I'm trying to dowload a file using Python 3's urllib, but I get some html garbage instead of the actual file. However, if I use the browser, I can download the file just fine. A minimum non-working example:
download pdf files from urllib
I'm trying to get pdf files behind a domain that requires a username and password. I am able to get MechanicalSoup to enter my login credentials, however when I navigate to the pdf file I can view the pdf fine with MechanicalSoups launch_browser() but I cannot download the pdf. In the past (when using BeautifulSoup in python 2 for a site that didn't require authentication) I would just pass the url to urllib2 like so:
Look at the accompanying execution. I've utilized the requests module rather than urllib to do the download. In addition, I've utilized .select() technique rather than .find_all() to try not to utilize re.
I am trying to download a pdf file from a website with authentication and save it locally. This code appears to run but saves a pdf file that cannot be opened ("it is either not a supported file type or because the file has been damaged").
So first, lets import the libraries we need, and also note I downloaded the pdftohtml utility and placed that location as a system path on my Windows machine. Then we need to set a folder where we will download the files to on our local machine. Finally I create the base url for our meth labs.
Now to go from those sets of tuples to actually formatted data takes a bit of more work, and I used SPSS for that. See here for the full set of scripts used to download, parse and clean up the data. Basically it is alittle more complicated than just going from long to wide using the top marker for the data as some rows are off slightly. Also there is complications for long addresses being split across two lines. And finally there are just some data errors and fields being merged together. So that SPSS code solves a bunch of that. Also that includes scripts to geocode the to the city level using the Google geocoding API.
I was able to get your original function working with a small tweak--I used the requests library to download the file instead of urllib2. requests appears to pull the file with the loader referenced in the html you're getting from your current implementation. Try this:
So, if you can save a single file this easily, could you write a programto download a bunch of files? Could you step through trial IDs, forexample, and make your own copies of a whole bunch of them? Yep. You can learnhow to do that in Downloading Multiple Files using Query Strings,which we recommend after you have completed the introductory lessons in this series.
Next, we utilize the urllib library to open a connection to the supplied URL on Line 10. The raw byte-sequence from the request is then converted to a NumPy array on Line 11.
Our first example demonstrates Python's urllib2 module. The urllib2 module provides various methods to download data and interact with WWW content and protocols - all from within a Python script. urllib2 can be used to access files in different formats, such as HTML, XML, JSON, *.txt, PDF, etc., from the web. We will be using this module quite a lot in this workshop.
In this example, you import urlopen() from urllib.request. Using the context manager with, you make a request and receive a response with urlopen(). Then you read the body of the response and close the response object. With that, you display the first fifteen positions of the body, noting that it looks like an HTML document.
In this example, you import urlopen() from the urllib.request module. You use the with keyword with .urlopen() to assign the HTTPResponse object to the variable response. Then, you read the first fifty bytes of the response and then read the following fifty bytes, all within the with block. Finally, you close the with block, which executes the request and runs the lines of code within its block.
Aaron Swartz was a programmer and political activist, who infamously downloaded an estimated 4.8 million articles from the JSTOR database of academic articles. This led him to be prosecuted by the United States.
In the documentary "The Internet's Own Boy" about Aaron Swartz, there is a script that is referenced called KeepGrabbing.py. This script is what Aaron used to bulk download PDFs from the JSTOR database from within the MIT network.
It looks like this function called to custom url which was likely set up by Swartz himself, which had a list of URLs linking to PDFs to download. It is likely the case that Swartz had this grab page setup and would control and modify it from out side of the MIT campus in order to direct the script to download the PDFs he wanted. The first line in this function calls urllib.urlopen().read and saves the response of this call to a variable called 'r'. The urlopen function reads from a URL and returns a file-like object from the contents of the external resource that the URL points to. The read function that is called on this simply reads the byte contents and returns them.
This lambda expression will later be used as part of a subprocess call later in the script. It defines a curl request. The curl command is a command which allows youto transfer data to or from a URL, i.e upload or download from a url. The curl request is with a proxy to connect via (depending on the conditional above as mentioned). Next, it defines a cookie, which is simply the string TENACIOUS= followed by a random 3-digit number. This cookie, will make the server responding to this curl request think that it is coming from a real user as opposed to a script. The next thing this function does is define the output of this curl request: the pdf file name to a directory called pdfs. The rest of this lambda creates the url of the PDF from which to download the PDF with using curl.
First it calls the getblocks from earlier and saves the resulting list of PDFs to a variable called blocks.It then iterates over these, printing them to the console and then calling the line lambda from earlier in a subprocess.Popen call. Subprocess Popen will create a new process, in this case the curl request that will download the current PDF. Then the script will block until this subprocess finishes, i.e. it will wait until the PDF is finished downloading and then it will move on to the next PDF.
These will add the libraries "urllib" (python lib containing the code to download urls), FmWorkshopHelperFunctions (python lib containing helper functions for working with arrays) and NetCdfFile (DeltaShell code for working with NetCdf files).
Instead, I would recommend Zotero as an alternative type of solution. It lets you save article metadata from arXiv and many other sources in the click of a button. It can also download and archive PDFs automatically, on your PC or on their server (where you get a very limited amount of space for free and can pay to get more).
Searches and reports performed on this RCSB PDB website utilize data from the PDB archive. The PDB archive is maintained by the wwPDB at the main archive, files.wwpdb.org (data download details) and the versioned archive, files-versioned.wwpdb.org (versioning details).
All data are available via HTTPS and FTP. Note that FTP users should switch to binary mode before downloading data files. Note also that most web browsers (e.g., Chrome) have dropped support for FTP. You will need a separate FTP client for downloading via FTP protocol.
PDB entry files are available in several file formats (PDB, PDBx/mmCIF, XML, BinaryCIF), compressed or uncompressed, and with an option to download a file containing only "header" information (summary data, no coordinates).
Results of the weekly clustering of protein sequences in the PDB by MMseqs2 at 30%, 40%, 50%, 70%, 90%, 95%, and 100% sequence identity. Note that these files use polymer entity identifiers, instead of chain identifiers to avoid redundancy. The files are plain text with one cluster per line, sorted from largest cluster to smallest.
Now you know how to use threads and queues both in theory and in a practical way. Threads are especially useful when you are creating a user interface and you want to keep your interface usable. Without threads, the user interface would become unresponsive and would appear to hang while you did a large file download or a big query against a database. To keep that from happening, you do the long running processes in threads and then communicate back to your interface when you are done.
The Python urllib module allows us to access the website via Python code. This facility of urllib provides the flexibility to run GET, POST, and PUT methods and mock the JSON data in our Python code. We can download data, access websites, parse data, and modify the headers using the urllib library. There are two versions of urllib - urllib2 and urllib3. The urllib2 is used in Python 2, and urllib 3 is used in Python 3. The urllib3 has some advanced features.
The request_func() function takes a string as an argument, which will try to fetch the request from the URL with the urllib.request. If URL is not correct, it will catch the URLError. It also catches HTTPError's object that's raised an error. If there is no error, it will print the status and return the body and response.
The data downloaded from CDS climate could become very large in size. We want to process parts of the data one part at a time, summarize and aggregate over each part, and generate a file output file with aggregate statistics over the entire time period of interest. 2ff7e9595c
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