As our hearts and minds change to a decentralized economy, so must our infrastucture.
Krypto aims to build, support, and distribute these decentral networks.
We're always ready for coffee!
We're currently developing and building our product behind the scenes. If however you're interested, feel free to contact us. We're willing to talk about our and your vision on decentralized software and infrastucture!
As our hearts and minds change to a decentralized economy, so must our infrastucture.
Krypto aims to build, support, and distribute these decentral networks.
We're always ready for coffee!


21 packages : a - b - c - k - l - m - o - p - r - s - t
| arc | The ARC archive utility |
| base-cygwin | Initial base installation helper script |
| corrupt | A corrupt package |
| cygwin | The UNIX emulation engine |
| cygwin-devel | Core development files |
| keychain | Key manager for OpenSSH |
| libdns_sd-devel | Bonjour Zeroconf implementation |
| libdns_sd1 | Bonjour Zeroconf implementation |
| mDNSResponder | Bonjour Zeroconf implementation |
| obs-a | obsolete package A |
| obs-b | obsolete package B |
| openssh | The OpenSSH server and client programs |
| per-version | Per-version hint test package |
| per-version-replacement-hint-only | Per-version hint test package |
| perl-Net-SMTP-SSL | Perl distribution Net-SMTP-SSL |
| rpm-doc | Obsolete package for RPM package management system manual pages (extra text to so repr is not one line) |
| staleversion | Test package for stale version removal |
| test-c | test package C |
| test-d | test package D |
| test-e | test package E |
| testpackage | A test package (stuff & other stuff) |


The Chinese Treatment Centre has been helping patients in Canberra since 1999. At the Chinese Treatment Centre, you can expect honest, accurate and professional medical treatment and advice to help you solve your medical issues.
The director, Dr Charles Y Cai, is a highly qualified Chinese Medicine practitioner with over twenty years clinical experience and a sound knowledge of Chinese medicine that was passed to him from his father and grandfather.
By taking the time to properly diagnose ailments and prepare an effective treatment, Charles has helped many people to recover from a wide variety of painful and distressing conditions. Dr Cai has carried out successful research on the treatment of chronic condition and acute pain. The findings have been published in over 10 different journal and books.
Charles is a registered doctor in China and a full practicing member of Australian Acupuncture and Chinese Medicine Association (AACMA).
Qualifications:
Dr. Linda Liang graduated from Heilongjiang University of Chinese Medicine, China in 1978. She had worked at Harbin Chinese Medicine hospital as a Doctor and professor for 38 years. I have 5 Chinese herbal formulas been successfully approved by Heilongjiang food and drug authority in 1998, also I published articles and one book on Medical Science in China.
Specialized in TCM acupuncture technique with unique touching pulse diagnosis method to determinate the application of acupuncture points and Chinese Herbs in all kind of ailments. I do Special painless acupuncture technique to quickly get the best result while treating chronic conditions and pain, as well for women and children program.
Qualifications:
Dr Ming Chen was awarded the bachelor degree of Traditional Chinese Medicine (TCM) at Liaoning University of TCM in China in 1999. Then she went on to further study on Acupuncture and Chinese medicine, and graduated with Master and Ph.D degree in 2002 and 2006 from Shanghai Traditional Chinese Medical University, China.
She had been practicing as a registered Chinese medical doctor in China. She continued practice after coming to Australia, at the meantime she had also been involved in some research projects at the John Curtin School of Medical Research in ANU.
Ming acquires a wide range of knowledge and excellent practicing skills in Chinese medicine. She integrated Chinese herbal medicine, acupuncture, Chinese massage, cupping, guasha techniques with diet, exercises and life style consultation in a holistic approach for helping many chronic and complex conditions in internal medicine, muscular skeleton system and pain management.
Qualifications:
Dr Sali Tesseyman returned to Australia after five years of living, working and training in Japan. She does Japanese acupuncture, which use smaller and thinner needles, and very relaxing and cause minimal discomfort treatment. This form of acupuncture can be used to resolve complex or long standing conditions, by addressing the cause of the problem. After acupuncture, the bodies self healing abilities become activated helping the body to heal itself.
Her massage style is called Shiatsu, providing a therapeutic and intensified treatment, is very helpful for stress mind and tight whole body muscle treatment.
Qualifications:
Mr Hongxin Wei is a TCM Therapist with a Diploma of Acupuncture and Massage (China) and a Diploma of Remedial Massage（ACT College of Natural Therapies, Canberra）. He is a member of the Chinese Medicine Board of Australia, AHPRA （The Australian Health Practitioner Regulation Agency）and also member of ATMS (The Australian Traditional-Medicine Society).
Mr Wei has acquired a wide range of skills in Chinese massage, acupuncture, remedial massage, Cupping and Guasha techniques. He has more than ten years work experience as a Acupuncturist and Massage therapist in the centre of the city, providing massage service to office workers and surrounding residents. He has unique insights into the pain relief for customers, especially in the treatment of muscular pain. With his great personality and hospitality, he made very good relationship with all his customers.
Qualifications:
Ms Aina Craig started training in Professional Lymphatic Massage in Malaysia and Beauty Health Courses in Singapore which continued for over 9 years. Aina has also extended her skills to include Traditional Thai Massage and several other styles of massage therapy in Bangkok, Jakarta & Australia.
Now in Australia she is enjoying working as a massages therapist at Chinese Treatment Centre and having the pleasure of meeting all of our clients, as assisting in improving our client’s health through Lymphatic Drainage, Aromatherapy and Remedial Massage.
Qualifications:


KRLS: Kernel-Based Regularized Least Squares
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=KRLS
to link to this page.


“Canidia appears first, the locks of her dishevelled hair twined round with venomous and deadly serpents, ordering the wild fig-tree and the funereal cypress to be rooted up from the sepulchres on which they grew, and these, together with the egg of a toad smeared with blood, the plumage of the screech-owl, various herbs brought from Thessaly and Georgia, and bones torn from the jaws of a famished dog, to be burned in flames fed with perfumes from Colchis.” — William Godwin, Lives Of The Necromancers, Or: An Account Of The Most Eminent Persons In Successive Ages, Who Have Claimed For Themselves, Or To Whom Has Been Imputed By Others, The Exercise Of Magical Power


