Machine learning has become quite successful in several domains in the past decade. An important portion of this success can be associated to the availability of large amounts of data. However, collecting and labeling data can be costly and time-consuming in many fields such as speech recognition, text classification, image recognition, as well as in robotics. In addition to lack of labeled data, robot learning has a few other challenges that makes it particularly difficult:
-
Learning from demonstrations1 has recently been popular for many tasks. However, we cannot just rely on collecting demonstrations from humans to learn the desired behavior of a robot, since human experts usually provide suboptimal demonstrations or have difficulty operating a robot with more than a few degrees of freedom. Imagine you are to control a drone. Even if you are an expert on it, how optimal can you be on completing a task, e.g. following a specific trajectory as quickly as possible?
-
We could just use reinforcement learning to have the robot optimize for itself, but what will be the reward function? Beyond cases where it is easy to automatically measure success or failure, it is not just hard to come up with an analytical reward function, but it is also hard to have humans assign reward values on what robots do. Imagine you are watching a robot grasping an object. Could you reliably answer if you were asked: “On a scale of 0 to 10, how good was that?”. Even if you can answer, how precise can you be?
-
Both demonstrations and human reward labeling have another shared problem: scaling. Given that most supervised learning and reinforcement learning techniques need tons of data, it would take humans giving demonstrations or labeling rewards for years and years just to train one agent. This is clearly not practical.
So what can we do? One alternative is preference-based methods - instead of asking users to assign reward values, we will show them two options and ask them which one they would prefer. Furthermore, we are going to use active learning techniques to have the robot itself ask for queries that would give the most information and thus not need the human to toil the years just to teach the robot one skill. But, there’s an issue: while the use of active learning helps overcoming scalability issues regarding data size, it is computationally not practical as it needs to solve an optimization for each query selection. Luckily, we have a solution: combine preference learning and batch active learning to generate several queries at once! This is the premise of our CoRL paper “Batch Active Preference-Based Learning of Reward Functions”, which we will overview in this post.
Preference-based Learning
Let’s start with some background. Is Preference-based learning really a reliable machine learning technique?
In fact, psychologists studied this subject decades ago and concluded humans are pretty reliable on answering preference queries when the number of options to compare is low enough2 . In this post, we will focus on pairwise comparisons. Given we can now reliably collect data, the next natural question would be: How are we going to use these comparisons to learn the underlying reward function?
To develop our learning algorithm, we will first model the structure of the reward function. We will assume that the reward value of a trajectory is a linear function of some high-level features:
For example, for an autonomous driving task, these features could be the alignment of the car with the road and with the lane, the speed, the distance to the closest car, etc3. In this autonomous driving context, represents a trajectory, is the corresponding feature-vector and is a vector consisting of weights that define the reward function.
We then model how humans make their choices, again based on some psychology literature4 . Given two trajectories and , the difference on the reward values is simply . Then, the probability of user choosing is:
where , and it being either 1 or -1 shows the output of the query.
The way we are going to learn the weights vector, and by extension the reward function, is Bayesian: , where denotes the query number, and after each query we update the distribution over .
Up to this point, we have shown how preference-based learning can help in robot learning. However, there remains an important problem: How many such comparisons are needed to have the robot learn the reward function? And is it always possible to learn the reward function in this way?
Active Preference-based Learning
To reduce the number of required comparisons, we want to actively synthesize queries, i.e. we want to maximize the information received from each query to learn the reward function as quickly as possible. While optimal querying is NP-hard, Sadigh et al. showed that modeling this problem as a maximum volume removal problem works well in practice (see this paper). In the same work, they also showed the query selection problem can be formulated as:
where the robot actions and the initial state are assumed to be identical among the two query trajectories. The optimization objective can be approximated by sampling and the optimization can be locally solved. One can easily note that we want to generate the queries for which we are very unsure about the outcome with the current knowledge of . Another interpretation is that we want to maximize the conditional entropy of given . While the practicality of this method has been analyzed in this paper, query generation times remained a huge limitation.
Batch-Active Preference-based Learning
To speed up the query synthesization process, we can generate a batch of queries at once. This is again NP-hard to do optimally. Moreover, the queries to be generated are not independent. One query might carry a significant portion of information that another query already has. In this case, while both queries are individually very informative, having both of them in the batch is wasteful.
Then, we can describe the problem as follows. We have one feature difference vector for each query. And for each of them, we can compute the optimization objective given above. While these values will represent how much we desire that query to be in the batch, we also want -values to be as different as possible from each other.
The general approach to this problem is the following: Among queries, we first select of them that individually maximize the optimization objective. To further select queries from this preselected set to eliminate similarities between queries, we present four different methods:
-
- Greedy Selection: We simply select individual maximizers.
2 - Medoids Selection: We cluster -vectors using K-medoids algorithm into clusters and then we select the medoids.
3 - Boundary Medoids Selection: Medoids selection algorithm can be improved by only choosing the queries that correspond to the boundary. For that, we first take the convex hull of the preselected set and eliminate the queries that are inside this volume. Then, we apply K-medoids algorithm on the remaining vectors to cluster them into clusters, and then finally we select the medoids.
4 - Successive Elimination: An important observation is that the problem we are trying to solve while selecting of the queries out of the preselected set is actually similar to the max-sum diversification problem, where the aim is to select a fixed-size subset of points whose average distance to each other is maximized. Unfortunately, this problem is also known to be NP-hard. However, our problem is slightly different, because we have the optimization objective values that represent the value of each query to us. Hence, we propose the following algorithm: At every iteration of the algorithm, we select two closest points in the preselected set, and remove the one with lower information entropy (or optimization objective). And we repeat this until we end up with queries.
Theoretical Guarantees: In the paper, we have showed the convergence is guaranteed under some additional assumptions with greedy selection and successive elimination methods, as they will always keep the most informative query in the batch.
Experiments & Results
We did experiments with a simple linear dynamical system (LDS) and 5 different simulations from MuJoCo, OpenAI Gym, and a driving simulator presented in another work. We assumed a true reward function and attempted to estimate it using our methods with .
We evaluated each algorithm with a metric that quantifies how close the estimated reward function is to the true function after queries. So, how well did the various querying methods do?
There we have it: the greedy algorithm is significantly outperformed by the three other batch-active methods. The performances are ordered from the worst to the best as greedy, medoids, boundary medoids, and successive elimination. In fact successive elimination significantly outperformed medoid selection, too.
Simulated environments also led to similar results and showed the time-efficiency of batch-active methods. They also showed how local optima can potentially impair the non-batch active method.
In the table below, we show the average query time in seconds for each method.
Batch active results in a speed up of factor 15 to 50! As one might infer there is a tradeoff between how fast we generate queries and how fast we converge to the true reward function in terms of the number of queries.
Lastly, we perform usability studies by recruiting 10 human subjects to respond the queries for Driver and Tosser tasks. We have seen that our methods are able to efficiently learn different human preferences.
We also present demonstrative examples of the learning process for both simulation environments.
Driver:
Tosser:
What’s Next?
So, as our results showed Batch Active learning not only improves convergence but does it fast. We are very enthusiastic about the continuation of this work. Some potential directions that we are currently working on include, but are not limited to:
- Varying batch-sizes could increase performance, i.e. it is intuitively a good idea to start with small batches and then increase batch size as we start with no information.
- To improve the usability in complex tasks, an end-to-end framework that also learns the feature transformations would help a lot.
- While the current approach is useful with simulations, it is important to incorporate safety constraints when working with actual robots. In other words, we cannot simply generate trajectories with any input when the system of interest is safety-critical.
Hopefully, an extension of this work will one day make machine learning as successful in robotics as in the other domains where it already works wonders.
This post is based on the following paper:
Batch Active Preference-Based Learning of Reward Functions (arXiv)
Erdem Bıyık, Dorsa Sadigh
Proceedings of the 2nd Conference on Robot Learning (CoRL), October 2018
It was also shared on the iliad lab’s blog.
-
Also known as ‘Programming by Demonstration’, this is just what it sounds like: teaching a robot skills by demonstrating how to do them. ↩
-
See, for example, this famous paper. ↩
-
This assumption is actually pretty mild, because those high-level feature transformations of trajectories could be the neural network embeddings. ↩
-
See Luce’s choice axiom and this work. ↩


{{exception.error.message}}
Please provide a name
The Product ID you entered is already in use.
The Product ID cannot contain spaces or special characters.
Leave blank and we'll create one automatically
A brief description of the product
Provide a minimum quantity to receive a particular discount. You can define the discount currency in as many currencies as you wish. The discount price for currencies you do not provide will be calculated automatically based on current exchange rates.
Please provide a minimum quantity
Please provide a type
Please provide a valid weight
Filename: {{data.file.filename}}
Version: {{data.file.version}}
Size: {{data.file.bytes | bytesToMB}}
Created: {{data.file.date_created | date:'mediumDate'}} {{data.file.date_created | date:'shortTime'}}
Updated: {{data.file.date_modified | date:'mediumDate'}} {{data.file.date_modified | date:'shortTime'}}
Description: {{data.file.description}}
Remove Change
Type: Remote URLList
Created: {{data.license_service.date_created | date:'mediumDate'}} {{data.license_service.date_created | date:'shortTime'}}
URL: {{data.license_service.configuration.url}}
Description: {{data.license_service.description}}
Enter a positive number to increase or a negative number to decrease
If you don't allow oversells, customers will be prevented from buying more than the available inventory quantity.
Version: {data.{subscription_plan.version}}
Billing Period: {{data.subscription_plan.billing_interval}} {{data.subscription_plan.billing_interval_unit}}(s)
Trial Period: {{data.subscription_plan.trial_interval}} {{data.subscription_plan.trial_interval_unit}}(s)None
Total Billings: {{data.subscription_plan.end_at_billing_count}}Unlimited
Created: {{data.subscription_plan.date_created | date:'mediumDate'}} {{data.subscription_plan.date_created | date:'shortTime'}}
Description: {{data.subscription_plan.description}}
Select which products this product can be upgraded or downgraded to during an active subscription. These are usually variations of this product, such as lite, standard and pro versions. The products you select must contain the same subscription plan.
Select the other versions of this product that contain different subscription billing terms. For example, if this product is offered as a monthly, quarterly and annual subscription. The products you select must contain different subscription plans.


