blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 616 | content_id stringlengths 40 40 | detected_licenses listlengths 0 69 | license_type stringclasses 2
values | repo_name stringlengths 5 118 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringlengths 4 63 | visit_date timestamp[us] | revision_date timestamp[us] | committer_date timestamp[us] | github_id int64 2.91k 686M ⌀ | star_events_count int64 0 209k | fork_events_count int64 0 110k | gha_license_id stringclasses 23
values | gha_event_created_at timestamp[us] | gha_created_at timestamp[us] | gha_language stringclasses 220
values | src_encoding stringclasses 30
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 2 10.3M | extension stringclasses 257
values | content stringlengths 2 10.3M | authors listlengths 1 1 | author_id stringlengths 0 212 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fb7c06bff9e8b84d7c6d1b5379ef4d9189b6a382 | 5c1c52d5ee5c1aade09c78fe6ba41d101b9f1842 | /keyboard/inlinekeyboard/customer_keyboard.py | 563be0397913bf84602ac7a685a8d07d88e96e5a | [] | no_license | Hazernit/Bot123 | 0db4ab591e91c98f9b2d81d6fea89f8fd5da0e43 | f7ab5dd06c1cfeb03a318cd6f45367c1ab249a0c | refs/heads/main | 2023-03-26T08:19:28.764767 | 2021-03-24T18:17:18 | 2021-03-24T18:17:18 | 347,471,209 | 0 | 1 | null | 2021-03-24T18:17:19 | 2021-03-13T20:24:27 | Python | UTF-8 | Python | false | false | 4,680 | py | from aiogram import types
from aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup
# Start
def inlinkeyboard():
markup = InlineKeyboardMarkup(resize_keyboard = True, selective = True)
button_1 = InlineKeyboardButton('Исполнитель', callback_data='button_1')
button_2 = InlineKeyboardButton('Заказчик', callback_data='button_2')
button_6_admin = types.InlineKeyboardButton('Связь с админом', callback_data='button_6', url='https://t.me/i_Tele_2')
markup.add(button_1, button_2)
markup.row(button_6_admin)
return markup
def customer_keyboard1(): ######### клавы для предметов
markup = InlineKeyboardMarkup(resize_keyboard = True, selective = True)
button_3 = InlineKeyboardButton('Гуманитарий', callback_data='customer_button_1')
button_4 = InlineKeyboardButton('Программист', callback_data='customer_button_2')
button_5 = InlineKeyboardButton('Юрист', callback_data='customer_button_3')
button_6 = InlineKeyboardButton('Технарь', callback_data='customer_button_4')
button_7 = InlineKeyboardButton('Естественник', callback_data='customer_button_5')
button_8 = InlineKeyboardButton('Экономист', callback_data='customer_button_6')
button_9 = InlineKeyboardButton('⬅ Назад', callback_data='back1')
button_6_admin = types.InlineKeyboardButton('Связь с админом', callback_data='button_6',
url='https://t.me/i_Tele_2')
markup.add(button_3, button_4, button_5, button_6, button_7, button_8)
markup.row(button_9)
markup.row(button_6_admin)
return markup
def customer_keyboard_data():
markup = types.InlineKeyboardMarkup(resize_keyboard = True, selective = True)
button_customer_1 = types.InlineKeyboardButton('Как можно скорее', callback_data='customer_button_7')
button_customer_2 = types.InlineKeyboardButton('1-2 дня', callback_data='customer_button_8')
button_customer_3 = types.InlineKeyboardButton('2-4 дня', callback_data='customer_button_9')
button_customer_4 = types.InlineKeyboardButton('в течение 7 дней', callback_data='customer_button_10')
button_customer_5 = types.InlineKeyboardButton('ввести свой срок исполнения', callback_data='customer_button_11')
button_customer_6 = InlineKeyboardButton('⬅ Назад', callback_data='back2')
button_6_admin = types.InlineKeyboardButton('Связь с админом', callback_data='button_6',
url='https://t.me/i_Tele_2')
markup.add(button_customer_1, button_customer_2, button_customer_3, button_customer_4, button_customer_5,)
markup.row(button_customer_6)
markup.row(button_6_admin)
return markup
def customer_keyboard3_price():
markup = types.InlineKeyboardMarkup(resize_keyboard=True, selective=True)
button_customer_1 = types.InlineKeyboardButton('100руб', callback_data='button_customer_1_price')
button_customer_2 = types.InlineKeyboardButton('200руб', callback_data='button_customer_2_price')
button_customer_3 = types.InlineKeyboardButton('300руб', callback_data='button_customer_3_price')
button_customer_4 = types.InlineKeyboardButton('400руб', callback_data='button_customer_4_price')
button_customer_5 = types.InlineKeyboardButton('500руб', callback_data='button_customer_5_price')
button_customer_6 = types.InlineKeyboardButton('600руб', callback_data='button_customer_6_price')
button_customer_7 = types.InlineKeyboardButton('700руб', callback_data='button_customer_7_price')
button_customer_8 = types.InlineKeyboardButton('800руб', callback_data='button_customer_8_price')
button_customer_9 = types.InlineKeyboardButton('900руб', callback_data='button_customer_9_price')
button_customer_10 = types.InlineKeyboardButton('1000руб', callback_data='button_customer_10_price')
button_customer_11 = InlineKeyboardButton('⬅ Назад', callback_data='back3')
button_6_admin = types.InlineKeyboardButton('Связь с админом', callback_data='button_6',
url='https://t.me/i_Tele_2')
markup.add(button_customer_1, button_customer_2, button_customer_3, button_customer_4, button_customer_5,
button_customer_6, button_customer_7, button_customer_8, button_customer_9, button_customer_10)
markup.row(button_customer_11)
markup.row(button_6_admin)
return markup
| [
"noreply@github.com"
] | Hazernit.noreply@github.com |
fd175a741de17ed07e553935afb890a9a07aed6a | c289c9472ce1f6eb80a9831b930674d3b71e85e4 | /chap_10/template_pattern.py | dfe6451bc18122a530eb31b5d78523140cdc6b87 | [] | no_license | Jordan-Rowland/oop-practice | a6ba2ad9f6323f58b7e4ac521c2885e5bd5b9571 | b442c62bbc45ad599bb964b22f98d6237f2e0297 | refs/heads/master | 2020-07-10T00:22:51.433643 | 2019-11-20T05:48:17 | 2019-11-20T05:48:17 | 204,117,797 | 1 | 0 | null | 2019-11-20T05:48:18 | 2019-08-24T06:21:08 | Python | UTF-8 | Python | false | false | 2,448 | py | """
The template pattern is useful for removing duplicate code; it's intended to support the
DRY principle. It is designed for situations where we have several different tasks to
accomplish that have some, but not all, steps in common. The common steps are implemented
in a base class, and the distinct steps are overridden in subclasses to provide custom
behaviour. In some ways, it's like a generalized strategy pattern, except similar sections
of the algorithm are shared using a base class.
"""
import sqlite3
conn = sqlite3.connect('sales.db')
conn.execute("""
DROP TABLE IF EXISTS sales;
""")
conn.execute(
"CREATE TABLE sales (salesperson text, "
"amt currency, year integer, model text, new boolean)"
)
conn.execute("""
INSERT INTO sales VALUES
('Tim', 16000, 2010, 'Honda Fit', 'true'),
('Tim', 9000, 2006, 'Ford Focus', 'false'),
('Gayle', 8000, 2004, 'Dodge Neon', 'false'),
('Gayle', 28000, 2009, 'Ford Mustang', 'true'),
('Gayle', 50000, 2010, 'Lincoln Navigator', 'true'),
('Don', 20000, 2008, 'Toyota Prius', 'false');
""")
conn.commit()
conn.close()
class QueryTemplate:
def connect(self):
self.conn = sqlite3.connect('sales.db')
def construct_query(self):
raise NotImplementedError()
def do_query(self):
results = self.conn.execute(self.query)
self.results = results.fetchall()
def format_results(self):
output = []
for row in self.results:
row = [str(i) for i in row]
output.append(", ".join(row))
self.formatted_results = "\n".join(output)
def output_results(self):
raise NotImplementedError
def process_format(self):
self.connect()
self.construct_query()
self.do_query()
self.format_results()
self.output_results()
import datetime
class NewVehicleQuery(QueryTemplate):
def construct_query(self):
self.query = "SELECT* FROM sales WHERE new = 'true'"
def output_results(self):
print(self.formatted_results)
class UserGrossQuery(QueryTemplate):
def construct_query(self):
self.query = """
SELECT salesperson, sum(amt) FROM sales GROUP BY salesperson
"""
def output_results(self):
filename = f"Gross_Sales_{datetime.datetime.today().strftime('%Y%m%d')}"
with open(filename, "w") as outfile:
outfile.write(self.formatted_results)
| [
"36084892+Jordan-Rowland@users.noreply.github.com"
] | 36084892+Jordan-Rowland@users.noreply.github.com |
4e8ad11d0b63d59fba4ed5c53a72136da7a90273 | 7c24607e5c201e9a6d4ab86bb89f5aa882aa65bf | /sib_api_v3_sdk/models/create_smtp_template.py | ec035dcd91cc45904777e8a6325c77808f609663 | [
"MIT"
] | permissive | SportPursuit/APIv3-python-library | f9c715f59ada2efce1f8ff69d167e71bfc71b598 | a615e09ccb59d78fd9baa9f45e1a70f2f882fe16 | refs/heads/master | 2021-05-10T13:19:41.566860 | 2017-12-27T10:59:34 | 2017-12-27T10:59:34 | 118,469,772 | 0 | 0 | null | 2018-01-22T14:42:32 | 2018-01-22T14:42:31 | null | UTF-8 | Python | false | false | 11,754 | py | # coding: utf-8
"""
SendinBlue API
SendinBlue provide a RESTFul API that can be used with any languages. With this API, you will be able to : - Manage your campaigns and get the statistics - Manage your contacts - Send transactional Emails and SMS - and much more... You can download our wrappers at https://github.com/orgs/sendinblue **Possible responses** | Code | Message | | :-------------: | ------------- | | 200 | OK. Successful Request | | 201 | OK. Successful Creation | | 202 | OK. Request accepted | | 204 | OK. Successful Update/Deletion | | 400 | Error. Bad Request | | 401 | Error. Authentication Needed | | 402 | Error. Not enough credit, plan upgrade needed | | 403 | Error. Permission denied | | 404 | Error. Object does not exist | | 405 | Error. Method not allowed |
OpenAPI spec version: 3.0.0
Contact: contact@sendinblue.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class CreateSmtpTemplate(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'tag': 'str',
'sender': 'CreateSmtpTemplateSender',
'template_name': 'str',
'html_content': 'str',
'html_url': 'str',
'subject': 'str',
'reply_to': 'str',
'to_field': 'str',
'attachment_url': 'str',
'is_active': 'bool'
}
attribute_map = {
'tag': 'tag',
'sender': 'sender',
'template_name': 'templateName',
'html_content': 'htmlContent',
'html_url': 'htmlUrl',
'subject': 'subject',
'reply_to': 'replyTo',
'to_field': 'toField',
'attachment_url': 'attachmentUrl',
'is_active': 'isActive'
}
def __init__(self, tag=None, sender=None, template_name=None, html_content=None, html_url=None, subject=None, reply_to=None, to_field=None, attachment_url=None, is_active=None):
"""
CreateSmtpTemplate - a model defined in Swagger
"""
self._tag = None
self._sender = None
self._template_name = None
self._html_content = None
self._html_url = None
self._subject = None
self._reply_to = None
self._to_field = None
self._attachment_url = None
self._is_active = None
if tag is not None:
self.tag = tag
if sender is not None:
self.sender = sender
self.template_name = template_name
if html_content is not None:
self.html_content = html_content
if html_url is not None:
self.html_url = html_url
self.subject = subject
if reply_to is not None:
self.reply_to = reply_to
if to_field is not None:
self.to_field = to_field
if attachment_url is not None:
self.attachment_url = attachment_url
if is_active is not None:
self.is_active = is_active
@property
def tag(self):
"""
Gets the tag of this CreateSmtpTemplate.
Tag of the template
:return: The tag of this CreateSmtpTemplate.
:rtype: str
"""
return self._tag
@tag.setter
def tag(self, tag):
"""
Sets the tag of this CreateSmtpTemplate.
Tag of the template
:param tag: The tag of this CreateSmtpTemplate.
:type: str
"""
self._tag = tag
@property
def sender(self):
"""
Gets the sender of this CreateSmtpTemplate.
:return: The sender of this CreateSmtpTemplate.
:rtype: CreateSmtpTemplateSender
"""
return self._sender
@sender.setter
def sender(self, sender):
"""
Sets the sender of this CreateSmtpTemplate.
:param sender: The sender of this CreateSmtpTemplate.
:type: CreateSmtpTemplateSender
"""
self._sender = sender
@property
def template_name(self):
"""
Gets the template_name of this CreateSmtpTemplate.
Name of the template
:return: The template_name of this CreateSmtpTemplate.
:rtype: str
"""
return self._template_name
@template_name.setter
def template_name(self, template_name):
"""
Sets the template_name of this CreateSmtpTemplate.
Name of the template
:param template_name: The template_name of this CreateSmtpTemplate.
:type: str
"""
if template_name is None:
raise ValueError("Invalid value for `template_name`, must not be `None`")
self._template_name = template_name
@property
def html_content(self):
"""
Gets the html_content of this CreateSmtpTemplate.
Body of the message (HTML version). The field must have more than 10 characters. REQUIRED if htmlUrl is empty
:return: The html_content of this CreateSmtpTemplate.
:rtype: str
"""
return self._html_content
@html_content.setter
def html_content(self, html_content):
"""
Sets the html_content of this CreateSmtpTemplate.
Body of the message (HTML version). The field must have more than 10 characters. REQUIRED if htmlUrl is empty
:param html_content: The html_content of this CreateSmtpTemplate.
:type: str
"""
self._html_content = html_content
@property
def html_url(self):
"""
Gets the html_url of this CreateSmtpTemplate.
Url which contents the body of the email message. REQUIRED if htmlContent is empty
:return: The html_url of this CreateSmtpTemplate.
:rtype: str
"""
return self._html_url
@html_url.setter
def html_url(self, html_url):
"""
Sets the html_url of this CreateSmtpTemplate.
Url which contents the body of the email message. REQUIRED if htmlContent is empty
:param html_url: The html_url of this CreateSmtpTemplate.
:type: str
"""
self._html_url = html_url
@property
def subject(self):
"""
Gets the subject of this CreateSmtpTemplate.
Subject of the template
:return: The subject of this CreateSmtpTemplate.
:rtype: str
"""
return self._subject
@subject.setter
def subject(self, subject):
"""
Sets the subject of this CreateSmtpTemplate.
Subject of the template
:param subject: The subject of this CreateSmtpTemplate.
:type: str
"""
if subject is None:
raise ValueError("Invalid value for `subject`, must not be `None`")
self._subject = subject
@property
def reply_to(self):
"""
Gets the reply_to of this CreateSmtpTemplate.
Email on which campaign recipients will be able to reply to
:return: The reply_to of this CreateSmtpTemplate.
:rtype: str
"""
return self._reply_to
@reply_to.setter
def reply_to(self, reply_to):
"""
Sets the reply_to of this CreateSmtpTemplate.
Email on which campaign recipients will be able to reply to
:param reply_to: The reply_to of this CreateSmtpTemplate.
:type: str
"""
self._reply_to = reply_to
@property
def to_field(self):
"""
Gets the to_field of this CreateSmtpTemplate.
This is to personalize the «To» Field. If you want to include the first name and last name of your recipient, add [FNAME] [LNAME]. To use the contact attributes here, these must already exist in SendinBlue account
:return: The to_field of this CreateSmtpTemplate.
:rtype: str
"""
return self._to_field
@to_field.setter
def to_field(self, to_field):
"""
Sets the to_field of this CreateSmtpTemplate.
This is to personalize the «To» Field. If you want to include the first name and last name of your recipient, add [FNAME] [LNAME]. To use the contact attributes here, these must already exist in SendinBlue account
:param to_field: The to_field of this CreateSmtpTemplate.
:type: str
"""
self._to_field = to_field
@property
def attachment_url(self):
"""
Gets the attachment_url of this CreateSmtpTemplate.
Absolute url of the attachment (no local file). Extensions allowed xlsx, xls, ods, docx, docm, doc, csv, pdf, txt, gif, jpg, jpeg, png, tif, tiff and rtf
:return: The attachment_url of this CreateSmtpTemplate.
:rtype: str
"""
return self._attachment_url
@attachment_url.setter
def attachment_url(self, attachment_url):
"""
Sets the attachment_url of this CreateSmtpTemplate.
Absolute url of the attachment (no local file). Extensions allowed xlsx, xls, ods, docx, docm, doc, csv, pdf, txt, gif, jpg, jpeg, png, tif, tiff and rtf
:param attachment_url: The attachment_url of this CreateSmtpTemplate.
:type: str
"""
self._attachment_url = attachment_url
@property
def is_active(self):
"""
Gets the is_active of this CreateSmtpTemplate.
Status of template. isActive = true means template is active and isActive = false means template is inactive
:return: The is_active of this CreateSmtpTemplate.
:rtype: bool
"""
return self._is_active
@is_active.setter
def is_active(self, is_active):
"""
Sets the is_active of this CreateSmtpTemplate.
Status of template. isActive = true means template is active and isActive = false means template is inactive
:param is_active: The is_active of this CreateSmtpTemplate.
:type: bool
"""
self._is_active = is_active
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, CreateSmtpTemplate):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
| [
"mymac@Aakankshas-MacBook-Air.local"
] | mymac@Aakankshas-MacBook-Air.local |
70bbe8208649b16729cf28e1e4a6518b00610e12 | 0617c812e9bf58a2dbc1c1fef35e497b054ed7e4 | /venv/Lib/site-packages/pyrogram/raw/functions/account/check_username.py | d280838035783c8751c6caf5d199e15af0b780fc | [] | no_license | howei5163/my_framework | 32cf510e19a371b6a3a7c80eab53f10a6952f7b2 | 492c9af4ceaebfe6e87df8425cb21534fbbb0c61 | refs/heads/main | 2023-01-27T14:33:56.159867 | 2020-12-07T10:19:33 | 2020-12-07T10:19:33 | 306,561,184 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,138 | py | # Pyrogram - Telegram MTProto API Client Library for Python
# Copyright (C) 2017-2020 Dan <https://github.com/delivrance>
#
# This file is part of Pyrogram.
#
# Pyrogram is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Pyrogram is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with Pyrogram. If not, see <http://www.gnu.org/licenses/>.
from io import BytesIO
from pyrogram.raw.core.primitives import Int, Long, Int128, Int256, Bool, Bytes, String, Double, Vector
from pyrogram.raw.core import TLObject
from pyrogram import raw
from typing import List, Union, Any
# # # # # # # # # # # # # # # # # # # # # # # #
# !!! WARNING !!! #
# This is a generated file! #
# All changes made in this file will be lost! #
# # # # # # # # # # # # # # # # # # # # # # # #
class CheckUsername(TLObject): # type: ignore
"""Telegram API method.
Details:
- Layer: ``117``
- ID: ``0x2714d86c``
Parameters:
username: ``str``
Returns:
``bool``
"""
__slots__: List[str] = ["username"]
ID = 0x2714d86c
QUALNAME = "pyrogram.raw.functions.account.CheckUsername"
def __init__(self, *, username: str) -> None:
self.username = username # string
@staticmethod
def read(data: BytesIO, *args: Any) -> "CheckUsername":
# No flags
username = String.read(data)
return CheckUsername(username=username)
def write(self) -> bytes:
data = BytesIO()
data.write(Int(self.ID, False))
# No flags
data.write(String(self.username))
return data.getvalue()
| [
"houwei5163"
] | houwei5163 |
b7d132d47f8448aeb6077d1264063bf458f2674c | e73f0bd1e15de5b8cb70f1d603ceedc18c42b39b | /Project Euler/014 - Collatz sequance.py | d3aabfadf4ae8c3e4f5527c2ef44622211ca50e0 | [] | no_license | thran/the_code | cbfa3b8be86c3b31f76f6fbd1deb2013d3326a4a | ba73317ddc42e10791a829cc6e1a3460cc601c44 | refs/heads/master | 2023-01-05T14:39:16.708461 | 2022-12-25T08:37:39 | 2022-12-25T08:37:39 | 160,978,160 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 305 | py | def next_collatz(n):
if n % 2 == 0:
return n / 2
return 3 * n + 1
def collatz_lenght(n):
if n == 1: return 1
return collatz_lenght(next_collatz(n)) + 1
m = 0
best = 0
for i in range(1, 10**6):
l = collatz_lenght(i)
if m < l:
m = l
best = i
print m, best | [
"thran@centrum.cz"
] | thran@centrum.cz |
1bfd9456a2d4952ac88234000b9c62e07f23db6e | a59a468fa5931b037b3a206ff85f1bd085cb0400 | /openmp/mdpic1/mdpic1_py/fmdpic1.py | d2ef00498018ff0749ce0f9cb39209d0279f414a | [
"BSD-2-Clause",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | tsung1029/PIC-skeleton-codes | 43586293339c16a9f76938ea7dc9bbe1afd73aaf | 253d01556401417b96cf4f421d6359de0a7c4719 | refs/heads/master | 2023-08-16T09:24:20.937609 | 2020-12-28T20:22:22 | 2020-12-28T20:22:22 | 158,845,056 | 0 | 1 | null | 2019-07-02T21:06:16 | 2018-11-23T14:49:04 | Fortran | UTF-8 | Python | false | false | 16,871 | py | #-----------------------------------------------------------------------
# Skeleton 1-2/2D Darwin OpenMP PIC code
# written by Viktor K. Decyk, Adam Tableman, and Qiyang Hu, UCLA
import math
import numpy
from fmdpush1 import *
from dtimer import *
from fomplib import *
int_type = numpy.int32
double_type = numpy.float64
float_type = numpy.float32
complex_type = numpy.complex64
# indx = exponent which determines grid points in x direction:
# nx = 2**indx.
indx = 9
# npx = number of electrons distributed in x direction.
npx = 18432
# tend = time at end of simulation, in units of plasma frequency.
# dt = time interval between successive calculations.
# qme = charge on electron, in units of e.
tend = 10.0; dt = 0.1; qme = -1.0
# vtx/vty = thermal velocity of electrons in x/y direction
# vx0/vy0 = drift velocity of electrons in x/y direction.
vtx = 1.0; vty = 1.0; vx0 = 0.0; vy0 = 0.0
# vtx/vz0 = thermal/drift velocity of electrons in z direction
vtz = 1.0; vz0 = 0.0
# ax = smoothed particle size in x direction
# ci = reciprocal of velocity of light.
ax = .912871; ci = 0.1
# idimp = number of particle coordinates = 4
# ipbc = particle boundary condition: 1 = periodic
idimp = 4; ipbc = 1
# omx/omy/omz = magnetic field electron cyclotron frequency in x/y/z
omx = 0.4; omy = 0.0; omz = 0.0
# ndc = number of corrections in darwin iteration
ndc = 1
# wke/we = particle kinetic/electrostatic field energy
# wf/wm/wt = magnetic field/transverse electric field/total energy
wke = numpy.zeros((1),float_type)
we = numpy.zeros((1),float_type)
wf = numpy.zeros((1),float_type)
wm = numpy.zeros((1),float_type)
wt = numpy.zeros((1),float_type)
zero = 0.0
# mx = number of grids in x in sorting tiles
mx = 32
# xtras = fraction of extra particles needed for particle management
xtras = 0.2
# declare scalars for standard code
wpmax = numpy.empty((1),float_type)
wpmin = numpy.empty((1),float_type)
# declare scalars for OpenMP code
nppmx = numpy.empty((1),int_type)
irc = numpy.zeros((1),int_type)
# declare and initialize timing data
itime = numpy.empty((4),numpy.int32)
tdpost = 0.0; tguard = 0.0; tfft = 0.0; tfield = 0.0
tdjpost = 0.0; tdcjpost = 0.0; tpush = 0.0; tsort = 0.0
dtime = numpy.empty((1),double_type)
# nvp = number of shared memory nodes (0=default)
nvp = 0
#nvp = int(input("enter number of nodes: "))
# initialize for shared memory parallel processing
init_omp(nvp)
# initialize scalars for standard code
# np = total number of particles in simulation
# nx = number of grid points in x direction
np = npx; nx = int(math.pow(2,indx)); nxh = int(nx/2)
nxe = nx + 2; nxeh = int(nxe/2)
# mx1 = number of tiles in x direction
mx1 = int((nx - 1)/mx + 1)
# nloop = number of time steps in simulation
# ntime = current time step
nloop = int(tend/dt + .0001); ntime = 0
qbme = qme
affp = float(nx)/float(np)
# allocate data for standard code
# part = particle array
part = numpy.empty((idimp,np),float_type,'F')
# qe = electron charge density with guard cells
qe = numpy.empty((nxe),float_type,'F')
# fxe = smoothed longitudinal electric field with guard cells
fxe = numpy.empty((nxe),float_type,'F')
# cue = electron current density with guard cells
cue = numpy.empty((2,nxe),float_type,'F')
# dcu = acceleration density with guard cells
dcu = numpy.empty((2,nxe),float_type,'F')
# cus = transverse electric field with guard cells
cus = numpy.empty((2,nxe),float_type,'F')
# amu = momentum flux with guard cells
amu = numpy.empty((2,nxe),float_type,'F')
# exyze = smoothed total electric field with guard cells
exyze = numpy.empty((3,nxe),float_type,'F')
# byze = smoothed magnetic field with guard cells
byze = numpy.empty((2,nxe),float_type,'F')
# ffc, ffe = form factor arrays for poisson solvers
ffc = numpy.empty((nxh),complex_type,'F')
ffe = numpy.empty((nxh),complex_type,'F')
# mixup = bit reverse table for FFT
mixup = numpy.empty((nxh),int_type,'F')
# sct = sine/cosine table for FFT
sct = numpy.empty((nxh),complex_type,'F')
# kpic = number of particles in each tile
kpic = numpy.empty((mx1),int_type,'F')
# gxe, gyze = scratch arrays for fft
gxe = numpy.empty((nxe),float_type,'F')
gyze = numpy.empty((2,nxe),float_type,'F')
# prepare fft tables
wfft1rinit(mixup,sct,indx,nxh)
# calculate form factor: ffc
isign = 0
pois1(qe,fxe,isign,ffc,ax,affp,we,nx)
# initialize electrons
distr1h(part,vtx,vty,vtz,vx0,vy0,vz0,npx,idimp,np,nx,ipbc)
# find number of particles in each of mx, tiles: updates kpic, nppmx
dblkp1l(part,kpic,nppmx,idimp,np,mx,mx1,irc)
if (irc[0] != 0):
print "dblkp1l error, irc=", irc[0]
exit(0)
# allocate vector particle data
nppmx0 = int((1.0 + xtras)*nppmx)
ntmax = int(xtras*nppmx)
npbmx = int(xtras*nppmx)
# ppart = tiled particle array
ppart = numpy.empty((idimp,nppmx0,mx1),float_type,'F')
# ppbuff = buffer array for reordering tiled particle array
ppbuff = numpy.empty((idimp,npbmx,mx1),float_type,'F')
# ncl = number of particles departing tile in each direction
ncl = numpy.empty((2,mx1),int_type,'F')
# ihole = location/destination of each particle departing tile
ihole = numpy.empty((2,ntmax+1,mx1),int_type,'F')
# copy ordered particle data for OpenMP: updates ppart and kpic
ppmovin1l(part,ppart,kpic,nppmx0,idimp,np,mx,mx1,irc)
if (irc[0] != 0):
print "ppmovin1l overflow error, irc=", irc[0]
exit(0)
# sanity check
ppcheck1l(ppart,kpic,idimp,nppmx0,nx,mx,mx1,irc)
if (irc[0] != 0):
print "ppcheck1l error, irc=", irc[0]
exit(0)
# find maximum and minimum initial electron density
qe.fill(0.0)
gppost1l(ppart,qe,kpic,qme,nppmx0,idimp,mx,nxe,mx1)
aguard1l(qe,nx,nxe)
fwpminmx1(qe,qbme,wpmax,wpmin,nx,nxe)
wpm = 0.5*(wpmax[0] + wpmin[0])*affp
# accelerate convergence: update wpm
if (wpm <= 10.0):
wpm = 0.75*wpm
print "wpm=",wpm
q2m0 = wpm/affp
# calculate form factor: ffe
isign = 0
epois13(dcu,cus,isign,ffe,ax,affp,wpm,ci,wf,nx,nxeh,nxh)
# initialize transverse electric field
cus.fill(0.0)
# * * * start main iteration loop * * *
for ntime in xrange(0,nloop):
# print "ntime = ", ntime
# deposit current with OpenMP: updates cue
dtimer(dtime,itime,-1)
cue.fill(0.0)
gjppost1l(ppart,cue,kpic,qme,zero,nppmx0,idimp,nx,mx,nxe,mx1,ipbc)
dtimer(dtime,itime,1)
time = float(dtime)
tdjpost = tdjpost + time
# deposit charge with OpenMP: updates qe
dtimer(dtime,itime,-1)
qe.fill(0.0)
gppost1l(ppart,qe,kpic,qme,nppmx0,idimp,mx,nxe,mx1)
dtimer(dtime,itime,1)
time = float(dtime)
tdpost = tdpost + time
# add guard cells with standard procedure: updates qe, cue
dtimer(dtime,itime,-1)
aguard1l(qe,nx,nxe)
acguard1l(cue,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tguard = tguard + time
# transform charge to fourier space with standard procedure:
# updates qe, gxe
dtimer(dtime,itime,-1)
isign = -1
fft1rxx(qe,gxe,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# calculate longitudinal force/charge in fourier space with standard
# procedure: updates fxe, we
dtimer(dtime,itime,-1)
isign = -1
pois1(qe,fxe,isign,ffc,ax,affp,we,nx)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# transform longitudinal electric force to real space with standard
# procedure: updates fxe, gxe
dtimer(dtime,itime,-1)
isign = 1
fft1rxx(fxe,gxe,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# transform current to fourier space with standard procedure:
# updates cue, gyze
dtimer(dtime,itime,-1)
isign = -1
fft1r2x(cue,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# calculate magnetic field in fourier space with standard procedure:
# updates byze, wm
dtimer(dtime,itime,-1)
bbpois13(cue,byze,ffc,ci,wm,nx,nxeh,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# transform magnetic force to real space with standard procedure:
# updates byze, gyze
dtimer(dtime,itime,-1)
isign = 1
fft1r2x(byze,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# add constant to magnetic field with standard procedure: updates byze
dtimer(dtime,itime,-1)
baddext1(byze,omy,omz,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# copy guard cells with standard procedure: updates fxe, byze
dtimer(dtime,itime,-1)
dguard1l(fxe,nx,nxe)
cguard1l(byze,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tguard = tguard + time
# add longitudinal and old transverse electric fields with standard
# procedure: updates exyze
dtimer(dtime,itime,-1)
addvrfield13(exyze,cus,fxe,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# deposit electron acceleration density and momentum flux with OpenMP:
# updates dcu, amu
dtimer(dtime,itime,-1)
dcu.fill(0.0); amu.fill(0.0)
gdjppost1l(ppart,exyze,byze,dcu,amu,kpic,omx,qme,qbme,dt,idimp,
nppmx0,nx,mx,nxe,mx1)
# add old scaled electric field with standard procedure: updates dcu
ascfguard1l(dcu,cus,q2m0,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tdcjpost = tdcjpost + time
# add guard cells with standard procedure: updates dcu, amu
dtimer(dtime,itime,-1)
acguard1l(dcu,nx,nxe)
acguard1l(amu,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tguard = tguard + time
# transform acceleration density and momentum flux to fourier space
# with standard procedure: updates dcu, amu, gyze
dtimer(dtime,itime,-1)
isign = -1
fft1r2x(dcu,gyze,isign,mixup,sct,indx,nxe,nxh)
fft1r2x(amu,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# take transverse part of time derivative of current with standard
# procedure: updates dcu
dtimer(dtime,itime,-1)
adcuperp13(dcu,amu,nx,nxeh)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# calculate transverse electric field with standard procedure:
# updates cus, wf
dtimer(dtime,itime,-1)
isign = -1
epois13(dcu,cus,isign,ffe,ax,affp,wpm,ci,wf,nx,nxeh,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# transform transverse electric field to real space with standard
# procedure: updates cus, gyze
dtimer(dtime,itime,-1)
isign = 1
fft1r2x(cus,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# copy guard cells with standard procedure: updates cus
dtimer(dtime,itime,-1)
cguard1l(cus,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tguard = tguard + time
# add longitudinal and transverse electric fields with standard
# procedure: exyze = cus + fxe, updates exyze
# cus needs to be retained for next time step
dtimer(dtime,itime,-1)
addvrfield13(exyze,cus,fxe,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# inner iteration loop
for k in xrange(0,ndc):
# deposit electron current and acceleration density and momentum flux
# with OpenMP: updates cue, dcu, amu
dtimer(dtime,itime,-1)
cue.fill(0.0); dcu.fill(0.0); amu.fill(0.0)
gdcjppost1l(ppart,exyze,byze,cue,dcu,amu,kpic,omx,qme,qbme,dt,
idimp,nppmx0,nx,mx,nxe,mx1)
# add scaled electric field with standard procedure: updates dcu
ascfguard1l(dcu,cus,q2m0,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tdcjpost = tdcjpost + time
# add guard cells for current, acceleration density, and momentum flux
# with standard procedure: updates cue, dcu, amu
dtimer(dtime,itime,-1)
acguard1l(cue,nx,nxe)
acguard1l(dcu,nx,nxe)
acguard1l(amu,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tguard = tguard + time
# transform current to fourier space with standard procedure:
# update cue, gyze
dtimer(dtime,itime,-1)
isign = -1
fft1r2x(cue,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# calculate magnetic field in fourier space with standard procedure:
# updates byze, wm
dtimer(dtime,itime,-1)
bbpois13(cue,byze,ffc,ci,wm,nx,nxeh,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# transform magnetic force to real space with standard procedure:
# updates byze, gyze
dtimer(dtime,itime,-1)
isign = 1
fft1r2x(byze,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# add constant to magnetic field with standard procedure: updates bzye
dtimer(dtime,itime,-1)
baddext1(byze,omy,omz,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# transform acceleration density and momentum flux to fourier space
# with standard procedure: updates dcu, amu, gyze
dtimer(dtime,itime,-1)
isign = -1
fft1r2x(dcu,gyze,isign,mixup,sct,indx,nxe,nxh)
fft1r2x(amu,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# take transverse part of time derivative of current with standard
# procedure: updates dcu
dtimer(dtime,itime,-1)
adcuperp13(dcu,amu,nx,nxeh)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# calculate transverse electric field with standard procedure:
# updates cus, wf
dtimer(dtime,itime,-1)
isign = -1
epois13(dcu,cus,isign,ffe,ax,affp,wpm,ci,wf,nx,nxeh,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
# transform transverse electric field to real space with standard
dtimer(dtime,itime,-1)
isign = 1
fft1r2x(cus,gyze,isign,mixup,sct,indx,nxe,nxh)
dtimer(dtime,itime,1)
time = float(dtime)
tfft = tfft + time
# copy guard cells with standard procedure: updates byze, cus
dtimer(dtime,itime,-1)
cguard1l(byze,nx,nxe)
cguard1l(cus,nx,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tguard = tguard + time
# add longitudinal and transverse electric fields with standard
# procedure: exyze = cus + fxyze, updates exyze
# cus needs to be retained for next time step
dtimer(dtime,itime,-1)
addvrfield13(exyze,cus,fxe,nxe)
dtimer(dtime,itime,1)
time = float(dtime)
tfield = tfield + time
pass
# push particles with OpenMP:
wke[0] = 0.0
dtimer(dtime,itime,-1)
# updates ppart, wke
# gbppush13l(ppart,exyze,byze,kpic,omx,qbme,dt,dt,wke,idimp,nppmx0,nx,
# mx,nxe,mx1,ipbc)
# updates ppart, ncl, ihole, wke, irc
gbppushf13l(ppart,exyze,byze,kpic,ncl,ihole,omx,qbme,dt,dt,wke,idimp,
nppmx0,nx,mx,nxe,mx1,ntmax,irc)
dtimer(dtime,itime,1)
time = float(dtime)
tpush = tpush + time
if (irc[0] != 0):
print "gbppushf13l error, irc=", irc[0]
exit(0)
# reorder particles by tile with OpenMP:
dtimer(dtime,itime,-1)
# updates ppart, ppbuff, kpic, ncl, ihole, and irc
# pporder1l(ppart,ppbuff,kpic,ncl,ihole,idimp,nppmx0,nx,mx,mx1,npbmx,
# ntmax,irc)
# updates ppart, ppbuff, kpic, ncl, and irc
pporderf1l(ppart,ppbuff,kpic,ncl,ihole,idimp,nppmx0,mx1,npbmx,ntmax,
irc)
dtimer(dtime,itime,1)
time = float(dtime)
tsort = tsort + time
if (irc[0] != 0):
print "pporderf1l error, ntmax, irc=", ntmax, irc[0]
exit(0)
if (ntime==0):
wt = we + wm
print "Initial Total Field, Kinetic and Total Energies:"
print "%14.7e %14.7e %14.7e" % (wt, wke, wke + wt)
print "Initial Electrostatic, Transverse Electric and Magnetic " \
"Field Energies:"
print "%14.7e %14.7e %14.7e" % (we, wf, wm)
ntime = ntime + 1
# * * * end main iteration loop * * *
print "ntime, ndc = ", ntime, ndc
wt = we + wm
print "Final Total Field, Kinetic and Total Energies:"
print "%14.7e %14.7e %14.7e" % (wt, wke, wke + wt)
print "Final Electrostatic, Transverse Electric and Magnetic Field " \
"Energies:"
print "%14.7e %14.7e %14.7e" % (we, wf, wm)
print ""
print "deposit time = ", tdpost
print "current deposit time = ", tdjpost
print "current derivative deposit time = ", tdcjpost
tdpost = tdpost + tdjpost + tdcjpost
print "total deposit time = ", tdpost
print "guard time = ", tguard
print "solver time = ", tfield
print "fft time = ", tfft
print "push time = ", tpush
print "sort time = ", tsort
tfield = tfield + tguard + tfft
print "total solver time = ", tfield
time = tdpost + tpush + tsort
print "total particle time = ", time
wt = time + tfield
print "total time = ", wt
print ""
wt = 1.0e+09/(float(nloop)*float(np))
print "Push Time (nsec) = ", tpush*wt
print "Deposit Time (nsec) = ", tdpost*wt
print "Sort Time (nsec) = ", tsort*wt
print "Total Particle Time (nsec) = ", time*wt
| [
"benjum@benjum.local"
] | benjum@benjum.local |
f8e8f68f9380aedccf9d892cce4db13455634022 | 7ad730835bf6c3a5cc51ed8978e1a8a962640a29 | /cal.py | 8374d8b04eba34b4aff4b2385ca7feb080fb58f8 | [] | no_license | umair1440/Calculator-with-python- | 553e449ba993bd8b9cf3ebb727e0df12bf78ece9 | be9efd306a64dff1ec97d6d855a804c034b7a5a2 | refs/heads/main | 2023-03-31T17:47:09.607955 | 2021-04-08T10:58:16 | 2021-04-08T10:58:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,386 | py | val1 = float(input("Enter first number: "))
val2 = float(input("Enter second number: "))
opr = float(input("Please Select the operation numbere:\n 1: Sum \n 2: Subtract \n 3: Multiply \n 4: Divide \n 5: Reminder \n Enter the number of the Operation: "))
sum = float(val1 + val2)
sub = float(val1 - val2)
mul = float(val1 * val2)
div = float(val1 // val2)
rem = float(val1 % val2)
if opr == 1:
print("The Sum of these two numbers is: "+str(sum))
elif opr == 2 :
print("The subtraction of these two numbers is: "+ str(sub))
elif opr == 3 :
print("The Multiplication of the numbers is: "+str(mul))
elif opr == 4 :
print("The Division of these two numbers is: "+str(div))
elif opr == 5 :
print("The Reminder of these two numbers is: "+str(rem))
else:
opr = float(input("Please Select from the given:\n 1: Sum \n 2: Subtract \n 3: Multiply \n 4: Divide \n 5: Reminder \n Enter the number of the Operation: "))
if opr == 1:
print("The Sum of these two numbers is: "+str(sum))
elif opr == 2 :
print("The subtraction of these two numbers is: "+ str(sub))
elif opr == 3 :
print("The Multiplication of the numbers is: "+str(mul))
elif opr == 4 :
print("The Division of these two numbers is: "+str(div))
elif opr == 5 :
print("The Reminder of these two numbers is: "+str(rem))
else:
print("Your are not interssted so program is exited....") | [
"noreply@github.com"
] | umair1440.noreply@github.com |
44b80c569089638f50802259a2b208a0acc1f02b | ee58b29d8982cc6987b986ee616bc47b6a8d8aa3 | /python/dcp_367_merge_iterators.py | fa15d8ab6be1c94d2399b380055906ae31def2cf | [] | no_license | gubenkoved/daily-coding-problem | 7dd9e0a7ee6606a04cd50fa2766e650da1259f7b | ea8b352b1d3d1f44cd0f04ddaadf3e662f4c85bf | refs/heads/master | 2021-07-03T22:31:50.519730 | 2020-09-27T10:28:09 | 2020-09-27T10:28:09 | 172,369,604 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,255 | py | # This problem was asked by Two Sigma.
#
# Given two sorted iterators, merge it into one iterator.
#
# For example, given these two iterators:
#
# foo = iter([5, 10, 15])
# bar = iter([3, 8, 9])
# You should be able to do:
#
# for num in merge_iterators(foo, bar):
# print(num)
#
# # 3
# # 5
# # 8
# # 9
# # 10
# # 15
#
# Bonus: Make it work without pulling in the contents of the iterators in memory.
def merge(*iterators):
# python iterators do NOT allow to get the current value, so we will have
# to have a separate store
values = [next(iterator) for iterator in iterators]
while True:
if not iterators:
return
# pick the smallest
idx, val = min(enumerate(values), key=lambda x: x[1])
# advance the idx-th pointer
try:
iterator = iterators[idx]
values[idx] = next(iterator)
except StopIteration:
# exhausted iterator, remove it!
del iterators[idx]
del values[idx]
yield val
assert list(merge(iter([1, 2, 3]))) == [1, 2, 3]
assert list(merge(iter([5, 10, 15]), iter(3, 8, 9))) == [3, 5, 8, 9, 10, 15]
assert list(merge(iter([10, 20, 30]), iter([15, 25]), iter([17]))) == [10, 15, 17, 20, 25, 30] | [
"gubenkoved@gmail.com"
] | gubenkoved@gmail.com |
2fd8eefdad94c4d9a30d03c88f26f12edc57badd | 135e60a01581b2b321c3a1fb2105d7d1fdb7b431 | /main.py | 245e67a2a2a8f26fead9d963a91b714f46e7513d | [] | no_license | bryan0578/dynamic-site | 749712b38a8328c123e5ca3e7028c49c37ffbd9d | bb1ff31c85f6da9ad3f6ca41ce42cf77248555a7 | refs/heads/master | 2023-02-19T15:18:04.038142 | 2019-08-21T21:37:41 | 2019-08-21T21:37:41 | 76,591,594 | 0 | 0 | null | 2023-02-15T21:34:00 | 2016-12-15T19:58:36 | Python | UTF-8 | Python | false | false | 2,517 | py | '''
Bryan Cash
1/28/2015
DPW
Dynamic Site
'''
import webapp2
#I imported the ContentPage class from the page.py file
from page import ContentPage
#Imported the Shirt class from the data.py file
from data import Shirt
class MainHandler(webapp2.RequestHandler):
def get(self):
#I created an instance of Shirt
s = Shirt()
#I created an instance of Content Page
cp = ContentPage()
#started the conditional to handle the appropriate content
if self.request.GET:
#If the request.GET has an id that is equal to victorian_woman then the content page data will grab the matching information in the array and will print out the information using the print out method.
if self.request.GET['id'] == "victorian_woman":
cp.data = s.shirts[0]
self.response.write(cp.print_out())
#If the request.GET has an id that is equal to metal_skull then the content page data will grab the matching information in the array and will print out the information using the print out method.
elif self.request.GET['id'] == "metal_skull":
cp.data = s.shirts[1]
self.response.write(cp.print_out())
#If the request.GET has an id that is equal to three_b then the content page data will grab the matching information in the array and will print out the information using the print out method.
elif self.request.GET['id'] == "three_b":
cp.data = s.shirts[2]
self.response.write(cp.print_out())
#If the request.GET has an id that is equal to f_bomb then the content page data will grab the matching information in the array and will print out the information using the print out method.
elif self.request.GET['id'] == "f_bomb":
cp.data = s.shirts[3]
self.response.write(cp.print_out())
#If the request.GET has an id that is equal to hoody then the content page data will grab the matching information in the array and will print out the information using the print out method.
elif self.request.GET['id'] == "hoody":
cp.data = s.shirts[4]
self.response.write(cp.print_out())
#Otherwise it will print out the the fist set of data in the array
else:
cp.data = s.shirts[0]
self.response.write(cp.print_out())
app = webapp2.WSGIApplication([
('/', MainHandler)
], debug=True)
| [
"bcash0578@gmail.com"
] | bcash0578@gmail.com |
6393411ca9e3bb846f7b35f86dd34828e6d485b0 | 9dd8ce770fa4012ddba7bad095f70dfd71c62044 | /ex09_baumgartner_marion/randomWlak.py | ecab7ea26ff80e4dac4ed42bee4109fb43ea0459 | [] | no_license | marionb/CompPhysics | cfcd133315a0438871600a694969fde03323a15b | ef046c0f0219d805f1a529483820090a3f3e604e | refs/heads/master | 2020-12-02T21:37:59.646907 | 2013-01-17T09:45:05 | 2013-01-17T09:45:05 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,025 | py | """
randomWalk.py
Task: Simple Random Walk
"""
__author__ = "Marion Baumgartner (marion.baumgartner@uzh.ch)"
__date__ = "$Date: 16/11/201$"
import numpy as np
import random as rand
import matplotlib as mpl
from cmath import rect
import matplotlib.pyplot as plt
#global variable
T=10000
def RW(N,radius):
"""
Generates a random walk starting at the origin (0,0)
@ param N the amount of steps done in the random walk.
@ param r size of the step; set to one by default
@ return an array containig the possition after every stepp, in a two dimensional x-y plane represented with complex numbed.
"""
rand.seed()
#initial position is choosen randomly
r=np.sqrt(rand.random()) #
psi=rand.random()*2.0*np.pi;
p0=complex(radius*r*np.cos(psi),radius*r*np.sin(psi))
walk=[p0]
for i in range(N-1):#take 49 steps
r=np.sqrt(rand.random()) #
psi=rand.random()*2.0*np.pi;
p0+=complex(radius*r*np.cos(psi),radius*r*np.sin(psi))
walk.append(p0)
distance=abs(walk[0]-walk[len(walk)-1])
return walk, distance
def printWalk(path):
x=list()
y=list()
for num in path:
x.append(num.real)
y.append(num.imag)
plt.plot(x,y)
plt.show()
def endPts(T,N=10,stepSize=1):
"""
Fuction generates T randome walks an and adds the distance of the walk in an array.
@param N the amount of steps taken
@param T the amount of RW done
@param stepSize the size of one step taken by the random walker
"""
error1=0
walkedDist=0
for i in range(T):
path, dist=RW(N,stepSize)
walkedDist+=dist**2
error1+=dist**4
error1=error1/float(T)
R2=1/float(T)*walkedDist
error=np.sqrt(1/float(T)*(error1-R2**2))
#print error
return R2, error1/R2
N=range(2,100)
R2=[]
error=[]
rel=[]
for i in N:
dis, err=endPts(i)
R2.append(dis)
error.append(err)
#rel.append(err/dis)
#print "errors are", R2
#plt.subplot(111, yscale="log")
fig1=plt.figure()
ax=fig1.add_subplot(2,1,1)
ax.plot(N, R2,'ro')
ay=fig1.add_subplot(2,1,2)
ay.plot(N,error)
print error
plt.show()
def plotStat(T):
"""
Plot the Statistics for T random walks od the x distance the y distance and the total distance in histograms.
"""
xd=arange(0,50,1)
xxy=arange(-25,25,1)
foo=linspace(0,500,5000)
pdist, px, py=endPts(T)
print px
#hist(px, 100, 50, normed=1, facecolor='g', alpha=0.75)
subplot(4,1,1)
hist(px, xxy, normed=1, facecolor='g', histtype='step')
subplot(4,1,2)
hist(py,xxy, normed=1, facecolor='g', histtype='step')
subplot(4,1,3)
hist(pdist,xd, normed=1, facecolor='g', histtype='step')
xlabel(r"distances", fontsize = 12)
ylabel(r"probability", fontsize = 12)
subplot(4,1,4)
plot(foo, map(lambda x: Gaus(x,T), foo))
show()
def Gaus(r,N):
"""
plot the gausian function
"""
return 2*r/N*exp(-r*r/N)
| [
"marion.baumgartner@uzh.ch"
] | marion.baumgartner@uzh.ch |
4bd60b2710f545f82a96f3c453c1fe5e6af26c4e | 6caab8d886e8bd302d1994ff663cf5ccb5e11522 | /MyNotes_01/Step01/4-CORE/day02_15/demo04.py | 899097f7197eb4379f74f3afa0259428d5a3dcf2 | [] | no_license | ZimingGuo/MyNotes01 | 7698941223c79ee754b17296b9984b731858b238 | 55e6681da1a9faf9c0ec618ed60f5da9ecc6beb6 | refs/heads/master | 2022-07-30T21:30:32.100042 | 2020-05-19T16:59:09 | 2020-05-19T16:59:09 | 265,254,345 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,200 | py | # author: Ziming Guo
# time: 2020/2/24
"""
demo04：
异常处理
练习:exercise03.py
"""
def div_apple(apple_count):
# ValueError
person_count = int(input("请输入人数："))
# ZeroDivisionError
result = apple_count / person_count
print("每人%d个苹果"%result)
"""
try:
# 可能出错的代码
div_apple(10)
except Exception:
print("出错喽")
"""
"""
# "建议"分门别类的处理
try:
# 可能出错的代码
div_apple(10)
except ValueError:
print("输入的人数必须是整数")
except ZeroDivisionError:
print("输入的人数不能是零")
except Exception: # 这句话一般是写在最后的，以上错误都不属于才会执行这句话
print("未知错误")
"""
"""
try:
# 可能出错的代码
div_apple(10)
except Exception:
print("出错喽")
else:
# 如果异常，不执行else语句.
print("没有出错")
"""
try:
# 可能出错的代码
div_apple(10)
finally:
# 无论是否异常，一定会执行的代码.
print("finally")
# 作用：不能处理的错误，但是一定要执行的代码，就定义到finally语句中。
print("后续逻辑.....")
| [
"guoziming99999@icloud.com"
] | guoziming99999@icloud.com |
781039b9e7f59cfcbb56fa389751aca40a777204 | 2a1f9bad1b0c1ba21a952b473f895854051e43a3 | /script.py | 8379c5bf50b079a84d28a8fdfe35600a8e6e015f | [] | no_license | tyagi-iiitv/NFLLive | b6d8a0c0e3390d4581a1ab68a85f0b3dde4c28df | c9fcf82a0e3ade293574f4a3fd5a75d9b4dcb2a7 | refs/heads/master | 2021-08-30T23:29:16.802776 | 2017-12-19T18:22:12 | 2017-12-19T18:22:12 | 108,178,033 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,857 | py | import pandas as pd
import csv
import json
import requests
match_ids = pd.read_csv('data/game_data/pbp-2013_17.csv',usecols=['GameId'])
for match in list(match_ids['GameId']):
print(match)
filename = 'http://www.nfl.com/liveupdate/game-center/'+str(match)+'/'+str(match)+'_gtd.json'
json_data = json.loads(requests.get(filename).text)
match_id = str(match)
drives = list(json_data[match_id]['drives'].keys())
colnames_plays = ['down','time','desc','ydstogo','qtr','ydsnet','yrdln','sp','posteam','note']
colnames_drives = ['fds','result','penyds','ydsgained','numplays','postime']
file_plays = match_id + '_plays.csv'
file_drives = match_id + '_drives.csv'
with open(file_drives,'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=colnames_drives)
writer.writeheader()
for values in drives:
if values == 'crntdrv':
continue
fds = json_data[match_id]['drives'][values]['fds']
result = json_data[match_id]['drives'][values]['result']
penyds = json_data[match_id]['drives'][values]['penyds']
ydsgained = json_data[match_id]['drives'][values]['ydsgained']
numplays = json_data[match_id]['drives'][values]['numplays']
postime = json_data[match_id]['drives'][values]['postime']
writer.writerow({'fds':fds, 'result':result, 'penyds':penyds, 'ydsgained':ydsgained,
'numplays':numplays,'postime':postime})
csvfile.close()
with open(file_plays,'w')as csvfile:
writer = csv.DictWriter(csvfile,fieldnames=colnames_plays)
writer.writeheader()
for values in drives:
if values == 'crntdrv':
continue
plays = list(json_data[match_id]['drives'][values]['plays'].keys())
for play in plays:
down = json_data[match_id]['drives'][values]['plays'][play]['down']
time = json_data[match_id]['drives'][values]['plays'][play]['time']
desc = json_data[match_id]['drives'][values]['plays'][play]['desc']
ydstogo = json_data[match_id]['drives'][values]['plays'][play]['ydstogo']
qtr = json_data[match_id]['drives'][values]['plays'][play]['qtr']
ydsnet = json_data[match_id]['drives'][values]['plays'][play]['ydsnet']
yrdln = json_data[match_id]['drives'][values]['plays'][play]['yrdln']
sp = json_data[match_id]['drives'][values]['plays'][play]['sp']
posteam = json_data[match_id]['drives'][values]['plays'][play]['posteam']
writer.writerow({'down':down, 'time':time, 'desc':desc, 'ydstogo':ydstogo,
'qtr':qtr,'ydsnet':ydsnet,'yrdln':yrdln,'sp':sp,'posteam':posteam})
csvfile.close() | [
"ysoroka@cs.stonybrook.edu"
] | ysoroka@cs.stonybrook.edu |
1fb7ffd3b1c13b6671d5d9429e26a2a2fc85f549 | fd7072b657b8cfdc36304fda2d1d8dc5eae49517 | /team_formation/io.py | a2176ead413e18c14b7b2261db557ceee95450b5 | [
"MIT"
] | permissive | ubccapico/team-formation-script | a30d8c2c7eb6a769ebb31749758155d5ff79d90e | c2b0273703dad0d6495e2f997560820a4980ecc8 | refs/heads/master | 2023-05-26T22:36:10.165963 | 2019-11-07T23:27:55 | 2019-11-07T23:27:55 | 155,277,913 | 0 | 2 | MIT | 2023-05-22T21:35:49 | 2018-10-29T20:38:05 | Python | UTF-8 | Python | false | false | 940 | py | import os
import string
from team_formation import config
def get_course_folder_path(course_id):
course_folder_path = "{}/course_{}".format(get_root_folder(), course_id)
if not os.path.exists(course_folder_path):
os.makedirs(course_folder_path)
return course_folder_path
def get_root_folder():
folder_path = "{}/{:%Y-%m-%d %H:%M:%S}".format(
config.DATA_FOLDER,
config.SCRIPT_CURRENT_TIME
)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
return folder_path
def get_output_folder():
folder_path = "{}/output".format(get_root_folder())
if not os.path.exists(folder_path):
os.makedirs(folder_path)
return folder_path
def format_filename(name):
valid_chars = "-_.() %s%s" % (string.ascii_letters, string.digits)
filename = ''.join(char for char in name if char in valid_chars)
filename = filename.replace(' ','_')
return filename | [
"andrew.e.gardener@gmail.com"
] | andrew.e.gardener@gmail.com |
9ea5dffbf67dd928826f360d2e948cf168476c43 | 6d3ea5a1bc75687e8f3010c3cb000661394b1313 | /loginSystemAllFiles/loginSystem.py | b406467d480c578dc8b21f87acc0c1c45abd7968 | [] | no_license | renasustek/login | cd0b00a053851607daa878ba39d4d06538aa4fe2 | 59cf260b182b882f58c6288b7810a76f2a65fe56 | refs/heads/master | 2020-03-31T03:05:11.742406 | 2018-10-06T17:08:20 | 2018-10-06T17:08:20 | 151,851,815 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,047 | py | def newUser():
writingToFile = open("users.txt","a")
username = input("ENTER A USERNAME: ")
password = input("ENTER A PASSWORD: ")
reEnterPassword = input("RE-ENTER A PASSOWORD: ")
while password != reEnterPassword:
print("DIDNT RE ENTER PASSWORD CORRECTLY")
password = input("ENTER A PASSWORD: ")
reEnterPassword = input("ENTER A PASSOWORD: ")
writingToFile.write(username+","+password+"\n")
writingToFile.close()
def login():
takingFromFile = open("users.txt","r")
username = input("ENTER YOUR USERNAME: ")
password = input("ENTER YOUR PASSWORD: ")
for line in takingFromFile:
line = line.strip()
uName,pWord = line.split(",")
if username == uName and password == pWord:
print("hello,",uName)
#add where ever you want to go#()
print("INVALID DETAILS")
return takingFromFile()
takingFromFile.close()
| [
"noreply@github.com"
] | renasustek.noreply@github.com |
aa0f2d8c6cba5572e20c6ab5ff7617208fe2c210 | 84c6edfb564c7cbcef566370d754dbd79c8951f6 | /nested_admin/tests/nested_polymorphic/base.py | 49ad55cf521224c4a1af977c1643995fde8c18c4 | [
"BSD-3-Clause",
"BSD-2-Clause"
] | permissive | pasevin/django-nested-admin | 605e706a89726768423c9350e5621b3998a72daf | 5fee72ca74b79863bdc2a102aa47282dd6b770c9 | refs/heads/master | 2022-07-31T14:53:49.067508 | 2020-04-30T19:21:03 | 2020-04-30T19:21:03 | 263,924,432 | 0 | 0 | NOASSERTION | 2020-05-14T13:34:45 | 2020-05-14T13:34:45 | null | UTF-8 | Python | false | false | 11,389 | py | import six
from unittest import SkipTest
import django
from django.conf import settings
from django.apps import apps
from polymorphic.utils import get_base_polymorphic_model
from nested_admin.tests.base import BaseNestedAdminTestCase
from nested_admin.tests.utils import xpath_item, xpath_cls, is_sequence, is_integer, is_str
try:
from polymorphic.models import PolymorphicModel
except:
# Temporary until django-polymorphic supports django 3.0
if django.VERSION < (3, 0):
raise
else:
class PolymorphicModel(object):
pass
class BaseNestedPolymorphicTestCase(BaseNestedAdminTestCase):
@classmethod
def setUpClass(cls):
if django.VERSION >= (3, 0):
raise SkipTest(
'django-polymorphic not yet compatible with Django 3.0')
super(BaseNestedPolymorphicTestCase, cls).setUpClass()
def get_inline_model_names(self):
return self.selenium.execute_script("""
return (function getGroup($group) {
$group = (typeof $group === 'undefined') ? $('.djn-group-root') : $($group);
var $djnItems = $group.find([
'> .djn-fieldset > .djn-items',
'> .djn-items',
'> .tabular.inline-related > .djn-fieldset > .djn-items'
].join(', '));
var $forms = $djnItems.find('> .djn-inline-form:not(.djn-empty-form)');
return {
model: $group.attr('data-inline-model'),
items: $forms.toArray().map(el => ({
model: el.getAttribute('data-inline-model'),
groups: $(el).nearest('.djn-group:not([id*="-empty-"])').toArray().map(
g => getGroup(g)),
}))
}
})()""")
def _normalize_indexes(self, indexes, is_group=False, named_models=True):
norm_indexes = []
indexes = list(indexes or [])
group_index = None
if is_group:
if len(indexes) and is_sequence(indexes[-1]) and len(indexes[-1]) == 1:
group_index = indexes.pop()[0]
elif len(indexes) and is_str(indexes[-1]):
group_index = indexes.pop()
else:
indexes.append(None)
elif not indexes:
return indexes
inline_model_names = [self.get_inline_model_names()]
for level, level_indexes in enumerate(indexes):
if len(inline_model_names) == 0:
raise ValueError("Indexes depth greater than inline depth")
if level_indexes is None:
if not is_group:
raise ValueError("Unexpected None found in indexes")
if len(inline_model_names) > 1:
raise ValueError(
"Terminal index to inline class omitted in group-level "
"operation, but parent has more than one inline")
if named_models:
norm_indexes.append(inline_model_names[0]['model'])
else:
norm_indexes.append(0)
break
if not is_sequence(level_indexes) and not is_integer(level_indexes):
raise ValueError("Unexpected type %s in list of indexes" % (
type(level_indexes).__name__))
if is_integer(level_indexes):
if len(inline_model_names) > 1:
raise ValueError((
"indexes[%d] using shorthand integer value, but more "
"than one inline to choose from") % (level))
level_indexes = [0, level_indexes]
if is_sequence(level_indexes):
if len(level_indexes) != 2:
raise ValueError("Expected indexes[%d] to have len 2, got %d" % (
level, len(level_indexes)))
inline_index, inline_item = level_indexes
if is_str(inline_index):
lookup = inline_index
inline_index = None
for i, group in enumerate(inline_model_names):
if group['model'] == lookup:
inline_index = i
break
if any(i for i in group['items'] if i['model'] == lookup):
inline_index = i
break
inline_data = inline_model_names[inline_index]['items'][inline_item]
inline_model_name = inline_data['model']
inline_model_names = inline_data['groups']
if named_models:
norm_indexes.append([inline_model_name, inline_item])
else:
norm_indexes.append([inline_index, inline_item])
if group_index is not None:
if is_str(group_index):
lookup = group_index
group_index = None
for i, group in enumerate(inline_model_names):
if group['model'] == lookup:
group_index = i
break
if any(i for i in group['items'] if i['model'] == lookup):
group_index = i
break
if named_models:
norm_indexes.append(inline_model_names[group_index]['model'])
else:
norm_indexes.append(group_index)
return norm_indexes
def get_item(self, indexes):
indexes = self._normalize_indexes(indexes)
group_indexes = indexes[:-1]
model_id, item_index = indexes[-1]
app_label, model_name = model_id.split('-')
model_cls = apps.get_model(app_label, model_name)
if issubclass(model_cls, PolymorphicModel):
base_model_cls = get_base_polymorphic_model(model_cls)
else:
base_model_cls = model_cls
base_model_id = "%s-%s" % (
base_model_cls._meta.app_label, base_model_cls._meta.model_name)
try:
group = self.get_group(indexes=group_indexes + [base_model_id])
except TypeError:
group = self.get_group(indexes=group_indexes + [model_id])
group_id = group.get_attribute('id')
djn_items = self.selenium.find_element_by_css_selector(
"#%(id)s > .djn-fieldset > .djn-items, "
"#%(id)s > .tabular.inline-related > .djn-fieldset > .djn-items, "
"#%(id)s > .djn-items" % {'id': group_id})
model_name, item_index = indexes[-1]
return djn_items.find_element_by_xpath(
"./*[%s][%d]" % (xpath_item(), item_index + 1))
def delete_inline(self, indexes):
indexes = self._normalize_indexes(indexes)
model_id = indexes[-1][0]
app_label, model_name = model_id.split('-')
model_cls = apps.get_model(app_label, model_name)
if issubclass(model_cls, PolymorphicModel):
base_model_cls = get_base_polymorphic_model(model_cls)
else:
base_model_cls = model_cls
base_model_id = "%s-%s" % (
base_model_cls._meta.app_label, base_model_cls._meta.model_name)
item_id = self.get_item(indexes).get_attribute('id')
delete_selector = "#%s .djn-delete-handler.djn-model-%s" % (
item_id, base_model_id)
with self.clickable_selector(delete_selector) as el:
self.click(el)
if self.has_grappelli:
undelete_selector = "#%s.grp-predelete .grp-delete-handler.djn-model-%s" % (
item_id, base_model_id)
self.wait_until_clickable_selector(undelete_selector)
def add_inline(self, indexes=None, model=None, **kwargs):
model_name = "%s-%s" % (model._meta.app_label, model._meta.model_name)
if issubclass(model, PolymorphicModel):
base_model = get_base_polymorphic_model(model)
else:
base_model = model
base_model_identifier = "%s-%s" % (
base_model._meta.app_label, base_model._meta.model_name)
if indexes:
item = self.get_item(indexes)
group_el = self.selenium.execute_script(
'return $(arguments[0]).closest(".djn-group")[0]', item)
else:
group_el = self.get_group([base_model_identifier])
group_id = group_el.get_attribute('id')
error_desc = "%s in inline %s" % (model, indexes)
add_selector = "#%s .djn-add-item a.djn-add-handler.djn-model-%s" % (
group_id, base_model_identifier)
add_els = self.selenium.find_elements_by_css_selector(add_selector)
self.assertNotEqual(len(add_els), 0,
"No inline add handlers found for %s" % (error_desc))
self.click(add_els[0])
add_link_selector = "return $('.polymorphic-type-menu:visible [data-type=\"%s\"]')[0]" % (
model_name)
poly_add_link = self.selenium.execute_script(add_link_selector)
if poly_add_link:
poly_add_link.click()
indexes = self._normalize_indexes(indexes)
group_el = self.selenium.execute_script(
'return $(arguments[0]).closest(".djn-group")[0]', add_els[0])
group_id = group_el.get_attribute('id')
items_el = self.selenium.find_element_by_css_selector(
'#%(id)s > .djn-fieldset > .djn-items, '
"#%(id)s > .tabular.inline-related > .djn-fieldset > .djn-items, "
'#%(id)s > .djn-items' % {'id': group_id})
num_inlines = len(items_el.find_elements_by_xpath(
'./*[%s and not(%s)]' % (xpath_item(), xpath_cls('djn-empty-form'))))
new_index = num_inlines - 1
indexes.append([model_name, new_index])
for field_name, val in six.iteritems(kwargs):
self.set_field(field_name, val, indexes=indexes)
return indexes
def remove_inline(self, indexes):
item = self.get_item(indexes)
remove_handler = self.selenium.execute_script(
"return $(arguments[0]).nearest('.djn-remove-handler')[0]",
item)
self.click(remove_handler)
def get_num_inlines(self, indexes=None):
group = self.get_group(indexes=indexes)
group_id = group.get_attribute('id')
djn_items = self.selenium.find_element_by_css_selector(
"#%(id)s > .djn-fieldset > .djn-items, "
"#%(id)s > .tabular.inline-related > .djn-fieldset > .djn-items, "
"#%(id)s > .djn-items" % {'id': group_id})
selector = "> .djn-item:not(.djn-no-drag,.djn-item-dragging,.djn-thead,.djn-empty-form)"
return self.selenium.execute_script(
"return $(arguments[0]).find(arguments[1]).length",
djn_items, selector)
def get_group(self, indexes=None):
indexes = self._normalize_indexes(indexes, is_group=True)
model_name = indexes.pop()
expr_parts = []
for parent_model, parent_item_index in indexes:
expr_parts += ["/*[%s][count(preceding-sibling::*[%s]) = %d]" % (
xpath_item(parent_model), xpath_item(), parent_item_index)]
expr_parts += ["/*[@data-inline-model='%s' and %s]"
% (model_name, xpath_cls('djn-group'))]
expr = "/%s" % ("/".join(expr_parts))
return self.selenium.find_element_by_xpath(expr)
| [
"fdintino@gmail.com"
] | fdintino@gmail.com |
2a684e40a18e06d89fb16782da9604b0b0c9b552 | 6a79b7f79503e25aab15a4ea1cf3bde8cbee5d16 | /FP/Laboratory Assignments/Assignment 5-7/domain/GradeValidator.py | 3fece4bcc68c3ab259f8383947fc354d365d9070 | [] | no_license | birsandiana99/UBB-Work | 5b2bbc942cb34ae2dc7c3f1c3712ef53b55a28a4 | 20a7a0bdf4fb9c25370114dee61e0d85f7fcef2b | refs/heads/master | 2023-07-11T11:19:37.430561 | 2021-08-22T13:33:12 | 2021-08-22T13:33:12 | 398,805,779 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 831 | py | from domain.Grade import Grade
from domain.ValidatorException import ValidatorException
class Grade_Validator:
def validate(self, grade):
if type(grade) != Grade:
raise TypeError("Not a student!")
_errors = []
if type(grade.getStudentID) != int or grade.getStudentID() < 0:
_errors.append("Student ID must be an int bigger than 0!")
if type(grade.getDisciplineID()) != int or grade.getDisciplineID() < 0:
_errors.append("Discipline ID must be an int bigger than 0!")
if type(grade.getValue()) != int or grade.getValue()not in (1,10):
_errors.append("Value must be an integer between 1 and 10!")
if len(_errors) != 0:
raise ValidatorException(_errors)
| [
"56911032+birsandiana99@users.noreply.github.com"
] | 56911032+birsandiana99@users.noreply.github.com |
3e5cb51db897cdbb6486c584bbe310a72f96f141 | 9b4a691ce500a37a31ae63effa807e51de895dc9 | /scripts/calibrations.py | aeb08415b15dbe90d3603079fc254f7db9644eac | [] | no_license | elliottwarren/ClearFO_paper1 | b307b35f8b8c12a1097b517f4ff45e7949f60e04 | f54262d80ec30e9981236913efa6a335a6ac0e53 | refs/heads/master | 2022-11-21T20:04:44.019304 | 2020-07-29T14:22:36 | 2020-07-29T14:22:36 | 90,743,541 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 589,822 | py | """ Calibration data from Emma 15/05/2017"""
from numpy import nan
class A_KSS45W():
Dates = ['2011/01/01', '2011/01/02', '2011/01/03', '2011/01/04', '2011/01/05', '2011/01/06', '2011/01/07',
'2011/01/08', '2011/01/09', '2011/01/10', '2011/01/11', '2011/01/12', '2011/01/13', '2011/01/14',
'2011/01/15', '2011/01/16', '2011/01/17', '2011/01/18', '2011/01/19', '2011/01/20', '2011/01/21',
'2011/01/22', '2011/01/23', '2011/01/24', '2011/01/25', '2011/01/26', '2011/01/27', '2011/01/28',
'2011/01/29', '2011/01/30', '2011/01/31', '2011/02/01', '2011/02/02', '2011/02/03', '2011/02/04',
'2011/02/05', '2011/02/06', '2011/02/07', '2011/02/08', '2011/02/09', '2011/02/10', '2011/02/11',
'2011/02/12', '2011/02/13', '2011/02/14', '2011/02/15', '2011/02/16', '2011/02/17', '2011/02/18',
'2011/02/19', '2011/02/20', '2011/02/21', '2011/02/22', '2011/02/23', '2011/02/24', '2011/02/25',
'2011/02/26', '2011/02/27', '2011/02/28', '2011/03/01', '2011/03/02', '2011/03/03', '2011/03/04',
'2011/03/05', '2011/03/06', '2011/03/07', '2011/03/08', '2011/03/09', '2011/03/10', '2011/03/11',
'2011/03/12', '2011/03/13', '2011/03/14', '2011/03/15', '2011/03/16', '2011/03/17', '2011/03/18',
'2011/03/19', '2011/03/20', '2011/03/21', '2011/03/22', '2011/03/23', '2011/03/24', '2011/03/25',
'2011/03/26', '2011/03/27', '2011/03/28', '2011/03/29', '2011/03/30', '2011/03/31', '2011/04/01',
'2011/04/02', '2011/04/03', '2011/04/04', '2011/04/05', '2011/04/06', '2011/04/07', '2011/04/08',
'2011/04/09', '2011/04/10', '2011/04/11', '2011/04/12', '2011/04/13', '2011/04/14', '2011/04/15',
'2011/04/16', '2011/04/17', '2011/04/18', '2011/04/19', '2011/04/20', '2011/04/21', '2011/04/22',
'2011/04/23', '2011/04/24', '2011/04/25', '2011/04/26', '2011/04/27', '2011/04/28', '2011/04/29',
'2011/04/30', '2011/05/01', '2011/05/02', '2011/05/03', '2011/05/04', '2011/05/05', '2011/05/06',
'2011/05/07', '2011/05/08', '2011/05/09', '2011/05/10', '2011/05/11', '2011/05/12', '2011/05/13',
'2011/05/14', '2011/05/15', '2011/05/16', '2011/05/17', '2011/05/18', '2011/05/19', '2011/05/20',
'2011/05/21', '2011/05/22', '2011/05/23', '2011/05/24', '2011/05/25', '2011/05/26', '2011/05/27',
'2011/05/28', '2011/05/29', '2011/05/30', '2011/05/31', '2011/06/01', '2011/06/02', '2011/06/03',
'2011/06/04', '2011/06/05', '2011/06/06', '2011/06/07', '2011/06/08', '2011/06/09', '2011/06/10',
'2011/06/11', '2011/06/12', '2011/06/13', '2011/06/14', '2011/06/15', '2011/06/16', '2011/06/17',
'2011/06/18', '2011/06/19', '2011/06/20', '2011/06/21', '2011/06/22', '2011/06/23', '2011/06/24',
'2011/06/25', '2011/06/26', '2011/06/27', '2011/06/28', '2011/06/29', '2011/06/30', '2011/07/01',
'2011/07/02', '2011/07/03', '2011/07/04', '2011/07/05', '2011/07/06', '2011/07/07', '2011/07/08',
'2011/07/09', '2011/07/10', '2011/07/11', '2011/07/12', '2011/07/13', '2011/07/14', '2011/07/15',
'2011/07/16', '2011/07/17', '2011/07/18', '2011/07/19', '2011/07/20', '2011/07/21', '2011/07/22',
'2011/07/23', '2011/07/24', '2011/07/25', '2011/07/26', '2011/07/27', '2011/07/28', '2011/07/29',
'2011/07/30', '2011/07/31', '2011/08/01', '2011/08/02', '2011/08/03', '2011/08/04', '2011/08/05',
'2011/08/06', '2011/08/07', '2011/08/08', '2011/08/09', '2011/08/10', '2011/08/11', '2011/08/12',
'2011/08/13', '2011/08/14', '2011/08/15', '2011/08/16', '2011/08/17', '2011/08/18', '2011/08/19',
'2011/08/20', '2011/08/21', '2011/08/22', '2011/08/23', '2011/08/24', '2011/08/25', '2011/08/26',
'2011/08/27', '2011/08/28', '2011/08/29', '2011/08/30', '2011/08/31', '2011/09/01', '2011/09/02',
'2011/09/03', '2011/09/04', '2011/09/05', '2011/09/06', '2011/09/07', '2011/09/08', '2011/09/09',
'2011/09/10', '2011/09/11', '2011/09/12', '2011/09/13', '2011/09/14', '2011/09/15', '2011/09/16',
'2011/09/17', '2011/09/18', '2011/09/19', '2011/09/20', '2011/09/21', '2011/09/22', '2011/09/23',
'2011/09/24', '2011/09/25', '2011/09/26', '2011/09/27', '2011/09/28', '2011/09/29', '2011/09/30',
'2011/10/01', '2011/10/02', '2011/10/03', '2011/10/04', '2011/10/05', '2011/10/06', '2011/10/07',
'2011/10/08', '2011/10/09', '2011/10/10', '2011/10/11', '2011/10/12', '2011/10/13', '2011/10/14',
'2011/10/15', '2011/10/16', '2011/10/17', '2011/10/18', '2011/10/19', '2011/10/20', '2011/10/21',
'2011/10/22', '2011/10/23', '2011/10/24', '2011/10/25', '2011/10/26', '2011/10/27', '2011/10/28',
'2011/10/29', '2011/10/30', '2011/10/31', '2011/11/01', '2011/11/02', '2011/11/03', '2011/11/04',
'2011/11/05', '2011/11/06', '2011/11/07', '2011/11/08', '2011/11/09', '2011/11/10', '2011/11/11',
'2011/11/12', '2011/11/13', '2011/11/14', '2011/11/15', '2011/11/16', '2011/11/17', '2011/11/18',
'2011/11/19', '2011/11/20', '2011/11/21', '2011/11/22', '2011/11/23', '2011/11/24', '2011/11/25',
'2011/11/26', '2011/11/27', '2011/11/28', '2011/11/29', '2011/11/30', '2011/12/01', '2011/12/02',
'2011/12/03', '2011/12/04', '2011/12/05', '2011/12/06', '2011/12/07', '2011/12/08', '2011/12/09',
'2011/12/10', '2011/12/11', '2011/12/12', '2011/12/13', '2011/12/14', '2011/12/15', '2011/12/16',
'2011/12/17', '2011/12/18', '2011/12/19', '2011/12/20', '2011/12/21', '2011/12/22', '2011/12/23',
'2011/12/24', '2011/12/25', '2011/12/26', '2011/12/27', '2011/12/28', '2011/12/29', '2011/12/30',
'2011/12/31', '2012/01/01', '2012/01/02', '2012/01/03', '2012/01/04', '2012/01/05', '2012/01/06',
'2012/01/07', '2012/01/08', '2012/01/09', '2012/01/10', '2012/01/11', '2012/01/12', '2012/01/13',
'2012/01/14', '2012/01/15', '2012/01/16', '2012/01/17', '2012/01/18', '2012/01/19', '2012/01/20',
'2012/01/21', '2012/01/22', '2012/01/23', '2012/01/24', '2012/01/25', '2012/01/26', '2012/01/27',
'2012/01/28', '2012/01/29', '2012/01/30', '2012/01/31', '2012/02/01', '2012/02/02', '2012/02/03',
'2012/02/04', '2012/02/05', '2012/02/06', '2012/02/07', '2012/02/08', '2012/02/09', '2012/02/10',
'2012/02/11', '2012/02/12', '2012/02/13', '2012/02/14', '2012/02/15', '2012/02/16', '2012/02/17',
'2012/02/18', '2012/02/19', '2012/02/20', '2012/02/21', '2012/02/22', '2012/02/23', '2012/02/24',
'2012/02/25', '2012/02/26', '2012/02/27', '2012/02/28', '2012/02/29', '2012/03/01', '2012/03/02',
'2012/03/03', '2012/03/04', '2012/03/05', '2012/03/06', '2012/03/07', '2012/03/08', '2012/03/09',
'2012/03/10', '2012/03/11', '2012/03/12', '2012/03/13', '2012/03/14', '2012/03/15', '2012/03/16',
'2012/03/17', '2012/03/18', '2012/03/19', '2012/03/20', '2012/03/21', '2012/03/22', '2012/03/23',
'2012/03/24', '2012/03/26', '2012/03/27', '2012/03/28', '2012/03/29', '2012/03/30', '2012/03/31',
'2012/04/01', '2012/04/02', '2012/04/03', '2012/04/04', '2012/04/05', '2012/04/06', '2012/04/07',
'2012/04/08', '2012/04/09', '2012/04/10', '2012/04/11', '2012/04/12', '2012/04/13', '2012/04/14',
'2012/04/15', '2012/04/16', '2012/04/17', '2012/04/18', '2012/04/19', '2012/04/20', '2012/04/21',
'2012/04/22', '2012/04/23', '2012/04/24', '2012/04/25', '2012/04/26', '2012/04/27', '2012/04/28',
'2012/04/29', '2012/04/30', '2012/05/01', '2012/05/02', '2012/05/03', '2012/05/04', '2012/05/05',
'2012/05/06', '2012/05/07', '2012/05/08', '2012/05/09', '2012/05/10', '2012/05/11', '2012/05/12',
'2012/05/13', '2012/05/14', '2012/05/15', '2012/05/16', '2012/05/17', '2012/05/18', '2012/05/19',
'2012/05/20', '2012/05/21', '2012/05/22', '2012/05/23', '2012/05/24', '2012/05/25', '2012/05/26',
'2012/05/27', '2012/05/28', '2012/05/29', '2012/05/30', '2012/05/31', '2012/06/01', '2012/06/02',
'2012/06/03', '2012/06/04', '2012/06/05', '2012/06/06', '2012/06/07', '2012/06/08', '2012/06/09',
'2012/06/10', '2012/06/11', '2012/06/12', '2012/06/13', '2012/06/14', '2012/06/15', '2012/06/16',
'2012/06/17', '2012/06/18', '2012/06/19', '2012/06/20', '2012/06/21', '2012/06/22', '2012/06/23',
'2012/06/24', '2012/06/25', '2012/06/26', '2012/06/27', '2012/06/28', '2012/06/29', '2012/06/30',
'2012/07/01', '2012/07/02', '2012/07/03', '2012/07/04', '2012/07/05', '2012/07/06', '2012/07/07',
'2012/07/08', '2012/07/09', '2012/07/10', '2012/07/11', '2012/07/12', '2012/07/13', '2012/07/14',
'2012/07/15', '2012/07/16', '2012/07/17', '2012/07/18', '2012/07/19', '2012/07/20', '2012/07/21',
'2012/07/22', '2012/07/23', '2012/07/24', '2012/07/25', '2012/07/26', '2012/07/27', '2012/07/28',
'2012/07/29', '2012/07/30', '2012/07/31', '2012/08/01', '2012/08/02', '2012/08/03', '2012/08/04',
'2012/08/05', '2012/08/06', '2012/08/07', '2012/08/08', '2012/08/09', '2012/08/10', '2012/08/11',
'2012/08/12', '2012/08/13', '2012/08/14', '2012/08/15', '2012/08/16', '2012/08/17', '2012/08/18',
'2012/08/19', '2012/08/20', '2012/08/21', '2012/08/22', '2012/08/23', '2012/08/24', '2012/08/25',
'2012/08/26', '2012/08/27', '2012/08/28', '2012/08/29', '2012/08/30', '2012/08/31', '2012/09/01',
'2012/09/02', '2012/09/03', '2012/09/04', '2012/09/05', '2012/09/06', '2012/09/07', '2012/09/08',
'2012/09/09', '2012/09/10', '2012/09/11', '2012/09/12', '2012/09/13', '2012/09/14', '2012/09/15',
'2012/09/16', '2012/09/17', '2012/09/18', '2012/09/19', '2012/09/20', '2012/09/21', '2012/09/22',
'2012/09/23', '2012/09/24', '2012/09/25', '2012/09/26', '2012/09/27', '2012/09/28', '2012/09/29',
'2012/09/30', '2012/10/01', '2012/10/02', '2012/10/03', '2012/10/04', '2012/10/05', '2012/10/06',
'2012/10/07', '2012/10/08', '2012/10/09', '2012/10/10', '2012/10/11', '2012/10/12', '2012/10/13',
'2012/10/14', '2012/10/15', '2012/10/16', '2012/10/17', '2012/10/18', '2012/10/19', '2012/10/20',
'2012/10/21', '2012/10/22', '2012/10/23', '2012/10/24', '2012/10/25', '2012/10/26', '2012/10/27',
'2012/10/28', '2012/10/29', '2012/10/30', '2012/10/31', '2012/11/01', '2012/11/02', '2012/11/03',
'2012/11/04', '2012/11/05', '2012/11/06', '2012/11/07', '2012/11/08', '2012/11/09', '2012/11/10',
'2012/11/11', '2012/11/12', '2012/11/13', '2012/11/14', '2012/11/15', '2012/11/16', '2012/11/17',
'2012/11/18', '2012/11/19', '2012/11/20', '2012/11/21', '2012/11/22', '2012/11/23', '2012/11/24',
'2012/11/25', '2012/11/26', '2012/11/27', '2012/11/28', '2012/11/29', '2012/11/30', '2012/12/01',
'2012/12/02', '2012/12/03', '2012/12/04', '2012/12/05', '2012/12/06', '2012/12/07', '2012/12/08',
'2012/12/09', '2012/12/10', '2012/12/11', '2012/12/12', '2012/12/13', '2012/12/14', '2012/12/15',
'2012/12/16', '2012/12/17', '2012/12/18', '2012/12/19', '2012/12/20', '2012/12/21', '2012/12/22',
'2012/12/23', '2012/12/24', '2012/12/25', '2012/12/26', '2012/12/27', '2012/12/28', '2012/12/29',
'2012/12/30', '2012/12/31', '2013/01/01', '2013/01/02', '2013/01/03', '2013/01/04', '2013/01/05',
'2013/01/06', '2013/01/07', '2013/01/08', '2013/01/09', '2013/01/10', '2013/01/11', '2013/01/12',
'2013/01/13', '2013/01/14', '2013/01/15', '2013/01/16', '2013/01/17', '2013/01/18', '2013/01/19',
'2013/01/20', '2013/01/21', '2013/01/22', '2013/01/23', '2013/01/24', '2013/01/25', '2013/01/26',
'2013/01/27', '2013/01/28', '2013/01/29', '2013/01/30', '2013/01/31', '2013/02/01', '2013/02/02',
'2013/02/03', '2013/02/04', '2013/02/05', '2013/02/06', '2013/02/07', '2013/02/08', '2013/02/09',
'2013/02/10', '2013/02/11', '2013/02/12', '2013/02/13', '2013/02/14', '2013/02/15', '2013/02/16',
'2013/02/17', '2013/02/18', '2013/02/19', '2013/02/20', '2013/02/21', '2013/02/22', '2013/02/23',
'2013/02/24', '2013/02/25', '2013/02/26', '2013/02/27', '2013/02/28', '2013/03/01', '2013/03/02',
'2013/03/03', '2013/03/04', '2013/03/05', '2013/03/06', '2013/03/07', '2013/03/08', '2013/03/09',
'2013/03/10', '2013/03/11', '2013/03/12', '2013/03/13', '2013/03/14', '2013/03/15', '2013/03/16',
'2013/03/17', '2013/03/18', '2013/03/19', '2013/03/20', '2013/03/21', '2013/03/22', '2013/03/23',
'2013/03/24', '2013/03/25', '2013/03/26', '2013/03/27', '2013/03/28', '2013/03/29', '2013/03/30',
'2013/03/31', '2013/04/01', '2013/04/02', '2013/04/03', '2013/04/04', '2013/04/05', '2013/04/06',
'2013/04/07', '2013/04/08', '2013/04/09', '2013/04/10', '2013/04/11', '2013/04/12', '2013/04/13',
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# C_medians = [ 3.74936428 3.29192995 3.32400535 ..., nan nan nan]
C_stdevs = [0.44728923730944464, 0.16688252073952564, 0.34069729965896201, 0.28043673024410115, 0.26139850715296703,
nan, 0.16997267516910866, 0.22974219513187427, nan, 0.22015469974381502, 0.20658588787532084,
0.17225411505715665, 0.22222657737027859, 0.19431628662751249, 0.15757391105825194, 0.19917696870060245,
0.34364443767776587, nan, 0.26847940987916696, 0.19979142218304136, 0.21379110081639646,
0.19051355338654871, 0.14759866481100592, 0.28962247363090271, 0.26907258005279194, 0.26874166421022339,
0.20496061446602065, 0.2272648925370305, 0.43189705931601752, 0.23347397002657647, 0.27234383035910575,
0.34428228681662759, 0.1841514084684911, nan, 0.1106413307240738, 0.15308219888087207,
0.21251518162949823, 0.17881021131877861, nan, 0.35465107822551667, 0.30728504324041955,
0.34789505423762929, 0.25925302037277442, nan, 0.32136127304488116, 0.30239186142569324,
0.24730795532864788, nan, nan, nan, nan, nan, nan, 0.34128892005669259, 0.54002923119517754,
0.36490770071931117, 0.29642268418834505, 0.3134401070625098, 0.28937362042163628, 0.23378929125912501,
0.29427321776307164, 0.28338861998234288, 0.31213820630815625, 0.39028189762461646, 0.20727192202770772,
nan, nan, 0.21976474895509579, nan, 0.26955139038419235, 0.29526733500814067, 0.50613170587125411,
0.24668957505416592, nan, nan, nan, 0.2149419803992329, nan, nan, 0.22712418114179891,
0.25481578277012806, nan, nan, nan, nan, nan, nan, nan, 0.3859261857635975, 0.14953699284629382,
0.19561265710541412, nan, 0.20321497287865503, nan, 0.17702208794946869, 0.11115049545999355, nan,
0.22156453891014885, nan, nan, nan, nan, 0.13664507693036526, 0.11322572906925712, 0.22255804703889126,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.18032747447755237, nan, 0.15121444685305702, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.19190000542974317, 0.17464088786007728,
0.20070779679951734, 0.25633733815787191, 0.21988908796640269, 0.13787437574536376, 0.18802258518821127,
0.25312747710177236, nan, nan, nan, 0.16593159709293909, 0.12183438244254689, nan, nan,
0.17534161014674354, 0.14530938685906677, 0.1989788293796422, 0.22317029224606763, 0.30841436385300086,
nan, nan, nan, nan, nan, 0.14812642156032749, nan, 0.28633098694858011, 0.19969332486477834, nan,
0.25658645690485676, 0.24339433081634942, 0.12382125903215936, 0.19884579188760701, 0.29679083066007178,
0.30070819674077282, 0.17344921055723389, 0.17253823724073544, 0.15117544862111035, 0.18959279130393497,
0.20896555599299874, 0.13043078296313837, 0.27742559109324322, 0.22306193368660357, nan,
0.18454222027815051, 0.13368201405242769, nan, 0.35838250816851769, nan, nan, nan, nan, nan, nan,
0.17311932444504513, 0.1753808184101435, 0.1891886347509143, 0.24844069255995005, 0.29440115859953753,
0.26122749923792932, nan, 0.15924435541352516, 0.20075076682737641, 0.34031769636195219,
0.19366304998758582, 0.24030638812882771, 0.2140777391611427, 0.19753723531398301, 0.28939003497073945,
0.16946832849134116, 0.2583600110341196, nan, 0.25248374349079172, nan, nan, 0.21478683421524286,
0.30443650247203985, 0.27356298119896921, 0.30391191160774411, 0.18478008023848497, nan,
0.19327796419192564, nan, nan, 0.20946276588697924, 0.21506450258483056, 0.25235113736730547,
0.19447868804305807, 0.25060044201568299, nan, 0.20794670619440359, 0.22304785765276339,
0.28689154853029225, 0.27237156132177087, 0.18415062726557999, 0.2479715801769263, 0.26415213639452428,
0.31966051582485727, 0.39061866678021334, nan, 0.18112099944545923, nan, nan, 0.3682193601390481,
0.29017701697147152, 0.25184752560886187, 0.31309850484929375, nan, 0.24046989990207149,
0.16619820221052209, 0.17462658348326845, 0.26953257537452185, nan, nan, 0.25448693617396151,
0.26045796016097489, nan, 0.16735925555816178, nan, 0.22062554410231963, 0.2109330834017496,
0.40123840237098518, nan, 0.12244660050361204, nan, nan, nan, 0.13595441900877842, 0.25505441336514967,
0.3188344827490186, 0.31655818704911204, 0.19320134911326686, 0.24535137569700644, nan, nan, nan,
0.21923940084816465, 0.28652846745799782, 0.18804012968347958, nan, nan, nan, nan, nan,
0.32292256638294942, 0.24638729568556, 0.18497430369900372, 0.18271799579155129, 0.18030802180846153,
0.21383710156220465, 0.1779056169777963, 0.13994476392160243, 0.20735504213134887, 0.30555740926994251,
0.23728989255099572, 0.2661949352827549, nan, 0.31774521130115979, 0.28654205004827099, nan, nan, nan,
0.34229189618036976, 0.11145854648391541, 0.12110590309674295, 0.13534874135115077, 0.19606985675635707,
0.1825533172390498, 0.19816971122119759, 0.23278105454665288, 0.21649971186953595, 0.14743892744509129,
0.16833706246059263, 0.22174955772623128, 0.18248907078024257, 0.15270485921719534, 0.17499692819527182,
0.2945706987209078, 0.16629733998338278, nan, nan, 0.22216883491421086, nan, nan, 0.16407063934970997,
nan, 0.1838261067733612, 0.20184509909725723, 0.20506136759526331, 0.22823665614469871, nan, nan, nan,
nan, 0.27223765123596549, nan, 0.18314565609680175, nan, 0.13005285832341196, 0.11412764166235052,
0.14901832391207703, 0.12851791658895589, nan, 0.20750150843401707, nan, 0.17893579735902693,
0.1076590752012149, nan, nan, nan, 0.13997921576529507, nan, 0.2285341215852584, 0.20846536290634576,
nan, 0.169123998574569, nan, 0.14048396579033845, 0.24411391261747997, 0.14997525095004366,
0.17134424432179704, 0.19033863515918686, 0.21546310684741768, 0.15263055542052173, 0.16678041978790492,
0.13772340797041019, 0.12515212576922069, 0.11945069476438568, 0.17542627299324684, 0.15011566972314491,
0.1961909144051695, nan, nan, 0.15031965904953376, 0.20133830564589264, nan, nan, 0.078452688598141862,
0.10002487579975897, 0.13271211116762341, 0.15579730257442265, 0.15703543545344492, 0.21020856000069754,
0.13676858599882355, 0.17811944061775514, 0.15600706521404006, nan, 0.1200455933152464,
0.11898841622862448, nan, nan, 0.13691100323538349, 0.12300852138060078, 0.074563875333094415,
0.14407738432520856, nan, nan, nan, 0.1581058810516732, 0.152621533354099, 0.13419182841111277,
0.17494980991550305, 0.39310430153516845, 0.23780556734529379, nan, nan, nan, 0.20237811849451104,
0.40050613368253341, 0.32132597408154778, 0.33862271039032893, 0.48139309976656841, 0.2075676811332979,
0.273042615988488, 0.31028472412198455, nan, 0.29043422394148716, 0.20538622606886786, nan,
0.15964934812738765, 0.31765072023579882, 0.21224031278445135, 0.14485890777147867, nan,
0.34845431115531683, 0.22155580409694109, 0.18398751771497601, 0.21252724573078283, 0.2699450857920298,
0.32149357524834998, 0.25444565808939623, 0.29393939858779577, 0.37265800964831514, 0.23800291173135926,
0.13483992982703144, nan, 0.2689281821120213, nan, 0.20191315793581024, 0.22981374607497573,
0.20303934848595345, nan, 0.23037257520992752, 0.27488948818027115, 0.35108098319881498,
0.30819040392703967, 0.27519912292189214, 0.34055053291453152, nan, 0.15700222349430865,
0.20614754552955239, 0.22129972760821298, nan, 0.15854972355486008, nan, nan, nan, nan, nan, nan, nan,
nan, nan, 0.27995306951175841, nan, nan, 0.33941414388968483, 0.3289943083545005, 0.25727748014941881,
0.20254894477087548, 0.23267443117733067, 0.24722769521445723, 0.15128640583608011, 0.21933692620142356,
0.2464209471918471, 0.26748766735604945, nan, 0.22443563910372691, nan, 0.21160101554878258,
0.25456005284652944, 0.14217006782405689, 0.21413975525403023, 0.34343834158207442, 0.23477845773323247,
0.22620872787585541, nan, 0.16402255722257189, nan, nan, 0.16542482731155797, nan, 0.21159383922556718,
nan, 0.27933321382656245, 0.22443665042515756, 0.15919967898408835, 0.2001695867748097,
0.19007739073327631, 0.23805482925177324, 0.26604067591163705, 0.27371194897347545, 0.19125808727767538,
0.16502610012064278, 0.21804551502626512, nan, nan, 0.18448928694930242, 0.27522020404487268, nan, nan,
