text stringlengths 38 1.54M |
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from tkinter import *
from math import *
class InvestmentCalc:
def __init__(self):
window = Tk()
window.title("Investment Calculator")
frame0 = Frame(window)
frame0.pack()
Label(frame0, text = "Investment Amount:").grid(row = 1, column = 1, sticky = W)
self.v1 = StringVar()
Entry(frame0, textvariable = self.v1, justify = RIGHT).grid(row = 1, column = 2, padx = 5, pady = 2)
Label(frame0, text = "Years:").grid(row = 2, column = 1, sticky = W)
self.v2 = StringVar()
Entry(frame0, textvariable = self.v2, justify = RIGHT).grid(row = 2, column = 2, padx = 5, pady = 2)
Label(frame0, text = "Annual Interest Rate:").grid(row = 3, column = 1, sticky = W)
self.v3 = StringVar()
Entry(frame0, textvariable = self.v3, justify = RIGHT).grid(row = 3, column = 2, padx = 5, pady = 2)
Label(frame0, text = "Future Value:").grid(row = 4, column = 1, sticky = W)
self.v4 = StringVar()
Label(frame0, textvariable = self.v4).grid(row = 4, column = 2, sticky = E)
Button(frame0, command = self.calc, text = "Calculate").grid(row = 5, column = 2, sticky = E)
window.mainloop()
def calc(self):
self.v4.set(format(float(self.v1.get()) * (1 + float(self.v3.get())/1200)**(float(self.v2.get())*12), "10.2f"))
InvestmentCalc()
|
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from .server_properties_for_create import ServerPropertiesForCreate
class ServerPropertiesForReplica(ServerPropertiesForCreate):
"""The properties to create a new replica.
All required parameters must be populated in order to send to Azure.
:param version: Server version. Possible values include: '5.6', '5.7'
:type version: str or ~azure.mgmt.rdbms.mysql.models.ServerVersion
:param ssl_enforcement: Enable ssl enforcement or not when connect to
server. Possible values include: 'Enabled', 'Disabled'
:type ssl_enforcement: str or
~azure.mgmt.rdbms.mysql.models.SslEnforcementEnum
:param storage_profile: Storage profile of a server.
:type storage_profile: ~azure.mgmt.rdbms.mysql.models.StorageProfile
:param create_mode: Required. Constant filled by server.
:type create_mode: str
:param source_server_id: Required. The master server id to create replica
from.
:type source_server_id: str
"""
_validation = {
'create_mode': {'required': True},
'source_server_id': {'required': True},
}
_attribute_map = {
'version': {'key': 'version', 'type': 'str'},
'ssl_enforcement': {'key': 'sslEnforcement', 'type': 'SslEnforcementEnum'},
'storage_profile': {'key': 'storageProfile', 'type': 'StorageProfile'},
'create_mode': {'key': 'createMode', 'type': 'str'},
'source_server_id': {'key': 'sourceServerId', 'type': 'str'},
}
def __init__(self, **kwargs):
super(ServerPropertiesForReplica, self).__init__(**kwargs)
self.source_server_id = kwargs.get('source_server_id', None)
self.create_mode = 'Replica'
|
#pragma out
#pragma repy
try:
try:
raise Exception, "Exiting"
finally:
print "Hi" # should be printed
except Exception:
pass
|
import enum
from abc import ABC, abstractmethod
from model.list_pkg.entry import Entry
from model.observer_pkg.observer import Observer
import pymongo
from pymongo import MongoClient
import urllib.parse
class Account(Entry, ABC):
class Role(enum.Enum):
AUTHOR = "Author"
PCM = "PCM"
PCC = "PCC"
ADMIN = "Admin"
def __init__(self, account_id: int, username: str, password: str, role: Role, notifications: [], deadline: str = None):
self.account_id = account_id
self.username = username
self.password = password
self.role = role
self.notifications = notifications
self.deadline = deadline
def get_entry_id(self):
return self.account_id
def create_entry_dictionary(self):
return {
"accountID": self.account_id,
"username": self.username,
"password": self.password,
"role": self.role.value,
"notifications": self.notifications,
"deadline": self.deadline
}
def set_entry_attributes(self, attributes: {}):
self.account_id = attributes["accountID"]
self.username = attributes["username"]
self.password = attributes["password"]
self.notifications = attributes["notifications"]
self.deadline = attributes["deadline"]
role = attributes["role"]
if role == Account.Role.AUTHOR.value:
self.role = Account.Role.AUTHOR
elif role == Account.Role.PCM.value:
self.role = Account.Role.PCM
elif role == Account.Role.PCC.value:
self.role = Account.Role.PCC
elif role == Account.Role.ADMIN.value:
self.role = Account.Role.ADMIN
def set_deadline(self, deadline: str):
self.deadline = deadline
def get_deadline(self):
return self.deadline
def get_notifications(self):
return self.notifications
def add_notification(self, notification: str):
self.notifications.append(notification)
def remove_notification(self, notification: str):
self.notifications.remove(notification)
@abstractmethod
def change_password(self, oldpass: str, newpass: str):
pass
@abstractmethod
def update(self):
pass
@abstractmethod
def notify_account_change(self):
pass
|
import re
from django import forms
from django.contrib.sites.models import Site
from subdomains.conf import settings as subdomain_settings
from subdomains.models import Subdomain
class SubdomainForm(forms.ModelForm):
class Meta:
model = Subdomain
exclude = ('site', 'user',)
def clean_subdomain_text(self):
if not re.match(r'^[a-z0-9-]+$', self.cleaned_data['subdomain_text'].lower()):
raise forms.ValidationError('Subdomain can have only a-z, 0-9, - characters.')
elif self.cleaned_data['subdomain_text'].lower() in subdomain_settings.UNALLOWED_SUBDOMAINS:
raise forms.ValidationError('This subdomain name is reserved. Please choose another.')
return self.cleaned_data['subdomain_text']
def clean_domain(self):
if self.cleaned_data['domain'] == '':return None
return self.cleaned_data['domain']
def save(self, commit=True):
subdomain_obj = super(SubdomainForm, self).save(commit=False)
if commit:
subdomain_obj.save()
return subdomain_obj
|
import FileManager as fm;
import csv
import os.path
import time
import argparse
import glob
out_file ="places.txt"
in_folder="places"
search_pattern='/*/*INCPLACE.txt'
skip_first=True# skip the first line of subsequent files
directory = os.path.dirname(os.path.realpath(__file__))+"/"
# files from https://www.census.gov/geographies/reference-files/time-series/geo/name-lookup-tables.html
def main():
"""
open all the files and append them to the places
:param name:
:return:
"""
# start with a fresh file
open(directory+out_file, 'w').close()
o_file = open(directory+out_file, 'a+')
files = glob.glob(directory+in_folder+search_pattern)
for count, f in enumerate(files):
f_c = open(f, 'r')
if skip_first and count!=0:
lines= f_c.readlines()[1:]
for l in lines:
o_file.write(l)
else:
o_file.write(f_c.read())
f_c.close()
o_file.close()
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("-v", "--verbose", help="increase output verbosity",
action="count")
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
main()
|
# -*- coding:utf-8 -*-
import xlwt
import os
"""
将数据写入excel的脚本，简单版
"""
class ExcelWriteHelper:
@staticmethod
def write(title,data,excel_name,save_path=os.getcwd()):
"""
:param title: sheet中的标题
:param data: 标题对应的内容数据
:param excel_name: excel的文件名
:param save_path: excel文件的存放路径，默认为当前路径
:return:
"""
order_num = 0
book = xlwt.Workbook(encoding='utf-8')
sheet = book.add_sheet('Sheet1', cell_overwrite_ok=True)
for i in range(len(title)):
sheet.write(0, i, title[i])
for info in data:
order_num+=1
for j in range(len(info)):
sheet.write(order_num,j,info[j])
book.save(os.path.join(save_path,excel_name))
if __name__ == '__main__':
title = ["姓名", "年龄", "性别", "国籍", "职业"]
data=[["张三","20","男","中国","工程师"],
["李四", "30", "男", "中国", "工程师"],
["王五", "43", "男", "中国", "刀客"]
]
excel_name="infomation3.xlsx"
ExcelWriteHelper.write(title,data,excel_name,save_path=os.getcwd())
|
import math
from collections import defaultdict, namedtuple
DiscreteParameters = namedtuple(
'DiscreteParameters', ['S0', 'u_n', 'd_n', 'q_n', 'R_n', 'n'])
def convert_to_discrete(T, S0, r, sigma, c, n):
R_n = math.exp(r * T / n)
u_n = math.exp(sigma * math.sqrt(T / n))
d_n = 1 / u_n
q_n = (math.exp((r - c) * T/n) - d_n) / (u_n - d_n)
return DiscreteParameters(S0, u_n, d_n, q_n, R_n, n)
class Node(object):
def __init__(self, value=None):
self.value = value
self.values = defaultdict(lambda: None)
self.derived_value = None
self.futures_value = None
self.parent_up = None
self.parent_down = None
self.up = None
self.down = None
def __getattr__(self, item):
if item in self.values:
return self.values[item]
else:
raise AttributeError
class Lattice(object):
def __init__(self):
self.layers = []
class BinomialModel(object):
def __init__(self, S0, u, d, q, r, n):
self.S0 = S0
self.q = q
self.u = u
self.d = d
self.r = r
self.n = n
self.lattice = Lattice()
self.fill_lattice()
def fill_lattice(self):
self.lattice.layers = [[Node() for _ in range(i + 1)] for i in
range(self.n + 1)]
self.lattice.layers[0][0].value = self.S0
self.lattice.layers[0][0].values['stock'] = self.S0
for layer in range(self.n):
next_layer = self.lattice.layers[layer + 1]
for node_idx, node in enumerate(self.lattice.layers[layer]):
up_node = next_layer[node_idx + 1]
down_node = next_layer[node_idx]
node.up = up_node
node.down = down_node
up_node.value = node.value * self.u
up_node.values['stock'] = up_node.value
up_node.parent_up = node
down_node.value = node.value * self.d
down_node.values['stock'] = down_node.value
down_node.parent_down = node
def fill_futures_lattice(self):
for node in self.lattice.layers[-1]:
node.futures = node.value
for layer_idx in range(self.n - 1, -1, -1):
layer = self.lattice.layers[layer_idx]
for node in layer:
node.values['futures'] = (
self.q * node.up.futures +
(1 - self.q) * node.down.futures)
def american_option(self, k, call=True, futures=None):
def get_exercise_value(node_value, k):
if call:
return max(node_value - k, 0)
else:
return max(k - node_value, 0)
if futures:
self.fill_futures_lattice()
base_value = 'futures'
last_idx = futures
else:
base_value = 'stock'
last_idx = self.n
for node in self.lattice.layers[last_idx]:
node.values['option'] = get_exercise_value(
node.values[base_value], k)
early_exercise = last_idx
for layer_idx in range(last_idx - 1, -1, -1):
layer = self.lattice.layers[layer_idx]
for node in layer:
exercise_value = get_exercise_value(node.values[base_value], k)
continue_value = (
(1 / self.r) * (
self.q * node.up.option + (1 - self.q) *
node.down.option)
)
if exercise_value > continue_value:
early_exercise = min(early_exercise, layer_idx)
node.values['option'] = max(exercise_value, continue_value)
return self.lattice.layers[0][0].values['option'], early_exercise
def american_call(self, k, futures=None):
return self.american_option(k, call=True, futures=futures)
def american_put(self, k, futures=None):
return self.american_option(k, call=False, futures=futures)
|
import numpy as np
import math
pi = np.pi
def getLogLikelihood_k(means, weights, covariances, X, K):
# Log Likelihood estimation
#
# INPUT:
# means : Mean for each Gaussian KxD
# weights : Weight vector 1xK for K Gaussians
# covariances : Covariance matrices for each gaussian DxDxK
# X : Input data NxD
# where N is number of data points
# D is the dimension of the data points
# K is number of gaussians
#
# OUTPUT:
# logLikelihood : log-likelihood
N = X.shape[0] # number of samples
sub_total = [0] * N
logLikelihood = 0
for n in range(0, N, 1):
for k in range(0, K, 1):
#print(n, k)
#print('-1 Covariances: \n', np.linalg.inv(covariances[:, :, k]))
dis = np.matrix(X[n] - means[k])
#print('dis: \n', dis)
#print('dis.T: \n', dis.T)
inv_cov = np.linalg.inv(covariances[:, :, k])
det_cov = np.linalg.det(covariances[:, :, k])
mul = np.linalg.multi_dot([dis, inv_cov, dis.T])
#print(mul)
sub_total[n] += weights[k] * (1/(2 * pi * math.sqrt(det_cov))) * math.exp(-1/2 * mul)
logLikelihood += np.log(sub_total[n])
#####Insert your code here for subtask 6a#####
return logLikelihood
|
import os
import boto3
import datetime
from email.mime.multipart import MIMEMultipart
from email.mime.image import MIMEImage
from email.mime.text import MIMEText
from email import encoders
from email.mime.base import MIMEBase
from . import LOGGER
class Mail(object):
def __init__(
self,
subject,
text_content='',
from_email='',
to=[],
files=[],
body_images=[]
):
"""
Class to send notification mails.
"""
self.aws_mail_client = boto3.client('ses', region_name='eu-west-1')
self.sender = from_email
self.recipients = to
self.subject = subject
self.text = text_content
self.files = files
self.body_images = body_images
def __assemble(self):
# Message
self.message = MIMEMultipart()
self.message['From'] = self.sender
self.message['To'] = ', '.join(self.recipients)
self.message['Subject'] = self.subject
self.message.attach(MIMEText(self.text))
# Attachments
for file in self.files:
part = MIMEBase('application', "octet-stream")
part.set_payload(open(file, "rb").read())
encoders.encode_base64(part)
part.add_header('Content-Disposition', 'attachment; filename="%s"' % os.path.basename(file))
self.message.attach(part)
# Body Images
for file in self.body_images:
img = MIMEImage(open(file, "rb").read())
img.add_header('Content-ID', '<{}>'.format(file))
self.message.attach(img)
picture = MIMEText('<br><img src="cid:%s"><br>' % file, 'html')
self.message.attach(picture)
def attach(self, files):
for file in files:
self.files.append(file)
def add_image_to_body(self, files):
for file in files:
self.body_images.append(file)
def send(self):
"""Send notification email."""
assert self.aws_mail_client
assert self.recipients
self.__assemble()
try:
self.aws_mail_client.send_raw_email(
RawMessage={
'Data': self.message.as_string()
}
)
LOGGER.debug("Sent notification mail.")
except Exception as e:
LOGGER.error("Failed to send io: " + str(e))
raise e
if __name__ == '__main__':
from . import Mail
m = Mail("Spells QA", from_email='louis.guitton@dojomadness.com', to=['louis.guitton@dojomadness.com'],
text_content='Hello guys', body_images=['../research/summoners_rift.png'])
m.send()
m = Mail(
"Guides Data",
from_email="louis.guitton@dojomadness.com",
to=["louis.guitton@dojomadness.com"],
)
m.attach(['lolsumodatascience/qa_tests/guides_situation.csv'])
m.send()
Mail("Guides QA", from_email='louis.guitton@dojomadness.com', to=['louis.guitton@dojomadness.com'],
files=['lolsumodatascience/qa_tests/guide_scatter.html']).send()
|
def f(n, m, k, p_arr, s_arr, c_arr):
i_p_s_c_arr = []
for i in range(n):
i_p_s_c_arr.append((i + 1, p_arr[i], s_arr[i], (i + 1) in c_arr))
i_p_s_c_arr.sort(key=lambda x: (x[2], -x[1]))
count = 0
for i in range(1, n):
if i_p_s_c_arr[i][3] and i_p_s_c_arr[i][2] == i_p_s_c_arr[i - 1][2]:
count += 1
return count
# print(f"{f(7, 3, 1, [1, 5, 3, 4, 6, 7, 2], [1, 3, 1, 2, 1, 2, 3], [3])} = 1")
# print(f"{f(8, 4, 4, [1, 2, 3, 4, 5, 6, 7, 8], [4, 3, 2, 1, 4, 3, 2, 1], [3, 4, 5, 6])} = 2")
n, m, k = list(map(lambda _: int(_), input().split(' ')))
p_arr = list(map(lambda _: int(_), input().split(' ')))
s_arr = list(map(lambda _: int(_), input().split(' ')))
c_arr = list(map(lambda _: int(_), input().split(' ')))
print(f(n, m, k, p_arr, s_arr, c_arr))
# |
from django.contrib.auth import logout, login
from django.contrib.auth.forms import AuthenticationForm
from django.shortcuts import render, redirect, get_object_or_404
from .forms import UserForm, PostForm
from .models import Post
# Create your views here.
def signup(request):
if request.method =='POST':
form = UserForm(request.POST)
if form.is_valid():
user = form.save()
login(request, user)
return redirect('home')
else:
return redirect('/')
form = UserForm
return render(request, 'signup.html', {'form':form})
def login_view(request):
if request.method == 'POST':
form = AuthenticationForm(request=request, data=request.POST)
if form.is_valid():
user = form.get_user()
login(request, user)
return redirect('home')
else:
form = AuthenticationForm()
return render(request, 'login.html', {'form':form})
def logout_view(request):
logout(request)
return redirect("login")
def post_create(request):
if request.method == 'POST':
form = PostForm(request.POST)
if form.is_valid():
post=form.save(commit=False)
post.users = request.user
post.save()
return redirect('home')
else:
form = PostForm()
return render(request, 'post_create.html', {'form':form})
def posts_view(request):
if request.user.is_authenticated:
posts = Post.objects.filter(users=request.user)
else:
posts = Post.objects.all().order_by('created_at')
return render(request, 'home.html', {'posts':posts})
def post_edit(request, pk):
post = get_object_or_404(Post, pk=pk)
if request.method == "POST":
form = PostForm(request.POST, instance=post)
if form.is_valid():
post = form.save(commit=False)
post.users = request.user
post.save()
return redirect('home')
else:
form = PostForm(instance=post)
return render(request, 'post_edit.html', {'form': form})
def post_delete(request, pk):
post = Post.objects.get(pk=pk)
post.delete()
return redirect('home') |
import sys
sys.path.insert(0, '/var/www/html/saferouteapp')
from saferouteapp_backend import app as application |
"""Alibaba cloud OSS."""
from contextlib import contextmanager as _contextmanager
import re as _re
import oss2 as _oss # type: ignore
from oss2.models import PartInfo as _PartInfo # type: ignore
from oss2.exceptions import OssError as _OssError # type: ignore
from airfs._core.io_base import memoizedmethod as _memoizedmethod
from airfs._core.exceptions import (
ObjectNotFoundError as _ObjectNotFoundError,
ObjectPermissionError as _ObjectPermissionError,
ObjectNotASymlinkError as _ObjectNotASymlinkError,
ObjectNotImplementedError as _ObjectNotImplementedError,
)
from airfs.io import (
ObjectRawIOBase as _ObjectRawIOBase,
ObjectBufferedIOBase as _ObjectBufferedIOBase,
SystemBase as _SystemBase,
)
_ERROR_CODES = {
403: _ObjectPermissionError,
404: _ObjectNotFoundError,
409: _ObjectPermissionError,
}
@_contextmanager
def _handle_oss_error():
"""Handle OSS exception and convert to class IO exceptions.
Raises:
OSError subclasses: IO error.
"""
try:
yield
except _OssError as exception:
if exception.status in _ERROR_CODES:
raise _ERROR_CODES[exception.status](exception.details.get("Message", ""))
raise
class _OSSSystem(_SystemBase):
"""OSS system.
Args:
storage_parameters (dict): OSS2 Auth keyword arguments and endpoint.
This is generally OSS credentials and configuration.
unsecure (bool): If True, disables TLS/SSL to improve transfer performance.
But makes connection unsecure.
"""
__slots__ = ("_unsecure", "_endpoint")
SUPPORTS_SYMLINKS = True
_CTIME_KEYS = ("Creation-Date", "creation_date")
_MTIME_KEYS = ("Last-Modified", "last_modified")
def __init__(self, storage_parameters=None, *args, **kwargs):
try:
storage_parameters = storage_parameters.copy()
self._endpoint = storage_parameters.pop("endpoint")
except (AttributeError, KeyError):
raise ValueError('"endpoint" is required as "storage_parameters"')
_SystemBase.__init__(
self, storage_parameters=storage_parameters, *args, **kwargs
)
if self._unsecure:
self._endpoint = self._endpoint.replace("https://", "http://")
def copy(self, src, dst, other_system=None):
"""Copy an object of the same storage.
Args:
src (str): Path or URL.
dst (str): Path or URL.
other_system (airfs._core.io_system.SystemBase subclass): Unused.
"""
copy_source = self.get_client_kwargs(src)
copy_destination = self.get_client_kwargs(dst)
with _handle_oss_error():
bucket = self._get_bucket(copy_destination)
bucket.copy_object(
source_bucket_name=copy_source["bucket_name"],
source_key=copy_source["key"],
target_key=copy_destination["key"],
)
def get_client_kwargs(self, path):
"""Get base keyword arguments for the client for a specific path.
Args:
path (str): Absolute path or URL.
Returns:
dict: client args
"""
bucket_name, key = self.split_locator(path)
kwargs = dict(bucket_name=bucket_name)
if key:
kwargs["key"] = key
return kwargs
def _get_client(self):
"""OSS2 Auth client.
Returns:
oss2.Auth or oss2.StsAuth: client
"""
return (
_oss.StsAuth
if "security_token" in self._storage_parameters
else _oss.Auth if self._storage_parameters else _oss.AnonymousAuth
)(**self._storage_parameters)
def _get_roots(self):
"""Return URL roots for this storage.
Returns:
tuple of str or re.Pattern: URL roots
"""
return (
# OSS Scheme
# - oss://<bucket>/<key>
"oss://",
# URL (With common aliyuncs.com endpoint):
# - http://<bucket>.oss-<region>.aliyuncs.com/<key>
# - https://<bucket>.oss-<region>.aliyuncs.com/<key>
# Note: "oss-<region>.aliyuncs.com" may be replaced by another endpoint
_re.compile(
(r"^https?://[\w-]+.%s" % self._endpoint.split("//", 1)[1]).replace(
".", r"\."
)
),
)
def _get_bucket(self, client_kwargs):
"""Get bucket object.
Returns:
oss2.Bucket
"""
return _oss.Bucket(
self.client,
endpoint=self._endpoint,
bucket_name=client_kwargs["bucket_name"],
)
def islink(self, path=None, client_kwargs=None, header=None):
"""Returns True if the object is a symbolic link.
Args:
path (str): File path or URL.
client_kwargs (dict): Client arguments.
header (dict): Object header.
Returns:
bool: True if the object is Symlink.
"""
header = self.head(path, client_kwargs, header)
for key in ("x-oss-object-type", "type"):
try:
return header.pop(key) == "Symlink"
except KeyError:
continue
return False
def _head(self, client_kwargs):
"""Returns object HTTP header.
Args:
client_kwargs (dict): Client arguments.
Returns:
dict: HTTP header.
"""
with _handle_oss_error():
bucket = self._get_bucket(client_kwargs)
if "key" in client_kwargs:
return bucket.head_object(key=client_kwargs["key"]).headers
return bucket.get_bucket_info().headers
def _make_dir(self, client_kwargs):
"""Make a directory.
Args:
client_kwargs (dict): Client arguments.
"""
with _handle_oss_error():
bucket = self._get_bucket(client_kwargs)
if "key" in client_kwargs:
return bucket.put_object(key=client_kwargs["key"], data=b"")
return bucket.create_bucket()
def _remove(self, client_kwargs):
"""Remove an object.
Args:
client_kwargs (dict): Client arguments.
"""
with _handle_oss_error():
bucket = self._get_bucket(client_kwargs)
if "key" in client_kwargs:
return bucket.delete_object(key=client_kwargs["key"])
return bucket.delete_bucket()
@staticmethod
def _model_to_dict(model, ignore):
"""Convert OSS model to dict.
Args:
model (oss2.models.RequestResult): Model.
ignore (tuple of str): Keys to not insert to dict.
Returns:
dict: Model dict version.
"""
return {
attr: value
for attr, value in model.__dict__.items()
if not attr.startswith("_") and attr not in ignore
}
def _list_locators(self, max_results):
"""List locators.
Args:
max_results (int): The maximum results that should return the method.
Yields:
tuple: locator name str, locator header dict, has content bool
"""
with _handle_oss_error():
response = _oss.Service(self.client, endpoint=self._endpoint).list_buckets(
max_keys=max_results or 100
)
for bucket in response.buckets:
yield bucket.name, self._model_to_dict(bucket, ("name",)), True
def _list_objects(self, client_kwargs, path, max_results, first_level):
"""List objects.
Args:
client_kwargs (dict): Client arguments.
path (str): Path to list.
max_results (int): The maximum results that should return the method.
first_level (bool): If True, may only first level objects.
Yields:
tuple: object path str, object header dict, has content bool
"""
prefix = self.split_locator(path)[1]
index = len(prefix)
kwargs = dict(prefix=prefix)
if max_results:
kwargs["max_keys"] = max_results
bucket = self._get_bucket(client_kwargs)
while True:
with _handle_oss_error():
response = bucket.list_objects(**kwargs)
if not response.object_list:
raise _ObjectNotFoundError(path=path)
for obj in response.object_list:
yield obj.key[index:], self._model_to_dict(obj, ("key",)), False
if response.next_marker:
client_kwargs["marker"] = response.next_marker
else:
break
def read_link(self, path=None, client_kwargs=None, header=None):
"""Return the path linked by the symbolic link.
Args:
path (str): File path or URL.
client_kwargs (dict): Client arguments.
header (dict): Object header.
Returns:
str: Path.
"""
if client_kwargs is None:
client_kwargs = self.get_client_kwargs(path)
try:
key = client_kwargs["key"]
except KeyError:
raise _ObjectNotASymlinkError(path=path)
with _handle_oss_error():
return path.rsplit(key, 1)[0] + (
self._get_bucket(client_kwargs).get_symlink(symlink_key=key).target_key
)
def symlink(self, target, path=None, client_kwargs=None):
"""Create a symbolic link to target.
Args:
target (str): Target path or URL.
path (str): File path or URL.
client_kwargs (dict): Client arguments.
"""
if client_kwargs is None:
client_kwargs = self.get_client_kwargs(path)
target_client_kwargs = self.get_client_kwargs(target)
if client_kwargs["bucket_name"] != target_client_kwargs["bucket_name"]:
raise _ObjectNotImplementedError("Cross bucket symlinks are not supported")
try:
symlink_key = client_kwargs["key"]
target_key = target_client_kwargs["key"]
except KeyError:
raise _ObjectNotImplementedError(
"Symlinks to or from bucket root are not supported"
)
with _handle_oss_error():
return self._get_bucket(client_kwargs).put_symlink(target_key, symlink_key)
class OSSRawIO(_ObjectRawIOBase):
"""Binary OSS Object I/O.
Args:
name (path-like object): URL or path to the file which will be opened.
mode (str): The mode can be 'r', 'w', 'a' for reading (default), writing or
appending.
storage_parameters (dict): OSS2 Auth keyword arguments and endpoint.
This is generally OSS credentials and configuration.
unsecure (bool): If True, disables TLS/SSL to improve transfer performance.
But makes connection unsecure.
"""
_SYSTEM_CLASS = _OSSSystem
@property # type: ignore
@_memoizedmethod
def _bucket(self):
"""Bucket client.
Returns:
oss2.Bucket: Client.
"""
return self._system._get_bucket(self._client_kwargs)
@property # type: ignore
@_memoizedmethod
def _key(self):
"""Object key.
Returns:
str: key.
"""
return self._client_kwargs["key"]
def _read_range(self, start, end=0):
"""Read a range of bytes in stream.
Args:
start (int): Start stream position.
end (int): End stream position. 0 To not specify the end.
Returns:
bytes: number of bytes read
"""
if start >= self._size:
# EOF. Do not detect using 416 (Out of range) error, 200 returned.
return bytes()
with _handle_oss_error():
response = self._bucket.get_object(
key=self._key,
headers=dict(
Range=self._http_range(
start,
end if end <= self._size else self._size,
)
),
)
return response.read()
def _readall(self):
"""Read and return all the bytes from the stream until EOF.
Returns:
bytes: Object content
"""
with _handle_oss_error():
return self._bucket.get_object(key=self._key).read()
def _flush(self, buffer):
"""Flush the write buffers of the stream if applicable.
Args:
buffer (memoryview): Buffer content.
"""
with _handle_oss_error():
self._bucket.put_object(key=self._key, data=buffer.tobytes())
class OSSBufferedIO(_ObjectBufferedIOBase):
"""Buffered binary OSS Object I/O."""
__slots__ = ("_bucket", "_key", "_upload_id")
_RAW_CLASS = OSSRawIO
#: Minimal buffer_size in bytes (OSS multipart upload minimal part size)
MINIMUM_BUFFER_SIZE = 102400
def __init__(self, *args, **kwargs):
"""Init.
Args:
name (path-like object): URL or path to the file which will be opened.
mode (str): The mode can be 'r', 'w' for reading (default) or writing
buffer_size (int): The size of buffer.
max_buffers (int): The maximum number of buffers to preload in read mode or
awaiting flush in "write" mode. 0 for no limit.
max_workers (int): The maximum number of threads that can be used to execute
the given calls.
storage_parameters (dict): OSS2 Auth keyword arguments and endpoint.
This is generally OSS credentials and configuration.
unsecure (bool): If True, disables TLS/SSL to improve transfer performance.
But makes connection unsecure.