In MELODY there are three types of user.
Registered users are members of a project that have a registered GitHub account.
For instance, MELODY dashboard is developed and maintained by members of the Polifonia H2020 project. All project members have a GitHub account and are members of the GitHub organisation that published MELODY. Registered users create and modify all stories that appear in MELODY dashboard. When they publish a story, this appears in the left sidebar, along with a button to modify it.
External users with a GitHub account are users that have a GitHub account, but they are not registered as members of the project.
External users can create and modify only the stories they authored. Stories do not appear in MELODY dashboard. Instead, these are published (for free) in MELODY stories. If a non registered user wants to modify a story published in MELODY stories, after the login in MELODY dashboard, on the top-right menu a button named Modify your stories will appear, showing the list of user's stories.
External users without a GitHub account are curious users that do not have a GitHub account, but they want to try out MELODY dashboard.
This kind of user can access the dashboard and create their stories. However, at the end of the session the story is not saved anywhere (neither in MELODY dashboard, nor in MELODY stories). Therefore users are invited to immediately export their story.
All users can export any story as a web-ready document (HTML, CSS, JS) or PDF, as well as any chart or graph (as image or embeddable code snippet).
The welcome page gives you access to the most important functionalities.
The left sidebar includes data stories created by registered users, grouped by section. From the footer you can access the documentation (this document), while from the top right menu you can sign in with your GitHub credentials and create a new data story.
If you are a registered user, your stories will always be accessible from the left sidebar. From there you can modify, delete, or see the final result.
If you are an external user with a GitHub account, your stories will be published on MELODY stories. To access and modify your stories, use the button named Modify your stories on the top right menu (it shows when you are logged in).
If you are an external user without a GitHub account, your stories cannot be published nor modified. Once you create a story, you will be asked to export it, either as a web document or as a PDF.
To create a new story, click on Create a story, in the top right menu. If you haven't logged in yet, you will be redirected to another page and you will be asked whether you want to do it.
If you do not have a GitHub account, click on START NOW
If you have a GitHub account, click on SIGN IN. If you are a registered user, we will know!
You will be redirected to the setup page of your story.
Data stories are built on templates, a set of layout instructions for paginating contents. We plan to offer several templates. To date we have one template called statistics, which offers the following functionalities:
Only if you are a registered user, you will be asked to choose a section.
Sections are thematic groups including one or more stories, that appear on the left sidebar. Create a new section or select an existing one from the dropdown menu.
The title of the data story doesn't need much explanation. It's the first prominent communication tool in the narrative of the data story you're going to create. You can change it later.
Lastly, you must include the URL of an online SPARQL endpoint to retrieve data and populate the charts. Check carefully that it's working.
Once you submit your setup information, you are redirected to a user-friendly interface based on the template you've chosen.
If you are a registered user, a link to your datastory will appear in the left sidebar under the name of the section you chose.
Your task is to (1) select the components to be added to your canvas (e.g. texts, bart charts, text searches), (2) write a SPARQL query to populate the elements, and (3) browse the preview of results. The interface is WYSIWYG (What You See Is What You Get), meaning you will be able to (i) see immediately results of your queries as charts, (ii) modify the order of components in the page and (iii) preview the final data story to be published.
The first components you will be asked to fill in are the following:
You can change the default colors of your story. The new colors will be used in every chart and will create a duotone background effect on the secondary menu.
Similarly, the title, the subtitle, the introduction, and the curators of the story can be modified. The position of these elements in the canvas cannot be modified.
The Statistics components are designed for an article-like data story, where you can combine textual information with statistical information, such as counters and charts to create a sequential narrative.
You can add as many elements as you want. To add a new element use the respective button, and a box will appear on top. Each box can be moved up and down or removed.
Text boxes are meant to include free text descriptions. You can edit your content in a simple text editor that allows you to style text in bold, italics, underlined, ordered and unordered lists, and to include hypertext link. If you want to add a title, you can choose between two dimensions (heading 2 and heading 3), or normal if you want to render text as a paragraph.
Counters can be used to give some figures on the dataset, e.g. the number of individuals of a certain class. Counters are rendered as boxes including a counting and a label. The counting is the result of a SPARQL query, while the label is manually associated. To create a counter, fill in the two respective text areas:
The SPARQL query should look like follows:
SELECT (COUNT(?s) AS ?count)
WHERE {?s ?p <https://w3id.org/musow/vocab/repository>.}
The query automatically runs against the endpoint when moving the mouse out of the text area, and the resulting box appears on top.
You can choose between a few charts: Bar chart, Line chart, Doughnut chart and Scatter Plot.
For each chart, the following information must be provided:
However, it is important to check the requirements of each chart. It will be possible to do this at the time of its creation.
Overall, a SPARQL query to build a chart can be of two types:
Bart charts, line charts and doughnut charts
To build bar charts, line charts and doughnut charts you can perform both aggregating and non-aggregating queries. An aggregating query to plot a bar chart looks like follows:
SELECT ?label (COUNT(?content) AS ?count)
WHERE {
?content <https://schema.org/additionalType> ?type.
?type rdfs:label ?label .
} GROUP BY ?label
The query returns two variables (?label and ?count) that fit the requirements, hence no further operations are needed.
In case of non-aggregating queries, the query must return only one variable called ?label, and the operation count must be checked in order to plot results. For instance, the non-aggregating SPARQL query for a doughnut chart:
SELECT ?label
WHERE {
?content <https://schema.org/additionalType> ?type .
?type rdfs:label ?label .
}
The query returns the same values as the aggregating query above, provided that the checbox Count is selected.
Scatter plots
To build scatterplots you can perform one or more aggregating SPARQL queries that return two numerical variables: ?x and ?y. An aggregating query to plot a scatter plot looks like follows:
SELECT DISTINCT ?y ?x
WHERE {
?item wdt:P31 wd:Q11424.
?item p:P444 ?review_statement .
?review_statement ps:P444 ?y .
?review_statement pq:P447 wd:Q105584 .
?review_statement pq:P459 wd:Q108403393 .
?item p:P2142 ?box_office_statement .
?box_office_statement ps:P2142 ?x .
?box_office_statement pq:P3005 wd:Q30 .
}
The query returns two variables (?x and ?y) that represent the coordinates of each point on the Cartesian axes. In this particular case, the main query (aqua green) identifies films and asks for their U.S. box office revenue (x-axis) and review score (y-axis) in Rotten Tomatoes. A second query (orange) was added that again returns data on films rated by Rotten Tomatoes, with the difference being worldwide box office. Therefore, the structure remains the same: the main subject is fixed (movies), the variables (source of the box office data in this case) change.
The table field is used to display the results in a simple tabular form. To create a table, fill in the two respective text areas:
For instance, a query on Wikidata to return horses and some related information would look like follows:
SELECT DISTINCT ?horse ?horseLabel ?mother ?motherLabel ?father ?fatherLabel (year(?birthdate) as ?birthyear) (year(?deathdate) as ?deathyear) ?genderLabel
WHERE
{
?horse wdt:P31/wdt:P279* wd:Q726 .
OPTIONAL{?horse wdt:P25 ?mother .}
OPTIONAL{?horse wdt:P22 ?father .}
OPTIONAL{?horse wdt:P569 ?birthdate .}
OPTIONAL{?horse wdt:P570 ?deathdate .}
OPTIONAL{?horse wdt:P21 ?gender .}
SERVICE wikibase:label {
bd:serviceParam wikibase:language "[AUTO_LANGUAGE],fr,ar,be,bg,bn,ca,cs,da,de,el,en,es,et,fa,fi,he,hi,hu,hy,id,it,ja,jv,ko,nb,nl,eo,pa,pl,pt,ro,ru,sh,sk,sr,sv,sw,te,th,tr,uk,yue,vec,vi,zh"
}
}
ORDER BY ?horse
LIMIT 20
The SPARQL query does not have constraints on the variable names, and it can return as many variables as needed. However, to make results easy to read, labels can replace URIs if these are named ?{variable}Label. For instance, in the result table below, values of the variable ?horse (URIs) are replaced with values of the variable ?horseLabel, and include a link to the URI (?horse).
The Relations template provides an interactive, iterative, text search to show relations between data, designed according to authors' preferences. With Relations you can design a flow of searches, based on results of a previous one. You can combine results of multiple searches and highlight patterns, or you can let the reader discover new insights in a follow-your-nose fashion.
The main components are:
You can add as many searches as you want. Actions can be attached to any search results (even to results of actions or combinations). In this template, elements can be deleted but cannot be moved up/down, since anyway is the hierarchical order that matters.
A text search presents two areas to be filled in, namely:
Below the two areas, a preview of the final search box is shown. Once you fill in the query, you can immediately try the results.
While the SPARQL query does not have constraints on the variable names, and it can return as many variables as needed, it requires a placeholder to be included instead of the term to be searched. The placeholder is usually enquoted, and MUST be called
<<searchterm>>
For instance, a query on Wikidata to return a) museums whose name includes the search term (exact match in this case), and b) their country, would look like follows:
SELECT DISTINCT ?item ?itemLabel ?country ?countryLabel WHERE {
SERVICE wikibase:mwapi {
bd:serviceParam wikibase:endpoint "www.wikidata.org";
wikibase:api "EntitySearch";
mwapi:search <<searchterm>>;
mwapi:language "en".
?item wikibase:apiOutputItem mwapi:item.
}
?item (wdt:P279|wdt:P31) wd:Q207694 ; wdt:P17 ?country .
SERVICE wikibase:label {
bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en"
}
} LIMIT 20
SPARQL endpoints usually provide specific APIs for text searches. Therefore the query might significantly change depending on such services.
Once you type the above query, it returns the following result
N.B. The above query would usually return a table with four columns, one for each variable in the SELECT clause. To make results easy to read, labels can replace URIs if these are named ?{variable}Label. For instance, in the above table, values of the variable ?item (URIs) are replaced with values of the variable ?itemLabel, and include a link to the URI (?item).
Actions are SPARQL queries that can be attached to results of a previous search (or action). Again, results of the SPARQL query are shown as tables. In detail, actions are attached to columns of the table, i.e. a variable name of a previous SPARQL query, and these appear as buttons in the former table.
To add an action click on the button Add action below the previous SPARQL query
An action consists of two areas to be filled in, namely:
However, a placeholder MUST be included in the query in the following form
<<item>>
For instance, an action called show similar museums can be attached to the column item of the text search described above (the column name is called item too just by chance).
Multiple actions can be attached to the same column, as well as to different columns.
To preview the results of an action, go back to the preview of the initial text search, perform a search and click on the button that appears in results. The new query replaces the placeholder with the value of the cell where the action was selected.
In the above examples you can already see that results of an action (Show similar museums) can be further associated with new actions (e.g. shared artists). To attach an action to a table use the green button that appears next to column names, use the button you find below the latter.
The Maps components provide an interactive geographic map. Data points can be associated with tooltips with customisable content and filters can be applied to filter out points on the map.
The main elements of the template are:
In a story you can add only one map, but you can add as many filters as you want.
The initialise the map with data points you need two elements:
Once you type the query, you can immediately see a preview of the final map. Data points with the same location are grouped in bubbles, which expand/collapse on zoom. To prevent rerunning the query every time a change is done to the query, data points are cached. The button Rerun the query forces MELODY to perform again the query on demand.
For instance, a query on Wikidata to return museums in Brittany would look like follows:
SELECT DISTINCT ?point ?pointLabel ?villeIdLabel ?lat ?long
WHERE
{
?point wdt:P539 ?museofile.
?point wdt:P131* wd:Q12130.
?point wdt:P131 ?villeId.
?point p:P625 ?statement.
?statement psv:P625 ?node.
?node wikibase:geoLatitude ?lat.
?node wikibase:geoLongitude ?long.
SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
Clicking on each data point shows a sidebar. The content of the sidebar is defined in the same SPARQL query to retrieve the data points. In particular, each non-mandatory variable returned in the query appears in the tooltip in the same order as the corresponding variable is returned, separated by period. Following the previous example, we can show the label of the data point (?pointLabel) and the label of the corresponding city (?villeIdLabel). The URI of the data point is automatically added.
To add a filter to the map you need to specify two elements:
You can add as many filters as you want. Like with data points, to update the view, you must rerun the SPARQL query for data points first.
For instance, a query on Wikidata to return museums' cities in Brittany would look like follows:
SELECT DISTINCT ?point ?filter ?filterLabel
WHERE
{
?point wdt:P539 ?museofile.
?point wdt:P131* wd:Q12130.
?point wdt:P131 ?filter.
?filter rdfs:label ?filterLabel .
FILTER(LANG(?filterLabel) = "" || LANGMATCHES(LANG(?filterLabel), "en"))
}
Filters appear as sections of a left sidebar in the map. Each section (filter) can be expanded, and it includes a list of checkboxes that the final user can use to filter data points.
When you save your work, you are redirected to the preview of the data story.
The final data story presents elements on the canvas in the same order defined by the designer. In the secondary menu, a few metadata and the export buttons are shown.
Modify your story
If you are a registered user, in the left sidebar you will see the title of your story under the section you chose. Your story is already published, and you can always access and modify it from there.
 Be aware that your story will not be saved if you leave the page or you do not publish it!
If you are a user without any GitHub account, in the secondary menu only the Modify button will appear. You can continue modifying your story until you are happy, and you can export it. Be aware that your story will not be saved if you leave the page or you do not export it!
Export
The whole story can be exported as a PDF document or as a web document, ready to be published in you favourite platform. The exported story does not include the top menu and left sidebar. The export buttons appear in the secondary menu on the left of the story. Similarly, each chart can be saved as a .png image, or exported as an embeddable HTML code snippet.


Explore data extracted from the complete set of PubMed abstracts. With LINK you are able to explore how biomedical entities are linked together.
LINK is focused on three main entities: genes, diseases (including phenotypes) and drugs. When we cannot identify one of the three, key concepts are extracted via Natural Language Processing and used to fill the gaps
Use LINK to find new hypotheses for how a target affects disesae, repurpose existing compounds to a different disease, find new trends and connections made in the literature about your target or disease of interest.
Start typing in the search box
You'll see a list of entities that either match your query or are very highly related to what you are searching for. E.g. A search for "BRAF" will also suggest "metastatic melanoma".
Just select one or more entities from the drop down and press GO.
Selecting an entity from the suggested list will optimize the search to recognise all the synonyms we are aware of.
You can still search without selecting any entity if you want. Just type something and
press GO.
E.g. you could search for the PubMed ID of an article you are interested in and use LINK to show you
a map of related entites and concepts.
LINK is a service built on top of the data generated by the Open Targets Library Project
Using state of the art tools, we built a serverless pipeline (based on Apache Beam) to process all the publications released by PubMed in a couple of hours of computation in Google cloud.
We precompiled a set of dictionaries to recognise entities from:
We analyse each title and abstract with spacy and extract key concepts and semantic relations in the form of subject-predicate-object triples. This means that every time you see a link between two entities in our graphs, there is at least a sentence in one abstract linking them in a subject-predicate-object structure. For the sake of simplicity we currently consider the graph undirected.
You can click on any link in the graph, see the annotated sentences, and go to the original paper
We built a huge graph (more than 500 millions edges) but we are not using a graph database to store it. Everything is saved in the form of JSON files in a standard size Elasticsearch cluster. It is still possible to get the data and put it into a graph database (more coming).
We make use of the advanced querying capabilities of Elasticsearch to extract the set of nodes relevant to your query. Elasticsearch has a very nice Graph API that is able to return all the edges to build graph that we show. While the Elasticsearch Graph API works great and is lightning fast, it was not fully customisable to our needs, so we implemented a similar strategy in our REST API.
The LINK REST API will start from your query, build a graph of entities related to the query and then expand it for entities related to that seed graph, allowing you to have a bigger picture of the area you are interested in. It will also filter out hot nodes that are not useful in our view. E.g. "cancer" would dominate any graph it goes into but, being a very general term, would add very low value to the quality of the information provided
What about the UI? The nice graphs we show are built with linkurious.js, while the rest is a simple HTML5 page powered by Semantic UI
Thank you Open Targets
This website is released as a proof of concept. There is no guarantee it will be regularly updated in the future.


