IONzymes virus lipid IAVs influenza viral cells activity using (Figure peroxidation antiviral envelope HN, shown viruses Figure protein hemagglutinin nanoparticles {border-style MDCK subtypes titer treated IONzyme-treated infected px;} #ffffff;} high oxide mg/mL virus. IONzymes. TCID assay infection iron mice proteins results treatment clade represent Therefore, addition, titers PBS Virol avian control destruct inactivation including induce multiple neighboring pathogenicity peroxidase-like reduced supernatant IAVs. added cell matrix subtype (Log) China Jiangsu Statistical Student's analysis assessed containing demonstrated different electron enzyme-like evaluated hemagglutination indicated infectious lung mixed neuraminidase neutral px; representative showed t-test tissue transmission two-sided virus, {font-family **P<.. .±. IAVs, Influenza MDA S). according analyzed area catalytic concentrations doses facemask facemasks fluorescence font-size highly incubated indicating level lipoxidase natural peroxidase possess protective significance structure unpaired However, Sci Science USA). ability activities apoptosis biological cells. chicken collected contrast, culture data detection double; effects group host inactivate incubation induced intact means±s.d. mg/cm, non-enveloped protection provide result serum strain) strategy value virus) (A) (HN Data IONzyme Importantly, MOI Microbiol PCV- Together, [], acidic activity. annexin apoptotic border-color border-width bovine circovirus column comparison, complete described detect detected effect efficiency examined experiments. failed flow group. hemagglutinin, hydrogen independent infectivity inorganic integrity mean measured microscopy performed purified reduce resulting similar solid; supernatants synthesized typical untreated virulence work {font-style {font-weight (D) (the ..., Briefly, EID Foundation IAV), IONzymes, Infect NDV Newcastle Results abolish animal applications assay, assay. biosafety blot bold broad catalase catalase-like cells, cellular characterized clades, compared contributed courier; damage days disease early em; embryo exhibit experiments fluorescent followed genes image immunofluorescence infection. kidney layer liposomes lipoxidase-like magnetic membrane mg/mL) min, nanomaterials novel nucleoprotein observed p.i., pH. pathogenic perform peroxidation, peroxide phylogenetic placed porcine preparation presence previously. process. proliferation protein, protein. pt;} reactive recent replication reported surface threat times undetectable used various weight


01
At Shikksha Ek Abhiyaan Trust Regd. we help poor people send their children to school by paying for their fees and school supplies.
1,546
16
563
2,874
03
To start an open 'Apna school' where those children who are not going to any school get educated especially labour class children.


 Provides 4 read-only resources that deliver JSON reprsentations of some of the resources the full REST API exposes:
/searches
- A list of search IDs
/search
- Perform a search. Takes the query argument "searchid", which must be a valid search ID. Any other query arguments are passed on to the API client as search arguments.
/description
- Get an OpenSearch search description. Takes the query argument "searchid", which must be a valid search ID.
/ad
- An ad. Takes a single adid query argument.
Searches
Search
Search Description
Get ad


Penta Network Governance
By Penta Global on Fri, Jul 13, 2018
Overview
The primary goals of the Penta public blockchain are: decentralization, fairness and trust. In order to meet these, besides Penta, any public blockchain project needs to have a clear governance structure to avoid destructive actions or misaligned intentions by the core developers, or the block producers. Penta aims for a governance process that is both transparent and robust to corruption.
At the center of Penta’s mission to increase economic inclusion and the stability of its blockchain, is a lowering of barriers to entry and increased opportunities for stakeholders to participate in the ecosystem. Through fair incentive allocations, and stable core architecture, Penta has pioneered innovative technology and social governance mechanisms so as to thrive in the emerging Smart Economy.
Participation in the Consensus Process Node Types, Definitions:
- Common Nodes
Common Nodes synchronize and keep track of shared ledgers using the Penta Client. A Common Node votes to elect delegates/witnesses, or nominate to become a Delegate/Witness by pledging (staking) PNT. Common Nodes participate in maintaining the security and high-availability of the Penta network.
- Witness Nodes
Requirements for eligibility to function as a Witness Node are to receive a number of votes and to stake a small (same here, how much is staked) amount of PNT. Next to Common Nodes, Witnesses are next in terms of numbers populating the Penta network. Witnesses are essential during the BFT and for block validation. Summarizing, the required eligibility criteria for Witness Nodes:
Stake a small number of PNT;
Receive a small number of votes;
A specified minimum hardware and network configuration;
- Delegate Nodes
Common Nodes can nominate to become Delegate Nodes if certain criteria are met. As with Witness Nodes these are: a threshold number of votes, a specified amount of staked PNT, and a specified nominal hardware configuration. These requirements have to be met to participate in the block generation and consensus process. All Delegate Nodes have the same opportunity (probability) to participate in the consensus process, with responsibilities for block creation and block validation. Summarizing, the required eligibility criteria for Delegate Nodes:
Well-known to the community and a significant principal to drive the ecosystem;
Stake a large amount of PNT and receive a high number of votes;
Very stable, high-availability infrastructure and network configuration;
Participate in validating the core architecture and infrastructure of the Penta testNet as a community maintained blockchain test environment;
Consensus Process Flow
- Pledge PNT
Common Nodes pledge (stake) a specified amount of PNT to participate in the voting process.
- Apply for participating in the consensus process
Delegate and Witness candidate nominations are broadcast to the entire Penta network. But, this process can be cancelled prior to any Delegates and Witnesses are elected.
- Vote
Penta Common Notes vote through an official Penta Client.
- PNT stake lock up
The PNT stake of a node will be locked after its successfully becoming a Witness or Delegate. A stake will be released only after a node successfully exits from the consensus process.
- Participate in consensus process
Delegates and Witnesses participating in the DSC consensus process are eligible to receive ‘compensation’ incentives. Any Delegate or Witness taking part in malicious attempts to sabotage the consensus process will be subjected to penalties and under serious circumstances, possible exclusion from any further consensus participation (banning).
- Application of an exit from consensus process
A delegate or witness may, and can easily, apply to exit (terminate participation) from the consensus process with the Penta Client. But, to promote the stability and high-availability of the network, early termination rules apply once a node puts forward an intent to exit request.
- Exit from consensus process
Having satisfied all the early termination rules nodes are allowed to exit the consensus process without penalties.
- Apply for PNT stake release on due date
After exiting from consensus process participation, nodes can request return of any pledged PNT.
- Time for PNT stake final release
It may take up to 7 calendar days to unlock, and for final return of pledged PNT.
Eligibility: Voting & Election rules
Nodes can vote for other nodes or themselves during the consensus process.
PNT held by all voting nodes will be locked before a election round, with 1 PNT representing 1 vote;
Votes nodes obtain will be counted and results updated after a certain numbers of blocks are generated. Election as a Delegate or Witness is based on the amount of PNT pledged and votes obtained, being automatic if all the eligibility criteria are met;
The Incentive Plan is divided into three parts:
Incentive Plan
PNT incentives originate from two sources:
A 50% reserve from the total 100 billion PNT token fund, or 50 billion of PNT
Transaction fee within the Penta Network
Penta Charity Program
The not-for-profit plan may designate certain Delegates as ‘nonprofit’ and any derived profits from those Delegates will be allocated to public benefit activities.
Penalties
Delegates or Witnesses discovered attempting sabotage of the network, or similar malicious behaviour during the consensus process, reported by a certain percentage of nodes will be subjected to penalties and then suspended from consensus participation for a specified period of time.
Discussion and Conclusions
The Penta governance model separates economic benefits available to consensus participants from governance responsibilities, to promote fairness and trust in a decentralized environment.
The entire Penta Network is involved in the governance process. To ensure and sustain a fair network it is important that the entire community is working towards the ‘common good’ (commonwealth) of the ecosystem. The Penta governance model seeks to achieve this by a fair distribution of benefits, and by looking to avoid the formation of self-serving cartels.
The Penta governance model optimizes the transparency, inclusiveness, stability and overall intrinsic value of the Penta ecosystem. The rights and responsibilities contained within reflect Penta’s operating philosophy, and its doctrines for fairness and inclusion. Under which, and with the cooperation of all the agents in the ecosystem: a secure, balanced, and sustainable network is created for the new economy.


