Boolean If True, embed owner info in VIP port 'name', else embed owner info in 'device_id' field.
neutron_rbac (optional)
Boolean If True, enable neutron rbac discovery of networks shared across tenants/projects.
nuage_organization (optional)
String Field deprecated in 20.1.1.
nuage_password (optional)
String Field deprecated in 20.1.1.
nuage_port (optional)
Integer Field deprecated in 20.1.1. format: int32
nuage_username (optional)
String Field deprecated in 20.1.1.
nuage_virtualip (optional)
Boolean Applicable only if allowed-address-pairs is disabled or unusable. VIP placement uses Nuage virtualIp if true, else redirectionTarget. Field deprecated in 20.1.1. Field introduced in 17.2.3.
nuage_vsd_host (optional)
String Nuage VSD host name or IP address. Field deprecated in 20.1.1.
password (optional)
String The password Avi Vantage will use when authenticating to Keystone.
port_security (optional)
Boolean If true, port-security extension (if detected) will be used instead of security-groups, allowed-address-pairs or interface-secondary-ips. If false, port-security extension is skipped. Field deprecated in 20.1.1.
privilege
String Access privilege. Enum options - NO_ACCESS, READ_ACCESS, WRITE_ACCESS.
prov_name (optional)
provider_vip_networks (optional)
array[OpenStackVipNetwork] A tenant can normally use its own networks and any networks shared with it. In addition, this setting provides extra networks that are usable by tenants. Field introduced in 18.1.2.
region (optional)
role_mapping (optional)
array[OpenStackRoleMapping] Defines the mapping from OpenStack role names to avi local role names. For an OpenStack role, this mapping is consulted only if there is no local Avi role with the same name as the OpenStack role. This is an ordered list and only the first matching entry is used. You can use '*' to match all OpenStack role names.
se_group_ref (optional)
String This field has been generalized for all clouds and has been renamed to se_group_template_uuid. It is a reference to an object of type ServiceEngineGroup. Field deprecated in 18.2.5.
security_groups (optional)
Boolean If false, security-groups extension will not be used. .
tenant_se (optional)
Boolean If true, then SEs will be created in the appropriate tenants, else SEs will be created in the admin_tenant.
usable_network_uuids (optional)
array[String] Deprecated, please use provider_vip_networks. Field deprecated in 18.2.2. Field introduced in 17.1.1.
use_admin_url (optional)
Boolean If admin URLs are either inaccessible or not to be accessed from Avi Controller, then set this to False.
use_internal_endpoints (optional)
Boolean Use internalURL for OpenStack endpoints instead of the default publicURL endpoints.
use_keystone_auth (optional)
Boolean Use keystone for user authentication.
use_nuagevip (optional)
Boolean If True, use nuage vip as device_owner of VIP ports, else use neutron LOADBALANCER. Field deprecated in 20.1.1. Field introduced in 17.2.1.
username
String The username Avi Vantage will use when authenticating to Keystone. For Keystone v3, provide the user information in user@domain format, unless that user belongs to the Default domain.
wildcard_access (optional)
Boolean If enabled, program SE security group with ingress rule to allow SSH (port 22) access from <IP_ADDRESS>/0. Field deprecated in 17.1.5. Field introduced in 17.1.3.
hypervisor
String Hypervisor type. Enum options - DEFAULT, VMWARE_ESX, KVM, VMWARE_VSAN, XEN. Field introduced in 17.2.1.
image_properties (optional)
array[Property] Custom properties to be associated with the SE image in Glance for this hypervisor type. Field introduced in 17.2.1.
avi_role
os_role
String Role name in OpenStack.
os_network_uuid (optional)
String Neutron network UUID. Field introduced in 18.1.2.
os_tenant_uuids (optional)
array[String] UUIDs of OpenStack tenants that should be allowed to use the specified Neutron network for VIPs. Use '*' to make this network available to all tenants. Field introduced in 18.1.2.
registry_namespace (optional)
String Namespace for the ServiceEngine image to be hosted in Openshift Integrated registry.
registry_service (optional)
String Name of the Integrated registry Service in Openshift.
registry_vip (optional)
IpAddr Static VIP for 'docker-registry' service in Openshift if Avi is proxying for this service.This VIP should be outside the cluster IP subnet in Kubernetes and within the subnet configured (but outside the available pool of IPs) in the East West IPAM profile configuration for this Cloud. For example, if kubernetes cluster VIP range is <IP_ADDRESS>/16 and subnet configured in East West IPAM profile is <IP_ADDRESS>/16, then <IP_ADDRESS> can be used for this vip and IP pool can start from <IP_ADDRESS> onwards. Use this static VIP in '--insecure-registry <this-vip> 5000' docker config if using an insecure registry or add this to the list of IPs/hostnames when generating certificates if using a secure TLS registry.
virtualservice_name
String Name of shared virtualservice. VirtualService will be created automatically by Cloud Connector. Field introduced in 17.1.1.
effect (optional)
String Effect to match. Enum options - NO_SCHEDULE, PREFER_NO_SCHEDULE, NO_EXECUTE. Field introduced in 17.2.14, 18.1.5, 18.2.1.
key (optional)
String Key to match. Field introduced in 17.2.14, 18.1.5, 18.2.1.
operator (optional)
String Operator to match. Enum options - EQUAL, EXISTS. Field introduced in 17.2.14, 18.1.5, 18.2.1.
toleration_seconds (optional)
Integer Pods that tolerate the taint with a specified toleration_seconds remain bound for the specified amount of time. Field introduced in 17.2.14, 18.1.5, 18.2.1. Unit is SECONDS. format: int32
value (optional)
String Value to match. Field introduced in 17.2.14, 18.1.5, 18.2.1.
end
Integer TCP/UDP port range end (inclusive). Allowed values are 1-65535. format: int32
start
Integer TCP/UDP port range start (inclusive). Allowed values are 1-65535. format: int32
name
String Property name. Field introduced in 17.2.1.
value (optional)
String Property value. Field introduced in 17.2.1.
host
String Proxy hostname or IP address.
password (optional)
port
username (optional)
access_key (optional)
app_sync_frequency (optional)
Integer Sync frequency in seconds with frameworks. format: int32
container_port_match_http_service (optional)
Boolean Perform container port matching to create a HTTP Virtualservice instead of a TCP/UDP VirtualService.
coredump_directory (optional)
String Directory to mount to check for core dumps on Service Engines. This will be mapped read only to /var/crash on any new Service Engines. This is a disruptive change.
disable_auto_backend_service_sync (optional)
Boolean Disable auto service sync for back end services.
disable_auto_frontend_service_sync (optional)
Boolean Disable auto service sync for front end services.
disable_auto_se_creation (optional)
docker_registry_se (optional)
east_west_placement_subnet (optional)
IpAddrPrefix Match against this prefix when placing east-west VSs on SEs .
enable_event_subscription (optional)
Boolean Enable Docker event subscription.
feproxy_container_port_as_service (optional)
Boolean For Front End proxies, use container port as service port.
feproxy_vips_enable_proxy_arp (optional)
Boolean Enable proxy ARP from Host interface for Front End proxies.
fleet_endpoint (optional)
String Optional fleet remote endpoint if fleet is used for SE deployment.
http_container_ports (optional)
array[Integer] List of container ports that create a HTTP Virtualservice instead of a TCP/UDP VirtualService. Defaults to 80.
nuage_controller (optional)
rancher_servers (optional)
array[String] List of Rancher servers; In case of a load balanced Rancher multi cluster, use Virtual IP of the cluster.
se_deployment_method (optional)
String Use Fleet/SSH for SE deployment. Enum options - SE_CREATE_FLEET, SE_CREATE_SSH, SE_CREATE_POD.
se_exclude_attributes (optional)
se_include_attributes (optional)
se_spawn_rate (optional)
Integer New SE spawn rate per minute. format: int32
se_volume (optional)
String Host volume to be used as a disk for Avi SE, This is a disruptive change.
secret_key (optional)
services_accessible_all_interfaces (optional)
Boolean Make service ports accessible on all Host interfaces in addition to East-West VIP and/or bridge IP. Usually enabled AWS clusters to export East-West services on Host interface.
ssh_se_deployment (optional)
SSHSeDeployment Parameters for SSH SE deployment. Field deprecated in 17.1.1.
ssh_user_ref (optional)
String Cloud connector user uuid for SSH to hosts. It is a reference to an object of type CloudConnectorUser. Field introduced in 17.1.1.
use_container_ip_port (optional)
Boolean Use container IP address port for pool instead of host IP address hostport. This mode is applicable if the container IP is reachable (not a private NATed IP) from other hosts in a routed environment for containers.
use_controller_image (optional)
Boolean If true, use controller generated SE docker image via fileservice, else use docker repository image as defined by docker_registry_se.
if_name (optional)
String Host interface name. Field introduced in 18.2.6.
network_namespace (optional)
String Network Namespace type used to add an route entry in a specific namespace. Enum options - LOCAL_NAMESPACE, HOST_NAMESPACE, OTHER_NAMESPACE. Field introduced in 18.2.6.
nexthop (optional)
IpAddr Host nexthop ip address. Field introduced in 18.2.6.
subnet
IpAddrPrefix Host subnet address. Field introduced in 18.2.6.
host_os (optional)
String Host OS distribution e.g. COREOS, UBUNTU, REDHAT. Field deprecated in 17.1.1.
password (optional)
String Password for ssh and/or sudo. Field deprecated in 17.1.1.
ssh_user (optional)
String Username for SSH access to hosts. Field deprecated in 17.1.1.
sudo_user (optional)
String Username for sudo. Field deprecated in 17.1.1.
segment_id
String Segment ID. Field introduced in 20.1.1.
tier1_lr_id
String Tier1 logical router ID. Field introduced in 20.1.1.
datacenter (optional)
String Datacenter for virtual infrastructure discovery.
management_ip_subnet (optional)
IpAddrPrefix Management subnet to use for Avi Service Engines.
management_network (optional)
String Management network to use for Avi Service Engines. It is a reference to an object of type VIMgrNWRuntime.
password (optional)
String The password Avi Vantage will use when authenticating with vCenter.
privilege
String Set the access mode to vCenter as either Read, which allows Avi to discover networks and servers, or Write, which also allows Avi to create Service Engines and configure their network properties. Enum options - NO_ACCESS, READ_ACCESS, WRITE_ACCESS.
username (optional)
String The username Avi Vantage will use when authenticating with vCenter.
vcenter_template_se_location (optional)
String Avi Service Engine Template in vCenter to be used for creating Service Engines.
vcenter_url (optional)
String vCenter hostname or IP address.
privilege
String vCloudAir access mode. Enum options - NO_ACCESS, READ_ACCESS, WRITE_ACCESS.
vca_host
String vCloudAir host address.
vca_instance
vca_mgmt_network
String vCloudAir management network.
vca_orgnization
String vCloudAir orgnization ID.
vca_password
vca_username
vca_vdc
String vCloudAir virtual data center name.