"""
_ObjectBufferedIOBase.__init__(self, *args, **kwargs)
self._bucket = self._raw._bucket
self._key = self._raw._key
self._upload_id = None
def _flush(self):
"""Flush the write buffers of the stream."""
if self._upload_id is None:
with _handle_oss_error():
self._upload_id = self._bucket.init_multipart_upload(
self._key
).upload_id
response = self._workers.submit(
self._bucket.upload_part,
key=self._key,
upload_id=self._upload_id,
part_number=self._seek,
data=self._get_buffer().tobytes(),
)
self._write_futures.append(dict(response=response, part_number=self._seek))
def _close_writable(self):
"""Close the object in "write" mode."""
parts = [
_PartInfo(
part_number=future["part_number"], etag=future["response"].result().etag
)
for future in self._write_futures
]
with _handle_oss_error():
try:
self._bucket.complete_multipart_upload(
key=self._key, upload_id=self._upload_id, parts=parts
)
except _OssError:
self._bucket.abort_multipart_upload(
key=self._key, upload_id=self._upload_id
)
raise
|
from django.contrib import admin
from .models import *
class AlunoAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = ('nome','ra','cod_energia','escola')
search_fields = (['nome','ra','cod_energia', 'escola'])
class AgenciaTransporteAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome'])
list_filter = ('nome','sre')
search_fields = (['nome'])
class EscolaAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome', 'municipio', 'cod_inep'])
list_filter = (['municipio'])
search_fields = (['nome', 'cod_inep'])
class MunicipioAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome', 'sre', 'cod_ibge'])
list_filter = (['sre'])
search_fields = (['nome', 'cod_ibge'])
class ReclamanteAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome', 'email'])
search_fields = (['nome', 'email'])
class ResponsavelAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['usuario','sre'])
list_filter = (['sre'])
search_fields = (['usuario'])
class SreAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['name'])
list_filter = ([])
search_fields = (['name'])
class ComentarioInline(admin.StackedInline):
model = Comentario
extra = 0
fields = ["responsavel", "texto"]
class ParecerFinalInline(admin.StackedInline):
model = ParecerFinal
extra = 0
fields = ["responsavel", "texto"]
class ReclamacaoAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = ('aluno','protocolo', 'sre_responsavel', 'setor', 'tipo', 'reclamante', 'status', 'rota')
list_filter = ('agencia_transporte', 'status', 'tipo', 'sre_responsavel')
search_fields = (['protocolo', 'aluno', 'rota', 'placa_veiculo', 'sre_responsavel'])
inlines = [ComentarioInline, ParecerFinalInline]
readonly_fields = ['protocolo', 'status']
def escola(self, obj):
return obj.aluno.escola
def tipo(self, obj):
return obj.tipo
def setor(self, obj):
return obj.tipo.setor
class TipoReclamacaoAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome'])
search_fields = (['nome'])
class SetorAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome'])
search_fields = (['nome'])
class TurnoAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome'])
search_fields = (['nome'])
class RotaAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_filter = ['turno']
list_display = (['nome', 'turno'])
search_fields = (['nome', 'turno'])
class PapelAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['nome'])
search_fields = (['nome'])
class RotaEscolaAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['rota', 'escola'])
search_fields = (['rota', 'escola'])
class RotaAlunoAdmin(admin.ModelAdmin):
empty_value_display = 'Nenhum'
list_display = (['rota', 'aluno'])
search_fields = (['rota', 'aluno'])
#admin.site.register(Municipio, MunicipioAdmin)
#admin.site.register(Escola, EscolaAdmin)
admin.site.register(AgenciaTransporte, AgenciaTransporteAdmin)
#admin.site.register(SRE, SreAdmin)
admin.site.register(Reclamante, ReclamanteAdmin)
admin.site.register(Reclamacao, ReclamacaoAdmin)
#admin.site.register(Aluno, AlunoAdmin)
admin.site.register(Responsavel, ResponsavelAdmin)
admin.site.register(TipoReclamacao, TipoReclamacaoAdmin)
admin.site.register(Setor, SetorAdmin)
admin.site.register(Turno, TurnoAdmin)
#admin.site.register(Rota, RotaAdmin)
admin.site.register(Papel, PapelAdmin)
# admin.site.register(Token, TokenAdmin)
#admin.site.register(RotaEscola, RotaEscolaAdmin)
#admin.site.register(RotaAluno, RotaAlunoAdmin)
|
#!/usr/bin/python
import os
virtenv = os.environ['APPDIR'] + '/virtenv/'
os.environ['PYTHON_EGG_CACHE'] = os.path.join(virtenv, 'lib/python2.6/site-packages')
virtualenv = os.path.join(virtenv, 'bin/activate_this.py')
try:
execfile(virtualenv, dict(__file__=virtualenv))
except:
pass
# new codes we adding for Django
import sys
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "cherry.settings")
from django.core.wsgi import get_wsgi_application
sys.path.append(os.path.join(os.environ['OPENSHIFT_REPO_DIR'], 'wsgi', os.environ['OPENSHIFT_APP_NAME']))
application = get_wsgi_application()
# application = django.core.handlers.wsgi.WSGIHandler()
|
#!usr/bin/python
score_C=int(input("请输入语文成绩："))
score_M=int(input("请输入数学成绩："))
score_E=int(input("请输入英语成绩："))
if score_C>score_M:
if score_M>score_E:
print(score_C)
print(score_E)
else:
if score_C>score_E:
print(score_C)
print(score_M)
else:
print(score_E)
print(score_M)
else:
if score_M<score_E:
print(score_E)
print(score_C)
else:
if score_C>score_E:
print(score_M)
print(score_E)
else:
print(score_M)
print(score_C)
average=(score_C+score_M+score_E)/3
print(average)
|
# Generated by Django 3.1.4 on 2021-01-08 08:16
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('myapi', '0003_auto_20210108_1359'),
]
operations = [
migrations.DeleteModel(
name='Category',
),
]
|
from django.db.models import Q, Count
from utils.file.export_task import ExportExcelTask
from reman.models import Batch, Repair, EcuRefBase
REMAN_DICT = {
'batch': [
('Numero de lot', 'batch_number'), ('Quantite', 'quantity'), ('Ref_REMAN', 'ecu_ref_base__reman_reference'),
('Client', 'customer'), ('Réparés', 'repaired'), ('Rebuts', 'rebutted'), ('Emballés', 'packed'),
('Total', 'total'), ('Date_de_Debut', 'start_date'), ('Date_de_fin', 'end_date'),
('Type_ECU', 'ecu_ref_base__ecu_type__technical_data'),
('HW_Reference', 'ecu_ref_base__ecu_type__hw_reference'), ('Fabriquant', 'ecu_ref_base__ecu_type__supplier_oe'),
('Actif', 'active')
],
'repair': [
('Numero_identification', 'identify_number'), ('Numero_lot', 'batch__batch_number'),
('Ref_REMAN', 'batch__ecu_ref_base__reman_reference'),
('Type_ECU', 'batch__ecu_ref_base__ecu_type__technical_data'),
('Fabriquant', 'batch__ecu_ref_base__ecu_type__supplier_oe'),
('HW_Reference', 'batch__ecu_ref_base__ecu_type__hw_reference'),
('Code_barre', 'barcode'), ('Nouveau_code_barre', 'new_barcode'), ('Code_defaut', 'default__code'),
('Libelle_defaut', 'default__description'), ('Commentaires_action', 'comment'), ('status', 'status'),
('Controle_qualite', 'quality_control'), ('Date_de_cloture', 'closing_date')
],
'base_ref': [
('Reference OE', 'ecu_type__ecumodel__oe_raw_reference'), ('REFERENCE REMAN', 'reman_reference'),
('Module Moteur', 'ecu_type__technical_data'), ('Réf HW', 'ecu_type__hw_reference'),
('FNR', 'ecu_type__supplier_oe'), ('CODE BARRE PSA', 'ecu_type__ecumodel__barcode'),
('REF FNR', 'ecu_type__ecumodel__former_oe_reference'), ('REF CAL OUT', 'ref_cal_out'),
('REF à créer ', 'ecu_type__spare_part__code_produit'), ('REF_PSA_OUT', 'ref_psa_out'),
('REQ_DIAG', 'req_diag'), ('OPENDIAG', 'open_diag'), ('REQ_REF', 'req_ref'), ('REF_MAT', 'ref_mat'),
('REF_COMP', 'ref_comp'), ('REQ_CAL', 'req_cal'), ('CAL_KTAG', 'cal_ktag'), ('REQ_STATUS', 'req_status'),
('STATUS', 'status'), ('TEST_CLEAR_MEMORY', 'test_clear_memory'), ('CLE_APPLI', 'cle_appli')
],
'created': [
('Cree par', 'created_by__username'), ('Cree_le', 'created_at'),
],
'updated': [
('Modifie_par', 'modified_by__username'), ('Modifie_le', 'modified_at')
],
'remanufacturing': [
('FACE PLATE', 'face_plate'), ('FAN', 'fan'), ('REAR BOLT', 'locating_pin'), ('METAL CASE', 'metal_case')
]
}
class ExportRemanIntoExcelTask(ExportExcelTask):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.fields = []
def run(self, *args, **kwargs):
excel_type = kwargs.pop('excel_type', 'xlsx')
model = kwargs.pop('table', 'batch')
filename = f"{model}"
if model == "base_ref_reman":
values_list = self.extract_ecurefbase(*args, **kwargs)
elif model == "repair_reman":
values_list = self.extract_repair(*args, **kwargs)
else:
values_list = self.extract_batch(*args, **kwargs)
destination_path = self.file(filename, excel_type, values_list)
return {
"detail": "Successfully export REMAN",
"data": {
"outfile": destination_path
}
}
def extract_batch(self, *args, **kwargs):
"""
Export Batch data to excel format
"""
data_list = REMAN_DICT['batch'] + REMAN_DICT['created']
repaired = Count('repairs', filter=Q(repairs__status="Réparé"))
rebutted = Count('repairs', filter=Q(repairs__status="Rebut"))
packed = Count('repairs', filter=Q(repairs__checkout=True))
queryset = Batch.objects.all().order_by('batch_number')
queryset = queryset.annotate(repaired=repaired, packed=packed, rebutted=rebutted, total=Count('repairs'))
self.header, self.fields = self.get_header_fields(data_list)
return queryset.values_list(*self.fields).distinct()
def extract_ecurefbase(self, *args, **kwargs):
"""
Export EcuRefBase data to excel format
"""
data_list = REMAN_DICT['base_ref']
queryset = EcuRefBase.objects.exclude(test_clear_memory__exact='').order_by('reman_reference')
self.header, self.fields = self.get_header_fields(data_list)
return queryset.values_list(*self.fields).distinct()
def extract_repair(self, *args, **kwargs):
"""
Export Repair data to excel format
"""
data_list = REMAN_DICT['repair'] + REMAN_DICT['remanufacturing'] + REMAN_DICT['created']
data_list += REMAN_DICT['updated']
queryset = Repair.objects.all().order_by('identify_number')
if kwargs.get('customer', None):
queryset = queryset.filter(batch__customer=kwargs.get('customer'))
if kwargs.get('batch_number', None):
queryset = queryset.filter(batch__batch_number=kwargs.get('batch_number'))
self.header, self.fields = self.get_header_fields(data_list)
values_list = queryset.values_list(*self.fields).distinct()
if "repair_parts" in kwargs.get('columns', []):
self.textCols = [len(data_list) + 1, len(data_list) + 2]
values_list = self._add_parts(values_list)
return values_list
def _add_parts(self, values_list):
self.header.extend(["Code_produit (PART)", "Quantité (PART)"])
new_values_list = []
for values in values_list:
values = list(values)
try:
product_code, quantity = "", ""
for part in Repair.objects.get(identify_number=values[0]).parts.all():
product_code += f"{part.product_code} \r\n"
quantity += f"{part.quantity} \r\n"
values.extend([product_code.strip(), quantity.strip()])
except Repair.DoesNotExist:
pass
finally:
new_values_list.append(values)
return new_values_list
|
from django import forms
from src.bo.Enum import TimePeriod, Index, TransactionType, PositionType
from src.bo.static.Calendar import Calendar
import models
from models import Portfolio, TCBond, Identifier, Equity, ModelPosition, TCSwap
from models import InterestRateCurve, Location, UserProfile, Transaction, Batch
from ajax_select.fields import AutoCompleteSelectField
from crispy_forms.helper import FormHelper
from crispy_forms.layout import Submit, Layout, Div
class HVaRParameters(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form validation. Can be changed at some point to request or session
self.user = user
super(HVaRParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
portfolio = AutoCompleteSelectField('portfolio',required=True)
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required = True)
endDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
stepSize = forms.IntegerField(required=True)
stepUnit = forms.ChoiceField(choices=TimePeriod.choices,required=True)
calendar = forms.ChoiceField(choices=Calendar.choices,required=True)
confLevel = forms.FloatField(required=True)
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
marketId = forms.CharField(required=True)
def clean(self):
try:
#TODO Maybe fix portfolio validation. Currently workaround used
#only done to validate the field
portfolio = self.cleaned_data['portfolio']
except KeyError:
raise forms.ValidationError('Some field does not exist')
if self.cleaned_data['endDate'] <= self.cleaned_data['startDate']:
raise forms.ValidationError('Start date must be before end date')
return self.cleaned_data
class HVaRParametersPreConfigured(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form valiadtion. Can be changed at some point to request or session
self.user = user
super(HVaRParametersPreConfigured, self).__init__(*args, **kwargs)
self.helper = FormHelper()
portfolio = AutoCompleteSelectField('portfolio',required=True)
config = AutoCompleteSelectField('hvarconfig',required=True)
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
def clean(self):
try:
portfolio = self.cleaned_data['portfolio']
config = self.cleaned_data['config']
except KeyError:
raise forms.ValidationError('Some field does not exist')
return self.cleaned_data
class ValuationReportParameters(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form validation. Can be changed at some point to request or session
self.user = user
super(ValuationReportParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
portfolio = AutoCompleteSelectField('portfolio',required=True)
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
marketId = forms.CharField(required=True)
def clean(self):
try:
portfolio = self.cleaned_data['portfolio']
except KeyError:
raise forms.ValidationError('Portfolio does not exist')
return self.cleaned_data
class LoadEquityPrices(forms.Form):
def __init__(self, *args, **kwargs):
super(LoadEquityPrices, self).__init__(*args, **kwargs)
self.helper = FormHelper()
equity = AutoCompleteSelectField('equity')
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
endDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
calendar = forms.ChoiceField(choices=Calendar.choices,required=True)
marketId = forms.CharField(required=True)
def clean(self):
if self.cleaned_data['endDate'] < self.cleaned_data['startDate']:
raise forms.ValidationError('Start date must be equal or before end date')
return self.cleaned_data
class LoadMissingMarketDataForPortfolioForm(forms.Form):
def __init__(self, *args, **kwargs):
super(LoadMissingMarketDataForPortfolioForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
portfolio = AutoCompleteSelectField('portfolio',required=True)
asOf = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
endDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
calendar = forms.ChoiceField(choices=Calendar.choices,required=True)
marketId = forms.CharField(required=True)
def clean(self):
if self.cleaned_data['endDate'] < self.cleaned_data['startDate']:
raise forms.ValidationError('Start date must be equal or before end date')
return self.cleaned_data
class PositionReportParameters(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form valiadtion. Can be changed at some point to request or session
self.user = user
super(PositionReportParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
portfolio = AutoCompleteSelectField('portfolio')
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
def clean(self):
try:
portfolio = self.cleaned_data['portfolio']
except KeyError:
raise forms.ValidationError('Portfolio does not exist')
return self.cleaned_data
class EquityPricesReportForm(forms.Form):
equity = AutoCompleteSelectField('equity', required=True)
marketId = forms.CharField(required=True)
def __init__(self, *args, **kwargs):
super(EquityPricesReportForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
def clean(self):
try:
equity = self.cleaned_data['equity']
except KeyError:
raise forms.ValidationError('Equity does not exist')
return self.cleaned_data
class PortfolioForm(forms.ModelForm):
def __init__(self, user, *args, **kwargs):
self.user = user
super(PortfolioForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = Portfolio
def clean(self):
formInputUser = self.cleaned_data['user']
if not self.user.username == formInputUser:
raise forms.ValidationError('Use your own user name as user')
return self.cleaned_data
class TCBondCalculatorForm(forms.ModelForm):
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),
input_formats=('%m/%d/%y',))
marketId = forms.CharField()
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),
input_formats=('%m/%d/%y',))
endDate = forms.DateField(widget=forms.DateInput(format = '%m/%d/%y'),
input_formats=('%m/%d/%y',))
def __init__(self, *args, **kwargs):
super(TCBondCalculatorForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
self.helper.layout = Layout(Div(Div('pricingDate', 'name', 'startDate', 'coupon', 'paymentFrequency', 'paymentCalendar',
css_class='large-6 columns'),
Div('marketId', 'ccy', 'endDate', 'basis', 'paymentRollRule', 'paymentCalendar',
css_class='large-6 columns'),
css_class="row"))
class Meta:
model = TCBond
def clean(self):
return self.cleaned_data
class TCSwapCalculatorForm(forms.ModelForm):
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),
input_formats=('%m/%d/%y',))
marketId = forms.CharField()
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),
input_formats=('%m/%d/%y',))
endDate = forms.DateField(widget=forms.DateInput(format = '%m/%d/%y'),
input_formats=('%m/%d/%y',))
def __init__(self, *args, **kwargs):
super(TCSwapCalculatorForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
self.helper.layout = Layout(Div(Div('pricingDate', 'name', 'startDate',
css_class='large-6 columns'),
Div('marketId', 'ccy', 'endDate',
css_class='large-6 columns'),
css_class="row"),
Div(Div('fixedCoupon','fixedBasis', 'fixedPaymentFrequency',
'fixedPaymentRollRule', 'fixedPaymentCalendar',
css_class="large-6 columns"),
Div('floatingIndex','floatingIndexTerm', 'floatingIndexNumTerms', 'floatingSpread',
'floatingBasis', 'floatingPaymentFrequency', 'floatingPaymentRollRule',
'floatingPaymentCalendar', 'floatingResetFrequency', 'floatingResetRollRule',
'floatingResetCalendar',
css_class="large-6 columns"),
css_class="row"))
class Meta:
model = TCSwap
def clean(self):
if self.cleaned_data['floatingIndex'] <> Index('LIBOR'):
raise forms.ValidationError('Only Libor currently implemented')
return self.cleaned_data
class IdentifierForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(IdentifierForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = Identifier
def clean(self):
return self.cleaned_data
class EquityForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(EquityForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = Equity
def clean(self):
return self.cleaned_data
class PositionForm(forms.ModelForm):
def __init__(self, user, *args, **kwargs):
super(PositionForm, self).__init__(*args, **kwargs)
self.fields['portfolio'].queryset = Portfolio.objects.filter(user=user)
self.helper = FormHelper()
class Meta:
model = ModelPosition
def clean(self):
positionType = self.cleaned_data['positionType']
ticker = self.cleaned_data['ticker']
if positionType not in tuple(x[0] for x in PositionType.choices):
raise forms.ValidationError('PositionType %s invalid' % positionType)
if not models.tickerExists(positionType, ticker):
raise forms.ValidationError('Ticker %s does not exist' % ticker)
return self.cleaned_data
class InterestRateCurveReportParameters(forms.ModelForm):
def __init__(self, *args, **kwargs):
#user is used in form valiadtion. Can be changed at some point to request or session
super(InterestRateCurveReportParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = InterestRateCurve
def clean(self):
term = self.cleaned_data['term']
return self.cleaned_data
class CorrelationReportParameters(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form valiadtion. Can be changed at some point to request or session
self.user = user
super(CorrelationReportParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
portfolio = AutoCompleteSelectField('portfolio', required=True)
benchmark = AutoCompleteSelectField('equity', required=True)
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
endDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
stepSize = forms.IntegerField(required=True)
stepUnit = forms.ChoiceField(choices=TimePeriod.choices, required=True)
calendar = forms.ChoiceField(choices=Calendar.choices, required=True)
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
marketId = forms.CharField(required=True)
def clean(self):
#TODO LOW Change the validation on the form to a form portfolio field
try:
portfolio = self.cleaned_data['portfolio']
benchmark = self.cleaned_data['benchmark']
except KeyError:
raise forms.ValidationError('One Field does not exist')
return self.cleaned_data
class LocationForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(LocationForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = Location
def clean(self):
return self.cleaned_data
class UserProfileForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(UserProfileForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = UserProfile
def clean(self):
return self.cleaned_data
class TCBondForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(TCBondForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = TCBond
def clean(self):
return self.cleaned_data
class TCSwapForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(TCSwapForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = TCSwap
def clean(self):
if self.cleaned_data['floatingIndex'] <> Index('LIBOR'):
raise forms.ValidationError('Only Libor currently implemented')
return self.cleaned_data
class TransactionForm(forms.ModelForm):
def __init__(self, user, *args, **kwargs):
super(TransactionForm, self).__init__(*args, **kwargs)
self.fields['portfolio'].queryset = Portfolio.objects.filter(user=user)
self.helper = FormHelper()
class Meta:
model = Transaction
exclude = ('reflectedInPosition')
def clean(self):
transactionType = self.cleaned_data['transactionType']
if transactionType not in tuple(x[0] for x in TransactionType.choices):
raise forms.ValidationError('TransactionType %s not valid' % transactionType)
positionType = self.cleaned_data['positionType']
if positionType not in tuple(x[0] for x in PositionType.choices):
raise forms.ValidationError('PositionType %s invalid' % positionType)
ticker = self.cleaned_data['ticker']
if not models.tickerExists(positionType, ticker):
raise forms.ValidationError('Ticker %s does not exist' % ticker)
return self.cleaned_data
class BatchForm(forms.ModelForm):
def __init__(self, *args, **kwargs):
super(BatchForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
class Meta:
model = Batch
class MultiBatchesForm(forms.Form):
def __init__(self, *args, **kwargs):
super(MultiBatchesForm, self).__init__(*args, **kwargs)
self.helper = FormHelper()
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
endDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
class PerformanceReportParameters(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form valiadtion. Can be changed at some point to request or session
self.user = user
super(PerformanceReportParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
endDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
marketId = forms.CharField(required=True)
def clean(self):
if self.cleaned_data['endDate'] == self.cleaned_data['startDate']:
raise forms.ValidationError('Start date cannot be equal to end date')
return self.cleaned_data
class AssetAllocationReportParameters(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form valiadtion. Can be changed at some point to request or session
self.user = user
super(AssetAllocationReportParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
pricingDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
marketId = forms.CharField(required=True)
def clean(self):
return self.cleaned_data
class NetWorthTrendReportParameters(forms.Form):
def __init__(self, user, *args, **kwargs):
#user is used in form valiadtion. Can be changed at some point to request or session
self.user = user
super(NetWorthTrendReportParameters, self).__init__(*args, **kwargs)
self.helper = FormHelper()
startDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
endDate = forms.DateField(widget = forms.DateInput(format = '%m/%d/%y'),input_formats = ('%m/%d/%y',),required=True)
marketId = forms.CharField(required=True)
def clean(self):
if self.cleaned_data['endDate'] == self.cleaned_data['startDate']:
raise forms.ValidationError('Start date cannot be equal to end date')
return self.cleaned_data
|
#NATURAL SELECTION
# #
# sim15d_mono_1of2.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 2 # Initial Number of Subpopns
d = 3 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_nat_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_nat_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_nat_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_nat_lomu_j_370005
#NATURAL SELECTION
# #
# sim15d_mono.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 1 # Initial Number of Subpopns
d = 6 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_nat_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_nat_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_mono_seq_subset_nat_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_nat_lomu_j_370005
#NATURAL SELECTION
# #
# sim15d_diphyletic.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 1 # Initial Number of Subpopns
d = 6 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_nat_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_nat_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_diphyletic_seq_subset_nat_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_nat_lomu_j_370005
#NATURAL SELECTION
# #
# sim15d_diphyletic_sep.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 1 # Initial Number of Subpopns
d = 6 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_a_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_b_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_c_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_d_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_e_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_f_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_g_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_h_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_i_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_nat_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_nat_lomu_j_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_nat_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaSelector(loci=5, fitness = [1,1-s1,1-s1], wildtype = 1, begin = j1, end = (j1+d1)),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_nat_lomu_j_370005
#ARTIFICIAL SELECTION
# #
# sim15d_mono_1of2.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 2 # Initial Number of Subpopns
d = 3 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_a_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_a_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_b_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_b_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_c_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_c_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_d_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_d_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_e_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_e_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_f_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_f_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_g_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_g_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_h_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_h_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_i_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_i_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_1of2_seq_subset_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_1of2_seq_subset_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_1of2_seq_subset_lomu_j_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_1of2_seq_subset_lomu_j_370005_sample_%d.phy sim15d_mono_1of2_seq_subset_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_1of2_lomu_j_370005
#ARTIFICIAL SELECTION
# #
# sim15d_mono.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 1 # Initial Number of Subpopns
d = 6 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_a_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_a_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_b_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_b_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_c_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_c_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_d_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_d_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_e_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_e_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_f_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_f_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_g_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_g_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_h_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_h_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_i_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_i_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_mono_seq_subset_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_mono_seq_subset_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_mono_seq_subset_lomu_j_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_mono_seq_subset_lomu_j_370005_sample_%d.phy sim15d_mono_seq_subset_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_mono_lomu_j_370005
#ARTIFICIAL SELECTION
# #
# sim15d_diphyletic.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 1 # Initial Number of Subpopns
d = 6 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_a_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_a_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_b_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_b_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_c_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_c_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_d_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_d_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_e_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_e_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_f_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_f_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_g_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_g_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_h_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_h_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_i_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_i_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_seq_subset_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_seq_subset_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_seq_subset_lomu_j_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_seq_subset_lomu_j_370005_sample_%d.phy sim15d_diphyletic_seq_subset_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_lomu_j_370005
#ARTIFICIAL SELECTION
# #
# sim15d_diphyletic_sep.py
#
# # Script to simulate a population with n subpops of i individuals, with population rebound,
# natural selection and sampling. Selection occuring at different periods. Equal events spacing
# 2 Initial subpopns, expanding to 6
# output as genotypes for Powermarker
# Selection at locus 4 by exclusion of minor allele (bottleneck) before splitting
# If other selection events before that at locus 4, this occurs before the split also
#
# phylip, fasta format
#
# #
# Author: Richard Stephens
# Created: July 30, 2013 09:04:33 AM
# Modified: July 30, 2013 09:04:47 AM
# #
import simuOpt
import simuPOP as sim
import math, os
from simuPOP.utils import export, saveCSV, Exporter, migrIslandRates, importPopulation, viewVars
from simuPOP.sampling import drawRandomSample
## Settings ###################################################################################
n = 1 # Initial Number of Subpopns
d = 6 # Divisor for subpopn splitting
i = 100000 # Number of Indivs/Subpopn
l = 30 # Number of Loci per Chromosome
c = 2 # Number of Chromosomes
g = 10 # Number of Steps (Generations) before expansion
t = 2500 # Total Number of Steps (Generations)
u = 0.005 # Forward Mutation Rate
v = 0.0005# Backward Mutation Rate
m = 0.0005 # Migration rate
e = 98 # Proportion of selfing
s1 = 0.008 # selection coefficient
q = 100000 # Maximum Population Size
r1 = 2.5e-4 #mean selection intensity as on chr 3H (based on a rate of 0.4Mb/cM) ie.(1/4/100)
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_a_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_a_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_a_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_a_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_a_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_a_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_a_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_b_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_b_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_b_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_b_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_b_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_b_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_b_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_c_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_c_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_c_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_c_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_c_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_c_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_c_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_d_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_d_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_d_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_d_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_d_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_d_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_d_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_e_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_e_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_e_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_e_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_e_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_e_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_e_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_f_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_f_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_f_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_f_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_f_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_f_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_f_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_g_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_g_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_g_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_g_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_g_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_g_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_g_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_h_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_h_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_h_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_h_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_h_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_h_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_h_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_i_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_i_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_i_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_i_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_i_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_i_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_i_370005
##################################################################################################
z1 = 500 # Number of Steps (Generations) before population splitting
j1 = 400 # Number of Steps (Generations) before selection event 1
j2 = 550 # Number of Steps (Generations) before selection event 2
j3 = 100 # Number of Steps (Generations) before selection event 3
d1 = 50 # duration of selection event 1 (in generations)
d2 = 50 # duration of selection event 2 (in generations)
d3 = 50 # duration of selection event 3 (in generations)
##################################################################################################
pop = sim.Population(size=[i]*n, loci=[l]*c, lociPos = list(range(0,30,1))*2, infoFields=['migrate_to','fitness'])
pop.setVirtualSplitter(sim.ProductSplitter([
sim.AffectionSplitter()
])
)
z=20
def demo(pop,gen):
global q, g
rate=2**15
# if .... stop growing
if gen >= g and all([x < q for x in pop.subPopSizes()]):
return ([(x+rate) for x in pop.subPopSizes(ancGen=-1)])
elif gen >= g and all([x > q for x in pop.subPopSizes()]):
return ([q for x in pop.subPopSizes(ancGen=-1)])
else:
return ([x for x in pop.subPopSizes(ancGen=-1)])
def sampleAndExport(pop):
sz = pop.subPopSizes()
new_sz = [x//2000 for x in sz]
sample = drawRandomSample(pop, new_sz)
export(sample, format='fstat', output='sim15d_diphyletic_sep_seq_subset_lomu_j_370005_sample_%d.dat' % pop.dvars().gen, gui=False),
export(sample, format='phylip', output='sim15d_diphyletic_sep_seq_subset_lomu_j_370005_sample_%d.phy' % pop.dvars().gen, alleleNames = ('A','C','G','T'), gui=False),
os.system('perl convert_diploid.pl N sim15d_diphyletic_sep_seq_subset_lomu_j_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_j_370005_merged_sample_%d.phy' % (pop.dvars().gen, pop.dvars().gen)),
os.system('perl phylip_to_fasta.pl sim15d_diphyletic_sep_seq_subset_lomu_j_370005_sample_%d.phy sim15d_diphyletic_sep_seq_subset_lomu_j_370005_sample_%d.fas' % (pop.dvars().gen, pop.dvars().gen)),
return True
simu = sim.Simulator(pop, rep=1)
simu.evolve(
initOps=[
sim.InitGenotype(loci = [3,7,21,36,50,54], freq=(0.0, 0.80, 0.20, 0.0)),
sim.InitGenotype(loci = [4,6,17,20,22,35,37,39,51,53,55], freq=(0.2, 0.0, 0.8, 0.0)),
sim.InitGenotype(loci = [5,19,23,38,52,58], freq=(0.0, 0.2, 0.0, 0.8)),
sim.InitGenotype(loci = [0,12,24,29,30,42,43,57,59], freq=(1.0, 0.0, 0.0, 0.0)),
sim.InitGenotype(loci = [1,9,14,16,26,33,44,46,47,49], freq=(0.0, 1.0, 0.0, 0.0)),
sim.InitGenotype(loci = [8,13,25,31,32,40,41,45], freq=(0.0, 0.0, 1.0, 0.0)),
sim.InitGenotype(loci = [2,10,11,15,18,27,28,34,48,56], freq=(0.0, 0.0, 0.0, 1.0)),
],
preOps=[
sim.SNPMutator(u = u, v = v),
sim.Migrator(rate=migrIslandRates(m, n), end = z1-1),
sim.SplitSubPops(subPops = [k for k in (list(range(n)))], proportions=[(1.0/d)]*d, randomize=True, at=z1),
sim.Migrator(rate=migrIslandRates(m, (n*d)), begin = z1+1),
sim.MaPenetrance(loci=5, penetrance=[1, 1, 0.0], wildtype=1),
sim.DiscardIf(True, subPops=[
(0, 'Affected')], at = j1),
sim.DiscardIf(True, subPops=[
(1, 'Affected')], at = j1+20)
# sim.MaSelector(loci=21, fitness = [1,1-s1,1-s1], wildtype = 2, begin = j2, end = (j2+d2)),
sim.MaSelector(loci=37, fitness = [1,1-s1,1-s1], wildtype = 0, begin = j3, end = (j3+d3)),
],
#mating scheme structure for sexless mating with variable proportion of selfing, with recombination
matingScheme=sim.HeteroMating([
sim.SelfMating(ops=sim.Recombinator(intensity=r1), weight=e),
sim.HomoMating(
chooser=sim.CombinedParentsChooser(
sim.RandomParentChooser(),
sim.RandomParentChooser()),
generator=sim.OffspringGenerator(
ops= [
sim.MendelianGenoTransmitter(),
sim.Recombinator(intensity=r1)
],
),
weight=100-e)
],
subPopSize=demo
),
postOps=[
sim.PyOperator(func=sampleAndExport, at = [0,500,t])
],
gen=(t+1),
)
print 'all done'
##sim15d_diphyletic_sep_lomu_j_370005
|
# -*- coding: utf-8 -*-
#
# Auxiliary functions for querying things/people
#
__all__ = []
def user_yesno(msg, default=None):
"""
Docstring
"""
# Parse optional `default` answer
valid = {"yes": True, "y": True, "ye":True, "no":False, "n":False}
if default is None:
suffix = " [y/n] "
elif default == "yes":
suffix = " [Y/n] "
elif default == "no":
suffix = " [y/N] "
# Wait for valid user input, if received return `True`/`False`
while True:
choice = input(msg + suffix).lower()
if default is not None and choice == "":
return valid[default]
elif choice in valid.keys():
return valid[choice]
else:
print("Please respond with 'yes' or 'no' (or 'y' or 'n').\n")
def user_input(msg, valid, default=None):
"""
Docstring
msg = str (message)
valid = list (avail. options, no need specifying 'a', and '[a]', code strips brackets)
default = str (default option, same as above)
"""
# Add trailing whitespace to `msg` if not already present and append
# default reply (if provided)
suffix = "" + " " * (not msg.endswith(" "))
if default is not None:
default = default.replace("[", "").replace("]","")
assert default in valid
suffix = "[Default: '{}'] ".format(default)
# Wait for valid user input and return choice upon receipt
while True:
choice = input(msg + suffix)
if default is not None and choice == "":
return default
elif choice in valid:
return choice
else:
print("Please respond with '" + \
"or '".join(opt + "' " for opt in valid) + "\n")
|
# Generated by Django 3.0.5 on 2020-06-01 08:51
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('holvi_orders', '0002_auto_20200331_1347'),
('fvh_courier', '0034_delete_userlocation'),
]
operations = [
migrations.CreateModel(
name='RequiredHolviProduct',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=128)),
('holvi_shop', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='required_product', to='holvi_orders.HolviWebshop')),
],
),
migrations.CreateModel(
name='IgnoredHolviProduct',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=128)),
('holvi_shop', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='ignored_products', to='holvi_orders.HolviWebshop')),
],
),
]
|
import dash
# from settings import PATH
# # external css
# icons = 'https://fonts.googleapis.com/icon?family=Material+Icons'
# external_stylesheets = [icons, {"href": icons, "rel": "stylesheet"}]
# external_scripts = [
# {"src": "https://code.jquery.com/jquery-3.4.1.min.js",
# "integrity": "sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=",
# "crossorigin": "anonymous"}
# ]
# assets_url_path=f"{PATH}/assets"
app = dash.Dash(__name__, meta_tags=[
# A description of the app, used by e.g.
# search engines when displaying search results.
{
'name': 'description',
'content': 'Quantz Stock Screener'
},
# A tag that tells Internet Explorer (IE)
# to use the latest renderer version available
# to that browser (e.g. Edge)
{
'http-equiv': 'X-UA-Compatible',
'content': 'IE=edge'
},
# A tag that tells the browser not to scale
# desktop widths to fit mobile screens.
# Sets the width of the viewport (browser)
# to the width of the device, and the zoom level
# (initial scale) to 1.
#
# Necessary for "true" mobile support.
{
'name': 'viewport',
'content': 'width=device-width, initial-scale=1.0'
}
])
app.config['suppress_callback_exceptions'] = True
app.title = "Stockscreener.dk - Free Financial Data For The Nordic Markets!"
app.index_string = '''
<!DOCTYPE html>
<html>
<head>
{%metas%}
<title>{%title%}</title>
{%favicon%}
{%css%}
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-152874377-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-152874377-1');
</script>
</head>
<body>
{%app_entry%}
<footer>
{%config%}
{%scripts%}
{%renderer%}
</footer>
</body>
</html>
'''
|
'''
파이썬의 조건문
쉬운 내용이라 대부분 스킵했음.
if / elif / else
중요한건 elif는 if의 조건문에 해당 안되면서, elif의 조건문을 만족할 때 분기가 걸림.
pass 키워드
아무것도 처리하고 싶지 않을 때 (디버깅 할 때 사용)
한줄일 경우에는 간략하게 표현 가능.
if score >= 80: result = "Success"
else: result = "Fail"
if ~ else를 한줄에 작성 가능.
score = 85
result = "Success" if score >= 80 else "Fail"
파이썬은 수학의 부등식을 그대로 쓸 수 있음.
x > 0 and x < 20은
0 < x < 20 과 같음.
