Work Experience
- Research Assistant at Nara Institute of Science and Technology (Nara, Japan)
- Developed a system to allow non-robot experrts to create and debug programs in robot behavior using visual programming and simulators
- Evaluated the system with 24 non-expert users using the System Usability Scale (SUS) and visualized the results using Plotly
- The subjective evaluation shows that more than 70% of non-expert users can utilize the proposed system to finish all the tasks and overall, the system had a good usability level
- Using Python, C++, OpenPose, SUS, NumPy, t-test, Plotly
- April 2020 - March 2023
- Business Development at Learn Corporation (Bangkok, Thailand)
- Designed python programming language curriculum for teachers and high school students
- Evaluated the pilot courses with high school teachers and students for feedback and improvement
- October 2017 - March 2018
- Teaching Assistant at Kasetsart University (Bangkok, Thailand)
- Assisted first year students in faculty of Engineer in the Information technology and programming classes (C# and Java languages)
- Supported the lecturers in checking homeworks and exams
- October 2013 - June 2017


Boolean LocalGeosToCodePage( char * str, word strSize, DosCodePage codePage, word defaultChar);
Convert a GEOS string to DOS text, using the specified code page. Any characters for which there is no DOS equivalent will be replaced by the passed default character.
Include: localize.h
word LocalGeosToCodePageChar( word ch, DosCodePage codePage, word defaultChar);
Convert a GEOS character to DOS text, using the specified code page. Any character for which there is no DOS equivalent will be replaced by the passed default character.
Include: localize.h
Boolean LocalGeosToDos( char * str, word strSize, word defaultChar);
Convert a GEOS string to DOS text. Any characters for which there is no DOS equivalent will be replaced by the passed default character.
Include: localize.h
word LocalGeosToDosChar( word ch, word defaultChar);
Convert a GEOS character to DOS text. Any character for which there is no DOS equivalent will be replaced by the passed default character.
Include: localize.h
DosCodePage LocalGetCodePage(void);
This routine returns the current code page, used by DOS to handle international character sets.
Include: localize.h
void LocalGetCurrencyFormat( LocalCurrencyFormat * buf, char * symbol);
This routine returns the current currency format and symbol.
Include: localize.h
void LocalGetDateTimeFormat( char * str, DateTimeFormat format);
This routine returns the user's preferred time and date formats.
Include: localize.h
word LocalGetLanguage(void); /* Returns a StandardLanguage value */
This routine returns the user machine's language.
MeasurementTypes LocalGetMeasurementType(void);
This routine returns the user preference between US and metric measurement systems.
Include: localize.h
void LocalGetNumericFormat( LocalNumericFormat * buf);
This routine returns the user's preferred format for numbers.
Include: localize.h
void LocalGetQuotes( LocalQuotes * quotes);
This routine returns the user's preferred quote marks.
Include: localize.h
Boolean LocalIsDateChar( word ch);
This routine returns true if the passed character could be part of a date or time.
Include: localize.h
Boolean LocalIsDosChar( word ch);
This routine returns true if the passed character is part of the DOS character set.
Include: localize.h
Boolean LocalIsNumChar( word ch);
This routine returns true if the passed character is a number or part of the number format.
Include: localize.h
Boolean LocalIsSymbol( word ch);
This routine returns true if the passed character is a symbol.
Include: localize.h
Boolean LocalIsTimeChar( word ch);
This routine returns true if the passed character is a number or part of the user's time format.
Include: localize.h


Jobs API
Not all queries return instantaneously, additionally some queries elements are used repeatedly. The query Jobs API provides a mechanism to submit graph traversals that will be evaluated asynchronously and can be retrieved at a later time.
Submitting a job
job = G.query().V().hasLabel("Planet").out().submit()
Getting job status
jinfo = G.getJob(job["id"])
Example job info:
{
"id": "job-326392951",
"graph": "test_graph_qd7rs7",
"state": "COMPLETE",
"count": "12",
"query": [{"v": []}, {"hasLabel": ["Planet"]}, {"as": "a"}, {"out": []}],
"timestamp": "2021-03-30T23:12:01-07:00"
}
Reading job results
for row in G.readJob(job["id"]):
print(row)
Search for jobs
Find jobs that match the prefix of the current request (example should find job from G.query().V().hasLabel(“Planet”).out())
jobs = G.query().V().hasLabel("Planet").out().out().count().searchJobs()
If there are multiple jobs that match the prefix of the search, all of them will be returned. It will be a client side job to decide which of the jobs to use as a starting point. This can either be the job with the longest matching prefix, or the most recent job. Note, that if the underlying database has changed since the job was run, adding additional steps to the traversal may produce inaccurate results.
Once job
has been selected from the returned list you can use these existing results and continue the traversal.
for res in G.resume(job["id"]).out().count():
print(res)


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An application that is meant to help catch Opinion Fraud in hotel reviews. Currently, it analyses a review and creates a true "Sentiment" score based on the context. This is done via a Keras Deep Neural Network model which was trained on Tripadvisor hotel review data. THe model is served via a Flask framework on Heroku. The reviews were stored in a SQL Lite Database and queried via DB Browser. 4&5 Star reviews were given a score of 1, 1&2 a score of 0. Another model was created to determine if a review was "incentivized" based on the context. This Tensorflow Categorical model will be served to the public in the near future.
View ProjectCreated a comprehensive bird sighting applicaiton using a Flask framework with Plotly and Leaflet visualizations. The user is able to search for birding hotspots by state/region or can also search for specific species. Bird data is being retrieved from the Ebird API and queried via Python scripts. Bird Images were scraped from the web and stored in a SQL Lite Database. The top 10 birding locations and top 5 species per state/region visualizations are populated via Javascript.Bird enthusiats are encouraged to use this interactive tool to view recent migration pattern species or to plan their next birding trip!
View ProjectA deep dive into animal shelter data for both Austin TX and Louisville KY. Pandas was the main explaratory tool to access API's from the City of Austin website and query into Data Frames. Heat maps using Google API's and Matplotlib visualizations were created to show the findings.
View Project


Pre-Primary & Primary School
Aaryans World School was established with a vision of providing learning through experience and activities.
Aaryans World School (AWS) has been established with a vision of providing world class education in India. AWS started its first school in Bhilarewadi, Pune with a
pre-primary, primary and secondary section.
Only unique people are remembered forever and if a youngster wants to be remembered than he/she has to give their best to the world. The challenge is to fight the hardest battle until you achieve your destination. — A P J Abdul Kalam
The function of education is to teach one to think intensively and to think critically. Intelligence plus character - that is the goal of true education. If a man neglects education, he walks lame to the end of his life. —Martin Luther King, Jr.
You have to stay in school. You have to. You have to go to college. You have to get your degree. Because that's the one thing people can't take away from you is your education. And it is worth the investment.— Michelle Obama
A perfect combination of classroom, cultural and educational activities.


{$ selectedLocation.name $} was inactive in the selected month. It was replaced by {$ selectedLocation.deprecated_to.join(' & ') $} on {$ selectedLocation.archived_on $}.
{$ selectedLocation.name $} was inactive in the selected month. It replaced {$ selectedLocation.deprecates.join(' & ') $} on {$ selectedLocation.deprecates_at $}.


Ghost of von Hellmann:
Who is calling me? Why am I stuck here? Why can't I sleep?
"Who are you? What's wrong?"


Sync song - As name suggests, play a song on multiple devices in sync. Web app starts playing song on multiple devices at specified time. Now default song loads. In future, you can upload your own song and if someone else uses your session ID then your song will be loaded on their device and played at the time you have specified.
How to use?
-> Session: A session is created by default, when you visit this web app.
-> Set Time: Set the time (in seconds) after which song should start.
-> Share Session ID: Share session ID with
friends near by. They have to enter the session ID in "Use Existing Session" Tab.
-> And see the magic happen!
Click on "Create Session" Tab => Enter the seconds after which song should start. => click "Set Time".
Click on "Use Existing Session" Tab => Enter the Session ID shared by your friend. => click "Set Time".


-
update_context
boolean update_context
-
SYNC_LOCK
java.lang.Object SYNC_LOCK
-
pixel_format
PixelFormat pixel_format
The requested pixel format
-
drawable
Drawable drawable
The drawable to share context with
-
attribs
ContextAttribs attribs
The ContextAttribs to use when creating the context
-
peer_info
org.lwjgl.opengl.PeerInfo peer_info
Context handle
-
context
org.lwjgl.opengl.ContextGL context
-
reentry_count
int reentry_count
re-entry counter for support for re-entrant
redrawing in paint(). It happens when using dialog boxes.
-
first_run
boolean first_run
Tracks whether initGL() needs to be called


back to index
I will expand on what I mean with this later but I felt it was important to start this page. Here is a teaser:
gist


At the end of each of our Python script there will be an if block. This is where our test code goes. For the purposes of the workshop we will minimize what is inside this if block. Normally you would keep all your tests available (probably written as their own functions, possibly as a separate included module).


Watch: Blac Youngsta Explains Why Yo Gotti Is The G.O.A.T – “I Made $1 Million In Less Than 223 Days”
by Team SOHHMemphis rapper Blac Youngsta has plenty to talk about. The hip-hop artist recently pulled through to Hot 97 to dish on his come-up, close-knit Yo Gotti relationship, grinding in a competitive music biz and more. Watch and comment below!


