Passionate about new technologies and web design for over 10 years, I now provide maintenance for your home or professional network or your website. Combative and inventive, by my side, there is no problem, only solutions!
During my various experiences and support which was offered to me by the various speakers at CESI as well as my colleagues at WebHelp, I knew how to forge different strengths.
Advanced, 2 years
Excel, SAP, PowerBI
Advanced, 5 years
Installation, maintenance
Advanced, 8 years
HTML4/5, CSS3/4
Advanced, 10 years
PostGreSQL, MySQL, MariaDB, Microsoft SQL
Beyond my main skills, my passion for networks and the web has led me to become versatile. Thanks to the support of CESI, learning platforms and the various projects that I have been able to carry out, today I want to share my know-how with each of you.
Familiar with discussions on the web, my linguistic skills are not limited to my native language. For over 10 years browsing websites, forums and chats from different application environments, I have been able to sharpen my language levels accordingly and learn to use them in a professional environment.
Since the acquisition of my first computer, 12 years ago, I did not stopped putting my skills to the test by servicing each of you! Thanks to you, my experience has not stopped growing to make me the professional of today. Join me and find out how you forged me.
To find out a little more about me, I invite you to discover my higher education path. Curious and combative, I knew how to seize different opportunities in order to work on skills essential to my different projects.
From 2018 to 2020, the CESI work-study college in Dijon offered me the opportunity to improve my skills in terms of IT network management. You will find the different works carried out in this context as well as the associated evaluation system.
A: Objective overachieved, B: Objective achieved, C: Objective partially achieved, D: Not reached
At the bottom of this section, you can also explore my different creations made as a freelance.
Click on images for further details.
Make no mistake! Although discreet on a daily basis, network technicians and developers are above all human and enjoy communicating. Don't be shy and let's start building relationships! In this section, you will find everything you need to start this new one.


BaseDB¶
-
class
Data_Reduction.GAVRT.
BaseDB
(host=None, user=None, pw=None, name=None, port=3306)¶ Bases:
object
This is a database superclass.
- Public attributes::
db - the database connection object c - the database cursor host - database host port - port used to connect to the database pw - user’s password user - authorized db user
- Methods::
connect - returns a connection to a database. check_db - reconnects to the database if a connection has been lost cursor -
Methods Summary
checkDB
()Reconnects to the database if the connection has been lost.
close
()Close a connection
commit
()Commits the most recent database transaction
connect
()Make a connection to the database.
cursor
()Creates a database cursor object; same as BaseDB.c but this is better because it handles disconnected a database
get
(*args)Executes a query of the database
getLastId
(table)ID of the last record
getLastRecord
(table)Returns the last record as a dictionary
getRecordById
(table, rec_id)Get the record with the given ID
get_as_dict
(*args, **kwargs)Executes a query of the database and returns the result as a dict
get_columns
(table)Returns information about the columns of a table
List the table names in the database.
get_rows_by_date
(table, columns, year, doy)Gets data from a table
get_rows_by_time
(table, columns, year, doy, utcs)Queries a table for quantities in columns at designated times
insertRecord
(table, rec)Inserts a record into the database; handles a disconnected database
report_table
(table, columns)Reports on the columns in a table
send_query
(query)Send a MySQL query
updateValues
(vald, table)Add row with updated values
Methods Documentation
-
checkDB
()¶ Reconnects to the database if the connection has been lost.
-
close
()¶ Close a connection
-
commit
()¶ Commits the most recent database transaction
-
connect
()¶ Make a connection to the database. Creates a cursor object.
Automatically invoked when an instance is created; can be called again if the connection is closed but the database object persists.
-
cursor
()¶ Creates a database cursor object; same as BaseDB.c but this is better because it handles disconnected a database
-
get
(*args)¶ Executes a query of the database
:param args : query to be executed
- Returns
record (dict)
-
getLastId
(table)¶ ID of the last record
:param table : the name of the table :type table : str
- Returns
ID (int)
-
getLastRecord
(table)¶ Returns the last record as a dictionary
:param table : name of the table (string)
- Returns
dict
-
getRecordById
(table, rec_id)¶ Get the record with the given ID
:param table : table name :type table : str
:param id : row ID :type id : int
- Returns
dict
-
get_as_dict
(*args, **kwargs)¶ Executes a query of the database and returns the result as a dict
At present, only keyword {‘asfloat’: True} is recognized and is the default if not given. It will convert to float any values for which it is possible.
If the query returns multiple rows, each value associated with a keyword will be a list. If nothing was found, an empty dictionary is returned.
:param db : database connection object
- Parameters
args – query to be executed
:param kwargs : a dictionary with keyword arguments.
@eturn: the record as a dictionary.
-
get_columns
(table)¶ Returns information about the columns of a table
-
get_data_index
()¶
-
get_public_tables
()¶ List the table names in the database.
- Returns
tuple of tuples of str
-
get_rows_by_date
(table, columns, year, doy)¶ Gets data from a table
:param table : table name :type table : str
:param columns : list of columns to be selected :type columns : list of str
:type year : int
:param doy : day of year :type doy : int
- Returns
dict of numpy arrays keyed on column name
-
get_rows_by_time
(table, columns, year, doy, utcs)¶ Queries a table for quantities in columns at designated times
This loops over a list of UTs. For each, it takes the first row it finds matching the date and time. So it has an effective resolution of one second.
:param table : table name :type table : str
:param columns : list of columns to be selected :type columns : list of str
:type year : int
:param doy : day of year :type doy : int
:param utcs : times to be selected; first occurrence is used :type utcs : list of unixtimes (seconds since the epoch)
- Returns
dict of numpy arrays keyed on column name
-
insertRecord
(table, rec)¶ Inserts a record into the database; handles a disconnected database
:param table : table name :type table : str
:param rec : a dictionary with column names as keys.
- Returns
record ID (int)
-
report_table
(table, columns)¶ Reports on the columns in a table
Response has keys: Extra, Default, Field, Key, Null, Type
:param table : table name :type table : str
:param columns : list of column names :type columns : list of str
- Returns
result of query
-
send_query
(query)¶ Send a MySQL query
:param crsr : cursor object for a database
:param query : MySQL query :type query : str
- Returns
str with query response
-
updateValues
(vald, table)¶ Add row with updated values
Add a new row to table with same values as previous row, except for keys in vald, which are updated with provided values. This is useful for keeping logs.
:param vald : updated values :type vald : dict
:param table : table name :type table : str


Page Maximal Repeats
Given a sequences, a repeat is a substring that occurs at at least 2 different positions. A maximal repeat is a repeat that cannot be extended to the left or to right to a longer repeat. The following example demonstrates how to iterate over all maximal repeats and output them.
#include <iostream> #include <seqan/index.h> using namespace seqan;
We begin with a @Class.String@ to store our sequence. Then we create an @Class.Index@ of this StringSet.
Afterwards we initialize a string with the sequence and build an index over it
int main() { String<char> myString = "How many wood would a woodchuck chuck."; typedef Index<String<char> > TMyIndex; TMyIndex myIndex(myString);
To find maximal repeats, we use SeqAn's MaxRepeatsIterator and set the minimum repeat length to 3.
typedef Iterator<TMyIndex, MaxRepeats>::Type TMaxRepeatIterator; TMaxRepeatIterator myRepeatIterator(myIndex, 3); while (!atEnd(myRepeatIterator)) { Iterator<TMaxRepeatIterator>::Type myRepeatPair(myRepeatIterator); while (!atEnd(myRepeatPair)) { std::cout << *myRepeatPair << ", "; ++myRepeatPair; } std::cout << repLength(myRepeatIterator) << " "; std::cout << "\t\"" << representative(myRepeatIterator) << '\"' << std::endl; ++myRepeatIterator; } return 0; }
A repeat can be represented by its length and positions it occurs at. $myRepeatIterator$ iterates over all repeat strings. Please note that in contrast to supermaximal repeats, given a maximal repeat string, not all pairs of its occurrences are maximal repeats. So we need an iterator to iterate over all maximal pairs of this repeat string. The @Spec.MaxRepeats Iterator@ can be seen as a container and be iterated for itself.
weese@tanne:~/seqan$ cd ../build weese@tanne:~/build$ make demo_index_maxrepeats weese@tanne:~/build$ ./bin/demo_index_maxrepeats < 8 , 21 >, 5 " wood" < 21 , 13 >, < 8 , 13 >, 3 " wo" < 26 , 32 >, 5 "chuck" weese@tanne:~/seqan/demos$


Some of the projects I have worked on include:
This is a resort website showcases the best holiday destination in South Coast Kenya.
Languages used to develop this website are:
This is the first website that I created on Github
To develop this website I used:
I am Anthony Njuguna Kiarie from Nyandarua County. Am a graduate of Jomo Kenyatta University of Agriculture & Technology. I graduated with a second class Upper Division in the year November, 2016. After graduating I worked as an intern at Moi Airbase as an IT support technician. I also worked in an E-commerce shop where I did Search Engine Optimization(SEO), posting items on WordPress site & Digital Marketing.
I have always wanted to be a developer, so that I can use my programming skills to develop solutions to real-life challenges. This drove me to join Moringa School to pursue this dream.
My Hobbies include:


Before Change
root_rank: rank of the process from which global variables will be broadcasted
to all other processes.
return tf.group(*[tf.assign(var, broadcast(var, root_rank))
for var in tf.global_variables()])
class BroadcastGlobalVariablesHook(tf.train.SessionRunHook):
After Change
root_rank: rank of the process from which global variables will be broadcasted
to all other processes.
return broadcast_variables(root_rank, tf.global_variables())
def broadcast_variables(root_rank, variables):
Broadcasts variables from root rank to all other processes.


D3plus 2.0 Workshop
Following along
To do this workshop you'll need a local copy of all the boilerplate code. You can find that here:
https://github.com/d3plus/d3plus-workshop


Numerical series predictors
All predictors should implement Predictor
interface.
For building a predictor ready to be plugged as listener to a source of
data, it must implement at the same time Listener
interface,
from com.metaos.datamgt
. To help in this task,
PredictorListener
interface has been created.
Simple predictors
Simple predictors include the core numerical tasks. Should be implemented
in an agnostically way, thus, without knowning anything about the type
of data they are receiving.
Subpackage simple
has been created for housing these
numerical algorithms.
Specific predictors
Created for specific tasks. For instance, to predict volume profiles,
price changes, act as proxies of other values...


Grids / Columns Fixed on Side
FIxed on Side:
This example shows how to fix columns on left or right side in TreeGrid directive. On right panel you can find controls with which you can determine whether the column is fixed and on which side. By default column is not fixed.
Each column object has a field which allows you to set the side at which column will be fixed.
- fixed - Determines the side at which column will be fixed. This field accepts one of the following values: 'none', 'left' or 'right'.
When column is fixed on left or right side, it is still resizable.
Column reordering is also avaliable withing the same group of columns base don their fixed value. However, you can't drag-drop a column fixed on left side and drop it over column which is not fixed or it is fixed on right side.
You can customize the appearance of fixed columns by using corresponding CSS styles. You can find more information in code of this sample.


betterlockscreen --lock- Change the lock screen background:
betterlockscreen -u path/to/image.png- Lock the screen, showing some custom text:
betterlockscreen -l pixel -t "custom lock screen text"- Lock the screen, with a custom monitor off timeout in seconds:
betterlockscreen --off 5 -l


Gives methods to create a catenary object and then plot it and get properties of it. Can construct from parameters or endpoints. Also can get catenary fitted to data.
| Version: | 1.1.2 |
| Depends: | R (≥ 2.15.0) |
| Imports: | ggplot2, boot, methods, tidyverse, dplyr, broom |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-05-04 |
| Author: | Jonathan Tuke [aut, cre], Matthew Roughan [aut] |
| Maintainer: | Jonathan Tuke <simon.tuke at adelaide.edu.au> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | catenary results |
| Reference manual: | catenary.pdf |
| Vignettes: |
Fitting a catenary to data |
| Package source: | catenary_1.1.2.tar.gz |
| Windows binaries: | r-devel: catenary_1.1.2.zip, r-release: catenary_1.1.2.zip, r-oldrel: catenary_1.1.2.zip |
| OS X binaries: | r-release: catenary_1.1.2.tgz, r-oldrel: catenary_1.1.2.tgz |
| Old sources: | catenary archive |
Please use the canonical form https://CRAN.R-project.org/package=catenary to link to this page.