Coordinator Signup
Emergency Coordinators can visualize the disaster response with our in-depth, real-time information feed. Our AI system does all the heavy lifting in the background - gathering information, monitoring, analyzing, and prioritizing; so that you can focus on making those critical decisions.


{% extends 'hr/base.html' %} {% block content %}
PAYSLIP FOR THE MONTH OF SEPTEMBER 2017 |
|||
|---|---|---|---|
| Employee Name: | John Doe | Employee ID: | SM0050 |
| Designation: | Web Developer | No Of Working Days: | 25 |
| Date Of Joining: | 25/11/16 | No Of Paid Days: | 24 |
| Bank: | ICICI | CTC: | 20000 |
Earnings |
Deductions |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Basic Salary | 7833 | Provident Fund(PF) Employer | 940 | ||||||||
| House Rent Allowance (H.R.A.) | 3133 | Provident Fund(PF) Employee | 940 | ||||||||
| Conveyance | 1567 | Professional Tax: | 200 | ||||||||
| PF Employer | 940 | Advance: | - | ||||||||
| Performance Allowance | 6110 | Income Tax Ded: | - | ||||||||
| Arrears | - | ||||||||||
| Others | - | ||||||||||
| Incentives | - | ||||||||||
| Total Earnings | 19583 | Total Deductions: | 2080 | ||||||||
| Net Salary: 17503(Seventeen thousand five hundred and three only.) |
1)Others will be deducted as applicable
2)Deduction may be due to Attandance Policy or unapproved leaves, the same will be on tha basis on absent and reporting time
3)Deductions can be applied due to lack of performance.
Authorized Signature*This is computer generated report hence signature is not required


---
layout: post
title: Do I want one?
tags:
- Business
- Personal Life
status: publish
type: post
published: true
meta: {}
---
My personnel friends (all one of them, if I remember to pay you) will agree that I am perhaps slightly well enveloped. Could this be the answer? Or will it just make me smell sweaty at work?
I think I shall have to put one on my Santa list
powered by performancing firefox


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object de.uniheidelberg.cl.swp.mlprocess.InstanceContainer
public class InstanceContainer
A container for WEKA Instances utilized by MLProcess
.
This is a temporary container for providing the attribute structure which WEKA requests.
| Constructor Summary | |
|---|---|
InstanceContainer()
|
| Method Summary | |
|---|---|
weka.core.Instance |
addCorefInstance(CoreferencePair corefPair,
java.lang.String acr)
Takes a single CoreferencePair and adds it to the Instances. |
weka.core.Instance |
addCorefInstance(CoreferencePair corefPair,
java.lang.String acr,
java.lang.String result)
Takes a single CoreferencePair and adds it to the Instances. |
java.util.Map<CoreferencePair,weka.core.Instance> |
addCorefInstances(java.util.List<CoreferencePair> corefPairs,
java.lang.String acr,
java.lang.String result)
Adds a List of CoreferencePair s to the list of Instances. |
void |
createAttributeStructure(java.util.List<AbstractFeatureExtractor> featureList)
Takes a list of AbstractFeatureExtractor and adds the corresponding feature as a
WEKA Attribute to the Instances structure. |
weka.core.Instances |
getInstances()
Getter for the Instances object which contains the stored Instance objects. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public InstanceContainer()
| Method Detail |
|---|
public void createAttributeStructure(java.util.List<AbstractFeatureExtractor> featureList)
AbstractFeatureExtractor
and adds the corresponding feature as a
WEKA Attribute to the Instances structure.
featureList
- The list of FeatureExtractors to be added.public weka.core.Instance addCorefInstance(CoreferencePair corefPair, java.lang.String acr, java.lang.String result)
CoreferencePair
and adds it to the Instances.
CoreferencePair
.
corefPair
- CoreferencePair
to be added.acr
- ACR-System.result
- Result of the ACR-System for the CoreferencePair
-candidate.
public weka.core.Instance addCorefInstance(CoreferencePair corefPair, java.lang.String acr)
CoreferencePair
and adds it to the Instances.
CoreferencePair
.
corefPair
- CoreferencePair
to be added.acr
- ACR-System.
public java.util.Map<CoreferencePair,weka.core.Instance> addCorefInstances(java.util.List<CoreferencePair> corefPairs, java.lang.String acr, java.lang.String result)
CoreferencePair
s to the list of Instances.
corefPairs
- CoreferencePair
s to be added.acr
- Related ACR-System.result
- Result of the related ACR-System.
CoreferencePair
s and their corresponding Instance objects.public weka.core.Instances getInstances()
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Must do this more often
Posting shouldn't be work, but it does feel like it sometimes.
But it feels like work sometimes. Having to think of a subject rather than just rambling on about nothing at all does seem a little tedious at times.
However I am digressing! When I re-vamped this blog I had all the good intentions of writing about things everyday, when in reality instead of picking subjects with substance (or not) I should have been concentrating on what the family was up to and writing things about us ... now that makes much more sense.
So what to write about from a family perspective for my first post back after such a long time away, yes, yes I know not as long as last time!
What have we done? Nothing? No, not really. Where have we been? Every where, well again, not really ...
Since July we've been the usual family trying to make life as enjoyable as possible, nothing out of the ordinary there really. Braden started playing ice hockey at the Gold Coast rink in the KRL, no not that mob-run (allegedly) league in Russia, but the Kids Recreational League. It is made up of three teams who play two weeks in a row and then have a bye, they have enough players to make two lines, one of older/bigger kids and the other of younger/smaller kids. The skill levels vary a great deal from those that can't really skate to those that have played at a state level. It is well run and Braden is really learning to play well, when it is not "too tired" that is.
So far he has only had one assist, not bad considering he doesn't really get into the action too much and likes to hang back, but he has also had a third and first star of the game. The third star was an encouragement award, but the first star was deserved. He played his best game ever that weekend, he was in on the plays, up and down the ice, making a supreme effort, happily staying on the ice and just doing a really good job overall. To say we were proud is an understatement.
Vicki and I have not been as active as we should be, but fingers crossed that will be changing soon with the startup of the development league, or C-grade. Where games will be run as a training/scrimmage session to allow us to get to know the positions, the rules and what is expected of us on the ice. This is to get us ready to move to B-grade for the next regular season, mainly because there were some "issues" with the season just completed, where new players were "inserted" into pre-existing teams and the outcome was not what I call friendly at all. The new league starts up in just over a week so we need to get signed up and ready to go, pretty excited about it actually!
Our life has not just been ice hockey, we've just recently come back from a vacation to the Hawaiian Islands, and this time I can really say that as we didn't just spend time on Oahu, we went to Hawai'i itself as well (also know as the Big Island) and spent a week there. Besides the abysmal attempt at customer service by both QANTAS and Jetstar airlines we made it there and back safely, in the end. Suffice it to say we will NEVER book a flight through one airline where some legs may be by another carrier, it just ends up with far too much finger pointing and a complete lack of professionalism on either parties behalf.
The vacation though was excellent, we had the best time and returned with many great memories, and photos. I will have to figure out if I can link to a shared photo stream or if I have to add photos here individually, only time will tell. I will write more detailed posts on some of the things that we did while on both islands as it is far too much for a single post (not to mention giving me subject matter that will span weeks instead of days ... oops I shouldn't readily admit that should I?).
As with all vacations it is always sad to have to come home, this time especially as Vicki didn't have a job to come back to after being "let go" due to financial re-structuring at her last job. It was a shame as she loved the company, the staff and the job, although I think the hardest part was leaving her friends behind who have more work to do to fill the gaps. On the bright side though she did find a new job in record time and the hours are now "school hours" which means she starts at 8:30am and finishes at 3:00pm daily, which means I do not have to pick up Braden every afternoon from school, although I could if I wanted to and will in situations where Vicki has to work back.
One thing the vacation has done is re-affirmed our desire to move to the United States of America, to that end we've both entered the Diversity Lottery (Green Card Lottery) for entry in 2014 as a fall back in case I cannot organise something else. The wonderful, yes sarcasm, thing is that we put our entry into the lottery in October but do not find out until May, and then have to wait for the next round of steps before getting the card in 2014. Patience is not really one of my virtues to be entirely honest!


Revision Poll
Subject
Select Subject
{{sub.subject_name}}
Topic
Select Topic
{{top.topic_name}}
Due Date
Create Poll


J Hus Makes His Triumphant Return With "Must Be"
It must be an opp.
by Aron A.Must Be
J Hus
Editor Rating: VERY HOTTTTT
User Rating: n/a - 0% (0)
2019 started on a rough note for J Hus but things made a turn shortly after. For those who were unaware, J Hus was arrested for carrying a knife in the U.K. Although he was freed on bail, he was sentenced to eight months in prison in late 2018. He spent a few months in prison before being released earlier this year and making his warm return on stage with Drake.
J Hus has been keeping low key since his release from prison, although he did appear on Skepta's new project. Now, he makes his official return with his new song, "Must Be." The rapper hops over jazz-infused, U.K. hip-hop production with an Afrobeats-esque melody. The rapper still keeps it to the U.K. roads on this one, reminding everyone if it looks like an opp, chills with an opp, hangs with an opp, then it must be...


