--- layout: default permalink: /modules/ title: Modules description: Extending the functionality of YaST across a variety of modules. columns: - categories: - title: "Network services" modules: - name: yast-auth-server url: http://github.com/yast/yast-auth-server desc: Configuration of server providing various authentication methods like Kerberos, LDAP or SSSD. - name: yast-dhcp-server url: http://github.com/yast/yast-dhcp-server desc: Configuration of DHCP server, including LDAP-related settings. - name: yast-dns-server url: http://github.com/yast/yast-dns-server desc: Comprehensive configuration of BIND DNS server. - name: yast-ftp-server url: http://github.com/yast/yast-ftp-server desc: Configuration of Pure-FTPd and vsftpd. - name: yast-http-server url: http://github.com/yast/yast-http-server desc: Comprehensive configuration of Apache2. - name: yast-instserver url: http://github.com/yast/yast-instserver desc: Configures installation server (NFS, HTTP or FTP) providing installation repositories. - name: yast-mail url: http://github.com/yast/yast-mail desc: Mail configuration. - name: yast-nfs-server url: http://github.com/yast/yast-nfs-server desc: Exports directories over NFS network protocol. - name: yast-nis-server url: http://github.com/yast/yast-nis-server desc: Server configuration for network information services (NIS, YP). - name: yast-samba-server url: http://github.com/yast/yast-samba-server desc: Server side of a file and printing services using Samba. - name: yast-slp-server url: http://github.com/yast/yast-slp-server desc: Configures Service Location Protocol server (OpenSLP). - name: yast-squid url: http://github.com/yast/yast-squid desc: Configures Squid proxy service. - name: yast-tftp-server url: http://github.com/yast/yast-tftp-server desc: Configures TFTP server (used for booting over network). - title: "Infrastructure & deployment" modules: - name: yast-add-on url: http://github.com/yast/yast-add-on desc: Allows to add/remove add-ons for base system. - name: yast-cluster url: http://github.com/yast/yast-cluster desc: Management of settings for high availability clusters. - name: yast-configuration-management url: http://github.com/yast/yast-configuration-management desc: Integration with Configuration Management Systems like Salt or Puppet. - name: yast-crowbar url: http://github.com/yast/yast-crowbar desc: Configuration of Crowbar, the orchestration tool used by SUSE Cloud. - name: yast-docker url: http://github.com/yast/yast-docker desc: Management of Docker daemon, images, and containers. - name: yast-drbd url: http://github.com/yast/yast-drbd desc: Configuration of Distributed Replicated Block Devices, a distributed storage system frequently used on high availability (HA) clusters. - name: yast-firstboot url: http://github.com/yast/yast-firstboot desc: Allows system administrators to setup a wizard to execute on the first run of a system. Useful for image-based deployments. - name: yast-geo-cluster url: http://github.com/yast/yast-geo-cluster desc: Management of Geo Clustering for high availability. - name: yast-iplb url: http://github.com/yast/yast-iplb desc: Configures IP load balancing. - name: yast-opensuse-mirror url: http://github.com/yast/yast-opensuse-mirror desc: Configures mirroring of openSUSE repositories locally. - name: yast-pos-installation url: http://github.com/yast/yast-pos-installation desc: Takes care of SUSE POS installation and upgrade. - name: yast-vm url: http://github.com/yast/yast-vm desc: Installs tools for using XEN or KVM virtualization. - title: "Development & diagnosis" modules: - name: openwsman-yast url: http://github.com/yast/yast-openwsman-yast desc: Remote access to YaST capabilities via an openwsman plugin, enabling the creation of new interfaces. - name: yast-cim url: http://github.com/yast/yast-cim desc: CIM API for YaST developers. - name: yast-dbus-server url: http://github.com/yast/yast-dbus-server desc: YaST DBus service providing DBus access to YaST components. - name: yast-journal url: http://github.com/yast/yast-journal desc: Displays the systemd journal in a convenient and user-friendly way. - name: yast-kdump url: http://github.com/yast/yast-kdump desc: Configures kdump (kernel dump) tool. - name: yast-ldap url: http://github.com/yast/yast-ldap desc: Allows YaST components to work with LDAP. It enables searching the LDAP tree and managing items on an LDAP server. - name: yast-packager url: http://github.com/yast/yast-packager desc: Libraries and modules for software management. - name: yast-support url: http://github.com/yast/yast-support desc: Allows you to collect system information for installation support in a standardized format. - name: yast-transfer url: http://github.com/yast/yast-transfer desc: A YaST agent for various transfer protocols like FTP, HTTP and TFTP. - name: yast-x11 url: http://github.com/yast/yast-x11 desc: Contains the programs and files for YaST2 X11 support. - name: yast-xml url: http://github.com/yast/yast-xml desc: The YaST XML agent. - categories: - title: "System configuration" modules: - name: yast-apparmor url: http://github.com/yast/yast-apparmor desc: Apparmor profile management. - name: yast-auth-client url: http://github.com/yast/yast-auth-client desc: Configuration of various authentication methods for system like Kerberos, LDAP or SSSD. - name: yast-bootloader url: http://github.com/yast/yast-bootloader desc: Bootloader installation and configuration. - name: yast-cio url: http://github.com/yast/yast-cio desc: Management of blacklisting channels for Input/Output for s390x. - name: yast-country url: http://github.com/yast/yast-country desc: Configuration of localization specific settings like language, keyboard layout, timezone, etc. - name: yast-fcoe-client url: http://github.com/yast/yast-fcoe-client desc: Settings to connect to a Fibre Channel over Ethernet (FCoE) network. - name: yast-firewall url: http://github.com/yast/yast-firewall desc: Management of SUSE Firewall. - name: yast-fonts url: http://github.com/yast/yast-fonts desc: Management of fontconfig settings to control font appearance, including font rendering algorithm and font family preference. - name: yast-iscsi-client url: http://github.com/yast/yast-iscsi-client desc: Configures iSCSI client. - name: yast-iscsi-lio-server url: http://github.com/yast/yast-iscsi-lio-server desc: Configures iSCSI LIO target. - name: yast-isns url: http://github.com/yast/yast-isns desc: Configures iSNS. - name: yast-multipath url: http://github.com/yast/yast-multipath desc: Configures multipath devices. - name: yast-network url: http://github.com/yast/yast-network desc: Configures network devices. - name: yast-nfs-client url: http://github.com/yast/yast-nfs-client desc: Configures mounts NFS directories from NFS servers. - name: yast-nis-client url: http://github.com/yast/yast-nis-client desc: Client side of network information services (NIS, YP), such as user and group information, and hostnames. - name: yast-ntp-client url: http://github.com/yast/yast-ntp-client desc: Time synchronization over network (NTP). - name: yast-online-update url: http://github.com/yast/yast-online-update desc: Updates your SUSE with the latest patches from online repositories. - name: yast-pam url: http://github.com/yast/yast-pam desc: User authentication plug-ins configuration (PAM). - name: yast-printer url: http://github.com/yast/yast-printer desc: Local and remote printing configuration. - name: yast-proxy url: http://github.com/yast/yast-proxy desc: Configures your system to use HTTP and FTP proxy. - name: yast-rdp url: http://github.com/yast/yast-rdp desc: Configure your graphical interface to allow remote connection via RDP. - name: yast-rear url: http://github.com/yast/yast-rear desc: Configure backup using Relax and Recover (ReaR). - name: yast-registration url: http://github.com/yast/yast-registration desc: Allows registering the system to the SUSE Customer Center (SCC) or Subscription Management Tool (SMT) server to receive security updates, extra software and customer support (SUSE Linux Enterprise only). - name: yast-reipl url: http://github.com/yast/yast-reipl desc: IBM zSeries specific configuration (loading IPL (initial program loader) from a running system. - name: yast-s390 url: http://github.com/yast/yast-s390 desc: IBM zSeries specific configuration (DASD disks, ZFCP disks, IUCV terminal, ...). - name: yast-samba-client url: http://github.com/yast/yast-samba-client desc: Client configuration for using file and printing services over SMB/CIFS networking protocol provided by Windows or Samba server. - name: yast-scanner url: http://github.com/yast/yast-scanner desc: Configures local (SCSI, USB) or network scanners. - name: yast-security url: http://github.com/yast/yast-security desc: Security settings (password and login settings, kernel options...) - name: yast-services-manager url: http://github.com/yast/yast-services-manager desc: Configures system services started at boot (systemd services). - name: yast-snapper url: http://github.com/yast/yast-snapper desc: Configures snapper (the Btrfs snapshotting tool), can browse snapshots. - name: yast-sound url: http://github.com/yast/yast-sound desc: Configures sound cards (usually needed just in some special cases, autoconfiguration should work out-of-box). - name: yast-storage-ng url: http://github.com/yast/yast-storage-ng desc: Disk and partition management. - name: yast-sudo url: http://github.com/yast/yast-sudo desc: Configures the sudo tool (to execute commands as another user or as root). - name: yast-sysconfig url: http://github.com/yast/yast-sysconfig desc: Editor for /etc/sysconfig files. - name: yast-tune url: http://github.com/yast/yast-tune desc: Hardware detection and expert system tuning. - name: yast-users url: http://github.com/yast/yast-users desc: Tool for managing system users and groups. ---


| Interface | Description |
|---|---|
| CloseRegistry |
Registry of
AutoCloseable objects. |
| DatabaseService |
A service that supplies the DataBase Connection
|
| SQLContext |
This encodes the differences between different databases and
dialects of SQL.
|
| Class | Description |
|---|---|
| DefaultDataBaseService |
Default implementation of the
DatabaseService
This gets connection parameters from the ConfigService but this is only queried
at the point where a database connection is required. |
| JNDIDatabaseService |
A
DatabaseService that also looks for a pooled connection via JNDID. |
| MysqlSQLContext | |
| PostgresqlSQLContext | |
| WrappedDatabaseService |
A
DatabaseService wrapper that can beused to generate a tabe
showing the current active connections |
DatabaseService


--- layout: post title: How news organizations can harness the sharing economy to reclaim the classifieds market date: 2014-05-01 21:10:45.000000000 +05:30 categories: [] tags: [] status: draft type: post published: false meta: {} author: login: cvlewis email<EMAIL_ADDRESS>display_name: Carl V. Lewis first_name: Carl V. last_name: Lewis excerpt: !ruby/object:Hpricot::Doc options: {} ---
For nearly a decade, newspaper executives have bemoaned the loss of the local classified ad market.


