Results for v-can

Standalone, embeddable Git operations via libgit2 — clone/fetch/push/commit/branch/merge, layered auth chain and async support

Fast domain availability checker. Asks authoritative TLD nameservers directly instead of WHOIS.

Pure-Rust core for vacant: rules, DNS, RDAP.

This is an old, deprecated version of the Ruby PostgreSQL driver that hasn't been maintained or supported since early 2008. You should install/require 'pg' instead. If you need the 'postgres' gem for legacy code that can't be converted, you can still install it using an explicit version, like so: gem install postgres -v '0.7.9.2008.01.28' gem uninstall postgres -v '>0.7.9.2008.01.28' If you have any questions, the nice folks in the Google group can help: http://goo.gl/OjOPP / ruby-pg@googlegroups.com

This is an old, deprecated version of the 'pg' gem that hasn't been maintained or supported since early 2008. You should install/require 'pg' instead. If you need ruby-pg for legacy code that can't be converted, you can still install it using an explicit version, like so: gem install ruby-pg -v '0.7.9.2008.01.28' gem uninstall ruby-pg -v '>0.7.9.2008.01.28' If you have any questions, the nice folks in the Google group can help: http://goo.gl/OjOPP / ruby-pg@googlegroups.com

Provide a executable client and a series of APIs of Power V firewall of Lenovo. You can use the client to interact with the firewall, and you can also program with the APIs.

kiki is a dead-simple module that you can include in your Models that provides convenient, DRY way of generating unique keys for your favorite K/V or NoSQL databases.

Wraps Sox's `play` command, allowing playslists, find, random and time limit. Once a song is playing you can: 'x' to previous 'c' to pause/play 'v' to next 'q' to quit 't' n to set a timer that will pause the music after n minutes

== Baf baf helps writing an user acceptance test suite with a dedicated library and cucumber steps. It can run and wait for programs in a modified environment, verify the exit status, the output streams and other side effects. It also supports interactive programs and writing to their standard input. Then, it provides a DSL to write the CLI: require 'baf/cli' module MyProgram class CLI < Baf::CLI def setup flag_version '0.1.2'.freeze option :c, :config, 'config', 'specify config file' do |path| @config_path = path end end def run usage! unless arguments.any? puts 'arguments: %s' % arguments puts 'config: %s' % @config_path if @config_path end end end MyProgram::CLI.run ARGV Which behaves this way: % ./my_program Usage: my_program [options] options: -c, --config config specify config file -h, --help print this message -V, --version print version zsh: exit 64 ./my_program % ./my_program --wrong-arg Usage: my_program [options] options: -c, --config config specify config file -h, --help print this message -V, --version print version zsh: exit 64 ./my_program --wrong-arg % ./my_program foo arguments ["foo"] % ./my_program -c some_file foo arguments ["foo"] config path some_file

Log2json lets you read, filter and send logs as JSON objects via Unix pipes. It is inspired by Logstash, and is meant to be compatible with it at the JSON event/record level so that it can easily work with Kibana. Reading logs is done via a shell script(eg, `tail`) running in its own process. You then configure(see the `syslog2json` or the `nginxlog2json` script for examples) and run your filters in Ruby using the `Log2Json` module and its contained helper classes. `Log2Json` reads from stdin the logs(one log record per line), parses the log lines into JSON records, and then serializes and writes the records to stdout, which then can be piped to another process for processing or sending it to somewhere else. Currently, Log2json ships with a `tail-log` script that can be run as the input process. It's the same as using the Linux `tail` utility with the `-v -F` options except that it also tracks the positions(as the numbers of lines read from the beginning of the files) in a few files in the file system so that if the input process is interrupted, it can continue reading from where it left off next time if the files had been followed. This feature is similar to the sincedb feature in Logstash's file input. Note: If you don't need the tracking feature(ie, you are fine with always tailling from the end of file with `-v -F -n0`), then you can just use the `tail` utility that comes with your Linux distribution.(Or more specifically, the `tail` from the GNU coreutils). Other versions of the `tail` utility may also work, but are not tested. The input protocol expected by Log2json is very simple and documented in the source code. ** The `tail-log` script uses a patched version of `tail` from the GNU coreutils package. A binary of the `tail` utility compiled for Ubuntu 12.04 LTS is included with the Log2json gem. If the binary doesn't work for your distribution, then you'll need to get GNU coreutils-8.13, apply the patch(it can be found in the src/ directory of the installed gem), and then replace the bin/tail binary in the directory of the installed gem with your version of the binary. ** P.S. If you know of a way to configure and compile ONLY the tail program in coreutils, please let me know! The reason I'm not building tail post gem installation is that it takes too long to configure &amp;&amp; make because that actually builds every utilties in coreutils. For shipping logs to Redis, there's the `lines2redis` script that can be used as the output process in the pipe. For shipping logs from Redis to ElasticSearch, Log2json provides a `redis2es` script. Finally here's an example of Log2json in action: From a client machine: tail-log /var/log/{sys,mail}log /var/log/{kern,auth}.log | syslog2json | queue=jsonlogs \ flush_size=20 \ flush_interval=30 \ lines2redis host.to.redis.server 6379 0 # use redis DB 0 On the Redis server: redis_queue=jsonlogs redis2es host.to.es.server Resources that help writing log2json filters: - look at log2json.rb source and example filters - http://grokdebug.herokuapp.com/ - http://www.ruby-doc.org/stdlib-1.9.3/libdoc/date/rdoc/DateTime.html#method-i-strftime

