What?
Ally McBeal is not a role model for future women litigators?
John Denvirs commentary on Ally McBeal and Michael
Hayes finds Ally to have much in common with Mary Tyler Moore. In her essay Dont
Call Me Ally, Lisa Friedman makes a strong case that Allys short skirts and
personal trials are no way to win an actual trial. Denvir is right about Ally
having more to do with relationships than the law. Friedman is also correct that David E.
Kellys "other" show, The Practice, presents more verisimilitude
about the legal profession.
However, Allys character indeed presents a strong
"professional in progress" role model. Her courtroom tactics may be feeble, but
she struggles to arrive at truths she can believe in. The shows extensive uses of
music direct our attention to how Ally puts herself on trial. Ally McBeal is more
about personal law, finding a balance between professional and private goals, hearing the
music of inner justice.
Obviously television shows have a long history of using
musical soundtracks, primarily to define the situation, mood, and style of the show. In Perry
Mason, for instance, questioning woodwinds play behind suspenseful scenes, and scenes
end with dramatic brass flourishes. In the shows theme song, the understated low
brass creeps into a forthright trumpet solo, a sort of musical analog to Perry himself, a
go-ahead, definitive guy. Under this musical conflict beat those menacing piano triplets.
This Cold War-era justice takes itself seriously.
More recent television shows make more complex musical
references for character insights or thematic commentary. Murphy Brown, investigative
reporter on FYI and seeker of justice, is heavy into Motown.  In Homicide: Life on the Streets, Andre
Braughers character Frank is unwilling to participate in his baby daughters
baptism. He questions a nun about where to find his lost faith. Back in the squad room, Death summons him yet again to a dreary
apartment house. There he finds a very old woman, dead of natural causes. Ravels Pavane
for a Dead Princess plays on a radio in the background. The music propels Frank to an
epiphany as he recalls the nuns words about finding the sacred in the everyday. At
that moment, his character receives at least a moment of grace, a partial restoration of
faith.
From the extensive uses of music in Ally McBeal, it
seems that David E. Kelley has learned from these techniques and more. Most of the songs
we hear on the show fall into the Classic Oldies category. As Lawrence Kasdans The
Big Chill showed, recycled hits from the sixties and seventies have a strong appeal
with baby boomers.
Ally McBeal uses music on many levels to convey
truths about living and working in law. The loudest example often comes last on the show.
After working all day as members of the Bar, lawyers from Cage and Fish practice lifting
elbows at another kind of bar. Here, dancing and singing provide a harmony to resolve
office conflicts. The law office is upstairs, while the bar is in the same building on the
ground floor. This set-up presents the metaphor of the well-rounded lawyer, exercising
upstairs logic on cases, and accessing a more visceral "lower level" to defuse
daily tensions.
The excellent singer Vonda Shepard appears nearly every
week as a bar regular to provide a free-wheeling, party atmosphere. Her piano is a
versatile magic barrel with upbeat rock, soul, and blues on tap. Shepard even has her own
best-selling CD featuring songs from the show, with a new release just out called By
7:30. In a few episodes, the big-voiced Jennifer Holliday joins the cast as the Cage
and Fish group attends a church with a mostly African-American congregation. This
situation is similar to when Murphy Brown, the quintessential WASP, tries to "get
down" singing Natural Woman with Aretha Franklin. Whereas Aretha ends up
telling Murphy not to sing so loudly, the Ally McBeal characters let loose along
with the rest of the congregation. The episodes of singing and dancing in the church are
similar to those in the bar. Both places are "big tents" of democracy,
cathedrals of expansive acceptance.
Office grind evolves into bump and grind at the bar. The
women employed at Cage and Fish all rock together for girlfriend bonding and to rise above
petty squabbles.  Georgia and
Billy dance close and make up from their argument du jour. The anal retentive John
Cage can forget "taking a moment" and discover why he is "drawn" to
Nell. On a recent show (aired April 19, 1999), Nell presents The Biscuit with a birthday
surprise at the bar, a live appearance by Johns musical idol Barry White.
Office pratfalls show clumsy surface embarrassments, but
dancing is the way to get in synch psychologically. Ensemble dancing provides a metaphor
for the blend of individualism and teamwork required of members in a large practice. In
the unisex bathroom, John Cage often bolsters his self- confidence by solo dancing to an
inner Barry White rendition. In one episode, Richard Fish walks in on John. Instead of
reacting with the expected ridicule, Fish joins his law partner in dance. Then Elaine sees
the two and falls into step with perfectly choreographed kicks and turns.
We are not surprised at Elaines participation. She
haunts the
bathroom, waiting for juicy gossip and sensuous dance moves. Several episodes have
featured a talent show at the bar with all the people from the firm showcasing their
talents. Elaine generally coordinates this effort so she can star. "This show is all
about me," she says.
But we are surprised when Ling arrives and adds her
high-booted leg to the chorus line. She will practice law with them, so the group also
makes room for her in the dance. It is noteworthy that Ally has never been a member of
this musical collaboration. More on that later.
Music expresses each characters ideal for developing
a workable personal style. In an episode aired the first season, John Cage tells Fish to
listen for the bells. When those golden bells ring, the character is ready, prepared to
fight in court with the best defense for the client. For Fish, the bells clunk together
more often than they ring. This clunk has happened so many times, the sound effect has an
additional connotation, less golden bells than brass balls. This motif fits Fish perfectly
as he blatantly asserts his masculine power in a way that is never separated from sexual
or monetary gains. In one episode (aired April 5, 1999), Fish says that getting fees from
both sides is "music to my ears."
The Biscuit used the bells to supplement his smile
therapy. What began as a parody of self-esteem exercises has evolved into a more poignant
search for understanding. Tracey Ullman has put in a few star turns as Allys wacky
therapist recommending that each person have a "song" to boost the ego. How can
we forget Traceys own bubbly dancing to her song entitled, of course, Tracy?
Traceys flamboyant style contrasts starkly with Allys feeble attempts to find
inner music.
Eventually, Ally conjures up I Know Something About
Love, but it plays in her head only at half-tempo. Later in the series, we see the
song work its magic, and not just on Ally. Ally stands on a street corner in downtown
Boston waiting for the crossing light. She thinks of her song and falls into the
energizing beat. Her shoulders shrug, her knees bounce, and before long, all the folks
waiting with her bebop to their inner songs. By the time the light changes, the sea of
humanity crosses the street in a scene reminiscent of the Monty Python sketch featuring
the Ministry of Silly Walks.
To be sure, the scene is comic, but in context, it might
be viewed as a metaphor for all of The Law. What starts out as an individual aberration,
if believed with enough fervency, has the power to change others. If enough join in, the
right aberration becomes mainstream.
For Ally, hearing a smooth music track in a group of
strangers is one thing, but to hear it in the office is quite another. I do not recall any
scene in which Ally joins her colleagues in a dance number. She dances, but with the
blow-up doll or alone in her apartment.
At one point, her solo dance is frantic, almost spastic.
She is Nora in Ibsens play A Dolls House. Then Allys roommate
Renee walks in, and the needle shrieks across the track. The "scratching needle"
sound effect breaks into her psyche to tell her that her dream music and reality are too
far apart. Allys budding confidence is quashed.
Allys roommate Renee already possesses a powerful
inner song. As a present for Ally, Renee sings a duet with Allys doctor boyfriend
Greg Butters at the bar. In their luminous, untroubled faces, we see that Renee and Greg
are happy inside the music. They are a better-matched couple than Ally and Greg, or Ally
and Billy, who have never sung together. The camera pans to Allys abandoned place at
the table.
Poor Ally! Whenever she starts on a musical flight of
fancy, the needle inevitably scratches across her inner LP.  She cannot feel the
snow dance in her waking life. She tells Tracey the song therapist that she has day dreams
in the office. She cannot wake up. Her inner voice is all wrong for the world out there.
The scratchy needle interrupts, and she startles with her trademark big eyes and shocked
gasp.
In the courtroom scenes of Ally McBeal, justice is
not always served. As in reality, money talks, the guilty may walk, and law practitioners
are fallible. However, to minimize these imperfections, David Kelley uses music to bring a
degree of resolution to each show. Music carries its own internal scale of justice.
Harmony, consonance, and the tonic key, or the musical "home," all push a melody
to its resolution. The familiar melodies of these classic hits capture the vitality and
assertiveness of the human spirit.
Ally shouldnt worry. Her inner music is already
there. She just needs to listen. The melody most true to her heart is the Ally McBeal
theme song that introduces every episode: Now Im ready to sing, I been
searchin my soul tonight, I see theres so much more tonight, Now I know I can
shine the light, To find the way back ho--ooo--ome."


