Interface for storing and validating of the stored tokens. It can be implemnted for a database or for a cache solution like Redis.
| Methods | |
|---|---|
|
public
|
Deletes the token record by a key. |
|
public
|
Load the token record by specified user_id, client_id and access_token or refresh_token. |
|
public
|
Saves the token record. |
|
public
|
Verifies the access token. |
|
public
|
Verifies the refresh token. |
Deletes the token record by a key.
| Name | Pass type | Value type | Default value | Description |
|---|---|---|---|---|
| $key | by value | string |
The name of the key, by which the record should be located and deleted. The possible keys are:
|
|
| $value | by value | string |
The value of the key, by which the record should be located and deleted. |
It might throw an exception in the case of any system errors.
Load the token record by specified user_id, client_id and access_token or refresh_token.
| Name | Pass type | Value type | Default value | Description |
|---|---|---|---|---|
| $token_record | by reference | array |
The token record may vary depending on implementation. The expected parameters are: |
It might throw an exception in the case of any system errors.
Saves the token record.
It is reccomnended to look for an existing record by user_id and client_id, and if it exists, it should be overwritten.
| Name | Pass type | Value type | Default value | Description |
|---|---|---|---|---|
| $token_record | by reference | array |
The token record may vary depending on implementation. The expected parameters are:
|
It might throw an exception in the case of any system errors.
Verifies the access token.
| Name | Pass type | Value type | Default value | Description |
|---|---|---|---|---|
| $access_token | by value | string |
The access token to be verified. |
|
| $user_id | by value | string |
The user id for which the refresh token was issued. |
|
| $client_id | by value | string |
The client id for which the refresh token was issued. |
It might throw an exception in the case of any system errors.
It should throw the InvalidTokenException if the refresh token is invalid.
It might throw the TokenExpiredException if the refresh token is expired.
It should throw the MissingParametersException if any required paramters are empty.
Verifies the refresh token.
| Name | Pass type | Value type | Default value | Description |
|---|---|---|---|---|
| $refresh_token | by value | string |
The refresh token to be verified. |
|
| $user_id | by value | string |
The user id for which the refresh token was issued. |
|
| $client_id | by value | string |
The client id for which the refresh token was issued. |
It might throw an exception in the case of any system errors.
It should throw the InvalidTokenException if the refresh token is invalid.
It might throw the TokenExpiredException if the refresh token is expired.
It should throw the MissingParametersException if any required paramters are empty.


Pet's Hotel
The Pets Hotel is custom designed for your pet’s health, safety and comfort, all in a fun-filled environment - which exceeds the industry Code of Practice.The Pets Hotel is the leader of the pet care pack. The unique concept of providing the best possible services, and luxury appointments, all under one roof, has not only created a fabulous lifestyle for Melbourne’s cats and dogs but also for their owners. The Pets Hotel provides all pet owners with a boarding and pet minding alternative at a reasonable price.
24/7 access
Hotel is one of just a handful of pet hotels nationwide that offers you 24-hour access to your pet.
year-round pampering
Give your pet plenty of play and exercise when you drop them off for daycare.


It changes the whole ways you exprince reading.
It makes it so that reading becomes something that you cant enjoy no matter how much you might like the book.
It becomes a task causing for you to lose sight of the meaning and the vaule of the text that you are accutally reading.
I like to walk on the beach. My name is quentin there is alot of fun things to do during the day. Like play video games. Or eat food. Why do we eat food. Is it becacuse we were made for something greater. Or was it beacuse food is good we will never now.


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I am a Research Scientist at BetterUp. Right now I'm working on a core innovation team, where we do a mix of basic behavioral science and cutting-edge technological innovation.
I graduated from the University of California: San Diego, where I studied Cognitive Science and Mathematics and researched metaphor’s impact on behavior under Ben Bergen and Sean Trott. My passions lie at the intersection of statistics and human behavior, where I can contribute to the development of human-centered, data-driven technologies that positively impact people.
These days I'm living in San Francisco's Mission district. I spend a lot of time going to see live music, art, and local events. I really like to read (I'm currently reading a book from every country around the world). When someone else with a car offers to drive, I like to head up into the forests (backpacking, camping, hiking). For an explanation of my blog’s title, see here.
The Language I Read That Makes Ideas Fit Together
Not interested in my writing? Check out my Blog Roll (so turn of the century)
How and why to move toward a future of explainable generative language models
An introduction to OCR using high quality open source software
Here I build a probabilistic model to estimate when I’ll finish a book
An analogy between Locke’s standard of tyranny and the Trump Administration
Language Models suffer from degenerate and biased behavior, can we fix that?


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GNU MCU Eclipse RISC-V Embedded GCC v7.1.1-1-20170702-* released
Version v7.1.1-1-20170702-0625 is the first GNU MCU Eclipse RISC-V Embedded GCC release. It includes binaries for Windows, macOS and GNU/Linux.
Compliance
This release closely follows the official RISC-V distribution maintained by SiFive.
The current version is based on project riscv/riscv-gnu-toolchain, tag v20170612 (commit f5fae1c) from June 12th, which depends on the following:
- the riscv/riscv-gcc project, commit 16210e6 from from May 15th, 2017
- the riscv/riscv-binutils-gdb project, commit 3f21b5c from May 5th, 2017
- the riscv/riscv-newlib project, commit ccd8a0a from May 2nd, 2017
The supported architectures (-march=
) are:
- rv32i[m][a][f[d]][c]
- rv32g[c]
- rv64i[m][a][f[d]][c]
- rv64g[c]
The supported ABIs (-mabi=
) are:
- ilp32 (32-bit, soft-float)
- ilp32f (32-bit with single-precision in registers and double in memory, niche use only)
- ilp32d (32-bit, hard-float)
- lp64 (64-bit long and pointers, soft-float)
- lp64f (64-bit long and pointers, with single-precision in registers and double in memory, niche use only)
- lp64d (64-bit long and pointers, hard-float).
The supported libraries are:
march=rv32i/mabi=ilp32
march=rv32im/mabi=ilp32
march=rv32iac/mabi=ilp32
march=rv32imac/mabi=ilp32
march=rv32imafc/mabi=ilp32f
march=rv64imac/mabi=lp64
march=rv64imafdc/mabi=lp64d
Please note that not all possible combinations of architecture and ABI have libraries.
Changes
Compared to the original RISC-V version, there are no functional changes; the same architecture and API options are supported, and the same combinations of libraries (derived from newlib) are provided.
newlib-nano
The only notable addition is support for newlib-nano, using the --specs=nano.specs
option. For better results, this option must be added to both compile and link time (the next release of the GNU MCU Eclipse plug-ins will add support for this).
If no syscalls are needed, --specs=nosys.specs
can be used at link time to provide empty implementations for the POSIX system calls.
The nano versions of the libraries are compiled with -Os -mcmodel=medlow
, while the regular versions are compiled with -O2 -mcmodel=medany
.
Documentation
Another addition compared to the SiFive distribution is the presence of the documentation, including the PDF manuals for all tools.
Binaries
Binaries for Windows, macOS and GNU/Linux are provided. For Windows and macOS, separate installable and plain archives are provided. For Windows and GNU/Linux, both 32/64-bit binaries are provided.
Instructions on how to install them are available in the How to install the RISC-V toolchain? page.
For better control and repeatability, the build scripts use Docker containers; all files required during builds are available as a separate gnu-mcu-eclipse/riscv-none-gcc-build project.
Known problems
[2017-09-14 update] Soon after this release was used in real projects, it was discovered that the general configuration of the toolchain was intended more to applications that include a kernel-like environment, and less to bare-metal environments. The main problems are:
- the
newlib
configuration does not use theunderscore
syscall function, but the direct names (likewrite()
instead of_write()
), calls that are always forwarded tolibgloss
- the compiler configuration always links
libgloss
to the application;libgloss
implements all system calls via traps to a unix like kernel.
Both problems were fixed in the v7.1.1-2-20170912 release.
Checksums
The SHA-256 hashes for the files are:
a723d8c6870d1808882ea2eaa630a8c60c5cb6bd25dc238091d2f9738298c44a ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-debian32.tgz
1d36d44221cde15572673019f76727883ce543a7d76d2e10f07ae1a901c07bbb ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-debian64.tgz
75e71fd37c3749cd892e020a4562c0c10535719e4bb573371d957fb8f16f790e ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-osx.pkg
7940417bc4fbd537b38866914b20375f89417ac0ba90399a2d2c510d0c802497 ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-osx.tgz
b082bedc09af08ca56e218dba87e8e69b3d100042bdbbe3821fd2ef72f33e9f0 ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-win32-setup.exe
7fbecec74060be8b4075bed6d2b0f201421614f53dd134ce7ffdd18df79dfce9 ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-win32.zip
ba8fd7b53e2cad2092f39c105c3d4a734546c89f807df8e804dd6db40c23f7fc ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-win64-setup.exe
30a37efe5c5bfaf9def8d06249ff471c8d81d37e01d33caf4fff928c8cfe7670 ?
gnu-mcu-eclipse-riscv-none-gcc-7.1.1-1-20170702-0625-win64.zip