Is it valid? {{f.valid | json}}
Is it submitted? {{f.submitted | json}}
what is its value? {{f.value | json}}


shortcut of RBatch::Cmd
# File lib/rbatch/cmd.rb, line 126 126: def cmd(cmd_str,opt = nil) 127: Cmd.new(cmd_str,opt).run 128: end
# File lib/rbatch.rb, line 21 21: def common_config 22: if File.exist?(RBatch.common_config_path) 23: yaml = YAML::load_file(RBatch.common_config_path) 24: if yaml 25: return yaml 26: else 27: # If file is emply , YAML::load_file is false 28: return nil 29: end 30: else 31: return nil 32: end 33: end
# File lib/rbatch.rb, line 18 18: def common_config_path 19: File.join(File.dirname(RBatch.program_name),"..","conf","rbatch.yaml") 20: end
Alias of RBatch::Config.new
# File lib/rbatch/config.rb, line 9 9: def config ; Config.new end
# File lib/rbatch.rb, line 34 34: def double_run_check 35: # double run check 36: if ( RBatch::common_config != nil && RBatch::common_config["forbid_double_run"] ) 37: lock_file="rbatch_lock_" + Digest::MD5.hexdigest(@@program_name) 38: if Dir.exists? RBatch::tmp_dir 39: Dir::foreach(RBatch::tmp_dir) do |f| 40: if (Regexp.new(lock_file) =~ f) 41: raise RBatchException, "Script double run is forbid about \"#{RBatch::program_name}\"" 42: end 43: end 44: end 45: # make lockfile 46: Tempfile::new(lock_file,RBatch::tmp_dir) 47: end 48: end
# File lib/rbatch.rb, line 50 50: def double_run_lock_file ; ; end
# File lib/rbatch.rb, line 7 7: def program_name ; @@program_name ; end
Disabled; run with --debug to generate this.
Generated with the Darkfish Rdoc Generator 1.1.6.


"INN "in the modern sense, unknown in the East. The khans or caravanserais, which correspond to the European inn, are not alluded to in the Old Testament. The “inn” mentioned in Exodus 4:24 was just the halting-place of the caravan. In later times khans were erected for the accommodation of travellers. In Luke 2:7 the word there so rendered denotes a place for loosing the beasts of their burdens. It is rendered“guest-chamber” in Mark 14:14 and Luke 22:11. In Luke 10:34 the word so rendered is different. That inn had an “inn-keeper,” who attended to the wants of travellers.
"INN "in the modern sense, unknown in the East. The khans or caravanserais, which correspond to the European inn, are not alluded to in the Old Testament. The “inn” mentioned in Exodus 4:24 was just the halting-place of the caravan. In later times khans were erected for the accommodation of travellers. In Luke 2:7 the word there so rendered denotes a place for loosing the beasts of their burdens. It is rendered“guest-chamber” in Mark 14:14 and Luke 22:11. In Luke 10:34 the word so rendered is different. That inn had an “inn-keeper,” who attended to the wants of travellers.


 What we learn throughout the evaluation process becomes the basis foclinical recommendations to the court such as specialized testing, treatmenteducation, advocacy, and follow-up.
Emergency Evaluations of Suicidal Youth
A clinician is called into court to evaluate whether a child requires hospital-level care due to a risk of self-harm or a risk of harming others. Many children appear before the Court with critical or emergency needs, for example, an evaluation of a 14-year-old girl who had been on the run for six weeks and told her court-appointed attorney that she wanted to kill herself "because nobody cares."
Comprehensive Psychosocial Evaluations
A clinician interviews the child and family, gathers collateral information, and submits a confidential report to the Judge. For example, this type of evaluation has uncovered the undiagnosed learning disability of a 13-year-old boy who was arrested for stealing. Since his mother was deceased and his father had been recently incarcerated, the ACS clinician worked with social services to match the boy with an appropriate living situation with other family members, to develop an educational plan, and find a mentor in the community.
Specialized Evaluations
ACS has expertise in highly specialized areas where careful evaluations are necessary to protect the child and the community. This includes violence risk, substance abuse, sexual offending, and firesetting behavior.


I am using Eclipse for development. I can run my program just fine in Eclipse. However, I want to create a Jar file that will run my program. Everytime I try to execute the generated JAR I get this error: Exception in thread "main" java.lang.ClassNotFoundException: org.sqlite.JDBC
What am I doing wrong? Thank in advance. --~--~---------~--~----~------------~-------~--~----~ Mailing List: http://groups.google.com/group/sqlitejdbc?hl=en To unsubscribe, send email to [EMAIL PROTECTED] -~----------~----~----~----~------~----~------~--~---


Enter a reference pressure and temperature. The plot will show values resulting from adiabatic changes in pressure above and below the reference point.


The way I react to doing something new and different that im not use to both for myself and when around others. I feel the pressure
when someone is depending on me, for example: when we did the looping exercise, I felt the pressure if I was going to be able to recall
everything being said within the 2 minutes. Because of that I missed some key things.
How I feel when a share my work with others. I like to see what others have done to try and be similar or make sure im on the right
track. I like to compare myself to others a lot to ensure there isn’t much discrepancy between what is ‘normal’ and what isn’t.
Ways in which I react when I hit a problem which I can’t resolve, release the stress, and then come back to the same problem again
and use my problem solving skills to tackle the issue through process of elimination of different problem solving techniques.
I think all three of these personal components sum up a lot of what represents a person and how they deal with things that effect themselves and others. I feel a lot of what is within ones values, empathy, and self awareness forms the foundation of a person. If can effect ones motivation to learning or even in programming if they don’t have the right self awareness or values. Another example is empathy and this can be a big component to working with others and how others can be effected. Throughout the core exercises, they have made me aware to these aspects and how they link into being a developer over just having the technical skills. With what I have already established and continually identifying/adapting what these are/mean to me (values, empathy, self awareness) throughout the EDA course and my career, is going to be paramount to my success as a developer.
My view on becoming a developer has always been seen as knowing the technical knowledge/language. There has not been any regard to aspects covered in the core exercises. The surprising factor for me is the importance to these aspects that make a successful, long-lasting modern-day developer. But throughout these aspects the most surprising aspect has been learning about myself and looking deeper into the way I react/respond to situations and then approaching a response. This has meant an incredible amount of learning about myself and not just as a developer but as a person in general. A lot of the things learnt about myself are applicable throughout any aspects of life.
The most challenging aspect for me was learning without direct guidance where someone is physically there to explain to me face to face and in the moment, when all has come to a stand still, facing a problem. These challenges were mostly faced during sprints 4/5. It required a great deal of persistence and learnings from the core exercises to keep going and working a solution to not having the direct guidance.
Because, the ideology of a modern-day developer is changing as we go into the future where group work is more and more common and being able to take a step back, understand the way that our mind/emotions work, and then executing a response is going to be a key component to becoming a successful developer. Core skills allows an individual to look at ones self, understand how they work, make changes where necessary and then come through with a response to any situation. This is my biggest and overall take-away from the core learning and how I perceive this aspect of foundations.
At the very beginning, it did feel like a big waste of time and I felt a sense of annoyance when I had to complete these activities part of core. It was difficult to talk about myself throughout these activities and a lot of me didn’t want to part take in them. But it was my mindset and values that keep reminding myself that this was required as part of learning and there was a big element of trust that I would be learning this as EDA(the experts) thought it was important to learn as a developer. Overall and coming to the end of foundations, I now can connect the dots and see its value and importance as a developer for problem solving, dealing with my own emotions, dealing with others around me and understanding what defines me as things to come to mind. There are probably a whole lot more things that have come form core which iv missed. But I think a lot of this learning and also what is to learn through bootcamp, will put me at an advantage.
Ideally, id like to build a long lasting career out of my learnings from EDA. Initially I see myself working under an employer to start off with, while I build experience within the industry. Once I feel I am confident I would like to start my own independent work which could be alongside part time work under an employer and then transitioning to fully independent work. Thereafter id ideally like to have a team of developers working under me where I am solely the person sourcing the work for a team of developers.
I feel my strengths and limitations haven’t changed a great deal since sprint 2. However I feel I know more about my strengths and limitations since I established them a few weeks ago. I am better at problem solving and at understanding my self as a person - how I react to certain things and how I deal with difficult situations, what helps me relieve stress, and now having developed a way of problem solving. More so I think my focus has been more on the limitations, to find ways for those limitations not to be a hinderance. I still feel what I mentioned is sprint 2 is still relevant but moving forward from there has been more about learning those obstacles to better understand how to overcome them.
I feel my strengths and limitations haven’t changed a great deal since sprint 2. However I feel I know more about my strengths and limitations since I established them a few weeks ago. I am better at problem solving and at understanding my self as a person - how I react to certain things and how I deal with difficult situations, what helps me relieve stress, and now having developed a way of problem solving. More so I think my focus has been more on the limitations, to find ways for those limitations not to be a hinderance. I still feel what I mentioned is sprint 2 is still relevant but moving forward from there has been more about learning those obstacles to better understand how to overcome them.
Definitely, speaking in-front of a large group of people is something that I have always struggled with and I feel this is going to be somethings that may come up in bootcamp as a non technical challenge for me. I do also find myself being easily distracted as I get more and more comfortable with my working environment. So being self aware of myself for distractions where they are likely to bring me down is going to be an important challenge for me. However distractions are somethings healthy for me when used correctly. Another challenge is going to be, working within time allocations for each aspect and if having major issues with something, to ask for help or problem solving rather than going around in circles. But more so for me, when things go right, sometimes I can get caught up in perfecting (design) aspects so having self control and knowing where to draw the line is going to be important.
Id really like to learn how to effectively present work specific to this career - how things are presented both verbally and visually. I feel like id really like to make sure that I have got time management on the lock down by the time iv finished bootcamp so that I don’t have issues with time management when in the work force. I want to make sure that I don’t make others problems my problem keeping my interested at the forefront. At times I get shy or scared of asking questions, I want to be able to be confident when I don’t know something, not to be afraid to ask in-front of a group setting or one on one. At times I get scared of what others will think of me so id like to get over this.
I want to keep on top of all work and assignments making sure that I don’t fall behind on any work or have to pull all nighters which are like a downward cycle. I feel like it’s important for me to keep on top of moments where I get stuck to not pretend like I know what I’m doing rather not be afraid of asking for help or not being afraid of looking dumb. Id like to keep a regular journal and make at least one note about my day. If I get time id like to make more notes revising what iv learnt that day. I think its also important that I commit more than just the class times to coding and the assignments.
Nimesh Govind
Dev Academy Student | Auckland, New Zealand
Phone<PHONE_NUMBER>
Email nimeshgovind74@gmail.com


