Django Cookie Cutter

Wondering which is the best way to structure your Django project? The Django Cookie Cutter frameworks help one create a project with a great structure quite easily. The startproject command initializes the directory structure and required files for a standard Django project, which may be fine for learning Django, but not good to be used in production. This is where Cookiecutter exerts its power. The Cookiecutter takes care of a lot of standard tasks like installing the software dependencies, setting up testing files, and including organizing common libraries like Bootstrap and AngularJS, also generating a software license and the README file.

Cookie Cutter provides various features like:

  •  Renders Django projects with 100% test coverage
  •  Twitter Bootstrap v4.0.0 – alpha
  •  End-to-end via Hitch
  •  AngularJS
  •  12-Factor based settings via django-environ
  •  Optimized development and production settings
  •  Registration via django-allauth
  •  Comes with custom user model ready to go.
  •  Grunt build for compass and livereload
  •  Basic Email configurations for sending emails via Mailgun
  •  Media storage using Amazon S3
  •  Docker support using docker-compose for development and production
  •  Procfile for deploying to Heroku

They also provide bonus features like:

  •  Serving static files from Amazon S3 or Whitenoise
  •  Configuration for Celery and Flower(Set up in Docker only)
  •  Integration with MailHog for local email testing
  •  Integration with Sentry for error logging
  •  Integration with NewRelic for performance monitoring
  •  Integration with Opbeat for performance monitoring

Getting Started

    There are few prerequisites to be followed for installing cookiecutter. They are:

  • pip
  • virtualenv
  • PostgreSQL

First, we need to create a virtualenv for your project and activate it:

$ mkvirtualenv <virtualenv name>
$ workon <virtualenv name>

Now, we can install Cookiecutter by running the command:

$ pip install cookiecutter

Switch to the directory where you want your project to be. Now, execute the following command to generate the django project:

$ cookiecutter https://github.com/pydanny/cookiecutter-django.git

The cookiecutter runs with the cookiecutter-django repo, asking us to enter project-specific details and thus, creating the project. Optional features are asked. We could install them if needed.

That’s it! Let’s build some awesome well structured Django applications with the cookie Cutter framework.

Artificial Intelligence: The Python Way

Artificial Intelligence has built an entirely new world of opportunities for developers to explore. AI allows Google to complete the question you ask, or Netflix to know what shows you’ll want to see next. It is used extensively by companies in customer service to improve workflows and employee productivity while driving self-service. There are programming languages like R, Java, Python, C# etc. which are ideal for AI-based projects.

The choice of one’s programming language depends on various factors like package ecosystems, ease to code, personal preferences etc. The skills of the developers matter more than any programming language. Python, a language favored by developers for a wide range of applications, but what makes Python ideal for projects involving AI?

Python, an Object Oriented, high level, interpreted programming language is a robust, developer friendly and highly useful, focusing on rapid application development (RAD) and don’t repeat yourself (DRY). It works perfectly to connect existing components together. Due to the ease of learning and adaptability of Python, it is one of the fastest growing languages. Python’s support and ever-evolving libraries make it a good choice for any projects, including AI.

The usage of Python is not limited to purpose. The growing popularity has allowed Python to enter into some of the most popular and complex processes like Artificial Intelligence (AI), Machine Learning (ML), natural language processing (NLP), data science etc.

Python has a huge stack of libraries and frameworks that facilitate coding and save development time. Libraries like NumPy, used for scientific computation, SciPy for advanced computation, and scikit-learn for data mining and data analysis, are the popular ones along with heavy-hitting frameworks as TensorFlow, CNTK, and Apache Spark. These libraries and frameworks are Python-first, while some, like PyTorch, are written specifically for Python, facilitating AI, ML, and deep learning.

Python is known for concise, readable code and ease of use, providing simplicity, particularly for new developers. This has several advantages for machine learning and deep learning. AI, ML and DL rely on complex algorithms and multi-stage workflows, so less complex the coding, the more they can focus on finding solutions to problems and achieving the goals of the project.

Python has a simple syntax resulting in faster in development than many programming languages, allowing the developer to quickly test algorithms without having to implement them. The easily readable code is invaluable for collaborative coding, or when machine learning or deep learning projects change hands between development teams. This is true if a project contains a great deal of custom business logic or third-party components.

Python is platform independent and provides the developer with the flexibility to provide an API from the existing programming language making it extremely flexible for the new Python developers. The same Python can run smoothly in any operating systems. Python is suitable for every purpose, be it scripting or OO approach. Python also works as a perfect backend language links various data structures together.

