Note: If you are only going to read from the Google Sheets document, and not edit or write data to the document, you can skip the process of making a Service Account. The only requirement is that the Google Sheets document is shared such that anyone with the link can read the document.
Create a Service Account
In this tutorial, we will be creating a Service Account, a user account that’s created to interact with a specific piece of software or service. In our case, this specific piece of software is the Google Sheets API. You can then reuse this Service account for other apps made with App Inventor.
1. Create a Google Developer Account
The first step to use the Google Sheets API is to create a Google Developer Account. A Google Developer Account is a Google Account that is activated to use developer tools, so if you have not already done so, begin by navigating to https://www.google.com and create a google account. Once you have done so, navigate to https://developers.google.com and sign in to your desired Google Account. This does not need to be the google account which will own the Google Sheets that you intend to use.
2. Create a Google Developer Project
Our next step is to create a coding project. Once you have logged into your Google Developer Account, navigate to https://console.developers.google.com/. If this is your first time using your Google Developer Account, you will be prompted to agree to the Terms of Service. Check the box and continue.
Next, you will be on the Developer Dashboard. If you have not created a project before, you can create your first project by selecting [CREATE PROJECT].
If you are using an existing Developer Account and already have a Project, you will be on a Project’s Dashboard. To create a new Project, click on the drop down menu at the top of the webpage.
At the top of the pop-up menu, select NEW PROJECT.
On the Creation page, give the project a name. For this tutorial, we will name it “App Inventor.” We don’t need to provide an organization, so we will leave it as No organization.
Click ‘Create’ to finish creating the project.
3. Enable the Google Sheeets API
The next step is to enable the Google Sheets API for your project. Click on the “+ Enable APIs and Services” button next to APIs & Services.
Enter in the search bar “Google Sheets API” and select the resulting API that appears. Then click the Enable button for the API Library.
4. Creating a Service Acccount
Now that we have enabled the Google Sheets API for this project, we want to create a service account. Simply put, this service account will look a lot like a google email account. After selecting the ‘Enable’ button for the Google Sheets API, you will be sent to the Overview tab for the Google Sheets API in your project. To the right of the screen, click ‘Create Credentials’ to get started with creating the Service Account.
On the next page, we are presented with a guide that will ask various questions about what kind of project we are making. In the first drop down menu under ‘Which API are you using?’, select the Google Sheets API.
After you select that, a follow-up question will appear below. Under the question “Where will you be calling the API from?”, select Other UI. Finally, under what data will you be accessing, select Application Data.
After entering the above information, click on the blue button labeled “What credentials do I need?” This will take you to the second part of creating the credentials. This part will allow you to customize the service account and should look like the figure below.
The name of the service account is not important. For this tutorial, I will name it “example-service.” You then set the role to Selecting Project > Editor, which should provide this service account with enough permissions. Make sure to save the entire text in the box labeled Service Account ID. You should notice that this will look a lot like an email address. You will want to copy this Service Account ID someplace so that we can use it later.
Make sure that “JSON” is selected under Key Type like it is above before pressing ‘Continue’. This will download a JSON file with the name “AppInventor-####.json.”
When using the Google Sheets API component, we will upload this json file in order to work properly. Make sure to keep this JSON safe!
Linking the Google Sheets Document
Now that we have created a Service Account, we want to provide the service account access to the Google Sheets files we will or read from.
1. Create the Google Sheets Document
The first step, if you haven’t already, is to create a Google Sheets document. The name of the Google Sheets document can be anything you’d like. If you already have a Google Sheets file you would like to use, then simply go to your Google Drive and navigate anywhere you would like to create the document. Then click New and select New Sheet.
2. Share the Google Sheets Document
After you have created the Google Sheets file that you would like to use, you simply need to share the Google Sheets file with the service account email we saved from earlier. This will provide the service account with the permissions you need. You can specify whether the service account can only read the data or whether the service account can read and write the data by changing the permissions between Viewer and Editor respectively.
3. Record Relevant Information
Finally, there are a handful of pieces of information about the google sheets document which you would need to record in order to properly access the file. You can also use this portion of the instructions as a reference for where to find the information later.
You can read more about these terms here.
The Spreadsheet ID
The spreadsheet ID can be found in the url of the Google Sheets Document. The url of the Google Sheets Document is typically in the form:
https://docs.google.com/spreadsheets/d/<Spreadsheet ID>/edit#gid=<Grid ID>
The spreadsheet ID corresponds to the ID of the Google Sheets File.
The Sheet Name
Within a spreadsheet are individual sheets, each of which have unique titles and ID numbers. In this component, the title of the page is called the sheetName.
A single spreadsheet document can have multiple pages. By default, when you create a new Google Sheets document, the spreadsheet has a single page with the name “Sheet1”. You can find this in the tabs at the bottom left corner of the screen like so:
You can add a new page to the document by pressing the “+” symbol. Doing so will create a new tab next to Sheet1 that will read Sheet2.
Each block in the Google Sheets Component will ask you to specify the title of the page (or the “Sheet Name”) you want to read from or edit. For example, if I want to edit values on Sheet2, then I would provide the text “Sheet2” in the sheetName slot.
Additionally, you can rename the sheet to whatever you’d like by right clicking on the tab and clicking rename. Note, doing so would require that you change the sheet name in the project as well to match.
The Grid ID
Similarly to the sheet name, this is used to refer to which page you would like to edit. However, unlike the sheet name, the grid ID cannot be customized by the user. Certain blocks require that the grid ID is provided instead of a sheet name. To find the grid ID, navigate to the page you want to reference by clicking on the tab. In the URL, you should see that the end should have “gid=” followed by a number.
https://docs.google.com/spreadsheets/d/<Spreadsheet ID>/edit#gid=<Grid ID>
Debugging Tips
Unable to resolve host "oauth2.googleapis.com"
If you get this error message, double check your device’s connection to the internet. This is a sign that the Android networking subsystem can’t resolve the hostname, so it’s not able to talk to DNS.
Attempt to invoke virtual method … on a null object reference.
If you get this error message, double check that you have linked your credentials in the designer properties. If your Designer property reads “None…” like it says in the image below, click on the box and select the .json file you got from the Service Account setup steps.
Unable to parse range: ______
If you get this error message, there may be an error with the range that you provided. This could mean that the sheetName you’ve provided does not actually exist, or that the reference you provided is not valid A1-notation. To learn more about A1 notation, see this reference: https://developers.google.com/sheets/api/guides/concepts#a1_notation


Russia detains activists after giant blow-up whale protest near Kremlin
MOSCOW (Reuters) - Russian police detained two activists on Monday after they floated a giant inflatable killer whale on the Moskva River outside the Kremlin to call on authorities to protect the creatures, protesters said.
Environmental campaign group Greenpeace said it had staged the stunt to urge authorities to ban the capture of whales for educational or cultural purposes after dozens of beluga and killer whales were caught last year to be sold to marine parks or aquariums in China.
The captured whales became a cause celebre when they were kept in cramped enclosures in Russia’s Far East that were likened by activists to a whale prison. The whales were released back into the wild earlier this year after the outcry.
On Monday, activists rowed into the river with a blow-up whale bearing the slogan: “Thanks for letting us go, don’t catch us anymore.”
Police detained the activists as well as a photographer for the Novaya Gazeta newspaper, Greenpeace said in a statement. The Interior Ministry did not immediately respond to a request for comment. Novaya Gazeta said its photographer was later released.
“Until there is a ban on catching killer whales, belugas and other types of whale for educational or cultural purposes ... they can be caught again and sold to China,” an activist said.


A comphrehensive list of programs, softwares, and technologies that the Octo-Docs team is using to collaborate. This includes the systems used to actually create the CrossDoc program.


Soon after daybreak, street vendors fill the streets of Tlaltenango, Mexico with stands of fruits, vegetables, meats, and other perishable goods. People from neighboring towns start to flow in, including me and my family who traveled thirty minutes by car from our ranch. I gaze at the various fruits and veggies that line the streets with color, my favorites being mangos, cucumbers, watermelon, and corn. The mood is lively and cheerful, and I feel a deep sense of comfort and familiarity, having visited here many times in my childhood.
This food market happens every Sunday and it is what keeps the town bustling with life. Aside from selling essential goods, the market is also integral to Mexican people’s culture and social lives. People come not only to shop, but also to catch up with neighbors and friends while bargaining with vendors about the freshness and price of each good.
Contrary to worldwide popular beliefs, the people of China would describe a typical wet market in the exact same way. There is no denying that some existing Chinese markets sell exotic animals, which is problematic as deadly diseases such as the coronavirus can emerge. However, the majority of wet markets do not sell wild animals at all, nor do people typically consume them.
The Chinese government permitting the reopening of wet markets has led to criticism from countries all around the world with experts, conservationists, and world leaders demanding China to ban them altogether. Tackling the root of the problem instead however is a lot more feasible, which solely calls for the banning of exotic animals for human consumption and non-evidence based traditional medicines.
When demanding a country to implement change, the nuances which have existed in their culture for thousands of years must be considered with respect. The use of wild animals in Chinese culture has existed for over 2,000 years, and while it is evident that this practice is far too risky to continue, the traditional wet market does not have to be banned completely. Not everyone has to sacrifice their culture for the Western-style grocery store, but they must adopt better safety precautions for the sake of the rest of the world’s health.
On April 30th, Beijing, China banned the consumption of wild animals to reduce the risk of future pandemics. This was a critical first step in the right direction, but the rest of China and countries with similar practices must adopt this legislation as well to globally end the commercial trade of wildlife. As well said by Mahatma Gandhi, “The greatness of a nation and its moral progress can be judged by the way its animals are treated.” It is catastrophic times like these when a nation truly starts to recognize the great linkage between the health of humans, wild animals, and the planet, a connection that has been long lost in decades past. With that, I am confident that we will come back from this pandemic more resilient and with the recognition that a healthy natural world is critical for a stable and prosperous future.


