Geospatial Analytics using Presto and Hive

Introduction – Geospatial Analytics using Presto and Hive

Geospatial Analytics is related to data that is used for locating anything on the globe, an uber driver to a man in a new neighbourhood place everybody uses geospatial data in some way or the other. Geospatial technology involves GPS (global positioning systems), GIS (geographical information systems), and RS (remote sensing). The data is collected in a humongous amount daily, and it needs to be extracted efficiently and judiciously to have better working software that requires it. This blog we will explore the topic Geospatial Analytics using Presto and Hive in depth. We start with the basics and then deep dive into all the details.

Why is Geospatial Data Necessary?

It is necessary for so many things and is used daily for various reasons. From commuting purposes for an ordinary man to data in missiles of a defence organization of a particular county, everything requires geospatial data. It is extracted from various resources. Every phone having an active internet connection somehow adds up to contributing to geospatial data, satellites collect data daily. It is of great use in everyday life, and so it requires a significant amount of attention. It can be used for various reasons, to help support natural hazards and, to know of disasters, global climate change, wildlife, natural resources, etc. It is used for satellite imagery too that could be for tactical or for weather forecasting purposes. Many tech giants like uber etc. use it on daily bases to help ease everyday life. A company has to be efficient in extracting the data efficiently and use it, to stand out in the market.

How to retrieve Geospatial Data?

Various methods could do this, but mainly Presto and hives are used to extract and reform the data that’s present in hundreds of petabyte and use it efficiently and make the lives of billions easy. This data is vital as it touches the mass majority and is used every second. GIS is a part of Geospatial data that helps in the collection, storage, manipulation, analyzation, and present spatial data. Whatever the situation is going on at local, regional or national level, if where is asked for, geospatial analytics come to play. Geospatial Analytics wouldn’t be effective without Visualization.

We create maps using abstract shapes and colors to reveal geographic patterns and tell stories about human existence. Visualize the maps by giving colors, shapes to understand geographic patterns and in the form of stories.

Taken From Article, Geospatial Visualization Tools and Techniques

#blogs #decision intelligence #artificial intelligence

What is GEEK

Buddha Community

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

Design the PCB using your favorite CAD or drawing software.
Print the top and bottom copper and top silk screen layers to a PDF file.
Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
Use a Gerber viewer to double-check the output against the original PCB design.
Make adjustments as needed.
Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl

Nigel Uys

1670711520

Tutorial Geospatial Analytics using Presto and Hive

Introduction to Geospatial Analytics

Geospatial Analytics is related to data that is used for locating anything on the globe, an uber driver to a man in a new neighbourhood place everybody uses its data in some way or the other. Its technology involves GPS (global positioning systems), GIS (geographical information systems), and RS (remote sensing). This blog we will explore the topic in depth. We start with the basics and then deep dive into all the details.

Why is it important?

It is necessary for so many things and is used daily for various reasons. From commuting purposes for an ordinary man to data in missiles of a defence organization of a particular county, everything requires its data. It is extracted from various resources. Every phone having an active internet connection somehow adds up to contributing to geospatial data, satellites collect data daily. It is of great use in everyday life, and so it requires a significant amount of attention. It can be used for various reasons, to help support natural hazards and, to know of disasters, global climate change, wildlife, natural resources, etc. It is used for satellite imagery too that could be for tactical or for weather forecasting purposes. Many tech giants like uber etc. use it on daily bases to help ease everyday life. A company has to be efficient in extracting the data efficiently and use it, to stand out in the market.

How to retrieve Geospatial Data?

Various methods could do this, but mainly Presto and hives are used to extract and reform the data that's present in hundreds of petabyte and use it efficiently and make the lives of billions easy. This data is vital as it touches the mass majority and is used every second. GIS is a part of its data that helps in the collection, storage, manipulation, analyzation, and present spatial data. Whatever the situation is going on at local, regional or national level, if where is asked for it come to play. It wouldn't be effective without Visualization.

Geospatial Analytics Using Presto

Presto is an open-source distributed SQL query, used to solve the question of any size or type. It runs on Hadoop. It supports many non-relational resources and Teradata. It can query data on its respective location, without moving the actual data to any separate system. The execution of the query runs parallel over a pure memory-based architecture, with most results returning within seconds. Many tech giants use it. It's a popular choice for undertaking interactive queries that are in data ranging in100s of PetaByte.

