content
stringlengths 7
1.05M
|
|---|
# -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license, see LICENSE.
"""
Submodule for useful exceptions
===============================
.. note:: not meant for user code in general, though possible.
"""
# Definition of handy colours for printing
_default = "\x1b[00m"
_green = "\x1b[01;32m"
_red = "\x1b[01;31m"
class InvalidOperationError(Exception):
"""Exception class for meaningless operations."""
def __init__(self, *args, **kwargs):
Exception.__init__(self, *args, **kwargs)
def __str__(self):
"""String representation."""
return _red + self.message + _default
class SkhepTypeError(Exception):
"""Exception class for non-instantiable classes."""
def __init__(self, name):
Exception.__init__(self, name)
self.message = "'{0}' is an abstract base class. Instantiate one of its subclasses instead.".format(
name
)
def __str__(self):
"""String representation."""
return _red + self.message + _default
|
class Node:
def __init__(self, data=None):
self.__data=data
self.__next=None
@property
def data(self):
return self.__data
@data.setter
def data(self, data):
self.__data=data
@property
def next(self):
return self.__next
@next.setter
def next(self, n):
self.__next = n
class LStack:
def __init__(self):
self.top=None
def empty(self):
if self.top is None:
return True
else:
return False
def push(self, data):
New_node = Node(data)
New_node.next = self.top
self.top = New_node
def pop(self):
if self.empty():
return
temp = self.top.data
self.top = self.top.next
return temp
def peek(self):
if self.empty():
return
return self.top.data
if __name__ =="__main__":
s = LStack()
s.push(1)
s.push(2)
s.push(3)
s.push(4)
s.push(5)
while not s.empty():
print(s.pop(), end=" ") |
def pkgs_raw(packages):
pkgs_raw_str = ""
for package in packages:
pkgs_raw_str += str(package) + "\n"
print(pkgs_raw_str)
def pkgs_traits(packages, traits_to_print):
pkgs_traits_str = ""
for package in packages:
pkgs_traits_str += "\n"
for trait in traits_to_print:
if trait in package:
pkgs_traits_str += "%s: %s\n" % (trait, package[trait])
print(pkgs_traits_str)
|
webgui = Runtime.create("webgui","WebGui")
# if you don't want the browser to
# autostart to homepage
#
# webgui.autoStartBrowser(false)
# set a different port number to listen to
# default is 8888
# webgui.setPort(7777)
# on startup the webgui will look for a "resources"
# directory (may change in the future)
# static html files can be placed here and accessed through
# the webgui service
# starts the websocket server
# and attempts to autostart browser
webgui.startService();
|
# coding=utf8
# Copyright 2018 JDCLOUD.COM
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# NOTE: This class is auto generated by the jdcloud code generator program.
class WatermarkTemplate(object):
def __init__(self, position=None, offsetUnit=None, offSetX=None, offSetY=None, sizeUnit=None, width=None, height=None, url=None, template=None):
"""
:param position: (Optional) 水印位置
- 取值范围：左上：1，右上：3， 左下：7，右下：9，默认：1
:param offsetUnit: (Optional) 偏移量单位
- 取值: percent,pixel
:param offSetX: (Optional) x轴偏移量
:param offSetY: (Optional) y轴偏移量
:param sizeUnit: (Optional) 水印大小单位
- 取值: percent,pixel
:param width: (Optional) 水印宽度
:param height: (Optional) 水印高度
:param url: (Optional) 水印地址
:param template: (Optional) 水印模板自定义名称
"""
self.position = position
self.offsetUnit = offsetUnit
self.offSetX = offSetX
self.offSetY = offSetY
self.sizeUnit = sizeUnit
self.width = width
self.height = height
self.url = url
self.template = template
|
class Solution:
max_time = 0
def numOfMinutes(self, n: int, headID: int, manager: List[int], informTime: List[int]) -> int:
global max_time
max_time=0
def dfs(adj,node,path_sum):
global max_time
if adj.get(node)==None:
max_time=max(max_time,path_sum)
return
else:
for i in adj[node]:
dfs(adj,i,path_sum+informTime[node])
adj={}
for i in range(len(manager)):
if adj.get(manager[i])!=None:
adj[manager[i]].append(i)
else:
adj[manager[i]]=[i]
max_path=0
dfs(adj,headID,0)
return max_time
|
def z_inicial(lista):
cont=0
if len(lista) == 0:
return
else:
for i in range(len(lista)):
if lista[i][0] == "z" or lista[i][0] == "Z":
cont+=1
return cont
lista=input().split()
print(z_inicial(lista))
|
"""
pyexcel_io.constants
~~~~~~~~~~~~~~~~~~~
Constants appeared in pyexcel
:copyright: (c) 2014-2017 by Onni Software Ltd.
:license: New BSD License
"""
# flake8: noqa
DEFAULT_NAME = 'pyexcel'
DEFAULT_SHEET_NAME = '%s_sheet1' % DEFAULT_NAME
MESSAGE_INVALID_PARAMETERS = "Invalid parameters"
MESSAGE_ERROR_02 = "No content, file name. Nothing is given"
MESSAGE_ERROR_03 = "cannot handle unknown content"
MESSAGE_WRONG_IO_INSTANCE = "Wrong io instance is passed for your file format."
MESSAGE_CANNOT_WRITE_STREAM_FORMATTER = "Cannot write content of file type %s to stream"
MESSAGE_CANNOT_READ_STREAM_FORMATTER = "Cannot read content of file type %s from stream"
MESSAGE_CANNOT_WRITE_FILE_TYPE_FORMATTER = "Cannot write content of file type %s to file %s"
MESSAGE_CANNOT_READ_FILE_TYPE_FORMATTER = "Cannot read content of file type %s from file %s"
MESSAGE_LOADING_FORMATTER = "The plugin for file type %s is not installed. Please install %s"
MESSAGE_EMPTY_ARRAY = "One empty row is found"
MESSAGE_IGNORE_ROW = "One row is ignored"
MESSAGE_DB_EXCEPTION = """
Warning: Bulk insertion got below exception. Trying to do it one by one slowly."""
FILE_FORMAT_CSV = 'csv'
FILE_FORMAT_TSV = 'tsv'
FILE_FORMAT_CSVZ = 'csvz'
FILE_FORMAT_TSVZ = 'tsvz'
FILE_FORMAT_ODS = 'ods'
FILE_FORMAT_XLS = 'xls'
FILE_FORMAT_XLSX = 'xlsx'
FILE_FORMAT_XLSM = 'xlsm'
DB_SQL = 'sql'
DB_DJANGO = 'django'
KEYWORD_TSV_DIALECT = 'excel-tab'
KEYWORD_LINE_TERMINATOR = 'lineterminator'
SKIP_DATA = -1
TAKE_DATA = 0
STOP_ITERATION = 1
|
''' Problem # 3
Write a program that takes the salary and grade from user.
It then adds 50% bonus if the grade is greater tha 15.
It adds 25% bonus if the grade is 15 or less and then it should display the salary
Pseudocode
1. Start
2. Take the salary and grade from the user
3. If the grade is greater than 15
then
1. Calculate the bonus using the formula 50 / 100 * salary # can also use 1.5
4. Otherwise do the following
1. Caculate the bonus using the formula 25 / 100 * salary # can also use 1.25
5. Calculate the total salary using salary = salary + bonus.
6. Display the total salary
7. End
# I need to create three variables:
# 1. Salary needs to be a floating point number
# 2. grade needs to be a interger
# 3. bonus should be a floating point number
Main
|
Real Salary
|
Real bonus
|
Interger grade
|
Input Salary
|
Input bonus
|
False | True
____if grade > 15__________________________
| |
Bonus = 25/100 * salary bonus = 50/100 * salary
|_______________________ 0 _____________|
|
salary = salary + bonus
|
Output"Your total salary is"salary and bonus
|
End
'''
salary = float(input("please enter the salary: "))
grade = int(input("please enter your grade: "))
if grade > 15:
bonus = 50 / 100 * salary
else:
bonus = 25 / 100 * salary
salary += bonus # salary = salary + bonus
print("Your total salary is", salary) # please enter the salary: 50000
# please enter your grade: 18
# Your total salary is 75000.0
|
## 1. Overview ##
f = open("movie_metadata.csv", 'r')
movie_metadata = f.read()
movie_metadata = movie_metadata.split('\n')
movie_data = []
for element in movie_metadata:
row = element.split(',')
movie_data.append(row)
print(movie_data[:5])
## 3. Writing Our Own Functions ##
def first_elts(nested_lists):
list_heads = []
for n_list in nested_lists:
list_heads.append(n_list[0])
return list_heads
movie_names = first_elts(movie_data)
print(movie_names)
## 4. Functions with Multiple Return Paths ##
def is_usa(movie):
origin_idx = 6
return True if movie[origin_idx] == "USA" else False
wonder_woman = ['Wonder Woman','Patty Jenkins','Color',141,'Gal Gadot','English','USA',2017]
wonder_woman_usa = is_usa(wonder_woman)
## 5. Functions with Multiple Arguments ##
wonder_woman = ['Wonder Woman','Patty Jenkins','Color',141,'Gal Gadot','English','USA',2017]
def is_usa(input_lst):
if input_lst[6] == "USA":
return True
else:
return False
def index_equals_str(input_lst, index, input_str):
return input_lst[index] == input_str
wonder_woman_in_color = index_equals_str(wonder_woman, 2, "Color")
## 6. Optional Arguments ##
def index_equals_str(input_lst,index,input_str):
if input_lst[index] == input_str:
return True
else:
return False
def counter(input_lst,header_row = False):
num_elt = 0
if header_row == True:
input_lst = input_lst[1:len(input_lst)]
for each in input_lst:
num_elt = num_elt + 1
return num_elt
def feature_counter(input_lst, index, input_str, header_row=False):
num_feature = 0
if header_row == True:
input_lst = input_lst[1:]
for row in input_lst:
num_feature += 1 if row[index] == input_str else 0
return num_feature
num_of_us_movies = feature_counter(movie_data, 6, "USA", True)
## 7. Calling a Function inside another Function ##
def feature_counter(input_lst, index, input_str, header_row = False):
num_elt = 0
if header_row == True:
input_lst = input_lst[1:]
for each in input_lst:
if each[index] == input_str:
num_elt += 1
return num_elt
def summary_statistics(input_lst):
input_lst = input_lst[1:]
num_japan_films = feature_counter(input_lst, 6, "Japan")
num_color_films = feature_counter(input_lst, 2, "Color")
num_films_in_english = feature_counter(input_lst, 5, "English")
summary_dict = {"japan_films": num_japan_films,
"color_films": num_color_films,
"films_in_english": num_films_in_english}
return summary_dict
summary = summary_statistics(movie_data) |
# -*- coding: utf-8 -*-
#
# Copyright (c) 2017 Hewlett Packard Enterprise Development LP
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
Manifest = {
'Name': 'temp_sensor_status_transition_monitor',
'Description': 'Network Analytics Agent Script to monitor'
'status transitions of all temperature sensors',
'Version': '1.0',
'Author': 'Aruba Networks'
}
class Policy(NAE):
def __init__(self):
self.variables['sensors_list'] = ''
uri1 = '/rest/v1/system/subsystems/*/*/temp_sensors/*?' \
'attributes=status'
self.m1 = Monitor(uri1, 'Sensor Status')
# MONITOR NORMAL STATE TRANSITIONS
# Normal -> Min
self.r1 = Rule('Sensor Status - Normal -> Min')
self.r1.condition(
'transition {} from "normal" to "min"', [self.m1])
self.r1.action(self.sensor_status_action_normal)
# Normal -> Max
self.r2 = Rule('Sensor Status - Normal -> Max')
self.r2.condition(
'transition {} from "normal" to "max"', [self.m1])
self.r2.action(self.sensor_status_action_normal)
# Low Critical -> Min
self.r3 = Rule('Sensor Status - Low Critical -> Min')
self.r3.condition(
'transition {} from "low_critical" to "min"', [self.m1])
self.r3.action(self.sensor_status_action_normal)
# Critical -> Max
self.r4 = Rule('Sensor Status - Critical -> Max')
self.r4.condition(
'transition {} from "critical" to "max"', [self.m1])
self.r4.action(self.sensor_status_action_normal)
# Fault -> Uninitialized
self.r5 = Rule('Sensor Status - Fault -> Uninitialized')
self.r5.condition(
'transition {} from "fault" to "uninitialized"', [self.m1])
self.r5.action(self.sensor_status_action_normal)
# Fault -> Normal
self.r6 = Rule('Sensor Status - Fault -> Normal')
self.r6.condition(
'transition {} from "fault" to "normal"', [self.m1])
self.r6.action(self.sensor_status_action_normal)
# Fault -> Min
self.r7 = Rule('Sensor Status - Fault -> Min')
self.r7.condition(
'transition {} from "fault" to "min"', [self.m1])
self.r7.action(self.sensor_status_action_normal)
# Fault -> Max
self.r8 = Rule('Sensor Status - Fault -> Max')
self.r8.condition(
'transition {} from "fault" to "max"', [self.m1])
self.r8.action(self.sensor_status_action_normal)
# MONITOR CRITICAL STATE TRANSITIONS
# Min -> Low Critical
self.r9 = Rule('Sensor Status - Min -> Low Critical')
self.r9.condition(
'transition {} from "min" to "low_critical"', [self.m1])
self.r9.action(self.sensor_status_action_critical)
# Max -> Critical
self.r10 = Rule('Sensor Status - Max -> Critical')
self.r10.condition(
'transition {} from "max" to "critical"', [self.m1])
self.r10.action(self.sensor_status_action_critical)
# Critical -> Emergency
self.r11 = Rule(
'Sensor Status - Critical -> Emergency')
self.r11.condition(
'transition {} from "critical" to "emergency"',
[self.m1])
self.r11.action(self.sensor_status_action_critical)
# Emergency -> Critical
self.r12 = Rule('Sensor Status - Emergency -> Critical')
self.r12.condition(
'transition {} from "emergency" to "critical"', [self.m1])
self.r12.action(self.sensor_status_action_critical)
# Uninitialized -> Fault
self.r13 = Rule('Sensor Status - Uninitialized -> Fault')
self.r13.condition(
'transition {} from "uninitialized" to "fault"', [self.m1])
self.r13.action(self.sensor_status_action_critical)
# Normal -> Fault
self.r14 = Rule('Sensor Status - Normal -> Fault')
self.r14.condition(
'transition {} from "normal" to "fault"', [self.m1])
self.r14.action(self.sensor_status_action_critical)
# Min -> Fault
self.r15 = Rule('Sensor Status - Min -> Fault')
self.r15.condition(
'transition {} from "min" to "fault"', [self.m1])
self.r15.action(self.sensor_status_action_critical)
# Low Critical -> Fault
self.r16 = Rule('Sensor Status - Low Critical -> Fault')
self.r16.condition(
'transition {} from "low_critical" to "fault"', [self.m1])
self.r16.action(self.sensor_status_action_critical)
# Max -> Fault
self.r17 = Rule('Sensor Status - Max -> Fault')
self.r17.condition(
'transition {} from "max" to "fault"', [self.m1])
self.r17.action(self.sensor_status_action_critical)
# Critical -> Fault
self.r18 = Rule('Sensor Status - Critical -> Fault')
self.r18.condition(
'transition {} from "critical" to "fault"', [self.m1])
self.r18.action(self.sensor_status_action_critical)
# Emergency -> Fault
self.r19 = Rule('Sensor Status - Emergency -> Fault')
self.r19.condition(
'transition {} from "emergency" to "fault"', [self.m1])
self.r19.action(self.sensor_status_action_critical)
# Fault -> Emergency
self.r20 = Rule('Sensor Status - Fault -> Emergency')
self.r20.condition(
'transition {} from "fault" to "emergency"', [self.m1])
self.r20.action(self.sensor_status_action_critical)
# Fault -> Critical
self.r21 = Rule('Sensor Status - Fault -> Critical')
self.r21.condition(
'transition {} from "fault" to "critical"', [self.m1])
self.r21.action(self.sensor_status_action_critical)
# Fault -> Low Critical
self.r22 = Rule('Sensor Status - Fault -> Low Critical')
self.r22.condition(
'transition {} from "fault" to "low_critical"', [self.m1])
self.r22.action(self.sensor_status_action_critical)
def sensor_status_action_critical(self, event):
self.logger.debug('********CRITICAL********')
self.logger.debug('LABEL = ' + event['labels'] +
'VALUE = ' + event['value'])
label = str(event['labels'])
labelsplit = label.split(",")
readsensor = labelsplit[1]
readsensorsplit = readsensor.split("=")
sensorname = str(readsensorsplit[1])
self.logger.debug('Sensor Name= ' + sensorname)
if self.variables['sensors_list'] != '':
findsensor = self.variables['sensors_list']
istrue = findsensor.find(sensorname)
if istrue == -1:
sensors_list = sensorname + self.variables['sensors_list']
self.variables['sensors_list'] = sensors_list
self.logger.debug('list of sensors : ' +
self.variables['sensors_list'])
self.setactions(sensorname)
else:
ActionSyslog('Sensor: ' + sensorname +
' is in Critical state')
ActionCLI('show environment temperature')
else:
self.variables['sensors_list'] = sensorname
self.logger.debug('list of sensors:' +
self.variables['sensors_list'])
self.setactions(sensorname)
def setactions(self, sensorname):
self.logger.debug('+++ CALLBACK: SENSOR STATUS - CRITICAL!')
self.set_alert_level(AlertLevel.CRITICAL)
ActionSyslog('Sensor: ' + sensorname +
' is in Critical state')
ActionCLI('show environment temperature')
def sensor_status_action_normal(self, event):
if self.get_alert_level() is not None:
if self.variables['sensors_list'] == '':
self.set_policy_status_normal()
else:
print('********NORMAL********')
label = str(event['labels'])
labelsplit = label.split(",")
readsensor = labelsplit[1]
readsensorsplit = readsensor.split("=")
sensorname = str(readsensorsplit[1])
'''
delete all Sensor Name's which moved back to
Normal state from Critical state
'''
index = 0
length = len(sensorname)
findsensor = self.variables['sensors_list']
index = findsensor.find(sensorname)
if index != -1:
# index = string.find(str, substr)
findsensor = findsensor[
0:index] + findsensor[
index + length:]
self.variables['sensors_list'] = findsensor
self.logger.debug('Sensor name deleted: ' + sensorname)
self.logger.debug('Current Sensors list: ' +
self.variables['sensors_list'])
ActionSyslog('Sensor ' + sensorname +
' is back to Normal')
if self.variables['sensors_list'] == '':
self.set_policy_status_normal()
def set_policy_status_normal(self):
self.remove_alert_level()
ActionSyslog('All Sensors are Normal')
|
#gwang_01.py
j = 0
for i in range (1000):
if (i % 3) == 0 or (i%5) == 0: j+=i
j
|
dp = [0 for i in range(301)];stair = [0 for i in range(301)]
n = int(input())
for i in range(n): stair[i] = int(input())
dp[0] = stair[0];dp[1] = stair[0]+stair[1];dp[2] = max(stair[0]+stair[2],stair[1]+stair[2])
for i in range(3,n): dp[i] = max(dp[i-2]+stair[i],dp[i-3]+stair[i-1]+stair[i])
print(dp[n-1]) |
# Static config for the wms metadata.
response_cfg = {
"Access-Control-Allow-Origin": "*", # CORS header
}
service_cfg = {
## Which web service(s) should be supported by this instance
"wcs": True,
"wms": True,
## Required config for WMS and/or WCS
# Service title - appears e.g. in Terria catalog
"title": "Digital Earth Australia - OGC Web Services",
# Service URL. Should a fully qualified URL
"url": "https://ows.services.dea.ga.gov.au",
"human_url": "dea.ga.gov.au/",
# Supported co-ordinate reference systems
"published_CRSs": {
"EPSG:3857": { # Web Mercator
"geographic": False,
"horizontal_coord": "x",
"vertical_coord": "y",
},
"EPSG:4326": { # WGS-84
"geographic": True,
"vertical_coord_first": True
},
"EPSG:3577": { # GDA-94, internal representation
"geographic": False,
"horizontal_coord": "x",
"vertical_coord": "y",
},
},
## Required config for WCS
# Must be a geographic CRS in the published_CRSs list. EPSG:4326 is recommended, but any geographic CRS should work.
"default_geographic_CRS": "EPSG:4326",
# Supported WCS formats
"wcs_formats": {
# Key is the format name, as used in DescribeCoverage XML
"GeoTIFF": {
# Renderer is the FQN of a Python function that takes:
# * A WCS Request object
# * Some ODC data to be rendered.
"renderer": "datacube_wms.wcs_utils.get_tiff",
# The MIME type of the image, as used in the Http Response.
"mime": "image/geotiff",
# The file extension to add to the filename.
"extension": "tif",
# Whether or not the file format supports multiple time slices.
"multi-time": False
},
"netCDF": {
"renderer": "datacube_wms.wcs_utils.get_netcdf",
"mime": "application/x-netcdf",
"extension": "nc",
"multi-time": True,
}
},
# The native wcs format must be declared in wcs_formats above.
"native_wcs_format": "GeoTIFF",
## Optional config for instances supporting WMS
# Max tile height/width. If not specified, default to 256x256
"max_width": 512,
"max_height": 512,
# Optional config for all services (WMS and/or WCS) - may be set to blank/empty, no defaults
"abstract": """Digital Earth Australia OGC Web Services""",
"keywords": [
"geomedian",
"WOfS",
"mangrove",
"landsat",
"australia",
"time-series",
],
"contact_info": {
"person": "Digital Earth Australia",
"organisation": "Geoscience Australia",
"position": "",
"address": {
"type": "postal",
"address": "GPO Box 378",
"city": "Canberra",
"state": "ACT",
"postcode": "2609",
"country": "Australia",
},
"telephone": "+61 2 6249 9111",
"fax": "",
"email": "[email protected]",
},
"fees": "",
"access_constraints": "© Commonwealth of Australia (Geoscience Australia) 2018. "
"This product is released under the Creative Commons Attribution 4.0 International Licence. "
"http://creativecommons.org/licenses/by/4.0/legalcode",
"preauthenticate_s3": True,
"geotiff_georeference_source": "INTERNAL"
}
layer_cfg = [
# Layer Config is a list of platform configs
{
# Name and title of the platform layer.
# Platform layers are not mappable. The name is for internal server use only.
"name": "Geomedian_AU_NBART",
"title": "Surface Reflectance",
"abstract": "Data is only visible at higher resolutions; when zoomed-out the available area will be displayed "
"as a shaded region. The surface reflectance geometric median (geomedian) is a pixel composite "
"mosaic of a time series of earth observations. The value of a pixel in a an annual geomedian "
"image is the statistical median of all observations for that pixel from a calendar year. "
"Annual mosaics are available for the following years: "
"Landsat 5: 1988 to 1999, 2004 to 2007, 2009 to 2011; "
"Landsat 7: 2000 to 2017; "
"Landsat 8: 2013 to 2017; "
"For more information, see http://pid.geoscience.gov.au/dataset/ga/120374",
# Products available for this platform.
# For each product, the "name" is the Datacube name, and the label is used
# to describe the label to end-users.
"products": [
{
# Included as a keyword for the layer
"label": "Landsat 8",
# Included as a keyword for the layer
"type": "Annual Geomedian",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "ls8_nbart_geomedian_annual",
# The Datacube name for the associated data product
"product_name": "ls8_nbart_geomedian_annual",
# The Datacube name for the associated pixel-quality product (optional)
# The name of the associated Datacube pixel-quality product
# "pq_dataset": "ls8_level1_usgs",
# The name of the measurement band for the pixel-quality product
# (Only required if pq_dataset is set)
# "pq_manual_data_merge": True,
# "data_manual_merge": True,
# "pq_band": "quality",
# "always_fetch_bands": [ "quality" ],
# Min zoom factor - sets the zoom level where the cutover from indicative polygons
# to actual imagery occurs.
"min_zoom_factor": 35.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'],
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"data_manual_merge": True,
"always_fetch_bands": [ ],
"apply_solar_corrections": False,
# Define layer wide legend graphic if no style is passed
# to GetLegendGraphic
"legend": {
# "url": ""
"styles": ["ndvi", "ndwi"]
},
# A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer
# If None or not specified, the product has no sub-layers.
# "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")),
# A prefix used to describe the sub-layer in the GetCapabilities response.
# E.g. sub-layer 109 will be described as "Landsat Path 109"
# "sub_product_label": "Landsat Path",
# Bands to include in time-dimension "pixel drill".
# Don't activate in production unless you really know what you're doing.
# "band_drill": ["nir", "red", "green", "blue"],
# Styles.
#
# See band_mapper.py
#
# The various available spectral bands, and ways to combine them
# into a single rgb image.
# The examples here are ad hoc
#
"styles": [
# Examples of styles which are linear combinations of the available spectral bands.
#
{
"name": "simple_rgb",
"title": "Simple RGB",
"abstract": "Simple true-colour image, using the red, green and blue bands",
"components": {
"red": {
"red": 1.0
},
"green": {
"green": 1.0
},
"blue": {
"blue": 1.0
}
},
# The raw band value range to be compressed to an 8 bit range for the output image tiles.
# Band values outside this range are clipped to 0 or 255 as appropriate.
"scale_range": [0.0, 3000.0]
},
{
"name": "infrared_green",
"title": "False colour - Green, SWIR, NIR",
"abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue",
"components": {
"red": {
"swir1": 1.0
},
"green": {
"nir": 1.0
},
"blue": {
"green": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
#
# Examples of non-linear heat-mapped styles.
{
"name": "ndvi",
"title": "NDVI - Red, NIR",
"abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation",
"index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]),
"needed_bands": ["red", "nir"],
"color_ramp": [
{
"value": -1.0,
"color": "#FFFFFF",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 0.1,
"color": "#A35F18"
},
{
"value": 0.2,
"color": "#B88512"
},
{
"value": 0.3,
"color": "#CEAC0E"
},
{
"value": 0.4,
"color": "#E5D609"
},
{
"value": 0.5,
"color": "#FFFF0C"
},
{
"value": 0.6,
"color": "#C3DE09"
},
{
"value": 0.7,
"color": "#88B808"
},
{
"value": 0.8,
"color": "#529400"
},
{
"value": 0.9,
"color": "#237100"
},
{
"value": 1.0,
"color": "#114D04"
}
]
},
{
"name": "ndwi",
"title": "NDWI - Green, SWIR",
"abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water",
"index_function": lambda data: (data["green"] - data["swir1"]) / (data["swir1"] + data["green"]),
"needed_bands": ["green", "swir1"],
"color_ramp": [
{
"value": -1.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 1.0,
"color": "#0303FF",
},
]
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "simple_rgb",
},
{
# Included as a keyword for the layer
"label": "Landsat 7",
# Included as a keyword for the layer
"type": "Annual Geomedian",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "ls7_nbart_geomedian_annual",
# The Datacube name for the associated data product
"product_name": "ls7_nbart_geomedian_annual",
# The Datacube name for the associated pixel-quality product (optional)
# The name of the associated Datacube pixel-quality product
# "pq_dataset": "ls8_level1_usgs",
# The name of the measurement band for the pixel-quality product
# (Only required if pq_dataset is set)
# "pq_manual_data_merge": True,
# "data_manual_merge": True,
# "pq_band": "quality",
# "always_fetch_bands": [ "quality" ],
# Min zoom factor - sets the zoom level where the cutover from indicative polygons
# to actual imagery occurs.
"min_zoom_factor": 35.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'],
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"data_manual_merge": True,
"always_fetch_bands": [],
"apply_solar_corrections": False,
# Define layer wide legend graphic if no style is passed
# to GetLegendGraphic
"legend": {
# "url": ""
"styles": ["ndvi", "ndwi"]
},
# A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer
# If None or not specified, the product has no sub-layers.
# "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")),
# A prefix used to describe the sub-layer in the GetCapabilities response.
# E.g. sub-layer 109 will be described as "Landsat Path 109"
# "sub_product_label": "Landsat Path",
# Bands to include in time-dimension "pixel drill".
# Don't activate in production unless you really know what you're doing.
# "band_drill": ["nir", "red", "green", "blue"],
# Styles.
#
# See band_mapper.py
#
# The various available spectral bands, and ways to combine them
# into a single rgb image.
# The examples here are ad hoc
#
"styles": [
# Examples of styles which are linear combinations of the available spectral bands.
#
{
"name": "simple_rgb",
"title": "Simple RGB",
"abstract": "Simple true-colour image, using the red, green and blue bands",
"components": {
"red": {
"red": 1.0
},
"green": {
"green": 1.0
},
"blue": {
"blue": 1.0
}
},
# The raw band value range to be compressed to an 8 bit range for the output image tiles.
# Band values outside this range are clipped to 0 or 255 as appropriate.
"scale_range": [0.0, 3000.0]
},
{
"name": "infrared_green",
"title": "False colour - Green, SWIR, NIR",
"abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue",
"components": {
"red": {
"swir1": 1.0
},
"green": {
"nir": 1.0
},
"blue": {
"green": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
#
# Examples of non-linear heat-mapped styles.
{
"name": "ndvi",
"title": "NDVI - Red, NIR",
"abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation",
"index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]),
"needed_bands": ["red", "nir"],
"color_ramp": [
{
"value": -1.0,
"color": "#FFFFFF",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 0.1,
"color": "#A35F18"
},
{
"value": 0.2,
"color": "#B88512"
},
{
"value": 0.3,
"color": "#CEAC0E"
},
{
"value": 0.4,
"color": "#E5D609"
},
{
"value": 0.5,
"color": "#FFFF0C"
},
{
"value": 0.6,
"color": "#C3DE09"
},
{
"value": 0.7,
"color": "#88B808"
},
{
"value": 0.8,
"color": "#529400"
},
{
"value": 0.9,
"color": "#237100"
},
{
"value": 1.0,
"color": "#114D04"
}
]
},
{
"name": "ndwi",
"title": "NDWI - Green, SWIR",
"abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water",
"index_function": lambda data: (data["green"] - data["swir1"]) / (data["swir1"] + data["green"]),
"needed_bands": ["green", "swir1"],
"color_ramp": [
{
"value": -1.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 1.0,
"color": "#0303FF",
},
]
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "simple_rgb",
},
{
# Included as a keyword for the layer
"label": "Landsat 5",
# Included as a keyword for the layer
"type": "Annual Geomedian",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "ls5_nbart_geomedian_annual",
# The Datacube name for the associated data product
"product_name": "ls5_nbart_geomedian_annual",
# The Datacube name for the associated pixel-quality product (optional)
# The name of the associated Datacube pixel-quality product
# "pq_dataset": "ls8_level1_usgs",
# The name of the measurement band for the pixel-quality product
# (Only required if pq_dataset is set)
# "pq_manual_data_merge": True,
# "data_manual_merge": True,
# "pq_band": "quality",
# "always_fetch_bands": [ "quality" ],
# Min zoom factor - sets the zoom level where the cutover from indicative polygons
# to actual imagery occurs.
"min_zoom_factor": 35.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'],
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"data_manual_merge": True,
"always_fetch_bands": [],
"apply_solar_corrections": False,
# Define layer wide legend graphic if no style is passed
# to GetLegendGraphic
"legend": {
# "url": ""
"styles": ["ndvi", "ndwi"]
},
# A function that extracts the "sub-product" id (e.g. path number) from a dataset. Function should return a (small) integer
# If None or not specified, the product has no sub-layers.
# "sub_product_extractor": lambda ds: int(s3_path_pattern.search(ds.uris[0]).group("path")),
# A prefix used to describe the sub-layer in the GetCapabilities response.
# E.g. sub-layer 109 will be described as "Landsat Path 109"
# "sub_product_label": "Landsat Path",
# Bands to include in time-dimension "pixel drill".
# Don't activate in production unless you really know what you're doing.
# "band_drill": ["nir", "red", "green", "blue"],
# Styles.
#
# See band_mapper.py
#
# The various available spectral bands, and ways to combine them
# into a single rgb image.
# The examples here are ad hoc
#
"styles": [
# Examples of styles which are linear combinations of the available spectral bands.
#
{
"name": "simple_rgb",
"title": "Simple RGB",
"abstract": "Simple true-colour image, using the red, green and blue bands",
"components": {
"red": {
"red": 1.0
},
"green": {
"green": 1.0
},
"blue": {
"blue": 1.0
}
},
# The raw band value range to be compressed to an 8 bit range for the output image tiles.
# Band values outside this range are clipped to 0 or 255 as appropriate.
"scale_range": [0.0, 3000.0]
},
{
"name": "infrared_green",
"title": "False colour - Green, SWIR, NIR",
"abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue",
"components": {
"red": {
"swir1": 1.0
},
"green": {
"nir": 1.0
},
"blue": {
"green": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
#
# Examples of non-linear heat-mapped styles.
{
"name": "ndvi",
"title": "NDVI - Red, NIR",
"abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation",
"index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]),
"needed_bands": ["red", "nir"],
"color_ramp": [
{
"value": -1.0,
"color": "#FFFFFF",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 0.1,
"color": "#A35F18"
},
{
"value": 0.2,
"color": "#B88512"
},
{
"value": 0.3,
"color": "#CEAC0E"
},
{
"value": 0.4,
"color": "#E5D609"
},
{
"value": 0.5,
"color": "#FFFF0C"
},
{
"value": 0.6,
"color": "#C3DE09"
},
{
"value": 0.7,
"color": "#88B808"
},
{
"value": 0.8,
"color": "#529400"
},
{
"value": 0.9,
"color": "#237100"
},
{
"value": 1.0,
"color": "#114D04"
}
]
},
{
"name": "ndwi",
"title": "NDWI - Green, SWIR",
"abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water",
"index_function": lambda data: (data["green"] - data["swir1"]) / (data["swir1"] + data["green"]),
"needed_bands": ["green", "swir1"],
"color_ramp": [
{
"value": -1.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 1.0,
"color": "#0303FF",
},
]
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "simple_rgb",
}
]
},
{
# Name and title of the platform layer.
# Platform layers are not mappable. The name is for internal server use only.
"name": "landsat8_barest_earth",
"title": "Barest Earth",
"abstract": """
A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent.
The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy.
Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted.
Mosaics are available for the following years:
Landsat 8: 2013 to 2017;""",
# Products available for this platform.
# For each product, the "name" is the Datacube name, and the label is used
# to describe the label to end-users.
"products": [
{
# Included as a keyword for the layer
"label": "Landsat 8",
# Included as a keyword for the layer
"type": "Barest Earth",
# Included as a keyword for the layer
"variant": "25m",
"abstract": """
A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent.
The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy.
Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted.
Mosaics are available for the following years:
Landsat 8: 2013 to 2017;""",
# The WMS name for the layer
"name": "ls8_barest_earth_mosaic",
# The Datacube name for the associated data product
"product_name": "ls8_barest_earth_mosaic",
# Min zoom factor - sets the zoom level where the cutover from indicative polygons
# to actual imagery occurs.
"min_zoom_factor": 500.0,
"max_datasets_wms": 1000,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: data[band] != data[band].attrs['nodata'],
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"data_manual_merge": True,
"always_fetch_bands": [],
"apply_solar_corrections": False,
# Define layer wide legend graphic if no style is passed
# to GetLegendGraphic
"legend": {
# "url": ""
"styles": ["ndvi"]
},
#
# See band_mapper.py
#
# The various available spectral bands, and ways to combine them
# into a single rgb image.
# The examples here are ad hoc
#
"styles": [
# Examples of styles which are linear combinations of the available spectral bands.
#
{
"name": "simple_rgb",
"title": "Simple RGB",
"abstract": "Simple true-colour image, using the red, green and blue bands",
"components": {
"red": {
"red": 1.0
},
"green": {
"green": 1.0
},
"blue": {
"blue": 1.0
}
},
# The raw band value range to be compressed to an 8 bit range for the output image tiles.
# Band values outside this range are clipped to 0 or 255 as appropriate.
"scale_range": [0.0, 3000.0]
},
{
"name": "infrared_green",
"title": "False colour - Green, SWIR, NIR",
"abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue",
"components": {
"red": {
"swir1": 1.0
},
"green": {
"nir": 1.0
},
"blue": {
"green": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
{
"name": "ndvi",
"title": "NDVI - Red, NIR",
"abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation",
"index_function": lambda data: (data["nir"] - data["red"]) / (data["nir"] + data["red"]),
"needed_bands": ["red", "nir"],
"color_ramp": [
{
"value": -1.0,
"color": "#FFFFFF",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 0.1,
"color": "#A35F18"
},
{
"value": 0.2,
"color": "#B88512"
},
{
"value": 0.3,
"color": "#CEAC0E"
},
{
"value": 0.4,
"color": "#E5D609"
},
{
"value": 0.5,
"color": "#FFFF0C"
},
{
"value": 0.6,
"color": "#C3DE09"
},
{
"value": 0.7,
"color": "#88B808"
},
{
"value": 0.8,
"color": "#529400"
},
{
"value": 0.9,
"color": "#237100"
},
{
"value": 1.0,
"color": "#114D04"
}
]
}
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "simple_rgb",
}
]
},
{
"name": "mangrove_cover",
"title": "Mangrove Canopy Cover",
"abstract": "",
"products": [
{
"label": "Mangrove Canopy Cover",
"abstract": """
Mangrove canopy cover version 1, 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577). Data is only visible at higher resolutions; when zoomed-out the available area will be displayed as a shaded region.
The mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each year between 1987 and 2016 for the entire Australian coastline.
The canopy cover classes are:
20-50% (pale green), 50-80% (mid green), 80-100% (dark green).
The product consists of a sequence (one per year) of 25 meter resolution maps that are generated by analysing the Landsat fractional cover (https://doi.org/10.6084/m9.figshare.94250.v1) developed by the Joint Remote Sensing Research Program and the Global Mangrove Watch layers (https://doi.org/10.1071/MF13177) developed by the Japanese Aerospace Exploration Agency.
The mangrove canopy cover version 1 product has the following caveats:
it underestimates the overall extent of mangroves.
it doesn’t detect small mangrove communities i.e. smaller estuaries in NSW and Victoria
that there is localised confusion between mangroves and wooded freshwater wetlands i.e. Melaleuca swamps, and
in some locations dense dwarf/shrub mangroves that are less than 2 metres tall may be mis-labelled as woodland/open forest/closed forest.""",
"type": "100km tile",
"variant": "25m",
"name": "mangrove_cover",
"product_name": "mangrove_cover",
"min_zoom_factor": 15.0,
"zoomed_out_fill_colour": [150, 180, 200, 160],
"time_zone": 9,
"extent_mask_func": lambda data, band: data["extent"] == 1,
"ignore_info_flags": [],
"data_manual_merge": False,
"always_fetch_bands": ["extent"],
"apply_solar_corrections": False,
"legend": {
"styles": ["mangrove"]
},
"styles": [
{
"name": "mangrove",
"title": "Mangrove Cover",
"abstract": "",
"value_map": {
"canopy_cover_class": [
{
"title": "Woodland",
"abstract": "(20% - 50% cover)",
"flags": {
"woodland": True
},
"color": "#9FFF4C"
},
{
"title": "Open Forest",
"abstract": "(50% - 80% cover)",
"flags": {
"open_forest": True
},
"color": "#5ECC00"
},
{
"title": "Closed Forest",
"abstract": "(>80% cover)",
"flags": {
"closed_forest": True
},
"color": "#3B7F00"
},
]
}
}
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "mangrove",
},
]
},
{
# Name and title of the platform layer.
# Platform layers are not mappable. The name is for internal server use only.
"name": "WOfS",
"title": "Water Observations from Space",
"abstract": "WOfS",
# Products available for this platform.
# For each product, the "name" is the Datacube name, and the label is used
# to describe the label to end-users.
"products": [
{
# Included as a keyword for the layer
"label": "WOfS Filtered Statistics",
# Included as a keyword for the layer
"type": "Filtered Water Summary",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "wofs_filtered_summary",
# The Datacube name for the associated data product
"product_name": "wofs_filtered_summary",
"abstract": """
Water Observations from Space - Filtered Statistics is a set of statistical summaries of the water observations contained in Water Observations from Space.
This product is Water Observations from Space - Filtered Statistics, consisting of a Confidence layer that compares the Water Observations from Space - Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the Water Observations from Space - Statistics water summary where Confidence is low.
The Filtered Water Summary provides the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out.
This layer is Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level.
This layer gives a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist.
For more information please see: http://dea-public-data.s3-ap-southeast-2.amazonaws.com/WOfS/filtered_summary/v2.1.0/Product%20Description.pdf""",
"min_zoom_factor": 15.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']),
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"legend": {
# "url": ""
"styles": ["WOfS_filtered_frequency"]
},
"styles": [
{
"name": "WOfS_filtered_frequency",
"title": "Filtered Water Summary",
"abstract": "WOfS filtered summary showing the frequency of Wetness",
"needed_bands": ["wofs_filtered_summary"],
"color_ramp": [
{
"value": 0.0,
"color": "#000000",
"alpha": 0.0
},
{
"value": 0.002,
"color": "#000000",
"alpha": 0.0
},
{
"value": 0.005,
"color": "#8e0101",
"alpha": 0.25
},
{
"value": 0.01,
"color": "#cf2200",
"alpha": 0.75
},
{
"value": 0.02,
"color": "#e38400"
},
{
"value": 0.05,
"color": "#e3df00"
},
{
"value": 0.1,
"color": "#a6e300"
},
{
"value": 0.2,
"color": "#62e300"
},
{
"value": 0.3,
"color": "#00e32d"
},
{
"value": 0.4,
"color": "#00e384"
},
{
"value": 0.5,
"color": "#00e3c8"
},
{
"value": 0.6,
"color": "#00c5e3"
},
{
"value": 0.7,
"color": "#0097e3"
},
{
"value": 0.8,
"color": "#005fe3"
},
{
"value": 0.9,
"color": "#000fe3"
},
{
"value": 1.0,
"color": "#5700e3"
}
],
"legend": {
"units": "%",
"radix_point": 0,
"scale_by": 100.0,
"major_ticks": 0.1
}
}
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "WOfS_filtered_frequency",
},
{
# Included as a keyword for the layer
"label": "WOfS Statistics",
# Included as a keyword for the layer
"type": "Wet Count",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "wofs_summary_wet",
# The Datacube name for the associated data product
"product_name": "wofs_summary",
"abstract": """
Water Observations from Space - Statistics is a set of statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time.
This product is Water Observations from Space - Statistics, a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own.
The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary.
This layer contains Wet Count: how many times water was detected in observations that were clear
For more information please see: http://dea-public-data.s3-ap-southeast-2.amazonaws.com/WOfS/summary/v2.1.0/Product%20Description.pdf""",
"min_zoom_factor": 15.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']),
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"legend": {
# "url": ""
"styles": ["water_observations"]
},
"styles": [
{
"name": "water_observations",
"title": "Wet Count",
"abstract": "WOfS summary showing the count of water observations",
"needed_bands": ["count_wet"],
"color_ramp": [
{
"value": 0,
"color": "#666666",
"alpha": 0
},
{
"value": 2,
"color": "#890000"
},
{
"value": 5,
"color": "#990000"
},
{
"value": 10,
"color": "#E38400"
},
{
"value": 25,
"color": "#E3DF00"
},
{
"value": 50,
"color": "#A6E300"
},
{
"value": 100,
"color": "#00E32D"
},
{
"value": 150,
"color": "#00E3C8"
},
{
"value": 200,
"color": "#0097E3"
},
{
"value": 250,
"color": "#005FE3"
},
{
"value": 300,
"color": "#000FE3"
},
{
"value": 350,
"color": "#000EA9"
},
{
"value": 400,
"color": "#5700E3",
"legend": {
"prefix": ">"
}
}
],
"legend": {
"radix_point": 0,
"scale_by": 1,
"major_ticks": 100
}
}
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "water_observations",
},
{
# Included as a keyword for the layer
"label": "WOfS Statistics",
# Included as a keyword for the layer
"type": "Clear Count",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "wofs_summary_clear",
# The Datacube name for the associated data product
"product_name": "wofs_summary",
"abstract": """
Water Observations from Space - Statistics is a set of statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time.
This product is Water Observations from Space - Statistics, a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own.
The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary.
This layer contains Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems)
For more information please see: http://dea-public-data.s3-ap-southeast-2.amazonaws.com/WOfS/summary/v2.1.0/Product%20Description.pdf""",
"min_zoom_factor": 15.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']),
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"legend": {
# "url": ""
"styles": ["clear_observations"]
},
"styles": [
{
"name": "clear_observations",
"title": "Clear Count",
"abstract": "WOfS summary showing the count of clear observations",
"needed_bands": ["count_clear"],
"color_ramp": [
{
"value": 0,
"color": "#FFFFFF",
"alpha": 0
},
{
"value": 10,
"color": "#B21800"
},
{
"value": 25,
"color": "#FF4400"
},
{
"value": 50,
"color": "#FF8000"
},
{
"value": 100,
"color": "#FFA200"
},
{
"value": 150,
"color": "#FFC000"
},
{
"value": 200,
"color": "#FFD500"
},
{
"value": 250,
"color": "#FFF300"
},
{
"value": 300,
"color": "#E6FF00"
},
{
"value": 350,
"color": "#BCFF00"
},
{
"value": 400,
"color": "#89FF00"
},
{
"value": 500,
"color": "#68C400"
},
{
"value": 600,
"color": "#44C400"
},
{
"value": 700,
"color": "#03B500"
},
{
"value": 800,
"color": "#039500"
},
{
"value": 1000,
"color": "#026900",
"legend": {
"prefix": ">"
}
}
],
"legend": {
"radix_point": 0,
"scale_by": 1,
"major_ticks": 100,
"axes_position": [0.05, 0.5, 0.89, 0.15]
}
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "clear_observations",
},
{
# Included as a keyword for the layer
"label": "WOfS Statistics",
# Included as a keyword for the layer
"type": "Water Summary",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "Water Observations from Space Statistics",
# The Datacube name for the associated data product
"product_name": "wofs_summary",
"abstract": """
Water Observations from Space - Statistics is a set of statistical summaries of the water observations contained in WOfS. The layers available are: the count of clear observations; the count of wet observations; the percentage of wet observations over time.
This product is Water Observations from Space - Statistics, a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own.
The confidence layer and filtered summary are contained in the Water Observations from Space Statistics - Filtered Summary product, which provide a noise-reduced view of the water summary.
This layer contains Water Summary: what percentage of clear observations were detected as wet (ie. the ratio of wet to clear as a percentage)
For more information please see: http://dea-public-data.s3-ap-southeast-2.amazonaws.com/WOfS/summary/v2.1.0/Product%20Description.pdf""",
"min_zoom_factor": 15.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']),
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"legend": {
# "url": ""
"styles": ["WOfS_frequency"]
},
"styles": [
{
"name": "WOfS_frequency",
"title": " Water Summary",
"abstract": "WOfS summary showing the frequency of Wetness",
"needed_bands": ["frequency"],
"color_ramp": [
{
"value": 0.0,
"color": "#000000",
"alpha": 0.0
},
{
"value": 0.002,
"color": "#000000",
"alpha": 0.0
},
{
"value": 0.005,
"color": "#8e0101",
"alpha": 0.25
},
{
"value": 0.01,
"color": "#cf2200",
"alpha": 0.75
},
{
"value": 0.02,
"color": "#e38400"
},
{
"value": 0.05,
"color": "#e3df00"
},
{
"value": 0.1,
"color": "#a6e300"
},
{
"value": 0.2,
"color": "#62e300"
},
{
"value": 0.3,
"color": "#00e32d"
},
{
"value": 0.4,
"color": "#00e384"
},
{
"value": 0.5,
"color": "#00e3c8"
},
{
"value": 0.6,
"color": "#00c5e3"
},
{
"value": 0.7,
"color": "#0097e3"
},
{
"value": 0.8,
"color": "#005fe3"
},
{
"value": 0.9,
"color": "#000fe3"
},
{
"value": 1.0,
"color": "#5700e3"
}
],
"legend": {
"units": "%",
"radix_point": 0,
"scale_by": 100.0,
"major_ticks": 0.1
}
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "WOfS_frequency",
},
{
# Included as a keyword for the layer
"label": "WOfS Filtered Statistics",
# Included as a keyword for the layer
"type": "Confidence",
# Included as a keyword for the layer
"variant": "25m",
# The WMS name for the layer
"name": "wofs_filtered_summary_confidence",
# The Datacube name for the associated data product
"product_name": "wofs_filtered_summary",
"abstract": """
Water Observations from Space - Filtered Statistics is a set of statistical summaries of the water observations contained in Water Observations from Space.
This product is Water Observations from Space - Filtered Statistics, consisting of a Confidence layer that compares the Water Observations from Space - Statistics water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the Water Observations from Space - Statistics water summary where Confidence is low.
The Filtered Water Summary provides the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out.
This layer is Confidence: the degree of agreement between water shown in the Water Summary and other national datasets.
The Confidence layer provides understanding of whether the water shown in the Water Summary agrees with where water should exist in the landscape, such as due to sloping land or whether water has been detected in a location by other means.
For more information please see: http://dea-public-data.s3-ap-southeast-2.amazonaws.com/WOfS/filtered_summary/v2.1.0/Product%20Description.pdf""",
"min_zoom_factor": 15.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']),
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
"legend": {
# "url": ""
"styles": ["wofs_confidence"]
},
"styles": [
{
"name": "wofs_confidence",
"title": "Confidence",
"abstract": "WOfS Confidence",
"needed_bands": ["confidence"],
"color_ramp": [
{
"value": 0,
"color": "#FFFFFF",
"alpha": 0
},
{
"value": 0.01,
"color": "#000000"
},
{
"value": 0.02,
"color": "#990000"
},
{
"value": 0.05,
"color": "#CF2200"
},
{
"value": 0.1,
"color": "#E38400"
},
{
"value": 0.25,
"color": "#E3DF00"
},
{
"value": 0.5,
"color": "#A6E300"
},
{
"value": 0.75,
"color": "#62E300"
},
{
"value": 1.0,
"color": "#00E32D"
}
],
"legend": {
"units": "%",
"radix_point": 0,
"scale_by": 100.0,
"major_ticks": 0.25
}
}
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "wofs_confidence",
}
],
},
{
# Name and title of the platform layer.
# Platform layers are not mappable. The name is for internal server use only.
"name": "Sentinel-2 NRT",
"title": "Near Real-Time",
"abstract": "This is a 30-day rolling archive of daily Sentinel-2 Near Real Time data. "
"Data is only visible at higher resolutions: when zoomed-out the available areas for that day "
"will be displayed as shaded regions. The Near Real-Time capability provides analysis-ready data "
"that is processed on receipt using the best-available ancillary information at the time to "
"provide atmospheric corrections. For more information see "
"http://pid.geoscience.gov.au/dataset/ga/122229",
# Products available for this platform.
# For each product, the "name" is the Datacube name, and the label is used
# to describe the label to end-users.
"products": [
{
# Included as a keyword for the layer
"label": "Sentinel 2B",
# Included as a keyword for the layer
"type": "",
# Included as a keyword for the layer
"variant": "Surface Reflectance",
"abstract":"""
This is a 30-day rolling archive of daily Sentinel-2 Near Real Time data. Data is only visible at higher resolutions: when zoomed-out the available areas for that day will be displayed as shaded regions. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections.
For more information see http://pid.geoscience.gov.au/dataset/ga/122229""",
# The WMS name for the layer
"name": "s2b_nrt_granule_nbar_t",
# The Datacube name for the associated data product
"product_name": "s2b_nrt_granule",
# The Datacube name for the associated pixel-quality product (optional)
# The name of the associated Datacube pixel-quality product
# "pq_dataset": "s2b_nrt_granule",
# The name of the measurement band for the pixel-quality product
# (Only required if pq_dataset is set)
# "pq_band": "pixel_quality",
# Min zoom factor - sets the zoom level where the cutover from indicative polygons
# to actual imagery occurs.
"min_zoom_factor": 15.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']),
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
# Define layer wide legend graphic if no style is passed
# to GetLegendGraphic
"legend": {
# "url": ""
"styles": ["ndvi", "ndwi"]
},
# Styles.
#
# See band_mapper.py
#
# The various available spectral bands, and ways to combine them
# into a single rgb image.
# The examples here are ad hoc
#
"styles": [
# Examples of styles which are linear combinations of the available spectral bands.
#
{
"name": "simple_rgb",
"title": "Simple RGB",
"abstract": "Simple true-colour image, using the red, green and blue bands",
"components": {
"red": {
"nbart_red": 1.0
},
"green": {
"nbart_green": 1.0
},
"blue": {
"nbart_blue": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
{
"name": "infrared_green",
"title": "False colour - Green, SWIR, NIR",
"abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue",
"components": {
"red": {
"nbart_swir_2": 1.0
},
"green": {
"nbart_nir_1": 1.0
},
"blue": {
"nbart_green": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
{
"name": "ndvi",
"title": "NDVI - Red, NIR",
"abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation",
"index_function": lambda data: (data["nbart_nir_1"] - data["nbart_red"]) / (
data["nbart_nir_1"] + data["nbart_red"]),
"needed_bands": ["nbart_red", "nbart_nir_1"],
"color_ramp": [
{
"value": -1.0,
"color": "#FFFFFF",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 0.1,
"color": "#A35F18"
},
{
"value": 0.2,
"color": "#B88512"
},
{
"value": 0.3,
"color": "#CEAC0E"
},
{
"value": 0.4,
"color": "#E5D609"
},
{
"value": 0.5,
"color": "#FFFF0C"
},
{
"value": 0.6,
"color": "#C3DE09"
},
{
"value": 0.7,
"color": "#88B808"
},
{
"value": 0.8,
"color": "#529400"
},
{
"value": 0.9,
"color": "#237100"
},
{
"value": 1.0,
"color": "#114D04"
}
]
},
{
"name": "ndwi",
"title": "NDWI - Green, SWIR",
"abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water",
"index_function": lambda data: (data["nbart_green"] - data["nbart_nir_1"]) / (
data["nbart_nir_1"] + data["nbart_green"]),
"needed_bands": ["nbart_green", "nbart_nir_1"],
"color_ramp": [
{
"value": -1.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 1.0,
"color": "#0303FF",
},
]
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "simple_rgb",
},
{
# Included as a keyword for the layer
"label": "Sentinel 2A",
# Included as a keyword for the layer
"type": "",
# Included as a keyword for the layer
"variant": "Surface Reflectance",
"abstract": """
This is a 30-day rolling archive of daily Sentinel-2 Near Real Time data. Data is only visible at higher resolutions: when zoomed-out the available areas for that day will be displayed as shaded regions. The Near Real-Time capability provides analysis-ready data that is processed on receipt using the best-available ancillary information at the time to provide atmospheric corrections.
For more information see http://pid.geoscience.gov.au/dataset/ga/122229""",
# The WMS name for the layer
"name": "s2a_nrt_granule_nbar_t",
# The Datacube name for the associated data product
"product_name": "s2a_nrt_granule",
# The Datacube name for the associated pixel-quality product (optional)
# The name of the associated Datacube pixel-quality product
# "pq_dataset": "s2b_nrt_granule",
# The name of the measurement band for the pixel-quality product
# (Only required if pq_dataset is set)
# "pq_band": "pixel_quality",
# Min zoom factor - sets the zoom level where the cutover from indicative polygons
# to actual imagery occurs.
"min_zoom_factor": 15.0,
# The fill-colour of the indicative polygons when zoomed out.
# Triplets (rgb) or quadruplets (rgba) of integers 0-255.
"zoomed_out_fill_colour": [150, 180, 200, 160],
# Time Zone. In hours added to UTC (maybe negative)
# Used for rounding off scene times to a date.
# 9 is good value for imagery of Australia.
"time_zone": 9,
# Extent mask function
# Determines what portions of dataset is potentially meaningful data.
"extent_mask_func": lambda data, band: (data[band] != data[band].attrs['nodata']),
# Flags listed here are ignored in GetFeatureInfo requests.
# (defaults to empty list)
"ignore_info_flags": [],
# Define layer wide legend graphic if no style is passed
# to GetLegendGraphic
"legend": {
# "url": ""
"styles": ["ndvi", "ndwi"]
},
# Styles.
#
# See band_mapper.py
#
# The various available spectral bands, and ways to combine them
# into a single rgb image.
# The examples here are ad hoc
#
"styles": [
# Examples of styles which are linear combinations of the available spectral bands.
#
{
"name": "simple_rgb",
"title": "Simple RGB",
"abstract": "Simple true-colour image, using the red, green and blue bands",
"components": {
"red": {
"nbart_red": 1.0
},
"green": {
"nbart_green": 1.0
},
"blue": {
"nbart_blue": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
{
"name": "infrared_green",
"title": "False colour - Green, SWIR, NIR",
"abstract": "False Colour image with SWIR1->Red, NIR->Green, and Green->Blue",
"components": {
"red": {
"nbart_swir_2": 1.0
},
"green": {
"nbart_nir_1": 1.0
},
"blue": {
"nbart_green": 1.0
}
},
"scale_range": [0.0, 3000.0]
},
{
"name": "ndvi",
"title": "NDVI - Red, NIR",
"abstract": "Normalised Difference Vegetation Index - a derived index that correlates well with the existence of vegetation",
"index_function": lambda data: (data["nbart_nir_1"] - data["nbart_red"]) / (
data["nbart_nir_1"] + data["nbart_red"]),
"needed_bands": ["nbart_red", "nbart_nir_1"],
"color_ramp": [
{
"value": -1.0,
"color": "#FFFFFF",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 0.1,
"color": "#A35F18"
},
{
"value": 0.2,
"color": "#B88512"
},
{
"value": 0.3,
"color": "#CEAC0E"
},
{
"value": 0.4,
"color": "#E5D609"
},
{
"value": 0.5,
"color": "#FFFF0C"
},
{
"value": 0.6,
"color": "#C3DE09"
},
{
"value": 0.7,
"color": "#88B808"
},
{
"value": 0.8,
"color": "#529400"
},
{
"value": 0.9,
"color": "#237100"
},
{
"value": 1.0,
"color": "#114D04"
}
]
},
{
"name": "ndwi",
"title": "NDWI - Green, SWIR",
"abstract": "Normalised Difference Water Index - a derived index that correlates well with the existence of water",
"index_function": lambda data: (data["nbart_green"] - data["nbart_nir_1"]) / (
data["nbart_nir_1"] + data["nbart_green"]),
"needed_bands": ["nbart_green", "nbart_nir_1"],
"color_ramp": [
{
"value": -1.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": -0.0,
"color": "#8F3F20",
"alpha": 0.0
},
{
"value": 0.0,
"color": "#8F3F20",
"alpha": 1.0
},
{
"value": 1.0,
"color": "#0303FF",
},
]
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "simple_rgb",
},
],
},
{
"name": "multi_scale_topographic_position",
"title": "Multi-Scale Topographic Position",
"abstract": "",
"products": [
{
"label": "Multi-Scale Topographic Position",
"abstract": """A Multi-scale topographic position image of Australia has been generated by combining
a topographic position index and topographic ruggedness. Topographic Position Index (TPI) measures
the topographic slope position of landforms. Ruggedness informs on the roughness of the surface and
is calculated as the standard deviation of elevations. Both these terrain attributes are therefore
scale dependent and will vary according to the size of the analysis window. Based on an algorithm
developed by Lindsay et al. (2015) we have generated multi-scale topographic position model over the
Australian continent using 3 second resolution (~90m) DEM derived from the Shuttle Radar Topography
Mission (SRTM). The algorithm calculates topographic position scaled by the corresponding ruggedness
across three spatial scales (window sizes) of 0.2-8.1 Km; 8.2-65.2 Km and 65.6-147.6 Km. The derived
ternary image captures variations in topographic position across these spatial scales (blue local,
green intermediate and red regional) and gives a rich representation of nested landform features that
have broad application in understanding geomorphological and hydrological processes and in mapping
regolith and soils over the Australian continent. Lindsay, J, B., Cockburn, J.M.H. and Russell,
H.A.J. 2015. An integral image approach to performing multi-scale topographic position analysis,
Geomorphology 245, 51–61.""",
"type": "1 degree tile",
"variant": "",
"name": "multi_scale_topographic_position",
"product_name": "multi_scale_topographic_position",
"min_zoom_factor": 15.0,
"zoomed_out_fill_colour": [150, 180, 200, 160],
"time_zone": 9,
"extent_mask_func": lambda data, band: data[band] != data[band].nodata,
"ignore_info_flags": [],
"data_manual_merge": False,
"always_fetch_bands": ["regional", "intermediate", "local"],
"apply_solar_corrections": False,
"legend": {
"url": "https://s3-ap-southeast-2.amazonaws.com/dea-public-data/multi-scale-topographic-position/mstp_legend.png",
# "styles": ["mstp_rgb"]
},
"styles": [
{
"name": "mstp_rgb",
"title": "Multi-scale Topographic Position",
"abstract": "red regional, green intermediate and blue local",
"components": {
"red": {
"regional": 1.0
},
"green": {
"intermediate": 1.0
},
"blue": {
"local": 1.0
}
},
# The raw band value range to be compressed to an 8 bit range for the output image tiles.
# Band values outside this range are clipped to 0 or 255 as appropriate.
"scale_range": [0.0, 255.0]
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "mstp_rgb",
},
]
},
{
"name": "weathering_intensity",
"title": "Weathering Intensity",
"abstract": "",
"products": [
{
"label": "Weathering Intensity",
"abstract": "Weathering intensity or the degree of weathering is an important characteristic of the "
"earth’s surface that has a significant influence on the chemical and physical properties "
"of surface materials. Weathering intensity largely controls the degree to which primary "
"minerals are altered to secondary components including clay minerals and oxides. The "
"degree of surface weathering is particularly important in Australia where variations in "
"weathering intensity correspond to the nature and distribution of regolith (weathered "
"bedrock and sediments) which mantles approximately 90% of the Australian continent. The "
"weathering intensity prediction has been generated using the Random Forest decision tree "
"machine learning algorithm. The algorithm is used to establish predictive relationships "
"between field estimates of the degree of weathering and a comprehensive suite of "
"covariate or predictive datasets. The covariates used to generate the model include "
"satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. "
"Correlations between the training dataset and the covariates were explored through the "
"generation of 300 random tree models. An r-squared correlation of 0.85 is reported using "
"5 K-fold cross-validation. The mean of the 300 models is used for predicting the "
"weathering intensity and the uncertainty in the weathering intensity is estimated at "
"each location via the standard deviation in the 300 model values. The predictive "
"weathering intensity model is an estimate of the degree of surface weathering only. The "
"interpretation of the weathering intensity is different for in-situ or residual "
"landscapes compared with transported materials within depositional landscapes. In "
"residual landscapes, weathering process are operating locally whereas in depositional "
"landscapes the model is reflecting the degree of weathering either prior to erosion and "
"subsequent deposition, or weathering of sediments after being deposited. The weathering "
"intensity model has broad utility in assisting mineral exploration in variably weathered "
"geochemical landscapes across the Australian continent, mapping chemical and physical "
"attributes of soils in agricultural landscapes and in understanding the nature and "
"distribution of weathering processes occurring within the upper regolith.",
"type": "1 degree tile",
"variant": "",
"name": "weathering_intensity",
"product_name": "weathering_intensity",
"min_zoom_factor": 15.0,
"zoomed_out_fill_colour": [150, 180, 200, 160],
"time_zone": 9,
"extent_mask_func": lambda data, band: data[band] != data[band].nodata,
"ignore_info_flags": [],
"data_manual_merge": False,
"always_fetch_bands": ["intensity"],
"apply_solar_corrections": False,
"legend": {
"styles": ["wii"]
},
"styles": [
{
"name": "wii",
"title": "Weathering Intensity",
"abstract": "Weather Intensity Index (0-6)",
"needed_bands": ["intensity"],
"color_ramp": [
{
'value': 0,
'color': '#ffffff',
'alpha': 0
},
{
'value': 1,
'color': '#2972a8',
'legend': {
'label': 'Low\nClass 1'
}
},
{
'value': 3.5,
'color': '#fcf24b'
},
{
'value': 6,
'color': '#a02406',
'legend': {
'label': 'High\nClass 6'
}
}
],
"legend": {
"legend_values": [1, 6],
"axes_position": [0.1, 0.5, 0.8, 0.15]
}
},
],
# Default style (if request does not specify style)
# MUST be defined in the styles list above.
# (Looks like Terria assumes this is the first style in the list, but this is
# not required by the standard.)
"default_style": "wii",
},
]
},
]
|
#!/usr/bin/env python3
#
# Advent of Code 2017 - Day 2
#
INPUTFILE = 'input.txt'
def load_input(infile):
lines = []
with open(infile, 'r') as fp:
for line in fp:
line = line.strip()
if line:
lines.append(line)
return lines
def checksum(sheet):
result = 0
for row in sheet.splitlines():
vals = [int(item.strip()) for item in row.strip().split()]
result += max(vals) - min(vals)
return result
def checksum2(sheet):
result = 0
def row_value(items):
for i, a in enumerate(items[:-1]):
for b in items[i+1:]:
if a % b == 0:
return a // b
raise ValueError("No divisible pair found in {}".format(items))
for row in sheet.splitlines():
vals = sorted([int(item.strip()) for item in row.strip().split()],
reverse=True)
result += row_value(vals)
return result
# PART 1
def example():
sheet = """ 5 1 9 5
7 5 3
2 4 6 8
"""
expected = 18
result = checksum(sheet)
print("result = {} (expected {})".format(result, expected))
assert result == expected
print('= ' * 32)
def part1(lines):
result = checksum("\n".join(lines))
print("checksum = {}".format(result))
print('= ' * 32)
# PART 2
def example2():
sheet = """5 9 2 8
9 4 7 3
3 8 6 5
"""
expected = 9
result = checksum2(sheet)
print("result = {} (expected {})".format(result, expected))
assert result == expected
print('= ' * 32)
def part2(lines):
result = checksum2("\n".join(lines))
print("checksum2 = {}".format(result))
print('= ' * 32)
if __name__ == '__main__':
example()
input = load_input(INPUTFILE)
part1(input)
example2()
part2(input)
|
'''
Шахматный ферзь ходит по диагонали, горизонтали или вертикали.
Даны две различные клетки шахматной доски, определите,
может ли ферзь попасть с первой клетки на вторую одним ходом.
