Python

Writing your first Dockerfile

In this tutorial, I will show you how to write your first Dockerfile. I got to learn Docker because I had to implement a Docker deployment for our GSoC project Open Event Server.

First up, what is Docker ? Basically saying, Docker is an open platform for people to build, ship and run applications anytime and anywhere. Using Docker, your app will be able to run on any platform that supports Docker. And the best part is, it will run in the same way on different platforms i.e. no cross-platform issues. So you build your app for the platform you are most comfortable with and then deploy it anywhere. This is the fundamental advantage of Docker and why it was created.

So let’s start our dive into Docker.

Docker works using Dockerfile (example), a file which specifies how Docker is supposed to build your application. It contains the steps Docker is supposed to follow to package your app. Once that is done, you can send this packaged app to anyone and they can run it on their system with no problems.

Let’s start with the project structure. You will have to keep Dockerfile at the root of your project. A basic project will look as follows –

- app.py
- Dockerfile
- requirements.txt
- some_app_folder/
-   some_file
-   some_file

Dockerfile starts with a base image that decides on which image your app should be built upon. Basically “Images” are nothing but apps. So for example you want your run your application in Ubuntu 14.04 VM, you use ubuntu:14.04 as the base image.

FROM ubuntu:14.04
MAINTAINER Your Name <your@email.com>

These are usually the first two lines of a Dockerfile and they specify the base image and Dockerfile maintainer respectively. You can look into Docker Hub for more base images.

Now that we have started our Dockerfile, it’s time to do something. Now think, if you are trying to run your app on a new system of Ubuntu, what will be the first step you will do… You update the package lists.

RUN apt-get update

You may possibly want to update the packages too.

RUN apt-get update
RUN apt-get upgrade -y

Let’s explain what’s happening. RUN is a Docker command which instructs to run something on the shell. Here we are running apt-get update followed by apt-get upgrade -y on the shell. There is no need for sudo as Docker already runs commands with root user previledges.

The next thing you will want to do now is to put your application inside the container (your Ubuntu VM). COPY command is just for that.

RUN mkdir -p /myapp
WORKDIR /myapp
COPY . .

Right now we were at the root of the ubuntu instance i.e. in parallel with /var, /home, /root etc. You surely don’t want to copy your files there. So we create a ‘myapp’ directory and set it as WORKDIR (project’s directory). From now on, all commands will run inside it.

Now that copying the app has been done, you may want to install it’s requirements.

RUN apt-get install -y python python-setuptools python-pip
RUN pip install -r requirements.txt

You might be thinking why am I installing Python here. Isn’t it present by default !? Well let me tell you that base image ‘ubuntu’ is not the Ubuntu you are used with. It just contains the bare essentials, not stuff like python, gcc, ruby etc. So you will have to install it on your own.

Similarly if you are installing some Python package that requires gcc, it will not work. When you are struck in a issue like that, try googling the error message and most likely you will find an answer. :grinning:

The last thing remaining now is to run your app. With this, your Dockerfile is complete.

CMD python app.py

Building the app

To build the app run the following command.

docker build -t myapp .

Then to run the app, execute docker run myapp.

Where to go next

Refer to the official Dockerfile reference to learn more Dockerfile commands. Also you may find my post on using Travis to test Docker applications interesting if you want to automate testing of your Docker application.

I will write more blog posts on Docker as I learn more. I hope you found this one useful.

 

{{ Repost from my personal blog http://aviaryan.in/blog/gsoc/dockerfile-basic.html }}

Continue ReadingWriting your first Dockerfile

PayPal Express Checkout in Python

As per the PayPal documentation …

Express Checkout is a fast, easy way for buyers to pay with PayPal. Express Checkout eliminates one of the major causes of checkout abandonment by giving buyers all the transaction details at once, including order details, shipping options, insurance choices, and tax totals.

The basic steps for using express checkout to receive one-time payments are:

  1. Getting the PayPal API credentials.
  2. Making a request to the API with the transaction details to get a token
  3. Using the token to send the users to the PayPal payment page
  4. Capturing the payment and charging the user after the user completes the payment at PayPal.

We will be using PayPal’s Classic NVP (Name-value pair) API for implementing this.

Getting PayPal API Credentials

To begin with, we’ll need API Credentials.
We’ll be using the Signature API credentials which consists of

  • API Username
  • API Password
  • Signature

To obtain these, you can follow the steps at Creating and managing NVP/SOAP API credentials – PayPal Developer.

You’ll be getting two sets of credentials. Sandbox and Live. We’ll just stick to the Sandbox for now.

Now, we need sandbox test accounts for making and receiving payments. Head over to Creating Sandbox Test Accounts – PayPal Developer and create two sandbox test accounts. One would be the facilitator and one would be the buyer.

PayPal NVP Servers

All the API actions will take place by making a request to the PayPal server. PayPal has 4 different NVP servers for 4 different purposes.

  1. https://api-3t.sandbox.paypal.com/nvp – Sandbox “testing” server for use with API signature credentials.
  2. https://api-3t.paypal.com/nvp– PayPal “live” production server for use with API signature credentials.
  3. https://api.sandbox.paypal.com/nvp – Sandbox “testing” server for use with API certificate credentials.
  4. https://api.paypal.com/nvp – PayPal “live” production server for use with API certificate credentials.

We’ll be using the Sandbox “testing” server for use with API signature credentials.

Creating a transaction and obtaining the token

To create a transaction, we’ll need to make a request with all the transaction details. We can use Python requests library to easily make the requests. All requests are POST.