Rustbelt Refresh 2016
Responsive Design Workshop
With Karen McGrane and Ethan Marcotte
Presented by Feras Deiratany
What is Responsive Design?
Why Responsive Design?
Mobile Growth
Responsive Design Values
Thinking "Mobile First"
Flexibility in Design
Flexibility in Delivery
Images in RWD
img { max-width: 100%; }
Compressive Images
Rollout
Responsive Retrofit
Beta
Pick A Page
Responsive is not a project. Responsive is a permanent new way of working.
—Scott Kelton Jones, Expedia


Hi, my name is Thomas
I'm a CS Student at UIUC who loves cool words like LAMP, NoSQL,
AGILE, POSIX, and REST. The things that excite me most are Big Data,
Cloud Computing, Human Computer Interaction. I've been coding since
Middle School, I have worked for eBay as a Software Engineer intern,
and I have published two papers as a co-author with the Data Mining
Research Group at Illinois.


Whether you are looking to drop in for Sunday brunch, or prefer a chilled Friday afternoon with your friends, we
are here to delight your taste buds with our unique flavors of wood-fired pizza.


Buckingham Palace: Queen’s new outfits won’t use real fur
LONDON (AP) - Buckingham Palace says new outfits designed for Queen Elizabeth II will not use real fur.
The palace said Wednesday the decision does not mean the queen will dispose of all the fur outfits she already has. The palace statement says “the queen will continue to re-wear existing outfits in her wardrobe.”
The decision pleased animal rights activists, who have sometimes criticized the monarch for the fur pieces in her collection of designer clothes.
Claire Bass, director of the Humane Society International/UK, says “we are thrilled Her Majesty has officially gone fur-free.” She says the queen’s decision will send a positive message that fur is no longer considered fashionable.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Create a shopping list and view it in AR
Explore the contents of a sample notice board
Make a pinboard with hand written/drawn content


pisa_analysis_tool package¶
Submodules¶
pisa_analysis_tool.bokeh_plot module¶
pisa_analysis_tool.correlation module¶
Running descriptive visuals¶
The following module offers visualization for descriptive statistics such as correlation plot, scatter plot, density plot, and histograms.
-
pisa_analysis_tool.correlation.
calculate_correlation
(df, colnames)¶ A general function to calculate and visualize correlations between selected variables
Parameters: df – a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors.
Parameters: colnames – a list of focal variables Returns: a plot of correlations
-
pisa_analysis_tool.correlation.
main
()¶ Run functions for correlations and visuals
-
pisa_analysis_tool.correlation.
visual_densityplot
(df, colnames)¶ Univariate density plot, as a way to look at distribution
Parameters: df – a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :param colnames: a list of focal variables :return: a density plot
-
pisa_analysis_tool.correlation.
visual_histplot
(df, colnames)¶ histogram plot :param df: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :param colnames: a list of focal variables :return: a histogram plot
-
pisa_analysis_tool.correlation.
visual_scatterplot
(df, colnames)¶ scatter plot :param df: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :param colnames: a list of focal variables :return: a scatter plot
pisa_analysis_tool.preprocessing module¶
-
pisa_analysis_tool.preprocessing.
add_lat_long
(filedir, country_dict)¶ read a csv file and add countries’ latitude and longitude data behind the last column. :param filedir: directory of csv file :param country_dict: a dictionary which keys are country names
and values are [lat, lon]Returns: new csv file and a list of countries which have PISA test score
-
pisa_analysis_tool.preprocessing.
get_score
(df_male, df_female, subject)¶ Get a country’s male and female score with respect to a subject. :param df_male: a data frame with males average score :param df_female: a data frame with females average score :param subject: subject name :return: two dictionaries
-
pisa_analysis_tool.preprocessing.
make_country_dict
()¶ extract country name with lat and lon into a dictionary :return: a dictionary which keys are country name and value are (lat, lon)
-
pisa_analysis_tool.preprocessing.
preprocessing
()¶ read original student_info data and convert them into 3 new csv file world_score_avg.csv: store countries’ average subject score world_score_male_avg.csv: store countries’ male average subject score world_score_female_avg.csv: store countries’ female average subject score :return: none
-
pisa_analysis_tool.preprocessing.
remove_country
(csv_dir, country_dict, country_lst)¶ extract the countries which have gender parity index and also in world country data :param csv_dir: a csv file which have gender coefficent. :param country_dict: a dictionary which keys are country name
and value are (lat, lon)Parameters: country_lst – a list of countries which have PISA test score Returns: a dictionary with country name and gender parity index
-
pisa_analysis_tool.preprocessing.
sort_by_country_avg
(df_count, subject, m_res, f_res)¶ Sort the countries with subject score from high to low. Filter the scores into 4 bins, calculate each bin’s gender coefficient. We also filter male and female mean score for each country into 4 bins, and get the average for each bin. :param df_count: average score for each country :param subject: subject name :param m_res: average score for male student in each country :param f_res: average score for female student in each country :return: 3 dictionaries
-
pisa_analysis_tool.preprocessing.
wb_country_data
(indicator, start=2015, end=2015)¶ grab gender parity index data from world bank api :param indicator: :param start: start year :param end: end year :return: a dataframe
pisa_analysis_tool.data_cleaning module¶
Cleaning data¶
The following module demonstrates data cleaning procedures such as recoding, missing data handling, standardization, and subsetting a small sample data for modeling.
-
pisa_analysis_tool.data_cleaning.
data_cleaning
(filename)¶ Function to clean data types, create log_score, effect-coding variables, and dealing with NAs.
Parameters: df – a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: a dataframe to csv file
-
pisa_analysis_tool.data_cleaning.
main
()¶ - Run functions of cleaning and subsetting the data based on multiple levels
to be used for modeling
param df: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: two csv files, one with complete data, one with subsetted data
-
pisa_analysis_tool.data_cleaning.
school_type
(value)¶ In the ‘School_type’ variable there are multiple responses labeled as ‘Invalid’, or ‘No response’. These will be coded along with system missing code ‘99’ as ‘NaN’. The ‘public’ and ‘private’ school type will be recoded as 1 and -1 respectively.
-
pisa_analysis_tool.data_cleaning.
small_sample
(df)¶ output a random sample data csv file for smaller sample analysis
pisa_analysis_tool.formula_creator module¶
Tool for generating formulas Given user’s input for lmer, creates a formula and its name
-
pisa_analysis_tool.formula_creator.
formula_creator
()¶ Creates a formula based on user’s input for Lmer, and a name describing the formula.
-
pisa_analysis_tool.formula_creator.
main
()¶ Generates the formula specified by the user
pisa_analysis_tool.get_WDI_data module¶
Getting World Bank data from wbdata pakcage¶
This module searches the indicators in the World Bank data and get a dataframe accordingly.
-
pisa_analysis_tool.get_WDI_data.
WB_country_data
(indicator, start=2015, end=2015)¶ A function for for getting gender indicator data for all countries
Parameters: - country_code – a string of three letters indicatoring country name
- indicator – the indicator of database, a string
- start – start date of the year
- end – end year
Returns: a dataframe of this indicator
-
pisa_analysis_tool.get_WDI_data.
country_data
(country_code, indicator, start=2015, end=2015)¶ A function for specific country lookup.
Parameters: - country_code – a string of three letters indicatoring country name
- indicator – the indicator of database, a string
- start – start date of the year
- end – end year
Returns: a dataframe of this indicator
-
pisa_analysis_tool.get_WDI_data.
main
()¶ Run to get gender equity indicator/coefficient for all countries
pisa_analysis_tool.get_students_info module¶
CSE583 Project Given a student file, extracts useful features and writes as a csv file
-
pisa_analysis_tool.get_students_info.
get_student_info
(input_file)¶ Takes a list of interested attributes, computes and extracts useful data from student-school datafile, stores data into a new csv file.
-
pisa_analysis_tool.get_students_info.
main
()¶ Extracts student infomation and generates a output csv file
pisa_analysis_tool.hlm_pymer4 module¶
Fitting and plotting multi-level models¶
This module runs models through pymer4 package for the examplar inquiry, which is to investigate the school-level and country-level factors that predict students’ science performance in the PISA 2015 data set, and mediate gender differences in science performance. Here each model category is exemplified with a selection of variables and we will show a demonstration of model fitting with regard to gender (female) and a group of other interested factors. The results show the model coefficients for each model.
-
pisa_analysis_tool.hlm_pymer4.
fixed_effect_3level_model
(dataframe)¶ Multi-level model_2_sci includes intercept, variables as fixed effect.
param dataframe: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: the model results
-
pisa_analysis_tool.hlm_pymer4.
fixeff_interaction_3level_model
(dataframe)¶ Multi-level model_3_sci includes intercept, variables as fixed, and an interaction term.
param dataframe: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: the model results
-
pisa_analysis_tool.hlm_pymer4.
main
()¶ Run the function of intercept-only model, gradually adding in predictors, and interactions, with focal interest variable of female and science outcome.
-
pisa_analysis_tool.hlm_pymer4.
mixeff_interaction_3level_model
(dataframe)¶ Multi-level model_4_sci includes intercept, school resource as random, others as fixed including
interaction.
param dataframe: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: the model results
-
pisa_analysis_tool.hlm_pymer4.
mixeff_multinteraction2level_model
(dataframe)¶ Multi-level model_5_sci includes intercept, multiple interactions and fixed effects,
and setting ESCS as random on country level.
param dataframe: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: the model results
-
pisa_analysis_tool.hlm_pymer4.
model_sum_visual
(model_x_sci, predictor, outcome)¶ A general function of plotting model results, including the coefficients with 95% confidence intervals, and cluster level estimates overlaid, as well as fit and residuals.
param model_x_sci: a list of lists of model results assigned to a name :param predictor: a string of predictor indicator :param outcome: a string of outcome variable
-
pisa_analysis_tool.hlm_pymer4.
random_effect_2level_model
(dataframe)¶ Multi-level model_1_sci includes intercept, variable as fixed and the interaction term random on country level.
param dataframe: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: the model results
-
pisa_analysis_tool.hlm_pymer4.
random_intercept_3level_model
(dataframe)¶ Multi-level model_0_sci includes grand-mean intercept and setting outcome of log science scores as random.
param dataframe: a data frame with student ID, school ID, country ID, science, math, reading, and other five selected variables as predictors. :return: the model results
pisa_analysis_tool.mapdata module¶
pisa_analysis_tool.merge_csv module¶
CSE583 Project This program merges student file or teacher file with school file accoding to ‘CNTSCHID’ and ‘SUBNATIO’
-
pisa_analysis_tool.merge_csv.
main
()¶ Merges teacher file with school file and student file with school file
-
pisa_analysis_tool.merge_csv.
merge
(file, output)¶ Merges file with school file according to ‘CNTSCHID’ and ‘SUBNATIO’, and generates a new output csv file
pisa_analysis_tool.plot_WDI module¶
-
pisa_analysis_tool.plot_WDI.
plot_indicators
(country_list, indicator, start=2000, end=2015)¶ A grand function that takes as input a list of country codes, then uses our function above WB_country_data to get a list of indicators for each country,then plot a line diagram. :param country_list: a list of strings of country code :param indicator: indicator index from wbdata :param start: start of the year :param end: end of the year :return: a plot
pisa_analysis_tool.students_info_helper module¶
CSE583 Project get_student_info helper functions
-
pisa_analysis_tool.students_info_helper.
extract_basic_df
(df, country_dict_df)¶ Takes 2 dataframes df and country_dict_df, extract basic student information, returns a new dataframe
-
pisa_analysis_tool.students_info_helper.
info
(df, id_num)¶ Parameters: - df – the dataframe of ‘nations.csv’
- id – the id of nation
Returns: a tuple - (string country name, string country
code, string continent)
-
pisa_analysis_tool.students_info_helper.
infos
(country_df, id_list)¶ Parameters: - country_df – the dataframe of ‘nations.csv’
- id_df – the dataframe of country id
Returns: a dataframe - (string country name, string
country code, string continent)
-
pisa_analysis_tool.students_info_helper.
interface
(df)¶ Takes a dataframe as parameter, gets variable names and output file name, returns a list with the given variables
-
pisa_analysis_tool.students_info_helper.
sav_to_dataframe
(file)¶ Converts a sav file to a pandas dataframe
-
pisa_analysis_tool.students_info_helper.
school2nation
(id_num)¶ Takes school id, returns nation id of the school.
-
pisa_analysis_tool.students_info_helper.
student2nation
(id_num)¶ Takes student id, returns nation id of the student.
-
pisa_analysis_tool.students_info_helper.
student2school
(df, id_num)¶ Takes the ‘IDs_sorted_by_student.csv’ dataframe and student id, returns school id of the student.
-
pisa_analysis_tool.students_info_helper.
student2school_helper
(df, id_num, start, end)¶ student2school’s helper function
pisa_analysis_tool.version module¶
Specifies current version of pymer4 to be used by setup.py and __init__.py