Open Access VIS
A collection of open access visualization research at the VIS 2017 conference.
Goals of this site
- Collect openly accessible papers, data, and other materials from VIS before the conference.
- Encourage authors to post papers, data, and materials.
- Encourage posting on reliable open access repositories to avoid "link rot".
- Get an overview of how many VIS papers are inaccessible outside of a pay wall.
Badges
This paper is available on a reliable open access repository.
This paper is available on an author or institution site.
This paper may not be legitimately available outside of a paywall.
The materials or source code needed to reproduce this work are available on a reliable open access repository. Full criteria.
The materials or source code needed to reproduce this work are available from some other source.
The materials or source code needed to reproduce this work may not be available.
The results or data are available on a reliable open access repository. Full criteria.
The results or data are available from some other source.
The results or data may not be available.
A simple explanation or interactive demonstration is available on a reliable open access repository. (Not sure if this is realistic)
A simple explanation or interactive demonstration is available.
A simple explanation or interactive demonstration may not be available.
How do I make my work open access?
I suggest using the Open Science Foundation (OSF) to post your work.
Sign up. Create a new project. And upload your paper, data, and materials. It takes under 5 minutes.
You can also connect your OSF project to a GitHub repository to avoid keeping track of multiple repositories.
Once you're done, make a GitHub issue with the URLs for your work.
I see missing info, incorrect info, or a bug. How can I fix it?
Help filling in missing or incorrect info is greatly appreciated. The data and code are on GitHub.
Thumbails are kept separately for now. Information for adding or improving thumbails is also on GitHub.
Acknowledgements
This site was built with Boostrap, D3, and a fuckton of StackOverflow searches.
Some icons are from Font-Awesome-SVG-PNG (Font Awesome by Dave Gandy).
Open Access logo by PlOS via Wikipedia
Open access materials and open access data symbols by Center for Open Science. Note that the "partial credit" versions were created by me.
Closed access science is CC-BA by Steve Haroz
Special thanks to the Walsh Whiskey Distillery whose booze made all of this possible.


Make taxonomicCoverage EML node
make_taxonomicCoverage( taxa.clean, authority = NA, authority.id = NA, rank = NA, path, write.file = TRUE )
| taxa.clean | (character) Taxa names as they appear in your dataset |
|---|---|
| authority | (character) Authority |
| authority.id | (character) ID of |
| rank | (character) Rank (e.g. "Genus", "Species") of |
| path | (character) Path of the directory to which taxonomicCoverage.xml will be written. Can also be the path of the directory containing taxa_map.csv, if using as inputs to this function. |
| write.file | (logical) Whether taxonomicCoverage.xml should be written to file. Default is |
The taxonomicClassification EML node for use in constructing EML metadata with the EML R library.
If write.file = TRUE
.
This function uses get_classification()
to expand taxa, resolved to supported authorities, into full taxonomic classification. Each level of classification is accompanied by an annotation (listing the authority
and authority.id
) and common names (only when authority
is ITIS or WORMS). Taxa resolved to unsupported authorities, or not resolved at all, will be listed as is defined in the taxa.clean
, authority
, and authority.id
arguments.
The name of this function is a bit misleading. The return value is actually a list of taxonomicClassification nodes, which occur immediately below taxonomicCoverage (i.e. ../taxonomicCoverage/taxonomicClassification).
if (FALSE) { # Set working directory setwd("/Users/me/Documents/data_packages/pkg_260") # For taxa resolved to a supported authority taxcov <- make_taxonomicCoverage( taxa.clean = c("Oncorhynchus tshawytscha", "Oncorhynchus nerka"), authority = c("WORMS", "WORMS"), authority.id = c("158075", "254569"), path = ".") # For taxa resolved to an unsupported authority taxcov <- make_taxonomicCoverage( taxa.clean = c("Taxon-1", "Taxon-2"), authority = c("https://some-authority.org", "https://some-authority.org"), authority.id = c("123", "456"), path = ".") # For taxa not resolved to an authority taxcov <- make_taxonomicCoverage( taxa.clean = c("Taxon-1", "Taxon-2"), path = ".") }


+++ title = "Ranong" lat = 9.961944 lon = 98.638889 +++
Ranong is a small town, and is the capital of one of Thailand's most rural and sparsely-populated provinces. Ranong is located on the upper west coast of Thailand's narrow southern half, and it looks over the watery divide that marks the Thai-Burmese border. Like most tourists, Ranong wasn't a destination as such for me: I just popped in here this morning, in order to make a "visa run" over the water to Myanmar (formerly Burma), in order to reset my 30-day visa-free period in Thailand.


Note: Keep fields blank if doesn't want to give more options!
Example: What __(A)__ language do you __(B)__ ?
Note: Keep fields blank if havn't enough gaps in question!


Fifth Week of RC
Day 2: i3 SkillShare
March 19, 2019
When I switched from Mac OSX to Ubuntu in November 2018, I wanted to find a tool that would replace ShiftIt, my old favorite tool for window management in Mac OSX. Friends from Lucy Parsons Labs recommended i3, and I've been using it since.
Today at Recurse, a group of us got together to do an i3 skillshare for about an hour. We started by pulling up the keyboard map for i3 and demo-ing some example commands. I learned that when a roomful of recursers is instructing you to try out different commands for splitting the screen, one of the hardest parts is agreeing on the difference between a horizontal and vertical split.
We also tried out the floating mode, which I still like less than sticking with a tiled layout. However, I was excited to learn that if you put a window in floating mode, then right click on the floating window while holding down your mod key, you can resize the window. Thanks, recursers, for all the tiling manager TILs.


A refreshing JavaScript Datepicker — lightweight, no dependencies, modular CSS.
Since version 1.0 Pikaday is a stable and battle tested date-picker. Feel free to use it however you like but please report any bugs or feature requests to the GitHub issue tracker, thanks!
Copyright © 2014 David Bushell | BSD & MIT license | Example by Ramiro Rikkert


Well, the neighborhood bully, he's just one man,
His enemies say he's on their land.
They got him outnumbered 'bout a million to one,
He got no place to 'scape to, no place to run.
He's the neighborhood bully.

He's the neighborhood bully.
Neighborhood bully, been driven out of every land,
He's wandered the earth an exiled man,
Seen his family scattered, people hounded and torn,
He's always on trial for just being born.
He's the neighborhood bully.
When he knocked out a lynch mob, he was criticized,
Old women condemned him, said he should apologize.
Then he destroyed a bomb factory, he––nobody was glad.
The bombs were meant for him, he was supposed to feel bad.
He's the neighborhood bully.
Well, the chance is against it and the odds are slim
That he'll live by the rules that the world makes for him.

He's the neighborhood bully.
Well, he got no allies to really speak of,
What he gets he must pay for – he don't get it out of love.
He buys obsolete weapons and he won't be denied,
But no one sends flesh and blood to fight by his side.
He's the neighborhood bully.
Well, he's surrounded by pacifists who all want peace,
They pray for it nightly that the bloodshed will cease,
And they wouldn't hurt a fly – to hurt one, they would weep,
They lay and they wait for this bully to fall asleep.
He's the neighborhood bully.
Every empire that's enslaved him is gone –
Egypt and Rome, even the great Babylon.
He's made a garden of paradise in the desert sand,
In bed with nobody, under no one's command.
He's the neighborhood bully.
Now, his holiest books have been trampled upon,
No contract he signed was worth what was it written on.
He took the crumbs of the world and he turned it into wealth,
Took sickness and disease and he turned it into health.
He's the neighborhood bully.
What's anybody indebted to him for?
"Nothing," they say, "He just like to cause war."
It's pride, and prejudice, and superstition, indeed,
They wait for this bully like a dog waits to feed.
Neighborhood bully.
What has he done to wear so many scars?
Does he change the course of rivers?
Does he pollute the moon and stars?

