1,078 skills found · Page 19 of 36
Tam2 / Sveltekit Openapi RemoteGenerate SvelteKit remote functions directly from OpenAPI/Swagger links, turning API specs into typed, ready-to-use client functions with minimal setup.
shogo54 / Markov BabblerFunctions used Markov Chains to generate random sentences.
vkazanov / ElpygenGenerate Python function/method stub for a call-like symbol under point
xgladius / Glad ExtExternal shellcode wrapper using naked functions to automatically generate payloads
fabiospampinato / ZeptoidA tiny isomorphic fast function for generating a cryptographically random hex string.
ivarref / Gen FnGenerate Datomic function literals from regular Clojure namespaces. On-prem.
Mantonherre / OpenWebUI Assistant OpenAICreate your assistant in the OpenAI dashboard, generate an API key, and use this code to integrate it into Open WebUI. Remember that it is a function, not a pipeline.
BabyFlokiCoin / Baby Floki Coin/** *Submitted for verification at BscScan.com on 2021-03-01 */ /** *Submitted for verification at BscScan.com on 2021-03-01 */ /** #BEE #LIQ+#RFI+#SHIB+#DOGE = #BEE #SAFEMOON features: 3% fee auto add to the liquidity pool to locked forever when selling 2% fee auto distribute to all holders I created a black hole so #Bee token will deflate itself in supply with every transaction 50% Supply is burned at start. */ pragma solidity ^0.6.12; // SPDX-License-Identifier: Unlicensed interface IERC20 { function totalSupply() external view returns (uint256); /** * @dev Returns the amount of tokens owned by `account`. */ function balanceOf(address account) external view returns (uint256); /** * @dev Moves `amount` tokens from the caller's account to `recipient`. * * Returns a boolean value indicating whether the operation succeeded. * * Emits a {Transfer} event. */ function transfer(address recipient, uint256 amount) external returns (bool); /** * @dev Returns the remaining number of tokens that `spender` will be * allowed to spend on behalf of `owner` through {transferFrom}. This is * zero by default. * * This value changes when {approve} or {transferFrom} are called. */ function allowance(address owner, address spender) external view returns (uint256); /** * @dev Sets `amount` as the allowance of `spender` over the caller's tokens. * * Returns a boolean value indicating whether the operation succeeded. * * IMPORTANT: Beware that changing an allowance with this method brings the risk * that someone may use both the old and the new allowance by unfortunate * transaction ordering. One possible solution to mitigate this race * condition is to first reduce the spender's allowance to 0 and set the * desired value afterwards: * https://github.com/ethereum/EIPs/issues/20#issuecomment-263524729 * * Emits an {Approval} event. */ function approve(address spender, uint256 amount) external returns (bool); /** * @dev Moves `amount` tokens from `sender` to `recipient` using the * allowance mechanism. `amount` is then deducted from the caller's * allowance. * * Returns a boolean value indicating whether the operation succeeded. * * Emits a {Transfer} event. */ function transferFrom(address sender, address recipient, uint256 amount) external returns (bool); /** * @dev Emitted when `value` tokens are moved from one account (`from`) to * another (`to`). * * Note that `value` may be zero. */ event Transfer(address indexed from, address indexed to, uint256 value); /** * @dev Emitted when the allowance of a `spender` for an `owner` is set by * a call to {approve}. `value` is the new allowance. */ event Approval(address indexed owner, address indexed spender, uint256 value); } /** * @dev Wrappers over Solidity's arithmetic operations with added overflow * checks. * * Arithmetic operations in Solidity wrap on overflow. This can easily result * in bugs, because programmers usually assume that an overflow raises an * error, which is the standard behavior in high level programming languages. * `SafeMath` restores this intuition by reverting the transaction when an * operation overflows. * * Using this library instead of the unchecked operations eliminates an entire * class of bugs, so it's recommended to use it always. */ library SafeMath { /** * @dev Returns the addition of two unsigned integers, reverting on * overflow. * * Counterpart to