Python is popular among the large community of developers. The ease of learning is attracting a huge crowd to learn this language. Though AI Projects needs experience in programming, Python can smoothen the learning curve. It is practically more easy to look for Python developers than to hunt for LISP or R or Prolog programmers. Its extended libraries and active community with an ever developing and improving code have led it to be one of the popular languages of the day. Python also boasts a large, active community of developers who willing to offer help and support, which can be invaluable when dealing with such complex projects.

While many other programming languages can also be used in AI projects, Python is in fact at the cutting edge and should be given significant consideration. Therefore, Python is the primary choice for an AI product.

GeoDjango

GeoDjango is an included contrib module to make easy GIS (Geographic Information System) web apps with location-based services. GeoDjango provides a toolbox of utilities for building GIS web applications and also bindings to popular spatial libraries such as GEOS, GDAL, and GeoIP, which can be used separately without Django in any Python application or interactively in the shell.

Let’s get started by installing Python 3.

$ python3 --version

Now let’s install GeoDjango dependencies by running the following commands.

$ sudo aptitude install gdal-bin libgdal-dev 
$ sudo aptitude install python3-gdal

Now, we have to create spatial databases. PostGIS 2 ships with PostgreSQL as an extension for spatial functionality. Geodjango also supports Sqlite (via Spatialite), MySql and Oracle backends.

$ createdb  <db name> 
$ psql <db name>
> CREATE EXTENSION postgis;

Let’s now get started with our project. Let’s call our project geodjango by running:

$ django-admin startproject geodjango

This will initialize the project. Now, we can create a django project within our project.

$ cd geodjango 
$ python manage.py startapp world

Let’s edit the database connection in geodjango/settings.py to match our setup.

DATABASES = {
'default': {
'ENGINE': 'django.contrib.gis.db.backends.postgis',
'NAME': 'geodjango',
'USER': 'geo',
},
}

In addition, modify the INSTALLED_APPS setting to include django.contrib.admin, django.contrib.gis, and world (your newly created application):

INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django.contrib.gis',
'world',
]

Geographic Data

World Borders

We can avail the data of the world borders from Here. Let’s create a data directory in the world application, download the world borders data, and unzip. Use the following commands:

$ mkdir world/data
$ cd world/data
$ wget https://thematicmapping.org/downloads/TM_WORLD_BORDERS-0.3.zip
$ unzip TM_WORLD_BORDERS-0.3.zip
$ cd ../..

The world borders ZIP file contains a set of data files which is one of the most popular geospatial data formats known as an ESRI Shapefile. When unzipped, the world borders dataset includes files with the following extensions:

  • .shp: Holds the vector data for the world borders geometries.
  • .shx: Spatial index file for geometries stored in the .shp.
  • .dbf: Database file for holding non-geometric attribute data (e.g., integer and character fields).
  • .prj: Contains the spatial reference information for the geographic data stored in the shapefile.

The GDAL ogrinfo utility allows examining the metadata of shapefiles or other vector data sources:

$ ogrinfo world/data/TM_WORLD_BORDERS-0.3.shp 
INFO: Open of `world/data/TM_WORLD_BORDERS-0.3.shp'
using driver `ESRI Shapefile' successful.
1: TM_WORLD_BORDERS-0.3 (Polygon)

ogrinfo tells us that the shapefile has one layer and that this layer contains polygon data. Let’s specify the layer name and use the -so option to get the important summary information:

$ ogrinfo -so world/data/TM_WORLD_BORDERS-0.3.shp TM_WORLD_BORDERS-0.3
INFO: Open of `world/data/TM_WORLD_BORDERS-0.3.shp'
      using driver `ESRI Shapefile' successful.

Layer name: TM_WORLD_BORDERS-0.3
Geometry: Polygon
Feature Count: 246
Extent: (-180.000000, -90.000000) - (180.000000, 83.623596)
Layer SRS WKT:
GEOGCS["GCS_WGS_1984",
    DATUM["WGS_1984",
        SPHEROID["WGS_1984",6378137.0,298.257223563]],
    PRIMEM["Greenwich",0.0],
    UNIT["Degree",0.0174532925199433]]
FIPS: String (2.0)
ISO2: String (2.0)
ISO3: String (3.0)
UN: Integer (3.0)
NAME: String (50.0)
AREA: Integer (7.0)
POP2005: Integer (10.0)
REGION: Integer (3.0)
SUBREGION: Integer (3.0)
LON: Real (8.3)
LAT: Real (7.3)

This detailed summary information tells us the number of features in the layer, the geographic bounds of the data, the spatial reference system (“SRS WKT”), as well as type information for each attribute field. For example, FIPS: String (2.0) indicates that the FIPS character field has a maximum length of 2. Similarly, LON: Real (8.3) is a floating-point field that holds a maximum of 8 digits, up to three decimal places.

