120 skills found · Page 3 of 4
indranil143 / Fade Tac Toe GameA stylish Python Tic Tac Toe game built with Pygame where each player’s oldest moves gradually fade and vanish — only 3 moves stay visible!, featuring winner detection, easy restart, sound effects, and a modern twist on a classic game.
heru299 / Script Copy-? Print this help message and exit -alertnotify=<cmd> Execute command when a relevant alert is received or we see a really long fork (%s in cmd is replaced by message) -assumevalid=<hex> If this block is in the chain assume that it and its ancestors are valid and potentially skip their script verification (0 to verify all, default: 0000000000000000000b9d2ec5a352ecba0592946514a92f14319dc2b367fc72, testnet: 000000000000006433d1efec504c53ca332b64963c425395515b01977bd7b3b0, signet: 0000002a1de0f46379358c1fd09906f7ac59adf3712323ed90eb59e4c183c020) -blockfilterindex=<type> Maintain an index of compact filters by block (default: 0, values: basic). If <type> is not supplied or if <type> = 1, indexes for all known types are enabled. -blocknotify=<cmd> Execute command when the best block changes (%s in cmd is replaced by block hash) -blockreconstructionextratxn=<n> Extra transactions to keep in memory for compact block reconstructions (default: 100) -blocksdir=<dir> Specify directory to hold blocks subdirectory for *.dat files (default: <datadir>) -blocksonly Whether to reject transactions from network peers. Automatic broadcast and rebroadcast of any transactions from inbound peers is disabled, unless the peer has the 'forcerelay' permission. RPC transactions are not affected. (default: 0) -conf=<file> Specify path to read-only configuration file. Relative paths will be prefixed by datadir location. (default: bitcoin.conf) -daemon Run in the background as a daemon and accept commands -datadir=<dir> Specify data directory -dbcache=<n> Maximum database cache size <n> MiB (4 to 16384, default: 450). In addition, unused mempool memory is shared for this cache (see -maxmempool). -debuglogfile=<file> Specify location of debug log file. Relative paths will be prefixed by a net-specific datadir location. (-nodebuglogfile to disable; default: debug.log) -includeconf=<file> Specify additional configuration file, relative to the -datadir path (only useable from configuration file, not command line) -loadblock=<file> Imports blocks from external file on startup -maxmempool=<n> Keep the transaction memory pool below <n> megabytes (default: 300) -maxorphantx=<n> Keep at most <n> unconnectable transactions in memory (default: 100) -mempoolexpiry=<n> Do not keep transactions in the mempool longer than <n> hours (default: 336) -par=<n> Set the number of script verification threads (-8 to 15, 0 = auto, <0 = leave that many cores free, default: 0) -persistmempool Whether to save the mempool on shutdown and load on restart (default: 1) -pid=<file> Specify pid file. Relative paths will be prefixed by a net-specific datadir location. (default: bitcoind.pid) -prune=<n> Reduce storage requirements by enabling pruning (deleting) of old blocks. This allows the pruneblockchain RPC to be called to delete specific blocks, and enables automatic pruning of old blocks if a target size in MiB is provided. This mode is incompatible with -txindex and -rescan. Warning: Reverting this setting requires re-downloading the entire blockchain. (default: 0 = disable pruning blocks, 1 = allow manual pruning via RPC, >=550 = automatically prune block files to stay under the specified target size in MiB) -reindex Rebuild chain state and block index from the blk*.dat files on disk -reindex-chainstate Rebuild chain state from the currently indexed blocks. When in pruning mode or if blocks on disk might be corrupted, use full -reindex instead. -settings=<file> Specify path to dynamic settings data file. Can be disabled with -nosettings. File is written at runtime and not meant to be edited by users (use bitcoin.conf instead for custom settings). Relative paths will be prefixed by datadir location. (default: settings.json) -startupnotify=<cmd> Execute command on startup. -sysperms Create new files with system default permissions, instead of umask 077 (only effective with disabled wallet functionality) -txindex Maintain a full transaction index, used by the getrawtransaction rpc call (default: 0) -version Print version and exit Connection options: -addnode=<ip> Add a node to connect to and attempt to keep the connection open (see the `addnode` RPC command help for more info). This option can be specified multiple times to add multiple nodes. -asmap=<file> Specify asn mapping used for bucketing of the peers (default: ip_asn.map). Relative paths will be prefixed by the net-specific datadir location. -bantime=<n> Default duration (in seconds) of manually configured bans (default: 86400) -bind=<addr>[:<port>][=onion] Bind to given address and always listen on it (default: 0.0.0.0). Use [host]:port notation for IPv6. Append =onion to tag any incoming connections to that address and port as incoming Tor connections (default: 127.0.0.1:8334=onion, testnet: 127.0.0.1:18334=onion, signet: 127.0.0.1:38334=onion, regtest: 127.0.0.1:18445=onion) -connect=<ip> Connect only to the specified node; -noconnect disables automatic connections (the rules for this peer are the same as for -addnode). This option can be specified multiple times to connect to multiple nodes. -discover Discover own IP addresses (default: 1 when listening and no -externalip or -proxy) -dns Allow DNS lookups for -addnode, -seednode and -connect (default: 1) -dnsseed Query for peer addresses via DNS lookup, if low on addresses (default: 1 unless -connect used) -externalip=<ip> Specify your own public address -forcednsseed Always query for peer addresses via DNS lookup (default: 0) -listen Accept connections from outside (default: 1 if no -proxy or -connect) -listenonion Automatically create Tor onion service (default: 1) -maxconnections=<n> Maintain at most <n> connections to peers (default: 125) -maxreceivebuffer=<n> Maximum per-connection receive buffer, <n>*1000 bytes (default: 5000) -maxsendbuffer=<n> Maximum per-connection send buffer, <n>*1000 bytes (default: 1000) -maxtimeadjustment Maximum allowed median peer time offset adjustment. Local perspective of time may be influenced by peers forward or backward by this amount. (default: 4200 seconds) -maxuploadtarget=<n> Tries to keep outbound traffic under the given target (in MiB per 24h). Limit does not apply to peers with 'download' permission. 0 = no limit (default: 0) -networkactive Enable all P2P network activity (default: 1). Can be changed by the setnetworkactive RPC command -onion=<ip:port> Use separate SOCKS5 proxy to reach peers via Tor onion services, set -noonion to disable (default: -proxy) -onlynet=<net> Make outgoing connections only through network <net> (ipv4, ipv6 or onion). Incoming connections are not affected by this option. This option can be specified multiple times to allow multiple networks. -peerblockfilters Serve compact block filters to peers per BIP 157 (default: 0) -peerbloomfilters Support filtering of blocks and transaction with bloom filters (default: 0) -permitbaremultisig Relay non-P2SH multisig (default: 1) -port=<port> Listen for connections on <port>. Nodes not using the default ports (default: 8333, testnet: 18333, signet: 38333, regtest: 18444) are unlikely to get incoming connections. -proxy=<ip:port> Connect through SOCKS5 proxy, set -noproxy to disable (default: disabled) -proxyrandomize Randomize credentials for every proxy connection. This enables Tor stream isolation (default: 1) -seednode=<ip> Connect to a node to retrieve peer addresses, and disconnect. This option can be specified multiple times to connect to multiple nodes. -timeout=<n> Specify connection timeout in milliseconds (minimum: 1, default: 5000) -torcontrol=<ip>:<port> Tor control port to use if onion listening enabled (default: 127.0.0.1:9051) -torpassword=<pass> Tor control port password (default: empty) -upnp Use UPnP to map the listening port (default: 0) -whitebind=<[permissions@]addr> Bind to