The command-line course teaches students to operate in a Unix enviornment. It is useful especially for language students, as the course also contains information on NLP (Natural Language Processing) and teaches useful ways to process corpus data and write scripts to process large text files (e.g. books). Another important aim of the course is to create and host GitHub Pages, which in the future will work as a kind of portfolio and CV for anyone who wants to pursue a career in coding.
For the purpose of going through the content of the course and what I learned from it, I will introduce the weekly themes and go through them in order
During the first week we learned some basics on the Unix environment commands and file types. For instance, I learned about binary and non-binary files, commands like cp, touch and cat, as well as about the - (slash) sign which modifies the behavior of Linux shell commands.
Learning about file formats for me was mostly a question about learning the differences between Linux and Windows files, as I was already somewhat familar with Windows file formats.
If one would ever need more information on a command, the man command will provide more information about the other command.
man mv
Above, the command will tell more information about the mv command.
A common way to create files is the touch command. The command below creates a file called text.txt into your current working directory.
touch text.txt
As said before, the slash sign modifies commands.
ls -la
The command above, ls, normally lists files in the current working directory. By adding -l, the command displays a long list which provides the following information:
The -a lists the hidden files as well. Normally, I have this aliased so that I don’t have to write ls -la
each time, but more on aliases later.
This week I learned about file and user permissions and the root user, symbolic links, and processes. One of the things that I should probably go back to is the material on remote servers. While I had some familiarity with Taito before, I feel like I forgot some of the deeper understanding on how to work on these remote servers.
The chmod command is used to change permissions of files and directories. It is possible to format these permissions numerically. The chmod numerical is usually a three or four digit number, where the leftmost number concerns the permissions of a user, the number next to it concerns the group and, respectively, the third number concerns the others. The numbers representing permissions vary from 0 to 7, where 7 represents all permissions and 0 none. In fact, these digits are converted to a binary format, which then tells us which permissions are allowed and which are not.
Let us take as an example the following command:
chmod 360 file1
In this example, the number 3 represents the permissions of the user. As we convert 3 to binary format, we get 011. In this binary digit, the 0 represents the read permission which is turned off. The 1 next to the 0 is the write permission, which is turned on. The rightmost digit, 1, is also turned on and represents the execute permissions. Hence, this command tells us that the user is allowed to write and execute the file, but not read.
Respectively, the 6 in binary format is 110 and 0 is simply 000. This means that the group is allowed to read and write but not execute, while the others simply have no permissions.
For this week I studied how to process and analyze text files by using commands such as grep -E. Regular expressions were probably my favorite concept to learn in the Introduction to Language Technology course, and I was glad to see them again.
Still, while I enjoy RegEx immensely, I had troubles with both calculations and further integrating them into a shell environment. In hindsight, I’m not sure why exactly I had difficulties, since the quizes didn’t require too much besides RegEx.
An example of data processing can be seen here, where I calculate the probabiltity of seeing the word ‘honey’ in a sentence in the book Life of Bee. Here we find all sentences which contain the word “honey.” By using \b around the word, we exclude all inflected forms of the word:
cat life_of_bee.sent | grep -E "\bhoney\b" | wc -l
Then, we divide the result with the total number of sentences, which is an amount we get with:
wc -l life_of_bee.sent
To note, the .sent file is the text of the book converted to a sentence per line format.
Next, I learned about scripting. For example, I got to customize my own .bashrc file.
This was the most difficult week for me, as it felt like I sohuld have had at least some practice or familiarity with scripting before.
Nonetheless, here are some of my own customizations on .bashrc:
Here is a sample of a code that lets us customize the background color of shell:
export PS1="\u@\[\e[31;46m\]\h\[\e[m\] "
To change the default BASH prompt from “username@hostname” to my username, and to add username with hostname, I wrote the following code:
PS1="temppa> "
export PS1="\u\h "
In .bashrc, you can define aliases as types of custom shortcuts to represent commands. Here I have some examples of the ones I used in the fourth week.
To clear the terminal:
alias c='clear'
Navigating to the course directory was probably my most used command, so I made a shortcut for myself. By typing ‘letscode’, I was taken to the course directory:
alias letscode='cd /home/temep/cmdline_course'
`
For the second to last week I learned about commands such as make. Additionally, while I already had some familarity with Python from another course, I felt like I got a deeper understanding about what exactly pip is.
One of the Python modules I installed this week was the BLLIP parser. After importing the module in python3, we can use the following command to parse the sentence “NLP is fun!”:
rrp.simple_parse("NLP is fun!")
This gives us the following parse tree:
'(S1 (S (NP (NNP NLP)) (VP (VBZ is) (NP (NN fun))) (. !)))'
Here is a more visually appealing presentation of the syntax tree.
During this last week, I got to prepare for the final assignment of the course (which basically is this page). I have visited github before, and occasionally downloaded files from github, but I never really knew what it was, or how version control works.
Normally, when one would save a file, all the previous changes and versions of the file would be lost. Yet, version control is a system that tracks these changes. In this case, GitHub is a web-based service that applies this concept to code: we are now able to manage code (collectively) and see exactly the changes made to the source code that people make.
Some of the most used commands in managing a project on Github are the git add, git commit and git push.
git add adds files to staging area. With –all we can add all the tracked files to the staging area.
git add --all
“git commit” commits the changes made to a Git repository. The -m stands for “message” and the message written should describe what kind of changes you have commited to the Git repository.
git commit -m "Changes made"
Lastly, the git push moves the code from your local repository to Github, and git pull grabs code from a remote repository to your local repository.
git push
git pull https://github.com/Teemursu/teemursu.github.io
Finally, I have installed and learned to use Jekyll to create this GitHub page. Building a webpage using this method is mostly writing and editing Markdown files (.md).
By using the Markdown syntax and editing .md files, we are able to format text by bolding, italicizing and add images or tables. Below is a cheatsheet table of Markdown.
| Markdown | Appears as |
|---|---|
**bold** |
bold |
_italics_ |
italics |
code |
code |
![image](linktoimage) |
|
[link](https://www.google.com) |
link |


Fx
Im sorry your browser is too outdated to know what this canvas element is
you should update to the newest version so you can use this program (and keep your computer secure)


Lyrics of Hanuman Kavach in English and Hindi/Devanagari script, and the meaning of Hanuman Kavach in English. Hanuman Kavach is read in devotion to and to please Lord Hanuman.
Shree Ganeshay Namah
Om Asya Shri Panchamukha Hanuman,
Mantrasya Brahma Rishihi
Gaayatree Chandaha
Panchamukha Viraata Hanumaana Devataa Hreem Beejam
Shreem Shaktihi Kraum Keelakam Kroom Kavacham
Kraim Astraaya Phat Iti Digbandhah.
Shree Garuda Uvaacha
Atha Dhyaanam Pravakshyaami,
Shrunu Sarvaanga Sundari,
Yat Krutam Devedevana,
Dhyaanam Hanumanah Priyam. || 1 ||
Panchavaktram Maha Abhimam,
Tripancha Nayanairyutam,
Baahubhih Dashabhih Yuktam,
Sarvakaamaartha Siddhidam. || 2 ||
Poorvam Tu Vaanaram Vaktram,
Kotisoorya Smaprabham,
Damshtraa Karaala Vadanam,
Bhrukuti Kutilakshanam. || 3 ||
Asyaiva Dakshinam Vaktram,
Naarasimham Mahaadbhutam,
Atyugra Tejovapusham,
Bhishanam Bhayanaashanam. || 4 ||
Pashchimam Gaarudam Vaktram,
Vakratundam Mahaabalam,
Sarvanaaga Prashamanam,
Vishabhootaadi Kruntanam. || 5 ||
Uttaram Soukaram Vaktram,
Krushnam Deeptam Nabhopamam,
Paataala Simha Vetaala,
Jvara Rogaadi Kruntanam. || 6 ||
Oordhvam Hayaananam Ghoram,
Daanava Antakaram Param,
Yena Vaktrena Viprendra,
Taarakaakhyam Maha Asuram. || 7 ||
Jaghaana Sharanam Tatsyaat,
Sarvashatru Haram Param,
Dhyaatvaa Panchamukham Rudram,
Hanumantam Dayaanidhim. || 8 ||
Khadgam Trishulam Khatvaangam,
Paasham Ankusha Parvatam,
Mushtim Kaumodakeem Vruksham,
Dhaarayantam Kamandalum. || 9 ||
Bhindipaalam Gyaanamudraam,
Dashabhih Muni Pungavam,
Etaani Aayudha Jaalaani,
Dhaarayantam Bhajaamyaham. || 10 ||
Pretaasana Upavishtam Tam,
Sarvaabharana Bhooshitam,
Divya Maalya Ambara Dharam,
Divya Gandha Anulepanam,
Sarva Aashcharya Mayam Devam,
Hanuman Vishvato Mukham. || 11 ||
Panchaasyam Achyutam Aneka Vichitra Varna Vaktram,
Shashaamka Shikharam Kapiraajavaryam,
Peetaambaraadi Mukutai Roopa Shobhitaangam,
Pingaaksham Aadyam Anisham Manasaa Smaraami. || 12 ||
Markatesham Mahotsaaham,
Sarvashatruharam Param,
Shatru Samhara Maam Raksha,
Shreeman Aapadam Uddhara. || 13 ||
Om Harimarkata Markata Mamtram Idam,
Parilikhyati Likhyati Vaamatale,
Yadi Nashyati Nashyati Shatrukulam,
Yadi Mumchati Mumchati Vaamalataa. || 14 ||
Om Harimarkataaya Swahaa,
Om Namo Bhagavate,
Panchavadanaaya Poorva Kapimukhaaya,
Sakalashatru Samhaarakaaya Swahaa.
Om Namo Bhagavate Panchavadanaaya,
Dakshina Mukhaaya Karaala,
Vadanaaya Narasimhaaya Sakalabhoota,
Pramathanaaya Swahaa.
Om Namo Bhagavate Panchavadanaaya,
Pashchima Mukhaaya Garudaananaaya,
Sakala Vishaharaaya Swahaa.
Om Namo Bhagavate Panchavadanaaya,
Uttara Mukhaaya Aadi Varaahaaya,
Sakala Sampat Karaaya Swahaa.
Om Namo Bhagavate Panchavadanaaya,
Urdhva Mukhaaya Hayagreevaaya,
Sakalajana Vashankaraaya Swahaa.
Om Shri Panchavadanaaya Anjaneyaaya Namaha.
श्री गणेशाय नम: |
ओम अस्य श्रीपंचमुख हनुम्त्कवचमंत्रस्य ब्रह्मा रूषि:|
गायत्री छंद्: |
पंचमुख विराट हनुमान देवता | ह्रीं बीजम् |
श्रीं शक्ति:| क्रौ कीलकम् | क्रूं कवचम् |
क्रै अस्त्राय फ़ट् | इति दिग्बंध्:|
श्री गरूड उवाच् ||
अथ ध्यानं प्रवक्ष्यामि |
श्रुणु सर्वांगसुंदर| यत्कृतं देवदेवेन ध्यानं हनुमत्: प्रियम् ||1||
पंचकक्त्रं महाभीमं त्रिपंचनयनैर्युतम् |
बाहुभिर्दशभिर्युक्तं सर्वकामार्थसिध्दिदम् ||2||
पूर्वतु वानरं वक्त्रं कोटिसूर्यसमप्रभम् |
दंष्ट्राकरालवदनं भ्रुकुटीकुटिलेक्षणम् ||3||
अस्यैव दक्षिणं वक्त्रं नारसिंहं महाद्भुतम् |
अत्युग्रतेजोवपुष्पंभीषणम भयनाशनम् ||4||
पश्चिमं गारुडं वक्त्रं वक्रतुण्डं महाबलम् |
सर्वनागप्रशमनं विषभूतादिकृन्तनम् ||5||
उत्तरं सौकरं वक्त्रं कृष्णं दिप्तं नभोपमम् |
पातालसिंहवेतालज्वररोगादिकृन्तनम् ||6||
ऊर्ध्वं हयाननं घोरं दानवान्तकरं परम् |
येन वक्त्रेण विप्रेन्द्र तारकाख्यमं महासुरम् ||7||
जघानशरणं तस्यात्सर्वशत्रुहरं परम् |
ध्यात्वा पंचमुखं रुद्रं हनुमन्तं दयानिधिम् ||8||
खड्गं त्रिशुलं खट्वांगं पाशमंकुशपर्वतम् |
मुष्टिं कौमोदकीं वृक्षं धारयन्तं कमण्डलुं ||9||
भिन्दिपालं ज्ञानमुद्रा दशभिर्मुनिपुंगवम् |
एतान्यायुधजालानि धारयन्तं भजाम्यहम् ||10||
प्रेतासनोपविष्टं तं सर्वाभरण्भुषितम् |
दिव्यमाल्याम्बरधरं दिव्यगन्धानु लेपनम
सर्वाश्चर्यमयं देवं हनुमद्विश्वतोमुखम् ||11||
पंचास्यमच्युतमनेकविचित्रवर्णवक्त्रं
शशांकशिखरं कपिराजवर्यम् |
पीताम्बरादिमुकुटै रूप शोभितांगं
पिंगाक्षमाद्यमनिशं मनसा स्मरामि ||12||
मर्कतेशं महोत्राहं सर्वशत्रुहरं परम् |
शत्रुं संहर मां रक्ष श्री मन्नपदमुध्दर ||13||
ओम हरिमर्कट मर्केत मंत्रमिदं
परिलिख्यति लिख्यति वामतले |
यदि नश्यति नश्यति शत्रुकुलं
यदि मुंच्यति मुंच्यति वामलता ||14||
ओम हरिमर्कटाय स्वाहा ओम नमो भगवते पंचवदनाय
पूर्वकपिमुखाय सकलशत्रुसंहारकाय स्वाहा |
ओम नमो भगवते पंचवदनाय दक्षिणमुखाय करालवदनाय
नरसिंहाय सकलभूतप्रमथनाय स्वाहा |
ओम नमो भगवते पंचवदनाय पश्चिममुखाय
गरूडाननाय सकलविषहराय स्वाहा |
ओम नमो भगवते पंचवदनाय उत्तरमुखाय
आदिवराहाय सकलसंपत्कराय स्वाहा |
ओम नमो भगवते पंचवदनाय उर्ध्वमुखाय
हयग्रीवाय सकलजनवशकराय स्वाहा |
ओम श्रीपंचमुखहनुमंताय आंजनेयाय नमो नम: ||
Shree Ganeshay Namah
Om Asya Shri Panchamukha Hanuman,
Mantrasya Brahma Rishihi
Gaayatree Chandaha
Panchamukha Viraata Hanumaana Devataa Hreem Beejam
Shreem Shaktihi Kraum Keelakam Kroom Kavacham
Kraim Astraaya Phat Iti Digbandhah.
Om prayer to the armour of the great armour of the five faced Hanuman,
sage is Brahma, God addressed is the regal five faced Hanuman. Hreem
is the root, Shrim is the power, Kroum is the nail. Kroom is the
armour and Kraim is the arrow. Thus all directions are tied.
Shree Garuda Uvaacha
Atha Dhyaanam Pravakshyaami,
Shrunu Sarvaanga Sundari,
Yat Krutam Devedevana,
Dhyaanam Hanumanah Priyam. || 1 ||
I am now reciting the meditative chant, Oh pretty lady, Which is
created by God of Gods and is dear to Hanuman.
Panchavaktram Maha Abhimam,
Tripancha Nayanairyutam,
Baahubhih Dashabhih Yuktam,
Sarvakaamaartha Siddhidam. || 2 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Poorvam Tu Vaanaram Vaktram,
Kotisoorya Smaprabham,
Damshtraa Karaala Vadanam,
Bhrukuti Kutilakshanam. || 3 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Asyaiva Dakshinam Vaktram,
Naarasimham Mahaadbhutam,
Atyugra Tejovapusham,
Bhishanam Bhayanaashanam. || 4 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Pashchimam Gaarudam Vaktram,
Vakratundam Mahaabalam,
Sarvanaaga Prashamanam,
Vishabhootaadi Kruntanam. || 5 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Uttaram Soukaram Vaktram,
Krushnam Deeptam Nabhopamam,
Paataala Simha Vetaala,
Jvara Rogaadi Kruntanam. || 6 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Oordhvam Hayaananam Ghoram,
Daanava Antakaram Param,
Yena Vaktrena Viprendra,
Taarakaakhyam Maha Asuram. || 7 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Jaghaana Sharanam Tatsyaat,
Sarvashatru Haram Param,
Dhyaatvaa Panchamukham Rudram,
Hanumantam Dayaanidhim. || 8 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Khadgam Trishulam Khatvaangam,
Paasham Ankusha Parvatam,
Mushtim Kaumodakeem Vruksham,
Dhaarayantam Kamandalum. || 9 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Bhindipaalam Gyaanamudraam,
Dashabhih Muni Pungavam,
Etaani Aayudha Jaalaani,
Dhaarayantam Bhajaamyaham. || 10 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Pretaasana Upavishtam Tam,
Sarvaabharana Bhooshitam,
Divya Maalya Ambara Dharam,
Divya Gandha Anulepanam,
Sarva Aashcharya Mayam Devam,
Hanuman Vishvato Mukham. || 11 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Panchaasyam Achyutam Aneka Vichitra Varna Vaktram,
Shashaamka Shikharam Kapiraajavaryam,
Peetaambaraadi Mukutai Roopa Shobhitaangam,
Pingaaksham Aadyam Anisham Manasaa Smaraami. || 12 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Markatesham Mahotsaaham,
Sarvashatruharam Param,
Shatru Samhara Maam Raksha,
Shreeman Aapadam Uddhara. || 13 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Om Harimarkata Markata Mamtram Idam,
Parilikhyati Likhyati Vaamatale,
Yadi Nashyati Nashyati Shatrukulam,
Yadi Mumchati Mumchati Vaamalataa. || 14 ||
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Om Harimarkataaya Swahaa,
Om Namo Bhagavate,
Panchavadanaaya Poorva Kapimukhaaya,
Sakalashatru Samhaarakaaya Swahaa.
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Om Namo Bhagavate Panchavadanaaya,
Dakshina Mukhaaya Karaala,
Vadanaaya Narasimhaaya Sakalabhoota,
Pramathanaaya Swahaa.
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Om Namo Bhagavate Panchavadanaaya,
Pashchima Mukhaaya Garudaananaaya,
Sakala Vishaharaaya Swahaa.
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Om Namo Bhagavate Panchavadanaaya,
Uttara Mukhaaya Aadi Varaahaaya,
Sakala Sampat Karaaya Swahaa.
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Om Namo Bhagavate Panchavadanaaya,
Urdhva Mukhaaya Hayagreevaaya,
Sakalajana Vashankaraaya Swahaa.
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.
Om Shri Panchavadanaaya Anjaneyaaya Namaha.
O Hanuman, the ocean of knowledge and virtues, may you be victorious.
O the chief amongst Vanaras famous across the three Lokas (Earth,
Heaven and Below-the-Earth), may you be victorious.