Log2json lets you read, filter and send logs as JSON objects via Unix pipes. It is inspired by Logstash, and is meant to be compatible with it at the JSON event/record level so that it can easily work with Kibana. Reading logs is done via a shell script(eg, `tail`) running in its own process. You then configure(see the `syslog2json` or the `nginxlog2json` script for examples) and run your filters in Ruby using the `Log2Json` module and its contained helper classes. `Log2Json` reads from stdin the logs(one log record per line), parses the log lines into JSON records, and then serializes and writes the records to stdout, which then can be piped to another process for processing or sending it to somewhere else. Currently, Log2json ships with a `tail-log` script that can be run as the input process. It's the same as using the Linux `tail` utility with the `-v -F` options except that it also tracks the positions(as the numbers of lines read from the beginning of the files) in a few files in the file system so that if the input process is interrupted, it can continue reading from where it left off next time if the files had been followed. This feature is similar to the sincedb feature in Logstash's file input. Note: If you don't need the tracking feature(ie, you are fine with always tailling from the end of file with `-v -F -n0`), then you can just use the `tail` utility that comes with your Linux distribution.(Or more specifically, the `tail` from the GNU coreutils). Other versions of the `tail` utility may also work, but are not tested. The input protocol expected by Log2json is very simple and documented in the source code. ** The `tail-log` script uses a patched version of `tail` from the GNU coreutils package. A binary of the `tail` utility compiled for Ubuntu 12.04 LTS is included with the Log2json gem. If the binary doesn't work for your distribution, then you'll need to get GNU coreutils-8.13, apply the patch(it can be found in the src/ directory of the installed gem), and then replace the bin/tail binary in the directory of the installed gem with your version of the binary. ** P.S. If you know of a way to configure and compile ONLY the tail program in coreutils, please let me know! The reason I'm not building tail post gem installation is that it takes too long to configure && make because that actually builds every utilties in coreutils. For shipping logs to Redis, there's the `lines2redis` script that can be used as the output process in the pipe. For shipping logs from Redis to ElasticSearch, Log2json provides a `redis2es` script. Finally here's an example of Log2json in action: From a client machine: tail-log /var/log/{sys,mail}log /var/log/{kern,auth}.log | syslog2json | queue=jsonlogs \ flush_size=20 \ flush_interval=30 \ lines2redis host.to.redis.server 6379 0 # use redis DB 0 On the Redis server: redis_queue=jsonlogs redis2es host.to.es.server Resources that help writing log2json filters: - look at log2json.rb source and example filters - http://grokdebug.herokuapp.com/ - http://www.ruby-doc.org/stdlib-1.9.3/libdoc/date/rdoc/DateTime.html#method-i-strftime

