|
|
| CConstituent (string _settling_model) |
| |
|
CConstituent & | operator== (const CConstituent &CC) |
| |
|
| CConstituent (const CConstituent &C) |
| |
|
double | get_capacity (string &i) const |
| |
|
double | get_exchange_rate (string &i) const |
| |
|
void | set_val (int i, double val) |
| |
|
void | set_val (string S, double val) |
| |
|
double | get_val (int i) |
| |
|
|
string | name |
| |
|
int | solid |
| |
|
double | conc |
| |
|
double | conc_0 |
| |
|
bool | influx |
| |
|
char | inlflux_model |
| |
|
vector< double > | rate_constant_influx |
| |
|
vector< double > | ini_conc |
| |
|
vector< bool > | set_ini_conc |
| |
|
vector< double > | equilibrium_conc |
| |
|
bool | equil_to_be_estimated |
| |
|
bool | rate_to_be_estimated |
| |
|
vector< double > | value_fixed |
| |
|
vector< double > | value_fixed_ts |
| |
|
bool | fixed_file |
| |
|
bool | fixed_val |
| |
|
vector< bool > | fixed_val_ts |
| |
|
vector< CBTC > | fixedts |
| |
|
vector< double > | fixedts_avg |
| |
|
vector< string > | rate_exchange_ptr |
| |
|
vector< double > | rate_exchange |
| |
|
double | diffusion |
| |
|
vector< string > | capacity_ptr |
| |
|
vector< double > | capacity |
| |
|
CStringOP | exchange_rate_scale_factor |
| |
|
vector< double > | exchange_params |
| |
|
double | vs =0 |
| |
|
CStringOP | vs_coefficient |
| |
|
string | settling_model |
| |
|
vector< double > | settling_parameters |
| |
|
bool | mobile = true |
| |
Definition at line 12 of file Constituent.h.
The documentation for this class was generated from the following files:


com.example.sulemaia.Adapter
Classes
CharacterEditorAdapter
CharacterSelectorAdapter
DialogCharacterEditorAdapter
DialogCharacterInformationAdapter
MainActivityAdapter


I am Roshni Sarda,a student. I m pursuing my engineering from Thadomal Shahani Engineering College,Mumbai.I started my journey as a learning web developer. Currently, my aim is to become a globally competent individual with programming skills along with business leadership/entrepreneurship skills and a peaceful soul!


The email or password is incorrect!
Please log in
Email address
Password
Remember me
Log in
Create a Letter of Life Account
Return to Home Page


Ask
As you start to observe & explore a system it's likely that you'll have some questions. As you learn stuff some questions will be answered, but some will not. Some of these unanswered questions might highlight parts of the system that could improve. If so, then the next step is to figure out what the problem is and how to solve it. It's important to spend a lot of time on this because solving problems starts with asking questions. You can't solve something you don't understand.
The most important thing is to have a clear and concise definition of the problem you're trying to solve. It should be obvious. If you're unclear about what you're doing, everything else is more difficult. Once you have a clear problem definition you can explore similar problems. This might provide intuition around how other problems were solved and the results of implementing various solutions. This can then help you form a hypothesis of what is likely to help solve your problem.
There are several tools and techniques that can help you define your problem and identify likely solutions. The first is to just write it down. Try to define the problem in a single sentence and make a list of things of similar problems and their solutions. Then ask a friend and see what they have to say. Apply Socratic questioning to further explore the problem and your thinking around it. Then once you have a few good ideas produce a design rationale explaining your thought process, the ideas considered, and why you think a particular solution might work well. Then you'll have a clear problem definition and solution hypothesis to test out.
Concepts
Problem Definition
Defining the question you want to ask or the problem you want to solve is one of the most important steps. If you don't know what problem you're solving how will you know when you've found a solution? Also, if people don't know what problem you're trying to solve they won't be able to work with you and help you find solutions.
- Can you state the problem in a single sentence?
- How will you know when the problem is solved?
- What are the constraints for any potential solutions?
Explore Similar Problems
History doesn't repeat, but it does rhyme. It's likely that someone somewhere has had to deal with a similar problem as the one you're exploring. It might be that the dynamics of the problem are similar, but it's applied in a different context. It might also be that the context is similar, but there was a slightly different problem. Regardless, building off of prior work can often save you a lot of time and energy.
- Can you find any problems similar to the one you're trying to solve?
- If so, could a solution to that problem be applied to your problem (potentially with modifications)?
Hypothesis
The point of running experiments is to validate results. There's an infinite amount of things we could do, but forming a hypothesis allows us to test things that seem the most probable. The hypothesis has to come before the experiment, otherwise you haven't proven anything other than your ability to cherry pick data and/or get lucky. By formulating a detailed hypothesis you'll have a better idea of what's working or what's not, and more importantly why not.
- What do you expect to happen if you do nothing?
- What do you expect to happen if you implement a solution?
Tools
Make a list
Just writing out your ideas can help you start to understand and organize them better.
You can even plot ideas on a spreadsheet and then rank them according to their attributes and trade-offs. The exercise of defining attributes can often help you better understand the problem and potential solutions, even if there isn't a clear solution yet.
In the initial stages (after brainstorming) a simple way to triage ideas is to first eliminate ideas as rationally as possible based on their characteristics. Then, once you've curated via logic use emotions to choose between the best options left. This way you eliminate choices that you might be biased towards, but can also move forward to choose a solution if there's no obvious way to discern between options.
Ask a friend
Getting feedback, esp from people with different viewpoints than your own, can help you better understand the problem you're trying to solve as well as where there might be gaps in your own thinking.
Socratic Questioning
Socratic Questioning is a process of evaluation that can help you explore alternative perspectives to understand a problem. This looks something like:
- Explaining your thought process to clarify your thinking.
- Look for (and challenging) assumptions in your problem definition or solution.
- Providing evidence to validate those assumptions.
- Asking what you might think if you were arguing against the idea.
- Explore the first and second order effects of the solution, and new problems that might arise as a result.
- Question the original question to determine if it's even the right problem to be solving to create the change you want.
Design Rationale
A design rationale explains the decisions made during a design process, and the reasons why those decisions were made. Its primary goal is to support designers by providing a means to record and communicate the argumentation and reasoning behind the design process. This often involves the questions you asked, the solutions considered, and why your proposed solution seems like the best choice.
- the reasons behind a design decision
- the justification for it
- the other alternatives considered
- the trade offs evaluated, an
- the argumentation that led to the decision
Notes
MOTIVATION
- knowledge is power
- asking the right questions
- solving the right problems
CONCEPTS
- problem definition
- explore similar problems
- anticipate results (form a hypothesis)
TOOLS
- make a list
- ask a friend
- socratic questioning
- design rationale


Craft Beef
Why Highland Craft Beef is Healthier
Colonial Farms is focused on producing and marketing a unique brand of Craft Beef referred to as Highland Craft Beef.
Highland Craft Beef is healthier and more nutritious for consumers. The beef is also flavorful and delicious. As a result of the Highland breed's thick coat of long hair, the beef is naturally lean. The beef is also higher in protein and other nutrients and lower in cholesterol compared to grain-fed beef.
Highland Craft Beef is generally considered more of a premium grass-fed beef since it marbles well and is rich in taste even when grass finished. Highland Craft Beef has old fashioned beef qualities that ensure tenderness and succulence with a satisfying flavor.
Our Highland Craft Beef has the following health and nutrition benefits:
- Leaner
- Fewer in calories
- Lower in cholesterol
- More protein
- More beta-carotene
- More omega-3 fatty acids
- More conjugated linoleic acid (CLA)
- A healthier ratio of omega-6 to omega-3 fatty acids
- More vitamin E
- Free of chemical pesticides
- Free of antibiotics
- Free of artificial growth hormones
- Free of MSG
- Free of animal byproducts
- Free of gluten
Craft Beef Defined
Colonial Farms is part of the growing "Craft Beef" movement. The beef industry is currently undergoing a huge transformation. There is a significant resurgence of small family farms raising specialty beef breeds and creating unique tastes of beef for sale at local restaurants and at local farmers markets. This industry transformation is similar to the explosion that occurred in the markets for specialty cheeses and craft beers.
Generally speaking, Craft Beef is often described in the following manner:
- Craft Beef is a premium beef from a specialty breed of cattle, like a Heritage breed, that is not a cross breed. Heritage breeds are historic breeds that are typically smaller and slower growing that have fallen out of favor in today's industrial farming business.
- Craft Beef is healthier and more nutritious because it is typically nutrient rich, higher in protein, lower in cholesterol, lower in calories, free of antibiotics and free of artificial growth hormones, among other benefits.
- Craft Beef is raised using sustainable agricultural practices and is 100% natural, pasture raised and grass-fed.
- Craft Beef is raised on family farms in small batches, not on large industrial farms.
- Craft Beef is raised humanely, under less stressful conditions. Less stressful beef tastes better and is more tender.


This package emulates an FPGA that can be configured with the Bitstream class and can also be read back to create bitstreams.


--- layout: page title: About Me description: Wofo News For You. background: '/img/bg-about.jpg' ---
Wofo News is a non profit news organisation. Read our latest posts and news stories here


Hi! I'm Shari.
I am a Responsive Web Developer who is passionate about using technology to enhance effective communication. When I am not working, you can find me experimenting in the kitchen or pursuing fiber arts, and nurturing my family.
I was born and raised in NY, and my kids were born there. I taught sciences to girls, and loved it. But growth and change are the best part of life, right? Now we live in Israel, and I have a new career. How marvelous is that?
My favorite hobbies are reading and working with wool. I spin, dye, crochet, knit, and weave, sew clothes and recently started quilting for my grandkids. I like baking and canning, and trying new recipes. Never a dull moment!
A fun fact about me: I have a partner, a wonderful and brilliant service dog. He does everything from helping me get around to doing laundry, and is always happy to have a job to do. What a blessing service animals are!