{% extends 'base.html' %} {% block title %}UWCS | Password change successful{% endblock %} {% block content %} Password change successful Your password was changed successfully. Return to your profile. {% endblock %}
Your password was changed successfully.
Return to your profile.


eog-resources
Businessmodel of EOG Resources
Customer Segments
EOG Resources serves a broad range of clients across various industrial and commercial sectors. The Company’s principal target markets are:
- Chemical Refining and Manufacturing, comprising producers of chemicals and petrochemicals;
- Energy and Power Generation, comprising energy production and transmission companies;
- Plastics and Rubber, comprising plastics and rubber manufacturers;
- Automotive and Automotive Aftermarket, comprising automotive manufacturers and aftermarket repair and maintenance service providers;
- Oil and Gas, including midstream natural resources companies and oil and gas distribution companies; and
- Pharmaceuticals, including various biopharmaceutical and life sciences companies. EOG Resources includes a number of high-profile companies from various industries. This includes Spectra Energy, Occidental Petroleum, Integrys Holding, Exxon Mobil, Agl Resources, Kinder Morgan, Hess Corp, and Qep Resources.
EOG Resources primarily serves clients in its native US. The Company, however, also serves an international customer base, including customers across Europe and Asia Pacific.
Value Propositions
EOG Resources provides value to its customers in the following ways:
- Its broad portfolio of production and exploration assets, with the Company operating a broad portfolio of production and exploration projects across the US, Trinidad and Tobago, Canada, China, and the UK;
- Its corporate responsibility activities, with the Company participating in a range of social, environmental and community projects alongside various no-profits and charitable organisations;
- Its operational expertise and technical experience, with the Company employing high-trained specialist personnel across the entirety of its enterprises, including technical engineering staff, led by a team of experienced industry executives;
- Its broad client base, with the Company providing products and services to clients operating across a range of industries, including petrochemicals and chemicals production, oil and gas, energy, and automotive;
- Its reputation and track record, with the Company established as a reliable producer of oil and gas, serving a broad range of clients, and named as a Fortune 500 company. ### Channels
EOG Resources operates a website at www.eogresources.com, through which it provides information on its products, activities, operating assets, and leadership. The nature of the Company’s products and services is not conducive with the operation of an online sales channel.
Instead, EOG Resources makes its sales through a direct in-house sales force, which is organised by geographic regions, operating out of the Company’s network of 15 offices across the US – in Texas, Colorado, New Mexico, Oklahoma, Pennsylvania, and Wyoming – and in Canada, Trinidad, and the UK. The Company additionally makes sales through various channel partners, including third-party retailers. While the Company operates its own distribution and logistics units, the Company also receives support form third-party distribution and logistics service providers.
EOG Resources manages and operates its own portfolio of exploration and production assets across the US, the UK, Trinidad and Tobago, Canada, and China. The Company principally utilises its own specialist personnel, supported by various contracting firms.
Customer Relationships
EOG Resources does not provide products or services to its clients on a self-service basis. The Company makes sales of its oil and gas products through its direct sales force, which works closely with its customers to establish the details of sales and distribution contracts on a client-by-client basis. Similarly, the Company works closely with its service customers in order to ensure their individual needs are met.
EOG Resources establishes long-standing relationships with its customers, providing ongoing sales and technical support, with most clients having their own dedicated account management teams. The Company provides a range of contact details on its website for its various international offices, with customers able contact the relevant geographic team over the phone.
EOG Resources does not maintain a significant social media presence, with the Company having only a LinkedIn account through which it can interact quickly and easily with existing and prospective clients.
Key Activities
EOG Resources is an international oil and gas company. It is engaged in the exploration for, development, production, and marketing of crude oil and natural gas, operating principally in in major producing basins across the US, Trinidad and Tobago, the UK, China, Canada, and select other international areas.
The Company provides products and services to clients across a broad range of industries including the chemicals and petrochemicals, oil and gas, plastics and rubber, and energy sectors.
EOG Resources has total estimated net proved reserves of approximately 2.12 billion barrels of oil equivalent, of which more than 1.10 billion barrels is crude oil and condensate reserves, approximately 383 million barrels include natural gas liquids reserves, and 637 million barrels includes natural gas reserves.
Key Partners
EOG Resources works in collaboration with a range of companies and organisations across its operating divisions and jurisdictions. These partners include:
- Supplier and Vendor Partners, comprising suppliers of services, technology, equipment, and resources that are utilised across the Company exploration and production projects, and vendors to which certain non-technical functions can be outsourced;
- Distribution and Logistics Partners, comprising various distribution and logistics service providers that assist in delivering, storing and transporting the Company’s crude oil and natural gas products;
- Contracting and Engineering Partners, comprising various contracting, engineering, and specialist construction firms that support the Company’s exploration and production activities
- Exploration and Production Partners, comprising other oil and gas companies that work in collaboration with the Company on joint projects; and
- Corporate Responsibility Partners, comprising a range of non-profits and charitable organisations with which the Company works on social and environmental projects. EOG Resources is a founder of Texan By Nature, a collaborative partnership between various natural resources businesses and conservation experts that aims to sustain Texas’s working lands, water supplies and wildlife. The Company was also, previously, a regular partner of Yates Petroleum, which it went on to acquire in 2016.
Key Resources
EOG Resources key resources are its production and exploration assets and equipment, its various licences and permits, its controlled oil and gas reserves, its logistics and distribution infrastructure, its sales and marketing channels, its partnerships, and its personnel.
EOG Resources owns and or leases a range of properties, most notably including its portfolio of operating assets, which span a net area of around 4.24 million acres of developed and undeveloped land across the US, China, Trinidad and Tobago, the UK, and Canada. The Company owns drilling, active well, production, distribution, and storage facilities across these jurisdictions, as well as a network of offices across North America, the UK, and Trinidad.
Cost Structure
EOG Resources incurs costs in relation to the operation and development of its operating assets, the procurement and maintenance of tools and equipment, the procurement of supplies and consumables, the operation of its logistics and distribution infrastructure, the operation of its sales and marketing channels, the management of its partnerships, and the retention of its personnel.
In 2015 EOG Resources recorded total operating expenses amounting to $15.44 billion. This includes marketing costs totalling $2.39 billion for the year, lease and well costs of $1.18 billion, and transportation costs totalling $849.32 million. The Company’s general and administrative costs – including the payment of salaries and benefits to its workforce of 2,760 employees – totalled $366.59 million.
Revenue Streams
EOG Resources generates revenue through the production and sale of crude oil and natural gas and related natural resources products. The Company divides its revenue by product category:
- Revenue derived from the sale of crude oil and condensates;
- Revenue derived from the sale of natural gas liquids; and
- Revenue derived from the sale of natural gas. EOG Resources additionally generates a portion of its revenue through the processing and marketing third-party crude oil, natural gas liquids, and natural gas, as well as gathering fees associated with gathering third-party natural gas and revenues from sales of EOG Resources-owned sand.
In 2015 EOG Resources generated total net operating revenue of $8.76 billion, down considerably on the 18.04 billion recorded by the Company in 2014. The majority of the Company’s revenue is generated through the sale of crude oil and condensates, which alone accounted in 2015 for $4.93 billion in operating revenue. Revenues from the sales of crude oil and condensate and natural gas liquids in 2015 represented approximately 83% of total wellhead revenues.


Public Facing Page
Pat's Salmon Cookies
Business, Profits, Synergy
New Store Info:
Location Name:
Min Hourly Customers:
Max Hourly Customers:
Avg Cookies Consumed:
Color Schemes:
Tiger
Ocean
Charcoal
Eggplant
Cookies Per Hour:
Employees Per Hour:


Jane Doe
Astronaut
555 Beverly Hills Blvd.
Beverly Hills, CA 90210
555.555.5555
<EMAIL_ADDRESS>Objective
Reclaim Mars from invaders.
Experience
-
European Space Agency - Space
Captain
Fall 2056 - Spring 2093
- Destroyed alien battleship
- Recovered from engine failure
-
McDonald's - Detriot
Assistant to the Regional Manager
July 2012 - January 2014
- Record for the fastest cheeseburger made
- Employee of the year
- Helped lead an amazing team
Skills
SFSDC
KIC
Quantum Mechanics
Technical Writing
Fencing
Juggling
Spanish
-
Expert
- 234 years of experience
Technology
Linux 3.92
Mac OS-XI
Microsoft Word
Volunteer
-
Save the Mutants - Albany, New York, Mexican Empire
Phone Marketer
Spring 2034 - Spring 2035
- Helped change public opinion on mutants by diligently harassing people.
Education
-
MEd Nuclear Fusion, University of Europa
August 2063
- Thesis on Fusion-Powered Nanobots
References
Available upon request


Hara's ex-boyfriend Choi Jong Bum turns SNS private after news of her death
by jennywillHara's ex-boyfriend Choi Jong Bum has turned his SNS private.
Hara had sued Choi Jong Bum for revenge porn. His sentence involved crimes of property damages, physical assault, threatening, and unnecessary force, was not found guilty of sexual assault in the form of illegal footage filming. He filed an appeal back in September for the Seoul District Court's initial ruling that found him guilty of assault.
As soon as news was released this morning that she had passed away, Choi Jong Bum changed his SNS to private. He has previously come under fire for promoting his hair salon on SNS.
If you or someone you know is at risk of self-harm or suicide, seek help as soon as possible by contacting agencies specializing in crisis intervention and suicide prevention in the United States and abroad.


| CS 391 - Special Topic: Machine Learning
Chapter 4 |
What assumption is made by DP methods that limit their applicability?
What is policy evaluation (a.k.a the prediction problem)?
What is the update rule for iterative policy evaluation?
What is a "full backup"?
If updates are done "in place", will the algorithm converge to correct values for V^pi? If you didn't update "in place", what additional data structures would you need?
Does the ordering of states for "in place" have any effect on convergence? If yes, how? If no, why not?
What is the termination condition for iterative policy evaluation?
3-Room Navigation: A robot's policy is to randomly navigate back and forth between three rooms {left, middle, right}. For each movement between rooms, there is a reward of -1 (i.e. a cost). Upon entering room left, the robot powers down (i.e. the episode ends; there are no further costs).
-Write the Bellman equations for V^pi.
-Solve them algebraically.
-Perform 4 iterations of iterative policy evaluation (not) in place.
What is policy improvement?
How would policy improvement apply to the 3-Room Navigation problem above?
What is policy iteration, and how does it relate to policy evaluation and policy improvement?
How would policy iteration apply to the 3-Room Navigation problem above?
Describe value iteration. How does it differ from policy iteration?
How does the 3-Room Navigation problem motivate use of value iteration?
What are asynchronous DP algorithms? What advantages do they have?
What backup constraint is necessary to guarantee convergence?
Describe generalized policy iteration. How is it generalized from both value iteration and policy iteration?
The methods of this chapter can be viewed as a continual relaxation of constraints. What constraints are removed at each stage?
Describe the general interaction between evaluation and improvement processes.
What is the time complexity of DP methods?
What is the curse of dimensionality and how does it apply to DP methods?
What do the authors note about he practical applicability of DP methods?
What is bootstrapping in the context of reinforcement learning?
Programming Assignment
HW4
Due Friday 2/21 at the beginning of class. An improvised, informal presentation of your work may be requested in class.
Choose one of the following problems:
and apply either policy iteration or value iteration to compute an optimal policy. Present the optimal policy in a clear form, and present data on the performance of the optimal policy.