{% extends './base.html' %} {% load staticfiles %} {% block content %}
The Gross Domestic Product (GDP) in India was worth 2073.54 billion US dollars in 2015. The GDP value of India represents 3.34 percent of the world economy. GDP in India averaged 582.30 USD Billion from 1970 until 2015, reaching an all time high of 2073.54 USD Billion in 2015 and a record low of 63.50 USD Billion in 1970.
The gross domestic product (GDP) measures of national income and output for a given country's economy. The gross domestic product (GDP) is equal to the total expenditures for all final goods and services produced within the country in a stipulated period of time.


Lallemand Munich Classic
No.106
OG:
1.073
FG:
1.020
No.45
OG:
1.048
FG:
1.012
under pitch for banana flavor.
No.49
OG:
1.045
FG:
1.010
under pitch for banana flavor.
No.105
OG:
1.051
FG:
1.012


{% extends "base.html" %} {% block title %}Pint - {{ user.nickname }}{% endblock %} {% block leftblocs %}
Welcome, oh great and powerful admin!
Humble am I in front of such power and authority...


John Smith
Yesterday
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque eleifend...
John Smith
Yesterday
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque eleifend...
John Smith
Yesterday
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque eleifend...
Read All Messages
Task 1
40% Complete
40% Complete (success)
Task 2
20% Complete
20% Complete
Task 3
60% Complete
60% Complete (warning)
Task 4
80% Complete
80% Complete (danger)
See All Tasks
User Profile
Settings
Logout
Home
New Search
Search Results
Visualize
Clusters
Report
Tutorial
Clear Fields
Estimated Results
0 documents
Submit
New Query
Main Query
Main Query:
Year Range
Document Types
MeSH Terms
MeSH Terms
Search
Organization Groups
Org. Label
Org. Value
Topics
Topic Label
Topic Value
Expert Groups
Expert Label
Expert Value
Topic Label
Topic Value
Organization Label
Organization Value
Expert Label
Expert Value


In our program, we are helping you change the way you think about common but potentially stressful situations in order to reduce your anxiety. During each session, you will be asked to read brief stories about situations that may generate anxiety and then complete word fragments by filling in one or more missing letters in a word. These tasks are not meant to be tricky or too difficult.
The exercises are designed to help you change the way you make sense of events that may cause anxiety. The program can feel a little repetitive, but the repetition is important so you develop a more balanced thinking style. Don’t worry if you don’t use smartphones or computers much — the tasks are not designed to be difficult, and you can always email questions to the team at<EMAIL_ADDRESS>This is an experimental program that is still in the testing phase, but prior research suggests this program may be able to reduce anxious thinking as effectively as therapy for some people. We want to determine if it can be helpful for you. If you would like to learn more about the science behind MindTrails, take a look at the "The Science of MindTrails" button at the top of the page.


Ren Zhengfei: No US company is willing to negotiate for our 5G technology
by Efe UdinRen Zhengfei: No US company is willing to negotiate for our 5G technology
3 (60%) 6 votes
Over the past two months, there have been reports that Huawei is willing to sell access to its 5G technology to an American company. However, in a recent dialogue with some professionals, Huawei’s CEO, Ren Zhengfei, said that the company is willing to sell access to its 5G technology to an American company but no one is willing to negotiate.
“This is a very big and very difficult decision. The big American companies have to think carefully. When someone is willing to discuss this issue, we will find an investment bank. The bank will help us intervene to discuss the contract and cooperation of the transaction.” Ren Zhengfei said.
Regarding why Huawei is particular about selling access to its 5G technology to an American company, Ren Zhengfei believes that the competition is “good”. According to him, the US is behind in 5G. Europe, South Korea, and Japan all have their own 5G technology so the only viable buyer should be the US. However, he didn’t deny the fact that another reason for selling to a US company is to re-establish cooperation. For him, if an American company has access, it will be convinced that the network is clean. However, he believes that technology companies are not the problem but the politicians.


"""distutils.dep_util Utility functions for simple, timestamp-based dependency of files and groups of files; also, function based entirely on such timestamp dependency analysis.""" import os from distutils.errors import DistutilsFileError def newer (source, target): """Return true if 'source' exists and is more recently modified than 'target', or if 'source' exists and 'target' doesn't. Return false if both exist and 'target' is the same age or younger than 'source'. Raise DistutilsFileError if 'source' does not exist. """ if not os.path.exists(source): raise DistutilsFileError("file '%s' does not exist" % os.path.abspath(source)) if not os.path.exists(target): return 1 from stat import ST_MTIME mtime1 = os.stat(source)[ST_MTIME] mtime2 = os.stat(target)[ST_MTIME] return mtime1 > mtime2 # newer () def newer_pairwise (sources, targets): """Walk two filename lists in parallel, testing if each source is newer than its corresponding target. Return a pair of lists (sources, targets) where source is newer than target, according to the semantics of 'newer()'. """ if len(sources) != len(targets): raise ValueError("'sources' and 'targets' must be same length") # build a pair of lists (sources, targets) where source is newer n_sources = [] n_targets = [] for i in range(len(sources)): if newer(sources[i], targets[i]): n_sources.append(sources[i]) n_targets.append(targets[i]) return (n_sources, n_targets) # newer_pairwise () def newer_group (sources, target, missing='error'): """Return true if 'target' is out-of-date with respect to any file listed in 'sources'. In other words, if 'target' exists and is newer than every file in 'sources', return false; otherwise return true. 'missing' controls what we do when a source file is missing; the default ("error") is to blow up with an OSError from inside 'stat()'; if it is "ignore", we silently drop any missing source files; if it is "newer", any missing source files make us assume that 'target' is out-of-date (this is handy in "dry-run" mode: it'll make you pretend to carry out commands that wouldn't work because inputs are missing, but that doesn't matter because you're not actually going to run the commands). """ # If the target doesn't even exist, then it's definitely out-of-date. if not os.path.exists(target): return 1 # Otherwise we have to find out the hard way: if *any* source file # is more recent than 'target', then 'target' is out-of-date and # we can immediately return true. If we fall through to the end # of the loop, then 'target' is up-to-date and we return false. from stat import ST_MTIME target_mtime = os.stat(target)[ST_MTIME] for source in sources: if not os.path.exists(source): if missing == 'error': # blow up when we stat() the file pass elif missing == 'ignore': # missing source dropped from continue # target's dependency list elif missing == 'newer': # missing source means target is return 1 # out-of-date source_mtime = os.stat(source)[ST_MTIME] if source_mtime > target_mtime: return 1 else: return 0 # newer_group ()