the given address and add permission flags to the peers connecting to it. Use [host]:port notation for IPv6. Allowed permissions: bloomfilter (allow requesting BIP37 filtered blocks and transactions), noban (do not ban for misbehavior; implies download), forcerelay (relay transactions that are already in the mempool; implies relay), relay (relay even in -blocksonly mode, and unlimited transaction announcements), mempool (allow requesting BIP35 mempool contents), download (allow getheaders during IBD, no disconnect after maxuploadtarget limit), addr (responses to GETADDR avoid hitting the cache and contain random records with the most up-to-date info). Specify multiple permissions separated by commas (default: download,noban,mempool,relay). Can be specified multiple times. -whitelist=<[permissions@]IP address or network> Add permission flags to the peers connecting from the given IP address (e.g. 1.2.3.4) or CIDR-notated network (e.g. 1.2.3.0/24). Uses the same permissions as -whitebind. Can be specified multiple times. Wallet options: -addresstype What type of addresses to use ("legacy", "p2sh-segwit", or "bech32", default: "bech32") -avoidpartialspends Group outputs by address, selecting all or none, instead of selecting on a per-output basis. Privacy is improved as an address is only used once (unless someone sends to it after spending from it), but may result in slightly higher fees as suboptimal coin selection may result due to the added limitation (default: 0 (always enabled for wallets with "avoid_reuse" enabled)) -changetype What type of change to use ("legacy", "p2sh-segwit", or "bech32"). Default is same as -addresstype, except when -addresstype=p2sh-segwit a native segwit output is used when sending to a native segwit address) -disablewallet Do not load the wallet and disable wallet RPC calls -discardfee=<amt> The fee rate (in BTC/kB) that indicates your tolerance for discarding change by adding it to the fee (default: 0.0001). Note: An output is discarded if it is dust at this rate, but we will always discard up to the dust relay fee and a discard fee above that is limited by the fee estimate for the longest target -fallbackfee=<amt> A fee rate (in BTC/kB) that will be used when fee estimation has insufficient data. 0 to entirely disable the fallbackfee feature. (default: 0.00) -keypool=<n> Set key pool size to <n> (default: 1000). Warning: Smaller sizes may increase the risk of losing funds when restoring from an old backup, if none of the addresses in the original keypool have been used. -maxapsfee=<n> Spend up to this amount in additional (absolute) fees (in BTC) if it allows the use of partial spend avoidance (default: 0.00) -mintxfee=<amt> Fees (in BTC/kB) smaller than this are considered zero fee for transaction creation (default: 0.00001) -paytxfee=<amt> Fee (in BTC/kB) to add to transactions you send (default: 0.00) -rescan Rescan the block chain for missing wallet transactions on startup -spendzeroconfchange Spend unconfirmed change when sending transactions (default: 1) -txconfirmtarget=<n> If paytxfee is not set, include enough fee so transactions begin confirmation on average within n blocks (default: 6) -wallet=<path> Specify wallet path to load at startup. Can be used multiple times to load multiple wallets. Path is to a directory containing wallet data and log files. If the path is not absolute, it is interpreted relative to <walletdir>. This only loads existing wallets and does not create new ones. For backwards compatibility this also accepts names of existing top-level data files in <walletdir>. -walletbroadcast Make the wallet broadcast transactions (default: 1) -walletdir=<dir> Specify directory to hold wallets (default: <datadir>/wallets if it exists, otherwise <datadir>) -walletnotify=<cmd> Execute command when a wallet transaction changes. %s in cmd is replaced by TxID and %w is replaced by wallet name. %w is not currently implemented on windows. On systems where %w is supported, it should NOT be quoted because this would break shell escaping used to invoke the command. -walletrbf Send transactions with full-RBF opt-in enabled (RPC only, default: 0) ZeroMQ notification options: -zmqpubhashblock=<address> Enable publish hash block in <address> -zmqpubhashblockhwm=<n> Set publish hash block outbound message high water mark (default: 1000) -zmqpubhashtx=<address> Enable publish hash transaction in <address> -zmqpubhashtxhwm=<n> Set publish hash transaction outbound message high water mark (default: 1000) -zmqpubrawblock=<address> Enable publish raw block in <address> -zmqpubrawblockhwm=<n> Set publish raw block outbound message high water mark (default: 1000) -zmqpubrawtx=<address> Enable publish raw transaction in <address> -zmqpubrawtxhwm=<n> Set publish raw transaction outbound message high water mark (default: 1000) -zmqpubsequence=<address> Enable publish hash block and tx sequence in <address> -zmqpubsequencehwm=<n> Set publish hash sequence message high water mark (default: 1000) Debugging/Testing options: -debug=<category> Output debugging information (default: -nodebug, supplying <category> is optional). If <category> is not supplied or if <category> = 1, output all debugging information. <category> can be: net, tor, mempool, http, bench, zmq, walletdb, rpc, estimatefee, addrman, selectcoins, reindex, cmpctblock, rand, prune, proxy, mempoolrej, libevent, coindb, qt, leveldb, validation. -debugexclude=<category> Exclude debugging information for a category. Can be used in conjunction with -debug=1 to output debug logs for all categories except one or more specified categories. -help-debug Print help message with debugging options and exit -logips Include IP addresses in debug output (default: 0) -logthreadnames Prepend debug output with name of the originating thread (only available on platforms supporting thread_local) (default: 0) -logtimestamps Prepend debug output with timestamp (default: 1) -maxtxfee=<amt> Maximum total fees (in BTC) to use in a single wallet transaction; setting this too low may abort large transactions (default: 0.10) -printtoconsole Send trace/debug info to console (default: 1 when no -daemon. To disable logging to file, set -nodebuglogfile) -shrinkdebugfile Shrink debug.log file on client startup (default: 1 when no -debug) -uacomment=<cmt> Append comment to the user agent string Chain selection options: -chain=<chain> Use the chain <chain> (default: main). Allowed values: main, test, signet, regtest -signet Use the signet chain. Equivalent to -chain=signet. Note that the network is defined by the -signetchallenge parameter -signetchallenge Blocks must satisfy the given script to be considered valid (only for signet networks; defaults to the global default signet test network challenge) -signetseednode Specify a seed node for the signet network, in the hostname[:port] format, e.g. sig.net:1234 (may be used multiple times to specify multiple seed nodes; defaults to the global default signet test network seed node(s)) -testnet Use the test chain. Equivalent to -chain=test. Node relay options: -bytespersigop Equivalent bytes per sigop in transactions for relay and mining (default: 20) -datacarrier Relay and mine data carrier transactions (default: 1) -datacarriersize Maximum size of data in data carrier transactions we relay and mine (default: 83) -minrelaytxfee=<amt> Fees (in BTC/kB) smaller than this are considered zero fee for relaying, mining and transaction creation (default: 0.00001) -whitelistforcerelay Add 'forcerelay' permission to whitelisted inbound peers with default permissions. This will relay transactions even if the transactions were already in the mempool. (default: 0) -whitelistrelay Add 'relay' permission to whitelisted inbound peers with default permissions. This will accept relayed transactions even when not relaying transactions (default: 1) Block creation options: -blockmaxweight=<n> Set maximum BIP141 block weight (default: 3996000) -blockmintxfee=<amt> Set lowest fee rate (in BTC/kB) for transactions to be included in block creation. (default: 0.00001) RPC server options: -rest Accept