'''
def condition_statement():
x = 15
if x>10:
print("큼.")
else:
pass
score = 85
result = "Success" if score >= 80 else "Fail"
print(result) |
from flask import session
from models.ACC_USER import AccUser
from utils.db_connection import DbSession
from utils.errors.parameter_errors import BadRequest
def login(username, password):
with DbSession() as db_session:
register_user = db_session.query(AccUser).filter(AccUser.username==username).first()
if register_user is None:
raise BadRequest(1, 'No such user')
else:
if register_user.password!=password:
raise BadRequest(2, 'Password error')
else:
userid=register_user.id;
session['userid']=userid
session['username']=username
session['logged_in']=True
return userid,register_user._as_dict()
|
from django.shortcuts import render,redirect
from django.http import HttpResponse
from CrimeReportingSystem.forms import UserRegistrationForm,MyProfileForm,ChangepassForm,ComplaintForm
from CrimeReportingSystem.models import MyProfile
# Create your views here.
def home(request):
return render(request,'html/home.html')
def aboutus(request):
return render(request,'html/aboutus.html')
def contactus(r):
return render(r,'html/contactus.html')
# def login(r):
# return render(r,'html/login.html')
def register(r):
if r.method == "POST":
p=UserRegistrationForm(r.POST)
if p.is_valid():
p.save()
return redirect('/login')
p=UserRegistrationForm()
return render(r,'html/register.html',{'u':p})
def profile(r):
return render(r,'html/profile.html')
def dashboard(r):
return render(r,'html/dashboard.html')
def changepass(request):
if request.method=="POST":
c=ChangepassForm(user=request.user,data=request.POST)
if c.is_valid():
c.save()
return redirect('/login')
c=ChangepassForm(user=request)
return render(request,'html/changepassword.html',{'t':c})
def complaint(req):
if req.method=="POST":
data=ComplaintForm(req.POST)
if data.is_valid():
subject='Confirmation_Complaint'
body="thank you for complaint"+req.POST['p_name']
receiver=req.POST['p_email']
sender=settings.EMAIL_HOST_USER
send_mail(subject,body,sender,[receiver])
data.save()
messages.success(req,"Successfully sent to your mail "+receiver)
return redirect('/')
form=ComplaintForm()
return render(req,'html/complaint.html',{'c':form})
def crud(request):
if request.method=="POST":
c=UserRegistrationForm(request.POST)
if c.is_valid():
c.save()
return render(request,'html/actions.html',{'o':c})
c=UserRegistrationForm()
return render(request,'html/actions.html',{'o':c})
def deletedata(req,id):
c=UserRegistration.objects.get(id=id)
c.delete()
return redirect('/crud')
|
__author__ = "jz-rolling"
import numpy as np
import tifffile
import nd2reader as nd2
from .helper_image import *
import pickle as pk
from .segmentation import *
from .optimize import *
from .particle import Particle
Version = '0.2.2'
class Patch:
def __init__(self):
# make sure that the number of images match the number of channels provided.
# modularize Image class
self.id = 0
self.data = {}
self.channels = []
self.ref_channel = ''
self.config = None
self.pixel_microns = 0.065
self.shape = (2048, 2048)
self.binary = None
self.contours = {}
self.cell_dict = {}
self._cluster_categories = None
self.RoG = None
self.Frangi = None
self.DoG = None
self.shapeindex = None
self.bbox=()
self.predictions = None
self.ridge=None
self._annotated_seeds = None
self._seed_watershed = None
self._binary_from_seed = None
self._pixel_annotation = None
self._pixel_features = None
self._pixel_features_proba = None
self._masked_coords = None
def load_data(self,
image_id,
image_dict,
ref_channel=-1):
self.id = image_id
self.data = image_dict
self.channels = list(image_dict.keys())
if isinstance(ref_channel, int):
self.ref_channel = self.channels[ref_channel]
elif isinstance(ref_channel, str):
self.ref_channel = ref_channel
else:
raise ValueError('reference channel should be integer or string.')
self.shape = self.data[self.ref_channel].shape
def load_config(self,
config):
self.config=config
def inherit(self, image_id, parent_obj, bbox, binary):
self.id = image_id
self.bbox = bbox
self.binary = binary
self.ref_channel = parent_obj.ref_channel
self.data = parent_obj._get_roi_data(bbox)
self.pixel_microns = parent_obj.pixel_microns
self.shape = self.binary.shape
self.config = parent_obj.config
x1, y1, x2, y2 = bbox
if parent_obj.RoG is not None:
self.RoG = parent_obj.RoG[x1:x2, y1:y2].copy()
if parent_obj.DoG is not None:
self.DoG = parent_obj.DoG[x1:x2, y1:y2]
if parent_obj.Frangi is not None:
self.Frangi = parent_obj.Frangi[x1:x2, y1:y2]
if parent_obj.shapeindex is not None:
self.shapeindex = parent_obj.shapeindex[x1:x2, y1:y2]
if parent_obj.config is not None:
self.config = parent_obj.config
def crop_edge(self):
from skimage import registration
cropped = float(self.config['image']['crop_edge'])
offset_correction = bool(int(self.config['image']['offset_correction']))
max_drift = float(self.config['image']['maximum_xy_drift'])
if 0 <= cropped < 0.4:
crop_width = int(cropped * self.shape[1])
crop_height = int(cropped * self.shape[0])
w1, w2 = crop_width, self.shape[1] - crop_width
h1, h2 = crop_height, self.shape[0] - crop_height
self.shape = (h2 - h1, w2 - w1)
else:
raise ValueError('Edge fraction should be no higher than 0.4 (40% from each side)!')
if offset_correction:
reference_image = self.data[self.ref_channel]
reference_image = 100 + reference_image.max() - reference_image
for channel, data in self.data.items():
if channel != self.ref_channel:
shift, error, _diff = registration.phase_cross_correlation(reference_image, data,
upsample_factor=10)
if max(np.abs(shift)) <= max_drift:
offset_image = shift_image(data, shift)
self.data[channel] = offset_image[h1:h2, w1:w2]
else:
self.data[channel] = self.data[channel][h1:h2, w1:w2]
else:
self.data[channel] = self.data[channel][h1:h2, w1:w2]
else:
for channel, data in self.data.items():
self.data[channel] = self.data[channel][h1:h2, w1:w2]
def enhance_brightfield(self, normalize=True, gamma=1.0, adjust_gamma=False):
"""
suppresses signal aberrations in phase contrast image using FFT bandpass filters
:param normalize: adjust exposure of brightfield image
:param gamma: user specified gamma correction value, default = 1
"""
perform_bandpass_correction = bool(int(self.config['image']['bandpass']))
ref_img = self.data[self.ref_channel].copy()
if perform_bandpass_correction:
ref_fft = fft(ref_img, subtract_mean=True)
fft_filters = bandpass_filter(pixel_microns=self.pixel_microns,
img_width=self.shape[1], img_height=self.shape[0],
high_pass_width=float(self.config['image']['bandpass_high']),
low_pass_width=float(self.config['image']['bandpass_low']))
ref_img = fft_reconstruction(ref_fft, fft_filters)
if normalize:
ref_img = adjust_image(ref_img, adjust_gamma=adjust_gamma, gamma=gamma)
self.data[self.ref_channel] = ref_img
del ref_fft, fft_filters, ref_img
def enhance_fluorescence(self,
normalize=False,
adjust_gamma=False,
gamma=1.0,
method='rolling_ball'):
"""
remove fluorescence background using the rolling ball method
:param normalize: adjust exposure and data depth
:param adjust_gamma: apply user specified gamma correction if True
:param gamma: user specified gama correction value, default = 1
:return:
"""
subtract_background = bool(int(self.config['image']['subtract_background']))
if subtract_background:
for channel, img in self.data.items():
if channel != self.ref_channel:
img = background_subtraction(img,method=method)
if normalize:
img = adjust_image(img, adjust_gamma=adjust_gamma, gamma=gamma)
self.data[channel] = (filters.gaussian(img, sigma=0.5) * 65535).astype(np.uint16)
del img
def segmentation_basic(self):
method, kwarg = self._get_binary_method()
self.binary, \
self.cluster_labels, \
self.regionprop_table = patch_segmentation_basic(self.data[self.ref_channel],
method=method, **kwarg)
self.regionprop_table = self.regionprop_table.set_index('label')
self.regionprop_table[['opt-x1','opt-y1','opt-x2','opt-y2','touching_edge']] = optimize_bbox_batch(self.shape,
self.regionprop_table)
def segmentation_shapeindex(self,
watershed_line=True):
threshold = (int(self.config['image']['shapeindex_low_bound']),
int(self.config['image']['shapeindex_high_bound']))
disk_radius = int(self.config['image']['opening_radius'])
min_seed_size = int(self.config['image']['min_seed_size'])
use_ridge = bool(int(self.config['image']['use_ridge']))
sigmas = tuple([float(x) for x in self.config['segmentation']['sato_sigmas'].split(',')])
if self.binary is None:
method = self._get_binary_method()
self.binary = make_mask(self.get_ref_image(),method=method, min_size=40)
shapeindex_sigma = float(self.config['image']['shapeindex_sigma'])
self.shapeindex = shape_index_conversion(self.get_ref_image(),
shapeindex_sigma=shapeindex_sigma)
if use_ridge:
self.ridge = filters.sato(self.get_ref_image(),
sigmas= sigmas,
mode='constant',
black_ridges=False)
target = self.ridge
else:
target = self.get_ref_image()
self.cluster_labels, \
self.regionprop_table = patch_segmentation_shapeindex(target,
self.binary,
self.shapeindex,
threshold,
disk_radius,
min_seed_size,
watershed_line,
ridge=self.ridge)
self.regionprop_table = self.regionprop_table.set_index('label')
self.regionprop_table[['opt-x1','opt-y1','opt-x2','opt-y2','touching_edge']] = optimize_bbox_batch(self.shape,
self.regionprop_table)
def find_contours(self, level=0.1, dilation=True):
labels = self.regionprop_table.index
n_contours = []
for label in labels:
x1,y1,x2,y2 = self.regionprop_table.loc[label][['opt-x1','opt-y1','opt-x2','opt-y2']].values.astype(int)
mask = (self.cluster_labels[x1:x2,y1:y2]==label).astype(np.int32)
data = self.get_ref_image()[x1:x2,y1:y2]
contours = find_contour_marching_squares(data, mask,
level=level,
dilation=dilation)
n_contours.append(len(contours))
self.contours[label] = contours
self.regionprop_table['n_contours'] = n_contours
def get_ref_image(self):
return self.data[self.ref_channel]
def _measure_RoG(self,s1=0.5,s2=5):
basic_measures = ['weighted_moments_hu','mean_intensity',
'min_intensity','max_intensity']
if self.RoG is None:
self.RoG = ratio_of_gaussian(self.get_ref_image(),s1,s2)
rog_measures = pd.DataFrame(measure.regionprops_table(self.cluster_labels,
intensity_image=self.RoG,
properties=basic_measures,
extra_properties=(std, skewness, kurtosis))).values
columns = ['RoG_weighted_hu-{}'.format(x) for x in range(0,7)] +\
['RoG_mean','RoG_min','RoG_max','RoG_std','RoG_skewness','RoG_kurtosis']
self.regionprop_table[columns] = rog_measures
def _measure_contour(self):
contour_data=[]
for label, contours in self.contours.items():
contour_angles = bend_angle(contours[0], window=3)
contour_data.append([np.std(contour_angles),
np.percentile(contour_angles,90),
np.percentile(contour_angles,10),
np.max(contour_angles),
np.min(contour_angles),
stats.skew(contour_angles),
np.median(contour_angles)])
self.regionprop_table[['bending_std','bending_90','bending_10',
'bending_max','bending_min','bending_skewness',
'bending_median']]=contour_data
def _measure_Frangi(self):
basic_measures = ['weighted_moments_hu', 'mean_intensity',
'min_intensity', 'max_intensity']
if self.Frangi is None:
self.Frangi = filters.frangi(self.get_ref_image(), scale_step=5)
frangi_measures = pd.DataFrame(measure.regionprops_table(self.cluster_labels,
intensity_image=self.Frangi,
properties=basic_measures,
extra_properties=(std, skewness, kurtosis))).values
columns = ['Frangi_weighted_hu-{}'.format(x) for x in range(0, 7)] + \
['Frangi_mean', 'Frangi_min', 'Frangi_max',
'Frangi_std', 'Frangi_skewness', 'Frangi_kurtosis']
self.regionprop_table[columns] = frangi_measures
def _measure_shapeindex(self, shapeindex_sigma=1):
basic_measures = ['weighted_moments_hu', 'mean_intensity',
'min_intensity', 'max_intensity']
if self.shapeindex is None:
self.shapeindex = shape_index_conversion(self.get_ref_image(), shapeindex_sigma=shapeindex_sigma)
shapeindex_measures = pd.DataFrame(measure.regionprops_table(self.cluster_labels,
intensity_image=self.shapeindex,
properties=basic_measures,
extra_properties=(std, skewness, kurtosis))).values
columns = ['Shapeindex_weighted_hu-{}'.format(x) for x in range(0, 7)] + \
['Shapeindex_mean', 'Shapeindex_min', 'Shapeindex_max',
'Shapeindex_std', 'Shapeindex_skewness', 'Shapeindex_kurtosis']
self.regionprop_table[columns] = shapeindex_measures
def _measure_DoG(self,s1=0.5,s2=10):
basic_measures = ['weighted_moments_hu', 'mean_intensity',
'min_intensity', 'max_intensity']
if self.DoG is None:
self.DoG = normalized_difference_of_gaussian(self.get_ref_image(), s1, s2)
dog_measures = pd.DataFrame(measure.regionprops_table(self.cluster_labels,
intensity_image=self.DoG,
properties=basic_measures,
extra_properties=(std, skewness, kurtosis))).values
columns = ['DoG_weighted_hu-{}'.format(x) for x in range(0, 7)] + \
['DoG_mean', 'DoG_min', 'DoG_max', 'DoG_std', 'DoG_skewness', 'DoG_kurtosis']
self.regionprop_table[columns] = dog_measures
def _hu_moments_log_transform(self):
for c in self.regionprop_table.columns.values:
if 'hu' in c:
self.regionprop_table[c] = hu_log10_transform(self.regionprop_table[c].values)
def cluster_classification(self, classifier):
self.regionprop_table['prediction'] = classifier.predict(self.regionprop_table)
def get_cluster_roi(self,label):
x1, y1, x2, y2 = self.regionprop_table.loc[label][['opt-x1', 'opt-y1', 'opt-x2', 'opt-y2']].values.astype(int)
return (x1,y1,x2,y2)
def get_cluster_data(self,label,channel):
x1,y1,x2,y2 = self.get_cluster_roi(label)
return self.data[channel][x1:x2,y1:y2]
def _get_roi_data(self,roi):
x1, y1, x2, y2 = roi
return {c:d[x1:x2, y1:y2] for c,d in self.data.items()}
def _get_cluster_mask(self,label):
x1, y1, x2, y2 = self.get_cluster_roi(label)
mask = (self.cluster_labels[x1:x2, y1:y2] == label) * 1
return mask
def _predict(self, classifier=None):
if classifier is not None:
self.predictions = classifier.predict(self.regionprop_table).astype(int)
else:
self.predictions = np.zeros(len(self.regionprop_table))
def _get_class_index(self, class_label=0):
return self.regionprop_table.index[np.where(self.predictions==class_label)].values
def _get_binary_method(self):
if int(self.config['segmentation']['binary_method']) == 0:
method='isodata'
elif int(self.config['segmentation']['binary_method']) == 1:
method='sauvola'
elif int(self.config['segmentation']['binary_method']) == 2:
method='legacy'
elif int(self.config['segmentation']['binary_method']) == 3:
method='combined'
kwarg = {'window_size':int(self.config['segmentation']['sauvola_window_size']),
'k':float(self.config['segmentation']['sauvola_k']),
'min_size':float(self.config['segmentation']['min_size']),
'block_sizes':np.array(self.config['segmentation']['block_sizes'].split(',')).astype(float),
'binary_opening':bool(int(self.config['segmentation']['binary_opening']))}
return method, kwarg
def _adopt_seeds(self,seed):
if seed.shape != self.shape:
raise ValueError("Seed image shape doesn't match")
self._annotated_seeds = measure.label(seed)
def _seed2annotation(self, overwrite=True):
for i, k in enumerate(self.regionprop_table.index):
x, y = np.array(self.regionprop_table.loc[k, 'coords']).T
members = np.unique(self._annotated_seeds[x, y])
if members[0] == 0:
if len(members) == 1:
self.predictions[i] = 2
elif len(members) > 2:
self.predictions[i] = 1
elif len(members) == 2:
self.predictions[i] = 0
else:
if len(members) == 1:
self.predictions[i] = 0
elif len(members) > 1:
self.predictions[i] = 1
if overwrite:
self.regionprop_table['annotation'] = self.predictions
|
# Python Standard Library Imports
# Third Party / PIP Imports
# HTK Imports
from htk.lib.yahoo.groups.message import YahooGroupsMessage
def yahoo_groups_message_parser(message_html):
"""Extracts the main message from a Yahoo Groups message
"""
yahoo_groups_message = YahooGroupsMessage(message_html)
message = yahoo_groups_message.message
return message
|
#!/usr/bin/env python
from robolink import * # API to communicate with robodk
from robodk import * # robodk robotics toolbox
import sys
from io import StringIO
import sys
#import StringIO
import contextlib
@contextlib.contextmanager
def stdoutIO(stdout=None):
old = sys.stdout
if stdout is None:
stdout = StringIO()
sys.stdout = stdout
yield stdout
sys.stdout = old
code = """
i = [0,1,2]
for j in i :
print(j)
"""
with stdoutIO() as s:
exec(code)
print("out:", s.getvalue())
from io import StringIO
def execute(code, _globals={}, _locals={}):
import sys
fake_stdout = StringIO()
__stdout = sys.stdout
sys.stdout = fake_stdout
try:
#try if this is expressions
ret = eval(code, _globals, _locals)
result = fake_stdout.getvalue()
sys.stdout = __stdout
if ret:
result += str(ret)
return result
except:
try:
exec(code, _globals, _locals)
except:
sys.stdout = __stdout
import traceback
buf = StringIO()
traceback.print_exc(file=buf)
return buf.getvalue()
else:
sys.stdout = __stdout
return fake_stdout.getvalue()
def test_execute():
cmdoutput = execute("z = 5", globals(), locals())
print("output of command", cmdoutput)
cmdoutput = execute("z", globals(), locals())
print("output of command", cmdoutput)
#cmdoutput = execute(code, globals(), locals())
#print("output of command", cmdoutput)
print()
code = "y = 5 + 2"
cmdoutput = execute(code, globals(), locals())
print("output of command", cmdoutput)
code = "print(y)"
cmdoutput = execute(code, globals(), locals())
print("output of command", cmdoutput)
code = "y"
cmdoutput = execute(code, globals(), locals())
print("output of command", cmdoutput)
print("quickrundone")
sys.exit()
#test_execute()
# Any interaction with RoboDK must be done through RDK:
RDK = Robolink()
RDK.AddFile('C:/RoboDK/Library/KUKA_KR_210_2.robot')
from http.server import BaseHTTPRequestHandler, HTTPServer
import json
# HTTPRequestHandler class
class testHTTPServer_RequestHandler(BaseHTTPRequestHandler):
# GET
def do_GET(self):
# Send response status code
self.send_response(200)
# Send headers
self.send_header('Content-type','text/html')
self.end_headers()
print("asdf");
# Send message back to client
message = "Hello world2!"
# Write content as utf-8 data
self.wfile.write(bytes(message, "utf8"))
return
# GET
def do_POST(self):
#with stdoutIO() as s:
# exec code
#print "out:", s.getvalue()
# Send response status code
self.send_response(200)
# Send headers
self.send_header('Content-type','text/html')
self.end_headers()
content_len = int(self.headers['content-length'])
post_body = self.rfile.read(content_len).decode('UTF-8')
print("code");
print(post_body); #show command
print()
#outputc = exec(post_body, globals() );
#print("result", outputc);
##with stdoutIO() as s:
## exec(post_body, globals())
##out = s.getvalue()
#test_execute():
out = ""
out = execute(post_body, globals())
#out = execute(post_body, globals(), locals())
#print "out:", s.getvalue()
print("result:")
print(out);
# Send message back to client
##message = "Hello worldg!"
# Write content as utf-8 data
##message = out;
if out == None:
out = "none"
self.wfile.write(bytes(out, "utf8"))
return
def run():
print('starting server...|')
# Server settings
# Choose port 8080, for port 80, which is normally used for a http server, you need root access
server_address = ('127.0.0.1', 8081)
httpd = HTTPServer(server_address, testHTTPServer_RequestHandler)
print('running server...')
httpd.serve_forever()
run()
print('after...')
from robolink import * # API to communicate with robodk
from robodk import * # robodk robotics toolbox
# Setup global parameters
BALL_DIAMETER = 100 # diameter of one ball
APPROACH = 100 # approach distance to grab each part, in mm
nTCPs = 6 # number of TCP's in the tool
#----------------------------------------------
# Function definitions
def box_calc(BALLS_SIDE=4, BALLS_MAX=None):
"""Calculate a list of points (ball center) as if the balls were stored in a box"""
if BALLS_MAX is None: BALLS_MAX = BALLS_SIDE**3
xyz_list = []
for h in range(BALLS_SIDE):
for i in range(BALLS_SIDE):
for j in range(BALLS_SIDE):
xyz_list = xyz_list + [[(i+0.5)*BALL_DIAMETER, (j+0.5)*BALL_DIAMETER, (h+0.5)*BALL_DIAMETER]]
if len(xyz_list) >= BALLS_MAX:
return xyz_list
return xyz_list
def pyramid_calc(BALLS_SIDE=4):
"""Calculate a list of points (ball center) as if the balls were place in a pyramid"""
#the number of balls can be calculated as: int(BALLS_SIDE*(BALLS_SIDE+1)*(2*BALLS_SIDE+1)/6)
BALL_DIAMETER = 100
xyz_list = []
sqrt2 = 2**(0.5)
for h in range(BALLS_SIDE):
for i in range(BALLS_SIDE-h):
for j in range(BALLS_SIDE-h):
height = h*BALL_DIAMETER/sqrt2 + BALL_DIAMETER/2
xyz_list = xyz_list + [[i*BALL_DIAMETER + (h+1)*BALL_DIAMETER*0.5, j*BALL_DIAMETER + (h+1)*BALL_DIAMETER*0.5, height]]
return xyz_list
def balls_setup(frame, positions):
"""Place a list of balls in a reference frame. The reference object (ball) must have been previously copied to the clipboard."""
nballs = len(positions)
step = 1/(nballs - 1)
for i in range(nballs):
newball = frame.Paste()
newball.setName('ball ' + str(i)) #set item name
newball.setPose(transl(positions[i])) #set item position with respect to parent
newball.setVisible(True, False) #make item visible but hide the reference frame
newball.Recolor([1-step*i, step*i, 0.2, 1]) #set RGBA color
def cleanup_balls(parentnodes):
"""Delete all child items whose name starts with \"ball\", from the provided list of parent items."""
todelete = []
for item in parentnodes:
todelete = todelete + item.Childs()
for item in todelete:
if item.Name().startswith('ball'):
item.Delete()
def TCP_On(toolitem, tcp_id):
"""Attach the closest object to the toolitem Htool pose,
furthermore, it will output appropriate function calls on the generated robot program (call to TCP_On)"""
toolitem.AttachClosest()
toolitem.RDK().RunMessage('Set air valve %i on' % (tcp_id+1))
toolitem.RDK().RunProgram('TCP_On(%i)' % (tcp_id+1));
def TCP_Off(toolitem, tcp_id, itemleave=0):
"""Detaches the closest object attached to the toolitem Htool pose,
furthermore, it will output appropriate function calls on the generated robot program (call to TCP_Off)"""
toolitem.DetachClosest(itemleave)
toolitem.RDK().RunMessage('Set air valve %i off' % (tcp_id+1))
toolitem.RDK().RunProgram('TCP_Off(%i)' % (tcp_id+1));
#----------------------------------------------------------
# The program starts here:
# Any interaction with RoboDK must be done through RDK:
RDK = Robolink()
# Turn off automatic rendering (faster)
RDK.Render(False)
#RDK.Set_Simulation_Speed(500); # set the simulation speed
# Gather required items from the station tree
robot = RDK.Item('Fanuc M-710iC/50')
robot_tools = robot.Childs()
#robottool = RDK.Item('MainTool')
frame1 = RDK.Item('Table 1')
frame2 = RDK.Item('Table 2')
# Copy a ball as an object (same as CTRL+C)
ballref = RDK.Item('reference ball')
ballref.Copy()
# Run a pre-defined station program (in RoboDK) to replace the two tables
prog_reset = RDK.Item('Replace objects')
prog_reset.RunProgram()
# Call custom procedure to remove old objects
cleanup_balls([frame1, frame2])
# Make a list of positions to place the objects
frame1_list = pyramid_calc(4)
frame2_list = pyramid_calc(4)
# Programmatically place the objects with a custom-made procedure
balls_setup(frame1, frame1_list)
# Delete previously generated tools
for tool in robot_tools:
if tool.Name().startswith('TCP'):
tool.Delete()
# Calculate tool frames for the suction cup tool of 6 suction cups
TCP_list = []
for i in range(nTCPs):
TCPi_pose = transl(0,0,100)*rotz((360/nTCPs)*i*pi/180)*transl(125,0,0)*roty(pi/2)
TCPi = robot.AddTool(TCPi_pose, 'TCP %i' % (i+1))
TCP_list.append(TCPi)
TCP_0 = TCP_list[0]
# Turn on automatic rendering
RDK.Render(True)
# Move balls
robot.setTool(TCP_list[0])
nballs_frame1 = len(frame1_list)
nballs_frame2 = len(frame2_list)
idTake = nballs_frame1 - 1
idLeave = 0
idTCP = 0
target_app_frame = transl(2*BALL_DIAMETER, 2*BALL_DIAMETER, 4*BALL_DIAMETER)*roty(pi)*transl(0,0,-APPROACH)
while idTake >= 0:
# ------------------------------------------------------------------
# first priority: grab as many balls as possible
# the tool is empty at this point, so take as many balls as possible (up to a maximum of 6 -> nTCPs)
ntake = min(nTCPs, idTake + 1)
# approach to frame 1
robot.setFrame(frame1)
robot.setTool(TCP_0)
robot.MoveJ([0,0,0,0,10,-200])
robot.MoveJ(target_app_frame)
# grab ntake balls from frame 1
for i in range(ntake):
TCPi = TCP_list[i]
robot.setTool(TCPi)
# calculate target wrt frame1: rotation about Y is needed since Z and X axis are inverted
target = transl(frame1_list[idTake])*roty(pi)*rotx(30*pi/180)
target_app = target*transl(0,0,-APPROACH)
idTake = idTake - 1
robot.MoveL(target_app)
robot.MoveL(target)
TCP_On(TCPi, i)
robot.MoveL(target_app)
# ------------------------------------------------------------------
# second priority: unload the tool
# approach to frame 2 and place the tool balls into table 2
robot.setTool(TCP_0)
robot.MoveJ(target_app_frame)
robot.MoveJ([0,0,0,0,10,-200])
robot.setFrame(frame2)
robot.MoveJ(target_app_frame)
for i in range(ntake):
TCPi = TCP_list[i]
robot.setTool(TCPi)
if idLeave > nballs_frame2-1:
raise Exception("No room left to place objects in Table 2")
# calculate target wrt frame1: rotation of 180 about Y is needed since Z and X axis are inverted
target = transl(frame2_list[idLeave])*roty(pi)*rotx(30*pi/180)
target_app = target*transl(0,0,-APPROACH)
idLeave = idLeave + 1
robot.MoveL(target_app)
robot.MoveL(target)
TCP_Off(TCPi, i, frame2)
robot.MoveL(target_app)
robot.MoveJ(target_app_frame)
# Move home when the robot finishes
robot.MoveJ([0,0,0,0,10,-200])
|
class MemoryAllocation(object):
_store_name = "address"
def __init__(self):
self._address_map = {}
def __setattr__(self, attr, value):
if attr == '_address_map':
return super(MemoryAllocation, self).__setattr__(attr, value)
self._address_map[attr] = value
def __getattr__(self, att):
return self._address_map[att]
def __deepcopy__(self, memo):
new_obj = MemoryAllocation()
new_obj._address_map = {k: v for k, v in self._address_map.items()}
return new_obj
class DataManager(object):
InfoCenterMap = {
MemoryAllocation._store_name: MemoryAllocation
}
def __init__(self, datas):
self.StorageCenter = {}
for data_name in datas:
data_cls = self.InfoCenterMap.get(data_name, None)
if data_cls is None:
raise ValueError("Unknown transformation method: {}".format(data_name))
datastorage = data_cls()
self.StorageCenter.update({data_name: datastorage})
@classmethod
def register_datamap(cls, data_cls, overwrite=False):
cls.InfoCenterMap[data_cls.name] = data_cls
def __getattr__(self, attr):
if attr == 'StorageCenter':
raise AttributeError('StorageCenter')
elif attr.startswith('__'):
return super(DataManager, self).__getattr__(attr)
cls_instance = self.StorageCenter[attr]
return cls_instance
def __setattr__(self, attr, value):
if attr == 'StorageCenter':
return super(DataManager, self).__setattr__(attr, value)
cls_instance = self.StorageCenter[attr]
k, v = value
cls_instance.__setattr__(k, v)
def group(self, tensor):
ret = {}
for cls_object in self.StorageCenter.values():
ans = cls_object.__getattr__(tensor)
ret.update({cls_object._store_name: ans})
return ret
|
import socket
import feiQ_data
import send_online_msg
def deal_msg(recv_data_):
"""处理消息数据"""
_recv_data = recv_data_.decode("gbk", errors = "ignore")
message_list = _recv_data.split(":", 5)
#用字典保存数据信息
msg_dict = dict()
msg_dict["version"] = message_list[0]
msg_dict["packet_numb"] = message_list[1]
msg_dict["user_name"] = message_list[2]
msg_dict["host_name"] = message_list[3]
msg_dict["command"] = message_list[4]
msg_dict["content"] = message_list[5]
return msg_dict
def deal_data(command_):
"""获取数据command的十六进制前六位和后两位"""
recv_command = int(command_) & 0x000000ff
additional_function = int(command_) & 0xffffff00
return recv_command, additional_function
def receive_message():
"""接收数据并根据数据反馈"""
while True:
recv_data, aim_broadcast = feiQ_data.udp_socket.recvfrom(1024)
feiq_data = deal_msg(recv_data)
command, command_option = deal_data(feiq_data["command"])
if command == feiQ_data.IPMSG_BR_ENTRY:
print("用户上线", aim_broadcast, "用户名", feiq_data["user_name"])
elif command == feiQ_data.IPMSG_BR_EXIT:
print("用户离线", aim_broadcast, "用户名", feiq_data["user_name"])
elif command == feiQ_data.IPMSG_SENDMSG:
print("收到消息：", aim_broadcast, "信息", feiq_data["content"] )
msg = send_online_msg.option(feiQ_data.IPMSG_RECVMSG)
send_online_msg.send_msg(msg, aim_broadcast)
|
from abc import ABC, abstractmethod
class Trainer(ABC):
def __init__(self, iteratinos, batch_size, num_workers, learning_rate, optimizer,
split, dataloader, loss_function):
'''
args:
iterations: number of iterations for training
batch_size: number of batch size
num_workers: number of works to load dataset
learning_rate:
optimizer:
'ADAM', ...
split:
train-test dataset split ratio, 0.0-1.0 for training data
dataloader:
pytorch dataloader
loss_function: 'bce'(binary cross entropy loss), 'w'(Wasserstein-1 Loss)
'''
self.iterations = iterations
self.batch_size = batch_size
self.num_workers = number_workers
self.learning_rate = learning_rate
self.optimizer = optimizer
self.split = split
self.dataloader = dataloader
self.loss_function = loss_function
@abstractmethod
def Train(self):
raise NonImplementedError
@abstractmethod
def Predict(self):
raise NonImplementedError |
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def flipEquiv(self, root1: TreeNode, root2: TreeNode) -> bool:
if root1 is None or root2 is None:
return root2 is None and root1 is None
if root1.val != root2.val: return False
left1 = root1.left.val if root1.left else None
left2 = root2.left.val if root2.left else None
if left1 == left2:
return self.flipEquiv(root1.left, root2.left) and self.flipEquiv(root1.right, root2.right)
return self.flipEquiv(root1.left, root2.right) and self.flipEquiv(root1.right, root2.left)
|
from bs4 import BeautifulSoup
from splinter import Browser
from webdriver_manager.chrome import ChromeDriverManager
import requests
import pandas as pd
def scrape():
executable_path = {'executable_path': ChromeDriverManager().install()}
browser = Browser('chrome', **executable_path, headless=False)
# Create new dictionary to store all scraped data
mars = {}
#Open News site with splinter
news_url = 'https://redplanetscience.com'
browser.visit(news_url)
html = browser.html
soup = BeautifulSoup(html, "html.parser")
#Scrape news titles and paragraph text as variables
news_title = soup.find('div', class_='content_title').text
news_p = soup.find('div', class_='article_teaser_body').text
#Add news data to dictionary
mars['news_title'] = news_title
mars['news_p'] = news_p
# Open Featured Image site with splinter
image_url = 'https://spaceimages-mars.com/'
browser.visit(image_url)
html = browser.html
soup = BeautifulSoup(html, "html.parser")
# Find relative path to image
relative_path = soup.find('img', class_='headerimage fade-in')['src']
# Create full path to image
featured_image_url = image_url+relative_path
# Add featured image to dictionary
mars['featured_image_url'] = featured_image_url
# Use Pandas to scrape Mars Facts table
facts_url = 'https://galaxyfacts-mars.com/'
table = pd.read_html(facts_url)[0]
# Rename columns
table.rename(columns={0:'Description', 1:'Mars', 2:'Earth'}, inplace = True)
# Change index
table.set_index('Description', inplace = True)
# Convert to html
mars_table = table.to_html()
# Add table to dictionary
mars['mars_table'] = mars_table
# Scrape High Res Images
# Set home page as variable
base_url = 'https://marshemispheres.com/'
# Create list of hemisphere links to open with splinter
pages = ['cerberus.html', 'schiaparelli.html', 'syrtis.html', 'valles.html']
# Create list to hold title and url
hemisphere_image_urls = []
# Loop through list of hemisphere pages and scrape Title and Image URL
for page in pages:
hemi_page = base_url+page
browser.visit(hemi_page)
html = browser.html
soup = BeautifulSoup(html, "html.parser")
title = soup.find('h2', class_='title').text
downloads = soup.find('div', class_='downloads')
relative_url = downloads.find('a')['href']
img_url = base_url+relative_url
hemisphere_image_urls.append({'title':title, 'img_url':img_url})
# Add list to dictionary
mars['hemisphere_image_urls'] = hemisphere_image_urls
# Quit the browser
browser.quit()
return mars |
from django.contrib import admin
from .models import Button
from .models import Slider
admin.site.register(Button)
admin.site.register(Slider)
# Register your models here.
|
from urllib.parse import quote
import json
def URLEncodeQuery(**kwargs):
"""Encodes kwarg values to URL-friendly strings
Ex. query="Redmi Phone" => {'query': 'Redmi%20Phone'}
Returns:
dict: object containing kwargs and URL-encoded kwarg values
"""
for kwarg in kwargs:
kwargs[kwarg] = quote(str(kwargs[kwarg]))
return kwargs
def get_queries_from_config(config_path: str='config.json') -> [dict]:
return json.load(open(config_path))['search_queries'] |
max_tentativas = 6
tentativas = 0
oculta = "teste"
digitadas = ""
acertou_tudo = False
while (tentativas < max_tentativas) and not acertou_tudo:
letra = raw_input("Digite uma letra: ")
digitadas = digitadas + letra
if letra in oculta: #acertou a letra digitada
print "A palavra é: ",
qtdeTracos = 0
for i in oculta: #passando pela palavra para exibir as letras que já acertou
if i in digitadas:
print i,
else:
print "_",
qtdeTracos += 1
print "" #enter
if qtdeTracos == 0: #ja acertou a palavra inteira
print "Parabéns! Você acertou tudo!"