Welcome to Luna's page
My favorite things to eat
- Tuna
- Greenies
- Dog toys
- Dog food
Five facts about me
- I am named after the cat from Sailor Moon
- Sometimes people get confused when they learn I'm a boy. Not sure why!
- I was adopted
- I have a black nose
- I love sleeping and purring at 3am.


Beer list
About MUSKOKA BREWERY
ABOUT THE BREWERY
Handcrafted, Small Batch Brewed
Nestled in the heart of Muskoka, Muskoka Brewery handcrafts premium beers as unique and refreshing as the region they’re from.
Founders Gary McMullen and Kirk Evans kicked open the doors of Muskoka Brewery in June of 1996. Currently owned and operated by Todd Lewin and Bob MacDonald, what started as a very small and dedicated team has quickly grown to a passionate band of beer lovers more than 130 strong.
In short, we have a passion for great beer and we hope you get a chance to enjoy it.
Interested in learning more about our journey from our small brewery on Taylor Road to now? Check out our History.
Contact Us<EMAIL_ADDRESS>OR<PHONE_NUMBER>.
WHY MUSKOKA?
There is only one Muskoka. It is a special place where rugged granite outcrops and lonely windswept pines meet the shimmering beauty of freshwater lakes. Unspoiled natural beauty is what people find here in Muskoka. This place is our home. It is at the heart of what we brew.
Go ahead, taste it.
OUR VALUES
We Are Muskoka Brewery
Manifesto-We
WE HAVE RESOLVE
We work hard and are unwavering in our beliefs. The connection that we feel to this place that we share has made us strong and determined. We are passionate about beer and we will make our mark. Competition only strengthens our resolve to be a leader.
WE ARE CARING
We care about each other and the well-being of our community. We are approachable, understanding, and patient. We strive to make our community a better place.
WE ARE AUTHENTIC
We draw inspiration from the natural wilderness that surrounds us. Our beers are a reflection of us; genuine and unfiltered.
WE ARE ALL UNIQUE
Our diversity makes us stronger as a team. We support and listen to each other. We teach and learn from each other. We laugh and celebrate together.
WE ARE THIRSTY
We are not afraid to venture off the beaten path to create and forge new ways of doing things. That is how we are built.
CONTACT US
Venture off the beaten path to Muskoka Brewery
1964 Muskoka Beach Road: Just 3 minutes off HWY 11 to 118W in Bracebridge.
Tours are always free and run every hour from 12 p.m. to 3 p.m. on Wednesdays through Sundays. We recommend calling ahead to reserve your spot and avoid disappointment. Got a large group looking to visit? Contact our Tap Room and we will be sure to accommodate.
Contact Us<EMAIL_ADDRESS>or call<PHONE_NUMBER>
https://muskokabrewery.com/


Kansas police reunite wayward camel, cow and donkey with owner after posting viral ‘Christmas Nativity’ photo
by Stephen SoraceWhy did a camel, a cow and a donkey cross the road?
Police in Kansas were left wondering just that after they discovered the unlikely animal trio roaming together on Sunday in Goddard, about 15 miles west of Wichita, in a scene they found reminiscent of a Christmas Nativity.
CALIFORNIA DEPUTIES HELP FREE HUGE BEAR NAMED 'T-SHIRT' STUCK IN DUMPSTER, VIDEO SHOWS
“Does anyone know the owners of these three friends traveling together (towards a Northern star) just East of Goddard,” the department wrote. “If we cannot locate the owner, we may be halfway towards a live nativity this Christmas season.”
The Goddard Police Department posted a photo on Facebook that showed the animals proceeding toward a golden light, playing into the seasonal theme.
Police updated their post a short time later, confirming they had found the animals' owner. Sedgwick County Sheriff’s Lt. Tim Meyers said the animals belonged to an employee of the nearby Tanganyika Wildlife Park.
But not before the photo set off a string of jokes – some of biblical proportions – on social media.
BELUGA WHALE NEAR NORTH POLE PLAYS FETCH WITH RUGBY FANS
One person who replied to the Facebook post asked, “Are there 3 wise looking men near?” Another speculated that they “may lead you to the Second Coming.”
Several were interested in their names, asking: “Are there (sic) names gold, frankincense and myrrh?”
“They are looking for the nativity scene, they are stars,” another wrote.
Others were just confused over the appearance of the animals in the Midwestern state, with one flabberghasted user writing: “Who owns a CAMEL in Kansas?”
CLICK HERE FOR THE FOX NEWS APP
It wasn’t immediately clear how the fugitive animals managed to escape the park.
The Associated Press contributed to this report.


This status is displayed in case the report creation process was successfully completed. Therefore, your report file is ready and it is available to download from the Reports module. Furthermore, it is possible to delete your report file. Once your report is deleted, it cannot be restored.
A new generating report with the Deploying status.


This is the sample template. Not much to see here. If you can read this, you can take comfort in knowing that everything appears to be working properly.


这里简单介绍一下MIC2017届实习生的所见所闻、所感所悟
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Hello! I am Akhil and I am currently pursuing my undergraduate degree in Computer Science and will be graduating in ’22. I always look forward to expanding the scope of my knowledge. I have inclination towards data science and machine learning. I had also taught a course on Flutter to my juniors under the PESU I/O Program. I enjoy sharing and discussing various topics with my fellow peers. I always believe in living in the present and not worry to much about what is not under my control while making the best of what I have. That’s it folks :)
The project we our working on deals with detecting Multisource Plagiarism in programs. Aim is perform a comparision at the functional level and use a Transformer Network to detect plagiarism.
I have a passion for Automobiles, Space, Technology and Photography. I have been really passionate about cars from my childhood days. Space inspires me and I always keep myself updated with the latest news in the space industry. I also like discussing about the latest tech and keep myself updated and curious. Besides all this I have a passion for photography. I am gradually honing my photography skills. I also enjoying cycling a lot. There is nothing better than going for an early morning ride to relieve yourself of all the unnecessary stress. I really enjoy having discussions on all these topics.


Cloud computing refers to a wide range of computing infrastructure services--from storage to software--that are delivered over the internet to a customer (normally a company). These services used to be in the scope of a company's IT department. But with cloud computing, these services can be handled by a third-party so companies can focus on their core business.
Models of Cloud Computing
Cloud Computing Services are broken into three main groups:
- Infrastructure as a Service (IaaS)
- Description: This is the lowest level of cloud computing. IaaS provides servers and storage, networking and security. Think of it like a company's traditional data center, but completely virtual. The customer accesses these services through a user interface. Examples include Microsoft Azure or Amazon Web Services.
- Benefits:
- You can purchase services individually as you need them. This makes IaaS scalable with businesses of all sizes
- Microsoft's page on IaaS states that new services can be deployed in less than a day. When you've decided you need a new component of IaaS, you can have it up and running that same day, rather than waiting weeks to set up local resources.
- Disaster recovery with the cloud is significantly easier than with a traditional data center. In an excerpt from a white paper on disaster recovery in cloud computing, Online Tech writes that your infrastructure "...can be safely and accurately transferred from one data center to a second data center without the burden of reloading each component of the server. This can dramatically reduce recovery times compared to conventional (non-virtualized) disaster recovery approaches..."
- Platform as a Service (PaaS)
- Description: This is the mid-tier of cloud computing. Along with all IaaS components, PaaS offers a suite of services to support a web application's entire lifecycle including development tools, database management, and business analytics.
- Benefits:
- All the benefits of IaaS plus...
- Reduces coding time. According to Microsoft's page on PaaS, PaaS tools come with "pre-coded application components built into the platform, such as workflow, directory services, security features, search, and so on."
- Since the development environment is virtual, you and your team can work on your application remotely
- All the tools you need to develop and manage a web app are included in PaaS
- Software as a Service (SaaS)
- Description: This is the most holistic version of cloud computing and also the most common. In addition to the services described in IaaS and PaaS, SaaS provides hosted applications, such as email, calendars, and customer Relationship Management (CRM).
- Benefits:
- All the benefits of IaaS and PaaS plus...
- Most applications can be run from the web, making your work accessible anywhere with an internet connection
- The customer isn't responsible for updating the software
When it comes to asking "which type of cloud computing is right for me?", what you should really ask yourself is, "how much of your company's IT do you want controlled by a third-party vendor?" This graphic from Stephen Watts of bmc blog breaks down your options:
If your needs are purely IT infrastructure based, go with an IaaS provider like Microsoft Azure or Digital Ocean. If you're concerned with building and deploying web apps quickly, without having to worry about software updates, look into PaaS providers like Heroku or Apache Stratos. If you want your company to deal with as little IT as possible, SaaS is right for you. Watts lists examples of all the different cloud computing types. Some examples of SaaS providers he mentions are Salesforce (CRM), Concur (Travel/Expense management), and Dropbox (file hosting).
Models for Implementing Cloud Computing
Cloud Computing services are deployed in one of four ways:
- Public Cloud
- Description: Public clouds are available to the public and data are created and stored on third-party servers. As server infrastructure belongs to service providers that manage and administer resources, the need for user companies to buy and maintain their own hardware is eliminated. Provider companies offer resources as a service on a free of charge or pay-per-use basis via an internet connection. Users can scale them when required.
- Benefits:
- Easy access to data,setup, and use
- Flexibility to add and reduce capacity
- Cost-effectiveness
- Continuous operation time
- Full maintenance>
- No need for software
- Private Cloud
- Description: The difference between public and private clouds is little from the technical point of view, as their designs are very similar. However, unlike in the public cloud, only one specific company owns a private cloud, which is why it is also called an internal or corporate cloud. Since these data center architectures reside within the firewall, they provide enhanced security. Even though one organization runs its workloads on a private basis, a third party can also manage it, and the server can be hosted externally or on premises of the user company.
- Benefits:
- Individual development
- Storage and network components are customizable
- High control over the corporate information
- High security,privacy and reliability
- Community Cloud
- Description: A community cloud deployment model is similar to a private one to a large extent; the only difference is the set of users. While a private type implies that only one company owns the server, in the case of a community cloud, several organizations with similar backgrounds share the same infrastructure and related resources.
- Benefits:
- Cost reduction
- Improved security,privacy and reliability
- Ease of data sharing and collaboration
- Hybrid Cloud
- Description:a hybrid cloud encompasses the best features of the above-mentioned cloud computing deployment models--public, private and community. It allows companies to mix and match facets of all three types that best suit their requirements. As an example, a company can balance its load by locating mission-critical workloads on a secure private cloud and deploy less sensitive ones to a public cloud. It not only safeguards and controls strategically important assets but does so in the most cost effective way possible for each specific case.
- Benefits:
- Improved security and privacy
- Enhanced scalability and flexibility
- Reasonable price
Resources
For more information on cloud computing, check out these great resources:
- Microsoft Azure - What is cloud computing?—Provides an overview of cloud computing including types and deployments
- Microsoft Azure - What is IaaS? —Provides an overview of the IaaS cloud type
- Microsoft Azure - What is PaaS?—Provides an overview of the PaaS cloud type
- Microsoft Azure - What is SaaS?—Provides an overview of the SaaS cloud type
- four-best-cloud-deployment-models-you-need-to-know
- cloud_deployment_models/index
- Online Tech - Benefits of Disaster Recovery in Cloud Computing—Excerpt of a white paper on disaster recovery. You can download the whole white paper at this link
- SaaS vs PaaS vs IaaS: What's the Difference and How to Choose—Provides an overview of cloud computing types and their differences. Includes a very informative graphic to illustrate the differences


Accept invitation from ?
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{% extends "layout/default.html" %} {% load bootstrap3 %} {% block title %}Request to Participate | {{ program.name }} | Early User Program{% endblock %} {% block content %}
{{ message }}
By participating in the Early User Program I understand that I will be working with a preview release which may be immature in some aspects.