'''
x1 = int(input())
y1 = int(input())
x2 = int(input())
y2 = int(input())
if x1 == x2 or y1 == y2:
print('YES')
elif (x2 - x1) / (y2 - y1) == 1 or (x2 - x1) / (y2 - y1) == -1:
print('YES')
else:
print('NO')
|
def read_only_properties(*args):
def class_rebuilder(cls):
def __setattr__(self, key, value):
if key in args and key in self.__dict__:
raise AttributeError("Can't modify %s" % key)
else:
super().__setattr__(key, value)
cls.__setattr__ = __setattr__
return cls
return class_rebuilder
|
# Copyright (c) 2013 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'variables': {
'chromium_code': 1,
},
'targets': [
{
'target_name': 'gfx_geometry',
'type': '<(component)',
'dependencies': [
'<(DEPTH)/base/base.gyp:base',
],
'defines': [
'GFX_IMPLEMENTATION',
],
'sources': [
'geometry/box_f.cc',
'geometry/box_f.h',
'geometry/cubic_bezier.h',
'geometry/cubic_bezier.cc',
'geometry/insets.cc',
'geometry/insets.h',
'geometry/insets_base.h',
'geometry/insets_f.cc',
'geometry/insets_f.h',
'geometry/matrix3_f.cc',
'geometry/matrix3_f.h',
'geometry/point.cc',
'geometry/point.h',
'geometry/point3_f.cc',
'geometry/point3_f.h',
'geometry/point_conversions.cc',
'geometry/point_conversions.h',
'geometry/point_f.cc',
'geometry/point_f.h',
'geometry/quad_f.cc',
'geometry/quad_f.h',
'geometry/rect.cc',
'geometry/rect.h',
'geometry/rect_conversions.cc',
'geometry/rect_conversions.h',
'geometry/rect_f.cc',
'geometry/rect_f.h',
'geometry/r_tree.h',
'geometry/r_tree_base.cc',
'geometry/r_tree_base.h',
'geometry/safe_integer_conversions.h',
'geometry/scroll_offset.cc',
'geometry/scroll_offset.h',
'geometry/size.cc',
'geometry/size.h',
'geometry/size_conversions.cc',
'geometry/size_conversions.h',
'geometry/size_f.cc',
'geometry/size_f.h',
'geometry/vector2d.cc',
'geometry/vector2d.h',
'geometry/vector2d_conversions.cc',
'geometry/vector2d_conversions.h',
'geometry/vector2d_f.cc',
'geometry/vector2d_f.h',
'geometry/vector3d_f.cc',
'geometry/vector3d_f.h',
],
# TODO(jdduke): Revisit optimization after gauging benefit, crbug/419051.
'includes': [
'../../build/android/increase_size_for_speed.gypi',
],
},
{
'target_name': 'gfx',
'type': '<(component)',
'dependencies': [
'<(DEPTH)/base/base.gyp:base',
'<(DEPTH)/base/base.gyp:base_i18n',
'<(DEPTH)/base/base.gyp:base_static',
'<(DEPTH)/base/third_party/dynamic_annotations/dynamic_annotations.gyp:dynamic_annotations',
'<(DEPTH)/skia/skia.gyp:skia',
'<(DEPTH)/third_party/harfbuzz-ng/harfbuzz.gyp:harfbuzz-ng',
'<(DEPTH)/third_party/icu/icu.gyp:icui18n',
'<(DEPTH)/third_party/icu/icu.gyp:icuuc',
'<(DEPTH)/third_party/libpng/libpng.gyp:libpng',
'<(DEPTH)/third_party/zlib/zlib.gyp:zlib',
'gfx_geometry',
],
# text_elider.h includes ICU headers.
'export_dependent_settings': [
'<(DEPTH)/skia/skia.gyp:skia',
'<(DEPTH)/third_party/icu/icu.gyp:icui18n',
'<(DEPTH)/third_party/icu/icu.gyp:icuuc',
],
'defines': [
'GFX_IMPLEMENTATION',
],
'include_dirs': [
'<(DEPTH)/third_party/icu/source/common'
],
'sources': [
'android/device_display_info.cc',
'android/device_display_info.h',
'android/gfx_jni_registrar.cc',
'android/gfx_jni_registrar.h',
'android/java_bitmap.cc',
'android/java_bitmap.h',
'android/shared_device_display_info.cc',
'android/shared_device_display_info.h',
'android/view_configuration.cc',
'android/view_configuration.h',
'animation/animation.cc',
'animation/animation.h',
'animation/animation_container.cc',
'animation/animation_container.h',
'animation/animation_container_element.h',
'animation/animation_container_observer.h',
'animation/animation_delegate.h',
'animation/linear_animation.cc',
'animation/linear_animation.h',
'animation/multi_animation.cc',
'animation/multi_animation.h',
'animation/slide_animation.cc',
'animation/slide_animation.h',
'animation/throb_animation.cc',
'animation/throb_animation.h',
'animation/tween.cc',
'animation/tween.h',
'break_list.h',
'canvas.cc',
'canvas.h',
'canvas_notimplemented.cc',
'canvas_paint_mac.h',
'canvas_paint_mac.mm',
'canvas_paint_win.cc',
'canvas_paint_win.h',
'canvas_skia.cc',
'canvas_skia_paint.h',
'codec/jpeg_codec.cc',
'codec/jpeg_codec.h',
'codec/png_codec.cc',
'codec/png_codec.h',
'color_utils.cc',
'color_utils.h',
'display.cc',
'display.h',
'display_change_notifier.cc',
'display_change_notifier.h',
'display_observer.cc',
'display_observer.h',
'font.cc',
'font.h',
'font_fallback.h',
'font_fallback_linux.cc',
'font_fallback_mac.cc',
'font_fallback_win.cc',
'font_fallback_win.h',
'font_list.cc',
'font_list.h',
'font_list_impl.cc',
'font_list_impl.h',
'font_render_params.cc',
'font_render_params.h',
'font_render_params_android.cc',
'font_render_params_linux.cc',
'font_render_params_mac.cc',
'font_render_params_win.cc',
'frame_time.h',
'gfx_export.h',
'gfx_paths.cc',
'gfx_paths.h',
'gpu_memory_buffer.cc',
'gpu_memory_buffer.h',
'image/canvas_image_source.cc',
'image/canvas_image_source.h',
'image/image.cc',
'image/image.h',
'image/image_family.cc',
'image/image_family.h',
'image/image_ios.mm',
'image/image_mac.mm',
'image/image_png_rep.cc',
'image/image_png_rep.h',
'image/image_skia.cc',
'image/image_skia.h',
'image/image_skia_operations.cc',
'image/image_skia_operations.h',
'image/image_skia_rep.cc',
'image/image_skia_rep.h',
'image/image_skia_source.h',
'image/image_skia_util_ios.h',
'image/image_skia_util_ios.mm',
'image/image_skia_util_mac.h',
'image/image_skia_util_mac.mm',
'image/image_util.cc',
'image/image_util.h',
'image/image_util_ios.mm',
'interpolated_transform.cc',
'interpolated_transform.h',
'linux_font_delegate.cc',
'linux_font_delegate.h',
'mac/coordinate_conversion.h',
'mac/coordinate_conversion.mm',
'mac/scoped_ns_disable_screen_updates.h',
'native_widget_types.h',
'nine_image_painter.cc',
'nine_image_painter.h',
'overlay_transform.h',
'pango_util.cc',
'pango_util.h',
'path.cc',
'path.h',
'path_aura.cc',
'path_win.cc',
'path_win.h',
'path_x11.cc',
'path_x11.h',
'platform_font.h',
'platform_font_android.cc',
'platform_font_ios.h',
'platform_font_ios.mm',
'platform_font_mac.h',
'platform_font_mac.mm',
'platform_font_ozone.cc',
'platform_font_pango.cc',
'platform_font_pango.h',
'platform_font_win.cc',
'platform_font_win.h',
'range/range.cc',
'range/range.h',
'range/range_mac.mm',
'range/range_win.cc',
'render_text.cc',
'render_text.h',
'render_text_harfbuzz.cc',
'render_text_harfbuzz.h',
'render_text_mac.cc',
'render_text_mac.h',
'render_text_ozone.cc',
'render_text_pango.cc',
'render_text_pango.h',
'render_text_win.cc',
'render_text_win.h',
'scoped_canvas.h',
'scoped_cg_context_save_gstate_mac.h',
'scoped_ns_graphics_context_save_gstate_mac.h',
'scoped_ns_graphics_context_save_gstate_mac.mm',
'screen.cc',
'screen.h',
'screen_android.cc',
'screen_aura.cc',
'screen_ios.mm',
'screen_mac.mm',
'screen_win.cc',
'screen_win.h',
'scrollbar_size.cc',
'scrollbar_size.h',
'selection_model.cc',
'selection_model.h',
'sequential_id_generator.cc',
'sequential_id_generator.h',
'shadow_value.cc',
'shadow_value.h',
'skbitmap_operations.cc',
'skbitmap_operations.h',
'skia_util.cc',
'skia_util.h',
'switches.cc',
'switches.h',
'sys_color_change_listener.cc',
'sys_color_change_listener.h',
'text_constants.h',
'text_elider.cc',
'text_elider.h',
'text_utils.cc',
'text_utils.h',
'text_utils_android.cc',
'text_utils_ios.mm',
'text_utils_skia.cc',
'transform.cc',
'transform.h',
'transform_util.cc',
'transform_util.h',
'ui_gfx_exports.cc',
'utf16_indexing.cc',
'utf16_indexing.h',
'vsync_provider.h',
'win/direct_write.cc',
'win/direct_write.h',
'win/dpi.cc',
'win/dpi.h',
'win/hwnd_util.cc',
'win/hwnd_util.h',
'win/scoped_set_map_mode.h',
'win/singleton_hwnd.cc',
'win/singleton_hwnd.h',
'win/window_impl.cc',
'win/window_impl.h',
],
'includes': [
'../../build/android/increase_size_for_speed.gypi',
],
'conditions': [
['OS=="ios"', {
'dependencies': [
'<(DEPTH)/ui/ios/ui_ios.gyp:ui_ios',
],
# iOS only uses a subset of UI.
'sources/': [
['exclude', '^codec/jpeg_codec\\.cc$'],
],
}, {
'dependencies': [
'<(libjpeg_gyp_path):libjpeg',
],
}],
# TODO(asvitkine): Switch all platforms to use canvas_skia.cc.
# http://crbug.com/105550
['use_canvas_skia==1', {
'sources!': [
'canvas_notimplemented.cc',
],
}, { # use_canvas_skia!=1
'sources!': [
'canvas_skia.cc',
],
}],
['OS=="win"', {
'sources': [
'gdi_util.cc',
'gdi_util.h',
'icon_util.cc',
'icon_util.h',
],
# TODO(jschuh): C4267: http://crbug.com/167187 size_t -> int
# C4324 is structure was padded due to __declspec(align()), which is
# uninteresting.
'msvs_disabled_warnings': [ 4267, 4324 ],
}],
['OS=="android"', {
'sources!': [
'animation/throb_animation.cc',
'display_observer.cc',
'selection_model.cc',
],
'dependencies': [
'gfx_jni_headers',
],
'link_settings': {
'libraries': [
'-landroid',
'-ljnigraphics',
],
},
}],
['use_aura==0 and toolkit_views==0', {
'sources!': [
'nine_image_painter.cc',
'nine_image_painter.h',
],
}],
['OS=="android" and use_aura==0', {
'sources!': [
'path.cc',
],
}],
['OS=="android" and use_aura==1', {
'sources!': [
'screen_android.cc',
],
}],
['OS=="android" and android_webview_build==0', {
'dependencies': [
'<(DEPTH)/base/base.gyp:base_java',
],
}],
['OS=="android" or OS=="ios"', {
'sources!': [
'render_text.cc',
'render_text.h',
'render_text_harfbuzz.cc',
'render_text_harfbuzz.h',
'text_utils_skia.cc',
],
}],
['use_x11==1', {
'dependencies': [
'../../build/linux/system.gyp:x11',
'x/gfx_x11.gyp:gfx_x11',
],
}],
['use_pango==1', {
'dependencies': [
'<(DEPTH)/build/linux/system.gyp:pangocairo',
],
'sources!': [
'platform_font_ozone.cc',
'render_text_ozone.cc',
],
}],
['desktop_linux==1 or chromeos==1', {
'dependencies': [
# font_render_params_linux.cc uses fontconfig
'<(DEPTH)/build/linux/system.gyp:fontconfig',
],
}],
],
'target_conditions': [
# Need 'target_conditions' to override default filename_rules to include
# the file on iOS.
['OS == "ios"', {
'sources/': [
['include', '^scoped_cg_context_save_gstate_mac\\.h$'],
],
}],
],
},
{
'target_name': 'gfx_test_support',
'type': 'static_library',
'sources': [
'image/image_unittest_util.cc',
'image/image_unittest_util.h',
'image/image_unittest_util_ios.mm',
'image/image_unittest_util_mac.mm',
'test/fontconfig_util_linux.cc',
'test/fontconfig_util_linux.h',
'test/gfx_util.cc',
'test/gfx_util.h',
'test/ui_cocoa_test_helper.h',
'test/ui_cocoa_test_helper.mm',
],
'dependencies': [
'../../base/base.gyp:base',
'../../skia/skia.gyp:skia',
'../../testing/gtest.gyp:gtest',
],
'conditions': [
['OS == "mac"', {
'link_settings': {
'libraries': [
'$(SDKROOT)/System/Library/Frameworks/AppKit.framework',
],
},
}],
['OS=="ios"', {
# The cocoa files don't apply to iOS.
'sources/': [
['exclude', 'cocoa']
],
}],
['OS=="linux"', {
'dependencies': [
'../../build/linux/system.gyp:fontconfig',
],
}],
],
},
],
'conditions': [
['OS=="android"' , {
'targets': [
{
'target_name': 'gfx_jni_headers',
'type': 'none',
'sources': [
'../android/java/src/org/chromium/ui/gfx/BitmapHelper.java',
'../android/java/src/org/chromium/ui/gfx/DeviceDisplayInfo.java',
'../android/java/src/org/chromium/ui/gfx/ViewConfigurationHelper.java',
],
'variables': {
'jni_gen_package': 'ui/gfx',
},
'includes': [ '../../build/jni_generator.gypi' ],
},
],
}],
],
}
|
class Solution(object):
def reformatNumber(self, number):
"""
:type number: str
:rtype: str
"""
number = number.replace('-' ,'').replace(' ', '')
ans = []
for i in range(0, len(number), 3):
ans.append(number[i:i+3])
if len(ans) >= 2 and len(ans[-1]) == 1:
ans[-1] = ans[-2][2] + ans[-1]
ans[-2] = ans[-2][:2]
return '-'.join(ans)
number = "1-23-45 6"
res = Solution().reformatNumber(number)
print(res) |
class BaseError(Exception):
"""Base package error."""
class InvalidModelInputError(BaseError):
"""Model input contains an error."""
|
#!/usr/bin/env python3
# ex6: String and Text
# Assign the string with 10 replacing the formatting character to variable 'x'
x = "There are %d types of people." % 10
# Assign the string with "binary" to variable 'binary'
binary = "binary"
# Assign the string with "don't" to variable 'do_not'
do_not = "don't"
# Assign the string with 'binary' and 'do_not' replacing the
# formatting character to variable 'y'
y = "Those who know %s and those who %s." % (binary, do_not) # Two strings inside of a string
# Print "There are 10 types of people."
print(x)
# Print "Those who know binary and those who don't."
print(y)
# Print "I said 'There are 10 types of people.'"
print("I said %r." % x) # One string inside of a string
# Print "I also said: 'Those who know binary and those who don't.'."
print("I also said: '%s'." % y) # One string inside of a string
# Assign boolean False to variable 'hilarious'
hilarious = False
# Assign the string with an unevaluated formatting character to 'joke_evaluation'
joke_evaluation = "Isn't that joke so funny?! %r"
# Print "Isn't that joke so funny?! False"
print(joke_evaluation % hilarious) # One string inside of a string
# Assign string to 'w'
w = "This is the left side of..."
# Assign string to 'e'
e = "a string with a right side."
# Print "This is the left side of...a string with a right side."
print(w + e) # Concatenate two strings with + operator
|
class QolsysException(Exception):
pass
class QolsysGwConfigIncomplete(QolsysException):
pass
class QolsysGwConfigError(QolsysException):
pass
class UnableToParseEventException(QolsysException):
pass
class UnknownQolsysControlException(QolsysException):
pass
class UnknownQolsysEventException(QolsysException):
pass
class UnknownQolsysSensorException(QolsysException):
pass
class MissingUserCodeException(QolsysException):
pass
class InvalidUserCodeException(QolsysException):
pass
|
## 3. Read the File Into a String ##
f = open("dq_unisex_names.csv", 'r')
names = f.read();
## 4. Convert the String to a List ##
f = open('dq_unisex_names.csv', 'r')
names = f.read()
names_list = names.split('\n')
first_five = names_list[0:5]
print(first_five)
## 5. Convert the List of Strings to a List of Lists ##
f = open('dq_unisex_names.csv', 'r')
names = f.read()
names_list = names.split('\n')
nested_list = []
for name in names_list:
nested_list.append(name.split(','))
print(nested_list[0:5])
## 6. Convert Numerical Values ##
print(nested_list[0:5])
numerical_list = []
for ele in nested_list:
numerical_list.append([ele[0], float(ele[1])])
## 7. Filter the List ##
# The last value is ~100 people
numerical_list[len(numerical_list)-1]
thousand_or_greater = []
for ele in numerical_list:
if ele[1] >= 1000:
thousand_or_greater.append(ele[0])
print(thousand_or_greater[0:10]) |
def main():
x_coords = []
x_lines = ["side 1 G", "side 1 5", "side 1 10", "side 1 15", "side 1 20", "side 1 25", "side 1 30", "side 1 35",
"side 1 40", "side 1 45", "50", "side 2 45", "side 2 40", "side 2 35", "side 2 30", "side 2 25",
"side 2 20", "side 2 15", "side 2 10", "side 2 5", "side 2 G"]
for i, line in enumerate(x_lines):
if i < 10:
if i > 0:
x_coords += ["{0} outside the {1}".format(x, line) for x in range(4, 0, -1)]
x_coords.append("on the {0}".format(line))
x_coords += ["{0} inside the {1}".format(x, line) for x in range(1, 4)]
elif i == 10:
x_coords += ["{0} outside the {1} on side 1".format(x, line) for x in range(4, 0, -1)]
x_coords.append("on the {0}".format(line))
x_coords += ["{0} outside the {1} on side 2".format(x, line) for x in range(1, 5)]
else:
x_coords += ["{0} inside the {1}".format(x, line) for x in range(3, 0, -1)]
x_coords.append("on the {0}".format(line))
if i + 1 < len(x_lines):
x_coords += ["{0} outside the {1}".format(x, line) for x in range(1, 5)]
y_coords = []
y_coords.append("on the front sideline")
y_coords += ["{0} behind the front sideline".format(x) for x in range(1, 50)]
y_coords += ["{0} in front of the front hash".format(x) for x in range(50, 0, -1)]
y_coords.append("on the front hash")
y_coords += ["{0} behind the front hash".format(x) for x in range(1, 50)]
y_coords += ["{0} in front of the back hash".format(x) for x in range(50, 0, -1)]
y_coords.append("on the back hash")
y_coords += ["{0} behind the back hash".format(x) for x in range(1, 50)]
y_coords += ["{0} in front of the back sideline".format(x) for x in range(50, 0, -1)]
y_coords.append("on the back sideline")
output = ""
for x in x_coords:
for y in y_coords:
output += "{0}, {1}. \n".format(x, y)
with open("output.txt", "w") as out:
out.write(output)
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
main()
# See PyCharm help at https://www.jetbrains.com/help/pycharm/
|
def main():
arr = []
while True:
arr.append(1)
if len(arr) >= 10:
break
return None
if __name__ == "__main__":
main()
|
"""
Shared constants.
Only put things here if they are used in more than one module. Otherwise just define
them in the module where they are used.
"""
EASYCRON_IPS = ["198.27.83.222", "192.99.21.124", "167.114.64.88", "167.114.64.21"]
|
'''
Given an array of meeting time intervals consisting of start and end times [[s1,e1],[s2,e2],...] (si < ei), find the minimum number of conference rooms required.
Example 1:
Input: [[0, 30],[5, 10],[15, 20]]
Output: 2
Example 2:
Input: [[7,10],[2,4]]
Output: 1
'''
# Definition for an interval.
# class Interval(object):
# def __init__(self, s=0, e=0):
# self.start = s
# self.end = e
class Solution(object):
def minMeetingRooms(self, intervals):
"""
:type intervals: List[Interval]
:rtype: int
"""
start = [x.start for x in intervals]
end = [x.end for x in intervals]
start.sort()
end.sort()
res = 0
idx = 0
for i in xrange(len(start)):
if start[i] < end[idx]:
res += 1
else:
idx += 1
return res
# Definition for an interval.
# class Interval(object):
# def __init__(self, s=0, e=0):
# self.start = s
# self.end = e
class Solution(object):
def minMeetingRooms(self, intervals):
"""
:type intervals: List[Interval]
:rtype: int
"""
start = sorted([x.start for x in intervals])
end = sorted([x.end for x in intervals])
res = 0
tmp = 0
i = 0
j = 0
while i < len(start):
if j < len(end):
if start[i] < end[j]:
if tmp > 0:
tmp -= 1
else:
res += 1
i += 1
else:
tmp += 1
j += 1
else:
if tmp > 0:
tmp -= 1
else:
res += 1
i += 1
return res
|
enums = {
'AcpAmplitudeCorrectionType': {
'values': [
{
'documentation': {
'description': ' All the frequency bins in the spectrum are compensated with a single external attenuation value that corresponds to the RF center frequency.'
},
'name': 'RF_CENTER_FREQUENCY',
'value': 0
},
{
'documentation': {
'description': ' An individual frequency bin in the spectrum is compensated with the external attenuation value corresponding to that frequency.'
},
'name': 'SPECTRUM_FREQUENCY_BIN',
'value': 1
}
]
},
'AcpAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The ACP measurement uses the RFMXSPECAN_ATTR_ACP_AVERAGING_COUNT attribute as the number of acquisitions over which the ACP measurement is averaged. '
},
'name': 'TRUE',
'value': 1
}
]
},
'AcpAveragingType': {
'values': [
{
'documentation': {
'description': ' The power spectrum is linearly averaged. RMS averaging reduces signal fluctuations but not the noise floor. '
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power spectrum is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power spectrum is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The peak power in the spectrum at each frequency bin is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The least power in the spectrum at each frequency bin is retained from one acquisition to the next. '
},
'name': 'MINIMUM',
'value': 4
}
]
},
'AcpCarrierMode': {
'values': [
{
'documentation': {
'description': ' The carrier power is not considered as part of the total carrier power.'
},
'name': 'PASSIVE',
'value': 0
},
{
'documentation': {
'description': ' The carrier power is considered as part of the total carrier power.'
},
'name': 'ACTIVE',
'value': 1
}
]
},
'AcpCarrierRrcFilterEnabled': {
'values': [
{
'documentation': {
'description': ' The channel power of the acquired carrier channel is measured directly.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement applies the RRC filter on the acquired carrier channel before measuring the carrier channel power.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AcpFftOverlapMode': {
'values': [
{
'documentation': {
'description': ' Disables the overlap between the chunks.'
},
'name': 'DISABLED',
'value': 0
},
{
'documentation': {
'description': ' Measurement sets the overlap based on the value you have set for the RFMXSPECAN_ATTR_ACP_FFT_WINDOW attribute. When you set the RFMXSPECAN_ATTR_ACP_FFT_WINDOW attribute to any value other than RFMXSPECAN_VAL_ACP_FFT_WINDOW_NONE, the number of overlapped samples between consecutive chunks is set to 50% of the value of the RFMXSPECAN_ATTR_ACP_SEQUENTIAL_FFT_SIZE attribute. When you set the RFMXSPECAN_ATTR_ACP_FFT_WINDOW attribute to RFMXSPECAN_VAL_ACP_FFT_WINDOW_NONE, the chunks are not overlapped and the overlap is set to 0%.'
},
'name': 'AUTOMATIC',
'value': 1
},
{
'documentation': {
'description': ' Measurement uses the overlap that you specify in the RFMXSPECAN_ATTR_ACP_FFT_OVERLAP attribute.'
},
'name': 'USER_DEFINED',
'value': 2
}
]
},
'AcpFftWindow': {
'values': [
{
'documentation': {
'description': ' Analyzes transients for which duration is shorter than the window length. You can also use this window type to separate two tones with frequencies close to each other but with almost equal amplitudes.'
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' Measures single-tone amplitudes accurately.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Analyzes transients for which duration is longer than the window length. You can also use this window type to provide better frequency resolution for noise measurements.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Analyzes closely-spaced sine waves.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Provides a good balance of spectral leakage, frequency resolution, and amplitude attenuation. Hence, this windowing is useful for time-frequency analysis.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Analyzes single tone because it has a low maximum side lobe level and a high side lobe roll-off rate. '
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Useful as a good general purpose window, having side lobe rejection greater than 90 dB and having a moderately wide main lobe. '
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Separates two tones with frequencies close to each other but with widely-differing amplitudes.'
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'AcpIFOutputPowerOffsetAuto': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'AcpMeasurementMethod': {
'values': [
{
'documentation': {
'description': ' The ACP measurement acquires the spectrum using the same signal analyzer setting across frequency bands. Use this method when measurement speed is desirable over higher dynamic range. '
},
'name': 'NORMAL',
'value': 0
},
{
'documentation': {
'description': ' The ACP measurement acquires the spectrum using the hardware-specific optimizations for different frequency bands. Use this method to get the best dynamic range.\n\n Supported devices: PXIe-5665/5668'
},
'name': 'DYNAMIC_RANGE',
'value': 1
},
{
'documentation': {
'description': ' The ACP measurement acquires I/Q samples for a duration specified by the RFMXSPECAN_ATTR_ACP_SWEEP_TIME_INTERVAL attribute. These samples are divided into smaller chunks. The size of each chunk is defined by the RFMXSPECAN_ATTR_ACP_SEQUENTIAL_FFT_SIZE attribute. The overlap between the chunks is defined by the RFMXSPECAN_ATTR_ACP_FFT_OVERLAP_MODE attribute. FFT is computed on each of these chunks. The resultant FFTs are averaged to get the spectrum and is used to compute ACP. If the total acquired samples is not an integer multiple of the FFT size, the remaining samples at the end of acquisition are not used for the measurement. Use this method to optimize ACP measurement speed. Accuracy of the results may be reduced when using this measurement method.'
},
'name': 'SEQUENTIAL_FFT',
'value': 2
}
]
},
'AcpMeasurementMode': {
'values': [
{
'documentation': {
'description': ' ACP measurement is performed on the acquired signal. '
},
'name': 'MEASURE',
'value': 0
},
{
'documentation': {
'description': ' Manual noise calibration of the signal analyzer is performed for the ACP measurement.'
},
'name': 'CALIBRATE_NOISE_FLOOR',
'value': 1
}
]
},
'AcpNoiseCalibrationAveragingAuto': {
'values': [
{
'documentation': {
'description': ' RFmx uses the averages that you set for the RFMXSPECAN_ATTR_ACP_NOISE_CALIBRATION_AVERAGING_COUNT attribute.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' When you set the RFMXSPECAN_ATTR_ACP_MEASUREMENT_METHOD attribute to RFMXSPECAN_VAL_ACP_MEASUREMENT_METHOD_NORMAL, RFmx uses a noise calibration averaging count of 32. When you set the RFMXSPECAN_ATTR_ACP_MEASUREMENT_METHOD attribute to RFMXSPECAN_VAL_ACP_MEASUREMENT_METHOD_DYNAMIC_RANGE and the sweep time is less than 5 ms, RFmx uses a noise calibration averaging count of 15. When you set the RFMXSPECAN_ATTR_ACP_MEASUREMENT_METHOD attribute to RFMXSPECAN_VAL_ACP_MEASUREMENT_METHOD_DYNAMIC_RANGE and the sweep time is greater than or equal to 5 ms, RFmx uses a noise calibration averaging count of 5.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AcpNoiseCalibrationDataValid': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'AcpNoiseCalibrationMode': {
'values': [
{
'documentation': {
'description': ' When you set the RFMXSPECAN_ATTR_ACP_MEASUREMENT_MODE attribute to RFMXSPECAN_VAL_ACP_MEASUREMENT_MODE_CALIBRATE_NOISE_FLOOR, you can initiate instrument noise calibration for the ACP measurement manually. When you set the RFMXSPECAN_ATTR_ACP_MEASUREMENT_MODE attribute to RFMXSPECAN_VAL_ACP_MEASUREMENT_MODE_MEASURE, you can initiate the ACP measurement manually.'
},
'name': 'MANUAL',
'value': 0
},
{
'documentation': {
'description': ' When you set the RFMXSPECAN_ATTR_ACP_NOISE_COMPENSATION_ENABLED to RFMXSPECAN_VAL_ACP_NOISE_COMPENSATION_ENABLED_TRUE, RFmx sets the Input Isolation Enabled attribute to Enabled and calibrates the instrument noise in the current state of the instrument. RFmx then resets the Input Isolation Enabled attribute and performs the ACP measurement, including compensation for noise of the instrument. RFmx skips noise calibration in this mode if valid noise calibration data is already cached. When you set the RFMXSPECAN_ATTR_ACP_NOISE_COMPENSATION_ENABLED attribute to RFMXSPECAN_VAL_ACP_NOISE_COMPENSATION_ENABLED_FALSE, RFmx does not calibrate instrument noise and only performs the ACP measurement without compensating for noise of the instrument.'
},
'name': 'AUTO',
'value': 1
}
]
},
'AcpNoiseCompensationEnabled': {
'values': [
{
'documentation': {
'description': ' Disables noise compensation.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables noise compensation.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AcpNoiseCompensationType': {
'values': [
{
'documentation': {
'description': ' Compensates for noise from the analyzer and the 50-ohm termination. The measured power values are in excess of the thermal noise floor.'
},
'name': 'ANALYZER_AND_TERMINATION',
'value': 0
},
{
'documentation': {
'description': ' Compensates for the analyzer noise only.'
},
'name': 'ANALYZER_ONLY',
'value': 1
}
]
},
'AcpOffsetEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the offset channel for ACP measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables the offset channel for ACP measurement.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AcpOffsetFrequencyDefinition': {
'values': [
{
'documentation': {
'description': ' The offset frequency is defined from the center of the closest carrier to the center of the offset channel.'
},
'name': 'CARRIER_CENTER_TO_OFFSET_CENTER',
'value': 0
},
{
'documentation': {
'description': ' The offset frequency is defined from the center of the closest carrier to the nearest edge of the offset channel.'
},
'name': 'CARRIER_CENTER_TO_OFFSET_EDGE',
'value': 1
}
]
},
'AcpOffsetPowerReferenceCarrier': {
'values': [
{
'documentation': {
'description': ' The measurement uses the power measured in the carrier closest to the offset channel center frequency, as the power reference.'
},
'name': 'CLOSEST',
'value': 0
},
{
'documentation': {
'description': ' The measurement uses the highest power measured among all the active carriers as the power reference.'
},
'name': 'HIGHEST',
'value': 1
},
{
'documentation': {
'description': ' The measurement uses the sum of powers measured in all the active carriers as the power reference.'
},
'name': 'COMPOSITE',
'value': 2
},
{
'documentation': {
'description': ' The measurement uses the power measured in the carrier that has an index specified by the RFMXSPECAN_ATTR_ACP_OFFSET_POWER_REFERENCE_SPECIFIC attribute, as the power reference.'
},
'name': 'SPECIFIC',
'value': 3
}
]
},
'AcpOffsetRrcFilterEnabled': {
'values': [
{
'documentation': {
'description': ' The channel power of the acquired offset channel is measured directly.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement applies the RRC filter on the acquired offset channel before measuring the offset channel power.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AcpOffsetSideband': {
'values': [
{
'documentation': {
'description': ' Configures a lower offset segment to the left of the leftmost carrier. '
},
'name': 'NEGATIVE',
'value': 0
},
{
'documentation': {
'description': ' Configures an upper offset segment to the right of the rightmost carrier. '
},
'name': 'POSITIVE',
'value': 1
},
{
'documentation': {
'description': ' Configures both negative and positive offset segments.'
},
'name': 'BOTH',
'value': 2
}
]
},
'AcpPowerUnits': {
'values': [
{
'documentation': {
'description': ' The absolute powers are reported in dBm.'
},
'name': 'DBM',
'value': 0
},
{
'documentation': {
'description': ' The absolute powers are reported in dBm/Hz.'
},
'name': 'DBM_PER_HZ',
'value': 1
}
]
},
'AcpRbwAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'AcpRbwFilterBandwidthDefinition': {
'values': [
{
'documentation': {
'description': ' Defines the RBW in terms of the 3dB bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_ACP_RBW_FILTER_TYPE attribute to RFMXSPECAN_VAL_ACP_RBW_FILTER_TYPE_FFT_BASED, RBW is the 3dB bandwidth of the window specified by the RFMXSPECAN_ATTR_ACP_FFT_WINDOW attribute.'
},
'name': '3_DB',
'value': 0
},
{
'documentation': {
'description': ' Defines the RBW in terms of the bin width of the spectrum computed using FFT when you set the RFMXSPECAN_ATTR_ACP_RBW_FILTER_TYPE attribute to FFT Based.'
},
'name': 'BIN_WIDTH',
'value': 2
}
]
},
'AcpRbwFilterType': {
'values': [
{
'documentation': {
'description': ' No RBW filtering is performed.'
},
'name': 'FFT_BASED',
'value': 0
},
{
'documentation': {
'description': ' An RBW filter with a Gaussian response is applied.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' An RBW filter with a flat response is applied.'
},
'name': 'FLAT',
'value': 2
}
]
},
'AcpSweepTimeAuto': {
'values': [
{
'documentation': {
'description': ' The measurement uses the sweep time that you specify in the RFMXSPECAN_ATTR_ACP_SWEEP_TIME_INTERVAL attribute.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement calculates the sweep time based on the value of the RFMXSPECAN_ATTR_ACP_RBW_FILTER_BANDWIDTH attribute.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AmpmAMToAMCurveFitType': {
'values': [
{
'name': 'LEAST_SQUARE',
'value': 0
},
{
'name': 'LEAST_ABSOLUTE_RESIDUAL',
'value': 1
},
{
'name': 'BISQUARE',
'value': 2
}
]
},
'AmpmAMToAMEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'AmpmAMToPMCurveFitType': {
'values': [
{
'name': 'LEAST_SQUARE',
'value': 0
},
{
'name': 'LEAST_ABSOLUTE_RESIDUAL',
'value': 1
},
{
'name': 'BISQUARE',
'value': 2
}
]
},
'AmpmAMToPMEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'AmpmAutoCarrierDetectionEnabled': {
'values': [
{
'documentation': {
'description': ' Disables auto detection of carrier offset and carrier bandwidth.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables auto detection of carrier offset and carrier bandwidth.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AmpmAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The AMPM measurement uses the RFMXSPECAN_ATTR_AMPM_AVERAGING_COUNT attribute as the number of acquisitions over which the signal for the AMPM measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AmpmCompressionPointEnabled': {
'values': [
{
'documentation': {
'description': ' Disables computation of compression points.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables computation of compression points.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AmpmCompressionPointGainReference': {
'values': [
{
'documentation': {
'description': ' Measurement computes the gain reference to be used for compression point calculation. The computed gain reference is also returned as RFMXSPECAN_ATTR_AMPM_RESULTS_MEAN_LINEAR_GAIN result.'
},
'name': 'AUTO',
'value': 0
},
{
'documentation': {
'description': ' Measurement uses the gain corresponding to the reference power that you specify for the RFMXSPECAN_ATTR_AMPM_COMPRESSION_POINT_GAIN_REFERENCE_POWER attribute as gain reference. The reference power can be configured as either input or output power based on the value of the RFMXSPECAN_ATTR_AMPM_REFERENCE_POWER_TYPE attribute.'
},
'name': 'REFERENCE_POWER',
'value': 1
}
]
},
'AmpmEqualizerMode': {
'values': [
{
'documentation': {
'description': ' Equalization is not performed.'
},
'name': 'OFF',
'value': 0
},
{
'documentation': {
'description': ' The equalizer is turned on to compensate for the effect of the channel.'
},
'name': 'TRAIN',
'value': 1
}
]
},
'AmpmEvmEnabled': {
'values': [
{
'documentation': {
'description': ' Disables EVM computation. NaN is returned as Mean RMS EVM.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables EVM computation.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AmpmFrequencyOffsetCorrectionEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement does not perform frequency offset correction.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement computes and corrects any frequency offset between the reference and the acquired waveforms.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AmpmIQOriginOffsetCorrectionEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'AmpmMeasurementSampleRateMode': {
'values': [
{
'documentation': {
'description': ' The acquisition sample rate is defined by the value of the RFMXSPECAN_ATTR_AMPM_MEASUREMENT_SAMPLE_RATE attribute.'
},
'name': 'USER',
'value': 0
},
{
'documentation': {
'description': ' The acquisition sample rate is set to match the sample rate of the reference waveform.'
},
'name': 'REFERENCE_WAVEFORM',
'value': 1
}
]
},
'AmpmReferencePowerType': {
'values': [
{
'documentation': {
'description': ' The instantaneous powers at the input port of device under test (DUT) forms the x-axis of AM to AM and AM to PM traces.'
},
'name': 'INPUT',
'value': 0
},
{
'documentation': {
'description': ' The instantaneous powers at the output port of DUT forms the x-axis of AM to AM and AM to PM traces.'
},
'name': 'OUTPUT',
'value': 1
}
]
},
'AmpmReferenceWaveformIdleDurationPresent': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'AmpmSignalType': {
'values': [
{
'documentation': {
'description': ' The reference waveform is a cellular or connectivity standard signal.'
},
'name': 'MODULATED',
'value': 0
},
{
'documentation': {
'description': ' The reference waveform is a continuous signal comprising of one or more tones.'
},
'name': 'TONES',
'value': 1
}
]
},
'AmpmSynchronizationMethod': {
'values': [
{
'documentation': {
'description': ' Synchronizes the acquired and reference waveforms assuming that sample rate is sufficient to prevent aliasing in intermediate operations. This method is recommended when the measurement sampling rate is high.'
},
'name': 'DIRECT',
'value': 1
},
{
'documentation': {
'description': ' Synchronizes the acquired and reference waveforms while ascertaining that intermediate operations are not impacted by aliasing. This method is recommended for non-contiguous carriers separated by a large gap, and/or when the measurement sampling rate is low. Refer to AMPM concept help for more information.'
},
'name': 'ALIAS_PROTECTED',
'value': 2
}
]
},
'AmpmThresholdEnabled': {
'values': [
{
'documentation': {
'description': ' All samples are considered for the AMPM measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Samples above the threshold level specified in the RFMXSPECAN_ATTR_AMPM_THRESHOLD_LEVEL attribute are considered for the AMPM measurement.'
},
'name': 'TRUE',
'value': 1
}
]
},
'AmpmThresholdType': {
'values': [
{
'documentation': {
'description': ' The threshold is relative to the peak power of the acquired samples.'
},
'name': 'RELATIVE',
'value': 0
},
{
'documentation': {
'description': ' The threshold is the absolute power, in dBm.'
},
'name': 'ABSOLUTE',
'value': 1
}
]
},
'Boolean': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'CcdfRbwFilterType': {
'values': [
{
'documentation': {
'description': ' The measurement does not use any RBW filtering.'
},
'name': 'NONE',
'value': 5
},
{
'documentation': {
'description': ' The RBW filter has a Gaussian response.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' The RBW filter has a flat response.'
},
'name': 'FLAT',
'value': 2
},
{
'documentation': {
'description': ' The RRC filter with the roll-off specified by the RFMXSPECAN_ATTR_CCDF_RBW_FILTER_RRC_ALPHA attribute is used as the RBW filter.'
},
'name': 'RRC',
'value': 6
}
]
},
'CcdfThresholdEnabled': {
'values': [
{
'documentation': {
'description': ' All samples are considered for the CCDF measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The samples above the threshold level specified in the RFMXSPECAN_ATTR_CCDF_THRESHOLD_LEVEL attribute are considered for the CCDF measurement.'
},
'name': 'TRUE',
'value': 1
}
]
},
'CcdfThresholdType': {
'values': [
{
'documentation': {
'description': ' The threshold is relative to the peak power of the acquired samples.'
},
'name': 'RELATIVE',
'value': 0
},
{
'documentation': {
'description': ' The threshold is the absolute power, in dBm.'
},
'name': 'ABSOLUTE',
'value': 1
}
]
},
'ChpAmplitudeCorrectionType': {
'values': [
{
'documentation': {
'description': ' All the frequency bins in the spectrum are compensated with a single external attenuation value that corresponds to the RF center frequency.'
},
'name': 'RF_CENTER_FREQUENCY',
'value': 0
},
{
'documentation': {
'description': ' An individual frequency bin in the spectrum is compensated with the external attenuation value corresponding to that frequency.'
},
'name': 'SPECTRUM_FREQUENCY_BIN',
'value': 1
}
]
},
'ChpAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The CHP measurement uses the RFMXSPECAN_ATTR_CHP_AVERAGING_COUNT attribute as the number of acquisitions over which the CHP measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'ChpAveragingType': {
'values': [
{
'documentation': {
'description': ' The power spectrum is linearly averaged. RMS averaging reduces signal fluctuations but not the noise floor. '
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power spectrum is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power spectrum is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The peak power in the spectrum at each frequency bin is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The least power in the spectrum at each frequency bin is retained from one acquisition to the next. '
},
'name': 'MINIMUM',
'value': 4
}
]
},
'ChpCarrierRrcFilterEnabled': {
'values': [
{
'documentation': {
'description': ' The channel power of the acquired channel is measured directly.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement applies the RRC filter on the acquired channel before measuring the channel power.'
},
'name': 'TRUE',
'value': 1
}
]
},
'ChpFftWindow': {
'values': [
{
'documentation': {
'description': ' Analyzes transients for which duration is shorter than the window length. You can also use this window type to separate two tones with frequencies close to each other but with almost equal amplitudes. '
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' Measures single-tone amplitudes accurately.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Analyzes transients for which duration is longer than the window length. You can also use this window type to provide better frequency resolution for noise measurements.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Analyzes closely-spaced sine waves.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Provides a good balance of spectral leakage, frequency resolution, and amplitude attenuation. Hence, this windowing is useful for time-frequency analysis.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Analyzes single tone because it has a low maximum side lobe level and a high side lobe roll-off rate. '
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Useful as a good general purpose window, having side lobe rejection greater than 90 dB and having a moderately wide main lobe. '
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Separates two tones with frequencies close to each other but with widely-differing amplitudes. '
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'ChpMeasurementMode': {
'values': [
{
'documentation': {
'description': ' CHP measurement is performed on the acquired signal.'
},
'name': 'MEASURE',
'value': 0
},
{
'documentation': {
'description': ' Manual noise calibration of the signal analyzer is performed for the CHP measurement.'
},
'name': 'CALIBRATE_NOISE_FLOOR',
'value': 1
}
]
},
'ChpNoiseCalibrationAveragingAuto': {
'values': [
{
'documentation': {
'description': ' RFmx uses the averages that you set for the RFMXSPECAN_ATTR_CHP_NOISE_CALIBRATION_AVERAGING_COUNT attribute. '
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' RFmx uses a noise calibration averaging count of 32.'
},
'name': 'TRUE',
'value': 1
}
]
},
'ChpNoiseCalibrationDataValid': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'ChpNoiseCalibrationMode': {
'values': [
{
'documentation': {
'description': ' When you set the RFMXSPECAN_ATTR_CHP_MEASUREMENT_MODE attribute to RFMXSPECAN_VAL_CHP_MEASUREMENT_MODE_CALIBRATE_NOISE_FLOOR, you can initiate instrument noise calibration for the CHP measurement manually. When you set the RFMXSPECAN_ATTR_CHP_MEASUREMENT_MODE attribute to RFMXSPECAN_VAL_CHP_MEASUREMENT_MODE_MEASURE, you can initiate the CHP measurement manually.'
},
'name': 'MANUAL',
'value': 0
},
{
'documentation': {
'description': ' When you set the RFMXSPECAN_ATTR_CHP_NOISE_COMPENSATION_ENABLED attribute to RFMXSPECAN_VAL_CHP_NOISE_COMPENSATION_ENABLED_TRUE, RFmx sets Input Isolation Enabled to Enabled and calibrates the intrument noise in the current state of the instrument. RFmx then resets the Input Isolation Enabled attribute and performs the CHP measurement, including compensation for noise of the instrument. RFmx skips noise calibration in this mode if valid noise calibration data is already cached. When you set the RFMXSPECAN_ATTR_CHP_NOISE_COMPENSATION_ENABLED attribute to RFMXSPECAN_VAL_CHP_NOISE_COMPENSATION_ENABLED_FALSE, RFmx does not calibrate instrument noise and performs only the CHP measurement without compensating for the noise contribution of the instrument.'
},
'name': 'AUTO',
'value': 1
}
]
},
'ChpNoiseCompensationEnabled': {
'values': [
{
'documentation': {
'description': ' Disables noise compensation.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables noise compensation.'
},
'name': 'TRUE',
'value': 1
}
]
},
'ChpNoiseCompensationType': {
'values': [
{
'documentation': {
'description': ' Compensates for noise from the analyzer and the 50 ohm termination. The measured power values are in excess of the thermal noise floor.'
},
'name': 'ANALYZER_AND_TERMINATION',
'value': 0
},
{
'documentation': {
'description': ' Compensates for the analyzer noise only.'
},
'name': 'ANALYZER_ONLY',
'value': 1
}
]
},
'ChpRbwAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'ChpRbwFilterBandwidthDefinition': {
'values': [
{
'documentation': {
'description': ' Defines the RBW in terms of the 3 dB bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_CHP_RBW_FILTER_TYPE attribute to RFMXSPECAN_VAL_CHP_RBW_FILTER_TYPE_FFT_BASED, RBW is the 3 dB bandwidth of the window specified by the RFMXSPECAN_ATTR_CHP_FFT_WINDOW attribute.'
},
'name': '3_DB',
'value': 0
},
{
'documentation': {
'description': ' Defines the RBW in terms of the spectrum bin width computed using an FFT when you set the RFMXSPECAN_ATTR_CHP_RBW_FILTER_TYPE attribute to FFT Based.'
},
'name': 'BIN_WIDTH',
'value': 2
}
]
},
'ChpRbwFilterType': {
'values': [
{
'documentation': {
'description': ' No RBW filtering is performed.'
},
'name': 'FFT_BASED',
'value': 0
},
{
'documentation': {
'description': ' An RBW filter with a Gaussian response is applied.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' An RBW filter with a flat response is applied. '
},
'name': 'FLAT',
'value': 2
}
]
},
'ChpSweepTimeAuto': {
'values': [
{
'documentation': {
'description': ' The measurement uses the sweep time that you specify in the RFMXSPECAN_ATTR_CHP_SWEEP_TIME_INTERVAL attribute. '
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement calculates the sweep time based on the value of the RFMXSPECAN_ATTR_CHP_RBW_FILTER_BANDWIDTH attribute.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DigitalEdgeTriggerEdge': {
'values': [
{
'documentation': {
'description': ' The trigger asserts on the rising edge of the signal.'
},
'name': 'RISING_EDGE',
'value': 0
},
{
'documentation': {
'description': ' The trigger asserts on the falling edge of the signal.'
},
'name': 'FALLING_EDGE',
'value': 1
}
]
},
'DigitalEdgeTriggerSource': {
'generate-mappings': True,
'values': [
{
'documentation': {
'description': ' The trigger is received on PFI 0.'
},
'name': 'PFI0',
'value': 'PFI0'
},
{
'documentation': {
'description': ' The trigger is received on PFI 1.'
},
'name': 'PFI1',
'value': 'PFI1'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 0.'
},
'name': 'PXI_TRIG0',
'value': 'PXI_Trig0'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 1.'
},
'name': 'PXI_TRIG1',
'value': 'PXI_Trig1'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 2.'
},
'name': 'PXI_TRIG2',
'value': 'PXI_Trig2'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 3.'
},
'name': 'PXI_TRIG3',
'value': 'PXI_Trig3'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 4.'
},
'name': 'PXI_TRIG4',
'value': 'PXI_Trig4'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 5.'
},
'name': 'PXI_TRIG5',
'value': 'PXI_Trig5'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 6.'
},
'name': 'PXI_TRIG6',
'value': 'PXI_Trig6'
},
{
'documentation': {
'description': ' The trigger is received on PXI trigger line 7.'
},
'name': 'PXI_TRIG7',
'value': 'PXI_Trig7'
},
{
'documentation': {
'description': ' The trigger is received on the PXI star trigger line. '
},
'name': 'PXI_STAR',
'value': 'PXI_STAR'
},
{
'documentation': {
'description': ' The trigger is received on the PXIe DStar B trigger line. '
},
'name': 'PXIE_DSTARB',
'value': 'PXIe_DStarB'
},
{
'documentation': {
'description': ' The trigger is received from the timer event.'
},
'name': 'TIMER_EVENT',
'value': 'TimerEvent'
}
]
},
'DpdApplyDpdCfrEnabled': {
'values': [
{
'documentation': {
'description': ' Disables CFR. The maximum increase in PAPR, after pre-distortion, is limited to 6 dB.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables CFR.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdApplyDpdCfrMethod': {
'values': [
{
'documentation': {
'description': ' Hard clips the signal such that the target PAPR is achieved.'
},
'name': 'CLIPPING',
'value': 0
},
{
'documentation': {
'description': ' Scales the peaks in the signal using weighted window function to get smooth peaks and achieve the target PAPR.'
},
'name': 'PEAK_WINDOWING',
'value': 1
},
{
'documentation': {
'description': ' Scales the peaks using modified sigmoid transfer function to get smooth peaks and achieve the target PAPR. This method does not support the filter operation.'
},
'name': 'SIGMOID',
'value': 2
}
]
},
'DpdApplyDpdCfrTargetPaprType': {
'values': [
{
'documentation': {
'description': ' Sets the target PAPR for pre-distorted waveform equal to the PAPR of input waveform.'
},
'name': 'INPUT_PAPR',
'value': 0
},
{
'documentation': {
'description': ' Sets the target PAPR equal to the value that you set for the Apply DPD CFR Target PAPR attribute.'
},
'name': 'CUSTOM',
'value': 1
}
]
},
'DpdApplyDpdCfrWindowType': {
'values': [
{
'documentation': {
'description': ' Uses the flat top window function to scale peaks.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Uses the Hanning window function to scale peaks.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Uses the Hamming window function to scale peaks.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Uses the Gaussian window function to scale peaks.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Uses the Blackman window function to scale peaks.'
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Uses the Blackman-Harris window function to scale peaks.'
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Uses the Kaiser-Bessel window function to scale peaks.'
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'DpdApplyDpdConfigurationInput': {
'values': [
{
'documentation': {
'description': ' Uses the computed DPD polynomial or lookup table for applying DPD on an input waveform using the same RFmx session handle. The configuration parameters for applying DPD such as the RFMXSPECAN_ATTR_DPD_DUT_AVERAGE_INPUT_POWER, RFMXSPECAN_ATTR_DPD_MODEL, RFMXSPECAN_ATTR_DPD_MEASUREMENT_SAMPLE_RATE, DPD polynomial, and lookup table are obtained from the DPD measurement configuration. '
},
'name': 'MEASUREMENT',
'value': 0
},
{
'documentation': {
'description': ' Applies DPD by using a computed DPD polynomial or lookup table on an input waveform. You must set the configuration parameters for applying DPD such as the RFMXSPECAN_ATTR_DPD_APPLY_DPD_USER_DUT_AVERAGE_INPUT_POWER, RFMXSPECAN_ATTR_DPD_APPLY_DPD_USER_DPD_MODEL, RFMXSPECAN_ATTR_DPD_APPLY_DPD_USER_MEASUREMENT_SAMPLE_RATE, DPD polynomial, and lookup table. '
},
'name': 'USER',
'value': 1
}
]
},
'DpdApplyDpdIdleDurationPresent': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'DpdApplyDpdLookupTableCorrectionType': {
'values': [
{
'documentation': {
'description': ' The measurement predistorts the magnitude and phase of the input waveform.'
},
'name': 'MAGNITUDE_AND_PHASE',
'value': 0
},
{
'documentation': {
'description': ' The measurement predistorts only the magnitude of the input waveform.'
},
'name': 'MAGNITUDE_ONLY',
'value': 1
},
{
'documentation': {
'description': ' The measurement predistorts only the phase of the input waveform.'
},
'name': 'PHASE_ONLY',
'value': 2
}
]
},
'DpdApplyDpdMemoryModelCorrectionType': {
'values': [
{
'documentation': {
'description': ' The measurement predistorts the magnitude and phase of the input waveform.'
},
'name': 'MAGNITUDE_AND_PHASE',
'value': 0
},
{
'documentation': {
'description': ' The measurement predistorts only the magnitude of the input waveform.'
},
'name': 'MAGNITUDE_ONLY',
'value': 1
},
{
'documentation': {
'description': ' The measurement predistorts only the phase of the input waveform.'
},
'name': 'PHASE_ONLY',
'value': 2
}
]
},
'DpdApplyDpdUserDpdModel': {
'values': [
{
'documentation': {
'description': ' This model computes the complex gain coefficients applied to linearize systems with negligible memory effects.'
},
'name': 'LOOKUP_TABLE',
'value': 0
},
{
'documentation': {
'description': ' This model computes the memory polynomial predistortion coefficients used to linearize systems with moderate memory effects.'
},
'name': 'MEMORY_POLYNOMIAL',
'value': 1
},
{
'documentation': {
'description': ' This model computes the generalized memory polynomial predistortion coefficients used to linearize systems with significant memory effects.'
},
'name': 'GENERALIZED_MEMORY_POLYNOMIAL',
'value': 2
}
]
},
'DpdApplyDpdUserLookupTableType': {
'values': [
{
'documentation': {
'description': ' Input powers in the LUT are specified in dBm.'
},
'name': 'LOG',
'value': 0
},
{
'documentation': {
'description': ' Input powers in the LUT are specified in watts.'
},
'name': 'LINEAR',
'value': 1
}
]
},
'DpdAutoCarrierDetectionEnabled': {
'values': [
{
'documentation': {
'description': ' Disables auto detection of carrier offset and carrier bandwidth.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables auto detection of carrier offset and carrier bandwidth.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The DPD measurement uses the RFMXSPECAN_ATTR_DPD_AVERAGING_COUNT attribute as the number of acquisitions over which the signal for the DPD measurement is averaged. '
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdFrequencyOffsetCorrectionEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement computes and corrects any frequency offset between the reference and the acquired waveforms.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement does not perform frequency offset correction.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdIQOriginOffsetCorrectionEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'DpdIterativeDpdEnabled': {
'values': [
{
'documentation': {
'description': ' RFmx computes the DPD Results DPD Polynomial without considering the value of the DPD Previous DPD Polynomial.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' RFmx computes the DPD Results DPD Polynomial based on the value of the DPD Previous DPD Polynomial.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdLookupTableAMToAMCurveFitType': {
'values': [
{
'name': 'LEAST_SQUARE',
'value': 0
},
{
'name': 'LEAST_ABSOLUTE_RESIDUAL',
'value': 1
},
{
'name': 'BISQUARE',
'value': 2
}
]
},
'DpdLookupTableAMToPMCurveFitType': {
'values': [
{
'name': 'LEAST_SQUARE',
'value': 0
},
{
'name': 'LEAST_ABSOLUTE_RESIDUAL',
'value': 1
},
{
'name': 'BISQUARE',
'value': 2
}
]
},
'DpdLookupTableThresholdEnabled': {
'values': [
{
'documentation': {
'description': ' All samples are considered for the DPD measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Only samples above the threshold level which you specify in the RFMXSPECAN_ATTR_DPD_LOOKUP_TABLE_THRESHOLD_LEVEL attribute are considered for the DPD measurement.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdLookupTableThresholdType': {
'values': [
{
'documentation': {
'description': ' The threshold is relative to the peak power of the acquired samples.'
},
'name': 'RELATIVE',
'value': 0
},
{
'documentation': {
'description': ' The threshold is the absolute power, in dBm.'
},
'name': 'ABSOLUTE',
'value': 1
}
]
},
'DpdLookupTableType': {
'values': [
{
'documentation': {
'description': ' Input powers in the LUT are specified in dBm.'
},
'name': 'LOG',
'value': 0
},
{
'documentation': {
'description': ' Input powers in the LUT are specified in watts.'
},
'name': 'LINEAR',
'value': 1
}
]
},
'DpdMeasurementSampleRateMode': {
'values': [
{
'documentation': {
'description': ' The acquisition sample rate is defined by the value of the RFMXSPECAN_ATTR_DPD_MEASUREMENT_SAMPLE_RATE attribute.'
},
'name': 'USER',
'value': 0
},
{
'documentation': {
'description': ' The acquisition sample rate is set to match the sample rate of the reference waveform.'
},
'name': 'REFERENCE_WAVEFORM',
'value': 1
}
]
},
'DpdModel': {
'values': [
{
'documentation': {
'description': ' This model computes the complex gain coefficients applied when performing digital predistortion to linearize systems with negligible memory effects.'
},
'name': 'LOOKUP_TABLE',
'value': 0
},
{
'documentation': {
'description': ' This model computes the memory polynomial predistortion coefficients used to linearize systems with moderate memory effects.'
},
'name': 'MEMORY_POLYNOMIAL',
'value': 1
},
{
'documentation': {
'description': ' This model computes the generalized memory polynomial predistortion coefficients used to linearize systems with significant memory effects.'
},
'name': 'GENERALIZED_MEMORY_POLYNOMIAL',
'value': 2
}
]
},
'DpdNmseEnabled': {
'values': [
{
'documentation': {
'description': ' Disables NMSE computation. NaN is returned as NMSE.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables NMSE computation.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdPreDpdCfrEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the CFR. The RFmxSpecAn_DPDApplyPreDPDSignalConditioning function returns an error when the CFR is disabled.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables the CFR.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdPreDpdCfrFilterEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the filter operation when performing CFR.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables filter operation when performing CFR. Filter operation is not supported when you set the RFMXSPECAN_ATTR_DPD_PRE_DPD_CFR_METHOD attribute to RFMXSPECAN_VAL_DPD_PRE_DPD_CFR_METHOD_SIGMOID.'
},
'name': 'TRUE',
'value': 1
}
]
},
'DpdPreDpdCfrMethod': {
'values': [
{
'documentation': {
'description': ' Hard clips the signal such that the target PAPR is achieved.'
},
'name': 'CLIPPING',
'value': 0
},
{
'documentation': {
'description': ' Scales the peaks in the signal using weighted window function to get smooth peaks and achieve the target PAPR.'
},
'name': 'PEAK_WINDOWING',
'value': 1
},
{
'documentation': {
'description': ' Scales the peaks using modified sigmoid transfer function to get smooth peaks and achieve the target PAPR. This method does not support the filter operation.'
},
'name': 'SIGMOID',
'value': 2
}
]
},
'DpdPreDpdCfrWindowType': {
'values': [
{
'documentation': {
'description': ' Uses the flat top window function to scale peaks.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Uses the Hanning window function to scale peaks.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Uses the Hamming window function to scale peaks.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Uses the Gaussian window function to scale peaks.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Uses the Blackman window function to scale peaks.'
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Uses the Blackman-Harris window function to scale peaks.'
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Uses the Kaiser-Bessel window function to scale peaks.'
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'DpdReferenceWaveformIdleDurationPresent': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'DpdSignalType': {
'values': [
{
'documentation': {
'description': ' The reference waveform is a cellular or connectivity standard signal.'
},
'name': 'MODULATED',
'value': 0
},
{
'documentation': {
'description': ' The reference waveform is a continuous signal comprising one or more tones.'
},
'name': 'TONES',
'value': 1
}
]
},
'DpdSynchronizationMethod': {
'values': [
{
'documentation': {
'description': ' Synchronizes the acquired and reference waveforms assuming that sample rate is sufficient to prevent aliasing in intermediate operations. This method is recommended when measurement sampling rate is high.'
},
'name': 'DIRECT',
'value': 1
},
{
'documentation': {
'description': ' Synchronizes the acquired and reference waveforms while ascertaining that intermediate operations are not impacted by aliasing. This method is recommended for non-contiguous carriers separated by a large gap, and/or when measurement sampling rate is low. Refer to DPD concept help for more information.'
},
'name': 'ALIAS_PROTECTED',
'value': 2
}
]
},
'DpdTargetGainType': {
'values': [
{
'documentation': {
'description': ' The DPD polynomial or lookup table is computed by assuming that the linearized gain expected from the DUT after applying DPD on the input waveform is equal to the average power gain provided by the DUT without DPD.'
},
'name': 'AVERAGE_GAIN',
'value': 0
},
{
'documentation': {
'description': ' The DPD polynomial or lookup table is computed by assuming that the linearized gain expected from the DUT after applying DPD on the input waveform is equal to the gain provided by the DUT, without DPD, to the parts of the reference waveform that do not drive the DUT into non-linear gain-expansion or compression regions of its input-output characteristics.\n\n The measurement computes the linear region gain as the average gain experienced by the parts of the reference waveform that are below a threshold which is computed as shown in the following equation:\n\n Linear region threshold (dBm) = Max {-25, Min {reference waveform power} + 6, DUT Average Input Power -15}'
},
'name': 'LINEAR_REGION_GAIN',
'value': 1
},
{
'documentation': {
'description': ' The DPD polynomial or lookup table is computed by assuming that the linearized gain expected from the DUT after applying DPD on the input waveform is equal to the average power gain provided by the DUT, without DPD, to all the samples of the reference waveform for which the magnitude is greater than the peak power in the reference waveform (dBm) - 0.5dB.'
},
'name': 'PEAK_INPUT_POWER_GAIN',
'value': 2
}
]
},
'FcntAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The FCnt measurement uses the RFMXSPECAN_ATTR_FCNT_AVERAGING_COUNT attribute as the number of acquisitions over which the FCnt measurement is averaged. '
},
'name': 'TRUE',
'value': 1
}
]
},
'FcntAveragingType': {
'values': [
{
'documentation': {
'description': ' The mean of the instantaneous signal phase difference over multiple acquisitions is used for the frequency measurement. '
},
'name': 'MEAN',
'value': 6
},
{
'documentation': {
'description': ' The maximum instantaneous signal phase difference over multiple acquisitions is used for the frequency measurement. '
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The minimum instantaneous signal phase difference over multiple acquisitions is used for the frequency measurement. '
},
'name': 'MINIMUM',
'value': 4
},
{
'documentation': {
'description': ' The maximum instantaneous signal phase difference over multiple acquisitions is used for the frequency measurement. The sign of the phase difference is ignored to find the maximum instantaneous value.'
},
'name': 'MINMAX',
'value': 7
}
]
},
'FcntRbwFilterType': {
'values': [
{
'documentation': {
'description': ' The measurement does not use any RBW filtering.'
},
'name': 'NONE',
'value': 5
},
{
'documentation': {
'description': ' The RBW filter has a Gaussian response.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' The RBW filter has a flat response.'
},
'name': 'FLAT',
'value': 2
},
{
'documentation': {
'description': ' The RRC filter with the roll-off specified by RFMXSPECAN_ATTR_FCNT_RBW_FILTER_RRC_ALPHA attribute is used as the RBW filter.'
},
'name': 'RRC',
'value': 6
}
]
},
'FcntThresholdEnabled': {
'values': [
{
'documentation': {
'description': ' All samples are considered for the FCnt measurement. '
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The samples above the threshold level specified in the RFMXSPECAN_ATTR_FCNT_THRESHOLD_LEVEL attribute are considered for the FCnt measurement. '
},
'name': 'TRUE',
'value': 1
}
]
},
'FcntThresholdType': {
'values': [
{
'documentation': {
'description': ' The threshold is relative to the peak power of the acquired samples.'
},
'name': 'RELATIVE',
'value': 0
},
{
'documentation': {
'description': ' The threshold is the absolute power, in dBm.'
},
'name': 'ABSOLUTE',
'value': 1
}
]
},
'FrequencyReferenceSource': {
'generate-mappings': True,
'values': [
{
'name': 'ONBOARD_CLOCK',
'value': 'OnboardClock'
},
{
'name': 'REF_IN',
'value': 'RefIn'
},
{
'name': 'PXI_CLK',
'value': 'PXI_Clk'
},
{
'name': 'CLK_IN',
'value': 'ClkIn'
}
]
},
'HarmAutoHarmonicsSetupEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'HarmAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The Harmonics measurement uses the RFMXSPECAN_ATTR_HARM_AVERAGING_COUNT attribute as the number of acquisitions over which the Harmonics measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'HarmAveragingType': {
'values': [
{
'documentation': {
'description': ' The power trace is linearly averaged. RMS averaging reduces signal fluctuations but not the noise floor. '
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power trace is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power trace is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The maximum instantaneous power in the power trace is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The minimum instantaneous power in the power trace is retained from one acquisition to the next.'
},
'name': 'MINIMUM',
'value': 4
}
]
},
'HarmHarmonicEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the harmonic for measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables the harmonic for measurement.'
},
'name': 'TRUE',
'value': 1
}
]
},
'HarmMeasurementMethod': {
'values': [
{
'documentation': {
'description': ' The harmonics measurement acquires the signal using the same signal analyzer setting across frequency bands. Use this method when the measurement speed is desirable over higher dynamic range.\n\n Supported devices: PXIe-5644/5645/5646, PXIe-5663/5665/5668'
},
'name': 'TIME_DOMAIN',
'value': 0
},
{
'documentation': {
'description': ' The harmonics measurement acquires the signal using the hardware-specific features, such as the IF filter and IF gain, for different frequency bands. Use this method to get the best dynamic range.\n\n Supported devices: PXIe-5665/5668'
},
'name': 'DYNAMIC_RANGE',
'value': 2
}
]
},
'HarmNoiseCompensationEnabled': {
'values': [
{
'documentation': {
'description': ' Disables compensation of the average harmonic powers for the noise floor of the signal analyzer.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables compensation of the average harmonic powers for the noise floor of the signal analyzer. The noise floor of the signal analyzer is measured for the RF path used by the harmonics measurement and cached for future use. If the signal analyzer or measurement parameters change, noise floors are measured again.\n\n Supported devices: PXIe-5663/5665/5668'
},
'name': 'TRUE',
'value': 1
}
]
},
'HarmRbwFilterType': {
'values': [
{
'name': 'GAUSSIAN',
'value': 1
},
{
'name': 'FLAT',
'value': 2
},
{
'name': 'NONE',
'value': 5
},
{
'name': 'RRC',
'value': 6
}
]
},
'IMAmplitudeCorrectionType': {
'values': [
{
'name': 'RF_CENTER_FREQUENCY',
'value': 0
},
{
'name': 'SPECTRUM_FREQUENCY_BIN',
'value': 1
}
]
},
'IMAutoIntermodsSetupEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IMAveragingEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IMAveragingType': {
'values': [
{
'name': 'RMS',
'value': 0
},
{
'name': 'LOG',
'value': 1
},
{
'name': 'SCALAR',
'value': 2
},
{
'name': 'MAXIMUM',
'value': 3
},
{
'name': 'MINIMUM',
'value': 4
}
]
},
'IMFftWindow': {
'values': [
{
'name': 'NONE',
'value': 0
},
{
'name': 'FLAT_TOP',
'value': 1
},
{
'name': 'HANNING',
'value': 2
},
{
'name': 'HAMMING',
'value': 3
},
{
'name': 'GAUSSIAN',
'value': 4
},
{
'name': 'BLACKMAN',
'value': 5
},
{
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'IMFrequencyDefinition': {
'values': [
{
'name': 'RELATIVE',
'value': 0
},
{
'name': 'ABSOLUTE',
'value': 1
}
]
},
'IMIFOutputPowerOffsetAuto': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IMIntermodEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IMIntermodSide': {
'values': [
{
'name': 'LOWER',
'value': 0
},
{
'name': 'UPPER',
'value': 1
},
{
'name': 'BOTH',
'value': 2
}
]
},
'IMLocalPeakSearchEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IMMeasurementMethod': {
'values': [
{
'name': 'NORMAL',
'value': 0
},
{
'name': 'DYNAMIC_RANGE',
'value': 1
},
{
'name': 'SEGMENTED',
'value': 2
}
]
},
'IMRbwFilterAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IMRbwFilterType': {
'values': [
{
'name': 'FFT_BASED',
'value': 0
},
{
'name': 'GAUSSIAN',
'value': 1
},
{
'name': 'FLAT',
'value': 2
}
]
},
'IMSweepTimeAuto': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IQBandwidthAuto': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IQDeleteRecordOnFetch': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'IQPowerEdgeTriggerLevelType': {
'values': [
{
'name': 'RELATIVE',
'value': 0
},
{
'name': 'ABSOLUTE',
'value': 1
}
]
},
'IQPowerEdgeTriggerSlope': {
'values': [
{
'name': 'RISING_SLOPE',
'value': 0
},
{
'name': 'FALLING_SLOPE',
'value': 1
}
]
},
'LimitedConfigurationChange': {
'values': [
{
'documentation': {
'description': ' This is the normal mode of RFmx operation. All configuration changes in RFmxInstr attributes or in personality attributes will be applied during RFmx Commit.'
},
'name': 'DISABLED',
'value': 0
},
{
'documentation': {
'description': ' Signal configuration and RFmxInstr configuration are locked after the first Commit or Initiate of the named signal configuration. Any configuration change thereafter either in RFmxInstr attributes or personality attributes will not be considered by subsequent RFmx Commits or Initiates of this signal. Use No Change if you have created named signal configurations for all measurement configurations but are setting some RFmxInstr attributes. Refer to the Limitations of the Limited Configuration Change Attribute topic for more details about the limitations of using this mode. '
},
'name': 'NO_CHANGE',
'value': 1
},
{
'documentation': {
'description': ' Signal configuration, other than center frequency, external attenuation, and RFInstr configuration, is locked after first Commit or Initiate of the named signal configuration. Thereafter, only the Center Frequency and RFMXSPECAN_ATTR_EXTERNAL_ATTENUATION attribute value changes will be considered by subsequent driver Commits or Initiates of this signal. Refer to the Limitations of the Limited Configuration Change Attribute topic for more details about the limitations of using this mode. '
},
'name': 'FREQUENCY',
'value': 2
},
{
'documentation': {
'description': ' Signal configuration, other than the reference level and RFInstr configuration, is locked after first Commit or Initiate of the named signal configuration. Thereafter only the RFMXSPECAN_ATTR_REFERENCE_LEVEL attribute value change will be considered by subsequent driver Commits or Initiates of this signal. If you have configured this signal to use an IQ Power Edge Trigger, NI recommends that you set the RFMXSPECAN_ATTR_IQ_POWER_EDGE_TRIGGER_LEVEL_TYPE to RFMXSPECAN_VAL_IQ_POWER_EDGE_TRIGGER_LEVEL_TYPE_RELATIVE so that the trigger level is automatically adjusted as you adjust the reference level. Refer to the Limitations of the Limited Configuration Change Attribute topic for more details about the limitations of using this mode. '
},
'name': 'REFERENCE_LEVEL',
'value': 3
},
{
'documentation': {
'description': ' Signal configuration, other than center frequency, reference level, external attenuation, and RFInstr configuration, is locked after first Commit or Initiate of the named signal configuration. Thereafter only Center Frequency, RFMXSPECAN_ATTR_REFERENCE_LEVEL, and RFMXSPECAN_ATTR_EXTERNAL_ATTENUATION attribute value changes will be considered by subsequent driver Commits or Initiates of this signal. If you have configured this signal to use an IQ Power Edge Trigger, NI recommends you set the RFMXSPECAN_ATTR_IQ_POWER_EDGE_TRIGGER_LEVEL_TYPE to RFMXSPECAN_VAL_IQ_POWER_EDGE_TRIGGER_LEVEL_TYPE_RELATIVE so that the trigger level is automatically adjusted as you adjust the reference level. Refer to the Limitations of the Limited Configuration Change Attribute topic for more details about the limitations of using this mode. '
},
'name': 'FREQUENCY_AND_REFERENCE_LEVEL',
'value': 4
},
{
'documentation': {
'description': ' Signal configuration, other than Selected Ports, Center frequency, Reference level, External attenuation, and RFInstr configuration, is locked after first Commit or Initiate of the named signal configuration. Thereafter only Selected Ports, Center Frequency, RFMXSPECAN_ATTR_REFERENCE_LEVEL, and RFMXSPECAN_ATTR_EXTERNAL_ATTENUATION attribute value changes will be considered by subsequent driver Commits or Initiates of this signal. If you have configured this signal to use an IQ Power Edge Trigger, NI recommends you set the RFMXSPECAN_ATTR_IQ_POWER_EDGE_TRIGGER_LEVEL_TYPE to RFMXSPECAN_VAL_IQ_POWER_EDGE_TRIGGER_LEVEL_TYPE_RELATIVE so that the trigger level is automatically adjusted as you adjust the reference level. Refer to the Limitations of the Limited Configuration Change Attribute topic for more details about the limitations of using this mode.'
},
'name': 'SELECTED_PORTS_FREQUENCY_AND_REFERENCE_LEVEL',
'value': 5
}
]
},
'MarkerNextPeak': {
'values': [
{
'name': 'NEXT_HIGHEST',
'value': 0
},
{
'name': 'NEXT_LEFT',
'value': 1
},
{
'name': 'NEXT_RIGHT',
'value': 2
}
]
},
'MarkerPeakExcursionEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'MarkerThresholdEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'MarkerTrace': {
'values': [
{
'name': 'ACP_SPECTRUM',
'value': 0
},
{
'name': 'CCDF_GAUSSIAN_PROBABILITIES_TRACE',
'value': 1
},
{
'name': 'CCDF_PROBABILITIES_TRACE',
'value': 2
},
{
'name': 'CHP_SPECTRUM',
'value': 3
},
{
'name': 'FCNT_POWER_TRACE',
'value': 4
},
{
'name': 'OBW_SPECTRUM',
'value': 5
},
{
'name': 'SEM_SPECTRUM',
'value': 6
},
{
'name': 'SPECTRUM',
'value': 7
},
{
'name': 'TXP_POWER_TRACE',
'value': 8
}
]
},
'MarkerType': {
'values': [
{
'name': 'OFF',
'value': 0
},
{
'name': 'NORMAL',
'value': 1
},
{
'name': 'DELTA',
'value': 3
}
]
},
'MeasurementTypes': {
'values': [
{
'name': 'ACP',
'value': 1
},
{
'name': 'CCDF',
'value': 2
},
{
'name': 'CHP',
'value': 4
},
{
'name': 'FCNT',
'value': 8
},
{
'name': 'HARMONICS',
'value': 16
},
{
'name': 'OBW',
'value': 32
},
{
'name': 'SEM',
'value': 64
},
{
'name': 'SPECTRUM',
'value': 128
},
{
'name': 'SPUR',
'value': 256
},
{
'name': 'TXP',
'value': 512
},
{
'name': 'AMPM',
'value': 1024
},
{
'name': 'DPD',
'value': 2048
},
{
'name': 'IQ',
'value': 4096
},
{
'name': 'IM',
'value': 8192
},
{
'name': 'NF',
'value': 16384
},
{
'name': 'PHASENOISE',
'value': 32768
},
{
'name': 'PAVT',
'value': 65536
}
]
},
'MechanicalAttenuationAuto': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'NFAveragingEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'NFCalibrationDataValid': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'NFCalibrationLossCompensationEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'NFColdSourceMode': {
'values': [
{
'name': 'MEASURE',
'value': 0
},
{
'name': 'CALIBRATE',
'value': 1
}
]
},
'NFDutInputLossCompensationEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'NFDutOutputLossCompensationEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'NFDutType': {
'values': [
{
'name': 'AMPLIFIER',
'value': 0
},
{
'name': 'DOWNCONVERTER',
'value': 1
},
{
'name': 'UPCONVERTER',
'value': 2
}
]
},
'NFExternalPreampPresent': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'NFFrequencyConverterFrequencyContext': {
'values': [
{
'name': 'RF',
'value': 0
},
{
'name': 'IF',
'value': 1
}
]
},
'NFFrequencyConverterSideband': {
'values': [
{
'name': 'LSB',
'value': 0
},
{
'name': 'USB',
'value': 1
}
]
},
'NFMeasurementMethod': {
'values': [
{
'name': 'Y_FACTOR',
'value': 0
},
{
'name': 'COLD_SOURCE',
'value': 1
}
]
},
'NFYFactorMode': {
'values': [
{
'name': 'MEASURE',
'value': 0
},
{
'name': 'CALIBRATE',
'value': 1
}
]
},
'NFYFactorNoiseSourceLossCompensationEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'ObwAmplitudeCorrectionType': {
'values': [
{
'documentation': {
'description': ' All the frequency bins in the spectrum are compensated with a single external attenuation value that corresponds to the RF center frequency.'