Geospatial Analytics Using Hive

It is a data warehouse infrastructure tool to process any structured data and developed on top of the Hadoop distributed file system. It resides on top of Hadoop to summarize Big Data and makes querying and analyzing of any kind of data accessible.

What is the architecture of Hive?

It is an ETL and Data Warehousing tool built on top of the Hadoop. It helps to perform many operations secure like :

Analysis of large data sets
Data encapsulation
Ad-hoc queries

What are its major components?

Client
Services
Processing & Resource Management
Distributed Storage

Hive Clients

It supports all the application written in languages like Java, Python, C++ etc. It is using Thrift, JDBC and ODBC drivers. It's easy to write its client application in the desired language. Its clients are categorized into three types:-

Thrift Clients: Apache Hive's servers are based on Thrift, so it's easy for it to serve all the request from the languages that support Thrift
JDBC Clients: It allows java apps to connect to it by using its JDBC driver
ODBC Clients: ODBC Driver will enable applications that support ODBC protocol to connect to it. It uses Thrift to communicate to its server.

Hive Services

It provides with various services like -

CLI(Command Line Interface) – It is the default shell provided by it, which helps to execute its queries and command directly.
Web Interface – It gives an option to execute queries and commands on a web-based GUI provided by it.
Server – It is built on Apache Thrift and is also knows as Thrift Server. It allows different clients to submit requests and retrieve the final result from it.
Driver – It is responsible for receiving the queries submitted by clients. It compiles, optimizes and executes the queries.

What is the architecture of Presto?

There is two central part in it: Coordinator and Worker. It is an open-source distributed system that can be run on multiple machines. Its distributed SQL query engine was built for fast analytic queries. Its deployment will include one Coordinator and any number of it.

Coordinator – Used to submit queries and manages parsing, planning, and scheduling query processing.
Worker – Processes the queries, adding more workers gives faster query processing.

What are its key components?

The key components of presto are:

Coordinator

It is the brain of any installation; it manages all the worker nodes for all the work comes related to queries. It gets results from workers and returns the final output to the client. It connects with workers and clients via REST.

Worker

It helps to execute the task and to process the data. These nodes share data amongst each other and get data from the Coordinator.

Catalogue

It contains information related to data, such as where the data is located, where the schema is located and the data source.

Tables and Schemas

It is similar to what it means in a relational database. The table is set of rows organized into named columns and schema is what you use to hold your tables.

Connector

lt issued to help it to integrate with the external data source.

Stage

To execute a query, Presto breaks it up into steps.

Tasks

Stages are implemented as a series of functions that might get distributed on Workers.

Drivers and Operators

Tasks contains one or more parallel drivers, and they are operators in memory. An operator consumes, transforms and produces data.

What are the deployment strategies?

The deployment strategies for Hive are listed below:

AWS

Amazon EMR is used to deploy its megastore. User can opt from three configurations that Amazon has to offer, namely – Embedded, Local or Remote. There are two options for creating an external Hive megastore for EMR:

By using AWS Glue data catalogue
Use Amazon RDS / Amazon Aurora

Cloud Dataproc

Apache Hive on Cloud Dataproc provides an efficient and flexible way by storing data of it in Cloud Storage and hosting its metastore in MySQL database on the Cloud SQL. It offers some advantages like flexibility and agility by letting user tailor cluster configuration for specific workloads and scale the cluster according to the need. It also helps in saving cost.

The deployment strategies for Presto

AWS

Amazon EMR allows to quickly spin up a managed EMR cluster with a presto query engine and run interactive analysis on the data stored in Amazon S3. It is used to run interactive queries. Its implementation can be built on the cloud on Amazon Web Services. Amazon EMR and Amazon Athena provides with building and implementation of it.

Cloud Dataproc

The cluster that includes its component can easily prepare in Presto.

What are the various ways to optimise?

The various ways to optimise are described below:

Hive

Tez-Execution Engine – It is an application framework built on Hadoop Yarn.
Usage of Suitable File Format – Usage of appropriate file format on the basis of data will drastically increase the query performance. ORC file format is best suited for the same.
Partitioning – By partitioning the entries into the different dataset, only the required data is called during the time of the execution of the query, thus making the performance more efficient and optimized.
Bucketing – It helps divide the datasets into more manageable parts, for this purpose bucketing is used. User can set the size of manageable pieces or Buckets too.
Vectorization – Vectorized query execution is used for more optimized performance of it. It happens by performing aggregation over batches of 1024 rows at once instead of the single row each time.
Cost-Based Optimization (CBO) – It performs optimization based on query cost. To use CBO parameters are to be set at the beginning of the query.
Indexing – Indexing helps increase optimization. It helps the speed of the process of executing queries by taking less time to do so.