My Team
Employee Name
Manager
ID
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Office Number
Employee Name
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ID
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ID
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School


Ocean [ay76], ocean, in Crystal Sea
Last updated Turn #106
Ocean [ay76], ocean, in Crystal Sea
------------------------------------------------------------------------
13: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
13: 10% damaged, arrived from Ocean [ay75], owner:
13: Dorothy [v1b], Pen, with 94 sailors
14: Pleasure Sloop [v5n], ship, 30 hulls, 1 rowing port, 90 sails,
14: 10% damaged, departed for Ocean [ay77].
Routes leaving Ocean:
North, to Ocean [ax76], 6 days
East, to Ocean [ay77], 6 days
South, to Ocean [az76], 6 days
West, to Ocean [ay75], 6 days


The old adage “build it and they will come” no longer works. In today’s digital era, a website is incomplete without a marketing plan to attract VISITors. A cleverly crafted digital marketing strategy can lead to big changes in your business. Access a new market, streamline operations, eliminate waste, or collect customer insights – whatever your goal, your online presence can help achieve it. We partner with businesses to obtain the greatest possible return on each marketing investment with strategic targeting, careful planning, and strong design.
We create more than websites. We help tell your organization’s story through creative design and a thoroughly considered marketing strategy that attracts new customers. Explore our portfolio to learn more about what services we offer and how we take our clients from “we need a new website” to “our website brings us business.”
President, CEO
Graphic - Web Designer
Software Development
Software Development
Account Manager
We are proud to live and work in Corpus Christi, TX, and to have had the opportunity to work with some of the area's finest businesses, including:
“They were there from the beginning– guiding us through the entire process and educating us.”


WITNESS LIST Business & Industry Committee April 16, 1997-2:00P HB 374 For: ALLEN, DANIEL (TX.WATER UTILITIES ASS.) BELL, DIANA L. (SELF) MAXEY, GLEN (SELF) MOORE, MARK (SELF) HB 484 Against: MINTZ, DAVID (TX.APARTMENT ASS.) HB 878 For: NAISHTAT, ELLIOTT (SELF) HB 1572 For: DAVILA, DIANA (SELF) O'NEILL, TERENCE (SELF) HB 1978 For: ADKINS, RAY (TX TENANTS ASS.) MINTZ, DAVID (TX.APARTMENT ASS.) REEVES, BILLY H. (CONSTABLES OF TEXAS) On: FUCHS, FRED (SELF) HB 2039 For: KARISCH, GLENN (PROBATE & TRUST LAWYERS) Against: McGIFFERT, LISA (CONSUMERS UNION) On: PERKINS, THOMAS P., JR. (TX.ATTY.GENERAL OFFICE) HB 2042 For: SMITH, TODD (SELF) HB 2290 For: ELLER, ROBERT (CONSTABLE) MINTZ, DAVID (TX.APARTMENT ASS.) NAISHTAT, ELLIOTT (SELF) REEVES, BILLY H. (CONSTABLE) WARNDOF, DONNA (CONSTABLE,PCT.5,TRAVIS) HB 2291 For: MINTZ, DAVID (TX.APARTMENT ASS.) NAISHTAT, ELLIOTT (SELF) HB 2771 For: COLEMAN, GARNET (SELF) SOIFER, JAN (TX.SCOTTISH RITE HOSPIT) Against: CROZIER, BRIAN W. (SELF) On: MORRIS, MARILYN (UNITED WAY OF TEXAS) PERKINS, PAMELA (TX.ATTORNEY GENERAL OFF) HB 2772 For: COLEMAN, GARNET (SELF) MORRIS, MARILYN (UNITED WAY OF TEXAS) SOIFER, JAN (TX.SCOTTISH RITE HOSPIT) On: PERKINS, PAMELA (TX.ATTORNEY GENERAL OFF) HB 2940 For: McCALL, BRIAN (SELF) HB 2966 On: PERKINS, THOMAS (TX.ATTORNEY GENERAL OFF) HB 2986 For: SMITH, TODD (SELF) THETFORD, ANITA (CITY OF HURST) HB 3021 For: ALLEN, RAY (SELF) HB 3251 For: HARTNETT, WILL (SELF) SOIFER, JAN (TX.SCOTTISH RITE HOSPIT) Against: CROZIER, BRIAN W. (SELF) HB 3464 For: CARLSON, PAT (TEXAS EAGLE FORUM) CHISUM, WARREN (SELF) ROBERTS, WYATT (TX.FAMILY ASSOCIATION) Against: GARCIA, DIANNE HARDY (LESBIAN/GAY RIGHTS TX.) O'NEILL, TERENCE (SELF) HB 3575 For: BUFKIN, BARBARA (SWISS RE SERVICE CORP.) BURNER, BURNIE (SWISS RE SERVICE CORP.) FONDREN, GENE (TX.WKS.COMP.FACILITY) On: FERGUSON, JAN (TX.PROPERTY & CASUALTY) MONTEMAYOR, JOSE (TDI)


Let's Change @ Science KMITL
Episode 5: Let’s voice your opinion. :The Dean
Faculty of Science, KMITL


