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vget | VIdeo download java library

 by   laserjim Java Version: Current License: No License

 by   laserjim Java Version: Current License: No License

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kandi X-RAY | vget Summary

vget is a Java library. vget has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Code taken from ytd2 and completely rewritten to support more web sites. Good examples here: https://github.com/pculture/miro/blob/master/tv/lib/flashscraper.py.
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kandi-support Support

  • vget has a low active ecosystem.
  • It has 16 star(s) with 169 fork(s). There are 2 watchers for this library.
  • It had no major release in the last 12 months.
  • vget has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of vget is current.
vget Support
Best in #Java
Average in #Java
vget Support
Best in #Java
Average in #Java

quality kandi Quality

  • vget has 0 bugs and 0 code smells.
vget Quality
Best in #Java
Average in #Java
vget Quality
Best in #Java
Average in #Java

securitySecurity

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

license License

  • vget does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
vget License
Best in #Java
Average in #Java
vget License
Best in #Java
Average in #Java

buildReuse

  • vget releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
vget Reuse
Best in #Java
Average in #Java
vget Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Downloads a video .
    • Extracts the videos encoded .
      • Extracts video info from the HTML .
        • Extracts a video .
          • Retry the proxy server .
            • Set the target file to resume .
              • Checks for FileNotFoundException .
                • Extracts the ID from a URL .
                  • Sets the state .
                    • Set the delay .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      vget Key Features

                      VIdeo download java library

                      Example Direct Download

                      copy iconCopydownload iconDownload
                      package com.github.axet.vget;
                      
                      import java.io.File;
                      import java.net.URL;
                      
                      public class Example {
                      
                          public static void main(String[] args) {
                              try {
                                  VGet v = new VGet(new URL("http://vimeo.com/52716355"), new File("/Users/axet/Downloads"));
                                  v.download();
                              } catch (Exception e) {
                                  throw new RuntimeException(e);
                              }
                          }
                      
                      }
                      

                      Example Application Managed Download

                      copy iconCopydownload iconDownload
                      package com.github.axet.vget;
                      
                      import java.io.File;
                      import java.net.URL;
                      import java.util.concurrent.atomic.AtomicBoolean;
                      
                      import com.github.axet.vget.info.VideoInfo;
                      import com.github.axet.wget.info.DownloadInfo;
                      import com.github.axet.wget.info.DownloadInfo.Part;
                      import com.github.axet.wget.info.DownloadInfo.Part.States;
                      
                      public class Example {
                      
                          VideoInfo info;
                          long last;
                      
                          public void run() {
                              try {
                                  AtomicBoolean stop = new AtomicBoolean(false);
                                  Runnable notify = new Runnable() {
                                      @Override
                                      public void run() {
                                          VideoInfo i1 = info;
                                          DownloadInfo i2 = i1.getInfo();
                      
                                          // notify app or save download state
                                          // you can extract information from DownloadInfo info;
                                          switch (i1.getState()) {
                                          case EXTRACTING:
                                          case EXTRACTING_DONE:
                                          case DONE:
                                              System.out.println(i1.getState());
                                              break;
                                          case RETRYING:
                                              System.out.println(i1.getState() + " " + i1.getDelay());
                                              break;
                                          case DOWNLOADING:
                                              long now = System.currentTimeMillis();
                                              if (now - 1000 > last) {
                                                  last = now;
                      
                                                  String parts = "";
                      
                                                  for (Part p : i2.getParts()) {
                                                      if (p.getState().equals(States.DOWNLOADING)) {
                                                          parts += String.format("Part#%d(%.2f) ", p.getNumber(),
                                                                  p.getCount() / (float) p.getLength());
                                                      }
                                                  }
                      
                                                  System.out.println(String.format("%s %.2f %s", i1.getState(),
                                                          i2.getCount() / (float) i2.getLength(), parts));
                                              }
                                              break;
                                          default:
                                              break;
                                          }
                                      }
                                  };
                      
                                  info = new VideoInfo(new URL("http://vimeo.com/52716355"));
                      
                                  VGet v = new VGet(info, new File("/Users/axet/Downloads"));
                      
                                  // optional. only if you dlike to get video title before start
                                  // download
                                  v.extract(stop, notify);
                                  System.out.println(info.getTitle());
                      
                                  v.download(stop, notify);
                              } catch (RuntimeException e) {
                                  throw e;
                              } catch (Exception e) {
                                  throw new RuntimeException(e);
                              }
                          }
                      
                          public static void main(String[] args) {
                              Example e = new Example();
                              e.run();
                          }
                      }
                      