Neighborhood bully.


jwstobsim.utils.
AncillaryData
Note
Units for the orbital period and ephemeris can be anything as long as they are consistent (e.g. both in days).
The orbital path is calculated based on t0 for primary transits and t_secondary for secondary eclipses.
bins_new
Calculate maximum error for transit light curve calculation.
plot (bool) – If True, plots the error in the light curve model as a function of separation of centers.
True
Truncation error (parts per million)
float
Contents:


{% extends "base.html" %} {% block content %} {%raw%}
LOTS of info goes here, blah blah and stuff about the app i guess? im not sure, its tough to think of stuff to write sometimes hahah i joke cos im writing now, woo this is tough and stuff... catcha latez


Our History
Our History
House of Hashimoto was originally founded by Japan immigrant Hiromori Hashimoto, and his wife, Manami.
The two opened their first restaurant in the heart of Wake Forest, NC, at the intersection of Rogers Rd. and Forestville Rd., then called "Hashi's Diner."
Almost going out of business in their first year, the Hashimotos persevered and began developing their roots.
Originally, items on the menu focused on popular foods such udon and uramaki, advertised as "noodles" and "inside-out sushi."
With steady success in their humble restaurant, the couple began to grow their family, and raised Fuyuki, Akiyo, Goro, Banri, and Asao.
The children became integral components of Hashi's Diner, and eventually came to manage the entire operation.
With Fuyuki's vision and leadership, she and all of her siblings saved most of their earnings with the hope to one day expand on their parents' dreams in coming to the U.S.
The children meticulously planned to grow the business, expanding their menu items to integrate more authentic Japanese flavors.
A major shift occurred. The Hashimoto family pivotted their business to invite a larger audience, while also embracing their Japanese heritage.
They closed down Hashi's Diner and set a new location, right in the heart of North Carolina.
The menu was upgraded to cater an audience with a refined pallete and intrigue for authentic Japanese cuisine.
And while Hiromori and Manami passed away before they could witness the fruits of their children's labor, they are forever remembered.
Namely, with the new location, the business was renamed House of Hashimoto in honor of Hiromori and Manami.
Right in the middle of downtown Raleigh on Front End Road, House of Hashimoto was become one of the city's premiere restaurants for Japanese gourmet.
Fuyuki continues to manage daily operations, now with the help of her daughter, Fumi, and husband, Atomo.
The restaurant now leads a staff over over 200 individuals for it's single location, and serves a wide selection of sake, teas, and traditional Japanese foods.
In an effort to provide true authenticity, all food items and ingredients on the menu are directly sourced from Japan.
Be sure to reserve your table and visit on Fridays and Saturday for live performances featuring the koto, Japan's national instrument.
When you dine in House of Hashimoto, you may find the menu items described in the native tongue. What you may know as a "california roll" may be described as an inside-out sushi roll. However, it is truly called uramaki.
That being said, we graciously invite you to come enjoy, appreciate, and learn about our authentic Japanese cuisine. Our staff members are highly skilled and knowledgeable, and are more than happy to provide you a 5-star experience you'll never forget.
Thank You | Arigatou | ありがとう


Data manipulation with Pandas (python)
April 23, 2016
This is a summary for reference of the great Pandas libary, which offers efficient manipulation of spreadsheet-like data in python.
Data Structures
Series
pd.Series([1,3,5,np.nan,6,8]) #from list
pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) #from array
pd.Series({'a' : 0., 'b' : 1., 'c' : 2.}) #from dict
Dataframe
pd.DataFrame(np.random.randn(6,4)) #from ndarray
pd.DataFrame({'A':1.0,'B':pd.Timestamp('20130102')}) #from dict
Other ways to construct and other constructors (from_dict, from_records, from_items) exist.
Panel
ref. Container for 3-dimensional data. Axis names: - items: axis 0, each item corresponds to a DataFrame contained inside. - major_axis: axis 1, it is the index (rows) of each of the DataFrames - minor_axis: axis 2, it is the columns of each of the DataFrames
Summarizing and basic values
Summary:
df.info()
print(df)
to_string(df) #tabular form
df.describe() #itself returns a df
s.describe(percentiles=[.05, .25, .75, .95])
df.describe(include='all') #for mixed type df-s
s.value_counts()
pd.value_counts()
df.head() #
df.tail() #
Values:
df.index #row index, axis=0
df.columns #col index, axis=1
df.values #returns a numpy array
Indexes
Creating:
pd.Index(['e', 'd', 'a', 'b'])
pd.Index(['e', 'd', 'a', 'b'], name='something')
pd.DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04'],
dtype='datetime64[ns]', freq='D', tz=None)
Multiindex
pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']])
pd.MultiIndex.from_tuples([('bar','one'), ('bar','two')], names=['first', 'second'])
index.get_level_values(0)
index.get_level_values('second')
Setting
data.index = index #set and add-hoc index
df.set_index('colB') #make a given column (or several) an index
reset_index #sets an integer index , previouly called delevel (deprecated)
Uniqueness:
df.index.is_unique
Index values of the Min/Max elements (note idxmin and idxmax are called argmin and argmax in NumPy)
s1.idxmin(), s1.idxmax()
df1.idxmin(axis=0)
Set operations
ref (note that +
and -
operators were deprecated for set operations on indexes).
a | b #a.union(b)
a & b #a.intersection(b)
a.difference(b)
idx1 ^ idx2 #idx1.sym_diff(idx2)
Reindex
s.reindex(['e', 'b', 'f', 'd']) #if f was missing, it's added with NaN values
df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])
ts2.reindex(ts.index, method='ffill') #Filling while reindexing]
reindex_like
align
Metadata:
- Indexes are “mostly immutable”, but it is possible to set and change their metadata. You can use the
rename
,set_names
,set_levels
, andset_labels
to set these attributes directly.
Types
The main types stored in pandas objects are float, int, bool, datetime64[ns], timedelta[ns] and object.
df.dtypes
get_dtype_counts() #return the number of columns of each type
df.select_dtypes(include=[bool])
df.select_dtypes(include=['number', 'bool'])
Conversion Try to force conversion of types from the object dtype to other types
df.convert_objects(convert_numeric=True)
df['D'].astype('float16')
df['B'].astype('category', categories=[1,2,3])
To force conversion to datetime64[ns], pass convert_dates=’coerce’. This will convert any datetime-like object to dates, forcing other values to NaT.
Datetiem, timedelta
pd.date_range('20130101 09:10:12',periods=4)
pd.period_range('20130101', periods=4,freq='D')
pd.timedelta_range('1 day 00:00:05',periods=4,freq='s')
Selecting
[ ]
and .
operators
They provide intuitive dict-like or numpy-like syntax, but have optimization limits. For production code, it is recommended to take advantage of the optimized pandas data access methods below.
s['a'] #dict-like
df['A'] #select a column; returns Series
df.A #like df['A'], as long as colname is a valid identifier
df[0:3] #row position selection
df['20130102':'20130104'] #row index selection
df[:,'A'] #col index selection
Boolena indexing
ref. Operators: |
(for or
), &
(for and
), and ~
(for not
)
s[s > 0]
s[~(s < 0)]
df[df['A'] > 0]
s[s.isin([2, 4, 6])]
s[s.index.isin([2, 4, 6])]
df.loc[:,df.loc['a']>0]
By either label or position ; .ix
operator
Note that when an axis is integer based, ONLY label based access and not positional access is supported. Thus, in such cases, it’s usually better to be explicit and use .iloc or .loc.
For all .ix, .loc and .iloc, axes left out of the specification are assumed to be :
( p.loc[‘a’] is equiv to p.loc[‘a’, :, :]).
[deprecated: irow, icol, iget_value]
By label (.loc
)
Note that both the start and end bound are included!
df.loc['a'] #cross section, equiv to `df.xs('a')`
df.loc[:'c'] #index labels up to 'c' (included!)
df.loc[5] #index label=5 , not the 5th element.
df.loc[['a','b','d'],:]
By position (.iloc
)
Note that the upper bond is excluded !
Note that out of range slice indexes are handled gracefully (ie, return empty object instead of error). However a single indexer out of bounds will raise an IndexError.
df.iloc[1] #cross-section (second row), equiv to `df.xs(1)`
s.iloc[:3] #first and second elements
df.iloc[1:5,2:4]
df.iloc[[1,3,5],[1,3]]
Scalar value access (at()
, iat()`)
df.at['foo', 'A'] #label based
df.iat[3, 0] #position based
df.at[dates[-1]+1, 'Z'] = 7 #appends 'Z', sets the given element, and the rest of cells in row and col are Nan
where()
and mask()
methods
ref.
By default, where()
returns a copy (there is a ‘inplace’ argument).
It Returns an output the same shape as the original data. For df-s, equivalent to df[df < 0]
mask()
is the inverse boolean operation of where()
.
s.where(s > 0) #Includes NaNs. Compare with s[s > 0], that would skip NaN-s
df.where(df < 0)
df.where(df < 0, -df) #Replaces False cases by the given 'other' argument
df.mask(df >= 0) #returns Nan where the condition is met
get()
method
ref
Using the get method, a missing label will return None if the label does not exist, or the specified default:
python
s.get('x', default=-1)
select() & lookup() methods
The select
method should be used only when there is no more direct way. select takes a function which operates on labels along axis and returns a boolean.
df.select(lambda x: x == 'A', axis=1)
The lookup method is a label-based “fancy indexing” function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair.
Datetime-like properties of series: dt
s.dt.hour
s[s.dt.day==2]
assign() (dplyr’s mutate
)
iris.assign(sepal_ratio = iris['SepalWidth'] / iris['SepalLength'])
iris.assign(sepal_ratio = lambda x: (x['SepalWidth'] /
....: x['SepalLength']))
query()
(experimental)
ref Selection using an expression. A use case for query() is when you have a collection of DataFrame objects that have a subset of column names (or index levels/names) in common
df.query('(a < b) & (b < c)')
df.query('a < b < c')
Sepcial types of selections
Missing data
np.NaN
, NaT
, and None
are considered missing by the isnull function.
pd.isnull(df)
df.isnull() #boolean array
df.A.notnull() #boolean array
df.dropna() #return df without na values
Dealing with duplicate data
df.duplicated(['colA', 'colB']) #boolean vector
df.drop_duplicated
s.nunique()
Random sample
Note that weigths can be assigned (with array list, ts or df column). random_state sets the seed.
s.sample() #one sampled row from series
s.sample(n=6, replace=True) #6 sampels rows with replacement
Discretization and quantiling
pd.cut(s, 4) #bins based
pd.qcut(s, 4) #quantile based
pd.cut(arr, [-np.inf, 0, np.inf])
Modifying
Setting:
The .loc
/.ix
/[]
operations can perform enlargement when setting a non-existant key for that axis.
Setting in general works by assignment after selection of an item or slice.
Rename
s.rename(str.upper)
df.rename(columns={'data':'Data', 'quality':'Quality'}, inplace=True)
Delete
del df['two']
df.drop(['a', 'd'], axis=0, inplace=True) #remove col
df.pop('three')
Sort
df.sort_index(axis=1, ascending=False)
df.sort(columns='B')
s.order()
s.nsmallest()
s.nlargest()
Arithmetics, logical operations, descriptive statistics
df.T.dot(df) #matrix multiplication
s1.dot(s1) #dot product of vectors
df[:5].T #transpose
(df+df).equals(df*2) #equals treats NaN-s as equals, as opposed to ==
df.count #Number of non-null observations
df.sum #Sum of values
df.mean #Mean of values
df.mad #Mean absolute deviation
df.median #Arithmetic median of values
df.min #Minimum
df.max #Maximum
df.mode #Mode
df.abs #Absolute Value
df.prod #Product of values
df.std #Unbiased standard deviation
df.var #Unbiased variance
df.sem #Unbiased standard error of the mean
df.df.skew #Unbiased skewness (3rd moment)
df.kurt #Unbiased kurtosis (4th moment)
df.quantile #Sample quantile (value at %)
df.cumsum #Cumulative sum
df.cumprod #Cumulative product
df.cummax #Cumulative maximum
df.cummin #Cumulative minimum
s.nunique() #number of unique non-null values in a Series
s.value_counts() #computes a histogram of a 1D array of values
Covariance and Correlation ref
s.cov(s2) #covariance bwteen series
df.cov() #covariance matrix
df['a'].corr(df['b'], method='spearman') #correlation
df.rank(1) #Rank columns
Moving (rolling) statistics ref
rolling_count #Number of non-null observations
rolling_sum #Sum of values
rolling_mean #Mean of values
etc
rolling_window #Moving window function
ewma #Exponentially weighted (EW) moving average
ewmstd #EW moving standard deviation
etc
Group by (split-apply-combine)
Group by
Grouping
grouped = obj.groupby(key)
grouped.get_group('bar')
grouped.describe()
The groups
attribute is a dict whose keys are the computed unique groups and corresponding values being the axis labels belonging to each group
Iteration can be done with for name, group in grouped: ...
Aggregation
grouped.mean().sort('C') #“dispatches” method calls to the groups
grouped.aggregate(np.sum)
grouped.size()
Transform
this retunrs an object that is indexed the same (same size) as the one being grouped
t = df.groupby('year').transform(lambda x: (x - x.mean()) / x.std())
t.groupby('year') #this should have mean 0 and std=1
Filter returns a subset of the original object
sf.groupby(sf).filter(lambda x: x.sum() > 2)
dff.groupby('B').filter(lambda x: len(x) > 2)
Function application & itereation
Tablewise Function Application: pipe()
ref:
df.pipe(h).pipe(g, arg1=1).pipe(f, arg2=2, arg3=3) #equivalent to f(g(h(df), arg1=1), arg2=2, arg3=3)
Row or Column-wise Function Application: apply()
ref:
Apply arbitrary functions along the axes of a df or panel:
df.apply(np.mean) #'vertical'
df.apply(np.mean, axis=1) #'horizontal'
df.apply(lambda x: x.max() - x.min())
df.apply(np.exp)
df.apply(pd.Series.interpolate)
Elementwise function application: applymap()
and map()
ref:
The function applymap()
on DataFrame and analogously map()
on Series accept any Python function taking a single value and returning a single value
df.applymap(lambda x: len(str(x)) )
Iteration
for col in df: print(col)
for col, series in df.iteritems(): ...
for row_index, row in df2.iterrows(): ...
for r in df2.itertuples() #iterator yielding a tuple with all elements in each row
Vectorized string methods
ref Series is equipped with a set of string processing methods. Applies to columns as well.
s.str.lower()
df.columns.str.strip()
df.columns.str.replace(' ','_')
s.str.split('_') #returns tuples
s.str.split('_', expand=True) #returns df
s.str.extract #uses regexps
s.str.replace #uses regexps
s.str.findall #uses regexps
and many other methods ref
Combining datasets
df1.combine_first(df2) #combine two DataFrame objects where missing values in one DataFrame are conditionally filled with like-labeled values from the other DataFrame
I/O
df = pd.read_csv(‘.\data\ex_data1.csv’, parse_dates=True, index_col=0) etc
Code snipptes
…
References
- Most of the basic contents are directly taken form the official pandas documentation: http://pandas.pydata.org/pandas-docs/stable/index.html
- Python for Data Analysis
Written with StackEdit.