0.18936333935533006, 0.27354588857643453, 0.1841729124511404, 0.23860711805972484, 0.17050649230062442,
nan, nan, nan, nan, nan, nan, nan, nan, 0.22306688453931572, 0.31333793024386919, 0.32822424474035305,
nan, 0.25663079658449256, nan, 0.2128624555346853, 0.20793183277045266, nan, nan, nan, nan,
0.15818609549039919, nan, nan, nan, 0.1627378591637518, 0.17808495335700009, 0.24696317601116444, nan,
nan, 0.27383408300267492, 0.17224869869621862, 0.19530759431577888, 0.13832089916385112,
0.23685095580477364, 0.2075858835631007, 0.23751887522859641, nan, 0.25303507724779611,
0.19188139463069703, 0.2163579474861905, 0.25602436974662812, 0.26262208430096512, 0.2954445476524768,
0.27976105766166914, 0.28234858361449405, 0.27843459448545332, nan, 0.30672111974417193,
0.31251378125138035, 0.19524346396274986, nan, 0.17675767548488114, 0.26626075758420142,
0.2898098002500677, 0.17365566255605938, 0.26745920996933831, 0.19439117290367861, 0.21806531307604887,
0.27322990843705419, 0.28300198623492379, nan, nan, nan, nan, nan, nan, 0.32078038476225679, nan,
0.21303916965860956, 0.2061465265857246, 0.24204671970566158, 0.14881697581141287, 0.15318772070463899,
0.24379346572983893, nan, 0.24150186084199182, 0.25910842343954937, 0.30383029009012191, nan, nan, nan,
nan, 0.28426817004730981, 0.39579632393874337, nan, nan, 0.29927044344409642, nan, nan,
0.3095150918738247, 0.26737789710317189, 0.18188338477567687, nan, 0.24122698052009492,
0.24363276791143587, 0.22599542654048796, nan, 0.15936008429022308, 0.18306005185913676, nan, nan,
0.22933335225911833, 0.23035712741544759, 0.19582501043154263, 0.48187081957306033, nan, nan, nan, nan,
nan, 0.21530767582053101, 0.33930796267740515, 0.24166452435384794, 0.14386558162730617,
0.19160280198592522, nan, 0.19509884028077662, 0.17489597157438869, 0.23993615485602232,
0.41901363811201425, 0.65263325342769751, 0.18458516615724535, nan, nan, nan, nan, 0.24883774171793904,
0.23929395678910595, 0.26382673168667981, nan, 0.16803978108851331, 0.15497396390075271,
0.23028550215673568, nan, nan, 0.20862118579249112, 0.20031653995637985, nan, nan, 0.28311550207558545,
0.15562587355384738, 0.17300935962347808, 0.17870160331398169, 0.35380617540650183, 0.47913385873256087,
0.19943381170453012, nan, nan, nan, 0.33793334592973839, 0.18825259802681735, 0.2612716639906405, nan,
nan, nan, 0.17491899936443417, 0.13300072061471988, 0.2121814181538107, 0.19389497863832686,
0.32768030077060245, 0.21696324154444396, 0.24238210923411063, 0.29819867031450914, nan,
0.25409705831492108, 0.24221090500955689, nan, 0.15757935039439014, 0.14761832739081487,
0.19761308664083949, 0.16499202397001039, 0.20415123039202965, nan, nan, 0.23696300679630999,
0.32444319548828793, 0.22829948805052472, 0.2064628935329943, 0.26162116119451628, nan,
0.1962993881545749, 0.16003248573710815, 0.1709998196833554, nan, nan, nan, 0.2129637245272516,
0.2170387199120366, 0.20101660790842185, 0.17335902987477475, 0.21367800165953577, 0.38675808784812254,
0.21207538553769256, nan, nan, nan, 0.22543416602507957, nan, 0.22551322261826401, 0.35190882815071417,
0.23084456887940139, nan, nan, 0.45840468725686506, 0.31876703162757908, nan, 0.20369910699629001,
0.2551386251310348, nan, 0.29987302002074051, 0.23680551679570871, 0.36729054004316514,
0.21951820831606109, 0.13296166760365638, nan, 0.19963515554695094, 0.22648767909944509, nan,
0.28684223758121941, 0.17642500592100552, 0.17910954450470484, nan, 0.20961945476746593,
0.17392322127206217, nan, 0.19698474550486014, 0.2434243818842014, 0.22626919055089778,
0.29462978303635917, 0.21310054347487667, 0.20543448938800515, 0.25480504611997301, 0.43515551502898253,
0.41871942756539604, nan, 0.21766813785674702, 0.43248985062681267, 0.29750479563659349, nan, nan,
0.33623169940490022, nan, nan, 0.44382789269594991, nan, nan, 0.40139107501667665, 0.67600732194846092,
nan, 0.21289387093125695, nan, 0.25697609412907491, nan, nan, 0.31168352718267667, 0.22822669726348765,
0.32858187899621222, 0.20175176276755655, nan, 0.26587899152528155, nan, 0.44545649487797362,
0.44641124973070617, nan, nan, nan, nan, 0.25267686176729931, 0.24397780513751982, 0.24223444921025777,
nan, nan, nan, 0.35030795369736401, 0.316384570084081, 0.14258355548783117, nan, nan,
0.22424011842697483, nan, 0.33612937785564329, 0.46042372233223061, 0.22683571480217024,
0.23636691816449651, 0.40957408267974271, nan, nan, nan, nan, nan, 0.38267101266015241,
0.32093674441584452, 0.32166916504830073, 0.28303997796268621, 0.30317128510012492, 0.28374575406955871,
0.2792961289301002, 0.29531203267111095, 0.36594201802499426, 0.28158661095590393, 0.41183742373230459,
0.34713517979710401, 0.32947044248070706, nan, nan, nan, nan, nan, nan, nan, 0.25654150946189935, nan,
0.24904909319383001, nan, nan, nan, nan, nan, nan, nan, nan, 0.3597880934550754, nan, nan,
0.35582113367826496, 0.42024549646873838, 0.2689752324235592, nan, 0.1883663162888746,
0.22525991013880989, nan, 0.21406274601690989, nan, nan, 0.44651491768033374, nan, nan, nan,
0.51099092173510274, 0.35961233129547604, 0.25673425234070529, 0.29637909195255291, nan, nan, nan, nan,
nan, nan, nan, nan, 0.29521744181271503, nan, 0.24289483446321747, 0.26749469412406218, nan,
0.2896350383789475, nan, 0.31019102381631081, nan, 0.32426229776252607, 0.3203427081118147, nan,
0.35868473044529892, 0.39282939681617302, 0.40104942560030976, nan, 0.26873479413912638, nan, nan, nan,
0.32998895005365292, 0.34337403454637205, 0.29124403085939687, 0.31694014939355164, 0.36791037387178072,
nan, nan, nan, 0.2226422044592867, 0.29308771630821806, nan, 0.26905072644945122, 0.21592202692381779,
0.34942552341137967, 0.40945613887089327, 0.3352519651471732, 0.3118580503875909, 0.235097226507643,
nan, nan, nan, 0.28541933362448124, nan, nan, 0.34193933840053348, 0.27083759604880786,
0.26088927520912875, 0.25098977522277927, 0.3450852916856435, nan, 0.45596043000226372,
0.32069603953539244, 0.36515030998844067, 0.29293989280086868, 0.22327941412291624, 0.19741807467882938,
0.46799841449200613, 0.33172225681368367, nan, nan, nan, nan, nan, 0.21028076471766297,
0.23500874133264449, 0.3028575224855502, nan, nan, nan, nan, nan, nan, nan, 0.3226105878145557,
0.3351075809652741, nan, nan, nan, 0.42199736937217175, nan, nan, 0.26868292754222783,
0.25879625243253102, 0.27167200920185874, 0.28447531116826702, 0.1668072010063579, 0.21550219593837444,
nan, nan, 0.33902722264723478, nan, nan, nan, 0.26700179286392689, 0.27540761986842416,
0.28413402314786707, nan, nan, 0.32385593912082794, 0.50098615808218627, 0.27694198170989098,
0.17545010470894185, 0.24869442603831754, nan, nan, nan, nan, nan, nan, nan, 0.26405971975739156, nan,
nan, nan, 0.3004550983623398, nan, nan, nan, nan, nan, nan, nan, nan, 0.20295571399794438, nan,
0.2818308329316519, 0.23945562249849983, 0.43037332112885485, nan, 0.27372778624439614,
0.22606761994625957, nan, 0.31373755519252683, 0.26600529713335669, 0.44826971384364489,
0.23759081763317308, 0.28273410230421564, 0.3075010552573007, 0.27382007182307655, nan, nan,
0.3862150026853936, nan, nan, nan, nan, 0.16805852437084309, 0.40690865438906654, 0.44016851880986407,
0.28328443122581459, 0.29793329963041421, nan, 0.31969803686336112, 0.32506424737093198,
0.33967124306848606, 0.21326705455137746, 0.11277241930160346, 0.41687040648780355, 0.31979165228202894,
0.23904153990004048, 0.2981667123755255, nan, nan, 0.29330855379879839, 0.22096962861076266, nan,
0.25639494385904987, nan, nan, 0.2535645429766834, nan, 0.22807795193987848, nan, nan, nan, nan,
0.1924158236665251, 0.21242696675008862, 0.3644920973734348, nan, nan, nan, 0.1264737977697224,
0.27365161769474378, 0.30696876967339271, 0.24753640020498949, nan, 0.8589707957510947, nan,
0.1985018254345994, nan, nan, 0.2451015740930684, 0.18059013460586296, 0.36580670135406995,
0.39887016542066833, 0.36381423609771329, 0.20457564955905588, 0.27971756818349158, 0.23497410286067261,
0.16795759737156779, nan, 0.22149558399359182, 0.28437118924206761, 0.30466848975437738,
0.32215900159399191, 0.18639508061969992, 0.23540952342091109, 0.18801420942953515, 0.18888711089257829,
0.20303874621358964, nan, 0.2824614507013396, 0.21588508496226311, 0.35826926824368505, nan,
0.30736058786542836, nan, nan, 0.16562569379717215, nan, 0.19264433967184813, 0.16932031889386617,
0.1916948428465565, 0.14326897899589017, nan, nan, 0.13934128804537493, nan, nan, nan, nan, nan, nan,
nan, nan, nan, 0.21250302916510994, nan, 0.22480519418176947, nan, nan, 0.24213836399306929, nan, nan,
nan, 0.24043978679011604, nan, nan, 0.27321880036321455, nan, nan, nan, 0.16383267004583599,
0.11739473497755221, 0.24554095554427005, 0.20127604211799741, nan, nan, 0.15992613352609206,
0.13529564482670176, nan, 0.18527522956711717, 0.16428772147703111, nan, 0.12699049201255849,
0.24171657595176685, nan, 0.12209963801659468, 0.27022267228126479, 0.12148879409024842, nan, nan,
0.20599145754219733, 0.19187479922320055, 0.19304009377493203, 0.15896279004334943, nan, nan,
0.11650367489520475, nan, 0.076292508426486466, nan, 0.28429819893113795, nan, 0.23628163182135636,
0.32401799916045648, nan, nan, nan, 0.23795248010917788, nan, 0.22684322105295995, 0.13790009502929851,
nan, nan, nan, nan, nan, 0.36880646089205182, 0.24482715888394382, nan, 0.30077416642868887, nan,
0.21307844855013228, nan, 0.24493159221503441, 0.22072220263995523, nan, nan, nan, nan, nan, nan, nan,
0.46681930729451487, 0.22883680499349227, 0.24331518119000087, 0.23366599488235187, nan, nan, nan, nan,
nan, nan, nan, 0.24677100058763915, nan, nan, nan, nan, 0.32730052336799736, nan, 0.33483413683377278,
nan, nan, 0.31778575073563237, nan, nan, nan, 0.25953938587791259, nan, nan, 0.28973831837663183,
0.21331660173751224, 0.20026482941365198, 0.19149564544037043, 0.20540150830324672, nan, nan, nan, nan,
nan, nan, 0.23237656078910821, 0.26830810380724052, 0.28267486017389448, nan, nan, nan,
0.25022064950629591, 0.26536031602605004, 0.32317637444906927, nan, nan, 0.28523006396448547,
0.2210045053931855, nan, nan, nan, 0.39142560136984922, nan, nan, 0.32380904370114233,
0.21831006899613234, 0.22982197452222747, 0.15873613838871303, 0.20363490791511971, nan,
0.28824117313877845, nan, nan, nan, 0.32588728776453757, nan, nan, nan, 0.33636521197534852,
0.24912806288795691, nan, nan, nan, 0.15300387814737082, 0.26402320860237449, 0.30290946112801992, nan,
nan, 0.30950768456473482, 0.35383367878928995, 0.31438987269463581, nan, nan, 0.21170274417127338,
0.19235371500896659, nan, nan, nan, nan, nan, 0.35478560750469601, nan, nan, nan, nan, nan,
0.37262092059823843, 0.29403312277799276, nan, nan, nan, 0.27115514427318682, nan, nan,
0.39656150812668245, nan, nan, nan, nan, 0.26249052481126084, 0.16600026407649332, 0.19181276695152905,
0.24455643947119232, nan, nan, nan, 0.23485190860609909, nan, nan, nan, 0.17420453243253067,
0.27862493694172552, nan, 0.19391049411996095, nan, 0.37778656928840543, 0.32952540005693359, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.32490075176875688,
0.22103440416438946, nan, nan, nan, 0.23461656876229822, 0.2519349618228357, 0.3691469956869477, nan,
nan, 0.36067538054025194, 0.36175906604386898, 0.35807509730028925, nan, nan, nan, nan, nan, nan, nan,
nan, 0.35231205572704616, 0.41119967649167377, 0.18525181074918171, 0.30782802377465562,
0.23993190295894939, 0.26116998054341339, 0.29146835582445008, 0.40906433511124041, 0.31613080422940326,
0.19539918329183684, 0.28633923979270781, nan, nan, nan, nan, nan, 0.31486950801621111,
0.41456741862970681, 0.14967268754888857, nan, nan, nan, 0.26118106239618649, 0.23331051104291028,
0.31272788624114767, 0.31606909295951741, nan, 0.58239581355636683, nan, nan, nan, 0.55878681006386599,
nan, 0.3289022956645824, nan, 0.4809689713879477, 0.45353033010971172, nan, 0.29133423376026063,
0.46475745214248526, 0.58345424287113512, nan, 0.45785398029752877, nan, nan, nan, nan, nan, nan, nan,
nan, 0.29522639675022583, 0.54509555765942319, 0.41769761969629532, 0.31953170279751231,
0.38269156400638576, 0.35203697151300961, nan, 0.34390284770619439, 0.35291130134500126,
0.29439704448338905, 0.36373480137600217, nan, 0.48985105848406352, nan, nan, nan, nan,
0.33814326714084397, 0.32446635282394654, 0.33199857793893717, 0.2578132654720573, 0.32486658193867762,
0.23672735108460244, 0.23271587284067202, nan, 0.27717636609303092, nan, nan, nan, nan,
0.4792033084509012, 0.51459298001261433, 0.33950866064113944, 0.67336294278654163, nan, nan, nan,
0.28940902793873102, 0.33507643175959312, 0.37750405967748607, 0.45455427774429669, nan, nan,
0.43396674173346372, nan, nan, 0.92153568091593951, nan, nan, 0.66645505589380283, 0.51547445372555978,
0.52451676381561596, 0.62131884762416156, 0.75289067921289743, nan, nan, 0.75694517284408935, nan, nan,
nan, 0.38507491188945964, 0.38897958275273792, 0.56590359325375295, nan, nan, nan, nan,
0.30615173649918542, 0.25358566531925519, nan, 0.19776113280772664, nan, nan, nan, nan,
0.18791363241674108, 0.14944542075848163, nan, 0.27295742153629882, 0.27260986876591337, nan, nan, nan,
nan, nan, nan, nan, nan, 0.3284793066879656, 0.28163838042385292, 0.22706509725091206,
0.26109318308805735, 0.25915898829431833, nan, nan, nan, 0.24985404645468259, 0.10701660661474335, nan,
nan, nan, nan, nan, nan, 0.22665629683066987, 0.24023366435345161, 0.37077777613164081, nan, nan, nan,
0.27067879436390591, 0.29436574402443105, nan, nan, nan, 0.26330832855902653, 0.26471417117499396,
0.29253596046094249, 0.41599614601115986, nan, nan, nan, nan, nan, nan, nan, 0.25914392002048769, nan,
0.31043441622760776, 0.45137064065647986, 0.29316555748480894, nan, nan, nan, nan, nan, nan,
0.37102035209270923, nan, nan, nan, nan, nan, nan, 0.53743370085633091, 0.61941931484276436,
0.36735086792751931, nan, nan, 0.3649163730739946, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, 0.3011101870865236, nan, 0.42059890549570578, 0.64942181127151788, 0.34653780860224437, nan,
0.47974378123454792, 0.29465906410331272, 0.33600806822235318, 0.35291959200042933, nan,
0.37979669379019887, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.35447434539829042, nan, nan, nan,
0.36090309036997748, 0.42189315263803934, nan, nan, nan, nan, 0.44507230697392625, nan, nan, nan, nan,
0.33115739012014345, nan, nan, nan, nan, nan, nan, nan, 0.45673303254859327, 0.40828768712457086, nan,
nan, nan, 0.21519616958416346, nan, nan, nan, nan, nan, 0.32281660077038093, 0.55785093590040791, nan,
nan, nan, nan, nan, 0.4344761963685515, 0.33003350377329566, 0.25631109232398452, 0.33658531946385534,
0.45290214952948454, 0.28940472532606726, 0.27023237634938901, 0.35549954299422454, nan,
0.42258056397282295, 0.30166384694641069, 0.21149989889561113, nan, nan, 0.32054250906094572, nan, nan,
nan, nan, nan, nan, 0.30971753897570886, 0.25631274308218394, 0.30216108199920955, 0.24189925205536769,
0.21117562239565102, nan, nan, nan, 0.18653286422539117, nan, nan, nan, nan, nan, nan,
0.23676829569013996, 0.32161308377326214, 0.26795491728373588, nan, nan, 0.28556338817199339,
0.56943700562172084, 0.16635643784586193, 0.17028704902178499, 0.23844492878801563, 0.19092788873242125,
0.18417486687721024, 0.1457246790272175, 0.12616292200939375, 0.15272734180338698, nan,
0.38164047386459016, 0.11992781575448451, 0.17116210026261261, 0.24988222578958, 0.2000497048413622,
nan, 0.15964770458718527, 0.12201644483892091, 0.16500909765853827, nan, nan, 0.1519976000065128,
0.32392710794526164, nan, 0.22475849418533955, 0.28722100667447825, 0.21029928543142579,
0.25225121272591999, nan, nan, 0.11539047869954544, 0.14654849535953884, 0.090473796976348594,
0.1619144469229877, 0.13901235055066927, nan, 0.17504526920276212, 0.20276118767637874, nan, nan, nan,
0.30937707625209665, 0.22778217490481209, 0.189794188153007, 0.34571538427511533, nan,
0.24854010898678666, 0.19919612844251702, nan, 0.18241235840501507, 0.17017654921641953, nan, nan,
0.19621296433172067, nan, nan, 0.38686255068236697, 0.19549934855433107, 0.18796834575881319,
0.23970900223851649, nan, 0.36664864276978754, 0.22176545723252494, 0.11935207420645141,
0.19059062659423118, 0.27115321215101645, nan, 0.33501146711893981, 0.23650942892077106, nan,
0.38742625472716896, 0.32419664210216387, 0.2493262547872126, 0.264503675808761, 0.27178586579919106,
0.31219378838855699, nan, 0.20933752413164738, nan, 0.19648833845285044, nan, 0.14941730948398774,
0.13454968423766892, nan, nan, 0.47567304368982938, 0.22338878223281552, 0.19341653317718879, nan, nan,
0.30177576379645887, 0.32694840606889586, 0.23978850652308406, 0.24477992478214972, 0.42772363074237174,
nan, 0.44829977231864798, 0.35853020614975362, 0.16413142157365146, 0.34744622150047194,
0.18048703456302412, 0.11566588177824362, 0.16731007863580089, 0.15153394104754225, 0.14806844066451674,
0.41893563448124688, 0.23500688903710293, nan, 0.26513285565361561, 0.26296277573625881,
0.18529598791617408, nan, 0.20258574948945207, 0.32152831711706253, nan, 0.31495966621989513,
0.23012013443095916, 0.31160563941407188, 0.34855498056155448, nan, nan, nan, 0.23983317701603815, nan,
0.36725990046715407, 0.23404362588157734, nan, 0.27799088488785573, 0.23322900271582925,
0.22511532120583852, 0.19083496520131366, nan, nan, nan, nan, 0.29354946945243632, nan, nan,
0.31580535662384923, 0.28256710638001331, nan, nan, nan, nan, 0.30920194108646137, nan,
0.33379884617596201, 0.2596575103658465, 0.50853387306424958, 0.33097888455665259, nan, nan, nan, nan,
nan]
C_modes = [3.6170212765957444, 3.2978723404255317, 3.436170212765957, 3.3936170212765955, 3.2180851063829787, nan,
3.0425531914893615, 3.1595744680851063, nan, 2.8829787234042552, 3.0, 3.25, 3.4148936170212765, 3.0,
3.1276595744680846, 3.0372340425531914, 2.9734042553191489, nan, 3.0478723404255317, 3.0638297872340425,
3.1648936170212765, 3.1755319148936172, 3.1702127659574466, 3.1914893617021276, 3.3404255319148932,
3.4095744680851059, 3.1010638297872339, 3.1542553191489358, 4.0904255319148941, 3.2819148936170213,
3.207446808510638, 3.4999999999999996, 3.1755319148936172, nan, 3.7659574468085104, 3.7287234042553186,
3.5744680851063828, 3.4095744680851059, nan, 3.686170212765957, 3.4627659574468082, 3.4627659574468082,
3.7074468085106385, nan, 3.3510638297872339, 3.5265957446808507, 3.5372340425531914, nan, nan, nan, nan,
nan, nan, 3.3510638297872339, 3.6702127659574466, 2.8457446808510638, 2.8138297872340425,
3.4468085106382977, 3.6914893617021276, 3.5797872340425529, 3.8723404255319145, 3.8989361702127656,
3.6382978723404258, 3.808510638297872, 2.8776595744680851, nan, nan, 3.5425531914893611, nan,
3.2659574468085104, 3.3829787234042552, 3.5851063829787235, 3.7021276595744674, nan, nan, nan,
2.4095744680851063, nan, nan, 2.5851063829787235, 2.4946808510638294, nan, nan, nan, nan, nan, nan, nan,
2.2021276595744679, 2.707446808510638, 2.4627659574468082, nan, 2.3723404255319149, nan,
2.4521276595744679, 2.2606382978723403, nan, 2.6702127659574471, nan, nan, nan, nan, 2.207446808510638,
2.1861702127659575, 2.1010638297872339, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
2.4414893617021276, nan, 2.2553191489361701, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
2.3351063829787231, 2.3776595744680851, 2.2340425531914891, 2.3404255319148937, 2.3457446808510638,
2.4680851063829787, 2.2819148936170213, 2.3351063829787231, nan, nan, nan, 2.6436170212765959,
2.1755319148936167, nan, nan, 2.3138297872340425, 2.3138297872340425, 2.1808510638297873,
2.4202127659574466, 2.1968085106382977, nan, nan, nan, nan, nan, 2.3244680851063833, nan,
2.2819148936170213, 2.1170212765957444, nan, 2.0797872340425534, 2.3191489361702127, 2.0691489361702127,
2.3617021276595742, 2.4840425531914896, 1.9787234042553192, 2.271276595744681, 2.1808510638297873,
2.2978723404255321, 2.3031914893617018, 2.3244680851063833, 2.2021276595744679, 2.1914893617021276,
2.4042553191489362, nan, 2.3563829787234041, 2.5691489361702127, nan, 2.3723404255319149, nan, nan, nan,
nan, nan, nan, 2.6648936170212765, 2.4468085106382977, 2.3563829787234041, 2.5797872340425529,
2.6117021276595747, 2.4255319148936172, nan, 2.6010638297872339, 2.75, 3.0744680851063828,
2.8670212765957444, 2.7446808510638299, 2.7819148936170208, 2.8297872340425534, 2.9414893617021276,
2.9680851063829783, 2.808510638297872, nan, 2.4627659574468082, nan, nan, 2.6702127659574471,
2.4574468085106385, 2.6489361702127656, 2.7606382978723403, 2.3617021276595742, nan, 2.7127659574468086,
nan, nan, 2.4574468085106385, 2.5053191489361701, 2.1010638297872339, 2.4095744680851063,
2.5585106382978724, nan, 2.4255319148936172, 2.2978723404255321, 2.5425531914893615, 2.3723404255319149,
2.7234042553191489, 2.6382978723404253, 2.6808510638297869, 2.8404255319148932, 2.3882978723404253, nan,
2.7234042553191489, nan, nan, 2.5851063829787235, 2.8404255319148932, 2.4042553191489362,
2.7765957446808511, nan, 2.2819148936170213, 2.3085106382978724, 2.3457446808510638, 2.4202127659574466,
nan, nan, 2.8936170212765955, 2.5638297872340425, nan, 2.4574468085106385, nan, 2.5265957446808511,
2.7872340425531914, 3.3351063829787235, nan, 2.728723404255319, nan, nan, nan, 2.7819148936170208,
2.8776595744680851, 2.3670212765957448, 2.6276595744680851, 2.7340425531914891, 2.8404255319148932, nan,
nan, nan, 3.1542553191489358, 2.7659574468085104, 2.4308510638297873, nan, nan, nan, nan, nan,
2.8404255319148932, 2.4574468085106385, 2.5159574468085104, 2.5531914893617018, 2.5319148936170213,
2.6170212765957448, 2.7872340425531914, 2.7872340425531914, 2.707446808510638, 2.75, 3.1382978723404253,
2.8670212765957444, nan, 2.8723404255319149, 2.6063829787234041, nan, nan, nan, 2.0797872340425534,
2.1063829787234041, 2.1170212765957444, 2.0585106382978724, 2.1808510638297873, 2.1648936170212765,
2.2499999999999996, 2.4042553191489362, 2.1276595744680851, 2.1595744680851063, 2.2340425531914891,
2.0957446808510638, 2.2978723404255321, 2.4468085106382977, 2.1170212765957444, 2.4042553191489362,
2.0585106382978724, nan, nan, 2.5691489361702127, nan, nan, 2.5744680851063828, nan, 3.2021276595744683,
3.228723404255319, 2.9893617021276597, 2.5478723404255317, nan, nan, nan, nan, 2.7021276595744679, nan,
2.2340425531914891, nan, 2.3670212765957448, 2.478723404255319, 2.3829787234042552, 2.4414893617021276,
nan, 2.5851063829787235, nan, 2.5106382978723403, 2.3617021276595742, nan, nan, nan, 2.6436170212765959,
nan, 2.5957446808510638, 2.4627659574468082, nan, 2.6755319148936167, nan, 2.4734042553191489,
2.4042553191489362, 2.4414893617021276, 2.4468085106382977, 2.3510638297872339, 2.6436170212765959,
2.9734042553191489, 2.8936170212765955, 2.75, 2.6914893617021276, 2.6223404255319145, 2.6648936170212765,
2.6436170212765959, 2.6808510638297869, nan, nan, 2.6170212765957448, 2.5053191489361701, nan, nan,
2.436170212765957, 2.4680851063829787, 2.3031914893617018, 2.6276595744680851, 2.3138297872340425,
2.1914893617021276, 2.1170212765957444, 2.0851063829787235, 2.0319148936170213, nan, 1.8882978723404256,
1.9627659574468084, nan, nan, 2.2499999999999996, 2.1276595744680851, 2.3138297872340425,
2.5585106382978724, nan, nan, nan, 2.1755319148936167, 2.2553191489361701, 2.3723404255319149,
2.4627659574468082, 2.7606382978723403, 2.4734042553191489, nan, nan, nan, 2.2340425531914891,
2.5904255319148937, 2.7127659574468086, 2.6595744680851063, 2.2446808510638299, 2.3563829787234041,
2.6063829787234041, 2.8670212765957444, nan, 2.5478723404255317, 2.8191489361702127, nan,
2.7819148936170208, 2.9521276595744679, 2.7872340425531914, 2.4574468085106385, nan, 2.6595744680851063,
2.6595744680851063, 2.5319148936170213, 2.7234042553191489, 2.8457446808510638, 2.4734042553191489,
2.9521276595744679, 2.9414893617021276, 3.0478723404255317, 2.9893617021276597, 3.2127659574468082, nan,
2.9095744680851063, nan, 2.8191489361702127, 2.7765957446808511, 2.4414893617021276, nan,
2.8882978723404253, 3.228723404255319, 3.4202127659574466, 3.4627659574468082, 3.3617021276595747,
3.7553191489361697, nan, 2.2659574468085104, 1.8723404255319149, 2.1595744680851063, nan,
2.2340425531914891, nan, nan, nan, nan, nan, nan, nan, nan, nan, 2.2765957446808507, nan, nan,
2.7393617021276593, 2.0372340425531914, 2.436170212765957, 2.3457446808510638, 2.4095744680851063,
2.5638297872340425, 2.3510638297872339, 2.0531914893617023, 2.1329787234042552, 2.7234042553191489, nan,
2.1489361702127656, nan, 2.1914893617021276, 2.3510638297872339, 2.207446808510638, 2.1010638297872339,
2.5957446808510638, 2.2978723404255321, 2.3829787234042552, nan, 2.0425531914893615, nan, nan,
2.1648936170212765, nan, 2.3776595744680851, nan, 2.5319148936170213, 2.6968085106382977,
2.5851063829787235, 2.5212765957446805, 2.3404255319148937, 2.3776595744680851, 2.8882978723404253,
2.3670212765957448, 2.5797872340425529, 2.4468085106382977, 2.5265957446808511, nan, nan,
2.2978723404255321, 2.5957446808510638, nan, nan, 2.3191489361702127, 2.6223404255319145,
2.5159574468085104, 2.4627659574468082, 3.4468085106382977, nan, nan, nan, nan, nan, nan, nan, nan,
2.6170212765957448, 2.9468085106382977, 2.2553191489361701, nan, 2.5797872340425529, nan,
2.6595744680851063, 2.6436170212765959, nan, nan, nan, nan, 2.3670212765957448, nan, nan, nan,
2.3138297872340425, 2.6117021276595747, 2.5, nan, nan, 2.1755319148936167, 2.3989361702127661,
2.1276595744680851, 2.2553191489361701, 2.5531914893617018, 2.2925531914893615, 2.1914893617021276, nan,
2.3510638297872339, 2.5425531914893615, 2.1702127659574466, 2.3510638297872339, 2.8031914893617023,
2.8031914893617023, 2.8989361702127661, 2.5638297872340425, 2.6329787234042552, nan, 2.6861702127659575,
2.2819148936170213, 2.5585106382978724, nan, 2.3510638297872339, 2.75, 2.5159574468085104,
2.7393617021276593, 2.6382978723404253, 2.6914893617021276, 2.6968085106382977, 2.8457446808510638,
2.5691489361702127, nan, nan, nan, nan, nan, nan, 2.5585106382978724, nan, 2.7553191489361701,
2.3297872340425529, 2.6755319148936167, 2.5053191489361701, 2.3510638297872339, 2.6010638297872339, nan,
2.6436170212765959, 2.0638297872340425, 2.3244680851063833, nan, nan, nan, nan, 2.8031914893617023,
2.5106382978723403, nan, nan, 2.5372340425531914, nan, nan, 2.9521276595744679, 2.6755319148936167,
2.6755319148936167, nan, 2.5159574468085104, 2.9946808510638294, 2.707446808510638, nan,
2.6329787234042552, 2.2553191489361701, nan, nan, 2.3882978723404253, 2.6542553191489362,
2.6223404255319145, 2.9521276595744679, nan, nan, nan, nan, nan, 2.8510638297872339, 2.771276595744681,
2.8617021276595742, 2.9414893617021276, 2.8351063829787231, nan, 2.8617021276595742, 2.8244680851063828,
3.0531914893617018, 2.8510638297872339, 2.8351063829787231, 2.8723404255319149, nan, nan, nan, nan,
2.2393617021276597, 2.8670212765957444, 2.6595744680851063, nan, 2.4627659574468082, 2.5691489361702127,
2.5904255319148937, nan, nan, 2.3776595744680851, 2.2819148936170213, nan, nan, 2.7446808510638299,
2.4574468085106385, 2.5797872340425529, 2.478723404255319, 2.6648936170212765, 2.478723404255319,
2.808510638297872, nan, nan, nan, 2.8829787234042552, 2.6276595744680851, 2.4946808510638294, nan, nan,
nan, 2.4255319148936172, 2.5106382978723403, 2.4574468085106385, 2.6063829787234041, 2.7393617021276593,
2.75, 2.6276595744680851, 2.7393617021276593, nan, 2.7659574468085104, 2.5691489361702127, nan,
2.5851063829787235, 2.4893617021276593, 2.5797872340425529, 2.6702127659574471, 2.6010638297872339, nan,
nan, 2.6702127659574471, 2.8031914893617023, 3.5159574468085104, 3.4042553191489362, 2.5797872340425529,
nan, 2.6329787234042552, 2.6808510638297869, 2.7446808510638299, nan, nan, nan, 2.5, 2.7606382978723403,
2.8351063829787231, 2.7765957446808511, 2.5425531914893615, 2.8191489361702127, 2.9680851063829783, nan,
nan, nan, 2.6170212765957448, nan, 2.8191489361702127, 3.0265957446808507, 3.2446808510638299, nan, nan,
3.0159574468085109, 3.5053191489361706, nan, 2.9893617021276597, 3.3085106382978724, nan,
3.2872340425531914, 3.3351063829787235, 3.4414893617021276, 3.0212765957446805, 3.1914893617021276, nan,
3.0957446808510638, 3.1489361702127661, nan, 3.4734042553191489, 3.0585106382978724, 2.8936170212765955,
nan, 3.457446808510638, 3.2127659574468082, nan, 3.0744680851063828, 3.1861702127659575,
3.1808510638297869, 3.3829787234042552, 3.5106382978723403, 3.2925531914893615, 3.3829787234042552,
3.3882978723404253, 3.9893617021276593, nan, 3.4627659574468082, 3.3510638297872339, 3.4521276595744683,
nan, nan, 5.0106382978723403, nan, nan, 4.1489361702127656, nan, nan, 3.5265957446808507,
3.7659574468085104, nan, 3.6117021276595747, nan, 4.0, nan, nan, 3.2234042553191489, 3.5851063829787235,
3.6223404255319145, 3.7872340425531914, nan, 3.6489361702127656, nan, 3.228723404255319,
3.6914893617021276, nan, nan, nan, nan, 3.6595744680851059, 3.4148936170212765, 3.3563829787234041, nan,
nan, nan, 3.5319148936170213, 3.3191489361702127, 3.25, nan, nan, 3.9627659574468082, nan,
3.9680851063829783, 4.1436170212765955, 3.8510638297872344, 3.9095744680851063, 3.686170212765957, nan,
nan, nan, nan, nan, 3.6595744680851059, 3.4202127659574466, 3.5904255319148937, 3.6010638297872339, 3.75,
3.7074468085106385, 3.3085106382978724, 3.2234042553191489, 3.8031914893617018, 3.4308510638297869,
4.0638297872340425, 3.6117021276595747, 3.7606382978723403, nan, nan, nan, nan, nan, nan, nan,
2.9042553191489362, nan, 3.1702127659574466, nan, nan, nan, nan, nan, nan, nan, nan, 3.457446808510638,
nan, nan, 3.4521276595744683, 3.6595744680851059, 3.3776595744680851, nan, 3.0797872340425529,
3.5106382978723403, nan, 3.4734042553191489, nan, nan, 3.0904255319148937, nan, nan, nan,
3.5744680851063828, 3.457446808510638, 3.2234042553191489, 3.0585106382978724, nan, nan, nan, nan, nan,
nan, nan, nan, 3.1063829787234041, nan, 3.686170212765957, 3.4042553191489362, nan, 3.8510638297872344,
nan, 3.7765957446808511, nan, 3.4255319148936172, 3.2659574468085104, nan, 4.2021276595744679,
3.5851063829787235, 3.2446808510638299, nan, 3.5319148936170213, nan, nan, nan, 3.4893617021276593,
3.1808510638297869, 3.3776595744680851, 3.4787234042553195, 3.1861702127659575, nan, nan, nan,
2.9042553191489362, 3.0797872340425529, nan, 3.1170212765957448, 3.1063829787234041, 3.25,
3.6648936170212769, 3.1755319148936172, 3.25, 3.2925531914893615, nan, nan, nan, 3.7606382978723403, nan,
nan, 3.7074468085106385, 3.5585106382978724, 3.5372340425531914, 3.8989361702127656, 3.5372340425531914,
nan, 3.3882978723404253, 3.5531914893617018, 3.8829787234042552, 3.8936170212765959, 3.4999999999999996,
3.1861702127659575, 4.1382978723404253, 3.521276595744681, nan, nan, nan, nan, nan, 3.8404255319148937,
3.686170212765957, 3.9574468085106385, nan, nan, nan, nan, nan, nan, nan, 4.1968085106382977,
4.0851063829787231, nan, nan, nan, 3.3138297872340421, nan, nan, 3.0, 3.0159574468085109,
2.9042553191489362, 3.2872340425531914, 3.3404255319148932, 3.0425531914893615, nan, nan,
3.2659574468085104, nan, nan, nan, 3.0106382978723403, 3.3191489361702127, 3.3510638297872339, nan, nan,
3.4148936170212765, 3.1968085106382977, 3.2659574468085104, 2.8936170212765955, 3.4627659574468082, nan,
nan, nan, nan, nan, nan, nan, 3.6436170212765955, nan, nan, nan, 3.7340425531914896, nan, nan, nan, nan,
nan, nan, nan, nan, 3.1702127659574466, nan, 3.3457446808510638, 3.1861702127659575, 3.3723404255319145,
nan, 3.0691489361702127, 2.8191489361702127, nan, 3.0531914893617018, 3.3989361702127656, 3.25,
3.3457446808510638, 3.1702127659574466, 3.2446808510638299, 3.6808510638297873, nan, nan,
2.9627659574468086, nan, nan, nan, nan, 3.0, 3.0159574468085109, 3.4734042553191489, 3.4255319148936172,
3.6648936170212769, nan, 3.5691489361702122, 3.7234042553191489, 3.5851063829787235, 3.4521276595744683,
3.3989361702127656, 3.1223404255319149, 3.5957446808510634, 3.2712765957446805, 3.4734042553191489, nan,
nan, 3.3297872340425529, 3.3670212765957444, nan, 3.7021276595744674, nan, nan, 3.2659574468085104, nan,
3.3244680851063828, nan, nan, nan, nan, 3.4521276595744683, 3.3510638297872339, 3.4414893617021276, nan,
nan, nan, 3.3563829787234041, 3.1808510638297869, 2.9255319148936167, 2.8138297872340425, nan,
3.5425531914893611, nan, 3.2765957446808511, nan, nan, 3.0851063829787231, 3.1542553191489358,
3.0744680851063828, 3.4468085106382977, 3.5744680851063828, 3.2712765957446805, 3.4999999999999996,
3.3031914893617023, 3.2765957446808511, nan, 3.4042553191489362, 4.1276595744680851, 3.7819148936170208,
3.7074468085106385, 3.457446808510638, 3.6276595744680851, 3.5797872340425529, 3.3989361702127656,
3.686170212765957, nan, 3.6063829787234041, 3.7659574468085104, 3.8457446808510634, nan,
4.3191489361702127, nan, nan, 2.1382978723404258, nan, 2.2021276595744679, 2.1595744680851063,
2.3138297872340425, 2.3776595744680851, nan, nan, 3.5053191489361706, nan, nan, nan, nan, nan, nan, nan,
nan, nan, 3.7127659574468082, nan, 3.6170212765957444, nan, nan, 3.1436170212765955, nan, nan, nan,
3.5106382978723403, nan, nan, 3.7127659574468082, nan, nan, nan, 2.3563829787234041, 2.228723404255319,
2.3989361702127661, 2.4095744680851063, nan, nan, 2.3723404255319149, 2.1489361702127656, nan,
2.5585106382978724, 2.3989361702127661, nan, 2.5159574468085104, 2.3882978723404253, nan,
2.6329787234042552, 2.1542553191489362, 2.6170212765957448, nan, nan, 2.4095744680851063,
2.5159574468085104, 2.6648936170212765, 2.6276595744680851, nan, nan, 2.5585106382978724, nan,
2.6170212765957448, nan, 2.9468085106382977, nan, 2.5585106382978724, 2.6914893617021276, nan, nan, nan,
2.728723404255319, nan, 2.6968085106382977, 2.8670212765957444, nan, nan, nan, nan, nan,
2.8989361702127661, 2.8510638297872339, nan, 2.6861702127659575, nan, 2.5478723404255317, nan,
2.8297872340425534, 3.1382978723404253, nan, nan, nan, nan, nan, nan, nan, 3.5159574468085104,
3.0478723404255317, 3.0904255319148937, 3.2393617021276593, nan, nan, nan, nan, nan, nan, nan,
3.2606382978723403, nan, nan, nan, nan, 3.0159574468085109, nan, 3.1755319148936172, nan, nan,
3.3989361702127656, nan, nan, nan, 2.6595744680851063, nan, nan, 2.6595744680851063, 2.9574468085106385,
3.2819148936170213, 2.978723404255319, 3.0638297872340425, nan, nan, nan, nan, nan, nan,
2.6595744680851063, 3.2234042553191489, 3.1914893617021276, nan, nan, nan, 2.6861702127659575,
2.9414893617021276, 3.0319148936170213, nan, nan, 2.6382978723404253, 2.3563829787234041, nan, nan, nan,
2.9255319148936167, nan, nan, 2.9946808510638294, 2.9414893617021276, 2.8617021276595742,
2.9946808510638294, 3.0638297872340425, nan, 2.9468085106382977, nan, nan, nan, 3.2978723404255317, nan,
nan, nan, 2.8670212765957444, 2.8829787234042552, nan, nan, nan, 2.9521276595744679, 3.1542553191489358,
3.2712765957446805, nan, nan, 2.6861702127659575, 2.6968085106382977, 3.2606382978723403, nan, nan,
3.207446808510638, 3.2819148936170213, nan, nan, nan, nan, nan, 3.5904255319148937, nan, nan, nan, nan,
nan, 2.5319148936170213, 2.7021276595744679, nan, nan, nan, 3.1595744680851063, nan, nan,
3.2765957446808511, nan, nan, nan, nan, 3.2872340425531914, 2.7659574468085104, 3.0053191489361701,
3.0851063829787231, nan, nan, nan, 2.8404255319148932, nan, nan, nan, 3.1914893617021276,
3.0159574468085109, nan, 3.1968085106382977, nan, 2.9840425531914891, 3.5319148936170213, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 3.5319148936170213,
3.4255319148936172, nan, nan, nan, 3.1648936170212765, 3.4734042553191489, 3.686170212765957, nan, nan,
3.7872340425531914, 3.6276595744680851, 3.6329787234042552, nan, nan, nan, nan, nan, nan, nan, nan,
4.0531914893617023, 3.8510638297872344, 3.8351063829787231, 3.75, 3.9627659574468082, 3.8936170212765959,
4.1170212765957448, 4.0478723404255312, 3.9255319148936167, 4.0851063829787231, 4.6914893617021276, nan,
nan, nan, nan, nan, 3.7659574468085104, 3.6276595744680851, 3.5585106382978724, nan, nan, nan,
4.0478723404255312, 3.8882978723404249, 3.978723404255319, 4.042553191489362, nan, 4.2446808510638299,
nan, nan, nan, 4.8829787234042552, nan, 3.6595744680851059, nan, 4.3085106382978724, 5.2978723404255312,
nan, 4.4361702127659575, 4.9680851063829792, 4.8670212765957448, nan, 3.9202127659574466, nan, nan, nan,
nan, nan, nan, nan, nan, 5.0744680851063828, 5.0106382978723403, 5.0478723404255321, 5.0797872340425529,
5.457446808510638, 5.1382978723404253, nan, 5.4255319148936172, 5.707446808510638, 5.1914893617021276,
5.4840425531914887, nan, 5.2606382978723403, nan, nan, nan, nan, 4.6595744680851059, 4.4414893617021276,
4.4521276595744679, 4.3670212765957439, 4.8776595744680851, 4.4468085106382977, 4.292553191489362, nan,
4.6276595744680851, nan, nan, nan, nan, 4.4361702127659575, 4.8510638297872344, 5.0053191489361701,
6.3563829787234036, nan, nan, nan, 5.1170212765957448, 5.6010638297872335, 5.2021276595744679,
5.4255319148936172, nan, nan, 5.6276595744680851, nan, nan, 6.8936170212765955, nan, nan,
6.4787234042553186, 6.4202127659574471, 6.6117021276595738, 6.6329787234042552, 7.4361702127659575, nan,
nan, 6.7234042553191493, nan, nan, nan, 5.9840425531914887, 6.1436170212765955, 5.9361702127659566, nan,
nan, nan, nan, 2.7446808510638299, 2.9308510638297873, nan, 2.8723404255319149, nan, nan, nan, nan,
2.4627659574468082, 2.8031914893617023, nan, 3.1117021276595742, 2.978723404255319, nan, nan, nan, nan,
nan, nan, nan, nan, 3.7234042553191489, 4.2340425531914887, 3.0372340425531914, 2.7340425531914891,
3.4414893617021276, nan, nan, nan, 3.1861702127659575, 3.1808510638297869, nan, nan, nan, nan, nan, nan,
3.8457446808510634, 3.2393617021276593, 3.1382978723404253, nan, nan, nan, 3.4414893617021276,
3.7553191489361697, nan, nan, nan, 3.5265957446808507, 4.0319148936170208, 3.4893617021276593,
3.6702127659574466, nan, nan, nan, nan, nan, nan, nan, 3.9521276595744679, nan, 4.6808510638297873,
4.4893617021276597, 4.6010638297872335, nan, nan, nan, nan, nan, nan, 4.6276595744680851, nan, nan, nan,
nan, nan, nan, 5.5638297872340416, 4.6276595744680851, 4.8510638297872344, nan, nan, 5.3723404255319149,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 5.3031914893617023, nan, 5.3404255319148941,
5.5212765957446805, 5.2712765957446805, nan, 6.2819148936170208, 4.5904255319148932, 4.2606382978723403,
4.4521276595744679, nan, 4.2340425531914887, nan, nan, nan, nan, nan, nan, nan, nan, nan,
4.4680851063829783, nan, nan, nan, 4.6489361702127665, 4.9893617021276588, nan, nan, nan, nan,
5.1861702127659575, nan, nan, nan, nan, 3.914893617021276, nan, nan, nan, nan, nan, nan, nan,
4.542553191489362, 4.7925531914893611, nan, nan, nan, 4.7393617021276588, nan, nan, nan, nan, nan,
4.9414893617021276, 5.1223404255319149, nan, nan, nan, nan, nan, 3.1648936170212765, 3.4946808510638299,
3.1223404255319149, 3.521276595744681, 2.9946808510638294, 2.8297872340425534, 3.1117021276595742,
3.5106382978723403, nan, 3.0, 3.5531914893617018, 3.3829787234042552, nan, nan, 3.1755319148936172, nan,
nan, nan, nan, nan, nan, 3.4414893617021276, 3.6648936170212769, 3.6755319148936167, 2.9521276595744679,
3.4042553191489362, nan, nan, nan, 3.4148936170212765, nan, nan, nan, nan, nan, nan, 3.9202127659574466,
3.6648936170212769, 3.3404255319148932, nan, nan, 3.9042553191489362, 2.6542553191489362,
2.8031914893617023, 2.5053191489361701, 2.7925531914893615, 2.6010638297872339, 2.8723404255319149,
2.7925531914893615, 2.8457446808510638, 2.8670212765957444, nan, 3.6595744680851059, 3.0744680851063828,
2.9893617021276597, 2.8989361702127661, 2.7553191489361701, nan, 2.8404255319148932, 2.8563829787234045,
2.6223404255319145, nan, nan, 3.0691489361702127, 2.8297872340425534, nan, 3.0053191489361701,
3.0053191489361701, 2.7606382978723403, 2.5106382978723403, nan, nan, 2.8510638297872339,
2.8297872340425534, 2.8882978723404253, 2.808510638297872, 2.8191489361702127, nan, 2.978723404255319,
2.9734042553191489, nan, nan, nan, 3.2234042553191489, 3.1063829787234041, 3.2872340425531914,
3.436170212765957, nan, 3.4627659574468082, 3.3776595744680851, nan, 3.2819148936170213,
3.4308510638297869, nan, nan, 3.436170212765957, nan, nan, 3.0797872340425529, 3.0319148936170213,
3.0478723404255317, 3.0904255319148937, nan, 3.1063829787234041, 3.0053191489361701, 3.1914893617021276,
2.8936170212765955, 3.0265957446808507, nan, 2.8829787234042552, 3.5265957446808507, nan,
3.8351063829787231, 3.4042553191489362, 3.2393617021276593, 3.2180851063829787, 3.5159574468085104,
3.2234042553191489, nan, 3.4680851063829787, nan, 3.6382978723404258, nan, 3.8723404255319145,
3.8351063829787231, nan, nan, 4.2234042553191493, 3.6808510638297873, 3.6489361702127656, nan, nan,
3.7553191489361697, 4.0106382978723403, 2.6436170212765959, 2.7606382978723403, 2.6489361702127656, nan,
3.1329787234042552, 2.5265957446808511, 2.7021276595744679, 2.9946808510638294, 2.9042553191489362,
3.0319148936170213, 3.0319148936170213, 2.8989361702127661, 2.8510638297872339, 3.0638297872340425,
2.9148936170212765, nan, 2.9414893617021276, 2.9946808510638294, 3.0904255319148937, nan,
3.2606382978723403, 2.9627659574468086, nan, 3.0159574468085109, 3.2180851063829787, 2.8989361702127661,
3.3297872340425529, nan, nan, nan, 4.414893617021276, nan, 3.6010638297872339, 3.521276595744681, nan,
3.3191489361702127, 3.3989361702127656, 3.3936170212765955, 3.3723404255319145, nan, nan, nan, nan,
3.686170212765957, nan, nan, 3.6010638297872339, 3.0212765957446805, nan, nan, nan, nan,
4.0638297872340425, nan, 3.9202127659574466, 4.0265957446808507, 3.8031914893617018, 4.2553191489361701,
nan, nan, nan, nan, nan]
profile_total = [1336, 3637, 1561, 1512, 581, 137, 170, 650, 0, 565, 1869, 362, 871, 1605, 111, 772, 760, 167, 304,
1126, 1126, 824, 522, 1329, 1717, 1056, 3630, 2893, 2246, 2283, 1960, 1550, 562, 26, 212, 216,
1189, 1203, 16, 1300, 304, 861, 374, 92, 207, 492, 593, 44, 5, 35, 47, 0, 59, 777, 147, 1493, 381,
433, 479, 1074, 1146, 1146, 1159, 1993, 577, 16, 0, 489, 45, 620, 719, 928, 620, 0, 0, 94, 275, 0,
0, 2054, 1215, 10, 0, 0, 5, 0, 0, 0, 982, 565, 1455, 15, 215, 148, 1408, 838, 100, 200, 0, 0, 34,
26, 694, 1292, 140, 74, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1712, 0, 1285, 33, 0, 0, 0, 5, 0, 0, 0, 56, 168,
6, 18, 310, 315, 266, 328, 588, 623, 443, 1613, 32, 116, 132, 113, 190, 10, 0, 957, 1869, 1778,
1318, 490, 82, 0, 10, 15, 41, 1007, 141, 309, 188, 119, 259, 355, 119, 711, 379, 2004, 797, 151,
814, 298, 755, 276, 126, 212, 33, 794, 449, 0, 977, 53, 63, 5, 34, 0, 0, 165, 210, 351, 189, 247,
142, 22, 159, 671, 283, 165, 538, 463, 757, 413, 1190, 314, 168, 756, 105, 0, 870, 1903, 1690, 577,
445, 0, 153, 39, 0, 772, 702, 166, 249, 804, 105, 180, 1278, 1290, 1291, 1435, 296, 1407, 719,
1686, 42, 405, 40, 118, 564, 341, 195, 362, 137, 201, 1879, 4543, 2669, 53, 108, 233, 611, 72, 220,
30, 420, 2177, 893, 7, 177, 8, 60, 47, 103, 262, 255, 1553, 2957, 487, 32, 0, 8, 494, 1017, 277, 0,
0, 0, 0, 0, 151, 1883, 2345, 181, 272, 1488, 1004, 2849, 1102, 1671, 1397, 385, 0, 349, 552, 24, 0,
80, 1192, 280, 156, 328, 267, 130, 124, 1368, 1186, 1988, 1303, 341, 618, 553, 241, 524, 1171, 5,
42, 764, 5, 6, 288, 0, 482, 766, 391, 228, 150, 26, 0, 0, 412, 142, 178, 6, 292, 765, 1719, 672,
18, 449, 29, 121, 163, 13, 5, 10, 357, 38, 245, 715, 15, 141, 27, 257, 634, 583, 166, 127, 332,
1676, 838, 1473, 212, 4365, 3882, 4928, 653, 0, 5, 2681, 676, 22, 63, 354, 195, 791, 1033, 3768,
3579, 2036, 2138, 1711, 0, 238, 420, 0, 37, 822, 390, 115, 801, 29, 34, 6, 1853, 626, 380, 1149,
389, 396, 0, 0, 0, 334, 852, 695, 723, 1060, 2067, 2262, 526, 0, 591, 694, 72, 1656, 807, 2681,
1264, 58, 424, 1160, 1878, 841, 1475, 1075, 485, 1547, 1083, 1104, 305, 82, 120, 66, 648, 1750,
496, 0, 1562, 1063, 267, 296, 1288, 1694, 0, 1393, 182, 216, 48, 209, 6, 0, 0, 0, 0, 0, 0, 0, 0,
941, 0, 0, 278, 453, 899, 824, 1921, 808, 285, 317, 315, 537, 94, 279, 33, 175, 492, 201, 1208,
361, 685, 376, 43, 379, 81, 47, 355, 11, 338, 49, 204, 568, 738, 2109, 571, 278, 330, 374, 590,
2442, 730, 59, 44, 215, 298, 99, 0, 704, 758, 4558, 640, 280, 40, 0, 0, 0, 0, 0, 0, 0, 698, 703,
314, 0, 215, 133, 307, 299, 5, 28, 24, 20, 2632, 164, 0, 53, 137, 460, 389, 115, 0, 256, 304, 205,
120, 200, 870, 611, 25, 613, 636, 168, 588, 644, 542, 843, 175, 783, 106, 926, 516, 835, 29, 256,
329, 425, 1664, 1204, 1221, 935, 993, 870, 0, 0, 0, 0, 0, 14, 1214, 36, 158, 222, 333, 445, 285,
279, 79, 911, 634, 388, 0, 0, 0, 0, 637, 224, 41, 6, 746, 0, 0, 526, 211, 386, 76, 671, 461, 507,
72, 124, 178, 160, 0, 1765, 953, 307, 655, 0, 0, 0, 0, 5, 1102, 387, 513, 351, 1481, 13, 1904, 624,
131, 247, 406, 354, 68, 86, 61, 23, 268, 398, 582, 95, 2265, 590, 155, 66, 59, 282, 257, 86, 83,
243, 626, 644, 395, 519, 252, 1051, 38, 40, 67, 339, 639, 664, 0, 0, 0, 1157, 702, 390, 899, 734,
1275, 652, 168, 109, 114, 1433, 62, 246, 779, 3447, 3180, 196, 18, 69, 2634, 1232, 880, 673, 1286,
0, 1576, 815, 198, 0, 28, 64, 365, 572, 544, 2127, 229, 409, 504, 0, 55, 21, 341, 17, 1046, 2042,
1212, 118, 0, 550, 846, 35, 282, 203, 15, 2125, 163, 278, 472, 272, 77, 455, 847, 55, 840, 374,
606, 24, 322, 543, 46, 1935, 3944, 1729, 495, 1539, 2966, 114, 650, 495, 144, 1060, 686, 1842, 0,
197, 343, 171, 0, 333, 38, 111, 1884, 2370, 41, 190, 72, 803, 92, 0, 321, 903, 2003, 819, 127, 474,
178, 864, 1211, 44, 0, 0, 0, 512, 549, 1222, 17, 0, 0, 1174, 1929, 157, 0, 132, 1634, 5, 764, 1523,
1930, 2522, 1357, 45, 0, 0, 0, 0, 1996, 1732, 166, 617, 223, 521, 435, 485, 756, 267, 525, 327,
341, 0, 0, 93, 0, 0, 35, 114, 180, 163, 679, 10, 0, 5, 14, 22, 37, 0, 0, 366, 0, 70, 615, 513, 379,
55, 141, 258, 0, 336, 0, 0, 1765, 77, 20, 197, 612, 491, 275, 813, 0, 0, 0, 0, 169, 62, 48, 0, 874,
57, 548, 431, 57, 170, 0, 1108, 83, 1448, 1379, 0, 845, 2509, 2194, 237, 369, 64, 0, 0, 691, 1444,
1546, 936, 985, 104, 164, 0, 1360, 517, 0, 2165, 4198, 3238, 1124, 1882, 419, 264, 162, 149, 0,
174, 0, 34, 408, 431, 803, 1135, 296, 41, 1140, 678, 562, 419, 183, 1150, 1323, 436, 5, 0, 0, 0, 0,
139, 1571, 1226, 0, 28, 0, 0, 0, 0, 0, 1454, 789, 0, 0, 10, 592, 101, 8, 392, 232, 115, 2391, 438,
860, 109, 41, 168, 7, 0, 0, 828, 1692, 450, 47, 125, 335, 279, 404, 646, 254, 102, 20, 0, 44, 0, 5,
15, 249, 153, 0, 78, 390, 137, 0, 0, 20, 0, 0, 142, 138, 144, 142, 399, 1538, 1019, 32, 305, 361,
88, 246, 861, 124, 1659, 803, 808, 2441, 0, 0, 480, 23, 0, 0, 11, 627, 191, 272, 320, 1355, 91,
966, 1187, 1187, 294, 73, 427, 241, 458, 707, 0, 39, 692, 224, 31, 275, 126, 108, 410, 24, 236, 20,
76, 44, 5, 188, 397, 274, 0, 35, 106, 390, 364, 208, 325, 20, 295, 0, 471, 84, 0, 1692, 1497, 412,
282, 287, 752, 465, 899, 1260, 9, 3062, 1450, 2183, 1688, 1149, 4017, 1071, 1249, 1628, 12, 1183,
2909, 432, 21, 1006, 0, 0, 362, 0, 592, 1488, 685, 1021, 5, 0, 227, 29, 0, 0, 0, 43, 9, 49, 0, 80,
257, 52, 136, 0, 91, 108, 32, 9, 133, 474, 148, 77, 167, 119, 25, 152, 385, 176, 735, 615, 32, 0,
164, 477, 0, 158, 425, 54, 937, 1224, 101, 99, 136, 126, 18, 21, 386, 1060, 750, 407, 33, 0, 155,
39, 90, 19, 509, 87, 498, 280, 140, 0, 0, 754, 0, 1843, 1513, 0, 0, 0, 25, 0, 439, 1001, 0, 203,
59, 179, 15, 372, 644, 222, 0, 0, 0, 0, 0, 0, 773, 1768, 886, 746, 78, 0, 0, 83, 76, 78, 0, 447,
20, 51, 38, 60, 343, 84, 382, 0, 28, 539, 29, 98, 0, 569, 0, 0, 521, 816, 980, 143, 138, 130, 259,
19, 18, 0, 0, 162, 173, 276, 0, 114, 147, 1654, 533, 947, 10, 119, 542, 278, 0, 38, 0, 282, 5, 0,
278, 2008, 821, 860, 731, 236, 215, 0, 38, 11, 225, 17, 0, 128, 443, 432, 57, 0, 0, 427, 294, 467,
115, 53, 212, 960, 1097, 33, 183, 641, 845, 5, 115, 11, 11, 0, 342, 0, 12, 0, 0, 0, 426, 346, 8, 5,
43, 260, 21, 21, 222, 39, 0, 43, 129, 562, 705, 695, 310, 61, 66, 141, 307, 60, 0, 22, 185, 199,
72, 942, 22, 317, 561, 0, 0, 0, 0, 92, 0, 0, 11, 0, 28, 15, 30, 19, 0, 79, 0, 0, 349, 326, 0, 0,
12, 1469, 1635, 1598, 0, 70, 396, 254, 352, 83, 0, 29, 0, 10, 10, 94, 0, 745, 289, 120, 185, 156,
935, 746, 1042, 1487, 1316, 185, 56, 0, 116, 30, 83, 245, 276, 321, 154, 186, 55, 555, 713, 133,
245, 86, 781, 0, 44, 93, 275, 131, 219, 0, 406, 420, 0, 566, 2737, 674, 58, 530, 205, 64, 0, 41,
104, 5, 95, 79, 287, 446, 644, 1374, 2168, 332, 49, 1916, 1809, 1112, 587, 18, 951, 0, 11, 78, 69,
677, 4463, 1644, 227, 591, 1267, 151, 0, 180, 98, 46, 20, 5, 435, 873, 307, 1683, 0, 60, 27, 1951,
1407, 1612, 1149, 91, 52, 514, 148, 104, 420, 138, 203, 1410, 261, 482, 3409, 1833, 5, 24, 506, 61,
5, 11, 350, 352, 400, 212, 5, 7, 20, 1014, 375, 5, 357, 7, 16, 130, 0, 1644, 1370, 0, 842, 1298, 0,
0, 7, 43, 77, 84, 0, 0, 556, 425, 443, 835, 964, 78, 78, 83, 629, 289, 40, 0, 0, 0, 0, 0, 291, 559,
808, 0, 0, 5, 310, 1060, 0, 0, 195, 1785, 478, 219, 464, 16, 10, 0, 0, 107, 54, 70, 503, 11, 632,
444, 351, 67, 10, 7, 0, 0, 49, 1096, 0, 41, 145, 18, 5, 243, 399, 1235, 500, 36, 135, 755, 33, 128,
148, 16, 80, 0, 0, 0, 0, 0, 22, 185, 298, 0, 325, 683, 528, 14, 324, 886, 206, 421, 0, 327, 10, 24,
0, 0, 0, 78, 21, 6, 0, 450, 7, 0, 248, 1199, 1048, 40, 145, 0, 23, 711, 0, 45, 5, 38, 337, 141,
166, 0, 30, 0, 121, 0, 354, 271, 0, 64, 94, 205, 199, 27, 0, 0, 41, 874, 791, 0, 95, 95, 21, 5,
398, 820, 728, 340, 297, 240, 2052, 335, 5, 395, 1620, 786, 0, 0, 321, 64, 116, 214, 91, 55, 195,
244, 279, 244, 135, 589, 103, 0, 21, 661, 0, 0, 0, 23, 0, 0, 415, 370, 802, 77, 115, 309, 279, 220,
436, 1503, 1354, 138, 1269, 566, 1176, 12, 1768, 368, 3662, 1046, 624, 17, 1097, 428, 313, 0, 29,
364, 866, 70, 139, 295, 1053, 503, 69, 30, 309, 1086, 354, 945, 452, 0, 1032, 1067, 42, 5, 118,
316, 768, 361, 2543, 15, 1361, 552, 0, 94, 543, 17, 17, 247, 40, 54, 146, 271, 117, 235, 31, 1308,
833, 264, 602, 189, 48, 618, 555, 0, 645, 1075, 654, 346, 547, 389, 10, 736, 106, 1130, 22, 1712,
430, 0, 93, 589, 1256, 884, 13, 0, 150, 1224, 347, 2254, 272, 63, 601, 220, 1052, 308, 241, 586,
1792, 299, 918, 545, 1377, 71, 1207, 257, 408, 59, 799, 320, 68, 368, 872, 136, 517, 61, 0, 0, 459,
0, 1984, 559, 0, 2500, 3011, 2037, 1457, 0, 122, 27, 66, 665, 27, 168, 553, 155, 30, 0, 0, 111,
459, 339, 325, 603, 301, 400, 0, 57, 72, 49, 0]
peak_total = [55.0, 312.0, 70.0, 64.0, 29.0, 7.0, 14.0, 43.0, 0, 33.0, 103.0, 25.0, 57.0, 84.0, 12.0, 51.0, 42.0,
10.0, 27.0, 84.0, 69.0, 54.0, 41.0, 58.0, 94.0, 59.0, 198.0, 163.0, 78.0, 116.0, 126.0, 90.0, 40.0,
3.0, 22.0, 18.0, 72.0, 85.0, 2.0, 63.0, 25.0, 35.0, 19.0, 7.0, 12.0, 22.0, 35.0, 4.0, 1.0, 4.0, 5.0,
0, 3.0, 30.0, 13.0, 50.0, 29.0, 30.0, 24.0, 55.0, 51.0, 49.0, 47.0, 58.0, 42.0, 2.0, 0, 39.0, 4.0,
30.0, 37.0, 33.0, 32.0, 0, 0, 5.0, 17.0, 0, 0, 113.0, 77.0, 2.0, 0, 0, 1.0, 0, 0, 0, 44.0, 45.0, 89.0,
3.0, 12.0, 10.0, 114.0, 101.0, 8.0, 15.0, 0, 0, 3.0, 4.0, 65.0, 132.0, 15.0, 5.0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 117.0, 0, 109.0, 5.0, 0, 0, 0, 2.0, 0, 0, 0, 10.0, 10.0, 2.0, 3.0, 23.0, 29.0, 17.0, 18.0, 51.0,
50.0, 27.0, 90.0, 3.0, 8.0, 8.0, 12.0, 27.0, 2.0, 0, 66.0, 189.0, 105.0, 98.0, 26.0, 6.0, 0, 2.0, 2.0,
6.0, 73.0, 9.0, 14.0, 12.0, 9.0, 14.0, 23.0, 12.0, 58.0, 49.0, 82.0, 52.0, 14.0, 70.0, 19.0, 58.0,
30.0, 12.0, 15.0, 4.0, 58.0, 48.0, 0, 45.0, 5.0, 5.0, 2.0, 3.0, 0, 0, 21.0, 17.0, 21.0, 14.0, 18.0,
11.0, 4.0, 15.0, 47.0, 16.0, 13.0, 33.0, 39.0, 58.0, 20.0, 91.0, 18.0, 8.0, 46.0, 8.0, 0, 74.0, 87.0,
88.0, 32.0, 34.0, 0, 13.0, 3.0, 0, 54.0, 62.0, 11.0, 21.0, 47.0, 9.0, 14.0, 75.0, 64.0, 72.0, 109.0,
16.0, 80.0, 49.0, 92.0, 5.0, 32.0, 4.0, 9.0, 32.0, 16.0, 15.0, 19.0, 10.0, 14.0, 164.0, 382.0, 122.0,
5.0, 8.0, 12.0, 32.0, 7.0, 16.0, 4.0, 25.0, 121.0, 38.0, 2.0, 21.0, 2.0, 7.0, 6.0, 12.0, 15.0, 13.0,
74.0, 172.0, 30.0, 5.0, 0, 2.0, 35.0, 73.0, 17.0, 0, 0, 0, 0, 0, 11.0, 80.0, 175.0, 18.0, 19.0, 102.0,
93.0, 278.0, 64.0, 108.0, 81.0, 35.0, 0, 19.0, 26.0, 3.0, 0, 8.0, 76.0, 35.0, 19.0, 29.0, 19.0, 15.0,
11.0, 76.0, 92.0, 145.0, 103.0, 19.0, 41.0, 42.0, 28.0, 40.0, 97.0, 2.0, 6.0, 50.0, 2.0, 2.0, 22.0, 0,
35.0, 53.0, 26.0, 18.0, 10.0, 5.0, 0, 0, 36.0, 10.0, 16.0, 2.0, 30.0, 91.0, 126.0, 62.0, 4.0, 28.0,
2.0, 16.0, 26.0, 3.0, 2.0, 1.0, 36.0, 6.0, 25.0, 50.0, 4.0, 14.0, 4.0, 21.0, 42.0, 48.0, 12.0, 15.0,
30.0, 127.0, 59.0, 149.0, 31.0, 418.0, 264.0, 422.0, 48.0, 0, 2.0, 203.0, 44.0, 4.0, 9.0, 62.0, 28.0,
57.0, 89.0, 362.0, 228.0, 295.0, 151.0, 149.0, 0, 28.0, 55.0, 0, 9.0, 65.0, 40.0, 22.0, 79.0, 5.0,
5.0, 3.0, 146.0, 50.0, 40.0, 73.0, 19.0, 22.0, 0, 0, 0, 31.0, 31.0, 33.0, 32.0, 67.0, 110.0, 134.0,
38.0, 0, 40.0, 51.0, 9.0, 137.0, 40.0, 161.0, 112.0, 8.0, 34.0, 76.0, 140.0, 77.0, 83.0, 43.0, 27.0,
80.0, 50.0, 55.0, 36.0, 10.0, 14.0, 9.0, 40.0, 100.0, 37.0, 0, 89.0, 45.0, 13.0, 18.0, 56.0, 59.0, 0,
105.0, 15.0, 15.0, 4.0, 15.0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 47.0, 0, 0, 15.0, 27.0, 40.0, 48.0, 135.0,
45.0, 27.0, 20.0, 22.0, 29.0, 5.0, 21.0, 4.0, 12.0, 30.0, 18.0, 79.0, 23.0, 42.0, 24.0, 4.0, 32.0,
8.0, 6.0, 29.0, 2.0, 26.0, 6.0, 12.0, 30.0, 63.0, 123.0, 31.0, 20.0, 22.0, 21.0, 49.0, 166.0, 49.0,
4.0, 4.0, 16.0, 15.0, 10.0, 0, 51.0, 36.0, 295.0, 35.0, 22.0, 3.0, 0, 0, 0, 0, 0, 0, 0, 39.0, 33.0,
18.0, 0, 14.0, 10.0, 25.0, 21.0, 1.0, 4.0, 4.0, 4.0, 201.0, 10.0, 0, 7.0, 13.0, 33.0, 26.0, 7.0, 0,
20.0, 21.0, 15.0, 13.0, 16.0, 53.0, 36.0, 3.0, 41.0, 42.0, 11.0, 31.0, 31.0, 28.0, 39.0, 13.0, 35.0,
8.0, 38.0, 25.0, 63.0, 5.0, 27.0, 16.0, 24.0, 135.0, 71.0, 89.0, 54.0, 51.0, 55.0, 0, 0, 0, 0, 0, 3.0,
55.0, 5.0, 14.0, 15.0, 24.0, 38.0, 21.0, 19.0, 8.0, 48.0, 37.0, 26.0, 0, 0, 0, 0, 35.0, 13.0, 4.0,
3.0, 35.0, 0, 0, 25.0, 14.0, 28.0, 6.0, 42.0, 23.0, 33.0, 9.0, 14.0, 17.0, 9.0, 0, 108.0, 59.0, 23.0,
24.0, 0, 0, 0, 0, 1.0, 79.0, 19.0, 35.0, 43.0, 105.0, 3.0, 137.0, 51.0, 13.0, 12.0, 21.0, 30.0, 5.0,
7.0, 7.0, 4.0, 14.0, 20.0, 38.0, 7.0, 175.0, 58.0, 11.0, 6.0, 5.0, 20.0, 20.0, 6.0, 6.0, 16.0, 65.0,
46.0, 38.0, 30.0, 16.0, 87.0, 8.0, 7.0, 8.0, 23.0, 41.0, 38.0, 0, 0, 0, 76.0, 66.0, 37.0, 61.0, 34.0,
83.0, 38.0, 12.0, 10.0, 12.0, 102.0, 6.0, 25.0, 75.0, 247.0, 265.0, 17.0, 3.0, 6.0, 180.0, 77.0, 56.0,
38.0, 69.0, 0, 93.0, 59.0, 21.0, 0, 4.0, 8.0, 27.0, 41.0, 37.0, 129.0, 15.0, 16.0, 30.0, 0, 7.0, 3.0,
21.0, 2.0, 61.0, 118.0, 76.0, 10.0, 0, 21.0, 33.0, 6.0, 21.0, 15.0, 2.0, 91.0, 12.0, 15.0, 30.0, 26.0,
7.0, 31.0, 69.0, 4.0, 61.0, 28.0, 40.0, 4.0, 24.0, 50.0, 6.0, 107.0, 246.0, 86.0, 25.0, 86.0, 181.0,
11.0, 25.0, 19.0, 8.0, 58.0, 21.0, 86.0, 0, 10.0, 17.0, 9.0, 0, 12.0, 3.0, 10.0, 84.0, 66.0, 5.0,
12.0, 8.0, 42.0, 7.0, 0, 17.0, 61.0, 68.0, 63.0, 8.0, 28.0, 6.0, 32.0, 50.0, 5.0, 0, 0, 0, 37.0, 34.0,
71.0, 2.0, 0, 0, 51.0, 76.0, 18.0, 0, 10.0, 87.0, 1.0, 32.0, 47.0, 109.0, 144.0, 51.0, 4.0, 0, 0, 0,
0, 70.0, 74.0, 11.0, 35.0, 19.0, 27.0, 23.0, 23.0, 33.0, 14.0, 19.0, 17.0, 14.0, 0, 0, 8.0, 0, 0, 6.0,
9.0, 12.0, 10.0, 39.0, 1.0, 0, 2.0, 4.0, 2.0, 5.0, 0, 0, 15.0, 0, 4.0, 25.0, 25.0, 27.0, 5.0, 12.0,
17.0, 0, 26.0, 0, 0, 73.0, 8.0, 2.0, 9.0, 21.0, 17.0, 15.0, 34.0, 0, 0, 0, 0, 6.0, 4.0, 3.0, 0, 39.0,
6.0, 35.0, 22.0, 5.0, 14.0, 0, 49.0, 6.0, 59.0, 61.0, 0, 35.0, 92.0, 83.0, 8.0, 21.0, 5.0, 0, 0, 29.0,
54.0, 71.0, 41.0, 44.0, 7.0, 10.0, 0, 85.0, 27.0, 0, 114.0, 237.0, 121.0, 38.0, 90.0, 21.0, 17.0, 9.0,
7.0, 0, 12.0, 0, 4.0, 19.0, 34.0, 46.0, 72.0, 19.0, 4.0, 45.0, 32.0, 26.0, 23.0, 17.0, 73.0, 41.0,
23.0, 2.0, 0, 0, 0, 0, 13.0, 84.0, 57.0, 0, 3.0, 0, 0, 0, 0, 0, 65.0, 32.0, 0, 0, 1.0, 22.0, 7.0, 2.0,
20.0, 15.0, 11.0, 137.0, 35.0, 50.0, 8.0, 3.0, 11.0, 2.0, 0, 0, 45.0, 72.0, 24.0, 3.0, 7.0, 16.0,
13.0, 22.0, 45.0, 19.0, 5.0, 2.0, 0, 5.0, 0, 1.0, 2.0, 16.0, 8.0, 0, 6.0, 24.0, 10.0, 0, 0, 2.0, 0, 0,
8.0, 8.0, 17.0, 7.0, 22.0, 98.0, 42.0, 4.0, 19.0, 21.0, 7.0, 17.0, 52.0, 11.0, 108.0, 39.0, 40.0,
121.0, 0, 0, 22.0, 3.0, 0, 0, 2.0, 45.0, 12.0, 12.0, 17.0, 76.0, 6.0, 40.0, 47.0, 61.0, 23.0, 14.0,
22.0, 15.0, 27.0, 34.0, 0, 5.0, 35.0, 14.0, 4.0, 17.0, 8.0, 9.0, 27.0, 2.0, 16.0, 5.0, 7.0, 4.0, 2.0,
15.0, 25.0, 12.0, 0, 4.0, 6.0, 40.0, 19.0, 12.0, 25.0, 4.0, 15.0, 0, 33.0, 10.0, 0, 85.0, 98.0, 16.0,
20.0, 17.0, 48.0, 28.0, 68.0, 119.0, 2.0, 187.0, 77.0, 94.0, 73.0, 79.0, 275.0, 74.0, 78.0, 131.0,
2.0, 58.0, 183.0, 21.0, 2.0, 42.0, 0, 0, 29.0, 0, 44.0, 105.0, 47.0, 81.0, 1.0, 0, 19.0, 4.0, 0, 0, 0,
6.0, 3.0, 5.0, 0, 6.0, 15.0, 4.0, 11.0, 0, 5.0, 11.0, 4.0, 2.0, 10.0, 28.0, 8.0, 7.0, 11.0, 8.0, 2.0,
9.0, 38.0, 20.0, 54.0, 37.0, 8.0, 0, 19.0, 42.0, 0, 12.0, 33.0, 9.0, 111.0, 78.0, 8.0, 13.0, 11.0,
14.0, 4.0, 2.0, 31.0, 66.0, 60.0, 31.0, 4.0, 0, 17.0, 7.0, 15.0, 4.0, 28.0, 8.0, 34.0, 18.0, 9.0, 0,
0, 56.0, 0, 91.0, 138.0, 0, 0, 0, 3.0, 0, 19.0, 54.0, 0, 13.0, 4.0, 14.0, 2.0, 25.0, 52.0, 10.0, 0, 0,
0, 0, 0, 0, 25.0, 90.0, 48.0, 39.0, 8.0, 0, 0, 7.0, 7.0, 6.0, 0, 26.0, 3.0, 3.0, 4.0, 9.0, 18.0, 7.0,
19.0, 0, 3.0, 27.0, 3.0, 5.0, 0, 28.0, 0, 0, 23.0, 60.0, 76.0, 11.0, 15.0, 7.0, 10.0, 2.0, 2.0, 0, 0,
11.0, 13.0, 21.0, 0, 8.0, 7.0, 86.0, 28.0, 37.0, 2.0, 8.0, 28.0, 20.0, 0, 3.0, 0, 18.0, 1.0, 0, 18.0,
145.0, 61.0, 78.0, 46.0, 9.0, 11.0, 0, 3.0, 2.0, 11.0, 2.0, 0, 6.0, 24.0, 27.0, 5.0, 0, 0, 35.0, 17.0,
24.0, 10.0, 3.0, 15.0, 46.0, 43.0, 3.0, 9.0, 43.0, 59.0, 1.0, 9.0, 2.0, 3.0, 0, 16.0, 0, 2.0, 0, 0, 0,
21.0, 22.0, 2.0, 1.0, 5.0, 14.0, 2.0, 2.0, 12.0, 5.0, 0, 4.0, 7.0, 30.0, 56.0, 55.0, 19.0, 6.0, 6.0,
9.0, 22.0, 6.0, 0, 4.0, 13.0, 13.0, 6.0, 75.0, 2.0, 15.0, 26.0, 0, 0, 0, 0, 5.0, 0, 0, 2.0, 0, 3.0,
2.0, 3.0, 2.0, 0, 7.0, 0, 0, 22.0, 27.0, 0, 0, 2.0, 81.0, 96.0, 81.0, 0, 6.0, 20.0, 14.0, 17.0, 5.0,
0, 3.0, 0, 2.0, 2.0, 6.0, 0, 29.0, 13.0, 13.0, 13.0, 11.0, 85.0, 34.0, 36.0, 76.0, 91.0, 13.0, 5.0, 0,
6.0, 3.0, 6.0, 13.0, 11.0, 26.0, 10.0, 8.0, 5.0, 33.0, 37.0, 11.0, 13.0, 6.0, 33.0, 0, 4.0, 4.0, 11.0,
7.0, 12.0, 0, 14.0, 15.0, 0, 32.0, 77.0, 19.0, 5.0, 19.0, 7.0, 8.0, 0, 6.0, 8.0, 1.0, 4.0, 4.0, 16.0,
16.0, 24.0, 58.0, 88.0, 20.0, 5.0, 73.0, 64.0, 47.0, 25.0, 2.0, 30.0, 0, 1.0, 4.0, 5.0, 26.0, 171.0,
79.0, 15.0, 25.0, 70.0, 11.0, 0, 11.0, 5.0, 4.0, 4.0, 3.0, 15.0, 24.0, 18.0, 34.0, 0, 5.0, 4.0, 89.0,
55.0, 57.0, 35.0, 4.0, 4.0, 21.0, 8.0, 5.0, 11.0, 6.0, 10.0, 29.0, 11.0, 13.0, 75.0, 34.0, 1.0, 2.0,
17.0, 3.0, 1.0, 2.0, 15.0, 13.0, 13.0, 8.0, 1.0, 1.0, 2.0, 57.0, 22.0, 1.0, 26.0, 2.0, 3.0, 8.0, 0,
88.0, 123.0, 0, 43.0, 75.0, 0, 0, 2.0, 6.0, 5.0, 5.0, 0, 0, 23.0, 25.0, 24.0, 42.0, 45.0, 8.0, 7.0,
8.0, 32.0, 32.0, 5.0, 0, 0, 0, 0, 0, 19.0, 31.0, 43.0, 0, 0, 1.0, 18.0, 59.0, 0, 0, 7.0, 80.0, 27.0,
11.0, 15.0, 2.0, 1.0, 0, 0, 9.0, 4.0, 5.0, 26.0, 2.0, 35.0, 18.0, 19.0, 5.0, 2.0, 2.0, 0, 0, 6.0,
45.0, 0, 3.0, 6.0, 3.0, 1.0, 9.0, 15.0, 28.0, 21.0, 3.0, 8.0, 30.0, 3.0, 6.0, 5.0, 2.0, 3.0, 0, 0, 0,
0, 0, 2.0, 8.0, 15.0, 0, 12.0, 21.0, 25.0, 2.0, 12.0, 50.0, 11.0, 28.0, 0, 15.0, 2.0, 3.0, 0, 0, 0,
4.0, 3.0, 1.0, 0, 24.0, 1.0, 0, 10.0, 43.0, 36.0, 4.0, 6.0, 0, 3.0, 25.0, 0, 3.0, 1.0, 4.0, 16.0, 8.0,
8.0, 0, 4.0, 0, 7.0, 0, 12.0, 11.0, 0, 5.0, 5.0, 19.0, 8.0, 2.0, 0, 0, 3.0, 37.0, 26.0, 0, 6.0, 7.0,
2.0, 1.0, 15.0, 34.0, 37.0, 16.0, 16.0, 18.0, 106.0, 14.0, 1.0, 16.0, 76.0, 53.0, 0, 0, 16.0, 6.0,
10.0, 10.0, 7.0, 4.0, 7.0, 16.0, 19.0, 14.0, 12.0, 33.0, 8.0, 0, 3.0, 43.0, 0, 0, 0, 2.0, 0, 0, 25.0,
21.0, 41.0, 5.0, 7.0, 17.0, 16.0, 20.0, 37.0, 79.0, 85.0, 15.0, 112.0, 56.0, 113.0, 3.0, 62.0, 40.0,
237.0, 69.0, 53.0, 2.0, 89.0, 48.0, 27.0, 0, 4.0, 34.0, 46.0, 5.0, 15.0, 24.0, 71.0, 30.0, 7.0, 5.0,
39.0, 100.0, 49.0, 68.0, 43.0, 0, 82.0, 85.0, 5.0, 2.0, 10.0, 28.0, 57.0, 28.0, 98.0, 3.0, 68.0, 37.0,
0, 11.0, 44.0, 3.0, 2.0, 17.0, 4.0, 6.0, 11.0, 22.0, 19.0, 19.0, 3.0, 74.0, 63.0, 35.0, 39.0, 14.0,
5.0, 37.0, 35.0, 0, 27.0, 41.0, 39.0, 22.0, 24.0, 21.0, 2.0, 60.0, 8.0, 67.0, 3.0, 144.0, 48.0, 0,
8.0, 28.0, 83.0, 71.0, 2.0, 0, 14.0, 57.0, 19.0, 104.0, 16.0, 10.0, 36.0, 17.0, 100.0, 15.0, 22.0,
59.0, 147.0, 36.0, 99.0, 24.0, 85.0, 8.0, 85.0, 17.0, 30.0, 7.0, 64.0, 29.0, 5.0, 17.0, 66.0, 11.0,
20.0, 5.0, 0, 0, 29.0, 0, 83.0, 31.0, 0, 147.0, 141.0, 110.0, 109.0, 0, 7.0, 3.0, 6.0, 31.0, 4.0,
10.0, 29.0, 13.0, 3.0, 0, 0, 7.0, 22.0, 10.0, 22.0, 37.0, 11.0, 31.0, 0, 4.0, 3.0, 4.0, 0]
# C_medians_wv= [ nan nan nan ..., nan nan nan]
C_stdevs_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.47405914591298681,
0.43810832869676392, nan, 0.27027066056268878, nan, 0.55123159587216009, nan, 0.43680343956612028,
nan, nan, nan, nan, nan, nan, nan, nan, 0.27721315524645029, 0.45693902779619527,
0.36008110218803868, 0.30402347839784799, 0.38289427028517375, 0.42627652765385654, nan,
0.33469954726475265, 0.36993757547405742, 0.28186303433779059, 0.41043179209382913, nan,
0.45126218415751268, nan, nan, nan, nan, 0.30476176399229904, 0.28287000690775937,
0.32801141994449173, 0.25078950137079131, 0.37015404253755685, 0.25087500361963233,
0.21654955267173931, nan, nan, nan, nan, nan, nan, 0.45063523348757312, 0.51445595367480079,
0.29654698534985452, 0.64990910525256351, nan, nan, nan, 0.29271416219435503, 0.31816039920933342,
0.33283167408810493, 0.45074690565232445, nan, nan, 0.38589761952371915, nan, nan, nan, nan, nan,
nan, nan, nan, 0.57466009902038939, nan, nan, nan, 0.72181744388275226, nan, nan, nan,
0.35750336525201665, 0.35527556001908389, 0.55033414357868171, nan, nan, nan, nan,
0.29013920408234722, 0.26563458642391224, nan, 0.2103071488021179, nan, nan, nan, nan,
0.16838428597617475, 0.13637581378605637, nan, 0.2613078223231704, 0.27257970413296262, nan, nan,
nan, nan, nan, nan, nan, nan, 0.32212925021122157, 0.26629409506176033, 0.18865533746313956,
0.23177263227765171, 0.24781293931144879, nan, nan, nan, 0.24469015602789468, 0.10410440383282329,
nan, nan, nan, nan, nan, nan, 0.24135192967446573, 0.21529128167620712, 0.34646603283740396, nan,
nan, nan, 0.25195136146921443, 0.2828736267764867, nan, nan, nan, 0.2407519875643139,
0.22643064230886992, 0.26911358473782632, 0.40264428148517328, nan, nan, nan, nan, nan, nan, nan,
0.26974748772702678, nan, 0.26574968584509184, 0.42140491552826287, 0.28626780673486962, nan, nan,
nan, nan, nan, nan, 0.31791646642548521, nan, nan, nan, nan, nan, 0.45523106168607036,
0.50489514355156428, 0.55345818011755277, 0.29286952780233405, nan, nan, 0.36943869060974294, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.26318945670247629, nan, 0.3703794001757138,
0.61548586122631543, 0.32372423108403925, nan, 0.40951687820158478, 0.2888172357565742,
0.25210359313819025, 0.29620022599750045, nan, 0.31388475963709234, nan, nan, nan, nan, nan, nan,
nan, nan, nan, 0.3597778034458064, nan, nan, nan, 0.3391022752379631, 0.38616680048688756, nan, nan,
nan, nan, 0.48606979806864781, nan, nan, nan, nan, 0.30861778514126376, nan, nan, nan, nan, nan, nan,
nan, 0.41315135864201025, nan, nan, nan, nan, 0.22596536226654992, nan, nan, nan, nan, nan,
0.31061632280852702, 0.50878412687618668, nan, nan, nan, nan, nan, 0.37716910674811838,
0.27753603136400956, 0.21742895486236327, nan, 0.31861335454235606, 0.25243627306040056,
0.22776590380641559, 0.36099717377208068, nan, 0.34712174633864096, 0.31082540669888314,
0.15963996195604729, nan, nan, 0.30505333752726443, nan, nan, 0.25160320460600427, nan, nan, nan,
0.27151348117732854, 0.22775114554126966, 0.25185820087164179, nan, nan, nan, nan, nan,
0.16290984488725058, nan, nan, nan, nan, nan, nan, 0.20436458933729806, 0.31002381149465957,
0.25481689441074606, nan, nan, 0.28544547358381456, 0.55576070727205884, 0.14854611948362034,
0.16555468927891212, 0.22356108398365171, 0.17359351991394234, 0.15132630036947259,
0.14055997766706371, 0.10896890210169873, 0.13115599796336169, nan, 0.39265537013516888,
0.12092711809670909, 0.15816286565994472, 0.21706217531724908, 0.1924401792769834, nan,
0.13739224114780332, 0.12258982157229183, 0.15380523261341539, nan, nan, 0.14042465582736466,
0.29579526036729276, nan, 0.17609414385401406, 0.25224381091054843, 0.17857152024935702,
0.25429588270456577, nan, nan, 0.11317696331286888, nan, 0.084682565614150523, 0.15265335545558034,
0.15043823424852559, nan, 0.15952598708329188, 0.18681776896589769, nan, nan, 0.26773528067176905,
0.28947717939010387, 0.19051517517093927, 0.1827593017878007, 0.35601519156156641, nan,
0.21976654929029307, 0.17757070535430955, nan, 0.14742677799360285, 0.12799343656784401, nan, nan,
0.15580335285890612, nan, nan, nan, 0.19304197974157039, 0.16171488013370541, 0.26034959381892597,
nan, 0.35077157209342164, 0.18786107424032955, 0.11922780103465336, 0.17180085113181101,
0.25630777406667787, nan, 0.27642800182216393, nan, nan, 0.35307492461354711, 0.34033699090670616,
0.27928236245433663, 0.2400023776368897, 0.23995585287277305, 0.27345374178342724, nan,
0.18192433665241037, nan, 0.18434404152755984, nan, 0.14654292087457116, nan, nan, nan, nan, nan,
0.18954992571981616, nan, nan, nan, nan, 0.20905803790554087, 0.23660464987207883,
0.38169581937500924, nan, 0.43853030904724583, 0.32014951473681513, 0.15184468242071988,
0.35254822120286694, nan, nan, 0.14443212481285858, 0.16497063147763064, 0.13396013661103864,
0.42444362589261359, 0.20897368826233387, nan, 0.24251612621474358, 0.24324570666264619, nan, nan,
nan, nan, nan, nan, nan, nan, 0.34027099423889473, nan, nan, nan, 0.22807596371970418, nan,
0.34457442761665324, nan, nan, nan, nan, 0.22087694920196504, 0.17370576580858987, nan,
0.37184509005115557, nan, nan, 0.28900305022814393, nan, 0.39395715395254072, 0.2966882048803311,
0.24763889671900344, nan, nan, nan, nan, 0.24472844265026708, 0.51409772964814682,
0.27923111610157125, 0.20055634236831996, 0.54867506340223349, 0.38315253033576757, nan, nan, nan,
nan, nan]
C_modes_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 3.7234042553191489,
4.7234042553191484, nan, 3.8989361702127656, nan, 3.5585106382978724, nan, 3.6010638297872339, nan,
nan, nan, nan, nan, nan, nan, nan, 4.3457446808510642, 4.2340425531914887, 4.207446808510638,
4.7340425531914896, 4.9521276595744679, 4.787234042553191, nan, 4.8989361702127656,
4.9255319148936163, 4.6702127659574462, 5.0744680851063828, nan, 4.7287234042553195, nan, nan, nan,
nan, 4.1436170212765955, 3.8882978723404249, 4.0, 4.2553191489361701, 4.3563829787234045,
4.0372340425531918, 3.8829787234042552, nan, nan, nan, nan, nan, nan, 4.2393617021276597,
4.5265957446808507, 4.3617021276595747, 5.4840425531914887, nan, nan, nan, 4.5212765957446805,
5.1117021276595738, 4.462765957446809, 4.4042553191489358, nan, nan, 5.0265957446808507, nan, nan,
nan, nan, nan, nan, nan, nan, 6.2765957446808507, nan, nan, nan, 6.2180851063829792, nan, nan, nan,
5.414893617021276, 5.2393617021276597, 5.6329787234042552, nan, nan, nan, nan, 2.3776595744680851,
2.75, nan, 2.6382978723404253, nan, nan, nan, nan, 2.2499999999999996, 2.5212765957446805, nan,
2.5797872340425529, 2.6276595744680851, nan, nan, nan, nan, nan, nan, nan, nan, 3.0585106382978724,
3.7180851063829787, 2.6170212765957448, 2.5638297872340425, 2.8670212765957444, nan, nan, nan,
2.8138297872340425, 2.7925531914893615, nan, nan, nan, nan, nan, nan, 3.4840425531914891,
2.7446808510638299, 2.6755319148936167, nan, nan, nan, 3.3776595744680851, 3.3351063829787235, nan,
nan, nan, 3.1808510638297869, 3.5691489361702122, 3.3351063829787235, 3.1063829787234041, nan, nan,
nan, nan, nan, nan, nan, 3.4787234042553195, nan, 4.1861702127659575, 4.1117021276595738,
3.8670212765957448, nan, nan, nan, nan, nan, nan, 4.2819148936170208, nan, nan, nan, nan, nan,
3.5904255319148937, 4.4468085106382977, 4.1968085106382977, 4.0, nan, nan, 4.9734042553191484, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 4.5159574468085104, nan, 4.8031914893617014,
5.2925531914893611, 5.2340425531914896, nan, 4.7446808510638299, 4.0904255319148941,
3.3563829787234041, 3.9202127659574466, nan, 3.1382978723404253, nan, nan, nan, nan, nan, nan, nan,
nan, nan, 3.9414893617021272, nan, nan, nan, 3.9468085106382977, 4.1489361702127656, nan, nan, nan,
nan, 3.7234042553191489, nan, nan, nan, nan, 3.436170212765957, nan, nan, nan, nan, nan, nan, nan,
3.8670212765957448, nan, nan, nan, nan, 4.1808510638297864, nan, nan, nan, nan, nan,
4.3031914893617023, 4.462765957446809, nan, nan, nan, nan, nan, 2.6861702127659575,
3.0851063829787231, 2.7765957446808511, nan, 2.8563829787234045, 2.3723404255319149,
2.6436170212765959, 2.6595744680851063, nan, 2.4574468085106385, 2.8510638297872339,
2.8404255319148932, nan, nan, 2.6914893617021276, nan, nan, 2.8297872340425534, nan, nan, nan,
2.8297872340425534, 2.9148936170212765, 2.6702127659574471, nan, nan, nan, nan, nan,
2.9148936170212765, nan, nan, nan, nan, nan, nan, 3.4255319148936172, 3.0425531914893615,
2.728723404255319, nan, nan, 3.3457446808510638, 2.3723404255319149, 2.5212765957446805,
2.2234042553191489, 2.3085106382978724, 2.2021276595744679, 2.4202127659574466, 2.5372340425531914,
2.4148936170212765, 2.4946808510638294, nan, 3.3297872340425529, 2.7978723404255317,
2.7021276595744679, 2.4840425531914896, 2.3670212765957448, nan, 2.436170212765957,
2.6223404255319145, 2.2127659574468086, nan, nan, 2.8510638297872339, 2.4946808510638294, nan,
2.5904255319148937, 2.6117021276595747, 2.4095744680851063, 2.4414893617021276, nan, nan,
2.3510638297872339, nan, 2.4202127659574466, 2.4574468085106385, 2.436170212765957, nan,
2.7021276595744679, 2.6914893617021276, nan, nan, 2.6595744680851063, 2.8297872340425534, 2.75,
3.0372340425531914, 3.0319148936170213, nan, 2.9095744680851063, 2.9468085106382977, nan,
3.0638297872340425, 2.978723404255319, nan, nan, 2.9468085106382977, nan, nan, nan,
2.7765957446808511, 2.6010638297872339, 2.808510638297872, nan, 2.6276595744680851,
2.5904255319148937, 2.8510638297872339, 2.6436170212765959, 2.9680851063829783, nan,
2.6595744680851063, nan, nan, 3.2021276595744683, 2.7819148936170208, 2.7606382978723403,
3.0372340425531914, 3.1489361702127661, 2.728723404255319, nan, 3.1489361702127661, nan,
3.2925531914893615, nan, 3.3882978723404253, nan, nan, nan, nan, nan, 3.3244680851063828, nan, nan,
nan, nan, 2.4308510638297873, 2.7872340425531914, 2.5106382978723403, nan, 2.771276595744681,
2.3989361702127661, 2.4202127659574466, 2.6861702127659575, nan, nan, 2.6382978723404253,
2.7180851063829787, 2.4893617021276593, 2.6702127659574471, 2.5851063829787235, nan,
2.6861702127659575, 2.7234042553191489, nan, nan, nan, nan, nan, nan, nan, nan, 3.2978723404255317,
nan, nan, nan, 4.2234042553191493, nan, 3.2393617021276593, nan, nan, nan, nan, 2.8670212765957444,
2.9574468085106385, nan, 4.0159574468085104, nan, nan, 3.3723404255319145, nan, 3.3829787234042552,
3.2127659574468082, 2.5638297872340425, nan, nan, nan, nan, 3.436170212765957, 3.6542553191489362,
3.4414893617021276, 3.4202127659574466, 3.3617021276595747, 3.978723404255319, nan, nan, nan, nan,
nan]
class C_MR():
Dates= ['2014/01/01', '2014/01/02', '2014/01/03', '2014/01/04', '2014/01/05', '2014/01/06', '2014/01/07',
'2014/01/08', '2014/01/09', '2014/01/10', '2014/01/11', '2014/01/12', '2014/01/13', '2014/01/14',
'2014/01/15', '2014/01/16', '2014/01/17', '2014/01/18', '2014/01/19', '2014/01/20', '2014/01/21',
'2014/01/22', '2014/01/23', '2014/01/24', '2014/01/25', '2014/01/26', '2014/01/27', '2014/01/28',
'2014/01/29', '2014/01/30', '2014/01/31', '2014/02/01', '2014/02/02', '2014/02/03', '2014/02/04',
'2014/02/05', '2014/02/06', '2014/02/07', '2014/02/08', '2014/02/09', '2014/02/10', '2014/02/11',
'2014/02/12', '2014/02/13', '2014/02/14', '2014/02/15', '2014/02/16', '2014/02/17', '2014/02/18',
'2014/02/19', '2014/02/20', '2014/02/21', '2014/02/22', '2014/02/23', '2014/02/24', '2014/02/25',
'2014/02/26', '2014/02/27', '2014/02/28', '2014/03/01', '2014/03/02', '2014/03/03', '2014/03/04',
'2014/03/05', '2014/03/06', '2014/03/07', '2014/03/08', '2014/03/09', '2014/03/10', '2014/03/11',
'2014/03/12', '2014/03/13', '2014/03/14', '2014/03/15', '2014/03/16', '2014/03/17', '2014/03/18',
'2014/03/19', '2014/03/20', '2014/03/21', '2014/03/22', '2014/03/23', '2014/03/24', '2014/03/25',
'2014/03/26', '2014/03/27', '2014/03/28', '2014/03/29', '2014/03/30', '2014/03/31', '2014/04/01',
'2014/04/02', '2014/04/03', '2014/04/04', '2014/04/05', '2014/04/07', '2014/04/08', '2014/04/09',
'2014/04/10', '2014/04/11', '2014/04/12', '2014/04/13', '2014/04/14', '2014/04/15', '2014/04/16',
'2014/04/17', '2014/04/18', '2014/04/19', '2014/04/20', '2014/04/21', '2014/04/22', '2014/04/23',
'2014/04/24', '2014/04/25', '2014/04/26', '2014/04/27', '2014/04/28', '2014/04/29', '2014/04/30',
'2014/05/01', '2014/05/02', '2014/05/03', '2014/05/04', '2014/05/05', '2014/05/06', '2014/05/07',
'2014/05/08', '2014/05/09', '2014/05/10', '2014/05/11', '2014/05/12', '2014/05/13', '2014/05/14',
'2014/05/15', '2014/05/16', '2014/05/17', '2014/05/18', '2014/05/19', '2014/05/20', '2014/05/21',
'2014/05/22', '2014/05/23', '2014/05/24', '2014/05/25', '2014/05/26', '2014/05/27', '2014/05/28',
'2014/05/29', '2014/05/30', '2014/05/31', '2014/06/01', '2014/06/02', '2014/06/03', '2014/06/04',
'2014/06/05', '2014/06/06', '2014/06/07', '2014/06/08', '2014/06/09', '2014/06/10', '2014/06/11',
'2014/06/12', '2014/06/13', '2014/06/14', '2014/06/15', '2014/06/16', '2014/06/17', '2014/06/18',
'2014/06/19', '2014/06/20', '2014/06/21', '2014/06/22', '2014/06/23', '2014/06/24', '2014/06/25',
'2014/06/26', '2014/06/27', '2014/06/28', '2014/06/29', '2014/06/30', '2014/07/01', '2014/07/02',
'2014/07/03', '2014/07/04', '2014/07/05', '2014/07/06', '2014/07/07', '2014/07/08', '2014/07/09',
'2014/07/10', '2014/07/11', '2014/07/12', '2014/07/13', '2014/07/14', '2014/07/15', '2014/07/16',
'2014/07/17', '2014/07/18', '2014/07/19', '2014/07/20', '2014/07/21', '2014/07/22', '2014/07/23',
'2014/07/24', '2014/07/25', '2014/07/26', '2014/07/27', '2014/07/28', '2014/07/29', '2014/07/30',
'2014/07/31', '2014/08/01', '2014/08/02', '2014/08/03', '2014/08/04', '2014/08/05', '2014/08/06',
'2014/08/07', '2014/08/08', '2014/08/09', '2014/08/10', '2014/08/11', '2014/08/12', '2014/08/13',
'2014/08/14', '2014/08/15', '2014/08/16', '2014/08/17', '2014/08/18', '2014/08/19', '2014/08/20',
'2014/08/21', '2014/08/22', '2014/08/23', '2014/08/24', '2014/08/25', '2014/08/26', '2014/08/27',
'2014/08/28', '2014/08/29', '2014/08/30', '2014/08/31', '2014/09/01', '2014/09/02', '2014/09/03',
'2014/09/04', '2014/09/05', '2014/09/06', '2014/09/07', '2014/09/08', '2014/09/09', '2014/09/10',
'2014/09/11', '2014/09/12', '2014/09/13', '2014/09/14', '2014/09/15', '2014/09/16', '2014/09/17',
'2014/09/18', '2014/09/19', '2014/09/20', '2014/09/21', '2014/09/22', '2014/09/23', '2014/09/24',
'2014/09/25', '2014/09/26', '2014/09/27', '2014/09/28', '2014/09/29', '2014/09/30', '2014/10/01',
'2014/10/02', '2014/10/03', '2014/10/04', '2014/10/05', '2014/10/06', '2014/10/07', '2014/10/08',
'2014/10/09', '2014/10/10', '2014/10/11', '2014/10/12', '2014/10/13', '2014/10/14', '2014/10/15',
'2014/10/16', '2014/10/17', '2014/10/18', '2014/10/19', '2014/10/20', '2014/10/21', '2014/10/22',
'2014/10/23', '2014/10/24', '2014/10/25', '2014/10/26', '2014/10/27', '2014/10/28', '2014/10/29',
'2014/10/30', '2014/10/31', '2014/11/01', '2014/11/09', '2014/11/10', '2014/11/11', '2014/12/09',
'2014/12/10', '2014/12/11', '2014/12/12', '2014/12/13', '2014/12/14', '2014/12/15', '2014/12/16',
'2014/12/17', '2014/12/18', '2014/12/19', '2014/12/20', '2014/12/22', '2014/12/23', '2014/12/24',
'2014/12/26', '2014/12/27', '2014/12/29', '2014/12/31', '2015/01/01', '2015/01/03', '2015/01/04',
'2015/01/05', '2015/01/06', '2015/01/08', '2015/01/10', '2015/01/11', '2015/01/12', '2015/01/14',
'2015/01/15', '2015/01/16', '2015/01/17', '2015/01/18', '2015/01/19', '2015/01/20', '2015/01/21',
'2015/01/22', '2015/01/23', '2015/01/24', '2015/01/26', '2015/01/28', '2015/01/29', '2015/01/30',
'2015/01/31', '2015/02/01', '2015/02/02', '2015/02/03', '2015/02/04', '2015/02/05', '2015/02/06',
'2015/02/07', '2015/02/10', '2015/02/11', '2015/02/12', '2015/02/13', '2015/02/14', '2015/02/15',
'2015/02/16', '2015/02/18', '2015/02/20', '2015/02/21', '2015/02/22', '2015/02/23', '2015/02/26',
'2015/02/27', '2015/03/01', '2015/03/02', '2015/03/03', '2015/03/04', '2015/03/05', '2015/03/06',
'2015/03/07', '2015/03/12', '2015/03/13', '2015/03/14', '2015/03/16', '2015/03/17', '2015/03/19',
'2015/03/20', '2015/03/21', '2015/03/22', '2015/03/23', '2015/03/24', '2015/03/26', '2015/03/30',
'2015/03/31', '2015/04/02', '2015/04/03', '2015/04/04', '2015/04/06', '2015/04/09', '2015/04/10',
'2015/04/11', '2015/04/12', '2015/04/13', '2015/04/14', '2015/04/15', '2015/04/16', '2015/04/17',
'2015/04/18', '2015/04/19', '2015/04/20', '2015/04/21', '2015/04/22', '2015/04/23', '2015/04/24',
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C_stdevs = [0.050203652141332704, 0.061879047567400799, nan, 0.12714529528624111, 0.072889894860341919, nan, nan,
0.080953633991651411, 0.069998064850669839, 0.14610442042422939, 0.074847607550985823,
0.097143215239622244, 0.076168131705190684, nan, 0.071760850059911008, 0.12490380535163649,
0.084479490273535005, 0.095659550348909592, 0.11025474316085891, 0.1275043402472642,
0.079570575749013425, 0.064404432022591301, 0.1168429506004709, nan, nan, 0.11743260431132641,
0.086020290131700955, 0.09813705147759863, 0.080941783183885008, 0.22484714156601843,
0.13875685969314569, 0.075118541626699051, nan, 0.085503570434937518, 0.082615705765981609, nan,
0.068385934569236501, 0.084488053121863693, 0.050536073133598487, 0.049162006943115719,
0.10300747323169965, 0.10642533916350774, 0.084731044907442099, 0.12526892969986236,
0.094861859407795943, nan, nan, 0.08194720618338043, 0.077673625098207022, 0.10666057124091916,
0.082001855663699372, nan, nan, 0.053865416246217025, nan, 0.072493136851060513, 0.032229008491942879,
nan, 0.11303102947831348, 0.12852173728716043, 0.072156021294004183, 0.11872411406539778,
0.15165643230312159, nan, nan, 0.20512055673507787, 0.11846194714481136, nan, 0.12584448932719294,
0.053933454764088271, nan, nan, nan, 0.098816746821419138, nan, 0.21315978145725603, 0.11682479795629257,
nan, 0.1278404722882725, nan, 0.099021593726722826, 0.092877858229396759, 0.084770805320158807,
0.11518919614883341, 0.10577474420064056, 0.15441661958518857, nan, nan, nan, nan, nan, nan, nan,
0.14497145759879645, 0.1178497637756693, 0.096521219539082631, 0.098823458650153684, nan,
0.10234427193523686, 0.09185948465107345, 0.12882250634757991, 0.12218312998754872, nan, nan, nan,
0.092999036756659964, 0.075816872832843565, 0.11869670088498099, nan, nan, 0.12944604428497947,
0.14675348476823613, 0.13707403914778576, nan, 0.11294875580904801, 0.13736437496038351, nan,
0.23648391583866121, nan, 0.10471477805142573, 0.099678643647731782, nan, nan, nan, 0.11178338842746083,
0.07922893069002368, 0.064566199060159743, 0.06745208869103736, 0.082695448875170621,
0.076918148207263032, 0.12461436893454229, 0.1084346610493203, nan, 0.0892345006474001, nan, nan, nan,
nan, 0.11466673144493963, 0.13250822965442347, 0.12444366410699054, 0.11006971554804509,
0.10924750555954725, 0.11469124817734694, 0.23186794901517949, 0.2802804630060502, 0.12803627254684233,
0.11406846686157225, 0.12212122554096043, 0.10557736978439394, nan, 0.16808572246930614,
0.10044832353976502, 0.11023352397851896, 0.09125021243634264, nan, 0.1148805096807225, nan,
0.16566018735180124, 0.094414959958692812, 0.12258624612335756, nan, nan, 0.1279980136561695,
0.07830166829368701, 0.081529603142336038, 0.060702230372468496, 0.082732413828936785,
0.1397593089625184, 0.13460993239747016, nan, nan, nan, 0.11814077333136137, 0.084677121525786145,
0.048836322884826228, 0.12107158314692779, 0.12230534363329638, 0.11681313059146409, 0.15527567417555088,
nan, nan, nan, 0.061932565887614921, 0.11007191501161312, 0.11415594764821062, 0.13459681207298102,
0.1536827776877204, 0.24779996533864909, 0.1493557834702641, 0.12137492327011434, 0.12868235347691542,
0.11862277343750442, 0.085420128682657775, 0.09332362497828578, 0.12274418841165541, 0.18504697624478075,
nan, nan, 0.13473171654961966, 0.14812040010963015, 0.12914583266512777, 0.10887444025173672, nan, nan,
0.13285138596924789, 0.19458216179350657, 0.17499453102817025, 0.16333042779109513, 0.1674641367665923,
nan, 0.13190360148996261, 0.13125155560949647, 0.10060927159647061, nan, 0.12013353891481571,
0.16240904326433539, nan, nan, 0.086791780781520111, 0.13872796814952293, nan, 0.078265759231918114,
0.12034995054947609, 0.14398746932958353, 0.10255251072753693, 0.10197022632098679, 0.066031994452670217,
0.096602077052842239, 0.10991225280453075, 0.15379111225614581, 0.15694575953775067, 0.10471930749401118,
0.084869962783548261, 0.10940701621274922, 0.059658319589756419, 0.056744840551612191,
0.1394526313542325, 0.13601341649284684, 0.10184068100602542, 0.099386148182044318, 0.092765226679110205,
0.13209712375588598, 0.12917203076249897, nan, nan, 0.15942436728111894, nan, 0.14615362342320581, nan,
nan, nan, nan, nan, 0.086613548727367359, 0.11776020711432503, 0.11892269968656954, nan,
0.067757636896569717, nan, nan, 0.12600436020106337, 0.081288318271204002, nan, nan, nan,
0.094842158528912424, 0.09070029325359516, 0.12051424365641634, 0.10715708729650121, nan,
0.12032268953869658, 0.122598406237027, 0.17881362965252312, 0.1092292841990211, 0.092884347144354173,
nan, 0.069242197798203009, 0.10978987937749288, 0.08475729448523707, nan, 0.11945353214107927,
0.079231849955712585, 0.077148014185607197, nan, 0.089763238982684818, 0.11589961176413814,
0.10234968993134756, 0.061216328700177375, 0.085971584581197857, 0.073844011466959431,
0.086765266999111856, 0.09720370627087016, nan, 0.079345700176180353, 0.10989228090025389,
0.12662222699029776, 0.099448116839533737, 0.099251375209046658, 0.10943992739963188,
0.040393293997971882, nan, nan, 0.083797472431652045, nan, 0.080297434094230249, 0.067540120021953004,
0.056112677673678502, 0.03492750432959979, 0.12120278653549042, 0.089367678990803862, nan,
0.068469922762606508, 0.28287811892267717, 0.10183862208618442, 0.070455828570080134,
0.061705838085913137, 0.098311258420567232, 0.069757451211612601, 0.050833898331538056,
0.061560202869767253, 0.082099141823639671, nan, 0.091143660865628834, nan, 0.082122605176216917,
0.080240023239046193, 0.1414416096835234, 0.094654757272530934, 0.081254193195360883,
0.080183858547041326, 0.071616212022751299, 0.053153865145817381, nan, 0.069561116977589543, nan, nan,
nan, nan, 0.11504510498349491, 0.12271606438236979, 0.11348215336835694, 0.15793497094917533, nan,
0.14944212986619931, 0.10900120022451423, 0.076540968238783091, 0.072197783995211109,
0.086679977665250332, 0.087611935268595245, 0.094177710907384582, 0.073118851867939025, nan,
0.09046605068987372, 0.13472335557192511, 0.07588577302049794, 0.066885228967178592,
0.086114611498139065, 0.14967504503285786, 0.12607192602248085, 0.090047019630387776,
0.065065971781750359, 0.1101158451285823, 0.11740761269202966, nan, 0.077101039836095522,
0.10997215187286963, 0.080695415091016312, nan, nan, 0.11892646263641996, nan, nan, nan,
0.14335246571693935, 0.069768846622945066, 0.070176601835593683, 0.086011939332564666, nan, nan, nan,
0.074673803759028212, nan, nan, 0.099449957247351686, 0.11728155481877497, 0.07278673435378781,
0.10651440889827686, 0.11559853009846992, 0.10824749393807402, 0.07125717516854424, 0.062760781956238174,
0.094520205660462431, 0.097932468410268064, 0.14520847004634296, 0.12157435039616074,