We’ll be calling the SetExpressCheckout method of the NVP API to obtain the token.

import requests  
import urlparse

data = {  
    'USER': credentials['USER'],
    'PWD': credentials['PWD'],
    'SIGNATURE': credentials['SIGNATURE'],
    'SUBJECT': credentials['FACILITATOR_EMAIL'],
    'METHOD': 'SetExpressCheckout',
    'VERSION': 93,
    'PAYMENTREQUEST_0_PAYMENTACTION': 'SALE',
    'PAYMENTREQUEST_0_AMT': 100,
    'PAYMENTREQUEST_0_CURRENCYCODE': 'USD',
    'RETURNURL': 'http://localhost:5000/paypal/return/',
    'CANCELURL': 'http://localhost:5000/paypal/cancel/'
}
response = requests.post('https://api-3t.sandbox.paypal.com/nvp', data=data)  
token = dict(urlparse.parse_qsl(response.text))['TOKEN']

Here,

  • USER represents your Sandbox API Username.
  • PWD represents your Sanbox API Password.
  • SIGNATURE represents your Sandbox Signature.
  • SUBJECT represents the facilitator’s email ID.
  • PAYMENTREQUEST_0_AMT is the total transaction amount.
  • PAYMENTREQUEST_0_CURRENCYCODE is the 3 digit ISO 4217 Currency code.
  • RETURNURL is where the user will be sent to after the transaction
  • CANCELURL is where the user will be sent to if he/she cancels the transaction.

A URL-Encoded, Name-value pair response would be obtained. We can decode that into a dict by using Python’s urlparse modules.

From the response, we’re extracting the TOKEN which we will use to generate the payment URL for the user.

This token has to be retained since we’ll be using it in further steps of the process.

Redirecting the user to PayPal for Approval

With the token we obtained, we can form the payment URL.

https://www.sandbox.paypal.com/cgi-bin/webscr?cmd=_express-checkout&token=<TOKEN>

We’ll have to send the user to that URL. Once the user completes the transaction at PayPal, he/she will be returned to the RETURNURL where we’ll further process the transaction.

Obtaining approved payment details and capturing the payment

Once the user completes the transaction and gets redirected back to RETURNURL, we’ll have to obtain the confirmed payment details from PayPal. For that we can again use the token ID that we obtained before.

We’ll now be making a request to the GetExpressCheckoutDetails method of the API.

import requests  
import urlparse

data = {  
    'USER': credentials['USER'],
    'PWD': credentials['PWD'],
    'SIGNATURE': credentials['SIGNATURE'],
    'SUBJECT': credentials['FACILITATOR_EMAIL'],
    'METHOD': 'GetExpressCheckoutDetails',
    'VERSION': 93,
    'TOKEN': TOKEN
}

response = requests.post('https://api-3t.sandbox.paypal.com/nvp', data=data)  
result = dict(urlparse.parse_qsl(response.text))  
payerID = result['PAYERID']

A URL-Encoded, Name-value pair response would be obtained. We can decode that into a dict by using Python’s urlparse modules.

This will provide us with information about the transaction such as transaction time, transaction amount, charges, transaction mode, etc.

But, we’re more interested in the PAYERID which we’ll need to capture/collect the payment. The money is not transferred to the facilitators account until it is captured/collected. So, be sure to collect it.

To collect it, we’ll be making another request to the DoExpressCheckoutPaymentmethod of the API using the token and the PAYERID.

import requests  
import urlparse

data = {  
    'USER': credentials['USER'],
    'PWD': credentials['PWD'],
    'SIGNATURE': credentials['SIGNATURE'],
    'SUBJECT': credentials['FACILITATOR_EMAIL'],
    'METHOD': 'DoExpressCheckoutPayment',
    'VERSION': 93,
    'TOKEN': TOKEN,
    'PAYERID': payerID,
    'PAYMENTREQUEST_0_PAYMENTACTION': 'SALE',
    'PAYMENTREQUEST_0_AMT': 100,
    'PAYMENTREQUEST_0_CURRENCYCODE': 'USD',
}

response = requests.post('https://api-3t.sandbox.paypal.com/nvp', data=data)  
result = dict(urlparse.parse_qsl(response.text))  
status = result['ACK']

All the details have to be the same as the ones provided while obtaining the token. Once we make the request, we’ll again get a URL-Encoded, Name-value pair response. We can decode that into a dict by using Python’s urlparsemodules.

From the response, ACK (Acknowledgement status) will provide us with the status of the payment.

  • Success — A successful operation.
  • SuccessWithWarning — A successful operation; however, there are messages returned in the response that you should examine.
  • Failure — The operation failed; the response also contains one or more error messages explaining the failure.
  • FailureWithWarning — The operation failed and there are messages returned in the response that you should examine.

And, we have completed the PayPal transaction flow for Express Checkout. These are just the basics and might miss a few stuff. I suggest you go through the following links too for a better understanding of everything:

For Reference:
  1. PayPal Name-Value Pair API Basics – PayPal Developer
  2. How to Create One-Time Payments Using Express Checkout – PayPal Developer
Continue ReadingPayPal Express Checkout in Python

Design Your Own Experiments With PSLab

PSLab, with its simple and open architecture allows programmers, hobbyists to use the tool for various measurements and to develop new experiments with simple python code.