This is a small personal design project for an ear training tool using solfège. It was conceived as an exercise in interface design as well as an introduction to newer HTML5/CSS developments.
I attempted to accomplish this project relatively quickly with as little JavaScript as I could get away with. As a result, most of the effects you see are heavily implemented via CSS with little consideration given to browser compatibility or progressive enhancement. My use of jQuery is largely limited to handling the tone generation and toggling classes.
Since I am not primarily a front-end developer (if you couldn't tell already), I thought it would be a good opportunity to round out my skillset.
Right now, it is still a work in progress. I am currently planning on:
Simply press the icon in the center of the screen to play a tone and activate the radial menu. Hover over the name of the tone you think it is, and the ring will turn green if you are correct, red if not.
NOTE: A new scenario will be generated automatically after you guess a tone correctly. The tones are currently limited to a single octave of the C Major scale based on a tonic of C4 (Middle C), although C5 is missing.
This project utilizes techniques from inspired by several different sources. In the interest of due credit, here is a list:
A copy of the code can be accessed via Github.
Have any questions or comments? Contact me via e-mail at andy.lo<at>gmx.com.
Note: These values are based on scientific pitch notation and the standard 88-key piano, and are not relative to the selected key. The 0th octave notes are currently removed due to faulty sound samples. The single 8th octave note has been removed for my own convenience.


Andre Weiner, Richard Semaan
TU Braunschweig, ISM, Flow Modeling and
Control Group
Euromech Colloquium 612, March 30, 2022
Slice of local Mach number $Ma$; $Re_\infty=10^7$, $Ma_\infty=0.75$, $\alpha=4^\circ$.
Unsteady flow and interaction phenomena at high speed stall conditions
Improved understanding and prediction of flight envelop via:
Modal decomp. = spatial structures + temp. behavior
spatial mode
temporal coefficient
Spectra resulting from standard dynamic mode decomposition (DMD); influence of rank parameter.
Spectra resulting from standard dynamic mode decomposition (DMD); influence of rank parameter.
Problem - determining hyperparameters manually is not always an option:
$\rightarrow$ need for robust automated workflow
The state vector $\mathbf{x}_n$:
$\rightarrow$ $\mathbf{x}_1$, $\mathbf{x}_2$, ..., $\mathbf{x}_N$
$N$ - number of snapshots
Important: non-uniformly sampled fields should be weighted with square-root of volume/area!
Definition of data matrices:
$$ \mathbf{X} = \left[ \begin{array}{cccc} | & | & & | \\ \mathbf{x}_1 & \mathbf{x}_2 & ... & \mathbf{x}_{N-1} \\ | & | & & | \\ \end{array}\right],\quad \mathbf{X}^\prime = \left[ \begin{array}{cccc} | & | & & | \\ \mathbf{x}_2 & \mathbf{x}_3 & ... & \mathbf{x}_{N} \\ | & | & & | \\ \end{array}\right] $$
$\mathbf{x}_n$ - state vector snapshot at timestep $n$
Singular value decomposition (SVD):
$$ \underset{\tilde{\mathbf{X}}, rank(\tilde{\mathbf{X}})=r}{\mathrm{argmin}} ||\mathbf{X}-\tilde{\mathbf{X}}||_2 = \mathbf{U}_r\mathbf{\Sigma}_r\mathbf{V}_r^\ast $$
Dynamic mode decomposition (DMD):
$$ \underset{\mathbf{A}}{\mathrm{argmin}} ||\mathbf{X}^\prime-\mathbf{AX}||_2 = \mathbf{X}^\prime\mathbf{V}\mathbf{\Sigma}^{-1}\mathbf{U}^\ast $$
$\rightarrow r$ is a DMD hyperparameter!
How to choose $r$?
Optimizing mode amplitudes:
$$ \underset{\mathbf{b}}{\mathrm{argmin}} ||\mathbf{X}-\mathbf{\Phi D}_\mathbf{b}\mathbf{V}_\lambda||_2 $$
More details: Sparsity-promoting DMD
How to select important modes?
$$ I_i = |\mathbf{\phi}_i|^2 \sum\limits_{j=1}^N b_i \lambda_i^{j-1} $$
More details: K. Kou and W. Zhang
Other tested DMD variants:
See also: pyDMD
Final recipe:
Implementation details: flowTorch - a Python library for the analysis and modeling of fluid flows
Setup motivated by exp. investigations of McDevitt and Okuno 1985:
Simulation approach in a nutshell:
Various views of the computational mesh.
Pressure coefficient $c_p$ at pre-onset conditions, 2D, $\alpha = 2^\circ$.
Pressure coefficient $c_p$ at post-onset conditions, 2D, $\alpha = 4^\circ$.
Influence of volume-weighting on left-singular vectors.
Influence of snapshots/cycle on dominant $\bar{f}$.
DMD buffet mode recon. based on surface $c_p$.
DMD buffet mode recon. based on slice of $u_x$.
Reconstruction of another dominant DMD mode; based on slice of $u_x$.
Iso-contours of reconstructed $|\mathbf{u}|$ for two dominant DMD modes.
github.com/AndreWeiner/naca0012_shock_buffet
{a.weiner|r.semaan}@tu-braunschweig.de


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/barber.20paradox.html ---
Can someone please help me with the barber paradox proof in lean?
theorem barber_paradox : ¬ (∀ x : men, shaves barber x ↔ ¬ shaves x x)
Do you have a specific question?
https://leanprover.zulipchat.com/#narrow/stream/113488-general/topic/Logic.20.26.20Proof
theorem barber_paradox : ¬ (∀ x : men, shaves barber x ↔ ¬ shaves x x)
See barber_is_dead
(and a tactic mode proof above it) here: https://github.com/abhimanyupallavisudhir/lean/blob/master/logic_theorems.lean
Ha ha are you revising Cantor's theorem :-)
I don't think you need to be classical for pants on fire. I had to prove this in cantor at the end
example (P : Prop) (Q : Prop) : ¬ P → (P → Q) := by intros;contradiction
etc. Your instinct as a mathematician is to case split, but it's often not necessary.
Last updated: Apr 24 2020 at 19:02 UTC


Tamara Jo Pelham
Republican
Environmental consultant/chemist; technical writer; Tetra Tech, Inc.
BS - Chemistry (1991); California Certified Environmental Assessor (or something like that), 1.5 years into an MBA (Cal State Fullerton approx 1993); OSHA Hazardous Waste Operations Worker-trained; former staff engineer for the state of Nevada.
80015
I purchased political signs for the 2020 election (first time, ever).
Eastern Hills Church.
I was Cub Scout Committee Chair years 2017-2019 (approximately), but that's not really anything you will likely care about. I have not significantly engaged in the political process in my lifetime. What I can say for myself is that I understand how to navigate regulation and law; I can write and communicate effectively and persuasively (although I really need to practice if I have to publicly speak); I look for references and precedent to substantiate my decisions and opinions; I am data-driven and have conducted/reported numerous statistical interpretations for compliance purposes; and I am self-sacrificing in my efforts to meet hard deadlines.
Two weeks ago, I probably would not have taken the plunge to volunteer for this, but right now, I feel like our political system/s and framework are vulnerable to chaos, distrust, and ambiguity. The only way I can see to either change my perception or begin to be part of the solution to fix whatever might be wrong is to get involved. Then I saw your emails looking for volunteers. I've hesitated to apply for the past few days, but today it feels like the right thing to do.
Since I'm not exactly sure where or how conflicts will arise, I don't think there's a specific answer to this request. What I can say is that I promise I won't yell; I won't throw a temper tantrum; I have friends of all persuasions, so I don't have premeditated 'taboos' assigned to anyone of any persuasion; I refer, first and foremost, to data and written word/regulation/requirement; I have 2 kids (girl age 17, boy age 12) and neither think that I'm fair when I settle a dispute between them, so I figure I'm on the right track; and I frequently refer to the poem by Edwin Markham, 'There is a destiny that makes us brothers. No one walks his way alone, for all that we send into the lives of others, comes back into our own.' I like that poem; it's true.
Again, I'm a data-head and a reg-head. I wallow around in mounds of data and figure out what to do with it and what it means; then I write the report/s so that someone else that isn't a data-head or reg-head can understand it. I write; I've written many, many documents. Some I actually know how to construct from the beginning, and some I don't, but even if I don't, I find the way and I've not missed a deadline yet. The hardest part of any project is just getting it started, so whether the first phases of any project end up scratched or not, just getting started is a victory in-and-of itself. And lastly, I know (oh, so well) the 80/20 rule, and it's real. We will spend 80% of our time, energy, and effort to finish and perfect the last 20% of the details of this project. This is logic I am quite certain of.
Female


Pull requests gladly accepted
Webmin is a web-based, server configuration tool – or, control panel, like cPanel and Plesk – and its versatility allows even novice Linux users to deploy, and administer, a full-fledged mail server.
First, follow the steps outlined in How To Create Your First DigitalOcean Droplet Virtual Server to deploy the latest release of Ubuntu 12.04 Server
.
A 32-bit operating system is recommended for cloud servers with less than 3 GB of RAM – this is especially true for servers with 1 GB, or less, of RAM. Processes can require significantly more memory on the 64-bit architecture. On servers with a limited amount of RAM, any performance benefits that one might gain from a 64-bit operating system would be diluted by having less memory available for buffers and caching.
SSH Keys
For increased security, it is advisable that you:
- Create your droplet with pre-installed SSH keys. See How To Use SSH Keys with DigitalOcean Droplets (Windows users: Refer to the article cited, next); and
- Disable password logins. See How To Create SSH Keys with PuTTY to Connect to a VPS | DigitalOcean.
Then, complete the steps outlined in Initial Server Setup with Ubuntu 12.04 | DigitalOcean.
For low-memory systems
If you deployed a cloud server with 512MB of RAM, you’re best advised to add swap. See How To Add Swap on Ubuntu 12.04 | DigitalOcean. Allowing Webmin to swap out infrequently-used processes, and data, leaves more memory available for active processes.
/etc/hosts
Next, follow the steps outlined in Setting the Hostname & Fully Qualified Domain Name (FQDN) on Ubuntu 12.04 or CentOS 6.4.
Now, follow the steps in How to Install Webmin on an Ubuntu Cloud Server | DigitalOcean to deploy the Webmin control panel.
This article assumes that you do not wish to manage your DNS records via Webmin.
One of Webmin’s benefits is that its capabilities can be expanded via modules. Modules make it easy to add new functionality to your server.
In order for mail to start flowing through your new mail server, an MX record
needs to be added to your DNS records. See How To Set Up a Host Name with DigitalOcean.
How To use an SPF Record to Prevent Spoofing & Improve E-mail Reliability | DigitalOcean
As always, if you need help with the steps outlined in this How-To, look to the DigitalOcean Community for assistance by posing your question(s), below.
Pull requests gladly accepted