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#include <LogFileManager.h>
Public Member Functions | |
| ~LogFileManager () | |
| LogFile * | CreateLog (const std::string &name, bool isDefault=false, LogFile::LoggingLevel logLevel=LogFile::LoggingLevel::Normal, std::ios_base::openmode mode=std::ios::out, std::ostream *stream=0) |
| LogFile * | GetLog (const std::string &name) |
| LogFile * | GetDefaultLog () const |
| void | SetDefaultLog (LogFile *log) |
| void | SetLoggingLevel (LogFile::LoggingLevel lvl) |
| std::string | lead_in (LogFile::ErrorCode errCode=LogFile::ErrorCode::Remark) |
| void | LogMessage (const std::string &msg, LogFile::ErrorCode errCode=LogFile::ErrorCode::Remark, bool appendNewline=true) |
| void | PushSection (const std::string &name) |
| void | PopSection () |
| template<typename T> | |
| std::ostream & | operator<< (const T &val) |
Static Public Member Functions | |
| static LogFileManager & | getInstance () |
| static LogFileManager * | getInstancePtr () |
An application will typically create a log file name <AppName>.log. However, if logging is to occur within libraries used by the application (and written to <AppName>.log, it is necessary to have a way to retrieve the appropriate log file without knowing the name. The log file manager is a singleton, and there is only one manager per application instance. The manager should be used to create all log files, and will allow the application to set the default log file. The manager can handle multiple log files, which can be retrieved by name, but the default file can be retrieved anywhere.
| cmd::LogFileManager::~LogFileManager | ( | ) | [inline] |
Destructor
| static LogFileManager& cmd::LogFileManager::getInstance | ( | ) | [inline, static] |
Get the manager instance
| static LogFileManager* cmd::LogFileManager::getInstancePtr | ( | ) | [inline, static] |
Get a pointer to the manager instance
| LogFile* cmd::LogFileManager::CreateLog | ( | const std::string & | name, | |
| bool | isDefault = false , |
|||
| LogFile::LoggingLevel | logLevel = LogFile::LoggingLevel::Normal , |
|||
| std::ios_base::openmode | mode = std::ios::out , |
|||
| std::ostream * | stream = 0 | |||
| ) | [inline] |
Create a new log file
| name | Name of the log file, also used as an index in the log list | |
| isDefault | Indicates if this log file is the default log file | |
| mode | Indicates the mode in which the file is opened | |
| stream | If null, the output is not redirected. Otherwise, this is the stream to which output is directed. |
| LogFile* cmd::LogFileManager::GetLog | ( | const std::string & | name | ) | [inline] |
Get a pointer to a specified log file
| LogFile* cmd::LogFileManager::GetDefaultLog | ( | ) | const [inline] |
Get the default log file
| void cmd::LogFileManager::SetDefaultLog | ( | LogFile * | log | ) | [inline] |
Set the default log file
| void cmd::LogFileManager::SetLoggingLevel | ( | LogFile::LoggingLevel | lvl | ) | [inline] |
Set the logging level of the default log file
| std::string cmd::LogFileManager::lead_in | ( | LogFile::ErrorCode | errCode = LogFile::ErrorCode::Remark |
) | [inline] |
Get the lead-in string for the default log file
| void cmd::LogFileManager::LogMessage | ( | const std::string & | msg, | |
| LogFile::ErrorCode | errCode = LogFile::ErrorCode::Remark , |
|||
| bool | appendNewline = true | |||
| ) | [inline] |
Write a log message to the default log file
| void cmd::LogFileManager::PushSection | ( | const std::string & | name | ) | [inline] |
Push a new section onto the stack
| void cmd::LogFileManager::PopSection | ( | ) | [inline] |
Pop a section from the stack
| std::ostream& cmd::LogFileManager::operator<< | ( | const T & | val | ) | [inline] |
Allow remarks to be posted using the '<<' operator


Run Utils¶
Table of Contents
ExperimentGrid¶
Spinning Up ships with a tool called ExperimentGrid for making hyperparameter ablations easier. This is based on (but simpler than) the rllab tool called VariantGenerator.
-
class
spinup.utils.run_utils.
ExperimentGrid
(name='')[source]¶ Tool for running many experiments given hyperparameter ranges.
-
add
(key, vals, shorthand=None, in_name=False)[source]¶ Add a parameter (key) to the grid config, with potential values (vals).
By default, if a shorthand isn’t given, one is automatically generated from the key using the first three letters of each colon-separated term. To disable this behavior, change
DEFAULT_SHORTHAND
in thespinup/user_config.py
file toFalse
.Parameters: - key (string) – Name of parameter.
- vals (value or list of values) – Allowed values of parameter.
- shorthand (string) – Optional, shortened name of parameter. For
example, maybe the parameter
steps_per_epoch
is shortened tosteps
. - in_name (bool) – When constructing variant names, force the inclusion of this parameter into the name.
-
run
(thunk, num_cpu=1, data_dir=None, datestamp=False)[source]¶ Run each variant in the grid with function ‘thunk’.
Note: ‘thunk’ must be either a callable function, or a string. If it is a string, it must be the name of a parameter whose values are all callable functions.
Uses
call_experiment
to actually launch each experiment, and gives each variant a name usingself.variant_name()
.Maintenance note: the args for ExperimentGrid.run should track closely to the args for call_experiment. However,
seed
is omitted because we presume the user may add it as a parameter in the grid.
-
variant_name
(variant)[source]¶ Given a variant (dict of valid param/value pairs), make an exp_name.
A variant’s name is constructed as the grid name (if you’ve given it one), plus param names (or shorthands if available) and values separated by underscores.
Note: if
seed
is a parameter, it is not included in the name.
-
variants
()[source]¶ Makes a list of dicts, where each dict is a valid config in the grid.
There is special handling for variant parameters whose names take the form
'full:param:name'
.The colons are taken to indicate that these parameters should have a nested dict structure. eg, if there are two params,
Key Val 'base:param:one'
1 'base:param:two'
2 the variant dict will have the structure
variant = { base: { param : { a : 1, b : 2 } } }
-
Calling Experiments¶
-
spinup.utils.run_utils.
call_experiment
(exp_name, thunk, seed=0, num_cpu=1, data_dir=None, datestamp=False, **kwargs)[source]¶ Run a function (thunk) with hyperparameters (kwargs), plus configuration.
This wraps a few pieces of functionality which are useful when you want to run many experiments in sequence, including logger configuration and splitting into multiple processes for MPI.
There’s also a SpinningUp-specific convenience added into executing the thunk: if
env_name
is one of the kwargs passed to call_experiment, it’s assumed that the thunk accepts an argument calledenv_fn
, and that theenv_fn
should make a gym environment with the givenenv_name
.The way the experiment is actually executed is slightly complicated: the function is serialized to a string, and then
run_entrypoint.py
is executed in a subprocess call with the serialized string as an argument.run_entrypoint.py
unserializes the function call and executes it. We choose to do it this way—instead of just calling the function directly here—to avoid leaking state between successive experiments.Parameters: - exp_name (string) – Name for experiment.
- thunk (callable) – A python function.
- seed (int) – Seed for random number generators.
- num_cpu (int) – Number of MPI processes to split into. Also accepts ‘auto’, which will set up as many procs as there are cpus on the machine.
- data_dir (string) – Used in configuring the logger, to decide where
to store experiment results. Note: if left as None, data_dir will
default to
DEFAULT_DATA_DIR
fromspinup/user_config.py
. - **kwargs – All kwargs to pass to thunk.
-
spinup.utils.run_utils.
setup_logger_kwargs
(exp_name, seed=None, data_dir=None, datestamp=False)[source]¶ Sets up the output_dir for a logger and returns a dict for logger kwargs.
If no seed is given and datestamp is false,
output_dir = data_dir/exp_name
If a seed is given and datestamp is false,
output_dir = data_dir/exp_name/exp_name_s[seed]
If datestamp is true, amend to
output_dir = data_dir/YY-MM-DD_exp_name/YY-MM-DD_HH-MM-SS_exp_name_s[seed]
You can force datestamp=True by setting
FORCE_DATESTAMP=True
inspinup/user_config.py
.Parameters: - exp_name (string) – Name for experiment.
- seed (int) – Seed for random number generators used by experiment.
- data_dir (string) – Path to folder where results should be saved.
Default is the
DEFAULT_DATA_DIR
inspinup/user_config.py
. - datestamp (bool) – Whether to include a date and timestamp in the name of the save directory.
Returns: logger_kwargs, a dict containing output_dir and exp_name.