public REST requests (default: 0) -rpcallowip=<ip> Allow JSON-RPC connections from specified source. Valid for <ip> are a single IP (e.g. 1.2.3.4), a network/netmask (e.g. 1.2.3.4/255.255.255.0) or a network/CIDR (e.g. 1.2.3.4/24). This option can be specified multiple times -rpcauth=<userpw> Username and HMAC-SHA-256 hashed password for JSON-RPC connections. The field <userpw> comes in the format: <USERNAME>:<SALT>$<HASH>. A canonical python script is included in share/rpcauth. The client then connects normally using the rpcuser=<USERNAME>/rpcpassword=<PASSWORD> pair of arguments. This option can be specified multiple times -rpcbind=<addr>[:port] Bind to given address to listen for JSON-RPC connections. Do not expose the RPC server to untrusted networks such as the public internet! This option is ignored unless -rpcallowip is also passed. Port is optional and overrides -rpcport. Use [host]:port notation for IPv6. This option can be specified multiple times (default: 127.0.0.1 and ::1 i.e., localhost) -rpccookiefile=<loc> Location of the auth cookie. Relative paths will be prefixed by a net-specific datadir location. (default: data dir) -rpcpassword=<pw> Password for JSON-RPC connections -rpcport=<port> Listen for JSON-RPC connections on <port> (default: 8332, testnet: 18332, signet: 38332, regtest: 18443) -rpcserialversion Sets the serialization of raw transaction or block hex returned in non-verbose mode, non-segwit(0) or segwit(1) (default: 1) -rpcthreads=<n> Set the number of threads to service RPC calls (default: 4) -rpcuser=<user> Username for JSON-RPC connections -rpcwhitelist=<whitelist> Set a whitelist to filter incoming RPC calls for a specific user. The field <whitelist> comes in the format: <USERNAME>:<rpc 1>,<rpc 2>,...,<rpc n>. If multiple whitelists are set for a given user, they are set-intersected. See -rpcwhitelistdefault documentation for information on default whitelist behavior. -rpcwhitelistdefault Sets default behavior for rpc whitelisting. Unless rpcwhitelistdefault is set to 0, if any -rpcwhitelist is set, the rpc server acts as if all rpc users are subject to empty-unless-otherwise-specified whitelists. If rpcwhitelistdefault is set to 1 and no -rpcwhitelist is set, rpc server acts as if all rpc users are subject to empty whitelists. -server Accept command line and JSON-RPC commands ~ $
HlaingPhyoAung / SqlmapUsage: python sqlmap.py [options] Options: -h, --help Show basic help message and exit -hh Show advanced help message and exit --version Show program's version number and exit -v VERBOSE Verbosity level: 0-6 (default 1) Target: At least one of these options has to be provided to define the target(s) -d DIRECT Connection string for direct database connection -u URL, --url=URL Target URL (e.g. "http://www.site.com/vuln.php?id=1") -l LOGFILE Parse target(s) from Burp or WebScarab proxy log file -x SITEMAPURL Parse target(s) from remote sitemap(.xml) file -m BULKFILE Scan multiple targets given in a textual file -r REQUESTFILE Load HTTP request from a file -g GOOGLEDORK Process Google dork results as target URLs -c CONFIGFILE Load options from a configuration INI file Request: These options can be used to specify how to connect to the target URL --method=METHOD Force usage of given HTTP method (e.g. PUT) --data=DATA Data string to be sent through POST --param-del=PARA.. Character used for splitting parameter values --cookie=COOKIE HTTP Cookie header value --cookie-del=COO.. Character used for splitting cookie values --load-cookies=L.. File containing cookies in Netscape/wget format --drop-set-cookie Ignore Set-Cookie header from response --user-agent=AGENT HTTP User-Agent header value --random-agent Use randomly selected HTTP User-Agent header value --host=HOST HTTP Host header value --referer=REFERER HTTP Referer header value -H HEADER, --hea.. Extra header (e.g. "X-Forwarded-For: 127.0.0.1") --headers=HEADERS Extra headers (e.g. "Accept-Language: fr\nETag: 123") --auth-type=AUTH.. HTTP authentication type (Basic, Digest, NTLM or PKI) --auth-cred=AUTH.. HTTP authentication credentials (name:password) --auth-file=AUTH.. HTTP authentication PEM cert/private key file --ignore-401 Ignore HTTP Error 401 (Unauthorized) --proxy=PROXY Use a proxy to connect to the target URL --proxy-cred=PRO.. Proxy authentication credentials (name:password) --proxy-file=PRO.. Load proxy list from a file --ignore-proxy Ignore system default proxy settings --tor Use Tor anonymity network --tor-port=TORPORT Set Tor proxy port other than default --tor-type=TORTYPE Set Tor proxy type (HTTP (default), SOCKS4 or SOCKS5) --check-tor Check to see if Tor is used properly --delay=DELAY Delay in seconds between each HTTP request --timeout=TIMEOUT Seconds to wait before timeout connection (default 30) --retries=RETRIES Retries when the connection timeouts (default 3) --randomize=RPARAM Randomly change value for given parameter(s) --safe-url=SAFEURL URL address to visit frequently during testing --safe-post=SAFE.. POST data to send to a safe URL --safe-req=SAFER.. Load safe HTTP request from a file --safe-freq=SAFE.. Test requests between two visits to a given safe URL --skip-urlencode Skip URL encoding of payload data --csrf-token=CSR.. Parameter used to hold anti-CSRF token --csrf-url=CSRFURL URL address to visit to extract anti-CSRF token --force-ssl Force usage of SSL/HTTPS --hpp Use HTTP parameter pollution method --eval=EVALCODE Evaluate provided Python code before the request (e.g. "import hashlib;id2=hashlib.md5(id).hexdigest()") Optimization: These options can be used to optimize the performance of sqlmap -o Turn on all optimization switches --predict-output Predict common queries output --keep-alive Use persistent HTTP(s) connections --null-connection Retrieve page length without actual HTTP response body --threads=THREADS Max number of concurrent HTTP(s) requests (default 1) Injection: These options can be used to specify which parameters to test for, provide custom injection payloads and optional tampering scripts -p TESTPARAMETER Testable parameter(s) --skip=SKIP Skip testing for given parameter(s) --skip-static Skip testing parameters that not appear dynamic --dbms=DBMS Force back-end DBMS to this value --dbms-cred=DBMS.. DBMS authentication credentials (user:password) --os=OS Force back-end DBMS operating system to this value --invalid-bignum Use big numbers for invalidating values --invalid-logical Use logical operations for invalidating values --invalid-string Use random strings for invalidating values --no-cast Turn off payload casting mechanism --no-escape Turn off string escaping mechanism --prefix=PREFIX Injection payload prefix string --suffix=SUFFIX Injection payload suffix string --tamper=TAMPER Use given script(s) for tampering injection data Detection: These options can be used to customize the detection phase --level=LEVEL Level of tests to perform (1-5, default 1) --risk=RISK Risk of tests to perform (1-3, default 1) --string=STRING String to match when query is evaluated to True --not-string=NOT.. String to match when query is evaluated to False --regexp=REGEXP Regexp to match when query is evaluated to True --code=CODE HTTP code to match when query is evaluated to True --text-only Compare pages based only on the textual content --titles Compare pages based only on their titles Techniques: These options can be used to tweak testing of specific SQL injection techniques --technique=TECH SQL injection techniques to use (default "BEUSTQ") --time-sec=TIMESEC Seconds to delay the DBMS response (default 5) --union-cols=UCOLS Range of columns to test for UNION query SQL injection --union-char=UCHAR Character to use for bruteforcing number of columns --union-from=UFROM Table to use in FROM part of UNION query SQL injection --dns-domain=DNS.. Domain name used for DNS exfiltration attack --second-order=S.. Resulting page URL searched for second-order response Fingerprint: -f, --fingerprint Perform an extensive DBMS version fingerprint Enumeration: These options can be used to enumerate the back-end database management system information, structure and data contained