checksum

I'm thinking to create a checksum of a dict to know if it was modified or not For the moment i have that: >>> import hashlib >>> import pickle >>> d = {'k': 'v', 'k2': 'v2'} >>> z = pickle.dumps(d) >>> hashlib.md5(z).hexdigest() '8521955ed8c63c554744058c9888dc30' Perhaps a better solution exists? Note: I want to create an unique id of a dict to create a good Etag. EDIT: I can have abstract data in the dict. Something like this: reduce(lambda x,y : x^y, [hash(item) for item in d.items()]) Take the hash of each (key, value) tuple in the dict and XOR them alltogether. @katrielalex If the dict

I need to calculate the checksum of a csv file. The checksum will change every time the data in the file is changed. I found nothing useful over the internet in this regard. First of all, this problem is not specific to JSP. JSP is just a HTML code generator. Writing Java code in a JSP file instead of a normal Java class doesn't make it a JSP problem. You would help yourself more if you concentrate on solving future Java problems using the "Java" keyword, not using the "JSP" keyword. Said that, you can just use MessageDigest which you update with the bytes read from the file. FileInputStream

I have a Master Detail form in my Oracle APEX application. When I am trying to update data in this form, I am getting below error. Current version of data in database has changed since user initiated update process. current row version identifier = "26D0923D8A5144D6F483C2B9815D07D3" application row version identifier = "1749BCD159359424E1EE00AC1C3E3FCB" (Row 1) I have cleared browser cache and try to update. But it not worked. How can I solve this? I have experienced similar problem where my detail records set has timestamp fields. By default master detail wizard creates the timestamp fields

I want to be able to do a checksum based on a list of files in a local dir. Then be able to get those files checksum and compare it to the checksum of the same files on a remote system. I know I can get the with the following # Local File - stat: path: "{{ playbook_dir }}/roles/common/files/myfile.dat" checksum_algorithm: sha1 delegate_to: localhost run_once: true register: localsha_result # Remote file - stat: path: "{{ rmt_dest_dir }}/myfile.dat" checksum_algorithm: sha1 register: sha_result and I have tried to loop through the files that I want to checksum with: # Gather Files - name:

I am experiencing a problem where the tcp checksum generated by the sample program (copied below) doesn't match with the checksum calculated by wireshark. Can some one please point me where i am going wrong. Here i tried two ways tcp_checksum get_ipv6_udptcp_checksum. with both these, getting two different values and both are not matching with the wireshark value. I am copying here the IP and TCP Header details. IP Header: 0000 60 00 00 00 00 2a 06 80 10 80 a2 b1 00 00 00 00 0010 00 00 00 00 00 1e 00 00 ff 00 00 00 00 00 00 00 0020 00 00 00 00 00 00 00 24 TCP Header: 0000 04 22 00 50 00 01 e0

I need to communicate with a RS232 device, I have no specs or information available. I send a 16 byte command and get a 16 byte result back. The last byte looks like some kind of crc or checksum, I have tried using this http://miscel.dk/MiscEl/miscelCRCandChecksum.html with no luck. Anyone can reverse engineer the crc/checksum algorithm? here is some data captured with an RS-232 monitor program: 01 80 42 00 00 00 00 00 00 00 00 00 00 00 01 B3 01 80 42 00 00 00 00 00 00 00 00 00 00 00 02 51 01 80 42 00 00 00 00 00 00 00 00 00 00 00 03 0F 01 80 42 00 00 00 00 00 00 00 00 00 00 00 04 8C 01 80 42

Are there any clever algorithms for computing high-quality checksums on millions or billions of prime numbers? I.e. with maximum error-detection capability and perhaps segmentable? Motivation: Small primes - up to 64 bits in size - can be sieved on demand to the tune of millions per second, by using a small bitmap for sieving potential factors (up to 2^32-1) and a second bitmap for sieving the numbers in the target range. Algorithm and implementation are reasonably simple and straightforward but the devil is in the details: values tend to push against - or exceed - the limits of builtin