compression

I have gzipped data from HTTP reply. I have following code: def gzipDecode(self, content): import StringIO import gzip outFilePath = 'test' compressedFile = StringIO.StringIO(content) decompressedFile = gzip.GzipFile(fileobj=compressedFile) with open(outFilePath, 'w') as outfile: outfile.write(decompressedFile.read()) data = '' with open(outFilePath, 'r') as myfile: data=myfile.read().replace('\n', '') return data which decompress input gzipped content and return string (http reply is gzipped json). - It works. But I need it without creating test file - all in memory. I modified it to: def

For instance, consider the DFT or DCT. Precisely, what would be the differences between an image transformed by sub-blocks, and an image transformed whole? Is the resulting file size smaller? Is the algorithm more efficient? Does the transformed image look different? Thanks. ctrl-alt-delor They are designed so they can be implemented using parallel hardware. Each block is independent, and can be calculated on a different computing node, or shared out to as many nodes as you have. Also as noted in an answer to Why JPEG compression processes image by 8x8 blocks? the computational complexity is

The application i'm developing needs to compress xml files into zip files and send them through http requests to a web service. As I dont need to keep the zip files, i'm just performing the compression in memory. The web service is denying my requests because the zip files are apparently malformed. I know there is a solution in this question which works perfectly, but it uses a StreamWriter . My problem with that solution is that StreamWriter requires an encoding or assumes UTF-8 , and I do not need to know the enconding of the xml files. I just need to read the bytes from those files, and

When using mod_deflate in Apache2, Apache will chunk gzipped content, setting the Transfer-encoding: chunked header. While this results in a faster download time, I cannot display a progress bar. If I handle the compression myself in PHP, I can gzip it completely first and set the Content-length header, so that I can display a progress bar to the user. Is there any setting that would change Apache's default behavior, and have Apache set a Content-length header instead of chunking the response, so that I don't have to handle the compression myself? You could maybe play with the sendBufferSize

I want to extract .rar files using cmd shell so I wrote this code: string commandLine = @"c:\progra~1\winrar\winrar e c:\download\TestedU.rar c:\download"; ProcessStartInfo PSI = new ProcessStartInfo("cmd.exe"); PSI.RedirectStandardInput = true; PSI.RedirectStandardOutput = true; PSI.RedirectStandardError = true; PSI.UseShellExecute = false; Process p = Process.Start(PSI); StreamWriter SW = p.StandardInput; StreamReader SR = p.StandardOutput; SW.WriteLine(commandLine); SW.Close(); The first time it worked fine, the second time it displayed nothing. Use SevenZipSharp as it's a bit better way of

I have a .gz file and I need to get the name of files inside it using python. This question is the same as this one The only difference is that my file is .gz not .tar.gz so the tarfile library did not help me here I am using requests library to request a URL. The response is a compressed file. Here is the code I am using to download the file response = requests.get(line.rstrip(), stream=True) if response.status_code == 200: with open(str(base_output_dir)+"/"+str(current_dir)+"/"+str(count)+".gz", 'wb') as out_file: shutil.copyfileobj(response.raw, out_file) del response This code downloads

I'm using sklearns OrthogonalMatchingPursuit to get a sparse coding of a signal using a dictionary learned by a KSVD algorithm . However, during the fit I get the following RuntimeWarning: /usr/local/lib/python2.7/dist-packages/sklearn/linear_model/omp.py:391: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. copy_X=copy_X, return_path=return_path) In those cases the results are indeed not satisfactory. I don't get the point of this warning as it is common in sparse coding to have an