},
'name': 'RF_CENTER_FREQUENCY',
'value': 0
},
{
'documentation': {
'description': ' An individual frequency bin in the spectrum is compensated with the external attenuation value corresponding to that frequency.'
},
'name': 'SPECTRUM_FREQUENCY_BIN',
'value': 1
}
]
},
'ObwAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The OBW measurement uses the RFMXSPECAN_ATTR_OBW_AVERAGING_COUNT attribute as the number of acquisitions over which the OBW measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'ObwAveragingType': {
'values': [
{
'documentation': {
'description': ' The power spectrum is linearly averaged. RMS averaging reduces signal fluctuations but not the noise floor. '
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power spectrum is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power spectrum is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The peak power in the spectrum at each frequency bin is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The least power in the spectrum at each frequency bin is retained from one acquisition to the next. '
},
'name': 'MINIMUM',
'value': 4
}
]
},
'ObwFftWindow': {
'values': [
{
'documentation': {
'description': ' Analyzes transients for which duration is shorter than the window length. You can also use this window type to separate two tones with frequencies close to each other but with almost equal amplitudes. '
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' Measures single-tone amplitudes accurately.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Analyzes transients for which duration is longer than the window length. You can also use this window type to provide better frequency resolution for noise measurements.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Analyzes closely-spaced sine waves.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Provides a good balance of spectral leakage, frequency resolution, and amplitude attenuation. Hence, this windowing is useful for time-frequency analysis.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Analyzes single tone because it has a low maximum side lobe level and a high side lobe roll-off rate. '
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Useful as a good general purpose window, having side lobe rejection greater than 90 dB and having a moderately wide main lobe. '
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Separates two tones with frequencies close to each other but with widely-differing amplitudes. '
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'ObwPowerUnits': {
'values': [
{
'documentation': {
'description': ' The absolute powers are reported in dBm.'
},
'name': 'DBM',
'value': 0
},
{
'documentation': {
'description': ' The absolute powers are reported in dBm/Hz.'
},
'name': 'DBM_PER_HZ',
'value': 1
}
]
},
'ObwRbwAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'ObwRbwFilterBandwidthDefinition': {
'values': [
{
'documentation': {
'description': ' Defines the RBW in terms of the 3 dB bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_OBW_RBW_FILTER_TYPE attribute to RFMXSPECAN_VAL_OBW_RBW_FILTER_TYPE_FFT_BASED, RBW is the 3 dB bandwidth of the window specified by the RFMXSPECAN_ATTR_OBW_FFT_WINDOW attribute.'
},
'name': '3_DB',
'value': 0
},
{
'documentation': {
'description': ' Defines the RBW in terms of the spectrum bin width computed using an FFT when you set the RFMXSPECAN_ATTR_OBW_RBW_FILTER_TYPE attribute to FFT Based.'
},
'name': 'BIN_WIDTH',
'value': 2
}
]
},
'ObwRbwFilterType': {
'values': [
{
'documentation': {
'description': ' No RBW filtering is performed.'
},
'name': 'FFT_BASED',
'value': 0
},
{
'documentation': {
'description': ' The RBW filter has a Gaussian response.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' The RBW filter has a flat response.'
},
'name': 'FLAT',
'value': 2
}
]
},
'ObwSweepTimeAuto': {
'values': [
{
'documentation': {
'description': ' The measurement uses the sweep time that you specify in the RFMXSPECAN_ATTR_OBW_SWEEP_TIME_INTERVAL attribute. '
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement calculates the sweep time based on the value of the RFMXSPECAN_ATTR_OBW_RBW_FILTER_BANDWIDTH attribute.'
},
'name': 'TRUE',
'value': 1
}
]
},
'PavtFrequencyOffsetCorrectionEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the frequency offset correction.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables the frequency offset correction. The measurement computes and corrects any frequency offset between the reference and the acquired waveforms.'
},
'name': 'TRUE',
'value': 1
}
]
},
'PavtFrequencyTrackingEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the drift correction for the measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables the drift correction. The measurement corrects and reports the frequency offset per segment.'
},
'name': 'TRUE',
'value': 1
}
]
},
'PavtMeasurementIntervalMode': {
'values': [
{
'documentation': {
'description': ' The time offset from the start of segment and the duration over which the measurement is performed is uniform for all segments and is given by the RFMXSPECAN_ATTR_PAVT_MEASUREMENT_OFFSET attribute and the RFMXSPECAN_ATTR_PAVT_MEASUREMENT_LENGTH attribute respectively.'
},
'name': 'UNIFORM',
'value': 0
},
{
'documentation': {
'description': ' The time offset from the start of segment and the duration over which the measurement is performed is configured separately for each segment and is given by the RFMXSPECAN_ATTR_PAVT_SEGMENT_MEASUREMENT_OFFSET attribute and the RFMXSPECAN_ATTR_PAVT_SEGMENT_MEASUREMENT_LENGTH attribute respectively.'
},
'name': 'VARIABLE',
'value': 1
}
]
},
'PavtMeasurementLocationType': {
'values': [
{
'documentation': {
'description': ' The measurement is performed over a single record across multiple segments separated in time. The measurement locations of the segments are specified by the RFMXSPECAN_ATTR_PAVT_SEGMENT_START_TIME attribute. The number of segments is equal to the number of segment start times.'
},
'name': 'TIME',
'value': 0
},
{
'documentation': {
'description': ' The measurement is performed across segments obtained in multiple records, where each record is obtained when a trigger is received. The number of segments is equal to the number of triggers (records).'
},
'name': 'TRIGGER',
'value': 1
}
]
},
'PavtPhaseUnwrapEnabled': {
'values': [
{
'documentation': {
'description': ' Phase measurement results are wrapped within +/-180 degrees.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Phase measurement results are unwrapped.'
},
'name': 'TRUE',
'value': 1
}
]
},
'PavtSegmentType': {
'values': [
{
'documentation': {
'description': ' Phase and amplitude is measured in this segment.'
},
'name': 'PHASE_AND_AMPLITUDE',
'value': 0
},
{
'documentation': {
'description': ' Amplitude is measured in this segment.'
},
'name': 'AMPLITUDE',
'value': 1
},
{
'documentation': {
'description': ' Frequency error is measured in this segment.'
},
'name': 'FREQUENCY_ERROR_MEASUREMENT',
'value': 2
}
]
},
'PhaseNoiseCancellationEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'PhaseNoiseFftWindow': {
'values': [
{
'name': 'NONE',
'value': 0
},
{
'name': 'FLAT_TOP',
'value': 1
},
{
'name': 'HANNING',
'value': 2
},
{
'name': 'HAMMING',
'value': 3
},
{
'name': 'GAUSSIAN',
'value': 4
},
{
'name': 'BLACKMAN',
'value': 5
},
{
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'PhaseNoiseIntegratedNoiseRangeDefinition': {
'values': [
{
'name': 'NONE',
'value': 0
},
{
'name': 'MEASUREMENT',
'value': 1
},
{
'name': 'CUSTOM',
'value': 2
}
]
},
'PhaseNoiseRangeDefinition': {
'values': [
{
'name': 'MANUAL',
'value': 0
},
{
'name': 'AUTO',
'value': 1
}
]
},
'PhaseNoiseSmoothingType': {
'values': [
{
'name': 'NONE',
'value': 0
},
{
'name': 'LINEAR',
'value': 1
},
{
'name': 'LOGARITHMIC',
'value': 2
},
{
'name': 'MEDIAN',
'value': 3
}
]
},
'PhaseNoiseSpurRemovalEnabled': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'RFAttenuationAuto': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'SemAmplitudeCorrectionType': {
'values': [
{
'documentation': {
'description': ' All the frequency bins in the spectrum are compensated with a single external attenuation value that corresponds to the RF center frequency.'
},
'name': 'RF_CENTER_FREQUENCY',
'value': 0
},
{
'documentation': {
'description': ' An individual frequency bin in the spectrum is compensated with the external attenuation value corresponding to that frequency.'
},
'name': 'SPECTRUM_FREQUENCY_BIN',
'value': 1
}
]
},
'SemAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The SEM measurement uses the RFMXSPECAN_ATTR_SEM_AVERAGING_COUNT attribute as the number of acquisitions over which the SEM measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SemAveragingType': {
'values': [
{
'documentation': {
'description': ' The power spectrum is linearly averaged. RMS averaging reduces signal fluctuations but not the noise floor. '
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power spectrum is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power spectrum is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The peak power in the spectrum at each frequency bin is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The least power in the spectrum at each frequency bin is retained from one acquisition to the next. '
},
'name': 'MINIMUM',
'value': 4
}
]
},
'SemCarrierEnabled': {
'values': [
{
'documentation': {
'description': ' The carrier power is not considered as part of the total carrier power.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The carrier power is considered as part of the total carrier power.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SemCarrierRbwAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'SemCarrierRbwFilterBandwidthDefinition': {
'values': [
{
'documentation': {
'description': ' Defines the RBW in terms of the 3 dB bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_SEM_CARRIER_RBW_FILTER_TYPE attribute to RFMXSPECAN_VAL_SEM_CARRIER_RBW_FILTER_TYPE_FFT_BASED, RBW is the 3 dB bandwidth of the window specified by the RFMXSPECAN_ATTR_SEM_FFT_WINDOW attribute.'
},
'name': '3_DB',
'value': 0
},
{
'documentation': {
'description': ' Defines the RBW in terms of the spectrum bin width computed using an FFT when you set the RFMXSPECAN_ATTR_SEM_CARRIER_RBW_FILTER_TYPE attribute to FFT Based.'
},
'name': 'BIN_WIDTH',
'value': 2
}
]
},
'SemCarrierRbwFilterType': {
'values': [
{
'documentation': {
'description': ' No RBW filtering is performed.'
},
'name': 'FFT_BASED',
'value': 0
},
{
'documentation': {
'description': ' The RBW filter has a Gaussian response.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' The RBW filter has a flat response.'
},
'name': 'FLAT',
'value': 2
}
]
},
'SemCarrierRrcFilterEnabled': {
'values': [
{
'documentation': {
'description': ' The channel power of the acquired carrier channel is measured directly.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement applies the RRC filter on the acquired carrier channel before measuring the carrier channel power.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SemCompositeMeasurementStatus': {
'values': [
{
'name': 'FAIL',
'value': 0
},
{
'name': 'PASS',
'value': 1
}
]
},
'SemFftWindow': {
'values': [
{
'documentation': {
'description': ' Analyzes transients for which duration is shorter than the window length. You can also use this window type to separate two tones with frequencies close to each other but with almost equal amplitudes. '
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' Measures single-tone amplitudes accurately.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Analyzes transients for which duration is longer than the window length. You can also use this window type to provide better frequency resolution for noise measurements.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Analyzes closely-spaced sine waves.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Provides a good balance of spectral leakage, frequency resolution, and amplitude attenuation. Hence, this windowing is useful for time-frequency analysis.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Analyzes single tone because it has a low maximum side lobe level and a high side lobe roll-off rate. '
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Useful as a good general purpose window, having side lobe rejection greater than 90 dB and having a moderately wide main lobe. '
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Separates two tones with frequencies close to each other but with widely-differing amplitudes. '
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'SemLowerOffsetMeasurementStatus': {
'values': [
{
'name': 'FAIL',
'value': 0
},
{
'name': 'PASS',
'value': 1
}
]
},
'SemOffsetAbsoluteLimitMode': {
'values': [
{
'documentation': {
'description': ' The line specified by the RFMXSPECAN_ATTR_SEM_OFFSET_ABSOLUTE_LIMIT_START and RFMXSPECAN_ATTR_SEM_OFFSET_ABSOLUTE_LIMIT_STOP attribute values as the two ends is considered as the mask.'
},
'name': 'MANUAL',
'value': 0
},
{
'documentation': {
'description': ' The two ends of the line are coupled to the value of the RFMXSPECAN_ATTR_SEM_OFFSET_ABSOLUTE_LIMIT_START attribute.'
},
'name': 'COUPLE',
'value': 1
}
]
},
'SemOffsetEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the offset segment for the SEM measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables the offset segment for the SEM measurement.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SemOffsetFrequencyDefinition': {
'values': [
{
'documentation': {
'description': ' The start frequency and stop frequency are defined from the center of the closest carrier channel bandwidth to the center of the offset segment measurement bandwidth.\n\nMeasurement Bandwidth = Resolution Bandwidth * Bandwidth Integral.'
},
'name': 'CARRIER_CENTER_TO_MEASUREMENT_BANDWIDTH_CENTER',
'value': 0
},
{
'documentation': {
'description': ' The start frequency and stop frequency are defined from the center of the closest carrier channel bandwidth to the nearest edge of the offset segment measurement bandwidth.'
},
'name': 'CARRIER_CENTER_TO_MEASUREMENT_BANDWIDTH_EDGE',
'value': 1
},
{
'documentation': {
'description': ' The start frequency and stop frequency are defined from the nearest edge of the closest carrier channel bandwidth to the center of the nearest offset segment measurement bandwidth.'
},
'name': 'CARRIER_EDGE_TO_MEASUREMENT_BANDWIDTH_CENTER',
'value': 2
},
{
'documentation': {
'description': ' The start frequency and stop frequency are defined from the nearest edge of the closest carrier channel bandwidth to the edge of the nearest offset segment measurement bandwidth.'
},
'name': 'CARRIER_EDGE_TO_MEASUREMENT_BANDWIDTH_EDGE',
'value': 3
}
]
},
'SemOffsetLimitFailMask': {
'values': [
{
'documentation': {
'description': ' The measurement fails if the power in the segment exceeds both the absolute and relative masks.'
},
'name': 'ABSOLUTE_AND_RELATIVE',
'value': 0
},
{
'documentation': {
'description': ' The measurement fails if the power in the segment exceeds either the absolute or relative mask.'
},
'name': 'ABSOLUTE_OR_RELATIVE',
'value': 1
},
{
'documentation': {
'description': ' The measurement fails if the power in the segment exceeds the absolute mask.'
},
'name': 'ABSOLUTE',
'value': 2
},
{
'documentation': {
'description': ' The measurement fails if the power in the segment exceeds the relative mask.'
},
'name': 'RELATIVE',
'value': 3
}
]
},
'SemOffsetRbwAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'SemOffsetRbwFilterBandwidthDefinition': {
'values': [
{
'documentation': {
'description': ' Defines the RBW in terms of the 3dB bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_SEM_OFFSET_RBW_FILTER_TYPE attribute to RFMXSPECAN_VAL_SEM_RBW_FILTER_TYPE_FFT_BASED, RBW is the 3dB bandwidth of the window specified by the RFMXSPECAN_ATTR_SEM_FFT_WINDOW attribute.'
},
'name': '3_DB',
'value': 0
},
{
'documentation': {
'description': ' Defines the RBW in terms of the spectrum bin width computed using FFT when you set the RFMXSPECAN_ATTR_SEM_OFFSET_RBW_FILTER_TYPE attribute to FFT Based.'
},
'name': 'BIN_WIDTH',
'value': 2
}
]
},
'SemOffsetRbwFilterType': {
'values': [
{
'documentation': {
'description': ' No RBW filtering is performed.'
},
'name': 'FFT_BASED',
'value': 0
},
{
'documentation': {
'description': ' The RBW filter has a Gaussian response.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' The RBW filter has a flat response.'
},
'name': 'FLAT',
'value': 2
}
]
},
'SemOffsetRelativeLimitMode': {
'values': [
{
'documentation': {
'description': ' The line specified by the RFMXSPECAN_ATTR_SEM_OFFSET_RELATIVE_LIMIT_START and RFMXSPECAN_ATTR_SEM_OFFSET_RELATIVE_LIMIT_STOP attribute values as the two ends is considered as the mask.'
},
'name': 'MANUAL',
'value': 0
},
{
'documentation': {
'description': ' The two ends of the line are coupled to the value of the RFMXSPECAN_ATTR_SEM_OFFSET_RELATIVE_LIMIT_START attribute.'
},
'name': 'COUPLE',
'value': 1
}
]
},
'SemOffsetSideband': {
'values': [
{
'documentation': {
'description': ' Configures a lower offset segment to the left of the leftmost carrier. '
},
'name': 'NEGATIVE',
'value': 0
},
{
'documentation': {
'description': ' Configures an upper offset segment to the right of the rightmost carrier. '
},
'name': 'POSITIVE',
'value': 1
},
{
'documentation': {
'description': ' Configures both negative and positive offset segments.'
},
'name': 'BOTH',
'value': 2
}
]
},
'SemPowerUnits': {
'values': [
{
'documentation': {
'description': ' The absolute powers are reported in dBm.'
},
'name': 'DBM',
'value': 0
},
{
'documentation': {
'description': ' The absolute powers are reported in dBm/Hz.'
},
'name': 'DBM_PER_HZ',
'value': 1
}
]
},
'SemReferenceType': {
'values': [
{
'documentation': {
'description': ' The power reference is the integrated power of the closest carrier.'
},
'name': 'INTEGRATION',
'value': 0
},
{
'documentation': {
'description': ' The power reference is the peak power of the closest carrier.'
},
'name': 'PEAK',
'value': 1
}
]
},
'SemSweepTimeAuto': {
'values': [
{
'documentation': {
'description': ' The measurement uses the sweep time that you specify in the RFMXSPECAN_ATTR_SEM_SWEEP_TIME_INTERVAL attribute.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement calculates the sweep time based on the value of the RFMXSPECAN_ATTR_SEM_OFFSET_RBW_FILTER_BANDWIDTH and RFMXSPECAN_ATTR_SEM_CARRIER_RBW_FILTER_BANDWIDTH attributes.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SemUpperOffsetMeasurementStatus': {
'values': [
{
'name': 'FAIL',
'value': 0
},
{
'name': 'PASS',
'value': 1
}
]
},
'SpectrumAmplitudeCorrectionType': {
'values': [
{
'documentation': {
'description': ' All the frequency bins in the spectrum are compensated with a single external attenuation value that corresponds to the RF center frequency.'
},
'name': 'RF_CENTER_FREQUENCY',
'value': 0
},
{
'documentation': {
'description': ' An individual frequency bin in the spectrum is compensated with the external attenuation value corresponding to that frequency.'
},
'name': 'SPECTRUM_FREQUENCY_BIN',
'value': 1
}
]
},
'SpectrumAnalysisInput': {
'values': [
{
'documentation': {
'description': ' Measurement analyzes the acquired I+jQ data, resulting generally in a spectrum that is not symmetric around 0 Hz. Spectrum trace result contains both positive and negative frequencies. Since the RMS power of the complex envelope is 3.01 dB higher than that of its equivalent real RF signal, the spectrum trace result of the acquired I+jQ data is scaled by -3.01 dB. '
},
'name': 'IQ',
'value': 0
},
{
'documentation': {
'description': ' Measurement ignores the Q data from the acquired I+jQ data and analyzes I+j0, resulting in a spectrum that is symmetric around 0 Hz. Spectrum trace result contains positive frequencies only. Spectrum of I+j0 data is scaled by +3.01 dB to account for the power of the negative frequencies that are not returned in the spectrum trace.'
},
'name': 'I_ONLY',
'value': 1
},
{
'documentation': {
'description': ' Measurement ignores the I data from the acquired I+jQ data and analyzes Q+j0, resulting in a spectrum that is symmetric around 0 Hz. Spectrum trace result contains positive frequencies only. Spectrum of Q+j0 data is scaled by +3.01 dB to account for the power of the negative frequencies that are not returned in the spectrum trace.'
},
'name': 'Q_ONLY',
'value': 2
}
]
},
'SpectrumAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The spectrum measurement uses the RFMXSPECAN_ATTR_SPECTRUM_AVERAGING_COUNT attribute as the number of acquisitions over which the spectrum measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpectrumAveragingType': {
'values': [
{
'documentation': {
'description': ' The power spectrum is linearly averaged. RMS averaging reduces signal fluctuations but not the noise floor. '
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power spectrum is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power spectrum is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The peak power in the spectrum at each frequency bin is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The least power in the spectrum at each frequency bin is retained from one acquisition to the next. '
},
'name': 'MINIMUM',
'value': 4
}
]
},
'SpectrumDetectorType': {
'values': [
{
'documentation': {
'description': ' The detector is disabled.'
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' The middle sample in the bucket is detected.'
},
'name': 'SAMPLE',
'value': 1
},
{
'documentation': {
'description': ' The maximum value of the samples within the bucket is detected if the signal only rises or if the signal only falls. If the signal, within a bucket, both rises and falls, then the maximum and minimum values of the samples are detected in alternate buckets.'
},
'name': 'NORMAL',
'value': 2
},
{
'documentation': {
'description': ' The maximum value of the samples in the bucket is detected.'
},
'name': 'PEAK',
'value': 3
},
{
'documentation': {
'description': ' The minimum value of the samples in the bucket is detected.'
},
'name': 'NEGATIVE_PEAK',
'value': 4
},
{
'documentation': {
'description': ' The average RMS of all the samples in the bucket is detected.'
},
'name': 'AVERAGE_RMS',
'value': 5
},
{
'documentation': {
'description': ' The average voltage of all the samples in the bucket is detected. '
},
'name': 'AVERAGE_VOLTAGE',
'value': 6
},
{
'documentation': {
'description': ' The average log of all the samples in the bucket is detected.'
},
'name': 'AVERAGE_LOG',
'value': 7
}
]
},
'SpectrumFftWindow': {
'values': [
{
'documentation': {
'description': ' Analyzes transients for which duration is shorter than the window length. You can also use this window type to separate two tones with frequencies close to each other but with almost equal amplitudes. '
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' Measures single-tone amplitudes accurately.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Analyzes transients for which duration is longer than the window length. You can also use this window type to provide better frequency resolution for noise measurements.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Analyzes closely-spaced sine waves.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Provides a good balance of spectral leakage, frequency resolution, and amplitude attenuation. Hence, this windowing is useful for time-frequency analysis.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Analyzes single tone because it has a low maximum side lobe level and a high side lobe roll-off rate. '
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Useful as a good general purpose window, having side lobe rejection greater than 90 dB and having a moderately wide main lobe. '
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Separates two tones with frequencies close to each other but with widely-differing amplitudes. '
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'SpectrumMeasurementMode': {
'values': [
{
'documentation': {
'description': ' Spectrum measurement is performed on the acquired signal. '
},
'name': 'MEASURE',
'value': 0
},
{
'documentation': {
'description': ' Manual noise calibration of the signal analyzer is performed for the spectrum measurement.'
},
'name': 'CALIBRATE_NOISE_FLOOR',
'value': 1
}
]
},
'SpectrumNoiseCalibrationAveragingAuto': {
'values': [
{
'documentation': {
'description': ' RFmx uses the averages that you set for the RFMXSPECAN_ATTR_SPECTRUM_NOISE_CALIBRATION_AVERAGING_COUNT attribute.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' RFmx uses a noise calibration averaging count of 32.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpectrumNoiseCalibrationDataValid': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'SpectrumNoiseCalibrationMode': {
'values': [
{
'documentation': {
'description': ' When you set the RFMXSPECAN_ATTR_SPECTRUM_MEASUREMENT_MODE attribute to RFMXSPECAN_VAL_SPECTRUM_MEASUREMENT_MODE_CALIBRATE_NOISE_FLOOR, you can initiate instrument noise calibration for the spectrum measurement manually. When you set the RFMXSPECAN_ATTR_SPECTRUM_MEASUREMENT_MODE attribute to RFMXSPECAN_VAL_SPECTRUM_MEASUREMENT_MODE_MEASURE, you can initiate the spectrum measurement manually.'
},
'name': 'MANUAL',
'value': 0
},
{
'documentation': {
'description': ' When you set the RFMXSPECAN_ATTR_SPECTRUM_NOISE_COMPENSATION_ENABLED attribute to RFMXSPECAN_VAL_SPECTRUM_NOISE_COMPENSATION_ENABLED_TRUE, RFmx sets the Input Isolation Enabled attribute to Enabled and calibrates the intrument noise in the current state of the instrument. RFmx then resets the Input Isolation Enabled attribute and performs the spectrum measurement, including compensation for noise from the instrument. RFmx skips noise calibration in this mode if valid noise calibration data is already cached. When you set the RFMXSPECAN_ATTR_SPECTRUM_NOISE_COMPENSATION_ENABLED attribute to RFMXSPECAN_VAL_SPECTRUM_NOISE_COMPENSATION_ENABLED_FALSE, RFmx does not calibrate instrument noise and performs only the spectrum measurement without compensating for the noise from the instrument.'
},
'name': 'AUTO',
'value': 1
}
]
},
'SpectrumNoiseCompensationEnabled': {
'values': [
{
'documentation': {
'description': ' Disables compensation of the spectrum for the noise floor of the signal analyzer.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables compensation of the spectrum for the noise floor of the signal analyzer. The noise floor of the signal analyzer is measured for the RF path used by the Spectrum measurement and cached for future use. If signal analyzer or measurement parameters change, noise floors are measured again.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpectrumNoiseCompensationType': {
'values': [
{
'documentation': {
'description': ' Compensates for noise from the analyzer and the 50 ohm termination. The measured power values are in excess of the thermal noise floor.'
},
'name': 'ANALYZER_AND_TERMINATION',
'value': 0
},
{
'documentation': {
'description': ' Compensates for the analyzer noise only.'
},
'name': 'ANALYZER_ONLY',
'value': 1
}
]
},
'SpectrumPowerUnits': {
'values': [
{
'documentation': {
'description': ' The absolute powers are reported in dBm.'
},
'name': 'DBM',
'value': 0
},
{
'documentation': {
'description': ' The absolute powers are reported in dBm/Hz.'
},
'name': 'DBM_PER_HZ',
'value': 1
},
{
'documentation': {
'description': ' The absolute powers are reported in dBW.'
},
'name': 'DBW',
'value': 2
},
{
'documentation': {
'description': ' The absolute powers are reported in dBV.'
},
'name': 'DBV',
'value': 3
},
{
'documentation': {
'description': ' The absolute powers are reported in dBmV.'
},
'name': 'DBMV',
'value': 4
},
{
'documentation': {
'description': ' The absolute powers are reported in dBuV.'
},
'name': 'DBUV',
'value': 5
},
{
'documentation': {
'description': ' The absolute powers are reported in W.'
},
'name': 'WATTS',
'value': 6
},
{
'documentation': {
'description': ' The absolute powers are reported in volts.'
},
'name': 'VOLTS',
'value': 7
},
{
'documentation': {
'description': ' The absolute powers are reported in volts2.'
},
'name': 'VOLTS_SQUARED',
'value': 8
}
]
},
'SpectrumRbwAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'SpectrumRbwFilterBandwidthDefinition': {
'values': [
{
'documentation': {
'description': ' Defines the RBW in terms of the 3dB bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_SPECTRUM_RBW_FILTER_TYPE attribute to RFMXSPECAN_VAL_SPECTRUM_RBW_FILTER_TYPE_FFT_BASED, RBW is the 3dB bandwidth of the window specified by the RFMXSPECAN_ATTR_SPECTRUM_FFT_WINDOW attribute.'
},
'name': '3_DB',
'value': 0
},
{
'documentation': {
'description': ' Defines the RBW in terms of the 6dB bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_SPECTRUM_RBW_FILTER_TYPE attribute to FFT Based, RBW is the 6dB bandwidth of the window specified by the RFMXSPECAN_ATTR_SPECTRUM_FFT_WINDOW attribute.'
},
'name': '6_DB',
'value': 1
},
{
'documentation': {
'description': ' Defines the RBW in terms of the spectrum bin width computed using FFT when you set the RFMXSPECAN_ATTR_SPECTRUM_RBW_FILTER_TYPE attribute to FFT Based.'
},
'name': 'BIN_WIDTH',
'value': 2
},
{
'documentation': {
'description': ' Defines the RBW in terms of the ENBW bandwidth of the RBW filter. When you set the RFMXSPECAN_ATTR_SPECTRUM_RBW_FILTER_TYPE attribute to FFT Based, RBW is the ENBW bandwidth of the window specified by the RFMXSPECAN_ATTR_SPECTRUM_FFT_WINDOW attribute.'
},
'name': 'ENBW',
'value': 3
}
]
},
'SpectrumRbwFilterType': {
'values': [
{
'documentation': {
'description': ' No RBW filtering is performed.'
},
'name': 'FFT_BASED',
'value': 0
},
{
'documentation': {
'description': ' The RBW filter has a Gaussian response.'
},
'name': 'GAUSSIAN',
'value': 1
},
{
'documentation': {
'description': ' The RBW filter has a flat response.'
},
'name': 'FLAT',
'value': 2
}
]
},
'SpectrumSweepTimeAuto': {
'values': [
{
'documentation': {
'description': ' The measurement uses the sweep time that you specify in the RFMXSPECAN_ATTR_SPECTRUM_SWEEP_TIME_INTERVAL attribute. '
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The measurement calculates the sweep time based on the value of the RFMXSPECAN_ATTR_SPECTRUM_RBW_FILTER_BANDWIDTH attribute.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpectrumVbwFilterAutoBandwidth': {
'values': [
{
'documentation': {
'description': ' Specify the video bandwidth in the RFMXSPECAN_ATTR_SPECTRUM_VBW_FILTER_BANDWIDTH attribute. The RFMXSPECAN_ATTR_SPECTRUM_VBW_FILTER_VBW_TO_RBW_RATIO attribute is disregarded in this mode.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Specify video bandwidth in terms of the VBW to RBW ratio. The value of the video bandwidth is then computed by using the RFMXSPECAN_ATTR_SPECTRUM_VBW_FILTER_VBW_TO_RBW_RATIO attribute and the RFMXSPECAN_ATTR_SPECTRUM_RBW_FILTER_BANDWIDTH attribute. The value of the RFMXSPECAN_ATTR_SPECTRUM_VBW_FILTER_BANDWIDTH attribute is disregarded in this mode.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpurAbsoluteLimitMode': {
'values': [
{
'name': 'MANUAL',
'value': 0
},
{
'name': 'COUPLE',
'value': 1
}
]
},
'SpurAmplitudeCorrectionType': {
'values': [
{
'documentation': {
'description': ' All the frequency bins in the spectrum are compensated with a single external attenuation value that corresponds to the RF center frequency.'
},
'name': 'RF_CENTER_FREQUENCY',
'value': 0
},
{
'documentation': {
'description': ' An individual frequency bin in the spectrum is compensated with the external attenuation value corresponding to that frequency.'
},
'name': 'SPECTRUM_FREQUENCY_BIN',
'value': 1
}
]
},
'SpurAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The Spur measurement uses the RFMXSPECAN_ATTR_SPUR_AVERAGING_COUNT attribute as the number of acquisitions over which the Spur measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpurAveragingType': {
'values': [
{
'documentation': {
'description': ' The power spectrum is linearly averaged. RMS averaging reduces signal fluctuations but not the noise floor. '
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power spectrum is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power spectrum is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The peak power in the spectrum at each frequency bin is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The least power in the spectrum at each frequency bin is retained from one acquisition to the next. '
},
'name': 'MINIMUM',
'value': 4
}
]
},
'SpurFftWindow': {
'values': [
{
'documentation': {
'description': ' Analyzes transients for which duration is shorter than the window length. You can also use this window type to separate two tones with frequencies close to each other but with almost equal amplitudes. '
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' Measures single-tone amplitudes accurately.'
},
'name': 'FLAT_TOP',
'value': 1
},
{
'documentation': {
'description': ' Analyzes transients for which duration is longer than the window length. You can also use this window type to provide better frequency resolution for noise measurements.'
},
'name': 'HANNING',
'value': 2
},
{
'documentation': {
'description': ' Analyzes closely-spaced sine waves.'
},
'name': 'HAMMING',
'value': 3
},
{
'documentation': {
'description': ' Provides a balance of spectral leakage, frequency resolution, and amplitude attenuation. This windowing is useful for time-frequency analysis.'
},
'name': 'GAUSSIAN',
'value': 4
},
{
'documentation': {
'description': ' Analyzes single tone because it has a low maximum side lobe level and a high side lobe roll-off rate. '
},
'name': 'BLACKMAN',
'value': 5
},
{
'documentation': {
'description': ' Useful as a good general purpose window, having side lobe rejection greater than 90 dB and having a moderately wide main lobe. '
},
'name': 'BLACKMAN_HARRIS',
'value': 6
},
{
'documentation': {
'description': ' Separates two tones with frequencies close to each other but with widely-differing amplitudes.'
},
'name': 'KAISER_BESSEL',
'value': 7
}
]
},
'SpurMeasurementStatus': {
'values': [
{
'name': 'FAIL',
'value': 0
},
{
'name': 'PASS',
'value': 1
}
]
},
'SpurRangeDetectorType': {
'values': [
{
'documentation': {
'description': ' The detector is disabled.'
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' The middle sample in the bucket is detected.'
},
'name': 'SAMPLE',
'value': 1
},
{
'documentation': {
'description': ' The maximum value of the samples within the bucket is detected if the signal only rises or if the signal only falls. If the signal, within a bucket, both rises and falls, then the maximum and minimum values of the samples are detected in alternate buckets.'
},
'name': 'NORMAL',
'value': 2
},
{
'documentation': {
'description': ' The maximum value of the samples in the bucket is detected.'
},
'name': 'PEAK',
'value': 3
},
{
'documentation': {
'description': ' The minimum value of the samples in the bucket is detected.'
},
'name': 'NEGATIVE_PEAK',
'value': 4
},
{
'documentation': {
'description': ' The average RMS of all the samples in the bucket is detected.'
},
'name': 'AVERAGE_RMS',
'value': 5
},
{
'documentation': {
'description': ' The average voltage of all the samples in the bucket is detected. '
},
'name': 'AVERAGE_VOLTAGE',
'value': 6
},
{
'documentation': {
'description': ' The average log of all the samples in the bucket is detected.'
},
'name': 'AVERAGE_LOG',
'value': 7
}
]
},
'SpurRangeEnabled': {
'values': [
{
'documentation': {
'description': ' Disables the acquisition of the frequency range.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Enables measurement of Spurs in the frequency range.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpurRangeStatus': {
'values': [
{
'name': 'FAIL',
'value': 0
},
{
'name': 'PASS',
'value': 1
}
]
},
'SpurRangeVbwFilterAutoBandwidth': {
'values': [
{
'documentation': {
'description': ' Specify the video bandwidth in the RFMXSPECAN_ATTR_SPUR_RANGE_VBW_FILTER_BANDWIDTH attribute. The Spur VBW to RBW Ratio attribute is disregarded in this mode.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Specify video bandwidth in terms of the VBW to RBW ratio. The value of the video bandwidth is then computed by using the RFMXSPECAN_ATTR_SPUR_RANGE_VBW_FILTER_VBW_TO_RBW_RATIO attribute and the RFMXSPECAN_ATTR_SPUR_RANGE_RBW_FILTER_BANDWIDTH attribute. The value of the RFMXSPECAN_ATTR_SPUR_RANGE_VBW_FILTER_BANDWIDTH attribute is disregarded in this mode.'
},
'name': 'TRUE',
'value': 1
}
]
},
'SpurRbwAutoBandwidth': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'SpurRbwFilterBandwidthDefinition': {
'values': [
{
'name': '3_DB',
'value': 0
},
{
'name': 'BIN_WIDTH',
'value': 2
},
{
'name': 'ENBW',
'value': 3
}
]
},
'SpurRbwFilterType': {
'values': [
{
'name': 'FFT_BASED',
'value': 0
},
{
'name': 'GAUSSIAN',
'value': 1
},
{
'name': 'FLAT',
'value': 2
}
]
},
'SpurSweepTimeAuto': {
'values': [
{
'name': 'FALSE',
'value': 0
},
{
'name': 'TRUE',
'value': 1
}
]
},
'TriggerMinimumQuietTimeMode': {
'values': [
{
'documentation': {
'description': ' The minimum quiet time for triggering is the value of the RFMXSPECAN_ATTR_TRIGGER_MINIMUM_QUIET_TIME_DURATION attribute. '
},
'name': 'MANUAL',
'value': 0
},
{
'documentation': {
'description': ' The measurement computes the minimum quiet time used for triggering.'
},
'name': 'AUTO',
'value': 1
}
]
},
'TriggerType': {
'values': [
{
'documentation': {
'description': ' No Reference Trigger is configured.'
},
'name': 'NONE',
'value': 0
},
{
'documentation': {
'description': ' The Reference Trigger is not asserted until a digital edge is detected. The source of the digital edge is specified using the RFMXSPECAN_ATTR_DIGITAL_EDGE_TRIGGER_SOURCE attribute.'
},
'name': 'DIGITAL_EDGE',
'value': 1
},
{
'documentation': {
'description': ' The Reference Trigger is asserted when the signal changes past the level specified by the slope (rising or falling), which is configured using the RFMXSPECAN_ATTR_IQ_POWER_EDGE_TRIGGER_SLOPE attribute.'
},
'name': 'IQ_POWER_EDGE',
'value': 2
},
{
'documentation': {
'description': ' The Reference Trigger is not asserted until a software trigger occurs. '
},
'name': 'SOFTWARE',
'value': 3
}
]
},
'TxpAveragingEnabled': {
'values': [
{
'documentation': {
'description': ' The measurement is performed on a single acquisition.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The TXP measurement uses the RFMXSPECAN_ATTR_TXP_AVERAGING_COUNT attribute as the number of acquisitions over which the TXP measurement is averaged.'
},
'name': 'TRUE',
'value': 1
}
]
},
'TxpAveragingType': {
'values': [
{
'documentation': {
'description': ' The power trace is linearly averaged.'
},
'name': 'RMS',
'value': 0
},
{
'documentation': {
'description': ' The power trace is averaged in a logarithmic scale.'
},
'name': 'LOG',
'value': 1
},
{
'documentation': {
'description': ' The square root of the power trace is averaged.'
},
'name': 'SCALAR',
'value': 2
},
{
'documentation': {
'description': ' The maximum instantaneous power in the power trace is retained from one acquisition to the next.'
},
'name': 'MAXIMUM',
'value': 3
},
{
'documentation': {
'description': ' The minimum instantaneous power in the power trace is retained from one acquisition to the next.'
},
'name': 'MINIMUM',
'value': 4
}
]
},
'TxpRbwFilterType': {
'values': [
{
'documentation': {
'description': ' The RBW filter has a Gaussian response.'
},
'name': 'NONE',
'value': 1
},
{
'documentation': {
'description': ' The RBW filter has a flat response.'
},
'name': 'GAUSSIAN',
'value': 2
},
{
'documentation': {
'description': ' The measurement does not use any RBW filtering.'
},
'name': 'FLAT',
'value': 5
},
{
'documentation': {
'description': ' The RRC filter with the roll-off specified by the RFMXSPECAN_ATTR_TXP_RBW_FILTER_ALPHA attribute is used as the RBW filter.'
},
'name': 'RRC',
'value': 6
}
]
},
'TxpThresholdEnabled': {
'values': [
{
'documentation': {
'description': ' All the acquired samples are considered for the TXP measurement.'
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' The samples above the threshold level specified in the RFMXSPECAN_ATTR_TXP_THRESHOLD_LEVEL attribute are considered for the TXP measurement.'
},
'name': 'TRUE',
'value': 1
}
]
},
'TxpThresholdType': {
'values': [
{
'documentation': {
'description': ' The threshold is relative to the peak power of the acquired samples.'
},
'name': 'RELATIVE',
'value': 0
},
{
'documentation': {
'description': ' The threshold is the absolute power, in dBm.'
},
'name': 'ABSOLUTE',
'value': 1
}
]
},
'TxpVbwFilterAutoBandwidth': {
'values': [
{
'documentation': {
'description': ' Specify the video bandwidth in the RFMXSPECAN_ATTR_TXP_VBW_FILTER_BANDWIDTH attribute. The RFMXSPECAN_ATTR_TXP_VBW_FILTER_VBW_TO_RBW_RATIO attribute is disregarded in this mode. '
},
'name': 'FALSE',
'value': 0
},
{
'documentation': {
'description': ' Specify video bandwidth in terms of the VBW to RBW ratio. The value of the video bandwidth is then computed by using the RFMXSPECAN_ATTR_TXP_VBW_FILTER_VBW_TO_RBW_RATIO attribute and the RFMXSPECAN_ATTR_TXP_RBW_FILTER_BANDWIDTH attribute. The value of the RFMXSPECAN_ATTR_TXP_VBW_FILTER_BANDWIDTH attribute is disregarded in this mode.'
},
'name': 'TRUE',
'value': 1
}
]
}
}
|
for letra in 'Laiana Nardi':
print(letra)
print("For loop letra!")
for contador in range(2,8):
print(contador)
print("For Loop contador!")
friends = ['John','Terry','Eric','Michael','George']
# for friend in friends:
# print(friend)
for index in range(len(friends)):
print(friends[index])
for friend in friends:
if friend == 'Eric':
print('Found ' + friend + '!')
break
# continue
print(friend)
for friend in friends:
for number in [1,2,3]:
print(friend, number)
print("For Loop friend done!")
|
"""Result class definitions."""
class _WriteResult(object):
"""Base class for write result classes."""
def __init__(self, acknowledged=True):
self.acknowledged = acknowledged # here only to PyMongo compat
class InsertOneResult(_WriteResult):
"""The return type for :meth:`~tinymongo.TinyMongoCollection.insert_one`.
"""
__slots__ = ("__inserted_id", "__acknowledged", "__eid")
def __init__(self, eid, inserted_id, acknowledged=True):
self.__eid = eid
self.__inserted_id = inserted_id
super(InsertOneResult, self).__init__(acknowledged)
@property
def inserted_id(self):
"""The inserted document's _id."""
return self.__inserted_id
@property
def eid(self):
"""The inserted document's tinyDB eid."""
return self.__eid
class InsertManyResult(_WriteResult):
"""The return type for :meth:`~tinymongo.TinyMongoCollection.insert_many`.
"""
__slots__ = ("__inserted_ids", "__acknowledged", "__eids")
def __init__(self, eids, inserted_ids, acknowledged=True):
self.__eids = eids
self.__inserted_ids = inserted_ids
super(InsertManyResult, self).__init__(acknowledged)
@property
def inserted_ids(self):
"""A list of _ids of the inserted documents, in the order provided."""
return self.__inserted_ids
@property
def eids(self):
"""A list of _ids of the inserted documents, in the order provided."""
return self.__eids
class UpdateResult(_WriteResult):
"""The return type for :meth:`~tinymongo.TinyMongoCollection.update_one`,
:meth:`~tinymongo.TinyMongoCollection.update_many`, and
:meth:`~tinymongo.TinyMongoCollection.replace_one`.
"""
__slots__ = ("__raw_result", "__acknowledged")
def __init__(self, raw_result, acknowledged=True):
self.__raw_result = raw_result
super(UpdateResult, self).__init__(acknowledged)
@property
def raw_result(self):
"""The raw result document returned by the server."""
return self.__raw_result
@property
def matched_count(self):
"""The number of documents matched for this update."""
# TODO: Implement this
@property
def modified_count(self):
"""The number of documents modified.
"""
# TODO: Implement this
@property
def upserted_id(self):
"""The _id of the inserted document if an upsert took place. Otherwise
``None``.
"""
# TODO: Implement this
class DeleteResult(_WriteResult):
"""The return type for :meth:`~tinymongo.TinyMongoCollection.delete_one`
and :meth:`~tinymongo.TinyMongoCollection.delete_many`"""
__slots__ = ("__raw_result", "__acknowledged")
def __init__(self, raw_result, acknowledged=True):
self.__raw_result = raw_result
super(DeleteResult, self).__init__(acknowledged)
@property
def raw_result(self):
"""The raw result document returned by the server."""
return self.__raw_result
@property
def deleted_count(self):
"""The number of documents deleted."""
if isinstance(self.raw_result, list):
return len(self.raw_result)
else:
return self.raw_result
|
num = int(input())
def ListOfNum(num):
L = []
while num != 0:
x = num%10
num = num//10
L.append(x)
return L
def Multiply(L):
result = 1
for i in L:
result = result *i
return result
L = ListOfNum(num) #L = [2,6,8]
result = 0
while len(L) > 1:
result +=1
x = Multiply(L)
L = ListOfNum(x)
print(result)
number = int(input("enter number"))
product = 1
persistence = 0
print(number)
while number > 9:
for digit in range(0, len(str(number))):
product *= int(str(number)[digit])
print(product)
persistence += 1
number = product
product = 1
print("persistence:", persistence) |
class Term:
def __init__(self, name, type="constant"):
"""
Args:
value (string):
type (str): ``constant '' or ``variable''
"""
self.name = name
self.type = type
@classmethod
def new_term(cls, name, type="constant"):
return cls(name, type)
def __hash__(self):
return hash(self.type + str(self.name))
def __str__(self):
return self.name
def __eq__(self, other):
if isinstance(other, Term):
return self.type == other.type and self.name == other.name
return False
|
def average(nums):
"""Find mean of a list of numbers."""
return sum(nums) / len(nums)
def test_average():
"""
>>> test_average()
"""
assert 12.0 == average([3, 6, 9, 12, 15, 18, 21])
assert 20 == average([5, 10, 15, 20, 25, 30, 35])
assert 4.5 == average([1, 2, 3, 4, 5, 6, 7, 8])
if __name__ == "__main__":
"""Call average module to find mean of a specific list of numbers."""
print(average([2, 4, 6, 8, 20, 50, 70])) |
# -*- coding: utf-8; -*-
#
# @file __init__.py
# @brief module sub-package init.
# @author Frédéric SCHERMA (INRA UMR1095)
# @date 2016-02-03
# @copyright Copyright (c) 2016 INRA
# @license MIT (see LICENSE file)
# @details
AUTH_ANY = 0
AUTH_GUEST = 1
AUTH_USER = 2
AUTH_STAFF = 3
AUTH_SUPER_USER = 4
AUTH_TYPE = (
(0, AUTH_ANY, "auth-any"),
(1, AUTH_GUEST, "auth-guest"),
(2, AUTH_USER, "auth-user"),
(3, AUTH_STAFF, "auth-staff"),
(4, AUTH_SUPER_USER, "auth-superuser"),
)
|
# -*- coding: utf-8 -*-
'''
File name: code\rooms_of_doom\sol_327.py
Author: Vaidic Joshi
Date created: Oct 20, 2018
Python Version: 3.x
'''
# Solution to Project Euler Problem #327 :: Rooms of Doom
#
# For more information see:
# https://projecteuler.net/problem=327
# Problem Statement
'''
A series of three rooms are connected to each other by automatic doors.
Each door is operated by a security card. Once you enter a room the door automatically closes and that security card cannot be used again. A machine at the start will dispense an unlimited number of cards, but each room (including the starting room) contains scanners and if they detect that you are holding more than three security cards or if they detect an unattended security card on the floor, then all the doors will become permanently locked. However, each room contains a box where you may safely store any number of security cards for use at a later stage.
If you simply tried to travel through the rooms one at a time then as you entered room 3 you would have used all three cards and would be trapped in that room forever!
However, if you make use of the storage boxes, then escape is possible. For example, you could enter room 1 using your first card, place one card in the storage box, and use your third card to exit the room back to the start. Then after collecting three more cards from the dispensing machine you could use one to enter room 1 and collect the card you placed in the box a moment ago. You now have three cards again and will be able to travel through the remaining three doors. This method allows you to travel through all three rooms using six security cards in total.
It is possible to travel through six rooms using a total of 123 security cards while carrying a maximum of 3 cards.
Let C be the maximum number of cards which can be carried at any time.
Let R be the number of rooms to travel through.
Let M(C,R) be the minimum number of cards required from the dispensing machine to travel through R rooms carrying up to a maximum of C cards at any time.
For example, M(3,6)=123 and M(4,6)=23.And, ΣM(C,6)=146 for 3 ≤ C ≤ 4.
You are given that ΣM(C,10)=10382 for 3 ≤ C ≤ 10.
Find ΣM(C,30) for 3 ≤ C ≤ 40.
'''
# Solution
# Solution Approach
'''
'''
|
l=[]
fo=open("EnglishWords.txt","r")
st=fo.read()
st=st.split()
for line in st:
if line[0]=='t':
l.append(line)
print(len(l))
|
# Faça um Programa que peça um número e então mostre a mensagem O número informado foi [número].
def chamar_numero(s):
"""Função que pede um número com uma str especifica e retorna o número digitado com uma frase, primeira tentativa
:param s:
:return:
"""
if s == 1:
resultado = (input('Digite um número:'))
print(f'O número informado foi: {resultado}')
elif s != 1:
print('tente novamente')
class Mandar:
def __init__(self, numero=0, frase='O número informado foi:'):
self.frase = frase
self.numero = numero
def envio(self):
return f'{self.frase} {self.numero}'
if __name__ == '__main__':
mandar = Mandar()
mandar.numero = 100
print(mandar.envio())
chamar_numero(1)
x = int(input('Insira um número inteiro: ')) # resposta certa, corrigido da internet
print('O número inserido foi:', x)
|
"""
@project: parser
@file: node.py
@author: Guillaume Sottas
@date: 05.04.2018
"""
node_to_class = dict()
def a2l_node_type(node_type):
def wrapper(cls):
node_to_class[node_type] = cls
cls._node = node_type
return cls
return wrapper
class A2lNode(object):
__slots__ = '_node', '_parent', '_children'
def __init__(self, *args, **kwargs):
if not isinstance(self.__slots__, tuple):
raise ValueError('__slot__ attribute must be a list (maybe \',\' is missing at the end?).')
self._parent = None
self._children = list()
for attribute, value in args:
attr = getattr(self, attribute)
if isinstance(attr, list):
attr.append(value)
elif attr is None:
setattr(self, attribute, value)
else:
raise AttributeError(attribute)
if isinstance(value, A2lNode):
value.set_parent(self)
self.add_children(value)
def set_parent(self, a2l_node):
self._parent = a2l_node
def add_children(self, a2l_node):
self._children.append(a2l_node)
def get_properties(self):
return (p for p in self.__slots__ if not p.startswith('_'))
def node(self):
return self._node
def get_node(self, node_name):
nodes = list()
for node in self._children:
if node.node() == node_name:
nodes.append(node)
nodes += node.get_node(node_name)
return nodes
def get_json(self):
tmp = dict(node=self.node())
for p in self.properties:
v = getattr(self, p)
if isinstance(v, A2lNode):
tmp[p] = v.json
elif isinstance(v, list):
tmp[p] = list()
for e in v:
if isinstance(e, A2lNode):
tmp[p].append(e.json)
else:
tmp[p].append(e)
else:
tmp[p] = v
return tmp
properties = property(fget=get_properties)
json = property(fget=get_json)
@a2l_node_type('ROOT')
class A2lFile(A2lNode):
__slots__ = 'asap2_version', 'a2ml_version', 'project'
def __init__(self, args):
self.asap2_version = None
self.a2ml_version = None
self.project = None
super(A2lFile, self).__init__(*args)
@a2l_node_type('VERSION')
class Version(A2lNode):
__slots__ = 'version_no', 'upgrade_no'
def __init__(self, version_no, upgrade_no):
self.version_no = version_no
self.upgrade_no = upgrade_no
super(Version, self).__init__()
@a2l_node_type('A2ML_VERSION')
class A2MLVersion(Version):
pass
@a2l_node_type('ADDRESS_MAPPING')
class AddressMapping(A2lNode):
__slots__ = 'orig_address', 'mapping_address', 'length'
def __init__(self, orig_address, mapping_address, length):
self.orig_address = orig_address
self.mapping_address = mapping_address
self.length = length
super(AddressMapping, self).__init__()
@a2l_node_type('ANNOTATION')
class Annotation(A2lNode):
__slots__ = 'annotation_label', 'annotation_origin', 'annotation_text'
def __init__(self, args):
self.annotation_label = None
self.annotation_origin = None
self.annotation_text = None
super(Annotation, self).__init__(*args)
@a2l_node_type('ANNOTATION_TEXT')
class AnnotationText(A2lNode):
__slots__ = 'annotation_text',
def __init__(self, args):
self.annotation_text = list()
super(AnnotationText, self).__init__(*args)
@a2l_node_type('ASAP2_VERSION')
class ASAP2Version(Version):
pass
@a2l_node_type('AVAILABLE_EVENT_LIST')
class AvailableEventList(A2lNode):
__slots__ = 'event',
def __init__(self, args):
self.event = list()
super(AvailableEventList, self).__init__(*args)
@a2l_node_type('AXIS_DESCR')
class AxisDescr(A2lNode):
__slots__ = 'attribute', 'input_quantity', 'conversion', 'max_axis_points', 'lower_limit', 'upper_limit', \
'read_only', 'format', 'annotation', 'axis_pts_ref', 'max_grad', 'monotony', 'byte_order', \
'extended_limits', 'fix_axis_par', 'fix_axis_par_dist', 'fix_axis_par_list', 'deposit', 'curve_axis_ref'
def __init__(self, attribute, input_quantity, conversion, max_axis_points, lower_limit, upper_limit, args):
self.attribute = attribute
self.input_quantity = input_quantity
self.conversion = conversion
self.max_axis_points = max_axis_points
self.lower_limit = lower_limit
self.upper_limit = upper_limit
self.read_only = None
self.format = None
self.annotation = list()
self.axis_pts_ref = None
self.max_grad = None
self.monotony = None
self.byte_order = None
self.extended_limits = None
self.fix_axis_par = None
self.fix_axis_par_dist = None
self.fix_axis_par_list = None
self.deposit = None
self.curve_axis_ref = None
super(AxisDescr, self).__init__(*args)
@a2l_node_type('AXIS_PTS')
class AxisPts(A2lNode):
__slots__ = 'name', 'long_identifier', 'address', 'input_quantity', 'deposit', 'max_diff', 'conversion', \
'max_axis_points', 'lower_limit', 'upper_limit', 'display_identifier', 'read_only', 'format', \
'deposit', 'byte_order', 'function_list', 'ref_memory_segment', 'guard_rails', 'extended_limits', \
'annotation', 'if_data_axis_pts', 'calibration_access', 'ecu_address_extension'
def __init__(self, name, long_identifier, address, input_quantity, deposit, max_diff, conversion,
max_axis_points, lower_limit, upper_limit, args):
self.name = name
self.long_identifier = long_identifier
self.address = address
self.input_quantity = input_quantity
self.deposit = deposit
self.max_diff = max_diff
self.conversion = conversion
self.max_axis_points = max_axis_points
self.lower_limit = lower_limit
self.upper_limit = upper_limit
self.display_identifier = None
self.read_only = None
self.format = None
self.deposit = None
self.byte_order = None
self.function_list = None
self.ref_memory_segment = None
self.guard_rails = None
self.extended_limits = None
self.annotation = list()
self.if_data_axis_pts = list()
self.calibration_access = None
self.ecu_address_extension = None
super(AxisPts, self).__init__(*args)
class AxisPtsXYZ(A2lNode):
__slots__ = 'position', 'data_type', 'index_incr', 'addressing'
def __init__(self, position, data_type, index_incr, addressing):
self.position = position
self.data_type = data_type
self.index_incr = index_incr
self.addressing = addressing
super(AxisPtsXYZ, self).__init__()
@a2l_node_type('AXIS_PTS_X')
class AxisPtsX(AxisPtsXYZ):
pass
@a2l_node_type('AXIS_PTS_Y')
class AxisPtsY(AxisPtsXYZ):
pass
@a2l_node_type('AXIS_PTS_Z')
class AxisPtsZ(AxisPtsXYZ):
pass
class AxisRescale(A2lNode):
__slots__ = 'position', 'data_type', 'max_number_of_rescale_pairs', 'index_incr', 'addressing'
def __init__(self, position, data_type, max_number_of_rescale_pairs, index_incr, addressing):
self.position = position
self.data_type = data_type
self.max_number_of_rescale_pairs = max_number_of_rescale_pairs
self.index_incr = index_incr
self.addressing = addressing
super(AxisRescale, self).__init__()
@a2l_node_type('AXIS_RESCALE_X')
class AxisRescaleX(AxisRescale):
pass
@a2l_node_type('AXIS_RESCALE_Y')
class AxisRescaleY(AxisRescale):
pass
@a2l_node_type('AXIS_RESCALE_Z')
class AxisRescaleZ(AxisRescale):
pass
@a2l_node_type('BIT_OPERATION')
class BitOperation(A2lNode):
__slots__ = 'left_shift', 'right_shift', 'sign_extend'
def __init__(self, args):
self.left_shift = None
self.right_shift = None
self.sign_extend = None
super(BitOperation, self).__init__(*args)
@a2l_node_type('CALIBRATION_METHOD')
class CalibrationMethod(A2lNode):
__slots__ = 'method', 'version', 'calibration_handle'
def __init__(self, method, version, args):
self.method = method
self.version = version
self.calibration_handle = list()
super(CalibrationMethod, self).__init__(*args)
@a2l_node_type('CHARACTERISTIC')
class Characteristic(A2lNode):
__slots__ = 'name', 'long_identifier', 'type', 'address', 'deposit', 'max_diff', 'conversion', 'lower_limit', \
'upper_limit', 'display_identifier', 'format', 'byte_order', 'bit_mask', 'function_list', 'number', \
'extended_limits', 'read_only', 'guard_rails', 'map_list', 'max_refresh', 'dependent_characteristic', \
'virtual_characteristic', 'ref_memory_segment', 'annotation', 'comparison_quantity', \
'if_data_characteristic', 'axis_descr', 'calibration_access', 'matrix_dim', 'ecu_address_extension','symbol_link'
def __init__(self, name, long_identifier, type, address, deposit, max_diff, conversion, lower_limit,
upper_limit, args):
self.name = name
self.long_identifier = long_identifier
self.type = type
self.address = address
self.deposit = deposit
self.max_diff = max_diff
self.conversion = conversion
self.lower_limit = lower_limit
self.upper_limit = upper_limit
self.display_identifier = None
self.format = None
self.byte_order = None
self.bit_mask = None
self.function_list = None
self.number = None
self.symbol_link=None
self.extended_limits = None
self.read_only = None
self.guard_rails = None
self.map_list = None
self.max_refresh = None
self.dependent_characteristic = None
self.virtual_characteristic = None
self.ref_memory_segment = None
self.annotation = list()
self.comparison_quantity = None
self.if_data_characteristic = list()
self.axis_descr = list()
self.calibration_access = None
self.matrix_dim = None
self.ecu_address_extension = None
super(Characteristic, self).__init__(*args)
@a2l_node_type('CHECKSUM')
class Checksum(A2lNode):
__slots__ = 'checksum_dll', 'max_block_size'
def __init__(self, checksum_dll, max_block_size):
self.checksum_dll = checksum_dll
self.max_block_size = max_block_size
super(Checksum, self).__init__()
@a2l_node_type('COEFFS')
class Coeffs(A2lNode):
__slots__ = 'a', 'b', 'c', 'd', 'e', 'f'
def __init__(self, a, b, c, d, e, f):
self.a = a
self.b = b
self.c = c
self.d = d
self.e = e
self.f = f
super(Coeffs, self).__init__()
@a2l_node_type('COMPU_METHOD')
class CompuMethod(A2lNode):
__slots__ = 'name', 'long_identifier', 'conversion_type', 'format', 'unit', 'formula', 'coeffs', 'coeffs_linear', \
'compu_tab_ref', 'ref_unit'
def __init__(self, name, long_identifier, conversion_type, format, unit, args):
self.name = name
self.long_identifier = long_identifier
self.conversion_type = conversion_type
self.format = format
self.unit = unit
self.formula = None
self.coeffs = None
self.coeffs_linear = None # TODO: should be removed, according to 1.51.
self.compu_tab_ref = None
self.ref_unit = None
super(CompuMethod, self).__init__(*args)
@a2l_node_type('COMPU_TAB')
class CompuTab(A2lNode):
__slots__ = 'name', 'long_identifier', 'conversion_type', 'number_value_pairs', 'in_val_out_val', 'default_value', \
'default_value_numeric'
def __init__(self, name, long_identifier, conversion_type, number_value_pairs, args):
self.name = name
self.long_identifier = long_identifier
self.conversion_type = conversion_type
self.number_value_pairs = number_value_pairs
self.in_val_out_val = None
self.default_value = None
self.default_value_numeric = None # TODO: should be removed, according to 1.51.
super(CompuTab, self).__init__(*args)
@a2l_node_type('COMPU_VTAB')
class CompuVTab(A2lNode):
__slots__ = 'name', 'long_identifier', 'conversion_type', 'number_value_pairs', 'compu_vtab_in_val_out_val', \
'default_value'
def __init__(self, name, long_identifier, conversion_type, number_value_pairs, args):
self.name = name
self.long_identifier = long_identifier
self.conversion_type = conversion_type
self.number_value_pairs = number_value_pairs
self.compu_vtab_in_val_out_val = None # TODO: replace with in_val_out_val...
self.default_value = None
super(CompuVTab, self).__init__(*args)
@a2l_node_type('COMPU_VTAB_RANGE')
class CompuVTabRange(A2lNode):
__slots__ = 'name', 'long_identifier', 'number_value_triples', 'compu_vtab_range_in_val_out_val', 'default_value'
def __init__(self, name, long_identifier, number_value_triples, args):
self.name = name
self.long_identifier = long_identifier
self.number_value_triples = number_value_triples
self.compu_vtab_range_in_val_out_val = None # TODO: replace with in_val_min_in_val_max_out_val...
