512 skills found · Page 9 of 18
goerz / Convert Encoding.pyPython script for converting text files between encodings (stable)
soasme / DogeonDSON encoder/decoder for Python
delb-xml / Delb PyA Python library that provides an ergonomic, DOM-like model for XML encoded text documents.
Rushikesh8983 / MastersDataScience Deep Learning ProjectLanguage Translation In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French. Get the Data Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus. """ DON'T MODIFY ANYTHING IN THIS CELL """ import helper import problem_unittests as tests source_path = 'data/small_vocab_en' target_path = 'data/small_vocab_fr' source_text = helper.load_data(source_path) target_text = helper.load_data(target_path) Explore the Data Play around with view_sentence_range to view different parts of the data. view_sentence_range = (0, 10) """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np print('Dataset Stats') print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()}))) sentences = source_text.split('\n') word_counts = [len(sentence.split()) for sentence in sentences] print('Number of sentences: {}'.format(len(sentences))) print('Average number of words in a sentence: {}'.format(np.average(word_counts))) print() print('English sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) print() print('French sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) Dataset Stats Roughly the number of unique words: 227 Number of sentences: 137861 Average number of words in a sentence: 13.225277634719028 English sentences 0 to 10: new jersey is sometimes quiet during autumn , and it is snowy in april . the united states is usually chilly during july , and it is usually freezing in november . california is usually quiet during march , and it is usually hot in june . the united states is sometimes mild during june , and it is cold in september . your least liked fruit is the grape , but my least liked is the apple . his favorite fruit is the orange , but my favorite is the grape . paris is relaxing during december , but it is usually chilly in july . new jersey is busy during spring , and it is never hot in march . our least liked fruit is the lemon , but my least liked is the grape . the united states is sometimes busy during january , and it is sometimes warm in november . French sentences 0 to 10: new jersey est parfois calme pendant l' automne , et il est neigeux en avril . les états-unis est généralement froid en juillet , et il gèle habituellement en novembre . california est généralement calme en mars , et il est généralement chaud en juin . les états-unis est parfois légère en juin , et il fait froid en septembre . votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme . son fruit préféré est l'orange , mais mon préféré est le raisin . paris est relaxant en décembre , mais il est généralement froid en juillet . new jersey est occupé au printemps , et il est jamais chaude en mars . notre fruit est moins aimé le citron , mais mon moins aimé est le raisin . les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre . Implement Preprocessing Function Text to Word Ids As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids. However, you need to add the <EOS> word id at the end of target_text. This will help the neural network predict when the sentence should end. You can get the <EOS> word id by doing: target_vocab_to_int['<EOS>'] You can get other word ids using source_vocab_to_int and target_vocab_to_int. def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): """ Convert source and target text to proper word ids :param source_text: String that contains all the source text. :param target_text: String that contains all the target text. :param source_vocab_to_int: Dictionary to go from the source words to an id :param target_vocab_to_int: Dictionary to go from the target words to an id :return: A tuple of lists (source_id_text, target_id_text) """ # TODO: Implement Function source_id_text = [[source_vocab_to_int[word] for word in sentence.split()] \ for sentence in source_text.split('\n')] target_id_text = [[target_vocab_to_int[word] for word in sentence.split()] + [target_vocab_to_int['<EOS>']] \ for sentence in target_text.split('\n')] return source_id_text, target_id_text """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_text_to_ids(text_to_ids) Tests Passed Preprocess all the data and save it Running the code cell below will preprocess all the data and save it to file. """ DON'T MODIFY ANYTHING IN THIS CELL """ helper.preprocess_and_save_data(source_path, target_path, text_to_ids) Check Point This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk. import problem_unittests as tests """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np import helper (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() Check the Version of TensorFlow and Access to GPU This will check to make sure you have the correct version of TensorFlow and access to a GPU """ DON'T MODIFY ANYTHING IN THIS CELL """ from distutils.version import LooseVersion import warnings import tensorflow as tf from tensorflow.python.layers.core import Dense # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer' print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) TensorFlow Version: 1.1.0 Default GPU Device: /gpu:0 Build the Neural Network You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below: model_inputs process_decoder_input encoding_layer decoding_layer_train decoding_layer_infer decoding_layer seq2seq_model Input Implement the model_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders: Input text placeholder named "input" using the TF Placeholder name parameter with rank 2. Targets placeholder with rank 2. Learning rate placeholder with rank 0. Keep probability placeholder named "keep_prob" using the TF Placeholder name parameter with rank 0. Target sequence length placeholder named "target_sequence_length" with rank 1 Max target sequence length tensor named "max_target_len" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0. Source sequence length placeholder named "source_sequence_length" with rank 1 Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) def model_inputs(): """ Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences. :return: Tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) """ # TODO: Implement Function inputs = tf.placeholder(tf.int32, [None, None], 'input') targets = tf.placeholder(tf.int32, [None, None]) learning_rate = tf.placeholder(tf.float32, []) keep_prob = tf.placeholder(tf.float32, [], 'keep_prob') target_sequence_length = tf.placeholder(tf.int32, [None], 'target_sequence_length') max_target_len = tf.reduce_max(target_sequence_length) source_sequence_length = tf.placeholder(tf.int32, [None], 'source_sequence_length') return inputs, targets, learning_rate, keep_prob, target_sequence_length, max_target_len, source_sequence_length """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_model_inputs(model_inputs) Tests Passed Process Decoder Input Implement process_decoder_input by removing the last word id from each batch in target_data and concat the GO ID to the begining of each batch. def process_decoder_input(target_data, target_vocab_to_int, batch_size): """ Preprocess target data for encoding :param target_data: Target Placehoder :param target_vocab_to_int: Dictionary to go from the target words to an id :param batch_size: Batch Size :return: Preprocessed target data """ # TODO: Implement Function go = tf.constant([[target_vocab_to_int['<GO>']]]*batch_size) # end = tf.slice(target_data, [0, 0], [-1, batch_size]) end = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) return tf.concat([go, end], 1) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_process_encoding_input(process_decoder_input) Tests Passed Encoding Implement encoding_layer() to create a Encoder RNN layer: Embed the encoder input using tf.contrib.layers.embed_sequence Construct a stacked tf.contrib.rnn.LSTMCell wrapped in a tf.contrib.rnn.DropoutWrapper Pass cell and embedded input to tf.nn.dynamic_rnn() from imp import reload reload(tests) def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size): """ Create encoding layer :param rnn_inputs: Inputs for the RNN :param rnn_size: RNN Size :param num_layers: Number of layers :param keep_prob: Dropout keep probability :param source_sequence_length: a list of the lengths of each sequence in the batch :param source_vocab_size: vocabulary size of source data :param encoding_embedding_size: embedding size of source data :return: tuple (RNN output, RNN state) """ # TODO: Implement Function embed = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size) def lstm_cell(): lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) return tf.contrib.rnn.DropoutWrapper(lstm, keep_prob) stacked_lstm = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(num_layers)]) # initial_state = stacked_lstm.zero_state(source_sequence_length, tf.float32) return tf.nn.dynamic_rnn(stacked_lstm, embed, source_sequence_length, dtype=tf.float32) # initial_state=initial_state) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_encoding_layer(encoding_layer) Tests Passed Decoding - Training Create a training decoding layer: Create a tf.contrib.seq2seq.TrainingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, output_layer, keep_prob): """ Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell :param dec_embed_input: Decoder embedded input :param target_sequence_length: The lengths of each sequence in the target batch :param max_summary_length: The length of the longest sequence in the batch :param output_layer: Function to apply the output layer :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing training logits and sample_id """ # TODO: Implement Function helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_train_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) # for tensorflow 1.2: # dec_train_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) return dec_train_logits # keep_prob/dropout not used? """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_train(decoding_layer_train) Tests Passed Decoding - Inference Create inference decoder: Create a tf.contrib.seq2seq.GreedyEmbeddingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob): """ Create a decoding layer for inference :param encoder_state: Encoder state :param dec_cell: Decoder RNN Cell :param dec_embeddings: Decoder embeddings :param start_of_sequence_id: GO ID :param end_of_sequence_id: EOS Id :param max_target_sequence_length: Maximum length of target sequences :param vocab_size: Size of decoder/target vocabulary :param decoding_scope: TenorFlow Variable Scope for decoding :param output_layer: Function to apply the output layer :param batch_size: Batch size :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing inference logits and sample_id """ # TODO: Implement Function start_tokens = tf.constant([start_of_sequence_id]*batch_size) helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_infer_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) # for tensorflow 1.2: # dec_infer_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) return dec_infer_logits """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_infer(decoding_layer_infer)
coodoing / Py Unicodepython unicode and encoding code
kdschlosser / PyIRDecoderIR encoder/decoder for Python.
s373r / Steganography Png DecoderA Python script for extracting encoded text from PNG images
e2crawfo / Hrr ScalingPython/CUDA code to create a spiking neural network that can encode and traverse the Wordnet lexical database.
andreypopp / JsonpublishConfigurable JSON encoder for publishing Python objects as JSON documents
raeudigerRaeffi / GeneralizedReedSolomonPython Implementation of A Class of Generalized RS-Codes with Faster Encoding and Decoding Algorithms
zTrix / ReprsBlazing fast repr and eval like string encoding/decoding for python/go!
taiypeo / H.264 Enc DecA Python library based on gstreamer-1.0 for encoding and decoding RTP H.264 streams
DolbyUUU / Byte Pair Encoding BPE Subword Tokenization Implementation PythonByte-Pair Encoding (BPE) (subword-based tokenization) algorithm implementaions from scratch with python
XiaohuiGuoEartha / NLP Project Abstractive SummarizationUsed Python “keras” package to develop a unidirectional LSTM based deep learning model “encoder-decoder networks” for title generation on news data set. Improved the architecture by using attention. Created tensorboard visualizations to show the validation accuracy and loss. Evaluated the summaries using the ROUGE score.
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
BINOD-XD / Python EncryptPython Encoder
vash3d / Pigpio EncoderPython module for the KY040 rotary encoder.
bonniee / UltrasonicAn ultrasonic encoder / decoder for Python. Final project for COS597G.
Beatrice-Raws / VapourSynth TCPClipPython class for distributed video processing and encoding
Muterra / Py SmartyparseSmartyparse: A python library for smart dynamic binary de/encoding.
