Consent Management Platform

🛡 Easily manage user consents and the cookie widget.

• ✅ Managing cookies and providers in bulk
• ✅ Summary statistics on user consent positivity including their detailed histories
• ✅ Direct integration with 68publishers/cookie-consent
• ✅ Configurable storages standardized by Google
• ✅ Automatic cookie detection using Crawler including proposals for changes
• ✅ Support for multiple environments (web, mobile app, etc.)
• ✅ Wide possibilities of integration with your projects
• ✅ Automated reporting
• ✅ Support of authentication with Azure AD
• And many more!

• About CMP
• Getting Started
  • Prerequisites
  • Installation
• Product Documentation
• Development Guide

The CMP is a standalone application for storing user’s cookie consents and managing the cookies themselves on any website or application.

• Docker
• Make
• Mkcert

$ sudo vi /etc/hosts

127.0.0.1 cmp.local
::1 cmp.local

$ git clone https://github.com/68publishers/consent-management-platform cmp
$ cd cmp
$ cp .env.dist .env
$ make init-with-certs
$ make fixtures

Visit https://cmp.local and sign in via admin@68publishers.io / admin credentials.

See Makefile for other useful commands and the Development Guide for information about ENV variables etc.

For documentation of the application from a user perspective, please go to the Product Documentation.

For more technical information, please go to the Development Guide.

Status: Maintained
Version: 1.0.0
Download now »
Report Bug · View Changelog ·

This guide is only for the Lenovo ThinkPad L540. I am NOT responsible for any harm you cause to your device. This guide is provided “as-is” and all steps taken are done at your own risk.

# Monterey (12)
python macrecovery.py -b Mac-E43C1C25D4880AD6 -m 00000000000000000 download

Did you find any bugs or just have some questions? Feel free to provide your feedback using the Discussions tab.

This repo is licensed under the MIT License.

The following files are licensed under the MIT License:

• FixShutdown-USB-SSDT (Fixing the Shutdown/Sleep Problems of USB
• SSDT-ALS0 (Fake ambient light sensor)
• SSDT-ECRW (ACPI driver for OEM hardware (YogaSMC))
• SSDT-HPET (IRQ Conflicts fix (Needs _CRS to XCRS Rename))
• SSDT-PLUG (Plugin type to 1 for CPU0/PR00)
• SSDT-PNLF (PNLF device for WhateverGreen)
• SSDT-THINK (ThinkVPC (YogaSMC))
• UTPMap.kext (USB Mapping for MacBookPro11,4)

OpenCore is licensed under the BSD 3-Clause License.

Copyright (c) 2022 breakfastdeep. <muhteremking@gmail.de>

Simulator of Asteroid Malformation Under Stress, package designed for Taylor et al 2023 (link forthcoming). Questions on its use should be directed to astertaylor@uchicago.edu.

This code simulates the deformation of minor bodies, assuming that they are homogenous incompressible fluid masses. They are initialized as ellipsoids and the Navier-Stokes equations are interatively solved to investigate the deformation of the body over time. This package is highly modular, and allows for user-defined output functions, size, and trajectories.

SAMUS is structured as a single large class, which allows for variables to be stored and for arbitrary function calls. A single high-fidelity simulation run can be quite lengthy, and so this allows for ease of debugging and investigation. It utilizes Python3.8 and above, and depends on the numpy, FEniCS, DOLFIN, UFL, SciPy, pandas, quaternion, and mpipy packages.

Further description of SAMUS can be found in Taylor et al 2023 (found on the ArXiv here (LINK)) and in the in-line documentation.

NOTE: SAMUSv1.0.0 assumes that the object follows a body-frame fixed non-principal axis rotation, which is not fully physical. This rotation is primarily used to compute the deforming forces, but otherwise is unenforced. If complex rotation is necessary for your usecase, testing and potential modification are recommended.

Examples of SAMUS’s use are given in the examples folder.

