Results for d-installer

Generate systemd service files for your apps

The Infinity Engine Mod Installer is a tool designed to automate the installation of mods for Infinity Engine games such as Baldur's Gate, Icewind Dale, and Planescape: Torment. It uses a file called 'weidu.log' to determine which mods to install and how to install them.

System service for D-Installer, an experimental YaST-based installer.

Command line interface for D-Installer service

Ruby bindings for D-BUS. This module allows Ruby programs to interface with the D-BUS message bus installed on newer Unix operating systems.

== DESCRIPTION: Charlie is a library for genetic algorithms (GA) and genetic programming (GP). == FEATURES: - Quickly develop GAs by combining several parts (genotype, selection, crossover, mutation) provided by the library. - Sensible defaults are provided with any genotype, so often you only need to define a fitness function. - Easily replace any of the parts by your own code. - Test different strategies in GA, and generate reports comparing them. Example report: http://charlie.rubyforge.org/example_report.html == INSTALL: * sudo gem install charlie == EXAMPLES: This example solves a TSP problem (also quiz #142): N=5 CITIES = (0...N).map{|i| (0...N).map{|j| [i,j] } }.inject{|a,b|a+b} class TSP < PermutationGenotype(CITIES.size) def fitness d=0 (genes + [genes[0]]).each_cons(2){|a,b| a,b=CITIES[a],CITIES[b] d += Math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 ) } -d # lower distance -> higher fitness. end use EdgeRecombinationCrossover, InversionMutator end Population.new(TSP,20).evolve_on_console(50) This example finds a polynomial which approximates cos(x) class Cos < TreeGenotype([proc{3*rand-1.5},:x], [:-@], [:+,:*,:-]) def fitness -[0,0.33,0.66,1].map{|x| (eval_genes(:x=>x) - Math.cos(x)).abs }.max end use TournamentSelection(4) end Population.new(Cos).evolve_on_console(500)

# EventReporter EventReporter is a CSV parser and sorter. you can load a CSV and then search it. ## Installation $ gem install the_only_event_reporter_ever $ gem list event_reporter -d ## Usage After installation run: $ event_reporter Then Type 'load <filename>' to load records from a CSV $ Load event_attendees.csv Try these commands $ Find first_name sarah $Queue Print $Queue Save to <filename> ### Saving the queue accepts extensions JSON, XML, TXT, CSV. ## Contributing 1. Fork it 2. Create your feature branch (`git checkout -b my-new-feature`) 3. Commit your changes (`git commit -am 'Add some feature'`) 4. Push to the branch (`git push origin my-new-feature`) 5. Create new Pull Request

= Backup utility for database, folders and files Backs up a MySQL database, folders and files to a default folder (~/backup) or to a specified folder. If the --cron switch is provided the specified database and files are not backed up rather a cron job of the provided command is added to crontab. == Install The application can be installed with $ gem install syc-backup == Usage Backup a database to the default folder _~/backup_ $ sycbackup -d database -uuser -ppass Backup a MySQL database, a directory and files to the default folder $ sycbackup -d database -uuser -ppass -f directory,file1,file2 Specify a backup folder $ sycbackup backup/folder -d database -uuser -ppass -f directory,file1,file2 Override files in the backup folder if they exist $ sycbackup backup/folder --override -f directory,file1,file2 Don't compress the backup $ sycbackup --no-compress -f directory,file1,file2 Create a cron job that is scheduled every day at 2:30 $ sycbackup -d database -uuser -ppass -f directory,file1 --cron 30,2,*,*,* If the user or password contains characters as '(' you have to escape them. A password like 123(56 has to be provided with pass\"123\(56\". == Usage of --override and --no-compress Whether the backup directory and the backup files are time stamped depends how --override and --no-compress is set. The results are shown in the table below. --override --no-compress backup directory backup file(s) 0 0 w/o timestamp w/ timestamp 1 0 w/o timestamp w/ timestamp 0 1 w/ timestamp uncompressed 1 1 w/o timestamp uncompressed == Supported Platform syc-backup has been tested with 1.9.3 == Notes The application backs up the MySQL database with _mysqldump_. The dumpfile has the form yyyymmdd-HHMMSS_databasename.sql. After the files are backed up the dumpfile will be deleted. If the --no-compress is provided the files are copied to the backup folder. Otherwise they are compressed with _tar cfz YYYYmmdd-HHMMSS_syc-backup.tar.gz_. If the --override switch is not provided the backup directory will be added a timestamp. So if you create a cron job you should every now and then delete obsolete backup folders. The source contains lib/backup/file_backup.rb which is not used in the application. == Tests The tests create folders and files and will be deleted after the tests finish. _MySQLBackup_ needs to run a MySQL database with a database _test_ and a user _user_ with the password _pass_. The test files live in the test folder and begin with test_. There is a rake file available which can be used to run all tests with $ rake test == Links * [http://sugaryourcoffee.github.com/syc-backup] - RubyDoc * [http://www.github.com/sugaryourcoffee/syc-backup] - Source code on GitHub * [http://syc.dyndns.org/drupal/content/backup-drupal-database] - Development notebook * [https://rubygems.org/gems/syc-backup] - RubyGems