0.11232571096508297, nan, nan, 0.12680686592867538, 0.090787723809198959, 0.14857835881556813, nan, nan,
nan, nan, 0.094206258417282099, 0.10365848953646495, nan, nan, 0.099292637355001587,
0.090155929507261812, 0.10922428772970598, 0.13523090939593288, 0.10231775446563313, 0.12149683390398651,
0.10720792409847733, 0.099941148125475915, 0.084482302358758615, nan, 0.069657920691409858,
0.087798301435345713, 0.096530348759439916, 0.080169355325691416, nan, 0.099920268178140745,
0.093685559137288307, 0.08821007145445714, 0.10322641651495346, nan, nan, nan, 0.14100652710912509,
0.088777597597234928, 0.091398264781861055, 0.065933291134990857, 0.074763647419114454,
0.099340519682326114, 0.09222593992501732, nan, 0.15273925028175678, 0.16377918113685122,
0.15870096478544013, 0.093479855856497138, 0.090529301846518084, 0.068326229194158289,
0.083580086547186946, 0.099348473068508789, 0.12936244979416267, 0.12109579570707628,
0.080478040626402722, 0.085018643861733259, 0.1002221033509597, nan, nan, 0.083352371143761039, nan,
0.10484002424402994, 0.085261988141618908, 0.065618298840223563, nan, nan, 0.098462951649222683,
0.13680300130476505, 0.073371639357795476, nan, 0.12782778165519751, 0.10818928065941279,
0.079892077792240709, 0.11929729450774781, 0.092409164679514308, 0.11162151275575591,
0.10677963351134535, nan, nan, 0.11188669699275039, nan, 0.1197165203803742, nan, nan, nan,
0.17467397749664235, nan, nan, 0.096801963711887415, 0.075659651498738545, 0.10144754104162594,
0.078067613293493154, nan, nan, 0.2226931049199084, 0.10935636620393038, 0.082791357150930736,
0.095024602315956802, 0.10265098763925957, 0.1617687055651531, 0.13580573731693138, nan,
0.23924287176145478, 0.11134440904603776, 0.11822126305313395, 0.16605809291959581, 0.11083126495085995,
nan, 0.090521174504658644, 0.082862841831586931, 0.074546079544958205, 0.13221632958918766,
0.1574744275348407, 0.14210806250261396, 0.12048985269974091, 0.11881159339141843, nan,
0.10487846653394632, 0.097014805768137979, 0.061382267390392098, 0.092509819920919445,
0.1800981464178352, 0.096247478777099105, 0.12713612660132559, 0.12078504207367838, 0.30933744384663414,
0.2401163568328957, 0.12659040736900992, 0.1137900089032598, 0.16489068140352686, 0.13225430430028123,
nan, nan, 0.17587007470153773, 0.088532290834046096, 0.086084455721044867, nan, 0.11681239692865938,
0.1110221203070491, 0.10609943870635839, 0.071426324107765185, 0.10926418062265099, nan,
0.28368153334348395, 0.14103621940935004, 0.092018855995425544, 0.12220927399333374, 0.16604179707777825,
0.1183515092777388, 0.12423016610289374, 0.11307512583237095, 0.097442572386330403, 0.1017133255057696,
0.090222620098453526, 0.076786502826407296, nan, nan, 0.11024807972426813, 0.21072879259427557,
0.093476681089171579, 0.098969926446308254, 0.090505035325794389, 0.11416393276033933,
0.10102503702135801, 0.10189587315224508, 0.10671558593772146, 0.10556997962661729, 0.13092401946854687,
0.077017078986876805, 0.10299268515700655, 0.085244270040542233, nan, 0.12121514912121356,
0.066266298115349745, nan, nan, nan, nan, nan, nan, 0.085088529127500465, 0.13039138222596131,
0.095349477214479575, 0.098547423731168093, 0.10347371044589125, 0.16777362298511292, nan,
0.050634024389032903, 0.097532238307466199, 0.065137145261060056, 0.09834058752207088,
0.095060578227773876, 0.057470219590836341, 0.10245787686367917, 0.089393335734036752,
0.080583244662431039, nan, 0.066467237435945986, 0.054304341653689192, 0.055307293324680575, nan,
0.1750333282940775, 0.12133085081390757, 0.17793209360813547, 0.065661671777124328, 0.09628857690437706,
0.11070526421513591, nan, 0.1075834378515865, 0.10762187787521077, 0.13752416740475676,
0.10544339783521341, 0.066288421622667182, 0.10721982167087662, 0.073216062409482377,
0.045728201425657262, 0.067052146918664554, 0.096734010498549697, 0.079927127765606829,
0.068682275961359604, 0.050637781494669666, 0.053865200346603644, 0.044878568359736873,
0.047132204814214172, nan, 0.069860859020403066, 0.15323560397268965, 0.04758453211239095,
0.16280972649371286, 0.1274426046085656, 0.058644678857946934, 0.1176807406068888, 0.070554274635178021,
0.09150159968715782, 0.066472922495708728, 0.039494803470605001, 0.049074589637424455,
0.04109705493781976, 0.039031390875723278, 0.067042144031886436, 0.15810017992250402, nan,
0.069218625051062618, 0.079452781087020197, 0.069068377339116804, nan, 0.11322633132232465,
0.10422007917995893, 0.085827679803136214, 0.067762140269735552, 0.13854452067170711, nan,
0.063988384136159551, 0.093691564441878455, 0.089132947080649033, 0.070961186298253737, nan,
0.15662604388238885, 0.052482778324738674, nan, nan, 0.074275522749512116, 0.039513074055710196,
0.066431005944333835, nan, 0.056885456413698139, 0.092618298761083698, nan, 0.16070275371619847,
0.069246875176338457, 0.10148415463872817, 0.069991446033012245, 0.16155322976104619,
0.19214520199818294, 0.075570810202473901, 0.089846821919557535, 0.071594932990379723, nan,
0.094635421114176263, 0.075363322466705587, 0.045751244081147655, 0.094014866241872372,
0.079136714486756465, nan, 0.15362599554376127, 0.083959192939129748, nan, 0.12894640039367702,
0.10054470631457516, 0.086415565547854567, 0.072290198182110993, 0.09113827251286663,
0.073930880615789757, nan, 0.05580166784662946, 0.06720802437641786, 0.049316583116703616,
0.10114616286543424, 0.060149080143670756, 0.05024321597188372, nan, 0.10494259582443358,
0.14495079884409384, 0.076825029743123344, 0.054980638714509922, 0.065485452130998892, nan,
0.10435905380342403, 0.081704987756298197, 0.092536055958535007, 0.12316289405248082,
0.17809202466913895, 0.08490219129640432, 0.11827045860611889, 0.18822170468293925, 0.090549417806344956,
0.11894485488153088, 0.063398045965687988, 0.066987576689720038, 0.062815601374735144,
0.089101992397557728, 0.086090328859270704, 0.20403673769971975, 0.1940928507315709, 0.11313491737768153,
0.084809734579767704, 0.13482452908390613, 0.13246948341726483, 0.078241893904879248,
0.15754444078334656, 0.12019174909250872, 0.16440884143661672, 0.10282359558236952, 0.11013057060117588,
0.07867286916396668, 0.1093198865172342, 0.11929499228990828, 0.15173261385401673, nan, nan,
0.08371709599798767, nan, 0.14689224064170231, 0.15364135531058096, nan, 0.073134834087784609,
0.073851492345722386, 0.076620190847042413, 0.06777209076006209, nan, 0.15571931210577436,
0.08166690036523841, 0.085333657516596634, 0.058268568281619443, nan, 0.12503568946439536,
0.089168113977678806, 0.1170634750877864, 0.11856361883837185, 0.090733655584121026, nan,
0.24704959789063674, 0.14150022359991193, 0.18725849919336923, 0.10276879646500199, 0.084377533144105565,
0.12611395324818378, 0.10281183493525523, nan, nan, nan, 0.10461710565807061, 0.14020219796293795,
0.11508362940273718, 0.12218014127695484, 0.25280485149308174, 0.088494796146906918,
0.067601651339872101, nan, nan, nan, 0.13204868563734681, 0.13513283191141157, 0.13243901069754022,
0.074520728945019307, 0.1243874744014825, 0.094908040524814705, 0.072595086204380141,
0.13256462541714445, 0.10014584903556356, 0.078954975990317269, nan, nan, nan, nan, nan, nan,
0.094626917152024995, 0.13885449333826289, 0.17898510539321855, 0.092531950691658552,
0.11068001479770655, 0.088666908705020461, 0.074082444793945088, nan, 0.11364954669893715,
0.10603359584932309, 0.12916560208137531, 0.079408587264243699, 0.071914545158756088,
0.14579230827738826, 0.11707428605128907, 0.08344646955782864, 0.091011511258385761, 0.10518584258948432,
nan, nan, 0.1073257998450058, 0.092294524036661912, 0.057810544126671179, 0.040000715564569717,
0.055122571894829483, 0.10989580564311004, 0.14864098578239515, 0.083557792378560369, nan, nan, nan,
0.095356257866422142, nan, 0.15892279792764316, 0.12102527910155655, 0.1121214998785033,
0.093463500735476962, 0.11792630758540629, 0.1641839741964071, 0.13667406322805259, 0.11018996628414374,
0.1062411233801883, 0.1520895830457383, 0.12526371167725217, 0.10535348025111048, 0.12602310721027635,
0.1057628484693746, 0.17882296237440098, 0.1107957010791715, 0.097260810434295991, 0.067351170616306688,
0.078750826991474662, 0.099325612509943331, 0.081223869849891578, 0.09554852783407293,
0.11454336730124606, 0.093250443183636275, 0.094794291348064968, 0.25519001935753122, nan,
0.083118565754581666, 0.1617917464536536, 0.059931901445016235, 0.069169204785936744,
0.12055087215629062, 0.11054848012384202, 0.12369032979495793, 0.081939961217499524, 0.13728283621205525,
0.095057352175634643, nan, nan, nan, 0.16832931057086439, 0.2182349981219032, nan, 0.11059525474308562,
0.13065715992423024, 0.1247878383902057, 0.13848271414840213, 0.21988047316829959, 0.19813575140097445,
0.22707120699831171, 0.24612635444240463, 0.21605982034584159, 0.11692074444178918, 0.14121531530463416,
0.12521758111329603, 0.1527039598301965, nan, 0.15520102256491422, 0.12873540195459432, nan,
0.15700258683716392, 0.17488748555108274, 0.20387956185281139, 0.16085832477757017, 0.13957059374285161,
nan, nan, nan, nan, 0.37659126597436304, 0.060431412405282645, 0.10278120542682423, 0.11851351967803488,
nan, nan, nan, nan, nan, 0.24741530703980083, 0.19293666395228898, nan, 0.25129515041593803, nan,
0.11151750965079585, 0.10522979006090978, 0.14772247636880273, 0.17298482839863027, 0.17319374909420987,
0.12228282458723869, nan, 0.10562927934421687, 0.097268872984135835, nan, 0.18660728898779039, nan, nan,
nan, 0.2228244296014896, 0.098031391927553418, 0.14931048021962368, 0.31147679676860118,
0.19858687401038819, nan, 0.17592091093410173, 0.17573515709993909, 0.13885815972785159,
0.41187090878793686, nan, 0.14170061632070838, 0.12130612206819553, 0.10185460731967518, nan,
0.17331637903217692, 0.154834526315861, nan, 0.14885393795534541, nan, 0.086823118073009242,
0.11008665647426973, 0.16485333373144198, 0.15103276889420761, 0.15575449252223383, 0.16929870975945394,
0.18400706514509538, 0.13076135873987998, 0.16390607385771105, 0.15237950764538533, 0.13574008327828951,
nan, 0.10880896827109682, 0.078282925023923255, 0.16478556319228543, 0.13012832601705923,
0.11640363812817932, 0.120172794139403, 0.074217006081159095, 0.35361707979138141, 0.15879856696319111,
0.09184145201948693, 0.13701049289475092, 0.14728327515239167, nan, nan, 0.15323738300554798, nan,
0.14052625059657928, 0.11054508667878131, 0.097601359491648915, 0.1029095935121887, 0.13034571661192373,
0.25751349880873098, 0.14899179270387142, 0.10440359094041242, 0.12054933596471162, 0.13991380024934733,
0.11674707556115409, 0.13192481986293958, 0.099226073226684544, 0.12731884580894129,
0.083556765769025002, nan, nan, 0.12886555199528688, 0.11203529992315686, 0.18011580464302515,
0.099197963362847766, 0.066171065521254846, nan, 0.2180957313607487, 0.1574160253900295, nan, nan, nan,
0.074982890383863646, 0.096268585111438626, 0.2075192985531695, nan, nan, 0.12191170407697914, nan,
0.1381533341806595, nan, 0.2211345280716413, nan, 0.10263687404621372, 0.17036344388733141, nan,
0.16915446319057154, 0.19661296951612117, nan, 0.21288217344749624, 0.11065412455993975,
0.19424358935926408, 0.1052602144952674, nan, 0.090382843336048263, nan, 0.10774055075781079,
0.13352898221186277, nan, nan, nan, nan, 0.22217114132680621]
C_modes = [1.4574468085106382, 1.3563829787234043, nan, 1.3882978723404256, 1.3723404255319149, nan, nan,
1.1968085106382977, 1.1542553191489362, 1.1968085106382977, 1.2340425531914894, 1.3138297872340425,
1.2234042553191489, nan, 1.3085106382978724, 1.2819148936170213, 1.25, 1.2340425531914894,
1.2659574468085106, 1.3882978723404256, 1.2819148936170213, 1.2872340425531914, 1.2287234042553192, nan,
nan, 1.4414893617021276, 1.3191489361702127, 1.3138297872340425, 1.2819148936170213, 1.5319148936170213,
1.4308510638297871, 1.4202127659574466, nan, 1.3351063829787235, 1.3936170212765957, nan,
1.2021276595744681, 1.0957446808510638, 1.2765957446808509, 1.2287234042553192, 1.2712765957446808,
1.2978723404255319, 1.3670212765957446, 1.0478723404255319, 1.2765957446808509, nan, nan,
1.3670212765957446, 1.3297872340425532, 1.1968085106382977, 1.2765957446808509, nan, nan,
1.2978723404255319, nan, 1.1914893617021276, 1.2021276595744681, nan, 1.303191489361702,
1.2393617021276595, 1.25, 1.3457446808510638, 1.303191489361702, nan, nan, 1.2925531914893618,
1.5478723404255319, nan, 1.3882978723404256, 1.4148936170212767, nan, nan, nan, 1.2393617021276595, nan,
1.4202127659574466, 1.4734042553191489, nan, 1.3989361702127658, nan, 1.2074468085106382,
1.2765957446808509, 1.2765957446808509, 1.3723404255319149, 1.4627659574468084, 1.5425531914893615, nan,
nan, nan, nan, nan, nan, nan, 1.25, 1.1648936170212765, 1.2340425531914894, 1.1117021276595744, nan,
1.2819148936170213, 1.3510638297872339, 1.3085106382978724, 1.5904255319148934, nan, nan, nan,
1.2606382978723403, 1.1063829787234043, 1.1808510638297871, nan, nan, 1.3085106382978724,
1.4946808510638299, 1.3563829787234043, nan, 1.3723404255319149, 1.3829787234042552, nan,
1.904255319148936, nan, 1.3457446808510638, 1.2180851063829785, nan, nan, nan, 1.2446808510638296,
1.3297872340425532, 1.3617021276595744, 1.303191489361702, 1.2925531914893618, 1.303191489361702,
1.2393617021276595, 1.2393617021276595, nan, 1.3191489361702127, nan, nan, nan, nan, 1.2925531914893618,
1.4893617021276595, 1.3404255319148934, 1.2180851063829785, 1.3563829787234043, 1.1595744680851063,
1.2659574468085106, 1.2872340425531914, 1.1861702127659575, 1.3989361702127658, 1.2553191489361701,
1.2978723404255319, nan, 1.3989361702127658, 1.2340425531914894, 1.2021276595744681, 1.3404255319148934,
nan, 1.3510638297872339, nan, 1.3989361702127658, 1.2234042553191489, 1.1329787234042552, nan, nan,
1.2287234042553192, 1.2127659574468086, 1.2446808510638296, 1.2553191489361701, 1.2393617021276595,
1.4574468085106382, 1.3138297872340425, nan, nan, nan, 1.2553191489361701, 1.1648936170212765,
1.2606382978723403, 1.3563829787234043, 1.2712765957446808, 1.2180851063829785, 1.0265957446808511, nan,
nan, nan, 1.2819148936170213, 1.3297872340425532, 1.3882978723404256, 1.1914893617021276,
1.2074468085106382, 1.1010638297872339, 1.1010638297872339, 1.3138297872340425, 1.5319148936170213,
1.3723404255319149, 1.3404255319148934, 1.303191489361702, 1.4893617021276595, 1.2127659574468086, nan,
nan, 1.4893617021276595, 1.5159574468085106, 1.5106382978723403, 1.425531914893617, nan, nan,
1.2659574468085106, 1.5319148936170213, 1.2819148936170213, 1.4734042553191489, 1.3617021276595744, nan,
1.4680851063829787, 1.3989361702127658, 1.303191489361702, nan, 1.4521276595744681, 1.303191489361702,
nan, nan, 1.1968085106382977, 1.2659574468085106, nan, 1.1010638297872339, 1.1117021276595744,
1.1595744680851063, 1.3351063829787235, 1.2340425531914894, 1.3297872340425532, 1.2340425531914894,
1.1595744680851063, 1.1542553191489362, 1.2340425531914894, 1.2180851063829785, 1.1702127659574468,
1.2819148936170213, 1.3244680851063828, 1.3297872340425532, 1.3297872340425532, 1.3085106382978724,
1.1861702127659575, 1.3510638297872339, 1.25, 1.3563829787234043, 1.5159574468085106, nan, nan,
1.6276595744680851, nan, 1.3404255319148934, nan, nan, nan, nan, nan, 1.4202127659574466,
1.3829787234042552, 1.4946808510638299, nan, 1.946808510638298, nan, nan, 1.5691489361702127,
1.3085106382978724, nan, nan, nan, 1.3351063829787235, 1.3457446808510638, 1.3776595744680851,
1.4148936170212767, nan, 1.5159574468085106, 1.3351063829787235, 1.2925531914893618, 1.2712765957446808,
1.3351063829787235, nan, 1.0957446808510638, 1.2659574468085106, 1.2234042553191489, nan,
1.1861702127659575, 1.2021276595744681, 1.1968085106382977, nan, 1.4148936170212767, 1.2978723404255319,
1.2553191489361701, 1.2819148936170213, 1.2127659574468086, 1.3404255319148934, 1.2340425531914894,
1.2180851063829785, nan, 1.3244680851063828, 1.303191489361702, 1.2021276595744681, 1.2340425531914894,
1.3085106382978724, 1.3989361702127658, 1.3191489361702127, nan, nan, 1.3936170212765957, nan,
1.2925531914893618, 1.2393617021276595, 1.1648936170212765, 1.2234042553191489, 1.1329787234042552,
1.1223404255319149, nan, 1.25, 1.2340425531914894, 1.2978723404255319, 1.2606382978723403,
1.2606382978723403, 1.2340425531914894, 1.2659574468085106, 1.3829787234042552, 1.3191489361702127,
1.3457446808510638, nan, 1.2021276595744681, nan, 1.3297872340425532, 1.3297872340425532,
1.2393617021276595, 1.3510638297872339, 1.3351063829787235, 1.3723404255319149, 1.2234042553191489,
1.1542553191489362, nan, 1.2234042553191489, nan, nan, nan, nan, 1.3563829787234043, 1.3882978723404256,
1.3989361702127658, 1.4361702127659575, nan, 1.1648936170212765, 1.0957446808510638, 1.2925531914893618,
1.1595744680851063, 1.2819148936170213, 1.2978723404255319, 1.2393617021276595, 1.2872340425531914, nan,
1.303191489361702, 1.3191489361702127, 1.1063829787234043, 1.0585106382978722, 1.2074468085106382,
1.3510638297872339, 1.553191489361702, 1.5372340425531914, 1.2021276595744681, 1.2127659574468086,
1.3936170212765957, nan, 1.1170212765957446, 1.1010638297872339, 1.1648936170212765, nan, nan,
1.3351063829787235, nan, nan, nan, 1.2074468085106382, 0.97340425531914898, 1.0851063829787233,
1.1914893617021276, nan, nan, nan, 1.2872340425531914, nan, nan, 1.4202127659574466, 1.6170212765957446,
1.2712765957446808, 1.1170212765957446, 1.4148936170212767, 1.1063829787234043, 1.1117021276595744,
1.2765957446808509, 1.2819148936170213, 1.2872340425531914, 1.2872340425531914, 1.2127659574468086,
1.2659574468085106, nan, nan, 1.4095744680851063, 1.2021276595744681, 1.1861702127659575, nan, nan, nan,
nan, 1.1329787234042552, 1.1968085106382977, nan, nan, 1.2659574468085106, 1.2872340425531914,
1.2712765957446808, 1.4095744680851063, 1.0957446808510638, 1.0159574468085106, 1.0159574468085106,
1.2234042553191489, 1.053191489361702, nan, 1.1968085106382977, 1.3138297872340425, 1.3404255319148934,
1.2872340425531914, nan, 1.3723404255319149, 1.3563829787234043, 1.3457446808510638, 1.3351063829787235,
nan, nan, nan, 1.175531914893617, 1.1117021276595744, 1.1702127659574468, 1.1010638297872339,
1.1595744680851063, 1.2074468085106382, 1.1010638297872339, nan, 1.3829787234042552, 1.3510638297872339,
1.2872340425531914, 1.3563829787234043, 1.303191489361702, 1.2393617021276595, 1.1861702127659575,
1.1329787234042552, 1.1382978723404253, 1.2606382978723403, 1.053191489361702, 1.2446808510638296,
1.1329787234042552, nan, nan, 1.1276595744680851, nan, 1.1595744680851063, 1.2925531914893618,
1.3085106382978724, nan, nan, 1.2021276595744681, 1.2074468085106382, 1.2127659574468086, nan,
1.3191489361702127, 1.2340425531914894, 1.1223404255319149, 1.303191489361702, 1.25, 1.2287234042553192,
1.2287234042553192, nan, nan, 1.2819148936170213, nan, 1.3297872340425532, nan, nan, nan,
1.4521276595744681, nan, nan, 1.0851063829787233, 1.1702127659574468, 1.2393617021276595,
1.3191489361702127, nan, nan, 1.3404255319148934, 1.4308510638297871, 1.3776595744680851,
1.3936170212765957, 1.4574468085106382, 1.7074468085106382, 1.3829787234042552, nan, 1.3351063829787235,
1.4308510638297871, 1.3670212765957446, 1.574468085106383, 1.2606382978723403, nan, 1.1436170212765957,
1.2872340425531914, 1.2819148936170213, 1.3404255319148934, 1.3829787234042552, 1.303191489361702,
1.2872340425531914, 1.4521276595744681, nan, 1.4840425531914891, 1.4787234042553192, 1.4148936170212767,
1.3244680851063828, 1.3936170212765957, 1.4414893617021276, 1.3936170212765957, 1.4627659574468084,
1.4308510638297871, 1.5053191489361701, 1.3936170212765957, 1.3936170212765957, 1.303191489361702,
1.3191489361702127, nan, nan, 1.1648936170212765, 1.303191489361702, 1.3191489361702127, nan,
1.0797872340425532, 1.1968085106382977, 1.3191489361702127, 1.3138297872340425, 1.1276595744680851, nan,
1.3244680851063828, 1.4148936170212767, 1.3138297872340425, 1.425531914893617, 1.1117021276595744,
1.2074468085106382, 1.1542553191489362, 1.1914893617021276, 1.2393617021276595, 1.175531914893617,
1.1861702127659575, 1.2180851063829785, nan, nan, 1.2765957446808509, 1.2712765957446808,
1.2074468085106382, 1.2765957446808509, 1.2074468085106382, 1.3244680851063828, 1.1542553191489362,
1.3138297872340425, 1.3563829787234043, 1.3936170212765957, 1.2659574468085106, 1.3723404255319149,
1.2925531914893618, 1.1382978723404253, nan, 1.2606382978723403, 1.3244680851063828, nan, nan, nan, nan,
nan, nan, 1.4627659574468084, 1.3510638297872339, 1.303191489361702, 1.2393617021276595,
1.3351063829787235, 1.3989361702127658, nan, 1.3989361702127658, 1.3297872340425532, 1.2127659574468086,
1.2340425531914894, 1.2446808510638296, 1.0851063829787233, 1.2446808510638296, 1.1861702127659575,
1.2819148936170213, nan, 1.2393617021276595, 1.2606382978723403, 1.2446808510638296, nan,
1.5425531914893615, 1.5372340425531914, 1.175531914893617, 1.2021276595744681, 1.2712765957446808,
1.4787234042553192, nan, 1.7340425531914894, 1.3457446808510638, 1.3297872340425532, 1.3085106382978724,
1.2659574468085106, 1.3191489361702127, 1.3085106382978724, 1.3510638297872339, 1.2606382978723403,
1.3457446808510638, 1.1861702127659575, 1.1861702127659575, 1.2287234042553192, 1.2606382978723403,
1.2659574468085106, 1.2021276595744681, nan, 1.1329787234042552, 1.1436170212765957, 1.2872340425531914,
1.2872340425531914, 1.3617021276595744, 1.2287234042553192, 1.2393617021276595, 1.1914893617021276,
1.1648936170212765, 1.2872340425531914, 1.2553191489361701, 1.2127659574468086, 1.2659574468085106,
1.2606382978723403, 1.2553191489361701, 1.1914893617021276, nan, 1.303191489361702, 1.2712765957446808,
1.2978723404255319, nan, 1.1063829787234043, 1.0478723404255319, 1.2074468085106382, 1.3936170212765957,
1.3404255319148934, nan, 1.303191489361702, 1.2978723404255319, 1.2234042553191489, 1.3351063829787235,
nan, 1.1808510638297871, 1.2765957446808509, nan, nan, 1.3297872340425532, 1.2606382978723403, 1.5, nan,
1.2872340425531914, 1.2340425531914894, nan, 1.3138297872340425, 1.3244680851063828, 1.2712765957446808,
1.2659574468085106, 1.2606382978723403, 1.2553191489361701, 1.1436170212765957, 1.1808510638297871,
1.1595744680851063, nan, 1.1914893617021276, 1.175531914893617, 1.2127659574468086, 1.1223404255319149,
1.2765957446808509, nan, 1.1170212765957446, 1.2127659574468086, nan, 1.4574468085106382,
1.1436170212765957, 1.053191489361702, 1.1170212765957446, 1.2180851063829785, 1.0904255319148937, nan,
1.2712765957446808, 1.303191489361702, 1.2553191489361701, 1.2180851063829785, 1.3776595744680851,
1.4095744680851063, nan, 1.3723404255319149, 1.3404255319148934, 1.1968085106382977, 1.1329787234042552,
1.1489361702127661, nan, 1.2287234042553192, 1.2925531914893618, 1.2553191489361701, 1.3351063829787235,
1.3563829787234043, 1.3670212765957446, 1.404255319148936, 1.303191489361702, 1.3989361702127658,
1.2606382978723403, 1.25, 1.3989361702127658, 1.3829787234042552, 1.4095744680851063, 1.3085106382978724,
1.4095744680851063, 1.4414893617021276, 1.3617021276595744, 1.425531914893617, 1.404255319148936,
1.4148936170212767, 1.3351063829787235, 1.3297872340425532, 1.1914893617021276, 1.0957446808510638,
1.1914893617021276, 1.2021276595744681, 1.2446808510638296, 1.1968085106382977, 1.1808510638297871,
1.3191489361702127, nan, nan, 1.5851063829787233, nan, 1.4734042553191489, 1.3457446808510638, nan,
1.1223404255319149, 1.1808510638297871, 1.1223404255319149, 1.1382978723404253, nan, 1.5319148936170213,
1.1808510638297871, 1.175531914893617, 1.1861702127659575, nan, 1.0851063829787233, 1.175531914893617,
1.0691489361702129, 1.0797872340425532, 1.1914893617021276, nan, 1.6010638297872342, 1.3244680851063828,
1.3776595744680851, 1.1276595744680851, 1.1382978723404253, 1.2765957446808509, 1.2659574468085106, nan,
nan, nan, 1.2287234042553192, 1.1436170212765957, 1.3138297872340425, 1.2127659574468086,
1.0372340425531914, 1.1329787234042552, 1.1914893617021276, nan, nan, nan, 1.2340425531914894,
1.1968085106382977, 1.4202127659574466, 1.1276595744680851, 1.1382978723404253, 1.0638297872340425,
0.97872340425531901, 1.0319148936170213, 1.1914893617021276, 1.2021276595744681, nan, nan, nan, nan, nan,
nan, 1.3989361702127658, 1.5585106382978724, 1.2819148936170213, 1.4361702127659575, 1.3297872340425532,
1.0904255319148937, 1.0904255319148937, nan, 1.3244680851063828, 1.2978723404255319, 1.1861702127659575,
1.2553191489361701, 1.2872340425531914, 1.2234042553191489, 1.2872340425531914, 1.2606382978723403,
1.2553191489361701, 1.2287234042553192, nan, nan, 1.2819148936170213, 1.2234042553191489,
1.1063829787234043, 1.1648936170212765, 1.1648936170212765, 1.1914893617021276, 1.4202127659574466,
1.2925531914893618, nan, nan, nan, 1.3457446808510638, nan, 1.404255319148936, 1.2659574468085106,
1.3138297872340425, 1.2446808510638296, 1.2553191489361701, 1.2659574468085106, 1.303191489361702,
1.2340425531914894, 1.3138297872340425, 1.2446808510638296, 1.2234042553191489, 1.2659574468085106,
1.2446808510638296, 1.1808510638297871, 1.0425531914893618, 1.3351063829787235, 1.2074468085106382,
1.2446808510638296, 1.1861702127659575, 1.2234042553191489, 1.3191489361702127, 1.1010638297872339,
1.2234042553191489, 1.2872340425531914, 1.3829787234042552, 1.2553191489361701, nan, 1.2127659574468086,
1.2712765957446808, 1.2925531914893618, 1.3244680851063828, 1.2340425531914894, 1.1968085106382977,
1.2872340425531914, 1.1648936170212765, 1.2819148936170213, 1.3085106382978724, nan, nan, nan,
1.5265957446808509, 1.4521276595744681, nan, 1.3085106382978724, 1.4095744680851063, 1.4840425531914891,
1.3936170212765957, 1.6968085106382977, 1.7340425531914894, 1.7925531914893618, 1.675531914893617,
1.7553191489361701, 1.8936170212765957, 1.7925531914893618, 1.6436170212765957, 1.5797872340425532, nan,
1.675531914893617, 1.4893617021276595, nan, 1.5372340425531914, 1.6861702127659572, 1.8457446808510638,
1.7765957446808509, 1.675531914893617, nan, nan, nan, nan, 1.7340425531914894, 1.8244680851063828,
1.7659574468085106, 1.7765957446808509, nan, nan, nan, nan, nan, 1.7765957446808509, 1.9255319148936172,
nan, 1.8670212765957448, nan, 1.6542553191489362, 1.7606382978723405, 1.675531914893617,
1.7127659574468086, 1.8085106382978722, 1.8670212765957448, nan, 1.8510638297872337, 1.7553191489361701,
nan, 1.7553191489361701, nan, nan, nan, 1.9627659574468084, 1.6595744680851063, 1.9148936170212765,
2.0053191489361701, 2.0106382978723403, nan, 1.904255319148936, 1.6968085106382977, 1.9521276595744681,
2.0, nan, 1.7499999999999998, 1.8297872340425529, 1.7659574468085106, nan, 1.7712765957446805,
1.7446808510638296, nan, 1.8723404255319149, nan, 1.6542553191489362, 1.8936170212765957,
1.803191489361702, 1.6702127659574466, 1.7340425531914894, 1.8617021276595744, 1.6436170212765957,
1.7180851063829785, 1.7872340425531914, 1.8563829787234041, 1.7180851063829785, nan, 1.4946808510638299,
1.5638297872340423, 1.5904255319148934, 1.6595744680851063, 1.5957446808510638, 1.6914893617021276,
1.7180851063829785, 1.7127659574468086, 1.8457446808510638, 1.6542553191489362, 1.6010638297872342,
1.7393617021276597, nan, nan, 1.574468085106383, nan, 1.7393617021276597, 1.574468085106383,
1.5106382978723403, 1.3297872340425532, 1.4627659574468084, 1.6223404255319149, 1.4840425531914891,
1.5159574468085106, 1.5, 1.6542553191489362, 1.6117021276595744, 1.6382978723404256, 1.8510638297872337,
1.7765957446808509, 1.6648936170212765, nan, nan, 1.5638297872340423, 1.728723404255319,
1.6010638297872342, 1.6117021276595744, 1.6117021276595744, nan, 1.7127659574468086, 1.4680851063829787,
nan, nan, nan, 1.5957446808510638, 1.7499999999999998, 1.5478723404255319, nan, nan, 2.2819148936170213,
nan, 1.7234042553191489, nan, 1.6702127659574466, nan, 1.6329787234042552, 1.6117021276595744, nan,
1.8563829787234041, 2.0531914893617023, nan, 1.9893617021276595, 2.0531914893617023, 1.7819148936170213,
1.904255319148936, nan, 1.8563829787234041, nan, 1.9255319148936172, 1.946808510638298, nan, nan, nan,
nan, 1.8829787234042552]
C_medians_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, 0.98731115293788574, 1.1381168011979534, 1.0979270915376955,
1.0048265220473018, nan, 1.1091838281009792, 1.1024037018639943, 1.1338150741923787,
1.147925288726662, 1.1347333531286752, 1.1317739266462561, 1.1011694589701522, 1.2115893760536358,
1.1538749350751061, 1.213582223170633, nan, 1.1537723322097808, nan, 1.2242174899053915, nan,
1.1588940844484017, 1.2276509983367074, 1.1751313572134079, 1.2049446902301779, 1.0879522197842841,
1.0272784300431148, nan, 1.1223441256855711, nan, nan, nan, nan, 1.2844595678778445,
1.3252818667911233, 1.2450750909640758, 1.4026582502814682, nan, 1.0818643603891784,
1.0035229858727215, 1.1905407723124204, 1.146805928180463, 1.2006390106034166, 1.1315738535671636,
1.132216155415418, nan, nan, 1.1907433903591691, nan, nan, nan, nan, 1.2235455596428004, nan, nan,
1.1501386205457504, 1.108437322525734, nan, nan, 1.0112017284891719, 1.0791349076167951,
1.0224041546880338, nan, nan, 1.2059449916979113, nan, nan, 1.1395854564443568, 1.070457931082883,
0.88433663410747521, 0.96201564713382748, 1.0562039422014324, nan, nan, nan, 1.1807688260503792,
1.5890022353134361, nan, 1.3007208808452864, 1.4894772553305597, 1.0745206282773383,
0.99314311329734284, 1.204097630131757, 1.0213023244330406, 0.97026873058200336, 1.1295489777011474,
1.1129517110568523, 1.1727119599071882, 1.1218160845032066, 1.1107805369848298, 1.1850542224712879,
nan, nan, 1.3272980987610057, 1.0994038024304154, 1.0915532140229258, nan, nan, nan, nan,
1.0870769850035136, 1.0822688712014621, nan, nan, 1.109717784288724, 1.1438574701161248,
1.211000364146585, 1.1876934610267242, 1.018113770576069, 0.96309122544774683, 0.97199898897904635,
1.0185379192207384, 0.96262008236888863, nan, 1.0031406053052561, 1.1455888156305023,
1.1855020514612635, 1.154268190881977, nan, 1.1984183290644375, 1.2074048586675179,
1.2045765632362713, 1.2970188072157005, nan, nan, nan, 1.0737493179665614, 0.99240218398561164,
1.0914072358166846, 0.96898394199816507, 1.060294472610817, 1.0502426515197647, 0.99957181247599514,
nan, 1.1811190985056981, 1.1546654769878997, 1.1875592579894261, 1.1457570087761695,
1.1622233183155657, 1.0797035143201463, 1.0608729319529742, 0.99805626650398083, 0.98689937632652658,
1.0955584338062354, nan, 1.1267647924072275, 1.0089898574164842, nan, nan, 0.98209460943142846, nan,
1.0977065209635029, 1.1143738709299003, 1.1342279341871959, nan, nan, 1.0680728312106633,
1.0848742391856174, 1.0744566762551828, nan, 1.1703922963740832, 1.1222094767021311,
0.98747160155859204, 1.0652237218732061, 1.1396212612025867, 0.98722489196326135, 1.0290928549113891,
nan, nan, 1.002639131808519, nan, 1.0454163784534429, nan, nan, nan, 1.1994420132924839, nan, nan,
0.9884325612622793, 0.97394863330860593, 1.0558482357371655, 1.1539710601479305, nan, nan,
1.1712781903889611, 1.1875301556896216, 1.1624340066591117, 1.1928160085863042, 1.1853562149014676,
1.3350554765482023, 1.2139902252813322, nan, nan, 1.2048968900932693, 1.1960694834149759,
1.1399969564721446, 1.0022906607661048, nan, 1.0063787287466162, 1.0734842230547652,
1.1264723341881133, 1.1485043993950912, 1.0929303422412147, 1.0913868138547016, 1.1328905211479299,
nan, nan, 1.2310704464629714, 1.2141332710902608, 1.1125542026039899, 1.1484886131078351,
1.1558115237910895, nan, 1.1549662724755843, 1.2748565808070964, 1.257655980914097,
1.246004433839339, 1.2685826367133484, 1.1657234144624713, 1.1393598177167168, 1.0829501170637428,
nan, 1.1048704225365886, 1.0830186819251917, 1.1045650175113195, 1.1423258158596743, nan, nan, nan,
1.1210246892290394, nan, nan, nan, 1.1188384802308433, 1.2676161449398471, 1.136279462912519, nan,
0.98645195519332052, 0.98283920039024764, 0.97381455795665262, 1.0119494318784705, 1.037809202234975,
1.0343391778247519, 1.0605405773212437, 1.0580951428874625, nan, nan, 1.0485632578468387,
1.0857636594896927, 1.0409515154424442, 1.1028458712009555, 1.0328717806714165, 1.0490051066293611,
1.082332059633057, 1.1434008913508897, 1.1504717160624014, 1.1189279741148945, nan, nan, nan, nan,
nan, 1.1094935224393179, 1.1479635660120517, nan, nan, nan, nan, nan, nan, 1.2786000841960967,
1.1879759961889365, 1.0930575088211123, nan, nan, 1.268270098257319, nan, 1.263869714825868,
1.1583200700592451, 1.0216487024245053, 1.046546164229516, 1.1229538654189051, 1.0134249110848081,
1.0715523181063604, 1.0388878838879305, 1.0828216361856096, nan, 1.0683157489900488,
1.092692280734431, 1.0594165319255946, nan, 1.3696007017394383, nan, 1.0840115058936457,
1.0896724951691656, 1.1317556555885377, 1.4036635696503699, nan, 1.6276933252794927,
1.1990085474544541, 1.1479120088890942, 1.1327842074299526, 1.1091439209311023, 1.1832400342391425,
1.1266228862207948, 1.201165645325188, 1.1612843738054175, 1.1210816720007299, 1.0559125689168976,
1.0327965233858269, 1.077654391388525, 1.0791142713233213, 1.1162344219740883, 1.0875800148408512,
nan, 0.97055603254574763, 1.0368245781657204, 1.1811274701188723, 1.1703611890481003,
1.2081150632989643, 1.0434229459658988, 1.096189942876562, 1.0362470710413869, 1.0359953771585482,
1.1275076677479194, 1.1368965950945018, 1.0908496914126298, nan, 1.0482035834016772,
1.1376675964224012, 1.0345262949496148, nan, 1.1629060769050386, 1.1884865329461816,
1.1753155051917397, nan, 1.0318506133948246, 0.99821199715126407, 1.060954181331738,
1.2765083626864879, 1.1795061012380048, nan, 1.1309152178344886, 1.1656654176026364,
1.0765786541529079, 1.1849191907369458, nan, 1.1136726272402853, 1.1408655086833379, nan, nan,
1.2037576230994742, 1.1093379256464426, 1.3828353791673447, nan, 1.1634941403772119,
1.0580155470262862, nan, nan, 1.1817855753618347, 1.1040493794012187, 1.1399229281435646,
1.1355604919660334, 1.1125200331527254, 1.0551373815573581, 1.0094720436568605, 1.0363232030294132,
nan, 1.0548075923946889, 1.0723741837746585, 1.104608133664253, 1.0181433059718452,
1.1463428994581037, nan, 1.0186533427407336, nan, nan, 1.3099818607456695, 1.0822061297360519,
0.97384109327123836, 1.0762689783358361, 1.0926065188343033, 1.0119356154368961, nan,
1.1289534319579353, 1.1343492808496125, 1.1231564773671532, 1.125405007821938, 1.2164165694780191,
nan, nan, 1.2160103552743107, nan, nan, 1.0645298570578026, 1.050560144332906, nan, nan, nan,
1.1107600012637189, 1.2383090950141784, 1.2522817012052418, 1.1933927366467645, 1.3142498296778795,
1.2161438904857591, 1.2539128598549261, 1.1939628500519435, nan, nan, 1.2188914975791736,
1.2195342589056517, 1.15321710445912, 1.2515879636850451, 1.3964301869193156, 1.2270407204492808,
1.2341240310607697, 1.2490186277494959, 1.2997651750838268, nan, nan, nan, nan, nan, nan, nan, nan,
1.1194992375044306, 1.2467749385195057, nan, nan, 1.5092761775577248, nan, 1.3233369335656904, nan,
nan, nan, nan, 1.012596340585866, 1.0049629840239016, nan, 1.3995121257135232, 1.0499830392926106,
1.0109379280093524, 1.0884715671336489, nan, 1.0203142003463892, 1.0431526963994542,
0.95888404837856644, 1.0130981633478133, nan, nan, 1.2339671180883305, 1.1311292347260884,
1.1698762784355541, 1.0056973527136841, 1.0295048297708824, 1.0581106506442384, 1.0890764168502023,
nan, nan, nan, 0.99282917807014637, 1.000693522841182, 1.1406835965895759, 1.0886845478354856,
0.94437071498172909, 1.0063094024455732, 1.0210878228535207, nan, nan, nan, 1.1303721277939291,
1.1480627808673864, nan, nan, 1.0867489957031604, nan, nan, 0.95276330530439179, nan,
1.091659993352788, nan, nan, nan, nan, nan, nan, 1.1429408674327024, 1.5173469078351625,
1.2316033421201196, 1.3153027564443251, 1.2488687191582575, 0.95286478575316469, 0.9474706044373441,
nan, nan, 1.0893732414372708, 1.1115928019871673, 1.1024813095990513, 1.1195903478417861,
1.019282303611061, 1.1142310725423143, 1.0846719522466111, 1.0632237625764205, 1.0809012139581409,
nan, nan, 1.1857001583197331, 1.0944378316986687, 1.0073780228779388, nan, 1.0591740155714293,
1.0996804830043123, 1.3231914093141308, nan, nan, nan, nan, 1.1847069015899807, nan,
1.098445587853742, 1.0716773908140846, 1.1112887491091585, 1.0876799727958111, 1.0288790842469007,
1.0758603096899595, 1.1317398359130308, 1.070340436161888, 1.0959107918790421, 1.0613310208015008,
1.049844662081455, 1.055191101348584, 1.0922253078426443, 1.0101414664420767, 1.0334733588135943,
1.0779397756467515, 0.97774473083245983, 1.0973563637933967, 1.0675668886877108, 1.0888659211908847,
nan, 0.95795106076774772, 1.0472274766454963, 1.1182183377811414, 1.1502232993199462,
1.0543297421082134, nan, 1.0654581893876283, 1.0227909282313292, 1.1322965089729451,
1.1448824812929221, 1.0662176603674822, 1.0095044030454341, 1.007688975607089, 1.030843368451269,
1.1134598296531972, 1.1112906729232563, nan, nan, nan, 1.2324929440039989, 1.310167463052917, nan,
1.1101313743842542, 1.0921504275466984, 1.125749408217773, 1.1698768214295558, 1.4542064755694613,
1.5308751631830813, 1.6158971324890556, 1.4694437486591689, nan, 1.5963805843437413,
1.4252144691530058, 1.4101398824862914, 1.3354825615345658, nan, 1.4940310177089564,
1.2681527149540235, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
1.4809966454456285, 1.3755737651328275, nan, nan, 2.1089174925193293, nan, 1.5835883442555638, nan,
1.6253661847072225, nan, 1.4709277506609408, 1.5047089837312089, nan, 1.7603845873292439,
1.8910448102715853, nan, 1.9432852692260798, 1.9328052372281079, 1.6519339236002661,
1.716103774672443, nan, 1.6815888892975108, nan, 1.7108689107395039, 1.7844380347975757, nan, nan,
nan, nan, 1.7790000520803735]
C_stdevs_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, 0.056715387579872024, 0.035140856094424702, 0.09666965709281268,
0.091885877308190519, nan, 0.060466332639981218, 0.23873360970081323, 0.085537881625414688,
0.062077970591622594, 0.059114898052885799, 0.099524460348683563, 0.060323287987146042,
0.0483469284740995, 0.061584036236542072, 0.096268717873429152, nan, 0.07936488363788563, nan,
0.075992650804403483, nan, 0.14134604365790457, 0.084700021289026733, 0.071783037457843354,
0.079423550608023516, 0.089742188601547423, 0.055184679174809965, nan, 0.064139545354193014, nan, nan,
nan, nan, 0.10930415220715854, 0.11980771744048765, 0.10214281324137338, 0.15118284112441757, nan,
0.16716664926127689, 0.097744327402536246, 0.07698337782382271, 0.072374498193297013,
0.08033047716249074, 0.083794546420454363, 0.08753696868473701, nan, nan, 0.085088196740113253, nan,
nan, nan, nan, 0.13800902267240769, nan, nan, 0.060603825287758581, 0.10604154725036208, nan, nan,
0.069253147129451881, 0.11032233140438212, 0.072006862909261338, nan, nan, 0.12722309347298774, nan,
nan, 0.10238554658221989, 0.13014183251772188, 0.063907218659460752, 0.062448463403374149,
0.078173316495039016, nan, nan, nan, 0.074549321601468752, 0.1119772527480349, nan,
0.096738112428294304, 0.11793881044583773, 0.06169664845711291, 0.096408975659749471,
0.11629610018468867, 0.10106313506667987, 0.068705225812914855, 0.060894533132490707,
0.10095438314282663, 0.098536050896200669, 0.13813625150565489, 0.12052501919147603,
0.099546675104171298, nan, nan, 0.14125530096110386, 0.082701017724490747, 0.13669419497538773, nan,
nan, nan, nan, 0.088228909189174143, 0.10108654740454229, nan, nan, 0.09707526927814146,
0.081347588032789936, 0.082780987405776288, 0.14871468255225342, 0.08994787882924378,
0.11170446698043622, 0.096993394523298124, 0.086951670202920772, 0.070502193956258216, nan,
0.071148696251725652, 0.089271358023314373, 0.089377989067009903, 0.089224451241761579, nan,
0.11549761774546492, 0.07500133848025653, 0.080262657134420656, 0.10686602701779532, nan, nan, nan,
0.1073985238430592, 0.072380974415306026, 0.07424065618938637, 0.056034587338810733,
0.076361973198848271, 0.089351478212629343, 0.080166842008730255, nan, 0.11139102656268475,
0.14447686856527542, 0.12986976975542283, 0.074074263829487424, 0.074909904453101125,
0.056988200312554159, 0.091501004688166382, 0.088798574176460754, 0.11221274873744036,
0.12141201564922771, nan, 0.073537511221875768, 0.087296785360817628, nan, nan, 0.07021283947036086,
nan, 0.087581968652133524, 0.074167667799610371, 0.057123347675840049, nan, nan, 0.076502737166646143,
0.12490601764260617, 0.072276470381423291, nan, 0.1129444834590568, 0.095417946006214935,
0.064310509054819928, 0.10466900900520794, 0.10302497744364729, 0.092806327184254703,
0.088501049935905282, nan, nan, 0.088933060092323185, nan, 0.09350682177291926, nan, nan, nan,
0.19350961075744505, nan, nan, 0.080853170328481203, 0.061645691871908465, 0.082843766262359911,
0.08624395297318696, nan, nan, 0.18403654090901816, 0.09787813481154134, 0.070386855510771534,
0.079428839821778813, 0.10040610733497986, 0.15997918318390256, 0.14739633946303052, nan, nan,
0.11023525243531283, 0.094190548059148477, 0.12429665356571228, 0.084229962087173704, nan,
0.090894351138972845, 0.071532380526754077, 0.073899453616717473, 0.11847973887853855,
0.11834128633757665, 0.11532697581384785, 0.10071860810604193, nan, nan, 0.087715325866804375,
0.074341724866918743, 0.049207908993323855, 0.078677113935140819, 0.16243220596782504, nan,
0.11987781242049472, 0.10287386110521209, 0.28287979048276013, 0.20556631566260394,
0.11664917628121814, 0.11217327655538152, 0.13490656719572144, 0.11706724374166938, nan,
0.055567206469817679, 0.13750964181295736, 0.081557687678912943, 0.07569576794800692, nan, nan, nan,
0.10027216014608203, nan, nan, nan, 0.22970158678639277, 0.11319802415564406, 0.080536794822397839,
nan, 0.14837695719412561, 0.094124623932951942, 0.1016132388719623, 0.10772545144607676,
0.071731924240092018, 0.084446972167156284, 0.092370343918188283, 0.063775042984833163, nan, nan,
0.09899564547268172, 0.16162748057568022, 0.10283579439849921, 0.092875649861350329,
0.073274811732062656, 0.086785063922363959, 0.10510017395829584, 0.085330223730329391,
0.092082854873352368, 0.094251467337077996, nan, nan, nan, nan, nan, 0.10402409468157114,
0.061015066646688279, nan, nan, nan, nan, nan, nan, 0.073341194263601528, 0.11372717687039839,
0.089282905440668303, nan, nan, 0.17133124650315418, nan, 0.043684907395730284, 0.088799646573801641,
0.055101315191319541, 0.089054465829559531, 0.10435858176999591, 0.055081207088262675,
0.09695411042656954, 0.088606022474235963, 0.066685462434576945, nan, 0.065143555779601861,
0.045450449780249993, 0.04794245134020151, nan, 0.17561532228800464, nan, 0.12897707868785691,
0.059757079191092084, 0.094216896948756013, 0.091125615347423153, nan, 0.10039337712635744,
0.095770589127884789, 0.12415037308438155, 0.098242123111432433, 0.058503184710716437,
0.096932670709699797, 0.066877756206847896, 0.045618334157811434, 0.057416788189104555,
0.084758947456692962, 0.078108443329821697, 0.06633479169680849, 0.044302932328152467,
0.044013430451984269, 0.048394441884985241, 0.047888510361081812, nan, 0.056556842421071576,
0.13946999782498676, 0.046804740275804058, 0.1417580120055969, 0.11154708350423626,
0.051857240444562906, 0.10418136926909236, 0.066608736413366773, 0.085936734457045763,
0.062822812175250792, 0.054250221874481523, 0.04639039347512048, nan, 0.033726926456554479,
0.075861366566475491, 0.14422592215582031, nan, 0.064526957865694429, 0.071730379147922962,
0.059000709734862926, nan, 0.10054952953982668, 0.0976303765875981, 0.074979813045088078,
0.063917580231054816, 0.14147742244746686, nan, 0.058095826752206203, 0.091438472063906054,
0.091345822816861322, 0.06104639060222964, nan, 0.12998233073910187, 0.038977295504418995, nan, nan,
0.067628426021882251, 0.034391542289325623, 0.063530270132855407, nan, 0.056511853435886744,
0.10208035137952889, nan, nan, 0.062004944166284583, 0.095478608836719756, 0.078220124298383389,
0.12258764609346903, 0.18263968276418086, 0.074034233030511765, 0.081805753770822151,
0.073258532520865074, nan, 0.088986498158372285, 0.064476775085557159, 0.051183896478680324,
0.083678625011948241, 0.071490190269739906, nan, 0.13380070477779291, nan, nan, 0.11737008335064049,
0.10757037313986836, 0.088049055605728344, 0.063930481045831836, 0.076541325703590152,
0.065849889374093667, nan, 0.051376050993661548, 0.059664914451787177, 0.044697975605671757,
0.097651875739730915, 0.066966739416918997, nan, nan, 0.1014160665052446, nan, nan,
0.053207546585905229, 0.057283259671513612, nan, nan, nan, 0.080378635207220425, 0.10903945101816752,
0.17715272234819998, 0.076334405602089428, 0.12400034687738763, 0.17001296637502564,
0.081361554571017292, 0.12017548290648976, nan, nan, 0.055242529813065808, 0.098236255965845426,
0.075598081059227981, 0.17906682053267228, 0.19677388475542698, 0.10263164202272872,
0.079934233445685926, 0.12090845323657311, 0.12344710327766432, nan, nan, nan, nan, nan, nan, nan,
nan, 0.12313802811422361, 0.1465354120404041, nan, nan, 0.079902360987137627, nan,
0.14018675520490292, nan, nan, nan, nan, 0.072610321818508197, 0.062405986789751219, nan,
0.14102586980529722, 0.071565697796151684, 0.066479080169629529, 0.055908934070389264, nan,
0.10755881565280698, 0.086489780313915923, 0.095500478225071836, 0.10488601640454628, nan, nan,
0.27911024930389539, 0.11617729733649998, 0.19093450490465558, 0.08889295511330346,
0.068344141065032879, 0.11739819963958271, 0.11895517998361832, nan, nan, nan, 0.087205202429068818,
0.12004602146133099, 0.10748138625452541, 0.12209293759787848, 0.22382553075905298,
0.07760236141418228, 0.057547737075860667, nan, nan, nan, 0.11987269271608247, 0.11047190219664692,
nan, nan, 0.12569981605644995, nan, nan, 0.11499106610020117, nan, 0.072894011318643939, nan, nan,
nan, nan, nan, nan, 0.076170873043014328, 0.14709376909436991, 0.1735832961610711,
0.085213760998979593, 0.10698786145575366, 0.078478865111566404, 0.066079194792994511, nan, nan,
0.090807970401886595, 0.11528052066377378, 0.080349232731254328, 0.063725479606276059,
0.12534793101974101, 0.1020974479048197, 0.078785535612433052, 0.086909064705002093,
0.10425059201418543, nan, nan, 0.089111281327670211, 0.089296654662998257, 0.05250248082987935, nan,
0.053727287544771546, 0.094616908338377284, 0.12781494158467618, nan, nan, nan, nan,
0.086358931213948359, nan, 0.13174826947717386, 0.12058679906719291, 0.098691621035870994,
0.089941567758241234, 0.097074847115966006, 0.13900065880889464, 0.12594107589972026,
0.095499689196565984, 0.091200480132569692, 0.12016821852135409, 0.10843787835657634,
0.096283103831455219, 0.098178640613527338, 0.098428658487077658, 0.1459213709883751,
0.10758699738804398, 0.089101216608136663, 0.073295746787303176, 0.05849893654530619,
0.096144735714639676, nan, 0.083030797430897904, 0.080384628143477951, 0.082047311526363464,
0.099893574049180381, 0.21489219095076784, nan, 0.082416051998054116, 0.12881088323180326,
0.052788065906604932, 0.066111362274064694, 0.088657622656057422, 0.097639804348788273,
0.098223120253968399, 0.066502866779930739, 0.091282834415883279, 0.092582465279673148, nan, nan, nan,
0.13148773271694245, 0.17980016161066092, nan, 0.090293982851216828, 0.11377818587238414,
0.08584168245153885, 0.11237284331446876, 0.20745791192672217, 0.1795568305862848,
0.21801475203313395, 0.21110887527877736, nan, 0.10194659971462595, 0.16220078736145335,
0.10715011826593573, 0.14586402775804144, nan, 0.14791927819598907, 0.10440703559251757, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.083667845059236384,
0.18781492372378156, nan, nan, 0.12371322757292466, nan, 0.13140207639201398, nan,
0.22109264888775484, nan, 0.10510009208697768, 0.16760580418105031, nan, 0.17147806848315128,
0.2111499894771349, nan, 0.20329941657371378, 0.1035162408857722, 0.20029898220637524,
0.10037992328414654, nan, 0.080921464434161372, nan, 0.10356516011798893, 0.13948010786050802, nan,
nan, nan, nan, 0.22549608867859672]
C_modes_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, 0.9414893617021276, 1.1436170212765957, 1.1117021276595744,
0.92021276595744683, nan, 1.1063829787234043, 1.0425531914893618, 1.1595744680851063,
1.175531914893617, 1.1382978723404253, 1.1436170212765957, 1.0957446808510638, 1.2074468085106382,
1.1436170212765957, 1.25, nan, 1.1542553191489362, nan, 1.1861702127659575, nan, 1.2287234042553192,
1.2074468085106382, 1.1861702127659575, 1.2180851063829785, 1.1329787234042552, 1.0372340425531914,
nan, 1.1170212765957446, nan, nan, nan, nan, 1.2925531914893618, 1.2712765957446808,
1.2446808510638296, 1.3989361702127658, nan, 0.98936170212765961, 0.95744680851063824,
1.1808510638297871, 1.0904255319148937, 1.2393617021276595, 1.1117021276595744, 1.1117021276595744,
nan, nan, 1.1861702127659575, nan, nan, nan, nan, 1.1861702127659575, nan, nan, 1.1489361702127661,
1.1382978723404253, nan, nan, 1.0053191489361701, 0.9627659574468086, 1.0, nan, nan,
1.2234042553191489, nan, nan, 1.1329787234042552, 1.0638297872340425, 0.85106382978723405,
0.93617021276595747, 1.0797872340425532, nan, nan, nan, 1.2340425531914894, 1.553191489361702, nan,
1.303191489361702, 1.5159574468085106, 1.0691489361702129, 1.0, 1.2180851063829785, 1.0585106382978722,
0.92021276595744683, 1.1276595744680851, 1.1702127659574468, 1.2074468085106382, 1.0478723404255319,
1.0691489361702129, 1.1276595744680851, nan, nan, 1.3351063829787235, 1.0797872340425532,
1.0106382978723405, nan, nan, nan, nan, 1.0691489361702129, 1.1010638297872339, nan, nan,
1.0904255319148937, 1.1808510638297871, 1.1329787234042552, 1.2978723404255319, 0.98936170212765961,
0.91489361702127647, 0.87234042553191482, 1.0265957446808511, 0.93085106382978722, nan,
0.99468085106382975, 1.1223404255319149, 1.2021276595744681, 1.2127659574468086, nan,
1.2606382978723403, 1.1968085106382977, 1.2287234042553192, 1.3882978723404256, nan, nan, nan, 1.0,
0.96808510638297862, 1.0904255319148937, 0.94680851063829785, 1.0106382978723405, 1.0638297872340425,
0.97872340425531901, nan, 1.1648936170212765, 1.1276595744680851, 1.0851063829787233,
1.1595744680851063, 1.1436170212765957, 1.0797872340425532, 1.0851063829787233, 1.0265957446808511,
0.87234042553191482, 1.0797872340425532, nan, 1.1329787234042552, 0.94680851063829785, nan, nan,
0.98404255319148937, nan, 1.0691489361702129, 1.1276595744680851, 1.1382978723404253, nan, nan,
1.0212765957446808, 1.0585106382978722, 1.1223404255319149, nan, 1.1276595744680851,
1.0691489361702129, 0.99468085106382975, 1.1117021276595744, 1.1595744680851063, 0.98404255319148937,
1.0265957446808511, nan, nan, 1.0, nan, 1.053191489361702, nan, nan, nan, 1.1808510638297871, nan, nan,
0.90425531914893609, 0.93617021276595747, 1.0691489361702129, 1.1648936170212765, nan, nan,
1.2021276595744681, 1.2127659574468086, 1.1436170212765957, 1.2287234042553192, 1.1595744680851063,
1.3191489361702127, 1.2021276595744681, nan, nan, 1.2021276595744681, 1.1063829787234043,
1.2234042553191489, 1.0, nan, 0.93085106382978722, 1.0691489361702129, 1.1382978723404253,
1.1861702127659575, 1.0691489361702129, 1.1063829787234043, 1.1117021276595744, nan, nan, 1.25,
1.1914893617021276, 1.1117021276595744, 1.1489361702127661, 1.1063829787234043, nan,
1.0904255319148937, 1.303191489361702, 1.1914893617021276, 1.2127659574468086, 1.2446808510638296,
1.0957446808510638, 1.0744680851063828, 1.053191489361702, nan, 1.0851063829787233,
0.97872340425531901, 1.1382978723404253, 1.1436170212765957, nan, nan, nan, 1.1063829787234043, nan,
nan, nan, 1.0797872340425532, 1.2127659574468086, 1.1595744680851063, nan, 0.88297872340425532,
0.97340425531914898, 0.9627659574468086, 0.93085106382978722, 1.0212765957446808, 1.0372340425531914,
1.0478723404255319, 1.0319148936170213, nan, nan, 1.0585106382978722, 1.0372340425531914,
0.9627659574468086, 1.1063829787234043, 1.0319148936170213, 1.0957446808510638, 1.0904255319148937,
1.1489361702127661, 1.1329787234042552, 1.0904255319148937, nan, nan, nan, nan, nan,
1.0106382978723405, 1.1329787234042552, nan, nan, nan, nan, nan, nan, 1.2712765957446808,
1.2074468085106382, 1.1170212765957446, nan, nan, 1.1808510638297871, nan, 1.25, 1.1382978723404253,
1.0265957446808511, 1.0265957446808511, 1.1223404255319149, 1.0106382978723405, 1.0265957446808511,
1.0106382978723405, 1.0691489361702129, nan, 1.0585106382978722, 1.0744680851063828,
1.0691489361702129, nan, 1.4468085106382977, nan, 1.1276595744680851, 1.0957446808510638,
1.2074468085106382, 1.404255319148936, nan, 1.6276595744680851, 1.175531914893617, 1.1223404255319149,
1.1170212765957446, 1.1276595744680851, 1.1808510638297871, 1.1702127659574468, 1.2074468085106382,
1.1542553191489362, 1.1276595744680851, 1.0585106382978722, 0.93617021276595747, 1.0957446808510638,
1.0904255319148937, 1.1382978723404253, 1.0957446808510638, nan, 0.97872340425531901,
0.97340425531914898, 1.1861702127659575, 1.1648936170212765, 1.2074468085106382, 1.0425531914893618,
1.1489361702127661, 1.0638297872340425, 1.0, 1.1329787234042552, 1.1489361702127661,
1.0957446808510638, nan, 1.053191489361702, 1.1329787234042552, 1.0106382978723405, nan,
1.1489361702127661, 1.175531914893617, 1.175531914893617, nan, 0.98404255319148937,
0.95744680851063824, 1.0638297872340425, 1.2872340425531914, 1.1542553191489362, nan,
1.1276595744680851, 1.1595744680851063, 1.0265957446808511, 1.1542553191489362, nan,
1.1436170212765957, 1.1436170212765957, nan, nan, 1.1808510638297871, 1.0957446808510638,
1.3617021276595744, nan, 1.1489361702127661, 1.0372340425531914, nan, nan, 1.1808510638297871,
1.1329787234042552, 1.0744680851063828, 1.0797872340425532, 1.0638297872340425, 1.1010638297872339,
0.99468085106382975, 1.0478723404255319, nan, 0.99468085106382975, 1.0797872340425532,
1.1117021276595744, 1.0159574468085106, 1.1170212765957446, nan, 0.9627659574468086, nan, nan,
1.3191489361702127, 0.9627659574468086, 0.89893617021276584, 1.0319148936170213, 1.0425531914893618,
0.98404255319148937, nan, 1.1436170212765957, 1.1595744680851063, 1.1436170212765957,
1.1702127659574468, 1.2074468085106382, nan, nan, 1.2287234042553192, nan, nan, 1.0797872340425532,
1.0106382978723405, nan, nan, nan, 1.0904255319148937, 1.1702127659574468, 1.1595744680851063,
1.2074468085106382, 1.3138297872340425, 1.1595744680851063, 1.2712765957446808, 1.2234042553191489,
nan, nan, 1.1968085106382977, 1.3085106382978724, 1.1489361702127661, 1.0744680851063828,
1.2819148936170213, 1.2021276595744681, 1.1914893617021276, 1.2393617021276595, 1.2872340425531914,
nan, nan, nan, nan, nan, nan, nan, nan, 1.1382978723404253, 1.2074468085106382, nan, nan,
1.4787234042553192, nan, 1.3510638297872339, nan, nan, nan, nan, 1.0159574468085106,
0.98404255319148937, nan, 1.3989361702127658, 0.98936170212765961, 1.0106382978723405,
1.0904255319148937, nan, 0.91489361702127647, 1.0265957446808511, 0.93617021276595747,
0.98404255319148937, nan, nan, 0.95212765957446799, 1.1010638297872339, 1.0, 1.0053191489361701,
1.0106382978723405, 1.0638297872340425, 1.1436170212765957, nan, nan, nan, 1.0106382978723405,
0.95744680851063824, 1.1595744680851063, 1.1010638297872339, 0.90957446808510645, 0.99468085106382975,
1.0372340425531914, nan, nan, nan, 1.1063829787234043, 1.2393617021276595, nan, nan,
1.1489361702127661, nan, nan, 0.91489361702127647, nan, 1.0797872340425532, nan, nan, nan, nan, nan,
nan, 1.2446808510638296, 1.5691489361702127, 1.1595744680851063, 1.3138297872340425,
1.2180851063829785, 0.91489361702127647, 0.91489361702127647, nan, nan, 1.1276595744680851,
1.0319148936170213, 1.1170212765957446, 1.1170212765957446, 1.0319148936170213, 1.1276595744680851,
1.1170212765957446, 1.0425531914893618, 1.053191489361702, nan, nan, 1.1702127659574468,
1.0957446808510638, 1.0106382978723405, nan, 1.0638297872340425, 1.0797872340425532,
1.2553191489361701, nan, nan, nan, nan, 1.1861702127659575, nan, 1.1223404255319149,
1.0478723404255319, 1.0691489361702129, 1.1329787234042552, 0.98936170212765961, 1.0797872340425532,
1.1329787234042552, 1.0372340425531914, 1.0851063829787233, 1.0053191489361701, 1.0478723404255319,
1.0638297872340425, 1.0585106382978722, 0.96808510638297862, 0.99468085106382975, 1.2021276595744681,
0.95212765957446799, 1.1382978723404253, 1.0425531914893618, 1.1170212765957446, nan,
0.93085106382978722, 0.99468085106382975, 1.1489361702127661, 1.2127659574468086, 1.053191489361702,
nan, 1.0744680851063828, 0.95212765957446799, 1.1117021276595744, 1.1223404255319149,
1.0797872340425532, 1.0159574468085106, 1.0372340425531914, 1.0159574468085106, 1.1436170212765957,
1.1276595744680851, nan, nan, nan, 1.1542553191489362, 1.2872340425531914, nan, 1.1329787234042552,
1.0957446808510638, 1.0904255319148937, 1.1808510638297871, 1.3670212765957446, 1.5053191489361701,
1.6702127659574466, 1.4574468085106382, nan, 1.5904255319148934, 1.6276595744680851,
1.4680851063829787, 1.2819148936170213, nan, 1.4521276595744681, 1.2978723404255319, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.4574468085106382, 1.3723404255319149,
nan, nan, 2.0744680851063828, nan, 1.6170212765957446, nan, 1.574468085106383, nan, 1.4095744680851063,
1.3936170212765957, nan, 1.7021276595744681, 1.6223404255319149, nan, 1.8723404255319149,
1.9095744680851063, 1.5691489361702127, 1.7606382978723405, nan, 1.6808510638297873, nan,
1.6648936170212765, 1.7925531914893618, nan, nan, nan, nan, 1.7074468085106382]
profile_total = [54, 192, 41, 185, 92, 31, 22, 214, 462, 140, 74, 293, 226, 15, 467, 122, 306, 371, 757, 623, 547,
706, 107, 0, 0, 79, 388, 296, 157, 716, 701, 50, 0, 233, 492, 0, 150, 477, 62, 1023, 1162, 112, 143,
119, 195, 0, 0, 262, 1070, 896, 444, 59, 16, 149, 6, 79, 86, 36, 875, 544, 1037, 453, 297, 0, 11,
1000, 528, 0, 2439, 1747, 0, 0, 0, 213, 0, 757, 1446, 0, 434, 75, 137, 433, 138, 542, 927, 149, 0,
0, 0, 0, 0, 0, 0, 1980, 905, 376, 297, 15, 605, 1311, 157, 173, 0, 0, 0, 583, 185, 290, 58, 39,
1077, 158, 1349, 25, 195, 843, 94, 101, 22, 196, 951, 33, 0, 0, 760, 1149, 1114, 233, 184, 705, 318,
684, 73, 113, 0, 0, 0, 0, 307, 198, 368, 451, 466, 98, 278, 285, 1742, 1055, 2557, 408, 21, 269,
1060, 432, 224, 0, 186, 13, 612, 230, 145, 6, 0, 768, 2643, 2237, 1172, 1240, 886, 797, 24, 48, 43,
367, 333, 59, 409, 456, 511, 221, 35, 7, 0, 345, 697, 630, 133, 206, 267, 1566, 1682, 161, 603,
1149, 1248, 211, 394, 7, 47, 287, 688, 95, 155, 0, 24, 175, 472, 663, 967, 486, 55, 351, 503, 104,
41, 184, 583, 71, 89, 196, 313, 59, 79, 205, 294, 1174, 1414, 1216, 1111, 192, 245, 351, 690, 222,
172, 45, 191, 324, 463, 200, 1394, 257, 621, 673, 5, 5, 106, 0, 383, 0, 0, 37, 0, 40, 158, 153, 250,
0, 127, 0, 0, 552, 524, 0, 0, 76, 2062, 3181, 3257, 825, 14, 105, 946, 390, 528, 183, 11, 795, 152,
74, 12, 107, 115, 137, 0, 738, 742, 371, 127, 494, 222, 1343, 77, 0, 941, 1683, 2950, 3629, 1328,
99, 49, 6, 0, 183, 65, 133, 723, 110, 111, 84, 208, 55, 477, 850, 834, 2156, 2794, 391, 291, 2701,
1728, 1144, 26, 1322, 0, 603, 1183, 91, 746, 4941, 1956, 608, 271, 5, 376, 52, 10, 16, 43, 472,
1175, 675, 2927, 0, 156, 41, 1817, 1147, 298, 443, 799, 695, 0, 158, 1441, 271, 785, 2316, 4251,
3115, 65, 115, 923, 519, 52, 516, 444, 829, 41, 64, 448, 31, 5, 61, 202, 90, 1844, 1626, 0, 0, 0,
90, 71, 0, 663, 617, 796, 1527, 1192, 319, 172, 372, 95, 492, 1048, 2543, 1382, 0, 0, 374, 637,
1668, 0, 0, 0, 27, 380, 2268, 0, 0, 669, 2304, 463, 441, 1979, 195, 156, 161, 75, 20, 770, 408, 231,
841, 74, 1012, 712, 551, 156, 61, 31, 9, 86, 652, 1413, 168, 199, 294, 198, 48, 1397, 1551, 1713,
1166, 133, 150, 1066, 135, 384, 959, 116, 674, 197, 0, 21, 89, 29, 152, 1341, 1449, 0, 0, 2080,
1811, 887, 21, 925, 579, 56, 925, 1746, 691, 2200, 56, 53, 602, 50, 307, 0, 0, 0, 183, 0, 13, 70,
76, 478, 1095, 50, 0, 152, 1105, 2119, 2490, 1558, 1061, 731, 9, 213, 2324, 106, 637, 381, 10, 340,
686, 1893, 1067, 844, 484, 84, 699, 0, 896, 121, 362, 1145, 677, 89, 342, 2830, 272, 1376, 593, 724,
442, 795, 64, 41, 325, 2043, 1359, 0, 171, 446, 350, 142, 109, 64, 556, 1333, 838, 717, 580, 1151,
3375, 986, 116, 1167, 4783, 1059, 0, 0, 890, 228, 442, 583, 263, 470, 837, 959, 578, 848, 304, 826,
535, 356, 5, 224, 781, 6, 0, 5, 12, 0, 0, 476, 525, 1908, 1382, 89, 277, 0, 77, 555, 603, 1086, 250,
538, 2331, 2063, 1117, 20, 2828, 1061, 1415, 15, 2600, 95, 118, 547, 311, 78, 0, 181, 551, 1030,
1212, 2774, 282, 1453, 436, 4279, 1925, 777, 61, 458, 1489, 485, 460, 6, 279, 167, 356, 1292, 85,
97, 647, 1054, 645, 120, 47, 405, 1875, 895, 1277, 579, 0, 885, 1190, 71, 23, 146, 380, 1047, 478,
2531, 19, 1333, 249, 769, 799, 0, 106, 570, 13, 23, 232, 80, 204, 0, 80, 470, 14, 353, 862, 321,
217, 1061, 1416, 253, 262, 320, 29, 1411, 934, 338, 698, 428, 31, 840, 594, 0, 749, 1217, 629, 407,
695, 380, 5, 882, 166, 1184, 68, 2006, 533, 7, 280, 750, 1526, 969, 174, 0, 149, 1740, 371, 2542,
394, 106, 520, 358, 875, 657, 269, 659, 1949, 739, 1806, 85, 1049, 2078, 631, 1850, 140, 649, 94,
578, 433, 163, 440, 1123, 286, 660, 247, 0, 0, 627, 20, 2306, 825, 0, 2863, 3427, 2618, 2384, 5,
143, 100, 131, 746, 5, 346, 650, 275, 164, 89, 0, 292, 921, 691, 488, 1076, 730, 807, 0, 62, 41,
150, 155, 532, 1652, 352, 2719, 2075, 0, 0, 25, 287, 767, 1166, 72, 389, 627, 97, 102, 1074, 915,
33, 0, 0, 0, 5, 0, 229, 410, 419, 81, 1307, 594, 49, 50, 234, 738, 2742, 571, 2079, 856, 499, 1001,
2583, 1149, 0, 35, 1790, 3171, 1703, 315, 3426, 2971, 2095, 52, 0, 0, 69, 1234, 21, 294, 761, 1662,
916, 549, 1297, 936, 359, 506, 161, 1799, 2598, 790, 537, 476, 849, 678, 569, 1187, 1602, 684, 177,
349, 2115, 1666, 232, 92, 916, 823, 2160, 514, 859, 529, 819, 2540, 2936, 2061, 0, 0, 0, 616, 353,
0, 1938, 1286, 828, 1752, 1206, 1540, 772, 976, 131, 598, 523, 422, 98, 68, 1000, 270, 0, 289, 471,
183, 1564, 2779, 50, 48, 34, 0, 436, 252, 1474, 860, 0, 0, 0, 94, 32, 999, 895, 11, 759, 21, 1852,
382, 854, 1672, 2946, 2326, 143, 986, 378, 0, 171, 0, 0, 33, 600, 3241, 1591, 521, 2674, 56, 628,
201, 394, 294, 35, 1766, 787, 1002, 58, 668, 1092, 5, 176, 84, 756, 920, 676, 829, 343, 1201, 964,
197, 129, 151, 156, 99, 164, 3521, 1652, 864, 933, 436, 102, 175, 585, 1364, 3374, 2001, 66, 0, 643,
0, 479, 1754, 759, 546, 182, 703, 700, 335, 707, 875, 220, 1088, 1183, 319, 331, 93, 74, 1668, 169,
260, 1815, 634, 0, 158, 1191, 13, 0, 0, 794, 4194, 1685, 0, 10, 337, 17, 1280, 58, 714, 5, 1347,
1033, 0, 1275, 281, 61, 1058, 189, 442, 1389, 31, 380, 0, 1437, 92, 0, 0, 0, 0, 1847]
peak_total = [12.0, 36.0, 6.0, 22.0, 17.0, 7.0, 5.0, 38.0, 81.0, 20.0, 14.0, 37.0, 36.0, 2.0, 78.0, 26.0, 53.0, 53.0,
85.0, 109.0, 81.0, 172.0, 20.0, 0, 0, 12.0, 63.0, 53.0, 24.0, 58.0, 68.0, 12.0, 0, 35.0, 82.0, 0, 28.0,
71.0, 17.0, 244.0, 174.0, 13.0, 26.0, 18.0, 30.0, 0, 0, 40.0, 162.0, 110.0, 59.0, 8.0, 8.0, 33.0, 2.0,
12.0, 43.0, 6.0, 84.0, 78.0, 167.0, 49.0, 31.0, 0, 5.0, 145.0, 52.0, 0, 268.0, 396.0, 0, 0, 0, 29.0, 0,
76.0, 163.0, 0, 60.0, 6.0, 16.0, 57.0, 22.0, 49.0, 103.0, 17.0, 0, 0, 0, 0, 0, 0, 0, 177.0, 93.0, 45.0,
33.0, 4.0, 67.0, 176.0, 23.0, 27.0, 0, 0, 0, 74.0, 30.0, 28.0, 6.0, 9.0, 121.0, 15.0, 126.0, 4.0, 24.0,
72.0, 8.0, 12.0, 3.0, 24.0, 114.0, 5.0, 0, 0, 75.0, 158.0, 202.0, 50.0, 33.0, 114.0, 39.0, 79.0, 8.0,
16.0, 0, 0, 0, 0, 29.0, 21.0, 36.0, 59.0, 59.0, 12.0, 40.0, 29.0, 187.0, 107.0, 219.0, 41.0, 5.0, 24.0,
149.0, 45.0, 38.0, 0, 21.0, 4.0, 44.0, 32.0, 13.0, 3.0, 0, 93.0, 538.0, 355.0, 276.0, 207.0, 72.0,
84.0, 3.0, 8.0, 9.0, 35.0, 42.0, 16.0, 38.0, 43.0, 49.0, 19.0, 4.0, 3.0, 0, 64.0, 71.0, 85.0, 20.0,
25.0, 36.0, 178.0, 167.0, 15.0, 56.0, 179.0, 167.0, 27.0, 37.0, 2.0, 5.0, 29.0, 54.0, 11.0, 18.0, 0,
4.0, 16.0, 31.0, 62.0, 83.0, 36.0, 9.0, 36.0, 56.0, 21.0, 8.0, 19.0, 57.0, 8.0, 10.0, 25.0, 25.0, 10.0,
13.0, 21.0, 28.0, 135.0, 206.0, 248.0, 172.0, 21.0, 23.0, 38.0, 73.0, 34.0, 18.0, 13.0, 42.0, 30.0,
51.0, 25.0, 183.0, 29.0, 55.0, 66.0, 1.0, 1.0, 12.0, 0, 30.0, 0, 0, 7.0, 0, 4.0, 20.0, 16.0, 27.0, 0,
22.0, 0, 0, 61.0, 92.0, 0, 0, 9.0, 266.0, 504.0, 355.0, 104.0, 2.0, 20.0, 137.0, 44.0, 56.0, 21.0, 6.0,
117.0, 19.0, 12.0, 4.0, 15.0, 21.0, 20.0, 0, 97.0, 71.0, 39.0, 29.0, 73.0, 40.0, 326.0, 13.0, 0, 144.0,
193.0, 497.0, 599.0, 259.0, 13.0, 14.0, 3.0, 0, 28.0, 10.0, 19.0, 107.0, 23.0, 32.0, 15.0, 30.0, 10.0,
85.0, 101.0, 116.0, 325.0, 544.0, 82.0, 86.0, 613.0, 306.0, 187.0, 8.0, 154.0, 0, 86.0, 180.0, 11.0,
89.0, 715.0, 354.0, 101.0, 70.0, 3.0, 64.0, 9.0, 2.0, 4.0, 6.0, 53.0, 156.0, 109.0, 241.0, 0, 21.0,
11.0, 403.0, 159.0, 40.0, 59.0, 161.0, 125.0, 0, 26.0, 157.0, 39.0, 136.0, 311.0, 385.0, 258.0, 11.0,
24.0, 124.0, 62.0, 9.0, 84.0, 48.0, 139.0, 6.0, 10.0, 67.0, 7.0, 2.0, 7.0, 27.0, 19.0, 282.0, 339.0, 0,
0, 0, 15.0, 10.0, 0, 69.0, 67.0, 118.0, 152.0, 112.0, 40.0, 26.0, 110.0, 16.0, 82.0, 106.0, 266.0,
137.0, 0, 0, 57.0, 94.0, 166.0, 0, 0, 0, 7.0, 56.0, 283.0, 0, 0, 78.0, 269.0, 51.0, 35.0, 219.0, 24.0,
22.0, 20.0, 14.0, 4.0, 148.0, 68.0, 34.0, 119.0, 9.0, 163.0, 82.0, 72.0, 16.0, 7.0, 7.0, 1.0, 13.0,
79.0, 176.0, 39.0, 29.0, 33.0, 34.0, 8.0, 103.0, 158.0, 118.0, 134.0, 19.0, 26.0, 181.0, 22.0, 31.0,
130.0, 22.0, 90.0, 33.0, 0, 3.0, 12.0, 7.0, 18.0, 197.0, 262.0, 0, 0, 235.0, 212.0, 147.0, 3.0, 81.0,
65.0, 13.0, 97.0, 278.0, 83.0, 264.0, 9.0, 10.0, 77.0, 10.0, 32.0, 0, 0, 0, 16.0, 0, 2.0, 13.0, 14.0,
65.0, 193.0, 7.0, 0, 18.0, 133.0, 307.0, 362.0, 212.0, 72.0, 71.0, 3.0, 19.0, 223.0, 11.0, 46.0, 41.0,
4.0, 39.0, 90.0, 364.0, 153.0, 78.0, 49.0, 11.0, 63.0, 0, 105.0, 29.0, 65.0, 157.0, 65.0, 13.0, 39.0,
392.0, 35.0, 122.0, 140.0, 72.0, 33.0, 107.0, 9.0, 10.0, 29.0, 335.0, 215.0, 0, 21.0, 52.0, 47.0, 25.0,
16.0, 9.0, 49.0, 116.0, 143.0, 86.0, 49.0, 165.0, 432.0, 104.0, 19.0, 145.0, 622.0, 198.0, 0, 0, 85.0,
25.0, 59.0, 64.0, 45.0, 59.0, 88.0, 119.0, 82.0, 105.0, 37.0, 122.0, 65.0, 46.0, 2.0, 27.0, 131.0, 2.0,
0, 2.0, 3.0, 0, 0, 65.0, 49.0, 250.0, 183.0, 11.0, 23.0, 0, 22.0, 83.0, 131.0, 150.0, 35.0, 106.0,
326.0, 343.0, 206.0, 4.0, 573.0, 219.0, 272.0, 4.0, 208.0, 13.0, 17.0, 106.0, 40.0, 12.0, 0, 22.0,
77.0, 80.0, 130.0, 510.0, 60.0, 197.0, 123.0, 638.0, 313.0, 145.0, 14.0, 113.0, 321.0, 133.0, 105.0,
3.0, 71.0, 34.0, 98.0, 188.0, 19.0, 19.0, 73.0, 190.0, 107.0, 29.0, 13.0, 101.0, 557.0, 286.0, 217.0,
141.0, 0, 139.0, 163.0, 17.0, 5.0, 26.0, 52.0, 172.0, 80.0, 239.0, 6.0, 225.0, 64.0, 170.0, 118.0, 0,
16.0, 130.0, 3.0, 10.0, 36.0, 19.0, 30.0, 0, 20.0, 55.0, 4.0, 56.0, 127.0, 62.0, 38.0, 158.0, 221.0,
46.0, 50.0, 65.0, 7.0, 208.0, 172.0, 99.0, 114.0, 70.0, 10.0, 110.0, 78.0, 0, 106.0, 140.0, 76.0, 66.0,
107.0, 68.0, 1.0, 210.0, 36.0, 273.0, 12.0, 514.0, 170.0, 2.0, 43.0, 73.0, 291.0, 180.0, 37.0, 0, 30.0,
245.0, 49.0, 282.0, 54.0, 15.0, 70.0, 51.0, 131.0, 85.0, 43.0, 158.0, 385.0, 146.0, 426.0, 13.0, 82.0,
272.0, 85.0, 309.0, 26.0, 132.0, 13.0, 103.0, 121.0, 26.0, 48.0, 189.0, 40.0, 61.0, 26.0, 0, 0, 83.0,
7.0, 148.0, 60.0, 0, 478.0, 442.0, 461.0, 532.0, 1.0, 16.0, 12.0, 20.0, 143.0, 2.0, 34.0, 118.0, 45.0,
17.0, 13.0, 0, 22.0, 108.0, 49.0, 81.0, 173.0, 77.0, 93.0, 0, 6.0, 6.0, 18.0, 20.0, 57.0, 177.0, 35.0,
418.0, 354.0, 0, 0, 5.0, 27.0, 58.0, 151.0, 12.0, 36.0, 73.0, 17.0, 14.0, 205.0, 157.0, 7.0, 0, 0, 0,
2.0, 0, 26.0, 37.0, 32.0, 12.0, 162.0, 69.0, 12.0, 7.0, 34.0, 77.0, 269.0, 89.0, 350.0, 91.0, 54.0,
169.0, 319.0, 221.0, 0, 5.0, 217.0, 368.0, 308.0, 76.0, 736.0, 340.0, 147.0, 11.0, 0, 0, 5.0, 147.0,
2.0, 35.0, 81.0, 213.0, 161.0, 67.0, 129.0, 85.0, 48.0, 54.0, 20.0, 244.0, 314.0, 76.0, 64.0, 42.0,
83.0, 77.0, 113.0, 170.0, 206.0, 134.0, 27.0, 41.0, 262.0, 198.0, 32.0, 7.0, 132.0, 67.0, 399.0, 83.0,
97.0, 68.0, 95.0, 409.0, 452.0, 294.0, 0, 0, 0, 49.0, 40.0, 0, 271.0, 123.0, 78.0, 188.0, 76.0, 141.0,
59.0, 61.0, 12.0, 73.0, 48.0, 53.0, 13.0, 7.0, 113.0, 26.0, 0, 21.0, 33.0, 22.0, 121.0, 273.0, 6.0,
6.0, 4.0, 0, 35.0, 61.0, 219.0, 97.0, 0, 0, 0, 7.0, 5.0, 58.0, 77.0, 1.0, 55.0, 3.0, 181.0, 50.0, 77.0,
119.0, 230.0, 246.0, 9.0, 136.0, 50.0, 0, 16.0, 0, 0, 4.0, 34.0, 395.0, 113.0, 40.0, 194.0, 10.0, 61.0,
20.0, 67.0, 15.0, 6.0, 164.0, 109.0, 94.0, 8.0, 83.0, 100.0, 2.0, 19.0, 10.0, 114.0, 113.0, 73.0, 99.0,
34.0, 118.0, 75.0, 17.0, 16.0, 13.0, 17.0, 10.0, 24.0, 695.0, 173.0, 82.0, 98.0, 43.0, 24.0, 22.0,
39.0, 235.0, 305.0, 195.0, 6.0, 0, 59.0, 0, 57.0, 350.0, 99.0, 69.0, 21.0, 68.0, 84.0, 57.0, 96.0,
96.0, 28.0, 135.0, 146.0, 52.0, 47.0, 9.0, 10.0, 141.0, 27.0, 78.0, 306.0, 138.0, 0, 16.0, 273.0, 4.0,
0, 0, 109.0, 480.0, 273.0, 0, 2.0, 34.0, 3.0, 150.0, 8.0, 67.0, 2.0, 177.0, 88.0, 0, 108.0, 19.0, 7.0,
71.0, 29.0, 33.0, 193.0, 5.0, 47.0, 0, 207.0, 14.0, 0, 0, 0, 0, 143.0]
class B_RGS():
Dates = ['2011/01/01', '2011/01/02', '2011/01/03', '2011/01/04', '2011/01/05', '2011/01/06', '2011/01/07',
'2011/01/08', '2011/01/09', '2011/01/10', '2011/01/11', '2011/01/12', '2011/01/13', '2011/01/14',
'2011/01/15', '2011/01/16', '2011/01/17', '2011/01/18', '2011/01/19', '2011/01/20', '2011/01/21',
'2011/01/22', '2011/01/23', '2011/01/24', '2011/01/25', '2011/01/26', '2011/01/27', '2011/01/28',
'2011/01/29', '2011/01/30', '2011/01/31', '2011/02/01', '2011/02/02', '2011/02/03', '2011/02/04',
'2011/02/05', '2011/02/06', '2011/02/07', '2011/02/08', '2011/02/09', '2011/02/10', '2011/02/11',
'2011/02/12', '2011/02/13', '2011/02/14', '2011/02/15', '2011/02/16', '2011/02/17', '2011/02/18',
'2011/02/19', '2011/02/20', '2011/02/21', '2011/02/22', '2011/02/23', '2011/02/24', '2011/02/25',
'2011/02/26', '2011/02/27', '2011/02/28', '2011/03/01', '2011/03/02', '2011/03/03', '2011/03/04',
'2011/03/05', '2011/03/06', '2011/03/07', '2011/03/08', '2011/03/09', '2011/03/10', '2011/03/11',
'2011/03/12', '2011/03/13', '2011/03/14', '2011/03/15', '2011/03/16', '2011/03/17', '2011/03/18',
'2011/03/19', '2011/03/20', '2011/03/21', '2011/03/22', '2011/03/23', '2011/03/24', '2011/03/25',
'2011/03/26', '2011/03/27', '2011/03/28', '2011/03/29', '2011/03/30', '2011/03/31', '2011/04/01',
'2011/04/02', '2011/04/03', '2011/04/04', '2011/04/05', '2011/04/06', '2011/04/07', '2011/04/08',
'2011/04/09', '2011/04/10', '2011/04/11', '2011/04/12', '2011/04/13', '2011/04/14', '2011/04/15',
'2011/04/16', '2011/04/17', '2011/04/18', '2011/04/19', '2011/04/20', '2011/04/21', '2011/04/22',
'2011/04/23', '2011/04/24', '2011/04/25', '2011/04/26', '2011/04/27', '2011/04/28', '2011/04/29',
'2011/04/30', '2011/05/01', '2011/05/02', '2011/05/03', '2011/05/04', '2011/05/05', '2011/05/06',
'2011/05/07', '2011/05/08', '2011/05/09', '2011/05/10', '2011/05/11', '2011/05/12', '2011/05/13',
'2011/05/14', '2011/05/15', '2011/05/16', '2011/05/17', '2011/05/18', '2011/05/19', '2011/05/20',
'2011/05/21', '2011/05/22', '2011/05/23', '2011/05/24', '2011/05/25', '2011/05/26', '2011/05/27',
'2011/05/28', '2011/05/29', '2011/05/30', '2011/05/31', '2011/06/01', '2011/06/02', '2011/06/03',
'2011/06/04', '2011/06/05', '2011/06/06', '2011/06/07', '2011/06/08', '2011/06/09', '2011/06/10',
'2011/06/11', '2011/06/12', '2011/06/13', '2011/06/14', '2011/06/15', '2011/06/16', '2011/06/17',
'2011/06/18', '2011/06/19', '2011/06/20', '2011/06/21', '2011/06/22', '2011/06/23', '2011/06/24',
'2011/06/25', '2011/06/26', '2011/06/27', '2011/06/28', '2011/06/29', '2011/06/30', '2011/07/01',
'2011/07/02', '2011/07/03', '2011/07/04', '2011/07/05', '2011/07/06', '2011/07/07', '2011/07/08',
'2011/07/09', '2011/07/10', '2011/07/11', '2011/07/12', '2011/07/13', '2011/07/14', '2011/07/15',
'2011/07/16', '2011/07/17', '2011/07/18', '2011/07/19', '2011/07/20', '2011/07/21', '2011/07/22',
'2011/07/23', '2011/07/24', '2011/07/25', '2011/07/26', '2011/07/27', '2011/07/28', '2011/07/29',
'2011/07/30', '2011/07/31', '2011/08/01', '2011/08/02', '2011/08/03', '2011/08/04', '2011/08/05',
'2011/08/06', '2011/08/07', '2011/08/08', '2011/08/09', '2011/08/10', '2011/08/11', '2011/08/12',
'2011/08/13', '2011/08/14', '2011/08/15', '2011/08/16', '2011/08/17', '2011/08/18', '2011/08/19',
'2011/08/20', '2011/08/21', '2011/08/22', '2011/08/23', '2011/08/24', '2011/08/25', '2011/08/26',
'2011/08/27', '2011/08/28', '2011/08/29', '2011/08/30', '2011/08/31', '2011/09/01', '2011/09/02',
'2011/09/03', '2011/09/04', '2011/09/05', '2011/09/06', '2011/09/07', '2011/09/08', '2011/09/09',
'2011/09/10', '2011/09/11', '2011/09/12', '2011/09/13', '2011/09/14', '2011/09/15', '2011/09/19',
'2011/09/20', '2011/09/21', '2011/09/22', '2011/09/23', '2011/09/24', '2011/09/25', '2011/09/26',
'2011/09/27', '2011/09/28', '2011/09/29', '2011/09/30', '2011/10/01', '2011/10/02', '2011/10/03',
'2011/10/04', '2011/10/05', '2011/10/06', '2011/10/07', '2011/10/08', '2011/10/09', '2011/10/10',
'2011/10/11', '2011/10/12', '2011/10/13', '2011/10/14', '2011/10/19', '2011/10/20', '2011/10/21',
'2011/10/22', '2011/10/23', '2011/10/24', '2011/10/25', '2011/10/26', '2011/10/27', '2011/10/28',
'2011/10/29', '2011/10/30', '2011/10/31', '2011/11/01', '2011/11/02', '2011/11/03', '2011/11/04',
'2011/11/05', '2011/11/06', '2011/11/07', '2011/11/08', '2011/11/09', '2011/11/10', '2011/11/11',
'2011/11/12', '2011/11/13', '2011/11/14', '2011/11/15', '2011/11/16', '2011/11/17', '2011/11/18',
'2011/11/19', '2011/11/20', '2011/11/21', '2011/11/22', '2011/11/23', '2011/11/24', '2011/11/25',
'2011/11/26', '2011/11/27', '2011/11/28', '2011/11/29', '2011/11/30', '2011/12/01', '2011/12/02',
'2011/12/03', '2011/12/04', '2011/12/05', '2011/12/06', '2011/12/07', '2011/12/08', '2011/12/09',
'2011/12/10', '2011/12/11', '2011/12/12', '2011/12/13', '2011/12/14', '2011/12/15', '2011/12/16',
'2011/12/17', '2011/12/18', '2011/12/19', '2011/12/20', '2011/12/21', '2011/12/22', '2011/12/23',
'2011/12/24', '2011/12/25', '2011/12/26', '2011/12/27', '2011/12/28', '2011/12/29', '2011/12/30',
'2011/12/31', '2012/01/01', '2012/01/02', '2012/01/03', '2012/01/04', '2012/01/05', '2012/01/06',
'2012/01/07', '2012/01/08', '2012/01/09', '2012/01/10', '2012/01/11', '2012/01/12', '2012/01/13',
'2012/01/14', '2012/01/15', '2012/01/16', '2012/01/17', '2012/01/18', '2012/01/19', '2012/01/20',
'2012/01/21', '2012/01/22', '2012/01/23', '2012/01/24', '2012/01/25', '2012/01/26', '2012/01/27',
'2012/01/28', '2012/01/29', '2012/01/30', '2012/01/31', '2012/02/01', '2012/02/02', '2012/02/03',
'2012/02/04', '2012/02/05', '2012/02/06', '2012/02/07', '2012/02/08', '2012/02/09', '2012/02/10',
'2012/02/11', '2012/02/12', '2012/02/13', '2012/02/14', '2012/02/15', '2012/02/16', '2012/02/17',
'2012/02/18', '2012/02/19', '2012/02/20', '2012/02/21', '2012/02/22', '2012/02/23', '2012/02/24',
'2012/02/25', '2012/02/26', '2012/02/27', '2012/02/28', '2012/02/29', '2012/03/01', '2012/03/02',
'2012/03/03', '2012/03/04', '2012/03/05', '2012/03/06', '2012/03/07', '2012/03/08', '2012/03/09',
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'2015/05/24', '2015/05/25', '2015/05/26', '2015/05/27', '2015/05/28', '2015/05/29', '2015/05/30',
'2015/05/31', '2015/06/01', '2015/06/02', '2015/06/03', '2015/06/04', '2015/06/05', '2015/06/06',
'2015/06/07', '2015/06/08', '2015/06/09', '2015/06/10', '2015/06/11', '2015/06/12', '2015/06/13',
'2015/06/14', '2015/06/15', '2015/06/16', '2015/06/17', '2015/06/18', '2015/06/19', '2015/06/20',
'2015/06/21', '2015/06/22', '2015/06/23', '2015/06/24', '2015/06/25', '2015/06/26', '2015/06/27',
'2015/06/28', '2015/06/29', '2015/06/30', '2015/07/01', '2015/07/02', '2015/07/03', '2015/07/04',
'2015/07/05', '2015/07/06', '2015/07/07', '2015/07/08', '2015/07/09', '2015/07/10', '2015/07/11',
'2015/07/12', '2015/07/13', '2015/07/14', '2015/07/15', '2015/07/16', '2015/07/17', '2015/07/18',
'2015/07/19', '2015/07/20', '2015/07/21', '2015/07/22', '2015/07/23', '2015/07/24', '2015/07/25',
'2015/07/26', '2015/07/27', '2015/07/28', '2015/07/29', '2015/07/30', '2015/07/31', '2015/08/01',
'2015/08/02', '2015/08/03', '2015/08/04', '2015/08/05', '2015/08/06', '2015/08/07', '2015/08/08',
'2015/08/09', '2015/08/10', '2015/08/11', '2015/08/12', '2015/08/13', '2015/08/14', '2015/08/15',
'2015/08/16', '2015/08/17', '2015/08/18', '2015/08/19', '2015/08/20', '2015/08/21', '2015/08/22',
'2015/08/23', '2015/08/24', '2015/08/25', '2015/08/26', '2015/08/27', '2015/08/28', '2015/08/29',
'2015/08/30', '2015/08/31', '2015/09/01', '2015/09/02', '2015/09/03', '2015/09/04', '2015/09/05',
'2015/09/06', '2015/09/07', '2015/09/08', '2015/09/09', '2015/09/10', '2015/09/11', '2015/09/12',
'2015/09/13', '2015/09/14', '2015/09/15', '2015/09/16', '2015/09/17', '2015/09/18', '2015/09/19',
'2015/09/20', '2015/09/21', '2015/09/22', '2015/09/23', '2015/09/24', '2015/09/25', '2015/09/26',
'2015/09/27', '2015/09/28', '2015/09/29', '2015/09/30', '2015/10/01', '2015/10/02', '2015/10/05',
'2015/10/06', '2015/10/07', '2015/10/08', '2015/10/09', '2015/10/10', '2015/10/11', '2015/10/12',
'2015/10/13', '2015/10/14', '2015/10/15', '2015/10/16', '2015/10/17', '2015/10/18', '2015/10/19',
'2015/10/20', '2015/10/21', '2015/10/22', '2015/10/23', '2015/10/24', '2015/10/25', '2015/10/26',
'2015/10/27', '2015/10/28', '2015/10/29', '2015/10/30', '2015/10/31', '2015/11/01', '2015/11/02',
'2015/11/03', '2015/11/04', '2015/11/05', '2015/11/06', '2015/11/07', '2015/11/08', '2015/11/09',
'2015/11/10', '2015/11/11', '2015/11/12', '2015/11/13', '2015/11/14', '2015/11/15', '2015/11/16',
'2015/11/17', '2015/11/18', '2015/11/19', '2015/11/20', '2015/11/21', '2015/11/22', '2015/11/23',
'2015/11/24', '2015/11/25', '2015/11/26', '2015/11/27', '2015/11/28', '2015/11/29', '2015/11/30',
'2015/12/01', '2015/12/02', '2015/12/03', '2015/12/04', '2015/12/05', '2015/12/06', '2015/12/07',
'2015/12/08', '2015/12/09', '2015/12/10', '2015/12/11', '2015/12/12', '2015/12/13', '2015/12/14',
'2015/12/15', '2015/12/16', '2015/12/17', '2015/12/18', '2015/12/19', '2015/12/20', '2015/12/21',
'2015/12/22', '2015/12/23', '2015/12/24', '2015/12/25', '2015/12/26', '2015/12/27', '2015/12/28',
'2015/12/29', '2015/12/30', '2015/12/31', '2016/01/01', '2016/01/02', '2016/01/03', '2016/01/04',
'2016/01/07', '2016/01/08', '2016/01/09', '2016/01/10', '2016/01/11', '2016/01/12', '2016/01/13',
'2016/01/14', '2016/01/15', '2016/01/16', '2016/01/17', '2016/01/18', '2016/01/19', '2016/01/20',
'2016/01/21', '2016/01/22', '2016/01/23', '2016/01/24', '2016/01/25', '2016/01/26', '2016/01/27',
'2016/01/28', '2016/01/29', '2016/01/30', '2016/01/31', '2016/02/01', '2016/02/02', '2016/02/03',
'2016/02/04', '2016/02/05', '2016/02/06', '2016/02/07', '2016/02/08', '2016/02/09', '2016/02/10',
'2016/02/11', '2016/02/12', '2016/02/13', '2016/02/14', '2016/02/15', '2016/02/16', '2016/02/17',
'2016/02/18', '2016/02/19', '2016/02/20', '2016/02/21', '2016/02/22', '2016/02/23', '2016/02/24',
'2016/02/25', '2016/02/26', '2016/02/27', '2016/02/28', '2016/02/29', '2016/03/01', '2016/03/02',
'2016/03/03', '2016/03/04', '2016/03/05', '2016/03/06', '2016/03/07', '2016/03/08', '2016/03/09',
'2016/03/10', '2016/03/11', '2016/03/12', '2016/03/13', '2016/03/14', '2016/03/15', '2016/03/16',
'2016/03/17', '2016/03/18', '2016/03/19', '2016/03/20', '2016/03/21', '2016/03/22', '2016/03/23',
'2016/03/24', '2016/03/25', '2016/03/26', '2016/03/27', '2016/03/28', '2016/03/29', '2016/03/30',
'2016/03/31', '2016/04/01', '2016/04/02', '2016/04/03', '2016/04/04', '2016/04/05', '2016/04/06',
'2016/04/07', '2016/04/08', '2016/04/09', '2016/04/10', '2016/04/11', '2016/04/12', '2016/04/13',
'2016/04/14', '2016/04/15', '2016/04/16', '2016/04/17', '2016/04/18', '2016/04/19', '2016/04/20',
'2016/04/21', '2016/04/22', '2016/04/23', '2016/04/24', '2016/04/25', '2016/04/26', '2016/04/27',
'2016/04/28', '2016/04/29', '2016/04/30', '2016/05/01', '2016/05/02', '2016/05/03', '2016/05/04',
'2016/05/05', '2016/05/06', '2016/05/07', '2016/05/08', '2016/05/09', '2016/05/10', '2016/05/11',
'2016/05/12', '2016/05/13', '2016/05/14', '2016/05/15', '2016/05/16', '2016/05/17', '2016/05/18',
'2016/05/19', '2016/05/20', '2016/05/21', '2016/05/22', '2016/05/23', '2016/05/24', '2016/05/25',
'2016/05/26', '2016/05/27', '2016/05/28', '2016/05/29', '2016/05/30', '2016/05/31', '2016/06/01',
'2016/06/02', '2016/06/03', '2016/06/04', '2016/06/05', '2016/06/06', '2016/06/07', '2016/06/08',
'2016/06/09', '2016/06/10', '2016/06/11', '2016/06/12', '2016/06/13', '2016/06/14']
C_stdevs = [0.27400563763242414, 0.078565169119915679, 0.20041084755359595, 0.15668518976953533, 0.15597436064052103,
nan, 0.13589350598577005, 0.14522198881770104, nan, 0.11690620453602178, 0.12832499095430044,
0.10205248328041006, 0.10790896136413998, 0.11505237962118245, 0.061715049267738561, 0.10971295772751523,
0.16337789014715456, 0.14691232926565714, 0.071423760746673295, 0.12628788512192929, 0.14566734749728855,
0.13857675725810567, 0.10487927247660921, 0.17705434036800341, 0.18256614287340411, 0.19353941566856958,
0.13891357295950396, 0.14189749255894069, 0.24063007305910741, 0.14246342151953315, 0.19573014348791407,
0.311566999601255, 0.11122385108090854, nan, nan, nan, 0.14316540555789209, nan, 0.20271839173702461,
0.24320239823718126, 0.27374910675362729, 0.18824159838988669, nan, nan, nan, 0.15740964466663698,
0.15429088678488956, nan, 0.15628959462659922, 0.11407534484768916, nan, nan, 0.1914847192270466,
0.19073251232821795, 0.13999390710072529, 0.22506513440571838, 0.17043262463210565, 0.20863596071854709,
0.19054907735780208, 0.16571013605545093, 0.16103215895129164, 0.16957624661225396, 0.21090472034972124,
0.25036444547213332, 0.12435135661971729, nan, nan, 0.15511631495243214, nan, 0.19694252414584043,
0.17649951839456238, 0.41516156695746315, 0.15762414568689834, nan, nan, 0.20138281124570942,
0.23989973301553161, nan, nan, 0.22058493768811702, 0.19391186801041405, nan, nan, nan, nan, nan, nan,
nan, 0.39397620001525291, 0.12910330288542504, 0.17337506733208138, nan, 0.23845247492411586,
0.22306825192877608, 0.15506433594820065, 0.12584718520081092, 0.21110062313373293, 0.20564971064225351,
nan, nan, nan, nan, 0.15665540571392128, 0.11214367521640442, 0.14801264570170353, 0.13371145897006717,
nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.14957820936944868, nan, 0.11149845111805234,
0.13862909084320085, nan, nan, nan, nan, nan, nan, nan, nan, 0.25939636659117199, nan, nan,
0.22395594998329918, 0.099813335197524625, 0.15413798279070778, 0.17700266845616972, 0.17277569848984209,
0.10392163178901835, 0.13696179180698428, 0.17644363532905058, 0.20880675059127762, 0.1695737052583505,
nan, 0.14493327498347397, 0.062507943371580138, nan, nan, 0.14850666439360161, 0.10856872457285058,
0.14464321637368593, 0.17702238165153977, 0.20268876728159996, 0.16500831700399082, nan, nan, nan,
0.15894659290663371, 0.11964957460470919, nan, nan, nan, 0.19719360769418975, 0.20666695620376221,
0.18340301207941595, 0.26486622116542002, 0.12067211842011893, 0.20151238239700728, 0.25273764508982061,
0.180140756331227, 0.17589830489555261, 0.13769854077159266, 0.16676614701028247, 0.21003509131806616,
0.11702995484085855, nan, 0.27916783687178409, nan, 0.13867887628914879, 0.090297059174395886, nan,
0.2591373081357819, nan, 0.22270818030313344, nan, nan, nan, nan, 0.11809098242199853,
0.16966133904789449, 0.16918798703308852, 0.13979614830161055, 0.15961414459051387, 0.21447265626732312,
nan, 0.25774023988828293, 0.19189501716069146, 0.21213016095353479, 0.14389495625872573,
0.14928058443079439, 0.2159338410142683, 0.12867804469239918, 0.2635107849498346, 0.12079294358722786,
