Hanami support plugin for vim that gives you faster navigation between semantically
associated files, like Action <-> View, Entity -> Repository or Spec -> Entity.
Spec <-> Entity association works if code lives in lib and specs in ‘spec’ directoties.
Installation
Add this to your .vimrc or nvim/init.vim:
Plug 'sovetnik/vim-hanami'
Usage
The plugin registers <Leader>s(SpecToggle) and <Leader>x(RepoToggle) in normal mode for toggle files.
Some public commands:
:HanamiAlterToggle toggles between entity and repo.
:HanamiSpecToggle toggles between lib and spec.
:HanamiProject returnes project name from .hanamirc:HanamiTemplate returnes template engine from .hanamirc
Toggles
Assume we have generated a hanami entity or action.
Suppose you run hanami g model fnord and get files:
Translations in languages other than English are machine translated and are not yet accurate. No errors have been fixed yet as of March 21st 2021. Please report translation errors here. Make sure to backup your correction with sources and guide me, as I don’t know languages other than English well (I plan on getting a translator eventually) please cite wiktionary and other sources in your report. Failing to do so will result in a rejection of the correction being published.
Note: due to limitations with GitHub’s interpretation of markdown (and pretty much every other web-based interpretation of markdown) clicking these links will redirect you to a separate file on a separate page that isn’t the intended page. You will be redirected to the .github folder of this project, where the README translations are hosted.
Translations are currently done with Bing translate and DeepL. Support for Google Translate translations is coming to a close due to privacy concerns.
Try it out! The sponsor button is right up next to the watch/unwatch button.
Version history
Version history currently unavailable
No other versions listed
Software status
All of my works are free some restrictions. DRM (Digital Restrictions Management) is not present in any of my works.
This sticker is supported by the Free Software Foundation. I never intend to include DRM in my works.
I am using the abbreviation “Digital Restrictions Management” instead of the more known “Digital Rights Management” as the common way of addressing it is false, there are no rights with DRM. The spelling “Digital Restrictions Management” is more accurate, and is supported by Richard M. Stallman (RMS) and the Free Software Foundation (FSF)
This section is used to raise awareness for the problems with DRM, and also to protest it. DRM is defective by design and is a major threat to all computer users and software freedom.
Translations in languages other than English are machine translated and are not yet accurate. No errors have been fixed yet as of March 21st 2021. Please report translation errors here. Make sure to backup your correction with sources and guide me, as I don’t know languages other than English well (I plan on getting a translator eventually) please cite wiktionary and other sources in your report. Failing to do so will result in a rejection of the correction being published.
Note: due to limitations with GitHub’s interpretation of markdown (and pretty much every other web-based interpretation of markdown) clicking these links will redirect you to a separate file on a separate page that isn’t the intended page. You will be redirected to the .github folder of this project, where the README translations are hosted.
Translations are currently done with Bing translate and DeepL. Support for Google Translate translations is coming to a close due to privacy concerns.
Try it out! The sponsor button is right up next to the watch/unwatch button.
Version history
Version history currently unavailable
No other versions listed
Software status
All of my works are free some restrictions. DRM (Digital Restrictions Management) is not present in any of my works.
This sticker is supported by the Free Software Foundation. I never intend to include DRM in my works.
I am using the abbreviation “Digital Restrictions Management” instead of the more known “Digital Rights Management” as the common way of addressing it is false, there are no rights with DRM. The spelling “Digital Restrictions Management” is more accurate, and is supported by Richard M. Stallman (RMS) and the Free Software Foundation (FSF)
This section is used to raise awareness for the problems with DRM, and also to protest it. DRM is defective by design and is a major threat to all computer users and software freedom.
Bu Streamlit uygulaması, arXiv’deki güncel makine öğrenmesi makalelerini çeker ve kullanıcıya gösterir. Kullanıcılar, makalelerin başlıklarını ve özetlerini Türkçeye çevirebilir, makaleleri beğenebilir ve GitHub bağlantılarını görüntüleyebilir.
git clone https://github.com/kullanici_adi/proje_repo.git
cd proje_repo
Bağımlılıkları Yükleme:
Proje dizininde requirements.txt dosyası bulunmaktadır. Bu dosyadaki bağımlılıkları yüklemek için aşağıdaki komutu çalıştırın:
pip install -r requirements.txt
Çalıştırma
Streamlit Uygulamasını Başlatma:
Proje dizininde aşağıdaki komutu çalıştırarak uygulamayı başlatın:
streamlit run app.py
Tarayıcıda Görüntüleme:
Uygulama başlatıldıktan sonra, tarayıcınızda otomatik olarak açılacaktır. Eğer açılmazsa, terminalde gösterilen URL’yi tarayıcınıza yapıştırın (örneğin: http://localhost:8501).
Katkıda Bulunma
Eğer bu projeye katkıda bulunmak isterseniz, lütfen aşağıdaki adımları takip edin:
Projeyi fork edin.
Yeni bir branch oluşturun (git checkout -b yeni-ozellik).