There are two primary methods of installing SAMUS:

1. Install through the Python Package Index (PyPI):
   pip install SAMUS
2. Install the developer’s version on GitHub:
   git clone https://github.com/astertaylor/SAMUS
cd SAMUS
python setup.py install (Note that this command may require a sudo instruction.)

setup.py: Setup file for the package.
LICENSE.txt: Text file containing the license for use of this code.
README.md: Markdown file with basic documentation.
SAMUSfig.jpg: JPG file with the flowchart shown below, for ease of understanding.

Folder containing the build documents. Auto-generated with pip.

Folder containing documentation and examples of use.

Folder containing package itself.

modelFile.py: Primary file, containing the SAMUS model class.
__init__.py: Initializing file for the package.

meshes

Folder containing meshes for use by SAMUS. Users should not have to interact with this.

__init__.py: Initializing file for the subpackage.
3ball.geo: GMSH file which contains the simple spherical mesh. Used to create the various improvements.
3ball?.msh: GMSH file containing the mesh, which has undergone ? refinements by splitting.
3ball?.xml: xml file containing the mesh, which has undergone ? refinements by splitting.

testing

Folder containing scripts for testing SAMUS and the discretizations it uses.

__init__.py: Initializing file for the subpackage.
example_traj.csv: csv file with simple, short trajectory information, for use in validations.
example_traj.txt: txt file with simple, short trajectory information, for use in validations.
hyperbolic_traj.csv: csv file with hyperbolic trajectory information, for use in validations.
hyperbolic_traj.txt: txt file with hyperbolic trajectory information, for use in validations.
mesh_validation.py: Python script which runs simulations, validating the use of the lowest-refined mesh via doubling test.
modularity_example.py: Python script which runs simulations and demonstrates the modular use of SAMUS.
trajectory_jump_validation.py: Python script which validates the use of the trajectory jump method via doubling test.
euler_step_validation.py: Python script which validates the use of Euler finite-difference time steps via a doubling test.

logs

Folder containing the outputs from these testing simulations.

coarse_mesh_7.txt: Running log from a coarse-mesh run, run by mesh_validation.py.
Outputs_coarse_mesh_7.csv: Output log from from a coarse-mesh run, run by mesh_validation.py.
finer_mesh_7.txt: Running log from a finer-mesh run, run by mesh_validation.py.
Outputs_finer_mesh_7.csv: Output log from from a finer-mesh run, run by mesh_validation.py. Compare this file to Outputs_coarse_mesh_7.csv to demonstrate that the mesh usage is validated.
standard_tolerance_7.txt: Running log from a standard-tolerance run, run by trajectory_jump_validation.py.
Outputs_standard_tolerance_7.csv: Output log from from a standard-tolerance run, run by trajectory_jump_validation.py.
halved_tolerance_7.txt: Running log from a halved-tolerance run, run by trajectory_jump_validation.py.
Outputs_halved_tolerance_7.csv: Output log from from a halved-tolerance run, run by trajectory_jump_validation.py. Compare this file to Outputs_standard_tolerance_7.csv to demonstrate that the trajectory jump usage is validated.
standard_timestep_7.txt: Running log from a standard-timestep run, run by euler_step_validation.py.
Outputs_standard_timestep_7.csv: Output log from from a standard-tolerance run, run by euler_step_validation.py.
doubled_timestep_7.txt: Running log from a double-timestep run, run by euler_step_validation.py.
Outputs_doubled_timestep_7.csv: Output log from from a double-timestep run, run by euler_step_validation.py. Compare this file to Outputs_standard_timestep_7.csv to demonstrate that the usage of a Euler finite-difference timestep is validated.

examples

Folder containing examples of SAMUS‘s use. There are .ipynb and .py files for both examples. The .ipynb files have greater documentation, and the .py files are more efficient to run.