Settings
Clock Display:
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About Yellow Light
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Yellow Light was built by a small, easy-going team of designers and developers who simply want to encourage others to occassionally slow down and take a break.
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Before Change
if isinstance(base_feat.primitive, AddNumeric):
num_add_as_base_feat += 1
assert num_add_feats == 3
assert num_add_as_base_feat == 9
def test_transform_consistency():
After Change
max_depth=3)
feats = dfs_obj.build_features()
add_feats = [f for f in feats if isinstance(f.primitive, AddNumeric)]
// Check that there are no two AddNumeric features with the same base
// features.
unordered_args = set()
for f in add_feats:
arg1, arg2 = f.base_features
args_set = frozenset({arg1.unique_name(), arg2.unique_name()})
unordered_args.add(args_set)
assert len(add_feats) == len(unordered_args)
def test_transform_consistency():


Help with filling in the Attendance Allowance form
Filling in the Attendance Allowance form can be difficult - there are some personal questions which can be emotionally draining. Don't worry though - help is available and you don't have to fill in the form alone.
A specialist at your nearest Citizens Advice can help you with the form, or even fill it in for you.
The form is 31 pages and can take a long time to fill in so you might need to ask for an appointment if you want help from your nearest Citizens Advice.
You can ask a friend, relative or carer if you'd rather they help you with the form.
If you're terminally ill you'll only need to answer some of the questions. Read more about .
What you should write about on the formAdd reference
The Department for Work and Pensions (DWP) will use your form to decide whether to give you Attendance Allowance. They'll be looking to see:
- how much help you need
- how often you need help
- what sort of help you need
You don't have to be getting any help at the moment - the important thing is that you need it.
Questions 27-43 ask about your care needs with personal tasks. It's really important that you use the blank boxes to explain if you need:
- someone to help you with personal tasks - for example washing, getting out of bed or getting dressed - at least 3 times during the day
- someone to supervise you throughout the day to make sure you stay safe - for example to stop you falling or to look after you if you have seizures or blackouts
- someone to help you with personal tasks repeatedly (2 or more times) during the night or just once if it's for 20 minutes or more - for example if you need help getting out of bed, going to the toilet or changing the sheets if you have an accident
- someone to watch over you or supervise you during the night to make sure you're safe - they need to help you at least 3 times, or just once if it's for 20 minutes or more, for example in case you have a fit while you're asleep, or to stop you falling over if you have to get up
Keep a diaryAdd reference
It's worth keeping a diary of your needs for at least a week before you fill in the form. You can use our diary template.
The diary can give you a good idea of your 'care needs' - this is the help you need to complete personal tasks.
Help with personal tasksdoes not mean help with things like housework or gardening or other tasks around the home.
Personal tasks means things like:
- getting in or out of bed
- having a bath or shower
- getting dressed
- being reminded or encouraged to eat or drink
Remember to include any help you need during the night too.
You should also write on the diary how many times you need help with tasks like getting out of chairs.
If you get help to fill in the form from Contact your nearest Citizens Advice , remember to take your diary with you.
Tips for filling in the formAdd reference
You should read all these tips before you start filling in your form.
Write in the blank boxesAdd reference
It's really important that you explain the help or supervision you need in the blank boxes for Q27-43. Don't just tick the boxes or write how many times you need help or supervision.
Don't expect the person making the decision to know about your conditionAdd reference
The person making a decision on Attendance Allowance won't be a medical expert, so don't assume they'll know about your condition. It's important you give as much information as possible on the form about how much help you need. Don't think that any detail is too small to include. Use an extra sheet of paper if you need to.
Remember to say how many times you need help with tasksAdd reference
It's really important that you explain how many times you need or get help each day for the 'care needs' questions. For example if you need help getting to and from the toilet, remember to write the total amount for the day. Filling in our diary template will help you.
It's fine to repeat yourself in different answersAdd reference
You might feel like you're repeating some of your answers. It's fine to write about the same thing again if it's relevant to more than one question. For example if you have mobility problems and have difficulty getting to the toilet, you should explain this in Q28. You should also explain you have difficulty getting to the toilet in Q31 - moving around indoors.
Mention the adaptations you use in the relevant questionAdd reference
You're asked to list all the adaptations and aids you use in Q25 but you should also write about them in the relevant 'care needs' questions. For example if you use a grab rail to get in and out of the shower you should also mention it in Q29.
Explain if you have good days and bad daysAdd reference
If your illness or disability means you have good and bad days try to explain how many of each you have in an average week. Filling in our diary template can help you do this. Explain what help you need on a good day and on a bad day.
Explain if tasks take you longerAdd reference
If you can do some personal tasks on your own but they take you a long time, explain this on the form. It might help if you can compare how long it takes you to a friend or someone else you know who doesn't have your condition.
Remember to write about the help you needAdd reference
Remember you don't actually have to be getting any help to get Attendance Allowance - the important thing is that you need it and that you explain why you need it on the form.
Example answers for Q27-29Add reference
You'll need to write in a lot of blank boxes on the Attendance Allowance form. Read our examples to help you get an idea of what to write.
Example answer for Q27
James has depression and his wife, Doreen, has to encourage him to get out of bed in the morning.
He writes in the blank box for Q27: 'My depression makes me feel like it's not worth getting out of bed in the morning. On a bad day - about 3 times a week - I'm awake at 4am and can't get back to sleep. I don't want to get up but I don't want to stay in bed either - I just don't want to do anything. Doreen will talk to me and encourage me to get up and tell me what we can do that day if I feel up to it. If Doreen's out visiting a friend I'll just lay there until she comes home as I can't face getting up.
On a good day, Doreen will still need to encourage me to do things like have dinner or go to bed - otherwise I'll just stay up until 3am because I'm worried I won't be able to sleep again and it will make my depression worse."
Example answer for Q28
Judith has dementia and is sometimes incontinent during the night. Her husband, Jeremy helps her.
She writes in the blank box for Q28: "Sometimes I don't realise I need to go to the toilet when I'm asleep and I have an accident. Jeremy sleeps in the same room and knows when it happens as I get upset when I realise. He helps me get out of bed and into the shower. He then helps me put on some clean pyjamas and he changes the sheets. He also puts the washing machine on so that the sheets are clean in the morning and he'll then sit by my bed comforting me until I go back to sleep. All this usually takes about 45 minutes - I don't know what I'd do if Jeremy didn't help me."
Example answer for Q29
Sue has arthritis in her knees. Her niece, Rose, helps her shower in the morning.
She writes in the blank box for Q29: "I struggle to get in and out of the shower by myself. The last time I did I slipped over and banged my head. I've got a grab rail to hold while I'm in the shower but Rose still has to help me get up the step to get in and then help me down when I get out. She helps me 5 days a week - on the days she doesn't come I don't shower - it would take me too long to get in and out by myself and I'm worried I'll have another fall. I'm getting a special shower seat fitted to help while I'm washing but I'll still need help up and down the step."
Get someone who knows you to fill in Q49Add reference
Q49 on the form asks for a 'statement from someone who knows you'. You don't have to get someone to fill it in but it's best if you do. It can be anyone who knows about your illness or disability and how it affects your ability to do personal tasks.
It's best if you can get a healthcare professional you've seen recently to fill in Q49 - for example a doctor, consultant or Macmillan nurse.
If you can't get a healthcare professional you can ask a friend, relative or carer. Before they write on the form, talk to them about how your illness or disability affects you. Tell them that the DWP wants to know how much help you need with personal tasks. It's worth asking them to read the advice on this page before they answer Q49.
The box for Q49 is quite small - if they run out of space you can get them to continue on a separate sheet of paper. You can use our template for this.
Before you send the formAdd reference
Read through the form again before you send it to make sure you've answered everything properly.
Make a copy of the form in case you need to refer to it later, or in case it gets lost.


JIRA Projects
An overview of all created project.Form Jira Project all required fields for creating new project entry
Project Tickets All tickets created.
The Buttons extension for DataTables provides a common set of options, API methods and styling to display buttons on a page that will interact with a DataTable. The core library provides the based framework upon which plug-ins can built.
| Id | Name | ShortName | Created On | Updated On |
|---|


This Activity extends to Preference Activity, and implements all the settings required by the app.
More...
Inherits PreferenceActivity.
|
| void | onCreate (Bundle savedInstanceState) |
| | The method is executed first when the activity is created. More...
|
| |
| void | onPostCreate (Bundle savedInstanceState) |
| |
|
| void | setupSimplePreferencesScreen () |
| | Shows the simplified settings UI if the device configuration if the device configuration dictates that a simplified, single-pane UI should be shown. More...
|
| |
This Activity extends to Preference Activity, and implements all the settings required by the app.
| static void org.buildmlearn.appstore.activities.SettingsActivity.bindPreferenceSummaryToValue |
( |
Preference |
preference | ) |
|
|
staticprivate |
Binds a preference's summary to its value.
More specifically, when the preference's value is changed, its summary (line of text below the preference title) is updated to reflect the value. The summary is also immediately updated upon calling this method. The exact display format is dependent on the type of preference.
- See also
- sBindPreferenceSummaryToValueListener
| static boolean org.buildmlearn.appstore.activities.SettingsActivity.isSimplePreferences |
( |
Context |
context | ) |
|
|
staticprivate |
Determines whether the simplified settings UI should be shown.
This is true if this is forced via ALWAYS_SIMPLE_PREFS, or the device doesn't have newer APIs like PreferenceFragment, or the device doesn't have an extra-large screen. In these cases, a single-pane "simplified" settings UI should be shown.
| static boolean org.buildmlearn.appstore.activities.SettingsActivity.isXLargeTablet |
( |
Context |
context | ) |
|
|
staticprivate |
Helper method to determine if the device has an extra-large screen.
For example, 10" tablets are extra-large.
| void org.buildmlearn.appstore.activities.SettingsActivity.onBackPressed |
( |
| ) |
|
This method is called when the back button is pressed.
It makes the changes to the preferences and navigates to the Home Page clearing all of the back stacks.
| void org.buildmlearn.appstore.activities.SettingsActivity.onBuildHeaders |
( |
List< Header > |
target | ) |
|
| void org.buildmlearn.appstore.activities.SettingsActivity.onCreate |
( |
Bundle |
savedInstanceState | ) |
|
|
protected |
The method is executed first when the activity is created.
- Parameters
-
| savedInstanceState | The bundle stores all the related parameters, if it has to be used when resuming the app. |
| boolean org.buildmlearn.appstore.activities.SettingsActivity.onIsMultiPane |
( |
| ) |
|
| void org.buildmlearn.appstore.activities.SettingsActivity.onPostCreate |
( |
Bundle |
savedInstanceState | ) |
|
|
protected |
| void org.buildmlearn.appstore.activities.SettingsActivity.setupSimplePreferencesScreen |
( |
| ) |
|
|
private |
Shows the simplified settings UI if the device configuration if the device configuration dictates that a simplified, single-pane UI should be shown.
| final boolean org.buildmlearn.appstore.activities.SettingsActivity.ALWAYS_SIMPLE_PREFS = false |
|
staticprivate |
Determines whether to always show the simplified settings UI, where settings are presented in a single list.
When false, settings are shown as a master/detail two-pane view on tablets. When true, a single pane is shown on tablets.
| Context org.buildmlearn.appstore.activities.SettingsActivity.mContext |
|
staticprivate |
| int org.buildmlearn.appstore.activities.SettingsActivity.numberOfInstalledApps =6 |
|
private |
| final Preference.OnPreferenceChangeListener org.buildmlearn.appstore.activities.SettingsActivity.sBindPreferenceSummaryToValueListener |
|
staticprivate |
Initial value:= new Preference.OnPreferenceChangeListener() {
@Override
public boolean onPreferenceChange(Preference preference, Object value) {
String stringValue = value.toString();
if (preference.getKey().equals("number_featured_apps")) {
int x;
EditTextPreference txtCategories = (EditTextPreference) preference;
try {
x = Integer.parseInt(stringValue);
if (x < 3) x = 3;
else if (x > SplashActivity.appList.size()) x = SplashActivity.appList.size();
} catch (Exception e) {
x = 6;
}
txtCategories.setText(x + "");
preference.setSummary(x + "");
} else preference.setSummary(stringValue);
return true;
}
}
A preference value change listener that updates the preference's summary to reflect its new value.
The documentation for this class was generated from the following file:
- C:/Users/Srujan/Documents/GitHub/BuildmLearn-Store/Android/source-code/AppStore/app/src/main/java/org/buildmlearn/appstore/activities/SettingsActivity.java