== Easily add colors, boxes, repetitions and emojis to your terminal output using pipes (|). Install using the Ruby Gem: > gem install pipetext Includes a library module which can be included in your code: require 'pipetext' class YellowPrinter include PipeText def print(string) write('|Y' + string + '|n') end end printer = YellowPrinter.new printer.print('This is yellow') The gem includes a command line interface too: > pipetext > pipetext '|Ccyan|n' Easily set your bash prompt colors using pipetext: > PS1=$(pipetext '|$|g\u|n@|g\h|n:|g\w|n$ ') Works with files: > pipetext <filename> Works with pipes too: > echo '|RRed test |u1f49c|n' | pipetext --- | pipe || & ampersand && Toggle (&) background color mode |& smoke |s white |W black text on white background |k&w red |r bright red |R red background &r green |g bright green |G green background &g blue |b bright blue |B blue background &b cyan |c bright cyan |C cyan background &c yellow |y bright yellow |Y yellow background &y magenta |m bright magenta |M magenta background &m --- Hex RGB color codes: Foreground |#RRGGBB Background &#RRGGBB Palette colors (256) using Hex: |p33&pF8 Clear Screen |! black with white background |K&w Blinking |@ white with magenta background |w&m invert |i smoke with green background |s&g Underlined |_ red with cyan background |r&c Italics |~ bright red with blue background |R&b Bold |+ green with yellow background |g&y Faint |. bright green with red background |G&r Crossed out |x normal color and background |n&n Escape Sequence |\ Center text using current position and line end number |{text to center} Add spaces to line end |; Set line end |]# Set current x,y cursor position |[x,y] Terminal bell |[bell] Move cursor up 1 line |^ Hide cursor |h Move cursor down 1 line |v Unhide cursor |H Move cursor forward 1 character |> Sleep timer in seconds |[#s] Move cursor back 1 character |< Sleep timer in milliseconds |[#ms] Capture variable |(variable name=data) Display variable |(variable name) Add to variable |(variable name+=data) Subtract from variable |(variable name-=data) Multiple variable |(variable name*=data) Divide variable |(variable name/=data) Copy variable to current number |(#variable name) |$ toggles [ and ] around empty sequences automatically for bash command prompts --- Emojis: https://unicode.org/emoji/charts/full-emoji-list.html |[Abbreviated CLDR Short Name] 😍 |[smiling face with heart-eyes] or ⚙ |[gear] 💤 |[zzz] 👨 |[man] 😍 |[sm f w he e] ✔ |U2714 ❌ |U274c ☮ |u262E 💎 |u1f48e 💜 |u1f49c --- Single or double line box mode with |- or |= ┌──┬──┐ ╔══╦══╗ +--+--+ <-- Draw this with this: |15 |-[--v--] |=[--v--] |o[--v--] │ │ │ ║ ║ ║ | | | |15 |-! ! ! |=! ! ! |o! ! ! 123456789012345├──┴──┤ ╠══╩══╣ +--+--+ |y1234567890|g12345|n|->--^--< |=>--^--< |o>--^--< 15 Spaces │ │ ║ ║ | | |c15|n Spaces|6 |-! ! |=! ! |o! ! (|15 ) └─────┘ ╚═════╝ +-----+ (||15 )|9 |-{-----} |={-----} |o{-----} ┌──────────────────┐ ╔════════════════════╗ |-[|18-]|4 |g&m|=[|20-]|n&n|O │ │ ║ ║ |-!|18 !|4 |g&m|=!|20 !|n&n|O ├──────────────────┤ ╠════════════════════╣ |->|18-<|4 &m|g|=>|20-<|n&n|O │ │ ║ ║ |-!|18 !|4 |g&m|=!|20 !|n&n|O └──────────────────┘ ╚════════════════════╝ |-{|18-}|4 |g&m|={|20-}|n&n|O --- Repetition using | followed by the number of characters to repeat and then the character to repeat. |15* does the * character 15 times like this: *************** --- ==Use the ++pipetext++ command to see other options and examples.