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Ampion Inc.
Mailing Address:
3-6540 Kestrel RdMISSISSAUGA, Ontario
L5T 2C8
Location Address:
3-6540 Kestrel RdMISSISSAUGA, Ontario
L5T 2C8
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Company Profile
Ampion Inc. possesses the required technical knowledge and expertise, facilities and the state of the art diagnostic gadgets/equipment to test and repair complex electronic circuit board assemblies found in aerospace, military, communications, medical, mainframe and industrial industries. Ampion is a contract manufacturer and our technical staff is fully trained to diagnose and repair electronic products such as printed circuit boards (PCB),drives, power supply modules, controllers and displays.
 Our company handles all orders regardless of availability of National Stock Numbers (NSN). Our well trained and proficient technical staff is ideally suited for military, aerospace, naval and industrial equipment manufacturing, design and repair.
Full profile
Full profile
Contact information
Company description
334290 - Other Communications Equipment Manufacturing
334410 - Semiconductor and Other Electronic Component Manufacturing
334511 - Navigational and Guidance Instruments Manufacturing
541330 - Engineering Services
Product / Service / Licensing
Ampion is well positioned to be a formidable global supply chain partner being a defense contractor with a Canadian CAGE code L0CZ4 Our company handles all orders regardless of availability of National Stock Numbers (NSN). Our well trained and proficient technical staff is ideally suited for military, aerospace, naval and industrial equipment manufacturing, design and repair.
3040-Miscellaneous Power Transmission Equipment
3605-Food Products Machinery and Equipment
3670-Specialized Semiconductor, Microcircuit, and Printed Circuit Board Manufacturing Machinery
3690-Specialized Ammunition and Ordnance Machinery and Related Equipment
3693-Industrial Assembly Machines
3695-Miscellaneous Special Industry Machinery
5865-Electronic Countermeasures, Counter- Countermeasures and Quick Reaction Capability Equipment
5961-Semiconductor Devices and Associated Hardware
5962-Microcircuits, Electronic
5963-Electronic Modules
5975-Electrical Hardware and Supplies
5980-Optoelectronic Devices and Associated Hardware
5998-Electrical and Electronic Assemblies; Boards, Cards, and Associated Hardware
5999-Miscellaneous Electrical and Electronic Components
6625-Electrical and Electronic Properties Measuring and Testing Instruments
AC00-Defense Systems
AD00-Defense - Other
J000-Maintenance, Repair, Modification, Rebuilding and Installation of Goods/Equipment
R001-Specifications Development Services
R002-Technology Sharing/Utilization Services
R005-Technical Assistance
R007-Systems Engineering Services
R008-Engineering and Technical Services (incl. Mechanical, Electrical, Chemical, Electronic Engineering)
Technology profile
Ampion is a prime defense contractor operating under the Canadian CAGE code L0CZ4 serving the Defense and Aerospace Industries and has specialization in reverse engineering, design and manufacturing of complex RF military equipment, digital, analog and various obsolescent electronics circuit boards and assemblies.
We invite you to visit us at our Electronic Circuits Repair and Technology Center in Mississauga Ontario or for additional information about Ampion and its services, please visit the Canadian Government website.
Market profile
Geographic markets:
Export experience:
- United States
Sector information
Classification codes
Total plant size:
Distribution of employees:
Ampion is a contract manufacturer and supplier of NSN (National Stock Numbers) electronic parts for the Industrial Automation Products, Medical and Robotics Industries.Please review our NSN (National Stock Numbers) products list. If you need any information regarding repair, rework or prototyping any of the following electronic products, please contact us at<EMAIL_ADDRESS>Unique applications:
PROJECTS PERFORMED BY AMPION by NSN numbers:1005-01-151-6431
1430-01-057-2629
6130-01-060-2938
6130-01-072-0508
6130-01-091-7767
6130-01-072-4412
6130-01-176-3196
6130-01-072-1531
5998-01-040-9727
6110-01-088-6676
6110-01-088-6676
5998-01-501-9981
5998-01-501-9977
5998-01-501-5907
5820-01-501-5296
5985-01-294-9788
5985-01-294-9788
5996-01-097-6225
5820-01-212-2091
6130-01-472-1690
6340-00-173-9074
5895-01-443-9174
5998-01-447-3845
5998-01-410-7407
5998-01-083-8968
5998-01-084-1331
5998-01-084-1331
5998-01-083-8968
5998-01-084-1331
5999-01-087-8648
6130-01-260-9518
6130-01-339-9865
5996-01-372-2152
5840-01-263-7626
5895-01-239-6816
6130-01-232-9599
5998-01-280-1459
5895-01-272-8047
5998-01-532-1911
5961-00-000-0150
5961-00-001-2913
6110-01-091-7827
5998-01-092-7807
5998-01-040-9727
5998-01-120-3085
6130-01-149-0623
6130-01-363-6038
6130-01-112-1451
6130-01-035-1259
6130-01-028-1701
6130-01-098-3610
6130-01-098-3610
6130-01-035-1256
6130-01-033-0809
6130-01-033-9919
6130-01-182-1237
5963-01-387-5260
6130-01-346-5836
5996-01-074-8161
5996-01-074-8161
2920-01-552-1144
2990-01-522-3588
4020-01-335-3120
4020-01-335-3120
5120-01-115-1895
5340-00-024-3878
5340-01-236-5200
5340-01-345-5166
5355-01-071-2587
5820-01-179-2792
5840-01-153-8702
5869-01-064-3767
5895-00-183-7693
5895-00-238-4596
5895-01-185-5412
5895-01-280-3439
5895-01-282-1093
5895-01-352-4588
5915-01-146-8951
5940-01-375-6983
5945-01-363-4347
5955-01-534-2174
5962-01-177-0726
5962-01-178-2492
5962-01-223-1281
5962-01-223-1599
5962-01-243-6081
5962-01-304-5004
5962-01-356-1461
5975-01-556-9356
5975-01-563-6532
5985-00-722-1397
5985-00-826-8156
5985-01-085-8484
5985-01-138-2515
5985-01-142-8188
5985-01-156-9096
5985-01-436-7030
5995-00-097-8288
5995-01-094-1563
5995-01-306-7092
5995-01-308-3596
5995-01-357-3108
5995-01-379-7406
5995-01-386-7156
5995-01-449-9958
5995-01-548-0411
5996-00-908-8269
5996-01-060-0233
5996-01-091-0954
5996-01-097-3066
5996-01-099-4305
5996-01-264-8374
5996-01-287-8311
5996-01-298-6012
5996-01-459-3548
5998-00-450-6829
5998-00-464-1064
5998-00-629-3029
5998-01-021-7305
5998-01-048-2926
5998-01-060-9464
5998-01-066-1351
5998-01-079-4171
5998-01-080-5412
5998-01-082-9890
5998-01-085-5149
5998-01-086-7795
5998-01-096-7198
5998-01-112-8593
5998-01-126-8768
5998-01-146-0826
5998-01-175-4116
5998-01-175-4117
5998-01-176-5360
5998-01-176-5360
5998-01-196-2939
5998-01-197-4603
5998-01-201-6842
5998-01-209-1594
5998-01-232-7830
5998-01-279-5935
5998-01-337-9913
5998-01-354-7330
5998-01-379-7753
5998-01-418-5486
5998-01-418-7023
5998-01-435-2258
5998-01-513-1441
5998-01-528-8333
5998-01-531-6077
5998-01-545-4168
5998-01-565-1103
6020-01-237-4459
6021-01-515-7456
6110-01-125-1660
6110-01-382-7730
6130-01-084-5304
6130-01-325-2181
6130-01-423-3123
6130-01-532-0289
6150-01-130-2586
6150-01-513-3337
6150-01-556-3038
6210-00-938-9525
6220-01-257-2587
6695-01-392-9859
Key / Major clients:
Ampion Inc is seeking expressions of interest from United States of America and European Union based Defense OEMs for the purpose of fulfilling offset and IRB obligations for sales to the Government of Canada. Ampion Inc. in Canada operates under CAGE code L0CZ4.Ampion provides printed circuit boards repair, diagnostic and reverse engineering services for a diverse range of military, industrial and medical electronics equipment. We invite you to visit us at our Electronics Boards Assemblies Repair and Technology facility in Mississauga, ON, CanadaThird-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
334290 - Other Communications Equipment Manufacturing
334410 - Semiconductor and Other Electronic Component Manufacturing
334511 - Navigational and Guidance Instruments Manufacturing
541330 - Engineering Services
Products, services and licensing
Product / Service / Licensing
Ampion is well positioned to be a formidable global supply chain partner being a defense contractor with a Canadian CAGE code L0CZ4 Our company handles all orders regardless of availability of National Stock Numbers (NSN). Our well trained and proficient technical staff is ideally suited for military, aerospace, naval and industrial equipment manufacturing, design and repair.
3040-Miscellaneous Power Transmission Equipment
3605-Food Products Machinery and Equipment
3670-Specialized Semiconductor, Microcircuit, and Printed Circuit Board Manufacturing Machinery
3690-Specialized Ammunition and Ordnance Machinery and Related Equipment
3693-Industrial Assembly Machines
3695-Miscellaneous Special Industry Machinery
5865-Electronic Countermeasures, Counter- Countermeasures and Quick Reaction Capability Equipment
5961-Semiconductor Devices and Associated Hardware
5962-Microcircuits, Electronic
5963-Electronic Modules
5975-Electrical Hardware and Supplies
5980-Optoelectronic Devices and Associated Hardware
5998-Electrical and Electronic Assemblies; Boards, Cards, and Associated Hardware
5999-Miscellaneous Electrical and Electronic Components
6625-Electrical and Electronic Properties Measuring and Testing Instruments
AC00-Defense Systems
AD00-Defense - Other
J000-Maintenance, Repair, Modification, Rebuilding and Installation of Goods/Equipment
R001-Specifications Development Services
R002-Technology Sharing/Utilization Services
R005-Technical Assistance
R007-Systems Engineering Services
R008-Engineering and Technical Services (incl. Mechanical, Electrical, Chemical, Electronic Engineering)
Technology
Technology profile
Ampion is a prime defense contractor operating under the Canadian CAGE code L0CZ4 serving the Defense and Aerospace Industries and has specialization in reverse engineering, design and manufacturing of complex RF military equipment, digital, analog and various obsolescent electronics circuit boards and assemblies.
We invite you to visit us at our Electronic Circuits Repair and Technology Center in Mississauga Ontario or for additional information about Ampion and its services, please visit the Canadian Government website.
Market
Market profile
Geographic markets:
Export experience:
- United States
Sector
Sector information
Classification codes
Total plant size:
Distribution of employees:
Ampion is a contract manufacturer and supplier of NSN (National Stock Numbers) electronic parts for the Industrial Automation Products, Medical and Robotics Industries.Please review our NSN (National Stock Numbers) products list. If you need any information regarding repair, rework or prototyping any of the following electronic products, please contact us at<EMAIL_ADDRESS>Unique applications:
PROJECTS PERFORMED BY AMPION by NSN numbers:1005-01-151-6431
1430-01-057-2629
6130-01-060-2938
6130-01-072-0508
6130-01-091-7767
6130-01-072-4412
6130-01-176-3196
6130-01-072-1531
5998-01-040-9727
6110-01-088-6676
6110-01-088-6676
5998-01-501-9981
5998-01-501-9977
5998-01-501-5907
5820-01-501-5296
5985-01-294-9788
5985-01-294-9788
5996-01-097-6225
5820-01-212-2091
6130-01-472-1690
6340-00-173-9074
5895-01-443-9174
5998-01-447-3845
5998-01-410-7407
5998-01-083-8968
5998-01-084-1331
5998-01-084-1331
5998-01-083-8968
5998-01-084-1331
5999-01-087-8648
6130-01-260-9518
6130-01-339-9865
5996-01-372-2152
5840-01-263-7626
5895-01-239-6816
6130-01-232-9599
5998-01-280-1459
5895-01-272-8047
5998-01-532-1911
5961-00-000-0150
5961-00-001-2913
6110-01-091-7827
5998-01-092-7807
5998-01-040-9727
5998-01-120-3085
6130-01-149-0623
6130-01-363-6038
6130-01-112-1451
6130-01-035-1259
6130-01-028-1701
6130-01-098-3610
6130-01-098-3610
6130-01-035-1256
6130-01-033-0809
6130-01-033-9919
6130-01-182-1237
5963-01-387-5260
6130-01-346-5836
5996-01-074-8161
5996-01-074-8161
2920-01-552-1144
2990-01-522-3588
4020-01-335-3120
4020-01-335-3120
5120-01-115-1895
5340-00-024-3878
5340-01-236-5200
5340-01-345-5166
5355-01-071-2587
5820-01-179-2792
5840-01-153-8702
5869-01-064-3767
5895-00-183-7693
5895-00-238-4596
5895-01-185-5412
5895-01-280-3439
5895-01-282-1093
5895-01-352-4588
5915-01-146-8951
5940-01-375-6983
5945-01-363-4347
5955-01-534-2174
5962-01-177-0726
5962-01-178-2492
5962-01-223-1281
5962-01-223-1599
5962-01-243-6081
5962-01-304-5004
5962-01-356-1461
5975-01-556-9356
5975-01-563-6532
5985-00-722-1397
5985-00-826-8156
5985-01-085-8484
5985-01-138-2515
5985-01-142-8188
5985-01-156-9096
5985-01-436-7030
5995-00-097-8288
5995-01-094-1563
5995-01-306-7092
5995-01-308-3596
5995-01-357-3108
5995-01-379-7406
5995-01-386-7156
5995-01-449-9958
5995-01-548-0411
5996-00-908-8269
5996-01-060-0233
5996-01-091-0954
5996-01-097-3066
5996-01-099-4305
5996-01-264-8374
5996-01-287-8311
5996-01-298-6012
5996-01-459-3548
5998-00-450-6829
5998-00-464-1064
5998-00-629-3029
5998-01-021-7305
5998-01-048-2926
5998-01-060-9464
5998-01-066-1351
5998-01-079-4171
5998-01-080-5412
5998-01-082-9890
5998-01-085-5149
5998-01-086-7795
5998-01-096-7198
5998-01-112-8593
5998-01-126-8768
5998-01-146-0826
5998-01-175-4116
5998-01-175-4117
5998-01-176-5360
5998-01-176-5360
5998-01-196-2939
5998-01-197-4603
5998-01-201-6842
5998-01-209-1594
5998-01-232-7830
5998-01-279-5935
5998-01-337-9913
5998-01-354-7330
5998-01-379-7753
5998-01-418-5486
5998-01-418-7023
5998-01-435-2258
5998-01-513-1441
5998-01-528-8333
5998-01-531-6077
5998-01-545-4168
5998-01-565-1103
6020-01-237-4459
6021-01-515-7456
6110-01-125-1660
6110-01-382-7730
6130-01-084-5304
6130-01-325-2181
6130-01-423-3123
6130-01-532-0289
6150-01-130-2586
6150-01-513-3337
6150-01-556-3038
6210-00-938-9525
6220-01-257-2587
6695-01-392-9859