Geographic Models

Defining a Geographic Model

Let’s create a GeoDjango model to represent the data:

from django.contrib.gis.db import models

class WorldBorder(models.Model):
    # Regular Django fields corresponding to the attributes in the
    # world borders shapefile.
    name = models.CharField(max_length=50)
    area = models.IntegerField()
    pop2005 = models.IntegerField('Population 2005')
    fips = models.CharField('FIPS Code', max_length=2)
    iso2 = models.CharField('2 Digit ISO', max_length=2)
    iso3 = models.CharField('3 Digit ISO', max_length=3)
    un = models.IntegerField('United Nations Code')
    region = models.IntegerField('Region Code')
    subregion = models.IntegerField('Sub-Region Code')
    lon = models.FloatField()
    lat = models.FloatField()

    # GeoDjango-specific: a geometry field (MultiPolygonField)
    mpoly = models.MultiPolygonField()

    # Returns the string representation of the model.
    def __str__(self):
        return self.name

The default spatial reference system for geometry fields is WGS84 . In other words, the field coordinates are in longitude, latitude pairs in units of degrees. To use a different coordinate system, let’s set the SRID of the geometry field with the srid argument. Use an integer representing the coordinate system’s EPSG code.

Run migrate

After defining your model, we need to sync it with the database. First, we need to create a database migration. Run the following command:

$ python manage.py makemigrations
Migrations for 'world':
  world/migrations/0001_initial.py:
    - Create model WorldBorder

You may inspect the raw code generated by the above migration:

$ python manage.py sqlmigrate world 0001

The output:

BEGIN;
--
-- Create model WorldBorder
--
CREATE TABLE "world_worldborder" (
    "id" serial NOT NULL PRIMARY KEY,
    "name" varchar(50) NOT NULL,
    "area" integer NOT NULL,
    "pop2005" integer NOT NULL,
    "fips" varchar(2) NOT NULL,
    "iso2" varchar(2) NOT NULL,
    "iso3" varchar(3) NOT NULL,
    "un" integer NOT NULL,
    "region" integer NOT NULL,
    "subregion" integer NOT NULL,
    "lon" double precision NOT NULL,
    "lat" double precision NOT NULL
    "mpoly" geometry(MULTIPOLYGON,4326) NOT NULL
)
;
CREATE INDEX "world_worldborder_mpoly_id" ON "world_worldborder" USING GIST ( "mpoly" );
COMMIT;

Now, let’s run migrate to execute the sql:

$ python manage.py migrate
Operations to perform:
  Apply all migrations: admin, auth, contenttypes, sessions, world
Running migrations:
  ...
  Applying world.0001_initial... OK

GDAL Interface

GeoDjango also includes a Pythonic interface to GDAL’s powerful OGR library that can work with all the vector data sources that OGR supports.

First, let’s invoke the Django shell:

$ python manage.py shell

We can determine the path of World Border data using Python’s built-in os module:

>>> import os
>>> import world
>>> world_shp = os.path.abspath(os.path.join(os.path.dirname(world.__file__),
...                             'data', 'TM_WORLD_BORDERS-0.3.shp'))

Now, open the world borders shapefile using GeoDjango’s DataSource interface:

>>> from django.contrib.gis.gdal import DataSource
>>> ds = DataSource(world_shp)
>>> print(ds)
/ ... /geodjango/world/data/TM_WORLD_BORDERS-0.3.shp (ESRI Shapefile)

Data source objects can have different layers of geospatial features; however, shapefiles are only allowed to have one layer:

>>> print(len(ds))
1
>>> lyr = ds[0]
>>> print(lyr)
TM_WORLD_BORDERS-0.3

We can see the layer’s geometry type the features it contains:

>>> print(lyr.geom_type)
Polygon
>>> print(len(lyr))
246

The Layer may also have a spatial reference system associated with it. If it does, the srs attribute will return a SpatialReference object:

>>> srs = lyr.srs
>>> print(srs)
GEOGCS["GCS_WGS_1984",
    DATUM["WGS_1984",
        SPHEROID["WGS_1984",6378137.0,298.257223563]],
    PRIMEM["Greenwich",0.0],
    UNIT["Degree",0.0174532925199433]]
>>> srs.proj4 # PROJ.4 representation
'+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs '

This shapefile is in the popular WGS84 spatial reference system.