Değişikliklerinizi yapın ve commit edin (git commit -am 'Yeni özellik eklendi').
This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.
This corpus has been collected from free or free for research sources at the Internet:
A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages.
A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available.
A list of 450 SMS ham messages collected from Caroline Tag’s PhD Thesis
Finally, incorporation of the SMS Spam Corpus. It has 1,002 SMS ham messages and 322 spam messages and it is public available at. This corpus has been used in the academic researches:
Class
count
percentage
Spam
747
13.41 %
Ham
4825
86.59 %
Objective:
Prediction of a SMS into SPAM or NOT A SPAM so that developers come up with the application that can filter messages them based on the prediction
Hurdles –
Looking for external spam_words, to get the spam-word-count to avoid Out of the vocabulary words and biasing towards category ‘SPAM’
Reducing the False positive at the minimum cost of False negative (Better Tradeoff between Precision & Recall)
Imbalanced Dataset
Skills Aquired-
Text processing / cleaning
Vectorization (Bag of words/TFIDF)
Classification (Logistic regression)
Synthetic minority oversampling technique (SMOTE)
Limitations of project
The semantics(exact meanings/context) of words are not taken into account
Sometimes/Rarely Model may end up predicting an important message as spam (False positives)
when out of the vocabulary word will be encountered.
Model needs to be continuously updated to escape out of the vocabulary words,
Model should incorporate the new slangs, spamwords in the emerging social media.
This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.
This corpus has been collected from free or free for research sources at the Internet:
A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages.
A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available.
A list of 450 SMS ham messages collected from Caroline Tag’s PhD Thesis
Finally, incorporation of the SMS Spam Corpus. It has 1,002 SMS ham messages and 322 spam messages and it is public available at. This corpus has been used in the academic researches:
Class
count
percentage
Spam
747
13.41 %
Ham
4825
86.59 %
Objective:
Prediction of a SMS into SPAM or NOT A SPAM so that developers come up with the application that can filter messages them based on the prediction
Hurdles –
Looking for external spam_words, to get the spam-word-count to avoid Out of the vocabulary words and biasing towards category ‘SPAM’
Reducing the False positive at the minimum cost of False negative (Better Tradeoff between Precision & Recall)
Imbalanced Dataset
Skills Aquired-
Text processing / cleaning
Vectorization (Bag of words/TFIDF)
Classification (Logistic regression)
Synthetic minority oversampling technique (SMOTE)
Limitations of project
The semantics(exact meanings/context) of words are not taken into account
Sometimes/Rarely Model may end up predicting an important message as spam (False positives)
when out of the vocabulary word will be encountered.
Model needs to be continuously updated to escape out of the vocabulary words,
Model should incorporate the new slangs, spamwords in the emerging social media.
Startrade is an advanced roleplay community with a a discord server and a custom bot built from the ground up. Endless player opportunities and an emergent economy, as well as effective server moderation and general task automation are just a few of the goals of Startrade.
You can join the community here: https://discord.gg/vcCyNFt
#Features:
Automated economy with multiple ways of earning income
Buy and sell dozens of commodities across dozens of locations
Monitor and reward user activity, ignoring spam with smart techniques
Detailed and customizable permissioning
Moderation assistance
Rule enforcement
#Detailed Features:
Monitor activity and provide ‘activity points’ based on message complexity, pace and word variety instead of simply number of messages sent
Assign members a ‘Verified’ role upon acceptance of the rules in order to gain access to the server
Add each member to a database upon registration to keep track of personal money, investments and inventory
Create new items and register new locations with a command, saving them to the database
Edit and remove items and locations with commands
Shop browse command:
Browse by category
Browse all items
Show details and image of a specific item
Fuzzy matching
Buy and sell commodities at different prices depending on location
Allow players to transfer to a new channel after reaching sufficient chat activity in the previous one to prevent spam, command to travel to a new channel
Different locations have fully configurable buy and sell prices, and may not buy or sell everything
Prices vary even at the same location slightly from moment to moment
Commands to view all possible buys and sells that you can afford, or below a given price
Pull list of locations and buy and sell prices from a google sheet using the google api, to allow comfortable editing of these values
Invest money in a non-withdrawable account for small % returns over time, automatic configurable payouts on the hour
Top command to list users by money, invested money, or activity
Nuanced dice rolling command
Roll one die or a hundred thousand; specifiy number of dice or not, specify number of sides or not
Configurable defaults
Change display results depending on number of dice rolled
Display automatically tallied total when rolling more than one die
In the command specify meaning for die rolls, that is then mapped onto the results
Commands to check your balance, send others money, buy and sell items, view your items
Remind users arbitrary messages on request, and remind other users
Emoji polls
Custom permissions system with Authorization levels 1-10 for management commands
Commands to check and update member authorization, list all authorized members, etc.
Ignore Channels
Log all server messages and embeds to organized log files
Log message edits and deletions, including bulk deletions, to a logging channel
Time, echo and ping commands, commands to message a user, change playing status
Configurable server prefix that can vary from server to server
A ‘viral’ Certified Literate role: May be assigned by anyone with the role and indicates a high quality of roleplay. There is an automatically assigned currency reward for achieving this, and appropriate logging.