__init__.py: Initializing file for the subpackage.
example_traj.csv: .csv file with simple, short trajectory information, for use in validations.
example_traj.txt: .txt file with simple, short trajectory information, for use in validations.
Basic_Usage_Example.ipynb: A Jupyter Notebook file with a very basic example of SAMUS‘s use. This file has relatively extensive documentation, and should be used to gain greater understanding of how SAMUS works.
basic_usage_example.py: A Python script with a very basic example of SAMUS‘s use. This file should be run by the learner, as it is capable of being run with mpirun and runs significantly faster than the corresponding .ipynb file.
Modularity_Example.ipynb: A Jupyter Notebook file with an example of SAMUS‘s modular functionalities. This file has relatively extensive documentation, and should be used to gain greater understanding of how SAMUS works.
modularity_example.py: A Python script with an example of SAMUS‘s modular functionalities. This file should be run by the learner, as it is capable of being run with mpirun and runs significantly faster than the corresponding .ipynb file.

logs

Folder containing the outputs from these example simulations.

basic_example_6.txt: Running log from the basic example, run by basic_usage_example.py.
Outputs_basic_example_6.csv: Output log from from the basic example, run by basic_usage_example.py.
modularity_example_6.txt: Running log from the basic example, run by modularity_example.py.
Outputs_modularity_example_6.csv: Output log from from the basic example, run by modularity_example.py.

Below is a flowchart demonstrating the basic use case of SAMUS, as laid out in the various example files.

Aster Taylor
astertaylor@uchicago.edu | aster.taylor8587@gmail.com
University of Chicago, Department of Astrophysics

Simulator of Asteroid Malformation Under Stress, package designed for Taylor et al 2023 (link forthcoming). Questions on its use should be directed to astertaylor@uchicago.edu.

This code simulates the deformation of minor bodies, assuming that they are homogenous incompressible fluid masses. They are initialized as ellipsoids and the Navier-Stokes equations are interatively solved to investigate the deformation of the body over time. This package is highly modular, and allows for user-defined output functions, size, and trajectories.

SAMUS is structured as a single large class, which allows for variables to be stored and for arbitrary function calls. A single high-fidelity simulation run can be quite lengthy, and so this allows for ease of debugging and investigation. It utilizes Python3.8 and above, and depends on the numpy, FEniCS, DOLFIN, UFL, SciPy, pandas, quaternion, and mpipy packages.

Further description of SAMUS can be found in Taylor et al 2023 (found on the ArXiv here (LINK)) and in the in-line documentation.

NOTE: SAMUSv1.0.0 assumes that the object follows a body-frame fixed non-principal axis rotation, which is not fully physical. This rotation is primarily used to compute the deforming forces, but otherwise is unenforced. If complex rotation is necessary for your usecase, testing and potential modification are recommended.

Examples of SAMUS’s use are given in the examples folder.

There are two primary methods of installing SAMUS:

1. Install through the Python Package Index (PyPI):
   pip install SAMUS
2. Install the developer’s version on GitHub:
   git clone https://github.com/astertaylor/SAMUS
cd SAMUS
python setup.py install (Note that this command may require a sudo instruction.)

setup.py: Setup file for the package.
LICENSE.txt: Text file containing the license for use of this code.
README.md: Markdown file with basic documentation.
SAMUSfig.jpg: JPG file with the flowchart shown below, for ease of understanding.

Folder containing the build documents. Auto-generated with pip.

Folder containing documentation and examples of use.

Folder containing package itself.

modelFile.py: Primary file, containing the SAMUS model class.
__init__.py: Initializing file for the package.

meshes

Folder containing meshes for use by SAMUS. Users should not have to interact with this.

__init__.py: Initializing file for the subpackage.
3ball.geo: GMSH file which contains the simple spherical mesh. Used to create the various improvements.
3ball?.msh: GMSH file containing the mesh, which has undergone ? refinements by splitting.
3ball?.xml: xml file containing the mesh, which has undergone ? refinements by splitting.

testing

Folder containing scripts for testing SAMUS and the discretizations it uses.