Code Quiz
(1 of 10)
Q.
Inside which HTML element do we write JavaScript?
﹤js﹥
﹤script﹥
﹤javaScript﹥
﹤scripting﹥
Next


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.apache.xpath.axes.NodeSequence.IteratorCache
private static final class NodeSequence.IteratorCache
Each NodeSequence object has an iterator which is "walked". As an iterator is walked one obtains nodes from it. As those nodes are obtained they may be cached, making the next walking of a copy or clone of the iterator faster. This field (m_cache) is a reference to such a cache, which is populated as the iterator is walked.
Note that multiple NodeSequence objects may hold a reference to the same cache, and also (and this is important) the same iterator. The iterator and its cache may be shared among many NodeSequence objects.
If one of the NodeSequence objects walks ahead of the others it fills in the cache. As the others NodeSequence objects catch up they get their values from the cache rather than the iterator itself, so the iterator is only ever walked once and everyone benefits from the cache.
At some point the cache may be complete due to walking to the end of one of the copies of the iterator, and the cache is then marked as "complete". and the cache will have no more nodes added to it.
Its use-count is the number of NodeSequence objects that use it.
| Field Summary | |
|---|---|
private boolean |
m_isComplete2
true if the associated iterator is exhausted and all nodes obtained from it are in the cache. |
private int |
m_useCount2
|
private NodeVector |
m_vec2
A list of nodes already obtained from the iterator. |
| Constructor Summary | |
|---|---|
NodeSequence.IteratorCache()
|
| Method Summary | |
|---|---|
private NodeVector |
getVector()
Get the cached list of nodes obtained from the iterator so far. |
private void |
increaseUseCount()
This method is called when yet another NodeSequence object uses, or shares this same cache. |
private boolean |
isComplete()
Returns true if no cache is complete and immutable. |
private void |
setCacheComplete(boolean b)
Call this method with 'true' if the iterator is exhausted and the cached list is complete, or no longer growing. |
private void |
setVector(NodeVector nv)
Sets the NodeVector that holds the growing list of nodes as they are appended to the cached list. |
private int |
useCount()
Returns count of how many NodeSequence objects share this IteratorCache object. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
private NodeVector m_vec2
Both an iterator and its corresponding cache can be shared by multiple NodeSequence objects.
For example, consider three NodeSequence objects ns1, ns2 and ns3 doing such sharing, and the nodes to be obtaind from the iterator being the sequence { 33, 11, 44, 22, 55 }.
If ns3.nextNode() is called 3 times the the underlying iterator will have walked through 33, 11, 55 and these three nodes will have been put in the cache.
If ns2.nextNode() is called 2 times it will return 33 and 11 from the cache, leaving the iterator alone.
If ns1.nextNode() is called 6 times it will return 33 and 11 from the cache, then get 44, 22, 55 from the iterator, and appending 44, 22, 55 to the cache. On the sixth call it is found that the iterator is exhausted and the cache is marked complete.
Should ns2 or ns3 have nextNode() called they will know that the cache is complete, and they will obtain all subsequent nodes from the cache.
Note that the underlying iterator, though shared is only ever walked once.
private boolean m_isComplete2
private int m_useCount2
| Constructor Detail |
|---|
NodeSequence.IteratorCache()
| Method Detail |
|---|
private int useCount()
private void increaseUseCount()
private void setVector(NodeVector nv)
private NodeVector getVector()
private void setCacheComplete(boolean b)
private boolean isComplete()
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


NOT FOUND
Oh, a wise guy, huh! Think you can choose an undefined route. Get it together and choose an option from the main menu, above.