--- layout: default-it ---
A Bitcoin address is similar to a physical address or an email. It is the only information you need to provide for someone to pay you with Bitcoin. It is an identifier of 26-35 alphanumeric characters, beginning with the number 1 or 3, that represents a possible destination for a Bitcoin payment. Addresses can be generated at no cost by any user of Bitcoin. An important difference, however, is that each address should only be used for a single transaction.
Bit is a common unit used to designate a sub-unit of a bitcoin - 1,000,000 bits is equal to 1 bitcoin (BTC or B⃦). This unit is usually more convenient for pricing tips, goods and services.
Bitcoin - with capitalization, is used when describing the concept of Bitcoin, or the entire network itself. e.g. "I was learning about the Bitcoin protocol today."
bitcoin - without capitalization, is used to describe bitcoins as a unit of account. e.g. "I sent ten bitcoins today."; it is also often abbreviated BTC or XBT.
A block is a record in the block chain that contains and confirms many waiting transactions. Roughly every 10 minutes, on average, a new block including transactions is appended to the block chain through mining.
The block chain is a public record of Bitcoin transactions in chronological order. The block chain is shared between all Bitcoin users. It is used to verify the permanence of Bitcoin transactions and to prevent double spending.
BTC is a common unit used to designate one bitcoin (B⃦).
Confirmation means that a transaction has been processed by the network and is highly unlikely to be reversed. Transactions receive a confirmation when they are included in a block and for each subsequent block. Even a single confirmation can be considered secure for low value transactions, although for larger amounts like 1000 US$, it makes sense to wait for 6 confirmations or more. Each confirmation exponentially decreases the risk of a reversed transaction.
Cryptography is the branch of mathematics that lets us create mathematical proofs that provide high levels of security. Online commerce and banking already uses cryptography. In the case of Bitcoin, cryptography is used to make it impossible for anybody to spend funds from another user's wallet or to corrupt the block chain. It can also be used to encrypt a wallet, so that it cannot be used without a password.
If a malicious user tries to spend their bitcoins to two different recipients at the same time, this is double spending. Bitcoin mining and the block chain are there to create a consensus on the network about which of the two transactions will confirm and be considered valid.
The hash rate is the measuring unit of the processing power of the Bitcoin network. The Bitcoin network must make intensive mathematical operations for security purposes. When the network reached a hash rate of 10 Th/s, it meant it could make 10 trillion calculations per second.
Bitcoin mining is the process of making computer hardware do mathematical calculations for the Bitcoin network to confirm transactions and increase security. As a reward for their services, Bitcoin miners can collect transaction fees for the transactions they confirm, along with newly created bitcoins. Mining is a specialized and competitive market where the rewards are divided up according to how much calculation is done. Not all Bitcoin users do Bitcoin mining, and it is not an easy way to make money.
Peer-to-peer refers to systems that work like an organized collective by allowing each individual to interact directly with the others. In the case of Bitcoin, the network is built in such a way that each user is broadcasting the transactions of other users. And, crucially, no bank is required as a third party.
A private key is a secret piece of data that enables transaction outputs from a public key which has transaction inputs. through a cryptographic signature. Private key(s) are generally generated and stored using a computer using Bitcoin wallet software. There can be several different formats. Private keys must be kept secret and never be revealed because they enable bitcoins to be spent.
Many third-parties like to generate and store private keys. Almost all major Bitcoin thefts, which have amounted to hundreds of millions of dollars of value, have resulted because people trusted third parties to hold the private keys.
The safest and most secure way to generate and store Bitcoin private keys is with Armory using an offline computer (cold storage). Thus, whoever holds the private keys makes the rules.
A public key can be mathematically derived from a private key and is used for the Bitcoin address. There can be several different formats.
A cryptographic signature is a mathematical mechanism that allows someone to prove ownership. In the case of Bitcoin, a Bitcoin wallet and its private key(s) are linked by some mathematical magic. When your Bitcoin software signs a transaction with the appropriate private key, the whole network can see that the signature matches the bitcoins being spent. However, there is no way for the world to guess your private key to steal your hard-earned bitcoins.
A Bitcoin wallet is loosely the equivalent of a physical wallet on the Bitcoin network. The wallet actually contains your private key(s) which allow you to spend the bitcoins allocated to it in the block chain. Each Bitcoin wallet can show you the total balance of all bitcoins it controls and lets you pay a specific amount to a specific person, just like a real wallet. This is different to credit cards where you are charged by the merchant.


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Hello! I'm Nikhil Rao, a designer & developer based in India. I use a variety of different frameworks over 3 different languages
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Introduction to Programming
How the Internet Works
The Web is a collection of HTML (hyper text markup language) documents.
It was started in the early 1990s, and now has over 30 billion pages!
Anything can exist on the web, including Text, Music, PDFs, Videos and HTML.
The web can be broken down in to 3 major pieces - A computer with a browser (such as Safari, Internet Explorer, or Chrome), servers, remotely located computers that host the files that make up the web, and the Internet, the go between for the browser and servers.
The browser makes requests to servers using HTTP protocols via the Internet, the browser then reads HTML, interpreting and displaying the page.
What is Coding?
Coding is writing in a language that computers can understand. Computers are considered "dumb" because they simply follow directions. So it is very important to give them clear directions with proper formatting (syntax) or the computer will return errors.
1. Tags
Tags are always contained within a set of angled brackets, and usually will occur in a set- opening and closing. They look like this < tag > < /tag >. Content is found between tags.
So for example, to create bold text, you could say:
< b > Title < /b >
2. Element
An Element is the information within a set of tags. Elements can have either inline or block behavior.
3. Attributes
Attributes are information assigned to an element.
5. White Space
White Space does not naturally exist in coding, so you must force it with break and paragraph tags.