Trim an audio or video file using ffmpeg - Works with all formats supported by ffmpeg, including mp3, mp4, mkv, and many more. - Seeks to the nearest frame positions by re-encoding the media. - Reduces file size procduced by OBS Studio by over 80 percent. - Can be used as a Ruby gem. - Installs the 'trim' command. When run as a command, output files are named by adding a 'trim.' prefix to the media file name, e.g. 'dir/trim.file.ext'. By default, the trim command does not overwrite pre-existing output files. When trimming is complete, the trim command displays the trimmed file, unless the -q option is specified Command-line Usage: trim [OPTIONS] dir/file.ext start [[to|for] end] - The start and end timecodes have the format [HH:[MM:]]SS[.XXX] Note that decimal seconds may be specified, bug frames may not; this is consistent with how ffmpeg parses timecodes. - end defaults to end of the audio/video file OPTIONS are: -d Enable debug output. -f Overwrite output file if present. -h Display help information. -v Verbose output. -V Do not @view the trimmed file when complete. Examples: # Crop dir/file.mp4 from 15.0 seconds to the end of the video, save to demo/trim.demo.mp4: trim demo/demo.mp4 15 # Crop dir/file.mkv from 3 minutes, 25 seconds to 9 minutes, 35 seconds, save to demo/trim.demo.mp4: trim demo/demo.mp4 3:25 9:35 # Same as the previous example, using optional 'to' syntax: trim demo/demo.mp4 3:25 to 9:35 # Save as the previous example, but specify the duration instead of the end time by using the for keyword: trim demo/demo.mp4 3:25 for 6:10

# Sangoro A Ruby program to change the exif creation time stamp of JPEGs or PNGs.<br> # Installation To use the Sangoro tool you require: <ul> <li> <a href="https://www.ruby-lang.org/en/downloads/"><code>ruby</code></a> (v>=2.3.3) </ul> as well as the following Ruby gems: <ul> <li><code>fastimage</code> <li><code>fileutils</code> <li><code>gtk3</code> <li><code>mini_exiftool</code></li> </ul> On Mac you might need the exiftool installed. I recommend installing it using the Brew package manager: ```brew install exiftool``` Get the Sangoro tool by typing ```gem install sangoro``` in your command line. This will install the sangoro gem as well as the gems mentined above. Now you can just run ```sangoro``` in your command line. # Usage 1. Select a JPEG/PNG file by clicking on "Select image" 2. You will see the file name and the creation date & time on the right side, if available. 3. Now you can specify by how many hours, minutes and/or seconds you want the time stamp to move. You also need to choose whether to move the timestamp forward or back. 4. If you want to apply this change to all images in the folder, check the box below. 5. Click "Apply". 6. You are done. The exif creation timestamp of the selected image(s) was adjusted as specified. # Remarks If you have any remarks, bugs, questions etc. please tell me, I'd be happy to help.