Presto

File format - Usage of ORC file format is best suited for optimizing the execution of queries while using it.
It can join automatically if the feature is enabled.
Dynamic filter feature optimizes the use of JOIN queries
It has added a new connector configuration to skip corrupt records in input formats other than orc, parquet and rcfile.
By setting task.max-worker-threads in config.properties, number of CPU cores into hyper-threads per core on a worker node.
Splits can be used for efficient and optimized use in executing the queries in Presto.

What are the advantages?

The advantages of Hive and Presto are:

Hive

It is a stable query engine and has a large and active community
Its queries are similar to that of SQL, which are easy to understand by RDBMS professionals
It supports ORC, TextFile, RCFile, Avro and Parquet file Formats

Presto

It supports file formats like ORC, Parquet and RCFile formats, eliminating the need for data transformation.
It works well with Amazon S3 queries and Storage, it can query data in mere seconds even if the data is of the size of petabytes.
It also has an active community.

Geospatial Analytics Using Presto and Hive

Modelling geospatial data has quite many complexities. Well, Known Texts are used to model different locations on the map. Various types like point and polygon shapes are used for these purposes. The Spatial Library is used for spatial processing in it with User-Defined Functions and SerDes. Through allowing this library in it, queries may be created using its Query Language (HQL), which is somewhat close to SQL. You will, therefore, stop complex MapReduce algorithms and stick to a more common workflow. Its plugin is running in production at Uber. All GeoSpatial traffic at Uber, more than 90% of it is completed within 5 minutes. Compared with brute force its MapReduce execution, Uber's Geospatial Plugin is more than 50X faster, leading to greater efficiency.

Summing up

Presto has the edge over Hive as it can be used to process unstructured data too, and query processing in it is faster than that in it. The data is collected in a humongous amount daily, and it needs to be extracted efficiently and judiciously to have better working software that requires it.

Discover more about What is Data Visualization? Best Tools and Modules
Click to explore about Data Analytics in Insurance Industry

Original article source at: https://www.xenonstack.com/

#analytics #presto #hive #geospatial

Jackson Crist

1618209540

Measuring Crop Health Using Deep Learning – Notes From Tiger Analytics

Agrochemical companies manufacture a range of offerings for yield maximisation, pest resistance, hardiness, water quality and availability and other challenges facing farmers. These companies need to measure the efficacy of their products in real-world conditions, not just controlled experimental environments. Single-crop farms are divided into plots and a specific intervention performed in each. For example, hybrid seeds are sown in one plot while another is treated with fertilisers, and so on. The relative performance of each treatment is assessed by tracking the plants’ health in the plot where that treatment was administered.

#featured #deep learning solution #tiger analytics #tiger analytics deep learning #tiger analytics deep learning solution #tiger analytics machine learning #tiger analytics ml #tiger analytics ml-powered digital twin

Solwin Infotech

1620729846

Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
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Kiwanuka Timothy

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Using Singular Value Separation in Python and Numpy (linalg.svd)

In this pythonn - Numpy tutorial we will learn about Numpy linalg.svd: Singular Value Decomposition in Python. In mathematics, a singular value decomposition (SVD) of a matrix refers to the factorization of a matrix into three separate matrices. It is a more generalized version of an eigenvalue decomposition of matrices. It is further related to the polar decompositions.

In Python, it is easy to calculate the singular decomposition of a complex or a real matrix using the numerical python or the numpy library. The numpy library consists of various linear algebraic functions including one for calculating the singular value decomposition of a matrix.

In machine learning models, singular value decomposition is widely used to train models and in neural networks. It helps in improving accuracy and in reducing the noise in data. Singular value decomposition transforms one vector into another without them necessarily having the same dimension. Hence, it makes matrix manipulation in vector spaces easier and efficient. It is also used in regression analysis.

Syntax of Numpy linalg.svd() function

The function that calculates the singular value decomposition of a matrix in python belongs to the numpy module, named linalg.svd() .