This vignette is intended to get you up and running using ‘postpack’ to process your MCMC output by showcasing some of the main features.
‘postpack’ requires that your MCMC samples are stored in
mcmc.list
objects (class and methods supplied by the ‘coda’
package). MCMC samples should be produced from at least two chains –
single chain output has not been tested with ‘postpack’ functions. Some
MCMC interfaces produce this output by default, while others require an
extra step to convert to mcmc.list
.
JAGS
mcmc.list
output is returned by:
rjags::coda.samples()
jagsUI::jags.basic()
$samples
element of
jagsUI::jags()
NIMBLE
To obtain mcmc.list
output, users should set the
samplesAsCodaMCMC = TRUE
argument when calling
nimble::runMCMC()
.
Stan
The output of rstan::stan()
can be converted to
mcmc.list
format using
rstan::As.mcmc.list()
.
WinBUGS/OpenBUGS
The output of both:
can be converted to mcmc.list
format using
coda::as.mcmc.list()
or
postpack::post_convert()
.
If your samples were generated another way (e.g., a custom MCMC
algorithm or a package like ‘MCMCpack’), check out
?postpack::post_convert
to see how you may be able to
reformat them.
This vignette uses an example mcmc.list
object called
cjs
to illustrate the main features of ‘postpack’, see
?cjs
or vignette("example-mcmclists")
for more
details. To follow along, load it and ‘postpack’ into your session:
You can return the dimensions of the MCMC run using:
post_dim(cjs)
## burn post_burn thin chains saved params
## 11000 50000 200 2 500 21
These elements should be self-explanatory, but see
?post_dim
for details. You can extract one of these
dimensional elements quickly by specifying the types
argument, e.g., notice that there are 21 saved nodes by running
post_dim(cjs, types = "params")
.
The parameter names of these nodes will be crucial in subsetting
them, so you should be able to check them out quickly at any time. This
is the purpose of the get_params()
function:
get_params(cjs)
## [1] "B0" "sig_B0" "B1" "sig_B1" "b0" "b1" "SIG" "p"
This shows you the base node names that were monitored during model
fitting. If you wish to see the element indices associated with each
node as well, supply the type = "base_index"
argument:
get_params(cjs, type = "base_index")
## [1] "B0" "sig_B0" "B1" "sig_B1" "b0[1]" "b0[2]"
## [7] "b0[3]" "b0[4]" "b0[5]" "b1[1]" "b1[2]" "b1[3]"
## [13] "b1[4]" "b1[5]" "SIG[1,1]" "SIG[2,1]" "SIG[1,2]" "SIG[2,2]"
## [19] "p[2]" "p[3]" "p[4]"
A cornerstone of ‘postpack’ is the post_summ()
function,
which is for extracting posterior summaries for particular nodes of
interest.
## sig_B0 sig_B1
## mean 0.8440230 0.243479572
## sd 0.6187583 0.262166509
## 50% 0.6820298 0.178356796
## 2.5% 0.2766108 0.005735707
## 97.5% 2.4394721 0.820640886
The output is in a simple, easily subsettable matrix format (unlike
coda::summary.mcmc()
), which makes plotting these summaries
more straight-forward. Presented by default are posterior mean, standard
deviation, and the 50%, 2.5%, and 97.5% quantiles. You can report other
quantiles using probs
if desired and round them using
digits
:
## sig_B0 sig_B1
## mean 0.844 0.243
## sd 0.619 0.262
## 2.5% 0.277 0.006
## 25% 0.490 0.081
## 50% 0.682 0.178
## 75% 1.035 0.319
## 97.5% 2.439 0.821
One of the key features is that the params
argument
selects nodes based on regular expressions, so all elements of the
"b0"
node can be summarized with:
post_summ(cjs, "b0")
## b0[1] b0[2] b0[3] b0[4] b0[5]
## mean 1.3753033 2.3192679 1.751486 1.5200421 1.0824184
## sd 0.2067011 0.3564031 0.238889 0.2032504 0.2077227
## 50% 1.3674581 2.2779459 1.728931 1.5075479 1.0754654
## 2.5% 0.9614656 1.7836702 1.324638 1.1597562 0.6748646
## 97.5% 1.8053107 3.1633955 2.287703 1.9605019 1.5194312
Several ‘postpack’ functions accept the params
argument
(always in the second argument if it is present) to specify queries from
the mcmc.list
passed to post
, so learning to
use it is key. More information on using regular expressions to extract
particular nodes can be found in
vignette("pattern-matching")
.
Estimates of the uncertainty associated with MCMC sampling in the
mean and quantiles can be obtained using the mcse
argument
(which calls mcmcse::mcse()
and
mcmcse::mcse.q()
):
post_summ(cjs, "^B", mcse = TRUE)
## B0 B1
## mean 1.59031238 0.415103100
## sd 0.49113008 0.212836348
## 50% 1.56878458 0.397436350
## 2.5% 0.55133227 0.061825217
## 97.5% 2.58753866 0.863829917
## mcse_mean 0.01359695 0.011192343
## mcse_50% 0.01724213 0.009013355
## mcse_2.5% 0.18139264 0.025923187
## mcse_97.5% 0.06398231 0.042813775
Summaries for each chain can be obtained using the
by_chain = TRUE
argument:
post_summ(cjs, "^B", by_chain = TRUE)
## , , chain1
##
## B0 B1
## mean 1.5571300 0.4180811
## sd 0.4614706 0.2401460
## 50% 1.5502780 0.3988950
## 2.5% 0.5012706 0.0608274
## 97.5% 2.5107643 0.8543241
##
## , , chain2
##
## B0 B1
## mean 1.6234948 0.41212506
## sd 0.5178997 0.18191380
## 50% 1.5922041 0.39674020
## 2.5% 0.5687639 0.07524337
## 97.5% 2.6084912 0.87875912
‘postpack’ features two primary ways of diagnosing the
convergence/adequate sample behavior of MCMC chains: numerically and
visually. Both methods use the params
argument to allow
users to have control over which nodes get diagnostics.
post_summ()
includes two additional arguments for
obtaining numerical diagnostics:
## sig_B0 sig_B1
## mean 0.8440230 2.434796e-01
## sd 0.6187583 2.621665e-01
## 50% 0.6820298 1.783568e-01
## 2.5% 0.2766108 5.735707e-03
## 97.5% 2.4394721 8.206409e-01
## Rhat 1.0220000 1.043000e+00
## neff<PHONE_NUMBER> 4.320000e+02
neff = TRUE
triggers a call to
coda::effectiveSize()
to estimate the number of MCMC
samples that are independent and Rhat = TRUE
triggers a
call to coda::gelman.diag()
to calculate the commonly used
Rhat convergence diagnostic (numbers near 1 are ideal, greater than 1.1
may be problematic). "Rhat"
will always be rounded to three
digits, and "neff"
will always be rounded to an integer,
regardless of the value of the digits
argument.
Viewing the density and trace plot for a parameter is another common
way to evaluate algorithm convergence. The diag_plots()
function serves this purpose (which, unlike
coda:::plot.mcmc()
, allows plotting the densities color
coded by chain and only for specific nodes):
diag_plots(cjs, params = "SIG")
Options exist to:
show_diags
argument, which accepts value of
"always"
, "never"
, or
"if_poor_Rhat"
with the last one being the default. See
?diag_plots
for details).ext_device
argument).dims
argument).layout
argument).save
argument, which then requires that you enter the file
argument, which is the file name of the PDF complete with the
".pdf"
extension).keep_percent = 0.8
is passed to post_thin()
,
and would discard 20% of the samples prior to trace plotting. This
thinning does not affect the density plot visual – all retained samples
are plotted there.The dims
and layout
arguments are set to
"auto"
by default – for adjustment of these settings, see
?diag_plots
.
The more chains there are in the mcmc.list
object passed
to the post
argument, the more colors will be
displayed.
Often you will want to take a subset out of your output while retaining each saved posterior sample, for example, to plot a histogram of the samples or to calculate the posterior of some derived quantity as though it had been included as part of the model code.
b0_samps = post_subset(cjs, "b0")
By default, the output will be a mcmc.list
which allows
it to play nicely with the rest of the ‘postpack’ functions (e.g.,
get_params(b0_samps)
). However, performing plotting or
calculation tasks on posterior samples might be easier if they were
combined across chains and stored as a matrix (nodes as columns, rows as
samples):
b0_samps = post_subset(cjs, "b0", matrix = TRUE)
Note that if you wish to retain the chain and iteration number of
each posterior sample when converting to a matrix with
post_subset()
, you can pass the optional logical arguments
chains
and iters
(both are FALSE
by default).
head(post_subset(cjs, "b0", matrix = TRUE, chains = TRUE, iters = TRUE))
## CHAIN ITER b0[1] b0[2] b0[3] b0[4] b0[5]
## [1,] 1 11200 1.275155 2.632859 1.860819 1.708103 0.8804635
## [2,] 1 11400 1.308511 2.077802 1.518087 1.198710 0.9843441
## [3,] 1 11600 1.338846 2.473211 1.358235 1.156759 0.8060235
## [4,] 1 11800 1.654078 2.703028 1.498338 1.279799 1.0339362
## [5,] 1 12000 1.118555 2.313318 1.819527 1.189711 0.9702624
## [6,] 1 12200 1.188926 2.653793 1.633183 1.539144 0.8438322
In some cases, it may be easier to keep all nodes
except those matched by params
. For this
this, you can use post_remove()
(if
ask = TRUE
, you will be prompted to verify that you wish to
remove the nodes that were matched – this is the default):
# check out param names get_params(cjs)
## [1] "B0" "sig_B0" "B1" "sig_B1" "b0" "b1" "SIG" "p"
# remove all SIG nodes cjs2 = post_remove(cjs, "SIG", ask = FALSE) # did it work? get_params(cjs2)
## [1] "B0" "sig_B0" "B1" "sig_B1" "b0" "b1" "p"
array_format()
Notice that the "SIG"
node is a matrix (there are two
dimensions of element indices in the node names):
(SIG_ests = post_summ(cjs, "SIG", digits = 2))
## SIG[1,1] SIG[2,1] SIG[1,2] SIG[2,2]
## mean 1.09 0.05 0.05 0.13
## sd 3.19 0.26 0.26 0.45
## 50% 0.47 0.02 0.02 0.03
## 2.5% 0.08 -0.33 -0.33 0.00
## 97.5% 5.95 0.58 0.58 0.67
You may want to create a matrix that stores the posterior means (or
any other summary statistic) in the same format as they would be found
in the model. For this, you can use array_format()
:
array_format(SIG_ests["mean",])
## [,1] [,2]
## [1,] 1.09 0.05
## [2,] 0.05 0.13
Although this is a basic example, this function becomes more useful for higher dimensional nodes – dimensions between 2 and 10 are currently supported.
array_format()
requires a vector of named elements,
where the element names contain the correct indices to place them in
(e.g., "SIG[1,1]"
and "SIG[2,2]"
). Based on
the indices in the element names, array_format()
determines
the dimensions of the object in the model and places the elements in the
correct location.
array_format()
will respect missing values. As an
example, suppose the the model did not specify what
"SIG[2,1]"
should be. In this case, it will not be returned
as a tracked node element in the mcmc.list
(at least in
JAGS). We can simulate this behavior by removing that node from the
output, and seeing that array_format()
inserts an
NA
in the proper location:
cjs2 = post_remove(cjs, "SIG[2,1]", ask = FALSE) array_format(post_summ(cjs2, "SIG")["mean",])
## [,1] [,2]
## [1,] 1.094471 0.04547222
## [2,] NA 0.12787612
vcov_decomp()
The "SIG"
node represents a variance-covariance matrix
in the model. Sometimes it is desirable to decompose this matrix into a
vector of standard deviations and a correlation matrix. Rather than
perform this calculation on the posterior summary of "SIG"
,
we can perform it for each posterior sample to obtain a posterior of the
correlation matrix. This is the purpose of
vcov_decomp()
:
SIG_decomp = vcov_decomp(cjs, param = "SIG")
## Decomposing variance-covariance matrix node: SIG (2x2)
##
##
Note the characteristics of the output obtained:
class(SIG_decomp)
## [1] "mcmc.list"
## burn post_burn thin chains saved params
## TRUE TRUE TRUE TRUE TRUE FALSE
get_params(SIG_decomp, type = "base_index")
## [1] "sigma[1]" "sigma[2]" "rho[1,1]" "rho[2,1]" "rho[1,2]" "rho[2,2]"
The nodes "sigma[1]"
and "sigma[2]"
represent the square root of the diagonal elements
"SIG[1,1]"
and "SIG[2,2]"
(and are thus the
same as the "sig_B0"
and "sig_B1"
nodes stored
in cjs
), and the "rho"
elements represent to
correlation matrix – and posterior samples exist now for each. The names
used for these newly-created nodes can be changed using the
sigma_base_name
and rho_base_name
arguments.
You can now build the posterior median correlation matrix:
array_format(post_summ(SIG_decomp, "rho")["50%",])
## [,1] [,2]
## [1,] 1.0000000 0.2685727
## [2,] 0.2685727 1.0000000
When using vcov_decomp()
, you are recommended to always
keep check = TRUE
, which will ensure that the samples are
from a valid variance-covariance matrix prior to performing the
calculation. Setting invert = TRUE
will take the inverse of
the matrix from each posterior sample prior to performing the
calculations (e.g., if you had monitored a precision matrix rather than
a covariance matrix).
post_thin()
If your downstream analyses of the posterior samples require many
calculations, then it may be advantageous to develop the code with a
smaller but otherwise identical version of the posterior output before
unleashing them on the full output. You can thin the chains at
quasi-evenly spaced intervals using post_thin()
:
post_thin(cjs, keep_percent = 0.25)
Which would retain 25% of the samples from each chain, and return the
result as an mcmc.list
object. You may instead use the
keep_iters
argument to specify the number of iterations you
wish to keep per chain.
post_bind()
It may be desirable to combine posterior samples from the same MCMC run together in a single object. This case may arise when calculating derived quantities, and for organizational purposes you want to have them in one object as opposed to two. The two objects must have the same number of chains and saved iterations, and should be calculated from the same model run.
mcmc.list
sThe derived quantities stored in SIG_decomp
from above
are stored as an mcmc.list
object and were calculated from
the same model using consistent rules, so have the same dimensions. This
makes it easy to use:
cjs = post_bind(post1 = cjs, post2 = SIG_decomp)
and note that you have the new calculated nodes in you main object:
get_params(cjs)
## [1] "B0" "sig_B0" "B1" "sig_B1" "b0" "b1" "SIG" "p"
## [9] "sigma" "rho"
You can now quickly verify that "sig_B0"
and
"sig_B1"
are the same as "sigma[1]"
and
"sigma[2]"
, respectively:
post_summ(cjs, "sig")
## sig_B0 sig_B1 sigma[1] sigma[2]
## mean 0.8440230 0.243479572 0.8440230 0.243479572
## sd 0.6187583 0.262166509 0.6187583 0.262166509
## 50% 0.6820298 0.178356796 0.6820298 0.178356796
## 2.5% 0.2766108 0.005735707 0.2766108 0.005735707
## 97.5% 2.4394721 0.820640886 2.4394721 0.820640886
mcmc.list
and one matrix
Suppose instead that your derived quantities are stored as a matrix:
iterations along the rows and quantities along the columns. Binding this
to an mcmc.list
object involves:
1.) Obtaining the matrix of the derived quantity(ies),
2.) Deciding on a name for each of your quantities, and
3.) Binding the derived list to the main list.
For step (1), generate such a matrix of derived quantities now,
representing the inverse logit transformation of each of the
"b0"
elements:
# extract the raw samples from cjs in matrix form b0_samps = post_subset(cjs, "b0", matrix = TRUE) # perform a derived quantity calculation eb0_samps = exp(b0_samps)/(1 + exp(b0_samps))
Now you have a derived quantity, so for step (2) change the names that each quantity will be stored as:
Finally, for step (3) combine the samples using
post_bind()
:
cjs = post_bind(post1 = cjs, post2 = eb0_samps)
If the two objects contain duplicate node names, the values from the
object passed to the post2
argument of
post_bind()
will have the suffix supplied under the
argument dup_id
("_p2"
by default), and a
warning will be returned.