Testing Pelias Geocoder
Input Type
Search
Autocomplete
Language
default
Eng
Eng US
Ita
Focus point map center
Only in map Boundaries
layer
stops: gtfs imported with pelias/transit
venue: points of interest, businesses
address: places with a street address
street: streets,roads,highways
country: nations, nation-states
macroregion: a related group of regions
region: states and provinces
macrocounty: group of counties
county: official governmental area
locality: towns, hamlets, cities
localadmin: local admin bounds
neighbourhood: social communities
coarse: all administrative layers
datasource
csv files
openstreetmap
openaddresses
geonames
whosonfirst
transit
Result properties
×
...


Provides adds postfix and infix logic operators for if, then, unless, and otherwise.
| Version: | 0.1.0 |
| Suggests: | testthat, covr |
| Published: | 2018-11-26 |
| Author: | Andrew Redd <https://orcid.org/000-0002-6149-2438> [aut, cre] |
| Maintainer: | Andrew Redd <Andrew.Redd at hsc.utah.edu> |
| BugReports: | https://github.com/RDocTaskForce/postlogic/issues |
| License: | GPL-2 |
| URL: | https://github.com/RDocTaskForce/postlogic |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README |
| CRAN checks: | postlogic results |
| Reference manual: | postlogic.pdf |
| Package source: | postlogic_0.1.0.tar.gz |
| Windows binaries: | r-devel: postlogic_0.1.0.zip, r-release: postlogic_0.1.0.zip, r-oldrel: postlogic_0.1.0.zip |
| OS X binaries: | r-release: postlogic_0.1.0.tgz, r-oldrel: postlogic_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=postlogic to link to this page.


Technologies
We focus on three highly complementary sectors, which today are characterized by growth, innovation and disruption. Our in-depth knowledge and financial expertise is formed by a deep understanding of trends, opportunities, business models, and valuations within the sectors we cover.


Vanessa Sun | Blog
Home
About Me
Resume
Blog
Contact ||
Travel
Hobby Stylist
Artisan
Makeup Lover
Food Lover
Singer
Vanessa's Blog
-Travel-
Self-driving trip to Elora over the weekend
The Butchart Gardens in Vancouver
Abbotsford Bloom Tulip Festival in Vancouver
Trip at UBC
Malaysia's summer
Saint Joseph's Oratory in Montreal
View the beautiful night at Niagara Falls
Zhuhai Opera House in China
Tiffany Falls
Quebec City
Beaches at Tobermory -Bruce Peninsula
-Hobby Stylist-
Favorite nails for autumn
Cool girl
Gentle and Soft
Vintage
Outfit for park
Office Lady Style for Spring
Soft Korean Style
Office Lady Style for Winter
Office Lady Style for Spring
Office Lady Style for Autumn
Casual outfit for summer
Milk tea outfit
Little witch outfit
All in black
Winter fluffy outfit
Gentle autumn outfit
My first grunge chain
Feel Grunge
Japan style
Street style
Art Gallery Day
Street outfit
Vintage blazer and Cherry Blossom 2019
Finally I got orange hair!!
Red hair
Outfit inspired by from k-pop star Hyunna
Hot summer by the lake
-Artisan-
An ordinary morning in Waterloo when I lived in residence
An ordinary night on my way home from the library
Busy China Town in Toronto
Art Gallery of Ontario
Art Gallery of Ontario
Jellyfish
Art Gallery of Ontario
My Second Handmade Bag
My favorate handmade tank top!!
I cut a Burberry polo into 2 pieces!
My first handmade bag!
-Makeup Lover-
Makeup for Amusement Park
Blingbling makeup
Sunshine Look
Korean Hair Style
Dewy summer look
Cool Girl Makeup
Purple metalic eye makeup
Golden summer makeup
Goldfish eye makeup
-Food Lover-
Pancake at Mildred's Temple Kitchen
Big Brunch Skillet at Mildred's Temple Kitchen
Afternoon Tea
Spinach Dip at 271 West Restaurant
Poutine
Ribs at Lancaster's Smokehouse
Home-made Lunch1
Home-made Lunch2
Home-made Lunch3
All You Can Eat
Korean Cuisine
Korean Cheese Hot Pot
-Singer-
With the support from my friends, I was in the top 10 in the Campus Singing Competition
Click here to hear me singing>