in the tables. Moreover you can run your own SQL statements -a, --all Retrieve everything -b, --banner Retrieve DBMS banner --current-user Retrieve DBMS current user --current-db Retrieve DBMS current database --hostname Retrieve DBMS server hostname --is-dba Detect if the DBMS current user is DBA --users Enumerate DBMS users --passwords Enumerate DBMS users password hashes --privileges Enumerate DBMS users privileges --roles Enumerate DBMS users roles --dbs Enumerate DBMS databases --tables Enumerate DBMS database tables --columns Enumerate DBMS database table columns --schema Enumerate DBMS schema --count Retrieve number of entries for table(s) --dump Dump DBMS database table entries --dump-all Dump all DBMS databases tables entries --search Search column(s), table(s) and/or database name(s) --comments Retrieve DBMS comments -D DB DBMS database to enumerate -T TBL DBMS database table(s) to enumerate -C COL DBMS database table column(s) to enumerate -X EXCLUDECOL DBMS database table column(s) to not enumerate -U USER DBMS user to enumerate --exclude-sysdbs Exclude DBMS system databases when enumerating tables --pivot-column=P.. Pivot column name --where=DUMPWHERE Use WHERE condition while table dumping --start=LIMITSTART First query output entry to retrieve --stop=LIMITSTOP Last query output entry to retrieve --first=FIRSTCHAR First query output word character to retrieve --last=LASTCHAR Last query output word character to retrieve --sql-query=QUERY SQL statement to be executed --sql-shell Prompt for an interactive SQL shell --sql-file=SQLFILE Execute SQL statements from given file(s) Brute force: These options can be used to run brute force checks --common-tables Check existence of common tables --common-columns Check existence of common columns User-defined function injection: These options can be used to create custom user-defined functions --udf-inject Inject custom user-defined functions --shared-lib=SHLIB Local path of the shared library File system access: These options can be used to access the back-end database management system underlying file system --file-read=RFILE Read a file from the back-end DBMS file system --file-write=WFILE Write a local file on the back-end DBMS file system --file-dest=DFILE Back-end DBMS absolute filepath to write to Operating system access: These options can be used to access the back-end database management system underlying operating system --os-cmd=OSCMD Execute an operating system command --os-shell Prompt for an interactive operating system shell --os-pwn Prompt for an OOB shell, Meterpreter or VNC --os-smbrelay One click prompt for an OOB shell, Meterpreter or VNC --os-bof Stored procedure buffer overflow exploitation --priv-esc Database process user privilege escalation --msf-path=MSFPATH Local path where Metasploit Framework is installed --tmp-path=TMPPATH Remote absolute path of temporary files directory Windows registry access: These options can be used to access the back-end database management system Windows registry --reg-read Read a Windows registry key value --reg-add Write a Windows registry key value data --reg-del Delete a Windows registry key value --reg-key=REGKEY Windows registry key --reg-value=REGVAL Windows registry key value --reg-data=REGDATA Windows registry key value data --reg-type=REGTYPE Windows registry key value type General: These options can be used to set some general working parameters -s SESSIONFILE Load session from a stored (.sqlite) file -t TRAFFICFILE Log all HTTP traffic into a textual file --batch Never ask for user input, use the default behaviour --binary-fields=.. Result fields having binary values (e.g. "digest") --charset=CHARSET Force character encoding used for data retrieval --crawl=CRAWLDEPTH Crawl the website starting from the target URL --crawl-exclude=.. Regexp to exclude pages from crawling (e.g. "logout") --csv-del=CSVDEL Delimiting character used in CSV output (default ",") --dump-format=DU.. Format of dumped data (CSV (default), HTML or SQLITE) --eta Display for each output the estimated time of arrival --flush-session Flush session files for current target --forms Parse and test forms on target URL --fresh-queries Ignore query results stored in session file --hex Use DBMS hex function(s) for data retrieval --output-dir=OUT.. Custom output directory path --parse-errors Parse and display DBMS error messages from responses --save=SAVECONFIG Save options to a configuration INI file --scope=SCOPE Regexp to filter targets from provided proxy log --test-filter=TE.. Select tests by payloads and/or titles (e.g. ROW) --test-skip=TEST.. Skip tests by payloads and/or titles (e.g. BENCHMARK) --update Update sqlmap Miscellaneous: -z MNEMONICS Use short mnemonics (e.g. "flu,bat,ban,tec=EU") --alert=ALERT Run host OS command(s) when SQL injection is found --answers=ANSWERS Set question answers (e.g. "quit=N,follow=N") --beep Beep on question and/or when SQL injection is found --cleanup Clean up the DBMS from sqlmap specific UDF and tables --dependencies Check for missing (non-core) sqlmap dependencies --disable-coloring Disable console output coloring --gpage=GOOGLEPAGE Use Google dork results from specified page number --identify-waf Make a thorough testing for a WAF/IPS/IDS protection --skip-waf Skip heuristic detection of WAF/IPS/IDS protection --mobile Imitate smartphone through HTTP User-Agent header --offline Work in offline mode (only use session data) --page-rank Display page rank (PR) for Google dork results --purge-output Safely remove all content from output directory --smart Conduct thorough tests only if positive heuristic(s) --sqlmap-shell Prompt for an interactive sqlmap shell --wizard Simple wizard interface for beginner users
pengiwn / QR Token GrabberDiscord Image Token Grabber About A Python script that automatically generates a Nitro scam QR code and grabs the Discord token when scanned. This tool demonstrates how people can trick others into scanning their Discord login QR Code, and gain access to their account. Use for Educational Purposes only. img1 Demonstration qr-code Info Download the latest version of chromedriver, and replace the old chromedriver.exe with the new one. If you get any errors, scroll down to troubleshoot to learn more. Usage If you dont have python installed, download python 3.7.6 and make sure you click on the 'ADD TO PATH' option during the installation. Install the required modules > pip install -r requirements.txt or double click pip_install_requirements.bat Type python QR_Generator.py in cmd to run or double click run_script.bat Wait for the discord_gift.png to be generated. Send the image to the victim and make them scan it. QR Code only lasts about 2 minutes. Make sure you send a fresh one to the victim and he is ready to scan. When the QR Code is scanned, you will automatically be logged in to their account and the script will grab the Discord token. Troubleshoot Make sure your chromedriver.exe file is the same version as your current Chrome web browser version. To check your current Chrome version, paste chrome://settings/help in Google Chrome. if Chrome crashes, Make sure your chromedriver.exe file is the same version as your Chrome web browser version Download the latest version chromedriver.exe here: https://chromedriver.chromium.org/downloads Then replace the chromedriver.exe file in the folder.
ajaybhatiya1234 / DEEP FACE Dectection01 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── base_deepfake (1) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── both_deepfake (3) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │ ├── DeepFakeDetection │ ├── Deepfakes │ ├── Face2Face │ ├── FaceSwap │ └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.
TheKysek / MiNodePython 3 implementation of the Bitmessage protocol. Designed only to route objects inside the network.
Roibal / Geotechnical Engineering Python CodeThe purpose of this github repository is to post Geotechnical Engineering related software I have written in Python 3.3. Code is for research purposes only. Requires MatPlotLib for examples and data visualization.