self.default_value = None
super(CompuVTabRange, self).__init__(*args)
@a2l_node_type('DAQ')
class Daq(A2lNode):
__slots__ = 'daq_config_type', 'max_daq', 'max_event_channel', 'min_daq', 'optimisation_type', \
'address_extension', 'identification_field_type', 'granularity_odt_entry', 'max_odt_entry_size_daq', \
'overload_indication', 'prescaler_supported', 'resume_supported', 'daq_list', 'timestamp_supported', \
'event', 'EVENT', 'IDENT', 'NUMERIC','stim'
def __init__(self,
daq_config_type,
max_daq,
max_event_channel,
min_daq,
optimisation_type,
address_extension,
identification_field_type,
granularity_odt_entry,
max_odt_entry_size_daq,
overload_indication,
args):
self.daq_config_type = daq_config_type
self.max_daq = max_daq
self.max_event_channel = max_event_channel
self.min_daq = min_daq
self.optimisation_type = optimisation_type
self.address_extension = address_extension
self.identification_field_type = identification_field_type
self.granularity_odt_entry = granularity_odt_entry
self.max_odt_entry_size_daq = max_odt_entry_size_daq
self.overload_indication = overload_indication
self.prescaler_supported = None
self.resume_supported = None
self.daq_list = list()
self.timestamp_supported = list()
self.stim = list()
self.event = list()
self.EVENT = list()
self.IDENT = list()
self.NUMERIC = list()
super(Daq, self).__init__(*args)
@a2l_node_type('DAQ_EVENT')
class DaqEvent(A2lNode):
__slots__ = 'name', 'available_event_list', 'default_event_list'
def __init__(self, name, args):
self.name = name
self.available_event_list = list()
self.default_event_list = list()
super(DaqEvent, self).__init__(*args)
@a2l_node_type('DAQ_LIST')
class DaqList(A2lNode):
__slots__ = 'daq_list_number', 'daq_list_type', 'max_odt', 'max_odt_entries', 'first_pid', 'event_fixed', \
'predefined'
def __init__(self, daq_list_number, args):
self.daq_list_number = daq_list_number
self.daq_list_type = None
self.max_odt = None
self.max_odt_entries = None
self.first_pid = None
self.event_fixed = None
self.predefined = list()
super(DaqList, self).__init__(*args)
@a2l_node_type('DAQ_LIST_CAN_ID')
class DaqListCanId(A2lNode):
__slots__ = 'identifier', 'daq_list_can_id_type_fixed', 'daq_list_can_id_type_variable'
def __init__(self, identifier, args):
self.identifier = identifier
self.daq_list_can_id_type_fixed = None
self.daq_list_can_id_type_variable = None
super(DaqListCanId, self).__init__(*args)
@a2l_node_type('DEFAULT_EVENT_LIST')
class DefaultEventList(A2lNode):
__slots__ = 'event',
def __init__(self, args):
self.event = list()
super(DefaultEventList, self).__init__(*args)
@a2l_node_type('DEF_CHARACTERISTIC')
class DefCharacteristic(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(DefCharacteristic, self).__init__(*args)
@a2l_node_type('DEPENDENT_CHARACTERISTIC')
class DependentCharacteristic(A2lNode):
__slots__ = 'formula', 'characteristic'
def __init__(self, formula, args):
self.formula = formula
self.characteristic = list()
super(DependentCharacteristic, self).__init__(*args)
class DistOp(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(DistOp, self).__init__()
@a2l_node_type('DIST_OP_X')
class DistOpX(DistOp):
pass
@a2l_node_type('DIST_OP_Y')
class DistOpY(DistOp):
pass
@a2l_node_type('DIST_OP_Z')
class DistOpZ(DistOp):
pass
@a2l_node_type('EVENT')
class Event(A2lNode):
__slots__ = 'name', 'short_name', 'event_channel_number', 'daq_list_type', 'max_daq_list', 'time_cycle', \
'time_unit', 'priority'
def __init__(self, name, short_name, event_channel_number, daq_list_type, max_daq_list, time_cycle, time_unit,
priority):
self.name = name
self.short_name = short_name
self.event_channel_number = event_channel_number
self.daq_list_type = daq_list_type
self.max_daq_list = max_daq_list
self.time_cycle = time_cycle
self.time_unit = time_unit
self.priority = priority
super(Event, self).__init__()
@a2l_node_type('EVENT_GROUP')
class EventGroup(A2lNode):
__slots__ = 'raster_grp_name', 'short_name', 'raster_id'
def __init__(self, raster_grp_name, short_name, raster_id):
self.raster_grp_name = raster_grp_name
self.short_name = short_name
self.raster_id = raster_id
super(EventGroup, self).__init__()
@a2l_node_type('FIX_AXIS_PAR')
class FixAxisPar(A2lNode):
__slots__ = 'offset', 'shift', 'numberapo'
def __init__(self, offset, shift, numberapo):
self.offset = offset
self.shift = shift
self.numberapo = numberapo
super(FixAxisPar, self).__init__()
@a2l_node_type('FIX_AXIS_PAR_DIST')
class FixAxisParDist(FixAxisPar):
__slots__ = 'offset', 'distance', 'numberapo'
def __init__(self, offset, distance, numberapo):
self.offset = offset
self.distance = distance
self.numberapo = numberapo
super(FixAxisPar, self).__init__()
class FixNoAxisPts(A2lNode):
__slots__ = 'number_of_axis_points',
def __init__(self, number_of_axis_points):
self.number_of_axis_points = number_of_axis_points
super(FixNoAxisPts, self).__init__()
@a2l_node_type('FIX_NO_AXIS_PTS_X')
class FixNoAxisPtsX(FixNoAxisPts):
pass
@a2l_node_type('FIX_NO_AXIS_PTS_Y')
class FixNoAxisPtsY(FixNoAxisPts):
pass
@a2l_node_type('FIX_NO_AXIS_PTS_Z')
class FixNoAxisPtsZ(FixNoAxisPts):
pass
@a2l_node_type('FNC_VALUES')
class FncValues(A2lNode):
__slots__ = 'position', 'data_type', 'index_mode', 'addresstype'
def __init__(self, position, data_type, index_mode, addresstype):
self.position = position
self.data_type = data_type
self.index_mode = index_mode
self.addresstype = addresstype
super(FncValues, self).__init__()
@a2l_node_type('FORMULA')
class Formula(A2lNode):
__slots__ = 'f', 'formula_inv'
def __init__(self, f, args):
self.f = f
self.formula_inv = None
super(Formula, self).__init__(*args)
@a2l_node_type('FRAME')
class Frame(A2lNode):
__slots__ = 'name', 'long_identifier', 'scaling_unit', 'rate', 'frame_measurement', 'if_data_frame'
def __init__(self, name, long_identifier, scaling_unit, rate, args):
self.name = name
self.long_identifier = long_identifier
self.scaling_unit = scaling_unit
self.rate = rate
self.frame_measurement = None
self.if_data_frame = list()
super(Frame, self).__init__(*args)
@a2l_node_type('FRAME_MEASUREMENT')
class FrameMeasurement(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(FrameMeasurement, self).__init__(*args)
@a2l_node_type('FUNCTION')
class Function(A2lNode):
__slots__ = 'name', 'long_identifier', 'annotation', 'def_characteristic', 'ref_characteristic', 'in_measurement', \
'out_measurement', 'loc_measurement', 'sub_function', 'function_version'
def __init__(self, name, long_identifier, args):
self.name = name
self.long_identifier = long_identifier
self.annotation = list()
self.def_characteristic = None
self.ref_characteristic = None
self.in_measurement = None
self.out_measurement = None
self.loc_measurement = None
self.sub_function = None
self.function_version = None
super(Function, self).__init__(*args)
@a2l_node_type('FUNCTION_LIST')
class FunctionList(A2lNode):
__slots__ = 'name',
def __init__(self, args):
self.name = list()
super(FunctionList, self).__init__(*args)
@a2l_node_type('GROUP')
class Group(A2lNode):
__slots__ = 'group_name', 'group_long_identifier', 'annotation', 'root', 'ref_characteristic', 'ref_measurement', \
'function_list', 'sub_group'
def __init__(self, group_name, group_long_identifier, args):
self.group_name = group_name
self.group_long_identifier = group_long_identifier
self.annotation = list()
self.root = None
self.ref_characteristic = None
self.ref_measurement = None
self.function_list = None
self.sub_group = None
super(Group, self).__init__(*args)
@a2l_node_type('HEADER')
class Header(A2lNode):
__slots__ = 'comment', 'version', 'project_no'
def __init__(self, comment, args):
self.comment = comment
self.version = None
self.project_no = None
super(Header, self).__init__(*args)
@a2l_node_type('IDENTIFICATION')
class Identification(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(Identification, self).__init__()
@a2l_node_type('if_data_frame')
class IfDataFrame(A2lNode):
__slots__ = 'name',
def __init__(self, name, args):
self.name = name
super(IfDataFrame, self).__init__(*args)
@a2l_node_type('if_data_memory_segment')
class IfDataMemorySegment(A2lNode):
__slots__ = 'name', 'address_mapping', 'segment', 'generic_parameter'
def __init__(self, name, args):
self.name = name
self.address_mapping = list()
self.segment = list()
self.generic_parameter = list()
super(IfDataMemorySegment, self).__init__(*args)
@a2l_node_type('if_data_module')
class IfDataModule(A2lNode):
__slots__ = 'name', 'source', 'raster', 'event_group', 'seed_key', 'checksum', 'tp_blob', 'tp_data','if_data_module_unsupported_element'
def __init__(self, name, args):
self.name = name
self.source = list()
self.raster = list()
self.event_group = list()
self.seed_key = None
self.checksum = None
self.tp_blob = None
self.tp_data = None
self.if_data_module_unsupported_element=None
super(IfDataModule, self).__init__(*args)
@a2l_node_type('if_data_xcp')
class IfDataXcp(A2lNode):
__slots__ = 'protocol_layer', 'daq', 'pag', 'pgm', 'segment', 'daq_event', 'xcp_on_can', 'generic_parameter_list'
def __init__(self, args):
self.protocol_layer = list()
self.daq = list()
self.pag = list()
self.pgm = list()
self.segment = list()
self.daq_event = list()
self.xcp_on_can = list()
self.generic_parameter_list = None
super(IfDataXcp, self).__init__(*args)
@a2l_node_type('IN_MEASUREMENT')
class InMeasurement(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(InMeasurement, self).__init__(*args)
@a2l_node_type('LOC_MEASUREMENT')
class LocMeasurement(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(LocMeasurement, self).__init__(*args)
@a2l_node_type('MAX_REFRESH')
class MaxRefresh(A2lNode):
__slots__ = 'scaling_unit', 'rate'
def __init__(self, scaling_unit, rate):
self.scaling_unit = scaling_unit
self.rate = rate
super(MaxRefresh, self).__init__()
@a2l_node_type('MEASUREMENT')
class Measurement(A2lNode):
__slots__ = 'name', 'long_identifier', 'data_type', 'conversion', 'resolution', 'accuracy', 'lower_limit', \
'upper_limit', 'display_identifier', 'read_write', 'format', 'array_size', 'bit_mask', \
'bit_operation', 'byte_order', 'max_refresh', 'virtual', 'function_list', 'ecu_address', 'error_mask', \
'ref_memory_segment', 'annotation', 'if_data_xcp', 'if_data_measurement', 'matrix_dim', \
'ecu_address_extension','symbol_link'
def __init__(self, name, long_identifier, data_type, conversion, resolution, accuracy, lower_limit,
upper_limit, args):
self.name = name
self.long_identifier = long_identifier
self.data_type = data_type
self.conversion = conversion
self.resolution = resolution
self.accuracy = accuracy
self.lower_limit = lower_limit
self.upper_limit = upper_limit
self.display_identifier = None
self.read_write = None
self.symbol_link = None
self.format = None
self.array_size = None
self.bit_mask = None
self.bit_operation = None
self.byte_order = None
self.max_refresh = None
self.virtual = None
self.function_list = None
self.ecu_address = None
self.error_mask = None
self.ref_memory_segment = None
self.annotation = list()
self.if_data_xcp = list()
self.if_data_measurement = list()
self.matrix_dim = None
self.ecu_address_extension = None
super(Measurement, self).__init__(*args)
@a2l_node_type('MEMORY_LAYOUT')
class MemoryLayout(A2lNode):
__slots__ = 'prg_type', 'address', 'size', 'offset', 'if_data_memory_layout'
def __init__(self, prg_type, address, size, offset, args):
self.prg_type = prg_type
self.address = address
self.size = size
self.offset = offset
self.if_data_memory_layout = list()
super(MemoryLayout, self).__init__(*args)
@a2l_node_type('MEMORY_SEGMENT')
class MemorySegment(A2lNode):
__slots__ = 'name', 'long_identifier', 'prg_type', 'memory_type', 'attribute', 'address', 'size', 'offset', \
'if_data_memory_segment', 'if_data_xcp'
def __init__(self, name, long_identifier, prg_type, memory_type, attribute, address, size, offset, args):
self.name = name
self.long_identifier = long_identifier
self.prg_type = prg_type
self.memory_type = memory_type
self.attribute = attribute
self.address = address
self.size = size
self.offset = offset
self.if_data_memory_segment = list()
self.if_data_xcp = list()
super(MemorySegment, self).__init__(*args)
@a2l_node_type('MODULE')
class Module(A2lNode):
__slots__ = 'name', 'long_identifier', 'a2ml', 'mod_par', 'mod_common', 'if_data_xcp', 'if_data_module', \
'characteristic', 'axis_pts', 'measurement', 'compu_method', 'compu_tab', 'compu_vtab', \
'compu_vtab_range', 'function', 'group', 'record_layout', 'variant_coding', 'frame', 'user_rights', \
'unit'
def __init__(self, name, long_identifier, args):
self.name = name
self.long_identifier = long_identifier
self.a2ml = None
self.mod_par = None
self.mod_common = None
self.if_data_xcp = None
self.if_data_module = list()
self.characteristic = list()
self.axis_pts = list()
self.measurement = list()
self.compu_method = list()
self.compu_tab = list()
self.compu_vtab = list()
self.compu_vtab_range = list()
self.function = list()
self.group = list()
self.record_layout = list()
self.variant_coding = None
self.frame = None
self.user_rights = list()
self.unit = list()
super(Module, self).__init__(*args)
@a2l_node_type('MOD_COMMON')
class ModCommon(A2lNode):
__slots__ = 'comment', 's_rec_layout', 'deposit', 'byte_order', 'data_size', 'alignment_byte', 'alignment_word', \
'alignment_long', 'alignment_float32_ieee', 'alignment_float64_ieee','alignment_int64'
def __init__(self, comment, args):
self.comment = comment
self.s_rec_layout = None
self.deposit = None
self.byte_order = None
self.data_size = None
self.alignment_byte = None
self.alignment_word = None
self.alignment_long = None
self.alignment_int64 = None
self.alignment_float32_ieee = None
self.alignment_float64_ieee = None
super(ModCommon, self).__init__(*args)
@a2l_node_type('MOD_PAR')
class ModPar(A2lNode):
__slots__ = 'comment', 'version', 'addr_epk', 'epk', 'supplier', 'customer', 'customer_no', 'user', 'phone_no', \
'ecu', 'cpu_type', 'no_of_interfaces', 'ecu_calibration_offset', 'calibration_method', \
'memory_layout', 'memory_segment', 'system_constant'
def __init__(self, comment, args):
self.comment = comment
self.version = None
self.addr_epk = list()
self.epk = None
self.supplier = None
self.customer = None
self.customer_no = None
self.user = None
self.phone_no = None
self.ecu = None
self.cpu_type = None
self.no_of_interfaces = None
self.ecu_calibration_offset = None
self.calibration_method = list()
self.memory_layout = list()
self.memory_segment = list()
self.system_constant = list()
super(ModPar, self).__init__(*args)
class NoAxisPts(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(NoAxisPts, self).__init__()
@a2l_node_type('NO_AXIS_PTS_X')
class NoAxisPtsX(NoAxisPts):
pass
@a2l_node_type('NO_AXIS_PTS_Y')
class NoAxisPtsY(NoAxisPts):
pass
@a2l_node_type('NO_AXIS_PTS_Z')
class NoAxisPtsZ(NoAxisPts):
pass
class NoRescale(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(NoRescale, self).__init__()
@a2l_node_type('NO_RESCALE_X')
class NoRescaleX(NoRescale):
pass
@a2l_node_type('NO_RESCALE_Y')
class NoRescaleY(NoRescale):
pass
@a2l_node_type('NO_RESCALE_Z')
class NoRescaleZ(NoRescale):
pass
class Offset(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(Offset, self).__init__()
@a2l_node_type('OFFSET_X')
class OffsetX(Offset):
pass
@a2l_node_type('OFFSET_Y')
class OffsetY(Offset):
pass
@a2l_node_type('OFFSET_Z')
class OffsetZ(Offset):
pass
@a2l_node_type('OUT_MEASUREMENT')
class OutMeasurement(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(OutMeasurement, self).__init__(*args)
@a2l_node_type('PAG')
class Pag(A2lNode):
__slots__ = 'max_segments', 'freeze_supported'
def __init__(self, max_segments, args):
self.max_segments = max_segments
self.freeze_supported = None
super(Pag, self).__init__(*args)
@a2l_node_type('PGM')
class Pgm(A2lNode):
__slots__ = 'mode', 'max_sectors', 'max_cto_pgm', 'sector', 'generic_parameter_list'
def __init__(self, mode, max_sectors, max_cto_pgm, args):
self.mode = mode
self.max_sectors = max_sectors
self.max_cto_pgm = max_cto_pgm
self.sector = list()
self.generic_parameter_list = None
super(Pgm, self).__init__(*args)
@a2l_node_type('PROJECT')
class Project(A2lNode):
__slots__ = 'name', 'long_identifier', 'header', 'module'
def __init__(self, name, long_identifier, args):
self.name = name
self.long_identifier = long_identifier
self.header = None
self.module = list()
super(Project, self).__init__(*args)
@a2l_node_type('PROTOCOL_LAYER')
class ProtocolLayer(A2lNode):
__slots__ = 'xcp_protocol_layer_version', 't1', 't2', 't3', 't4', 't5', 't6', 't7', 'max_cto', 'max_dto'
def __init__(self, xcp_protocol_layer_version, t1, t2, t3, t4, t5, t6, t7, max_cto, max_dto):
self.xcp_protocol_layer_version = xcp_protocol_layer_version
self.t1 = t1
self.t2 = t2
self.t3 = t3
self.t4 = t4
self.t5 = t5
self.t6 = t6
self.t7 = t7
self.max_cto = max_cto
self.max_dto = max_dto
super(ProtocolLayer, self).__init__()
@a2l_node_type('RASTER')
class Raster(A2lNode):
__slots__ = 'raster_name', 'short_name', 'raster_id', 'scaling_unit', 'rate'
def __init__(self, raster_name, short_name, raster_id, scaling_unit, rate):
self.raster_name = raster_name
self.short_name = short_name
self.raster_id = raster_id
self.scaling_unit = scaling_unit
self.rate = rate
super(Raster, self).__init__()
@a2l_node_type('RECORD_LAYOUT')
class RecordLayout(A2lNode):
__slots__ = 'name', 'fnc_values', 'identification', 'axis_pts_x', 'axis_pts_y', 'axis_pts_z', 'axis_rescale_x', \
'axis_rescale_y', 'axis_rescale_z', 'no_axis_pts_x', 'no_axis_pts_y', 'no_axis_pts_z', 'no_rescale_x', \
'no_rescale_y', 'no_rescale_z', 'fix_no_axis_pts_x', 'fix_no_axis_pts_y', 'fix_no_axis_pts_z', \
'src_addr_x', 'src_addr_y', 'src_addr_z', 'rip_addr_x', 'rip_addr_y', 'rip_addr_z', 'rip_addr_w', \
'shift_op_x', 'shift_op_y', 'shift_op_z', 'offset_x', 'offset_y', 'offset_z', 'dist_op_x', \
'dist_op_y', 'dist_op_z', 'alignment_byte', 'alignment_word', 'alignment_long', \
'alignment_float32_ieee', 'alignment_float64_ieee', 'reserved','alignment_int64'
def __init__(self, name, args):
self.name = name
self.fnc_values = None
self.identification = None
self.axis_pts_x = None
self.axis_pts_y = None
self.axis_pts_z = None
self.axis_rescale_x = None
self.axis_rescale_y = None
self.axis_rescale_z = None
self.no_axis_pts_x = None
self.no_axis_pts_y = None
self.no_axis_pts_z = None
self.no_rescale_x = None
self.no_rescale_y = None
self.no_rescale_z = None
self.fix_no_axis_pts_x = None
self.fix_no_axis_pts_y = None
self.fix_no_axis_pts_z = None
self.src_addr_x = None
self.src_addr_y = None
self.src_addr_z = None
self.rip_addr_x = None
self.rip_addr_y = None
self.rip_addr_z = None
self.rip_addr_w = None
self.shift_op_x = None
self.shift_op_y = None
self.shift_op_z = None
self.offset_x = None
self.offset_y = None
self.offset_z = None
self.dist_op_x = None
self.dist_op_y = None
self.dist_op_z = None
self.alignment_byte = None
self.alignment_word = None
self.alignment_long = None
self.alignment_float32_ieee = None
self.alignment_float64_ieee = None
self.alignment_int64 = None
self.reserved = list()
super(RecordLayout, self).__init__(*args)
@a2l_node_type('REF_CHARACTERISTIC')
class RefCharacteristic(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(RefCharacteristic, self).__init__(*args)
@a2l_node_type('REF_GROUP')
class RefGroup(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(RefGroup, self).__init__(*args)
@a2l_node_type('REF_MEASUREMENT')
class RefMeasurement(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(RefMeasurement, self).__init__(*args)
@a2l_node_type('RESERVED')
class Reserved(A2lNode):
__slots__ = 'position', 'data_size'
def __init__(self, position, data_size):
self.position = position
self.data_size = data_size
super(Reserved, self).__init__()
class RipAddr(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(RipAddr, self).__init__()
@a2l_node_type('RIP_ADDR_X')
class RipAddrX(RipAddr):
pass
@a2l_node_type('RIP_ADDR_Y')
class RipAddrY(RipAddr):
pass
@a2l_node_type('RIP_ADDR_Z')
class RipAddrZ(RipAddr):
pass
@a2l_node_type('RIP_ADDR_W')
class RipAddrW(RipAddr):
pass
@a2l_node_type('SECTOR')
class Sector(A2lNode):
__slots__ = 'name', 'sector_number', 'address', 'length', 'erase_number', 'program_number', 'programming_method'
def __init__(self, name, sector_number, address, length, erase_number, program_number, programming_method):
self.name = name
self.sector_number = sector_number
self.address = address
self.length = length
self.erase_number = erase_number
self.program_number = program_number
self.programming_method = programming_method
super(Sector, self).__init__()
@a2l_node_type('SEED_KEY')
class SeedKey(A2lNode):
__slots__ = 'cal_dll', 'daq_dll', 'pgm_dll'
def __init__(self, cal_dll, daq_dll, pgm_dll):
self.cal_dll = cal_dll
self.daq_dll = daq_dll
self.pgm_dll = pgm_dll
super(SeedKey, self).__init__()
@a2l_node_type('SEGMENT')
class Segment(A2lNode):
__slots__ = 'segment_logical_number', 'number_of_pages', 'address_extension', 'compression_method', \
'encryption_method', 'checksum', 'page'
def __init__(self, segment_logical_number, number_of_pages, address_extension, compression_method,
encryption_method, args):
self.segment_logical_number = segment_logical_number
self.number_of_pages = number_of_pages
self.address_extension = address_extension
self.compression_method = compression_method
self.encryption_method = encryption_method
self.checksum = None
self.page = None
super(Segment, self).__init__(*args)
class ShiftOp(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(ShiftOp, self).__init__()
@a2l_node_type('SHIFT_OP_X')
class ShiftOpX(ShiftOp):
pass
@a2l_node_type('SHIFT_OP_Y')
class ShiftOpY(ShiftOp):
pass
@a2l_node_type('SHIFT_OP_Z')
class ShiftOpZ(ShiftOp):
pass
@a2l_node_type('SI_EXPONENTS')
class SiExponents(A2lNode):
__slots__ = 'length', 'mass', 'time', 'electric_current', 'temperature', 'amount_of_substance', 'luminous_intensity'
def __init__(self, length, mass, time, electric_current, temperature, amount_of_substance,
luminous_intensity):
self.length = length
self.mass = mass
self.time = time
self.electric_current = electric_current
self.temperature = temperature
self.amount_of_substance = amount_of_substance
self.luminous_intensity = luminous_intensity
super(SiExponents, self).__init__()
@a2l_node_type('SOURCE')
class Source(A2lNode):
__slots__ = 'name', 'scaling_unit', 'rate', 'display_identifier', 'qp_blob', 'qp_data'
def __init__(self, name, scaling_unit, rate, args):
self.name = name
self.scaling_unit = scaling_unit
self.rate = rate
self.display_identifier = None
self.qp_blob = None
self.qp_data = None
super(Source, self).__init__(*args)
class SrcAddr(A2lNode):
__slots__ = 'position', 'data_type'
def __init__(self, position, data_type):
self.position = position
self.data_type = data_type
super(SrcAddr, self).__init__()
@a2l_node_type('SRC_ADDR_X')
class SrcAddrX(SrcAddr):
pass
@a2l_node_type('SRC_ADDR_Y')
class SrcAddrY(SrcAddr):
pass
@a2l_node_type('SRC_ADDR_Z')
class SrcAddrZ(SrcAddr):
pass
@a2l_node_type('SUB_FUNCTION')
class SubFunction(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(SubFunction, self).__init__(*args)
@a2l_node_type('SUB_GROUP')
class SubGroup(A2lNode):
__slots__ = 'identifier',
def __init__(self, args):
self.identifier = list()
super(SubGroup, self).__init__(*args)
@a2l_node_type('SYSTEM_CONSTANT')
class SystemConstant(A2lNode):
__slots__ = 'name', 'value'
def __init__(self, name, value):
self.name = name
self.value = value
super(SystemConstant, self).__init__()
@a2l_node_type('TIMESTAMP_SUPPORTED')
class TimestampSupported(A2lNode):
__slots__ = 'timestamp_ticks', 'size', 'unit', 'timestamp_fixed'
def __init__(self, timestamp_ticks, size, unit, args):
self.timestamp_ticks = timestamp_ticks
self.size = size
self.unit = unit
self.timestamp_fixed = None
super(TimestampSupported, self).__init__(*args)
@a2l_node_type('STIM')
class Stim(A2lNode):
def __init__(self,args):
super(Stim, self).__init__(*args)
@a2l_node_type('UNIT')
class Unit(A2lNode):
__slots__ = 'name', 'long_identifier', 'display', 'type', 'si_exponents', 'ref_unit', 'unit_conversion'
def __init__(self, name, long_identifier, display, type, args):
self.name = name
self.long_identifier = long_identifier
self.display = display
self.type = type
self.si_exponents = None
self.ref_unit = None
self.unit_conversion = None
super(Unit, self).__init__(*args)
@a2l_node_type('UNIT_CONVERSION')
class UnitConversion(A2lNode):
__slots__ = 'gradient', 'offset'
def __init__(self, gradient, offset):
self.gradient = gradient
self.offset = offset
super(UnitConversion, self).__init__()
@a2l_node_type('USER_RIGHTS')
class UserRights(A2lNode):
__slots__ = 'user_level_id', 'read_only', 'ref_group'
def __init__(self, user_level_id, args):
self.user_level_id = user_level_id
self.read_only = None
self.ref_group = list()
super(UserRights, self).__init__(*args)
@a2l_node_type('VARIANT_CODING')
class VariantCoding(A2lNode):
__slots__ = 'var_separator', 'var_naming', 'var_criterion', 'var_forbidden_comb', 'var_characteristic'
def __init__(self, args):
self.var_separator = None
self.var_naming = None
self.var_criterion = list()
self.var_forbidden_comb = list()
self.var_characteristic = list()
super(VariantCoding, self).__init__(*args)
@a2l_node_type('VAR_ADDRESS')
class VarAddress(A2lNode):
__slots__ = 'address',
def __init__(self, args):
self.address = list()
super(VarAddress, self).__init__(*args)
@a2l_node_type('VAR_CHARACTERISTIC')
class VarCharacteristic(A2lNode):
__slots__ = 'name', 'criterion_name', 'var_address'
def __init__(self, name, criterion_name, args):
self.name = name
self.criterion_name = criterion_name
self.var_address = None
super(VarCharacteristic, self).__init__(args)
@a2l_node_type('VAR_CRITERION')
class VarCriterion(A2lNode):
__slots__ = 'name', 'long_identifier', 'value', 'var_measurement', 'var_selection_characteristic'
def __init__(self, name, long_identifier, value, args):
self.name = name
self.long_identifier = long_identifier
self.value = value
self.var_measurement = None
self.var_selection_characteristic = None
super(VarCriterion, self).__init__(*args)
@a2l_node_type('VAR_FORBIDDEN_COMB')
class VarForbiddenComb(A2lNode):
__slots__ = 'criterion_name', 'criterion_value'
def __init__(self, *args):
self.criterion_name = list()
self.criterion_value = list()
super(VarForbiddenComb, self).__init__(*args)
@a2l_node_type('VIRTUAL_CHARACTERISTIC')
class VirtualCharacteristic(A2lNode):
__slots__ = 'formula', 'characteristic'
def __init__(self, formula, args):
self.formula = formula
self.characteristic = list()
super(VirtualCharacteristic, self).__init__(*args)
@a2l_node_type('XCP_ON_CAN')
class XcpOnCan(A2lNode):
__slots__ = 'identifier', 'can_id_broadcast', 'can_id_master', 'can_id_slave', 'baudrate', 'sample_point', \
'sample_rate', 'btl_cycles', 'sjw', 'sync_edge', 'daq_list_can_id'
def __init__(self, identifier, args):
self.identifier = identifier
self.can_id_broadcast = None
self.can_id_master = None
self.can_id_slave = None
self.baudrate = None
self.sample_point = None
self.sample_rate = None
self.btl_cycles = None
self.sjw = None
self.sync_edge = None
self.daq_list_can_id = list()
super(XcpOnCan, self).__init__(*args)
def a2l_node_factory(node_type, *args, **kwargs):
try:
return node_to_class[node_type](*args, **kwargs)
except KeyError:
raise NotImplementedError(str(node_type))
except:
raise
|
# -*- coding: utf-8 -*-
# Copyright: (c) 2019-2021, Dell EMC
"""Module for PowerStore constants"""
# HTTP constants
GET = 'GET'
POST = 'POST'
PUT = 'PUT'
DELETE = 'DELETE'
PATCH = 'PATCH'
# Default Connection Timeout in seconds
TIMEOUT = 120.0
# Pagination Constants
# offset is 0 and limit is 99 for first request
# offset for second request
OFFSET = 100
# max number of items limit in a response
MAX_LIMIT = 2000
# Query params
SELECT_ALL_VOLUME = {"select": "id,name,description,type,wwn,appliance_id,"
"state,size,creation_timestamp,"
"protection_policy_id,performance_policy_id,"
"protection_policy(name,id),"
"performance_policy(name,id),"
"is_replication_destination,"
"migration_session_id,"
"protection_data,location_history,type_l10n,"
"state_l10n,host_group(name,id),host(name,id),"
"volume_groups(name,id)"
}
SELECT_ALL_HOST = {"select": "id,name,description,os_type,"
"host_group_id,"
"host_initiators,os_type_l10n"
}
SELECT_ALL_HOST_GROUP = {"select": "name,id,description,hosts(id,name)"}
SELECT_ALL_VG = {"select": "id,name,description,creation_"
"timestamp, member_type,"
"is_protectable, protection_policy_id,"
"migration_session_id,"
"is_write_order_consistent,"
"placement_rule,type,"
"is_replication_destination,protection_data,"
"is_importing,location_history,"
"member_type_l10n,type_l10n,volumes"}
SELECT_ALL_VOL_GROUP = {"select": "id,name,description,creation_"
"timestamp,"
"is_protectable, protection_policy_id,"
"protection_policy(name,id),"
"migration_session_id,"
"is_write_order_consistent,"
"placement_rule,type,"
"is_replication_destination,protection_data,"
"is_importing,location_history,"
"type_l10n,volumes(name,id)"}
SELECT_ID_AND_NAME = {"select": "id,name"}
# For getting the list of appliances (mentioned in configuration.py)
SELECT_ID_NAME_AND_MODEL = {"select": "id,name,model"}
SELECT_ID = {"select": "id"}
SELECT_VERSION = {"select": "release_version"}
SELECT_ID_AND_PATH = {"select": "id,path"}
SELECT_ALL_HOST_VOLUME_MAPPING = {"select": "id, host_id, host_group_id,"
"logical_unit_number"}
SELECT_ALL_FILESYSTEM = {"select": "id,name, description,"
"parent_id, filesystem_type, size_total,size_used,"
"access_policy,locking_policy,"
"folder_rename_policy, is_smb_sync_writes_enabled,"
"is_smb_op_locks_enabled, is_smb_no_notify_enabled,"
"is_smb_notify_on_access_enabled,"
"is_smb_notify_on_write_enabled,"
"smb_notify_on_change_dir_depth,"
"is_async_MTime_enabled, is_quota_enabled,"
"grace_period, default_hard_limit,"
"default_soft_limit, creation_timestamp,"
"expiration_timestamp, last_refresh_timestamp,"
"last_writable_timestamp, is_modified,access_type,"
"creator_type, filesystem_type_l10n,"
"access_policy_l10n, locking_policy_l10n,"
"folder_rename_policy_l10n, access_type_l10n,"
"creator_type_l10n,nas_server(name,id),"
"protection_policy(name,id)"}
SELECT_ALL_NAS_SERVER = {"select": "id,name, description, operational_status,"
"current_node_id,preferred_node_id,"
"default_unix_user,default_windows_user,"
"current_unix_directory_service,"
"is_username_translation_enabled,"
"is_auto_user_mapping_enabled,"
"production_IPv4_interface_id,"
"production_IPv6_interface_id,"
"backup_IPv4_interface_id,"
"backup_IPv6_interface_id,"
"current_preferred_IPv4_interface_id,"
"current_preferred_IPv6_interface_id,"
"operational_status_l10n,"
"current_unix_directory_service_l10n,"
"file_interfaces(name,id,ip_address),"
"nfs_servers,smb_servers,"
"file_ldaps,file_nises,file_systems(id,name)"
}
SELECT_ALL_SMB_SHARE = {"select": "id,name,path,description,umask,"
"is_continuous_availability_enabled,"
"is_encryption_enabled,"
"is_ABE_enabled,is_branch_cache_enabled,"
"offline_availability,"
"file_system(id,name,filesystem_type,"
"nas_server(id,name))"}
# Select all tree quota
SELECT_ALL_TREE_QUOTA = {"select": "id,path,description,"
"is_user_quotas_enforced,state,"
"hard_limit,soft_limit,"
"remaining_grace_period,size_used,"
"file_system(id,name,filesystem_type,"
"nas_server(id,name))"}
# Select all user quota
SELECT_ALL_USER_QUOTA = {"select": "id,tree_quota_id,uid,unix_name,"
"windows_name,windows_sid,state,"
"hard_limit,soft_limit,"
"remaining_grace_period,size_used,"
"state_l10n,file_system(id,name,"
"filesystem_type,nas_server(id,name)),"
"tree_quota(path,description,hard_limit,"
"soft_limit,remaining_grace_period,"
"size_used)"}
# Select All NFS Export
SELECT_ALL_NFS_EXPORT = {"select": "id, name, file_system(id, name, "
"filesystem_type, nas_server(id, name)), "
"path, description, default_access, "
"min_security, nfs_owner_username, "
"no_access_hosts, read_only_hosts, "
"read_only_root_hosts, read_write_hosts, "
"read_write_root_hosts, anonymous_UID, "
"anonymous_GID, is_no_SUID, "
"default_access_l10n, min_security_l10n"}
# SELECT JOB DETAILS
JOB_DETAILS_QUERY = {
'select': 'id,resource_action,resource_type,resource_id,resource_name,'
'description_l10n,state,start_time,phase,end_time,'
'estimated_completion_time,progress_percentage,parent_id,'
'root_id,user,response_body,step_order,'
'resource_action_l10n,resource_type_l10n,state_l10n,phase_l10n'
}
# Select cluster details
CLUSTER_DETAILS_QUERY = {
'select': 'id,global_id,name,management_address,'
'storage_discovery_address,master_appliance_id,'
'appliance_count,physical_mtu,is_encryption_enabled,'
'compatibility_level,state,state_l10n'
}
# Network details
NETWORK_DETAILS_QUERY = {
'select': 'id,type,ip_version,vlan_id,prefix_length,'
'gateway,mtu,type_l10n,ip_version_l10n'
}
# Role details
ROLE_DETAILS_QUERY = {
'select': 'id,name,is_built_in,description'
}
# IP pool details
IP_DETAILS_QUERY = {
'select': 'id,network_id,ip_port_id,appliance_id,node_id,address,'
'purposes,purposes_l10n'
}
# CHAP config details
CHAP_CONFIG_DETAILS_QUERY = {
'select': 'id,mode,mode_l10n'
}
# Service config details
SERVICE_CONFIG_DETAILS_QUERY = {
'select': 'id,appliance_id,is_ssh_enabled'
}
# Service user details
SERVICE_USER_DETAILS_QUERY = {
'select': 'id,name,is_built_in,is_default_password'
}
# Local user details
LOCAL_USER_DETAILS_QUERY = {
'select': 'id,name,is_built_in,is_locked,is_default_password,role_id'
}
# IP port details
IP_PORT_DETAILS_QUERY = {
'select': 'id,partner_id,target_iqn,available_usages,current_usages,'
'bond_id,eth_port_id,veth_port_id,available_usages_l10n,'
'current_usages_l10n'
}
# vCenter details
VCENTER_DETAILS_QUERY = {
'select': 'id,instance_uuid,address,username'
}
# Appliance details
APPLIANCE_DETAILS_QUERY = {
'select': 'id,name,service_tag,express_service_code,model,nodes,'
'veth_ports,maintenance_windows,fc_ports,sas_ports,eth_ports,'
'software_installed,virtual_volumes,hardware,volumes,'
'ip_pool_addresses'
}
# Select all Snapshot
EQUALS = 'eq.'
# API endpoints
# Software version
GET_SOFTWARE_VERSION = 'https://{0}/api/rest/software_installed'
# Volume endpoints
VOLUME_CREATE_URL = 'https://{0}/api/rest/volume'
GET_VOLUME_LIST_URL = VOLUME_CREATE_URL
GET_VOLUME_DETAILS_URL = 'https://{0}/api/rest/volume/{1}'
MODIFY_VOLUME_URL = 'https://{0}/api/rest/volume/{1}'
DELETE_VOLUME_URL = MODIFY_VOLUME_URL
MAP_VOLUME_TO_HOST_URL = 'https://{0}/api/rest/volume/{1}/attach'
UNMAP_VOLUME_FROM_HOST_URL = 'https://{0}/api/rest/volume/{1}/detach'
RESTORE_VOLUME_FROM_SNAPSHOT_URL = 'https://{0}/api/rest/volume/{1}/restore'
GET_VOLUME_BY_NAME_URL = VOLUME_CREATE_URL
CREATE_VOLUME_SNAPSHOT_URL = 'https://{0}/api/rest/volume/{1}/snapshot'
# Host endpoints
GET_HOST_LIST_URL = 'https://{0}/api/rest/host'
GET_HOST_DETAILS_URL = 'https://{0}/api/rest/host/{1}'
CREATE_HOST_URL = GET_HOST_LIST_URL
MODIFY_HOST_URL = 'https://{0}/api/rest/host/{1}'
DELETE_HOST_URL = MODIFY_HOST_URL
ATTACH_HOST_URL = 'https://{0}/api/rest/host/{1}/attach'
DETACH_HOST_URL = 'https://{0}/api/rest/host/{1}/detach'
GET_HOST_BY_NAME_URL = GET_HOST_LIST_URL
# Hostgroup endpoints
GET_HOST_GROUP_LIST_URL = 'https://{0}/api/rest/host_group'
CREATE_HOST_GROUP_URL = GET_HOST_GROUP_LIST_URL
GET_HOST_GROUP_DETAILS_URL = 'https://{0}/api/rest/host_group/{1}'
MODIFY_HOST_GROUP_URL = GET_HOST_GROUP_DETAILS_URL
DELETE_HOST_GROUP_URL = GET_HOST_GROUP_DETAILS_URL
GET_HOST_GROUP_BY_NAME_URL = 'https://{0}/api/rest/host_group'
GET_HOSTS_BY_HOST_GROUP = GET_HOST_GROUP_LIST_URL
# Volume Group Endpoints
GET_VOLUME_GROUP_LIST_URL = 'https://{0}/api/rest/volume_group'
CREATE_VOLUME_GROUP_URL = GET_VOLUME_GROUP_LIST_URL
GET_VOLUME_GROUP_DETAILS_URL = 'https://{0}/api/rest/volume_group/{1}'
MODIFY_VOLUME_GROUP_URL = GET_VOLUME_GROUP_DETAILS_URL
DELETE_VOLUME_GROUP_URL = GET_VOLUME_GROUP_DETAILS_URL
ADD_MEMBERS_TO_VOLUME_GROUP_URL = \
'https://{0}/api/rest/volume_group/{1}/add_members'
REMOVE_MEMBERS_FROM_VOLUME_GROUP_URL = \
'https://{0}/api/rest/volume_group/{1}/remove_members'
GET_VOLUME_GROUP_BY_NAME_URL = GET_VOLUME_GROUP_LIST_URL
GET_VOLUMES_FROM_VOLUME_GROUP = GET_VOLUME_GROUP_LIST_URL
CREATE_VOLUME_GROUP_SNAPSHOT_URL = \
'https://{0}/api/rest/volume_group/{1}/snapshot'
# Cluster endpoints
GET_CLUSTER = 'https://{0}/api/rest/cluster'
# Node endpoints
GET_NODE = 'https://{0}/api/rest/node'
# Snapshot Rule endpoints
SNAPSHOT_RULE_LIST_URL = 'https://{0}/api/rest/snapshot_rule'
SNAPSHOT_RULE_OBJECT_URL = 'https://{0}/api/rest/snapshot_rule/{1}'
# Replication rule endpoints
REPLICATION_RULE_LIST_URL = 'https://{0}/api/rest/replication_rule'
REPLICATION_RULE_OBJECT_URL = 'https://{0}/api/rest/replication_rule/{1}'
# Replication session endpoints
REPLICATION_SESSION_LIST_URL = 'https://{0}/api/rest/replication_session'
REPLICATION_SESSION_OBJECT_URL = 'https://{0}/api/rest/replication_session/{1}'
REPLICATION_SESSION_SYNC_URL = 'https://{0}/api/rest/replication_session/{1}/sync'
REPLICATION_SESSION_PAUSE_URL = 'https://{0}/api/rest/replication_session/{1}/pause'
REPLICATION_SESSION_RESUME_URL = 'https://{0}/api/rest/replication_session/{1}/resume'
REPLICATION_SESSION_FAILOVER_URL = 'https://{0}/api/rest/replication_session/{1}/failover'
REPLICATION_SESSION_REPROTECT_URL = 'https://{0}/api/rest/replication_session/{1}/reprotect'
# Remote system endpoints
REMOTE_SYSTEM_LIST_URL = 'https://{0}/api/rest/remote_system'
REMOTE_SYSTEM_OBJECT_URL = 'https://{0}/api/rest/remote_system/{1}'
# Protection Policy endpoint
PROTECTION_POLICY_LIST_URL = 'https://{0}/api/rest/policy'
PROTECTION_POLICY_OBJECT_URL = 'https://{0}/api/rest/policy/{1}'
# Host Volume Mapping endpoints
HOST_VOLUME_MAPPING_URL = 'https://{0}/api/rest/host_volume_mapping'
# NAS Server endpoints
GET_NAS_SERVER_LIST_URL = 'https://{0}/api/rest/nas_server'
GET_NAS_SERVER_DETAILS_URL = 'https://{0}/api/rest/nas_server/{1}'
GET_NAS_SERVER_DETAILS_BY_NAME_URL = GET_NAS_SERVER_LIST_URL
MODIFY_NAS_SERVER_URL = GET_NAS_SERVER_DETAILS_URL
# NFS Export endpoints
GET_NFS_EXPORT_LIST_URL = 'https://{0}/api/rest/nfs_export'
GET_NFS_EXPORT_DETAILS_URL = 'https://{0}/api/rest/nfs_export/{1}'
GET_NFS_EXPORT_DETAILS_BY_NAME_URL = GET_NFS_EXPORT_LIST_URL
CREATE_NFS_EXPORT_URL = GET_NFS_EXPORT_LIST_URL
MODIFY_NFS_EXPORT_URL = GET_NFS_EXPORT_DETAILS_URL
DELETE_NFS_EXPORT_URL = GET_NFS_EXPORT_DETAILS_URL
# SMB Share endpoints
GET_SMB_SHARE_LIST_URL = 'https://{0}/api/rest/smb_share'
CREATE_SMB_SHARE_URL = GET_SMB_SHARE_LIST_URL
GET_SMB_SHARE_DETAILS_URL = 'https://{0}/api/rest/smb_share/{1}'
MODIFY_SMB_SHARE_URL = GET_SMB_SHARE_DETAILS_URL
DELETE_SMB_SHARE_URL = GET_SMB_SHARE_DETAILS_URL
# File Tree Quota endpoints
GET_TREE_QUOTA_LIST_URL = 'https://{0}/api/rest/file_tree_quota'
CREATE_TREE_QUOTA_URL = GET_TREE_QUOTA_LIST_URL
GET_TREE_QUOTA_DETAILS_URL = 'https://{0}/api/rest/file_tree_quota/{1}'
MODIFY_TREE_QUOTA_URL = GET_TREE_QUOTA_DETAILS_URL
DELETE_TREE_QUOTA_URL = GET_TREE_QUOTA_DETAILS_URL
# File User Quota endpoints
GET_USER_QUOTA_LIST_URL = 'https://{0}/api/rest/file_user_quota'
CREATE_USER_QUOTA_URL = GET_USER_QUOTA_LIST_URL
GET_USER_QUOTA_DETAILS_URL = 'https://{0}/api/rest/file_user_quota/{1}'
MODIFY_USER_QUOTA_URL = GET_USER_QUOTA_DETAILS_URL
# File System endpoints
GET_FILE_SYSTEM_LIST_URL = 'https://{0}/api/rest/file_system'
GET_FILESYSTEM_DETAILS_URL = 'https://{0}/api/rest/file_system/{1}'
GET_FILESYSTEM_DETAILS_BY_NAME_URL = GET_FILE_SYSTEM_LIST_URL
CREATE_FILESYSTEM_URL = GET_FILESYSTEM_DETAILS_BY_NAME_URL
DELETE_FILESYSTEM_URL = GET_FILESYSTEM_DETAILS_URL
MODIFY_FILESYSTEM_URL = GET_FILESYSTEM_DETAILS_URL
CREATE_FILESYSTEM_SNAPSHOT_URL = 'https://{0}/api/rest/file_system/{1}/' \
'snapshot'
GET_SNAPSHOTS_FILESYSTEM_URL = GET_FILE_SYSTEM_LIST_URL
# Network endpoints
GET_NETWORK_DETAILS_URL = 'https://{0}/api/rest/network/{1}'
GET_NETWORK_LIST_URL = 'https://{0}/api/rest/network'
MODIFY_NETWORK_URL = GET_NETWORK_DETAILS_URL
ADD_REMOVE_IP_PORTS = 'https://{0}/api/rest/network/{1}/scale'
# Role endpoints
GET_ROLE_LIST_URL = 'https://{0}/api/rest/role'
GET_ROLE_DETAILS_URL = 'https://{0}/api/rest/role/{1}'
# Logout endpoint
LOGOUT_URL = 'https://{0}/api/rest/logout'
# Login session endpoint
LOGIN_SESSION = 'https://{0}/api/rest/login_session'
# Local_user endpoints
GET_LOCAL_USER_LIST_URL = 'https://{0}/api/rest/local_user'
# IP Pool Address endpoint
GET_IP_POOL_LIST_URL = 'https://{0}/api/rest/ip_pool_address'
# Cluster endpoints
GET_CLUSTER_DETAILS_URL = 'https://{0}/api/rest/cluster/{1}'
GET_CLUSTER_LIST_URL = 'https://{0}/api/rest/cluster'
MODIFY_CLUSTER_URL = GET_CLUSTER_DETAILS_URL
# CHAP config endpoints
GET_CHAP_CONFIG_LIST_URL = 'https://{0}/api/rest/chap_config'
GET_CHAP_CONFIG_DETAILS_URL = 'https://{0}/api/rest/chap_config/{1}'
MODIFY_CHAP_CONFIG_URL = GET_CHAP_CONFIG_DETAILS_URL
# Service config endpoints
GET_SERVICE_CONFIG_LIST_URL = 'https://{0}/api/rest/service_config'
GET_SERVICE_CONFIG_DETAILS_URL = 'https://{0}/api/rest/service_config/{1}'
MODIFY_SERVICE_CONFIG_URL = GET_SERVICE_CONFIG_DETAILS_URL
# Service user endpoints
GET_SERVICE_USER_LIST_URL = 'https://{0}/api/rest/service_user'
GET_SERVICE_USER_DETAILS_URL = 'https://{0}/api/rest/service_user/{1}'
MODIFY_SERVICE_USER_URL = GET_SERVICE_USER_DETAILS_URL
# Local user endpoints
GET_LOCAL_USER_LIST_URL = 'https://{0}/api/rest/local_user'
GET_LOCAL_USER_DETAILS_URL = 'https://{0}/api/rest/local_user/{1}'
MODIFY_LOCAL_USER_URL = GET_LOCAL_USER_DETAILS_URL
DELETE_LOCAL_USER_URL = GET_LOCAL_USER_DETAILS_URL
CREATE_LOCAL_USER_URL = GET_LOCAL_USER_LIST_URL
# IP port endpoints
GET_IP_PORT_DETAILS_URL = 'https://{0}/api/rest/ip_port/{1}'
# Job endpoints
GET_JOB_DETAILS_URL = 'https://{0}/api/rest/job/{1}'
# vCenter endpoints
GET_VCENTER_LIST_URL = 'https://{0}/api/rest/vcenter'
GET_VCENTER_DETAILS_URL = 'https://{0}/api/rest/vcenter/{1}'
MODIFY_VCENTER_URL = GET_VCENTER_DETAILS_URL
# Appliance endpoints
GET_APPLIANCE_LIST_URL = 'https://{0}/api/rest/appliance'
GET_APPLIANCE_DETAILS_URL = 'https://{0}/api/rest/appliance/{1}'
|
# Faça um algoritmo em Python que leia dois valores inteiros A e B se os valores forem iguais deverá se somar os dois, caso contrário multiplique A por B. Ao final de qualquer um dos cálculos deve-se atribuir o resultado para uma variável C e mostrar seu conteúdo na tela.
A = int(input("Digite o valor de A: "))
B = int(input("Digite o valor de B: "))
if A == B:
C = A + B
else:
C = A * B
print(C)
|
#!/usr/bin/python3
#-- Use pprint module..
x=[[1,2,3,4],[11,22,33,44],[111,222,333,444]]
#-- Use printf style formatting..
x = 22/7
#-- Write some multivariate for loops..
for x,y in [[1,2]]:
print('x',x,'y',y)
for x in enumerate(range(5)):
print('x',x)
#-- Compute diagonal sums with lambdas..
m=[
[ 5, 2, 3, 4, 1],
[ 10, 40, 30, 20, 50],
[ 100, 200, 300, 400, 500],
[ 1001, 4000, 3000, 2000, 5000],
[50000,20000,30000,40000,10000]
]
#-- Use a list comprehension to produce a list of lists..
m=8
n=9
#-- Use a dictionary comprehension..
#-- Obtain user input to fill a list with integers (use split and strip)..
#-- Open a file, write to a file..
#-- Develop a class..
#-- Use list unpacking to pass variable number of args..
#-- Use dictionary unpacking to combine two dictionaries..
#-- Use 'zip()' in an example..
#-- Use regular expressions..
#-- Convert this to a simpler expression using 'in'..
#
# if socket.gethostname() == "bristle" or socket.gethostname() == "rete":
# DEBUG = False
# else:
# DEBUG = True
#-- Write something using closures..
#-- Write something using decorators..
|
def e_letra(a):
return a in 'AaBbCcDdEeFfGgHhIiJjKkLlMmNnOoPpQqRrSsTUuVvWwXxYyZz'
def main():
a = str(input("Digite uma consoante ou vogal: "))
resultado = e_letra(a)
print(f'{a} é faz parte do alfabeto? {resultado} ')
if __name__ == '__main__':
main() |
# -*- coding: utf-8 -*-
class ExampleLibraryException(Exception):
'''It is a good practice to throw library specific exceptions so
that you know where the exception is comming'''
pass
class ExampleLibrary(object):
'''Libraries should be documented according to Robot Framework User Guide'''
def library_keyword(self):
'''Document keywords as well'''
return True
|
def getNumericVal(number):
if number == 1:
return 3
elif number == 2:
return 3
elif number == 3:
return 5
elif number == 4:
return 4
elif number == 5:
return 4
elif number == 6:
return 3
elif number == 7:
return 5
elif number == 8:
return 5
elif number == 9:
return 4
def lessThan20(number):
if number < 10:
return getNumericVal(number)
if number == 10:
return 3
elif number == 11:
return 6
elif number == 12:
return 6
elif number == 13:
return 8
elif number == 14:
return 8
elif number == 15:
return 7
elif number == 16:
return 7
elif number == 17:
return 9
elif number == 18:
return 8
elif number == 19:
return 8
def lessThan100(number):
if number < 20:
return lessThan20(number)
value1 = number % 10
value2 = number // 10
if value2 == 2:
if value1 == 0:
return 6
else:
return 6 + getNumericVal(value1)
elif value2 == 3:
if value1 == 0:
return 6
else:
return 6 + getNumericVal(value1)
elif value2 == 4:
if value1 == 0:
return 5
else:
return 5 + getNumericVal(value1)
elif value2 == 5:
if value1 == 0:
return 5
else:
return 5 + getNumericVal(value1)
elif value2 == 6:
if value1 == 0:
return 5
else:
return 5 + getNumericVal(value1)
elif value2 == 7:
if value1 == 0:
return 7
else:
return 7 + getNumericVal(value1)
elif value2 == 8:
if value1 == 0:
return 6
else:
return 6 + getNumericVal(value1)
elif value2 == 9:
if value1 == 0:
return 6
else:
return 6 + getNumericVal(value1)
def lessThan1000(number):
temp = number
value1 = temp % 10
temp = temp // 10
value2 = temp % 10
temp = temp // 10
value3 = temp
if value1 == 0 and value2 == 0:
return getNumericVal(value3) + 7
else:
return getNumericVal(value3) + 7 + 3 + lessThan100(10 * value2 + value1)
def countLetters(max_val):
count = 0
for i in range(1, max_val + 1):
if i <= 9:
count += getNumericVal(i)
elif i < 100:
count += lessThan100(i)
elif i < 1000:
count += lessThan1000(i)
elif i == 1000:
count += 11
return count
print(countLetters(1000))
|
class BaseError(Exception):
pass
class RecordNotFound(BaseError):
pass
|
# liczby Fibonacciego
def fibRek(n):
if n == 0 or n == 1:
return n
else:
return fibRek(n-1) + fibRek(n-2)
def fib(n):
"""Wypisuje liczby Fibonacciego mniejsze niz n
"""
a, b = 0, 1
while b < n:
print (b)
a, b = b, a+b
def fib2(n):
"""Zwraca liste liczb Fibonacciego mniejszych niz n
"""
result = []
a, b = 0, 1
while b < n:
result.append(b)
a, b = b, a+b
return result
def silnia(n):
if n == 0:
return 1
elif n > 0:
return n * silnia(n-1)
else:
return (-1)*silnia((-1)*n)
def a(n):
return n
def b(n):
return a(n)
|
"""
Copyright 2018 abbas ehsanfar
"""
"""
genfigs: generate figures
""" |
## Para operações de saída, o CST deve ser preenchido com os valores de 01 a 49 ou 99.
##
##
def exec(conexao):
cursor = conexao.cursor()
print("RULE 08 - Inicializando",end=' ')
update = " UPDATE principal SET "
update = update + " r2 = \"49\" "
update = update + " WHERE 1=1 "
update = update + " and r1 = \"C181\" "
update = update + " and r2 in (\"00\",\"99\") "
update = update + " and r3 in (\"5152\",\"5102\") "
cursor.execute(update)
conexao.commit()
print('-',end=' ')
update = " UPDATE principal SET "
update = update + " r2 = \"49\" "
update = update + " WHERE 1=1 "
update = update + " and r1 = \"C185\" "
update = update + " and r2 in (\"00\",\"99\") "
update = update + " and r3 in (\"5152\",\"5102\") "
cursor.execute(update)
conexao.commit()
print('-',end=' ')
print("Finalizado") |
class ArmatureActuator:
bone = None
constraint = None
influence = None
mode = None
secondary_target = None
target = None
weight = None
|
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def mergeTwoLists(self, list1: Optional[ListNode], list2: Optional[ListNode]) -> Optional[ListNode]:
cur = dummy = ListNode()
while list1 and list2:
if list1.val < list2.val:
cur.next = list1
list1, cur = list1.next, list1
else:
cur.next = list2
list2, cur = list2.next, list2
if list1 or list2:
cur.next = list1 if list1 else list2
return dummy.next |
file_name = "nameslist.txt"
names_list = open(file_name, "rt").read().splitlines(False)
print(names_list)
names_count = {}
for name in names_list:
if name not in names_count:
names_count[name] = 0
names_count[name] += 1
print(names_count) |
n, d = map(int,input().split())
l=[]
for i in range(n):
l1=list(map(int,input().split()))
l.append(l1)
l=sorted(l)
i, j, ans, s = 0, 0, 0, 0
while j<n:
if l[i][0]+d > l[j][0]:
s+=l[j][1]
j+=1
else:
s-=l[i][1]
i+=1
ans=max(ans,s)
print(ans) |
class Atom:
def __init__(self):
pass
def __repr__(self):
raise NotImplementedError()
class Tmp(Atom):
__slots__ = ['tmp_idx']
def __init__(self, tmp_idx):
super(Tmp, self).__init__()
self.tmp_idx = tmp_idx
def __repr__(self):
return "<Tmp %d>" % self.tmp_idx
def __eq__(self, other):
return type(other) is Tmp and \
self.tmp_idx == other.tmp_idx
def __hash__(self):
return hash(('tmp', self.tmp_idx))
class Register(Atom):
__slots__ = ['reg_offset', 'size']
def __init__(self, reg_offset, size):
super(Register, self).__init__()
self.reg_offset = reg_offset
self.size = size
def __repr__(self):
return "<Reg %d<%d>>" % (self.reg_offset, self.size)
def __eq__(self, other):
return type(other) is Register and \
self.reg_offset == other.reg_offset and \
self.size == other.size
def __hash__(self):
return hash(('reg', self.reg_offset, self.size))
class MemoryLocation(Atom):
__slots__ = ['addr', 'size']
def __init__(self, addr, size):
super(MemoryLocation, self).__init__()
self.addr = addr
self.size = size
def __repr__(self):
return "<Mem %s<%d>>" % (hex(self.addr) if type(self.addr) is int else self.addr, self.size)
@property
def bits(self):
return self.size * 8
@property
def symbolic(self):
return not type(self.addr) is int
def __eq__(self, other):
return type(other) is MemoryLocation and \
self.addr == other.addr and \
self.size == other.size
def __hash__(self):
return hash(('mem', self.addr, self.size))
class Parameter(Atom):
__slots__ = ['value', 'type_', 'meta']
def __init__(self, value, type_=None, meta=None):
super(Parameter, self).__init__()
self.value = value
self.type_ = type_
self.meta = meta
def __repr__(self):
type_ = ', type=%s' % self.type_ if self.type_ is not None else ''
meta = ', meta=%s' % self.meta if self.meta is not None else ''
return '<Param %s%s%s>' % (self.value, type_, meta)
def __eq__(self, other):
return type(other) is Parameter and \
self.value == other.value and \
self.type_ == other.type_ and \
self.meta == other.meta
|
'''Elabore um programa que calcule o valor a ser pago por um produto,
considerando o seu preço normal e condição de pagamento:
– à vista dinheiro/cheque: 10% de desconto
– à vista no cartão: 5% de desconto
– em até 2x no cartão: preço formal
– 3x ou mais no cartão: 20% de juros'''
print("{:=^40}".format(" LOJAS GUANABARA "))
preco = float(input("Preço das compras R$ "))
print("FORMAS DE PAGAMENTO")
print("[ 1 ] à vista Dinheiro\Cheque")
print("[ 2 ] à vista no Cartão")
print("[ 3 ] 2x no Cartão")
print("[ 4 ] 3x no Cartão")
opcao = int(input("Qual é a opção: "))
if opcao == 1:
print("Sua compra de R${:.2f} vai custar R${:.2f} no final".format(preco,(preco - preco * 10/100)))
elif opcao == 2:
print("Sua compra de R${:.2f} vai custar R${:.2f} no final".format(preco,(preco - preco * 5/100)))
elif opcao == 3:
print("Sua compra de R${:.2f} sera parcelada em 2x sem juros.\nO valor de cada parcela será R${:.2f}.".format(preco, preco/2))
elif opcao == 4:
parcelas = int(input("Quantas parcelas: "))
precoFinal = preco + (preco * 20/100)
print("Sua compra sera parcelada em {}x de R${:.2f} COM JUROS.".format(parcelas,precoFinal/parcelas))
print("Sua compra de R${:.2f} vai custar R${:.2f} no final.".format(preco, precoFinal))
else:
print("Opção invalida, tente novamente")
|
# Utilities and color maps for plotting
# Colours from https://s-rip.ees.hokudai.ac.jp/mediawiki/index.php/Notes_for_Authors
reanalysis_color = {
'MERRA2' :'#e21f26',
'MERRA' :'#f69999',
'ERAI' :'#295f8a',
'ERA5' :'#5f98c6',
'ERA40' :'#afcbe3',
'JRA55' :'#723b7a',
'JRA55C' :'#ad71b5',
'JRA25' :'#d6b8da',
'NCEP1' :'#f57e20',
'NCEP2' :'#fdbf6e',
'20CRV2C' :'#ec008c',
'20CRV2' :'#f799D1',
'CERA20C' :'#00aeef',
'ERA20C' :'#60c8e8',
'CFSR' :'#34a048',
'REM' :'#b35b28', # reanalysis ensemble mean
'OTHER' :'#ffd700',
'OBS' :'#000000', # observations black
'OBS2' :'#777777',
} # observations grey
|
# maximum_subarray_sum.py
# https://www.codewars.com/kata/54521e9ec8e60bc4de000d6c
def max_sequence(arr):
if len(arr) == 0:
return 0
all_elements_negative = True
for item in arr:
if item > 0:
all_elements_negative = False
break
if all_elements_negative:
return 0
maximum_sum = max(arr)
current_sum = 0
max_subarray = []
for i in range(len(arr)):
current_sum = 0
for j in range(i, len(arr)):
current_sum += arr[j]
print(i, j, current_sum, arr[i:j+1])
if current_sum > maximum_sum:
maximum_sum = current_sum
max_subarray = arr[i:j+1]
return maximum_sum
if __name__ == "__main__":
print(max_sequence([-2, 1, -3, 4, -1, 2, 1, -5, 4]))
|
"""
PASSENGERS
"""
numPassengers = 22944
passenger_arriving = (
(5, 6, 5, 9, 4, 1, 2, 3, 2, 1, 1, 1, 0, 7, 6, 8, 4, 8, 2, 3, 1, 0, 1, 1, 1, 0), # 0
(7, 6, 3, 10, 7, 1, 1, 1, 6, 1, 2, 1, 0, 3, 5, 7, 7, 5, 4, 5, 0, 2, 2, 0, 1, 0), # 1
(5, 3, 10, 5, 3, 3, 4, 2, 1, 1, 1, 1, 0, 9, 9, 4, 3, 5, 2, 4, 2, 4, 1, 1, 0, 0), # 2
(7, 12, 10, 9, 4, 3, 10, 0, 2, 0, 0, 1, 0, 9, 9, 7, 3, 10, 1, 1, 0, 1, 2, 0, 0, 0), # 3
(8, 6, 6, 8, 6, 3, 3, 8, 1, 2, 1, 1, 0, 13, 8, 4, 7, 5, 3, 2, 1, 6, 0, 1, 0, 0), # 4
(9, 12, 7, 8, 6, 3, 5, 5, 5, 2, 2, 1, 0, 4, 11, 8, 6, 7, 4, 5, 3, 0, 0, 1, 0, 0), # 5
(6, 8, 6, 4, 8, 3, 1, 4, 5, 5, 0, 3, 0, 6, 7, 3, 3, 10, 6, 6, 4, 1, 4, 1, 1, 0), # 6
(4, 4, 10, 5, 7, 6, 8, 4, 6, 3, 0, 2, 0, 10, 6, 3, 6, 7, 4, 1, 2, 0, 2, 3, 1, 0), # 7
(10, 7, 7, 11, 6, 1, 7, 8, 4, 1, 1, 0, 0, 11, 16, 9, 3, 8, 8, 4, 2, 4, 2, 1, 2, 0), # 8
(6, 6, 6, 11, 2, 3, 1, 1, 3, 1, 0, 1, 0, 9, 8, 7, 5, 6, 5, 3, 0, 0, 3, 0, 0, 0), # 9
(7, 8, 10, 5, 6, 4, 2, 5, 2, 0, 1, 1, 0, 13, 12, 5, 5, 9, 6, 3, 0, 4, 2, 2, 3, 0), # 10
(8, 15, 6, 15, 14, 3, 4, 0, 1, 2, 2, 1, 0, 8, 12, 10, 3, 8, 4, 4, 3, 3, 2, 0, 3, 0), # 11
(11, 13, 9, 12, 5, 2, 4, 3, 1, 2, 2, 0, 0, 11, 12, 8, 8, 9, 5, 5, 2, 6, 5, 4, 0, 0), # 12
(11, 14, 3, 13, 3, 4, 6, 4, 6, 2, 1, 1, 0, 13, 8, 7, 6, 6, 4, 3, 1, 2, 5, 6, 1, 0), # 13
(10, 11, 4, 9, 12, 4, 8, 5, 3, 0, 3, 0, 0, 8, 8, 6, 9, 7, 5, 3, 3, 2, 4, 3, 1, 0), # 14
(11, 7, 8, 14, 11, 3, 5, 3, 4, 5, 0, 1, 0, 11, 10, 6, 10, 6, 5, 3, 4, 4, 4, 2, 1, 0), # 15
(14, 17, 7, 10, 9, 3, 3, 6, 5, 3, 0, 1, 0, 11, 10, 6, 8, 8, 6, 4, 6, 6, 3, 5, 2, 0), # 16
(7, 11, 8, 5, 10, 3, 4, 5, 5, 2, 1, 1, 0, 9, 8, 6, 6, 9, 4, 6, 5, 4, 4, 4, 0, 0), # 17
(10, 10, 8, 13, 12, 4, 3, 5, 3, 1, 1, 0, 0, 14, 11, 6, 6, 3, 10, 4, 3, 2, 5, 4, 2, 0), # 18
(16, 6, 10, 8, 5, 4, 8, 3, 7, 4, 0, 0, 0, 5, 15, 6, 12, 12, 6, 5, 5, 6, 4, 1, 2, 0), # 19
(15, 10, 7, 9, 11, 7, 2, 4, 3, 4, 0, 1, 0, 9, 16, 9, 6, 9, 6, 7, 3, 4, 6, 1, 0, 0), # 20
(17, 17, 8, 17, 11, 4, 5, 5, 8, 4, 1, 2, 0, 10, 10, 11, 4, 8, 9, 6, 1, 5, 9, 2, 1, 0), # 21
(17, 19, 15, 12, 12, 4, 7, 5, 8, 3, 2, 2, 0, 11, 13, 10, 9, 5, 9, 3, 6, 7, 3, 3, 0, 0), # 22
(7, 11, 6, 12, 11, 4, 7, 3, 3, 1, 2, 0, 0, 19, 9, 4, 5, 9, 9, 5, 5, 5, 2, 4, 2, 0), # 23
(15, 11, 8, 8, 15, 7, 5, 4, 6, 2, 3, 0, 0, 13, 8, 12, 6, 11, 6, 5, 2, 7, 4, 2, 1, 0), # 24
(18, 17, 17, 5, 5, 7, 8, 6, 6, 1, 2, 0, 0, 14, 6, 6, 4, 13, 4, 4, 1, 8, 5, 0, 0, 0), # 25
(14, 10, 8, 9, 11, 4, 4, 4, 4, 1, 2, 2, 0, 19, 11, 9, 6, 9, 11, 6, 1, 1, 4, 5, 1, 0), # 26
(12, 17, 12, 11, 8, 12, 4, 8, 4, 1, 6, 1, 0, 15, 5, 7, 10, 4, 7, 2, 2, 3, 4, 1, 1, 0), # 27
(15, 17, 16, 14, 12, 3, 5, 5, 5, 1, 4, 2, 0, 6, 8, 9, 8, 4, 4, 5, 1, 4, 0, 3, 3, 0), # 28
(14, 16, 10, 15, 14, 7, 3, 5, 8, 0, 3, 0, 0, 15, 3, 10, 9, 9, 8, 3, 1, 6, 5, 2, 1, 0), # 29
(13, 15, 10, 6, 6, 4, 10, 1, 7, 3, 1, 0, 0, 11, 13, 7, 8, 12, 8, 7, 3, 3, 2, 1, 0, 0), # 30
(15, 15, 15, 15, 9, 4, 4, 3, 1, 3, 1, 1, 0, 8, 17, 8, 7, 9, 5, 7, 2, 7, 5, 4, 3, 0), # 31
(13, 11, 16, 11, 10, 3, 8, 9, 5, 2, 3, 0, 0, 4, 10, 12, 5, 12, 8, 3, 5, 3, 4, 2, 0, 0), # 32
(11, 18, 10, 12, 10, 4, 4, 3, 7, 0, 6, 1, 0, 14, 13, 12, 4, 9, 7, 7, 1, 6, 0, 0, 1, 0), # 33
(7, 16, 18, 13, 11, 3, 5, 9, 8, 4, 0, 2, 0, 13, 10, 8, 3, 10, 4, 4, 1, 1, 3, 0, 1, 0), # 34
(18, 12, 9, 14, 7, 4, 4, 2, 4, 2, 1, 1, 0, 6, 5, 2, 12, 9, 8, 4, 4, 5, 2, 1, 1, 0), # 35
(14, 10, 13, 15, 5, 6, 5, 2, 2, 3, 1, 0, 0, 9, 10, 5, 11, 15, 2, 4, 2, 6, 5, 3, 0, 0), # 36
(12, 12, 7, 10, 12, 3, 7, 4, 3, 1, 3, 1, 0, 7, 4, 7, 7, 5, 5, 4, 2, 3, 3, 3, 0, 0), # 37
(13, 17, 15, 10, 13, 2, 8, 8, 3, 5, 2, 1, 0, 12, 13, 11, 6, 8, 9, 6, 4, 5, 4, 2, 0, 0), # 38
(14, 5, 20, 5, 8, 3, 4, 3, 10, 2, 3, 1, 0, 14, 8, 12, 5, 8, 9, 6, 6, 4, 2, 1, 1, 0), # 39
(9, 8, 5, 8, 5, 2, 2, 2, 5, 2, 1, 1, 0, 10, 5, 7, 10, 12, 12, 7, 1, 7, 2, 1, 2, 0), # 40
(11, 9, 8, 8, 4, 4, 9, 7, 6, 3, 3, 0, 0, 8, 14, 5, 13, 7, 4, 7, 2, 6, 3, 0, 0, 0), # 41
(11, 12, 8, 9, 6, 5, 5, 6, 1, 1, 0, 0, 0, 5, 4, 9, 6, 15, 6, 4, 3, 3, 2, 1, 2, 0), # 42
(11, 21, 9, 11, 9, 6, 7, 9, 3, 2, 0, 1, 0, 17, 13, 12, 7, 9, 8, 4, 2, 2, 5, 3, 1, 0), # 43
(13, 17, 10, 14, 10, 7, 6, 2, 3, 1, 2, 1, 0, 8, 12, 6, 3, 3, 3, 5, 4, 6, 4, 3, 0, 0), # 44
(14, 17, 6, 8, 12, 8, 6, 4, 7, 3, 3, 1, 0, 13, 10, 11, 9, 11, 4, 6, 4, 1, 3, 3, 1, 0), # 45
(12, 17, 12, 9, 10, 4, 11, 7, 3, 3, 1, 0, 0, 14, 5, 6, 8, 8, 3, 6, 2, 3, 3, 1, 0, 0), # 46
(18, 15, 9, 9, 16, 6, 3, 3, 4, 0, 4, 0, 0, 9, 7, 6, 6, 10, 8, 8, 2, 5, 4, 3, 2, 0), # 47
(10, 10, 10, 10, 7, 5, 3, 4, 3, 6, 0, 0, 0, 10, 10, 6, 8, 10, 5, 3, 2, 4, 1, 3, 0, 0), # 48
(13, 7, 11, 13, 7, 1, 3, 4, 7, 4, 0, 0, 0, 11, 11, 9, 7, 8, 7, 3, 1, 4, 5, 2, 0, 0), # 49
(17, 16, 11, 12, 11, 2, 1, 6, 9, 0, 2, 1, 0, 10, 10, 10, 13, 4, 3, 4, 2, 3, 3, 1, 1, 0), # 50
(11, 16, 12, 13, 11, 5, 9, 3, 2, 2, 1, 1, 0, 12, 5, 7, 6, 8, 6, 4, 1, 2, 4, 1, 2, 0), # 51