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Team 7 is a group of goal-oriented, hardworking individuals that collaborate together to bring tasks to life. We view challenges as building blocks in the framework of learning; as an opportunity for growth and development.
Marius-Nicolae Toc - Chairman
Kayla Phillips Sanchez - Secretary
Pranav Bheemsetty - Director of Primary Research
Yuvraj Singh - Director of Secondary Research
Yitong Zhong - Assistant Director of Secondary Research
Daniil Plohih - Director of Presentation Production
Emily Radesch - Co-Director of Content Creation
Ahdra Merali - Co-Director of Content Creation
Coordinating our team was sometimes very difficult, especially due to our large size (8 group members), and the differences in our schedules. It seemed like every time we agreed upon a time there was at least one person who had a schedule clash. We tried to solve this problem by using software to coordinate our meetings. A survey was put out before each meeting, and each person checked off the times they were available at. This helped pick out the most popular time in a much quicker and more efficient manner.
At the beginning we also had problems assigning clear responsibilities. We wanted everyone to be involved in all sections of the project, but we were lacking coordination and accountability. This was solved by dividing the team up into pairs, where each pair had a main task. The first pair focused on primary research, the second pair on secondary research, the third pair on report writing, and the fourth pair on presentation and design. While we all ended up doing a little bit of each, this ultimately helped us hold people accountable and meet our deadlines.
Besides problems with scheduling and planning, we found that our team worked well together overall. We all got to know each other better through this project, and have become good friends. We all approached this project as a learning experience, making sure that each person was involved in each task, rather than completely separating ourselves into different groups. We made sure that we all learned how to do primary research and secondary research, and that we all contributed to the report and presentation. However, we also used each person’s strengths to our advantage. For example, we had a film major in our group, allowing us to make a professional looking website. We also had four computer science students, which helped us with website design, and group members who really enjoy writing, helping us with the report.
Our biggest problem was trying to get the project finalized at the end. We found that we had left many of the important things for the last moment, and had to meet several times to complete the last few but very important tasks. If we had to do this again, we would have definitely planned out our work better, aiming to finish our project at least a week in advance.
Overall, this project was a great learning experience, and allowed us to not only improve our interviewing, presentation, and writing skills, but also to plan and work as a group. After having done this, we feel that we are much more prepared for future group work, and can better deal with splitting responsibilities and tasks. Apart from all the learning, we also enjoyed spending time with each other and making new friends. We will definitely stay friends well after this class, and can help each other in our next modules, such as economics.


--- title: Conference permalink: "conference.html" order: 7 --- {% include head.html %}
{% include navdata.html %} {% include header.html %}Note: This page anticipates when a web conference is scheduled. The actual date may change as circumstances require. Check with your instructor for the correct dates and times.
To participate:
Note: You might need to run the Launcher utility when you first join the course. Blackboard will prompt you to "Download the Blackboard Collaborate Launcher".
One or two days after the live session, you can use the same link to play a recording of the Collaborate session. Remember to select the Recordings tab on the Collaborate page and search for the date of the live broadcast.


HTTP, Promises, and TDD (Week 6) - Learning Objectives
HTTP (W6D1) - Learning Objectives
HTTP
Match the header fields of HTTP with a bank of definitions.
Host: Root path of our URI (typically a domain like appacademy.io, could also be an IP address)
User-Agent: Information about which browser the request originated from
Referer: The URL that you’re coming from (such as when you click a link to a new site)
Accept: What the client can receive. May be expansive to accept all kinds of data, or limited, such as only accepting application/json
Content-*: Defines details about the body, indicating what format it is in, such as application/json or application/x-www.form-urlencoded
Location: A server typically adds this to a response so that the client can perform a redirection
Expires: When the response should be considered stale and needs to be refetched. Often used to cache a response so that subsequent requests can load directly from the cache instead of hitting the server.
Set-Cookie: The server is telling the client to create/update a key/value pair in its cookies.
Matching HTTP verbs (GET, PUT, PATCH, POST, DELETE) to their common uses.
GET: Direct requests. Do not contain a body, simply asking for data.
PUT: Update a resource on the server. Contain the whole resource to be updated.
PATCH: Update a resource on the server. Does not need to have the whole resource, usually just the identifier and what fields are being updated.
POST: Creating a new resource on the server. Usually what is generated when we submit a form, with the form’s data being passed in the body of the request.
DELETE: Destroy a resource on the server, such as removing a product, or logging out a user (destroying their session)
Match common HTTP status codes (200, 302, 400, 401, 402, 403, 404, 500) to their meanings.
100s: Informational
200s: Successful
200 OK: received and fulfilled, typically with a body that has the requested data
300s: Redirection
302 Found: the resource has moved. We usually see this with a Location header, where a browser will automatically redirect the request to the new location.
400s: Client Error
400 Bad Request: General response that the server couldn’t understand your request. Often seen with typos, if a more specific 404 is not issued.
401 Unauthorized: The resource may exist, but you’re not allowed to see it unless you are authorized. (Try logging in with valid credentials before sending the request again.)
403 Forbidden: The resource may exist, but you’re not allowed to see it, even if you are logged in. Can also be seen if you’re trying to perform an action that is not allowed (such as creating a duplicate record). Maybe this is a resource that you need special permissions for, like admin access.
404 Not Found: The resource doesn’t exist. It may be that it hasn’t been created, or that you just had a typo in what you were requesting.
500s: Server Error
500 Internal Server Error: The server tried to process your request, but something went wrong, typically there was some kind of runtime error in the server code due to your request.
Send a simple HTTP request to google.com
We can use netcat in the terminal to make a connection to a URL and send an HTTP request
nc google.com 80 opens our connection to google.com
After we make our connection, we specify the three parts of an HTTP request:
Request line
Headers
Body
GET / HTTP/1.1 creates the request-line, indicating our verb (GET), URI (/), and version (HTTP/1.1)
any other headers we would like (optional), such as Accept: application/json
body of the request (optional), such as myKey=myValue
Write a very simple HTTP server using ‘http’ in node with paths that will result in the common HTTP status codes.
We can instantiate a Promise object using the new keyword.
The Promise takes in a callback that we can invoke, taking in two arguments: typically labelled resolve and reject.
resolve is invoked when we want to indicate our function has successfully completed. A value can be passed as the successful return value.
reject is invoked when we want to indicate that our function failed in some way. A value can be passed as the fail value (what would be used in a catch or the second argument to then).
Be comfortable with using the following tags. This is review/tangential material and will not be tested directly on the assessment, but if it would appear in a problem, you should know what it is doing.
the root element of the HTML document
valid child elements are
and
- contains metadata for the HTML
- often will include a <title> as well as <link> and <script async> tags
what appears in the tab or title bar of the browser
allows us to link another file, we’ll often see it for our css
rel attribute specifies the relation of the link (“stylesheet”, less common may be “icon”, “author”, etc.)
href attribute specifies the URL of the linked file (can be an absolute url or relative path to file in your project)
allows us to add JavaScript to be run
can include code between tags, or provide a src
src attribute specifies the URL of the linked script (if this attribute is present, the


Care Card
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John Goff
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Wife
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01234 567890
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Beer.
Allergic to hard work.
Support details go here
Medical details go here