Tiered compilation, introduced in Java SE 7, brings client startup speeds to the server VM. Normally, a server VM uses the interpreter to collect profiling information about methods that is fed into the compiler. In the tiered scheme, in addition to the interpreter, the client compiler is used to generate compiled versions of methods that collect profiling information about themselves. Since the compiled code is substantially faster than the interpreter, the program executes with greater performance during the profiling phase. In many cases, a startup that is even faster than with the client VM can be achieved because the final code produced by the server compiler may be already available during the early stages of application initialization. The tiered scheme can also achieve better peak performance than a regular server VM because the faster profiling phase allows a longer period of profiling, which may yield better optimization.
Tiered compilation is the default mode for the server VM. Both 32 and 64 bit modes are supported, as well as compressed oops (see the
next section). Use the -XX:-TieredCompilation
flag with the
java
command to disable tiered compilation.
An "oop", or ordinary object pointer in Java Hotspot parlance, is a managed pointer to an object. An oop is normally the same size as a native machine pointer, which means 64 bits on an LP64 system. On an ILP32 system, maximum heap size is somewhat less than 4 gigabytes, which is insufficient for many applications. On an LP64 system, the heap used by a given program might have to be around 1.5 times larger than when it is run on an ILP32 system. This requirement is due to the expanded size of managed pointers. Memory is inexpensive, but these days bandwidth and cache are in short supply, so significantly increasing the size of the heap and only getting just over the 4 gigabyte limit is undesirable.
Managed pointers in the Java heap point to objects which are aligned on 8-byte address boundaries. Compressed oops represent managed pointers (in many but not all places in the JVM software) as 32-bit object offsets from the 64-bit Java heap base address. Because they're object offsets rather than byte offsets, they can be used to address up to four billion objects (not bytes), or a heap size of up to about 32 gigabytes. To use them, they must be scaled by a factor of 8 and added to the Java heap base address to find the object to which they refer. Object sizes using compressed oops are comparable to those in ILP32 mode.
The term decode is used to express the operation by which a 32-bit compressed oop is converted into a 64-bit native address into the managed heap. The inverse operation is referred to as encoding.
Compressed oops is supported and enabled by default in Java SE 6u23 and
later.
In Java SE 7, use of compressed oops is the default for 64-bit JVM
processes when -Xmx
isn't specified and for values of
-Xmx
less than 32 gigabytes. For JDK 6 before the 6u23
release, use the
-XX:+UseCompressedOops
flag with the java
command to enable the feature.
When using compressed oops in a 64-bit Java Virtual Machine process, the JVM software asks the operating system to reserve memory for the Java heap starting at virtual address zero. If the operating system supports such a request and can reserve memory for the Java heap at virtual address zero, then zero-based compressed oops are used.
Use of zero-based compressed oops means that a 64-bit pointer can be decoded from a 32-bit object offset without adding in the Java heap base address. For heap sizes less than 4 gigabytes, the JVM software can use a byte offset instead of an object offset and thus also avoid scaling the offset by 8. Encoding a 64-bit address into a 32-bit offset is correspondingly efficient.
For Java heap sizes up around 26 gigabytes, any of Solaris, Linux, and Windows operating systems will typically be able to allocate the Java heap at virtual address zero.
Escape analysis is a technique by which the Java Hotspot Server Compiler can analyze the scope of a new object's uses and decide whether to allocate it on the Java heap.
Escape analysis is supported and enabled by default in Java SE 6u23 and later.
The Java Hotspot Server Compiler implements the flow-insensitive escape analysis algorithm described in:
[Choi99] Jong-Deok Choi, Manish Gupta, Mauricio Seffano, Vugranam C. Sreedhar, Sam Midkiff, "Escape Analysis for Java", Procedings of ACM SIGPLAN OOPSLA Conference, November 1, 1999
Based on escape analysis, an object's escape state might be one of the following:
GlobalEscape
– An object escapes the method
and thread. For example, an object stored in a static field, or,
stored in a field of an escaped object, or, returned as the result
of the current method.ArgEscape
– An object passed as an argument
or referenced by an argument but does not globally escape during a
call. This state is determined by analyzing the bytecode of called
method.NoEscape
– A scalar replaceable object,
meaning its allocation could be removed from generated code.After escape analysis, the server compiler eliminates scalar replaceable object allocations and associated locks from generated code. The server compiler also eliminates locks for all non-globally escaping objects. It does not replace a heap allocation with a stack allocation for non-globally escaping objects.
Some scenarios for escape analysis are described next.
The server compiler might eliminate certain object allocations. Consider the example where a method makes a defensive copy of an object and returns the copy to the caller.
public class Person { private String name; private int age; public Person(String personName, int personAge) { name = personName; age = personAge; } public Person(Person p) { this(p.getName(), p.getAge()); } public int getName() { return name; } public int getAge() { return age; } } public class Employee { private Person person; // makes a defensive copy to protect against modifications by caller public Person getPerson() { return new Person(person) }; public void printEmployeeDetail(Employee emp) { Person person = emp.getPerson(); // this caller does not modify the object, so defensive copy was unnecessary System.out.println ("Employee's name: " + person.getName() + "; age: " + person.getAge()); } }
The method makes a copy to prevent modification of the original
object by the caller. If the compiler determines that the
getPerson
method is being invoked in a loop, it will
inline that method. In addition, through escape analysis, if the
compiler determines that the original object is never modified, it
might optimize and eliminate the call to make a copy.
The server compiler might eliminate synchronization blocks
(lock elision) if it determines that an object is thread
local. For example, methods of classes such as
StringBuffer
and Vector
are synchronized
because they can be accessed by different threads. However, in most
scenarios, they are used in a thread local manner. In cases where
the usage is thread local, the compiler might optimize and remove
the synchronization blocks.
The Parallel Scavenger garbage collector has been extended to take advantage of machines with NUMA (Non Uniform Memory Access) architecture. Most modern computers are based on NUMA architecture, in which it takes a different amount of time to access different parts of memory. Typically, every processor in the system has a local memory that provides low access latency and high bandwidth, and remote memory that is considerably slower to access.
In the Java HotSpot Virtual Machine, the NUMA-aware allocator has been implemented to take advantage of such systems and provide automatic memory placement optimizations for Java applications. The allocator controls the eden space of the young generation of the heap, where most of the new objects are created. The allocator divides the space into regions each of which is placed in the memory of a specific node. The allocator relies on a hypothesis that a thread that allocates the object will be the most likely to use the object. To ensure the fastest access to the new object, the allocator places it in the region local to the allocating thread. The regions can be dynamically resized to reflect the allocation rate of the application threads running on different nodes. That makes it possible to increase performance even of single-threaded applications. In addition, "from" and "to" survivor spaces of the young generation, the old generation, and the permanent generation have page interleaving turned on for them. This ensures that all threads have equal access latencies to these spaces on average.
The NUMA-aware allocator is available on the Solaris™ operating system starting in Solaris 9 12/02 and on the Linux operating system starting in Linux kernel 2.6.19 and glibc 2.6.1.
The NUMA-aware allocator can be turned on with the
-XX:+UseNUMA
flag in conjunction with the selection of
the Parallel Scavenger garbage collector. The Parallel Scavenger
garbage collector is the default for a server-class machine. The
Parallel Scavenger garbage collector can also be turned on
explicitly by specifying the -XX:+UseParallelGC
option.
The -XX:+UseNUMA
flag was added in Java SE 6u2.
Note: There was a known bug in the Linux Kernel that may cause the JVM to crash when being run with -XX:UseNUMA
. The bug was fixed in 2012, so this should not affect the latest versions of the Linux Kernel. To see if your Kernel has this bug, you can run the native reproducer.
When evaluated against the SPEC JBB 2005 benchmark on an 8-chip Opteron machine, NUMA-aware systems showed the following performance increases:


- AbstractRESTService<KEY,VALUE> - Class in com.reactivecascade.rest
-
AFile base class for implementations which handle REST activities in an asynchronous manner
- AbstractRESTService(String, IThreadType, IThreadType) - Constructor for class com.reactivecascade.rest.AbstractRESTService
-
Create a new REST service using the specified asynchronous implementation with an appropriate
default
IThreadType
for reading split writing.
- AbstractThreadType - Class in com.reactivecascade.util
-
The baseline implementation of ThreadType convenience classes.
- AbstractThreadType(String, ExecutorService, BlockingQueue<Runnable>) - Constructor for class com.reactivecascade.util.AbstractThreadType
-
Create an asynchronous onFireAction handler that embodies certain rules for threading split concurrency
in a set of lambda-friendly methods
- add(KEY) - Method in class com.reactivecascade.rest.PrioritizedGettable
-
Add an item to the prioritized collection of URLs
- addAllAsync(Collection<T>) - Method in class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- addAllAsync(T...) - Method in class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- addAndGet(int) - Method in class com.reactivecascade.reactive.ReactiveInteger
-
Add two integers in a thread-safe manner
- addAndGet(long) - Method in class com.reactivecascade.reactive.ReactiveLong
-
Add two longs in a thread-safe manner
- addAsync(T) - Method in class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- addOriginToReason(String, ImmutableValue<String>) - Static method in class com.reactivecascade.Async
-
- addState(IN, IActionTwo<IN, IN>) - Method in class com.reactivecascade.functional.StateMachine
-
The action that should be performed on the next transition toKey the specified state
- AltArrayAdapter<T> - Class in com.reactivecascade.reactive.ui
-
Manipulate an ArrayAdapter
safely from any thread
- AltArrayAdapter(Context, int) - Constructor for class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- AltArrayAdapter(Context, int, int) - Constructor for class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- AltArrayAdapter(Context, int, T[]) - Constructor for class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- AltArrayAdapter(Context, int, int, T[]) - Constructor for class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- AltArrayAdapter(Context, int, List<T>) - Constructor for class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- AltArrayAdapter(Context, int, int, List<T>) - Constructor for class com.reactivecascade.reactive.ui.AltArrayAdapter
-
- AltFuture<IN,OUT> - Class in com.reactivecascade.functional
-
A present-time representation of one of many possible alternate future results
- AltFuture(IThreadType, IAction<IN>) - Constructor for class com.reactivecascade.functional.AltFuture
-
Create a
Runnable
which will be executed one time on the
IThreadType
implementation toKey perform an
IBaseAction
- AltFuture(IThreadType, IActionOne<IN>) - Constructor for class com.reactivecascade.functional.AltFuture
-
Constructor
- AltFuture(IThreadType, IActionR<IN, OUT>) - Constructor for class com.reactivecascade.functional.AltFuture
-
Create a
Runnable
which will be executed one time on the
IThreadType
implementation toKey perform an
IBaseAction
- AltFuture(IThreadType, IActionOneR<IN, OUT>) - Constructor for class com.reactivecascade.functional.AltFuture
-
Create a
Runnable
which will be executed one time on the
IThreadType
implementation toKey perform an
IBaseAction
- AltFutureFuture<IN,OUT> - Class in com.reactivecascade.util
-
A
Future
which can be used to safely wait for the results
from an
IAltFuture
.
- AltFutureFuture(IAltFuture<IN, OUT>) - Constructor for class com.reactivecascade.util.AltFutureFuture
-
- AltWeakReference<T> - Class in com.reactivecascade.util
-
A WeakReference
which also returns true
as normal, and also if
equality tests positive for two AltWeakReference objects pointed to.
- AltWeakReference(T) - Constructor for class com.reactivecascade.util.AltWeakReference
-
- AltWeakReference(T, ReferenceQueue<? super T>) - Constructor for class com.reactivecascade.util.AltWeakReference
-
- assertNotDone() - Method in class com.reactivecascade.functional.SettableAltFuture
-
- assertThreadSafe() - Method in class com.reactivecascade.util.AltFutureFuture
-
- assertTrue(String, boolean) - Static method in class com.reactivecascade.Async
-
In DEBUG builds only, check the condition specified.
- assertUIThread() - Static method in class com.reactivecascade.Async
-
A runtime assertion you may use at the beginning of code to mark your contract for code
which must run on the main system thread.
- assertWorkerThread() - Static method in class com.reactivecascade.Async
-
A runtime assertion you may use at the beginning of code to mark your contract for code
which must run in the default background task thread pool.
- Async - Class in com.reactivecascade
-
"Any sufficiently advanced technology is indistinguishable from magic" -Arthur C Clarke
- AsyncBuilder - Class in com.reactivecascade
-
- AsyncBuilder(Context) - Constructor for class com.reactivecascade.AsyncBuilder
-
Create a new AsyncBuilder
that will execute as long as the specified
Context
will execute.
- asyncBuilder - Static variable in class com.reactivecascade.AsyncBuilder
-
- awaitDone(IAltFuture<IN, OUT>, long) - Method in class com.reactivecascade.test.TestUtil
-
Run a unit of work on the specified thread
- awaitDoneNoErrorStackTraces(IAltFuture<IN, OUT>, long) - Method in class com.reactivecascade.test.TestUtil
-
Run a unit of work on the specified thread
- awaitTermination(long, TimeUnit) - Method in class com.reactivecascade.util.UIExecutorService
-