"""
.. _ad_from_string:
Create Ads Using Long Descriptive Text (top-down approach)
==========================================================
Many times you have long descriptive text about your products, especially on
their respective landing pages. The allowed length of text ads has become
considerably long on many platforms. On Google Ads for example, you have slots
of 30, 30, 30, 90, and 90 characters, for a total of 270. That's more than
enough space to talk about the main features of your product.
The question is, how do you utilize that long description text that has all the
details that you want, and make sure it fits correctly within the limits given
by the platform you are using?
The :func:`ad_from_string` function does exactly that. Given a long string, it
divides it into slots of any given length that you specify, and if any text
remains it will be appended to the end of the returned list.
Another important benefit of this is that you can take those long descriptions
(or write them) once, and then you can easily split them into different slots
based on the ad format and the platform you are using.
Here is a quick overview of the available parameters and options:
.. glossary::
s
The string that you want to split. This would typically be available
on the landing pages of each product.
slots
The lengths that you want to split into. Note that although the default
uses Google Ads' text ad template, you can change it to any other
group of slots, with more or fewer slots of different lengths.
sep
The separator by which to split the text. The default is :attr:`None`
which splits the text by whitespace, but you can change it to something
else if needed. Sometimes you might want the text split by hyphens
(URLs for example) so you can split by that character.
capitalize
The default is :attr:`False` which leaves the capitalization of
:attr:`s` intact. If you set it to :attr:`True` then the first letter
of each word would be capitalized.
**Example**
Note that in any case, the returned list of characters is longer than the
provided slots by one. So if you provide five slots, for example, the function
will always return a list of length six.
This is to ensure that the remainder of the text is not lost if it is longer,
so you know what is missing. In case you have shorter text, you will still have
one element more than the provided slots to ensure consistency.
.. thebe-button::
Run this code
.. code-block::
:class: thebe, thebe-init
import advertools as adv
desc_text = "Get the latest gadget online. The GX12 model comes with 13 things that do a lot of good stuff for your health. Start shopping now."
len(desc_text) # 130
Now let's see how this same description can be utilized in different scenarios
Google Text Ads
^^^^^^^^^^^^^^^
Since this is shorter than the default Google values, you will get extra empty
slots (with an additional last one).
.. thebe-button::
Run this code
.. code-block::
:class: thebe, thebe-init
adv.ad_from_string(desc_text) # default values (Google text ads)
.. code-block::
['Get the latest gadget online.',
'The GX12 model comes with 13',
'things that do a lot of good',
'stuff for your health. Start shopping now.',
'',
'',
'',
'']
Facebook Feed Ads
^^^^^^^^^^^^^^^^^
In this case, it is also shorter than the default value, so you get an extra
space.
.. thebe-button::
Run this code
.. code-block::
:class: thebe, thebe-init
adv.ad_from_string(desc_text, [125, 25, 30]) # Facebook feed ads
.. code-block:
['Get the latest gadget online. The GX12 model comes with 13 things that do a lot of good stuff for your health. Start shopping',
'now.',
'',
'']
Since it might not look good having just one word in the second slot, and an
empty last one, you might want to change it as follows:
.. thebe-button::
Run this code
.. code-block::
:class: thebe, thebe-init
adv.ad_from_string(desc_text, [90, 25, 30])
.. code-block::
['Get the latest gadget online. The GX12 model comes with 13 things that do a lot of good',
'stuff for your health.',
'Start shopping now.',
'']
Facebook Instant Article Ad
^^^^^^^^^^^^^^^^^^^^^^^^^^^
Here is a case where our text is longer than the provided limitations, so we
end up having an extra space that is not used:
.. thebe-button::
Run this code
.. code-block::
:class: thebe, thebe-init
adv.ad_from_string(desc_text, [25, 30]) # Facebook instant article ad
.. code-block::
['Get the latest gadget',
'online. The GX12 model comes',
'with 13 things that do a lot of good stuff for your health. Start shopping now.']
"""
import string
[docs]def ad_from_string(s, slots=(30, 30, 30, 90, 90, 15, 15), sep=None,
capitalize=False):
"""Convert string :attr:`s` to an ad by splitting it into groups of words.
Each group would have a length of at most the allowed length for that slot.
If the total length of :attr:`s` exceeds the total allowed length, all
remaining characters would be grouped in the last element of the
returned list.
:param str s: a string of characters, with no restrictions on length
:param list slots: an iterable of integers for the maximum lengths for
each slot
:param str sep: character(s) by which to split :attr:`s`
:param bool capitalize: whether or not to capitalize each word after
grouping. Setting it as False would not change the
capitalization of the input string
:returns text_ad: a list of strings
>>> ad_from_string('this is a short ad')
['this is a short ad', '', '', '', '', '', '', '']
>>> ad_from_string('this is a longer ad and will take the first two slots')
['this as a longer ad and would', 'take the first two slots',
'', '', '', '', '', '']
>>> ad_from_string("Slots can be changed the way you want", (10, 15, 10))
['Slots can', 'be changed the', 'way you', 'want']
>>> ad_from_string("The capitalization REMAinS as IS bY DefAULt",
... (10, 15, 10))
['The', 'capitalization', 'REMAinS as', 'IS bY DefAULt']
>>> ad_from_string("set captialize=True to capitalize first letters",
... capitalize=True)
['Set Captialize=true To', 'Capitalize First Letters',
'', '', '', '', '', '']
"""
str_words = s.split(sep=sep)
text_ad = ['' for x in range(len(slots)+1)]
counter = 0
for i, slot in enumerate(slots):
while counter <= len(str_words) - 1:
if len(text_ad[i] + str_words[counter]) + 1 > slot:
break
text_ad[i] += (' ' + str_words[counter] if text_ad[i]
else str_words[counter])
counter += 1
text_ad[-1] = (sep.join(str_words[counter:])
if sep is not None else ' '.join(str_words[counter:]))
return [string.capwords(x) if capitalize else x for x in text_ad]


@* * Copyright 2016 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @(applicationConfig: config.ApplicationConfig = config.ApplicationConfig, calculationResponse:CalculationResponse)(implicit request: Request[_]) @import uk.gov.hmrc.play.views.html._ @import uk.gov.hmrc.play.views.html.helpers._ @import play.i18n._ @import config.ApplicationConfig.globalErrors @gmp_main(applicationConfig = applicationConfig, title = Messages.get("gmp.results.title"),supportLinkEnabled = false) { @if(calculationResponse.globalErrorCode > 0){ @includes.global_error(calculationResponse.globalErrorCode) } else { @if(calculationResponse.calculationPeriods.length > 1){ @if(calculationResponse.hasErrors) { @includes.multi_error(calculationResponse) } else { @includes.multi_results(calculationResponse) } } else { @if(calculationResponse.calculationPeriods.head.errorCode == 0){ @includes.single_result(calculationResponse) }else{ @includes.global_error(calculationResponse.calculationPeriods.head.errorCode) } } } @includes.member_details_result(calculationResponse) @if(!calculationResponse.hasErrors){


DSC-RIT Recruitment Test 2020 Question Paper
Topic: Mobile Development
Q1: What is an adapter in Android?
Create a generic class abstraction over the main class
Create child views to represent the parent view items
Create separate activity which adapts to the child activity
Provides a plug-in feature to mitigate need for creating additional libraries
Q2: What is not true about a web view used in Android?
WebView is a UI component
WebView can render static HTML
WebView can also render dynamic web pages
None of the above
Q3: Which compiler compiles android code?
JVM
DVM
Blue-J
None of the above
Q4: Which kernel is used in Android?
Customized Linux 3.6
Unix 1.0
Symantec 1.2
Huawei 3.0
Q5: Is it possible to have an activity without UI to perform actions?
No
Yes
Maybe
Wrong question
Q6: Which of the following is correct?
Margin is specifying the extra space left on all four sides in layout
Padding is used to offset the content of a view by specific px or dp
Both A & B
None of the above
Q7: What is Manifest.xml in Android?
It has information about layout in an application
It has information about activities in an application
It has all the information about the application
None of the above
Q8: Which media file format is not supported in Android?
MP4
AVI
MIDI
MPEG
Q9: What are the functionalities in AsyncTask in Android?
OnPreExecution()
OnPostExecution()
DoInBackground()
OnProgressUpdate()
Q10: What is the latest release of Android Studio?
3.6
3.5
3.4
3.3
Submit