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title: "Week 3 Lesson Plan"
permalink: /week3.html/
author_profile: true
---
{% include base_path %}
The reading worksheet file can be found here.
The participation form can be found here.
To navigate to individual lesson plans:
Tuesday, May 28th
Wednesday, May 29th
Thursday, May 30th
Friday, May 31st
Tuesday, May 28th - Language (#9/28 of class):
Learning Objectives:
Resources: Pitch Rubric, Slides, Pitch Sheets (1, 2, 3), & Quiz 2, Quiz 2 key
- LO1: Continue to build a supportive classroom culture & discuss science communication
- Handouts on Science Communication Elevator pitches
- Determine components as a class for rubric on SciComm pitch
- LO2: Define, identify, and apply previous constructs that we have discussed in class
- Quiz on material covered since last quiz: multiple choice and short answer
- LO3: Describe the basic fundamental principles of language
- Discuss real-world implications of language interventions, language theories, and development
(e.g., this) as another subarea in psychology that interacts with cognitive psychology
- Discuss Goldstein chapter 11 on language as well as recent language research from the SciAm article
- LO4: Summarize and critically analyze academic journal articles
- With the language research as our background, we can now discuss the methodology used to infer cognition, language development, etc.
Readings
Articles:
I asked how accessible & interesting students found each article on a scale of 1-5 (Not At All Accessible/Interesting vs. Very Accessible/Interesting). Yu et al. (2019) Accessibility (N=7), M = 4.43, SD = 0.79; Interesting M = 4, SD = 0.82; Bergelson & Aislin (2017) Accessibility (N=7), M = 4.43, SD = 0.79; Interesting M = 4.14, SD = 0.90
- Bergelson, E., & Aslin, R. N. (2017). Nature and origins of the lexicon in 6-mo-olds. Proceedings of the National Academy of Sciences, 114(49), 12916–12921. https://doi.org/10.1073/pnas.1712966114. PDF here
- Yu, C., Suanda, S. H., & Smith, L. B. (2019). Infant sustained attention but not joint attention to objects at 9 months predicts vocabulary at 12 and 15 months. Developmental Science, 22(1), e12735. https://doi.org/10.1111/desc.12735. PDF here
Science News:
- Gutman, R. (2017, November 20). The Connected Vocabularies of Six-Month-Olds. Retrieved from The Atlantic website: https://www.theatlantic.com/science/archive/2017/11/babies-language-vocabularies/546320/. PDF here
- Hayakawa, S., & Marian, V. (2019). How Language Shapes the Brain. Retrieved from Scientific American Blog Network website: https://blogs.scientificamerican.com/observations/how-language-shapes-the-brain/. PDF here
Assignments Due:
- Note that we will have our second quiz today - expect feedback from me later in the day
- List of potential scientists (multiple in case of duplicates) that you want to cover for Wikipedia profile
- Reading worksheet - if you want to continue filling this out
►Return to the top of the page
Wednesday, May 29th - Working Memory / Cognitive Training (#10/28 of class):
Learning Objectives:
Resources: Slides, Brain Training Activity Packets (1,2,3,4,5,6,7,8,9)
- LO1: Continue to build a supportive classroom culture & discuss science communication
- In class practice of your written SciComm pitches to a partner
- Discuss what Duong article meant in terms of science journalism - https://www.eurekalert.org/
- Towards the end of class, can discuss how research on working memory applies to science writing (e.g., sentence construction)
- LO2: Describe the basic foundamental principles of working memory & the claims underlying cognitive training
- Discussion of Goldstein, Chpt 5 and historical perspectives on short-term & working memory
- Recall what we learned on attention last week & its relation to working memory
- LO3: Summarize and critically analyze academic journal articles
- Compare and contrast the evidence for & against working memory training & how science communicators cover this topic
- Critically discuss the academic journal articles: what does this mean for society at large?
Readings
Articles:
I asked how accessible & interesting students found each article on a scale of 1-5 (Not At All Accessible/Interesting vs. Very Accessible/Interesting). Kable et al. (2017) Accessibility (N=6), M = 4.17, SD = 0.75; Interesting M = 4.17, SD = 0.75; Yin et al. (2019) Accessibility (N=6), M = 4.17, SD = 0.75; Interesting M = 4.17, SD = 0.75
- Kable, J. W., Caulfield, M. K., Falcone, M., McConnell, M., Bernardo, L., Parthasarathi, T., … Lerman, C. (2017). No Effect of Commercial Cognitive Training on Brain Activity, Choice Behavior, or Cognitive Performance. Journal of Neuroscience, 37(31), 7390–7402. https://doi.org/10.1523/JNEUROSCI.2832-16.2017. PDF here
- Yin, S., Sui, J., Chiu, Y.-C., Chen, A., & Egner, T. (2019). Automatic Prioritization of Self-Referential Stimuli in Working Memory. Psychological Science, 30(3), 415–423. https://doi.org/10.1177/0956797618818483. PDF here
Science News:
- Noe, A. (2017). More Bad News For Brain-Training Games. Retrieved from NPR.org website: https://www.npr.org/sections/13.7/2017/07/14/536759455/more-bad-news-for-brain-training-games. PDF here
- Duong, Y. (2019). It’s Not Your Fault -- Your Brain is Self-Centered. Retrieved from https://today.duke.edu/2019/03/its-not-your-fault-your-brain-self-centered. PDF here
Assignments Due:
- Science Communication Pitch of Yu et al. (2019), Kable et al. (2017), MacCormack and Linquist (2019), Siegel et al. (2018), Wechsler et al. (2018), Kang and Wheatley (2017), OR Middlebrooks et al. (2017); you can't use the same article you did for your tweet, headline, or opening paragraph, and we will try to make sure everyone in class has a different article for the pitch.
- Reading worksheet - if you want to continue filling this out
- Expect feedback from me on your opening paragraph assignment
►Return to the top of the page
Thursday, May 30th - LTM: Structure (Episodic Memory) (#11/28 of class):
Learning Objectives:
Resources: Slides
- LO1: Continue to build a supportive classroom culture & discuss science communication
- Paragraph-by-paragraph breakdown of the scientist summary piece we read for today
- Comparison between what the actual article (JoN) discussed vs. what the scientist summary piece did
- Discuss reverse outlines - for multi paragraph expansion of SciComm & scientist summary pieces
- LO2: Describe the basic fundamental principles of long-term memory structure
- What is episodic memory? How does it relate to knowledge? Autobiographical memory? etc. Discuss Goldstein Chpt 6
- LO3: Summarize and critically analyze academic journal articles
- Now that research has been contextualized, what makes a memory its own event? How are memories constructed (cc: Science News piece from today)? Different perspectives on event segmentation.
Readings
Articles:
I asked how accessible & interesting students found each article on a scale of 1-5 (Not At All Accessible/Interesting vs. Very Accessible/Interesting). Ben-Yakov and Henson (2018) Accessibility (N=9), M = 4.11, SD = 1.17; Interesting M = 3.89, SD = 0.93; Uitvlugt and Healey (2019) Accessibility (N=9), M = 4.33, SD = 0.87; Interesting M = 4.11, SD = 0.93
- Uitvlugt, M. G., & Healey, M. K. (2019). Temporal Proximity Links Unrelated News Events in Memory. Psychological Science, 30(1), 92–104. https://doi.org/10.1177/0956797618808474. PDF here
- Ben-Yakov, A., & Henson, R. N. (2018). The Hippocampal Film Editor: Sensitivity and Specificity to Event Boundaries in Continuous Experience. Journal of Neuroscience, 38(47), 10057–10068. https://doi.org/10.1523/JNEUROSCI.0524-18.2018. PDF here
Science Summary:
- Williams, A. N., Postans, M., & Hodgetts, C. J. (2019). How the Human Brain Segments Continuous Experience. Journal of Neuroscience, 39(17), 3172–3174. https://doi.org/10.1523/JNEUROSCI.3041-18.2019. PDF here
Science News:
- Shute, N. (2014). Our Brains Rewrite Our Memories, Putting Present In The Past. Retrieved from NPR.org website: https://www.npr.org/sections/health-shots/2014/02/04/271527934/our-brains-rewrite-our-memories-putting-present-in-the-past. PDF here
Assignments Due:
►Return to the top of the page
Friday, May 31st - LTM: Processes & Mechanisms (#12/28 of class):
Learning Objectives:
Resources: Slides
- LO1: Continue to build a supportive classroom culture & discuss science communication
- Discuss Wikipedia profile
- Discuss a rubric for science summary pieces based off of the ripples article + previous Journal club article
- LO2: Describe the basic mechanisms of memory processes
- Discuss Goldstein chapter 7 about memory processes
- Review LTM: structure materialk
- LO3: Summarize and critically analyze academic journal articles
- Now that the research has been contextualized on what are considered basic memory processes, how does our previous discussion on WEIRD samples apply here? Are these basic memory principles?
- What are real-world implications? What are ongoing questions in the memory field?
Readings
Articles:
I asked how accessible & interesting students found each article on a scale of 1-5 (Not At All Accessible/Interesting vs. Very Accessible/Interesting). Vaz et al. (2019) Accessibility (N=7), M = 3.71, SD = 1.38; Interesting M = 3.29, SD = 1.11
- Chapter 7 from the 4th edition of Bruce Goldstein’s Cognitive Psychology textbook https://www.cengage.com/c/cognitive-psychology-connecting-mind-research-and-everyday-experience-4e-goldstein/9781285763880. PDF here
- Vaz, A. P., Inati, S. K., Brunel, N., & Zaghloul, K. A. (2019). Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory. Science, 363(6430), 975–978. https://doi.org/10.1126/science.aau8956. PDF here
Science Summary:
- Gelinas, J. (2019). Ripples for memory retrieval in humans. Science, 363(6430), 927–928. https://doi.org/10.1126/science.aaw6767. PDF here
Assignments Due:
- Expect feedback from me on your SciComm pitch assignment
- Multiple paragraphs of SciComm article - whatever you chose for your opening paragraph, you can expand on, and you should incorporate feedback from that assignment.
- Reading worksheet - if you want to continue filling this out
►Return to the top of the page