--- layout: post title: Slicehost firewalls date: 2009-08-24 06:59:59.000000000 -07:00 categories: - Technology tags: [] status: publish type: post published: true ---
Installed Apache, configured your DNS but STILL having trouble accessing your domain at slicehost? You probably need to adjust your iptables (firewall) rules.
This article really helped me understand iptables at a very basic level. It's been a couple of years since I really delved into linux and things have become really nice.
I also found out that slicehost correctly configures your firewall pretty tight, allowing new connections for ssl only. Wow, another thing done right at slicehost!
From: http://ping.fm/mIqx2


spocc: Interface to Species Occurrence Data Sources
A programmatic interface to many species occurrence data sources,
including Global Biodiversity Information Facility ('GBIF'), 'USGSs'
Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist',
Berkeley 'Ecoinformatics' Engine, 'eBird', Integrated Digitized
'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information
System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes
functionality for retrieving species occurrence data, and combining
those data.
| Version: |
0.9.0 |
| Imports: |
utils, rgbif (≥ 1.0.0), rbison (≥ 0.6.0), rebird (≥ 1.0.0), rvertnet (≥ 0.7.0), ridigbio (≥ 0.3.5), lubridate (≥ 1.5.0), crul (≥ 0.3.4), whisker (≥ 0.3-2), jsonlite (≥ 1.4), data.table (≥ 1.9.6), tibble (≥ 1.2), wicket (≥ 0.3.0) |
| Suggests: |
roxygen2 (≥ 6.1.1), testthat, knitr, taxize (≥ 0.8.4) |
| Published: |
2018-11-15 |
| Author: |
Scott Chamberlain [aut, cre],
Karthik Ram [ctb],
Ted Hart [ctb],
rOpenSci [fnd] (https://ropensci.org/) |
| Maintainer: |
Scott Chamberlain <myrmecocystus at gmail.com> |
| BugReports: |
https://github.com/ropensci/spocc/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/ropensci/spocc |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
spocc results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=spocc
to link to this page.


turtle.io is a HTTP 1.1 web server with a focus on simplicity.
All you need to do is install it, and tell it what directory holds your web sites, & which hostnames to answer for.
Getting Started
Install the module with: npm install turtle.io
Create a script to load & start a server. You could use sample.js in the turtle.io directory (./node_modules/turtle.io) as a template, or see the examples below
[Optional] Edit config.json in the turtle.io directory to configure server defaults; you can override defaults by passing server.start() an Object
Documentation
API documentation is available at api.turtle.io. Configuration details are available on the wiki.
Examples
turtle.io requires a default virtual host to be specified, because it is the failover when a request can't be routed.
Virtual hosts
Virtual host keys are the hostname, and the value is the directory relative to "root".
turtle.io
turtle.io is a HTTP 1.1 web server with a focus on simplicity.
All you need to do is install it, and tell it what directory holds your web sites, & which hostnames to answer for.
Getting Started
npm install turtle.io
sample.js
in the turtle.io directory (./node_modules/turtle.io) as a template, or see the examples belowconfig.json
in the turtle.io directory to configure server defaults; you can override defaults by passing server.start() an ObjectDocumentation
API documentation is available at api.turtle.io. Configuration details are available on the wiki.
Examples
turtle.io requires a default virtual host to be specified, because it is the failover when a request can't be routed.
Virtual hosts
Virtual host keys are the hostname, and the value is the directory relative to "root".
Proxy routes
This example has
/api
act as a reverse proxy to another service.License
Copyright (c) 2014 Jason Mulligan
Licensed under the BSD-3 license.


Unconsciously, one person or one thing is walking into your heart, and I have scarcely remembered when I began my writing. All I can remember is that I started to keep a diary in a notebook with a plastic cover which I felt as if I had found a priceless treasure.
When I had an English class in junior high school, I had a strong interest in English. Although Chinese and English are different languages, I remain enthusiastic and interested in Chinese writing and English learning until today.
I spent more time in learning English than in writing article before I joined Steemit. After all, it’s almost unrealistic to earn a living by writing on Steemit. However, if you learn English well, you would have a chance to show your talents and realize your ambition.
Steemit is such a good platform where you can not only develop your writing skills but also earn more money. Why not go ahead with it?
With the popularization and application of computer, I rarely write with a pen, but when I start with a first draft some characters escape me for the moment which I have always talked about. There are even some wrongly written characters in my writings. Out of practice for a long time, everything will become unfamiliar.
But all of this has been completely changed by Steemit.
Full of talented men and overwhelming posts with beautiful pictures, Steemit is an arena where only the strong stand out from the competition. If you want to get the upvotes from the big fish or whales, you should produce high - quality articles.
English learning is set in a secondary place. I begin to search for knowledge that I could need but know only a bit, and hunt up my daughter’s textbooks and reading materials to read it carefully. I also take time to read Readers, Youth Digest, etc carefully.
Even in the course of English learning, I also pay attention to some of the knowledge points in case of need.
From a self-learner of English major, I begin to become that of Chinese language major, all of which benefits from Steemit.
Although the road is endless and faraway, I still want to pursue the truth in the world.
**Brief Biography of Old Wu, or 老吴 in Chinese:
Old Wu, male, born on Dec. 12, 1968, Chinese nationality, is a good man and has been studying English for over 30 years. He teaches himself English, and he does not believe in fate, so he keeps working hard. Humorous and optimistic, he will become your good friend. **
This page is synchronized from the post: Steemit Has Changed My Study Habit


AWS::WAFRegional::WebACL
The AWS::WAFRegional::WebACL
resource creates an AWS WAF Regional web access control group
(ACL) containing the rules that identify the Amazon CloudFront (CloudFront) web requests
that you want to
allow, block, or count. For more information, see CreateWebACL in the
AWS WAF Regional API Reference.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::WAFRegional::WebACL", "Properties" : { "DefaultAction" :
Action
, "MetricName" :String
, "Name" :String
, "Rules" : [Rule, ...
] } }
YAML
Type: "AWS::WAFRegional::WebACL" Properties: DefaultAction:
Action
MetricName:String
Name:String
Rules: -Rule
Properties
DefaultAction
-
The action that you want AWS WAF to take when a request doesn't match the criteria in any of the rules that are associated with the web ACL.
Required: Yes
Type: AWS WAF Regional WebACL Action
Update requires: No interruption
MetricName
-
A friendly name or description for the Amazon CloudWatch metric of this web ACL. For valid values, see the
MetricName
parameter of the CreateWebACL action in the AWS WAF Regional API Reference.Required: Yes
Type: String
Update requires: Replacement
Name
-
A friendly name or description of the web ACL.
Required: Yes
Type: String
Update requires: Replacement
Rules
-
The rules to associate with the web ACL and the settings for each rule.
Required: No
Type: List of AWS WAF Regional WebACL Rules
Update requires: No interruption
Return Values
Ref
When the logical ID of this resource is provided to the Ref
intrinsic function, Ref
returns the resource name, such as
1234a1a-a1b1-12a1-abcd-a123b123456
.
For more information about using the Ref
function, see Ref.
Examples
Create a Web ACL
The following example defines a web ACL that allows, by default, any web request. However, if the request matches any rule, AWS WAF blocks the request. AWS WAF evaluates each rule in priority order, starting with the lowest value.
JSON
"MyWebACL": { "Type": "AWS::WAFRegional::WebACL", "Properties": { "Name": "WebACL to with three rules", "DefaultAction": { "Type": "ALLOW" }, "MetricName" : "MyWebACL", "Rules": [ { "Action" : { "Type" : "BLOCK" }, "Priority" : 1, "RuleId" : { "Ref" : "MyRule" } }, { "Action" : { "Type" : "BLOCK" }, "Priority" : 2, "RuleId" : { "Ref" : "BadReferersRule" } }, { "Action" : { "Type" : "BLOCK" }, "Priority" : 3, "RuleId" : { "Ref" : "SqlInjRule" } } ] } }
YAML
MyWebACL: Type: "AWS::WAFRegional::WebACL" Properties: Name: "WebACL to with three rules" DefaultAction: Type: "ALLOW" MetricName: "MyWebACL" Rules: - Action: Type: "BLOCK" Priority: 1 RuleId: Ref: "MyRule" - Action: Type: "BLOCK" Priority: 2 RuleId: Ref: "BadReferersRule" - Action: Type: "BLOCK" Priority: 3 RuleId: Ref: "SqlInjRule"


{% block no_container_content %}{% endblock %}
{% comment %}
this exists so that if there is an error in oTree's channel consumers,
such as data export or rooms, the user gets some notification rather than it silently
failing.
don't put it in global base template even though it could be useful there too,
because we don't want to pollute "view source"
in user-defined pages
{% endcomment %}
{% endblock body_main %}


I’m Femi Oladipupo, an MIT Senior pursuing a Bachelor of Science in Computer Science and Engineering (Course 6-3) as well as a minor in Comparative Media Studies (Course CMS).
I have about 7 years of programming experience. I started with amateur game development projects in high school, and continued in university through coursework and undergraduate research.


