text:
---
layout: song
title: '"Father and Son" by Cat Stevens'
song_title: Father and Son
artist: Cat Stevens
year: 1970
category: full_song
slug: "333"
date_published: 2020-10-18
yt_video_id: ANoZhO7lENQ
tags: [Full Song Lesson, 1970s, Key of G, Folk, Classic Rock]
pdf_version: musicnotes
pdf_numpages: 2
musicnotes_url: https://www.musicnotes.com/l/DgbMX
---

## Video lesson

{% include youtube_embed.html yt_id = page.yt_video_id %}

{% include pdf-module.html slug = page.slug pdf_numpages = page.pdf_numpages pdf_version = page.pdf_version has_patreon_url = page.patreon_lesson_url patreon_url = page.patreon_lesson_url free_pdf_available = page.free_pdf_available free_pdf_url = page.free_pdf_url song_name = page.song_title pdf_for_sale = page.pdf_for_sale musicnotes_url = page.musicnotes_url %}

## Editor's notes

Hey friends! Here's a brand new guitar lesson showing you how to play Father and Son, the 1970 from Cat Stevens' 4th album (Tea for the Tillerman). In this lesson I'll explain the chords, talk about the progressions, demonstrate two different strumming options, and then show you a few different ways to play the intro riff (tabs included). Whether you want to keep it simple play it exactly like Cat Stevens -- this lesson has you covered. Thanks for everyone who requested this song, there's been quite a lot of you who have asked for this one recently!

Video timestamps:

- 0:00 Lesson overview
- 2:27 Chord shapes
- 5:10 Progression & basic strum
- 11:25 How Cat Stevens strums it
- 14:56 Intro riff: chords & strumming
- 17:47 Intro riff: ending lick (2 options)
- 22:54 Full playthrough

## My other Cat Stevens lessons

### Wild World

[Get the PDF](https://playsongnotes.com/lessons/282) for Wild World.

### The Wind

[Get the PDF](https://playsongnotes.com/lessons/254) for The Wind

url: https://raw.githubusercontent.com/davidpots/playsongnotes/923e07f479ef5fdfa9e7f3e6ce44c36ae7053038/_lessons/333 - cat stevens father and son.md

repository: davidpots/playsongnotes

filename: 333 - cat stevens father and son.md

gha_language: HTML

repo_name: davidpots/playsongnotes


text:
# tde-migration-demo
Simple demo migrating an encrypted database from SQL Server on Windows to Linux.

## Prerequisites
 * Windows machine running SQL Server
 * Linux machine running SQL Server

## Steps
**01-setup.sql**

   * Connect to SQL Server on Windows (e.g. using SSMS)
   * Create a certificate
   * Create a new database, 'TestDb'
   * Turn on [Transparent Data Encryption](https://msdn.microsoft.com/en-us/library/bb934049.aspx)
   * Back up the database and the certificate to files

**02-bash-commands.txt**

   * Copy the database and certificate files to Linux machine
   * ssh to Linux machine
   * chown and chmod files so the mssql account can read/write

**03-restore.sql**

   * Connect to SQL Server on Linux (e.g. using SSMS)
   * Re-create the certificate from file
   * Restore the database from file

## Useful links
 * [aka.ms/sqldev](aka.ms/sqldev) - Build a simple app with SQL Server in any language, on any platform
 * [msdn.microsoft.com/commandline/wsl/install_guide] (https://msdn.microsoft.com/commandline/wsl/install_guide) - Enable Bash on Windows 10

license: MIT

repository: twright-msft/mssql-test-scripts

branch_name: refs/heads/master

extension: md

gha_language: SQLPL

license_type: permissive

repo_name: twright-msft/mssql-test-scripts


text:
# Odette FTP 2 (OFTP2) Client and Library

## Purpose

Odette FTP 2 (OFTP2) is a protocol specified in [RFC 5024](https://tools.ietf.org/html/rfc5024). It is a file transfer protocol with roots in the ancient B2B world of the 1980ies. In contrast to FTP it has no notion of directories but just transfers files between locations. Locations are identified using a so called _ODETTE ID_, which is formatted according to ISO-6523.

This project is a clean room implementation of the OFTP2 protocol, just using the specification found in the RFC. No other implementation was consulted or reviewed. Therefore, it can be expected to be free of external IP. See the [license](LICENSE).

The purpose is not to implement the protocol to its full extent but to provide the means to communication with an OFTP2 server as a client sending files to the server. For example, this project currently does not support the server side of the protocol. Nevertheless, regarding the protocol messages, the complete OFTP2 specification is implemented, making a server implementation at least feasible.

From this project, you get

  * an OFTP2 library for sending files to a server
  * a command-line utility using this library as a proof of concept

The library has more functionality than the command-line utility exposes. Therefore, if you want to do more fancy stuff, have a look at the `OFTP2Client` struct and its methods.

## Structure of project

  * [cmd/oft2/main.go](cmd/oft2/main.go) command line tool, to interact with OFTP2 servers based on the [cobra](https://github.com/spf13/cobra) framework
  * [internal/liboftp2](internal/liboftp2) Library to talk to OFTP2 servers
    * [client](internal/liboftp2/client) The client functions
    * [wire](internal/liboftp2/wire) Implementation of the different messages, clients and servers can exchange

## Talking to a server

### Odette ID

To talk to an OFTP2 server, you need a valid Odette ID that is configured as a communication partner on the server. If you know such an id, you can ask the server for its own id, with the `id` subcommand:

```
$ oftp2 id -s <IP_ADDRESS> -i MYODETTEID
```

You can try `LOCAL` and `O1999MENDELSONDE` which sometimes work out-of-the-box due to the server implementation.

The format of the ID is: `Onnnnccccccccccccccssssss`

  * `O`: the first character is always `O`
  * `nnnn`: a four-digit number with leading zeros, called the _International Code Designator_ (_ICD_). Only the numbers 0002-0189 are globally registered (see [ICD list](https://www.cyber-identity.com/download/ICD-list.pdf)). In Germany, you can expect to see `0013` and `0177` quite often because they are registered to the [VDA](https://www.vda.de) and [Odette consortium](https://www.odette.org).
  * `cccccccccccccc`: A 14 character _Organisation Code_ that normally consists of the character set `[A-Z\-0-9]`. Which characters are allowed, depends on the [ICD](https://www.cyber-identity.com/download/ICD-list.pdf).
  * `ssssss`: a 6 character computer sub address that normally consists of the character set `[A-Z\-0-9]`.

The ID has not to take up the full 25 characters but can be shorter. In this case, it is padded with blanks to the right.

If the Odette id starts with `O013` the next six characters are numeric, e.g. `O0013000243AEG`.

### Server Capabilities

Using a valid Odette ID for the sender and knowing the ID of the server (e.g. determined via the ID command), you can query the server for its capabilities using the `query` sub command of the tool.

```
$ oftp2 query -s <IP_ADDRESS> -i MYODETTEID
```

A typical answer may be:

```
Data received from remote system:
------------------------------------------
OdetteId                : SERVERODETTEID
Max buffer size         : 10240
Capability              : Location can only receive files.
Compression supported   : false
Restart supported       : false
Special logic supported : false
Authentication supported: false
User data               :
------------------------------------------
```

The server can only receive files, does not support compression, authentication, or resuming a file transfer session. "Special logic" refers to additional processing steps performed with data received. "User data" is additional data, configured for the Odette ID you provided as the sender.

### Sending a file

If the server does not require authentication - which is common - you can simply send data to the server if you have a valid Odette ID. This is done with the `send` sub command, for example:

```
$ oftp2 send -s <IP_ADDRESS> -i MYODETTEID SERVERODETTEID /tmp/filetosend DATASETNAME
```

The `SERVERODETTEID` and be the ID determined by the `id` sub command or another ID, the server is configured to receive files for. `DATASETNAME` is an arbitrary name chosen for the data. The command above send ths file `/tmp/filetosend` to the sever `<IP_ADDRESS>`.

## Fuzzing

This project was created during a security assessment to find vulnerabilities in an OFTP2 installation. Therefore, the `OFTP2Client` structure contains a field `Fuzzer` for a callback function that allows you to mess around with the data send over the wire to the server. To use it, implement a new command in [cmd/oft2/cmd](cmd/oft2/cmd/) and provide a callback function for fuzzing the data. Please keep in mind that the fuzzer gets the raw data which is afterwards directly written to the TCP socket, so it is up to you to maintain the right length and header information if you change the data.

## Limitations

  * The tool provides only client functionality, i.e. sending files. Receiving files is not implemented yet, but the messages are already understood by the tool.
  * The command line tool does note provide commands for authentication but the library already supports the necessary protocol steps.


text:
[//]: # (Image References)

[image1]: ./example/cameras3.png "Cameras3"
[image2]: ./example/modelError.png "ModelPerf"

# **Behavioral Cloning for Autonomous Driving**

I build a learner to drive autonomously a simulation bot. The learner tells the simulation bot the steering angle depending on the images captured by three cameras mounted on the bot.

The simulation bot is able to complete autonomously the first track of the Udacity simulator.