0.16306355710368628, 0.25948779894025259, 0.18315087492653553, nan, nan, 0.1610892360458816,
0.21615516049567415, 0.20762983842500918, 0.25818793412535151, 0.19576897121736833, nan,
0.25200418696581894, 0.10602169889680488, nan, 0.18144421280609646, 0.18844046499602085,
0.15920178680697145, 0.17171409037673221, 0.1670624810566583, nan, nan, 0.14664900044402304,
0.16372400936393605, 0.20330642523275849, 0.14336456065319061, 0.15398114975491523, 0.141159031940402,
0.2518709061187232, 0.25739430023013499, 0.15772412776344388, 0.14091874722806824, 0.25411293742979618,
0.19432014078814042, 0.1880270473700604, 0.21534217593531316, 0.18101658174950527, 0.26480577913982833,
0.27493786345128568, 0.2366615497499937, 0.13907989901413462, 0.12622593864741205, 0.21239528345558445,
nan, 0.15632336650234271, 0.18653900043314933, 0.1753101229324831, nan, 0.13889811053653844, nan,
0.23632087533179874, 0.14493442845905802, 0.21505500284636425, nan, 0.093505109557105059, nan,
0.19486208613693062, nan, 0.17128879634771846, 0.12555329403377352, 0.16473819247793389, nan, nan, nan,
0.16726327060709459, 0.19110328175972194, 0.1624855843805677, nan, nan, nan, nan, nan,
0.17632694874047539, 0.16254288614636572, 0.12452800990685173, 0.089307557889397315, 0.1376391597964518,
0.14879856663560623, 0.14319590252634118, 0.090625857994648784, 0.14610351537993108, 0.18878253365032446,
0.16060774473261846, 0.12260858565540039, nan, 0.13163695767787462, 0.32391871105650866,
0.12390835197742912, 0.094072006085702972, 0.11578574881991198, 0.15163414700572678, 0.36546305896023829,
0.23626651725872938, 0.21504125288368245, 0.16388562333565818, 0.12366943807108122, 0.14372982971181725,
0.12084508942365924, 0.13778449424633263, 0.14108558033539631, 0.13653156497159907, 0.18901201822990296,
0.15811460286344542, nan, nan, 0.2265475358592684, nan, nan, 0.14590687306341052, nan,
0.14848651854014103, 0.18322718696008766, 0.19623194755337484, 0.22326483673529599, 0.16430715627465131,
nan, nan, nan, 0.22792857830214774, nan, nan, nan, 0.11160300236108758, 0.077747404026344114,
0.12385164818020976, 0.11014600829549022, nan, 0.18763082493171065, nan, 0.12054067034787858,
0.10373193686232926, nan, 0.068800943153351418, nan, 0.12307168131628929, nan, 0.59015922106929897,
0.18900462291114248, nan, 0.095717187111074589, nan, 0.11223678589231517, 0.20663660392101535,
0.18145884425598183, 0.12265195035107716, 0.33980804348165006, 0.15144963163080657, 0.11944788188859996,
0.11865278791937685, 0.098615879116035504, 0.11245936701810251, 0.080959410359860304,
0.10476147412204349, 0.10506099023091225, 0.15346263220546871, nan, nan, 0.16063720624648078,
0.11769152834952376, nan, nan, 0.059362786502148879, 0.073926254396074031, 0.11268374667485156,
0.11835820716581785, 0.13013462777952417, 0.2032796712732593, 0.10405940234902596, 0.12069348805205589,
0.10505185265615651, nan, 0.083961735744956051, 0.10628220239526852, nan, 0.066264697558354804,
0.10212489370512093, 0.1047518641691963, 0.060082563958171589, 0.10206385623847387, nan,
0.20440203032035917, nan, 0.11692672007623064, 0.11553605471712561, 0.11871069626449149,
0.14237203247969107, 0.26865766322783341, 0.1888287156402253, nan, nan, nan, 0.1130687290521344,
0.24613494353163978, 0.3241455078279436, 0.26884539822031772, 0.3538644865922172, 0.16713786661140398,
0.17233863321882303, 0.1681066472313201, nan, 0.17085879791699579, 0.27291364920900352, nan,
0.11362324779918716, 0.24498622522001695, 0.16861663484891456, 0.1118711742862797, 0.17207177646262153,
0.26351952437644077, 0.17056643330230223, 0.11479568533246046, 0.12774529971707713, 0.21907935239191154,
0.2230013610873128, 0.21203268928097518, 0.24808830213317595, 0.19676019199044054, 0.1669817540234603,
0.086516912147941938, 0.12245635224915018, 0.23909038295666279, nan, 0.1570644273518492,
0.16544636945928734, 0.11862997149485828, nan, 0.14039621233939617, 0.208835571856191,
0.17806439224803086, 0.15891603137828389, 0.20237045204517173, 0.21306215350817598, nan,
0.11828543576360412, 0.14840167531614476, 0.16623188541991071, 0.12755466579419117, 0.12913662925214872,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.20954989571798266, nan, nan, 0.24177934751955749,
0.19702100363496444, 0.22695065896123448, 0.14874474815213448, 0.19112138296104322, 0.21937791360545941,
0.15085098262641403, 0.16457768668918546, 0.25563147871815978, 0.19622746978417693, 0.29353247124575776,
0.22372214920567488, 0.13351666442217458, 0.17350305999018237, 0.43628554707684286, 0.2997340211171638,
0.19195216717853616, 0.22881987506395873, 0.2013648082202612, nan, nan, 0.17910236556262477,
0.20297287074917095, nan, 0.17668158840855522, nan, 0.14361712887770145, nan, 0.23285220575771282,
0.2442041638810786, 0.13432869818698007, 0.16508621838227541, 0.16032106949743163, 0.23278511845172428,
0.19763317481583459, 0.28450172990086336, 0.1665405976635331, 0.13529410522660809, 0.17578679710904679,
0.21402991101914051, nan, 0.16166797758892773, 0.25360966145654934, 0.20298062158848779,
0.089764824242694899, 0.163767038538285, 0.23060198040111687, 0.16639931334232055, 0.2662324771299176,
0.162473413844343, nan, nan, nan, nan, nan, nan, nan, nan, 0.24792936924919876, 0.34032278384161818,
0.33858138298696627, nan, 0.23109792882725677, 0.18675554720213131, 0.15958432573736842,
0.20148885178316747, nan, nan, nan, nan, 0.15281759958708446, 0.30515053493784033, nan, nan,
0.13911998479133139, 0.1741173033822033, 0.27143901077157218, nan, nan, 0.32555714298487576,
0.15235852234145747, 0.20467968843070664, 0.17747995150628823, 0.2551558571178017, 0.24300958092627228,
0.26092784515382705, nan, 0.2420914858727036, 0.21146620854868753, 0.25899247992327012,
0.28012641778741948, 0.29021233622711501, 0.38668350428983184, 0.2954802202925178, 0.25206118479186718,
0.33789246492161285, 0.42318751145332156, 0.3272661317939986, 0.33983430742296195, 0.20526159694133317,
nan, 0.21618890046897829, 0.34449534328294157, 0.2719131575842581, 0.19690156933806474,
0.27219675135807198, 0.1718340168326912, 0.25767191572025894, 0.35170135431088939, 0.25152321093043595,
nan, nan, nan, nan, nan, nan, 0.38955898581430382, nan, nan, 0.42614153707498742, 0.24560980224819309,
0.30292074452980766, 0.25207304702766248, nan, nan, 0.35909307117030659, 0.34778201256849639,
0.41431466094949748, nan, nan, nan, nan, 0.45034502130916837, 0.42380683163605026, nan, nan,
0.37908935102615055, nan, nan, 0.51217017438921819, nan, 0.49299405062275814, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22423147981476779, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.30010549348101956, nan,
0.12910405834398694, 0.10096135678781355, 0.14215302940361577, 0.18158067666496649, 0.22449388729311609,
0.27767682183983722, 0.15384566469338587, 0.28447568045475474, 0.14979957642917116, 0.2008837715383387,
0.20254638803109107, nan, 0.17512911174168203, 0.10048154655264513, 0.14047075401655573,
0.11277106954731636, 0.14353557322788779, nan, 0.15685928350084063, 0.13945394414672493,
0.1961489664873784, 0.22795273245233105, 0.17808139582725477, 0.21256516684698301, nan,
0.13429286319305186, 0.099082453367466267, 0.14244982547132909, nan, nan, nan, 0.1430650259104278,
0.24527015201547997, 0.22238824906777327, 0.10294837680677486, 0.14906999939442145, 0.32299949817966228,
0.1904350055887408, nan, 0.10111634903351539, nan, 0.29371391413147235, nan, 0.14948248253920934,
0.1980911631319362, 0.14165362612037194, 0.19973396812271327, nan, 0.23444619661226457,
0.14787963005894159, nan, 0.13943838660259367, 0.1846090233557863, nan, 0.2014121458634312,
0.13035078368879374, 0.43115923855456711, 0.17227300316499405, 0.096613252600730434, 0.06715764156389685,
0.14918060772694886, 0.26423308797473205, 0.098842242537089703, 0.1502248145900596, 0.2523167466870776,
0.1639057270948068, nan, 0.10480543739786129, 0.098804673033968948, 0.19025807349220339,
0.13615246644499221, 0.1333182237390228, 0.15595765119079122, 0.15599854516587244, 0.12532941251872509,
0.12450426588553432, 0.16449250372657231, 0.30365092930113385, 0.15488126251620768, 0.35686457439397767,
0.16969864015143377, 0.21475082155343636, 0.18232373107328273, nan, nan, 0.17794594191802945,
0.23585574609350368, 0.15583592466685786, 0.22156503020451976, nan, 0.31255977861213791,
0.19895396709604124, 0.34354044082215945, nan, 0.31886644637218026, 0.08019026334176825,
0.10920037378955638, nan, nan, 0.2577894250984572, 0.15379147818288003, 0.19696834853170161,
0.13044419610262695, nan, 0.20216971387837621, 0.27287801209219309, 0.25753367561780516,
0.25382490347551956, nan, nan, nan, nan, 0.18976129317038917, 0.12471036798409263, 0.14563896808042745,
nan, nan, nan, 0.24032560585198187, 0.18088853015947778, 0.072581045545512896, nan, 0.14030678525569537,
0.15456934276437434, nan, 0.24763292027674819, 0.23929523023825569, 0.14705924660856728,
0.16483939550704171, 0.22782611805782446, nan, nan, nan, nan, nan, 0.30416621023667412,
0.19517599614215339, 0.14060184006195708, 0.29077104457377939, 0.21055159104790983, 0.19073310279878197,
0.20698022858611015, 0.18877204104986361, 0.18894841905990292, 0.22716919976014707, 0.34508749032528174,
0.21532916747140882, 0.25183268699499267, nan, nan, nan, nan, nan, nan, 0.12623211456050584,
0.16001009448436876, nan, 0.21212933278563886, nan, nan, nan, nan, nan, nan, nan, nan,
0.23894331373797906, nan, 0.22261019443269742, 0.23247451242665257, 0.23877042133702539,
0.2293658136335178, nan, 0.11798622752134315, 0.14560030089078244, nan, 0.15650728699394098, nan, nan,
0.21800410933770381, nan, nan, nan, 0.31427943730500207, 0.2338567901520153, 0.1897203016160352,
0.16115169959910575, nan, 0.068646145017263877, nan, nan, 0.24205698736251802, 0.25294651855618833,
0.22230085700825122, nan, 0.21587522917292185, nan, 0.18727609189143651, 0.18117229504681895,
0.19971089972212291, 0.21353383171631157, nan, 0.16601397440755322, 0.29640392627026196,
0.21190687788424006, 0.20575329432537123, nan, 0.25074791516809247, 0.2734655556939804, nan, nan, nan,
nan, nan, nan, 0.11842653774685784, 0.23106755628435588, 0.32113808529148985, 0.2481664185438964,
0.19370713629183386, nan, 0.16500126051160452, 0.22767443375469332, nan, 0.15240039113309053,
0.15760434755374786, 0.23446622447978543, 0.29148544166567752, 0.19156169143655874, 0.19225907484739746,
0.16126406673922064, 0.24040554713912804, 0.23741199000857999, nan, 0.16767680496791643, nan, nan,
0.28684363072183161, 0.1765265470844038, 0.15648077417527509, 0.17808940383300495, 0.21078357465121728,
nan, 0.36277225511437933, 0.17027430330935603, 0.26441324345300066, 0.23796833011865809,
0.16454440041635987, 0.15632414039854878, 0.21984444016672552, 0.22520611278536934, nan, nan, nan, nan,
nan, 0.20226969020362903, 0.13346338214206507, 0.17181159355635331, nan, nan, nan, nan, nan, nan, nan,
0.25455307514669084, 0.27244911369511854, nan, nan, nan, 0.21719904616032065, 0.23725742462253011, nan,
0.21160149907466036, 0.16327933772444136, 0.2814101822736747, 0.17766936082044624, 0.10255145702838851,
nan, nan, nan, nan, nan, nan, nan, nan, 0.16693865878773451, nan, nan, nan, nan, nan,
0.19037381416161642, 0.083272453307969077, nan, nan, nan, nan, nan, nan, nan, 0.16263590671890574,
0.2135749449825026, nan, nan, 0.25030671846645297, 0.19954333717927616, nan, nan, 0.2641481173976945,
nan, nan, 0.21611325765046394, 0.23912312651181383, 0.21988571821463107, 0.32271814696005496,
0.20972849966920556, 0.18965920521067536, 0.24257295548219923, nan, 0.18141534622844008,
0.16991861539269498, 0.16312961871486112, 0.19730993369091612, 0.17943676931372107, 0.24589873873634052,
0.14450627081940165, nan, nan, nan, 0.25084377882081926, nan, nan, nan, nan, 0.15927162737179709,
0.27962903768217834, 0.28509113704371325, 0.21795262175049812, 0.18260092386456858, nan,
0.20000965878625784, 0.25200464521849064, 0.20855937487012735, 0.26056253874749413, nan,
0.22156420305630226, 0.19304848339387434, 0.18772210524073007, 0.22548420568171687, nan, nan,
0.14912771265215824, 0.1982890501490277, nan, 0.16890020221895094, 0.18180243110942543, nan,
0.14422501873234367, nan, 0.27673728463548242, nan, 0.15342342165435399, nan, nan, 0.25012254314444321,
0.23627103625995191, 0.19623757535265596, nan, nan, 0.17786664789466233, 0.092875007466209955,
0.24822114313985147, 0.15954508447543594, 0.17354413096087873, nan, 0.2875865743241936, nan,
0.11612244354157628, 0.079689448073840791, nan, 0.16805457492222189, 0.14001853027267899,
0.26406390365552385, 0.26801908356067233, 0.18259631962825909, 0.13636326351768593, 0.14936873460079492,
0.15175515135867992, 0.10149706165110262, nan, 0.13472585654182614, 0.21278925137383536,
0.17403864244300907, 0.21634221214307525, 0.099023804227593354, 0.14752042204328603, 0.10450819693221201,
0.11865112758647083, 0.15356841940217489, nan, 0.1728275288331986, 0.14704548218056748,
0.23232623633890406, nan, 0.19149687814514388, nan, nan, 0.26785875905627438, nan, 0.19884922156563292,
0.12428438385943329, 0.17736397792722153, 0.12959676125205591, nan, nan, 0.085782380613665193, nan, nan,
nan, nan, 0.16065640243275961, nan, nan, nan, 0.11771815046697613, 0.1193124100498746, nan,
0.077579163221749278, nan, nan, nan, nan, nan, 0.092292271683566879, 0.15332592738223971,
0.26355259460462793, 0.13537986026982007, 0.14954871891270857, 0.20304753494722647, nan,
0.14063917241590815, nan, 0.24392661839375437, 0.12212140185030765, 0.17447223144549576,
0.13248736923594667, 0.19575095349333696, 0.15113340626690994, nan, nan, nan, 0.094465147691368601,
0.125406816269161, 0.20198450323468276, 0.095825001405847987, 0.20744114224477767, 0.19386032873645304,
0.049888495566417529, nan, 0.13268953599301866, 0.1421952277859117, nan, 0.15157663074535957,
0.13690218794352155, nan, 0.1089398903528417, 0.1764064796050534, 0.16120577830224145, nan, nan, nan,
nan, nan, nan, nan, 0.12155632903708019, nan, nan, nan, nan, nan, nan, nan, nan, 0.16412001169755303,
nan, 0.10161432362884425, 0.17380577671832317, 0.18093305079081892, nan, nan, 0.14524197384317888,
0.15313711997100868, nan, 0.15024440758658933, 0.076966543560617315, nan, nan, nan, 0.1595174652589707,
nan, 0.29201184538747071, 0.1476934660478752, nan, 0.19402269484261941, nan, nan, 0.11221849120372596,
nan, 0.14811578377391255, 0.17106739344083915, 0.26707555537564082, nan, nan, nan, nan, nan, nan, nan,
0.24389916890947541, nan, nan, nan, nan, nan, nan, nan, 0.1375289529480552, 0.22201084088937054, nan,
nan, 0.21540048255509431, nan, nan, nan, 0.25072528286034917, 0.18773148792803621, nan, nan,
0.21528854535130976, 0.14853798438389312, 0.12980176356620227, 0.10089106490526249, 0.18989455274383293,
0.17144435301594219, 0.16240051359434318, 0.31788786777896166, nan, nan, nan, 0.21089493002834503,
0.46427909827656438, 0.15948666431874192, 0.17105952849079997, 0.13878397864349662, 0.35449475765590888,
0.34726315600529256, 0.21800248321469101, 0.24048895142157528, 0.18712633649740398, nan, nan, nan,
0.20999378785166919, 0.24712215157048503, 0.14592082245043231, nan, 0.21570468735989493, nan,
0.49298050824502843, 0.19420870720785141, nan, nan, nan, 0.22496959149168699, 0.15213600409337749,
0.14865533605613221, 0.10556916463243117, 0.14858496588165199, 0.23345337727631749, 0.26471389169243259,
nan, nan, nan, 0.2120260349922575, nan, nan, 0.24247291074441371, 0.19594405144100538,
0.16731129146175161, 0.15504472423989027, nan, nan, nan, 0.095675277139546197, 0.2044635697593479,
0.24116244249990942, nan, nan, nan, nan, 0.33249922468129539, nan, 0.28619513633709381,
0.13787334281963837, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.23783285533766266,
nan, nan, 0.19295659901328865, 0.17587558085018962, nan, nan, nan, nan, 0.15064357220339425, nan,
0.24729485300840215, nan, nan, 0.1768345034188791, 0.20802455523672406, 0.19798799886391139,
0.1799483667169228, 0.0979721806994409, 0.1957970807942894, 0.13651330036674636, 0.17730622065561047,
0.21381223663015347, 0.18070389211082555, 0.16204622283730427, nan, nan, 0.10241681781393741,
0.17048192006030538, 0.15432493369184999, nan, 0.10076567749558329, 0.18479359083173255,
0.18442301775444694, 0.17696009177342209, nan, nan, nan, nan, 0.24451114755027753, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, 0.20668568612528948, 0.11641880127712113, nan, nan, nan,
0.14490954441148135, 0.14220671306749766, 0.17333915924120208, nan, 0.21593493784730367,
0.2963384128459175, 0.18110208894659963, nan, nan, 0.11770956794774239, nan, nan, nan, nan,
0.10462697140973698, 0.13300759029978426, nan, 0.14529266814591446, 0.18366773206084117,
0.16846073292865724, 0.090090069720565769, 0.15895013911406916, 0.1848613066480484, 0.13451824659430731,
nan, nan, 0.17861346327959457, 0.19341481385230755, 0.17232592437357919, 0.16354325128624886,
0.085832127197646255, 0.14106652320097149, 0.094915312769235302, nan, nan, 0.13901425951789481, nan, nan,
nan, nan, nan, 0.090157280925067973, nan, nan, nan, 0.14560604994454207, nan, 0.14713302453603333,
0.17331061290279023, 0.25161667047613007, 0.25962679672760602, nan, nan, 0.31927860900628524,
0.25229397741220405, nan, nan, 0.27599500900597068, 0.19828867740318126, 0.25313929838421584,
0.26454338883930179, nan, 0.16009519668961947, 0.26066177853626826, 0.32848993568764884,
0.17169328179490176, 0.25635669748493134, 0.41528210786932901, 0.1307849053509155, 0.22203873625500725,
nan, nan, 0.089784817027716415, nan, 0.21876979845381245, nan, 0.19331849750326166, 0.25573868138232092,
0.17446350992711487, 0.14929744210881901, 0.14009652313097654, 0.2552674796539916, 0.14121231279235696,
0.16913976435571176, 0.13398680171524605, 0.13939629189219185, 0.16995979314784135, nan, nan,
0.20821102382626241, nan, nan, nan, 0.20275329198699715, 0.18157558062283718, nan, 0.19871183832877745,
0.18559013870930965, 0.18406046973725512, 0.15101521372781371, nan, nan, nan, 0.20423048672089289,
0.21121911023185519, nan, nan, nan, nan, 0.20325772064846201, 0.23179445354699038, 0.22412625664575966,
0.34350408124023707, nan, nan, nan, 0.11526041769476773, 0.132648868264887, 0.19723291885095998,
0.16972575758207878, 0.24706959297745015, 0.21799989613772427, 0.17209914442655569, 0.21871157887766607,
nan, 0.27230378399761185, 0.228797133355928, 0.1670761025039946, 0.16090569083086928,
0.29696512656398616, 0.18573130223177012, 0.10961459326376252, 0.15394183443130566, 0.15724818677751018,
0.39196081214227274, 0.23826176972158039, nan, nan, nan, 0.26316907114488186, 0.26358885832703022, nan,
0.1612280909996153, nan, nan, nan, nan, 0.19094108490517175, 0.15143305923781589, nan,
0.23639109455806376, 0.24427192195706698, nan, nan, nan, nan, nan, nan, nan, nan, nan,
0.24716802332249249, 0.30811875625298157, 0.23361952314239814, 0.29870376577591451, 0.26268595185273258,
nan, nan, nan, 0.21371107090802488, 0.21440044728413235, nan, 0.11486301202679126, 0.10544017569576013,
nan, 0.3631268698908488, 0.3467121141231746, 0.30076179047620338, 0.34504541046950293,
0.30201681766966509, nan, nan, nan, nan, 0.26030646616493941, 0.18180409201782266, 0.36811519375707985,
nan, nan, nan, nan, 0.1054052653896648, 0.13751654925167747, nan, nan, 0.16915569102467282,
0.12742504637030286, 0.12986073137677889, 0.18805431576750792, 0.14276544495120916, nan,
0.17366493544673808, 0.12865286201334852, nan, nan, 0.10322840480485683, 0.12490717852096857,
0.070871156159557178, 0.1112347052743464, nan, 0.13636173446365485, 0.12567821989598602,
0.12583512457011306, 0.14610535108568393, nan, nan, nan, nan, 0.11926622442671143, 0.14694913952802321,
nan, nan, 0.20037251139479939, 0.13633925358096161, nan, 0.24877090721871345, 0.23956759430865338,
0.24791640377587015, 0.13210094455340363, 0.17193263956468757, 0.10587043448685221, 0.12751162248944811,
nan, 0.19692361175846626, 0.20770863603219258, nan, 0.13435827468710734, 0.17778154136199584, nan, nan,
nan, nan, 0.16259773738660008, 0.15299389780638889, 0.097472701845315049, nan, nan, 0.1613015194215536,
0.15844993522923684, 0.1282102106193074, nan, 0.30505832739199029, 0.19615430155648247, nan,
0.28515151371767811, 0.14193264799098371, 0.17076343108976594, 0.1781864206907979, nan, nan,
0.14244629364009073, nan, 0.18206969583811869, nan, nan, nan, 0.18177825951822602, nan, nan, nan, nan,
0.12728109334979423, 0.14821107162282213, nan, nan, nan, 0.18658291731316057, 0.13142015302356722,
0.159015325630746, 0.18253385939350891, 0.20403512203464502, 0.19844508800960356, nan, nan,
0.16998641287437616, nan, 0.17533470776726406, nan, nan, 0.20328302621201905, 0.1324843815845233,
0.099824647844258735, 0.15253909139704558, 0.25979282274394783, 0.21481020652262747, nan,
0.19670088791004164, nan, 0.1752278428052762, nan, 0.11450051333460222, 0.17879403173264596,
0.17520644663443824, nan, 0.23159475905890325, 0.1937221064555586, 0.54620861784709418,
0.22289664802431222, 0.23986663538109668, 0.29031737503791216, 0.31593622236788976, 0.15739689700290188,
nan, nan, nan, 0.16800197908616699, 0.1705758341654976, nan, nan, 0.3333325933784011,
0.21626515127130133, nan, nan, nan, 0.44197846599669355, 0.22381276969862363, 0.1546411535321493,
0.24710016410594354, 0.29668607393673463, 0.25593024909076323, 0.19171995673869233, 0.22410357180694057,
nan, 0.21661500400336958, 0.20193559795839564, 0.16312521806015345, nan, nan, 0.19917403345662871, nan,
0.27441726036151776, 0.21861660548445749, 0.15989814113132542, nan, 0.23619686380889326,
0.20710036336717683, 0.15378929498317584, 0.17684182432528514, 0.19586635516228096, 0.1407701762245725,
0.19735222658300652, nan, nan, nan, 0.12537739949734514, nan, nan, nan, nan, nan, 0.24968611131291124,
0.20933329390719108, 0.12985137518304044, nan, nan, nan, nan, 0.14562503009707686, 0.14745879982645593,
0.20501676630005025, nan, 0.17065514921219443, 0.18931609431890731, 0.19672837178118177,
0.23457338043690928, 0.13248622473145816, 0.13023761765139008, 0.099447624890245917, 0.13759412990854114,
nan, 0.29450000038049617, nan, nan, 0.13880604049697889, 0.16130897877969508, nan, nan,
0.2001981808535172, 0.19431518775941037, 0.23683550107069726, 0.19812328147644953, 0.12622394806259726,
0.18107414475813671, 0.17271441005608418, 0.10207061183212539, 0.12810565067688945, 0.20824720916785003,
0.13554653754022275, nan, 0.079167461561106017, 0.12137853062490347, 0.1104665981265785,
0.079742310651020262, nan, 0.11962097396800654, nan, 0.092821428771281814, 0.18729293766927677, nan,
0.20483121333051674, 0.12155595426118564, 0.066007213480279686, 0.13653246128672991,
0.089932242748972921, nan, 0.06889158512272156, 0.073975982564367104, 0.058064418171585412,
0.084535862360483827, 0.11662580675813265, nan, 0.089904155332572289, 0.096171030608072555,
0.088466506658396796, nan, 0.090509777746181089, 0.12464711308466872, 0.097057581003082316, nan,
0.17459403644894614, nan, 0.091458611800839695, 0.10804958040955771, 0.091733234151077661,
0.08321394766519738, nan, nan, 0.1125379249059382, nan, nan, nan, nan, 0.11882681366105768, nan, nan,
nan, nan, 0.093035172320033305, 0.10592360119856491, 0.10224986350909514, nan, nan, 0.1110988964351093,
0.17416774682119421, nan, 0.20102659024143976, 0.11129452935732077, 0.072401278039562908,
0.14652755151809743, 0.13457694851842475, nan, 0.20021200578612669, 0.12367930335597234, nan,
0.18583244889323858, 0.17947827426188651, 0.1287614110128372, 0.11059207313222709, 0.18063552886437351,
0.14435573431402826, nan, 0.11631340067008272, nan, 0.079362950586972708, nan, 0.062447724781645078,
0.068354365564970626, nan, 0.13782416182551885, 0.28145672773238473, 0.13943709379902944,
0.088178076503432429, nan, nan, 0.11130411588767256, 0.13377305818617394, 0.16847607568001288,
0.1528637249197316, 0.20230586597603584, nan, 0.19372386019053117, 0.19547707807322429,
0.11659673918391801, 0.25062377728776941, 0.12788147161039279, 0.10851003979585923, 0.094733745732395902,
0.15645725128923299, 0.1015638540949006, nan, 0.36880380375263844, 0.13465947075835111,
0.068734091766199087, 0.18029203116650008, 0.14224837625218714, 0.13638329463129251, 0.11413453362903603,
0.14457300077029012, 0.098531636493594268, 0.16877786882566376, 0.20577783753090861, 0.13099823267968153,
0.21580538018393011, 0.16179806587092083, 0.23833277879475917, nan, nan, 0.13383435474103919, nan,
0.20872978318975033, 0.14055217431543418, nan, 0.12511883708174018, 0.12389337198361439,
0.14290590412039561, 0.10840569682647623, nan, 0.20455653425497125, nan, nan, 0.098513817486459612, nan,
0.22017063964018427, 0.17097200791899009, 0.17159339365086501, 0.18622120915296195, nan, nan, nan,
0.19060951050252933, 0.1786357509251652, 0.13438359480934323, 0.16938755654821827, 0.18212879183244346,
0.1506736229741589, nan, nan, nan, nan, 0.23831537973529013, 0.19167799999635118, 0.19612718058205048,
0.17262909972176688, 0.12204613059838251, 0.10100505581803663, nan, nan, nan, 0.19482966175996613,
0.22237535224006497, 0.23861194984526449, nan, 0.15149799615762646, 0.19499113765835502, nan, nan,
0.11327581168308413, 0.13415508489351274, nan, nan, nan, nan, nan, nan, 0.13500853341941285,
0.36678168768616781, 0.25763226000565492, 0.12558999576094504, 0.15566650885402666, 0.13407924515347416,
nan, nan, 0.19173373212498587, 0.15500072798858258, 0.1795967203727539, 0.10809246766239107,
0.097948892855195049, 0.17494195591048564, 0.19505514138897254, 0.11662270057106702, 0.12600676429925162,
0.14153670773021099, nan, nan, 0.13497166284105702, 0.12343001491027591, 0.076316183627488501,
0.065183924108733887, 0.085269295438382783, 0.15743026925790246, 0.18948113953610418,
0.21610631377579118, nan, nan, nan, 0.12825822313438004, nan, 0.18898471929083155, 0.19340483032992672,
0.13830413334152536, 0.1578520018182929]
C_modes = [2.2978723404255321, 1.9840425531914891, 1.9946808510638296, 2.0851063829787235, 1.8829787234042552, nan,
2.021276595744681, 1.9202127659574468, nan, 1.8989361702127661, 1.9414893617021276, 2.0851063829787235,
2.0797872340425534, 1.946808510638298, 1.7393617021276597, 1.9414893617021276, 2.0372340425531914,
1.8510638297872337, 2.0053191489361701, 1.9734042553191489, 1.9680851063829787, 2.0106382978723403,
2.0797872340425534, 2.1861702127659575, 1.9893617021276595, 2.2819148936170213, 2.0159574468085104,
2.0957446808510638, 2.5585106382978724, 2.0372340425531914, 2.0904255319148932, 2.3776595744680851,
2.021276595744681, nan, nan, nan, 2.4734042553191489, nan, 2.6117021276595747, 2.6117021276595747,
2.0585106382978724, 2.1755319148936167, nan, nan, nan, 2.1702127659574466, 2.1489361702127656, nan,
3.1861702127659575, 2.3244680851063833, nan, nan, 2.5478723404255317, 2.1010638297872339,
2.2872340425531914, 2.2340425531914891, 1.8563829787234041, 2.1808510638297873, 2.478723404255319,
2.3776595744680851, 2.4574468085106385, 2.5638297872340425, 2.4095744680851063, 2.1329787234042552,
2.1329787234042552, nan, nan, 2.3989361702127661, nan, 2.3031914893617018, 2.5, 2.4468085106382977,
2.2659574468085104, nan, nan, 2.6861702127659575, 2.3085106382978724, nan, nan, 2.5957446808510638,
2.5159574468085104, nan, nan, nan, nan, nan, nan, nan, 2.1755319148936167, 2.4680851063829787,
2.3617021276595742, nan, 2.0904255319148932, 2.2872340425531914, 2.271276595744681, 2.1436170212765955,
2.6968085106382977, 2.3882978723404253, nan, nan, nan, nan, 1.8191489361702129, 1.8138297872340425,
1.8723404255319149, 1.8936170212765957, nan, nan, nan, nan, nan, nan, nan, nan, nan, 2.0957446808510638,
nan, 1.9946808510638296, 2.4680851063829787, nan, nan, nan, nan, nan, nan, nan, nan, 1.6861702127659572,
nan, nan, 1.6648936170212765, 1.9255319148936172, 1.6702127659574466, 1.7819148936170213,
1.8404255319148937, 1.9148936170212765, 1.803191489361702, 1.803191489361702, 1.7340425531914894,
1.8776595744680848, nan, 2.0319148936170213, 1.7127659574468086, nan, nan, 1.7659574468085106,
1.7446808510638296, 1.7393617021276597, 1.9680851063829787, 1.7234042553191489, 1.6968085106382977, nan,
nan, nan, 1.8297872340425529, 1.8510638297872337, nan, nan, nan, 2.0053191489361701, 1.6914893617021276,
1.7819148936170213, 1.8297872340425529, 2.0691489361702127, 1.9202127659574468, 1.6542553191489362,
1.957446808510638, 1.7021276595744681, 1.8457446808510638, 1.8829787234042552, 1.8882978723404256,
1.8138297872340425, nan, 1.8563829787234041, nan, 1.8936170212765957, 1.8829787234042552, nan,
1.7872340425531914, nan, 1.9361702127659572, nan, nan, nan, nan, 1.8829787234042552, 1.8882978723404256,
1.9734042553191489, 1.8617021276595744, 1.9521276595744681, 1.9734042553191489, nan, 2.1968085106382977,
2.1170212765957444, 2.0744680851063828, 2.0425531914893615, 2.0531914893617023, 1.9361702127659572,
1.9361702127659572, 2.0851063829787235, 2.1117021276595747, 2.0531914893617023, 2.0904255319148932,
2.0053191489361701, nan, nan, 2.1276595744680851, 2.0744680851063828, 2.1968085106382977,
2.1755319148936167, 2.0531914893617023, nan, 2.0319148936170213, 2.4308510638297873, nan,
2.0691489361702127, 2.1808510638297873, 1.8776595744680848, 1.904255319148936, 1.9308510638297871, nan,
nan, 1.6648936170212765, 1.8297872340425529, 1.7819148936170213, 1.7819148936170213, 1.9095744680851063,
1.8457446808510638, 1.8829787234042552, 1.6489361702127658, 1.9202127659574468, 1.8563829787234041,
2.4627659574468082, 1.6170212765957446, 1.7127659574468086, 1.9148936170212765, 2.1117021276595747,
1.8138297872340425, 1.8138297872340425, 2.0, 1.7234042553191489, 1.7393617021276597, 1.8670212765957448,
nan, 2.0585106382978724, 2.3989361702127661, 1.9840425531914891, nan, 1.9680851063829787, nan,
2.0851063829787235, 1.9734042553191489, 2.1436170212765955, nan, 1.7925531914893618, nan,
1.8457446808510638, nan, 1.7765957446808509, 1.7127659574468086, 1.7925531914893618, nan, nan, nan,
1.9414893617021276, 1.8138297872340425, 1.8457446808510638, nan, nan, nan, nan, nan, 2.1117021276595747,
1.9893617021276595, 1.8617021276595744, 1.7393617021276597, 1.8085106382978722, 1.9361702127659572,
1.9255319148936172, 1.8776595744680848, 1.8510638297872337, 1.9308510638297871, 2.0531914893617023,
2.0106382978723403, nan, 1.7074468085106382, 1.6276595744680851, 1.803191489361702, 1.8457446808510638,
1.7659574468085106, 1.8351063829787233, 1.7446808510638296, 2.0585106382978724, 1.7659574468085106,
1.7393617021276597, 1.7659574468085106, 1.7393617021276597, 1.9095744680851063, 1.9361702127659572,
1.8404255319148937, 1.7553191489361701, 2.0851063829787235, 1.7712765957446805, nan, nan,
1.9202127659574468, nan, nan, 2.3085106382978724, nan, 2.5904255319148937, 2.6755319148936167,
2.5319148936170213, 2.1861702127659575, 2.1861702127659575, nan, nan, nan, 2.3882978723404253, nan, nan,
nan, 1.9680851063829787, 2.1063829787234041, 2.1489361702127656, 2.0319148936170213, nan,
1.9414893617021276, nan, 2.1117021276595747, 1.8457446808510638, nan, 1.8989361702127661, nan,
2.2180851063829787, nan, 2.0797872340425534, 1.8989361702127661, nan, 2.0957446808510638, nan,
2.0478723404255317, 2.0, 2.0106382978723403, 1.957446808510638, 1.9095744680851063, 2.0425531914893615,
2.2021276595744679, 2.0053191489361701, 2.0691489361702127, 2.1382978723404258, 2.0053191489361701,
2.0106382978723403, 2.0478723404255317, 1.9787234042553192, nan, nan, 2.0957446808510638,
2.0106382978723403, nan, nan, 1.9521276595744681, 1.9308510638297871, 1.9893617021276595,
2.1382978723404258, 1.9680851063829787, 1.7712765957446805, 1.7393617021276597, 1.6808510638297873,
1.6542553191489362, nan, 1.7872340425531914, 1.6968085106382977, nan, 2.0904255319148932,
1.8882978723404256, 1.7978723404255317, 1.8670212765957448, 1.957446808510638, nan, 1.8723404255319149,
nan, 1.7872340425531914, 1.6702127659574466, 1.8670212765957448, 1.9840425531914891, 2.2659574468085104,
1.904255319148936, nan, nan, nan, 1.7712765957446805, 1.9734042553191489, 2.0319148936170213,
2.1117021276595747, 1.6648936170212765, 1.9308510638297871, 1.9414893617021276, 2.1170212765957444, nan,
2.0053191489361701, 1.9946808510638296, nan, 2.0531914893617023, 2.2978723404255321, 2.2021276595744679,
2.0053191489361701, 1.6382978723404256, 2.0478723404255317, 2.1276595744680851, 1.9787234042553192,
2.0585106382978724, 2.1595744680851063, 1.6861702127659572, 1.9521276595744681, 2.0, 2.4414893617021276,
2.2978723404255321, 2.3297872340425529, 2.6914893617021276, 2.1808510638297873, nan, 1.9893617021276595,
1.9734042553191489, 1.8297872340425529, nan, 2.0, 2.1755319148936167, 2.2446808510638299,
2.5425531914893615, 2.6595744680851063, 2.707446808510638, nan, 1.8191489361702129, 1.8244680851063828,
1.7127659574468086, 1.7127659574468086, 1.7180851063829785, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, 2.1117021276595747, nan, nan, 2.3191489361702127, 1.8404255319148937, 1.8457446808510638,
2.0159574468085104, 1.957446808510638, 1.9840425531914891, 2.0372340425531914, 1.6542553191489362,
1.6117021276595744, 1.8936170212765957, 2.0638297872340425, 1.9787234042553192, 1.8882978723404256,
2.1489361702127656, 2.0744680851063828, 1.8670212765957448, 1.7499999999999998, 1.7765957446808509,
1.7234042553191489, nan, nan, 1.8085106382978722, 1.9148936170212765, nan, 1.7340425531914894, nan,
1.957446808510638, nan, 2.228723404255319, 2.3776595744680851, 2.1436170212765955, 2.0425531914893615,
1.9946808510638296, 1.8297872340425529, 2.2978723404255321, 2.0478723404255317, 2.0372340425531914,
2.021276595744681, 2.0, 1.7872340425531914, nan, 1.8457446808510638, 1.7925531914893618,
2.3936170212765955, 2.1223404255319149, 2.1010638297872339, 2.021276595744681, 2.2606382978723403,
2.4627659574468082, 3.3138297872340421, nan, nan, nan, nan, nan, nan, nan, nan, 2.478723404255319,
2.6436170212765959, 2.707446808510638, nan, 2.0797872340425534, 2.3351063829787231, 2.3085106382978724,
2.5372340425531914, nan, nan, nan, nan, 2.2553191489361701, 2.3510638297872339, nan, nan,
2.2127659574468086, 2.3138297872340425, 2.3670212765957448, nan, nan, 2.2127659574468086,
2.4521276595744679, 2.4414893617021276, 2.1702127659574466, 2.8191489361702127, 2.5904255319148937,
2.8404255319148932, nan, 2.7765957446808511, 2.8882978723404253, 2.6276595744680851, 2.8936170212765955,
2.7978723404255317, 3.1648936170212765, 3.1223404255319149, 2.75, 2.2659574468085104, 2.207446808510638,
3.2340425531914891, 3.0478723404255317, 2.8936170212765955, nan, 2.5744680851063828, 2.7021276595744679,
2.7553191489361701, 2.9308510638297873, 2.9627659574468086, 2.8670212765957444, 2.9574468085106385,
3.3510638297872339, 2.7819148936170208, nan, nan, nan, nan, nan, nan, 3.1542553191489358, nan, nan,
3.1223404255319149, 3.3085106382978724, 3.228723404255319, 3.3085106382978724, nan, nan,
3.5319148936170213, 3.0, 3.25, nan, nan, nan, nan, 3.6648936170212769, 4.0478723404255312, nan, nan,
3.8031914893617018, nan, nan, 4.0212765957446805, nan, 4.0531914893617023, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 4.537234042553191, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 4.8670212765957448, nan, 1.8138297872340425,
1.957446808510638, 1.904255319148936, 1.7553191489361701, 1.9840425531914891, 2.0053191489361701,
1.9255319148936172, 1.8882978723404256, 1.5851063829787233, 1.946808510638298, 1.9255319148936172, nan,
1.9840425531914891, 1.8191489361702129, 1.9521276595744681, 2.0372340425531914, 1.8297872340425529, nan,
2.1648936170212765, 2.1063829787234041, 2.2127659574468086, 2.75, 2.5851063829787235, 1.8723404255319149,
nan, 1.9308510638297871, 1.9946808510638296, 1.8297872340425529, nan, nan, nan, 1.9627659574468084,
1.8829787234042552, 1.9680851063829787, 1.9202127659574468, 1.8244680851063828, 1.8191489361702129,
1.9308510638297871, nan, 2.0797872340425534, nan, 1.8191489361702129, nan, 1.8776595744680848,
1.9255319148936172, 2.0904255319148932, 1.8457446808510638, nan, 1.9734042553191489, 2.4946808510638294,
nan, 1.8617021276595744, 2.0, nan, 2.2606382978723403, 2.0851063829787235, 2.0159574468085104,
1.9893617021276595, 2.1648936170212765, 2.2234042553191489, 2.1329787234042552, 2.0319148936170213,
2.1436170212765955, 2.1382978723404258, 2.1117021276595747, 1.9734042553191489, nan, 2.1117021276595747,
2.0851063829787235, 2.1648936170212765, 2.0797872340425534, 2.0904255319148932, 2.1489361702127656,
2.207446808510638, 2.2659574468085104, 2.1329787234042552, 2.0744680851063828, 2.5851063829787235,
2.3829787234042552, 1.9148936170212765, 1.8723404255319149, 2.4042553191489362, 2.1063829787234041, nan,
nan, 2.5638297872340425, 2.6595744680851063, 2.4946808510638294, 2.3936170212765955, nan,
2.0797872340425534, 2.0585106382978724, 2.271276595744681, nan, 2.2021276595744679, 1.9202127659574468,
2.0691489361702127, nan, nan, 1.8617021276595744, 2.0638297872340425, 2.2872340425531914,
2.1063829787234041, nan, 2.1808510638297873, 2.5691489361702127, 2.1117021276595747, 2.0797872340425534,
nan, nan, nan, nan, 2.207446808510638, 2.1170212765957444, 2.0691489361702127, nan, nan, nan,
2.3191489361702127, 2.0904255319148932, 2.1170212765957444, nan, 2.478723404255319, 2.4521276595744679,
nan, 2.7765957446808511, 2.6329787234042552, 2.4255319148936172, 2.5744680851063828, 2.6861702127659575,
nan, nan, nan, nan, nan, 2.2872340425531914, 2.1808510638297873, 2.021276595744681, 2.3882978723404253,
2.3882978723404253, 2.4521276595744679, 2.0904255319148932, 2.3670212765957448, 2.3723404255319149,
2.2765957446808507, 2.8829787234042552, 2.7234042553191489, 2.5159574468085104, nan, nan, nan, nan, nan,
nan, 2.4734042553191489, 2.4202127659574466, nan, 2.4148936170212765, nan, nan, nan, nan, nan, nan, nan,
nan, 2.5106382978723403, nan, 2.5053191489361701, 2.3351063829787231, 2.3936170212765955,
2.1914893617021276, nan, 2.0638297872340425, 2.2659574468085104, nan, 2.2499999999999996, nan, nan,
2.0372340425531914, nan, nan, nan, 2.3723404255319149, 2.1648936170212765, 2.1808510638297873,
2.2606382978723403, nan, 2.3191489361702127, nan, nan, 2.3457446808510638, 2.6010638297872339,
3.0106382978723403, nan, 2.3723404255319149, nan, 2.5212765957446805, 2.1063829787234041,
2.0904255319148932, 2.4202127659574466, nan, 2.1117021276595747, 2.207446808510638, 2.2978723404255321,
2.2127659574468086, nan, 2.9574468085106385, 2.5319148936170213, nan, nan, nan, nan, nan, nan,
2.2393617021276597, 2.7021276595744679, 2.0478723404255317, 2.4148936170212765, 2.271276595744681, nan,
2.0106382978723403, 2.0957446808510638, nan, 2.1063829787234041, 2.228723404255319, 2.3670212765957448,
2.6170212765957448, 2.3085106382978724, 2.1914893617021276, 2.1436170212765955, 2.5851063829787235,
2.1542553191489362, nan, 2.6755319148936167, nan, nan, 2.5904255319148937, 2.3085106382978724,
2.3829787234042552, 2.4042553191489362, 2.3882978723404253, nan, 2.2765957446808507, 2.3138297872340425,
2.5265957446808511, 2.5638297872340425, 2.4202127659574466, 2.5, 2.436170212765957, 2.3191489361702127,
nan, nan, nan, nan, nan, 2.3882978723404253, 2.1489361702127656, 2.4521276595744679, nan, nan, nan, nan,
nan, nan, nan, 2.5957446808510638, 2.5957446808510638, nan, nan, nan, 2.3563829787234041,
2.5372340425531914, nan, 2.2021276595744679, 2.1702127659574466, 2.0425531914893615, 2.4148936170212765,
2.4840425531914896, nan, nan, nan, nan, nan, nan, nan, nan, 2.1542553191489362, nan, nan, nan, nan, nan,
2.271276595744681, 2.1648936170212765, nan, nan, nan, nan, nan, nan, nan, 2.3776595744680851,
2.3776595744680851, nan, nan, 2.5904255319148937, 2.5744680851063828, nan, nan, 3.0425531914893615, nan,
nan, 2.3617021276595742, 1.9627659574468084, 2.4946808510638294, 1.8776595744680848, 2.3404255319148937,
2.2021276595744679, 2.4148936170212765, nan, 2.1170212765957444, 1.9255319148936172, 1.9255319148936172,
2.2606382978723403, 2.2234042553191489, 2.3563829787234041, 2.2765957446808507, nan, nan, nan,
2.2127659574468086, nan, nan, nan, nan, 2.5425531914893615, 2.3404255319148937, 2.3297872340425529,
2.271276595744681, 2.5106382978723403, nan, 2.5425531914893615, 2.4946808510638294, 2.6223404255319145,
2.2446808510638299, nan, 2.1808510638297873, 2.3031914893617018, 2.2340425531914891, 2.3723404255319149,
nan, nan, 2.1808510638297873, 2.2234042553191489, nan, 2.4148936170212765, 2.2393617021276597, nan,
2.3936170212765955, nan, 2.3297872340425529, nan, 2.0957446808510638, nan, nan, 2.3138297872340425,
2.3563829787234041, 2.3563829787234041, nan, nan, 2.271276595744681, 2.1808510638297873,
2.0957446808510638, 1.9202127659574468, 2.0531914893617023, nan, 2.0744680851063828, nan,
2.2606382978723403, 2.1808510638297873, nan, 2.1542553191489362, 2.2765957446808507, 2.2925531914893615,
2.2659574468085104, 2.207446808510638, 2.1808510638297873, 2.1702127659574466, 2.1276595744680851,
2.0904255319148932, nan, 2.1861702127659575, 2.7606382978723403, 2.4574468085106385, 2.5159574468085104,
2.3138297872340425, 2.3510638297872339, 2.2606382978723403, 2.3351063829787231, 2.4680851063829787, nan,
2.4148936170212765, 2.5425531914893615, 2.3617021276595742, nan, 2.8829787234042552, nan, nan,
2.8191489361702127, nan, 2.3617021276595742, 2.1755319148936167, 2.0638297872340425, 1.9361702127659572,
nan, nan, 2.3191489361702127, nan, nan, nan, nan, 2.1542553191489362, nan, nan, nan, 2.1276595744680851,
2.1702127659574466, nan, 2.0638297872340425, nan, nan, nan, nan, nan, 2.0957446808510638,
2.0106382978723403, 2.2393617021276597, 2.0957446808510638, 2.1702127659574466, 2.1702127659574466, nan,
2.1648936170212765, nan, 2.0797872340425534, 1.9946808510638296, 2.1170212765957444, 2.3297872340425529,
1.9202127659574468, 2.2127659574468086, nan, nan, nan, 2.1648936170212765, 2.1329787234042552,
2.1436170212765955, 2.1436170212765955, 2.1968085106382977, 2.1914893617021276, 2.1063829787234041, nan,
2.0744680851063828, 1.9414893617021276, nan, 2.1914893617021276, 1.9255319148936172, nan,
2.1755319148936167, 2.2393617021276597, 2.1276595744680851, nan, nan, nan, nan, nan, nan, nan,
2.0159574468085104, nan, nan, nan, nan, nan, nan, nan, nan, 1.5904255319148934, nan, 1.5159574468085106,
1.7340425531914894, 1.7180851063829785, nan, nan, 1.6489361702127658, 1.9893617021276595, nan,
1.7872340425531914, 1.803191489361702, nan, nan, nan, 1.5851063829787233, nan, 1.9680851063829787,
1.9414893617021276, nan, 1.7872340425531914, nan, nan, 1.6117021276595744, nan, 1.8670212765957448,
1.9255319148936172, 2.1276595744680851, nan, nan, nan, nan, nan, nan, nan, 2.021276595744681, nan, nan,
nan, nan, nan, nan, nan, 2.3085106382978724, 2.0425531914893615, nan, nan, 2.2499999999999996, nan, nan,
nan, 2.3882978723404253, 2.0319148936170213, nan, nan, 1.904255319148936, 2.2446808510638299,
2.2553191489361701, 2.1010638297872339, 2.3031914893617018, 2.1542553191489362, 2.1170212765957444,
2.0744680851063828, nan, nan, nan, 2.228723404255319, 1.8723404255319149, 2.0797872340425534,
2.0265957446808511, 1.9840425531914891, 2.0691489361702127, 2.2553191489361701, 2.228723404255319,
2.3404255319148937, 2.2765957446808507, nan, nan, nan, 2.2234042553191489, 1.8882978723404256,
1.957446808510638, nan, 2.1010638297872339, nan, 2.6382978723404253, 2.0851063829787235, nan, nan, nan,
2.1755319148936167, 2.1329787234042552, 2.1542553191489362, 2.1755319148936167, 2.2127659574468086,
2.4946808510638294, 2.1968085106382977, nan, nan, nan, 2.4148936170212765, nan, nan, 2.1808510638297873,
2.1117021276595747, 2.1648936170212765, 2.1861702127659575, nan, nan, nan, 2.2765957446808507,
2.3244680851063833, 2.2340425531914891, nan, nan, nan, nan, 2.3563829787234041, nan, 2.4840425531914896,
2.4042553191489362, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 2.3297872340425529, nan,
nan, 1.7446808510638296, 1.6489361702127658, nan, nan, nan, nan, 1.7659574468085106, nan,
1.4734042553191489, nan, nan, 1.5904255319148934, 1.8882978723404256, 1.9521276595744681,
1.7499999999999998, 1.8085106382978722, 1.904255319148936, 1.4734042553191489, 1.8404255319148937,
1.8244680851063828, 1.7606382978723405, 1.6170212765957446, nan, nan, 1.7712765957446805,
2.1755319148936167, 1.8457446808510638, nan, 1.9255319148936172, 1.7021276595744681, 2.0372340425531914,
2.1808510638297873, nan, nan, nan, nan, 2.021276595744681, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, 2.2978723404255321, 1.9893617021276595, nan, nan, nan, 2.0319148936170213,
2.0957446808510638, 2.1436170212765955, nan, 2.0904255319148932, 2.1914893617021276, 2.1914893617021276,
nan, nan, 1.803191489361702, nan, nan, nan, nan, 1.946808510638298, 1.9202127659574468, nan,
2.2446808510638299, 2.0691489361702127, 2.0319148936170213, 2.1170212765957444, 2.0159574468085104,
2.1648936170212765, 2.0319148936170213, nan, nan, 2.0053191489361701, 2.0797872340425534,
2.021276595744681, 2.0, 2.1276595744680851, 2.2340425531914891, 2.0585106382978724, nan, nan,
2.1489361702127656, nan, nan, nan, nan, nan, 1.9202127659574468, nan, nan, nan, 1.8829787234042552, nan,
2.1755319148936167, 2.1808510638297873, 2.0159574468085104, 2.2446808510638299, nan, nan,
2.7872340425531914, 2.8191489361702127, nan, nan, 2.2553191489361701, 2.6861702127659575,
2.4574468085106385, 2.75, nan, 2.6170212765957448, 2.8510638297872339, 2.7978723404255317,
2.4840425531914896, 2.5744680851063828, 2.8829787234042552, 2.808510638297872, 2.6914893617021276, nan,
nan, 2.6382978723404253, nan, 2.2659574468085104, nan, 2.6223404255319145, 2.4734042553191489,
2.5744680851063828, 2.6223404255319145, 2.5531914893617018, 2.4893617021276593, 2.5531914893617018,
2.7180851063829787, 2.7234042553191489, 2.6223404255319145, 2.5797872340425529, nan, nan,
2.3776595744680851, nan, nan, nan, 2.7606382978723403, 2.7659574468085104, nan, 2.5, 2.6063829787234041,
2.5851063829787235, 2.5425531914893615, nan, nan, nan, 2.5265957446808511, 2.2872340425531914, nan, nan,
nan, nan, 2.4680851063829787, 2.7021276595744679, 2.4308510638297873, 2.8404255319148932, nan, nan, nan,
2.4946808510638294, 2.7872340425531914, 2.6755319148936167, 2.5, 2.4042553191489362, 2.6755319148936167,
2.6755319148936167, 2.8191489361702127, nan, 3.0851063829787231, 2.8723404255319149, 2.978723404255319,
2.6968085106382977, 3.0585106382978724, 2.9574468085106385, 2.4521276595744679, 2.5957446808510638,
2.5638297872340425, 2.2393617021276597, 2.228723404255319, nan, nan, nan, 2.5372340425531914,
2.728723404255319, nan, 2.4042553191489362, nan, nan, nan, nan, 2.6861702127659575, 2.6968085106382977,
nan, 2.8191489361702127, 2.7765957446808511, nan, nan, nan, nan, nan, nan, nan, nan, nan,
3.228723404255319, 4.042553191489362, 2.7606382978723403, 2.4946808510638294, 3.1063829787234041, nan,
nan, nan, 2.8031914893617023, 3.2606382978723403, nan, 3.0425531914893615, 3.0372340425531914, nan,
3.3617021276595747, 3.1170212765957448, 3.2446808510638299, 3.2606382978723403, 3.0851063829787231, nan,
nan, nan, nan, 3.7127659574468082, 2.8138297872340425, 2.7925531914893615, nan, nan, nan, nan,
1.5638297872340423, 1.6861702127659572, nan, nan, 1.7872340425531914, 1.6276595744680851,
1.9095744680851063, 1.7765957446808509, 1.574468085106383, nan, 1.7127659574468086, 1.675531914893617,
nan, nan, 1.5053191489361701, 1.6648936170212765, 1.6436170212765957, 1.675531914893617, nan,
1.8404255319148937, 1.8563829787234041, 1.8457446808510638, 1.6808510638297873, nan, nan, nan, nan,
1.6170212765957446, 1.6808510638297873, nan, nan, 1.7446808510638296, 1.5159574468085106, nan, 2.0,
1.904255319148936, 1.803191489361702, 2.0265957446808511, 2.1170212765957444, 2.0478723404255317,
1.9893617021276595, nan, 1.9680851063829787, 2.0106382978723403, nan, 1.9840425531914891,
1.7180851063829785, nan, nan, nan, nan, 1.728723404255319, 1.8936170212765957, 1.9680851063829787, nan,
nan, 2.0797872340425534, 2.0957446808510638, 2.0425531914893615, nan, 2.3404255319148937,
2.2340425531914891, nan, 2.0265957446808511, 1.9095744680851063, 1.904255319148936, 1.9095744680851063,
nan, nan, 1.9680851063829787, nan, 1.7553191489361701, nan, nan, nan, 1.6382978723404256, nan, nan, nan,
nan, 1.7872340425531914, 1.8882978723404256, nan, nan, nan, 1.8882978723404256, 1.8882978723404256,
1.957446808510638, 1.9946808510638296, 2.228723404255319, 2.0957446808510638, nan, nan, 1.957446808510638,
nan, 1.9787234042553192, nan, nan, 1.8510638297872337, 1.904255319148936, 1.957446808510638,
1.9521276595744681, 2.1542553191489362, 1.9840425531914891, nan, 2.0053191489361701, nan,
2.1382978723404258, nan, 2.4521276595744679, 2.2872340425531914, 2.436170212765957, nan,
2.4202127659574466, 2.5265957446808511, 2.5159574468085104, 2.8297872340425534, 2.6170212765957448,
2.2659574468085104, 2.1276595744680851, 2.1861702127659575, nan, nan, nan, 2.2872340425531914,
2.4255319148936172, nan, nan, 2.3404255319148937, 2.3510638297872339, nan, nan, nan, 2.0425531914893615,
2.3829787234042552, 2.1914893617021276, 2.5, 2.1063829787234041, 2.2978723404255321, 2.2499999999999996,
2.4148936170212765, nan, 2.1914893617021276, 2.3936170212765955, 2.3829787234042552, nan, nan,
2.4734042553191489, nan, 2.2180851063829787, 2.1329787234042552, 2.1914893617021276, nan,
2.3510638297872339, 2.228723404255319, 2.4734042553191489, 2.3617021276595742, 2.3617021276595742,
2.2180851063829787, 2.1914893617021276, nan, nan, nan, 2.3882978723404253, nan, nan, nan, nan, nan,
2.2499999999999996, 2.1063829787234041, 2.0265957446808511, nan, nan, nan, nan, 2.3670212765957448,
2.5053191489361701, 2.2872340425531914, nan, 2.1968085106382977, 2.5638297872340425, 2.4468085106382977,
2.8457446808510638, 2.4627659574468082, 2.2446808510638299, 2.3351063829787231, 2.3404255319148937, nan,
2.6542553191489362, nan, nan, 2.207446808510638, 2.207446808510638, nan, nan, 3.0585106382978724,
2.5585106382978724, 2.5744680851063828, 2.3829787234042552, 2.3936170212765955, 2.5265957446808511,
2.3031914893617018, 2.4414893617021276, 2.436170212765957, 2.3936170212765955, 1.728723404255319, nan,
1.7925531914893618, 1.904255319148936, 1.8989361702127661, 1.8510638297872337, nan, 1.6382978723404256,
nan, 1.9840425531914891, 1.7180851063829785, nan, 1.8297872340425529, 1.8510638297872337,
1.5797872340425532, 1.7180851063829785, 1.6702127659574466, nan, 1.6170212765957446, 1.6542553191489362,
1.6223404255319149, 1.6117021276595744, 1.6223404255319149, nan, 1.7180851063829785, 1.6968085106382977,
1.6968085106382977, nan, 1.7234042553191489, 1.675531914893617, 1.6010638297872342, nan,
2.1170212765957444, nan, 1.8563829787234041, 1.6595744680851063, 1.728723404255319, 1.7925531914893618,
nan, nan, 1.8617021276595744, nan, nan, nan, nan, 2.1010638297872339, nan, nan, nan, nan,
1.7127659574468086, 1.7021276595744681, 1.7712765957446805, nan, nan, 1.904255319148936,
1.8776595744680848, nan, 1.8457446808510638, 1.7872340425531914, 1.8244680851063828, 1.6276595744680851,
1.9148936170212765, nan, 1.6648936170212765, 1.7819148936170213, nan, 2.1436170212765955,
1.9095744680851063, 1.9148936170212765, 1.8989361702127661, 2.1063829787234041, 1.803191489361702, nan,
2.0159574468085104, nan, 2.1436170212765955, nan, 2.0265957446808511, 2.0265957446808511, nan,
1.8670212765957448, 1.957446808510638, 2.0957446808510638, 1.9095744680851063, nan, nan,
1.904255319148936, 2.0, 1.803191489361702, 1.8936170212765957, 2.1276595744680851, nan,
2.2819148936170213, 2.0478723404255317, 2.0372340425531914, 2.0265957446808511, 1.8617021276595744,
2.0744680851063828, 2.0531914893617023, 1.9946808510638296, 1.8457446808510638, nan, 1.7978723404255317,
1.8563829787234041, 1.7499999999999998, 1.9202127659574468, 1.9255319148936172, 1.8670212765957448,
1.8723404255319149, 1.904255319148936, 1.7340425531914894, 1.8351063829787233, 1.7553191489361701,
1.9361702127659572, 1.7819148936170213, 1.8351063829787233, 2.1170212765957444, nan, nan,
2.6223404255319145, nan, 2.0744680851063828, 2.0585106382978724, nan, 1.8989361702127661,
2.0372340425531914, 2.0851063829787235, 2.1010638297872339, nan, 2.4946808510638294, nan, nan,
1.8191489361702129, nan, 1.8670212765957448, 1.8457446808510638, 1.6861702127659572, 1.5265957446808509,
nan, nan, nan, 1.9414893617021276, 1.9148936170212765, 1.9840425531914891, 1.9734042553191489,
1.9202127659574468, 1.9148936170212765, nan, nan, nan, nan, 1.8510638297872337, 1.9414893617021276,
2.0531914893617023, 1.6117021276595744, 1.8244680851063828, 1.946808510638298, nan, nan, nan,
1.7127659574468086, 1.8510638297872337, 2.2872340425531914, nan, 1.946808510638298, 1.8617021276595744,
nan, nan, 2.0, 1.9734042553191489, nan, nan, nan, nan, nan, nan, 1.8404255319148937, 1.6808510638297873,
1.8617021276595744, 2.0106382978723403, 1.9414893617021276, 1.4946808510638299, nan, nan,
2.0106382978723403, 1.8989361702127661, 1.6542553191489362, 1.7659574468085106, 1.7393617021276597,
1.6010638297872342, 1.6489361702127658, 1.7393617021276597, 1.6914893617021276, 1.6489361702127658, nan,
nan, 1.7446808510638296, 1.6489361702127658, 1.5851063829787233, 1.6276595744680851, 1.6648936170212765,
1.7659574468085106, 2.0691489361702127, 2.0265957446808511, nan, nan, nan, 1.675531914893617, nan,
1.6489361702127658, 1.6914893617021276, 1.6968085106382977, 1.675531914893617]
profile_total = [1062, 4062, 1703, 1855, 551, 17, 105, 796, 0, 598, 2009, 627, 1204, 1479, 87, 969, 1066, 120, 202,
1472, 1214, 719, 1501, 1393, 2180, 893, 3721, 2876, 1858, 2013, 1780, 1953, 925, 6, 21, 0, 312, 18,
94, 1422, 404, 1164, 46, 24, 132, 566, 676, 0, 140, 114, 136, 0, 129, 702, 501, 1764, 360, 478, 528,
1080, 1108, 1890, 1957, 2564, 381, 0, 0, 681, 55, 781, 1203, 1013, 839, 0, 0, 289, 272, 0, 71, 2231,
1557, 6, 0, 0, 55, 0, 0, 0, 810, 580, 1674, 34, 226, 137, 1890, 1166, 308, 291, 0, 0, 36, 110, 884,
1339, 416, 711, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1766, 0, 235, 273, 0, 0, 0, 5, 0, 7, 0, 134, 204, 17, 29,
458, 70, 388, 428, 637, 573, 471, 1644, 111, 430, 172, 130, 174, 27, 0, 1001, 2167, 2145, 1906, 487,
140, 10, 0, 10, 178, 885, 0, 10, 66, 390, 296, 420, 176, 757, 353, 2402, 862, 119, 848, 281, 1136,
406, 69, 297, 66, 1153, 508, 0, 987, 0, 118, 83, 21, 0, 19, 183, 195, 299, 237, 334, 332, 66, 477,
2140, 337, 294, 606, 616, 917, 655, 1429, 560, 321, 770, 108, 0, 954, 2153, 1627, 539, 667, 0, 252,
85, 0, 919, 816, 241, 246, 131, 30, 5, 969, 1424, 1699, 1820, 360, 1736, 661, 1847, 197, 574, 179,
70, 622, 356, 278, 462, 363, 174, 2142, 4878, 3588, 53, 178, 348, 922, 90, 223, 0, 94, 2550, 953, 5,
216, 23, 165, 0, 1253, 3377, 786, 25, 0, 5, 593, 1368, 465, 0, 0, 0, 0, 0, 201, 1868, 2582, 197,
432, 1849, 1623, 3246, 1504, 2302, 2335, 260, 47, 542, 1033, 331, 175, 370, 200, 187, 357, 1369,
1527, 2103, 1444, 262, 690, 516, 372, 1063, 975, 5, 0, 274, 0, 34, 482, 0, 468, 420, 476, 250, 125,
0, 0, 0, 402, 112, 105, 0, 228, 755, 1882, 784, 12, 523, 63, 232, 235, 5, 51, 17, 287, 48, 304, 713,
35, 153, 25, 244, 618, 456, 183, 144, 548, 1759, 914, 1533, 200, 4544, 3679, 4898, 693, 10, 39,
3236, 624, 14, 57, 328, 94, 786, 1099, 3713, 3928, 2404, 2240, 1879, 0, 247, 368, 0, 85, 1351, 586,
107, 891, 21, 146, 21, 1753, 595, 273, 1231, 435, 386, 0, 0, 0, 410, 504, 748, 633, 1055, 1984,
2391, 512, 0, 489, 597, 24, 1612, 1610, 2948, 1440, 86, 787, 1186, 2012, 1023, 2560, 1223, 383,
2036, 2150, 1491, 117, 220, 105, 70, 685, 1965, 551, 0, 1758, 1363, 311, 429, 1112, 2062, 0, 1325,
245, 434, 105, 171, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1265, 38, 29, 373, 495, 505, 865, 2410, 822, 266,
292, 322, 840, 265, 182, 156, 157, 585, 260, 1362, 337, 784, 25, 34, 430, 96, 91, 335, 10, 303, 73,
386, 958, 1901, 2356, 624, 434, 457, 525, 563, 2282, 798, 196, 42, 253, 271, 255, 81, 986, 818,
5101, 1077, 440, 127, 18, 0, 0, 0, 8, 0, 0, 870, 617, 527, 5, 450, 248, 393, 334, 29, 54, 24, 14,
2854, 226, 0, 70, 166, 240, 347, 138, 8, 480, 313, 222, 139, 272, 1151, 826, 62, 659, 454, 200, 696,
638, 817, 973, 210, 673, 337, 844, 747, 926, 113, 250, 364, 261, 1543, 1110, 1189, 981, 1238, 626,
16, 0, 0, 0, 0, 5, 1178, 66, 113, 353, 394, 294, 201, 257, 55, 1020, 659, 520, 0, 0, 0, 0, 494, 352,
26, 7, 538, 0, 5, 311, 129, 196, 0, 55, 16, 7, 26, 5, 12, 6, 0, 49, 21, 11, 117, 0, 0, 0, 0, 0, 8,
181, 5, 0, 76, 62, 0, 0, 22, 25, 0, 0, 10, 0, 12, 19, 87, 247, 51, 447, 516, 207, 1156, 807, 1251,
706, 269, 109, 167, 1366, 82, 224, 896, 3825, 3428, 229, 0, 283, 3155, 1561, 827, 750, 1258, 0,
1989, 887, 188, 0, 26, 0, 364, 792, 664, 1843, 419, 222, 691, 0, 281, 0, 255, 22, 1075, 1808, 1673,
180, 0, 491, 1097, 33, 351, 249, 25, 2576, 232, 416, 560, 263, 130, 388, 1079, 89, 925, 293, 335,
29, 284, 397, 90, 2166, 4100, 2116, 596, 2079, 3123, 146, 890, 199, 188, 1052, 800, 2207, 18, 33,
290, 397, 133, 975, 80, 199, 1068, 2477, 13, 236, 78, 1128, 52, 5, 370, 928, 2328, 836, 130, 682,
197, 871, 1704, 43, 35, 0, 0, 647, 1068, 3166, 37, 5, 0, 1164, 2158, 100, 0, 174, 2010, 0, 1099,
1750, 1986, 3196, 1802, 26, 0, 15, 0, 0, 2211, 2888, 183, 697, 401, 560, 577, 327, 965, 337, 848,
298, 353, 0, 23, 55, 0, 0, 0, 130, 323, 128, 949, 11, 13, 7, 0, 36, 114, 0, 0, 453, 0, 335, 805,
722, 447, 86, 102, 392, 35, 708, 11, 0, 1868, 74, 44, 172, 644, 584, 388, 914, 51, 59, 0, 49, 329,
552, 512, 0, 1100, 19, 847, 735, 219, 246, 6, 1404, 207, 2133, 2157, 121, 1084, 2025, 82, 18, 6, 47,
0, 0, 67, 816, 1841, 240, 249, 0, 1343, 390, 0, 2319, 5154, 3953, 1449, 1990, 758, 288, 155, 327, 0,
134, 0, 28, 316, 537, 1336, 1747, 635, 116, 1185, 965, 896, 521, 266, 1225, 1961, 1060, 6, 0, 0, 0,
0, 284, 1775, 1554, 0, 146, 9, 0, 0, 0, 0, 2481, 1532, 0, 0, 102, 949, 285, 62, 378, 334, 206, 2630,
490, 97, 6, 0, 5, 0, 0, 0, 76, 626, 5, 95, 40, 20, 23, 241, 57, 7, 25, 0, 0, 0, 0, 47, 1038, 299,
49, 102, 467, 249, 0, 0, 433, 0, 0, 230, 386, 149, 180, 522, 1798, 1366, 16, 328, 517, 121, 505,
899, 304, 1518, 5, 0, 0, 731, 0, 0, 0, 95, 817, 328, 345, 649, 1893, 69, 1298, 1473, 1463, 249, 41,
373, 596, 795, 1365, 10, 35, 758, 435, 20, 411, 225, 83, 431, 12, 225, 17, 152, 150, 42, 221, 456,
903, 24, 41, 261, 325, 331, 179, 401, 20, 484, 30, 503, 145, 0, 1835, 1873, 272, 284, 406, 818, 604,
996, 1482, 39, 3206, 1840, 2411, 1822, 890, 4215, 1103, 1306, 1729, 0, 1213, 3557, 603, 21, 1609, 0,
0, 689, 0, 544, 1011, 550, 800, 0, 0, 62, 21, 6, 0, 83, 915, 0, 32, 0, 104, 444, 40, 152, 27, 147,
90, 31, 0, 110, 482, 89, 99, 320, 238, 40, 400, 87, 207, 607, 728, 573, 599, 674, 33, 24, 22, 79,
356, 277, 192, 582, 621, 26, 5, 397, 460, 0, 146, 572, 53, 954, 1232, 183, 47, 33, 0, 0, 0, 0, 29,
64, 57, 0, 0, 0, 0, 0, 0, 0, 663, 41, 739, 507, 347, 0, 0, 916, 519, 0, 2225, 1727, 0, 0, 0, 115, 0,
796, 1222, 0, 364, 101, 78, 298, 127, 550, 788, 318, 0, 0, 0, 0, 0, 0, 0, 882, 115, 64, 61, 0, 0, 0,
29, 110, 662, 22, 66, 925, 70, 35, 0, 182, 842, 0, 0, 781, 921, 1092, 181, 104, 445, 211, 414, 6,
77, 0, 195, 284, 161, 213, 101, 204, 325, 1466, 1003, 1955, 65, 0, 157, 763, 243, 125, 0, 140, 0,
602, 218, 41, 0, 0, 436, 2481, 1431, 799, 1001, 578, 489, 0, 18, 0, 355, 135, 10, 352, 444, 402,
151, 0, 0, 0, 288, 510, 523, 24, 5, 10, 35, 384, 26, 182, 173, 0, 0, 0, 6, 0, 8, 6, 0, 70, 0, 0, 0,
281, 0, 57, 309, 336, 28, 5, 54, 32, 109, 67, 364, 101, 18, 170, 185, 1035, 1065, 842, 743, 194,
105, 260, 664, 160, 41, 33, 186, 227, 476, 87, 1078, 190, 592, 587, 24, 10, 35, 0, 321, 0, 0, 5, 0,
65, 45, 80, 128, 0, 42, 0, 0, 920, 277, 0, 0, 29, 1911, 2812, 1251, 13, 658, 435, 418, 68, 7, 571,
39, 68, 6, 49, 85, 140, 0, 572, 526, 242, 107, 335, 68, 1200, 28, 0, 1212, 1605, 2499, 3345, 1305,
249, 68, 22, 0, 187, 58, 122, 42, 74, 58, 72, 0, 0, 0, 299, 0, 172, 366, 728, 761, 0, 12, 167, 469,
49, 0, 360, 123, 681, 950, 0, 736, 3845, 671, 120, 442, 431, 213, 1138, 43, 6, 126, 80, 251, 28,
204, 758, 628, 2321, 2556, 378, 127, 2509, 2385, 1480, 991, 10, 35, 1416, 0, 0, 83, 397, 1252, 63,
122, 708, 4911, 1822, 0, 0, 7, 276, 135, 60, 33, 14, 30, 357, 976, 376, 2756, 0, 10, 35, 2066, 1871,
2160, 1165, 224, 429, 745, 556, 0, 151, 1150, 163, 408, 2104, 367, 236, 380, 575, 376, 517, 18, 45,
98, 1122, 409, 7, 522, 10, 44, 154, 26, 1474, 1409, 0, 986, 1357, 0, 51, 0, 5, 14, 59, 28, 0, 0,
242, 447, 571, 768, 910, 95, 6, 17, 122, 852, 10, 348, 65, 0, 288, 1120, 1792, 812, 1072, 0, 0, 0,
0, 281, 489, 825, 0, 0, 0, 81, 235, 2125, 0, 0, 536, 2296, 422, 223, 1675, 52, 118, 122, 63, 0, 144,
308, 106, 625, 16, 852, 555, 584, 145, 56, 18, 0, 50, 322, 1507, 26, 56, 321, 175, 51, 895, 1171,
1679, 781, 72, 152, 921, 71, 224, 728, 59, 461, 142, 0, 13, 66, 0, 114, 792, 893, 0, 0, 1362, 1290,
899, 10, 790, 458, 11, 744, 1361, 613, 1209, 12, 16, 559, 16, 236, 0, 0, 0, 105, 0, 0, 23, 11, 303,
795, 32, 0, 71, 717, 1752, 2350, 549, 929, 471, 6, 88, 1780, 46, 220, 104, 74, 273, 347, 1281, 708,
451, 226, 10, 336, 0, 496, 11, 264, 788, 242, 87, 204, 2358, 193, 710, 536, 437, 243, 169, 5, 0,
134, 1649, 1140, 0, 81, 314, 190, 59, 68, 59, 461, 947, 690, 553, 422, 642, 2341, 455, 15, 718,
2075, 1018, 0, 0, 600, 110, 262, 431, 224, 115, 609, 525, 367, 442, 159, 809, 184, 54, 0, 61, 645,
0, 0, 0, 0, 99, 455, 1228, 801, 10, 166, 0, 110, 387, 413, 408, 90, 537, 1589, 1676, 293, 209, 1169,
559, 1371, 0, 2507, 24, 56, 384, 202, 20, 0, 167, 569, 809, 968, 2825, 259, 1159, 251, 4189, 1229,
655, 21, 417, 1139, 313, 297, 0, 153, 69, 404, 1143, 82, 92, 420, 58, 118, 97, 30, 381, 1370, 476,
976, 476, 0, 958, 1104, 58, 8, 105, 338, 775, 10, 879, 30, 1012, 219, 653, 749, 5, 14, 222, 0, 0,
53, 7, 113, 0, 10, 91, 0, 109, 420, 195, 29, 46, 215, 333, 49, 1576, 972, 277, 641, 524, 8, 607,
885, 0, 519, 1059, 564, 396, 717, 373, 7, 748, 13, 457, 29, 1915, 516, 0, 173, 763, 1471, 821, 48,
0, 89, 1440, 357, 2289, 474, 59, 419, 371, 873, 556, 227, 582, 1830, 292, 594, 13, 1039, 1556, 114,
1462, 197, 627, 94, 940, 289, 213, 453, 982, 473, 660, 216, 0, 0, 413, 7, 2518, 633, 0, 2939, 3243,
2505, 2019, 0, 145, 76, 56, 808, 24, 329, 659, 152, 185, 0, 0, 147, 736, 573, 432, 604, 639, 658, 0,
59, 55, 21, 227, 371, 1544, 368, 2327, 1653, 0, 0, 5, 145, 523, 770, 82, 183, 410, 72, 63, 936, 751,
22, 0, 0, 0, 0, 0, 242, 285, 428, 128, 1548, 199, 0, 5, 816, 1031, 2452, 589, 1901, 672, 291, 780,
2219, 797, 0, 26, 1684, 3475, 2076, 616, 3596, 3043, 2087, 110, 0, 0, 83, 1363, 0, 121, 574, 1586,
708]
peak_total = [63.0, 666.0, 120.0, 134.0, 48.0, 2.0, 12.0, 67.0, 0, 61.0, 167.0, 75.0, 155.0, 128.0, 20.0, 95.0, 83.0,
12.0, 45.0, 190.0, 93.0, 64.0, 156.0, 89.0, 178.0, 63.0, 303.0, 245.0, 99.0, 179.0, 128.0, 119.0, 95.0,
1.0, 6.0, 0, 29.0, 4.0, 15.0, 74.0, 30.0, 92.0, 6.0, 4.0, 9.0, 44.0, 58.0, 0, 11.0, 15.0, 9.0, 0, 16.0,
72.0, 47.0, 101.0, 32.0, 52.0, 33.0, 82.0, 75.0, 134.0, 116.0, 120.0, 43.0, 0, 0, 58.0, 7.0, 53.0,
79.0, 34.0, 69.0, 0, 0, 16.0, 19.0, 0, 6.0, 117.0, 119.0, 2.0, 0, 0, 7.0, 0, 0, 0, 46.0, 52.0, 110.0,
4.0, 15.0, 11.0, 155.0, 114.0, 22.0, 18.0, 0, 0, 5.0, 10.0, 61.0, 156.0, 36.0, 82.0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 142.0, 0, 27.0, 26.0, 0, 0, 0, 1.0, 0, 2.0, 0, 10.0, 14.0, 4.0, 6.0, 32.0, 12.0, 38.0, 27.0,
45.0, 64.0, 49.0, 100.0, 13.0, 33.0, 10.0, 15.0, 46.0, 3.0, 0, 88.0, 292.0, 166.0, 155.0, 33.0, 14.0,
2.0, 0, 2.0, 16.0, 82.0, 0, 1.0, 8.0, 36.0, 22.0, 30.0, 24.0, 71.0, 48.0, 113.0, 65.0, 12.0, 100.0,
30.0, 89.0, 49.0, 6.0, 23.0, 9.0, 91.0, 65.0, 0, 53.0, 0, 11.0, 5.0, 2.0, 0, 3.0, 17.0, 21.0, 24.0,
18.0, 28.0, 24.0, 6.0, 25.0, 133.0, 28.0, 24.0, 48.0, 62.0, 86.0, 39.0, 165.0, 48.0, 21.0, 57.0, 10.0,
0, 84.0, 122.0, 121.0, 35.0, 50.0, 0, 24.0, 11.0, 0, 72.0, 69.0, 19.0, 17.0, 12.0, 4.0, 2.0, 72.0,
93.0, 136.0, 159.0, 26.0, 168.0, 47.0, 106.0, 21.0, 74.0, 12.0, 12.0, 39.0, 22.0, 19.0, 38.0, 19.0,
14.0, 227.0, 505.0, 233.0, 6.0, 16.0, 25.0, 81.0, 8.0, 25.0, 0, 13.0, 188.0, 56.0, 3.0, 32.0, 4.0,
13.0, 0, 126.0, 320.0, 68.0, 3.0, 0, 1.0, 62.0, 133.0, 36.0, 0, 0, 0, 0, 0, 16.0, 117.0, 265.0, 31.0,
44.0, 166.0, 205.0, 453.0, 149.0, 219.0, 175.0, 41.0, 7.0, 99.0, 104.0, 36.0, 22.0, 47.0, 23.0, 17.0,
23.0, 113.0, 146.0, 184.0, 127.0, 33.0, 73.0, 48.0, 35.0, 61.0, 89.0, 2.0, 0, 21.0, 0, 4.0, 45.0, 0,
40.0, 30.0, 32.0, 15.0, 14.0, 0, 0, 0, 25.0, 9.0, 10.0, 0, 28.0, 126.0, 166.0, 81.0, 3.0, 48.0, 5.0,
22.0, 24.0, 1.0, 11.0, 3.0, 33.0, 6.0, 33.0, 54.0, 5.0, 24.0, 5.0, 30.0, 57.0, 57.0, 22.0, 15.0, 55.0,
183.0, 97.0, 190.0, 27.0, 676.0, 421.0, 546.0, 54.0, 3.0, 7.0, 302.0, 67.0, 2.0, 10.0, 59.0, 15.0,
71.0, 127.0, 432.0, 312.0, 333.0, 198.0, 246.0, 0, 33.0, 46.0, 0, 20.0, 152.0, 65.0, 22.0, 142.0, 9.0,
11.0, 6.0, 169.0, 51.0, 33.0, 106.0, 25.0, 33.0, 0, 0, 0, 50.0, 29.0, 54.0, 37.0, 76.0, 151.0, 180.0,
39.0, 0, 40.0, 31.0, 3.0, 173.0, 115.0, 197.0, 157.0, 15.0, 64.0, 105.0, 240.0, 117.0, 195.0, 79.0,
28.0, 118.0, 137.0, 106.0, 19.0, 24.0, 11.0, 10.0, 55.0, 139.0, 70.0, 0, 134.0, 97.0, 33.0, 37.0, 66.0,
102.0, 0, 139.0, 28.0, 38.0, 12.0, 31.0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 71.0, 4.0, 3.0, 22.0, 45.0,
34.0, 75.0, 197.0, 50.0, 22.0, 21.0, 26.0, 52.0, 16.0, 12.0, 16.0, 13.0, 41.0, 21.0, 109.0, 21.0, 48.0,
5.0, 3.0, 28.0, 15.0, 9.0, 29.0, 2.0, 23.0, 7.0, 22.0, 56.0, 153.0, 155.0, 54.0, 29.0, 33.0, 30.0,
40.0, 212.0, 58.0, 14.0, 4.0, 22.0, 19.0, 20.0, 11.0, 80.0, 58.0, 335.0, 48.0, 33.0, 10.0, 3.0, 0, 0,
0, 2.0, 0, 0, 52.0, 28.0, 23.0, 2.0, 30.0, 16.0, 39.0, 25.0, 4.0, 5.0, 4.0, 3.0, 202.0, 12.0, 0, 10.0,
16.0, 16.0, 17.0, 6.0, 2.0, 25.0, 30.0, 12.0, 11.0, 18.0, 55.0, 36.0, 4.0, 36.0, 30.0, 12.0, 39.0,
29.0, 30.0, 42.0, 11.0, 30.0, 15.0, 44.0, 40.0, 67.0, 10.0, 18.0, 19.0, 19.0, 119.0, 55.0, 108.0, 56.0,
62.0, 44.0, 2.0, 0, 0, 0, 0, 1.0, 49.0, 5.0, 10.0, 14.0, 33.0, 16.0, 12.0, 9.0, 6.0, 38.0, 21.0, 29.0,
0, 0, 0, 0, 16.0, 16.0, 3.0, 3.0, 17.0, 0, 1.0, 14.0, 7.0, 11.0, 0, 4.0, 2.0, 2.0, 3.0, 1.0, 1.0, 1.0,
0, 4.0, 2.0, 1.0, 5.0, 0, 0, 0, 0, 0, 1.0, 14.0, 1.0, 0, 5.0, 6.0, 0, 0, 3.0, 3.0, 0, 0, 2.0, 0, 1.0,
3.0, 6.0, 13.0, 5.0, 37.0, 77.0, 21.0, 73.0, 53.0, 102.0, 57.0, 30.0, 12.0, 18.0, 97.0, 8.0, 32.0,
105.0, 414.0, 397.0, 21.0, 0, 29.0, 317.0, 141.0, 49.0, 55.0, 97.0, 0, 184.0, 110.0, 21.0, 0, 3.0, 0,
34.0, 60.0, 49.0, 201.0, 33.0, 14.0, 46.0, 0, 34.0, 0, 22.0, 5.0, 91.0, 172.0, 146.0, 15.0, 0, 38.0,
87.0, 3.0, 36.0, 24.0, 4.0, 161.0, 21.0, 40.0, 47.0, 47.0, 27.0, 45.0, 126.0, 13.0, 95.0, 36.0, 40.0,
5.0, 34.0, 60.0, 11.0, 180.0, 370.0, 131.0, 39.0, 184.0, 301.0, 14.0, 40.0, 18.0, 16.0, 131.0, 48.0,
154.0, 3.0, 5.0, 23.0, 24.0, 13.0, 83.0, 7.0, 23.0, 85.0, 111.0, 2.0, 21.0, 12.0, 121.0, 10.0, 2.0,
18.0, 91.0, 133.0, 149.0, 9.0, 43.0, 17.0, 68.0, 109.0, 10.0, 3.0, 0, 0, 64.0, 99.0, 254.0, 4.0, 1.0,
0, 78.0, 141.0, 17.0, 0, 18.0, 142.0, 0, 54.0, 96.0, 142.0, 226.0, 115.0, 3.0, 0, 3.0, 0, 0, 102.0,
161.0, 16.0, 53.0, 51.0, 45.0, 43.0, 24.0, 69.0, 21.0, 28.0, 18.0, 17.0, 0, 3.0, 4.0, 0, 0, 0, 16.0,
25.0, 9.0, 65.0, 1.0, 2.0, 2.0, 0, 3.0, 7.0, 0, 0, 24.0, 0, 23.0, 47.0, 47.0, 37.0, 9.0, 13.0, 37.0,
4.0, 65.0, 2.0, 0, 113.0, 10.0, 7.0, 9.0, 33.0, 30.0, 32.0, 72.0, 5.0, 15.0, 0, 8.0, 18.0, 28.0, 34.0,
0, 70.0, 2.0, 65.0, 58.0, 18.0, 23.0, 1.0, 101.0, 13.0, 120.0, 145.0, 9.0, 53.0, 85.0, 7.0, 3.0, 1.0,
5.0, 0, 0, 13.0, 44.0, 81.0, 20.0, 17.0, 0, 120.0, 30.0, 0, 175.0, 347.0, 193.0, 65.0, 145.0, 52.0,
21.0, 12.0, 20.0, 0, 14.0, 0, 3.0, 19.0, 40.0, 112.0, 143.0, 51.0, 8.0, 68.0, 79.0, 65.0, 36.0, 21.0,
103.0, 112.0, 70.0, 2.0, 0, 0, 0, 0, 26.0, 148.0, 102.0, 0, 8.0, 2.0, 0, 0, 0, 0, 145.0, 75.0, 0, 0,
6.0, 51.0, 18.0, 8.0, 31.0, 28.0, 15.0, 174.0, 64.0, 10.0, 1.0, 0, 1.0, 0, 0, 0, 8.0, 44.0, 2.0, 6.0,
6.0, 2.0, 3.0, 17.0, 12.0, 2.0, 4.0, 0, 0, 0, 0, 6.0, 76.0, 20.0, 4.0, 7.0, 44.0, 22.0, 0, 0, 27.0, 0,
0, 14.0, 20.0, 13.0, 15.0, 36.0, 155.0, 75.0, 3.0, 26.0, 47.0, 13.0, 45.0, 60.0, 24.0, 140.0, 2.0, 0,
0, 53.0, 0, 0, 0, 7.0, 62.0, 16.0, 23.0, 45.0, 143.0, 6.0, 92.0, 77.0, 116.0, 23.0, 7.0, 26.0, 45.0,
62.0, 78.0, 2.0, 5.0, 56.0, 38.0, 2.0, 37.0, 17.0, 10.0, 45.0, 2.0, 21.0, 3.0, 12.0, 10.0, 4.0, 25.0,
43.0, 68.0, 2.0, 4.0, 22.0, 44.0, 34.0, 18.0, 45.0, 5.0, 46.0, 4.0, 60.0, 23.0, 0, 126.0, 142.0, 20.0,
16.0, 35.0, 77.0, 73.0, 107.0, 224.0, 8.0, 315.0, 109.0, 146.0, 106.0, 115.0, 437.0, 138.0, 121.0,
184.0, 0, 125.0, 314.0, 41.0, 4.0, 96.0, 0, 0, 33.0, 0, 37.0, 90.0, 40.0, 69.0, 0, 0, 11.0, 3.0, 1.0,
0, 9.0, 121.0, 0, 4.0, 0, 13.0, 48.0, 6.0, 31.0, 3.0, 10.0, 8.0, 5.0, 0, 17.0, 57.0, 11.0, 12.0, 28.0,
28.0, 4.0, 35.0, 8.0, 14.0, 57.0, 53.0, 54.0, 59.0, 69.0, 6.0, 4.0, 4.0, 14.0, 44.0, 26.0, 24.0, 40.0,
46.0, 11.0, 1.0, 39.0, 38.0, 0, 13.0, 48.0, 7.0, 121.0, 90.0, 13.0, 8.0, 7.0, 0, 0, 0, 0, 4.0, 11.0,
9.0, 0, 0, 0, 0, 0, 0, 0, 42.0, 5.0, 83.0, 47.0, 30.0, 0, 0, 90.0, 40.0, 0, 174.0, 276.0, 0, 0, 0,
15.0, 0, 38.0, 118.0, 0, 26.0, 10.0, 8.0, 39.0, 10.0, 40.0, 69.0, 19.0, 0, 0, 0, 0, 0, 0, 0, 49.0, 8.0,
6.0, 8.0, 0, 0, 0, 5.0, 13.0, 43.0, 4.0, 7.0, 68.0, 6.0, 4.0, 0, 17.0, 48.0, 0, 0, 39.0, 79.0, 114.0,
20.0, 15.0, 33.0, 16.0, 23.0, 2.0, 9.0, 0, 17.0, 16.0, 17.0, 21.0, 11.0, 11.0, 17.0, 81.0, 58.0, 108.0,
10.0, 0, 9.0, 44.0, 18.0, 12.0, 0, 11.0, 0, 27.0, 25.0, 4.0, 0, 0, 34.0, 245.0, 131.0, 116.0, 98.0,
33.0, 27.0, 0, 2.0, 0, 21.0, 8.0, 2.0, 20.0, 26.0, 33.0, 12.0, 0, 0, 0, 49.0, 41.0, 30.0, 4.0, 1.0,
4.0, 6.0, 22.0, 3.0, 13.0, 19.0, 0, 0, 0, 2.0, 0, 2.0, 2.0, 0, 5.0, 0, 0, 0, 19.0, 0, 6.0, 21.0, 29.0,
6.0, 2.0, 5.0, 4.0, 14.0, 8.0, 24.0, 10.0, 3.0, 12.0, 15.0, 86.0, 121.0, 119.0, 51.0, 19.0, 11.0, 24.0,
47.0, 16.0, 3.0, 5.0, 25.0, 18.0, 39.0, 8.0, 150.0, 17.0, 43.0, 49.0, 3.0, 2.0, 4.0, 0, 19.0, 0, 0,
2.0, 0, 8.0, 3.0, 7.0, 9.0, 0, 6.0, 0, 0, 57.0, 35.0, 0, 0, 4.0, 133.0, 240.0, 92.0, 2.0, 48.0, 27.0,
30.0, 4.0, 1.0, 51.0, 3.0, 10.0, 2.0, 5.0, 13.0, 17.0, 0, 56.0, 37.0, 21.0, 20.0, 26.0, 11.0, 170.0,
4.0, 0, 91.0, 118.0, 254.0, 350.0, 181.0, 22.0, 11.0, 3.0, 0, 20.0, 8.0, 10.0, 7.0, 4.0, 9.0, 13.0, 0,
0, 0, 22.0, 0, 20.0, 29.0, 52.0, 56.0, 0, 1.0, 13.0, 31.0, 5.0, 0, 26.0, 11.0, 37.0, 53.0, 0, 64.0,
187.0, 27.0, 11.0, 29.0, 21.0, 24.0, 62.0, 5.0, 2.0, 23.0, 8.0, 14.0, 5.0, 17.0, 41.0, 60.0, 197.0,
206.0, 32.0, 19.0, 215.0, 216.0, 124.0, 89.0, 1.0, 7.0, 90.0, 0, 0, 10.0, 27.0, 88.0, 10.0, 13.0, 46.0,
283.0, 160.0, 0, 0, 2.0, 30.0, 12.0, 10.0, 4.0, 3.0, 3.0, 23.0, 66.0, 28.0, 96.0, 0, 1.0, 7.0, 250.0,
172.0, 120.0, 78.0, 15.0, 28.0, 65.0, 42.0, 0, 12.0, 75.0, 12.0, 38.0, 107.0, 27.0, 29.0, 31.0, 44.0,
21.0, 32.0, 2.0, 4.0, 10.0, 59.0, 28.0, 3.0, 39.0, 3.0, 3.0, 10.0, 4.0, 81.0, 134.0, 0, 47.0, 76.0, 0,
6.0, 0, 1.0, 2.0, 6.0, 3.0, 0, 0, 16.0, 21.0, 30.0, 30.0, 47.0, 10.0, 1.0, 3.0, 15.0, 43.0, 2.0, 40.0,
11.0, 0, 14.0, 37.0, 75.0, 35.0, 52.0, 0, 0, 0, 0, 20.0, 35.0, 42.0, 0, 0, 0, 9.0, 30.0, 167.0, 0, 0,
37.0, 212.0, 33.0, 18.0, 146.0, 7.0, 11.0, 14.0, 8.0, 0, 19.0, 34.0, 16.0, 71.0, 3.0, 126.0, 54.0,
65.0, 11.0, 5.0, 3.0, 0, 4.0, 35.0, 131.0, 7.0, 7.0, 23.0, 17.0, 3.0, 48.0, 67.0, 106.0, 72.0, 11.0,
16.0, 98.0, 6.0, 17.0, 45.0, 7.0, 42.0, 13.0, 0, 4.0, 8.0, 0, 12.0, 63.0, 107.0, 0, 0, 104.0, 98.0,
91.0, 3.0, 46.0, 27.0, 4.0, 34.0, 162.0, 43.0, 109.0, 3.0, 2.0, 48.0, 4.0, 15.0, 0, 0, 0, 11.0, 0, 0,
3.0, 4.0, 29.0, 95.0, 5.0, 0, 7.0, 49.0, 153.0, 225.0, 41.0, 55.0, 30.0, 1.0, 6.0, 131.0, 10.0, 17.0,
9.0, 7.0, 24.0, 37.0, 175.0, 74.0, 28.0, 16.0, 2.0, 24.0, 0, 42.0, 2.0, 29.0, 61.0, 20.0, 7.0, 17.0,
166.0, 13.0, 47.0, 49.0, 30.0, 18.0, 17.0, 1.0, 0, 9.0, 131.0, 89.0, 0, 7.0, 15.0, 15.0, 8.0, 10.0,
7.0, 20.0, 52.0, 58.0, 33.0, 24.0, 46.0, 143.0, 29.0, 3.0, 41.0, 130.0, 95.0, 0, 0, 35.0, 10.0, 20.0,
28.0, 23.0, 10.0, 38.0, 35.0, 38.0, 33.0, 14.0, 81.0, 15.0, 5.0, 0, 8.0, 53.0, 0, 0, 0, 0, 8.0, 35.0,
74.0, 86.0, 2.0, 8.0, 0, 10.0, 36.0, 40.0, 24.0, 9.0, 46.0, 116.0, 108.0, 18.0, 19.0, 139.0, 63.0,
125.0, 0, 103.0, 3.0, 6.0, 34.0, 15.0, 2.0, 0, 14.0, 46.0, 43.0, 59.0, 273.0, 20.0, 87.0, 35.0, 358.0,
110.0, 72.0, 3.0, 69.0, 121.0, 61.0, 42.0, 0, 22.0, 9.0, 54.0, 108.0, 9.0, 17.0, 52.0, 16.0, 11.0,
14.0, 6.0, 74.0, 208.0, 103.0, 148.0, 116.0, 0, 109.0, 133.0, 11.0, 3.0, 19.0, 38.0, 102.0, 3.0, 53.0,
5.0, 129.0, 31.0, 96.0, 93.0, 3.0, 7.0, 38.0, 0, 0, 8.0, 3.0, 15.0, 0, 2.0, 9.0, 0, 19.0, 53.0, 30.0,
8.0, 5.0, 32.0, 39.0, 5.0, 145.0, 148.0, 46.0, 59.0, 71.0, 3.0, 66.0, 73.0, 0, 46.0, 109.0, 56.0, 45.0,
61.0, 38.0, 3.0, 102.0, 3.0, 77.0, 4.0, 385.0, 128.0, 0, 18.0, 48.0, 145.0, 104.0, 8.0, 0, 11.0, 119.0,
26.0, 178.0, 33.0, 8.0, 38.0, 36.0, 103.0, 43.0, 27.0, 84.0, 248.0, 48.0, 106.0, 3.0, 49.0, 123.0,
25.0, 160.0, 22.0, 70.0, 11.0, 119.0, 41.0, 25.0, 29.0, 110.0, 39.0, 45.0, 16.0, 0, 0, 42.0, 2.0,
134.0, 56.0, 0, 244.0, 266.0, 268.0, 268.0, 0, 12.0, 8.0, 10.0, 97.0, 5.0, 41.0, 63.0, 15.0, 17.0, 0,
0, 9.0, 48.0, 36.0, 53.0, 71.0, 48.0, 68.0, 0, 5.0, 4.0, 3.0, 13.0, 24.0, 84.0, 34.0, 233.0, 185.0, 0,
0, 1.0, 13.0, 37.0, 60.0, 10.0, 18.0, 37.0, 9.0, 7.0, 116.0, 98.0, 3.0, 0, 0, 0, 0, 0, 26.0, 20.0,
22.0, 17.0, 123.0, 20.0, 0, 2.0, 52.0, 85.0, 166.0, 74.0, 260.0, 56.0, 24.0, 108.0, 182.0, 112.0, 0,
3.0, 148.0, 336.0, 305.0, 104.0, 507.0, 241.0, 128.0, 11.0, 0, 0, 9.0, 133.0, 0, 13.0, 39.0, 157.0,
71.0]
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, 2.1448735239803902, 2.6037525211067925, nan, 2.2603083372232011, nan,
2.6127995049108428, 2.3231829409682794, 2.3374613586189987, 2.5333765192662261, 2.4449311199186092,
2.4386506908989127, nan, nan, 2.4576114359534245, nan, 2.1857947651244354, nan, 2.3095607089862469,
2.2424211745262577, 2.2444700179119015, 2.3542033608702848, 2.3158396859283155, 2.2889190909678199,
2.2848790088448983, 2.3735441027606732, 2.4216188000805947, 2.2881723542537316, 2.37585720991044,
nan, nan, 2.2974896465011545, nan, nan, 2.1384660272016247, 2.4661167197555898, nan, nan, nan,
2.3095097865415131, 2.2068729988441298, 2.2452686393071497, nan, nan, nan, 2.2923466174034584,
2.1239721325958976, 2.0655419107975344, nan, nan, nan, 2.2938051980444465, 2.5144728278545569,
2.2529072072253924, 2.8050105904416616, nan, nan, nan, 2.2960034676893004, 2.570145415632175,
2.3627255984861075, 2.4493302911993609, 2.4629406755195085, 2.4089204673282918, 2.4211463701989948,
nan, nan, nan, nan, nan, nan, nan, nan, 2.228858208128587, 2.3497639613082284, 2.2532161783982367,
2.3156294168552836, 2.2798710040703187, nan, nan, nan, 2.2776023805030574, 2.52459009822031, nan,
2.33481100102565, nan, nan, 2.2271900344589755, nan, 2.1720304040492655, 2.3688846031746547, nan,
2.4320120484108196, 2.555183856505459, nan, nan, nan, nan, nan, nan, nan, nan, nan,
3.0151055007757011, 3.704733481340214, 2.5652317103877751, 2.3993012273465388, 2.9077950736568403,
2.2632581430154324, nan, nan, 2.3975037631319709, 2.7976136747291549, nan, 2.754699857755754, nan,
nan, 3.205425843995644, 3.0576220989005805, 2.7460992504201438, 2.875313643702448, 2.897542971226811,
nan, nan, nan, nan, 3.5696313273929459, 2.622066298889175, 2.4080447621867607, nan, nan, nan, nan,
1.4699275472770501, 1.4870221294134942, nan, nan, 1.5965483102332503, 1.5630188715728266,
1.6892643802936267, 1.5402595115275644, 1.4335268315383827, nan, 1.4769882847674045,
1.4299635288360966, nan, nan, 1.3854702559822774, 1.501780936637815, 1.5155408995876409,
1.4839672038269558, nan, 1.6616364065246436, 1.5958216240048992, 1.5794950514333932, nan, nan, nan,
nan, nan, 1.4447410186884408, 1.6246126107075605, nan, nan, 1.4852286568169746, 1.4362529526671575,
nan, 1.6988220971805246, 1.7504395548000464, 1.7329181354358214, 1.6952757582074098, nan,
1.806889138828454, 1.8615287068687265, nan, 1.6286799534071075, 1.739688278638001, nan,
1.7243707967024053, 1.5556292332472019, nan, nan, nan, nan, 1.6489354407402395, 1.622050748789523,
1.6959886603266507, nan, nan, 1.8549848918279035, 1.8602351535124053, 1.8212905015849632, nan,
2.091499604660195, 2.022092581933582, nan, 1.8736448024784731, 1.7647859216618669, 1.516239148777361,
1.6182098851610016, nan, nan, 1.5662859626619599, nan, 1.6439625902550259, nan, nan, nan,
1.412741387987521, nan, nan, nan, nan, 1.517377019889786, 1.690487924026387, nan, nan, nan,
1.7259569152635024, 1.6625164542556012, 1.6851326848747772, 1.7152687875007464, 1.9899437087937055,
1.845718296855591, nan, nan, 1.6870337116837417, nan, 1.5932638624072817, 1.6759765665302311, nan,
1.501152191308291, 1.7021802640370207, 1.7073455342611084, 1.7280062645731986, 1.6830487364952784,
1.7057306241488941, nan, nan, nan, 1.8668925981079514, nan, 1.9860757141796705, 2.0222967078683527,
2.0217638824703275, nan, 2.1100770201807575, 2.181914324056331, 2.4877055478817729,
2.2750837238876311, 2.3041217747473195, 1.9105146010678244, 1.7419170952032264, 1.7571477043483177,
nan, nan, nan, 2.0020520563528232, 2.1259998971166509, nan, nan, nan, 2.0292582652733024, nan, nan,
nan, 2.0840462066972085, 2.1057413304775006, 1.9331571603718931, nan, 1.9549607879751751,
1.8728030475590078, 1.874594163478893, 1.9971019948917874, nan, 2.0098242048285888,
2.0703251164315226, 2.0739609835996218, nan, nan, 2.0960705821981866, 2.2232587404578257,
1.9566902430458863, 1.9827170929862858, 1.8776149374547277, nan, 2.087203512921969,
2.0188941412562729, 2.0821890736180149, 2.0077136038802896, nan, nan, nan, nan, nan, nan,
1.9893042848348048, nan, nan, nan, nan, nan, 2.0375290622639852, 1.8163658548810004, nan, nan, nan,
nan, 2.1574380958333652, 2.0359166530653399, 2.0897267745640042, 2.127762987541677, 2.10215548985814,
2.0773613939419433, 2.1726876834333284, 2.1778731592698914, 2.2961852941362508, 2.1464537311264515,
2.0023732381629378, 2.0324804035063737, 2.0102974932564086, nan, 2.4410364806565825, nan, nan,
2.0240260800859202, 2.0530516833454509, nan, nan, 2.850572273390287, 2.2103029330267878,
2.240207203151229, 2.1675191100782039, 2.0944558448335662, 2.2028847204927153, 2.0880557175070384,
2.2223424271774856, 2.1931498215509917, 2.0699987828691562, 1.5706275058497439, nan,
1.6134271328015455, 1.6631808122715623, 1.6957753442720838, 1.6003905956936773, nan,
1.4627356344787112, nan, 1.8495945390984865, 1.5984088052855017, 1.6057120913344112,
1.5641889806412983, 1.654201698997589, 1.3847397803668846, 1.504211420095694, 1.4615179144154709,
nan, 1.4242336835074001, nan, 1.3872796033158397, 1.4653314490793354, 1.3954486651357976, nan,
1.5224598976467503, 1.5447595552710527, nan, nan, 1.5491007698915025, 1.4622659289005775,
1.4345595762789016, nan, 1.9038790833224399, nan, 1.5847995246358049, 1.6046181210658921,
1.528730805874694, 1.6237472804035893, nan, nan, 1.5817190944866453, nan, nan, 1.6596670336622821,
nan, 1.9363860120957868, nan, nan, nan, nan, nan, 1.6019801727486116, 1.5544482520703073, nan, nan,
1.6565314723605868, 1.636210448291582, nan, 1.6579808332545529, 1.6310694043476397,
1.6990578436625592, 1.5180392431486975, 1.7559490720807169, nan, 1.5519202375604602, nan, nan,
1.9422582938991289, 1.7551975586144617, 1.6503845774232762, 1.7779326507693551, 1.8446375591311477,
1.649489709222324, nan, 1.8284222677509308, nan, 1.8945292884429386, nan, 1.7955954900511086, nan,
nan, 1.7822160336641895, nan, nan, 1.7377586183507421, nan, nan, nan, nan, 1.6333938211003511,
1.8102209441461374, 1.8759530923220327, nan, 2.0987972926444867, 1.8580945104750064,
1.84133210701099, 1.9295711398334332, nan, nan, 1.7854199587438815, 1.8484756408510878,
1.6116412317099815, nan, 1.8609225177625102, 1.6672197043482229, 1.5624177448768499,
1.6974379909312332, 1.6322043873929857, nan, nan, nan, nan, nan, nan, nan, nan, 1.7025425535917793,
1.9550349623528032, nan, nan, 2.4613358700128054, nan, 2.0152083067431397, nan, nan, nan, nan,
1.8131628853926731, 1.8311742766546337, nan, 2.3557920552023415, nan, 1.6259563402809181,
1.6936089078564018, nan, 1.6433326010968696, 1.6087062205674676, 1.4746483365964476,
1.490604459513293, nan, nan, nan, 1.6544097226362873, 1.6891929033797293, 1.7294555835141676,
1.7436355588622801, 1.6858054521489878, 1.7646519990939891, nan, nan, nan, nan, 1.7043074531254496,
1.8670352355930091, 1.8032702186994545, 1.4458284821131162, 1.5951426428955346, 1.6360843262290763,
nan, nan, nan, 1.6442412663913777, 1.8015915957689947, nan, nan, 1.9558670004451568, nan, nan, nan,
nan, 1.8234097132612694, nan, nan, nan, nan, nan, nan, 1.4994202184074636, 1.9988634813092903,
1.7507811479138744, 1.8217456094872635, 1.8544432480853166, 1.3552287024762664, nan, nan, nan,
1.5499994745445396, 1.5679686199376213, 1.5465924154852706, 1.5181325165476154, 1.3816450247128009,
1.4751268223826584, 1.4973310771521808, 1.432540843701624, 1.4571435304808307, nan, nan,
1.5859767687237567, 1.5026368839901041, 1.4122282968142958, nan, 1.5245536497436636,
1.5571732754181082, 1.8398249045283666, nan, nan, nan, nan, 1.5789954402898745, nan, nan,
1.4749096121844549, 1.4834155713000503, 1.4921570196665583]
C_stdevs_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, 0.25140752646507386, 0.27027212190321215, nan, 0.13647717773854209, nan,
0.3090557159054213, 0.16339964767631862, 0.22882926344414514, 0.42291686026210351, 0.1181546946270231,
0.21551780701417833, nan, nan, 0.10151800094073797, nan, 0.23663093667238125, nan,
0.18942752521150691, 0.21643696913028071, 0.15221166162539429, 0.13507341537984638,
0.13551987192560364, 0.23412125350741989, 0.12754009472654604, 0.15224065975885884,
0.13175714695269655, 0.13650024254055268, 0.19407317759048964, nan, nan, 0.18862433404804219, nan,
nan, 0.15556813201775116, 0.18326889153085188, nan, nan, nan, 0.16602436582719116,
0.16376434248749261, 0.15456620533198911, nan, nan, nan, 0.20564416482590481, 0.20667227401692514,
0.11276363272977327, nan, nan, nan, 0.18983953462334652, 0.22294312476802311, 0.20445454762484741,
0.32669042827476347, nan, nan, nan, 0.11909732120121702, 0.14658918893732736, 0.17106603908398113,
0.17015488683061694, 0.22465537441660297, 0.20972498537227136, 0.15629533528567952, nan, nan, nan,
nan, nan, nan, nan, nan, 0.097178590425383474, 0.13789032098172438, 0.13766721306676791,
0.37740985163775281, 0.20718340878030841, nan, nan, nan, 0.25453352560379622, 0.2780264163296729, nan,
0.18483313861259315, nan, nan, 0.19652652749715677, nan, 0.17100835534864531, 0.1387220180749934, nan,
0.22077776373637828, 0.23262206316544695, nan, nan, nan, nan, nan, nan, nan, nan, nan,
0.23992620122338465, 0.31767392740162381, 0.19553668453543144, 0.26618601870202413,
0.25647077733492357, 0.14115885298219022, nan, nan, 0.18180285294774512, 0.2140442592354401, nan,
0.11539886808496155, nan, nan, 0.38530840246538461, 0.33008620852906345, 0.30561709961921729,
0.3386269097329021, 0.27232375620750621, nan, nan, nan, nan, 0.26512988002223425, 0.16201366085368318,
0.336353622073289, nan, nan, nan, nan, 0.099370453062266995, 0.13054304678203235, nan, nan,
0.17587625958326727, 0.11532407928765818, 0.10277631413227348, 0.19630716878842699,
0.12976430402661779, nan, 0.17762036986537938, 0.099070767472685783, nan, nan, 0.11179239127178606,
0.12917579763534962, 0.067447636956822002, 0.12390977238475882, nan, 0.16141822685146101,
0.09641791872990188, 0.10620132518188982, nan, nan, nan, nan, nan, 0.10528656597608542,
0.12418406782508461, nan, nan, 0.1813103426236459, 0.12886568611279137, nan, 0.18812544699624936,
0.19928052301347643, 0.20490720169372012, 0.10589020094920452, nan, 0.096763424272466647,
0.13410604819709265, nan, 0.17261427133400134, 0.21829899275509887, nan, 0.11434597639263214,
0.15896633441123939, nan, nan, nan, nan, 0.14688062543423422, 0.12879178409262243,
0.083877123219435773, nan, nan, 0.13881489845853126, 0.14887839449420015, 0.11260893771151831, nan,