Chip Goines (Harvard University and Jeffrey C. Witt (Loyola University Maryland)
In Collaboration with Kate Lynch and Doug Emery (University of Pennsylvania) and Marina Rustow and the Princeton Geniza Lab (Princeton University)
Vatican, Vatican City, June 9, 2017
Slide Deck: http://lombardpress.org/slides/2017-06-09-vatican-ldn


Bitwise dynamic programming uses the concepts of dynamic programming to use the results of subproblems to solve larger problems. We can also incorporate bitwise manipulation, which allows us to act on subsets. Often, this strategy can be used to optimize a naive bash into something with complexity, or something similar. This is significant - complexity allows us to reach about , while a complexity of lets us reach about . In particular, if a problem's constraints include , there are pretty good chances that some sort of bitwise DP is involved.
We'll start by solving an extremely simple problem - count the number of permutations of N objects. For now, let's ignore the combinatorial solution. Here, the "naive" solution would involve looping over every permutation. Instead, let's define an array dp[subset] as the number of ways to arrange a subset of the N objects. In order to calculate this for a particular subset, we can first pick an element to appear last in the permutation and then add the number of ways we can arrange the remaining objects. In other words, .
For implementation, we'll treat an integer as a subset where each bit of the integer represents one of the objects. To perform operations on these subsets, you can refer to the previous bitwise lesson summary. Finally, we have to ensure that we process the subsets in the correct order, to ensure that all dp states we rely on have already been calculated. But here, we can just process the subsets in numerical order, because when we exclude an object from a subset the result is guaranteed to be less. With those in mind, you can solve the problem.
#include<bits/stdc++.h> using namespace std; int main() { int n = 8; int dp[1<<n]; // 1 way to arrange 0 objects dp[0] = 1; for (int subset = 1; subset < (1<<n); subset++) { // initialize to 0 dp[subset] = 0; // for each object in the subset for (int i = 0; i < n; i++) if ((subset & 1<<i) > 0) { // add number of ways to arrange every other object dp[subset] += dp[subset ^ 1<<i]; } } // output answer for entire set cout << dp[(1<<8) - 1] << endl; }
Now we can use this technique to solve the travelling salesman problem more efficiently. In the travelling salesman problem, we must find a shortest path that visits every node through a graph. Many versions will make the graph fully connected (where every pair of nodes is connected by an edge), but if they aren't connected we can just fill in edges with infinite weight. In the naive solution, we can test every permutation of nodes and find the one with minimal distance. This solution is , but we can do better.
As with before, we'll define a dp state by a subset. But in order to use prior results to calculate answers for the current subset, we'll also need to store the last node visited. So dp[subset][last] will be the minimum distance needed to visit subset nodes and end at last. Let subset2 be equal to all of subset, except for last. Then we can do casework on the node in subset2 that we end on, and add on the distance from that node to last. In other words,
#include<bits/stdc++.h> using namespace std; int main() { // take inputs int n; cin >> n; int dist[n][n]; for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { cin >> dist[i][j]; } } // initialize dp array int dp[1<<n][n]; fill(dp[0], dp[1<<n], 1e9); for (int i = 0; i < n; i++) dp[1<<i][i]=0; // iterate through subset, last, and second-last for (int subset = 0; subset < (1<<n); subset++) { for (int i = 0; i < n; i++) if (subset&1<<i) { int subset2 = subset ^ 1<<i; for (int j = 0; j < n; j++) if ((subset2&1<<j)>0) { int test = dp[subset2][j] + dist[j][i]; dp[subset][i]=min(dp[subset][i], test); } } } // test possible ending cities to find the answer int ans = 1e9; for (int i = 0; i < n; i++) ans = min(ans, dp[(1<<n)-1][i]); cout << ans << endl; }
Original problem: Atcoder - Matching.
The naive solution is simple: we can test every possible matching by noting that the first man has women to match with, the second has to match with, and so on. In total, the complexity of this approach is .
On first glance, it may seem that the bitwise DP solution would involve dp[subsetA][subsetB], which counts the number of ways to match subsetA men with subsetB women. The complexity of this approach would be , which is better than our previous but still too slow. However, the key observation is that we can assume that the subset of men will just be the first k men (where k is the size of the subset). And so we can set dp[subset] equal to the number of ways to match a subset of women with the first k men. Our dp transition will involve picking a woman to be paired with the k'th man, and adding the number of way to match the rest of the women with the first k-1 men. So the dp transition will be
#include <bits/stdc++.h> using namespace std; const int MOD = 1e9+7; int main() { // read input int n; cin >> n; int a[n][n]; for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { cin >> a[i][j]; } } // initialize dp array vector<int> dp(1<<n, 0); dp[0]=1; for (int subset = 1; subset < 1<<n; subset++) { // index of last man int k = __builtin_popcount(subset)-1; // for each compatible woman for (int last = 0; last < n; last++) { if ((subset & (1<<last)) > 0 && a[k][last]==1) { // add # ways to arrange rest of subset dp[subset] += dp[subset ^ (1<<last)]; dp[subset] %= MOD; } } } // output answer cout << dp[(1<<n)-1] << endl; }


A professional package that comes with hunderds of UI components, forms, tables, charts, dashboards, pages and svg icons. Each one is fully customizable, responsive and easy to use.
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This is a preview of the Azure voice font service. Use this tool to record your voice, and generate the training data for the service. Read the text on the screen as accurately as you can. You can re-record as many times as you like. Recordings should be under one minute each, so speak at a natural pace. You can use as many sessions as you like, so be sure to take breaks and finish and export each time.


mixparams = mdnfwd(net, x) [mixparams, y, z] = mdnfwd(net, x) [mixparams, y, z, a] = mdnfwd(net, x)
mixparams = mdnfwd(net, x)
takes a mixture density network data
structure net
and a matrix x
of input vectors, and forward
propagates the inputs through the network to generate a structure
mixparams
which contains the parameters of several mixture models.
Each row of x
represents
one input vector and the corresponding row of the matrices in mixparams
represents the parameters of a mixture model for the conditional probability
of target vectors given the input vector. This is not represented as an array
of gmm
structures to improve the efficiency of MDN training.
The fields in mixparams
are
type = 'mdnmixes' ncentres = number of mixture components dimtarget = dimension of target space mixcoeffs = mixing coefficients centres = means of Gaussians: stored as one row per pattern covars = covariances of Gaussians nparams = number of parameters
[mixparams, y, z] = mdnfwd(net, x)
also generates a matrix y
of
the outputs of the MLP and a matrix z
of the hidden
unit activations where each row corresponds to one pattern.
[mixparams, y, z, a] = mlpfwd(net, x)
also returns a matrix a
giving the summed inputs to each output unit, where each row
corresponds to one pattern.
mdn
, mdn2gmm
, mdnerr
, mdngrad
, mlpfwd
Copyright (c) Ian T Nabney (1996-9)
David J Evans (1998)


I’m more than happy with the build and simplicity of Ikea furniture for almost anything but when it comes to a couch, I want something big, robust and super comfortable. Since moving we’ve now got everything we need and it’s time to start replacing our crappiest things that have simply done the job with some better things. I’ve always wanted a corner lounge and our current couches are old hand-me-downs.
We quickly found a great corner lounge for $2300, much too expensive.
A couple of weeks later we resumed the search and found the exact same couch on sale for $1600! A massive $700 off and well within our price range, great! We measured our lounge room and went to the furniture store, measuring tape in hand, ready to lay down the cash.
We found the couch, sat in the couch, loved the couch and then measured the couch. Too big, no way it’d fit. We browsed the other 80+ couches in the store, none came anywhere near the comfort, practicality (it has a built-in bed) or price of the original brilliant lounge.
We came home and slumped into our ridiculously low, vinyl, old, two seater lounges, dejected.
Then I had a brilliant idea, a complete rearrangement of the lounge room into a layout so crazy it just might work. The more we thought about, the more we measured and mapped out, the more possible and just plain superior to our current setup it looked! Great! We can buy the couch, first thing next weekend!
This afternoon I check the website looking for a phone number so I could ask about getting it up our stairs and into the apartment. Horror! The couch is no longer on sale for $1600 but for $2000! Fuck!
I call the store, begging for the sale price of just two days ago, when we were ready to buy. He told me the probably-bullshit story of their suppliers upping the price but agreed to meet me half way, $1800. I reluctantly accepted.


Promoter
Name
Name
Trading
List the items briefly
Address line 1
Address line 2
Address line 3
1999
05
Proprietorship firm
Indian Bank
Naranpura Branch
10*****
I******835
*********
**********


Select this option if you want the Kiuwan Local Analyzer process
to use the Jenkins proxy settings when connecting to Kiuwan.
You can configure this settings in the Advanced configuration
tab on the Plugin Manager section.
NOTE: these proxy settings will only apply to Kiuwan Local Analyzer. Connections made
by the plugin itself will always use the configured Jenkins proxy.