Catalog¶
Collections of SED
objects can be stored and analyzed in a Catalog
object. One can be initialized and populated with an SED
object using the add_SED()
method.
from sedkit import Catalog, VegaSED
vega = VegaSED()
cat1 = Catalog(name='My New Catalog')
cat1.add_SED(vega)
Catalogs can be merged with the addition operator.
from sedkit import SED
sirius = SED('Sirius', spectral_type='A1V', method_list=['find_2MASS', 'find_WISE'])
cat2 = Catalog('My Second Catalog')
cat2.add_SED(sirius)
cat = cat1 + cat2
To check the table of data and calculated parameters, just call the results`
property. The wavelength and flux density units of all the SEDs can be checked and set with the wave_units`
and flux_units`
properties.
cat.results
import astropy.units as q
cat.wave_units = q.AA
cat.flux_units = q.W / q.m**3
Additional columns of data can be added to the results table with the add_column()
method.
rv = np.array([-13.9, -5.5]) * q.km / q.s
rv_unc = np.array([0.9, 0.1]) * q.km / q.s
cat.add_column('radial_velocity', rv, rv_unc)
Data for individual columns (and associated uncertainties when applicable) can be retrieved by passing the desired column names to the get_data()
method.
spt_data, plx_data = cat.get_data('spectral_type', 'parallax')
The SED
object for a source can be retrieved with the get_SED()
method.
vega = cat.get_SED('Vega')
An interactive scatter plot of any two numeric columns can be made by passing the desired x and y parameter names from the results table to the plot()
method. Photometric colors can be calculated by passing two photometric band names with a -
sign. The order
argument accepts an integer and plots a polynomial of the given order. For busy plots, individual sources can be identified by passing the SED name to the identify
argument. Similarly, setting the argument label_points=True
prints the name of each source next to its data point.
cat.plot('Lbol', 'spectral_type', order=1) # Lbol v. SpT plot with first order polynomial fit
cat.plot('spectral_type', '2MASS.J-2MASS.H') # SpT v. J-H color plot
cat.plot('age', 'distance', identify=['Vega']) # Age v. Dist with Vega circled in red
cat.plot('parallax', 'mbol', label_points=True) # Plx v. mbol with labeled points
The SEDs can be plotted for visual comparison with the plot_SEDs()
method. The can be normalized to 1 by setting the argument normalize=True
.
cat.plot_SEDs('*', normalize=True) # Plot of all SEDs
cat.plot_SEDs(['Vega', 'Sirius']) # Normalized plot of Vega and Sirius
The results table, photometry, and plots of each SED can be exported to a zip file or directory with the export()
method.
cat.export('/path/to/target/dir', zip=True)
The whole Catalog
object can be serialized and loaded with the save()
and load()
methods, respectively.
cat_file = '/path/to/cat.p'
cat.save(cat_file)
new_cat = Catalog('A-type stars')
new_cat.load(cat_file)
A catalog can also be made from an ASCII file with column names name
, ra
, and dec
by passing the filepath to the from_file()
method. For each source in the list, an SED is created, the methods in the run_methods
argument are run, and the SED is added to the catalog.
source_list = '/path/to/sources.csv'
new_cat = Catalog()
new_cat.from_file(source_list, run_methods=['find_2MASS', 'find_WISE', 'find_Gaia'])


Renewal
Renewal Process
MemberName
{{ option.MemberName }}
Search
Scheme
Please select Scheme
{{Scheme.SchemeName}}
Scheme is required
PlanName
Please select Plan
{{plan.PlanName}}
Plan is required
Member Name
MemberNo
New Date
New Date is required
Renewal Date
Amount
Renew


In the SkyRaid network we use discord a lot to communicate and talk about the server but also to have fun and just talk to other members of the community. We have fun bots to mess around with so why not give it a try. It even works in the web.
"All-in-one voice and text chat for gamers that's free, secure, and works on both your desktop and phone. Stop paying for TeamSpeak servers and hassling with Skype. Simplify your life."


Welcome to Quindar
Hi there, this is the first simple Web page served by Express Web server.
I now use a new HTTP PORT


I am a highly motivated self-starter seeking to combine my passion for software development and experience in data management and analysis.
Between online courses, personal projects and on the job experience I have built a broad portfolio of skills and technology that might be combined in any number of tech roles to complement any number of teams.
I have built this site to demo some of the things I do, inside and out of work. Fun words include Java, R, Python, css, HTML, Javascript, MongoDB, NoSQL, iOS programming, Google App Engine, SciKit-Learn and the magic Hadoop.


Shinpuru
John doe
Setting
Another action
Something else here
Logout
Setting
Logout
Dashboard
List User
List Theme
Setting Sent Mail
Dashboard
Dashboard
Hello, Comming soon!


