text:
# Behavioral Cloning Project

[![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive)

Overview
---
This repository contains starting files for the Behavioral Cloning Project.

In this project, you will use what you've learned about deep neural networks and convolutional neural networks to clone driving behavior. You will train, validate and test a model using Keras. The model will output a steering angle to an autonomous vehicle.

We have provided a simulator where you can steer a car around a track for data collection. You'll use image data and steering angles to train a neural network and then use this model to drive the car autonomously around the track.

We also want you to create a detailed writeup of the project. Check out the [writeup template](https://github.com/udacity/CarND-Behavioral-Cloning-P3/blob/master/writeup_template.md) for this project and use it as a starting point for creating your own writeup. The writeup can be either a markdown file or a pdf document.

To meet specifications, the project will require submitting five files:
* model.py (script used to create and train the model)
* drive.py (script to drive the car - feel free to modify this file)
* model.h5 (a trained Keras model)
* a report writeup file (either markdown or pdf)
* video.mp4 (a video recording of your vehicle driving autonomously around the track for at least one full lap)

This README file describes how to output the video in the "Details About Files In This Directory" section.

Creating a Great Writeup
---
A great writeup should include the [rubric points](https://review.udacity.com/#!/rubrics/432/view) as well as your description of how you addressed each point.  You should include a detailed description of the code used (with line-number references and code snippets where necessary), and links to other supporting documents or external references.  You should include images in your writeup to demonstrate how your code works with examples.

All that said, please be concise!  We're not looking for you to write a book here, just a brief description of how you passed each rubric point, and references to the relevant code :).

You're not required to use markdown for your writeup.  If you use another method please just submit a pdf of your writeup.

The Project
---
The goals / steps of this project are the following:
* Use the simulator to collect data of good driving behavior
* Design, train and validate a model that predicts a steering angle from image data
* Use the model to drive the vehicle autonomously around the first track in the simulator. The vehicle should remain on the road for an entire loop around the track.
* Summarize the results with a written report

### Dependencies
This lab requires:

* [CarND Term1 Starter Kit](https://github.com/udacity/CarND-Term1-Starter-Kit)

The lab enviroment can be created with CarND Term1 Starter Kit. Click [here](https://github.com/udacity/CarND-Term1-Starter-Kit/blob/master/README.md) for the details.

The following resources can be found in this github repository:
* drive.py
* video.py
* writeup_template.md

The simulator can be downloaded from the classroom. In the classroom, we have also provided sample data that you can optionally use to help train your model.

## Details About Files In This Directory

### `drive.py`

Usage of `drive.py` requires you have saved the trained model as an h5 file, i.e. `model.h5`. See the [Keras documentation](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) for how to create this file using the following command:
```sh
model.save(filepath)
```

Once the model has been saved, it can be used with drive.py using this command:

```sh
python drive.py model.h5
```

The above command will load the trained model and use the model to make predictions on individual images in real-time and send the predicted angle back to the server via a websocket connection.

Note: There is known local system's setting issue with replacing "," with "." when using drive.py. When this happens it can make predicted steering values clipped to max/min values. If this occurs, a known fix for this is to add "export LANG=en_US.utf8" to the bashrc file.

#### Saving a video of the autonomous agent

```sh
python drive.py model.h5 run1
```

The fourth argument, `run1`, is the directory in which to save the images seen by the agent. If the directory already exists, it'll be overwritten.

```sh
ls run1

[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_424.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_451.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_477.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_528.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_573.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_618.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_697.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_723.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_749.jpg
[2017-01-09 16:10:23 EST]  12KiB 2017_01_09_21_10_23_817.jpg
...
```

The image file name is a timestamp of when the image was seen. This information is used by `video.py` to create a chronological video of the agent driving.

### `video.py`

```sh
python video.py run1
```

Creates a video based on images found in the `run1` directory. The name of the video will be the name of the directory followed by `'.mp4'`, so, in this case the video will be `run1.mp4`.

Optionally, one can specify the FPS (frames per second) of the video:

```sh
python video.py run1 --fps 48
```

Will run the video at 48 FPS. The default FPS is 60.

#### Why create a video

1. It's been noted the simulator might perform differently based on the hardware. So if your model drives succesfully on your machine it might not on another machine (your reviewer). Saving a video is a solid backup in case this happens.
2. You could slightly alter the code in `drive.py` and/or `video.py` to create a video of what your model sees after the image is processed (may be helpful for debugging).

### Tips
- Please keep in mind that training images are loaded in BGR colorspace using cv2 while drive.py load images in RGB to predict the steering angles.

---
---
---
---
---

# **Behavioral Cloning**

## Writeup

## Project: Behavioral Cloning Project

### Solution by luk6xff (June 2019)

---

**Behavioral Cloning Project**

The goals / steps of this project are the following:
* Use the simulator to collect data of good driving behavior
* Build, a convolution neural network in Keras that predicts steering angles from images
* Train and validate the model with a training and validation set
* Test that the model successfully drives around track one without leaving the road
* Summarize the results with a written report

[//]: # (Image References)

[image1]: ./examples/Distribution_of_steering_angles_in_training_dataset.png "Model Visualization"
[image2]: ./examples/Sample_dataset_images.png "Sample dataset"
[image3]: ./examples/preprocessed_images.png "Preprocessed images"
[image4]: ./examples/generated_data.png "Generated image"

## Rubric Points
### Here I will consider the [rubric points](https://review.udacity.com/#!/rubrics/432/view) individually and describe how I addressed each point in my implementation.

---
### Files Submitted & Code Quality

#### 1. Submission includes all required files and can be used to run the simulator in autonomous mode

My project includes the following files:
* model.py containing the script to create and train the model
* drive.py for driving the car in autonomous mode
* model.h5 containing a trained convolution neural network
* P4_SOLUTION.ipynb python notebook containing the code to create and train the model
* README.md (section Writeup) summarizing the results

#### 2. Submission includes functional code
Using the Udacity provided simulator and my drive.py file, the car can be driven autonomously around the track by executing
```sh
python drive.py model.h5
```

#### 3. Submission code is usable and readable

The `model.ipynb` (`model.py`) is just an extracted part of it) file contains the code for training and saving the convolution neural network. The file shows the pipeline I used for training and validating the model, and it contains comments to explain how the code works.

### Model Architecture and Training Strategy

#### 1. An appropriate model architecture has been employed

The CNN is a slightly modified Keras implementation of this [paper](https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf)(https://devblogs.nvidia.com/deep-learning-self-driving-cars/) from NVIDIA Corporation.
It is a deep convolution network which works well with supervised image classification / regression problems. As the NVIDIA model is well documented, I was able to focus how to adjust the training images to produce the best result with some adjustments to the model to avoid overfitting and adding non-linearity to improve the prediction.

I have added the following adjustments to the model:
* Lambda layer to normalized input images to avoid saturation and make gradients work better.
* An additional dropout layer to avoid overfitting after the convolution layers.
* ELU for activation function for each layer except for the output layer to introduce non-linearity.

The trained model successfully steered the simulated car around track-1 of the beta-simulator provided as part of this project.

A lambda layer normalises the image such that pixel values are in the range (-1,1). This helps with better numerical conditioning of the optimisation problem. The cropping layer takes the input camera images and crops out areas from the top (60 pixels - sky) and bottom (20 pixels - hood) that are largely irrelevant to training. This also reduces computational load as the images are now smaller.

The final structure of my CNN:
* Image normalization -lambda
* Image cropping (60 pixxels from top, 20 pixels from the bottom)
* Convolution: 5x5, filter: 24, strides: 2x2, activation: ELU
* Convolution: 5x5, filter: 36, strides: 2x2, activation: ELU
* Convolution: 5x5, filter: 48, strides: 2x2, activation: ELU
* Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
* Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
* Drop out (0.25)
* Fully connected: neurons: 100, activation: ELU
* Fully connected: neurons: 50, activation: ELU
* Fully connected: neurons: 10, activation: ELU
* Fully connected: neurons: 1 (output)

#### 2. Attempts to reduce overfitting in the model

The model contains dropout layer in order to reduce overfitting after the fully connected layer with rate of 0.25.
The model was trained and validated on different data sets to ensure that the model was not overfitting. The model was tested by running it through the simulator and ensuring that the vehicle could stay on the track.

#### 3. Model parameter tuning

The model used an adam optimizer, so the learning rate was not tuned manually.

#### 4. Appropriate training data

Training data was chosen to keep the vehicle driving on the road. I used a combination of center lane driving and recovering from the left and right sides of the road.

For details about how I created the training data, see the next section.

### Model Architecture and Training Strategy

#### 1. Solution Design Approach

The overall strategy for deriving a model architecture was to use the described earlier CNN designed by NVIDIA team.

In order to gauge how well the model was working, I split my image and steering angle data into a training and validation set. I found that my first model had a low mean squared error on the training set but a high mean squared error on the validation set. This implied that the model was overfitting.

To combat the overfitting, I modified the model so that I added dropout after fully conected layer.

The final step was to run the simulator to see how well the car was driving around track 1. There were a few spots where the vehicle fell off the track. To improve the driving behavior in these cases, I recorded more streams and .

At the end of the process, the vehicle is able to drive autonomously around the track without leaving the road.

#### 2. Final Model Architecture

The final model architecture consisted of a convolution neural network described in previous chapter.

Here is a visualization of the architecture (note: visualizing the architecture is optional according to the project rubric)

```
_______________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
lambda_3 (Lambda)            (None, 160, 320, 3)       0
_________________________________________________________________
cropping2d_3 (Cropping2D)    (None, 80, 320, 3)        0
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 38, 158, 24)       1824
_________________________________________________________________
activation_17 (Activation)   (None, 38, 158, 24)       0
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 17, 77, 36)        21636
_________________________________________________________________
activation_18 (Activation)   (None, 17, 77, 36)        0
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 7, 37, 48)         43248
_________________________________________________________________
activation_19 (Activation)   (None, 7, 37, 48)         0
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 5, 35, 64)         27712
_________________________________________________________________
activation_20 (Activation)   (None, 5, 35, 64)         0
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 3, 33, 64)         36928
_________________________________________________________________
activation_21 (Activation)   (None, 3, 33, 64)         0
_________________________________________________________________
flatten_3 (Flatten)          (None, 6336)              0
_________________________________________________________________
dense_9 (Dense)              (None, 100)               633700
_________________________________________________________________
activation_22 (Activation)   (None, 100)               0
_________________________________________________________________
dropout_3 (Dropout)          (None, 100)               0
_________________________________________________________________
dense_10 (Dense)             (None, 50)                5050
_________________________________________________________________
activation_23 (Activation)   (None, 50)                0
_________________________________________________________________
dense_11 (Dense)             (None, 10)                510
_________________________________________________________________
activation_24 (Activation)   (None, 10)                0
_________________________________________________________________
dense_12 (Dense)             (None, 1)                 11
=================================================================
Total params: 770,619
Trainable params: 770,619
Non-trainable params: 0
_________________________________________________________________
```

#### 3. Creation of the Training Set & Training Process
To load data I used OpenCV to load the images, by default the images are read by OpenCV in BGR format but we need to convert to RGB as in drive.py it is processed in RGB format.
I noticed that steering angles sample data has a huge peak around 0 as shown in picture below, which means that the model may have a bias to go straight.
If we include the left and right cameras with an steering angle offset, this can fix the problem
![alt text][image1]

To capture good driving behavior, I first recorded two laps on track one using center lane driving. Here is an example image of center lane driving:
![alt text][image2]

Normalization and cropping of the image is done in Network layers (Lambda, Cropping).