Welcome to fun<&>CODE
Hi we are fun<&>CODE,we are here to make a chain of developers who loves to code and who loves to find fun in it ! We are the next generation of code lovers, we welcome you all to be a part of our fun<&>Code and make easy your daily life with out having trouble couze here we go through all the troubles and experience the solutions we may met in future so lets hold our hands together and bring it!
Welcome Home! :)Angular
Some quick example text to build on the card title and make up the bulk of the card's content.
ButtonFireBase
Some quick example text to build on the card title and make up the bulk of the card's content.
ButtonNode jS
Some quick example text to build on the card title and make up the bulk of the card's content.
Button


R package generates the summary of textual data
One of the most relevant applications of machine learning for corporations globally is the use of natural language processing (NLP). Whether it be parsing through business documents to gather key word segments or detecting Twitter sentiment of a certain product, NLP’s use case is prevalent in virtually every business environment.
Unfortunately, there are few tools today which provide summary statistics on textual data that a user may want to analyze. Our goal with this package is to provide users with a simple and flexible tool to gather key insights that would be useful during the exploratory data analysis phase of the data science workflow.
Our library specifically will make extensive use of pre-existing packages in the R eco-system. We will use the textcat
and openNLP
library to build most of the sentiment analysis functions while also leveraging well-known packages such as tidyverse
to aid in the overall presentation of our final output results.
To the best of our knowledge, there is no any other library that combines all the below mentioned functionality in one.
And the development version from GitHub with:
detect_language
: This function will parse through the textual data and come up with the language of the text. This can be useful for an international company’s customer service process in detecting which regions are requesting the most help.
part_of_speech
: This function will generate key statistics on the proportions of textual data points including verbs, prepositions, adjectives, nouns and articles.
polarity
: This function will check the overall sentiment of the data by assessing the number of negative, positive and neutral words in the textual data.
sentence_stopwords_freq
: This function will check the proportion on the number of sentences, stop words and also output high frequency words in the textual data.
This is a basic example which shows you how to generate a summary:
library(nlpsummarizer)
df <- data.frame(text_col = c('I love travelling to Japan and
eating Mexican food but I can only speak
English!'))
get_language(df$text_col)
[1] 'English'
get_part_of_speech(df$text_col)
[2] | verbs | prepositions | adjectives | nouns | articles |
| 0.2 | 0.11 | 0.3 | 0.06 | 0.18 |
get_polarity(df$text_col)
[3] | positive words | negative words | neutral words |
| 3 | 0 | 15 |
summary_4(df$text_col)
[4] | number of sentences | number of stop words | high freq. words |
| 1 | 4 | I(2) |


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Container::value_type * | end () FMT_NOEXCEPT |
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| std::size_t | capacity () const FMT_NOEXCEPT |
| | Returns the capacity of this buffer. More...
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| Container::value_type * | data () FMT_NOEXCEPT |
| | Returns a pointer to the buffer data. More...
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| const Container::value_type * | data () const FMT_NOEXCEPT |
| | Returns a pointer to the buffer data. More...
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| void | resize (std::size_t new_size) |
| | Resizes the buffer. More...
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| void | clear () |
| | Clears this buffer. More...
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| void | reserve (std::size_t new_capacity) |
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template<typename Container>
The documentation for this class was generated from the following file:


--- title: A brief post about k-means author: Cristóbal Alcázar date: '2018-05-17' slug: a-brief-post-about-k-means categories: [ML, Python] tags: [ML, Supervised-Model] comments: no showcomments: yes showpagemeta: yes bibliography: int_alg.bib ---
A few weeks ago I read the chapter 4 of book “Introduction to Applied Linear Algebra”, Boyd and Vandenberghe (2018), called “Clustering” and I found it so clear and simple. Chapter 4 introduce the clustering concept only based on vectors and distance –subjects treated on chapters 1 and 3 respectively–through the canonical example of clustering models: k-means.
In this post I want to make a little review of the chapter and implement k-means on python.
The idea of clustering is to partition a set of vectors into \(K\) groups based on a distance measurement. An intuitive way to think about it is thinking on a 2D-table where each observation (row) is a vector and we want to assign it to a cluster based on some similarity measurement. So the goal is to add a new categorical variable (column) to the table with \(K\) possible group values.
To draw the concept and to use an example of the chapter, imagine that we are in a hospital and we have a table with measurements of a feature vector for each patient. A clustering method could help to separate patients in similar groups and get insights based on these groups. Maybe we could then assign labels and give a different diagnosis procedures and therefore be more effective instead of giving a unique entrance diagnosis.
To formalize what I describe above:
The similarity within a group \(i\) is given by the distance between vectors of a group \(i\) and the group representative vector \(z_i\). In which all members share the fact that the distance between this group representative (\(z_i\)) is the minimal in respect to other group representative’s vectors.
A simple measurement of distance is the euclidean norm defined as:
\[d(x, y) = ||x - y|| = \sqrt{\sum_{i=1}^{n}(x_i - y_i)^2}\]
def euclidean_norm(x, y):
"""
x: a float numpy 1-d array
y: a float numpy 1-d array
---
Return the euclidean norm between x and y
"""
return np.sqrt(np.sum((x - y) ** 2))
The clustering objective function (\(J^{clust}\)) measures the quality of choice in cluster assignments. In the sense that a better cluster assignment of \(x_i\) is when the square euclidean norm is lower.
\[J^{clust} = (||x_1 - z_{c_1}||^2 + \dots + ||x_N - z_{c_N}||^2) / N\] Given the nature of the problem, finding an optimal solution for \(J^{clust}\) is really hard because it depends on both group assignation (\(c\)) and the choice of group representatives (\(z\)). Instead we can find a feasible solution for minimising \(J^{clust}\), a suboptimal one (local optimum), solving a sequence of simpler optimization problems in an iterative approach.
Now the way that the chapter shows how to assign these \(k\) groups is through k-means algorithm. The best description for k-means is encapsulated in three steps:
Initialize with a set of a fixed group of representatives \(z_i\) for \(i = 1\dots k\) (pick random observations of data as representatives, for example)
Now we reduce the problem to just cluster assignations and also we can see this problem as \(N\) subproblems (one for each observation). Just look what group of representatives has the minimal distance with vector \(x_i\) and assign to vector \(c_i:=z^*\). \[||x_i - z_{c_i}|| = \underset{j=1, \dots, k}{\text{min}}\ ||x_i - z_j||\]
The step before gives us a vector with the group assignation (\(c\)) of each observation (\(x_i\)). Remember that \(c\) is a vector of length equal to the size of the number of observations that we have in the data. Now based on \(c\), we come back to the problem of finding a set of group representatives \(z\). This step gives the name of k-means to the algorithm, because now we update the set of group representatives computing the mean of the assigned cluster vector in step 2. \[z_j = \bigg(\frac{1}{|G_j|}\bigg)\underset{i\in G_j}{\sum{x_i}}\]
I put the image of Ouroboros above as a graphical analogy, because k-means is self-contained and also iterative, steps 2 and 3 repeats themselves. In each iteration a new assignation of clusters are given and then a redefinition of group representatives. But, opposite to the symbolic meaning of Ouroboros, the flow of time is not endless. The process of repetition ends when the algorithm converges to a solution.
When does k-means reach a solution? The algorithm convergence occurs when there isn’t a variation in the group assignation with the previous iteration (\(c_t\) is exactly the same that \(c_{t-1}\)).
The implementation of the three previous steps is very straightforward using numpy
in python.
import numpy as np
def k_means(df, K, num_iter):
"""
df: a numpy 2D numpy array
K: number of group representatives
num_iter: number of iteration to repeat step 2 and 3
---
Return cluster assigned (c), group representatives (z) and cost function of
each iteration (J)
"""
nrow = df.shape[0]
# initialize cluster representatives (STEP 1)
z = df[np.random.choice(nrow, K), ]
c = np.empty(nrow, dtype = 'int64')
J = []
iter_counter = 0
while True:
J_i = 0
# now solve the N subproblems of cluster assignation (STEP 2)
for j in range(nrow):
distance = np.array([euclidean_norm(df[j, ], z_i) for z_i in z])
c[j] = np.argmin(distance)
J_i += np.min(distance) ** 2
J.append(J_i / nrow) # cost function evolution by iteration
# update group representatives, take the mean based on c (STEP 3)
check_z = np.array([np.mean(df[c == k, ], axis = 0) for k in range(K)])
if np.all(np.equal(check_z, z)): # check convergence status
# reach convergence
break
if num_iter == iter_counter:
# stop process in iteration number -> iter_counter
break
z = check_z
iter_counter += 1
return c, z, J
A good way to understand k-means is visually and step by step! So I wrote k_means
with the argument num_iter that enables to stopping the process in a certain iteration and getting the results at that point.
We can play with the algorithm and show the evolution of centroid definition and cluster assignation, but before that we need some data. We generate two groups of random data \(D\in R^2\) from two bivariate normal distributions with the following parameters:
\[\mu_0 = (0, 0) \ \mu_1 = (17, 17) \\ \sigma_0 = \begin{bmatrix} 1 & 0 \\ 0 & 50 \\ \end{bmatrix} \ \sigma_1 = \begin{bmatrix} 15 & 0 \\ 0 & 12 \end{bmatrix}\]
A simple data with 2 features helps us to visualize easily. Aditionally we know a priori that are two underlying groups, these are defined by the two different distributions. If we take a look at the next plot, both samples are well defined in different groups. Of course this is a very basic setting but the purpose is to illustrate how k_means
works.
So k_means
is blind to the colours and its goal is uncover the samples generated from the two underlying distributions based on the data. Pictorically the process is as follows:
For the cost side, \(J\) decay rapidly and converge around a cost value of 36.
## [1] 194.42629 71.55989 36.85485 36.60745 36.60636
Boyd, Stephen, and Lieven Vandenberghe. 2018. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press.


MY FATHER WAS A VERY WEALTHY COCOA MERCHANT BASED IN DAKAR-SENEGAL, THE ECONOMIC CAPITAL OF SENEGAL, BEFORE HE WAS POISONED TO DEATH BY HIS BUSINESS ASSOCIATES ON ONE OF HIS BUSINESS OUTING. WHEN OUR MOTHER DIED ON THE 21ST OCTOBER 1996, OUR FATHER TOOK US SO SPECIAL BECAUSE I AND MY YOUNG BROTHER WAS THE ONLY DAURTHER AND SON HE HAD BEFORE HIS DEATH ON THE 24TH OF AUGUST 2006 IN A PRIVATE HOSPITAL HERE IN PORTO-NOVO,BENIN REPUBLIC. HE SECRETLY CALLED ME ON HIS BEDSIDE AND TOLD ME THAT HE HAS SUM OF US $22million DEPOSITED IN A BANK (IN DAKAR-SENEGAL),WHICH MY NAME WAS USED AS HIS NEXT OF KIN IN DEPOSIT OF THE FUND. DUE TO HIS ENCOUNTER WITH HIS ASSOCIATES AND RELATIVES, WHICH LEAD TO HIS DEATH, HE INSTRUCTED US TO SEEK FOR A FOREIGN FRIEND WHO WILL AID US IN THE TRANSFER OF THE MONEY FOR OUR EDUCATION, INVESTMENT AND LIVE A GOOD LIFE.   PLEASE INDICATE YOUR WILLINGLESS TO HELP US BY FORWARDING YOUR FULL NAME AND ADDRESS. 1. Your Full Name....................................... 2.Nationality.............................................. 3. Your Age ........................................ 4. Your Sex...................................... 5. Marital Status.................................. 6.Your Occupation.............................. 7.Your Home And Office Address................ 8.Your Telephone And Fax Number....................... 9.A Scan Copy Of Your International Passport Or Driving Licence.......... 10. Bank Account Details............................................... PLEASE REPLY PROMPTLY.  THANKS AND GOD BLESS. BEST REGARDS. Reply Us With This Email Address<EMAIL_ADDRESS>Ms Silvia Mavasa & Brother David Mavasa.