(13, 11, 5, 11, 10, 4, 4, 3, 4, 2, 2, 2, 0, 10, 10, 8, 9, 8, 5, 8, 5, 1, 5, 3, 0, 0), # 52
(10, 12, 13, 12, 6, 5, 5, 3, 6, 2, 0, 1, 0, 11, 12, 6, 7, 15, 5, 2, 5, 4, 5, 1, 2, 0), # 53
(14, 8, 11, 8, 8, 4, 3, 0, 4, 3, 3, 0, 0, 14, 7, 6, 8, 8, 5, 5, 1, 3, 5, 3, 0, 0), # 54
(12, 18, 11, 9, 8, 7, 5, 6, 3, 0, 1, 0, 0, 15, 9, 10, 9, 10, 6, 0, 4, 2, 1, 2, 0, 0), # 55
(17, 11, 7, 13, 8, 4, 3, 5, 3, 2, 1, 0, 0, 18, 7, 8, 7, 9, 5, 1, 5, 5, 7, 2, 1, 0), # 56
(8, 12, 13, 6, 11, 3, 6, 4, 5, 3, 0, 0, 0, 7, 6, 6, 7, 15, 3, 3, 4, 2, 2, 2, 1, 0), # 57
(7, 11, 6, 14, 9, 5, 5, 4, 6, 5, 1, 2, 0, 11, 14, 6, 6, 11, 9, 1, 3, 5, 7, 2, 2, 0), # 58
(13, 10, 7, 8, 7, 5, 4, 5, 5, 3, 1, 1, 0, 10, 19, 7, 2, 3, 4, 2, 2, 5, 6, 2, 1, 0), # 59
(16, 8, 9, 14, 11, 2, 6, 1, 8, 3, 0, 1, 0, 17, 10, 7, 6, 6, 7, 8, 3, 3, 6, 4, 0, 0), # 60
(18, 15, 15, 10, 4, 5, 7, 4, 3, 3, 4, 1, 0, 14, 10, 12, 2, 11, 7, 10, 5, 3, 4, 1, 0, 0), # 61
(11, 9, 9, 12, 12, 4, 1, 2, 5, 4, 3, 0, 0, 9, 14, 9, 6, 6, 3, 6, 4, 9, 5, 2, 1, 0), # 62
(17, 8, 12, 8, 11, 2, 4, 4, 5, 3, 3, 1, 0, 12, 6, 10, 6, 6, 4, 6, 3, 2, 5, 0, 1, 0), # 63
(15, 10, 12, 5, 12, 6, 2, 1, 6, 0, 2, 2, 0, 18, 13, 10, 7, 8, 5, 5, 3, 8, 1, 3, 1, 0), # 64
(13, 14, 13, 7, 6, 3, 14, 5, 6, 4, 2, 0, 0, 17, 13, 7, 2, 11, 7, 4, 4, 6, 2, 2, 1, 0), # 65
(7, 12, 14, 10, 7, 4, 4, 2, 2, 2, 0, 2, 0, 9, 9, 6, 6, 10, 5, 3, 3, 7, 2, 3, 1, 0), # 66
(9, 8, 11, 9, 9, 7, 2, 4, 5, 3, 0, 0, 0, 11, 11, 4, 7, 7, 5, 5, 6, 8, 5, 0, 1, 0), # 67
(7, 17, 4, 4, 15, 6, 5, 9, 8, 4, 2, 0, 0, 10, 9, 7, 12, 9, 5, 6, 3, 5, 3, 1, 2, 0), # 68
(8, 8, 8, 14, 8, 3, 3, 2, 6, 1, 3, 0, 0, 12, 11, 8, 5, 7, 3, 3, 2, 5, 7, 1, 0, 0), # 69
(13, 10, 6, 9, 7, 4, 3, 3, 3, 0, 2, 1, 0, 9, 6, 4, 7, 8, 5, 4, 3, 6, 2, 2, 2, 0), # 70
(11, 4, 8, 16, 9, 3, 5, 2, 2, 2, 3, 3, 0, 14, 10, 6, 5, 5, 4, 5, 3, 3, 5, 1, 0, 0), # 71
(11, 14, 11, 8, 11, 5, 7, 3, 5, 1, 2, 0, 0, 13, 16, 7, 5, 6, 9, 7, 3, 10, 5, 2, 1, 0), # 72
(14, 9, 14, 12, 14, 7, 4, 2, 1, 3, 0, 1, 0, 11, 9, 6, 9, 10, 4, 4, 4, 6, 3, 2, 1, 0), # 73
(12, 18, 17, 12, 7, 4, 5, 3, 6, 1, 2, 1, 0, 7, 12, 8, 5, 11, 4, 3, 6, 5, 5, 3, 1, 0), # 74
(16, 18, 10, 19, 11, 2, 5, 2, 8, 4, 1, 2, 0, 11, 7, 6, 6, 12, 1, 5, 0, 5, 4, 2, 1, 0), # 75
(10, 7, 9, 15, 12, 5, 7, 3, 6, 1, 1, 0, 0, 8, 8, 9, 7, 9, 9, 7, 3, 6, 0, 0, 1, 0), # 76
(17, 13, 12, 9, 14, 4, 7, 0, 5, 1, 1, 1, 0, 11, 6, 6, 3, 12, 8, 7, 3, 4, 4, 1, 4, 0), # 77
(19, 13, 9, 10, 13, 7, 6, 7, 5, 1, 1, 1, 0, 8, 12, 5, 3, 10, 4, 3, 4, 2, 6, 3, 0, 0), # 78
(7, 11, 6, 8, 6, 4, 1, 6, 5, 4, 3, 2, 0, 12, 9, 5, 4, 14, 7, 6, 2, 2, 6, 2, 3, 0), # 79
(13, 15, 5, 13, 11, 7, 8, 0, 6, 1, 0, 1, 0, 14, 7, 14, 7, 14, 2, 3, 2, 1, 1, 2, 0, 0), # 80
(12, 7, 10, 12, 8, 2, 2, 2, 4, 1, 1, 2, 0, 11, 9, 9, 5, 9, 4, 3, 1, 4, 1, 4, 2, 0), # 81
(8, 10, 9, 9, 13, 4, 2, 6, 10, 1, 4, 0, 0, 15, 12, 9, 2, 11, 6, 3, 3, 4, 2, 0, 1, 0), # 82
(11, 8, 7, 11, 10, 0, 3, 5, 2, 2, 3, 2, 0, 7, 13, 9, 4, 10, 0, 3, 2, 3, 7, 2, 1, 0), # 83
(9, 10, 12, 16, 7, 6, 3, 3, 2, 3, 2, 0, 0, 6, 14, 5, 8, 18, 5, 3, 2, 2, 1, 3, 3, 0), # 84
(15, 12, 11, 11, 12, 8, 6, 2, 5, 2, 1, 0, 0, 10, 9, 9, 5, 9, 8, 4, 3, 5, 3, 3, 2, 0), # 85
(9, 12, 12, 9, 11, 8, 8, 1, 3, 3, 1, 1, 0, 10, 6, 2, 1, 9, 3, 2, 3, 5, 5, 1, 0, 0), # 86
(6, 11, 9, 10, 5, 4, 4, 3, 6, 4, 2, 0, 0, 8, 11, 2, 8, 9, 6, 8, 4, 4, 5, 4, 0, 0), # 87
(16, 15, 6, 12, 8, 3, 4, 4, 1, 2, 2, 2, 0, 13, 9, 5, 7, 7, 7, 3, 2, 4, 1, 3, 0, 0), # 88
(21, 8, 15, 7, 11, 2, 3, 2, 1, 2, 2, 0, 0, 22, 15, 3, 9, 10, 8, 3, 3, 3, 1, 1, 0, 0), # 89
(14, 14, 11, 9, 12, 2, 6, 5, 2, 3, 1, 1, 0, 9, 11, 7, 9, 7, 9, 4, 4, 5, 7, 2, 0, 0), # 90
(9, 5, 9, 9, 11, 4, 5, 3, 2, 2, 1, 0, 0, 13, 8, 8, 3, 8, 5, 3, 3, 4, 1, 2, 4, 0), # 91
(11, 8, 9, 8, 6, 3, 3, 6, 4, 0, 0, 2, 0, 11, 10, 9, 4, 9, 5, 5, 2, 5, 4, 3, 0, 0), # 92
(10, 12, 10, 10, 11, 2, 5, 1, 2, 5, 3, 0, 0, 7, 7, 7, 11, 12, 7, 4, 5, 2, 5, 3, 0, 0), # 93
(11, 12, 16, 7, 5, 5, 4, 4, 3, 3, 1, 0, 0, 8, 9, 8, 8, 12, 2, 4, 7, 5, 3, 3, 0, 0), # 94
(13, 8, 8, 6, 13, 5, 6, 3, 4, 1, 3, 1, 0, 13, 14, 9, 5, 5, 7, 8, 0, 7, 4, 3, 0, 0), # 95
(11, 7, 8, 9, 11, 5, 1, 4, 3, 3, 1, 1, 0, 12, 11, 7, 7, 13, 8, 4, 2, 3, 4, 0, 1, 0), # 96
(10, 11, 9, 9, 6, 1, 7, 3, 3, 2, 1, 0, 0, 10, 5, 8, 8, 4, 5, 7, 2, 4, 4, 2, 0, 0), # 97
(13, 6, 4, 16, 8, 3, 3, 6, 4, 2, 1, 0, 0, 15, 12, 7, 7, 11, 1, 2, 3, 5, 10, 0, 1, 0), # 98
(14, 11, 10, 9, 11, 8, 3, 3, 3, 0, 2, 2, 0, 11, 13, 7, 7, 10, 2, 5, 4, 7, 2, 2, 3, 0), # 99
(21, 10, 11, 5, 10, 9, 5, 1, 7, 3, 0, 0, 0, 8, 10, 4, 6, 8, 4, 5, 7, 5, 4, 2, 1, 0), # 100
(14, 16, 13, 8, 5, 1, 3, 3, 6, 2, 4, 2, 0, 11, 7, 6, 4, 8, 5, 1, 3, 2, 5, 0, 1, 0), # 101
(10, 9, 14, 13, 15, 3, 8, 1, 5, 2, 1, 1, 0, 9, 11, 7, 5, 5, 5, 5, 1, 4, 2, 3, 0, 0), # 102
(8, 11, 11, 9, 10, 3, 3, 6, 5, 1, 0, 1, 0, 8, 6, 10, 7, 8, 3, 5, 1, 4, 3, 2, 2, 0), # 103
(11, 8, 9, 9, 8, 1, 4, 3, 6, 1, 2, 0, 0, 22, 9, 8, 9, 4, 2, 2, 3, 5, 1, 2, 1, 0), # 104
(16, 6, 8, 15, 6, 4, 0, 4, 5, 1, 0, 1, 0, 15, 7, 3, 6, 7, 2, 6, 1, 2, 2, 1, 1, 0), # 105
(13, 11, 7, 17, 9, 2, 2, 2, 5, 0, 2, 0, 0, 6, 7, 11, 1, 9, 5, 4, 1, 1, 5, 1, 0, 0), # 106
(16, 5, 9, 9, 11, 6, 0, 5, 4, 2, 2, 1, 0, 9, 6, 4, 3, 12, 5, 4, 3, 6, 4, 2, 1, 0), # 107
(19, 13, 10, 9, 7, 5, 1, 2, 4, 3, 0, 1, 0, 13, 16, 7, 5, 8, 5, 2, 8, 1, 0, 3, 2, 0), # 108
(12, 5, 4, 10, 4, 3, 2, 3, 8, 0, 2, 1, 0, 12, 6, 9, 7, 7, 2, 1, 1, 5, 4, 3, 0, 0), # 109
(14, 10, 9, 8, 4, 6, 2, 3, 3, 1, 0, 0, 0, 8, 10, 6, 6, 11, 8, 3, 4, 6, 4, 2, 0, 0), # 110
(16, 9, 10, 7, 12, 4, 6, 3, 1, 2, 1, 2, 0, 15, 16, 5, 5, 9, 3, 5, 3, 4, 7, 2, 0, 0), # 111
(10, 5, 6, 14, 3, 2, 4, 2, 5, 2, 1, 1, 0, 13, 5, 6, 5, 6, 7, 1, 1, 1, 9, 1, 2, 0), # 112
(15, 11, 7, 9, 8, 3, 3, 3, 2, 1, 0, 0, 0, 11, 4, 7, 4, 6, 4, 3, 1, 2, 2, 1, 1, 0), # 113
(12, 9, 6, 5, 11, 6, 7, 7, 2, 3, 3, 0, 0, 22, 10, 5, 5, 4, 7, 1, 4, 2, 4, 1, 1, 0), # 114
(10, 6, 8, 8, 6, 4, 3, 2, 4, 3, 0, 0, 0, 15, 15, 6, 3, 8, 4, 5, 6, 5, 2, 1, 2, 0), # 115
(14, 9, 14, 7, 9, 2, 2, 5, 3, 1, 1, 1, 0, 17, 7, 8, 4, 8, 5, 6, 3, 6, 4, 2, 2, 0), # 116
(6, 9, 7, 8, 7, 5, 4, 4, 5, 1, 2, 0, 0, 11, 8, 8, 9, 6, 6, 4, 2, 7, 3, 3, 0, 0), # 117
(17, 8, 13, 6, 6, 2, 2, 6, 2, 0, 0, 1, 0, 10, 9, 6, 5, 14, 2, 2, 4, 4, 9, 0, 0, 0), # 118
(9, 7, 13, 11, 11, 6, 2, 2, 6, 2, 3, 3, 0, 13, 6, 4, 5, 10, 3, 0, 2, 5, 2, 2, 0, 0), # 119
(13, 9, 13, 11, 9, 1, 2, 2, 1, 4, 0, 0, 0, 13, 11, 10, 2, 11, 5, 6, 2, 4, 6, 2, 1, 0), # 120
(12, 8, 12, 8, 9, 3, 5, 6, 7, 1, 2, 2, 0, 13, 13, 5, 10, 6, 4, 3, 1, 5, 0, 3, 0, 0), # 121
(10, 3, 7, 6, 6, 3, 6, 5, 6, 2, 3, 1, 0, 16, 7, 10, 3, 7, 9, 3, 1, 1, 4, 2, 0, 0), # 122
(14, 7, 15, 17, 9, 10, 0, 1, 7, 1, 2, 0, 0, 16, 11, 1, 5, 7, 3, 5, 3, 3, 6, 0, 0, 0), # 123
(10, 9, 8, 13, 5, 10, 3, 5, 2, 2, 1, 1, 0, 13, 10, 11, 6, 7, 2, 1, 2, 1, 3, 3, 1, 0), # 124
(11, 5, 11, 11, 8, 8, 5, 1, 10, 4, 2, 0, 0, 13, 3, 6, 8, 6, 4, 3, 1, 4, 4, 2, 1, 0), # 125
(9, 2, 7, 8, 4, 4, 3, 3, 4, 2, 3, 1, 0, 9, 9, 10, 5, 9, 2, 6, 3, 3, 1, 2, 0, 0), # 126
(12, 12, 8, 3, 6, 7, 1, 2, 3, 0, 0, 2, 0, 9, 13, 6, 6, 6, 3, 3, 2, 4, 2, 3, 2, 0), # 127
(14, 6, 7, 12, 10, 0, 6, 3, 8, 0, 2, 0, 0, 14, 6, 8, 3, 7, 4, 2, 2, 3, 3, 2, 1, 0), # 128
(18, 8, 10, 4, 2, 3, 2, 3, 2, 3, 2, 0, 0, 9, 8, 8, 5, 8, 0, 1, 0, 6, 1, 1, 0, 0), # 129
(11, 11, 8, 8, 9, 3, 2, 1, 5, 1, 1, 0, 0, 15, 8, 4, 8, 9, 7, 2, 5, 6, 5, 1, 1, 0), # 130
(13, 12, 11, 8, 8, 6, 1, 4, 4, 2, 3, 2, 0, 8, 12, 3, 1, 4, 7, 6, 5, 7, 3, 2, 0, 0), # 131
(8, 7, 9, 8, 5, 6, 1, 3, 0, 1, 2, 0, 0, 16, 13, 5, 6, 9, 5, 4, 0, 9, 4, 1, 0, 0), # 132
(11, 5, 14, 14, 10, 4, 10, 4, 6, 2, 2, 0, 0, 8, 5, 7, 4, 9, 3, 0, 2, 4, 9, 3, 0, 0), # 133
(11, 3, 6, 5, 11, 4, 3, 0, 4, 2, 1, 0, 0, 11, 10, 6, 3, 8, 4, 3, 1, 4, 5, 3, 0, 0), # 134
(5, 9, 3, 8, 9, 4, 3, 2, 11, 1, 1, 0, 0, 12, 6, 6, 8, 8, 4, 3, 1, 1, 3, 1, 0, 0), # 135
(8, 9, 15, 2, 10, 5, 4, 2, 6, 3, 1, 0, 0, 10, 8, 5, 5, 12, 4, 6, 2, 3, 3, 1, 2, 0), # 136
(8, 1, 12, 5, 5, 3, 4, 3, 5, 4, 0, 1, 0, 16, 8, 9, 4, 8, 4, 6, 1, 2, 4, 3, 1, 0), # 137
(7, 4, 11, 5, 10, 3, 1, 4, 2, 0, 3, 1, 0, 13, 7, 4, 6, 9, 6, 1, 8, 5, 4, 2, 0, 0), # 138
(5, 7, 8, 8, 7, 4, 3, 5, 3, 6, 4, 0, 0, 16, 6, 7, 5, 9, 4, 4, 2, 3, 1, 1, 1, 0), # 139
(10, 8, 14, 9, 9, 5, 4, 2, 3, 0, 0, 1, 0, 4, 13, 8, 5, 11, 5, 2, 3, 6, 2, 4, 1, 0), # 140
(10, 10, 6, 11, 6, 3, 3, 2, 4, 0, 0, 0, 0, 8, 7, 6, 7, 9, 5, 3, 0, 6, 3, 3, 1, 0), # 141
(9, 5, 9, 10, 10, 1, 5, 2, 5, 2, 1, 0, 0, 10, 9, 7, 2, 9, 3, 2, 3, 6, 0, 1, 2, 0), # 142
(8, 5, 12, 6, 10, 1, 4, 3, 5, 1, 0, 2, 0, 6, 9, 8, 6, 5, 2, 1, 3, 3, 2, 2, 0, 0), # 143
(7, 4, 10, 11, 8, 2, 1, 3, 7, 0, 0, 0, 0, 9, 12, 4, 3, 11, 3, 5, 1, 2, 0, 1, 0, 0), # 144
(2, 10, 4, 4, 4, 2, 1, 1, 7, 1, 0, 0, 0, 14, 7, 2, 4, 5, 3, 4, 3, 4, 4, 1, 0, 0), # 145
(5, 11, 14, 12, 9, 4, 1, 5, 4, 0, 1, 0, 0, 10, 9, 6, 6, 11, 5, 4, 3, 3, 4, 2, 0, 0), # 146
(13, 11, 2, 9, 7, 4, 1, 2, 0, 0, 2, 0, 0, 10, 15, 9, 6, 9, 4, 1, 5, 5, 5, 1, 0, 0), # 147
(7, 3, 11, 6, 8, 1, 4, 2, 5, 2, 0, 0, 0, 10, 10, 3, 8, 5, 3, 3, 2, 4, 1, 2, 2, 0), # 148
(10, 6, 5, 8, 9, 4, 4, 1, 3, 2, 3, 1, 0, 8, 8, 6, 6, 10, 5, 5, 2, 3, 2, 1, 1, 0), # 149
(8, 8, 14, 10, 9, 7, 3, 2, 4, 2, 1, 2, 0, 6, 9, 12, 1, 15, 2, 1, 1, 2, 6, 2, 0, 0), # 150
(9, 5, 10, 6, 7, 5, 3, 1, 2, 2, 1, 3, 0, 11, 5, 2, 5, 7, 2, 3, 5, 1, 5, 0, 1, 0), # 151
(8, 9, 6, 12, 10, 4, 4, 2, 4, 1, 1, 2, 0, 8, 9, 10, 6, 8, 3, 2, 3, 6, 3, 0, 1, 0), # 152
(11, 7, 13, 12, 7, 6, 3, 5, 3, 0, 1, 0, 0, 14, 9, 4, 1, 7, 6, 1, 3, 6, 3, 2, 2, 0), # 153
(10, 14, 8, 8, 9, 7, 4, 2, 4, 0, 3, 3, 0, 14, 8, 4, 5, 4, 4, 1, 2, 3, 4, 0, 2, 0), # 154
(7, 6, 7, 7, 13, 1, 3, 2, 6, 5, 1, 2, 0, 9, 10, 2, 6, 3, 6, 2, 2, 7, 4, 2, 0, 0), # 155
(6, 5, 10, 11, 9, 7, 2, 2, 2, 2, 1, 0, 0, 13, 7, 6, 2, 2, 3, 2, 5, 9, 3, 0, 0, 0), # 156
(7, 6, 14, 8, 6, 8, 6, 1, 5, 2, 2, 0, 0, 8, 10, 4, 4, 8, 3, 4, 3, 3, 1, 1, 1, 0), # 157
(6, 3, 2, 14, 5, 4, 5, 1, 1, 1, 0, 0, 0, 6, 14, 6, 8, 6, 7, 1, 4, 7, 1, 1, 0, 0), # 158
(7, 8, 10, 10, 6, 4, 2, 5, 5, 1, 1, 0, 0, 6, 8, 6, 3, 7, 3, 2, 1, 5, 1, 0, 0, 0), # 159
(6, 5, 7, 4, 7, 4, 1, 3, 3, 2, 0, 0, 0, 10, 5, 5, 9, 5, 7, 3, 3, 3, 3, 0, 0, 0), # 160
(7, 6, 5, 5, 1, 3, 5, 2, 1, 2, 1, 0, 0, 11, 6, 8, 2, 7, 3, 3, 5, 4, 5, 2, 1, 0), # 161
(5, 5, 6, 11, 14, 4, 2, 1, 4, 2, 1, 2, 0, 8, 9, 7, 3, 3, 1, 5, 1, 2, 3, 3, 1, 0), # 162
(3, 8, 6, 6, 8, 2, 2, 3, 2, 0, 3, 0, 0, 11, 6, 7, 4, 4, 6, 1, 2, 4, 2, 0, 0, 0), # 163
(10, 6, 8, 10, 8, 1, 4, 1, 4, 1, 0, 1, 0, 8, 7, 7, 4, 5, 5, 1, 1, 4, 1, 2, 0, 0), # 164
(9, 8, 7, 4, 7, 2, 2, 1, 1, 2, 3, 0, 0, 6, 10, 8, 7, 14, 4, 3, 2, 3, 1, 3, 2, 0), # 165
(5, 8, 9, 6, 5, 3, 5, 3, 2, 2, 2, 0, 0, 8, 9, 2, 2, 8, 6, 2, 1, 4, 0, 3, 0, 0), # 166
(4, 4, 6, 6, 10, 2, 5, 1, 4, 0, 3, 3, 0, 9, 2, 4, 3, 4, 1, 3, 1, 2, 2, 1, 0, 0), # 167
(3, 7, 7, 9, 10, 3, 4, 2, 1, 1, 0, 1, 0, 9, 4, 4, 2, 3, 3, 2, 2, 2, 3, 2, 1, 0), # 168
(3, 4, 3, 4, 5, 3, 3, 2, 0, 1, 0, 2, 0, 12, 9, 7, 2, 12, 5, 0, 4, 4, 5, 1, 1, 0), # 169
(5, 4, 2, 10, 5, 5, 5, 4, 4, 0, 3, 0, 0, 6, 6, 5, 5, 5, 4, 2, 2, 5, 3, 1, 0, 0), # 170
(8, 8, 7, 6, 5, 4, 4, 2, 6, 0, 2, 0, 0, 4, 7, 4, 4, 4, 4, 0, 1, 1, 1, 1, 0, 0), # 171
(4, 3, 7, 4, 6, 4, 1, 2, 4, 3, 1, 1, 0, 11, 5, 1, 7, 3, 3, 2, 4, 1, 3, 1, 0, 0), # 172
(10, 7, 7, 7, 10, 3, 2, 2, 4, 1, 1, 0, 0, 5, 3, 5, 4, 4, 6, 2, 1, 2, 4, 0, 1, 0), # 173
(3, 3, 4, 10, 2, 1, 0, 1, 1, 1, 0, 0, 0, 6, 1, 2, 2, 3, 1, 0, 3, 2, 4, 0, 0, 0), # 174
(6, 3, 3, 6, 5, 1, 3, 2, 2, 0, 1, 0, 0, 9, 8, 1, 2, 9, 3, 6, 0, 4, 1, 0, 2, 0), # 175
(5, 3, 2, 3, 3, 3, 2, 2, 3, 0, 0, 0, 0, 6, 8, 6, 3, 6, 0, 3, 1, 6, 0, 1, 1, 0), # 176
(3, 4, 6, 2, 3, 0, 2, 2, 0, 0, 0, 1, 0, 7, 4, 5, 2, 3, 2, 3, 1, 3, 1, 1, 0, 0), # 177
(7, 4, 6, 3, 1, 2, 1, 2, 0, 1, 0, 2, 0, 4, 3, 3, 3, 3, 2, 2, 1, 0, 1, 1, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(6.025038694046121, 6.630346271631799, 6.253539875535008, 7.457601328636119, 6.665622729131534, 3.766385918444806, 4.9752427384486975, 5.583811407575308, 7.308118874601608, 4.749618018626843, 5.046318196662723, 5.877498093967408, 6.100656255094035), # 0
(6.425192582423969, 7.06807283297371, 6.666415909596182, 7.950173103931939, 7.106988404969084, 4.015180300851067, 5.303362729516432, 5.951416467486849, 7.79069439159949, 5.062776830732579, 5.3797153631473575, 6.265459992977225, 6.503749976927826), # 1
(6.8240676107756775, 7.504062205069175, 7.077650742656896, 8.440785245597752, 7.546755568499692, 4.262982137414934, 5.630182209552845, 6.317550297485303, 8.271344168253059, 5.3746965300246545, 5.711787778531575, 6.651879182463666, 6.905237793851628), # 2
(7.220109351775874, 7.936584602323736, 7.485613043183825, 8.927491689038488, 7.983194011202282, 4.508808747102135, 5.954404369977547, 6.680761388993408, 8.74816219310531, 5.684139238111417, 6.041218094192859, 7.035222821916553, 7.30352736750507), # 3
(7.611763378099177, 8.363910239142928, 7.8886714796436435, 9.408346369659084, 8.41457352455579, 4.751677448878401, 6.27473240221015, 7.039598233433898, 9.219242454699248, 5.9898670766012145, 6.36668896150869, 7.413958070825716, 7.69702635952778), # 4
(7.9974752624202115, 8.784309329932306, 8.285194720503021, 9.881403222864472, 8.839163900039136, 4.990605561709457, 6.589869497670269, 7.392609322229511, 9.682678941577871, 6.290642167102395, 6.686883031856559, 7.786552088680978, 8.084142431559393), # 5
(8.375690577413598, 9.196052089097401, 8.673551434228639, 10.344716184059582, 9.255234929131252, 5.224610404561036, 6.898518847777515, 7.738343146802986, 10.136565642284177, 6.58522663122331, 7.000482956613939, 8.15147203497217, 8.463283245239527), # 6
(8.744854895753962, 9.597408731043757, 9.052110289287162, 10.796339188649354, 9.661056403311065, 5.452709296398865, 7.199383643951502, 8.075348198577062, 10.578996545361173, 6.872382590572303, 7.306171387158321, 8.507185069189115, 8.832856462207822), # 7
(9.103413790115921, 9.986649470176918, 9.419239954145274, 11.234326172038713, 10.054898114057503, 5.673919556188667, 7.491167077611837, 8.402172968974469, 11.008065639351846, 7.150872166757728, 7.602630974867185, 8.852158350821643, 9.1912697441039), # 8
(9.449812833174102, 10.362044520902426, 9.773309097269644, 11.656731069632603, 10.43502985284949, 5.88725850289618, 7.772572340178144, 8.717365949417955, 11.421866912799208, 7.419457481387929, 7.888544371118013, 9.184859039359576, 9.536930752567395), # 9
(9.782497597603118, 10.721864097625819, 10.11268638712695, 12.061607816835945, 10.79972141116596, 6.091743455487129, 8.042302623070025, 9.019475631330252, 11.818494354246257, 7.676900656071257, 8.162594227288288, 9.503754294292742, 9.868247149237932), # 10
(10.099913656077605, 11.064378414752648, 10.435740492183857, 12.447010349053675, 11.14724258048584, 6.286391732927242, 8.2990611177071, 9.307050506134097, 12.196041952235992, 7.921963812416062, 8.423463194755499, 9.807311275110973, 10.183626595755133), # 11
(10.400506581272174, 11.387857686688436, 10.740840080907047, 12.810992601690733, 11.475863152288053, 6.470220654182243, 8.541551015508974, 9.578639065252224, 12.552603695311413, 8.153409072030685, 8.669833924897121, 10.093997141304081, 10.48147675375864), # 12
(10.68272194586145, 11.690572127838744, 11.026353821763193, 13.151608510152052, 11.78385291805152, 6.642247538217868, 8.768475507895266, 9.832789800107378, 12.886273572015517, 8.369998556523484, 8.900389069090641, 10.362279052361904, 10.760205284888082), # 13
(10.945005322520059, 11.970791952609106, 11.290650383218976, 13.46691200984255, 12.069481669255188, 6.801489703999841, 8.978537786285592, 10.068051202122295, 13.195145570891304, 8.5704943875028, 9.113811278713541, 10.610624167774272, 11.018219850783076), # 14
(11.185802283922625, 12.22678737540506, 11.53209843374105, 13.754957036167182, 12.33101919737797, 6.946964470493895, 9.17044104209955, 10.282971762719706, 13.477313680481783, 8.753658686576989, 9.308783205143303, 10.837499647031004, 11.253928113083257), # 15
(11.40355840274376, 12.456828610632158, 11.749066641796109, 14.01379752453086, 12.5667352938988, 7.077689156665751, 9.34288846675677, 10.476099973322352, 13.730871889329944, 8.918253575354395, 9.483987499757415, 11.041372649621927, 11.465737733428254), # 16
(11.59671925165809, 12.659185872695934, 11.939923675850823, 14.241487410338534, 12.774899750296605, 7.192681081481142, 9.494583251676852, 10.64598432535298, 13.95391418597878, 9.06304117544336, 9.638106813933359, 11.220710335036866, 11.652056373457699), # 17
(11.763730403340244, 12.832129376001928, 12.103038204371856, 14.436080628995134, 12.953782358050306, 7.290957563905803, 9.62422858827942, 10.791173310234312, 14.144534558971316, 9.186783608452243, 9.76982379904861, 11.373979862765658, 11.811291694811214), # 18
(11.903037430464838, 12.973929334955693, 12.236778895825895, 14.595631115905576, 13.101652908638838, 7.37153592290545, 9.730527667984072, 10.910215419389093, 14.300826996850533, 9.288242995989393, 9.877821106480653, 11.499648392298115, 11.941851359128435), # 19
(12.013085905706498, 13.082855963962754, 12.339514418679602, 14.718192806474825, 13.216781193541133, 7.4334334774458215, 9.812183682210435, 11.00165914424006, 14.420885488159437, 9.36618145966315, 9.96078138760698, 11.59618308312407, 12.042143028048988), # 20
(12.09232140173984, 13.15717947742867, 12.409613441399662, 14.801819636107782, 13.297437004236105, 7.475667546492642, 9.86789982237811, 11.064052976209947, 14.502804021441024, 9.419361121081865, 10.01738729380507, 11.662051094733352, 12.110574363212494), # 21
(12.139189491239494, 13.195170089758973, 12.445444632452743, 14.844565540209402, 13.341890132202689, 7.497255449011639, 9.89637927990672, 11.095945406721498, 14.544676585238298, 9.44654410185389, 10.046321476452407, 11.695719586615787, 12.145553026258591), # 22
(12.156472036011166, 13.199668312757202, 12.449907818930042, 14.849916975308643, 13.353278467239116, 7.5, 9.899764802711205, 11.099392592592592, 14.54991148148148, 9.44975072702332, 10.049949644594088, 11.69987709190672, 12.15), # 23
(12.169214895640982, 13.197044444444446, 12.449177777777777, 14.849258333333335, 13.359729136337823, 7.5, 9.8979045751634, 11.0946, 14.549209999999999, 9.44778074074074, 10.049549494949495, 11.698903703703703, 12.15), # 24
(12.181688676253897, 13.191872427983538, 12.447736625514404, 14.84795524691358, 13.366037934713404, 7.5, 9.894238683127572, 11.085185185185185, 14.547824074074073, 9.443902606310013, 10.048756079311634, 11.696982167352537, 12.15), # 25
(12.19389242285764, 13.184231275720165, 12.445604115226338, 14.846022530864197, 13.372204642105325, 7.5, 9.888824061970466, 11.071325925925926, 14.54577148148148, 9.438180850480109, 10.047576580621024, 11.694138820301784, 12.15), # 26
(12.205825180459962, 13.174199999999997, 12.4428, 14.843474999999998, 13.378229038253057, 7.5, 9.881717647058824, 11.0532, 14.54307, 9.430679999999999, 10.046018181818182, 11.6904, 12.15), # 27
(12.217485994068602, 13.161857613168722, 12.439344032921811, 14.8403274691358, 13.384110902896081, 7.5, 9.87297637375938, 11.030985185185186, 14.539737407407406, 9.421464581618656, 10.04408806584362, 11.685792043895749, 12.15), # 28
(12.2288739086913, 13.147283127572017, 12.43525596707819, 14.83659475308642, 13.389850015773865, 7.5, 9.862657177438878, 11.004859259259257, 14.535791481481482, 9.410599122085047, 10.041793415637859, 11.680341289437584, 12.15), # 29
(12.239987969335797, 13.130555555555555, 12.430555555555555, 14.832291666666666, 13.395446156625884, 7.5, 9.850816993464052, 10.974999999999998, 14.53125, 9.398148148148149, 10.039141414141413, 11.674074074074072, 12.15), # 30
(12.25082722100983, 13.11175390946502, 12.42526255144033, 14.827433024691356, 13.400899105191609, 7.5, 9.837512757201647, 10.941585185185184, 14.52613074074074, 9.384176186556926, 10.0361392442948, 11.667016735253773, 12.15), # 31
(12.261390708721144, 13.09095720164609, 12.419396707818928, 14.822033641975308, 13.406208641210513, 7.5, 9.822801404018398, 10.904792592592594, 14.520451481481482, 9.368747764060357, 10.032794089038532, 11.659195610425241, 12.15), # 32
(12.271677477477477, 13.068244444444444, 12.412977777777778, 14.816108333333332, 13.411374544422076, 7.5, 9.806739869281046, 10.8648, 14.51423, 9.351927407407407, 10.02911313131313, 11.650637037037034, 12.15), # 33
(12.28168657228657, 13.04369465020576, 12.406025514403291, 14.809671913580246, 13.416396594565759, 7.5, 9.789385088356331, 10.821785185185183, 14.507484074074075, 9.33377964334705, 10.025103554059108, 11.641367352537722, 12.15), # 34
(12.291417038156167, 13.01738683127572, 12.398559670781895, 14.802739197530862, 13.421274571381044, 7.5, 9.77079399661099, 10.775925925925925, 14.500231481481482, 9.314368998628257, 10.020772540216983, 11.631412894375858, 12.15), # 35
(12.300867920094007, 12.989399999999998, 12.3906, 14.795324999999998, 13.426008254607403, 7.5, 9.751023529411764, 10.727400000000001, 14.492489999999998, 9.293759999999999, 10.016127272727273, 11.620800000000001, 12.15), # 36
(12.310038263107828, 12.95981316872428, 12.382166255144032, 14.787444135802469, 13.430597423984304, 7.5, 9.730130622125392, 10.676385185185184, 14.484277407407406, 9.272017174211248, 10.01117493453049, 11.609555006858711, 12.15), # 37
(12.31892711220537, 12.928705349794239, 12.37327818930041, 14.779111419753086, 13.435041859251228, 7.5, 9.708172210118615, 10.62305925925926, 14.475611481481481, 9.249205048010975, 10.005922708567153, 11.597704252400549, 12.15), # 38
(12.327533512394384, 12.896155555555554, 12.363955555555556, 14.770341666666667, 13.439341340147644, 7.5, 9.68520522875817, 10.567599999999999, 14.466510000000001, 9.225388148148149, 10.000377777777777, 11.585274074074073, 12.15), # 39
(12.335856508682596, 12.86224279835391, 12.354218106995884, 14.761149691358025, 13.443495646413021, 7.5, 9.661286613410796, 10.510185185185186, 14.456990740740741, 9.200631001371743, 9.99454732510288, 11.572290809327848, 12.15), # 40
(12.343895146077754, 12.82704609053498, 12.344085596707819, 14.751550308641974, 13.447504557786841, 7.5, 9.636473299443233, 10.450992592592593, 14.44707148148148, 9.174998134430727, 9.988438533482979, 11.558780795610424, 12.15), # 41
(12.3516484695876, 12.790644444444444, 12.333577777777778, 14.741558333333334, 13.45136785400857, 7.5, 9.610822222222222, 10.3902, 14.436770000000001, 9.148554074074074, 9.982058585858585, 11.54477037037037, 12.15), # 42
(12.35911552421987, 12.753116872427984, 12.322714403292181, 14.731188580246913, 13.455085314817683, 7.5, 9.584390317114499, 10.327985185185186, 14.426104074074072, 9.121363347050755, 9.97541466517022, 11.530285871056241, 12.15), # 43
(12.366295354982311, 12.714542386831276, 12.31151522633745, 14.72045586419753, 13.458656719953654, 7.5, 9.557234519486807, 10.264525925925927, 14.415091481481479, 9.09349048010974, 9.968513954358398, 11.515353635116599, 12.15), # 44
(12.37318700688266, 12.674999999999999, 12.299999999999999, 14.709375, 13.462081849155954, 7.5, 9.529411764705882, 10.2, 14.403749999999999, 9.065, 9.961363636363636, 11.499999999999998, 12.15), # 45
(12.379789524928656, 12.634568724279836, 12.288188477366253, 14.697960802469135, 13.465360482164058, 7.5, 9.500978988138465, 10.134585185185186, 14.392097407407405, 9.035956433470506, 9.953970894126448, 11.484251303155007, 12.15), # 46
(12.386101954128042, 12.59332757201646, 12.276100411522634, 14.686228086419751, 13.46849239871744, 7.5, 9.471993125151295, 10.068459259259258, 14.380151481481482, 9.006424307270233, 9.946342910587354, 11.468133882030179, 12.15), # 47
(12.392123339488554, 12.551355555555554, 12.263755555555555, 14.674191666666667, 13.471477378555573, 7.5, 9.442511111111111, 10.001800000000001, 14.367930000000001, 8.976468148148147, 9.938486868686867, 11.451674074074074, 12.15), # 48
(12.397852726017943, 12.508731687242797, 12.251173662551441, 14.661866358024692, 13.474315201417928, 7.5, 9.412589881384651, 9.934785185185184, 14.355450740740741, 8.946152482853224, 9.930409951365506, 11.434898216735254, 12.15), # 49
(12.403289158723938, 12.46553497942387, 12.23837448559671, 14.649266975308642, 13.477005647043978, 7.5, 9.38228637133866, 9.867592592592592, 14.342731481481481, 8.91554183813443, 9.922119341563786, 11.417832647462278, 12.15), # 50
(12.408431682614292, 12.421844444444444, 12.225377777777776, 14.636408333333332, 13.479548495173196, 7.5, 9.351657516339868, 9.8004, 14.329790000000001, 8.88470074074074, 9.913622222222223, 11.400503703703704, 12.15), # 51
(12.413279342696734, 12.377739094650208, 12.21220329218107, 14.62330524691358, 13.481943525545056, 7.5, 9.320760251755022, 9.733385185185183, 14.316644074074073, 8.853693717421125, 9.904925776281331, 11.382937722908094, 12.15), # 52
(12.417831183979011, 12.333297942386832, 12.198870781893005, 14.609972530864196, 13.484190517899036, 7.5, 9.28965151295086, 9.666725925925926, 14.303311481481483, 8.822585294924554, 9.89603718668163, 11.365161042524004, 12.15), # 53
(12.42208625146886, 12.2886, 12.185399999999998, 14.596425, 13.486289251974602, 7.5, 9.258388235294117, 9.600599999999998, 14.28981, 8.79144, 9.886963636363634, 11.347199999999999, 12.15), # 54
(12.426043590174027, 12.24372427983539, 12.171810699588478, 14.5826774691358, 13.488239507511228, 7.5, 9.227027354151536, 9.535185185185185, 14.276157407407407, 8.760322359396433, 9.877712308267864, 11.329080932784636, 12.15), # 55
(12.429702245102245, 12.198749794238683, 12.158122633744856, 14.568744753086419, 13.49004106424839, 7.5, 9.195625804889858, 9.470659259259259, 14.262371481481482, 8.729296899862826, 9.868290385334829, 11.310830178326475, 12.15), # 56
(12.433061261261258, 12.153755555555556, 12.144355555555556, 14.554641666666665, 13.49169370192556, 7.5, 9.164240522875817, 9.407200000000001, 14.24847, 8.698428148148148, 9.85870505050505, 11.292474074074073, 12.15), # 57
(12.436119683658815, 12.108820576131688, 12.130529218106995, 14.540383024691355, 13.493197200282209, 7.5, 9.132928443476155, 9.344985185185184, 14.23447074074074, 8.667780631001373, 9.848963486719043, 11.274038957475994, 12.15), # 58
(12.438876557302644, 12.064023868312757, 12.116663374485597, 14.525983641975307, 13.494551339057814, 7.5, 9.101746502057614, 9.284192592592593, 14.220391481481482, 8.637418875171468, 9.839072876917319, 11.255551165980796, 12.15), # 59
(12.441330927200491, 12.019444444444444, 12.102777777777776, 14.511458333333334, 13.495755897991843, 7.5, 9.070751633986927, 9.225, 14.20625, 8.607407407407408, 9.829040404040404, 11.237037037037037, 12.15), # 60
(12.443481838360098, 11.975161316872429, 12.08889218106996, 14.496821913580245, 13.496810656823774, 7.5, 9.040000774630839, 9.167585185185185, 14.192064074074073, 8.577810754458161, 9.818873251028807, 11.218522908093279, 12.15), # 61
(12.445328335789204, 11.931253497942386, 12.075026337448561, 14.482089197530865, 13.497715395293081, 7.5, 9.009550859356088, 9.112125925925925, 14.177851481481481, 8.548693443072702, 9.808578600823045, 11.20003511659808, 12.15), # 62
(12.44686946449555, 11.887799999999999, 12.0612, 14.467275, 13.498469893139227, 7.5, 8.979458823529411, 9.0588, 14.16363, 8.520119999999999, 9.798163636363636, 11.1816, 12.15), # 63
(12.448104269486876, 11.844879835390946, 12.047432921810698, 14.452394135802468, 13.499073930101698, 7.5, 8.94978160251755, 9.007785185185186, 14.149417407407407, 8.492154951989026, 9.787635540591094, 11.1632438957476, 12.15), # 64
(12.449031795770926, 11.802572016460903, 12.033744855967079, 14.437461419753085, 13.49952728591996, 7.5, 8.920576131687243, 8.959259259259259, 14.135231481481481, 8.464862825788751, 9.777001496445942, 11.144993141289435, 12.15), # 65
(12.449651088355436, 11.760955555555556, 12.020155555555556, 14.422491666666666, 13.499829740333487, 7.5, 8.891899346405228, 8.913400000000001, 14.12109, 8.438308148148147, 9.766268686868687, 11.126874074074076, 12.15), # 66
(12.44996119224815, 11.720109465020576, 12.00668477366255, 14.407499691358023, 13.499981073081754, 7.5, 8.863808182038246, 8.870385185185187, 14.10701074074074, 8.412555445816187, 9.755444294799851, 11.108913031550067, 12.15), # 67
(12.44974993737699, 11.679898367184387, 11.993287139917694, 14.392370088566828, 13.499853546356814, 7.49986081390032, 8.836218233795575, 8.830012620027434, 14.092905418381346, 8.3875445299766, 9.74434318624845, 11.09103602627969, 12.149850180041152), # 68
(12.447770048309177, 11.639094623655915, 11.979586111111109, 14.376340217391302, 13.498692810457515, 7.49876049382716, 8.808321817615935, 8.790118518518518, 14.078157407407408, 8.362567668845314, 9.731835406698563, 11.072662768031188, 12.148663194444444), # 69
(12.443862945070673, 11.597510951812026, 11.965522119341562, 14.35930454911433, 13.49639917695473, 7.496593507087334, 8.779992161473643, 8.75034293552812, 14.062683470507546, 8.33750342935528, 9.717778663831295, 11.05370731355137, 12.14631880144033), # 70
(12.438083592771514, 11.555172202309835, 11.951100102880657, 14.341288204508858, 13.493001694504963, 7.49339497027892, 8.751241991446784, 8.710699039780522, 14.046506652949246, 8.312352431211167, 9.702224844940634, 11.034183524655257, 12.142847865226338), # 71
(12.430486956521738, 11.51210322580645, 11.936324999999998, 14.322316304347826, 13.488529411764706, 7.4892, 8.722084033613445, 8.671199999999999, 14.02965, 8.287115294117646, 9.685225837320575, 11.014105263157894, 12.13828125), # 72
(12.421128001431383, 11.46832887295898, 11.921201748971193, 14.302413969404187, 13.48301137739046, 7.48404371284865, 8.69253101405171, 8.631858984910837, 14.012136556927299, 8.261792637779392, 9.666833528265105, 10.993486390874303, 12.132649819958848), # 73
(12.410061692610485, 11.423873994424532, 11.905735288065841, 14.281606320450885, 13.47647664003873, 7.477961225422954, 8.662595658839667, 8.59268916323731, 13.993989368998628, 8.236385081901073, 9.647099805068226, 10.972340769619521, 12.125984439300412), # 74
(12.397342995169081, 11.378763440860213, 11.889930555555553, 14.25991847826087, 13.468954248366014, 7.470987654320988, 8.6322906940554, 8.553703703703704, 13.97523148148148, 8.210893246187362, 9.626076555023921, 10.950682261208575, 12.118315972222222), # 75
(12.383026874217212, 11.33302206292314, 11.873792489711933, 14.237375563607086, 13.460473251028805, 7.463158116140832, 8.601628845776993, 8.514915775034293, 13.955885939643347, 8.185317750342934, 9.60381566542619, 10.928524727456498, 12.10967528292181), # 76
(12.367168294864912, 11.286674711270411, 11.857326028806582, 14.214002697262478, 13.451062696683609, 7.454507727480566, 8.570622840082535, 8.476338545953361, 13.935975788751714, 8.15965921407246, 9.580369023569023, 10.905882030178327, 12.10009323559671), # 77
(12.349822222222222, 11.23974623655914, 11.84053611111111, 14.189824999999999, 13.440751633986928, 7.445071604938271, 8.53928540305011, 8.437985185185186, 13.915524074074073, 8.133918257080609, 9.55578851674641, 10.882768031189086, 12.089600694444444), # 78
(12.331043621399177, 11.192261489446436, 11.823427674897118, 14.164867592592591, 13.429569111595256, 7.434884865112025, 8.5076292607578, 8.399868861454047, 13.894553840877913, 8.108095499072055, 9.530126032252346, 10.859196592303805, 12.07822852366255), # 79
(12.310887457505816, 11.144245320589407, 11.806005658436213, 14.139155595813206, 13.417544178165095, 7.423982624599908, 8.475667139283697, 8.362002743484226, 13.873088134430727, 8.082191559751472, 9.503433457380826, 10.835181575337522, 12.066007587448558), # 80
(12.289408695652174, 11.09572258064516, 11.788274999999999, 14.112714130434783, 13.40470588235294, 7.412399999999999, 8.443411764705882, 8.3244, 13.851149999999999, 8.05620705882353, 9.475762679425838, 10.810736842105262, 12.052968749999998), # 81
(12.26666230094829, 11.046718120270809, 11.770240637860082, 14.085568317230273, 13.391083272815298, 7.40017210791038, 8.410875863102444, 8.28707379972565, 13.828762482853223, 8.030142615992899, 9.447165585681375, 10.785876254422064, 12.039142875514404), # 82
(12.242703238504205, 10.997256790123457, 11.751907510288065, 14.057743276972623, 13.376705398208665, 7.387334064929126, 8.378072160551463, 8.250037311385459, 13.805948628257887, 8.003998850964253, 9.417694063441433, 10.760613674102954, 12.0245608281893), # 83
(12.21758647342995, 10.947363440860215, 11.733280555555554, 14.029264130434782, 13.361601307189543, 7.373920987654321, 8.345013383131029, 8.213303703703703, 13.78273148148148, 7.977776383442266, 9.3874, 10.734962962962962, 12.009253472222222), # 84
(12.191366970835569, 10.897062923138192, 11.714364711934154, 14.000155998389696, 13.345800048414427, 7.359967992684042, 8.311712256919229, 8.176886145404664, 13.759134087791493, 7.951475833131606, 9.356335282651072, 10.708937982817124, 11.9932516718107), # 85
(12.164099695831096, 10.846380087614497, 11.695164917695474, 13.970444001610307, 13.32933067053982, 7.34551019661637, 8.278181507994145, 8.14079780521262, 13.73517949245542, 7.925097819736949, 9.32455179868864, 10.682552595480471, 11.976586291152262), # 86
(12.135839613526569, 10.795339784946236, 11.67568611111111, 13.940153260869563, 13.312222222222223, 7.330582716049382, 8.244433862433862, 8.10505185185185, 13.710890740740743, 7.8986429629629615, 9.292101435406698, 10.655820662768031, 11.959288194444444), # 87
(12.106641689032028, 10.74396686579052, 11.655933230452675, 13.90930889694042, 13.29450375211813, 7.315220667581161, 8.210482046316468, 8.069661454046638, 13.686290877914953, 7.8721118825143215, 9.259036080099238, 10.628756046494837, 11.941388245884776), # 88
(12.076560887457505, 10.69228618080446, 11.63591121399177, 13.877936030595812, 13.276204308884047, 7.299459167809785, 8.176338785720048, 8.034639780521262, 13.661402949245542, 7.845505198095699, 9.225407620060253, 10.601372608475922, 11.922917309670781), # 89
(12.045652173913043, 10.640322580645162, 11.615625, 13.846059782608696, 13.257352941176471, 7.283333333333333, 8.142016806722689, 7.999999999999999, 13.636250000000002, 7.818823529411764, 9.191267942583732, 10.573684210526315, 11.90390625), # 90
(12.013970513508676, 10.588100915969731, 11.59507952674897, 13.813705273752015, 13.237978697651899, 7.266878280749885, 8.107528835402473, 7.965755281207133, 13.610855075445818, 7.79206749616719, 9.15666893496367, 10.54570471446105, 11.884385931069957), # 91
(11.981570871354446, 10.535646037435285, 11.574279732510288, 13.78089762479871, 13.218110626966835, 7.250129126657521, 8.07288759783749, 7.9319187928669415, 13.585241220850481, 7.7652377180666505, 9.121662484494063, 10.517447982095156, 11.864387217078187), # 92
(11.948508212560386, 10.482982795698925, 11.553230555555555, 13.74766195652174, 13.197777777777778, 7.2331209876543205, 8.03810582010582, 7.898503703703704, 13.55943148148148, 7.738334814814813, 9.0863004784689, 10.488927875243665, 11.84394097222222), # 93
(11.914837502236535, 10.43013604141776, 11.531936934156379, 13.714023389694042, 13.177009198741224, 7.215888980338362, 8.003196228285553, 7.865523182441701, 13.53344890260631, 7.7113594061163555, 9.050634804182172, 10.460158255721609, 11.823078060699588), # 94
(11.880613705492932, 10.377130625248904, 11.510403806584362, 13.680007045088566, 13.155833938513677, 7.198468221307727, 7.968171548454772, 7.832990397805213, 13.507316529492455, 7.684312111675945, 9.014717348927874, 10.431152985344015, 11.801829346707818), # 95
(11.845891787439614, 10.323991397849465, 11.488636111111111, 13.645638043478261, 13.134281045751633, 7.180893827160493, 7.933044506691564, 7.800918518518519, 13.481057407407405, 7.657193551198256, 8.9786, 10.401925925925926, 11.780225694444445), # 96
(11.810726713186616, 10.270743209876544, 11.466638786008229, 13.610941505636069, 13.112379569111596, 7.163200914494741, 7.897827829074016, 7.769320713305898, 13.454694581618655, 7.63000434438796, 8.942334644692538, 10.372490939282363, 11.758297968106996), # 97
(11.775173447843981, 10.217410911987256, 11.444416769547324, 13.575942552334944, 13.090158557250062, 7.145424599908551, 7.86253424168021, 7.738210150891632, 13.428251097393689, 7.602745110949729, 8.905973170299486, 10.342861887228358, 11.736077031893004), # 98
(11.739286956521738, 10.16401935483871, 11.421975, 13.540666304347825, 13.06764705882353, 7.1276, 7.827176470588236, 7.707599999999999, 13.40175, 7.575416470588234, 8.869567464114832, 10.313052631578946, 11.71359375), # 99
(11.703122204329933, 10.110593389088011, 11.39931841563786, 13.505137882447665, 13.044874122488501, 7.109762231367169, 7.791767241876174, 7.677503429355281, 13.375214334705076, 7.548019043008149, 8.833169413432572, 10.28307703414916, 11.690878986625515), # 100
(11.6667341563786, 10.057157865392274, 11.376451954732511, 13.469382407407409, 13.021868796901476, 7.091946410608139, 7.756319281622114, 7.647933607681755, 13.348667146776405, 7.5205534479141445, 8.796830905546694, 10.252948956754024, 11.667963605967076), # 101
(11.630177777777778, 10.003737634408603, 11.353380555555555, 13.433425, 12.998660130718955, 7.074187654320988, 7.720845315904139, 7.618903703703703, 13.32213148148148, 7.4930203050108934, 8.760603827751195, 10.222682261208577, 11.644878472222222), # 102
(11.593508033637502, 9.950357546794105, 11.3301091563786, 13.39729078099839, 12.975277172597435, 7.056521079103795, 7.685358070800336, 7.590426886145404, 13.295630384087792, 7.465420234003066, 8.724540067340067, 10.192290809327847, 11.621654449588474), # 103
(11.556779889067812, 9.897042453205893, 11.30664269547325, 13.361004871175522, 12.951748971193416, 7.03898180155464, 7.649870272388791, 7.562516323731138, 13.269186899862826, 7.437753854595336, 8.6886915116073, 10.161788462926864, 11.598322402263374), # 104
(11.520048309178742, 9.843817204301073, 11.28298611111111, 13.324592391304348, 12.928104575163397, 7.021604938271605, 7.614394646747589, 7.535185185185185, 13.242824074074074, 7.410021786492375, 8.653110047846889, 10.131189083820663, 11.574913194444443), # 105
(11.483368259080336, 9.790706650736759, 11.259144341563784, 13.288078462157811, 12.904373033163884, 7.004425605852766, 7.578943919954813, 7.508446639231824, 13.216564951989024, 7.382224649398854, 8.617847563352825, 10.100506533824273, 11.551457690329217), # 106
(11.446794703882626, 9.737735643170053, 11.235122325102882, 13.251488204508856, 12.880583393851365, 6.987478920896206, 7.543530818088553, 7.482313854595337, 13.190432578875171, 7.354363063019446, 8.582955945419101, 10.069754674752724, 11.527986754115226), # 107
(11.410382608695652, 9.684929032258065, 11.210925000000001, 13.214846739130435, 12.856764705882352, 6.9708, 7.508168067226889, 7.4568, 13.16445, 7.326437647058824, 8.548487081339712, 10.038947368421054, 11.504531250000001), # 108
(11.374186938629451, 9.632311668657906, 11.18655730452675, 13.178179186795488, 12.832946017913338, 6.954423959762231, 7.472868393447913, 7.431918244170096, 13.138640260631002, 7.298449021221656, 8.514492858408648, 10.008098476644285, 11.48112204218107), # 109
(11.338262658794058, 9.579908403026684, 11.162024176954734, 13.141510668276974, 12.809156378600823, 6.938385916780978, 7.437644522829707, 7.407681755829903, 13.113026406035663, 7.270397805212619, 8.4810251639199, 9.977221861237457, 11.457789994855966), # 110
(11.302664734299517, 9.527744086021507, 11.137330555555558, 13.104866304347826, 12.785424836601306, 6.922720987654322, 7.402509181450357, 7.384103703703703, 13.087631481481482, 7.242284618736383, 8.448135885167463, 9.946331384015595, 11.434565972222222), # 111
(11.26744813025586, 9.47584356829948, 11.112481378600824, 13.068271215780998, 12.76178044057129, 6.907464288980339, 7.367475095387949, 7.361197256515775, 13.062478532235938, 7.214110081497618, 8.41587690944533, 9.915440906793732, 11.411480838477365), # 112
(11.232605068443652, 9.424318342543142, 11.087541393902482, 13.031800658990448, 12.738210816208445, 6.892643723057416, 7.332631156388123, 7.339023082536727, 13.037655373510344, 7.185965683935275, 8.38430868738344, 9.884631523805313, 11.388532681011865), # 113
(11.197777077480078, 9.373676620230642, 11.062854810025941, 12.995747305532804, 12.71447202547959, 6.8782255302358815, 7.298421850092694, 7.317853511406144, 13.013542842855673, 7.158378201495339, 8.353493204535836, 9.85429460653557, 11.365530496992042), # 114
(11.162861883604794, 9.323936638419655, 11.038436319248781, 12.960101406218135, 12.69048921346632, 6.864172214998518, 7.264871580229873, 7.297683185134451, 12.990149974402547, 7.131390393585692, 8.323385413712511, 9.824445099070621, 11.342407957992451), # 115
(11.127815847885161, 9.275025937550042, 11.014238627980648, 12.924799380319685, 12.666226231660534, 6.8504506527445175, 7.231925781033471, 7.278456375478791, 12.967417607073395, 7.104952030139456, 8.293927117525778, 9.795027836984815, 11.319128711707068), # 116
(11.092595331388527, 9.226872058061664, 10.990214442631183, 12.889777647110693, 12.641646931554133, 6.837027718873069, 7.199529886737303, 7.260117354196302, 12.945286579790643, 7.079012881089755, 8.26506011858794, 9.7659876558525, 11.295656405829869), # 117
(11.057156695182252, 9.179402540394388, 10.96631646961004, 12.8549726258644, 12.61671516463901, 6.8238702887833655, 7.167629331575178, 7.2426103930441155, 12.923697731476722, 7.053522716369711, 8.236726219511308, 9.737269391248018, 11.271954688054828), # 118
(11.02145630033369, 9.132544924988075, 10.942497415326867, 12.820320735854047, 12.591394782407065, 6.810945237874599, 7.136169549780907, 7.225879763779374, 12.902591901054052, 7.028431305912446, 8.208867222908193, 9.708817878745721, 11.247987206075917), # 119
(10.985450507910194, 9.08622675228259, 10.918709986191313, 12.785758396352874, 12.565649636350196, 6.7982194415459585, 7.105095975588303, 7.209869738159211, 12.88190992744507, 7.003688419651087, 8.181424931390898, 9.680577953919956, 11.223717607587115), # 120
(10.949095678979122, 9.040375562717795, 10.894906888613024, 12.75122202663412, 12.539443577960302, 6.7856597751966365, 7.0743540432311764, 7.1945245879407675, 12.861592649572199, 6.979243827518755, 8.154341147571738, 9.652494452345065, 11.199109540282393), # 121
(10.912348174607825, 8.994918896733553, 10.871040829001652, 12.716648045971025, 12.512740458729281, 6.773233114225823, 7.043889186943341, 7.179788584881178, 12.841580906357867, 6.955047299448572, 8.127557674063022, 9.6245122095954, 11.174126651855724), # 122
(10.875164355863662, 8.949784294769728, 10.847064513766842, 12.681972873636832, 12.485504130149028, 6.76090633403271, 7.013646840958606, 7.16560600073758, 12.821815536724504, 6.931048605373665, 8.101016313477052, 9.596576061245305, 11.148732590001085), # 123
(10.837500583813984, 8.904899297266184, 10.822930649318243, 12.647132928904783, 12.457698443711445, 6.748646310016486, 6.983572439510783, 7.151921107267111, 12.802237379594539, 6.9071975152271525, 8.074658868426143, 9.56863084286913, 11.122891002412453), # 124
(10.79931321952615, 8.860191444662783, 10.798591942065508, 12.612064631048112, 12.429287250908427, 6.736419917576347, 6.953611416833687, 7.138678176226909, 12.78278727389039, 6.88344379894216, 8.048427141522602, 9.540621390041217, 11.096565536783794), # 125
(10.760558624067514, 8.815588277399392, 10.774001098418278, 12.576704399340064, 12.400234403231872, 6.724194032111481, 6.923709207161124, 7.12582147937411, 12.763406058534501, 6.859737226451811, 8.022262935378736, 9.51249253833592, 11.069719840809094), # 126
(10.721193158505432, 8.771017335915868, 10.749110824786205, 12.540988653053878, 12.370503752173677, 6.711935529021078, 6.893811244726913, 7.113295288465854, 12.744034572449289, 6.836027567689229, 7.9961080526068535, 9.484189123327578, 11.042317562182317), # 127
(10.681173183907255, 8.72640616065208, 10.72387382757894, 12.504853811462798, 12.340059149225747, 6.699611283704333, 6.863862963764858, 7.101043875259275, 12.72461365455718, 6.8122645925875345, 7.969904295819269, 9.455655980590546, 11.014322348597444), # 128
(10.640455061340337, 8.681682292047888, 10.698242813206127, 12.468236293840057, 12.308864445879973, 6.687188171560433, 6.833809798508775, 7.089011511511512, 12.705084143780608, 6.788398071079854, 7.943593467628284, 9.426837945699162, 10.985697847748446), # 129
(10.598995151872039, 8.63677327054316, 10.672170488077414, 12.431072519458903, 12.276883493628256, 6.6746330679885695, 6.803597183192475, 7.077142468979701, 12.685386879042001, 6.764377773099308, 7.9171173706462135, 9.397679854227782, 10.956407707329298), # 130
(10.556749816569713, 8.591606636577751, 10.645609558602457, 12.39329890759257, 12.244080143962494, 6.661912848387936, 6.773170552049771, 7.06538101942098, 12.665462699263783, 6.740153468579022, 7.890417807485361, 9.36812654175075, 10.926415575033973), # 131
(10.51367541650071, 8.546109930591532, 10.618512731190895, 12.354851877514305, 12.210418248374584, 6.648994388157723, 6.7424753393144705, 7.053671434592488, 12.645252443368385, 6.715674927452118, 7.863436580758037, 9.33812284384241, 10.89568509855645), # 132
(10.469728312732395, 8.500210693024362, 10.59083271225238, 12.315667848497341, 12.175861658356423, 6.63584456269712, 6.711456979220387, 7.041957986251359, 12.624696950278231, 6.690891919651718, 7.8361154930765515, 9.307613596077111, 10.864179925590703), # 133
(10.424864866332113, 8.453836464316106, 10.562522208196564, 12.275683239814922, 12.14037422539991, 6.622430247405318, 6.6800609060013345, 7.0301849461547326, 12.603737058915753, 6.665754215110948, 7.808396347053214, 9.2765436340292, 10.831863703830699), # 134
(10.379041438367224, 8.406914784906629, 10.53353392543309, 12.234834470740294, 12.103919800996945, 6.60871831768151, 6.648232553891121, 7.018296586059743, 12.582313608203375, 6.640211583762931, 7.78022094530033, 9.244857793273022, 10.798700080970423), # 135
(10.332214389905081, 8.35937319523579, 10.50382057037161, 12.193057960546687, 12.066462236639419, 6.594675648924887, 6.615917357123561, 7.0062371777235315, 12.560367437063528, 6.6142137955407865, 7.751531090430213, 9.212500909382928, 10.764652704703844), # 136
(10.28434008201304, 8.311139235743456, 10.473334849421772, 12.150290128507349, 12.027965383819241, 6.580269116534637, 6.583060749932466, 6.993950992903235, 12.537839384418639, 6.587710620377641, 7.722268585055167, 9.179417817933263, 10.729685222724932), # 137
(10.235374875758456, 8.26214044686949, 10.442029468993221, 12.106467393895516, 11.988393094028302, 6.565465595909957, 6.5496081665516455, 6.981382303355987, 12.514670289191137, 6.560651828206615, 7.692375231787501, 9.145553354498373, 10.693761282727667), # 138
(10.185275132208682, 8.212304369053752, 10.409857135495608, 12.06152617598443, 11.947709218758497, 6.550231962450032, 6.515505041214911, 6.968475380838929, 12.490800990303445, 6.532987188960836, 7.661792833239527, 9.110852354652607, 10.656844532406023), # 139
(10.133997212431076, 8.16155854273611, 10.376770555338585, 12.015402894047332, 11.905877609501735, 6.534535091554055, 6.480696808156076, 6.955174497109195, 12.466172326677999, 6.5046664725734225, 7.630463192023552, 9.07525965397031, 10.618898619453978), # 140
(10.081497477492995, 8.109830508356424, 10.342722434931792, 11.968033967357464, 11.862862117749904, 6.518341858621218, 6.445128901608954, 6.9414239239239235, 12.440725137237216, 6.4756394489775015, 7.598328110751885, 9.03872008802583, 10.579887191565495), # 141
(10.027732288461786, 8.057047806354559, 10.307665480684884, 11.919355815188064, 11.818626594994903, 6.501619139050712, 6.408746755807351, 6.927167933040253, 12.41440026090353, 6.445855888106193, 7.565329392036836, 9.001178492393512, 10.539773896434559), # 142
(9.972658006404808, 8.003137977170377, 10.27155239900751, 11.86930485681237, 11.773134892728635, 6.484333808241727, 6.371495804985082, 6.912350796215319, 12.387138536599375, 6.415265559892623, 7.531408838490711, 8.962579702647707, 10.49852238175514), # 143
(9.916230992389421, 7.948028561243743, 10.234335896309313, 11.817817511503627, 11.726350862442994, 6.466452741593456, 6.333321483375959, 6.896916785206259, 12.358880803247171, 6.383818234269912, 7.496508252725821, 8.922868554362758, 10.456096295221217), # 144
(9.858407607482972, 7.891647099014518, 10.195968678999947, 11.764830198535073, 11.67823835562988, 6.4479428145050885, 6.294169225213792, 6.880810171770211, 12.329567899769344, 6.351463681171185, 7.460569437354474, 8.881989883113016, 10.41245928452676), # 145
(9.79914421275282, 7.83392113092257, 10.156403453489059, 11.71027933717995, 11.62876122378119, 6.428770902375816, 6.253984464732396, 6.863975227664311, 12.299140665088327, 6.318151670529565, 7.423534194988978, 8.839888524472823, 10.367574997365741), # 146
(9.73839716926632, 7.774778197407756, 10.115592926186292, 11.654101346711496, 11.577883318388821, 6.4089038806048295, 6.212712636165577, 6.846356224645698, 12.267539938126548, 6.283831972278175, 7.385344328241643, 8.796509314016532, 10.321407081432142), # 147
(9.676122838090825, 7.714145838909944, 10.0734898035013, 11.596232646402955, 11.525568490944673, 6.38830862459132, 6.170299173747152, 6.827897434471509, 12.234706557806435, 6.248454356350137, 7.345941639724779, 8.751797087318483, 10.27391918441993), # 148
(9.612277580293695, 7.651951595868995, 10.030046791843732, 11.536609655527563, 11.471780592940645, 6.366952009734479, 6.126689511710929, 6.80854312889888, 12.200581363050405, 6.211968592678576, 7.3052679320506915, 8.705696679953029, 10.225074954023084), # 149
(9.546817756942277, 7.588123008724775, 9.985216597623232, 11.475168793358565, 11.416483475868631, 6.344800911433499, 6.08182908429072, 6.788237579684948, 12.165105192780901, 6.174324451196612, 7.2632650078316905, 8.658152927494514, 10.174838037935576), # 150
(9.47969972910393, 7.522587617917144, 9.93895192724945, 11.411846479169196, 11.359640991220532, 6.321822205087566, 6.03566332572034, 6.7669250585868514, 12.128218885920345, 6.135471701837373, 7.2198746696800855, 8.609110665517285, 10.123172083851381), # 151
(9.41087985784601, 7.455272963885967, 9.89120548713204, 11.346579132232701, 11.301216990488243, 6.297982766095876, 5.9881376702335976, 6.744549837361729, 12.089863281391164, 6.095360114533979, 7.175038720208185, 8.558514729595691, 10.070040739464476), # 152
(9.340314504235872, 7.386106587071107, 9.841929983680641, 11.279303171822319, 11.241175325163667, 6.273249469857618, 5.939197552064303, 6.721056187766714, 12.049979218115787, 6.053939459219555, 7.128698962028299, 8.506309955304076, 10.015407652468832), # 153
(9.267960029340873, 7.315016027912428, 9.79107812330491, 11.209955017211291, 11.179479846738696, 6.247589191771985, 5.888788405446274, 6.696388381558948, 12.008507535016639, 6.011159505827223, 7.080797197752734, 8.45244117821679, 9.959236470558428), # 154
(9.193772794228362, 7.241928826849794, 9.73860261241449, 11.138471087672853, 11.116094406705235, 6.220968807238165, 5.836855664613313, 6.670490690495563, 11.965389071016153, 5.966970024290105, 7.0312752299938, 8.396853233908178, 9.901490841427231), # 155
(9.117709159965697, 7.166772524323065, 9.684456157419032, 11.06478780248025, 11.050982856555176, 6.193355191655353, 5.7833447637992395, 6.643307386333702, 11.920564665036752, 5.921320784541327, 6.980074861363805, 8.339490957952586, 9.842134412769221), # 156
(9.039725487620235, 7.089474660772107, 9.628591464728181, 10.988841580906724, 10.984109047780422, 6.164715220422736, 5.728201137237862, 6.614782740830498, 11.873975156000865, 5.874161556514009, 6.927137894475059, 8.280299185924363, 9.781130832278372), # 157
(8.957617135686286, 7.008543744926709, 9.568310344682827, 10.907723497981491, 10.912417327045196, 6.133229371580532, 5.6701280651134285, 6.582956342819247, 11.821994509918916, 5.824039099549372, 6.870714903046731, 8.217119477033206, 9.715783031298415), # 158
(8.858744120374082, 6.915678383519373, 9.488085382083584, 10.804772590546143, 10.818229571737954, 6.088427577608523, 5.601855316062859, 6.536656239317259, 11.743712713466573, 5.762737192918494, 6.800900322742793, 8.13763502841973, 9.630513176304232), # 159
(8.741846513885172, 6.810116074248857, 9.386305149547066, 10.67829301249063, 10.699704157616154, 6.0292095552572205, 5.5226924980605405, 6.4747190274328155, 11.636910272674381, 5.689446782235472, 6.716711410331447, 8.040602338665416, 9.523704730672296), # 160
(8.607866465503152, 6.692545041696563, 9.26405636629237, 10.529487004508074, 10.558071749138534, 5.956292689884377, 5.433217735208252, 6.397920639731736, 11.50299572039882, 5.604789831805125, 6.618889985519648, 7.926920962689085, 9.396448853782916), # 161
(8.457746124511628, 6.563653510443886, 9.122425751538595, 10.359556807291591, 10.394563010763845, 5.870394366847746, 5.334009151607771, 6.307037008779842, 11.343377589496363, 5.509388305932277, 6.508177868014344, 7.797490455409552, 9.2498367050164), # 162
(8.292427640194196, 6.424129705072228, 8.962500024504841, 10.16970466153432, 10.210408606950825, 5.772231971505087, 5.22564487136088, 6.20284406714295, 11.159464412823487, 5.40386416892175, 6.38531687752249, 7.653210371745638, 9.084959443753055), # 163
(8.11285316183446, 6.2746618501629845, 8.785365904410211, 9.961132807929381, 10.006839202158226, 5.662522889214155, 5.108703018569359, 6.086117747386882, 10.952664723236667, 5.2888393850783615, 6.251048833751035, 7.494980266616163, 8.902908229373192), # 164
(7.9199648387160195, 6.115938170297558, 8.592110110473802, 9.735043487169902, 9.785085460844789, 5.541984505332703, 4.983761717334986, 5.957633982077455, 10.724387053592375, 5.164935918706936, 6.106115556406933, 7.323699694939943, 8.704774221257123), # 165
(7.714704820122476, 5.948646890057345, 8.383819361914712, 9.492638939949002, 9.546378047469256, 5.41133420521849, 4.851399091759543, 5.818168703780493, 10.476039936747087, 5.0327757341122945, 5.9512588651971345, 7.140268211635801, 8.491648578785155), # 166
(7.498015255337426, 5.773476234023744, 8.161580377952045, 9.235121406959811, 9.291947626490375, 5.27128937422927, 4.712193265944809, 5.668497845061811, 10.209031905557278, 4.892980795599256, 5.787220579828592, 6.94558537162255, 8.264622461337595), # 167
(7.2708382936444735, 5.591114426778154, 7.926479877804897, 8.963693128895455, 9.02302486236689, 5.122567397722799, 4.5667223639925645, 5.509397338487231, 9.924771492879426, 4.746173067472646, 5.614742520008257, 6.740550729819013, 8.024787028294753), # 168
(7.034116084327218, 5.402249692901975, 7.67960458069237, 8.67955634644906, 8.740840419557543, 4.965885661056833, 4.4155645100045895, 5.341643116622574, 9.624667231570005, 4.592974514037284, 5.434566505443081, 6.526063841144007, 7.773233439036942), # 169
(6.78879077666926, 5.207570256976605, 7.422041205833562, 8.383913300313743, 8.44662496252108, 4.8019615495891275, 4.259297828082663, 5.166011112033656, 9.310127654485486, 4.434007099597989, 5.247434355840019, 6.3030242605163505, 7.5110528529444665), # 170
(6.5358045199542, 5.007764343583441, 7.154876472447573, 8.077966231182643, 8.141609155716246, 4.631512448677438, 4.098500442328566, 4.983277257286299, 8.982561294482347, 4.269892788459586, 5.054087890906017, 6.072331542854863, 7.239336429397638), # 171
(6.276099463465638, 4.803520177303883, 6.879197099753504, 7.762917379748876, 7.827023663601784, 4.45525574367952, 3.9337504768440783, 4.794217484946325, 8.643376684417062, 4.101253544926895, 4.855268930348032, 5.834885243078365, 6.959175327776763), # 172
(6.010617756487176, 4.59552598271933, 6.596089806970453, 7.43996898670557, 7.504099150636442, 4.27390881995313, 3.7656260557309795, 4.599607727579548, 8.293982357146106, 3.9287113333047374, 4.651719293873013, 5.59158491610567, 6.671660707462155), # 173
(5.740301548302412, 4.384469984411181, 6.306641313317521, 7.110323292745848, 7.174066281278959, 4.088189062856022, 3.5947053030910503, 4.400223917751792, 7.935786845525956, 3.752888117897936, 4.444180801187913, 5.3433301168556016, 6.37788372783412), # 174
(5.466092988194946, 4.171040406960834, 6.01193833801381, 6.775182538562841, 6.838155719988083, 3.898813857745954, 3.421566343026069, 4.196841988028875, 7.570198682413086, 3.574405863011309, 4.233395271999683, 5.091020400246977, 6.078935548272969), # 175
(5.188934225448382, 3.9559254749496873, 5.713067600278413, 6.43574896484967, 6.497598131222556, 3.7065005899806795, 3.2467872996378175, 3.9902378709766184, 7.1986264006639695, 3.3938865329496806, 4.020104526015276, 4.835555321198615, 5.7759073281590085), # 176
(4.909767409346319, 3.7398134129591414, 5.411115819330436, 6.09322481229946, 6.1536241794411275, 3.511966644917956, 3.0709462970280748, 3.781187499160839, 6.822478533135084, 3.2119520920178695, 3.8050503829416424, 4.5778344346293345, 5.4698902268725496), # 177
(4.629534689172356, 3.5233924455705936, 5.107169714388976, 5.748812321605339, 5.807464529102536, 3.3159294079155393, 2.894621459298621, 3.5704668051473587, 6.443163612682903, 3.0292245045207, 3.588974662485735, 4.318757295457952, 5.161975403793902), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(5, 6, 5, 9, 4, 1, 2, 3, 2, 1, 1, 1, 0, 7, 6, 8, 4, 8, 2, 3, 1, 0, 1, 1, 1, 0), # 0
(12, 12, 8, 19, 11, 2, 3, 4, 8, 2, 3, 2, 0, 10, 11, 15, 11, 13, 6, 8, 1, 2, 3, 1, 2, 0), # 1
(17, 15, 18, 24, 14, 5, 7, 6, 9, 3, 4, 3, 0, 19, 20, 19, 14, 18, 8, 12, 3, 6, 4, 2, 2, 0), # 2
(24, 27, 28, 33, 18, 8, 17, 6, 11, 3, 4, 4, 0, 28, 29, 26, 17, 28, 9, 13, 3, 7, 6, 2, 2, 0), # 3
(32, 33, 34, 41, 24, 11, 20, 14, 12, 5, 5, 5, 0, 41, 37, 30, 24, 33, 12, 15, 4, 13, 6, 3, 2, 0), # 4
(41, 45, 41, 49, 30, 14, 25, 19, 17, 7, 7, 6, 0, 45, 48, 38, 30, 40, 16, 20, 7, 13, 6, 4, 2, 0), # 5
(47, 53, 47, 53, 38, 17, 26, 23, 22, 12, 7, 9, 0, 51, 55, 41, 33, 50, 22, 26, 11, 14, 10, 5, 3, 0), # 6
(51, 57, 57, 58, 45, 23, 34, 27, 28, 15, 7, 11, 0, 61, 61, 44, 39, 57, 26, 27, 13, 14, 12, 8, 4, 0), # 7
(61, 64, 64, 69, 51, 24, 41, 35, 32, 16, 8, 11, 0, 72, 77, 53, 42, 65, 34, 31, 15, 18, 14, 9, 6, 0), # 8
(67, 70, 70, 80, 53, 27, 42, 36, 35, 17, 8, 12, 0, 81, 85, 60, 47, 71, 39, 34, 15, 18, 17, 9, 6, 0), # 9
(74, 78, 80, 85, 59, 31, 44, 41, 37, 17, 9, 13, 0, 94, 97, 65, 52, 80, 45, 37, 15, 22, 19, 11, 9, 0), # 10
(82, 93, 86, 100, 73, 34, 48, 41, 38, 19, 11, 14, 0, 102, 109, 75, 55, 88, 49, 41, 18, 25, 21, 11, 12, 0), # 11
(93, 106, 95, 112, 78, 36, 52, 44, 39, 21, 13, 14, 0, 113, 121, 83, 63, 97, 54, 46, 20, 31, 26, 15, 12, 0), # 12
(104, 120, 98, 125, 81, 40, 58, 48, 45, 23, 14, 15, 0, 126, 129, 90, 69, 103, 58, 49, 21, 33, 31, 21, 13, 0), # 13