README ====== This is a simple API to evaluate information retrieval results. It allows you to load ranked and unranked query results and calculate various evaluation metrics (precision, recall, MAP, kappa) against a previously loaded gold standard. Start this program from the command line with: retreval -l <gold-standard-file> -q <query-results> -f <format> -o <output-prefix> The options are outlined when you pass no arguments and just call retreval You will find further information in the RDOC documentation and the HOWTO section below. If you want to see an example, use this command: retreval -l example/gold_standard.yml -q example/query_results.yml -f yaml -v INSTALLATION ============ If you have RubyGems, just run gem install retreval You can manually download the sources and build the Gem from there by `cd`ing to the folder where this README is saved and calling gem build retreval.gemspec This will create a gem file called which you just have to install with `gem install <file>` and you're done. HOWTO ===== This API supports the following evaluation tasks: - Loading a Gold Standard that takes a set of documents, queries and corresponding judgements of relevancy (i.e. "Is this document relevant for this query?") - Calculation of the _kappa measure_ for the given gold standard - Loading ranked or unranked query results for a certain query - Calculation of _precision_ and _recall_ for each result - Calculation of the _F-measure_ for weighing precision and recall - Calculation of _mean average precision_ for multiple query results - Calculation of the _11-point precision_ and _average precision_ for ranked query results - Printing of summary tables and results Typically, you will want to use this Gem either standalone or within another application's context. Standalone Usage ================ Call parameters --------------- After installing the Gem (see INSTALLATION), you can always call `retreval` from the commandline. The typical call is: retreval -l <gold-standard-file> -q <query-results> -f <format> -o <output-prefix> Where you have to define the following options: - `gold-standard-file` is a file in a specified format that includes all the judgements - `query-results` is a file in a specified format that includes all the query results in a single file - `format` is the format that the files will use (either "yaml" or "plain") - `output-prefix` is the prefix of output files that will be created Formats ------- Right now, we focus on the formats you can use to load data into the API. Currently, we support YAML files that must adhere to a special syntax. So, in order to load a gold standard, we need a file in the following format: * "query" denotes the query * "documents" these are the documents judged for this query * "id" the ID of the document (e.g. its filename, etc.) * "judgements" an array of judgements, each one with: * "relevant" a boolean value of the judgment (relevant or not) * "user" an optional identifier of the user Example file, with one query, two documents, and one judgement: - query: 12th air force germany 1957 documents: - id: g5701s.ict21311 judgements: [] - id: g5701s.ict21313 judgements: - relevant: false user: 2 So, when calling the program, specify the format as `yaml`. For the query results, a similar format is used. Note that it is necessary to specify whether the result sets are ranked or not, as this will heavily influence the calculations. You can specify the score for a document. By "score" we mean the score that your retrieval algorithm has given the document. But this is not necessary. The documents will always be ranked in the order of their appearance, regardless of their score. Thus in the following example, the document with "07" at the end is the first and "25" is the last, regardless of the score. --- query: 12th air force germany 1957 ranked: true documents: - score: 0.44034874 document: g5701s.ict21307 - score: 0.44034874 document: g5701s.ict21309 - score: 0.44034874 document: g5701s.ict21311 - score: 0.44034874 document: g5701s.ict21313 - score: 0.44034874 document: g5701s.ict21315 - score: 0.44034874 document: g5701s.ict21317 - score: 0.44034874 document: g5701s.ict21319 - score: 0.44034874 document: g5701s.ict21321 - score: 0.44034874 document: g5701s.ict21323 - score: 0.44034874 document: g5701s.ict21325 --- query: 1612 ranked: true documents: - score: 1.0174774 document: g3290.np000144 - score: 0.763108 document: g3201b.ct000726 - score: 0.763108 document: g3400.ct000886 - score: 0.6359234 document: g3201s.ct000130 --- **Note**: You can also use the `plain` format, which will load the gold standard in a different way (but not the results): my_query my_document_1 false my_query my_document_2 true See that every query/document/relevancy pair is separated by a tabulator? You can also add the user's ID in the fourth column if necessary. Running the evaluation ----------------------- After you have specified the input files and the format, you can run the program. If needed, the `-v` switch will turn on verbose messages, such as information on how many judgements, documents and users there are, but this shouldn't be necessary. The program will first load the gold standard and then calculate the statistics for each result set. The output files are automatically created and contain a YAML representation of the results. Calculations may take a while depending on the amount of judgements and documents. If there are a thousand judgements, always consider a few seconds for each result set. Interpreting the output files ------------------------------ Two output files will be created: - `output_avg_precision.yml` - `output_statistics.yml` The first lists the average precision for each query in the query result file. The second file lists all supported statistics for each query in the query results file. For example, for a ranked evaluation, the first two entries of such a query result statistic look like this: --- 12th air force germany 1957: - :precision: 0.0 :recall: 0.0 :false_negatives: 1 :false_positives: 1 :true_negatives: 2516 :true_positives: 0 :document: g5701s.ict21313 :relevant: false - :precision: 0.0 :recall: 0.0 :false_negatives: 1 :false_positives: 2 :true_negatives: 2515 :true_positives: 0 :document: g5701s.ict21317 :relevant: false You can see the precision and recall for that specific point and also the number of documents for the contingency