#19 - Remote Working
Show Notes
06:35 - Remote : Office Not Required
Contact us and send feedback at<EMAIL_ADDRESS>Looking forward to sharing more stuffs with all of you.
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LTI Link: 📋copy
Define the readings students must complete by selecting chapters and subchapters of the textbook.
Select the problems which compose this assignment. You can drag and drop rows to reorder.
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low-light shooter
Galaxy S11 may come with a "Bright Night Sensor" for low-light photography
The Galaxy S11 may deliver significantly improved low-light performance over the S10.
by Babu MohanWhat you need to know
- A new trademark filing by Samsung suggests the Galaxy S11 could come equipped with a 'Bright Night Sensor.'
- Since the Galaxy S11 and S11+ will have a total of five cameras on the back, it is certainly plausible that one of them could be used exclusively for low-light photography.
- The Galaxy S11 will be Samsung's first flagship phone with a 108MP camera and may also offer 5x optical zoom.
Leaked CAD-based renders of the Galaxy S11 and Galaxy S11+ that surfaced online recently had revealed that Samsung's next flagship phones will have a total of five cameras on the back. While rumors have suggested that the Galaxy S11 series will have a 108MP primary camera, we still don't know much about the rest of the sensors.
Samsung has now filed a trademark application (via LetsGoDigital) for a "Bright Night Sensor" with the European Union Intellectual Property Office (EUIPO), which suggests it could be one of the sensors on the back of the Galaxy S11. Bright Night was actually the name of the Galaxy S10's dedicated Night Mode feature, although Samsung no longer calls it that.
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The "Bright Night Sensor", on the other hand, will be an actual sensor that might be used for shooting in low-light. Sadly, however, the trademark filing does not reveal any information regarding the sensor, except that it is a "camera image sensor for smartphones."
Regardless of whether or not the Galaxy S11 series will have a "Bright Night Sight" sensor, they are expected to be huge upgrades over the Galaxy S10 and Note 10 series phones in the camera department. Thanks to the 108MP sensor, the phones will be capable of recording videos at up to 8K resolution. The Galaxy S11 series is also expected to have a telephoto lens with 5x optical zoom.
Samsung Galaxy S11: News, Leaks, Release Date, Specs, and Rumors!


Access Runtime 2002 ダウンロード
Nov 16 2020 You have access to the same runtime API and language capabilities with each app.
Access runtime 2002 ダウンロード. In 2002 Microsoft released NET Framework a development platform for creating Windows apps. Winamp is a media player for Microsoft Windows originally developed by Justin Frankel and Dmitry Boldyrev by their company Nullsoft which they later sold to AOL in 1999 for 80 million. Python 213 April 9 2002 Download Release Notes.
Visionary sponsors like Google help to host Python downloads. IBM Community offers a constant stream of freshly updated content including featured blogs and forums for discussion and collaboration. Microsoft バーコード コントロール 90 はMicrosoft Office Access のフォームやレポートに挿入できるバーコード シンボルを表示する ActiveX コントロールです本ダウンロードは Microsoft バーコード コントロール 90 がサポートするメソッドやプロパティについてのリファレンスがコンパイ.
Historically most but not all Python releases have. It was then acquired by Radionomy in 2014. IMPORTANT - Read this agreement Agreement carefully before installing the software programs in this installation.
Apr 20 2021 Power Geez 2002 - Shareware - more info. 0c end-user redistributable that developers may include with their products. Access to the latest white papers webcasts presentations and research uniquely for members by members.
Descargar wincc runtime advance. 185 2014 1529 arXiv13091692 including counter-terms. View Symantec Enterprise Security product availability and maintenance schedule.
Service Status How to Subscribe. These older versions of the JRE and JDK are provided to help developers debug issues in older systems. Microsoft Officeの上位版に同梱され同社の関係データベース管理システムであるMicrosoft SQL Serverに似たソフトウェアである.
View all customer successes Oracle hardware customer successes. 竹中工務店 wizdom ダウンロード. End-User Runtime installs a number of runtime libraries from the legacy DirectX SDK for some games that use D3DX9 D3DX10 D3DX11 XAudio 2.
A guide to building information modeling for owners managers designers engineers and contractors. Visual Studio 2008 VC 90 SP1. Mar 04 2021 Download Oxygen XML Editor and test it for free for 30 days requires a trial license keyThis allows you to evaluate the software for free before making a purchase decision.
More Microsoft Office Professional Plus<PHONE_NUMBER>1. Documentation View release notes installation implementation administration user documentation. Python 201 June 22 2001 Download Release Notes.
21 2001 Download Release Notes. All Python releases are Open Source. Feb 23 2021.
Since version 2 it has been sold as freemium and supports extensibility with plug-ins and skins and features music visualization playlist and a media library. They are not updated with the latest security patches and are not recommended for use in production. TI Connect CE Software 5x for Windows.
Access 2002 で間違いないのでしょうか Windows 10 に対応しているのは Office 2010 以降なので更新が適用できなくても仕方ないと思います なお0x80096004 については更新プログラムの証明書が古い際のエラーなのでその通りですね. FeynArts started out in 1990 as a Macsyma code written by Hagen Eck and Sepp Kblbeck which could produce diagrams in the Standard Model but soon got ported to the Mathematica platform. 143 2002 54 hep-ph0105349 and Comput.
Apr 11 2021 Y. Access training and education for Symantec Products. Microsoft Office AccessマイクロソフトオフィスアクセスはマイクロソフトがMicrosoft Windows 向けに販売しているデータベース管理システムのソフトウェアである.
Oracle hardware is used by thousands of customers worldwide to run and protect business-critical Oracle Databases and applications with high performance scale and security while reducing. 0 works perfect with Windows 10 I downloaded 2. Oracle Java Archive The Oracle Java Archive offers self-service download access to some of our historical Java releases.
Obtained in the United States or Canada. We dont have any change log information yet for version 339 of SourceTree. Download the Microsoft Visual C 2010 Service Pack 1 Redistributable Package MFC Security UpdateThis is the latest supported Visual C redistributable package update for Visual Studio 2010.
The Microsoft Security Response Center is part of the defender community and on the front line of security response evolution. Up to 148 TBhour of throughput speeds access to offline storage and archives. Product Lifecycle Details.
This License applies to TI Connect CE Software for Windows. Sometimes publishers take a little while to make this information available so please check back in a few days to see if it has been updated.


Browse Ionic Framework components, api, plugins, theming, and CLI documentation and guides.
Browse service configuration, user admin, and troubleshooting docs and guides for Ionic Services.
Stop by and say hello. The Forum is the best place to connect, ask a question, or help out other Ionic developers!
Take a look and get coding! Our documentation covers all you need to know to get an app up and running in minutes.


Use arrow keys to navigate
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Lane, D. M., Napier, H. A., Peres, S. C., & Sándor, A. (2005). Hidden costs of graphical user interfaces: Failure to make the transition from menus and icon toolbars to keyboard shortcuts. International Journal of Human-Computer Interaction, 18(2), 133-144.Six participants performed common commands using menu selection, icon toolbars, and keyboard shortcuts. The keyboard shortcuts were, as expected, the most efficient. We conclude that even experienced users are inefficient in their use of graphical interfaces.
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{% ifnotequal execute "convert" %}
This is a dry run. Nothing has been changed.
Please check the results.
Dry run
It's probably too late though.