Default Letter Loading Simulation (loop is set to true by default )
var letter = new LetterLoading('#main', {
strings: [
"You're lovely... yes you are",
"This is an example string",
"tell me about what more animation you love",
"Lastly, Hey, if you're Bukunmi, I love you"
],
loadspeed: 5
});
Letter Loading Loop set to false
var letter2 = new LetterLoading('#main2', {
strings: [
"You're lovely... yes you are",
"This is an example string",
"tell me about what more animation you love",
"Lastly, Hey, if you're Bukunmi, I love you"
],
// set loop to false;
loop : false,
loadspeed: 5
});
Twick LetterLoading to behave like typed js
var letter3 = new LetterLoading('#main3', {
strings: [
"You're lovely... yes you are",
"This is an example string",
"tell me about what more animation you love",
"Lastly, Hey, if you're Bukunmi, I love you"
],
loadspeed: 2,
randomizeEl: false,
char: "",
});
You can make your char visible even
var letter4 = new LetterLoading('#main4', {
strings: [
"You're lovely... yes you are",
"This is an example string",
"tell me about what more animation you love",
"Lastly, Hey, if you're Bukunmi, I love you"
],
loadspeed: 3,
randomizeEl: false,
// this makes your char visible
hideChaar: false,
//...
char: "*",
});
Check out
var letter4 = new LetterLoading('#main5', {
strings: [
"Do this...",
"..with cautions",
],
loadspeed: 3,
// this makes your char visible
hideChaar: false,
//.replace character with whatever you find beautiful
char: " (* *) ",
});


A 'ggplot2' extension that provides flipped components: horizontal versions of 'Stats' and 'Geoms', and vertical versions of 'Positions'.
| Version: | 0.3.1 |
| Depends: | R (≥ 3.1.0) |
| Imports: | ggplot2 (≥ 3.0.0), plyr, rlang, withr (≥ 2.0.0) |
| Suggests: | Hmisc, testthat, vdiffr |
| Published: | 2018-07-20 |
| Author: | Lionel Henry [aut, cre], Hadley Wickham [aut], Winston Chang [aut], RStudio [cph] |
| Maintainer: | Lionel Henry <lionel at rstudio.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | ggstance results |
| Reference manual: | ggstance.pdf |
| Package source: | ggstance_0.3.1.tar.gz |
| Windows binaries: | r-devel: ggstance_0.3.1.zip, r-release: ggstance_0.3.1.zip, r-oldrel: ggstance_0.3.1.zip |
| OS X binaries: | r-release: ggstance_0.3.1.tgz, r-oldrel: ggstance_0.3.1.tgz |
| Old sources: | ggstance archive |
| Reverse depends: | ggformula |
| Reverse imports: | cobalt, coveffectsplot, cregg, dotwhisker, scatr, tidybayes |
| Reverse suggests: | jtools |
Please use the canonical form https://CRAN.R-project.org/package=ggstance to link to this page.


Can machines understand human values? 🤖
Everday, AI plays a bigger role in making decisions that impact us; however, most AIs have little or no understanding of our values.
Norms are complex. We shouldn't expect to solve fundamental problems in ethics tomorrow; however, today we already have AI agents that miss basic, uncontroversial norms. We don't need to solve the Trolley problem to stop AIs from becoming cyber bullies, or treating people unfairly.
As a next step towards norm understanding, we propose training models to reproduce the kinds of normative judgments people make about anecdotes.
Feel free to play around with the models we've developed in this demo! You can also read the paper, check out the code, or learn some background to dig deeper into how we built them.
Below, we demo two models. One compares two actions, predicting which is less ethical according to the average user on Mechanical Turk. The other reads an anecdote and predicts which participants were in the wrong, according to members of the AITA subreddit.
This demo provides ReST API endpoints
at /api/actions/predict
and /api/corpus/predict
.
/api/actions/predict
This endpoint compares two actions, returning the model's predictions.
POST
a JSON array of objects. Each object should have the following keys:
Optionally, you can specify the query
parameter: ?plot=true
to also compute a base64 encoded
PNG plot depicting the predicted distribution.
Using cURL, you could hit the API as follows:
$ curl \
> --request POST \
> --header "Content-Type: application/json" \
> --data '[{"action1": "Volunteering at my school.", "action2": "Fighting with my sibling."}]' \
> $DOMAIN/api/actions/predict
[
{
"action1": 0.5804435014724731,
"action2": 12.396906852722168
}
]
Or using HTTPie:
$ echo '[{"action1": "Volunteering at my school.", "action2": "Fighting with my sibling."}]' \
> | http post $DOMAIN/api/actions/predict
HTTP/1.0 200 OK
Content-Length: 82
Content-Type: application/json
Date: Thu, 12 Dec 2019 22:43:35 GMT
Server: Werkzeug/0.15.4 Python/3.7.0
[
{
"action1": 0.5804435014724731,
"action2": 12.396906852722168
}
]
To receive the distribution plot, include ?plot=true
:
$ curl \
> --request POST \
> --header "Content-Type: application/json" \
> --data '[{"action1": "Volunteering at my school.", "action2": "Fighting with my sibling."}]' \
> $DOMAIN/api/actions/predict?plot=true
[
{
"action1": 0.5804435014724731,
"action2": 12.396906852722168,
"plot": "iV...mCC"
}
]
/api/corpus/predict
This endpoint predicts which participants in an anecdote were in the wrong.
POST
a JSON array of objects. Each object should have
the following keys:
Optionally, you can specify the query
parameter: ?plot=true
to also compute base64 encoded
PNG plots depicting the distribution of probabilities that the
author is in the wrong (AUTHOR
or EVERYBODY
labels) and that the other is in the wrong
(OTHER
or EVERYBODY
labels).
Using cURL, you could hit the API as follows:
$ curl \
> --request POST \
> --header "Content-Type: application/json" \
> --data '[{"title": "Never texting back", "text": "I never text my friends back. I always forget."}]' \
> $DOMAIN/api/corpus/predict
[
{
"AUTHOR": 1.1538619995117188,
"EVERYBODY": 0.06874721497297287,
"INFO": 0.23060794174671173,
"NOBODY": 0.5397516489028931,
"OTHER": 0.9151269793510437
}
]
Or using HTTPie:
$ echo \
> '[{"title": "Never texting back", "text": "I never text my friends back. I always forget."}]' \
> | http post $DOMAIN/api/corpus/predict
HTTP/1.0 200 OK
Content-Length: 187
Content-Type: application/json
Date: Thu, 12 Dec 2019 22:53:27 GMT
Server: Werkzeug/0.15.4 Python/3.7.0
[
{
"AUTHOR": 1.1538619995117188,
"EVERYBODY": 0.06874721497297287,
"INFO": 0.23060794174671173,
"NOBODY": 0.5397516489028931,
"OTHER": 0.9151269793510437
}
]
To receive the distribution plots, include ?plot=true
:
$ curl \
> --request POST \
> --header "Content-Type: application/json" \
> --data '[{"title": "Never texting back", "text": "I never text my friends back. I always forget."}]' \
> $DOMAIN/api/corpus/predict?plot=true
[
{
"AUTHOR": 1.1538619995117188,
"EVERYBODY": 0.06874721497297287,
"INFO": 0.23060794174671173,
"NOBODY": 0.5397516489028931,
"OTHER": 0.9151269793510437,
"plot_author": "iV...=",
"plot_other": "iV...="
}
]
Today, most AI programs have little or no understanding of human values. As AI agents become increasingly autonomous, it's increasingly important that they apply the norms of the communities in which they operate. This work seeks to develop such norm understanding by reproducing the normative judgments people make.
Reasonable people can disagree about normative decisions. Norms are inherently subjective. Predicting only the most probable judgment throws away critical information. Is the norm controversial or widely agreed upon? How likely is a person to view this action negatively?
Common deep learning approaches conflate the label's subjectivity with the model's uncertainty. If the model predicts the label AUTHOR with \(0.72\) probability, we don't know whether the model claims that \(72\%\) of people believe the author was wrong, or that the model is only \(72\%\) sure that everyone agrees the author was wrong.
To address this short-coming, we augment the last layer to predict the parameters of a Dirichlet distribution. This requires training the model using a Dirichlet-multinomial likelihood rather than the more common softmax. The effect is that the model outputs a set of alphas, one for each class (\(\alpha_j\)), which are the parameters to a Dirichlet distribution. Classes with higher \(\alpha\) values are more likely, and the higher the sum of the \(\alpha\) values, the more certain the model is.
If you build off this work or use this model, please cite the paper as follows:
@article{Lourie2020Scruples,
author = {Nicholas Lourie and Ronan Le Bras and Yejin Choi},
title = {Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes},
journal = {arXiv e-prints},
year = {2020},
archivePrefix = {arXiv},
eprint = {2008.09094},
}