Then I repeated this process on track two in order to get more data points.
To augment the data sat, I also flipped images and angles. In augmenting after flipping multiply the steering angle by a factor of -1 to get the steering angle for the flipped image. For example, here is a set of normal and flipped images:
![alt text][image4]

I used a generator to generate the data so as to avoid loading all the images in the memory and instead generate it at the run time in batches of 32. Even Augmented images are generated inside the generators.

After splitting samples data, number of training samples: 11740,
and number of validation samples: 2935 (20%).

I used this training data for training the model. The validation set helped determine if the model was over or under fitting.

#### Finally used Model tuning parameter:
* No of epochs= 5
* Optimizer Used- Adam
* Learning Rate- Default 0.001
* Validation Data split- 0.15
* Generator batch size= 32
* Correction factor- 0.2
* Loss Function Used- MSE(Mean Squared Error as it is efficient for regression problem).

### Outcome

The model can drive on the track 1 without any problem. It does not work well for track 2.

- [Track 1](run_track1.mp4)
- [Track 2](run_track2.mp4)

### References
- NVIDIA model: https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/
- Udacity Self-Driving Car Simulator: https://github.com/udacity/self-driving-car-sim


text:
# Empirically Mearsuring Concentration: Fundamental Limits on Intrinsic Robustness

The goal of this project [(link to the original ArXiv paper)](https://arxiv.org/abs/1905.12202):
* Develop a systematic framework to measure concentration for arbitrary distributions

* Theoretically, prove that the empirical concentration with respect to special collection of subsets will converge to the actual concentration asymptotically

* Empirically, propose algorithms for measuring concentration of benchmark image distributions under both  and  distance metrics

# Installation & Usage
The code was developed using Python3 on [Anaconda](https://www.anaconda.com/download/#linux)

* Install Pytorch 0.4.1:
```text
conda update -n base conda && conda install pytorch=0.4.1 torchvision -c pytorch -y
```

* Install dependencies:
```text
pip install --upgrade pip && pip install scipy sklearn numpy torch setproctitle
```

* Example for empirically measuring concentraion under  metric:

  * First, precompute the distance to the k-th nearest neighours for each training example
    ```text
    python preliminary.py --dataset mnist --metric infinity --k 50
    ```
  * Next, run the proposed algorithm that finds a robust error region under
    ```text
    python main_infinity.py --dataset mnist --metric infinity --epsilon 0.3 --q 0.629 --clusters 10
    ```

* Example for empirically measuring concentraion under  metric:

  * First, precompute the nearest neighbor indices for each training example
    ```text
    python preliminary.py --dataset cifar --metric euclidean --alpha 0.05
    ```
  * Next, run the proposed algorithm that finds a robust error region under
    ```text
    python main_euclidean.py --dataset cifar --metric euclidean --epsilon 0.2453 --alpha 0.05 --clusters 5
    ```

# What is in this respository?
* ```load_data.py```: defines argparser and dataloaders for several benchmark image datasets
* ```preliminary.py```: finds the k-nearest neighbors for each example in a given training dataset
* ```main_infinity.py```: main function for emprically measuring concentration under  metric based on complement of union of hyperrectangles
* ```main_euclidean.py```: main function for emprically measuring concentration under  metric based on union of balls
* ```tune_infinity.py```: implements the tuning method (gird search for #clusters & binary search for q) for optimal concentration under  metric
* ```tune_euclidean.py```: implements the tuning method (grid search for #clusters) for optimal concentration under  metric
* ```baseline.py```: implements the baseline method that heuristically estimates concentration using linear hyperplane proposed in [Gilmer et al. (2018)](https://arxiv.org/abs/1801.02774)

license: MIT

repository: yaodongyu/Measure-Concentration

branch_name: refs/heads/master

extension: md

license_type: permissive

repo_name: yaodongyu/Measure-Concentration


text:
# pycon-monitoring-workshop
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/50af7728784642b78123b9cf4fa0f7bd)](https://app.codacy.com/app/kwahome/pycon-monitoring-workshop?utm_source=github.com&utm_medium=referral&utm_content=kwahome/pycon-monitoring-workshop&utm_campaign=Badge_Grade_Dashboard)
[![Build Status](https://travis-ci.com/kwahome/pycon-monitoring-workshop.svg?branch=master)](https://travis-ci.com/kwahome/pycon-monitoring-workshop)
[![codecov](https://codecov.io/gh/kwahome/pycon-monitoring-workshop/branch/master/graph/badge.svg)](https://codecov.io/gh/kwahome/pycon-monitoring-workshop)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

### Brief
Reliability and stability of your services is highly dependent on understanding the state of your infrastructure and systems. Troubleshooting them effectively necessitates real-time performance, data presented at fine granularity.

One of the best ways to gain this insight is with a robust monitoring system that gathers metrics, visualizes data, and alerts operators when things appear to be broken.

Metrics, monitoring, and alerting are thus interleaved concepts that together form the basis of a monitoring system. They have the ability to provide visibility into the health of your systems, help you understand trends in usage or behavior, and to understand the impact of changes you make. If the metrics fall outside of your expected ranges or thresholds, these systems can send notifications to prompt an operator to take a look, and can then assist in surfacing information to help identify the possible causes.

### Objectives:

- Reasons for a monitoring system
- Historical challenges to effective monitoring
- Monitoring Strategy: Choosing what and where to monitor
- Important qualities of a Metrics, Monitoring, and Alerting System
- Designing/architecting applications for monitoring & observability

## Messaging/Notification Service
To adequately demonstrate implementation of a monitoring system in a Python application/service, a messaging service has been built.

The service is designed to handle messages of varying kind (push, SMS).

A sample payload to the notifications endpoint:

```
{
  "messageId": "y7sdxl24df",
  "senderId": "0700123456",
  "recipients": [
    "0700987654"
  ],
  "messageType": "dummy",
  "channel": "africas-talking",
  "message": "This is an example message",
  "priority": "normal",
  "callback": "https://mydomain.com/callback/y7sdxl24df"
}
```

The API is also documented in a [swagger doc](https://github.com/kwahome/pycon-monitoring-workshop/blob/master/docs/api/swagger/send-message-1.0.yml) which can be rendered via a swagger ui available on `localhost` port `9001`

Below are the overall architectural and sequence diagrams:

###### 1. Architectural overview

![](docs/architecture/architecture-diagram.png)

###### 2. Sequence diagram

![](docs/architecture/messaging-service-sequence.png)

## Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

#### Prerequisites

For your local development environment, you will need the following installed:

- docker
- docker-compose

Follow instructions in this [link](https://docs.docker.com/install/) for a guide on installing docker and docker-compose

#### Cloning & set up pre-commit hook

- Clone the repository and navigate to the project root
- Set up pre-commit hook by running this command from the project root

```
pip install flake8 && cp devops/scripts/commit-checks.sh .git/hooks/pre-commit
```

#### Start the app

Run `docker-compose up` to fire up the notification service app & critical dependencies (database, rabbitmq, redis)

Open another terminal and run the below command to start netdata collectors
```
docker-compose exec worker  netdata  && docker-compose exec nginx  netdata  && docker-compose exec db  netdata && docker-compose exec web  netdata && docker-compose exec redis netdata && docker-compose exec rabbitmq netdata
```

Hit this url http://localhost:80/ to access the app. This will send a request to the nginx LB that in turn proxies it to the web server backends.

- Run `docker ps -a` to see all docker containers running on your development machine:

- To access your running container bash, run `docker exec -it <CONTAINER-ID> bash`. This will launch you into the container

- Do the same for the database container if you need to access it.

#### Running Tests

To run local tests, run