One of the main target group, the PSLab is aimed at, is high-school science teachers and students, who may or may-not be familiar with the computer programming. For such users it is difficult to design or develop new experiments on their own. They may also find it difficult to fetch the data and plot required graphs, if a ready-made GUI is not available for that particular experiment.

To enable such users to quickly design a simple experiment for studying various phenomena, we have developed a simple Experiment Designer GUI. This incorporates few controls, read-back elements and easy functions to select parameters and plot graphs.

The screen shot of the ‘Design Your Own Experiment’ GUI along with the App-window is here..

experiment designer1

Experiment Designer allows the user to define the control and read-back sequences of parameters and execute them.

Features of “Design Your Own Experiment” GUI

  • Configure Experiment : Here user can select the required channels ( manual / sweep / read-back). One can also add a derived channel for measuring some physical quantity, for example ‘current’.
  • Make Measurements : Selected channels are displayed. User can make measurements individually for each step or  can sweep in auto mode.
  • Plot and View Plots: Enables user to plot selected parameters. Acquired plots can be selectively displayed or deleted.
  • Save Plots: Data acquired can be save in a spreadsheet.
  • Save Profile : Experiment profile can be saved for repeating the experiment in future. Saved profiles can be loaded from “Load Profile” tab.

Example : Diode IV Characteristics Experiment

For this experiment one needs the following…

  • A variable voltage source : Needs to be swept from Voltage A to  B (say from 0V to 5V)
  • Current Monitoring : Needs to be read for every value of Voltage
  • Plotting and analytics :  Tools to plot the parameters and save data

Schematic Circuit diagram:

diode IV

CH3 monitors the voltage drop across the diode. PV1 is varied in steps, and for each step the current is calculated from the difference between voltages at PV1 and CH3, and the known value of the resistor. For example for 1K resistor, current through the diode is given by

I = (PV1-CH3)/1K

Procedure :

Step 1. Connect Fossasia PSLab to the pc. Connect the components –  Diode from CH3 to Ground and  1k resistor from PV1 to CH3

Step 2. From the terminal Run

Experiments

The App-window will pop-up. Click on ‘Design your own Experiment’ button to get the experiment designer GUI.

experiment designer2

Step 3: Select channels

Sweep Channel PV1 – Sweep from 0.00V -5.00V in 200 steps

Read-back Channel CH3 – for monitoring voltage across the diode

Derived Channel – To measure Current. Type the equation to calculate the current,   (PV1()-CH3())/1000

Step 4. Click on ‘Prepare Experiment‘ to get measurements screen. Click on ‘Evaluate All Rows‘ to make the measurements.

Experiment designer3

Step 5. Select the required columns and click on ‘Plot Selected Columns‘, a message window will pop-up, here user can select the Axes for plotting the graph. On clicking  ‘Plot‘, view plots screen will be displayed.

plotsdiodeiv

One can repeat the experiment and plot multiple curves and save them in a spreadsheet. Acquired plots can be selectively displayed or deleted.

Step 6. The entire design ( Experiment Profile)  of the experiment can be saved for repeating the experiment in future. Saved profiles can be loaded from “Load Profile” tab.

experiment designer profile
This is a very important value add to PSLab Apps. It has enabled PSLab to reach out and help users, who do not have any background in programming. Now ‘designing your own experiments’ has become super easy 🙂 🙂 🙂

Continue ReadingDesign Your Own Experiments With PSLab

Integrating Stripe in the Flask web framework

{ Repost from my personal blog @ https://blog.codezero.xyz/integrating-stripe-in-flask }

Stripe is a developer and a user-friendly payment infrastructure provider. Stripe provides easy to use SDKs in different programming languages allowing us to easily collect payments on our website or mobile application.

Flask is a web microframework for Python based on Werkzeug, Jinja 2. Flask makes building web applications in python a breeze.

Make sure you have your Flask app ready. Let’s start with installing the required dependency. The Stripe python SDK. You can get it by running.

pip install stripe

Don’t forget to add the same in your requirements.txt. (if you have one that is.)

Now, head over to Stripe: Register and create a new Stripe account to get your test keys. If you don’t wish to create an account at this time, you can use the following test keys, but you’ll not be able to see the payments in the stripe dashboard.

  • Publishable Key: pk_test_6pRNASCoBOKtIshFeQd4XMUh
  • Secret Key: sk_test_BQokikJOvBiI2HlWgH4olfQ2

We’ll need to set the secret key in the SDK.

import stripe

STRIPE_PUBLISHABLE_KEY = 'pk_test_6pRNASCoBOKtIshFeQd4XMUh'  
STRIPE_SECRET_KEY = 'sk_test_BQokikJOvBiI2HlWgH4olfQ2'

stripe.api_key = STRIPE_SECRET_KEY

Let’s create a page with a form for us to handle the Stripe payment.

<!DOCTYPE html>  
<html>  
<head>  
    <title>Pay now</title>
</head>  
<body>  
    <h4>Pay $250.00 by clicking on the button below.</h4>
    <form action="/payment" method="POST">
        <script src="https://checkout.stripe.com/checkout.js" 
                class="stripe-button"
                data-key="pk_test_6pRNASCoBOKtIshFeQd4XMUh"
                data-description="A payment for the Hello World project"
                data-name="HelloWorld.com"
                data-image="/images/logo/hw_project.png"
                data-amount="25000"></script>
    </form>
</body>  
</html>

We’re using Stripe’s Checkout library to get the payment details from the user and process. Also, keep in mind that the checkout library has to be loaded directly from https://checkout.stripe.com/checkout.js. Downloading it and serving locally will not work.