My Team
Caitlin
Manager
ID: 12
Email:
<EMAIL_ADDRESS>Office number: 100
Cody
Engineer
ID: 14
Email:
<EMAIL_ADDRESS>GitHub:
@codythedog
Rizzo
Intern
ID: 9
Email:
<EMAIL_ADDRESS>School: The School of Nine Lives


Hello.
My name is {{FIRST_NAME}} {{LAST_NAME}} and this is my resume my story
- Tel:{{PHONE_NUMBER}}
- Email:{{EMAIL}}
- Address:{{ STREET }}, {{ CITY }}
- Birthday:{{BIRTHDAY}}
01. Experience
{% foreach(experiences) %}
{% endforeach %}
[[TIME_LETTER]]
[[COMPANY_NAME]] / [[POSITION]]
[[DESCRIPTION]]
02. Education
{% foreach(education) %}
{% endforeach %}
[[TIME_LETTER]]
[[ COLLEGE_NAME ]]
[[ SUBJECT ]] - [[ QUALIFICATION ]]
03. Skill
-
{% foreach(skills) %}
-
[[ NAME ]]
{% endforeach %}
04. Interest
{{HOBBIES}}


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Room details
Sector
Room Flags
Exits
Path
#2s6e4s
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Contents
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Equipped with
an item
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sold for coins
May contain
some object
level 0
1000 coins
Containing
item


| Object Name | Attribute name | Type | Description | Constraints | Default Value |
|---|---|---|---|---|---|
|
Use of telecom service. |
|||||
|
String |
Phone number used. |
||||
|
BigDecimal |
Price of use of the service. |
||||
|
ZonedDateTime |
Timestamp of use. |
ZonedDateTime.now() |
|||
|
Monthly bill. |
|||||
|
YearMonth |
Which month of the bill. |
@NotNull() @PastOrPresent() |
|||
|
Contract concerned by the bill. |
@NotNull() |
||||
|
Set |
Bill contents. |
new HashSet<>() |
|||
|
PaymentState, Enumeration |
Bill payment state |
@NotNull() |
PaymentState.WAITING |
||
|
Phone call type access. |
|||||
|
Duration |
Phone call duration |
||||
|
Telecom contract |
|||||
|
Long |
Contract identifier. Generate by the system and communicate to client. |
||||
|
Contract customer. |
|||||
|
Customer of the telecom service |
|||||
|
String |
Email of the customer. |
@Email() @NotBlank() |
|||
|
Set |
Customer’s contracts. |
new HashSet<>() |
|||
|
Enumeration |
Bill payment state values. |
||||
|
Wainting payment by the client. |
|||||
|
Client has payed. |
|||||
|
SMS type access. |


Now that we have defined Brownian motion, we can utilise it as a building block to start constructing stochastic differential equations (SDE). We need SDE in order to discuss how functions f=f(s) and their derivatives with respect to S behave, where S is a stock price determined by a Brownian motion.
Some of the rules of ordinary calculus do not work as expected in a stochastic world. We need to modify them to take into account both the random behaviour of Brownian motion as well as its non-differentiable nature. We will begin by discussing stochastic integrals, which will lead us naturally to the concept of an SDE.
A stochastic integral of the function f=f(t) is a function
W=W(t),t ∈ [0,T] given by:
where tk=kt/N.
Note that the function f is non-anticipatory, in the sense that it is evaluated within the summation sign at time tk-1. This means that it has no information as to what the random variable at X(tk) is. Supposing that f represented some portfolio allocation based on B, then if it were not evaluated at tk−1, but rather at tk, we would be able to anticipate the future and modify the portfolio accordingly.
The previous expression provided for W(t) is an integral expression and thus is well-defined for a non-differentiable variable, B(t), due the property of finiteness as well as the chosen mean and variance. However, we wish to be able to write it in differential form:
One can consider the term dB as being a normally distributed random variable with zero mean and variance dt. The formal definition is provided:
Let B(t) be a Brownian motion. If W(t) is a sequence of random variables, such that for all t,
is a random variable with mean and variance that rae o(δt), then:
is a stochastic differential equation for W(t).
A sequence of random variables given by the above is termed an Ito drift-diffusion process, or simply an Ito process or a stochastic process.
It can be seen that μ and σ are both functions of t and W. μ has the interpretation of a non-stochastic drift coefficient, while σ represents the coefficient of volatility - it is multiplied by the stochastic dB term. Hence, stochastic differential equations have both a non-stochastic and stochastic component.
In the following section on geometric Brownian motion, a stochastic differential equation will be utilised to model asset price movements.
The usual model for the time-evolution of an asset price S(t) is given by the geometric Brownian motion, represented by the following stochastic differential equation:
Note that the coefficients μ and σ, representing the drift and volatility of the asset, respectively, are both constant in this model. In more sophisticated models they can be made to be functions of t,S(t) and other stochastic processes.
The solution S(t) can be found by the application of Ito's Lemma to the stochastic differential equation.
Dividing through by S(t) in the above equation leads to:
Notice that the left hand side of this equation looks similar to the derivative of logS(t). Applying Ito's Lemma to logS(t) gives:
This becomes:
This is an Ito drift-diffusion process. It is a standard Brownian motion with a drift term. Since the above formula is simply shorthand for an integral formula, we can write this as:
Finally,taking the exponential of this equation gives:
This is the solution the stochastic differential equation. In fact it is one of the only analytical solutions that can be obtained from stochastic differential equations.
Ito's Lemma is a key component in the Ito Calculus, used to determine the derivative of a time-dependent function of a stochastic process. It performs the role of the chain rule in a stochastic setting, analogous to the chain rule in ordinary differential calculus. Ito's Lemma is a cornerstone of quantitative finance and it is intrinsic to the derivation of the Black-Scholes equation for contingent claims (options) pricing.
It is necessary to understand the concepts of Brownian motion, stochastic differential equations and geometric Brownian motion before proceeding.
One of the most fundamental tools from ordinary calculus is the chain rule. It allows the calculation of the derivative of chained functional composition. Formally, if W(t) is a continuous function, and:
Then the chain rule states:
When f has t as a direct dependent parameter also, we require additional terms and partial derivatives. In this instance, the chain rule is given by:
In order to model an asset price distribution correctly in a log-normal fashion, a stochastic version of the chain rule will be used to solve a stochastic differential equation representing geometric Brownian motion.
The primary task is now to correctly extend the ordinary calculus version of the chain rule to be able to cope with random variables.
Let B(t) be a Brownian motion and W(t) be an Ito drift-diffusion process which satisfies the stochastic differential equation:
If f(w,t)∈C2(R2,R) then f(W(t),t) is also and Ito drift-diffusion with its differential given by:
With dW(t)2 given by: dt2=0, dtdB(t)=0∧dB(t)2=dt.
Now that we have derived Ito's Lemma, we are in a position to derive the Black-Scholes equation.
Suppose we wish to price a vanilla European contingent claim C, on a time-varying asset S, which is set to mature at T. We shall assume that S follows a geometric Brownian motion with mean growth rate of μ and volatility σ. r will represent the continuously compounding risk free interest rate.r , μ and σ are not functions of time, t, or the asset price S and so are fixed for the duration of the option's lifetime.
Since our option price,C , is a function of time t and the price of the asset S, we will use the notation C=C(S,t) to represent the price of the option. Note that we are assuming at this stage that C exists and is well-defined. We will later show this to be a justified claim.
The first step is to utilise Ito's Lemma on the function C(S,t) to give us a SDE:
Our asset price is modelled by a geometric Brownian motion, the expression for which is recalled here. Note that μ and σ are constant - i.e. not functions of S or t:
We can substitute this expression into Ito's Lemma to obtain:
The thrust of our derivation argument will essentially be to say that a fully hedged portfolio, with all risk eliminated, will grow at the risk free rate. Thus, we need to determine how our portfolio changes in time. Specifically, we are interested in the infinitesimal change of a mixture of a call option and a quantity of assets. The quantity will be denoted by Δ. Hence:
This leads us to a choice for Δ which will eliminate the term associated with the randomness. If we set δ = -∂C/∂S(S,t), we receive:
Note that we have glossed over the issue of what the derivative of Δ is. We will return to this later.
This technique is known as Delta-Hedging and provides us with a portfolio that is free of randomness. This is how we can apply the argument that it should grow at the risk free rate, otherwise, as with our previous arguments, we would have an arbitrage opportunity. Hence the growth rate of our delta-hedged portfolio must be equal the continuously compounding risk free rate, r.
Thus we are able to state that:
If we rearrange this equation, and using shorthand notation to drop the dependence on (S,T) we arrive at the famous Black-Scholes equation for the value of our contingent claim:
Although we have derived the equation, we do not yet possess enough conditions in order to provide a unique solution. The equation is a second-order linear partial differential equation (PDE) and without boundary conditions (such as a payoff function for our contingent claim), we will not be able to solve it.
One payoff function we can use is that of a European call option struck at K. This has a payoff function at expiry, T, of:
We are now in a position to solve the Black-Scholes equation.
It has already been outlined that the reader is to be familiar with the Black-Scholes formula for the pricing of European Vanilla Calls and Puts. For completeness, the price of a European Vanilla Call, C(S,t) is given below, where S is the underlying asset, K is the strike price, r is the risk-free rate, T is the time to maturity and σ is the (constant) volatility of the underlying S(N is described below):
With d1 and d2 defined as follows:
Thanks to Put-Call Parity, we are also able to price a European Vanilla Put P(S,t) with the following formula:
The remaining function we have yet to describe is N. This is the cumulative distribution function of the standard normal distribution. The formula for N is given by:
In addition, we would like to have closed form solutions for the "Greeks", which are the option price sensitivities to the underlying variables and parameters. For this reason we also need the formula for the probability density function of the standard normal distribution which is given below:
With our ticker symbols saved in a list, we can iterate through the list and query the Alpha Vantage API for daily price data.
from math import exp, log, sqrt, pi
from statistics import norm_pdf, norm_cfd
def norm_pdf(x):
"""
Standard normal probability density function
"""
return (1.0/((2*pi)**0.5))*exp(-0.5*x*x)
def norm_cdf(x):
"""
An approximation to the cumulative distribution
function for the standard normal distribution:
N(x) = \frac{1}{sqrt(2*\pi)} \int^x_{-\infty} e^{-\frac{1}{2}s^2} ds
"""
k = 1.0/(1.0+0.2316419*x)
k_sum = k * (0.319381530 + k * (-0.356563782 + \
k * (1.781477937 + k * (-1.821255978 + 1.330274429 * k))))
if x >= 0.0:
return (1.0 - (1.0 / ((2 * pi)**0.5)) * exp(-0.5 * x * x) * k_sum)
else:
return 1.0 - norm_cdf(-x)
def d_j(j, S, K, r, v, T):
"""
d_j = \frac{log(\frac{S}{K})+(r+(-1)^{j-1} \frac{1}{2}v^2)T}{v sqrt(T)}
"""
return (log(S/K) + (r + ((-1)**(j-1))*0.5*v*v)*T)/(v*(T**0.5))
def vanilla_call_price(S, K, r, v, T):
"""
Price of a European call option struck at K, with
spot S, constant rate r, constant vol v (over the
life of the option) and time to maturity T
"""
return S * norm_cdf(d_j(1, S, K, r, v, T)) - \
K*exp(-r*T) * norm_cdf(d_j(2, S, K, r, v, T))
def vanilla_put_price(S, K, r, v, T):
"""
Price of a European put option struck at K, with
spot S, constant rate r, constant vol v (over the
life of the option) and time to maturity T
"""
return -S * norm_cdf(-d_j(1, S, K, r, v, T)) + \
K*exp(-r*T) * norm_cdf(-d_j(2, S, K, r, v, T))