from typing import Tuple, Optional, List, overload, Union, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
import torch
from labml.internal.analytics import cache as _cache
from labml.internal.analytics.altair import density as _density
from labml.internal.analytics.altair import histogram as _histogram
from labml.internal.analytics.altair import scatter as _scatter
from labml.internal.analytics.altair import binned_heatmap as _binned_heatmap
from labml.internal.analytics.indicators import IndicatorCollection as _IndicatorCollection
def _remove_names_prefix(names: List[Union[str, List[str]]]) -> List[str]:
if len(names) == 0:
return []
if isinstance(names[0], list):
common = names[0]
else:
common = None
for n in names:
if common is None:
break
if not isinstance(n, list):
common = None
merge = []
for x, y in zip(common, n):
if x != y:
merge.append(None)
else:
merge.append(x)
common = merge
res = []
for n in names:
if isinstance(n, list):
if common is not None:
n = [p for i, p in enumerate(n) if i > len(common) or p != common[i]]
n = '-'.join(n)
res.append(n)
return res
[docs]class IndicatorCollection(_IndicatorCollection):
r"""
You can get a indicator collection with :func:`runs`.
>>> from labml import analytics
>>> indicators = analytics.runs('1d3f855874d811eabb9359457a24edc8')
You can reference individual indicators as attributes.
>>> train_loss = indicators.train_loss
You can add multiple indicator collections
>>> losses = indicators.train_loss + indicators.validation_loss
"""
pass
[docs]def runs(*uuids: str):
r"""
This is used to analyze runs.
It fetches all the log indicators.
Arguments:
uuids (str): UUIDs of the runs. You can
get this from `labml.ai app <https://github.com/labmlai/labml/tree/master/app>`_
Example:
>>> from labml import analytics
>>> indicators = analytics.runs('1d3f855874d811eabb9359457a24edc8')
"""
indicators = None
for r in uuids:
run = _cache.get_run(r)
indicators = indicators + run.indicators
return indicators
[docs]def get_run(uuid: str):
r"""
Returns ``Run`` object
"""
return _cache.get_run(uuid)
[docs]def set_preferred_db(db: str):
"""
Set the preference to load data from.
Arguments:
db (str): Either ``tensorboard`` or ``sqlite``
"""
if db not in ['tensorboard', 'sqlite']:
raise ValueError('db should be one of tensorboard or sqlite')
_cache.set_preferred_db(db)
@overload
def distribution(indicators: IndicatorCollection, *,
names: Optional[List[str]] = None,
levels: int = 5, alpha: int = 0.6,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
@overload
def distribution(series: List[Union[np.ndarray, 'torch.Tensor']], *,
names: Optional[List[str]] = None,
levels: int = 5, alpha: int = 0.6,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
@overload
def distribution(series: List[Union[np.ndarray, 'torch.Tensor']],
step: np.ndarray, *,
names: Optional[List[str]] = None,
levels: int = 5, alpha: int = 0.6,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
@overload
def distribution(series: Union[np.ndarray, 'torch.Tensor'], *,
names: Optional[List[str]] = None,
levels: int = 5, alpha: int = 0.6,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
[docs]def distribution(*args: any,
names: Optional[List[str]] = None,
levels: int = 5, alpha: int = 0.6,
height: int = 400, width: int = 800, height_minimap: int = 100):
r"""
Creates a distribution plot distribution with Altair
This has multiple overloads
.. function:: distribution(indicators: IndicatorCollection, *, names: Optional[List[str]] = None, levels: int = 5, alpha: int = 0.6, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
.. function:: distribution(series: Union[np.ndarray, torch.Tensor], *, names: Optional[List[str]] = None, levels: int = 5, alpha: int = 0.6, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
.. function:: distribution(series: List[Union[np.ndarray, torch.Tensor]], *, names: Optional[List[str]] = None, levels: int = 5, alpha: int = 0.6, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
.. function:: distribution(series: List[Union[np.ndarray, torch.Tensor]], step: np.ndarray, *, names: Optional[List[str]] = None, levels: int = 5, alpha: int = 0.6, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
Arguments:
indicators(IndicatorCollection): Set of indicators to be plotted
series(List[np.ndarray]): List of series of data
step(np.ndarray): Steps
Keyword Arguments:
names(List[str]): List of names of series
levels: how many levels of the distribution to be plotted
alpha: opacity of the distribution
height: height of the visualization
width: width of the visualization
height_minimap: height of the view finder
Return:
The Altair visualization
Example:
>>> from labml import analytics
>>> indicators = analytics.runs('1d3f855874d811eabb9359457a24edc8')
>>> analytics.distribution(indicators)
"""
series = None
step = None
if len(args) == 1:
if isinstance(args[0], _IndicatorCollection):
series, names_ = _cache.get_indicators_data(args[0])
if not series:
raise ValueError("No series found")
if names is None:
names = names_
elif isinstance(args[0], list):
series = args[0]
else:
series = [args[0]]
elif len(args) == 2:
series = args[0]
step = args[1]
if names is None:
digits = len(str(len(series)))
names = [str(i + 1).zfill(digits) for i in range(len(series))]
if series is None:
raise ValueError("distribution should be called with an indicator collection"
" or a series. Check documentation for details.")
names = _remove_names_prefix(names)
tables = _density.data_to_table(series, names, step)
return _density.render(
tables,
levels=levels,
alpha=alpha,
width=width,
height=height,
height_minimap=height_minimap)
[docs]def histogram(series: Union[np.ndarray, 'torch.Tensor'], *,
low: Optional[float] = None, high: Optional[float] = None,
height: int = 400, width: int = 800, height_minimap: int = 100):
r"""
Creates a histogram with Altair
Arguments:
series(Union[np.ndarray, torch.Tensor]): Data
Keyword Arguments:
low: values less than this are ignored
high: values greater than this are ignored
height: height of the visualization
width: width of the visualization
height_minimap: height of the view finder
Return:
The Altair visualization
"""
table = _histogram.data_to_table(series, low, high)
return _histogram.render(
table,
width=width,
height=height,
height_minimap=height_minimap)
@overload
def scatter(indicators: IndicatorCollection, x_indicators: IndicatorCollection, *,
names: Optional[List[str]] = None, x_name: Optional[str] = None,
noise: Optional[Tuple[float, float]] = None,
circle_size: int = 20,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
@overload
def scatter(series: List[np.ndarray],
x_series: np.ndarray, *,
names: Optional[List[str]] = None, x_name: Optional[str] = None,
noise: Optional[Tuple[float, float]] = None,
circle_size: int = 20,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
[docs]def scatter(*args: any,
names: Optional[List[str]] = None, x_name: Optional[str] = None,
noise: Optional[Tuple[float, float]] = None,
circle_size: int = 20,
height: int = 400, width: int = 800, height_minimap: int = 100):
r"""
Creates a scatter plot with Altair
This has multiple overloads
.. function:: scatter(indicators: IndicatorCollection, x_indicators: IndicatorCollection, *, names: Optional[List[str]] = None, x_name: Optional[str] = None, noise: Optional[Tuple[float, float]] = None, circle_size: int = 20, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
.. function:: scatter(series: List[np.ndarray], x_series: np.ndarray, *, names: Optional[List[str]] = None, x_name: Optional[str] = None, noise: Optional[Tuple[float, float]] = None, circle_size: int = 20, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
Arguments:
indicators(IndicatorCollection): Set of indicators to be plotted
x_indicators(IndicatorCollection): Indicator for x-axis
series(List[np.ndarray]): List of series of data
x_series(np.ndarray): X series of data
Keyword Arguments:
names(List[str]): List of names of series
name(str): Name of X series
noise: Noise to be added to spread out the scatter plot
circle_size: size of circles in the plot
height: height of the visualization
width: width of the visualization
height_minimap: height of the view finder
Return:
The Altair visualization
:Example:
>>> from labml import analytics
>>> indicators = analytics.runs('1d3f855874d811eabb9359457a24edc8')
>>> analytics.scatter(indicators.validation_loss, indicators.train_loss)
"""
series = None
x_series = None
if len(args) == 2:
if isinstance(args[0], _IndicatorCollection) and isinstance(args[1], _IndicatorCollection):
series, names_ = _cache.get_indicators_data(args[0])
x_series, x_name_ = _cache.get_indicators_data(args[1])
if len(x_series) != 1:
raise ValueError("There should be exactly one series for x-axis")
if not series:
raise ValueError("No series found")
x_series = x_series[0]
if x_name is None:
x_name = x_name_[0]
if names is None:
names = names_
elif isinstance(args[0], list):
series = args[0]
x_series = args[1]
if series is None:
raise ValueError("scatter should be called with an indicator collection"
" or a series. Check documentation for details.")
if x_name is None:
x_name = 'x'
if names is None:
names = [f'{i + 1}' for i in range(len(series))]
tables = [_scatter.data_to_table(s, x_series, noise) for s in series]
names = _remove_names_prefix(names)
return _scatter.render(
tables,
names=names,
x_name=x_name,
width=width,
height=height,
height_minimap=height_minimap,
circle_size=circle_size)
@overload
def binned_heatmap(indicators: IndicatorCollection, x_indicators: IndicatorCollection, *,
names: Optional[List[str]] = None, x_name: Optional[str] = None,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
@overload
def binned_heatmap(series: List[np.ndarray],
x_series: np.ndarray, *,
names: Optional[List[str]] = None, x_name: Optional[str] = None,
height: int = 400, width: int = 800, height_minimap: int = 100):
...
[docs]def binned_heatmap(*args: any,
names: Optional[List[str]] = None, x_name: Optional[str] = None,
height: int = 400, width: int = 800, height_minimap: int = 100):
r"""
Creates a scatter plot with Altair
This has multiple overloads
.. function:: binned_heatmap(indicators: IndicatorCollection, x_indicators: IndicatorCollection, *, names: Optional[List[str]] = None, x_name: Optional[str] = None, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
.. function:: binned_heatmap(series: List[np.ndarray], x_series: np.ndarray, *, names: Optional[List[str]] = None, x_name: Optional[str] = None, height: int = 400, width: int = 800, height_minimap: int = 100)
:noindex:
Arguments:
indicators(IndicatorCollection): Set of indicators to be plotted
x_indicators(IndicatorCollection): Indicator for x-axis
series(List[np.ndarray]): List of series of data
x_series(np.ndarray): X series of data
Keyword Arguments:
names(List[str]): List of names of series
name(str): Name of X series
noise: Noise to be added to spread out the scatter plot
circle_size: size of circles in the plot
height: height of the visualization
width: width of the visualization
height_minimap: height of the view finder
Return:
The Altair visualization
Example:
>>> from labml import analytics
>>> indicators = analytics.runs('1d3f855874d811eabb9359457a24edc8')
>>> analytics.scatter(indicators.validation_loss, indicators.train_loss)
"""
series = None
x_series = None
if len(args) == 2:
if isinstance(args[0], _IndicatorCollection) and isinstance(args[1], _IndicatorCollection):
series, names_ = _cache.get_indicators_data(args[0])
x_series, x_name_ = _cache.get_indicators_data(args[1])
if len(x_series) != 1:
raise ValueError("There should be exactly one series for x-axis")
if not series:
raise ValueError("No series found")
x_series = x_series[0]
if x_name is None:
x_name = x_name_[0]
if names is None:
names = names_
elif isinstance(args[0], list):
series = args[0]
x_series = args[1]
if series is None:
raise ValueError("scatter should be called with an indicator collection"
" or a series. Check documentation for details.")
if x_name is None:
x_name = 'x'
if names is None:
names = [f'{i + 1}' for i in range(len(series))]
tables = [_binned_heatmap.data_to_table(s, x_series) for s in series]
names = _remove_names_prefix(names)
return _binned_heatmap.render(
tables,
names=names,
x_name=x_name,
width=width,
height=height,
height_minimap=height_minimap)
[docs]def indicator_data(indicators: IndicatorCollection) -> Tuple[List[np.ndarray], List[List[str]]]:
r"""
Returns a tuple of a list of series and a list of names of series.
Each series, `S` is a timeseries of histograms of shape `[T, 10]`,
where `T` is the number of timesteps.
`S[:, 0]` is the `global_step`.
`S[:, 1:10]` represents the distribution at basis points
`0, 6.68, 15.87, 30.85, 50.00, 69.15, 84.13, 93.32, 100.00`.
Example:
>>> from labml import analytics
>>> indicators = analytics.runs('1d3f855874d811eabb9359457a24edc8')
>>> analytics.indicator_data(indicators)
"""
series, names = _cache.get_indicators_data(indicators)
if not series:
raise ValueError("No series found")
return series, names
[docs]def artifact_data(indicators: IndicatorCollection) -> Tuple[List[any], List[str]]:
r"""
Returns a tuple of a list of series and a list of names of series.
Each series, ``S`` is a timeseries of histograms of shape ``[T, 10]``,
where ``T`` is the number of timesteps.
``S[:, 0]`` is the `global_step`.
``S[:, 1:10]`` represents the distribution at basis points:
``0, 6.68, 15.87, 30.85, 50.00, 69.15, 84.13, 93.32, 100.00``.
Example:
>>> from labml import analytics
>>> indicators = analytics.runs('1d3f855874d811eabb9359457a24edc8')
>>> analytics.artifact_data(indicators)
"""
series, names = _cache.get_artifacts_data(indicators)
if not series:
raise ValueError("No series found")
return series, names


{% extends "layouts/default.html" %} {% block title %}Welcome to Caveman2{% endblock %} {% block content %}
The Ruby on Rails Tutorial is a book and screencast series to teach web development with Ruby on Rails. This is the sample application for the tutorial.
{% endblock %}


Generic VM/Sandbox Detection
Malware can use WMI to do generic detection of VM and sandbox environments. For example, if there is less than 2GB of physical memory or if there is only a single processor core, the OS is likely to be running in a
virtual machine.


org.vertx.scala.core
A distributed lightweight event bus which can encompass multiple vert.
Represents a message on the event bus.
A RegisteredHandler can be unregistered at a later point in time.
Companion object for EventBus.
Companion object for Message.


YashWardhan Blogs
My blog will give frequent updates of the recent bestselling novels
and tell the viewers about their plot, price and other necessary
information.Content related to school education especially Social science and Science is also posted.


| HTTP::Headers::Fast - faster implementation of HTTP::Headers |
HTTP::Headers::Fast - faster implementation of HTTP::Headers
use HTTP::Headers::Fast; # and, same as HTTP::Headers.
HTTP::Headers::Fast is a perl class for parsing/writing HTTP headers.
The interface is same as HTTP::Headers.
HTTP::Headers is a very good. But I needed a faster implementation, fast =)
as_string method sorts the header names.But, sorting is bit slow.
In this method, stringify the instance of HTTP::Headers::Fast without sorting.
If you want HTTP::Headers::Fast to pretend like it's really HTTP::Headers, you can try the following hack:
unshift @HTTP::Headers::Fast::ISA, 'HTTP::Headers';
HTTP::Headers 5.818, HTTP::Headers::Fast 0.01
-- push_header Rate orig fast orig 144928/s -- -20% fast 181818/s 25% --
-- push_header_many Rate orig fast orig 74627/s -- -16% fast 89286/s 20% --
-- get_date Rate orig fast orig 34884/s -- -14% fast 40541/s 16% --
-- set_date Rate orig fast orig 21505/s -- -19% fast 26525/s 23% --
-- scan Rate orig fast orig 57471/s -- -1% fast 57803/s 1% --
-- get_header Rate orig fast orig 120337/s -- -24% fast 157729/s 31% --
-- set_header Rate orig fast orig 79745/s -- -30% fast 113766/s 43% --
-- get_content_length Rate orig fast orig 182482/s -- -77% fast 793651/s 335% --
-- as_string Rate orig fast orig 23753/s -- -41% fast 40161/s 69% --
Tokuhiro Matsuno<EMAIL_ADDRESS>Daisuke Maki
And HTTP::Headers' originally written by Gisle Aas.
Markstos
Tatsuhiko Miyagawa
This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.
| HTTP::Headers::Fast - faster implementation of HTTP::Headers |


Simply showing my personal gmail would attract spam. So instead...
...this is a placeholder for a form that will feed submissions to a backend, which in turn sends to my personal gmail.
Since Github Pages doesn't support backend stuff, offload this to something like getsimpleform.com


Hey grrrrl!
Welcome!. This website aims to be a great source of tech talks by under-represented communities in the tech industry
Feel free to take a look around, or send us a talk you think we're missing.