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SAR
Section: Linux User's Manual (1)Updated: MAY 2009
Index Return to Main Contents
NAME
sar - Collect, report, or save system activity information.SYNOPSIS
sar [ -A ] [ -b ] [ -B ] [ -C ] [ -d ] [ -h ] [ -i interval ] [ -m ] [ -p ] [ -q ] [ -r ] [ -R ] [ -S ] [ -t ] [ -u [ ALL ] ] [ -v ] [ -V ] [ -w ] [ -W ] [ -y ] [ -n { keyword [,...] | ALL } ] [ -I { int [,...] | SUM | ALL | XALL } ] [ -P { cpu [,...] | ALL } ] [ -o [ filename ] | -f [ filename ] ] [ -s [ hh:mm:ss ] ] [ -e [ hh:mm:ss ] ] [ interval [ count ] ]DESCRIPTION
The sar command writes to standard output the contents of selected cumulative activity counters in the operating system. The accounting system, based on the values in the count and interval parameters, writes information the specified number of times spaced at the specified intervals in seconds. If the interval parameter is set to zero, the sar command displays the average statistics for the time since the system was started. If the interval parameter is specified without the count parameter, then reports are generated continuously. The collected data can also be saved in the file specified by the -o filename flag, in addition to being displayed onto the screen. If filename is omitted, sar uses the standard system activity daily data file, the /var/log/sa/sadd file, where the dd parameter indicates the current day. By default all the data available from the kernel are saved in the data file.The sar command extracts and writes to standard output records previously saved in a file. This file can be either the one specified by the -f flag or, by default, the standard system activity daily data file.
Without the -P flag, the sar command reports system-wide (global among all processors) statistics, which are calculated as averages for values expressed as percentages, and as sums otherwise. If the -P flag is given, the sar command reports activity which relates to the specified processor or processors. If -P ALL is given, the sar command reports statistics for each individual processor and global statistics among all processors.
You can select information about specific system activities using flags. Not specifying any flags selects only CPU activity.
The default version of the sar command (CPU utilization report) might be one of the first facilities the user runs to begin system activity investigation, because it monitors major system resources. If CPU utilization is near 100 percent (user + nice + system), the workload sampled is CPU-bound.
If multiple samples and multiple reports are desired, it is convenient to specify an output file for the sar command. Run the sar command as a background process. The syntax for this is:
sar -o datafile interval count >/dev/null 2>&1 &
All data is captured in binary form and saved to a file (datafile). The data can then be selectively displayed with the sar command using the -f option. Set the interval and count parameters to select count records at interval second intervals. If the count parameter is not set, all the records saved in the file will be selected. Collection of data in this manner is useful to characterize system usage over a period of time and determine peak usage hours.
Note: The
sar
command only reports on local activities.
OPTIONS
- -A
- This is equivalent to specifying -bBdqrRSuvwWy -I SUM -I XALL -n ALL -u ALL -P ALL.
- -b
-
Report I/O and transfer rate statistics.
The following values are displayed:
tps
-
- Total number of transfers per second that were issued to physical devices. A transfer is an I/O request to a physical device. Multiple logical requests can be combined into a single I/O request to the device. A transfer is of indeterminate size.
rtps
- Total number of read requests per second issued to physical devices.
wtps
- Total number of write requests per second issued to physical devices.
bread/s
- Total amount of data read from the devices in blocks per second. Blocks are equivalent to sectors with 2.4 kernels and newer and therefore have a size of 512 bytes. With older kernels, a block is of indeterminate size.
bwrtn/s
- Total amount of data written to devices in blocks per second.
-
- -B
-
Report paging statistics. Some of the metrics below are available
only with post 2.5 kernels. The following values are displayed:
pgpgin/s
-
- Total number of kilobytes the system paged in from disk per second. Note: With old kernels (2.2.x) this value is a number of blocks per second (and not kilobytes).
pgpgout/s
- Total number of kilobytes the system paged out to disk per second. Note: With old kernels (2.2.x) this value is a number of blocks per second (and not kilobytes).
fault/s
- Number of page faults (major + minor) made by the system per second. This is not a count of page faults that generate I/O, because some page faults can be resolved without I/O.
majflt/s
- Number of major faults the system has made per second, those which have required loading a memory page from disk.
pgfree/s
- Number of pages placed on the free list by the system per second.
pgscank/s
- Number of pages scanned by the kswapd daemon per second.
pgscand/s
- Number of pages scanned directly per second.
pgsteal/s
- Number of pages the system has reclaimed from cache (pagecache and swapcache) per second to satisfy its memory demands.
%vmeff
- Calculated as pgsteal / pgscan, this is a metric of the efficiency of page reclaim. If it is near 100% then almost every page coming off the tail of the inactive list is being reaped. If it gets too low (e.g. less than 30%) then the virtual memory is having some difficulty. This field is displayed as zero if no pages have been scanned during the interval of time.
-
- -C
- When reading data from a file, tell sar to display comments that have been inserted by sadc.
- -d
-
Report activity for each block device (kernels 2.4 and newer only).
When data is displayed, the device specification
dev m-n
is generally used (
DEV
column).
m
is the major number of the device.
With recent kernels (post 2.5),
n
is the minor number of the device, but is only a sequence number with
pre 2.5 kernels. Device names may also be pretty-printed if option -p
is used (see below). Values for fields avgqu-sz, await, svctm and %util
may be unavailable and displayed as 0.00 with some 2.4 kernels.
Note that disk activity depends on sadc options "-S DISK" and "-S XDISK"
to be collected. The following values are displayed:
tps
-
- Indicate the number of transfers per second that were issued to the device. Multiple logical requests can be combined into a single I/O request to the device. A transfer is of indeterminate size.
rd_sec/s
- Number of sectors read from the device. The size of a sector is 512 bytes.
wr_sec/s
- Number of sectors written to the device. The size of a sector is 512 bytes.
avgrq-sz
- The average size (in sectors) of the requests that were issued to the device.
avgqu-sz
- The average queue length of the requests that were issued to the device.
await
- The average time (in milliseconds) for I/O requests issued to the device to be served. This includes the time spent by the requests in queue and the time spent servicing them.
svctm
- The average service time (in milliseconds) for I/O requests that were issued to the device.
%util
- Percentage of CPU time during which I/O requests were issued to the device (bandwidth utilization for the device). Device saturation occurs when this value is close to 100%.
-
- -e [ hh:mm:ss ]
- Set the ending time of the report. The default ending time is 18:00:00. Hours must be given in 24-hour format. This option can be used when data are read from or written to a file (options -f or -o ).
- -f [ filename ]
- Extract records from filename (created by the -o filename flag). The default value of the filename parameter is the current daily data file, the /var/log/sa/sadd file. The -f option is exclusive of the -o option.
- -h
- Display a short help message then exit.
- -i interval
- Select data records at seconds as close as possible to the number specified by the interval parameter.
- -I { int [,...] | SUM | ALL | XALL }
- Report statistics for a given interrupt. int is the interrupt number. Specifying multiple -I int parameters on the command line will look at multiple independent interrupts. The SUM keyword indicates that the total number of interrupts received per second is to be displayed. The ALL keyword indicates that statistics from the first 16 interrupts are to be reported, whereas the XALL keyword indicates that statistics from all interrupts, including potential APIC interrupt sources, are to be reported. Note that interrupt statistics depend on sadc option "-S INT" to be collected.
- -m
-
Report power management statistics.
Note that these statistics depend on sadc option "-S POWER" to be collected.
The following value is displayed:
MHz
-
- CPU clock frequency in MHz.
-
- -n { keyword [,...] | ALL }
-
Report network statistics.
Possible keywords are DEV, EDEV, NFS, NFSD, SOCK, IP, EIP, ICMP, EICMP, TCP, ETCP, UDP, SOCK6, IP6, EIP6, ICMP6, EICMP6 and UDP6.
With the DEV keyword, statistics from the network devices are reported. The following values are displayed:
IFACE
-
- Name of the network interface for which statistics are reported.
rxpck/s
- Total number of packets received per second.
txpck/s
- Total number of packets transmitted per second.
rxkB/s
- Total number of kilobytes received per second.
txkB/s
- Total number of kilobytes transmitted per second.
rxcmp/s
- Number of compressed packets received per second (for cslip etc.).
txcmp/s
- Number of compressed packets transmitted per second.
rxmcst/s
- Number of multicast packets received per second.
With the EDEV keyword, statistics on failures (errors) from the network devices are reported. The following values are displayed:
IFACE
- Name of the network interface for which statistics are reported.
rxerr/s
- Total number of bad packets received per second.
txerr/s
- Total number of errors that happened per second while transmitting packets.
coll/s
- Number of collisions that happened per second while transmitting packets.
rxdrop/s
- Number of received packets dropped per second because of a lack of space in linux buffers.
txdrop/s
- Number of transmitted packets dropped per second because of a lack of space in linux buffers.
txcarr/s
- Number of carrier-errors that happened per second while transmitting packets.
rxfram/s
- Number of frame alignment errors that happened per second on received packets.
rxfifo/s
- Number of FIFO overrun errors that happened per second on received packets.
txfifo/s
- Number of FIFO overrun errors that happened per second on transmitted packets.
With the NFS keyword, statistics about NFS client activity are reported. The following values are displayed:
call/s
- Number of RPC requests made per second.
retrans/s
- Number of RPC requests per second, those which needed to be retransmitted (for example because of a server timeout).
read/s
- Number of 'read' RPC calls made per second.
write/s
- Number of 'write' RPC calls made per second.
access/s
- Number of 'access' RPC calls made per second.
getatt/s
- Number of 'getattr' RPC calls made per second.
With the NFSD keyword, statistics about NFS server activity are reported. The following values are displayed:
scall/s
- Number of RPC requests received per second.
badcall/s
- Number of bad RPC requests received per second, those whose processing generated an error.
packet/s
- Number of network packets received per second.
udp/s
- Number of UDP packets received per second.
tcp/s
- Number of TCP packets received per second.
hit/s
- Number of reply cache hits per second.
miss/s
- Number of reply cache misses per second.
sread/s
- Number of 'read' RPC calls received per second.
swrite/s
- Number of 'write' RPC calls received per second.
saccess/s
- Number of 'access' RPC calls received per second.
sgetatt/s
- Number of 'getattr' RPC calls received per second.
With the SOCK keyword, statistics on sockets in use are reported (IPv4). The following values are displayed:
totsck
- Total number of sockets used by the system.
tcpsck
- Number of TCP sockets currently in use.
udpsck
- Number of UDP sockets currently in use.
rawsck
- Number of RAW sockets currently in use.
ip-frag
- Number of IP fragments currently in use.
tcp-tw
- Number of TCP sockets in TIME_WAIT state.
With the IP keyword, statistics about IPv4 network traffic are reported. Note that IPv4 statistics depend on sadc option "-S SNMP" to be collected. The following values are displayed (formal SNMP names between square brackets):
irec/s
- The total number of input datagrams received from interfaces per second, including those received in error [ipInReceives].
fwddgm/s
- The number of input datagrams per second, for which this entity was not their final IP destination, as a result of which an attempt was made to find a route to forward them to that final destination [ipForwDatagrams].
idel/s
- The total number of input datagrams successfully delivered per second to IP user-protocols (including ICMP) [ipInDelivers].
orq/s
- The total number of IP datagrams which local IP user-protocols (including ICMP) supplied per second to IP in requests for transmission [ipOutRequests]. Note that this counter does not include any datagrams counted in fwddgm/s.
asmrq/s
- The number of IP fragments received per second which needed to be reassembled at this entity [ipReasmReqds].
asmok/s
- The number of IP datagrams successfully re-assembled per second [ipReasmOKs].
fragok/s
- The number of IP datagrams that have been successfully fragmented at this entity per second [ipFragOKs].
fragcrt/s
- The number of IP datagram fragments that have been generated per second as a result of fragmentation at this entity [ipFragCreates].
With the EIP keyword, statistics about IPv4 network errors are reported. Note that IPv4 statistics depend on sadc option "-S SNMP" to be collected. The following values are displayed (formal SNMP names between square brackets):
ihdrerr/s
- The number of input datagrams discarded per second due to errors in their IP headers, including bad checksums, version number mismatch, other format errors, time-to-live exceeded, errors discovered in processing their IP options, etc. [ipInHdrErrors]
iadrerr/s
- The number of input datagrams discarded per second because the IP address in their IP header's destination field was not a valid address to be received at this entity. This count includes invalid addresses (e.g., <IP_ADDRESS>) and addresses of unsupported Classes (e.g., Class E). For entities which are not IP routers and therefore do not forward datagrams, this counter includes datagrams discarded because the destination address was not a local address [ipInAddrErrors].
iukwnpr/s
- The number of locally-addressed datagrams received successfully but discarded per second because of an unknown or unsupported protocol [ipInUnknownProtos].
idisc/s
- The number of input IP datagrams per second for which no problems were encountered to prevent their continued processing, but which were discarded (e.g., for lack of buffer space) [ipInDiscards]. Note that this counter does not include any datagrams discarded while awaiting re-assembly.
odisc/s
- The number of output IP datagrams per second for which no problem was encountered to prevent their transmission to their destination, but which were discarded (e.g., for lack of buffer space) [ipOutDiscards]. Note that this counter would include datagrams counted in fwddgm/s if any such packets met this (discretionary) discard criterion.
onort/s
- The number of IP datagrams discarded per second because no route could be found to transmit them to their destination [ipOutNoRoutes]. Note that this counter includes any packets counted in fwddgm/s which meet this 'no-route' criterion. Note that this includes any datagrams which a host cannot route because all of its default routers are down.
asmf/s
- The number of failures detected per second by the IP re-assembly algorithm (for whatever reason: timed out, errors, etc) [ipReasmFails]. Note that this is not necessarily a count of discarded IP fragments since some algorithms can lose track of the number of fragments by combining them as they are received.
fragf/s
- The number of IP datagrams that have been discarded per second because they needed to be fragmented at this entity but could not be, e.g., because their Don't Fragment flag was set [ipFragFails].
With the ICMP keyword, statistics about ICMPv4 network traffic are reported. Note that ICMPv4 statistics depend on sadc option "-S SNMP" to be collected. The following values are displayed (formal SNMP names between square brackets):
imsg/s
- The total number of ICMP messages which the entity received per second [icmpInMsgs]. Note that this counter includes all those counted by ierr/s.
omsg/s
- The total number of ICMP messages which this entity attempted to send per second [icmpOutMsgs]. Note that this counter includes all those counted by oerr/s.
iech/s
- The number of ICMP Echo (request) messages received per second [icmpInEchos].
iechr/s
- The number of ICMP Echo Reply messages received per second [icmpInEchoReps].
oech/s
- The number of ICMP Echo (request) messages sent per second [icmpOutEchos].
oechr/s
- The number of ICMP Echo Reply messages sent per second [icmpOutEchoReps].
itm/s
- The number of ICMP Timestamp (request) messages received per second [icmpInTimestamps].
itmr/s
- The number of ICMP Timestamp Reply messages received per second [icmpInTimestampReps].
otm/s
- The number of ICMP Timestamp (request) messages sent per second [icmpOutTimestamps].
otmr/s
- The number of ICMP Timestamp Reply messages sent per second [icmpOutTimestampReps].
iadrmk/s
- The number of ICMP Address Mask Request messages received per second [icmpInAddrMasks].
iadrmkr/s
- The number of ICMP Address Mask Reply messages received per second [icmpInAddrMaskReps].
oadrmk/s
- The number of ICMP Address Mask Request messages sent per second [icmpOutAddrMasks].
oadrmkr/s
- The number of ICMP Address Mask Reply messages sent per second [icmpOutAddrMaskReps].
With the EICMP keyword, statistics about ICMPv4 error messages are reported. Note that ICMPv4 statistics depend on sadc option "-S SNMP" to be collected. The following values are displayed (formal SNMP names between square brackets):
ierr/s
- The number of ICMP messages per second which the entity received but determined as having ICMP-specific errors (bad ICMP checksums, bad length, etc.) [icmpInErrors].
oerr/s
- The number of ICMP messages per second which this entity did not send due to problems discovered within ICMP such as a lack of buffers [icmpOutErrors].
idstunr/s
- The number of ICMP Destination Unreachable messages received per second [icmpInDestUnreachs].
odstunr/s
- The number of ICMP Destination Unreachable messages sent per second [icmpOutDestUnreachs].
itmex/s
- The number of ICMP Time Exceeded messages received per second [icmpInTimeExcds].
otmex/s
- The number of ICMP Time Exceeded messages sent per second [icmpOutTimeExcds].
iparmpb/s
- The number of ICMP Parameter Problem messages received per second [icmpInParmProbs].
oparmpb/s
- The number of ICMP Parameter Problem messages sent per second [icmpOutParmProbs].
isrcq/s
- The number of ICMP Source Quench messages received per second [icmpInSrcQuenchs].
osrcq/s
- The number of ICMP Source Quench messages sent per second [icmpOutSrcQuenchs].
iredir/s
- The number of ICMP Redirect messages received per second [icmpInRedirects].
oredir/s
- The number of ICMP Redirect messages sent per second [icmpOutRedirects].
With the TCP keyword, statistics about TCPv4 network traffic are reported. Note that TCPv4 statistics depend on sadc option "-S SNMP" to be collected. The following values are displayed (formal SNMP names between square brackets):
active/s
- The number of times TCP connections have made a direct transition to the SYN-SENT state from the CLOSED state per second [tcpActiveOpens].
passive/s
- The number of times TCP connections have made a direct transition to the SYN-RCVD state from the LISTEN state per second [tcpPassiveOpens].
iseg/s
- The total number of segments received per second, including those received in error [tcpInSegs]. This count includes segments received on currently established connections.
oseg/s
- The total number of segments sent per second, including those on current connections but excluding those containing only retransmitted octets [tcpOutSegs].
With the ETCP keyword, statistics about TCPv4 network errors are reported. Note that TCPv4 statistics depend on sadc option "-S SNMP" to be collected. The following values are displayed (formal SNMP names between square brackets):
atmptf/s
- The number of times per second TCP connections have made a direct transition to the CLOSED state from either the SYN-SENT state or the SYN-RCVD state, plus the number of times per second TCP connections have made a direct transition to the LISTEN state from the SYN-RCVD state [tcpAttemptFails].
estres/s
- The number of times per second TCP connections have made a direct transition to the CLOSED state from either the ESTABLISHED state or the CLOSE-WAIT state [tcpEstabResets].
retrans/s
- The total number of segments retransmitted per second - that is, the number of TCP segments transmitted containing one or more previously transmitted octets [tcpRetransSegs].
isegerr/s
- The total number of segments received in error (e.g., bad TCP checksums) per second [tcpInErrs].
orsts/s
- The number of TCP segments sent per second containing the RST flag [tcpOutRsts].
With the UDP keyword, statistics about UDPv4 network traffic are reported. Note that UDPv4 statistics depend on sadc option "-S SNMP" to be collected. The following values are displayed (formal SNMP names between square brackets):
idgm/s
- The total number of UDP datagrams delivered per second to UDP users [udpInDatagrams].
odgm/s
- The total number of UDP datagrams sent per second from this entity [udpOutDatagrams].
noport/s
- The total number of received UDP datagrams per second for which there was no application at the destination port [udpNoPorts].
idgmerr/s
- The number of received UDP datagrams per second that could not be delivered for reasons other than the lack of an application at the destination port [udpInErrors].
With the SOCK6 keyword, statistics on sockets in use are reported (IPv6). Note that IPv6 statistics depend on sadc option "-S IPV6" to be collected. The following values are displayed:
tcp6sck
- Number of TCPv6 sockets currently in use.
udp6sck
- Number of UDPv6 sockets currently in use.
raw6sck
- Number of RAWv6 sockets currently in use.
ip6-frag
- Number of IPv6 fragments currently in use.
With the IP6 keyword, statistics about IPv6 network traffic are reported. Note that IPv6 statistics depend on sadc option "-S IPV6" to be collected. The following values are displayed (formal SNMP names between square brackets):
irec6/s
- The total number of input datagrams received from interfaces per second, including those received in error [ipv6IfStatsInReceives].
fwddgm6/s
- The number of output datagrams per second which this entity received and forwarded to their final destinations [ipv6IfStatsOutForwDatagrams].
idel6/s
- The total number of datagrams successfully delivered per second to IPv6 user-protocols (including ICMP) [ipv6IfStatsInDelivers].
orq6/s
- The total number of IPv6 datagrams which local IPv6 user-protocols (including ICMP) supplied per second to IPv6 in requests for transmission [ipv6IfStatsOutRequests]. Note that this counter does not include any datagrams counted in fwddgm6/s.
asmrq6/s
- The number of IPv6 fragments received per second which needed to be reassembled at this interface [ipv6IfStatsReasmReqds].
asmok6/s
- The number of IPv6 datagrams successfully reassembled per second [ipv6IfStatsReasmOKs].
imcpck6/s
- The number of multicast packets received per second by the interface [ipv6IfStatsInMcastPkts].
omcpck6/s
- The number of multicast packets transmitted per second by the interface [ipv6IfStatsOutMcastPkts].
fragok6/s
- The number of IPv6 datagrams that have been successfully fragmented at this output interface per second [ipv6IfStatsOutFragOKs].
fragcr6/s
- The number of output datagram fragments that have been generated per second as a result of fragmentation at this output interface [ipv6IfStatsOutFragCreates].
With the EIP6 keyword, statistics about IPv6 network errors are reported. Note that IPv6 statistics depend on sadc option "-S IPV6" to be collected. The following values are displayed (formal SNMP names between square brackets):
ihdrer6/s
- The number of input datagrams discarded per second due to errors in their IPv6 headers, including version number mismatch, other format errors, hop count exceeded, errors discovered in processing their IPv6 options, etc. [ipv6IfStatsInHdrErrors]
iadrer6/s
- The number of input datagrams discarded per second because the IPv6 address in their IPv6 header's destination field was not a valid address to be received at this entity. This count includes invalid addresses (e.g., ::0) and unsupported addresses (e.g., addresses with unallocated prefixes). For entities which are not IPv6 routers and therefore do not forward datagrams, this counter includes datagrams discarded because the destination address was not a local address [ipv6IfStatsInAddrErrors].
iukwnp6/s
- The number of locally-addressed datagrams received successfully but discarded per second because of an unknown or unsupported protocol [ipv6IfStatsInUnknownProtos].
i2big6/s
- The number of input datagrams that could not be forwarded per second because their size exceeded the link MTU of outgoing interface [ipv6IfStatsInTooBigErrors].
idisc6/s
- The number of input IPv6 datagrams per second for which no problems were encountered to prevent their continued processing, but which were discarded (e.g., for lack of buffer space) [ipv6IfStatsInDiscards]. Note that this counter does not include any datagrams discarded while awaiting re-assembly.
odisc6/s
- The number of output IPv6 datagrams per second for which no problem was encountered to prevent their transmission to their destination, but which were discarded (e.g., for lack of buffer space) [ipv6IfStatsOutDiscards]. Note that this counter would include datagrams counted in fwddgm6/s if any such packets met this (discretionary) discard criterion.
inort6/s
- The number of input datagrams discarded per second because no route could be found to transmit them to their destination [ipv6IfStatsInNoRoutes].
onort6/s
- The number of locally generated IP datagrams discarded per second because no route could be found to transmit them to their destination [unknown formal SNMP name].
asmf6/s
- The number of failures detected per second by the IPv6 re-assembly algorithm (for whatever reason: timed out, errors, etc.) [ipv6IfStatsReasmFails]. Note that this is not necessarily a count of discarded IPv6 fragments since some algorithms can lose track of the number of fragments by combining them as they are received.
fragf6/s
- The number of IPv6 datagrams that have been discarded per second because they needed to be fragmented at this output interface but could not be [ipv6IfStatsOutFragFails].
itrpck6/s
- The number of input datagrams discarded per second because datagram frame didn't carry enough data [ipv6IfStatsInTruncatedPkts].
With the ICMP6 keyword, statistics about ICMPv6 network traffic are reported. Note that ICMPv6 statistics depend on sadc option "-S IPV6" to be collected. The following values are displayed (formal SNMP names between square brackets):
imsg6/s
- The total number of ICMP messages received by the interface per second which includes all those counted by ierr6/s [ipv6IfIcmpInMsgs].
omsg6/s
- The total number of ICMP messages which this interface attempted to send per second [ipv6IfIcmpOutMsgs].
iech6/s
- The number of ICMP Echo (request) messages received by the interface per second [ipv6IfIcmpInEchos].
iechr6/s
- The number of ICMP Echo Reply messages received by the interface per second [ipv6IfIcmpInEchoReplies].
oechr6/s
- The number of ICMP Echo Reply messages sent by the interface per second [ipv6IfIcmpOutEchoReplies].
igmbq6/s
- The number of ICMPv6 Group Membership Query messages received by the interface per second [ipv6IfIcmpInGroupMembQueries].
igmbr6/s
- The number of ICMPv6 Group Membership Response messages received by the interface per second [ipv6IfIcmpInGroupMembResponses].
ogmbr6/s
- The number of ICMPv6 Group Membership Response messages sent per second [ipv6IfIcmpOutGroupMembResponses].
igmbrd6/s
- The number of ICMPv6 Group Membership Reduction messages received by the interface per second [ipv6IfIcmpInGroupMembReductions].
ogmbrd6/s
- The number of ICMPv6 Group Membership Reduction messages sent per second [ipv6IfIcmpOutGroupMembReductions].
irtsol6/s
- The number of ICMP Router Solicit messages received by the interface per second [ipv6IfIcmpInRouterSolicits].
ortsol6/s
- The number of ICMP Router Solicitation messages sent by the interface per second [ipv6IfIcmpOutRouterSolicits].
irtad6/s
- The number of ICMP Router Advertisement messages received by the interface per second [ipv6IfIcmpInRouterAdvertisements].
inbsol6/s
- The number of ICMP Neighbor Solicit messages received by the interface per second [ipv6IfIcmpInNeighborSolicits].
onbsol6/s
- The number of ICMP Neighbor Solicitation messages sent by the interface per second [ipv6IfIcmpOutNeighborSolicits].
inbad6/s
- The number of ICMP Neighbor Advertisement messages received by the interface per second [ipv6IfIcmpInNeighborAdvertisements].
onbad6/s
- The number of ICMP Neighbor Advertisement messages sent by the interface per second [ipv6IfIcmpOutNeighborAdvertisements].
With the EICMP6 keyword, statistics about ICMPv6 error messages are reported. Note that ICMPv6 statistics depend on sadc option "-S IPV6" to be collected. The following values are displayed (formal SNMP names between square brackets):
ierr6/s
- The number of ICMP messages per second which the interface received but determined as having ICMP-specific errors (bad ICMP checksums, bad length, etc.) [ipv6IfIcmpInErrors]
idtunr6/s
- The number of ICMP Destination Unreachable messages received by the interface per second [ipv6IfIcmpInDestUnreachs].
odtunr6/s
- The number of ICMP Destination Unreachable messages sent by the interface per second [ipv6IfIcmpOutDestUnreachs].
itmex6/s
- The number of ICMP Time Exceeded messages received by the interface per second [ipv6IfIcmpInTimeExcds].
otmex6/s
- The number of ICMP Time Exceeded messages sent by the interface per second [ipv6IfIcmpOutTimeExcds].
iprmpb6/s
- The number of ICMP Parameter Problem messages received by the interface per second [ipv6IfIcmpInParmProblems].
oprmpb6/s
- The number of ICMP Parameter Problem messages sent by the interface per second [ipv6IfIcmpOutParmProblems].
iredir6/s
- The number of Redirect messages received by the interface per second [ipv6IfIcmpInRedirects].
oredir6/s
- The number of Redirect messages sent by the interface by second [ipv6IfIcmpOutRedirects].
ipck2b6/s
- The number of ICMP Packet Too Big messages received by the interface per second [ipv6IfIcmpInPktTooBigs].
opck2b6/s
- The number of ICMP Packet Too Big messages sent by the interface per second [ipv6IfIcmpOutPktTooBigs].
With the UDP6 keyword, statistics about UDPv6 network traffic are reported. Note that UDPv6 statistics depend on sadc option "-S IPV6" to be collected. The following values are displayed (formal SNMP names between square brackets):
idgm6/s
- The total number of UDP datagrams delivered per second to UDP users [udpInDatagrams].
odgm6/s
- The total number of UDP datagrams sent per second from this entity [udpOutDatagrams].
noport6/s
- The total number of received UDP datagrams per second for which there was no application at the destination port [udpNoPorts].
idgmer6/s
- The number of received UDP datagrams per second that could not be delivered for reasons other than the lack of an application at the destination port [udpInErrors].
The ALL keyword is equivalent to specifying all the keywords above and therefore all the network activities are reported.
-
- -o [ filename ]
- Save the readings in the file in binary form. Each reading is in a separate record. The default value of the filename parameter is the current daily data file, the /var/log/sa/sadd file. The -o option is exclusive of the -f option. All the data available from the kernel are saved in the file (in fact, sar calls its data collector sadc with the option "-S ALL". See sadc(8) manual page).
- -P { cpu [,...] | ALL }
- Report per-processor statistics for the specified processor or processors. Specifying the ALL keyword reports statistics for each individual processor, and globally for all processors. Note that processor 0 is the first processor.
- -p
- Pretty-print device names. Use this option in conjunction with option -d. By default names are printed as dev m-n where m and n are the major and minor numbers for the device. Use of this option displays the names of the devices as they (should) appear in /dev. Name mappings are controlled by /etc/sysconfig/sysstat.ioconf.
- -q
-
Report queue length and load averages. The following values are displayed:
runq-sz
-
- Run queue length (number of tasks waiting for run time).
plist-sz
- Number of tasks in the task list.
ldavg-1
- System load average for the last minute. The load average is calculated as the average number of runnable or running tasks (R state), and the number of tasks in uninterruptible sleep (D state) over the specified interval.
ldavg-5
- System load average for the past 5 minutes.
ldavg-15
- System load average for the past 15 minutes.
-
- -r
-
Report memory utilization statistics.
The following values are displayed:
kbmemfree
-
- Amount of free memory available in kilobytes.
kbmemused
- Amount of used memory in kilobytes. This does not take into account memory used by the kernel itself.
%memused
- Percentage of used memory.
kbbuffers
- Amount of memory used as buffers by the kernel in kilobytes.
kbcached
- Amount of memory used to cache data by the kernel in kilobytes.
kbcommit
- Amount of memory in kilobytes needed for current workload. This is an estimate of how much RAM/swap is needed to guarantee that there never is out of memory.
%commit
-
Percentage of memory needed for current workload in relation to the total amount of memory (RAM+swap).
This number may be greater than 100% because the kernel usually overcommits memory.
-
- -R
-
Report memory statistics. The following values are displayed:
frmpg/s
-
- Number of memory pages freed by the system per second. A negative value represents a number of pages allocated by the system. Note that a page has a size of 4 kB or 8 kB according to the machine architecture.
bufpg/s
- Number of additional memory pages used as buffers by the system per second. A negative value means fewer pages used as buffers by the system.
campg/s
- Number of additional memory pages cached by the system per second. A negative value means fewer pages in the cache.
-
- -s [ hh:mm:ss ]
- Set the starting time of the data, causing the sar command to extract records time-tagged at, or following, the time specified. The default starting time is 08:00:00. Hours must be given in 24-hour format. This option can be used only when data are read from a file (option -f ).
- -S
-
Report swap space utilization statistics.
The following values are displayed:
kbswpfree
-
- Amount of free swap space in kilobytes.
kbswpused
- Amount of used swap space in kilobytes.
%swpused
- Percentage of used swap space.
kbswpcad
- Amount of cached swap memory in kilobytes. This is memory that once was swapped out, is swapped back in but still also is in the swap area (if memory is needed it doesn't need to be swapped out again because it is already in the swap area. This saves I/O).
%swpcad
- Percentage of cached swap memory in relation to the amount of used swap space.
-
- -t
- When reading data from a daily data file, indicate that sar should display the timestamps in the original locale time of the data file creator. Without this option, the sar command displays the timestamps in the user's locale time.
- -u [ ALL ]
-
Report CPU utilization. The
ALL
keyword indicates that all the CPU fields should be displayed.
The report may show the following fields:
%user
-
- Percentage of CPU utilization that occurred while executing at the user level (application). Note that this field includes time spent running virtual processors.
%usr
- Percentage of CPU utilization that occurred while executing at the user level (application). Note that this field does NOT include time spent running virtual processors.
%nice
- Percentage of CPU utilization that occurred while executing at the user level with nice priority.
%system
- Percentage of CPU utilization that occurred while executing at the system level (kernel). Note that this field includes time spent servicing hardware and software interrupts.
%sys
- Percentage of CPU utilization that occurred while executing at the system level (kernel). Note that this field does NOT include time spent servicing hardware or software interrupts.
%iowait
- Percentage of time that the CPU or CPUs were idle during which the system had an outstanding disk I/O request.
%steal
- Percentage of time spent in involuntary wait by the virtual CPU or CPUs while the hypervisor was servicing another virtual processor.
%irq
- Percentage of time spent by the CPU or CPUs to service hardware interrupts.
%soft
- Percentage of time spent by the CPU or CPUs to service software interrupts.
%guest
- Percentage of time spent by the CPU or CPUs to run a virtual processor.
%idle
- Percentage of time that the CPU or CPUs were idle and the system did not have an outstanding disk I/O request.
Note: On SMP machines a processor that does not have any activity at all (0.00 for every field) is a disabled (offline) processor.
-
- -v
-
Report status of inode, file and other kernel tables.
The following values are displayed:
dentunusd
-
- Number of unused cache entries in the directory cache.
file-nr
- Number of file handles used by the system.
inode-nr
- Number of inode handlers used by the system.
pty-nr
- Number of pseudo-terminals used by the system.
-
- -V
- Print version number then exit.
- -w
-
Report task creation and system switching activity.
proc/s
-
- Total number of tasks created per second.
cswch/s
- Total number of context switches per second.
-
- -W
-
Report swapping statistics. The following values are displayed:
pswpin/s
-
- Total number of swap pages the system brought in per second.
pswpout/s
- Total number of swap pages the system brought out per second.
-
- -y
-
Report TTY device activity. The following values are displayed:
rcvin/s
-
- Number of receive interrupts per second for current serial line. Serial line number is given in the TTY column.
xmtin/s
- Number of transmit interrupts per second for current serial line.
framerr/s
- Number of frame errors per second for current serial line.
prtyerr/s
- Number of parity errors per second for current serial line.
brk/s
- Number of breaks per second for current serial line.
ovrun/s
- Number of overrun errors per second for current serial line.
Note that with recent 2.6 kernels, these statistics can be retrieved only by root.
-
ENVIRONMENT
The sar command takes into account the following environment variables:
- S_TIME_FORMAT
-
If this variable exists and its value is
ISO
then the current locale will be ignored when printing the date in the report header.
The
sar
command will use the ISO 8601 format (YYYY-MM-DD) instead.
- S_TIME_DEF_TIME
- If this variable exists and its value is UTC then sar will save its data in UTC time (data will still be displayed in local time). sar will also use UTC time instead of local time to determine the current daily data file located in the /var/log/sa directory. This variable may be useful for servers with users located across several timezones.
EXAMPLES
sar -u 2 5- Report CPU utilization for each 2 seconds. 5 lines are displayed.
sar -I 14 -o int14.file 2 10
- Report statistics on IRQ 14 for each 2 seconds. 10 lines are displayed. Data are stored in a file called int14.file.
sar -r -n DEV -f /var/log/sa/sa16
- Display memory and network statistics saved in daily data file 'sa16'.
sar -A
- Display all the statistics saved in current daily data file.
BUGS
/proc filesystem must be mounted for the sar command to work.All the statistics are not necessarily available, depending on the kernel version used.
FILES
/var/log/sa/sadd-
Indicate the daily data file, where the
dd
parameter is a number representing the day of the month.
AUTHOR
Sebastien Godard (sysstat <at> orange.fr)SEE ALSO
sadc(8), sa1(8), sa2(8), sadf(1), isag(1), pidstat(1), mpstat(1), iostat(1), vmstat(8)http://pagesperso-orange.fr/sebastien.godard/
Index
This document was created by man2html, using the manual pages.
Time: 05:29:10 GMT, December 24, 2015