Image Segmentation DeepLabV3 on Android¶
Author: Jeff Tang
Reviewed by: Jeremiah Chung
Introduction¶
Semantic image segmentation is a computer vision task that uses semantic labels to mark specific regions of an input image. The PyTorch semantic image segmentation DeepLabV3 model can be used to label image regions with 20 semantic classes including, for example, bicycle, bus, car, dog, and person. Image segmentation models can be very useful in applications such as autonomous driving and scene understanding.
In this tutorial, we will provide a step-by-step guide on how to prepare and run the PyTorch DeepLabV3 model on Android, taking you from the beginning of having a model you may want to use on Android to the end of having a complete Android app using the model. We will also cover practical and general tips on how to check if your next favorable pre-trained PyTorch models can run on Android, and how to avoid pitfalls.
Note
Before going through this tutorial, you should check out PyTorch Mobile for Android and give the PyTorch Android HelloWorld example app a quick try. This tutorial will go beyond the image classification model, usually the first kind of model deployed on mobile. The complete code repo for this tutorial is available here.
Learning Objectives¶
In this tutorial, you will learn how to:
- Convert the DeepLabV3 model for Android deployment.
- Get the output of the model for the example input image in Python and compare it to the output from the Android app.
- Build a new Android app or reuse an Android example app to load the converted model.
- Prepare the input into the format that the model expects and process the model output.
- Complete the UI, refactor, build and run the app to see image segmentation in action.
Pre-requisites¶
- PyTorch 1.6 or 1.7
- torchvision 0.7 or 0.8
- Android Studio 3.5.1 or above with NDK installed
Steps¶
1. Convert the DeepLabV3 model for Android deployment¶
The first step to deploying a model on Android is to convert the model into the TorchScript format.
Note
Not all PyTorch models can be converted to TorchScript at this time because a model definition may use language features that are not in TorchScript, which is a subset of Python. See the Script and Optimize Recipe for more details.
Simply run the script below to generate the scripted model deeplabv3_scripted.pt:
import torch
# use deeplabv3_resnet50 instead of resnet101 to reduce the model size
model = torch.hub.load('pytorch/vision:v0.7.0', 'deeplabv3_resnet50', pretrained=True)
model.eval()
scriptedm = torch.jit.script(model)
torch.jit.save(scriptedm, "deeplabv3_scripted.pt")
The size of the generated deeplabv3_scripted.pt model file should be around 168MB. Ideally, a model should also be quantized for significant size reduction and faster inference before being deployed on an Android app. To have a general understanding of quantization, see the Quantization Recipe and the resource links there. We will cover in detail how to correctly apply a quantization workflow called Post Training Static Quantization to the DeepLabV3 model in a future tutorial or recipe.
2. Get example input and output of the model in Python¶
Now that we have a scripted PyTorch model, let’s test with some example inputs to make sure the model works correctly on Android. First, let’s write a Python script that uses the model to make inferences and examine inputs and outputs. For this example of the DeepLabV3 model, we can reuse the code in Step 1 and in the DeepLabV3 model hub site. Add the following code snippet to the code above:
from PIL import Image
from torchvision import transforms
input_image = Image.open("deeplab.jpg")
preprocess = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)
with torch.no_grad():
output = model(input_batch)['out'][0]
print(input_batch.shape)
print(output.shape)
Download deeplab.jpg from here, then run the script above and you will see the shapes of the input and output of the model:
torch.Size([1, 3, 400, 400])
torch.Size([21, 400, 400])
So if you provide the same image input deeplab.jpg of size 400x400 to the model on Android, the output of the model should have the size [21, 400, 400]. You should also print out at least the beginning parts of the actual data of the input and output, to be used in Step 4 below to compare with the actual input and output of the model when running in the Android app.
3. Build a new Android app or reuse an example app and load the model¶
First, follow Step 3 of the Model Preparation for Android recipe to use our model in an Android Studio project with PyTorch Mobile enabled. Because both DeepLabV3 used in this tutorial and MobileNet v2 used in the PyTorch HelloWorld Android example are computer vision models, you can also get the HelloWorld example repo to make it easier to modify the code that loads the model and processes the input and output. The main goal in this step and Step 4 is to make sure the model deeplabv3_scripted.pt generated in Step 1 can indeed work correctly on Android.
Now let’s add deeplabv3_scripted.pt and deeplab.jpg used in Step 2 to the Android Studio project and modify the onCreate method in the MainActivity to resemble:
Module module = null;
try {
module = Module.load(assetFilePath(this, "deeplabv3_scripted.pt"));
} catch (IOException e) {
Log.e("ImageSegmentation", "Error loading model!", e);
finish();
}
Then set a breakpoint at the line finish() and build and run the app. If the app doesn’t stop at the breakpoint, it means that the scripted model in Step 1 has been successfully loaded on Android.
4. Process the model input and output for model inference¶
After the model loads in the previous step, let’s verify that it works with expected inputs and can generate expected outputs. As the model input for the DeepLabV3 model is an image the same as that of the MobileNet v2 in the HelloWorld example, we will reuse some of the code in the MainActivity.java file from HelloWorld for input processing. Replace the code snippet between line 50 and 73 in MainActivity.java with the following code:
final Tensor inputTensor = TensorImageUtils.bitmapToFloat32Tensor(bitmap,
TensorImageUtils.TORCHVISION_NORM_MEAN_RGB,
TensorImageUtils.TORCHVISION_NORM_STD_RGB);
final float[] inputs = inputTensor.getDataAsFloatArray();
Map<String, IValue> outTensors =
module.forward(IValue.from(inputTensor)).toDictStringKey();
// the key "out" of the output tensor contains the semantic masks
// see https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101
final Tensor outputTensor = outTensors.get("out").toTensor();
final float[] outputs = outputTensor.getDataAsFloatArray();
int width = bitmap.getWidth();
int height = bitmap.getHeight();
Note
The model output is a dictionary for the DeepLabV3 model so we use toDictStringKey to correctly extract the result. For other models, the model output may also be a single tensor or a tuple of tensors, among other things.
With the code changes shown above, you can set breakpoints after final float[] inputs and final float[] outputs, which populate the input tensor and output tensor data to float arrays for easy debugging. Run the app and when it stops at the breakpoints, compare the numbers in inputs and outputs with the model input and output data you see in Step 2 to see if they match. For the same inputs to the models running on Android and Python, you should get the same outputs.
Warning
You may see different model outputs with the same image input when running on an Android emulator due to some Android emulator’s floating point implementation issue. So it is best to test the app on a real Android device.
All we have done so far is to confirm that the model of our interest can be scripted and run correctly in our Android app as in Python. The steps we walked through so far for using a model in an iOS app consumes the bulk, if not most, of our app development time, similar to how data preprocessing is the heaviest lift for a typical machine learning project.
5. Complete the UI, refactor, build and run the app¶
Now we are ready to complete the app and the UI to actually see the processed result as a new image. The output processing code should be like this, added to the end of the code snippet in Step 4:
int[] intValues = new int[width * height];
// go through each element in the output of size [WIDTH, HEIGHT] and
// set different color for different classnum
for (int j = 0; j < width; j++) {
for (int k = 0; k < height; k++) {
// maxi: the index of the 21 CLASSNUM with the max probability
int maxi = 0, maxj = 0, maxk = 0;
double maxnum = -100000.0;
for (int i=0; i < CLASSNUM; i++) {
if (outputs[i*(width*height) + j*width + k] > maxnum) {
maxnum = outputs[i*(width*height) + j*width + k];
maxi = i; maxj = j; maxk= k;
}
}
// color coding for person (red), dog (green), sheep (blue)
// black color for background and other classes
if (maxi == PERSON)
intValues[maxj*width + maxk] = 0xFFFF0000; // red
else if (maxi == DOG)
intValues[maxj*width + maxk] = 0xFF00FF00; // green
else if (maxi == SHEEP)
intValues[maxj*width + maxk] = 0xFF0000FF; // blue
else
intValues[maxj*width + maxk] = 0xFF000000; // black
}
}
The constants used in the code above are defined in the beginning of the class MainActivity:
private static final int CLASSNUM = 21;
private static final int DOG = 12;
private static final int PERSON = 15;
private static final int SHEEP = 17;
The implementation here is based on the understanding of the DeepLabV3 model which outputs a tensor of size [21, width, height] for an input image of width*height. Each element in the width*height output array is a value between 0 and 20 (for a total of 21 semantic labels described in Introduction) and the value is used to set a specific color. Color coding of the segmentation here is based on the class with the highest probability, and you can extend the color coding for all classes in your own dataset.
After the output processing, you will also need to call the code below to render the RGB intValues array to a bitmap instance outputBitmap before displaying it on an ImageView:
Bitmap bmpSegmentation = Bitmap.createScaledBitmap(bitmap, width, height, true);
Bitmap outputBitmap = bmpSegmentation.copy(bmpSegmentation.getConfig(), true);
outputBitmap.setPixels(intValues, 0, outputBitmap.getWidth(), 0, 0,
outputBitmap.getWidth(), outputBitmap.getHeight());
imageView.setImageBitmap(outputBitmap);
The UI for this app is also similar to that for HelloWorld, except that you do not need the TextView to show the image classification result. You can also add two buttons Segment and Restart as shown in the code repo to run the model inference and to show back the original image after the segmentation result is shown.
Now when you run the app on an Android emulator or preferably an actual device, you will see screens like the following:
Recap¶
In this tutorial, we described what it takes to convert a pre-trained PyTorch DeepLabV3 model for Android and how to make sure the model can run successfully on Android. Our focus was to help you understand the process of confirming that a model can indeed run on Android. The complete code repo is available here.
More advanced topics such as quantization and using models via transfer learning or of your own on Android will be covered soon in future demo apps and tutorials.


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College Football Fans Are Furious With CBS Announcer Gary Danielson
by Andrew HolleranCollege football fans are furious with CBS analyst Gary Danielson for how he insensitively handled the Georgia photographer’s scary injury on the sideline of Saturday’s game at Auburn.
A Bulldogs photographer was knocked out on the sideline late in the first half of Saturday afternoon’s game at Auburn. The female journalist was down, motionless, for several minutes. She was eventually stretchered off the field and taken to the hospital.
Thankfully, the Georgia photographer has since been released from the hospital. She appears to be OK.
Danielson, meanwhile, continues to get criticized for how he handled everything.
Danielson first did not realize that it was a photographer who was knocked out. He thought it was a player. Danielson then laughed when Brad Nessler mentioned that the photographer was not getting up. Finally, he thought it was a man, not a woman.
The longtime CBS analyst could not have handled it worse.
“Gary Danielson has called every single element of this collision and injury incorrectly,” longtime college football writer Pat Forde tweeted, summing it up best.
The photographer is thankfully OK, which is most important, but CBS cannot be happy with how its lead SEC analyst handled such a frightening moment.


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This attribute specifies the speaking pitch for the utterance.
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Speech synthesis engines or voices may constrain the minimum and maximum rates further.
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This attribute specifies the speaking rate for the utterance.
It is relative to the default rate for this voice.
1 is the default rate supported by the speech synthesis engine or specific voice
(which should correspond to a normal speaking rate).
2 is twice as fast, and 0.5 is half as fast. Values below 0.1 or above 10 are strictly disallowed,
but speech synthesis engines or specific voices may constrain the minimum and maximum rates further,
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even if you specify a value larger than 3.
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This attribute specifies the speech synthesis voice that the web application wishes to use.
When a SpeechSynthesisUtterance object is created this attribute must be initialized to null.
If, at the time of the speak() method call,
this attribute has been set to one of the SpeechSynthesisVoice objects returned by getVoices(),
then the user agent must use that voice. If this attribute is unset or null at the time of the speak()
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--- ---
|
TGUI
v0.7-dev
|
Class to store the position or size of a widget. More...
#include <Layout.hpp>
Public Member Functions | |
| Layout2d (sf::Vector2f constant={0, 0}) | |
| Default constructor to implicitly construct from an sf::Vector2f. More... | |
| Layout2d (Layout layoutX, Layout layoutY) | |
| Constructor to create the Layout2d from two Layout classes. More... | |
| Layout2d (const char *expression) | |
| Construct the layout based on a string which will be parsed to determine the value of the layout. More... | |
| Layout2d (const std::string &expression) | |
| Constructs the layout based on a string which will be parsed to determine the value of the layout. More... | |
| sf::Vector2f | getValue () const |
| Return the cached value of the layout. More... | |
| Layout2d | operator+ () |
| Unary plus operator for the Layout class. | |
| Layout2d | operator- () |
| Unary minus operator for the Layout class. | |
| Layout2d | operator+= (Layout2d right) |
| += operator for the Layout2d class | |
| Layout2d | operator-= (Layout2d right) |
| -= operator for the Layout2d class | |
| Layout2d | operator*= (Layout right) |
| *= operator for the Layout2d class | |
| Layout2d | operator/= (Layout right) |
| /= operator for the Layout2d class | |
| Layout2d | operator%= (Layout right) |
| %= operator for the Layout2d class (floating point modulo) | |
Public Attributes | |
| Layout | x |
| Layout to store the x component. | |
| Layout | y |
| Layout to store the y component. | |
Class to store the position or size of a widget.
You don't have to create an instance of this class, sf::Vector2f is implicitly converted to this class.
| tgui::Layout2d::Layout2d | ( | sf::Vector2f | constant = {0, 0} | ) |
Default constructor to implicitly construct from an sf::Vector2f.
| constant | Value of the layout |
| tgui::Layout2d::Layout2d | ( | const char * | expression | ) |
Construct the layout based on a string which will be parsed to determine the value of the layout.
| expression | String to parse |
| tgui::Layout2d::Layout2d | ( | const std::string & | expression | ) |
Constructs the layout based on a string which will be parsed to determine the value of the layout.
| expression | String to parse |
| sf::Vector2f tgui::Layout2d::getValue | ( | ) | const |
Return the cached value of the layout.