                      Central Maven Repo

                      copy iconCopydownload iconDownload
                      <dependency>
                        <groupId>com.github.axet</groupId>
                        <artifactId>vget</artifactId>
                        <version>1.0.40</version>
                      </dependency>
                      

                      Is there a numpy function to replace str to int values based on list index

                      copy iconCopydownload iconDownload
                      icing_type_to_idx = {icinig_type: idx for idx, icing_type in enumerate(icing_types)}
                      arr = np.array([icing_type_to_idx[ice] for ice in forecast_icing])
                      
                      import numpy as np
                      icing_types=[
                          "none",
                          "l-mixed",
                          "l-rime",
                          "l-clear",
                          "m-mixed",
                          "m-rime",
                          "m-clear",
                      ]
                      
                      
                      forecast_icing=np.array([
                          "none", 
                          "none", 
                          "l-rime",
                          "m-rime"
                      ])
                      
                      np.vectorize({b:a for a,b in enumerate(icing_types)}.get)(forecast_icing)
                      
                      In [103]: arr = np.array(forecast_icing)
                      In [104]: idx,arr
                      Out[104]: 
                      (array(['none', 'l-mixed', 'l-rime', 'l-clear', 'm-mixed', 'm-rime',
                              'm-clear'], dtype='<U7'),
                       array(['none', 'none', 'l-rime', 'm-rime'], dtype='<U6'))
                      
                      In [105]: arr[:,None]==idx
                      Out[105]: 
                      array([[ True, False, False, False, False, False, False],
                             [ True, False, False, False, False, False, False],
                             [False, False,  True, False, False, False, False],
                             [False, False, False, False, False,  True, False]])
                      
                      In [106]: np.where(_)
                      Out[106]: (array([0, 1, 2, 3]), array([0, 0, 2, 5]))
                      
                      In [107]: _[1]
                      Out[107]: array([0, 0, 2, 5])
                      
                      In [103]: arr = np.array(forecast_icing)
                      In [104]: idx,arr
                      Out[104]: 
                      (array(['none', 'l-mixed', 'l-rime', 'l-clear', 'm-mixed', 'm-rime',
                              'm-clear'], dtype='<U7'),
                       array(['none', 'none', 'l-rime', 'm-rime'], dtype='<U6'))
                      
                      In [105]: arr[:,None]==idx
                      Out[105]: 
                      array([[ True, False, False, False, False, False, False],
                             [ True, False, False, False, False, False, False],
                             [False, False,  True, False, False, False, False],
                             [False, False, False, False, False,  True, False]])
                      
                      In [106]: np.where(_)
                      Out[106]: (array([0, 1, 2, 3]), array([0, 0, 2, 5]))
                      
                      In [107]: _[1]
                      Out[107]: array([0, 0, 2, 5])
                      
                      In [103]: arr = np.array(forecast_icing)
                      In [104]: idx,arr
                      Out[104]: 
                      (array(['none', 'l-mixed', 'l-rime', 'l-clear', 'm-mixed', 'm-rime',
                              'm-clear'], dtype='<U7'),
                       array(['none', 'none', 'l-rime', 'm-rime'], dtype='<U6'))
                      
                      In [105]: arr[:,None]==idx
                      Out[105]: 
                      array([[ True, False, False, False, False, False, False],
                             [ True, False, False, False, False, False, False],
                             [False, False,  True, False, False, False, False],
                             [False, False, False, False, False,  True, False]])
                      
                      In [106]: np.where(_)
                      Out[106]: (array([0, 1, 2, 3]), array([0, 0, 2, 5]))
                      
                      In [107]: _[1]
                      Out[107]: array([0, 0, 2, 5])
                      
                      In [103]: arr = np.array(forecast_icing)
                      In [104]: idx,arr
                      Out[104]: 
                      (array(['none', 'l-mixed', 'l-rime', 'l-clear', 'm-mixed', 'm-rime',
                              'm-clear'], dtype='<U7'),
                       array(['none', 'none', 'l-rime', 'm-rime'], dtype='<U6'))
                      
                      In [105]: arr[:,None]==idx
                      Out[105]: 
                      array([[ True, False, False, False, False, False, False],
                             [ True, False, False, False, False, False, False],
                             [False, False,  True, False, False, False, False],
                             [False, False, False, False, False,  True, False]])
                      
                      In [106]: np.where(_)
                      Out[106]: (array([0, 1, 2, 3]), array([0, 0, 2, 5]))
                      
                      In [107]: _[1]
                      Out[107]: array([0, 0, 2, 5])
                      

                      Community Discussions

                      Trending Discussions on vget
                      • Is there a numpy function to replace str to int values based on list index
                      Trending Discussions on vget

                      QUESTION

                      Is there a numpy function to replace str to int values based on list index

                      Asked 2021-Nov-08 at 16:31

                      Is there way to accomplish the code below with out the for loop?