Preprocessing¶
integer_encode¶
protlearn.preprocessing.integer_encode(X, *, padding=False)
Encode amino acids as integers.
This function converts amino acids into their corresponding integers based on the specified notation, starting at 1. Zeros are reserved for optional padding. This is particularly useful for preparing a sequence-based model such as a long short-term memory (LSTM) or a gated recurrent unit (GRU).
Parameters¶
- X: string, fasta, or a list thereof
Dataset of amino acid sequences.
- padding: bool, default=False
False : sequences are returned in their original lengths
True : sequences will be padded with zeros to the length of the longest sequence in the dataset
Returns¶
- enc: ndarray of shape (n_samples,) if padding=False or (n_samples, max_len) if padding=True
Contains the label-encoded peptide sequences.
- amino_acids: amino acid order of enc array
This serves as a lookup for the encoded sequences.
Examples¶
>>> from protlearn.preprocessing import integer_encode
>>> seq = 'ARKLYPGPGEERNK'
>>> enc, aa = integer_encode(seq)
>>> enc
array([ 1, 15, 9, 10, 20, 13, 6, 13, 6, 4, 4, 15, 12, 9])
>>> aa
'ACDEFGHIKLMNPQRSTVWY'
Below is an example using multiple sequences and padding. If padding=True
,
sequences of unequal lengths will be posteriorly padded with zeros to the length
of the longest sequence in the dataset.
>>> from protlearn.preprocessing import integer_encode
>>> seqs = ['ARKLY', 'EERNPAA', 'QEPGPGLLLK']
>>> enc, aa = integer_encode(seqs, padding=True)
>>> enc
array([[ 1, 15, 9, 10, 20, 0, 0, 0, 0, 0],
[ 4, 4, 15, 12, 13, 1, 1, 0, 0, 0],
[14, 4, 13, 6, 13, 6, 10, 10, 10, 9]])
>>> aa
'ACDEFGHIKLMNPQRSTVWY'
remove_duplicates¶
protlearn.preprocessing.remove_duplicates(X, *, verbose=1)
Remove duplicate sequences.
This function detects and removes duplicate sequences from the dataset.
Parameters¶
- X: string, fasta, or a list thereof
Dataset of amino acid sequences.
- verbose: int, default=1
0 : no information on duplicates is printed
1 : prints number of duplicates removed
2 : prints duplicate sequences and number of times present
Returns¶
- Y: list of length n_samples minus the number of duplicates
Dataset containing only unique sequences.
Examples¶
>>> from protlearn.preprocessing import remove_duplicates
>>> seqs = ['ARKLY', 'EERNPAA', 'ARKLY', 'QEPGPGLLLK']
>>> seqs = remove_duplicates(seqs)
>>> seqs
['EERNPAA', 'QEPGPGLLLK', 'ARKLY']
remove_unnatural¶
protlearn.preprocessing.remove_unnatural(X)
Remove sequences containing unnatural amino acids.
This function removes sequences containing amino acids other than the 20 natural ones.
Parameters¶
- X: string, fasta, or a list thereof
Dataset of amino acid sequences.
Returns¶
- Y: list of length n_samples minus the number of sequences containing unnatural amino acids
Dataset containing only sequences comprised of natural amino acids.
Examples¶
>>> from protlearn.preprocessing import remove_unnatural
>>> seqs = ['ARKLY', 'EERNPJAB', 'QEPGPGLLLK']
>>> seqs = remove_unnatural(seqs)
>>> seqs
['ARKLY', 'QEPGPGLLLK']


The classic. Simple dahl with red lentils and garlic.
Serves 4
Wash the lentils until the water runs clear.
Put lentils and water in a saucepan.
Add salt, ground coriander, turmeric, and red chilli.
Cook on medium heat for 5-10 mins. Stir occasionally. Add more water if the dahl starts to dry. Add green chilli now if you like it hot.
Once the lentils are cooked, make the Tahka:
Heat oil in a frying pan over medium heat. Add garlic and cook until it sizzles and starts to brown slightly.
Quickly add to the dahl and cover for a minute.
Stir the dahl and add fresh coriander.
Simmer for 5 mins. Credit: Ms Zartasia Khan of Machester, London and New York
Want another recipe or wondering about us? Get clickin'
About OhMyDahling More recipes


About Us
As a team, we were assigned to create a website called HairCare to help stylists and customers find one another and make sure they're a good match. Nobody likes trying out a new hairstylist to afterward leave the salon frustrated and dissapointed. This website allows stylists and customers to create their own accounts. The customers can search for nearby stylists based on their address, and view a collection of photos and review their past work. Stylists can create a detailed profile and upload photos of their past work.


Train the Trainer Programmes
Train the Trainer Programmes are learning courses that provide new trainers with methods and techniques in order to provide training for others. Virtual Train the Trainer Programmes are the same, but take place via a virtual platform; such as Zoom or Microsoft Teams.
In this page, you will find everything that constitutes a Train the Trainer Programme, why it is beneficial and how to build your own Train the Trainer Programme, along with a range of helpful tips and insights from the Virtual Training Team about our own Programme.
What is a Train the Trainer Programme?
A Train the Trainer Programme is a learning & development framework run for facilitators or subject matter experts to enable them to go on to train other employees within their organisations and beyond.
Typically, groups of 8-12 attend a series of Masterclasses in order to learn the necessary skills and techniques to make them capable trainers / instructors in their field. It provides coaching, practice and feedback which are invaluable in setting up new trainers for success.
Train the Trainer Programmes were more recently adopted in the virtual workspace; facilitating a broader reach to remote working colleagues by providing online Train the Trainer Courses and allowing the training of trainers to take place on a much wider and more consistent scale.
Why do we need Train the Trainer Courses
Train the Trainer Courses can provide individuals with not only platform techniques, the confidence they need to deliver and the design know-how, but also helps to reinforce the other critical skills they require in order to facilitate successful workshops and ensure learning transfer and employee knowledge retention.
To stay competitive in today’s modern world, organisations need to implement continuous, results-driven business training to maintain productivity and profitability. This means ensuring corporate trainers are equipped with practical skills and up-to-date training strategies in order to achieve this.
Why should you get upskilled with Virtual Train the Trainer?
What skills should a Virtual Trainer have?
Having a strong facilitator can be the difference between achieving learning transfer and not. Here are seven skills that we feel are required to be a truly effective trainer in the modern workplace.- Technology skills
- Strong communication skills
- Advanced research skills
- Organizational skills
- Ability to assess employees
- Adaptability
- Enthusiasm for learning
Having facilitators that are skilled across these key areas will be hugely beneficial in providing impactful, engaging and successful training sessions for all employees.
20 Tips To Facilitate A Virtual Workshop – The Rookie’s Handbook
What is a Train the Trainer model?
Simply, a group of trainers attend a concise Training Programme that focuses on both specific training content and how to deliver this training content successfully to others. The expected outcome is for these trainers to learn new knowledge and skills and from there they can go on to successfully coach more people within the organisation including new employees.
What are the benefits of the Train the Trainer model?
The main advantage of the Train the Trainer model is its effectiveness to impart new skills and learnings onto a range of people within an organisation, with the additional benefit of offering fantastic professional development for the employees involved.
It is also cost-effective; allows for a greater consistency in the delivery of training including tailored learning to suit learners and facilitates more effective follow up training for all employees.
It is important to get Virtual Training right the first time. Changing opinions after a negative experience can be difficult.
This client had worked internally to move their training to a virtual approach and struggled. The programme had been halted and the client needed assistance to re-engage their trainers to successfully make the move to virtual training. Read more here to find out how we changed their negative Virtual Training experience to a positive one .
For more insights, please visit our Train the Trainer Programme
How do I build a Train the Trainer Programme?
If you decide that a Train the Trainer Programme is the right way forward in your organisation, there are a few things that need to be taken into consideration in order to build an effective approach.
- Be clear on the purpose of the programme What are the main goals you want to achieve? Do you want to build up a roster of trainers who can be called upon to deliver a wide range of development training? The answers you decide on here will inform which areas you need to focus on when deciding the training curriculum.
- Establish progress measurement You need to set up measurable objectives for your programme. Tracking the progress of your new virtual trainers competency will help you to judge the effectiveness of your programme. This is essential to being able to evaluate and improve your Train the Trainer Programme over time.
- Decide on your training curriculum Now that you have decided on the objective, your Train the Trainer model will be based on encouraging your trainee virtual trainers to improve their subject matter knowledge and delivery skills. The curriculum needs to reflect this. It can be helpful to define the course by session; decide what the focus will be and what content you want the trainers to deliver to the participants.
- Create your training materials In order to achieve consistently high results through the delivery of your training, you should design and provide all the materials that your new virtual trainers will need when they come to deliver the training to their colleagues. This can be anything from slide decks to participant handouts.
What makes an effective training session?
Once all the planning has been done, the preparation taken care of and the objectives are set then the training space is ready. But how do you ensure it is an effective experience?
Here are 10 techniques to help you deliver an effective Virtual Train the Trainer Workshop.
- Let your participants know what you are going to cover; include in the introduction a brief overview of the training session’s main points
- Provide plenty of information; explain key points, demonstrate procedures and share all the information the participants need to know
- Conclude with a summary of the initial overview highlighting key points enabling participants to retain information
- Use interactive training approaches as much as possible; get participants to practice processes and techniques during the session
- Involve the participants; ask them to share their experiences on the training topic. Hearing different voices keeps the training varied and engaging
- Tee questions up before asking participants to answer them; this ensures all participants know what question is being asked of them
- Start and finish on time; don’t hold up the session waiting for late arrivals and don’t run over trying to cram everything in. You can always run another session for further discussions
- Allow time for breaks - especially in longer sessions
- Encourage feedback in the training session; participant input is invaluable for improvement
- Analyze the session as you go; always be on the lookout for techniques or methods that work well, so you can incorporate them into future sessions
What about Train the Trainer Programme certification?
Being able to display visual representations of your credentials after your name online is a great way to strengthen credibility and professional standing within the Learning & Development sector. Attending a Virtual Train the Trainer Certification Programme can achieve this.
An accredited Train the Trainer Certification Programme allows you to visually communicate to potential clients that you have successfully completed the required workshops to obtain the certification.
Currently, when trainers attend VTT’s Virtual Train the Trainer Programme, once all the sessions are completed, they receive a certificate and badge to display on LinkedIn to showcase their achievement.
Train the Trainer Course
A Train the Trainer Course is specially designed to help trainers and coaches improve their skills for training others and can be undertaken for a variety of reasons.
Case study: Developing a Train the Trainer for a Public Body
This client contacted us when they had recognised that their training needed to adapt and evolve with their modern workplace. The primary challenges for the client were reduced physical working space; less room availability and a more geographically spread workforce.
Types of Train the Trainer Courses
There are three categorisations of traditional in-person Train the Trainer Courses. While there are a myriad of options on how to structure a Train the Trainer Course, it heavily depends on specific needs and timeframe. Here are the three most common Train the Trainer Course formats.
Short intense session
Long, scattered sessions
Long, intense sessions
Usually scheduled over 2-3 days, spanning roughly 20 learning hours. It covers the essentials and typically is used to provide subject matter experts with facilitation and even some training design skills. The overall goal is to empower attendees to go on to deliver their own effective training sessions in their fields of expertise.
Usually one day per week over 10-20 days, roughly 80-120 learning hours. If there is no availability for short intense sessions, then choosing a format of recurring weekly training can be a good second option.
Usually a full 1-2 week event, roughly 40-80 learning hours. This is the next step up from the basic essentials. It allows for more complex interactions between participants and coaches, and more opportunity to explore complex interpersonal topics that facilitators need to master. This can be a great option if you have a team that needs to work together later on and the necessary time available in which to accomplish it.
Complex Global Train the Trainer in Sales
The client wanted to take their training existing programme globally and virtually for their inside sales teams. This required training their trainers to deliver virtually whilst converting the existing programme into a virtual, interactive one.
Topics to include in Virtual Train the Trainer Courses
- Team building techniques - show facilitators some effective interactive exercises and icebreakers to help learners become familiar with each other
- Presentation skills - how to boost your presenting abilities
- Facilitation skills - allow trainers to practice facilitation skills needed to get group conversations flowing effectively
- Virtual Design - share how to set up slide decks to work most efficiently in the virtual environment, fostering engagement and interaction
- Visualisation - explore ways to use visuals in a virtual training session
- Participant management - share ways to keep learners focused and effectively manage their energy levels, while being able to deal with challenging situations
- Group facilitation - learn facilitation techniques to have a balanced mix of interaction
- Experiential learning - use real case studies to investigate and debrief in order to enhance learning transfer
- Group dynamics - demonstrate how to adjust to the dynamics of different groups
Train the Trainer Course for new Virtual Trainers
Training, as we all know, is a critical piece to driving success in any organisation. Enhanced skills and knowledge across all levels of a business invariably improve competency and productivity.
Benefits for new Virtual Trainers currently include :-- They can instantly get back to work, by delivering the backlog of training from recent months and help with virtual onboarding
- They can also assist colleagues in the journey of getting back to work and helping them to adapt to new circumstances and ways of working remotely or in offices
- Improving your virtual delivery skills also helps to further other key virtual areas such as providing webinars or creating vlogs and podcasts
- The training budget can reach further as costs per head are reduced thanks to no travel, no hotels and no meeting venues required
- Trainers can manage their work-life balance better as delivering training virtually allows for less disruption to a working day
- Learn skills that they can carry proudly into the future and add to their CV
Find out why getting upskilled for the future is a great idea!.
Our Train the Virtual Trainer Programme
The modules below make up the Virtual Training Team’s complete Virtual Train the Trainer Programme. Whatever your starting point, we will give you the tools to design and deliver brilliant Virtual Training. We can flex and tailor the process to best meet your needs.
Virtual Delivery Masterclass
Virtual Design Masterclass
Interaction Masterclass
Practical Masterclass
In the first Masterclass of the Train the Trainer Programme, we cover how to set up and begin a Virtual Workshop that creates learner engagement and involvement right from the outset. We share over 100 tips, techniques and processes that boost interactivity and learning transfer.
In this Train the Trainer Masterclass, we share our unique 8-step Building Block Design approach to structuring content and process. We use the science of learning and our sequencing template to focus on how to design Virtual Training that delivers results.
This Train the Trainer Masterclass focuses on how to develop conversations and interaction in the virtual environment. We explore how the language we use and the way we ask questions can help or hinder interaction. We also share our structure for initiating group conversations using failsafe methodology.
In the final steps of the Train the Trainer Masterclasses, participants prepare and deliver a section of a Virtual Workshop in order to put into practice all they have learned. The group shares feedback along with a VTT master coach who will give guidance and share best practice. Our clients agree this Masterclass is a must have!
Why The Virtual Training Team
We know that more organisations are making the move to embrace Virtual Training. As a Virtual Training Company, we just want to help make it a success for your learners, your L&D team and ultimately your business.
We do this by using our 10+ years of expertise in the world of Virtual Training to:
- Build Virtual Training into your learning strategy
- Design virtual learning solutions for you
- Deliver interactive Virtual Workshops for you
- Train your trainers to design and deliver interactive Virtual Workshops
- Ready your learners for the virtual world
- Be easy and fun to work with!
Make your move to virtual and get upskilled now.