---
## Dependencies
* [Driving Simulator](https://github.com/udacity/self-driving-car-sim)
* Python 3.x
* NumPy
* OpenCV
* Random
* Pandas
* sklearn
* Keras 1.2

---
## Goals / Steps
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

---
## Rubric Points
---
### 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:
* This README.md summarizing the results
* model.py (or model.ipynb) 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
* video.mp4 recording the bot completing the first track

#### 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 resource/run5
```

Images of the ride are saved in the directory _resource/run5_. The _video.mp4_ is generated as:
```sh
python video.py resource/run5
cp resource/run5.mp4 video.mp4
```

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

The _model.py_ (alternatively, _model.ipynb_) 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.

In the file, a generator, named _dataGenerator(df, batch_size = 100, augment=True)_ is provided (line 139). The final model architecture is trained both using and not using the generator and both versions of the model are suitable. The model.h5 file contains data of the trained network that uses the _dataGenerator_ with argument _batch_size=32_, that basically means 32*3*2 = 192 images for each batch (3 cameras and each image flipped)

### Model Architecture and Training Strategy

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

The architecture is defined in the definition _MyKerasArchitecture(inputShape, NewSyntax = False)_ in model.py, line 87.
It consists of a series of convolutional layers and fully connected layers. It includes RELU and MaxPool layers to introduce nonlinearity and the data is normalized using a Keras lambda layer.

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

Previous version of the model contained dropout layers in an attempt to reduce overfitting. However, the performance was not better. The final model uses Max Pool layers after activated each convolutional layer in order to reduce model complexity.

#### 3. Model parameter tuning

The model used an adam optimizer, so the learning rate was not tuned manually (model.py line 206).

#### 4. Appropriate training data

Training data was chosen to keep the vehicle driving on the road. I used a combination of center lane driving, recovering from the left and right sides of the road. I drive about three tracks (approximately 10 minutes driving) and collected 9100 images for each camera, for a total of 27300 images. Each of these 27300 images is flipped, yielding 54600 images of size (160, 320, 3)

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 have a series of convolutional layers, that requires less parameters as they share parameters among connections, followed by a few fully connected layers.

At each subsequent layer, I increase the number of filters. The initial layer has 3 channels (RGB). Subsequent layers have a number of filters that are multiple of 3, up until 81 filters.

At each subsequent layer I reduce the picture size by applying cropping, convolution and pooling with "valid" padding (shrink the size of picture).

The final fully connected layers reduces the size from 8019 to 200, to 50, to 1. That is the steering angle. At each of those layer the RELU activation is applied.

In order to gauge how well the model was working, I split my image and steering angle data into a training and validation set (model.py, line 192). The training set is randomly sampled to be 2/3 of the sample.

Previous versions of the model were not successful completing the track. The vehicle bot failed in specific spots. I added additional convolutional layer and increased the size of the fully connected layer to be 200. Overall, it seemed that with a model having around 1500000 to 2500000 parameters, train on my sample of 2/3 * 54600 images, is be suitable to successfully complete the first track autonomously.

#### 2. Final Model Architecture

The final model architecture (model.py, lines 18-24) consisted of a convolution neural network with the following layers and layer sizes. The model has 10 layers, excluding image cropping and flattening layers.

| Layer (type)                     | Output Shape          | Param #     | Connected to            |
|:--------------------------------:|:---------------------:|:-----------:|------------------------:|
| input_1 (InputLayer)             | (None, 160, 320, 3)   | 0           |                         |
| lambda_1 (Lambda)                | (None, 160, 320, 3)   | 0           | input_1[0][0]           |
| cropping2d_1 (Cropping2D)        | (None, 80, 320, 3)    | 0           | lambda_1[0][0]          |
| convolution2d_1 (Convolution2D)  | (None, 75, 315, 9)    | 981         | cropping2d_1[0][0]      |
| maxpooling2d_1 (MaxPooling2D)    | (None, 37, 157, 9)    | 0           | convolution2d_1[0][0]   |
| convolution2d_2 (Convolution2D)  | (None, 32, 152, 27)   | 8775        | maxpooling2d_1[0][0]    |
| maxpooling2d_3 (MaxPooling2D)    | (None, 16, 76, 27)    | 0           | convolution2d_2[0][0]   |
| convolution2d_3 (Convolution2D)  | (None, 11, 71, 81)    | 78813       | maxpooling2d_3[0][0]    |
| maxpooling2d_4 (MaxPooling2D)    | (None, 5, 35, 81)     | 0           | convolution2d_3[0][0]   |
| convolution2d_4 (Convolution2D)  | (None, 3, 33, 81)     | 59130       | maxpooling2d_4[0][0]    |
| flatten_1 (Flatten)              | (None, 8019)          | 0           | convolution2d_4[0][0]   |
| dense_1 (Dense)                  | (None, 200)           | 1604000     | flatten_1[0][0]         |
| dense_2 (Dense)                  | (None, 50)            | 10050       | dense_1[0][0]           |
| dense_3 (Dense)                  | (None, 1)             | 51          | dense_2[0][0]           |
|__________________________________|_______________________|_____________|_________________________|
| Total params: 1,761,800																		   |
| Trainable params: 1,761,800																	   |
| Non-trainable params: 0																		   |

The training and validation error for each epoch is as below:
![alt text][image2]

#### 3. Creation of the Training Set & Training Process

To capture good driving behavior, I first recorded two laps on track one using center lane driving as much as possible.

I then recorded driving another 2 tracks were I drive from the other way. Around the curves, I steer quite considerably (instead of braking) to recover the vehicle from left to right and then to the center (or from right to the left and then to the center of the lane).

The following is an example image, captured while driving, from respectively left camera, central camera, and right camera,
![alt text][image1]

To augment the training data, each image and its steering measurement is flipped. Totally I have a training sample of 2/3 * 54600 images.

The training dataset contained in folder _data/_ is not uploaded in this repository. For an alternative training set, it is possible to use the one [provided by Udacity](https://s3.amazonaws.com/video.udacity-data.com/topher/2016/December/584f6edd_data/data.zip)

---
## Resources
* Udacity Self-Driving Car [Nanodegree](https://www.udacity.com/course/self-driving-car-engineer-nanodegree--nd013)
* Udacity project assignment and template on [GitHub](https://github.com/udacity/CarND-Behavioral-Cloning-P3)
* Udacity project [rubric](https://review.udacity.com/#!/rubrics/432/view)
* Udacity Driving Simulator on [GitHub](https://github.com/udacity/self-driving-car-sim)
* Training [Data](https://s3.amazonaws.com/video.udacity-data.com/topher/2016/December/584f6edd_data/data.zip)


text:
## Writeup

---

**Advanced Lane Finding Project**

The goals / steps of this project are the following:

* Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
* Apply a distortion correction to raw images.
* Use color transforms, gradients, etc., to create a thresholded binary image.
* Apply a perspective transform to rectify binary image ("birds-eye view").
* Detect lane pixels and fit to find the lane boundary.
* Determine the curvature of the lane and vehicle position with respect to center.
* Warp the detected lane boundaries back onto the original image.
* Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

[//]: # (Image References)

[image1]: ./output_images/original_img_undist_img.jpg "Undistorted"
[image2]: ./output_images/undist_test1.jpg "Road Transformed"
[image3]: ./output_images/combined_binary_test1.jpg "Binary Example"
[image4]: ./output_images/perspective_transform_test1.jpg "Warp Example"
[image5]: ./output_images/histgram_test1.jpeg "Fit Visual"
[image6]: ./output_images/decect_lane_test1.jpg
[image7]: ./output_images/warp_detect_lane.jpg "Output"
[video1]: ./output_images/project_video.mp4 "Video"

## [Rubric](https://review.udacity.com/#!/rubrics/571/view) Points

### Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

---

### Writeup / README

#### 1. Provide a Writeup / README that includes all the rubric points and how you addressed each one.  You can submit your writeup as markdown or pdf.  [Here](https://github.com/udacity/CarND-Advanced-Lane-Lines/blob/master/writeup_template.md) is a template writeup for this project you can use as a guide and a starting point.

You're reading it!

### Camera Calibration

#### 1. Briefly state how you computed the camera matrix and distortion coefficients. Provide an example of a distortion corrected calibration image.

The code for this step is contained in the first code cell of the IPython notebook located in "Advanced-Lane-Lines.ipynb"

I start by preparing "object points", which will be the (x, y, z) coordinates of the chessboard corners in the world. Here I am assuming the chessboard is fixed on the (x, y) plane at z=0, such that the object points are the same for each calibration image.  Thus, `objp` is just a replicated array of coordinates, and `objpoints` will be appended with a copy of it every time I successfully detect all chessboard corners in a test image.  `imgpoints` will be appended with the (x, y) pixel position of each of the corners in the image plane with each successful chessboard detection.

I then used the output `objpoints` and `imgpoints` to compute the camera calibration and distortion coefficients using the `cv2.calibrateCamera()` function.  I applied this distortion correction to the test image using the `cv2.undistort()` function and obtained this result:

## Undistored chessboard
![alt text][image1]

### Pipeline (single images)

### 1. Provide an example of a distortion-corrected image.

To demonstrate this step, I will describe how I apply the distortion correction to one of the test images like this one:
I apply "cv2.calibrateCamera()"and "cv2.undistort".
The code for this step is contained in the 3rd cell of the IPython notebook located in "Advanced-Lane-Lines.ipynb"

## Undistored image
![alt text][image2]

### 2. Describe how (and identify where in your code) you used color transforms, gradients or other methods to create a thresholded binary image.  Provide an example of a binary image result.

I used a combination of color and gradient thresholds to generate a binary image   Here's an example of my output for this step.

①：I use 'cv2.cvtColor(image, cv2.COLOR_BGR2HLS)',I apply HLS color space.
BecauseS value is a fairly robust job of picking up the lines under very different color and contrast conditions.
Threshold color channel is 170 ≦ thre ＜ 255

②：I use 'cv2.Sobel', I apply sobel x threshold.
I think that sobelx is doing robust job of lane lines.
Threshold sobelx is 15 ≦ thre ＜ 100

Then, An combine image was created by combining ① and ②.

The code for this step is contained in the 4th cell of the IPython notebook located in "Advanced-Lane-Lines.ipynb"

![alt text][image3]

### 3. Describe how (and identify where in your code) you performed a perspective transform and provide an example of a transformed image.

The code for my perspective transform includes a function called `perspective_transform()`, which appears in the 5th cell of the IPython notebook located in "Advanced-Lane-Lines.ipynb"
The `perspective_transform()` function takes as inputs an image (`img`), as well as source (`src`) and destination (`dst`) points.  I chose the hardcode the source and destination points. I determined based on references.

