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telescope | An elegant debug assistant for the Laravel framework

 by   laravel PHP Version: v4.8.2 License: MIT

 by   laravel PHP Version: v4.8.2 License: MIT

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kandi X-RAY | telescope Summary

telescope is a PHP library typically used in Institutions, Learning, Administration, Public Services, Logging, Laravel applications. telescope has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.
Laravel Telescope is an elegant debug assistant for the Laravel framework. Telescope provides insight into the requests coming into your application, exceptions, log entries, database queries, queued jobs, mail, notifications, cache operations, scheduled tasks, variable dumps and more. Telescope makes a wonderful companion to your local Laravel development environment.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • telescope has a medium active ecosystem.
  • It has 4215 star(s) with 458 fork(s). There are 75 watchers for this library.
  • There were 9 major release(s) in the last 12 months.
  • There are 0 open issues and 618 have been closed. On average issues are closed in 20 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of telescope is v4.8.2
telescope Support
Best in #PHP
Average in #PHP
telescope Support
Best in #PHP
Average in #PHP

quality kandi Quality

  • telescope has 0 bugs and 0 code smells.
telescope Quality
Best in #PHP
Average in #PHP
telescope Quality
Best in #PHP
Average in #PHP

securitySecurity

  • telescope has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • telescope code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
telescope Security
Best in #PHP
Average in #PHP
telescope Security
Best in #PHP
Average in #PHP

license License

  • telescope is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
telescope License
Best in #PHP
Average in #PHP
telescope License
Best in #PHP
Average in #PHP

buildReuse

  • telescope releases are available to install and integrate.
  • It has 3898 lines of code, 433 functions and 133 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
telescope Reuse
Best in #PHP
Average in #PHP
telescope Reuse
Best in #PHP
Average in #PHP
Top functions reviewed by kandi - BETA

kandi has reviewed telescope and discovered the below as its top functions. This is intended to give you an instant insight into telescope implemented functionality, and help decide if they suit your requirements.

  • Create the table .
    • Get request data .
      • Store the entries in the queue .
        • Register the Transescope service provider .
          • Format the response .
            • Get the composer for the event .
              • Format the event listeners .
                • Record an incoming exception .
                  • Record a failed job .
                    • Get properties from target class .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      telescope Key Features

                      An elegant debug assistant for the Laravel framework.

                      telescope Examples and Code Snippets

                      See all related Code Snippets

                      How to create a SPARQL query to search Wikidata item descriptions

                      copy iconCopydownload iconDownload
                      WHERE {   hint:Query hint:optimizer "None".   
                             SERVICE wikibase:mwapi {     
                               bd:serviceParam wikibase:api "Search";                     
                               wikibase:endpoint "www.wikidata.org";                     
                               mwapi:srsearch "space telescope".     
                               ?item wikibase:apiOutputItem mwapi:title .   }      
                               SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". } 
                               
                      } 
                      
                      limit 10
                      

                      Why link in inertiajs/vuejs is not reactive?

                      copy iconCopydownload iconDownload
                      import { createInertiaApp, Link } from '@inertiajs/inertia-vue'
                      Vue.component('inertia-link', Link)
                      
                        <inertia-link :href="route('profile.index') ">{{ $page.props.user.name}}</inertia-link>
                      
                      import { createInertiaApp, Link } from '@inertiajs/inertia-vue'
                      Vue.component('inertia-link', Link)
                      
                        <inertia-link :href="route('profile.index') ">{{ $page.props.user.name}}</inertia-link>
                      

                      Why I can not reference app layout file adding inertiajs to my project?

                      copy iconCopydownload iconDownload
                      mix.webpackConfig({
                          resolve: {
                              alias: {
                                  ziggy: path.resolve('vendor/tightenco/ziggy/dist'),
                                  '@Layouts': path.resolve(__dirname, 'path/to/layouts')
                              },
                          },
                      });
                      
                      import AppLayout from '@Layouts/AppLayout'
                      
                      mix.webpackConfig({
                          resolve: {
                              alias: {
                                  ziggy: path.resolve('vendor/tightenco/ziggy/dist'),
                                  '@Layouts': path.resolve(__dirname, 'path/to/layouts')
                              },
                          },
                      });
                      
                      import AppLayout from '@Layouts/AppLayout'
                      