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Autoregressions¶
This notebook introduces autoregression modeling using the AutoReg
model. It also covers aspects of ar_select_order
assists in selecting models that minimize an information criteria such as the AIC. An autoregressive model has dynamics given by
AutoReg
also permits models with:
Deterministic terms (
trend
)n
: No deterministic termc
: Constant (default)ct
: Constant and time trendt
: Time trend onlySeasonal dummies (
seasonal
)True
includes \(s-1\) dummies where \(s\) is the period of the time series (e.g., 12 for monthly)Exogenous variables (
exog
)A
DataFrame
orarray
of exogenous variables to include in the modelOmission of selected lags (
lags
)If
lags
is an iterable of integers, then only these are included in the model.
The complete specification is
where:
\(d_i\) is a seasonal dummy that is 1 if \(mod(t, period) = i\). Period 0 is excluded if the model contains a constant (
c
is intrend
).\(t\) is a time trend (\(1,2,\ldots\)) that starts with 1 in the first observation.
\(x_{t,j}\) are exogenous regressors. Note these are time-aligned to the left-hand-side variable when defining a model.
\(\epsilon_t\) is assumed to be a white noise process.
This first cell imports standard packages and sets plats to appear inline.
[1]:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as pdr
import seaborn as sns
from statsmodels.tsa.ar_model import AutoReg, ar_select_order
from statsmodels.tsa.api import acf, pacf, graphics
/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
from pandas.util.testing import assert_frame_equal
This cell sets the plotting style, registers pandas date converters for matplotlib, and sets the default figure size.
[2]:
sns.set_style('darkgrid')
pd.plotting.register_matplotlib_converters()
# Default figure size
sns.mpl.rc('figure',figsize=(16, 6))
The first set of examples uses the month-over-month growth rate in U.S. Housing starts that has not been seasonally adjusted. The seasonality is evident by the regular pattern of peaks and troughs. We set the frequency for the time series to “MS” (month-start) to avoid warnings when using AutoReg
.
[3]:
data = pdr.get_data_fred('HOUSTNSA', '1959-01-01', '2019-06-01')
housing = data.HOUSTNSA.pct_change().dropna()
# Scale by 100 to get percentages
housing = 100 * housing.asfreq('MS')
fig, ax = plt.subplots()
ax = housing.plot(ax=ax)
We can start with an AR(3). While this is not a good model for this data, it demonstrates the basic use of the API.
[4]:
mod = AutoReg(housing, 3)
res = mod.fit()
res.summary()
[4]:
| Dep. Variable: | HOUSTNSA | No. Observations: | 725 |
|---|---|---|---|
| Model: | AutoReg(3) | Log Likelihood | -2993.442 |
| Method: | Conditional MLE | S.D. of innovations | 15.289 |
| Date: | Fri, 21 Feb 2020 | AIC | 5.468 |
| Time: | 13:53:22 | BIC | 5.500 |
| Sample: | 05-01-1959 | HQIC | 5.480 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 1.1228 | 0.573 | 1.961 | 0.050 | 0.000 | 2.245 |
| HOUSTNSA.L1 | 0.1910 | 0.036 | 5.235 | 0.000 | 0.120 | 0.263 |
| HOUSTNSA.L2 | 0.0058 | 0.037 | 0.155 | 0.877 | -0.067 | 0.079 |
| HOUSTNSA.L3 | -0.1939 | 0.036 | -5.319 | 0.000 | -0.265 | -0.122 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | 0.9680 | -1.3298j | 1.6448 | -0.1499 |
| AR.2 | 0.9680 | +1.3298j | 1.6448 | 0.1499 |
| AR.3 | -1.9064 | -0.0000j | 1.9064 | -0.5000 |
AutoReg
supports the same covariance estimators as OLS
. Below, we use cov_type="HC0"
, which is White’s covariance estimator. While the parameter estimates are the same, all of the quantities that depend on the standard error change.
[5]:
res = mod.fit(cov_type="HC0")
res.summary()
[5]:
| Dep. Variable: | HOUSTNSA | No. Observations: | 725 |
|---|---|---|---|
| Model: | AutoReg(3) | Log Likelihood | -2993.442 |
| Method: | Conditional MLE | S.D. of innovations | 15.289 |
| Date: | Fri, 21 Feb 2020 | AIC | 5.468 |
| Time: | 13:53:22 | BIC | 5.500 |
| Sample: | 05-01-1959 | HQIC | 5.480 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 1.1228 | 0.601 | 1.869 | 0.062 | -0.055 | 2.300 |
| HOUSTNSA.L1 | 0.1910 | 0.035 | 5.499 | 0.000 | 0.123 | 0.259 |
| HOUSTNSA.L2 | 0.0058 | 0.039 | 0.150 | 0.881 | -0.070 | 0.081 |
| HOUSTNSA.L3 | -0.1939 | 0.036 | -5.448 | 0.000 | -0.264 | -0.124 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | 0.9680 | -1.3298j | 1.6448 | -0.1499 |
| AR.2 | 0.9680 | +1.3298j | 1.6448 | 0.1499 |
| AR.3 | -1.9064 | -0.0000j | 1.9064 | -0.5000 |
[6]:
sel = ar_select_order(housing, 13)
sel.ar_lags
res = sel.model.fit()
res.summary()
[6]:
| Dep. Variable: | HOUSTNSA | No. Observations: | 725 |
|---|---|---|---|
| Model: | AutoReg(13) | Log Likelihood | -2676.157 |
| Method: | Conditional MLE | S.D. of innovations | 10.378 |
| Date: | Fri, 21 Feb 2020 | AIC | 4.722 |
| Time: | 13:53:22 | BIC | 4.818 |
| Sample: | 03-01-1960 | HQIC | 4.759 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 1.3615 | 0.458 | 2.970 | 0.003 | 0.463 | 2.260 |
| HOUSTNSA.L1 | -0.2900 | 0.036 | -8.161 | 0.000 | -0.360 | -0.220 |
| HOUSTNSA.L2 | -0.0828 | 0.031 | -2.652 | 0.008 | -0.144 | -0.022 |
| HOUSTNSA.L3 | -0.0654 | 0.031 | -2.106 | 0.035 | -0.126 | -0.005 |
| HOUSTNSA.L4 | -0.1596 | 0.031 | -5.166 | 0.000 | -0.220 | -0.099 |
| HOUSTNSA.L5 | -0.0434 | 0.031 | -1.387 | 0.165 | -0.105 | 0.018 |
| HOUSTNSA.L6 | -0.0884 | 0.031 | -2.867 | 0.004 | -0.149 | -0.028 |
| HOUSTNSA.L7 | -0.0556 | 0.031 | -1.797 | 0.072 | -0.116 | 0.005 |
| HOUSTNSA.L8 | -0.1482 | 0.031 | -4.803 | 0.000 | -0.209 | -0.088 |
| HOUSTNSA.L9 | -0.0926 | 0.031 | -2.960 | 0.003 | -0.154 | -0.031 |
| HOUSTNSA.L10 | -0.1133 | 0.031 | -3.665 | 0.000 | -0.174 | -0.053 |
| HOUSTNSA.L11 | 0.1151 | 0.031 | 3.699 | 0.000 | 0.054 | 0.176 |
| HOUSTNSA.L12 | 0.5352 | 0.031 | 17.133 | 0.000 | 0.474 | 0.596 |
| HOUSTNSA.L13 | 0.3178 | 0.036 | 8.937 | 0.000 | 0.248 | 0.388 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | 1.0913 | -0.0000j | 1.0913 | -0.0000 |
| AR.2 | 0.8743 | -0.5018j | 1.0080 | -0.0829 |
| AR.3 | 0.8743 | +0.5018j | 1.0080 | 0.0829 |
| AR.4 | 0.5041 | -0.8765j | 1.0111 | -0.1669 |
| AR.5 | 0.5041 | +0.8765j | 1.0111 | 0.1669 |
| AR.6 | 0.0056 | -1.0530j | 1.0530 | -0.2491 |
| AR.7 | 0.0056 | +1.0530j | 1.0530 | 0.2491 |
| AR.8 | -0.5263 | -0.9335j | 1.0716 | -0.3317 |
| AR.9 | -0.5263 | +0.9335j | 1.0716 | 0.3317 |
| AR.10 | -0.9525 | -0.5880j | 1.1194 | -0.4120 |
| AR.11 | -0.9525 | +0.5880j | 1.1194 | 0.4120 |
| AR.12 | -1.2928 | -0.2608j | 1.3189 | -0.4683 |
| AR.13 | -1.2928 | +0.2608j | 1.3189 | 0.4683 |
plot_predict
visualizes forecasts. Here we produce a large number of forecasts which show the string seasonality captured by the model.
[7]:
fig = res.plot_predict(720, 840)
plot_diagnositcs
indicates that the model captures the key features in the data.
[8]:
fig = plt.figure(figsize=(16,9))
fig = res.plot_diagnostics(fig=fig, lags=30)
Seasonal Dummies¶
AutoReg
supports seasonal dummies which are an alternative way to model seasonality. Including the dummies shortens the dynamics to only an AR(2).
[9]:
sel = ar_select_order(housing, 13, seasonal=True)
sel.ar_lags
res = sel.model.fit()
res.summary()
[9]:
| Dep. Variable: | HOUSTNSA | No. Observations: | 725 |
|---|---|---|---|
| Model: | Seas. AutoReg(2) | Log Likelihood | -2652.556 |
| Method: | Conditional MLE | S.D. of innovations | 9.487 |
| Date: | Fri, 21 Feb 2020 | AIC | 4.541 |
| Time: | 13:53:24 | BIC | 4.636 |
| Sample: | 04-01-1959 | HQIC | 4.578 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 1.2726 | 1.373 | 0.927 | 0.354 | -1.418 | 3.963 |
| seasonal.1 | 32.6477 | 1.824 | 17.901 | 0.000 | 29.073 | 36.222 |
| seasonal.2 | 23.0685 | 2.435 | 9.472 | 0.000 | 18.295 | 27.842 |
| seasonal.3 | 10.7267 | 2.693 | 3.983 | 0.000 | 5.449 | 16.005 |
| seasonal.4 | 1.6792 | 2.100 | 0.799 | 0.424 | -2.437 | 5.796 |
| seasonal.5 | -4.4229 | 1.896 | -2.333 | 0.020 | -8.138 | -0.707 |
| seasonal.6 | -4.2113 | 1.824 | -2.309 | 0.021 | -7.786 | -0.636 |
| seasonal.7 | -6.4124 | 1.791 | -3.581 | 0.000 | -9.922 | -2.902 |
| seasonal.8 | 0.1095 | 1.800 | 0.061 | 0.952 | -3.419 | 3.638 |
| seasonal.9 | -16.7511 | 1.814 | -9.234 | 0.000 | -20.307 | -13.196 |
| seasonal.10 | -20.7023 | 1.862 | -11.117 | 0.000 | -24.352 | -17.053 |
| seasonal.11 | -11.9554 | 1.778 | -6.724 | 0.000 | -15.440 | -8.470 |
| HOUSTNSA.L1 | -0.2953 | 0.037 | -7.994 | 0.000 | -0.368 | -0.223 |
| HOUSTNSA.L2 | -0.1148 | 0.037 | -3.107 | 0.002 | -0.187 | -0.042 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | -1.2862 | -2.6564j | 2.9514 | -0.3218 |
| AR.2 | -1.2862 | +2.6564j | 2.9514 | 0.3218 |
The seasonal dummies are obvious in the forecasts which has a non-trivial seasonal component in all periods 10 years in to the future.
[10]:
fig = res.plot_predict(720, 840)
[11]:
fig = plt.figure(figsize=(16,9))
fig = res.plot_diagnostics(lags=30, fig=fig)
Seasonal Dynamics¶
While AutoReg
does not directly support Seasonal components since it uses OLS to estimate parameters, it is possible to capture seasonal dynamics using an over-parametrized Seasonal AR that does not impose the restrictions in the Seasonal AR.
[12]:
yoy_housing = data.HOUSTNSA.pct_change(12).resample("MS").last().dropna()
_, ax = plt.subplots()
ax = yoy_housing.plot(ax=ax)
We start by selecting a model using the simple method that only chooses the maximum lag. All lower lags are automatically included. The maximum lag to check is set to 13 since this allows the model to next a Seasonal AR that has both a short-run AR(1) component and a Seasonal AR(1) component, so that
when expanded. AutoReg
does not enforce the structure, but can estimate the nesting model
We see that all 13 lags are selected.
[13]:
sel = ar_select_order(yoy_housing, 13)
sel.ar_lags
[13]:
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
It seems unlikely that all 13 lags are required. We can set glob=True
to search all \(2^{13}\) models that include up to 13 lags.
Here we see that the first three are selected, as is the 7th, and finally the 12th and 13th are selected. This is superficially similar to the structure described above.
After fitting the model, we take a look at the diagnostic plots that indicate that this specification appears to be adequate to capture the dynamics in the data.
[14]:
sel = ar_select_order(yoy_housing, 13, glob=True)
sel.ar_lags
res = sel.model.fit()
res.summary()
[14]:
| Dep. Variable: | HOUSTNSA | No. Observations: | 714 |
|---|---|---|---|
| Model: | Restr. AutoReg(13) | Log Likelihood | 589.177 |
| Method: | Conditional MLE | S.D. of innovations | 0.104 |
| Date: | Fri, 21 Feb 2020 | AIC | -4.496 |
| Time: | 13:53:33 | BIC | -4.444 |
| Sample: | 02-01-1961 | HQIC | -4.476 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 0.0035 | 0.004 | 0.875 | 0.382 | -0.004 | 0.011 |
| HOUSTNSA.L1 | 0.5640 | 0.035 | 16.167 | 0.000 | 0.496 | 0.632 |
| HOUSTNSA.L2 | 0.2347 | 0.038 | 6.238 | 0.000 | 0.161 | 0.308 |
| HOUSTNSA.L3 | 0.2051 | 0.037 | 5.560 | 0.000 | 0.133 | 0.277 |
| HOUSTNSA.L7 | -0.0903 | 0.030 | -2.976 | 0.003 | -0.150 | -0.031 |
| HOUSTNSA.L12 | -0.3791 | 0.034 | -11.075 | 0.000 | -0.446 | -0.312 |
| HOUSTNSA.L13 | 0.3354 | 0.033 | 10.254 | 0.000 | 0.271 | 0.400 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | -1.0309 | -0.2682j | 1.0652 | -0.4595 |
| AR.2 | -1.0309 | +0.2682j | 1.0652 | 0.4595 |
| AR.3 | -0.7454 | -0.7417j | 1.0515 | -0.3754 |
| AR.4 | -0.7454 | +0.7417j | 1.0515 | 0.3754 |
| AR.5 | -0.3172 | -1.0221j | 1.0702 | -0.2979 |
| AR.6 | -0.3172 | +1.0221j | 1.0702 | 0.2979 |
| AR.7 | 0.2419 | -1.0573j | 1.0846 | -0.2142 |
| AR.8 | 0.2419 | +1.0573j | 1.0846 | 0.2142 |