It’s Ada Lovelace Day again and so, in a combination of genuinely heartfelt celebration and bare-faced self-promotion, here’s a link to our article from this time last year, Women Who Have Changed Machinima.
If anything, women have become even more prominent in Machinima and Anymation over the past twelve months. It’s particularly obvious in the more abstract creative communities such as Moviestorm or IClone, but even the communities for supposedly male-oriented games have many prominent female bloggers, movie-makers and celebrity players.
So, let’s once again raise a glass to Ada, and to the women who make our community the vibrant, fascinating and challenging arena it is.


Contact Me
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<EMAIL_ADDRESS>https://github.com/chibuikeIg
+234<PHONE_NUMBER>
Get In Touch
Feel Free To Drop Us A Line Below
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Can't we find a more simple way to do this ?
It seems main problems we had with virtualization have gone !
Cool !
But...
Much better !
You can easily share then with people.
For instance by using docker and hub.docker.com
And they are actually used by many big companies
Google use them since a decade, with a container orchestration system called Borg.
Wait, a container orchestration system ? What is this ?
Look at technologies like


Wedding Car Decorations Artificial Flowers Car Decorations as colourful as they can get. We make sure the groom rides like the king on the most important day of his life. Ideal investment for Wedding Event Organisers and T.V. Serial sets. Best Decorations at competitive rates.


Cast: Eric Bana, Simon Lyndon, Vince Colosimo, Kate Beahan, David Field, Dan Wyllie, Bill Young, Kenny Graham
Andrew Dominik (Writer/Director) Michele Bennett (Producer) Michael Gudinski (Co-Producer) Al Clark (Executive Producer) Martin Fabinyi (Executive Producer) Ken Sallows (Editor) Mick Harvey (Original Music)
We here in Australia were founded - partially at least - as a big prison. >From 1788 England dumped its poor, tired and huddled convicts down here. No statue with an engraved welcome waiting for them, just a hostile, dry and hot open air continent/prison.
To me, this explains two characteristics that people find puzzling about the Australian psyche:
1) Our dry, dark "gallows" humour. 2) Our fascination with Bad Boys - the darker elements of our otherwise shiny, happy society
A classic example of the former is in "Breaker Morant." As Morant is about to be executed - by an English firing squad - the Aussie yells out to the gunmen "shoot straight you bastards, don't make a mess of it".
One of our major folk heroes is a Bad Boy. Mr Edward "Ned" Kelly. Bushranger, circa 1880. Our favourite song is probably Waltzing Matilda. It's about a sheep thief, who eludes the law by drowning himself.
And so to "Chopper". Not to compare him to Mr Kelly, nor to imply he is anything like a folk hero, but to put him - and this movie - into the broader context of the Australian mind-set. Well, that's the theory anyway.
Mark Brandon "Chopper" Read is real. Very much alive and living in Tasmania. To say he has lead a colourful life is to not understand the word colour. He started out in the 1970's as a streetfighter, moved on to standover man and underworld executioner. In 1991, whilst still in prison, he wrote his memoirs. The book "Chopper: From the Inside" was a phenomenal success. Part confession, part bragging and all wrapped up in Read's disarming, chatty and funny style. He even wrote poetry about some of his victims.
He became one of our bestselling authors and has written many a sequel book. And now we have the movie.
So how to make a film about a bloke who claims to have killed 19 people (but never an 'innocent member of the public') and yet makes jokes and writes poems about such things? Should it be judgemental or just feed us the data and let us decide?
It's a mercy that debut writer/director Andrew Dominik went for the latter. To be fair, it's not 'data' in the true sense; the film starts with a disclaimer implying that some liberty was taken with the facts per se. However the result is still stunning.
"Chopper" is a snappy, stylish mixture of extreme violence and wicked humour. Using unusual colours and playing with the film speed, Dominik transports us into the complex and intriguing world of Chopper.Brutal.Funny.Com.
He even uses poetry in one memorable scene, with all characters giving their rhyming lines to camera with the film strangely sped up. The result is all surreal and jerky; like a coloured-up version of an ultra-violent Charlie Chaplin. Oh, they are waxing lyrical about their roles in the same murder. The victim is in there too. You don't know if you should laugh or be appalled.
In the lead role Eric Bana is mesmerising. Best known here for his TV comic skills, Bana's electric performance dominates and carries the film. Bana is hardly ever off the frame. "Chopper" is a complex role, which Bana captures with great aplomb. He stabs someone and then we see the shock and concern - for the victim - build up in his eyes. The two dimensional pulp fiction character suddenly spawns an unexpected third dimension.
You may recall the disarming style of "Muriel's Wedding" - nearly every time there was a blissfully happy event, it ended in tragedy or despair. "Chopper" has the reverse; probably reflecting Read's own style. Episodes start off fairly friendly, but you know that it's going to end in mayhem. Chopper is rated R here and is not for the feint hearted.
Everything works - the writing, direction and postproduction. Bana and his excellent support cast bring it all together. The film doesn't take sides in a complex and dangerous playing field. Walking that fine line and still being an adventurous and entertaining movie is just one of its many triumphs.
5 out of 5. Ear ear.
Copyright (C) David Sidwell 2000
--
Regards, ---
David Sidwell (Melbourne, Australia<EMAIL_ADDRESS><EMAIL_ADDRESS>http://www.users.bigpond.com/dsidwell/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| Reasons we can share personal information | Do We Share? | Can you limit this sharing? |
|---|---|---|
| For our everyday business:
Purposes such as to process your transactions, maintain your account(s), respond to court orders and legal investigations, or report to credit bureaus. |
Yes | No |
| For our marketing purposes: To offer you other products and services from our company. |
[Field reason2] | [Field optout2] Opt Out |
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| Who is providing this notice? | This notice reflects the policy of: [Legal Entity Name] + [, other if applicable] |
| How does [company alias] protect your personal information? |
To protect your personal information from unauthorized access and use we use security measures that comply with federal law. These measures include:
|
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We collect your information, for example, when you:
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Federal law gives you the right to limit some but not all sharing related to:
|
| What happens when I limit sharing for an account I hold jointly with someone else? | [If Institution provides opt-out option] Your choices will apply only to your [account/policy], unless you tell us otherwise. - Your choices will apply to everyone on the [account/policy], unless you tell us otherwise. - Sharing may not be limited. |


{extend name="vehicle@common/base" /} {block name="leftmenu"}{include file="common/leftMenu" /} {/block}{block name="main"} {include file="common/ie8warning" /}