---
title: "45+ Cult Of Fire Logo
PNG"
description: "There are no ratings yet."
date: "2020-01-21"
categories:
- "Logo"
images:
- "https://upload.wikimedia.org/wikipedia/en/2/2b/The_Cult_Fire_Woman.jpg"
featuredImage: "https://static.politico.com/17/b2/83b345c845a09470f0051702ff05/politico-5.jpg"
featured_image: "https://i.ytimg.com/vi/2OTsi_0zttU/maxresdefault.jpg"
image: "https://beyondeyesshop.com/wp-content/uploads/2018/01/3.CULT-OF-FIRE-Kali-logo-1.jpg"
---
45+ Cult Of Fire Logo
PNG. Price is for one patch! 12 cm (4.72 inch) height:
Threads have high strength and resistant color. Design your own fire logo for free. First logo of cult of fire from their ma page:
We have 189 free fire vector logos, logo templates and icons.
3 cm (1.18 inch) product description: Cult of fire фото исполнителя cult of fire. Price is for one patch! See cult of fire pictures, photo shoots, and listen online to the latest music.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/list.20of.20length.20n.20from.20.60fin.20n.20-.3E.20X.60.html ---
I have f : fin n -> X
and I want [f(0),f(1),...f(n-1)]. I could list.map if I had the list [0 : fin n, 1 : fin n, ...]
. I could use list.pmap
assuming I can find a proof that s \in range(n) -> s < n
. Is there an idiomatic way to do this?
Thanks! I'm not sure I was even aware of that file!
there's also https://github.com/leanprover/lean/blob/72a965986fa5aeae54062e98efb3140b2c4e79fd/library/init/data/list/basic.lean for list defs from core
I want (of_fn {n} f).length = n
. I'll prove it myself I guess.
oh there's a huge section on it in data.list.basic
indeed!
oh wow data.list.basic imports data.list.defs! I had always assumed that basic was the first one, as a general rule.
Usually basic
is indeed the most basic file in a folder.
In this case, mathlib tactics want to use definitions on lists, and basic properties about list want to use mathlib tactics, which (probably) caused list definitions to be separated out from list.basic
.
Last updated: Apr 24 2020 at 19:02 UTC


Your project has been loaded. You can find some statistics, such as number of classes/methods/etc, by navigating in the menu to Project > Project info
or pressing Ctrl+I
(Cmd+I
on MacOS).

To do that, either navigate in the menu to Analysis > Run
or press Ctrl+R
(Cmd+R
on MacOS).
Tip: You can cancel the analysis at any time by pressing the Cancel
button in the progress dialog.
Tip: You can view this message again by navigating in the menu to Help > Browse Help > Running the analysis
.