```
docker-compose run app ./devops/scripts/run_test.sh
```

Coverage reports will be written to htmlcov/index.html

## Observability: Metrics, Monitoring, Alerting
All metrics data from the nodes is streamed into the [netdata](https://github.com/netdata/netdata) master from which it’s visualized (real-time analysis on netdata) & streamed to [prometheus](https://prometheus.io/) for storage (and further visualization with [grafana](https://grafana.com/)

The diagram below illustrates how netdata has been integrated

![](docs/architecture/netdata-integration.png)

## Resources
- https://www.digitalocean.com/community/tutorials/an-introduction-to-metrics-monitoring-and-alerting
- https://www.scalyr.com/community/guides/zen-and-the-art-of-system-monitoring
- https://opensource.com/article/18/4/metrics-monitoring-and-python
- https://opensource.com/article/18/4/metrics-monitoring-and-python
- [grafana](https://grafana.com/)
- [netdata](https://github.com/netdata/netdata)
- [netdata monitoring ephemeral nodes](https://github.com/netdata/netdata/tree/master/streaming#monitoring-ephemeral-nodes)
- [prometheus](https://prometheus.io/)

## Contributing
Please read [CONTRIBUTING.md](https://github.com/kwahome/pycon-monitoring-workshop/blob/master/CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.

## Versioning
We use [SemVer](https://semver.org/) for versioning. For the versions available, see the tags on this repository.

## License
This software is licensed under the MIT License. See the [LICENSE](https://github.com/kwahome/pycon-monitoring-workshop/blob/master/LICENSE) file in the top distribution directory for the full license text.

repository: nknganda/pycon-monitoring-workshop

gha_license_id: MIT

repo_name: nknganda/pycon-monitoring-workshop


text:
## 2018-05-16

#### python
* [Embedding / Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors):100+ Chinese Word Vectors 上百种预训练中文词向量
* [chrissimpkins / Crunch](https://github.com/chrissimpkins/Crunch):Insane(ly slow but wicked good) PNG image optimization
* [cchen156 / Learning-to-See-in-the-Dark](https://github.com/cchen156/Learning-to-See-in-the-Dark):
* [tensorflow / models](https://github.com/tensorflow/models):Models and examples built with TensorFlow
* [vinta / awesome-python](https://github.com/vinta/awesome-python):A curated list of awesome Python frameworks, libraries, software and resources
* [ansible / ansible](https://github.com/ansible/ansible):Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy. Avoid writing scripts or custom code to deploy and update your applications — automate in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com/ansible/
* [iyanuashiri / meethub](https://github.com/iyanuashiri/meethub):This is a Python/Django based event management system. A meetup clone.
* [keras-team / keras](https://github.com/keras-team/keras):Deep Learning for humans
* [django / django](https://github.com/django/django):The Web framework for perfectionists with deadlines.
* [rg3 / youtube-dl](https://github.com/rg3/youtube-dl):Command-line program to download videos from YouTube.com and other video sites
* [toddmotto / public-apis](https://github.com/toddmotto/public-apis):A collective list of public JSON APIs for use in web development.
* [Justin-Tan / generative-compression](https://github.com/Justin-Tan/generative-compression):TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression
* [pypa / pipenv](https://github.com/pypa/pipenv):Python Development Workflow for Humans.
* [requests / requests](https://github.com/requests/requests):Python HTTP Requests for Humans™
✨
🍰
✨
* [pallets / flask](https://github.com/pallets/flask):The Python micro framework for building web applications.
* [scrapy / scrapy](https://github.com/scrapy/scrapy):Scrapy, a fast high-level web crawling & scraping framework for Python.
* [littlecodersh / ItChat](https://github.com/littlecodersh/ItChat):A complete and graceful API for Wechat. 微信个人号接口、微信机器人及命令行微信，三十行即可自定义个人号机器人。
* [donnemartin / system-design-primer](https://github.com/donnemartin/system-design-primer):Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
* [YadiraF / PRNet](https://github.com/YadiraF/PRNet):The source code of 'Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network'.
* [kalliope-project / kalliope](https://github.com/kalliope-project/kalliope):Kalliope is a modular always-on voice controlled personal assistant designed for home automation.
* [scikit-learn / scikit-learn](https://github.com/scikit-learn/scikit-learn):scikit-learn: machine learning in Python
* [tzutalin / labelImg](https://github.com/tzutalin/labelImg):🤘
LabelImg is a graphical image annotation tool and label object bounding boxes in images
* [josephmisiti / awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning):A curated list of awesome Machine Learning frameworks, libraries and software.
* [linkedin / shiv](https://github.com/linkedin/shiv):shiv is a command line utility for building fully self contained Python zipapps as outlined in PEP 441, but with all their dependencies included.
* [eriklindernoren / Keras-GAN](https://github.com/eriklindernoren/Keras-GAN):Keras implementations of Generative Adversarial Networks.

#### swift
* [shadowsocks / ShadowsocksX-NG](https://github.com/shadowsocks/ShadowsocksX-NG):Next Generation of ShadowsocksX
* [objcio / app-architecture](https://github.com/objcio/app-architecture):Sample Code of the App Architecture Book
* [serhii-londar / open-source-mac-os-apps](https://github.com/serhii-londar/open-source-mac-os-apps):🚀
Awesome list of open source applications for macOS.
* [vsaravind007 / nodeScratchpad](https://github.com/vsaravind007/nodeScratchpad):Evaluate Nodejs Code Snippets From Menubar!
💻
* [vsouza / awesome-ios](https://github.com/vsouza/awesome-ios):A curated list of awesome iOS ecosystem, including Objective-C and Swift Projects
* [raywenderlich / swift-algorithm-club](https://github.com/raywenderlich/swift-algorithm-club):Algorithms and data structures in Swift, with explanations!
* [vapor / vapor](https://github.com/vapor/vapor):💧
A server-side Swift web framework.
* [huri000 / SwiftEntryKit](https://github.com/huri000/SwiftEntryKit):SwiftEntryKit is a banner presenter library for iOS. It can be used to easily display pop-ups and notification-like views within your iOS apps.
* [Juanpe / SkeletonView](https://github.com/Juanpe/SkeletonView):An elegant way to show users that something is happening and also prepare them to which contents he is waiting
* [ReactiveX / RxSwift](https://github.com/ReactiveX/RxSwift):Reactive Programming in Swift
* [apple / swift-nio](https://github.com/apple/swift-nio):Event-driven network application framework for high performance protocol servers & clients, non-blocking.
* [matteocrippa / awesome-swift](https://github.com/matteocrippa/awesome-swift):A collaborative list of awesome Swift libraries and resources. Feel free to contribute!
* [Alamofire / Alamofire](https://github.com/Alamofire/Alamofire):Elegant HTTP Networking in Swift
* [lhc70000 / iina](https://github.com/lhc70000/iina):The modern video player for macOS.
* [apollographql / apollo-ios](https://github.com/apollographql/apollo-ios):📱
A strongly-typed, caching GraphQL client for iOS, written in Swift
* [dkhamsing / open-source-ios-apps](https://github.com/dkhamsing/open-source-ios-apps):📱
Collaborative List of Open-Source iOS Apps
* [SunshineBrother / SwiftTools](https://github.com/SunshineBrother/SwiftTools):技术不够，努力来凑；封装不了一个伟大的开源框架，但是仍然想要有一个自己的开源项目，所以就准备把一些项目中的常用工具类给打出来，省的每一次写项目的时候都要重写一遍
* [mxcl / PromiseKit](https://github.com/mxcl/PromiseKit):Promises for Swift & ObjC
* [xmartlabs / Eureka](https://github.com/xmartlabs/Eureka):Elegant iOS form builder in Swift
* [scribd / Weaver](https://github.com/scribd/Weaver):Declarative, easy-to-use and safe Dependency Injection framework for Swift (iOS/macOS/Linux)
* [glushchenko / fsnotes](https://github.com/glushchenko/fsnotes):Notes manager for macOS/iOS
* [onevcat / Kingfisher](https://github.com/onevcat/Kingfisher):A lightweight, pure-Swift library for downloading and caching images from the web.
* [SwiftyJSON / SwiftyJSON](https://github.com/SwiftyJSON/SwiftyJSON):The better way to deal with JSON data in Swift.
* [gltwy / LTScrollView](https://github.com/gltwy/LTScrollView):ScrollView嵌套ScrolloView解决方案（初级、进阶)， 支持OC / Swift，实现原理：http://blog.csdn.net/glt_code/article/details/78576628
* [typelift / SwiftCheck](https://github.com/typelift/SwiftCheck):QuickCheck for Swift

#### javascript
* [DIYgod / RSSHub](https://github.com/DIYgod/RSSHub):🍰
使用 RSS 连接全世界
* [RelaxedJS / ReLaXed](https://github.com/RelaxedJS/ReLaXed):Create PDF documents using web technologies
* [xx45 / dayjs](https://github.com/xx45/dayjs):⏰
Day.js 2KB immutable date library alternative to Moment.js with the same modern API
* [olistic / warriorjs](https://github.com/olistic/warriorjs):An exciting game of programming and Artificial Intelligence
* [nhnent / tui.calendar](https://github.com/nhnent/tui.calendar):🍞
📅
A JavaScript calendar that has everything you need.
* [vuejs / vue](https://github.com/vuejs/vue):🖖
A progressive, incrementally-adoptable JavaScript framework for building UI on the web.
* [kusti8 / proton-native](https://github.com/kusti8/proton-native):A React environment for cross platform native desktop apps
* [facebook / react](https://github.com/facebook/react):A declarative, efficient, and flexible JavaScript library for building user interfaces.
* [facebook / create-react-app](https://github.com/facebook/create-react-app):Create React apps with no build configuration.
* [isomorphic-git / isomorphic-git](https://github.com/isomorphic-git/isomorphic-git):A pure JavaScript implementation of git for node and browsers!
* [botpress / botpress](https://github.com/botpress/botpress):The
🤖
platform that ⚡ thousands of bots worldwide
* [justjavac / free-programming-books-zh_CN](https://github.com/justjavac/free-programming-books-zh_CN):📚
免费的计算机编程类中文书籍，欢迎投稿
* [axios / axios](https://github.com/axios/axios):Promise based HTTP client for the browser and node.js
* [airbnb / javascript](https://github.com/airbnb/javascript):JavaScript Style Guide
* [sallar / github-contributions-chart](https://github.com/sallar/github-contributions-chart):Generate an image of all your Github contributions
* [marktext / marktext](https://github.com/marktext/marktext):📝
Next generation markdown editor, running on platforms of MacOS Windows and Linux.
* [mui-org / material-ui](https://github.com/mui-org/material-ui):React components that implement Google's Material Design.
* [nodejs / node](https://github.com/nodejs/node):Node.js JavaScript runtime
✨
🐢
🚀
✨
* [facebook / react-native](https://github.com/facebook/react-native):A framework for building native apps with React.
* [GoogleChrome / puppeteer](https://github.com/GoogleChrome/puppeteer):Headless Chrome Node API
* [layerJS / layerJS](https://github.com/layerJS/layerJS):layerJS: Javascript UI composition framework
* [tmcw / big](https://github.com/tmcw/big):presentations for busy messy hackers
* [tensorflow / tfjs](https://github.com/tensorflow/tfjs):A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
* [typicode / json-server](https://github.com/typicode/json-server):Get a full fake REST API with zero coding in less than 30 seconds (seriously)
* [css-doodle / css-doodle](https://github.com/css-doodle/css-doodle):🎨
A web component for drawing patterns with css.

#### go
* [google / gvisor](https://github.com/google/gvisor):Container Runtime Sandbox
* [TruthHun / DocHub](https://github.com/TruthHun/DocHub):参考百度文库，使用Beego（Golang）开发的开源文库系统
* [umputun / remark](https://github.com/umputun/remark):comment engine
* [ethereum / go-ethereum](https://github.com/ethereum/go-ethereum):Official Go implementation of the Ethereum protocol
* [github / gh-ost](https://github.com/github/gh-ost):GitHub's Online Schema Migrations for MySQL
* [kubernetes / kubernetes](https://github.com/kubernetes/kubernetes):Production-Grade Container Scheduling and Management
* [gocolly / colly](https://github.com/gocolly/colly):Elegant Scraper and Crawler Framework for Golang
* [golang / go](https://github.com/golang/go):The Go programming language
* [truechain / truechain-consensus-core](https://github.com/truechain/truechain-consensus-core):TrueChain Consensus Protocol
* [avelino / awesome-go](https://github.com/avelino/awesome-go):A curated list of awesome Go frameworks, libraries and software
* [astaxie / build-web-application-with-golang](https://github.com/astaxie/build-web-application-with-golang):A golang ebook intro how to build a web with golang
* [gin-gonic / gin](https://github.com/gin-gonic/gin):Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.
* [gohugoio / hugo](https://github.com/gohugoio/hugo):The world’s fastest framework for building websites.
* [fatedier / frp](https://github.com/fatedier/frp):A fast reverse proxy to help you expose a local server behind a NAT or firewall to the internet.
* [snail007 / goproxy](https://github.com/snail007/goproxy):Proxy is a high performance HTTP(S), websocket, TCP, UDP, Socks5 proxy server implemented by golang. Now, it supports chain-style proxies,nat forwarding in different lan,TCP/UDP port forwarding, SSH forwarding.Proxy是golang实现的高性能http,https,websocket,tcp,socks5代理服务器,支持内网穿透,链式代理,通讯加密,智能HTTP,SOCKS5代理,域名黑白名单,跨平台,KCP协议支持,集成外部API。
* [meqio / meq](https://github.com/meqio/meq):A modern messaging platform for MQ、Message Push、IM、IoT etc,our goal is to be the best messaging platform in the world, like the iphone to others
* [moby / moby](https://github.com/moby/moby):Moby Project - a collaborative project for the container ecosystem to assemble container-based systems
* [containous / traefik](https://github.com/containous/traefik):Træfik, a modern reverse proxy
* [getlantern / lantern](https://github.com/getlantern/lantern):🔴
Lantern Latest Download https://github.com/getlantern/lantern/releases/tag/latest
🔴
蓝灯最新版本下载 https://github.com/getlantern/forum/issues/833
🔴
* [istio / istio](https://github.com/istio/istio):An open platform to connect, manage, and secure microservices.
* [iikira / BaiduPCS-Go](https://github.com/iikira/BaiduPCS-Go):百度网盘客户端 - Go语言编写
* [radondb / radon](https://github.com/radondb/radon):RadonDB is an open source, cloud-native MySQL database for building global, scalable cloud services
* [pa-m / sklearn](https://github.com/pa-m/sklearn):attempt to port bits of sklearn to golang go
* [hyperledger / fabric](https://github.com/hyperledger/fabric):Read-only mirror of https://gerrit.hyperledger.org/r/#/admin/projects/fabric
* [coreos / etcd](https://github.com/coreos/etcd):Distributed reliable key-value store for the most critical data of a distributed system

repository: bonfy/github-trending

gha_language: Python

repo_name: bonfy/github-trending


text:
# NNTDice_Roller
d100 dice roller with support for modifiers for the Ninja Ninja Tactics community.

To add new modifiers, use the included GUI tool through the (Create) button, or modify the "Modifiers.txt" file according to the existing format. To modify existing modifiers, directly modify the "Modifiers.txt" file. A GUI tool for this may be included in later versions.

I encourage you to freely modify the "Modifiers.txt" file, as it may be inaccurate. If you see an inaccuracy, or a modifier has updated, feel free to open an issue or edit it yourself and launch a commit request.

![alt text](https://i.imgur.com/JXrjM7y.png "Example Picture")
![alt text](https://imgur.com/Evw8oso.png "Example Picture")


text:
# Laravel RAID Storage

[![Latest Version on Github](https://img.shields.io/github/v/tag/phpguus/laravel-raid-storage-driver?style=plastic)](https://github.com/phpguus/laravel-raid-storage-driver)
[![Latest version on Packagist](https://img.shields.io/packagist/v/phpguus/laravel-raid-storage-driver?style=plastic)](https://packagist.org/packages/phpguus/laravel-raid-storage-driver)
[![StyleCI](https://styleci.io/repos/209052284/shield?branch=master&style=plastic)](https://styleci.io/repos/209052284)
[![Quality Score](https://img.shields.io/scrutinizer/quality/g/phpguus/laravel-raid-storage-driver?style=plastic)](https://img.shields.io/scrutinizer/quality/g/phpguus/laravel-raid-storage-driver)
[![Total Downloads](https://img.shields.io/packagist/dt/phpguus/laravel-raid-storage-driver?style=plastic)](https://packagist.org/packages/phpguus/laravel-raid-storage-driver)

laravel-raid-storage-driver provides encapsulation of the
[flysystem-raid](https://github.com/phpguus/flysystem-raid) package for Laravel
applications.

## Installation

Require the package using composer