| AR.9 | 0.7840 | -0.8303j | 1.1420 | -0.1296 |
| AR.10 | 0.7840 | +0.8303j | 1.1420 | 0.1296 |
| AR.11 | 1.0730 | -0.2386j | 1.0992 | -0.0348 |
| AR.12 | 1.0730 | +0.2386j | 1.0992 | 0.0348 |
| AR.13 | 1.1193 | -0.0000j | 1.1193 | -0.0000 |
[15]:
fig = plt.figure(figsize=(16,9))
fig = res.plot_diagnostics(fig=fig, lags=30)
We can also include seasonal dummies. These are all insignificant since the model is using year-over-year changes.
[16]:
sel = ar_select_order(yoy_housing, 13, glob=True, seasonal=True)
sel.ar_lags
res = sel.model.fit()
res.summary()
[16]:
| Dep. Variable: | HOUSTNSA | No. Observations: | 714 |
|---|---|---|---|
| Model: | Restr. Seas. AutoReg(13) | Log Likelihood | 590.875 |
| Method: | Conditional MLE | S.D. of innovations | 0.104 |
| Date: | Fri, 21 Feb 2020 | AIC | -4.469 |
| Time: | 13:53:44 | BIC | -4.346 |
| Sample: | 02-01-1961 | HQIC | -4.422 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 0.0167 | 0.014 | 1.215 | 0.224 | -0.010 | 0.044 |
| seasonal.1 | -0.0179 | 0.019 | -0.931 | 0.352 | -0.056 | 0.020 |
| seasonal.2 | -0.0121 | 0.019 | -0.630 | 0.528 | -0.050 | 0.026 |
| seasonal.3 | -0.0210 | 0.019 | -1.089 | 0.276 | -0.059 | 0.017 |
| seasonal.4 | -0.0223 | 0.019 | -1.157 | 0.247 | -0.060 | 0.015 |
| seasonal.5 | -0.0224 | 0.019 | -1.160 | 0.246 | -0.060 | 0.015 |
| seasonal.6 | -0.0212 | 0.019 | -1.096 | 0.273 | -0.059 | 0.017 |
| seasonal.7 | -0.0101 | 0.019 | -0.520 | 0.603 | -0.048 | 0.028 |
| seasonal.8 | -0.0095 | 0.019 | -0.491 | 0.623 | -0.047 | 0.028 |
| seasonal.9 | -0.0049 | 0.019 | -0.252 | 0.801 | -0.043 | 0.033 |
| seasonal.10 | -0.0084 | 0.019 | -0.435 | 0.664 | -0.046 | 0.030 |
| seasonal.11 | -0.0077 | 0.019 | -0.400 | 0.689 | -0.046 | 0.030 |
| HOUSTNSA.L1 | 0.5630 | 0.035 | 16.160 | 0.000 | 0.495 | 0.631 |
| HOUSTNSA.L2 | 0.2347 | 0.038 | 6.248 | 0.000 | 0.161 | 0.308 |
| HOUSTNSA.L3 | 0.2075 | 0.037 | 5.634 | 0.000 | 0.135 | 0.280 |
| HOUSTNSA.L7 | -0.0916 | 0.030 | -3.013 | 0.003 | -0.151 | -0.032 |
| HOUSTNSA.L12 | -0.3810 | 0.034 | -11.149 | 0.000 | -0.448 | -0.314 |
| HOUSTNSA.L13 | 0.3373 | 0.033 | 10.327 | 0.000 | 0.273 | 0.401 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | -1.0305 | -0.2681j | 1.0648 | -0.4595 |
| AR.2 | -1.0305 | +0.2681j | 1.0648 | 0.4595 |
| AR.3 | -0.7447 | -0.7414j | 1.0509 | -0.3754 |
| AR.4 | -0.7447 | +0.7414j | 1.0509 | 0.3754 |
| AR.5 | -0.3172 | -1.0215j | 1.0696 | -0.2979 |
| AR.6 | -0.3172 | +1.0215j | 1.0696 | 0.2979 |
| AR.7 | 0.2416 | -1.0568j | 1.0841 | -0.2142 |
| AR.8 | 0.2416 | +1.0568j | 1.0841 | 0.2142 |
| AR.9 | 0.7837 | -0.8304j | 1.1418 | -0.1296 |
| AR.10 | 0.7837 | +0.8304j | 1.1418 | 0.1296 |
| AR.11 | 1.0724 | -0.2383j | 1.0986 | -0.0348 |
| AR.12 | 1.0724 | +0.2383j | 1.0986 | 0.0348 |
| AR.13 | 1.1192 | -0.0000j | 1.1192 | -0.0000 |
Industrial Production¶
We will use the industrial production index data to examine forecasting.
[17]:
data = pdr.get_data_fred('INDPRO', '1959-01-01', '2019-06-01')
ind_prod = data.INDPRO.pct_change(12).dropna().asfreq('MS')
_, ax = plt.subplots(figsize=(16,9))
ind_prod.plot(ax=ax)
[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fec8d4cd5d0>
We will start by selecting a model using up to 12 lags. An AR(13) minimizes the BIC criteria even though many coefficients are insignificant.
[18]:
sel = ar_select_order(ind_prod, 13, 'bic')
res = sel.model.fit()
res.summary()
[18]:
| Dep. Variable: | INDPRO | No. Observations: | 714 |
|---|---|---|---|
| Model: | AutoReg(13) | Log Likelihood | 2318.848 |
| Method: | Conditional MLE | S.D. of innovations | 0.009 |
| Date: | Fri, 21 Feb 2020 | AIC | -9.411 |
| Time: | 13:53:45 | BIC | -9.313 |
| Sample: | 02-01-1961 | HQIC | -9.373 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 0.0012 | 0.000 | 2.896 | 0.004 | 0.000 | 0.002 |
| INDPRO.L1 | 1.1504 | 0.035 | 32.940 | 0.000 | 1.082 | 1.219 |
| INDPRO.L2 | -0.0747 | 0.053 | -1.407 | 0.159 | -0.179 | 0.029 |
| INDPRO.L3 | 0.0043 | 0.053 | 0.081 | 0.935 | -0.099 | 0.107 |
| INDPRO.L4 | 0.0027 | 0.053 | 0.052 | 0.959 | -0.100 | 0.106 |
| INDPRO.L5 | -0.1383 | 0.052 | -2.642 | 0.008 | -0.241 | -0.036 |
| INDPRO.L6 | 0.0085 | 0.052 | 0.163 | 0.871 | -0.094 | 0.111 |
| INDPRO.L7 | 0.0375 | 0.052 | 0.720 | 0.471 | -0.065 | 0.139 |
| INDPRO.L8 | -0.0235 | 0.052 | -0.453 | 0.651 | -0.125 | 0.078 |
| INDPRO.L9 | 0.0945 | 0.052 | 1.824 | 0.068 | -0.007 | 0.196 |
| INDPRO.L10 | -0.0844 | 0.052 | -1.627 | 0.104 | -0.186 | 0.017 |
| INDPRO.L11 | 0.0024 | 0.052 | 0.047 | 0.962 | -0.099 | 0.104 |
| INDPRO.L12 | -0.3809 | 0.052 | -7.367 | 0.000 | -0.482 | -0.280 |
| INDPRO.L13 | 0.3589 | 0.033 | 10.916 | 0.000 | 0.294 | 0.423 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | -1.0411 | -0.2889j | 1.0804 | -0.4569 |
| AR.2 | -1.0411 | +0.2889j | 1.0804 | 0.4569 |
| AR.3 | -0.7774 | -0.8064j | 1.1201 | -0.3721 |
| AR.4 | -0.7774 | +0.8064j | 1.1201 | 0.3721 |
| AR.5 | -0.2760 | -1.0530j | 1.0886 | -0.2908 |
| AR.6 | -0.2760 | +1.0530j | 1.0886 | 0.2908 |
| AR.7 | 0.2719 | -1.0515j | 1.0861 | -0.2097 |
| AR.8 | 0.2719 | +1.0515j | 1.0861 | 0.2097 |
| AR.9 | 0.8019 | -0.7297j | 1.0842 | -0.1175 |
| AR.10 | 0.8019 | +0.7297j | 1.0842 | 0.1175 |
| AR.11 | 1.0224 | -0.2214j | 1.0461 | -0.0339 |
| AR.12 | 1.0224 | +0.2214j | 1.0461 | 0.0339 |
| AR.13 | 1.0578 | -0.0000j | 1.0578 | -0.0000 |
We can also use a global search which allows longer lags to enter if needed without requiring the shorter lags. Here we see many lags dropped. The model indicates there may be some seasonality in the data.
[19]:
sel = ar_select_order(ind_prod, 13, 'bic', glob=True)
sel.ar_lags
res_glob = sel.model.fit()
res_glob.summary()
[19]:
| Dep. Variable: | INDPRO | No. Observations: | 714 |
|---|---|---|---|
| Model: | Restr. AutoReg(13) | Log Likelihood | 2313.420 |
| Method: | Conditional MLE | S.D. of innovations | 0.009 |
| Date: | Fri, 21 Feb 2020 | AIC | -9.421 |
| Time: | 13:53:53 | BIC | -9.382 |
| Sample: | 02-01-1961 | HQIC | -9.406 |
| - 06-01-2019 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 0.0013 | 0.000 | 2.986 | 0.003 | 0.000 | 0.002 |
| INDPRO.L1 | 1.0882 | 0.013 | 80.869 | 0.000 | 1.062 | 1.115 |
| INDPRO.L5 | -0.1057 | 0.016 | -6.649 | 0.000 | -0.137 | -0.075 |
| INDPRO.L12 | -0.3852 | 0.034 | -11.404 | 0.000 | -0.451 | -0.319 |
| INDPRO.L13 | 0.3587 | 0.032 | 11.315 | 0.000 | 0.297 | 0.421 |
| Real | Imaginary | Modulus | Frequency | |
|---|---|---|---|---|
| AR.1 | -1.0518 | -0.2762j | 1.0875 | -0.4591 |
| AR.2 | -1.0518 | +0.2762j | 1.0875 | 0.4591 |
| AR.3 | -0.7718 | -0.7796j | 1.0970 | -0.3742 |
| AR.4 | -0.7718 | +0.7796j | 1.0970 | 0.3742 |
| AR.5 | -0.2714 | -1.0479j | 1.0825 | -0.2903 |
| AR.6 | -0.2714 | +1.0479j | 1.0825 | 0.2903 |
| AR.7 | 0.2820 | -1.0573j | 1.0943 | -0.2085 |
| AR.8 | 0.2820 | +1.0573j | 1.0943 | 0.2085 |
| AR.9 | 0.8021 | -0.7558j | 1.1021 | -0.1203 |
| AR.10 | 0.8021 | +0.7558j | 1.1021 | 0.1203 |
| AR.11 | 1.0189 | -0.2198j | 1.0423 | -0.0338 |
| AR.12 | 1.0189 | +0.2198j | 1.0423 | 0.0338 |
| AR.13 | 1.0578 | -0.0000j | 1.0578 | -0.0000 |
plot_predict
can be used to produce forecast plots along with confidence intervals. Here we produce forecasts starting at the last observation and continuing for 18 months.
[20]:
ind_prod.shape
[20]:
(714,)
[21]:
fig = res_glob.plot_predict(start=714, end=732)
The forecasts from the full model and the restricted model are very similar. I also include an AR(5) which has very different dynamics
[22]:
res_ar5 = AutoReg(ind_prod, 5).fit()
predictions = pd.DataFrame({"AR(5)": res_ar5.predict(start=714, end=726),
"AR(13)": res.predict(start=714, end=726),
"Restr. AR(13)": res_glob.predict(start=714, end=726)})
_, ax = plt.subplots()
ax = predictions.plot(ax=ax)
The diagnostics indicate the model captures most of the the dynamics in the data. The ACF shows a patters at the seasonal frequency and so a more complete seasonal model (SARIMAX
) may be needed.
[23]:
fig = plt.figure(figsize=(16,9))
fig = res_glob.plot_diagnostics(fig=fig, lags=30)
Forecasting¶
Forecasts are produced using the predict
method from a results instance. The default produces static forecasts which are one-step forecasts. Producing multi-step forecasts requires using dynamic=True
.
In this next cell, we produce 12-step-heard forecasts for the final 24 periods in the sample. This requires a loop.
Note: These are technically in-sample since the data we are forecasting was used to estimate parameters. Producing OOS forecasts requires two models. The first must exclude the OOS period. The second uses the predict
method from the full-sample model with the parameters from the shorter sample model that excluded the OOS period.
[24]:
import numpy as np
start = ind_prod.index[-24]
forecast_index = pd.date_range(start, freq=ind_prod.index.freq, periods=36)
cols = ['-'.join(str(val) for val in (idx.year, idx.month)) for idx in forecast_index]
forecasts = pd.DataFrame(index=forecast_index,columns=cols)
for i in range(1, 24):
fcast = res_glob.predict(start=forecast_index[i], end=forecast_index[i+12], dynamic=True)
forecasts.loc[fcast.index, cols[i]] = fcast
_, ax = plt.subplots(figsize=(16, 10))
ind_prod.iloc[-24:].plot(ax=ax, color="black", linestyle="--")
ax = forecasts.plot(ax=ax)
Comparing to SARIMAX¶
SARIMAX
is an implementation of a Seasonal Autoregressive Integrated Moving Average with eXogenous regressors model. It supports:
Specification of seasonal and nonseasonal AR and MA components
Inclusion of Exogenous variables
Full maximum-likelihood estimation using the Kalman Filter
This model is more feature rich than AutoReg
. Unlike SARIMAX
, AutoReg
estimates parameters using OLS. This is faster and the problem is globally convex, and so there are no issues with local minima. The closed-form estimator and its performance are the key advantages of AutoReg
over SARIMAX
when comparing AR(P) models. AutoReg
also support seasonal dummies, which can be used with SARIMAX
if the user includes them as exogenous regressors.
[25]:
from statsmodels.tsa.api import SARIMAX
sarimax_mod = SARIMAX(ind_prod, order=((1,5,12,13),0, 0), trend='c')
sarimax_res = sarimax_mod.fit()
sarimax_res.summary()
[25]:
| Dep. Variable: | INDPRO | No. Observations: | 714 |
|---|---|---|---|
| Model: | SARIMAX([1, 5, 12, 13], 0, 0) | Log Likelihood | 2302.146 |
| Date: | Fri, 21 Feb 2020 | AIC | -4592.293 |
| Time: | 13:54:00 | BIC | -4564.867 |
| Sample: | 01-01-1960 | HQIC | -4581.701 |
| - 06-01-2019 | |||
| Covariance Type: | opg |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| intercept | 0.0011 | 0.000 | 2.627 | 0.009 | 0.000 | 0.002 |
| ar.L1 | 1.0798 | 0.010 | 106.388 | 0.000 | 1.060 | 1.100 |
| ar.L5 | -0.0849 | 0.011 | -7.467 | 0.000 | -0.107 | -0.063 |
| ar.L12 | -0.4392 | 0.026 | -16.935 | 0.000 | -0.490 | -0.388 |
| ar.L13 | 0.4032 | 0.025 | 15.831 | 0.000 | 0.353 | 0.453 |
| sigma2 | 9.179e-05 | 3.13e-06 | 29.366 | 0.000 | 8.57e-05 | 9.79e-05 |
| Ljung-Box (Q): | 177.22 | Jarque-Bera (JB): | 928.85 |
|---|---|---|---|
| Prob(Q): | 0.00 | Prob(JB): | 0.00 |
| Heteroskedasticity (H): | 0.38 | Skew: | -0.63 |
| Prob(H) (two-sided): | 0.00 | Kurtosis: | 8.44 |
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[26]:
sarimax_params = sarimax_res.params.iloc[:-1].copy()
sarimax_params.index = res_glob.params.index
params = pd.concat([res_glob.params, sarimax_params], axis=1, sort=False)
params.columns = ["AutoReg", "SARIMAX"]
params
[26]:
| AutoReg | SARIMAX | |
|---|---|---|
| intercept | 0.001292 | 0.001140 |
| INDPRO.L1 | 1.088215 | 1.079829 |
| INDPRO.L5 | -0.105668 | -0.084904 |
| INDPRO.L12 | -0.385171 | -0.439175 |
| INDPRO.L13 | 0.358738 | 0.403241 |