The CSS dimension properties allow you to control the height and width of an element.
This element has a width of 100%.
The height
and width
properties are used to set the height and width of an element.
The height
and width
can be set to auto (this is default. Means that the browser calculates the height and width), or be specified in length values, like px, cm, etc., or in percent (%) of the containing block.
Note: The height
and width
properties do not include padding, borders, or margins; they set the height/width of the area inside the padding, border, and margin of the element!
The following example shows a

element with a height of 100 pixels and a width of 500 pixels:
div {
width: 500px;
height: 100px;
border: 3px solid #73AD21;
}
The max-width
property is used to set the maximum width of an element.
The max-width
can be specified in length values, like px, cm, etc., or in percent (%) of the containing block, or set to none (this is default. Means that there is no maximum width).
The problem with the

above occurs when the browser window is smaller than the width of the element (500px). The browser then adds a horizontal scrollbar to the page.
Using max-width
instead, in this situation, will improve the browser's handling of small windows.
Tip: Drag the browser window to smaller than 500px wide, to see the difference between the two divs!
Note: The value of the max-width
property overrides width
.
The following example shows a

element with a height of 100 pixels and a max-width of 500 pixels:
div {
max-width: 500px;
height: 100px;
border: 3px solid #73AD21;
}


THE IMPACT OF DEMAND FORECASTING ON SUPPLY CHAIN PERFORMANCE
Supply chain according to Harland (1996), comprises of the various activities involved in the conversion of goods from the raw state to the final state for consumer usage and consumption. This process of goods conversion as supported by McCormack and Kasper (2002), involves planning, design, and regulation of the flow of material, information and finance along the supply chain to satisfy customer’s needs. However, the globalisation of markets and liberalisation of economies has culminated in the growing competitiveness and volatility in the present day market. This has intensified the need for companies to engage in supply chain process evaluations and improvements through demand forecast accuracy for efficient business performance (Nenni et., al. 2013).
Demand forecasting is essential for maximum customer satisfaction in supply chain processes (Giannakis and Croom 2004). It is a process of knowing what quantities of products are needed at what place and time. Many techniques of demand forecasting are in use today, however the exponential smoothing and moving average are the two popular time series methods of forecasting (Ibrahim and Dauda 2012).
In modern day businesses, firms have assembled structures for sourcing of raw materials, conversion of materials into finished products or creating services, storing and evenly distribution of same products to customers and consumers (Krajewski, 2002). Organisation’s supply chain is liable for its achievement or losses that has resulted in the need for a proper regulation and coordination amongst supply chain associates (Lockamy, A. & McCormack, K. 2004). Over the past years, in an attempt to advance the long-term efficacy of the supply chain, Madhani (2013), agrees that many organisations have collaborated with other supply chain members for efficiency. This concept has resulted in achieving maximum customer satisfaction and company’s profitability.
Veiga et., al. (2010) agree that the accuracy of a company’s demand forecast has a direct impact on the services rendered to consumers, the actual cost of the supply chain and the level of safety stock units (SKU) thus for a company to remain successful, necessitates a high level of accuracy during forecasting which revolves around products, time, geographies, medium and characteristics. Forecasting is a key management performance tool for the fulfilment of customer expectations on delivery, however the success depends on demand information created by inventory policies of the downstream phase in the supply chain process (Reiner and Fichtinger, 2009). Nonetheless, information varies across the supply network based on the needs of businesses for timely replenishment of products and there is need to explore ways to modify, renovate and restructure the complete network so as to accurately utilise available information to forecast products demand for maximum customer’s satisfaction.
While supply chain management is widely adopted by retailing companies, the aspect of accurate demand forecast is still questionable and less understood (McCormack and Kasper 2002). Accurate demand forecasting is problematical in the sense that there are still likelihood of tension and downturn to firms who struggle to remain competitively positioned. They face challenges of converting their operations from a static to a dynamic platform within interconnected roles across enterprise supply chain and this undertaking is very complex because supply chain management encompasses managing of many frame-work, establishing strategically inclined capabilities not only within the company itself, but also among other firms that are members of their value-adding networks (Lockamy, A. & McCormack, K. 2004).
Consumer’s demand characterised by both growth and seasonal influences present a very difficult forecasting problem due to the fluctuating demand patterns (Fildes and Hastings (1994). There are comprehensive forecasting techniques available for predicting the future demand for higher supply chain performance but researchers have proved that some of these techniques pose various problems during forecasting (Benkachcha et., al. 2013). All forecasts depend on previous sales history hence stringent and rigorous routine monitoring and maintenance is required to ensure that forecasts are authentic.
According to Madhani (2013), there is a significant relationship between effective supply chain management and efficient customer response, a management idea that deals on how to collaborate in retailing industries with the sole aim of ensuring maximum customer’s satisfaction. Industries in the supply chain such as suppliers, intermediaries, logistics and transportation providers must communicate and interact properly to ensure meeting up with customer’s expectations effectively (Simatupang & Sridharan 2006).
Companies become dependent on each other and must collaborate in supply chain network to coordinate their activities. Mentzer et al., (2000) agree that limited attention has been rendered towards obtaining the features that represent different areas of collaboration. This according to Min & Yu (2004), highlights why it is important to understand the aspect of collaboration which if neglected can lead to distortion in information sharing and inappropriate replenishment of products that lead to inefficiencies and low supply chain dynamics (Ahmad & Ullah 2013).
Ismail and Abu (2013) found out that making accurate estimates is very vital to both forecasting and all productive system as they form the basis of planning and estimating the quantity of goods customers will consume however, the growing and changing customer’s needs appear to make it difficult to accurately predict demand. A key concern is whether customer’s needs are accurately predicted, sourced and delivered at the right time. However, it is important to highlight that for a company to remain competitively superior, requires identifying the right market signals through demand sensing capabilities so as to provide steady supply of needed products at the right place and at the right time thereby creating an effective response (Veiga et., al. 2010).
According to Lunenburg (2012) demand forecast is the background of all planning as it gives estimates of future expectancies. Over the past years, different forecasting techniques have been established and applied to the practical problem of forecasting with varying level of success (Benkachcha et., al. 2013). Most companies admit that they have a comprehensive historical demand database which they use for forecasting, but the fact still remains that these records display sales histories and not demand histories (Veiga et., al. 2010).
Apparently, to forecast for future demand, one ought to reinforce the sales records with predictions of demand lost. The concern thus is, what quantity of products would have been sold when a company is out of stock? Researchers have proved that forecasting of future demand will identify the quantities of products to be sourced, procured, processed and shipped (Matthew et., al. 2013). To remain competitively advantageous, most companies no longer wait for customers to demand for products before they react, hence they tend to predict and plan for future demand and act promptly as the customer’s needs and orders arises.
Demand forecast is very vital since the process involved in transforming raw materials to finished goods and shipping them from suppliers to end users can be time consuming hence the need for efficient planning (Lunenburg 2012). Accurate demand forecasting is increasingly proving difficult to companies due to noisy data, new products, non-availability of history data, shorter product life cycles, changing seasonal patterns, varying customer demand and increased product variety (Veiga et., al. 2010).
These issues have rendered the demand pattern unpredictable, but forecasting is highly important as it boosts the competitive advantage of a firm and encourages savings as a result of inventory investment for decision making and planning, but Chase (1997), suggested that customer’s demand can be estimated through a set of causal factors such as commodity price, seasonality, advertising, costs and sales promotion. Patil et., al. (2012) supported this statement by identifying that many factors such as time, resources, advertising, promotions, unusual weather conditions, price changes, events, price, vacations, public holidays, input and output components ought to be considered as they can positively or negatively affect the accuracy of a forecast.
REFERENCES Ahmad, S. & Ullah, A. (2013) Driving Forces of Collaboration in Supply Chain: A Review, Journal of Contemporary Research in Business, Vol. 5, No. 7, pp. 40-42.
Benkachcha, S., Benhra, J. & El Hassani.H. (2013) Causal Method and Time Series Forecasting model based on Artificial Neural Network, International Journal of Computer Applications, Vol. 75, No.7, p. 37.
Chase, C. W. Jr., (1997), Integrating Market Response Models in Sales Forecasting, Journal of Business Forecasting. Vol. 2, p. 27.
Fildes, R. and Hastings, R. (1994) The Organisation and Improvement of Market Forecasting, Journal of the Operational Research Society, Vol. 45, No.1 pp.2-12.
Giannakis, M. and Croom, S. R. (2004), Toward the Development of a Supply Chain Management Paradigm: A Conceptual Framework, Journal of Supply Chain Management, Vol.40, No.2, pp25-30.
Harland, C., M. (1996) Supply Chain Management: Relationships, Chains and Networks, British Journal of Management, vol. 7, Special Issue.
Ibrahim L. K and Dauda U. (2012) Modeling Monthly Rainfall Time Series Using ETS Space and SARIMA Models, International Journal of Physics and Mathematical Research, Vol. 1, No. 1, pp. 11-13.
Ismail, Z. and Abu, N. (2013) A Study On New Product Demand Forecasting Based On Bass Diffusion Model, International Journal of Mathematics and Statistics, Vol. 9, No.2. pp.84-85.
Krajewski, L. (2002) Reflections on Operations Management Research, Journal of Operations and Technology, Organisation Science, Vol.3, pp. 381-396.
Lockamy, A. & McCormack, K. (2004) The Development of a Supply Chain Management Process Maturity Model Using the Concepts of Business Process Orientation, An International Journal of Supply Chain Management, Vol. 9, No. 4, P. 272, 276.
Lunenburg, F. C. (2012) Human Resource Planning: Forecasting Demand and Supply, International Journal of Business Management Administration Vol. 15, No.1, pp4.
Madhani, P. M. (2013) Making a Conceptual Framework for Supply Chain Management and Marketing Integration, Journal of Management, Vol. 11, No.3, pp55-60.
Mathew, A., Somasekaran E.M & Jenson J. E. (2013) Demand Forecasting For Economic Order Quantity in Inventory Management, International Journal of Scientific and Research Publications, Vol. 3, Issue 10, p.1.
McCormack, K. and Kasper, K. (2002), The Extended Supply Chain; A Statistical Study, An International Journal, Emerald Publisher, Vol. 9, No. 2. pp. 133-135.
Mentzer, J.M., Min, S. and Zacharia, Z.G. (2000), The nature of inter-firm partnering in supply Chain management, Journal of Retailing, Vol. 76 No. 4, pp. 549-551.
Min, H. & Yu, W. (2004) Collaborative Planning, Forecasting and Replenishment: Demand Planning in Supply Chain Management, The Fourth International Conference on Electronic Business, pp58-60.
Nenni, M. E., Giustiniano, L. & Pirolo, L. (2013) Demand Forecasting in the Fashion Industry: A Review, International Journal of Engineering Business Management, Special Issue on Innovations in Fashion Industry, pp1-3.
Patil, D.P., Shrotri, A.P., and Dandekar, A.R. (2012) Management of Uncertainty In Supply Chain, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, Issue 5, pp. 303-305.
Reiner, G., and Fichtinger, J. (2009) Demand Forecasting for Supply Processes in Consideration of Pricing and Market Information- International Journal of production Economics, vol. 118, Issue 1, P1.
Simatupang, T. M. & Sridharan, R. (2006) An integrative framework for supply chain collaboration, International Journal of Logistics and Management, Vol. 16, No. 2, p.258.
Veiga, C. R. P. D., Veiga, C. P. D., Duclós, L. C. (2010) The Accuracy of Demand Forecast Models as a Critical Factor in the Financial Performance of the Food Industry, Future Studies Research Journal, Vol. 2, Nos. 2, pp.84-88.