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NY judge orders Trump to pay US$2m in damages to charities
NEW YORK, Nov 8 — A New York judge yesterday ordered Donald Trump to pay US$2 million (RM8.25 million) for using his former charity to further his political and business interests, adding to the president’s legal woes.
Justice Saliann Scarpulla of the New York Supreme Court told him to pay the damages to a group of non-profit organisations to settle a civil lawsuit brought by state Attorney General Letitia James, a Democrat.
James filed the lawsuit against the Trump Foundation in June last year, accusing it of “persistently illegal conduct” that included improper coordination between the charity and his campaign team during his 2016 White House bid.
Trump agreed to shut down his personal charity in December but the suit moved ahead regardless as prosecutors sought millions of dollars in restitution and penalties.
“(The ruling) is a major victory in our efforts to protect charitable assets and hold accountable those who would abuse charities for personal gain,” James said in a statement.
The suit accused Trump of using foundation funds to settle lawsuits, promote his Trump-branded hotels, and for personal spending, including the purchase of a portrait of himself to display at one of his golf clubs.
It also said that in early 2016 while Trump was running for president, he organised what was billed as a Trump Foundation fundraiser but was actually a campaign event.
The ruling was the third legal setback Trump suffered in his native New York this week.
On Monday, a US appeals court ruled that he must release eight years of tax returns which he promised during the campaign to make public after an audit but has since refused to share.
The same day, US magazine columnist E. Jean Carroll, who accuses Trump of rape, sued the president for defamation after he accused her of making up the allegation. — AFP