{{ post.title }}
{% if post.subtitle %}{{ post.subtitle }}
{% endif %}
{{ post.content | truncatewords: 50 | strip_html | xml_escape}}
[Read More]
---
layout: page
title: "FIN-TECH to Finance SPACE-TECH"
subtitle: I write about :-
1. The potential impact of blockchain on established monetary & banking systems.
2. Digital Investment Assets that can support evolved Financially Engineered Products.
3. Dapps that i am working upon.
4. How DLT based monetary systems can be created that can fuel space-tech research and space exploration.
css: "/css/index.css"
meta-title: "Rahul Mittal - Fin-Tech Systems Architect"
meta-description: "Distributed Ledger based Financial Systems Architect and Developer, currently working with Societe Generale."
use-site-title: true
bigimg:
- "/img/newCover.jpg" : "The Hero's Journey"
---


- Current Investment
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₦ 0.00- Transaction
- Funding
- Investment Payout
- Wallet Payout
Fund wallet via bank transfer
You can now fund your wallet by simply making a bank or online transfer to a unique account number.
Wallet
Balance
Withdrawable: ₦ 0.00
Non withdrawable: ₦ 0.00
₦ 0.00
Total Payout
last payout
All payments made directly (as card/online/cash transfers from the farm & commodity page or as reinvestments from a complete investment) would not show up here. Please check the “Transactions” tab for direct payments. Only payments made into the wallet would show up here.
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Withdrawable: ₦ 0.00
Non withdrawable: ₦ 0.00
₦ 0.00
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last payout
All payments made directly (as card/online/cash transfers from the farm & commodity page or as reinvestments from a complete investment) would not show up here. Please check the “Transactions” tab for direct payments. Only payments made into the wallet would show up here.
Wallet
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Withdrawable: ₦ 0.00
Non withdrawable: ₦ 0.00
₦ 0.00
Total Payout
last payout
All payments made directly (as card/online/cash transfers from the farm & commodity page or as reinvestments from a complete investment) would not show up here. Please check the “Transactions” tab for direct payments. Only payments made into the wallet would show up here.
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- Invite Friends
- Redeemed referrals
- Pending referrals
Share code
GET
2.5%
of your invite’s first
investment on the platform.
They should use the referral code upon signup
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referrals
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To be spent on the platform on an investment
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- Profile
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Print Current Investment Certificate
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gvfs-mount - Mounts the locations
gvfs-mount [OPTION...] [LOCATION...]
gvfs-mount --unmount-scheme=SCHEME
gvfs-mount --list [OPTION...]
gvfs-mount --monitor [OPTION...]
gvfs-mount provides commandline access to various aspects of GIOs mounting functionality.
Mounting refers to the traditional concept of arranging multiple file systems and devices in a single tree, rooted at /. Classical mounting happens in the kernel and is controlled by the mount utility. GIO expands this concept by introducing mount daemons that can make file systems available to GIO applications without kernel involvement.
The following options are understood:
-h, --help
Prints a short help text and exits.
--version
Shows the version number and exits.
-m, --mountable
Mount as a mountable.
-d, --device=DEVICE
Mount the volume with the given device file.
-u, --unmount
Unmount the locations.
-e, --eject
Eject the locations.
-s, --unmount-scheme=SCHEME
Unmount all mounts with the given scheme.
-l, --list
List all known mounts.
-o, --monitor
Monitor mount and unmount events.
-i, --detail
Show extra information for --list and --monitor.
On success 0 is returned, a non-zero failure code otherwise.
ls(1), gvfs-info(1)


Working father and full stack developer trainee
My goal is to become a full-stack developer. I learn useful stuff at Freecodecamp and I will keep trying until I make it. My newfound love is Javascript and I practice it daily on Codewars, Hackerrank and other sites.
I am also a Filemaker Pro professional developer and coder with 22 years experience. I have been developing FileMaker Pro solutions since FileMaker Pro 3. I have built solutions from start as well as supported existing databases.
I love my work and I am good at it. I always give my 100% and deliver quality work in a timely fashion.
Here is myFCC Profile
Thank you for your interest in my work. If you like to know more about me please use the fully functional form below to send me your message.
I will reply within 10 hours to your message.
You can use the social media buttons bellow to find me around the web.


The class Implementation was implemented based on the class teCounted writed by Ricardo Cartaxo and Gilberto Câmara and founded in the geographic library TerraLib.
More...
#include <handlebody.h>
The class Implementation was implemented based on the class teCounted writed by Ricardo Cartaxo and Gilberto Câmara and founded in the geographic library TerraLib.
◆ Body() [1/2]
Constructor: zero references when the object is being built.
◆ ~Body()
◆ Body() [2/2]
| Body::Body |
( |
const Body & |
| ) |
|
|
private |
◆ attach()
Increases the number of references to this object.
◆ detach()
Decreases the number of references to this object. Destroy it if there are no more references to it
◆ operator=()
◆ refCount()
Returns the number of references to this object.
◆ refCount_
The documentation for this class was generated from the following file:


Cardi B's Social Media "Freestyle" Goes Viral
by Yohance Kyles (@HUEYmixwitRILEY)Were the "Bodak Yellow" rapper's bars 🔥 or 🗑?
(AllHipHop News) Cardi B had been lying low on social media for the last several days. However, the Bronx native showed back up online Monday night to offer her followers some new rhymes.
A video of Cardi rapping a "freestyle" in a kitchen went viral. The clip picked up more than 1 million views on Twitter and 7 million views on Instagram in under 8 hours.
"Just [a little] something something ......hair on healthy." wrote Cardi in the vid's caption. Previously, the Grammy winner uploaded images of herself in the studio to Instagram Stories which led her fans to believe she is currently recording her sophomore album.
Cardi B released her debut LP, Invasion of Privacy, in 2018. The former #1 has been certified 3x-Platinum by the RIAA and spent 83 weeks on the Billboard 200 chart. Invasion of Privacy hosts the hit singles "Bodak Yellow (Money Moves)," "I Like It," "Be Careful," and "Bartier Cardi."


{% load i18n %}
Some of the products in your cart are eligible to receive a discount of {{ pct }} when you enter the code "{{ code }}" during our "{{ desc }}" offer. This discount ends on {{ enddate }}.
{% endblocktrans %} {% endwith %}{% endwith %}{% endwith %}{% endwith %}


Description Logics (DLs) is a family of formalisms used to represent knowledge of a domain. They are equipped with a formal logic-based semantics. Knowledge representation systems based on description logics provide various inference capabilities that deduce implicit knowledge from the explicitly represented knowledge. In this thesis we investigate the Proof Theory for DLs. We introduce Sequent Calculi and Natural Deduction for some DLs (ALC, ALCQ). Cut-elimination and Normalization are proved for the calculi. It is argued that those systems can improve the extraction of computational content from DLs proofs for explanations purpose.


Writing Lab 2
What We are Going to Do Today
- Start with Our Research Questions
- Discuss Our Search Strategy
- If we need, conduct a search on our computers
- Identify the list of articles
- Identify the five articles
Layout of The Report
- 3000 words
- Title, Abstract, Methods, Body
- Tables and References Excluded from Word Count
Suggested Workflow
How to Critique Articles on ...
Basic Principles Remain the same
- PEICO-CBC
- How were the participants recruited? (Random, Nonrandom)
- How was the Exposure Measured?
- What was the Intervention?
- Who administered the Intervention?
- What was compared with?
- How Was Outcomes Measured?
How to Critique 2
- For Each:
- How Was Chance Accounted For? (Sample Size and Power)
- What are the Possible Biases?
- How did the Authors Control for Biases?
- What Alternative Explanations are Possible?
- How did the Authors Control for Confounding Variables?
How Will You Identify your Five Articles?
- On the basis of most recent?
- On the basis of best evidence?
- Others?
Let's Work in 10 minute blocks with 5 minutes break
Finally, we share our work together


Materials
Work Measurements
Results
Target RPM
{{ rpm }}
Suggested gear(s)
- {{ gear.name }}: {{ gear.speed }} Difference: {{ gear.getPercentDiff(rpm) }}%
Suggests which gear to use when machining various materials. Works with a Grizzly G4000.
{{ rpm }}


Everyone has a dream and our dream is to encourage people to explore this beautiful world as they travel with us. It’s our passion and we have lived it to the fullest since 1977. It all started 36 years back when this passion took form of an organization and became the most Everyone has a dream and our dream is to encourage people to explore this beautiful world as they travel with us. It’s our passion and we have lived it to the fullest since 1977. It all started 36 years back when this passion took form of an organization and became the most trustworthy Travel Partner for over half a million happy customers… and counting!Travel Partner for over half a million happy customers… and counting!.


Disgusting moment creepy marathon runner gropes female TV reporter live on air
by Jimmy McCloskeyA TV reporter has hit out at a creepy marathon runner who left her lost for words after groping her live on air.
Alexandrea Bozarjian was assaulted while covering a race in Savannah, Georgia, on Saturday morning for local TV station WSAV.
A man in sunglasses could be seen smirking as he violated the young journalist, who was stunned into momentary silence by the incident.
Bozarjian has since taken to Twitter to hit out at her attacker, saying: ‘To the man who smacked my butt on live TV this morning: You violated, objectified, and embarrassed me.
‘No woman should EVER have to put up with this at work or anywhere!! Do better.’
She wrote the comment above a video shared by another tweeter called Tonya who branded Bozarijan’s attacker a ‘jerk.’
Savannah Police Department said they have since been in touch with Bozarjian, and are waiting to see whether she would like to take formal action against him.
Spokesman Keturah Greene told NBC News: ‘We’ve talked to her about the situation. I can’t discuss it further, but it’s up to Alexandrea on how she wants to handle the situation.
‘All I can say is we have made contact with Alexandrea and we are definitely going to be working with her in any capacity on how she’d like to move forward with this incident.’
A spokesman for Savannah Sports Council, which organizes the city’s marathon, says the groper – who has not been identified – has been banned from all future races.