The WordCount examples demonstrate how to set up a processing pipeline that can read text, tokenize the text lines into individual words, and perform a frequency count on each of those words. The Beam SDKs contain a series of these four successively more detailed WordCount examples that build on each other. The input text for all the examples is a set of Shakespeare’s texts.
Each WordCount example introduces different concepts in the Beam programming model. Begin by understanding Minimal WordCount, the simplest of the examples. Once you feel comfortable with the basic principles in building a pipeline, continue on to learn more concepts in the other examples.
Minimal WordCount demonstrates a simple pipeline that can read from a text file, apply transforms to tokenize and count the words, and write the data to an output text file. This example hard-codes the locations for its input and output files and doesn’t perform any error checking; it is intended to only show you the “bare bones” of creating a Beam pipeline. This lack of parameterization makes this particular pipeline less portable across different runners than standard Beam pipelines. In later examples, we will parameterize the pipeline’s input and output sources and show other best practices.
To run this example in Java:
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.MinimalWordCount
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.MinimalWordCount \
-Dexec.args="--inputFile=pom.xml --output=counts --runner=ApexRunner" -Papex-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.MinimalWordCount \
-Dexec.args="--runner=FlinkRunner --inputFile=pom.xml --output=counts" -Pflink-runner
$ mvn package exec:java -Dexec.mainClass=org.apache.beam.examples.MinimalWordCount \
-Dexec.args="--runner=FlinkRunner --flinkMaster=<flink master> --filesToStage=target/word-count-beam-bundled-0.1.jar \
--inputFile=/path/to/quickstart/pom.xml --output=/tmp/counts" -Pflink-runner
You can monitor the running job by visiting the Flink dashboard at http://<flink master>:8081
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.MinimalWordCount \
-Dexec.args="--runner=SparkRunner --inputFile=pom.xml --output=counts" -Pspark-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.MinimalWordCount \
-Dexec.args="--runner=DataflowRunner --gcpTempLocation=gs://<your-gcs-bucket>/tmp \
--inputFile=gs://apache-beam-samples/shakespeare/* --output=gs://<your-gcs-bucket>/counts" \
-Pdataflow-runner
To view the full code in Java, see MinimalWordCount.
To run this example in Python:
python -m apache_beam.examples.wordcount_minimal --input README.md --output counts
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
# As part of the initial setup, install Google Cloud Platform specific extra components.
pip install apache-beam[gcp]
python -m apache_beam.examples.wordcount_minimal --input gs://dataflow-samples/shakespeare/kinglear.txt \
--output gs://<your-gcs-bucket>/counts \
--runner DataflowRunner \
--project your-gcp-project \
--temp_location gs://<your-gcs-bucket>/tmp/
To view the full code in Python, see wordcount_minimal.py.
Key Concepts:
The following sections explain these concepts in detail along with excerpts of the relevant code from the Minimal WordCount pipeline.
The first step in creating a Beam pipeline is to create a PipelineOptions
object. This object lets us set various options for our pipeline, such as the pipeline runner that will execute our pipeline and any runner-specific configuration required by the chosen runner. In this example we set these options programmatically, but more often command-line arguments are used to set PipelineOptions
.
You can specify a runner for executing your pipeline, such as the DataflowRunner
or SparkRunner
. If you omit specifying a runner, as in this example, your pipeline will be executed locally using the DirectRunner
. In the next sections, we will specify the pipeline’s runner.
PipelineOptions options = PipelineOptionsFactory.create();
// In order to run your pipeline, you need to make following runner specific changes:
//
// CHANGE 1/3: Select a Beam runner, such as DataflowRunner or FlinkRunner.
// CHANGE 2/3: Specify runner-required options.
// For DataflowRunner, set project and temp location as follows:
// DataflowPipelineOptions dataflowOptions = options.as(DataflowPipelineOptions.class);
// dataflowOptions.setRunner(DataflowRunner.class);
// dataflowOptions.setProject("SET_YOUR_PROJECT_ID_HERE");
// dataflowOptions.setTempLocation("gs://SET_YOUR_BUCKET_NAME_HERE/AND_TEMP_DIRECTORY");
// For FlinkRunner, set the runner as follows. See {@code FlinkPipelineOptions}
// for more details.
// options.setRunner(FlinkRunner.class);
options = PipelineOptions()
google_cloud_options = options.view_as(GoogleCloudOptions)
google_cloud_options.project = 'my-project-id'
google_cloud_options.job_name = 'myjob'
google_cloud_options.staging_location = 'gs://your-bucket-name-here/staging'
google_cloud_options.temp_location = 'gs://your-bucket-name-here/temp'
options.view_as(StandardOptions).runner = 'DataflowRunner'
The next step is to create a Pipeline object with the options we’ve just constructed. The Pipeline object builds up the graph of transformations to be executed, associated with that particular pipeline.
Pipeline p = Pipeline.create(options);
p = beam.Pipeline(options=options)
The Minimal WordCount pipeline contains several transforms to read data into the pipeline, manipulate or otherwise transform the data, and write out the results. Each transform represents an operation in the pipeline.
Each transform takes some kind of input (data or otherwise), and produces some output data. The input and output data is represented by the SDK class PCollection
. PCollection
is a special class, provided by the Beam SDK, that you can use to represent a data set of virtually any size, including unbounded data sets.
Figure 1: The pipeline data flow.
The Minimal WordCount pipeline contains five transforms:
A text file Read
transform is applied to the Pipeline object itself, and produces a PCollection
as output. Each element in the output PCollection represents one line of text from the input file. This example uses input data stored in a publicly accessible Google Cloud Storage bucket (“gs://”).
p.apply(TextIO.read().from("gs://apache-beam-samples/shakespeare/*"))
p | beam.io.ReadFromText(
'gs://dataflow-samples/shakespeare/kinglear.txt')
A ParDo transform that invokes a DoFn
(defined in-line as an anonymous class) on each element that tokenizes the text lines into individual words. The input for this transform is the PCollection
of text lines generated by the previous TextIO.Read
transform. The ParDo
transform outputs a new PCollection
, where each element represents an individual word in the text.
.apply("ExtractWords", ParDo.of(new DoFn<String, String>() {
@ProcessElement
public void processElement(ProcessContext c) {
// \p{L} denotes the category of Unicode letters,
// so this pattern will match on everything that is not a letter.
for (String word : c.element().split("[^\\p{L}]+")) {
if (!word.isEmpty()) {
c.output(word);
}
}
}
}))
# The Flatmap transform is a simplified version of ParDo.
| 'ExtractWords' >> beam.FlatMap(lambda x: re.findall(r'[A-Za-z\']+', x))
The SDK-provided Count
transform is a generic transform that takes a PCollection
of any type, and returns a PCollection
of key/value pairs. Each key represents a unique element from the input collection, and each value represents the number of times that key appeared in the input collection.
In this pipeline, the input for Count
is the PCollection
of individual words generated by the previous ParDo
, and the output is a PCollection
of key/value pairs where each key represents a unique word in the text and the associated value is the occurrence count for each.
.apply(Count.<String>perElement())
| beam.combiners.Count.PerElement()
The next transform formats each of the key/value pairs of unique words and occurrence counts into a printable string suitable for writing to an output file.
The map transform is a higher-level composite transform that encapsulates a simple ParDo
. For each element in the input PCollection
, the map transform applies a function that produces exactly one output element.
.apply("FormatResults", MapElements.via(new SimpleFunction<KV<String, Long>, String>() {
@Override
public String apply(KV<String, Long> input) {
return input.getKey() + ": " + input.getValue();
}
}))
| beam.Map(lambda (word, count): '%s: %s' % (word, count))
A text file write transform. This transform takes the final PCollection
of formatted Strings as input and writes each element to an output text file. Each element in the input PCollection
represents one line of text in the resulting output file.
.apply(TextIO.write().to("wordcounts"));
| beam.io.WriteToText('gs://my-bucket/counts.txt')
Note that the Write
transform produces a trivial result value of type PDone
, which in this case is ignored.
Run the pipeline by calling the run
method, which sends your pipeline to be executed by the pipeline runner that you specified when you created your pipeline.
p.run().waitUntilFinish();
result = p.run()
Note that the run
method is asynchronous. For a blocking execution instead, run your pipeline appending the waitUntilFinish
method.
This WordCount example introduces a few recommended programming practices that can make your pipeline easier to read, write, and maintain. While not explicitly required, they can make your pipeline’s execution more flexible, aid in testing your pipeline, and help make your pipeline’s code reusable.
This section assumes that you have a good understanding of the basic concepts in building a pipeline. If you feel that you aren’t at that point yet, read the above section, Minimal WordCount.
To run this example in Java:
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
-Dexec.args="--inputFile=pom.xml --output=counts" -Pdirect-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
-Dexec.args="--inputFile=pom.xml --output=counts --runner=ApexRunner" -Papex-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
-Dexec.args="--runner=FlinkRunner --inputFile=pom.xml --output=counts" -Pflink-runner
$ mvn package exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
-Dexec.args="--runner=FlinkRunner --flinkMaster=<flink master> --filesToStage=target/word-count-beam-bundled-0.1.jar \
--inputFile=/path/to/quickstart/pom.xml --output=/tmp/counts" -Pflink-runner
You can monitor the running job by visiting the Flink dashboard at http://<flink master>:8081
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
-Dexec.args="--runner=SparkRunner --inputFile=pom.xml --output=counts" -Pspark-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WordCount \
-Dexec.args="--runner=DataflowRunner --gcpTempLocation=gs://<your-gcs-bucket>/tmp \
--inputFile=gs://apache-beam-samples/shakespeare/* --output=gs://<your-gcs-bucket>/counts" \
-Pdataflow-runner
To view the full code in Java, see WordCount.
To run this example in Python:
python -m apache_beam.examples.wordcount --input README.md --output counts
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
# As part of the initial setup, install Google Cloud Platform specific extra components.
pip install apache-beam[gcp]
python -m apache_beam.examples.wordcount --input gs://dataflow-samples/shakespeare/kinglear.txt \
--output gs://<your-gcs-bucket>/counts \
--runner DataflowRunner \
--project your-gcp-project \
--temp_location gs://<your-gcs-bucket>/tmp/
To view the full code in Python, see wordcount.py.
New Concepts:
ParDo
with an explicit DoFn
PipelineOptions
The following sections explain these key concepts in detail, and break down the pipeline code into smaller sections.
When using ParDo
transforms, you need to specify the processing operation that gets applied to each element in the input PCollection
. This processing operation is a subclass of the SDK class DoFn
. You can create the DoFn
subclasses for each ParDo
inline, as an anonymous inner class instance, as is done in the previous example (Minimal WordCount). However, it’s often a good idea to define the DoFn
at the global level, which makes it easier to unit test and can make the ParDo
code more readable.
// In this example, ExtractWordsFn is a DoFn that is defined as a static class:
static class ExtractWordsFn extends DoFn<String, String> {
...
@ProcessElement
public void processElement(ProcessContext c) {
...
}
}
# In this example, the DoFns are defined as classes:
class FormatAsTextFn(beam.DoFn):
def process(self, element):
word, count = element
yield '%s: %s' % (word, count)
formatted = counts | beam.ParDo(FormatAsTextFn())
If you have a processing operation that consists of multiple transforms or ParDo
steps, you can create it as a subclass of PTransform
. Creating a PTransform
subclass allows you to create complex reusable transforms, can make your pipeline’s structure more clear and modular, and makes unit testing easier.
In this example, two transforms are encapsulated as the PTransform
subclass CountWords
. CountWords
contains the ParDo
that runs ExtractWordsFn
and the SDK-provided Count
transform.
When CountWords
is defined, we specify its ultimate input and output; the input is the PCollection<String>
for the extraction operation, and the output is the PCollection<KV<String, Long>>
produced by the count operation.
public static class CountWords extends PTransform<PCollection<String>,
PCollection<KV<String, Long>>> {
@Override
public PCollection<KV<String, Long>> expand(PCollection<String> lines) {
// Convert lines of text into individual words.
PCollection<String> words = lines.apply(
ParDo.of(new ExtractWordsFn()));
// Count the number of times each word occurs.
PCollection<KV<String, Long>> wordCounts =
words.apply(Count.<String>perElement());
return wordCounts;
}
}
public static void main(String[] args) throws IOException {
Pipeline p = ...
p.apply(...)
.apply(new CountWords())
...
}
class CountWords(beam.PTransform):