0.28941601174362203, 0.15476023968131877, nan, 0.2480480372475555, 0.15394693321066685,
0.15471072675281894, 0.14605698734182862, nan, nan, 0.11117035136027739, nan, 0.15408058834711469,
nan, nan, nan, 0.15425488176887198, nan, nan, nan, nan, 0.11430152120161795, 0.14216535432363678, nan,
nan, nan, 0.16152694926869732, 0.1174657917638379, 0.13466471694295826, 0.18644172680422613,
0.20710535226600926, 0.21042256860792846, nan, nan, 0.16597222076506812, nan, 0.13371081892215436,
0.14153705868841274, nan, 0.19697362513723957, 0.13044481914065015, 0.093719083597176026,
0.15378194900501757, 0.18759321865077108, 0.16726493186241007, nan, nan, nan, 0.17773882584002956,
nan, 0.093645779448510602, 0.16525299072984961, 0.14484593466438442, nan, 0.24967154953514351,
0.17556514529149894, 0.48817049934792794, 0.16765891818985626, 0.2389829162173418,
0.24896876295483761, 0.35927264489022037, 0.12650190792299776, nan, nan, nan, 0.16436547803279278,
0.14097385466300524, nan, nan, nan, 0.2117075399777173, nan, nan, nan, 0.36397327058150653,
0.19559161662954419, 0.14926287658186202, nan, 0.23755006851444657, 0.19530987085539675,
0.15701225587141829, 0.20004514431103923, nan, 0.18456137111873658, 0.17781371142074723,
0.13720759724336576, nan, nan, 0.19035922660064886, 0.28879246210565407, 0.28598955571083634,
0.22877223387452836, 0.15040982819894697, nan, 0.22923318159048509, 0.21563098434965985,
0.12670860971650383, 0.15522672480851105, nan, nan, nan, nan, nan, nan, 0.10913941107125862, nan, nan,
nan, nan, nan, 0.21606801129943837, 0.2132624417966644, nan, nan, nan, nan, 0.11758632412008478,
0.11991417653711031, 0.11615757180865444, 0.15541228739496876, 0.14133844356418165,
0.16905313169497618, 0.17428440669170167, 0.19835445667320545, 0.18516416466382912,
0.15504166591732302, 0.13651811194100494, 0.084507752342638909, 0.1122547189373439, nan,
0.30787470624856378, nan, nan, 0.12585486780109831, 0.18413807212608396, nan, nan,
0.18031862739876142, 0.17548700575897191, 0.21264882069979615, 0.19470342015123415,
0.12195785777019109, 0.17316153455397443, 0.16001877077813512, 0.099219704388852914,
0.1174233696974212, 0.18200787440793953, 0.12723328660988351, nan, 0.06998724649271533,
0.0973291760696917, 0.10610878946030172, 0.082131397692432884, nan, 0.093593131629846532, nan,
0.089128730247280893, 0.1624725837482818, 0.14437549902050797, 0.1735129032021151,
0.10694949390291189, 0.058262708000431353, 0.14420409721762595, 0.089127602323629443, nan,
0.069370637036632876, nan, 0.048936220931651044, 0.078191514566609943, 0.10524439965497658, nan,
0.085225928000782866, 0.088619160520699417, nan, nan, 0.077805860899214124, 0.1278510750364476,
0.09236886950957017, nan, 0.17372312770065543, nan, 0.080700771716399916, 0.09671189540025249,
0.083925313360818904, 0.066177627538084857, nan, nan, 0.10907485538793264, nan, nan,
0.069523609965853966, nan, 0.11106511945451124, nan, nan, nan, nan, nan, 0.099242945479602884,
0.11626899997838132, nan, nan, 0.10351857388150087, 0.14710551108950537, nan, 0.18455419024839501,
0.095320705201969669, 0.067366747504544328, 0.13256795787164335, 0.118768633640742, nan,
0.1643966830536692, nan, nan, 0.17048088569750688, 0.18437020072536967, 0.14699630236169897,
0.095667148002126279, 0.16791920142831052, 0.13311193837040225, nan, 0.095293662026924103, nan,
0.071998162903536805, nan, 0.057164310478396971, nan, nan, 0.12385969129564986, nan, nan,
0.08674727666507577, nan, nan, nan, nan, 0.14611105013573528, 0.14416769183742192,
0.18113927785056169, nan, 0.19949229535894697, 0.17724879414505179, 0.1031436712292243,
0.23190031995043198, nan, nan, 0.083289320814900425, 0.15799078522823096, 0.084940405763101592, nan,
0.36706406068366876, 0.1196822920549345, 0.066092399710274474, 0.1623667280697921,
0.13400203772068769, nan, nan, nan, nan, nan, nan, nan, nan, 0.16853565856852684, 0.23444075984985141,
nan, nan, 0.12557989638757336, nan, 0.19764887508277085, nan, nan, nan, nan, 0.12189470584637509,
0.096741859070037781, nan, 0.19070200126163828, nan, 0.12123786491389899, 0.09489291125513899, nan,
0.18616365635423668, 0.15751103998398067, 0.14021192669537186, 0.17190564454627374, nan, nan, nan,
0.15566010587900214, 0.20157852095854381, 0.12751550002340645, 0.13921774631667341,
0.19730362913317201, 0.1736502465499575, nan, nan, nan, nan, 0.21178193531743758, 0.18741641234912854,
0.19351193474549505, 0.14915295555830768, 0.10774937756226473, 0.086025575040343125, nan, nan, nan,
0.18222689831671104, 0.17713184812228572, nan, nan, 0.16263176012465874, nan, nan, nan, nan,
0.12979238065550572, nan, nan, nan, nan, nan, nan, 0.1104951587460309, 0.38850603298903685,
0.24642786711317974, 0.11360505776132512, 0.14970195661704502, 0.12085634173581203, nan, nan, nan,
0.13292752878784436, 0.165004043101703, 0.10980533991463068, 0.087215087153656751, 0.1541186062364211,
0.17988073557913636, 0.11176138227660665, 0.10811213910888018, 0.14298948320470589, nan, nan,
0.11832659694497054, 0.12021994994189032, 0.073117901363629431, nan, 0.085586504367618971,
0.13335160826289955, 0.16626020848035444, nan, nan, nan, nan, 0.11724555400499893, nan, nan,
0.14922659096963609, 0.118269655892822, 0.15730833073562617]
C_modes_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, 1.9840425531914891, 2.5212765957446805, nan, 2.2872340425531914, nan,
2.771276595744681, 2.3085106382978724, 2.3404255319148937, 2.5053191489361701, 2.4574468085106385,
2.3776595744680851, nan, nan, 2.4308510638297873, nan, 2.3085106382978724, nan, 2.2872340425531914,
2.1329787234042552, 2.228723404255319, 2.3563829787234041, 2.2872340425531914, 2.3191489361702127,
2.271276595744681, 2.4042553191489362, 2.4414893617021276, 2.2553191489361701, 2.4680851063829787, nan,
nan, 2.1861702127659575, nan, nan, 2.1382978723404258, 2.4202127659574466, nan, nan, nan,
2.2340425531914891, 2.2340425531914891, 2.2234042553191489, nan, nan, nan, 2.3138297872340425,
1.9734042553191489, 1.946808510638298, nan, nan, nan, 2.2127659574468086, 2.4734042553191489,
2.2446808510638299, 2.7819148936170208, nan, nan, nan, 2.2819148936170213, 2.5904255319148937,
2.3457446808510638, 2.3031914893617018, 2.4255319148936172, 2.4095744680851063, 2.4148936170212765,
nan, nan, nan, nan, nan, nan, nan, nan, 2.1968085106382977, 2.3563829787234041, 2.207446808510638,
2.3617021276595742, 2.3829787234042552, nan, nan, nan, 2.0797872340425534, 2.5531914893617018, nan,
2.3351063829787231, nan, nan, 2.1276595744680851, nan, 2.1861702127659575, 2.4042553191489362, nan,
2.5212765957446805, 2.5159574468085104, nan, nan, nan, nan, nan, nan, nan, nan, nan,
2.9308510638297873, 3.4627659574468082, 2.4095744680851063, 2.1276595744680851, 2.8297872340425534,
2.228723404255319, nan, nan, 2.3351063829787231, 2.8776595744680851, nan, 2.7340425531914891, nan, nan,
3.75, 2.7978723404255317, 2.6276595744680851, 2.6436170212765959, 2.6914893617021276, nan, nan, nan,
nan, 3.4255319148936172, 2.5957446808510638, 2.3882978723404253, nan, nan, nan, nan,
1.4414893617021276, 1.553191489361702, nan, nan, 1.675531914893617, 1.5265957446808509,
1.6595744680851063, 1.6914893617021276, 1.4468085106382977, nan, 1.4680851063829787,
1.4148936170212767, nan, nan, 1.3085106382978724, 1.5106382978723403, 1.5319148936170213,
1.4734042553191489, nan, 1.6702127659574466, 1.5691489361702127, 1.5797872340425532, nan, nan, nan,
nan, nan, 1.4840425531914891, 1.5691489361702127, nan, nan, 1.4308510638297871, 1.3989361702127658,
nan, 1.7659574468085106, 1.6382978723404256, 1.553191489361702, 1.6542553191489362, nan,
1.728723404255319, 1.8617021276595744, nan, 1.7127659574468086, 1.7127659574468086, nan,
1.6223404255319149, 1.6063829787234041, nan, nan, nan, nan, 1.5053191489361701, 1.5957446808510638,
1.6968085106382977, nan, nan, 1.803191489361702, 1.7872340425531914, 1.8351063829787233, nan,
2.0585106382978724, 2.0319148936170213, nan, 1.7765957446808509, 1.7446808510638296,
1.6861702127659572, 1.5691489361702127, nan, nan, 1.6223404255319149, nan, 1.5212765957446808, nan,
nan, nan, 1.3085106382978724, nan, nan, nan, nan, 1.4627659574468084, 1.675531914893617, nan, nan, nan,
1.5904255319148934, 1.6595744680851063, 1.7499999999999998, 1.6648936170212765, 1.9521276595744681,
1.7819148936170213, nan, nan, 1.7074468085106382, nan, 1.5265957446808509, 1.6489361702127658, nan,
1.5, 1.675531914893617, 1.7074468085106382, 1.7553191489361701, 1.7712765957446805, 1.7553191489361701,
nan, nan, nan, 1.8989361702127661, nan, 2.021276595744681, 2.0372340425531914, 1.9308510638297871, nan,
2.2021276595744679, 2.1808510638297873, 2.1861702127659575, 2.271276595744681, 2.2127659574468086,
1.9255319148936172, 1.728723404255319, 1.8829787234042552, nan, nan, nan, 2.0851063829787235,
2.0478723404255317, nan, nan, nan, 1.9734042553191489, nan, nan, nan, 1.6489361702127658,
2.0691489361702127, 1.9521276595744681, nan, 1.7659574468085106, 1.8723404255319149,
1.8138297872340425, 1.9787234042553192, nan, 2.0265957446808511, 2.1861702127659575,
2.0478723404255317, nan, nan, 1.9680851063829787, 2.1861702127659575, 2.1010638297872339,
1.957446808510638, 1.8297872340425529, nan, 2.1489361702127656, 1.9361702127659572, 2.0797872340425534,
1.946808510638298, nan, nan, nan, nan, nan, nan, 2.0478723404255317, nan, nan, nan, nan, nan,
2.0638297872340425, 1.8776595744680848, nan, nan, nan, nan, 2.1063829787234041, 2.0425531914893615,
2.1329787234042552, 2.1276595744680851, 2.0851063829787235, 2.021276595744681, 2.0851063829787235,
2.0425531914893615, 2.2872340425531914, 2.1329787234042552, 2.0106382978723403, 2.0159574468085104,
2.0265957446808511, nan, 2.4840425531914896, nan, nan, 2.0053191489361701, 2.3829787234042552, nan,
nan, 2.8776595744680851, 2.1276595744680851, 2.1436170212765955, 2.1808510638297873,
2.0797872340425534, 2.0585106382978724, 2.0478723404255317, 2.2021276595744679, 2.1755319148936167,
2.0372340425531914, 1.5851063829787233, nan, 1.6010638297872342, 1.6808510638297873,
1.7021276595744681, 1.6170212765957446, nan, 1.3829787234042552, nan, 1.803191489361702,
1.5265957446808509, 1.6170212765957446, 1.5106382978723403, 1.6648936170212765, 1.3829787234042552,
1.3936170212765957, 1.5212765957446808, nan, 1.425531914893617, nan, 1.3989361702127658,
1.4414893617021276, 1.3617021276595744, nan, 1.5, 1.5212765957446808, nan, nan, 1.5265957446808509,
1.5212765957446808, 1.4574468085106382, nan, 1.9202127659574468, nan, 1.6223404255319149,
1.4893617021276595, 1.4787234042553192, 1.6117021276595744, nan, nan, 1.5797872340425532, nan, nan,
1.6595744680851063, nan, 1.9308510638297871, nan, nan, nan, nan, nan, 1.5691489361702127,
1.5372340425531914, nan, nan, 1.6489361702127658, 1.6648936170212765, nan, 1.6170212765957446,
1.6010638297872342, 1.6968085106382977, 1.4946808510638299, 1.7819148936170213, nan,
1.4361702127659575, nan, nan, 1.946808510638298, 1.6648936170212765, 1.6276595744680851,
1.7446808510638296, 1.8882978723404256, 1.6276595744680851, nan, 1.8244680851063828, nan,
1.8989361702127661, nan, 1.7819148936170213, nan, nan, 1.7553191489361701, nan, nan, 1.728723404255319,
nan, nan, nan, nan, 1.6595744680851063, 1.7819148936170213, 1.8191489361702129, nan,
2.0585106382978724, 1.7872340425531914, 1.8297872340425529, 1.8989361702127661, nan, nan,
1.7978723404255317, 1.8882978723404256, 1.6010638297872342, nan, 1.8882978723404256,
1.6223404255319149, 1.5372340425531914, 1.7180851063829785, 1.5425531914893615, nan, nan, nan, nan,
nan, nan, nan, nan, 1.6117021276595744, 1.8191489361702129, nan, nan, 2.436170212765957, nan,
2.0159574468085104, nan, nan, nan, nan, 1.7819148936170213, 1.8191489361702129, nan,
2.3085106382978724, nan, 1.4680851063829787, 1.6489361702127658, nan, 1.6489361702127658,
1.7021276595744681, 1.4680851063829787, 1.4893617021276595, nan, nan, nan, 1.6542553191489362,
1.4946808510638299, 1.7234042553191489, 1.7127659574468086, 1.5797872340425532, 1.8404255319148937,
nan, nan, nan, nan, 1.5797872340425532, 1.9893617021276595, 1.8510638297872337, 1.4468085106382977,
1.5691489361702127, 1.675531914893617, nan, nan, nan, 1.5319148936170213, 1.9202127659574468, nan, nan,
1.8563829787234041, nan, nan, nan, nan, 1.8297872340425529, nan, nan, nan, nan, nan, nan,
1.4840425531914891, 1.3936170212765957, 1.6968085106382977, 1.7499999999999998, 1.8989361702127661,
1.2393617021276595, nan, nan, nan, 1.5, 1.5, 1.5372340425531914, 1.5212765957446808,
1.2925531914893618, 1.3989361702127658, 1.5265957446808509, 1.4414893617021276, 1.4840425531914891,
nan, nan, 1.5053191489361701, 1.5, 1.404255319148936, nan, 1.5319148936170213, 1.5851063829787233,
1.7659574468085106, nan, nan, nan, nan, 1.5159574468085106, nan, nan, 1.3457446808510638,
1.4627659574468084, 1.4521276595744681]
class D_NK():
Dates = ['2011/03/09', '2011/03/10', '2011/03/11', '2011/03/12', '2011/03/13', '2011/03/14', '2011/03/15',
'2011/03/16', '2011/03/17', '2011/03/18', '2011/03/19', '2011/03/20', '2011/03/21', '2011/03/22',
'2011/03/23', '2011/03/24', '2011/03/25', '2011/03/26', '2011/03/27', '2011/03/28', '2011/03/29',
'2011/03/30', '2011/03/31', '2011/04/01', '2011/04/02', '2011/04/03', '2011/04/04', '2011/04/05',
'2011/04/06', '2011/04/07', '2011/04/08', '2011/04/09', '2011/04/10', '2011/04/11', '2011/04/12',
'2011/04/13', '2011/04/14', '2011/04/15', '2011/04/16', '2011/04/17', '2011/04/18', '2011/04/19',
'2011/04/20', '2011/04/21', '2011/04/22', '2011/04/23', '2011/04/24', '2011/04/25', '2011/04/26',
'2011/04/27', '2011/04/28', '2011/04/29', '2011/04/30', '2011/05/01', '2011/05/02', '2011/05/03',
'2011/05/04', '2011/05/05', '2011/05/06', '2011/05/07', '2011/05/08', '2011/05/09', '2011/05/10',
'2011/05/11', '2011/05/12', '2011/05/13', '2011/05/14', '2011/05/15', '2011/05/16', '2011/05/17',
'2011/05/18', '2011/05/19', '2011/05/20', '2011/05/21', '2011/05/22', '2011/05/23', '2011/05/24',
'2011/05/25', '2011/05/26', '2011/05/27', '2011/05/28', '2011/05/29', '2011/05/30', '2011/05/31',
'2011/06/01', '2011/06/02', '2011/06/03', '2011/06/04', '2011/06/05', '2011/06/06', '2011/06/07',
'2011/06/08', '2011/06/09', '2011/06/10', '2011/06/11', '2011/06/12', '2011/06/13', '2011/06/14',
'2011/06/15', '2011/06/16', '2011/06/17', '2011/06/18', '2011/06/19', '2011/06/20', '2011/06/21',
'2011/06/22', '2011/06/23', '2011/06/24', '2011/06/25', '2011/06/26', '2011/06/27', '2011/06/28',
'2011/06/29', '2011/06/30', '2011/07/01', '2011/07/02', '2011/07/03', '2011/07/04', '2011/07/05',
'2011/07/06', '2011/07/07', '2011/07/08', '2011/07/09', '2011/07/10', '2011/07/11', '2011/07/12',
'2011/07/13', '2011/07/14', '2011/07/15', '2011/07/16', '2011/07/17', '2011/07/18', '2011/07/19',
'2011/07/20', '2011/07/21', '2011/07/22', '2011/07/23', '2011/07/24', '2011/07/25', '2011/07/26',
'2011/07/27', '2011/07/28', '2011/07/29', '2011/07/30', '2011/07/31', '2011/08/01', '2011/08/02',
'2011/08/03', '2011/08/04', '2011/08/05', '2011/08/06', '2011/08/07', '2011/08/08', '2011/08/09',
'2011/08/10', '2011/08/11', '2011/08/12', '2011/08/13', '2011/08/14', '2011/08/15', '2011/08/16',
'2011/08/17', '2011/08/18', '2011/08/19', '2011/08/20', '2011/08/21', '2011/08/22', '2011/08/23',
'2011/08/24', '2011/08/25', '2011/08/26', '2011/08/27', '2011/08/28', '2011/08/29', '2011/08/30',
'2011/08/31', '2011/09/01', '2011/09/02', '2011/09/03', '2011/09/04', '2011/09/05', '2011/09/06',
'2011/09/07', '2011/09/08', '2011/09/09', '2011/09/10', '2011/09/11', '2011/09/12', '2011/09/13',
'2011/09/14', '2011/09/15', '2011/09/16', '2011/09/17', '2011/09/18', '2011/09/19', '2011/09/20',
'2011/09/21', '2011/09/22', '2011/09/23', '2011/09/24', '2011/09/25', '2011/09/26', '2011/09/27',
'2011/09/28', '2011/09/29', '2011/09/30', '2011/10/01', '2011/10/02', '2011/10/03', '2011/10/04',
'2011/10/05', '2011/10/06', '2011/10/07', '2011/10/08', '2011/10/09', '2011/10/10', '2011/10/11',
'2011/10/12', '2011/10/13', '2011/10/14', '2011/10/15', '2011/10/16', '2011/10/17', '2011/10/18',
'2011/10/19', '2011/10/20', '2011/10/21', '2011/10/22', '2011/10/23', '2011/10/24', '2011/10/25',
'2011/10/26', '2011/10/27', '2011/10/28', '2011/10/29', '2011/10/30', '2011/10/31', '2011/11/01',
'2011/11/02', '2011/11/03', '2011/11/04', '2011/11/05', '2011/11/06', '2011/11/07', '2011/11/08',
'2011/11/09', '2011/11/10', '2011/11/11', '2011/11/12', '2011/11/13', '2011/11/14', '2011/11/15',
'2011/11/16', '2011/11/17', '2011/11/18', '2011/11/19', '2011/11/20', '2011/11/21', '2011/11/22',
'2011/11/23', '2011/11/24', '2011/11/25', '2011/11/26', '2011/11/27', '2011/11/28', '2011/11/29',
'2011/11/30', '2011/12/01', '2011/12/02', '2011/12/03', '2011/12/04', '2011/12/05', '2011/12/06',
'2011/12/07', '2011/12/08', '2011/12/09', '2011/12/10', '2011/12/11', '2011/12/12', '2011/12/13',
'2011/12/14', '2011/12/15', '2011/12/16', '2011/12/17', '2011/12/18', '2011/12/19', '2011/12/20',
'2011/12/21', '2011/12/22', '2011/12/23', '2011/12/24', '2011/12/25', '2011/12/26', '2011/12/27',
'2011/12/28', '2011/12/29', '2011/12/30', '2011/12/31', '2012/01/01', '2012/01/02', '2012/01/03',
'2012/01/04', '2012/01/05', '2012/01/06', '2012/01/07', '2012/01/08', '2012/01/09', '2012/01/10',
'2012/01/11', '2012/01/12', '2012/01/13', '2012/01/14', '2012/01/15', '2012/01/16', '2012/01/17',
'2012/01/18', '2012/01/19', '2012/01/20', '2012/01/21', '2012/01/22', '2012/01/23', '2012/01/24',
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'2015/03/04', '2015/03/05', '2015/03/06', '2015/03/07', '2015/03/08', '2015/03/09', '2015/03/10',
'2015/03/11', '2015/03/12', '2015/03/13', '2015/03/14', '2015/03/15', '2015/03/16', '2015/03/17',
'2015/03/18', '2015/03/19', '2015/03/20', '2015/03/21', '2015/03/22', '2015/03/23', '2015/03/24',
'2015/03/25', '2015/03/26', '2015/03/27', '2015/03/28', '2015/03/29', '2015/03/30', '2015/03/31',
'2015/04/01', '2015/04/02', '2015/04/03', '2015/04/04', '2015/04/05', '2015/04/06', '2015/04/07',
'2015/04/08', '2015/04/09', '2015/04/10', '2015/04/11', '2015/04/12', '2015/04/13', '2015/04/14',
'2015/04/15', '2015/04/16', '2015/04/17', '2015/04/18', '2015/04/19', '2015/04/20', '2015/04/21',
'2015/04/22', '2015/04/23', '2015/04/24', '2015/04/25', '2015/04/26', '2015/04/27', '2015/04/28',
'2015/04/29', '2015/04/30', '2015/05/01', '2015/05/02', '2015/05/03', '2015/05/04', '2015/05/05',
'2015/05/06', '2015/05/07', '2015/05/08', '2015/05/09', '2015/05/10', '2015/05/11', '2015/05/12',
'2015/05/13', '2015/05/14', '2015/05/15', '2015/05/16', '2015/05/17', '2015/05/18', '2015/05/19',
'2015/05/20', '2015/05/21', '2015/05/22', '2015/05/23', '2015/05/24', '2015/05/25', '2015/05/26',
'2015/05/27', '2015/05/28', '2015/05/29', '2015/05/30', '2015/05/31', '2015/06/01', '2015/06/02',
'2015/06/03', '2015/06/04', '2015/06/05', '2015/06/06', '2015/06/07', '2015/06/08', '2015/06/09',
'2015/06/10', '2015/06/11', '2015/06/12', '2015/06/13', '2015/06/14', '2015/06/15', '2015/06/16',
'2015/06/17', '2015/06/18', '2015/06/19', '2015/06/20', '2015/06/21', '2015/06/22', '2015/06/23',
'2015/06/24', '2015/06/25', '2015/06/26', '2015/06/27', '2015/06/28', '2015/06/29', '2015/06/30',
'2015/07/01', '2015/07/02', '2015/07/03', '2015/07/04', '2015/07/05', '2015/07/06', '2015/07/07',
'2015/07/08', '2015/07/09', '2015/07/10', '2015/07/11', '2015/07/12', '2015/07/13', '2015/07/14',
'2015/07/15', '2015/07/16', '2015/07/17', '2015/07/18', '2015/07/19', '2015/07/20', '2015/07/21',
'2015/07/22', '2015/07/23', '2015/07/24', '2015/07/25', '2015/07/26', '2015/07/27', '2015/07/28',
'2015/07/29', '2015/07/30', '2015/07/31', '2015/08/01', '2015/08/02', '2015/08/03', '2015/08/04',
'2015/08/05', '2015/08/06', '2015/08/07', '2015/08/08', '2015/08/09', '2015/08/10', '2015/08/11',
'2015/08/12', '2015/08/13', '2015/08/14', '2015/08/15', '2015/08/16', '2015/08/17', '2015/08/18',
'2015/08/19', '2015/08/20', '2015/08/21', '2015/08/22', '2015/08/23', '2015/08/24', '2015/08/25',
'2015/08/26', '2015/08/27', '2015/08/28', '2015/08/29', '2015/08/30', '2015/08/31', '2015/09/01',
'2015/09/02', '2015/09/03', '2015/09/04', '2015/09/05', '2015/09/06', '2015/09/07', '2015/09/08',
'2015/09/09', '2015/09/10', '2015/09/11', '2015/09/12', '2015/09/13', '2015/09/14', '2015/09/15',
'2015/09/16', '2015/09/17', '2015/09/18', '2015/09/19', '2015/09/20', '2015/09/22', '2015/09/23',
'2015/09/24', '2015/09/25', '2015/09/26', '2015/09/27', '2015/09/28', '2015/09/29', '2015/09/30',
'2015/10/01', '2015/10/02', '2015/10/03', '2015/10/04', '2015/10/05', '2015/10/06', '2015/10/07',
'2015/10/08', '2015/10/09', '2015/10/10', '2015/10/11', '2015/10/12', '2015/10/13', '2015/10/14',
'2015/10/15', '2015/10/16', '2015/10/17', '2015/10/18', '2015/10/19', '2015/10/20', '2015/10/21',
'2015/10/22', '2015/10/23', '2015/10/24', '2015/10/25', '2015/10/26', '2015/10/27', '2015/10/28',
'2015/10/29', '2015/10/30', '2015/10/31', '2015/11/01', '2015/11/02', '2015/11/03', '2015/11/04',
'2015/11/05', '2015/11/06', '2015/11/07', '2015/11/08', '2015/11/09', '2015/11/10', '2015/11/11',
'2015/11/12', '2015/11/13', '2015/11/14', '2015/11/15', '2015/11/16', '2015/11/17', '2015/11/18',
'2015/11/19', '2015/11/20', '2015/11/21', '2015/11/22', '2015/11/23', '2015/11/24', '2015/11/25',
'2015/11/26', '2015/11/27', '2015/11/28', '2015/11/29', '2015/11/30', '2015/12/01', '2015/12/02',
'2015/12/03', '2015/12/04', '2015/12/05', '2015/12/06', '2015/12/07', '2015/12/08', '2015/12/09',
'2015/12/10', '2015/12/11', '2015/12/12', '2015/12/13', '2015/12/14', '2015/12/15', '2015/12/16',
'2015/12/17', '2015/12/18', '2015/12/19', '2015/12/20', '2015/12/21', '2015/12/22', '2015/12/23',
'2015/12/24', '2015/12/25', '2015/12/26', '2015/12/27', '2015/12/28', '2015/12/29', '2015/12/30',
'2015/12/31', '2016/01/01', '2016/01/02', '2016/01/03', '2016/01/04', '2016/01/05', '2016/01/06',
'2016/01/07', '2016/01/08', '2016/01/09', '2016/01/10', '2016/01/11', '2016/01/12', '2016/01/13',
'2016/01/14', '2016/01/15', '2016/01/16', '2016/01/17', '2016/01/18', '2016/01/19', '2016/01/20',
'2016/01/21', '2016/01/22', '2016/01/23', '2016/01/24', '2016/01/25', '2016/01/26', '2016/01/27',
'2016/01/28', '2016/01/29', '2016/01/30', '2016/01/31', '2016/02/01', '2016/02/02', '2016/02/03',
'2016/02/04', '2016/02/05', '2016/02/06', '2016/02/07', '2016/02/08', '2016/02/09', '2016/02/10',
'2016/02/11', '2016/02/12', '2016/02/13', '2016/02/14', '2016/02/15', '2016/02/16', '2016/02/17',
'2016/02/18', '2016/02/19', '2016/02/20', '2016/02/21', '2016/02/22', '2016/02/23', '2016/02/24',
'2016/02/25', '2016/02/26', '2016/02/27', '2016/02/28', '2016/02/29', '2016/03/01', '2016/03/02',
'2016/03/03', '2016/03/04', '2016/03/05', '2016/03/06', '2016/03/07', '2016/03/08', '2016/03/09',
'2016/03/10', '2016/03/11', '2016/03/12', '2016/03/13', '2016/03/14', '2016/03/15', '2016/03/16',
'2016/03/17', '2016/03/18', '2016/03/19', '2016/03/20', '2016/03/21', '2016/03/22', '2016/03/23',
'2016/03/24', '2016/03/25', '2016/03/26', '2016/03/27', '2016/03/28', '2016/03/29', '2016/03/30',
'2016/03/31', '2016/04/01', '2016/04/02', '2016/04/03', '2016/04/04', '2016/04/05', '2016/04/06',
'2016/04/07', '2016/04/08', '2016/04/09', '2016/04/10', '2016/04/11', '2016/04/12', '2016/04/13',
'2016/04/14', '2016/04/15', '2016/04/16', '2016/04/17', '2016/04/18', '2016/04/19', '2016/04/20',
'2016/04/21', '2016/04/22', '2016/04/23', '2016/04/24', '2016/04/25', '2016/04/26', '2016/04/27',
'2016/04/28', '2016/04/29', '2016/04/30', '2016/05/01', '2016/05/02', '2016/05/03', '2016/05/04',
'2016/05/05', '2016/05/06', '2016/05/07', '2016/05/08', '2016/05/09', '2016/05/10', '2016/05/11',
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'2016/05/19', '2016/05/20', '2016/05/21', '2016/05/22', '2016/05/23', '2016/05/24', '2016/05/25',
'2016/05/26', '2016/05/27', '2016/05/28', '2016/05/29', '2016/05/30', '2016/05/31', '2016/06/01',
'2016/06/02', '2016/06/03', '2016/06/04', '2016/06/05', '2016/06/06', '2016/06/07', '2016/06/08',
'2016/06/09', '2016/06/10', '2016/06/11', '2016/06/12', '2016/06/13', '2016/06/14', '2016/06/15',
'2016/06/16', '2016/06/17', '2016/06/18', '2016/06/19', '2016/06/20', '2016/06/21', '2016/06/22',
'2016/06/23']
C_stdevs = [0.10338110288770763, nan, 0.12951078637151478, 0.15739686475236536, 0.27382092538983865,
0.11973163918714794, nan, nan, 0.16487151341808023, 0.17566528258021749, nan, nan, 0.16659878500577061,
0.12333995854819103, nan, nan, nan, nan, nan, nan, nan, 0.21736953819522351, 0.26146617719003656,
0.10386124786506923, nan, 0.1233636409765257, 0.14553562186520358, 0.096968120313199208,
0.080506774133086184, 0.11067885417486688, 0.086752889232984395, nan, nan, nan, nan,
0.096372368931333621, 0.074200859939718825, 0.12867234193386651, 0.077658672673859785, nan, nan, nan,
nan, nan, nan, nan, nan, nan, 0.12454235136853761, nan, 0.094790919862268327, 0.10169138054053975, nan,
nan, nan, nan, nan, nan, nan, 0.16107081526218497, 0.14628708526100728, nan, nan, 0.12279367189982902,
0.10644061466601171, 0.11102234723878385, 0.11766068695025, 0.090992040524238205, 0.080482682456203228,
0.11340902621851527, 0.13587710343240489, nan, 0.12798137479790103, 0.14287717071363679,
0.11024101447520332, 0.048064749875346834, nan, nan, 0.11066182431149479, 0.085390926315243795,
0.1130230216958456, 0.1119976082154959, 0.15056655412429029, 0.15636417627235041, nan, nan, nan,
0.12767151594347112, 0.087216382388013339, nan, 0.15352043616170116, 0.10891100084028681,
0.11556075401991152, 0.11472562687908837, 0.19186704282927494, 0.073369404892571585, 0.12047122326463128,
0.14878886824254722, 0.14483013591989533, 0.15536593770172286, 0.11752370865036485, 0.10602788893147967,
0.12988571856716274, 0.14005839598917355, 0.1083039726140851, 0.11831237395906312, 0.17634808414283484,
nan, 0.10193169470488994, 0.08210808399863545, nan, 0.19292636849077266, nan, nan, nan, nan, nan, nan,
0.13484110048094378, 0.12220167565367607, 0.13903499634081559, 0.12688510036331904, 0.13999684595644221,
0.1312969393103616, nan, 0.1098476621968828, 0.10863860099806381, 0.16232352637216874,
0.11367053893782671, 0.14757508932061181, 0.12105439415313589, 0.092605311833922344, 0.17397736258176183,
0.097485104104253067, 0.19152540019750533, 0.20816713110086299, 0.18877884185863003, 0.13013242591095381,
nan, 0.10390250931245926, 0.15399920451026006, 0.14402142646309521, 0.19110175366965071,
0.1272526433476199, nan, 0.085563880488248742, nan, nan, 0.17727579112554745, 0.16675888625605506,
0.13085756362593848, 0.21302458308514619, 0.15844547432278766, 0.098470027841042695, 0.12662690761480819,
0.10860535712200495, 0.15135930608457288, 0.11991573824669412, 0.11409585632840458, 0.1389657152264617,
0.12482097925192197, 0.36356630005601176, 0.18492719228996218, 0.24579325531316845, 0.14014640614702267,
0.14052000319544247, 0.16440449178940336, 0.1373988837532637, 0.17039170835949588, 0.15484655072753825,
0.14651761894896273, 0.2465845544979767, 0.12771841386452917, 0.092965089826083355, 0.095895240552693925,
0.15267379887751298, nan, nan, 0.16251037763527582, 0.15709616347359476, nan, 0.095070003502512171, nan,
0.13033592514160339, 0.12881214335559057, 0.15359407413344758, nan, 0.077980595733668112, nan,
0.11526044654909857, nan, 0.057863124542134117, 0.11424671951970231, 0.21649233933812681,
0.17041814370498737, 0.11020840430316732, 0.10930454831114528, nan, nan, nan, 0.1267641702808667,
0.14556719087260009, 0.14469130790709964, nan, nan, nan, nan, nan, 0.12757667133606132,
0.16662928691383538, 0.11387861160908518, 0.11756284501572853, 0.1214661513633644, 0.12150915112434221,
0.12526685494944473, 0.089998498019261949, 0.12951720845180417, 0.15511593911627841, 0.15974488754022081,
0.14403429431618803, nan, 0.15409607876802758, 0.13635166027367598, nan, nan, 0.1088781728762812,
0.18775556255232378, 0.076386088503572078, 0.070565925209220365, 0.10032698992857074,
0.12664930576207148, 0.17775600198048447, 0.18626931276253356, 0.17969143522483111, 0.10725259668259149,
0.0995498578634883, 0.11501627854882167, 0.13302270039759917, 0.10404291988587221, 0.11716784810180299,
0.12305657277756149, 0.13158820980254785, 0.16357489240763068, nan, 0.14150037883204422,
0.17639105448845691, nan, nan, 0.14645472352155625, nan, 0.12676755477545126, 0.12673954345051666, nan,
0.24219143239387358, 0.18133964944313233, nan, nan, nan, 0.19400749453626881, nan, 0.10078255903921803,
nan, 0.07516822670763329, 0.081085118765325148, 0.12792407432560596, 0.093988390744904587, nan,
0.12765664489476974, 0.17152883559702592, 0.065203050079258251, 0.097986007121722202, nan, nan, nan,
0.097259103378865855, nan, 0.20242310222946983, 0.16739509376829864, nan, 0.11639014624679118, nan,
0.080488027141938243, 0.14328936814797533, 0.1601475031864831, 0.1114299271096649, 0.14615756684210426,
0.19281293816025219, 0.082701773542521242, 0.12044699520213967, 0.08301739377153472,
0.093558110558726509, 0.075844884272452576, 0.09754265743492839, 0.090872842806842907,
0.16753686384045721, nan, nan, 0.10536813213420107, 0.085137647833504304, nan, nan, 0.053485424674702672,
0.079193237261585253, 0.09056821301851832, 0.090655053352077325, 0.09215230145244975, 0.1657570489705226,
0.092500878582798429, 0.095380823951133745, 0.083160655673355649, nan, 0.085684031167945318,
0.072061180275769438, nan, nan, 0.093626225207425667, 0.103083331579267, 0.053829842559040351,
0.093947054106836445, 0.08995301594237616, nan, 0.056810028627061131, 0.10506667537535851,
0.11309519776493958, 0.10942200523686496, 0.12184928389903231, 0.13929584732566261, nan, nan, nan, nan,
0.13705717653611774, 0.26805497029496633, 0.13610127284929149, 0.19778615372361705, 0.25338045061333869,
0.12165979624720012, 0.14592927665527253, 0.18224606199781948, nan, 0.12210417774946085,
0.14031135667271435, nan, 0.096935975872422975, 0.22645218729091582, 0.13700855306817858,
0.082487179767291835, 0.089623792763926666, 0.16956642175367259, 0.095131603539343371,
0.073438925616703482, 0.098429587513603478, 0.21862276797100058, 0.16886534340952661,
0.15560548129081983, 0.19785487189450029, 0.15656400598535286, 0.16873143099987103, nan, nan, nan, nan,
0.10487847526531244, 0.11522831789897357, 0.07736319618409547, nan, 0.12494669829946765,
0.1711757456784104, nan, 0.11501551395436961, 0.17871942371032823, 0.19448613218287047, nan,
0.083599387598676286, 0.13901527458798704, 0.25783140530884435, 0.083366985931525597,
0.14882423507961579, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.27602300542693547, nan, nan,
0.18281694143660204, 0.15159215087517311, nan, 0.13853920901626177, 0.18299053624797815,
0.15152982211232638, 0.087311128050064829, 0.13480863041171914, 0.17759046288305541, 0.18075710386619304,
nan, 0.28597878913296398, 0.084857716415993323, 0.14341846022470953, 0.1510864039182554,
0.17011131297852836, 0.17214706610079067, 0.1726440010579906, 0.1634092539418, 0.12669214230031897, nan,
0.13346726042267792, nan, nan, 0.09626354735361782, nan, 0.11145783136157757, nan, 0.21668459610615262,
0.20796384193876513, 0.10320668598217736, 0.11567147750986555, 0.11309578794567007, 0.22402694727053168,
0.17552310901193824, 0.18381034716736872, 0.12127135615042049, 0.094658357260741177, 0.14057434899902863,
0.14670224892458242, nan, 0.12161479657388877, 0.16064247101935625, 0.12854353501489751,
0.092876258540429926, 0.12171030228801555, 0.17018563091998909, 0.11028809352813668, 0.14797445790488695,
0.11161298055786113, 0.15844367820747574, nan, nan, nan, nan, nan, nan, nan, 0.13699392881279171,
0.29005452264413006, 0.14924469449452074, nan, 0.1173858957271296, 0.13491009863195497,
0.13438549267060942, 0.13278001273310777, nan, nan, nan, nan, 0.11742362377191386, nan, nan, nan,
0.087957110641244826, 0.13211061749114356, 0.14932001706566553, 0.21926650379794438, nan,
0.23829078884249824, 0.10065762429850478, 0.12775182176114019, 0.1279120693855369, 0.15572200826654573,
0.20406104611278031, 0.1806302692740844, nan, 0.27196072217753481, 0.23917725640909951, nan,
0.45239877824985952, 0.26785892211446016, 0.2572312931843731, 0.19408336960097208, 0.3434165714296169,
0.15935961674263238, 0.19154260563732783, 0.24230870680994063, 0.24675989923766917, 0.21400916831291605,
nan, 0.22509488156083135, 0.33022945627807576, 0.2204110382407708, 0.82404863343011603,
0.6449824873679576, 0.69711781590298838, 0.40283010087496501, 0.78591794871173337, 0.22076459030157847,
nan, nan, nan, nan, nan, nan, 0.38924878668818164, nan, 0.11783911721934152, 0.14429551063352908,
0.13594513039257961, 0.13284990760461485, 0.1365209331007875, 0.28563515862742689, nan,
0.31246573543894662, 0.3837646381193206, nan, nan, nan, nan, nan, 0.38708798188450078,
0.42308593942481504, nan, nan, 0.19967112991175592, nan, nan, 0.25600706581806765, 0.24885359440174543,
0.15339768411894592, 0.20025227116584721, 0.14484088601970463, 0.31073167286907705, 0.15751924515376461,
nan, 0.11247068209829277, 0.11882443455450596, 0.16493240528898312, nan, 0.18241235034225792,
0.21034800242056448, 0.30638120044942785, 0.35759676414600744, nan, nan, nan, nan, nan,
0.20613276949037226, 0.19108542456206085, 0.36477148131711562, 0.10477740299026191, 0.12242764022736602,
nan, 0.15323523818974341, 0.15314778012020874, 0.14192899819799068, nan, 0.24827059967647785,
0.25117469263403874, nan, 0.10208730929285179, nan, nan, 0.18810098861111446, 0.17127674972577045,
0.21598813636274281, nan, 0.12122694739897803, 0.1173015160660153, nan, nan, 0.16976396515429279,
0.14163396840126954, 0.18751259037759421, nan, nan, 0.11925237820742632, 0.16281754061746048,
0.14911706668078484, 0.10510274796933693, 0.20197615891064294, 0.18029059711336481, 0.11769570367384627,
nan, 0.073484168709870012, 0.097308446973190549, 0.25221545625498099, 0.14576352407917814,
0.16155444852443163, nan, nan, nan, 0.15122860627341575, 0.10773346943068454, 0.15799653102504171,
0.1708978166072363, 0.20254046688962069, 0.16712795357261789, 0.18242518509997174, 0.325402239003171,
nan, 0.19582081139044386, 0.14498715580341856, nan, 0.12125319460558413, 0.12671041163243688,
0.17983366291067207, 0.11455416155406624, 0.15187864030399634, nan, 0.12957307595983827,
0.13016096309128783, 0.33219893604478146, 0.21857141472833572, 0.17949889814019146, 0.22228253750699054,
nan, 0.13906509767147252, 0.12443971396942649, nan, nan, nan, nan, 0.17686931745415221,
0.19087363925221004, 0.18559128532506927, 0.10217539032956412, 0.15602178479360446, 0.19255587862928905,
0.14588172945967048, nan, 0.13144926213209715, nan, 0.17988278806802063, nan, 0.16301216563835558,
0.27537052730224959, 0.18609787524240615, 0.12994159572553771, nan, 0.16999959109086593,
0.15572160687083331, 0.1094444337231911, 0.1309303863116569, 0.15941516221837396, nan,
0.20016357395947149, 0.15374451260632438, 0.15959856307058834, 0.17944973097028197, 0.070225277633306535,
0.09865818128369816, 0.13788729725642457, 0.1198119120342122, 0.096957559536424112, 0.16236772596958671,
0.091567604696218774, 0.093373759806163664, nan, 0.084624610415287488, 0.12600498055432227,
0.31065200841002133, 0.12115174985786874, 0.13549146810421295, 0.14611014736716063, 0.14581321502915801,
0.14774854087868053, 0.14811499907170106, nan, 0.19195213683933085, 0.18277598095720091,
0.26330883420363349, 0.099934563538237126, 0.17369767080753418, 0.14781293116814578, nan, nan,
0.1541340159168911, nan, nan, 0.17509563241234097, nan, nan, 0.17082620060911188, 0.25433357704407383,
nan, 0.083893134780816281, 0.10154362805146444, 0.099198188237178367, 0.070298699742059587, nan,
0.15776318403780451, 0.10843287166586711, 0.15357908092533176, 0.076494813551023152, 0.11039867403239617,
0.11831547736112379, 0.11613477068857657, 0.11581378044043389, 0.2580008780342351, nan, nan, nan, nan,
0.1006941158273488, 0.092306067915026713, 0.10034805317183228, nan, nan, nan, 0.13783721680478073,
0.14442435227026926, nan, nan, 0.096317617873744668, 0.1084188535337246, nan, 0.17196057278226401,
0.21271681162628317, 0.10723178798451144, 0.12049531770283844, 0.18103992773213615, nan, nan,
0.12829862453513355, nan, nan, 0.23344281557907851, 0.17222773249211867, 0.12963972222991477,
0.13804865085559193, 0.13439733841895518, 0.12956606460324568, 0.1193309104207793, 0.13329836949848151,
0.14710458034047089, 0.20855177812099873, 0.29431984460966254, 0.18090815912374611, 0.18652238311549979,
nan, nan, nan, nan, nan, 0.20630720383519485, nan, 0.12762133215896729, nan, 0.18852816325558666, nan,
nan, nan, nan, nan, nan, nan, nan, 0.21324860550485572, nan, 0.17820207694771473, 0.19970398247160653,
0.13641680981933016, 0.12203138078137539, nan, 0.084343663790310983, 0.11840200516590524, nan,
0.12518610857719745, nan, nan, 0.15158864994030935, 0.068137447015053698, nan, nan, 0.30928887444615205,
0.17405330098334754, 0.13271538755517476, 0.16023335094255139, nan, nan, nan, nan, 0.13731822985791772,
0.18184821317737038, 0.15023751170804903, nan, 0.15158366095743123, 0.1039479375084017,
0.14518110029942155, 0.23683129214590209, 0.15831193496816584, 0.10844878622906226, nan,
0.12057405042914644, nan, 0.18199825191752525, 0.16143214073126486, nan, 0.20777023555984586,
0.22280434789605111, 0.16654129027693576, 0.17325081980228077, 0.15567474510721929, 0.15481035186150324,
nan, nan, 0.15322060167569637, 0.18025388066151285, 0.15883228040747627, 0.21753680621929439, nan,
0.13217072478910391, nan, 0.15221780914817518, 0.11543304777127672, nan, 0.14394243626717293,
0.13063801703052036, 0.1855038920706048, 0.21845811401214216, 0.15569991427505378, 0.14500279460581872,
0.13367361473022824, 0.15545780463129696, 0.17558989375486561, nan, 0.16771934757767074, nan, nan,
0.28411727476075738, 0.13316593603889851, 0.11508630613984863, 0.10838376489383292, 0.14504160827411319,
nan, 0.15220926497581427, 0.15134159651841272, 0.1345472800259401, 0.14982362039438671,
0.11815043301211001, 0.12086895521436994, 0.16504642669549088, 0.15891535040759086, nan, nan, nan, nan,
nan, 0.11408949121012579, 0.12446429317842428, 0.14572822790448439, nan, 0.18050037416288001, nan, nan,
nan, nan, nan, 0.17471311640697751, 0.19958267804582827, nan, nan, nan, 0.34255810504926471,
0.16874350176413211, nan, 0.14513414081624523, 0.12122792055504504, 0.29117234719768292,
0.15023796612369625, 0.076740827026667013, 0.16444220304126542, 0.14139497233521869, nan,
0.14548810301342002, 0.15424270276165344, 0.19421347465880184, 0.15602221918766027, 0.15888407771971591,
0.17759202577223165, 0.10077489179671391, 0.14943028961533192, 0.3143240556484263, nan, nan, nan, nan,
nan, nan, 0.16801433637733876, nan, nan, nan, 0.31598144630704111, 0.1482495721179409, nan, nan, nan,
nan, nan, 0.20615238257773544, 0.19871989851374811, nan, nan, 0.15462039748190398, 0.14373710684816499,
0.26067713153585764, nan, 0.17484208184184707, 0.15099677352502064, 0.12179997017652096,
0.13315642971720734, 0.29398730632747505, 0.10382075327328079, 0.1050723032240564, 0.15654681922932456,
0.15081706709109119, 0.14396351944488972, nan, nan, 0.20409253277470804, nan, nan, nan, nan,
0.1487708453082503, 0.3723902582116323, 0.26511814483947455, 0.21591883550441407, 0.23653384618270745,
nan, 0.21584593859478662, 0.22091806159099001, 0.187249429136919, 0.18958458446607437, nan,
0.23222524369476341, 0.31643800489063512, 0.25350952466115939, 0.25845033154131464, nan, nan,
0.1887897877926264, 0.16378635401594571, nan, 0.19529912100542729, 0.15834440679085748, nan,
0.17596805793272627, nan, 0.18986374923195867, nan, nan, nan, nan, 0.086561954020805618,
0.10132344839035337, 0.192936996951065, nan, nan, 0.13518559579499947, 0.061394478027387615,
0.14132074876003922, 0.10996789089484184, 0.099931960105900677, 0.076849272419971762,
0.16872601864390196, nan, 0.08332568413682527, 0.064084282340850229, nan, 0.14016550131941194,
0.09819186548890832, 0.15517581370448177, 0.27078074182023321, 0.10715859352794414, 0.088785448193605981,
0.15394059691998146, 0.084296229901372102, 0.11337686947866991, 0.083515698064442756,
0.099387621176066768, 0.14125878638988762, 0.11113595053240956, 0.11917480824111155,
0.064036655565029577, 0.12643131881097053, 0.065966476269921595, 0.080624621785261308,
0.11106112120247116, nan, 0.10658454087812289, 0.11001425393546412, 0.16161882110820752, nan,
0.13760824803433913, nan, nan, 0.11673721476017733, nan, 0.096766240400781148, 0.099858067359144695,
0.30820757662583603, 0.086916738575387703, nan, nan, 0.054397839355722122, nan, nan, nan, nan,
0.16788484965576628, nan, nan, nan, 0.079060040041368032, 0.07133357908455, 0.066916603785347306,
0.071789488119065437, nan, 0.13523079422581791, 0.10183756624145711, nan, nan, 0.12532332382693542,
0.088000591663491071, 0.13614760123733186, 0.10273091388900669, 0.12732209339148443,
0.096550796366086211, nan, 0.096216430698486649, 0.063251792287885675, 0.15418764121495598,
0.11335938829322867, 0.15621667596796449, 0.097022447524173258, 0.10531904210194032,
0.092002712511532886, 0.077885437905271596, nan, nan, nan, 0.1052417836365838, 0.11788084217299188,
0.08040884630146107, 0.15147730046726526, 0.1726266633519721, nan, nan, 0.11860565936069825,
0.098846390135996207, nan, 0.08642134677009923, 0.11322390803540279, nan, 0.072661890610403387,
0.13952654181082216, 0.10444774870005359, nan, nan, 0.074997062106678461, nan, nan, 0.10655746956830256,
0.12689551454451042, 0.11560573741987913, 0.1210543383570777, nan, 0.131311565107462,
0.064185930644328468, nan, 0.10238177613495926, nan, nan, 0.16376264025849144, 0.1354975573712387,
0.094074005266735691, 0.16345479357405357, 0.19526942213737672, nan, nan, 0.13350341061171467,
0.14864238148170422, nan, 0.14181098399639627, 0.065148193190252141, nan, nan, nan, 0.17769516028950286,
nan, 0.45242284978155828, 0.15866724655665324, nan, 0.17542640663800746, nan, 0.11501070986763968,
0.1310478584756182, 0.10275669652752134, 0.12972607943622957, 0.12584561327090793, 0.1586575169924625,
nan, nan, nan, nan, nan, nan, nan, 0.23646844670549394, 0.1277077006758581, 0.11808880173934769,
0.12193788560786718, 0.17735354906958711, nan, 0.14691470405910451, 0.12027758134218987,
0.15016609126574498, 0.15219481862816706, nan, nan, nan, 0.18315343493789613, 0.1300737388429607,
0.19331969423983938, nan, nan, 0.16035199308741752, nan, 0.18907380630027745, nan, nan,
0.20739296401610377, nan, nan, nan, nan, 0.17873747725387584, nan, nan, nan, 0.20396957108790509,
0.13521163233196565, 0.12758850729414914, 0.097784054458120329, 0.14369106107605373, 0.1440828764815463,
0.18798853467262341, 0.22993736066271922, nan, 0.18075299518955779, nan, nan, nan, nan,
0.21875586141609737, 0.21255002395359576, 0.18924109703668904, 0.17666504977681102, 0.16773401990972817,
0.12953192220677276, 0.28276025892580314, 0.23907086910901429, 0.17450417182254535, 0.16916026258171052,
0.17886529868522794, 0.14184480359938254, nan, 0.23225194980141473, 0.15845587308366965,
0.17705162689419204, 0.14564689115564419, nan, 0.13572836311761052, nan, 0.20688022687836236,
0.1416847974284729, nan, nan, nan, 0.16776720761202457, 0.12206071515063585, 0.11393422616369213,
0.080497392285612909, 0.10526253398989301, 0.19832799878933635, 0.1786544540292801, nan, nan, nan,
0.1712111653910435, 0.18942389924090536, 0.097202126726228452, 0.18290090717130986, 0.17489182340372852,
0.23003222439043078, 0.2352374207539375, nan, nan, nan, 0.076183470589327368, 0.13381203612131196,
0.18186948577008308, 0.13415612149277997, 0.17082905290149492, 0.37551508583763898, 0.19027569829320065,
0.17223138290975873, 0.21648699866423018, 0.1674589936500073, 0.13109445057691138, 0.13629582725597567,
0.20046918681634682, 0.25786372854357331, nan, nan, 0.21238675580939081, 0.22411991137181614, nan, nan,
nan, nan, nan, 0.3816751084557799, 0.23908980496844928, 0.30944003406661302, 0.22823090264220258, nan,
0.25353374699508874, 0.1709294357098628, nan, nan, nan, 0.27733974498323116, nan, 0.17412727651792034,
0.15305738668227234, 0.20944425759264784, nan, nan, 0.24298026560829894, 0.25910045343522181,
0.18430701138985947, 0.11387294623357226, 0.1003846362226174, 0.14168849864571498, 0.21901019673216199,
0.18498887100691747, 0.21951905755573967, 0.16293960465452614, 0.17773845459170343, nan, nan,
0.11278944627949, 0.21181693249011466, 0.13345762638391395, 0.16281647061559165, 0.12295403367205225,
0.22943771517656469, 0.18500713907581298, 0.2634921296908424, nan, nan, nan, nan, 0.2334472302755736,
nan, nan, nan, nan, nan, nan, nan, 0.19206272479326056, nan, 0.12795206602543932, nan, nan,
0.18088536944764005, 0.087846606592004425, nan, nan, nan, 0.12410520423802705, 0.1357359307796728,
0.23622356270207326, 0.13976292445047853, nan, 0.12477033631754762, 0.19342703952259224,
0.22991158987395444, 0.17973426231982664, 0.15386420321919492, nan, 0.099938521411486653,
0.15869076175077457, nan, nan, nan, 0.12822409757337691, 0.15548310804203952, nan, 0.12944278068421236,
0.16641916225076497, 0.11595352282586788, 0.065462289901270676, 0.13383571295469554, 0.11150247180663224,
0.074421950602185136, nan, nan, 0.039380180256856689, nan, nan, 0.17647918361924106, 0.11453955551216725,
0.23605693123336072, 0.080037426986393684, 0.10226648759138225, nan, nan, nan, nan, 0.10167384996313124,
0.12600148984581838, 0.16449983918151659, 0.10546564467242102, 0.15410577483517221, 0.17094607525728109,
nan, nan, 0.13614416267597274, 0.15730335879723142, nan, nan, 0.15953155635739422, 0.14252657991101178,
nan, nan, 0.14618047448617583, 0.20689404599026603, 0.10587734624762174, 0.12122776323653861,
0.13039614573327912, 0.10394364798838883, 0.077449439679710602, 0.071125011416783943,
0.10715191862572851, 0.11106059398879735, nan, 0.11130884592823663, 0.20160776091345864,
0.1079676658421358, nan, 0.051073850796730935, 0.21960523609100538, 0.084938123278398989, nan, nan,
0.11210854487121853, nan, nan, nan, nan, 0.083746370526917177, 0.10649513934676925, 0.069285557618745122,
0.099465982281508478, 0.060863012629926466, 0.059368995873272899, nan, 0.086391124575470976,
0.14582822351480362, nan, nan, nan, nan, 0.16302848077919257, 0.097800260714218726, 0.15785005622822523,
0.1850691756815592, nan, 0.11815535340537495, nan, 0.050152887800470225, 0.071440740904700109,
0.10835927542037517, 0.095108427796969805, 0.11453852337265565, 0.099871260685915883,
0.12240884678028403, 0.10052404923859404, nan, 0.094052544406761349, 0.10701884696928503,
0.095463869841982255, 0.10043516025409978, 0.14073681149726949, 0.081263019805314377,
0.13366023326145726, 0.16209600404201838, 0.17265700097465461, nan, nan, 0.32804938517727361,
0.18066123849557891, nan, 0.086156990199458577, 0.092165309511629018, 0.09509390804067297,
0.16895096150222103, 0.37100596586207363, nan, 0.098980290045197927, 0.11304902329189399,
0.12809949430962345, 0.09934112171642874, nan, 0.07885478236660412, nan, nan, 0.15556623255361135, nan,
0.099179969544000268, 0.094466286993388382, nan, 0.15175850706870414, 0.1382611134635405, nan,
0.089761814723978664, nan, nan, 0.058227482559802958, nan, nan, nan, nan, 0.1155747930499787,
0.13731645042068499, 0.10143042510946232, 0.17680654020067152, 0.15011892592103315, 0.10822966932252623,
nan, 0.10839603364375737, 0.25012058183455876, 0.1220849482827364, nan, 0.06125789846443154,
0.10455986315666012, nan, 0.16104796212044359, 0.19672859573818516, 0.17535290965300654,
0.14394951068698469, 0.13938072015526781, nan, nan, nan, nan, 0.18452187496773531, 0.11427582133998505,
0.2072138592018708, nan, nan, nan, 0.23116658417449781, 0.12908670369373226, 0.16801732830924399, nan,
nan, 0.19735510079897911, 0.14574098780066749, 0.19417295631703987, 0.22580982158387167,
0.16395158123250753, 0.16550487984547901, nan, 0.13655069788251981, nan, nan, 0.10638236038262111,
0.14502514486850746, 0.1578382804974483, 0.14671050973831279, 0.10790869620537223, 0.13234265273829643,
0.11308962324992691, 0.14567933553061466, 0.11690554550144816, 0.11127426489868175, nan, nan,
0.1886232616218686, 0.12490496971790736, 0.13572032230370321, 0.075755998908557082, 0.11246061717454188,
0.28761477682212733, 0.21693107798892683, nan, 0.19294549595331817, 0.2394219056082891,
0.2184411689870614, 0.1273486835178099, nan, 0.11662750682340212, 0.10934728761317286, nan,
0.20164888978630865, 0.1375060785499968, 0.095737913641382275, 0.12225731545062477, 0.14436381508902443,
nan, nan, nan, nan, 0.14291723560628622, 0.11859864543887283, 0.09361072564293392, nan, nan,
0.14202422838868894, 0.14780041822877671, 0.11006221605655592, 0.12941846974018714, 0.19189432668591047,
0.22441272868541087, 0.14184540082896852, 0.21115080971623656, 0.13085116435488164, 0.16208269721937851,
0.15731963569719987, 0.22029780357316972, nan, 0.15294576539159166, nan, 0.1851710342315811, nan, nan,
nan, 0.2728329708641386, nan, nan, nan, 0.1601246289732467, 0.14517095125404469, 0.11886862298206628,
0.21254906238212062, nan, nan, 0.25257437262449645, 0.15146497296028, 0.14820604576660751,
0.19345937094907883, 0.23998849638337821, 0.19866834503071473, nan, 0.36480305878024011,
0.18756828479533663, 0.15515435527416865, 0.20101432719297552, 0.24323267476266111, nan,
0.14768711001643628, 0.1117327270032988, 0.096207298100661406, 0.16290743701552762, 0.28293872881879073,
0.30235117379495796, nan, 0.26678081955414795, nan, 0.1826736726890163, 0.069054552564912358,
0.11212500597000276, 0.17402371041910467, 0.20740533401286576, nan, 0.17563007708397377,
0.18873038877643875, nan, 0.24629407553444158, 0.21720938283322022, 0.20693803325420768,
0.25820125440725178, 0.31910968351215846, nan, nan, 0.19338549685369275, 0.14987647930887713,
0.14925918889076648, nan, 0.19542300546098573, 0.19812092284558375, 0.14441832807334343,
0.24829304970751065, 0.18124096458999917, 0.10395571350002517, 0.19723985637263747, 0.14487410893307526,
0.11413126735465659, 0.15804708808515361, 0.23959729074106287, 0.15628495500154496, 0.16937488474291215,
0.13465277588271285, nan, 0.2126483885053102, 0.12597094025867558, 0.10503192549181488, nan, nan,
0.16238458693924004, 0.3353053442584864, 0.13889938619070732, 0.13293036343468939, 0.11445738569269183,
0.15967311884867696, 0.14255527064233123, 0.16248752022970511, 0.19016094611013221, nan,
0.095139403177254042, 0.12854205563305288, nan, nan, 0.13544957272309599, 0.16603846039740724, nan, nan,
nan, nan, nan, nan, 0.11388542851647959, 0.19879723711105141, 0.13125490896610423, 0.11725080852462999,
nan, 0.29926860305466918, nan, 0.085711329613237824, 0.12816069194174717, 0.11017334303822936,
0.124923209485459, 0.096943386495473988, 0.085532469020808521, 0.13639236248084438, 0.14395553376065701,
0.13815466550544861, 0.095091479573302204, 0.094080924929592843, 0.086371530895012857,
0.09437344153932517, nan, 0.24524053686148398, 0.19081300865930967, nan, 0.10754066590403061,
0.12846820257696848, nan, nan, 0.1276483181617119, 0.15335446063261776, 0.22801177680301743,
0.12898432366449999, 0.10278463090993109, 0.13049531124936092, 0.11481236256184377, 0.071506809582051648,
0.10378839969178608, 0.18377904934418562, 0.10095523038649662, 0.06805415257923253, 0.082485355828329249,
0.090262158729339084, 0.059218830557932624, 0.067608536701551106, nan, 0.1254689348968159,
0.20682401252680807, 0.08152426321348466, 0.27762810749425459, 0.1325035541488388, 0.088045386058186251,
0.11392899704073065, 0.10318799256474781, 0.19926324891859584, 0.068674126498955101, nan,
0.072270952918736614, 0.056789455651857508, 0.057506055514949486, 0.085812349658574205,
0.12315694864909869, nan, 0.083826956944624026, 0.090342182968951135, nan, 0.057489146311252279,
0.077324277205675904, 0.17125079609822572, 0.10436962962366833, 0.11175896950021262, 0.20048711713766207,
nan, 0.088132285120304785, 0.063704582769846141, 0.088005442110350482, 0.082292705783065259, nan, nan,
0.083241061967690294, nan, nan, 0.080848649922591678, nan, 0.084956745221329871, nan, nan,
0.1353135758719452, nan, 0.11457411438256328, 0.11486879460752727, 0.10022353287436304,
0.10288361238454399, 0.15032847758600021, 0.24918628101033388, 0.33739861635965868, 0.10279526354742392,
0.1541205212091098, nan, 0.1909417875106279, 0.10483647183568939, 0.0826582805031877,
0.11047792955968246, 0.11571857849244281, nan, 0.22185168918836481, 0.12514540159314327, nan,
0.15786354650588399, 0.18089749759713541, 0.12155147496066789, 0.091852590448040394, 0.12482077008285807,
0.10567671792202488, nan, 0.079692723782507469, 0.080209149993379594, 0.08851783812406315, nan,
0.097396623646609309, 0.10274421612722717, nan, 0.18305611476990485, 0.19436304240150554,
0.14021391718930268, 0.088290289667844274, nan, nan, 0.094398222159369147, 0.11185130493600288,
0.19217465242373788, 0.13974798757189291, 0.21574295387011524, 0.080500538488832291, 0.18162195708825252,
0.22329953314152412, 0.16412219371297473, 0.25107605984549652, 0.11549596445596945, 0.08126389391200077,
0.09091619421023743, 0.13472240737929311, 0.081332360917433441, nan, 0.2564246220452393,
0.13871367305459631, 0.11437562488351796, 0.15168897763000949, 0.15157204363051868, 0.10504622642917411,
0.10337381950304139, 0.17063351535637802, 0.23325016327836087, 0.14728410130353581, 0.20964469490347831,
0.12255484512152533, 0.1564713807911339, 0.20305655184728252, 0.22808687575667497, nan, nan,
0.11726913873103592, nan, 0.17182398532704449, 0.13769096044910073, nan, 0.090631395851236946,
0.090162499644713484, 0.12641516239582415, 0.085280045114670938, nan, nan, 0.12628967951890413,
0.12730023019844097, 0.077093249470432362, nan, 0.28938509902515863, 0.13616778274309915,
0.08839231711245249, 0.18515410141228492, nan, nan, 0.30450075096782786, 0.11315568157186619,
0.17044831685033826, 0.098146660804049712, 0.13341910025753878, 0.15655971468972546, 0.15735351137725762,
nan, nan, nan, 0.1359419682170295, 0.18922071505472735, 0.17017293007682396, 0.18432409829684887,
0.13487319631615777, 0.12710105280659917, 0.091771611416134019, nan, nan, nan, 0.24794110818039211,
0.20190818678455222, 0.17865558226381253, 0.11597253291205432, 0.21457745457572999, 0.15969145296097365,
nan, 0.15499536262184549, 0.39317367050523366, 0.12439871412410437, nan, nan, nan, nan, nan, nan,
0.14353384772529465, 0.2394579412927787, 0.30765174113708665, nan, 0.14085229508627606,
0.12505141737318898, nan, nan, 0.30740839625780908, 0.17170797938010682, 0.16992362191989041,
0.14088695719893335, 0.10988116595319052, 0.17133737631786505, 0.17875628623047934, 0.12190335482422862,
0.12076697139631738, 0.17238485400656534, nan, nan, 0.17148987122029946, 0.1421063412542386,
0.084251091013684345, 0.077167991339082739, 0.089609553485628324, 0.1507850147047734,
0.21722649479579376, 0.20687916230862544, nan, nan, nan, 0.13581807163109885, nan, 0.27174063229692547,
0.21649222360458706, 0.15781409966293833, 0.16396794949612692, 0.16046551897034636, 0.16773485198584648,
0.14303570964873155, 0.17575404893185156, 0.14254201969460176, 0.12561962834694407, 0.14447273204047634,
0.20442031358416063, nan]
C_modes = [1.7659574468085106, nan, 1.6117021276595744, 1.6914893617021276, 1.8989361702127661, 1.9414893617021276,
nan, nan, 2.0957446808510638, 1.9627659574468084, nan, nan, 1.7499999999999998, 1.8138297872340425, nan,
nan, nan, nan, nan, nan, nan, 1.8936170212765957, 1.5585106382978724, 1.3989361702127658, nan,
1.3882978723404256, 1.1914893617021276, 1.5106382978723403, 1.3829787234042552, 1.5691489361702127,
1.5265957446808509, nan, nan, nan, nan, 1.4202127659574466, 1.5265957446808509, 1.5, 1.4095744680851063,
nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.7021276595744681, nan, 1.6276595744680851,
1.9627659574468084, nan, nan, nan, nan, nan, nan, nan, 1.3244680851063828, 1.4468085106382977, nan, nan,
1.3617021276595744, 1.553191489361702, 1.5, 1.4468085106382977, 1.5638297872340423, 1.5851063829787233,
1.4893617021276595, 1.4680851063829787, nan, 1.5159574468085106, 1.5212765957446808, 1.4414893617021276,
1.4414893617021276, nan, nan, 1.4095744680851063, 1.4574468085106382, 1.404255319148936,
1.5957446808510638, 1.5053191489361701, 1.3244680851063828, nan, nan, nan, 1.7180851063829785,
1.6063829787234041, nan, 1.5212765957446808, 1.3351063829787235, 1.4840425531914891, 1.4202127659574466,
1.4202127659574466, 1.4787234042553192, 1.5638297872340423, 1.5425531914893615, 1.5904255319148934,
1.6648936170212765, 1.5159574468085106, 1.5638297872340423, 1.404255319148936, 1.5638297872340423,
1.6329787234042552, 1.5372340425531914, 1.5478723404255319, nan, 1.5478723404255319, 1.553191489361702,
nan, 1.6117021276595744, nan, nan, nan, nan, nan, nan, 1.8510638297872337, 1.7021276595744681,
1.7021276595744681, 1.5212765957446808, 1.574468085106383, 1.675531914893617, nan, 1.6968085106382977,
1.728723404255319, 1.6861702127659572, 1.7606382978723405, 1.7021276595744681, 1.6436170212765957,
1.5851063829787233, 1.5957446808510638, 1.6489361702127658, 1.7978723404255317, 1.4521276595744681,
1.4893617021276595, 1.4787234042553192, nan, 1.7340425531914894, 1.728723404255319, 2.0319148936170213,
1.8085106382978722, 1.6542553191489362, nan, 1.7553191489361701, nan, nan, 1.7553191489361701,
1.7340425531914894, 1.6117021276595744, 1.7021276595744681, 1.574468085106383, 1.4521276595744681,
1.425531914893617, 1.3882978723404256, 1.6010638297872342, 1.5478723404255319, 1.4840425531914891,
1.5319148936170213, 1.5106382978723403, 1.5478723404255319, 1.3776595744680851, 1.6436170212765957,
1.4893617021276595, 1.6489361702127658, 1.553191489361702, 1.6223404255319149, 1.574468085106383,
1.5851063829787233, 1.6117021276595744, 1.8776595744680848, 1.5159574468085106, 1.4893617021276595,
1.4946808510638299, 1.6489361702127658, nan, nan, 1.9734042553191489, 1.7340425531914894, nan,
1.5638297872340423, nan, 1.6117021276595744, 1.5691489361702127, 1.8457446808510638, nan,
1.6223404255319149, nan, 1.425531914893617, nan, 1.6223404255319149, 1.6861702127659572,
1.4095744680851063, 1.6702127659574466, 1.6329787234042552, 1.6489361702127658, nan, nan, nan,
1.7499999999999998, 1.6861702127659572, 1.6063829787234041, nan, nan, nan, nan, nan, 2.1382978723404258,
2.0, 1.7234042553191489, 1.8457446808510638, 1.7074468085106382, 1.8138297872340425, 1.8351063829787233,
1.8138297872340425, 1.7553191489361701, 1.8617021276595744, 2.0319148936170213, 1.8776595744680848, nan,
1.8989361702127661, 1.7340425531914894, nan, nan, 1.3617021276595744, 1.4468085106382977,
1.5159574468085106, 1.6382978723404256, 1.5106382978723403, 1.6170212765957446, 1.6063829787234041,
1.5053191489361701, 1.7021276595744681, 1.5691489361702127, 1.5691489361702127, 1.6170212765957446,
1.5691489361702127, 1.728723404255319, 1.6861702127659572, 1.6648936170212765, 2.0265957446808511,
1.4627659574468084, nan, 2.1063829787234041, 1.8829787234042552, nan, nan, 2.0585106382978724, nan,
2.5744680851063828, 2.5372340425531914, nan, 2.0904255319148932, 2.0159574468085104, nan, nan, nan, 2.0,
nan, 1.6382978723404256, nan, 1.7340425531914894, 1.8563829787234041, 1.6808510638297873,
1.8989361702127661, nan, 1.5691489361702127, 1.5265957446808509, 1.6648936170212765, 1.6010638297872342,
nan, nan, nan, 1.7659574468085106, nan, 1.6436170212765957, 1.6489361702127658, nan, 1.7446808510638296,
nan, 1.6861702127659572, 1.6542553191489362, 1.7446808510638296, 1.7446808510638296, 1.7180851063829785,
1.675531914893617, 1.803191489361702, 1.6223404255319149, 1.7765957446808509, 1.8457446808510638,
1.803191489361702, 1.803191489361702, 1.7712765957446805, 1.7234042553191489, nan, nan,
1.8297872340425529, 1.7978723404255317, nan, nan, 1.7872340425531914, 1.7234042553191489,
1.7021276595744681, 1.8829787234042552, 1.7393617021276597, 1.7393617021276597, 1.404255319148936,
1.4521276595744681, 1.4361702127659575, nan, 1.3723404255319149, 1.3563829787234043, nan, nan,
1.6010638297872342, 1.4840425531914891, 1.5851063829787233, 1.7340425531914894, 1.4946808510638299, nan,
1.7446808510638296, 1.5212765957446808, 1.5372340425531914, 1.5, 1.5159574468085106, 1.957446808510638,
nan, nan, nan, nan, 1.4787234042553192, 1.6117021276595744, 1.7234042553191489, 1.7180851063829785,
1.4361702127659575, 1.6117021276595744, 1.6914893617021276, 1.7340425531914894, nan, 1.6542553191489362,
1.7606382978723405, nan, 1.6968085106382977, 1.7446808510638296, 1.7819148936170213, 1.6063829787234041,
1.3776595744680851, 1.675531914893617, 1.6117021276595744, 1.6117021276595744, 1.904255319148936,
1.7446808510638296, 1.4095744680851063, 1.6170212765957446, 1.6861702127659572, 2.0797872340425534,
1.9255319148936172, nan, nan, nan, nan, 1.425531914893617, 1.5106382978723403, 1.4414893617021276, nan,
1.6117021276595744, 1.8404255319148937, nan, 2.3031914893617018, 2.1914893617021276, 2.4414893617021276,
nan, 1.5638297872340423, 1.5585106382978724, 1.6329787234042552, 1.3670212765957446, 1.5106382978723403,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.7180851063829785, nan, nan, 1.9680851063829787,
1.5265957446808509, nan, 1.6542553191489362, 1.5638297872340423, 1.6010638297872342, 1.7021276595744681,
1.404255319148936, 1.3882978723404256, 1.6382978723404256, nan, 1.5691489361702127, 1.5319148936170213,
1.4946808510638299, 1.5, 1.6542553191489362, 1.6702127659574466, 1.7127659574468086, 1.4148936170212767,
1.5159574468085106, nan, 1.574468085106383, nan, nan, 1.3510638297872339, nan, 1.2978723404255319, nan,
1.7234042553191489, 2.0319148936170213, 1.5372340425531914, 1.6063829787234041, 1.4840425531914891,
1.6595744680851063, 1.574468085106383, 1.5904255319148934, 1.4680851063829787, 1.6170212765957446,
1.6382978723404256, 1.3404255319148934, nan, 1.4893617021276595, 1.728723404255319, 1.3244680851063828,
1.5053191489361701, 1.6276595744680851, 1.3989361702127658, 1.6010638297872342, 1.675531914893617,
2.4202127659574466, 2.1914893617021276, nan, nan, nan, nan, nan, nan, nan, 1.8829787234042552,
2.3244680851063833, 1.7978723404255317, nan, 1.4946808510638299, 1.6702127659574466, 1.6276595744680851,
1.6702127659574466, nan, nan, nan, nan, 1.6063829787234041, nan, nan, nan, 1.6117021276595744,
1.4787234042553192, 1.5691489361702127, 1.8776595744680848, nan, 1.5638297872340423, 1.7340425531914894,
1.6542553191489362, 1.7127659574468086, 1.8829787234042552, 2.021276595744681, 2.0851063829787235, nan,
3.1170212765957448, 3.2819148936170213, nan, 2.4414893617021276, 2.3297872340425529, 2.0531914893617023,
2.1542553191489362, 1.7446808510638296, 1.9840425531914891, 1.8563829787234041, 2.2180851063829787,
1.8882978723404256, 2.0265957446808511, nan, 2.2553191489361701, 1.6808510638297873, 2.1223404255319149,
4.2712765957446805, 5.7287234042553195, 4.0212765957446805, 4.1329787234042552, 3.8138297872340425,
2.3670212765957448, nan, nan, nan, nan, nan, nan, 2.9574468085106385, nan, 1.7872340425531914,
1.6861702127659572, 2.0744680851063828, 2.0053191489361701, 2.0106382978723403, 1.8297872340425529, nan,
2.6595744680851063, 2.5478723404255317, nan, nan, nan, nan, nan, 4.5851063829787231, 5.0531914893617023,
nan, nan, 2.2021276595744679, nan, nan, 2.0425531914893615, 2.0372340425531914, 2.0159574468085104,
1.9680851063829787, 2.2553191489361701, 1.9627659574468084, 1.8617021276595744, nan, 2.021276595744681,
1.957446808510638, 2.1542553191489362, nan, 1.9734042553191489, 2.0691489361702127, 2.5,
2.021276595744681, nan, nan, nan, nan, nan, 2.4255319148936172, 2.2925531914893615, 1.8936170212765957,
2.0638297872340425, 2.0053191489361701, nan, 2.0904255319148932, 2.1382978723404258, 2.2925531914893615,
nan, 2.4680851063829787, 3.228723404255319, nan, 1.7978723404255317, nan, nan, 2.2340425531914891,
2.2021276595744679, 1.9734042553191489, nan, 2.1542553191489362, 2.228723404255319, nan, nan,
2.0904255319148932, 1.803191489361702, 1.7925531914893618, nan, nan, 1.9893617021276595,
1.9095744680851063, 2.1382978723404258, 1.9787234042553192, 1.904255319148936, 2.0053191489361701,
1.9840425531914891, nan, 2.1542553191489362, 2.1063829787234041, 2.0425531914893615, 1.9734042553191489,
2.0319148936170213, nan, nan, nan, 1.9946808510638296, 2.3031914893617018, 2.2340425531914891,
2.2180851063829787, 2.3404255319148937, 2.0372340425531914, 2.0265957446808511, 2.021276595744681, nan,
2.0106382978723403, 2.0106382978723403, nan, 2.1968085106382977, 2.0638297872340425, 2.1436170212765955,
2.0585106382978724, 2.1223404255319149, nan, 2.1914893617021276, 2.1010638297872339, 2.2765957446808507,
2.9148936170212765, 2.8617021276595742, 2.2234042553191489, nan, 2.1755319148936167, 2.1808510638297873,
nan, nan, nan, nan, 1.9255319148936172, 1.7819148936170213, 1.8351063829787233, 1.9148936170212765,
1.7180851063829785, 2.0425531914893615, 1.8404255319148937, nan, 2.1170212765957444, nan,
1.7553191489361701, nan, 1.7446808510638296, 1.7606382978723405, 2.0, 1.7819148936170213, nan,
2.0478723404255317, 2.3351063829787231, 1.9255319148936172, 1.7925531914893618, 1.7978723404255317, nan,
2.1010638297872339, 2.1861702127659575, 1.946808510638298, 2.0106382978723403, 2.0319148936170213,
2.0957446808510638, 1.9680851063829787, 1.8829787234042552, 1.803191489361702, 2.0, 2.0265957446808511,
1.8829787234042552, nan, 1.9893617021276595, 1.8244680851063828, 1.6170212765957446, 1.9202127659574468,
1.9787234042553192, 1.9521276595744681, 2.1170212765957444, 2.0, 1.9893617021276595, nan,
1.957446808510638, 1.6489361702127658, 1.4734042553191489, 1.4468085106382977, 1.6968085106382977,
1.7659574468085106, nan, nan, 2.2234042553191489, nan, nan, 1.7074468085106382, nan, nan,
1.6648936170212765, 1.6648936170212765, nan, 1.6223404255319149, 1.7021276595744681, 1.6968085106382977,
1.5691489361702127, nan, 1.6914893617021276, 1.6436170212765957, 1.5797872340425532, 1.6276595744680851,
1.5319148936170213, 1.5691489361702127, 1.5478723404255319, 1.6489361702127658, 1.6861702127659572, nan,
nan, nan, nan, 1.6648936170212765, 1.6063829787234041, 1.5638297872340423, nan, nan, nan,
1.8297872340425529, 1.5904255319148934, nan, nan, 1.7499999999999998, 1.9946808510638296, nan,
2.1117021276595747, 2.1436170212765955, 1.9095744680851063, 1.946808510638298, 2.0585106382978724, nan,
nan, 2.0425531914893615, nan, nan, 1.9627659574468084, 1.7712765957446805, 1.7765957446808509,
1.7712765957446805, 1.728723404255319, 1.7127659574468086, 1.6489361702127658, 1.7074468085106382,
1.7872340425531914, 1.8723404255319149, 1.7180851063829785, 2.1595744680851063, 2.0957446808510638, nan,
nan, nan, nan, nan, 0.40425531914893614, nan, 1.9202127659574468, nan, 1.8244680851063828, nan, nan, nan,
nan, nan, nan, nan, nan, 2.2446808510638299, nan, 2.2499999999999996, 1.8510638297872337,
1.6702127659574466, 1.8351063829787233, nan, 1.6702127659574466, 1.6914893617021276, nan,
1.7127659574468086, nan, nan, 1.7553191489361701, 1.9414893617021276, nan, nan, 1.8723404255319149,
1.6276595744680851, 1.6914893617021276, 1.7978723404255317, nan, nan, nan, nan, 1.6968085106382977,
1.8829787234042552, 1.9255319148936172, nan, 1.7393617021276597, 1.9361702127659572, 1.9255319148936172,
1.7127659574468086, 1.8138297872340425, 1.7819148936170213, nan, 1.6542553191489362, nan,
1.7180851063829785, 1.728723404255319, nan, 2.4095744680851063, 2.2021276595744679, 1.7819148936170213,
1.5797872340425532, 1.8510638297872337, 1.7234042553191489, nan, nan, 1.6436170212765957,
1.8510638297872337, 2.0159574468085104, 1.6436170212765957, nan, 1.7393617021276597, nan,
1.6329787234042552, 1.7234042553191489, nan, 1.7127659574468086, 1.7872340425531914, 1.8191489361702129,
1.904255319148936, 1.9255319148936172, 1.9148936170212765, 1.8244680851063828, 1.8404255319148937,
1.728723404255319, nan, 2.0319148936170213, nan, nan, 2.0106382978723403, 2.0265957446808511,
1.9627659574468084, 1.8510638297872337, 1.7606382978723405, nan, 1.8882978723404256, 1.9202127659574468,
1.904255319148936, 1.9095744680851063, 1.803191489361702, 1.9095744680851063, 1.9308510638297871,
1.946808510638298, nan, nan, nan, nan, nan, 1.9095744680851063, 1.8191489361702129, 2.0425531914893615,
nan, 2.1489361702127656, nan, nan, nan, nan, nan, 2.0425531914893615, 2.4148936170212765, nan, nan, nan,
1.8617021276595744, 1.8989361702127661, nan, 1.8351063829787233, 1.7659574468085106, 1.7393617021276597,
2.0106382978723403, 2.0904255319148932, 1.9148936170212765, 1.7553191489361701, nan, 1.8989361702127661,
1.8776595744680848, 2.0265957446808511, 1.9680851063829787, 1.9361702127659572, 1.8829787234042552,
2.0053191489361701, 1.9680851063829787, 2.0106382978723403, nan, nan, nan, nan, nan, nan,
2.2606382978723403, nan, nan, nan, 2.1382978723404258, 2.2446808510638299, nan, nan, nan, nan, nan,
2.1170212765957444, 2.0319148936170213, nan, nan, 2.1914893617021276, 2.0372340425531914,
2.3882978723404253, nan, 2.1542553191489362, 1.8670212765957448, 1.7446808510638296, 2.0957446808510638,
1.5478723404255319, 1.5904255319148934, 1.6117021276595744, 1.7872340425531914, 1.7606382978723405,
1.9787234042553192, nan, nan, 1.6595744680851063, nan, nan, nan, nan, 2.228723404255319,
2.3882978723404253, 2.7553191489361701, 2.6861702127659575, 2.7234042553191489, nan, 2.7021276595744679,
2.5585106382978724, 2.6223404255319145, 2.3617021276595742, nan, 2.5053191489361701, 2.707446808510638,
2.7340425531914891, 2.8457446808510638, nan, nan, 2.6861702127659575, 2.6702127659574471, nan,
2.5691489361702127, 2.3244680851063833, nan, 2.5425531914893615, nan, 2.4414893617021276, nan, nan, nan,
nan, 1.5904255319148934, 1.6489361702127658, 1.7606382978723405, nan, nan, 1.5638297872340423,
1.6542553191489362, 1.5159574468085106, 1.4893617021276595, 1.5053191489361701, 1.5265957446808509,
1.4787234042553192, nan, 1.5638297872340423, 1.6489361702127658, nan, 1.5957446808510638,
1.675531914893617, 1.6329787234042552, 1.5797872340425532, 1.6063829787234041, 1.5638297872340423,
1.4893617021276595, 1.5, 1.4521276595744681, 1.553191489361702, 1.6170212765957446, 1.803191489361702,
1.5106382978723403, 1.6276595744680851, 1.4574468085106382, 1.4680851063829787, 1.4521276595744681,
1.4361702127659575, 1.553191489361702, nan, 1.4946808510638299, 1.5372340425531914, 1.6170212765957446,
nan, 1.8510638297872337, nan, nan, 1.4946808510638299, nan, 1.6063829787234041, 1.6063829787234041,
1.4202127659574466, 1.4574468085106382, nan, nan, 1.5319148936170213, nan, nan, nan, nan,
1.5585106382978724, nan, nan, nan, 1.5691489361702127, 1.5904255319148934, 1.5638297872340423,
1.5106382978723403, nan, 1.4308510638297871, 1.574468085106383, nan, nan, 1.5904255319148934,
1.5159574468085106, 1.5691489361702127, 1.5851063829787233, 1.6170212765957446, 1.5319148936170213, nan,
1.6170212765957446, 1.6968085106382977, 1.5265957446808509, 1.5478723404255319, 1.6063829787234041,
1.803191489361702, 1.6542553191489362, 1.6010638297872342, 1.6382978723404256, nan, nan, nan,
1.5797872340425532, 1.6117021276595744, 1.5585106382978724, 1.6861702127659572, 1.7712765957446805, nan,
nan, 1.6489361702127658, 1.6063829787234041, nan, 1.675531914893617, 1.5425531914893615, nan,
1.6117021276595744, 1.6595744680851063, 1.6170212765957446, nan, nan, 1.7606382978723405, nan, nan,
1.5957446808510638, 1.7872340425531914, 1.675531914893617, 1.7446808510638296, nan, 1.7234042553191489,
1.675531914893617, nan, 1.6276595744680851, nan, nan, 1.803191489361702, 1.5585106382978724,
1.5904255319148934, 1.7553191489361701, 1.6861702127659572, nan, nan, 1.6542553191489362,
1.9361702127659572, nan, 1.7234042553191489, 1.7765957446808509, nan, nan, nan, 1.6276595744680851, nan,
1.8191489361702129, 1.8936170212765957, nan, 1.7978723404255317, nan, 1.3936170212765957,
1.5372340425531914, 1.6117021276595744, 1.6702127659574466, 1.7659574468085106, 1.8936170212765957, nan,
nan, nan, nan, nan, nan, nan, 1.8670212765957448, 1.803191489361702, 1.803191489361702,
1.8244680851063828, 1.6063829787234041, nan, 1.8244680851063828, 1.8617021276595744, 2.0053191489361701,
2.1329787234042552, nan, nan, nan, 1.8670212765957448, 1.8723404255319149, 1.7499999999999998, nan, nan,
1.9148936170212765, nan, 2.0478723404255317, nan, nan, 2.1276595744680851, nan, nan, nan, nan,
1.957446808510638, nan, nan, nan, 2.2499999999999996, 2.1968085106382977, 2.3031914893617018,
2.2234042553191489, 2.1542553191489362, 2.3351063829787231, 2.1755319148936167, 2.0372340425531914, nan,
1.8776595744680848, nan, nan, nan, nan, 1.8138297872340425, 1.9148936170212765, 1.7712765957446805,
1.8829787234042552, 1.8617021276595744, 1.6702127659574466, 1.7978723404255317, 1.9627659574468084,
1.6329787234042552, 1.9521276595744681, 1.8723404255319149, 1.8936170212765957, nan, 1.9255319148936172,
1.8882978723404256, 1.6808510638297873, 1.7127659574468086, nan, 1.8510638297872337, nan,
1.8191489361702129, 1.7659574468085106, nan, nan, nan, 1.8563829787234041, 1.8244680851063828,
1.8191489361702129, 1.8829787234042552, 1.8563829787234041, 2.0904255319148932, 1.9308510638297871, nan,
nan, nan, 2.1968085106382977, 1.6861702127659572, 1.8244680851063828, 1.9414893617021276,
1.7765957446808509, 1.7712765957446805, 1.6276595744680851, nan, nan, nan, 1.7606382978723405,
1.7872340425531914, 1.9255319148936172, 1.7712765957446805, 1.6489361702127658, 1.5691489361702127,
1.6595744680851063, 1.8617021276595744, 1.9627659574468084, 1.8776595744680848, 1.7553191489361701,
1.8244680851063828, 1.8457446808510638, 1.7765957446808509, nan, nan, 2.6010638297872339,
2.2978723404255321, nan, nan, nan, nan, nan, 2.0425531914893615, 2.1010638297872339, 2.3404255319148937,
2.2872340425531914, nan, 2.4308510638297873, 2.2499999999999996, nan, nan, nan, 1.9095744680851063, nan,
2.1755319148936167, 1.7393617021276597, 1.957446808510638, nan, nan, 1.6223404255319149,
2.0106382978723403, 1.9095744680851063, 1.8297872340425529, 1.9521276595744681, 1.803191489361702,
1.4574468085106382, 1.7234042553191489, 1.7553191489361701, 1.8138297872340425, 1.7712765957446805, nan,
nan, 1.9095744680851063, 1.8617021276595744, 1.9787234042553192, 1.8351063829787233, 1.9627659574468084,
1.8510638297872337, 2.1436170212765955, 1.9148936170212765, nan, nan, nan, nan, 2.1276595744680851, nan,
nan, nan, nan, nan, nan, nan, 1.8936170212765957, nan, 2.6648936170212765, nan, nan, 2.1010638297872339,
1.7765957446808509, nan, nan, nan, 1.7872340425531914, 1.8138297872340425, 1.9148936170212765,
1.8989361702127661, nan, 2.0744680851063828, 2.0265957446808511, 2.1223404255319149, 1.9893617021276595,
1.6702127659574466, nan, 1.6117021276595744, 1.5957446808510638, nan, nan, nan, 1.7659574468085106,
1.6702127659574466, nan, 1.9308510638297871, 1.9095744680851063, 1.8404255319148937, 1.8138297872340425,
1.7180851063829785, 1.7553191489361701, 1.8510638297872337, nan, nan, 1.5212765957446808, nan, nan,
1.4574468085106382, 1.6648936170212765, 1.7074468085106382, 1.5425531914893615, 1.5691489361702127, nan,
nan, nan, nan, 1.6595744680851063, 1.6968085106382977, 1.7446808510638296, 1.7499999999999998,
1.6648936170212765, 1.7499999999999998, nan, nan, 2.0851063829787235, 2.0425531914893615, nan, nan,
1.9361702127659572, 2.0478723404255317, nan, nan, 1.9414893617021276, 2.1117021276595747,
1.7765957446808509, 1.8404255319148937, 1.7340425531914894, 1.8563829787234041, 1.7659574468085106,
1.9361702127659572, 1.7659574468085106, 1.9095744680851063, nan, 1.904255319148936, 1.803191489361702,
1.9734042553191489, nan, 1.9202127659574468, 1.4308510638297871, 1.5212765957446808, nan, nan,
1.4734042553191489, nan, nan, nan, nan, 1.5106382978723403, 1.4361702127659575, 1.425531914893617,
1.553191489361702, 1.5106382978723403, 1.4521276595744681, nan, 1.5265957446808509, 1.5106382978723403,
nan, nan, nan, nan, 1.4840425531914891, 1.5585106382978724, 1.4521276595744681, 1.9202127659574468, nan,
1.5106382978723403, nan, 1.4787234042553192, 1.6063829787234041, 1.5957446808510638, 1.4361702127659575,
1.5106382978723403, 1.5904255319148934, 1.4787234042553192, 1.553191489361702, nan, 1.5372340425531914,
1.5585106382978724, 1.5159574468085106, 1.4574468085106382, 1.6010638297872342, 1.4361702127659575,
1.8404255319148937, 1.8351063829787233, 1.9734042553191489, nan, nan, 1.5904255319148934,
1.8138297872340425, nan, 1.4361702127659575, 1.5053191489361701, 1.4574468085106382, 1.4680851063829787,
1.5585106382978724, nan, 1.4361702127659575, 1.5319148936170213, 1.6382978723404256, 1.5319148936170213,
nan, 1.5957446808510638, nan, nan, 1.2765957446808509, nan, 1.4361702127659575, 1.5797872340425532, nan,
1.5797872340425532, 1.5957446808510638, nan, 1.7765957446808509, nan, nan, 1.6542553191489362, nan, nan,
nan, nan, 1.9893617021276595, 2.1329787234042552, 1.6968085106382977, 1.5212765957446808,
1.7978723404255317, 1.5319148936170213, nan, 1.5212765957446808, 1.4308510638297871, 1.6170212765957446,
nan, 1.574468085106383, 1.5851063829787233, nan, 1.7659574468085106, 1.5797872340425532,
1.6808510638297873, 1.7553191489361701, 1.7659574468085106, nan, nan, nan, nan, 1.9255319148936172,
1.6489361702127658, 1.5478723404255319, nan, nan, nan, 1.6595744680851063, 1.6595744680851063,
1.8936170212765957, nan, nan, 1.8510638297872337, 1.957446808510638, 2.0957446808510638,
1.9361702127659572, 1.6436170212765957, 1.5904255319148934, nan, 1.7978723404255317, nan, nan,
1.7074468085106382, 1.7446808510638296, 1.957446808510638, 1.8510638297872337, 1.803191489361702,
1.8617021276595744, 1.6648936170212765, 1.4680851063829787, 1.6010638297872342, 1.553191489361702, nan,
nan, 1.5797872340425532, 1.5053191489361701, 1.5319148936170213, 1.4734042553191489, 1.5691489361702127,
1.5212765957446808, 1.4627659574468084, nan, 1.6595744680851063, 1.7819148936170213, 1.6382978723404256,
1.7340425531914894, nan, 1.8723404255319149, 1.7659574468085106, nan, 1.7925531914893618,
1.8776595744680848, 1.5691489361702127, 1.8829787234042552, 1.6382978723404256, nan, nan, nan, nan,
1.8297872340425529, 1.8297872340425529, 1.8351063829787233, nan, nan, 1.7234042553191489,
1.6648936170212765, 1.7234042553191489, 1.8617021276595744, 1.9361702127659572, 2.0159574468085104,
1.8882978723404256, 1.8351063829787233, 1.8989361702127661, 1.8138297872340425, 1.7712765957446805,
1.8563829787234041, nan, 1.8404255319148937, nan, 2.0638297872340425, nan, nan, nan, 2.436170212765957,
nan, nan, nan, 1.7819148936170213, 1.8510638297872337, 2.0638297872340425, 2.0797872340425534, nan, nan,
2.0265957446808511, 2.021276595744681, 2.0585106382978724, 2.1542553191489362, 2.4255319148936172,
2.0531914893617023, nan, 1.7925531914893618, 2.1755319148936167, 2.2819148936170213, 2.2819148936170213,
2.5851063829787235, nan, 1.8244680851063828, 1.9308510638297871, 1.9946808510638296, 2.0425531914893615,
2.2446808510638299, 2.2765957446808507, nan, 2.0797872340425534, nan, 2.4095744680851063,
2.5265957446808511, 2.5691489361702127, 2.3563829787234041, 2.4521276595744679, nan, 2.3936170212765955,
2.5797872340425529, nan, 2.808510638297872, 2.5425531914893615, 2.0319148936170213, 1.9308510638297871,
2.0585106382978724, nan, nan, 2.228723404255319, 2.2606382978723403, 2.2978723404255321, nan,
2.1276595744680851, 2.0851063829787235, 2.0425531914893615, 2.0744680851063828, 1.6276595744680851,
1.4787234042553192, 1.7021276595744681, 1.8563829787234041, 1.6436170212765957, 1.7978723404255317,
1.5691489361702127, 1.728723404255319, 1.6595744680851063, 1.7765957446808509, nan, 1.7180851063829785,
1.803191489361702, 1.7659574468085106, nan, nan, 1.6595744680851063, 1.8244680851063828,
1.7127659574468086, 1.7872340425531914, 1.7234042553191489, 1.8670212765957448, 1.8617021276595744,
1.8351063829787233, 2.0372340425531914, nan, 1.7872340425531914, 1.7340425531914894, nan, nan,
1.7234042553191489, 1.7393617021276597, nan, nan, nan, nan, nan, nan, 2.0744680851063828,
1.8936170212765957, 1.803191489361702, 1.7234042553191489, nan, 1.7765957446808509, nan,
1.8776595744680848, 1.8085106382978722, 1.904255319148936, 1.9414893617021276, 1.957446808510638,
1.7021276595744681, 1.9734042553191489, 1.946808510638298, 2.0478723404255317, 2.1382978723404258,
1.957446808510638, 1.946808510638298, 1.9361702127659572, nan, 2.3457446808510638, 2.3351063829787231,
nan, 1.8138297872340425, 1.7925531914893618, nan, nan, 2.6063829787234041, 2.0638297872340425,
2.0053191489361701, 1.8244680851063828, 1.8670212765957448, 1.9148936170212765, 1.8829787234042552,
1.9787234042553192, 1.8138297872340425, 1.574468085106383, 1.553191489361702, 1.425531914893617,
1.5957446808510638, 1.675531914893617, 1.6489361702127658, 1.5797872340425532, nan, 1.4308510638297871,
1.5053191489361701, 1.6382978723404256, 1.574468085106383, 1.6063829787234041, 1.574468085106383,
1.574468085106383, 1.6914893617021276, 1.6382978723404256, 1.6702127659574466, nan, 1.6223404255319149,
1.6223404255319149, 1.6276595744680851, 1.6808510638297873, 1.5797872340425532, nan, 1.7234042553191489,
1.7446808510638296, nan, 1.6968085106382977, 1.6861702127659572, 1.5053191489361701, 1.728723404255319,
1.8457446808510638, 1.8351063829787233, nan, 1.7925531914893618, 1.8244680851063828, 1.7180851063829785,
1.8351063829787233, nan, nan, 1.8563829787234041, nan, nan, 1.8404255319148937, nan, 1.9255319148936172,
nan, nan, 1.6436170212765957, nan, 1.9255319148936172, 1.8297872340425529, 1.8297872340425529,
1.8882978723404256, 1.8776595744680848, 1.8882978723404256, 1.8617021276595744, 1.7978723404255317,
1.8882978723404256, nan, 1.6276595744680851, 1.675531914893617, 1.7659574468085106, 1.5106382978723403,
1.7819148936170213, nan, 1.6542553191489362, 1.8351063829787233, nan, 2.0, 1.8723404255319149,
1.7978723404255317, 1.8244680851063828, 1.9734042553191489, 1.7659574468085106, nan, 1.9095744680851063,
2.0106382978723403, 1.9361702127659572, nan, 2.0957446808510638, 2.1595744680851063, nan,
1.9787234042553192, 2.2340425531914891, 2.0904255319148932, 1.9680851063829787, nan, nan,
1.7606382978723405, 1.8085106382978722, 1.7446808510638296, 1.9521276595744681, 1.8989361702127661,
1.7446808510638296, 2.0319148936170213, 1.7446808510638296, 1.7872340425531914, 2.0851063829787235,
1.8563829787234041, 1.9734042553191489, 1.9680851063829787, 1.9308510638297871, 1.7553191489361701, nan,
1.7446808510638296, 1.7340425531914894, 1.6861702127659572, 1.7819148936170213, 1.7712765957446805,
1.9202127659574468, 1.9202127659574468, 1.8244680851063828, 1.7765957446808509, 1.6702127659574466,
1.6542553191489362, 1.8351063829787233, 1.675531914893617, 1.7234042553191489, 1.9680851063829787, nan,
nan, 1.9946808510638296, nan, 1.5851063829787233, 1.4734042553191489, nan, 1.4574468085106382,
1.5478723404255319, 1.5638297872340423, 1.5319148936170213, nan, nan, 1.5319148936170213,
1.5265957446808509, 1.574468085106383, nan, 1.3989361702127658, 1.5478723404255319, 1.3244680851063828,
1.3776595744680851, nan, nan, 1.5053191489361701, 1.5904255319148934, 1.7446808510638296,
1.6010638297872342, 1.6170212765957446, 1.6117021276595744, 1.6595744680851063, nan, nan, nan,
1.7765957446808509, 1.6595744680851063, 1.8404255319148937, 1.6968085106382977, 1.3989361702127658,
1.6010638297872342, 1.5265957446808509, nan, nan, nan, 1.5319148936170213, 1.5053191489361701,
1.7925531914893618, 1.4521276595744681, 1.5851063829787233, 1.6117021276595744, nan, 1.4414893617021276,
1.6436170212765957, 1.6489361702127658, nan, nan, nan, nan, nan, nan, 2.2021276595744679,