Hulkenberg: "I have not signed anything and I won’t do in the foreseeable future"
by Joseph TyrrellNico Hulkenberg's time in Formula 1 is coming to an end but attention will turn to where he will race next season. German touring cars and IndyCar have all been rumoured and the current Renault driver is not ruling out the latter.
Hulkenberg is set to be replaced by Esteban Ocon at Renault next season with the Frenchman teaming up with Daniel Ricciardo. The only possible F1 seat left for Hulkenberg is at Williams but that seems reserved for Nicholas Latifi.
With his future yet to be decided the German driver is not ruling out a drive in IndyCar but just wants to see out his final F1 season strongly.
“I have not signed anything and I won’t do in the foreseeable future,” Hulkenberg told Racer.
“I received several calls from different series’ teams, but nothing is really happening at the moment.
“I guess, yes (IndyCar is a consideration). To be honest, my state of mind is pretty relaxed. I want to finish the season as successfully as possible.
“And after that rewind, a little bit, take a moment or two or three for myself, see what I want to do, see what seems of interest."


| Opcode* | Instruction | Op/En | 64-Bit Mode | Compat/Leg Mode | Description |
|---|---|---|---|---|---|
| NP 0F AE F8 | SFENCE | ZO | Valid | Valid | Serializes store operations. |
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| ZO | NA | NA | NA | NA |
Orders processor execution relative to all memory stores prior to the SFENCE instruction. The processor ensures that every store prior to SFENCE is globally visible before any store after SFENCE becomes globally visible. The SFENCE instruction is ordered with respect to memory stores, other SFENCE instructions, MFENCE instructions, and any serializing instructions (such as the CPUID instruction). It is not ordered with respect to memory loads or the LFENCE instruction.
Weakly ordered memory types can be used to achieve higher processor performance through such techniques as out-of-order issue, write-combining, and write-collapsing. The degree to which a consumer of data recognizes or knows that the data is weakly ordered varies among applications and may be unknown to the producer of this data. The SFENCE instruction provides a performance-efficient way of ensuring store ordering between routines that produce weakly-ordered results and routines that consume this data.
This instruction’s operation is the same in non-64-bit modes and 64-bit mode.
Specification of the instruction's opcode above indicates a ModR/M byte of F8. For this instruction, the processor ignores the r/m field of the ModR/M byte. Thus, SFENCE is encoded by any opcode of the form 0F AE Fx, where x is in the range 8-F.
Wait_On_Following_Stores_Until(preceding_stores_globally_visible);
void _mm_sfence(void)
#UD If CPUID.01H:EDX.SSE[bit 25] = 0.
If the LOCK prefix is used.


| 1 | 2 | 3 |
| 4 | 5 | 6 |
| 7 | 8 | 9 |
| 0 |
| Winer | Hot Dog |
| Nacho | Popcorn |
| Winer | Hot Dog |
| Nacho | Popcorn |
| Winer | Hot Dog |
| Winer | Hot Dog |
| Nacho | Popcorn |
| Winer | Hot Dog |
| Nacho | Popcorn |
| Winer | Hot Dog |
| Cola | Spezi |
| Wasser | Limo |
| Cola | Spezi |
| Wasser | Limo |
| Cola | Spezi |
| Wasser | Limo |
| Cola | Spezi |
| Eingabe |
| Cancel |
| Sum | 0 |
| Name | Price |


Folders
<Go Back>
pow-1.view
pow-4.view
pow-min.view
3-pow-test-wrong
pow-2.view
pow-full.view
pow-nan.view
beforeafter.view
pow-3.view
Folders: 10


--- title: Card keywords: fastai sidebar: home_sidebar summary: "A Mimimal example of using nbdev" description: "A Mimimal example of using nbdev" nb_path: "00_card.ipynb" ---
I am following nbdev.fast.ai's minimal example that uses code from Think Python by Allen Downey.
"""This module contains a code example related to
Think Python, 2nd Edition
by Allen Downey
http://thinkpython2.com
Copyright 2015 Allen Downey
License: http://creativecommons.org/licenses/by/4.0/
"""
from __future__ import print_function, division
import random
class Card:
"""Represents a standard playing card.
Attributes:
suit: integer 0-3
rank: integer 1-13
"""
suit_names = ["Clubs", "Diamonds", "Hearts", "Spades"]
rank_names = [None, "Ace", "2", "3", "4", "5", "6", "7",
"8", "9", "10", "Jack", "Queen", "King"]
def __init__(self, suit=0, rank=2):
self.suit = suit
self.rank = rank
def __str__(self):
"""Returns a human-readable string representation."""
return '%s of %s' % (Card.rank_names[self.rank],
Card.suit_names[self.suit])
def __eq__(self, other):
"""Checks whether self and other have the same rank and suit.
returns: boolean
"""
return self.suit == other.suit and self.rank == other.rank
def __lt__(self, other):
"""Compares this card to other, first by suit, then rank.
returns: boolean
"""
t1 = self.suit, self.rank
t2 = other.suit, other.rank
return t1 < t2
def __repr__(self): return self.__str__()
Docs, tests and source code can be combined into the same context.
Comments and tests are rendered by the documentation system.
The assert
statements will automatically become tests that are run by the continuous integration system that is set up by default in nbdev
in your GitHub repository.
Card
is a class that represents a single card in a deck of cards. For example:
Card(suit=2, rank=11)
c = Card(suit=1,rank =3)
assert str(c) == '3 of Diamonds'
c2 = Card(suit=2, rank =11)
assert str(c2)=='Jack of Hearts'
You can do comparisons of cards too
assert c2 > c


Code Co-op machines keep exchanging synchronization information (check-in scripts). E-mail provides a very efficient way to exchange scripts.
You can configure Code Co-op to communicate with Outlook or Outlook Express using the MAPI interface.
Often a better option is to let Code Co-op use its own built-in e-mail client that can communicate directly with an e-mail server. For that, you will need a dedicated e-mail account (address). You can use a free Gmail account for that purpose.
(You can also usea Hotmail (Windows Live) account by selecting "I have a POP3/SMTP account", and using Advanced E-mail Options to separately configure Hotmail SMTP and POP3.)


Script MoonProject
Module for creating Coding projects
Info:
- Release: 01/01/2021
- License: MIT
- Author: letiulthecode
| data |
The data table contains data for a basic enviorment |
| dynamicdata |
The Dynamic Data table contains data for a dynamic enviorment |
| BuildDynamic () |
Build the program without flags and output files |
| BuildRunDynamic () |
Build and run without flags and giving output filename
This is used for debug like BuildRun |
| Update () |
Update an output file |
| Run () |
Run compiled file |
| checknil (Value) |
Check if an value is null or is a empty string
For advoid the permission is denied to open the output
file |
| Clean (File) |
remove/Delete the file on param 'file' |
| Test () |
Make an C file and test its out,
After running it will clean; |
| new (name, name) |
MoonProject.new will create a new project
Returns nil if he dont reconigze the language. |
-
IncludeData (index, include)
-
Insert a custom value in data table.
Parameters:
See also:
-
IncludeDyData (index, include)
-
Insert a custom value in dynamicdata table.
Parameters:
See also:
-
Info ()
-
Show information of Current version, changelog, etc
-
data
-
The data table contains data for a basic enviorment
-
dynamicdata
-
The Dynamic Data table contains data for a dynamic enviorment
-
Build ()
-
Just Build up the program,
Returns:
Compiled File
-
Run ()
-
Run compiled file
Returns:
Compiled File Result
-
Update ()
-
Update an output file
Returns:
Updated file
-
BuildRun ()
-
Build the source program and run.
This is used for debugging
Returns:
Compiled Code Result
-
BuildDynamic ()
-
Build the program without flags and output files
Returns:
Compiled File
-
BuildRunDynamic ()
-
Build and run without flags and giving output filename
This is used for debug like BuildRun
Returns:
Compiled Code Result
-
Update ()
-
Update an output file
Returns:
Updated file
-
Run ()
-
Run compiled file
Returns:
Compiled File Result
-
checknil (Value)
-
Check if an value is null or is a empty string
For advoid the permission is denied to open the output
file
Parameters:
-
Clean (File)
-
remove/Delete the file on param 'file'
Parameters:
Returns:
FIle Deleted
-
Test ()
-
Make an C file and test its out,
After running it will clean;
Returns:
"Hello World!"
-
new (name, name)
-
MoonProject.new will create a new project
Returns nil if he dont reconigze the language.
Parameters:
- name
of the language
- name
of the language
Returns:
Project