Example_lakes.Rmd
This notebook demonstrates the use of extRatum
package drawing on natural environment data. extRatum
provides summary statistics of local geospatial features within a given geographic area. It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). The geospatial features can be of any geospatial data type, including point, polygon or line data.
In this example, we focus on natural environment characteristics.
We make use of openly available geospatial data to calculate the area covered by water bodies in each National Park in England.
## Warning: package 'sf' was built under R version 4.0.3
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
## Loading required package: sp
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:raster':
##
## intersect, select, union
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
First, we read in the boundaries of Great Britain that will help with plotting the results using raster
package .
GBR <- getData("GADM", country="GBR", level=0)
Then, we read in the boundaries of National Parks in England, published by Natural England.
The data downloaded from: https://naturalengland-defra.opendata.arcgis.com/datasets/d333c7529754444894e2d7f5044d1bbf_0.
# read in the national parks boundaries
national_parks <- st_read("lake_data/National_Parks_(England)/National_Parks__England____Natural_England.shp")
## Reading layer `National_Parks__England____Natural_England' from data source `C:\Users\User\Desktop\extRatum\vignettes\lake_data\National_Parks_(England)\National_Parks__England____Natural_England.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 246828.2 ymin: 56378.39 xmax: 652373.5 ymax: 633117
## projected CRS: OSGB 1936 / British National Grid
# plot the boundaries using a static map
tmap_mode("plot")
## tmap mode set to plotting
#tmap_mode("view") #use this code for creating an interactive map
tm_shape(national_parks) +
tm_borders(col = "green") +
tm_shape(GBR) +
tm_borders()
Finally, we read in the water bodies boundaries in England published by the Environment Agency.
The data downloaded from: https://data.gov.uk/dataset/33dcd836-3813-4233-a3ca-856358312415/wfd-lake-water-bodies-cycle-1
# read in the water bodies boundaries
water_bodies <- st_read("lake_data/EA_WFDLakeWaterBodiesCycle1_SHP_Full/data/WFD_Lake_Water_Bodies_Cycle_1.shp")
## Reading layer `WFD_Lake_Water_Bodies_Cycle_1' from data source `C:\Users\User\Desktop\extRatum\vignettes\lake_data\EA_WFDLakeWaterBodiesCycle1_SHP_Full\data\WFD_Lake_Water_Bodies_Cycle_1.shp' using driver `ESRI Shapefile'
## Simple feature collection with 620 features and 12 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 143019 ymin: 24071 xmax: 650347 ymax: 643896
## projected CRS: OSGB 1936 / British National Grid
# create a map
tm_shape(water_bodies) +
tm_borders(col = "blue") +
tm_shape(GBR) +
tm_borders()
extRatum
functionsFinally, it is time to run the areal_calc()
function to calculate the area covered by water bodies in each National Park in England.
Note that we have to pass a planar coordinate system so taht the algorithm can calculate areas. In this example we use the British National Grid.
# run the function from extRatum package
water_coverage <- areal_calc(
water_bodies,
national_parks,
unique_id_code = "FID",
crs = "epsg:27700"
)
The output of this function will be a dataframe containing:
Given that everything is measured in sqm, the ratio represents what is the % of area covered by water bodies by sqm. In this way, we have a relative measure that can be compared across all National Parks and is independent of their size.
We can also transform the calculated values in sqkm by dividing the value in sqm by 1,000,000. This can be done as follows.
water_coverage$AreaCovered_sqkm <- water_coverage$AreaCovered /1000000
head(water_coverage)
## # A tibble: 6 x 5
## FID TotalArea AreaCovered Ratio AreaCovered_sqkm
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1<PHONE_NUMBER>. 478289. 0.000289 0.478
## 2 2 693121787. 1573645. 0.00227 1.57
## 3 3<PHONE_NUMBER>. 2862833. 0.00131 2.86
## 4 4<PHONE_NUMBER>. 9812351. 0.00682 9.81
## 5 5 955747461. 1811909. 0.00190 1.81
## 6 6<PHONE_NUMBER>. 702244. 0.000487 0.702
Finally, we append the data calculated to the original table of National Parks.
# perform a join of tables
national_parks_v2 <- left_join(national_parks, water_coverage, by = "FID")
We can now create two maps: a first map showing the total area covered by water bodies.
# create a choropleth map
tm_shape(national_parks_v2) +
tm_fill("AreaCovered",
style = "fisher",
palette = "Blues",
alpha = 0.6,
id="NAME",
title="Area covered by water bodies (in m2)") +
tm_legend(position = c("right", "top")) +
tm_layout(legend.outside = TRUE) +
tm_shape(GBR) +
tm_borders()
And a second map that shows the ratio of water bodies to the total area of each National Park.
# create a choropleth map
tm_shape(national_parks_v2) +
tm_fill("Ratio",
style = "fisher",
palette = "Blues",
alpha = 0.6,
id="NAME",
title="Ratio of water bodies to total area of National Parks") +
tm_legend(position = c("right", "top")) +
tm_layout(legend.outside = TRUE) +
tm_shape(GBR) +
tm_borders()