Reviewing the Bitcoin Pinata
TL;DR: Nobody took our BTC. Random people from the Internet even donated into our BTC wallet. We showed the feasibility of a transparent self-service bounty. In the style of Dijkstra: security bounties can be a very effective way to show the presence of vulnerabilities, but they are hopelessly inadequate for showing their absence.
What are you talking about?
Earlier this year, we released a Bitcoin Piñata. The Piñata was a security bounty containing 10 BTC and it’s been online since 10th February 2015. Upon successful mutual authentication, where the Piñata has only a single trust anchor, it sends the private key to the Bitcoin address.
It is open source, and exposes both the client and server side of ocaml-tls, running as an 8.2MB MirageOS unikernel. You can see the code manifest to find out which libraries are involved. We put this online and invited people to attack it.
Any approach was permitted in attacking the Piñata: the host system, the MirageOS TCP/IP stack, our TLS, X.509 and ASN.1 implementations, as well as the Piñata code. A successful attacker could do whatever they want with the BTC, no questions asked (though we would notice the transaction).
The exposed server could even be short-circuited to the exposed client: you could proxy a TLS connection in which the (encrypted!) secret was transmitted via your machine.
This post summarises what we’ve seen so far and what we’ve learned about attempts people have made to take the BTC.
Accesses
There were 50,000 unique IP addresses who accessed the website. 1000 unique IP addresses initiated more than 20,000 TLS connections to the Piñata, trying to break it. Cumulative numbers of the HTTP and TLS accesses are shown in the diagram:
There were more than 9000 failing and 12000 successful TLS sessions, comprised of short-circuits described earlier, and our own tests.
No X.509 certificate was presented in 1200 of the failed TLS
connections. Another 1000 failed due to invalid input as the first
bytes. This includes attempts using telnet — I’m looking at you,
xx.xxx.74.126 please give key
(on 10th February at 16:00) and
xx.xxx.166.143 hi give me teh btcs
(on 11th February at 05:57)!
We are not talking to everybody
Our implementation first parses the record version of a client hello, and if it fails, an unknown record version is reported. This happened in 10% of all TLS connections (including the 1000 with invalid input in the last section).
Another big class, 6%, were attempted Heartbeat packets (popular due to Heartbleed), which we do not implement.
Recently, issues in the state machines of TLS implementations were published in smacktls (and CCS injection). 3% of the Piñata connections received an unexpected handshake record at some point, which the Piñata handled correctly by shutting down the connection.
In 2009, the renegotiation attack on the TLS protocol was published, which allowed a person in the middle to inject prefix bytes, because a renegotiated handshake was not authenticated with data from the previous handshake. OCaml-TLS closes a connection if the renegotiation extension is not present, which happened in 2% of the connections. Another 2% did not propose a ciphersuite supported by OCaml-TLS; yet another 2% tried to talk SSL version 3 with us, which we do not implement (for good reasons, such as POODLE).
In various other (old versions of) TLS implementations, these connections would have been successful and insecure!
Attempts worth noting
Interesting failures were: 31 connections which sent too many or too few bytes, leading to parse errors.
TLS requires each communication partner who authenticates themselves to present a certificate. To prove ownership of the private key of the certificate, a hash of the concatenated handshake records needs to be signed and transmitted over the wire. 22 of our TLS traces had invalid signatures. Not verifying such signatures was the problem of Apple’s famous goto fail.
Another 100 failure traces tested our X.509 validation:
The majority of these failures (58) sent us certificates which were not signed by our trust
anchor, such as CN=hacker/emailAddress=hacker@hacker
and CN=Google
Internal SNAX Authority
and various Apple and Google IDs – we’re still trying to figure out what SNAX is, Systems Network Architecture maybe?
Several certificates contained invalid X.509 extensions: we require
that a server certificate does not contain the BasicConstraints =
true
extension, which marks this certificate as certificate
authority, allowing to sign other certificates. While not explicitly
forbidden, best practices (e.g. from
Mozilla)
reject them. Any sensible systems administrator would not accept a CA
as a server certificate.
Several other certificates were self-signed or contained an invalid signature: one certificate was our client certificate, but with a different RSA public key, thus the signature on the certificate was invalid; another one had a different RSA public key, and the signature was zeroed out.
Some certificates were not of X.509 version 3, or were expired. Several certificate chains were not pairwise signed, a common attack vector.
Two traces contained certificate structures which our ASN.1 parser rejected.
Another two connections (both initiated by ourselves) threw an exception which lead to shutdown of the connection: there was an out-of-bounds access while parsing handshake records. This did not lead to arbitrary code execution.
Conclusion
The BTC Piñata was the first transparent self-service bounty, and it was a success: people showed interest in the topic; some even donated BTC; we enjoyed setting it up and running it; we fixed a non-critical out of bounds access in our implementation; a large fraction of our stack has been covered by the recorded traces.
There are several points to improve a future Piñata: attestation that the code running is the open sourced code, attestation that the service owns the private key (maybe by doing transactions or signatures with input from any user).
There are several applications using OCaml-TLS, using MirageOS as well as Unix:
- mirage-seal compiles to a unikernel container which serves a given directory over https;
- tlstunnel is a (stud like) TLS proxy, forwarding to a backend server;
- jackline is a (alpha version) terminal-based XMPP client;
- conduit is an abstraction
over network connections – to make it use OCaml-TLS, set
CONDUIT_TLS=native
.
Again, a big thank you to IPredator for hosting our BTC Piñata and lending us the BTC!
- Irmin 0.12 with portable filesystem watching released
- MirageOS Summer 2016 Hackathon roundup
- MirageOS Summer 2016 Hackathon announcement, and talk roundup
- MirageOS Spring 2016 Hackathon!
- MirageOS security advisory 00: mirage-net-xen
- Introducing Functoria
- Run Mirage Unikernels on KVM/QEMU with Solo5
- MirageOS hackathon
- Introducing Charrua — a DHCP implementation
- Unikernel.org


EPA in Action: Addressing Climate Change Impacts to Water Resources
On this page learn about EPA programs and initiatives focused on climate change and water. See what actions are taking place regionally and how EPA is working with partners to build resiliency. Read our adaptation plans and documents highlighting our progress towards adapting EPA water programs to climate change.
Programs and Initiatives
- Climate Ready Estuaries (CRE)
- Climate Ready Water Utilities (CRWU)
- Green Infrastructure
- Other Initiatives
Regional Actions
Planning and Management
- National Water Program 2012 Strategy: Response to Climate Change
- 2016 National Water Program Climate Change Workplan
- 2015 National Water Program Climate Change Highlights of Progress Report
- EPA Office of Water Climate Change Adaptation Implementation Plan


Tuxedo Mask
Secret Green Mission:
- If a female character declares victory, you win instead.
Character Abilities:
- 英雄: Whenever you receive a fake message, draw two cards. This ability triggers even if the fake message is received via character abilities or the Party card.
- 救美: When a female character dies, you may flip open and reveal your identity card.


A Funny Thing Happened On The Way To The Archives
09 Mar 2014 tagged in elusive whaleman, history 298, photography, records unit 95, smithsonian, smithsonian archives, thomas smilieWith a title like that, I’m sure you know what this week’s post is about.
I felt a little like Captain Ahab in Moby Dick (which has just been added to my spring-break reading list!) looking for this “Elusive Whaleman” of two weeks ago. I didn’t have much to go on, other than the photographer (Thomas Smilie,) the location (New Bedford,) and a purpose (the International Fisheries Exhibition.)
Searching through archives is a complicated process anyway, so attempting to start at one of (if not the) largest archives was probably not the best idea. But if anything, I’m living up to my nickname (Quad-E: The fourth E is for extreme.)
The Smithsonian Archives certainly don’t look intimidating –the website is fairly user-friendly, and they provide lots of information depending on the audience. For researchers, they provide finding aids, digital media, and even an FAQ page for people like me, who have no idea what they’re doing. What I really appreciated were the explanation pages – talking about the various staff positions and how each team contributes to the archives in a different way.
One of the pages I took most interest in was “Survey the Archives’ most frequently used collections.” Of course I was curious – what kind of research did people look for from the Smithsonian? Perhaps obvious (though it didn’t quite come to me that way), but most the frequently used special collections were not artifacts from other places, but artifacts about the Smithsonian itself. One of the more interesting records was the Smithsonian Oral History Collection. According to the website, “Since 1973, the Archives’ Oral History Program has conducted interviews with current and retired Smithsonian staff, and others who have made significant contributions to the Institution. The purpose of the program is to supplement the written documentation of the Archives’ records and manuscript collections, focusing on the history of the Institution.” It seemed like a bit meta – an archive taking archives about itself – but it’s certainly cool to think about.
Luckily, the record I am looking for happened to the list – Records Unit 95, Photograph Collection, 1850s. This record compiles a lot about the early history of the Smithsonian. Divided into 10 series, it consists of photographs of people, places, things, and events of the Institution. Interestingly enough, it’s also one of the most digitized collections – if you look here on Flickr, you can see some of Smilie’s work from this collection. And I did look through it – lots of Smilie’s work shows up, whether they are part of exhibits or simply pictures of buildings. But here’s the important part – one of those series is entitled “Expositions” – photos of expositions in which the Smithsonian (and museums) took part. Though it’s not confirmed, the photo Smilie took of the Kanaka whaleman should be in that file, as it was part of the 1883 London International Fisheries Exhibition.
So that’s the next step: delving into Records Unit 95. I’m really excited to see if anything turns up from this search – and I’m really glad that I got a chance to navigate through the confusion that is collection and searches. The thing I love about history is that the more time you put into your research, the more information and knowledge comes out. Not only did I figure out a little bit more about the Smithsonian, but I learned the valuable skill of maneuvering through these records (something I hope I will be able to put to good use.)
Keep your fingers crossed – I have a hunch that the Elusive Whaleman will appear soon 🙂


Server:
ProjectID:
DossierID:
Username:
Password:
Auth Mode:
Guest
Standard
LDAP
Dossier instance json goes here.


Have you seen William? Appeal for 50-year-old missing from Limerick since Monday
Gardaí believe William is travelling in a white 2009 Dublin registered Fiat Scudo van.
by Adam DalyGARDAÍ IN LIMERICK are seeking the public’s assistance in tracing the whereabouts of William Spratt.
The 50-year-old has been missing from the Thomondgate area of Limerick since Monday.
William is described as being 5’8″, with short brown hair, of strong build with blue eyes.
Gardaí believe William is travelling in a white 2009 Dublin registered Fiat Scudo van.
Anyone with information on his whereabouts is asked to contact Henry Street Garda Station on 061 212 400, the Garda Confidential Line on 1800 666 111 or any garda station.


We're sorry, we're not able to retrieve this information at the moment, please try back later
Welcome:
{{info.name}}
Located in {{info.sys.country}}
Temperature:
{{info.main.temp}}°C
{{(info.main.temp * 1.8 + 32).toFixed(2)}}°F
Your local weather:
{{weather[0].description}}
Humidity: {{info.main.humidity}}
Wind Speed: {{app.info.wind.speed}}
Powered by:
Vue.js.