from contextlib import contextmanager
import copy
import itertools
import torch
import torch.cuda.comm
import torch.distributed as dist
if dist.is_available():
from torch.distributed.distributed_c10d import _get_default_group
from ..modules import Module
from .replicate import replicate
from .scatter_gather import scatter_kwargs, gather
from .parallel_apply import parallel_apply
from torch.cuda._utils import _get_device_index
def _find_tensors(obj):
r"""
Recursively find all tensors contained in the specified object.
"""
if isinstance(obj, torch.Tensor):
return [obj]
if isinstance(obj, (list, tuple)):
return itertools.chain(*map(_find_tensors, obj))
if isinstance(obj, dict):
return itertools.chain(*map(_find_tensors, obj.values()))
return []
[docs]class DistributedDataParallel(Module):
r"""Implements distributed data parallelism that is based on
``torch.distributed`` package at the module level.
This container parallelizes the application of the given module by
splitting the input across the specified devices by chunking in the batch
dimension. The module is replicated on each machine and each device, and
each such replica handles a portion of the input. During the backwards
pass, gradients from each node are averaged.
The batch size should be larger than the number of GPUs used locally.
See also: :ref:`distributed-basics` and :ref:`cuda-nn-dataparallel-instead`.
The same constraints on input as in :class:`torch.nn.DataParallel` apply.
Creation of this class requires that ``torch.distributed`` to be already
initialized, by calling :func:`torch.distributed.init_process_group`.
``DistributedDataParallel`` can be used in the following two ways:
(1) Single-Process Multi-GPU
In this case, a single process will be
spawned on each host/node and each process will operate on all the GPUs
of the node where it's running. To use ``DistributedDataParallel`` in
this way, you can simply construct the model as the following:
>>> torch.distributed.init_process_group(backend="nccl")
>>> model = DistributedDataParallel(model) # device_ids will include all GPU devices by default
(2) Multi-Process Single-GPU
This is the highly recommended way to use ``DistributedDataParallel``, with
multiple processes, each of which operates on a single GPU. This is
currently the fastest approach to do data parallel training using PyTorch
and applies to both single-node(multi-GPU) and multi-node data
parallel training. It is proven to be significantly faster than
:class:`torch.nn.DataParallel` for single-node multi-GPU data
parallel training.
Here is how to use it: on each host with N GPUs, you should spawn up N
processes, while ensuring that each process individually works on a single GPU
from 0 to N-1. Therefore, it is your job to ensure that your training script
operates on a single given GPU by calling:
>>> torch.cuda.set_device(i)
where i is from 0 to N-1. In each process, you should refer the following
to construct this module:
>>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
>>> model = DistributedDataParallel(model, device_ids=[i], output_device=i)
In order to spawn up multiple processes per node, you can use either
``torch.distributed.launch`` or ``torch.multiprocessing.spawn``
.. note:: ``nccl`` backend is currently the fastest and
highly recommended backend to be used with Multi-Process Single-GPU
distributed training and this applies to both single-node and multi-node
distributed training
.. note:: This module also supports mixed-precision distributed training.
This means that your model can have different types of parameters such
as mixed types of fp16 and fp32, the gradient reduction on these
mixed types of parameters will just work fine.
Also note that ``nccl`` backend is currently the fastest and highly
recommended backend for fp16/fp32 mixed-precision training.
.. note:: If you use ``torch.save`` on one process to checkpoint the module,
and ``torch.load`` on some other processes to recover it, make sure that
``map_location`` is configured properly for every process. Without
``map_location``, ``torch.load`` would recover the module to devices
where the module was saved from.
.. warning::
This module works only with the ``gloo`` and ``nccl`` backends.
.. warning::
Constructor, forward method, and differentiation of the output (or a
function of the output of this module) is a distributed synchronization
point. Take that into account in case different processes might be
executing different code.
.. warning::
This module assumes all parameters are registered in the model by the
time it is created. No parameters should be added nor removed later.
Same applies to buffers.
.. warning::
This module assumes all parameters are registered in the model of each
distributed processes are in the same order. The module itself will
conduct gradient all-reduction following the reverse order of the
registered parameters of the model. In other words, it is users'
responsibility to ensure that each distributed process has the exact
same model and thus the exact same parameter registration order.
.. warning::
This module assumes all buffers and gradients are dense.
.. warning::
This module doesn't work with :func:`torch.autograd.grad` (i.e. it will
only work if gradients are to be accumulated in ``.grad`` attributes of
parameters).
.. warning::
If you plan on using this module with a ``nccl`` backend or a ``gloo``
backend (that uses Infiniband), together with a DataLoader that uses
multiple workers, please change the multiprocessing start method to
``forkserver`` (Python 3 only) or ``spawn``. Unfortunately
Gloo (that uses Infiniband) and NCCL2 are not fork safe, and you will
likely experience deadlocks if you don't change this setting.
.. warning::
Forward and backward hooks defined on :attr:`module` and its submodules
won't be invoked anymore, unless the hooks are initialized in the
:meth:`forward` method.
.. warning::
You should never try to change your model's parameters after wrapping
up your model with DistributedDataParallel. In other words, when
wrapping up your model with DistributedDataParallel, the constructor of
DistributedDataParallel will register the additional gradient
reduction functions on all the parameters of the model itself at the
time of construction. If you change the model's parameters after
the DistributedDataParallel construction, this is not supported and
unexpected behaviors can happen, since some parameters' gradient
reduction functions might not get called.
.. note::
Parameters are never broadcast between processes. The module performs
an all-reduce step on gradients and assumes that they will be modified
by the optimizer in all processes in the same way. Buffers
(e.g. BatchNorm stats) are broadcast from the module in process of rank
0, to all other replicas in the system in every iteration.
Args:
module (Module): module to be parallelized
device_ids (list of int or torch.device): CUDA devices. This should
only be provided when the input module resides on a single
CUDA device. For single-device modules, the ``i``th
:attr:`module` replica is placed on ``device_ids[i]``. For
multi-device modules and CPU modules, device_ids must be None
or an empty list, and input data for the forward pass must be
placed on the correct device. (default: all devices for
single-device modules)
output_device (int or torch.device): device location of output for
single-device CUDA modules. For multi-device modules and
CPU modules, it must be None, and the module itself
dictates the output location. (default: device_ids[0] for
single-device modules)
broadcast_buffers (bool): flag that enables syncing (broadcasting) buffers of
the module at beginning of the forward function.
(default: ``True``)
process_group: the process group to be used for distributed data
all-reduction. If ``None``, the default process group, which
is created by ```torch.distributed.init_process_group```,
will be used. (default: ``None``)
bucket_cap_mb: DistributedDataParallel will bucket parameters into
multiple buckets so that gradient reduction of each
bucket can potentially overlap with backward computation.
:attr:`bucket_cap_mb` controls the bucket size in MegaBytes (MB)
(default: 25)
find_unused_parameters (bool): Traverse the autograd graph of all tensors
contained in the return value of the wrapped
module's ``forward`` function.
Parameters that don't receive gradients as
part of this graph are preemptively marked
as being ready to be reduced. Note that all
``forward`` outputs that are derived from
module parameters must participate in
calculating loss and later the gradient
computation. If they don't, this wrapper will
hang waiting for autograd to produce gradients
for those parameters. Any outputs derived from
module parameters that are otherwise unused can
be detached from the autograd graph using
``torch.Tensor.detach``. (default: ``False``)
check_reduction: when setting to ``True``, it enables DistributedDataParallel
to automatically check if the previous iteration's
backward reductions were successfully issued at the
beginning of every iteration's forward function.
You normally don't need this option enabled unless you
are observing weird behaviors such as different ranks
are getting different gradients, which should not
happen if DistributedDataParallel is correctly used.
(default: ``False``)
Attributes:
module (Module): the module to be parallelized
Example::
>>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
>>> net = torch.nn.DistributedDataParallel(model, pg)
"""
def __init__(self, module, device_ids=None,
output_device=None, dim=0, broadcast_buffers=True,
process_group=None, bucket_cap_mb=25,
find_unused_parameters=False,
check_reduction=False):
super(DistributedDataParallel, self).__init__()
assert any((p.requires_grad for p in module.parameters())), (
"DistributedDataParallel is not needed when a module "
"doesn't have any parameter that requires a gradient."
)