zu == Unzipper (in the tradition of `uz`, but better). Works for .tgz, .xz, .zip, .deb, .rpm — you name it. (Literally. If you find an archive that it doesn't open, let me know about it and I'll add that.) If you have an archive sitting there of format `xyz`, then `zu foo.xyz` should take care of it. It will: - Know how to extract the archive (based on extension ┈ though a version that detects based on `file` is something we're considering) - Guard against impoliteness. That is, if the archive only has one file, it will be permitted to extract into the current directory, otherwise it will first `mkdir foo; cd foo` then extract there. (The directory name will be the archive file minus the extension.) - Download the file first, using `wget`, if the arg starts with `http:`, `https:`, or `ftp:` - Remove the archive file if you pass `-d` Dependencies ------------ `zu` doesn't strive to be dependency-free by any means. For starters, it expects Ruby. Then it simply delegates to `unzip`, `gunzip`, `tar`, etc. Not sure if I ever plan on changing this. The main purpose is to optimize the command-line extraction of archives on a configured box. Installation ------------ 1. Have Ruby 1.8 (with gems) or 1.9 2. `gem install zu` Feedback -------- Tell us. (exad-zu@sharpsaw.worg)[mailto:exad-zu@sharpsaw.org]

# ruby unshare (runshare) This tool allows to unshare Linux namespaces. The implementation is similar to the unshare(1) tool. ## Installation Add this line to your application's Gemfile: ```ruby gem 'runshare' ``` And then execute: $ bundle Or install it yourself as: $ gem install runshare ## Usage > require "runshare" > RUnshare::unshare For example: cat > test.rb require "runshare" pid = RUnshare::unshare( :clone_newpid => true, :clone_newns => true, :clone_newcgroup => true, :clone_newipc => true, :clone_newuts => true, :clone_newnet => true, :clone_newtime => true, :fork => true, :mount_proc => "/proc", # docker export $(docker create hello-world) | tar -xf - -C rootfs :root => "/tmp/rootfs" ) if pid == 0 # child puts "--- #{Process.pid}" if system("/hello") != true raise "bad" end puts "--- done" else # parent puts "-- unshare=#{pid}, pid=#{Process.pid}" puts "-- exit=#{Process.waitpid(pid)}" end ^D sudo ruby -I ./lib ./test.rb ## Quick start $ rake compile && echo 'require "runshare"; RUnshare::unshare(:clone_newuts => true)' | irb install -c tmp/x86_64-linux/runshare/2.4.10/runshare.so lib/runshare/runshare.so cp tmp/x86_64-linux/runshare/2.4.10/runshare.so tmp/x86_64-linux/stage/lib/runshare/runshare.so Switch to inspect mode. require "runshare"; RUnshare::unshare ## Ruby <2.5 If your app is single threaded and you are observing: eval:1: warning: pthread_create failed for timer: Invalid argument, scheduling broken Just ignore it with some degree of bravity. You also can silence it by setting: $VERBOSE = nil ## Development After checking out the repo, run `bin/setup` to install dependencies. Then, run `rake spec` to run the tests. You can also run `bin/console` for an interactive prompt that will allow you to experiment. To install this gem onto your local machine, run `bundle exec rake install`. To release a new version, update the version number in `version.rb`, and then run `bundle exec rake release`, which will create a git tag for the version, push git commits and tags, and push the `.gem` file to [rubygems.org](https://rubygems.org). ## Contributing Bug reports and pull requests are welcome on GitHub at https://github.com/sitano/runshare. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant](http://contributor-covenant.org) code of conduct. ## License The gem is available as open source under the terms of the [MIT License](https://opensource.org/licenses/MIT).