In addition, shapefiles also support attribute fields that may contain additional data. Here are the fields on the World Borders layer:

>>> print(lyr.fields)
['FIPS', 'ISO2', 'ISO3', 'UN', 'NAME', 'AREA', 'POP2005', 'REGION', 'SUBREGION', 'LON', 'LAT']

The following code will let you examine the OGR types (e.g. integer or string) associated with each of the fields:

>>> [fld.__name__ for fld in lyr.field_types]
['OFTString', 'OFTString', 'OFTString', 'OFTInteger', 'OFTString', 'OFTInteger', 'OFTInteger', 'OFTInteger', 'OFTInteger', 'OFTReal', 'OFTReal']

We can iterate over each feature in the layer and extract information from both the feature’s geometry as well as the feature’s attribute fields (whose values are accessed via get() method):

>>> for feat in lyr:
...    print(feat.get('NAME'), feat.geom.num_points)
...
Guernsey 18
Jersey 26
South Georgia South Sandwich Islands 338
Taiwan 363

Layer objects may be sliced:

>>> lyr[0:2]
[<django.contrib.gis.gdal.feature.Feature object at 0x2f47690>, <django.contrib.gis.gdal.feature.Feature object at 0x2f47650>]

And individual features may be retrieved by their feature ID:

>>> feat = lyr[234]
>>> print(feat.get('NAME'))
San Marino

Boundary geometries may be exported as WKT and GeoJSON. Both are plaintext representations of shapes which are originally encoded in binary.

>>> geom = feat.geom
>>> print(geom.wkt)
POLYGON ((12.415798 43.957954,12.450554 ...
>>> print(geom.json)
{ "type": "Polygon", "coordinates": [ [ [ 12.415798, 43.957954 ], [ 12.450554, 43.979721 ], ...

LayerMapping

To import the data, use a LayerMapping in a Python script. Create a file called load.py inside the world application, with the following code:

import os
from django.contrib.gis.utils import LayerMapping
from .models import WorldBorder

world_mapping = {
    'fips' : 'FIPS',
    'iso2' : 'ISO2',
    'iso3' : 'ISO3',
    'un' : 'UN',
    'name' : 'NAME',
    'area' : 'AREA',
    'pop2005' : 'POP2005',
    'region' : 'REGION',
    'subregion' : 'SUBREGION',
    'lon' : 'LON',
    'lat' : 'LAT',
    'mpoly' : 'MULTIPOLYGON',
}

world_shp = os.path.abspath(
    os.path.join(os.path.dirname(__file__), 'data', 'TM_WORLD_BORDERS-0.3.shp'),
)

def run(verbose=True):
    lm = LayerMapping(WorldBorder, world_shp, world_mapping, transform=False)
    lm.save(strict=True, verbose=verbose)

Let’s invoke the Django shell from the geodjango project directory:

$ python manage.py shell

Next, let’s import the load module, call the run routine:

>>> from world import load
>>> load.run()

grinspect

The ogrinspect command introspects a GDAL-supported vector data source (e.g., a shapefile) and generates a model definition and LayerMapping dictionary automatically.

The general usage of the command goes as follows:

$ python manage.py ogrinspect [options] <data_source> <model_name> [options]

data_source is the path to the GDAL-supported data source and model_name is the name to use for the model. Command-line options may be used to further define how the model is generated.

For example, the following command nearly reproduces the WorldBorder model and mapping dictionary created above, automatically:

$ python manage.py ogrinspect world/data/TM_WORLD_BORDERS-0.3.shp WorldBorder \
    --srid=4326 --mapping --multi

The command produces the following output, which may be copied directly into the models.py of a GeoDjango application:

# This is an auto-generated Django model module created by ogrinspect.
from django.contrib.gis.db import models

class WorldBorder(models.Model):
    fips = models.CharField(max_length=2)
    iso2 = models.CharField(max_length=2)
    iso3 = models.CharField(max_length=3)
    un = models.IntegerField()
    name = models.CharField(max_length=50)
    area = models.IntegerField()
    pop2005 = models.IntegerField()
    region = models.IntegerField()
    subregion = models.IntegerField()
    lon = models.FloatField()
    lat = models.FloatField()
    geom = models.MultiPolygonField(srid=4326)