```bash
composer require phpguus/laravel-raid-storage-driver
```

## Usage

In `config/filesystems.php`, you can now create a disk (in the `disks` subarray)
that has the "raid" driver.

```php
'redundantStorage' => [
    'driver' => 'raid',
    'raidLevel' => 1,
    'disks' => [
         'diskOne', 'diskTwo', 'diskThree'
    ];
];
```

This allows to use the disk as simple as in

```php
Storage::disk('redundantStorage')->write('myfile.txt', 'Something!');
```

... or in case you are handling uploaded files:

```php
$file->store('', 'redundantStorage');
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](./LICENSE.md)

repository: Munyola/laravel-raid-storage-driver

repo_name: Munyola/laravel-raid-storage-driver


text:
# Contributing

For architecture and build instructions see
[doc/architecture.md](./doc/architecture.md).

We welcome all contributions to the Oryx project: questions and answers, bug
reports, feature requests and source code. Please submit these using GitHub
issues and pull requests.

Most contributions require you to agree to a Contributor License Agreement
(CLA) declaring that you have the right to, and actually do, grant us the
rights to use your contribution. For details, visit
<https://cla.microsoft.com>.

When you submit a pull request, a bot will determine whether you need to
provide a CLA and decorate the PR appropriately (e.g., label, comment).
Simply follow the instructions provided by the bot. You will only need to do
this once across all repositories using our CLA.

## Conduct

This project has adopted the [Microsoft Open Source Code of
Conduct](https://opensource.microsoft.com/codeofconduct/). For more
information see the [Code of Conduct
FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact
[opencode<EMAIL_ADDRESS>with any additional
questions or comments.

repository: microsoft/Oryx

branch_name: refs/heads/main

gha_language: C#

gha_license_id: NOASSERTION

repo_name: microsoft/Oryx


text:
Classic RBDoom 3 BFG Edition Launcher - https://github.com/MadDeCoDeR/CRBDL.git

This is a launcher for the DOOM 3 BFG Edition Source port Classic RBDOOM 3 BFG - https://github.com/MadDeCoDeR/Classic-RBDOOM-3-BFG.

The launcher is written on C# with .NET and Windows Forms. The main goal is to allow you to manage some launch arguments
better than writing them on console.

repository: MadDeCoDeR/CRBDL

repo_name: MadDeCoDeR/CRBDL


text:
# Java8

# Output of NumericStream Program

*************************************************
# of Words used by Shakesphere :23688
# of scrabbles words are :79339
*************************************************
Testing Our Functions by giving a sample word ...
Hello as a word scores using intScore 8
Hello as a word scores using score 8
*************************************************
The best word using Comparator.comparing() is :honorificabilitudinitatibus
The best word using Comparator.comparingInt() is :honorificabilitudinitatibus
*************************************************
*************************************************
The best word after filtering using Comparator.comparing() is :whizzing
The best word after filtering using Comparator.comparingInt() is :whizzing
*************************************************
Using IntSummaryStatistics the statistics are IntSummaryStatistics{count=14277, sum=147277, min=2, average=10.315683, max=33}
*************************************************


text:
[![Build Status](https://travis-ci.org/bfops/bit-svo.svg?branch=master)](https://travis-ci.org/bfops/bit-svo)

`bit-svo` is a Rust sparse voxel octree implementation. It doesn't have a whole lot,
but features will be added as [Playform](https://github.com/bfops/playform) grows.

If you feel like something's missing from here, poke me/file an issue/submit a PR!

Things to note:

  * Voxel widths are exponents of 2.
  * Voxel positions are expressed as multiples of their widths, so that the positions can always be integers. This means that voxels positions are aligned to their widths.
  * The tree width is an exponent of 2 (the same in all dimensions).
  * The tree is always centered around (0, 0, 0).
  * The tree expands dynamically on insert.
  * The benches run at around `1us`/insert through 30 levels of depth (world width of `2^30` voxels).

repository: ConnorBP/bit-svo

extension: markdown

repo_name: ConnorBP/bit-svo


text:
# Redmine GitHub plugin

_redmine_github_ is a Redmine plugin for connecting a local Redmine installation to a remote GitHub repository. The plugin allows to:

- Syncronize remote GitHub repository to a local Git one - all Git-related Redmine features can be used
- Attach pull request (PR) status icons to issues - will change in real time, when pul request status change - created, approved, merged etc.
- Connect commit comment to issues via [Redmine commit comments keywords](<(https://www.redmine.org/projects/redmine/wiki/RedmineSettings#Referencing-issues-in-commit-messages)>)

## Getting started

### 1. Install the plugin

```shell
cd {LOCAL_REDMINE_DIRECTORY}/plugins
```

#### From downloaded release archive file

```shell
tar xvzpf redmine_github....
```

#### or via Git clone

```shell
git clone https://github.com/agileware-jp/redmine_github.git
```

#### Install gems and migrate the database

```shell
cd {LOCAL_REDMINE_DIRECTORY}
bundle install
bundle exec rake redmine:plugins:migrate
```

#### Restart you Redmine

After restart, also check if plugin is listed in the installed Redmine plugins list - _(Administration|Plugins)_

### 2. Add the repository to Redmine

For given project, in \_(Settings|Repositories|New Repository) form enter:

- _SCM_ - **Github**
- _Identifier_ - unique repository identifier
- _URL_ - GitHub repository HTTPS URL ([clone address](https://docs.github.com/en/get-started/getting-started-with-git/about-remote-repositories#about-remote-repositories), starting with `https://`)
- _Access Token_ - [personal access token](https://help.github.com/en/articles/creating-a-personal-access-token-for-the-command-line)
- _Webhook Secret_ - [webhook secret](https://developer.github.com/webhooks/securing/)

After pressing _'Create'_ button, bare-clone repository will be created inside your Redmine install directory - `{LOCAL_REDMINE_DIRECTORY}/repositories/` path.

> Note the **repository ID** in the _'Edit'_ and _'Delete'_ links - you will need this for the next step (webhook url)

### 3. Connecting GitHub to Redmine

1. Go to the repository _Settings_ interface on GitHub.
2. Under _Webhooks_ add a new webHook:

- The _Payload URL_ needs to be of the format: `[redmine_url]/redmine_github/:repository_id/webhook` (for example `http://redmine.example.com/redmine_github/1/webhook`). Repository ID is the one of the **created in the previous step repository**
- _Content type_: **application/json**
- _Secret_: **same as Webhook Secret inside you Redmine repository settings**
- _Which events would you like to trigger this webhook?_ - _Pull requests, Pull request reviews, Pull request review comments, Pushes, Statuses, Commit comments_

### 4. Configure commit comments keywords

In _(Administration|Settings)_ , _Repositories_ tab configure [commit comments keywords](https://www.redmine.org/projects/redmine/wiki/RedmineSettings#Referencing-issues-in-commit-messages).

## Documentation

Read [redmine_github plugin wiki pages](https://github.com/agileware-jp/redmine_github/wiki) (still WIP).

## License

Copyright © 2019 [Agileware Inc.](http://agileware.jp)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

gha_language: Ruby


text:
# ActiveRecord Analyze

This gem adds an `analyze` method to Active Record query objects. It executes `EXPLAIN ANALYZE` on a query SQL.

You can check out this blog post for more info on how to [debug and fix slow queries in Rails apps](https://pawelurbanek.com/slow-rails-queries).