# HebCal Determines the date of Passover for a Gregorian year. Also includes boolean functions to check whether a date is a Jewish holiday, Fast Day or Rosh Chodesh. Supported languages: Javascript Ruby ## Installation as a Ruby gem Add this line to your application's Gemfile: gem 'hebcal' And then execute: $ bundle Or install it yourself as: $ gem install hebcal ## General notes 1. 3- and 4-digit years are supported, so the domain of valid years is 100-9999. For years before the Gregorian transition (1582), the transition is ignored and the proleptic Gregorian calendar is used. ## Ruby Version ### To Run Unit Tests $ rake test ### To Use #### Calculating the date of Passover 1. At the top of the file where the class is defined, declare `require `hebcal`` 1. In the class, declare `include HebCal::Passover` 1. `WhenIsPesach(yyyy)` returns a Ruby Time object representing midnight on the first day of passover, where `yyyy` is the Gregorian year Note that the date returned is the first day of Pesach, not the day on which Pesach begins at sunset. #### Finding out if a date is a holiday 1. At the top of the file where the class is defined, declare `require `hebcal`` 1. In the desired class, declare `include HebCal::Holidays` 1. `IsPesach(d)` returns true iff d is a Ruby Time object representing a date during Pesach. Note that the day on which Pesach begins at sunset returns false. 1. The following functions work in a similar way to `IsPesach()`: 1. `IsShavuot()`, `IsRoshHashanah()`, `IsYomKippur()`, `IsSukkot()` 1. `IsRegel()`: `IsPesach() || IsShavuot || IsSukkot()` 1. `IsMoed()`: Hol HaMoed Pesach or Hol HaMoed Sukkot 1. `IsYomTov()`: `IsPesach() || IsShavuot() || IsRoshHashanah() || IsSukkot()) && !IsMoed()` Note that IsYomTov(yk) == false, where yk is the date of Yom Kippur. 1. `IsPurim()`, `IsHanuka()` 1. `Is10Tevet()`, `IsTaanitEster()`, `Is17Tamuz()`, `Is9Av()`, `IsFastOfGedalia()` 1. `IsTaanit()`: `Is10Tevet() || IsTaanitEster() || Is17Tamuz() || Is9Av() || IsFastOfGedalia()` 1. `IsRoshChodesh()` ## Javascript Version ### To Run Unit Tests 1. Open index.html in a browser. You should see a lot of green text saying that tests passed. If not, javascript may not be enabled in your browser. Scroll down to the bottom and verify that the summary says all tests passed. ### To Use 1. Include the javascript source file in your HTML page 1. If using Ruby On Rails, you can declare `//= require hebcal` at the top of a javascript or coffeescript file 1. To include the script explicitly in an html file, `<script src="app/assets/javascripts/hebcal/passover.js" type="text/javascript"></script>` 1. All date formats are YYYY-mm-dd, where month is index from 1 (i.e. 1 == January, not the usual javascript index of 0 == January!) and YYYY is the Gregorian year. 1. $.whenIsPesach(yyyy) returns a date in the above format, where yyyy is the Gregorian year. Note that the date returned is the first day of Pesach, not the day on which Pesach begins at sunset. 1. $.isPesach(d) returns true iff d is a date during Pesach, in the above format. Note that the day on which Pesach begins at sunset returns false. 1. The following functions work in a similar way to $.isPesach(): 1. $.isShavuot(), $.isRoshHashanah(), $.isYomKippur(), $.isSukkot(); 1. $.isRegel(): $.isPesach() || $.isShavuot() || $.isSukkot(); 1. $.isMoed(): Hol HaMoed Pesach or Hol HaMoed Sukkot; 1. $.isYomTov(): ($.isPesach() || $.isSukkot() || $.isShavuot() || $.isRoshHashanah()) && !$.isMoed(); Note that isYomTov(yk) == false, where yk is the date of Yom Kippur. 1. `$.isPurim()`, `$.isHanuka()` 1. `$.isRoshChodesh()` 1. `$.is10Tevet()`, `$.isTaanitEster()`, `$.is17Tamuz()`, `$.is9Av()`, `$.isFastOfGedalia()` 1. `$.isTaanit()`: `$.is10Tevet() || $.isTaanitEster() || $.is17Tamuz() || $.is9Av() || $.isFastOfGedalia()`

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

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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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