```python
src = np.float32([(581,477),(699,477),(896,675),(384,675)])
dst = np.float32([(384,0),(896,0),(896,720),(384,720)])
```

I verified that my perspective transform was working as expected by drawing the `src` and `dst` points onto a test image and its warped counterpart to verify that the lines appear parallel in the warped image.

![alt text][image4]

### 4. Describe how (and identify where in your code) you identified lane-line pixels and fit their positions with a polynomial?

1st：I take a histogram along all the columns in the lower half of the image like this.
![alt text][image5]

2nd：I use 'find_lane_pixels()'function,the lane lines part was detected using this function.

Then,the following images were created as done in the lesson,
![alt text][image6]

The code for this step is contained in the 6th cell of the IPython notebook located in "Advanced-Lane-Lines.ipynb"

### 5. Describe how (and identify where in your code) you calculated the radius of curvature of the lane and the position of the vehicle with respect to center.

The left and right curvature are calculated in 'measure_curvature_real()' function using ym_per_pix = 30/720 [m/pixel],  In the 7th code cell in "Advanced-Lane-Lines.ipynb"

    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])

Then , I can assume the camera is mounted at the center of the car, such that the lane center is the midpoint at the bottom of the image between the two lines.

    position = (left_fitx + right_fitx) / 2
    distance_from_center = img_size[0]/2 - position
    vehicle_pos = distance_from_center * xm_per_pix

### 6. Provide an example image of your result plotted back down onto the road such that the lane area is identified clearly.

Finally, fills the space between two fitted lines, and un-warps (warps back) the image using M_inv, a matrix just source points src and destination points dst are opposite from warping matrix M.

![alt text][image7]

I build pipeline in the 9th cell of the IPython notebook located in "Advanced-Lane-Lines.ipynb"

The code for this step is contained in the 8th cell of the IPython notebook located in "Advanced-Lane-Lines.ipynb"

---

### Pipeline (video)

### 1. Provide a link to your final video output.  Your pipeline should perform reasonably well on the entire project video (wobbly lines are ok but no catastrophic failures that would cause the car to drive off the road!).

Here's a [link to my video result](./output_images/project_video.mp4)

---

### Discussion

### 1. Briefly discuss any problems / issues you faced in your implementation of this project.  Where will your pipeline likely fail?  What could you do to make it more robust?

The lane line bends around 40 ～　42 seconds In this video.
The line may not be detected correctly.
So I have another opportunity , I would like to try another color space or line detection algorithm.(HSV.. and so on.)

repository: shoito-its/CarND-Advanced-Lane-Lines

repo_name: shoito-its/CarND-Advanced-Lane-Lines


text:
---
id: events-4
title: Event props
sidebar_label: Event props
---

All events are available through props. The following event are available on both Android and iOS:

### `onNativeAdLoaded`

This event returns a data object which contains all the ad related information incase you need it. Usually you wont need this because everything is loaded automatically in the views.

| Value                 | Description                                                                                | Platform |
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | -------- |
| `headline:string`     | Title of the native ad.                                                                                | All      |
| `tagline:string`      | Description of the native ad.                                                                                | All      |
| `advertiser:string`   | Advertiser of the native ad.                                                                                | All      |
| `price:string`        | If the ad is for a paid app or service, price value.                                                                                | All      |
| `aspectRatio:number`  | Aspect ratio of the Content loaded inside MediaView. You should use this to calculate the correct width and height of the MediaView. | All      |
| `store:string`        | If ad is for an app, then name of the store.                                                                                | All      |
| `icon:string`         | Icon / Logo url of the adveriser or the product advertised in ad.                                                                    | All      |
| `images:Array<Image>` | Array of image urls recieved with the ad. The `Image` object contains `url`,`width` and `height` of image.                           | All      |
| `callToAction:string` | callToAction text value for the native ad.                                                                                | All      |
| `rating:number`       | If ad is for an app, then its rating on the respective store.                                                                        | All      |
| `video:boolean`       | if ad has video content or not.                                                                                | All      |

### `onAdFailedToLoad`

Called when ad has failed to load and returns reason due to which ad was not loaded.

### `onAdLoaded`

Called when ad has successfully loaded without any errors.

### `onAdOpened`

Called when ad is opened.

### `onAdClosed`

Called when ad is closed.

### `onAdLeftApplication`

Called when ad is loaded but user has left the application

### `onAdImpression`

User impression has been recorded

### `onAdClicked`

User has clicked on the ad.

gha_language: Java

gha_license_id: MIT


text:
# 11 - AWS Account Management #
_____________________________________________

This section dives more into health dashboards, organizations of accounts and administrative aspects of your AWS accounts.

This note is broken down into the following sections:

1.  [AWS Service Health Dashboards](#aws-service-health-dashboards)
    - [AWS Personal Health Dashboard](#aws-personal-health-service-dashboard)
2.  [AWS Organizations Overview](#aws-organizations-overview)
    - [Multi Account Strategies](#multi-account-strategies)
    - [Organization Units](#organization-units)
    - [Service Control Policies (SCP)](#service-control-policies-scp)
    - [Migration of accounts](#migration-of-accounts)
3.  [AWS Service Catalog Overview](#aws-service-catalog-overview)
4.  [AWS Billing Alarms](#aws-billing-alarms)
5.  [AWS Cost Explorer](#aws-cost-explorer)
6.  [AWS Budgets](#aws-budgets)
7.  [AWS Cost Allocation Tags](#aws-cost-allocation-tags)

_____________________________________________

## AWS Service Health Dashboards ##

This shows health of the services on all regions.
- can show historical information for each day
- has an RSS feed you cna subscribe to
- you can check out service health on https://status.aws.amazon.com

![](../Images/shd.png)

### AWS Personal Health Dashboard ###

You can also personalize your view of the services health dashboard.
- global service
- show how AWS outages directly impact you
- show impact on your resources
- list issues and actions you can do to remediate them
- to access the personal health dashboard, just click the orange tab in the services health dashboard page (see picture above)

![](../Images/shd-personal.png)
_____________________________________________

## AWS Organizations Overview ##

This is a global service that allows you to manage multiple AWS accounts in your organization.
- main account is the **master** while the rest are called **member** accounts
- member accounts can only be part of one organization
- member accounts can be moved fromone organization to another
- **consolidated billing** across all accounts
- **single payment method**
- **pricing benefit from aggregated usage**
- an API is available to automate AWS account creation
- you can use **tagging standards** for billing purposes
- you can enable cloudTrail on all accounts to send logs to a central S3 account
- you can enable CloudWatch logs to be send to a central logging account

### Multi Account Strategies ###

You may want to create multiple accounts in your environment for various reasons.
- create an account per department, pper cost center, per environment, etc.
- based on regulatory restrictions (using SCP)
- to have separate per-account service
- isolated account logging

### Organization Units ###

We organize organization accounts using **organization units**.

![](../Images/ou.png)

Inside your organization, you can have multiple OUs as well.

![](../Images/root-ou.png)

### Service Control Policies (SCP) ###

These are policies that allows you to blacklist or whitelist IAM actions.
- can be applied at the root, OU, or Account level
- applied to ALL the **Users** and **Roles**, including the **root**
- **does not affect service-linked roles**
- by default, SCP does not allow anything
- SCP must have an **explicit allow**
- use cases:
    - access restriction to certain services,
    - enforce PCI compliance by explicitly disabling services
- policy at the OU level supersedes policy at the account level

![](../Images/scp-1.png)

Note that there are two strategies we can take in designing our SCP. Blacklist certain services or whitelist only a certain services.

![](../Images/scp-2.png)

### Migration of accounts ###

![](../Images/scp-3.png)

_____________________________________________

## AWS Service Catalog Overview ##

A quick, self-service portal to launch a set of authorized products pre-defined by admins.
- includes VMs, databases, storage options, etc.
- create and manage portfolio of IT services approved internally
- products are CloudFormation templates
- CloudFormation helps ensure consistency and standardization by admins
- all deployed products are centrally managed
- helps with governance, compliance, and consistency
- integrations with ServiceNow, etc.

![](../Images/service-catalog.png)
_____________________________________________

## AWS Billing Alarms ##

Recall that billing alarm data for all the regions in your account is **stored in CloudWatch us-east-1 region**.
- it's for actual costs, not project costs
- you can set threshold or alarm when you're expense goes over a certain limit
_____________________________________________

## AWS Cost Explorer ##

There are three costing tool available to use, the first one is **AWS Cost Explorer**.

- **graphical tool to view and analyze your costs and usage**
- review costs associated with your AWS account or organization
- forecast spending for the next 3 months
- access to default reports
- API to build custom cost management applications
- allows you to **view reservation summary**
- get **recommendations** for which instances to purchase
_____________________________________________

## AWS Budgets ##

AWS Budgets is almost similar with Cost Explorer, except **you can now send alarms when costs exceeds budgets**.

- there are four types of budgets:
    - **Cost**
    - **Usage**
    - **Reservation**
    - **Savings Plan**

- for reserved instances (RI):
    - track utilization
    - support EC2, redshift, elasticache

- up to **5 SNS notifications per budget**

- can filter by any of the following:
    - Service
    - Linked Accounts
    - Tag
    - etc.

- **first 2 budgets are free**

_____________________________________________

## AWS Cost Allocation Tags ##

Tags are used to track resources that relate to each other. We can then use **Cost Allocation Tags** to enable detailed costing reports.
- just like tags, but **show up as columns in Reports**
- cost allocation tags will just **appear in the Billing console**
- can take up to 24 hours to show up in report
- **two types of cost allocation tags:**

    - **AWS Generated Cost Allocation Tags**
        - automatically applied to the resource you create
        - start with the prefix *aws: createdBy*
        - not applied to resources created before activation

    - **User Tags**
        - tags defined by the user
        - starts with the prefix *user*
_____________________________________________

repository: joseeden/notes-aws-sysops

repo_name: joseeden/notes-aws-sysops


text:
# DROP Exposure CSA Dynamics Package

DROP Exposure CSA Dynamics Package implements the CSA Numeraire Basis/Measure Dynamics.

## Class Components

 * [***FundingBasisEvolver***](https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/exposure/csadynamics/FundingBasisEvolver.java)
 FundingBasisEvolver implements a Two Factor Stochastic Funding Model Evolver with a Log Normal
 Forward Process and a Mean Reverting Diffusion Process for the Funding Spread.

 * [***NumeraireInducedMeasureShift***](https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/exposure/csadynamics/NumeraireInducedMeasureShift.java)
 NumeraireInducedMeasureShift computes the Shift of the Forward Terminal Distribution between the
 Non-CSA and the CSA Cases.

# References

 * Antonov, A., and M. Arneguy (2009): Analytical Formulas for Pricing CMS Products in the LIBOR Market Model
 	with Stochastic Volatility https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=1352606 eSSRN

 * Burgard, C., and M. Kjaer (2009): Modeling and successful Management of Credit Counter-party Risk of
 	Derivative Portfolios ICBI Conference Rome

 * Gregory, J. (2009): Being Two-faced over Counter-party Credit Risk Risk 20 (2) 86-90

 * Johannes, M., and S. Sundaresan (2007): Pricing Collateralized Swaps Journal of Finance 62
 	383-410

 * Piterbarg, V. (2010): Funding Beyond Discounting: Collateral Agreements and Derivatives Pricing
 	Risk 21 (2) 97-102

## DROP Specifications

 * Main                     => https://lakshmidrip.github.io/DROP/
 * Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
 * GitHub                   => https://github.com/lakshmiDRIP/DROP
 * Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
 * Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
 * Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
 * Release Versions         => https://lakshmidrip.github.io/DROP/version.html
 * Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
 * Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues

repository: fairhopeweb/DROP

repo_name: fairhopeweb/DROP


text:
# Serverless Secured Image/Video Gallery Website

password protected Image/Video Gallery built ontop of AWS Services

> this Project started as a simple Terraform Port of [AWSPics](https://github.com/jpsim/AWSPics), so most ideas comes from there.

## Demo

[Demo Deployment](https://gallery-demo.digitalkaoz.net/)

use these credentials: `demouser` : `demouser`

## Whats inside

* AWS Cloudfront (delivers the website)
* AWS Certificate Manager (ssl all the things)
* AWS S3 (for storing original images/videos, the encoded/resized ones, the final static website)
* AWS Transcoder Service (transcode videos into a web usable format)
* AWS Lambda (handling login, triggering video encoding, resizing images, building the static website)
* AWS Cognito (for user management)
* AWS KMS (encrypt what could be encrypted, cloudfront cant read from encrypted buckets :( )
* AWS Cloudwatch (trigger the website build every hour, collect all the logs)

## Differences to AWSPics

* Terraform instead of Cloudformation (for obvious reasons ;) )
* AWS Cognito for User Management instead of `.htaccess`
* Video Support
* Image Sourcesets
* [GatsbyJS](https://www.gatsbyjs.org/) for static Site Generation
* Album Post through simple Markdown File and `frontmatter`

## Prerequisites

1.
Docker for building the lambda functions

2.
a certificate for your `domain` or `certdomain` should already exists (in `us-east-1` for cloudfront)

3.
the encrypted cloudfront private key

create and download a cloudfront private key [here](https://console.aws.amazon.com/iam/home?region=us-east-1#/security_credential), note the ID

put the absolute path to file into the variable `cloudfront_private_key_file`
put the key_pair ID into `cloudfront_key_pair`

> thats needed to generate signed cloudfront cookies and the ability to login

4.
a user in your [AWS Cognito User Pool](https://eu-central-1.console.aws.amazon.com/cognito/home?region=eu-central-1#) after the stack is initially completed

> so your users can login

## Usage