| | |
- Bio.PDB.Chain.Chain(Bio.PDB.Entity.Entity)
-
- UpdChain
- Bio.PDB.Model.Model(Bio.PDB.Entity.Entity)
-
- UpdModel
class UpdChain(Bio.PDB.Chain.Chain) |
| |
Custom chain class that inherits attributes and methods from the biopython chain class. |
| |
- Method resolution order:
- UpdChain
- Bio.PDB.Chain.Chain
- Bio.PDB.Entity.Entity
- builtins.object
Methods defined here:
- __hash__(self)
- Returns the hash value of the object. Used to avoid the existence of more than one instance of the object with the same ID in dictionaries or sets.
- __init__(self, chainObject)
- Method to initialize the object after its creation. The attributes are needed to correctly set the relationships between parent and child classes.
Considerations: in order to set CustomChain as child of CustomModel, CustomChain will have to remove
its original biopython chain model as parent.
- get_common_atoms(self, other)
- Compares the list of atoms of two chains and returns an even tuple of atoms.
- get_sequence(self)
- Checks what type of sequence has the chain according to the nature of its first residue.
Reads the sequence in the format present in the object and returns it in a more conventional format.
- has_equivalent(self, other_sequence)
- Checks if the sequence of the current chain object is equivalent (very similar or identical) to another sequence.
Considerations: pairwise alignment is performed and the cutoff is set to 0.95. If the alignment score is above the cutoff,
the function returns True. Otherwise it returns False.
Data and other attributes defined here:
- dna_dict = {'DA': 'A', 'DC': 'C', 'DG': 'G', 'DT': 'T'}
- protein_dict = {'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D', 'CYS': 'C', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', ...}
- rna_dict = {'A': 'A', 'C': 'C', 'G': 'G', 'U': 'U'}
Methods inherited from Bio.PDB.Chain.Chain:
- __contains__(self, id)
- Check if a residue with given id is present in this chain.
Arguments:
- id - (string, int, string) or int
- __delitem__(self, id)
- Delete item.
Arguments:
- id - (string, int, string) or int
- __ge__(self, other)
- Validate if id is greater or equal than other.id.
- __getitem__(self, id)
- Return the residue with given id.
The id of a residue is (hetero flag, sequence identifier, insertion code).
If id is an int, it is translated to (" ", id, " ") by the _translate_id
method.
Arguments:
- id - (string, int, string) or int
- __gt__(self, other)
- Validate if id is greater than other.id.
- __le__(self, other)
- Validate if id is less or equal than other id.
- __lt__(self, other)
- Validate if id is less than other.id.
- __repr__(self)
- Return the chain identifier.
- get_atoms(self)
- Return atoms from residues.
- get_residues(self)
- Return residues.
- get_unpacked_list(self)
- Return a list of undisordered residues.
Some Residue objects hide several disordered residues
(DisorderedResidue objects). This method unpacks them,
ie. it returns a list of simple Residue objects.
- has_id(self, id)
- Return 1 if a residue with given id is present.
The id of a residue is (hetero flag, sequence identifier, insertion code).
If id is an int, it is translated to (" ", id, " ") by the _translate_id
method.
Arguments:
- id - (string, int, string) or int
Methods inherited from Bio.PDB.Entity.Entity:
- __eq__(self, other)
- Test for equality. This compares full_id including the IDs of all parents.
- __iter__(self)
- Iterate over children.
- __len__(self)
- Return the number of children.
- __ne__(self, other)
- Test for inequality.
- add(self, entity)
- Add a child to the Entity.
- copy(self)
- Copy entity recursively.
- detach_child(self, id)
- Remove a child.
- detach_parent(self)
- Detach the parent.
- get_full_id(self)
- Return the full id.
The full id is a tuple containing all id's starting from
the top object (Structure) down to the current object. A full id for
a Residue object e.g. is something like:
("1abc", 0, "A", (" ", 10, "A"))
This corresponds to:
Structure with id "1abc"
Model with id 0
Chain with id "A"
Residue with id (" ", 10, "A")
The Residue id indicates that the residue is not a hetero-residue
(or a water) because it has a blank hetero field, that its sequence
identifier is 10 and its insertion code "A".
- get_id(self)
- Return the id.
- get_iterator(self)
- Return iterator over children.
- get_level(self)
- Return level in hierarchy.
A - atom
R - residue
C - chain
M - model
S - structure
- get_list(self)
- Return a copy of the list of children.
- get_parent(self)
- Return the parent Entity object.
- insert(self, pos, entity)
- Add a child to the Entity at a specified position.
- set_parent(self, entity)
- Set the parent Entity object.
- transform(self, rot, tran)
- Apply rotation and translation to the atomic coordinates.
:param rot: A right multiplying rotation matrix
:type rot: 3x3 Numeric array
:param tran: the translation vector
:type tran: size 3 Numeric array
Examples
--------
This is an incomplete but illustrative example::
from numpy import pi, array
from Bio.PDB.vectors import Vector, rotmat
rotation = rotmat(pi, Vector(1, 0, 0))
translation = array((0, 0, 1), 'f')
entity.transform(rotation, translation)
Data descriptors inherited from Bio.PDB.Entity.Entity:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- id
- Return identifier.
|
class UpdModel(Bio.PDB.Model.Model) |
| |
Custom model class that inherits attributes and methods from the biopython model class.
Considerations: it allows to have chains with repeated IDs. |
| |
- Method resolution order:
- UpdModel
- Bio.PDB.Model.Model
- Bio.PDB.Entity.Entity
- builtins.object
Methods defined here:
- add(self, entity)
- Adds a child to the Entity.
Methods inherited from Bio.PDB.Model.Model:
- __init__(self, id, serial_num=None)
- Initialize.
Arguments:
- id - int
- serial_num - int
- __repr__(self)
- Return model identifier.
- get_atoms(self)
- Return atoms.
- get_chains(self)
- Return chains.
- get_residues(self)
- Return residues.
Methods inherited from Bio.PDB.Entity.Entity:
- __contains__(self, id)
- Check if there is a child element with the given id.
- __delitem__(self, id)
- Remove a child.
- __eq__(self, other)
- Test for equality. This compares full_id including the IDs of all parents.
- __ge__(self, other)
- Test greater or equal.
- __getitem__(self, id)
- Return the child with given id.
- __gt__(self, other)
- Test greater than.
- __hash__(self)
- Hash method to allow uniqueness (set).
- __iter__(self)
- Iterate over children.
- __le__(self, other)
- Test less or equal.
- __len__(self)
- Return the number of children.
- __lt__(self, other)
- Test less than.
- __ne__(self, other)
- Test for inequality.
- copy(self)
- Copy entity recursively.
- detach_child(self, id)
- Remove a child.
- detach_parent(self)
- Detach the parent.
- get_full_id(self)
- Return the full id.
The full id is a tuple containing all id's starting from
the top object (Structure) down to the current object. A full id for
a Residue object e.g. is something like:
("1abc", 0, "A", (" ", 10, "A"))
This corresponds to:
Structure with id "1abc"
Model with id 0
Chain with id "A"
Residue with id (" ", 10, "A")
The Residue id indicates that the residue is not a hetero-residue
(or a water) because it has a blank hetero field, that its sequence
identifier is 10 and its insertion code "A".
- get_id(self)
- Return the id.
- get_iterator(self)
- Return iterator over children.
- get_level(self)
- Return level in hierarchy.
A - atom
R - residue
C - chain
M - model
S - structure
- get_list(self)
- Return a copy of the list of children.
- get_parent(self)
- Return the parent Entity object.
- has_id(self, id)
- Check if a child with given id exists.
- insert(self, pos, entity)
- Add a child to the Entity at a specified position.
- set_parent(self, entity)
- Set the parent Entity object.
- transform(self, rot, tran)
- Apply rotation and translation to the atomic coordinates.
:param rot: A right multiplying rotation matrix
:type rot: 3x3 Numeric array
:param tran: the translation vector
:type tran: size 3 Numeric array
Examples
--------
This is an incomplete but illustrative example::
from numpy import pi, array
from Bio.PDB.vectors import Vector, rotmat
rotation = rotmat(pi, Vector(1, 0, 0))
translation = array((0, 0, 1), 'f')
entity.transform(rotation, translation)
Data descriptors inherited from Bio.PDB.Entity.Entity:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- id
- Return identifier.
| |


Longview man killed, suspect arrested
LONGVIEW, Wash. (AP) - Police have arrested a 25-year-old man in the death of a Longview man.
KATU-TV reports on Sunday night, police went to a residence after a woman reported that she had found her 73-year-old landlord, Arthur Mahlum, dead.
Longview police say officers in Barstow, California arrested person of interest David J.E. Daniel Jr. on a warrant in that state Thursday.
Longview police said detectives flew to California, interviewed him and then arrested him, accusing him of shooting and killing Mahlum.
Daniel faces a charge of first-degree murder.
Police say Mahlum’s vehicle was found in Barstow.
It wasn’t known if Daniel has a lawyer.
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Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Twinkle Khanna visits grandmother Betty Kapadia in the hospital
by Poonam JainVeteran actor Dimple Kapadia’s mother, Betty Kapadia, was hospitalised on November 14. She was admitted to the ICU at the Hinduja hospital in Mumbai. Earlier in the day there were rumours of Dimple Kapadia being hospitalised after pictures of Twinkle Khanna and Akshay Kumar outside the same hospital were doing the rounds on the internet.
But Dimple refuted the rumours saying,"I am alive and kicking. It's my mother, who has been hospitalised. I don't want to talk about it.  She is better now. I need all the prayers and best wishes.”
Visiting grandmother Betty in the hospital, Twinkle was snapped outside. Check out the pictures below.