pvcell¶
This module contains the PVcell
object which is used by modules, strings and systems.
PVcell¶
-
class
pvmismatch.pvmismatch_lib.pvcell.
PVcell
(Rs=0.004267236774264931, Rsh=10.01226369025448, Isat1_T0=2.28618816125344e-11, Isat2_T0=1.117455042372326e-06, Isc0_T0=6.3056, aRBD=0.0001036748445065697, bRBD=0.0, VRBD=-5.527260068445654, nRBD=3.284628553041425, Eg=1.1, alpha_Isc=0.0003551, Tcell=298.15, Ee=1.0, pvconst=<PVconstants(npts=101)>)¶ Class for PV cells.
Parameters: - Rs – series resistance [ohms]
- Rsh – shunt resistance [ohms]
- Isat1_T0 – first saturation diode current at ref temp [A]
- Isat2_T0 – second saturation diode current [A]
- Isc0_T0 – short circuit current at ref temp [A]
- aRBD – reverse breakdown coefficient 1
- bRBD – reverse breakdown coefficient 2
- VRBD – reverse breakdown voltage [V]
- nRBD – reverse breakdown exponent
- Eg – band gap [eV]
- alpha_Isc – short circuit current temp coeff [1/K]
- Tcell – cell temperature [K]
- Ee – incident effective irradiance [suns]
- pvconst (
PVconstants
) – configuration constants object
-
Aph
¶ Photogenerated current coefficient, non-dimensional.
-
Ee
= None¶ [suns] incident effective irradiance on cell
-
Eg
= None¶ [eV] band gap of cSi
-
Icell
= None¶ cell currents on IV curve [A]
-
Igen
¶ Photovoltaic generated light current (AKA IL or Iph) Returns Igen : numpy.ndarray of float, PV generated light current [A]
Photovoltaic generated light current is zero if irradiance is zero.
-
Isat1
¶ Diode one saturation current at Tcell in amps.
-
Isat1_T0
= None¶ [A] diode one sat. current at T0
-
Isat2
¶ Diode two saturation current at Tcell in amps.
-
Isat2_T0
= None¶ [A] diode two saturation current
-
Isc0
¶ Short circuit current at Tcell in amps.
-
Isc0_T0
= None¶ [A] short circuit current at T0
-
Pcell
= None¶ cell power on IV curve [W]
-
Rs
= None¶ [ohm] series resistance
-
Rsh
= None¶ [ohm] shunt resistance
-
Tcell
= None¶ [K] cell temperature
-
VRBD
= None¶ [V] reverse breakdown voltage
-
Vcell
= None¶ cell voltages on IV curve [V]
-
Voc
¶ Estimate open circuit voltage of cells. Returns Voc : numpy.ndarray of float, estimated open circuit voltage
-
VocSTC
= None¶ estimated Voc at STC [V]
-
Vt
¶ Thermal voltage in volts.
-
aRBD
= None¶ reverse breakdown coefficient 1
-
alpha_Isc
= None¶ [1/K] short circuit temp. coeff.
-
bRBD
= None¶ reverse breakdown coefficient 2
-
calcCell
()¶ Calculate cell I-V curves. Returns (Icell, Vcell, Pcell) : tuple of numpy.ndarray of float
-
calcIcell
(Vcell)¶ Calculate Icell as a function of Vcell. :param Vcell: cell voltage [V] :return: Icell
-
calcVcell
(Icell)¶ Calculate Vcell as a function of Icell. :param Icell: cell current [A] :return: Vcell
-
static
f_Icell
(Icell, Vcell, Igen, Rs, Vt, Isat1, Isat2, Rsh)¶ Objective function for Icell. :param Icell: cell current [A] :param Vcell: cell voltage [V] :param Igen: photogenerated current at Tcell and Ee [A] :param Rs: series resistance [ohms] :param Vt: thermal voltage [V] :param Isat1: first diode saturation current at Tcell [A] :param Isat2: second diode saturation current [A] :param Rsh: shunt resistance [ohms] :return: residual = (Icell - Icell0) [A]
-
nRBD
= None¶ reverse breakdown exponent
-
plot
()¶ Plot cell I-V curve. Returns cellPlot : matplotlib.pyplot figure
-
pvconst
= None¶ configuration constants
-
update
(**kwargs)¶ Update user-defined constants.


This article was co-authored along with Scott Kurth in 2017, based on our experience at SVDS. The article was first published on the SVDS Blog.
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Data maturity is a useful tool for measuring the progress being made against your transformation. Recently, we were working with a multi-billion dollar industrial device company that was just beginning their Internet of Things (IoT) transformation: integrating software services with their physical devices. The vision was set and small experiments were being run throughout the organization—but the real work of building had not yet begun.
The company’s overarching strategy made a lot of sense—building a higher-margin services business from the key position their devices play in their customers’ workflows. We were asked to help them develop a data strategy and accompanying architecture to make that possible. Fundamentally, they needed a plan to use analytics and device data to create new services their clients would value.
While this company certainly had major technology investments in its future, some of the most urgent things necessary for this transformation to be successful had little to do with technology:
- New incentives to change required: Like many established product companies, the organization was structured around distinct, mature product lines. With very different P&Ls and incentives, we were told: “Careers are made within the business units, not the company.” There was an inherent skepticism for investing in unproven growth, especially if the effort was performed by a centralized function.
- Lack of experience with data rights: At this time, a very small percentage of overall revenue was produced through software. To sidestep questions of license management, the default behavior was to “make stuff free so that we do not have to create licenses.”
- New customer relationships (and approach to product development): Getting the most value from device data meant integrating it with some of the customers’ own data sources to provide greater context to end users. This meant expanding or revisiting customer relationships to embrace data sharing, requiring updates to processes in sales management (i.e., around channel or partnership agreements) and overarching application strategies (e.g., creating standards and cultivating ISV ecosystems).
In working together, we helped the industrial product company identify solutions for incentivizing technical investment. We identified policies required around data ownership, custody, and consent as a precursor to becoming an integrator and reseller in the supply chain of data services. We helped them update their architecture to support these services. It is not uncommon for an SVDS maturity assessment to reveal a broad set of opportunities.
Ultimately, our client was equipped with a broader perspective on what they needed to launch their software business and a roadmap to build all aspects of their data capabilities, ensuring that their transformation would be more successful. A clear roadmap helped them fill in the gaps between here and there, illuminating the concrete steps they needed to achieve their vision. Although transformation does not have a fixed end point, milestones are important—change should be incremental enough that the organization can see progress.
Although it won’t give you a full picture of your organization, these exercises are a good starting point to help you gain a better sense of your company’s data maturity and spark productive exchanges with your colleagues. Is your organization using data just for generating reports or focusing on use cases in mission-critical areas? Or is it putting data first in every business activity? Knowing where you stand will increase your understanding of your organization’s potential, provide a baseline for measuring progress, and give you a framework for thinking about your data operations and how they might compare with the competition’s and your industry’s as a whole.


Go back
WASD: Rotates the rocket Shift: Turns on the rocket thruster Up and Down arrows: Alters the zoom rate


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{% import 'helper_macros.html' as helpers %}
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nips nips2004 nips2004-73 nips2004-73-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: John Winn, Andrew Blake
Abstract: We present an extension to the Jojic and Frey (2001) layered sprite model which allows for layers to undergo afﬁne transformations. This extension allows for afﬁne object pose to be inferred whilst simultaneously learning the object shape and appearance. Learning is carried out by applying an augmented variational inference algorithm which includes a global search over a discretised transform space followed by a local optimisation. To aid correct convergence, we use bottom-up cues to restrict the space of possible afﬁne transformations. We present results on a number of video sequences and show how the model can be extended to track an object whose appearance changes throughout the sequence. 1
[1] J. Y. A. Wang and E. H. Adelson. Representing moving images with layers. In IEEE Transactions on Image Processing, volume 3, pages 625–638, 1994.
[2] N. Jojic and B. Frey. Learning ﬂexible sprites in video layers. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2001.
[3] B. Frey and N. Jojic. Fast, large-scale transformation-invariant clustering. In Advances in Neural Information Processing Systems 14, 2001.
[4] M. K. Titsias and C. K. I. Williams. Fast unsupervised greedy learning of multiple objects and parts from video. 2004. To appear in Proc. Generative-Model Based Vision Workshop, Washington DC, USA.
[5] C.K.I. Williams and M. K. Titsias. Greedy learning of multiple objects in images using robust statistics and factorial learning. Neural Computation, 16(5):1039–1062, 2004.
[6] M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. In M. I. Jordan, editor, Learning in Graphical Models, pages 105–162. Kluwer, 1998.
[7] C. M. Bishop, J. M. Winn, and D. Spiegelhalter. VIBES: A variational inference engine for Bayesian networks. In Advances in Neural Information Processing Systems, volume 15, 2002.
[8] J. M. Winn and C. M. Bishop. Variational Message Passing. 2004. To appear in Journal of Machine Learning Research. Available from http://johnwinn.org.
[9] A. Jepson, D. Fleet, and T. El-Maraghi. Robust online appearance models for visual tracking. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, volume I, pages 415–422, 2001.