```tf
//terraform.tfvars

domain = "gallery-demo.digitalkaoz.net"
region = "eu-central-1"
certdomain = "digitalkaoz.net" #only if your domain is a subdomain
cloudfront_key_pair = "XYZ"
cloudfront_private_key_file = "/path/to/cloudfront_private_key.pem"
website_config = {
    title = "website title"
    subline = "sub headline"
    short_code = "the websites short name"
    author = "the author"
}
```

```tf
//main.tf

provider "aws" {
  region  = "eu-central-1"
  profile = "default"
  version = "~> 1.11"
}

provider "aws" {
  alias   = "us"
  region  = "us-east-1"
  profile = "default"
  version = "~> 1.11"
}

module "ssl_private_image_gallery" {
  source = "github.com/digitalkaoz/tf-private-static-image-video-gallery"

  region                           = "${var.region}"
  domain                           = "${var.domain}"
  certdomain                       = "${var.certdomain}"
  cloudfront_key_pair              = "${var.cloudfront_key_pair}"
  cloudfront_private_key_file      = "${var.cloudfront_private_key_file}"
  website_config                   = "${var.website_config}"
}
```

you have to wait until your Cloudfront Distribution is done deploying, so grab a coffee (~20min) before going on

### uploading images and videos

simply drop your files (categorized by folders) inside the `source` bucket into the folder `original`

### providing folder metadata

simply create a markdown file inside the folder named `post.md`

### creating users

simply create them inside `AWS Cognito`

## TODO

* encrypt more stuff
* handle building of lambda functions outside of terraform?! would fix the needless terraform state changes but would need another tooling step :/
* extract image metadata for later usage (e.g. geolocated on a worldmap)
* certificate generation with DNS validation
* remove/publish gatsby lambda patches/hacks
* Fix Terraform Building of function code ordering = build -> package -> upload -> create_function (sometimes its wrong)
* sometimes the build lambda ist uploaded somehow strange and errors in gatsby site config validation "path should not be null"
* use correct lambda source code hash to minimize tainted resources (sha1, sha256 ? )
* remove css build chain for gatsby in lambda to remove custom `html.js` and hardcoded `static/main.css`


text:
This is a live view of openHABs log. For performance reasons
this view is limited to 1000 entries.

You can filter by log messages. This is a case sensitive search.

If you switch to "Events" you will see the events changes
instead of the regular log file.

repository: Schnilz/paperui-ng

repo_name: Schnilz/paperui-ng


text:
# vue-typescript
Vue2.0+Typescript 的一套模板

> vue-typescript

## Build Setup