News story
Qualifications regulators work together through important reforms
Regulators for England, Wales and Northern Ireland describe the common features and key differences in GCSEs and A levels in each country.
GCSEs, AS and A levels in England, Wales and Northern Ireland are changing. The regulators of these qualifications in each jurisdiction are overseeing the changes and have today (Tuesday 29 November) issued a
joint statement
.
In future there will be differences in most subjects between jurisdictions. Some of these will be immediately noticeable, such as the grading scales used for GCSEs or the relationship between AS and A levels. Others are less apparent, for example, differences in the content or the assessment methods used within subjects.
While there will be some differences, the qualifications will continue to share important, common features irrespective of where they are studied. In particular, they will remain of the same size and equally aim to recognise the achievements of the same cohort of students. Each regulator is also committed to carrying forward the standards set in existing qualifications to the reformed ones.
A summary of the common features and key differences between the GCSEs, AS and A levels offered in the three countries can be seen in
the infographic we have produced
.


disarm_gears.frames package¶
Submodules¶
disarm_gears.frames.point_pattern module¶
-
class
disarm_gears.frames.point_pattern.
PointPattern
(points, attributes=None, crs=None)¶ Bases:
object
-
__init__
(points, attributes=None, crs=None)¶ General class to handle point pattern observations in space.
- Parameters
points – Set of coordinates. Numpy array, shape = [n_points, 2].
attributes – Attributes associated to the points (optional). Numpy array or pandas DataFrame, shape = [n_points, m].
crs – Coordinate reference system (optional). String.
-
attributes_array
()¶ Return an array of attributes associated to the region, if any.
- Returns
Array of attributes. Numpy array or None(if there are no attributes).
-
make_attributes_series
(knots, var_name='knot')¶ Make a (time) series of attributes, by repeating them along the knots.
-
make_grid
(resolution=(10, 10), bounded=True)¶ Return a regular grid of points across the region.
- Parameters
resolution – Number of horizontal and vertical cells in the squared grid. Tuple of integers (n, m).
bounded – Whether to return all cells in a squared grid or just the cells within the boundary. Boolean (default True)
- Returns
Grid. Numpy array, shape [n, 2]
-
set_boundary
(B)¶ Set a boundary to delimit the spatial region.
- Parameters
B – Polygon(s) that define the region. Geopandas DataFrame.
-
disarm_gears.frames.sparse_grid module¶
-
class
disarm_gears.frames.sparse_grid.
SparseGrid
(x_lim, y_lim, n_cols=10, n_rows=10, tag_prefix='')¶ Bases:
object
-
__init__
(x_lim, y_lim, n_cols=10, n_rows=10, tag_prefix='')¶ General class to define a spatial frame composed of regular polygons, based on a grid of size n_cols x n_rows
- Parameters
x_lim – Minimum and Maximum values in the horizontal axis. Tupple of floats.
y_lim – Minimum and Maximum values in the vertical axis. Tupple of floats.
n_cols – Number of columns in which the horizontal axis is divided. Integer.
n_rows – Number of columns in which the vertical axis is divided. Integer
tag_prefix – Prefix to use as id of the polygons in the grid. String.
-
add_polygon_from_tag
(tag)¶ Incorporate a polygon to the sparse_grid GeoDataFrame
- Parameters
tag – id of a polygon String
-
add_polygon_from_xy
(X)¶ Incorporate a polygon to the sparse_grid GeoDataFrame
- Parameters
X – Points withing the grid Numpy array of dimensions (n, 2)
-
get_col
(x)¶ Get the column in the grid to which a value x corresponds
- Parameters
x – Coordinate in the horizontal axis Float
- Returns
Column number Integer
-
get_row
(y)¶ Get the row in the grid to which a value y corresponds
- Parameters
y – Coordinate in the vertical axis Float
- Returns
Row number Integer
-
get_simplified
(tolerance=0.0001)¶ Simplify adjacent polygons in sparse_grid
- Parameters
tolerance – Points in a simplified geometry will be no more than tolerance distance from the original. (see geopandas.GeoDataFrame.simplify). float
- Returns
Simplified polygons object. GeoDataFrame
-
ij_from_tag
(tag)¶ Get the location of a polygon within the grid based on its tag (or id)
- Parameters
tag – id of a polygon String
- Returns
Location (i, j) of a polygon Tuple of integers
-
tag_from_ij
(i, j)¶ Get the tag (or id) of a polygon based on its location within the grid
- Parameters
i – Column number within the grid Integer
j – Row number within the grid Integer
- Returns
Tag String
-
tag_from_xy
(x, y)¶ Get the tag (or id) of a polygon based on a pair of coordinates located within it
- Parameters
x – Coordinate in the horizontal axis Float
y – Coordinate in the vertical axis Float
- Returns
Tag String
-
disarm_gears.frames.tile_pattern module¶
-
class
disarm_gears.frames.tile_pattern.
TilePattern
(geometries, attributes=None, crs=None)¶ Bases:
disarm_gears.frames.point_pattern.PointPattern
-
__init__
(geometries, attributes=None, crs=None)¶ Define a spatial region based on a GeoDataFrame .
- Parameters
geometries – Geometries that delimit the region. geopandas GeoSeries.
attributes – Attributes associated to the geometries (optional). Numpy array or pandas DataFrame, shape = [n_points, m].
crs – Coordinate reference system (optional). String.
-
locate
(X)¶ Associate geometries in the region with a set of locations X.
- Parameters
X – Set of coordinates. Numpy array, shape = [n, 2]
- Returns
Array with tiles per point (-1 for points outside the spatial frame). Numpy array of integers.
-
marked_points_to_frame
(X, Y, fun='mean', group_by=None)¶ From a set of values Y associated to a set of locations X, compute the statistic fun per geometry in the region.
- Parameters
X – Set of coordinates. Numpy array, shape = [n, 2]
Y – Set of values at locations X. Numpy array of size n
fun – Statistic function to compute per region. String.
group_by – Variable that should be used to group X in addition to the geometries (optional). None or numpy array of size n.
- Returns
Dataframe with statistics per geometry-group_by. pandas DataFrame.
-
points_to_frame
(X, group_by=None)¶ From a set of points X, count the number of points per geometry in the region.
- Parameters
X – Set of coordinates. Numpy array, shape = [n, 2].
group_by – Variable that should be used to group X in addition to the geometries (optional). None or numpy array of size n.
- Returns
Dataframe with point counts per geometry-group_by. pandas DataFrame.
-
set_boundary
(B)¶ Set a boundary to delimit the spatial region.
- Parameters
B – Polygon(s) that define the region. Geopandas DataFrame.
-
disarm_gears.frames.timeframe module¶
-
class
disarm_gears.frames.timeframe.
Timeframe
(start, length, by='day', step=1, end=None, directive='%Y-%m-%d')¶ Bases:
object
-
__init__
(start, length, by='day', step=1, end=None, directive='%Y-%m-%d')¶ Define a timeframe for a model.
- Parameters
start – Initial date of the timeframe. Date encoded as: integer, float, string or datetime. Can be a None value if end is specified.
length – Length of the timeframe. Positive integer.
by – Units of time in which the frame is measured. String, one of ‘day’, ‘month’, ‘year’.
step – Number of units per time point. Positive integer.
end – Last date of the timeframe (optional and only used if start is None). Date encoded as: integer, float, string or datetime.
directive – Date format representation (default ‘%Y-%m-%d’). String.
-
which_knot
(x)¶ Identify the knot (point within the timeframe) to which a date x belongs.
- Parameters
x – Array of dates. Numpy array, shape [n, ]. Elements may be of type: integer, float, string, datetime.
- Returns
Array with knots per date (-1 for dates outside the timeframe). Numpy array of integers.
-


Vivvianne Kimani was born in Nairobi. Since her parents were civil servants, her family move from Nairobi to Busia to Isebania, and to Kisumu.
All this while she was doing her primary and secondary schooling. She finished this and joined University of Nairobi to do Geography and economics.Later on she did a Master of Arts in Environmental management in the same institution.
Vivvianne is looking forward to building apps and engaging in projects that will improve the livelihoods of people and make the world a better place.
home