The script tag, accepts a lot of parameters. A few important ones are,

  • data-key – The Publishable Key.
  • data-amount – The amount to be charged to the user in the lowest denomination of the currency. (For example, 5 USD should be represented as 500 cents)
  • data-name – The name of your site or company that will be displayed to the user.
  • data-image – The path to an image file (maybe a logo) that you’d like to be displayed to the user.

More configuration options can be seen at Stripe: Detailed Checkout Guide.

This script would automatically create a Pay with Card button which would open the stripe Checkout lightbox when clicked by the user.

Once the payment process is completed the following parameters are submitted to the form’s action endpoint (the form inside which this script is located), along with any other elements that were in the form.

  • stripeToken – The ID of the token representing the payment details
  • stripeEmail – The email address the user entered during the Checkout process

Along with the Billing address details and Shipping address details if applicable and enabled

We’ll need to write a Flask method to handle the input that were submitted by Stripe to proceed with the transaction and charge the user.

Let’s add a new Flask route to respond when submitting the form.

@app.route('/payment', methods=['POST'])
def payment_proceed():  
    # Amount in cents
    amount = 25000

    customer = stripe.Customer.create(
        email=request.form['stripeEmail'],
        source=request.form['stripeToken']
    )

    charge = stripe.Charge.create(
        amount=amount,
        currency='usd',
        customer=customer.id,
        description='A payment for the Hello World project'
    )

    return render_template('payment_complete.html')

We’re now creating a new Stripe customer along with the stripeToken as the source parameter. The card details are stored by stripe as a token. And using this token ID, Stripe will be able to retrieve it to make the charge.

We’re creating a charge object with the amount in the lowest denomination of the currency, the currency name, the customer ID, and an optional description. This will charge the customer. On a successful transaction, a charge object would be returned. Else, an exception will be thrown.

For more information regarding the Charge object and the various other APIs available fro consumption in Stripe, checkout the Stripe API Guide.

Continue ReadingIntegrating Stripe in the Flask web framework

PSLab Communication Function Calls

Prerequisite reading:

Communicating with Pocket Science Lab via USB and capturing and plotting sine waves

Interfacing with the hardware of PSLab, fetching the data and plotting it is very simple and straight forward. Various sensors can be connected to PSLab and data can be fetched with a simple python code as shown in the following example…

>>> from PSL import sciencelab
>>> I = sciencelab.connect()     # Initializing: Returns None if device isn't found. The initialization process connects to tty device and loads calibration values.
# An example function that measures voltage present at the specified analog input
>>> print I.get_average_voltage('CH1')
# An example to capture and plot data
>>> I.set_gain('CH1', 3) # set input CH1 to +/-4V range 
>>> I.set_sine1(1000) # generate 1kHz sine wave on output W1 
>>> x,y = I.capture1('CH1', 1000, 10) # digitize CH1 1000 times, with 10 usec interval 
>>> plot(x,y) 
>>> show()

# An example function to get data from magnetometer sensor connected to PSLab
>>> from PSL.SENSORS import HMC5883L #A 3-axis magnetometer >>> M = HMC5883L.connect() >>> Gx,Gy,Gz = M.getRaw()

The module sciencelab.py contains all the functions required for communicating with PSLab hardware. It also contains some utility functions. The class ScienceLab() contains methods that can be used to interact with the PSLab.

After initiating this class, all the features built into the device can be accessed  using various function calls.

Capture1 : for capturing one trace

capture1(ch, ns, tg)

Arguments

  • ch  : Channel to select as input. [‘CH1′..’CH3′,’SEN’]
  • ns  :  Number of samples to fetch. Maximum 10000
  • tg   :  Time gap between samples in microseconds
#Example >>> x,y = I.capture1('CH1', 1000, 10) # digitize CH1 1000 times, with 10 usec interval

Returns : Arrays X(timestamps),Y(Corresponding Voltage values)

Capture2 : for capturing two traces

capture2(ns, tg, TraceOneRemap='CH1')

Arguments

  • ns :  Number of samples to fetch. Maximum 5000
  • tg  :  Time gap between samples in microseconds
  • TraceOneRemap :   Choose the analogue input for channel 1 (Like MIC OR SEN). It is connected to CH1 by default. Channel 2 always reads CH2.
#Example 
>>> x,y1,y2 = I.capture2(1600,1.75,'CH1') # digitize CH1 and CH2, 1600 times, with 1.75 usec interval

Returns: Arrays X(timestamps),Y1(Voltage at CH1),Y2(Voltage at CH2)

Capture4 : for capturing four taces

capture4(ns, tg, TraceOneRemap='CH1')

Arguments

  • ns:   Number of samples to fetch. Maximum 2500
  • tg :   Time gap between samples in microseconds. Minimum 1.75uS
  • TraceOneRemap :   Choose the analogue input for channel 1 (Like MIC OR SEN). It is connected to CH1 by default. Channel 2 always reads CH2, channel 3 always reads CH3 and MIC is channel 4 (CH4)
#Example
>>> x,y1,y2,y3,y4 = I.capture4(800,1.75) # digitize CH1-CH4, 800 times, with 1.75 usec interval

Returns: Arrays X(timestamps),Y1(Voltage at CH1),Y2(Voltage at CH2),Y3(Voltage at CH3),Y4(Voltage at CH4)

Capture_multiple : for capturing multiple traces

capture_multiple(samples, tg, *args)