__init__.py: Initializing file for the subpackage.
example_traj.csv: csv file with simple, short trajectory information, for use in validations.
example_traj.txt: txt file with simple, short trajectory information, for use in validations.
hyperbolic_traj.csv: csv file with hyperbolic trajectory information, for use in validations.
hyperbolic_traj.txt: txt file with hyperbolic trajectory information, for use in validations.
mesh_validation.py: Python script which runs simulations, validating the use of the lowest-refined mesh via doubling test.
modularity_example.py: Python script which runs simulations and demonstrates the modular use of SAMUS.
trajectory_jump_validation.py: Python script which validates the use of the trajectory jump method via doubling test.
euler_step_validation.py: Python script which validates the use of Euler finite-difference time steps via a doubling test.

logs

Folder containing the outputs from these testing simulations.

coarse_mesh_7.txt: Running log from a coarse-mesh run, run by mesh_validation.py.
Outputs_coarse_mesh_7.csv: Output log from from a coarse-mesh run, run by mesh_validation.py.
finer_mesh_7.txt: Running log from a finer-mesh run, run by mesh_validation.py.
Outputs_finer_mesh_7.csv: Output log from from a finer-mesh run, run by mesh_validation.py. Compare this file to Outputs_coarse_mesh_7.csv to demonstrate that the mesh usage is validated.
standard_tolerance_7.txt: Running log from a standard-tolerance run, run by trajectory_jump_validation.py.
Outputs_standard_tolerance_7.csv: Output log from from a standard-tolerance run, run by trajectory_jump_validation.py.
halved_tolerance_7.txt: Running log from a halved-tolerance run, run by trajectory_jump_validation.py.
Outputs_halved_tolerance_7.csv: Output log from from a halved-tolerance run, run by trajectory_jump_validation.py. Compare this file to Outputs_standard_tolerance_7.csv to demonstrate that the trajectory jump usage is validated.
standard_timestep_7.txt: Running log from a standard-timestep run, run by euler_step_validation.py.
Outputs_standard_timestep_7.csv: Output log from from a standard-tolerance run, run by euler_step_validation.py.
doubled_timestep_7.txt: Running log from a double-timestep run, run by euler_step_validation.py.
Outputs_doubled_timestep_7.csv: Output log from from a double-timestep run, run by euler_step_validation.py. Compare this file to Outputs_standard_timestep_7.csv to demonstrate that the usage of a Euler finite-difference timestep is validated.

examples

Folder containing examples of SAMUS‘s use. There are .ipynb and .py files for both examples. The .ipynb files have greater documentation, and the .py files are more efficient to run.

__init__.py: Initializing file for the subpackage.
example_traj.csv: .csv file with simple, short trajectory information, for use in validations.
example_traj.txt: .txt file with simple, short trajectory information, for use in validations.
Basic_Usage_Example.ipynb: A Jupyter Notebook file with a very basic example of SAMUS‘s use. This file has relatively extensive documentation, and should be used to gain greater understanding of how SAMUS works.
basic_usage_example.py: A Python script with a very basic example of SAMUS‘s use. This file should be run by the learner, as it is capable of being run with mpirun and runs significantly faster than the corresponding .ipynb file.
Modularity_Example.ipynb: A Jupyter Notebook file with an example of SAMUS‘s modular functionalities. This file has relatively extensive documentation, and should be used to gain greater understanding of how SAMUS works.
modularity_example.py: A Python script with an example of SAMUS‘s modular functionalities. This file should be run by the learner, as it is capable of being run with mpirun and runs significantly faster than the corresponding .ipynb file.

logs

Folder containing the outputs from these example simulations.

basic_example_6.txt: Running log from the basic example, run by basic_usage_example.py.
Outputs_basic_example_6.csv: Output log from from the basic example, run by basic_usage_example.py.
modularity_example_6.txt: Running log from the basic example, run by modularity_example.py.
Outputs_modularity_example_6.csv: Output log from from the basic example, run by modularity_example.py.

Below is a flowchart demonstrating the basic use case of SAMUS, as laid out in the various example files.

Aster Taylor
astertaylor@uchicago.edu | aster.taylor8587@gmail.com
University of Chicago, Department of Astrophysics

Experience the first Quantic Dream game in proper widescreen for the first time since 1999. No more stretching or field of view reduction at 16:9 or wider.