Aryia-Behroziuan / NumpyQuickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see https://scipy.org/install.html for instructions. Learner profile This tutorial is intended as a quick overview of algebra and arrays in NumPy and want to understand how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this tutorial might be of help. Learning Objectives After this tutorial, you should be able to: Understand the difference between one-, two- and n-dimensional arrays in NumPy; Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops; Understand axis and shape properties for n-dimensional arrays. The Basics NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array. ndarray.shape the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size the total number of elements of the array. This is equal to the product of the elements of shape. ndarray.dtype an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize. ndarray.data the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities. An example >>> import numpy as np a = np.arange(15).reshape(3, 5) a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) a.shape (3, 5) a.ndim 2 a.dtype.name 'int64' a.itemsize 8 a.size 15 type(a) <class 'numpy.ndarray'> b = np.array([6, 7, 8]) b array([6, 7, 8]) type(b) <class 'numpy.ndarray'> Array Creation There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. >>> >>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') A frequent error consists in calling array with multiple arguments, rather than providing a single sequence as an argument. >>> >>> a = np.array(1,2,3,4) # WRONG Traceback (most recent call last): ... TypeError: array() takes from 1 to 2 positional arguments but 4 were given >>> a = np.array([1,2,3,4]) # RIGHT array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. >>> >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) The type of the array can also be explicitly specified at creation time: >>> >>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64. >>> >>> np.zeros((3, 4)) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) To create sequences of numbers, NumPy provides the arange function which is analogous to the Python built-in range, but returns an array. >>> >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: >>> >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) See also array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.Generator.rand, numpy.random.Generator.randn, fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. >>> >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3) # 2d array >>> print(b) [[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] >>> >>> c = np.arange(24).reshape(2,3,4) # 3d array >>> print(c) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] See below to get more details on reshape. If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: >>> >>> print(np.arange(10000)) [ 0 1 2 ... 9997 9998 9999] >>> >>> print(np.arange(10000).reshape(100,100)) [[ 0 1 2 ... 97 98 99] [ 100 101 102 ... 197 198 199] [ 200 201 202 ... 297 298 299] ... [9700 9701 9702 ... 9797 9798 9799] [9800 9801 9802 ... 9897 9898 9899] [9900 9901 9902 ... 9997 9998 9999]] To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions. >>> >>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported Basic Operations Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. >>> >>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False]) Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: >>> >>> A = np.array( [[1,1], ... [0,1]] ) >>> B = np.array( [[2,0], ... [3,4]] ) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. >>> >>> rg = np.random.default_rng(1) # create instance of default random number generator >>> a = np.ones((2,3), dtype=int) >>> b = rg.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ... numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind' When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). >>> >>> a = np.ones(3, dtype=np.int32) >>> b = np.linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. >>> >>> a = rg.random((2,3)) >>> a array([[0.82770259, 0.40919914, 0.54959369], [0.02755911, 0.75351311, 0.53814331]]) >>> a.sum() 3.1057109529998157 >>> a.min() 0.027559113243068367 >>> a.max() 0.8277025938204418 By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: >>> >>> b = np.arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]]) Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”(ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>> >>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([2., 0., 6.]) See also all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, invert, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. >>> >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) # equivalent to a[0:6:2] = 1000; # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, 1000, 27, 1000, 1, 1000]) >>> for i in a: ... print(i**(1/3.)) ... 9.999999999999998 1.0 9.999999999999998 3.0 9.999999999999998 4.999999999999999 5.999999999999999 6.999999999999999 7.999999999999999 8.999999999999998 Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: >>> >>> def f(x,y): ... return 10*x+y ... >>> b = np.fromfunction(f,(5,4),dtype=int) >>> b array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]]) >>> b[2,3] 23 >>> b[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) When fewer indices are provided than the number of axes, the missing indices are considered complete slices: >>> >>> b[-1] # the last row. Equivalent to b[-1,:] array([40, 41, 42, 43]) The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...]. The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:], x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:]. >>> >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays) ... [ 10, 12, 13]], ... [[100,101,102], ... [110,112,113]]]) >>> c.shape (2, 2, 3) >>> c[1,...] # same as c[1,:,:] or c[1] array([[100, 101, 102], [110, 112, 113]]) >>> c[...,2] # same as c[:,:,2] array([[ 2, 13], [102, 113]]) Iterating over multidimensional arrays is done with respect to the first axis: >>> >>> for row in b: ... print(row) ... [0 1 2 3] [10 11 12 13] [20 21 22 23] [30 31 32 33] [40 41 42 43] However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: >>> >>> for element in b.flat: ... print(element) ... 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43 See also Indexing, Indexing (reference), newaxis, ndenumerate, indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: >>> >>> a = np.floor(10*rg.random((3,4))) >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.shape (3, 4) The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: >>> >>> a.ravel() # returns the array, flattened array([3., 7., 3., 4., 1., 4., 2., 2., 7., 2., 4., 9.]) >>> a.reshape(6,2) # returns the array with a modified shape array([[3., 7.], [3., 4.], [1., 4.], [2., 2.], [7., 2.], [4., 9.]]) >>> a.T # returns the array, transposed array([[3., 1., 7.], [7., 4., 2.], [3., 2., 4.], [4., 2., 9.]]) >>> a.T.shape (4, 3) >>> a.shape (3, 4) The order of the elements in the array resulting from ravel() is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a[0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape() can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: >>> >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.resize((2,6)) >>> a array([[3., 7., 3., 4., 1., 4.], [2., 2., 7., 2., 4., 9.]]) If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: >>> >>> a.reshape(3,-1) array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) See also ndarray.shape, reshape, resize, ravel Stacking together different arrays Several arrays can be stacked together along different axes: >>> >>> a = np.floor(10*rg.random((2,2))) >>> a array([[9., 7.], [5., 2.]]) >>> b = np.floor(10*rg.random((2,2))) >>> b array([[1., 9.], [5., 1.]]) >>> np.vstack((a,b)) array([[9., 7.], [5., 2.], [1., 9.], [5., 1.]]) >>> np.hstack((a,b)) array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: >>> >>> from numpy import newaxis >>> np.column_stack((a,b)) # with 2D arrays array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) >>> a = np.array([4.,2.]) >>> b = np.array([3.,8.]) >>> np.column_stack((a,b)) # returns a 2D array array([[4., 3.], [2., 8.]]) >>> np.hstack((a,b)) # the result is different array([4., 2., 3., 8.]) >>> a[:,newaxis] # view `a` as a 2D column vector array([[4.], [2.]]) >>> np.column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 3.], [2., 8.]]) >>> np.hstack((a[:,newaxis],b[:,newaxis])) # the