def expand(self, pcoll):
return (pcoll
# Convert lines of text into individual words.
| 'ExtractWords' >> beam.FlatMap(
lambda x: re.findall(r'[A-Za-z\']+', x))
# Count the number of times each word occurs.
| beam.combiners.Count.PerElement())
counts = lines | CountWords()
You can hard-code various execution options when you run your pipeline. However, the more common way is to define your own configuration options via command-line argument parsing. Defining your configuration options via the command-line makes the code more easily portable across different runners.
Add arguments to be processed by the command-line parser, and specify default values for them. You can then access the options values in your pipeline code.
public static interface WordCountOptions extends PipelineOptions {
@Description("Path of the file to read from")
@Default.String("gs://dataflow-samples/shakespeare/kinglear.txt")
String getInputFile();
void setInputFile(String value);
...
}
public static void main(String[] args) {
WordCountOptions options = PipelineOptionsFactory.fromArgs(args).withValidation()
.as(WordCountOptions.class);
Pipeline p = Pipeline.create(options);
...
}
class WordCountOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument('--input',
help='Input for the pipeline',
default='gs://my-bucket/input')
options = PipelineOptions(argv)
with beam.Pipeline(options=options) as p:
The Debugging WordCount example demonstrates some best practices for instrumenting your pipeline code.
To run this example in Java:
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.DebuggingWordCount \
-Dexec.args="--inputFile=pom.xml --output=counts" -Pdirect-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.DebuggingWordCount \
-Dexec.args="--inputFile=pom.xml --output=counts --runner=ApexRunner" -Papex-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.DebuggingWordCount \
-Dexec.args="--runner=FlinkRunner --inputFile=pom.xml --output=counts" -Pflink-runner
$ mvn package exec:java -Dexec.mainClass=org.apache.beam.examples.DebuggingWordCount \
-Dexec.args="--runner=FlinkRunner --flinkMaster=<flink master> --filesToStage=target/word-count-beam-bundled-0.1.jar \
--inputFile=/path/to/quickstart/pom.xml --output=/tmp/counts" -Pflink-runner
You can monitor the running job by visiting the Flink dashboard at http://<flink master>:8081
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.DebuggingWordCount \
-Dexec.args="--runner=SparkRunner --inputFile=pom.xml --output=counts" -Pspark-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.DebuggingWordCount \
-Dexec.args="--runner=DataflowRunner --gcpTempLocation=gs://<your-gcs-bucket>/tmp \
--inputFile=gs://apache-beam-samples/shakespeare/* --output=gs://<your-gcs-bucket>/counts" \
-Pdataflow-runner
To view the full code in Java, see DebuggingWordCount.
To run this example in Python:
python -m apache_beam.examples.wordcount_debugging --input README.md --output counts
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
This runner is not yet available for the Python SDK.
# As part of the initial setup, install Google Cloud Platform specific extra components.
pip install apache-beam[gcp]
python -m apache_beam.examples.wordcount_debugging --input gs://dataflow-samples/shakespeare/kinglear.txt \
--output gs://<your-gcs-bucket>/counts \
--runner DataflowRunner \
--project your-gcp-project \
--temp_location gs://<your-gcs-bucket>/tmp/
To view the full code in Python, see wordcount_debugging.py.
New Concepts:
PAssert
The following sections explain these key concepts in detail, and break down the pipeline code into smaller sections.
Each runner may choose to handle logs in its own way.
// This example uses .trace and .debug:
public class DebuggingWordCount {
public static class FilterTextFn extends DoFn<KV<String, Long>, KV<String, Long>> {
...
public void processElement(ProcessContext c) {
if (...) {
...
LOG.debug("Matched: " + c.element().getKey());
} else {
...
LOG.trace("Did not match: " + c.element().getKey());
}
}
}
}
# [START example_wordcount_debugging_aggregators]
import logging
class FilterTextFn(beam.DoFn):
"""A DoFn that filters for a specific key based on a regular expression."""
def __init__(self, pattern):
self.pattern = pattern
# A custom metric can track values in your pipeline as it runs. Create
# custom metrics matched_word and unmatched_words.
self.matched_words = Metrics.counter(self.__class__, 'matched_words')
self.umatched_words = Metrics.counter(self.__class__, 'umatched_words')
def process(self, element):
word, _ = element
if re.match(self.pattern, word):
# Log at INFO level each element we match. When executing this pipeline
# using the Dataflow service, these log lines will appear in the Cloud
# Logging UI.
logging.info('Matched %s', word)
# Add 1 to the custom metric counter matched_words
self.matched_words.inc()
yield element
else:
# Log at the "DEBUG" level each element that is not matched. Different
# log levels can be used to control the verbosity of logging providing
# an effective mechanism to filter less important information. Note
# currently only "INFO" and higher level logs are emitted to the Cloud
# Logger. This log message will not be visible in the Cloud Logger.
logging.debug('Did not match %s', word)
# Add 1 to the custom metric counter umatched_words
self.umatched_words.inc()
If you execute your pipeline using DirectRunner
, it will print the log messages directly to your local console.
If you execute your pipeline using DataflowRunner
, you can use Stackdriver Logging. Stackdriver Logging aggregates the logs from all of your Dataflow job’s workers to a single location in the Google Cloud Platform Console. You can use Stackdriver Logging to search and access the logs from all of the workers that Dataflow has spun up to complete your Dataflow job. Logging statements in your pipeline’s DoFn
instances will appear in Stackdriver Logging as your pipeline runs.
If you execute your pipeline using DataflowRunner
, you can control the worker log levels. Dataflow workers that execute user code are configured to log to Stackdriver Logging by default at “INFO” log level and higher. You can override log levels for specific logging namespaces by specifying: --workerLogLevelOverrides={"Name1":"Level1","Name2":"Level2",...}
. For example, by specifying --workerLogLevelOverrides={"org.apache.beam.examples":"DEBUG"}
when executing this pipeline using the Dataflow service, Stackdriver Logging would contain only “DEBUG” or higher level logs for the package in addition to the default “INFO” or higher level logs.
The default Dataflow worker logging configuration can be overridden by specifying --defaultWorkerLogLevel=<one of TRACE, DEBUG, INFO, WARN, ERROR>
. For example, by specifying --defaultWorkerLogLevel=DEBUG
when executing this pipeline with the Dataflow service, Cloud Logging would contain all “DEBUG” or higher level logs. Note that changing the default worker log level to TRACE or DEBUG will significantly increase the amount of logs output.
Note: This section is yet to be added. There is an open issue for this (BEAM-792).
Note: This section is yet to be added. There is an open issue for this (BEAM-791).
Note: This section is yet to be added. There is an open issue for this (BEAM-2285).
PAssert
is a set of convenient PTransforms in the style of Hamcrest’s collection matchers that can be used when writing Pipeline level tests to validate the contents of PCollections. PAssert
is best used in unit tests with small data sets, but is demonstrated here as a teaching tool.
Below, we verify that the set of filtered words matches our expected counts. Note that PAssert
does not produce any output, and pipeline will only succeed if all of the expectations are met. See DebuggingWordCountTest for an example unit test.
public static void main(String[] args) {
...
List<KV<String, Long>> expectedResults = Arrays.asList(
KV.of("Flourish", 3L),
KV.of("stomach", 1L));
PAssert.that(filteredWords).containsInAnyOrder(expectedResults);
...
}
This feature is not yet available in the Beam SDK for Python.
This example, WindowedWordCount
, counts words in text just as the previous examples did, but introduces several advanced concepts.
New Concepts:
The following sections explain these key concepts in detail, and break down the pipeline code into smaller sections.
To run this example in Java:
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WindowedWordCount \
-Dexec.args="--inputFile=pom.xml --output=counts" -Pdirect-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WindowedWordCount \
-Dexec.args="--inputFile=pom.xml --output=counts --runner=ApexRunner" -Papex-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WindowedWordCount \
-Dexec.args="--runner=FlinkRunner --inputFile=pom.xml --output=counts" -Pflink-runner
$ mvn package exec:java -Dexec.mainClass=org.apache.beam.examples.WindowedWordCount \
-Dexec.args="--runner=FlinkRunner --flinkMaster=<flink master> --filesToStage=target/word-count-beam-bundled-0.1.jar \
--inputFile=/path/to/quickstart/pom.xml --output=/tmp/counts" -Pflink-runner
You can monitor the running job by visiting the Flink dashboard at http://<flink master>:8081
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WindowedWordCount \
-Dexec.args="--runner=SparkRunner --inputFile=pom.xml --output=counts" -Pspark-runner
$ mvn compile exec:java -Dexec.mainClass=org.apache.beam.examples.WindowedWordCount \
-Dexec.args="--runner=DataflowRunner --gcpTempLocation=gs://<your-gcs-bucket>/tmp \
--inputFile=gs://apache-beam-samples/shakespeare/* --output=gs://<your-gcs-bucket>/counts" \
-Pdataflow-runner
To view the full code in Java, see WindowedWordCount.
Note: WindowedWordCount is not yet available for the Python SDK.
Beam allows you to create a single pipeline that can handle both bounded and unbounded types of input. If the input is unbounded, then all PCollections of the pipeline will be unbounded as well. The same goes for bounded input. If your input has a fixed number of elements, it’s considered a ‘bounded’ data set. If your input is continuously updating, then it’s considered ‘unbounded’.
Recall that the input for this example is a set of Shakespeare’s texts, finite data. Therefore, this example reads bounded data from a text file:
public static void main(String[] args) throws IOException {
Options options = ...
Pipeline pipeline = Pipeline.create(options);
PCollection<String> input = pipeline
.apply(TextIO.read().from(options.getInputFile()))
This feature is not yet available in the Beam SDK for Python.
Each element in a PCollection
has an associated timestamp. The timestamp for each element is initially assigned by the source that creates the PCollection
and can be adjusted by a DoFn
. In this example the input is bounded. For the purpose of the example, the DoFn
method named AddTimestampsFn
(invoked by ParDo
) will set a timestamp for each element in the PCollection
.
.apply(ParDo.of(new AddTimestampFn()));
This feature is not yet available in the Beam SDK for Python.
Below is the code for AddTimestampFn
, a DoFn
invoked by ParDo
, that sets the data element of the timestamp given the element itself. For example, if the elements were log lines, this ParDo
could parse the time out of the log string and set it as the element’s timestamp. There are no timestamps inherent in the works of Shakespeare, so in this case we’ve made up random timestamps just to illustrate the concept. Each line of the input text will get a random associated timestamp sometime in a 2-hour period.
static class AddTimestampFn extends DoFn<String, String> {
private final Instant minTimestamp;
private final Instant maxTimestamp;
AddTimestampFn(Instant minTimestamp, Instant maxTimestamp) {
this.minTimestamp = minTimestamp;
this.maxTimestamp = maxTimestamp;
}
@ProcessElement
public void processElement(ProcessContext c) {
Instant randomTimestamp =
new Instant(
ThreadLocalRandom.current()
.nextLong(minTimestamp.getMillis(), maxTimestamp.getMillis()));
/**
* Concept #2: Set the data element with that timestamp.
*/
c.outputWithTimestamp(c.element(), new Instant(randomTimestamp));
}
}
This feature is not yet available in the Beam SDK for Python.
Beam uses a concept called Windowing to subdivide a PCollection
according to the timestamps of its individual elements. PTransforms that aggregate multiple elements, process each PCollection
as a succession of multiple, finite windows, even though the entire collection itself may be of infinite size (unbounded).
The WindowedWordCount
example applies fixed-time windowing, wherein each window represents a fixed time interval. The fixed window size for this example defaults to 1 minute (you can change this with a command-line option).
PCollection<String> windowedWords = input
.apply(Window.<String>into(
FixedWindows.of(Duration.standardMinutes(options.getWindowSize()))));
This feature is not yet available in the Beam SDK for Python.
You can reuse existing PTransforms that were created for manipulating simple PCollections over windowed PCollections as well.
PCollection<KV<String, Long>> wordCounts = windowedWords.apply(new WordCount.CountWords());
This feature is not yet available in the Beam SDK for Python.
When our input is unbounded, the same is true of our output PCollection
. We need to make sure that we choose an appropriate, unbounded sink. Some output sinks support only bounded output, while others support both bounded and unbounded outputs. By using a FilenamePolicy
, we can use TextIO
to files that are partitioned by windows. We use a composite PTransform
that uses such a policy internally to write a single sharded file per window.
In this example, we stream the results to a BigQuery table. The results are then formatted for a BigQuery table, and then written to BigQuery using BigQueryIO.Write.
wordCounts
.apply(MapElements.via(new WordCount.FormatAsTextFn()))
.apply(new WriteOneFilePerWindow(output, options.getNumShards()));
This feature is not yet available in the Beam SDK for Python.


Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.2.0) |
| Imports: | ggplot2, MASS, orthopolynom, quantmod, rsm, stats4 |
| Published: | 2017-03-10 |
| Author: | Derek S. Young [aut, cre] |
| Maintainer: | Derek S. Young <derek.young at uky.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | HoRM citation info |
| Materials: | NEWS |
| CRAN checks: | HoRM results |
| Reference manual: | HoRM.pdf |
| Package source: | HoRM_0.1.1.tar.gz |
| Windows binaries: | r-devel: HoRM_0.1.1.zip, r-release: HoRM_0.1.1.zip, r-oldrel: HoRM_0.1.1.zip |
| OS X binaries: | r-release: HoRM_0.1.1.tgz, r-oldrel: HoRM_0.1.1.tgz |
| Old sources: | HoRM archive |
Please use the canonical form https://CRAN.R-project.org/package=HoRM to link to this page.


Israel has some of the best food in the world. Fresh, bold, colorful. A culinary light unto the nations. Israelis have known this for a long time and it seems like the world has finally realized it too, thanks to beautiful and popular cookbooks by chefs like Yotam Ottolenghi, Sami Tamimi, Michael Solomonov and Einat Admoni, and thanks to the many award winning Israeli restaurants springing up like mushrooms after the rain in American and European cities.
Naturally, on your visit to Israel, you’ll want to try it all. But it’s hard to know where to start.
And since the variety is so big, and there are so many different cuisines to sample, especially if you want to dive into the ethnic Jewish cuisines, I’m dividing my restaurant recommendations into three categories: The first focuses on chefs’ restaurants, specifically those with unique interpretations of local, Israeli or diaspora cuisine.
In the last 20 or so years, Israeli chefs realized they don’t need to look far for inspiration. Instead, they started looking inside their home kitchens, or more accurately, the kitchens of their grandmothers, and learning the secrets to the foods they grew up with. Those grandmothers came from all across the Jewish diaspora, from Morocco to Kurdistan, Libya, Iran and Turkey (not many Ashkenazi grandmothers have ignited a spark of culinary curiosity in Israel just yet). Talented chefs added their touch to the traditional cuisines of the Middle East, particularly the Palestinian and Mediterranean cuisines (mostly Italian). In other words, the new Israeli cuisine, is, well, Israeli.
If only the term “Israeli cuisine” were easy to explain, like French, or American.


WITNESS LIST Finance Committee May 12, 2005 -12:30P HB 3016 FOR: King, Don Director, Transactional and Property Tax (Cardinal Health), Dublin, OH SB 222 FOR BILL/FOR COMMITTEE SUBSTITUTE: Brummett, Elizabeth Student, UT Vice President of Student Government (Students), Austin, TX Chaney, Brent Student, Former UT Austin Student Body President (Students), Austin, TX Windle, Michael Student (Self), Austin, TX Registering, but not testifying: For Bill/For Committee Substitute: Becha, Daniel Student - Legislative Relations Agency Internal Director (UT - Austin Student Government), Austin, TX Zucker, Charles Executive Director (Texas Faculty Association), Austin, TX SB 1325 ON BILL/ON COMMITTEE SUBSTITUTE: Glura, Ruth-Ellen Assistant D.A. (Travis County D.A.'s Office), Austin, TX SB 1863 FOR BILL/FOR COMMITTEE SUBSTITUTE: Sebree, Ben Association Manager (Texas Oil and Gas Association), Austin, TX ON BILL/ON COMMITTEE SUBSTITUTE: Homer, Andrew Government Relations Director (Texas Public Employers Association), Austin, TX Pyka, Kathy Deputy Executive Director (Railroad Commission), Austin, TX


1 Thus, they reckon that for a hundred and six years Egypt was in great misery and the temples so long shut were never opened. The people hate the memory of these two kings so much that they do not much wish to name them, and call the pyramids after the shepherd Philitis, who then pastured his flocks in this place.


Larger Text
Use OpNames
Live Update
Load new SPIR-V file
Loading and parsing SPIR-V Header files, should be quick!
SPIR-V Visualizer
Based on SPIR-V grammar
Select SPIR-V binary file to load
How to use


Legendary Close to Deal With Frank Miller for 'Sin City' TV Series
Robert Rodriguez, who co-directed 2005 film adaptation, is also close to returning
by Tim BaysingerLegendary is close to a deal with Frank Miller for the rights to develop a TV series based on his “Sin City” graphic novels, an individual with knowledge of the deal tells TheWrap.
Robert Rodriguez, who co-directed the 2005 film adaptation with Miller, is also close to returning as an executive producer. The film starred Bruce Willis, Jessica Alba, Rosario Dawson, Clive Owen and Mickey Rourke. A sequel, “Sin City: A Dame to Kill For,” was released in 2014.
“Dame to Kill For” producer Stephen L’Heureux, and Silenn Thomas, who heads Miller’s production company, would also serve as producers. The deal with Miller includes a “hard R” animated prequel series.
Also Read:
“Sin City” is based on a series of neo-noir graphic novels from Miller for Darkhorse Comics. The events were presented as an anthology, with intertwining stories between the various characters. Both films were produced by Dimension Films, with the first one grossing $158 million around the world and earning critical praise. The 2014 sequel was panned by critics and earned just $39 million globally.
Deadline was first to report on the TV series.


Climb Tracker
Dashboard
Cards
Add A Climb
5.10
c
Sport
5.6
+
Trad
v
4
Boulder
Route Name
Stars
Date
Grade
Desc
Asc
Tick List
Upload a ticklist of climbs as a CSV file matching
this
file format.
Single Climb
Manually add a climb by completing the form.
Date
Mountain Project ID
Style
Lead
Follow
Top Rope
Submit


Introduction
This documentation aims to provide all the information you need to work with our API.
Base URL
http://localhost
Authenticating requests
This API is not authenticated.
Endpoints
Login API
Example request:
curl --request POST \
"http://localhost/api/login" \
--header "Content-Type: application/json" \
--header "Accept: application/json" \
--data "{
\"email\"<EMAIL_ADDRESS>\"password\": \"jejc\"
}"
const url = new URL(
"http://localhost/api/login"
);
const headers = {
"Content-Type": "application/json",
"Accept": "application/json",
};
let body = {
"email"<EMAIL_ADDRESS>"password": "jejc"
};
fetch(url, {
method: "POST",
headers,
body: JSON.stringify(body),
}).then(response => response.json());
Received response:
Request failed with error:
POST api/dashboard
Example request:
curl --request POST \
"http://localhost/api/dashboard" \
--header "Content-Type: application/json" \
--header "Accept: application/json"
const url = new URL(
"http://localhost/api/dashboard"
);
const headers = {
"Content-Type": "application/json",
"Accept": "application/json",
};
fetch(url, {
method: "POST",
headers,
}).then(response => response.json());
Received response:
Request failed with error:
User API Get all the user by pagination
Example request:
curl --request POST \
"http://localhost/api/users" \
--header "Content-Type: application/json" \
--header "Accept: application/json" \
--data "{
\"page\": 11
}"
const url = new URL(
"http://localhost/api/users"
);
const headers = {
"Content-Type": "application/json",
"Accept": "application/json",
};
let body = {
"page": 11
};
fetch(url, {
method: "POST",
headers,
body: JSON.stringify(body),
}).then(response => response.json());
Received response:
Request failed with error:


tunic-encoder is a tool for converting English phonemes into Tunic symbols.
You can input using either IPA symbols or the ASCII variants shown in the chart below. For example, instead of entering the IPA /ʃ/, which is probably not on your keyboard, you can type the ASCII code sh
.
The input box does not accept regular English words. For example, entering “secret” will not give you the correct answer. Instead, you need to enter the phonemic transcription /sikrɪt/ (sikriht
). You can find IPA transcriptions in dictionaries like Wiktionary.
The encoder will greedily combine phonemes into Tunic symbols. For example, /ʌweɪ/ (uhwei
) will render /ʌw/ as a single Tunic character. If you instead want /weɪ/ to be rendered as one unit, you need to separate them using a dot like this: /ʌ·weɪ/ (uh.wei
).
The /ə/ phoneme does not seem to have a unique translation in Tunic. It usually translates to /ʌ/, but can also translate to /ɛ/ or /ʊ/ depending on circumstances.
The tool renders both standard IPA as well as Tunic script.
There is no feature to export or share. You'll just have to take a screenshot.
To increase the size of the text, use your native browser capabilities (e.g. Ctrl + =).
Here's the story of my journey: Deciphering Tunic.


Name: getTable($objects_table) as $object) { echo ''.$object[0].''; } ?>
Pickup date/time:
Due date/time:Advanced...
# of weekly repetitions:
Name:
Net ID:


Reports any implementations of the Object.finalize() method
which are not declared protected. finalize() should be declare protected,
to prevent it from being explicitly invoked by other classes.
Example:
class X {
public void finalize() {
// todo cleanup
}
}
After the quick fix is applied the result looks like:
class X {
protected void finalize() {
// todo cleanup
}
}