(114, 131, 102, 134, 93, 44, 66, 53, 48, 23, 17, 15, 0, 134, 137, 96, 78, 110, 63, 52, 24, 35, 35, 24, 14, 0), # 14
(125, 138, 110, 148, 104, 47, 71, 56, 52, 28, 17, 16, 0, 145, 147, 102, 88, 116, 68, 55, 28, 39, 39, 26, 15, 0), # 15
(139, 155, 117, 158, 113, 50, 74, 62, 57, 31, 17, 17, 0, 156, 157, 108, 96, 124, 74, 59, 34, 45, 42, 31, 17, 0), # 16
(146, 166, 125, 163, 123, 53, 78, 67, 62, 33, 18, 18, 0, 165, 165, 114, 102, 133, 78, 65, 39, 49, 46, 35, 17, 0), # 17
(156, 176, 133, 176, 135, 57, 81, 72, 65, 34, 19, 18, 0, 179, 176, 120, 108, 136, 88, 69, 42, 51, 51, 39, 19, 0), # 18
(172, 182, 143, 184, 140, 61, 89, 75, 72, 38, 19, 18, 0, 184, 191, 126, 120, 148, 94, 74, 47, 57, 55, 40, 21, 0), # 19
(187, 192, 150, 193, 151, 68, 91, 79, 75, 42, 19, 19, 0, 193, 207, 135, 126, 157, 100, 81, 50, 61, 61, 41, 21, 0), # 20
(204, 209, 158, 210, 162, 72, 96, 84, 83, 46, 20, 21, 0, 203, 217, 146, 130, 165, 109, 87, 51, 66, 70, 43, 22, 0), # 21
(221, 228, 173, 222, 174, 76, 103, 89, 91, 49, 22, 23, 0, 214, 230, 156, 139, 170, 118, 90, 57, 73, 73, 46, 22, 0), # 22
(228, 239, 179, 234, 185, 80, 110, 92, 94, 50, 24, 23, 0, 233, 239, 160, 144, 179, 127, 95, 62, 78, 75, 50, 24, 0), # 23
(243, 250, 187, 242, 200, 87, 115, 96, 100, 52, 27, 23, 0, 246, 247, 172, 150, 190, 133, 100, 64, 85, 79, 52, 25, 0), # 24
(261, 267, 204, 247, 205, 94, 123, 102, 106, 53, 29, 23, 0, 260, 253, 178, 154, 203, 137, 104, 65, 93, 84, 52, 25, 0), # 25
(275, 277, 212, 256, 216, 98, 127, 106, 110, 54, 31, 25, 0, 279, 264, 187, 160, 212, 148, 110, 66, 94, 88, 57, 26, 0), # 26
(287, 294, 224, 267, 224, 110, 131, 114, 114, 55, 37, 26, 0, 294, 269, 194, 170, 216, 155, 112, 68, 97, 92, 58, 27, 0), # 27
(302, 311, 240, 281, 236, 113, 136, 119, 119, 56, 41, 28, 0, 300, 277, 203, 178, 220, 159, 117, 69, 101, 92, 61, 30, 0), # 28
(316, 327, 250, 296, 250, 120, 139, 124, 127, 56, 44, 28, 0, 315, 280, 213, 187, 229, 167, 120, 70, 107, 97, 63, 31, 0), # 29
(329, 342, 260, 302, 256, 124, 149, 125, 134, 59, 45, 28, 0, 326, 293, 220, 195, 241, 175, 127, 73, 110, 99, 64, 31, 0), # 30
(344, 357, 275, 317, 265, 128, 153, 128, 135, 62, 46, 29, 0, 334, 310, 228, 202, 250, 180, 134, 75, 117, 104, 68, 34, 0), # 31
(357, 368, 291, 328, 275, 131, 161, 137, 140, 64, 49, 29, 0, 338, 320, 240, 207, 262, 188, 137, 80, 120, 108, 70, 34, 0), # 32
(368, 386, 301, 340, 285, 135, 165, 140, 147, 64, 55, 30, 0, 352, 333, 252, 211, 271, 195, 144, 81, 126, 108, 70, 35, 0), # 33
(375, 402, 319, 353, 296, 138, 170, 149, 155, 68, 55, 32, 0, 365, 343, 260, 214, 281, 199, 148, 82, 127, 111, 70, 36, 0), # 34
(393, 414, 328, 367, 303, 142, 174, 151, 159, 70, 56, 33, 0, 371, 348, 262, 226, 290, 207, 152, 86, 132, 113, 71, 37, 0), # 35
(407, 424, 341, 382, 308, 148, 179, 153, 161, 73, 57, 33, 0, 380, 358, 267, 237, 305, 209, 156, 88, 138, 118, 74, 37, 0), # 36
(419, 436, 348, 392, 320, 151, 186, 157, 164, 74, 60, 34, 0, 387, 362, 274, 244, 310, 214, 160, 90, 141, 121, 77, 37, 0), # 37
(432, 453, 363, 402, 333, 153, 194, 165, 167, 79, 62, 35, 0, 399, 375, 285, 250, 318, 223, 166, 94, 146, 125, 79, 37, 0), # 38
(446, 458, 383, 407, 341, 156, 198, 168, 177, 81, 65, 36, 0, 413, 383, 297, 255, 326, 232, 172, 100, 150, 127, 80, 38, 0), # 39
(455, 466, 388, 415, 346, 158, 200, 170, 182, 83, 66, 37, 0, 423, 388, 304, 265, 338, 244, 179, 101, 157, 129, 81, 40, 0), # 40
(466, 475, 396, 423, 350, 162, 209, 177, 188, 86, 69, 37, 0, 431, 402, 309, 278, 345, 248, 186, 103, 163, 132, 81, 40, 0), # 41
(477, 487, 404, 432, 356, 167, 214, 183, 189, 87, 69, 37, 0, 436, 406, 318, 284, 360, 254, 190, 106, 166, 134, 82, 42, 0), # 42
(488, 508, 413, 443, 365, 173, 221, 192, 192, 89, 69, 38, 0, 453, 419, 330, 291, 369, 262, 194, 108, 168, 139, 85, 43, 0), # 43
(501, 525, 423, 457, 375, 180, 227, 194, 195, 90, 71, 39, 0, 461, 431, 336, 294, 372, 265, 199, 112, 174, 143, 88, 43, 0), # 44
(515, 542, 429, 465, 387, 188, 233, 198, 202, 93, 74, 40, 0, 474, 441, 347, 303, 383, 269, 205, 116, 175, 146, 91, 44, 0), # 45
(527, 559, 441, 474, 397, 192, 244, 205, 205, 96, 75, 40, 0, 488, 446, 353, 311, 391, 272, 211, 118, 178, 149, 92, 44, 0), # 46
(545, 574, 450, 483, 413, 198, 247, 208, 209, 96, 79, 40, 0, 497, 453, 359, 317, 401, 280, 219, 120, 183, 153, 95, 46, 0), # 47
(555, 584, 460, 493, 420, 203, 250, 212, 212, 102, 79, 40, 0, 507, 463, 365, 325, 411, 285, 222, 122, 187, 154, 98, 46, 0), # 48
(568, 591, 471, 506, 427, 204, 253, 216, 219, 106, 79, 40, 0, 518, 474, 374, 332, 419, 292, 225, 123, 191, 159, 100, 46, 0), # 49
(585, 607, 482, 518, 438, 206, 254, 222, 228, 106, 81, 41, 0, 528, 484, 384, 345, 423, 295, 229, 125, 194, 162, 101, 47, 0), # 50
(596, 623, 494, 531, 449, 211, 263, 225, 230, 108, 82, 42, 0, 540, 489, 391, 351, 431, 301, 233, 126, 196, 166, 102, 49, 0), # 51
(609, 634, 499, 542, 459, 215, 267, 228, 234, 110, 84, 44, 0, 550, 499, 399, 360, 439, 306, 241, 131, 197, 171, 105, 49, 0), # 52
(619, 646, 512, 554, 465, 220, 272, 231, 240, 112, 84, 45, 0, 561, 511, 405, 367, 454, 311, 243, 136, 201, 176, 106, 51, 0), # 53
(633, 654, 523, 562, 473, 224, 275, 231, 244, 115, 87, 45, 0, 575, 518, 411, 375, 462, 316, 248, 137, 204, 181, 109, 51, 0), # 54
(645, 672, 534, 571, 481, 231, 280, 237, 247, 115, 88, 45, 0, 590, 527, 421, 384, 472, 322, 248, 141, 206, 182, 111, 51, 0), # 55
(662, 683, 541, 584, 489, 235, 283, 242, 250, 117, 89, 45, 0, 608, 534, 429, 391, 481, 327, 249, 146, 211, 189, 113, 52, 0), # 56
(670, 695, 554, 590, 500, 238, 289, 246, 255, 120, 89, 45, 0, 615, 540, 435, 398, 496, 330, 252, 150, 213, 191, 115, 53, 0), # 57
(677, 706, 560, 604, 509, 243, 294, 250, 261, 125, 90, 47, 0, 626, 554, 441, 404, 507, 339, 253, 153, 218, 198, 117, 55, 0), # 58
(690, 716, 567, 612, 516, 248, 298, 255, 266, 128, 91, 48, 0, 636, 573, 448, 406, 510, 343, 255, 155, 223, 204, 119, 56, 0), # 59
(706, 724, 576, 626, 527, 250, 304, 256, 274, 131, 91, 49, 0, 653, 583, 455, 412, 516, 350, 263, 158, 226, 210, 123, 56, 0), # 60
(724, 739, 591, 636, 531, 255, 311, 260, 277, 134, 95, 50, 0, 667, 593, 467, 414, 527, 357, 273, 163, 229, 214, 124, 56, 0), # 61
(735, 748, 600, 648, 543, 259, 312, 262, 282, 138, 98, 50, 0, 676, 607, 476, 420, 533, 360, 279, 167, 238, 219, 126, 57, 0), # 62
(752, 756, 612, 656, 554, 261, 316, 266, 287, 141, 101, 51, 0, 688, 613, 486, 426, 539, 364, 285, 170, 240, 224, 126, 58, 0), # 63
(767, 766, 624, 661, 566, 267, 318, 267, 293, 141, 103, 53, 0, 706, 626, 496, 433, 547, 369, 290, 173, 248, 225, 129, 59, 0), # 64
(780, 780, 637, 668, 572, 270, 332, 272, 299, 145, 105, 53, 0, 723, 639, 503, 435, 558, 376, 294, 177, 254, 227, 131, 60, 0), # 65
(787, 792, 651, 678, 579, 274, 336, 274, 301, 147, 105, 55, 0, 732, 648, 509, 441, 568, 381, 297, 180, 261, 229, 134, 61, 0), # 66
(796, 800, 662, 687, 588, 281, 338, 278, 306, 150, 105, 55, 0, 743, 659, 513, 448, 575, 386, 302, 186, 269, 234, 134, 62, 0), # 67
(803, 817, 666, 691, 603, 287, 343, 287, 314, 154, 107, 55, 0, 753, 668, 520, 460, 584, 391, 308, 189, 274, 237, 135, 64, 0), # 68
(811, 825, 674, 705, 611, 290, 346, 289, 320, 155, 110, 55, 0, 765, 679, 528, 465, 591, 394, 311, 191, 279, 244, 136, 64, 0), # 69
(824, 835, 680, 714, 618, 294, 349, 292, 323, 155, 112, 56, 0, 774, 685, 532, 472, 599, 399, 315, 194, 285, 246, 138, 66, 0), # 70
(835, 839, 688, 730, 627, 297, 354, 294, 325, 157, 115, 59, 0, 788, 695, 538, 477, 604, 403, 320, 197, 288, 251, 139, 66, 0), # 71
(846, 853, 699, 738, 638, 302, 361, 297, 330, 158, 117, 59, 0, 801, 711, 545, 482, 610, 412, 327, 200, 298, 256, 141, 67, 0), # 72
(860, 862, 713, 750, 652, 309, 365, 299, 331, 161, 117, 60, 0, 812, 720, 551, 491, 620, 416, 331, 204, 304, 259, 143, 68, 0), # 73
(872, 880, 730, 762, 659, 313, 370, 302, 337, 162, 119, 61, 0, 819, 732, 559, 496, 631, 420, 334, 210, 309, 264, 146, 69, 0), # 74
(888, 898, 740, 781, 670, 315, 375, 304, 345, 166, 120, 63, 0, 830, 739, 565, 502, 643, 421, 339, 210, 314, 268, 148, 70, 0), # 75
(898, 905, 749, 796, 682, 320, 382, 307, 351, 167, 121, 63, 0, 838, 747, 574, 509, 652, 430, 346, 213, 320, 268, 148, 71, 0), # 76
(915, 918, 761, 805, 696, 324, 389, 307, 356, 168, 122, 64, 0, 849, 753, 580, 512, 664, 438, 353, 216, 324, 272, 149, 75, 0), # 77
(934, 931, 770, 815, 709, 331, 395, 314, 361, 169, 123, 65, 0, 857, 765, 585, 515, 674, 442, 356, 220, 326, 278, 152, 75, 0), # 78
(941, 942, 776, 823, 715, 335, 396, 320, 366, 173, 126, 67, 0, 869, 774, 590, 519, 688, 449, 362, 222, 328, 284, 154, 78, 0), # 79
(954, 957, 781, 836, 726, 342, 404, 320, 372, 174, 126, 68, 0, 883, 781, 604, 526, 702, 451, 365, 224, 329, 285, 156, 78, 0), # 80
(966, 964, 791, 848, 734, 344, 406, 322, 376, 175, 127, 70, 0, 894, 790, 613, 531, 711, 455, 368, 225, 333, 286, 160, 80, 0), # 81
(974, 974, 800, 857, 747, 348, 408, 328, 386, 176, 131, 70, 0, 909, 802, 622, 533, 722, 461, 371, 228, 337, 288, 160, 81, 0), # 82
(985, 982, 807, 868, 757, 348, 411, 333, 388, 178, 134, 72, 0, 916, 815, 631, 537, 732, 461, 374, 230, 340, 295, 162, 82, 0), # 83
(994, 992, 819, 884, 764, 354, 414, 336, 390, 181, 136, 72, 0, 922, 829, 636, 545, 750, 466, 377, 232, 342, 296, 165, 85, 0), # 84
(1009, 1004, 830, 895, 776, 362, 420, 338, 395, 183, 137, 72, 0, 932, 838, 645, 550, 759, 474, 381, 235, 347, 299, 168, 87, 0), # 85
(1018, 1016, 842, 904, 787, 370, 428, 339, 398, 186, 138, 73, 0, 942, 844, 647, 551, 768, 477, 383, 238, 352, 304, 169, 87, 0), # 86
(1024, 1027, 851, 914, 792, 374, 432, 342, 404, 190, 140, 73, 0, 950, 855, 649, 559, 777, 483, 391, 242, 356, 309, 173, 87, 0), # 87
(1040, 1042, 857, 926, 800, 377, 436, 346, 405, 192, 142, 75, 0, 963, 864, 654, 566, 784, 490, 394, 244, 360, 310, 176, 87, 0), # 88
(1061, 1050, 872, 933, 811, 379, 439, 348, 406, 194, 144, 75, 0, 985, 879, 657, 575, 794, 498, 397, 247, 363, 311, 177, 87, 0), # 89
(1075, 1064, 883, 942, 823, 381, 445, 353, 408, 197, 145, 76, 0, 994, 890, 664, 584, 801, 507, 401, 251, 368, 318, 179, 87, 0), # 90
(1084, 1069, 892, 951, 834, 385, 450, 356, 410, 199, 146, 76, 0, 1007, 898, 672, 587, 809, 512, 404, 254, 372, 319, 181, 91, 0), # 91
(1095, 1077, 901, 959, 840, 388, 453, 362, 414, 199, 146, 78, 0, 1018, 908, 681, 591, 818, 517, 409, 256, 377, 323, 184, 91, 0), # 92
(1105, 1089, 911, 969, 851, 390, 458, 363, 416, 204, 149, 78, 0, 1025, 915, 688, 602, 830, 524, 413, 261, 379, 328, 187, 91, 0), # 93
(1116, 1101, 927, 976, 856, 395, 462, 367, 419, 207, 150, 78, 0, 1033, 924, 696, 610, 842, 526, 417, 268, 384, 331, 190, 91, 0), # 94
(1129, 1109, 935, 982, 869, 400, 468, 370, 423, 208, 153, 79, 0, 1046, 938, 705, 615, 847, 533, 425, 268, 391, 335, 193, 91, 0), # 95
(1140, 1116, 943, 991, 880, 405, 469, 374, 426, 211, 154, 80, 0, 1058, 949, 712, 622, 860, 541, 429, 270, 394, 339, 193, 92, 0), # 96
(1150, 1127, 952, 1000, 886, 406, 476, 377, 429, 213, 155, 80, 0, 1068, 954, 720, 630, 864, 546, 436, 272, 398, 343, 195, 92, 0), # 97
(1163, 1133, 956, 1016, 894, 409, 479, 383, 433, 215, 156, 80, 0, 1083, 966, 727, 637, 875, 547, 438, 275, 403, 353, 195, 93, 0), # 98
(1177, 1144, 966, 1025, 905, 417, 482, 386, 436, 215, 158, 82, 0, 1094, 979, 734, 644, 885, 549, 443, 279, 410, 355, 197, 96, 0), # 99
(1198, 1154, 977, 1030, 915, 426, 487, 387, 443, 218, 158, 82, 0, 1102, 989, 738, 650, 893, 553, 448, 286, 415, 359, 199, 97, 0), # 100
(1212, 1170, 990, 1038, 920, 427, 490, 390, 449, 220, 162, 84, 0, 1113, 996, 744, 654, 901, 558, 449, 289, 417, 364, 199, 98, 0), # 101
(1222, 1179, 1004, 1051, 935, 430, 498, 391, 454, 222, 163, 85, 0, 1122, 1007, 751, 659, 906, 563, 454, 290, 421, 366, 202, 98, 0), # 102
(1230, 1190, 1015, 1060, 945, 433, 501, 397, 459, 223, 163, 86, 0, 1130, 1013, 761, 666, 914, 566, 459, 291, 425, 369, 204, 100, 0), # 103
(1241, 1198, 1024, 1069, 953, 434, 505, 400, 465, 224, 165, 86, 0, 1152, 1022, 769, 675, 918, 568, 461, 294, 430, 370, 206, 101, 0), # 104
(1257, 1204, 1032, 1084, 959, 438, 505, 404, 470, 225, 165, 87, 0, 1167, 1029, 772, 681, 925, 570, 467, 295, 432, 372, 207, 102, 0), # 105
(1270, 1215, 1039, 1101, 968, 440, 507, 406, 475, 225, 167, 87, 0, 1173, 1036, 783, 682, 934, 575, 471, 296, 433, 377, 208, 102, 0), # 106
(1286, 1220, 1048, 1110, 979, 446, 507, 411, 479, 227, 169, 88, 0, 1182, 1042, 787, 685, 946, 580, 475, 299, 439, 381, 210, 103, 0), # 107
(1305, 1233, 1058, 1119, 986, 451, 508, 413, 483, 230, 169, 89, 0, 1195, 1058, 794, 690, 954, 585, 477, 307, 440, 381, 213, 105, 0), # 108
(1317, 1238, 1062, 1129, 990, 454, 510, 416, 491, 230, 171, 90, 0, 1207, 1064, 803, 697, 961, 587, 478, 308, 445, 385, 216, 105, 0), # 109
(1331, 1248, 1071, 1137, 994, 460, 512, 419, 494, 231, 171, 90, 0, 1215, 1074, 809, 703, 972, 595, 481, 312, 451, 389, 218, 105, 0), # 110
(1347, 1257, 1081, 1144, 1006, 464, 518, 422, 495, 233, 172, 92, 0, 1230, 1090, 814, 708, 981, 598, 486, 315, 455, 396, 220, 105, 0), # 111
(1357, 1262, 1087, 1158, 1009, 466, 522, 424, 500, 235, 173, 93, 0, 1243, 1095, 820, 713, 987, 605, 487, 316, 456, 405, 221, 107, 0), # 112
(1372, 1273, 1094, 1167, 1017, 469, 525, 427, 502, 236, 173, 93, 0, 1254, 1099, 827, 717, 993, 609, 490, 317, 458, 407, 222, 108, 0), # 113
(1384, 1282, 1100, 1172, 1028, 475, 532, 434, 504, 239, 176, 93, 0, 1276, 1109, 832, 722, 997, 616, 491, 321, 460, 411, 223, 109, 0), # 114
(1394, 1288, 1108, 1180, 1034, 479, 535, 436, 508, 242, 176, 93, 0, 1291, 1124, 838, 725, 1005, 620, 496, 327, 465, 413, 224, 111, 0), # 115
(1408, 1297, 1122, 1187, 1043, 481, 537, 441, 511, 243, 177, 94, 0, 1308, 1131, 846, 729, 1013, 625, 502, 330, 471, 417, 226, 113, 0), # 116
(1414, 1306, 1129, 1195, 1050, 486, 541, 445, 516, 244, 179, 94, 0, 1319, 1139, 854, 738, 1019, 631, 506, 332, 478, 420, 229, 113, 0), # 117
(1431, 1314, 1142, 1201, 1056, 488, 543, 451, 518, 244, 179, 95, 0, 1329, 1148, 860, 743, 1033, 633, 508, 336, 482, 429, 229, 113, 0), # 118
(1440, 1321, 1155, 1212, 1067, 494, 545, 453, 524, 246, 182, 98, 0, 1342, 1154, 864, 748, 1043, 636, 508, 338, 487, 431, 231, 113, 0), # 119
(1453, 1330, 1168, 1223, 1076, 495, 547, 455, 525, 250, 182, 98, 0, 1355, 1165, 874, 750, 1054, 641, 514, 340, 491, 437, 233, 114, 0), # 120
(1465, 1338, 1180, 1231, 1085, 498, 552, 461, 532, 251, 184, 100, 0, 1368, 1178, 879, 760, 1060, 645, 517, 341, 496, 437, 236, 114, 0), # 121
(1475, 1341, 1187, 1237, 1091, 501, 558, 466, 538, 253, 187, 101, 0, 1384, 1185, 889, 763, 1067, 654, 520, 342, 497, 441, 238, 114, 0), # 122
(1489, 1348, 1202, 1254, 1100, 511, 558, 467, 545, 254, 189, 101, 0, 1400, 1196, 890, 768, 1074, 657, 525, 345, 500, 447, 238, 114, 0), # 123
(1499, 1357, 1210, 1267, 1105, 521, 561, 472, 547, 256, 190, 102, 0, 1413, 1206, 901, 774, 1081, 659, 526, 347, 501, 450, 241, 115, 0), # 124
(1510, 1362, 1221, 1278, 1113, 529, 566, 473, 557, 260, 192, 102, 0, 1426, 1209, 907, 782, 1087, 663, 529, 348, 505, 454, 243, 116, 0), # 125
(1519, 1364, 1228, 1286, 1117, 533, 569, 476, 561, 262, 195, 103, 0, 1435, 1218, 917, 787, 1096, 665, 535, 351, 508, 455, 245, 116, 0), # 126
(1531, 1376, 1236, 1289, 1123, 540, 570, 478, 564, 262, 195, 105, 0, 1444, 1231, 923, 793, 1102, 668, 538, 353, 512, 457, 248, 118, 0), # 127
(1545, 1382, 1243, 1301, 1133, 540, 576, 481, 572, 262, 197, 105, 0, 1458, 1237, 931, 796, 1109, 672, 540, 355, 515, 460, 250, 119, 0), # 128
(1563, 1390, 1253, 1305, 1135, 543, 578, 484, 574, 265, 199, 105, 0, 1467, 1245, 939, 801, 1117, 672, 541, 355, 521, 461, 251, 119, 0), # 129
(1574, 1401, 1261, 1313, 1144, 546, 580, 485, 579, 266, 200, 105, 0, 1482, 1253, 943, 809, 1126, 679, 543, 360, 527, 466, 252, 120, 0), # 130
(1587, 1413, 1272, 1321, 1152, 552, 581, 489, 583, 268, 203, 107, 0, 1490, 1265, 946, 810, 1130, 686, 549, 365, 534, 469, 254, 120, 0), # 131
(1595, 1420, 1281, 1329, 1157, 558, 582, 492, 583, 269, 205, 107, 0, 1506, 1278, 951, 816, 1139, 691, 553, 365, 543, 473, 255, 120, 0), # 132
(1606, 1425, 1295, 1343, 1167, 562, 592, 496, 589, 271, 207, 107, 0, 1514, 1283, 958, 820, 1148, 694, 553, 367, 547, 482, 258, 120, 0), # 133
(1617, 1428, 1301, 1348, 1178, 566, 595, 496, 593, 273, 208, 107, 0, 1525, 1293, 964, 823, 1156, 698, 556, 368, 551, 487, 261, 120, 0), # 134
(1622, 1437, 1304, 1356, 1187, 570, 598, 498, 604, 274, 209, 107, 0, 1537, 1299, 970, 831, 1164, 702, 559, 369, 552, 490, 262, 120, 0), # 135
(1630, 1446, 1319, 1358, 1197, 575, 602, 500, 610, 277, 210, 107, 0, 1547, 1307, 975, 836, 1176, 706, 565, 371, 555, 493, 263, 122, 0), # 136
(1638, 1447, 1331, 1363, 1202, 578, 606, 503, 615, 281, 210, 108, 0, 1563, 1315, 984, 840, 1184, 710, 571, 372, 557, 497, 266, 123, 0), # 137
(1645, 1451, 1342, 1368, 1212, 581, 607, 507, 617, 281, 213, 109, 0, 1576, 1322, 988, 846, 1193, 716, 572, 380, 562, 501, 268, 123, 0), # 138
(1650, 1458, 1350, 1376, 1219, 585, 610, 512, 620, 287, 217, 109, 0, 1592, 1328, 995, 851, 1202, 720, 576, 382, 565, 502, 269, 124, 0), # 139
(1660, 1466, 1364, 1385, 1228, 590, 614, 514, 623, 287, 217, 110, 0, 1596, 1341, 1003, 856, 1213, 725, 578, 385, 571, 504, 273, 125, 0), # 140
(1670, 1476, 1370, 1396, 1234, 593, 617, 516, 627, 287, 217, 110, 0, 1604, 1348, 1009, 863, 1222, 730, 581, 385, 577, 507, 276, 126, 0), # 141
(1679, 1481, 1379, 1406, 1244, 594, 622, 518, 632, 289, 218, 110, 0, 1614, 1357, 1016, 865, 1231, 733, 583, 388, 583, 507, 277, 128, 0), # 142
(1687, 1486, 1391, 1412, 1254, 595, 626, 521, 637, 290, 218, 112, 0, 1620, 1366, 1024, 871, 1236, 735, 584, 391, 586, 509, 279, 128, 0), # 143
(1694, 1490, 1401, 1423, 1262, 597, 627, 524, 644, 290, 218, 112, 0, 1629, 1378, 1028, 874, 1247, 738, 589, 392, 588, 509, 280, 128, 0), # 144
(1696, 1500, 1405, 1427, 1266, 599, 628, 525, 651, 291, 218, 112, 0, 1643, 1385, 1030, 878, 1252, 741, 593, 395, 592, 513, 281, 128, 0), # 145
(1701, 1511, 1419, 1439, 1275, 603, 629, 530, 655, 291, 219, 112, 0, 1653, 1394, 1036, 884, 1263, 746, 597, 398, 595, 517, 283, 128, 0), # 146
(1714, 1522, 1421, 1448, 1282, 607, 630, 532, 655, 291, 221, 112, 0, 1663, 1409, 1045, 890, 1272, 750, 598, 403, 600, 522, 284, 128, 0), # 147
(1721, 1525, 1432, 1454, 1290, 608, 634, 534, 660, 293, 221, 112, 0, 1673, 1419, 1048, 898, 1277, 753, 601, 405, 604, 523, 286, 130, 0), # 148
(1731, 1531, 1437, 1462, 1299, 612, 638, 535, 663, 295, 224, 113, 0, 1681, 1427, 1054, 904, 1287, 758, 606, 407, 607, 525, 287, 131, 0), # 149
(1739, 1539, 1451, 1472, 1308, 619, 641, 537, 667, 297, 225, 115, 0, 1687, 1436, 1066, 905, 1302, 760, 607, 408, 609, 531, 289, 131, 0), # 150
(1748, 1544, 1461, 1478, 1315, 624, 644, 538, 669, 299, 226, 118, 0, 1698, 1441, 1068, 910, 1309, 762, 610, 413, 610, 536, 289, 132, 0), # 151
(1756, 1553, 1467, 1490, 1325, 628, 648, 540, 673, 300, 227, 120, 0, 1706, 1450, 1078, 916, 1317, 765, 612, 416, 616, 539, 289, 133, 0), # 152
(1767, 1560, 1480, 1502, 1332, 634, 651, 545, 676, 300, 228, 120, 0, 1720, 1459, 1082, 917, 1324, 771, 613, 419, 622, 542, 291, 135, 0), # 153
(1777, 1574, 1488, 1510, 1341, 641, 655, 547, 680, 300, 231, 123, 0, 1734, 1467, 1086, 922, 1328, 775, 614, 421, 625, 546, 291, 137, 0), # 154
(1784, 1580, 1495, 1517, 1354, 642, 658, 549, 686, 305, 232, 125, 0, 1743, 1477, 1088, 928, 1331, 781, 616, 423, 632, 550, 293, 137, 0), # 155
(1790, 1585, 1505, 1528, 1363, 649, 660, 551, 688, 307, 233, 125, 0, 1756, 1484, 1094, 930, 1333, 784, 618, 428, 641, 553, 293, 137, 0), # 156
(1797, 1591, 1519, 1536, 1369, 657, 666, 552, 693, 309, 235, 125, 0, 1764, 1494, 1098, 934, 1341, 787, 622, 431, 644, 554, 294, 138, 0), # 157
(1803, 1594, 1521, 1550, 1374, 661, 671, 553, 694, 310, 235, 125, 0, 1770, 1508, 1104, 942, 1347, 794, 623, 435, 651, 555, 295, 138, 0), # 158
(1810, 1602, 1531, 1560, 1380, 665, 673, 558, 699, 311, 236, 125, 0, 1776, 1516, 1110, 945, 1354, 797, 625, 436, 656, 556, 295, 138, 0), # 159
(1816, 1607, 1538, 1564, 1387, 669, 674, 561, 702, 313, 236, 125, 0, 1786, 1521, 1115, 954, 1359, 804, 628, 439, 659, 559, 295, 138, 0), # 160
(1823, 1613, 1543, 1569, 1388, 672, 679, 563, 703, 315, 237, 125, 0, 1797, 1527, 1123, 956, 1366, 807, 631, 444, 663, 564, 297, 139, 0), # 161
(1828, 1618, 1549, 1580, 1402, 676, 681, 564, 707, 317, 238, 127, 0, 1805, 1536, 1130, 959, 1369, 808, 636, 445, 665, 567, 300, 140, 0), # 162
(1831, 1626, 1555, 1586, 1410, 678, 683, 567, 709, 317, 241, 127, 0, 1816, 1542, 1137, 963, 1373, 814, 637, 447, 669, 569, 300, 140, 0), # 163
(1841, 1632, 1563, 1596, 1418, 679, 687, 568, 713, 318, 241, 128, 0, 1824, 1549, 1144, 967, 1378, 819, 638, 448, 673, 570, 302, 140, 0), # 164
(1850, 1640, 1570, 1600, 1425, 681, 689, 569, 714, 320, 244, 128, 0, 1830, 1559, 1152, 974, 1392, 823, 641, 450, 676, 571, 305, 142, 0), # 165
(1855, 1648, 1579, 1606, 1430, 684, 694, 572, 716, 322, 246, 128, 0, 1838, 1568, 1154, 976, 1400, 829, 643, 451, 680, 571, 308, 142, 0), # 166
(1859, 1652, 1585, 1612, 1440, 686, 699, 573, 720, 322, 249, 131, 0, 1847, 1570, 1158, 979, 1404, 830, 646, 452, 682, 573, 309, 142, 0), # 167
(1862, 1659, 1592, 1621, 1450, 689, 703, 575, 721, 323, 249, 132, 0, 1856, 1574, 1162, 981, 1407, 833, 648, 454, 684, 576, 311, 143, 0), # 168
(1865, 1663, 1595, 1625, 1455, 692, 706, 577, 721, 324, 249, 134, 0, 1868, 1583, 1169, 983, 1419, 838, 648, 458, 688, 581, 312, 144, 0), # 169
(1870, 1667, 1597, 1635, 1460, 697, 711, 581, 725, 324, 252, 134, 0, 1874, 1589, 1174, 988, 1424, 842, 650, 460, 693, 584, 313, 144, 0), # 170
(1878, 1675, 1604, 1641, 1465, 701, 715, 583, 731, 324, 254, 134, 0, 1878, 1596, 1178, 992, 1428, 846, 650, 461, 694, 585, 314, 144, 0), # 171
(1882, 1678, 1611, 1645, 1471, 705, 716, 585, 735, 327, 255, 135, 0, 1889, 1601, 1179, 999, 1431, 849, 652, 465, 695, 588, 315, 144, 0), # 172
(1892, 1685, 1618, 1652, 1481, 708, 718, 587, 739, 328, 256, 135, 0, 1894, 1604, 1184, 1003, 1435, 855, 654, 466, 697, 592, 315, 145, 0), # 173
(1895, 1688, 1622, 1662, 1483, 709, 718, 588, 740, 329, 256, 135, 0, 1900, 1605, 1186, 1005, 1438, 856, 654, 469, 699, 596, 315, 145, 0), # 174
(1901, 1691, 1625, 1668, 1488, 710, 721, 590, 742, 329, 257, 135, 0, 1909, 1613, 1187, 1007, 1447, 859, 660, 469, 703, 597, 315, 147, 0), # 175
(1906, 1694, 1627, 1671, 1491, 713, 723, 592, 745, 329, 257, 135, 0, 1915, 1621, 1193, 1010, 1453, 859, 663, 470, 709, 597, 316, 148, 0), # 176
(1909, 1698, 1633, 1673, 1494, 713, 725, 594, 745, 329, 257, 136, 0, 1922, 1625, 1198, 1012, 1456, 861, 666, 471, 712, 598, 317, 148, 0), # 177
(1916, 1702, 1639, 1676, 1495, 715, 726, 596, 745, 330, 257, 138, 0, 1926, 1628, 1201, 1015, 1459, 863, 668, 472, 712, 599, 318, 148, 0), # 178
(1916, 1702, 1639, 1676, 1495, 715, 726, 596, 745, 330, 257, 138, 0, 1926, 1628, 1201, 1015, 1459, 863, 668, 472, 712, 599, 318, 148, 0), # 179
)
passenger_arriving_rate = (
(6.025038694046121, 6.077817415662483, 5.211283229612507, 5.593200996477089, 4.443748486087689, 2.197058452426137, 2.4876213692243487, 2.3265880864897115, 2.4360396248672025, 1.187404504656711, 0.8410530327771206, 0.4897915078306174, 0.0, 6.100656255094035, 5.38770658613679, 4.205265163885603, 3.562213513970132, 4.872079249734405, 3.257223321085596, 2.4876213692243487, 1.5693274660186693, 2.2218742430438443, 1.8644003321590301, 1.0422566459225016, 0.5525288559693167, 0.0), # 0
(6.425192582423969, 6.479066763559234, 5.555346591330152, 5.9626298279489545, 4.737992269979389, 2.342188508829789, 2.651681364758216, 2.479756861452854, 2.5968981305331633, 1.265694207683145, 0.8966192271912263, 0.5221216660814355, 0.0, 6.503749976927826, 5.743338326895789, 4.483096135956131, 3.7970826230494343, 5.193796261066327, 3.4716596060339957, 2.651681364758216, 1.6729917920212778, 2.3689961349896946, 1.9875432759829852, 1.1110693182660305, 0.589006069414476, 0.0), # 1
(6.8240676107756775, 6.878723687980077, 5.8980422855474135, 6.330588934198314, 5.031170378999795, 2.4867395801587113, 2.8150911047764224, 2.6323126239522097, 2.7571147227510195, 1.3436741325061639, 0.9519646297552626, 0.5543232652053055, 0.0, 6.905237793851628, 6.09755591725836, 4.759823148776313, 4.031022397518491, 5.514229445502039, 3.6852376735330936, 2.8150911047764224, 1.7762425572562224, 2.5155851894998973, 2.1101963113994384, 1.179608457109483, 0.625338517089098, 0.0), # 2
(7.220109351775874, 7.275202552130091, 6.238010869319854, 6.695618766778866, 5.322129340801521, 2.6301384358095787, 2.9772021849887733, 2.7836505787472534, 2.9160540643684367, 1.4210348095278544, 1.0068696823654766, 0.5862685684930461, 0.0, 7.30352736750507, 6.448954253423507, 5.0343484118273825, 4.263104428583563, 5.8321081287368735, 3.8971108102461547, 2.9772021849887733, 1.8786703112925562, 2.6610646704007603, 2.2318729222596225, 1.247602173863971, 0.6613820501936447, 0.0), # 3
(7.611763378099177, 7.666917719214351, 6.573892899703036, 7.056259777244312, 5.609715683037193, 2.7718118451790676, 3.137366201105075, 2.9331659305974576, 3.0730808182330827, 1.4974667691503039, 1.0611148269181152, 0.6178298392354764, 0.0, 7.69702635952778, 6.79612823159024, 5.305574134590575, 4.492400307450911, 6.146161636466165, 4.10643230283644, 3.137366201105075, 1.9798656036993338, 2.8048578415185963, 2.3520865924147714, 1.3147785799406073, 0.6969925199285775, 0.0), # 4
(7.9974752624202115, 8.052283552437947, 6.904328933752518, 7.411052417148355, 5.892775933359424, 2.9111865776638504, 3.2949347488351344, 3.080253884262296, 3.2275596471926233, 1.5726605417755992, 1.1144805053094267, 0.6488793407234149, 0.0, 8.084142431559393, 7.137672747957563, 5.572402526547132, 4.7179816253267965, 6.455119294385247, 4.312355437967215, 3.2949347488351344, 2.079418984045607, 2.946387966679712, 2.4703508057161185, 1.3808657867505036, 0.7320257774943589, 0.0), # 5
(8.375690577413598, 8.42971441500595, 7.227959528523866, 7.758537138044686, 6.170156619420834, 3.047689402660605, 3.4492594238887575, 3.2243096445012442, 3.3788552140947257, 1.6463066578058279, 1.1667471594356567, 0.6792893362476808, 0.0, 8.463283245239527, 7.472182698724488, 5.833735797178282, 4.938919973417482, 6.757710428189451, 4.514033502301742, 3.4492594238887575, 2.176921001900432, 3.085078309710417, 2.586179046014896, 1.4455919057047733, 0.7663376740914501, 0.0), # 6
(8.744854895753962, 8.797624670123444, 7.543425241072636, 8.097254391487015, 6.440704268874043, 3.1807470895660046, 3.599691821975751, 3.3647284160737763, 3.5263321817870574, 1.7180956476430762, 1.2176952311930538, 0.708932089099093, 0.0, 8.832856462207822, 7.798252980090021, 6.088476155965268, 5.154286942929227, 7.052664363574115, 4.7106197825032865, 3.599691821975751, 2.2719622068328604, 3.2203521344370216, 2.699084797162339, 1.508685048214527, 0.7997840609203132, 0.0), # 7
(9.103413790115921, 9.154428680995508, 7.849366628454395, 8.425744629029035, 6.703265409371668, 3.309786407776723, 3.7455835388059184, 3.5009054037393623, 3.669355213117282, 1.7877180416894325, 1.2671051624778642, 0.7376798625684703, 0.0, 9.1912697441039, 8.114478488253173, 6.335525812389321, 5.363154125068296, 7.338710426234564, 4.901267565235107, 3.7455835388059184, 2.3641331484119448, 3.351632704685834, 2.8085815430096788, 1.5698733256908792, 0.8322207891814098, 0.0), # 8
(9.449812833174102, 9.498540810827224, 8.144424247724704, 8.742548302224453, 6.956686568566327, 3.4342341266894385, 3.886286170089072, 3.6322358122574814, 3.8072889709330693, 1.8548643703469827, 1.3147573951863356, 0.7654049199466314, 0.0, 9.536930752567395, 8.419454119412945, 6.573786975931678, 5.564593111040947, 7.614577941866139, 5.0851301371604745, 3.886286170089072, 2.453024376206742, 3.4783432842831634, 2.914182767408151, 1.6288848495449408, 0.8635037100752023, 0.0), # 9
(9.782497597603118, 9.828375422823667, 8.427238655939124, 9.046205862626959, 7.19981427411064, 3.5535170157008253, 4.021151311535013, 3.7581148463876053, 3.9394981180820854, 1.9192251640178146, 1.3604323712147148, 0.7919795245243952, 0.0, 9.868247149237932, 8.711774769768347, 6.802161856073574, 5.757675492053442, 7.878996236164171, 5.261360784942648, 4.021151311535013, 2.5382264397863037, 3.59990713705532, 3.015401954208987, 1.685447731187825, 0.8934886748021517, 0.0), # 10
(10.099913656077605, 10.142346880189926, 8.696450410153215, 9.335257761790256, 7.431495053657226, 3.667061844207558, 4.14953055885355, 3.8779377108892072, 4.065347317411997, 1.980490953104016, 1.40391053245925, 0.8172759395925812, 0.0, 10.183626595755133, 8.99003533551839, 7.019552662296249, 5.9414728593120465, 8.130694634823994, 5.42911279524489, 4.14953055885355, 2.619329888719684, 3.715747526828613, 3.1117525872634197, 1.7392900820306432, 0.9220315345627208, 0.0), # 11
(10.400506581272174, 10.438869546131066, 8.95070006742254, 9.60824445126805, 7.650575434858702, 3.7742953816063087, 4.270775507754487, 3.99109961052176, 4.184201231770471, 2.0383522680076718, 1.444972320816187, 0.8411664284420068, 0.0, 10.48147675375864, 9.252830712862075, 7.224861604080934, 6.115056804023014, 8.368402463540942, 5.587539454730464, 4.270775507754487, 2.6959252725759346, 3.825287717429351, 3.2027481504226842, 1.790140013484508, 0.9489881405573698, 0.0), # 12
(10.68272194586145, 10.716357783852182, 9.188628184802662, 9.863706382614039, 7.85590194536768, 3.8746443972937565, 4.384237753947633, 4.096995750044741, 4.295424524005172, 2.0924996391308714, 1.4833981781817738, 0.8635232543634921, 0.0, 10.760205284888082, 9.498755797998411, 7.416990890908868, 6.277498917392613, 8.590849048010345, 5.735794050062637, 4.384237753947633, 2.7676031409241117, 3.92795097268384, 3.287902127538014, 1.8377256369605324, 0.974214343986562, 0.0), # 13
(10.945005322520059, 10.973225956558347, 9.408875319349146, 10.100184007381912, 8.046321112836791, 3.967535660666574, 4.489268893142796, 4.195021334217623, 4.398381856963768, 2.1426235968757004, 1.518968546452257, 0.8842186806478561, 0.0, 11.018219850783076, 9.726405487126415, 7.594842732261284, 6.4278707906271, 8.796763713927536, 5.873029867904672, 4.489268893142796, 2.833954043333267, 4.023160556418396, 3.3667280024606385, 1.8817750638698296, 0.997565996050759, 0.0), # 14
(11.185802283922625, 11.207888427454638, 9.610082028117542, 10.316217777125386, 8.220679464918646, 4.052395941121439, 4.585220521049775, 4.284571567799878, 4.4924378934939275, 2.1884146716442476, 1.551463867523884, 0.9031249705859171, 0.0, 11.253928113083257, 9.934374676445087, 7.757319337619419, 6.565244014932741, 8.984875786987855, 5.998400194919829, 4.585220521049775, 2.894568529372456, 4.110339732459323, 3.4387392590417964, 1.9220164056235085, 1.0188989479504218, 0.0), # 15
(11.40355840274376, 11.418759559746144, 9.790888868163425, 10.510348143398145, 8.377823529265866, 4.128652008055021, 4.671444233378385, 4.36504165555098, 4.5769572964433145, 2.2295633938385993, 1.5806645832929027, 0.920114387468494, 0.0, 11.465737733428254, 10.121258262153432, 7.9033229164645125, 6.688690181515796, 9.153914592886629, 6.111058317771373, 4.671444233378385, 2.9490371486107296, 4.188911764632933, 3.503449381132716, 1.958177773632685, 1.0380690508860133, 0.0), # 16
(11.59671925165809, 11.604253716637938, 9.949936396542352, 10.6811155577539, 8.51659983353107, 4.1957306308639994, 4.747291625838426, 4.435826802230409, 4.651304728659593, 2.2657602938608403, 1.60635113565556, 0.9350591945864056, 0.0, 11.652056373457699, 10.28565114045046, 8.031755678277799, 6.79728088158252, 9.302609457319186, 6.2101575231225725, 4.747291625838426, 2.9969504506171427, 4.258299916765535, 3.5603718525846344, 1.9899872793084707, 1.0549321560579947, 0.0), # 17
(11.763730403340244, 11.7627852613351, 10.08586517030988, 10.82706047174635, 8.63585490536687, 4.253058578945052, 4.81211429413971, 4.49632221259763, 4.7148448529904385, 2.2966959021130613, 1.6283039665081016, 0.9478316552304716, 0.0, 11.811291694811214, 10.426148207535187, 8.141519832540508, 6.890087706339182, 9.429689705980877, 6.294851097636682, 4.81211429413971, 3.0378989849607514, 4.317927452683435, 3.6090201572487843, 2.0171730340619765, 1.0693441146668274, 0.0), # 18
(11.903037430464838, 11.892768557042718, 10.197315746521578, 10.946723336929182, 8.734435272425891, 4.300062621694845, 4.865263833992036, 4.5459230914121225, 4.766942332283511, 2.3220607489973486, 1.6463035177467755, 0.9583040326915097, 0.0, 11.941851359128435, 10.541344359606605, 8.231517588733878, 6.9661822469920445, 9.533884664567022, 6.364292327976972, 4.865263833992036, 3.071473301210604, 4.367217636212946, 3.648907778976395, 2.039463149304316, 1.0811607779129746, 0.0), # 19
(12.013085905706498, 11.992617966965858, 10.282928682233003, 11.038644604856119, 8.811187462360754, 4.336169528510063, 4.9060918411052175, 4.5840246434333585, 4.806961829386479, 2.341545364915788, 1.66013023126783, 0.9663485902603393, 0.0, 12.042143028048988, 10.62983449286373, 8.30065115633915, 7.024636094747362, 9.613923658772958, 6.417634500806702, 4.9060918411052175, 3.097263948935759, 4.405593731180377, 3.679548201618707, 2.0565857364466007, 1.0902379969968963, 0.0), # 20
(12.09232140173984, 12.060747854309614, 10.341344534499719, 11.101364727080837, 8.86495800282407, 4.360806068787375, 4.933949911189055, 4.6100220734208115, 4.834268007147008, 2.3548402802704667, 1.669564548967512, 0.9718375912277795, 0.0, 12.110574363212494, 10.690213503505571, 8.34782274483756, 7.064520840811399, 9.668536014294016, 6.454030902789136, 4.933949911189055, 3.1148614777052677, 4.432479001412035, 3.7004549090269463, 2.068268906899944, 1.096431623119056, 0.0), # 21
(12.139189491239494, 12.095572582279058, 10.371203860377285, 11.133424155157051, 8.894593421468459, 4.373399011923457, 4.94818963995336, 4.623310586133957, 4.848225528412765, 2.361636025463473, 1.674386912742068, 0.9746432988846491, 0.0, 12.145553026258591, 10.721076287731139, 8.37193456371034, 7.084908076390418, 9.69645105682553, 6.47263482058754, 4.94818963995336, 3.1238564370881834, 4.447296710734229, 3.7111413850523514, 2.0742407720754574, 1.0995975074799145, 0.0), # 22
(12.156472036011166, 12.099695953360769, 10.374923182441702, 11.137437731481482, 8.902185644826076, 4.375, 4.949882401355603, 4.624746913580247, 4.8499704938271595, 2.3624376817558304, 1.6749916074323483, 0.9749897576588934, 0.0, 12.15, 10.724887334247827, 8.37495803716174, 7.087313045267489, 9.699940987654319, 6.474645679012346, 4.949882401355603, 3.125, 4.451092822413038, 3.7124792438271617, 2.0749846364883404, 1.0999723593964337, 0.0), # 23
(12.169214895640982, 12.09729074074074, 10.374314814814815, 11.13694375, 8.906486090891882, 4.375, 4.9489522875817, 4.62275, 4.849736666666666, 2.3619451851851854, 1.6749249158249162, 0.9749086419753087, 0.0, 12.15, 10.723995061728393, 8.37462457912458, 7.085835555555555, 9.699473333333332, 6.47185, 4.9489522875817, 3.125, 4.453243045445941, 3.7123145833333346, 2.074862962962963, 1.099753703703704, 0.0), # 24
(12.181688676253897, 12.092549725651576, 10.373113854595337, 11.135966435185185, 8.910691956475603, 4.375, 4.947119341563786, 4.618827160493828, 4.8492746913580245, 2.3609756515775038, 1.6747926798852726, 0.9747485139460449, 0.0, 12.15, 10.722233653406493, 8.373963399426362, 7.08292695473251, 9.698549382716049, 6.466358024691359, 4.947119341563786, 3.125, 4.455345978237801, 3.711988811728396, 2.0746227709190674, 1.0993227023319616, 0.0), # 25
(12.19389242285764, 12.085545336076818, 10.371336762688616, 11.134516898148147, 8.914803094736882, 4.375, 4.944412030985233, 4.613052469135803, 4.84859049382716, 2.3595452126200276, 1.674596096770171, 0.9745115683584822, 0.0, 12.15, 10.719627251943303, 8.372980483850855, 7.078635637860081, 9.69718098765432, 6.458273456790124, 4.944412030985233, 3.125, 4.457401547368441, 3.71150563271605, 2.0742673525377233, 1.0986859396433473, 0.0), # 26
(12.205825180459962, 12.076349999999996, 10.369, 11.132606249999998, 8.918819358835371, 4.375, 4.940858823529412, 4.6055, 4.84769, 2.35767, 1.674336363636364, 0.9742000000000002, 0.0, 12.15, 10.7162, 8.371681818181818, 7.073009999999999, 9.69538, 6.4477, 4.940858823529412, 3.125, 4.459409679417686, 3.7108687500000004, 2.0738000000000003, 1.09785, 0.0), # 27
(12.217485994068602, 12.065036145404662, 10.366120027434842, 11.13024560185185, 8.92274060193072, 4.375, 4.93648818687969, 4.596243827160494, 4.846579135802468, 2.3553661454046644, 1.6740146776406037, 0.9738160036579792, 0.0, 12.15, 10.711976040237769, 8.370073388203018, 7.066098436213991, 9.693158271604936, 6.434741358024692, 4.93648818687969, 3.125, 4.46137030096536, 3.710081867283951, 2.073224005486969, 1.0968214677640604, 0.0), # 28
(12.2288739086913, 12.051676200274349, 10.362713305898492, 11.127446064814816, 8.926566677182576, 4.375, 4.931328588719439, 4.585358024691358, 4.845263827160494, 2.3526497805212623, 1.6736322359396434, 0.9733617741197987, 0.0, 12.15, 10.706979515317785, 8.368161179698216, 7.057949341563786, 9.690527654320988, 6.419501234567901, 4.931328588719439, 3.125, 4.463283338591288, 3.709148688271606, 2.0725426611796984, 1.0956069272976683, 0.0), # 29
(12.239987969335797, 12.036342592592591, 10.358796296296296, 11.12421875, 8.930297437750589, 4.375, 4.925408496732026, 4.572916666666666, 4.84375, 2.3495370370370376, 1.6731902356902357, 0.9728395061728394, 0.0, 12.15, 10.701234567901233, 8.365951178451178, 7.048611111111112, 9.6875, 6.402083333333333, 4.925408496732026, 3.125, 4.4651487188752945, 3.7080729166666675, 2.0717592592592595, 1.094212962962963, 0.0), # 30
(12.25082722100983, 12.019107750342934, 10.354385459533608, 11.120574768518516, 8.933932736794405, 4.375, 4.918756378600824, 4.558993827160494, 4.842043580246913, 2.346044046639232, 1.6726898740491336, 0.9722513946044812, 0.0, 12.15, 10.694765340649292, 8.363449370245666, 7.038132139917694, 9.684087160493826, 6.382591358024691, 4.918756378600824, 3.125, 4.466966368397203, 3.70685825617284, 2.070877091906722, 1.0926461591220853, 0.0), # 31
(12.261390708721144, 12.000044101508914, 10.349497256515773, 11.11652523148148, 8.937472427473676, 4.375, 4.911400702009199, 4.543663580246914, 4.84015049382716, 2.3421869410150897, 1.672132348173089, 0.9715996342021036, 0.0, 12.15, 10.687595976223138, 8.360661740865444, 7.026560823045267, 9.68030098765432, 6.36112901234568, 4.911400702009199, 3.125, 4.468736213736838, 3.705508410493828, 2.069899451303155, 1.0909131001371744, 0.0), # 32
(12.271677477477477, 11.979224074074073, 10.344148148148149, 11.11208125, 8.94091636294805, 4.375, 4.903369934640523, 4.527, 4.838076666666666, 2.3379818518518523, 1.6715188552188551, 0.9708864197530863, 0.0, 12.15, 10.679750617283949, 8.357594276094275, 7.013945555555555, 9.676153333333332, 6.3378000000000005, 4.903369934640523, 3.125, 4.470458181474025, 3.704027083333334, 2.06882962962963, 1.0890203703703705, 0.0), # 33
(12.28168657228657, 11.956720096021947, 10.338354595336076, 11.107253935185184, 8.944264396377172, 4.375, 4.894692544178166, 4.509077160493827, 4.835828024691358, 2.333444910836763, 1.670850592343185, 0.9701139460448103, 0.0, 12.15, 10.671253406492912, 8.354252961715924, 7.000334732510288, 9.671656049382715, 6.312708024691357, 4.894692544178166, 3.125, 4.472132198188586, 3.7024179783950624, 2.0676709190672153, 1.0869745541838134, 0.0), # 34
(12.291417038156167, 11.932604595336077, 10.332133058984912, 11.102054398148146, 8.947516380920696, 4.375, 4.885396998305495, 4.489969135802469, 4.83341049382716, 2.328592249657065, 1.6701287567028307, 0.969284407864655, 0.0, 12.15, 10.662128486511202, 8.350643783514153, 6.985776748971193, 9.66682098765432, 6.285956790123457, 4.885396998305495, 3.125, 4.473758190460348, 3.7006847993827163, 2.0664266117969827, 1.0847822359396435, 0.0), # 35
(12.300867920094007, 11.906949999999998, 10.3255, 11.096493749999999, 8.950672169738269, 4.375, 4.875511764705882, 4.46975, 4.830829999999999, 2.32344, 1.6693545454545458, 0.9684000000000001, 0.0, 12.15, 10.6524, 8.346772727272727, 6.970319999999999, 9.661659999999998, 6.257650000000001, 4.875511764705882, 3.125, 4.475336084869134, 3.6988312500000005, 2.0651, 1.08245, 0.0), # 36
(12.310038263107828, 11.879828737997256, 10.318471879286694, 11.090583101851852, 8.953731615989536, 4.375, 4.865065311062696, 4.448493827160494, 4.828092469135802, 2.3180042935528125, 1.668529155755082, 0.9674629172382261, 0.0, 12.15, 10.642092089620485, 8.34264577877541, 6.954012880658436, 9.656184938271604, 6.227891358024691, 4.865065311062696, 3.125, 4.476865807994768, 3.696861033950618, 2.063694375857339, 1.0799844307270234, 0.0), # 37
(12.31892711220537, 11.851313237311386, 10.311065157750342, 11.084333564814814, 8.956694572834152, 4.375, 4.854086105059308, 4.426274691358025, 4.825203827160493, 2.312301262002744, 1.6676537847611925, 0.9664753543667125, 0.0, 12.15, 10.631228898033836, 8.33826892380596, 6.936903786008231, 9.650407654320986, 6.196784567901235, 4.854086105059308, 3.125, 4.478347286417076, 3.6947778549382724, 2.0622130315500686, 1.0773921124828534, 0.0), # 38
(12.327533512394384, 11.821475925925924, 10.303296296296297, 11.07775625, 8.959560893431762, 4.375, 4.842602614379085, 4.4031666666666665, 4.82217, 2.3063470370370376, 1.6667296296296297, 0.9654395061728396, 0.0, 12.15, 10.619834567901233, 8.333648148148148, 6.919041111111111, 9.64434, 6.164433333333333, 4.842602614379085, 3.125, 4.479780446715881, 3.6925854166666676, 2.0606592592592596, 1.0746796296296297, 0.0), # 39
(12.335856508682596, 11.790389231824417, 10.295181755829903, 11.070862268518518, 8.962330430942014, 4.375, 4.830643306705398, 4.3792438271604945, 4.818996913580246, 2.3001577503429362, 1.6657578875171468, 0.9643575674439875, 0.0, 12.15, 10.60793324188386, 8.328789437585733, 6.900473251028807, 9.637993827160493, 6.1309413580246925, 4.830643306705398, 3.125, 4.481165215471007, 3.690287422839507, 2.059036351165981, 1.0718535665294926, 0.0), # 40
(12.343895146077754, 11.758125582990397, 10.286737997256516, 11.06366273148148, 8.96500303852456, 4.375, 4.818236649721617, 4.354580246913581, 4.81569049382716, 2.293749533607682, 1.6647397555804966, 0.9632317329675355, 0.0, 12.15, 10.595549062642888, 8.323698777902482, 6.881248600823045, 9.63138098765432, 6.096412345679013, 4.818236649721617, 3.125, 4.48250151926228, 3.6878875771604944, 2.0573475994513033, 1.0689205075445818, 0.0), # 41
(12.3516484695876, 11.724757407407406, 10.277981481481483, 11.056168750000001, 8.967578569339047, 4.375, 4.805411111111111, 4.32925, 4.812256666666666, 2.287138518518519, 1.663676430976431, 0.9620641975308644, 0.0, 12.15, 10.582706172839506, 8.318382154882155, 6.861415555555555, 9.624513333333333, 6.06095, 4.805411111111111, 3.125, 4.483789284669523, 3.6853895833333343, 2.055596296296297, 1.0658870370370372, 0.0), # 42
(12.35911552421987, 11.690357133058985, 10.268928669410151, 11.048391435185184, 8.970056876545122, 4.375, 4.7921951585572495, 4.3033271604938275, 4.80870135802469, 2.280340836762689, 1.6625691108617036, 0.9608571559213536, 0.0, 12.15, 10.569428715134888, 8.312845554308517, 6.841022510288067, 9.61740271604938, 6.024658024691359, 4.7921951585572495, 3.125, 4.485028438272561, 3.682797145061729, 2.0537857338820307, 1.062759739368999, 0.0), # 43
(12.366295354982311, 11.65499718792867, 10.259596021947875, 11.040341898148148, 8.972437813302435, 4.375, 4.778617259743403, 4.2768858024691365, 4.805030493827159, 2.2733726200274353, 1.6614189923930665, 0.9596128029263833, 0.0, 12.15, 10.555740832190216, 8.307094961965332, 6.820117860082305, 9.610060987654318, 5.987640123456791, 4.778617259743403, 3.125, 4.486218906651217, 3.6801139660493836, 2.0519192043895753, 1.0595451989026066, 0.0), # 44
(12.37318700688266, 11.618749999999999, 10.25, 11.03203125, 8.974721232770635, 4.375, 4.764705882352941, 4.25, 4.80125, 2.2662500000000003, 1.6602272727272729, 0.9583333333333333, 0.0, 12.15, 10.541666666666664, 8.301136363636363, 6.79875, 9.6025, 5.95, 4.764705882352941, 3.125, 4.487360616385318, 3.677343750000001, 2.0500000000000003, 1.0562500000000001, 0.0), # 45
(12.379789524928656, 11.581687997256516, 10.240157064471878, 11.023470601851852, 8.976906988109372, 4.375, 4.750489494069233, 4.222743827160494, 4.797365802469135, 2.258989108367627, 1.6589951490210748, 0.9570209419295841, 0.0, 12.15, 10.527230361225422, 8.294975745105374, 6.77696732510288, 9.59473160493827, 5.9118413580246925, 4.750489494069233, 3.125, 4.488453494054686, 3.6744902006172846, 2.048031412894376, 1.0528807270233198, 0.0), # 46
(12.386101954128042, 11.543883607681755, 10.230083676268862, 11.014671064814813, 8.978994932478294, 4.375, 4.7359965625756475, 4.195191358024691, 4.793383827160493, 2.2516060768175588, 1.657723818431226, 0.955677823502515, 0.0, 12.15, 10.512456058527663, 8.288619092156129, 6.754818230452675, 9.586767654320987, 5.873267901234568, 4.7359965625756475, 3.125, 4.489497466239147, 3.6715570216049387, 2.046016735253773, 1.049443964334705, 0.0), # 47
(12.392123339488554, 11.505409259259258, 10.219796296296296, 11.00564375, 8.980984919037049, 4.375, 4.7212555555555555, 4.167416666666667, 4.78931, 2.244117037037037, 1.656414478114478, 0.9543061728395063, 0.0, 12.15, 10.497367901234567, 8.28207239057239, 6.73235111111111, 9.57862, 5.834383333333334, 4.7212555555555555, 3.125, 4.490492459518524, 3.6685479166666677, 2.0439592592592595, 1.0459462962962964, 0.0), # 48
(12.397852726017943, 11.466337379972563, 10.209311385459534, 10.996399768518518, 8.982876800945284, 4.375, 4.706294940692326, 4.139493827160494, 4.78515024691358, 2.2365381207133064, 1.6550683252275846, 0.9529081847279379, 0.0, 12.15, 10.481990032007316, 8.275341626137923, 6.709614362139918, 9.57030049382716, 5.795291358024691, 4.706294940692326, 3.125, 4.491438400472642, 3.665466589506174, 2.0418622770919073, 1.0423943072702333, 0.0), # 49
(12.403289158723938, 11.426740397805213, 10.198645404663925, 10.986950231481481, 8.984670431362652, 4.375, 4.69114318566933, 4.111496913580247, 4.78091049382716, 2.228885459533608, 1.6536865569272978, 0.9514860539551899, 0.0, 12.15, 10.466346593507089, 8.268432784636488, 6.686656378600823, 9.56182098765432, 5.756095679012346, 4.69114318566933, 3.125, 4.492335215681326, 3.6623167438271613, 2.0397290809327853, 1.038794581618656, 0.0), # 50
(12.408431682614292, 11.38669074074074, 10.187814814814814, 10.977306249999998, 8.986365663448797, 4.375, 4.675828758169934, 4.0835, 4.776596666666666, 2.2211751851851855, 1.6522703703703707, 0.9500419753086421, 0.0, 12.15, 10.450461728395062, 8.261351851851853, 6.663525555555555, 9.553193333333333, 5.7169, 4.675828758169934, 3.125, 4.493182831724399, 3.659102083333334, 2.037562962962963, 1.0351537037037037, 0.0), # 51
(12.413279342696734, 11.34626083676269, 10.176836076817558, 10.967478935185184, 8.98796235036337, 4.375, 4.660380125877511, 4.055577160493827, 4.772214691358024, 2.2134234293552817, 1.6508209627135553, 0.9485781435756746, 0.0, 12.15, 10.434359579332419, 8.254104813567777, 6.640270288065844, 9.544429382716048, 5.677808024691357, 4.660380125877511, 3.125, 4.493981175181685, 3.655826311728396, 2.035367215363512, 1.0314782578875175, 0.0), # 52
(12.417831183979011, 11.305523113854596, 10.165725651577505, 10.957479398148147, 8.989460345266023, 4.375, 4.64482575647543, 4.0278024691358025, 4.767770493827161, 2.205646323731139, 1.6493395311136052, 0.9470967535436672, 0.0, 12.15, 10.418064288980338, 8.246697655568026, 6.616938971193416, 9.535540987654322, 5.638923456790124, 4.64482575647543, 3.125, 4.4947301726330116, 3.65249313271605, 2.0331451303155013, 1.0277748285322361, 0.0), # 53
(12.42208625146886, 11.26455, 10.154499999999999, 10.94731875, 8.9908595013164, 4.375, 4.629194117647058, 4.000249999999999, 4.7632699999999994, 2.1978600000000004, 1.6478272727272725, 0.9456, 0.0, 12.15, 10.401599999999998, 8.239136363636362, 6.593579999999999, 9.526539999999999, 5.60035, 4.629194117647058, 3.125, 4.4954297506582, 3.649106250000001, 2.0309, 1.0240500000000001, 0.0), # 54
(12.426043590174027, 11.223413923182441, 10.143175582990398, 10.93700810185185, 8.992159671674152, 4.375, 4.613513677075768, 3.9729938271604937, 4.758719135802469, 2.1900805898491087, 1.6462853847113108, 0.9440900777320531, 0.0, 12.15, 10.384990855052584, 8.231426923556553, 6.570241769547325, 9.517438271604938, 5.562191358024691, 4.613513677075768, 3.125, 4.496079835837076, 3.645669367283951, 2.02863511659808, 1.0203103566529494, 0.0), # 55
(12.429702245102245, 11.182187311385459, 10.131768861454047, 10.926558564814814, 8.993360709498926, 4.375, 4.597812902444929, 3.946108024691358, 4.754123827160494, 2.182324224965707, 1.6447150642224717, 0.9425691815272064, 0.0, 12.15, 10.368260996799268, 8.223575321112358, 6.54697267489712, 9.508247654320988, 5.524551234567902, 4.597812902444929, 3.125, 4.496680354749463, 3.6421861882716056, 2.02635377229081, 1.0165624828532238, 0.0), # 56
(12.433061261261258, 11.140942592592593, 10.120296296296297, 10.915981249999998, 8.994462467950372, 4.375, 4.582120261437908, 3.9196666666666675, 4.74949, 2.1746070370370374, 1.6431175084175085, 0.9410395061728396, 0.0, 12.15, 10.351434567901233, 8.215587542087542, 6.523821111111111, 9.49898, 5.487533333333334, 4.582120261437908, 3.125, 4.497231233975186, 3.638660416666667, 2.0240592592592597, 1.0128129629629632, 0.0), # 57
(12.436119683658815, 11.09975219478738, 10.108774348422497, 10.905287268518517, 8.995464800188138, 4.375, 4.5664642217380775, 3.8937438271604936, 4.744823580246913, 2.1669451577503436, 1.641493914453174, 0.939503246456333, 0.0, 12.15, 10.334535711019662, 8.20746957226587, 6.50083547325103, 9.489647160493826, 5.451241358024691, 4.5664642217380775, 3.125, 4.497732400094069, 3.6350957561728396, 2.0217548696844996, 1.0090683813443075, 0.0), # 58
(12.438876557302644, 11.05868854595336, 10.097219478737998, 10.89448773148148, 8.996367559371876, 4.375, 4.550873251028807, 3.868413580246914, 4.74013049382716, 2.1593547187928674, 1.63984547948622, 0.9379625971650665, 0.0, 12.15, 10.31758856881573, 8.1992273974311, 6.478064156378601, 9.48026098765432, 5.41577901234568, 4.550873251028807, 3.125, 4.498183779685938, 3.6314959104938276, 2.0194438957476, 1.0053353223593966, 0.0), # 59
(12.441330927200491, 11.017824074074072, 10.085648148148147, 10.88359375, 8.997170598661228, 4.375, 4.535375816993463, 3.84375, 4.735416666666667, 2.1518518518518523, 1.6381734006734008, 0.9364197530864199, 0.0, 12.15, 10.300617283950617, 8.190867003367003, 6.455555555555556, 9.470833333333333, 5.3812500000000005, 4.535375816993463, 3.125, 4.498585299330614, 3.6278645833333343, 2.0171296296296295, 1.0016203703703705, 0.0), # 60
(12.443481838360098, 10.977231207133059, 10.0740768175583, 10.872616435185183, 8.997873771215849, 4.375, 4.520000387315419, 3.819827160493827, 4.730688024691357, 2.1444526886145407, 1.6364788751714678, 0.9348769090077733, 0.0, 12.15, 10.283645999085506, 8.182394375857339, 6.4333580658436205, 9.461376049382714, 5.347758024691358, 4.520000387315419, 3.125, 4.498936885607924, 3.624205478395062, 2.0148153635116604, 0.9979301097393691, 0.0), # 61
(12.445328335789204, 10.936982373113853, 10.062521947873801, 10.861566898148148, 8.998476930195388, 4.375, 4.504775429678044, 3.796719135802469, 4.72595049382716, 2.137173360768176, 1.6347631001371743, 0.9333362597165068, 0.0, 12.15, 10.266698856881574, 8.17381550068587, 6.411520082304527, 9.45190098765432, 5.315406790123457, 4.504775429678044, 3.125, 4.499238465097694, 3.620522299382717, 2.0125043895747603, 0.9942711248285323, 0.0), # 62
(12.44686946449555, 10.897149999999998, 10.051, 10.85045625, 8.998979928759484, 4.375, 4.4897294117647055, 3.7745, 4.721209999999999, 2.13003, 1.6330272727272728, 0.9318000000000001, 0.0, 12.15, 10.249799999999999, 8.165136363636364, 6.390089999999999, 9.442419999999998, 5.2843, 4.4897294117647055, 3.125, 4.499489964379742, 3.616818750000001, 2.0102, 0.99065, 0.0), # 63
(12.448104269486876, 10.857806515775033, 10.039527434842249, 10.839295601851852, 8.999382620067799, 4.375, 4.474890801258775, 3.7532438271604947, 4.716472469135802, 2.123038737997257, 1.6312725900985157, 0.9302703246456334, 0.0, 12.15, 10.232973571101967, 8.156362950492579, 6.369116213991769, 9.432944938271604, 5.254541358024692, 4.474890801258775, 3.125, 4.499691310033899, 3.613098533950618, 2.00790548696845, 0.9870733196159123, 0.0), # 64
(12.449031795770926, 10.819024348422495, 10.0281207133059, 10.828096064814813, 8.999684857279973, 4.375, 4.4602880658436215, 3.7330246913580245, 4.711743827160493, 2.1162157064471883, 1.6295002494076571, 0.9287494284407863, 0.0, 12.15, 10.216243712848648, 8.147501247038285, 6.348647119341564, 9.423487654320986, 5.226234567901234, 4.4602880658436215, 3.125, 4.499842428639987, 3.609365354938272, 2.0056241426611803, 0.9835476680384088, 0.0), # 65
(12.449651088355436, 10.780875925925926, 10.016796296296297, 10.81686875, 8.999886493555657, 4.375, 4.445949673202614, 3.7139166666666674, 4.70703, 2.1095770370370373, 1.6277114478114478, 0.9272395061728398, 0.0, 12.15, 10.199634567901235, 8.138557239057238, 6.328731111111111, 9.41406, 5.199483333333334, 4.445949673202614, 3.125, 4.499943246777828, 3.6056229166666673, 2.0033592592592595, 0.9800796296296298, 0.0), # 66
(12.44996119224815, 10.743433676268861, 10.005570644718793, 10.805624768518516, 8.999987382054503, 4.375, 4.431904091019123, 3.695993827160495, 4.702336913580247, 2.103138861454047, 1.625907382466642, 0.9257427526291724, 0.0, 12.15, 10.183170278920894, 8.12953691233321, 6.30941658436214, 9.404673827160494, 5.1743913580246925, 4.431904091019123, 3.125, 4.499993691027251, 3.6018749228395066, 2.0011141289437586, 0.9766757887517148, 0.0), # 67
(12.44974993737699, 10.706573503252354, 9.994405949931412, 10.794277566425121, 8.999902364237876, 4.37491880810852, 4.418109116897788, 3.6791719250114308, 4.6976351394604485, 2.0968861324941503, 1.624057197708075, 0.9242530021899743, 0.0, 12.149850180041152, 10.166783024089716, 8.120285988540376, 6.290658397482449, 9.395270278920897, 5.1508406950160035, 4.418109116897788, 3.1249420057918, 4.499951182118938, 3.598092522141708, 1.9988811899862826, 0.9733248639320324, 0.0), # 68
(12.447770048309177, 10.669170071684588, 9.982988425925925, 10.782255163043477, 8.999128540305009, 4.374276954732511, 4.404160908807968, 3.6625493827160494, 4.692719135802469, 2.090641917211329, 1.621972567783094, 0.9227218973359325, 0.0, 12.148663194444444, 10.149940870695255, 8.10986283891547, 6.271925751633985, 9.385438271604938, 5.127569135802469, 4.404160908807968, 3.1244835390946504, 4.499564270152504, 3.5940850543478264, 1.996597685185185, 0.9699245519713263, 0.0), # 69
(12.443862945070673, 10.63105170582769, 9.971268432784635, 10.769478411835749, 8.997599451303152, 4.373012879134278, 4.389996080736822, 3.645976223136717, 4.687561156835848, 2.0843758573388205, 1.619629777305216, 0.921142276129281, 0.0, 12.14631880144033, 10.13256503742209, 8.09814888652608, 6.25312757201646, 9.375122313671696, 5.104366712391404, 4.389996080736822, 3.123580627953056, 4.498799725651576, 3.5898261372785836, 1.9942536865569274, 0.9664592459843356, 0.0), # 70
(12.438083592771514, 10.592241185450682, 9.959250085733881, 10.755966153381644, 8.995334463003308, 4.371147065996037, 4.375620995723392, 3.629457933241884, 4.682168884316415, 2.078088107802792, 1.6170374741567726, 0.9195152937212715, 0.0, 12.142847865226338, 10.114668230933985, 8.085187370783862, 6.234264323408375, 9.36433776863283, 5.081241106538638, 4.375620995723392, 3.1222479042828835, 4.497667231501654, 3.5853220511272155, 1.9918500171467763, 0.962931016859153, 0.0), # 71
(12.430486956521738, 10.552761290322579, 9.946937499999999, 10.74173722826087, 8.99235294117647, 4.3687000000000005, 4.3610420168067225, 3.6129999999999995, 4.67655, 2.071778823529412, 1.614204306220096, 0.917842105263158, 0.0, 12.13828125, 10.096263157894736, 8.07102153110048, 6.215336470588234, 9.3531, 5.058199999999999, 4.3610420168067225, 3.1205000000000003, 4.496176470588235, 3.5805790760869574, 1.9893874999999999, 0.959341935483871, 0.0), # 72
(12.421128001431383, 10.512634800212398, 9.934334790809327, 10.72681047705314, 8.98867425159364, 4.36569216582838, 4.346265507025855, 3.5966079103795154, 4.670712185642433, 2.0654481594448484, 1.6111389213775176, 0.916123865906192, 0.0, 12.132649819958848, 10.07736252496811, 8.055694606887588, 6.196344478334543, 9.341424371284866, 5.035251074531322, 4.346265507025855, 3.118351547020271, 4.49433712579682, 3.5756034923510476, 1.9868669581618656, 0.9556940727465817, 0.0), # 73
(12.410061692610485, 10.471884494889155, 9.921446073388202, 10.711204740338164, 8.984317760025819, 4.3621440481633895, 4.331297829419833, 3.5802871513488794, 4.664663122999542, 2.0590962704752687, 1.607849967511371, 0.9143617308016269, 0.0, 12.125984439300412, 10.057979038817894, 8.039249837556856, 6.177288811425805, 9.329326245999084, 5.012402011888431, 4.331297829419833, 3.115817177259564, 4.4921588800129095, 3.5704015801127222, 1.9842892146776405, 0.9519894995353778, 0.0), # 74
(12.397342995169081, 10.430533154121862, 9.908275462962962, 10.694938858695652, 8.97930283224401, 4.358076131687243, 4.3161453470277, 3.5640432098765435, 4.6584104938271595, 2.052723311546841, 1.604346092503987, 0.9125568551007147, 0.0, 12.118315972222222, 10.038125406107861, 8.021730462519935, 6.158169934640522, 9.316820987654319, 4.989660493827161, 4.3161453470277, 3.112911522633745, 4.489651416122005, 3.5649796195652184, 1.9816550925925924, 0.9482302867383512, 0.0), # 75
(12.383026874217212, 10.388603557679545, 9.894827074759945, 10.678031672705314, 8.973648834019203, 4.353508901082153, 4.300814422888497, 3.5478815729309554, 4.651961979881115, 2.046329437585734, 1.6006359442376985, 0.9107103939547083, 0.0, 12.10967528292181, 10.01781433350179, 8.003179721188491, 6.138988312757201, 9.30392395976223, 4.967034202103338, 4.300814422888497, 3.1096492150586803, 4.486824417009601, 3.5593438909017725, 1.978965414951989, 0.9444185052435952, 0.0), # 76
(12.367168294864912, 10.34611848533121, 9.881105024005485, 10.660502022946858, 8.967375131122406, 4.34846284103033, 4.285311420041268, 3.531807727480567, 4.645325262917238, 2.0399148035181156, 1.5967281705948373, 0.9088235025148608, 0.0, 12.10009323559671, 9.997058527663466, 7.983640852974187, 6.119744410554345, 9.290650525834476, 4.944530818472794, 4.285311420041268, 3.106044886450236, 4.483687565561203, 3.5535006743156203, 1.9762210048010973, 0.9405562259392011, 0.0), # 77