acertou_tudo = True
else:
tentativas += 1
print "-> Você errou pela %d vez!"%tentativas
if acertou_tudo == False:
print "Você perdeu. Tente novamente."
|
import os
import pathlib
from flightsparser.cache import LocalCache, RedisCache
class TestLocalCache:
def test_get_cache_elements_empty(self):
cache = LocalCache()
cache.cache_path = f"{pathlib.Path(__file__).parent.absolute()}/local_cache.pkl"
cache.remove_cache()
elements = cache.get_cache_elements()
assert isinstance(elements, list)
assert elements == []
def test_add_cache(self):
cache = LocalCache()
cache.cache_path = f"{pathlib.Path(__file__).parent.absolute()}/local_cache.pkl"
cache.remove_cache()
cache.add_element("aaaa", [123, 4354, 54545])
elements = cache.get_cache_elements()
assert isinstance(elements, list)
assert elements == ["aaaa"]
cache.add_element("bbbb", [23243, 45354, 5454545])
elements = cache.get_cache_elements()
assert isinstance(elements, list)
assert elements == ["aaaa", "bbbb"]
def test_get_element(self):
cache = LocalCache()
cache.cache_path = f"{pathlib.Path(__file__).parent.absolute()}/local_cache.pkl"
data = cache.get_element("ssdsd")
assert data == []
data = cache.get_element("aaaa")
assert data == [123, 4354, 54545]
def test_remove_cache(self):
cache = LocalCache()
cache.cache_path = f"{pathlib.Path(__file__).parent.absolute()}/local_cache.pkl"
cache.remove_cache()
assert os.path.exists(cache.cache_path) is False
class TestRedisCache:
def test_get_cache_elements_empty(self):
cache = RedisCache()
cache.remove_cache()
elements = cache.get_cache_elements()
assert isinstance(elements, list)
assert elements == []
def test_add_cache(self):
cache = RedisCache()
cache.remove_cache()
cache.add_element("aaaa", [123, 4354, 54545])
elements = cache.get_cache_elements()
assert isinstance(elements, list)
assert elements == ["aaaa"]
cache.add_element("bbbb", [23243, 45354, 5454545])
elements = cache.get_cache_elements()
assert isinstance(elements, list)
assert len(elements) == 2
assert "aaaa" in elements
assert "bbbb" in elements
def test_get_element(self):
cache = RedisCache()
data = cache.get_element("ssdsd")
assert data == []
data = cache.get_element("aaaa")
assert data == [123, 4354, 54545]
def test_remove_cache(self):
cache = LocalCache()
cache.remove_cache()
elements = cache.get_cache_elements()
assert isinstance(elements, list)
assert elements == []
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# !/usr/bin/python
# -*- coding: utf-8 -*-
import sqlite3
# Подключаемся к базе данных
con = sqlite3.connect('dbase1')
curs = con.cursor()
# Создаем таблицу
curs.execute(
'''
create table diafilms(name text, path text, type text, )''')
|
1# -*- coding: utf-8 -*-
"""
Created on Tue Jun 8 03:54:05 2021
@author: sarangbhagwat
"""
import thermosteam as tmo
import biosteam as bst
from biosteam.process_tools import SystemFactory
from biorefineries.BDO import units, facilities
from biorefineries.BDO.process_settings import price
import numpy as np
__all__ = (
'create_concentration_evaporator_sys',
'create_separation_system_oleyl_alcohol',
)
@SystemFactory(
ID='BDO_separation_sys',
ins=[dict(ID='filtered_fermentation_effluent',
phase='l', T=323.15, P=101325,
H2O=3663, AceticAcid=0.5861,
Glucose=27.61, BDO=191.1,
GlucoseOligomer=6.5, Extract=62.82,
Xylose=33.96, XyloseOligomer=2.741,
Cellobiose=0.8368, Mannose=2.573,
MannoseOligomer=0.06861, Galactose=6.132,
GalactoseOligomer=0.1635, Arabinose=12.53,
ArabinoseOligomer=0.334, SolubleLignin=0.4022,
Protein=0.04538, Enzyme=23.95,
FermMicrobe=0.07988, Furfural=0.1058,
Acetoin=1.678, HMF=0.04494,
Glucan=0.003135, Mannan=3.481e-05,
Galactan=8.295e-05, Xylan=0.00139,
Arabinan=0.0001694, Lignin=0.00331,
Ash=0.02955, units='kmol/hr')],
outs=[dict(ID='BDO'),
dict(ID='wastewater_b')],
)
def create_concentration_evaporator_sys(ins, outs):
"""
Create a separation system for BDO using ethanol and DPHP for the
"salting out" effect.
Parameters
----------
ins : stream
Fermentation effluent.
outs : stream sequence
[0] BDO
[1] Unreacted acetoin
[2] Wastewater
Examples
--------
>>> from biorefineries import BDO
>>> import biosteam as bst
>>> bst.settings.set_thermo(BDO.BDO_chemicals)
>>> BDO_separation_sys = create_separation_system_DPHP()
>>> BDO_separation_sys.simulate()
>>> BDO_separation_sys.show()
System: BDO_separation_sys
ins...
[0] filtered_fermentation_effluent
phase: 'l', T: 323.15 K, P: 101325 Pa
flow (kmol/hr): H2O 3.66e+03
AceticAcid 0.586
Glucose 27.6
2,3-Butanediol 191
GlucoseOligomer 6.5
Extract 62.8
Xylose 34
...
outs...
[0] BDO
phase: 'l', T: 454.81 K, P: 101325 Pa
flow (kmol/hr): H2O 9.06e-06
Ethanol 0.109
Glucose 0.277
2,3-Butanediol 189
GlucoseOligomer 0.0652
Extract 0.63
Xylose 0.341
...
[1] unreacted_acetoin
phase: 'l', T: 435.47 K, P: 101325 Pa
flow (kmol/hr): 2,3-Butanediol 0.0945
3-Hydroxybutanone 1.59
[2] wastewater
phase: 'l', T: 374.32 K, P: 101325 Pa
flow (kmol/hr): H2O 3.66e+03
Ethanol 0.454
AceticAcid 0.586
Glucose 27.3
2,3-Butanediol 2.03
Dipotassium hydrogen phosphate 4.12
GlucoseOligomer 6.43
...
"""
filtered_fermentation_effluent, = ins
conc_aqueous_broth, wastewater_b = outs
F401 = bst.units.MultiEffectEvaporator('F401', ins=filtered_fermentation_effluent, outs=('F401_0', 'wastewater_b'),
P = (101325, 73581, 50892, 32777, 20000), V = 0.5)
target_BDO_x = 0.03
def get_x(chem_ID, stream):
return stream.imol[chem_ID]/sum(stream.imol[[i.ID for i in stream.vle_chemicals]])
def F401_specification():
instream = F401.ins[0]
# ratio = target_water_x/get_x('Water', instream)
ratio = get_x('BDO', instream)/target_BDO_x
# no need to check for ratio>1 becasue our target_water_x is consistently lower than the max possible titer
F401.V = 1. - ratio
F401._run()
F401.specification = F401_specification
F401_H = bst.HXutility('F401_H', ins=F401-0, outs='cooled_broth', T=25.+273.15, rigorous=True)
wastewater_b = F401.outs[1]
T601 = bst.StorageTank('T601', ins=F401_H-0,
tau=7*24, V_wf=0.9,
vessel_type='Floating roof',
vessel_material='Carbon steel')
T601.line = 'Conc. aq. broth storage tank'
T601_P = bst.Pump('T601_P', ins=T601-0, outs=('conc_aqueous_broth',), P=101325)
conc_aqueous_broth = T601_P-0
@SystemFactory(
ID='BDO_separation_sys',
ins=[dict(ID='filtered_fermentation_effluent',
phase='l', T=323.15, P=101325,
H2O=3663, AceticAcid=0.5861,
Glucose=27.61, BDO=191.1,
GlucoseOligomer=6.5, Extract=62.82,
Xylose=33.96, XyloseOligomer=2.741,
Cellobiose=0.8368, Mannose=2.573,
MannoseOligomer=0.06861, Galactose=6.132,
GalactoseOligomer=0.1635, Arabinose=12.53,
ArabinoseOligomer=0.334, SolubleLignin=0.4022,
Protein=0.04538, Enzyme=23.95,
FermMicrobe=0.07988, Furfural=0.1058,
Acetoin=1.678, HMF=0.04494,
Glucan=0.003135, Mannan=3.481e-05,
Galactan=8.295e-05, Xylan=0.00139,
Arabinan=0.0001694, Lignin=0.00331,
Ash=0.02955, units='kmol/hr')],
outs=[dict(ID='BDO'),
dict(ID='unreacted_acetoin'),
dict(ID='wastewater')],
)
def create_separation_system_oleyl_alcohol(ins, outs):
"""
Create a separation system for BDO using ethanol and DPHP for the
"salting out" effect.
Parameters
----------
ins : stream
Fermentation effluent.
outs : stream sequence
[0] BDO
[1] Unreacted acetoin
[2] Wastewater
Examples
--------
>>> from biorefineries import BDO as bdo
>>> import biosteam as bst
>>> bst.settings.set_thermo(bdo.BDO_chemicals)
>>> BDO_separation_sys = bdo.create_separation_system_oleyl_alcohol()
>>> BDO_separation_sys.simulate()
>>> BDO_separation_sys.show()
System: BDO_separation_sys
ins...
[0] filtered_fermentation_effluent
phase: 'l', T: 323.15 K, P: 101325 Pa
flow (kmol/hr): H2O 3.66e+03
AceticAcid 0.586
Glucose 27.6
2,3-Butanediol 191
GlucoseOligomer 6.5
Extract 62.8
Xylose 34
...
outs...
[0] BDO
phase: 'l', T: 407.71 K, P: 20265 Pa
flow (kmol/hr): H2O 0.108
2,3-Butanediol 163
OleylAlcohol 0.0246
3-Hydroxybutanone 0.000715
[1] unreacted_acetoin
phase: 'g', T: 385.71 K, P: 20265 Pa
flow (kmol/hr): 2,3-Butanediol 0.0815
3-Hydroxybutanone 1.43
[2] wastewater
phase: 'l', T: 343.2 K, P: 101325 Pa
flow (kmol/hr): H2O 3.66e+03
AceticAcid 0.586
Glucose 27.6
2,3-Butanediol 28.1
OleylAlcohol 0.22
GlucoseOligomer 6.5
Extract 62.8
...
"""
filtered_fermentation_effluent, = ins
BDO, unreacted_acetoin, wastewater = outs
oleyl_alcohol = bst.Stream('oleyl_alcohol', price=price.get('OleylAlcohol', 0.))
solvent_recycle = bst.Stream('')
# 7-day storage time, similar to ethanol's in Humbird et al.
T605 = bst.StorageTank('T605', ins=oleyl_alcohol,
tau=7*24, V_wf=0.9,
vessel_type='Floating roof',
vessel_material='Carbon steel')
T605.line = 'Oleyl alcohol storage tank'
T605_P = bst.Pump('T605_P', ins=T605-0, P=101325)
M402 = bst.Mixer('M402', ins=(T605_P-0, solvent_recycle))
preheated_stream = bst.Stream()
D407 = bst.BinaryDistillation('D407',
ins=preheated_stream,
LHK=('Water', 'BDO'),
partial_condenser=False,
k=1.1,
product_specification_format='Composition',
y_top=0.99999, x_bot=0.93)
# product_specification_format='Recovery',
# Lr=0.5, Hr=0.999)
# D407.target_BDO_x = 0.07
# def get_x(chem_ID, stream):
# return stream.imol[chem_ID]/sum(stream.imol['AceticAcid', 'Furfural', 'HMF', 'BDO', 'Water'])
# def D407_f(Lr):
# D407.Hr = 0.999
# D407.Lr = Lr
# D407._run()
# BDO_x = get_x('BDO', D407.outs[1])
# return get_x('BDO', D407.outs[1]) - max(get_x('BDO', D407.ins[0]), D407.target_BDO_x)
# D407.specification = bst.BoundedNumericalSpecification(D407_f, 0.001, 0.999)
D407_Pb = bst.Pump('D407_Pb', D407-1, P=101325.)
H407_b = bst.HXprocess('H407_b',
ins=[filtered_fermentation_effluent, D407_Pb-0],
outs=[preheated_stream, ''],
)
S402 = bst.units.MultiStageMixerSettlers('S402',
ins = (H407_b-1, M402-0),
partition_data={
'K': np.array([1/1.940224889932903, 1/0.16864361850509718, 1/0.37, 1/1.940224889932903, 1/10000,
10000, 10000, 10000, 10000,
10000, 10000, 10000, 10000]),
'IDs': ('2,3-Butanediol', 'Water', 'Ethanol', 'Acetoin', 'OleylAlcohol',
'Xylose', 'GlucoseOligomer', 'Extract', 'XyloseOligomer',
'Arabinose', 'ArabinoseOligomer', 'SolubleLignin', 'Enzyme'),
'phi' : 0.5,
},
N_stages = 20,
)
@S402.add_specification(run=True)
def adjust_S402_split():
feed = S402.ins[0]
Water = feed.imass['Water']
required_solvent = 8 * Water
oleyl_alcohol, recycle = M402.ins
oleyl_alcohol.imass['OleylAlcohol'] = max(0, required_solvent- recycle.imass['OleylAlcohol'])
if recycle.imass['OleylAlcohol'] > required_solvent:
recycle.imass['OleylAlcohol'] = required_solvent
M402._run()
S402_Pr = bst.Pump('S402_Pr', ins=S402-0, P=101325)
S402_Pe = bst.Pump('S402_Pe', ins=S402-1, P=101325)
D401_H = bst.HXprocess('D401_H', ins=[S402_Pe-0, None], outs=['', solvent_recycle], dT=15.)
D401 = bst.units.BinaryDistillation('D401', ins=D401_H-0,
outs=('D401_g', 'D401_l'),
LHK=('BDO', 'OleylAlcohol'),
partial_condenser=True,
is_divided=True,
product_specification_format='Recovery',
Lr=0.9995, Hr=0.9999, k=1.1,
P=0.06 * 101325,
vessel_material = 'Stainless steel 316')
D401_Pb = bst.Pump('D401_Pb', ins=D401-1, P=101325)
D401_Pb-0-1-D401_H
D402 = bst.units.ShortcutColumn('D402', ins=D401-0,
outs=('D402_g', 'D402_l'),
LHK=('Water', 'Acetoin'),
partial_condenser=False,
is_divided=True,
P=0.2 * 101325,
product_specification_format='Recovery',
Lr=0.9995, Hr=0.9995, k=1.1,
vessel_material = 'Stainless steel 316')
D402_Pd = bst.Pump('D402_Pd', D402-0, P=101325)
D402_Pb = bst.Pump('D402_Pb', D402-1, P=101325)
M403 = bst.Mixer('M403', [S402_Pr-0, D402_Pd-0, D407-0], wastewater)
D403x = bst.units.BinaryDistillation('D403x', ins=D402_Pb-0,
outs=('D403x_g', 'D403x_l'),
LHK=('Acetoin', 'BDO'),
partial_condenser=False,
is_divided=True,
P=0.2 * 101325,
product_specification_format='Recovery',
Lr=0.995, Hr=0.999, k=1.2,
vessel_material = 'Stainless steel 316')
D403x_H = bst.HXutility('D403x_H', D403x-0, T=305.15, rigorous=True)
D403x_Pd = bst.Pump('D403x_Pd', D403x_H-0, unreacted_acetoin, P=101325)
D403x_Pb = bst.Pump('D403x_Pb', D403x-1, BDO, P=101325)
|
#--================================================
# Loops
#--================================================
#---------------------------------------
# Definite Loop
for i in [5, 4, 3, 2, 1]:
print(i)
print('Blastoff!')
# A Definite Loop with Strings
friends = ['Joseph', 'Glenn', 'Sally']
for friend in friends:
print('Happy New Year:', friend)
print('Done!')
n = 5
while n > 0:
print(n)
n -= 1
print('End')
print(n)
#----------------------------------------
# Finishing the iteration with "break"
# The breakstatement ends the current loop and jumps to the
# statement immediately following the loop
while True:
line = input('> ')
if line == 'done':
break
print(line)
print('Done!')
#----------------------------------------
# The iteration with "continue"
# The continue statement ends the current iteration and jumps to the
# top of the loop and starts the next iteration
# continue checks the logic in the "while" again
# so, it's a way to begin execution over in the "while" body without getting all the way to the end first
while True:
line = input('> ')
if line[0] == '#':
print('continue executes and loop starts again')
continue
if line == 'done': break
print("after continue")
print(line)
print('Done!')
|
from nltk import NaiveBayesClassifier
from nltk.tokenize import word_tokenize
from itertools import chain
from textblob.classifiers import NaiveBayesClassifier
from text import training_data
from textblob import TextBlob
import sys
import pickle
test = [
('the beer was good.', 'pos'),
('I do not enjoy my job', 'neg'),
("I ain't feeling dandy today.", 'neg'),
("I feel amazing!", 'pos'),
('Gary is a friend of mine.', 'pos'),
("I can't believe I'm doing this.", 'neg')
]
f = open('algorithm.pickle', 'rb')
classifer = pickle.load(f)
print(classifer.classify('THis is amazing'))
print(classifer.classify('This looks so good'))
print(classifer.accuracy(test))
f.close() |
import pytest
from takler.core import NodeStatus
from takler.core.expression_parser import parse_trigger
from takler.core.expression_ast import (
AstOpEq, AstOpGt, AstOpGe,
AstOpOr, AstOpAnd,
AstNodePath, AstVariablePath, AstNodeStatus, AstInteger
)
def test_node_path():
expr_cases = [
"/flow1/task1 == complete",
"./task1 == aborted",
"../container1/task1 == complete",
"../../container1/task1 == complete",
"/flow1/00/container1/task1 == complete"
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
assert isinstance(ast.left, AstNodePath)
def test_variable_path():
expr_cases = [
"/flow1/task1:event1 == set",
"./task1:meter1 >= 20",
"../container1/task1:event2 == set",
"../../container1/task1:meter2 > 10"
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
assert isinstance(ast.left, AstVariablePath)
def test_op_eq():
expr_cases = [
"/flow1/task1 == complete",
"/flow1/task1 eq complete",
"/flow1/task1 EQ complete",
"/flow1/task1 eQ complete",
"/flow1/task1 Eq complete",
"/flow1/task1:event1 == set",
"/flow1/task1:meter1 == 10", # not suggested.
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
assert isinstance(ast, AstOpEq)
def test_op_gt():
expr_cases = [
"/flow1/task1:meter1 > 20"
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
assert isinstance(ast, AstOpGt)
def test_op_ge():
expr_cases = [
"/flow1/task1:meter1 >= 20"
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
assert isinstance(ast, AstOpGe)
def test_op_and():
expr_cases = [
"/flow1/task1 == complete and /flow1/task2 == complete",
"/flow1/task1 == complete AND /flow1/task2 == complete",
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
assert isinstance(ast, AstOpAnd)
def test_op_or():
expr_cases = [
"/flow1/task1 == complete or /flow1/task2 == complete",
"/flow1/task1 == complete OR /flow1/task2 == complete",
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
assert isinstance(ast, AstOpOr)
def test_status_complete():
expr_cases = [
"/flow1/task1 == complete",
"/flow1/task1 == COMPLETE",
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
ast_right = ast.right
assert isinstance(ast_right, AstNodeStatus)
assert ast_right.value() == NodeStatus.complete
def test_status_abort():
expr_cases = [
"/flow1/task1 == aborted",
"/flow1/task1 == ABORTED",
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
ast_right = ast.right
assert isinstance(ast_right, AstNodeStatus)
assert ast_right.value() == NodeStatus.aborted
def test_event_set():
expr_cases = [
"/flow1/task1:event1 == set",
"/flow1/task1:event1 == SET"
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
ast_right = ast.right
assert isinstance(ast_right, AstInteger)
assert ast_right.value() == 1
def test_event_unset():
expr_cases = [
"/flow1/task1:event1 == unset",
"/flow1/task1:event1 == UNSET"
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
ast_right = ast.right
assert isinstance(ast_right, AstInteger)
assert ast_right.value() == 0
def test_meter_value():
expr_cases = [
"/flow1/task1:meter1 == 10",
"/flow1/task1:meter1 > 10",
"/flow1/task1:meter1 >= 10",
]
for expr_string in expr_cases:
ast = parse_trigger(expr_string)
ast_right = ast.right
assert isinstance(ast_right, AstInteger)
assert ast_right.value() == 10
|
import json
from pony.orm import *
from email.utils import parseaddr
from datetime import datetime
import re
from app.db import Institution, InstitutionType, Phone, InstitutionPhone, UserInstitution
from app.address_controller import Address
import app.user_controller
class CRUDInstitution():
phone_pattern = None
def __init__(self):
self.phone_pattern = re.compile("\(\d\d\) (\d{9}|\d{8})$")
def validate_email(self, email_str):
return "@" in parseaddr(email_str)[1]
def is_phone_valid(self, phone_str):
return self.phone_pattern.match(phone_str)
@db_session
def create_institution(self, **kwargs):
if self.validate_email(kwargs["email"]):
phones = kwargs["phone"]
del kwargs["phone"]
inst = Institution(create_date=datetime.now(), active=True, last_update=datetime.now(), **kwargs)
inst.flush()
for phone in phones:
if self.phone_pattern.match(phone):
self.add_phone(institution_id=inst.institution_id, phone_number=phone)
commit()
result = { "institution_id": inst.institution_id,
"name": inst.name,
"city_id": inst.city_id.city_id,
"state": inst.city_id.state_id.state,
"city_name": inst.city_id.city,
"type": inst.institution_type_id.description,
"type_id": inst.institution_type_id.institution_type_id,
"email": inst.email,
"site": inst.site, "active": inst.active,
"street": inst.street, "number": inst.number,
"complement": inst.complement, "district": inst.district,
"postal_code": inst.postal_code,
"phone": phones,
"create_date": inst.create_date.strftime("%d/%m/%Y %H:%M"),
"last_update": inst.last_update.strftime("%d/%m/%Y %H:%M")}
return json.dumps(result)
else:
raise ValueError("Email not valid")
@db_session
def list_institution(self, query):
if query:
institutions = select(inst for inst in Institution if inst.name.lower().startswith(query) and inst.active==True)
else:
institutions = select(inst for inst in Institution if inst.active==True)
results = []
for institution in institutions:
institution_data = self.get_institution(institution_id=institution.institution_id, export_json=False)
results.append(institution_data)
return json.dumps(results)
@db_session
def get_institution(self, institution_id, export_json=True):
inst = Institution.get(institution_id=institution_id)
phones_inst = select(inst_phone for inst_phone in InstitutionPhone if inst_phone.institution_id==inst)
phones = []
address = Address()
city_data = address.get_city(inst.city_id.city_id)
for phone_inst in phones_inst:
phone = Phone.get(phone_id=phone_inst.phone_id.phone_id)
phones.append(phone.number)
response = {"institution_id": institution_id, "name": inst.name,
"city_id": inst.city_id.city_id,
"city": inst.city_id.city, "state": city_data["state"].state,
"type": inst.institution_type_id.description,
"institution_type_id": inst.institution_type_id.institution_type_id,
"email": inst.email,
"site": inst.site, "active": inst.active, "street": inst.street,
"number": inst.number, "complement": inst.complement,
"phone": phones,
"district": inst.district, "postal_code": inst.postal_code,
"create_date": inst.create_date.strftime("%d/%m/%Y %H:%M"),
"last_update": inst.last_update.strftime("%d/%m/%Y %H:%M")}
if export_json:
response = json.dumps(response)
return response
@db_session
def alter_instution(self, institution_id, **kwargs):
inst = Institution.get(institution_id=institution_id)
phones = kwargs["phone"]
del kwargs["phone"]
inst.set(**kwargs)
#for phone in phones:
# if self.is_phone_valid(phone_str=phone):
# self.update_phone(institution_id=institution_id, phone_number=phone)
commit()
return self.get_institution(institution_id=institution_id)
@db_session
def delete_institution(self, institution_id):
try:
inst = Institution.get(institution_id=institution_id)
inst.activate = False
commit()
return {"message": "institution deleted"}
except Exception as ex:
print(ex)
return {"message": "error to delele institution"}
@db_session
def add_institution_type(self, description):
result = InstitutionType(description=description)
commit()
return json.dumps({"id": result.institution_type_id,
"description": result.description})
@db_session
def list_institution_type(self):
result = select(inst_type for inst_type in InstitutionType)
result_json = json.dumps([{"id": i.institution_type_id, "description": i.description} for i in result])
return result_json
@db_session
def update_institution_type(self, id, description):
result = InstitutionType.get(institution_type_id=id)
result.description = description
commit()
@db_session
def remove_institution_type(self, id):
inst_type = InstitutionType.get(institution_type_id=id)
inst_type.delete()
commit()
@db_session
def get_institutions(self, institution_id):
inst = Institution.get(institution_id=institution_id)
return inst
@db_session
def list_institution_phones(self, institution_id, size=10):
inst = Institution.get(user_id=institution_id)
phones_institutions = select( inst_phone for inst_phone in InstitutionPhone if inst_phone.institution_id == inst)
phones = []
for phone_inst in phones_institutions:
phone = Phone.get(phone_id=phone_inst.phone_id.phone_id)
phone_json = {"institution_id": institution_id, "phone_id":phone_inst.phone_id.phone_id, "number": phone.number}
phones.append(phone_json)
return json.dumps(phones)
#TODO termoinar phones
@db_session
def add_phone(self, institution_id, phone_number):
if self.is_phone_valid(phone_str=phone_number):
phone = Phone(number=phone_number)
phone.flush()
inst_phone = InstitutionPhone( institution_id=institution_id,
phone_id=phone.phone_id
)
inst_phone.flush()
response = {"institution_id": institution_id, "phone_id": phone.phone_id, "phone_number": phone_number}
else:
response = {"message": "phone not valide", "type": "ERROR"}
return json.dumps(response)
@db_session
def remove_phone(self, institution_id, phone_id):
institution = Institution.get(institution_id=institution_id)
phone = Phone.get(phone_id=phone_id)
phone_institution = InstitutionPhone.get(institution_id=institution, phone_id=phone)
phone.delete()
phone_institution.delete()
commit()
@db_session
def update_phone(self, institution_id, phone_number):
#TODO conSERTAR UPLOAD DE TELEFONE
if self.is_phone_valid(phone_str=phone_number):
institution = Institution.get(institution_id=institution_id)
phone = Phone.get(number=phone_number)
phones_inst = select(phone_inst for phone_inst in InstitutionPhone if institution_id==institution)
for phone_inst in phones_inst:
phone = Phone.get(phone_id=phone_inst.phone_id.phone_id)
phone.number = phone_number
commit()
return json.dumps({"institution_id": institution_id, "phone_id": phone_id, "phone_number": phone_number})
return {"message": "phone not valide", "type": "ERROR"}
@db_session
def list_linked_users(self, institution_id, type="user"):
inst_id = Institution.get(institution_id=institution_id)
if institution_id is None:
users_inst = select(user for user in UserInstitution if
user.user_id.role_id.description == "institution")
else:
users_inst = select(user for user in UserInstitution if
user.institution_id==inst_id and user.user_id.role_id.description==type)
user = app.user_controller.UserController()
users = []
for user_inst in users_inst:
user_data = user.get_user(user_inst.user_id.user_id, export_json=False)
user_data["status"] = user_inst.status
user_data["institution_id"] = user_inst.institution_id.institution_id
if user_data["status"] in ["PENDING", "APPROVED"]:
users.append(user_data)
return users
@db_session
def approve_user(self, institution_id, user_id):
user = UserInstitution.get(institution_id=institution_id, user_id=user_id)
resp = {}
if user.status == "PENDING":
user.status = "APPROVED"
user_obj = app.user_controller.UserController()
resp = user_obj.get_user(id=user.user_id.user_id, export_json=False)
resp["status"] = "APPROVED"
else:
raise
commit()
return resp
@db_session
def remove_user(self, institution_id, user_id):
user = UserInstitution.get(user_id=user_id, institution_id=institution_id)
resp = {}
user.status = "INTS_REMOV"
commit()
user_obj = app.user_controller.UserController()
resp = user_obj.get_user(id=user.user_id.user_id, export_json=False)
resp["status"] = "INTS_REMOV"
return resp |
import torch
import torch.nn as nn
import utils.batch_norm
import utils.whitening
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
def compute_bn_stats(state_dict):
# state_dict = state_dict = torch.load(path) #'/home/sroy/.torch/models/resnet50-19c8e357.pth'
bn_key_names = []
for name, param in state_dict.items():
if name.find('bn') != -1:
bn_key_names.append(name)
elif name.find('downsample') != -1:
bn_key_names.append(name)
# keeping only the batch norm specific elements in the dictionary
bn_dict = {k: v for k, v in state_dict.items() if k in bn_key_names}
return bn_dict
class whitening_scale_shift(nn.Module):
def __init__(self, planes, group_size, running_mean, running_variance, track_running_stats=True, affine=True):
super(whitening_scale_shift, self).__init__()
self.planes = planes
self.group_size = group_size
self.track_running_stats = track_running_stats
self.affine = affine
self.running_mean = running_mean
self.running_variance = running_variance
self.wh = utils.whitening.WTransform2d(self.planes,
self.group_size,
running_m=self.running_mean,
running_var=self.running_variance,
track_running_stats=self.track_running_stats)
if self.affine:
self.gamma = nn.Parameter(torch.ones(self.planes, 1, 1))
self.beta = nn.Parameter(torch.zeros(self.planes, 1, 1))
def forward(self, x):
out = self.wh(x)
if self.affine:
out = out * self.gamma + self.beta
return out
# class Bottleneck_rt(nn.Module):
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, layer, sub_layer, bn_dict, group_size=4, stride=1, downsample=None, rt = False):
super(Bottleneck, self).__init__()
self.expansion = 4
self.conv1 = conv1x1(inplanes, planes)
if layer == 1:
self.bns1 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.wh.running_variance'],
affine=False)
self.bnt1 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.wh.running_variance'],
affine=False)
self.bnt1_aug = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.wh.running_variance'],
affine=False)
self.gamma1 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.gamma'])
self.beta1 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.beta'])
else:
self.bns1 = utils.batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.running_var'],
affine=False)
self.bnt1 = utils.batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.running_var'],
affine=False)
self.bnt1_aug = utils.batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn1.running_var'],
affine=False)
self.gamma1 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.weight'].view(-1, 1, 1))
self.beta1 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.bias'].view(-1, 1, 1))
self.conv2 = conv3x3(planes, planes, stride)
if layer == 1:
self.bns2 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.wh.running_variance'],
affine=False)
self.bnt2 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.wh.running_variance'],
affine=False)
self.bnt2_aug = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.wh.running_variance'],
affine=False)
self.gamma2 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.gamma'])
self.beta2 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.beta'])
else:
self.bns2 = utils.batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.running_var'],
affine=False)
self.bnt2 = utils.batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.running_var'],
affine=False)
self.bnt2_aug = utils.batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn2.running_var'],
affine=False)
self.gamma2 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.weight'].view(-1, 1, 1))
self.beta2 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.bias'].view(-1, 1, 1))
self.conv3 = conv1x1(planes, planes * self.expansion)
if layer == 1:
self.bns3 = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.wh.running_variance'],
affine=False)
self.bnt3 = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.wh.running_variance'],
affine=False)
self.bnt3_aug = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.wh.running_variance'],
affine=False)
self.gamma3 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.gamma'])
self.beta3 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.beta'])
else:
self.bns3 = utils.batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.running_var'],
affine=False)
self.bnt3 = utils.batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.running_var'],
affine=False)
self.bnt3_aug = utils.batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(
sub_layer) + '.bn3.running_var'],
affine=False)
self.gamma3 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.weight'].view(-1, 1, 1))
self.beta3 = nn.Parameter(
bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.bias'].view(-1, 1, 1))
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
if self.downsample is not None:
if layer == 1:
self.downsample_bns = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(
layer) + '.0.downsample_bn.wh.running_mean'],
running_variance=bn_dict['layer' + str(
layer) + '.0.downsample_bn.wh.running_variance'],
affine=False)
self.downsample_bnt = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(
layer) + '.0.downsample_bn.wh.running_mean'],
running_variance=bn_dict['layer' + str(
layer) + '.0.downsample_bn.wh.running_variance'],
affine=False)
self.downsample_bnt_aug = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(
layer) + '.0.downsample_bn.wh.running_mean'],
running_variance=bn_dict['layer' + str(
layer) + '.0.downsample_bn.wh.running_variance'],
affine=False)
self.downsample_gamma = nn.Parameter(
bn_dict['layer' + str(layer) + '.0.downsample_bn.gamma'])
self.downsample_beta = nn.Parameter(
bn_dict['layer' + str(layer) + '.0.downsample_bn.beta'])
else:
self.downsample_bns = utils.batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(
layer) + '.0.downsample_bn.running_mean'],
running_v=bn_dict['layer' + str(
layer) + '.0.downsample_bn.running_var'],
affine=False)
self.downsample_bnt = utils.batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(
layer) + '.0.downsample_bn.running_mean'],
running_v=bn_dict['layer' + str(
layer) + '.0.downsample_bn.running_var'],
affine=False)
self.downsample_bnt_aug = utils.batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(
layer) + '.0.downsample_bn.running_mean'],
running_v=bn_dict['layer' + str(
layer) + '.0.downsample_bn.running_var'],
affine=False)
self.downsample_gamma = nn.Parameter(
bn_dict['layer' + str(layer) + '.0.downsample_bn.weight'].view(-1, 1, 1))
self.downsample_beta = nn.Parameter(
bn_dict['layer' + str(layer) + '.0.downsample_bn.bias'].view(-1, 1, 1))
def forward(self, x):
if self.training:
# to do
identity = x
out = self.conv1(x)
out_s, out_t, out_t_dup = torch.split(
out, split_size_or_sections=out.shape[0] // 3, dim=0)
out = torch.cat((self.bns1(out_s), torch.cat((self.bnt1(out_t), self.bnt1_aug(
out_t_dup)), dim=0)), dim=0) * self.gamma1 + self.beta1
out = self.relu(out)
out = self.conv2(out)
out_s, out_t, out_t_dup = torch.split(
out, split_size_or_sections=out.shape[0] // 3, dim=0)
out = torch.cat((self.bns2(out_s), torch.cat((self.bnt2(out_t), self.bnt2_aug(
out_t_dup)), dim=0)), dim=0) * self.gamma2 + self.beta2
out = self.relu(out)
out = self.conv3(out)
out_s, out_t, out_t_dup = torch.split(
out, split_size_or_sections=out.shape[0] // 3, dim=0)
out = torch.cat((self.bns3(out_s), torch.cat((self.bnt3(out_t), self.bnt3_aug(
out_t_dup)), dim=0)), dim=0) * self.gamma3 + self.beta3
if self.downsample is not None:
identity = self.downsample(x)
identity_s, identity_t, identity_t_dup = torch.split(
identity, split_size_or_sections=identity.shape[0] // 3, dim=0)
identity = torch.cat((self.downsample_bns(identity_s),
torch.cat((self.downsample_bnt(identity_t), self.downsample_bnt_aug(identity_t_dup)), dim=0)), dim=0) * self.downsample_gamma + self.downsample_beta
out = out.clone() + identity
out = self.relu(out)
else:
identity = x
out = self.conv1(x)
out = self.bnt1(out) * self.gamma1 + self.beta1
out = self.relu(out)
out = self.conv2(out)
out = self.bnt2(out) * self.gamma2 + self.beta2
out = self.relu(out)
out = self.conv3(out)
out = self.bnt3(out) * self.gamma3 + self.beta3
if self.downsample is not None:
identity = self.downsample(x)
identity = self.downsample_bnt(
identity) * self.downsample_gamma + self.downsample_beta
out = out.clone() + identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, state_dict, num_classes=65, zero_init_residual=False, group_size=4, rt=False):
super(ResNet, self).__init__()
self.inplanes = 64
if rt:
self.bn_dict = state_dict
else:
self.bn_dict = compute_bn_stats(state_dict)
self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
stride=2, padding=3, bias=False)
if rt:
self.bns1 = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bns1.wh.running_mean'],
running_variance=self.bn_dict['bns1.wh.running_variance'],
affine=False)
self.bnt1 = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bnt1.wh.running_mean'],
running_variance=self.bn_dict['bnt1.wh.running_variance'],
affine=False)
self.bnt1_aug = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bnt1.wh.running_mean'],
running_variance=self.bn_dict['bnt1.wh.running_variance'],
affine=False)
self.gamma1 = nn.Parameter(self.bn_dict['gamma1'])
self.beta1 = nn.Parameter(self.bn_dict['beta1'])
else:
self.bns1 = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bn1.wh.running_mean'],
running_variance=self.bn_dict['bn1.wh.running_variance'],
affine=False)
self.bnt1 = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bn1.wh.running_mean'],
running_variance=self.bn_dict['bn1.wh.running_variance'],
affine=False)
self.bnt1_aug = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bn1.wh.running_mean'],
running_variance=self.bn_dict['bn1.wh.running_variance'],
affine=False)
self.gamma1 = nn.Parameter(self.bn_dict['bn1.gamma'])
self.beta1 = nn.Parameter(self.bn_dict['bn1.beta'])
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(
block, 64, layers[0], self.bn_dict, layer=1)
self.layer2 = self._make_layer(
block, 128, layers[1], self.bn_dict, stride=2, layer=2)
self.layer3 = self._make_layer(
block, 256, layers[2], self.bn_dict, stride=2, layer=3)
self.layer4 = self._make_layer(
block, 512, layers[3], self.bn_dict, stride=2, layer=4)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc_out = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, bn_dict, layer=1, group_size=4, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
# nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, layer, 0,
bn_dict, group_size, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,
layer, i, bn_dict, group_size))
return nn.Sequential(*layers)
def forward(self, x):
if self.training:
x = self.conv1(x)
x_s, x_t, x_t_dup = torch.split(
x, split_size_or_sections=x.shape[0] // 3, dim=0)
x = torch.cat((self.bns1(x_s), torch.cat((self.bnt1(x_t), self.bnt1_aug(
x_t_dup)), dim=0)), dim=0) * self.gamma1 + self.beta1
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc_out(x)
else:
x = self.conv1(x)
x = self.bnt1(x) * self.gamma1 + self.beta1
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc_out(x)
return x
|
"""A basic police lights effect."""