Copyright © Yariv Sadan 2006-2007
Authors: Yariv Sadan<EMAIL_ADDRESS>[web site: http://yarivsblog.com].
Smerl: Simple Metaprogramming for Erlang
Smerl is an Erlang library that simplifies the creation and manipulation of Erlang modules in runtime.
You don't need to know Smerl in order to use ErlyWeb; Smerl is included in ErlyWeb because ErlyWeb uses it internally.
Smerl uses Erlang's capabilities for hot code swapping and abstract syntax tree transformations to do its magic. Smerl is inspired by the rdbms_codegen.erl module in the RDBMS application written by Ulf Wiger. RDBMS is part of Jungerl (http://jungerl.sf.net).
Here's a quick example illustrating how to use Smerl:test_smerl() -> M1 = smerl:new(foo), {ok, M2} = smerl:add_func(M1, "bar() -> 1 + 1."), smerl:compile(M2), foo:bar(), % returns 2`` smerl:has_func(M2, bar, 0). % returns true
New functions can be expressed either as strings of Erlang code or as abstract forms. For more information, read the Abstract Format section in the ERTS User's guide (http://erlang.org/doc/doc-5.5/erts-5.5/doc/html/absform.html#4).
Using the abstract format, the 3rd line of the above example would be written as{ok,M2} = smerl:add_func(M1, {function,1,bar,0, [{clause,1,[],[], [{op,1,'+',{integer,1,1},{integer,1,1}}]}]).
The abstact format may look more verbose in this example, but it's also easier to manipulate in code.
abstract datatype: func_form()
The abstract form for the function, as described in the ERTS Users' manual.
abstract datatype: meta_mod()
A data structure holding the abstract representation for a module.
| add_func/2 | Add a new function to the meta_mod and return the resulting meta_mod. |
| add_func/3 | Add a new function to the meta_mod and return the new MetaMod record. |
| compile/1 | Compile the module represented by the meta_mod and load the resulting BEAM into the emulator. |
| compile/2 | Compile the module represented by the meta_mod and load the resulting BEAM into the emulator. |
| curry/2 | Get the curried form for the function and parameter(s). |
| curry/4 | Curry the function from the module with the given param(s). |
| curry/5 | Curry the function from the module or meta_mod with the param(s), and return its renamed form. |
| curry_add/3 | Add the curried form of the function in the meta_mod with its curried form. |
| curry_add/4 | Add the curried form of the function in the meta_mod with its curried form. |
| curry_add/5 | Curry the function form from the meta_mod, then add it to the other meta_mod with a new name. |
| curry_add/6 | Curry the function in the module, rename the curried form, and add it to the meta_mod. |
| curry_replace/3 | Replace the function in the meta_mod with its curried form. |
| curry_replace/4 | Replace the function in the meta_mod with its curried form. |
| embed_all/2 | Apply the embed_params function with the list of {Name, Value} pairs to all forms in the meta_mod. |
| embed_params/2 | This function takes a function form and list of name/value pairs, and replaces all the function's parameters that whose names match an element from the list with the predefined value. |
| embed_params/4 | Apply embed_params/2 to a function from the meta_mod and
add the resulting function to the meta_mod, and return the resulting
meta_mod. |
| embed_params/5 | Apply embed_params/2 to the function from the meta_mod and add the resulting function to the meta_mod after renaming the function. |
| extend/2 | extend/2 Add all the parent module's functions that are missing from the child module to the child module. |
| extend/3 | Similar to extend/2, with the addition of the 'ArityDiff' parameter, which indicates the difference in arities Smerl should use when figuring out which functions to generate based on the modules' exports. |
| extend/4 | |
| for_file/1 | Equivalent to for_file(SrcFilePath, []). |
| for_file/2 | Create a meta_mod for a module from its source file. |
| for_module/1 | Equivalent to for_module(ModuleName, []). |
| for_module/2 | Create a meta_mod tuple for an existing module. |
| get_attribute/2 | Get the value a the module's attribute. |
| get_export_all/1 | Get the export_all value for the module. |
| get_exports/1 | Return the list of exports in the meta_mod. |
| get_forms/1 | Return the list of function forms in the meta_mod. |
| get_func/3 | Get the form for the function with the specified arity in the meta_mod. |
| get_module/1 | Return the module name for the meta_mod. |
| has_func/3 | Check whether the meta_mod has a function with the given name and arity. |
| new/1 | Create a new meta_mod for a module with the given name. |
| remove_export/3 | Remove the export from the list of exports in the meta_mod. |
| remove_func/3 | Try to remove the function from the meta_mod. |
| rename/2 | Change the name of the function represented by the form. |
| replace_func/2 | Replace an existing function with the new one. |
| set_export_all/2 | Set the export_all value for the module. |
| set_exports/2 | Set the meta_mod's export list to the new list. |
| set_forms/2 | |
| set_module/2 | Set the meta_mod's module name. |
| to_src/1 | Return the pretty-printed source code for the module. |
| to_src/2 | Write the pretty printed source code for the module to the file with the given file name. |
add_func(MetaMod::meta_mod(), Form::func_form() | string()) -> {ok, NewMod::meta_mod()} | {error, parse_error}
Add a new function to the meta_mod and return the resulting meta_mod. This function calls add_func(MetaMod, Form, true).
add_func(MetaMod::meta_mod(), Func::func_form() | string(), Export::boolean()) -> {ok, NewMod::meta_mod()} | {error, parse_error}
Add a new function to the meta_mod and return the new MetaMod record. Export is a boolean variable indicating if the function should be added to the module's exports.
compile(MetaMod::meta_mod()) -> ok | {error, Error}
Compile the module represented by the meta_mod and load the resulting BEAM into the emulator. This function calls compile(MetaMod, [report_errors, report_warnings]).
compile(MetaMod::meta_mod(), Options::[term()]) -> ok | {error, Error}
Compile the module represented by the meta_mod and load the resulting BEAM into the emulator. 'Options' is a list of options as described in the 'compile' module in the Erlang documentation.
If the 'outdir' option is provided, the .beam file is written to the destination directory.curry(Form::func_form(), Param::term() | [term()]) -> {ok, NewForm::func_form()} | {error, Err}
Get the curried form for the function and parameter(s). Currying involves replacing one or more of the function's leading parameters with predefined values.
curry(ModName::atom(), Name::atom(), Arity::integer(), Params::term() | [term()]) -> {ok, NewForm} | {error, Err}
Curry the function from the module with the given param(s)
curry(Module::atom() | meta_mod(), Name::atom(), Arity::integer(), Params::term() | [terms()], NewName::atom()) -> {ok, NewForm} | {error, Err}
Curry the function from the module or meta_mod with the param(s), and return its renamed form.
curry_add(MetaMod::meta_mod(), Form::func_form(), Params::term() | [term()]) -> {ok, NewMetaMod::meta_mod()} | {error, Err}
Add the curried form of the function in the meta_mod with its curried form.
curry_add(MetaMod::meta_mod(), Name::atom(), Arity::integer(), Params::term() | [term()]) -> {ok, NewMetaMod::meta_mod()} | {error, Err}
Add the curried form of the function in the meta_mod with its curried form.
curry_add(MetaMod::meta_mod(), Name::atom(), Arity::integer(), Params::[term()], NewName::atom()) -> {ok, NewMod::meta_mod()} | {error, Err}
Curry the function form from the meta_mod, then add it to the other meta_mod with a new name.
curry_add(MetaMod::meta_mod(), Module::atom() | meta_mod(), Name::atom(), Arity::integer(), Params::term() | [term()], NewName::atom()) -> {ok, NewMod::meta_mod()} | {error, Error}
Curry the function in the module, rename the curried form, and add it to the meta_mod.
curry_replace(MetaMod::meta_mod(), Form::func_form(), Params::term() | [term()]) -> {ok, NewMetaMod::meta_mod()} | {error, Err}
Replace the function in the meta_mod with its curried form.
curry_replace(MetaMod::meta_mod(), Name::atom(), Arity::integer(), Params::term() | list()) -> {ok, NewMetaMod::meta_mod()} | {error, Err}
Replace the function in the meta_mod with its curried form.
embed_all(MetaMod::meta_mod(), Vals::[{Name::atom(), Value::term()}]) -> NewMetaMod::meta_mod()
Apply the embed_params function with the list of {Name, Value} pairs to all forms in the meta_mod. Exports for functions whose arities change due to the embedding are preserved.
embed_params(Func::func_form(), Vals::[{Name::atom(), Value::term()}]) -> NewForm::func_form()
This function takes a function form and list of name/value pairs, and replaces all the function's parameters that whose names match an element from the list with the predefined value.
embed_params(MetaMod::meta_mod(), Name::atom(), Arity::integer(), Values::proplist()) -> {ok, NewMetaMod::meta_mod()} | {error, Err}
Apply embed_params/2
to a function from the meta_mod and
add the resulting function to the meta_mod, and return the resulting
meta_mod.
embed_params(MetaMod::meta_mod(), Name::atom(), Arity::integer(), Values::proplist(), NewName::atom()) -> {ok, NewMetaMod::meta_mod()} | {error, Err}
Apply embed_params/2 to the function from the meta_mod and add the resulting function to the meta_mod after renaming the function.
extend(Parent::atom() | meta_mod(), Child::atom() | meta_mod()) -> NewChildMod::meta_mod()
extend/2 Add all the parent module's functions that are missing from the child module to the child module. The new functions in the child module are shallow: they have the name and arity as their corresponding functions in the parent meta_mod, but instead of implementing their logic they call the parent module's functions.
extend(Parent::atom() | meta_mod(), Child::atom() | meta_mod(), ArityDiff::integer()) -> NewChildMod::meta_mod()
Similar to extend/2, with the addition of the 'ArityDiff' parameter, which indicates the difference in arities Smerl should use when figuring out which functions to generate based on the modules' exports. This is sometimes useful when calling extend() followed by embed_all().
Equivalent to for_file(SrcFilePath, []).
for_file(SrcFilePath::string(), IncludePaths::[string()]) -> {ok, meta_mod()} | {error, invalid_module}
Create a meta_mod for a module from its source file.
Equivalent to for_module(ModuleName, []).
for_module(ModuleName::atom() | string(), IncludePaths::[string()]) -> {ok, meta_mod()} | {error, Error}
Create a meta_mod tuple for an existing module. If ModuleName is a string, it is interpreted as a file name (this is the same as calling {link smerl:for_file}). If ModuleName is an atom, Smerl attempts to find its abstract represtation either from its source file or from its .beam file directly (if it has been compiled with debug_info). If the abstract representation can't be found, this function returns an error.
The IncludePaths parameter is used when 'ModuleName' is a file name.get_attribute(MetaMod::meta_mod(), AttName::atom()) -> {ok, Val} | error
Get the value a the module's attribute.
get_export_all(MetaMod::meta_mod) -> true | false
Get the export_all value for the module.
get_exports(MetaMod::meta_mod()) -> [{FuncName::atom(), Arity::integer()}]
Return the list of exports in the meta_mod.
get_forms(MetaMod::meta_mod()) -> [Form]
Return the list of function forms in the meta_mod.
get_func(MetaMod::meta_mod() | atom(), FuncName::atom(), Arity::integer()) -> {ok, func_form()} | {error, Err}
Get the form for the function with the specified arity in the meta_mod.
Return the module name for the meta_mod.
has_func(MetaMod::meta_mod(), FuncName::atom(), Arity::integer()) -> bool()
Check whether the meta_mod has a function with the given name and arity.
new(Module::atom()) -> meta_mod()
Create a new meta_mod for a module with the given name.
remove_export(MetaMod::meta_mod(), FuncName::atom(), Arity::integer()) -> NewMod::meta_mod()
Remove the export from the list of exports in the meta_mod.
remove_func(MetaMod::meta_mod(), FuncName::string(), Arity::integer()) -> NewMod::meta_mod()
Try to remove the function from the meta_mod. If the function exists, the new meta_mod is returned. Otherwise, original meta_mod is returned.
rename(Form::func_form(), NewName::atom()) -> {ok, NewForm::func_form()} | {error, Err}
Change the name of the function represented by the form.
replace_func(MetaMod::meta_mod(), Function::string() | func_form()) -> {ok, NewMod::meta_mod()} | {error, Error}
Replace an existing function with the new one. If the function doesn't exist the new function is added to the meta_mod. This is tantamount to calling smerl:remove_func followed by smerl:add_func.
set_export_all(MetaMod::meta_mod(), Val::true | false) -> NewMetaMod::meta_mod()
Set the export_all value for the module.
set_exports(MetaMod::meta_mod(), Exports::[{FuncName::atom(), Arity::integer()}]) -> NewMod::meta_mod()
Set the meta_mod's export list to the new list.
set_module(MetaMod::meta_mod(), NewName::atom()) -> NewMod::meta_mod()
Set the meta_mod's module name.
to_src(MetaMod::meta_mod()) -> string()
Return the pretty-printed source code for the module.
to_src(MetaMod::meta_mod(), FileName::string()) -> ok | {error, Error}
Write the pretty printed source code for the module to the file with the given file name.