(12.349822222222222, 10.30310071684588, 9.867113425925925, 10.64236875, 8.960501089324618, 4.3429584362139915, 4.269642701525055, 3.5158271604938274, 4.638508024691357, 2.0334795642701526, 1.5926314194577353, 0.9068973359324239, 0.0, 12.089600694444444, 9.975870695256662, 7.963157097288676, 6.100438692810457, 9.277016049382715, 4.922158024691359, 4.269642701525055, 3.1021131687242796, 4.480250544662309, 3.5474562500000006, 1.9734226851851853, 0.9366455197132618, 0.0), # 78
(12.331043621399177, 10.259573031992566, 9.8528563957476, 10.623650694444443, 8.953046074396838, 4.337016171315348, 4.2538146303789, 3.4999453589391867, 4.631517946959304, 2.0270238747680143, 1.5883543387087244, 0.9049330493586505, 0.0, 12.07822852366255, 9.954263542945155, 7.941771693543622, 6.081071624304041, 9.263035893918609, 4.899923502514861, 4.2538146303789, 3.097868693796677, 4.476523037198419, 3.5412168981481487, 1.97057127914952, 0.9326884574538697, 0.0), # 79
(12.310887457505816, 10.215558210540289, 9.838338048696844, 10.604366696859904, 8.945029452110063, 4.330656531016613, 4.2378335696418485, 3.4841678097850943, 4.624362711476909, 2.0205478899378684, 1.5839055762301377, 0.9029317979447936, 0.0, 12.066007587448558, 9.932249777392729, 7.919527881150689, 6.061643669813604, 9.248725422953818, 4.877834933699132, 4.2378335696418485, 3.093326093583295, 4.4725147260550315, 3.5347888989533023, 1.967667609739369, 0.9286871100491174, 0.0), # 80
(12.289408695652174, 10.171079032258064, 9.8235625, 10.584535597826088, 8.936470588235293, 4.3239, 4.221705882352941, 3.4685000000000006, 4.617049999999999, 2.014051764705883, 1.5792937799043065, 0.9008947368421053, 0.0, 12.052968749999998, 9.909842105263158, 7.8964688995215315, 6.042155294117647, 9.234099999999998, 4.855900000000001, 4.221705882352941, 3.0885, 4.468235294117647, 3.5281785326086967, 1.9647125, 0.9246435483870968, 0.0), # 81
(12.26666230094829, 10.126158276914907, 9.808533864883403, 10.564176237922705, 8.927388848543531, 4.316767062947722, 4.205437931551222, 3.4529474165523544, 4.6095874942844075, 2.007535653998225, 1.5745275976135626, 0.8988230212018388, 0.0, 12.039142875514404, 9.887053233220225, 7.8726379880678135, 6.022606961994674, 9.219174988568815, 4.834126383173296, 4.205437931551222, 3.0834050449626584, 4.4636944242717655, 3.521392079307569, 1.9617067729766806, 0.9205598433559008, 0.0), # 82
(12.242703238504205, 10.080818724279835, 9.793256258573388, 10.543307457729467, 8.917803598805776, 4.30927820454199, 4.189036080275732, 3.4375155464106077, 4.6019828760859625, 2.0009997127410637, 1.569615677240239, 0.8967178061752463, 0.0, 12.0245608281893, 9.863895867927708, 7.848078386201194, 6.00299913822319, 9.203965752171925, 4.812521764974851, 4.189036080275732, 3.078055860387136, 4.458901799402888, 3.5144358192431566, 1.9586512517146777, 0.9164380658436215, 0.0), # 83
(12.21758647342995, 10.035083154121864, 9.777733796296296, 10.521948097826087, 8.907734204793028, 4.301453909465021, 4.1725066915655145, 3.4222098765432096, 4.5942438271604935, 1.994444095860567, 1.5645666666666667, 0.8945802469135803, 0.0, 12.009253472222222, 9.840382716049382, 7.8228333333333335, 5.9833322875817, 9.188487654320987, 4.791093827160494, 4.1725066915655145, 3.0724670781893004, 4.453867102396514, 3.5073160326086965, 1.9555467592592592, 0.9122802867383514, 0.0), # 84
(12.191366970835569, 9.988974346210009, 9.761970593278463, 10.500116998792272, 8.897200032276285, 4.293314662399025, 4.1558561284596145, 3.4070358939186103, 4.58637802926383, 1.9878689582829019, 1.5593892137751788, 0.8924114985680938, 0.0, 11.9932516718107, 9.81652648424903, 7.796946068875894, 5.963606874848704, 9.17275605852766, 4.769850251486054, 4.1558561284596145, 3.0666533302850176, 4.448600016138142, 3.500038999597425, 1.9523941186556926, 0.9080885769281828, 0.0), # 85
(12.164099695831096, 9.942515080313289, 9.745970764746229, 10.477833001207731, 8.886220447026545, 4.284880948026216, 4.139090753997072, 3.391999085505258, 4.578393164151806, 1.9812744549342376, 1.5540919664481068, 0.8902127162900394, 0.0, 11.976586291152262, 9.792339879190433, 7.770459832240534, 5.943823364802712, 9.156786328303612, 4.748798719707362, 4.139090753997072, 3.0606292485901543, 4.443110223513273, 3.4926110004025777, 1.9491941529492458, 0.9038650073012082, 0.0), # 86
(12.135839613526569, 9.895728136200717, 9.729738425925925, 10.455114945652172, 8.874814814814815, 4.276173251028807, 4.122216931216931, 3.3771049382716045, 4.570296913580247, 1.9746607407407408, 1.5486835725677832, 0.8879850552306694, 0.0, 11.959288194444444, 9.76783560753736, 7.743417862838915, 5.923982222222222, 9.140593827160494, 4.727946913580246, 4.122216931216931, 3.054409465020576, 4.437407407407408, 3.4850383152173916, 1.9459476851851853, 0.8996116487455198, 0.0), # 87
(12.106641689032028, 9.84863629364131, 9.713277692043896, 10.431981672705316, 8.863002501412087, 4.2672120560890106, 4.105241023158234, 3.3623589391860995, 4.562096959304984, 1.9680279706285808, 1.5431726800165397, 0.8857296705412365, 0.0, 11.941388245884776, 9.743026375953601, 7.715863400082698, 5.904083911885741, 9.124193918609969, 4.707302514860539, 4.105241023158234, 3.0480086114921505, 4.431501250706043, 3.477327224235106, 1.9426555384087794, 0.8953305721492102, 0.0), # 88
(12.076560887457505, 9.801262332404088, 9.696592678326475, 10.40845202294686, 8.850802872589364, 4.258017847889041, 4.088169392860024, 3.3477665752171926, 4.553800983081847, 1.9613762995239252, 1.537567936676709, 0.8834477173729935, 0.0, 11.922917309670781, 9.717924891102928, 7.687839683383544, 5.884128898571774, 9.107601966163694, 4.68687320530407, 4.088169392860024, 3.041441319920744, 4.425401436294682, 3.469484007648954, 1.9393185356652953, 0.8910238484003719, 0.0), # 89
(12.045652173913043, 9.753629032258065, 9.6796875, 10.384544836956522, 8.838235294117647, 4.248611111111111, 4.071008403361344, 3.333333333333333, 4.545416666666667, 1.9547058823529415, 1.5318779904306221, 0.881140350877193, 0.0, 11.90390625, 9.692543859649122, 7.65938995215311, 5.864117647058823, 9.090833333333334, 4.666666666666666, 4.071008403361344, 3.0347222222222223, 4.419117647058823, 3.461514945652175, 1.9359375, 0.8866935483870969, 0.0), # 90
(12.013970513508676, 9.705759172972254, 9.662566272290809, 10.360278955314012, 8.825319131767932, 4.239012330437433, 4.053764417701236, 3.319064700502972, 4.536951691815272, 1.948016874041798, 1.526111489160612, 0.8788087262050875, 0.0, 11.884385931069957, 9.66689598825596, 7.630557445803059, 5.844050622125392, 9.073903383630544, 4.646690580704161, 4.053764417701236, 3.027865950312452, 4.412659565883966, 3.4534263184380047, 1.9325132544581618, 0.8823417429974777, 0.0), # 91
(11.981570871354446, 9.657675534315677, 9.64523311042524, 10.335673218599032, 8.812073751311223, 4.2292419905502205, 4.036443798918745, 3.304966163694559, 4.528413740283494, 1.941309429516663, 1.5202770807490107, 0.8764539985079298, 0.0, 11.864387217078187, 9.640993983587226, 7.601385403745053, 5.823928288549988, 9.056827480566987, 4.626952629172383, 4.036443798918745, 3.0208871361073006, 4.406036875655611, 3.4452244061996784, 1.9290466220850482, 0.8779705031196072, 0.0), # 92
(11.948508212560386, 9.609400896057348, 9.62769212962963, 10.310746467391306, 8.798518518518518, 4.219320576131687, 4.01905291005291, 3.2910432098765434, 4.51981049382716, 1.9345837037037037, 1.5143834130781502, 0.8740773229369722, 0.0, 11.84394097222222, 9.614850552306692, 7.57191706539075, 5.80375111111111, 9.03962098765432, 4.607460493827161, 4.01905291005291, 3.0138004115226336, 4.399259259259259, 3.436915489130436, 1.925538425925926, 0.8735818996415772, 0.0), # 93
(11.914837502236535, 9.56095803796628, 9.609947445130317, 10.285517542270531, 8.784672799160816, 4.209268571864045, 4.0015981141427766, 3.277301326017376, 4.511149634202103, 1.9278398515290893, 1.5084391340303622, 0.8716798546434675, 0.0, 11.823078060699588, 9.588478401078142, 7.54219567015181, 5.783519554587267, 9.022299268404206, 4.588221856424326, 4.0015981141427766, 3.0066204084743178, 4.392336399580408, 3.4285058474235113, 1.9219894890260634, 0.8691780034514802, 0.0), # 94
(11.880613705492932, 9.512369739811495, 9.592003172153635, 10.260005283816424, 8.770555959009117, 4.199106462429508, 3.984085774227386, 3.2637459990855056, 4.5024388431641515, 1.9210780279189867, 1.5024528914879791, 0.869262748778668, 0.0, 11.801829346707818, 9.561890236565347, 7.512264457439896, 5.763234083756959, 9.004877686328303, 4.569244398719708, 3.984085774227386, 2.9993617588782198, 4.385277979504559, 3.4200017612721423, 1.9184006344307272, 0.8647608854374088, 0.0), # 95
(11.845891787439614, 9.463658781362009, 9.573863425925927, 10.234228532608697, 8.756187363834421, 4.188854732510288, 3.966522253345782, 3.250382716049383, 4.493685802469135, 1.9142983877995645, 1.4964333333333335, 0.8668271604938274, 0.0, 11.780225694444445, 9.5350987654321, 7.482166666666667, 5.742895163398693, 8.98737160493827, 4.5505358024691365, 3.966522253345782, 2.9920390946502056, 4.3780936819172105, 3.411409510869566, 1.9147726851851854, 0.8603326164874555, 0.0), # 96
(11.810726713186616, 9.414847942386832, 9.555532321673525, 10.208206129227051, 8.74158637940773, 4.178533866788599, 3.948913914537008, 3.237216963877458, 4.484898193872885, 1.9075010860969905, 1.4903891074487565, 0.864374244940197, 0.0, 11.758297968106996, 9.508116694342165, 7.451945537243782, 5.7225032582909705, 8.96979638774577, 4.532103749428441, 3.948913914537008, 2.984667047706142, 4.370793189703865, 3.402735376409018, 1.911106464334705, 0.8558952674897121, 0.0), # 97
(11.775173447843981, 9.365960002654985, 9.53701397462277, 10.181956914251208, 8.72677237150004, 4.168164349946655, 3.931267120840105, 3.22425422953818, 4.476083699131229, 1.9006862777374327, 1.484328861716581, 0.8619051572690299, 0.0, 11.736077031893004, 9.480956729959328, 7.421644308582906, 5.702058833212297, 8.952167398262459, 4.513955921353452, 3.931267120840105, 2.9772602499618963, 4.36338618575002, 3.3939856380837368, 1.9074027949245542, 0.8514509093322715, 0.0), # 98
(11.739286956521738, 9.317017741935484, 9.5183125, 10.15549972826087, 8.711764705882352, 4.157766666666667, 3.913588235294118, 3.2115, 4.46725, 1.893854117647059, 1.4782612440191387, 0.859421052631579, 0.0, 11.71359375, 9.453631578947368, 7.391306220095694, 5.681562352941175, 8.9345, 4.4961, 3.913588235294118, 2.9698333333333333, 4.355882352941176, 3.385166576086957, 1.9036625000000003, 0.8470016129032258, 0.0), # 99
(11.703122204329933, 9.268043939997343, 9.49943201303155, 10.128853411835749, 8.696582748325667, 4.147361301630848, 3.895883620938087, 3.1989597622313672, 4.458404778235025, 1.8870047607520377, 1.4721949022387621, 0.8569230861790968, 0.0, 11.690878986625515, 9.426153947970063, 7.36097451119381, 5.661014282256112, 8.91680955647005, 4.4785436671239145, 3.895883620938087, 2.9624009297363205, 4.348291374162834, 3.376284470611917, 1.89988640260631, 0.8425494490906678, 0.0), # 100
(11.6667341563786, 9.219061376609584, 9.480376628943759, 10.102036805555556, 8.681245864600983, 4.136968739521414, 3.878159640811057, 3.1866390032007312, 4.449555715592135, 1.8801383619785366, 1.4661384842577825, 0.8544124130628354, 0.0, 11.667963605967076, 9.398536543691188, 7.330692421288911, 5.640415085935608, 8.89911143118427, 4.461294604481024, 3.878159640811057, 2.9549776710867244, 4.340622932300492, 3.367345601851853, 1.8960753257887522, 0.8380964887826896, 0.0), # 101
(11.630177777777778, 9.170092831541218, 9.461150462962962, 10.07506875, 8.665773420479303, 4.126609465020577, 3.8604226579520695, 3.174543209876543, 4.44071049382716, 1.8732550762527238, 1.4601006379585326, 0.8518901884340482, 0.0, 11.644878472222222, 9.37079207277453, 7.300503189792663, 5.61976522875817, 8.88142098765432, 4.44436049382716, 3.8604226579520695, 2.947578189300412, 4.332886710239651, 3.358356250000001, 1.8922300925925928, 0.8336448028673837, 0.0), # 102
(11.593508033637502, 9.121161084561264, 9.4417576303155, 10.047968085748792, 8.650184781731623, 4.116303962810547, 3.842679035400168, 3.162677869227252, 4.43187679469593, 1.8663550585007669, 1.4540900112233446, 0.8493575674439874, 0.0, 11.621654449588474, 9.342933241883859, 7.270450056116723, 5.599065175502299, 8.86375358939186, 4.427749016918153, 3.842679035400168, 2.940217116293248, 4.325092390865811, 3.3493226952495982, 1.8883515260631, 0.8291964622328422, 0.0), # 103
(11.556779889067812, 9.072288915438735, 9.422202246227709, 10.020753653381641, 8.634499314128943, 4.10607271757354, 3.8249351361943953, 3.151048468221308, 4.423062299954275, 1.8594384636488344, 1.44811525193455, 0.8468157052439055, 0.0, 11.598322402263374, 9.314972757682959, 7.24057625967275, 5.578315390946502, 8.84612459990855, 4.411467855509831, 3.8249351361943953, 2.9329090839811003, 4.317249657064472, 3.3402512177938815, 1.884440449245542, 0.8247535377671579, 0.0), # 104
(11.520048309178742, 9.023499103942651, 9.402488425925926, 9.99344429347826, 8.618736383442265, 4.09593621399177, 3.8071973233737944, 3.1396604938271606, 4.414274691358024, 1.8525054466230941, 1.4421850079744816, 0.8442657569850553, 0.0, 11.574913194444443, 9.286923326835607, 7.210925039872408, 5.557516339869281, 8.828549382716048, 4.395524691358025, 3.8071973233737944, 2.9256687242798356, 4.309368191721132, 3.331148097826088, 1.8804976851851853, 0.8203181003584229, 0.0), # 105
(11.483368259080336, 8.974814429842029, 9.382620284636488, 9.966058846618358, 8.602915355442589, 4.0859149367474465, 3.7894719599774067, 3.12851943301326, 4.405521650663008, 1.8455561623497139, 1.436307927225471, 0.8417088778186895, 0.0, 11.551457690329217, 9.258797656005584, 7.181539636127354, 5.53666848704914, 8.811043301326016, 4.379927206218564, 3.7894719599774067, 2.918510669105319, 4.301457677721294, 3.3220196155394537, 1.8765240569272976, 0.81589222089473, 0.0), # 106
(11.446794703882626, 8.926257672905882, 9.362601937585735, 9.938616153381641, 8.58705559590091, 4.076029370522787, 3.7717654090442765, 3.117630772748057, 4.396810859625057, 1.838590765754862, 1.4304926575698504, 0.8391462228960604, 0.0, 11.527986754115226, 9.230608451856664, 7.152463287849251, 5.515772297264585, 8.793621719250114, 4.36468308184728, 3.7717654090442765, 2.911449550373419, 4.293527797950455, 3.312872051127215, 1.8725203875171472, 0.8114779702641711, 0.0), # 107
(11.410382608695652, 8.877851612903227, 9.3424375, 9.911135054347826, 8.571176470588235, 4.0663, 3.7540840336134447, 3.107, 4.3881499999999996, 1.8316094117647064, 1.4247478468899522, 0.8365789473684213, 0.0, 11.504531250000001, 9.202368421052633, 7.12373923444976, 5.494828235294118, 8.776299999999999, 4.3498, 3.7540840336134447, 2.9045, 4.285588235294117, 3.3037116847826096, 1.8684875000000005, 0.8070774193548388, 0.0), # 108
(11.374186938629451, 8.82961902960308, 9.322131087105625, 9.883634390096615, 8.555297345275559, 4.056747309861302, 3.7364341967239567, 3.0966326017375403, 4.379546753543667, 1.8246122553054145, 1.4190821430681082, 0.8340082063870239, 0.0, 11.48112204218107, 9.174090270257262, 7.09541071534054, 5.473836765916242, 8.759093507087334, 4.335285642432557, 3.7364341967239567, 2.8976766499009297, 4.277648672637779, 3.294544796698873, 1.864426217421125, 0.8026926390548256, 0.0), # 109
(11.338262658794058, 8.78158270277446, 9.301686814128946, 9.85613300120773, 8.539437585733882, 4.047391784788904, 3.7188222614148536, 3.0865340649291264, 4.371008802011888, 1.8175994513031553, 1.4135041939866502, 0.8314351551031215, 0.0, 11.457789994855966, 9.145786706134334, 7.067520969933251, 5.452798353909465, 8.742017604023776, 4.321147690900777, 3.7188222614148536, 2.8909941319920742, 4.269718792866941, 3.2853776670692443, 1.8603373628257893, 0.7983257002522237, 0.0), # 110
(11.302664734299517, 8.733765412186381, 9.281108796296298, 9.82864972826087, 8.523616557734204, 4.038253909465022, 3.7012545907251786, 3.0767098765432097, 4.3625438271604935, 1.8105711546840961, 1.4080226475279107, 0.8288609486679663, 0.0, 11.434565972222222, 9.117470435347629, 7.040113237639553, 5.431713464052287, 8.725087654320987, 4.307393827160493, 3.7012545907251786, 2.8844670781893007, 4.261808278867102, 3.2762165760869575, 1.8562217592592598, 0.7939786738351257, 0.0), # 111
(11.26744813025586, 8.686189937607857, 9.26040114883402, 9.801203411835749, 8.507853627047526, 4.029354168571865, 3.6837375476939744, 3.0671655235482396, 4.354159510745312, 1.803527520374405, 1.402646151574222, 0.8262867422328111, 0.0, 11.411480838477365, 9.08915416456092, 7.013230757871109, 5.4105825611232135, 8.708319021490624, 4.294031732967535, 3.6837375476939744, 2.8781101204084747, 4.253926813523763, 3.2670678039452503, 1.8520802297668042, 0.7896536306916234, 0.0), # 112
(11.232605068443652, 8.638958480664547, 9.239617828252069, 9.773850494242836, 8.492140544138962, 4.02070883845016, 3.6663155781940615, 3.057926284390303, 4.3458851245034475, 1.7964914209838192, 1.3973847812305735, 0.8237192936504428, 0.0, 11.388532681011865, 9.06091223015487, 6.9869239061528665, 5.389474262951456, 8.691770249006895, 4.281096798146424, 3.6663155781940615, 2.8719348846072568, 4.246070272069481, 3.257950164747613, 1.8479235656504138, 0.7853598618785952, 0.0), # 113
(11.197777077480078, 8.592536901878088, 9.219045675021619, 9.746810479149604, 8.47631468365306, 4.012298225970931, 3.649210925046347, 3.04910562975256, 4.337847614285224, 1.7895945503738353, 1.3922488674226394, 0.8211912172112975, 0.0, 11.365530496992042, 9.033103389324271, 6.961244337113197, 5.368783651121505, 8.675695228570447, 4.268747881653584, 3.649210925046347, 2.8659273042649507, 4.23815734182653, 3.248936826383202, 1.8438091350043238, 0.7811397183525536, 0.0), # 114
(11.162861883604794, 8.546941918551349, 9.198696932707318, 9.7200760546636, 8.46032614231088, 4.004100458749136, 3.6324357901149367, 3.0407013271393546, 4.330049991467515, 1.7828475983964234, 1.3872309022854188, 0.8187037582558852, 0.0, 11.342407957992451, 9.005741340814735, 6.936154511427093, 5.348542795189269, 8.66009998293503, 4.256981857995097, 3.6324357901149367, 2.8600717562493823, 4.23016307115544, 3.2400253515545345, 1.8397393865414637, 0.7769947198683046, 0.0), # 115
(11.127815847885161, 8.502107109420871, 9.178532189983873, 9.693599535239764, 8.444150821107023, 3.9960962141009686, 3.6159628905167356, 3.0326901564494966, 4.322472535691132, 1.7762380075348645, 1.3823211862542963, 0.8162523197487347, 0.0, 11.319128711707068, 8.97877551723608, 6.911605931271482, 5.328714022604592, 8.644945071382264, 4.245766219029295, 3.6159628905167356, 2.854354438643549, 4.222075410553511, 3.231199845079922, 1.835706437996775, 0.7729188281291702, 0.0), # 116
(11.092595331388527, 8.457966053223192, 9.158512035525986, 9.66733323533302, 8.427764621036088, 3.988266169342624, 3.5997649433686516, 3.0250488975817924, 4.315095526596881, 1.769753220272439, 1.3775100197646568, 0.8138323046543751, 0.0, 11.295656405829869, 8.952155351198124, 6.887550098823283, 5.309259660817316, 8.630191053193762, 4.23506845661451, 3.5997649433686516, 2.8487615495304452, 4.213882310518044, 3.222444411777674, 1.8317024071051975, 0.768906004838472, 0.0), # 117
(11.057156695182252, 8.414452328694855, 9.138597058008367, 9.6412294693983, 8.411143443092673, 3.980591001790297, 3.583814665787589, 3.0177543304350483, 4.307899243825574, 1.7633806790924282, 1.3727877032518847, 0.811439115937335, 0.0, 11.271954688054828, 8.925830275310684, 6.863938516259424, 5.290142037277283, 8.615798487651148, 4.224856062609067, 3.583814665787589, 2.843279286993069, 4.205571721546336, 3.213743156466101, 1.8277194116016737, 0.7649502116995325, 0.0), # 118
(11.02145630033369, 8.3714995145724, 9.118747846105723, 9.615240551890535, 8.394263188271376, 3.973051388760183, 3.5680847748904534, 3.0107832349080725, 4.300863967018017, 1.757107826478112, 1.3681445371513656, 0.8090681565621435, 0.0, 11.247987206075917, 8.899749722183577, 6.840722685756828, 5.271323479434335, 8.601727934036035, 4.215096528871301, 3.5680847748904534, 2.8378938491144163, 4.197131594135688, 3.2050801839635126, 1.8237495692211447, 0.761045410415673, 0.0), # 119
(10.985450507910194, 8.329041189592374, 9.098924988492762, 9.589318797264655, 8.377099757566796, 3.965628007568476, 3.5525479877941515, 3.0041123908996714, 4.293969975815023, 1.7509221049127721, 1.3635708218984832, 0.8067148294933297, 0.0, 11.223717607587115, 8.873863124426626, 6.817854109492416, 5.252766314738315, 8.587939951630046, 4.20575734725954, 3.5525479877941515, 2.8325914339774827, 4.188549878783398, 3.1964395990882193, 1.8197849976985525, 0.7571855626902159, 0.0), # 120
(10.949095678979122, 8.287010932491311, 9.079089073844187, 9.56341651997559, 8.359629051973535, 3.9583015355313718, 3.5371770216155882, 2.9977185783086533, 4.2871975498573995, 1.7448109568796892, 1.3590568579286233, 0.8043745376954222, 0.0, 11.199109540282393, 8.848119914649644, 6.795284289643115, 5.234432870639067, 8.574395099714799, 4.196806009632114, 3.5371770216155882, 2.8273582396652652, 4.179814525986767, 3.1878055066585307, 1.8158178147688375, 0.753364630226483, 0.0), # 121
(10.912348174607825, 8.245342322005756, 9.059200690834711, 9.537486034478269, 8.341826972486187, 3.951052649965064, 3.5219445934716704, 2.9915785770338243, 4.2805269687859555, 1.7387618248621435, 1.3545929456771704, 0.8020426841329501, 0.0, 11.174126651855724, 8.82246952546245, 6.772964728385852, 5.216285474586429, 8.561053937571911, 4.1882100078473545, 3.5219445934716704, 2.8221804642607595, 4.170913486243093, 3.1791620114927572, 1.8118401381669422, 0.7495765747277962, 0.0), # 122
(10.875164355863662, 8.20396893687225, 9.039220428139036, 9.511479655227625, 8.323669420099352, 3.9438620281857477, 3.506823420479303, 2.9856691669739917, 4.273938512241501, 1.7327621513434166, 1.3501693855795087, 0.7997146717704421, 0.0, 11.148732590001085, 8.796861389474863, 6.750846927897544, 5.1982864540302485, 8.547877024483002, 4.1799368337635885, 3.506823420479303, 2.8170443058469625, 4.161834710049676, 3.170493218409209, 1.8078440856278073, 0.7458153578974774, 0.0), # 123
(10.837500583813984, 8.162824355827334, 9.01910887443187, 9.485349696678588, 8.30513229580763, 3.9367103475096172, 3.4917862197553915, 2.979967128027963, 4.267412459864846, 1.7267993788067886, 1.345776478071024, 0.7973859035724276, 0.0, 11.122891002412453, 8.771244939296702, 6.728882390355119, 5.180398136420364, 8.534824919729692, 4.171953979239149, 3.4917862197553915, 2.8119359625068694, 4.152566147903815, 3.1617832322261967, 1.803821774886374, 0.7420749414388487, 0.0), # 124
(10.79931321952615, 8.121842157607551, 8.998826618387923, 9.459048473286083, 8.286191500605618, 3.9295782852528696, 3.4768057084168436, 2.9744492400945455, 4.260929091296797, 1.7208609497355405, 1.3414045235871004, 0.7950517825034348, 0.0, 11.096565536783794, 8.745569607537782, 6.707022617935502, 5.16258284920662, 8.521858182593594, 4.164228936132364, 3.4768057084168436, 2.806841632323478, 4.143095750302809, 3.153016157762029, 1.799765323677585, 0.7383492870552321, 0.0), # 125
(10.760558624067514, 8.080955920949442, 8.978334248681898, 9.432528299505048, 8.266822935487914, 3.9224465187316975, 3.461854603580562, 2.969092283072546, 4.254468686178167, 1.7149343066129532, 1.3370438225631227, 0.7927077115279934, 0.0, 11.069719840809094, 8.719784826807926, 6.685219112815614, 5.144802919838858, 8.508937372356334, 4.156729196301565, 3.461854603580562, 2.801747513379784, 4.133411467743957, 3.144176099835017, 1.79566684973638, 0.7346323564499494, 0.0), # 126
(10.721193158505432, 8.040099224589545, 8.957592353988504, 9.405741489790408, 8.247002501449117, 3.915295725262296, 3.4469056223634564, 2.9638730368607726, 4.248011524149763, 1.7090068919223076, 1.3326846754344757, 0.7903490936106315, 0.0, 11.042317562182317, 8.693840029716947, 6.6634233771723785, 5.127020675766921, 8.496023048299525, 4.149422251605082, 3.4469056223634564, 2.7966398037587825, 4.1235012507245585, 3.1352471632634704, 1.7915184707977012, 0.7309181113263225, 0.0), # 127
(10.681173183907255, 7.999205647264407, 8.93656152298245, 9.3786403585971, 8.226706099483831, 3.908106582160861, 3.431931481882429, 2.958768281358031, 4.241537884852393, 1.703066148146884, 1.328317382636545, 0.7879713317158789, 0.0, 11.014322348597444, 8.667684648874667, 6.641586913182724, 5.109198444440651, 8.483075769704786, 4.1422755939012434, 3.431931481882429, 2.791504701543472, 4.1133530497419155, 3.1262134528657004, 1.7873123045964903, 0.7272005133876734, 0.0), # 128
(10.640455061340337, 7.958208767710564, 8.91520234433844, 9.351177220380043, 8.205909630586648, 3.9008597667435865, 3.4169048992543876, 2.95375479646313, 4.235028047926869, 1.697099517769964, 1.3239322446047141, 0.7855698288082636, 0.0, 10.985697847748446, 8.641268116890899, 6.619661223023571, 5.0912985533098905, 8.470056095853739, 4.135256715048382, 3.4169048992543876, 2.7863284048168473, 4.102954815293324, 3.117059073460015, 1.783040468867688, 0.7234735243373241, 0.0), # 129
(10.598995151872039, 7.917042164664562, 8.893475406731179, 9.323304389594178, 8.18458899575217, 3.893535956326666, 3.4017985915962377, 2.948809362074875, 4.228462293014, 1.6910944432748274, 1.3195195617743691, 0.7831399878523152, 0.0, 10.956407707329298, 8.614539866375466, 6.5975978088718445, 5.073283329824481, 8.456924586028, 4.128333106904826, 3.4017985915962377, 2.781097111661904, 4.092294497876085, 3.1077681298647266, 1.7786950813462359, 0.7197311058785967, 0.0), # 130
(10.556749816569713, 7.8756394168629384, 8.87134129883538, 9.294974180694428, 8.162720095974995, 3.886115828226296, 3.3865852760248853, 2.943908758092075, 4.221820899754594, 1.685038367144756, 1.3150696345808937, 0.7806772118125626, 0.0, 10.926415575033973, 8.587449329938186, 6.575348172904468, 5.055115101434266, 8.443641799509187, 4.121472261328905, 3.3865852760248853, 2.77579702016164, 4.081360047987498, 3.0983247268981433, 1.7742682597670765, 0.7159672197148127, 0.0), # 131
(10.51367541650071, 7.833934103042237, 8.848760609325746, 9.266138908135728, 8.140278832249722, 3.878580059758672, 3.3712376696572353, 2.9390297644135366, 4.215084147789462, 1.6789187318630299, 1.310572763459673, 0.7781769036535342, 0.0, 10.89568509855645, 8.559945940188875, 6.552863817298364, 5.0367561955890885, 8.430168295578923, 4.114641670178951, 3.3712376696572353, 2.770414328399051, 4.070139416124861, 3.088712969378577, 1.7697521218651495, 0.7121758275492944, 0.0), # 132
(10.469728312732395, 7.791859801938998, 8.825693926876983, 9.236750886373006, 8.117241105570947, 3.870909328239987, 3.3557284896101933, 2.934149160938066, 4.2082323167594105, 1.67272297991293, 1.306019248846092, 0.7756344663397593, 0.0, 10.864179925590703, 8.531979129737351, 6.53009624423046, 5.018168939738788, 8.416464633518821, 4.107808825313293, 3.3557284896101933, 2.764935234457133, 4.058620552785474, 3.078916962124336, 1.765138785375397, 0.7083508910853636, 0.0), # 133
(10.424864866332113, 7.749350092289764, 8.802101840163804, 9.206762429861191, 8.093582816933273, 3.863084310986436, 3.3400304530006673, 2.929243727564472, 4.201245686305251, 1.6664385537777375, 1.3013993911755357, 0.7730453028357667, 0.0, 10.831863703830699, 8.503498331193432, 6.506996955877678, 4.9993156613332115, 8.402491372610502, 4.100941218590261, 3.3400304530006673, 2.7593459364188826, 4.046791408466636, 3.0689208099537315, 1.7604203680327608, 0.7044863720263422, 0.0), # 134
(10.379041438367224, 7.706338552831077, 8.777944937860909, 9.17612585305522, 8.069279867331296, 3.8550856853142146, 3.3241162769455603, 2.92429024419156, 4.194104536067791, 1.6600528959407332, 1.2967034908833885, 0.7704048161060852, 0.0, 10.798700080970423, 8.474452977166937, 6.483517454416942, 4.980158687822199, 8.388209072135583, 4.094006341868184, 3.3241162769455603, 2.753632632367296, 4.034639933665648, 3.0587086176850744, 1.755588987572182, 0.7005762320755525, 0.0), # 135
(10.332214389905081, 7.6627587622994735, 8.753183808643008, 9.144793470410015, 8.044308157759612, 3.8468941285395175, 3.3079586785617807, 2.9192654907181383, 4.186789145687842, 1.653553448885197, 1.2919218484050357, 0.7677084091152441, 0.0, 10.764652704703844, 8.444792500267685, 6.459609242025177, 4.96066034665559, 8.373578291375685, 4.086971687005394, 3.3079586785617807, 2.7477815203853697, 4.022154078879806, 3.0482644901366727, 1.750636761728602, 0.6966144329363159, 0.0), # 136
(10.28434008201304, 7.618544299431501, 8.72777904118481, 9.112717596380511, 8.018643589212827, 3.838490317978539, 3.291530374966233, 2.9141462470430146, 4.179279794806213, 1.6469276550944107, 1.2870447641758613, 0.764951484827772, 0.0, 10.729685222724932, 8.41446633310549, 6.435223820879306, 4.940782965283231, 8.358559589612426, 4.079804745860221, 3.291530374966233, 2.741778798556099, 4.0093217946064135, 3.037572532126838, 1.7455558082369622, 0.6925949363119547, 0.0), # 137
(10.235374875758456, 7.573628742963698, 8.701691224161017, 9.079850545421637, 7.992262062685534, 3.8298549309474748, 3.2748040832758227, 2.908909293064995, 4.1715567630637125, 1.6401629570516543, 1.2820625386312503, 0.7621294462081979, 0.0, 10.693761282727667, 8.383423908290176, 6.410312693156252, 4.920488871154961, 8.343113526127425, 4.0724730102909925, 3.2748040832758227, 2.735610664962482, 3.996131031342767, 3.02661684847388, 1.7403382448322038, 0.6885117039057909, 0.0), # 138
(10.185275132208682, 7.527945671632606, 8.67488094624634, 9.046144631988323, 7.965139479172331, 3.8209686447625186, 3.2577525206074553, 2.903531408682887, 4.163600330101148, 1.6332467972402094, 1.276965472206588, 0.7592376962210506, 0.0, 10.656844532406023, 8.351614658431556, 6.38482736103294, 4.899740391720627, 8.327200660202296, 4.064943972156042, 3.2577525206074553, 2.729263317687513, 3.9825697395861654, 3.0153815439961082, 1.7349761892492683, 0.6843586974211461, 0.0), # 139
(10.133997212431076, 7.481428664174767, 8.647308796115487, 9.011552170535499, 7.937251739667823, 3.811812136739866, 3.240348404078038, 2.897989373795498, 4.155390775559333, 1.626166618143356, 1.2717438653372588, 0.7562716378308593, 0.0, 10.618898619453978, 8.31898801613945, 6.358719326686294, 4.878499854430067, 8.310781551118666, 4.0571851233136975, 3.240348404078038, 2.7227229548141896, 3.9686258698339114, 3.003850723511834, 1.7294617592230976, 0.6801298785613425, 0.0), # 140
(10.081497477492995, 7.4340112993267216, 8.61893536244316, 8.976025475518098, 7.908574745166602, 3.802366084195711, 3.222564450804477, 2.892259968301635, 4.146908379079072, 1.6189098622443758, 1.2663880184586478, 0.7532266740021526, 0.0, 10.579887191565495, 8.285493414023676, 6.331940092293238, 4.856729586733126, 8.293816758158144, 4.049163955622289, 3.222564450804477, 2.7159757744255075, 3.954287372583301, 2.9920084918393663, 1.7237870724886322, 0.675819209029702, 0.0), # 141
(10.027732288461786, 7.385627155825012, 8.58972123390407, 8.939516861391049, 7.879084396663268, 3.792611164446249, 3.2043733779036754, 2.8863199721001056, 4.138133420301177, 1.6114639720265487, 1.2608882320061394, 0.7500982076994595, 0.0, 10.539773896434559, 8.251080284694053, 6.304441160030697, 4.834391916079644, 8.276266840602354, 4.040847960940148, 3.2043733779036754, 2.7090079746044635, 3.939542198331634, 2.9798389537970165, 1.7179442467808141, 0.6714206505295467, 0.0), # 142
(9.972658006404808, 7.336209812406179, 8.559626999172925, 8.901978642609278, 7.848756595152423, 3.7825280548076745, 3.185747902492541, 2.880146165089716, 4.129046178866458, 1.6038163899731561, 1.2552348064151186, 0.746881641887309, 0.0, 10.49852238175514, 8.215698060760397, 6.276174032075593, 4.811449169919467, 8.258092357732917, 4.032204631125603, 3.185747902492541, 2.701805753434053, 3.9243782975762116, 2.967326214203093, 1.7119253998345851, 0.6669281647641981, 0.0), # 143
(9.916230992389421, 7.285692847806764, 8.528613246924428, 8.86336313362772, 7.817567241628662, 3.772097432596183, 3.1666607416879793, 2.8737153271692746, 4.119626934415724, 1.5959545585674784, 1.2494180421209704, 0.7435723795302299, 0.0, 10.456096295221217, 8.179296174832528, 6.247090210604851, 4.787863675702434, 8.239253868831447, 4.023201458036985, 3.1666607416879793, 2.6943553089972734, 3.908783620814331, 2.954454377875907, 1.7057226493848856, 0.6623357134369786, 0.0), # 144
(9.858407607482972, 7.234009840763308, 8.496640565833289, 8.823622648901305, 7.785492237086586, 3.7612999751279688, 3.147084612606896, 2.867004238237588, 4.109855966589781, 1.5878659202927967, 1.2434282395590792, 0.7401658235927514, 0.0, 10.41245928452676, 8.141824059520264, 6.217141197795395, 4.763597760878389, 8.219711933179562, 4.013805933532623, 3.147084612606896, 2.6866428393771202, 3.892746118543293, 2.9412075496337686, 1.699328113166658, 0.6576372582512099, 0.0), # 145
(9.79914421275282, 7.181094370012356, 8.463669544574216, 8.782709502884963, 7.752507482520793, 3.750116359719226, 3.126992232366198, 2.8599896781934633, 4.099713555029442, 1.5795379176323916, 1.2372556991648298, 0.7366573770394019, 0.0, 10.367574997365741, 8.103231147433421, 6.186278495824149, 4.738613752897173, 8.199427110058885, 4.0039855494708485, 3.126992232366198, 2.67865454265659, 3.8762537412603963, 2.927569834294988, 1.6927339089148434, 0.6528267609102142, 0.0), # 146
(9.73839716926632, 7.126880014290443, 8.42966077182191, 8.740576010033621, 7.71858887892588, 3.7385272636861506, 3.1063563180827884, 2.8526484269357075, 4.0891799793755155, 1.570957993069544, 1.2308907213736073, 0.7330424428347111, 0.0, 10.321407081432142, 8.06346687118182, 6.154453606868036, 4.712873979208631, 8.178359958751031, 3.9937077977099906, 3.1063563180827884, 2.670376616918679, 3.85929443946294, 2.9135253366778744, 1.6859321543643822, 0.6478981831173131, 0.0), # 147
(9.676122838090825, 7.071300352334116, 8.394574836251083, 8.697174484802217, 7.6837123272964485, 3.726513364344937, 3.085149586873576, 2.8449572643631287, 4.078235519268811, 1.5621135890875346, 1.2243236066207965, 0.729316423943207, 0.0, 10.27391918441993, 8.022480663375276, 6.1216180331039824, 4.686340767262602, 8.156471038537623, 3.9829401701083804, 3.085149586873576, 2.6617952602463837, 3.8418561636482242, 2.899058161600739, 1.6789149672502168, 0.6428454865758287, 0.0), # 148
(9.612277580293695, 7.014288962879912, 8.358372326536443, 8.652457241645672, 7.647853728627096, 3.71405533901178, 3.0633447558554643, 2.8368929703745334, 4.0668604543501345, 1.5529921481696445, 1.2175446553417821, 0.7254747233294191, 0.0, 10.225074954023084, 7.980221956623609, 6.08772327670891, 4.658976444508932, 8.133720908700269, 3.971650158524347, 3.0633447558554643, 2.6528966707226997, 3.823926864313548, 2.8841524138818913, 1.671674465307289, 0.637662632989083, 0.0), # 149
(9.546817756942277, 6.955779424664377, 8.321013831352694, 8.606376595018924, 7.610988983912421, 3.7011338650028747, 3.04091454214536, 2.828432324868728, 4.0550350642603, 1.5435811127991534, 1.2105441679719486, 0.7215127439578762, 0.0, 10.174838037935576, 7.936640183536638, 6.0527208398597425, 4.630743338397459, 8.1100701285206, 3.95980525481622, 3.04091454214536, 2.6436670464306244, 3.8054944919562104, 2.8687921983396416, 1.6642027662705388, 0.632343584060398, 0.0), # 150
(9.47969972910393, 6.895705316424048, 8.282459939374542, 8.558884859376896, 7.573093994147021, 3.6877296196344136, 3.01783166286017, 2.8195521077445216, 4.042739628640115, 1.5338679254593437, 1.203312444946681, 0.7174258887931072, 0.0, 10.123172083851381, 7.891684776724178, 6.016562224733405, 4.601603776378029, 8.08547925728023, 3.9473729508423303, 3.01783166286017, 2.6340925854531525, 3.7865469970735104, 2.8529616197922993, 1.6564919878749085, 0.6268823014930954, 0.0), # 151
(9.41087985784601, 6.83400021689547, 8.242671239276701, 8.509934349174525, 7.534144660325495, 3.6738232802225945, 2.9940688351167988, 2.8102290989007206, 4.029954427130388, 1.5238400286334952, 1.1958397867013644, 0.713209560799641, 0.0, 10.070040739464476, 7.84530516879605, 5.979198933506821, 4.5715200859004845, 8.059908854260776, 3.9343207384610093, 2.9940688351167988, 2.6241594858732817, 3.7670723301627476, 2.836644783058176, 1.6485342478553402, 0.6212727469904974, 0.0), # 152
(9.340314504235872, 6.770597704815181, 8.201608319733868, 8.459477378866739, 7.4941168834424445, 3.659395524083611, 2.9695987760321514, 2.800440078236131, 4.016659739371929, 1.513484864804889, 1.1881164936713833, 0.7088591629420063, 0.0, 10.015407652468832, 7.797450792362069, 5.940582468356916, 4.5404545944146655, 8.033319478743858, 3.9206161095305836, 2.9695987760321514, 2.6138539457740078, 3.7470584417212223, 2.81982579295558, 1.6403216639467737, 0.6155088822559257, 0.0), # 153
(9.267960029340873, 6.705431358919725, 8.159231769420758, 8.407466262908468, 7.4529865644924636, 3.644427028533658, 2.944394202723137, 2.7901618256495615, 4.002835845005546, 1.5027898764568062, 1.1801328662921224, 0.7043700981847325, 0.0, 9.959236470558428, 7.748071080032056, 5.900664331460612, 4.508369629370417, 8.005671690011091, 3.9062265559093863, 2.944394202723137, 2.603162163238327, 3.7264932822462318, 2.802488754302823, 1.631846353884152, 0.6095846689927024, 0.0), # 154
(9.193772794228362, 6.638434757945644, 8.115502177012075, 8.35385331575464, 7.4107296044701565, 3.62889847088893, 2.9184278323066564, 2.779371121039818, 3.988463023672051, 1.4917425060725265, 1.1718792049989668, 0.6997377694923482, 0.0, 9.901490841427231, 7.6971154644158295, 5.859396024994833, 4.4752275182175785, 7.976926047344102, 3.8911195694557454, 2.9184278323066564, 2.5920703363492357, 3.7053648022350782, 2.7846177719182137, 1.6231004354024152, 0.6034940689041496, 0.0), # 155
(9.117709159965697, 6.569541480629476, 8.070380131182526, 8.298590851860187, 7.367321904370117, 3.612790528465623, 2.8916723818996197, 2.7680447443057092, 3.9735215550122502, 1.480330196135332, 1.163345810227301, 0.6949575798293822, 0.0, 9.842134412769221, 7.644533378123204, 5.816729051136504, 4.440990588405995, 7.9470431100245005, 3.875262642027993, 2.8916723818996197, 2.5805646631897305, 3.6836609521850585, 2.766196950620063, 1.6140760262365055, 0.5972310436935888, 0.0), # 156
(9.039725487620235, 6.498685105707764, 8.023826220606818, 8.241631185680044, 7.322739365186948, 3.59608387857993, 2.864100568618931, 2.756159475346041, 3.957991718666955, 1.4685403891285025, 1.1545229824125098, 0.6900249321603636, 0.0, 9.781130832278372, 7.590274253763999, 5.772614912062549, 4.405621167385506, 7.91598343733391, 3.8586232654844577, 2.864100568618931, 2.568631341842807, 3.661369682593474, 2.7472103952266815, 1.6047652441213638, 0.5907895550643424, 0.0), # 157
(8.957617135686286, 6.424498432849483, 7.973591953902356, 8.180792623486118, 7.274944884696797, 3.5777171334219773, 2.8350640325567142, 2.742898476174686, 3.9406648366396384, 1.4560097748873433, 1.1451191505077887, 0.6847599564194339, 0.0, 9.715783031298415, 7.532359520613772, 5.7255957525389425, 4.368029324662029, 7.881329673279277, 3.840057866644561, 2.8350640325567142, 2.555512238158555, 3.6374724423483986, 2.7269308744953733, 1.5947183907804712, 0.5840453120772259, 0.0), # 158
(8.858744120374082, 6.3393718515594255, 7.906737818402987, 8.103579442909608, 7.212153047825302, 3.551582753604972, 2.8009276580314295, 2.7236067663821912, 3.9145709044888575, 1.4406842982296237, 1.133483387123799, 0.6781362523683109, 0.0, 9.630513176304232, 7.459498776051419, 5.667416935618994, 4.322052894688871, 7.829141808977715, 3.813049472935068, 2.8009276580314295, 2.5368448240035515, 3.606076523912651, 2.7011931476365363, 1.5813475636805976, 0.5763065319599479, 0.0), # 159
(8.741846513885172, 6.242606401394785, 7.821920957955889, 8.008719759367974, 7.133136105077435, 3.517038907233379, 2.7613462490302703, 2.6977995947636733, 3.8789700908914604, 1.4223616955588683, 1.119451901721908, 0.6700501948887847, 0.0, 9.523704730672296, 7.370552143776631, 5.59725950860954, 4.267085086676604, 7.757940181782921, 3.7769194326691427, 2.7613462490302703, 2.512170648023842, 3.5665680525387176, 2.669573253122658, 1.5643841915911778, 0.5675096728540715, 0.0), # 160
(8.607866465503152, 6.134832954888515, 7.7200469719103095, 7.897115253381055, 7.038714499425689, 3.4745040690992197, 2.716608867604126, 2.66580026655489, 3.8343319067996067, 1.4011974579512814, 1.1031483309199415, 0.6605767468907572, 0.0, 9.396448853782916, 7.266344215798328, 5.515741654599707, 4.203592373853843, 7.668663813599213, 3.7321203731768464, 2.716608867604126, 2.481788620785157, 3.5193572497128445, 2.632371751127019, 1.5440093943820619, 0.557712086808047, 0.0), # 161
(8.457746124511628, 6.016682384573562, 7.602021459615496, 7.769667605468694, 6.929708673842563, 3.424396713994519, 2.6670045758038854, 2.627932086991601, 3.781125863165454, 1.3773470764830695, 1.0846963113357242, 0.6497908712841294, 0.0, 9.2498367050164, 7.147699584125422, 5.42348155667862, 4.132041229449208, 7.562251726330908, 3.6791049217882414, 2.6670045758038854, 2.4459976528532277, 3.4648543369212814, 2.5898892018228983, 1.5204042919230993, 0.5469711258703239, 0.0), # 162
(8.292427640194196, 5.888785562982875, 7.468750020420702, 7.6272784961507405, 6.806939071300549, 3.367135316711301, 2.61282243568044, 2.5845183613095624, 3.719821470941162, 1.3509660422304377, 1.0642194795870819, 0.6377675309788032, 0.0, 9.084959443753055, 7.015442840766835, 5.321097397935408, 4.052898126691312, 7.439642941882324, 3.6183257058333878, 2.61282243568044, 2.405096654793786, 3.4034695356502747, 2.5424261653835805, 1.4937500040841403, 0.5353441420893524, 0.0), # 163
(8.11285316183446, 5.751773362649402, 7.321138253675176, 7.470849605947036, 6.67122613477215, 3.3031383520415907, 2.5543515092846794, 2.5358823947445344, 3.650888241078889, 1.3222098462695906, 1.0418414722918394, 0.6245816888846804, 0.0, 8.902908229373192, 6.870398577731482, 5.209207361459196, 3.966629538808771, 7.301776482157778, 3.550235352642348, 2.5543515092846794, 2.3593845371725646, 3.335613067386075, 2.4902832019823458, 1.4642276507350354, 0.5228884875135821, 0.0), # 164
(7.9199648387160195, 5.606276656106095, 7.160091758728169, 7.301282615377426, 6.5233903072298585, 3.2328242947774104, 2.491880858667493, 2.482347492532273, 3.5747956845307916, 1.2912339796767343, 1.0176859260678224, 0.610308307911662, 0.0, 8.704774221257123, 6.713391387028281, 5.088429630339111, 3.873701939030202, 7.149591369061583, 3.4752864895451823, 2.491880858667493, 2.309160210555293, 3.2616951536149292, 2.433760871792476, 1.432018351745634, 0.5096615141914632, 0.0), # 165
(7.714704820122476, 5.452926315885899, 6.9865161349289275, 7.119479204961751, 6.364252031646171, 3.156611619710786, 2.4256995458797714, 2.4242369599085385, 3.492013312249029, 1.2581939335280738, 0.9918764775328559, 0.5950223509696502, 0.0, 8.491648578785155, 6.545245860666151, 4.959382387664279, 3.7745818005842207, 6.984026624498058, 3.393931743871954, 2.4256995458797714, 2.254722585507704, 3.1821260158230853, 2.373159734987251, 1.3973032269857855, 0.4957205741714455, 0.0), # 166
(7.498015255337426, 5.292353214521765, 6.801316981626705, 6.926341055219858, 6.194631750993583, 3.074918801633741, 2.3560966329724047, 2.361874102109088, 3.403010635185759, 1.2232451988998143, 0.9645367633047655, 0.5787987809685459, 0.0, 8.264622461337595, 6.366786590654004, 4.822683816523827, 3.669735596699442, 6.806021270371518, 3.3066237429527234, 2.3560966329724047, 2.196370572595529, 3.0973158754967915, 2.308780351739953, 1.360263396325341, 0.4811230195019787, 0.0), # 167
(7.2708382936444735, 5.125188224546641, 6.605399898170748, 6.722769846671591, 6.015349908244593, 2.9881643153382993, 2.2833611819962822, 2.2955822243696797, 3.308257164293142, 1.1865432668681617, 0.9357904200013762, 0.5617125608182512, 0.0, 8.024787028294753, 6.178838169000762, 4.678952100006881, 3.559629800604484, 6.616514328586284, 3.2138151141175517, 2.2833611819962822, 2.1344030823844995, 3.0076749541222965, 2.2409232822238643, 1.3210799796341497, 0.46592620223151293, 0.0), # 168
(7.034116084327218, 4.952062218493477, 6.399670483910309, 6.509667259836794, 5.827226946371695, 2.8967666356164865, 2.2077822550022947, 2.2256846319260726, 3.2082224105233346, 1.1482436285093212, 0.9057610842405137, 0.5438386534286673, 0.0, 7.773233439036942, 5.982225187715339, 4.528805421202568, 3.444730885527963, 6.416444821046669, 3.1159584846965016, 2.2077822550022947, 2.0691190254403473, 2.9136134731858476, 2.1698890866122653, 1.2799340967820618, 0.450187474408498, 0.0), # 169
(6.78879077666926, 4.773606068895221, 6.185034338194635, 6.2879349752353075, 5.631083308347386, 2.8011442372603246, 2.1296489140413315, 2.1525046300140236, 3.103375884828495, 1.1085017748994974, 0.8745723926400033, 0.525252021709696, 0.0, 7.5110528529444665, 5.777772238806654, 4.372861963200016, 3.325505324698492, 6.20675176965699, 3.013506482019633, 2.1296489140413315, 2.0008173123288033, 2.815541654173693, 2.0959783250784363, 1.2370068676389272, 0.4339641880813838, 0.0), # 170
(6.5358045199542, 4.59045064828482, 5.962397060372978, 6.058474673386982, 5.427739437144163, 2.701715595061839, 2.049250221164283, 2.0763655238692915, 2.994187098160782, 1.0674731971148967, 0.8423479818176697, 0.5060276285712387, 0.0, 7.239336429397638, 5.566303914283624, 4.211739909088348, 3.2024195913446896, 5.988374196321564, 2.906911733417008, 2.049250221164283, 1.9297968536155994, 2.7138697185720817, 2.019491557795661, 1.1924794120745956, 0.4173136952986201, 0.0), # 171
(6.276099463465638, 4.403226829195226, 5.7326642497945866, 5.822188034811656, 5.218015775734522, 2.5988991838130535, 1.9668752384220392, 1.9975906187276353, 2.881125561472354, 1.025313386231724, 0.8092114883913387, 0.4862404369231972, 0.0, 6.959175327776763, 5.348644806155168, 4.046057441956694, 3.075940158695172, 5.762251122944708, 2.7966268662186895, 1.9668752384220392, 1.8563565598664666, 2.609007887867261, 1.9407293449372194, 1.1465328499589174, 0.40029334810865697, 0.0), # 172
(6.010617756487176, 4.212565484159386, 5.4967415058087115, 5.579976740029178, 5.002732767090961, 2.4931134783059927, 1.8828130278654898, 1.916503219824812, 2.7646607857153684, 0.9821778333261846, 0.7752865489788355, 0.4659654096754725, 0.0, 6.671660707462155, 5.125619506430197, 3.8764327448941778, 2.9465334999785533, 5.529321571430737, 2.6831045077547366, 1.8828130278654898, 1.7807953416471376, 2.5013663835454807, 1.859992246676393, 1.0993483011617424, 0.38296049855994424, 0.0), # 173
(5.740301548302412, 4.019097485710249, 5.2555344277646014, 5.332742469559387, 4.782710854185972, 2.3847769533326795, 1.7973526515455251, 1.8334266323965802, 2.645262281841985, 0.9382220294744842, 0.7406968001979856, 0.44527750973796687, 0.0, 6.37788372783412, 4.898052607117634, 3.7034840009899272, 2.814666088423452, 5.29052456368397, 2.5667972853552126, 1.7973526515455251, 1.7034121095233423, 2.391355427092986, 1.7775808231864625, 1.0511068855529204, 0.3653724987009318, 0.0), # 174
(5.466092988194946, 3.823453706380764, 5.009948615011508, 5.08138690392213, 4.558770479992055, 2.2743080836851397, 1.7107831715130346, 1.748684161678698, 2.5233995608043616, 0.8936014657528275, 0.7055658786666139, 0.4242517000205815, 0.0, 6.078935548272969, 4.666768700226395, 3.5278293933330693, 2.680804397258482, 5.046799121608723, 2.4481578263501773, 1.7107831715130346, 1.6245057740608142, 2.2793852399960275, 1.6937956346407106, 1.0019897230023018, 0.3475867005800695, 0.0), # 175
(5.188934225448382, 3.62626501870388, 4.760889666898678, 4.8268117236372525, 4.331732087481704, 2.1621253441553967, 1.6233936498189088, 1.6625991129069244, 2.3995421335546565, 0.8484716332374204, 0.670017421002546, 0.4029629434332179, 0.0, 5.7759073281590085, 4.432592377765396, 3.35008710501273, 2.5454148997122603, 4.799084267109313, 2.327638758069694, 1.6233936498189088, 1.5443752458252833, 2.165866043740852, 1.6089372412124179, 0.9521779333797357, 0.3296604562458073, 0.0), # 176
(4.909767409346319, 3.4281622952125463, 4.5092631827753635, 4.569918609224595, 4.102416119627418, 2.0486472095354746, 1.5354731485140374, 1.5754947913170163, 2.2741595110450277, 0.8029880230044676, 0.6341750638236071, 0.3814862028857779, 0.0, 5.4698902268725496, 4.196348231743556, 3.1708753191180357, 2.408964069013402, 4.548319022090055, 2.2056927078438227, 1.5354731485140374, 1.4633194353824817, 2.051208059813709, 1.5233062030748654, 0.9018526365550728, 0.31165111774659515, 0.0), # 177
(4.629534689172356, 3.2297764084397107, 4.255974761990814, 4.311609241204004, 3.8716430194016906, 1.9342921546173981, 1.4473107296493104, 1.4876945021447328, 2.147721204227634, 0.7573061261301752, 0.5981624437476226, 0.3598964412881627, 0.0, 5.161975403793902, 3.958860854169789, 2.9908122187381125, 2.271918378390525, 4.295442408455268, 2.082772303002626, 1.4473107296493104, 1.3816372532981414, 1.9358215097008453, 1.437203080401335, 0.8511949523981628, 0.29361603713088286, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 132
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 133
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 134
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 135
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 136
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 137
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 138
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 139
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 140
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 141
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 142
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 143
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 144
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 145
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 146
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 147
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 148
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 149
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 150
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 151
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 152
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 153
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 154
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 155
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 156
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 157
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 158
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 159
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 160
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
36, # 1
)
|
distancia = float(input('Qual é a distância da sua viagem? '))
print('Você está prestes a começar uma viagem de {} Km.'.format(distancia))
preco = distancia * 0.50 if distancia <= 200 else distancia * 0.45
print('E o preço da sua passagem será de R$ {:.2f}.'.format(preco))
|
# -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
AIRFLOW_VAR_NAME_FORMAT_MAPPING = {
'AIRFLOW_CONTEXT_DAG_ID': {'default': 'airflow.ctx.dag_id',
'env_var_format': 'AIRFLOW_CTX_DAG_ID'},
'AIRFLOW_CONTEXT_TASK_ID': {'default': 'airflow.ctx.task_id',
'env_var_format': 'AIRFLOW_CTX_TASK_ID'},
'AIRFLOW_CONTEXT_EXECUTION_DATE': {'default': 'airflow.ctx.execution_date',
'env_var_format': 'AIRFLOW_CTX_EXECUTION_DATE'},
'AIRFLOW_CONTEXT_DAG_RUN_ID': {'default': 'airflow.ctx.dag_run_id',
'env_var_format': 'AIRFLOW_CTX_DAG_RUN_ID'}
}
def context_to_airflow_vars(context, in_env_var_format=False):
"""
Given a context, this function provides a dictionary of values that can be used to
externally reconstruct relations between dags, dag_runs, tasks and task_instances.
Default to abc.def.ghi format and can be made to ABC_DEF_GHI format if
in_env_var_format is set to True.
:param context: The context for the task_instance of interest.
:type context: dict
:param in_env_var_format: If returned vars should be in ABC_DEF_GHI format.
:type in_env_var_format: bool
:return: task_instance context as dict.
"""
params = dict()
if in_env_var_format:
name_format = 'env_var_format'
else:
name_format = 'default'
task_instance = context.get('task_instance')
if task_instance and task_instance.dag_id:
params[AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_DAG_ID'][
name_format]] = task_instance.dag_id
if task_instance and task_instance.task_id:
params[AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_TASK_ID'][
name_format]] = task_instance.task_id
if task_instance and task_instance.execution_date:
params[
AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_EXECUTION_DATE'][
name_format]] = task_instance.execution_date.isoformat()
dag_run = context.get('dag_run')
if dag_run and dag_run.run_id:
params[AIRFLOW_VAR_NAME_FORMAT_MAPPING['AIRFLOW_CONTEXT_DAG_RUN_ID'][
name_format]] = dag_run.run_id
return params
|
x = int(input())
y = float(input())
gasto = x / y
print('{:.3f} km/l'.format(gasto))
|
# Created by MechAviv
# ID :: [931050000]
# Hidden Street : Extraction Room 1
sm.curNodeEventEnd(True)
sm.setTemporarySkillSet(0)
sm.setInGameDirectionMode(True, True, False, False)
sm.setStandAloneMode(True)
def failMessage(crack):
sm.chatScript("Tap the Control Key repeatedly to break the wall.")
sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0)
sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/" + str(crack), 6600, 0, 0, 1, 0, False, 0)
if not "1" in sm.getQRValue(23206):
sm.createQuestWithQRValue(23206, "1")
sm.levelUntil(10)
sm.sendDelay(3000)
sm.showFieldEffect("demonSlayer/text12", 0)
sm.sendDelay(5000)
sm.forcedInput(1)
sm.sendDelay(10)
sm.forcedInput(0)
sm.setSpeakerID(2159311)
sm.removeEscapeButton()
sm.setPlayerAsSpeaker()
sm.setSpeakerType(3)
sm.sendNext("........")
sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/14", 2000, 130, 50, 10, 0, False, 0)
sm.sendDelay(2000)
sm.setSpeakerID(2159311)
sm.removeEscapeButton()
sm.setPlayerAsSpeaker()
sm.setSpeakerType(3)
sm.sendNext("(I think I hear something...)")
sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/15", 2000, -130, 50, 10, 0, False, 0)
sm.sendDelay(2000)
sm.setSpeakerID(2159311)
sm.removeEscapeButton()
sm.setPlayerAsSpeaker()
sm.setSpeakerType(3)
sm.sendNext("(Where am I? Am I still alive...?)")
sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/16", 2000, 130, 50, 10, 0, False, 0)
sm.sendDelay(2000)
sm.setSpeakerID(2159311)
sm.removeEscapeButton()
sm.setPlayerAsSpeaker()
sm.setSpeakerType(3)
sm.sendNext("(Ugh... My energy... Something is stealing my energy!)")
sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/17", 2000, -130, 50, 10, 0, False, 0)
sm.sendDelay(2000)
sm.setSpeakerID(2159311)
sm.removeEscapeButton()
sm.setPlayerAsSpeaker()
sm.setSpeakerType(3)
sm.sendNext("(I must escape before they drain all my power!)")
sm.setPatternInputCount(0)
sm.chatScript("Tap the Control Key repeatedly to break the wall.")
sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0)
while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7:
failMessage(0)
sm.setPatternInputCount(0)
sm.playSound("demonSlayer/punch", 100)
sm.playSound("demonSlayer/crackEgg", 100)
sm.chatScript("Tap the Control Key repeatedly to break the wall.")
sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0)
sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/0", 6600, 0, 0, 1, 0, False, 0)
while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7:
failMessage(0)
sm.setPatternInputCount(0)
sm.playSound("demonSlayer/punch", 100)
sm.playSound("demonSlayer/crackEgg", 100)
sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/7", 2000, 130, 100, 10, 0, False, 0)
sm.chatScript("Tap the Control Key repeatedly to break the wall.")
sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0)
sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/0", 6600, 0, 0, 1, 0, False, 0)
while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7:
failMessage(0)
sm.setPatternInputCount(0)
sm.playSound("demonSlayer/punch", 100)
sm.playSound("demonSlayer/crackEgg", 100)
sm.chatScript("Tap the Control Key repeatedly to break the wall.")
sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0)
sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/1", 6600, 0, 0, 1, 0, False, 0)
while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7:
failMessage(1)
sm.setPatternInputCount(0)
sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/0", 3600, 0, 0, 1, 0, False, 0)
sm.sendDelay(3000)
sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/1", 3600, 0, 0, 1, 0, False, 0)
sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg1/1", 2000, -130, 50, 10, 0, False, 0)
sm.playSound("demonSlayer/crackEgg", 100)
sm.sendDelay(1000)
sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/2", 9000, 0, 0, 1, 0, False, 0)
sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg1/2", 2000, 130, 50, 10, 0, False, 0)
sm.sendDelay(1000)
sm.playSound("demonSlayer/breakEgg", 100)
sm.showFieldEffect("demonSlayer/whiteOut", 0)
sm.warpInstanceIn(931050020, 0)
|
class HTTPError(Exception):
pass
class VersionSpecificationError(Exception):
pass
|
"""文字列基礎
数値文字関連の判定メソッド
isdecimal, isdigit, isnumericの結果の違い
[説明ページ]
https://tech.nkhn37.net/python-isxxxxx/#isdecimalisdigitisnumeric
"""
print("--- 半角数字('12345')")
print('12345'.isdecimal())
print('12345'.isdigit())
print('12345'.isnumeric())
print("--- 上付き数字、下付き数字('⁰₀')")
print('⁰₀'.isdecimal())
print('⁰₀'.isdigit())
print('⁰₀'.isnumeric())
print("--- 全角数字('１２３４５')")
print('１２３４５'.isdecimal())
print('１２３４５'.isdigit())
print('１２３４５'.isnumeric())
print("--- 漢数字('一弐参肆伍')")
print('一弐参肆伍'.isdecimal())
print('一弐参肆伍'.isdigit())
print('一弐参肆伍'.isnumeric())
print("--- ローマ数字('ⅠⅡⅢⅣⅤ')")
print('ⅠⅡⅢⅣⅤ'.isdecimal())
print('ⅠⅡⅢⅣⅤ'.isdigit())
print('ⅠⅡⅢⅣⅤ'.isnumeric())
|
# -*- coding: utf-8 -*-
'''
Splunk User State Module
.. versionadded:: 2016.3.0.