Api Documentation¶
This section contains more granular documentation of the classes and functions available to the api.
Automated Runner¶
-
class
Runner
(path, *, filesystem=None)[source]¶ This class handles orchestration of downloading and storing the backup. Options are set in a yaml configuration file. There is an
example
you can use as a starting point.Parameters: - path – (required) absolute path to the file on the system or relative to the FS object supplied in the filesystem parameter
- filesystem – (keyword only) a pyfilesystem2 FS object where the yaml config file is located.
Useful Methods/Classes¶
Download Table¶
-
class
DownloadTable
(base_key, table_name, api_key=None, progress=False, compression=True, fields={}, discard_attach=False)[source]¶ Downloads all data from a table including atachments.
Parameters: - base_key – base id from airtable api url (starts ‘app’)
- table_name – the table name to download
- api_key – the airtable api key. If an environment variable ‘AIRTABLE_API_KEY’ is set this is not required.
- compression – whether to compress attachment data
- fields – Store the field
- discard_attach – if true, attachment data will not be downloaded, url and other info will be preservered
File I/O¶
-
join_files
(tmpfs, outfs)[source]¶ Join the backup json files into a single package (tarball, zip).
Parameters: - tmpfs – the temporary fs where the backup is stored.
- outfs – the filesystem to copy to (should be TarFS or ZipFS). things like compression and encoding should be specified at instantiation.
-
write_out_backup
(backing_store_fs, *, filepath=None, filesystem=None, prefix='')[source]¶ Write the backup data to its final location. A backing store is required and either a filepath to the packaged backup or the tmp filesystem is required.
Parameters: - backing_store_fs (required) – a pyfilesystem2 object to be the final storage location of the backup. (should be OSFS, S3FS, FTPFS, etc.) Can be a single object or list of filesystem objects for copying to multiple backing stores.
- filepath – path to the zip or tar file containing the backup data (if desired). Can be a path object or str.
- filesystem – the TmpFS containing the backup data.
- prefix – a parent directory for the files to be saved under. This is can be a good place to encode some information about the backup. A slash will be appended to the prefix to create a directory or pseudo-directory structure.
-
write_to_file
(downloadtable, tmpfs, prefix='', suffix='', fields={})[source]¶ Write out the table data to a file.
Parameters: - downloadtable – A download.DownloadTable object for the table to be saved
- tmpfs – the temporary filesystem (from pyfilesystem2) to write the file to.
- prefix – A prefix for a the file name. include a / for directories
- suffix – A suffix to append to the file name
- fields – a dict of the fields and type of data in the field for easier restoring.


This week, we added something more. We add the vibration functionality, a voting and timer system which allows users to see the time and able to vote for the murderer. We also added the third room - Benny's room.
What Every Member Did
- Jack: Implmented vibration on interacting with objects and modified inventory so that items in inventory are no longer children of inventory
- Zhifu: Added colliders to all items in Benny's room.
- Diana: decimate all the models in meeting room, lucas's room and benny's room. Also find and position all the models in Benny's room.
- Sean: Implemented the time synchronization system and a rough framework for the voting system
Update on Code
Diana: I edited *index.html* to add in the furnitures of Benny's room. Now benny's room look furnished.
Zhifu: Edited index.html and define-collider.js to add colliders to all items in Benny's room.
Jack: Implmented vibration and modified inventory
Sean: I used Croquet to synchronizing the game time for every player and synchronizing the voting system as well as display the result.
Update on Ideas
Not a lot of change of ideas. Stay tuned!
Plan for next week
Milestone: expand the environments to the fourth room
Description: Add a fourth room with similar functionality to the other room in MVP with enough details and interactions.
The fourth room to be constructed is Peter’s room. Peter is the only employee of the Happiness Inn.  He believes that suicide is the best way to cure people who have anxiety and depression, so he set up a routine and cause the four deaths that happen in Lucas’s room. The color theme of Peter’s room is dark blue and grey. The major furnitures include a bed, a TV stand, a long sofa, coffee table, desk, chair, and a lamp.  Some hints include the books he read, a diary he has on the desk where he said goodbye to every victim, and melatonin that are on his bedside table.
- Jack: Work on script display
- Zhifu: Add colliders to items in Peter's room.
- Diana: Set up Peter's room with static objects, outline interactive objects; hopefully making progress on the blocking issue.
- Sean: The time and voting system still have lots of bugs. I will fix them in the upcoming week.
Blockers
- Diana: the oculus browser cannot support our projects now. It crashes without finish loading all the models, but the same project does work on Firefox. I tried Meeting + Lucas, Meeting + Benny all work. I have also decimate the models. But the crash is still going.