Task Developer's Guide
In Pants, code that does "real build work" (e.g., downloads prebuilt artifacts, compiles Java code, runs tests) lives in Tasks. To add a feature to Pants so that it can, e.g., compile a new language, you want to write a new Task.
This page documents how to develop a Pants Task, enabling you to teach
pants how to do things it does not already know how to do today. To see
the Tasks that are built into Pants, look over
src/python/pants/backend/*/tasks/*.py
. The code makes more sense if
you know the concepts from internals. The rest of this page introduces
some concepts especially useful when defining a Task.
Hello Task
To implement a Task, you define a subclass of
pants.task.task.Task
and define an execute
method for that class. The execute
method does
the work.
The Task can see (and affect) the state of the build via its .context
member, a
pants.goal.context.Context.
Which targets to act on? A typical Task wants to act on all "in
play" targets that match some predicate. Here, "'in play' targets" means
those targets the user specified on the command line, the targets needed
to build those targets, the targets needed to build those targets,
etc. Call self.context.targets()
to get these. This method takes an
optional parameter, a predicate function; this is useful for filtering
just those targets that match some criteria.
Task Installation: Associate Task with Goal[s]
Defining a Task is nice, but doesn't hook it up so users can get to it.
Install a task to make it available to users. To do this, you register
it with Pants, associating it with a goal. A plugin's register.py
registers goals in its register_goals
function. Here's an excerpt from
Pants' own Python
backend:
def register_goals(): task(name='python-binary-create', action=PythonBinaryCreate).install('binary') task(name='pytest', action=PytestRun).install('test') task(name='py', action=PythonRun).install('run') task(name='py', action=PythonRepl).install('repl') task(name='setup-py', action=SetupPy).install()
That task(...)
is a name for
pants.goal.task_registrar.TaskRegistrar
. Calling its install
method
installs the task in a goal with the same name. To install a task in
goal foo
, use Goal.by_name('foo').install
. You can install more than
one task in a goal; e.g., there are separate tasks to run Java tests and
Python tests; but both are in the test
goal.
Products: How one Task consumes the output of another
One task might need to consume the "products" (outputs) of another. E.g., the Java test runner
task uses Java .class
files that the Java compile task produces. Pants
tasks keep track of this in the
pants.goal.products.ProductMapping
that is provided in the task's context at self.context.products
.
The ProductMapping
is basically a dict. Calling
self.context.products.get('jar_dependencies')
looks up
jar_dependencies
in that dict. Tasks can set/change the value stored
at that key; later tasks can read (and perhaps further change) that
value. That value might be, say, a dictionary that maps target specs to
file paths.
product_types
and require_data
: Why "test" comes after "compile"
It might only make sense to run your Task after some other Task has finished. E.g., Pants has separate tasks to compile Java code and run Java tests; it only makes sense to run those tests after compiling. To tell Pants about these inter-task dependencies...
The "early" task class defines a product_types
class method that
returns a list of strings:
def product_types(cls): return ['runtime_classpath']
The "late" task defines a prepare
method that calls
round_manager.require_data
to "require" one of those same strings:
def prepare(cls, options, round_manager): super(DuplicateDetector, cls).prepare(options, round_manager) round_manager.require_data('runtime_classpath')
Pants uses this information to determine which tasks must run first to prepare data required by other tasks. (If one task requires data that no task provides, Pants errors out.)
A task can have more than one product type. You might want to know which type[s] were require
d
by other tasks. If one product is especially "expensive" to make, perhaps your task should only
do so if another task will use it. Use self.context.products.isrequired
to find out if a task
required a product type:
catalog = self.context.products.isrequired(self.jvmdoc().product_type) if catalog and self.combined: raise TaskError( 'Cannot provide {} target mappings for combined output'.format(self.jvmdoc().product_type))
Task Implementation Versioning
Tasks may optionally specify an implementation version. This is useful to be sure that cached objects from previous runs of pants using an older version are not used. If you change a task class in a way that will impact its outputs you should update the version. Implementation versions are set with the class method implementation_version.
Class FooTask(Task): @classmethod def implementation_version(cls): return super(FooTask, cls).implementation_version() + [('FooTask', 1)]
We store both a version number and the name of the class in order to disambiguate changes in different classes that have the same implementation version set.
Task Configuration
Tasks may be configured via the options system.
To define an option, implement your Task's register_options
class method and call the passed-in register
function:
def register_options(cls, register): super(Checkstyle, cls).register_options(register) register('--skip', type=bool, fingerprint=True, help='Skip checkstyle.') register('--configuration', advanced=True, type=file_option, fingerprint=True, help='Path to the checkstyle configuration file.') register('--properties', advanced=True, type=dict_with_files_option, default={}, fingerprint=True, help='Dictionary of property mappings to use for checkstyle.properties.')
Option values are available via self.get_options()
:
# Did user pass in the --my-option CLI flag (or set it in pants.ini)? if self.get_options().my_option:
Scopes
Every task has an options scope: If the task is registered as my-task
in goal my-goal
, then its
scope is my-goal.my-task
, unless goal and task are the same string, in which case the scope is simply
that string. For example, the ZincCompile
task has scope compile.zinc
, and the filemap
task has the scope filemap
.
The scope is used to set options values. E.g., the value of self.get_options().my_option
for a
task with scope scope
is set by, in this order:
- The value of the cmd-line flag --scope-my-option
.
- The value of the environment variable PANTS_SCOPE_MY_OPTION
.
- The value of the pants.ini var my_option
in section scope
.
Note that if the task being run is specified explicitly on the command line, you can omit the
scope from the cmd-line flag name. For example, instead of
./pants compile --compile-java-foo-bar
you can do ./pants compile.java --foo-bar
. See
Invoking Pants for more information.
Fine-tuning Options
When calling the register
function, passing a few additional arguments
will affect the behaviour of the registered option. The most common parameters are:
type
: Constrains the type of the option. Takes a python type constructor (one ofbool
,str
,int
,float
,list
,dict
), or a special option type constructor liketarget_option
from pants.option.custom_types. If not specified, the option will be a string.default
: Sets a default value that will be used if the option is not specified by the user.advanced
: Indicates that an option is intended either for use by power users, or for use in only in pants.ini, rather than from the command-line. By default, advanced options are not displayed in./pants help
.fingerprint
: Indicates that the value of the registered option affects the products of the task, such that changing the option would result in different products. WhenTrue
, changing the option will cause targets built by the task to be invalidated and rebuilt.
GroupTask
Task
s may be grouped together under a parent GroupTask
.
Specifically, the JVM compile tasks:
jvm_compile = GroupTask.named( 'jvm-compilers', product_type=['compile_classpath', 'classes_by_source'], flag_namespace=['compile']) jvm_compile.add_member(AptCompile) jvm_compile.add_member(ZincCompile)
A GroupTask
allows its constituent tasks to 'claim' targets for
processing, and can iterate between those tasks until all work is done.
This allows, e.g., Java code to depend on Scala code which itself
depends on some other Java code.
JVM Tool Bootstrapping
If you want to integrate an existing JVM-based tool with a pants task, Pants must be able to bootstrap it. That is, a running Pants will need to fetch the tool and create a classpath with which to run it.
Your job as a task developer is to set up the arguments passed to your tool (e.g.: source file names to compile) and do something useful after the tool has run. For example, a code generation tool would identify targets that own IDL sources, pass those sources as arguments to the code generator, create targets of the correct type to own generated sources, and mutate the targets graph rewriting dependencies on targets owning IDL sources to point at targets that own the generated code.
The Scalastyle tool enforces style policies for scala code. The Pants Scalastyle task shows some useful idioms for JVM tasks.
- Inherit
NailgunTask
to avoid starting up a new JVM. - Specify the tool executable as a Pants
jar
; Pants knows how to download and run those. - Let organizations/users override the jar in
pants.ini
; it makes it easy to use/test a new version.
Enabling Artifact Caching For Tasks
An artifact is the output file produced by a task processing some target.
For example, the artifacts of a JavaCompile
task on a target
java_library
would be the .class
files produced by compiling the library.
In this scenario, the java_library
(i.e. the target) would have its sources
used to compute a cache key, and the .class
files would be used as the cached value.
If your task follows an isolated strategy where each target produces artifacts into its own directory, then the task will be able to take advantage of automatic target workdir caching. If your task has more complicated behavior (for example, all targets produce artifacts into the same directory), then check out the manual caching section below.
Automatic target workdir caching
Automatic target workdir caching works by assigning a results directory to
each VersionedTarget (VT) of the InvalidationCheck yielded by Task->invalidated
.
A task operating on a given VT should place the resulting artifacts in the VT's
results_dir
. After exiting the invalidated
context block, these artifacts
will be automatically uploaded to the artifact cache.
This interface for caching is disabled by default. To enable, override
Task->cache_target_dirs
to return True.
Manual caching
Manual caching is much more complicated than automatic target workdir caching,
and as such should only be used for non-standard usecases. Instead of placing
artifacts in known target directories, artifacts may be placed anywhere -- although
it is now the responsibility of the task developer to manually upload
VT / artifact pairs to the cache. Here is a template for how manual caching
would be implemented in the execute
method:
def execute(self): targets = self.context.targets() with self.invalidated(targets) as invalidation_check: # for each VersionedTarget in invalidation_check.invalid_vts, # run your task on the target and remember where the output files are output_files = do_some_work() if self.artifact_cache_writes_enabled(): # build a list of (VersionedTarget, output_files) pairs pairs = match_up(invalidation_check.invalid_vts, output_files) self.update_artifact_cache(pairs)
The above implementation writes each target/artifact (key/val) pair to the cache independently of all other targets. However you might want to write multiple targets and their artifacts together under a single cache key. A good example of this is Ivy resolution, where the set of resolved 3rd party dependencies is a property of all targets taken together, not of each target individually.
To implement caching for groupings of targets, you can use a VersionedTargetSet
in place of a VersionedTarget
, and group the invalid_vts
within
VersionedTargetSet
s. If you choose to do this, however, you must override the
check_artifact_cache_for
method in your task to return the groupings
of targets you want to read (VersionedTargetSet
s). If you don't, you will miss
the cache, because by default Pants reads each target from the cache
independently.
def check_artifact_cache_for(self, invalidation_check): return [VersionedTargetSet.from_versioned_targets(invalidation_check.invalid_vts)]