# Auto-generated `LayerMapping` dictionary for WorldBorder model
worldborders_mapping = {
    'fips' : 'FIPS',
    'iso2' : 'ISO2',
    'iso3' : 'ISO3',
    'un' : 'UN',
    'name' : 'NAME',
    'area' : 'AREA',
    'pop2005' : 'POP2005',
    'region' : 'REGION',
    'subregion' : 'SUBREGION',
    'lon' : 'LON',
    'lat' : 'LAT',
    'geom' : 'MULTIPOLYGON',
}

Spatial Queries

Spatial Lookups

GeoDjango adds spatial lookups to the Django ORM. We can find the country in the WorldBorder table that a point belongs to. Let’s fire up the management shell:

$ python manage.py shell

Define the point of interest:

>>> pnt_wkt = 'POINT(-95.3385 29.7245)'

The pnt_wkt string represents the point at -95.3385 degrees longitude, 29.7245 degrees latitude. The geometry is in a format known as Well Known Text (WKT), a standard issued by the Open Geospatial Consortium (OGC). [ Import the WorldBorder model, and perform a contains lookup using the pnt_wkt as the parameter:

>>> from world.models import WorldBorder
>>> WorldBorder.objects.filter(mpoly__contains=pnt_wkt)
<QuerySet [<WorldBorder: United States>]>

We can also use a GEOS geometry object. Here, we can combine the intersects spatial lookup with the get method to retrieve only the WorldBorder instance for San Marino instead of a queryset:

>>> from django.contrib.gis.geos import Point
>>> pnt = Point(12.4604, 43.9420)
>>> WorldBorder.objects.get(mpoly__intersects=pnt)
<WorldBorder: San Marino>

Automatic Spatial Transformations

When doing spatial queries, GeoDjango automatically transforms geometries if they’re in a different coordinate system.

>>> from django.contrib.gis.geos import GEOSGeometry, Point
>>> pnt = Point(954158.1, 4215137.1, srid=32140)

pnt may also be constructed with EWKT, an “extended” form of WKT that includes the SRID:

>>> pnt = GEOSGeometry('SRID=32140;POINT(954158.1 4215137.1)')

GeoDjango’s ORM will automatically wrap geometry values in transformation SQL, allowing the developer to work at a higher level of abstraction:

>>> qs = WorldBorder.objects.filter(mpoly__intersects=pnt)
>>> print(qs.query) # Generating the SQL
SELECT "world_worldborder"."id", "world_worldborder"."name", "world_worldborder"."area",
"world_worldborder"."pop2005", "world_worldborder"."fips", "world_worldborder"."iso2",
"world_worldborder"."iso3", "world_worldborder"."un", "world_worldborder"."region",
"world_worldborder"."subregion", "world_worldborder"."lon", "world_worldborder"."lat",
"world_worldborder"."mpoly" FROM "world_worldborder"
WHERE ST_Intersects("world_worldborder"."mpoly", ST_Transform(%s, 4326))
>>> qs # printing evaluates the queryset
<QuerySet [<WorldBorder: United States>]>

Lazy Geometries

GeoDjango loads geometries in a standardized textual representation. When the geometry field is first accessed, GeoDjango creates a GEOSGeometry object, exposing powerful functionality, such as serialization properties for popular geospatial formats:

>>> sm = WorldBorder.objects.get(name='San Marino')
>>> sm.mpoly
<MultiPolygon object at 0x24c6798>
>>> sm.mpoly.wkt # WKT
MULTIPOLYGON (((12.4157980000000006 43.9579540000000009, 12.4505540000000003 43.9797209999999978, ...
>>> sm.mpoly.wkb # WKB (as Python binary buffer)
<read-only buffer for 0x1fe2c70, size -1, offset 0 at 0x2564c40>
>>> sm.mpoly.geojson # GeoJSON
'{ "type": "MultiPolygon", "coordinates": [ [ [ [ 12.415798, 43.957954 ], [ 12.450554, 43.979721 ], ...

This includes access to all of the advanced geometric operations provided by the GEOS library:

>>> pnt = Point(12.4604, 43.9420)
>>> sm.mpoly.contains(pnt)
True
>>> pnt.contains(sm.mpoly)
False

Putting your data on the map

Geographic Admin

GeoDjango extends Django’s admin application with support for editing geometry fields.

Basics

GeoDjango also supplements the Django admin by allowing users to create and modify geometries on a JavaScript slippy map.

Let’s create a file called admin.py inside the world application with the following code:

from django.contrib.gis import admin
from .models import WorldBorder

admin.site.register(WorldBorder, admin.GeoModelAdmin)

Next, edit the urls.py in the geodjango application folder as follows:

from django.contrib.gis import admin
from django.urls import include, path

urlpatterns = [
    path('admin/', admin.site.urls),
]

Create an admin user:

$ python manage.py createsuperuser

Next, start up the Django development server:

$ python manage.py runserver

Browse in to http://localhost:8000/admin/, and log in with the user you just created. Browse to any of the WorldBorder entries. The borders may be edited by clicking on a polygon and dragging the vertices to the desired position.