Solidity's `+` operator. * * Requirements: * * - Addition cannot overflow. */ function add(uint256 a, uint256 b) internal pure returns (uint256) { uint256 c = a + b; require(c >= a, "SafeMath: addition overflow"); return c; } /** * @dev Returns the subtraction of two unsigned integers, reverting on * overflow (when the result is negative). * * Counterpart to Solidity's `-` operator. * * Requirements: * * - Subtraction cannot overflow. */ function sub(uint256 a, uint256 b) internal pure returns (uint256) { return sub(a, b, "SafeMath: subtraction overflow"); } /** * @dev Returns the subtraction of two unsigned integers, reverting with custom message on * overflow (when the result is negative). * * Counterpart to Solidity's `-` operator. * * Requirements: * * - Subtraction cannot overflow. */ function sub(uint256 a, uint256 b, string memory errorMessage) internal pure returns (uint256) { require(b <= a, errorMessage); uint256 c = a - b; return c; } /** * @dev Returns the multiplication of two unsigned integers, reverting on * overflow. * * Counterpart to Solidity's `*` operator. * * Requirements: * * - Multiplication cannot overflow. */ function mul(uint256 a, uint256 b) internal pure returns (uint256) { // Gas optimization: this is cheaper than requiring 'a' not being zero, but the // benefit is lost if 'b' is also tested. // See: https://github.com/OpenZeppelin/openzeppelin-contracts/pull/522 if (a == 0) { return 0; } uint256 c = a * b; require(c / a == b, "SafeMath: multiplication overflow"); return c; } /** * @dev Returns the integer division of two unsigned integers. Reverts on * division by zero. The result is rounded towards zero. * * Counterpart to Solidity's `/` operator. Note: this function uses a * `revert` opcode (which leaves remaining gas untouched) while Solidity * uses an invalid opcode to revert (consuming all remaining gas). * * Requirements: * * - The divisor cannot be zero. */ function div(uint256 a, uint256 b) internal pure returns (uint256) { return div(a, b, "SafeMath: division by zero"); } /** * @dev Returns the integer division of two unsigned integers. Reverts with custom message on * division by zero. The result is rounded towards zero. * * Counterpart to Solidity's `/` operator. Note: this function uses a * `revert` opcode (which leaves remaining gas untouched) while Solidity * uses an invalid opcode to revert (consuming all remaining gas). * * Requirements: * * - The divisor cannot be zero. */ function div(uint256 a, uint256 b, string memory errorMessage) internal pure returns (uint256) { require(b > 0, errorMessage); uint256 c = a / b; // assert(a == b * c + a % b); // There is no case in which this doesn't hold return c; } /** * @dev Returns the remainder of dividing two unsigned integers. (unsigned integer modulo), * Reverts when dividing by zero. * * Counterpart to Solidity's `%` operator. This function uses a `revert` * opcode (which leaves remaining gas untouched) while Solidity uses an * invalid opcode to revert (consuming all remaining gas). * * Requirements: * * - The divisor cannot be zero. */ function mod(uint256 a, uint256 b) internal pure returns (uint256) { return mod(a, b, "SafeMath: modulo by zero"); } /** * @dev Returns the remainder of dividing two unsigned integers. (unsigned integer modulo), * Reverts with custom message when dividing by zero. * * Counterpart to Solidity's `%` operator. This function uses a `revert` * opcode (which leaves remaining gas untouched) while Solidity uses an * invalid opcode to revert (consuming all remaining gas). * * Requirements: * * - The divisor cannot be zero. */ function mod(uint256 a, uint256 b, string memory errorMessage) internal pure returns (uint256) { require(b != 0, errorMessage); return a % b; } } abstract contract Context { function _msgSender() internal view virtual returns (address payable) { return msg.sender; } function _msgData() internal view virtual returns (bytes memory) { this; // silence state mutability warning without generating bytecode - see https://github.com/ethereum/solidity/issues/2691 return msg.data; } } /** * @dev Collection of functions related to the address type */ library Address { /** * @dev Returns true if `account` is a contract. * * [IMPORTANT] * ==== * It is unsafe to assume that an address for which this function