Escape from North Korea
March 14th, 2017
I watched videos about the experiences of people who have escaped from North Korea and my questions and comments on the videos are below.
I was absent for the first video, so my questions and comments are not posted here.
Hyeonseo Lee
March 14th, 2017
The video I watched was titled "Escape from North Korea" and it was a TED talk.
My questions and comments are as follows:
- How was that the first experience when she had heard of poverty in her country was when she was seven years old?
- Why did the 1990s famine in North Korea hit? What caused it? Was it directly related to the new lack of support from the Soviet Union?
- There is so much death, from famine and attempted escape mentioned in the video.
Zeina
March 16th, 2017
This video, from Mashable Docs, is actually about escaping from Syria.
My questions and comments are as follows:
- I am surprised that civilians are in danger as much as they are in Syria.
- Is there no "normal" person in Syria whose home or workplace or school is not targeted for bombings?
- I find it impressive that they were able to escape and now live a life that is normal for the United States.
Mohammed and Shofar
March 16th, 2017
These people have escaped from Burma (now Myanmar), and tell their story in this video by the Globe and Mail.
My questions and comments are as follows:
- Was there a reason other than racial segregation that the Burmese government prohibited these students from attending school?
- What was life like in Burma prior to the escape?

- Why are they hated and targeted by the government of Burma?
- I find it sad that the vast majority of the people who escaped on the boat that Mohammed was on died.


Welcome!! Welocome!! Welcome!! Let me introduce myself My name is in the heading above I am still a human
This website is still under construction phase I will keep improving it as I learn to codehhhh


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