import time
from logipy import logi_led
logi_led.logi_led_init()
time.sleep(2)
while True:
logi_led.logi_led_set_lighting(100, 0, 0)
time.sleep(0.1)
logi_led.logi_led_set_lighting(0, 0, 0)
time.sleep(0.1)
logi_led.logi_led_set_lighting(100, 0, 0)
time.sleep(0.1)
logi_led.logi_led_set_lighting(0, 0, 0)
time.sleep(0.8)
logi_led.logi_led_set_lighting(0, 0, 100)
time.sleep(0.1)
logi_led.logi_led_set_lighting(0, 0, 0)
time.sleep(0.1)
logi_led.logi_led_set_lighting(0, 0, 100)
time.sleep(0.1)
logi_led.logi_led_set_lighting(0, 0, 0)
time.sleep(0.8)
logi_led.logi_led_set_lighting(100, 100, 100)
time.sleep(0.1)
logi_led.logi_led_set_lighting(0, 0, 0)
time.sleep(0.1)
logi_led.logi_led_set_lighting(100, 100, 100)
time.sleep(0.1)
logi_led.logi_led_set_lighting(0, 0, 0)
time.sleep(0.8)
logi_led.logi_led_shutdown()
|
import pandas
import matplotlib.pyplot as plt
import numpy as np
import time
import seaborn as sns
sns.set()
# df = pandas.read_csv("results/latin_cube_integration_results.csv", header=0)
df = pandas.read_csv("lc_2000.csv")
print(df.head())
df.columns = ["iterations", "samples", "area", "computationtime"]
print(df.describe())
df_new = df.loc[df['iterations'] == 2000]
print(df_new.describe())
total_points = np.arange(1000, 100000, 1000)
means = []
variances = []
for points in total_points:
means.append(df_new[df_new['samples'] == points]['area'].mean())
variances.append(df_new[df_new['samples'] == points]['area'].var())
# plt.scatter(df_new['samples'], df_new['area'])
plt.plot(total_points, means)
plt.title("Latin Hypercube integration")
lowerlims = [means[i] - variances[i] for i in range(len(variances))]
upperlims = [means[i] + variances[i] for i in range(len(variances))]
plt.fill_between(total_points, lowerlims, upperlims, facecolor='blue', alpha=0.5)
plt.xlabel("Amount of samples (darts)")
plt.ylabel("Area of the Mandelbrot set")
plt.ylim(1.50, 1.52)
plt.savefig("lhc_" + str(time.time()) + ".png") |
import numpy as np
class MaxPoolLayer(object):
def __init__(self, size=2):
"""
MaxPool layer
Ok to assume non-overlapping regions
"""
self.locs = None # to store max locations
self.size = size # size of the pooling
def forward(self, x):
"""
Compute "forward" computation of max pooling layer
Parameters
----------
x : np.array
The input data of size number of training samples x number
of input channels x number of rows x number of columns
Returns
-------
np.array
The output of the maxpooling
Stores
-------
self.locs : np.array
The locations of the maxes (needed for back propagation)
"""
result = np.zeros((x.shape[0], x.shape[1], x.shape[2] // self.size, x.shape[3] // self.size))
location = np.zeros((x.shape[0], x.shape[1], x.shape[2], x.shape[3]))
r_e, c_e = self.size * (x.shape[2] // self.size), self.size * (x.shape[3] // self.size)
r_s, c_s = 0, 0
maxi = 0
for i in xrange(x.shape[0]):
for j in xrange(x.shape[1]):
m, n = x[i][j].shape[:2]
ny = m // self.size
nx = n // self.size
mat_pad = x[i, j, :ny * self.size, :nx * self.size, ...]
new_shape = (ny, self.size, nx, self.size) + x[i][j].shape[2:]
result[i][j] = np.nanmax(mat_pad.reshape(new_shape), axis=(1, 3))
for k in xrange(r_s, r_e - self.size + 1, self.size):
for l in xrange(c_s, c_e - self.size + 1, self.size):
temp = x[i, j, k:k + self.size, l:l + self.size]
index1, index2 = np.unravel_index(np.argmax(temp, axis=None), temp.shape)
# print index1, index2
# t_index1 = index1 + k
# t_index2 = index2 + l
# location[i, j, t_index1, t_index2] = 1
for in1 in xrange(self.size):
for in2 in xrange(self.size):
if temp[in1, in2] == temp[index1, index2]:
location[i, j, in1 + k, in2 + l] = 1
self.locs = np.copy(location)
return result
#raise NotImplementedError
def backward(self, y_grad):
"""
Compute "backward" computation of maxpool layer
Parameters
----------
y_grad : np.array
The gradient at the output
Returns
-------
np.array
The gradient at the input
"""
output = np.zeros((self.locs.shape[0], self.locs.shape[1], self.locs.shape[2], self.locs.shape[3])).astype('float64')
for i in xrange(y_grad.shape[0]):
for j in xrange(y_grad.shape[1]):
for q in xrange(0, y_grad.shape[2]):
for k in xrange(0, y_grad.shape[3]):
for in1 in xrange(q * self.size, q * self.size + self.size):
for in2 in xrange(k * self.size, k * self.size + self.size):
if self.locs[i, j, in1, in2] == 1:
output[i, j, in1, in2] = y_grad[i, j, q, k]
return output
#raise NotImplementedError
def update_param(self, lr):
pass
|
# Equal weight portfolio in case no clear over/under wight asset allocation signal exists
def equal_weight(marked_portfolio):
target_value = marked_portfolio
# Calculate the total value of holdings by portfolio
target_value['PortfolioValue'] = target_value['Value'].groupby(target_value['Portfolio']).transform('sum')
# Calculate the number of non cash holdings by portfolio
target_value['PortfolioHoldingCount'] = target_value.groupby(target_value['Portfolio'])['symbol'].transform('count')
# Calculate the target market value of each holding within the portfolio net of trade fee
target_value['TargetValue'] = (target_value['PortfolioValue'] / target_value['PortfolioHoldingCount']) - 10
return target_value
|
class Solution:
def firstUniqChar(self, s: str) -> int:
unique_letters = sorted(set(s), key=s.index)
for letter in unique_letters:
if s.count(letter) == 1:
return s.index(letter)
return -1
|
#! /usr/bin/python
import sys, math
import pdb_lib
###
# This programs was writen by Trent E. Balius, the Shoichet Group, UCSF, 2017
# It counts how meny waters are nearby a extreme point
###
#def cal_dists(atom1,atom2):
# d2 = (atom1.X - atom2.X)**2 + (atom1.Y - atom2.Y)**2 + (atom1.Z - atom2.Z)**2
# return math.sqrt(d2)
def in_voxel(atom1,atom2,val):
boolval = False
#print math.fabs(atom1.X - atom2.X), val
if ((math.fabs(atom1.X - atom2.X) <= val) and
(math.fabs(atom1.Y - atom2.Y) <= val) and
(math.fabs(atom1.Z - atom2.Z) <= val)):
boolval = True
return boolval
#################################################################################################################
#################################################################################################################
def main():
if len(sys.argv) != 3: # if no input
print "This function takes as input two pdb files"
print "calculates distances and writes out a report"
print len(sys.argv)
return
pdb_file1 = sys.argv[1]
pdb_file2 = sys.argv[2]
#pdb_out = sys.argv[3]
print "center of voxal, file 1: " + pdb_file1
print "list of waters, file 2: " + pdb_file2
pdb1 = pdb_lib.read_pdb(pdb_file1)
pdb2 = pdb_lib.read_pdb(pdb_file2)
atomcount = [] # count how meny atoms are in each voxel
# intialize
for i in range(len(pdb1)):
atomcount.append(0)
i = 0
for voxelatom in pdb1:
for atom in pdb2:
#print atom.atomname
if atom.atomname.replace(" ","") != "O":
continue
if (in_voxel(voxelatom,atom,0.25)):
atomcount[i] = atomcount[i] + 1
i = i + 1
for i in range(len(pdb1)):
print "voxel%d, %d"%(i, atomcount[i])
print len(pdb2)/3
#output_pdb(ave_pdb, pdb_out)
#################################################################################################################
#################################################################################################################
main()
|
# Copyright 2021
#
# 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.
# libraries
# ---------------------------------------------------------------
# Minimal Python binding for Turbonomic Data Ingestion Framework
# ---------------------------------------------------------------
import json
import time
def del_none(d):
"""
Delete keys with the value ``None`` in a dictionary, recursively.
This alters the input so you may wish to ``copy`` the dict first.
"""
for key, value in list(d.items()):
if value is None:
del d[key]
elif isinstance(value, dict):
del_none(value)
return d # For convenience
class Topology:
def __init__(self, version="v1", scope=""):
self.version = version
self.updateTime = int(time.time())
self.scope = scope
self.topology = []
def AddEntity(self, entity):
self.topology.append(entity)
def ToJSON(self):
return json.dumps(self, default=lambda o: del_none(o.__dict__))
class DIFEntity:
def __init__(self, uid, entity_type, name=None):
self.uniqueId = uid
self.type = entity_type
self.matchIdentifiers = None
self.hostedOn = None
self.partOf = None
self.metrics = {}
if name:
self.name = name
else:
self.name = uid
def AddMetric(self, metric_type, metric_kind, value, key=None):
if metric_type not in self.metrics:
metric_list = [DIFMetricVal()]
self.metrics[metric_type] = metric_list
else:
metric_list = self.metrics[metric_type]
if len(metric_list) < 1:
return
metric = metric_list[0]
if metric_kind == "average":
metric.average = value
elif metric_kind == "capacity":
metric.capacity = value
if key:
metric.key = key
return self
def Matching(self, matching_id):
if not self.matchIdentifiers:
self.matchIdentifiers = DIFMatchingIdentifiers(matching_id)
return self
def HostedOn(self, ip_address, host_type='virtualMachine'):
if not self.hostedOn:
self.hostedOn = {'hostType': [host_type], 'ipAddress': ip_address}
return self
def PartOf(self, uid, entity):
if not self.partOf:
self.partOf = [{'uniqueId': uid, 'entity': entity}]
return self
class DIFMatchingIdentifiers:
def __init__(self, ip_address):
self.ipAddress = ip_address
class DIFMetricVal:
def __init__(self):
self.average = None
self.capacity = None
self.key = None
|
import subprocess
from . import constants
from . import exceptions
def send_command(command):
"""
Function to send the IR command through LIRC. Make sure that LIRC is properly configured or this could raise
exceptions.
:param command: string representing the raw IR command to send
:return: nothing, but sends and IR command through LIRC
"""
try:
subprocess.call(['irsend', 'SEND_ONCE', constants.RC_MODE, command])
except FileNotFoundError:
raise exceptions.LircError
def create_command(channel=1, output='R', speed=0, brake=False):
"""
This function generates an IR command based upon user input.
When no IR command can be generated based upon the user input, an exception is raised.
:param channel: integer representing the channel to control (1 to 4)
:param output: string representing the output to control (R or B)
:param speed: integer representing speed and direction (-7 up to 7)
:param brake: boolean indicating that the brake was hit
:return: string representing the raw IR command
"""
if channel in range(1, constants.CHANNELS + 1) and output in constants.OUTPUTS:
command = None
if brake:
command = 'BRAKE'
elif speed in range(0, constants.MAX_SPEED + 1):
command = '{}'.format(speed)
elif speed in range(-constants.MAX_SPEED, 0):
command = 'M{}'.format(abs(speed))
if command:
return '{}{}_{}'.format(channel, output, command)
raise exceptions.CommandError
|
'''
calcula as raízes de uma equação do 2o grau:
ax² + bx + c=0
Para ela existir, o coeficiente 'a' deve ser diferente de zero.
No caso de a ser igual a zero, envie uma mensagem de erro ao usuário.
Caso o delta seja maior ou igual a zero, as raízes serão reais.
Caso o delta seja negativo, exiba a mensagem: As raízes são números complexos.
'''
def delta_r(a, b, c):
delta = (b**2)-(4*a*c)
return delta
def equacao(a, b, c):
global delta
from math import sqrt
if (a != 0):
if (delta >= 0):
xI = (-(b) + sqrt(delta))/(2*a)
xII = (-(b) - sqrt(delta))/(2*a)
print("Solução:", xI, "e", xII)
else:
print("As raízes são números complexos.")
else:
print("Coeficiente 'a' inválido!")
return
a = eval(input("Digite o coeficiente 'a': "))
b = eval(input("Digite o coeficiente 'b': "))
c = eval(input("Digite o coeficiente 'c': "))
delta = delta_r(a,b,c)
equacao(a, b, c)
|
from utility import *
from scipy.stats import poisson
from sklearn.manifold import MDS
import numpy as np
from scipy.stats import multivariate_normal
from numpy import argmax,log
from random import randint,uniform,shuffle
import math
import scipy as sc
def update_frag_topic(ecount_matrix,components,frag_group,frag_topic,topic_num,topic_count,topic_assign_vec,labels):
for c in range(len(frag_topic)):
for i in range(len(frag_topic[c])):
topic_portion=get_gaussian_topic_portion([components[c][b] for b in frag_group[c][i]],labels,topic_assign_vec,topic_num,ecount_matrix,topic_count)[:-1]
'''
topic_portion=[0.0]*topic_num
total_nodes=float(sum(topic_count))
for j in range(topic_num):
topic_portion[j]=topic_count[j]/total_nodes
'''
normalize(topic_portion)
picked_topic=np.random.choice(topic_num,1,p=topic_portion)[0]
frag_topic[c][i]=picked_topic
return frag_topic
def update_frag_group(frag_group,frag_topic,frag_mean,frag_var,frag_group_nums,dimensionlen):
t=[]
for i in range(dimensionlen):
t.append([0.0]*dimensionlen)
t[i][i]=1.0
for c in range(len(frag_group)):
remove_list=[]
for f in range(len(frag_group[c])):
if len(frag_group[c][f])==0:
remove_list.append(f)
# print 'f',np.delete(frag_group[c] ,remove_list,axis=0).tolist(),frag_group[c]
frag_group[c]=np.delete(frag_group[c] ,remove_list,axis=0).tolist()
frag_group[c].append([])
#print 'sd',frag_group[c]
frag_topic[c]=np.delete(frag_topic[c] ,remove_list,axis=0).tolist()
frag_topic[c].append(0)
frag_mean[c]=np.delete(frag_mean[c] ,remove_list,axis=0).tolist()
frag_mean[c].append([0.0]*dimensionlen)
frag_var[c]=np.delete(frag_var[c] ,remove_list,axis=0).tolist()
frag_var[c].append(t)
frag_group_nums[c]=len(frag_group[c])
def add_topic(ecount_matrix,topic_count,topic_assign_vec,topic_size_list,topic_num):
ec=list(ecount_matrix)
ec.append([0]*len(ecount_matrix[0]))
tc=list(topic_count)
tc.append(0)
ts=list(topic_size_list)
ts.append(1)
return ec,tc,ts,topic_num+1
def remove_empty_topic(ecount_matrix,topic_count,topic_assign_vec,topic_size_list,topic_num):
remove_list=[]
for i in range(topic_num):
if topic_count[i]==0:
remove_list.append(i)
for j in range(len(topic_assign_vec)):
t=0
for i in remove_list:
if topic_assign_vec[j]>i:
t+=1
topic_assign_vec[j]-=t
return np.delete( ecount_matrix,remove_list,axis=0).tolist(),np.delete(topic_count ,remove_list,axis=0).tolist(),np.delete(topic_size_list ,remove_list,axis=0).tolist(),(topic_num-len(remove_list))
def update_topic_size(topic_size_list,frag_group,frag_topic):
topic_num=len(topic_size_list)
topic_count_list=[0]*topic_num
for t in range(topic_num):
topic_size_list[t]=0.0
for c in range(len(frag_group)):
for f in range(len(frag_group[c])):
if len(frag_group[c][f])<2:continue
topic_count_list[frag_topic[c][f]]+=1.0
topic_size_list[frag_topic[c][f]]+=len(frag_group[c][f])
for t in range(topic_num):
if topic_count_list[t]<2:continue
topic_size_list[t]/=topic_count_list[t]
#def GC_perplexity(components,point_vec,ecount_matrix,topic_count,labels,frag_assign_vec,topic_assign_vec,frag_mean,frag_var,topic_num,beta):
def get_dis_from_point(dis_matrix,latent_point_vec):
D=np.zeros(shape=(len(dis_matrix),len(dis_matrix)))
for i in range(len(dis_matrix)):
for j in range(i+1,len(dis_matrix)):
D[i][j]=np.linalg.norm(latent_point_vec[i]-latent_point_vec[j])
D[j][i]=D[i][j]
return D
def GC_perplexity(components,latent_point_vec,ecount_matrix,topic_count,labels,frag_assign_vec,topic_assign_vec,frag_mean,frag_var,topic_num,dis_matrices,beta):
label_num=len(set(labels))
A=np.zeros(shape=(topic_num,label_num))
for i in range(topic_num):
for j in range(label_num):
A[i][j]=(ecount_matrix[i][j]+beta)/(float(topic_count[i])+label_num*beta)
p=0.0
for i in range(len(components)):
D=get_dis_from_point(dis_matrices[i],latent_point_vec[i])
for j in range(len(components[i])):
p+=-log(A[topic_assign_vec[components[i][j]]][labels[components[i][j]]])-log(multivariate_normal.pdf(latent_point_vec[i][j], mean=frag_mean[i][frag_assign_vec[i][j]], cov=frag_var[i][frag_assign_vec[i][j]],allow_singular=True))
'''
for j in range(len(components[i])):
for k in range(len(components[i])):
p+=-log(single_normal_pdf(D[j][k],dis_matrices[i][j][k],1))
'''
return np.exp(p/len(labels))
def get_geodesic_vec(latent_point_vec,dis_matrix,group_node):
gradient=0.0
# print latent_point_vec[0]-latent_point_vec[1]
for i in range(len(dis_matrix)):
if i==group_node:continue
e_dis=np.linalg.norm(latent_point_vec[group_node]-latent_point_vec[i])
# if e_dis<=0.01:continue
gradient+=((e_dis-dis_matrix[i][group_node])/e_dis)*(latent_point_vec[group_node]-latent_point_vec[i])
return latent_point_vec[group_node]-2*(gradient)/len(dis_matrix)
def sample_vec(group_node,latent_point_vec,mean,topic_var,dis_matrix,sample_num=7):
dimensionlen=len(mean)
#vec_samples=np.random.multivariate_normal(mean,var([],[],dimensionlen,coef=1) , sample_num)
vec_samples=np.random.multivariate_normal(mean,topic_var , sample_num)
portion=[1.0]*sample_num
for i in range(sample_num):
for n in range(len(dis_matrix)):
if group_node==n or dis_matrix[group_node][n]>7:continue
# if single_normal_pdf(np.linalg.norm(vec_samples[i]-latent_point_vec[n]),dis_matrix[group_node][n],1)==0:
# print vec_samples[i],latent_point_vec[n],dis_matrix[group_node][n]
portion[i]*=single_normal_pdf(np.linalg.norm(vec_samples[i]-latent_point_vec[n]),dis_matrix[group_node][n],1)
# portion[i]=np.exp(portion[i])
normalize(portion)
#print portion
pick_vec=np.random.choice(sample_num,1,p=portion)[0]
return vec_samples[pick_vec]
def single_normal_pdf(x,m,std):
return np.exp(-((x-m)**2)/2.0)
def update_position_vec(latent_point_vec,dis_matrices,frag_group,frag_mean):
dimensionlen=len(latent_point_vec[0][0])
tv=np.eye(dimensionlen)
for c in range(len(frag_group)):
node_num=float(len(latent_point_vec[c]))
for f in range(len(frag_group[c])):
for group_node in frag_group[c][f]:
#geodesic_vec=get_geodesic_vec(latent_point_vec[c],dis_matrices[c],group_node)
#latent_point_vec[c][group_node]=np.random.multivariate_normal((np.array(frag_mean[c][f])+node_num*geodesic_vec)/(node_num+1),var(frag_group[c][f],latent_point_vec[c],dimensionlen,coef=node_num+1) , 1)[0]
v=sample_vec(group_node,latent_point_vec[c],frag_mean[c][f],tv,dis_matrices[c])
latent_point_vec[c][group_node]=v
#latent_point_vec[c][group_node]=np.random.multivariate_normal((np.array(frag_mean[c][f])+geodesic_vec)/2.0,var([],[],dimensionlen,coef=0.5) , 1)[0]
def get_frag_portion(node,matrix,topic_size_list,component,frag_group,frag_topic,point_vec,frag_mean,frag_var,labels,frag_num,topic_assign_vec,topic_num,ecount_matrix,topic_count,topic_masks,beta=0.05):
label_num=len(set(labels))
frag_portion=[0]*frag_num
f_len=[]
old_frag=0
for i in range(frag_num):
for j in frag_group[i]:
if j==node:old_frag=i
f_len.append(len(frag_group[i]))
for i in range(frag_num):
#frag_portion[i]=multivariate_normal.pdf(point_vec[node], mean=frag_mean[i], cov=frag_var[i],allow_singular=True)*((float(ecount_matrix[labels[component[node]]][frag_topic[i]])+beta)/(topic_count[frag_topic[i]]+beta*label_num))
connected=False
if len(frag_group[i])>0:
for j in frag_group[i]:
if matrix[component[node]][component[j]]==1 or matrix[component[j]][component[node]]==1:
connected=True
break
if connected==False and len(frag_group[i])>0:
frag_portion[i]=0.0
continue
# print (topic_size_list[frag_topic[i]]**len(frag_group[i]))/float(np.math.factorial(len(frag_group[i])))*np.exp(-topic_size_list[frag_topic[i]])
if len(frag_group[i])==0:
frag_portion[i]=normal_pdf(frag_mean[i],point_vec[node])*((float(ecount_matrix[frag_topic[i]][labels[component[node]]])+beta)/(topic_count[frag_topic[i]]+beta*label_num))*(topic_size_list[frag_topic[i]]**1)/float(np.math.factorial(1))*np.exp(-topic_size_list[frag_topic[i]])
else:
if i!=old_frag:
frag_portion[i]=normal_pdf(frag_mean[i],point_vec[node])*((float(ecount_matrix[frag_topic[i]][labels[component[node]]])+beta)/(topic_count[frag_topic[i]]+beta*label_num))
#*(topic_size_list[frag_topic[i]]**(f_len[i]+1))/float(np.math.factorial(f_len[i]+1))*np.exp(-topic_size_list[frag_topic[i]])
else:
frag_portion[i]=normal_pdf(frag_mean[i],point_vec[node])*((float(ecount_matrix[frag_topic[i]][labels[component[node]]])+beta)/(topic_count[frag_topic[i]]+beta*label_num))
#*(topic_size_list[frag_topic[i]]**(f_len[i]))/float(np.math.factorial(f_len[i]))*np.exp(-topic_size_list[frag_topic[i]])
#if sum(frag_portion)==0:
#print frag_portion
# print "zeros:",normal_pdf(frag_mean[i],point_vec[node]),((float(ecount_matrix[frag_topic[i]][labels[component[node]]])+beta)/(topic_count[frag_topic[i]]+beta*label_num)),(topic_size_list[frag_topic[i]]**f_len[i])/float(np.math.factorial(f_len[i]))*np.exp(-topic_size_list[frag_topic[i]]),normal_pdf(frag_mean[i],point_vec[node])*((float(ecount_matrix[frag_topic[i]][labels[component[node]]])+beta)/(topic_count[frag_topic[i]]+beta*label_num))*(topic_size_list[frag_topic[i]]**f_len[i])/float(np.math.factorial(len(frag_group[i])))*np.exp(-topic_size_list[frag_topic[i]])
if sum(frag_portion)==0 and len(frag_portion)<3:
for i in range(len(frag_portion)):
frag_portion[0]=1
normalize(frag_portion)
return frag_portion
def get_gaussian_topic_portion(frag,labels,topic_assign_vec,topic_num,ecount_matrix,topic_count,beta=0.5,tau=50):
label_num=len(set(labels))
topic_portion=[0]*(topic_num+1)
total_nodes=len(topic_assign_vec)
tau=float(tau)
if len(frag)==0:
for i in range(topic_num):
topic_portion[i]=1.0/topic_num
topic_portion[-1]=tau/(total_nodes+tau)
normalize(topic_portion)
return topic_portion
s=1.0
for i in range(topic_num):
s=1.0
for node in frag:
# print ecount_matrix[labels[node]],i
s*=(float(ecount_matrix[i][labels[node]])+beta)/(topic_count[i]+beta*label_num)
s*=((topic_count[i]+tau)/(total_nodes+tau))
topic_portion[i]=s
normalize(topic_portion)
topic_portion[-1]=(tau/(total_nodes+tau))*((beta/(label_num*beta))**len(frag))
normalize(topic_portion)
return topic_portion
def mean(frag,point_vec):
s=[0.0]*len(point_vec[0])
n=float(len(frag))
if n==0:return s
for d in range(len(point_vec[0])):
for f_id in frag:
s[d]+=point_vec[f_id][d]
for d in range(len(point_vec[0])):
s[d]/=(n)
return s
def var(frag,point_vec,dimensionlen,coef=1.0):
a=coef
t=[]
for i in range(dimensionlen):
t.append([0.0]*dimensionlen)
t[i][i]=1.0/((1.0/a)+len(frag))
return t
def update_transition(matrix,labels,components,frag_group,frag_topic,transition_count_matrices,pair_count_matrices,ind_label=[]):
component_num=len(components)
for t in range(len(transition_count_matrices)):
for i in range(len(transition_count_matrices[t])):
for j in range(len(transition_count_matrices[t][i])):
transition_count_matrices[t][i][j]=0
pair_count_matrices[t][i][j]=0
for i in range(component_num):
for k in range(len(frag_group[i])):
for j in frag_group[i][k]:
for d in frag_group[i][k]:
if j==d :continue
if matrix[components[i][j]][components[i][d]]==1:
transition_count_matrices[frag_topic[i][k]][labels[components[i][j]]][labels[components[i][d]]]+=1
t=0
if float(pair_count_matrices[frag_topic[i][k]][labels[components[i][j]]][labels[components[i][d]]])!=0:
t=float(pair_count_matrices[frag_topic[i][k]][labels[components[i][j]]][labels[components[i][d]]])/float(pair_count_matrices[frag_topic[i][k]][labels[components[i][j]]][labels[components[i][d]]])
print ind_label[labels[components[i][j]]],ind_label[labels[components[i][d]]],frag_topic[i][k],t
pair_count_matrices[frag_topic[i][k]][labels[components[i][j]]][labels[components[i][d]]]+=1
'''
for t in range(len(transition_count_matrices)):
for i in range(len(transition_count_matrices[0])):
for j in range(len(transition_count_matrices[0])):
if transition_count_matrices[t][i][j]>0:
print ind_label[i],ind_label[j]
'''
def PSM_Flow(matrix,labels,dis_matrices,components,topic_num,iter_num,component_cluster_num=[],beta=0.05,dimensionlen=10,ind_label=[],tau=0.5):
node_num= len(labels)
print set(labels)
label_num= len(set(labels))
dis_point_vec=[]
latent_point_vec=[]
component_num=len(components)
topic_assign_vec=[0]*node_num
topic_portion=[0]*topic_num
topic_size_list=[5]*topic_num
perplexity_list=[]
frag_assign_vec=[]
frag_portion=[]
frag_topic=[]
frag_group=[]
new_frag_group=[]
# dimensionlen=len(point_vec[0][0])
frag_mean=[]
frag_var=[]
frag_group_nums=[]
for i in range(component_num):
frag_assign_vec.append([])
frag_topic.append([])
frag_mean.append([])
latent_point_vec.append([])
frag_var.append([])
frag_group.append([])
new_frag_group.append([])
if component_cluster_num!=[]:
frag_group_nums.append(component_cluster_num[i])
else:
frag_group_nums.append(len(components[i]))
for j in range(len(components[i])):
#latent_point_vec[i].append([0.0]*dimensionlen)
# latent_point_vec[i].append(np.zeros(dimensionlen))
latent_point_vec[i].append(np.random.multivariate_normal(np.zeros(dimensionlen),var([],[],dimensionlen) , 1)[0])
for i in range(component_num):
frag_assign_vec[i]=[0]*len(components[i])
frag_topic[i]=[0]*len(components[i])
for j in range(frag_group_nums[i]):
#frag_mean[i].append([0.0]*len(point_vec[0]))
frag_mean[i].append([0.0]*dimensionlen)
frag_group[i].append([])
new_frag_group[i].append([])
t=[]
for i in range(dimensionlen):
t.append([0.0]*dimensionlen)
t[i][i]=1.0
for i in range(component_num):
for j in range(len(frag_mean[i])):
frag_var[i].append(t)
ecount_matrix=np.zeros(shape=(topic_num,label_num),dtype=np.int)
logit_coef_matrix=np.zeros(shape=(label_num,dimensionlen),dtype=np.float)
topic_masks=np.ones(shape=(topic_num,label_num),dtype=np.int)
topic_count=np.zeros(shape=(topic_num))
#initialize
###ini frag assign
for i in range(component_num):
for j in range(len(components[i])):
picked_frag=np.random.choice(frag_group_nums[i],1)[0]
frag_group[i][picked_frag].append(j)
update_position_vec(latent_point_vec,dis_matrices,frag_group,frag_mean)
#print latent_point_vec[0]
#init topic
for i in range(component_num):
for k in range(frag_group_nums[i]):
picked_topic=np.random.choice(topic_num,1)[0]
frag_topic[i][k]=picked_topic
for j in frag_group[i][k]:
ecount_matrix[picked_topic][labels[components[i][j]]]+=1
topic_assign_vec[components[i][j]]=picked_topic
topic_count[picked_topic]+=len(frag_group[i][k])
frag_mean[i][k]=mean(frag_group[i][k],latent_point_vec[i])
s=0
######
for i in range(iter_num):
scan_order=range(node_num)
shuffle(scan_order)
new_frag_group=[]
for c in range(component_num):
new_frag_group.append([])
for f in range(frag_group_nums[c]):new_frag_group[c].append([])
update_position_vec(latent_point_vec,dis_matrices,frag_group,frag_mean)
##update mean var
for c in range(component_num):
for f in range(frag_group_nums[c]):
frag_mean[c][f]=np.random.multivariate_normal(mean(frag_group[c][f],latent_point_vec[c]),var(frag_group[c][f],latent_point_vec[c],dimensionlen,coef=1.0/(max(len(frag_group[c][f]),1))) , 1)[0]
###
##update doc membership
for c in range(component_num):
for group_node in range(len(components[c])):
#frag_portion=get_frag_portion(group_node,components[c],frag_group[c],frag_topic[c],latent_point_vec[c],frag_mean[c],frag_var[c],labels,frag_group_nums[c],topic_assign_vec,topic_num,ecount_matrix,topic_count,topic_masks,beta=beta)
frag_portion=get_frag_portion(group_node,matrix,topic_size_list,components[c],frag_group[c],frag_topic[c],latent_point_vec[c],frag_mean[c],[],labels,frag_group_nums[c],topic_assign_vec,topic_num,ecount_matrix,topic_count,topic_masks,beta=beta)
picked_frag=np.random.choice(frag_group_nums[c],1,p=frag_portion)[0]
new_frag_group[c][picked_frag].append(group_node)
frag_assign_vec[c][group_node]=picked_frag
frag_group=new_frag_group
for c in range(component_num):
for f in range(frag_group_nums[c]):
topic_portion=get_gaussian_topic_portion([components[c][b] for b in frag_group[c][f]],labels,topic_assign_vec,topic_num,ecount_matrix,topic_count,beta=beta,tau=tau)
# print topic_portion
picked_topic=np.random.choice(topic_num+1,1,p=topic_portion)[0]
if picked_topic==topic_num:#open new topic
ecount_matrix,topic_count,topic_size_list,topic_num=add_topic(ecount_matrix,topic_count,topic_assign_vec,topic_size_list,topic_num)
frag_topic[c][f]=picked_topic
for group_node in frag_group[c][f]:
old_topic=topic_assign_vec[components[c][group_node]]
ecount_matrix[old_topic][labels[components[c][group_node]]]-=1
topic_count[old_topic]-=1
topic_assign_vec[components[c][group_node]]=picked_topic
ecount_matrix[picked_topic][labels[components[c][group_node]]]+=1
topic_count[picked_topic]+=1
#update_topic_masks(ecount_matrix,topic_masks,jump_prop=0.01)
ecount_matrix,topic_count,topic_size_list,topic_num=remove_empty_topic(ecount_matrix,topic_count,topic_assign_vec,topic_size_list,topic_num)
frag_topic= update_frag_topic(ecount_matrix,components,frag_group,frag_topic,topic_num,topic_count,topic_assign_vec,labels)
# update_frag_group(frag_group,frag_topic,frag_mean,frag_var,frag_group_nums,dimensionlen)
# print 'f',frag_group[1],frag_group[0]
update_topic_size(topic_size_list,frag_group,frag_topic)
# print topic_size_list
print "iter_num",i
# print np.array(ecount_matrix).tolist()
# print frag_group[2]
print "topic_num",topic_num
# print 'effective topic_num:',effective_topic_num(ecount_matrix,15)
#print topic_masks
#print 'effective topic number:',effective_topic_num(np.array(ecount_matrix).transpose())
perplexity_list.append(GC_perplexity(components,latent_point_vec,ecount_matrix,topic_count,labels,frag_assign_vec,topic_assign_vec,frag_mean,frag_var,topic_num,dis_matrices,beta))
print 'perplexity:',perplexity_list[-1]
transition_count_matrices=[]
pair_count_matrices=[]
for t in range(topic_num):
transition_count_matrices.append(np.zeros(shape=(label_num,label_num)))
pair_count_matrices.append(np.zeros(shape=(label_num,label_num)))
transition_matrices=[]
'''
update_transition(matrix,labels,components,frag_group,frag_topic,transition_count_matrices,pair_count_matrices,ind_label=ind_label)
for t in range(topic_num):
transition_matrices.append(np.zeros(shape=(label_num,label_num)))
for t in range(topic_num):
for i in range(label_num):
for j in range(label_num):
if pair_count_matrices[t][i][j]>0:
transition_matrices[t][i][j]=float(transition_count_matrices[t][i][j])/float(pair_count_matrices[t][i][j])
'''
# print transition_count_matrices[0],pair_count_matrices[0],transition_matrices[0]
# print transition_count_matrices[1],pair_count_matrices[1],transition_matrices[1]
# print transition_matrices[0],transition_matrices[1],transition_matrices[2]
'''
for i in range(0,len(matrix)-5):
for j in range(0,len(matrix)-5):
if matrix[i][j]==1:
print ind_label[labels[i]],ind_label[labels[j]]
'''
'''
for t in range(len(transition_count_matrices)):
for i in range(len(transition_count_matrices[0])):
for j in range(len(transition_count_matrices[0])):
if transition_count_matrices[t][i][j]>0.5:
print ind_label[i],ind_label[j]
'''
return topic_assign_vec,latent_point_vec,frag_mean,frag_group,frag_topic,transition_matrices,perplexity_list
|
# Generated by Django 3.0.5 on 2020-04-29 11:30
from django.db import migrations, models
import django.utils.timezone
import sorl.thumbnail.fields
import tinymce.models
class Migration(migrations.Migration):
dependencies = [
('mysite', '0005_portfolio_url'),
]
operations = [
migrations.CreateModel(
name='Testimonial',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=255)),
('description', tinymce.models.HTMLField()),
('image', sorl.thumbnail.fields.ImageField(upload_to='uploads/testimonial')),
('status', models.PositiveSmallIntegerField(choices=[(1, 'Active'), (0, 'Inactive')], default=1)),
('published_date', models.DateTimeField(auto_now_add=True)),
],
),
migrations.AddField(
model_name='portfolio',
name='image',
field=sorl.thumbnail.fields.ImageField(default=django.utils.timezone.now, upload_to='uploads/portfolio'),
preserve_default=False,
),
]
|
"""Module to hold the ServicesInvoice resource."""