GeoDjango turns Django into an effective geographic Web framework. GeoDjango eases the effort of making GIS (Geographic information system) web apps with location-based services.

IoT: How It’s Changing Our World

Our world is ever evolving. From smart assistants to driverless cars, all our childhood fantasies are now turning into realities. Technology is on the rise and soon, our textbook ideas are coming into life, rising up to the challenges of our changing world.

Internet of Things (IoT) has grown incredibly, facilitating the technological boom across various industries. IoT is changing our world for a better tomorrow.

IoT is creating a new, quantifiable and measurable world, where people and businesses manage their assets in an informed way, making more timely and better-informed decisions about what is needed to be done. This new connected world brings fundamental changes to society and to their consumers. IoT creates many practical improvements in our world, increasing our convenience, health, and safety while improving energy efficiency and comfort by analyzing the world. Business Insider predicts that by 2020 there will be over 24 billion users.  IoT is in fact, bring change to our world we know in various ways.

Smart Appliances

Smart appliances help us to manage our household chores better. The devices like a smart refrigerator which help us remind the provisions needed to buy or smart lighting systems helping us save our electricity bills come quite handy in our everyday lives.

For example, Samsung has brought its Family Hub refrigerator, which does everything from playing hit tunes to managing your grocery list. Grocery management has a  simple concept. Three cameras are placed to take photos of the interior of your fridge each time when the door is opened. The photos can be accessed from a smartphone to see what goodies you need to buy.

Healthcare

IoT is well-suited for healthcare. It collects and monitors data from devices so that more and more doctors and the hospital will make use of this emerging technology to track the vital statistics of their patients. Hospitals have already begun to use smart beds to inform nurses when a patient leaves their bed, which can also adjust to a comfortable posture for an individual patient. There are microbots used to track detect the changes in temperature and other statistics of a patient.

Cleaner and Smarter cities

IoT helps cities improve their public health. Cities suffering from chronic pollution, such as Delhi and Beijing, have begun to deploy sensors designed to alert residents when the containment levels are dangerously high.

Internet-linked sensors, along with software and a data analytics platforms have enabled cities to provide smart water technology, automated street lighting, remote-controlled irrigation for parks and fountains, smarter waste management, digital bus routes, and smart parking meters. The IoT-enabled urban services have reduced traffic jams and pollution, as well as conserving water, light and energy usage. IoT based solutions are often used to control traffic jams. Solar energy based IoT devices helps to cut down our natural resources.

Smart Agriculture

From massive agribusiness players to small organic farmers, farmers and producers all over the world are users of IoT to reduce their consumption of water, fertilizers, cut waste and to improve the quality or yield of their products. From tracking the climate to closely monitoring the temperature changes and humidity levels, IoT plays a major part in agriculture as perishable goods move from field to warehouse to store in order to extend their shelf life and eliminate waste.

California’s recent historic drought forced farmers to search for innovative ways to reduce water consumption. Technology helps them with tools like drone imagery and soil sensors, measuring real-time conditions. This precision agriculture can enable farmers to cut water and fertilizer use by up to 40 percent, without reducing yields.

Abundant Food Supply

Farmers may adapt to the new technology at a slow pace, but eventually, it will do the job. As farmers realize the benefits of assessing potential conditions of a crop in real time, analyzing and knowing exactly how to proceed with the farming techniques, the more they will use smart devices in farming.

More the usage of smart techniques for farming, more the product. This will lead to an abundance of food supply around the world.

Renewable Energy

Renewable energy will be on a rise as devices will depend on green sources for power. Transportation is a field where major changes will be visible, while connected homes will easily run on solar and wind power. A Smart Grid may soon come in place, making power to be distributed more efficiently causing fewer blackouts and brownouts. Smart meters and thermostats will also make an impact.

IoT has limitless possibilities on changing this world to a better one. The world is evolving and so is our technology. The changes in the upcoming years is almost impossible to imagine. IoT empowers the users to be more informed and make data-based decisions about shared resources. Whether we live in a crowded city or on a remote farm, the Internet of Things can take us to a better future.

Why Outsource Software Development?