returns * false is an externally-owned account (EOA) and not a contract. * * Among others, `isContract` will return false for the following * types of addresses: * * - an externally-owned account * - a contract in construction * - an address where a contract will be created * - an address where a contract lived, but was destroyed * ==== */ function isContract(address account) internal view returns (bool) { // According to EIP-1052, 0x0 is the value returned for not-yet created accounts // and 0xc5d2460186f7233c927e7db2dcc703c0e500b653ca82273b7bfad8045d85a470 is returned // for accounts without code, i.e. `keccak256('')` bytes32 codehash; bytes32 accountHash = 0xc5d2460186f7233c927e7db2dcc703c0e500b653ca82273b7bfad8045d85a470; // solhint-disable-next-line no-inline-assembly assembly { codehash := extcodehash(account) } return (codehash != accountHash && codehash != 0x0); } /** * @dev Replacement for Solidity's `transfer`: sends `amount` wei to * `recipient`, forwarding all available gas and reverting on errors. * * https://eips.ethereum.org/EIPS/eip-1884[EIP1884] increases the gas cost * of certain opcodes, possibly making contracts go over the 2300 gas limit * imposed by `transfer`, making them unable to receive funds via * `transfer`. {sendValue} removes this limitation. * * https://diligence.consensys.net/posts/2019/09/stop-using-soliditys-transfer-now/[Learn more]. * * IMPORTANT: because control is transferred to `recipient`, care must be * taken to not create reentrancy vulnerabilities. Consider using * {ReentrancyGuard} or the * https://solidity.readthedocs.io/en/v0.5.11/security-considerations.html#use-the-checks-effects-interactions-pattern[checks-effects-interactions pattern]. */ function sendValue(address payable recipient, uint256 amount) internal { require(address(this).balance >= amount, "Address: insufficient balance"); // solhint-disable-next-line avoid-low-level-calls, avoid-call-value (bool success, ) = recipient.call{ value: amount }(""); require(success, "Address: unable to send value, recipient may have reverted"); } /** * @dev Performs a Solidity function call using a low level `call`. A * plain`call` is an unsafe replacement for a function call: use this * function instead. * * If `target` reverts with a revert reason, it is bubbled up by this * function (like regular Solidity function calls). * * Returns the raw returned data. To convert to the expected return value, * use https://solidity.readthedocs.io/en/latest/units-and-global-variables.html?highlight=abi.decode#abi-encoding-and-decoding-functions[`abi.decode`]. * * Requirements: * * - `target` must be a contract. * - calling `target` with `data` must not revert. * * _Available since v3.1._ */ function functionCall(address target, bytes memory data) internal returns (bytes memory) { return functionCall(target, data, "Address: low-level call failed"); } /** * @dev Same as {xref-Address-functionCall-address-bytes-}[`functionCall`], but with * `errorMessage` as a fallback revert reason when `target` reverts. * * _Available since v3.1._ */ function functionCall(address target, bytes memory data, string memory errorMessage) internal returns (bytes memory) { return _functionCallWithValue(target, data, 0, errorMessage); } /** * @dev Same as {xref-Address-functionCall-address-bytes-}[`functionCall`], * but also transferring `value` wei to `target`. * * Requirements: * * - the calling contract must have an ETH balance of at least `value`. * - the called Solidity function must be `payable`. * * _Available since v3.1._ */ function functionCallWithValue(address target, bytes memory data, uint256 value) internal returns (bytes memory) { return functionCallWithValue(target, data, value, "Address: low-level call with value failed"); } /** * @dev Same as {xref-Address-functionCallWithValue-address-bytes-uint256-}[`functionCallWithValue`], but * with `errorMessage` as a fallback revert reason when `target` reverts. * * _Available since v3.1._ */ function functionCallWithValue(address target, bytes memory data, uint256 value, string memory errorMessage) internal returns (bytes memory) { require(address(this).balance >= value, "Address: insufficient balance for call"); return _functionCallWithValue(target, data, value, errorMessage); } function _functionCallWithValue(address