public interface
FieldCache
Created: May 19, 2004 11:13:14 AM
FieldCacheSanityChecker
| Modifier and Type | Interface and Description |
|---|---|
static interface
|
FieldCache.ByteParser
Interface to parse bytes from document fields.
|
static class
|
FieldCache.CacheEntry
EXPERT: A unique Identifier/Description for each item in the FieldCache.
|
static class
|
FieldCache.CreationPlaceholder
|
static interface
|
FieldCache.DoubleParser
Interface to parse doubles from document fields.
|
static interface
|
FieldCache.FloatParser
Interface to parse floats from document fields.
|
static interface
|
FieldCache.IntParser
Interface to parse ints from document fields.
|
static interface
|
FieldCache.LongParser
Interface to parse long from document fields.
|
static interface
|
FieldCache.Parser
Marker interface as super-interface to all parsers.
|
static interface
|
FieldCache.ShortParser
Interface to parse shorts from document fields.
|
static class
|
FieldCache.StringIndex
Expert: Stores term text values and document ordering data.
|
| Modifier and Type | Field and Description |
|---|---|
static
FieldCache
|
DEFAULT
Expert: The cache used internally by sorting and range query classes.
|
static
FieldCache.ByteParser
|
DEFAULT_BYTE_PARSER
The default parser for byte values, which are encoded by
Byte.toString(byte)
|
static
FieldCache.DoubleParser
|
DEFAULT_DOUBLE_PARSER
The default parser for double values, which are encoded by
Double.toString(double)
|
static
FieldCache.FloatParser
|
DEFAULT_FLOAT_PARSER
The default parser for float values, which are encoded by
Float.toString(float)
|
static
FieldCache.IntParser
|
DEFAULT_INT_PARSER
The default parser for int values, which are encoded by
Integer.toString(int)
|
static
FieldCache.LongParser
|
DEFAULT_LONG_PARSER
The default parser for long values, which are encoded by
Long.toString(long)
|
static
FieldCache.ShortParser
|
DEFAULT_SHORT_PARSER
The default parser for short values, which are encoded by
Short.toString(short)
|
static
FieldCache.DoubleParser
|
NUMERIC_UTILS_DOUBLE_PARSER
A parser instance for double values encoded with
NumericUtils
, e.g.
|
static
FieldCache.FloatParser
|
NUMERIC_UTILS_FLOAT_PARSER
A parser instance for float values encoded with
NumericUtils
, e.g.
|
static
FieldCache.IntParser
|
NUMERIC_UTILS_INT_PARSER
A parser instance for int values encoded by
NumericUtils.intToPrefixCoded(int)
, e.g.
|
static
FieldCache.LongParser
|
NUMERIC_UTILS_LONG_PARSER
A parser instance for long values encoded by
NumericUtils.longToPrefixCoded(long)
, e.g.
|
static int
|
STRING_INDEX
Indicator for StringIndex values in the cache.
|
| Modifier and Type | Method and Description |
|---|---|
byte[]
|
getBytes
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none is
found, reads the terms in
field
as a single byte and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
byte[]
|
getBytes
(
IndexReader
reader,
String
field,
FieldCache.ByteParser
parser)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as bytes and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
byte[]
|
getBytes
(
IndexReader
reader,
String
field,
FieldCache.ByteParser
parser,
boolean setDocsWithField)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as bytes and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
FieldCache.CacheEntry
[]
|
getCacheEntries
()
EXPERT: Generates an array of CacheEntry objects representing all items
currently in the FieldCache.
|
Bits
|
getDocsWithField
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
and returns a bit set at the size of
reader.maxDoc()
, with turned on bits for each docid that
does have a value for this field.
|
double[]
|
getDoubles
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none is
found, reads the terms in
field
as integers and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
double[]
|
getDoubles
(
IndexReader
reader,
String
field,
FieldCache.DoubleParser
parser)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as doubles and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
double[]
|
getDoubles
(
IndexReader
reader,
String
field,
FieldCache.DoubleParser
parser,
boolean setDocsWithField)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as doubles and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
float[]
|
getFloats
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if
none is found, reads the terms in
field
as floats and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
float[]
|
getFloats
(
IndexReader
reader,
String
field,
FieldCache.FloatParser
parser)
Checks the internal cache for an appropriate entry, and if
none is found, reads the terms in
field
as floats and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
float[]
|
getFloats
(
IndexReader
reader,
String
field,
FieldCache.FloatParser
parser,
boolean setDocsWithField)
Checks the internal cache for an appropriate entry, and if
none is found, reads the terms in
field
as floats and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
PrintStream
|
getInfoStream
()
counterpart of
setInfoStream(PrintStream)
|
int[]
|
getInts
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none is
found, reads the terms in
field
as integers and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
int[]
|
getInts
(
IndexReader
reader,
String
field,
FieldCache.IntParser
parser)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as integers and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
int[]
|
getInts
(
IndexReader
reader,
String
field,
FieldCache.IntParser
parser,
boolean setDocsWithField)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as integers and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
long[]
|
getLongs
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none is
found, reads the terms in
field
as longs and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
long[]
|
getLongs
(
IndexReader
reader,
String
field,
FieldCache.LongParser
parser)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as longs and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
long[]
|
getLongs
(
IndexReader
reader,
String
field,
FieldCache.LongParser
parser,
boolean setDocsWithField)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as longs and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
short[]
|
getShorts
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none is
found, reads the terms in
field
as shorts and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
|
short[]
|
getShorts
(
IndexReader
reader,
String
field,
FieldCache.ShortParser
parser)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as shorts and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
short[]
|
getShorts
(
IndexReader
reader,
String
field,
FieldCache.ShortParser
parser,
boolean setDocsWithField)
Checks the internal cache for an appropriate entry, and if none is found,
reads the terms in
field
as shorts and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
|
FieldCache.StringIndex
|
getStringIndex
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none
is found reads the term values in
field
and returns
an array of them in natural order, along with an array telling
which element in the term array each document uses.
|
String
[]
|
getStrings
(
IndexReader
reader,
String
field)
Checks the internal cache for an appropriate entry, and if none
is found, reads the term values in
field
and returns an array
of size
reader.maxDoc()
containing the value each document
has in the given field.
|
void
|
purge
(
IndexReader
r)
Expert: drops all cache entries associated with this
reader.
|
void
|
purgeAllCaches
()
EXPERT: Instructs the FieldCache to forcibly expunge all entries
from the underlying caches.
|
void
|
setInfoStream
(
PrintStream
stream)
If non-null, FieldCacheImpl will warn whenever
entries are created that are not sane according to
FieldCacheSanityChecker
.
|
static final int STRING_INDEX
static final FieldCache DEFAULT
static final FieldCache.ByteParser DEFAULT_BYTE_PARSER
Byte.toString(byte)
static final FieldCache.ShortParser DEFAULT_SHORT_PARSER
Short.toString(short)
static final FieldCache.IntParser DEFAULT_INT_PARSER
Integer.toString(int)
static final FieldCache.FloatParser DEFAULT_FLOAT_PARSER
Float.toString(float)
static final FieldCache.LongParser DEFAULT_LONG_PARSER
Long.toString(long)
static final FieldCache.DoubleParser DEFAULT_DOUBLE_PARSER
Double.toString(double)
static final FieldCache.IntParser NUMERIC_UTILS_INT_PARSER
NumericUtils.intToPrefixCoded(int)
, e.g. when indexed
via
NumericField
/
NumericTokenStream
.
static final FieldCache.FloatParser NUMERIC_UTILS_FLOAT_PARSER
NumericUtils
, e.g. when indexed
via
NumericField
/
NumericTokenStream
.
static final FieldCache.LongParser NUMERIC_UTILS_LONG_PARSER
NumericUtils.longToPrefixCoded(long)
, e.g. when indexed
via
NumericField
/
NumericTokenStream
.
static final FieldCache.DoubleParser NUMERIC_UTILS_DOUBLE_PARSER
NumericUtils
, e.g. when indexed
via
NumericField
/
NumericTokenStream
.
Bits getDocsWithField( IndexReader reader, String field) throws IOException
field
and returns a bit set at the size of
reader.maxDoc()
, with turned on bits for each docid that
does have a value for this field.
IOException
byte[] getBytes( IndexReader reader, String field) throws IOException
field
as a single byte and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the single byte values.
IOException
- If any error occurs.
byte[] getBytes( IndexReader reader, String field, FieldCache.ByteParser parser) throws IOException
field
as bytes and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the bytes.
parser
- Computes byte for string values.
IOException
- If any error occurs.
byte[] getBytes( IndexReader reader, String field, FieldCache.ByteParser parser, boolean setDocsWithField) throws IOException
field
as bytes and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the bytes.
parser
- Computes byte for string values.
setDocsWithField
- If true then
getDocsWithField(org.apache.lucene.index.IndexReader, java.lang.String)
will
also be computed and stored in the FieldCache.
IOException
- If any error occurs.
short[] getShorts( IndexReader reader, String field) throws IOException
field
as shorts and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the shorts.
IOException
- If any error occurs.
short[] getShorts( IndexReader reader, String field, FieldCache.ShortParser parser) throws IOException
field
as shorts and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the shorts.
parser
- Computes short for string values.
IOException
- If any error occurs.
short[] getShorts( IndexReader reader, String field, FieldCache.ShortParser parser, boolean setDocsWithField) throws IOException
field
as shorts and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the shorts.
parser
- Computes short for string values.
setDocsWithField
- If true then
getDocsWithField(org.apache.lucene.index.IndexReader, java.lang.String)
will
also be computed and stored in the FieldCache.
IOException
- If any error occurs.
int[] getInts( IndexReader reader, String field) throws IOException
field
as integers and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the integers.
IOException
- If any error occurs.
int[] getInts( IndexReader reader, String field, FieldCache.IntParser parser) throws IOException
field
as integers and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the integers.
parser
- Computes integer for string values.
IOException
- If any error occurs.
int[] getInts( IndexReader reader, String field, FieldCache.IntParser parser, boolean setDocsWithField) throws IOException
field
as integers and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the integers.
parser
- Computes integer for string values.
setDocsWithField
- If true then
getDocsWithField(org.apache.lucene.index.IndexReader, java.lang.String)
will
also be computed and stored in the FieldCache.
IOException
- If any error occurs.
float[] getFloats( IndexReader reader, String field) throws IOException
field
as floats and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the floats.
IOException
- If any error occurs.
float[] getFloats( IndexReader reader, String field, FieldCache.FloatParser parser) throws IOException
field
as floats and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the floats.
parser
- Computes float for string values.
IOException
- If any error occurs.
float[] getFloats( IndexReader reader, String field, FieldCache.FloatParser parser, boolean setDocsWithField) throws IOException
field
as floats and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the floats.
parser
- Computes float for string values.
setDocsWithField
- If true then
getDocsWithField(org.apache.lucene.index.IndexReader, java.lang.String)
will
also be computed and stored in the FieldCache.
IOException
- If any error occurs.
long[] getLongs( IndexReader reader, String field) throws IOException
field
as longs and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the longs.
IOException
- If any error occurs.
long[] getLongs( IndexReader reader, String field, FieldCache.LongParser parser) throws IOException
field
as longs and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the longs.
parser
- Computes integer for string values.
IOException
- If any error occurs.
long[] getLongs( IndexReader reader, String field, FieldCache.LongParser parser, boolean setDocsWithField) throws IOException
field
as longs and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the longs.
parser
- Computes integer for string values.
setDocsWithField
- If true then
getDocsWithField(org.apache.lucene.index.IndexReader, java.lang.String)
will
also be computed and stored in the FieldCache.
IOException
- If any error occurs.
double[] getDoubles( IndexReader reader, String field) throws IOException
field
as integers and returns an array
of size
reader.maxDoc()
of the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the doubles.
IOException
- If any error occurs.
double[] getDoubles( IndexReader reader, String field, FieldCache.DoubleParser parser) throws IOException
field
as doubles and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the doubles.
parser
- Computes integer for string values.
IOException
- If any error occurs.
double[] getDoubles( IndexReader reader, String field, FieldCache.DoubleParser parser, boolean setDocsWithField) throws IOException
field
as doubles and returns an array of
size
reader.maxDoc()
of the value each document has in the
given field.
reader
- Used to get field values.
field
- Which field contains the doubles.
parser
- Computes integer for string values.
setDocsWithField
- If true then
getDocsWithField(org.apache.lucene.index.IndexReader, java.lang.String)
will
also be computed and stored in the FieldCache.
IOException
- If any error occurs.
String [] getStrings( IndexReader reader, String field) throws IOException
field
and returns an array
of size
reader.maxDoc()
containing the value each document
has in the given field.
reader
- Used to get field values.
field
- Which field contains the strings.
IOException
- If any error occurs.
FieldCache.StringIndex getStringIndex( IndexReader reader, String field) throws IOException
field
and returns
an array of them in natural order, along with an array telling
which element in the term array each document uses.
reader
- Used to get field values.
field
- Which field contains the strings.
IOException
- If any error occurs.
FieldCache.CacheEntry [] getCacheEntries()
NOTE: These CacheEntry objects maintain a strong reference to the Cached Values. Maintaining references to a CacheEntry the IndexReader associated with it has garbage collected will prevent the Value itself from being garbage collected when the Cache drops the WeakReference.
void purgeAllCaches()
EXPERT: Instructs the FieldCache to forcibly expunge all entries from the underlying caches. This is intended only to be used for test methods as a way to ensure a known base state of the Cache (with out needing to rely on GC to free WeakReferences). It should not be relied on for "Cache maintenance" in general application code.
void purge( IndexReader r)
void setInfoStream( PrintStream stream)
FieldCacheSanityChecker
.
PrintStream getInfoStream()
setInfoStream(PrintStream)


What's happening at Pâtissez
Be the first to know when we release a new video by subscribing to our official Facebook Developers YouTube channel! http://www.youtube.com/facebookdevelopersPosted by Facebook Developers on Wednesday, May 29, 2013
Be the first to know when we release a new video by subscribing to our official Facebook Developers YouTube channel! http://www.youtube.com/facebookdevelopers


In Pictures: Boris Johnson reaffirms pledge to ‘get Brexit done’
24 Nov 2019, 16:22
Meanwhile Jeremy Corbyn visited Essex.
Prime Minister Boris Johnson launches the Tory manifesto at Telford International Centre
Prime Minister Boris Johnson launched the Tory manifesto on Sunday, repeating his mantra to “get Brexit done” and setting out a plan to “unite the country”.
Meanwhile, Labour leader Jeremy Corbyn met supporters in Thurrock, Essex.
Prime Minister Boris Johnson arrives at Euston station in London, as he heads to Telford for the launch of his party’s manifesto (Stefan Rousseau/PA)
Mr Johnson looks over the manifesto during the train journey (Stefan Rousseau/PA)
Next stop for the Prime Minister was Wolverhampton station (Stefan Rousseau/PA)
Protesters await the arrival of Mr Johnson in Telford (Stefan Rousseau/PA)
Mr Johnson launches the Tory manifesto in Telford, West Midlands (Stefan Rousseau/PA)
Mr Johnson addresses the party faithful (Stefan Rousseau/PA)
Meanwhile, Labour leader Jeremy Corbyn was on the campaign trail in Thurrock, Essex (Aaron Chown/PA)
Mr Corbyn championed his party’s manifesto (Aaron Chown/PA)
The Labour leader met supporters in Thurrock (Aaron Chown/PA)
Brexit Party leader Nigel Farage continued his visit to the North East, stopping off at Seaham, County Durham (Henry Clare/PA)
All this campaigning is thirsty work… (Henry Clare/PA)


My dream is to work at a company where I can contribute to and benefit from a culture of continuous learning. To work collaboratively in an agile team environment. To be safe to fail, and encouraged to innovate. If it sounds like I am the right fit for your team, I'd love to hear from you.