result is the same array([[4., 3.], [2., 8.]]) On the other hand, the function row_stack is equivalent to vstack for any input arrays. In fact, row_stack is an alias for vstack: >>> >>> np.column_stack is np.hstack False >>> np.row_stack is np.vstack True In general, for arrays with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) >>> >>> np.r_[1:4,0,4] array([1, 2, 3, 0, 4]) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also hstack, vstack, column_stack, concatenate, c_, r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: >>> >>> a = np.floor(10*rg.random((2,12))) >>> a array([[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.], [8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]]) # Split a into 3 >>> np.hsplit(a,3) [array([[6., 7., 6., 9.], [8., 5., 5., 7.]]), array([[0., 5., 4., 0.], [1., 8., 6., 7.]]), array([[6., 8., 5., 2.], [1., 8., 1., 0.]])] # Split a after the third and the fourth column >>> np.hsplit(a,(3,4)) [array([[6., 7., 6.], [8., 5., 5.]]), array([[9.], [7.]]), array([[0., 5., 4., 0., 6., 8., 5., 2.], [1., 8., 6., 7., 1., 8., 1., 0.]])] vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of objects or their data. >>> >>> a = np.array([[ 0, 1, 2, 3], ... [ 4, 5, 6, 7], ... [ 8, 9, 10, 11]]) >>> b = a # no new object is created >>> b is a # a and b are two names for the same ndarray object True Python passes mutable objects as references, so function calls make no copy. >>> >>> def f(x): ... print(id(x)) ... >>> id(a) # id is a unique identifier of an object 148293216 # may vary >>> f(a) 148293216 # may vary View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. >>> >>> c = a.view() >>> c is a False >>> c.base is a # c is a view of the data owned by a True >>> c.flags.owndata False >>> >>> c = c.reshape((2, 6)) # a's shape doesn't change >>> a.shape (3, 4) >>> c[0, 4] = 1234 # a's data changes >>> a array([[ 0, 1, 2, 3], [1234, 5, 6, 7], [ 8, 9, 10, 11]]) Slicing an array returns a view of it: >>> >>> s = a[ : , 1:3] # spaces added for clarity; could also be written "s = a[:, 1:3]" >>> s[:] = 10 # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Deep Copy The copy method makes a complete copy of the array and its data. >>> >>> d = a.copy() # a new array object with new data is created >>> d is a False >>> d.base is a # d doesn't share anything with a False >>> d[0,0] = 9999 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Sometimes copy should be called after slicing if the original array is not required anymore. For example, suppose a is a huge intermediate result and the final result b only contains a small fraction of a, a deep copy should be made when constructing b with slicing: >>> >>> a = np.arange(int(1e8)) >>> b = a[:100].copy() >>> del a # the memory of ``a`` can be released. If b = a[:100] is used instead, a is referenced by b and will persist in memory even if del a is executed. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like Conversions ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat Manipulations array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack Questions all, any, nonzero, where Ordering argmax, argmin, argsort, max, min, ptp, searchsorted, sort Operations choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum Basic Statistics cov, mean, std, var Basic Linear Algebra cross, dot, outer, linalg.svd, vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Advanced indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices >>> >>> a = np.arange(12)**2 # the first 12 square numbers >>> i = np.array([1, 1, 3, 8, 5]) # an array of indices >>> a[i] # the elements of a at the positions i array([ 1, 1, 9, 64, 25]) >>> >>> j = np.array([[3, 4], [9, 7]]) # a bidimensional array of indices >>> a[j] # the same shape as j array([[ 9, 16], [81, 49]]) When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. >>> >>> palette = np.array([[0, 0, 0], # black ... [255, 0, 0], # red ... [0, 255, 0], # green ... [0, 0, 255], # blue ... [255, 255, 255]]) # white >>> image = np.array([[0, 1, 2, 0], # each value corresponds to a color in the palette ... [0, 3, 4, 0]]) >>> palette[image] # the (2, 4, 3) color image array([[[ 0, 0, 0], [255, 0, 0], [ 0, 255, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 255], [255, 255, 255], [ 0, 0, 0]]]) We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. >>> >>> a = np.arange(12).reshape(3,4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> i = np.array([[0, 1], # indices for the first dim of a ... [1, 2]]) >>> j = np.array([[2, 1], # indices for the second dim ... [3, 3]]) >>> >>> a[i, j] # i and j must have equal shape array([[ 2, 5], [ 7, 11]]) >>> >>> a[i, 2] array([[ 2, 6], [ 6, 10]]) >>> >>> a[:, j] # i.e., a[ : , j] array([[[ 2, 1], [ 3, 3]], [[ 6, 5], [ 7, 7]], [[10, 9], [11, 11]]]) In Python, arr[i, j] is exactly the same as arr[(i, j)]—so we can put i and j in a tuple and then do the indexing with that. >>> >>> l = (i, j) # equivalent to a[i, j] >>> a[l] array([[ 2, 5], [ 7, 11]]) However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. >>> >>> s = np.array([i, j]) # not what we want >>> a[s] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: index 3 is out of bounds for axis 0 with size 3 # same as a[i, j] >>> a[tuple(s)] array([[ 2, 5], [ 7, 11]]) Another common use of indexing with arrays is the search of the maximum value of time-dependent series: >>> >>> time = np.linspace(20, 145, 5) # time scale >>> data = np.sin(np.arange(20)).reshape(5,4) # 4 time-dependent series >>> time array([ 20. , 51.25, 82.5 , 113.75, 145. ]) >>> data array([[ 0. , 0.84147098, 0.90929743, 0.14112001], [-0.7568025 , -0.95892427, -0.2794155 , 0.6569866 ], [ 0.98935825, 0.41211849, -0.54402111, -0.99999021], [-0.53657292, 0.42016704, 0.99060736, 0.65028784], [-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]... >>> time_max array([ 82.5 , 20. , 113.75, 51.25]) >>> data_max array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ]) >>> np.all(data_max == data.max(axis=0)) True You can also use indexing with arrays as a target to assign to: >>> >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a[[1,3,4]] = 0 >>> a array([0, 0, 2, 0, 0]) However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: >>> >>> a = np.arange(5) >>> a[[0,0,2]]=[1,2,3] >>> a array([2, 1, 3, 3, 4]) This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: >>> >>> a = np.arange(5) >>> a[[0,0,2]]+=1 >>> a array([1, 1, 3, 3, 4]) Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: >>> >>> a = np.arange(12).reshape(3,4) >>> b = a > 4 >>> b # b is a boolean with a's shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7, 8, 9, 10, 11]) This property can be very useful in assignments: >>> >>> a[b] = 0 # All elements of 'a' higher than 4 become 0 >>> a array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]]) You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set: >>> import numpy as np import matplotlib.pyplot as plt def mandelbrot( h,w, maxit=20 ): """Returns an image of the Mandelbrot fractal of size (h,w).""" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ] c = x+y*1j z = c divtime = maxit + np.zeros(z.shape, dtype=int) for i in range(maxit): z = z**2 + c diverge = z*np.conj(z) > 2**2 # who is diverging div_now = diverge & (divtime==maxit) # who is diverging now divtime[div_now] = i # note when z[diverge] = 2 # avoid diverging too much return divtime plt.imshow(mandelbrot(400,400)) ../_images/quickstart-1.png The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: >>> >>> a = np.arange(12).reshape(3,4) >>> b1 = np.array([False,True,True]) # first dim selection >>> b2 = np.array([True,False,True,False]) # second dim selection >>> >>> a[b1,:] # selecting rows array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:,b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1,b2] # a weird thing to do array([ 4, 10]) Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: >>> >>> a = np.array([2,3,4,5]) >>> b = np.array([8,5,4]) >>> c = np.array([5,4,6,8,3]) >>> ax,bx,cx = np.ix_(a,b,c) >>> ax array([[[2]], [[3]], [[4]], [[5]]]) >>> bx array([[[8], [5], [4]]]) >>> cx array([[[5, 4, 6, 8, 3]]]) >>> ax.shape, bx.shape, cx.shape ((4, 1, 1), (1, 3, 1), (1, 1, 5)) >>> result = ax+bx*cx >>> result array([[[42, 34, 50, 66, 26], [27, 22, 32, 42, 17], [22, 18, 26, 34, 14]], [[43, 35, 51, 67, 27], [28, 23, 33, 43, 18], [23, 19, 27, 35, 15]], [[44, 36, 52, 68, 28], [29, 24, 34, 44, 19], [24, 20, 28, 36, 16]], [[45, 37, 53, 69, 29], [30, 25, 35, 45, 20], [25, 21, 29, 37, 17]]]) >>> result[3,2,4] 17 >>> a[3]+b[2]*c[4] 17 You could also implement the reduce as follows: >>> >>> def ufunc_reduce(ufct, *vectors): ... vs = np.ix_(*vectors) ... r = ufct.identity ... for v in vs: ... r = ufct(r,v) ... return r and then use it as: >>> >>> ufunc_reduce(np.add,a,b,c) array([[[15, 14, 16, 18, 13], [12, 11, 13, 15, 10], [11, 10, 12, 14, 9]], [[16, 15, 17, 19, 14], [13, 12, 14, 16, 11], [12, 11, 13, 15, 10]], [[17, 16, 18, 20, 15], [14, 13, 15, 17, 12], [13, 12, 14, 16, 11]], [[18, 17, 19, 21, 16], [15, 14, 16, 18, 13], [14, 13, 15, 17, 12]]]) The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays. Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. >>> >>> import numpy as np >>> a = np.array([[1.0, 2.0], [3.0, 4.0]]) >>> print(a) [[1. 2.] [3. 4.]] >>> a.transpose() array([[1., 3.], [2., 4.]]) >>> np.linalg.inv(a) array([[-2. , 1. ], [ 1.5, -0.5]]) >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I" >>> u array([[1., 0.], [0., 1.]]) >>> j = np.array([[0.0, -1.0], [1.0, 0.0]]) >>> j @ j # matrix product array([[-1., 0.], [ 0., -1.]]) >>> np.trace(u) # trace 2.0 >>> y = np.array([[5.], [7.]]) >>> np.linalg.solve(a, y) array([[-3.], [ 4.]]) >>> np.linalg.eig(j) (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j]])) Parameters: square matrix Returns The eigenvalues, each repeated according to its multiplicity. The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]`` . Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: >>> >>> a = np.arange(30) >>> b = a.reshape((2, -1, 3)) # -1 means "whatever is needed" >>> b.shape (2, 5, 3) >>> b array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]], [[15, 16, 17], [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]]) Vector Stacking How do we construct a 2D array from a list of equally-sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y]. In NumPy this works via the functions column_stack, dstack, hstack and vstack, depending on the dimension in which the stacking is to be done. For example: >>> >>> x = np.arange(0,10,2) >>> y = np.arange(5) >>> m = np.vstack([x,y]) >>> m array([[0, 2, 4, 6, 8], [0, 1, 2, 3, 4]]) >>> xy = np.hstack([x,y]) >>> xy array([0, 2, 4, 6, 8, 0, 1, 2, 3, 4]) The logic behind those functions in more than two dimensions can be strange. See also NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and a vector of the bin edges. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. >>> import numpy as np rg = np.random.default_rng(1) import matplotlib.pyplot as plt # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = rg.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins plt.hist(v, bins=50, density=1) # matplotlib version (plot) # Compute the histogram with numpy and then plot it (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) plt.plot(.5*(bins[1:]+bins[:-1]), n) ../_images/quickstart-2.png Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary © Copyright 2008-2020, The SciPy community. Last updated on Jun 29, 2020. Created using Sphinx 2.4.4.
waldiTM / Python MysqlprotoPlain Python implementation of the MySQL protocol. It uses asyncio, so works only with Python 3.3+. It only implements the server partfor now.
ced-mos / Line Drawerline-drawer recreates a given image by only drawing it by simple straight lines. Implementation inspired by linify.me and written in Python 3.
ovrsea / AdapTable"adapTable" is an unofficial open source tool developed in python by Ovrsea in order to help other data teams administrate more efficiently dashboards, cards and tables in Metabase. This script has been tested on Metabase version 0.45.3 only for now.
mojihack / TgTelegram messenger CLI Build Status Command-line interface for Telegram. Uses readline interface. API, Protocol documentation Documentation for Telegram API is available here: http://core.telegram.org/api Documentation for MTproto protocol is available here: http://core.telegram.org/mtproto Upgrading to version 1.0 First of all, the binary is now in ./bin folder and is named telegram-cli. So be careful, not to use old binary. Second, config folder is now ${HOME}/.telegram-cli Third, database is not compatible with older versions, so you'll have to login again. Fourth, in peer_name '#' are substitued to '@'. (Not applied to appending of '#%d' in case of two peers having same name). Installation Clone GitHub Repository git clone --recursive https://github.com/vysheng/tg.git && cd tg Python Support Python support is currently limited to Python 2.7 or Python 3.1+. Other versions may work but are not tested. Linux and BSDs Install libs: readline, openssl and (if you want to use config) libconfig, liblua, python and libjansson. If you do not want to use them pass options --disable-libconfig, --disable-liblua, --disable-python and --disable-json respectively. On Ubuntu/Debian use: sudo apt-get install libreadline-dev libconfig-dev libssl-dev lua5.2 liblua5.2-dev libevent-dev libjansson-dev libpython-dev make On gentoo: sudo emerge -av sys-libs/readline dev-libs/libconfig dev-libs/openssl dev-lang/lua dev-libs/libevent dev-libs/jansson dev-lang/python On Fedora: sudo dnf install lua-devel openssl-devel libconfig-devel readline-devel libevent-devel libjansson-devel python-devel On Archlinux: yaourt -S telegram-cli-git On FreeBSD: pkg install libconfig libexecinfo lua52 python On OpenBSD: pkg_add libconfig libexecinfo lua python On openSUSE: sudo zypper in lua-devel libconfig-devel readline-devel libevent-devel libjansson-devel python-devel libopenssl-devel Then, ./configure make Other methods to install on linux On Gentoo: use ebuild provided. On Arch: https://aur.archlinux.org/packages/telegram-cli-git Mac OS X The client depends on readline library and libconfig, which are not included in OS X by default. You have to install these libraries manually. If using Homebrew: brew install libconfig readline lua python libevent jansson export CFLAGS="-I/usr/local/include -I/usr/local/Cellar/readline/6.3.8/include" export LDFLAGS="-L/usr/local/lib -L/usr/local/Cellar/readline/6.3.8/lib" ./configure && make Thanks to @jfontan for this solution. If using MacPorts: sudo port install libconfig-hr sudo port install readline sudo port install lua51 sudo port install python34 sudo port install libevent export CFLAGS="-I/usr/local/include -I/opt/local/include -I/opt/local/include/lua-5.1" export LDFLAGS="-L/usr/local/lib -L/opt/local/lib -L/opt/local/lib/lua-5.1" ./configure && make Install these ports: devel/libconfig devel/libexecinfo lang/lua52 Then build: env CC=clang CFLAGS=-I/usr/local/include LDFLAGS=-L/usr/local/lib LUA=/usr/local/bin/lua52 LUA_INCLUDE=-I/usr/local/include/lua52 LUA_LIB=-llua-5.2 ./configure make Other UNIX If you manage to launch it on other UNIX, please let me know. Contacts If you would like to ask a question, you can write to my telegram or to the github (or both). To contact me via telegram, you should use import_card method with argument 000653bf:0738ca5d:5521fbac:29246815:a27d0cda Usage bin/telegram-cli -k <public-server-key> By default, the public key is stored in tg-server.pub in the same folder or in /etc/telegram-cli/server.pub. If not, specify where to find it: bin/telegram-cli -k tg-server.pub Client support TAB completion and command history. Peer refers to the name of the contact or dialog and can be accessed by TAB completion. For user contacts peer name is Name Lastname with all spaces changed to underscores. For chats it is it's title with all spaces changed to underscores For encrypted chats it is <Exсlamation mark> Name Lastname with all spaces changed to underscores. If two or more peers have same name, number is appended to the name. (for example A_B, A_B#1, A_B#2 and so on) Supported commands Messaging msg <peer> Text - sends message to this peer fwd <user> <msg-seqno> - forward message to user. You can see message numbers starting client with -N chat_with_peer <peer> starts one on one chat session with this peer. /exit or /quit to end this mode. add_contact <phone-number> <first-name> <last-name> - tries to add contact to contact-list by phone rename_contact <user> <first-name> <last-name> - tries to rename contact. If you have another device it will be a fight mark_read <peer> - mark read all received messages with peer delete_msg <msg-seqno> - deletes message (not completly, though) restore_msg <msg-seqno> - restores delete message. Impossible for secret chats. Only possible short time (one hour, I think) after deletion Multimedia send_photo <peer> <photo-file-name> - sends