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{% if (tweets|length) == 0 %}
No tweets were found!
{% endif %} {% for tweet in tweets %} {% include "tweet.html" %} {% endfor %}


| Class |
Description |
|
ASTNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
AnnotatedNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
AnnotationNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
AstToTextHelper
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
ClassCodeExpressionTransformer
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
ClassCodeVisitorSupport
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
ClassHelper
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
ClassNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
CodeVisitorSupport
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
CompileUnit
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
ConstructorNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
DynamicVariable
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
EnumConstantClassNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
FieldNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
GenericsType
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
ImportNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
InnerClassNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
InterfaceHelperClassNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
MethodNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
MixinASTTransformation
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
MixinNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
ModuleNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
PackageNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
Parameter
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
PropertyNode
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |
|
VariableScope
|
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. |


This code suppprts entropy-merge discretization. This is an bottom-up entropy-based supervised clustering algorithm
that divided numerics into (say) 16 bins, then recursively merges adjacent bins in the splits are less informative than the combination.
At its start, this code uses recursive Fastmap to find a few best
examples, then a sample of the rest
. bins.lua
then prunes bins that have similar distributions in best
and `rest
This is the main file of hw5. All tests are run here and all the meat is created in the other files
I kept the main running the same for simplicty of automation on my side
from tests import *
import config
def main(options: dict, HELP, funs):
This function runs the tests specified by the user on the run (defined in -h or –help)
saved, fails, passes = {}, 0, 0
for k,v in cli(settings(HELP)).items():
options[k] = v
saved[k] = v
if options["help"]:
print(HELP)
else:
for what in funs:
if options["go"] == "all" or what == options["go"]:
Since I added the surveys as tests, this ignores them unless specified to keep them seperate
if options["go"] == "all" and ("Survey" in what or "Every" == what):
continue
for k,v in saved.items():
options[k] = v
config.Seed = options["seed"]
if funs[what]() == False:
fails = fails + 1
print("❌ fail:", "\t" + what)
else:
passes += 1
print("✅ pass:","\t" + what)
print("\n🔆 %s\n" % str({"pass": passes, "fail": fails, "success": 100*passes/(passes+fails)}))
exit(fails)
egs = {}
def eg(key,string,fun):
This is the base function for creating tests which will be run in main
global egs
egs[key]=fun
It also adds a way to call that test function into the help
config.Help = config.Help + (" -g %s\t%s\n" % (key,string))
eg("the", "show settings", lambda: oo(config.the))
eg("rand", "demo random number generation", checkRand)
eg("some","demo of reservoir sampling", checkSome)
eg("nums","demo of NUM", checkNums)
eg("syms","demo SYMS", checkSyms)
eg("csv","reading csv files", checkCsv)
eg("data", "showing data sets", checkData)
eg("clone","replicate structure of a DATA", checkClone)
eg("cliffs","stats tests", checkCliffs)
eg("dist","distance test", checkDist)
eg("half","divide data in halg", checkHalf)
eg("tree","make snd show tree of clusters", checkTree)
TODO Start here. Cliffs says its false for acc when it should be true. Need to check either that specifically or each of the “close” values I have ignored up to this point
eg("sway","optimizing", checkSway)
eg("bins", "find deltas between best and rest", checkBins)
main(config.the, config.Help, egs)


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In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
real_images_ = tf.placeholder(tf.float32,(None,image_width, image_height, image_channels))
z_random_ = tf.placeholder(tf.float32,(None, z_dim))
learning_rate_ = tf.placeholder(tf.float32)
return (real_images_, z_random_, learning_rate_)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
#input layer 28x28x3
x1 = tf.layers.conv2d(images, 64, 5, strides = 2, padding = 'same')
x1 = tf.maximum(0.2*x1, x1)
#layer now 14x14x64
x2 = tf.layers.conv2d(x1, 128, 5, strides = 2, padding = 'same')
x2 = tf.layers.batch_normalization(x2, training=True)
x2 = tf.maximum(0.2*x2, x2)
#layer now 7x7x128
x3 = tf.layers.conv2d(x2, 256, 5, strides = 2, padding = 'same')
x3 = tf.layers.batch_normalization(x3, training=True)
x3 = tf.maximum(0.2*x3, x3)
#layer now 4x4x256
# Flatten it
flat = tf.reshape(x3, (-1, 4*4*256))
logits = tf.layers.dense(flat,1)
out = tf.sigmoid(logits)
return (out,logits)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope('generator', reuse= not is_train):
z1 = tf.layers.dense(z,(7*7*512))
# Reshape it to start the convolutional stack
x1 = tf.reshape(z1, (-1, 7, 7, 512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(0.2 * x1, x1)
# 7x7x512 now
x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(0.2 * x2, x2)
# 14x14x256 now
logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
# 28x28x3 now
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth = 0.1
g_model = generator(input_z, out_channel_dim, is_train=True)
d_out_real, d_logits_real = discriminator(input_real, reuse=False)
d_out_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits = d_logits_real, labels = tf.ones_like(d_out_real)* (1 - smooth)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits = d_logits_fake, labels = tf.zeros_like(d_out_real)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits = d_logits_fake, labels = tf.ones_like(d_out_fake)))
d_loss = d_loss_real + d_loss_fake
return (d_loss,g_loss)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(d_loss, var_list = d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(g_loss, var_list = g_vars)
return (d_train_opt,g_train_opt)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
input_real, input_z, l_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, l_rate, beta1)
samples, losses = [], []
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps += 1
# TODO: Train Model
batch_images = batch_images * 2
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, l_rate: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, l_rate: learning_rate})
if steps % 10 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps %100 == 0:
show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 128
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


This is some text.
There are many functions on jQuery.
For instance
$("element").hover -> when mouse hovers over it
$("element").click -> when mouse clicks on it
$("element").html -> gets the HTML property of it.
$("element").attr("src", "http://www.ecowebhosting.co.uk/") -> to change an attribute INSIDE the element (for example, an iframe.


Preparation time
1-2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 6-8
Dietary
This posh trifle by James Martin combines homemade cake, pistachio nuts, kirsch and lashings of cream… Heaven.
For the custard, gently heat the milk, cream, vanilla pods and vanilla seeds in a saucepan.
In a bowl, whisk the sugar and egg yolks until pale and creamy.
When the milk mixture is just about to boil, pour it onto the egg mixture, whisking continuously until all of the liquid has been incorporated. Return the mixture to the pan and cook over a low heat, whisking all the time, until the custard has thickened enough to coat the back of a wooden spoon.
Strain the custard into a bowl and set aside to cool. Once cool, chill in the fridge for at least an hour.
Meanwhile, for the pistachio sponge, preheat the oven to 200C/180C Fan/Gas 6. Grease a 23cm/9in springform cake tin with a little melted butter, dust the insides with flour, then line the base with a circle of baking parchment.
Whisk the eggs and sugar in a large bowl for 4-5 minutes, or until pale, thick and creamy. Whisk in the pistachio paste and mix for a further minute, until completely incorporated.
Quickly but carefully, fold the flour and melted butter into the mixture. Pour the cake batter into the prepated tin and bake for 20-25 minutes, or until golden-brown and cooked through. (The cake is cooked through when a skewer inserted into the centre comes out clean.) Allow to cool in the tin for 10 minutes, then turn out onto a wire rack and set aside until competely cool.
Meanwhile, in a food processor, blend 300g/10½oz of the raspberries to a purée with two tablespoons of cold water. Set aside.
Toast the flaked almonds for a few seconds in a small, dry frying pan set over a medium heat. When they start to brown, tip them onto a plate to stop them from cooking further.
Whip the cream with the icing sugar until soft peaks form when the whisk is removed.
Cut the cooled pistachio cake into 2cm/1in-thick slices.
To assemble the trifle, arrange a layer of sponge pieces in the bottom of a large glass serving bowl. Drizzle over a little kirsch, then spread over some of the raspberry purée and sprinkle a handful of raspberries on top. Pour over a little of the cold custard. Repeat the layering process until all of the ingredients have been used up.
To finish, top the trifle with the whipped cream and sprinkle over the extra raspberries, the flaked almonds and the chopped pistachios. Serve immediately, or chill until ready to serve.


{% extends "jTemplate.html" %} {% block title %}Crossword Preview{% endblock %} {% block head %} {{ super() }} {% endblock %} {% block content %}
Select each box an type the letter you think should be there. When finished, you can check your answer or move straight on.


--- layout: post title: How mockito works inside category : lessons tags : [mockito] --- {% include JB/setup %}
public class CashRegister {
private final Printer printer;
public CashRegister(Printer printer) {
this.printer = printer;
}
public void process(Purchase purchase) {
printer.print(purchase.asString());
}
}
We can use mockito like this:
@Test
public void shouldCallAsStringInPurchaseAndPrintInPrinterWhenCashRegisterCallProcess() {
Purchase mockPurchase = mock(Purchase.class);
Printer mockPrinter = mock(Printer.class);
CashRegister cashRegister = new CashRegister(mockPrinter);
cashRegister.process(mockPurchase);
verify(mockPurchase).asString();
verify(mockPrinter).print(mockPurchase.asString());
}
The test case above is to verify the asString method in Purchase and the print method in Printer are called (of cause it is called, but we are just talking about how mockito mocks.)
So, how does mockito do this inside? We can implement by ourselves like this:
1. create a new class that extends the dependency class just for test.
public class PrinterMock extends Printer {
public boolean isPrinterUsed = false;
@Override
public void print(String printThis) {
isPrinterUsed = true;
}
}
2. verify the print method in Printer is used when call process method.
@Test
public void shouldUsePrinterWhenProcessIsCalled() {
PrinterMock printerMock = new PrinterMock();
PurchaseStub purchaseStub = new PurchaseStub(null);
CashRegister cashRegister = new CashRegister(printerMock);
cashRegister.process(purchaseStub);
assertTrue(printerMock.isPrinterUsed);
}
Mockito's implementation is more complex than the above shown, but the is theory the same.


Upset at league, Kaepernick moves scheduled tryout
by Reuters(Reuters) - Upset at what he called preconditions imposed by the National Football League, quarterback Colin Kaepernick moved his scheduled tryout on Saturday away from the Atlanta Falcons training facility.
The former starting quarterback for the San Francisco 49ers is seeking a return to the NFL three years after his protests against racial injustice roiled the sport.
Kaepernik, 32, accused the NFL of not acting in a forthright manner in organizing the workout and said the tryout would be held at another Atlanta location later on Saturday.
"From the outset, Mr. Kaepernick requested a legitimate process and from the outset the NFL league office has not provided one," a statement said.
"Most recently, the NFL has demanded that as a precondition to the workout, Mr. Kaepernick sign an unusual liability waiver that addresses employment-related issues and rejected the standard liability waiver from physical injury proposed by Mr. Kaepernick’s representatives."
The statement said Kaepernick had requested all media be allowed into the workout to observe and film it.
"The NFL denied this request,” it added
The NFL was not immediately available for comment.