``` bash
# install dependencies
npm install

# serve with hot reload at localhost:8888
npm run dev

# build for production with minification
npm run build

# build for production and view the bundle analyzer report
npm run build --report
```

## resources
* [vue-class-component](https://github.com/vuejs/vue-class-component)
* [vue-property-decorator](https://github.com/kaorun343/vue-property-decorator)

repository: ztktct/vue-typescript

repo_name: ztktct/vue-typescript


text:
Voyage to Venus
====

Student Name: Greg Lyons

netID: gml14

Project: Game (approx. 20 hours?)

Game Name: Voyage to Venus

Backstory: For the first time, NASA is sending a manned mission to Venus. As pilot of the spacecraft, it is your job to safely navigate the ship to Venus (Level 1).  Venus is 162 million miles away – the counter in the top left of the screen keeps track of distance traveled (in millions of miles).  At 162, you will reach Venus.  If you crash along the way, you must restart from the beginning.  NASA has predicted a possible meteor shower during your journey to Venus – not a problem, right?

After you reach Venus, you will begin the return journey home (Level 2).  If the ship crashes along the way, you restart from the Level 2 checkpoint rather than the start of the game.  Again, the return trip to earth will take 162 million miles.  Reach home safely, and you’ll be an international hero.

[CONTROLS]

[UP] and [DOWN]……Move spaceship

[SPACE]…………………Fire laser cannon

[R]…………………Reload laser cannon (5 shots per reload)

[ESC]………………………Pause

[ENTER]……………Resume from pause

Goal: To make two safe trips: from Earth to Venus (Level 1), and then from Venus back to Earth (Level 2).

Basic mechanics:  The background screen moves continuously to the left while the spaceship stays laterally suspended on the left side of the screen, giving the illusion of the ship's motion from left to right (for Level 1; reversed for Level 2).  The up and down arrow keys will be used to move the ship vertically to avoid meteors, alien spaceships, and alien projectiles.  The user's spaceship is also be equipped with a projectile weapon to fire at obstacles and destroy them.

The game is implemented with a series of states, progressing from the opening splash screen, to level 1, to a mid-game splash screen, to level 2, and then to a final splash screen.  There is an additional pause state for ingame pausing.  Using states allows for methods to be shared and interpreted based on state rather than writing isolated, specific sections of code for each part of the game.

Levels: The first level (Earth to Venus) features a meteor shower - a heavy barrage of obstacles to avoid.  The second level (Venus to Earth) features fewer meteors but introduces alien spaceships that move up and down with the player and shoot projectiles straight ahead.

The main class for running the game is Voyage.java.

Image files for the game are included in the src package.

Cheat keys:
- Holding [SHIFT] triggers God Mode, which freezes all obstacles on the screen.  However it also freezes the mile counter.
- Pressing [BACKSLASH] jumps immediately to gameplay for level 2

*NOTE: Many of the in-game values for things like speed, timing, size, etc. may seem arbitrary, but they have been carefully tested to ensure the best gameplay experience.  Small tweaks that improve gameplay are better than sticking to a rigid system of numbering.  Some of the methods use math based on the state, which helps with the directional switch between the first and second level.

repository: gregory-lyons/voyage-to-venus

repo_name: gregory-lyons/voyage-to-venus


text:
---
layout: post
title: kbeznak parmatonic
---

## Chao Wan
netID:cw940505
Texas A&M University, College Station.
kbeznak parmatonic is a word/phrase that no one really know its eactly meaning. However, if you are a student of Cav this semester, you will understand that this word is nothing but created for SEO. You can find a detail definition of [SEO](https://en.wikipedia.org/wiki/Search_engine_optimization) here.

## How search engines operate
There are several stategies for SEO. But before that, let's take a deep look at how search engines operate. Search engines have two major functions: crawling and building an index, and providing search users with a ranked list of the websites they've determined are the most relevant.

### [kbeznak parmatonic for Bing search engine](http://www.bing.com/search?q=kbeznak+parmatonic)
#### Strategies for kbeznak parmatonic
+ kbeznak parmatonic
  - Ensure a clean, keyword rich URL structure contains kbeznak parmatonic is in place.
  - Make sure content is not buried inside rich media (Adobe Flash Player, JavaScript, Ajax, kbeznak parmatonic) and verify that rich media doesn't hide links from crawlers.
  - Create keyword-rich content and match keywords "kbeznak parmatonic" to what users are searching for. Produce fresh content regularly.
  - Don’t put the text that you want indexed inside images. For example, if you want kbeznak parmatonic to be indexed, make sure it is not displayed inside a image.

### [kbeznak parmatonic for Google search engine](http://www.google.com/search?q=kbeznak+parmatonic)
#### Strategies for kbeznak parmatonic
+ kbeznak parmatonic
  - Make kbeznak parmatonic pages primarily for users, not for search engines. Don't deceive your users or present different content to search engines than you display to users, a practice commonly referred to as "cloaking."
  - Make a site of kbeznak parmatonic with a clear hierarchy and text links. Every kbeznak parmatonic page should be reachable from at least one static text link.
  - Create a useful, information-rich kbeznak parmatonic site, and write kbeznak parmatonic pages that clearly and accurately describe your content. Make sure that your <title> elements and ALT attributes are descriptive and accurate.
  - Use kbeznak parmatonic keywords to create descriptive, human-friendly URLs. Provide one version of a URL to reach a document, using 301 redirects or the rel="canonical" attribute to address duplicate content.

## Data for kbeznak parmatonic until now:

Want to watch video of kbeznak parmatonic?
[Click Here](https://www.youtube.com/watch?v=YLrnC7OOh14).


text:
# Django Tutorial Part 1: Create the Polls App

This is the 1st part of the tutorial. Before you start make sure you read the [intro](README.md)

## Start the Django Polls Tutorial

When you add new code, like new apps, features, bugfixes etc, it's better to use a separate branch, and keep the master branch stable. When the master branch is stable, deployment is just pushing it to production. As long as the master is stable, you can experiment and develop freely on other branches, and safely deploy with the master branch.

So, to add the polls app, start with a new branch.

**Make sure you are in the `site_repo` directory:**

	you@dev-machine: git branch polls-app
	you@dev-machine: git checkout polls-app

Create the polls app:

	you@dev-machine: django-admin startapp polls

What happened?

	you@dev-machine: git status

	# On branch polls-app
	# Untracked files:
	#   (use "git add <file>..." to include in what will be committed)
	#
	#	polls/
	nothing added to commit but untracked files present (use "git add" to track)track)

Django created the app directory, polls.
The directory contains several files, based on the default app template:

	you@dev-machine: ls polls
	admin.py  __init__.py  migrations  models.py  tests.py  views.py

Add the files, and commit:

	you@dev-machine: git add .
	you@dev-machine: git commit -m 'added polls app'

Now we have to tell django to use the polls app. It's not enough to create the app directory, we also have to declate it in the `settings.py` file:

	you@dev-machine: nano settings.py

Then look for the `INSTALLED_APPS` key, and edit, so it will look like this:

	INSTALLED_APPS = (
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'site_repo.home',
    'site_repo.polls'
    )

The basic app files are in place, we can start to write the app.
Edit the app models:

	you@dev-machine: nano polls/models.py

After the edit, the file should look like this:

	from django.db import models

	class Question(models.Model):
   	    question_text = models.CharField(max_length=200)
   	    pub_date = models.DateTimeField('date published')

	class Choice(models.Model):
   		question = models.ForeignKey(Question)
    	choice_text = models.CharField(max_length=200)
    	votes = models.IntegerField(default=0)

A model is a python class that django uses to access the database table. The django "models" are an abstraction layer, of the underlying database tables, with additional functionality (provided by django). When you code, you interact with models, and then django interact with the database behind the scenes.

Since the app's models are **not** database tables, once you define the model class, django needs to create the actual database table. But first, we tell django that we have updated the models.

In the `site_repo` directory:

	you@dev-machine: cd ..
	you@dev-machine: ./manage.py makemigrations polls

	Migrations for 'polls':
	0001_initial.py:
    - Create model Choice
    - Create model Question
    - Add field question to choice

See what happened:

	you@dev-machine: cd site_repo
	you@dev-machine: git status

	# On branch polls-app
	# Changes not staged for commit:
	#   (use "git add <file>..." to update what will be committed)
	#   (use "git checkout -- <file>..." to discard changes in working directory)
	#
	#	modified:   polls/models.py
	#	modified:   settings.py
	#
	# Untracked files:
	#   (use "git add <file>..." to include in what will be committed)
	#
	#	polls/migrations/0001_initial.py

Note the changes of two tracked files: `settings.py`, and the `polls/models.py`.

And there is a new file: `polls/migrations/0001_initial.py`.
Django added a migrations file, with specific instructions how to change the database to match the models.

It's time to tell django to apply these changes to the database. Django handles the SQL schema updates to the database:

	you@dev-machine: cd ..
	you@dev-machine: ./manage.py migrate

	Operations to perform:
	  Synchronize unmigrated apps: staticfiles, messages
	  Apply all migrations: admin, contenttypes, polls, auth, sessions
	Synchronizing apps without migrations:
	  Creating tables...
	    Running deferred SQL...
	  Installing custom SQL...
	Running migrations:
	  Rendering model states... DONE
	  Applying polls.0001_initial... OK

OK! Django just applied the `0001_initial` migration to the database.
Check it:

	you@dev-machine: mysql -uroot -p
	mysql > use django_db;

	Reading table information for completion of table and column names
	You can turn off this feature to get a quicker startup with -A

	Database changed

	mysql> show_tables;

	+----------------------------+
	| Tables_in_django_db        |
	+----------------------------+
	| auth_group                 |
	| auth_group_permissions     |
	| auth_permission            |
	| auth_user                  |
	| auth_user_groups           |
	| auth_user_user_permissions |
	| django_admin_log           |
	| django_content_type        |
	| django_migrations          |
	| django_session             |
	| polls_choice               |
	| polls_question             |
	+----------------------------+
	12 rows in set (0.00 sec)

The database contains (for now) 12 tables. 10 tables are used by django, and the last two tables are the new polls app tables.

By default, django names the tables as "app-name_model-name".
To see the tables' fields:

	mysql > describe polls_question;

	+---------------+--------------+------+-----+---------+----------------+
	| Field         | Type         | Null | Key | Default | Extra          |
	+---------------+--------------+------+-----+---------+----------------+
	| id            | int(11)      | NO   | PRI | NULL    | auto_increment |
	| question_text | varchar(200) | NO   |     | NULL    |                |
	| pub_date      | datetime     | NO   |     | NULL    |                |
	+---------------+--------------+------+-----+---------+----------------+
	3 rows in set (0.00 sec)

	mysql > describe polls_choice;

	+-------------+--------------+------+-----+---------+----------------+
	| Field       | Type         | Null | Key | Default | Extra          |
	+-------------+--------------+------+-----+---------+----------------+
	| id          | int(11)      | NO   | PRI | NULL    | auto_increment |
	| choice_text | varchar(200) | NO   |     | NULL    |                |
	| votes       | int(11)      | NO   |     | NULL    |                |
	| question_id | int(11)      | NO   | MUL | NULL    |                |
	+-------------+--------------+------+-----+---------+----------------+
	4 rows in set (0.02 sec)

The django migration converted the models' definitions in python to SQL, and created the actual database tables.
By default, django adds an "id" auto_increment field to each table.

*Note: The question_id field in the polls_choices table is a MUL index, a multiple index, which django will use for the "many" side of the one-to-many relation question-choices (in the `polls/models.py` file, there is a `ForeignKey` field for the choices model)*

Most of the time you will work with django models, but once in a while you will need to use direct sql to run some complex queries or improve preformance. For now, exit MySQL.

**Interim Summary:**

1. You created the polls app
2. You added the polls app to the site `INSTALLED_APPS`, in `settings.py`
3. You added polls models
4.  You created a migration
5.  You applied the migration to the **local** database

Seems like a progress, so it's a good time to commit.
From the `site_repo` directory:

	you@dev-machine: git add .
	you@dev-machine: git status
	# On branch polls-app

	# Changes to be committed:
	#   (use "git reset HEAD <file>..." to unstage)
	#
	#	new file:   polls/migrations/0001_initial.py
	#	modified:   polls/models.py
	#	modified:   settings.py
	#

All the changes are staged.

Commit:

	you@dev-machine: git commit -m "polls models and migrations"

And push:

	you@dev-machine:  git push origin polls-app

	Counting objects: 13, done.
	Compressing objects: 100% (11/11), done.
	Writing objects: 100% (11/11), 1.69 KiB, done.
	Total 11 (delta 3), reused 0 (delta 0)
	To django@<IP_ADDRESS>:/home/django/site_repo.git
	 * [new branch]      polls-app -> polls-app

Everything so far is on the polls-app branch. The master branch didn't change at all. We will merge the polls-app to master only when it's tested, stable, and ready for deployment.

Great Work!

In the next tutorial, you will enter some manual data with the django admin.
Continue to [Part 2: The Django Admin](tutorial_part2.md)

Support this project with my affiliate link|
-------------------------------------------|
https://www.linode.com/?r=cc1175deb6f3ad2f2cd6285f8f82cefe1f0b3f46|

repository: Aviah/one-click-django-polls-tutorial

repo_name: Aviah/one-click-django-polls-tutorial


text:
> [Go to French version](/fr/Règlements/Décrets,%20ordonnances%20et%20règlements%20statutaires/2007/71.md)

# Order Varying Telecom Decision CRTC 2006-15

**SOR/2007-71**

Enabling authorities:
- [TELECOMMUNICATIONS ACT](/en/Acts/Statutes%20of%20Canada/1993/c.%2038.md)

Date registered: 4 April 2007

----------

Whereas, on April 6, 2006, the Canadian Radio-television and Telecommunications Commission (the Commission) rendered Telecom Decision CRTC 2006-15, Forbearance from the regulation of retail local exchange services (the Decision);

Whereas the Commission determined that it was prepared to forbear from regulating retail local exchange services provided by Incumbent Local Exchange Carriers (ILECs) when the ILEC could demonstrate that, in the relevant market, rivalrous behaviour exists and that it has experienced a 25% market share loss, complied with specified competitor quality of service standards throughout the six-month period preceding its application for forbearance, implemented approved Competitor Services tariffs and provided competitors with access to its Operational Support Systems;

Whereas the Commission determined that local forbearance regions (LFRs) were the appropriate geographic component of a relevant market;

Whereas the Commission considered providing ILECs with greater regulatory flexibility prior to forbearance and the circumstances under which it might lessen or remove existing competitive safeguards for promotions, as defined in Telecom Decision CRTC 2005-25, Promotions of local wireline services, lessen or remove the local winback rule set out in Telecom Decision CRTC 2005-28, Regulatory framework for voice communication services using Internet Protocol, as amended by Telecom Decision CRTC 2005-28-1 and confirmed by Telecom Decision CRTC 2006-53, permit the *ex parte* filing of tariff applications for promotions, and permit the waiving of service charges for local residential winbacks;

Whereas subsection 12(1) of the [Telecommunications Act](/en/Acts/Statutes%20of%20Canada/1993/c.%2038.md) (the Act) provides that, within one year after a decision by the Commission, the Governor in Council may vary the decision on petition in writing presented to the Governor in Council within 90 days after the decision;

Whereas petitions by the Government of Saskatchewan and the Coalition for Competitive Telecommunications and a joint petition by Aliant Telecom Inc., Bell Canada, Saskatchewan Telecommunications and Telus Communications Company were presented in writing to the Governor in Council within 90 days after the Decision;

Whereas, in accordance with subsection 12(4) of the Act, notices of receipt of those petitions were published in the Canada Gazette by the Minister of Industry on June 3, 2006 and July 22, 2006, indicating that the petitions and any petition or submission made in response to them could be inspected and copied at the website strategis.gc.ca;

Whereas, in accordance with section 13 of the Act, the Minister of Industry has notified a minister designated by the government of each province of the Minister’s intention to make a recommendation to the Governor in Council for the purposes of an order under section 12 of the Act and has provided an opportunity for each of them to consult with the Minister;