target, bytes memory data, uint256 weiValue, string memory errorMessage) private returns (bytes memory) { require(isContract(target), "Address: call to non-contract"); // solhint-disable-next-line avoid-low-level-calls (bool success, bytes memory returndata) = target.call{ value: weiValue }(data); if (success) { return returndata; } else { // Look for revert reason and bubble it up if present if (returndata.length > 0) { // The easiest way to bubble the revert reason is using memory via assembly // solhint-disable-next-line no-inline-assembly assembly { let returndata_size := mload(returndata) revert(add(32, returndata), returndata_size) } } else { revert(errorMessage); } } } } /** * @dev Contract module which provides a basic access control mechanism, where * there is an account (an owner) that can be granted exclusive access to * specific functions. * * By default, the owner account will be the one that deploys the contract. This * can later be changed with {transferOwnership}. * * This module is used through inheritance. It will make available the modifier * `onlyOwner`, which can be applied to your functions to restrict their use to * the owner. */ contract Ownable is Context { address private _owner; address private _previousOwner; uint256 private _lockTime; event OwnershipTransferred(address indexed previousOwner, address indexed newOwner); /** * @dev Initializes the contract setting the deployer as the initial owner. */ constructor () internal { address msgSender = _msgSender(); _owner = msgSender; emit OwnershipTransferred(address(0), msgSender); } /** * @dev Returns the address of the current owner. */ function owner() public view returns (address) { return _owner; } /** * @dev Throws if called by any account other than the owner. */ modifier onlyOwner() { require(_owner == _msgSender(), "Ownable: caller is not the owner"); _; } /** * @dev Leaves the contract without owner. It will not be possible to call * `onlyOwner` functions anymore. Can only be called by the current owner. * * NOTE: Renouncing ownership will leave the contract without an owner, * thereby removing any functionality that is only available to the owner. */ function renounceOwnership() public virtual onlyOwner { emit OwnershipTransferred(_owner, address(0)); _owner = address(0); } /** * @dev Transfers ownership of the contract to a new account (`newOwner`). * Can only be called by the current owner. */ function transferOwnership(address newOwner) public virtual onlyOwner { require(newOwner != address(0), "Ownable: new owner is the zero address"); emit OwnershipTransferred(_owner, newOwner); _owner = newOwner; } function geUnlockTime() public view returns (uint256) { return _lockTime; } //Locks the contract for owner for the amount of time provided function lock(uint256 time) public virtual onlyOwner { _previousOwner = _owner; _owner = address(0); _lockTime = now + time; emit OwnershipTransferred(_owner, address(0)); } //Unlocks the contract for owner when _lockTime is exceeds function unlock() public virtual { require(_previousOwner == msg.sender, "You don't have permission to unlock"); require(now > _lockTime , "Contract is locked until 7 days"); emit OwnershipTransferred(_owner, _previousOwner); _owner = _previousOwner; } } // pragma solidity >=0.5.0; interface IUniswapV2Factory { event PairCreated(address indexed token0, address indexed token1, address pair, uint); function feeTo() external view returns (address); function feeToSetter() external view returns (address); function getPair(address tokenA, address tokenB) external view returns (address pair); function allPairs(uint) external view returns (address pair); function allPairsLength() external view returns (uint); function createPair(address tokenA, address tokenB) external returns (address pair); function setFeeTo(address) external; function setFeeToSetter(address) external; } // pragma solidity >=0.5.0; interface IUniswapV2Pair { event Approval(address indexed owner, address indexed spender, uint value); event Transfer(address indexed from, address indexed to, uint value); function name() external pure returns (string memory); function symbol() external pure returns (string memory); function decimals() external pure returns (uint8); function totalSupply() external view returns (uint); function