Save a dog’s life by adopting or fostering today!
Once we receive your application, we will arrange a time for a phone call. This is to find out more about you and the kind of dog you’re interested in adopting or fostering. If the phone call has been successful, there will be a homecheck. Your details will be forwarded onto an external homechecking team who will contact you to arrange a visit. It is required that all members of the household are home for the homecheck. Once this visit has been done, we will inform you if your application has been successful.
If you are looking to adopt, we will then ask for the adoption fee of £340 once you have passed your homecheck.
If you are looking to foster, then please note that foster applications are for emergency backup, unless otherwise agreed. Once you have passed the application to foster, you will be added to a list of backup fosters for emergencies (i.e. when dogs are no longer able to stay in their current homes for whatever reason). Slip leads, as well as flea and worm treatment will be provided. Vet bills are covered provided you give us notice before visiting the vet (unless in an emergency) and that an itemised receipt (including the dog’s name) is emailed to us so that we can reimburse you. We ask that you ensure they have an ID tag with your details on it as required by the law, and that you provide the dog with food.
All dogs are fully vaccinated, microchipped, and de-flea and wormed before transport from Romania.
If the dog is older than 6 months, they will be neutered, otherwise you will be required to neuter them when they are old enough.
Dogs over 6 months old cannot be rehomed with children under 5 unless they are currently living in a home with young children.
Please note that dogs who are cat tested in Romania may still react towards cats in the UK. It does not guarantee that they are okay with every cat.
Transport is by road, usually leaving Romania on a Thursday and arriving on Saturday or Sunday. We always endeavour to home drop the dog where possible as this is the safest way for them to arrive safely.
Please don't panic you don’t hear back straight away! Everyone involved is a volunteer, so sometimes it takes a few days to find a homechecker local to you!
Donate now to help us save more dogs! We need donations to help feed, vaccinate and neuter dogs that are still in Romania. We also need donations to help pay for transport and vet bills for our foster dogs in the UK.
Want to send a monthly donation? Just set up a recurring payment via bank transfer to Jade Ahangari 23-14-70 95979164
£5 a month helps pay for a slip lead to train dogs to trust humans
£10 a month helps pay for 1 month of flea and worm treatment for 1 dog
£15 a month helps feed 1 dog for 1 month
£25 a month helps spay/neuter 1 dog
£35 a month helps us pay for 1 dog’s foster care for 1 month
£50 a month helps us fully vaccinate 1 dog
Or donate a one-off amount now with the link below which will take you to our PayPal.


An anchor point. This ID is the target for references by href and conref attributes and for external applications that refer to DITA content.


|
Order Book
0.0.1
|
Declaration & implementation of the order book library. More...
Classes | |
| struct | Order |
| POD order representation. More... | |
| struct | OrderBook |
| Bid and ask tables, and functions to add/cancel orders. More... | |
Typedefs | |
| using | Price = int |
| Define a new type to represent prices. More... | |
| using | Quantity = int |
| Define a new type to represent quantities. More... | |
| using | Asks = std::map< Price, std::vector< Order > > |
| Using std:map with default ordering, to hit the smallest asks first when we place a bid. More... | |
| using | Bids = std::map< Price, std::vector< Order >, std::greater< Price > > |
| Using std:map with default ordering, to hit the largest bids first when we place an ask. More... | |
Enumerations | |
| enum | OrderSide { BUY, SELL } |
| Strongly-typed order side. | |
| enum | OrderType { NEW, CANCEL } |
| Strongly-typed order type. | |
Functions | |
| bool | operator== (Order const &left, Order const &right) |
| Enable use of ASSERT_EQ on Order objects in the test suite. More... | |
| std::vector< std::pair< OrderType, Order > > | read_orders_file (std::ifstream &) |
| Deserialize text into Order objects. | |
Declaration & implementation of the order book library.
Library implementation
Using std:map with default ordering, to hit the smallest asks first when we place a bid.
We could easily reserve a few limits to reduce the number of allocations.
Using std:map with default ordering, to hit the largest bids first when we place an ask.
We could easily reserve a few limits to reduce the number of allocations.
| using ob::Price = typedef int |
Define a new type to represent prices.
Ideally this would be a strong typedef.
| using ob::Quantity = typedef int |
Define a new type to represent quantities.
Ideally this would be a strong typedef.


title = "homepage" url = "/" layout = "default" is_hidden = 0 [session] security = "all" ==
Athletic fit is made for athletes and is slim through the shoulders and body
Sizes: XS,S,M,L


Contact Us
Tire Changers
Brake Lathes
Wheel Balancers
Lift Rack
Other
Contact Us
Tire Changers
Center Clamp Slider - TC37/TC39 - Tire Changer Splash
center clamp
The Revolution™ fully-automatic tire changer is the easiest tire changer to use in the industry and uses the same procedure for all tire and wheel assemblies.
The Auto34S tire changer combines simple operation and unmatched capability to easily and safely change the world's toughest tires and wheels.
Easy-to-use leverless center clamp tire changers with bead press arm to assist in mounting of difficult assemblies.
Easy-to-use conventional center clamp tire changers with bead press arm to assist in mounting of difficult assemblies.
The quick clamp design of the Hunter TC3300 rim clamp tire machine allows for fast, simple clamping of popular wheel designs.
Hunter also provides a full-line of tire changer accessories to enhance serviceability of specialized applications.
Table Top - Tire Changer Splash Page
table top tire changers
The Hunter TCX57 and TCX56 leverless conventional tire changers combine traditional design elements with advanced capabilities to provide superior service with simple operation.
Hunter's TCX53 performance tire changer with bead press system is the heaviest tire changing equipment in its class and offers a long service life.
Hunter's TCX51 professional tire changer is the heaviest model in its class and offers a long service life.
The TCX50 conventional table top tire changer is the heaviest model in its class, featuring a long service life and an easy-to-use adjustable tabletop.
Hunter also provides a full-line of table top tire changer accessories to enhance serviceability of specialized tire changing equipment applications.
choosing the right tire changing equipment
The proliferation of sizes and types of tires make choosing the best tire changer to fit every need more difficult than in times past.
Let Hunter help you make the decision of choosing tire changing equipment easier. What is better for your shop: center clamp or table top?
Learn more about Hunter’s family of table top tire changing equipment including the Revolution™, TCX57, TCX56, TCX53, TCX51 and TCX50 tire changers.
Learn more about Hunter’s family of center clamp tire changing equipment including the Revolution™, Auto34S, TC3700 and TC3300 tire changers.
Featured Videos
documents
Tire Changer Accessories Brochure
Choosing the right model for your shop


Strength-of-Materials Lab
Strength-of-Materials Lab
>
Universal Spring Testing Machine
Universal Spring Testing Machine
ASSIGNMENT
Q1. What is helical tension spring? How it is differ from helical compression spring?
Q2. What is spring index?
Q3. What is stiffness of spring?
Q4.Measure the inner and outer diameter of the bike and cycle spring?
Q5. What are active and inactive coils of spring?