Details
- class: card
- settings: none
- defaults: none
Examples
Basic cards
Cards are a set of defaults for making content look like a card on the page:
Welcome to the club.
Don't forget to validate parking!

Welcome to the club.

Don't forget to validate parking!

Welcome to the club.
Don't forget to validate parking!

Welcome to the club.

Don't forget to validate parking!

Cards have an undecorated default look, but classes like inset, dark and no-border can rectify that:
Welcome to the club.
Don't forget to validate parking!
Welcome to the club.
Don't forget to validate parking!

Welcome to the club.

Don't forget to validate parking!

Welcome to the club.

Don't forget to validate parking!

Advanced cards
Card layout
In general, cards stack their child elements. But Decent's layout composables can be used to achieve more complicated layouts:
The Mouser
If there's a rat about, the criminal cat gang goes to The Mouser.
Since the card uses an all-margins paradigm, the no-margin class can be used to make elements bleed to the edge, like the headline above.
Fuzzy Mittens
Fuzzy runs catnip all through the neighborhood.
Featuring content
Cards with an img or .overlay component up front will feature it, making it fill the full width of the card. The overlay component can be good for putting information or buttons in useful spots on the feature image.
Dog
His name is ironic or something. Don't cross him. His meow is worse than his bite.
Scratches
Nobody knows if his name refers to the scratches that cover his body, or those he's dished out to others. Everyone knows better than to ask.


UP10TION's Lee Jin Hyuk treats fans to stage performance video of 'Villain'
by danisurstLee Jin Hyuk has just unveiled a stage video for his song "Villain"!
His agency TOP Media shared the video, which features a live performance of the song, through their official YouTube channel, titling it a 'special video.' In the video, the UP10TION rapper is seen joined by back-up dancers, putting on a dynamic performance.
Meanwhile, "Villain" is track off Lee Jin Hyuk's debut solo album 'S.O.L,' which was released back on November 4 KST.
Check out the full video above!


streamloader Module
The streamloader object takes care of loading an html into a stream (string object) and at the same time distributes this stream as a number of sections.
The streamloader object returns a pormise with an html string loaded via ajax.
Use it's load() method to get started.
Example:
loader = new Loader();
loader.load('http://so.me/html/')
The loader.html
will contain the raw html
stream, loader.sections
the distributed node
elements as array of arrays (of elements).
This module provides the following classes:


c/2 cd/1 flush/0 h/0
h/1 import_file/1 l/1 ls/1
m/0 pwd/0 r/0 r/1
s/1 t/1 v/0 v/1
iex(2)> pwd
/Users/alco/Dropbox/home/projects/elixir-talk
:ok
iex(3)> h m
* def m()
Prints the list of all loaded modules with paths
to their corresponding .beam files.
:ok
```
---
# Inspecting (2)
```
iex(1)> h String
# String
A String in Elixir is a UTF-8 encoded binary.
[...]
iex(2)> h List.wrap/1
* def wrap(list)
Wraps the argument in a list.
If the argument is already a list, returns the list.
If the argument is nil, returns an empty list.
## Examples
[...]
:ok
```
---
# Inspecting (3)
## Macros lurking
```
iex(1)> String.split/1
** (UndefinedFunctionError) undefined function:
String.split/0
iex(2)> String.split() / 1
** (UndefinedFunctionError) undefined function:
String.split/0
iex(3)> h h/1
* defmacro h(other)
Prints the documentation for the given module
or for the given function/arity pair.
```
---
class: middle
# The future
---
# The future
The future looks bright for Elixir, but I'm more interested in the present.
Join the community and start hacking!
Everyone can contribute!
---
class: center, middle
# Thanks!
Alexei Sholik
[@true_droid](https://twitter.com/true_droid)
[github.com/alco](https://github.com/alco)
![img](data/logo.png)
[elixir-lang.org](http://elixir-lang.org) | [tryelixir.org](http://tryelixir.org/)


Jack Russell Terrier
| Jack Russell Terrier Picture | |
| Breed Information | |
|---|---|
| Popularity | 2017: #782016: #902015: #104 |
| Name | Jack Russell Terrier |
| Other names | Russell Terrier, JRT, Jack |
| Origin | United Kingdom |
| Breed Group | Terrier (AKC:2012)(UKC) |
| Size | Small |
| Type | Purebred |
| Life span | 13-16 years |
| Temperament | AlertCheerfulCourageousEnergeticIndependent |
| Height | 8-15 inches (20-38 cm) |
| Weight | 11-18 pounds (5-8 kg) |
| Colors | Black & TanBlackWhite |
| Litter Size | 4-8 puppies |
| Puppy Price | Average $700 - $1000 USD |
| Breed Characteristics | |
| Adaptability | 5 stars |
| Apartment Friendly | 1 starsThe Jack Russell Terrier will do okay in an apartment if it is sufficiently exercised. It is very active indoors and will do best with at least an average-sized yard. |
| Barking Tendencies | 5 starsFrequent |
| Cat Friendly | 2 stars |
| Child Friendly | 4 starsGood with Kids: This is a suitable breed for kids and is known to be playful, energetic, and affectionate around them. |
| Dog Friendly | 2 stars |
| Exercise Needs | 5 starsThe Jack Russell Terrier is a pleasant companion when it is sufficiently exercised; however if it does not get enough, it may become a nuisance. It needs to be taken on a long, daily, brisk walk. In addition, he will be in his glory with space to run, hunt and play. |
| Grooming | 2 starsLow Maintenance: All three Jack Russell Terrier coat types are relatively low maintenance. Jack Russell Terriers should be brushed with a rubber brush when shedding to remove dead hairs. |
| Health Issues | 3 starsHypoallergenic: No |
| Intelligence | 2 starsRanking: (#). (See All Rankings) |
| Playfulness | 4 stars |
| Shedding Level | 5 starsConstant Shedding: Expect this dog to shed frequently. Be prepared to vacuum often. Brushing will reduce shedding as well as make the coat softer and cleaner. |
| Stranger Friendly | 3 stars |
| Trainability | 5 starsEasy Training: The Jack Russell Terrier learns quickly but will always try to have its own way. Consistency in training is key. It will try to win you over with its mischievous charm, but do not be fooled! Jack Russell Terriers are able to learn a variety of games and excel in events such as dog agility and Earthdog competitions. They require firm and consistent handling. The Jack Russell Terrier is an intelligent and bold dog. |
| Watchdog Ability | 4 stars |
| Jack Russell Terrier Puppy Picture | ||
| Puppy Names | ||
|---|---|---|
| Rank | Male | Female |
| 01 | Charlie | Lucy |
| 02 | Milo | Lola |
| 03 | Jack | Coco |
| 04 | Tucker | Emma |
| 05 | Bear | Sadie |
| 06 | Murphy | Luna |
| 07 | Toby | Ella |
| 08 | Cody | Sophie |
| 09 | Jax | Ellie |
| 10 | Gizmo | Angel |
| See All Names › |
| Overview |
|---|
|
The Russell Terrier is a strong, hardy, earth-working Terrier. He is full of life and moves with confidence that matches his keen expression. The breed's handy size, small flexible chest, nose, strong voice and fearless nature make it an excellent specimen to work vermin below ground. Its weatherproof coat may be smooth, broken or rough and is predominantly white with tan and/or black markings. The Jack Russell Terrier is a bold, friendly, active and alert hunting terrier, built for work underground. This breed is notoriously fearless and requires little encouragement to go to ground. Aggression towards anything other than legitimate quarry detracts from the dog’s ability as a working terrier, and should be discouraged as much as possible. This is a high-energy breed and is happiest in an environment where there is lots of regular activity. |
| History |
| The Reverend John Russell was a 19th century parson with a passion for fox hunting, for which he developed a well-known strain of fox hunting terriers. From this strain was developed the Parson Russell Terrier and the Jack Russell Terrier. The Jack Russell Terrier was a smaller, longer-bodied, shorter-legged dog that was used almost exclusively to hunt vermin and bolt rabbits. For years, Jack Russell Terrier breeders referred to these dogs as “puddin’ dogs” or “puds,” and some-times just “shorties.” In England, Ireland, and Australia, the longer-legged square dog is known as the Parson Russell Terrier while the lower, longer dog is called the Jack Russell Terrier. |


Pasta With Red Sauce
Rana Mahdy
Founder of Skimmed Zebda
Ingredients List:
- 8 ounces uncooked pasta
- 2 tablespoons butter
- 2-3 cloves garlic minced
- 2 tablespoons tomato paste
- 1 (14 fluid ounce) can tomato sauce
- 3/4 cup heavy/whipping cream
- 1/4 teaspoon Italian seasoning
- Salt & pepper to taste
- Freshly grated parmesan cheese for serving, to taste
Directions:
- Boil a large, salted pot of water for your pasta and cook it al dente according to package instructions.
- Meanwhile (once the pasta starts to cook), add the butter to a skillet over medium-high heat.
- Once it melts, add the garlic and sauté for about a minute, stirring constantly.
- Add in the tomato paste, tomato sauce, cream, and Italian seasoning. Stir until it's nice and smooth. Let the sauce cook for about 5 minutes or until it's thickened to your liking (it should be very gently bubbling, so you may need to turn down the heat).
- Season the sauce with salt & pepper as needed (I'm pretty generous with both). If the sauce is a little too acidic/tangy for your liking, add in a pinch of sugar.
-
Drain the pasta and toss with the sauce (add a splash of hot pasta water if the sauce gets too thick).
Serve immediately with plenty of parmesan cheese over top.


An interesting question was posted on Stack Overflow dated July 23, 2011. The question was - what is the most efficient way of finding all the factors of a number in Python?
Do note that the author was seeking all possible factors and not just the prime factors. This is a fun problem to solve and reminds me of a time when I used to goof around all day with Project Euler problems.
The topmost answer from @agf is how most of us would try and approach this problem -
from functools import reduce
def factors(n):
return set(reduce(list.__add__,
([i, n//i] for i in range(1, int(n**0.5) + 1) if n % i == 0)))
The above code is simple, elegant, and suprisingly fast for numbers below 1016. As concisely described by @agf - the code tries all the numbers between 1 and sqrt(n)+1. If a number is found to divisible by n, the number (i) along with n/i are added to the list.
Question is can we do better with minimal effort?
GNU coreutils has a utility called factor. As mentioned on Wikipedia -
In some variants of Unix, it is classified as a "game" more than a serious utility, and therefore documented in section 6.Can we use this game to our advantage? Yes, we can! Although this utility finds prime factors instead of all factors, we can try and find all possible combinations of prime factors and get the list of factors. The utility is blazingly fast as seen in the below example -
We can integrate the results into Python using the standard library subprocess -
import subprocess
def find_prime_factors(n):
process = subprocess.Popen("factor {}".format(n).split(), stdout=subprocess.PIPE)
output, error = process.communicate()
return [int(num) for num in output.decode().split()[1:]]
With this, we are very close to the actual solution, all we need to do is find all the numbers which can be generated by multiplying the prime factors. This will provide us all the factors of that number. For example:
num = 18
prime_factors = [2, 3, 3]
Multiplying all possible combinations of the above prime_factors list gives:
(2) = 2, (3) = 3, (2,3) = 6, (3,3) = 9, (2,3,3) = 18
Adding 1 at the beginning, we get all factors of 18: [1, 2, 3, 6, 9, 18]
The above logic can be implemented in Python as:
import itertools
from functools import reduce
def all_factors(n):
prime_factors = find_prime_factors(n)
factors = set({1})
# using powerset()
# as described in https://docs.python.org/2/library/itertools.html#recipes
combinations = itertools.chain.from_iterable(
itertools.combinations(prime_factors, r)
for r in range(1, len(prime_factors) + 1)
)
for subset in combinations:
factors.add(reduce(lambda x, y: x*y, subset))
return factors
Won't this method be inefficient if the number of prime factors is large?
Yes, as the number of prime factors increases, this method becomes less efficient. As taught in a probability class, the number of combinations of length R for a List of length N is NCR = N!/(R!*(N-R)!). A good way to imagine this is by plotting 10X for a range X. For 1013, we get prime factors as 213513. Finding all the combinations for N=26 is not a trivial task for the above code. However, it is a breeze for the Stack Overflow code. The below chart will help visualize this -
Based on my observation, the method all_factors(n) can easily fetch factors when no. of prime factors (N) is less than 20. To prove this, we can find factors of randomly selected numbers between the range 1-1030. Here is a simple test case:
import random
import time
def test(fact, no_test_to_perform):
max_time = 0
for i in range(no_test_to_perform):
begin = time.time()
factors = fact(random.randint(1, 10**30))
end = time.time() - begin
max_time = max(max_time, end)
return max_time
The results for the method all_factors were:
As we can see even for 10000 randomly chosen integers (range 1-1030), the maximum time all_factors took was around 7.14 seconds. This is impressive! By comparison, here is how disastrous the Stack Overflow code was on the above test:
While this was just one test, I could have easily wasted hours without observing any improvement in its test performance.
How does the command-line utility - factor work? From its source code:
Algorithm:
(1) Perform trial division using a small primes table, but without hardware division since the primes table store inverses modulo the word base. (The GMP variant of this code doesn't make use of the precomputed inverses, but instead relies on GMP for fast divisibility testing.)
(2) Check the nature of any non-factored part using Miller-Rabin for detecting composites, and Lucas for detecting primes.
(3) Factor any remaining composite part using the Pollard-Brent rho algorithm or if USE_SQUFOF is defined to 1, try that first. Status of found factors are checked again using Miller-Rabin and Lucas.
We prefer using Hensel norm in the divisions, not the more familiar Euclidian norm, since the former leads to much faster code. In the Pollard-Brent rho code and the prime testing code, we use Montgomery's trick of multiplying all n-residues by the word base, allowing cheap Hensel reductions mod n.
I will try and discuss factor's implementation in a future blog post. The bottom line is, for large ranges and no. of prime factors < 20, the all_factors method gives a great performance. A curious reader can try and implement a hybrid model to get the best of both worlds.
Also, I tried using Sympy's factorint (as mentioned by few in the Stack Overflow thread) and found it to be noticeably slower than my all_factors implementation.
Lastly, is it considered a good practice to call a command-line utility in Python?
For production, probably not.
Update (25/04/20): I have performed 5 fold validation on all_factors. For this test, range is 1-1030 and mean time of X iterations (X=[10, 100, 1000, 10000]) is taken into consideration. On an average, all_factors requires 0.025 seconds to compute the factors. Here are the results -


5.0 LiDAR Data Formats
There is support for a number of different LiDAR file formats.
-
LAS - Version 1.2 and 1.3 - all point formats
Note: for now the Java LAS library does not read the GEOTIFF coordinate reference system information so
it is assume that the x/y/z coordinates are geographic - i.e., they are longitude/latitude/meters.
-
GLAS - GLAS record formats 5, 6, 12, 13, 14, 15 and GLANL
Note: the tiling won't work for the "non-flat" formats - everything except GLANL.
-
LVIS - Version 1.2 and 1.3 - all points formats (LCE, LGE and LGW)
-
ATM, McORDS, PARIS text formats
-
General CSV text formats - See below


Using Helm Charts with Configurator
This blog will focus on the following motives:
- Installing Configurator using the helm chart.
- Customizing Configurator helm chart based on requirements.
- Contributing back to the Configurator project.
System Requirements
Make sure that you have installed helm in your machine and you are connected to a Kubernetes cluster. The chart is qualified for helm version > v3 & Kube version v1.20.8. Follow the documentation from the link to install helm: https://helm.sh/docs/helm/helm_version/
helm versionversion.BuildInfo{Version:"v3.0.2", GitCommit:"19e47ee3283ae98139d98460de796c1be1e3975f", GitTreeState:"clean", GoVersion:"go1.13.5"}
Installing Configurator using helm chart
Follow the below steps to directly deploy the Configurator helm package. Make sure that a namespace ‘configurator’ already exists in your cluster. If not, create a namespace with the following command.
kubectl create namespace configurator
Add the configurator helm repository, by executing the following command :
helm repo add gopaddle_configurator https://gopaddle-io.github.io/configurator/helm/
Once the command is executed, verify the repository by running the command below. You must see the configurator_helm repo in the list.
helm repo list
The output must be similar to this:
NAME URL
hashicorp https://helm.releases.hashicorp.com
gopaddle_configurator https://gopaddleio.github.io/configurator/helm/
Once you’ve verified the repo, install the helm chart with the following command: helm install <release_name> <repo_name/chart_name>
helm install release1.0.0 gopaddle_configurator/configurator
This installs the Configurator CRDs and the controller in the ‘configurator’ namespace. After you install the helm chart, verify by listing the resources in the corresponding namespace using the following commands.
kubectl get pods -n configuratorkubectl get crds -n configuratorkubectl get serviceaccounts -n configuratorkubectl get clusterrolebindings -n configurator
The configurator is now ready for use. Here is a reference blog on how to use configurator with the deployments: https://blog.gopaddle.io/2021/04/01/strange-things-you-never-knew-about-kubernetes-configmaps-on-day-one/
Customizing Configurator helm chart based on requirements
Sometimes, you may wish to change the Configurator image name, Docker repository, image tag or even include other service charts along with Configurator. Modifying the Configurator helm is pretty straightforward. Make sure you’ve cloned the Configurator GitHub project before proceeding with the next steps.
To clone the project, run the following command:
git clone https://github.com/gopaddle-io/configurator.git
The helm package needs to be unpacked to modify the helm chart. The zip file will be present at the path configurator/helm in the Configurator project. Choose this option when you want to modify the helm chart configuration. Unzip the file with the following command.
tar -zxvf <path to .tgz file>
This will extract the contents of the chart in a folder. Once you extract, the helm chart’s file system tree will look like this:
configurator├── charts├── Chart.yaml├── crds│ ├── crd-customConfigMap.yaml│ └── crd-customSecret.yaml├── templates│ ├── configurator-clusterrolebinding.yaml│ ├── configurator-clusterrole.yaml│ ├── configurator-deployment.yaml│ ├── configurator-serviceaccount.yaml│ └── tests└── values.yaml
The crds directory contains the custom resource definition files — crd-customConfigMap.yaml & crd-customSecret.yaml. The templates directory contains the resource’s yaml files, in our case, it contains the roles and role bindings and the configurator service definitions. The charts directory is empty by default. This folder can be used to add your application charts that use Configurator Custom Resource. The Chart.yaml file contains information about the helm, like the chart’s name, description, type etc.
# Default values for my_chart.# This is a YAML-formatted file.# Declare variables to be passed into your templates.replicaCount: 1replicas: 1namespace: configuratorimage: gopaddle/configurator:latest
You can edit the values.yaml file to your requirements like changing the namespace, replica_count or the image name, docker repository or the image tag. Make sure that the namespace used in the values.yaml exists in the cluster before you do a helm install. Once the necessary configuration is done, execute the following command to install the charts into your cluster: helm install <release_name> <chart_name>
helm install release1.0.0 configurator
This will install the helm chart inside the cluster with the new configurations.
Contributing back to the configurator project
To contribute the helm changes back to the Configurator project, you need to package the helm chart with the following command :
helm package <path to helm chart>
This command will package the charts to a .tgz file. After packaging the helm, you need to give a pull request for code review & merge.
You can take a look at this open-source project @ https://github.com/gopaddle-io/configurator.git.
For any queries on how to use or how to contribute to the project, you can reach us on our discord server — https://discord.gg/dr24Z4BmP8
Image courtesy — https://www.freepik.com/vectors/technology Technology vector created by stories — www.freepik.com


YachtZoe!
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I love Katkootie my beautiful wife— Mohamed Alimam (@mohamed_al_imam) December 30, 2014
I love Katkootie my beautiful wife


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My name is Danill Yudhistira Setiawan.I live in a small village called Rawa indah with my mother. My mother's name is Syamti. She is 47 years old. Her job is a trader. She has got a few hobbies including cooking, reading recipe books. My cat name is Koneng, Koneng is very cute and lazy. My cat hobby is eating and play, ok lets talk about my hobbies my first hobby is reading books, why i likes books because when i'm reading books it feels like science and creativity entered my brain, my second hobby is coding, i know coding when i'm 12 years old in my junior high school in the it course, my third hobby is riding bycicle next my favorite food is noodle, tempe, tahu and meetbals.Sometimes when i'm alone i'm coding with my laptop or my pc. Thank you very much for your attention, i hope you enjoy my website and give suggestion to me about my website.


Note: Classtab.org is the original site that this one is based upon. That being said, this site is an effort to build upon an already great site.
Most of the base of the rankings comes from the easy tag from the original site. Other rankings are of my own judgement which might or might not be entirely accurate. Find something you disagree with? Open a pull request from the Github repo linked on the source button above. Don't know how? Open an issue and tell me what you want done.
Now as to how the difficulties are determined, it's primarily me strumming through the song and watching videos at this point. At this time I'm still somewhere around Albeniz, so feel free to jump in as my current method may take several months of educational experience.