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Delicious homemade pizza that explodes with flavour made fresh. Fadhili's pizzeria has created a menu featuring local ingredients, precisely cooked and brought together on the plate to create a lasting impression so you keep coming back for more.
Fadhili's Pizzeria is an upscale casual restaurant serving authentic Neapolitan Pizza in a warm and lively modern atmosphere. Come in or call today and experience the great flavour of Fadhili's!!


Rent-A-Place is a company that strives to give their clients clean and affordable apartment rentals. We are focused on providing apartments that are safe for all of its occupants, while maintaining a style and luxury that our clients love.
View our Apartment listings


Trading as: usernamehere
GBPBIT
| Name | Action | Quantity | Rate |
|---|---|---|---|
| Iain | BUY | 50 | 1.25 |
| Bob | BUY | 50 | 1.5 |
| Milo | BUY | 1 | 1.2 |
| Davide | SELL | 500 | 1.9 |
GBPBIT
| Status | Name | Action | Quantity | Rate |
|---|---|---|---|---|
| Pending | Iain | BUY | 50 | 1.25 |
| Completed | Iain | BUY | 50 | 1.5 |
| Completed | Iain | BUY | 1 | 1.2 |
| Completed | Iain | SELL | 500 | 1.9 |
GBPBIT
| Name | Action | Quantity | Rate |
|---|---|---|---|
| Iain | BUY | 50 | 1.25 |
| Bob | BUY | 50 | 1.5 |
| Milo | BUY | 1 | 1.2 |
| Davide | SELL | 500 | 1.9 |


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This is a heading (Center Aligned)
Sr No.
Name
Address
Phone no
FirstName
LastName
Data
Data
Data
Data
Data
Data
Data
Data
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Data
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This is a link to another webpage


Versions can be specified in one of two ways:
-
Component Versions:
Each component-version pair should be on a separate line.
Separate each component and version number with a colon. Example- My Component:2.5
-
SNAPSHOT:
Specify the name of a single SNAPSHOT to deploy. This snapshot will be ignored if the
'Deploy With Snapshot' option is selected in the 'Create Snapshot of Build Environment'.
Indicate a Snapshot by prepending the word SNAPSHOT and an equals to the Snapshot name. Example- SNAPSHOT=Name


{% load i18n %} {% load block_helper %}
{% blocktrans %} Sometimes public authorities require a written letter with a signature from you. We can send a letter as a fax with your signature, if you provide it below. {% endblocktrans %}
{% endif %}{% blocktrans %} We will only place your signature under requests and messages that you send. {% endblocktrans %}
{% blocktrans %} Please write your signature in the box below by using your mouse on Desktop or your finger on touch screens. {% endblocktrans %}


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Installing the NewRelic .NET agent on an Alpine Docker image
Alpine is a small Linux distribution, commonly used in containers like Docker due to its size.
In order to install the .NET agent on an Alpine, you need to modify your dockerfile to download the compressed agent binaries and manually install them. This involves a bit of file, folder and permission manipulation, as well as setting up a symlink.

Once your application gets traffic, your application name should show in the New Relic UI.
Newest Posts
- Installing the NewRelic .NET agent on an Alpine Docker image
- An implementation of the Capital Controls kata in Python
- Trigger validation of a dropdown box when tabbing over it in KnockoutJS
- The Capital Controls Kata
- Github Pages offers HTTPS for one custom domain. However you can point more than one domain to your pages (with caveats).


Welcome to Programming Robots Study Group [PRSG]
Programming Robots Study Group is a group of robot enthusiasts. Our mission is to create and enhance a low cost, open source, ROS platform robots. Our platform uses a variety of sensors, actuators and microcontrollers.
Get Started
We’ve created designs and how to's to get you started. Get a project overview from our group repository.
Participate
Be part of the team:
- Look for 'Programming Robots Study Group' on Robot Garden's meetup.
- Attend our meetings virtually. See meetup for schedule times.
- Join our Slack channel.
- Join our forum.
- Check out our YouTube videos.
Goals
2017 Goal: Competition robots, both table top and outdoor find and retrieve tasks. 2016 Goal: Robots autonomously making plans using swarm algorithms, loosely organized by a central command. 2015 Goal: ROS robots based upon Magician Chassis.
Support or Contact
Want to contact us directly? Need help being added to slack? Contact us and we’ll help you sort it out.


The following comma splices have been found in your text:
The comma splice occurs when a comma is used to connect two independent clauses.
Example: Jim usually gets on with everybody, he is an understanding person.
If you have two independent clauses that need to be separated, you can do so by using a full stop:
Jim usually gets on with everybody. He is an understanding person.