                      Removing words from sentence when in lookup dataframe

                      copy iconCopydownload iconDownload
                      import re
                      stopwords = ["Ford", "Hyundai", "Toyota", "Volkswagen", "Volvo"]
                      tests = ["Something about a Ford doing some car stuff",
                               "Hyundai is another car manufacturer",
                               "Not everyone buys cars. Some people buy trucks from Toyota.",
                               "Volkswagen is a German company.",
                               "A lot of car brands like Toyota, Volkswagen, Volvo, do things"] 
                      stopwards_lower = [word.lower() for word in stopwords]
                      delimiters = " ", "...", ",", "."
                      for test in tests:
                          querywords = list(filter(None, re.split('|'.join(map(re.escape, delimiters)), test)))
                          resultwords  = [word for word in querywords if word.lower() not in stopwards_lower]
                          result = ' '.join(resultwords)
                          print(result)
                      
                      stopwords = ["ford", "hyundai", "toyota", "volkswagen", "volvo"]
                      tests = ["Something about a Ford doing some car stuff",
                               "Hyundai is another car manufacturer",
                               "Not everyone buys cars. Some people buy trucks from Toyota",
                               "Volkswagen is a German company.",
                               "A lot of car brands like Toyota Volkswagen Volvo do things"] 
                      querywords = pd.Series(tests)
                      
                      resultwords  = [word for sentence in querywords for word in sentence.split(' ') if word.lower() not in stopwords]
                      result = ' '.join(resultwords)
                      

                      Transforming sentences to Numbers using SciKit-Learn’s CountVectorizer()

                      copy iconCopydownload iconDownload
                      from sklearn.feature_extraction.text import CountVectorizer
                      #instantiate the class
                      cv = CountVectorizer()
                      #vector is a sparse matrix storing individual words as "bag of words" model
                      vector = cv.fit_transform(df["clean_Review"].copy())
                      
                      
                      from sklearn.feature_extraction.text import CountVectorizer
                      import pandas as pd
                      
                      #sample text corpus
                      corpus = pd.Series(["aa bb cc dd ee","bb cc dd ee","cc dd ee","dd ee","ee","ee ff"])
                      #instantiate the class
                      cv = CountVectorizer()
                      vector = cv.fit_transform(corpus)
                      
                      print(corpus)
                      
                      0    aa bb cc dd ee
                      1       bb cc dd ee
                      2          cc dd ee
                      3             dd ee
                      4                ee
                      5             ee ff
                      dtype: object
                      
                      print(vector.toarray())
                      
                      array([[1, 1, 1, 1, 1, 0],
                             [0, 1, 1, 1, 1, 0],
                             [0, 0, 1, 1, 1, 0],
                             [0, 0, 0, 1, 1, 0],
                             [0, 0, 0, 0, 1, 0],
                             [0, 0, 0, 0, 1, 1]])
                      
                      from sklearn.feature_extraction.text import CountVectorizer
                      #instantiate the class
                      cv = CountVectorizer()
                      #vector is a sparse matrix storing individual words as "bag of words" model
                      vector = cv.fit_transform(df["clean_Review"].copy())
                      
                      
                      from sklearn.feature_extraction.text import CountVectorizer
                      import pandas as pd
                      
                      #sample text corpus
                      corpus = pd.Series(["aa bb cc dd ee","bb cc dd ee","cc dd ee","dd ee","ee","ee ff"])
                      #instantiate the class
                      cv = CountVectorizer()
                      vector = cv.fit_transform(corpus)
                      
                      print(corpus)
                      