1. Download dgVoodoo 2.
2. Copy D3DImm.dll and DDraw.dll from \MS\x86\ of the archive to the game folder (next to the Runtime file).

• As an option, also copy dgVoodooCpl.exe and dgVoodoo.conf from the archive and run the exe to remove the dgVoodoo watermark or configure the visuals (e.g. disabling mipmapping under DirectX will make the game textures look sharper).

1. Download and unpack the fix archive offered here.
2. Launch the game and the tool.
3. Press the hotkey as displayed to toggle the fix in real time.

All trainers based on CE components may trigger some anti-virus software.

Tested on the latest Steam version at 2560×1080, 3440×1440, 3840×1080, 5760×1080 and 11520×1080. The UI spans, making it undesirable for triple monitor setups.

You can buy me a coffee or become a patron.

a qrcode component for Server-Sent Event.

The package can be installed via NPM:

npm install sseqrcode --save

SSE: Using server-sent events on MDN.

                +-------+                  +---------+                         +---------+
                | user  |                  | browser |                         | server  |
                +-------+                  +---------+                         +---------+
                    |                           |                                   |
                    |                           | request login                     |
                    |                           |---------------------------------->|
                    |                ---------\ |                                   |
                    |                | onInit |-|                                   |
                    |                |--------| |                                   |
                    |                           |                                   |
                    |                           |        send('qrcode', base64/url) |
                    |                           |<----------------------------------|
                    |              -----------\ |                                   |
                    |              | onQrcode |-|                                   |
                    |              |----------| |                                   |
                    |                           |                                   |
                    |      present QRCode image |                                   |
                    |<--------------------------|                                   |
                    |                           |                                   |
                    |                           |        send('pending', 'pending') |
                    |                           |<----------------------------------|
                    |             ------------\ |                                   |
                    |             | onPending |-|                                   |
                    |             |-----------| |                                   |
------------------\ |                           |                                   |
| scan the QRCode |-|                           |                                   |
|-----------------| |                           |                                   |
                    |                           |                                   |
                    | access the login url      |                                   |
                    |-------------------------------------------------------------->|
                    |                           |                                   |
                    |                           |      send('scanned', 'logged in') |
                    |                           |<----------------------------------|
                    |             ------------\ |                                   |
                    |             | onScanned |-|                                   |
                    |             |-----------| |                                   |
                    |                           |                                   |

import React from 'react'
import { SSEQRCode } from 'SSEQRCode'

class App extends React.Component {
  handleScan = ret => {
    alert(`Logged in as ${ret}`)
  }

  render() {
    return (
      <div>
        <SSEQRCode
          sseURL='/api/sse'
          onScanned={this.handleScan} />
      </div>
    )
  }
}

function onQrcode(data)

where:

• data – string the received message from server, should be base64 or URL of QRCode image

function onPending(data)

where:

• data – string the received message from server

function onScanned(data)

where:

• data – string the received message from server, can be used for notification

function onError(data)

where:

• data – string the received message from server or the error message

function onQRCodeLoaded()

you can use this prop to control loading indicator.

For example,

class Spin extends React.component {
  state = {
    loading: true,
  }

  handleLoaded = () => {
    this.setState({ loading: false })
  }

  render() {
    return (
      <div style={{ border: `1px solid ${this.state.loading ? 'grey' : 'red'}` }}>
        <SSEQRCode
          sseURL="/api/sse"
          onQRCodeLoaded={this.handleLoaded} />
      </div>
    )
  }
}

为中文 Stable Diffusion Taiyi(太乙) 准备的容器运行环境，内置 Web UI，干净透明，开箱即用。

如果你本地已经准备好了运行 Docker 的环境，并且有一张显存在 4G 到 8G 之间的显卡，可以尝试使用下面这个镜像，镜像在 DockerHub 上的尺寸为 8GB（官方镜像 10G+）

如果你手头没有显卡，也不想使用云主机，那么可以等等后续不需要 GPU 的“模型把玩”文章，或者翻阅之前有关模型的文章 😀

docker pull soulteary/stable-diffusion:taiyi-0.1

想运行“太乙”，除了需要下载“模型游乐场”镜像之外，我们还需要获取“太乙模型”文件：

git clone https://huggingface.co/IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1