Whereas a copy of the proposed Order Varying Telecom Decision CRTC 2006–15 was published in the Canada Gazette, Part I, on December 16, 2006 and a reasonable opportunity was thereby afforded to interested persons to make representations with respect to the proposed Order;

Whereas subsection 34(1) of the Act provides that the Commission may make a determination to refrain, in whole or in part and conditionally or unconditionally, from the exercise of any power or the performance of any duty under sections 24, 25, 27, 29 and 31 of the Act in relation to a telecommunications service or class of services provided by a Canadian carrier, if the Commission finds as a question of fact that to refrain would be consistent with the Canadian telecommunications policy objectives set out in section 7 of the Act;

Whereas the Canadian telecommunications policy objectives include rendering reliable and affordable telecommunications services of high quality accessible to Canadians in both urban and rural areas in all regions of Canada, enhancing the efficiency and competitiveness, at the national and international levels, of Canadian telecommunications, fostering increased reliance on market forces for the provision of telecommunications services and ensuring that regulation, when required, is efficient and effective;

Whereas subsection 34(2) of the Act provides that the Commission shall make a determination to refrain, to the extent that it considers appropriate, conditionally or unconditionally, from the exercise of any power or the performance of any duty under sections 24, 25, 27, 29 and 31 of the Act in relation to a telecommunications service or class of services provided by a Canadian carrier, where the Commission finds as a question of fact that the service or class of service is or will be subject to competition sufficient to protect the interests of users;

Whereas the evolution of competitive telecommunications markets in Canada has accelerated with the deployment of Internet Protocol technology;

Whereas, in light of the evolving competition, the Governor in Council made, on December 14, 2006, the Order Issuing a Direction to the CRTC on Implementing the Canadian Telecommunication Policy Objectives;

Whereas that Order directs that the Commission should rely on market forces to the maximum extent feasible as the means of achieving the Canadian telecommunications policy objectives and that, when relying on regulation to achieve those objectives, the Commission should use measures that are efficient and proportionate to their purpose and that interfere with the operation of competitive market forces to the minimum extent necessary;

Whereas the Governor in Council considers that facilities-based competition is a durable form of competition that delivers the greatest benefits to consumers, imposes competitive market discipline on incumbents and strengthens investment in telecommunications infrastructure;

Whereas the Governor in Council considers that local business markets and local residential markets should be considered separately;

Whereas the Governor in Council considers that it is important to adopt simple and streamlined criteria for de-regulating the services of incumbent telephone companies in order to avoid imposing an unnecessary regulatory burden that could deny or delay the benefits of competitive rivalry to consumers;

Whereas the Governor in Council considers that LFRs are not the appropriate geographic component of a relevant market, as they are too vast to retain administrative practicality and do not reflect a social and economic community of interest;

Whereas the Governor in Council considers that local exchanges are the appropriate geographic component of a relevant market, as they often reflect a social and economic community of interest and are less likely than LFRs to contain pockets of uncontested customers;

Whereas the Governor in Council considers that the provision of competitor services by an ILEC, in accordance with the competitor quality of service standards, supports sustainable competition, and that it is appropriate that an ILEC demonstrate that it has, on average, met those standards prior to any granting of forbearance under section 34 of the Act;

Whereas the Governor in Council considers that the use of mobile wireless technology by consumers is increasing and will likely continue to increase, and that for many consumers the exclusive use of mobile wireless services is an increasingly attractive alternative to wireline local exchange services;

Whereas the Governor in Council considers that the removal of marketing restrictions imposed by the Commission on ILECs will foster an increased reliance on market forces and enhance competitive market rivalry;

Whereas the Governor in Council considers that it would be consistent with Canadian telecommunications policy objectives for the Commission, in accordance with section 34 of the Act, to grant forbearance from regulating the provision of retail local exchange services when the criteria set out in this Order are satisfied;

And whereas the Governor in Council considers that retail local exchange services, for which forbearance is granted under section 34 of the Act based on the criteria set out in this Order, will be subject to competition that is sufficient to protect the interests of users and will not unduly impair the establishment or continuance of a competitive market;

Therefore, Her Excellency the Governor General in Council, on the recommendation of the Minister of Industry, pursuant to subsection 12(1) of the [Telecommunications Act](/en/Acts/Statutes%20of%20Canada/1993/c.%2038.md)[a], varies Telecom Decision CRTC 2006-15 as follows:

a: S.C. 1993, c. 38

**1.** Paragraphs 141 to 168 of the Decision are replaced by the following:

**141.** The Commission considers that, for the purposes of a local forbearance application by an ILEC, a local exchange is the appropriate geographic component of the relevant market.

**2.** Paragraphs 242 to 281 of the Decision are replaced by the following:

**242.** The Commission considers that, if an ILEC can satisfy the following criteria, then the requirements of section 34 of the Act for a forbearance determination will have been met and the Commission may therefore grant local forbearance in accordance with that section:

**a)** the ILEC demonstrates that one of the following circumstances exists in the relevant market:

**(i)** that the ILEC does not have market power, based on the criteria set out in paragraph 213,

**(ii)** that, if the ILEC offers residential local exchange services, there are, in addition to the ILEC, at least 2 independent facilities-based telecommunications service providers, including providers of mobile wireless services, each of which offers local exchange services in the market and is capable of serving at least 75% of the number of residential local exchange service lines that the ILEC is capable of serving, and at least one of which, in addition to the ILEC, is a facilities-based, fixed-line telecommunications service provider, or

**(iii)** that, if the ILEC offers business local exchange services, there is, in addition to the ILEC, at least one other independent facilities-based, fixed-line telecommunications service provider that offers local exchange services in the market and is capable of serving at least 75% of the number of business local exchange service lines that the ILEC is capable of serving;

**b)** the ILEC demonstrates that, during a six-month period, beginning no earlier than eight months before its application for local forbearance and ending at any time before the Commission’s decision respecting the application,

**(i)** it met, on average, the quality of service standard for each indicator set out in Appendix B, as defined in Telecom Decision CRTC 2005-20, Finalization of quality of service rate rebate plan for competitors, with respect to the services provided to competitors in its territory, and

**(ii)** it did not consistently provide any of those competitors with services that were below those quality of service standards.

**243.** For the purposes of subparagraphs 242a)(ii) and (iii) and paragraph 523, the Commission considers that a telecommunications service provider is independent if it does not have the same owner as, and is not affiliated with, any other service provider referred to in the respective subparagraph. Further, for the purpose of those provisions, the Commission considers that a facilities-based telecommunications service provider is one that provides services in the relevant market either by using its own facilities and services or by using a combination of its own facilities and services together with those leased from other service providers.

**3.** Paragraphs 483 to 488 of the Decision are replaced by the following:

**483.** The Commission removes the existing competitive safeguards for promotions, as defined in Telecom Decision CRTC 2005-25, Promotions of local wireline services, removes the local winback rule as set out in Telecom Decision CRTC 2005-28, Regulatory framework for voice communication services using Internet Protocol, as amended by Telecom Decision CRTC 2005-28-1 and confirmed by Telecom Decision CRTC 2006-53, permits the *ex parte* filing of tariff applications for promotions and permits the waiving of service charges for residential local winbacks.

**4.** Paragraphs 512 to 535 of the Decision are replaced by the following:

*Application for local forbearance*

**512.** The Commission will establish a process for dealing with applications for local forbearance filed pursuant to the framework set out in this Decision.

**513.** In an application for local forbearance, the ILEC should provide evidence that it has satisfied all the criteria referred to in paragraph 242 or indicate that it will make the demonstration referred to in paragraph 242b) after filing the application. Each application should only be with reference to one relevant market and should list the tariff items and associated tariff numbers of the services for which the ILEC is requesting local forbearance.

**514.** An application for local forbearance should be accompanied by a draft communications plan for the Commission’s approval. The plan should describe how the ILEC intends to explain local forbearance to customers in the relevant market, provide information concerning the ongoing availability of stand-alone PES in the market and provide contact information for customers who have questions or concerns.

**515.** If an applicant ILEC is making applications for a number of contiguous local exchanges, the ILEC may request that the Commission determine those applications together.

**516.** The applicant ILEC will, prior to filing, serve a copy of its application on all registered CLECs and any other known TSP providing local exchange service in the relevant market. Other parties that may be interested in commenting on such applications for local forbearance will be responsible for monitoring the Commission’s website in order to be aware of when such applications have been filed.

**517.** The Commission will post the application to its website once it is received. The Commission notes that applications that are deficient in providing all of the required information, or that are not accompanied by all of the required documents, will be returned to the applicant ILEC.

**518.** The Commission notes that it may request that the applicant ILEC or others provide further information or documents that it considers necessary to determine whether local forbearance is warranted.

**519.** In establishing a process for dealing with applications for local forbearance, the Commission considers it important to provide a process that allows for timely decisions to be rendered for those applications. Consequently, if the ILEC provides all the required information and documents in the required form, the Commission undertakes, subject to paragraphs 520 to 522, to complete its analysis of, and render its decision respecting applications which rely on paragraph 242a)(ii) or (iii) within 120 days after the day on which the application is received.

**520.** Applications for local forbearance that propose a division of business local exchange services into multiple relevant markets will not be subject to the undertaking.

**521.** If the applicant ILEC indicates in its application that it will make the demonstration referred to in paragraph 242b) after filing the application, the Commission will commence its analysis of the application, but the application will not be subject to the undertaking.

**522.** Applications for local forbearance that relate to local exchanges that are located wholly or partially within the census metropolitan area of Calgary, Edmonton, Halifax, Hamilton, London, Montreal, Ottawa-Gatineau, Quebec City, Toronto, Vancouver or Winnipeg, will be given priority by the Commission.

*Special circumstance*

**523.** If, prior to granting local forbearance, the Commission is informed that the ILEC’s application is based on competition in the relevant market from an independent fixed-line telecommunications service provider that, including all of its affiliates, has less than 20,000 local exchange services customers in Canada, the forbearance will not be effective until at least 18 months after the day on which the service provider began providing local exchange services in that market.

**5.** Appendix A of the Decision is repealed.

**6.** Appendix B of the Decision is replaced by the following:

**Appendix B**

**Competitor Quality of Service Indicators**

**Indicator**
**Title**
**Standard**

Indicator 1.8
New Unbundled Type A and B Loop Order Service Intervals Met
90% or more

Indicator 1.9
Migrated Unbundled Type A and B Loop Order Service Intervals Met
90% or more

Indicator 1.10
LNP Order (Stand-alone) Service Interval Met
90% or more

Indicator 1.11
Competitor Interconnection Trunk Order Service Interval Met
90% or more

Indicator 1.12
Local Service Requests, Confirmed Due Dates Met
90% or more

Indicator 1.19
Confirmed Due Dates Met — CDN Services and Type C Loops
90% or more

Indicator 2.7
Competitor Out-of-Service Trouble Reports Cleared within 24 hours
80% or more

Indicator 2.9
Competitor Degraded Trouble Reports Cleared within 48 hours
90% or more

Indicator 2.10
Mean Time to Repair (MTTR) — CDN Services and Type C Loops
4 hours or less

**7.** Appendix D and Appendix E of the Decision are repealed.

**8.** The Decision shall otherwise continue to apply, but any provisions in the Decision that are inconsistent with this Order shall be interpreted in accordance with this Order to the extent of the inconsistency.

> SOR/2007-71, err.(F), Vol. 142, No. 5..

repository: TravisMadill/CanadianLaws

path: /en/Regulations/Statutory Orders and Regulations/2007/71.md

repo_name: TravisMadill/CanadianLaws


text:
---
title: "Install Dependencies"
metaTitle: "Install Dependencies | Remix Fullstack GraphQL Tutorial"
metaDescription: "You will setup a solid foundation for your Remix app"
---

import GithubLink from "../../src/GithubLink.js";

<GithubLink link="https://github.com/hasura/learn-graphql/blob/master/tutorials/frontend/remix-firebase/app-final/package.json" text="package.json" />

1. In our `package.json` we'll add the dependencies for our app, make sure everything is the latest version, and modify the scripts section. Please copy the package.json from the Github link to your folder.

   ```json
   {
     "private": true,
     "name": "remix-app-template",
     "description": "",
     "license": "",
     "dependencies": {
       "@remix-run/react": "1.2.3",
       "@remix-run/serve": "1.2.3",
       "firebase": "9.6.7",
       "firebase-admin": "10.0.2",
       "graphql": "16.3.0",
       "graphql-request": "4.0.0",
       "graphql-tag": "2.12.6",
       "react": "17.0.2",
       "react-dom": "17.0.2",
       "react-firebaseui": "6.0.0",
       "react-use": "17.3.2",
       "remix": "1.2.3",
       "remix-utils": "2.5.0",
       "zod": "3.12.1",
       "cross-env": "7.0.3"
     },
     "devDependencies": {
       "@graphql-codegen/cli": "2.6.2",
       "@graphql-codegen/typescript": "2.4.5",
       "@graphql-codegen/typescript-graphql-request": "4.3.7",
       "@graphql-codegen/typescript-operations": "2.3.2",
       "@remix-run/dev": "1.2.3",
       "@remix-run/eslint-config": "1.2.3",
       "@types/react": "17.0.39",
       "@types/react-dom": "17.0.12",
       "eslint": "8.10.0",
       "typescript": "4.6.2"
     },
     "engines": {
       "node": ">=14"
     },
     "sideEffects": false,
     "scripts": {
       "build": "cross-env NODE_ENV=production remix build",
       "dev": "cross-env NODE_ENV=development remix dev",
       "postinstall": "remix setup node",
       "start": "cross-env NODE_ENV=production remix-serve build",
       "codegen": "node -r dotenv/config node_modules/.bin/gql-gen"
     }
   }
   ```

1. Run `npm install`

repository: hasura/learn-graphql

gha_language: JavaScript

repo_name: hasura/learn-graphql


text:
# jQuery Iframe Transport

jQuery Ajax transport plugin for iframe-based file uploads. For documentation, see:

[https://cmlenz.github.io/jquery-iframe-transport/](https://cmlenz.github.io/jquery-iframe-transport/)

# Demo

To run the included simple demo application, you'll need [node.js](http://nodejs.org/).

In the `jquery-iframe-transport` source directory, run:

    $ npm install

That should download and install all dependencies required for the build, including running the demo app. Then simply run:

    $ grunt connect

Point your browser to http://localhost:3000/ and you should be able to play with the demo.

repository: tonyberrynd/SugarDockerized

repo_name: tonyberrynd/SugarDockerized


text:
# MultiRadio module

MultiRadio is a multi standard RF platform for MCU and embedded devices. It includes an high speed WiFi with TCP and UDP capabilities and a maximum througput up to 24 Mbps, in parallel with an Bluetooth Low energy interface for low data rate and simple applications, moreover LoRaWAN connectivity allows long range communications.
All the protocols are easily accesible by SPI interface, and throught lightweight APIs.

## 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

The FT2232H Mini Module requires USB device drivers, available free from