JS-only implementation of HMAC DRBG.
```js const DRBG = require(‘hmac-drbg’); const hash = require(‘hash.js’);
const d = new DRBG({
hash: hash.sha256,
entropy: ‘0123456789abcdef’,
nonce: ‘0123456789abcdef’,
pers: ‘0123456789abcdef’ /* or null
*/
});
d.generate(32, ‘hex’); ```
This software is licensed under the MIT License.
Copyright Fedor Indutny, 2017.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Hi, I'm Marino. Over the last seven years I have been working as a bridge inspector and structural engineer in building design. I have recently graduated from Fullstack Academy of Code and made a transition into full stack web developement. My expertise is in Javascript and the NERD (Node.js, Express, React/Redux, Databases with PostrgreSQL) stack but am open to learning new technolgies.
An e-commerce web application where users can create, buy and certify their opinions.
A web app that allows users to schedule and find pick up basketball games in NYC.
An Electron based GUI of Github where users can view, commit, push and pull from their repositories.
Apart from being a web developer, I enjoy playing baskteball and guitar.


category: '' date: 2022-03-06 16:38:57 UTC-06:00 description: '' link: '' slug: how-to-practice-basic-skills tags: 'zergs' title: How to practice basic skills type: text
When you play StarCraft, the most important thing is the basic skills


So you've stealthed, punched, and conned your way through the gauntlet of fearsome jungle animals from last week's epsiode. Well done! Your reward: a bounty of sustenance, in the form of a lush coconut tree grove. You'll feast tonight on their tender white meat and savor the sweet juices so prized by urban yoga enthusiasts, if only you can figure out how to use the damn things. And you've got to handle not just the one coconut, but a whole trove of 'em. What to do?
Let's talk about classes. Classes are the blueprints for objects. If your projects calls for an object, and then additional similar objects, classes are your way to greatness. Once you can catch a squid and havest its ink sacs, you'll want to tatoo this on the back of your hand: "An object is an instances of a class." Just like back on the mainland, your rusted-out grocery hauling machine was a one member of that illustrious category known as the '92 Chevy Monte Carlo, an object is a specific manifestation of an abstract class.
All well and good, but how does that help your current predicament. Well, you need a way to keep track of all these coconuts, right?
class Coconut
end
Hey, that wasn't so bad. But these coconuts just sit there, they don't do anything. You've got plans, BIG plans for these glorious fruit things, with their whole burlap-on-the-outside, ice-cream-on-the-inside schtick. They've got so many uses: You can drink em, you can eat em, you can throw em, you can squash bugs with em. But they also have special properties you'll want to record, particularly how much water and meat are left in each one. Let's see how that last thing works. Restarting...
class Coconut
def initialize
@water = 10
@meat = 10
end
# ...
Wow, now you can track their contents whenever you pluck a new one (by assigning Coconut.new
). The @ tells us that 'water' and 'meat' are instance variables. Each coconut objects will be able to interact with these attributes, but changes in one coconut will not affect another. In other words, they're the sole purview to their instance (i.e. object). Every class needs an initialize
method, but from there it's up to your imagination:
coconut_status = :intact
def tap # gotta get that juice out somehow
if conconut_status == :intact
coconut_status = :tapped
puts "You smash a hole in the nut's jungle-forged exterior."
end
end
def drink
raise StandardError.new("Tap it before you lap it!") if coconut_status == :intact
if @water > 0
@water -= 1
puts "You take one life-saving gulp from the fuzzy bowling ball."
else
puts "Drier than P. G. Wodehouse."
end
end
def crack_open # and now for the meat
if coconut_status != :opened
coconut_status = :opened
puts "The shatter of the broken husk echoes throughout the grove, the last cry of a vengeful earth."

else
puts "You gnash fruitlessly at the dessicated hull."
end
# ...
So we've solved the food situation, but what about those annoying bugs hopping around? This would be a bad time to acquire malaria, far as you are from any source of Canada Dry quinine-infused tonic water. Luckily, we can also define methods that let us use our object to interact with other objects.
def smash(bug)
bug.delete
puts "Victory... but at what cost? The omnipresent buzzing grows in judgmental menace."
end
end


W.P.(C) No.18479 of 2018 02. 28.11.2018 Since learned Additional Government Advocate accepts notice on behalf of opposite party nos.1, 2 and 4, three extra copies of the writ application be served on him by tomorrow (29.11.2018), enabling him to take instruction in the matter. Issue notice to the opposite party no.3 by speed post with A.D. indicating therein that the matter is likely to be disposed of at the stage of admission. Requisites shall be filed by Friday (30.11.2018), failing which, the interim order shall stand dismissed without any further reference to the Bench. List this matter on 07.01.2019 at 2.00 P.M. for hearing. ........................... Dr.A.K.Rath, J.
I.A. No.15637 of 2018
03. 28.11.2018 Heard Mr. Pitambar Acharya, learned Senior Advocate for the petitioner.
Mr. Acharya, learned Senior Advocate for the petitioner submits that the report dated 22.11.2018, Annexure-2, it is stated that there was no discharge of effluent to the outside of the premises. The Unit has given work order to M/s. S.J. Environmental Solution, Bhubaneswar for installation of effluent treatment plant of capacity 5 KLD for effluent generated from process and treatment plant of capacity 30 KLD for treatment of contaminated surface runoff. Thereafter, by notice dated uks 22.11.2018, the Regional Officer, State Pollution Control 2 Board, Angul issued a direction for closure of the Unit under Section 21 & 31(A) of the Air (Prevention and Control of Pollution) Act, 1981 read with Section 25 & 33(A) of Water (Prevention and Control of Pollution) Act, 1974. He further submits that at this juncture if the Unit is closed, then it is not possible on the part of the petitioner to install the effluent treatment plant (in short 'ETP').
In view of the submission of learned counsel for the petitioner, it is directed that in the interim, the order dated 22.11.2018, passed by the Regional Officer, State Pollution Control Board, Angul, opposite party no.2 under Annexure-2 shall remain stayed till the next date.
...........................
Dr.A.K.Rath, J.
3 4BK B


--- title: "Outreach" layout: single excerpt: "Engagement: Tableau in the Classroom" read_time: false header: overlay_image: carlos-muza-hpjSkU2UYSU-unsplash.jpg teaser: blackboard.jpg caption: "Credit: Carlos Muza (Unsplash)" ---
One of the fun projects I was able to work on over the past few months was in creating a quick diagnostics dashboard for a live survey at a conference. The ask was simple:
The survey was 20 questions long, with one free-form question, and the other 20 questions as rating from 1-5 (strongly disagree to strongly agree) where the interesting measure was the average. What was the fastest and easiest way to do this?
So, in summary, using Tableau Public (free), Tableau Desktop (monthly license, or free for students/teachers), and Google Sheets, we created a customized survey platform that collected results from the audience via web browser, calculated any measures that were necessary, and displayed the results on a query refresh to help demonstrate the survey in a tangible context.


I am a Physics PhD student at Stanford University, studying experimental particle physics and cosmology. Currently, I work on the ATLAS experiment at the SLAC Group. More generally, I am interested in the intersection of particle physics and cosmology, the interface between theories and experiments, the applications of deep learning and other novel algorithms in physics, and artificial intelligence. I am also very passionate about physics education and career counseling.
Outside of physics, I spend time writing, playing and watching basketball, playing around with non-physical datasets, and (most importantly) sleeping.
테스팅용 한국어 (Korean) 문단입니다. 한글에는 "Noto Sans Korean" 가 적용돼있고, 영어 폰트는 기존의 것과 같습니다.
Before coming to Stanford, I did my undergraduate at University of Rochester. I graduated in May 2017 with a B.S. in Physics & Astronomy with Highest Distinction and a B.A. in Mathematics. As an undergrad, I worked with Prof. Regina Demina on cosmological data analysis research, preparing for the DESI project.


#MondayMotivation Ft. Rajkummar Rao – The Man With No Plan B
Rajkummar has been an inspiration to every aspiring actor in Bollywood.
by Koimoi.com TeamContinuing the legacy of #MondayMotivation, we are here with yet another motivational quote from Rajkummar Rao. Yes, it’s not from any film but it’s his from his personal interview’s excerpts.
Rajkummar has been an inspiration to every aspiring actor in Bollywood. Without any filmy background, the actor who has achieved so much, reveals how there never was a plan B.
Stick to this space for more ‘Filmy Dialogues’. We’ll be back on Thursday with a nostalgia-filled #ThrowbackThursday.


Priceless diamonds, rubies seized in massive German museum heist
by Luke HurstThieves have carried out a heist at a museum in Germany in what could be the largest museum theft in post-Second World War history.
Jewels and other treasures worth up to a billion euros from Dresden's Gruenes Gewoelbe - or Green Vault museum - were seized, according to Germany’s Bild newspaper.
"Priceless" diamonds were stolen, according to police, who said at a press conference two people are wanted in connection with the burglary, but so far no arrests had been made.
Police did not rule out the possibility of other suspects being involved.
Security camera footage showed the perpetrators entering through a window, shattering the glass of a display case and taking the contents, before going back out the window and fleeing.
The thieves stole three "priceless" ensembles of early 18th century jewelry in diamond, ruby and emerald, Dresden police and museum officials said.
Two fires in the city centre may also be associated with the escape, police said.
Investigators have sealed off the museum, which houses one of Europe’s largest collections of jewellery.
The museum confirmed the burglary, which was at the historic part the collection in the Grünes Gewölbe.
The collection was founded in the 18th century by August the Strong, Elector of Saxony and later King of Poland.
The thieves couldn’t get hold of its best known treasure - the 41-carat Dresden "Green Diamond" - which is currently on loan to New York's Metropolitan Museum of Art.
Museum director Marion Ackermann said it would be impossible to sell the stolen works on the open market.
"We are talking here of objects of immeasurable cultural value," said her colleague Dirk Syndram. "It's almost world cultural heritage. There is nowhere else a collection of jewelry in this form, quality and quantity."
Michael Kretschmer, the head of Saxony, said: "The works in the Green Vault and the Palace were built up by the people of Saxony with many centuries of hard work," adding the theft was a blow for the whole state.


Quack Channel
# bread
# pond
# quack
Wow! Welcome to my first spankin app!
Thanks for droppin by!
Today's Date:
Do thing
game


Before Change
def conv1d(in_channels, out_channels, kernel_size, dropout=0, std_mul=4.0, **kwargs):
from .conv import Conv1d
m = Conv1d(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((std_mul * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return nn.utils.weight_norm(m)
def conv_transpose1d(in_channels, out_channels, kernel_size, dropout=0,
After Change
def conv1d(in_channels, out_channels, kernel_size, dropout=0, std_mul=4.0, **kwargs):
from .conv import Conv1d
m = Conv1d(in_channels, out_channels, kernel_size, **kwargs)
m = m_modification(m,in_channels,dropout,std_mul)
return nn.utils.weight_norm(m)
def conv_transpose1d(in_channels, out_channels, kernel_size, dropout=0,


Before MB Bookshelf can recognize your books, a collection needs to be added to either the Default Media Library or a users media library with the collection type set to Books. MB Bookshelf will do the rest
In order to view books on MBC you will need to install the client plugin for MB Bookshelf. It is available for download from the MB Classic configurator. Once installed there is no configuration necessary, just click your book and the plugin will pass the request to the operating system. You will need to have an appropriate viewer installed. Users are free to use their favorite programs but I recommend Calibre for epub/mobi & ComicRack for cbz/cbr.
There is no required setup for this client. As above, the book will be passed to the operating system to open. You will need appropriate viewers on the device. As Android doesn’t natively see Windows shares, the books are downloaded to the devices download folder before being opened.
To view books with MBT, you will need to add at least one Custom Player. The player needs to be set to External Player, and the MediaType specified as Book. Select the file extensions this player will open and then provide the path to the executable used to view the books Note: as MBT is still in development, these directions may change


404
The requested page was not found! Try searching the site for another page!
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Batman and Robin? Try Flatman and Ribbon. A movie review by "Average Joe" Barlow (c) Copyright 1998
STARRING: Arnold Schwarzenegger, George Clooney, Uma Thurman, Chris O'Donnell, Alicia Silverstone DIRECTOR: Joel Schumacher WRITER: Akiva Goldsman RATED/YEAR: PG-13/1997 LENGTH: 125 mins
It's always painful to witness the degredation of someone you dearly love... to watch, helpless, as a once strong family member succumbs to illness or the ravages of old age. Gradually the strength and personality traits that you remember so fondly vanish as the person dwindles away, replaced by a shell of their former self. They may still breathe, and perhaps even move around from time to time, but it's obvious that their spark is gone. Watching it happen to my grandfather was difficult enough... and now I get to see the same thing occur to the Batman film series. Oh joy. I was a big fan of the dark vision Tim Burton brought to the first two Batman outings. Gotham actually breathed on the silver screen; it was sinister and full of secrets. Michael Keaton didn't so much play Batman as BECOME him. The cherry on top was the great characterization, far more in keeping with Bob Kane's original comic books than the campy sixties television show. Gone were the WHAPS! and POWS! of Adam West, replaced with great action sequences and an intense score from composer extrordinaire Danny Elfman. I even went so far as to buy one of those goofy t-shirts with the bat logo boldly emblazoned on it. (Yes, that's right... I was one of THOSE guys.) Situations beyond my control prevented me from seeing the third film ("Batman Forever"), helmed by director Joel Shumaucher and starring Val Kilmer as the caped crusader, but when the fourth installment appeared in theaters, I was more than game. Another trip to Gotham, and with the lovely and talented Uma Thurman to boot? How could I possibly resist? Ho boy. How our naievity mocks us. "Batman and Robin" starts off with the obligatory action sequence, in which Batman (George Clooney) and his increasingly annoying sidekick Robin (Chris O'Donnell) attempt to foil Mr. Freeze (Arnold Schwarzenegger) in a diamond robbery. In an interesting interpretation of physics, Freeze launches our heroes high into the atmosphere in some sort of rocket-thingy whose purpose is never explained. Batman urgently tells Robin that they must stop the rocket, because it will soon explode and turn Gotham into a huge crater. Then, in an amazingly bold feat of self-contradiction, B&R set the rocket to self-destruct. How do they escape? Simple. They skateboard back down through the atmosphere to Gotham. Yes, you read that right. Skateboard. Through the air. Back down to Gotham. Read it again so your brain absorbs the absurdity of the situation. Then witness how B&R boldly blow up the rocket mere moments after explaining the importance of not letting it explode. Next we're off to the Amazon jungle to enjoy the cliched ramblings of a Dr. Clayton Forrester look-alike as he attempts to turn a 98-pound weakling into a sumu wrestler via some sort of venom-juice. He is of course successful, and all goes well until Dr. Unattractive (Uma Thurman) uncovers the secret plot. The mad scientist isn't happy, and kills her to demonstrate this.  Lucky guy: not only does he get to smooch one of the most beautiful women currently working in the movie industry, but he gets to exit the film very early to boot. Back at Wayne Manor, Bruce Wayne's aged butler Alfred gets a lovely surprise on the doorstep: Alicia Silverstone, who plays his British niece, has come for an unannounced visit (and apparently left her accent at home). They fondle each other for a bit in one of the most disturbing (yet not actually dirty) displays of affection ever captured on film. Robin becomes infatuated with her, and follows her one night when she steals a motorcycle to go racing. She of course gets in trouble and he saves her, because, after all, she's only a woman. In one of my favorite scenes, Robin and the future Batgirl find themselves dangling off a bridge. Robin has somehow managed to wrap his foot around a metal bar, which assumedly prevents him from falling off the bridge into the rushing water below. Visualize with me, now: we have our wormy hero, hanging off the bridge by one foot. He is holding on to Alicia Silverstone. That's two of them, dangling precariously by one foot. Then, we cut to... the two of them safely back at Wayne Manor. No explanation whatsoever. To quote Lenny from the Simpsons: "We won't bore you with the details of our miraculous escape." It gets worse. In an attempt to assumedly add a comic-book feel to the film, the director has inserted cartoon sound effects into the fight scenes. For example, when one of the evil henchmen falls down a staircase, we hear a sound similar to Wile E. Coyote falling off a cliff. Whether this was supposed to inspire laughter is unknown. It did get a reaction from me, though I doubt very much it was the one the director wanted. In the most vomitous feel-good ending of the summer, Mr. Freeze helps Batman cure Alfred of a crippling disease. It just so happens that Freeze is the world's leading expert on this particular disease. That's pushing believability as it is... but would you believe Freeze just happens to carry the cure for the disease in his armband? My, isn't that convenient. Next, stir in a lousy soundtrack and lots of people flying around on wires that are almost invisible during the fight scenes. Add a dash of some of the most poorly-handled special effects of the summer, sprinkled in with some gratingly bad puns courtesy of screenwriter Akiva Goldsman. Is the bile rising yet? Arnold's acting has all the depth and range I've come to expect from his post-Conan movies, which is to say, none at all. His films, however, are more about attitude than acting, and I can't really hold his performance against him. In a movie like "The Running Man," where the story is strong enough to let me ignore his terrible line-reads, it's not a problem... but in "Batman and Robin," it's simply one more thing to bristle at. It doesn't help that most of his dialogue consists of groanably bad puns, either. And not bad in a fun kind of way; just really *awful*. Alicia Silverstone is fine in her role, but it's so poorly written that it could do her professional reputation more harm than good. Her character alternates between strong and fragile, determined and waffling. Poor Alicia; I know she tried hard, and I don't fault her. Same with Uma and Chris O'Donnell; no problems with them personally, except they should've had the will-power to turn down a script this bad. This movie also attempts to set the Women's Rights movement back thirty years. Every woman in this film is portrayed very badly, from the aforementioned Alicia Silverstone to Bruce Wayne's marriage-hungry girlfriend (Elle MacPherson). And Uma?  True enough. If the movie has one good thing, it's George Clooney. Many folks are in strong disagreement with me, but I thought his performance as Batman was at least as strong as Keaton's. I don't watch TV so I've never seen whatever show he's on ("ER," maybe?), but I liked him here. Unfortunately, no matter how talented Clooney may be as an actor, he can't carry a film when the script has him doing nothing more than standing around, staring, and occasionally barking such helpful lines as "Let's go!" and "That was close!" Despite all that, Clooney was surprisingly likeable... which says quite a bit about his screen presence. One thing the film gets right is the depiction of Gotham itself. Like Burton, Schumacher knows how to create memorable images of a city caught in the struggle of good and evil. Some of the scenes of frozen city streets (courtesy of Mr. Freeze) are particularly well done. The lighting, costumes and sets are all lovely... but a few pretty pictures aren't enough to save this clunker. In conclusion: There are worse movies in existence, but it still hurts to encounter a film this far off the mark. With a better script, this could've been the ultimate Batman extravaganza; instead, one is left with what could only generously be called a mediocre film. Better luck next time, Mr. Schumacher. We'll be waiting... same bat time, same bat channel.
RATING: 1.5 stars (out of a possible five)
This review was originally rewritten: June 22, 1997 Revised: July 2, 1998
Copyright (c) 1998 by Joe Barlow. This review may be freely distributed as long as ABSOLUTELY NO CHANGES are made and this disclaimer remains attached. It may not reproduced for profit without the written consent of the author. If you have comments or questions, please send them to: jbarlow at earthling dot net (substituting the appropriate symbols, to discourage spam).
----- "Average Joe" Barlow MiSTie #73097<EMAIL_ADDRESS>http://www.ipass.net/~jbarlow {Remove YOUR PANTS to e-mail me.}
"More Reba! More Garth! And... Wynona!" -TV's Frank, Mystery Science Theater 3OOO
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