2.3297872340425529, 1.8723404255319149, nan, 1.8244680851063828, 1.553191489361702, nan, nan,
1.6968085106382977, 1.7446808510638296, 1.5957446808510638, 1.7021276595744681, 1.7074468085106382,
1.4680851063829787, 1.5851063829787233, 1.6223404255319149, 1.6702127659574466, 1.7978723404255317, nan,
nan, 1.8723404255319149, 1.7234042553191489, 1.5797872340425532, 1.5904255319148934, 1.6808510638297873,
1.7180851063829785, 2.2393617021276597, 1.7393617021276597, nan, nan, nan, 1.7978723404255317, nan,
1.8191489361702129, 1.7872340425531914, 1.8829787234042552, 1.8138297872340425, 1.675531914893617,
1.6595744680851063, 1.7393617021276597, 1.5851063829787233, 1.5957446808510638, 1.574468085106383,
1.6010638297872342, 1.6702127659574466, nan]
profile_total = [611, 66, 705, 783, 993, 827, 0, 0, 187, 482, 0, 0, 1913, 888, 0, 0, 0, 8, 0, 0, 0, 608, 470, 1828,
47, 397, 143, 2032, 966, 171, 288, 0, 0, 24, 17, 702, 753, 102, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1640,
0, 1410, 75, 0, 0, 0, 0, 0, 0, 5, 192, 237, 0, 0, 252, 154, 318, 318, 437, 794, 491, 1625, 5, 180,
198, 174, 143, 0, 0, 865, 2006, 2141, 1746, 474, 109, 0, 0, 0, 156, 1028, 0, 241, 220, 306, 318,
433, 132, 755, 447, 2349, 360, 212, 864, 254, 703, 383, 128, 406, 69, 1163, 502, 0, 889, 41, 65, 18,
0, 0, 5, 130, 181, 319, 290, 259, 187, 32, 117, 947, 134, 286, 529, 713, 942, 579, 1622, 604, 265,
751, 125, 0, 839, 2145, 1695, 520, 365, 0, 163, 43, 0, 885, 725, 201, 193, 841, 98, 415, 1167, 1413,
1630, 1644, 277, 1556, 612, 1723, 136, 570, 100, 124, 751, 322, 208, 580, 319, 275, 2138, 4872,
2530, 60, 28, 289, 790, 48, 208, 51, 544, 2613, 757, 0, 234, 45, 172, 0, 93, 205, 381, 1485, 3029,
904, 18, 0, 0, 577, 1595, 591, 0, 0, 0, 0, 0, 92, 1231, 1737, 213, 372, 1516, 1974, 3243, 1275,
2013, 1745, 183, 0, 524, 534, 17, 23, 604, 920, 305, 138, 268, 375, 135, 200, 1222, 1621, 2018,
1597, 355, 713, 588, 243, 752, 986, 29, 102, 1095, 11, 24, 516, 0, 260, 427, 93, 253, 741, 50, 0, 0,
332, 74, 109, 8, 253, 675, 1355, 675, 6, 330, 82, 69, 257, 5, 0, 0, 387, 23, 265, 743, 13, 145, 9,
246, 593, 510, 186, 197, 310, 1494, 1112, 1567, 308, 4312, 3867, 4979, 610, 19, 32, 3344, 573, 5,
44, 253, 71, 670, 809, 3522, 3727, 2565, 2424, 1870, 0, 112, 290, 0, 35, 1441, 550, 53, 699, 63, 99,
54, 2066, 565, 281, 1522, 380, 133, 5, 0, 0, 234, 225, 473, 505, 631, 2085, 2240, 355, 0, 602, 455,
17, 1769, 1052, 2848, 1343, 90, 697, 763, 1854, 1295, 2704, 1041, 454, 2091, 2052, 1217, 0, 0, 43,
45, 705, 1102, 481, 0, 1071, 1485, 128, 202, 1113, 1896, 0, 1036, 275, 343, 94, 123, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 598, 22, 0, 391, 501, 44, 901, 2311, 640, 280, 441, 338, 601, 134, 131, 98, 128, 324,
414, 1350, 247, 582, 436, 75, 270, 71, 64, 412, 10, 274, 26, 252, 484, 1614, 2350, 394, 167, 437,
372, 411, 2294, 861, 123, 39, 239, 290, 96, 157, 470, 865, 4960, 674, 355, 113, 5, 0, 0, 0, 0, 0, 0,
594, 676, 216, 0, 271, 263, 259, 413, 51, 33, 11, 0, 2738, 81, 5, 65, 156, 356, 531, 316, 0, 462,
348, 175, 193, 157, 1101, 803, 13, 427, 468, 67, 403, 671, 533, 1067, 246, 601, 310, 1020, 815, 999,
91, 333, 347, 537, 1292, 455, 597, 378, 605, 232, 0, 0, 0, 0, 0, 17, 996, 84, 154, 357, 197, 266,
157, 275, 70, 825, 545, 103, 0, 0, 0, 0, 406, 504, 83, 0, 606, 0, 0, 555, 136, 268, 129, 644, 567,
536, 28, 249, 195, 256, 5, 1697, 930, 494, 783, 38, 0, 0, 0, 0, 998, 292, 626, 568, 1211, 34, 2361,
638, 192, 144, 480, 424, 59, 120, 78, 71, 523, 911, 611, 35, 1785, 743, 90, 73, 213, 229, 316, 0,
113, 475, 862, 777, 478, 509, 194, 1082, 30, 79, 139, 444, 600, 629, 0, 0, 0, 1187, 384, 185, 1112,
655, 1160, 737, 229, 44, 231, 1453, 51, 289, 654, 3431, 3220, 165, 0, 217, 2048, 1204, 578, 572,
1210, 0, 1923, 959, 0, 0, 0, 48, 311, 779, 493, 1635, 323, 260, 549, 0, 229, 9, 254, 52, 1167, 1683,
1636, 92, 0, 289, 710, 74, 191, 261, 0, 2246, 163, 476, 603, 353, 102, 438, 1138, 98, 980, 251, 335,
39, 333, 321, 87, 2395, 4069, 1980, 740, 1482, 3127, 95, 405, 167, 139, 789, 503, 2006, 6, 0, 160,
48, 11, 1314, 152, 30, 1825, 2095, 27, 228, 75, 855, 83, 5, 284, 839, 2352, 900, 114, 642, 171, 537,
1357, 5, 0, 0, 0, 661, 668, 2224, 10, 0, 0, 915, 1705, 34, 0, 110, 1666, 0, 660, 1391, 1671, 3395,
1481, 35, 0, 295, 0, 0, 2090, 2850, 290, 409, 322, 663, 461, 359, 937, 105, 395, 290, 132, 0, 0, 6,
0, 0, 102, 16, 245, 5, 783, 5, 0, 0, 0, 5, 55, 0, 0, 432, 0, 237, 695, 759, 430, 7, 111, 513, 22,
612, 0, 0, 1585, 67, 45, 183, 507, 537, 373, 694, 15, 12, 0, 79, 292, 608, 98, 0, 1098, 67, 700,
732, 280, 203, 0, 1245, 65, 1939, 1897, 48, 765, 2456, 2626, 203, 250, 140, 0, 0, 725, 1649, 1510,
1266, 52, 80, 0, 1275, 606, 0, 1555, 4702, 3194, 1416, 1708, 515, 281, 178, 344, 5, 118, 0, 39, 328,
506, 1214, 1475, 680, 64, 873, 892, 1135, 355, 168, 1197, 1984, 774, 13, 0, 0, 0, 0, 96, 1652, 1564,
0, 192, 74, 0, 0, 0, 0, 326, 322, 0, 6, 48, 608, 309, 0, 598, 286, 139, 2795, 409, 1081, 202, 54,
1368, 1898, 283, 531, 487, 572, 745, 207, 318, 0, 0, 41, 0, 0, 15, 649, 127, 30, 85, 542, 172, 0, 0,
49, 0, 0, 264, 323, 98, 91, 299, 1495, 994, 6, 338, 462, 127, 428, 1016, 201, 1917, 761, 1066, 2736,
0, 0, 634, 0, 0, 0, 0, 794, 312, 205, 512, 1435, 44, 1103, 1358, 1466, 163, 18, 502, 483, 927, 1284,
5, 25, 288, 424, 14, 464, 259, 71, 375, 11, 156, 33, 113, 37, 11, 156, 440, 1017, 25, 18, 246, 242,
330, 130, 422, 65, 466, 0, 482, 122, 0, 1614, 1493, 323, 235, 352, 740, 484, 1072, 1330, 47, 3069,
1772, 2268, 1876, 760, 4350, 1197, 1334, 1780, 10, 1270, 3675, 670, 11, 1550, 0, 0, 628, 0, 440,
1094, 424, 918, 0, 0, 89, 27, 0, 0, 45, 1094, 10, 6, 0, 101, 268, 80, 170, 5, 174, 101, 0, 0, 185,
506, 133, 74, 355, 107, 6, 430, 137, 176, 556, 829, 446, 448, 595, 50, 18, 6, 74, 301, 309, 159,
549, 463, 33, 0, 277, 446, 0, 169, 380, 43, 927, 1258, 117, 41, 81, 112, 12, 29, 320, 798, 881, 404,
58, 66, 189, 0, 96, 24, 23, 707, 526, 1013, 466, 440, 0, 0, 1051, 618, 0, 2412, 1884, 0, 0, 0, 217,
0, 666, 1513, 0, 208, 66, 190, 375, 118, 453, 882, 375, 0, 0, 0, 15, 0, 0, 0, 1507, 2442, 1458, 956,
332, 41, 709, 1308, 347, 193, 0, 0, 0, 638, 116, 133, 92, 0, 870, 38, 929, 32, 98, 753, 123, 0, 0,
175, 803, 6, 0, 0, 671, 1096, 1061, 136, 128, 517, 245, 360, 35, 176, 0, 0, 0, 0, 263, 237, 291,
398, 513, 181, 258, 378, 1867, 935, 2287, 176, 31, 333, 1144, 465, 272, 0, 158, 0, 624, 239, 91, 16,
0, 427, 2443, 2086, 1172, 976, 644, 736, 10, 0, 33, 459, 192, 99, 424, 534, 449, 344, 0, 5, 0, 290,
686, 599, 103, 167, 169, 1873, 1164, 262, 525, 1093, 1026, 167, 230, 7, 20, 202, 379, 27, 107, 0,
48, 37, 234, 870, 832, 399, 109, 389, 375, 59, 8, 86, 409, 37, 287, 168, 307, 88, 64, 124, 182,
1027, 1070, 1159, 973, 172, 252, 323, 529, 120, 98, 43, 262, 277, 492, 237, 1568, 196, 640, 442, 5,
6, 84, 0, 499, 10, 0, 63, 0, 11, 71, 53, 209, 0, 136, 0, 0, 546, 534, 0, 0, 27, 2100, 3192, 3115,
871, 5, 119, 1158, 596, 236, 151, 0, 628, 98, 78, 0, 48, 127, 141, 0, 932, 680, 247, 118, 406, 201,
492, 0, 0, 65, 0, 0, 194, 283, 372, 503, 158, 0, 43, 14, 0, 325, 180, 483, 306, 825, 800, 0, 0, 204,
440, 0, 0, 758, 822, 0, 0, 1406, 789, 184, 349, 164, 131, 129, 71, 80, 171, 0, 404, 270, 1429, 0,
607, 1731, 1212, 33, 36, 1478, 5, 0, 0, 48, 1219, 380, 254, 494, 1501, 241, 15, 233, 304, 21, 35,
34, 45, 495, 1215, 801, 2931, 31, 132, 40, 505, 635, 2298, 1121, 341, 458, 1089, 941, 0, 193, 1307,
282, 835, 2261, 291, 1130, 4830, 3714, 35, 21, 912, 369, 92, 489, 557, 606, 228, 705, 15, 87, 166,
1200, 421, 19, 497, 0, 45, 134, 24, 2022, 1678, 0, 1301, 1364, 0, 99, 0, 6, 71, 35, 0, 0, 0, 730,
507, 672, 926, 1224, 321, 5, 217, 295, 1203, 11, 322, 117, 6, 533, 876, 2517, 1352, 1204, 0, 0, 0,
0, 328, 556, 1559, 0, 0, 0, 101, 175, 1974, 0, 0, 486, 2171, 385, 357, 1783, 98, 125, 222, 29, 5,
551, 147, 139, 548, 108, 730, 720, 661, 225, 210, 35, 0, 112, 823, 1662, 94, 248, 219, 265, 44,
1304, 2173, 1981, 1206, 108, 168, 1043, 116, 373, 842, 108, 656, 219, 0, 0, 94, 17, 190, 1361, 1545,
0, 0, 2113, 1678, 971, 100, 1115, 856, 163, 975, 1635, 834, 2100, 255, 96, 696, 56, 425, 0, 0, 0,
211, 0, 32, 44, 108, 361, 828, 174, 0, 91, 1093, 1784, 2402, 1558, 553, 547, 0, 167, 1859, 170, 420,
304, 67, 403, 539, 1379, 847, 655, 367, 29, 486, 5, 738, 55, 346, 799, 675, 50, 242, 2663, 145,
1140, 544, 661, 488, 800, 18, 0, 213, 1811, 1257, 0, 311, 529, 295, 80, 123, 122, 478, 960, 895,
670, 660, 1008, 2811, 701, 50, 1063, 2396, 1087, 0, 14, 1094, 213, 282, 679, 268, 264, 794, 844,
343, 0, 898, 462, 103, 19, 146, 751, 6, 0, 5, 6, 0, 0, 538, 518, 2010, 1561, 88, 134, 0, 132, 547,
595, 988, 152, 511, 2223, 2012, 1196, 287, 2420, 940, 1532, 22, 2566, 146, 127, 713, 507, 65, 0,
351, 568, 1177, 1217, 2538, 409, 1394, 375, 4290, 1045, 763, 50, 396, 998, 307, 423, 0, 244, 145,
424, 1066, 77, 124, 516, 769, 492, 76, 21, 401, 1660, 915, 1303, 564, 5, 693, 1114, 49, 49, 110,
437, 898, 597, 2492, 19, 1255, 217, 813, 788, 0, 45, 542, 0, 25, 205, 10, 120, 13, 50, 463, 18, 305,
881, 226, 161, 1035, 1589, 163, 422, 272, 11, 1292, 961, 259, 543, 454, 11, 742, 636, 0, 411, 1208,
665, 259, 715, 367, 6, 687, 84, 926, 31, 1859, 427, 12, 181, 965, 1396, 951, 85, 0, 179, 1588, 371,
2666, 352, 59, 436, 345, 673, 503, 292, 493, 1725, 690, 1438, 38, 1310, 2088, 646, 1975, 163, 518,
117, 813, 421, 120, 345, 1109, 534, 333, 232, 0, 0, 355, 26, 2698, 608, 0, 2917, 3347, 2739, 2739,
0, 35, 116, 57, 664, 19, 343, 617, 164, 143, 80, 0, 151, 988, 696, 362, 967, 628, 425, 0, 111, 84,
184, 212, 491, 1580, 289, 2649, 1993, 0, 0, 0, 205, 812, 1242, 116, 338, 529, 51, 109, 857, 814, 22,
0, 0, 0, 0, 0, 195, 481, 470, 5, 1271, 469, 41, 36, 974, 1122, 2314, 725, 1872, 465, 240, 777, 2892,
934, 0, 0, 1255, 3218, 1470, 651, 3360, 3056, 1938, 284, 0, 55, 0, 1357, 0, 269, 596, 1649, 750,
551, 1180, 683, 1946, 1108, 267, 1718, 2613, 0]
peak_total = [72.0, 7.0, 57.0, 95.0, 38.0, 87.0, 0, 0, 18.0, 38.0, 0, 0, 127.0, 101.0, 0, 0, 0, 2.0, 0, 0, 0, 44.0,
42.0, 188.0, 8.0, 39.0, 17.0, 248.0, 143.0, 24.0, 39.0, 0, 0, 4.0, 4.0, 100.0, 103.0, 13.0, 17.0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 165.0, 0, 177.0, 11.0, 0, 0, 0, 0, 0, 0, 2.0, 25.0, 23.0, 0, 0, 35.0, 17.0, 38.0,
29.0, 72.0, 119.0, 51.0, 125.0, 1.0, 20.0, 24.0, 19.0, 37.0, 0, 0, 102.0, 360.0, 203.0, 222.0, 40.0,
12.0, 0, 0, 0, 15.0, 120.0, 0, 22.0, 24.0, 34.0, 29.0, 35.0, 24.0, 91.0, 63.0, 176.0, 31.0, 29.0,
112.0, 25.0, 55.0, 56.0, 11.0, 42.0, 9.0, 156.0, 84.0, 0, 60.0, 6.0, 9.0, 3.0, 0, 0, 1.0, 16.0, 21.0,
34.0, 29.0, 22.0, 23.0, 3.0, 15.0, 116.0, 17.0, 27.0, 52.0, 74.0, 133.0, 43.0, 234.0, 36.0, 17.0, 55.0,
15.0, 0, 122.0, 172.0, 149.0, 40.0, 39.0, 0, 24.0, 6.0, 0, 79.0, 100.0, 23.0, 19.0, 86.0, 12.0, 37.0,
126.0, 105.0, 154.0, 222.0, 29.0, 152.0, 45.0, 157.0, 14.0, 73.0, 14.0, 16.0, 72.0, 27.0, 22.0, 59.0,
19.0, 32.0, 279.0, 587.0, 190.0, 5.0, 6.0, 21.0, 63.0, 9.0, 30.0, 9.0, 52.0, 235.0, 51.0, 0, 38.0, 9.0,
20.0, 0, 25.0, 24.0, 24.0, 180.0, 320.0, 104.0, 5.0, 0, 0, 66.0, 136.0, 54.0, 0, 0, 0, 0, 0, 13.0,
75.0, 167.0, 24.0, 52.0, 154.0, 231.0, 503.0, 125.0, 241.0, 149.0, 30.0, 0, 47.0, 53.0, 2.0, 5.0,
112.0, 100.0, 42.0, 23.0, 36.0, 37.0, 16.0, 14.0, 90.0, 188.0, 203.0, 166.0, 32.0, 91.0, 68.0, 25.0,
69.0, 102.0, 5.0, 12.0, 75.0, 3.0, 4.0, 38.0, 0, 25.0, 46.0, 8.0, 14.0, 87.0, 6.0, 0, 0, 32.0, 8.0,
12.0, 2.0, 41.0, 98.0, 122.0, 80.0, 2.0, 37.0, 14.0, 16.0, 31.0, 1.0, 0, 0, 43.0, 5.0, 30.0, 66.0, 3.0,
21.0, 3.0, 35.0, 61.0, 60.0, 19.0, 28.0, 37.0, 202.0, 122.0, 223.0, 54.0, 658.0, 436.0, 655.0, 45.0,
4.0, 6.0, 372.0, 72.0, 1.0, 7.0, 53.0, 16.0, 70.0, 110.0, 538.0, 349.0, 400.0, 265.0, 272.0, 0, 16.0,
60.0, 0, 9.0, 155.0, 72.0, 16.0, 95.0, 24.0, 10.0, 15.0, 190.0, 59.0, 35.0, 159.0, 32.0, 10.0, 1.0, 0,
0, 29.0, 15.0, 55.0, 42.0, 89.0, 195.0, 266.0, 30.0, 0, 94.0, 40.0, 5.0, 213.0, 103.0, 252.0, 194.0,
14.0, 70.0, 129.0, 334.0, 152.0, 211.0, 87.0, 35.0, 122.0, 154.0, 92.0, 0, 0, 7.0, 7.0, 94.0, 116.0,
73.0, 0, 91.0, 97.0, 8.0, 22.0, 74.0, 107.0, 0, 132.0, 24.0, 28.0, 15.0, 20.0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 40.0, 3.0, 0, 30.0, 56.0, 8.0, 82.0, 241.0, 69.0, 34.0, 41.0, 24.0, 43.0, 10.0, 16.0, 13.0, 17.0,
33.0, 31.0, 129.0, 31.0, 50.0, 37.0, 10.0, 28.0, 8.0, 9.0, 58.0, 2.0, 32.0, 3.0, 15.0, 48.0, 155.0,
243.0, 43.0, 21.0, 35.0, 34.0, 37.0, 269.0, 72.0, 14.0, 5.0, 29.0, 24.0, 12.0, 21.0, 38.0, 78.0, 474.0,
66.0, 35.0, 13.0, 1.0, 0, 0, 0, 0, 0, 0, 57.0, 28.0, 20.0, 0, 26.0, 24.0, 25.0, 36.0, 7.0, 4.0, 3.0, 0,
275.0, 10.0, 2.0, 7.0, 21.0, 33.0, 45.0, 20.0, 0, 35.0, 37.0, 21.0, 25.0, 15.0, 59.0, 50.0, 2.0, 20.0,
28.0, 4.0, 17.0, 29.0, 34.0, 71.0, 17.0, 42.0, 30.0, 50.0, 42.0, 64.0, 7.0, 26.0, 21.0, 31.0, 54.0,
15.0, 30.0, 19.0, 21.0, 16.0, 0, 0, 0, 0, 0, 2.0, 39.0, 7.0, 29.0, 29.0, 19.0, 26.0, 16.0, 22.0, 9.0,
33.0, 30.0, 6.0, 0, 0, 0, 0, 15.0, 20.0, 5.0, 0, 49.0, 0, 0, 36.0, 12.0, 24.0, 12.0, 59.0, 28.0, 42.0,
3.0, 24.0, 22.0, 21.0, 2.0, 121.0, 55.0, 32.0, 37.0, 7.0, 0, 0, 0, 0, 75.0, 27.0, 38.0, 71.0, 130.0,
7.0, 204.0, 69.0, 28.0, 8.0, 33.0, 26.0, 4.0, 20.0, 9.0, 8.0, 51.0, 75.0, 49.0, 7.0, 182.0, 72.0, 8.0,
8.0, 15.0, 19.0, 26.0, 0, 8.0, 53.0, 85.0, 56.0, 74.0, 38.0, 15.0, 134.0, 8.0, 17.0, 23.0, 40.0, 61.0,
57.0, 0, 0, 0, 91.0, 37.0, 22.0, 80.0, 52.0, 103.0, 69.0, 22.0, 7.0, 21.0, 127.0, 10.0, 43.0, 62.0,
284.0, 354.0, 17.0, 0, 23.0, 222.0, 112.0, 38.0, 41.0, 81.0, 0, 149.0, 91.0, 0, 0, 0, 5.0, 29.0, 70.0,
46.0, 169.0, 30.0, 20.0, 45.0, 0, 27.0, 3.0, 28.0, 6.0, 110.0, 194.0, 136.0, 11.0, 0, 32.0, 58.0, 11.0,
24.0, 26.0, 0, 184.0, 14.0, 60.0, 73.0, 55.0, 18.0, 44.0, 134.0, 12.0, 120.0, 35.0, 54.0, 8.0, 43.0,
51.0, 12.0, 231.0, 394.0, 140.0, 65.0, 136.0, 290.0, 9.0, 35.0, 17.0, 20.0, 118.0, 33.0, 180.0, 2.0, 0,
14.0, 5.0, 3.0, 149.0, 10.0, 5.0, 188.0, 153.0, 5.0, 29.0, 13.0, 84.0, 14.0, 2.0, 35.0, 93.0, 186.0,
136.0, 12.0, 66.0, 28.0, 53.0, 94.0, 1.0, 0, 0, 0, 89.0, 84.0, 242.0, 3.0, 0, 0, 99.0, 160.0, 9.0, 0,
18.0, 169.0, 0, 52.0, 100.0, 166.0, 307.0, 109.0, 6.0, 0, 31.0, 0, 0, 122.0, 177.0, 26.0, 62.0, 51.0,
73.0, 44.0, 36.0, 94.0, 12.0, 24.0, 24.0, 11.0, 0, 0, 1.0, 0, 0, 22.0, 4.0, 25.0, 2.0, 69.0, 1.0, 0, 0,
0, 1.0, 5.0, 0, 0, 26.0, 0, 18.0, 54.0, 86.0, 54.0, 2.0, 21.0, 54.0, 5.0, 73.0, 0, 0, 154.0, 12.0, 5.0,
9.0, 40.0, 38.0, 35.0, 54.0, 4.0, 3.0, 0, 6.0, 27.0, 44.0, 11.0, 0, 80.0, 11.0, 69.0, 51.0, 29.0, 26.0,
0, 135.0, 6.0, 119.0, 144.0, 7.0, 39.0, 112.0, 191.0, 21.0, 20.0, 18.0, 0, 0, 61.0, 111.0, 128.0, 73.0,
8.0, 11.0, 0, 126.0, 82.0, 0, 126.0, 370.0, 226.0, 90.0, 123.0, 57.0, 27.0, 19.0, 31.0, 1.0, 11.0, 0,
7.0, 23.0, 47.0, 128.0, 163.0, 74.0, 7.0, 76.0, 79.0, 104.0, 44.0, 21.0, 133.0, 163.0, 64.0, 3.0, 0, 0,
0, 0, 15.0, 147.0, 144.0, 0, 16.0, 7.0, 0, 0, 0, 0, 23.0, 26.0, 0, 2.0, 4.0, 35.0, 26.0, 0, 48.0, 29.0,
11.0, 207.0, 59.0, 85.0, 20.0, 6.0, 105.0, 149.0, 18.0, 47.0, 36.0, 51.0, 85.0, 22.0, 27.0, 0, 0, 5.0,
0, 0, 3.0, 46.0, 10.0, 5.0, 6.0, 51.0, 17.0, 0, 0, 5.0, 0, 0, 17.0, 24.0, 7.0, 10.0, 24.0, 116.0, 45.0,
1.0, 27.0, 34.0, 14.0, 41.0, 90.0, 37.0, 252.0, 63.0, 83.0, 225.0, 0, 0, 49.0, 0, 0, 0, 0, 86.0, 22.0,
11.0, 34.0, 95.0, 4.0, 65.0, 76.0, 118.0, 15.0, 3.0, 48.0, 32.0, 46.0, 60.0, 1.0, 3.0, 20.0, 34.0, 3.0,
38.0, 18.0, 8.0, 35.0, 3.0, 13.0, 5.0, 10.0, 5.0, 2.0, 28.0, 62.0, 80.0, 5.0, 4.0, 29.0, 46.0, 34.0,
16.0, 59.0, 12.0, 65.0, 0, 68.0, 27.0, 0, 134.0, 173.0, 34.0, 32.0, 47.0, 88.0, 52.0, 199.0, 217.0,
11.0, 487.0, 197.0, 319.0, 201.0, 151.0, 663.0, 234.0, 154.0, 313.0, 4.0, 162.0, 643.0, 76.0, 4.0,
142.0, 0, 0, 75.0, 0, 52.0, 138.0, 33.0, 118.0, 0, 0, 19.0, 7.0, 0, 0, 9.0, 115.0, 4.0, 2.0, 0, 19.0,
45.0, 18.0, 36.0, 1.0, 17.0, 13.0, 0, 0, 27.0, 66.0, 17.0, 12.0, 35.0, 18.0, 2.0, 51.0, 29.0, 22.0,
60.0, 69.0, 65.0, 51.0, 94.0, 12.0, 5.0, 2.0, 7.0, 42.0, 37.0, 24.0, 40.0, 35.0, 9.0, 0, 31.0, 47.0, 0,
25.0, 48.0, 10.0, 191.0, 132.0, 16.0, 6.0, 8.0, 20.0, 2.0, 5.0, 35.0, 80.0, 86.0, 45.0, 6.0, 17.0,
29.0, 0, 15.0, 8.0, 6.0, 46.0, 57.0, 132.0, 46.0, 43.0, 0, 0, 121.0, 43.0, 0, 217.0, 370.0, 0, 0, 0,
21.0, 0, 39.0, 162.0, 0, 21.0, 7.0, 22.0, 41.0, 16.0, 38.0, 109.0, 34.0, 0, 0, 0, 4.0, 0, 0, 0, 91.0,
198.0, 140.0, 122.0, 25.0, 8.0, 72.0, 133.0, 45.0, 19.0, 0, 0, 0, 43.0, 13.0, 12.0, 8.0, 0, 71.0, 3.0,
63.0, 6.0, 10.0, 56.0, 9.0, 0, 0, 10.0, 52.0, 2.0, 0, 0, 39.0, 96.0, 106.0, 20.0, 14.0, 48.0, 24.0,
22.0, 8.0, 15.0, 0, 0, 0, 0, 17.0, 18.0, 24.0, 31.0, 43.0, 18.0, 14.0, 26.0, 116.0, 60.0, 133.0, 15.0,
5.0, 26.0, 81.0, 35.0, 25.0, 0, 15.0, 0, 41.0, 28.0, 10.0, 4.0, 0, 49.0, 325.0, 223.0, 202.0, 117.0,
39.0, 61.0, 2.0, 0, 5.0, 33.0, 17.0, 12.0, 32.0, 34.0, 41.0, 25.0, 0, 2.0, 0, 46.0, 74.0, 42.0, 13.0,
13.0, 26.0, 123.0, 83.0, 19.0, 42.0, 88.0, 101.0, 12.0, 18.0, 3.0, 4.0, 16.0, 25.0, 3.0, 10.0, 0, 5.0,
5.0, 16.0, 45.0, 49.0, 24.0, 7.0, 27.0, 30.0, 7.0, 1.0, 8.0, 26.0, 4.0, 23.0, 18.0, 24.0, 9.0, 9.0,
13.0, 14.0, 81.0, 118.0, 165.0, 110.0, 14.0, 18.0, 29.0, 42.0, 17.0, 9.0, 7.0, 38.0, 23.0, 45.0, 17.0,
166.0, 19.0, 50.0, 28.0, 1.0, 2.0, 8.0, 0, 23.0, 2.0, 0, 8.0, 0, 2.0, 6.0, 8.0, 17.0, 0, 16.0, 0, 0,
39.0, 80.0, 0, 0, 5.0, 210.0, 286.0, 273.0, 81.0, 2.0, 16.0, 101.0, 54.0, 20.0, 12.0, 0, 72.0, 11.0,
8.0, 0, 7.0, 16.0, 15.0, 0, 87.0, 56.0, 42.0, 22.0, 36.0, 21.0, 111.0, 0, 0, 18.0, 0, 0, 20.0, 39.0,
27.0, 85.0, 20.0, 0, 10.0, 3.0, 0, 42.0, 27.0, 48.0, 44.0, 71.0, 76.0, 0, 0, 22.0, 30.0, 0, 0, 56.0,
69.0, 0, 0, 115.0, 50.0, 22.0, 48.0, 16.0, 19.0, 26.0, 12.0, 11.0, 19.0, 0, 50.0, 29.0, 156.0, 0,
133.0, 141.0, 191.0, 7.0, 10.0, 151.0, 4.0, 0, 0, 7.0, 158.0, 38.0, 51.0, 55.0, 324.0, 59.0, 5.0, 37.0,
31.0, 5.0, 4.0, 9.0, 6.0, 68.0, 163.0, 94.0, 158.0, 5.0, 30.0, 10.0, 114.0, 84.0, 233.0, 119.0, 38.0,
56.0, 186.0, 124.0, 0, 26.0, 212.0, 38.0, 110.0, 242.0, 40.0, 107.0, 312.0, 228.0, 6.0, 5.0, 73.0,
24.0, 10.0, 67.0, 62.0, 81.0, 25.0, 65.0, 3.0, 12.0, 24.0, 121.0, 57.0, 3.0, 59.0, 0, 5.0, 19.0, 4.0,
227.0, 273.0, 0, 138.0, 137.0, 0, 17.0, 0, 2.0, 13.0, 5.0, 0, 0, 0, 72.0, 45.0, 85.0, 56.0, 92.0, 36.0,
2.0, 29.0, 33.0, 108.0, 2.0, 63.0, 16.0, 3.0, 52.0, 62.0, 196.0, 115.0, 104.0, 0, 0, 0, 0, 35.0, 64.0,
127.0, 0, 0, 0, 11.0, 18.0, 139.0, 0, 0, 33.0, 150.0, 26.0, 30.0, 130.0, 11.0, 10.0, 25.0, 3.0, 3.0,
75.0, 16.0, 14.0, 54.0, 21.0, 104.0, 74.0, 66.0, 27.0, 22.0, 6.0, 0, 11.0, 86.0, 197.0, 19.0, 29.0,
19.0, 30.0, 5.0, 77.0, 172.0, 106.0, 118.0, 9.0, 20.0, 141.0, 10.0, 36.0, 73.0, 14.0, 61.0, 22.0, 0, 0,
9.0, 4.0, 18.0, 146.0, 190.0, 0, 0, 188.0, 179.0, 143.0, 15.0, 79.0, 55.0, 14.0, 69.0, 207.0, 62.0,
176.0, 17.0, 10.0, 59.0, 7.0, 29.0, 0, 0, 0, 11.0, 0, 3.0, 7.0, 12.0, 41.0, 102.0, 22.0, 0, 8.0, 52.0,
156.0, 237.0, 108.0, 37.0, 36.0, 0, 13.0, 119.0, 21.0, 23.0, 18.0, 6.0, 34.0, 63.0, 203.0, 89.0, 40.0,
26.0, 3.0, 27.0, 2.0, 61.0, 13.0, 36.0, 70.0, 41.0, 4.0, 23.0, 200.0, 8.0, 78.0, 60.0, 43.0, 32.0,
57.0, 3.0, 0, 16.0, 159.0, 116.0, 0, 23.0, 38.0, 33.0, 13.0, 11.0, 16.0, 32.0, 81.0, 102.0, 60.0, 44.0,
100.0, 280.0, 56.0, 4.0, 95.0, 258.0, 131.0, 0, 3.0, 81.0, 24.0, 32.0, 67.0, 29.0, 25.0, 83.0, 69.0,
25.0, 0, 109.0, 60.0, 10.0, 4.0, 19.0, 100.0, 2.0, 0, 2.0, 2.0, 0, 0, 60.0, 50.0, 242.0, 159.0, 7.0,
11.0, 0, 18.0, 69.0, 99.0, 98.0, 22.0, 74.0, 207.0, 150.0, 140.0, 38.0, 313.0, 118.0, 255.0, 4.0,
125.0, 11.0, 8.0, 89.0, 40.0, 5.0, 0, 34.0, 58.0, 82.0, 116.0, 304.0, 56.0, 153.0, 121.0, 442.0, 95.0,
116.0, 13.0, 64.0, 171.0, 55.0, 63.0, 0, 37.0, 19.0, 68.0, 121.0, 11.0, 23.0, 68.0, 85.0, 48.0, 15.0,
3.0, 68.0, 331.0, 193.0, 182.0, 124.0, 2.0, 93.0, 143.0, 6.0, 11.0, 23.0, 38.0, 99.0, 57.0, 147.0, 5.0,
174.0, 41.0, 161.0, 108.0, 0, 8.0, 85.0, 0, 6.0, 35.0, 3.0, 20.0, 8.0, 6.0, 44.0, 3.0, 38.0, 86.0,
28.0, 20.0, 177.0, 146.0, 24.0, 70.0, 29.0, 4.0, 145.0, 128.0, 47.0, 51.0, 64.0, 3.0, 75.0, 55.0, 0,
41.0, 86.0, 65.0, 33.0, 68.0, 45.0, 2.0, 102.0, 14.0, 141.0, 4.0, 341.0, 59.0, 4.0, 20.0, 80.0, 144.0,
115.0, 10.0, 0, 42.0, 167.0, 42.0, 213.0, 36.0, 11.0, 41.0, 45.0, 84.0, 31.0, 34.0, 85.0, 214.0, 91.0,
249.0, 5.0, 76.0, 189.0, 71.0, 217.0, 26.0, 58.0, 18.0, 95.0, 60.0, 11.0, 28.0, 130.0, 52.0, 20.0,
19.0, 0, 0, 40.0, 4.0, 152.0, 53.0, 0, 318.0, 380.0, 423.0, 519.0, 0, 6.0, 18.0, 15.0, 103.0, 4.0,
38.0, 104.0, 25.0, 15.0, 10.0, 0, 11.0, 139.0, 74.0, 57.0, 138.0, 47.0, 58.0, 0, 10.0, 5.0, 19.0, 17.0,
38.0, 113.0, 29.0, 282.0, 224.0, 0, 0, 0, 16.0, 63.0, 133.0, 16.0, 28.0, 42.0, 7.0, 11.0, 121.0, 98.0,
4.0, 0, 0, 0, 0, 0, 18.0, 23.0, 32.0, 1.0, 123.0, 49.0, 7.0, 5.0, 61.0, 88.0, 167.0, 78.0, 195.0, 39.0,
21.0, 81.0, 301.0, 133.0, 0, 0, 98.0, 248.0, 181.0, 95.0, 439.0, 250.0, 103.0, 20.0, 0, 6.0, 0, 101.0,
0, 19.0, 40.0, 135.0, 71.0, 44.0, 105.0, 60.0, 153.0, 85.0, 36.0, 143.0, 205.0, 0]
C_medians_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, 1.6376927133947077, 1.8828972624575941, nan, nan, nan, 1.8966736773185338, 1.5841924152937064,
1.6707567994922792, 1.6410390115501343, 1.6865850605144077, 1.5330269368564837, 1.7734041302960348,
nan, 1.5195258393797428, nan, 1.652043126703973, 1.5975315482152859, 1.7310340527922436, nan,
1.6944824522868636, 1.4348262948635151, 1.35417358561295, nan, nan, 1.3465684884286306, nan, nan,
nan, nan, 1.3479520625047532, 1.3747165854173564, 1.2900216140957739, 1.3705397447750098,
1.3169464851626187, 1.2912989308812213, nan, 1.3759680690693747, 1.3354473856001592, nan, nan, nan,
nan, 1.3591095888977809, 1.4513333231940047, 1.3221794213142477, 1.6525226803040052, nan,
1.2871441545241689, 1.3025935817975085, 1.336022386385652, 1.4969592266202829, 1.3688209874164405,
1.4222065193701479, 1.3918218654369554, 1.4150838586382588, 1.3661509463949164, nan, nan,
1.3949002693596226, nan, nan, nan, nan, nan, nan, 1.6502748163554424, nan, nan, nan,
1.467889807171838, nan, nan, 1.3163569965506798, 1.3493607872580782, 1.3476721438672383,
1.4750745437545525, 1.3716006123123266, nan, 1.2814349881708076, 1.3673777569943435,
1.4181105158995164, 1.4456931709772134, nan, 1.4209919363247616, nan, nan, 1.2190711095671758, nan,
1.2787236345046145, 1.3961616906134304, nan, 1.428344010185274, 1.4690137425787293, nan,
1.6459619443312064, nan, nan, 1.4404142338869748, nan, nan, nan, nan, 1.7916660293977296,
1.976491956415771, 1.4750183974051372, 1.3353843328175605, 1.6169702128525314, 1.343417887066219,
nan, 1.399546643069582, 1.3151543461795774, 1.4841045868135896, nan, 1.4391679653746294,
1.4349264613299904, nan, 1.5510457506424928, 1.5375699088768582, 1.479591381577658,
1.5151743513071456, 1.591033859802923, nan, nan, nan, nan, 1.8459714291410623, 1.4673597531048552,
1.4023605521686111, nan, nan, nan, 1.5775511035774508, 1.6517100502189888, 1.6377904255113966, nan,
nan, 1.7137504734977125, 1.7244438058720433, 1.8550355329176429, 1.7397079600775727,
1.5080320331006241, nan, 1.4439713138255326, 1.5079020017580622, nan, nan, 1.3985736966607458,
1.606194514180959, 1.6775170238010859, 1.6298563444863972, 1.5160490672660838, 1.5948247273139315,
1.3092338420896599, 1.2944618099305307, 1.4082057936393344, 1.3133218717918091, nan, nan,
1.4907512551824982, 1.3376211444927235, 1.4235326901484853, 1.2661157715312534, 1.3906154182302313,
1.3691557551730154, 1.2929799562176156, nan, 1.4927865711395498, 1.5079183036414312,
1.5587326939912236, 1.4971870794086259, 1.4922649113691597, 1.6388366538758239, 1.5958018364010944,
1.5269651144249261, 1.5581146871476852, 1.58055403424631, nan, 1.6109774129304151,
1.4487610464108978, nan, nan, 1.3760490002369612, nan, 1.5794016447355144, 1.5518259642848871,
1.57465233159566, nan, nan, 1.5387966438920933, 1.5225036178099005, 1.5243278989456059,
1.5845944326071679, 1.685851043467355, 1.6887949167199117, 1.5704423669559053, 1.692197678204332,
1.7108683490392314, 1.4951180220686884, 1.5467985248437475, 1.6177083189926766, 1.6186829775544869,
1.5422246355099296, nan, 1.738846693938515, nan, nan, nan, 2.1176426847254453, nan, nan, nan,
1.5290813938074865, 1.6296407581999492, 1.8184269707963443, 1.7885437055466333, nan, nan,
1.9701704460876659, 1.7291081485942057, 1.7857113224463337, 1.8365971687586866, 2.0559115389406979,
1.8883318759723511, nan, nan, 1.8140310820991794, 1.8634822235996682, 1.7553915240916349,
1.8704762300018194, nan, 1.584468310718735, 1.6924440792408888, 1.7460891495353006,
1.7617638008203373, 1.7903071489723428, 1.7852829994934907, nan, nan, nan, 2.0619430627283908,
2.0243223575512492, 1.9911201143473896, 2.0195981168278672, 2.0699803084544128, nan,
2.1304504377022404, 2.247809056485103, nan, 2.3032916956153331, 2.2911763243518544,
1.7814396843192286, 1.7189692513234665, 1.7072634034321494, nan, nan, 1.9159487967183431,
1.9506057006540702, 2.0313682941666178, nan, nan, nan, 1.8125496655667011, nan, nan, nan,
1.3678713604825889, 1.5769739515262189, 1.4586632037713647, nan, 1.4228402460042973,
1.4047147205443409, 1.375206078860701, 1.4994389549561151, nan, 1.4883683237657446,
1.5590862892635002, 1.5432966874444976, nan, nan, 1.4886126144948328, 1.5412183052238146,
1.5035776628627096, 1.5446543158345116, 1.5013493689923854, 1.5101467415708374, 1.659422970111375,
1.6375370217864789, 1.7209173257990718, nan, nan, nan, nan, nan, 1.4568898031897439,
1.5278689628409128, nan, nan, nan, nan, nan, nan, 1.7974055310246917, 1.7252765984764995,
1.5424977167820648, nan, nan, 1.6990666188233154, nan, 1.6932882789795054, 1.5852138825631479,
1.6117487149418666, 1.6238611135809826, 1.6514123520986934, 1.5807552494430432, 1.6821115395125312,
1.6725569762494465, 1.709758758665461, 1.8469319005113005, 1.6740934565495968, 1.6718622898812918,
1.6261347245254785, nan, 1.9715924916524641, 2.0982990306553377, nan, 1.6354231660008569,
1.5944773234698415, nan, nan, 2.4671213113956223, 1.7885626316269789, 1.7751070385091074,
1.6981950658933882, 1.6209270215899896, 1.6870657616545897, 1.6682427459103926, 1.7635492701839048,
1.6861389995423846, 1.3960218452456115, 1.3515430314777168, 1.2578219277438161, 1.4156649318354639,
1.4229514921700512, 1.4767677022344354, 1.4410866905564721, nan, 1.2609584682148105,
1.3497047030856522, 1.4982074670015622, 1.4153818884045832, 1.4384356419350675, 1.3478661688320741,
1.4292055806901069, 1.4640923858210584, 1.4518041457541708, 1.5171403034846258, nan,
1.4234543518853253, nan, 1.3642425154630322, 1.4943016224486581, 1.3595253097649698, nan,
1.5314567047945502, 1.5388678665222932, nan, 1.5100289139755745, 1.5024665614986357,
1.4611833715170892, 1.4765311873167795, 1.7518528359387593, 1.7135352780658086, nan,
1.6087429986515456, 1.639378865653474, 1.5220853419527818, 1.6277213187400179, nan, nan,
1.6107641389213929, nan, nan, 1.6660274496650782, nan, 1.7806749863917597, nan, nan,
1.437486000497574, nan, nan, 1.6854176034421975, 1.6349477743505967, 1.7100478685697864,
1.6312086962455012, 1.7139235157679802, 1.7247433641200047, 1.592901090732318, 1.6511098137164462,
nan, 1.5186202972540994, 1.5511232398105641, 1.5772950259558849, 1.4401751044543432,
1.6420668566959682, nan, 1.5100399788504384, nan, nan, 1.8076769984343275, 1.7231445614716576,
1.5774351528556052, 1.7033949921898202, 1.7513493046865325, 1.5988774462687407, nan,
1.7356872899843176, 1.7982193008679139, 1.7289472073050174, nan, 1.8464977042680508, nan, nan,
1.7850851293131722, nan, nan, 1.8152630942025398, nan, nan, nan, nan, 1.5508483210379203,
1.7361181620428912, 1.7567217303185456, 1.5043194912496856, 1.8356391492085071, 1.6142150935940958,
1.6668306762701577, 1.9640787012826653, nan, nan, 1.7196245163987118, 1.6851029572721612,
1.5184945903720704, nan, 1.7104070393454871, 1.5420189885358817, 1.5771487124435049,
1.5770823526510127, 1.5927106846479788, nan, nan, nan, nan, nan, nan, nan, nan, 1.6839469331503236,
1.8704205806343555, nan, nan, 1.8865759517032918, nan, 1.5970902822423754, nan, nan, nan, nan,
1.3499410554210516, 1.3423776463338153, nan, nan, 1.3952930356350253, nan, 1.4204289606742293, nan,
1.2989270108292894, 1.3901818523936993, 1.1674714757399618, 1.3030554282421234, nan, nan,
1.361982387208438, 1.3641385590306672, 1.5434368835352505, 1.4080258887377168, 1.3952816140969284,
1.4164905998640978, 1.4285022102367502, nan, 1.8090893401032713, nan, 1.399671022048903,
1.4061772787865627, 1.5571540204885792, 1.5039031058187153, 1.273422835537676, 1.3537785723839302,
1.3627224273359424, nan, nan, nan, 1.4529968229041077, 1.4470713541519156, nan, nan,
1.4660995162071664, nan, nan, 1.3683151981289912, nan, 1.536632971328213, nan, nan, nan, nan, nan,
nan, 1.7329535090700534, 2.1255195028274958, 1.6249919626868659, nan, 1.7141600716874161,
1.3213261143822603, nan, nan, nan, 1.4801427357134538, 1.5049740267045184, 1.4913535780092007,
1.4740830539918419, 1.3466345631366705, 1.4242898078765101, 1.3884486253396584, 1.4391665773579254,
1.5651021649344115, nan, nan, 1.6859608033764091, 1.5699414796896751, 1.4384597753183583, nan,
1.5505678582504114, 1.5917238447638282, 1.935216224728632, 1.6605575046257099, nan, nan, nan,
1.6675504801514147, nan, 1.6602350772839505, 1.5617791934176726, 1.5591291164779792,
1.5420396569625208, 1.3599351649385318, 1.3939533958428683, 1.5783956740343932, 1.4071965263744528,
1.4302016805188629, 1.4436958315505568, 1.4029625837703577, 1.4583558029986907, nan]
C_stdevs_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, 0.15024783826787202, 0.13164195190966019, nan, nan, nan, 0.19288963733496631,
0.095650377550584212, 0.11527593705729203, 0.14977919083725313, 0.094690742492415617,
0.07500640412168752, 0.066967396438988219, nan, 0.12531612384916199, nan, 0.09673737324428551,
0.17816438941834914, 0.094973826325920191, nan, 0.04312962799520767, 0.19555731564496753,
0.10006445591208916, nan, nan, 0.098206792214886499, nan, nan, nan, nan, 0.072562272986694501,
0.11261576744427924, 0.073239928133540785, 0.11774966912268699, 0.060170052421767248,
0.059335389041597075, nan, 0.10403998897294765, 0.14755323713128468, nan, nan, nan, nan,
0.15369507287551623, 0.091461755020323526, 0.14305060270794856, 0.17238195807282961, nan,
0.13165193825718796, 0.054656769510553559, 0.045326128392811728, 0.065621640285427649,
0.094064820923081313, 0.095491588808401792, 0.10963931071595512, 0.10418854660929298,
0.11899215930704557, nan, nan, 0.087092567092776962, nan, nan, nan, nan, nan, nan,
0.14938520188079443, nan, nan, nan, 0.2979666076925172, nan, nan, 0.076323200075888539,
0.082528017638502607, 0.083396494241022009, 0.16747435106858766, 0.34162418069256928, nan,
0.089948873949629457, 0.11004094175984726, 0.12643248053027437, 0.10866537854520943, nan,
0.079515140790193534, nan, nan, 0.14177329466527233, nan, 0.087915949029133869, 0.085473514631223976,
nan, 0.13745628148046413, 0.12504962827466826, nan, 0.081367591965779917, nan, nan,
0.058464044703503248, nan, nan, nan, nan, 0.11230702133212074, 0.14079863905397227,
0.085648945740408058, 0.15886717544518703, 0.15387165863932503, 0.09335148732936048, nan,
0.093497559896459595, 0.20497537422671083, 0.12292564688839734, nan, 0.060844077902937561,
0.11188439326571918, nan, 0.14957246750669498, 0.19184322286691255, 0.17299263421484468,
0.13563206769637989, 0.12597947566789233, nan, nan, nan, nan, 0.21044437440502564,
0.10273823763167496, 0.18830567701612652, nan, nan, nan, 0.18079741291192006, 0.13036187056777937,
0.16442494596799315, nan, nan, 0.21455910968429859, 0.13302577516780675, 0.13843520774083032,
0.2351129995868794, 0.14518154858763088, nan, 0.28674442749406803, 0.12059122285974959, nan, nan,
0.1027304257047614, 0.14072062005815988, 0.15662823549553262, 0.16135310872123362,
0.090612686768633072, 0.13827782270156128, 0.07441425575999501, 0.13549985693194397,
0.12626880806512422, 0.096816045032642378, nan, nan, 0.18394871346311603, 0.10082529696189856,
0.1077253984904163, 0.067839154851738484, 0.11045122593458287, 0.23419177114490974,
0.17133222133098844, nan, 0.13049728626023385, 0.19786790828124232, 0.17677815326866186,
0.10247627151916176, 0.13648919553921748, 0.096171604393479354, 0.1232948943769016,
0.14212487260773865, 0.1691125584289539, 0.14828727622315288, nan, 0.10864716150144467,
0.12444713232072252, nan, nan, 0.093762312150746568, nan, 0.12914186174728423, 0.10344013777044284,
0.080804934552587249, nan, nan, 0.11093221059001569, 0.10611777444568904, 0.10544415832661005,
0.095608319464988767, 0.16049003112635354, 0.18332727005449556, 0.11343987206937506,
0.1620558884575152, 0.14160676012227683, 0.12998915046721538, 0.13164680252053765, 0.1812353336058688,
0.19043266605203613, 0.12257592477323974, nan, 0.15323865392688593, nan, nan, nan,
0.24916565528573303, nan, nan, nan, 0.124062453693604, 0.10724402346483845, 0.12703180569013867,
0.16994914535190883, nan, nan, 0.21678769965902778, 0.11991755459327791, 0.12920373091032128,
0.16692300349204209, 0.240100300753949, 0.21447606033682629, nan, nan, 0.18443722222606401,
0.13424809179755004, 0.14319837967717197, 0.1759530644943994, nan, 0.14349994637167024,
0.10581985354330473, 0.096953052875185186, 0.15301801479557406, 0.21539494599834325,
0.23780571218959104, nan, nan, nan, 0.16737224280973215, 0.055148014432746875, 0.091260822413015433,
0.14682138797301938, 0.17055266138634023, nan, 0.18934095212116547, 0.16271834398350019, nan,
0.21438251383183649, 0.2012562366616, 0.1900959789778647, 0.23904625471186314, 0.28919587733552155,
nan, nan, 0.1613404770781767, 0.14095054856047071, 0.13591215657923325, nan, nan, nan,
0.12257026411246522, nan, nan, nan, 0.15448617904510839, 0.14812727146492261, 0.11021259183859662,
nan, 0.19265331113032194, 0.12096207251571368, 0.14496779118153044, 0.13513932730702868, nan,
0.14980064660388706, 0.11971233706039663, 0.078000799080941902, nan, nan, 0.14524440031643263,
0.26543045702486462, 0.14928382254271752, 0.13011937468284809, 0.10620337097400519,
0.12454870215326973, 0.14595709197536111, 0.14963122124979414, 0.16392380768274925, nan, nan, nan,
nan, nan, 0.10919674832649962, 0.14462685852196647, nan, nan, nan, nan, nan, nan, 0.10501265206390412,
0.18649899427021177, 0.13220439849886179, nan, nan, 0.29097700559218054, nan, 0.07567033524873143,
0.12279706621075881, 0.08893529403524579, 0.10019143800411884, 0.12161863616026007,
0.078966356125718604, 0.12859043940742784, 0.14244302818492624, 0.11367626561925241,
0.10969291440937612, 0.095668465307781836, 0.073561547202547456, 0.088595518212800428, nan,
0.25155634325428727, 0.21989392535052082, nan, 0.092286667347642459, 0.12205527961928833, nan, nan,
0.11812051759917737, 0.14069005837231663, 0.19203356423559009, 0.12034607984595619,
0.086758803037951901, 0.11902647650796651, 0.10644354968991165, 0.064272548909802968,
0.088983870489964895, 0.16559294947350012, 0.096861426057413796, 0.078520467586937065,
0.073562804624951614, 0.070933063925250742, 0.061427418167172371, 0.0673032894843167, nan,
0.10216103015479219, 0.17746772506924743, 0.084341635955477973, 0.24537861060883676,
0.11957603960297475, 0.069929414103640619, 0.10860450615956839, 0.095968524855916115,
0.18532819051822336, 0.06136814057925627, nan, 0.071634486043298709, nan, 0.048549571809584337,
0.099090597094958044, 0.11081758912011365, nan, 0.07891190653021965, 0.082000040969842355, nan,
0.050137113438045368, 0.064063622768877906, 0.16952291787334864, 0.10550010377187478,
0.10700917496724249, 0.20719913804895801, nan, 0.081206650042099732, 0.064928016358666463,
0.099319257512171688, 0.068552634641182941, nan, nan, 0.073872028937550807, nan, nan,
0.08286012814079223, nan, 0.078371614988323068, nan, nan, 0.13467081437651263, nan, nan,
0.10374816753048194, 0.10247273078523123, 0.10531630976169246, 0.13846652780458521,
0.2606818458699815, 0.28068103319500737, 0.090462283760170223, 0.14328136827342167, nan,
0.17117041820813539, 0.0934136216630614, 0.096233874304792189, 0.098365181315134137,
0.10616736661042923, nan, 0.18822987891005602, nan, nan, 0.14366367256538015, 0.18273882025371091,
0.12733731470692405, 0.095340034063620308, 0.11471961652880872, 0.098185497462345173, nan,
0.077041703652307572, 0.051537278806162511, 0.080982045638885253, nan, 0.10436981447988848, nan, nan,
0.15784258717287572, nan, nan, 0.089816136471677105, nan, nan, nan, nan, 0.16617426997275131,
0.13275326510291077, 0.21303912269028713, 0.072104651961052202, 0.18459187870237287,
0.20019499216414433, 0.14723911155615019, 0.24801766840592418, nan, nan, 0.080714039933517279,
0.15480359253227199, 0.070747218169872922, nan, 0.25950350159965074, 0.12458434104341157,
0.10857225824282513, 0.13607514174018917, 0.14344364567143889, nan, nan, nan, nan, nan, nan, nan, nan,
0.21049589188857343, 0.2388178471914153, nan, nan, 0.10890190519067942, nan, 0.16294178349963878, nan,
nan, nan, nan, 0.10719856591923083, 0.077483406785552836, nan, nan, 0.10496178039097719, nan,
0.07119203148719204, nan, 0.24735208438430817, 0.12929165125372991, 0.077567493119789929,
0.16657442561914729, nan, nan, 0.32985216139101559, 0.095529137865164565, 0.18930245912260024,
0.10743139596972491, 0.10893460349966187, 0.17301839075962705, 0.18039409961123662, nan,
0.23437539892933323, nan, 0.10883275937176817, 0.16448427233073667, 0.16251472131126582,
0.17835244329882402, 0.11228935112480136, 0.10966860320323721, 0.079109741374334283, nan, nan, nan,
0.23076121657678714, 0.16130998016419584, nan, nan, 0.19221711317753856, nan, nan,
0.13736715532187566, nan, 0.11990296430223198, nan, nan, nan, nan, nan, nan, 0.11857663290869755,
0.26056274112572769, 0.28522502940068412, nan, 0.13770901080831216, 0.11299734150682358, nan, nan,
nan, 0.14586004535363556, 0.15722291202535066, 0.13564433189679753, 0.089484679892602767,
0.14427776022078129, 0.15341847319782309, 0.11018418467474822, 0.10293621710497679,
0.16563768704779183, nan, nan, 0.15019365149846231, 0.14097530839718364, 0.080592932136307097, nan,
0.087418624282176963, 0.13062009540331002, 0.19180944467302702, 0.19616950518606976, nan, nan, nan,
0.12620133716201185, nan, 0.23395937138089221, 0.16564076323464821, 0.14610252768560231,
0.16276740712146914, 0.13656138559838918, 0.13954006016747394, 0.12745191747806195,
0.14323154012925915, 0.11417899433123038, 0.10136161998610761, 0.11792469586838351,
0.16337190944019281, nan]
C_modes_wv = [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, 1.6063829787234041, 1.8829787234042552, nan, nan, nan, 1.8936170212765957, 1.5212765957446808,
1.6117021276595744, 1.5638297872340423, 1.675531914893617, 1.4840425531914891, 1.7553191489361701, nan,
1.5106382978723403, nan, 1.6329787234042552, 1.5797872340425532, 1.7021276595744681, nan,
1.7074468085106382, 1.2127659574468086, 1.3829787234042552, nan, nan, 1.3085106382978724, nan, nan,
nan, nan, 1.3404255319148934, 1.3351063829787235, 1.2872340425531914, 1.4361702127659575,
1.2978723404255319, 1.2765957446808509, nan, 1.2659574468085106, 1.3723404255319149, nan, nan, nan,
nan, 1.3510638297872339, 1.4202127659574466, 1.303191489361702, 1.7340425531914894, nan,
1.2606382978723403, 1.303191489361702, 1.3244680851063828, 1.5053191489361701, 1.4202127659574466,
1.5159574468085106, 1.3670212765957446, 1.4202127659574466, 1.3138297872340425, nan, nan,
1.3510638297872339, nan, nan, nan, nan, nan, nan, 1.7074468085106382, nan, nan, nan,
1.3404255319148934, nan, nan, 1.2765957446808509, 1.2925531914893618, 1.2978723404255319,
1.303191489361702, 1.3563829787234043, nan, 1.2712765957446808, 1.3989361702127658, 1.4361702127659575,
1.4414893617021276, nan, 1.4468085106382977, nan, nan, 1.1329787234042552, nan, 1.2659574468085106,
1.4095744680851063, nan, 1.4308510638297871, 1.4680851063829787, nan, 1.6436170212765957, nan, nan,
1.4840425531914891, nan, nan, nan, nan, 1.7819148936170213, 1.904255319148936, 1.4787234042553192,
1.425531914893617, 1.6595744680851063, 1.4095744680851063, nan, 1.3829787234042552, 1.2393617021276595,
1.4468085106382977, nan, 1.3882978723404256, 1.5053191489361701, nan, 1.6648936170212765,
1.4574468085106382, 1.4414893617021276, 1.4893617021276595, 1.5797872340425532, nan, nan, nan, nan,
1.8776595744680848, 1.4521276595744681, 1.3085106382978724, nan, nan, nan, 1.4361702127659575,
1.728723404255319, 1.7234042553191489, nan, nan, 1.7127659574468086, 1.6436170212765957,
1.803191489361702, 1.728723404255319, 1.5106382978723403, nan, 1.2819148936170213, 1.5106382978723403,
nan, nan, 1.3989361702127658, 1.6382978723404256, 1.728723404255319, 1.7127659574468086,
1.5638297872340423, 1.4840425531914891, 1.3351063829787235, 1.2606382978723403, 1.5212765957446808,
1.2872340425531914, nan, nan, 1.5106382978723403, 1.3404255319148934, 1.425531914893617,
1.2659574468085106, 1.4787234042553192, 1.3138297872340425, 1.2872340425531914, nan,
1.5319148936170213, 1.4202127659574466, 1.3936170212765957, 1.5, 1.5425531914893615,
1.6117021276595744, 1.6063829787234041, 1.5265957446808509, 1.6063829787234041, 1.4946808510638299,
nan, 1.6382978723404256, 1.425531914893617, nan, nan, 1.3085106382978724, nan, 1.5851063829787233,
1.5106382978723403, 1.5691489361702127, nan, nan, 1.5106382978723403, 1.5212765957446808,
1.5319148936170213, 1.5425531914893615, 1.7180851063829785, 1.6648936170212765, 1.5585106382978724,
1.6702127659574466, 1.7074468085106382, 1.4893617021276595, 1.5265957446808509, 1.5478723404255319,
1.6170212765957446, 1.5957446808510638, nan, 1.7446808510638296, nan, nan, nan, 2.0585106382978724,
nan, nan, nan, 1.5, 1.6276595744680851, 1.8297872340425529, 1.7872340425531914, nan, nan,
2.0585106382978724, 1.6914893617021276, 1.8404255319148937, 1.803191489361702, 2.0106382978723403,
1.803191489361702, nan, nan, 1.8617021276595744, 1.8936170212765957, 1.7978723404255317,
1.8563829787234041, nan, 1.574468085106383, 1.6968085106382977, 1.7499999999999998, 1.7978723404255317,
1.7446808510638296, 1.803191489361702, nan, nan, nan, 2.1489361702127656, 2.0319148936170213,
2.0053191489361701, 2.0265957446808511, 2.1595744680851063, nan, 2.0744680851063828,
2.2659574468085104, nan, 2.2606382978723403, 2.3297872340425529, 1.7127659574468086,
1.7393617021276597, 1.6542553191489362, nan, nan, 2.0053191489361701, 1.9840425531914891,
2.0478723404255317, nan, nan, nan, 1.803191489361702, nan, nan, nan, 1.3670212765957446,
1.5638297872340423, 1.4414893617021276, nan, 1.3404255319148934, 1.3882978723404256,
1.3457446808510638, 1.5638297872340423, nan, 1.5, 1.5691489361702127, 1.4840425531914891, nan, nan,
1.3563829787234043, 1.4787234042553192, 1.5478723404255319, 1.5638297872340423, 1.5797872340425532,
1.4627659574468084, 1.6968085106382977, 1.5851063829787233, 1.7021276595744681, nan, nan, nan, nan,
nan, 1.4574468085106382, 1.5159574468085106, nan, nan, nan, nan, nan, nan, 1.8297872340425529,
1.7712765957446805, 1.5478723404255319, nan, nan, 1.574468085106383, nan, 1.6808510638297873,
1.5372340425531914, 1.6117021276595744, 1.6276595744680851, 1.574468085106383, 1.5425531914893615,
1.6276595744680851, 1.6117021276595744, 1.6968085106382977, 1.728723404255319, 1.6808510638297873,
1.6702127659574466, 1.6170212765957446, nan, 2.1489361702127656, 1.9946808510638296, nan,
1.6489361702127658, 1.5691489361702127, nan, nan, 2.4946808510638294, 1.803191489361702,
1.7180851063829785, 1.6968085106382977, 1.5957446808510638, 1.6861702127659572, 1.6914893617021276,
1.7606382978723405, 1.6542553191489362, 1.6010638297872342, 1.3829787234042552, 1.2393617021276595,
1.4148936170212767, 1.4468085106382977, 1.4574468085106382, 1.4787234042553192, nan,
1.2234042553191489, 1.2819148936170213, 1.4734042553191489, 1.4202127659574466, 1.4308510638297871,
1.3085106382978724, 1.3617021276595744, 1.4574468085106382, 1.3776595744680851, 1.5265957446808509,
nan, 1.4468085106382977, nan, 1.3723404255319149, 1.5159574468085106, 1.3404255319148934, nan,
1.5053191489361701, 1.5638297872340423, nan, 1.5106382978723403, 1.4787234042553192,
1.3138297872340425, 1.5, 1.7553191489361701, 1.6808510638297873, nan, 1.6329787234042552,
1.6329787234042552, 1.4627659574468084, 1.6276595744680851, nan, nan, 1.6117021276595744, nan, nan,
1.6436170212765957, nan, 1.7499999999999998, nan, nan, 1.3882978723404256, nan, nan,
1.6595744680851063, 1.6436170212765957, 1.6170212765957446, 1.6117021276595744, 1.6276595744680851,
1.6861702127659572, 1.5851063829787233, 1.6861702127659572, nan, 1.425531914893617, 1.5585106382978724,
1.5478723404255319, 1.3510638297872339, 1.6861702127659572, nan, 1.4734042553191489, nan, nan,
1.7234042553191489, 1.6117021276595744, 1.4414893617021276, 1.7234042553191489, 1.7499999999999998,
1.6223404255319149, nan, 1.6808510638297873, 1.803191489361702, 1.728723404255319, nan,
1.8457446808510638, nan, nan, 1.7872340425531914, nan, nan, 1.8723404255319149, nan, nan, nan, nan,
1.553191489361702, 1.803191489361702, 1.6861702127659572, 1.5638297872340423, 1.8138297872340425,
1.5797872340425532, 1.6170212765957446, 1.9627659574468084, nan, nan, 1.7393617021276597,
1.7872340425531914, 1.5265957446808509, nan, 1.553191489361702, 1.5159574468085106, 1.4893617021276595,
1.5691489361702127, 1.6117021276595744, nan, nan, nan, nan, nan, nan, nan, nan, 1.5265957446808509,
1.8244680851063828, nan, nan, 1.8085106382978722, nan, 1.6063829787234041, nan, nan, nan, nan,
1.3138297872340425, 1.3244680851063828, nan, nan, 1.3723404255319149, nan, 1.4202127659574466, nan,
1.2340425531914894, 1.3776595744680851, 1.1648936170212765, 1.2978723404255319, nan, nan,
1.2074468085106382, 1.3404255319148934, 1.5797872340425532, 1.425531914893617, 1.3563829787234043,
1.3244680851063828, 1.5425531914893615, nan, 1.7021276595744681, nan, 1.3563829787234043,
1.3191489361702127, 1.5265957446808509, 1.5691489361702127, 1.2340425531914894, 1.3617021276595744,
1.3776595744680851, nan, nan, nan, 1.2819148936170213, 1.3297872340425532, nan, nan,
1.4787234042553192, nan, nan, 1.25, nan, 1.4946808510638299, nan, nan, nan, nan, nan, nan,
1.6702127659574466, 2.2393617021276597, 1.6223404255319149, nan, 1.6968085106382977,
1.2127659574468086, nan, nan, nan, 1.4840425531914891, 1.4680851063829787, 1.4627659574468084,
1.5159574468085106, 1.3191489361702127, 1.4308510638297871, 1.4361702127659575, 1.4308510638297871,
1.5319148936170213, nan, nan, 1.6223404255319149, 1.5851063829787233, 1.4308510638297871, nan,
1.5319148936170213, 1.5319148936170213, 1.946808510638298, 1.6648936170212765, nan, nan, nan,
1.6276595744680851, nan, 1.6276595744680851, 1.5319148936170213, 1.5159574468085106,
1.6223404255319149, 1.3670212765957446, 1.4308510638297871, 1.5478723404255319, 1.3723404255319149,
1.4308510638297871, 1.4308510638297871, 1.3457446808510638, 1.4680851063829787, nan] | [
"e.l.warren@pgr.reading.ac.uk"
] | e.l.warren@pgr.reading.ac.uk |
320d6f5c8384d268d0631cbc28ba6c0da4cd77a3 | ae151e30a29f682eb8501e38f54749672c0f4baa | /util/plot_results.py | 3a7b91d933be655ccd3c3bd3b3fa31478a40394f | [
"Apache-2.0"
] | permissive | margorczynski/dist-ga | ccb8d91e84b63e827e4d303fe4f00f760ac6fb62 | e2d7a74428d85aa3c75b066c3e8b77947469d0b1 | refs/heads/master | 2023-03-14T15:48:05.623965 | 2021-03-04T23:48:58 | 2021-03-04T23:48:58 | 344,290,512 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 556 | py | from kafka import KafkaConsumer
import matplotlib.pyplot as plt
from decimal import Decimal
consumer = KafkaConsumer('dist-ga-chromosome-with-fitness', auto_offset_reset = 'earliest', value_deserializer=lambda m: m.decode('utf-8'))
y = []
x = []
i = 0
plt.ion()
ax = plt.gca()
ax.set_autoscale_on(True)
line, = ax.plot(x, y)
for msg in consumer:
yn = Decimal(msg.value)
y.append(yn)
x.append(i)
i = i + 1
line.set_xdata(x)
line.set_ydata(y)
ax.relim()
ax.autoscale_view(True,True,True)
plt.draw()
plt.pause(0.1) | [
"margorczynski@gmail.com"
] | margorczynski@gmail.com |
433dc5b4b254f190af96f7d0530c0099a8aaf14f | 112053ecef86c1c4d5f267409cbcb5aa9d321461 | /spyder/28_zakres_zmiennych.py | 69695d8f38295719593c3387f143f5bdc363f998 | [] | no_license | LukasKodym/py-tests | 76565d56f7906339bbe6ea5f3ac541735992cf87 | bced8f101a157290db8581955ac9044c16bfae8d | refs/heads/master | 2021-05-23T09:20:18.139628 | 2020-07-07T17:49:34 | 2020-07-07T17:49:34 | 253,217,988 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 746 | py | # -*- coding: utf-8 -*-
# %%
##
i = 2
j = i
i = 3
# %%
##
a = 5
def fun_1():
print(a)
fun_1()
# %%
##
a = 5
def fun_2():
a = 4
print(a)
fun_2()
# %%
##
def fun_3():
x = 4
print(x)
fun_3()
print(x)
# %%
##
tech = 'Python'
def change_tech(new_tech):
tech = new_tech
print(tech)
print(tech)
change_tech('Java')
print(tech)
# %%
##
tech = 'Python'
def change_tech(new_tech):
global tech
tech = new_tech
print(tech)
print(tech)
change_tech('Java')
print(tech)
# %%
##
level = 0
def f1():
level = 1
def f2():
nonlocal level
level = 2
print('funkcja f2: ', level)
f2()
print('funkcja f1: ', level)
f1()
print('funkcja globalnie: ', level)
| [
"lukasz.kodym@gmail.com"
] | lukasz.kodym@gmail.com |
2370ac32a8e67503a055689617e940e29a76ed26 | 33adecb88734b28842c99bef9f1d6bed8f97f6f5 | /Mask_Detector/mask_detector.py | 79e06932ef27a99c51c6911010cca3aec4080aa1 | [] | no_license | srinisriram/Mask_Detector | 0ad991c0c71a6b519c257c0438e4d592d9781077 | e708090b8521a70996296908f1a8b45c19240777 | refs/heads/master | 2023-01-28T09:23:42.195058 | 2020-12-13T16:42:21 | 2020-12-13T16:42:21 | 321,108,820 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,016 | py | # Import necessary packages
import threading
import time
import cv2
from detect import detect
from play_audioMask import PlayAudio
from tensorflow.keras.models import load_model
from vars import prototxt_path, face_model_path, mask_model_path, min_mask_confidence, video_cam_index
# Load all the models, and start the camera stream
faceModel = cv2.dnn.readNet(prototxt_path, face_model_path)
faceModel.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)
maskModel = load_model(mask_model_path)
stream = cv2.VideoCapture(video_cam_index)
AudioPlay = False
playAudio = False
def thread_for_when_to_play_audio():
"""
This function is used for playing the alarm if a person is not wearing a mask.
:return:
"""
global playAudio
while True:
if playAudio:
play_audio()
def play_audio():
"""
This function is used for playing the alarm if a person is not wearing a mask.
:return:
"""
global AudioPlay
global playAudio
SoundThread = threading.Thread(target=PlayAudio.play_audio_file)
print("[INFO]: Starting Sound Thread")
if not AudioPlay:
AudioPlay = True
SoundThread.start()
time.sleep(3)
AudioPlay = False
playAudio = False
print("[INFO]: Stopping Sound Thread")
def thread_for_mask_detection():
global faceModel
global maskModel
global stream
global playAudio
while True:
# Read frame from the stream
ret, frame = stream.read()
# Run the detect function on the frame
(locations, predictions) = detect(frame, faceModel, maskModel)
# Go through each face detection.
for (box, pred) in zip(locations, predictions):
# Extract the prediction and bounding box coords
(startX, startY, endX, endY) = box
(mask, withoutMask) = pred
confidence = max(mask, withoutMask) * 100
# Determine the class label and make actions accordingly
if mask >= withoutMask:
if confidence > min_mask_confidence:
label = 'Mask ' + str(confidence)
print(label)
color = (0, 255, 0)
else:
if confidence > min_mask_confidence:
label = 'No Mask ' + str(confidence)
print(label)
playAudio = True
color = (0, 0, 255)
# Place label and Bounding Box
#cv2.putText(frame, label, (startX, startY - 10),
# cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
#cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
# show the frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# break from loop if key pressed is q
if key == ord("q"):
break
if __name__ == "__main__":
t1 = threading.Thread(target=thread_for_when_to_play_audio)
t1.start()
thread_for_mask_detection()
| [
"srinivasssriram06@gmail.com"
] | srinivasssriram06@gmail.com |
8f3823824cc1e26c2da63e585869f6b849c13d75 | 380d49186aa1d0f17106ce05fb2a97b08db874ad | /Ejercicio7a.py | 79bbd3343a99e769336c54b6fe1caf86c4ed2543 | [] | no_license | Aguu21/Python | d743e52c90e9a19a395603d0937d2c48f57ed06f | 7971aabd18972a8e09fb96b7c49d5d8dc7a905db | refs/heads/master | 2023-05-02T02:30:47.422179 | 2021-04-29T01:30:14 | 2021-04-29T01:30:14 | 361,845,429 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 315 | py | def yearBisiestoVoF(year):
'''Decide si un year es o no bisiesto'''
if (year % 4 == 0):
if (year % 100 == 0):
if (year % 400 == 0):
total = True
else: False
else:
total = True
else:
total = False
return total
| [
"arpineda@alumno.huergo.edu.ar"
] | arpineda@alumno.huergo.edu.ar |
f56d1e046965e9b27701d9026a480a4aeab506c5 | 1fb65a0b9a9303621cc9c5a3d6dd8b1143cb34b2 | /mynewsite/urls.py | eeefbfa3e099e0e81e3bfdca792a7dcd671e5ee0 | [] | no_license | s9200801/mynewsite | 28e1e9025eeb5a918557a136fc00dc23a9f84833 | bb7d41fa973778622070f0951b44b28cb68ddf20 | refs/heads/master | 2022-12-21T11:44:18.627500 | 2018-10-31T17:55:50 | 2018-10-31T17:55:50 | 155,570,112 | 0 | 0 | null | 2022-12-08T01:16:02 | 2018-10-31T14:22:18 | Python | UTF-8 | Python | false | false | 1,206 | py | """mynewsite URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url,include
from django.contrib import admin
from myapp import views
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^captcha/',include('captcha.urls')),
url(r'^accounts/',include('registration.backends.default.urls')),
url(r'^list/$',views.listing),
url(r'^$',views.index),
url(r'^(\d+)/(\w+)/$',views.index),
url(r'^post/$',views.post2db),
url(r'^contact/$',views.contact),
url(r'^login/$',views.login),
url(r'^logout/$',views.logout),
url(r'^userinfo/$',views.userinfo),
]
| [
"k9200801@gmail.com"
] | k9200801@gmail.com |
b4b59dc7adfb84a3c1e6cec344658bb281d76d77 | 6d8492d87418fec09e3050f922b766950d0ed2c2 | /src/python/setup.py | d0512e87e126a2d9fb022c8ca103a4b0a90b7c1d | [] | no_license | HumanAmplification/Template-Compositor | a6e49378a568b4b995553fe22b487c607172e0f2 | f909767a9549be434dd19be2533dff127696a57e | refs/heads/master | 2016-09-10T00:12:13.165321 | 2012-03-19T08:16:18 | 2012-03-19T08:16:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,350 | py | import sys, os, urllib2, shutil, string
# To guarantee that we have setuptools
# This approach was appropriated from ez_setup.py:
# http://peak.telecommunity.com/dist/ez_setup.py
version = "0.6c11"
download_base = "http://pypi.python.org/packages/%s/s/setuptools/" % sys.version[:3]
egg_name = "setuptools-%s-py%s.egg" % (version,sys.version[:3])
url = download_base + egg_name
saveto = os.path.join(os.curdir, egg_name)
if os.path.exists(saveto):
# Remove existing
os.remove(saveto)
src = urllib2.urlopen(url)
if src:
dst = open(saveto,"wb")
egg = None
if dst:
data = src.read()
dst.write(data)
dst.close()
egg = os.path.realpath(saveto)
sys.path.insert(0, egg)
else:
print "Unable to open egg file %s" % saveto
src.close()
# Bootstrap setuptools
if egg is not None:
import setuptools
setuptools.bootstrap_install_from = egg
else:
print "Unable to retrieve egg."
else:
print "Unable to open URL %s" % url
from setuptools import setup
PROJECT_NAME = "Compositor"
VERSION = "0.2"
setup(
name=PROJECT_NAME,
version=VERSION,
description="Template renderer!",
url="https://github.com/HumanAmplification/Template-Compositor",
packages=[
"compositor",
"compositor.app"
],
entry_points = {
'console_scripts' : [
'compositor = compositor.app.compositor_app:main'
]
},
package_data = {
},
zip_safe=True,
install_requires=[
"Jinja2"
],
# Authorship metadata
author="Mark A Christensen",
license="CC Attribution 3.0",
keywords="templating jinja jinja2 christensen"
)
# Remove crap from sudo install command
# because no one wants compiler droppings everywhere.
egg_filename = string.replace(string.replace(egg_name,"'","_")," ","_")
if os.path.exists(egg_filename):
os.remove(egg_filename)
if os.path.exists("dist"):
shutil.rmtree("dist")
project_filename = string.replace(string.replace(PROJECT_NAME,"'","_")," ","_")
if os.path.exists("%s.egg-info" % project_filename):
shutil.rmtree("%s.egg-info" % project_filename)
if os.path.exists("build"):
shutil.rmtree("build")
| [
"christensen.mark.a@gmail.com"
] | christensen.mark.a@gmail.com |
3f69878decbab4658b8c118e2066348aebf24e86 | 05ad79e3698bb484a7c0272a15610d3da955a5f3 | /Python_learning_notes/code/深浅拷贝/深浅拷贝.py | ad815beebfa40d70c970fe8c4d70130ae02ac40f | [] | no_license | zhangpeng0v0/Python_learning_notes | 5bcd3292f5d770c8cc2e895433698a7aa372ef42 | 8f2dc18bb13a07ec92c489d28afd66da1186bea9 | refs/heads/master | 2022-01-22T18:29:44.391295 | 2019-06-13T05:59:59 | 2019-06-13T05:59:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 550 | py | # l1=[1,2,3]
# l2=l1 # 浅拷贝
# l3=l1.copy() # 浅拷贝
#
# print(l1,l2,l3)
# print(id(l1),id(l2),id(l3))
# print(id(l1[0]),id(l2[0]),id(l3[0]))
# a=257
# b=257
# print(id(a),id(b))
l1=[[1,2,3],[4,5,6],[7,8,9]]
l2=l1.copy() # 外--拷贝 内层不拷贝
# print(l1)
# print(l2)
# print(id(l1),id(l2))
# print(id(l1[0]),id(l2[0]))
# l1[0][0]=99
# print(l1)
# print(l2)
import copy
l3=copy.deepcopy(l1)
print(l1)
print(l2)
print(l3)
print(id(l1),id(l2),id(l3))
print(id(l1[0]),id(l2[0]),id(l3[0]))
l1[0][0]=99
print(l1)
print(l2)
print(l3) | [
"deerking007@163.com"
] | deerking007@163.com |
dda479fe3985fbe635d716f2b72e44d05c545d36 | 016109b9f052ffd037e9b21fa386b36089b05813 | /checkTree.py | 824b6551f6e8aaa158948abc4cfda4bca896f43e | [] | no_license | nsshayan/DataStructuresAndAlgorithms | 9194508c5227c5c8c60b9950917a4ea8da8bbab2 | 2f7ee1bc8f4b53c35d1cce62e898a9695d99540a | refs/heads/master | 2022-09-29T21:15:33.803558 | 2022-09-08T17:14:59 | 2022-09-08T17:14:59 | 73,257,752 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 308 | py | '''
Given :-
the number of nodes in a graph
the degree of each of the vertices
Find whether the given graph is tree or not
'''
N = int(raw_input())
Degree = raw_input().split(" ")
sum = 0
for i in range(len(Degree)):
sum = sum + int(Degree[i])
if sum/2 == N-1:
print "YES"
else :
print "NO" | [
"nsshayan89@gmail.com"
] | nsshayan89@gmail.com |
22847f17d0308d18decfe12910b02874f28be680 | 9151b02b211a54242a808e385e0e10857be89c80 | /Coord.py | f5d5a3267020fd95c685fac38a40ce0bbe62888d | [] | no_license | HogwartsHoboGame/HH-Game | 812548d0dcdde46cf4e7a56b6834db77d4cbeba5 | bdd113399a1ae58633a12d8fd6e7e3dd2c62d33c | refs/heads/master | 2021-04-09T07:27:27.721006 | 2020-04-10T13:37:47 | 2020-04-10T13:37:47 | 248,850,692 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 444 | py | import pygame
class Coord:
def __init__(self, x, y, width, height):
self.x = x
self.y = y
self.width = width
self.height = height
def almost_intersect(self, other):
bottom = self.y + 50
other_top = other.y
return not (bottom <= other_top-10)
def intersects(self, other):
bottom = self.y + 50
other_top = other.y
return not (bottom <= other_top)
| [
"filipegorodscy@gmail.com"
] | filipegorodscy@gmail.com |
7fa15c0970c59b8b9ac69849a9bd094513c39bdb | 81a55c0b1b9ee769f8bae9999105b0a6eed67e48 | /UsageSlots.py | 653573559f6cc9b86f3f3da5f6ff118f4cad999e | [] | no_license | kumaya/python-programs | e9eec86b988b48f4076708e1a9bcd001f6951059 | 9a47194ba25f100938dede3cdfdf76edd61b69e0 | refs/heads/master | 2022-04-30T19:36:12.561816 | 2022-04-28T11:21:40 | 2022-04-28T11:21:40 | 37,153,156 | 5 | 3 | null | 2019-04-01T17:55:15 | 2015-06-09T19:22:11 | Python | UTF-8 | Python | false | false | 599 | py | class foo(object):
pass
class bar(object):
__slots__ = ['a', 'b']
print "Evaluation for class foo() ===>"
ob1 = foo()
ob1.a = 1
print "Value of foo().a: ", ob1.a
# print dir(ob1)
print "__dict__ attribute: ", ob1.__dict__
print ""
print "Evaluation for class bar() ===>"
ob2 = bar()
# print dir(ob2)
try:
print ob2.__dict__
except AttributeError:
print "bar() does not contain __dict__ attribute"
print "type(__slots__): ", type(ob2.__slots__)
try:
ob2.c = 3
print ob2.c
except AttributeError:
print "Cannot assign attribute 'c' to class because of defined __slots__"
| [
"mayank@acelearningco.com"
] | mayank@acelearningco.com |
b77a10b3a9edac761144a086f8984e32473d00cd | d7cf99fbec59c69d696087c9e3d7cc3c3aa7693b | /remove_nan_from_data.py | f680cbcbbfd36a38b4222bb1746976fb6dc6c6ea | [] | no_license | FerdinandEiteneuer/SCYNet | 85683f009c9e0a76bd7278df3a430ef2f4b603a9 | 897c1c0bdc2b17b24fbf7c3aae83a2e2a9d7f8ae | refs/heads/master | 2021-06-20T05:38:12.901762 | 2017-08-01T08:53:17 | 2017-08-01T08:53:17 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 393 | py | import numpy as np
import sys
path = sys.argv[1]
x=np.load(path)
print '\ndata shape:', x.shape
delidx=[]
for i,e in enumerate(x):
if np.isnan(sum(e)):
delidx.append(i)
print 'found %s nans\n' % len(delidx)
if len(delidx) != 0:
y=np.delete(x,delidx,0)
print 'new data shape:', y.shape
np.save(path,y)
print 'saved to path %s' % path
else:
print 'do nothing'
| [
"Ferdinand.Eiteneuer@rwth-aachen.de"
] | Ferdinand.Eiteneuer@rwth-aachen.de |
f6a4d499c0e7af27c1a1f6d7adc7ed199e6ed8d1 | 3d044656c60055f2917b7518f2a443b02449c316 | /src/estudiantes/domain/listar.py | 7b4650493cb1e8bf0fa6623bfdfff1536e2a1cc9 | [] | no_license | Onlychief/FuncionHexagonal | 15db789d9dbf02799cc79acee262c890e2980c02 | ab78a8188251389cfc3b864ba055d7d6dc086cab | refs/heads/master | 2023-05-30T07:25:49.236801 | 2021-06-08T17:06:22 | 2021-06-08T17:06:22 | 375,085,996 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 273 | py | class ListarEstudiantes():
def __init__(self, DB):
self.DB = DB
def run(self):
cursor = self.DB.cursor()
cursor.execute('select * from estudiantes')
estudiantes = cursor.fetchall()
cursor.close()
return (estudiantes) | [
"jhonatanjaramillo8@gmail.com"
] | jhonatanjaramillo8@gmail.com |
b6faf20877f683beab77c503370315724c92cdac | 5fb579602489728ac47e195bd15838eb632aece4 | /tests/test_utils.py | 99a1fd9fe74c0728ba2a92baf3a1f722c68f4174 | [
"MIT"
] | permissive | Cesare-Liu/cryptokit | 6101701f3daec60ce8ca2f8a2bb464a58ccae20e | bfb90c229279c3c755bdbedfe659d7d5b6e65b51 | refs/heads/master | 2020-03-27T10:38:20.714133 | 2018-06-07T06:15:51 | 2018-06-07T06:15:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,410 | py | # coding: utf-8
"""test utils."""
from __future__ import unicode_literals
import datetime
from unittest import TestCase
from cryptokit.rsa import RSACrypto
from cryptokit.utils import (load_pfx, generate_certificate, generate_pfx,
get_pubkey_from_pfx)
class UtilTestCase(TestCase):
"""RSACrypto useage test."""
def setUp(self):
private_key = RSACrypto.generate_private_key(2048)
RSACrypto.dump_private_key_pem(private_key)
self.private_key = private_key
self.public_key = private_key.public_key()
validity = datetime.timedelta(days=365)
self.not_valid_before = datetime.datetime.today()
self.not_valid_after = self.not_valid_before + validity
payload = {
'common_name': 'CA',
'country_name': 'CN',
'email_address': 'codingcat@gmail.com',
'org_name': '数字认证中心',
'company_name': '编程猫科技',
'state_or_province': '浙江省',
'locality_name': '杭州市',
'private_key': self.private_key,
'public_key': self.public_key,
'serial_number': 9219100179121295299
}
self.payload = payload
def test_generate_certificate(self):
"""Test generate certificate."""
cert = generate_certificate(
self.not_valid_before, self.not_valid_after, **self.payload)
self.assertEqual(cert.serial_number, self.payload['serial_number'])
def test_generate_pfx(self):
"""Test generate pfx."""
cert = generate_certificate(
self.not_valid_before, self.not_valid_after, **self.payload)
pfx = generate_pfx(
cert, self.payload['company_name'], self.private_key)
pkcs12 = load_pfx(pfx)
self.assertEqual(
cert.serial_number,
pkcs12.get_certificate().get_serial_number()
)
def test_get_pubkey_from_pfx(self):
"""Test get_pubkey_from_pfx."""
cert = generate_certificate(
self.not_valid_before, self.not_valid_after, **self.payload)
pfx_file = generate_pfx(
cert, self.payload['company_name'], self.private_key)
pubkey = get_pubkey_from_pfx(pfx_file, password=None)
self.assertEqual(cert.public_key().public_numbers(),
pubkey.public_numbers())
| [
"istommao@gmail.com"
] | istommao@gmail.com |
72a70e77e70f3a661b07734e3bb73024c84bd945 | c7611c71ff52721a0995d976595e4aa69effde2a | /latest_version/format.py | c711216bc1b9cd5382b18a835e37440b9049490b | [
"BSD-3-Clause"
] | permissive | Alovez/CodeView | 3ab535501c445712e75c5c180a7a11f485a051ad | a835800219b26c7a578d457f59d11e4f27a13d59 | refs/heads/master | 2021-01-01T03:45:45.491871 | 2016-05-12T10:00:46 | 2016-05-12T10:00:46 | 58,630,414 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,398 | py | import os
from tree import node
from tree import tree
def writeContent(bookname, dirtree):
headnode = dirtree.gethead()
temp = open("template/content.Template")
templines = temp.readlines()
temp.close()
lines = templines[0:7]
lines.append("<h2>" + bookname + "</h2>\n")
lines.append("<ol>\n")
lines.extend(contentlines(headnode, dirtree))
lines.append("</ol>\n")
lines.extend(templines[7:])
contentPath = headnode.getfpath()+"/content.html"
content = open(contentPath,"w")
content.writelines(lines)
content.close()
def writeOPF(bookname, dirtree):
headnode = dirtree.gethead()
temp = open("template/opf.Template")
templines = temp.readlines()
temp.close()
lines = templines[0:5]
lines.append("<dc:title>"+bookname+"</dc:title>\n")
lines.extend(templines[5:24])
m, s = opflines(dirtree,headnode)
lines.extend(m)
lines.extend(templines[24:27])
lines.extend(s)
lines.extend(templines[27:])
opfPath = headnode.getfpath()+"/"+bookname+".opf"
opf = open(opfPath, "w")
opf.writelines(lines)
opf.close()
def tohtml(dirtree):
headnode = dirtree.gethead()
temp = open("template/code.Template")
templines = temp.readlines()
temp.close()
codelines(dirtree, headnode, templines)
def contentlines(cnode, dirtree):
wlist = []
flist = cnode.getchildren()
for step in flist:
name = step.getdata()
path = dirtree.relativepath(step)+".html"
wlist.append("<li><a href='"+path+"'>"+name+"</a>\n")
if step.getdegree() != 0:
wlist.append("<ol>\n")
wlist.extend(contentlines(step, dirtree))
wlist.append("</ol>\n")
wlist.append("</li>\n")
return wlist
def opflines(dirtree, headnode):
manifestlist = []
snipe = []
counter = 0
l = dirtree.ergodic(headnode)
for step in l:
counter+=1
href = dirtree.relativepath(step)+".html"
n = str(counter)
manifestlist.append('<item id="chap'+n+'" href="'+href+'" media-type="text/html"/>\n')
snipe.append('<itemref idref="chap'+n+'"/>\n')
return manifestlist, snipe
def codelines(dirtree, headnode, templines):
l = dirtree.ergodic(headnode)
for step in l:
codepath = step.getfpath()+step.getdata()
if step.getdegree() != 0:
wline = ["dir of \n",step.getdata()+"\n"]
else:
code = open(codepath)
wline = code.readlines()
code.close()
wcode = open(codepath+".html", "w")
wcode.writelines(templines[0:8])
wcode.writelines(wline)
wcode.writelines(templines[8:])
wcode.close()
| [
"Ruinand@live.com"
] | Ruinand@live.com |
bb4dfdc980715b8c2c3269fd90841ca6c39f7da3 | 37078f41c9a9b00b09213360759be6c9b417fa31 | /MongoDB-phase1.py | 32894e726ca92ec5d9d2739f097b020b9f88f144 | [] | no_license | apapadakuni/EC500-Mongodb | e2c80a59e7c8f8a886e0fd40bb651fa9b5b0ea2c | 55045f9d6c856567e530093a9766d3bf4090e081 | refs/heads/master | 2020-03-08T12:56:39.223755 | 2018-04-05T20:29:16 | 2018-04-05T20:29:16 | 128,143,957 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,393 | py | import pprint
import urllib, json
import pymongo
import os
def MakeDataBase():
#This part reads the json file from the github link provided
#Then stores it in an array called data
url = "https://gist.githubusercontent.com/tdreyno/4278655/raw/7b0762c09b519f40397e4c3e100b097d861f5588/airports.json"
response = urllib.urlopen(url)
data = json.loads(response.read())
#Now we demonstrate how to create that data in our own mongodb database
client = pymongo.MongoClient()
db = client.AirportsData
db.AirportsData.insert(data)
#----------------------------------------------------------
def Search(category, value):
client = pymongo.MongoClient()
db = client.AirportsData
specific = db.AirportsData.find_one({category : value}) #returns the object with those credentials
print('The properties are: \n')
pprint.pprint(specific)
#----------------------------------------------------------
def Update():
print("Enter the properties of the new Airport you want to add: ")
code = raw_input('Enter the code: ')
lat = raw_input('Enter the latitude: ')
lon = raw_input('Enter the longitude: ')
name = raw_input('Enter the name: ')
city = raw_input('Enter the city: ')
state = raw_input('Enter the state: ')
country = raw_input('Enter the country: ')
woeid = raw_input('Enther the woeid: ')
tz = raw_input('Enter the tz: ')
phone = raw_input('Enter the phone: ')
email = raw_input('Enter the email: ')
url = raw_input('Enter the url: ')
runway = raw_input('Enter the runway length: ')
elev = raw_input('Enter the elevation: ')
icao = raw_input('Enter the icao: ')
flights = raw_input('Enter the direct_flights: ')
carrierss = raw_input('Enter the carriers: ')
newone = {"code" : code, "lat":lat, "lon" : lon, "name":name, "city":city,
"state":state, "country":country, "woeid":woeid, "tz": tz, "phone":phone,
"email":email, "url":url,"runway_length": runway, "elev":elev, "icao":icao,
"direct_flights":flights, "carriers":carrierss}
client = pymongo.MongoClient()
db = client.AirportsData
db.AirportsData.insert(newone)
if __name__ == '__main__':
#os.system("mongod") #need this to connect to client so that we can enter our data
MakeDataBase()
category = raw_input("Are you looking for a city, a code, an airport name, etc. ? ")
value = raw_input("What is the keyword of what you are looking for? ")
Search(category, value)
Update()
| [
"noreply@github.com"
] | apapadakuni.noreply@github.com |
d152111c4317b9090484c966da3a4671a305c7de | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02755/s084222637.py | a56cd5d0e5b0b0922a2417c7c93736a84b7a05d5 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 136 | py | a,b=map(int,input().split())
for i in range(100001):
if int(i*0.08) == a and int(i*0.1) == b:
print(i)
break
else:
print(-1) | [
"66529651+Aastha2104@users.noreply.github.com"
] | 66529651+Aastha2104@users.noreply.github.com |
76da4859384e1d8a645aaf5c79f6116f3d66c864 | 38c35956be6343855914b1c58b8fbd2e40c6e615 | /Strings/2023.py | 449cb787bc1f493dc6d2d3557856b2f76693cf95 | [] | no_license | LucasBarbosaRocha/URI | b43e4f4a6b3beed935f24839001bea354411c4bd | 2c9bcc13300a9f6243242e483c8f9ec3296a88ad | refs/heads/master | 2020-06-25T05:06:51.297824 | 2019-08-22T04:50:11 | 2019-08-22T04:50:11 | 199,210,037 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 343 | py | lista = []
l = ""
while True:
try:
entrada = input()
l = l + entrada + "+"
except :
break
#print (l)
l = l[:len(l) - 1]
original = l.split("+")
lista = l.lower()
lista = lista.split("+")
lista.sort()
escolhido = lista[len(lista) - 1]
for i in range(len(original)):
if (escolhido == original[i].lower()):
print (original[i])
break
| [
"lucas.lb.rocha@gmail.com"
] | lucas.lb.rocha@gmail.com |
d91861d848b2bb3390d1f2e3ce8a76537336ca9c | bf9618c432a4c8d3f6350cfb25ae4adccd230d12 | /torchalign/backbone/mobilenet.py | a61933f66684886452d08e3432f160344edef2db | [] | no_license | mackenbaron/H3R-Eg | 409b6272053fce0ac60124114b37099e4742e7b5 | 52a8bde2efd4d77ce3ca76dd248610736245b29e | refs/heads/master | 2023-09-06T08:47:32.500720 | 2021-11-25T06:06:55 | 2021-11-25T06:06:55 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,929 | py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
__all__ = ['MobileNetV2', 'mobilenetv2']
class Block(nn.Module):
"""
Bottleneck Residual Block
"""
def __init__(self, in_channels, out_channels, expansion=1, stride=1):
super(Block, self).__init__()
if expansion == 1:
self.conv = nn.Sequential(
nn.Conv2d(in_channels, in_channels, 3, stride, 1, groups=in_channels, bias=False),
nn.BatchNorm2d(in_channels),
nn.ReLU6(inplace=True),
nn.Conv2d(in_channels, out_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(out_channels),
)
else:
channels = expansion * in_channels
self.conv = nn.Sequential(
nn.Conv2d(in_channels, channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(channels),
nn.ReLU6(inplace=True),
nn.Conv2d(channels, channels, 3, stride, 1, groups=channels, bias=False),
nn.BatchNorm2d(channels),
nn.ReLU6(inplace=True),
nn.Conv2d(channels, out_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(out_channels),
)
self.residual = (stride == 1) and (in_channels == out_channels)
def forward(self, x):
out = self.conv(x)
if self.residual:
out = out + x
return out
class MobileNetV2(nn.Module):
def __init__(self, config):
super(MobileNetV2, self).__init__()
in_channels = config[0][1]
features = [nn.Sequential(
nn.Conv2d(3, in_channels, 3, 2, 1, bias=False),
nn.BatchNorm2d(in_channels),
nn.ReLU6(inplace=True)
)]
for expansion, out_channels, blocks, stride in config[1:]:
for i in range(blocks):
features.append(Block(in_channels, out_channels, expansion, stride if i == 0 else 1))
in_channels = out_channels
self.features = nn.Sequential(*features)
def forward(self, x):
c2 = self.features[:4](x)
c3 = self.features[4:7](c2)
c4 = self.features[7:14](c3)
kwargs = {'size': c2.shape[-2:],'mode': 'bilinear','align_corners': False}
return torch.cat([F.interpolate(xx,**kwargs) for xx in [c2,c3,c4]], 1)
def mobilenetv2(pretrained=False, **kwargs):
"""Constructs a MobileNetv2 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
config = [
(1, 32, 1, 1),
(1, 16, 1, 1),
(6, 24, 2, 2),
(6, 32, 3, 2),
(6, 64, 4, 2),
(6, 96, 3, 1),
]
model = MobileNetV2(config, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['mobilenetv2']), strict=False)
return model
| [
"bayu0826@desktop.shared.sydney.edu.au"
] | bayu0826@desktop.shared.sydney.edu.au |
e8b0ffb2fe1de76223cfd6d5f844f77a6a81d54f | d0abbc6476344bfc3ec1fdafc055dc3b3e30d505 | /produccion/urls.py | ef72a3fb6f6843e854f88532ccd234d92cc2c5d4 | [] | no_license | albor1962/Projecto-tambo | 2a684e852e790ecc5f5fb9f81e7348540f8266f2 | 34ed1cb40a5fc4c4b94a44521de9ebfb530abec6 | refs/heads/master | 2023-03-02T08:33:30.160237 | 2021-02-04T21:35:55 | 2021-02-04T21:35:55 | 336,083,291 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 818 | py | from django.urls import path, include
from produccion.views import RegistroCrear,RegistroList,DatosProductivosUpdate,DatosProductivosDelete, \
ExistenciaList,ProLecheList,IngresoAlimentos_list
urlpatterns = [
path('registro_list/', RegistroList.as_view(), name='registro_list'),
path('registro_crear/', RegistroCrear.as_view(), name='registro_crear'),
path('registro_editar/<int:pk>/', DatosProductivosUpdate.as_view(), name='registro_editar'),
path('registro_eliminar/<int:pk>/', DatosProductivosDelete.as_view(), name='registro_eliminar'),
path('existencia_list/', ExistenciaList.as_view(), name='existencia_list'),
path('leche_list/', ProLecheList.as_view(), name='leche_list'),
path('ingreso_alimentos_list/', IngresoAlimentos_list.as_view(), name='ingreso_alimentos_list'),
] | [
"albertofborella@gmail.com"
] | albertofborella@gmail.com |
168e8af874264edf1c218a1454505bd863301676 | 417386323f761d678c32e261bb18368085af0a00 | /core/forms.py | f29d04dff273eb6bfda742d4b762f23ce96952e6 | [] | no_license | YvesHouedande/ecomWebsite | fc437b3d044a98e60ef5fc31d41b75a42d800fca | 1337f01067db6d50a095b73e59845978ea79c49b | refs/heads/master | 2023-06-04T16:36:09.517488 | 2021-06-21T11:23:44 | 2021-06-21T11:23:44 | 375,876,742 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 992 | py | from django import forms
from .models import BillingInfo
class BillingInfoForm(forms.ModelForm):
class Meta:
model = BillingInfo
exclude = ['session_order', 'order']
widgets = {
'address':forms.TextInput(attrs={'placeholder':'Street Address'}),
'appartement':forms.TextInput(attrs={'placeholder':'Apartment. suite, unite ect ( optinal )'}),
'account_password':forms.TextInput(attrs={'type':'password'}),
'notes':forms.TextInput(attrs={'placeholder':'put some notes about your order'}),
'create_account':forms.TextInput(attrs={'type':'checkbox', 'id':'acc'})
# 'create_account':forms.BooleanField(required=False)
}
class AccountForm(forms.Form):
username = forms.CharField(max_length=100)
email = forms.EmailField(max_length=100)
password = forms.CharField(max_length=100, widget=forms.TextInput(attrs={'type':'password'}))
| [
"yveshouedandedocteur@gmail.com"
] | yveshouedandedocteur@gmail.com |
58412d85187532f9f42d4f40e1c022211b03d8f3 | 4e1af52e60dd997fca04be3485e157292cf84b6a | /2020/tests/test_day08.py | c14b7db55c5cf09e4fcbd77b7364367ebec9a8fd | [
"MIT"
] | permissive | JesperDramsch/advent-of-code | e0173d4e78cf274ae461b39d619f56a03ef54773 | ccad3d578be473bf44dea7284c2f99fd67f3271c | refs/heads/main | 2023-01-13T07:18:30.772913 | 2022-12-25T16:12:02 | 2022-12-25T16:39:53 | 160,000,829 | 7 | 1 | null | null | null | null | UTF-8 | Python | false | false | 654 | py | import sys
import pytest
sys.path.insert(0, ".")
from util import Day
from day08 import *
@pytest.fixture(scope="function")
def day():
day = Day(8)
day.load(typing=str)
return day
def test_example(day):
data = """nop +0
acc +1
jmp +4
acc +3
jmp -3
acc -99
acc +1
jmp -4
acc +6"""
day.load(data, typing=str)
assert main(day, part=1) == 5
def test_example_p2(day):
data = """nop +0
acc +1
jmp +4
acc +3
jmp -3
acc -99
acc +1
jmp -4
acc +6"""
day.load(data, typing=str)
assert main(day, part=2) == 8
def test_part1(day):
assert main(day, part=1) == 1753
def test_part2(day):
assert main(day, part=2) == 733
| [
"jesper@dramsch.net"
] | jesper@dramsch.net |
7299a477bdfc231d68fe9525e482af079a89dddd | 1b912a429f614cafd0363fb601703b44dc30674a | /networking_fortinet/tests/tempest_plugin/tests/fwaas_client.py | 4819aae33af335d77970222e992ecdca36f95342 | [
"Apache-2.0"
] | permissive | samsu/networking-fortinet | 5e655d9519f926a59db9e8409e215989fbab3e6b | f9c99bcfbae7d328d0de815fb68fe3b6719c9050 | refs/heads/master | 2020-04-12T05:42:26.026646 | 2017-01-17T21:19:07 | 2017-01-17T21:19:07 | 61,332,457 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,996 | py | # Copyright (c) 2015 Midokura SARL
# 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.
import time
from tempest import config
from tempest import exceptions
from tempest.lib.common.utils import data_utils
from tempest.lib import exceptions as lib_exc
from neutron.plugins.common import constants as p_const
from networking_fortinet.tests.tempest_plugin.services import client
CONF = config.CONF
class FWaaSClientMixin(object):
@classmethod
def resource_setup(cls):
super(FWaaSClientMixin, cls).resource_setup()
manager = cls.manager
cls.firewalls_client = client.FirewallsClient(
manager.auth_provider,
CONF.network.catalog_type,
CONF.network.region or CONF.identity.region,
endpoint_type=CONF.network.endpoint_type,
build_interval=CONF.network.build_interval,
build_timeout=CONF.network.build_timeout,
**manager.default_params)
cls.firewall_policies_client = client.FirewallPoliciesClient(
manager.auth_provider,
CONF.network.catalog_type,
CONF.network.region or CONF.identity.region,
endpoint_type=CONF.network.endpoint_type,
build_interval=CONF.network.build_interval,
build_timeout=CONF.network.build_timeout,
**manager.default_params)
cls.firewall_rules_client = client.FirewallRulesClient(
manager.auth_provider,
CONF.network.catalog_type,
CONF.network.region or CONF.identity.region,
endpoint_type=CONF.network.endpoint_type,
build_interval=CONF.network.build_interval,
build_timeout=CONF.network.build_timeout,
**manager.default_params)
def create_firewall_rule(self, **kwargs):
body = self.firewall_rules_client.create_firewall_rule(
name=data_utils.rand_name("fw-rule"),
**kwargs)
fw_rule = body['firewall_rule']
self.addCleanup(self._delete_wrapper,
self.firewall_rules_client.delete_firewall_rule,
fw_rule['id'])
return fw_rule
def create_firewall_policy(self, **kwargs):
body = self.firewall_policies_client.create_firewall_policy(
name=data_utils.rand_name("fw-policy"),
**kwargs)
fw_policy = body['firewall_policy']
self.addCleanup(self._delete_wrapper,
self.firewall_policies_client.delete_firewall_policy,
fw_policy['id'])
return fw_policy
def create_firewall(self, **kwargs):
body = self.firewalls_client.create_firewall(
name=data_utils.rand_name("fw"),
**kwargs)
fw = body['firewall']
self.addCleanup(self._delete_wrapper,
self.delete_firewall_and_wait,
fw['id'])
return fw
def delete_firewall_and_wait(self, firewall_id):
self.firewalls_client.delete_firewall(firewall_id)
self._wait_firewall_while(firewall_id, [p_const.PENDING_DELETE],
not_found_ok=True)
def _wait_firewall_ready(self, firewall_id):
self._wait_firewall_while(firewall_id,
[p_const.PENDING_CREATE,
p_const.PENDING_UPDATE])
def _wait_firewall_while(self, firewall_id, statuses, not_found_ok=False):
start = int(time.time())
if not_found_ok:
expected_exceptions = (lib_exc.NotFound)
else:
expected_exceptions = ()
while True:
try:
fw = self.firewalls_client.show_firewall(firewall_id)
except expected_exceptions:
break
status = fw['firewall']['status']
if status not in statuses:
time.sleep(3)
break
if int(time.time()) - start >= self.firewalls_client.build_timeout:
msg = ("Firewall %(firewall)s failed to reach "
"non PENDING status (current %(status)s)") % {
"firewall": firewall_id,
"status": status,
}
raise exceptions.TimeoutException(msg)
time.sleep(1)
| [
"susltd.su@gmail.com"
] | susltd.su@gmail.com |
5a3ddff5b46ae937ecf174c1b3385b1c2baafc7f | 5bcacd9f90a7a059721cffd1260bc86496edb342 | /CNNbreast/CNNbreast.py | 129dfa7f01d209f9bbf6337e630b42d4b05b1dab | [] | no_license | min6434/CNTK_Breast | af2e229380cde61c6488c33b8c6459ad8e800be0 | cdc6e4d5463ccfc4bdf826dc91df51632834ed92 | refs/heads/master | 2021-01-20T00:53:42.906874 | 2017-06-21T05:37:41 | 2017-06-21T05:37:41 | 89,209,433 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,672 | py | # Import the relevant components
from __future__ import print_function
from CNNFunctions import *
ImagSize = 256
Job_ID = 'SygDNXzjqQxPAWC2A7Pes3L2m9EBY2dJ' # Job ID obtained by the cvedia-cli
# model dimensions
image_height = ImagSize
image_width = ImagSize
num_channels = 3
num_classes = 2
dir_path = os.path.dirname(os.path.realpath(__file__))
os.chdir(dir_path)
DataPath = os.getcwd()
for i in range(2):
DataPath = os.path.abspath(os.path.join(DataPath, os.pardir))
DataPath = os.path.join(DataPath,'data')
#DataPath = os.path.join(DataPath,Job_ID)
# Change map text files into the CNTK format
print("converting cvedia map to cntk map...", end = '')
nTrain = changeCvediaToCNTKmap(os.path.join(DataPath,'train.txt'), os.path.join(DataPath,'train_total_cntk.txt'))
nTest = changeCvediaToCNTKmap(os.path.join(DataPath,'test.txt'), os.path.join(DataPath,'test_total_cntk.txt'))
print("finished!")
print("Number of training samples: {}\nNumber of test samples: {}\n".format(nTrain, nTest))
# Calculate average pixel data and put them into the XML for CNTK
print("calculating an average image...", end = '')
meanImg = saveMean(os.path.join(DataPath,'train_total_cntk.txt'), image_height, image_width, num_channels, nTrain)
saveMeanXML(os.path.join(DataPath,'breast_mean.xml'), meanImg, ImagSize)
print("finished!")
# Mix map data
print("Mixing the training data...", end = '')
MixCNTKmap(os.path.join(DataPath,'train_total_cntk.txt'), os.path.join(DataPath,'train_total_cntk_mixed.txt'))
print("finished!")