Arguments

  • samples:   Number of samples to fetch. Maximum 10000/(total specified channels)
  • tg :   Time gap between samples in microseconds.
  • *args :   channel names
# Example 
>>> from pylab import * 
>>> I=interface.Interface() 
>>> x,y1,y2,y3,y4 = I.capture_multiple(800,1.75,'CH1','CH2','MIC','SEN') 
>>> plot(x,y1) 
>>> plot(x,y2) 
>>> plot(x,y3) 
>>> plot(x,y4) 
>>> show()

Returns: Arrays X(timestamps),Y1,Y2 …

Capture_fullspeed : fetches oscilloscope traces from a single oscilloscope channel at a maximum speed of 2MSPS

capture_fullspeed(chan, amples, tg, *args)

Arguments

  • chan:   channel name ‘CH1’ / ‘CH2’ … ‘SEN’
  • tg :   Time gap between samples in microseconds. minimum 0.5uS
  • *args :   specify if SQR1 must be toggled right before capturing. ‘SET_LOW’ will set it to 0V, ‘SET_HIGH’ will set it to 5V. if no arguments are specified, a regular capture will be executed.
# Example
>>> from pylab import *
>>> I=interface.Interface()
>>> x,y = I.capture_fullspeed('CH1',2000,1)
>>> plot(x,y)               
>>> show()

Returns: timestamp array ,voltage_value array

Set_gain : Set the gain of selected PGA

set_gain(channel, gain)

Arguments

  • channel:   ‘CH1’ , ‘CH2’
  • gain :   (0-7) -> (1x,2x,4x,5x,8x,10x,16x,32x)

Note: The gain value applied to a channel will result in better resolution for small amplitude signals.

# Example
>>> I.set_gain('CH1',7)  #gain set to 32x on CH1
Get_average_voltage : Return the voltage on the selected channel
get_average_voltage(channel_name, **kwargs)
Arguments

  • channel_name:    ‘CH1’,’CH2’,’CH3’, ‘MIC’,’IN1’,’SEN’
  • **kwargs :   Samples to average can be specified. eg. samples=100 will average a hundred readings
# Example 
>>> print I.get_average_voltage('CH4')
1.002

Get_freq : Frequency measurement on IDx. Measures time taken for 16 rising edges of input signal. returns the frequency in Hertz

get_average_voltage(channel='Fin', timeout=0.1)
Arguments

  • channel :    The input to measure frequency from. ‘ID1’ , ‘ID2’, ‘ID3’, ‘ID4’, ‘Fin’
  • timeout :   This is a blocking call which will wait for one full wavelength before returning the calculated frequency. Use the timeout option if you’re unsure of the input signal. returns 0 if timed out
# Example
>>> I.sqr1(4000,25)
>>> print I.get_freq('ID1')
4000.0

Return float: frequency

Get_states : Gets the state of the digital inputs. returns dictionary with keys ‘ID1’,’ID2’,’ID3’,’ID4’
get_states()
#Example
>>> print get_states()
{'ID1': True, 'ID2': True, 'ID3': True, 'ID4': False}
Get_state : Returns the logic level on the specified input (ID1,ID2,ID3, or ID4)
get_state(input_id)
Arguments

  • input_id :    The input channel ‘ID1’ -> state of ID1 ‘ID4’ -> state of ID4
#Example
>>> print I.get_state(I.ID1)
False
Set_state : Set the logic level on digital outputs SQR1,SQR2,SQR3,SQR4
set_state(**kwargs)
Arguments

  • **kwargs :    SQR1,SQR2,SQR3,SQR4 states(0 or 1)
#Example
>>> I.set_state(SQR1=1, SQR2=0) #sets SQR1 HIGH, SQR2 LOw, but leave SQR3,SQR4 untouched.










	

	Continue ReadingPSLab Communication Function Calls





			
		


	


	
								
							
								
								
	


		


			



	

		Flask-SocketIO Notifications
	












	
In the previous post I explained about configuring Flask-SocketIO, Nginx and Gunicorn. This post includes integrating Flask-SocketIO library to display notifications to users in real time.


Flask Config


For development we use the default web server that ships with Flask. For this, Flask-SocketIO fallsback to long-polling as its transport mechanism, instead of WebSockets. So to properly test SocketIO I wanted to work directly with Gunicorn (hence the previous post about configuring development environment). Also, not everyone needs to be bothered with the changes required to run it.


class DevelopmentConfig(Config):
    DEVELOPMENT = True
    DEBUG = True

    # If Env Var `INTEGRATE_SOCKETIO` is set to 'true', then integrate SocketIO
    socketio_integration = os.environ.get('INTEGRATE_SOCKETIO')
    if socketio_integration == 'true':
        INTEGRATE_SOCKETIO = True
    else:
        INTEGRATE_SOCKETIO = False

    # Other stuff



SocketIO is integrated (in development env) if the developer has set the INTEGRATE_SOCKETIO environment variable to “true”. In Production, our application runs on Gunicorn, and SocketIO integration must always be there.


Flow


To send message to a particular connection (or a set of connections) Flask-SocketIO provides Rooms. The connections are made to join a room and the message is sent in the room. So to send message to a particular user we need him to join a room, and then send the message in that room. The room name needs to be unique and related to just one user. The User database Ids could be used. I decided to keep user_{id} as the room name for a user with id {id}. This information (room name) would be needed when making the user join a room, so I stored it for every user that logged in.