The syntax of the numpy linalg.svd () is as follows:

numpy.linalg.svd(A, full_matrices=True, compute_uv=True, hermitian=False)

You can customize the true and false boolean values based on your requirements.

The parameters of the function are given below:

A->array_like: This is the required matrix whose singular value decomposition is being calculated. It can be real or complex as required. It’s dimension should be >= 2.
full_matrices->boolean value(optional): If set to true, then the Hermitian transpose of the given matrix is a square, if it’s false then it isn’t.
compute_uv->boolen value(optional): It determines whether the Hermitian transpose is to be calculated or not in addition to the singular value decomposition.
hermitian->boolean value(optional): The given matrix is considered hermitian(that is symmetric, with real values) which might provide a more efficient method for computation.

The function returns three types of matrices based on the parameters mentioned above:

S->array_like: The vector containing the singular values in the descending order with dimensions same as the original matrix.
u->array_like: This is an optional solution that is returned when compute_uv is set to True. It is a set of vectors with singular values.
v-> array_like: Set of unitary arrays only returned when compute_uv is set to True.

It raises a LinALgError when the singular values diverse.

Prerequisites for setup

Before we dive into the examples, make sure you have the numpy module installed in your local system. This is required for using linear algebraic functions like the one discussed in this article. Run the following command in your terminal.

pip install numpy

That’s all you need right now, let’s look at how we will implement the code in the next section.

To calculate Singular Value Decomposition (SVD) in Python, use the NumPy library’s linalg.svd() function. Its syntax is numpy.linalg.svd(A, full_matrices=True, compute_uv=True, hermitian=False), where A is the matrix for which SVD is being calculated. It returns three matrices: S, U, and V.

Example 1: Calculating the Singular Value Decomposition of a 3×3 Matrix

In this first example we will take a 3X3 matrix and compute its singular value decomposition in the following way:

#importing the numpy module
import numpy as np
#using the numpy.array() function to create an array
A=np.array([[2,4,6],
       [8,10,12],
       [14,16,18]])
#calculatin all three matrices for the output
#using the numpy linalg.svd function
u,s,v=np.linalg.svd(A, compute_uv=True)
#displaying the result
print("the output is=")
print('s(the singular value) = ',s)
print('u = ',u)
print('v = ',v)

The output will be:

the output is=
s(the singular value) =  [3.36962067e+01 2.13673903e+00 8.83684950e-16]
u =  [[-0.21483724  0.88723069  0.40824829]
 [-0.52058739  0.24964395 -0.81649658]
 [-0.82633754 -0.38794278  0.40824829]]
v =  [[-0.47967118 -0.57236779 -0.66506441]
 [-0.77669099 -0.07568647  0.62531805]
 [-0.40824829  0.81649658 -0.40824829]]

Example 1

Example 2: Calculating the Singular Value Decomposition of a Random Matrix

In this example, we will be using the numpy.random.randint() function to create a random matrix. Let’s get into it!

#importing the numpy module
import numpy as np
#using the numpy.array() function to craete an array
A=np.random.randint(5, 200, size=(3,3))
#display the created matrix
print("The input matrix is=",A)
#calculatin all three matrices for the output
#using the numpy linalg.svd function
u,s,v=np.linalg.svd(A, compute_uv=True)
#displaying the result
print("the output is=")
print('s(the singular value) = ',s)
print('u = ',u)
print('v = ',v)

The output will be as follows:

The input matrix is= [[ 36  74 101]
 [104 129 185]
 [139 121 112]]
the output is=
s(the singular value) =  [348.32979681  61.03199722  10.12165841]
u =  [[-0.3635535  -0.48363012 -0.79619769]
 [-0.70916514 -0.41054007  0.57318554]
 [-0.60408084  0.77301925 -0.19372034]]
v =  [[-0.49036384 -0.54970618 -0.67628871]
 [ 0.77570499  0.0784348  -0.62620264]
 [ 0.39727203 -0.83166766  0.38794824]]

Example 2

Wrapping Up

In this article, we explored the concept of singular value decomposition in mathematics and how to calculate it using Python’s numpy module. We used the linalg.svd() function to compute the singular value decomposition of both given and random matrices. Numpy provides an efficient and easy-to-use method for performing linear algebra operations, making it highly valuable in machine learning, neural networks, and regression analysis. Keep exploring other linear algebraic functions in numpy to enhance your mathematical toolset in Python.

Article source at: https://www.askpython.com

#python #numpy