| Jump to DART Documentation Main Index |
This module acts as an intermediary between DART compliant models and the filter. At one time the assim_model_type, which combines a state vector and a time_type, was envisioned as being fundamental to how DART views model states. This paradigm is gradually being abandoned so that model state vectors and times are handled as separate data types. It is important to call static_init_assim_model before using routines in assim_model_mod. Interfaces to work with model time stepping, restart files, and computations about the locations of model state variables and the distance between observations and state variables. Many of the interfaces are passed through nearly directly to the model_mod.
A note about documentation style. Optional arguments are enclosed in brackets [like this].
This module does not have a namelist.
types_mod location_mod (model dependent choice) time_manager_mod utilities_mod model_mod netcdf typeSizes (part of netcdf)
type assim_model_type private real(r8), pointer :: state_vector(:) type(time_type) :: time integer :: model_size integer :: copyID end type assim_model_type
This type is used to represent both the state and time of a state from a model.
| Component | Description |
|---|---|
| state_vector | A one dimensional representation of the model state vector. |
| time | The time of the model state. |
| model_s | Size of the model state vector. |
| copyID | Not used in present implementation. |
type netcdf_file_type integer :: ncid integer :: Ntimes integer :: NtimesMAX real(r8), pointer :: rtimes(:) type(time_type), pointer :: times(:) character(len = 80) :: fname end type netcdf_file_type
Basically, we want to keep a local mirror of the unlimited dimension coordinate variable (i.e. time) because dynamically querying it causes unacceptable performance degradation over "long" integrations.
| Component | Description |
|---|---|
| ncid | The netcdf file unit id. |
| Ntimes | The current working length. |
| NtimesMAX | Allocated length. |
| rtimes | Times as real (r8). |
| times | Times as time_types. |
| fname | Netcdf file name. |
Initializes the assim_model class. Must be called before any other assim_model_mod interfaces are used. Also calls the static initialization for the underlying model. There are no arguments.
type(netcdf_file_type) :: init_diag_output character (len = *), intent(in) :: FileName character (len = *), intent(in) :: global_meta_data integer, intent(in) :: copies_of_field_per_time character (len = *), intent(in) :: meta_data_per_copy(copies_of_field_per_time) integer, optional, intent(in) :: lagID
Initializes a netCDF file for output of state space diagnostics. A handle to the channel on which the file is opened is returned.
| ncFileID | Identifier for the netcdf file is returned. This is not an integer unit number, but a derived type containing additional information about the opened file. |
| FileName | Name of file to open. |
| global_meta_data | Global metadata that describes the contents of this file. |
| copies_of_field_per_time | Number of copies of data to be written at each time. For instance, these could be the prior ensemble members, prior ensemble mean, prior ensemble spread, posterior ensemble members, posterior spread and mean, etc.. |
| meta_data_per_copy | Metadata describing each of the copies. |
| lagID | If using the smoother, which lag number this output is for. |
integer :: get_model_size
Returns the size of the model state vector. This is a direct pass through to the model_mod.
type(time_type) :: get_closest_state_time_to type(time_type), intent(in) :: model_time type(time_type), intent(in) :: time
Returns the closest time that a model is capable of advancing a given state to a specified time. For instance, what is the closest time to 12GMT 01 January, 2004 that a model state at 00GMT 01 January, 2004 can be advanced? If the model time is past the time, the model time is returned (new feature in releases after Hawaii).
| var | The closest time to which the model can be advanced is returned. |
| model_time | The time of a model state vector. |
| time | A time that one would like to get close to with the model. |
Pass through to model_mod. See model_mod documentation for arguments and description.
type(time_type) :: get_model_time type(assim_model_type), intent(in) :: assim_model
Returns time from an assim_model type.
| var | Returned time from assim_model |
| assim_model | Assim_model type from which to extract time |
real(r8) :: get_model_state_vector(model_size) type(assim_model_type), intent(in) :: assim_model
Returns the state vector component from an assim_model_type.
| var | Returned state vector |
| assim_model | Input assim_model_type |
type(assim_model_type), intent(out) :: model_out type(assim_model_type), intent(in) :: model_in
Copies one assim_model_type to another.
| model_out | Copy. |
| model_in | Data to be copied. |
real(r8), intent(in) :: x(:) type(location_type), intent(in) :: location integer, intent(in) :: loctype real(r8), intent(out) :: obs_vals integer, intent(out) :: istatus
Interpolates a given model state variable type to a location given the model state vector. Nearly direct call to model_interpolate in model_mod. See model_mod for the error return values in istatus.
| x | Model state vector. |
| location | Location to which to interpolate. |
| loctype | Type of variable to interpolate. |
| obs_vals | Returned interpolated value. |
| istatus | Returned as 0 if all is well, else various errors. |
type(assim_model_type), intent(inout) :: assim_model type(time_type), intent(in) :: time
Sets the time in an assim_model_type.
| assim_model | Set the time in this assim_model_type. |
| time | Set to this time |
type(assim_model_type), intent(inout) :: assim_model real(r8), intent(in) :: state(:)
Set the state in an assim_model_type.
| assim_model | Set the state vector in this assim_model_type. |
| state | The state vector to be inserted. |
type(assim_model_type), intent(in) :: assim_model integer, intent(in) :: funit type(time_type), optional, intent(in) :: target_time
Writes a restart from an assim_model_type with an optional target_time.
| assim_model | Write a restart from this assim_model_type. |
| funit | Integer file unit id open for output of restart files. |
| target_time | If present, put this target time at the front of the restart file. |
type(assim_model_type), intent(out) :: assim_model integer, intent(in) :: funit type(time_type), optional, intent(out) :: target_time
Read a state restart file into assim_model_type. Optionally read a prepended target time.
| assim_model | Read the time and state vector from restart into this. |
| funit | File id that has been opened for reading restart files. |
| target_time | If present, read a target time from the front of the file into this. |
type(netcdf_file_type), intent(inout) :: ndFileID type(assim_model_type), intent(in) :: state integer, optional, intent(in) :: copy_index
Writes one copy of the state time and vector to a netCDF file.
| ndFileID | An identifier for a netCDF file |
| state | State vector and time |
| copy_index | Which copy of state is to be output |
Called to clean-up at end of assim_model use. For now just passes through to model_mod.
integer, intent(in) :: file_id type(assim_model_type), intent(inout) :: state integer, intent(out) :: copy_index
Used to read in a particular copy of the state vector from an open state diagnostics file.
| file_id | Integer descriptor (channel number) for a diagnostics file being read. |
| state | Assim_model_type to read in data. |
| copy_index | Which copy of state to be read. |
integer :: init_diag_input character(len=*), intent(in) :: file_name character(len=*), intent(out) :: global_meta_data integer, intent(out) :: model_size integer, intent(out) :: copies_of_field_per_time
Opens a state diagnostic file and reads the global meta data, model size, and number of data copies.
| var | Returns the unit number on which the file is open. |
| file_name | File name of state diagnostic file. |
| global_meta_data | Global metadata string from file. |
| model_size | Size of model. |
| copies_of_field_per_time | Number of copies of the state vector at each time. |
type(assim_model_type), intent(inout) :: state
Creates storage for an assim_model_type.
| state | An assim_model_type that needs storage created. |
integer, intent(in) :: file_id integer, intent(in) :: model_size_out integer, intent(in) :: num_copies type(location_type), intent(out) :: location(model_size_out) character(len = *) :: meta_data_per_copy(num_copies)
Reads meta-data describing state vectors in a state diagnostics file. Given the file, the model_size, and the number of copies, returns the locations of each state variable and the text description of each copy.
| file_id | Integer channel open to state diagostic file being read |
| Model_size_out | model size |
| num_copies | Number of copies of state in file |
| location | Returned locations for state vector |
| meta_data_per_copy | Meta data describing what is in each copy of state vector |
integer :: finalize_diag_output type(netcdf_file_type), intent(inout) :: ncFileID
Used to complete writing on and open netcdf file. An error return is provided for passing to the netcdf error handling routines.
| var | Returns an error value. |
| ncFileID | Netcdf file id of an open file. |
type(time_type), intent(out) :: model_time real(r8), intent(out) :: model_state(:) integer, intent(in) :: funit type(time_type), optional, intent(out) :: target_time
Reads a model time and state, and optionally a prepended target time, from a state restart file.
| model_time | Returned time of model state |
| model_state | Returned model state. |
| funit | Channel open for reading a state restart file. |
| target_time | If present, this time is read from the front of the restart file. |
type(netcdf_file_type), intent(inout) :: ncFileID type(time_type), intent(in) :: model_time real(r8), intent(in) :: model_state(:) integer, optional, intent(in) :: copy_index
Write a state vector to a state diagnostics netcdf file.
| ncFileID | Unit for a state vector netcdf file open for output. |
| model_time | The time of the state to be output |
| model_state | A model state vector to be output. |
| copy_index | Which copy of state vector is to be written, default is copy 1 |
type(time_type), intent(in) :: model_time real(r8), intent(in) :: model_state(:) integer, intent(in) :: funit type(time_type), optional, intent(in) :: target_time
Writes a model time and state vector to a restart file and optionally prepends a target time.
| model_time | Time of model state. |
| model_state | Model state vector. |
| funit | Channel of file open for restart output. |
| target_time | If present, time to be prepended to state time / vector. |
Passes through to pert_model_state in model_mod. See model_mod documentation for arguments and details.
integer :: nc_append_time type(netcdf_file_type), intent(inout) :: ncFileID type(time_type), intent(in) :: time
Appends the time to the time coordinate variable of the netcdf file. The new length of the time variable is returned. Requires that time is a coordinate variable AND it is the unlimited dimension.
| var | Returns new length of time variable. |
| ncFileID | Points to open netcdf file. |
| time | The next time to be added to the file. |
integer :: nc_write_calendar_atts type(netcdf_file_type), intent(in) :: ncFileID integer, intent(in) :: TimeVarID
Sets up the metadata for the appropriate calendar being used in the time manager an writes it to a netcdf file.
| var | Returns a netcdf error code. |
| ncFileID | Netcdf file id pointing to a file open for writing. |
| TimeVarID | The index of the time variable in the netcdf file. |
integer :: nc_get_tindex type(netcdf_file_type), intent(inout) :: ncFileID type(time_type), intent(in) :: statetime
Returns the index of a time from the time variable in a netcdf file. This function has been replaced with more efficient approaches and may be deleted from future releases.
| var | The index of the time in the netcdf file. |
| ncFileID | File id for an open netcdf file. |
| statetime | The time to be found in the netcdf file. |
type(time_type) :: get_model_time_step
This passes through to model_mod. See model_mod documentation for arguments and details.
| var | Returns time step of model. |
integer :: open_restart_read character(len=*), intent(in) :: file_name
Opens a restart file for readig.
| var | Returns a file descriptor (channel number). |
| file_name | Name of restart file to be open for reading. |
integer :: open_restart_write character(len=*), intent(in) :: file_name
Open a restart file for writing.
| var | Returns a file descriptor (channel) for a restart file. |
| file_name | File name of restart file to be opened. |
integer, intent(in) :: file_unit
Closes a restart file.
| file_unit | File descriptor (channel number) of open restart file. |
Advances a model by one step. Pass through to model_mod. See model_mod documentation for arguments and details.
type(time_type), intent(out) :: time real(r8), intent(out) :: x
Obtains an initial condition from models that support this option.
| time | the valid time of the model state |
| x | the initial model state |
type(r8), intent(in) :: ens_mean(:)
An array of length model_size containing the ensemble means. This is a direct pass through to the model_mod.
| ens_mean | Array of length model_size containing the mean for each entry in the state vector. |
type(get_close_type), intent(inout) :: gc type(r8), intent(in) :: maxdist
Sets the threshold distance. Anything closer than this is deemed to be close. This is a direct pass through to the model_mod, which in turn can pass through to the location_mod.
| gc | Data for efficiently finding close locations. |
| maxdist | Anything closer than this distance is a close location. |
type(get_close_type), intent(in) :: gc type(location_type), intent(in) :: base_obs_loc integer, intent(in) :: base_obs_kind type(location_type), intent(in) :: obs(:) integer, intent(in) :: obs_kind(:) integer, intent(out) :: num_close integer, intent(out) :: close_ind(:) real(r8), optional, intent(out) :: dist(:)
Given a single location and a list of other locations, returns the indices of all the locations close to the single one along with the number of these and the distances for the close ones. The observation kinds are passed in to allow more sophisticated distance computations to be done if needed. This is a direct pass through to the model_mod, which in turn can pass through to the location_mod.
| gc | Data for efficiently finding close locations. |
| base_obs_loc | Single given location. |
| base_obs_kind | Kind of the single location. |
| obs | List of observations from which close ones are to be found. |
| obs_kind | Kind associated with observations in obs list. |
| num_close | Number of observations close to the given location. |
| close_ind | Indices of those locations that are close. |
| dist | Distance between given location and the close ones identified in close_ind. |
type(get_close_type), intent(inout) :: gc integer, intent(in) :: num type(location_type), intent(in) :: obs(:)
Initialize storage for efficient identification of locations close to a given location. Allocates storage for keeping track of which 'box' each observation in the list is in. This is a direct pass through to the model_mod, which in turn can pass through to the location_mod.
| gc | Data for efficiently finding close locations. |
| num | The number of locations in the list. |
| obs | The location of each element in the list, not used in 1D implementation. |
| filename | purpose/comment |
|---|---|
| filter_restart | specified in &filter_nml:restart_in_filename |
| filter_restart | specified in &filter_nml:restart_out_filename |
| input.nml | to read namelists |
| Routine | Message | Comment |
|---|---|---|
| init_diag_output | Compiler does not support required kinds of variables. | NetCDF-f90 interface function byteSizeOK returned FALSE |
| init_diag_output and various nc_XXX | various NetCDF-f90 messages | Returned by one of the NetCDF calls in this subroutine. Consult the NetCDF manual. |
| get_diag_input_copy_meta_data | expected to read "locat" got ... | The header of the metadata for the copies of the data in diagnostic input file is not = 'locat' |
| set_model_state_vector | state vector has length # model size (#) does not match. | Check your model resolution and fields included in the state vector. |
| aread_state_restart | read error is : # | Unable to read model state from assim_model_state_ic# file. # is error condition retured by read statement. |
| open_restart_read | OPEN status was # | Failed to open file listed for reason #. |
| aoutput_diagnostics | model time (d,s) (#,#) is index # in ncFileID # | Time index for file listed is < 0 |
| ainput_diagnostics | expected "copy", got _____' | Trying to read diagnostic state output header. |
| nc_append_time | "time" expected to be rank-1 | ndims /= 1 The time array of the NetCDF file should be 1-dimensional |
| nc_append_time | unlimited dimension expected to be slowest-moving | dimids(1) /= unlimitedDimID |
| nc_append_time | time mirror and netcdf file time dimension out-of-sync | lngth /= ncFileId%Ntimes |
| nc_append_time | various NetCDF-f90 error messages | Returned from one of the NetCDF calls in this subroutine. Consult the NetCDF manual. |
| nc_get_tindex | trouble deep ... can go no farther. Stopping. | timeindex < -1 |
| nc_get_tindex | Model time preceeds earliest netCDF time. | Time of current assim_model is earlier than all the times on the NetCDF file to which the state is to be written by aoutput_diagnostics. |
| nc_get_tindex | subsequent netCDF time (days, seconds) # # | Time of current assim_model is in the midst of the times on the NetCDF file to which the state is to be written by aoutput_diagnostics, but doesn't match any of them. Very bad. |
| nc_get_tindex | various NetCDF-f90 error messages | Returned from one of the NetCDF calls in this subroutine. Consult the NetCDF manual. |
| nc_write_calendar_atts | various NetCDF-f90 error messages | Returned from one of the NetCDF calls in this subroutine. Consult the NetCDF manual. |
none at this time
none at this time
N/A
DART software - Copyright UCAR. This open source software is provided by UCAR, "as is", without charge, subject to all terms of use at http://www.image.ucar.edu/DAReS/DART/DART_download