List of all animations by categories
animations
A list of lists
https://github.com/animate-css/animate.css


Post questions to the Ebean forum at: Ebean google group
For Java: The query beans or the code using the query beans is not being enhanced.
For Kotlin: The query beans are not being enhanced.
querybean-packages
entry in ebean.mf
For Java:
We need to enhance both the code that is using the query beans and the query beans themselves.
The packages specified by querybean-packages
needs to include the code that is using
the query beans.
For Kotlin: For Kotlin we only need to enhance the query beans themselves (and not the calling code).
For Kotlin the querybean-packages
just needs to include the query beans.
Ebean does not think the bean in question is an entity bean, or we are explicitly registering entity beans (via ServerConfig.addClass()) and have not added the entity bean to that.
@Entity
annotation
If the entity beans are being explicit registered via serverConfig.addClass()
then it could
be that this entity bean class needs to also be included.
Caused by: java.lang.RuntimeException: Error with association to [class org.example.domain.Contact] from [org.example.domain.Customer.contacts]. Is class org.example.domain.Contact registered? at io.ebeaninternal.server.deploy.parse.AnnotationAssocManys.readToOne(AnnotationAssocManys.java:476) at io.ebeaninternal.server.deploy.parse.AnnotationAssocManys.read(AnnotationAssocManys.java:73) at io.ebeaninternal.server.deploy.parse.AnnotationAssocManys.parse(AnnotationAssocManys.java:64) at io.ebeaninternal.server.deploy.parse.ReadAnnotations.readAssociations(ReadAnnotations.java:50) ... 37 more
io.ebean.test : ebean-test-config
is a test dependency
See docs / testing for setting up ebean-test-config
ERROR io.ebean.Ebean - Error trying to create the default EbeanServer java.lang.RuntimeException: DataSource user is null? at org.avaje.datasource.pool.ConnectionPool.<init>(ConnectionPool.java:207) at org.avaje.datasource.core.Factory.createPool(Factory.java:15) at io.ebeaninternal.server.core.DefaultContainer.getDataSourceFromConfig(DefaultContainer.java:273) at io.ebeaninternal.server.core.DefaultContainer.setDataSource(DefaultContainer.java:217) ...
See docs / tooling for more details on enhancement


Literal Patterns
You want to ask Pyke a question. The simplest questions are just asking "Is statement X true?". Going back to Pyke's family_relations example, your Python program might want to know whether it's really true that Bruce's parents are Thomas and Norma? So it would ask whether the following statement is true:
family.son_of(Bruce, Thomas, Norma)
Pyke would search the facts that's it's been told about and answer "thumbs up" because you've told it before that this statement is true and it has remembered that.
In this case, all of the statement's arguments are literal patterns. You might consider literal patterns to be input to Pyke. You're passing Bruce, Thomas and Norma into Pyke. And Pyke just answers "thumbs up" or "thumbs down".
Literal patterns look exactly like data. Thus, your question would look exactly like you see above.


Value Type:
Initial Value:
true.
Description:
If false, escape characters and package prefixes are not output when an expression is printed.
If true, an attempt is made to print an expression in such a way that it can be read again to produce an equal expression. (This is only a guideline; not a requirement. See *print-readably*.)
For more specific details of how the value of *print-escape* affects the printing of certain types, see Section 22.1.3 (Default Print-Object Methods).
Examples:
(let ((*print-escape* t)) (write #\a)) >> #\a => #\a (let ((*print-escape* nil)) (write #\a)) >> a => #\a
Affected By:
See Also:
Notes:
princ effectively binds *print-escape* to false. prin1 effectively binds *print-escape* to true.


Quick Access to all your favourite websites, a Note Taker, a Random Fact Teller and a Reminder!
Quick Access to all your favourite websites, a Note Taker, a Random Fact Teller and a Reminder!
Do you know?? There is a fact😮related to every number! Enter a random number🤔and get an associated fact !!!
Want to set a reminder to track your usage time?
Please enter your desired time!


--- layout: page title: Afternoon, a story type: gallery ---
Michael Joyce’s Afternoon, a story
is widely considered to be the first example of a born-digital hypertext story. In the twenty years since its publication, Afternoon
’s complex rumination on memory and loss has become a touchstone for critiques of electronic literature, and has even been collected in the Norton Anthology of Postmodern Fiction. This gallery shows a Java port of the original, which continues to be published as software by Eastgate Systems.
How to run Afternoon, a story
:
1.
Click on the picture for instructions.
2.
Click GO.
Hypertext built in Storyspace
Emulated in Java
How to reset Afternoon, a story
:
1.
Refresh the webpage.


Weekly Dividend Boosts: 58 For November 11 And Beyond
by Fredrik ArnoldSummary
- Barron's Weekly posts a list of stocks announcing "dividend payment boosts" on the very last page of its financial data section. Here is your dog catcher 11/11/19 update.
- Last week, Barron's listed 58 stocks boosting dividends. The increases ranged from $.000004 per month to $.95 semi-annually, and ranged from 0.092% to 475.0% for this batch.
- Energy and real estate sector stocks most populated the high yield top 10, however, financials, consumer cyclical, funds, healthcare, communication services, technology, and industrials supplemented the top twenty.
- 10 top dividend-boosting stocks ranged from 12% to 144% in net gains calculated from broker targets plus dividends less broker fees.
- $5k invested in this week's lowest-priced five top-yield dividend-boosted dogs showed 25.75% more net gain than that from $5k invested in all 10. Little, lower-priced dividend-boosted dogs ruled this mid-November pack.
Foreword
Any collection of stocks is more clearly understood when subjected to yield-based (dog catcher) analysis; these dividend-boosted batches are perfect for the Arnold dog catcher process. Here is your November 11 data from Barron's for 58 dividend-paying stocks supplemented by dog catcher analysis powered by YCharts.
Actionable Conclusions (1-10): Analysts Estimated 12.03% To 144% Net Gains For 10 Top Dividend-Boosted Stocks Come November 11, 2020
Five of 10 top dividend-boosted stocks by yield were among the top ten gainers for the coming year based on analyst 1-year target prices. (They are tinted gray in the chart below.) Thus, this yield-based forecast for these booster dogs was graded by Wall St. Wizards as 50% accurate.
Source: YCharts.com
Projections were based on estimated dividends from $1k invested in each of the highest-yielding stocks and their aggregate one-year analyst median target prices, as reported by YCharts. Note: One-year target prices by lone analysts were not applied. 10 probable profit-generating trades projected to November 11, 2020 were:
Quarterhill Inc. (QTRH) was projected to net $1,439.95, based on the median of estimates from three analysts, plus dividends. The Beta number showed this estimate subject to risks 40% less than the market as a whole.
Oasis Midstream Partners (OMP) was projected to net $344.17, based on the median of target estimates from eight analysts, plus dividends, less broker fees. A Beta number was not available for OMP.
Uniti Group (UNIT) was projected to net $283.87, based on the median of target price estimates from three analysts, plus annual dividend, less broker fees. The Beta number showed this estimate subject to risks 52% less than the market a s a whole.
Chesapeake Granite Wash Trust (CHKR) was projected to net $210.00, based on dividends alone, less broker fees. The Beta number showed this estimate subject to risks 17% above the market as a whole.
Enbridge Inc. (ENB) was projected to net $164.75, based on dividends, plus the median of target prices estimated by 22 analysts, less broker fees. The Beta number showed this estimate subject to risk 42% under the market as a whole.
Canadian Natural Resources Ltd. (CNQ) was projected to net $164.06, based on dividends, plus the median of target price estimates from 25 analysts, less broker fees. The Beta number showed this estimate subject to risk 17% more than the market as a whole.
Cedar Fair LP (FUN) was projected to net $153.57, based on a median of target price estimates from nine analysts, plus dividends, less broker fees. The Beta number showed this estimate subject to risks 50% under the market as a whole.
Acadia Realty Trust (AKR) was projected to net $147.17, based on a median of target price estimates from six analysts plus annual dividends, less broker fees. The Beta number showed this estimate subject to risk 34% less than the market as a whole.
Delek US Holdings Inc. (DK) was projected to net $131.16, based on dividends, plus the median of target price estimates from fifteen analysts, less broker fees. The Beta number showed this estimate subject to risks 68% over the market as a whole.
Shore Bancshares Inc (SHBI) was projected to net $120.32, based on dividends, plus the median of target price estimates from two analysts, less broker fees. The Beta number showed this estimate subject to risks 45% under the market as a whole.
The average net gain in dividend and price was estimated at 31.59% on $10k invested as $1k in each of these 10 stocks. These gain estimates were subject to average risks 18% less than the market as a whole.
Actionable Conclusion (11): (Bear Alerts) Analysts Predicted Two Dividend-Boosted Dog To Show A 11.53% And 15.7% Losses to November 11, 2020
The probable losing trades revealed by Y-Charts were:
Source: YCharts.com
Aircastle Ltd. (AYR) projected a loss of $115.28 based on dividends and the median of the target price estimates from eight analysts including broker fees. The Beta number showed this estimate subject to risks 50% more than the market as a whole.
Cogent Communications (CCOI) projected a loss of $157.04 based on dividends and the median of the target price estimates from fifteen analysts including broker fees. The Beta number showed this estimate subject to risks 44% less than the market as a whole.
The average net loss in price including dividends was estimated at 13.6% on $2k invested as $1k in each of these two stocks. These loss estimates were subject to average risks 3% more than the market as a whole.
Source: flickr.com
The Dividend Dogs Rule
Stocks earned the "dog" moniker by exhibiting three traits: (1) paying reliable, repeating dividends, (2) their prices fell to where (3) yield (dividend/price) grew higher than their peers. Thus, the highest yielding stocks in any collection became known as "dogs." More precisely, these are, in fact, best called, "underdogs."
58 Dividend Boosted Stocks By Target Gains
Source: YCharts.com
58 Dividend Boosted Stocks By Yield
Source: YCharts.com
Actionable Conclusions (12-21): 10 Top Dividend Boosted Stocks By Yield
Top ten dividend boosted stocks reported in Barron's 11/11/19 by yield represented five of eleven Morningstar sectors plus one fund. The top of four energy dogs placed first, Chesapeake Granite Wash Trust [1]. The other three energy dogs placed second, third, and ninth: Oasis Midstream Partners LP [2], Sabine Royalty Trust (SBR) [3], Enbridge Inc. [9].