Description
This special function should only be called within Language Definition types. It walks the body of xdom:element tree node. The DocLang example shows a sample use.
Function walk:body
|
@x:func walk:body(elem~xdom:element, |
||||
|
Semantics
The pass result. This will be null for bind1 and bind2 and the value produced by evaluating the body in exec.


Script Filter Input
The Script Filter input is one of the most powerful workflow objects, allowing you to populate Alfred's results with your own custom items.
Note: Both the JSON and the legacy XML Output Formats are available as examples within Alfred's Workflow preferences. Select the [+] button > Getting Started and choose "Script Filter Output" to install the workflow.
Take a look at the JSON Format page for more details.
Jump to:
Configuring the Script Filter
Script Filters are configured like any other workflow input in that they require a keyword, title and icon.
The placeholder title and subtitle is displayed in Alfred's results until the Script Filter keyword is matched or the Script Filter is run. When the Script Filter is first run, the subtext is replaced by the "Please Wait" subtext which gives the user feedback while waiting for the initial returned result items.
Placeholder text is no longer required, but is recommended. The placeholder title and subtitle appear until you start typing your actual search query text. Without a placeholder, your filter will still work, but will not appear until you start typing your search query text.
The keyword works much like any other keyword throughout Alfred, it allows him to know when you want to run your Script Filter.
Keyword argument types
There are some subtle nuances regarding the keyword argument types which are worth understanding as it can help get the most out of your filter.
Argument Required
The Placeholder text and subtext are shown until the user types an argument. The script is not run until an argument is present. Once an argument is typed, the "Please Wait" subtext is shown until the initial script results are returned.
Argument Optional
The script is run immediately on the keyword matching, with the placeholder text and "Please Wait" subtext being shown until the initial script results are returned.
No Argument
The script is run immediately on keyword matching with the placeholder text and "Please Wait" subtext being shown until the initial script results are returned. If an argument is typed, Alfred no longer sees this Script Filter as relevant and any related results are removed from Alfred.
XML Output Format
The XML version is available for legacy, but new workflows should use the JSON format instead.
Script Behaviour
You can control the script execution behaviour when using a Script Filter input, including the queueing mode, queueing delay and trimming of whitespaces in arguments. You'll find this under the "Run Behaviour" button.
Queue Mode
The options for the 'Queue Mode' are self explanatory, you can either wait until the previous script finishes before running the script again with the latest argument, or you can terminate the currently running script and run a new instance of the script with the latest argument.
You can experiment to see which mode suits your script better. As a general rule, if you have a slow script, it may be better to use the 'terminate' option. If you have a script which needs to finish each time and is quite fast, then use the 'queue' option.
Queue Delay
This option allows you tweak the behaviour of when a script is queued (or run). It allows you to hold back from running the script if the user is currently typing, which is beneficial if your script is slow or uses a web API. It can also prevent flooding.
If you select the Automatic option, Alfred profiles how fast the user is typing and tries to only run the script when the user has stopped typing.
Unless you have a reason not to, it's recommended that you select "Always run immediately for first typed arg character" as this may make your script input perceptually faster to the user.
Argument Whitespaces Trimming
This option allows you to tell Alfred whether the whitespaces in your arg should be trimmed. By default, the option is set to trim irrelevant spaces to prevent a script from being re-run unnecessarily if there are multiple spaces.
If the spaces are significant and should be retained as part of your argument, use the "Don't trim arg spaces, as they are significant" option instead.
Alfred Filters Results
You can choose to run the script once, and allow Alfred to filter the results from all results returned by the script.
This is a highly efficient way to return results fast as Alfred handles the filtering, rather than running the script multiple times as additional characters are typed and the search is refined.
Match Mode
You can set the match mode Alfred uses to filter the results from the configuration cog to the right of the "Alfred Filters Results" checkbox. These modes pair well with the "match" property in the Script Filter JSON output.
Where phrase means the title or match property, the match modes are:
Exact from word boundary
This behaviour is the default behaviour for filtering, and matches pre-Alfred 3.5 behaviour. Results will match if the search term is an exact match from a word boundary in the phrase.
e.g. "My Family Photos" will match with the queries "My Family Photos", "Family Photos", "Photos".
Exact from start (anchored)
This is the most strict matching mode. Results will match only if they are an exact anchored match of the phrase.
e.g. "My Family Photos" will match with the queries "My Family Photos", "My Family".
Word matching
This is loose matching, and will match words in the phrase in any order.
e.g. "My Family Photos" will match with the queries "My Family Photos", "Photos Family", "Ph Fa".
Useful Resources
- The script runs (language, escaping, script) in the same way as a Run Script action.
- You'll find additional information and useful discussion relating to workflows on the Alfred forum
- Upload your best workflows to Packal, a great user-generated workflows and themes repository
- Github provides hosting suitable for your workflows


Sneha Venkatesh Yelimeli
About
Sneha is a Computer Science Masters student at UC San Diego. She is pursuing her interest in the field of machine learning and data sciences during her graduate studies there. She is highly passionate about computer science and technology. She wishes to pursue opportunities that will help her accomplish her goal of mapping technology solutions to unresolved real world problems.
Projects
-
Product Recommendations
- Built a real time product recommendation engine based on browsing patterns and user history for 15+ online retail merchants.
- Implemented collaborative filtering algorithms over map-reduce to scale for over 100 million users and products with a serving latency of < 400ms.
- This product is currently live with user coverage of ~90% and is already increasing click-through-rate.
- Technologies: Java, Hadoop, Cascading, Cassandra Db, AWS EMR and EC2.
-
Calatrava - Cross Platform Mobile Application Development Framework
- Part of the core team that built ‘Calatrava’ (calatrava.github.com) – an open-source framework for cross platform mobile application development.
- Calatrava provides fidelity of native user experience (by leveraging platform specific components) and minimizes cost (by sharing logic and views across platforms).
- Used Calatrava to develop Apps on Android, iOS, BB & Mobile Web for a leading US airline.
- Technologies: HTML5, Coffee Script (JS), Single Page Architecture, Node JS, Objective-C, Java.
-
Dating Website matching algorithm
- Completely revamped the core algorithm, user experience & performance of a dating website for a leading news and media company in UK.
- Developed a weighted scoring algorithm to match prospective partners based on statistics, user data & behavior. This significantly improved the revenue and the users’ interactions observed by analytics.
- Also worked on front-end development, performance improvements, interactive messaging & several other key features of the website.
- Technologies: Scala, Django-python (client side templating), Solr (text search engine), Mongo Db.


--- layout: page title: Hi, I'm Jiawei Yu subtitle: Algorithm Engineer in Speech Processing. use-site-title: true ---
"There is nothing as practical as a good theory." - Kurt Lewin
"The test of all knowledge is experiment." - Richard Feynman
My name is Jiawei Yu. Currently, an algorithm engineer in speech processing at Intengine Technology Corporation. Before that, I obtained M.S. degree from the Department of Information Science at Beijing Language and Culture University, advised by Prof. Jinsong Zhang. My research fields include language recognition using articulatory features, multilingual speech recognition and speaker recognition using speaker embeddings.
Jul, 2022 - Present
Intengine Technology Corporation, Beijing, China
Speech algorithm engineer.
Worked on multilingual speech recognition and key words spotting.
Worked on grapheme-to-phoneme and speech forced alignment for Cantonese and Bahasa Indonesia.
Sep, 2020 - Sep, 2021
SAIT Lab, Beijing Language and Culture University, Beijing, China
Research Assistant, supervised by Prof. Jinsong Zhang.
Researched on language recognition using articulatory features.
Researched on rectified goodness of pronunciation algorithm for pronunciation proficiency evaluation.
Sep. 2018 – July. 2019
Center for Speech and Language Technologies(CSLT), Tsinghua University, Beijing, China
Research intern, supervised by Prof. Dong Wang.
Researched on speaker recognition using attention mechanism.
Researched on zero-resource language recognition.
Reviewer
International Conference on Asian Language Processing (IALP) 2019
Asia-Pacific Signal and Information Processing Association (APSIPA) 2019
Presentation
Articulatory Features Based TDNN Model for Language Recognition, Qinghai, Aug, 2019
Zero-resource Language Recognition, Lanzhou, Nov, 2019
An Overview of Speaker Recognition, CSLT internal, Nov, 2018
Best Student Paper Award, National Conference on Man-Machine Speech Communication (NCMMSC), Qinghai, 2019.
2nd Prize of Excellent Undergraduate Scholarship, Beijing Language and Culture University, 2018.
6th Place in the Oriental Language Recognition (OLR) 2018 Challenge, Hawaii, 2018.
1st Prize Scholarship in four consecutive years (1%), Changchun University of Technology, 2012-2016.
1st Prize in Jilin Contest District in China Contemporary Undergraduate Mathematical Contest in Modeling, Changchun, 2014.
Bronze Medal in The ACM International Collegiate Programming Contest (ACM-ICPC) Asia Regional Contest Changchun Site, Changchun, 2014.


class Solution: def longestPalindrome(self, word1: str, word2: str) -> int: w = word1+word2 n = len(w) dp = [[0]*n for _ in range(n)] for i in range(n): dp[i][i] = 1 res = 0 for d in range(1,n): for i in range(n-d): j = i+d if w[i]==w[j]: dp[i][j] = 2+dp[i+1][j-1] if i < len(word1) and j >= len(word1): res = max(res,dp[i][j]) else: dp[i][j] = max(dp[i][j-1],dp[i+1][j]) return res


hTools2¶
hTools2, a UFO-based Python-powered font production toolkit.
-
class
hDialog
[source]¶ An object collecting constants and default settings for dialogs.
Height of standard button in dialogs.
Size of individual button in nudge button set.
-
padding
= 10¶ Padding in dialogs.
-
padding_x
= 10¶ Horizontal padding in dialogs (deprecated).
-
padding_y
= 10¶ Vertical padding in dialogs (deprecated).
-
progress_bar
= 18¶ The height of the progress bar.
-
read_only
= False¶ Can nummerical text input be edited directly with the keyboard? A boolean. Set to
True
as default.
-
size_style
= 'small'¶ The size style of text and standard dialog elements. Possible options are
small
,regular
,mini
.
-
spinner_height
= 46¶ Height of a spinner element (value and buttons).
Size of square button in cross nudge button set.
-
text_height
= 20¶ Height of text elements in dialogs.
-
text_input
= 18¶ Height of text elements in dialogs.
-
verbose
= True¶ Print (or not) messages to the console when running scripts.
-
width
= 123¶ The default width of vertical dialogs (palettes).