## Installation

In your Gemfile:

```ruby

gem 'activerecord-analyze'

```

## Options

The `analyze` method supports the following EXPLAIN query options ([PostgreSQL docs reference](https://www.postgresql.org/docs/12/sql-explain.html)):

```
VERBOSE [ boolean ]
COSTS [ boolean ]
SETTINGS [ boolean ]
BUFFERS [ boolean ]
TIMING [ boolean ]
SUMMARY [ boolean ]
FORMAT { TEXT | XML | JSON | YAML }
```

You can execute it like that:

```ruby

User.all.analyze(
  format: :json,
  verbose: true,
  costs: true,
  settings: true,
  buffers: true,
  timing: true,
  summary: true
)

# EXPLAIN (FORMAT JSON, ANALYZE, VERBOSE, COSTS, SETTINGS, BUFFERS, TIMING, SUMMARY)
# SELECT "users".* FROM "users"
# [
#   {
#     "Plan": {
#       "Node Type": "Seq Scan",
#       "Parallel Aware": false,
#       "Relation Name": "users",
#       "Schema": "public",
#       "Alias": "users",
#       "Startup Cost": 0.00,
#       "Total Cost": 11.56,
#       "Plan Rows": 520,
#       "Plan Width": 127,
#       "Actual Startup Time": 0.006,
#       "Actual Total Time": 0.007,
#       "Actual Rows": 2,
#       "Actual Loops": 1,
#       "Output": ["id", "team_id", "email"],
#       "Shared Hit Blocks": 1,
#       "Shared Read Blocks": 0,
#       "Shared Dirtied Blocks": 0,
#       "Shared Written Blocks": 0,
#       "Local Hit Blocks": 0,
#       "Local Read Blocks": 0,
#       "Local Dirtied Blocks": 0,
#       "Local Written Blocks": 0,
#       "Temp Read Blocks": 0,
#       "Temp Written Blocks": 0,
#       "I/O Read Time": 0.000,
#       "I/O Write Time": 0.000
#     },
#     "Settings": {
#       "cpu_index_tuple_cost": "0.001",
#       "cpu_operator_cost": "0.0005",
#       "cpu_tuple_cost": "0.003",
#       "effective_cache_size": "10800000kB",
#       "max_parallel_workers_per_gather": "1",
#       "random_page_cost": "2",
#       "work_mem": "100MB"
#     },
#     "Planning Time": 0.033,
#     "Triggers": [
#     ],
#     "Execution Time": 0.018
#   }
# ]

```

Optionally you can disable running the `ANALYZE` query and only generate the plan:

```ruby

User.all.analyze(analyze: false)

# EXPLAIN ANALYZE for: SELECT "users".* FROM "users"
#                         QUERY PLAN
# ----------------------------------------------------------
#  Seq Scan on users  (cost=0.00..15.20 rows=520 width=127)

```

### Disclaimer

It is a bit experimental and can break with new Rails release.

repository: harssh/activerecord-analyze

repo_name: harssh/activerecord-analyze


text:
How to build these docs
=======================

We use [Docusaurus 2](https://v2.docusaurus.io/) for our docs, a modern static website generator.

### Requirements

Assuming you have [Node.js (LTS recommended)](https://nodejs.org/en/download/) installed (which includes npm), you can install Docusaurus with:

You will need to install [Yarn](https://yarnpkg.com/getting-started/install) before you can build the site.

```sh
npm install -g yarn
```

### Installation

Once you have installed yarn, navigate to docs directory & set-up the dependencies.

```sh
cd docs
yarn
```

### Local Development

```sh
yarn start
```

This command starts a local development server and opens up a browser window. Usually at the URL <http://localhost:3000>
Most changes are reflected live without having to restart the server.

If you get an error starting yarn mentioning `update.latest` such as

```sh
>yarn start

if (notifier.update && semver.gt(this.update.latest, this.update.current)) {
TypeError: Cannot read property 'latest' of undefined
    at Object.<anonymous> (E:\GitHubRepos\OpenRefine\docs\node_modules\@docusaurus\core\bin\docusaurus.js:49:46)
error Command failed with exit code 1.
```

then it is likely that you will need to first run

```sh
yarn upgrade
```

which will update all dependencies and store them in the `yarn.lock` file to also be committed.

### Next version of OpenRefine docs

If you wish to work on the next version of docs for OpenRefine (`master` branch) then you will need to:

1. Git checkout our `master` branch
2. Edit files under `docs/docs/`
3. Preview changes with the URL kept pointing to <http://localhost:3000/next> which will automatically
   show changes live with yarn after you save a file.

### Build

```sh
yarn build
```

This command generates static content into the `build` directory and can be served using any static contents hosting service.
### (Optional) Test Build Locally
You can locally test ([with parameters](https://docusaurus.io/docs/cli#docusaurus-serve-sitedir)) the static content in the `build` directory (in case you don't have access to a hosting service) by using:

```sh
yarn serve
```

or to build and then serve locally with one command:

```sh
yarn serve --build
```

### Deployment

```sh
GIT_USER=<Your GitHub username> USE_SSH=true yarn deploy
```

If you are using GitHub pages for hosting, this command is a convenient way to build the website
and push to the `gh-pages` branch.

### Translations

It is now possible to translate the [OpenRefine docs](https://docs.openrefine.org/) via the
Crowdin platform:

<https://crowdin.com/project/openrefine>

Unfortunately, unlike Weblate, we need to manually invite anyone who
wants to contribute translations. Feel free to request an invite by emailing us at<EMAIL_ADDRESS>We can also add languages, depending on interest.

Your translations will not be immediately published on <https://docs.openrefine.org>, it will take a few days
(at the next commit on the master branch).
The translated pages will first appear under <https://docs.openrefine.org/next/> (the documentation for the development version).
When we publish a version, the translations for that version, we will take a snapshot of the translations during that time.
We will trial this process for 3.5.

repository: wetneb/OpenRefine

gha_language: Java

repo_name: wetneb/OpenRefine


text:
# 12. Comparison of Identifiers

Date: 2019-12-17

## Status

Accepted

- Amended by [20. Constraints on Identifier Values](0020-identifier-constraints.md)

## Context

Identifiers (the names and keys used to identify applications and handlers) must
be compared by engines to determine if two such entities are to be considered
equivalent.

The documentation specifies that such keys must be non-empty UTF-8 strings
consisting of printable characters without whitespace, but it did not previously
specify how such strings would be compared.

These identifiers are either mostly or entirely immutable and generated as part
of the source code. They do not need to be parsed and validated from user input.

## Decision

In keeping with current behavior, we've decided to specify byte-wise comparison
semantics for identifiers.

## Consequences

Existing tooling and engine implementations, such as `configkit` and the
in-memory engine implementation in `testkit` do not need to be changed.
Identifiers can be compared using Go's standard comparison operators.

It is possible that two identifiers may appear to be equal but consist of
different byte sequences. For example, when a character with a diacritic mark is
represented with a single codepoint, versus with combining characters. This is
considered unlikely to present problems in practice, but nonetheless it is worth
mentioning that the onus is on the application developer to normalize any UTF-8
identifiers.

repository: dogmatiq/dogma

gha_language: Go

repo_name: dogmatiq/dogma


text:
# Getting Started
This guide only applies to the following APIs: Ecommerce, Recurring and Login.

If you are using an e-commerce platform, integration partner or PSP, please see the respective resources:

* [Ecommerce platform](https://vipps.no/produkter-og-tjenester/bedrift/ta-betalt-paa-nett/ta-betalt-paa-nett/#kom-i-gang-med-vipps-pa-nett-category-1)
* [Partner](https://vipps.no/produkter-og-tjenester/bedrift/ta-betalt-paa-nett/ta-betalt-paa-nett/#kom-i-gang-med-vipps-pa-nett-category-3)
* [PSP](https://vipps.no/produkter-og-tjenester/bedrift/ta-betalt-paa-nett/ta-betalt-paa-nett/#kom-i-gang-med-vipps-pa-nett-category-2)

Document version 3.1.0.

## Table of contents
  - [Get credentials](#get-credentials)
  - [Getting the API keys](#getting-the-api-keys)
    - [List of sale units](#list-of-sale-units)
    - [API keys for a salesunit](#api-keys-for-a-salesunit)
    - [API key details](#api-key-details)
  - [API keys for different use](#api-keys-for-different-use)
- [API products](#api-products)
  - [Vipps-API](#vipps-api)
  - [Legacy API products](#legacy-api-products)
- [Quick overview of how to make an API call](#quick-overview-of-how-to-make-an-api-call)
  - [Get an access token](#get-an-access-token)
    - [Request](#request)
    - [Response](#response)
  - [HTTP response codes](#http-response-codes)
- [Questions?](#questions)

## Get credentials

The fastest way to get a Vipps developer account for the test and production
environments, is to apply for a product at [vipps.no](https://vipps.no/produkter-og-tjenester/bedrift/).
If you have questions about your customer relationship, please
[contact customer service](https://www.vipps.no/kontakt-oss/bedrift/).

**Please note:** You will need a Norwegian organization number to apply for Vipps services.

Test credentials are normally available 24 hours after we receive the application.
When the application has been processed you will receive an email, and the
API keys can be retrieved by logging in to
[portal.vipps.no](https://portal.vipps.no)
with BankID. This normally takes a few days.

**Please note:** If you plan to use Vipps through a Partner or a PSP, you will have to
order the appropriate Vipps solution, and then contact the Partner or PSP
to have _them_ help you with developer access.

## Getting the API keys

Please note that API keys are _not_ available for
[Vippsnummer](https://www.vipps.no/produkter-og-tjenester/bedrift/ta-betalt-i-butikk/ta-betalt-med-vipps/).
You may need to order
[Vipps På Nett](https://www.vipps.no/produkter-og-tjenester/bedrift/ta-betalt-paa-nett/ta-betalt-paa-nett/),
[Vipps Faste Betalinger](https://www.vipps.no/produkter-og-tjenester/bedrift/faste-betalinger/faste-betalinger/)
or
[Vipps Logg Inn](https://www.vipps.no/produkter-og-tjenester/bedrift/logg-inn-med-vipps/logg-inn-med-vipps/).
See
[Vipps products](https://vipps.no/produkter-og-tjenester/bedrift/)
for an overview of all Vipps products, and
also the
[Vipps plugins](https://github.com/vippsas/vipps-developers#plugins).

API keys are available in the Vipps portal:
The merchant's administrator can log in with BankID at
[portal.vipps.no](https://portal.vipps.no)
and retrieve the API keys under the `Utvikler` menu item.

All Vipps customers must have a Norwegian organization number, and the customer
agreements are signed with BankID. Since [portal.vipps.no](https://portal.vipps.no)
gives access to API keys for both the test and production environment, we require
BankID for logging in.
The customer's administrator can create additional users on
[portal.vipps.no](https://portal.vipps.no).

If you do not have BankID, you will need the administrator to log in and provide
the API keys to you.