整个仓库尺寸比较大（大概有 18GB），需要花费一些时间：

Cloning into 'Taiyi-Stable-Diffusion-1B-Chinese-v0.1'...
remote: Enumerating objects: 157, done.
remote: Counting objects: 100% (157/157), done.
remote: Compressing objects: 100% (155/155), done.
remote: Total 157 (delta 77), reused 0 (delta 0), pack-reused 0
Receiving objects: 100% (157/157), 3.06 MiB | 22.25 MiB/s, done.
Resolving deltas: 100% (77/77), done.
Filtering content: 100% (5/5), 8.92 GiB | 11.48 MiB/s, done.

原始项目启用了 git lfs，所以添加不添加 --depth 参数没有差别，耐心等待模型下载完毕之后，我们编写一个容器编排文件，来启动模型应用：

version: "2"
services:

  taiyi:
    image: soulteary/stable-diffusion:taiyi-0.1
    container_name: taiyi
    restart: always
    runtime: nvidia
    ipc: host
    ports:
      - "7860:7860"
    volumes:
      - ./Taiyi-Stable-Diffusion-1B-Chinese-v0.1:/stable-diffusion-webui/models/Taiyi-Stable-Diffusion-1B-Chinese-v0.1

将上面的内容保存为 docker-compose.yml 之后，执行 docker compose up -d，稍等片刻，在浏览器访问启动服务的 IP 地址和对应端口，比如：http://localhost:7860，就能够正常使用啦。

模型运行起来，当然是要玩一把了，我使用博客首页的古诗“醉里不知天在水，满船清梦压星河”为主题，尝试生成了一张图，看起来效果还不错：

想要快速上手中文 Stable Diffusion 模型的同学，看到这里就可以啦。

如果你想了解如何从零开始配置 GPU 云服务器环境，或者想了解这个 Stable Diffusion 容器运行环境是如何构建的，可以继续阅读这篇文章。

• “封神榜”模型
• “太乙”的 Web UI
• Huggingface 项目页面

The Face Recognition Attendance System is a Python-based application that uses OpenCV, Tkinter, and other libraries to take images of students, train a machine learning model on the images, and track student attendance based on face recognition. It allows users to:

1. Capture face images of students and save them.
2. Train a model to recognize faces.
3. Track student attendance and log it in a CSV file.

• Capture Student Faces: Takes images of students for training the face recognition model.
• Train Model: Trains the model using the images captured, allowing the system to recognize students’ faces.
• Mark Attendance: Marks student attendance by recognizing faces during real-time video capture.
• Save Attendance: Saves attendance data in a CSV file.
• GUI Interface: The application uses Tkinter for a user-friendly interface.
• Notification System: Displays real-time notifications to the user on various actions, such as saving images or training the model.

Before running the application, make sure you have the following Python libraries installed:

• opencv-python
• Pillow
• pandas
• numpy
• tkinter (Usually pre-installed with Python)
• csv
• datetime

To install the required libraries, run:

pip install opencv-python Pillow pandas numpy

1. StudentDetails.csv: Stores student roll numbers and names.
2. TrainingImage/: Directory where student face images are saved.
3. TrainingImageLabel/Trainner.yml: Saved machine learning model for face recognition.
4. Attendance/: Directory where attendance logs are saved.
5. haarcascade_frontalface_default.xml: Pre-trained classifier for face detection (downloadable from OpenCV repository).

1. Start the Application:
   Run the script to launch the Face Recognition Attendance System.

   python attendance_system.py

2. Capture Student Faces:

   • Enter the Roll No and Student Name in the provided fields.
   • Click on Take Images.
   • The system will open a webcam window, and the student will need to stay in front of the camera for face detection.
   • Once enough images are captured, the system will notify the user.

3. Train the Model:

   • Click on Train Model to train the face recognition model using the captured images.
   • The model will be saved as Trainner.yml for future use.