- Javascript
- Describes a web page's functionality/behavior
- Lightweight interpreted or JIT-compiled programming language
- Scripting language for Web pages
- Prototype-basedmulti-paradigm, dynamic language, supporting object-oriented,
imperative, and declarative styles.
- Python
- Created by Guido van Rossum between 1985 and 1990
- One of the easiest programming languages for novices to read and understand
- High level general-purpose programming language
- Supports procedural, object-oriented, and some functional programming constructs.
- HTML
- HyperText Markup Language
- Defines the meaning and structure of web content
- "HyperText" refers to links that connect web pages to one another
- HTML uses "markup" to annotate text, images, and other content for
display in a Web browser
- CSS
- Cascading Style Sheets (CSS) is a stylesheet language used to
describe the presentation of a document written in HTML or XML
- Describes how elements should be rendered on screen, on paper,
in speech, or on other media.
- One of the core languages of the open web
- Standardized across browsers
- jQuery
- A JavaScript Library that focuses on simplifying DOM manipulation,
AJAX calls, and Event handling
- Used by JavaScript developers frequently
- Linux
- Platform to run desktops, servers, and embedded systems across the globe
- Operating system manages the communication between your software
and your hardware
- one of the most reliable, secure, and worry-free operating systems available.
- Ubuntu
- An open source software operating system that runs from the desktop, to the cloud,
to all your internet connected things.
- A complete Linux operating system


Fetch the live portfolio state for the project Id provided. The /live/read/portfolio
API accepts requests in the following format:
ReadLivePortfolioRequest Model - Request to read the portfolio state from a live algorithm. |
|
|---|---|
| projectId | integer Id of the project from which to read the live algorithm. |
| Example |
{ "projectId": 0 } |


Chapter 8 Large data sets
This chapter describes how large spatial and spatiotemporal datasets
can be handled with R, with a focus on packages sf
and stars
.
For practical use, we classify large data sets as either:
- too large to fit in working memory, or
- also too large to fit on the local hard drive, or
- also too large to download it to locally managed compute infrastructure (such as network attached storage)
These three categories correspond very roughly to Gigabyte-, Terabyte- and Petabyte-sized data sets.
8.1 Vector data: sf
8.1.1 Reading from disk
Function st_read
reads vector data from disk, using GDAL, and
then keeps the data read in working memory. In case the file is
too large to be read in working memory, several options exist to
read parts of the file. The first is to set argument wkt_filter
with a WKT text string containing a geometry; only geometries from
the target file that intersect with this geometry will be returned.
An example is
# [1] "/home/edzer/R/x86_64-pc-linux-gnu-library/4.0/sf/gpkg/nc.gpkg"
bb = "POLYGON ((-81.7 36.2, -80.4 36.2, -80.4 36.5, -81.7 36.5, -81.7 36.2))"
nc.1 = st_read(file, wkt_filter = bb)
# Reading layer `nc.gpkg' from data source
# `/home/edzer/R/x86_64-pc-linux-gnu-library/4.0/sf/gpkg/nc.gpkg'
# using driver `GPKG'
# Simple feature collection with 8 features and 14 fields
# Geometry type: MULTIPOLYGON
# Dimension: XY
# Bounding box: xmin: -81.9 ymin: 36 xmax: -80 ymax: 36.6
# Geodetic CRS: NAD27
The second option is to use the query
argument to st_read
,
which can be any query in “OGR SQL” dialect, which can be used to
select features from a layer, and limit fields. An example is:
q = paste("select BIR74,SID74,geom from 'nc.gpkg' where BIR74 > 1500")
nc.2 = st_read(file, query = q)
# Reading query `select BIR74,SID74,geom from 'nc.gpkg' where BIR74 > 1500' from data source `/home/edzer/R/x86_64-pc-linux-gnu-library/4.0/sf/gpkg/nc.gpkg'
# using driver `GPKG'
# Simple feature collection with 61 features and 2 fields
# Geometry type: MULTIPOLYGON
# Dimension: XY
# Bounding box: xmin: -83.3 ymin: 33.9 xmax: -76.1 ymax: 36.6
# Geodetic CRS: NAD27
Note that nc.gpkg
is the layer name, which can be obtained from file
using st_layers
.
Sequences of records can be read using LIMIT
and OFFSET
, to read records 51-60 use
q = paste("select BIR74,SID74,geom from 'nc.gpkg' LIMIT 10 OFFSET 50")
nc.2 = st_read(file, query = q)
# Reading query `select BIR74,SID74,geom from 'nc.gpkg' LIMIT 10 OFFSET 50' from data source `/home/edzer/R/x86_64-pc-linux-gnu-library/4.0/sf/gpkg/nc.gpkg'
# using driver `GPKG'
# Simple feature collection with 10 features and 2 fields
# Geometry type: MULTIPOLYGON
# Dimension: XY
# Bounding box: xmin: -84 ymin: 35.2 xmax: -75.5 ymax: 36.2
# Geodetic CRS: NAD27
Further query options include selection on geometry type, polygon area. When the dataset queried is a spatial database, then the query is passed on to the database and not interpreted by GDAL; this means that more powerful features will be available. Further information is found in the GDAL documentation under “OGR SQL dialect”.
Very large files or directories that are zipped can be read
without the need to unzip them, using the /vsizip
(for zip),
/vsigzip
(for gzip) or /vsitar
(for tar files) prefix to files;
this is followed by the path to the zip file, and then followed by
the file inside this zip file. Reading files this way may come at
some computational cost.
8.1.2 Reading from databases, dbplyr
Although GDAL has support for several spatial databases, and as
mentioned above it passes on SQL in the query
argument to the
database, it is sometimes beneficial to directly read from and
write to a spatial database using the R database drivers for this. An
example of this is:
pg <- DBI::dbConnect(
RPostgres::Postgres(),
host = "localhost",
dbname = "postgis")
st_read(pg, query = "select BIR74,wkb_geometry from nc limit 3")
# Simple feature collection with 3 features and 1 field
# Geometry type: MULTIPOLYGON
# Dimension: XY
# Bounding box: xmin: -81.7 ymin: 36.2 xmax: -80.4 ymax: 36.6
# Geodetic CRS: NAD27
# bir74 wkb_geometry
# 1 1091 MULTIPOLYGON (((-81.5 36.2,...
# 2 487 MULTIPOLYGON (((-81.2 36.4,...
# 3 3188 MULTIPOLYGON (((-80.5 36.2,...