photo to peer send_video <peer> <video-file-name> - sends video to peer send_text <peer> <text-file-name> - sends text file as plain messages load_photo/load_video/load_video_thumb/load_audio/load_document/load_document_thumb <msg-seqno> - loads photo/video/audio/document to download dir view_photo/view_video/view_video_thumb/view_audio/view_document/view_document_thumb <msg-seqno> - loads photo/video to download dir and starts system default viewer fwd_media <msg-seqno> send media in your message. Use this to prevent sharing info about author of media (though, it is possible to determine user_id from media itself, it is not possible get access_hash of this user) set_profile_photo <photo-file-name> - sets userpic. Photo should be square, or server will cut biggest central square part Group chat options chat_info <chat> - prints info about chat chat_add_user <chat> <user> - add user to chat chat_del_user <chat> <user> - remove user from chat rename_chat <chat> <new-name> create_group_chat <chat topic> <user1> <user2> <user3> ... - creates a groupchat with users, use chat_add_user to add more users chat_set_photo <chat> <photo-file-name> - sets group chat photo. Same limits as for profile photos. Search search <peer> pattern - searches pattern in messages with peer global_search pattern - searches pattern in all messages Secret chat create_secret_chat <user> - creates secret chat with this user visualize_key <secret_chat> - prints visualization of encryption key. You should compare it to your partner's one set_ttl <secret_chat> <ttl> - sets ttl to secret chat. Though client does ignore it, client on other end can make use of it accept_secret_chat <secret_chat> - manually accept secret chat (only useful when starting with -E key) Stats and various info user_info <user> - prints info about user history <peer> [limit] - prints history (and marks it as read). Default limit = 40 dialog_list - prints info about your dialogs contact_list - prints info about users in your contact list suggested_contacts - print info about contacts, you have max common friends stats - just for debugging show_license - prints contents of GPLv2 help - prints this help get_self - get our user info Card export_card - print your 'card' that anyone can later use to import your contact import_card <card> - gets user by card. You can write messages to him after that. Other quit - quit safe_quit - wait for all queries to end then quit
mdscruggs / GaA lightweight genetic algorithm library written in pure Python (currently only 3.x).
kabikaj / JabalinJabalín is an application for generating verbs in Modern Standard Arabic. The application is implemented in python language version 3. The project includes a lexicon of 15.452 lemmas that can be used as the input of the system. The system generates all verbs as morphologically regular starting from the verbal root. Irregularities are considered phonological alterations affecting the superficial level of the form and as such they are treated in a later stage. The system classifies all verbs in only two conjugational classes. Jabalín has been developed in LLI-UAM, The Laboratorio de Lingüística Informática, Universidad Autónoma de Madrid.
llinkz / DirectKiwia Python 2/3 GUI for fast connect to KiwiSDR around the world (audio only)
Symefa / Gib LangThe purpose of this repo is showcasing the possibility of creating Turing-complete language using only 3 symbols. And to test my ability using python language.
Dieudomtechn / Create A Free Proxy Server With Google App EngineHere’s one such proxy site that you can build for your friends in China or even for your personal use (say for accessing blocked sites from office). This is created using Google App Engine and, contrary to what you may think, the setup is quite simple. 1. Go to appengine.google.com and sign-in using your Google Account. 2. Click the “Create an Application” button. Since this is your first time, Google will send a verification code via SMS to your mobile phone number. Type the code and you’re all set to create apps with Google App Engine. 3. Pick an Application Identifier and it becomes the sub-domain* of your proxy server. Give your app a title (say Proxy Server), set the Authentication Option as “Open to all users”, agree to the terms and create the application. (screenshot) 4. OK, now that we have reserved the APP ID, it’s time to create and upload the proxy server application to Google App Engine. Go to python.org, download the 2.7 Installer and install Python. If you are on Mac, Python 2.7 is already installed on your computer. 5. Download this zip file and extract it to your desktop. The zip file contains a couple of HTML, YAML and Python (.py) files that you can view inside WordPad. 6. Go to code.google.com, download the Google App Engine SDK for Python and follow the wizard to install the SDK on your computer. When the installation wizard has finished, click the “Run Launcher” button to open the App Engine Program. 7. Choose Edit -> Preferences inside the Google App Engine Launcher program from the desktop and set the correct values (see screenshot) for the Python Path, App Engine SDK and the Text Editor (set this is as WordPad or write.exe and not notepad.exe). 8. Click File – > Add Existing Application under the Google App Launcher program and browse to the folder that contain the index.yaml and other files that you extracted in Step 5. Once the project is added to App Engine, select the project and click Edit to replace “YOUR_APP_ID” with your App ID (screenshot). Save and close the file. 9. Click Deploy, enter you Google account credentials and, within a minute or two, your online proxy server will be deployed and become ready for use (screenshot). The public URL (or web address) of your new proxy server will be your_app_id.appspot.com (replace your_app_id with your App Engine Identifier). [*] The sub-domain or the App ID will uniquely identify your App Engine application. For this example, we’ll use labnol-proxy-server as the Application Identifier though you are free to choose any other unique name. Next Steps – Setting up a Free Proxy with Google You can edit the main.html file to change the appearance of your proxy website. You can even add code for Google Analytics and Google AdSense code to monetize your proxy server. The proxy server is public on the web (open to everyone) but you can add a layer of authentication so that only Google Account users who are logged-in can use your proxy server. If you have made any changes to your HTML files, you can upload the latest version to Google App Engine either by clicking the “Deploy” button again or use the following command – appcfg.py update <app-directory> This proxy works with Flash videos (like YouTube and ABC News) though not with Hulu. As some of you have suggested, web domains with the word “proxy” or “proxies” are banned at workplaces so you may avoid using them in your appspot.com proxy address. Though there exist proxy servers for accessing secure (https) sites, this is a basic proxy server that won’t work with sites that require logins (like Gmail). The proxy server code is available on Github and is fork of the Mirrorr project.
Optixal / Sshpwn:bomb: A modular framework and program for synchronous pwning with ssh, powered by Python 3, for educational and controlled penetration testing purposes only.
psarkerbd / Simple CalculatorIt is a calculator software developed by python. Python version is 3.x. It is my first project on python Graphical User Interface. This project always take the two numbers and do 4 operations (Addition, Subtraction, Multiplication and Division) between that two numbers. User can only entry two numerical values otherwise it will give Error message
Qwof / Tiktok Auto ClaimerTikTok-AutoClaimer Working TikTok Username Auto-Claimer/Sniper/Swapper which will autoclaim username if it´s available Usage Python 3.6 or above is required. Windows OS Only(can be easily changed). Check your Google Chrome version. Download chromedriver.exe with the matching version here to autoclaimer.py's file directory. pip/3 install -r requirements.txt run autoclaimer.py with Python 3.6 Enter Username you want to claim Login into your TikTok Account type "True" to start checking Info chromedriver version MUST be same version as your chrome version Navigate to the three dots at the top right corner of Google Chrome. Select "Help", and click "About Google Chrome". Additional Auto Claimer may be slow but it is still faster than manual swap