# Create image readers
reader_train = create_reader(os.path.join(DataPath,'train_total_cntk_mixed.txt'), os.path.join(DataPath,'breast_mean.xml'), image_width, image_height, num_channels, num_classes, True)
reader_test = create_reader(os.path.join(DataPath,'test_total_cntk.txt'), os.path.join(DataPath,'breast_mean.xml'), image_width, image_height, num_channels, num_classes, False)
pred_basic_model_bn = train_and_evaluate(reader_train, reader_test, image_width, image_height, num_channels, num_classes,\
nTrain, nTest, max_epochs=10, model_func=create_basic_model_with_batch_normalization)
label_lookup = ["healty tissue", "metastases"]
nTotal = 0
nFalse = 0
for line in open(os.path.join(DataPath,'test_total_cntk.txt'), 'r'):
imgFile, label = line.split('\t')
result = eval(pred_basic_model_bn, imgFile, meanImg)
nTotal += 1
if result != int(label):
print("real value: ", label_lookup[int(label)], end = ", ")
print("network result: ", label_lookup[result])
nFalse += 1
print( "Accuracy {}%".format( (nTotal-nFalse)/nTotal*100 ) ) | [
"min6434@gmail.com"
] | min6434@gmail.com |
ad89193a453ddab7679f17c37753ee3f9dc66835 | c8d0058906618aae42220a9a12fe935b3d9cf9d6 | /ciro/ciro/wsgi.py | 5bcd72ca60ff7a20d8e712622faec946a43374b7 | [] | no_license | pinyaskin/sok-dev | 5c4d651a9ea5ef8db978d780e2b98f4e4b61c11e | d17e7b0e49bca89ccca92d25118ceecd452a23be | refs/heads/main | 2023-08-13T22:14:28.565824 | 2021-10-03T07:46:38 | 2021-10-03T07:46:38 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 385 | py | """
WSGI config for ciro project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ciro.settings')
application = get_wsgi_application()
| [
"youngdodik@youngdodik.youngdodik"
] | youngdodik@youngdodik.youngdodik |
26a2ffc0e8ba5bcfbea22bfc24e1b16584ba13d1 | 86d499787fb35024db798b0c1dbfa7a6936854e9 | /py_web/django-real-estate/real_estate/asgi.py | a5c0e708311521f0d1353ba0081337f614c51f1f | [] | no_license | Tomtao626/python-note | afd1c82b74e2d3a488b65742547f75b49a11616e | e498e1e7398ff66a757e161a8b8c32c34c38e561 | refs/heads/main | 2023-04-28T08:47:54.525440 | 2023-04-21T17:27:25 | 2023-04-21T17:27:25 | 552,830,730 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 399 | py | """
ASGI config for real_estate project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/4.0/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'real_estate.settings')
application = get_asgi_application()
| [
"tp320670258@gmail.com"
] | tp320670258@gmail.com |
77fc462cc02717562bc3410fce204dda71f24772 | eac8cdc4e280c8b72f370932d0637c3e90310ffe | /data_creation.py | 577b0d552440014bb4f88adc36c5571bce9d79ad | [] | no_license | shotauchida007/flask-leaflet-vue | 7cd5fc46a7c3a1458c3fa2ded96a98bad733924b | aea8099c9ad4ba9be9ca2c8ba90bf53f8f25669e | refs/heads/main | 2023-05-05T16:37:45.972361 | 2021-05-30T02:24:58 | 2021-05-30T02:24:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 735 | py | import pandas as pd
import define_db
def init_table():
# table init
define_db.db.drop_all()
define_db.db.create_all()
def data_init():
# delete
define_db.db.session.query(define_db.Kyoto_m).delete()
# insert
df = pd.read_csv("data/kyoto_polygon.csv", dtype=str)
for i in range(len(df)):
id=df.iloc[i][0]
prefectures=df.iloc[i][1]
city=df.iloc[i][2]
ward=df.iloc[i][3]
town=df.iloc[i][4]
polygon=df.iloc[i][5]
row = define_db.Kyoto_m(id, prefectures, city, ward, town, polygon)
# registration
define_db.db.session.add(row)
# commit
define_db.db.session.commit()
init_table()
data_init() | [
"59258940+remia007@users.noreply.github.com"
] | 59258940+remia007@users.noreply.github.com |
b207fefd6ccd6d562f1572e2426380b43b6f1983 | 76563ffc91a6f35ffab2986693f0124a3a3aaf86 | /Crawler/2-DecisionTreeClassifier.py | 4f7047571f73169fdfd436414573723cf4d2f024 | [] | no_license | zelenkastiot/FCSE-Data-Mining | ab7aea21402742c518857a1c871d3e0a033f8581 | 6e1ffbada09784bb846af54aefc57fe0eb257a17 | refs/heads/master | 2023-02-27T17:14:10.457335 | 2021-02-07T22:13:20 | 2021-02-07T22:13:20 | 289,999,697 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,994 | py | """
Created on 15-Jan-21
@author: Kiril Zelenkovski
"""
import math
from sklearn.preprocessing import OrdinalEncoder, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import CategoricalNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
# Read csv
dataset = pd.read_csv("dataset.csv")
dataset = dataset.drop("Address Region", 1)
dataset = dataset.drop("Street Address", 1)
dataset = dataset.drop("Address Locality", 1)
dataset = dataset.drop("Postal Code", 1)
dataset = dataset.drop("Price", 1)
dataset = dataset.drop("Beds", 1)
print(dataset)
columns = dataset.columns.tolist()
dataset = dataset.values.tolist()
# Use Ordinal Encoder to encode categorical features as an integer array
encoder = OrdinalEncoder()
encoder.fit([dataset[j][:-1] for j in range(0, len(dataset))])
# Split dataset 75% train, 25% test
# test_csv = dataset[math.ceil(0.75 * len(dataset)):]
# train_csv = dataset[0:math.ceil(0.75 * len(dataset))]
X_dataset = [dataset[j][:-1] for j in range(0, len(dataset))]
y_dataset = [dataset[j][-1] for j in range(0, len(dataset))]
X, X_test, y, y_test = train_test_split(X_dataset,
y_dataset,
test_size=0.2,
random_state=42)
# Call encoder.transform or encoder.fit_transform to transform the data (because it is strings and int)
X = encoder.transform(X)
# Decision Tree Classifier: A non-parametric supervised learning method used for classification
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
# Fit Decision Tree Classifier according to X, y
classifier.fit(X, y)
# Call encoder.transform to transform the data
X_test = encoder.transform(X_test)
# Print accuracy using imported metrics
y_predicted = [classifier.predict([x])[0] for x in X_test]
print(f'DecisionTreeClassifier accuracy: {accuracy_score(y_test, y_predicted, normalize=True):.4f}')
# Print depth for classifier
print('Depth:', classifier.get_depth())
# Print # of leaves for classifier
print('Number of leaves:', classifier.get_n_leaves())
# Load importance of features in list
feature_importance = list(classifier.feature_importances_)
# Most and least important feature
most_important_feature = feature_importance.index(max(feature_importance))
least_important_feature = feature_importance.index(min(feature_importance))
# Print both
print('Most important feature:', columns[most_important_feature])
print('Least important feature:', columns[least_important_feature])
print(feature_importance)
for i in range(0, len(feature_importance)):
print(columns[feature_importance.index(feature_importance[i])])
print(y_predicted)
print(y_test)
le = LabelEncoder()
le.fit([dataset[j][-1] for j in range(0, len(dataset))])
list(le.classes_)
y_predicted = le.transform(y_predicted)
y_test = le.transform(y_test)
print(y_predicted)
print(y_test) | [
"zelenkastiot@gmail.com"
] | zelenkastiot@gmail.com |
83a94a19c21c202ba36604da5d55b206e20765f6 | 1f740c6ca396047b59e17406ce8d4954e398c5ab | /goplan/models.py | 3956a35294318583e6112ed7ce98bc9c4dd35a47 | [] | no_license | atkinson/goplan-beanstalk | 9a108b7f2d48e0852db44a88a7547e40a896d13a | 9e387d3b6e64749f277627f8feb14f9ac8b587dc | refs/heads/master | 2016-09-01T19:53:57.868147 | 2011-08-01T13:25:53 | 2011-08-01T13:25:53 | 1,549,486 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 837 | py | """
____ _ _ _ _ _ _
| _ \(_) ___| |__ / \ | |_| | __(_)_ __ ___ ___ _ __
| |_) | |/ __| '_ \ / _ \| __| |/ /| | '_ \/ __|/ _ \| '_ \
| _ <| | (__| | | | / ___ \ |_| < | | | | \__ \ (_) | | | |
|_| \_\_|\___|_| |_| /_/ \_\__|_|\_\|_|_| |_|___/\___/|_| |_|
Copyright 2011 (atkinsonr@gmail.com / @tkinson)
"""
from django.db import models
from beanstalk.models import Repo
class Project(models.Model):
""" A Goplan project """
alias = models.CharField(max_length = 32, unique=True)
name = models.CharField(max_length = 128)
description = models.TextField()
archived = models.BooleanField()
repo = models.ForeignKey(Repo, blank=True, null=True, related_name='project')
def __unicode__(self):
return self.name | [
"atkinsonr@gmail.com"
] | atkinsonr@gmail.com |
10d05c89c0ff7a9fce0804e934d2c4a031104371 | 48a37c5d50f2eab042408ff7eaab2bf973c8f402 | /main/models.py | 4ad8654d1b1a33022080248957fed505f4f760af | [] | no_license | Jerome4914/DojoReads | ca4f3fa4a563842eceb5ce46ea66f8656b47143c | 7862b7dabbbbea597b8669326dc90923972777fe | refs/heads/main | 2023-07-21T00:43:54.592336 | 2021-07-19T01:33:18 | 2021-07-19T01:33:18 | 387,155,326 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,335 | py | from django.db import models
from datetime import datetime
import bcrypt, re
# Create your models here.
class UserManager(models.Manager):
def registration_validator(self, postData):
errors = {}
EMAIL_REGEX = re.compile(r'^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\.[a-zA-Z]+$')
# PASSWORD_REGEX = re.compile(r'^(?=.*?[A-Z][a-zA-Z!"#\$%&\(\)\*\+,-\.\/:;<=>\?@[\]\^_\{\}~]]$')
if len(postData['first_name']) < 2:
errors['first_name'] = "Name must be more than 2 characters"
if len(postData['alias']) < 2:
errors['alias'] = "Alias Name must be more than 2 characters"
if len(postData['email']) == 0:
errors['register_email'] = "You must enter an email"
if not EMAIL_REGEX.match(postData['email']):
errors['register_email'] = "Invalid email address"
current_users = User.objects.filter(email=postData['email'])
if len(current_users) > 0:
errors['register_email'] = "That email already exists"
if len(postData['password']) < 8:
errors['register_password'] = "Password should be at least 8 characters"
if (postData['password']) != (postData['confirm_password']):
errors['register_password'] = "Passwords do not match"
# if not PASSWORD_REGEX.match(postData['password']):
# errors['register_password'] = "Password must contain a special character and 1 uppercase letter"
return errors
def login_validator(self, postData):
errors = {}
current_users = User.objects.filter(email=postData['email'])
if len(current_users) != 1:
errors['login_email'] = "User does not exist"
elif bcrypt.checkpw(postData['password'].encode(), current_users[0]. password.encode()) != True:
errors['login_password'] = "Email or Password do not match"
if len(postData['email']) == 0:
errors['login_email'] = "Email must be entered"
if len(postData['password']) < 8:
errors['login_password'] = "Password should be at least 8 characters"
return errors
class BookManager(models.Manager):
def book_validator(self, postData):
errors = {}
if len(postData['title']) < 2:
errors['title'] = "Title should be at least 2 characters"
return errors
class AuthorManager(models.Manager):
def author_validator(self, postData):
errors = {}
if len(postData['author_name']) < 2:
errors['author_name'] = "Author Name should be at least 2 characters"
author_in_db = Author.objects.filter(name=postData['author_name'])
if len(author_in_db) >= 1:
errors['author_name'] = "Author already exists"
return errors
class ReviewManager(models.Manager):
def review_validator(self, postData):
errors = {}
if len(postData['content']) < 10:
errors["content"] = "Review should be at least 10 characters"
return errors
class User(models.Model):
first_name = models.CharField(max_length=50)
alias = models.CharField(max_length=50)
email = models.CharField(max_length=50)
password = models.CharField(max_length=50)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects = UserManager()
#"user_reviews"
class Book(models.Model):
title = models.CharField(max_length=100)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects = BookManager()
#"authors"
#"book_reviews"
class Author(models.Model):
name = models.CharField(max_length=50)
books = models.ManyToManyField(Book, related_name="authors")
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects = AuthorManager()
class Review(models.Model):
content = models.TextField()
rating = models.IntegerField()
user_review = models.ForeignKey(User, related_name="user_reviews", on_delete=models.CASCADE)
book_reviewed = models.ForeignKey(Book, related_name="book_reviews", on_delete=models.CASCADE)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
objects = ReviewManager()
| [
"jeremyballew@hotmail.com"
] | jeremyballew@hotmail.com |
11fef142a4476363324a88a5de430f2ce53ae47f | 09d25cc5111d4a998794c56f152d3403d96a9be3 | /svm/svm.py | 31a1da3a5142acef8c78781fcdc7633e8fe10b45 | [] | no_license | mindew/SCOPEVALVE | 6643da91c769f296f6c10b2bb5e0b77616b719e9 | 8c5088cc0f61affe621c346d8b5d7ead0be6f816 | refs/heads/master | 2020-09-14T02:27:37.871099 | 2020-05-07T20:47:54 | 2020-05-07T20:47:54 | 222,985,569 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,451 | py | import pandas as pd
from scipy import signal
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
import pywt
from pyOpenBCI import OpenBCICyton
import numpy as np
categories = ['Pasta','Pies','Salads']
dataset = []
numDataPoints = 5000
def store_data(sample):
dataset.append(sample.channels_data)
if(len(dataset) is numDataPoints):
filterAndClassify(dataset)
board = OpenBCICyton(port='/dev/ttyUSB*')
def trainClassifier():
data = pd.read_csv("multi_classifier.csv")
training_set, test_set = train_test_split(data, test_size = 0.2, random_state = 1)
X_train = training_set.iloc[:,0:4].values
X_test = test_set.iloc[:,0:4].values
i = 0
for category in categories:
classifiers[i] = SVC(kernel='linear', random_state = 1, probability=True)
classifiers[i].fit(X_train,training_set[category])
Y_pred = classifiers[i].predict(X_test)
pred = classifiers[i].predict_log_proba(X_test)
print(category)
print(Y_pred)
print(pred)
print('Test accuracy is {}'.format(accuracy_score(test_set[category],Y_pred)))
return classifiers
def filterAndClassify(dataset):
#format dataset
fEMG0 = dataset[:,1]
fEMG1 = dataset[:,3]
#Perform the wavelet transform on both datasets
wavelet0 = pywt.dwt(fEMG0,'db4')
wavelet1 = pywt.dwt(fEMG1,'db4')
#filter EMG data
Fs = 250 #Hz
BandB,BandA = signal.butter(4,[30,500],'hp',fs=Fs,output='ba')
NotchB,NotchA = signal.iirnotch(50,10,Fs)
#apply filter to fEMG0
bandPass0 = signal.lfilter(BandB,BandA,wavelet0)
notched0 = signal.lfilter(NotchB,NotchA,bandPass0)
#apply filter to fEMG1
bandPass1 = signal.lfilter(BandB,BandA,wavelet1)
notched1 = signal.lfilter(NotchB,NotchA,bandPass1)
#apply moving average filter to both datasetss
convolveFilter = np.ones((1,31))/31
filtered0 = np.convolve(notched0,convolveFilter,mode='full')
filtered1 = np.convolve(notched1,convolveFilter,mode='full')
classifiers = trainClassifier()
testData = [filtered0, filtered1]
for num in range(0,len(categories)):
outPrediction[:,num] = classifiers[num].predict(testData)
# take a window based on button press
# automatic game interference every 5th or 20th move
# Andrew Ng Coursera Class
# Fei Fei Li
# Data science
# temporal features in addition to power features
# Convolution
# EMG Project with Neurotech class
# Do we need help getting things over the finish line
| [
"bryan.werth@students.olin.edu"
] | bryan.werth@students.olin.edu |
2ebbafa1c2d6e457a74cceb59b8ab893eab097ca | c5f58af61e3577ded52acda210f4f664651b598c | /template/mmdetection/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py | 6c517c9bfc6efebd56f35173b33505ea42865e03 | [
"Apache-2.0",
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | hojihun5516/object-detection-level2-cv-02 | 0a4ee5cea9a77ef5d43fb61a4b37fe3a87cb0eac | bc8a08286935b31b8e7e597c4b1ca2cbbaeb9109 | refs/heads/master | 2023-08-31T09:50:59.150971 | 2021-10-16T15:00:19 | 2021-10-16T15:00:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,470 | py | _base_ = "../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py"
norm_cfg = dict(type="BN", requires_grad=True)
model = dict(
neck=dict(
_delete_=True,
type="FPG",
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
add_extra_convs=True,
start_level=1,
stack_times=9,
paths=["bu"] * 9,
same_down_trans=None,
same_up_trans=dict(
type="conv",
kernel_size=3,
stride=2,
padding=1,
norm_cfg=norm_cfg,
inplace=False,
order=("act", "conv", "norm"),
),
across_lateral_trans=dict(
type="conv", kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=("act", "conv", "norm")
),
across_down_trans=dict(
type="interpolation_conv",
mode="nearest",
kernel_size=3,
norm_cfg=norm_cfg,
order=("act", "conv", "norm"),
inplace=False,
),
across_up_trans=None,
across_skip_trans=dict(
type="conv", kernel_size=1, norm_cfg=norm_cfg, inplace=False, order=("act", "conv", "norm")
),
output_trans=dict(type="last_conv", kernel_size=3, order=("act", "conv", "norm"), inplace=False),
norm_cfg=norm_cfg,
skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0,), ()],
)
)
evaluation = dict(interval=2)
| [
"hojihun5516@daum.net"
] | hojihun5516@daum.net |
0fba05d67a157c5a66cc87794cff56dde4c7b6e9 | 4bcbebef80e295b0fed851c3ae0140db3db62ad3 | /showsapp/urls.py | 8404640ae33b14a4234012793073abb62241b3a0 | [] | no_license | firoz1905/CodingDojo_PythonStack | 42f8208a0c2a770784b7be11357ead89e6cd6ce8 | 4ba11027ffc5ce8a85c66fda214fdbe541673bf2 | refs/heads/master | 2023-02-24T03:16:17.210573 | 2021-02-09T02:18:41 | 2021-02-09T02:18:41 | 337,266,553 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 387 | py | from django.urls import path
from . import views
urlpatterns = [
path('',views.reroute),
path('shows',views.shows),
path('new',views.new),
path('create',views.create),
path('shows/<int:show_id>',views.show,name="show_info"),
path('shows/<int:show_id>/edit',views.edit,name="edit_show"),
path('shows/<int:show_id>/destroy',views.delete,name="delete_show"),
]
| [
"syedfiroz2010@gmail.com"
] | syedfiroz2010@gmail.com |
c89927df7078e8bf390e1f73ca56617223ac32d4 | cef4f2e3357577bf56d3181dba988d0006d796b9 | /Projects/CourseInfo/Services/BussinessLogicServices/CourseService-old.py | 3ecae3d629720953d59c8dacbef0d7c8def24fd4 | [] | no_license | IshaShah27/E6156F21 | 5256715399f58d5f03dc6b4b8cf8e3920eb55bc7 | 8769203cf61a5ca96d5baa5ad1be34b1031ffffe | refs/heads/main | 2023-08-26T22:07:13.469515 | 2021-10-18T21:31:01 | 2021-10-18T21:31:01 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,613 | py | import Services.DataAccessServices.CourseWorksAdapter as cw_adapter
class Student():
def __init__(self, context, j_data):
self._context = context
self.id = j_data["id"]
self.user_id = j_data["sis_user_id"]
self.login_id = j_data["login_id"]
name_fields = j_data["sortable_name"].split(",")
self.name = {
"last_name": name_fields[0],
"first_name": name_fields[1]
}
def to_json(self):
result = {}
result["id"] = self.id
result["name"] = self.name
result["user_id"] = self.user_id
result["login_id"] = self.login_id
return result
class Course():
_field_list = ["id", "name", "uuid", "course_code_full", "course_no", "section", "year", "semester"]
def __init__(self, context, j_data):
self._context = context
self.id = j_data["id"]
self.name = j_data["name"]
self.uuid = j_data["uuid"]
self.course_code_full = j_data["course_code"]
course_code_fields = self.course_code_full.split("_")
self.course_no = course_code_fields[0]
self.section = course_code_fields[1]
self.year = course_code_fields[2]
self.semester = course_code_fields[3][0]
def to_json(self):
result = {}
for f in Course._field_list:
result[f] = getattr(self, f)
return result
@classmethod
def set_context(cls, context):
cls._context = context
@classmethod
def get_courses(cls, role=None):
res = cw_adapter.Adapter.set_context(cls._context)
res = cw_adapter.Adapter.get_courses(role=role)
if res is not None and len(res) > 0:
result = []
for j_data in res:
result.append(Course(cls._context, j_data))
else:
result = None
return result
@classmethod
def get_course(cls, course_id):
res = cw_adapter.Adapter.set_context(cls._context)
res = cw_adapter.Adapter.get_courses(course_id=course_id)
if res is not None and len(res) > 0:
res_in = res[0]
result = Course(cls._context, res_in)
else:
result = None
return result
def get_students(self):
res = cw_adapter.Adapter.set_context(self._context)
res = cw_adapter.Adapter.get_students(self.id)
if res[0] == 200:
result = []
for j_data in res[1]:
result.append(Student(self._context, j_data))
else:
result = None
return result
| [
"donff2@aol.com"
] | donff2@aol.com |
e32ac73c3af16ed8be75891963807a7fb28d0ba1 | bc441bb06b8948288f110af63feda4e798f30225 | /next_builder_sdk/model/flowable/process_instance_pb2.py | a763c2c5d25ebaa8d7f7708cb7b4d2a1e212f761 | [
"Apache-2.0"
] | permissive | easyopsapis/easyops-api-python | 23204f8846a332c30f5f3ff627bf220940137b6b | adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0 | refs/heads/master | 2020-06-26T23:38:27.308803 | 2020-06-16T07:25:41 | 2020-06-16T07:25:41 | 199,773,131 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | true | 10,254 | py | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: process_instance.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from next_builder_sdk.model.flowable import process_variable_pb2 as next__builder__sdk_dot_model_dot_flowable_dot_process__variable__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='process_instance.proto',
package='flowable',
syntax='proto3',
serialized_options=_b('ZBgo.easyops.local/contracts/protorepo-models/easyops/model/flowable'),
serialized_pb=_b('\n\x16process_instance.proto\x12\x08\x66lowable\x1a\x36next_builder_sdk/model/flowable/process_variable.proto\"\xb9\x03\n\x17\x46lowableProcessInstance\x12\n\n\x02id\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x13\n\x0b\x62usinessKey\x18\x03 \x01(\t\x12\x11\n\tsuspended\x18\x04 \x01(\x08\x12\r\n\x05\x65nded\x18\x05 \x01(\x08\x12\x1b\n\x13processDefinitionId\x18\x06 \x01(\t\x12\x1d\n\x15processDefinitionName\x18\x07 \x01(\t\x12$\n\x1cprocessDefinitionDescription\x18\x08 \x01(\t\x12\x12\n\nactivityId\x18\t \x01(\t\x12\x13\n\x0bstartUserId\x18\n \x01(\t\x12\x12\n\ncallbackId\x18\x0b \x01(\t\x12\x14\n\x0c\x63\x61llbackType\x18\x0c \x01(\t\x12\x13\n\x0breferenceId\x18\r \x01(\t\x12\x15\n\rreferenceType\x18\x0e \x01(\t\x12\x10\n\x08tenantId\x18\x0f \x01(\t\x12\x11\n\tcompleted\x18\x10 \x01(\x08\x12\x11\n\tstartTime\x18\x11 \x01(\t\x12\x34\n\tvariables\x18\x12 \x03(\x0b\x32!.flowable.FlowableProcessVariableBDZBgo.easyops.local/contracts/protorepo-models/easyops/model/flowableb\x06proto3')
,
dependencies=[next__builder__sdk_dot_model_dot_flowable_dot_process__variable__pb2.DESCRIPTOR,])
_FLOWABLEPROCESSINSTANCE = _descriptor.Descriptor(
name='FlowableProcessInstance',
full_name='flowable.FlowableProcessInstance',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='flowable.FlowableProcessInstance.id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='name', full_name='flowable.FlowableProcessInstance.name', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='businessKey', full_name='flowable.FlowableProcessInstance.businessKey', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='suspended', full_name='flowable.FlowableProcessInstance.suspended', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='ended', full_name='flowable.FlowableProcessInstance.ended', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='processDefinitionId', full_name='flowable.FlowableProcessInstance.processDefinitionId', index=5,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='processDefinitionName', full_name='flowable.FlowableProcessInstance.processDefinitionName', index=6,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='processDefinitionDescription', full_name='flowable.FlowableProcessInstance.processDefinitionDescription', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='activityId', full_name='flowable.FlowableProcessInstance.activityId', index=8,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='startUserId', full_name='flowable.FlowableProcessInstance.startUserId', index=9,
number=10, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='callbackId', full_name='flowable.FlowableProcessInstance.callbackId', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='callbackType', full_name='flowable.FlowableProcessInstance.callbackType', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='referenceId', full_name='flowable.FlowableProcessInstance.referenceId', index=12,
number=13, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='referenceType', full_name='flowable.FlowableProcessInstance.referenceType', index=13,
number=14, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='tenantId', full_name='flowable.FlowableProcessInstance.tenantId', index=14,
number=15, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='completed', full_name='flowable.FlowableProcessInstance.completed', index=15,
number=16, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='startTime', full_name='flowable.FlowableProcessInstance.startTime', index=16,
number=17, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='variables', full_name='flowable.FlowableProcessInstance.variables', index=17,
number=18, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=93,
serialized_end=534,
)
_FLOWABLEPROCESSINSTANCE.fields_by_name['variables'].message_type = next__builder__sdk_dot_model_dot_flowable_dot_process__variable__pb2._FLOWABLEPROCESSVARIABLE
DESCRIPTOR.message_types_by_name['FlowableProcessInstance'] = _FLOWABLEPROCESSINSTANCE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
FlowableProcessInstance = _reflection.GeneratedProtocolMessageType('FlowableProcessInstance', (_message.Message,), {
'DESCRIPTOR' : _FLOWABLEPROCESSINSTANCE,
'__module__' : 'process_instance_pb2'
# @@protoc_insertion_point(class_scope:flowable.FlowableProcessInstance)
})
_sym_db.RegisterMessage(FlowableProcessInstance)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
| [
"service@easyops.cn"
] | service@easyops.cn |
8c626ab93dbf410bba8151b8cdd660481d96c411 | a2c575fe2cf4afa40ec2adb8d5b98ec47693665b | /thread_api/model_builder.py | 1408f69c6ff601cbc6c4b8fa998e7f5224c3adaf | [] | no_license | cosmicBboy/confesh-bots | b530ba866fee5d276a8428670f2b2fb3a3f1ca3b | e1115a7c3f3cfb13d5b2e185c0b9410ccc09f5e4 | refs/heads/master | 2021-03-19T08:28:25.579876 | 2018-04-12T20:13:05 | 2018-04-12T20:13:05 | 44,482,435 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,796 | py | '''Module for Building a Model
Train a Word2Vec Model based on secret and comment text on www.confesh.com
1. Read secret and comment text
2. Train a Word2Vec model
3. Serialize model to S3
'''
import logging
import pandas as pd
import mongo_creds as creds
import json
import sys
import smart_open as so
from collections import OrderedDict
from argparse import ArgumentParser
from gensim.models import Word2Vec
from stream_mongo import MongoStreamer
from preprocessor import TextPreprocessor
from s3_utils import create_model_key
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO, stream=sys.stdout)
tp = TextPreprocessor()
class Word2VecModelBuilder(object):
def __init__(self, params):
self.model = Word2Vec
self.params = params
def fit(self, train_docs):
token_list = [tp.preprocess(d['text']) for d in train_docs]
self.model = self.model(token_list, **self.params)
def save_model(self, model_name, document_ids):
s3_keys = self._get_s3_keys(model_name)
self.model.save(s3_keys['model'])
with so.smart_open(s3_keys['params'], 'wb') as fout:
fout.write(json.dumps(self.params, sort_keys=True))
with so.smart_open(s3_keys['doc_ids'], 'wb') as fout:
for i in document_ids:
fout.write(i + '\n')
def load_model(self, model_name):
s3_keys = self._get_s3_keys(model_name)
self.model = self.model.load(s3_keys['model'])
def _get_s3_keys(self, model_name):
return {
'model': create_model_key(model_name, 'model', 'w2v'),
'params': create_model_key(model_name, 'params', 'json'),
'doc_ids': create_model_key(model_name, 'doc_ids', 'txt')
}
| [
"niels.bantilan@gmail.com"
] | niels.bantilan@gmail.com |
bc0c6b1c9cc4cf8e39e23237c192fe34e4c2a41d | 65c78b3947161dbe84cc433a3e96bc2a3b6cf44f | /offline_test.py | eadd40e45bac108545622728048af2cdaedd7029 | [] | no_license | Weiran1996/Surface-recognition | b074f3a29f4af896f2d6816ef38f4284c277279d | ec071d68d52c218a76bcdf43fd82486ef2cb5e8d | refs/heads/master | 2023-07-25T06:16:46.880565 | 2019-08-13T02:25:39 | 2019-08-13T02:25:39 | 202,046,493 | 1 | 0 | null | 2023-07-06T21:42:33 | 2019-08-13T02:24:07 | Python | UTF-8 | Python | false | false | 647 | py | from kmx62_sample_algorithm_jacob import Algorithm
import plot
# work around to plot...
class kwargs:
column_separator=';'
column_header=None
plot.kwargs = kwargs
def main():
a=Algorithm(None, None)
data = plot.loader('../Slot Car Tests (Jacob)/kmx62_50Hz_accel_skid_y.txt')
for row in data.iterrows():
row_number, data = row
ax, ay, az, mx, my, mz, temp = data['ax'], data['ay'], data['az'], data['mx'], data['my'], data['mz'], data['temp']
#print(row_number, ax, ay, az, mx, my, mz, temp)
a.feed([10, ax, ay, az, mx, my, mz, temp])
if __name__ == '__main__':
main() | [
"noreply@github.com"
] | Weiran1996.noreply@github.com |
5ef14af8b01c47efbecc92326569f004285947a7 | e7e59e95317eef570211d40c392cff655391806d | /automlbenchmark/frameworks/autosklearn/venv/lib/python3.8/site-packages/smac/runhistory/runhistory.py | bfce29ce8ba82cbc919f7bad614ffeff006d9f2d | [
"MIT"
] | permissive | odahviing-dov/CurL-AutoML | 9fd33a0f2d26ae4ab87cc33f23c3fec923d2ec9f | 97d28a907912223d075de42f49aaa441d1b350a8 | refs/heads/master | 2023-03-03T20:11:25.827624 | 2021-02-08T22:22:16 | 2021-02-08T22:22:16 | 337,226,671 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 28,917 | py | import collections
from enum import Enum
import json
import typing
import numpy as np
from smac.configspace import Configuration, ConfigurationSpace
from smac.tae import StatusType
from smac.utils.logging import PickableLoggerAdapter
__author__ = "Marius Lindauer"
__copyright__ = "Copyright 2015, ML4AAD"
__license__ = "3-clause BSD"
__maintainer__ = "Marius Lindauer"
__email__ = "lindauer@cs.uni-freiburg.de"
__version__ = "0.0.1"
# NOTE class instead of collection to have a default value for budget in RunKey
class RunKey(collections.namedtuple('RunKey', ['config_id', 'instance_id', 'seed', 'budget'])):
__slots__ = ()
def __new__(
cls, # No type annotation because the 1st argument for a namedtuble is always the class type,
# see https://docs.python.org/3/reference/datamodel.html#object.__new__
config_id: int,
instance_id: typing.Optional[str],
seed: typing.Optional[int],
budget: float = 0.0,
) -> 'RunKey':
return super().__new__(cls, config_id, instance_id, seed, budget)
# NOTE class instead of collection to have a default value for budget/source_id in RunInfo
class RunInfo(
collections.namedtuple(
'RunInfo',
['config', 'instance', 'instance_specific', 'seed', 'cutoff', 'capped', 'budget', 'source_id']
)
):
__slots__ = ()
def __new__(
cls, # No type annotation because the 1st argument for a namedtuble is always the class type,
# see https://docs.python.org/3/reference/datamodel.html#object.__new__
config: Configuration,
instance: typing.Optional[str],
instance_specific: str,
seed: int,
cutoff: typing.Optional[float],
capped: bool,
budget: float = 0.0,
# In the context of parallel runs, one will have multiple suppliers of
# configurations. source_id is a new mechanism to track what entity launched
# this configuration
source_id: int = 0,
) -> 'RunInfo':
return super().__new__(cls, config, instance, instance_specific, seed,
cutoff, capped, budget, source_id)
InstSeedKey = collections.namedtuple(
'InstSeedKey', ['instance', 'seed'])
InstSeedBudgetKey = collections.namedtuple(
'InstSeedBudgetKey', ['instance', 'seed', 'budget'])
RunValue = collections.namedtuple(
'RunValue', ['cost', 'time', 'status', 'starttime', 'endtime', 'additional_info'])
class EnumEncoder(json.JSONEncoder):
"""Custom encoder for enum-serialization
(implemented for StatusType from tae).
Using encoder implied using object_hook as defined in StatusType
to deserialize from json.
"""
def default(self, obj: object) -> typing.Any:
if isinstance(obj, StatusType):
return {"__enum__": str(obj)}
return json.JSONEncoder.default(self, obj)
class DataOrigin(Enum):
"""
Definition of how data in the runhistory is used.
* ``INTERNAL``: internal data which was gathered during the current
optimization run. It will be saved to disk, used for building EPMs and
during intensify.
* ``EXTERNAL_SAME_INSTANCES``: external data, which was gathered by running
another program on the same instances as the current optimization run
runs on (for example pSMAC). It will not be saved to disk, but used both
for EPM building and during intensify.
* ``EXTERNAL_DIFFERENT_INSTANCES``: external data, which was gathered on a
different instance set as the one currently used, but due to having the
same instance features can still provide useful information. Will not be
saved to disk and only used for EPM building.
"""
INTERNAL = 1
EXTERNAL_SAME_INSTANCES = 2
EXTERNAL_DIFFERENT_INSTANCES = 3
class RunHistory(object):
"""Container for target algorithm run information.
Most importantly, the runhistory contains an efficient mapping from each evaluated configuration to the
empirical cost observed on either the full instance set or a subset. The cost is the average over all
observed costs for one configuration:
* If using budgets for a single instance, only the cost on the highest observed budget is returned.
* If using instances as the budget, the average cost over all evaluated instances is returned.
* Theoretically, the runhistory object can handle instances and budgets at the same time. This is
neither used nor tested.
* Capped runs are not included in this cost.
Note
----
Guaranteed to be picklable.
Attributes
----------
data : collections.OrderedDict()
TODO
config_ids : dict
Maps config -> id
ids_config : dict
Maps id -> config
num_runs_per_config : dict
Maps config_id -> number of runs
Parameters
----------
overwrite_existing_runs : bool (default=True)
If set to ``True`` and a run of a configuration on an instance-budget-seed-pair already exists,
it is overwritten.
"""
def __init__(
self,
overwrite_existing_runs: bool = False
) -> None:
"""Constructor
Parameters
----------
overwrite_existing_runs: bool
allows to overwrites old results if pairs of
algorithm-instance-seed were measured
multiple times
"""
self.logger = PickableLoggerAdapter(
self.__module__ + "." + self.__class__.__name__
)
# By having the data in a deterministic order we can do useful tests
# when we serialize the data and can assume it's still in the same
# order as it was added.
self.data = collections.OrderedDict() # type: typing.Dict[RunKey, RunValue]
# for fast access, we have also an unordered data structure
# to get all instance seed pairs of a configuration.
# This does not include capped runs.
self._configid_to_inst_seed_budget = {} # type: typing.Dict[int, typing.Dict[InstSeedKey, typing.List[float]]]
self.config_ids = {} # type: typing.Dict[Configuration, int]
self.ids_config = {} # type: typing.Dict[int, Configuration]
self._n_id = 0
# Stores cost for each configuration ID
self._cost_per_config = {} # type: typing.Dict[int, float]
# Stores min cost across all budgets for each configuration ID
self._min_cost_per_config = {} # type: typing.Dict[int, float]
# runs_per_config maps the configuration ID to the number of runs for that configuration
# and is necessary for computing the moving average
self.num_runs_per_config = {} # type: typing.Dict[int, int]
# Store whether a datapoint is "external", which means it was read from
# a JSON file. Can be chosen to not be written to disk
self.external = {} # type: typing.Dict[RunKey, DataOrigin]
self.overwrite_existing_runs = overwrite_existing_runs
def add(
self,
config: Configuration,
cost: float,
time: float,
status: StatusType,
instance_id: typing.Optional[str] = None,
seed: typing.Optional[int] = None,
budget: float = 0.0,
starttime: float = 0.0,
endtime: float = 0.0,
additional_info: typing.Optional[typing.Dict] = None,
origin: DataOrigin = DataOrigin.INTERNAL,
force_update: bool = False,
) -> None:
"""Adds a data of a new target algorithm (TA) run;
it will update data if the same key values are used
(config, instance_id, seed)
Parameters
----------
config : dict (or other type -- depending on config space module)
Parameter configuration
cost: float
Cost of TA run (will be minimized)
time: float
Runtime of TA run
status: str
Status in {SUCCESS, TIMEOUT, CRASHED, ABORT, MEMOUT}
instance_id: str
String representing an instance (default: None)
seed: int
Random seed used by TA (default: None)
budget: float
budget (cutoff) used in intensifier to limit TA (default: 0)
starttime: float
starting timestamp of TA evaluation
endtime: float
ending timestamp of TA evaluation
additional_info: dict
Additional run infos (could include further returned
information from TA or fields such as start time and host_id)
origin: DataOrigin
Defines how data will be used.
force_update: bool (default: False)
Forces the addition of a config to the history
"""
if config is None:
raise TypeError('Configuration to add to the runhistory must not be None')
elif not isinstance(config, Configuration):
raise TypeError(
'Configuration to add to the runhistory is not of type Configuration, but %s' % type(config)
)
# Get the config id
config_id_tmp = self.config_ids.get(config)
if config_id_tmp is None:
self._n_id += 1
self.config_ids[config] = self._n_id
config_id = typing.cast(int, self.config_ids.get(config))
self.ids_config[self._n_id] = config
else:
config_id = typing.cast(int, config_id_tmp)
# Construct keys and values for the data dictionary
k = RunKey(config_id, instance_id, seed, budget)
v = RunValue(cost, time, status, starttime, endtime, additional_info)
# Each runkey is supposed to be used only once. Repeated tries to add
# the same runkey will be ignored silently if not capped.
if self.overwrite_existing_runs or force_update or self.data.get(k) is None:
self._add(k, v, status, origin)
elif status != StatusType.CAPPED and self.data[k].status == StatusType.CAPPED:
# overwrite capped runs with uncapped runs
self._add(k, v, status, origin)
elif status == StatusType.CAPPED and self.data[k].status == StatusType.CAPPED and cost > self.data[k].cost:
# overwrite if censored with a larger cutoff
self._add(k, v, status, origin)
def _add(self, k: RunKey, v: RunValue, status: StatusType,
origin: DataOrigin) -> None:
"""Actual function to add new entry to data structures
TODO
"""
self.data[k] = v
self.external[k] = origin
# Capped data is added above
# Do not register the cost until the run has completed
if origin in (DataOrigin.INTERNAL, DataOrigin.EXTERNAL_SAME_INSTANCES) \
and status not in [StatusType.CAPPED, StatusType.RUNNING]:
# also add to fast data structure
is_k = InstSeedKey(k.instance_id, k.seed)
self._configid_to_inst_seed_budget[k.config_id] = self._configid_to_inst_seed_budget.get(k.config_id, {})
if is_k not in self._configid_to_inst_seed_budget[k.config_id].keys():
# add new inst-seed-key with budget to main dict
self._configid_to_inst_seed_budget[k.config_id][is_k] = [k.budget]
elif k.budget not in is_k:
# append new budget to existing inst-seed-key dict
self._configid_to_inst_seed_budget[k.config_id][is_k].append(k.budget)
# if budget is used, then update cost instead of incremental updates
if not self.overwrite_existing_runs and k.budget == 0:
# assumes an average across runs as cost function aggregation, this is used for algorithm configuration
# (incremental updates are used to save time as getting the cost for > 100 instances is high)
self.incremental_update_cost(self.ids_config[k.config_id], v.cost)
else:
# this is when budget > 0 (only successive halving and hyperband so far)
self.update_cost(config=self.ids_config[k.config_id])
if k.budget > 0:
if self.num_runs_per_config[k.config_id] != 1: # This is updated in update_cost
raise ValueError('This should not happen!')
def update_cost(self, config: Configuration) -> None:
"""Store the performance of a configuration across the instances in
self.cost_per_config and also updates self.runs_per_config;
Note
----
This method ignores capped runs.
Parameters
----------
config: Configuration
configuration to update cost based on all runs in runhistory
"""
config_id = self.config_ids[config]
# removing duplicates while keeping the order
inst_seed_budgets = list(dict.fromkeys(self.get_runs_for_config(config, only_max_observed_budget=True)))
self._cost_per_config[config_id] = self.average_cost(config, inst_seed_budgets)
self.num_runs_per_config[config_id] = len(inst_seed_budgets)
all_inst_seed_budgets = list(dict.fromkeys(self.get_runs_for_config(config, only_max_observed_budget=False)))
self._min_cost_per_config[config_id] = self.min_cost(config, all_inst_seed_budgets)
def incremental_update_cost(self, config: Configuration, cost: float) -> None:
"""Incrementally updates the performance of a configuration by using a
moving average;
Parameters
----------
config: Configuration
configuration to update cost based on all runs in runhistory
cost: float
cost of new run of config
"""
config_id = self.config_ids[config]
n_runs = self.num_runs_per_config.get(config_id, 0)
old_cost = self._cost_per_config.get(config_id, 0.)
self._cost_per_config[config_id] = ((old_cost * n_runs) + cost) / (n_runs + 1)
self.num_runs_per_config[config_id] = n_runs + 1
def get_cost(self, config: Configuration) -> float:
"""Returns empirical cost for a configuration.
See the class docstring for how the costs are computed. The costs are not re-computed, but are read from cache.
Parameters
----------
config: Configuration
Returns
-------
cost: float
Computed cost for configuration
"""
config_id = self.config_ids.get(config)
return self._cost_per_config.get(config_id, np.nan) # type: ignore[arg-type] # noqa F821
def get_runs_for_config(self,
config: Configuration, only_max_observed_budget: bool) -> typing.List[InstSeedBudgetKey]:
"""Return all runs (instance seed pairs) for a configuration.
Note
----
This method ignores capped runs.
Parameters
----------
config : Configuration from ConfigSpace
Parameter configuration
only_max_observed_budget : bool
Select only the maximally observed budget run for this configuration
Returns
-------
instance_seed_budget_pairs : list<tuples of instance, seed, budget>
"""
config_id = self.config_ids.get(config)
runs = self._configid_to_inst_seed_budget.get(config_id, {}).copy() # type: ignore[arg-type] # noqa F821
# select only the max budget run if specified
if only_max_observed_budget:
for k, v in runs.items():
runs[k] = [max(v)]
# convert to inst-seed-budget key
rval = [InstSeedBudgetKey(k.instance, k.seed, budget) for k, v in runs.items() for budget in v]
return rval
def get_all_configs(self) -> typing.List[Configuration]:
"""Return all configurations in this RunHistory object
Returns
-------
parameter configurations: list
"""
return list(self.config_ids.keys())
def get_all_configs_per_budget(
self,
budget_subset: typing.Optional[typing.List] = None,
) -> typing.List[Configuration]:
"""
Return all configs in this RunHistory object that have been run on one of these budgets
Parameter
---------
budget_subset: list
Returns
-------
parameter configurations: list
"""
if budget_subset is None:
return self.get_all_configs()
configs = []
for c, i, s, b in self.data.keys():
if b in budget_subset:
configs.append(self.ids_config[c])
return configs
def get_min_cost(self, config: Configuration) -> float:
"""Returns the lowest empirical cost for a configuration, across all runs (budgets)
See the class docstring for how the costs are computed. The costs are not re-computed, but are read from cache.
Parameters
----------
config: Configuration
Returns
-------
min_cost: float
Computed cost for configuration
"""
config_id = self.config_ids.get(config)
return self._min_cost_per_config.get(config_id, np.nan) # type: ignore[arg-type] # noqa F821
def empty(self) -> bool:
"""Check whether or not the RunHistory is empty.
Returns
-------
emptiness: bool
True if runs have been added to the RunHistory,
False otherwise
"""
return len(self.data) == 0
def save_json(self, fn: str = "runhistory.json", save_external: bool = False) -> None:
"""
saves runhistory on disk
Parameters
----------
fn : str
file name
save_external : bool
Whether to save external data in the runhistory file.
"""
data = [([int(k.config_id),
str(k.instance_id) if k.instance_id is not None else None,
int(k.seed),
float(k.budget) if k[3] is not None else 0], list(v))
for k, v in self.data.items()
if save_external or self.external[k] == DataOrigin.INTERNAL]
config_ids_to_serialize = set([entry[0][0] for entry in data])
configs = {id_: conf.get_dictionary()
for id_, conf in self.ids_config.items()
if id_ in config_ids_to_serialize}
config_origins = {id_: conf.origin
for id_, conf in self.ids_config.items()
if (id_ in config_ids_to_serialize and conf.origin is not None)}
with open(fn, "w") as fp:
json.dump({"data": data,
"config_origins": config_origins,
"configs": configs}, fp, cls=EnumEncoder, indent=2)
def load_json(self, fn: str, cs: ConfigurationSpace) -> None:
"""Load and runhistory in json representation from disk.
Overwrites current runhistory!
Parameters
----------
fn : str
file name to load from
cs : ConfigSpace
instance of configuration space
"""
try:
with open(fn) as fp:
all_data = json.load(fp, object_hook=StatusType.enum_hook)
except Exception as e:
self.logger.warning(
'Encountered exception %s while reading runhistory from %s. '
'Not adding any runs!',
e,
fn,
)
return
config_origins = all_data.get("config_origins", {})
self.ids_config = {
int(id_): Configuration(
cs, values=values, origin=config_origins.get(id_, None)
) for id_, values in all_data["configs"].items()
}
self.config_ids = {config: id_ for id_, config in self.ids_config.items()}
self._n_id = len(self.config_ids)
# important to use add method to use all data structure correctly
for k, v in all_data["data"]:
self.add(config=self.ids_config[int(k[0])],
cost=float(v[0]),
time=float(v[1]),
status=StatusType(v[2]),
instance_id=k[1],
seed=int(k[2]),
budget=float(k[3]) if len(k) == 4 else 0,
starttime=v[3],
endtime=v[4],
additional_info=v[5])
def update_from_json(
self,
fn: str,
cs: ConfigurationSpace,
origin: DataOrigin = DataOrigin.EXTERNAL_SAME_INSTANCES,
) -> None:
"""Update the current runhistory by adding new runs from a json file.
Parameters
----------
fn : str
File name to load from.
cs : ConfigSpace
Instance of configuration space.
origin : DataOrigin
What to store as data origin.
"""
new_runhistory = RunHistory()
new_runhistory.load_json(fn, cs)
self.update(runhistory=new_runhistory, origin=origin)
def update(
self,
runhistory: 'RunHistory',
origin: DataOrigin = DataOrigin.EXTERNAL_SAME_INSTANCES,
) -> None:
"""Update the current runhistory by adding new runs from a RunHistory.
Parameters
----------
runhistory: RunHistory
Runhistory with additional data to be added to self
origin: DataOrigin
If set to ``INTERNAL`` or ``EXTERNAL_FULL`` the data will be
added to the internal data structure self._configid_to_inst_seed_budget
and be available :meth:`through get_runs_for_config`.
"""
# Configurations might be already known, but by a different ID. This
# does not matter here because the add() method handles this
# correctly by assigning an ID to unknown configurations and re-using
# the ID
for key, value in runhistory.data.items():
config_id, instance_id, seed, budget = key
cost, time, status, start, end, additional_info = value
config = runhistory.ids_config[config_id]
self.add(config=config, cost=cost, time=time,
status=status, instance_id=instance_id, starttime=start, endtime=end,
seed=seed, budget=budget, additional_info=additional_info,
origin=origin)
def _cost(
self,
config: Configuration,
instance_seed_budget_keys: typing.Optional[typing.Iterable[InstSeedBudgetKey]] = None,
) -> typing.List[float]:
"""Return array of all costs for the given config for further calculations.
Parameters
----------
config : Configuration
Configuration to calculate objective for
instance_seed_budget_keys : list, optional (default=None)
List of tuples of instance-seeds-budget keys. If None, the run_history is
queried for all runs of the given configuration.
Returns
-------
Costs: list
Array of all costs
"""
try:
id_ = self.config_ids[config]
except KeyError: # challenger was not running so far
return []
if instance_seed_budget_keys is None:
instance_seed_budget_keys = self.get_runs_for_config(config, only_max_observed_budget=True)
costs = []
for i, r, b in instance_seed_budget_keys:
k = RunKey(id_, i, r, b)
costs.append(self.data[k].cost)
return costs
def average_cost(
self,
config: Configuration,
instance_seed_budget_keys: typing.Optional[typing.Iterable[InstSeedBudgetKey]] = None,
) -> float:
"""Return the average cost of a configuration.
This is the mean of costs of all instance-seed pairs.
Parameters
----------
config : Configuration
Configuration to calculate objective for
instance_seed_budget_keys : list, optional (default=None)
List of tuples of instance-seeds-budget keys. If None, the run_history is
queried for all runs of the given configuration.
Returns
----------
Cost: float
Average cost
"""
costs = self._cost(config, instance_seed_budget_keys)
if costs:
return float(np.mean(costs))
return np.nan
def sum_cost(
self,
config: Configuration,
instance_seed_budget_keys: typing.Optional[typing.Iterable[InstSeedBudgetKey]] = None,
) -> float:
"""Return the sum of costs of a configuration.
This is the sum of costs of all instance-seed pairs.
Parameters
----------
config : Configuration
Configuration to calculate objective for
instance_seed_budget_keys : list, optional (default=None)
List of tuples of instance-seeds-budget keys. If None, the run_history is
queried for all runs of the given configuration.
Returns
----------
sum_cost: float
Sum of costs of config
"""
return float(np.sum(self._cost(config, instance_seed_budget_keys)))
def min_cost(
self,
config: Configuration,
instance_seed_budget_keys: typing.Optional[typing.Iterable[InstSeedBudgetKey]] = None,
) -> float:
"""Return the minimum cost of a configuration
This is the minimum cost of all instance-seed pairs.
Parameters
----------
config : Configuration
Configuration to calculate objective for
instance_seed_budget_keys : list, optional (default=None)
List of tuples of instance-seeds-budget keys. If None, the run_history is
queried for all runs of the given configuration.
Returns
----------
min_cost: float
minimum cost of config
"""
costs = self._cost(config, instance_seed_budget_keys)
if costs:
return float(np.min(costs))
return np.nan
def compute_all_costs(self, instances: typing.Optional[typing.List[str]] = None) -> None:
"""Computes the cost of all configurations from scratch and overwrites
self.cost_perf_config and self.runs_per_config accordingly;
Note
----
This method is only used for ``merge_foreign_data`` and should be removed.
Parameters
----------
instances: typing.List[str]
list of instances; if given, cost is only computed wrt to this instance set
"""
self._cost_per_config = {}
self.num_runs_per_config = {}
for config, config_id in self.config_ids.items():
# removing duplicates while keeping the order
inst_seed_budgets = list(dict.fromkeys(self.get_runs_for_config(config, only_max_observed_budget=True)))
if instances is not None:
inst_seed_budgets = list(
filter(
lambda x: x.instance in typing.cast(typing.List, instances), inst_seed_budgets
)
)
if inst_seed_budgets: # can be empty if never saw any runs on <instances>
self._cost_per_config[config_id] = self.average_cost(config, inst_seed_budgets)
self._min_cost_per_config[config_id] = self.min_cost(config, inst_seed_budgets)
self.num_runs_per_config[config_id] = len(inst_seed_budgets)
def get_instance_costs_for_config(self, config: Configuration) -> typing.Dict[str, typing.List[float]]:
""" Returns the average cost per instance (across seeds) for a configuration
If the runhistory contains budgets, only the highest budget for a configuration is returned.
Note
----
This is used by the pSMAC facade to determine the incumbent after the evaluation.
Parameters
----------
config : Configuration from ConfigSpace
Parameter configuration
Returns
-------
cost_per_inst: dict<instance name<str>, cost<float>>
"""
runs_ = self.get_runs_for_config(config, only_max_observed_budget=True)
cost_per_inst = {} # type: typing.Dict[str, typing.List[float]]
for inst, seed, budget in runs_:
cost_per_inst[inst] = cost_per_inst.get(inst, [])
rkey = RunKey(self.config_ids[config], inst, seed, budget)
vkey = self.data[rkey]
cost_per_inst[inst].append(vkey.cost)
cost_per_inst = dict([(inst, np.mean(costs)) for inst, costs in cost_per_inst.items()])
return cost_per_inst
| [
"lucas11_games@hotmail.com"
] | lucas11_games@hotmail.com |
59c6bf6445976ab8bf68c5d4fe2ab7034cfe83f1 | 6704f79ac1216f01da9466f780127999e3c0ea4e | /ablog/ablog/settings.py | 65d20cdc258a1202854481389841360644280361 | [] | no_license | Abhinavgoel77/django_assignment | 6a087d881c06d02f792a4ff025d9bcf23b27b280 | 557c1f406ef7249fe4320b4ccfe08bac5ab6d93e | refs/heads/main | 2023-07-02T11:56:48.787964 | 2021-08-03T17:48:40 | 2021-08-03T17:48:40 | 392,402,978 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,325 | py | """
Django settings for ablog project.
Generated by 'django-admin startproject' using Django 3.2.6.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.2/ref/settings/
"""
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-=ej@c+v%7)0cd))&_!mr1)^7+*u*4u(-%i8p2r9t!rue0+jaig'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'miniblog',
'members',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'ablog.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'ablog.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.2/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',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.2/howto/static-files/
STATIC_URL = '/static/'
LOGIN_REDIRECT_URL = 'home'
LOGOUT_REDIRECT_URL = 'home'
# Default primary key field type
# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
| [
"Abhinavgoel77@gmail.com"
] | Abhinavgoel77@gmail.com |
1a95ee42312d01afa32e915af2536f8f124984c7 | 2c74bb301f1ed83b79254944183ac5a18a639fdf | /homeassistant/components/denonavr/receiver.py | 28969d2579256202064fcda1e7a514fa6498a181 | [
"Apache-2.0"
] | permissive | Adminiuga/home-assistant | 5bec93007ddac1a268cc359bf7e48530c5f73b38 | dcf68d768e4f628d038f1fdd6e40bad713fbc222 | refs/heads/dev | 2023-02-22T22:03:31.013931 | 2022-11-09T00:27:20 | 2022-11-09T00:27:20 | 123,929,062 | 5 | 4 | Apache-2.0 | 2023-02-22T06:14:31 | 2018-03-05T14:11:09 | Python | UTF-8 | Python | false | false | 2,668 | py | """Code to handle a DenonAVR receiver."""
from __future__ import annotations
from collections.abc import Callable
import logging
from denonavr import DenonAVR
_LOGGER = logging.getLogger(__name__)
class ConnectDenonAVR:
"""Class to async connect to a DenonAVR receiver."""
def __init__(
self,
host: str,
timeout: float,
show_all_inputs: bool,
zone2: bool,
zone3: bool,
async_client_getter: Callable,
) -> None:
"""Initialize the class."""
self._async_client_getter = async_client_getter
self._receiver: DenonAVR | None = None
self._host = host
self._show_all_inputs = show_all_inputs
self._timeout = timeout
self._zones: dict[str, str | None] = {}
if zone2:
self._zones["Zone2"] = None
if zone3:
self._zones["Zone3"] = None
@property
def receiver(self) -> DenonAVR | None:
"""Return the class containing all connections to the receiver."""
return self._receiver
async def async_connect_receiver(self) -> bool:
"""Connect to the DenonAVR receiver."""
await self.async_init_receiver_class()
assert self._receiver
if (
self._receiver.manufacturer is None
or self._receiver.name is None
or self._receiver.model_name is None
or self._receiver.receiver_type is None
):
_LOGGER.error(
"Missing receiver information: manufacturer '%s', name '%s', model '%s', type '%s'",
self._receiver.manufacturer,
self._receiver.name,
self._receiver.model_name,
self._receiver.receiver_type,
)
return False
_LOGGER.debug(
"%s receiver %s at host %s connected, model %s, serial %s, type %s",
self._receiver.manufacturer,
self._receiver.name,
self._receiver.host,
self._receiver.model_name,
self._receiver.serial_number,
self._receiver.receiver_type,
)
return True
async def async_init_receiver_class(self) -> None:
"""Initialize the DenonAVR class asynchronously."""
receiver = DenonAVR(
host=self._host,
show_all_inputs=self._show_all_inputs,
timeout=self._timeout,
add_zones=self._zones,
)
# Use httpx.AsyncClient getter provided by Home Assistant
receiver.set_async_client_getter(self._async_client_getter)
await receiver.async_setup()
self._receiver = receiver
| [
"noreply@github.com"
] | Adminiuga.noreply@github.com |
8bfbca51d0b37ee289502c1fbaaf5efe3b2fda3e | debffca14a39dbeaf6af2f1b73ea530913e2cdad | /astromodels/tests/test_load_xspec_models.py | e10e14154daf319d6f29bd8409ebbdc01001fd9b | [
"BSD-3-Clause"
] | permissive | BjoernBiltzinger/astromodels | 6986695abfc4510a62254854fd0977b1e96e192f | d94a3d3bc607def2b5e3cd145c3922e0a00a7b15 | refs/heads/master | 2022-11-03T19:28:16.949036 | 2019-03-12T17:05:59 | 2019-03-12T17:05:59 | 175,420,543 | 0 | 0 | BSD-3-Clause | 2019-03-13T12:53:03 | 2019-03-13T12:53:03 | null | UTF-8 | Python | false | false | 641 | py | import pytest
import astropy.units as u
try:
from astromodels.xspec import *
except:
has_XSPEC = False
else:
has_XSPEC = True
# This defines a decorator which can be applied to single tests to
# skip them if the condition is not met
skip_if_xspec_is_not_available = pytest.mark.skipif(not has_XSPEC,
reason="XSPEC not available")
@skip_if_xspec_is_not_available
def test_xspec_load():
# no need to do anything really
s = XS_phabs() * XS_powerlaw() + XS_bbody()
print(s(1.0))
s.set_units(u.keV, 1 / (u.keV * u.cm**2 * u.s))
print(s(1.0 * u.keV))
| [
"giacomo.vianello@gmail.com"
] | giacomo.vianello@gmail.com |
915de1274fd700485b7168a42f9633c46ddcb52e | 3ab0ceafbc60bb6ecf96cda18f15b3f060c0d8d0 | /MartianBCI/Blocks/block_qrs_detect.py | c7ad80b372ee46d52d5a3543d1713b34cb6ce136 | [] | no_license | neuroph12/MartianBCI | 9fbbc25ce0f05d0aee85d92ce8e9947a0c4e16e1 | d6b814affbc15561223bf183149128c129f0e503 | refs/heads/master | 2020-09-12T08:55:15.026731 | 2019-06-19T21:33:40 | 2019-06-19T21:33:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,572 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 29 23:47:04 2017
@author: MartianMartin
"""
import sys
import numpy as np
import scipy.signal
import scipy.ndimage
def detect_beats(
ecg, # The raw ECG signal
rate, # Sampling rate in HZ
# Window size in seconds to use for
ransac_window_size=5.0,
# Low frequency of the band pass filter
lowfreq=5.0,
# High frequency of the band pass filter
highfreq=15.0,
):
"""
ECG heart beat detection based on
http://link.springer.com/article/10.1007/s13239-011-0065-3/fulltext.html
with some tweaks (mainly robust estimation of the rectified signal
cutoff threshold).
"""
ransac_window_size = int(ransac_window_size*rate)
lowpass = scipy.signal.butter(1, highfreq/(rate/2.0), 'low')
highpass = scipy.signal.butter(1, lowfreq/(rate/2.0), 'high')
# TODO: Could use an actual bandpass filter
ecg_low = scipy.signal.filtfilt(*lowpass, x=ecg)
ecg_band = scipy.signal.filtfilt(*highpass, x=ecg_low)
# Square (=signal power) of the first difference of the signal
decg = np.diff(ecg_band)
decg_power = decg**2
# Robust threshold and normalizator estimation
thresholds = []
max_powers = []
for i in range(len(decg_power)/ransac_window_size):
sample = slice(i*ransac_window_size, (i+1)*ransac_window_size)
d = decg_power[sample]
thresholds.append(0.5*np.std(d))
max_powers.append(np.max(d))
threshold = 0.5*np.std(decg_power)
threshold = np.median(thresholds)
max_power = np.median(max_powers)
decg_power[decg_power < threshold] = 0
decg_power /= max_power
decg_power[decg_power > 1.0] = 1.0
square_decg_power = decg_power**2
shannon_energy = -square_decg_power*np.log(square_decg_power)
shannon_energy[~np.isfinite(shannon_energy)] = 0.0
mean_window_len = int(rate*0.125+1)
lp_energy = np.convolve(shannon_energy, [1.0/mean_window_len]*mean_window_len, mode='same')
#lp_energy = scipy.signal.filtfilt(*lowpass2, x=shannon_energy)
lp_energy = scipy.ndimage.gaussian_filter1d(lp_energy, rate/8.0)
lp_energy_diff = np.diff(lp_energy)
zero_crossings = (lp_energy_diff[:-1] > 0) & (lp_energy_diff[1:] < 0)
zero_crossings = np.flatnonzero(zero_crossings)
zero_crossings -= 1
return zero_crossings
def plot_peak_detection(ecg, rate):
import matplotlib.pyplot as plt
dt = 1.0/rate
t = np.linspace(0, len(ecg)*dt, len(ecg))
plt.plot(t, ecg)
peak_i = detect_beats(ecg, rate)
plt.scatter(t[peak_i], ecg[peak_i], color='red')
plt.show()
if __name__ == '__main__':
time = np.linspace(0,2,500)
signal = np.sin(2*np.pi*time)
plot_peak_detection(signal, 250)
| [
"martinmolina147@gmail.com"
] | martinmolina147@gmail.com |
0aab616a8c0ca629a1f4e434c91a20302f47285b | 6fa701cdaa0d83caa0d3cbffe39b40e54bf3d386 | /google/monitoring/metricsscope/v1/monitoring-metricsscope-v1-py/google/monitoring/metricsscope_v1/__init__.py | 1530640d664a0943bb90109a53b0c43ead78fa5b | [
"Apache-2.0"
] | permissive | oltoco/googleapis-gen | bf40cfad61b4217aca07068bd4922a86e3bbd2d5 | 00ca50bdde80906d6f62314ef4f7630b8cdb6e15 | refs/heads/master | 2023-07-17T22:11:47.848185 | 2021-08-29T20:39:47 | 2021-08-29T20:39:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,521 | py | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# 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.
#
from .services.metrics_scopes import MetricsScopesClient
from .services.metrics_scopes import MetricsScopesAsyncClient
from .types.metrics_scope import MetricsScope
from .types.metrics_scope import MonitoredProject
from .types.metrics_scopes import CreateMonitoredProjectRequest
from .types.metrics_scopes import DeleteMonitoredProjectRequest
from .types.metrics_scopes import GetMetricsScopeRequest
from .types.metrics_scopes import ListMetricsScopesByMonitoredProjectRequest
from .types.metrics_scopes import ListMetricsScopesByMonitoredProjectResponse
from .types.metrics_scopes import OperationMetadata
__all__ = (
'MetricsScopesAsyncClient',
'CreateMonitoredProjectRequest',
'DeleteMonitoredProjectRequest',
'GetMetricsScopeRequest',
'ListMetricsScopesByMonitoredProjectRequest',
'ListMetricsScopesByMonitoredProjectResponse',
'MetricsScope',
'MetricsScopesClient',
'MonitoredProject',
'OperationMetadata',
)
| [
"bazel-bot-development[bot]@users.noreply.github.com"
] | bazel-bot-development[bot]@users.noreply.github.com |
066cc1e491e790f9ea09cfb173747b1c262f770a | 916b15e84713168e1c7e1c5be2d838dce70bfce0 | /challenges/backend-challenge/src/app.py | 12a2e67620cb78bd1f45efbddd1d85d2bf98efc2 | [
"MIT"
] | permissive | davidazael/Interview | c8a59424809d07a1e2eb6422a0b01614b5319828 | 6504b334eeafa1b3165f23d03884c9a91c325401 | refs/heads/master | 2023-04-29T03:10:31.547425 | 2021-05-17T15:33:28 | 2021-05-17T15:33:28 | 366,903,880 | 0 | 0 | MIT | 2021-05-17T15:33:29 | 2021-05-13T01:50:55 | XSLT | UTF-8 | Python | false | false | 5,771 | py | from flask import Flask, request, redirect, url_for
import ast
import os
import json
app = Flask(__name__)
app.config.from_pyfile('../config.py')
path = os.getcwd() + '/src/data'
new_json_data = 'data.json'
def format_incoming_data(incoming_data: str):
'''
Uses `ast.literal_eval` to convert incoming data to a data type that it fits best.
Because we are expecting a string that is of json list format we can assume that it will be converted to a list.
'''
# Convert the incoming data.
formatted_inc = ast.literal_eval(incoming_data)
# Is it converted into a list?
if isinstance(formatted_inc, list):
# Is the list of size 500?
if len(formatted_inc) == 500:
# utilized any for quicker execution.
# are any values in data list of type other than int?
if not any(not isinstance(e, int) for e in formatted_inc):
return formatted_inc
else:
# List was of the right size
# But the contents within were not of integer type.
raise TypeError
else:
# List size is not 500
raise IndexError
else:
# If it isn't converted into a list.
# Meaning the incoming values are not of correct type.
raise ValueError
def get_json_file_data(filename=new_json_data) -> dict:
'''
Returns Dictionary from JSON file.
Uses JSON to load data. If the file is empty, it returns a dictionary that displays null.
'''
# By default it has read capabilities.
with open('{0}/{1}'.format(path, filename)) as f:
try:
data = json.load(f)
return data
except Exception as e:
data = {'data': None}
return data
def write_json_data_to_file(inc_data: list, filename=new_json_data) -> None:
'''
Opens File 'data.json' JSON dump's incoming data.
Returns None.
'''
with open('{0}/{1}'.format(path, filename), 'w') as f:
data = {'data': sorted(inc_data)}
res = json.dump(data, f)
return res
def binary_insert(lst: list, patch_val: int) -> list:
''' Binary Insert given a Sorted list. '''
# size halfpoint is 250 because we know that list can only be size of 500
# but for reusability we will use len()
size = len(lst)
# Indexes: low -> midpoint -> high
# smallest value in asc sorted list should be first in list
low = 0
# largest value in asc sorted list should be last in list
high = size-1
mp = (high + low) // 2
mp_val = lst[mp]
# Check if new # is even in between lowest and highest
if patch_val not in range(lst[low], lst[high]):
if patch_val <= lst[low]:
# If new value is less than lowest, insert before.
lst.insert(0, patch_val)
return lst
elif patch_val >= lst[high]:
# If new value is greater than highest, append.
lst.append(patch_val)
return lst
# While low index is below high index search to see if the value belongs
# within
while low < high:
# midpoint index value. Floor operator rounds down -> int
mp = (high + low) // 2 # Calculate midpoint of any two values
# mp_val: midpoint value || value that will be used for comparison
mp_val = lst[mp]
# is midpoint exaclty inbetween the low and high bound?
if low + 1 == mp or high-1 == mp:
# is lst[low] <= patch_val < lst[high] ?
if patch_val in range(lst[low], lst[high]):
lst.insert(mp, patch_val)
return lst
elif patch_val < lst[low]:
lst.insert(low-1, patch_val)
return lst
elif patch_val >= lst[high]:
lst.insert(high+1, patch_val)
return lst
# If the incoming patch value is a duplicate, just place it in at that
# location and quit.
if patch_val == mp_val:
lst.insert(mp, patch_val)
return lst
elif patch_val > mp_val:
low = mp+1
continue
elif patch_val < mp_val:
high = mp-1
continue
# read/retrive
@app.route('/data/', defaults={'inc_data': ''}, methods=['GET'])
@app.route('/data/<inc_data>', methods=['POST']) # create
@app.route('/data/<inc_data>', methods=['PATCH']) # update existing data
def process_incoming(inc_data=''):
if request.method == 'GET':
# Get data from JSON data file.
return get_json_file_data(new_json_data)
elif request.method == 'POST':
# Insert new data, re-writing existing file data.
try:
formated_data = format_incoming_data(inc_data)
write_json_data_to_file(formated_data)
return get_json_file_data(new_json_data)
except Exception as e:
message = f'{e.__class__.__name__}: {e.__class__.__doc__}'
return redirect(url_for('dataIndexError', exception=message))
elif request.method == 'PATCH':
# Appending data to json file.
# Get existing_data from existing JSON data file.
existing_data = get_json_file_data(new_json_data)
# Insert in order. Update JSON data file.
write_json_data_to_file(binary_insert(
existing_data['data'], int(inc_data)))
return get_json_file_data(new_json_data)
else:
return redirect(url_for('root'))
@app.route('/<exception>')
def dataIndexError(exception):
return f'{exception} \n Please provide a JSON formatted list of integers that is of size exactly 500.'
@app.route('/')
def root():
return "Hello! Please provide a JSON formatted list of integers that is of size 500!"
| [
"davidazaelbernal@gmail.com"
] | davidazaelbernal@gmail.com |
b4d83d9b56e607732cd70a9353169eb6c897b04c | 2a2435c1955f61727c9968ea87a599d6e999c1bd | /core/migrations/0010_billingaddress.py | 88d7b724fb03f794792b0d947aca41ec9c668d05 | [] | no_license | mahmoudabuelnaga/dje-commerce | 9a5ba483b568613860d55c6062a01cd08ff9466c | 964917da53dc6045c4374943fce68d7de0edad37 | refs/heads/master | 2020-12-15T17:59:11.443834 | 2020-02-23T23:55:29 | 2020-02-23T23:55:29 | 235,202,147 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,042 | py | # Generated by Django 2.2 on 2020-01-20 02:04
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django_countries.fields
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('core', '0009_auto_20200119_0012'),
]
operations = [
migrations.CreateModel(
name='BillingAddress',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('street_address', models.CharField(max_length=255)),
('apartment_address', models.CharField(max_length=255)),
('countries', django_countries.fields.CountryField(max_length=746, multiple=True)),
('zip', models.CharField(max_length=100)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"mahmoudaboelnaga392@gmail.com"
] | mahmoudaboelnaga392@gmail.com |
0591acb80b5effd608d63527a482dd61d89b324f | 28d0f521e37865ff12f0245100ddc0e7e06370db | /bubble_sort.py | 76af5c74f879318ba40eb67c82cc318273b6ef0e | [] | no_license | McEnos/sorting_algorithms | ae5c8514f05bc45ad5f90bf96c030d96f18824fc | 96598fc46ea1f3371350ca7087c81f6f5355e48e | refs/heads/master | 2020-05-18T05:03:51.701304 | 2019-04-30T06:14:09 | 2019-04-30T06:14:09 | 184,193,060 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 331 | py | def bubble_sort(data):
for i in range(len(data)):#traverse through all data elements
for j in range(0,len(data)-i-1):#last i elements are already in place,travrse the data from 0 to n-i-1
if data[j] > data[j+1]:#swap if the element found is greater than the next element
data[j],data[j+1] = data[j+1],data[j]
return data | [
"joshuamcenos@gmail.com"
] | joshuamcenos@gmail.com |
d1df2196740c711b82b68bf80a7a61e19b6efa9f | 84b99814136f134fc2238a266225ed8342b6ede0 | /artemis_pcie/artemis_pcie.py | 467fc4772e63824248331ac13bb98a51834d7d05 | [
"MIT"
] | permissive | CospanDesign/nysa-artemis-pcie-platform | 1f25f3e940c43acc631d9c0cb1ecdee26e846af0 | 844be4b14b27520eb4bb15b8a2f18d7797b91943 | refs/heads/master | 2021-01-21T04:35:18.492160 | 2016-07-04T20:25:43 | 2016-07-04T20:25:43 | 48,339,973 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,373 | py | """ artemis_pcie
Concrete interface for Nysa on the artemis_pcie board
"""
__author__ = 'you@example.com'
import sys
import os
import time
from collections import OrderedDict
from array import array as Array
from nysa.cbuilder.sdb import SDBError
from nysa.host.nysa import Nysa
from nysa.host.nysa import NysaError
from nysa.host.nysa import NysaCommError
from nysa.host.driver.utils import dword_to_array
from nysa.host.driver.utils import array_to_dword
from nysa.common.print_utils import print_32bit_hex_array
IDWORD = 0xCD15DBE5
CMD_COMMAND_RESET = 0x0080
CMD_PERIPHERAL_WRITE = 0x0081
CMD_PERIPHERAL_WRITE_FIFO = 0x0082
CMD_PERIPHERAL_READ = 0x0083
CMD_PERIPHERAL_READ_FIFO = 0x0084
CMD_MEMORY_WRITE = 0x0085
CMD_MEMORY_READ = 0x0086
CMD_DMA_WRITE = 0x0087
CMD_DMA_READ = 0x0088
CMD_PING = 0x0089
CMD_READ_CONFIG = 0x008A
BAR0_ADDR = 0x00000000
STATUS_BUFFER_ADDRESS = 0x01000000
WRITE_BUFFER_A_ADDRESS = 0x02000000
WRITE_BUFFER_B_ADDRESS = 0x03000000
READ_BUFFER_A_ADDRESS = 0x04000000
READ_BUFFER_B_ADDRESS = 0x05000000
BUFFER_SIZE = 0x00000400
MAX_PACKET_SIZE = 0x40
#Register Values
HDR_STATUS_BUF_ADDR = "status_buf"
HDR_BUFFER_READY = "hst_buffer_rdy"
HDR_WRITE_BUF_A_ADDR = "write_buffer_a"
HDR_WRITE_BUF_B_ADDR = "write_buffer_b"
HDR_READ_BUF_A_ADDR = "read_buffer_a"
HDR_READ_BUF_B_ADDR = "read_buffer_b"
HDR_BUFFER_SIZE = "dword_buffer_size"
HDR_INDEX_VALUEA = "index value a"
HDR_INDEX_VALUEB = "index value b"
HDR_DEV_ADDR = "device_addr"
STS_DEV_STATUS = "device_status"
STS_BUF_RDY = "dev_buffer_rdy"
STS_BUF_POS = "hst_buf_addr"
STS_INTERRUPT = "interrupt"
HDR_AUX_BUFFER_READY = "hst_buffer_rdy"
REGISTERS = OrderedDict([
(HDR_STATUS_BUF_ADDR , "Address of the Status Buffer on host computer" ),
(HDR_BUFFER_READY , "Buffer Ready (Controlled by host)" ),
(HDR_WRITE_BUF_A_ADDR , "Address of Write Buffer 0 on host computer" ),
(HDR_WRITE_BUF_B_ADDR , "Address of Write Buffer 1 on host computer" ),
(HDR_READ_BUF_A_ADDR , "Address of Read Buffer 0 on host computer" ),
(HDR_READ_BUF_B_ADDR , "Address of Read Buffer 1 on host computer" ),
(HDR_BUFFER_SIZE , "Size of the buffer on host computer" ),
(HDR_INDEX_VALUEA , "Value of Index A" ),
(HDR_INDEX_VALUEB , "Value of Index B" ),
(HDR_DEV_ADDR , "Address to read from or write to on device" ),
(STS_DEV_STATUS , "Device Status" ),
(STS_BUF_RDY , "Buffer Ready Status (Controller from device)" ),
(STS_BUF_POS , "Address on Host" ),
(STS_INTERRUPT , "Interrupt Status" ),
(HDR_AUX_BUFFER_READY , "Buffer Ready (Controlled by host)" )
])
SB_READY = "ready"
SB_WRITE = "write"
SB_READ = "read"
SB_FIFO = "flag_fifo"
SB_PING = "ping"
SB_READ_CFG = "read_cfg"
SB_UNKNOWN_CMD = "unknown_cmd"
SB_PPFIFO_STALL = "ppfifo_stall"
SB_HOST_BUF_STALL = "host_buf_stall"
SB_PERIPH = "flag_peripheral"
SB_MEM = "flag_mem"
SB_DMA = "flag_dma"
SB_INTERRUPT = "interrupt"
SB_RESET = "reset"
SB_DONE = "done"
SB_CMD_ERR = "error"
STATUS_BITS = OrderedDict([
(SB_READY , "Ready for new commands" ),
(SB_WRITE , "Write Command Enabled" ),
(SB_READ , "Read Command Enabled" ),
(SB_FIFO , "Flag: Read/Write FIFO" ),
(SB_PING , "Ping Command" ),
(SB_READ_CFG , "Read Config Request" ),
(SB_UNKNOWN_CMD , "Unknown Command" ),
(SB_PPFIFO_STALL , "Stall Due to Ping Pong FIFO" ),
(SB_HOST_BUF_STALL , "Stall Due to Host Buffer" ),
(SB_PERIPH , "Flag: Peripheral Bus" ),
(SB_MEM , "Flag: Memory" ),
(SB_DMA , "Flag: DMA" ),
(SB_INTERRUPT , "Device Initiated Interrupt" ),
(SB_RESET , "Reset Command" ),
(SB_DONE , "Command Done" ),
(SB_CMD_ERR , "Error executing command" )
])
ARTEMIS_MEMORY_OFFSET = 0x0100000000
class ArtemisPcie(Nysa):
def __init__(self, path, status = None):
Nysa.__init__(self, status)
self.path = path
self.dev = None
self.dev = os.open(path, os.O_RDWR)
def set_command_mode(self):
#XXX: Change this to a seperate file
os.lseek(self.dev, 0, os.SEEK_END)
def set_data_mode(self):
#XXX: Change this to a seperate file
os.lseek(self.dev, 0, os.SEEK_SET)
def set_dev_addr(self, address):
self.dev_addr = address
reg = NysaPCIEConfig.get_config_reg(HDR_DEV_ADDR)
self.write_pcie_reg(reg, address)
def write_pcie_reg(self, address, data):
d = Array('B')
d.extend(dword_to_array(address))
d.extend(dword_to_array(data))
self.set_command_mode()
#self.dev.write(d)
os.write(self.dev, d)
self.set_data_mode()
def write_pcie_command(self, command, count, address):
d = Array('B')
d.extend(dword_to_array(command))
d.extend(dword_to_array(count))
d.extend(dword_to_array(address))
self.set_command_mode()
#self.dev.write(d)
os.write(self.dev, d)
self.set_data_mode()
def read(self, address, length = 1, disable_auto_inc = False):
"""read
Generic read command used to read data from a Nysa image
Args:
length (int): Number of 32 bit words to read from the FPGA
address (int): Address of the register/memory to read
disable_auto_inc (bool): if true, auto increment feature will be disabled
Returns:
(Array of unsigned bytes): A byte array containtin the raw data
returned from Nysa
Raises:
NysaCommError: When a failure of communication is detected
"""
#print "Read"
d = Array('B')
if length == 0:
length = 1
command = 0x00000002
d.extend(dword_to_array(IDWORD))
if address >= ARTEMIS_MEMORY_OFFSET:
address -= ARTEMIS_MEMORY_OFFSET
command |= 0x10000
if disable_auto_inc:
command |= 0x20000
d.extend(dword_to_array(command))
d.extend(dword_to_array(length))
d.extend(dword_to_array(address))
hdr_byte_len = len(d)
hdr_dword_len = hdr_byte_len / 4
self.write_pcie_command(CMD_PERIPHERAL_WRITE, hdr_dword_len, 0x00)
os.write(self.dev, d)
self.write_pcie_command(CMD_PERIPHERAL_READ, length + hdr_dword_len, 0x00)
#print "Read Command"
#print_32bit_hex_array(d)
data = Array('B', os.read(self.dev, ((length * 4) + hdr_byte_len)))
#print "Data:"
#print_32bit_hex_array(data)
return data[hdr_byte_len:]
def write(self, address, data, disable_auto_inc = False):
"""write
Generic write command usd to write data to a Nysa image
Args:
address (int): Address of the register/memory to read
data (array of unsigned bytes): Array of raw bytes to send to the
device
disable_auto_inc (bool): if true, auto increment feature will be disabled
Returns:
Nothing
Raises:
AssertionError: This function must be overriden by a board specific
implementation
"""
while (len(data) % 4) != 0:
data.append(0x00)
length = len(data) / 4
d = Array('B')
command = 0x00000001
d.extend(dword_to_array(IDWORD))
if address >= ARTEMIS_MEMORY_OFFSET:
address -= ARTEMIS_MEMORY_OFFSET
command |= 0x10000
if disable_auto_inc:
command |= 0x20000
d.extend(dword_to_array(command))
d.extend(dword_to_array(length))
d.extend(dword_to_array(address))
d.extend(data)
#print "Write Command"
self.write_pcie_command(CMD_PERIPHERAL_WRITE, (len(d) / 4), 0x00)
#print "Data:"
#print_32bit_hex_array(d)
os.write(self.dev, d)
def ping(self):
"""ping
Pings the Nysa image
Args:
Nothing
Returns:
Nothing
Raises:
NysaCommError: When a failure of communication is detected
"""
return
#raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
def reset(self):
"""reset
Software reset the Nysa FPGA Master, this may not actually reset the
entire FPGA image
Args:
Nothing
Returns:
Nothing
Raises:
NysaCommError: A failure of communication is detected
"""
self.write_pcie_command(CMD_COMMAND_RESET, 0, 0)
#raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
def is_programmed(self):
"""
Returns True if the FPGA is programmed
Args:
Nothing
Returns (Boolean):
True: FPGA is programmed
False: FPGA is not programmed
Raises:
NysaCommError: A failure of communication is detected
"""
return True
#raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
def get_sdb_base_address(self):
"""
Return the base address of the SDB (This is platform specific)
Args:
Nothing
Returns:
32-bit unsigned integer of the address where the SDB can be read
Raises:
Nothing
"""
return 0x00000000
def wait_for_interrupts(self, wait_time = 1):
"""wait_for_interrupts
listen for interrupts for the specified amount of time
Args:
wait_time (int): the amount of time in seconds to wait for an
interrupt
Returns:
(boolean):
True: Interrupts were detected
False: No interrupts detected
Raises:
NysaCommError: A failure of communication is detected
"""
raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
def register_interrupt_callback(self, index, callback):
""" register_interrupt
Setup the thread to call the callback when an interrupt is detected
Args:
index (Integer): bit position of the device
if the device is 1, then set index = 1
callback: a function to call when an interrupt is detected
Returns:
Nothing
Raises:
Nothing
"""
#raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
return
def unregister_interrupt_callback(self, index, callback = None):
""" unregister_interrupt_callback
Removes an interrupt callback from the reader thread list
Args:
index (Integer): bit position of the associated device
EX: if the device that will receive callbacks is 1, index = 1
callback: a function to remove from the callback list
Returns:
Nothing
Raises:
Nothing (This function fails quietly if ther callback is not found)
"""
#raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
return
def get_board_name(self):
return "artemis_pcie"
def upload(self, filepath):
"""
Uploads an image to a board
Args:
filepath (String): path to the file to upload
Returns:
Nothing
Raises:
NysaError:
Failed to upload data
AssertionError:
Not Implemented
"""
raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
def program (self):
"""
Initiate an FPGA program sequence, THIS DOES NOT UPLOAD AN IMAGE, use
upload to upload an FPGA image
Args:
Nothing
Returns:
Nothing
Raises:
AssertionError:
Not Implemented
"""
raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
def ioctl(self, name, arg = None):
"""
Platform specific functions to execute on a Nysa device implementation.
For example a board may be capable of setting an external voltage or
reading configuration data from an EEPROM. All these extra functions
cannot be encompused in a generic driver
Args:
name (String): Name of the function to execute
args (object): A generic object that can be used to pass an
arbitrary or multiple arbitrary variables to the device
Returns:
(object) an object from the underlying function
Raises:
NysaError:
An implementation specific error
"""
raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
def list_ioctl(self):
"""
Return a tuple of ioctl functions and argument types and descriptions
in the following format:
{
[name, description, args_type_object],
[name, description, args_type_object]
...
}
Args:
Nothing
Raises:
AssertionError:
Not Implemented
"""
raise AssertionError("%s not implemented" % sys._getframe().f_code.co_name)
| [
"cospan@gmail.com"
] | cospan@gmail.com |
3ed27b9d927cef8365bae343c2f4ea643b86044d | f0b3d4c9e6a5f8f4454adedf91db1b80c89401a7 | /22lab.py | bcf05a296b44755172aa6d967d6c35198bacf783 | [] | no_license | akotwicka/Learning_Python_Udemy | 5b31656858e8d729cc0274b3b873f9d3852e67b9 | c3d1c93d914ae1f2d4f497181ac41de39aeb0ce0 | refs/heads/master | 2020-06-24T18:28:42.294106 | 2019-08-06T10:45:34 | 2019-08-06T10:45:34 | 199,046,024 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,274 | py | color = ["red", "orange", "green", "violet", "blue", "yellow"]
def Colors(color_list, n):
colors = color_list.copy()
colors = colors[:n]
return colors
for i in range(len(Colors(color, 6))):
print(Colors(color, 6)[:i+1])
tekst = "Korporacja (z łac. corpo – ciało, ratus – szczur; pol. ciało szczura) – organizacja, która pod przykrywką prowadzenia biznesu włada dzisiejszym światem. Wydawać się może utopijnym miejscem realizacji pasji zawodowych. W rzeczywistości jednak nie jest wcale tak kolorowo. Korporacja służy do wyzyskiwania człowieka w imię postępu. Rządzi w niej prawo dżungli. "
tekst = tekst.split(sep = " ")[1:12]
tekst[0] = tekst[0].strip("(")
tekst[-1] = tekst[-1].strip(")")
x = ""
for i in range(len(tekst)):
x = x + tekst[i] + " "
print(x)
definition = "Korporacja (z łac. corpo – ciało, ratus – szczur; pol. ciało szczura) – organizacja, która pod przykrywką prowadzenia biznesu włada dzisiejszym światem. Wydawać się może utopijnym miejscem realizacji pasji zawodowych. W rzeczywistości jednak nie jest wcale tak kolorowo. Korporacja służy do wyzyskiwania człowieka w imię postępu. Rządzi w niej prawo dżungli. "
print(definition[definition.index('(')+1:definition.index(')')])
| [
"a_kotwicka@wp.pl"
] | a_kotwicka@wp.pl |
24687fafca2fcd4af426515100e73a74848007a8 | c4eef62faf22791ae426430c3054044eb98d469e | /201812/cird.py | 547572c2787595490570e580c77ecadb3d6c027c | [] | no_license | yeung66/codesAboutCCF | 85075344c2bc6f3afcca02edb51d1064bc5f4f5d | e16e1f6515ecc2747acfad34ad02f9dbb04ad2be | refs/heads/master | 2020-04-28T08:54:36.017148 | 2019-04-20T07:07:59 | 2019-04-20T07:07:59 | 175,147,020 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,626 | py | class IPA:
def __init__(self,init):
if '/' in init:
prefix,self.length = init.split('/')
ips = list(map(int,prefix.split('.')))
while len(ips)<4:
ips.append(0)
self.ip = tuple(ips)
else:
ips = list(map(int,init.split('.')))
self.length = len(ips)*8
while ips.__len__()<4:
ips.append(0)
self.ip = tuple(ips)
self.length = int(self.length)
self.bin = "".join(['%08d'%int(bin(i)[2:]) for i in self.ip])
def __lt__(self,right):
return self.ip<right.ip or self.ip==right.ip and self.length<right.length
def __str__(self):
return '%d.%d.%d.%d/%d'%(self.ip[0],self.ip[1],self.ip[2],self.ip[3],self.length)
def inSub(self,ip):
return self.bin[:self.length]==ip.bin[:self.length]
n = int(input())
ip_list = []
for _ in range(n):
new = IPA(input())
ip_list.append(new)
ip_list.sort()
i = 0
while i<len(ip_list)-1:
if ip_list[i].inSub(ip_list[i+1]):
ip_list.pop(i+1)
else:
i+=1
i = 0
while i<len(ip_list)-1:
if ip_list[i].length==ip_list[i+1].length:
new_length = ip_list[i].length-1
if new_length<0 or ip_list[i].bin[new_length]=='1':
i+=1
continue
ip_list[i].length=new_length
if ip_list[i].inSub(ip_list[i+1]):
#ip_list[i] = temp
ip_list.pop(i+1)
if i!=0:i-=1
else:
ip_list[i].length=new_length+1
i+=1
else:
i+=1
for ip in ip_list:
print(ip) | [
"yeunghl@whu.edu.cn"
] | yeunghl@whu.edu.cn |
377539c4026f018f1f2791420808df494af04c20 | d6cc56d95e410b931368f351ecde661a86a5ecb8 | /pythonprac/hello.py | 996b3e186bf84f08a8b15e571606aef7ca6e0e33 | [] | no_license | minjaae/sparta-web | 539c92e1f8c52c5e1027c0b0c694f2dd657e5101 | 352ba6de5d9347d63576957a857a3217e6bdb16a | refs/heads/master | 2023-06-17T00:12:04.149464 | 2021-07-07T12:35:18 | 2021-07-07T12:35:18 | 383,409,769 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 919 | py | import requests
from bs4 import BeautifulSoup
from pymongo import MongoClient
client = MongoClient('localhost', 27017)
db = client.dbsparta
headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'}
data = requests.get('https://movie.naver.com/movie/sdb/rank/rmovie.nhn?sel=pnt&date=20200303',headers=headers)
soup = BeautifulSoup(data.text, 'html.parser')
# 코딩 시작
trs = soup.select('#old_content > table > tbody > tr') #select는 결과가 리스트로 나옴
for tr in trs:
a_tag=tr.select_one('td.title > div > a')
if a_tag is not None:
rank = tr.select_one('td:nth-child(1) > img')['alt']
title = a_tag.text
star =tr.select_one('td.point').text
doc = {
'rank':rank,
'title':title,
'star':star
}
db.movies.insert_one(doc)
| [
"mjj3238@naver.com"
] | mjj3238@naver.com |
e72fb5148e9d6560555da3cb66069e5cb311d78e | 147519505f3c47e5f10d9679e07d3719931b9fd0 | /my_contacts/contacts/views.py | 177a81dfd5a303c238013aa4c1cbcc9b156afbe2 | [] | no_license | grbalmeida/hello-django | 85ed28d8d47a9a2e072f3eecd13d22fb2e977a31 | 9ef261ba5faeac3de8d36eeb7efa8974e5d1e661 | refs/heads/master | 2020-08-12T10:10:48.554349 | 2019-12-20T01:18:33 | 2019-12-20T01:18:33 | 214,748,310 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,670 | py | from django.shortcuts import render, get_object_or_404, Http404, redirect
from django.core.paginator import Paginator
from django.db.models import Q, Value
from django.db.models.functions import Concat
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from .models import Contact
@login_required(redirect_field_name='login')
def index(request):
contacts = Contact.objects.order_by('-id').filter(
show=True
)
paginator = Paginator(contacts, 2)
page = request.GET.get('p')
contacts = paginator.get_page(page)
return render(request, 'contacts/index.html', {
'contacts': contacts
})
@login_required(redirect_field_name='login')
def see_contact(request, contact_id):
contact = get_object_or_404(Contact, id=contact_id)
if not contact.show:
raise Http404()
return render(request, 'contacts/see_contact.html', {
'contact': contact
})
@login_required(redirect_field_name='login')
def search(request):
term = request.GET.get('term')
if term is None or not term:
messages.add_message(
request,
messages.WARNING,
'Term field cannot be empty'
)
return redirect('index')
fields = Concat('name', Value(' '), 'last_name')
contacts = Contact.objects.annotate(
full_name=fields
).filter(
Q(full_name__icontains=term) |
Q(phone__icontains=term)
)
paginator = Paginator(contacts, 2)
page = request.GET.get('p')
contacts = paginator.get_page(page)
return render(request, 'contacts/search.html', {
'contacts': contacts
})
| [
"g.r.almeida@live.com"
] | g.r.almeida@live.com |
43e7893cc914a9eb0c55d26c2d6fa8acb07c1fe4 | f6271c96a61986f0f948f11c9e531531c6dc2009 | /prog14.py | 39e67bf4d5f1125f633b55a05ca25f01f2dcb0fe | [] | no_license | MD-AZMAL/Project-Euler | 27f82ef442180bb05cd66d7865ef9741e30634d2 | ed526ee971a6095e90a6126bcbf716acc0fc9399 | refs/heads/master | 2018-12-22T12:28:09.927382 | 2018-10-30T15:14:40 | 2018-10-30T15:14:40 | 119,384,023 | 0 | 1 | null | 2018-10-30T15:14:41 | 2018-01-29T13:11:09 | Python | UTF-8 | Python | false | false | 982 | py | """
The following iterative sequence is defined for the set of positive integers:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
Using the rule above and starting with 13, we generate the following sequence:
13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.
Which starting number, under one million, produces the longest chain?
NOTE: Once the chain starts the terms are allowed to go above one million.
"""
max_ = 0
num = -1
def getNoOfTerms(n):
cnt = 1
while n > 1:
cnt += 1
if n % 2 == 0:
n /= 2
else:
n = 3 * n + 1
return cnt
print(getNoOfTerms(13))
for i in range(1000000):
n = getNoOfTerms(i)
if n > max_:
max_ = n
num = i
print(num)
| [
"noreply@github.com"
] | MD-AZMAL.noreply@github.com |
f1dfd4a19256e5e0c785c0139e4943e09ada9895 | 620b55599516ae465ca137005e718abd3ea1a38a | /lib/dataset/dataloader/__init__.py | 5024254245bec8bfb8790aefc6d2ce1c6da4eff9 | [] | no_license | daxiongpro/3DSSD-pytorch | b3366f9ae30e33524ed261fcc0defce942bdc4d8 | d0c856e4c1b6cb69810fc0b0562ebc9a42e5bace | refs/heads/master | 2023-02-25T07:10:39.761805 | 2021-01-29T09:27:56 | 2021-01-29T09:27:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 287 | py | from lib.core.config import cfg
from .kitti_dataloader import KittiDataset
# from .nuscenes_dataloader import NuScenesDataset
def choose_dataset():
dataset_dict = {
'KITTI': KittiDataset,
# 'NuScenes': NuScenesDataset,
}
return dataset_dict[cfg.DATASET.TYPE] | [
"34833553+qiqihaer@users.noreply.github.com"
] | 34833553+qiqihaer@users.noreply.github.com |
74c545b44ad5ca43f27f1b23262f0f185917b49c | c6878b4b4f3b00fbc8df675fcb58732f1d3bd44a | /100randombeasts.py | ba209008dca5a05d62c16065b0df457b3a5035c0 | [
"MIT"
] | permissive | rootoftwo/swn2e_beastgen | eed710a2aeac97d96785c04cdda6e50f71be5e65 | b2e293283290f471a6eb9eaf9e3f81af5485ca29 | refs/heads/master | 2021-09-01T22:51:45.815779 | 2017-12-29T02:03:44 | 2017-12-29T02:03:44 | 115,365,833 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,108 | py | import random
animal_name1 = ['Abominable', 'Agitated', 'Bane', 'Blight', 'Brood', 'Chaos', 'Cruel', 'Cursed', 'Damned', 'Decay', 'Dirty', 'Dust', 'Eternal', 'Fang', 'Fetid', 'Flail', 'Flame', 'Fog', 'Foul', 'Frost', 'Fungus', 'Gas', 'Ghoul', 'Greater', 'Grime', 'Groaning', 'Haunt', 'Herd', 'Howling', 'Infernal', 'Jelly', 'Lesser', 'Mantis', 'Mold', 'Murk', 'Noxious', 'Outlandish', 'Phase', 'Plague', 'Poison', 'Putrid', 'Radiant', 'Razor', 'Retch', 'Rot', 'Savage', 'Screeching', 'Shadow', 'Slime', 'Smog', 'Spore', 'Stealth', 'Stink', 'Tall', 'Terror', 'Toxic', 'Ugly', 'Vicious', 'Vortex', 'Warp', 'Wave', 'Web', 'Wisp']
animal_name2 = ['Assassin', 'Aura', 'Babbler', 'Beast', 'Behemoth', 'Blast', 'Blob', 'Brute', 'Charmer', 'Creeper', 'Critter', 'Enveloper', 'Fang', 'Fisher', 'Freak', 'Frill', 'Ghost', 'Grasper', 'Herder', 'Horror', 'Howler', 'Jelly', 'Lump', 'Lure', 'Mammoth', 'Maw', 'Mirage', 'Morph', 'Nightmare', 'Ooze', 'Orb', 'Pest', 'Ripper', 'Seeker', 'Sentinel', 'Snare', 'Spawn', 'Stinger', 'Strider', 'Strike', 'Swarm', 'Tangler', 'Wing']
armor_class = ['13', '14', '14', '15', '15', '15', '16', '17', '18']
hit_dice = ['1', '1', '1', '1', '1','1', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '3', '3', '3', '3', '4', '4', '5', '6', '7', '8', '9', '10']
movement_rate = ['10', '15', '15', '15', '20']
number_attacks = ['1', '1', '1', '1', '1', '2', '2', '3']
first_attack = ['1 bite', '1 bite', '1 bite', '1 bite', '1 bite', '1 claw', '1 claw', '1 claw', '1 gore', '1 pincer', '1 kick', '1 tentacle', '1 beak']
second_attack = ['1 claw', '1 claw', '1 claw', '2 claws', '1 tentacle', '1 squeeze', '1 trample', '1 touch', '1 breath', '1 sting', '1 spit' ]
third_attack = ['+ poison', '+ poison', '+ poison', '+ poison', '+ poison', '+ paralysis', '+ paralysis', '+ paralysis', '+ convulsions', '+ convulsions', '+ hallucinations', '+ hallucinations', '+ blindness', '+ death']
roll_damage1 = ['1d3', '1d3', '1d4', '1d4', '1d4', '1d4', '1d4', '1d4', '1d6', '1d6', '1d6', '1d8', '1d10', '1d12']
roll_damage2 = ['1d3', '1d3', '1d4', '1d4', '1d4', '1d4', '1d4', '1d4', '1d6', '1d6', '1d6', '1d8', '1d10', '1d12']
beast_morale = ['7', '8', '9', '9', '9', '9', '9', '9', '10', '11', '12']
basic_animal = ['Amphibian, froggish or newtlike', 'Bird, winged and feathered', 'Fish, scaled and torpedo-bodied', 'Insect, beetle-like or fly-winged', 'Mammal, hairy and fanged', 'Reptile, lizardlike and long-bodied', 'Spider, many-legged and fat', 'Exotic, made of wholly alien elements']
body_plan = ['Humanoid', 'Quadruped', 'Many-legged', 'Bulbous', 'Amorphous']
limb_novelty = ['Wings', 'Many joints', 'Tentacles', 'Opposable thumbs', 'Retractable', 'Varying sizes']
skin_novelty = ['Hard shell', 'Exoskeleton', 'Odd texture', 'Molts regularly', 'Harmful to touch', 'Wet or slimy']
main_weapon = ['Teeth or mandibles', 'Claws', 'Poison', 'Harmful discharge', 'Pincers', 'Horns']
animal_size = ['Cat-sized', 'Wolf-sized', 'Calf-sized', 'Bull-sized', 'Hippo-sized', 'Elephant-sized']
option_predator = ['Hunts in kin-group packs', 'Favors ambush attacks', 'Cripples prey and waits for death', 'Pack supports alpha-beast attack', 'Lures or drives prey into danger', 'Hunts as a lone, powerful hunter', 'Only is predator at certain times', 'Breeds at tremendous rates']
option_prey = ['Moves in vigilant herds', 'Exists in small family groups', 'They all team up on a single foe', 'They go berserk when near death', 'They are violent in certain seasons', 'They are vicious if threatened', 'Symbiotic creature protects them', 'Breeds at tremendous rates']
option_scavenger = ['Never attacks unwounded prey', 'Uses other beasts as harriers', 'Always flees if significantly hurt', 'Poisons prey, waits for it to die', 'Disguises itself as its prey', 'Remarkably stealthy', 'Summons predators to weak prey', 'Steals prey from weaker predator']
harmful_discharge = ['Acidic spew doing its damage on a hit', 'Toxic spittle or cloud, use adjacent chart', 'Super-heated or super-chilled spew', 'Sonic drill or other disabling noise', 'Natural laser or plasma discharge', 'Nauseating stench or disabling chemical', 'Equipment-melting corrosive', 'Explosive pellets or chemical catalysts']
option_poison = ['Death', 'Paralysis', '1d4 dmg per onset interval', 'Convulsions', 'Blindness', 'Hallucinations']
option_onset = ['Instant', '1 round', '1d6 rounds', '1 minute', '1d6 minutes', '1 hour']
option_duration = ['1d6 rounds', '1 minute', '10 minutes', '1 hour', '1d6 hours', '1d6 days']
for i in range(100):
print('Beast Type: {name1} {name2} \n AC: {ac} \n HD: {hd} \n #Attacks {atk}: {atk1}/{atk2}/{atk3} \n Damage: {d1}/{d2}/+special \n Movement Rate: {mv}m \n Morale: {ml} \n Skills: +{hd} \n Saves: (16-{hd})+ \n Basic Animal: {animal} \n Body Plan: {body} \n Limb Novelty: {limb} \n Skin Novelty: {skin} \n Main Weapon: {weapon} \n Size: {size} \n Predator: {predator} \n Prey: {prey} \n Scavenger: {scavenger} \n Harmful Discharge: {discharge} \n Poison: {poison} \n Onset: {onset} \n Duration: {duration} \n'.format(
name1=random.choice(animal_name1),
name2=random.choice(animal_name2),
ac=random.choice(armor_class),
hd=random.choice(hit_dice),
mv=random.choice(movement_rate),
atk=random.choice(number_attacks),
atk1=random.choice(first_attack),
atk2=random.choice(second_attack),
atk3=random.choice(third_attack),
d1=random.choice(roll_damage1),
d2=random.choice(roll_damage2),
ml=random.choice(beast_morale),
animal=random.choice(basic_animal),
body=random.choice(body_plan),
limb=random.choice(limb_novelty),
skin=random.choice(skin_novelty),
weapon=random.choice(main_weapon),
size=random.choice(animal_size),
predator=random.choice(option_predator),
prey=random.choice(option_prey),
scavenger=random.choice(option_scavenger),
discharge=random.choice(harmful_discharge),
poison=random.choice(option_poison),
onset=random.choice(option_onset),
duration=random.choice(option_duration)))
| [
"noreply@github.com"
] | rootoftwo.noreply@github.com |
2dfe56c39a906c5e5a663caacef4016c38661685 | fe024ec4f26a1e4d596f6c436934944211096221 | /Q-Routing-Protocol/agents/q_agent_discount.py | d26f68f4ae9d4c9ad2524d86f5b602ae3b04c98f | [] | no_license | All-Usernames-Already-Taken/Deep_Q_Routing | 6fed6374e00a2b9182bbd540dae85d1e1416e4fe | 5109fb5be3b889412e30fbd52d64d56453911a84 | refs/heads/master | 2020-04-05T05:29:09.784568 | 2019-02-05T17:04:27 | 2019-02-05T17:04:27 | 156,597,706 | 0 | 1 | null | 2018-12-18T17:24:43 | 2018-11-07T19:29:24 | Python | UTF-8 | Python | false | false | 41,564 | py | import numpy as np
import tensorflow as tf
class NetworkQAgent(object):
"""
Agent implementing Q-learning for the NetworkSimulatorEnv.
"""
def __init__(
self,
nodes,
actions,
node,
edges_from_node,
node_to_node,
absolute_node_edge_tuples,
destinations,
n_features,
learning_rate,
total_layers,
layer_size,
layer_type,
mean_val,
std_val,
constant_val,
activation_type
):
self.config = { # cg: reset configuration for each node in the graph
"init_mean": 0.0, # Initialize Q values with this mean
"init_std": 0.0, # Initialize Q values with this standard deviation
"learning_rate": 0.7,
"eps": 0.1, # Epsilon in epsilon greedy policies
"discount": 1,
"n_iter": 1000} # Number of iterations
self.activation_type = activation_type
self.constant_val = constant_val
self.destinations = destinations
self.episode_observation = []
self.episode_observation2 = []
self.episode_actions = []
self.episode_rewards = []
self.episode_observation_temp = []
self.episode_actions_temp = []
self.hist_resources = []
self.hist_action = []
self.learning_rate = learning_rate
self.layer_type = layer_type
self.links = node_to_node
self.link_num = absolute_node_edge_tuples
self.mean_val = mean_val
self.node = node
self.n_actions = edges_from_node[self.node]
self.n_features = n_features
self.n_links = edges_from_node
self.num_nodes = nodes
self.num_actions = actions
self.total_layers = total_layers
self.q = []
self.std_val = std_val
self.session = tf.Session()
self._build_net() # Model
self.session.run(tf.global_variables_initializer())
# observations = tf.placeholder(shape=[None, self.n_actions], dtype=tf.float32)
# actions = tf.placeholder(shape=[None], dtype=tf.float32)
# rewards = tf.placeholder(shape=[None], dtype=tf.float32)
# self._build_net_auto(total_layers,layer_size,layer_type,mean_val,std_val,constant_val,activation_type)
@staticmethod
def normalize_weights(x):
"""Compute softmax values for each sets of scores in x."""
"""?!--> ????? This is not SoftMax """
return x / x.sum(axis=0) # only difference
@staticmethod
def next_mini_batch(x_, y_, z_, batch_size):
"""""""?!--> what are these x, y, and z, representative of?"""
"""Create a vector with batch_size quantity of random integers; generate a mini-batch therefrom???."""
permutation = np.random.permutation(x_.shape[0])
permutation = permutation[:batch_size]
x_batch = x_[permutation, :]
y_batch = y_[permutation]
z_batch = z_[permutation]
return x_batch, y_batch, z_batch
# called in initializer
def _build_net(self):
"""
tf.name_scope is a context manager for defining Python operations
tf.placeholder returns a `Tensor` that may be used as a handle for feeding a value, but not evaluated directly.
"""
with tf.name_scope('inputs'):
self.tf_observations = \
tf.placeholder(
dtype=tf.float32,
shape=[None, self.n_features],
name="observations"
)
self.tf_observations_2 = \
tf.placeholder(
dtype=tf.float32,
shape=[None, self.n_features],
name="observations2"
)
self.tf_action_number = \
tf.placeholder(
dtype=tf.int32,
shape=[None, ],
name="actions_num"
)
self.tf_vt = \
tf.placeholder(
dtype=tf.float32,
shape=[None, ],
name=None
)
"""
tf.layers.dense -
Description:
Functional interface for the densely-connected layer that implements the operation:
activation(inputs * kernel + bias)
where activation is the activation function passed as the activation argument (if not None), kernel is a
weights matrix created by the layer, and bias is a bias vector created by the layer (only if use_bias is
True).
Inputs:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix. If None (default), weights are
initialized using the default initializer used by tf.get_variable.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the kernel after being updated by an
Optimizer (e.g. used to implement norm constraints or value constraints for layer
weights). The function must take as input the unprojected variable and must return
the projected variable (which must have the same shape). Constraints are not safe to
use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the bias after being updated by an
Optimizer.
trainable: Boolean, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer by the same name.
Output:
tensor the same shape as inputs except the last dimension is of size units
"""
# https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/7_Policy_gradient_softmax/RL_brain.py
# --> Forward Connected Layer 1
self.layer = tf.layers.dense(
inputs=self.tf_observations,
units=50,
activation=None, # tf.nn.relu, # tanh activation
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
# --> Forward Connected Layer 2
layer2 = tf.layers.dense(
inputs=self.layer,
units=25,
activation=tf.nn.relu, # tf.nn.relu, # tanh activation
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
# --> Forward Connected Layer 3
layer3 = tf.layers.dense(
inputs=layer2,
units=15,
activation=tf.nn.sigmoid, # tf.nn.relu, # tanh activation
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
# --> Forward Connected Layer 4
self.all_act = tf.layers.dense(
inputs=layer3,
units=self.n_actions,
activation=tf.nn.relu,
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
"""
tf.nn.softmax -
Aliases:
tf.math.softmax
tf.nn.softmax
Description:
Computes softmax activations.
This function performs the equivalent of softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis)
Args:
logits: A non-empty Tensor. Must be one of the following types: half, float32, float64.
axis: The dimension softmax would be performed on. The default is -1 which indicates the last dimension.
name: A name for the operation (optional).
dim: Deprecated alias for axis.
Returns:
A Tensor. Has the same type and shape as logits.
Raises:
InvalidArgumentError: if logits is empty or axis is beyond the last dimension of logits.
"""
# use SoftMax to convert to probability
self.action_probabilities = tf.nn.softmax(logits=self.all_act, name="action_probabilities")
with tf.name_scope('loss'):
one_hot_tensor = \
tf.one_hot(
indices=self.tf_action_number,
depth=self.n_actions,
on_value=None,
off_value=None,
axis=None,
dtype=None,
name="one_hot_tensor"
)
neg_logarithm_action_probabilities = \
-tf.log(
x=self.action_probabilities,
name="negative_log_action_probabilities"
)
"""
tf.math.reduce_sum
Aliases:
tf.math.reduce_sum
tf.reduce_sum
Description:
Computes the sum of elements across dimensions of a tensor. (deprecated arguments)
Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the
tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are
retained with length 1.
If axis is None, all dimensions are reduced, and a tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range
[-rank(input_tensor), rank(input_tensor)).
keepdims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.
keep_dims: Deprecated alias for keepdims.
Returns:
The reduced tensor, of the same dtype as the input_tensor.
"""
self.neg_log_prob = \
tf.reduce_sum(
input_tensor=neg_logarithm_action_probabilities * one_hot_tensor,
axis=1,
name="reduce_sum",
reduction_indices=None
)
# Reward guided loss
self.loss = \
tf.reduce_mean(
input_tensor=self.neg_log_prob * self.tf_vt,
axis=None,
name="reduce_mean",
reduction_indices=None
)
print("Why is there a print command here, and why print help?")
with tf.name_scope('train'):
self.train_op = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
"""
tf.session.run
Descriptions:
Runs operations and evaluates tensors in fetches.
This method runs one "step" of TensorFlow computation, by running the necessary graph fragment to execute
every Operation and evaluate every Tensor in fetches, substituting the values in feed_dict for the
corresponding input values.
Args:
fetches: A single graph element, a list of graph elements, or a dictionary whose values are graph elements
or lists of graph elements (described above).
feed_dict: A dictionary that maps graph elements to values (described above).
options: A [RunOptions] protocol buffer
run_metadata: A [RunMetadata] protocol buffer
Returns:
Either a single value if fetches is a single graph element, or a list of values if fetches is a list, or a
dictionary with the same keys as fetches if that is a dictionary (described above). Order in which fetches
operations are evaluated inside the call is undefined.
***The fetches argument may be a single graph element, or an arbitrarily nested list, tuple, namedtuple, dict,
or OrderedDict containing graph elements at its leaves. A graph element can be one of the following types:
An tf.Operation. The corresponding fetched value will be None.
A tf.Tensor. The corresponding fetched value will be a numpy ndarray containing the value of that tensor
A tf.SparseTensor. The corresponding fetched value will be a tf.SparseTensorValue containing the value
of that sparse tensor.
A get_tensor_handle op. The corresponding fetched value will be a numpy ndarray containing the handle of
that tensor.
A string which is the name of a tensor or operation in the graph.
The value returned by run() has the same shape as the fetches argument, where the leaves are replaced by the
corresponding values returned by TensorFlow.
The optional feed_dict argument allows the caller to override the value of tensors in the graph. Each key in
feed_dict can be one of the following types:
If the key is a tf.Tensor, the value may be a Python scalar, string, list, or numpy ndarray that can
be converted to the same dtype as that tensor. Additionally, if the key is a tf.placeholder, the
shape of the value will be checked for compatibility with the placeholder.
If the key is a tf.SparseTensor, the value should be a tf.SparseTensorValue.
If the key is a nested tuple of Tensors or SparseTensors, the value should be a nested tuple with
the same structure that maps to their corresponding values as above.
Each value in feed_dict must be convertible to a numpy array of the dtype of the corresponding key.
The optional options argument expects a [RunOptions] proto. The options allow controlling the behavior of
this particular step (e.g. turning tracing on).
The optional run_metadata argument expects a [RunMetadata] proto. When appropriate, the non-Tensor output of
this step will be collected there. For example, when users turn on tracing in options, the profiled info
will be collected into this argument and passed back.
"""
def choose_action(self, observation, valid):
prob_weights = \
self.session.run(
fetches=self.action_probabilities,
feed_dict={self.tf_observations: observation},
options=None,
run_metadata=None
)
valid_weights = prob_weights * valid
valid_prob = self.normalize_weights(valid_weights[0])
action = \
np.random.choice(
a=range(prob_weights.shape[1]),
size=None,
replace=True,
p=valid_prob.ravel()
)
return action
def choose_action2(self, observation):
prob_weights = \
self.session.run(
fetches=self.action_probabilities,
feed_dict={self.tf_observations: observation},
options=None,
run_metadata=None
)
action = \
np.random.choice(
a=range(prob_weights.shape[1]),
size=None,
replace=True,
p=prob_weights.ravel()
)
return action
def store_transition(self, state, action, reward):
self.episode_observation.append(state)
self.episode_actions.append(action)
self.episode_rewards.append(reward)
def store_transition_temp(self, state, action):
self.episode_observation_temp.append(state)
self.episode_actions_temp.append(action)
def store_transition_episode(self, reward):
ep_as_temp = len(self.episode_actions_temp)
for i in range(0, ep_as_temp):
self.store_transition(
self.episode_observation_temp[i],
self.episode_actions_temp[i],
reward
)
def learn5(self, iteration):
episode_observation = len(self.episode_observation)
self.episode_observation2 = np.array(self.episode_observation).reshape(episode_observation, self.n_features)
discounted_episode_rewards_norm = self._discount_and_norm_rewards()
# print('self.episode_observation2.shape =', self.episode_observation2.shape)
# print ('np.vstack(self.episode_observation2).shape =',np.vstack(self.episode_observation2).shape)
x_batch, y_batch, z_batch = \
self.next_mini_batch(
self.episode_observation2,
np.array(self.episode_actions),
np.array(discounted_episode_rewards_norm),
episode_observation
)
_, loss, log_probabilities, act_val = \
self.session.run(
fetches=[self.train_op, self.loss, self.neg_log_prob, self.all_act],
feed_dict={
self.tf_observations: x_batch, # shape=[None, n_obs]
self.tf_action_number: y_batch, # shape=[None, ]
self.tf_vt: z_batch, # shape=[None, ]
},
options=None,
run_metadata=None
)
if iteration % 1 == 0:
self.episode_observation, selfimport numpy as np
import tensorflow as tf
class NetworkQAgent(object):
"""
Agent implementing Q-learning for the NetworkSimulatorEnv.
"""
def __init__(
self,
nodes,
actions,
node,
edges_from_node,
node_to_node,
absolute_node_edge_tuples,
destinations,
n_features,
learning_rate,
total_layers,
layer_size,
layer_type,
mean_val,
std_val,
constant_val,
activation_type
):
self.config = { # cg: reset configuration for each node in the graph
"init_mean": 0.0, # Initialize Q values with this mean
"init_std": 0.0, # Initialize Q values with this standard deviation
"learning_rate": 0.7,
"eps": 0.1, # Epsilon in epsilon greedy policies
"discount": 1,
"n_iter": 1000} # Number of iterations
self.activation_type = activation_type
self.constant_val = constant_val
self.destinations = destinations
self.episode_observation = []
self.episode_observation2 = []
self.episode_actions = []
self.episode_rewards = []
self.episode_observation_temp = []
self.episode_actions_temp = []
self.hist_resources = []
self.hist_action = []
self.learning_rate = learning_rate
self.layer_type = layer_type
self.links = node_to_node
self.link_num = absolute_node_edge_tuples
self.mean_val = mean_val
self.node = node
self.n_actions = edges_from_node[self.node]
self.n_features = n_features
self.n_links = edges_from_node
self.num_nodes = nodes
self.num_actions = actions
self.total_layers = total_layers
self.q = []
self.std_val = std_val
self.session = tf.Session()
self._build_net() # Model
self.session.run(tf.global_variables_initializer())
# observations = tf.placeholder(shape=[None, self.n_actions], dtype=tf.float32)
# actions = tf.placeholder(shape=[None], dtype=tf.float32)
# rewards = tf.placeholder(shape=[None], dtype=tf.float32)
# self._build_net_auto(total_layers,layer_size,layer_type,mean_val,std_val,constant_val,activation_type)
@staticmethod
def normalize_weights(x):
"""Compute softmax values for each sets of scores in x."""
"""?!--> ????? This is not SoftMax """
return x / x.sum(axis=0) # only difference
@staticmethod
def next_mini_batch(x_, y_, z_, batch_size):
"""""""?!--> what are these x, y, and z, representative of?"""
"""Create a vector with batch_size quantity of random integers; generate a mini-batch therefrom???."""
permutation = np.random.permutation(x_.shape[0])
permutation = permutation[:batch_size]
x_batch = x_[permutation, :]
y_batch = y_[permutation]
z_batch = z_[permutation]
return x_batch, y_batch, z_batch
# called in initializer
def _build_net(self):
"""
tf.name_scope is a context manager for defining Python operations
tf.placeholder returns a `Tensor` that may be used as a handle for feeding a value, but not evaluated directly.