from fintoc.mixins import ResourceMixin
class ServicesInvoice(ResourceMixin):
"""Represents a Fintoc Services Invoice."""
|
from typing import List
from sklearn.svm import LinearSVC
from arg.counter_arg.runner_qck.qck_datagen import load_qk
from arg.qck.decl import QKUnit, KDP
from cache import load_from_pickle
def main():
split = "training"
qk_list: List[QKUnit] = load_qk(split)
svclassifier: LinearSVC = load_from_pickle("svclassifier")
feature_extractor = load_from_pickle("feature_extractor")
def get_score(k: KDP) -> float:
text = " ".join(k.tokens)
x = feature_extractor.transform([text])
s = svclassifier._predict_proba_lr(x)
return s[0][0]
for q, k_list in qk_list:
print("Query:", q.text)
for kdp in k_list:
score = get_score(kdp)
if score > 0.8:
print(score, " ".join(kdp.tokens))
if __name__ == "__main__":
main() |
'''
Created on Jul 3, 2018
@author: Pravesh
'''
from config import Session
from tables import Issue
session=Session()
result=session.query(Issue).filter(Issue.id=="1").first()
print(result) |
import configuration
import inference_utils
import inference_wrapper
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
#for file_pattern in FLAGS.input_files.split(","):
# filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
config_sess = tf.ConfigProto()
config_sess.gpu_options.allow_growth = True
with tf.Session(graph=g, config=config_sess) as sess:
# Load the model from checkpoint.
restore_fn(sess)
test_path = r'C:\Users\PSIML-1.PSIML-1\Desktop\projekti\Image-Captioning\test_gradient'
for filename in filenames:
full_fname = os.path.join(test_path, filename)
with tf.gfile.GFile(full_fname, "rb") as f:
image = f.read()
initial_state = model.feed_image(sess, image)
for i in range(20):
softmax, new_states, metadata = model.inference_step(sess, input_feed, state_feed)
if __name__ == "__main__":
#tf.app.run()
main(None) |
'''
已知文本文件，以 \n 为行结束符，
每行包含两个字符串 key和value，
中间用 \t 分割，key和value均有可能重复出现，
输入文件内容格式举例：
2687694 18070300
2687694 18070300
2687694 18070500
2687694 18070500
2687697 15050000
2687697 15050000
2687697 15050500
2687697 15050500
请写程序统计下列信息：
1） 每个key对应多少不同的唯一value？
2） 每个不同的value出现次数是多少？
并按value次数从大到小输出结果文件
(key1:value1,count1;value2,count2....\n
key2:value1,count1;value2,count2....)
输出文件格式举例：
2687694:18070300,2;18070500,2
2687697:15050000,2;15050500,2
'''
data = '''2687694\t18070300
2687694\t18070300
2687694\t18070500
2687694\t18070500
2687697\t15050000
2687697\t15050000
2687697\t15050500
2687697\t15050500
'''
# 先将数据按行分类
data_list = data.splitlines()
# 建立keys字典
dicts = {}
# 遍历数据 构建符合格式要求的字典
for data in data_list:
k = data.split('\t')[0].strip()
v = data.split('\t')[1].strip()
# 判断记录是否在字典里
if k not in dicts:
dicts[k] = {v: 1}
else:
if v not in dicts[k]:
dicts[k][v] = 1
else:
dicts[k][v] += 1
# 将数据格式化输出
for k, v in dicts.items():
# 打印k
print(k, end=':')
# 获取v_dict 的长度
lens = len(v)
i = 0
# 将count值排序
sorted(v.items(), key=lambda item: item[1], reverse=True)
for name, c in v.items():
i += 1
print('{},{}'.format(name, c), end=';')
# 格式化输出，换行
if i == lens:
print('')
|
from flask import Flask,request,jsonify,Response
from flask_pymongo import PyMongo,ObjectId
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
app.config["MONGO_URI"] = "mongodb://localhost:27017/flask"
mongo = PyMongo(app)
db= mongo.db.users
@app.route('/users', methods=['POST'])
def create():
ID = db.insert({
'name':request.json['name'],
'email':request.json['email'],
'password':request.json['password'],
})
response=jsonify(str(ObjectId(ID)))
return response
@app.route('/users', methods=['GET'])
def get():
users= []
for doc in db.find():
users.append({
'id':str(ObjectId(doc['_id'])),
'name':doc['name'],
'email':doc['email'],
'password':doc['password']
})
return jsonify(users)
@app.route('/user/<id>', methods=['GET'])
def getOne(id):
user=db.find_one({'_id':ObjectId(id)})
return jsonify({
'id':str(str(ObjectId(user['_id']))),
'name':user['name'],
'email':user['email'],
'password':user['password']
})
@app.route('/users/<id>', methods=['PUT'])
def update(id):
db.update_one({
'_id': ObjectId(id)},
{'$set':{'name':request.json['name'],'email':request.json['email'],'password':request.json['password'] }}
)
respose = jsonify({"message":"user" + id + "actualizado"})
return respose
@app.route("/users/<id>", methods=['DELETE'])
def delete(id):
db.delete_one({'_id': ObjectId(id)})
respose = jsonify({"message":"user" + id + "borrado"})
return respose
if __name__ == '__main__':
app.run(debug=True)
|
from sklearn import tree
features = [[140,1],
[130,1],
[150,0],
[170,0]]
#apple = 0
#orange = 1
labels = [0,0,1,1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(features,labels)
print(clf.predict([[120,1]])) |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""A lightweight Application framwork.
"""
from __future__ import (
division, print_function, absolute_import, unicode_literals)
# Standard libraries.
import argparse
# ID: $Id$"
__date__ = "$Date$"[6:-1]
__scm_version__ = "$Revision$"[10:-1]
__author__ = "`Berthold Höllmann <berthold.hoellmann@dnvgl.com>`__"
__copyright__ = "Copyright © 2010 by DNV GL SE"
class Application(object):
"""
Base Class for Applications.
This class is a base class applications. It allows access to the
program options from all parts of the program.
:CVariables:
args
parsed options as from `argparse.ArgumentParser.parse_args`
_optionList
option list for creating `ArgumentParser` instance
_usage
usage information
_version
version information for actual application
_description
description for actual application
_minArgs
required minimum length of args
_maxArgs
allowed maximum length of args
"""
args = None
_optionList = None
_usage = None
_version = None
_description = None
def __init__(self, args=None):
parser = argparse.ArgumentParser(
usage=self._usage, description=self._description)
parser.add_argument('--version', action='version',
version='%(prog)s {}'.format(self._version))
for (name, options) in self._optionList:
parser.add_argument(*name, **options)
parser.add_argument('--factor', action='store', default=1. / 1000.,
metavar="FACTOR", type=float,
help="""Factor for length units.
DEFAULT: %(default)s""")
Application.args = parser.parse_args(args)
def __call__(self):
return self.main()
# Local Variables:
# mode: python
# compile-command: "cd ../../;python setup.py test"
# End:
|
# -*- coding: utf-8 -*-
# 版权所有 2019 深圳米筐科技有限公司（下称“米筐科技”）
#
# 除非遵守当前许可，否则不得使用本软件。
#
# * 非商业用途（非商业用途指个人出于非商业目的使用本软件，或者高校、研究所等非营利机构出于教育、科研等目的使用本软件）：
# 遵守 Apache License 2.0（下称“Apache 2.0 许可”），
# 您可以在以下位置获得 Apache 2.0 许可的副本：http://www.apache.org/licenses/LICENSE-2.0。
# 除非法律有要求或以书面形式达成协议，否则本软件分发时需保持当前许可“原样”不变，且不得附加任何条件。
#
# * 商业用途（商业用途指个人出于任何商业目的使用本软件，或者法人或其他组织出于任何目的使用本软件）：
# 未经米筐科技授权，任何个人不得出于任何商业目的使用本软件（包括但不限于向第三方提供、销售、出租、出借、转让本软件、
# 本软件的衍生产品、引用或借鉴了本软件功能或源代码的产品或服务），任何法人或其他组织不得出于任何目的使用本软件，
# 否则米筐科技有权追究相应的知识产权侵权责任。
# 在此前提下，对本软件的使用同样需要遵守 Apache 2.0 许可，Apache 2.0 许可与本许可冲突之处，以本许可为准。
# 详细的授权流程，请联系 public@ricequant.com 获取。
import sys
import datetime
from pprint import pformat
import logbook
import jsonpickle.ext.numpy as jsonpickle_numpy
import six
from rqalpha import const
from rqalpha.core.strategy_loader import FileStrategyLoader, SourceCodeStrategyLoader, UserFuncStrategyLoader
from rqalpha.core.strategy import Strategy
from rqalpha.core.strategy_context import StrategyContext
from rqalpha.core.executor import Executor
from rqalpha.data.base_data_source import BaseDataSource
from rqalpha.data.data_proxy import DataProxy
from rqalpha.environment import Environment
from rqalpha.events import EVENT, Event
from rqalpha.execution_context import ExecutionContext
from rqalpha.interface import Persistable
from rqalpha.mod import ModHandler
from rqalpha.model.bar import BarMap
from rqalpha.utils import create_custom_exception, RqAttrDict, init_rqdatac_env
from rqalpha.utils.exception import CustomException, is_user_exc, patch_user_exc
from rqalpha.utils.i18n import gettext as _
from rqalpha.utils.log_capture import LogCapture
from rqalpha.utils.persisit_helper import PersistHelper
from rqalpha.utils.logger import system_log, user_system_log, user_log
jsonpickle_numpy.register_handlers()
def _adjust_start_date(config, data_proxy):
origin_start_date, origin_end_date = config.base.start_date, config.base.end_date
start, end = data_proxy.available_data_range(config.base.frequency)
config.base.start_date = max(start, config.base.start_date)
config.base.end_date = min(end, config.base.end_date)
config.base.trading_calendar = data_proxy.get_trading_dates(config.base.start_date, config.base.end_date)
if len(config.base.trading_calendar) == 0:
raise patch_user_exc(
ValueError(
_(u"There is no data between {start_date} and {end_date}. Please check your"
u" data bundle or select other backtest period.").format(
start_date=origin_start_date, end_date=origin_end_date)))
config.base.start_date = config.base.trading_calendar[0].date()
config.base.end_date = config.base.trading_calendar[-1].date()
def create_base_scope():
from . import user_module
from copy import copy
return copy(user_module.__dict__)
def init_persist_helper(env, ucontext, executor, config):
if not config.base.persist:
return None
persist_provider = env.persist_provider
if persist_provider is None:
raise RuntimeError(_(u"Missing persist provider. You need to set persist_provider before use persist"))
persist_helper = PersistHelper(persist_provider, env.event_bus, config.base.persist_mode)
env.set_persist_helper(persist_helper)
persist_helper.register('user_context', ucontext)
persist_helper.register('global_vars', env.global_vars)
persist_helper.register('universe', env._universe)
if isinstance(env.event_source, Persistable):
persist_helper.register('event_source', env.event_source)
persist_helper.register('portfolio', env.portfolio)
for name, module in six.iteritems(env.mod_dict):
if isinstance(module, Persistable):
persist_helper.register('mod_{}'.format(name), module)
# broker will restore open orders from account
if isinstance(env.broker, Persistable):
persist_helper.register('broker', env.broker)
persist_helper.register('executor', executor)
return persist_helper
def init_strategy_loader(env, source_code, user_funcs, config):
if source_code is not None:
return SourceCodeStrategyLoader(source_code)
elif user_funcs is not None:
return UserFuncStrategyLoader(user_funcs)
else:
return FileStrategyLoader(config.base.strategy_file)
def get_strategy_apis():
from rqalpha import api
return {n: getattr(api, n) for n in api.__all__}
def init_rqdatac(rqdatac_uri):
try:
import rqdatac
except ImportError:
system_log.info(_('rqdatac is not available, some apis will not function properly'))
return
try:
init_rqdatac_env(rqdatac_uri)
rqdatac.init()
except ValueError as e:
system_log.warn(_('rqdatac init failed, some apis will not function properly: {}').format(str(e)))
def run(config, source_code=None, user_funcs=None):
env = Environment(config)
persist_helper = None
init_succeed = False
mod_handler = ModHandler()
try:
# avoid register handlers everytime
# when running in ipython
set_loggers(config)
init_rqdatac(getattr(config.base, 'rqdatac_uri', None))
system_log.debug("\n" + pformat(config.convert_to_dict()))
env.set_strategy_loader(init_strategy_loader(env, source_code, user_funcs, config))
mod_handler.set_env(env)
mod_handler.start_up()
if not env.data_source:
env.set_data_source(BaseDataSource(config.base.data_bundle_path, getattr(config.base, "future_info", {})))
if env.price_board is None:
from rqalpha.data.bar_dict_price_board import BarDictPriceBoard
env.price_board = BarDictPriceBoard()
env.set_data_proxy(DataProxy(env.data_source, env.price_board))
_adjust_start_date(env.config, env.data_proxy)
# FIXME
start_dt = datetime.datetime.combine(config.base.start_date, datetime.datetime.min.time())
env.calendar_dt = start_dt
env.trading_dt = start_dt
assert env.broker is not None
assert env.event_source is not None
if env.portfolio is None:
from rqalpha.portfolio import Portfolio
env.set_portfolio(Portfolio(config.base.accounts, config.base.init_positions))
ctx = ExecutionContext(const.EXECUTION_PHASE.GLOBAL)
ctx._push()
env.event_bus.publish_event(Event(EVENT.POST_SYSTEM_INIT))
scope = create_base_scope()
scope.update({"g": env.global_vars})
scope.update(get_strategy_apis())
scope = env.strategy_loader.load(scope)
if config.extra.enable_profiler:
enable_profiler(env, scope)
ucontext = StrategyContext()
executor = Executor(env)
persist_helper = init_persist_helper(env, ucontext, executor, config)
user_strategy = Strategy(env.event_bus, scope, ucontext)
env.user_strategy = user_strategy
env.event_bus.publish_event(Event(EVENT.BEFORE_STRATEGY_RUN))
if persist_helper:
with LogCapture(user_log) as log_capture:
user_strategy.init()
else:
user_strategy.init()
if config.extra.context_vars:
for k, v in six.iteritems(config.extra.context_vars):
if isinstance(v, RqAttrDict):
v = v.__dict__
setattr(ucontext, k, v)
if persist_helper:
env.event_bus.publish_event(Event(EVENT.BEFORE_SYSTEM_RESTORED))
if persist_helper.restore(None):
user_system_log.info(_('system restored'))
else:
log_capture.replay()
env.event_bus.publish_event(Event(EVENT.POST_SYSTEM_RESTORED))
init_succeed = True
bar_dict = BarMap(env.data_proxy, config.base.frequency)
executor.run(bar_dict)
env.event_bus.publish_event(Event(EVENT.POST_STRATEGY_RUN))
if env.profile_deco:
output_profile_result(env)
except CustomException as e:
if init_succeed and persist_helper and env.config.base.persist_mode == const.PERSIST_MODE.ON_CRASH:
persist_helper.persist()
code = _exception_handler(e)
mod_handler.tear_down(code, e)
except Exception as e:
if init_succeed and persist_helper and env.config.base.persist_mode == const.PERSIST_MODE.ON_CRASH:
persist_helper.persist()
exc_type, exc_val, exc_tb = sys.exc_info()
user_exc = create_custom_exception(exc_type, exc_val, exc_tb, config.base.strategy_file)
code = _exception_handler(user_exc)
mod_handler.tear_down(code, user_exc)
else:
if persist_helper and env.config.base.persist_mode == const.PERSIST_MODE.ON_NORMAL_EXIT:
persist_helper.persist()
result = mod_handler.tear_down(const.EXIT_CODE.EXIT_SUCCESS)
system_log.debug(_(u"strategy run successfully, normal exit"))
return result
def _exception_handler(e):
user_system_log.exception(_(u"strategy execute exception"))
if not is_user_exc(e.error.exc_val):
system_log.exception(_(u"strategy execute exception"))
return const.EXIT_CODE.EXIT_INTERNAL_ERROR
return const.EXIT_CODE.EXIT_USER_ERROR
def enable_profiler(env, scope):
# decorate line profiler
try:
import line_profiler
except ImportError:
raise RuntimeError('--enable-profiler needs line_profiler')
import inspect
env.profile_deco = profile_deco = line_profiler.LineProfiler()
for name in scope:
obj = scope[name]
if getattr(obj, "__module__", None) != "rqalpha.user_module":
continue
if inspect.isfunction(obj):
scope[name] = profile_deco(obj)
if inspect.isclass(obj):
for key, val in six.iteritems(obj.__dict__):
if inspect.isfunction(val):
setattr(obj, key, profile_deco(val))
def output_profile_result(env):
stdout_trap = six.StringIO()
env.profile_deco.print_stats(stdout_trap)
profile_output = stdout_trap.getvalue()
profile_output = profile_output.rstrip()
six.print_(profile_output)
env.event_bus.publish_event(Event(EVENT.ON_LINE_PROFILER_RESULT, result=profile_output))
def set_loggers(config):
from rqalpha.utils.logger import user_log, user_system_log, system_log
from rqalpha.utils.logger import init_logger
from rqalpha.utils import logger
extra_config = config.extra
init_logger()
for log in [system_log, user_system_log]:
log.level = getattr(logbook, config.extra.log_level.upper(), logbook.NOTSET)
user_log.level = logbook.DEBUG
if extra_config.log_level.upper() != "NONE":
if extra_config.user_log_disabled:
user_log.disable()
else:
user_log.enable()
if extra_config.user_system_log_disabled:
user_system_log.disable()
else:
user_system_log.enable()
for logger_name, level in extra_config.logger:
getattr(logger, logger_name).level = getattr(logbook, level.upper())
|
from django.shortcuts import render
from django.http import JsonResponse
from rest_framework import permissions, status
from rest_framework.permissions import IsAuthenticated
from rest_framework.decorators import api_view, authentication_classes, permission_classes
from rest_framework.views import APIView
from rest_framework.authentication import TokenAuthentication
from rest_framework.response import Response
from .serializers import UserSerializer, UserSerializerWithToken, EventSerializer
from . models import User, Event
@api_view(['GET'])
def apiOverview(request):
api_urls = {
'User List': '/user-list/',
'User Detail View': '/user-detail/<str:pk>/',
'User Create': '/user-create/',
'User Update': '/user-update/<str:pk>/',
'User Delete': '/user-delete/<str:pk>/',
'EventList': '/event-list/',
'Event Detail View': '/event-detail/<str:pk>/',
'Event Create': '/event-create/',
'Event Update': '/event-update/<str:pk>/',
'Event Delete': '/event-delete/<str:pk>/'
}
return Response(api_urls)
################# USER VIEWS #################
@api_view(['GET'])
def current_user(request):
serializer = UserSerializer(request.user)
return Response(serializer.data)
class UserList(APIView):
permission_classes = (permissions.AllowAny,)
def post(self, request, format = None):
serializer = UserSerializerWithToken(data = request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, print("User created succesfully!"))
return Response(serializer.errors, print("Something went wrong with create!!!"))
@api_view(['GET'])
def userList(request):
users = User.objects.all()
serializer = UserSerializer(users, many = True)
return Response(serializer.data)
@api_view(['GET'])
def userDetail(request, pk):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
user = User.objects.get(id = pk)
serializer = UserSerializer(user, many = False)
return Response(serializer.data)
@api_view(['PUT'])
def userUpdate(request, pk):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
user = User.objects.get(id = pk)
print("THIS IS THE DATA BEING RECIEVED = ", request.data)
serializer = UserSerializer(instance = user, data = request.data)
if serializer.is_valid():
serializer.save()
print("DATA = ", serializer.data)
return Response(serializer.data, print("User updated!!!!"))
print("ERRORS = ", serializer.errors)
return Response(serializer.errors, print("Update Failed!!!"))
@api_view(['DELETE'])
def userDelete(request, pk):
user = User.objects.get(id = pk)
user.delete()
return Response('User successfully deleted!')
################# EVENT VIEWS #################
@api_view(['POST'])
def eventCreate(request):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
serializer = EventSerializer(data = request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, print(serializer.data, "Event created!!"))
else:
return Response(serializer.data, print(serializer.errors, "Error in event Create View"))
@api_view(['GET'])
def eventList(request):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
users = User.objects.all()
events = Event.objects.all()
serializer = EventSerializer(events, many = True)
return Response(serializer.data)
@api_view(['GET'])
def eventDetail(request, pk):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
event = Event.objects.get(id = pk)
print(event)
serializer = EventSerializer(event, many = False)
return Response(serializer.data)
@api_view(['GET'])
def hostingList(request, pk):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
print(pk)
user = User.objects.get(id = pk)
events = Event.objects.filter(event_by_user_id = user.id)
serializer = EventSerializer(events, many = True)
print(serializer.data)
return Response(serializer.data)
@api_view(['GET'])
def attendingList(request, pk):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
user = User.objects.get(id = pk)
attending_event = user.users_going_related.all()
serializer = EventSerializer(attending_event, many = True)
return Response(serializer.data)
@api_view(['GET'])
def notAttendingList(request, pk):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
user = User.objects.get(id = pk)
event_not_attending = Event.objects.exclude(users_going = user.id)
serializer = EventSerializer(event_not_attending, many = True)
return Response(serializer.data)
@api_view(['POST'])
def joinEvent(request, pk, id):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
event = Event.objects.get(id = pk)
user = User.objects.get(id = id)
event.users_going.add(user)
return Response(print("JOINED!!!!!!!"))
@api_view(['POST'])
def leaveEvent(request, pk, id):
authentication_classes(TokenAuthentication, )
permission_classes = (permissions.IsAuthenticated, )
event = Event.objects.get(id = pk)
user = User.objects.get(id = id)
event.users_going.remove(user)
return Response(print("LEFT!!!!!!!")) |
import PIL
import cv2
import requests
import zbarlight
video_capture = cv2.VideoCapture(1)
call_set = set()
def get_webcam_image():
# Capture frame-by-frame
ret, frame = video_capture.read()
cv2.imshow("Webcam", frame)
# Convert the CV frame to PIL image
return PIL.Image.fromarray(frame)
def decode_image(image):
# Decode the QR code in PIL image
return zbarlight.scan_codes('qrcode', image)
def make_http_call(url):
if url in call_set:
print(f"{url} is called: ignore it.")
return
r = requests.get(url)
if r.status_code == 200:
print(f"Successfully requests to {url}")
call_set.add(url)
if __name__ == '__main__':
while True:
image = get_webcam_image()
decode_value = decode_image(image)
if decode_value:
for value in decode_value:
make_http_call(value)
# Press q to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
|
import time
from math import *
from sys import *
from groups import *
# also checks default value
def check_initial_conditions(argv):
if len(argv) < 2:
print "Usage: python blur.py <input file> [neighbor reach]"
exit()
elif len(argv) == 2:
return 4
else:
return argv[2]
def in_file(argv):
try:
f = open(argv[1], 'r')
return f
except:
print 'Could not open "' + argv + '" for reading.'