```
http://www.ftdichip.com
```

which are used to make the FT2232H on the Mini Module appear as a two virtual COM ports (VCP). This
then allows the user to communicate with the USB interface via a standard PC serial emulation port. Another FTDI USB driver, the D2XX driver, can also be used with application software to directly
access the FT2232H on the Mini Module though a DLL.

GAP

```
https://github.com/pulp-platform/pulp-sdk
```

Must use
```
tag 2018.09.01
```

Clone or download this folder into local machine

## Deployment

### Expansion board description

The Sterling-LWB STM Expansion Board is designed for use with the ST 32F411EDISCOVERY
The expansion board routes pins from the STM32F4xx MCU to interface with the Sterling-LWB module. In addition, the Expansion Board includes SPI Flash and a USB/JTAG debugger.
The board provides a single micro-USB interface for JTAG programming and access to the UART1 serial interface of the STM32F411 MCU. The JTAG interface is provided via a standard FTDI FT2232H IC with its VID and PID updated to be a WICED debugger (VID: 0x0a5c PID: 0x43fa).

The USB SWD debug interface on the Discovery board is not used. The WICED SDK assumes a JTAG debugger, which is provided on the Expansion Board.

In order to use the Sterling-LWB module, the conflicting components must be removed on the 32F411EDISCOVERY Discovery board.

The Sterling-LWB expansion board requires a 1A +5VDC power supply to operate. The barrel connector (J3) is configured for 5VDC center positive. J3 powers both the Sterling-LWB expansion board and the attached Discovery Board. For access to the JTAG and USB-to-serial adapter on the Sterling-LWB expansion board, attach a Windows PC via micro-USB cable to the micro-USB port on the Sterling-LWB expansion board (J1).

### Wire connection between GAP and the ST 32F411EDISCOVERY

*Wires length must be shorter than 20 cm*

| GAPUINO | 32F411EDISCOVERY |
| --- | --- |
| SPI1_SCLK | PB13 |
| SPI1_MISO | PB14 |
| SPI1_MOSI | PB15 |
| SPI1_CS0 | PC1 |
| GPIO_A19 | PC2 |
| GPIO_A5 | PB7 |
| GND | GND |

## Sterling-LWB firmware loading

Open the Command Prompt,
Go to path

```
/multi_radio/43xxx_Wi-Fi/
```

start the .bat file
```
First_Load_ExtFlash.bat
```

## License

[![CC0](https://licensebuttons.net/p/zero/1.0/88x31.png)](https://creativecommons.org/publicdomain/zero/1.0/)

gha_language: C


text:
## Features

The Jupyter Kernel Gateway provides a rich set of features and options:

* [`jupyter-websocket` mode](#jupyter-websocket-mode) which provides a
  Jupyter Notebook server-compatible API for requesting kernels and
  communicating with them using Websockets
* [`notebook-http` mode](#notebook-http-mode) which maps HTTP requests to
  cells in annotated notebooks
* Option to set a shared authentication token and require it from clients
* Options to set CORS headers for servicing browser-based clients
* Option to set a custom base URL (e.g., for running under tmpnb)
* Option to limit the number kernel instances a gateway server will launch
  (e.g., to force scaling at the container level)
* Option to pre-spawn a set number of kernel instances
* Option to set a default kernel language to use when one is not specified
  in the request
* Option to pre-populate kernel memory from a notebook
* Option to serve annotated notebooks as HTTP endpoints, see
  [notebook-http](#notebook-http-mode)
* Option to allow downloading of the notebook source when running
  `notebook-http` mode
* Automatic [Swagger spec](http://swagger.io/introducing-the-open-api-initiative/)
  for a notebook-defined API in `notebook-http` mode
* A CLI for launching the kernel gateway: `jupyter kernelgateway OPTIONS`
* A Python 2.7 and 3.3+ compatible implementation

gha_language: Python


text:
---
layout: post
title:      "Fetching with React Hooks"
date:       2021-06-18 20:30:32 -0400
permalink:  fetching_with_react_hooks
---

So with class components there were a lot of `connect` method calls & calls to `mapStateToProps` & `mapDispatchToProps` along with the layers of indirection like actions and reducers.

With hooks, things are a lot clearer and simpler.

This is the source for a simple project that makes fetch calls via react hooks [[source](https://github.com/mrarthurwhite/fetch_hooks_react_demo)]. This is just the link to the source and some source code but I will elaborate upon the various details soon. This is just a placeholder for elaboration later.

And this is the source code :

index.js
```
import React from 'react';
import ReactDOM from 'react-dom';
import './index.css';
import App from './App';
import { createStore } from 'redux'; // this allows us to create store
import { Provider } from 'react-redux';
// we pass the store obj to top level container Provider
import rootReducer from './reducers/rootReducer.js';

const store = createStore(rootReducer);

const rootElement =   document.getElementById('root')
// added Provider to wrap around App
ReactDOM.render(
  <Provider store={store}>
      <App />
  </Provider> ,
  rootElement
);

```
Before we go into using `useEffect` to make a fetch call allow me to go over the ouline of the code:

`useEffect(  fetchdata(),[ empty_array__so_as_to_fetch_only_once] );`

If you want to run an effect and clean it up only once (on mount and unmount), you can pass an empty array ([]) as a second argument. This tells React that your effect doesn’t depend on any values from props or state, so it never needs to re-run.

The API for `useEffect` is :

``` useEffect( () => { . . . return cleanup; },[var_n_whose_change_triggers_useEffect . . .] ); ```

So here is the code which actually makes a call to the backend:

App.js
```
import './App.css';
import { useSelector, useDispatch } from 'react-redux';
import {  useEffect, useState } from 'react';

import React from 'react';

function App() {
  const [words, setWords] = useState([]);

  let fetchData = () => {
                          fetch('http://localhost:4000/words')
                          .then(response => response.json())
                          .then (data =>
                                            {
                                                  setWords(data);
                                            }
                                          )
                        };

  useEffect(fetchData, [] );

    return (
    <>
    {
      words.map(w=> <div>{w.word}</div>)
    }
    </>
  );
}

export default App;