                      0    aa bb cc dd ee
                      1       bb cc dd ee
                      2          cc dd ee
                      3             dd ee
                      4                ee
                      5             ee ff
                      dtype: object
                      
                      print(vector.toarray())
                      
                      array([[1, 1, 1, 1, 1, 0],
                             [0, 1, 1, 1, 1, 0],
                             [0, 0, 1, 1, 1, 0],
                             [0, 0, 0, 1, 1, 0],
                             [0, 0, 0, 0, 1, 0],
                             [0, 0, 0, 0, 1, 1]])
                      
                      from sklearn.feature_extraction.text import CountVectorizer
                      #instantiate the class
                      cv = CountVectorizer()
                      #vector is a sparse matrix storing individual words as "bag of words" model
                      vector = cv.fit_transform(df["clean_Review"].copy())
                      
                      
                      from sklearn.feature_extraction.text import CountVectorizer
                      import pandas as pd
                      
                      #sample text corpus
                      corpus = pd.Series(["aa bb cc dd ee","bb cc dd ee","cc dd ee","dd ee","ee","ee ff"])
                      #instantiate the class
                      cv = CountVectorizer()
                      vector = cv.fit_transform(corpus)
                      
                      print(corpus)
                      
                      0    aa bb cc dd ee
                      1       bb cc dd ee
                      2          cc dd ee
                      3             dd ee
                      4                ee
                      5             ee ff
                      dtype: object
                      
                      print(vector.toarray())
                      
                      array([[1, 1, 1, 1, 1, 0],
                             [0, 1, 1, 1, 1, 0],
                             [0, 0, 1, 1, 1, 0],
                             [0, 0, 0, 1, 1, 0],
                             [0, 0, 0, 0, 1, 0],
                             [0, 0, 0, 0, 1, 1]])
                      
                      from sklearn.feature_extraction.text import CountVectorizer
                      #instantiate the class
                      cv = CountVectorizer()
                      #vector is a sparse matrix storing individual words as "bag of words" model
                      vector = cv.fit_transform(df["clean_Review"].copy())
                      
                      
                      from sklearn.feature_extraction.text import CountVectorizer
                      import pandas as pd
                      
                      #sample text corpus
                      corpus = pd.Series(["aa bb cc dd ee","bb cc dd ee","cc dd ee","dd ee","ee","ee ff"])
                      #instantiate the class
                      cv = CountVectorizer()
                      vector = cv.fit_transform(corpus)
                      
                      print(corpus)
                      
                      0    aa bb cc dd ee
                      1       bb cc dd ee
                      2          cc dd ee
                      3             dd ee
                      4                ee
                      5             ee ff
                      dtype: object
                      
                      print(vector.toarray())
                      
                      array([[1, 1, 1, 1, 1, 0],
                             [0, 1, 1, 1, 1, 0],
                             [0, 0, 1, 1, 1, 0],
                             [0, 0, 0, 1, 1, 0],
                             [0, 0, 0, 0, 1, 0],
                             [0, 0, 0, 0, 1, 1]])
                      

                      Porter Stemmer algorithm not working through the sentences row by row

                      copy iconCopydownload iconDownload
                      words = [ps.stem(x) for x in w]
                      
                      w = word_tokenize(line)
                      words = []
                      for x in w:
                        words.append(ps.stem(x))
                      
                      words = [ps.stem(x) for x in w]
                      
                      w = word_tokenize(line)
                      words = []
                      for x in w:
                        words.append(ps.stem(x))
                      

                      Why parameter set in put method is not passed?

                      copy iconCopydownload iconDownload
                      Route::put('ads/{ad}/activate', [AdController::class, 'activate']);
                      
                      Route::model('ad', Ad::class);
                      
                      Route::put('ads/{ad}/activate', [AdController::class, 'activate']);
                      
                      Route::model('ad', Ad::class);
                      
                      Route::put('ads/{ad}/activate', [AdController::class, 'activate']);
                      

                      how to stop letter repeating itself python

                      copy iconCopydownload iconDownload
                      sorted_jumbled_word = sorted(a)
                      for word in val1:
                          if len(sorted_jumbled_word) == len(word) and sorted(word) == sorted_jumbled_word:
                              print(word)
                      