**Please note:** Vipps can not provide API keys in any other way, such as by
email, as the API keys give access to transferring money.
Vipps will never ask for your API keys, and you should keep them secret.

### List of sale units
Click `Show keys` on the relevant sale unit to display credentials.

![portal.vipps.no: The API products for a sales unit](images/portalvippsno-salesunit-products.png)

### API keys for a salesunit
![portal.vipps.no: The API keys for a sales unit](images/portalvippsno-salesunit-keys.png)

### API key details

| Name                     | Format | Example                              |  Description                     |
| ------------------------ | ------ | ------------------------------------ | -------------------------------- |
| `client_id`              | GUID   | fb492b5e-7907-4d83-bc20-c7fb60ca35de | Client ID for the merchant (the "username")     |
| `client_secret`          | Base64 | Y8Kteew6GE3ZmeycEt6egg==             | Client Secret for the merchant (the "password") |
| `Vipps-Subscription-Key` | Base64 | 0f14ebcab0eb4b29ae0cb90d91b4a84a     | Subscription key for the product |

**Please note:** `Vipps-Subscription-Key` was previously called `Ocp-Apim-Subscription-Key`.
The legacy name `Ocp-Apim-Subscription-Key` _must still be used in requests and code_,
but we are working to make `Vipps-Subscription-Key` work ASAP.

## API keys for different use

The same API keys are used for many types of integration:
Direct integration,
native apps for iOS and Android,
point of sale integrations,
[WooCommerce](https://github.com/vippsas/vipps-woocommerce),
[Magento](https://github.com/vippsas/vipps-magento) (both 1 and 2),
[Episerver](https://github.com/vippsas/vipps-episerver),
[Shopify](https://github.com/vippsas/vipps-shopify),
[Drupal](https://github.com/vippsas/vipps-drupal),
[other plugins](https://github.com/vippsas/vipps-developers#plugins)
and any other solution based on the Vipps eCom API.

# API products

An _API product_ is a "package" consisting of one or more APIs.

Vipps has previously had separate API keys for each API product, and a set of
API keys were directly linked to one sale unit. This meant that customers
needed to keep track of multiple API keys, and that access to a new API
required a new sale unit.

This is no longer the case: New customers now get the `Vipps-API` API product.

The Vipps-API API product give access to all current APIs, and the API keys for
the Vipps eCom v1 and v2 APIs have been “upgraded” to include the same APIs as
Vipps API.

## Vipps-API

This API product includes the following APIs:

| API          | Description                           | Documentation                                    |
| ------------ | ------------------------------------- | ------------------------------------------------ |
| Access Token | Required to obtain a JWT              | See: [Get an access token](#get-an-access-token) |
| eCom v2      | eCommerce, including express checkout | https://github.com/vippsas/vipps-ecom-api        |
| Recurring    | Recurring API                         | https://github.com/vippsas/vipps-recurring-api   |
| Vipps Log In | Identification                        | https://github.com/vippsas/vipps-login-api       |

Vipps-API also includes some _legacy_ APIs:

| API              | Description                                                      | Documentation                                    |
| ---------------- | ---------------------------------------------------------------- | ------------------------------------------------ |
| eCom v1          | eCommerce, legacy version, to be phased out on September 1 2020. | https://github.com/vippsas/vipps-ecom-api-v1     |
| Signup and Login | Legacy API (superseded by Vipps Log In), end of life Dec 31 2019 | https://github.com/vippsas/vipps-signuplogin-api |

See more details about the
[phase-out of eCom API v1](https://github.com/vippsas/vipps-ecom-api/blob/master/v1-deprecation.md)
on September 1 2020.

If you are still using a legacy API, you should upgrade as soon as possible.
The best way to keep up to date of changes is to
[star the API repository on GitHub](https://help.github.com/en/articles/about-stars).

## Legacy API products

Old API products have the `ZZZ` prefix to make them easy to distinguish,
and to always sort them at the bottom of lists.
One example is `ZZZ eCommerce-Classic-Services-API`.

The legacy API products prefixed with `ZZZ` have been updated to also include the
current, corresponding APIs in Vipps-API.
This means that a sale unit that could previously only use eCom v1,
can now also use eCom v2, with the same API keys as before.

The legacy API products still work, and there is no need to "upgrade" to Vipps-API,
unless there are technical problems.

The current APIs offer substantial improvements over the legacy APIs, and
we strongly recommend upgrading to the current version as soon as possible.

# Quick overview of how to make an API call

## Get an access token

The Access Token API provides the JWT bearer token used in the `Authorization`
header, required in all Vipps API calls.

The API keys needed the access token are the same as for the API products,
see [API key details](#api-key-details).

### Request

```
POST https://apitest.vipps.no/accessToken/get
client_id: fb492b5e-7907-4d83-bc20-c7fb60ca35de
client_secret: Y8Kteew6GE3ZmeycEt6egg==
Ocp-Apim-Subscription-Key: 0f14ebcab0eb4b29ae0cb90d91b4a84a
```

### Response

```
HTTP 200 OK
{
  "token_type": "Bearer",
  "expires_in": "86398",
  "ext_expires_in": "0",
  "expires_on": "1495271273",
  "not_before": "1495184574",
  "resource": "00000002-0000-0000-c000-000000000000",
  "access_token": "eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1Ni <snip>"
}
```

JWT properties:

| Name             | Description                                                                      |
| ---------------- | -------------------------------------------------------------------------------- |
| `token_type`     | It’s a `Bearer` token. The word `Bearer` should be added before the token        |
| `expires_in`     | Token expiry duration in seconds.                                                |
| `ext_expires_in` | Extra expiry time. Not used.                                                     |
| `expires_on`     | Token expiry time in epoch time format.                                          |
| `not_before`     | Token creation time in epoch time format.                                        |
| `resource`       | For the product for which token has been issued.                                 |
| `access_token`   | The actual access token that needs to be used in `Authorization` request header. |

**Please note:** The JWT (access token) is valid for 1 hour in MT (test) and 24 hours in Production.
To be sure that you are using correct time please use `expires_in` or `expires_on`.

### HTTP response codes

This API returns the following HTTP statuses in the responses:

| HTTP status        | Description                                             |
| ------------------ | ------------------------------------------------------- |
| `200 OK`           | Request successful.                                     |
| `400 Bad Request`  | Invalid request, see the `error` for details.           |
| `401 Unauthorized` | Invalid authorization.                                  |
| `403 Forbidden`    | Authentication ok, but credentials lacks authorization. |
| `500 Server Error` | An internal Vipps problem.                              |

**Please note:** `Vipps-Subscription-Key` was previously called `Ocp-Apim-Subscription-Key`. The legacy name `Ocp-Apim-Subscription-Key` must still be used in request headers. We will at some point phase out the old name completely, but it is not trivial and will take some time.

## Questions?

We're always happy to help with code or other questions you might have!
Please create an [issue](https://github.com/vippsas/vipps-developers/issues),
a [pull request](https://github.com/vippsas/vipps-developers/pulls),
or [contact us](https://github.com/vippsas/vipps-developers/blob/master/contact.md).

Sign up for our [Technical newsletter for developers](https://github.com/vippsas/vipps-developers/tree/master/newsletters).

repository: jvoron/vipps-developers

repo_name: jvoron/vipps-developers


text:
# bosh-concourse-tasks

Execute [BOSH](https://bosh.io/) tasks via [Concourse](https://concourse.ci/) tasks.

## Director Tasks

Requires `client`, `client_secret`, and `environment`; and accepts `ca_cert` parameters.

 * `clean-up.yml`
 * `clean-up-all.yml`

### Deployment Tasks

Also requires `deployment` parameter.

 * `run-errand.yml` (requires `errand` parameter)

#### Instance Group / ID Tasks

Also accepts `instance_group` and `id` parameters.

 * `recreate.yml`
 * `ssh.yml` (requires `command` parameter)
 * `start.yml`
 * `stop.yml`
 * `stop-hard.yml`
 * `take-snapshot.yml`

## Example

```yaml
meta:
  bosh_environment: &bosh_environment
    ca_cert: ((bosh_ca_cert))
    client: ((bosh_client))
    client_secret: ((bosh_client_secret))
    environment: ((bosh_environment))
  bosh_deployment: &bosh_deployment
    <<: *bosh_environment
    deployment: legacy
jobs:
- name: start
  plan:
  - get: weekday-start
    trigger: true
  - get: bosh-concourse-tasks
  - task: start-database
    file: bosh-concourse-tasks/start.yml
    params:
      <<: *bosh_deployment
      instance_group: database
  - task: start-app-server
    file: bosh-concourse-tasks/start.yml
    params:
      <<: *bosh_deployment
      instance_group: app-server
  serial_groups:
  - restart
- name: stop
  plan:
  - get: weekday-stop
    trigger: true
  - get: bosh-concourse-tasks
  - task: stop-database
    file: bosh-concourse-tasks/stop.yml
    params:
      <<: *bosh_deployment
      instance_group: database
  - task: stop-app-server
    file: bosh-concourse-tasks/stop.yml
    params:
      <<: *bosh_deployment
      instance_group: app-server
  serial_groups:
  - restart
- name: clean-up
  plan:
  - get: daily
    trigger: true
  - get: bosh-concourse-tasks
  - task: clean-up
    file: bosh-concourse-tasks/clean-up-all.yml
    params: *bosh_environment
resources:
- name: bosh-concourse-tasks
  type: git
  source:
    uri: https://github.com/dpb587/bosh-concourse-tasks.git
- name: daily
  type: timer
  source:
    interval: "24h"
- name: weekday-start
  type: timer
  source:
    location: America/Denver
    start: "07:00"
    stop: "08:00"
    days: [ Monday, Tuesday, Wednesday, Thursday, Friday ]
- name: weekday-stop
  type: timer
  source:
    location: America/Denver
    start: "20:00"
    stop: "21:00"
    days: [ Monday, Tuesday, Wednesday, Thursday, Friday ]
```

## License

[MIT License](LICENSE)


text:
# Contribution Guidelines

This project is maintained by [Jon](https://github.com/jonhadfield/) and [Johannes](https://github.com/jrabensc)

If you have created or discovered something awesome that relates to Standard Notes then please submit a [Pull Request](https://docs.github.com/en/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request).
Alternatively, let us know via the Standard Notes [slack channel](https://standardnotes.org/slack) or [Discord](https://discord.gg/9VNW3kK554) and we'll add it for you.