                      I’m assigning int values to str based on its index position.

                      import numpy as np
                      icing_types=[
                          "none",
                          "l-mixed",
                          "l-rime",
                          "l-clear",
                          "m-mixed",
                          "m-rime",
                          "m-clear",
                      ]
                      idx=np.array(icing_types)
                      #validtime=basetime+index hr
                      forecast_icing=[
                          "none", 
                          "none", 
                          "l-rime",
                          "m-rime"
                      ]
                      arr=np.array([np.where(ice==idx)for ice in forecast_icing]).flatten()
                      
                      
                      update:

                      I put together some benchmarks for two of the answers, both worked pretty well but as @chris mentoned @hpaulj solution was a fair bit faster.

                      To put into context I am parsing a large text file into a DataFrame. the file has 186 rows and 144 columns, most of the data is in a numeric format. Except for icing, turbulence, and precip. So I needed a method to convert the str value to an int to determine the Delta in a forecast with respect to time.

                      df = pd.DataFrame()
                      dfa = np.array(df)
                      
                      for i, k in enumerate(df.index):
                          if k == '###UA SECTION###':  # skip ua section header
                              continue
                      
                          lvl, key = self._make_lvl_key(k)  # key
                      
                          try:
                              val = dfa[i].astype(float)
                      

                      A ValueError exception occurs when the array cannot be cast to a float dtype.

                      The None string values are inconsistent throughout the file, so everything that would be considered as None is set as "none".

                      _get_wxidx is a simple method that returns the correct index for for string data being processed based on it's key value. keys are generally, turbulence, icing, and precip.

                          except ValueError:  # value error occurs when array values are not numerical
                              # array of strings -> lowercase, stripped, string values
                              aos = np.char.strip(np.char.lower(dfa[i].astype(str)))
                              aos[aos == ''] = 'none'
                              aos[aos == 'null'] = 'none'
                      
                              idx = self._get_wxidx(key)
                      
                      Benchmarks:

                      In both methods any value greater than 10 is to be assumed as a None type value, in which case the it is set to a negative value.

                      Where method:
                              def _where():
                                  a1 = np.where(aos[:, None] == idx)[1]
                                  a2 = np.where(a1 < 10, a1, -1)
                                  return a2
                      
                      Vector method:
                              vget = np.vectorize({b: a for a, b in enumerate(idx)}.get)
                      
                              def _vector():
                                  a1 = vget(aos)
                                  a2 = np.where(a1 < 10, a1, -1)
                                  return a2
                      
                      Running benchmarks

                      The __main__ function calls to parse two separate text file. within the benchmark it was set to run each function 1,000 times. 2,000 times each function and 4,000 total, converting 324,000 rows each with 144 columns of string data into int values.

                              # benchmark range
                              _r = 1000
                              # _where benchmark
                              t1S = datetime.now()
                              for i in range(_r):
                                  _where()
                              t1E = datetime.now()
                              t1D = (t1E-t1S).total_seconds()
                              t1time.append(t1D)
                      
                              # _vector benchmark
                              t2S = datetime.now()
                              for i in range(_r):
                                  _vector()
                              t2E = datetime.now()
                              t2D = (t2E-t2S).total_seconds()
                              t2time.append(t2D)
                      
                      
                      results:
                      where benchmark
                      rows processed: 81000
                      1.245809 seconds
                      
                      vector benchmark
                      rows processed: 81000
                      2.235406 seconds
                      
                      where benchmark
                      rows processed: 81000
                      1.229645 seconds
                      
                      vector benchmark
                      rows processed: 81000
                      2.2235739999999997 seconds
                      

                      ANSWER

                      Answered 2021-Nov-07 at 21:50

                      There is not, but I am not 100% sure.

                      Anyway, even if there was, you can get much better performance by using mapping dict (icing_type -> idx):

                      icing_type_to_idx = {icinig_type: idx for idx, icing_type in enumerate(icing_types)}
                      arr = np.array([icing_type_to_idx[ice] for ice in forecast_icing])
                      

                      Source https://stackoverflow.com/questions/69876680

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install vget

                      You can download it from GitHub.
                      You can use vget like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the vget component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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