@expose('/login/', methods=('GET', 'POST'))
    def login_view(self):
        if request.method == 'GET':
            # Render template
        if request.method == 'POST':
            # Take email and password from form and check if 
            # user exists. If he does, log him in.
            login.login_user(user)

            # Store user_id in session for socketio use
            session['user_id'] = login.current_user.id

            # Redirect



After the user logs in, a connection request from the client is sent to the server. With this connection request the connection handler at server makes the user join a room (based on the user_id stored previously).


@socketio.on('connect', namespace='/notifs')
def connect_handler():
    if current_user.is_authenticated():
        user_room = 'user_{}'.format(session['user_id'])
        join_room(user_room)
        emit('response', {'meta': 'WS connected'})



The client side is somewhat similar to this:


<script src="{{ url_for('static', filename='path/to/socket.io-client/socket.io.js') }}"></script>
<script type="text/javascript">
$(document).ready(function() {
    var namespace = '/notifs';

    var socket = io.connect(location.protocol + "//" + location.host + namespace, {reconnection: false});

    socket.on('response', function(msg) {
        console.log(msg.meta);
        // If `msg` is a notification, display it to the user.
    });
});
</script>



Namespaces helps when making multiple connections over the same socket.


So now that the user has joined a room we can send him notifications. The notification data sent to the client should be standard, so the message always has the same format. I defined a get_unread_notifs method for the User class that fetches unread notifications.


class User(db.Model):
    # Other stuff

    def get_unread_notifs(self, reverse=False):
        """Get unread notifications with titles, humanized receiving time
        and Mark-as-read links.
        """
        notifs = []
        unread_notifs = Notification.query.filter_by(user=self, has_read=False)
        for notif in unread_notifs:
            notifs.append({
                'title': notif.title,
                'received_at': humanize.naturaltime(datetime.now() - notif.received_at),
                'mark_read': url_for('profile.mark_notification_as_read', notification_id=notif.id)
            })

        if reverse:
            return list(reversed(notifs))
        else:
            return notifs



This class method is used when a notification is added in the database and has to be pushed into the user SocketIO room.


def create_user_notification(user, action, title, message):
    """
    Create a User Notification
    :param user: User object to send the notification to
    :param action: Action being performed
    :param title: The message title
    :param message: Message
    """
    notification = Notification(user=user,
                                action=action,
                                title=title,
                                message=message,
                                received_at=datetime.now())
    saved = save_to_db(notification, 'User notification saved')

    if saved:
        push_user_notification(user)

def push_user_notification(user):
    """
    Push user notification to user socket connection.
    """
    user_room = 'user_{}'.format(user.id)
    emit('response',
         {'meta': 'New notifications',
          'notif_count': user.get_unread_notif_count(),
          'notifs': user.get_unread_notifs()},
         room=user_room,
         namespace='/notifs')










	

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		Programmer principles
	












	
As programmers we develop our programming skills and learn something every single day. We write code and solve many troubles. But is our aim to simply write code? I am sure it is not. I think writing code just for doing it is not interesting, and it’s definitely not Open Event team’s objective. Personally, I like reading code like a poem. We should always try to eliminate bad practises and ugly code. There are a few principles how to do it. Let me share them with you now.


SOLID principle


SOLID  is a mnemonic acronym introduced by Michael Feathers, and it simply means five basic principles of object oriented programming. These principles, when applied together, make it more likely that a programmer will create a system that is easy to maintain and extend over time. They are guidelines that can be applied while working on software to remove code smells by causing the programmer to refactor the software’s source code.  It is also a part of an overall strategy of agile. So, here they are:


S – Single responsibility principle


This principle means that there should never be more than one reason for a class to change.


In other words, a class should have only one potential change in a software’s specification. You should not add everything into your class. The best practise here is to check if the logic you are introducing should be in this class or not. Responsibility is the heart of this principle, so to rephrase there should never be more than one responsibility per class. Use layers for a help. And try to divide big classes into smaller ones. 


O – Open/closed principle


Software entities like classes, module and functions should be open for extension, but closed for modification.


All of them should be private by default. 


To make an object behaving differently without modifying it use abstractions, or place behavior(responsibility) in derivative classes. If properties of the abstracted class need to be compared or organized together, another abstraction should handle this. This is the basis of the “keep all object variables private” argument.


L – Liskov substitution principle


Functions that use pointers or references to base classes have to be able to use objects of derived classes without knowing/alerting the correctness of a program


A great example you can find here. If you are using a method defined at a base class upon an abstracted class, the function must be implemented properly on the subtype class. A great example provided here http://williamdurand.fr/2013/07/30/from-stupid-to-solid-code/  you can find below. 


“ A rectangle is a plane figure with four right angles. It has a width, and a height. Now, take a look at the following pseudo-code:


rect = new Rectangle();



rect.width  = 10;

rect.height = 20;



assert 10 == rect.width

assert 20 == rect.height




We simply set a width and a height on a Rectangle instance, and then we assert that both properties are correct. So far, so good.


Now we can improve our definition by saying that a rectangle with four sides of equal length is called a square. A square is a rectangle so we can create aSquare class that extends the Rectangle one, and replace the first line above by the one below:


rect = new Square();




According to the definition of a square, its width is equal to its height. Can you spot the problem? The first assertion will fail because we had to change the behavior of the setters in the Square class to fit the definition “


I – Interface segregation principle


Many client-specific interfaces are better than one general-purpose interface.