This state is used to ensure presence of users in splunk.
.. code-block:: yaml
ensure example test user 1:
splunk.present:
- name: 'Example TestUser1'
- email: [email protected]
'''
def __virtual__():
'''
Only load if the splunk module is available in __salt__
'''
return 'splunk' if 'splunk.list_users' in __salt__ else False
def present(email, profile="splunk", **kwargs):
'''
Ensure a user is present
.. code-block:: yaml
ensure example test user 1:
splunk.user_present:
- realname: 'Example TestUser1'
- name: 'exampleuser'
- email: '[email protected]'
- roles: ['user']
The following parameters are required:
email
This is the email of the user in splunk
'''
name = kwargs.get('name')
ret = {
'name': name,
'changes': {},
'result': None,
'comment': ''
}
target = __salt__['splunk.get_user'](email, profile=profile, user_details=True)
if not target:
if __opts__['test']:
ret['comment'] = 'User {0} will be created'.format(name)
return ret
# create the user
result = __salt__['splunk.create_user'](
email, profile=profile, **kwargs
)
if result:
ret['changes'].setdefault('old', None)
ret['changes'].setdefault('new', 'User {0} exists'.format(name))
ret['result'] = True
else:
ret['result'] = False
ret['comment'] = 'Failed to create {0}'.format(name)
return ret
else:
ret['comment'] = 'User {0} set to be updated.'.format(name)
if __opts__['test']:
ret['result'] = None
return ret
# found a user... updating
result = __salt__['splunk.update_user'](
email, profile, **kwargs
)
if isinstance(result, bool) and result:
# no update
ret['result'] = None
ret['comment'] = "No changes"
else:
diff = {}
for field in ['name', 'realname', 'roles', 'defaultApp', 'tz', 'capabilities']:
if field == 'roles':
diff['roles'] = list(set(target.get(field, [])).symmetric_difference(set(result.get(field, []))))
elif target.get(field) != result.get(field):
diff[field] = result.get(field)
newvalues = result
ret['result'] = True
ret['changes']['diff'] = diff
ret['changes']['old'] = target
ret['changes']['new'] = newvalues
return ret
def absent(email, profile="splunk", **kwargs):
'''
Ensure a splunk user is absent
.. code-block:: yaml
ensure example test user 1:
splunk.absent:
- email: '[email protected]'
- name: 'exampleuser'
The following parameters are required:
email
This is the email of the user in splunk
name
This is the splunk username used to identify the user.
'''
user_identity = kwargs.get('name')
ret = {
'name': user_identity,
'changes': {},
'result': None,
'comment': 'User {0} is absent.'.format(user_identity)
}
target = __salt__['splunk.get_user'](email, profile=profile)
if not target:
ret['comment'] = 'User {0} does not exist'.format(user_identity)
ret['result'] = True
return ret
if __opts__['test']:
ret['comment'] = "User {0} is all set to be deleted".format(user_identity)
ret['result'] = None
return ret
result = __salt__['splunk.delete_user'](email, profile=profile)
if result:
ret['comment'] = 'Deleted user {0}'.format(user_identity)
ret['changes'].setdefault('old', 'User {0} exists'.format(user_identity))
ret['changes'].setdefault('new', 'User {0} deleted'.format(user_identity))
ret['result'] = True
else:
ret['comment'] = 'Failed to delete {0}'.format(user_identity)
ret['result'] = False
return ret
|
#!/usr/bin/env python
#fn: copy.py
# write specifi contents of alignment_py.py to blast_py.py
INPUT = open('alignment_py.py','r')
OUTPUT = open('blast_py.py','a')
lnum = 0
for line in INPUT:
lnum += 1
line = line.strip('\n')
if lnum > 6 and lnum < 138:
OUTPUT.write(line+'\n')
|
# ============================================================================
# 第三章 暖冷房負荷と外皮性能
# 第三節 熱貫流率及び線熱貫流率
# Ver.17（住宅・住戸の外皮性能の計算プログラム Ver.3.0.0～）
# ============================================================================
## 外皮の詳細計算
### 1 概要
"""
主に、
3章2節8.1 外皮平均熱貫流率(section3_2_8.py>calc_U_A)……①
3章2節8.2 暖房期の平均日射熱取得率(section3_2_8.py>calc_eta_A_H)……②
3章2節8.3 冷房期の外皮平均熱貫流率(section3_2_8.py>calc_eta_A_C)……③
を求めたい。
部位ごとの熱貫流率・熱橋の線熱貫流率の値(①の計算に必要)は3章3節、
日射熱取得率の値(②③の計算に必要)は3章4節を参照して計算する。
"""
### 2 入力
"""
入力XML(共有：XML定義表20200901.XLSM参照)から辞書を作成し、①～③の計算を行う関数に引数として渡す。
辞書作成の基本方針は、
A. XMLの属性は、ノード名をKey、値をValueとして辞書に入れる
B. XMLの子要素は、ノード名をKey、子要素をA・Bのルールに則って辞書にしたものをValueとして辞書にいれる。
同じノード名の子要素が 繰り返される場合、同様に辞書化したもののリストをValueとする。
とする。
従って、辞書は下記の 4データ構造 に示す階層構造を持つ。
"""
#### 2.1 XML→辞書変換例
"""
XML
<Envelope>
<Region>○○</Region>
<Wall Name="部位1" Area="○○" ... >
<GeneralPart Type="○○" Area="○○" ... >
<SolidLayer Thickness="○○" ... />
<SolidLayer Thickness="○○" ... />
<AirLayer Type="○○" ... />
</GeneralPart>
<GeneralPart ... >
</GeneralPart>
</Wall>
<Wall Name="部位2" Area="○○" ...>
<GeneralPart Type="○○" Area="○○" ... >
...
</GeneralPart>
...
</Wall>
</Envelope>
↓↓↓
Python
{
'Region':○○, ← 属性のノード名をKey、値をValueとする
'Wall':[ ← 子要素のノード名をKeyとする・繰り返しがあるためValueはリスト
{
'Name':'部位1', 'Area':○○, ... ,
'GeneralPart':[
{
'Type':'○○',
'Area':'○○',
'SolidLayer':[
{'Thickness':'○○', ... },
{'Thickness':'○○', ... }
],
'AirLayer':[
{'Type':'○○', ... }
],
},
{
...
}
] ← GeneralPart終わり
...
}, ← 部位1終わり
{
'Name':'部位2','Area':○○, ... ,
'GeneralPart':[ { ... } ],
...
}
] ← Wall終わり
}
"""
#### 2.2 繰り返しのある要素について
"""
繰り返しのある要素(部分や層)の要素数上限は暫定で設定した。
部分に含まれる層のリストは先頭要素を一番外側(外気側)とみなしている。
"""
#### 2.3 未入力項目の扱いについて
"""
本プログラムでは基本的に、未入力項目はKey/Valueペアがないとみなす。(⇒XML定義表中の全ての項目が辞書のKeyになっている必要はない。)
一部、未入力はValueがNoneになるという前提で処理する(section3_3_5.py>calc_R_i_k_l等)例外もあり。
"""
### 3 出力
"""
①～③の計算を行う関数は、入力XMLを辞書化したものに計算結果を付加したものを返す。
計算結果の階層構造内での位置は下記の 4データ構造 に示す。
例
{
'Region':○○,
'Wall':[
{ 'Name':'部位1', 'Area':○○, ... , }
]
}
↓↓ 計算後 ↓↓
{
'Region':○○,
'Wall':[
{ 'Name':'部位1', 'Area':○○, ... ,
'GeneralPart_output':[...], ← 付加された計算結果
'U_i': ○○, ← 付加された計算結果
}
]
}
"""
#### 3.1 出力する計算結果
"""
仕様が決まっていないため、旧バージョンのXML定義表に載っていたものを参考に暫定的に設定した。
部位ごとの熱貫流率・線熱貫流率の他、窓を除く外皮等の熱貫流率の計算では、部分や層ごとの熱貫流率や熱抵抗の計算結果も含めた。
想定外の計算結果(途中でエラー、計算対象外など)は空欄や'ERROR'・'NaN'など現時点では統一できていない。
"""
### 4 データ構造 #### XML定義表20200901より作成 ####
"""
''で囲われていないもの(Envelope/Wall_direct/Wall_accurate等)は
辞書の形式(必要最小限のkey・valueペアの集合に名前を付けたもの・情報の整理が目的)を表す。
実際に動作させる際は、XML定義表中の全ての要素が含まれる入力データを関数に渡す。
"""
#### 4.1 辞書のKey名について
"""
XML定義表のノード名由来のもの(Name,Window,GammaC等)は大文字始まり、
プログラム中での処理の都合で追加したもの(part_Name,layer_name等)は小文字始まりとしている。
"""
######## 全体 ########
"""
Envelope---Envelope要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Name': 建物名称
|
|--'Description': 所在地
|
|--'Version': バージョン
|
|--'Region': 地域区分
|
|--'Wall': 窓を除く外皮等のリスト
| 形式: Wall_direct Wall_accurate Wall_simple Wall_rc Wall_steel
|
|--'Window': 窓のリスト
|
|--'Door': ドアのリスト
|
|--'Foundation': 基礎等のリスト
|
|--'LinearHeatBridge': 熱橋のリスト
形式: LinearHeatBridge_wood LinearHeatBridge_rc LinearHeatBridge_steel
"""
## 出力
"""
|--'U_A': 外皮平均熱貫流率
|--'eta_A_H': 暖房期の平均日射熱取得率
|--'eta_A_C': 冷房期の平均日射熱取得率
"""
######## 窓を除く外皮等 ########
###### 木造 ######
#### 直接指定 ####
"""
Wall_direct---Wall要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Name': 名前
|--'Area': 面積
|--'Method': 計算方法 'Direct'
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
|--'SolarGain': 日射取得率
| 'Yes'(日射熱取得が発生する部位) または 'No'(日射熱取得が発生しない部分)
|--'Direction': 方位
| 'Top'(屋根上面) または 'N'(北) または 'NE'(北東) または 'E'(東) または 'SE'(南東) または
| 'S'(南) または 'SW'(南西) または 'W'(西) または 'NW'(北西) または 'Bottom'(下面)
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
|--'UValue': 熱貫流率
|--'UValueInfo': 熱貫流率の入力根拠
"""
## 出力
"""
|--'U_i': 熱貫流率
"""
#### 詳細計算法 ####
"""
Wall_accurate---Wall要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Name': 名前
|--'Area': 面積
|--'Method': 計算方法 'Accurate'
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
|--'Direction': 方位
| 'Top'(屋根上面) または 'N'(北) または 'NE'(北東) または 'E'(東) または 'SE'(南東) または
| 'S'(南) または 'SW'(南西) または 'W'(西) または 'NW'(北西) または 'Bottom'(下面)
|--'SolarGain': 日射取得率
| 'Yes'(日射熱取得が発生する部位) または 'No'(日射熱取得が発生しない部分)
|--'Type': 部位の種類
| 'Roof'(屋根) または 'Ceiling'(天井) または 'ExternalWall'(外壁) または 'Floor'(床) または
| 'BoundaryWall'(界壁) または 'BoundaryCeiling'(上階側界床) または 'BoundaryFloor'(下階側界床)
|--'Outside': 室外側は外気か？
| 'Yes'(外気) または 'No'(外気以外（通気層、小屋裏、床裏等）)
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
|--'GeneralPart': 含まれる一般部分のリスト＝GeneralPart_accurateの形式を持つ辞書のリスト
| 例：'GeneralPart':[GeneralPart1,GeneralPart2,...,GeneralPart5]
|
|
GeneralPart_accurate---GeneralPart要素のノード名をkey、値をvalueとして持つ辞書
|
|--'part_Name':'GeneralPart'
|--'Area': 面積
|--'layer': 含まれる層のリスト＝SolidLayer_accurateまたはAirLayer_accurateの形式を持つ辞書のリスト
| [layer1,layer2,...] 先頭要素が一番外側(外気側)とする ※変更の可能性あり
|
SolidLayer_accurate---SolidLayer要素のノード名をkey、値をvalueとして持つ辞書
|
|--'layer_Name': 'SolidLayer'
|--'LambdaValue' : 熱伝導率
|--'Thickness': 厚さ
|--'Material': 素材名
|--'MaterialInfo': 熱伝導率の根拠
|--'ExternalReduction': 外張断熱の熱抵抗の低減
| 'Yes'(外張断熱で１層張りの下地併用) または 'No'(外張断熱で１層張りの下地併用ではない)
|
|
AirLayer_accurate--AirLayer要素のノード名をkey、値をvalueとして持つ辞書
|
|--'layer_Name': 'AirLayer'
|--'Type': 空気層の種類
'AirTight'(面材で密閉された空気層) または 'OnSiteNonConnected'(他の空間と連通していない空気層) または
'OnSiteConnected'(他の空間と連通している空気層)
"""
## 出力
"""
|
|--'GeneralPart_output': 含まれる一般部分の計算結果リスト＝GeneralPart_accurate_outputの形式を持つ辞書のリスト
| | [GeneralPart1,GeneralPart2,...,GeneralPart5]
| |
| |
| GeneralPart_accurate_output--暫定の形式
| |
| |--'R1': 層1の熱抵抗
| |--'R2': 層2の熱抵抗
| |--'R3': 層3の熱抵抗
| |--'R5': 層4の熱抵抗
| |--'R6': 層5の熱抵抗
| |--'R7': 層6の熱抵抗
| |--'Rse': 外気側表面熱伝達抵抗
| |--'Rsi': 室内側表面熱伝達抵抗
| |--'Ru_n': 部分の熱抵抗の合計
| |--'Un': 部分の熱貫流率
| |--'a': 面積比率
|
|--'U_i': 熱貫流率
"""
#### 簡略計算法 ####
"""
Wall_simple---Wall要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Name': 名前
|--'Area': 面積
|--'Method': 計算方法 'Simple'
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
|--'Direction': 方位
| 'Top'(屋根上面) または 'N'(北) または 'NE'(北東) または 'E'(東) または 'SE'(南東) または
| 'S'(南) または 'SW'(南西) または 'W'(西) または 'NW'(北西) または 'Bottom'(下面)
|--'SolarGain': 日射取得率
| 'Yes'(日射熱取得が発生する部位) または 'No'(日射熱取得が発生しない部分)
|--'Type': 部位の種類
| 'Roof'(屋根) または 'Ceiling'(天井) または 'ExternalWall'(外壁) または 'Floor'(床) または
| 'BoundaryWall'(界壁) または 'BoundaryCeiling'(上階側界床) または 'BoundaryFloor'(下階側界床)
|--'ConstructionMethod': 工法の種類
| 'FrameFloorBeam'(軸組構法（床梁工法）) または
| 'FrameSleeper'(軸組構法（束立大引工法）) または
| 'FrameRigidFloor'(軸組構法（剛床工法）) または
| 'FrameSameLevel'(軸組構法（床梁土台同面工法）) または
| 'FrameWall'(軸組構法（外壁）) または
| 'FrameCeiling'(軸組構法（天井）) または
| 'FrameRoof'(軸組構法（屋根）) または
| 'WallFloor'(枠組構法（床）) または
| 'WallWall'(枠組構法（外壁）) または
| 'WallRoof'(枠組構法（屋根）)
|--'InsulationPlace': 断熱箇所
| 'FloorJoistInterval'(根太間) または 'FloorBeamInterval'(大引間) または
| 'FloorJoistBeamInterval'(根太間＋大引間) または 'PillarInterval'(柱・間柱間) または
| 'StudInterval'(たて枠間) または
| 'RoofBeamInterval'(桁・梁間) または 'RafterInterval'(たるき間)または
|--'Outside': 室外側は外気か？
| 'Yes'(外気) または 'No'(外気以外（通気層、小屋裏、床裏等）)
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
|--'GeneralPart': 含まれる一般部分のリスト＝GeneralPart_simpleの形式を持つ辞書のリスト
| | 例：'GeneralPart':[GeneralPart1] (要素数は1を想定)要素数が2以上の場合でも一番先頭要素のみを計算に使用する
| |
| |
| GeneralPart_simple---GeneralPart要素のノード名をkey、値をvalueとして持つ辞書
| |
| |--'part_Name':'GeneralPart'
| |--'layer': 含まれる層のリスト＝SolidLayer_simpleまたはAirLayer_simpleの形式を持つ辞書のリスト
| | [layer1,layer2,...] 先頭要素が一番外側(外気側)とする ※変更の可能性あり
| |
| SolidLayer_simple---SolidLayer要素のノード名をkey、値をvalueとして持つ辞書
| |
| |--'layer_Name': 'SolidLayer'
| |--'LambdaValue' : 熱伝導率
| |--'Thickness': 厚さ
| |--'Material': 素材名
| |--'MaterialInfo': 熱伝導率の根拠
| |--'ExternalReduction': 外張断熱の熱抵抗の低減
| | 'Yes'(外張断熱で１層張りの下地併用) または 'No'(外張断熱で１層張りの下地併用ではない)
| |
| |
| AirLayer_simple--AirLayer要素のノード名をkey、値をvalueとして持つ辞書
| |
| |--'layer_Name': 'AirLayer'
| |--'Type': 空気層の種類
| 'AirTight'(面材で密閉された空気層) または 'OnSiteNonConnected'(他の空間と連通していない空気層) または
| 'OnSiteConnected'(他の空間と連通している空気層)
|
|
|--'MixPart': 含まれる断熱部＋熱橋部のリスト＝MixPart_simpleの形式を持つ辞書のリスト
| | ※該当する部分がない場合は空リスト
| | 例：'MixPart':[]/[MixPart1]/[MixPart1, MixPart2] (要素数は0or1or2を想定)
| | 要素が3つ以上ある場合は先頭から順に計算に使用する
| | 要素数は3章3節5.1.1(2)の表3-6における断熱部＋熱橋部の面積比率のとり方で決まる
| | 例えば、軸組構法の束立大引工法において根太間及び大引間に断熱する場合の床
| | (つまり、'Type':'Floor','ConstructionMethod':'FrameSleeper','InsulationPlace':'FloorJoistBeamInterval' の場合)
| | の場合、表3-2が参照されるため要素数は2。
| |
| |
| MixPart_simple--MixPart要素のノード名をkey、値をvalueとして持つ辞書
| |
| |--'part_Name':'MixPart'
| |--'Type': 種類
| | 'JoistIntervalAndBeam'(根太間断熱材＋大引材等)
| | 'JoistAndBeamInterval'(根太材＋大引間断熱材)
| |--'layer': 含まれる層のリスト＝SolidLayer_simpleまたはAirLayer_simpleの形式を持つ辞書のリスト
| | [layer1,layer2,...] 先頭要素が一番外側(外気側)とする ※変更の可能性あり
| |
| SolidLayer_simple---SolidLayer要素のノード名をkey、値をvalueとして持つ辞書
| |
| |--'layer_Name': 'SolidLayer'
| |--'LambdaValue' : 熱伝導率
| |--'Thickness': 厚さ
| |--'Material': 素材名
| |--'MaterialInfo': 熱伝導率の根拠
| |--'ExternalReduction': 外張断熱の熱抵抗の低減
| | 'Yes'(外張断熱で１層張りの下地併用) または 'No'(外張断熱で１層張りの下地併用ではない)
| |
| |
| AirLayer_simple--AirLayer要素のノード名をkey、値をvalueとして持つ辞書
| |
| |--'layer_Name': 'AirLayer'
| |--'Type': 空気層の種類
| 'AirTight'(面材で密閉された空気層) または 'OnSiteNonConnected'(他の空間と連通していない空気層) または
| 'OnSiteConnected'(他の空間と連通している空気層)
|
|
|--'HeatBridge': 含まれる熱橋部分（軸組部分）のリスト＝HeatBridge_simpleの形式を持つ辞書のリスト
| 例：'HeatBridge':[HeatBridge1] (要素数は1を想定)要素数が2以上の場合でも一番先頭要素のみを計算に使用する
|
|
HeatBridge_simple--HeatBridge要素のノード名をkey、値をvalueとして持つ辞書
|
|--'part_Name':'HeatBridge'
|--'Type': 種類
| 'Joist-Beam'(根太材＋大引材等)
| 'Pillar-HeatBridge'(構造部材等＋付加断熱層内熱橋部)
| 'Lintel-HeatBridge'(まぐさ＋付加断熱層内熱橋部)
|--'layer': 含まれる層のリスト＝SolidLayer_simpleまたはAirLayer_simpleの形式を持つ辞書のリスト
| [layer1,layer2,...] 先頭要素が一番外側(外気側)とする ※変更の可能性あり
|
SolidLayer_simple---SolidLayer要素のノード名をkey、値をvalueとして持つ辞書
|
|--'layer_Name': 'SolidLayer'
|--'LambdaValue' : 熱伝導率
|--'Thickness': 厚さ
|--'Material': 素材名
|--'MaterialInfo': 熱伝導率の根拠
|--'ExternalReduction': 外張断熱の熱抵抗の低減
| 'Yes'(外張断熱で１層張りの下地併用) または 'No'(外張断熱で１層張りの下地併用ではない)
|
|
AirLayer_simple--AirLayer要素のノード名をkey、値をvalueとして持つ辞書
|
|--'layer_Name': 'AirLayer'
|--'Type': 空気層の種類
'AirTight'(面材で密閉された空気層) または 'OnSiteNonConnected'(他の空間と連通していない空気層) または
'OnSiteConnected'(他の空間と連通している空気層)
"""
## 出力
"""
|
|--'Rse': 外気側表面熱伝達抵抗
|--'Rsi': 室内側表面熱伝達抵抗
|--'GeneralPart_output': 含まれる一般部分の計算結果＝GeneralPart_simple_outputの形式を持つ辞書
| |
| |
| GeneralPart_simple_output--暫定の形式
| |
| |--'R1': 層1の熱抵抗
| |--'R2': 層2の熱抵抗
| |--'R3': 層3の熱抵抗
| |--'R4': 層4の熱抵抗 ※層数変更可能性あり
| |--'Ru_n': 部分の熱抵抗の合計
| |--'Un': 部分の熱貫流率
| |--'a': 面積比率
|
|--'MixPart1_output': 含まれる一般部分+熱橋部分1の計算結果＝MixPart_simple_outputの形式を持つ辞書
| |
| |
| MixPart_simple_output--暫定の形式
| |
| |--'R1': 層1の熱抵抗
| |--'R2': 層2の熱抵抗
| |--'R3': 層3の熱抵抗
| |--'R4': 層4の熱抵抗 ※層数変更可能性あり
| |--'Ru_n': 部分の熱抵抗の合計
| |--'Un': 部分の熱貫流率
| |--'a': 面積比率
|
|--'MixPart2_output': 含まれる一般部分+熱橋部分2の計算結果＝MixPart_simple_outputの形式を持つ辞書
| |
| |
| MixPart_simple_output--暫定の形式
| |
| |--'R1': 層1の熱抵抗
| |--'R2': 層2の熱抵抗
| |--'R3': 層3の熱抵抗
| |--'R4': 層4の熱抵抗 ※層数変更可能性あり
| |--'Ru_n': 部分の熱抵抗の合計
| |--'Un': 部分の熱貫流率
| |--'a': 面積比率
|
|--'HeatBridge_output': 含まれる熱橋部分の計算結果＝HeatBridge_simple_outputの形式を持つ辞書
| |
| |
| HeatBridge_simple_output--暫定の形式
| |
| |--'R1': 層1の熱抵抗
| |--'R2': 層2の熱抵抗
| |--'R3': 層3の熱抵抗
| |--'R4': 層4の熱抵抗 ※層数変更可能性あり
| |--'Ru_n': 部分の熱抵抗の合計
| |--'Un': 部分の熱貫流率
| |--'a': 面積比率
|
|--'U_i': 熱貫流率
"""
###### 鉄筋コンクリート造等 ######
"""
Wall_rc---Wall要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Name': 名前
|--'Area': 面積
|--'Method': 計算方法 'RC'
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
|--'Direction': 方位
| 'Top'(屋根上面) または 'N'(北) または 'NE'(北東) または 'E'(東) または 'SE'(南東) または
| 'S'(南) または 'SW'(南西) または 'W'(西) または 'NW'(北西) または 'Bottom'(下面)
|--'SolarGain': 日射取得率
| 'Yes'(日射熱取得が発生する部位) または 'No'(日射熱取得が発生しない部分)
|--'Type': 部位の種類
| 'Roof'(屋根) または 'Ceiling'(天井) または 'ExternalWall'(外壁) または 'Floor'(床) または
| 'BoundaryWall'(界壁) または 'BoundaryCeiling'(上階側界床) または 'BoundaryFloor'(下階側界床)
|--'Outside': 室外側は外気か？
| 'Yes'(外気) または 'No'(外気以外（通気層、小屋裏、床裏等）)
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
|--'GeneralPart': 含まれる一般部分のリスト＝GeneralPart_rcの形式を持つ辞書のリスト
| 例：'GeneralPart':[GeneralPart1] (要素数は1を想定)要素数が2以上の場合でも一番先頭要素のみを計算に使用する
|
|
GeneralPart_rc---一般部分についてのパラメータ名をキー、パラメータの値を値として持つ辞書
|
|--'part_Name':'GeneralPart'
|--'layer': 含まれる層のリスト＝SolidLayer_rcまたはAirLayer_rcの形式を持つ辞書のリスト
| [layer1,layer2,...] 先頭要素が一番外側(外気側)とする ※変更の可能性あり
|
SolidLayer_rc---SolidLayer要素のノード名をkey、値をvalueとして持つ辞書
|
|--'layer_Name': 'SolidLayer'
|--'LambdaValue' : 熱伝導率
|--'Thickness': 厚さ
|--'Material': 素材名
|--'MaterialInfo': 熱伝導率の根拠
|--'ExternalReduction': 'No'(木造以外はデフォルトでNo)
|
|
AirLayer_rc--空気層についてのパラメータ名をキー、パラメータの値を値として持つ辞書
|
|--'layer_Name': 'AirLayer'
|--'Type': 空気層の種類
'AirTight'(面材で密閉された空気層) または 'OnSiteNonConnected'(他の空間と連通していない空気層) または
'OnSiteConnected'(他の空間と連通している空気層)
"""
## 出力
"""
|
|--'GeneralPart_output': 含まれる一般部分の計算結果＝GeneralPart_rc_outputの形式を持つ辞書
| |
| |
| GeneralPart_rc_output--暫定の形式
| |
| |--'R1': 層1の熱抵抗
| |--'R2': 層2の熱抵抗
| |--'R3': 層3の熱抵抗
| |--'R5': 層4の熱抵抗
| |--'R6': 層5の熱抵抗
| |--'R7': 層6の熱抵抗
| |--'Rse': 外気側表面熱伝達抵抗
| |--'Rsi': 室内側表面熱伝達抵抗
| |--'R_u': 一般部の熱抵抗の合計
| |--'U_g_i': 一般部の熱貫流率
|
|--'U_i': 熱貫流率 ※一般部の熱貫流率と同じ？
"""
###### 鉄骨造 ######
"""
Wall_steel---Wall要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Name': 名前
|--'Area': 面積
|--'Method': 計算方法 'Steel'
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
|--'Direction': 方位
| 'Top'(屋根上面) または 'N'(北) または 'NE'(北東) または 'E'(東) または 'SE'(南東) または
| 'S'(南) または 'SW'(南西) または 'W'(西) または 'NW'(北西) または 'Bottom'(下面)
|--'SolarGain': 日射取得率
| 'Yes'(日射熱取得が発生する部位) または 'No'(日射熱取得が発生しない部分)
|--'Type': 部位の種類
| 'Roof'(屋根) または 'Ceiling'(天井) または 'ExternalWall'(外壁) または 'Floor'(床) または
| 'BoundaryWall'(界壁) または 'BoundaryCeiling'(上階側界床) または 'BoundaryFloor'(下階側界床)
|--'Outside': 室外側は外気か？
| 'Yes'(外気) または 'No'(外気以外（通気層、小屋裏、床裏等）)
|--'ExteriorThermalResistance': 外装材＋断熱補強材の熱抵抗
| 'Over1.7'(1.7以上) または 'Under1.7'(1.7未満1.5以上) または
| 'Under1.5'(1.5未満1.3以上) または 'Under1.3'(1.3未満1.1以上) または
| 'Under1.1'(1.1未満0.9以上) または 'Under0.9'(0.9未満0.7以上) または
| 'Under0.7'(0.7未満0.5以上) または 'Under0.5'(0.5未満0.3以上) または
| 'Under0.3'(0.3未満0.1以上) または 'Under0.1'(0.1未満)
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
|--'GeneralPart': 含まれる一般部分のリスト＝GeneralPart_steelの形式を持つ辞書のリスト
| 例：'GeneralPart':[GeneralPart1] (要素数は1を想定)要素数が2以上の場合でも一番先頭要素のみを計算に使用する
|
|
GeneralPart_steel---一般部分についてのパラメータ名をキー、パラメータの値を値として持つ辞書
|
|--'part_Name':'GeneralPart'
|--'layer': 含まれる層のリスト＝SolidLayer_steelまたはAirLayer_steelの形式を持つ辞書のリスト
| [layer1,layer2,...] 先頭要素が一番外側(外気側)とする ※変更の可能性あり
|
SolidLayer_steel---SolidLayer要素のノード名をkey、値をvalueとして持つ辞書
|
|--'layer_Name': 'SolidLayer'
|--'LambdaValue' : 熱伝導率
|--'Thickness': 厚さ
|--'Material': 素材名
|--'MaterialInfo': 熱伝導率の根拠
|--'ExternalReduction': 'No'(木造以外はデフォルトでNo)
|
|
AirLayer_steel--空気層についてのパラメータ名をキー、パラメータの値を値として持つ辞書
|
|--'layer_Name': 'AirLayer'
|--'Type': 空気層の種類
'AirTight'(面材で密閉された空気層) または 'OnSiteNonConnected'(他の空間と連通していない空気層) または
'OnSiteConnected'(他の空間と連通している空気層)
"""
## 出力
"""
|
|--'GeneralPart_output': 含まれる一般部分の計算結果＝GeneralPart_steel_outputの形式を持つ辞書
| |
| |
| GeneralPart_steel_output--暫定の形式
| |
| |--'R1': 層1の熱抵抗
| |--'R2': 層2の熱抵抗
| |--'R3': 層3の熱抵抗
| |--'R5': 層4の熱抵抗
| |--'R6': 層5の熱抵抗
| |--'R7': 層6の熱抵抗
| |--'Rse': 外気側表面熱伝達抵抗
| |--'Rsi': 室内側表面熱伝達抵抗
| |--'R_u': 一般部の熱抵抗の合計
| |--'U_g_i': 一般部の熱貫流率
| |--'U_r_s': 補正熱貫流率
|
|--'U_i': 熱貫流率
"""
###### 土間床等の外周部 ######
"""
Foundation---Foundation要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Name': 名前
|--'Area': 土間床等の面積
|--'OuterLength': 外周の長さ
|--'PsiF': 線熱貫流率
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
"""
## 出力
"""
|
|--'U_F': 線熱貫流率
"""
###### 窓 ######
"""
Window---Window要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Method': 'Window' ※内部処理のため追加
|--'Name': 名前
|--'Direction': 方位
| 'Top'(屋根上面) または 'N'(北) または 'NE'(北東) または 'E'(東) または 'SE'(南東) または
| 'S'(南) または 'SW'(南西) または 'W'(西) または 'NW'(北西) または 'Bottom'(下面)
|--'SolarGain': 日射取得率
| 'Yes'(日射熱取得が発生する部位) または 'No'(日射熱取得が発生しない部分)
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
|--'WindowPart': 窓部分(WindowPart形式をもつ辞書)
|
|
WindowPart---窓部分についてのパラメータ名をキー、パラメータの値を値として持つ辞書
|
|--'IsSetWindow': 二重窓の入力
| 'Yes'(二重窓の入力あり) または 'No'(二重窓の入力なし)
|--'Area': 面積
|--'OuterHeatTransferOpeningArea': 二重窓における外気側窓の伝熱開口面積
|--'InternalHeatTransferOpeningArea': 二重窓における室内側窓の伝熱開口面積
|--'HasShade': 日除けの有無
| 'Yes'(あり) または 'No'(なし)
|--'SashSpec': 建具仕様
| 'WoodenOrResin'(木製建具又は樹脂製建具) または 'Mix'(木と金属の複合材料製建具又は樹脂と金属の複合材料製建具) または
| 'MetallicInsulated'(金属製熱遮断構造建具) または 'Metalic'(金属製建具)
|--'SashSpecForInnerWindow': 建具仕様(内窓)
| 'WoodenOrResin'(木製建具又は樹脂製建具) または 'Mix'(木と金属の複合材料製建具又は樹脂と金属の複合材料製建具) または
| 'MetallicInsulated'(金属製熱遮断構造建具) または 'Metalic'(金属製建具)
|--'GlassSpecForCategory': ガラス仕様（区分）
| 'Single'(単層) または '2Pair'(二層複層) または '3Pair'(三層以上の複層)
|--'Attachment': 付属品部材
| 'No'(なし) または 'Shutter'(シャッター) または 'Shoji'(障子) または 'WindbreakSpace'(風除室) または 'ExteriorBlind'(外付けブラインド)
|--'UValue': 熱貫流率
|--'UValueInfo': 熱貫流率の入力根拠
|--'UValueForInnerWindow': 熱貫流率（内窓）
|--'UValueInfoForInnerWindow': 熱貫流率の入力根拠（内窓）
|--'SolarHeatGainCoefficient': ガラスの垂直面日射熱取得率
|--'SolarHeatGainCoefficientInfo': ガラスの垂直面日射熱取得率の入力根拠
|--'GlassType': ガラス仕様の区分
| '3PairDoubleLowEG'(2枚以上のガラス表面にLow-E膜を使用したLow-E三層複層ガラス 日射取得型) または
| '3PairDoubleLowES'(2枚以上のガラス表面にLow-E膜を使用したLow-E三層複層ガラス 日射遮蔽型) または
| '3PairLowEG'(Low-E三層複層ガラス 日射取得型) または
| '3PairLowES'(Low-E三層複層ガラス 日射遮蔽型) または
| '3PairClear'(三層複層ガラス) または
| '2PairLowEG'(Low-E二層複層ガラス 日射取得型) または
| '2PairLowES'(Low-E二層複層ガラス 日射遮蔽型) または
| '2PairClear'(二層複層ガラス) または
| '2PairSingleClear'(単板ガラス2枚を組み合わせたもの) または
| 'SingleClear'(単板ガラス)
|--'SolarHeatGainCoefficientForInnerWindow': ガラスの垂直面日射熱取得率（内窓）
|--'SolarHeatGainCoefficientInfoForInnerWindow': ガラスの垂直面日射熱取得率の入力根拠（内窓）
|--'GlassTypeForInnerWindow': ガラス仕様の区分（内窓）
| '3PairDoubleLowEG'(2枚以上のガラス表面にLow-E膜を使用したLow-E三層複層ガラス 日射取得型) または
| '3PairDoubleLowES'(2枚以上のガラス表面にLow-E膜を使用したLow-E三層複層ガラス 日射遮蔽型) または
| '3PairLowEG'(Low-E三層複層ガラス 日射取得型) または
| '3PairLowES'(Low-E三層複層ガラス 日射遮蔽型) または
| '3PairClear'(三層複層ガラス) または
| '2PairLowEG'(Low-E二層複層ガラス 日射取得型) または
| '2PairLowES'(Low-E二層複層ガラス 日射遮蔽型) または
| '2PairClear'(二層複層ガラス) または
| '2PairSingleClear'(単板ガラス2枚を組み合わせたもの) または
| 'SingleClear'(単板ガラス)
|--'FcMethod': 窓の日射熱取得の計算方法
| 'No'(計算しない) または 'Simple'(簡易法) または 'Accurate'(詳細法)
|--'FrameRef': 枠の影響の有無
| 'Yes'(枠の影響がある場合) または 'No'(枠の影響がない場合)
|--'WindowTopToEaveHeight': 日除け下端から窓上端までの垂直方向の距離
|--'WindowHeigt': 窓の開口高さ寸法
|--'EaveDepth': 窓面からの日除けの張り出し寸法
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
"""
## 出力
"""
|--'U_i': 熱貫流率
"""
###### ドア ######
"""
Door---Door要素のノード名をkey、値をvalueとして持つ辞書
|
|--'Method': 'Door' ※内部処理のため追加
|--'Name': 名前
|--'Direction': 方位
| 'Top'(屋根上面) または 'N'(北) または 'NE'(北東) または 'E'(東) または 'SE'(南東) または
| 'S'(南) または 'SW'(南西) または 'W'(西) または 'NW'(北西) または 'Bottom'(下面)
|--'SolarGain': 日射取得率
| 'Yes'(日射熱取得が発生する部位) または 'No'(日射熱取得が発生しない部分)
|--'Adjacent': 隣接空間等の種類別
| 'Open'(外気) または 'Connected'(外気に通じる空間) または 'Close'(外気に通じていない空間又は外気に通じる床裏) または
| 'Separator'(住戸、住戸と同様の熱的環境の空間又は外気に通じていない床裏)
|--'DoorPart': ドア部分(DoorPart形式をもつ辞書)
|
|
DoorPart---ドア部分についてのパラメータ名をキー、パラメータの値を値として持つ辞書
|
|--'Area': 面積
|--'HasShade': 日除けの有無
| 'Yes'(あり) または 'No'(なし)
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
|--'UValue': 熱貫流率
|--'UValueInfo': 熱貫流率の入力根拠
|--'Attachment': 付属品部材
'No'(なし) または 'Shutter'(シャッター) または 'Shoji'(障子) または 'WindbreakSpace'(風除室)
"""
## 出力
"""
|--'U_i': 熱貫流率
"""
######## 熱橋 ########
###### 木造 ######
"""
LinearHeatBridge_wood--LinearHeatBridge要素のノード名をkey、値をvalueとして持つ辞書
|
|--'part_Name':'LinearHeatBridge'
|--'SolarGain': 日射取得率
| 'Yes'(有) または 'No'(無)
|--'StructureType': 構造の種別 'Wood'
|--'ComponentNames': 接する部位の名前のリスト
|--'Length':長さ
|--'LinearThermalTransmittance':線熱貫流率
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
"""
## 出力
"""
|--'Psi': 線熱貫流率
"""
###### 鉄筋コンクリート造等 ######
"""
LinearHeatBridge_rc--LinearHeatBridge要素のノード名をkey、値をvalueとして持つ辞書
|
|--'part_Name':'LinearHeatBridge'
|--'SolarGain': 日射取得率
| 'Yes'(有) または 'No'(無)
|--'StructureType': 構造の種別 'RC'
|--'ComponentNames': 接する部位の名前のリスト
|--'Length':長さ
|--'LinearThermalTransmittance':線熱貫流率 ※付録Cの表を見て入力する
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
"""
## 出力
"""
|--'Psi': 線熱貫流率
"""
###### 鉄骨造 ######
"""
LinearHeatBridge_steel--LinearHeatBridge要素のノード名をkey、値をvalueとして持つ辞書
|
|--'part_Name':'LinearHeatBridge'
|--'SolarGain': 日射取得率
| 'Yes'(有) または 'No'(無)
|--'StructureType': 構造の種別 'Steel'
|--'ComponentNames': 接する部位の名前(配列)
|--'Length':長さ
|--'ExteriorThermalResistance': 外装材＋断熱補強材の熱抵抗
| 'Over1.7'(1.7以上) または 'Under1.7'(1.7未満1.5以上) または
| 'Under1.5'(1.5未満1.3以上) または 'Under1.3'(1.3未満1.1以上) または
| 'Under1.1'(1.1未満0.9以上) または 'Under0.9'(0.9未満0.7以上) または
| 'Under0.7'(0.7未満0.5以上) または 'Under0.5'(0.5未満0.3以上) または
| 'Under0.3'(0.3未満0.1以上) または 'Under0.1'(0.1未満)
|--'Type':部位
| 'Column'(柱) または 'Beam'(梁)
|--'ColumnInterval': 柱見付寸法
| 'Over300'(300以上) または 'Under300'(300未満200以上) または
| 'Under200'(200未満100以上) または 'Under100'(100未満)
|--'BeamInterval': 梁見付寸法
| 'Over400'(400以上) または 'Under400'(400未満200以上) または 'Under200'(200未満)
|--'GammaH': 暖房期の日除けの効果係数
|--'GammaC': 冷房期の日除けの効果係数
"""
## 出力
"""
|--'Psi': 線熱貫流率
"""
|
"""WebSocket message class"""
class WebSocketMessage:
"""A class representing a WS message"""
# pylint: disable=redefined-builtin,invalid-name
__slots__ = ["type", "id", "data", "reply"]
def __init__(self, data: dict) -> None:
self.type = data["type"]
self.id = data.get("id")
self.data = data
self.reply = data.get("reply")
def success(self, data: object) -> dict:
"""Generates a success response"""
return {
"type": "reply",
"id": self.id,
"success": True,
"data": data
}
def error(self, type: str, message: str) -> dict:
"""Generates an error response"""
return {
"type": "reply",
"id": self.id,
"success": False,
"error": {
"type": type,
"message": message
}
}
|
__author__ = 'sanyi'
class IpsetError(Exception):
pass
class IpsetNotFound(Exception):
pass
class IpsetNoRights(Exception):
pass
class IpsetInvalidResponse(Exception):
pass
class IpsetCommandHangs(Exception):
pass
class IpsetSetNotFound(Exception):
pass
class IpsetEntryNotFound(Exception):
pass
|
# MODFLOW 6 version file automatically created using...make_release.py
# created on...February 18, 2021 08:23:34
major = 6
minor = 2
micro = 1
__version__ = '{:d}.{:d}.{:d}'.format(major, minor, micro)
|
def getFuel(data):
return [int(s) for s in data.split("\n")]
def fuelbois(n):
return (n // 3) - 2
def part1(data):
return sum(map(fuelbois, getFuel(data)))
def part2(data):
return sum(map(doTheThing, getFuel(data)))
def doTheThing(n):
s = fuelbois(n)
if s <= 0:
return 0
return s + doTheThing(s)
|
class Sidelink:
def __init__(self, name, link, subtitle, http=False, css_classes='sidelink', onclick=''):
self.name = name
self.link = link
self.subtitle = subtitle
self.http = http
self.css_classes = css_classes
self.onclick = onclick
class Sidebar:
def __init__(self, name, link, http=False):
self.name = name
self.link = link
self.http = http |
class InvalidNoteException(Exception):
pass
class InvalidModeException(Exception):
pass
class InvalidChord(Exception):
pass
|
# Middlewares
# https://docs.djangoproject.com/en/3.0/topics/http/middleware/
MIDDLEWARE = [
'whitenoise.middleware.WhiteNoiseMiddleware',
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'corsheaders.middleware.CorsMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
# Password validation
# https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
AUTH_USER_MODEL = 'users.User' # Custom User Model
|
# Copyright (c) 2016 EMC Corporation.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
class StoropsException(Exception):
message = 'Storops Error.'
class VNXException(StoropsException):
message = "VNX Error."
class VNXStorageGroupError(VNXException):
pass
class VNXAttachAluError(VNXException):
pass
class VNXAluAlreadyAttachedError(VNXAttachAluError):
message = (
'LUN already exists in the specified storage group',
'Requested LUN has already been added to this Storage Group')
class VNXDetachAluError(VNXStorageGroupError):
pass
class VNXDetachAluNotFoundError(VNXDetachAluError):
message = 'No such Host LUN in this Storage Group'
class VNXCreateStorageGroupError(VNXStorageGroupError):
pass
class VNXStorageGroupNameInUseError(VNXCreateStorageGroupError):
message = 'Storage Group name already in use'
class VNXNoHluAvailableError(VNXStorageGroupError):
pass
class VNXMigrationError(VNXException):
pass
class VNXLunNotMigratingError(VNXException):
pass
class VNXLunSyncCompletedError(VNXMigrationError):
error_code = 0x714a8021
class VNXTargetNotReadyError(VNXMigrationError):
message = 'The destination LUN is not available for migration'
class VNXSnapError(VNXException):
pass
class VNXDeleteAttachedSnapError(VNXSnapError):
error_code = 0x716d8003
class VNXCreateSnapError(VNXException):
message = 'Cannot create the snapshot.'
class VNXAttachSnapError(VNXSnapError):
message = 'Cannot attach the snapshot.'
class VNXDetachSnapError(VNXSnapError):
message = 'Cannot detach the snapshot.'
class VNXSnapAlreadyMountedError(VNXSnapError):
error_code = 0x716d8055
class VNXSnapNameInUseError(VNXSnapError):
error_code = 0x716d8005
class VNXSnapNotExistsError(VNXSnapError):
message = 'The specified snapshot does not exist.'
class VNXLunError(VNXException):
pass
class VNXCreateLunError(VNXLunError):
pass
class VNXLunNameInUseError(VNXCreateLunError):
error_code = 0x712d8d04
class VNXLunExtendError(VNXLunError):
pass
class VNXLunExpandSizeError(VNXLunExtendError):
error_code = 0x712d8e04
class VNXLunPreparingError(VNXLunError):
error_code = 0x712d8e0e
class VNXLunNotFoundError(VNXLunError):
message = 'Could not retrieve the specified (pool lun).'
class VNXDeleteLunError(VNXLunError):
pass
class VNXLunUsedByFeatureError(VNXLunError):
pass
class VNXCompressionError(VNXLunError):
pass
class VNXCompressionAlreadyEnabledError(VNXCompressionError):
message = 'Compression on the specified LUN is already turned on.'
class VNXConsistencyGroupError(VNXException):
pass
class VNXCreateConsistencyGroupError(VNXConsistencyGroupError):
pass
class VNXConsistencyGroupNameInUseError(VNXCreateConsistencyGroupError):
error_code = 0x716d8021
class VNXConsistencyGroupNotFoundError(VNXConsistencyGroupError):
message = 'Cannot find the consistency group'
class VNXPingNodeError(VNXException):
pass
class VNXMirrorException(VNXException):
pass
class VNXMirrorNameInUseError(VNXMirrorException):
message = 'Mirror name already in use'
class VNXMirrorPromotePrimaryError(VNXMirrorException):
message = 'Cannot remove or promote a primary image.'
class VNXMirrorNotFoundError(VNXMirrorException):
message = 'Mirror not found'
class VNXMirrorGroupNameInUseError(VNXMirrorException):
message = 'Mirror Group name already in use'
class VNXMirrorGroupNotFoundError(VNXMirrorException):
message = 'Unable to locate the specified group'
class VNXMirrorGroupAlreadyMemberError(VNXMirrorException):
message = 'The mirror is already a member of a group'
class VNXMirrorGroupMirrorNotMemberError(VNXMirrorException):
message = 'The specified mirror is not a member of the group'
class VNXMirrorGroupAlreadyPromotedError(VNXMirrorException):
message = 'The Consistency Group has no secondary images to promote'
|
#-------------------------------------------------------------------------------
# Name: Signals types (voltage and current)
# Author: d.Fathi
# Created: 31/03/2020
# Modified: 19/09/2021
# Copyright: (c) PyAMS 2020
# Licence: unlicense
#-------------------------------------------------------------------------------
voltage={'discipline':'electrical',
'nature':'potential',
'abstol': 1e-8,
'chgtol':1e-14,
'signalType':'voltage',
'unit':'V'
}
current={'discipline':'electrical',
'nature':'flow',
'abstol': 1e-8,
'chgtol':1e-14,
'signalType':'voltage',
'unit':'A'
}
electrical={
'potential':voltage,
'flow':current
}
|
# @Title: 整数转罗马数字 (Integer to Roman)
# @Author: KivenC
# @Date: 2019-01-24 15:32:34
# @Runtime: 144 ms
# @Memory: 6.9 MB
class Solution:
def intToRoman(self, num):
"""
:type num: int
:rtype: str
"""
ans = ''
vals = [1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1]
symbols = ['M', 'CM', 'D', 'CD', 'C', 'XC', 'L', 'XL', 'X', 'IX', 'V', 'IV', 'I']
for val, symbol in zip(vals, symbols):
while num >= val:
ans += symbol
num -= val
return ans
|
# Replaced with the current commit when building the wheels.
__commit__ = "{{CLOUDTIK_COMMIT_SHA}}"
__version__ = "0.9.0"
|
"""
Iniciar com letras maiusculas os nomes das variáveis
"""
nome = 'Francenylson' # nunca 1nome=
idade = 50
#idade = 32 #fica implícito que é um int
e_maior = idade > 18
altura = 1.85
peso = 93
imc = (peso/(altura*2))
print(nome, 'tem', idade, 'anos de idade e seu imc é', imc)
|
def configure(conf):
conf.env.ARCHITECTURE = 'mips'
conf.env.VALID_ARCHITECTURES = ['mips']
conf.env.ARCH_FAMILY = 'mips'
conf.env.ARCH_LP64 = False
conf.env.append_unique('DEFINES', ['_MIPS'])
|
# -*- coding: utf-8 -*-
# Copyright 2019 Cohesity Inc.
class SQLServerInstanceVersion(object):
"""Implementation of the 'SQLServerInstanceVersion' model.
Specifies the Server Instance Version.
Attributes:
build (int): Specfies the build.
major_version (int): Specfies the major version.
minor_version (int): Specfies the minor version.
revision (int): Specfies the revision.
version_string (string): Specfies the version string.
"""
# Create a mapping from Model property names to API property names
_names = {
"build":'build',
"major_version":'majorVersion',
"minor_version":'minorVersion',
"revision":'revision',
"version_string":'versionString'
}
def __init__(self,
build=None,
major_version=None,
minor_version=None,
revision=None,
version_string=None):
"""Constructor for the SQLServerInstanceVersion class"""
# Initialize members of the class
self.build = build
self.major_version = major_version
self.minor_version = minor_version
self.revision = revision
self.version_string = version_string
@classmethod
def from_dictionary(cls,
dictionary):
"""Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class.
"""
if dictionary is None:
return None
# Extract variables from the dictionary
build = dictionary.get('build')
major_version = dictionary.get('majorVersion')
minor_version = dictionary.get('minorVersion')
revision = dictionary.get('revision')
version_string = dictionary.get('versionString')
# Return an object of this model
return cls(build,
major_version,
minor_version,
revision,
version_string)
|
class DataGridViewCellStyleConverter(TypeConverter):
"""
Converts System.Windows.Forms.DataGridViewCellStyle objects to and from other data types.
DataGridViewCellStyleConverter()
"""
def CanConvertTo(self,*__args):
"""
CanConvertTo(self: DataGridViewCellStyleConverter,context: ITypeDescriptorContext,destinationType: Type) -> bool
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
destinationType: A System.Type that represents the type you want to convert to.
Returns: true if this converter can perform the conversion; otherwise,false.
"""
pass
def ConvertTo(self,*__args):
"""
ConvertTo(self: DataGridViewCellStyleConverter,context: ITypeDescriptorContext,culture: CultureInfo,value: object,destinationType: Type) -> object
context: An System.ComponentModel.ITypeDescriptorContext that provides a format context.
culture: A System.Globalization.CultureInfo. If null is passed,the current culture is
assumed.
value: The System.Object to convert.
destinationType: The System.Type to convert the value parameter to.
Returns: An System.Object that represents the converted value.
"""
pass
|
s=[1, 1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 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, 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, 0, 0, 0, 0]
j=0
for i in range(len(s)):
if s[i] > 1:
j=j+1
print("%d, %d") % ( i, s[i])
print("count: %d") % (j)
|
# encoding: utf-8
# module perfmon
# from C:\Python27\lib\site-packages\win32\perfmon.pyd
# by generator 1.147
# no doc
# no imports
# functions
def CounterDefinition(*args, **kwargs): # real signature unknown
pass
def LoadPerfCounterTextStrings(*args, **kwargs): # real signature unknown
pass
def ObjectType(*args, **kwargs): # real signature unknown
pass
def PerfMonManager(*args, **kwargs): # real signature unknown
pass
def UnloadPerfCounterTextStrings(*args, **kwargs): # real signature unknown
pass
# no classes
|
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
# Copyright (C) David Arnold (XOE Solutions).
# Author David Arnold (XOE Solutions), [email protected]
# Co-Authors Juan Pablo Aries (devCO), [email protected]
# Hector Ivan Valencia Muñoz (TIX SAS)
# Nhomar Hernandez (Vauxoo)
# Humberto Ochoa (Vauxoo)
{
'name': 'Colombia - Accounting',
'version': '0.8',
'category': 'Accounting/Localizations/Account Charts',
'description': 'Colombian Accounting and Tax Preconfiguration',
'author': 'David Arnold (XOE Solutions)',
'website': 'https://www.odoo.com/colombia',
'depends': [
'account',
'base_address_city',
'account_debit_note',
'l10n_latam_base',
],
'data': [
'data/l10n_co_chart_data.xml',
'data/account.account.template.csv',
'data/account_chart_template_data.xml',
'data/account.tax.group.csv',
'data/account_tax_template.xml',
'data/account_chart_template_configure_data.xml',
'data/l10n_latam.identification.type.csv',
],
'demo': [
'demo/demo_company.xml',
],
}
|
temperatura_celsus = 0
def convert_celsus(temperatura_celsus):
celsus = ((temperatura_celsus - 32) / 1.8)
return celsus
print(convert_celsus(100))
|
class AsyncIterWrapper:
"""Async wrapper for sync iterables
Copied from aitertools package.
"""
def __init__(self, iterable):
self._it = iter(iterable)
def __aiter__(self):
return self
async def __anext__(self):
try:
return next(self._it)
except StopIteration as e:
raise StopAsyncIteration() from e
def __repr__(self):
return '<AsyncIterWrapper {}>'.format(self._it)
async def alist(iterable):
return [value async for value in iterable]
async def anext(iterable):
return await iterable.__anext__()
|
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution:
def hasCycle(self, head: Optional[ListNode]) -> bool:
## without using any set
## Floyd's Tortoise and Hare method
## https://www.youtube.com/watch?v=gBTe7lFR3vc
slow, fast = head, head
while fast and fast.next:
slow = slow.next
fast = fast.next.next
## If there is a cycle at some point in time
## slow and fast will point to the same node
if slow == fast:
return True
return False
##################################################
## We will maintain a set of node
val_pos = set()
## We will traverse through the linked list till
## either it ends or we detect a cycle
while head:
## if the next node is already in the set then
## we have a loop
if head.next in val_pos:
return True
## Otherwise keep on adding the current node to
## the linked list
else:
val_pos.add(head)
head = head.next
## Return false in case we have not found any loop
return False
|
#question 2
#generate the series
#3 8 14 22 34 53 83 129 197 294
print("Enter the lenght of the series:")
seriesLength =int(input())
s1,s2,s3 = 1,5,3
gen_series = []
for itr in range(1,seriesLength):
gen_series.append(s3)
s3 += s2
s2 += s1
s1 += itr
result = (str(gen_series).replace(',',' '))[1:-1]
#print the series
print(result)
|
my_name = "Juan Manuel Young Hoyos"
print(my_name[0])
# * Both of them are equal
print(my_name[0:3])
print(my_name[:3])
# * Both of them are equal
print(my_name[1:len(my_name)])
print(my_name[1:])
# * Slicing with steps
print(my_name[1:len(my_name):2])
# * It brings me everything
print(my_name[::])
|
settlement = "ISHUV"
street1="REHOV1"
street2="REHOV2"
road1="KVISH1"
road2="KVISH2"
junction_name = "SHEM_ZOMET"
settlement_sign = "SEMEL_YISHUV"
street_sign = "SEMEL_RECHOV"
street_name = "SHEM_RECHOV"
table_number = "MS_TAVLA"
code = "KOD"
name = "NAME"
sign = "SEMEL"
urban_intersection="ZOMET_IRONI"
non_urban_intersection="ZOMET_LO_IRONI"
accident_year="SHNAT_TEUNA"
accident_month="HODESH_TEUNA"
accident_day="YOM_BE_HODESH"
accident_hour = "SHAA"
x_coordinate="X"
y_coordinate="Y"
accident_type="SUG_TEUNA" # part of the desciption
accident_severity="HUMRAT_TEUNA" # part of the desciption
igun="STATUS_IGUN" # part of the desciption
id="PK_TEUNA_FIKT"
home="BAYIT"
road_type="SUG_DEREH" # part of the desciption
day_type="SUG_YOM" # part of the desciption
road_shape="ZURAT_DEREH" # part of the desciption
unit="YEHIDA" # part of the desciption
one_lane = "HAD_MASLUL"
multi_lane = "RAV_MASLUL"
speed_limit = "MEHIRUT_MUTERET"
intactness = "TKINUT"
road_width = "ROHAV"
road_sign = "SIMUN_TIMRUR"
road_light = "TEURA"
road_control = "BAKARA"
weather = "MEZEG_AVIR"
road_surface = "PNE_KVISH"
road_object = "SUG_EZEM"
object_distance = "MERHAK_EZEM"
didnt_cross = "LO_HAZA"
cross_mode = "OFEN_HAZIYA"
cross_location = "MEKOM_HAZIYA"
cross_direction = "KIVUN_HAZIYA"
road1="KVISH1"
road2="KVISH2"
km="KM"
yishuv_symbol = "SEMEL_YISHUV"
geo_area = "THUM_GEOGRAFI"
day_night = "YOM_LAYLA"
day_in_week = "YOM_BASHAVUA"
traffic_light = "RAMZOR"
region = "MAHOZ"
district = "NAFA"
natural_area = "EZOR_TIVI"
minizipali_status = "MAAMAD_MINIZIPALI"
yishuv_shape = "ZURAT_ISHUV"
# Involved csv fields
involved_type = "SUG_MEORAV"
license_acquiring_date = "SHNAT_HOZAA"
age_group = "KVUZA_GIL"
sex = "MIN"
car_type = "SUG_REHEV_NASA_LMS"
safety_measures = "EMZAE_BETIHUT"
home_city = "SEMEL_YISHUV_MEGURIM"
injury_severity = "HUMRAT_PGIA"
injured_type = "SUG_NIFGA_LMS"
injured_position = "PEULAT_NIFGA_LMS"
population_type = "KVUTZAT_OHLUSIYA_LMS"
home_district = "MAHOZ_MEGURIM"
home_nafa = "NAFA_MEGURIM"
home_area = "EZOR_TIVI_MEGURIM"
home_municipal_status = "MAAMAD_MINIZIPALI_MEGURIM"
home_residence_type = "ZURAT_ISHUV_MEGURIM"
file_type = "SUG_TIK"
hospital_time = "PAZUAUSHPAZ_LMS"
medical_type = "ISS_LMS"
release_dest = "YAADSHIHRUR_PUF_LMS"
safety_measures_use = "SHIMUSHBEAVIZAREYBETIHUT_LMS"
late_deceased = "PTIRAMEUHERET_LMS"
# Vehicles data
engine_volume = "NEFAH"
manufacturing_year = "SHNAT_YITZUR"
driving_directions = "KIVUNE_NESIA"
vehicle_status = "MATZAV_REHEV"
vehicle_attribution = "SHIYUH_REHEV_LMS"
vehicle_type = "SUG_REHEV_LMS"
seats = "MEKOMOT_YESHIVA_LMS"
total_weight = "MISHKAL_KOLEL_LMS"
|
'''
Class that maps to the JSON TokenInfo
'''
class MTTokenInfo():
v = None
code = None
code_desc = None
token_id = None
timestamp_sec = None
timestamp_iso = None
hostname = None
is_dev_host = None
action = None
ip = None
def __init__(self, v: str, code: int, codeDesc: str, tokID: str,
timestampSec: int, timestampISO: str, hostname: str,
isDevHost: bool, action: str, ip: str) -> None:
super().__init__()
self.v = v
self.code = code
self.code_desc = codeDesc
self.token_id = tokID
self.timestamp_sec = timestampSec
self.timestamp_iso = timestampISO
self.hostname = hostname
self.is_dev_host = isDevHost
self.action = action
self.ip = ip
|
"""
Extrusion.
Converts a flat image into spatial data points and rotates the points
around the center.
"""
a = None
onetime = True
aPixels = None
values = None
angle = 0
def setup():
size(640, 360, P3D)
aPixels = [[0] * width for i in range(height)]
values = [[0] * width for i in range(height)]
noFill()
# Load the image into a array
# Extract the values and store in an array
a = loadImage("ystone08.jpg")
a.loadPixels()
for i in range(a.height):
for j in range(a.width):
aPixels[j][i] = a.pixels[i * a.width + j]
values[j][i] = int(blue(aPixels[j][i]))
def draw():
background(0)
translate(width / 2, height / 2, -height / 2)
scale(2.0)
# Update and constrain the angle
angle += 0.005
rotateY(angle)
# Display the image mass
for i in range(0, a.height, 4):
for j in range(0, a.width, 4):
stroke(values[j][i], 255)
line(j - a.width / 2, i - a.height / 2, -
values[j][i], j - a.width / 2, i - a.height / 2, -values[j][i] - 10)
|
test = {
'name': 'Problem 11',
'points': 1,
'suites': [
{
'cases': [
{
'code': r"""
scm> (define (square x) (* x x))
square
scm> square
(lambda (x) (* x x))
scm> (square 21)
441
scm> square ; check to make sure lambda body hasn't changed
(lambda (x) (* x x))
scm> (define square (lambda (x) (* x x)))
square
scm> (square (square 21))
194481
""",
'hidden': False,
'locked': False
},
{
'code': r"""
scm> ((lambda (x) (list x (list (quote quote) x))) (quote (lambda (x) (list x (list (quote quote) x)))))
((lambda (x) (list x (list (quote quote) x))) (quote (lambda (x) (list x (list (quote quote) x)))))
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': '',
'teardown': '',
'type': 'scheme'
},
{
'cases': [
{
'code': r"""
scm> (define (outer x y)
.... (define (inner z x)
.... (+ x (* y 2) (* z 3)))
.... (inner x 10))
outer
scm> (outer 1 2)
17
scm> (define (outer-func x y)
.... (define (inner z x)
.... (+ x (* y 2) (* z 3)))
.... inner)
outer-func
scm> ((outer-func 1 2) 1 10)
17
""",
'hidden': False,
'locked': False
},
{
'code': r"""
scm> (define square (lambda (x) (* x x)))
square
scm> (define (sum-of-squares x y) (+ (square x) (square y)))
sum-of-squares
scm> (sum-of-squares 3 4)
25
scm> (define double (lambda (x) (* 2 x)))
double
scm> (define compose (lambda (f g) (lambda (x) (f (g x)))))
compose
scm> (define apply-twice (lambda (f) (compose f f)))
apply-twice
scm> ((apply-twice double) 5)
20
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': '',
'teardown': '',
'type': 'scheme'
}
]
}
|
class Solution:
def twoSum(self, numbers: List[int], target: int) -> List[int]:
# O(N)
for idx, t in enumerate(numbers):
targ = target - t
# O(logN)
left, right = 0, len(numbers) - 1
ans = []
while left <= right:
mid = left + (right - left) // 2
if numbers[mid] < targ:
left = mid + 1
elif numbers[mid] > targ:
right = mid - 1
else:
if mid != idx:
ans.append(idx + 1)
ans.append(mid + 1)
break
else:
left = mid + 1
if ans:
break
return ans
|
class Sorter:
def __init__(self, group):
self.group = group
self.found = False
def __call__(self, x):
if x in self.group:
self.found = True
return (0, x)
return (1, x)
|
## Exceptions
__all__ = ['PyDSTool_Error', 'PyDSTool_BoundsError', 'PyDSTool_KeyError',
'PyDSTool_UncertainValueError', 'PyDSTool_TypeError',
'PyDSTool_ExistError', 'PyDSTool_AttributeError',
'PyDSTool_ValueError', 'PyDSTool_UndefinedError',
'PyDSTool_InitError', 'PyDSTool_ClearError',
'PyDSTool_ContError']
class PyDSTool_Error(Exception):
def __init__(self, value=None):
self.value = value
self.code = None
def __str__(self):
return repr(self.value)
def __repr__(self):
return repr(self.value)
class PyDSTool_UncertainValueError(PyDSTool_Error):
def __init__(self, value, varval=None):
if varval is None:
valstr = ''
else:
valstr = ' at variable = '+str(varval)
self.varval = varval
PyDSTool_Error.__init__(self, value+valstr)
class PyDSTool_BoundsError(PyDSTool_Error):
pass
class PyDSTool_KeyError(PyDSTool_Error):
pass
class PyDSTool_ValueError(PyDSTool_Error):
pass
class PyDSTool_TypeError(PyDSTool_Error):
pass
class PyDSTool_ExistError(PyDSTool_Error):
pass
class PyDSTool_UndefinedError(PyDSTool_Error):
pass
class PyDSTool_AttributeError(PyDSTool_Error):
pass
class PyDSTool_InitError(PyDSTool_Error):
pass
class PyDSTool_ClearError(PyDSTool_Error):
pass
class PyDSTool_ContError(PyDSTool_Error):
pass |
def modpower(x,y,mod):
ans = 1
x = x % mod
while y:
if ((y & 1) == 1) :
ans = (ans * x) % mod
y = y >> 1
x = (x * x) % mod
return ans
T=int(input())
for tt in range(1,T+1):
N,K=[int(x) for x in input().split()]
S=input()
copyS=list(S)
total=0
MOD=10**9 +7
middle=(N+1)//2
for i in range(0,middle):
available=min(ord(copyS[i])-ord('a'),K)
total+= (available*modpower(K,middle-i-1,MOD))%MOD
copyS[N-i-1]=copyS[i]
x=''
for i in copyS:
x=x+i
if x<S:
total+=1
total%=MOD
print("Case #{}:".format(tt),total%MOD)
|
load("@build_bazel_rules_apple//apple/bundling:entitlements.bzl",
entitlements_rule="entitlements")
load(
"@build_bazel_rules_apple//apple/bundling:entitlements.bzl",
"AppleEntitlementsInfo",
)
def _entitlements_writer_impl(ctx):
entitlement_info = ctx.attr.entitlements[AppleEntitlementsInfo]
out = ctx.new_file(ctx.attr.name + ".entitlements")
if not entitlement_info or not entitlement_info.final_entitlements:
# Create some dummy entitlements
cmd = ' '.join([
'touch', out.path, '\n',
])
ctx.action(
command=cmd,
mnemonic="EntitlementsWriter",
inputs=[],
outputs=[out]
)
return struct(
files=depset([out])
)
# Export the entitlements from link_inputs
entitlements_file = entitlement_info.final_entitlements
cmd = ' '.join([
'cp', entitlements_file.path, out.path, '\n',
])
ctx.action(
command=cmd,
mnemonic="EntitlementsWriter",
inputs=[entitlements_file],
outputs=[out]
)
return struct(
files=depset([out])
)
entitlements_writer = rule(
implementation=_entitlements_writer_impl,
attrs={
"entitlements": attr.label(mandatory=True)
},
output_to_genfiles=True
)
def export_entitlements(name,
entitlements,
provisioning_profile,
bundle_id,
platform_type):
"""Macro that configure entitlements for use outside of Bazel.
- Accept an entitlements rule name.
- Derive a "name" + "_out" target.
Finally, it uses `rules_apple` to build up an entitlements file.
"""
entitlements_rule(
name=name + "_exported",
entitlements=entitlements,
platform_type=platform_type,
provisioning_profile=provisioning_profile,
bundle_id=bundle_id,
visibility=["//visibility:public"]
)
# Send entitlements to the writer.
entitlements_writer(
name=name,
entitlements=":" + name + "_exported",
visibility=["//visibility:public"]
)
|
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
#
# Copyright 2011 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This module exposes constants used inside the tool."""
VERSION = 'v1.2-beta1'
CODEJAM_AGENT_NAME = 'cmdline-%s' % VERSION
USER_CONFIG_PATH = '{base_dir}/config/user_config.py'
CURRENT_CONFIG_PATH = '{base_dir}/config/current_config.py'
_runtime_constants = {}
def GetRuntimeConstant(key, default_value=None):
"""Get a constant from the runtime_constants dictionary.
Args:
key: Key used to index the dictionary when retrieving the stored value.
default_value: Value to be returned if the key is not in the dictionary.
"""
return _runtime_constants.get(key, default_value)
def SetRuntimeConstant(key, value):
"""Store a constant into the runtime_constants dictionary.
Args:
key: Key used to index the dictionary when retrieving the stored value.
value: Value to store in the dictionary.
"""
_runtime_constants[key] = value
|
"""A base presenter for common properties needed when rendering annotations."""
class AnnotationBasePresenter:
def __init__(self, annotation):
self.annotation = annotation
@property
def target(self):
target = {"source": self.annotation.target_uri}
if self.annotation.target_selectors:
target["selector"] = self.annotation.target_selectors
return [target]
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.