"""
with tf.name_scope('inputs'):
self.tf_observations = \
tf.placeholder(
dtype=tf.float32,
shape=[None, self.n_features],
name="observations"
)
self.tf_observations_2 = \
tf.placeholder(
dtype=tf.float32,
shape=[None, self.n_features],
name="observations2"
)
self.tf_action_number = \
tf.placeholder(
dtype=tf.int32,
shape=[None, ],
name="actions_num"
)
self.tf_vt = \
tf.placeholder(
dtype=tf.float32,
shape=[None, ],
name=None
)
"""
tf.layers.dense -
Description:
Functional interface for the densely-connected layer that implements the operation:
activation(inputs * kernel + bias)
where activation is the activation function passed as the activation argument (if not None), kernel is a
weights matrix created by the layer, and bias is a bias vector created by the layer (only if use_bias is
True).
Inputs:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix. If None (default), weights are
initialized using the default initializer used by tf.get_variable.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the kernel after being updated by an
Optimizer (e.g. used to implement norm constraints or value constraints for layer
weights). The function must take as input the unprojected variable and must return
the projected variable (which must have the same shape). Constraints are not safe to
use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the bias after being updated by an
Optimizer.
trainable: Boolean, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer by the same name.
Output:
tensor the same shape as inputs except the last dimension is of size units
"""
# https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/7_Policy_gradient_softmax/RL_brain.py
# --> Forward Connected Layer 1
self.layer = tf.layers.dense(
inputs=self.tf_observations,
units=50,
activation=None, # tf.nn.relu, # tanh activation
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
# --> Forward Connected Layer 2
layer2 = tf.layers.dense(
inputs=self.layer,
units=25,
activation=tf.nn.relu, # tf.nn.relu, # tanh activation
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
# --> Forward Connected Layer 3
layer3 = tf.layers.dense(
inputs=layer2,
units=15,
activation=tf.nn.sigmoid, # tf.nn.relu, # tanh activation
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
# --> Forward Connected Layer 4
self.all_act = tf.layers.dense(
inputs=layer3,
units=self.n_actions,
activation=tf.nn.relu,
use_bias=True,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=.1),
bias_initializer=tf.constant_initializer(1),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=None
)
"""
tf.nn.softmax -
Aliases:
tf.math.softmax
tf.nn.softmax
Description:
Computes softmax activations.
This function performs the equivalent of softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis)
Args:
logits: A non-empty Tensor. Must be one of the following types: half, float32, float64.
axis: The dimension softmax would be performed on. The default is -1 which indicates the last dimension.
name: A name for the operation (optional).
dim: Deprecated alias for axis.
Returns:
A Tensor. Has the same type and shape as logits.
Raises:
InvalidArgumentError: if logits is empty or axis is beyond the last dimension of logits.
"""
# use SoftMax to convert to probability
self.action_probabilities = tf.nn.softmax(logits=self.all_act, name="action_probabilities")
with tf.name_scope('loss'):
one_hot_tensor = \
tf.one_hot(
indices=self.tf_action_number,
depth=self.n_actions,
on_value=None,
off_value=None,
axis=None,
dtype=None,
name="one_hot_tensor"
)
neg_logarithm_action_probabilities = \
-tf.log(
x=self.action_probabilities,
name="negative_log_action_probabilities"
)
"""
tf.math.reduce_sum
Aliases:
tf.math.reduce_sum
tf.reduce_sum
Description:
Computes the sum of elements across dimensions of a tensor. (deprecated arguments)
Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the
tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are
retained with length 1.
If axis is None, all dimensions are reduced, and a tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range
[-rank(input_tensor), rank(input_tensor)).
keepdims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.
keep_dims: Deprecated alias for keepdims.
Returns:
The reduced tensor, of the same dtype as the input_tensor.
"""
self.neg_log_prob = \
tf.reduce_sum(
input_tensor=neg_logarithm_action_probabilities * one_hot_tensor,
axis=1,
name="reduce_sum",
reduction_indices=None
)
# Reward guided loss
self.loss = \
tf.reduce_mean(
input_tensor=self.neg_log_prob * self.tf_vt,
axis=None,
name="reduce_mean",
reduction_indices=None
)
print("Why is there a print command here, and why print help?")
with tf.name_scope('train'):
self.train_op = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
"""
tf.session.run
Descriptions:
Runs operations and evaluates tensors in fetches.
This method runs one "step" of TensorFlow computation, by running the necessary graph fragment to execute
every Operation and evaluate every Tensor in fetches, substituting the values in feed_dict for the
corresponding input values.
Args:
fetches: A single graph element, a list of graph elements, or a dictionary whose values are graph elements
or lists of graph elements (described above).
feed_dict: A dictionary that maps graph elements to values (described above).
options: A [RunOptions] protocol buffer
run_metadata: A [RunMetadata] protocol buffer
Returns:
Either a single value if fetches is a single graph element, or a list of values if fetches is a list, or a
dictionary with the same keys as fetches if that is a dictionary (described above). Order in which fetches
operations are evaluated inside the call is undefined.
***The fetches argument may be a single graph element, or an arbitrarily nested list, tuple, namedtuple, dict,
or OrderedDict containing graph elements at its leaves. A graph element can be one of the following types:
An tf.Operation. The corresponding fetched value will be None.
A tf.Tensor. The corresponding fetched value will be a numpy ndarray containing the value of that tensor
A tf.SparseTensor. The corresponding fetched value will be a tf.SparseTensorValue containing the value
of that sparse tensor.
A get_tensor_handle op. The corresponding fetched value will be a numpy ndarray containing the handle of
that tensor.
A string which is the name of a tensor or operation in the graph.
The value returned by run() has the same shape as the fetches argument, where the leaves are replaced by the
corresponding values returned by TensorFlow.
The optional feed_dict argument allows the caller to override the value of tensors in the graph. Each key in
feed_dict can be one of the following types:
If the key is a tf.Tensor, the value may be a Python scalar, string, list, or numpy ndarray that can
be converted to the same dtype as that tensor. Additionally, if the key is a tf.placeholder, the
shape of the value will be checked for compatibility with the placeholder.
If the key is a tf.SparseTensor, the value should be a tf.SparseTensorValue.
If the key is a nested tuple of Tensors or SparseTensors, the value should be a nested tuple with
the same structure that maps to their corresponding values as above.
Each value in feed_dict must be convertible to a numpy array of the dtype of the corresponding key.
The optional options argument expects a [RunOptions] proto. The options allow controlling the behavior of
this particular step (e.g. turning tracing on).
The optional run_metadata argument expects a [RunMetadata] proto. When appropriate, the non-Tensor output of
this step will be collected there. For example, when users turn on tracing in options, the profiled info
will be collected into this argument and passed back.
"""
def choose_action(self, observation, valid):
prob_weights = \
self.session.run(
fetches=self.action_probabilities,
feed_dict={self.tf_observations: observation},
options=None,
run_metadata=None
)
valid_weights = prob_weights * valid
valid_prob = self.normalize_weights(valid_weights[0])
action = \
np.random.choice(
a=range(prob_weights.shape[1]),
size=None,
replace=True,
p=valid_prob.ravel()
)
return action
def choose_action2(self, observation):
prob_weights = \
self.session.run(
fetches=self.action_probabilities,
feed_dict={self.tf_observations: observation},
options=None,
run_metadata=None
)
action = \
np.random.choice(
a=range(prob_weights.shape[1]),
size=None,
replace=True,
p=prob_weights.ravel()
)
return action
def store_transition(self, state, action, reward):
self.episode_observation.append(state)
self.episode_actions.append(action)
self.episode_rewards.append(reward)
def store_transition_temp(self, state, action):
self.episode_observation_temp.append(state)
self.episode_actions_temp.append(action)
def store_transition_episode(self, reward):
ep_as_temp = len(self.episode_actions_temp)
for i in range(0, ep_as_temp):
self.store_transition(
self.episode_observation_temp[i],
self.episode_actions_temp[i],
reward
)
def learn5(self, iteration):
episode_observation = len(self.episode_observation)
self.episode_observation2 = np.array(self.episode_observation).reshape(episode_observation, self.n_features)
discounted_episode_rewards_norm = self._discount_and_norm_rewards()
# print('self.episode_observation2.shape =', self.episode_observation2.shape)
# print ('np.vstack(self.episode_observation2).shape =',np.vstack(self.episode_observation2).shape)
x_batch, y_batch, z_batch = \
self.next_mini_batch(
self.episode_observation2,
np.array(self.episode_actions),
np.array(discounted_episode_rewards_norm),
episode_observation
)
_, loss, log_probabilities, act_val = \
self.session.run(
fetches=[self.train_op, self.loss, self.neg_log_prob, self.all_act],
feed_dict={
self.tf_observations: x_batch, # shape=[None, n_obs]
self.tf_action_number: y_batch, # shape=[None, ]
self.tf_vt: z_batch, # shape=[None, ]
},
options=None,
run_metadata=None
)
if iteration % 1 == 0:
self.episode_observation, self.episode_actions, self.episode_rewards = [], [], [] # empty episode data
def _discount_and_norm_rewards(self):
self.gamma, running_add = .99, 0
discounted_episode_rewards = np.zeros_like(self.episode_rewards)
for t in reversed(range(0, len(self.episode_rewards))):
running_add = running_add * self.gamma + self.episode_rewards[t]
discounted_episode_rewards[t] = running_add
discounted_episode_rewards -= np.mean(discounted_episode_rewards)
discounted_episode_rewards /= np.std(discounted_episode_rewards)
return discounted_episode_rewards
def act_nn2(self, resources_edges, resources_bbu):
edge_bbu_sum = resources_edges + resources_bbu
obs = np.array(edge_bbu_sum).reshape(1, self.n_features)
action = self.choose_action2(obs)
self.store_transition_temp(edge_bbu_sum, action)
next_node = self.links[self.node][action]
# l_num = self.link_num[self.node][action]
if resources_edges[self.link_num[self.node][action]] == 0:
action = -1
elif next_node in self.destinations:
if resources_bbu[self.destinations.index(next_node)] == 0:
action = -1
return action
.episode_actions, self.episode_rewards = [], [], [] # empty episode data
def _discount_and_norm_rewards(self):
self.gamma, running_add = .99, 0
discounted_episode_rewards = np.zeros_like(self.episode_rewards)
for t in reversed(range(0, len(self.episode_rewards))):
running_add = running_add * self.gamma + self.episode_rewards[t]
discounted_episode_rewards[t] = running_add
discounted_episode_rewards -= np.mean(discounted_episode_rewards)
discounted_episode_rewards /= np.std(discounted_episode_rewards)
return discounted_episode_rewards
def act_nn2(self, resources_edges, resources_bbu):
edge_bbu_sum = resources_edges + resources_bbu
obs = np.array(edge_bbu_sum).reshape(1, self.n_features)
action = self.choose_action2(obs)
self.store_transition_temp(edge_bbu_sum, action)
next_node = self.links[self.node][action]
# l_num = self.link_num[self.node][action]
if resources_edges[self.link_num[self.node][action]] == 0:
action = -1
elif next_node in self.destinations:
if resources_bbu[self.destinations.index(next_node)] == 0:
action = -1
return action
| [
"Joshua18510"
] | Joshua18510 |
8dc96ae6d44f834bc6be387acb6a7d8ae7d3e972 | a9eed4d7b8d5256af9f33363761683bba32f106f | /apps/organization/migrations/0006_auto_20180620_2140.py | 98e71397529b5d6e0e4d6500af697f01abd731dc | [] | no_license | cannon-liu/mkonline | 12735d4761663ba42fdd6fe781a2658a5db1b383 | 2a1c64c10ae67abe58c1bfcd77c564fd53957067 | refs/heads/master | 2020-03-28T22:19:08.747770 | 2018-09-18T06:17:50 | 2018-09-18T06:17:50 | 149,223,626 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 433 | py | # Generated by Django 2.0.6 on 2018-06-20 21:40
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('organization', '0005_auto_20180620_1655'),
]
operations = [
migrations.AlterField(
model_name='teacher',
name='image',
field=models.ImageField(upload_to='teacher/%Y/%m', verbose_name='教师图片'),
),
]
| [
"woliuliwen@163.com"
] | woliuliwen@163.com |
ddcfeae16d336d94be1b954d6367720b239e0f52 | e4a05ebe836e1a9a768421359c96800aec5421a8 | /films/admin.py | a3a1a9a7811b0d6cb3a88bbf599df7437678d039 | [
"BSD-2-Clause"
] | permissive | xbrln/filmworld | 952d32814554fd8e2cf120fe3afab6395aca96b6 | 5c028c99dd3a2bf843a0b30a692a92a7ad7d573f | refs/heads/master | 2021-05-27T08:51:46.509840 | 2014-06-20T13:21:21 | 2014-06-20T13:21:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 153 | py | from django.contrib import admin
from films.models import Film
from films.models import Director
admin.site.register(Film)
admin.site.register(Director) | [
"jfrancis@tndm.de"
] | jfrancis@tndm.de |
c46e3b1095f43e674cccba5498761b5e0a283dd0 | 0090ce688fb5cdaad854338b307dc64316352eec | /src/data/hfsp_benchmark.py | 4290a6bc91cb5951ccdb1c793918ab0f76474119 | [] | no_license | xujinxue/ShopSchedule | e5479c85307eb66923d3a32b91edf33e566e57ea | 6727a25f931f7a1bb6d7a66195d8ff33d7515cea | refs/heads/main | 2023-09-04T00:35:05.775148 | 2021-11-14T07:10:47 | 2021-11-14T07:10:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,394 | py | instance = {
"real1": """12 9
3 3 1 2 2 2 3 3 2 4 4 5 5 4 6 2 7 3 8 2 9 3
3 3 1 4 2 5 3 4 2 4 3 5 4 4 6 3 7 4 8 5 9 4
3 3 1 6 2 5 3 4 2 4 4 5 2 4 6 3 7 4 8 2 9 5
3 3 1 4 2 3 3 4 2 4 6 5 5 4 6 3 7 6 8 5 9 8
3 3 1 4 2 5 3 3 2 4 3 5 1 4 6 3 7 4 8 6 9 5
3 3 1 6 2 5 3 4 2 4 2 5 3 4 6 4 7 3 8 9 9 5
3 3 1 5 2 2 3 4 2 4 4 5 6 4 6 3 7 4 8 3 9 5
3 3 1 3 2 5 3 4 2 4 7 5 5 4 6 3 7 3 8 6 9 4
3 3 1 2 2 5 3 4 2 4 1 5 2 4 6 7 7 8 8 6 9 5
3 3 1 3 2 6 3 4 2 4 3 5 4 4 6 4 7 8 8 6 9 7
3 3 1 5 2 2 3 4 2 4 3 5 5 4 6 6 7 7 8 6 9 5
3 3 1 6 2 5 3 4 2 4 5 5 4 4 6 3 7 4 8 7 9 5""",
"real2": """12 10
4 3 1 45 2 48 3 50 3 4 35 5 35 6 30 2 7 30 8 35 2 9 25 10 26
4 3 1 45 2 50 3 45 3 4 35 5 36 6 35 2 7 35 8 34 2 9 25 10 30
4 3 1 50 2 45 3 46 3 4 35 5 36 6 36 2 7 31 8 34 2 9 30 10 31
4 3 1 50 2 48 3 48 3 4 34 5 38 6 35 2 7 32 8 33 2 9 27 10 31
4 3 1 45 2 46 3 48 3 4 30 5 35 6 50 2 7 34 8 32 2 9 28 10 31
4 3 1 45 2 45 3 45 3 4 30 5 35 6 50 2 7 33 8 32 2 9 30 10 26
4 3 1 47 2 50 3 47 3 4 31 5 30 6 35 2 7 35 8 31 2 9 29 10 25
4 3 1 50 2 45 3 48 3 4 32 5 30 6 34 2 7 34 8 30 2 9 24 10 27
4 3 1 48 2 46 3 46 3 4 33 5 34 6 30 2 7 34 8 30 2 9 25 10 25
4 3 1 45 2 47 3 47 3 4 33 5 33 6 30 2 7 35 8 34 2 9 32 10 26
4 3 1 46 2 50 3 45 3 4 34 5 30 6 50 2 7 30 8 35 2 9 31 10 25
4 3 1 48 2 50 3 47 3 4 35 5 31 6 35 2 7 32 8 30 2 9 25 10 26
""",
"real3": """14 13
8 2 1 110 2 110 3 3 360 4 360 5 360 1 6 50 1 7 100 1 8 160 3 9 200 10 200 11 200 1 12 50 1 13 100
8 2 1 110 2 110 3 3 360 4 360 5 360 1 6 50 1 7 100 1 8 160 3 9 200 10 200 11 200 1 12 50 1 13 100
8 2 1 110 2 110 3 3 360 4 360 5 360 1 6 50 1 7 100 1 8 160 3 9 200 10 200 11 200 1 12 50 1 13 100
8 2 1 120 2 120 3 3 420 4 420 5 420 1 6 50 1 7 90 1 8 170 3 9 220 10 220 11 220 1 12 70 1 13 110
8 2 1 120 2 120 3 3 420 4 420 5 420 1 6 50 1 7 90 1 8 170 3 9 220 10 220 11 220 1 12 70 1 13 110
8 2 1 140 2 140 3 3 350 4 350 5 350 1 6 50 1 7 120 1 8 170 3 9 150 10 150 11 150 1 12 60 1 13 140
8 2 1 140 2 140 3 3 350 4 350 5 350 1 6 50 1 7 120 1 8 170 3 9 150 10 150 11 150 1 12 60 1 13 140
8 2 1 140 2 140 3 3 350 4 350 5 350 1 6 50 1 7 120 1 8 170 3 9 150 10 150 11 150 1 12 60 1 13 140
8 2 1 140 2 140 3 3 350 4 350 5 350 1 6 50 1 7 120 1 8 170 3 9 150 10 150 11 150 1 12 60 1 13 140
8 2 1 140 2 140 3 3 380 4 380 5 380 1 6 60 1 7 110 1 8 220 3 9 240 10 240 11 240 1 12 70 1 13 160
8 2 1 140 2 140 3 3 380 4 380 5 380 1 6 60 1 7 110 1 8 220 3 9 240 10 240 11 240 1 12 70 1 13 160
8 2 1 140 2 140 3 3 380 4 380 5 380 1 6 60 1 7 110 1 8 220 3 9 240 10 240 11 240 1 12 70 1 13 160
8 2 1 140 2 140 3 3 380 4 380 5 380 1 6 60 1 7 110 1 8 220 3 9 240 10 240 11 240 1 12 70 1 13 160
8 2 1 140 2 140 3 3 380 4 380 5 380 1 6 60 1 7 110 1 8 220 3 9 240 10 240 11 240 1 12 70 1 13 160
""",
"real4": """10 10
5 2 1 4 2 4 2 3 5 4 5 2 5 3 6 5 2 7 1 8 3 2 9 5 10 1
5 2 1 4 2 5 2 3 4 4 1 2 5 5 6 1 2 7 4 8 4 2 9 5 10 3
5 2 1 4 2 1 2 3 1 4 3 2 5 4 6 1 2 7 5 8 5 2 9 4 10 4
5 2 1 1 2 2 2 3 4 4 2 2 5 2 6 3 2 7 1 8 2 2 9 1 10 1
5 2 1 4 2 1 2 3 3 4 3 2 5 2 6 1 2 7 2 8 3 2 9 5 10 1
5 2 1 3 2 5 2 3 1 4 3 2 5 3 6 4 2 7 2 8 2 2 9 1 10 1
5 2 1 2 2 1 2 3 1 4 1 2 5 3 6 4 2 7 5 8 5 2 9 5 10 3
5 2 1 5 2 5 2 3 2 4 2 2 5 1 6 1 2 7 2 8 5 2 9 3 10 2
5 2 1 5 2 3 2 3 2 4 4 2 5 2 6 3 2 7 1 8 3 2 9 4 10 3
5 2 1 3 2 1 2 3 5 4 4 2 5 5 6 3 2 7 2 8 2 2 9 4 10 2 """
}
best_known = {
"real1": 23,
"real2": 297,
"real3": 3570,
"real4": 22,
}
| [
"guangcanyang@yeah.net"
] | guangcanyang@yeah.net |
b5a5ba1c0e24d7354f326079564501e64807c6f8 | 722c299d5ae33b2cca8dffa32ecdde751712f707 | /hway/migrations/0045_province_cardtrans.py | 2a4dba2dc96f8e482ecdf3406b262f01e795bc61 | [] | no_license | PangQian1/html_highway | db6791742fa9893278a0bd1caabdde0f8de6055f | af3c94a765b404ec9a4e0a5dd41355313c031736 | refs/heads/master | 2020-04-30T07:38:45.053413 | 2019-07-24T07:35:53 | 2019-07-24T07:35:53 | 175,984,099 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 448 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.16 on 2018-11-03 07:47
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('hway', '0044_carddaycount'),
]
operations = [
migrations.AddField(
model_name='province',
name='cardtrans',
field=models.BigIntegerField(default=0),
),
]
| [
"1835896410@qq.com"
] | 1835896410@qq.com |
50d3fa769119f65fde8c60106790dd20765218bf | effce116340b7d937bd285e43b49e1ef83d56156 | /data_files/profiler.py | 721d79980232dad6801fb4dd8236482b83610596 | [] | no_license | DL2021Spring/CourseProject | a7c7ef57d69bc1b21e3303e737abb27bee3bd585 | 108cdd906e705e9d4d05640af32d34bfc8b124da | refs/heads/master | 2023-04-11T18:52:30.562103 | 2021-05-18T09:59:59 | 2021-05-18T09:59:59 | 365,733,976 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 353 | py | from cProfile import Profile
from pstats import Stats
__author__ = 'Daniel'
def demo():
f = lambda x: x
profiler = Profile()
profiler.runcall(f)
stats = Stats(profiler)
stats.strip_dirs()
stats.sort_stats('cumulative')
stats.print_stats()
stats.print_callers()
stats.print_callees()
| [
"1042448815@qq.com"
] | 1042448815@qq.com |
6a2a9c90259d6ec36841ae22fe3df2dca560ef82 | 4de6aade1bbee2c7b01d3b36b0e85f0d9223307b | /crawl.py | 691aa412d6948a03533a42a1051fdb956a76647e | [] | no_license | lukeputz92/pyWebCrawler | 5e6af692e9d791b42d10ce4bea0e773fe9b8d016 | 9d9f4172a0c40ea3cf7f31245835407d9cf18029 | refs/heads/master | 2020-04-23T00:34:29.685586 | 2019-02-15T01:39:59 | 2019-02-15T01:39:59 | 170,784,416 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,043 | py | '''
Author: Luke Putz
Title: Email Spider
Date: February 15, 2017
Description: Crawls a website to find em
stores them in an sqlite3 DB
'''
import os, sys, re, sqlite3, urllib.request, unicodedata
def remove_duplicates(line):
seen = set()
result = []
for item in line:
if item not in seen:
seen.add(item)
result.append(item)
return result
def create_table(cur):
cur.execute('''CREATE TABLE IF NOT EXISTS Directory
(ID INTEGER PRIMARY KEY,
FIRST TEXT,
LAST TEXT,
EMAIL TEXT);''')
print ("Table created successfully")
def insert(conn, cur, result, first, last):
for i in range ( len (first) ):
email = result[i]
fname = first[i]
lname = last[i]
cur.execute('INSERT INTO Directory (ID, FIRST, LAST, EMAIL) VALUES (Null, ?, ?, ?)', (fname, lname, email) )
conn.commit()
def display(cur):
for row in cur:
print ("ID = ", row[0])
print ("FNAME = ", row[1])
print ("LNAME = ", row[2])
print ("EMAIL = ", row[3], "\n")
def main():
if len(sys.argv) > 1:
with urllib.request.urlopen('https://www.ohio.edu/engineering/about/people/') as ins:
text = ins.read().decode('utf-8')
new_emails = re.findall('[a-z0-9\.\-+_]+@ohio.edu', text, re.I)
text = unicodedata.normalize('NFKD', text) #normalize the text
names = re.findall('(?:profile=(?:.*">))(.*)\s([a-z].*)(?:<\/a>)', text, re.I)
first, last = zip(*names) #split the list of tuples
first = list(first)
last = list(last)
#remove duplicates
result = remove_duplicates(new_emails)
try:
conn = sqlite3.connect(sys.argv[1])
cur = conn.cursor()
create_table(cur)
insert(conn, cur, result, first, last)
cur = conn.execute("SELECT ID, FIRST, LAST, EMAIL from Directory")
display(cur)
except sqlite3.Error as e:
print("An error occurred:", e.args[0])
else:
print('Usage: thisprogram.py database.db')
print(sys.argv[0])
exit(1)
if __name__ == '__main__':
main()
| [
"putzey@gmail.com"
] | putzey@gmail.com |
0ee9e858bbfb7ab9d20091f167f95e73f65c3ca0 | bd7a94697fdfa45bb67f5403e7fca44b71fbfd15 | /1_alimentation/3_agregeResultats.py | 5e8040e3ff8b9dab6b41a9695aa93732d3765339 | [] | no_license | cwamgis/VizElections | eee8ce2facf9e658bdd2ed680bf620452a62cc3c | 4370446ab9f98a4361f79fa53115b55fb61d9c9c | refs/heads/master | 2021-01-22T12:03:02.889810 | 2015-02-15T15:11:39 | 2015-02-15T15:11:39 | 30,079,919 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,655 | py | #!/usr/bin/python
# script permettant d agreger les donnees pour la consultation des resultats par arrondissement et scrutin
import pymongo
import yaml
import json
import time
# verif ok pour nbExprimes
print "debut agreg resultats des votes : "+ time.strftime('%d/%m/%y %H:%M',time.localtime())
# connexion a la base
db = pymongo.MongoClient().electionsdb
# Recuperation du nb d exprimes et du nb d inscrits par bureau de vote
#selection distincte de nbExprimes, nbInscrits pour chaque bureau de vote pour une election donnee
# on prend comme operateur min, peu importe le tout c est de recuperer la valeur pour chaque regroupement
print "agregation..."
agregParBureau = db.electionsBrutes.aggregate([{"$group":{"_id":{"insee":"$insee","nom":"$nom","prenom":"$prenom","libelleScrutin":"$libelleScrutin","dateScrutin":"$dateScrutin"},"nbVotes":{"$sum":"$nbVotes"},"nbExprimes":{"$sum":"$nbExprimes"}}}])
# On vide la table finale
db.electionsFinalesResultats.remove()
for dictionnaire in agregParBureau["result"]:
dictionnaireInsert = {}
dictionnaireInsert["insee"] = dictionnaire["_id"]["insee"]
dictionnaireInsert["nom"] = dictionnaire["_id"]["nom"]
dictionnaireInsert["prenom"] = dictionnaire["_id"]["prenom"]
dictionnaireInsert["libelleScrutin"] = dictionnaire["_id"]["libelleScrutin"]
dictionnaireInsert["dateScrutin"] = dictionnaire["_id"]["dateScrutin"]
dictionnaireInsert["nbExprimes"] = dictionnaire["nbExprimes"]
dictionnaireInsert["nbVotes"] = dictionnaire["nbVotes"]
db.electionsFinalesResultats.insert(yaml.load(json.dumps(dictionnaireInsert)))
print "fin agreg : "+ time.strftime('%d/%m/%y %H:%M',time.localtime())
| [
"cwamgis@hotmail.com"
] | cwamgis@hotmail.com |
0da675a13aa1ad121aeb38b46b3c096b6af733c4 | 055cc6365eb707547cd632371495711a8a5f5d9d | /heatmap.py | b154ef164cf63eed884dafacec6ab6bedb8dc69f | [] | no_license | xtudbxk/rgb_heatmap | 164ef8a181e119ce3ab69f15602e50efc8a223af | 6f1fbf22e91e93e0bc79a92acb0b372d8f2aa0b9 | refs/heads/master | 2020-04-10T12:24:50.343762 | 2018-12-09T09:06:30 | 2018-12-09T09:06:30 | 161,021,864 | 5 | 1 | null | null | null | null | UTF-8 | Python | false | false | 6,850 | py | import sys
import numpy as np
import skimage.io as imgio
import skimage.color as imgco
heatmap_colors = np.array([(0.0, 0.0, 1.0), (0.0, 0.015625, 1.0), (0.0, 0.03125, 1.0), (0.0, 0.046875, 1.0), (0.0, 0.0625, 1.0), (0.0, 0.078125, 1.0), (0.0, 0.09375, 1.0), (0.0, 0.109375, 1.0), (0.0, 0.125, 1.0), (0.0, 0.140625, 1.0),
(0.0, 0.15625, 1.0), (0.0, 0.171875, 1.0), (0.0, 0.1875, 1.0), (0.0, 0.203125, 1.0), (0.0, 0.21875, 1.0), (0.0, 0.234375, 1.0), (0.0, 0.25, 1.0), (0.0, 0.265625, 1.0), (0.0, 0.28125, 1.0), (0.0, 0.296875, 1.0),
(0.0, 0.3125, 1.0), (0.0, 0.328125, 1.0), (0.0, 0.34375, 1.0), (0.0, 0.359375, 1.0), (0.0, 0.375, 1.0), (0.0, 0.390625, 1.0), (0.0, 0.40625, 1.0), (0.0, 0.421875, 1.0), (0.0, 0.4375, 1.0), (0.0, 0.453125, 1.0),
(0.0, 0.46875, 1.0), (0.0, 0.484375, 1.0), (0.0, 0.5, 1.0), (0.0, 0.515625, 1.0), (0.0, 0.53125, 1.0), (0.0, 0.546875, 1.0), (0.0, 0.5625, 1.0), (0.0, 0.578125, 1.0), (0.0, 0.59375, 1.0), (0.0, 0.609375, 1.0),
(0.0, 0.625, 1.0), (0.0, 0.640625, 1.0), (0.0, 0.65625, 1.0), (0.0, 0.671875, 1.0), (0.0, 0.6875, 1.0), (0.0, 0.703125, 1.0), (0.0, 0.71875, 1.0), (0.0, 0.734375, 1.0), (0.0, 0.75, 1.0), (0.0, 0.765625, 1.0),
(0.0, 0.78125, 1.0), (0.0, 0.796875, 1.0), (0.0, 0.8125, 1.0), (0.0, 0.828125, 1.0), (0.0, 0.84375, 1.0), (0.0, 0.859375, 1.0), (0.0, 0.875, 1.0), (0.0, 0.890625, 1.0), (0.0, 0.90625, 1.0), (0.0, 0.921875, 1.0),
(0.0, 0.9375, 1.0), (0.0, 0.953125, 1.0), (0.0, 0.96875, 1.0), (0.0, 0.984375, 1.0), (0.0, 1.0, 1.0), (0.0, 1.0, 0.984375), (0.0, 1.0, 0.96875), (0.0, 1.0, 0.953125), (0.0, 1.0, 0.9375), (0.0, 1.0, 0.921875),
(0.0, 1.0, 0.90625), (0.0, 1.0, 0.890625), (0.0, 1.0, 0.875), (0.0, 1.0, 0.859375), (0.0, 1.0, 0.84375), (0.0, 1.0, 0.828125), (0.0, 1.0, 0.8125), (0.0, 1.0, 0.796875), (0.0, 1.0, 0.78125), (0.0, 1.0, 0.765625),
(0.0, 1.0, 0.75), (0.0, 1.0, 0.734375), (0.0, 1.0, 0.71875), (0.0, 1.0, 0.703125), (0.0, 1.0, 0.6875), (0.0, 1.0, 0.671875), (0.0, 1.0, 0.65625), (0.0, 1.0, 0.640625), (0.0, 1.0, 0.625), (0.0, 1.0, 0.609375),
(0.0, 1.0, 0.59375), (0.0, 1.0, 0.578125), (0.0, 1.0, 0.5625), (0.0, 1.0, 0.546875), (0.0, 1.0, 0.53125), (0.0, 1.0, 0.515625), (0.0, 1.0, 0.5), (0.0, 1.0, 0.484375), (0.0, 1.0, 0.46875), (0.0, 1.0, 0.453125),
(0.0, 1.0, 0.4375), (0.0, 1.0, 0.421875), (0.0, 1.0, 0.40625), (0.0, 1.0, 0.390625), (0.0, 1.0, 0.375), (0.0, 1.0, 0.359375), (0.0, 1.0, 0.34375), (0.0, 1.0, 0.328125), (0.0, 1.0, 0.3125), (0.0, 1.0, 0.296875),
(0.0, 1.0, 0.28125), (0.0, 1.0, 0.265625), (0.0, 1.0, 0.25), (0.0, 1.0, 0.234375), (0.0, 1.0, 0.21875), (0.0, 1.0, 0.203125), (0.0, 1.0, 0.1875), (0.0, 1.0, 0.171875), (0.0, 1.0, 0.15625), (0.0, 1.0, 0.140625),
(0.0, 1.0, 0.125), (0.0, 1.0, 0.109375), (0.0, 1.0, 0.09375), (0.0, 1.0, 0.078125), (0.0, 1.0, 0.0625), (0.0, 1.0, 0.046875), (0.0, 1.0, 0.03125), (0.0, 1.0, 0.015625), (0.0, 1.0, 0.0), (0.015625, 1.0, 0.0),
(0.03125, 1.0, 0.0), (0.046875, 1.0, 0.0), (0.0625, 1.0, 0.0), (0.078125, 1.0, 0.0), (0.09375, 1.0, 0.0), (0.109375, 1.0, 0.0), (0.125, 1.0, 0.0), (0.140625, 1.0, 0.0), (0.15625, 1.0, 0.0), (0.171875, 1.0, 0.0),
(0.1875, 1.0, 0.0), (0.203125, 1.0, 0.0), (0.21875, 1.0, 0.0), (0.234375, 1.0, 0.0), (0.25, 1.0, 0.0), (0.265625, 1.0, 0.0), (0.28125, 1.0, 0.0), (0.296875, 1.0, 0.0), (0.3125, 1.0, 0.0), (0.328125, 1.0, 0.0),
(0.34375, 1.0, 0.0), (0.359375, 1.0, 0.0), (0.375, 1.0, 0.0), (0.390625, 1.0, 0.0), (0.40625, 1.0, 0.0), (0.421875, 1.0, 0.0), (0.4375, 1.0, 0.0), (0.453125, 1.0, 0.0), (0.46875, 1.0, 0.0), (0.484375, 1.0, 0.0),
(0.5, 1.0, 0.0), (0.515625, 1.0, 0.0), (0.53125, 1.0, 0.0), (0.546875, 1.0, 0.0), (0.5625, 1.0, 0.0), (0.578125, 1.0, 0.0), (0.59375, 1.0, 0.0), (0.609375, 1.0, 0.0), (0.625, 1.0, 0.0), (0.640625, 1.0, 0.0),
(0.65625, 1.0, 0.0), (0.671875, 1.0, 0.0), (0.6875, 1.0, 0.0), (0.703125, 1.0, 0.0), (0.71875, 1.0, 0.0), (0.734375, 1.0, 0.0), (0.75, 1.0, 0.0), (0.765625, 1.0, 0.0), (0.78125, 1.0, 0.0), (0.796875, 1.0, 0.0),
(0.8125, 1.0, 0.0), (0.828125, 1.0, 0.0), (0.84375, 1.0, 0.0), (0.859375, 1.0, 0.0), (0.875, 1.0, 0.0), (0.890625, 1.0, 0.0), (0.90625, 1.0, 0.0), (0.921875, 1.0, 0.0), (0.9375, 1.0, 0.0), (0.953125, 1.0, 0.0),
(0.96875, 1.0, 0.0), (0.984375, 1.0, 0.0), (1.0, 1.0, 0.0), (1.0, 0.984375, 0.0), (1.0, 0.96875, 0.0), (1.0, 0.953125, 0.0), (1.0, 0.9375, 0.0), (1.0, 0.921875, 0.0), (1.0, 0.90625, 0.0), (1.0, 0.890625, 0.0),
(1.0, 0.875, 0.0), (1.0, 0.859375, 0.0), (1.0, 0.84375, 0.0), (1.0, 0.828125, 0.0), (1.0, 0.8125, 0.0), (1.0, 0.796875, 0.0), (1.0, 0.78125, 0.0), (1.0, 0.765625, 0.0), (1.0, 0.75, 0.0), (1.0, 0.734375, 0.0),
(1.0, 0.71875, 0.0), (1.0, 0.703125, 0.0), (1.0, 0.6875, 0.0), (1.0, 0.671875, 0.0), (1.0, 0.65625, 0.0), (1.0, 0.640625, 0.0), (1.0, 0.625, 0.0), (1.0, 0.609375, 0.0), (1.0, 0.59375, 0.0), (1.0, 0.578125, 0.0),
(1.0, 0.5625, 0.0), (1.0, 0.546875, 0.0), (1.0, 0.53125, 0.0), (1.0, 0.515625, 0.0), (1.0, 0.5, 0.0), (1.0, 0.484375, 0.0), (1.0, 0.46875, 0.0), (1.0, 0.453125, 0.0), (1.0, 0.4375, 0.0), (1.0, 0.421875, 0.0),
(1.0, 0.40625, 0.0), (1.0, 0.390625, 0.0), (1.0, 0.375, 0.0), (1.0, 0.359375, 0.0), (1.0, 0.34375, 0.0), (1.0, 0.328125, 0.0), (1.0, 0.3125, 0.0), (1.0, 0.296875, 0.0), (1.0, 0.28125, 0.0), (1.0, 0.265625, 0.0),
(1.0, 0.25, 0.0), (1.0, 0.234375, 0.0), (1.0, 0.21875, 0.0), (1.0, 0.203125, 0.0), (1.0, 0.1875, 0.0), (1.0, 0.171875, 0.0), (1.0, 0.15625, 0.0), (1.0, 0.140625, 0.0), (1.0, 0.125, 0.0), (1.0, 0.109375, 0.0),
(1.0, 0.09375, 0.0), (1.0, 0.078125, 0.0), (1.0, 0.0625, 0.0), (1.0, 0.046875, 0.0), (1.0, 0.03125, 0.0), (1.0, 0.015625, 0.0)])
def heatmap_with_color(heatmap): # shape: [h,w], value range [0,255]
if np.max(heatmap) <= 1.0:
heatmap = (255*heatmap).astype(np.uint8)
colors = heatmap_colors[np.unique(heatmap),:]
heatmap = imgco.label2rgb(heatmap,colors=colors)
return 255*heatmap
def heatmap_with_img(img,heatmap):
heatmap_max = np.max(heatmap)
heatmap_min = np.min(heatmap)
heatmap = (heatmap-heatmap_min)/(heatmap_max-heatmap_min)
heatmap_rgb = heatmap_with_color((255*heatmap).astype(np.uint8))
if len(img.shape) >= 3:
mixed = img*np.expand_dims(1-heatmap,axis=2)+heatmap_rgb*np.expand_dims(heatmap,axis=2)
else:
mixed = img*(1-heatmap)+heatmap_rgb*heatmap
return mixed.astype(np.uint8)
if __name__ == "__main__":
img = imgio.imread(sys.argv[1])
heatmap = imgio.imread(sys.argv[2])
heatmap1 = heatmap_with_color(heatmap)
heatmap2 = heatmap_with_img(img,heatmap)
imgio.imsave("%s/heatmap1.png"%sys.argv[3],heatmap1/255.0)
imgio.imsave("%s/heatmap2.png"%sys.argv[3],heatmap2/255.0)
| [
"xtudbxk@126.com"
] | xtudbxk@126.com |
67300f0f24df2ae400ac70a96ee0e52d1ed68647 | 2250a980f4b950461651134a7c85ad8b233cf6eb | /calc/urls.py | f812afbbe139710275917c3ab686508ec689204d | [] | no_license | rishikesh-web/Pythonproj | be2715d800733729fed6e9e8c02f7016ea045ff0 | 6be2b77a154061c2e6f727689dcbfaef2b466b16 | refs/heads/master | 2022-04-28T20:14:57.274389 | 2020-04-29T19:05:20 | 2020-04-29T19:05:20 | 259,711,365 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 152 | py | from django.urls import path
from . import views
urlpatterns = [
path('',views.home,name='home'),
path('add',views.add,name='add')
] | [
"54280231+rishikesh-web@users.noreply.github.com"
] | 54280231+rishikesh-web@users.noreply.github.com |
d691222353c6667ef62e2b41b2050b9b117c83ab | 63f6fb16206d95ef054392eaa53102cd7a9c93c7 | /Fermat.py | 77040068eb90b85a111f30e4eb0e55a0b1d8f663 | [] | no_license | akmalone/IntegerFactorisation | 8b516d438c7aaaa92779aa216fa343ca8343b4c8 | 26826d6dcc0da6c44a03f66704c6b4508f706990 | refs/heads/master | 2021-01-20T07:36:39.115467 | 2017-05-02T10:26:43 | 2017-05-02T10:26:43 | 90,017,226 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,713 | py | import math
import numpy as np
import matplotlib.pyplot as plt
def Fermat(n):
x = math.ceil(math.sqrt(n))
a = 0
if n%2==0:
return 2,n/2,a
while True:
y = math.sqrt(math.pow(x,2)-n)
a=a+1
if y==int(y):
p = x-y
q = x+y
return p,q,a
else:
x=x+1
N = []
ferm = []
Np = []
fermp = []
for n in range (3,5001,2):
p,q,a = Fermat(n)
if p==1:
Np.append(n)
fermp.append(a)
else:
N.append(n)
ferm.append(a)
plt.title("Fermat's Method")
plt.ylabel('Number of Iterations')
plt.xlabel('Value of N')
plt.plot(Np, fermp, 'k^', label='Prime Numbers')
plt.plot(N, ferm, 'w^', label='Composite Numbers')
plt.legend(loc=0, borderaxespad=0.)
plt.savefig('Fresult1.png',bbox_inches='tight')
plt.clf()
plt.title("Fermat's Method without primes")
plt.ylabel('Number of Iterations')
plt.xlabel('Value of N')
plt.plot(N, ferm, 'w^')
plt.plot(N, (3.0+np.asarray(N)/3.0)/2.0-np.sqrt(N), lw=2, c='red', label='p=3')
plt.plot(N, (5.0+np.asarray(N)/5.0)/2.0-np.sqrt(N), lw=2, c='orange', label='p=5')
plt.plot(N, (7.0+np.asarray(N)/7.0)/2.0-np.sqrt(N), lw=2, c='yellow', label='p=7')
plt.plot(N, (9.0+np.asarray(N)/9.0)/2.0-np.sqrt(N), lw=2, c='green', label='p=9')
plt.plot(N, (11.0+np.asarray(N)/11.0)/2.0-np.sqrt(N), lw=2, c='blue', label='p=11')
plt.plot(N, (13.0+np.asarray(N)/13.0)/2.0-np.sqrt(N), lw=2, c='indigo', label='p=13')
plt.plot(N, (15.0+np.asarray(N)/15.0)/2.0-np.sqrt(N), lw=2, c='violet', label='p=15')
plt.legend(loc=0, borderaxespad=0.)
plt.savefig('Fresult2.png',bbox_inches='tight')
plt.show()
| [
"noreply@github.com"
] | akmalone.noreply@github.com |
7999a913e722d8bffa82b7a7a4091385285620d2 | 097977a495b37339adc3ed76beb0c79885e86c99 | /scripts/c10_s8.py | 8db437d2ea15cc291694d9dfb8e03042e6a9f307 | [] | no_license | prvieiramatos/biopython | 0b9dd2f2a86221e1e69283d9be5b7e54d4b8b7b7 | c20b5512ef87a4f5243b522eee9e92853ad1bafd | refs/heads/master | 2023-05-06T16:14:08.086295 | 2015-09-17T12:50:02 | 2015-09-17T12:50:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 92 | py | from Bio import SeqIO
SeqIO.convert("NC_009934.gbk", "genbank", "NC_009934.fasta", "fasta") | [
"diogohenks@hotmail.com"
] | diogohenks@hotmail.com |
73b90647a7825fae9a69d6c4e3b7af28ad834442 | 2ff4b0b14818443eedff7ad0ba3a1ff4d46cb449 | /chainlink/tests/test_vrf.py | 25a585297218833b027c0763986a4e813c792814 | [
"MIT"
] | permissive | charry1729/ETH-DeFi-Testing-Contracts | f94ad97e98df3da3c47041cdfe47b88261bae067 | 146362801428b8a595f19428d081dd70865d8c00 | refs/heads/main | 2023-06-12T04:51:11.044402 | 2021-07-02T15:32:44 | 2021-07-02T15:32:44 | 387,858,502 | 1 | 0 | MIT | 2021-07-20T16:56:18 | 2021-07-20T16:56:18 | null | UTF-8 | Python | false | false | 2,590 | py | import time
import pytest
from brownie import VRFConsumer, convert, network
from scripts.helpful_scripts import (
get_account,
LOCAL_BLOCKCHAIN_ENVIRONMENTS,
)
def test_can_request_random_number(
get_vrf_coordinator, get_keyhash, get_link_token, chainlink_fee
):
# Arrange
vrf_consumer = VRFConsumer.deploy(
get_keyhash,
get_vrf_coordinator.address,
get_link_token.address,
chainlink_fee,
{"from": get_account()},
)
get_link_token.transfer(
vrf_consumer.address, chainlink_fee * 3, {"from": get_account()}
)
# Act
requestId = vrf_consumer.getRandomNumber.call({"from": get_account()})
assert isinstance(requestId, convert.datatypes.HexString)
def test_returns_random_number_local(
get_vrf_coordinator, get_keyhash, get_link_token, chainlink_fee
):
# Arrange
if network.show_active() not in LOCAL_BLOCKCHAIN_ENVIRONMENTS:
pytest.skip("Only for local testing")
vrf_consumer = VRFConsumer.deploy(
get_keyhash,
get_vrf_coordinator.address,
get_link_token.address,
chainlink_fee,
{"from": get_account()},
)
get_link_token.transfer(
vrf_consumer.address, chainlink_fee * 3, {"from": get_account()}
)
# Act
transaction_receipt = vrf_consumer.getRandomNumber({"from": get_account()})
requestId = vrf_consumer.getRandomNumber.call({"from": get_account()})
assert isinstance(transaction_receipt.txid, str)
get_vrf_coordinator.callBackWithRandomness(
requestId, 777, vrf_consumer.address, {"from": get_account()}
)
# Assert
assert vrf_consumer.randomResult() > 0
assert isinstance(vrf_consumer.randomResult(), int)
def test_returns_random_number_testnet(
get_vrf_coordinator,
get_keyhash,
get_link_token,
chainlink_fee,
):
# Arrange
if network.show_active() not in ["kovan", "rinkeby", "ropsten"]:
pytest.skip("Only for testnet testing")
vrf_consumer = VRFConsumer.deploy(
get_keyhash,
get_vrf_coordinator.address,
get_link_token.address,
chainlink_fee,
{"from": get_account()},
)
get_link_token.transfer(
vrf_consumer.address, chainlink_fee * 3, {"from": get_account()}
)
# Act
transaction_receipt = vrf_consumer.getRandomNumber({"from": get_account()})
assert isinstance(transaction_receipt.txid, str)
transaction_receipt.wait(1)
time.sleep(35)
# Assert
assert vrf_consumer.randomResult() > 0
assert isinstance(vrf_consumer.randomResult(), int)
| [
"gberkinfand@gmail.com"
] | gberkinfand@gmail.com |
f8275baf4b7fd32b3bd3239b0df271f161895f18 | b1fc7bfcc82fa633ea9b26a1b08e617af6e6f983 | /turtle_polygon.py | 28bc53488e224643797643b253b9f4672c3e5000 | [] | no_license | teodorlu/turtle-polygon | b3a708a1d36649310bd034f7968cebe5a38c474f | 0d9400e212cbfb818cdb04dff2ecf665b87417e8 | refs/heads/master | 2021-01-10T02:58:56.172121 | 2016-03-01T16:16:25 | 2016-03-01T16:16:25 | 52,891,398 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,100 | py | from turtle import *
import math
def draw_poly(polygon):
penup()
for line in polygon:
goto(line)
pendown()
penup()
def draw_letter(polygons):
for poly in polygons:
draw_poly(poly)
def map_letter(f, letter):
return map(lambda poly: map(f, poly), letter)
def ellipse(rx, ry, start=0, end=360, x0=0, y0=0):
ps = []
for part in range(start, end+1, 10):
theta = part/360 * 2*math.pi
x = x0 + rx*math.cos(theta)
y = y0 + ry*math.sin(theta)
ps.append((x,y))
return ps
def safe_lookup_char(c):
if c in alphabet:
return alphabet[c]
else:
return []
def rasterize_string(s):
x = 0
y = 0
dx = 100
polygons = []
for c in s:
letter = translate_letter(safe_lookup_char(c), (x, y))
x += 100
for line in letter:
yield line
def concat(xss):
for xs in xss:
for x in xs:
yield x
def translate_letter(letter, delta):
def translator(tup):
(x, y) = tup
(dx, dy) = delta
return (x + dx, y+dy)
return map_letter(translator, letter)
alphabet = {
't': [[(0, 100), (80,100)], [(40, 0), (40,100)]]
, 'e': [[(0,0), (0,100), (80,100)], [(0, 50), (60, 50)], [(0,0), (80, 0)]]
, 'o': [ellipse(40,50, x0=40, y0=50)]
, 'd': [ellipse(50, 50, start=-90, end=90, x0=0, y0=50), [(0, 0), (0, 100)]]
, 'r': [[(0,0), (0,100)], ellipse(40, 20, -90, 90, 0, 80), [(0, 60), (50, 0)]]
, ' ': []
, 'k': [[(0,0), (0,100)], [(80,100), (0, 50), (80,0)]]
, 'u': [[(0,100), (0,50)], ellipse(40, 50, -180, 0, 40, 50), [(80,50), (80,100)]]
, 'l': [[(0,100), (0,0), (80,0)]]
}
def scale(tup):
factor = .2
x, y = tup
return (factor*x, factor*y)
def italics(tup):
tantheta = .3
x, y = tup
return (x + y * tantheta, y)
def compose(*args):
def g(x):
for f in args:
x = f(x)
return x
return g
def translate(x, y):
def translate_mod(tup):
x0, y0 = tup
return (x0+x, y0+y)
return translate_mod
def plus1(x):
return x + 1
def main():
transform = compose(translate(-800, 0), scale, italics)
teodor = map_letter(transform, rasterize_string("teodor er kul"))
# perry = map_letter(scale, )
draw_letter(teodor)
done()
if __name__ == '__main__':
main()
| [
"teodor.heggelund@gmail.com"
] | teodor.heggelund@gmail.com |
16c598bd448e1f93457575568377174b4fb08f01 | 9ddb3f463f1efcc4b6b25beee2dfcf1804b379cf | /accounts/tests/test_authentication_backends.py | b8ac246f66e7cf3a19fb01f88fb89813fa1ed1e0 | [
"BSD-3-Clause"
] | permissive | risha700/django-accounts | 798173d3bc1b574bee06ba0dbe0f4aa8304a7d05 | 161fe35fe55c94c33812bbf3c1da7beb59a96524 | refs/heads/master | 2023-08-04T08:01:03.471029 | 2021-09-21T09:36:10 | 2021-09-21T09:36:10 | 317,861,119 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,775 | py | import os
from django.contrib.auth import get_user_model
from django.test import Client, RequestFactory
from django.http import HttpRequest
from accounts.authentication import PhoneAuthBackend, UsernameAuthBackend,EmailAuthBackend
from .utils import AccountsTestCase
User = get_user_model()
class TestAuthBackends(AccountsTestCase):
def setUp(self) -> None:
self.client = Client(enforce_csrf_checks=False)
self.user = self.initialize_user(is_active=False)
self.request = RequestFactory()
def test_username_backend(self):
with self.settings(AUTHENTICATION_BACKENDS=['accounts.authentication.UsernameAuthBackend']):
request = HttpRequest()
self.assertIsNotNone(
UsernameAuthBackend.authenticate(self, request, username='test_user', password='rrrr')
)
self.assertIsNone(
UsernameAuthBackend.authenticate(self, request, username='test_use', password='rrrr')
)
self.assertIsNone(UsernameAuthBackend.get_user(request, None))
self.assertIsNone(
UsernameAuthBackend.authenticate(self, request, username='test_user', password='rrr')
)
def test_phone_backend(self):
with self.settings(AUTHENTICATION_BACKENDS=['accounts.authentication.PhoneAuthBackend']):
request = HttpRequest()
self.assertIsNotNone(
PhoneAuthBackend.authenticate(self, request, username='+16469061833', password='rrrr')
)
self.assertIsNone(
PhoneAuthBackend.authenticate(self, request, username='+16469061834', password='rrrr')
)
self.assertIsNone(PhoneAuthBackend.get_user(request, None))
self.assertIsNone(
PhoneAuthBackend.authenticate(self, request, username='+16469061833', password='rrr')
)
def test_email_backend(self):
with self.settings(AUTHENTICATION_BACKENDS=['accounts.authentication.EmailAuthBackend']):
request = HttpRequest()
self.assertIsNotNone(
EmailAuthBackend.authenticate(self, request,
username='testuser@test.com', password='rrrr')
)
self.assertIsNone(
EmailAuthBackend.authenticate(self, request,
username='testuer@test.com', password='rrrr')
)
self.assertIsNone(EmailAuthBackend.get_user(request, None))
self.assertIsNone(
EmailAuthBackend.authenticate(self, request,
username='testuser@test.com', password='rrr')
)
| [
"ahbox@outlook.com"
] | ahbox@outlook.com |
045e91eefbb6784e11a0d581027f7438c82d7ee4 | 211874c8c72ad0ff1e4d30b29f2e179161a36195 | /lingvo/tasks/milan/params/dual_encoder_recipe.py | 34d43f560fae603a1f930703f68e0a0e586a149f | [
"Apache-2.0"
] | permissive | sailfish009/lingvo | d3308260d2365477e38c4b1b61bdaa4405172b1e | 432e1b0918459c28fcfbed0e6d1a2f48a962a80f | refs/heads/master | 2023-04-19T03:15:51.420821 | 2021-04-27T22:52:45 | 2021-04-27T22:53:38 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,743 | py | # Lint as: python3
# Copyright 2021 The TensorFlow Authors. 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.
# ==============================================================================
"""Helpers for defining Milan dual-encoder models."""
import functools
from lingvo.core import base_model_params
from lingvo.core import layers as lingvo_layers
from lingvo.core import optimizer
from lingvo.core import schedule
from lingvo.tasks.milan import constants
from lingvo.tasks.milan import dataset_spec
from lingvo.tasks.milan import dual_encoder
from lingvo.tasks.milan import input_generator
class RecipeError(Exception):
pass
class DualEncoderRecipe(base_model_params.SingleTaskModelParams):
"""Base class that simplifies configuration of Milan dual encoder models.
`DualEncoderRecipe` is a `SingleTaskModelParams` with extra builder-like
methods for configuring the dual encoder (the `Task()` params) and input
generators (`Train()`, `Dev()`, `Test()`).
In typical usage, model definitions subclass `DualEncoderRecipe`, call helper
methods in the constructor to configure the dual encoder, and specify a
`default_dataset` for the model to run on. For example::
@model_registry.RegisterSingleTaskModel
class MyExperiment(DualEncoderRecipe):
def __init__(self):
super().__init__()
self.AddModality(
'TEXT',
input_feature='text_feature',
id_feature='text_id',
encoder=MyTextEncoder.Params(),
encoder_output_dim=42)
# Preprocess the raw 'image_feature' input prior to encoding.
self.AddPreprocessor('image_feature', ImagePreprocessor.Params())
self.AddModality(
'IMAGE',
input_feature='image_feature',
id_feature='image_id',
encoder=MyImageEncoder.Params(),
encoder_output_dim=67)
@property
def default_dataset(self) -> DatasetSpec:
# Point to your dataset of choice
...
"""
def __init__(self):
# Define these members here to make pytype happy.
self.dataset = None
self.input_params = None
self.task_params = None
self.dataset = self._ChooseDatasetSpec()
# Base input params, be shared by both train and eval sets.
self.input_params = input_generator.MilanInputGenerator.Params().Set(
batch_size=64,
# Run input pipeline on each TPU host (vs. one for all hosts) to
# avoid input-boundedness.
use_per_host_infeed=True)
# Default optimization and checkpointer settings.
self.task_params = dual_encoder.MilanTask.Params()
self.task_params.train.Set(
clip_gradient_norm_to_value=1.0,
grad_norm_tracker=lingvo_layers.GradNormTracker.Params().Set(
name='grad_norm_tracker',
# Don't clip if the grad norm is already smaller than this.
grad_norm_clip_cap_min=0.1),
save_max_to_keep=2000,
save_keep_checkpoint_every_n_hours=0.1667, # At most every 10 min.
optimizer=optimizer.Adam.Params().Set(
beta1=0.9, beta2=0.999, epsilon=1e-8),
learning_rate=0.0001,
lr_schedule=schedule.StepwiseExponentialSchedule.Params().Set(
decay=0.999, num_steps_per_decay=1000),
tpu_steps_per_loop=100,
max_steps=40000)
def _ChooseDatasetSpec(self):
"""Returns the `DatasetSpec` to be used by the recipe."""
return self.default_dataset
@property
def default_dataset(self) -> dataset_spec.DatasetSpec:
"""Returns a default dataset for the recipe to use.
Subclasses should override this method to specify a dataset, or add logic
(elsewhere) to choose the dataset at runtime, falling back to this one
as the default.
"""
raise NotImplementedError()
@property
def encoder_configs(self):
return self.task_params.dual_encoder.encoder_configs
def AddModality(self, name: str, **kwargs):
config = dual_encoder.EncoderConfig().Set(**kwargs)
self.encoder_configs[name] = config
return config
def AddPreprocessor(self, input_feature, preprocessor):
self.input_params.preprocessors[input_feature] = preprocessor.Copy()
def StartFromCheckpoint(self, checkpoint_path: str):
"""Configures the recipe to start training from the given model checkpoint.
This is intended to be used in fine-tuning recipes. All variables, including
Adam accumulators, are loaded from the checkpoint except for global step
(so that it resets to 0 in new experiment) and grad norm tracker stats
(since gradients are likely to have different moments in the new
experiment).
Args:
checkpoint_path: Path of the checkpoint to start training from.
"""
self.task_params.train.init_from_checkpoint_rules = {
checkpoint_path: (
[('(.*)', '%s')],
# Don't load vars matching these regexes.
['.*grad_norm_tracker/.*', 'global_step'])
}
# Methods below implement the lingvo SingleTaskModelParams interface, allowing
# the recipe to be registered with `RegisterSingleTaskModel()`.
def Train(self):
"""Returns Params for the training dataset."""
dataset_fn = functools.partial(
self.dataset.Read,
split=constants.Split.TRAIN,
shuffle_buffer_size=1024)
return self.input_params.Copy().Set(name='Train', dataset_fn=dataset_fn)
def Dev(self):
"""Returns Params for the development dataset."""
dataset_fn = functools.partial(
self.dataset.Read, split=constants.Split.DEV, shuffle_buffer_size=0)
return self.input_params.Copy().Set(name='Dev', dataset_fn=dataset_fn)
def Test(self):
"""Returns Params for the test dataset."""
dataset_fn = functools.partial(
self.dataset.Read, split=constants.Split.TEST, shuffle_buffer_size=0)
return self.input_params.Copy().Set(name='Test', dataset_fn=dataset_fn)
def Task(self):
task_params = self.task_params.Copy()
if not task_params.dual_encoder.encoder_configs:
raise RecipeError('Must configure at least one encoder.')
assert task_params.dual_encoder.label_fn is None
task_params.dual_encoder.label_fn = self.dataset.Label
return task_params
| [
"copybara-worker@google.com"
] | copybara-worker@google.com |
8b12478f06aaa73f33206cb27054a25d36d6eefb | c31872017416d17f58ab0ed38fd38ea367848f57 | /plotColors.py | 0e8f0204af483b20ae3854d754412ace89b174f5 | [] | no_license | martinburch/film-prints | b885c141f6255032f982fe40dfde14f0524898e7 | 5b0039719214139041140b62313e0dfb95e563c0 | refs/heads/master | 2021-01-10T19:20:45.156069 | 2013-10-21T14:05:11 | 2013-10-21T14:05:11 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,785 | py | #!/usr/bin/env python
# encoding: utf-8
"""
plotColors.py
Plots color values on polar charts. Feed it CSV (TSV).
"""
import csv
"""
Demo of a line plot on a polar axis.
"""
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
movieList = ["Argo","Beasts_of_the_Southern_Wild","Django_Unchained","Les_Miserables","Life_of_Pi","Lincoln","Silver_Linings_Playbook","Zero_Dark_Thirty"]
for movie in movieList:
# Argo, manually
r = [0,0.043137255,0.043137255,0.207843137,0.101960784,0.121568627,0.160784314,0.17254902,0.274509804,0.145098039,0.203921569,0.133333333,0.117647059,0.137254902,0.117647059,0.070588235,0.188235294,0.078431373,0.168627451,0.235294118,0.254901961,0.090196078,0.180392157,0.058823529,0.062745098,0.101960784,0.098039216,0.070588235,0.207843137,0.101960784,0.094117647,0.074509804,0.08627451,0.08627451,0.235294118,0.274509804,0.101960784,0.08627451,0.08627451,0.062745098,0.054901961,0.078431373,0.074509804,0.039215686,0.070588235,0.074509804,0.105882353,0.094117647,0.137254902,0.149019608,0.105882353,0.098039216,0.105882353,0.094117647,0.08627451,0.105882353,0.101960784,0.105882353,0.08627451,0.156862745,0.098039216,0.207843137,0.137254902,0.066666667,0.090196078,0.066666667,0.133333333,0.156862745,0.082352941,0.098039216,0.152941176,0.105882353,0.11372549,0.11372549,0.192156863,0.105882353,0.121568627,0.231372549,0.066666667,0.17254902,0.137254902,0.078431373,0.035294118,0.043137255,0.039215686,0.035294118,0.039215686,0.035294118,0.08627451,0.117647059,0.035294118,0.094117647,0.015686275,0.058823529,0.074509804,0.11372549,0.074509804,0.078431373,0.094117647,0.043137255,0.090196078,0.015686275,0.058823529,0.019607843,0.08627451,0.043137255,0.035294118,0.08627451,0.082352941,0.066666667,0.054901961,0.039215686,0.031372549,0.058823529,0.066666667,0.070588235,0.062745098,0.090196078,0.066666667,0.070588235,0.082352941,0.066666667,0.074509804,0.054901961,0.066666667,0.066666667,0.078431373,0.066666667,0.066666667,0.082352941,0.090196078,0.043137255,0.062745098,0.035294118,0.266666667,0.28627451,0.270588235,0.239215686,0.305882353,0.247058824,0.223529412,0.2,0.101960784,0.098039216,0.133333333,0.070588235,0.074509804,0.105882353,0.133333333,0.121568627,0.074509804,0.043137255,0.043137255,0.039215686,0.047058824,0.129411765,0.031372549,0.031372549,0.070588235,0.129411765,0.121568627,0.117647059,0.047058824,0.031372549,0.047058824,0.047058824,0.062745098,0.078431373,0.074509804,0.070588235,0.050980392,0.031372549,0.050980392,0.043137255,0.039215686,0.074509804,0.054901961,0.054901961,0.054901961,0.101960784,0.023529412,0.015686275,0.023529412,0.039215686,0.054901961,0.066666667,0.133333333,0.066666667,0.023529412,0.023529412,0.039215686,0.074509804,0.023529412,0.082352941,0.141176471,0.094117647,0.08627451,0.145098039,0.082352941,0.035294118,0.105882353,0.062745098,0.054901961,0.031372549,0.023529412,0.082352941,0.02745098,0.035294118,0.043137255,0.015686275,0.047058824,0.035294118,0.054901961,0.047058824,0.152941176,0.160784314,0.211764706,0.188235294,0.133333333,0.054901961,0.050980392,0.047058824,0.156862745,0.019607843,0.02745098,0.180392157,0.019607843,0.117647059,0.047058824,0.090196078,0.070588235,0.098039216,0.121568627,0.11372549,0.117647059,0.054901961,0.02745098,0.066666667,0.066666667,0.098039216,0.003921569,0.035294118,0.094117647,0.066666667,0.125490196,0.058823529,0.062745098,0.223529412,0.019607843,0.007843137,0.054901961,0.031372549,0.047058824,0.031372549,0.082352941,0.039215686,0.02745098,0.062745098,0.137254902,0.023529412,0.031372549,0.047058824,0.066666667,0.196078431,0.054901961,0.054901961,0.039215686,0.054901961,0.121568627,0.082352941,0.11372549,0.050980392,0.070588235,0.02745098,0.039215686,0.035294118,0.054901961,0.031372549,0.02745098,0.066666667,0.062745098,0.047058824,0.050980392,0.035294118,0.031372549,0.02745098,0.31372549,0.341176471,0.266666667,0.258823529,0.364705882,0.349019608,0.266666667,0.329411765,0.364705882,0.188235294,0.352941176,0.317647059,0.317647059,0.298039216,0.180392157,0.039215686,0.149019608,0.043137255,0.105882353,0.054901961,0.031372549,0.058823529,0.043137255,0.050980392,0.043137255,0.043137255,0.035294118,0.023529412,0.058823529,0.054901961,0.117647059,0.070588235,0.047058824,0.02745098,0.043137255,0.074509804,0.133333333,0.101960784,0.054901961,0.117647059,0.058823529,0.047058824,0.054901961,0.047058824,0.050980392,0.078431373,0.039215686,0.062745098,0.058823529,0.094117647,0.031372549,0.054901961,0.074509804,0.047058824,0.125490196,0.015686275,0.023529412,0.019607843,0.019607843,0.031372549,0.345098039,0.031372549,0.121568627,0.207843137,0.109803922,0.101960784,0.043137255,0.082352941,0.074509804,0.035294118,0.02745098,0.094117647,0.121568627,0.298039216,0.02745098,0.156862745,0.207843137,0.317647059,0.192156863,0.258823529,0.2,0.039215686,0.043137255,0.043137255,0.074509804,0.043137255,0.039215686,0.015686275,0.215686275,0.047058824,0.02745098,0.109803922,0.117647059,0.019607843,0.047058824,0.050980392,0.035294118,0.090196078,0.082352941,0.035294118,0.247058824,0.188235294,0.109803922,0.070588235,0.058823529,0.050980392,0.054901961,0.274509804,0.219607843,0.070588235,0.015686275,0.090196078,0.011764706,0.192156863,0.043137255,0.050980392,0.105882353,0.11372549,0.023529412,0.039215686,0.145098039,0.105882353,0.094117647,0.098039216,0.054901961,0.003921569,0,0,0.156862745,0.192156863,0.082352941,0.011764706,0.02745098,0.015686275,0.098039216,0.078431373,0.149019608,0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theta = [0,3.712791318,3.712791318,0.592753331,5.79986336,1.182319816,1.200445973,1.166197273,0.538558741,1.075500188,0.765259749,1.262797047,0.977384381,1.436156642,1.256637061,1.570796327,0.981747704,1.047197551,1.266378434,1.151917306,0.644429262,1.411440178,1.365909849,0.977384381,0.85084801,0.684706091,0.753982237,0.756309343,2.548839323,1.409689011,0.872664626,0.771619248,2.427594323,2.475194212,3.176499239,3.440791954,0.765259749,0.951997774,0.856797996,1.047197551,1.121997376,0.942477796,1.047197551,1.361356817,0.989019909,0.826734909,1.279908118,1.047197551,0.777918181,0.799177079,0.736916795,0.712094335,0.736916795,0.698131701,0.71399833,0.736916795,0.72498292,0.736916795,0.809198108,0.890117919,0.795870139,0.711303997,0.807838111,0.985597695,0.728485253,0.739198271,0.954797767,0.811578102,0.747998251,0.670206433,0.671280482,0.853272079,0.397212864,0.505543645,0.470170329,0.387850945,0.506708493,0.550222442,0.369599136,1.427996661,0.957437761,3.036872898,1.978039819,2.475194212,1.884955592,1.861684535,1.884955592,1.978039819,0.856797996,1.640609497,1.628973969,0.567232007,1.308996939,1.117010721,1.267660194,3.322143956,0.992081891,0.680678408,0.654498469,1.427996661,0.728485253,2.094395102,0.767944871,2.303834613,0.71399833,0.856797996,1.163552835,0.904397885,0.997331001,0.800798127,0.897597901,1.361356817,1.832595715,0.767944871,0.800798127,0.814486984,0.719948316,0.682954925,0.739198271,0.698131701,0.598398601,0.800798127,0.606272266,0.747998251,0.61599856,0.677598415,0.628318531,0.677598415,0.739198271,0.648265151,0.81954591,0.951997774,2.945243113,1.279908118,0.600598596,0.559461705,0.516010388,0.583683881,0.590726824,0.515287684,0.459297172,0.554398704,0.805536578,0.879645943,0.677598415,1.047197551,0.881850569,0.77570189,0.923997839,1.047197551,0.716503588,1.047197551,1.142397329,1.884955592,0.959931089,0.634665183,2.35619449,2.487094184,0.930842268,0.72986496,0.743172456,0.733038286,0.959931089,1.570796327,1.134464014,1.570796327,0.85084801,0.785398163,0.93696623,0.872664626,0.72498292,0.785398163,1.047197551,0.951997774,1.151917306,1.322775854,1.121997376,1.570796327,1.121997376,2.779101194,3.141592654,2.35619449,1.221730476,0.837758041,0.822798076,0.862397983,0.646798488,2.648793806,1.396263402,3.141592654,0.942477796,0.551156606,1.745329252,3.291192304,0.639954059,0.872664626,6.235585418,0.622658003,0.548532051,0.930842268,0.426636039,0.327249235,0.822798076,0.654498469,3.141592654,2.842393353,3.291192304,3.374303221,3.427191986,3.141592654,3.316125579,3.257947937,3.365992129,3.316125579,3.490658504,3.371465287,3.354910673,3.381575426,3.387992077,3.440791954,3.383253627,3.490658504,3.403392041,3.141592654,3.141592654,3.50583528,3.560471674,3.420845334,3.228859116,3.551365608,3.432480862,3.47669587,3.344276051,3.358254216,3.246312409,3.216392479,1.196797201,3.079992798,3.141592654,3.392920066,3.141592654,0.465421134,3.228859116,2.89519323,3.337942194,2.862339973,0.589048623,0.367437737,3.141592654,3.141592654,3.365992129,2.879793266,0.785398163,1.439896633,0.598398601,1.570796327,1.196797201,0.654498469,0.478718881,3.141592654,3.141592654,2.705260341,0.492798848,0.335103216,3.141592654,3.291192304,3.036872898,3.141592654,3.445617749,3.390925404,3.394364476,0.644429262,3.199770295,2.692793703,2.513274123,2.443460953,2.917193178,2.35619449,1.047197551,2.771993518,1.308996939,3.141592654,3.463807285,3.374303221,3.534291735,3.590391604,0.772308194,0.710168454,0.769998199,0.745731589,0.731912267,0.78833973,0.877797947,0.760464888,0.788213211,0.392699082,0.651589587,0.659346606,0.698131701,0.633830097,3.574130773,1.570796327,0.551156606,3.046392876,0.814486984,2.617993878,3.141592654,3.071779484,2.951193099,2.980485338,3.046392876,2.951193099,3.02523737,2.792526803,2.792526803,2.767593528,0.523598776,3.083415012,2.967059728,2.692793703,1.047197551,0.385809624,0.338799208,0.483321947,2.991993003,0.628318531,3.001966313,2.792526803,2.767593528,1.919862177,1.611073156,3.298672286,2.513274123,0.785398163,0.837758041,0.741764932,1.178097245,1.570796327,0.661387927,0.872664626,0.523598776,3.141592654,3.316125579,3.351032164,3.351032164,3.665191429,3.546191707,0.785398163,3.276714918,3.378693986,3.253792391,3.342976798,1.427996661,3.291192304,3.251823975,3.02523737,3.141592654,3.447025273,3.546959448,3.403392041,2.991993003,3.586651613,3.57627843,3.245019572,3.312563682,3.443058615,0.677598415,3.246312409,1.713595993,1.237597106,1.212544533,1.237597106,1.780235837,0,3.465271897,1.134464014,2.543194053,0.710598338,0.453785606,2.932153143,0.436332313,0.483321947,1.279908118,0.682954925,3.191459204,2.67617152,0.315821484,0.327249235,3.253792391,3.083415012,2.722713633,3.061038996,0.448798951,0.35903916,0.336599213,0.407243492,2.617993878,3.323713967,2.443460953,0.470170329,2.951193099,3.141592654,0.659346606,3.466584997,2.268928028,2.932153143,3.424619019,3.257947937,3.490658504,3.309144262,1.271597026,2.094395102,0,0,3.42957198,0.555655843,3.440791954,2.792526803,3.141592654,3.141592654,3.68613538,3.455751919,3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colors = theta
area = [10 for s in range(599)]
ax = plt.subplot(111, polar=True)
c = scatter(theta, r, colors, cmap=cm.hsv)
c.set_alpha(0.75)
show()
break | [
"cmb@new-host-2.home"
] | cmb@new-host-2.home |
ca570fc3f6bac84c77a2c7ed692f80fdf74003e1 | d737fa49e2a7af29bdbe5a892bce2bc7807a567c | /software/qt_examples/src/pyqt-official/sql/cachedtable.py | fc5e0ccdcaaacec4422fb011786cc34c79471638 | [
"MIT",
"CC-BY-NC-SA-4.0",
"GPL-1.0-or-later",
"GPL-3.0-only"
] | permissive | TG-Techie/CASPER | ec47dfbfd6c3a668739ff4d707572e0b853518b4 | 2575d3d35e7dbbd7f78110864e659e582c6f3c2e | refs/heads/master | 2020-12-19T12:43:53.825964 | 2020-01-23T17:24:04 | 2020-01-23T17:24:04 | 235,736,872 | 0 | 1 | MIT | 2020-01-23T17:09:19 | 2020-01-23T06:29:10 | Python | UTF-8 | Python | false | false | 4,184 | py | #!/usr/bin/env python
#############################################################################
##
## Copyright (C) 2013 Riverbank Computing Limited.
## Copyright (C) 2010 Nokia Corporation and/or its subsidiary(-ies).
## All rights reserved.
##
## This file is part of the examples of PyQt.
##
## $QT_BEGIN_LICENSE:BSD$
## You may use this file under the terms of the BSD license as follows:
##
## "Redistribution and use in source and binary forms, with or without
## modification, are permitted provided that the following conditions are
## met:
## * Redistributions of source code must retain the above copyright
## notice, this list of conditions and the following disclaimer.
## * Redistributions in binary form must reproduce the above copyright
## notice, this list of conditions and the following disclaimer in
## the documentation and/or other materials provided with the
## distribution.
## * Neither the name of Nokia Corporation and its Subsidiary(-ies) nor
## the names of its contributors may be used to endorse or promote
## products derived from this software without specific prior written
## permission.
##
## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
## "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
## LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
## A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
## OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
## DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
## THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
## (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
## OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
## $QT_END_LICENSE$
##
#############################################################################
from PyQt5.QtCore import Qt
from PyQt5.QtWidgets import (QApplication, QDialog, QDialogButtonBox,
QHBoxLayout, QMessageBox, QPushButton, QTableView)
from PyQt5.QtSql import QSqlTableModel
import connection
class TableEditor(QDialog):
def __init__(self, tableName, parent=None):
super(TableEditor, self).__init__(parent)
self.model = QSqlTableModel(self)
self.model.setTable(tableName)
self.model.setEditStrategy(QSqlTableModel.OnManualSubmit)
self.model.select()
self.model.setHeaderData(0, Qt.Horizontal, "ID")
self.model.setHeaderData(1, Qt.Horizontal, "First name")
self.model.setHeaderData(2, Qt.Horizontal, "Last name")
view = QTableView()
view.setModel(self.model)
submitButton = QPushButton("Submit")
submitButton.setDefault(True)
revertButton = QPushButton("&Revert")
quitButton = QPushButton("Quit")
buttonBox = QDialogButtonBox(Qt.Vertical)
buttonBox.addButton(submitButton, QDialogButtonBox.ActionRole)
buttonBox.addButton(revertButton, QDialogButtonBox.ActionRole)
buttonBox.addButton(quitButton, QDialogButtonBox.RejectRole)
submitButton.clicked.connect(self.submit)
revertButton.clicked.connect(self.model.revertAll)
quitButton.clicked.connect(self.close)
mainLayout = QHBoxLayout()
mainLayout.addWidget(view)
mainLayout.addWidget(buttonBox)
self.setLayout(mainLayout)
self.setWindowTitle("Cached Table")
def submit(self):
self.model.database().transaction()
if self.model.submitAll():
self.model.database().commit()
else:
self.model.database().rollback()
QMessageBox.warning(self, "Cached Table",
"The database reported an error: %s" % self.model.lastError().text())
if __name__ == '__main__':
import sys
app = QApplication(sys.argv)
if not connection.createConnection():
sys.exit(1)
editor = TableEditor('person')
editor.show()
sys.exit(editor.exec_())
| [
"TGTechie01@gmail.com"
] | TGTechie01@gmail.com |
e023e74cd5e8ae9cd89c50f08ca87039c6efff42 | 7b3db94bb0dac00fe7735ce72103e4e5422f08c8 | /cable_resistance.py | 9f4196a4a480d679483bfeee169811385a21dee2 | [
"MIT"
] | permissive | mdbartos/RIPS | 5efa485138146179469c7fd539de527fc2b38b87 | ab654138ccdcd8cb7c4ab53092132e0156812e95 | refs/heads/master | 2021-01-23T03:53:52.636545 | 2015-08-06T06:44:28 | 2015-08-06T06:44:28 | 32,833,552 | 1 | 2 | null | 2015-05-22T00:51:23 | 2015-03-25T00:39:29 | Python | UTF-8 | Python | false | false | 7,139 | py | import numpy as np
import math
def R_dc(T_c, n, n_props, T_0=20):
"""
Compute DC Resistance of a stranded conductor cable at a given temperature.
T_c: Temperature of conductor (C).
n: Number of layers.
n_props: Cable properties, given as a dictionary of lists. Must contain the following keys:
'n_n' : Number of wires in layer n.
'd_w' : Diameter of wires in layer n (m).
'A_t' : Cross-sectional area of layer n (m2) -- replaces 'n_n' and 'd_w'.
'd_n' : Mean diameter of layer n (m).
'L_n' : Lay length of layer n (m).
'resistivity_0' : Resistivity of layer material at reference temperature T_0 (ohm-m).
'alpha_0' : Temperature coefficient of layer material at reference temperature T_0 (1/C)
T_0 : Reference temperature (typically 20 C).
EXAMPLE USAGE:
From Example 7.1 in Anders, G.J., "Rating of Electric Power Cables". IEEE Press (1997).
"Compute the resistance of the parallel combination of the skid wire and tape for model cable No. 3.
The cable shield consists of a mylar tape intercalated with a 7/8 in bronze tape--1 in lay, and a
single 0.1 in D-shaped bronze skid wire--1.5 in lay. The diameter over the tape is equal to 2.648 in.
Operating temperature is 60 C."
>>> example_props = {
'A_t' : [0.169e-5, 0.101e-4],
'd_n' : [0.0672, 0.0651],
'L_n' : [0.0254, 0.0381],
'resistivity_0' : [0.35e-7, 0.35e-7],
'alpha_0' : [0.003, 0.003]
}
>>> R_dc(60, 2, example_props)
0.019107042682762667
"""
def R_layer(i):
if 'A_t' in n_props.keys():
A_t = n_props['A_t'][i]
else:
d_w = n_props['d_w'][i]
n_n = n_props['n_n'][i]
A_t = (math.pi)*(d_w**2)*(n_n)/4.0
d_n = n_props['d_n'][i]
L_n = n_props['L_n'][i]
resistivity_0 = n_props['resistivity_0'][i]
alpha_0 = n_props['alpha_0'][i]
k_n = (1 + (math.pi*d_n/L_n)**2)**0.5
Rn_0 = (resistivity_0*k_n)/(A_t)
Rn = Rn_0*(1 + alpha_0*(T_c - T_0))
return Rn
call_layers = np.vectorize(R_layer)
R_layers = call_layers(np.arange(n))
if n > 1:
R_dc = (R_layers.prod())/(R_layers.sum())
else:
R_dc = R_layers.sum()
return R_dc
def R_ac(R_dc, grouping=1, d_c=None, s=None, material='aluminum', shape='round',
arrangement='stranded', treatment='untreated', f=60, prox_method='IEC_287',
pipe_correction=1):
"""
Compute AC Resistance of a conductor cable based on DC Resistance, accounting for
skin and proximity effects.
R_dc : DC Resistance of conductor (ohm/m).
grouping : Number of parallel cables or cable cores (used for proximity effect).
d_c : Conductor diameter (m) -- used only for proximity effect.
s : Spacing between parallel conductor centers (m) -- used only for proximity effect.
material : conductor material (aluminum or copper).
shape : conductor cross-sectional shape (round or sector-shaped).
arrangement : cable arrangement (stranded, compact or segmental).
treatment : whether cable is dried/impregnated (treated or untreated).
f : frequency (Hz)
prox_method : which method to use when calculating proximity effect (IEC_287 or Arnold_1941).
pipe_correction : For pipe-type cables, this value should be 1.5-1.7.
EXAMPLE USAGE:
From Example 7.3 in Anders, G.J., "Rating of Electric Power Cables". IEEE Press (1997).
"Compute the AC Resistance (at 90 C) of model cable No. 1 using the IEC 287 method (assume
that the cable is not dried or impregnated. The conductor is stranded copper.
The DC resistance at 90 C is 7.663e-5 ohm/m. The conductor diameter is 20.5 mm. Three
conductors run parallel and the spacing between conductor centers is 71.6 mm.
The frequency is 50 Hz.
>>> R_ac(7.663e-5, grouping=3, d_c=0.0205,s=0.0716, material='copper', f=50)
7.805533308599811e-05
>>> R_ac(7.663e-5, grouping=3, d_c=20.5,s=71.6, material='copper', f=50, prox_method='Arnold_1941')
7.806147003059088e-05
"""
constants = {
'copper' : {
'round' : {
'stranded' : {
'treated' : {'k_s': 1, 'k_p' : 0.8},
'untreated' : {'k_s': 1, 'k_p' : 1}
},
'compact' : {
'treated' : {'k_s': 1, 'k_p' : 0.8},
'untreated' : {'k_s': 1, 'k_p' : 1}
},
'segmental' : {
'treated' : {'k_s': 0.435, 'k_p' : 0.37},
'untreated' : {'k_s': 0.435, 'k_p' : 0.37}
}
},
'sector-shaped' : {
'treated' : {'k_s': 1, 'k_p' : 0.8},
'untreated' : {'k_s': 1, 'k_p' : 1}
},
},
'aluminum' : {
'round' : {
'stranded' : {
'treated' : {'k_s': 1, 'k_p' : 0.8},
'untreated' : {'k_s': 1, 'k_p' : 1}
},
'four segment' : {
'treated' : {'k_s': 0.28, 'k_p' : 0.8},
'untreated' : {'k_s': 0.28, 'k_p' : 1}
},
'five segment' : {
'treated' : {'k_s': 0.19, 'k_p' : 0.8},
'untreated' : {'k_s': 0.19, 'k_p' : 1}
},
'six segment' : {
'treated' : {'k_s': 0.12, 'k_p' : 0.8},
'untreated' : {'k_s': 0.12, 'k_p' : 1}
},
}
}
}
# Compute skin effects.
k_s = constants[material][shape][arrangement][treatment]['k_s']
x_s = (k_s*(10**-7)*8*math.pi*f/R_dc)**0.5
if x_s <= 2.8:
y_s = (x_s**4)/(192 + 0.8*x_s**4)
elif 2.8 < x_s <= 3.8:
y_s = -0.136 - 0.0177*x_s + 0.0563*x_s**2
else:
y_s = (x_s/2*(2**0.5)) - 11.0/15.0
# Compute proximity effects.
if grouping > 1:
k_p = constants[material][shape][arrangement][treatment]['k_p']
x_p = (k_p*(10**-7)*8*math.pi*f/R_dc)**0.5
a = (x_p**4)/(192 + 0.8*x_p**4)
y = d_c/s
if x_p > 2.8:
prox_method = 'Arnold_1941'
def IEC_287(grouping, a, y):
if grouping == 2:
y_p = 2.9*a*y
elif grouping == 3:
y_p = a*(y**2)*(0.312*y**2 + 1.18/(a + 0.27))
else:
y_p = 0
print('No valid grouping selected for calculation of proximity effects')
return y_p
def Arnold_1941(x_p, grouping, y):
if x_p <= 2.8:
A = (0.042 + 0.012*x_p**4)/(1 + 0.0236*x_p**4)
B = 0
G = (11*x_p**4)/(704 + 20*x_p**4)
H = (1.0/3.0)*(1 + 0.0283*x_p**4)/(1 + 0.0042*x_p**4)
elif 2.8 < x_p <= 3.8:
A = -0.223 + 0.237*x_p - 0.0154*x_p**2
B = 0
G = -1.04 + 0.72*x_p - 0.08*x_p**2
H = 0.095 + 0.119*x_p + 0.0384*x_p**2
else:
A = 0.75 - 1.128*(1/x_p)
B = 0.094 - 0.376*(1/x_p)
G = x_p/(4*(2**0.5)) - (1.0/8.0)
H = (2*x_p - 4.69)/(x_p - 1.16)
if grouping == 2:
y_p = (G*y**2)/(1 - A*y**2 - B*y**4)
elif grouping == 3:
y_p = (G*3*y**2)/(2 - (5.0/12.0)*H*y**2)
else:
y_p = 0
print('No valid grouping selected for calculation of proximity effects')
return y_p
if prox_method == 'IEC_287':
y_p = IEC_287(grouping, a, y)
elif prox_method == 'Arnold_1941':
y_p = Arnold_1941(x_p, grouping, y)
else:
y_p = 0
print('No valid method selected for calculation of proximity effects')
else:
y_p = 0
R_ac = R_dc*(1 + pipe_correction*(y_s + y_p))
return R_ac
def R_dc_T(R_dc, T_0, T_1, alpha_0):
"""
Convert DC Resistance at temperature T_0 to DC Resistance at temperature T_1.
R_dc : DC Resistance at temperature T_0 (ohm/m).
T_0 : Temperature to convert from (C).
T_1: Temperature to convert to (C).
alpha_0 : Temperature coefficient of layer material at reference temperature T_0 (1/C)
"""
R_dc_1 = R_dc*(1 + alpha_0*(T_1 - T_0))
return R_dc_1
| [
"matthew.d.bartos@gmail.com"
] | matthew.d.bartos@gmail.com |
faa3dcfd957b4b9e09e84e657f73c358adb704d3 | 78502d4cb568ef301020ab553a91984bb8cb6a02 | /test2.py | 930eac65ceb5f418c629e4a3dc895218c2ba3ca8 | [] | no_license | hackinsubho/FlowchartDrawer | 0322968df93b5de809f33be68a2b425473b6fd81 | 54a9f413adde50c70a4b3d63a653e13739527f54 | refs/heads/master | 2020-03-07T12:14:39.011000 | 2018-04-02T09:02:37 | 2018-04-02T09:02:37 | 127,473,731 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,877 | py | import pydot
import xlrd
ExcelFileName = 'example.xlsx'
workbook = xlrd.open_workbook(ExcelFileName)
worksheet1 = workbook.sheet_by_name("Sheet2")
num_rows1 = worksheet1.nrows
num_cols1 = worksheet1.ncols
result_data1 = []
for curr_col in range(0, num_cols1, 1):
row_data = []
for curr_row in range(0, num_rows1, 1):
data = worksheet1.cell_value(curr_row, curr_col) # Read the data in the current cell
# print(data)
row_data.append(data)
result_data1.append(row_data)
print(len(result_data1[1]))
M = "|".join(result_data1[0])
N = "|".join(result_data1[1])
callgraph = pydot.Dot(graph_type='digraph', fontname="Verdana")
callgraph.set_strict(1)
callgraph.set_label("Assignment Pass Due Scenarios")
cluster_foo=pydot.Cluster('Ids',label='')
cluster_foo.add_node(pydot.Node('foo', label="{"+M+"}|{"+N+"}", shape="record", orientation="180"))
callgraph.add_subgraph(cluster_foo)
ExcelFileName = 'example.xlsx'
workbook = xlrd.open_workbook(ExcelFileName)
worksheet = workbook.sheet_by_name("Sheet1")
num_rows = worksheet.nrows
num_cols = worksheet.ncols
result_data = []
for curr_row in range(0, num_rows, 1):
row_data = []
for curr_col in range(0, num_cols, 1):
data = worksheet.cell_value(curr_row, curr_col) # Read the data in the current cell
# print(data)
row_data.append(data)
result_data.append(row_data)
node = result_data[1]
cluster_graph=pydot.Cluster('graph', label='')
for i in range(len(result_data[1])):
node[i] = pydot.Node(result_data[1][i], style="rounded, filled", shape="box", rotate="")
cluster_graph.add_node(node[i])
j = 0
k = 1
for i in range(len(set(node))-1):
cluster_graph.add_edge(pydot.Edge(node[j], node[k], dir="forward", arrowhead="normal", style=""))
j += 1
k += 1
callgraph.add_subgraph(cluster_graph)
callgraph.write_png("test2.png") | [
"subho3010@gmail.com"
] | subho3010@gmail.com |
82391869f28320568b761e20d7149a6cf2f5318d | 7b9e6ce86ef732a596d9af86eacc4d1afad7ff65 | /docplanner/settings.py | f6180b97f94709d4a2c84d4d0a70605beb8c2546 | [] | no_license | Aniakacp/docplanner | 3bc3de98e401b01f4f1764d67637ad398acd4b26 | fe1d60528f6594056cb34c058cec0fc16465fc18 | refs/heads/master | 2023-07-13T14:10:51.791056 | 2021-08-22T13:48:36 | 2021-08-22T13:48:36 | 378,492,028 | 1 | 0 | null | 2021-08-22T13:48:36 | 2021-06-19T19:40:28 | Python | UTF-8 | Python | false | false | 3,344 | py | """
Django settings for docplanner project.
Generated by 'django-admin startproject' using Django 3.2.4.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.2/ref/settings/
"""
from pathlib import Path
import os
import sys
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-^l)gag()jsb#0x0a*re57-6f3z#rmmsn1dtg1y9b^uz&f5re#e'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'docplanner',
'project'
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'docplanner.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [BASE_DIR / 'templates'],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'docplanner.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.2/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',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.2/howto/static-files/
STATIC_URL = '/static/'
# Default primary key field type
# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
TIME_ZONE = 'UTC' | [
"aniakacp@op.pl"
] | aniakacp@op.pl |
9fc60961ec8cdf589ac40c7a9a1ed86cf073e0f3 | 2c95e0f7bb3f977306f479d5c99601ab1d5c61f2 | /olive/rpc/farmer_rpc_api.py | 2544c04855463194e9428596f772ea7d75e8b7b9 | [
"Apache-2.0"
] | permissive | Olive-blockchain/Olive-blockchain-CLI | d62444f8456467f8105531178d2ae53d6e92087d | 8c4a9a382d68fc1d71c5b6c1da858922a8bb8808 | refs/heads/main | 2023-07-19T03:51:08.700834 | 2021-09-19T16:05:10 | 2021-09-19T16:05:10 | 406,045,499 | 0 | 0 | Apache-2.0 | 2021-09-19T16:05:10 | 2021-09-13T16:20:38 | Python | UTF-8 | Python | false | false | 5,569 | py | from typing import Callable, Dict, List, Optional
from olive.farmer.farmer import Farmer
from olive.types.blockchain_format.sized_bytes import bytes32
from olive.util.byte_types import hexstr_to_bytes
from olive.util.ws_message import WsRpcMessage, create_payload_dict
class FarmerRpcApi:
def __init__(self, farmer: Farmer):
self.service = farmer
self.service_name = "olive_farmer"
def get_routes(self) -> Dict[str, Callable]:
return {
"/get_signage_point": self.get_signage_point,
"/get_signage_points": self.get_signage_points,
"/get_reward_targets": self.get_reward_targets,
"/set_reward_targets": self.set_reward_targets,
"/get_pool_state": self.get_pool_state,
"/set_payout_instructions": self.set_payout_instructions,
"/get_harvesters": self.get_harvesters,
"/get_pool_login_link": self.get_pool_login_link,
}
async def _state_changed(self, change: str, change_data: Dict) -> List[WsRpcMessage]:
if change == "new_signage_point":
sp_hash = change_data["sp_hash"]
data = await self.get_signage_point({"sp_hash": sp_hash.hex()})
return [
create_payload_dict(
"new_signage_point",
data,
self.service_name,
"wallet_ui",
)
]
elif change == "new_farming_info":
return [
create_payload_dict(
"new_farming_info",
change_data,
self.service_name,
"wallet_ui",
)
]
elif change == "new_plots":
return [
create_payload_dict(
"get_harvesters",
change_data,
self.service_name,
"wallet_ui",
)
]
return []
async def get_signage_point(self, request: Dict) -> Dict:
sp_hash = hexstr_to_bytes(request["sp_hash"])
for _, sps in self.service.sps.items():
for sp in sps:
if sp.challenge_chain_sp == sp_hash:
pospaces = self.service.proofs_of_space.get(sp.challenge_chain_sp, [])
return {
"signage_point": {
"challenge_hash": sp.challenge_hash,
"challenge_chain_sp": sp.challenge_chain_sp,
"reward_chain_sp": sp.reward_chain_sp,
"difficulty": sp.difficulty,
"sub_slot_iters": sp.sub_slot_iters,
"signage_point_index": sp.signage_point_index,
},
"proofs": pospaces,
}
raise ValueError(f"Signage point {sp_hash.hex()} not found")
async def get_signage_points(self, _: Dict) -> Dict:
result: List = []
for _, sps in self.service.sps.items():
for sp in sps:
pospaces = self.service.proofs_of_space.get(sp.challenge_chain_sp, [])
result.append(
{
"signage_point": {
"challenge_hash": sp.challenge_hash,
"challenge_chain_sp": sp.challenge_chain_sp,
"reward_chain_sp": sp.reward_chain_sp,
"difficulty": sp.difficulty,
"sub_slot_iters": sp.sub_slot_iters,
"signage_point_index": sp.signage_point_index,
},
"proofs": pospaces,
}
)
return {"signage_points": result}
async def get_reward_targets(self, request: Dict) -> Dict:
search_for_private_key = request["search_for_private_key"]
return self.service.get_reward_targets(search_for_private_key)
async def set_reward_targets(self, request: Dict) -> Dict:
farmer_target, pool_target = None, None
if "farmer_target" in request:
farmer_target = request["farmer_target"]
if "pool_target" in request:
pool_target = request["pool_target"]
self.service.set_reward_targets(farmer_target, pool_target)
return {}
async def get_pool_state(self, _: Dict) -> Dict:
pools_list = []
for p2_singleton_puzzle_hash, pool_dict in self.service.pool_state.items():
pool_state = pool_dict.copy()
pool_state["p2_singleton_puzzle_hash"] = p2_singleton_puzzle_hash.hex()
pools_list.append(pool_state)
return {"pool_state": pools_list}
async def set_payout_instructions(self, request: Dict) -> Dict:
launcher_id: bytes32 = hexstr_to_bytes(request["launcher_id"])
await self.service.set_payout_instructions(launcher_id, request["payout_instructions"])
return {}
async def get_harvesters(self, _: Dict):
return await self.service.get_harvesters()
async def get_pool_login_link(self, request: Dict) -> Dict:
launcher_id: bytes32 = bytes32(hexstr_to_bytes(request["launcher_id"]))
login_link: Optional[str] = await self.service.generate_login_link(launcher_id)
if login_link is None:
raise ValueError(f"Failed to generate login link for {launcher_id.hex()}")
return {"login_link": login_link}
| [
"87711356+Olive-blockchain@users.noreply.github.com"
] | 87711356+Olive-blockchain@users.noreply.github.com |
2a3d1c28c7276b646b9b6fdbdac1de1a704897d9 | 2597c80ac39208a71e417becbc8e695216019fd4 | /venv/Scripts/django-admin.py | e5c775d260ba1c33df700ab530f13f16d8f1444c | [] | no_license | sasakir23/note | 33e5a4eac04eeff242e2f218c29fa10c5d0b078a | 5dafda61459417f47fa748684afd8059c9c1d8dc | refs/heads/main | 2023-08-25T17:06:22.457470 | 2021-10-05T01:04:08 | 2021-10-05T01:04:08 | 353,916,323 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 681 | py | #!C:\Users\r_sas\note\venv\Scripts\python.exe
# When the django-admin.py deprecation ends, remove this script.
import warnings
from django.core import management
try:
from django.utils.deprecation import RemovedInDjango40Warning
except ImportError:
raise ImportError(
'django-admin.py was deprecated in Django 3.1 and removed in Django '
'4.0. Please manually remove this script from your virtual environment '
'and use django-admin instead.'
)
if __name__ == "__main__":
warnings.warn(
'django-admin.py is deprecated in favor of django-admin.',
RemovedInDjango40Warning,
)
management.execute_from_command_line()
| [
"{r_sasaki@c-crea.jp}"
] | {r_sasaki@c-crea.jp} |
b56cab8d64b1e5653f330cd1a4b60adc6af9c4af | 634e2c9a863299f1568846c6566d12bc1cfc757c | /Online_Class/settings.py | e932343682cd50350a1a8037e3faf14ac75f5435 | [] | no_license | cryp73r/django3-Online_Class-Pro | a00e5760a4dc91fe19518312c84f5d6ed3dc998b | 7219ffa3b391de6dd01aa2d252de5d1f749412bb | refs/heads/main | 2023-06-27T11:37:49.213406 | 2021-07-28T18:17:59 | 2021-07-28T18:17:59 | 375,724,295 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,460 | py | """
Django settings for Online_Class project.
Generated by 'django-admin startproject' using Django 3.1.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve(strict=True).parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'dkl/sjf*powflk_sdilsfdjd862#s)qsv(a41@oy(kx1vs^1#yii'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = False
ALLOWED_HOSTS = ['ocs.pythonanywhere.com']
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'classdetail',
'notice',
'appRelease',
'quizExam',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'Online_Class.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'Online_Class.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.1/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',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.1/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = BASE_DIR / 'static'
MEDIA_URL = '/app/'
MEDIA_ROOT = BASE_DIR / 'appRelease'
LOGIN_URL = '/login/'
DEFAULT_AUTO_FIELD='django.db.models.AutoField'
try:
from .local_settings import *
except ImportError:
print('No Local File. You must be on Production') | [
"priyanshusingh0610@gmail.com"
] | priyanshusingh0610@gmail.com |
ad61cd08febe3a4d82276bd72c51ad8c30ce0453 | 504b27bd000b39757a69c6631185c64ffdab083f | /__init__.py | 24a5f613ba2fe15f254305239782a7ef68d15a03 | [
"Apache-2.0"
] | permissive | Blubbaa/ikea-tradfri-skill | c7c8973be3103a0c8f7a034e61625065cc053318 | 80a57014c349bcead97e2aae6e1e72dc828563b5 | refs/heads/master | 2020-12-03T18:05:44.503581 | 2020-01-02T16:49:41 | 2020-01-02T16:49:41 | 231,422,786 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 332 | py | from mycroft import MycroftSkill, intent_file_handler
class IkeaTradfri(MycroftSkill):
def __init__(self):
MycroftSkill.__init__(self)
@intent_file_handler('tradfri.ikea.intent')
def handle_tradfri_ikea(self, message):
self.speak_dialog('tradfri.ikea')
def create_skill():
return IkeaTradfri()
| [
"jonas.fuchs@gmx.net"
] | jonas.fuchs@gmx.net |
d71786623fcc8a0422620d2a36f680c62d593598 | f9e6361373dd4ac3ae9d749962acc68e391767e0 | /flow_manager/DialogflowHandler.py | 7462001aff341e3c9733950e88f7c3951cf9f6bd | [
"Apache-2.0"
] | permissive | fingeredman/chatbot-with-teanaps | ff8d4130f57c813e2dd8e8f8ac9d6d27a90a15b3 | 2f4f93d36145015b5a714b55cf7163418023757f | refs/heads/master | 2022-09-30T09:48:27.209728 | 2022-09-18T03:52:33 | 2022-09-18T03:52:33 | 239,278,240 | 0 | 0 | Apache-2.0 | 2020-02-09T09:43:20 | 2020-02-09T09:32:55 | null | UTF-8 | Python | false | false | 3,041 | py | import dialogflow_v2 as dialogflow
import configure as con
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = con.DIALOGFLOW_CREDENTIAL_PATH
class DialogflowHandler():
def __init__(self, base_url = con.DIALOGFLOW_BASE_URL,
client_access_token = con.DIALOGFLOW_CLIENT_ACCESS_TOKEN,
developer_access_token = con.DIALOGFLOW_DEVELOPER_ACCESS_TOKEN,
project_id = con.DIALOGFLOW_PROJECT_ID,
language_code = con.DIALOGFLOW_LANGUAGE_CODE,
session_id = con.DIALOGFLOW_SESSTION_ID):
self.base_url = base_url
self.client_access_token = client_access_token
self.developer_access_token = developer_access_token
self.project_id = project_id
self.language_code = language_code
self.session_id = session_id
def get_intent(self, sentence):
session_client = dialogflow.SessionsClient()
session = session_client.session_path(self.project_id, self.session_id)
text_input = dialogflow.types.TextInput(text=sentence, language_code=self.language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
response = session_client.detect_intent(session=session, query_input=query_input)
intent_type = response.query_result.intent.display_name
probability = response.query_result.intent_detection_confidence
response = response.query_result.fulfillment_text
return (intent_type, probability, response)
def create_intent(self, intent_type, train_sentence_list, response_sentence_list):
intents_client = dialogflow.IntentsClient()
parent = intents_client.project_agent_path(self.project_id)
training_phrases = []
for training_phrases_part in train_sentence_list:
part = dialogflow.types.Intent.TrainingPhrase.Part(text=training_phrases_part)
training_phrase = dialogflow.types.Intent.TrainingPhrase(parts=[part])
training_phrases.append(training_phrase)
text = dialogflow.types.Intent.Message.Text(text=response_sentence_list)
message = dialogflow.types.Intent.Message(text=text)
intent = dialogflow.types.Intent(display_name=intent_type, training_phrases=training_phrases, messages=[message])
intents_client.create_intent(parent, intent)
def get_intent_list(self):
intent_list = []
intents_client = dialogflow.IntentsClient()
parent = intents_client.project_agent_path(self.project_id)
intents = intents_client.list_intents(parent)
for intent in intents:
intent_id = intent.name.split("/intents/")[1]
intent_type = intent.display_name
intent_list.append([intent_id, intent_type])
return intent_list
def delete_intent(self, intent_id):
intents_client = dialogflow.IntentsClient()
intent_path = intents_client.intent_path(self.project_id, intent_id)
intents_client.delete_intent(intent_path) | [
"noreply@github.com"
] | fingeredman.noreply@github.com |
aaa6aa548821da963e638937b213dc378966b3c7 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/55/usersdata/88/23890/submittedfiles/av2_p3_civil.py | ed8eac935d52a83bb78809e7cbded4971043205d | [] | no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 708 | py | # -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
def slinha(a,x):
soma=0
for j in range(0,a.shape[1],1):
soma=soma+a[x,j]
return soma
def scoluna(a,y):
soma=0
for i in range(0,a.shape[0],1):
soma=soma+a[i,y]
return soma
def somatorio(a,x,y):
soma=(slinha(a,x)+scoluna(a,y))-(2*a[x,y])
return soma
n=input('Dê a dimensão da matriz: ')
x=input('Digite a coordenada da linha: ')
y=input('Digite a coordenada da coluna: ')
a=np.zeros((n,n))
for i in range(0,a.shape[0],1):
for j in range(0,a.shape[1],1):
a[i,j]=input('Digite um elemento da matriz: ')
somatotal=somatorio(a,x,y)
print ('%d' %somatotal)
| [
"rafael.mota@ufca.edu.br"
] | rafael.mota@ufca.edu.br |
f3b68a0ba1243be0333cad46b25226c835fc1ba6 | 79d485476d5ad2b00a96e47d07e4e3cdbbc40f06 | /CodeForcesCrawler/asgi.py | c1054869870aadd37f7bd603d0cbf66ecf3838ab | [] | no_license | hancy0007/Codeforces-Crawler | 156a4b68671113ca807aa577f8472df5280379ac | a1809112e4c6183df80de3571f14d1815ae4fc6b | refs/heads/master | 2023-06-21T21:05:49.217514 | 2021-07-20T13:57:16 | 2021-07-20T13:57:16 | 387,808,443 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 411 | py | """
ASGI config for CodeForcesCrawler project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'CodeForcesCrawler.settings')
application = get_asgi_application()
| [
"himanshu.chaudhary@iitg.ac.in"
] | himanshu.chaudhary@iitg.ac.in |
1226709fcd8b065e93dc8912cc8f20a2d45e5593 | ef3fc47eb3ed2e1f4b01d58e5ad2cc4f78a29d4a | /myflask.py | e20391c5cc5eb23476799959a6fbfcdfd389e5e2 | [] | no_license | youngfreeFJS/2019-nCoV-census | a308e9bf272b58fa9f352b7b3fd7baf7175712e7 | 0aa64286e382271f9d2f2c6415b6928c6134f7e5 | refs/heads/master | 2020-12-22T22:29:36.976096 | 2020-01-29T09:48:18 | 2020-01-29T09:48:18 | 236,948,723 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,213 | py | from flask import Flask, request,jsonify,Response,make_response
from flask_apscheduler import APScheduler
import requests
import time
class Config(object): # 创建配置，用类
# 任务列表
JOBS = [
# { # 第一个任务
# 'id': 'job1',
# 'func': '__main__:job_1',
# 'args': (1, 2),
# 'trigger': 'cron', # cron表示定时任务
# 'hour': 19,
# 'minute': 27
# },
{ # 第二个任务，每隔5S执行一次
'id': 'job2',
'func': '__main__:method_test', # 方法名
'args': (1, 2), # 入参
'trigger': 'interval', # interval表示循环任务
'seconds': 5000,
}
]
provinceName = "湖北省"
cityName = "西安市"
class Lib:
def timestamp(self,timestamp):
# 转换成localtime
time_local = time.localtime(int(timestamp)/1000)
# 转换成新的时间格式(2016-05-05 20:28:54)
dt = time.strftime("%Y-%m-%d %H:%M:%S", time_local).split(" ")[0]
return dt
lib = Lib()
class Disease:
def __init__(self):
self.base_uri = "http://lab.isaaclin.cn"
self.history_msg = {}
def city(self):
pass
def province(self):
uri = self.base_uri + "/nCoV/api/area?province="+provinceName+"&latest=0"
r = requests.get(uri)
dicts = {}
for line in r.json()["results"]:
print(lib.timestamp(timestamp=line["updateTime"]))
dicts[lib.timestamp(timestamp=line["updateTime"])] = line["confirmedCount"]
return dicts
disease = Disease()
def method_test(a, b):
print(a + b)
app = Flask(__name__,static_url_path="")
app.config.from_object(Config()) # 为实例化的flask引入配置
@app.route('/')
def index():
return app.send_static_file('index.html')
##
@app.route("/api", methods=["POST", "GET"])
def check():
return make_response(jsonify(
{
"area":provinceName,
"count":disease.province()
}
))
if __name__ == '__main__':
scheduler = APScheduler()
scheduler.init_app(app)
scheduler.start()
app.run(debug=False) | [
"yangfei28@meituan.com"
] | yangfei28@meituan.com |
e22a506b1ed83dfebbd9561e339602c24447b699 | 00d50dc50f364d1f474dd8818387e6da8a437fa2 | /travelproject/bot/migrations/0003_auto_20210109_2052.py | 812e74f7f93af4bb5b69a0f5f8e0cb0d56bb99b1 | [] | no_license | nightted/TravelProject | 06a2ca43e8b83824a8b2a810ca55615170c655d5 | 15959ec46e15aef3c17ce09d10606b251df5e1b1 | refs/heads/master | 2023-03-21T16:59:56.726806 | 2021-03-16T07:55:14 | 2021-03-16T07:55:14 | 325,753,356 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 431 | py | # Generated by Django 3.1.4 on 2021-01-09 12:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('bot', '0002_auto_20210109_2019'),
]
operations = [
migrations.AlterField(
model_name='resturant',
name='nearby_hotel',
field=models.ManyToManyField(related_name='nearby_resturant', to='bot.Hotel'),
),
]
| [
"h5904098@gmail.com"
] | h5904098@gmail.com |
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