Take a look at our experiences
and check out USTP.
USTP ( University of Science and Technology of Southern Philippines ), or mostly known as what used to be Don Mariano, is a state university here in Cagayan De Oro, a prestige school that specializes in mostly computer mediated courses. To us first year students, this is a school of new oppurtunities, other than its excellent academics, the college experience including the extra-co seals the deal.
Since the school, specializes in tech, you can expect that facilities like Computer Laboratories are complete and well cared for. Most of our time here in school takes place in the ICT Building or Building 9, most of the learning that prepares us as future IT happens here. Even waiting for the next class, we wait by the spiral stairs almost everyday. Here we meet other fellow students and teachers that are also "techies".
Drawing building also known as building 13. We have our purposive communication class here every Tuesday and Friday 4:30pm - 6:00pm in the evening. This is also the place where we always get stuck when there is flood. When it's raining very hard, most of us are already worried and afraid especially when we didn't brought slippers. Nevertheless, experiencing flood is also one of the most memorable and fun experience in USTP especially when you are with your friends.
Main Pathway, we met random people exchange smiles multiple times yet we don't know who they are. Isn't it awkward? Many things happened in this pathway specifically at the Flag Pole Pathway this is where we signed our Attendance dor the First time as a Student in USTP. In the same area it is where the Rave Party held at, some programs during intramurals was also held here.You can not enjoy intramuralsif you did not seat at the Gutter of the road in the pathway.We also got to experience passing at the pathway with a knee level of flooded water. Students pass by at the main pathway,so always expect that it is really noisy.And also at the main pathway you can find every announcement or program the school have. So far it was really nice to experience such things.
IT Pathway or the Technology Street, is the way to Bldg.9 or the ICT Building. And also it is the way where we walk through if we're going to IT Building or our Department Offices. As you can see on the photo it is also where cars and vehicles passes through. on the Left side is the Bldg.9 and on the right side is the Bldg.5 , it is where we buy on the Vendo Machine. We use to stand by here when we are going home and discussing things or talking noisily, or even shouting and laughing together as we go home we bring happy momments with us that happened to our Day.
Black market is one of the mysterious place in USTP considering that only few people know this place and where it is located. As you can see in the photo above, black market has only 1 store and its located across the creek, you only need to shout and they will deliver it through a rope and a basket. The first time we heard the word "Black Market" we felt excited and curious at the same time. Also, the way they describe the place and how they transport the foods or things we bought made us more curious what it looks like. Black Market is also one of the things that made USTP unique.
Here is our Vlog about our experiences here in USTP CDO Campus.
<EMAIL_ADDRESS>+63 (917) 123 456 / +63 (917) 333 987
Misamis Oriental
9000, Cagayan de Oro City
CM RECTO


--- layout: a_base nav_light: RECORDING_TRAINING cssUrls: style.css jsUrls: script.js cssContentClassName: container pt-5 pb-5 ---


After graduating in 2017 with a Bachelor's Degree in Applied Information Technology,
I've spent the last year brushing up my skills in Java, HTML/CSS, and some JavaScript.
I am currently living in the Houston Area but commute back and forth between here and Baltimore.


Yes. Place the two button panels in a top panel. Use vertical layout for the content pane. Add the top panel and the result panel.
Box
Layout managers sometimes jam components together
in a way that makes the layout look cramped.
The class Box
can help in this situation.
A Box
object is a container similar to a panel,
but lacking some features.
The default layout manager of Box
is BoxLayout
.
The previous example programs could be re-written using Box
in place of JPanel
.
This would slightly simplify the code since it would rely on the
default layout manager rather than specifically setting it.
However, the most useful features of class Box
are
its methods that create invisible components.
These components correspond to rectangular areas of the screen.
They are used as spacers to separate the visible components.
If you have ever set type you know how useful spacers are in
creating an attractive page.
To create a fixed-sized invisible component use the method:
Box.createRigidArea( new Dimension( int width, int height ) )
The parameter to createRigidArea()
is a Dimension
object.


DESCRIPTION
The
sendmail utility is a local enqueuer for the
smtpd(8) daemon, compatible with
mailwrapper(5). The message is read on standard input (stdin) until
sendmail encounters an end-of-file. The
sendmail enqueuer is not intended to be used directly to send mail, but rather via a frontend known as a mail user agent.
Unless the optional -t flag is specified, one or more recipients must be specified on the command line.
The options are as follows:
-
-F
-
Set the sender's full name.
-
-f
-
Set the sender's address.
-
-t
-
Read the message's To:, Cc:, and Bcc: fields for recipients. The Bcc: field will be deleted before sending.
-
-v
-
Enable verbose output.
To maintain compatibility with Sendmail, Inc.'s implementation of sendmail, various other flags are accepted, but have no effect.
AUTHORS
OpenSMTPD is primarily developed by Gilles Chehade, Eric Faurot, and Charles Longeau, with contributions from various
OpenBSD hackers. It is distributed under the ISC license.
This manpage was written by Ryan Kavanagh<EMAIL_ADDRESS>for the Debian project and is distributed under the ISC license.


amazon-chime-sdk
/
com.amazonaws.services.chime.sdk.meetings.device
/
MediaDeviceType
/
AUDIO_HANDSET
AUDIO_HANDSET
AUDIO_HANDSET