                      Laravel Queued job execution with delay

                      copy iconCopydownload iconDownload
                      php artisan queue:work
                      
                      php artisan queue:listen
                      
                      php artisan queue:work
                      
                      php artisan queue:listen
                      

                      How to make Telescope ignore files inside node_modules?

                      copy iconCopydownload iconDownload
                      --ignore-file <PATH>...                  
                          Specifies a path to one or more .gitignore format rules files. These patterns
                          are applied after the patterns found in .gitignore and .ignore are applied
                          and are matched relative to the current working directory. Multiple additional
                          ignore files can be specified by using the --ignore-file flag several times.
                          When specifying multiple ignore files, earlier files have lower precedence
                          than later files.
                      
                      require('telescope').setup{
                        defaults = {
                          vimgrep_arguments = {
                            'rg',
                            '--color=never',
                            '--no-heading',
                            '--with-filename',
                            '--line-number',
                            '--column',
                            '--smart-case',
                            '--ignore-file',
                            '.gitignore'
                          },
                      
                      --ignore-file <PATH>...                  
                          Specifies a path to one or more .gitignore format rules files. These patterns
                          are applied after the patterns found in .gitignore and .ignore are applied
                          and are matched relative to the current working directory. Multiple additional
                          ignore files can be specified by using the --ignore-file flag several times.
                          When specifying multiple ignore files, earlier files have lower precedence
                          than later files.
                      
                      require('telescope').setup{
                        defaults = {
                          vimgrep_arguments = {
                            'rg',
                            '--color=never',
                            '--no-heading',
                            '--with-filename',
                            '--line-number',
                            '--column',
                            '--smart-case',
                            '--ignore-file',
                            '.gitignore'
                          },
                      

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on telescope
                      • How to create a SPARQL query to search Wikidata item descriptions
                      • Laravel notification toArray() not saving data to notifications table
                      • error with a deleted mysql table belong to laravel telescope
                      • Upgrading laravel into 8 I got illuminate/support support?
                      • Aligning text in a flexbox
                      • Why link in inertiajs/vuejs is not reactive?
                      • Why I can not reference app layout file adding inertiajs to my project?
                      • Removing words from sentence when in lookup dataframe
                      • Transforming sentences to Numbers using SciKit-Learn’s CountVectorizer()
                      • Porter Stemmer algorithm not working through the sentences row by row
                      Trending Discussions on telescope

                      QUESTION

                      How to create a SPARQL query to search Wikidata item descriptions

                      Asked 2022-Mar-23 at 06:06

                      I'd like to be able to find an item based on words in its descriptions.

                      This is what I'm trying to do, but clearly I don't know what I'm doing. Any help is appreciated.

                      SELECT ?item ?itemLabel WHERE {
                       
                        ?item schema:description ?desc.
                        FILTER(CONTAINS(LCASE(?desc), "space telescope"))
                        SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
                      }
                      
                      LIMIT 10
                      

                      ANSWER

                      Answered 2022-Mar-23 at 06:06

                      Solved by UninformedUser in the comments!

                      WHERE {   hint:Query hint:optimizer "None".   
                             SERVICE wikibase:mwapi {     
                               bd:serviceParam wikibase:api "Search";                     
                               wikibase:endpoint "www.wikidata.org";                     
                               mwapi:srsearch "space telescope".     
                               ?item wikibase:apiOutputItem mwapi:title .   }      
                               SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". } 
                               
                      } 
                      
                      limit 10
                      

                      Source https://stackoverflow.com/questions/71578210

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install telescope

                      You can download it from GitHub.
                      PHP requires the Visual C runtime (CRT). The Microsoft Visual C++ Redistributable for Visual Studio 2019 is suitable for all these PHP versions, see visualstudio.microsoft.com. You MUST download the x86 CRT for PHP x86 builds and the x64 CRT for PHP x64 builds. The CRT installer supports the /quiet and /norestart command-line switches, so you can also script it.

                      Support

                      Documentation for Telescope can be found on the Laravel website.

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