self.is_multi_device_module = len({p.device for p in module.parameters()}) > 1
self.is_cuda = all([p.device.type == 'cuda' for p in module.parameters()])
if not self.is_cuda or self.is_multi_device_module:
assert not device_ids and not output_device, (
"DistributedDataParallel device_ids and output_device arguments "
"only work with single-device CUDA modules, but got "
"device_ids {}, output_device {}, and module parameters {}."
).format(device_ids, output_device, {p.device for p in module.parameters()})
self.device_ids = None
self.output_device = None
else:
# Use all devices by default for single-device CUDA modules
if device_ids is None:
device_ids = list(range(torch.cuda.device_count()))
self.device_ids = list(map(lambda x: _get_device_index(x, True), device_ids))
if output_device is None:
output_device = device_ids[0]
self.output_device = _get_device_index(output_device, True)
if self.is_multi_device_module:
assert self.is_cuda, (
"DistributedDataParallel with multi-device module only works "
"with CUDA devices, but module parameters locate in {}."
).format({p.device for p in module.parameters()})
if process_group is None:
self.process_group = _get_default_group()
else:
self.process_group = process_group
self.dim = dim
self.module = module
self.broadcast_buffers = broadcast_buffers
self.find_unused_parameters = find_unused_parameters
self.require_backward_grad_sync = True
self.require_forward_param_sync = True
if check_reduction:
# This argument is no longer used since the reducer
# will ensure reduction completes even if some parameters
# do not receive gradients.
pass
MB = 1024 * 1024
# used for intra-node param sync and inter-node sync as well
self.broadcast_bucket_size = int(250 * MB)
# reduction bucket size
self.bucket_bytes_cap = int(bucket_cap_mb * MB)
# Sync params and buffers
module_states = list(self.module.state_dict().values())
if len(module_states) > 0:
self._distributed_broadcast_coalesced(
module_states,
self.broadcast_bucket_size)
self._ddp_init_helper()
def _ddp_init_helper(self):
"""
Initialization helper function that does the following:
(1) replicating the module from device[0] to the other devices
(2) bucketing the parameters for reductions
(3) resetting the bucketing states
(4) registering the grad hooks
(5) passing a handle of DDP to SyncBatchNorm Layer
"""
if self.device_ids and len(self.device_ids) > 1:
# only create replicas for single-device CUDA modules
#
# TODO: we don't need to replicate params in here. they're always going to
# be broadcasted using larger blocks in broadcast_coalesced, so it might be
# better to not pollute the caches with these small blocks
self._module_copies = replicate(self.module, self.device_ids, detach=True)
self._module_copies[0] = self.module
for module_copy in self._module_copies[1:]:
for param, copy_param in zip(self.module.parameters(), module_copy.parameters()):
copy_param.requires_grad = param.requires_grad
else:
self._module_copies = [self.module]
self.modules_params = [list(m.parameters()) for m in self._module_copies]
self.modules_buffers = [list(m.buffers()) for m in self._module_copies]
# Build tuple of (module, parameter) for all parameters that require grads.
modules_and_parameters = [
[
(module, parameter)
for module in replica.modules()
for parameter in filter(
lambda parameter: parameter.requires_grad,
module.parameters(recurse=False))
] for replica in self._module_copies]
# Build list of parameters.
parameters = [
list(parameter for _, parameter in replica)
for replica in modules_and_parameters]
# Checks if a module will produce a sparse gradient.
def produces_sparse_gradient(module):
if isinstance(module, torch.nn.Embedding):
return module.sparse
if isinstance(module, torch.nn.EmbeddingBag):
return module.sparse
return False
# Build list of booleans indicating whether or not to expect sparse
# gradients for the corresponding parameters.
expect_sparse_gradient = [
list(produces_sparse_gradient(module) for module, _ in replica)
for replica in modules_and_parameters]
# The bucket size limit is specified in the constructor.
# Additionally, we allow for a single small bucket for parameters
# that are defined first, such that their gradients don't spill into
# a much larger bucket, adding unnecessary latency after gradient
# computation finishes. Experiments showed 1MB is a reasonable value.
bucket_indices = dist._compute_bucket_assignment_by_size(
parameters[0],
[1024 * 1024, self.bucket_bytes_cap],
expect_sparse_gradient[0])
# Note: reverse list of buckets because we want to approximate the
# order in which their gradients are produced, and assume they
# are used in the forward pass in the order they are defined.
self.reducer = dist.Reducer(
parameters,
list(reversed(bucket_indices)),
self.process_group,
expect_sparse_gradient)
# passing a handle to torch.nn.SyncBatchNorm layer
self._passing_sync_batchnorm_handle(self._module_copies)
def __getstate__(self):
self._check_default_group()
attrs = copy.copy(self.__dict__)
del attrs['process_group']
del attrs['reducer']
return attrs
def __setstate__(self, state):
# If serializable, then the process group should be the default one
self.process_group = _get_default_group()
super(DistributedDataParallel, self).__setstate__(state)
self.__dict__.setdefault('require_forward_param_sync', True)
self.__dict__.setdefault('require_backward_grad_sync', True)
self._ddp_init_helper()
def _check_default_group(self):
pickle_not_supported = False
try:
if self.process_group != _get_default_group():
pickle_not_supported = True
except RuntimeError:
pickle_not_supported = True
if pickle_not_supported:
raise RuntimeError("DDP Pickling/Unpickling are only supported "
"when using DDP with the default process "
"group. That is, when you have called "
"init_process_group and have not passed "
"process_group argument to DDP constructor")
[docs] @contextmanager
def no_sync(self):
r"""
A context manager to disable gradient synchronizations across DDP
processes. Within this context, gradients will be accumulated on module
variables, which will later be synchronized in the first
forward-backward pass exiting the context.
Example::
>>> ddp = torch.nn.DistributedDataParallel(model, pg)
>>> with ddp.no_sync():
... for input in inputs:
... ddp(input).backward() # no synchronization, accumulate grads
... ddp(another_input).backward() # synchronize grads
"""
old_require_backward_grad_sync = self.require_backward_grad_sync
self.require_backward_grad_sync = False
try:
yield
finally:
self.require_backward_grad_sync = old_require_backward_grad_sync
def forward(self, *inputs, **kwargs):
if self.require_forward_param_sync:
self._sync_params()
if self.device_ids:
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
if len(self.device_ids) == 1:
output = self.module(*inputs[0], **kwargs[0])
else:
outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)
output = self.gather(outputs, self.output_device)
else:
output = self.module(*inputs, **kwargs)
if torch.is_grad_enabled() and self.require_backward_grad_sync:
self.require_forward_param_sync = True
# We'll return the output object verbatim since it is a freeform
# object. We need to find any tensors in this object, though,
# because we need to figure out which parameters were used during
# this forward pass, to ensure we short circuit reduction for any
# unused parameters. Only if `find_unused_parameters` is set.
if self.find_unused_parameters:
self.reducer.prepare_for_backward(list(_find_tensors(output)))
else:
self.reducer.prepare_for_backward([])
else:
self.require_forward_param_sync = False
return output
def scatter(self, inputs, kwargs, device_ids):
return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
def parallel_apply(self, replicas, inputs, kwargs):
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
def gather(self, outputs, output_device):
return gather(outputs, output_device, dim=self.dim)
def train(self, mode=True):
super(DistributedDataParallel, self).train(mode)
for module in self._module_copies[1:]:
module.train(mode)
def _distributed_broadcast_coalesced(self, tensors, buffer_size):
dist._broadcast_coalesced(self.process_group, tensors, buffer_size)
def _sync_params(self):
with torch.no_grad():
# only do intra-node parameters sync for replicated single-device
# CUDA modules
if self.device_ids and len(self.device_ids) > 1:
# intra-node parameter sync
result = torch.cuda.comm.broadcast_coalesced(
self.modules_params[0],
self.device_ids,
self.broadcast_bucket_size)
for tensors, module_params in zip(result[1:],
self.modules_params[1:]):
for tensor, param in zip(tensors, module_params):
param.set_(tensor)
# Assume we have just run the optimizer and zeroed the
# grads of the parameters on the root model. We need
# to zero the grads on all model replicas as well.
# This snippet is copied from torch.optim.Optimizer.
if param.grad is not None:
param.grad.detach_()
param.grad.zero_()
# module buffer sync
if self.broadcast_buffers and len(self.modules_buffers[0]) > 0:
# Synchronize buffers across processes.
# The process with rank 0 is considered the authoritative copy.
self._distributed_broadcast_coalesced(
self.modules_buffers[0],
self.broadcast_bucket_size)
# only do intra-node buffer sync for replicated single-device
# CUDA modules
if self.device_ids and len(self.device_ids) > 1:
# intra-node buffer sync
result = torch.cuda.comm.broadcast_coalesced(
self.modules_buffers[0],
self.device_ids,
self.broadcast_bucket_size)
for tensors, module_buffers in zip(result[1:],
self.modules_buffers[1:]):
for tensor, buffer in zip(tensors, module_buffers):
buffer.set_(tensor)
def _passing_sync_batchnorm_handle(self, module_copies):
for dev_idx, module in enumerate(module_copies):
for layer in module.modules():
if isinstance(layer, torch.nn.modules.SyncBatchNorm):
assert self.is_cuda, "SyncBatchNorm layers only work with CUDA modules"
layer._specify_ddp_gpu_num(
len(self.device_ids) if self.device_ids else 1)