table (true/false positives/negatives). Also, the document identifier is given. API Usage ========= Using this API in another ruby application is probably the more common use case. All you have to do is include the Gem in your Ruby or Ruby on Rails application. For details about available methods, please refer to the API documentation generated by RDoc. **Important**: For this implementation, we use the document ID, the query and the user ID as the primary keys for matching objects. This means that your documents and queries are identified by a string and thus the strings should be sanitized first. Loading the Gold Standard ------------------------- Once you have loaded the Gem, you will probably start by creating a new gold standard. gold_standard = GoldStandard.new Then, you can load judgements into this standard, either from a file, or manually: gold_standard.load_from_yaml_file "my-file.yml" gold_standard.add_judgement :document => doc_id, :query => query_string, :relevant => boolean, :user => John There is a nice shortcut for the `add_judgement` method. Both lines are essentially the same: gold_standard.add_judgement :document => doc_id, :query => query_string, :relevant => boolean, :user => John gold_standard << :document => doc_id, :query => query_string, :relevant => boolean, :user => John Note the usage of typical Rails hashes for better readability (also, this Gem was developed to be used in a Rails webapp). Now that you have loaded the gold standard, you can do things like: gold_standard.contains_judgement? :document => "a document", :query => "the query" gold_standard.relevant? :document => "a document", :query => "the query" Loading the Query Results ------------------------- Now we want to create a new `QueryResultSet`. A query result set can contain more than one result, which is what we normally want. It is important that you specify the gold standard it belongs to. query_result_set = QueryResultSet.new :gold_standard => gold_standard Just like the Gold Standard, you can read a query result set from a file: query_result_set.load_from_yaml_file "my-results-file.yml" Alternatively, you can load the query results one by one. To do this, you have to create the results (either ranked or unranked) and then add documents: my_result = RankedQueryResult.new :query => "the query" my_result.add_document :document => "test_document 1", :score => 13 my_result.add_document :document => "test_document 2", :score => 11 my_result.add_document :document => "test_document 3", :score => 3 This result would be ranked, obviously, and contain three documents. Documents can have a score, but this is optional. You can also create an Array of documents first and add them altogether: documents = Array.new documents << ResultDocument.new :id => "test_document 1", :score => 20 documents << ResultDocument.new :id => "test_document 2", :score => 21 my_result = RankedQueryResult.new :query => "the query", :documents => documents The same applies to `UnrankedQueryResult`s, obviously. The order of ranked documents is the same as the order in which they were added to the result. The `QueryResultSet` will now contain all the results. They are stored in an array called `query_results`, which you can access. So, to iterate over each result, you might want to use the following code: query_result_set.query_results.each_with_index do |result, index| # ... end Or, more simply: for result in query_result_set.query_results # ... end Calculating statistics ---------------------- Now to the interesting part: Calculating statistics. As mentioned before, there is a conceptual difference between ranked and unranked results. Unranked results are much easier to calculate and thus take less CPU time. No matter if unranked or ranked, you can get the most important statistics by just calling the `statistics` method. statistics = my_result.statistics In the simple case of an unranked result, you will receive a hash with the following information: * `precision` - the precision of the results * `recall` - the recall of the results * `false_negatives` - number of not retrieved but relevant items * `false_positives` - number of retrieved but nonrelevant * `true_negatives` - number of not retrieved and nonrelevantv items * `true_positives` - number of retrieved and relevant items In case of a ranked result, you will receive an Array that consists of _n_ such Hashes, depending on the number of documents. Each Hash will give you the information at a certain rank, e.g. the following to lines return the recall at the fourth rank. statistics = my_ranked_result.statistics statistics[3][:recall] In addition to the information mentioned above, you can also get for each rank: * `document` - the ID of the document that was returned at this rank * `relevant` - whether the document was relevant or not Calculating statistics with missing judgements ---------------------------------------------- Sometimes, you don't have judgements for all document/query pairs in the gold standard. If this happens, the results will be cleaned up first. This means that every document in the results that doesn't appear to have a judgement will be removed temporarily. As an example, take the following results: * A * B * C * D Our gold standard only contains judgements for A and C. The results will be cleaned up first, thus leading to: * A * C With this approach, we can still provide meaningful results (for precision and recall). Other statistics ---------------- There are several other statistics that can be calculated, for example the **F measure**. The F measure weighs precision and recall and has one parameter, either "alpha" or "beta". Get the F measure like so: my_result.f_measure :beta => 1 If you don't specify either alpha or beta, we will assume that beta = 1. Another interesting measure is **Cohen's Kappa**, which tells us about the inter-agreement of assessors. Get the kappa statistic like this: gold_standard.kappa This will calculate the average kappa for each pairwise combination of users in the gold standard. For ranked results one might also want to calculate an **11-point precision**. Just call the following: my_ranked_result.eleven_point_precision This will return a Hash that has indices at the 11 recall levels from 0 to 1 (with steps of 0.1) and the corresponding precision at that recall level.

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