|
| Endpoint[] | Endpoint [get, set] |
| | Connect to a single endpoint. More...
|
| |
| string | From [get, set] |
| | A number in E.164 format that identifies the caller. This must be one of your Nexmo virtual numbers, another value will result in the caller ID being unknown. If the caller is an app user, this option should be omitted. More...
|
| |
| string | EventType [get, set] |
| | Set to synchronous to: make the connect action synchronous enable eventUrl to return an NCCO that overrides the current NCCO when a call moves to specific states. More...
|
| |
| string | Timeout [get, set] |
| | If the call is unanswered, set the number in seconds before Nexmo stops ringing endpoint. The default value is 60. More...
|
| |
| string | Limit [get, set] |
| | Maximum length of the call in seconds. The default and maximum value is 7200 seconds (2 hours). More...
|
| |
| string | MachineDetection [get, set] |
| | Configure the behavior when Nexmo detects that a destination is an answerphone. Set to either: continue - Nexmo sends an HTTP request to event_url with the Call event machine hangup - end the Call More...
|
| |
| string[] | EventUrl [get, set] |
| | Set the webhook endpoint that Nexmo calls asynchronously on each of the possible Call States. If eventType is set to synchronous the eventUrl can return an NCCO that overrides the current NCCO when a timeout occurs. More...
|
| |
| string | EventMethod [get, set] |
| | The HTTP method Nexmo uses to make the request to eventUrl. The default value is POST. More...
|
| |
| string | RingbackTone [get, set] |
| | A URL value that points to a ringbackTone to be played back on repeat to the caller, so they don't hear silence. The ringbackTone will automatically stop playing when the call is fully connected. It's not recommended to use this parameter when connecting to a phone endpoint, as the carrier will supply their own ringbackTone. Example: "ringbackTone": "http://example.com/ringbackTone.wav". More...
|
| |
|
ActionType | Action [get, protected set] |
| |
|
| enum | ActionType {
record =1,
conversation =2,
connect =3,
talk =4,
stream =5,
input =6,
notify =7
} |
| |
|
override string | ToString () |
| |
◆ Endpoint
| Endpoint [] Nexmo.Api.Voice.Nccos.ConnectAction.Endpoint |
|
getset |
Connect to a single endpoint.
◆ EventMethod
| string Nexmo.Api.Voice.Nccos.ConnectAction.EventMethod |
|
getset |
The HTTP method Nexmo uses to make the request to eventUrl. The default value is POST.
◆ EventType
| string Nexmo.Api.Voice.Nccos.ConnectAction.EventType |
|
getset |
Set to synchronous to: make the connect action synchronous enable eventUrl to return an NCCO that overrides the current NCCO when a call moves to specific states.
◆ EventUrl
| string [] Nexmo.Api.Voice.Nccos.ConnectAction.EventUrl |
|
getset |
Set the webhook endpoint that Nexmo calls asynchronously on each of the possible Call States. If eventType is set to synchronous the eventUrl can return an NCCO that overrides the current NCCO when a timeout occurs.
◆ From
| string Nexmo.Api.Voice.Nccos.ConnectAction.From |
|
getset |
A number in E.164 format that identifies the caller. This must be one of your Nexmo virtual numbers, another value will result in the caller ID being unknown. If the caller is an app user, this option should be omitted.
◆ Limit
| string Nexmo.Api.Voice.Nccos.ConnectAction.Limit |
|
getset |
Maximum length of the call in seconds. The default and maximum value is 7200 seconds (2 hours).
◆ MachineDetection
| string Nexmo.Api.Voice.Nccos.ConnectAction.MachineDetection |
|
getset |
Configure the behavior when Nexmo detects that a destination is an answerphone. Set to either: continue - Nexmo sends an HTTP request to event_url with the Call event machine hangup - end the Call
◆ RingbackTone
| string Nexmo.Api.Voice.Nccos.ConnectAction.RingbackTone |
|
getset |
A URL value that points to a ringbackTone to be played back on repeat to the caller, so they don't hear silence. The ringbackTone will automatically stop playing when the call is fully connected. It's not recommended to use this parameter when connecting to a phone endpoint, as the carrier will supply their own ringbackTone. Example: "ringbackTone": "http://example.com/ringbackTone.wav".
◆ Timeout
| string Nexmo.Api.Voice.Nccos.ConnectAction.Timeout |
|
getset |
If the call is unanswered, set the number in seconds before Nexmo stops ringing endpoint. The default value is 60.
The documentation for this class was generated from the following file:
- C:/Users/slore/Documents/projects/slorello89/nexmo-dotnet/Nexmo.Api/Voice/Nccos/ConnectAction.cs


The percentage of images that get faceswapped.A lower percentage means less photos get swapped for a more subtle effect.


Inline Widget Demo
This demo runs JSHint over the code in the editor (which is the script used on this page), and inserts line widgets to display the warnings that JSHint comes up with.
This demo runs JSHint over the code in the editor (which is the script used on this page), and inserts line widgets to display the warnings that JSHint comes up with.


MicroPython remote control: mpremote¶
The mpremote
command line tool provides an integrated set of utilities to
remotely interact with and automate a MicroPython device over a serial
connection.
To use mpremote install it via pip
:
$ pip install mpremote
The simplest way to use this tool is just by invoking it without any arguments:
mpremote
This command automatically detects and connects to the first available USB
serial device and provides an interactive REPL. Serial ports are opened in
exclusive mode, so running a second (or third, etc) instance of mpremote
will connect to subsequent serial devices, if any are available.
Commands¶
For REPL access, running mpremote
without any arguments is usually all that
is needed. mpremote
also supports a set of commands given at the command
line which will perform various actions on remote MicroPython devices.
For commands that support multiple arguments (e.g. a list of files), the
argument list can be terminated with +
.
The full list of supported commands are:
connect to a specified device via a device-name shortcut:
$ mpremote <device-shortcut>
connect to specified device via name:
$ mpremote connect <device>
<device>
may be one of:list
: list available devicesauto
: connect to the first available USB serial portid:<serial>
: connect to the device with USB serial number<serial>
(the second entry in the output from theconnect list
command)port:<path>
: connect to the device with the given pathany valid device name/path, to connect to that device
disconnect current device:
$ mpremote disconnect
After a disconnect, auto soft-reset is enabled.
resume a previous
mpremote
session:$ mpremote resume
This disables auto soft-reset.
perform a soft-reset of the device:
$ mpremote soft-reset
This will clear out the Python heap and restart the interpreter. It also disables auto soft-reset.
enter the REPL on the connected device:
$ mpremote repl [options]
Options are:
--capture <file>
, to capture output of the REPL session to the given file--inject-code <string>
, to specify characters to inject at the REPL when Ctrl-J is pressed--inject-file <file>
, to specify a file to inject at the REPL when Ctrl-K is pressed
evaluate and print the result of a Python expression:
$ mpremote eval <string>
execute the given Python code:
$ mpremote exec <string>
run a script from the local filesystem:
$ mpremote run <file>
execute filesystem commands on the device:
$ mpremote fs <command>
<command>
may be:cat <file..>
to show the contents of a file or files on the devicels
to list the current directoryls <dirs...>
to list the given directoriescp [-r] <src...> <dest>
to copy files; use “:” as a prefix to specify a file on the devicerm <src...>
to remove files on the devicemkdir <dirs...>
to create directories on the devicermdir <dirs...>
to remove directories on the devicetouch <file..>
to create the files (if they don’t already exist)
edit a file on the device:
$ mpremote edit <files...>
The
edit
command will copy each file from the device to a local temporary directory and then launch your editor for each file (defined by the environment variable$EDITOR
). If the editor exits successfully, the updated file will be copied back to the device.install packages from micropython-lib (or GitHub) using the
mip
tool:$ mpremote mip install <packages...>
See Package management for more information.
edit a file on the device:
$ mpremote edit <files...>
The
edit
command will copy each file from the device to a local temporary directory and then launch your editor for each file (defined by the environment variable$EDITOR
). If the editor exits successfully, the updated file will be copied back to the device.install packages from micropython-lib (or GitHub) using the
mip
tool:$ mpremote mip install <packages...>
See Package management for more information.
mount the local directory on the remote device:
$ mpremote mount [options] <local-dir>
During usage, Ctrl-D will soft-reboot and normally reconnect the mount automatically. If the unit has a main.py running at startup however the remount cannot occur. In this case a raw mode soft reboot can be used: Ctrl-A Ctrl-D to reboot, then Ctrl-B to get back to normal repl at which point the mount will be ready.
Options are:
-l
,--unsafe-links
: By default an error will be raised if the device accesses a file or directory which is outside (up one or more directory levels) the local directory that is mounted. This option disables this check for symbolic links, allowing the device to follow symbolic links outside of the local directory.
unmount the local directory from the remote device:
$ mpremote umount
Multiple commands can be specified and they will be run sequentially.
Auto connection and soft-reset¶
Connection and disconnection will be done automatically at the start and end of the execution of the tool, if such commands are not explicitly given. Automatic connection will search for the first available USB serial device. If no action is specified then the REPL will be entered.
Once connected to a device, mpremote
will automatically soft-reset the
device if needed. This clears the Python heap and restarts the interpreter,
making sure that subsequent Python code executes in a fresh environment. Auto
soft-reset is performed the first time one of the following commands are
executed: mount
, eval
, exec
, run
, fs
. After doing a
soft-reset for the first time, it will not be done again automatically, until a
disconnect
command is issued.
Auto soft-reset behaviour can be controlled by the resume
command. And the
soft-reset
command can be used to perform an explicit soft reset.
Shortcuts¶
Shortcuts can be defined using the macro system. Built-in shortcuts are:
- ``devs``: list available devices (shortcut for ``connect list``)
a0
,a1
,a2
,a3
: connect to /dev/ttyACM?u0
,u1
,u2
,u3
: connect to /dev/ttyUSB?c0
,c1
,c2
,c3
: connect to COM?cat
,ls
,cp
,rm
,mkdir
,rmdir
,touch
,df
: filesystem commandsreset
: reset the devicebootloader
: make the device enter its bootloader
Any user configuration, including user-defined shortcuts, can be placed in the file
.config/mpremote/config.py
. For example:
commands = {
"c33": "connect id:334D335C3138",
"bl": "bootloader",
"double x=4": "eval x*2", # x is an argument, with default 4
"wl_scan": ["exec", """
import network
wl = network.WLAN()
wl.active(1)
for ap in wl.scan():
print(ap)
""",],
"test": ["mount", ".", "exec", "import test"],
}
Examples¶
mpremote
mpremote a1
mpremote connect /dev/ttyUSB0 repl
mpremote ls
mpremote a1 ls
mpremote exec "import micropython; micropython.mem_info()"
mpremote eval 1/2 eval 3/4
mpremote mount .
mpremote mount . exec "import local_script"
mpremote ls
mpremote cat boot.py
mpremote cp :main.py .
mpremote cp main.py :
mpremote cp :a.py :b.py
mpremote cp -r dir/ :
mpremote cp a.py b.py : + repl
mpremote mip install aioble
mpremote mip install github:org/repo@branch
mpremote mip install --target /flash/third-party functools


404 Its a dead end
Sorry Its an error/Broken Link
Try to see if its a typo
If not then it can be possible that website is under maintenance
sorry to show you this 404 error page
check the home page here for front page for possible updates
Thank You (NEXT) 🙃