---

## Standards

Ensure your pull request/suggestion adheres to the following:

- it must be an **original creation** and not something that re-hosts other people's work, including SN itself
- the link should be the name of the project, followed by a description, and then a period
- descriptions should be clear and concise
- the project must be made available under a [open source license](https://en.wikipedia.org/wiki/Open-source_license)

Thank you for your suggestions!

repository: jonhadfield/awesome-standard-notes

repo_name: jonhadfield/awesome-standard-notes


text:
# Forms of Assessment BMED360 (10 ECTS)

## - Midterm project (15%):
**Compulsory assignment on given topic ([Kiwi segmentation challenge](https://github.com/computational-medicine/BMED360-2021/blob/main/Midterm-Kiwi-Project/README.md))**

## - MCQ / Quiz from home (10%):
**Compulsory (about 50 "simple/basic" MCQs / quizzes across the entire curriculum; 60 min duration - digital from home; at least 60\% correct to pass)**

## - Personal project / final oral exam on Zoom (75%):

1. Pick a topic from the BMED 360 curriculum that fits your research interests or evokes your curiosity (the topic could be e.g. your own research project, a scientific article, a review of methods with pros and cons).

2. Prepare a presentation of the topic as if you should give a talk [make the title yourself] at a scientific meeting with a very motivated audience - but who might not be experts in your field.

3. For this purpose make a Google Slides (or PowerPoint, or Keynote, or Beamer) _presentation of 20 min duration_ and plan for a _5 min discussion_ with your audience  (i.e. sensor / examiner / student peers)

4. You might also write a manuscript or notes telling what to say during the presentation, but avoid reading directly from your manuscript during the talk as the notes are meant for rehearsal only.

5. The presentation produced in 3. must be sent/shared by email to the course coordinator at latest 2 hours before the exam.
  (You can ask for advice regarding topic selection or your planned talk in the early preparation phase)

6. At the oral exam (20 min talk using Zoom), the following criteria are **assessed**:
_scientific attitude_, _vocabulary_, and _balanced use of text, tables, and figures_; _clarity in presentation_ regarding problem formulation / introduction, methods description, results (if any), discussion, and conclusions; ability to bring enthusiasm for your topic to the audience; keeping time.

Share your presentation with the examiner / sensor before the exam (see 5.).

7.   **Overall grading (spring 2021) is pass / fail**    (previously: grading scale was A-F)


text:
# ImageJ Macros Related to Image Cropping

### mask_autocrop_macro.ijm

Designed to take photo & mask image pairs. Use the mask as a reference to crop the image. Created in response to [a thread on the ImageJ subreddit](https://www.reddit.com/r/ImageJ/comments/hpffhn/automate_image_processing_for_research_project/). Please include attribution if you re-use or adapt the code. This macro is made available under the [MIT License](https://github.com/MurphysLab/r_ImageJ/blob/master/cropping/MIT_License.md).

Example to show what kind of images the code uses and what the output is:

| x        | Photo | Mask |
|----------|-------|------|
| Original |     |      |
| Cropped  |     |    |

The code as uploaded presumes that the mask filenames are identical to those of the corresponding photos, with the exception of a suffix, which is currently "\_CR", per the user question and the sample images.

The images should be sorted into 3-4 directories. Input images need to be in separate directories (unless you want to edit the macro to sort that out). *e.g.*

![directory structure](https://github.com/MurphysLab/r_ImageJ/blob/master/cropping/images/directory_structure.png?raw=true)


text:
---
title: Symmetric Storage Gaxpy
categories:
- Matrix Computation
---

## Introduction
A matrix \\(\color{black}{A\in\mathcal{R}^{n\times n}}\\) is *symmetric* if \\(\color{black}{A^{T}=A}\\) and *skew-symmetric* if \\(\color{black}{A^{T}=-A}\\). Likewise, a matrix \\(\color{black}{A\in\mathcal{C}^{n\times n}}\\) is *hermitian* if \\(\color{black}{A^{H}=A}\\) and *skew-hermitian* if \\(\color{black}{A^{H}=-A}\\).

For such matrices, storage requirements can be halved by simply storing the lower triangle of elements.

For general \\(\color{black}{n}\\), we set

\\[\color{black}{A.vec((n-j/2)(j-1)+i)=a_{ij} 1\le j\le i \le n}\\]

```Matlab
for j = 1:n
    for i = 1:j-1
        y(i) = y(i) + A.vec((i-1)n - i(i-1)/2 + j)x(j)
    end
    for i = j:n
        y(i) = y(i) + A.vec((j-1)n - j(j-1)/2 + i)x(j)
    end
end
```

This algorithm requires the same \\(\color{black}{2n^2}\\) flops that an ordinary gaxpy requires.


text:
---
hrs_structure:
  division: '1'
  volume: '7'
  title: '21'
  chapter: '383'
  section: 383-126.5
type: hrs_section
tags:
  - Government
menu:
  hrs:
    identifier: HRS_0383-0126_0005
    parent: HRS0383
    name: 383-126.5 Evaluation of fund balance adequacy
weight: 77465
title: Evaluation of fund balance adequacy
full_title: 383-126.5 Evaluation of fund balance adequacy
---
**[§383-126.5] Evaluation of fund balance adequacy.** The director shall conduct an annual evaluation of the adequacy of the fund balance, taking into account conditions in the State and national economic trends, and submit a report of findings to the legislature no later than twenty days prior to the convening of each regular session. [L 1991, c 193, §1]

repository: SamMade/BlueAndKrabby

repo_name: SamMade/BlueAndKrabby


text:
# Badminton Availability Checker

Badminton Availability Checker is a Selenium Web Scrapper bot built in Python for checking available badminton courts in Singapore.

In particular this bot will check for all courts in:
* One PA
* Active SG

## Features

* One time set-up for your username and password to access active sg (bogus details can be used if you don't intend to check Active SG)
* Checks all available badminton courts for a particular day
* Saves the result into a `csv` file for easy reference

## Installation
I am assuming that you have no technical background and are starting from scratch.
I am also assuming you are using Windows (sorry Mac users (||^_^))

**STEP 1:** [Download latest version of python 3](https://www.python.org/downloads/windows/)

This should be the first link

_When installing:_
* check the box that says Add Python 3.x to PATH

**STEP 2:** Download the piloting driver for your _Chrome_ browser [here](https://chromedriver.chromium.org/downloads)
Make sure it's the right version
* In chrome, click the three dot in the upper right hand corner -> help -> about google chrome, should be 80....)

**STEP 3:** Almost done!

1. Download the files in this GitHub (Using the green button somewhere along the right hand side)
    * Extract them into a folder
2. Open your command prompt (windows key, type "cmd", should be the first application)
3. Navigate to where all the files are installed in
    * `cd Downloads\EXTRACTED_FILENAME_HERE` (if you placed the extracted folder in your downloads folder)
4. type `pip3 install -r requirements.txt`

wait.

Once everything's done, you're good to go!

## Usage

Navigate to the folder that contain the file on your command prompt
* `cd Downloads\EXTRACTED_FILENAME_HERE` (if you placed the extracted folder in your downloads folder)

run the command:
```bash
python main.py
```

## Possible features down the line
* Check a particular court for all available booking slots
* Check multiple courts for all available booking slots
* Save a list of favourite courts to check

## Contributing
Pull requests are welcome!

Feel free to open an issue if you found a bug or have any questions!

## License
[MIT](https://choosealicense.com/licenses/mit/)


text:
# Chicago Murders
Predict murders in Chicago based on the data available on [dnainfo](https://www.dnainfo.com) (and perhaps more sources in future).

## Demo plot (Time, Age, Cause)

![Time Age Cause plot](https://raw.githubusercontent.com/craftbase/chicago-murders/master/src/data/outputs/time_age_cause_plot.png)

repository: craftbase/chicago-murders

repo_name: craftbase/chicago-murders


text:
# Elf C Library

## Naming Standards

In this project, standards are loosely inspired by GNOME's naming standards, which can be accessed in this link.

Macros: e.g. ELF_DIE

Constants: e.g. ELF_GRAPH_ORIENTED

Functions*: e.g. say_hello()

Structs: e.g. ElfGraph

Methods: e.g. elfGraph_someActionToExecute()

Overloaded Methods: e.g. elfGraph_someActionToExecute_overloadType()

File Names: e.g. elf_graph.c

 * Functions shouldn't be exported to outside of the file.

Never forget the most important rule: follow same standards as the code that surrounds the code you are adding.

## Data Structures' Type and Implementation Ambiguity
It may happen that you will implement the same data structure for different specific types, or using different implementations.

For example, you may implement a generic list for void pointers, and one for integers. Or you may implement a set structure using hashtables and then one using union-find algorithms.

For when these happen, use the following standards:

**[namespace]\[specific_type]\[specific_implementation]\[data_structure]**

**e.g. ElfIntHashSet**

You may omit the type or implementation for one of the ambiguous instances. The priority goes to the most generic, which should be void pointer.

repository: matheushjs/ElfLibC

repo_name: matheushjs/ElfLibC


text:
# wpda-cdg-extensions
Extended Search Extension for WP Data Access by Charles Godwin

Refer to CHANGES.md for changes
=======

__This plugin works with both the premium version and free version of WP Data Access__

__Introduction__
This is an alternative search algorithm that looks for the occurrence of each space delimited token in the search string rather than the default of just one string.

__How to install__

Download the repository zip file from https://github.com/CharlesGodwin/wpda-cwg-extensions. Use the standard WordPress process to upload and add a plugin.

__How to search__

You can enter multiple words in the search box. Separate the words with spaces. The search will examine EACH record in the database for the existence of all the words. For example, if you type in the words __john__ __smith__, it will check each record to see if it contains the value __john__ AND the value __smith__. __JOHN__ is the same as __john__. Using __John smith__ is the same as __smith john__. The words you enter will be found in longer words. For example, it will find __john__ in Johnston and __smith__ in Smithsonian. The searched words may not all be in the same column in the record. If you want to search for a phrase or a word with spaces, then put the value in double quotes. Such as __“Saint Mary”__
or __“Le Moine”__.

**_Column Filter_**

Column filter will accept special keyword EMPTY and NOTEMPTY. They must be in upper case. The plugin will test for column is NULL or column = '' for **EMPTY**, and column is NOT NULL and column != '' for **NOTEMPTY**.

Column searches are not tokenized and the entire string, as entered, is used for a search.

Note that numeric columns are only searched if the token is a number. A numeric search is for an equal match of either integer or float, depending on the type of column. Most other columns use the MySQL __column LIKE "%token%"__ construct.

__Premium Version Search__

This plugin follows the premium version protocol of searching, and only columns marked as Queryable are searched.

The plugin will only work if search mode is set to Normal wildcard search.

license: Unlicense

repository: CharlesGodwin/wpda-cwg-extensions

gha_language: PHP

gha_license_id: Unlicense

repo_name: CharlesGodwin/wpda-cwg-extensions


text:
---
slug: commencement-throng-celebrates-with-class-of-2015
title: "Commencement throng celebrates with Class of 2015"
date: January 01 2020
---

'BECOME ADVOCATES FOR PUBLIC EDUCATION,' GRADUATES TOLD

  More than 8,600 well-wishers crowded the stands of Freeman Stadium May 16
  for the
  commencement ceremony at
  California State University, Monterey Bay.