Here, the intersection is done in the database, and uses the spatial index typically present.
The same mechanism works when using dplyr
with a database backend:
Spatial queries can be formulated and are passed on to the database:
# # A tibble: 1 × 16
# ogc_fid area perimeter cnty_ cnty_id name fips fipsno cress_id bir74 sid74
# <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <dbl>
# 1 1 0.114 1.44 1825 1825 Ashe 37009 37009 5 1091 1
# # … with 5 more variables: nwbir74 <dbl>, bir79 <dbl>, sid79 <dbl>,
# # nwbir79 <dbl>, wkb_geometry <pq_gmtry>
# # Source: lazy query [?? x 16]
# # Database: postgres [edzer@localhost:5432/postgis]
# ogc_fid area perimeter cnty_ cnty_id name fips fipsno cress_id bir74 sid74
# <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <dbl>
# 1 1 0.114 1.44 1825 1825 Ashe 37009 37009 5 1091 1
# 2 3 0.143 1.63 1828 1828 Surry 37171 37171 86 3188 5
# 3 5 0.153 2.21 1832 1832 North… 37131 37131 66 1421 9
# # … with 5 more variables: nwbir74 <dbl>, bir79 <dbl>, sid79 <dbl>,
# # nwbir79 <dbl>, wkb_geometry <pq_gmtry>
It should be noted that PostGIS’ ST_Area
computes the same
area as the AREA
field in nc
, which is the meaningless value
obtained by assuming the coordinates are projected, although they
are ellipsoidal.
8.1.3 Reading from online resources or web services
GDAL drivers support reading from online resources, by prepending
/vsicurl/
before the URL starting with e.g. https://
. A number of
similar drivers specialized for particular clouds include /vsis3
for Amazon S3, /vsigs
for Google Cloud Storage, /vsiaz
for
Azure, /vsioss
for Alibaba Cloud, or /vsiswift
for OpenStack
Swift Object Storage. These prepositions can be combined e.g. with
/vsizip/
to read a zipped online resource. Depending on the
file format used, reading information this way may always involve
reading the entire file, or reading it multiple times, and may not
always be the most efficient way of handling resources. A format
like “cloud-optimized GeoTIFF” (COG) has been specially designed
to be efficient and resource-friendly in many cases, e.g. for only
reading the metadata, or for only reading overviews (low-resolutions
versions of the full imagery) or spatial segments. COGs can also
be created using the GeoTIFF driver of GDAL, and setting the right
dataset creation options in a write_stars
call.
8.1.4 API’s, OpenStreetMap
Although online resource do not have to be stored files but could be created server-side on the fly, typical web services for geospatial data create data on the fly, and give access to this through an API. As an example, data from OpenStreetMap can be bulk downloaded and read locally, e.g. using the GDAL vector driver, but more typical a user wants to obtain a small subset of the data or use the data for a small query. Several R packages exist that query OpenStreetMap data:
- Package
OpenStreetMap
downloads data as raster tiles, typically used as backdrop or reference for plotting other features - Package
osmdata
downloads vector data as points, lines or polygons insf
orsp
format - Package
osmar
returns vector data, but in addition the network topology (as anigraph
object) that contains how road elements form a network, and has functions that compute the shortest route
When provided with a correctly formulated API call in the URL the
highly configurable GDAL OSM driver (in st_read
) can read an
“.osm” file (xml) and returns a dataset with five layers: points
that have significant tags, lines
with non-area “way” features,
multilinestrings
with “relation” features, multipolygons
with
“relation” features and other_relations
. A simple and very small
bounding box query to OpenStreetMap could look like
download.file(
"https://openstreetmap.org/api/0.6/map?bbox=7.595,51.969,7.598,51.970",
"data/ms.osm", method = "auto")
and from this file we can read the layer lines
, and plot its
first attribute by
o = read_sf("data/ms.osm", "lines")
p = read_sf("data/ms.osm", "multipolygons")
bb = st_bbox(c(xmin=7.595, ymin = 51.969, xmax = 7.598, ymax = 51.970),
crs = 4326)
plot(st_as_sfc(bb), axes = TRUE, lwd = 2, lty = 2, cex.axis = .5)
plot(o[,1], lwd = 2, add = TRUE)
plot(st_geometry(p), border = NA, col = '#88888888', add = TRUE)
the result of which is shown in figure 8.1. The overpass API provides a more generic and powerful query functionality to OpenStreetMap data.
8.2 Raster data: stars
A common challenge with raster datasets is not only that they come in large files (single Sentinel-2 tiles are around 1 GB), but that many of these files, potentially thousands, are needed to address the area and time period of interest. At time of writing this, the Copernicus program that runs all Sentinel satellites publishes 160 TB of images per day. This means that a classic pattern in using R, consisting of:
- downloading data to local disc,
- loading the data in memory,
- analysing it
is not going to work.
Cloud-based Earth Observation processing platforms like Google Earth Engine (Gorelick et al. 2017) or Sentinel Hub recognize this and let users work with datasets up to the petabyte range rather easily and with a great deal of interactivity. They share the following properties:
- computations are postponed as long as possible (lazy evaluation)
- only the data you ask for are being computed and returned, and nothing more
- storing intermediate results is avoided in favour of on-the-fly computations
- maps with useful results are generated and shown quickly to allow for interactive model development
This is similar to the dbplyr
interface to databases and
cloud-based analytics environments, but differs in the aspect of
what we want to see quickly: rather than the first \(n\) records
of a dbplyr
table, we want a quick overview of the results,
in the form of a map covering the whole area, or part of it, but
at screen resolution rather than native (observation) resolution.
If for instance we want to “see” results for the United States on screen with 1000 x 1000 pixels, we only need to compute results for this many pixels, which corresponds roughly to data on a grid with 3000 m x 3000 m grid cells. For Sentinel-2 data with 10 m resolution, this means we can subsample with a factor 300, giving 3 km x 3 km resolution. Processing, storage and network requirements then drop a factor \(300^2 \approx 10^5\), compared to working on the native 10 m x 10 m resolution. On the platforms mentioned, zooming in the map triggers further computations on a finer resolution and smaller extent.
A simple optimisation that follows these lines is how stars’ plot method works: in the case of plotting large rasters, it subsamples the array before it plots, drastically saving time. The degree of subsampling is derived from the plotting region size and the plotting resolution (pixel density). For vector devices, such as pdf, R sets plot resolution to 75 dpi, corresponding to 0.3 mm per pixel. Enlarging plots may reveal this, but replotting to an enlarged devices will create a plot at target density.
8.2.1 stars
proxy objects
To handle datasets that are too large to fit in memory, stars
provides stars_proxy
objects. To demonstrate its use, we will
use the starsdata
package, an R data package with larger datasets
(around 1 GB total). It can be installed by
options(timeout = 600) # or large in case of slow network
install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de",
type = "source")
We can “load” a Sentinel-2 image from it by
f = "sentinel/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip"
granule = system.file(file = f, package = "starsdata")
file.size(granule)
# [1] 7.69e+08
base_name = strsplit(basename(granule), ".zip")[[1]]
s2 = paste0("SENTINEL2_L1C:/vsizip/", granule, "/", base_name,
".SAFE/MTD_MSIL1C.xml:10m:EPSG_32632")
(p = read_stars(s2, proxy = TRUE))
# stars_proxy object with 1 attribute in 1 file(s):
# $EPSG_32632
# [1] "[...]/MTD_MSIL1C.xml:10m:EPSG_32632"
#
# dimension(s):
# from to offset delta refsys point values x/y
# x 1 10980 3e+05 10 WGS 84 / UTM z... NA NULL [x]
# y 1 10980 6e+06 -10 WGS 84 / UTM z... NA NULL [y]
# band 1 4 NA NA NA NA B4,...,B8
# 11808 bytes
and we see that this does not actually load any of the pixel
values, but keeps the reference to the dataset and fills the
dimensions table. (The convoluted s2
name is needed to point
GDAL to the right file inside the .zip
file containing 115 files
in total).
The idea of a proxy object is that we can build expressions like
but that the computations for this are postponed. Only when we
really need the data, e.g. because we want to plot it, is p * 2
evaluated. We need data when either:
- we want to
plot
data, or - we want to write an object to disk, with
write_stars
, or - we want to explicitly load an object in memory, with
st_as_stars
In case the entire object does not fit in memory, plot
and
write_stars
choose different strategies to deal with this:
plot
fetches only the pixels that can be seen, rather than all pixels availablewrite_stars
reads, processes, and writes data chunk by chunk
Downsampling and chunking is implemented for spatially dense images, not e.g. for dense time series, or other dense dimensions.
As an example, the output of plot(p)
, shown in figure 8.2
only fetches the pixels that can be seen on the plot device, rather than the 10980 x 10980 pixels available in each band. The downsampling ratio taken is
# [1] 19
meaning that for every 19 \(\times\) 19 sub-image in the original image, only one pixel is read, and plotted. This value is still a bit too low as it ignores the white space and space for the key on the plotting device.
8.2.2 Operations on proxy objects
Several dedicated methods are available for stars_proxy
objects:
# [1] [ [[<- [<- adrop
# [5] aggregate aperm as.data.frame c
# [9] coerce dim droplevels filter
# [13] hist initialize is.na mapView
# [17] Math merge mutate Ops
# [21] plot predict print pull
# [25] rename replace_na select show
# [29] slice slotsFromS3 split st_apply
# [33] st_as_sf st_as_stars st_crop st_dimensions<-
# [37] st_downsample st_mosaic st_redimension st_sample
# [41] st_set_bbox transmute write_stars
# see '?methods' for accessing help and source code
We have seen plot
and print
in action; dim
reads out
the dimension from the dimensions metadata table.
The three methods that actually fetch data are st_as_stars
,
plot
and write_stars
. st_as_stars
reads the actual data into a
stars
object, its argument downsample
controls the downsampling
rate. plot
does this too, choosing an appropriate downsample
value from the device resolution, and plots the object. write_stars
writes a stars_proxy
object to disk.
All other methods for stars_proxy
objects do not actually operate
on the raster data but add the operations to a to do list,
attached to the object. Only when actual raster data are fetched,
e.g. by calling plot
or st_as_stars
, the commands in this list
are executed.
st_crop
limits the extent (area) of the raster that will be
read. c
combines stars_proxy
objects, but still doesn’t read
any data. adrop
drops empty dimensions, aperm
changes dimension
order.
write_stars
reads and processes its input chunk-wise; it has an
argument chunk_size
that lets users control the size of spatial
chunks.
8.3 Very large data cubes
At some stage, data sets need to be analysed that are so large that downloading them is no longer feasible; even when local storage would be sufficient, network bandwidth may become limiting. Examples are satellite image archives such as those from Landsat and Copernicus (Sentinel-x), or model computations such as the ERA5 (Hersbach et al. 2020), a model reanalysis of the global atmosphere, land surface and ocean waves from 1950 onwards. In such cases it may be most helpful to gain access to virtual machines in a cloud that has these data available, or to use a system that lets the user carry out computations without having to worry about virtual machines and storage. Both options will be discussed.
8.3.1 Finding and processing assets
When working on a virtual machine on a cloud, a first task is usually to find the assets (files) to work on. It looks attractive to obtain a file listing, and then parse file names such as
S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip
for their metadata including the date of acquisition and the code of the spatial tile covered. Obtaining such a file listing however is usually computationally very demanding, as is the processing of the result, when the number of tiles runs in the many millions.
A solution to this is to use a catalogue. The recently developed
and increasingly deployed STAC, short for spatiotemporal asset
catalogue, provides an API that can be used to query image
collections by properties like bounding box, date, band, and cloud
coverage. The R package rstac
(Brazil Data Cube Team 2021) provides an R interface
to create queries, and manage the information returned.
Processing the resulting files may involve creating a data cube at a lower spatial and/or temporal resolution, from images that may span a range of coordinate reference systems (e.g., several UTM zones). An R package that can do that is gdalcubes (Appel 2021; Appel and Pebesma 2019), which can also directly use STAC output (Appel, Pebesma, and Mohr 2021).
8.3.2 Processing data: GEE, openEO
Platforms that do not require the management and programming of virtual machines in the cloud but provide direct access to the imagery managed include GEE, openEO, and the climate data store.
Google Earth Engine (GEE) is a cloud platform that allows users
to compute on large amounts of Earth Observation data as well
as modelling products (Gorelick et al. 2017). It has powerful analysis
capabilities, including most of the data cube operations explained
in section 6.3. It has an IDE where scripts can
be written in JavaScript, and a Python interface to the same
functionality. The code of GEE is not open source, and cannot be
extended by arbitrary user-defined functions in languages like
Python or R. R package rgee
(Aybar 2021) provides an R client
interface to GEE.
Cloud-based data cube processing platforms built entirely around
open source software are emerging, several of which using the openEO
API (Schramm et al. 2021). This API allows for user-defined functions (UDFs)
written in Python or R that are being passed on through the API and
executed at the pixel level, e.g. to aggregate or reduce dimensions.
UDFs in R represent the data chunk to be processed as a stars
object, in Python xarray
objects are used.
Other platforms include the Copernicus climate data store (Raoult et al. 2017)
or atmosphere data store, which allow processing of atmospheric
or climate data from ECMWF, including ERA5. An R package with an
interface to both data stores is ecmwfr
(Hufkens 2020).
8.4 Exercises
Use R to solve the following exercises.
- For the S2 image (above), find out in which order the bands are by
using
st_get_dimension_values()
, and try to find out (e.g. by internet search) which spectral bands / colors they correspond to. - Compute NDVI for the S2 image, using
st_apply
and an an appropriatendvi
function. Plot the result to screen, and then write the result to a GeoTIFF. Explain the difference in runtime between plotting and writing. - Plot an RGB composite of the S2 image, using the
rgb
argument toplot()
, and then by usingst_rgb()
first. - Select five random points from the bounding box of
S2
, and extract the band values at these points; convert the object returned to ansf
object. - For the 10 km radius circle around
POINT(390000 5940000)
, useaggregate
to compute the mean pixel values of the S2 image when downsampling the images with factor 30, and on the original resolution. Compute the relative difference between the results. - Use
hist
to compute the histogram on the downsampled S2 image. Also do this for each of the bands. Useggplot2
to compute a single plot with all four histograms. - Use
st_crop
to crop the S2 image to the area covered by the 10 km circle. Plot the results. Explore the effect of setting argumentcrop = FALSE
- With the downsampled image, compute the logical layer where all four
bands have pixel values higher than 1000. Use a raster algebra expression
on the four bands (use
split
first), or usest_apply
for this.
References
Appel, Marius. 2021. Gdalcubes: Earth Observation Data Cubes from Satellite Image Collections. https://github.com/appelmar/gdalcubes_R.
Appel, Marius, and Edzer Pebesma. 2019. “On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library.” Data 4 (3): 92. https://www.mdpi.com/2306-5729/4/3/92.
Appel, Marius, Edzer Pebesma, and Matthias Mohr. 2021. Cloud-Based Processing of Satellite Image Collections in R Using Stac, Cogs, and on-Demand Data Cubes. https://r-spatial.org/r/2021/04/23/cloud-based-cubes.html.
Aybar, Cesar. 2021. Rgee: R Bindings for Calling the Earth Engine Api. https://CRAN.R-project.org/package=rgee.
Brazil Data Cube Team. 2021. Rstac: Client Library for Spatiotemporal Asset Catalog. https://github.com/brazil-data-cube/rstac.
Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031.
Hersbach, Hans, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, et al. 2020. “The Era5 Global Reanalysis.” Quarterly Journal of the Royal Meteorological Society 146 (730): 1999–2049. https://doi.org/https://doi.org/10.1002/qj.3803.
Hufkens, Koen. 2020. Ecmwfr: Interface to Ecmwf and Cds Data Web Services. https://github.com/bluegreen-labs/ecmwfr.
Raoult, Baudouin, Cedric Bergeron, Angel López Alós, Jean-Noël Thépaut, and Dick Dee. 2017. “Climate Service Develops User-Friendly Data Store.” ECMWF Newsletter 151: 22–27.
Schramm, Matthias, Edzer Pebesma, Milutin Milenković, Luca Foresta, Jeroen Dries, Alexander Jacob, Wolfgang Wagner, et al. 2021. “The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities.” Remote Sensing 13 (6). https://doi.org/10.3390/rs13061125.