@JvmOverloads fun bySize(minSize: Int, maxSize: Int = Integer.MAX_VALUE): AssociationRuleFilter
Sets the minimum and maximum number of items an association rule's body and head must contain to be accepted by the filter.
minSize
- The minimum number of items, which should be set. The minimum number of
items must be at least 0
maxSize
- The maximum number of items, which should be set. The maximum number of
items must be at least the minimum number of items


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--- layout: wpcf7_contact_form title: Contact form 1 date: 2012-01-31 19:11:49.000000000 -05:00 categories: [] tags: [] status: publish type: wpcf7_contact_form published: true meta: _mail_2: |- a:8:{s:6:"active";s:0:"";s:7:"subject";s:14:"[your-subject]";s:6:"sender";s:26:"[your-name] <[your-email]>";s:4:"body";s:121:"Message body: [your-message] -- This mail is sent via contact form on Blog de Jorge Gamba http://www.jorgegamba.com/blog";s:9:"recipient";s:12:"[your-email]";s:18:"additional_headers";s:0:"";s:11:"attachments";s:0:"";s:8:"use_html";s:0:"";} _messages: a:22:{s:12:"mail_sent_ok";s:43:"Your message was sent successfully. Thanks.";s:12:"mail_sent_ng";s:93:"Failed to send your message. Please try later or contact the administrator by another method.";s:16:"validation_error";s:74:"Validation errors occurred. Please confirm the fields and submit it again.";s:12:"accept_terms";s:35:"Please accept the terms to proceed.";s:13:"invalid_email";s:28:"Email address seems invalid.";s:16:"invalid_required";s:31:"Please fill the required field.";s:17:"captcha_not_match";s:31:"Your entered code is incorrect.";s:13:"upload_failed";s:22:"Failed to upload file.";s:24:"upload_file_type_invalid";s:30:"This file type is not allowed.";s:21:"upload_file_too_large";s:23:"This file is too large.";s:23:"upload_failed_php_error";s:38:"Failed to upload file. Error occurred.";s:23:"quiz_answer_not_correct";s:27:"Your answer is not correct.";s:17:"akismet_says_spam";s:93:"Failed to send your message. Please try later or contact the administrator by another method.";s:4:"spam";s:93:"Failed to send your message. Please try later or contact the administrator by another method.";s:11:"invalid_url";s:18:"URL seems invalid.";s:11:"invalid_tel";s:31:"Telephone number seems invalid.";s:14:"invalid_number";s:28:"Number format seems invalid.";s:16:"number_too_small";s:25:"This number is too small.";s:16:"number_too_large";s:25:"This number is too large.";s:12:"invalid_date";s:26:"Date format seems invalid.";s:14:"date_too_early";s:23:"This date is too early.";s:13:"date_too_late";s:22:"This date is too late.";} _additional_settings: '' _mail: |- a:7:{s:7:"subject";s:14:"[your-subject]";s:6:"sender";s:26:"[your-name] <[your-email]>";s:4:"body";s:179:"From: [your-name] <[your-email]> Subject: [your-subject] Message Body: [your-message] -- This mail is sent via contact form on Blog de Jorge Gamba http://www.jorgegamba.com/blog";s:9:"recipient";s:23:"contacto@jorgegamba.com";s:18:"additional_headers";s:0:"";s:11:"attachments";s:0:"";s:8:"use_html";s:0:"";} _form: |-
Su Nombre (requerido)
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[submit "Enviar"]
[your-subject]
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From: [your-name] <[your-email]>
Subject: [your-subject]
Message Body:
[your-message]
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Shuffling your stack and revealing less than half of it, because of the Poor Vision event.
{% else %}Shuffling your stack and revealing half of it.
{% endif %}Revealed {% if resultpositive|length == 0 %} {{ resultnegative|length }} blank{{ resultnegative|length|pluralize }} {% elif resultnegative|length == 0 %} {{ resultpositive|length }} risk{{ resultpositive|length|pluralize }} {% else %} {{ resultpositive|length }} risk{{ resultpositive|length|pluralize }} and {{ resultnegative|length }} blank{{ resultnegative|length|pluralize }} {% endif %}
{% else %} {% ifequal episode.number 1 %} {% ifequal resultpositive|length 0 %}ElinI can’t inspect your safety as well as I’m used to. Here are the results… I advise you not to draw too many conclusions from this. It's fragmentary information at best.
{% endifequal %} {% ifequal resultpositive|length 1 %}ElinYour safety is magnificent. Couldn’t be better. Perhaps it's obvious, but I should remind you that I can only take a random sample of about half your safety. This is not nearly enough if you ask me, but you’ll have to deal with it. It’s just the nature of this business that we can never eliminate all risk.
{% endifequal %} {% if not poorvision and resultpositive|length == 2 %}ElinYour safety looks pretty solid. Perhaps it's obvious, but I should remind you that I can only take a random sample of about half your safety. This is not nearly enough if you ask me, but you’ll have to deal with it. Now, is it safe enough to start production? Ultimately, that’s up to you.
{% endif %} {% if resultpositive|length == 3 or poorvision and resultpositive|length == 2 %} {# If we have poor vision, it means two positives is worse than average. #}ElinYour safety seems to be pretty average. Perhaps it's obvious, but I should remind you that I can only take a random sample of about half your safety. This is not nearly enough if you ask me, but you’ll have to deal with it. Now, is it safe enough to start production? Ultimately, that’s up to you.
{% endif %} {% if resultpositive|length == 4 %}ElinYour safety is quite poor. Perhaps it's obvious, but I should remind you that I can only take a random sample of about half your safety. That’s what my training tells me anyway. Now, should you start production with odds like this? If you ask me, no way!
{% endif %} {% endifequal %} {% ifequal episode.number 2 %} {% ifequal resultpositive|length 0 %}ElinYour safety is extremely poor. Perhaps it's obvious, but I should remind you that I can only take a random sample of about half your safety. That’s what my training tells me anyway. Now, should you start production with odds like this? Are you kidding me?
{% endifequal %} {% ifequal resultpositive|length 1 %}ElinYour safety is magnificent. Couldn’t be better. Should you jump straight into production now? I guess so!
{% endifequal %} {% if not poorvision and resultpositive|length == 2 %}ElinYour safety looks pretty solid. Now, is it safe enough to start production? Ultimately, that’s up to you.
{% endif %} {% if resultpositive|length == 3 or poorvision and resultpositive|length == 2 %} {# If we have poor vision, it means two positives is worse than average. #}ElinYour safety seems to be pretty average. Now, is it safe enough to start production? Ultimately, that’s up to you.
{% endif %} {% ifequal resultpositive|length 4 %}ElinYour safety is really poor. Should you start production with odds like this? If you ask me, no way!
{% endifequal %} {% endifequal %} {% endif %}ElinYour safety is extremely poor. Should you start production with odds like this? Are you kidding me?
Please take note of these results. You can't review them later.
Continue


About
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- Software Engineer with 13 years of experience.
- Strong expertise in on premise infrastructure and cloud native development.
- Extensive experience in developing and maintaining scalable enterprise solutions.
- Technical lead engineer for multiple engineering teams.
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Experience:
- Senior Cloud Infrastructure Developer
Axonify Inc ( Aug 2022 - Present )
Waterloo, Ontario, Canada- Responsible for the design, implementation, and deployment of cloud infrastructure.
- Research security vulnerabilities and put together a mitigation plan.
- Champion the efforts for secure infrastructure & CI/CD.
- Work closely with the dev team, stakeholders & DevOps to facilitate software delivery.
- Design, code & execute performance tests, and identify bottlenecks.
- Ensure visibility into the software stack with various tools.
- Design, implement and test integration between different application suites.
- Senior Software Developer II
Nudge, an Axonify company ( Jul 2021 - Jul 2022 )
Greater Toronto Area, Canada- With Nudge Software Inc acquired by Axonify.
- Design, implement, and maintain Integration with SAP systems.
- Architect, design, and code for new modules and features.
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- Fix high priority production bugs and release.
- Championing adoption of infrastructure as code.
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Cority ( Jun 2020 - Jul 2021 )
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- Design, develop, and scale solutions.
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- Code for new features, enable QA to test and prepare DevOps for release.
- Fix high priority production bugs.
- Collaborate with client engineering teams
- Champion for code quality and best practices.
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Enviance (Jan 2017 - May 2020)
Greater San Diego Area- Architecting new solutions.
- Development of POCs for new implementations.
- Ensuring code quality.
- Investigations on upgrading technology stack.
- Fix high-priority production bugs.
- Decides and maintains a branching strategy.
- Build and maintain analytics tools including custom query languages.
- Onsite Technical Lead
Razorthink Inc (Feb 2014 - Jan 2017)
San Francisco Bay Area- Handling the offshore team.
- Collaborating with product management to turn the ideas to technical specifications.
- Communicating with Customer Success teams about new features and changes
- And above all, code some kick-ass products !
- Senior Software Engineer
Razorthink (Dec 2012 - Jan 2014)
Bangalore, Karnataka- Software developer in charge of the development of new sections as well as integrating the ColdFusion application with Java services and components.
- Software developer in charge of the development of new sections as well as integrating the ColdFusion application with Java services and components.
- Software Developer
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Techno Park, Trivandrum- Coding and fixing an enterprise-level web application with an amazing team of 20+ developers on a wide range of technologies starting from .Net framework to ColdFusion.