exit()
def avg_pixel(row_min, row_max, col_min, col_max, width, height, pixelList):
avgPixel = [0,0,0]
for i in range(max(row_min, 0), min(row_max, width)):
for j in range(max(col_min, 0), min(col_max, height)):
avgPixel[0] += int(pixelList[i][j][0])
avgPixel[1] += int(pixelList[i][j][1])
avgPixel[2] += int(pixelList[i][j][2])
print 'j:', j
time.sleep(.01)
print 'i:', i
time.sleep(.01)
totalPixels = (row_max - row_min+1) * (col_max - col_min+1)
for i in range(len(avgPixel)):
avgPixel[i] /= totalPixels
return avgPixel
def process_image(infile, blur):
# function breaks if there is a double space
outfile = open('blurred.ppm', 'w')
# writes header, width x height, color cap value to file
header = infile.readline()
width_height = infile.readline().strip('\n').split(' ')
color_cap = infile.readline()
width = int(width_height[0])
height = int(width_height[1])
outfile.write(header + str(width) + ' ' + str(height) + '\n' + color_cap)
pixelList = [value.strip('\n').split(' ') for value in infile]
# checks for misformatted file (if there is a random linebreak or a random double space)
for values in pixelList:
if '' in values:
values.remove('')
if ' ' in values:
values.remove(' ')
pixelList = groups_of_3(pixelList)
# sort pixelList by rows, cols
sortedList = []
for i in range(height):
tempList = []
for j in range(width):
tempList.append(pixelList[j + i * width])
sortedList.append(tempList)
# begin blur
for i in range(len(sortedList)): # row
row_min = i - blur
row_max = i + blur
for j in range(len(sortedList[i])): # pixel
col_min = j - blur
col_max = j + blur
avgPixel = avg_pixel(row_min, row_max, col_min, col_max, width, height, sortedList)
outfile.write(str(avgPixel[0]) + ' ' +
str(avgPixel[1]) + ' ' +
str(avgPixel[2]) + '\n')
def main(argv):
blur = check_initial_conditions(argv)
infile = in_file(argv)
process_image(infile, int(blur))
if __name__ == '__main__':
main(argv)
|
# -*- coding: utf-8 -*-
import utool as ut
ut.noinject(__name__, '[wbia.gui.__init__]', DEBUG=False)
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 5 20:42:24 2019
@author: nico
"""
import os
import numpy as np
from scipy import signal as sig
import matplotlib.pyplot as plt
from scipy.fftpack import fft
import scipy.io as sio
from time import time
import pandas as pd
os.system ("clear") # limpia la terminal de python
plt.close("all") #cierra todos los graficos
fig_sz_x = 14
fig_sz_y = 13
fig_dpi = 80 # dpi
fig_font_family = 'Ubuntu'
fig_font_size = 16
#%% cargo el archivo ECG_TP$.mat
# para listar las variables que hay en el archivo
#sio.whosmat('ECG_TP4.mat')
mat_struct = sio.loadmat('ECG_TP4.mat')
ecg_one_lead = mat_struct['ecg_lead']
ecg_one_lead = ecg_one_lead.flatten(1)
cant_muestras = len(ecg_one_lead)
#%% Defino la fs y el eje de tiempo
fs = 1000
tt = np.linspace(0, cant_muestras, cant_muestras)
#%% genero el filtro de mediana original
the_start = time()
median1 = sig.medfilt(ecg_one_lead, 201) #200 ms
median2 = sig.medfilt(median1, 601) #600 ms
the_end = time()
tiempodft = the_end - the_start
signal = ecg_one_lead - median2
del the_start, the_end
print('El tiempo demorado por este tipo de filtrado es: ',tiempodft)
#%% Graficos
plt.figure("Estimación de la interpolante", constrained_layout=True)
plt.title("Estimación de la interpolante")
plt.plot(tt, median2)
plt.xlabel('Muestras')
plt.ylabel("Amplitud ")
plt.axhline(0, color="black")
plt.axvline(0, color="black")
plt.grid()
plt.legend()
plt.show()
plt.figure("ECG", constrained_layout=True)
plt.title("ECG")
plt.plot(tt, ecg_one_lead, label='ECG original')
plt.plot(tt, signal, label='ECG filtrada')
plt.xlabel('Muestras')
plt.ylabel("Amplitud ")
plt.axhline(0, color="black")
plt.axvline(0, color="black")
plt.grid()
plt.legend()
plt.show()
#%% Zoom regions
# Segmentos de interés
regs_interes = (
np.array([1.6, 2.6]) *60*fs, # minutos a muestras
np.array([4, 5]) *60*fs, # minutos a muestras
np.array([10, 10.5]) *60*fs, # minutos a muestras
np.array([12, 12.7]) *60*fs, # minutos a muestras
np.array([14.6, 15.7]) *60*fs, # minutos a muestras
)
for ii in regs_interes:
# intervalo limitado de 0 a cant_muestras
zoom_region = np.arange(np.max([0, ii[0]]), np.min([cant_muestras, ii[1]]), dtype='uint')
#hace el clipeo para salvar a los indices otra forma es el modulo N (le sumas N para que ingrece
#por el otro extremo y queda circular en 'C' se hace x % 5 )
plt.figure(figsize=(fig_sz_x, fig_sz_y), dpi= fig_dpi, facecolor='w', edgecolor='k')
plt.plot(zoom_region, ecg_one_lead[zoom_region], label='ECG', lw=2)
plt.plot(zoom_region, signal[zoom_region], label='interpolante')
plt.title('ECG filtering example from ' + str(ii[0]) + ' to ' + str(ii[1]) )
plt.ylabel('Adimensional')
plt.xlabel('Muestras (#)')
axes_hdl = plt.gca()
axes_hdl.legend()
axes_hdl.set_yticks(())
plt.show()
#%% Medicion de la frecuencia de corte del filtro de multirate (me quedo con ek 95% de la energia, para eso utilizo la funcion cumsum)
K = 30
L = cant_muestras/K
ff2,Swelch = sig.welch(median2,fs=fs,nperseg=L,window='bartlett')
Swelch2 = 10*np.log10(Swelch)
plt.figure("Estimación de la señal interpolante con el método de Welch")
plt.title(" Estimación de la señal interpolante con el método de Welch")
plt.plot(ff2,Swelch2)
plt.xlabel('frecuecnia [Hz]')
plt.ylabel('Amplitud db')
plt.grid()
plt.show()
# calculo la frecuencia de corte con el 95% de la enrgia
energia=np.zeros((int(L/2)+1))
np.cumsum(Swelch, out=energia)
limfreq = energia < 0.95*energia[-1]
for ii in range(len(limfreq)) :
if limfreq[ii] == False:
freq = ii
break
# calculo la cantidad de pasadas
nyq_frec = fs / 2
cant_pasadas = nyq_frec/freq
cant_pasadas = np.log2(cant_pasadas) #porque cada pasada divide a la mitad
cant_pasadas = int(np.round(cant_pasadas))
#%% Genero la interpolante utiliziando la técnica multirate
the_start = time()
decimation = ecg_one_lead
for jj in range(cant_pasadas):
decimation = sig.decimate(decimation, 2)
median1_dec = sig.medfilt(decimation, 3) #200 ms
median2_dec = sig.medfilt(median1_dec, 5) #600 ms
interpolation = median2_dec
for jj in range(cant_pasadas):
interpolation = sig.resample(interpolation,2*len(interpolation))
signal_int = ecg_one_lead - interpolation[0:len(ecg_one_lead)]
the_end = time()
tiempodft_dec = the_end - the_start
del the_start, the_end
#%% Guardo un ECG limpio para el punto 5b
obj_arr = np.zeros((1), dtype=np.object)
obj_arr = signal_int
sio.savemat('./ECG_Limpio.mat', mdict={'ECG_Limpio': obj_arr})
#%% comparo los dos métodos en tiempo y en error absoluto
tiempo = tiempodft / tiempodft_dec
error = median2 - interpolation[0:len(ecg_one_lead)]
error_cuadratico = (median2 - interpolation[0:len(ecg_one_lead)])**2
valor_medio_real = np.mean(median2)
valor_medio_interpolate_signal = np.mean(interpolation)
sesgo = np.abs(valor_medio_real - valor_medio_interpolate_signal)
error_cuadratico_medio = np.mean(error_cuadratico)
error__medio = np.mean(error)
var_error = np.var(error, axis=0)
plt.figure("ECG 2", constrained_layout=True)
plt.title("ECG 2")
plt.plot(tt, ecg_one_lead, label='ECG original')
plt.plot(tt, signal, label='ECG filtrada completa')
plt.plot(tt, signal_int, label = 'ECG filtrada con resampleo')
plt.xlabel('Muestras')
plt.ylabel("Amplitud ")
plt.axhline(0, color="black")
plt.axvline(0, color="black")
plt.grid()
plt.legend()
plt.show()
plt.figure("Comapración de estimadores", constrained_layout=True)
plt.title("Comparación de estimadores")
plt.plot(tt, median2, label='est med original')
plt.plot(tt, interpolation[0:len(ecg_one_lead)], label='est med resampling')
plt.xlabel('Muestras')
plt.ylabel("Amplitud ")
plt.axhline(0, color="black")
plt.axvline(0, color="black")
plt.grid()
plt.legend()
plt.show()
plt.figure("Error cuadrático de estimadores", constrained_layout=True)
plt.title("Error cuadrático de estimadores")
plt.plot(tt, error_cuadratico, label='error cuadrático')
plt.plot(tt, np.ones((len(ecg_one_lead)))*error_cuadratico_medio, label='media')
plt.xlabel('Muestras')
plt.ylabel("Amplitud ")
plt.axhline(0, color="black")
plt.axvline(0, color="black")
plt.grid()
plt.legend()
plt.show()
plt.figure("Histograma de errores")
plt.hist(error, bins=50, alpha=1, edgecolor = 'black', linewidth=1, label="error")
plt.legend(loc = 'upper right')
plt.ylabel('frecuencia')
plt.xlabel('valores')
plt.title('Histograma de errores' )
plt.show()
#%% Zoom regions
# Segmentos de interés
regs_interes = (
np.array([1.6, 2.6]) *60*fs, # minutos a muestras
np.array([4, 5]) *60*fs, # minutos a muestras
np.array([10, 10.5]) *60*fs, # minutos a muestras
np.array([12, 12.7]) *60*fs, # minutos a muestras
np.array([14.6, 15.7]) *60*fs, # minutos a muestras
)
for ii in regs_interes:
# intervalo limitado de 0 a cant_muestras
zoom_region = np.arange(np.max([0, ii[0]]), np.min([cant_muestras, ii[1]]), dtype='uint')
#hace el clipeo para salvar a los indices otra forma es el modulo N (le sumas N para que ingece
#por el otro extremo y queda circular en 'C' se hace x % 5 )
plt.figure(figsize=(fig_sz_x, fig_sz_y), dpi= fig_dpi, facecolor='w', edgecolor='k')
plt.plot(zoom_region, ecg_one_lead[zoom_region], label='ECG', lw=2)
plt.plot(zoom_region, interpolation[zoom_region], label='interpolante resamplig')
plt.plot(zoom_region, median2[zoom_region], label='interpolante')
plt.title('ECG filtering example from ' + str(ii[0]) + ' to ' + str(ii[1]) )
plt.ylabel('Adimensional')
plt.xlabel('Muestras (#)')
axes_hdl = plt.gca()
axes_hdl.legend()
axes_hdl.set_yticks(())
plt.show()
#%% Presentación de resultados
tus_resultados_per = [
[ tiempodft,valor_medio_real, '-' , '-'], # <-- acá debería haber numeritos :)
[ tiempodft_dec, valor_medio_interpolate_signal, '-', '-'], # <-- acá debería haber numeritos :)
]
df = pd.DataFrame(tus_resultados_per, columns=['$tiempo', '$media', 'media_error', 'varianza'],
index=['interpolante real','interpolante resamplleada'])
print("\n")
print(df)
|
from flask import Flask,g
from proxy_pool.db import Reidis_client
__all__=['app']
app = Flask(__name__)
def get_conn():
if not hasattr(g,'redis_client'):
g.redis_client = Reidis_client()
return g.redis_client
@app.route('/')
def index():
return '<h1>欢迎进入代理池系统！</h1>'
@app.route('/get')
def get():
return get_conn().pop()
@app.route('/count')
def count():
return str(get_conn().queue_len) |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 20 15:09:11 2020
@author: ns2dumon
"""
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
import re
Directory=os.getcwd() + '/storage'
dirnames = [name for name in os.listdir(Directory) if os.path.isdir(os.path.join(Directory, name))]
# Goal transfer task
regstrs = [".*_Goal_.*",".*_NoGoal_.*", ".*_size_.*"]
tits = ["Goal_transfer_learning", "Latent_learning","Size_transfer_learning"]
ridxs = [14,16,14]
for k in range(len(regstrs)):
reg_compile = re.compile(regstrs[k])
subset_dirs = [dirname for dirname in dirnames if reg_compile.match(dirname)]
column_name = 'return_mean'
all_data = []
frame_arrays=[]
plt.figure()
for i in range(len(subset_dirs)):
model_dir = subset_dirs[i]
data = pd.read_csv(Directory + '/' + model_dir + "/log.csv")
frame_arrays.append(pd.to_numeric(data['frames'], errors='coerce').values)
all_data.append(pd.to_numeric(data[column_name], errors='coerce').values)
plt.plot(frame_arrays[-1],all_data[-1],alpha=0.7,linewidth=1)
runs = [r[ridxs[k]:] for r in subset_dirs]
plt.legend(runs)
plt.ylabel("Mean return")
plt.xlabel("Env observations")
plt.savefig(str(tits[k]) + '.png', dpi=300)
plt.savefig(str(tits[k]) + '.pdf')
plt.title(tits[k])
reg_compile = re.compile(".*_NoGoal_.*")
subset_dirs = np.array([dirname for dirname in dirnames if reg_compile.match(dirname)])
column_name = 'return_mean'
all_data = []
frame_arrays=[]
plt.figure()
for i in range(len(subset_dirs)):
model_dir = subset_dirs[i]
if model_dir != "MiniGrid_NoGoal_SR_image":
data = pd.read_csv(Directory + '/' + model_dir + "/log.csv")
frame_arrays.append(pd.to_numeric(data['frames'], errors='coerce').values)
all_data.append(pd.to_numeric(data[column_name], errors='coerce').values)
plt.plot(frame_arrays[-1],all_data[-1],alpha=0.7,linewidth=1)
runs = [r[16:] for r in subset_dirs[[0,2,3]]]
plt.legend(runs)
plt.ylabel("Mean return")
plt.xlabel("Env observations")
plt.savefig(str(tits[1]) + '2.png', dpi=300)
plt.savefig(str(tits[1]) + '2.pdf')
Directory=os.getcwd() + '/storage_oldi'
dirnames = [name for name in os.listdir(Directory) if os.path.isdir(os.path.join(Directory, name))]
reg_compile = re.compile(".*_Goal_.*")
subset_dirs = [dirname for dirname in dirnames if reg_compile.match(dirname)]
column_name = 'return_mean'
all_data = []
frame_arrays=[]
for i in range(len(subset_dirs)):
model_dir = subset_dirs[i]
data = pd.read_csv(Directory + '/' + model_dir + "/log.csv")
frame_arrays.append(pd.to_numeric(data['frames'], errors='coerce').values)
all_data.append(pd.to_numeric(data[column_name], errors='coerce').values)
runs = [r[14:] for r in subset_dirs]
runs = np.array(runs)
sub_rs = [[0,6],[3,5],[3,4],[2,6]]
for k in range(len(sub_rs)):
plt.figure()
for i in range(len(sub_rs[k])):
frs = frame_arrays[sub_rs[k][i]]
dat = all_data[sub_rs[k][i]]
fid = frs <= 80000
plt.plot(frs[fid],dat[fid],alpha=0.7,linewidth=1)
plt.legend(runs[sub_rs[k]])
plt.ylabel("Mean return")
plt.xlabel("Env observations")
plt.savefig('sr_params' + str(k) + '.png', dpi=300)
plt.savefig('sr_params' + str(k) + '.pdf')
|
# Generated by Django 2.1.3 on 2020-07-03 11:21
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='companyCompare',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=200)),
('ppp', models.FloatField(null=True)),
('plasticPpp', models.FloatField(null=True)),
],
),
]
|
import asyncio
from logging import getLogger
from typing import List, TYPE_CHECKING
from pymongo.errors import DuplicateKeyError
from sqlalchemy import update, delete
from sqlalchemy.exc import IntegrityError
from sqlalchemy.ext.asyncio import AsyncEngine
from virtool_core.models.group import GroupMinimal, Group
from virtool.authorization.client import AuthorizationClient
from virtool.data.errors import ResourceNotFoundError, ResourceConflictError
from virtool.data.events import emits, Operation, emit
from virtool.data.topg import both_transactions
from virtool.groups.db import update_member_users, fetch_complete_group
from virtool.groups.oas import UpdateGroupRequest
from virtool.groups.pg import SQLGroup
from virtool.mongo.utils import get_one_field, id_exists
from virtool.users.utils import generate_base_permissions
from virtool.utils import base_processor
if TYPE_CHECKING:
from virtool.mongo.core import Mongo
logger = getLogger("groups")
class GroupsData:
name = "groups"
def __init__(
self, authorization_client: AuthorizationClient, mongo: "Mongo", pg: AsyncEngine
):
self._authorization_client = authorization_client
self._mongo = mongo
self._pg = pg
async def find(self) -> List[GroupMinimal]:
"""
List all user groups.
:return: a list of all user groups
"""
return [
GroupMinimal(**base_processor(document))
async for document in self._mongo.groups.find()
]
async def get(self, group_id: str) -> Group:
"""
Get a single group by its ID.
:param group_id: the group's ID
:return: the group
"""
group = await fetch_complete_group(self._mongo, group_id)
if group:
return group
raise ResourceNotFoundError()
@emits(Operation.CREATE)
async def create(self, name: str) -> Group:
"""
Create new group with the given name.
:param name: the ID for the new group
:return: the group
:raises ResourceConflictError: if a group with the given name already exists
"""
try:
async with both_transactions(self._mongo, self._pg) as (
mongo_session,
pg_session,
):
document = await self._mongo.groups.insert_one(
{"name": name, "permissions": generate_base_permissions()},
session=mongo_session,
)
pg_session.add(
SQLGroup(
legacy_id=document["_id"],
name=name,
permissions=generate_base_permissions(),
)
)
except (DuplicateKeyError, IntegrityError):
raise ResourceConflictError("Group already exists")
return Group(**base_processor(document), users=[])
@emits(Operation.UPDATE)
async def update(self, group_id: str, data: UpdateGroupRequest) -> Group:
"""
Update the permissions for a group.
:param group_id: the id of the group
:param data: updates to the current group permissions or name
:return: the updated group
:raises ResourceNotFoundError: if the group does not exist
"""
if not await id_exists(self._mongo.groups, group_id):
raise ResourceNotFoundError
data = data.dict(exclude_unset=True)
async with both_transactions(self._mongo, self._pg) as (
mongo_session,
pg_session,
):
db_update = {}
if "name" in data:
db_update["name"] = data["name"]
if "permissions" in data:
permissions = await get_one_field(
self._mongo.groups, "permissions", {"_id": group_id}
)
db_update["permissions"] = {**permissions, **data["permissions"]}
if db_update:
await asyncio.gather(
pg_session.execute(
(
update(SQLGroup)
.where(SQLGroup.legacy_id == group_id)
.values(**db_update)
)
),
self._mongo.groups.update_one(
{"_id": group_id}, {"$set": db_update}, session=mongo_session
),
)
await update_member_users(self._mongo, group_id, session=mongo_session)
return await fetch_complete_group(self._mongo, group_id)
async def delete(self, group_id: str):
"""
Delete a group by its id.
Deletes the group in all backing databases. Updates all member user permissions
if they are affected by deletion of the group.
:param group_id: the id of the group to delete
:raises ResourceNotFoundError: if the group is not found
"""
group = await self.get(group_id)
async with both_transactions(self._mongo, self._pg) as (
mongo_session,
pg_session,
):
mongo_result, pg_result = await asyncio.gather(
self._mongo.groups.delete_one({"_id": group_id}, session=mongo_session),
pg_session.execute(
delete(SQLGroup).where(SQLGroup.legacy_id == group_id)
),
)
if not mongo_result.deleted_count:
raise ResourceNotFoundError
if not pg_result.rowcount:
logger.info("Deleted group not found in Postgres id=%s", group_id)
await update_member_users(
self._mongo, group_id, remove=True, session=mongo_session
)
emit(group, "groups", "delete", Operation.DELETE)
|
from collections import defaultdict
import pprint
from nltk import word_tokenize
import simple
def get_words(text):
words = word_tokenize(text)
clean_words = simple.clean_words(words)
return words, clean_words
def get_byte_ngram(text, n=2, cs=False):
if not cs:
text = text.lower()
ngrams = defaultdict(int)
for i in range(0, len(text)-n+1):
ng = text[i:i+n]
ngrams[ng] += 1
return ngrams
def get_word_ngram(text, n=2, clean=False):
ngrams = defaultdict(int)
words = word_tokenize(text)
if clean:
words = simple.clean_words(words)
for i in range(0, len(words)-n+1):
ng = tuple(words[i:i+n])
ngrams[ng] += 1
return ngrams
def get_word_ngrams(text):
ngram_dict = {'ngram_word': {}, 'ngram_word_clean': {}}
for n in range(2, 8):
ngrams = get_word_ngram(text, n)
ngram_dict['ngram_word'][n] = ngrams
ngrams = get_word_ngram(text, n, True)
ngram_dict['ngram_word_clean'][n] = ngrams
return ngram_dict
def get_byte_ngrams(text):
ngram_dict = {'ngram_byte': {}, 'ngram_byte_cs': {}}
for n in range(2, 8):
ngrams = get_byte_ngram(text, n)
ngram_dict['ngram_byte'][n] = ngrams
ngrams = get_byte_ngram(text, n, True)
ngram_dict['ngram_byte_cs'][n] = ngrams
return ngram_dict
if __name__ == '__main__':
text = '''Newspapers in India are classified into two categories according to the amount and completeness of
information in them. Newspapers in the first category have more information and truth. Those in the second category
do not have much information and sometimes they hide the truth. Newspapers in the first category have news
collected from different parts of the country and also from different countries. They also have a lot of sports and
business news and classified ads. The information they give is clear and complete and it is supported by showing
pictures. The best know example of this category is the Indian Express. Important news goes on the first page with
big headlines, photographs from different angles, and complete information. For example, in 1989-90, the Indian
prime minister, Rajive Ghandi, was killed by a terrorist using a bomb. This newspaper investigated the situation
and gave information that helped the CBI to get more support. They also showed diagrams of the area where the prime
minister was killed and the positions of the bodies after the attack. This helped the reader understand what
happened. Unlike newspaper in the first category, newspapers in the second category do not give as much
information. They do not have international news, sports, or business news and they do not have classified ads.
Also, the news they give is not complete. For example, the newspaper Hindi gave news on the death of the prime
minister, but the news was not complete. The newspaper didn't investigate the terrorist group or try to find out
why this happened. Also, it did not show any pictures from the attack or give any news the next day. It just gave
the news when it happened, but it didn't follow up. Therefore, newspapers in the first group are more popular than
those in the second group.'''
pprint.pprint(get_byte_ngrams(text))
pprint.pprint(get_word_ngrams(text))
|
N = 4
arr = [1,2,3,-2,5]
first = arr[0]
f = first
for i in range(1,len(arr)):
sec = f+ arr[i]
first = max(first, sec)
f = sec
print(first) |
sizes = [5, 7, 300, 90, 24, 50, 75]
print("Hello, I'm Thanh and here are my sheep's sizes: ")
print(sizes)
print("Now my biggest sheep has size", max(sizes), "let's shear it!")
index = sizes.index(max(sizes))
sizes[index] = 8
print('After shearing, here is my flock:')
print(sizes)
month = int(input('Number of months: '))
for i in range(month):
print('Month', i + 1, ':')
sizes = [x + 50 for x in sizes]
print("One month has passed, now here is my flock:")
print(sizes)
if i + 1 < month:
print("Now my biggest sheep has size", max(sizes), "let's shear it!")
index = sizes.index(max(sizes))
sizes[index] = 8
print('After shearing, here is my flock:')
print(sizes)
print("My flock has size in total:", sum(sizes))
print("I would get", sum(sizes), '* 2$ =', sum(sizes)*2 )
|
import os
import logging
import shutil
from openpyxl import Workbook
def clear_summary_path(path_to_summary):
""" Removes the summaries if it exists """
if os.path.exists(path_to_summary):
logging.info("Summaries Exists. Deleting the summaries at %s" % path_to_summary)
shutil.rmtree(path_to_summary)
class Node():
def __init__(self, name, state, reward = 0, parent = None, parent_action = None, best_q_value = None, action_name = None):
self.parent = parent
self.best_q_value = best_q_value
self.action_name = action_name
self.children = []
self.action_dict = {}
self.actions = []
self.state = state
self.name = name
self.best_child = None
self.best_action = None
self.parent_action = parent_action
self.final_state = None
def add_child(self, sub_node, action = None):
self.children.append(sub_node)
self.action_dict[str(action)] = sub_node |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 11 19:57:41 2015
@author: Feng-cong Li
"""
import os
import sys
from os.path import abspath, dirname, join
import inspect
import subprocess
import tempfile
from tkinter.filedialog import askopenfilename, asksaveasfilename
from tkinter import Tk
from wavesynlib.languagecenter.utils import auto_subs
def get_my_dir():
return abspath(dirname(__file__))
callerCode = '''
using System;
class ScriptCaller
{
static void Main()
{
System.Diagnostics.Process.Start("$scriptPath");
}
}
'''
def compileCaller(scriptPath, exeFileName):
codeFile = tempfile.NamedTemporaryFile(delete=False)
print(auto_subs(callerCode), file=codeFile)
codeFile.close()
try:
powershell = subprocess.Popen(['powershell.exe',
'-ExecutionPolicy', 'Unrestricted',
join(get_my_dir(), 'cscompiler.ps1'),
codeFile.name,
exeFileName,
'System.dll'
])
ret = powershell.wait()
finally:
os.remove(codeFile.name)
return ret
def main(argv):
scriptPath = askopenfilename()
exeFileName = asksaveasfilename(filetypes=[('Executable', '*.exe')])
if not os.path.splitext(exeFileName)[1]:
exeFileName += '.exe'
compileCaller(scriptPath, exeFileName)
return 0
if __name__ == '__main__':
root = Tk()
root.withdraw()
sys.exit(main(sys.argv)) |
# Load the AlchemyAPI module code.
import AlchemyAPI
# Create an AlchemyAPI object.
alchemyObj = AlchemyAPI.AlchemyAPI()
# Load the API key from disk.
alchemyObj.loadAPIKey("api_key.txt")
# Extract a ranked list of named entities from a text string, using the supplied parameters object.
result = alchemyObj.TextGetTargetedSentiment("Madonna enjoys tasty Pepsi. I hate Madonna style.", "Pepsi");
print result
|
import re
from pprint import pformat
import requests
from swiggy_order.constants import (
SWIGGY_URL,
CSRF_PATTERN,
SWIGGY_COOKIE,
SWIGGY_SEND_OTP_URL,
SWIGGY_VERIFY_OTP_URL,
STATUS_FLAG,
STATUS_MESSAGE,
CART_URL,
APPLY_COUPON_URL,
PLACE_ORDER_URL,
)
from swiggy_order.utils import log
session = requests.Session()
csrf_source_pattern = re.compile(CSRF_PATTERN)
def get_cookie(cookies, name):
return cookies.get_dict().get(name)
def validate_response(response):
try:
if response.json().get(STATUS_FLAG) != 0:
log.error(response.json().get(STATUS_MESSAGE))
raise ValueError(f"Non-zero {STATUS_FLAG}!")
except AttributeError:
log.error(response.text)
raise ValueError(response.text)
def get_otp(registered_phone, sw_cookie, csrf_token):
return session.post(
SWIGGY_SEND_OTP_URL,
headers={
"content-type": "application/json",
"Cookie": "__SW={}".format(sw_cookie),
"User-Agent": "Mozilla/Gecko/Firefox/65.0",
},
json={"mobile": registered_phone, "_csrf": csrf_token},
)
def verify_otp(otp, csrf_token):
return session.post(
SWIGGY_VERIFY_OTP_URL,
headers={
"content-type": "application/json",
"User-Agent": "Mozilla/Gecko/Firefox/65.0",
},
json={"otp": otp, "_csrf": csrf_token},
)
def make_connection():
response = session.get(SWIGGY_URL)
try:
csrf_token = csrf_source_pattern.search(response.text).group(1)
sw_cookie = get_cookie(response.cookies, SWIGGY_COOKIE)
return sw_cookie, csrf_token
except IndexError:
raise IndexError(
f"Pattern={CSRF_PATTERN} matched but csrf token not found in expected location."
)
except TypeError:
raise TypeError(
f"Expected response.txt to be str but found {type(response.text)} instead."
)
def login(registered_phone):
sw_cookie, csrf_token = make_connection()
otp_response = get_otp(registered_phone, sw_cookie, csrf_token)
if otp_response.json().get(STATUS_FLAG) != 0:
raise ValueError(otp_response.text)
sw_cookie, csrf_token = make_connection()
otp = input("Enter OTP: ")
response = verify_otp(otp, csrf_token)
validate_response(response)
log.debug(pformat(response.json()))
def update_cart(payload, quantity=1):
_, csrf_token = make_connection()
payload["_csrf"] = csrf_token
payload["cart"]["cartItems"][0]["quantity"] = quantity
response = session.post(CART_URL, json=payload)
validate_response(response)
log.debug(pformat(response.json()))
def apply_coupon_code(coupon_code=""):
if not coupon_code:
return
_, csrf_token = make_connection()
payload = {"couponCode": coupon_code, "_csrf": csrf_token}
response = session.post(APPLY_COUPON_URL, json=payload)
validate_response(response)
log.debug(pformat(response.json()))
def place_order(payment_method, address_id):
_, csrf_token = make_connection()
payload = {
"order": {
"payment_cod_method": payment_method,
"address_id": str(address_id),
"order_comments": "",
"force_validate_coupon": True,
},
"_csrf": csrf_token,
}
response = session.post(PLACE_ORDER_URL, json=payload)
validate_response(response)
log.debug(pformat(response.json()))
log.info("Order placed ✨🌟 🥘🥙🥗 🌟✨ !")
|
# -*- coding: utf-8 -*-
def human_readable_int_to_machine(size):
""" translates human readable integer format to integer
@param str Number that may optionally end with K, M, or G at
the end, to ease writting powers of ten
@return int
"""
multiplier = 1
size = size.upper()
if size[-1] == 'K': multiplier = 1000
elif size[-1] == 'M': multiplier = 1000000
elif size[-1] == 'G': multiplier = 1000000000
if multiplier > 1: size = size[:-1]
size = int(size)
return size * multiplier
|
import random
LETTER_POOL = {
'A': 9,
'B': 2,
'C': 2,
'D': 4,
'E': 12,
'F': 2,
'G': 3,
'H': 2,
'I': 9,
'J': 1,
'K': 1,
'L': 4,
'M': 2,
'N': 6,
'O': 8,
'P': 2,
'Q': 1,
'R': 6,
'S': 4,
'T': 6,
'U': 4,
'V': 2,
'W': 2,
'X': 1,
'Y': 2,
'Z': 1
}
SCORE_LIST = {
1: ["A", "E", "I", "O", "U", "L", "N", "R", "S", "T" ],
2: ["D", "G"],
3: ["B", "C", "M", "P"],
4: ["F", "H", "V", "W", "Y"],
5: ["K"],
8: ["J", "X"],
10: ["Q", "Z"]
}
"""
Create_list_of_letters function returns a list of all letters in.
Based on letter_pool there are total 98 letters in the list.
"""
def create_list_of_letters(LETTER_POOL):
list_of_letters = []
for letter, count in LETTER_POOL.items():
while count > 0:
list_of_letters.append(letter)
count = count - 1
return (list_of_letters)
"""
draw_letters function returns a list of random 10 letters
from create_list_of_letters function.
"""
def draw_letters():
list_of_letters = create_list_of_letters(LETTER_POOL)
list_of_random_letters = []
length_of_list = 10
while length_of_list > 0:
random_index = random.randint(0, (len(list_of_letters)-1))
list_of_random_letters.append(list_of_letters[random_index])
list_of_letters.remove(list_of_letters[random_index])
length_of_list = length_of_list - 1
return (list_of_random_letters)
"""
uses_available_letters returns true if every letter in the word is available in the letter bank.
returns false if not.
"""
def uses_available_letters(word, letter_bank):
letter_bank_copy = letter_bank[:]
condition = None
while len(word) > 0:
for letter in word:
if letter in letter_bank_copy:
letter_bank_copy.remove(letter)
condition = True
word = word.replace(letter,"")
else:
return False
return condition
"""
score_word function returns the total points for the word based on score chart.
"""
def score_word(word):
total_score = 0
if len(word) >= 7:
total_score += 8
for score, letters in SCORE_LIST.items():
for letter in word.upper():
if letter in letters:
total_score += score
return total_score
"""
get_highest_word_score function retuns a tuple with a word as a first element,
# and the highest score as a second element.
"""
def get_highest_word_score(word_list):
highest_score = 0
highest_score_word = ""
for word in word_list:
if score_word(word) > highest_score:
highest_score = score_word(word)
highest_score_word = word
elif score_word(word) == highest_score:
if len(highest_score_word) == len(word):
highest_score_word = highest_score_word
elif len(word) == 10:
return (word, highest_score)
elif len(highest_score_word) == 10:
return (highest_score_word, highest_score)
highest_score_word = min([highest_score_word, word], key=len )
return (highest_score_word, highest_score)
|
import requests
import pymorphy2
from tkinter import *
from googletrans import Translator
morph = pymorphy2.MorphAnalyzer()
root = Tk()
def kelvin_to_celsius(temp):
return round(temp - 273.15, 2)
def eng_to_rus(city):
translator = Translator(service_urls=['translate.googleapis.com'])
result = translator.translate(city, src='en', dest='ru')
p = morph.parse(result.text)[0]
city = p.inflect({'loct'}).word
if len(city) > 1:
city = city.split()[-1]
return city[0].upper() + city[1:]
def get_weather():
city = cityField.get()
api_key = '28eded49f31842a06cc280df5ab95800' # нужно получить на OpenWeathepMap ссылка: https://openweathermap.org/
url = 'http://api.openweathermap.org/data/2.5/weather?'
params = {'APPID': api_key, 'q': city}
result = requests.get(url, params=params)
weather = result.json()
info['text'] = f'Информация о погоде в {eng_to_rus(str(weather["name"]))}:\n\n' \
f'Средняя температура: {kelvin_to_celsius(weather["main"]["temp"])}°C\n' \
f'Скорость ветера: {weather["wind"]["speed"]}м/с\n' \
f'Облачность: {weather["clouds"]["all"]}%\n' \
f'Видимость: {weather["visibility"]}м'
def clear():
cityField.delete(0, 'end')
root['bg'] = '#fafafa'
root.title('Погодное приложение')
root.geometry('400x300')
root.resizable(width=False, height=False)
# Верхний прямоугольник
frame_top = Frame(root, bg='#ffb700', bd=5)
frame_top.place(relx=0.15, rely=0.03, relwidth=0.7, relheight=0.20)
# Нижний прямоугольник
frame_bottom = Frame(root, bg='#ffb700', bd=5)
frame_bottom.place(relx=0.15, rely=0.35, relwidth=0.7, relheight=0.55)
# Строка ввода
cityField = Entry(frame_top, bg='white', justify=CENTER, font=('Helvetica', 11))
cityField.pack()
# Кнопка
btn = Button(frame_top, text='Посмотреть погоду', command=get_weather, font=('Helvetica', 11))
btn.pack()
# Инфомарция о погоде
info = Label(frame_bottom, text='Информация о погоде', bg='#ffb700', font=('Helvetica', 11))
info.pack()
# Добавить кнопку clear
cls_btn = Button(frame_top, text='X', command=clear)
cls_btn.pack()
cls_btn.place(height=21, width=21, relx=0.82, rely=0)
root.mainloop()
|
class GameCharacter:
def __init__(self,name,hp,power):
self.name = name
self.hp = hp
self.power = power
def is_alive(self):
return self.hp > 0
def get_attacked(self,damage):
# 게임케릭터가 살아있으면 파라미터로받은 다른 케릭의 체력을 자신의 공격력만큼 깍음
if self.is_alive():
# if self.hp >= damage:
# self.hp = self.hp - damage
# else:
# self.hp = 0
# 위 4줄 한줄로 써보기
self.hp = self.hp - damage if self.hp >= damage else 0
# 아래는 이미 죽었는데 공격을 당한 경우를 보는것.
else:
print(f'{self.name}은 이미 죽었습니다.')
def attack(self,other_character):
# 게임케릭터가 살아있으면 파라미터로 받은 다른 케릭터의 체력을 깍는다.
if self.is_alive():
other_character.get_attacked(self.power)
def __str__(self):
return f'{self.name}님의 hp는 {str(self.hp)}만큼 남았습니다.'