Implementing methods that you don’t use is not recommended in this way. The idea here is to keep your components focused and try to minimize the dependencies between them. Enforcing that principle gives you low coupling, and high cohesion. 


D – Dependency inversion principle


This means that “one should depends upon abstractions, do not depend upon concretions” 


Interfaces should depend on other interfaces. Don’t add concrete classes to method signatures of an interface. However, use interfaces in your class methods.


So, we can also say that rather than working with classes that are tight coupled, use interfaces. This reduces dependency on implementation specifics and makes code more reusable. 


Why SOLID?


I hope all of you understand the importance of using SOLID principles in your everyday code practise. Finally, let me underline again the main arguments why you should starting following them now. The most important thing is that thanks to them you can create easy to maintain software, then you can reuse your code, and finally it helps you to test easier. Do you need anymore to be  persuaded  to do it? I think it’s that’s crucial advantages and they are enough. 


Source:


https://pl.wikipedia.org/wiki/SOLID_(programowanie_obiektowe)


https://scotch.io/bar-talk/s-o-l-i-d-the-first-five-principles-of-object-oriented-design


http://williamdurand.fr/2013/07/30/from-stupid-to-solid-code/


http://www.codeproject.com/Articles/60845/The-S-O-L-I-D-Object-Oriented-Programming-OOP-Prin









	

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		Read more about the article Mark Notifications Read on Click			
			
	

	






	

		Mark Notifications Read on Click
	












	
Screenshot from 2016-08-01 07:31:22


Notification has become a really important way of informing users about the various activities related to them in web apps. There are different types of notification such as web app notification, email notification, desktop notification, push notification, etc. We are going to primarily talk about web app notification and mainly about how to mark them as read.


Create Notification


Creating a notification is plain and simple. You have a json or an object which stores the notification message corresponding to a particular activity. Whenever that activity occurs in the backend, you call the send notification module, which adds the information to the database and shows it in the notification page. As simple as that.


Screenshot from 2016-08-01 07:48:08


Marking Notification as Read


The main functioning of this is plain and simple as well. You have a URL, which on getting a request from the user, marks the notification as read in the database. That’s it.


Screenshot from 2016-08-01 07:48:17


We know how to do this using a button or a link. But the question here is how to mark a notification as read on clicking any part of the notification?? The obvious answer is, well, put the entire notification inside an anchor tag and you are done, right? Well, it would work in many cases. But what if the design structure is such that this doesn’t work somehow. Somehow enclosing the notification inside a particular anchor tag doesn’t solve the purpose. What do we do then?


Identify Whether Inside a DIV


The main problem here actually is how to identify whether the click is inside the enclosing div or somewhere else. Once we solve this problem, we can send an ajax request to the mark read URL and our job is done.


Screenshot from 2016-08-01 07:52:58


So, to identify that a click is indeed inside a div, we use the event.target property of the event clicked. The target event property returns the element that triggered the event. So we check whether event.target has the “notification” class in our case. If it does not have the “notification” class we check in all it’s parent nodes. We get the parent nodes using the “parent()” function and check whether any of that has notification. If either of the 2 occurs, we consider that the click is inside the div. And thus mark the notification as read.


Screenshot from 2016-08-01 07:51:09


So, once this is done, we mark the notification as read in the backend and our job is done…









	

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		Communicating with Pocket Science Lab via USB and capturing and plotting sine waves
	












	
Design of PSLab combines the flexibility of Python programming language and the real-time measurement capability of micro-controllers.


PSLab, with its simple and open architecture allows users to use the tool for various measurements and to develop new experiments with simple functions written in python.


PSLab is interfaced and powered by USB port of the computer. For connecting external signals it has several input/output terminals as shown in the figure.


pslabdesign


Interfacing with the real world


Connecting to PSLab is as simple and straight forward as this…


>>> from PSL import sciencelab
>>> I = sciencelab.connect()     #Returns None if device isn't found
# An example function that measures voltage present at the specified analog input
>>> print I.get_average_voltage('CH1')


Various sensors can be connected to PSLab and data can be fetched with a simple python code as shown below…


>>> from PSL.SENSORS import HMC5883L #A 3-axis magnetometer
>>> M = HMC5883L.connect()
>>> Gx,Gy,Gz = M.getRaw()



The module sciencelab.py contains all the functions required for communicating with PSLab hardware. It also contains some utility functions. The class ScienceLab() contains methods that can be used to interact with the PSLab. The connect() function returns an object of this class if PSLab hardware is detected.


The initialization process does the following


* connects to tty device


* loads calibration values.


>>> from PSL import sciencelab
>>> I = sciencelab.connect()
>>> print I
<PSL.sciencelab.ScienceLab instance at 0x7fe9a7bf0e18>


After initiating this class, its various function calls will allow access to all the features built into the device. Some examples showing the use of few function calls are given below…


Example 1: Capturing and plotting a sine wave


The function call used,


capture1(self,ch,ns,tg,*args,**kwargs)


Arguments


    

  • ch  : Channel to select as input. [‘CH1′..’CH3′,’SEN’]
    • 

  • ns  :  Number of samples to fetch. Maximum 10000
    • 

  • tg   :  Time gap between samples in microseconds
    • 



Example Program


Connect WG1 to CH1 and run the following code.