Bot management commands: List users, direct database query, evaluate code snippet, delete single message by id, add an item to a user, add money to a user, distribute investment payouts on demand in addition to automatically, edit a bot message, delete multiple messages, kick ban or unban a member, or remove all the pins in a channel.
Load, unload and reload modules to change functionality without going offline
#Deployment:
Please note, this bot was created for Startrade specific use without broader deployment in mind. You are welcome to use or adapt the code for your own purposes (As long as you follow the license, which includes making freely available a copy of your complete associated source code) but its offered as is and I may introduce breaking changes without notice; this is an in-dev project.
Create a bot in the discord dev portal
Install postgresql. Create a db user with read/write access for the bot to use
Clone repo, obviously
install requirements.txt
Authorize google sheets api on your account and create a token.json file in your repo
Create a file called ‘privatevars.py’ in the base directory and populate it with:
TOKEN = ‘your token from dev portal’
DBUSER =
DBPASS = ‘your username and password from postgre
Create a database named ‘startrade’ in postgresql. Its hardcoded to use this name but you can change it in database.py or refactor the name out if you like
User management, including adding/editing/searching users, enable/disable users, set/unset as administrator.
Service management, including adding/editing/searching services, enable/disable services.
I18n, support English and Chinese out of box, you can add language as your need.
Customize login methods, support email + password by default, you can add custom login methods by plugins. You can also disable email login by settings.
You have to set all fields that begin with DB_, then run php artisan migrate to initial database schema.
CAS Server
Field
Default Value
Description
CAS_LOCK_TIMEOUT
5000
CAS ticket locking time, in milliseconds
CAS_TICKET_EXPIRE
300
CAS ticket expire time, in seconds
CAS_TICKET_LEN
32
CAS ticket length, it’s recommend at least 32
CAS_PROXY_GRANTING_TICKET_EXPIRE
7200
CAS proxy-granting ticket expire time, in seconds
CAS_PROXY_GRANTING_TICKET_LEN
64
CAS proxy-granting ticket length, it’s recommend at least 64
CAS_PROXY_GRANTING_TICKET_IOU_LEN
64
CAS proxy-granting ticket IOU length, it’s recommend at least 64
CAS_VERIFY_SSL
true
Whether to check ssl when calling pgt url
CAS_SERVER_ALLOW_RESET_PWD
true
allow user reset password by email
CAS_SERVER_ALLOW_REGISTER
true
allow user register
CAS_SERVER_DISABLE_PASSWORD_LOGIN
false
disable password login
CAS_SERVER_NAME
Central Authentication Service
The site name of your CAS Server
Setup behind reverse proxy
Field
Default Value
Description
TRUSTED_PROXIES
127.0.0.1
The IP of reserve proxy servers, separated by comma(,), you can specific IP or use s subnet such as 127.0.0.1 and 127.0.0.1/24, configurations below take effect only when visiting IP in this list
TRUSTED_HEADER_CLIENT_IP
X_FORWARDED_FOR
User’s real IP is stored in this request header
TRUSTED_HEADER_CLIENT_HOST
X_FORWARDED_HOST
The host user visited is stored in this request header
TRUSTED_HEADER_CLIENT_PROTO
X_FORWARDED_PROTO
The http protocol user used is stored in this request header
TRUSTED_HEADER_CLIENT_PORT
X_FORWARDED_PORT
The port user visited is stored in this request header
Initial database and create administrator
Execute php artisan migrate at the root directory of this project to initial database.
Execute php artisan make:admin --password=yourpassword to create an administrator account.
Tensorflow Cloud Installer, developed by Websoft9, is an automatic installation program of Tensorflow based on Ansible and shell. It helps user install Tensorflow and pre-configure required items automatically and users only need to run a command on Linux. It simplifies the complicated installation and initialization process.
System Requirement
System Requirement to install this repository are as following：
You can install it by thi Cloud Installer solution all in one. In addition, you can deploy image published on major Cloud Platform by Websoft9.
All-in-one Installer
Run the automatic installation script with root authority to start the installation. If necessary, users need to make interactive choices, and then wait patiently until the installation is successful.
LGPL-3.0, Additional Terms: It is not allowed to publish free or paid image based on this repository in any Cloud platform’s Marketplace.
Copyright (c) 2016-present, Websoft9
This program provided by Websoft9 contains a series of software with separate copyright notices and license terms. Your use of the source code for the software included is subject to the terms and conditions of its own license.
FAQ
How to install and view the latest release?
This repository install way is Package isntallation, you can view the version from Official URL.
We will check Release version regularly. Update and test this project to ensure that users can successfully install the required version of Tensorflow.
Can I run this repository on Ansible Tower?
Yes.
Although the results of the deploy by image are consistent with the results of deploy by script, what is the difference between the two deployment methods?
https://github.com/sovetnik/vim-hanami