Choose or create your party
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class Solution: def maxProfit(self, inventory: List[int], orders: int) -> int: l,r=0, max(inventory) mod = 10**9+7 while l<r: mid = (l+r+1)>>1 s = sum(max(x-mid,0) for x in inventory) if s>=orders: l = mid else: r = mid-1 # print(l) s = sum(max(x-l,0) for x in inventory) ans = 0 for c in inventory: if c>l: ans+= (l+1+c)*(c-l)//2 ans -= (s-orders)*(l+1) return ans%mod


golosICO - platform for holding ICO companies on the blockchain Golos! This platform is a thin client, that works without a backend (only frontend and blockchain) directly on the GitHub Pages (through CloudFlare).
We use:
Golos.js - the JavaScript API for Golos blockchain;
Bootstrap - the most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web;
jQuery - is a fast, small, and feature-rich JavaScript library;
SweetAlert2 - a beautiful, responsive, customizable, accessible replacement for JavaScript's popup boxes.


| Vendor Name: | MSC MAPLESOFT CONSULTING INC. |
|---|---|
| Reference Number: |<PHONE_NUMBER> |
| Contract Date: | 2009-08-25 |
| Description Of Work: | 446 Training Consultants |
| Contract Period: | 2009-10-19 to 2014-09-30 |
| Original Contract Value: | $713,615.89 |
| Contract Value: | $1,078,551.29 |
| Comments: | This contract includes one or more amendments. This contract was competitively sourced. This contract is a multi-year contract. Informatics Professional and Consulting Services - Training, EDP Systems |


Definition at line 5 of file PlayerStats.cs.
| void PlayerStats.setBeerCans |
( |
int |
beerCans | ) |
|
| void PlayerStats.setCash |
( |
int |
cash | ) |
|
| void PlayerStats.setExperience |
( |
int |
experience | ) |
|
| void PlayerStats.Start |
( |
| ) |
|
|
private |
Acquiring handle on text components.
Definition at line 34 of file PlayerStats.cs.
36 this.
playerStatText = this.gameObject.GetComponentsInChildren<Text>();
| void PlayerStats.Update |
( |
| ) |
|
|
private |
Updating text components with new values.
Definition at line 45 of file PlayerStats.cs.
| Text PlayerStats.beerCansText |
|
private |
| Text PlayerStats.cashText |
|
private |
| int PlayerStats.experience |
|
private |
| Text PlayerStats.experienceText |
|
private |
| Text [] PlayerStats.playerStatText |
|
private |
The documentation for this class was generated from the following file:


Sync Adapter
Without doubt, your app will communicate with the server and fetch some data for the the users to view, unless you have some extraordinary idea that is self sufficient within the app. Most likely, you will end up making frequent server calls to sync data. Here is where you will find the Sync adapter useful. For the Sync adapter you need an Account authenticator, which I have explained in my previous blog post. If you have not implemented an account authenticator, please do so. For someday when you end up sitting across Larry or Mark from you know where, you will thank me silently for the extra couple of billions that feature adds to your deal. I take 100% commission in the form of retweets and likes.
However, if you do not deal with user accounts and still want to use the Sync adapter, this post will show you how to create one. I will assume you have not incorporated an account authenticator. So, I will show the stub implementations
Why implement a Sync Adapter
Well there are many ways to sync data with server calls, but when you have a tool called Sync adapter, that should be sufficient enough for you to use it ;-). Well if not, then consider this, Sync adapter usage is encouraged by Google, because it provides substantial savings in battery power. This is accomplished by clubbing together many server calls. Whenever you make a server call, the device needs to wake up the radio (if not already awake). This causes some battery strain. The Sync adapter tries to reduce the battery strain by having the sync call performed at a time that is beneficial for most of the apps, so that radio is not switched on and off again and for different calls from different apps. One call to rule them all. So let’s get started. Create a android application with a MainActivity.java.
Step 1: Create a stub Account authenticator, authenticator service
This is what the previous tutorial was all about. If you followed that skip this step. Else, create the following two files:
Create the corresponding XML files and prefs files. Don’t forget to add this info. in the manifest
Step 2: Create a stub ContentProvider
Now, for the sync adapter to run, you will need a content provider. That is a big topic in itself, so go ahead and create one, I will wait for you…still waiting. Naah, just kidding, you can create a stub provider too:
Make sure to add this to the manifest
Step 3: Create a Sync Adapter
This is the end of stubby stuff. This is where we create SyncAdapter.java. The main sync operation happens from onPerformSync which will be called from the MainActivity.java
public void onPerformSync(
Account account,
Bundle extras,
String authority,
ContentProviderClient provider,
SyncResult syncResult) {
/*
* Put the data transfer code here.
*/
}
This is where you will perform the network operations, database calls. This happens on a separate thread, so you will not be blocking the UI thread. The following is a link to the file:
Step 4: Create the Sync service to call the Sync Adapter
The sync service is a service that will be running the sync adapter for you:
Make sure to add this to the manifest.
Step 5: Call Sync Adapter from main
First of write a method to create your account in the Sync adapter.
public static Account CreateSyncAccount(Context context) {
Account newAccount = new Account(
ACCOUNT, ACCOUNT_TYPE);
AccountManager accountManager =
(AccountManager) context.getSystemService(
ACCOUNT_SERVICE);
/*
* Add the account and account type, no password or user data
* If successful, return the Account object, otherwise report an error.
*/
if (accountManager.addAccountExplicitly(newAccount, null, null)) {
/*
* If you don't set android:syncable="true" in
* in your <provider> element in the manifest,
* then call context.setIsSyncable(account, AUTHORITY, 1)
* here.
*/
} else {
/*
* The account exists or some other error occurred. Log this, report it,
* or handle it internally.
*/
}
return newAccount;
}
Almost there. Now create the account and content resolver in onCreate and call the syncadapter as follows:
mAccount = CreateSyncAccount(this);
mResolver = getContentResolver();
Bundle syncSettingsBundle = new Bundle();
syncSettingsBundle.putBoolean(ContentResolver.SYNC_EXTRAS_MANUAL,
true);
syncSettingsBundle.putBoolean(ContentResolver.SYNC_EXTRAS_EXPEDITED,
true);
ContentResolver.requestSync(mAccount, AUTHORITY, syncSettingsBundle);
ContentResolver.setSyncAutomatically(mAccount, AUTHORITY, true);
ContentResolver.addPeriodicSync(
mAccount,
AUTHORITY,
Bundle.EMPTY,
60L);
Yes, you are done. That’s it. Five steps and you have implemented your sync adapter. Feel free to download the sample app from:
https://github.com/sastagi/primesyncdemo
The following are good links to learn more about sync adapter.