Outsourcing software development is one of the best options for businesses to march towards their digital transformation goals while cutting down the risks and maintaining a cost-savings approach. There are numerous companies who have understood the advantages of outsourcing and are now followers of this path. Budding entrepreneurs always attempt new methods to get things done in an inexpensive, faster and more productive manner. The technology has moved significantly forward in the present day, allowing professionals to do their job online anywhere, anytime. That is where outsourcing comes into the play. The business owner can get things done cheaper and faster job completion with a team of an outsourced skilled multidisciplinary team than an in-house team.

The experts work in the software development outsourcing companies perform as contractors and help the startup owners to reduce the overhead costs related to taxes, health care or compensations. Outsourcing may be a painstaking and time-consuming process for some. However, if one figure out how to build and set up a project with support from outside engineers, it improves the outcomes, economies, efficiencies, and sustainability for business. Outside software experts are compensated way less than their in-house counterparts. However, this is not the only benefit of outsourcing than employing someone. Here are various reasons why outsourcing is advantageous and beneficial.

Cost Efficiency

Enterprises look out for more job done with a lesser budget. Outsourcing software development cuts the cost while reducing the workload. Outsourcing eliminates the IT recruiting budget. Also, the freelancers cost less than offering someone a salary. The near-shoring partners from countries like India offer the same tasks to be done at a much lower cost than their U.S counterparts The upfront investment is nil, making development projects much more attractive.

Time Saving

With people working and deadlines to be completed, the product is guaranteed to have a shorter time-to-market delivery when outsourced. Having a dedicated software development team with experience in agile methodology allows the product to be market ready faster than the in-house counterparts. This improves the competitiveness greatly and gives a head start against the competitors.

Talent Pool

Outsourcing the software developers gives one immediate access to a well-qualified talent pool. There will be immediate access to some of the best and brightest technology professionals. Since the outsourcing company will be an expert in the tech field, they can make sure that the projects are done optimally and professionally. This allows the company to start their projects faster, minimize the mistakes and provide a quality, tested product in very less time.

Flexibility

When outsourcing, one does not have to spend time recruiting, hiring, training, and housing employees for the projects. One does not have to worry about increasing the employees as the project grows.

Focusing on Strategy

Having the software developed by an outsourcing company streamlines one’s business processes. Having a focused strategy is one of the most competitive advantages in the industry.

Access to Domain Knowledge

Expanding into new markets is one of the top go-to growth strategies for today’s competitive businesses. While you may begin with one idea for a useful app, working with innovative software experts is sure to open your eyes to emerging opportunities. Robust software development companies with multidisciplinary teams feature a variety of competency centers, like AI, Mobile, and .NET, so they can easily poise your enterprise for further expansion.

Improved Compliance

By outsourcing the software development process, one can gain access to international talents without the legal compliance one goes through while hiring employees. The outsourcing company takes care of all the legal paperwork and customs which helps one focus on other things regarding their business.

Enhanced Accuracy

Outsourcing helps a great deal in finding the right talents and easing off your human-resource problems. Even if finding the right candidates from one’s company, talented employees can be recruited from various countries by outsourcing.

Technological Advancement

Technology is ever evolving. and keeping up with the growing economy can be concerning. By outsourcing your software development, you can get access to new perspectives around the world, and meet innovative people with strong and diverse technical expertise.

Meeting Customer Expectations

A good outsourcing company helps in researching and identifying target customers’ personas. Taking the time to engage in personal development allows one to understand the intended audience better, and thus, building products suiting the client’s interest.

Outsourcing can take care of several functions. From management training to payroll software, outsourcing takes care of it all. Offshore development is growing into a giant industry. Outsourcing is the best solution in the age of globalization and technological advancement.

To summarize, the crucial factor in outsourcing companies is picking the right business partner. It is something that one can experience and face when building up their first startup. One need to collect all the data and information, acquaint with recommendations and check out the ratings to take the correct strategic decision.

Nuventure Connect has over a decade of experience serving offshore enterprises on various domains. Our technology expertise and passion for customer service has helped us become essential partners to our clients worldwide. Get in touch with us today to discuss how we can work together. You can also email us at business@nuventure.in

PIPENV – The Python’s Packaging Tool

About   

Pipenv, the official Python-recommended resource for managing package dependencies, letting us install and uninstall Python packages. Pipenv helped to solve a few problems associated with the typical workflow using pip, virtualenv, and traditional requirements.txt, consolidating and simplifying the development process to a single command line tool.