I Dreamed of Africa Starring Kim Basinger, Allison Daugherty Smith, Vincent Perez, Liam Aiken Screenplay by Paula Milne Based on the book by Kuki Gallmann Directed by Hugh Hudson
IN SHORT: I dream of a comfortable seat to snooze in.
Africa is a lovely continent and, when properly photographed, breathtaking in its beauty. Set an incredibly strong story against such a background (usually a foreigner against the Land and so forth) and the effect is magnified. When the story fails to connect, to stir the emotions in the audience, you have the equivalent of a travel brochure. Such is the case with director Hugh Hudson's I Dream of Africa, which hits your local movie screen with a sickening plop.
You can't put all the blame on Kim Basinger's one-dimensional portrayal of Kuki Gullman an Italy-based American widow who takes on a new marriage and a new life facing the extremely-alien, to her, African continent. Settling in [Kenya] with new husband Paolo (Vincent Perez) and kidlet son Emanuele (Liam Aiken). It's the job of the director to get the best and fullest performance that he can out of his cast, something the Oscar-winning Basinger is more than capable of. What makes it to the screen indicates to me, at least, that Hudson seems more concerned with avoiding controversial topics than with telling a compelling story. When, early in the flick, Paolo disappears for days at a time on a hunting expedition Kuki is p.o.'d because she hasn't learned Swahili yet and has no concept how to run an African estate. Ducking the question of European domination of African cultures -- heck unless I missed a title card I'm not even sure what the proper timeframe for this flick is, which is politically important to the background story-- we're left with a standard stranger in a strange land story with performances so bland that there is nothing that resembles a standard emotional story arc. Lovely scenery, though.
We also found we got suckered by the accents and poor background development in the first act (Italy). West Coast correspondent Paul Fischer corrects us:
Sometimes a film emerges that should be something special, that the original source material can only make for a solid drama. Alas, that's not the case with I Dreamed of Africa, a movie that puts new meaning into the word 'tedium'. But apart from the film's sluggish pace, the movie's additional faults lay with the film's problematic performances, a problem that exists as the film deals with character arcs. Let's begin with the obvious. This is a movie about an Italian family in Africa. True story. So the producers decide to cast a Frenchman and an American, and of course ensure that they each speak in their natural tongues. Beyond that, Basinger, as hauntingly lovely she is on screen, is simply incapable of neither carrying a film, nor evoking any real emotions. Try as she might, and she does deliver one or two nice moments, Basinger lacks the emotive range to make us believe in her character's journey. Without that belief, the film can't work, so suffers from the outset. Perez is even worse, giving a superficial performance, and there is no chemistry between the two actors. He is devilishly attractive, but one has the impression that he doesn't really grasp his character's dichotomy.
On average, a first run movie ticket will run you Eight Bucks. Were Cranky able to set his own price to I Dreamed of Africa, he would have paid...
$0.00
An utter waste of time and, in both our judgments, the worst drama we've seen this year.
Cranky Critic® is a Registered Trademark of, and Copyright © 2000 by, Chuck Schwartz. All Rights Reserved. Cranky on the web at www.crankycritic.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{% load static %}
{{ post.vote_total }} comment{{ post.vote_total|pluralize:"s" }}
You have already submitted your comment for this project
{% elif request.user.profile == post.author %}You cannot comment your own work
{% elif request.user.is_authenticated %} {% else %} Please login to leave a review {% endif %}{{ review.created }}
{{ review.body|linebreaksbr }}


Damn Daniel Reference for the Meme-Deprived
Long time no see! I’ve been busy with school lately, but that’s nothing new. This project specifically is one of many that has kept me busy over the past few weeks, but one that I certainly enjoyed working on.
For this project, we were asked to design a digital audio player (software implementation only - in C) using the university-provided LogicalStep FPGA Board for our ECE 224 (Embedded Microprocessor Systems) course. The idea was to develop a device that can play stereo wav files off a MicroSD card and send the output to a standard headphone jack. Aside from the basic functionality, it required that we develop a user interface that displays the file name on an LCD and incorporate push buttons to start/stop the song, as well as search through tracks.
Specifications
- The audio player must support .wav format audio files (stereo audio with 16-bit audio samples).
- The audio must be read from a MicroSD card.
- The audio output uses a standard headphone jack.
- The hardware must include an LCD that indicates the track number (as enumerated on the SD card) and the track title (filename) on the first line of the LCD. THe second line of the LCD must show the Player functiosn for STOPPED, PAUSED, PLAYBACK-NORMAL SPD, PLAYBACK - HALF SPD, PLAYBACK - DBL SPD, PLAYBACK - MONO LEFT_Audio.
- The player must only display track titles (the filename) that are actual wav files indicated by the .wav extension.
- The audio played must be free of distortion for all speeds.
User Interface/Push-Button Functionality:
When the WavePlayer is initialized it displays the first track title on the LCD and is then in the stopped mode. Pressing the Play/Pause button plays the currently selected track, as currently displayed on the LCD and then must automatically stop. Pressing the Play/Pause button while a track is playing pauses the track. When paused, the WavePlayer must remain on the current track at its current position and the audio must be silent. Pressing the Play/Pause button again will allow the WavePlayer to continue the playback from its current track position and then continue to the end of the track. At this point, the WavePlayer must stop and then set the current track position back to the beginning of the current track.
The Stop button stops a currently playing track and resets the track position to the beginning of the track. If a track is paused, the Stop button resets the track position to the beginning of the track.
The Previous button searches backwards through the tracks on the SD card (one track each time the button is pressed), and displays the current track name on the LCD if the button is pressed. Searching farther backward past the first track in the list should cause the last track to be displayed. If the button is pressed while a track is not playing (stopped or paused) then the player just moves to the start of the previous track and stops. If the button is pressed while a track is playing then the playing continues but it begins at the start of the previous track.
The Next button searches forwards through the tracks on the SD card (one track each time the button is pressed), and displays the newly searched track name on the LCD. Searching farther forward past the last track should cause the first track to be displayed. If the button is pressed while a track is not playing (stopped or paused) then the player just moves to the start of the next track and stops. If the button is pressed while a track is playing then the playing continues but it begins at the start of the next track.
Demonstration
And there you have it! Special thanks to my partner Marcus for keeping his composure during all the late-nights spent on debugging the software for the device.
That’s all from me today folks. Stay tuned for new content, thanks for reading!


Summit is Hawaii's global magazine of ideas, style and smart living. We're named for Queen Kapiolani's motto, "kulia i ka nuu," strive for the summit. Summit is available on fine newsstands throughout North America and the Asia-Pacific region.
ContactArchipelago Inc. PO Box 11934
Honolulu, Hawaii 96828
Inquiries


Across: 1. Customs if farts go wrong (7) 5. Big, loosely packed ice formations with air spaces inside (6) 10. Handiwork includes gate negating ... 27 of negations! (2) 11. Notice vehicle label had expiry on upper section originally, which would be helpful... (12) 12. ... with determination of date legal policy ends following 15 to 26 (10) 13,4dn,21dn. As established by the 30 2 28 1 27 16, status 23 16 26 members extend to one another: "Chosen People"? (4 8 6) 14. Setting out as new 15: unbelievably, greed nice! (9) 16. Commerce setting one free in mad rant (5) 19. What many did to become rich, draped in religious trappings (5) 20. Desire carrying one along perhaps, as rest react badly (9) 22. Naked power social leader is indebted to (4) 24. Where shareholders should sit in the power structure, by the 15! (5 5) 26. Establishment of zoo in a grant I arranged (12) 28. Working overnight? (2) 29. In the Deep South, nicely dressing appreciated around racket (6) 30. Lee rang about Grant perhaps ... or Lee (7) Down: 2. Embracing greed almost entirely? So be it, with the leadership in accord (9) 3. I had fired up over Washington's neighbour (5) 4. see 13 across 6. Former NSW premier happy? Yes, on vacation! (6) 7. Unit influenced by MDMA (3) 8. Stops holding up transit & secures openings? Quite the opposite! (5) 9. Worker, not paid at 7, arranged loan from abroad (13) 13. Warrant to intercept rogue entering market initially rejected (5) 15. Moving lures which are meant to get followed (5) 17. Runs all over road, I hear (9) 18. Give new meaning to returning Yemeni federal territory (8) 21. see 13 across 23. 9 dispute arising, extremely loud (5) 25. Measure at once limiting union (5) 27. Handiwork includes gate negating ... 10 of negations! (3)


404
Sorry, something went wrong
The page you are looking for might have been removed had its name changed or is temporarily unavailable.
Go to HomepageThe page you are looking for might have been removed had its name changed or is temporarily unavailable.
Go to Homepage


Chronos [ad8z], city, in Forest of the Lost [aq61], in Imperia
Last updated Turn #108_bad
Chronos [ad8z], city, in Forest of the Lost [aq61], in Imperia
------------------------------------------------------------------------
Routes leaving Chronos:
Out, forest, to Forest of the Lost [aq61], 1 day
Market report (tax of 25%):
trade who price qty wt/ea item
----- --- ----- --- ----- ----
buy ad8z 116 5 1 nazgul tail [319]
buy ad8z 27 17 10 giants' tongues [335]
buy ad8z 26 20 1 giant bird feathers [325]
buy ad8z 10 12 5 dried fish [255]
buy ad8z 4 15 1 woven baskets [94]
sell ad8z 14 25 30 wood [77]
sell ad8z 475 1 5 old book [bg5m]
sell ad8z 725 1 5 Manual of Shipcraft [ja8k]
Inner locations:
Public tower [b42], Construction Guild, defense 20
Public tower [j74], tower, defense 20
Public tower [y66], tower, defense 20
Public tower [j44], tower, defense 20
Public tower [w45], tower, defense 20
Public tower [t80], Trading Guild, defense 20


We'll help you apply for the Presidential Volunteer Service Award if you qualify.
Want to do research? We have lots of experience with helping students through prestigious competitions.
Intel Competition
Siemens Competition
Get involved with organizing an international conference in China!


This form allows you to backup an entire database (or selected rows) to
a file. Select the file write the backup to and the other options, then click
the Backup Now button to start the backup process. Written to the
file will be SQL statements that when executed will re-create the database
tables and their contents. This file can be executed using the command-line
mysql program or the Execute SQL page, in order to restore the
database.