  On a cool, overcast day, Hugo Morales urged the graduates to remember that
  they can make a difference.

  Mr. Morales, founder of Radio Bilingue and a member of the CSU Board of
  Trustees, was the keynote speaker at the university’s 19th annual
  commencement. More than 1,500 students were awarded bachelor’s and master’s
  degrees and teaching credentials before the capacity crowd.

  After congratulating the graduates, Mr. Morales reminded them that they are
  evidence of the value of public education. “I hope that you become advocates
  for public education; we cannot take public education for granted,” he said.
  He recounted how, in 1980, he organized farmworkers, teachers, students and
  artists to launch Radio Bilingüe as a single public radio station for the
  large Latino communities of Fresno and the surrounding San Joaquin Valley. The
  network now includes 11 stations in California, Arizona, Colorado, New Mexico
  and Texas. Those stations provide opportunities for diverse voices, especially
  young people, to tell their stories. “All of you have stories, and you should
  be proud of them. You have persevered and defied stereotypes. I encourage you
  to work to open doors for those following behind you so they can tell their
  stories.

  “As you begin the next chapter of your life, please don’t let anyone tell you
  that you can’t make a difference. You will find a way. Yes, it can be done.
  Si se puede. Your graduation today speaks volumes that it can be
  done.”

  President Eduardo Ochoa told the graduates that they came to the university
  community and built a community of their own. “We hope that is something you
  can take into the next chapters of your life. No matter where you go from
  here, there will be issues to face, causes to champion, people to help,” he
  said. "May you make that next place better than you found it, just as you did
  here at Cal State Monterey Bay.”

  Student speaker Lilyana Gross, winner of the President’s Award for Exemplary
  Student Achievement, was next on the podium. She reminded her classmates that
  as graduates of CSUMB, “we have something special to offer.

  "We value service, team work, community impact, sustainability and integrity
  because of our shared values shaped by the amazing individuals around us
  daily,” she said. “We are an unstoppable entity. Together in the future, we
  will make our mark on the world.”

  The ceremony opened with the traditional welcome in Spanish, Japanese, Chinese
  and Vietnamese, delivered by the members of the faculty.

  Dr. Linda Bynoe of the Liberal Studies Department; Dr. Rita Benmayor, Division
  of Humanities and Communication; Dr. William Head, Division of Science and
  Environmental Policy; and Dr. Rosalie Strong, School of Computing and Design,
  were awarded emeritus status acknowledging their service to the university.

  Decorated mortarboards were popular with members of the graduating
    class.

repository: csumb-archives/news-gatsby

gha_language: JavaScript

repo_name: csumb-archives/news-gatsby


text:
## Changes

* Improve Pahsin detection on Unicode

npm packages are available on:

* [@myanmartools/ng-zawgyi-detector - npm registry](https://www.npmjs.com/package/@myanmartools/ng-zawgyi-detector)
* [@myanmartools/ng-zawgyi-detector - github package registry](https://github.com/myanmartools/ng-zawgyi-detector/packages)

repository: mmzliveid/ng-zawgyi-detector

repo_name: mmzliveid/ng-zawgyi-detector


text:
# CompileTimeRandom

Single header library to generate random numbers at compile time using KISS random generator.
Internal states are represented by a single paramtererized type pseudo remembered by the c++ counter macro.
Additionally a custom random seed can be specified

repository: phisn/CompileTimeRandom

repo_name: phisn/CompileTimeRandom


text:
# USE CASE: 32 The population of the world should be accessible to the organisation.

## CHARACTERISTIC INFORMATION

### Goal in Context

As an *Information Analyst in the organisation*
I want *the population of the world*
So that *I can analyse the information*

### Scope

Organisation.

### Level

Primary task.

### Preconditions

Database contains world data.

### Success End Condition

A report with1 statistic is available for the information analyst containing the population of the world.

### Failed End Condition

No report is produced.

### Primary Actor

An information analyst of the organisation.

### Trigger

The information analyst of the organisation requires the population of the world.

## MAIN SUCCESS SCENARIO

1. The information analyst requires access to the population of the world.
3. The information analyst extracts the population of the world.
4. The information analyst now has the statistic available to them in a report.

## EXTENSIONS

None.

## SUB-VARIATIONS

None.

## SCHEDULE

**DUE DATE**: Release 1.0

repository: eoinkane/sem-coursework

repo_name: eoinkane/sem-coursework


text:
# NSynth
This repository contains material related to Udacity's Machine Learning Engineer Nando Degree (MLEND) capstone project. The objective of this project is to classify wave files (.wav) based on their instrument family.

Like many people, I listen to music on a daily basis. I'm also fascinated how streaming giants such as Apple, Spotify,  Tidal, and Pandora (just to name a few) can curate songs and recommend them to users. The first step to recommending

The findings of can be found in Report.pdf. The report is also available in this link:
https://drive.google.com/file/d/1sgq-3WGUuPyAjt-HeDCNTQkCAE9ZZ7vc/view?usp=sharing

The instrument classification using the NSynth dataset found here:
https://magenta.tensorflow.org/datasets/nsynth

Due to the size of the dataset it can't be stored on a repository.
The sampled training files are 60MB+ in size and can't be stored in this repo.

All scripts are written in python, and it is assumed that the user already has Anaconda or another python interpreter installed.
If not, please download Anaconda from the link below:
https://docs.anaconda.com/anaconda/install/

The required libraries for this repository are:
- numpy
- pandas
- matplotlib.pyplot
- librosa
- os
- glob
- re
- scipy
- time
- collections
- scipy
- keras
- pickle
- itertools
- sklearn

The above packages are most likely built in your Anaconda package with the exception of Librosa which needs to be installed.

The time required to run most scripts depends on the user's machine. For example, extracing spectral features from 50,000 files requires more than 3hours on a 2014 Macbook Pro.

For the convolutional neural network (CNN), it is recommended to use Amazon Web Services (AWS) or an equivalent cloud computing service. For this project the EC2 instance is p2.xlarge using <i>"Deep Learning AMI with Source Code -CUDA 8- Ubuntu"</i>.

## Summary of Results
The ultimate winner in terms of accuracy and effciency was Random Forests, at 65% efficiency. The second runner up was CNN at 56% accuracy. The plot below shows the confusion matrix for the model.

![RF_confusion_matrix](https://github.com/NadimKawwa/NSynth/blob/master/ConfusionMatrix/RF_Normalized_RandomSearch.png)

Because we used a subset of the data for training, the next logical step was a sensitivy test to see how wildy how results would vary if we had used another set. Unsurprisingly, using a whole net training set gave us 85% accuracy. This raises the question: could another subset of the data returned a worse performance?

It's important to keep in mind that the paramters we initialize will affect our results. The key factor for predicting success in any machine learning exercise is the data.

## Acknowledgments

The findings presented here could not have been possible without the generosity of researchers who made their findings public.
The list below is pasted from the full report.

[1] Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi. "Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders." 2017.

[2] Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.

[3] Introduction to machine learning with Python: a guide for data scientists
Andreas Müller-Sarah Guido - O'Reilly. - 2017

[4] The Physics Factbook
https://hypertextbook.com/facts/2003/ChrisDAmbrose.shtml

[5] International Electrotechnical Commission
http://www.electropedia.org/iev/iev.nsf/display?openform&ievref=801-30-04

[6] Center for Computer Research in Music and Acoustics at Stanford University
https://ccrma.stanford.edu/CCRMA/Courses/152/percussion.html

[7] Fitzgerald, Derry. “Harmonic/percussive separation using median filtering.” 13th International Conference on Digital Audio Effects (DAFX10), Graz, Austria, 2010.

[8] Meinard Müller and Sebastian Ewert “Chroma Toolbox: MATLAB implementations for extracting variants of chroma-based audio features” In Proceedings of the International Conference on Music Information Retrieval (ISMIR), 2011.

[9] Center for Computer Research in Music and Acoustics at Stanford University
https://ccrma.stanford.edu/~jos/st/Spectrograms.html

[10] Roger B. Dannenberg, Introduction to Computer Music Spectral Centroid

[11] Jiang, Dan-Ning, Lie Lu, Hong-Jiang Zhang, Jian-Hua Tao, and Lian-Hong Cai. “Music type classification by spectral contrast feature.” In Multimedia and Expo, 2002. ICME‘02. Proceedings. 2002 IEEE International Conference on, vol. 1, pp. 113-116. IEEE, 2002.

[12] arXiv:1412.6980 [cs.LG]

## Looking Ahead
I passed and obtained my degree, looking forward to more exciting projects!

![MLEND_NK](https://github.com/NadimKawwa/NSynth/blob/master/MLEND.png)

repository: NadimKawwa/NSynth

repo_name: NadimKawwa/NSynth


text:
# ski-resorts
[![travis-build](https://img.shields.io/travis/acareaga/ski-resorts.svg?style=flat-square)](https://travis-ci.org/acareaga/ski-resorts)
[![codecov coverage](https://img.shields.io/codecov/c/github/acareaga/ski-resorts.svg?style=flat-square)](https://codecov.io/github/acareaga/ski-resorts)
[![version](https://img.shields.io/npm/v/ski-resorts.svg?style=flat-square)](http://npm.im/ski-resorts)
[![downloads](https://img.shields.io/npm/dm/ski-resorts.svg?style=flat-square)](http://npm-stat.com/charts.html?package=ski-resorts&from=2015-12-22)
[![MIT License](https://img.shields.io/npm/l/ski-resorts.svg?style=flat-square)](http://opensource.org/licenses/MIT)

## Description

The `ski-resorts` module provides an easy to use library of all ski resorts in the United States.

## Installation

With [npm](https://www.npmjs.com/) do:

```
npm install ski-resorts
```

## Usage

Get the entire list:

```
skiResorts.all
```

Or, a random number of resorts:

```
skiResorts.random(3)
```

## Dependencies

Uses with the following versions:

```
"unique-random-array": "1.0.0",
"chai": "3.4.1",
"codecov.io": "0.1.6",
"commitizen": "2.4.6",
"cz-conventional-changelog": "1.1.5",
"ghooks": "1.0.1",
"istanbul": "0.4.1",
"mocha": "2.3.4",
"semantic-release": "^4.3.5"
```

## Built with `ski-resorts`

* [First Tracks](https://github.com/acareaga/first-tracks) - Weather, avalanche, and backcountry data for the snow community.

If you're integrating the module in a creative way, please submit a pull request so your application can be featured above.

## Contributing

To submit patches:
* Fork this repository
* Implement the desired feature with tests (and documentation if necessary)
* Submit a pull request

### Future

Features that would be nice to include:
* Integration with Forecast.io's weather API

Please open an issue if you have any other updates that would be nice to have.

## License

The `ski-resorts` module is released under the [MIT license](https://opensource.org/licenses/MIT).

repository: acareaga/ski-resorts

repo_name: acareaga/ski-resorts