example.simple_functions package¶
Submodules¶
example.simple_functions.example module¶
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
Copyright (C) 2017 Nanjing University, Nanjing, China LAMDA, http://lamda.nju.edu.cn
-
example.simple_functions.example.
result_analysis
(result, top)¶
example.simple_functions.fx module¶
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
Copyright (C) 2017 Nanjing University, Nanjing, China LAMDA, http://lamda.nju.edu.cn
-
example.simple_functions.fx.
ackley
(solution)¶
-
example.simple_functions.fx.
mixed_function
(solution)¶
-
example.simple_functions.fx.
sphere
(solution)¶


Say what? File not found. Uh, is the puppy mechanical in any way? Hello, little man.  In our darkest hour, we can stand erect, with proud upthrust bosoms.
I'll get my kit! Hey! I'm a porno-dealing monster, what do I care what you think? Quite possible. We live long and are celebrated poopers.
Goodbye, cruel world. Goodbye, cruel lamp.  Cruel though they may be… Really?! Bender! Ship! Stop bickering or I'm going to come back there and change your opinions manually!
It's toe-tappingly tragic! And I'm his friend Jesus. Aww, it's true. I've been hiding it for so long. Meh. Leela, Bender, we're going grave robbing. With a warning label this big, you know they gotta be fun!
So I really am important? How I feel when I'm drunk is correct? Is today's hectic lifestyle making you tense and impatient? Ooh, name it after me!


|
xpcc
|
The CAN bus driver for LPC11Cxx devices. More...
#include <xpcc/architecture/platform/driver/can/lpc/c_can.hpp>
Static Public Member Functions | |
| template<class SystemClock , uint32_t bitrate = Bitrate::kBps125, uint16_t tolerance = Tolerance::OnePercent> | |
| static void | initialize () |
| static bool | isMessageAvailable () |
| static bool | getMessage (can::Message &message) |
| static bool | isReadyToSend () |
| The CAN controller has a free slot to send a new message. More... | |
| static bool | sendMessage (const can::Message &message) |
| Send a message over the CAN. More... | |
| static BusState | getBusState () |
| Static Public Member Functions inherited from xpcc::Can | |
| template<class SystemClock , uint32_t bitrate = Bitrate::kBps125, uint16_t tolerance = Tolerance::OnePercent> | |
| static void | initialize (Mode startupMode) |
| Initializes the hardware and sets the baudrate. More... | |
| static bool | isMessageAvailable () |
Returns true if a message can be retrieved by calling getMessage . | |
| static bool | getMessage (can::Message &message) |
| Returns true if a message was copied into the message buffer. | |
| static bool | isReadyToSend () |
| The CAN controller has a free slot to send a new message. More... | |
| static bool | sendMessage (const can::Message &message) |
| Send a message over the CAN. More... | |
| static uint8_t | getReceiveErrorCounter () |
| Get Receive Error Counter. | |
| static uint8_t | getTransmitErrorCounter () |
| Get Transmit Error Counter. | |
| static BusState | getBusState () |
| Static Public Member Functions inherited from xpcc::Peripheral | |
| template<uint32_t available, uint32_t requested, uint16_t tolerance> | |
| static void | assertBaudrateInTolerance () |
| Since baudrates are usually generated by prescaling a system clock, only several distinct values can be generated. More... | |
Static Protected Member Functions | |
| static void | CAN_rx (uint8_t msg_obj_num) |
| Callback functions for the on-chip CAN driver library. More... | |
| static void | CAN_tx (uint8_t msg_obj_num) |
| Transmit Callback. More... | |
| static void | CAN_error (uint32_t error_info) |
| Error Callback. More... | |
Additional Inherited Members | |
| Public Types inherited from xpcc::Can | |
| enum | Mode : uint8_t { Mode::Normal, Mode::ListenOnly, Mode::LoopBack, Mode::ListenOnlyLoopBack } |
| Possible modes of the CAN controller. More... | |
| enum | Bitrate : uint32_t { kBps10, kBps20, kBps50, kBps100, kBps125, kBps250, kBps500, MBps1 } |
| Supported CAN bitrates; maybe different on a per device basis. | |
| enum | BusState : uint8_t { BusState::Connected, BusState::ErrorWarning, BusState::ErrorPassive, Off } |
The C_CAN block is available in LPC11Cxx parts only (LPC11C00 series). The LPC11C22 and LPC11C24 parts include an on-chip, high-speed transceiver. For these parts, the CAN_RXD and CAN_TXD signals are connected internally to the on-chip transceiver, and the transceiver signals are pinned out.
This class uses the on-chip CAN drivers.
There are 32 Message Objects in two groups of 16 Message Objects. (1 to 16 and 17 to 32).
In this class the following assumption is made: Message Objects 1 to 16 are used for reception and Message Objects 17 to 32 are used for transmission.
TODO Group Message Objects to a FIFO ?! p307 section <IP_ADDRESS>.1
TODO Filter
TODO Configuration
|
inlinestatic |
MAIN_CLOCK / SYSAHBCLKDIV should be 48 MHz.
48 MHz / (canclkdiv + 1) / (prescaler + 1) / 12 = bitrate
48 MHz 125 kbit 0x451f OK 48 MHz 250 kbit 0x450f OK 48 MHz 500 kbit 0x4507 OK 48 MHz 1000 kbit 0x4503 OK
|
static |
|
static |
|
staticprotected |
They are registered in initialize() and called from the interrupt handler of the on-chip CAN driver (pCAND->isr()) Receive Callback
The CAN message received callback function is called on the interrupt level by the CAN interrupt handler when a message was received.
| msg_obj_num | Message Object that contains a new message |
|
staticprotected |
Called on the interrupt level by the CAN interrupt handler after a message has been successfully transmitted on the bus.
| msg_obj_num | Message Object that was transmitted |
|
staticprotected |
Called on the interrupt level by the CAN interrupt handler after a message has been successfully transmitted on the bus.
| error_info | Error bitmask |


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We accept this thesis as conforming to the required standard
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THE UNIVERSITY OF BRITISH COLUMBIA
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Slideshow with images
Slideshow with images in a div with play and pause buttons (current slide not specified)
Slideshow individual slide links and next and prev (starting on the 3rd slide)
Manual Slideshow Events
Manual Slideshow listening for events
Images courtesy of Jim Knox's Wild Zoofari


--- layout: casework title: Casework 2b permalink: /casework-v3-b/ ---
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graph_template.Rd
Produces a line graph with labels at the end of the lines, with theming designed for the purpose. Returns a ggplot
object that can be further modified as normal. Requires that the **ggplot2**, **ggrepel**, and **paletteer** packages be installed.
graph_template( dt, date = "date", growth = "growth", origin = 0, filter = NULL, by = NULL, x_title = "Date", y_title = "Foot Traffic Growth", title = ifelse(is.null(by), "SafeGraph: Foot Traffic Growth", paste0("SafeGraph: Foot Traffic Growth by ", paste(by, collapse = ", "))), caption = "7-day moving average applied.", subtitle = NULL, label = !is.null(by), hline = TRUE, expand_right = NULL, palette = "ggsci::category20_d3", manual_palette = NULL, skip_theming = FALSE, line_opts = list(size = 1), label_opts = list(size = 14/ggplot2::.pt, hjust = -0.2, vjust = 0.5, direction = "y"), hline_opts = list(size = 0.5, linetype = "dashed", color = "black") )
| dt | A |
|---|---|
| date | Character variable indicating the date variable (x axis). |
| growth | Character variable indicating the growth variable (y axis). |
| origin | The value indicating no growth/initial value. |
| filter | A character variable describing a subset of the data to include, for example |
| by | A character variable of the variable name to assign to the |
| x_title | Axis title for x-axis. |
| y_title | Axis title for y-axis. |
| title | Graph title. |
| caption | Figure caption. |
| subtitle | Graph subtitle. |
| label | Should a text label be applied at the end of each line? |
| hline | Should a horizontal line at the |
| expand_right | Number of additional days to extend the x-axis by so as to fit the labels. Defaults to adding 33 percent more days so a quarter of the graph is reserved for labels. |
| palette | Discrete color palette from the **paletteer** package to be sent to |
| manual_palette | Manually-specified color palette to be sent to the |
| skip_theming | Don't apply the template theming, so you can apply your own. |
| line_opts | A named list of options to be sent to |
| label_opts | A named list of options to be sent to |
| hline_opts | A named list of options to be sent to |
# Generally you'd be doing this with data that comes from read_many_patterns() # But here's an example using randomly generated data dt <- data.table::data.table(date = rep(lubridate::ymd('2020-01-01') + lubridate::days(0:300),2), state_fips = c(rep(6, 301), rep(7,301)), visits_by_day = rpois(602, lambda = 10)) norm <- data.table::data.table(date = rep(lubridate::ymd('2020-01-01') + lubridate::days(0:300),2), state_fips = c(rep(6, 301), rep(7,301)), total_devices_seen = rpois(602, lambda = 10000)) processed_data <- processing_template(dt, norm = norm, by = 'state_fips') p <- graph_template(processed_data, by = 'state_fips') p


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Machine Learning, Natural Language Processing, Neural Networks, Web API and scraping, Data Cleaning and Analysis, and Data Visualization through Python libraries such as Pandas, Scikit-learn, JSON, NLTK, Matplotlib, and Seaborn
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Download and upload files
This module can be used to upload a file, download a file or delete a remote file.
- To download the /etc/passwd file:
./odat.py utlfile -s $SERVER -d $SID -U $USER -P $PASSWORD --test-module --getFile /etc/ passwd passwd.txt
- To upload the test.txt file:
./odat.py utlfile -s $SERVER -d $SID -U $USER -P $PASSWORD --putFile /tmp/ file.txt test.txt
- To delete the file.txt file stored in /tmp/:
./odat.py utlfile -s $SERVER -d $SID -U $USER -P $PASSWORD --removeFile /tmp/ file.txt


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