balanceOf(address owner) external view returns (uint); function allowance(address owner, address spender) external view returns (uint); function approve(address spender, uint value) external returns (bool); function transfer(address to, uint value) external returns (bool); function transferFrom(address from, address to, uint value) external returns (bool); function DOMAIN_SEPARATOR() external view returns (bytes32); function PERMIT_TYPEHASH() external pure returns (bytes32); function nonces(address owner) external view returns (uint); function permit(address owner, address spender, uint value, uint deadline, uint8 v, bytes32 r, bytes32 s) external; event Mint(address indexed sender, uint amount0, uint amount1); event Burn(address indexed sender, uint amount0, uint amount1, address indexed to); event Swap( address indexed sender, uint amount0In, uint amount1In, uint amount0Out, uint amount1Out, address indexed to ); event Sync(uint112 reserve0, uint112 reserve1); function MINIMUM_LIQUIDITY() external pure returns (uint); function factory() external view returns (address); function token0() external view returns (address); function token1() external view returns (address); function getReserves() external view returns (uint112 reserve0, uint112 reserve1, uint32 blockTimestampLast); function price0CumulativeLast() external view returns (uint); function price1CumulativeLast() external view returns (uint); function kLast() external view returns (uint); function mint(address to) external returns (uint liquidity); function burn(address to) external returns (uint amount0, uint amount1); function swap(uint amount0Out, uint amount1Out, address to, bytes calldata data) external; function skim(address to) external; function sync() external; function initialize(address, address) external; } // pragma solidity >=0.6.2; interface IUniswapV2Router01 { function factory() external pure returns (address); function WETH() external pure returns (address); function addLiquidity( address tokenA, address tokenB, uint amountADesired, uint amountBDesired, uint amountAMin, uint amountBMin, address to, uint deadline ) external returns (uint amountA, uint amountB, uint liquidity); function addLiquidityETH( address token, uint amountTokenDesired, uint amountTokenMin, uint amountETHMin, address to, uint deadline ) external payable returns (uint amountToken, uint amountETH, uint liquidity); function removeLiquidity( address tokenA, address tokenB, uint liquidity, uint amountAMin, uint amountBMin, address to, uint deadline ) external returns (uint amountA, uint amountB); function removeLiquidityETH( address token, uint liquidity, uint amountTokenMin, uint amountETHMin, address to, uint deadline ) external returns (uint amountToken, uint amountETH); function removeLiquidityWithPermit( address tokenA, address tokenB, uint liquidity, uint amountAMin, uint amountBMin, address to, uint deadline, bool approveMax, uint8 v, bytes32 r, bytes32 s ) external returns (uint amountA, uint amountB); function removeLiquidityETHWithPermit( address token, uint liquidity, uint amountTokenMin, uint amountETHMin, address to, uint deadline, bool approveMax, uint8 v, bytes32 r, bytes32 s ) external returns (uint amountToken, uint amountETH); function swapExactTokensForTokens( uint amountIn, uint amountOutMin, address[] calldata path, address to, uint deadline ) external returns (uint[] memory amounts); function swapTokensForExactTokens( uint amountOut, uint amountInMax, address[] calldata path, address to, uint deadline ) external returns (uint[] memory amounts); function swapExactETHForTokens(uint amountOutMin, address[] calldata path, address to, uint deadline) external payable returns (uint[] memory amounts); function swapTokensForExactETH(uint amountOut, uint amountInMax, address[] calldata path, address to, uint deadline) external returns (uint[] memory amounts); function swapExactTokensForETH(uint amountIn, uint amountOutMin, address[] calldata path, address to, uint deadline) external returns (uint[] memory amounts); function swapETHForExactTokens(uint amountOut, address[] calldata path, address to, uint deadline) external payable returns (uint[] memory amounts); function quote(uint amountA, uint reserveA, uint reserveB) external pure returns (uint amountB); function