```

The json I am getting from the api is:

```
[
{
"id": 1,
"word": "abase",
"definition": "Lower ; humiliate.",
"sentence": "Defeated, Queen Zenobia was forced to abase herself before the conquering Romans, who made her march in chains before the emperor in the procession celebrating his triumph.",
"category_id": 1,
"category": {
"name": "3,500 Basic"
}
},
{
"id": 2,
"word": "abash",
"definition": "embarrass.",
"sentence": "He was not at all abashed by her open admiration.",
"category_id": 1,
"category": {
"name": "3,500 Basic"
}
},
{
"id": 3,
"word": "abate",
"definition": "subside; decrease, lessen.",
"sentence": "Rather than leaving immediately, they waited for the storm to abate.",
"category_id": 2,
"category": {
"name": "Hot Prospect"
}
},
{
"id": 4,
"word": "abridge",
"definition": "condense or shorten.",
"sentence": "Because the publishers felt the public wanted a shorter version of War and Peace, they proceeded to abridge the novel.",
"category_id": 3,
"category": {
"name": "High Frequency"
}
}
]
```

extension: markdown


text:
# [Nuzlocke Generator](https://nuzlocke-generator.herokuapp.com/) · ![Travis](https://img.shields.io/travis/EmmaRamirez/nuzlocke-generator.svg?style=flat-square)  ![Coveralls github](https://img.shields.io/coveralls/github/EmmaRamirez/nuzlocke-generator.svg?style=flat-square) ![Codacy grade](https://img.shields.io/codacy/grade/a41d81bbd4ad4479a0c71a1739707bf4.svg?style=flat-square) ![GitHub release](https://img.shields.io/github/release/EmmaRamirez/nuzlocke-generator.svg?style=flat-square)

> https://nuzlocke-generator.herokuapp.com/

This is a beta. Expect everything to break. Save backups if you value them.

# Running Locally

In order to run this application locally, you'll need [NodeJS](https://nodejs.org/en/) and [Git](https://git-scm.com/). After installing those, start by opening the command prompt and running the following commands:

```bash
git clone git@github.com:EmmaRamirez/nuzlocke-generator.git
cd nuzlocke-generator
npm install
npm run serve
```

Note: closing the command prompt will stop the server!

You can also click the `Clone or Download` button and select to download it as a zip file. You'll then want to use the `cd` command to navigate to the nuzlocke-generator folder. I'm available on Discord (emma#4085) or through Github issues if you have any questions.

You should find it at `localhost:8080`. In order to check for updates, run the following

```bash
git pull origin master
```

And then re-run the server!

## Why is the Generator down?

Not sure. Still investigating. Blame Heroku, I hadn't deployed a single thing and yet somehow the app blew up.

## A Preview

![alt](./src/assets/media-one.png)

![alt](./src/assets/media-two.png)

## Features
- Record your nuzlocke with a flashy image
- Save data on each of your Pokémon as a `json` file
- Flexible style editing for multiple scenarios

If you have ideas for features of fixes, please tell me! I want to make this app as useful as possible.

## Roadmap
- Supporting custom games, badges, types, moves, etc
- Space for sharing your custom ruleset
- Importing from this format and potentially save files
- Supporting better theming & customization of styles
- Fixing one million bugs

## Hall of Fame

None yet.

## Legal

I don't own Pokemon or any of the images except those of the app itself. All rights belong to their respective parties, including The Pokemon Company International and Nintendo. This application itself independent of copyrighted content is licensed under MIT.

repository: JimboNuzlocke/nuzlocke-generator

repo_name: JimboNuzlocke/nuzlocke-generator


text:
---
layout: post
title: "A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation"
date: 2018-06-06 15:11:00
categories: arXiv_CV
tags: arXiv_CV Segmentation GAN CNN Semantic_Segmentation Deep_Learning
author: Holger R. Roth, Chen Shen, Hirohisa Oda, Takaaki Sugino, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori
mathjax: true
---

* content
{:toc}

##### Abstract
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most network architectures require severely downsampling or cropping the images to meet the memory limitations of today's GPU cards while still considering enough context in the images for accurate segmentation. In this work, we propose a novel approach that utilizes auto-context to perform semantic segmentation at higher resolutions in a multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a dataset of manually annotated abdominal organs and vessels from 377 clinical CT images used in gastric surgery, and achieve promising results with close to 90% Dice score on average. For additional evaluation, we perform separate testing on datasets from different sources and achieve competitive results, illustrating the robustness of the model and approach.

##### Abstract (translated by Google)
深度学习的最新进展，如3D全卷积网络（FCNs），已经改进了医学图像密集语义分割的最新技术。然而，大多数网络架构都需要严格的下采样或裁剪图像，以满足当今GPU卡的内存限制，同时仍然考虑图像中足够的上下文以进行精确分割。在这项工作中，我们提出了一种新颖的方法，利用自动上下文在堆叠3D FCN的多尺度金字塔中以更高分辨率执行语义分割。我们在来自胃手术的377张临床CT图像的手动标注的腹部器官和血管的数据集上训练和验证我们的模型，并且实现有希望的结果，平均接近90％的骰子评分。为了进一步评估，我们对来自不同来源的数据集进行单独测试并获得有竞争力的结果，说明模型和方法的稳健性。

##### URL
[http://arxiv.org/abs/1806.02237](http://arxiv.org/abs/1806.02237)

##### PDF
[http://arxiv.org/pdf/1806.02237](http://arxiv.org/pdf/1806.02237)


text:
# brzo-regex

Python 3.x implementation of a PCRE subset using Brzozowski derivatives

![Example](doc/example.png)

Pretty rough prototype with a very basic pyparsing parser.

repository: hlampesberger/brzo-regex

repo_name: hlampesberger/brzo-regex


text:
# ES7 async wrapper for csv package #

This is a wrapper for the popular `csv` package in NPM that can be used with the ES7 async-await pattern, instead of using callbacks.

## Documentation ##

For all documentation, please see the documentation for the [csv package](https://www.npmjs.com/package/csv).

## Usage examples ##

All parameters are the same as for the functions in the `csv` module, except that you need to omit the callback parameter.

If there is any error returned by the `csv` package, an exception will be thrown.

```js
const csv = require('async-csv');

let result1 = await csv.generate(options);
let result2 = await csv.parse(input, options);
let result3 = await csv.transform(data, handler, options); // `options` are not required
let result4 = await csv.stringify(data, options); // `options` are not required
```

## Feedback ##

Feedback, bug reports and pull requests are welcome. See the linked github repository.

license: Unlicense

repository: sanjitaraj/scraping-node

repo_name: sanjitaraj/scraping-node


text:
---
title: Business exploration
date: "2020-01-28T22:12:03.284Z"
description: "Explore your potential business"
---

## Business exploration
### Know your customer
+ Explore client's needs by communication, ask question in guidance
+ What's his trouble or headache, how does your product or service support
+ design your questions, explore on both man and their business.

### Pre sale
+ Show how about our products can solve their business headaches
+ Be confident to the queries, also be honest regarding the product's quality.

### Make a deal
+ Not every pre-sale can generate a deal finally, keep patient, contact with your counter-part continually for possible business break-through.

+ If failed this time, please still keep patient, wait the 2nd or 3rd deal.


text:
# Employee Summary
## Description
To create a template that stores employee information.
## Table of Contents
* [Installation](#installation)
* [Usage](#usage)
* [Contributing](#contributing)
* [Demostration](#demostration)
* [License](#license)
* [Questions](#questions)
## Installation
Use node to access question form and answer accordingly. You can also run tests by using "npm test" in the terminal.
## Usage
Using Node.js to render a html file.
## Contributing
David Johnson
## Demostration
<a href = "https://user-images.githubusercontent.com/72535444/105960014-81aa0780-6031-11eb-80cf-7b886884f489.mp4"> Live Demo </a>
## License
MIT
## Questions
Click [here](https://github.com/DavidMark24) to visit my Github profile.
Or you can email me at<EMAIL_ADDRESS>
## Screenshot of Example Template

![Screenshot (35)](https://user-images.githubusercontent.com/72535444/105959318-9afe8400-6030-11eb-82ad-e40627d9bc32.png)

branch_name: refs/heads/main


text:
## What is [Warden](https://github.com/austinkregel/warden)?
Warden is a dynamic model manager for the [Laravel Framework](https://laravel.com)

## What makes Warden's forms?
Warden was build using another package of mine called [PaperWork](https://github.com/austinkregel/formmodel) formerly called FormModel. PaperWork is a dynamic CRUD generator. If you're interested in PaperWork,
the [documentation is poorly written](https://github.com/austinkregel/formmodel/tree/master/docs)... (sorry about that... I'm working on it...)

## Installing Warden.

To install Warden,
```bash
composer require kregel/warden
```

Then once composer finishes, you must add Warden to the array of service providers in your `/config/app.php` file.

```php
Kregel\Warden\WardenServiceProvider::class,
```

Once the WardenServiceProvider is registered, you'll need to publish the configuration. You can do that with the following artisan command
```bash
php artisan vendor:publish --provider='Kregel\Warden\WardenServiceProvider'
```

## So... How do I use this?...

#### First
To start using Warden, you must open your `/config/kregel/warden.php`, and add a friendly name to your `models` array (it should be an associative array.). If you're making a model for blog post you might want use just use
`blog` or `post` (I'll just call it `post` for the reminder of the docs). Then inside the `post` array you must have a key/value pair of `'model'` and the class name.

So as an example,
```php
'models' => [
    'post' => [
        'model' => App\Post::class
    ]
]
```

#### Second
You must make sure that your model has fillable attributes. If it doesn't have anything in it's fillable array then Warden won't have any way to input any data to your model.

So for our example we might want

```php
protected $fillable = [
    'title', 'body', 'is_published', // And other attributes
];
```

#### Third
Go to your website `http://example.com/warden`. On the left side you'll see a list of registered models. Click on the one you want, for this demo it would be `post`.
Then click `New Post`, fill out your information for your model and click Submit. You can view your new model by
click on the desired model, and then clicking `List all Posts`

#### If you encounter any issues...
Some things to keep in mind...
* Make sure you ran a migration.
* Double check your spelling.
* Make sure the keys in the models array, are all lower case and are all singular.
* **Make sure you set your fillable**

#### What are some other ways to use it?
There are many ways to use Warden.

The most common way I use Warden is as a quick and dirty PHPMyAdmin, but for Laravel.

Another way I use warden is for a backend api. By this I mean, I put the route for
creating the given model in a form. I would not recommend it for production, but it's great for prototyping!

repository: austinkregel/Warden

gha_language: PHP

repo_name: austinkregel/Warden