Two real estate representatives placed fifth, and seventh, Arbor Realty Trust Inc (ABR) [5], and Uniti Group Inc. [7]. A single consumer cyclical stock placed sixth, Cedar Fair LP [6]. Then a single fund took eighth, General American Investors (GAM) [8].
Finally, the lone healthcare firm in the top ten placed tenth, AbbVie Inc. (ABBV) [10], to complete the November 11 top 10 dividend boosted list, by yield.
Actionable Conclusions: (22-31) Top 10 November 11 Dividend-Boosted Dogs Showed 8.27%-141.94% Upsides While (31) Five Downsiders Plunged -0.17% to -18.64%
Source: YCharts.com
To quantify top dog rankings, analyst mean price target estimates provide a "market sentiment" gauge of upside potential. Added to the simple high-yield metrics, analyst mean price target estimates became another tool to dig out bargains.
Analysts Forecast A 25.75% Advantage For Five Highest Yield, Lowest-Priced Dividend Boosted Stocks To 11/11/2020
10 top dividend-boosted dogs were culled by yield for this update. Yield (dividend/price) results provided by YCharts did the ranking.
Source: YCharts.com
As noted above, top 10 dividend boosted dogs screened 11/11/19 showing the highest dividend yields represented five of 11 Morningstar sectors and a fund.
Actionable Conclusions: Analysts Predicted Five Lowest-Priced Of The Top 10 Highest-Yield 11/4 Dividend Booster Dogs (32) Delivering 18.63% Vs. (33 ) 14.82% Net Gains by All 10 Come November 11, 2020
Source: YCharts.com
$5k invested as $1k in each of the five lowest-priced stocks in the top 10 dividend booster kennel by yield were predicted by analyst one-year targets to deliver 25.75% more gain than $5k invested as $.5k in all 10. The fourth lowest-priced selection, Oasis Midstream Partners LP was projected by broker targets to deliver the best net gains of 34.42%.
Source: YCharts.com
The five lowest-priced top yield dividend-boosted dogs as of November 11 were: Chesapeake Granite Wash Trust, Uniti Group Inc., Arbor Realty Trust Inc., Oasis Midstream Partners LP, Westlake Chemical Partners LP, with prices ranging from $0.68 to $22.99.
Five higher-priced dividend boosted dogs as of November 11 were: General American Investors, Enbridge Inc., Sabine Royalty Trust, Cedar Fair LP, AbbVie Inc., whose prices ranged from $38.22 to $68.44.
The distinction between five low-priced dividend dogs and the general field of 10 reflected Michael B. O'Higgins' "basic method" for beating the Dow. The scale of projected gains based on analyst targets added a unique element of "market sentiment" gauging upside potential. It provided a here-and-now equivalent of waiting a year to find out what might happen in the market. Caution is advised, since analysts are historically only 20% to 80% accurate on the direction of change and just 0% to 20% accurate on the degree of change.
The net gain/loss estimates above did not factor in any foreign or domestic tax problems resulting from distributions. Consult your tax advisor regarding the source and consequences of "dividends" from any investment.
Stocks listed above were suggested only as possible reference points for your Dividend Boosted Batch stock purchase or sale research process. These were not recommendations.
Source: Barron's Weekly & YCharts.com
Graphs and charts were compiled by Rydlun & Co., LLC from data derived from www.indexarb, YCharts, finance.yahoo, analyst mean target price by YCharts. Dog photo: flickr.com.
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Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
Additional disclosure: This article is for informational and educational purposes only and should not be construed to constitute investment advice. Nothing contained herein shall constitute a solicitation, recommendation or endorsement to buy or sell any security. Prices and returns on equities in this article except as noted are listed without consideration of fees, commissions, taxes, penalties, or interest payable due to purchasing, holding, or selling same.


On the Mornington Peninsula (one hour from Melbourne) next door to Portsea. Our apartment is a lazy 10-minute walk from the relaxed township of Sorrento that offers restaurants, cafes, a chemist, a supermarket, a butcher and lots more. It is located in a quiet avenue, just 4 houses from the beach. It is in easy walking distance of the ocean beaches and surf. Your private luxury apartment consists of a spacious living area with leather lounge suite overlooking a sun drenched, tree-filled private garden. A separate kitchenette has everything you need to make your stay relaxed and comfortable.  Two TVs are sure to make everyone happy! An undercover BBQ area with seating offers plenty of space to stretch out and enjoy that cup of freshly brewed coffee or a glass of wine. This is the perfect place for a home away from home.
There are more than 55 wineries to choose from - offering cellar door sales, restaurants, cafes and tours. Along the way discover a foodie’s paradise. Meet the growers, producers, providers and chefs. Experience the fun of picking your own produce or buying dinner ingredients at the farm gate. Dine with views of the sea, lakes and vineyards.
Another world awaits. Enjoy the thermal bathing area or book a private indoor or outdoor hot springs bath experience. Activities include reflexology treatments, foot and hand baths, sauna, steam baths, cold plunges and yoga. The cafe at the bath house provides light meals and gourmet pizzas. Feel revitalised and relaxed at the same time.
There's no better way to cross the Bay either with your car or as a foot passenger. It only takes 40 minutes. Spot the playful dolphins whilst enjoying a coffee and light refreshments. The ferries run every day of the year on the hour. Wonderful restaurants at historic Queenscliff on the Bellarine peninsula offer perfect lunchtime adventures.
We understand that pets are an important part of the family. We are pet friendly by arrangement - so please talk to us about your needs. We offer a fully enclosed backyard, shady trees, water bowls, food bowls and a lead if you forget one. Only 3 minutes from a leash free beach, an oval close by and if you want to sleep in we are happy to take your dog (depending on its size) for a walk in the morning.\
We offer a spacious living area overlooking a beautiful garden Modern and elegant bathroom Queen sized bedroom suite A private entrance allows you to come and go as you please. Car parking is close by with room for a boat as well! Your luxury apartment consists of a spacious separate living area with leather furniture overlooking a private garden for you to enjoy. A separate Queen sized bedroom suite with quality linen also overlooks the garden. The modern, elegant bathroom has a luxurious bath that entices you to unwind and relax whilst overlooking a private courtyard fernery. The kitchenette provides everything you will need for a relaxing breakfast. We include a coffee machine and a variety of coffee pods for your enjoyment. Two TVS will keep everyone happy along with a DVD player, stereo system and games galore. There is an undercover area complete with BBQ and shady seating that offers plenty of space to stretch out and enjoy that cup of freshly brewed coffee or glass of wine. Sit back and relax and take a breather. Read a book, go for a beach walk, have a sleep-in. It’s your private space to enjoy. Amenities include top quality bedding, fluffy towels, bath robes, vanity packs, hair dryer, beach towels and chairs, a picnic basket, coffee machine and pods, a microwave/convection oven, a toaster, a kettle, a sandwich maker and BBQ tools. We offer locally sourced provisions for a hearty breakfast together with home-made biscuits and chocolates.
The Mornington Peninsula is blessed with 19 courses that both challenge and excite. Many golf courses provide the most exhilarating views over Port Phillip Bay, Western Port and Bass Strait. The sand hills and coastal hinterland provide wonderful examples of links, resort and traditional courses. Visitors are always welcome.
Fabulous walking tracks in any direction for all ages. Beautiful beach walks, amazing cliff top strolls and for the more serious walkers, a 100km walk combines the Bay trial, National Parks and the two Bays walk through the hinterland.
Endless Fun for all the family. Sailing, fishing, kayaking, paddle boarding, snorkeling, scuba diving and surfing are all on offer, not to mention the cricket match on the beach.
The Mornington Peninsula hosts an array of exhibitions and galleries, featuring fine art, sculpture, ceramics, jewelry, hand blown glass and clothing. Small local galleries and craft markets are in abundance across the region.
Lovely, leisurely shopping - for pure pleasure - is listed as the number one activity for holidaymakers. Sorrento and Mornington will not disappoint. Bookshops, giftware, home living and of course the odd handbag or "must have that" top is yours for the exploring.


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Namespace ExpressionEvaluatorForDotNet
Classes
CSharpExpressionConfiguration
This is the default expression configuration used by the Evaluator. It has settings for evaluating math, string, and boolean expressions relative to C# syntax.
Evaluator
This is the main class used for evaluating expressions.
ExpressionConfigurationBase
This abstract base class is what all expression configurations must derive from.
ExpressionConfigurationOperator
This class represents an operator to an Operators.
ExpressionConfigurationOptions
This class represents the options for configuring an ExpressionConfigurationBase.
ExpressionGroup
A class that represents an inner expression. Contains the Left and Right Operands, the Operator type, and the Expression type.
ExpressionOperandTypes
This class groups operand types with the expression type and is used internally.
ExpressionResult
This class is what's returned after evaluating expressions. It contains three properties which hold information about the result of the evaluated expression.
HL7V2Component
This class refers to HL7V2 Components defined in V2 specification. Components exist inside HL7V2Field's and their HL7V2FieldRepetition's.
HL7V2ExpressionConfiguration
Use this configuration when using the Evaluator to evaluate HL7V2 flat file messages.
HL7V2Field
This class refers to HL7V2 Fields defined in V2 specification. Fields are the highest level of 'fields' in an HL7V2 message segment.
HL7V2FieldBase
This abstract base class is what all other 'field' like classes inherit from.
HL7V2FieldRepetition
This class refers to HL7V2 Field Repetitions defined in V2 specification. Field Repetitions exist within fields. You always have atleast one FieldRepetition if there's a Field, unless it's a special case, such as MSH.1.
HL7V2Message
This class refers to HL7V2 Messages defined in V2 specification.
HL7V2MessageSegment
This class refers to HL7V2 Message Segments defined in V2 specification. Message Segments make up an HL7V2Message.
HL7V2SubComponent
This class refers to HL7V2 Sub Components defined in V2 specification. Sub Components are the lowest level of 'fields' in an hl7v2 message segment.
Operand
This class is used for encapsulating an operand as part of an expression.
OperatorLocation
This class is used internally to identify the next operators location when evaluating inner expressions.
Enums
ExpressionType
The expression type is used to identify either an operand or expression if it's Math, String, or Boolean.
Operator
This enumeration serves to identify operators used within math, string, or boolean expressions.
OperatorPrecedence
This enumeration describes the precedence for how expressions should be evaluated. Options are Higher and Lower. Higher precedence operator expressions are solved before Lower.
OperatorType
This enumeration is used for determining what type an operator is. Options are MathString and Boolean.
VariableType
The variable types supported for the evaluator are Boolean, Int, Float and String.