Problems that PIPENV solves

The major issue with packaging in Python is the need of the pip library for package installation along with a library for creating a virtual environment, a library for managing virtual environments and the commands associated with those libraries, which is tiresome to manage. Pipenv comes with the package management and virtual environment support so that installing, uninstalling, tracking, and documenting the dependencies, creating, using and organizing the virtual environments can be done with a single tool. When you start a project with it, Pipenv will automatically create a virtual environment for that project if you are not using one already. Pipenv does this dependency management by abandoning the requirements.txt norm and uses a new document called a Pipfile. When you install a library with Pipenv, the pipfile is automatically updated with the details of that installation, including the version information and possibly the Git repository location, file path, and other information.

Pipenv makes it easier to manage complex interdependencies. The app might depend on a specific version of a library, and that library might depend on a specific version of another library, causing conflicting dependencies. Pipenv keeps track of the tree of the app’s interdependencies in a file called Pipfile.lock. The Pipfile.lock also verifies that the right versions of dependencies are used in production.

Pipenv indicates to other people who work on the project that it has a standardized way to install project dependencies and development and testing requirements. Using a workflow with pip and requirements files means that you may have one single requirements.txt file, or several requirements files for different environments. Multiple requirements files require extra context and precise documentation to help others to install the dependencies properly and work as expected, which has the potential to confuse colleagues and increase the maintenance burden. Using Pipenv, which gives the Pipfile, one can avoid these problems by managing dependencies for different environments.

Installing PIPENV

To install PIPENV, run the following command

pip install pipenv

To start using PIPENV in your project, run…

pipenv install project_name

An indicator of Pipenv creating a Pipfile for your project is shown.

You may also see some output from Pipenv saying it is creating a virtual environment for you if you are not using one already.

To generate a Pipfile.lock file, run:

pipenv lock

You can also run Python scripts with Pipenv with the following syntax.

pipenv run python script_name

To start a shell, run…

pipenv shell

If you would like to convert a project that currently uses a requirements.txt file to use Pipenv, you can use…

pipenv install requirements.txt

Using PIPENV makes the packaging in python easier, thus making your life easier.

Word of the Day – 5/11/14


WORD OF THE DAY


PERPLEX(Verb)
/pəˈplɛks/

Meaning:
To confuse or trouble with uncertainty or doubt.
To make confusedly intricate; complicate.

Usage:
Questions about the meaning of life have always perplexed humankind.
Her strange response perplexed me.
Their goal is not to perplex, but to amuse and excite.

Pronunciationhttp://www.macmillandictionary.com/pronunciation/british/perplex


IDIOM/PHRASE OF THE DAY


BE TOO CLEVER BY HALF

Meaning:
To be too confident of your own intelligence in a way that annoys other people.
(Of a person) annoyingly proud of their intelligence or skill, and liable to overreach themselves.

Usage:
At school he had a reputation for arrogance. ‘Too clever by half’ was how one former teacher described him.
She was too clever by half – always correcting the teacher or coming back with a smart answer.

Word of the Day – 28/10/14


WORD OF THE DAY


REPUDIATE(Verb)
/rɪˈpjuːdɪeɪt/

Meaning:
To refuse to accept or support (something).
To reject (something or someone).
To say or show that (something) is not true.

Usage:
He has publicly repudiated the government’s policies.
He published an article that repudiates the study’s claims.
The minister repudiated allegations of human rights abuses.
She has repudiated policies associated with previous party leaders.

Pronunciation http://www.macmillandictionary.com/pronunciation/british/repudiate


IDIOM/PHRASE OF THE DAY


TAKE THE EDGE OFF

Meaning:
To reduce the effect of something, usually something unpleasant.

Usage:
Have an apple – it’ll take the edge off your hunger for a while
His apology took the edge off her anger.
Her comments were quite cruel, and nothing could be said to take the edge off of them.
Ceiling fans can take the edge off summer heat.

Engineer’s Day in India

Sir MV

September 15 is celebrated every year in India as Engineer’s Day to commemorate the birthday of the legendary engineer Sir M. Visvesvaraya (1860-1962).

Internationally recognised for his genius in harnessing water resources, he was responsible for successful design and construction of several river dams, bridges and implementing irrigation and drinking water schemes all over India.

He served as the dewan of Mysore State and was considered to be the architect of the all-round development of Karnataka.

Among his most successful projects are the design and construction of the K.R. Sagar dam and its adjoining Brindavan Gardens, turn-around of the Bhadravati Iron and Steel Works, setting up of the Mysore Sandalwood Oil Factory.

Sir Mokshagundam Visvesvaraya KCIE (popularly known as Sir MV; born on 15 September 1860 was a notable Indian engineer, thus it is celebrated on this day. “Making Indian Engineering World-class” is theme of Engineers Day 2014