4. Mark Attendance:

   • Click on Mark Attendance to start face recognition.
   • The system will use the webcam to detect faces in real time and match them with the trained model.
   • Attendance will be saved in a timestamped CSV file under the Attendance/ folder.

5. Clear Fields:

   • Click on Clear to reset the input fields and messages.

6. Exit:

   • Click on Quit to close the application.

- Face_Recognition_Attendance_System/
    - StudentDetails.csv              # Stores student details (Roll No and Name)
    - TrainingImage/                  # Captured images of students' faces
    - TrainingImageLabel/             # Folder where the trained model (Trainner.yml) is stored
    - Attendance/                     # Folder to store the attendance logs
    - haarcascade_frontalface_default.xml  # Pre-trained face detection model (downloadable from OpenCV)
    - attendance_system.py            # Python script to run the system

• Roll No should be numeric, and the Name should only contain letters and spaces.
• The system uses LBPH (Local Binary Pattern Histogram) face recognition for training and prediction.
• The system assumes a webcam is available for face capture and recognition.
• The application saves attendance in a CSV file named Attendance_DDMMYYYY.csv.

• Ensure that the webcam is accessible and working properly.
• If the haarcascade_frontalface_default.xml file is missing, download it from here.
• If the StudentDetails.csv file is not found, the system will display an error message. Ensure the file is in the same directory as the script.

This Redmine plugin adds an issue dashboard that supports drag and drop for issues and various filters.

Redmine Dashboard 2 is compatible and tested with Redmine 6.0, 5.1, 5.0, and Ruby 3.3, 3.2, 3.1, and 3.0.

• Drag-n-drop of issues
• Configurable columns
• Grouping & Filtering
• Group folding
• Hierarchical parent issue view
• Include subproject issues
• Quick edit of assignee and progress

Rate plugin at redmine.org.

Please ask your questions, or tell us your stories or experience on GitHub Discussions.

1. Download the latest release.
2. Extract archive to <redmine>/plugins. Make sure the plugin directory is called <redmine>/plugins/redmine_dashboard/ (#11).
3. Install required dependencies by running bundle install --without development test in your Redmine directory. Note: Bitnami and other appliance are not officially supported and may need additional option e.g. --path vendor/bundle (#58).
4. A database migration is not needed. Restart Redmine.

1. Add the dashboard module to your project (Settings > Modules).
2. Configure dashboard permissions to your user roles (Administration > Roles and permissions). Users won’t see a Dashboard tab without having the View Dashboard permission.

1. Remove old plugin directory.
2. Follow installation steps for new release.

I appreciate any help and like Pull Requests. The main branch is the current stable branch for v2. The next version, Redmine Dashboard 3, a complete rewrite had been under development on the develop branch. Due to limited available time the project is in maintenance only mode but open to new contributors.

I gladly accept new translations or language additions for any version of Redmine Dashboard. I would prefer new translations via Transifex, but you can also send a Pull Request. Feel free to request new languages or to join the team directly on Transifex.

Redmine dashboard is licensed under the Apache License, Version 2.0. See LICENSE for more information.

This is a client-side library that can be used to instrument PHP apps for distributed trace collection, and to send those traces to Jaeger. See the OpenTracing PHP API for additional detail.

Please see CONTRIBUTING.md.

Jaeger client can be installed via Composer:

composer require jonahgeorge/jaeger-client-php

<?php

require_once 'vendor/autoload.php';

use Jaeger\Config;
use OpenTracing\GlobalTracer;

$config = new Config(
    [
        'sampler' => [
            'type' => Jaeger\SAMPLER_TYPE_CONST,
            'param' => true,
        ],
        'logging' => true,
    ],
    'your-app-name'
);
$config->initializeTracer();

$tracer = GlobalTracer::get();

$scope = $tracer->startActiveSpan('TestSpan', []);
$scope->close();

$tracer->flush();

List of supported samplers, for more info about samplers, please read Jaeger Sampling guide.

This sampler either samples everything, or nothing.