getAmountOut(uint amountIn, uint reserveIn, uint reserveOut) external pure returns (uint amountOut); function getAmountIn(uint amountOut, uint reserveIn, uint reserveOut) external pure returns (uint amountIn); function getAmountsOut(uint amountIn, address[] calldata path) external view returns (uint[] memory amounts); function getAmountsIn(uint amountOut, address[] calldata path) external view returns (uint[] memory amounts); } // pragma solidity >=0.6.2; interface IUniswapV2Router02 is IUniswapV2Router01 { function removeLiquidityETHSupportingFeeOnTransferTokens( address token, uint liquidity, uint amountTokenMin, uint amountETHMin, address to, uint deadline ) external returns (uint amountETH); function removeLiquidityETHWithPermitSupportingFeeOnTransferTokens( address token, uint liquidity, uint amountTokenMin, uint amountETHMin, address to, uint deadline, bool approveMax, uint8 v, bytes32 r, bytes32 s ) external returns (uint amountETH); function swapExactTokensForTokensSupportingFeeOnTransferTokens( uint amountIn, uint amountOutMin, address[] calldata path, address to, uint deadline ) external; function swapExactETHForTokensSupportingFeeOnTransferTokens( uint amountOutMin, address[] calldata path, address to, uint deadline ) external payable; function swapExactTokensForETHSupportingFeeOnTransferTokens( uint amountIn, uint amountOutMin, address[] calldata path, address to, uint deadline ) external; } contract SafeMoon is Context, IERC20, Ownable { using SafeMath for uint256; using Address for address; mapping (address => uint256) private _rOwned; mapping (address => uint256) private _tOwned; mapping (address => mapping (address => uint256)) private _allowances; mapping (address => bool) private _isExcludedFromFee; mapping (address => bool) private _isExcluded; address[] private _excluded; uint256 private constant MAX = ~uint256(0); uint256 private _tTotal = 1000000000 * 10**6 * 10**9; uint256 private _rTotal = (MAX - (MAX % _tTotal)); uint256 private _tFeeTotal; string private _name = "SafeMoon"; string private _symbol = "SAFEMOON"; uint8 private _decimals = 9; uint256 public _taxFee = 5; uint256 private _previousTaxFee = _taxFee; uint256 public _liquidityFee = 5; uint256 private _previousLiquidityFee = _liquidityFee; IUniswapV2Router02 public immutable uniswapV2Router; address public immutable uniswapV2Pair; bool inSwapAndLiquify; bool public swapAndLiquifyEnabled = true; uint256 public _maxTxAmount = 5000000 * 10**6 * 10**9; uint256 private numTokensSellToAddToLiquidity = 500000 * 10**6 * 10**9; event MinTokensBeforeSwapUpdated(uint256 minTokensBeforeSwap); event SwapAndLiquifyEnabledUpdated(bool enabled); event SwapAndLiquify( uint256 tokensSwapped, uint256 ethReceived, uint256 tokensIntoLiqudity ); modifier lockTheSwap { inSwapAndLiquify = true; _; inSwapAndLiquify = false; } constructor () public { _rOwned[_msgSender()] = _rTotal; IUniswapV2Router02 _uniswapV2Router = IUniswapV2Router02(0x05fF2B0DB69458A0750badebc4f9e13aDd608C7F); // Create a uniswap pair for this new token uniswapV2Pair = IUniswapV2Factory(_uniswapV2Router.factory()) .createPair(address(this), _uniswapV2Router.WETH()); // set the rest of the contract variables uniswapV2Router = _uniswapV2Router; //exclude owner and this contract from fee _isExcludedFromFee[owner()] = true; _isExcludedFromFee[address(this)] = true; emit Transfer(address(0), _msgSender(), _tTotal); } function name() public view returns (string memory) { return _name; } function symbol() public view returns (string memory) { return _symbol; } function decimals() public view returns (uint8) { return _decimals; } function totalSupply() public view override returns (uint256) { return _tTotal; } function balanceOf(address account) public view override returns (uint256) { if (_isExcluded[account]) return _tOwned[account]; return tokenFromReflection(_rOwned[account]); } function transfer(address recipient, uint256 amount) public override returns (bool) { _transfer(_msgSender(), recipient, amount); return true; } function allowance(address owner, address spender) public view override returns (uint256) { return _allowances[owner][spender]; } function approve(address spender, uint256 amount) public override returns (bool) { _approve(_msgSender(), spender, amount); return true; } function transferFrom(address