# 게임 캐릭터 인스턴스 생성
character_1 = GameCharacter('Ww영훈전사wW',200,30)
character_2 = GameCharacter('Xx지웅최고xX',100,50)
character_1.attack(character_2)
character_2.attack(character_1)
character_2.attack(character_1)
character_2.attack(character_1)
character_2.attack(character_1)
character_2.attack(character_1)
print(character_1)
print(character_2) |
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def isBalanced(self, root: TreeNode) -> bool:
if not root:
return 1
left=self.isBalanced(root.left)
if not left:
return False
right=self.isBalanced(root.right)
if not right:
return False
if abs(left-right)<=1:
return max(left,right)+1
else:
return False |
from sage.all import RealIntervalField, ComplexIntervalField, prod, vector, matrix, arccosh, Infinity
from snappy.verify.upper_halfspace.finite_point import FinitePoint
from snappy.raytracing.hyperboloid_utilities import complex_and_height_to_R13_time_vector, PSL2C_to_O13
class PrecisionExperiment:
def __init__(self, tiling_engine, bits_prec = 53):
self.RIF = RealIntervalField(bits_prec)
self.CIF = ComplexIntervalField(bits_prec)
self.baseTetInCenter = FinitePoint(
self.CIF(tiling_engine.baseTetInCenter.z),
self.RIF(tiling_engine.baseTetInCenter.t))
self.generator_matrices = {
g : m.change_ring(self.CIF)
for g, m
in tiling_engine.mcomplex.GeneratorMatrices.items() }
self.max_values = {}
for tile in tiling_engine.all_tiles():
if tile.word:
matrix = prod(self.generator_matrices[g] for g in tile.word)
tileCenter = FinitePoint(
self.CIF(tile.center.z),
self.RIF(tile.center.t))
err = tileCenter.dist(self.baseTetInCenter.translate_PSL(matrix)).upper()
l = len(tile.word)
self.max_values[l] = max(err, self.max_values.get(l, err))
#print("len=", l)
#print(tile.center)
#print(self.baseTetInCenter.translate_PSL(matrix))
def inner_prod(a, b):
a0, a1, a2, a3 = a
b0, b1, b2, b3 = b
return a0 * b0 - a1 * b1 - a2 * b2 - a3 * b3
def my_dist(a, b):
RIF = a.base_ring()
i = inner_prod(a,b)
if not i > 0.9:
raise Exception("Bad inner product", i)
return arccosh(i.intersection(RIF(1, Infinity)))
class SO13PrecisionExperiment:
def __init__(self, tiling_engine, bits_prec = 2 * 53):
self.RIF = RealIntervalField(bits_prec)
self.CIF = ComplexIntervalField(bits_prec)
self.baseTetInCenter = vector(
self.RIF,
complex_and_height_to_R13_time_vector(
tiling_engine.baseTetInCenter.z,
tiling_engine.baseTetInCenter.t))
self.generator_matrices = {
g : matrix(self.RIF,
PSL2C_to_O13(m))
for g, m
in tiling_engine.mcomplex.GeneratorMatrices.items() }
self.max_values = {}
for tile in tiling_engine.all_tiles():
if tile.word:
m = prod(self.generator_matrices[g] for g in tile.word)
tileCenter = vector(
self.RIF,
complex_and_height_to_R13_time_vector(
tile.center.z,
tile.center.t))
#print("=====")
#print(tileCenter)
#print(m * self.baseTetInCenter)
#print(inner_prod(m * self.baseTetInCenter, tileCenter).endpoints())
err = my_dist(m * self.baseTetInCenter, tileCenter).upper()
l = len(tile.word)
self.max_values[l] = max(err, self.max_values.get(l, err))
if __name__ == '__main__':
from snappy import Manifold
from spineTilingEngine import get_tiling_engine
import time
t = get_tiling_engine(Manifold("m015"), 2.0, 1000)
print(time.process_time())
p = SO13PrecisionExperiment(t)
for l in sorted(p.max_values.keys()):
print(l, p.max_values[l])
"""
SO13
# With cut_off 2.0, precision 53
1 3.14693332939488e-7
2 1.94607111421966e-6
3 8.14470808624817e-6
4 0.0000198918837387775
5 0.0000437288532913095
6 0.000177651761008844
7 0.000158143242844043
8 0.000264805666663187
9 0.000400416033450115
10 0.000424379709861500
11 0.000810140556132233
12 0.000843544180585911
13 0.000951147032856916
14 0.00169059297569973
15 0.00107394394002549
16 0.00272983953461852
"""
"""
# With cut_off 2.0, precision 106
1 3.270921281085800238169841633950e-15
2 1.958025535935146422497115696615e-14
3 8.202076444245559868708528727807e-14
4 2.056665304271714819705992280621e-13
5 4.395612855950487133117295347254e-13
6 1.837457195065786916202026100860e-12
7 1.642014914084237702968333803705e-12
8 2.775231119033088881471601358991e-12
9 4.219929997444452653216752991498e-12
10 4.405387882386405780612489783424e-12
11 8.354387324326215904772343210832e-12
12 8.686169163671624886197965679412e-12
13 9.801694308813672806298050836381e-12
14 1.714340993607170713978996529316e-11
15 1.105424563281960868139073605716e-11
16 2.812018996585752241579728815233e-11
"""
if __name__ == '__main__a':
from snappy import Manifold
from spineTilingEngine import get_tiling_engine
import time
t = get_tiling_engine(Manifold("m015"), 3.0, 1000)
print(time.process_time())
p = PrecisionExperiment(t)
for l in sorted(p.max_values.keys()):
print(l, p.max_values[l])
"""
PSL(2,C)
# With cut_off 3.0, precision 53
1 2.10734242554471e-8
2 2.10734242554471e-8
3 2.10734242554471e-8
4 2.10734242554471e-8
5 2.10734242554471e-8
6 2.10734242554471e-8
7 2.10734242554471e-8
8 2.10734242554471e-8
9 2.10734242554471e-8
10 2.10734242554471e-8
11 2.10734242554471e-8
12 2.10734242554471e-8
13 2.10734242554471e-8
14 2.10734242554471e-8
15 2.10734242554471e-8
16 2.10734242554471e-8
17 2.10734242554471e-8
18 2.10734242554471e-8
19 3.65002414998886e-8
20 4.21468485108941e-8
21 4.71216091538725e-8
22 4.71216091538725e-8
23 3.65002414998886e-8
24 5.16191365590357e-8
25 5.16191365590357e-8
26 5.57550398524693e-8
27 6.66400187462506e-8
28 5.57550398524693e-8
29 9.18569267238524e-8
30 8.68879540986613e-8
31 1.01064592348416e-7
32 8.94069671630860e-8
33 1.68587394043576e-7
34 3.44342589625457e-7
35 6.21219384243355e-7
36 5.20475184815312e-7
37 8.29393866662702e-7
38 8.92080637639480e-7
39 6.93184336996840e-7
40 1.24261747237373e-6
41 7.69396462644653e-7
"""
|
# -*- coding: utf-8 -*-
from openerp import models, fields, api
class Ddi(models.Model):
'''Ddi'''
_name = "pbx.ddi"
_description = "DDI"
_rec_name = 'number'
def _search_inuse(self, operator, value):
ids = set()
if operator == '=' and value == True:
self.env.cr.execute("SELECT id FROM pbx_ddi WHERE number IN (SELECT exten FROM pbx_extension)")
else:
self.env.cr.execute("SELECT id FROM pbx_ddi WHERE number NOT IN (SELECT exten FROM pbx_extension)")
res_ids = set(id[0] for id in self.env.cr.fetchall())
ids = ids and (ids & res_ids) or res_ids
if ids:
return [('id', 'in', tuple(ids))]
return [('id', '=', '0')]
@api.multi
def _compute_inuse(self):
for record in self:
found = self.env['pbx.extension'].search_count([('exten', '=', record.number)])
record.inuse = found > 0
number = fields.Char(string="Number", required=True)
# country = fields.Many2one(comodel_name='res.country',string="Country")
# state = fields.Many2one(comodel_name='res.country.state',string="State")
# city = fields.Char(string="City")
inuse = fields.Boolean(compute='_compute_inuse', string="In Use", search=_search_inuse)
_sql_constraints = [
('number', 'unique(number)',
'DDI Exists!'),
] |
from django.shortcuts import render
from playsound import playsound
from text_to_speech.TextToSpeech import TextToSpeech
from duddy import forms
from duddy import models
# Create your views here.
def index(request):
context = {}
# playsound('sounds/SampleAudio.mp3')
app = TextToSpeech()
app.get_token()
tts = request.POST.get('tts')
if tts:
filename = app.save_audio(tts)
playsound(filename)
# app.save_audio()
return render(request, 'index.html', context)
def repeat(request):
message = ""
if request.method == "POST":
form = forms.RepeatedMessageForm(request.POST)
if form.is_valid():
form.save()
app = TextToSpeech()
app.get_token()
app.save_audio(form.cleaned_data['messageText'], repeated=True)
else:
message = "Opgeslagen!"
form = forms.RepeatedMessageForm()
context = {
'form': form,
'message': message if message else None
}
return render(request, 'repeat.html', context)
def play(request):
sounds = models.RepeatedMessage.objects.all()
sound = request.GET.get('sound')
if sound:
app = TextToSpeech()
app.get_token()
pass
context = {
'sounds': sounds,
}
return render(request, 'play.html', context)
|
import unittest
from OdioPares import respuesta_pares
class PruebaOdioPares(unittest.TestCase):
def prueba(self, fun_solucion):
dict_pruebas = {
1:('101001','10'),
2: ('1', '1'),
3: ('0', '0'),
4: ('', 'Helado es el vacio'),
5: ('11', 'Helado es el vacio'),
6: ('0011', 'Helado es el vacio'),
7: ('1001', 'Helado es el vacio'),
8: ('101', '101'),
10: ('1001', 'Helado es el vacio'),
9: ('10001', '101'),
10: ('100001', 'Helado es el vacio'),
11: ('1000001', '101'),
}
sol = 'Error, tu funcion no regresa nada'
for p in dict_pruebas.values():
try:
sol = fun_solucion(arr=p[0])
self.assertEqual(sol, p[1])
except AssertionError as e:
print(f'Fallo! cadena={p[0]}, output={sol}, esperada={p[1]}')
25
t = PruebaOdioPares()
t.prueba(respuesta_pares) |
from sqlalchemy import (Table, Column, Integer, String, create_engine,
MetaData, ForeignKey)
from sqlalchemy.orm import mapper, create_session
from sqlalchemy.ext.declarative import declarative_base
e = create_engine('sqlite:///sqlite.db', echo=True)
Base = declarative_base(bind=e)
class Employee(Base):
__tablename__ = 'employees'
employee_id = Column(Integer, primary_key=True)
name = Column(String(50))
type = Column(String(30), nullable=False)
__mapper_args__ = {'polymorphic_on': type}
def __init__(self, name):
self.name = name
class Manager(Employee):
__tablename__ = 'managers'
__mapper_args__ = {'polymorphic_identity': 'manager'}
employee_id = Column(Integer, ForeignKey('employees.employee_id'),
primary_key=True)
manager_data = Column(String(50))
def __init__(self, name, manager_data):
super(Manager, self).__init__(name)
self.manager_data = manager_data
class Owner(Manager):
__tablename__ = 'owners'
__mapper_args__ = {'polymorphic_identity': 'owner'}
employee_id = Column(Integer, ForeignKey('managers.employee_id'),
primary_key=True)
owner_secret = Column(String(50))
def __init__(self, name, manager_data, owner_secret):
super(Owner, self).__init__(name, manager_data)
self.owner_secret = owner_secret
Base.metadata.drop_all()
Base.metadata.create_all()
db_session = create_session(bind=e, autoflush=True, autocommit=False)
o = Owner('nosklo', 'mgr001', 'ownerpwd')
db_session.add(o)
db_session.commit() |
import unittest
from Learning.TokenParser import *
class TokenParserTests(unittest.TestCase):
def setUp(self):
self.tokenizer = TokenParser()
def parse(self, token):
return self.tokenizer.parse(token)
def test_number_recognition(self):
self.assertEqual(self.tokenizer.NUMBER_TAG, self.parse("1234"))
def test_language_recognition(self):
self.assertEqual(self.tokenizer.LANGUAGE_TAG, self.parse("C++"))
self.assertEqual(self.tokenizer.LANGUAGE_TAG, self.parse("cpp"))
self.assertEqual(self.tokenizer.LANGUAGE_TAG, self.parse("Java"))
self.assertEqual(self.tokenizer.LANGUAGE_TAG, self.parse("js"))
def test_jira_recognition(self):
self.assertEqual(self.tokenizer.ISSUE_TAG, self.parse("FIX-128"))
# self.assertEqual(self.tokenizer.ISSUE_TAG, self.parse("FIX-ME-128"))
def test_entity_recognition(self):
self.assertEqual(self.tokenizer.ENTITY_NAME, self.parse("ENT"))
self.assertEqual(self.tokenizer.ENTITY_NAME, self.parse("SUB_ENT"))
self.assertEqual(self.tokenizer.ENTITY_NAME, self.parse("SUB_ENT_ID"))
self.assertEqual(self.tokenizer.ENTITY_NAME, self.parse("SUB-ENT"))
def test_path_recognition(self):
self.assertEqual(self.tokenizer.PATH_TAG, self.parse("include/hello/world"))
self.assertEqual(self.tokenizer.PATH_TAG, self.parse("/hello/world"))
def test_function_recognition(self):
self.assertEqual(self.tokenizer.FUNCTION_TAG, self.parse("helloWorld"))
self.assertEqual(self.tokenizer.FUNCTION_TAG, self.parse("hello_world"))
def test_qualified_function_recognition(self):
# The tokenizer transforms "::" to "." so we use "." here
self.assertEqual(self.tokenizer.FUNCTION_TAG, self.parse("ns.sub_ns.helloWorld"))
self.assertEqual(self.tokenizer.FUNCTION_TAG, self.parse("ns.sub_ns.hello_world"))
self.assertEqual(self.tokenizer.FUNCTION_TAG, self.parse("std.transform"))
def test_class_recognition(self):
self.assertEqual(self.tokenizer.CLASS_TAG, self.parse("ClassName"))
def test_qualified_class_recognition(self):
self.assertEqual(self.tokenizer.CLASS_TAG, self.parse("pack.package.ClassName"))
self.assertEqual(self.tokenizer.CLASS_TAG, self.parse("pack.sub-pack.ClassName"))
|
class Node:
def __init__(self,data=None,next=None):
self.data = data
self.next = next
class LinkedList:
def __init__(self):
self.head = None
def insert_at_beginning(self,data):
node = Node(data,self.head)
self.head = node
def print(self):
if self.head is None:
print("Linked list is empty")
return
itr = self.head
llstr = ''
while itr:
llstr+= str(itr.data)+'-->'
itr = itr.next
print(llstr)
def insert_at_end(self,data):
if self.head is None:
self.head = Node(data,None)
return
itr = self.head
while itr.next:
itr = itr.next
itr.next = Node(data,None)
def insert_values(self,data_list):
self.head = None
for data in data_list:
self.insert_at_end(data)
def get_length(self):
count = 0
itr = self.head
while itr:
count+=1
itr = itr.next
return count
if __name__ == "__main__":
ll = LinkedList()
ll.insert_values(["baba","mummy","bhai","baini"])
ll.print()
|
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
#--------------------------------------------------------------------------------------
#_1.Data object 생성하기
#pd.Series
s = pd.Series([1, 3, 5, np.nan, 6, 8])
#date_range를 통해 날짜 기간 배열 생성
dates = pd.date_range('20130101', periods =6)
#난수 생성 randn(x행, y열)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns = list('ABCD'))
df2 = pd.DataFrame({'A': 1.,
'B': pd.Timestamp('20130102'),
'C': pd.Series(1, index=list(range(4)), dtype='float32'),
'D': np.array([3]*4, dtype='int32'),
'E': pd.Categorical(['test', 'train', 'test', 'train']),
'F': 'foo'})
#--------------------------------------------------------------------------------------
#_2.데이터 확인하기
print(df.head(), df.tail())
print(df.values)
#columns별 간단한 통계정보
print(df.describe())
#전치행렬(Transposed matrix)
print(df.T)
#행, 열 Sorting하기
print(df.sort_index(axis=0, ascending=True))
print(df.sort_values(by='B'))
#데이터 선택하기 R이랑 거의 비슷함
print(df['A'])
print(df.A)
print(df[0:3])
print(df['20130102':'20130104'])
print(df['20130102':'20130102'])
print(df.loc[dates[0]])
print(df.loc['20130101'])
print(df.loc[:, ['A','B']])
print(df.loc['20130102':'20130104',['A','B']])
print(df.loc[dates[0], ['A', 'B']])
print(df.at[dates[0], 'A'])
print(df.iloc[[1,2,4],[0,2]])
print(df.iloc[:, 1:3])
#조건을 이용하여 선택하기
print(df[df.A>0])
print(df[df>0])
df2 = df.copy()
df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
print(df2)
#데이터 변경하기
s1 = pd.Series([1,2,3,4,5,6], index= pd.date_range('20130102', periods=6))
df['F']=s1
df['G']=s1
df['H']=s1
print('\n',df)
df.at[dates[0], 'A']=0
df.iat[0,1]=0
print(df)
#여러 값을 한번에 변경하기
df.loc[:,'D']=np.array([5]*len(df))
print(len(df.T))
#0보다 큰 값들을 음수로 바꾸기
df2 = df.copy()
df2[df2 >0] = -df2
print(df2)
#결측치 처리
#Reindex는 해당 축에 대하여 인덱스를 변경/추가/삭제 할 수 있따. 이는 복사된 데이터프레임을 반환함
print(df)
df1 = df.reindex(index = dates[0:4], columns = list(df.columns) + ['E'])
print('\n\n', df1)
df1.loc[dates[0]:dates[1], 'E'] = 1
print('\n\n', df1)
#결측치가 있는 레코드 떨구기
df1_drop = df1.dropna(how='any')
print('Dropped Na Value\n', df1)
#결측치 채우기
df1_fillna = df1.fillna(value=9.999)
print(df1_fillna)
#결측치 -> True/False
df1_bullna = pd.isna(df1)
print(df1_bullna)
#--------------------------------------------------------------------------------------
#_3.연산(Operations)
#Column을 기준으로 연산
print(df.mean())
#Index를 기준으로 연산
print(df.mean(1))
#index를 축으로 하여 계산하기
s = pd.Series([1,3,5, np.nan, 6,8], index = dates).shift(2)
print('\n\n',s)
df_subindex = df.sub(s, axis = 'index')
print(df_subindex)
#사용자 지정 함수 적용하기
print(df.apply(np.cumsum))
print(df.apply(lambda x: x.max() - x.min()))
#히스토그램
s = pd.Series(np.random.randint(0,7, size = 10))
print(s)
print(s.value_counts())
#문자열 관련 메소드
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog' 'cat'])
print(s.str.lower())
#데이터 쪼개기 잇기
df = pd.DataFrame(np.random.randn(10,4))
print(df)
pieces = [df[:3], df[3:7], df[7:]]
print(pieces)
pd.concat(pieces)
print(pd.concat(pieces))
#Join SQL 스타일의 합치기 기능
left = pd.DataFrame({'key': ['foo' 'foo'], 'lval': [1,2]})
right = pd.DataFrame({'key': ['foo', 'foo'], 'rval':[4,5]})
merged = pd.merge(left, right, on='key')
left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1,2]})
right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4,5]})
merged = pd.merge(left, right, on= 'key')
#Append 행 추가하기
df = pd.DataFrame(np.random.randn(8,4), columns=['A', 'B', 'C', 'D'])
s = df.iloc[3]
df.append(s, ignore_index=True) |
import py
from hippy.phpcompiler import compile_php, PHPLexerWrapper
from hippy.objspace import ObjSpace
from testing.directrunner import run_php_source, DirectInterpreter
from testing.test_interpreter import BaseTestInterpreter, MockInterpreter
class LiteralInterpreter(MockInterpreter):
def run_source(self, source, expected_warnings=[]):
output_w = MockInterpreter.run_source(self, source)
space = self.space
output = [space.str_w(v) for v in output_w]
return ''.join(output)
def compile(self, source):
return compile_php('<input>', source, self.space, self)
class DirectLiteralInterpreter(DirectInterpreter):
def run_source(self, source, expected_warnings=None):
s = run_php_source(source)
return s
class BaseTestPHP(BaseTestInterpreter):
interpreter = LiteralInterpreter
interpreter_direct = DirectLiteralInterpreter
def test_phplexerwrapper():
phplexerwrapper = PHPLexerWrapper(
'Foo\n<?php Echo 5 ?>\nBar\nBaz\n<? echo')
for expected in [('B_LITERAL_BLOCK', 'Foo\n', 1),
('T_ECHO', 'Echo', 2),
('T_LNUMBER', '5', 2),
(';', ';', 2),
('B_LITERAL_BLOCK', 'Bar\nBaz\n', 3),
('T_ECHO', 'echo', 5)]:
tok = phplexerwrapper.next()
assert (tok.name, tok.value, tok.getsourcepos()) == expected
tok = phplexerwrapper.next()
assert tok is None
def test_line_start_offset():
space = ObjSpace()
MockInterpreter(space)
bc = compile_php('<input>', 'Hi there\n', space)
assert bc.startlineno == 1
class TestPHPCompiler(BaseTestPHP):
def test_simple(self):
output = self.run('Foo <?php echo 5; ?> Bar')
assert output == 'Foo 5 Bar'
def test_simple_2(self):
output = self.run('Foo <? echo 5; ?> Bar')
assert output == 'Foo 5 Bar'
output = self.run('Foo<?echo 5;?>Bar')
assert output == 'Foo5Bar'
def test_windows_line_ending(self):
output = self.run("Foo<?php\r\necho 5;\r\n?>Bar")
assert output == "Foo5Bar"
def test_case_insensitive(self):
output = self.run('Foo <?phP echo 5; ?> Bar')
assert output == 'Foo 5 Bar'
def test_no_php_code(self):
output = self.run('Foo\n')
assert output == 'Foo\n'
output = self.run('\nFoo')
assert output == '\nFoo'
def test_eol_after_closing_tag(self):
output = self.run('Foo <?phP echo 5; ?>\nBar')
assert output == 'Foo 5Bar'
output = self.run('Foo <?phP echo 5; ?> \nBar')
assert output == 'Foo 5 \nBar'
output = self.run('Foo <?phP echo 5; ?>\n')
assert output == 'Foo 5'
output = self.run('Foo <?phP echo 5; ?>\n\n')
assert output == 'Foo 5\n'
output = self.run('Foo <?phP echo 5; ?> \n')
assert output == 'Foo 5 \n'
def test_end_in_comment_ignored_1(self):
output = self.run('Foo <? echo 5; /* ?> */ echo 6; ?> Bar')
assert output == 'Foo 56 Bar'
def test_end_in_comment_not_ignored_1(self):
output = self.run('Foo <? echo 5; //?>\necho 6; ?> Bar')
assert output == 'Foo 5echo 6; ?> Bar'
def test_end_in_comment_not_ignored_2(self):
output = self.run('Foo <? echo 5; #?>\necho 6; ?> Bar')
assert output == 'Foo 5echo 6; ?> Bar'
def test_double_end(self):
output = self.run('<?php echo 5; ?> echo 6; ?>\n')
assert output == '5 echo 6; ?>\n'
def test_multiple_blocks(self):
output = self.run('-<?echo 5;?>+<?echo 6;?>*')
assert output == '-5+6*'
def test_non_closing_last_block_of_code(self):
output = self.run('-<?echo 5;?>+<?echo 6;')
assert output == '-5+6'
def test_missing_semicolon_before_end(self):
output = self.run('-<?echo 5?>+')
assert output == '-5+'
def test_reuse_var(self):
output = self.run('<?$x=5?>----<?echo $x;')
assert output == '----5'
def test_multiple_use_of_block_of_text(self):
output = self.run('<?for($x=0; $x<5; $x++){?>-+-+-\n<?}')
assert output == '-+-+-\n' * 5
def test_automatic_echo_1(self):
output = self.run('abc<?=2+3?>def')
assert output == 'abc5def'
def test_automatic_echo_2(self):
output = self.run('abc<?=2+3,7-1?>def')
assert output == 'abc56def'
def test_automatic_echo_3(self):
output = self.run('abc<?=2+3,7-1; echo 8+1;?>def')
assert output == 'abc569def'
def test_automatic_echo_4(self):
output = self.run('abc<?=2+3?><?=6*7?>def')
assert output == 'abc542def'
def test_automatic_echo_5(self):
py.test.raises(Exception, self.run, 'abc<? =2+3?>def')
def test_automatic_echo_6(self):
output = self.run('abc<?=2+3?>\ndef<?=6*7?> \nghi')
assert output == 'abc5def42 \nghi'
def test_automatic_echo_7(self):
output = self.run('abc<?=2+3;')
assert output == 'abc5'
py.test.raises(Exception, self.run, 'abc<?=2+3')
def test_halt_compiler(self):
output = self.run('abc<?php echo 5;__halt_compiler();]]]]]]]]]]?>def')
assert output == 'abc5'
output = self.run('abc<?php echo 5;__halt_compiler()?>def')
assert output == 'abc5'
output = self.run('abc<?php echo __COMPILER_HALT_OFFSET__;\n'
'__halt_compiler() ;]]]]]]]]]]?>def')
assert output == 'abc59'
output = self.run('abc<?php echo __COMPILER_HALT_OFFSET__;\n'
'__halt_compiler() ?> def')
assert output == 'abc62'
output = self.run('abc<?php echo __COMPILER_HALT_OFFSET__;\n'
'__halt_compiler() ?>\n def')
assert output == 'abc63'
def test_heredoc(self):
output = self.run('''<? $x = <<< \tPHP
Hello World
PHP;
echo $x;
?>''')
assert output == 'Hello World'
def test_heredoc_2(self):
output = self.run('''<? $x = <<<PHP
Hello World
12
;;
"hello"
19x333
class var
PHP;
echo $x;
?>''')
assert output == 'Hello World\n12\n;;\n"hello"\n19x333\nclass var'
def test_heredoc_error(self):
input = '''<? $x = <<<PHP
Hello World
PH;
echo $x;
?>'''
py.test.raises(Exception, self.run, input)
def test_heredoc_escape(self):
output = self.run(r'''<? $x = <<<EOS
\n
\$variable
\"quotes\
EOS;
echo $x;
?>''')
assert output == '\n\n$variable\n\\"quotes\\'
def test_heredoc_NUL(self):
output = self.run(r'''<? $x = <<<EOS
Hello\0world
EOS;
echo $x;
?>''')
assert output == "Hello\0world"
output = self.run('''<? $x = <<<EOS
Hello\0world
EOS;
echo $x;
?>''')
assert output == "Hello\0world"
def test_heredoc_unfinished(self):
output = self.run(r'''<?
class T {
public function test($var) {
echo $var;
}
}
$t = new T;
$t->test(<<<HTML
test
HTML
);
?>''')
assert output == "test\n"
|
# Artificial Neural Network
# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
# Installing Tensorflow
# pip install tensorflow
# Installing Keras
# pip install --upgrade keras
# Part 1 - Data Preprocessing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Part 2 - Now let's make the ANN!
# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
# Initialising the ANN
classifier = Sequential()
# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
# classifier.add(Dropout(p = 0.1))
# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
# classifier.add(Dropout(p = 0.1))
# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
# Part 3 - Making predictions and evaluating the model
# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
# Predicting a single new observation
"""Predict if the customer with the following informations will leave the bank:
Geography: France
Credit Score: 600
Gender: Male
Age: 40
Tenure: 3
Balance: 60000
Number of Products: 2
Has Credit Card: Yes
Is Active Member: Yes
Estimated Salary: 50000"""
new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
new_prediction = (new_prediction > 0.5)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Part 4 - Evaluating, Improving and Tuning the ANN
#/////////////////////////////////////////////////////CROSS VALIDATION CV//////////////////////////////////////////////////
# Evaluating the ANN
from keras.wrappers.scikit_learn import KerasClassifier
#Keras Classifier is a Keras wrapper on top of ScikitLearn, so that, we can use Cross Valuation on it.
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense
def build_classifier():
classifier = Sequential()
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
return classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100)
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
#accuracies will have all the 10 accuracies of the 10 folds.
mean = accuracies.mean()
#get its mean()
variance = accuracies.std()
# Improving the ANN
#----------------------------------------------------------------------------------------------------------------------------
# ////////////////////////////////////////////////////DROPOUT REGULARIZATION/////////////////////////////////////////////////
# Dropout Regularization to reduce overfitting if needed
# Some neurons randomly become disabled when dropout called!
# Can be on one layer or several different layers. Advice -- add dropout to all the layers.
'''
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(p = 0.1)) - 10% neurons will be disabled, start with 0.1 and go till 0.5
# This has added dropout to this layer.
'''
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(p = 0.1))
#----------------------------------------------------------------------------------------------------------------------------
# ////////////////////////////////////////////////////HYPER PARAMTERES OPTIMIZATION/////////////////////////////////////////////////
#2 types of parameters --
# - Learnt paramteres -- learnt during the process, -- weights
# - Hyper Parameters -- Assumed constant, batch size, epochs, optimizer, etc
# - We will do hyperparamter optimization using gridsearch -- will try several values and give the ones giving the best results.
'''
# Part 1 - Data Preprocessing -- NEEDS TO BE DONE!
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
'''
# Tuning the ANN
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
def build_classifier(optimizer):
classifier = Sequential()
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
return classifier
classifier = KerasClassifier(build_fn = build_classifier)
# dictionary created for the hyperparameters we want to optimize.
# Keys ahve to tbe exact as the parameters of the functions -- exact name
# since, batch_size and epochs was not used, look above, we can give them values directly, but optimizer is used as a paramtee in build_classifier,
# so make it a variable and pass the variable in the function.
parameters = {'batch_size': [25, 32],
'epochs': [100, 500],
'optimizer': ['adam', 'rmsprop']}
#gridsearch has Cross Validation built into it, with the metrics and everything.
grid_search = GridSearchCV(estimator = classifier,
param_grid = parameters,
scoring = 'accuracy',
cv = 10)
grid_search = grid_search.fit(X_train, y_train)
best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_ |
def Sum(lst):
sum_negative=0
sum_even_positive=0
sum_odd_positive=0
for i in lst:
if i <0:
sum_negative+=i
elif i>0 and i%2 == 0:
sum_even_positive+=i
elif i>0 and i%2 != 0:
sum_odd_positive+=i
print('Sum of Negative = {}\nSum of Positive Even = {}'
'\nSum of Positive Odd = {}'.format(sum_negative,sum_even_positive,
sum_odd_positive))
lst= eval(input('Enter list of numbers: '))
Sum(lst)
|
import tkinter
import serial
import msvcrt
from tkinter import *
class Application(tkinter.Frame):
""" GUI """
def __init__(self, master):
""" Initialize the Frame"""
tkinter.Frame.__init__(self, master)
self.grid()
self.create_widgets()
self.updater()
def create_widgets(self):
self.button1 = tkinter.Button(m, text='Exit', width=20, command=destroy)
self.button1.place(x=400, y=0)
self.button2 = tkinter.Button(m, text='1 +', width=20, command=OnePlus)
self.button2.place(x=10, y=0)
self.button3 = tkinter.Button(m, text='1 -', width=20, command=OneMinus)
self.button3.place(x=150, y=0)
self.button4 = tkinter.Button(m, text='2 +', width=20, command=TwoPlus)
self.button4.place(x=10, y=20)
self.button5 = tkinter.Button(m, text='2 -', width=20, command=TwoMinus)
self.button5.place(x=150, y=20)
#self.text1 = Text(m, width=40, height=2)
#self.text1.place(x=400, y=50)
def run(self):
loop_active = True
#while loop_active:
tdata = ser.read(ser.inWaiting())
# time.sleep(1)
# data_left = ser.inWaiting()
#print(tdata)
if len(tdata) > 4:
#self.text1.delete(1.0, END)
#self.text1.insert('1.0', tdata)
process_data(tdata)
# else:
# input_data.join(map(chr, tdata))# += tdata
def updater(self):
self.run()
self.after(20, self.updater)
m = tkinter.Tk()
port = "COM9"
baud = 115200
ser = serial.Serial(port, baud, timeout=1)
input_data = ""
def OnePlus():
ser.write(b'1,+' + bytes([10]))#bytes([13, 10]))
def OneMinus():
ser.write(b'1,-' + bytes([10]))
def TwoPlus():
ser.write(b'2,+' + bytes([10]))
def TwoMinus():
ser.write(b'2,-' + bytes([10]))
def process_data(input_data):
#global Application.w1
global w1
global w2
global LED1
global LED2
global LED3
global LED4
x = input_data.decode().split(",")
#check if format is correct
if x[0] == 'S':
value1 = x[1].isdigit()
if value1:
w1.set(x[1])
value1 = x[2].isdigit()
if value1:
w2.set(x[2])
tdata = 'V,' + x[1] + ',' + x[2] + '\r\n'
text1.delete(1.0, END)
text1.insert('1.0', tdata)
ser.write(tdata.encode())
elif x[0] == 'B':
value1 = x[1].isdigit()
if value1:
if x[1] == "1":
LED1.place(x = 30,y = 200)
else:
LED1.place_forget()
value1 = x[2].isdigit()
if value1:
if x[2] == "1":
LED2.place(x=30, y=250)
else:
LED2.place_forget()
value1 = x[3].isdigit()
if value1:
if x[3] == "1":
LED3.place(x=30, y=300)
else:
LED3.place_forget()
value1 = x[4].isdigit()
if value1:
if x[4] == "1":
LED4.place(x=30, y=350)
else:
LED4.place_forget()
input_data = ""
def destroy():
global ser
global m
ser.close()
m.destroy()
m.title("Analog control test ")
m.minsize(width=100, height=100)
m.geometry('800x600+0+0')
w1 = Scale(m, from_=100, to=0)
w1.place(x = 10,y = 50)
w2 = Scale(m, from_=100, to=0) # , orient=HORIZONTAL)
w2.place(x = 50,y = 50) # .pack()
text1 = Text(m, width=40, height=2)
text1.place(x=400, y=50)
LED1 = Canvas(m, width=50, height=50)
LED1.place(x = 30,y = 200)
LED1.create_oval(10, 10, 40, 40, fill='blue') # outline="#f11",fill="#1f1", width=2)
LED2 = Canvas(m, width=50, height=50)
LED2.place(x = 30,y = 250)
LED2.create_oval(10, 10, 40, 40, fill='blue')
LED3 = Canvas(m, width=50, height=50)
LED3.place(x = 30,y = 300)
LED3.create_oval(10, 10, 40, 40, fill='blue')
LED4 = Canvas(m, width=50, height=50)
LED4.place(x = 30,y = 350)
LED4.create_oval(10, 10, 40, 40, fill='blue')
# open the serial port
if ser.isOpen():
print(ser.name + ' is open...')
APP = Application(m)
m.mainloop()
|
from .SwtAdapter import SwtAdapter
from .VdfAdapter import VdfAdapter
from .VscAdapter import VscAdapter
|
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