'sampler' => [
    'type' => Jaeger\SAMPLER_TYPE_CONST,
    'param' => true, // boolean wheter to trace or not
],

This sampler samples request by given rate.

'sampler' => [
    'type' => Jaeger\SAMPLER_TYPE_PROBABILISTIC,
    'param' => 0.5, // float [0.0, 1.0]
],

Samples maximum specified number of traces (requests) per second.

• psr/cache PSR-6 cache component to store and retrieve sampler state between requests. Cache component is passed to Jaeger\Config trough its constructor.
• hrtime() function, that can retrieve time in nanoseconds. You need either php 7.3 or PECL/hrtime extension.

'sampler' => [
    'type' => Jaeger\SAMPLER_TYPE_RATE_LIMITING,
    'param' => 100 // integer maximum number of traces per second,
    'cache' => [
        'currentBalanceKey' => 'rate.currentBalance' // string
        'lastTickKey' => 'rate.lastTick' // string
    ]
],

The library supports 3 ways of sending data to Jaeger Agent:

1. Zipkin.thrift over Compact protocol (socket – UDP) – default
2. Jaeger.thrift over Binary protocol (socket – UDP)
3. Jaeger.thrift over Binary protocol (HTTP)

If you want to enable “Jaeger.thrift over Binary protocol” one or other, than you need to set dispatch_mode config option or JAEGER_DISPATCH_MODE env variable.

Allowed values for dispatch_mode are:

• jaeger_over_binary_udp
• jaeger_over_binary_http
• zipkin_over_compact_udp

There are 3 constants available, so it is better to use them:

class Config
{
    const ZIPKIN_OVER_COMPACT_UDP   = "zipkin_over_compact_udp";
    const JAEGER_OVER_BINARY_UDP    = "jaeger_over_binary_udp";
    const JAEGER_OVER_BINARY_HTTP   = "jaeger_over_binary_http";
    ...
}

A possible config with custom dispatch_mode can look like this:

// config.php

use Jaeger\Config;

return [
    'sampler' => [
        'type' => Jaeger\SAMPLER_TYPE_CONST,
        'param' => true,
    ],
    'logging' => true,
    "tags" => [
        // process. prefix works only with JAEGER_OVER_HTTP, JAEGER_OVER_BINARY
        // otherwise it will be shown as simple global tag
        "process.process-tag-key-1" => "process-value-1", // all tags with `process.` prefix goes to process section
        "process.process-tag-key-2" => "process-value-2", // all tags with `process.` prefix goes to process section
        "global-tag-key-1" => "global-tag-value-1", // this tag will be appended to all spans
        "global-tag-key-2" => "global-tag-value-2", // this tag will be appended to all spans
    ],
    "local_agent" => [
        "reporting_host" => "localhost", 
//        You can override port by setting local_agent.reporting_port value   
        "reporting_port" => 6832
    ],
//     Different ways to send data to Jaeger. Config::ZIPKIN_OVER_COMPACT - default):
    'dispatch_mode' => Config::JAEGER_OVER_BINARY_UDP,
];

The full example you can see at examples directory.

By default, for each dispatch_mode there is default reporting_port config value. Table with default values you can see below:

In case you need IPv6 support you need to set ip_version Config variable. By default, IPv4 is used. There is an alias Config::IP_VERSION which you can use as an alternative to raw ip_version.

Example:

use Jaeger\Config;

$config = new Config(
    [
        "ip_version" => Config::IPV6, // <-- or use Config::IP_VERSION constant
        'logging' => true,
        'dispatch_mode' => Config::JAEGER_OVER_BINARY_UDP,
    ],
    'serviceNameExample',
);
$config->initializeTracer();

or

use Jaeger\Config;

$config = new Config(
    [
        Config::IP_VERSION => Config::IPV6, // <--
        'logging' => true,
        'dispatch_mode' => Config::JAEGER_OVER_BINARY_UDP,
    ],
    'serviceNameExample',
);
$config->initializeTracer();

Tests are located in the tests directory. See tests/README.md.

• Support Span baggage
• Support Tracer metrics
• Support Tracer error reporting

MIT License.