>>> from pylab import *
>>> from PSL import sciencelab
>>> I=sciencelab.connect()
>>> I.set_gain('CH1', 3) # set input CH1 to +/-4V range
>>> I.set_sine1(1000) # generate 1kHz sine wave on output W1
>>> x,y = I.capture1('CH1', 1000, 10) # digitize CH1 1000 times, with 10 usec interval
>>> plot(x,y)
>>> show()


For running the script in IDE, one should define source code encoding, add this to the top of your script:


# -*- coding: utf-8 -*-


The output of the program is here…


sine1


Example 2 : Capturing two sine waves and plotting


The function call used,


capture2(self,ns,tg,TraceOneRemap='CH1')


Arguments


    

  • ns :  Number of samples to fetch. Maximum 5000
    • 

  • tg  :  Time gap between samples in microseconds
    • 

  • TraceOneRemap :   Choose the analogue input for channel 1 (Like MIC OR SEN). It is connected to CH1 by default. Channel 2 always reads CH2.
    • 



Example Program


Connect WG1 to CH1, WG2 to CH2 and run the following code.


# -*- coding: utf-8 -*-

from pylab import *
from PSL import sciencelab
I=sciencelab.connect()
I.set_gain('CH1', 2) # set input CH1 to +/-4V range
I.set_gain('CH2', 3) # set input CH2 to +/-4V range
I.set_sine1(1000) # generate 1kHz sine wave on output W1
I.set_sine2(1000) # generate 1kHz sine wave on output W2

x,y1,y2 = I.capture2(1600,1.75,'CH1') 
plot(x,y1) #Plot of analog input CH1
plot(x,y2) #plot of analog input CH2
show()


The output of the program is here…sine2


Example 3 : Capturing four traces and plotting


The function call used,


capture4(self,ns,tg,TraceOneRemap='CH1')


Arguments


    

  • ns:   Number of samples to fetch. Maximum 2500
    • 

  • tg :   Time gap between samples in microseconds. Minimum 1.75uS
    • 

  • TraceOneRemap :   Choose the analogue input for channel 1 (Like MIC OR SEN). It is connected to CH1 by default. Channel 2 always reads CH2.
    • 



Example Program




Connect WG1 to CH1, WG2 to CH2, SQR1 to CH3 and transducer mic to MIC (CH4) and run the following code.


# -*- coding: utf-8 -*-

from pylab import *
from PSL import sciencelab
I=sciencelab.connect()
I.set_gain('CH1', 2) # set input CH1 to +/-4V range
I.set_gain('CH2', 3) # set input CH2 to +/-4V range
I.set_sine1(1000) # generate 1kHz sine wave on output W1
I.set_sine2(1000) # generate 1kHz sine wave on output W2
I.sqr1(2000,duty_cycle=50) # generate 1kHz square wave on output SQR1

x,y1,y2,y3,y4 = I.capture4(800,1.75)
plot(x,y1) #Plot of analog input CH1
plot(x,y2) #plot of analog input CH2
plot(x,y3) #plot of analog input CH3
plot(x,y4) #plot of analog input CH4 : MIC
show()


The output of the program is here…waves


Next To Do for GSoC-16


A detailed User manual and programmers manual with description of all function calls. ( Work in progress 🙂  )


Read:


    

  1. Post about installing PSLab
    2. 

  2. PSLab and ExpEYES and GSoC-16 work
    3. 










	

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		Can solving lint bugs be interesting?
	












	
Today I am going to present you how we’ve changed monotonous solving bugs into motivating process. 


PEP


Most developers need to improve their code quality. To do  that they can use style guide for e.g for Python code (PEP). PEP contains an index of all Python Enhancement Proposals.


Below you can find which logs PEP returned in a command line. 


Do you think that this logs’ presentation is  good enough to interest a developer? Will he solve these  thousands of bugs?


Undoubtedly, there are much information about errors and warnings so PEP returns long logs. But developer can not even know how to start solving bugs. And even if she/he finally starts, after each commit he/she needs to run that script again to check if quantity of bugs are increased or decreased. It seems to be endless, exhausting and very monotonous.  Nobody is encouraged to do it.


logi.png


Quality monitoring


Open Event team wants to increase our productivity and code quality. Therefore we use a tool which allow us to check code style, security, duplication complexity and test coverage on every commit. That tool is Codacy and it fulfils our requirements in 100%. It is very helpful because it adds comments to pull requests and enables developer quickly find where a bug is located. It’s very comfortable, because you don’t need to check issues in above awful logs results. Take a look how it looks in Codacy.


-DO NOT MERGE  Ticketing Flow by niranjan94 · Pull Request  1927 · fossasia open event orga server.png


Isn’t it clear? Of course that it’s. Codacy shows in which line issue ocurres and which type of issue it’s.


Awesome statistics dashboard


I’d like to give an answer how you can engage your team to solve issues and make this process more interesting. On the main page codacy tool welcomes you with great statistics about your project. 


open event orga server   Codacy   Dashboard


You can see number of issues, category like code complexity, code style, compatibility, documentation, error prone, performance, security and unused code. That params show in which stage of code quality your project is. I think that every developer’s aim is to have the highest code quality and increasing these statistics. But if project has many issues, developer sees only a few changes in project charts.


Define Goals 


Recently I’ve discovered how you can motivate yourself more. You can define a goal which you’d like achive. It can be goal of category or goal of file. For example Open Event team has defined goal for a specific file to achieve. If you define small separate goals, you can quicker see the results of your work.


open event orga server_2   Codacy   Goals


On the left sidebar you can find a item which is named “Goals”. In this area you can easily add your projects goals. Everything is user friendly so you shouldn’t have a problem  to create own goals.









	

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