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object java.util.BitSet
public class BitSet
This class implements a vector of bits that grows as needed. Each
component of the bit set has a boolean
value. The
bits of a BitSet
are indexed by nonnegative integers.
Individual indexed bits can be examined, set, or cleared. One
BitSet
may be used to modify the contents of another
BitSet
through logical AND, logical inclusive OR, and
logical exclusive OR operations.
By default, all bits in the set initially have the value
false
.
Every bit set has a current size, which is the number of bits of space currently in use by the bit set. Note that the size is related to the implementation of a bit set, so it may change with implementation. The length of a bit set relates to logical length of a bit set and is defined independently of implementation.
Unless otherwise noted, passing a null parameter to any of the
methods in a BitSet
will result in a
NullPointerException
.
A BitSet
is not safe for multithreaded use without
external synchronization.
| Constructor Summary | |
|---|---|
BitSet()
Creates a new bit set. |
|
BitSet(int nbits)
Creates a bit set whose initial size is large enough to explicitly represent bits with indices in the range 0 through
nbits-1 . |
| Method Summary | |
|---|---|
void |
and(BitSet set)
Performs a logical AND of this target bit set with the argument bit set. |
void |
andNot(BitSet set)
Clears all of the bits in this BitSet whose corresponding
bit is set in the specified BitSet . |
int |
cardinality()
Returns the number of bits set to true in this BitSet . |
void |
clear()
Sets all of the bits in this BitSet to false . |
void |
clear(int bitIndex)
Sets the bit specified by the index to false . |
void |
clear(int fromIndex,
int toIndex)
Sets the bits from the specified fromIndex(inclusive) to the specified toIndex(exclusive) to false . |
Object |
clone()
Cloning this BitSet produces a new BitSet
that is equal to it. |
boolean |
equals(Object obj)
Compares this object against the specified object. |
void |
flip(int bitIndex)
Sets the bit at the specified index to the complement of its current value. |
void |
flip(int fromIndex,
int toIndex)
Sets each bit from the specified fromIndex(inclusive) to the specified toIndex(exclusive) to the complement of its current value. |
boolean |
get(int bitIndex)
Returns the value of the bit with the specified index. |
BitSet |
get(int fromIndex,
int toIndex)
Returns a new BitSet composed of bits from this BitSet from fromIndex(inclusive) to toIndex(exclusive). |
int |
hashCode()
Returns a hash code value for this bit set. |
boolean |
intersects(BitSet set)
Returns true if the specified BitSet has any bits set to
true that are also set to true in this
BitSet . |
boolean |
isEmpty()
Returns true if this BitSet contains no bits that are set
to true . |
int |
length()
Returns the "logical size" of this BitSet : the index of
the highest set bit in the BitSet plus one. |
int |
nextClearBit(int fromIndex)
Returns the index of the first bit that is set to false
that occurs on or after the specified starting index. |
int |
nextSetBit(int fromIndex)
Returns the index of the first bit that is set to true
that occurs on or after the specified starting index. |
void |
or(BitSet set)
Performs a logical OR of this bit set with the bit set argument. |
void |
set(int bitIndex)
Sets the bit at the specified index to true . |
void |
set(int bitIndex,
boolean value)
Sets the bit at the specified index to the specified value. |
void |
set(int fromIndex,
int toIndex)
Sets the bits from the specified fromIndex(inclusive) to the specified toIndex(exclusive) to true . |
void |
set(int fromIndex,
int toIndex,
boolean value)
Sets the bits from the specified fromIndex(inclusive) to the specified toIndex(exclusive) to the specified value. |
int |
size()
Returns the number of bits of space actually in use by this BitSet to represent bit values. |
String |
toString()
Returns a string representation of this bit set. |
void |
xor(BitSet set)
Performs a logical XOR of this bit set with the bit set argument. |
| Methods inherited from class java.lang.Object |
|---|
finalize, getClass, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public BitSet()
false
.
public BitSet(int nbits)
0
through
nbits-1
. All bits are initially false
.
nbits
- the initial size of the bit set.
NegativeArraySizeException
- if the specified initial size
is negative.| Method Detail |
|---|
public void flip(int bitIndex)
bitIndex
- the index of the bit to flip.
IndexOutOfBoundsException
- if the specified index is negative.public void flip(int fromIndex, int toIndex)
fromIndex
- index of the first bit to flip.toIndex
- index after the last bit to flip.
IndexOutOfBoundsException
- if fromIndex is negative,
or toIndex is negative, or fromIndex is
larger than toIndex.public void set(int bitIndex)
true
.
bitIndex
- a bit index.
IndexOutOfBoundsException
- if the specified index is negative.public void set(int bitIndex, boolean value)
bitIndex
- a bit index.value
- a boolean value to set.
IndexOutOfBoundsException
- if the specified index is negative.public void set(int fromIndex, int toIndex)
true
.
fromIndex
- index of the first bit to be set.toIndex
- index after the last bit to be set.
IndexOutOfBoundsException
- if fromIndex is negative,
or toIndex is negative, or fromIndex is
larger than toIndex.public void set(int fromIndex, int toIndex, boolean value)
fromIndex
- index of the first bit to be set.toIndex
- index after the last bit to be setvalue
- value to set the selected bits to
IndexOutOfBoundsException
- if fromIndex is negative,
or toIndex is negative, or fromIndex is
larger than toIndex.public void clear(int bitIndex)
false
.
bitIndex
- the index of the bit to be cleared.
IndexOutOfBoundsException
- if the specified index is negative.public void clear(int fromIndex, int toIndex)
false
.
fromIndex
- index of the first bit to be cleared.toIndex
- index after the last bit to be cleared.
IndexOutOfBoundsException
- if fromIndex is negative,
or toIndex is negative, or fromIndex is
larger than toIndex.public void clear()
false
.
public boolean get(int bitIndex)
true
if the bit with the index bitIndex
is currently set in this BitSet
; otherwise, the result
is false
.
bitIndex
- the bit index.
IndexOutOfBoundsException
- if the specified index is negative.public BitSet get(int fromIndex, int toIndex)
fromIndex
- index of the first bit to include.toIndex
- index after the last bit to include.
IndexOutOfBoundsException
- if fromIndex is negative,
or toIndex is negative, or fromIndex is
larger than toIndex.public int nextSetBit(int fromIndex)
true
that occurs on or after the specified starting index. If no such
bit exists then -1 is returned.
To iterate over the true
bits in a BitSet
,
use the following loop:
for(int i=bs.nextSetBit(0); i>=0; i=bs.nextSetBit(i+1)) {
// operate on index i here
}
fromIndex
- the index to start checking from (inclusive).
IndexOutOfBoundsException
- if the specified index is negative.public int nextClearBit(int fromIndex)
false
that occurs on or after the specified starting index.
fromIndex
- the index to start checking from (inclusive).
IndexOutOfBoundsException
- if the specified index is negative.public int length()
BitSet
: the index of
the highest set bit in the BitSet
plus one. Returns zero
if the BitSet
contains no set bits.
BitSet
.public boolean isEmpty()
BitSet
contains no bits that are set
to true
.
BitSet
is empty.public boolean intersects(BitSet set)
BitSet
has any bits set to
true
that are also set to true
in this
BitSet
.
set
- BitSet
to intersect with
BitSet
intersects
the specified BitSet
.public int cardinality()
BitSet
.
BitSet
.public void and(BitSet set)
true
if and only if it both initially
had the value true
and the corresponding bit in the
bit set argument also had the value true
.
set
- a bit set.public void or(BitSet set)
true
if and only if it either already had the
value true
or the corresponding bit in the bit set
argument has the value true
.
set
- a bit set.public void xor(BitSet set)
true
if and only if one of the following
statements holds:
true
, and the
corresponding bit in the argument has the value false
.
false
, and the
corresponding bit in the argument has the value true
.
set
- a bit set.public void andNot(BitSet set)
BitSet
whose corresponding
bit is set in the specified BitSet
.
set
- the BitSet
with which to mask this
BitSet
.public int hashCode()
BitSet
. The algorithm used to compute it may
be described as follows.
Suppose the bits in the BitSet
were to be stored
in an array of long
integers called, say,
bits
, in such a manner that bit k
is
set in the BitSet
(for nonnegative values of
k
) if and only if the expression
((k>>6) < bits.length) && ((bits[k>>6] & (1L << (bit & 0x3F))) != 0)is true. Then the following definition of the
hashCode
method would be a correct implementation of the actual algorithm:
public int hashCode() { long h = 1234; for (int i = bits.length; --i >= 0; ) { h ^= bits[i] * (i + 1); } return (int)((h >> 32) ^ h); }Note that the hash code values change if the set of bits is altered.
Overrides the hashCode
method of Object
.
hashCode
in class Object
Object.equals(java.lang.Object)
,
Hashtable
public int size()
BitSet
to represent bit values.
The maximum element in the set is the size - 1st element.
public boolean equals(Object obj)
true
if and only if the argument is
not null
and is a Bitset
object that has
exactly the same set of bits set to true
as this bit
set. That is, for every nonnegative int
index k
,
((BitSet)obj).get(k) == this.get(k)must be true. The current sizes of the two bit sets are not compared.
Overrides the equals
method of Object
.
equals
in class Object
obj
- the object to compare with.
true
if the objects are the same;
false
otherwise.size()
public Object clone()
BitSet
produces a new BitSet
that is equal to it.
The clone of the bit set is another bit set that has exactly the
same bits set to true
as this bit set and the same
current size.
Overrides the clone
method of Object
.
clone
in class Object
size()
public String toString()
BitSet
contains a bit in the set
state, the decimal representation of that index is included in
the result. Such indices are listed in order from lowest to
highest, separated by ", " (a comma and a space) and
surrounded by braces, resulting in the usual mathematical
notation for a set of integers.
Overrides the toString
method of Object
.
Example:
BitSet drPepper = new BitSet();Now
drPepper.toString()
returns "{}
".
drPepper.set(2);Now
drPepper.toString()
returns "{2}
".
drPepper.set(4); drPepper.set(10);Now
drPepper.toString()
returns "{2, 4, 10}
".
toString
in class Object
|
JavaTM 2 Platform Standard Ed. 5.0 |
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


Leeds United considering bid for Burnley forward Matej Vydra?
Burnley forward Matej Vydra has reportedly emerged as a potential January transfer target for Leeds United.
by Darren PlantWhile Leeds currently sit in second position in the Championship table, the Yorkshire giants have only scored 23 goals from their 17 matches.
Despite having Patrick Bamford and Eddie Nketiah at his disposal, Marcelo Bielsa is said to be keen on strengthening in the final third at the beginning of 2020.
According to The Sun, the Argentine may be prepared to launch a £7.5m offer for Vydra, who is out-of-favour at Turf Moor.
The 27-year-old has only been provided with 58 minutes of Premier League football this season, which have come from four outings as a substitute.
While the Czech Republic international still has over 18 months remaining on his contract, the Clarets may be prepared to cash in on the attacker.
body check tags ::


{% extends "_base.html" %} {% block content %}
You will be adding the following permission to these people:
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This is a facinating city full of narrow lanes and medieval buildings. It has many specialist and unusual shops. A good place from which to visit Pompeii and Vesuvius.
Now that the Marina here is partially completed and the breakwater extension complete, this is a very good winter stopover port being so close to the Amalfi coast.
An extension of the Molo Di Levante breakwater is now complete making Marina d’Arechi a very well protected marina with good facilities and very helpful staff. About half the pontoons are completed with very good water pressure and power on each. There is also a good restaurant in the marina and cocktail bar. Security is reported to be excellent with gate codes on each pontoon and on the main gate. There are plans to add a lot more to the marina, but no fixed timescale, and latest reports are that construction is still on-going.
The disadvantage is that it is a drive to Salerno and to the nearest supermarkets. The marina organises car hire (which delivers to the marina) and taxis and there are bicycles which can be used free of charge.
Last updated June 2015.


This augmenter requires some keywords to look for a matching video. You can use the "dynamic" value of any of the target Web app's properties, or you can write a fixed value.