sender, address recipient, uint256 amount) public override returns (bool) { _transfer(sender, recipient, amount); _approve(sender, _msgSender(), _allowances[sender][_msgSender()].sub(amount, "ERC20: transfer amount exceeds allowance")); return true; } function increaseAllowance(address spender, uint256 addedValue) public virtual returns (bool) { _approve(_msgSender(), spender, _allowances[_msgSender()][spender].add(addedValue)); return true; } function decreaseAllowance(address spender, uint256 subtractedValue) public virtual returns (bool) { _approve(_msgSender(), spender, _allowances[_msgSender()][spender].sub(subtractedValue, "ERC20: decreased allowance below zero")); return true; } function isExcludedFromReward(address account) public view returns (bool) { return _isExcluded[account]; } function totalFees() public view returns (uint256) { return _tFeeTotal; } function deliver(uint256 tAmount) public { address sender = _msgSender(); require(!_isExcluded[sender], "Excluded addresses cannot call this function"); (uint256 rAmount,,,,,) = _getValues(tAmount); _rOwned[sender] = _rOwned[sender].sub(rAmount); _rTotal = _rTotal.sub(rAmount); _tFeeTotal = _tFeeTotal.add(tAmount); } function reflectionFromToken(uint256 tAmount, bool deductTransferFee) public view returns(uint256) { require(tAmount <= _tTotal, "Amount must be less than supply"); if (!deductTransferFee) { (uint256 rAmount,,,,,) = _getValues(tAmount); return rAmount; } else { (,uint256 rTransferAmount,,,,) = _getValues(tAmount); return rTransferAmount; } } function tokenFromReflection(uint256 rAmount) public view returns(uint256) { require(rAmount <= _rTotal, "Amount must be less than total reflections"); uint256 currentRate = _getRate(); return rAmount.div(currentRate); } function excludeFromReward(address account) public onlyOwner() { // require(account != 0x7a250d5630B4cF539739dF2C5dAcb4c659F2488D, 'We can not exclude Uniswap router.'); require(!_isExcluded[account], "Account is already excluded"); if(_rOwned[account] > 0) { _tOwned[account] = tokenFromReflection(_rOwned[account]); } _isExcluded[account] = true; _excluded.push(account); } function includeInReward(address account) external onlyOwner() { require(_isExcluded[account], "Account is already excluded"); for (uint256 i = 0; i < _excluded.length; i++) { if (_excluded[i] == account) { _excluded[i] = _excluded[_excluded.length - 1]; _tOwned[account] = 0; _isExcluded[account] = false; _excluded.pop(); break; } } } function _transferBothExcluded(address sender, address recipient, uint256 tAmount) private { (uint256 rAmount, uint256 rTransferAmount, uint256 rFee, uint256 tTransferAmount, uint256 tFee, uint256 tLiquidity) = _getValues(tAmount); _tOwned[sender] = _tOwned[sender].sub(tAmount); _rOwned[sender] = _rOwned[sender].sub(rAmount); _tOwned[recipient] = _tOwned[recipient].add(tTransferAmount); _rOwned[recipient] = _rOwned[recipient].add(rTransferAmount); _takeLiquidity(tLiquidity); _reflectFee(rFee, tFee); emit Transfer(sender, recipient, tTransferAmount); } function excludeFromFee(address account) public onlyOwner { _isExcludedFromFee[account] = true; } function includeInFee(address account) public onlyOwner { _isExcludedFromFee[account] = false; } function setTaxFeePercent(uint256 taxFee) external onlyOwner() { _taxFee = taxFee; } function setLiquidityFeePercent(uint256 liquidityFee) external onlyOwner() { _liquidityFee = liquidityFee; } function setMaxTxPercent(uint256 maxTxPercent) external onlyOwner() { _maxTxAmount = _tTotal.mul(maxTxPercent).div( 10**2 ); } function setSwapAndLiquifyEnabled(bool _enabled) public onlyOwner { swapAndLiquifyEnabled = _enabled; emit SwapAndLiquifyEnabledUpdated(_enabled); } //to recieve ETH from uniswapV2Router when swaping receive() external payable {} function _reflectFee(uint256 rFee, uint256 tFee) private { _rTotal = _rTotal.sub(rFee); _tFeeTotal = _tFeeTotal.add(tFee); } function _getValues(uint256 tAmount) private view returns (uint256, uint256, uint256, uint256, uint256, uint256) { (uint256 tTransferAmount, uint256 tFee, uint256 tLiquidity) = _getTValues(tAmount); (uint256 rAmount, uint256 rTransferAmount, uint256 rFee) = _getRValues(tAmount, tFee, tLiquidity, _getRate()); return (rAmount, rTransferAmount, rFee, tTransferAmount, tFee, tLiquidity); } function _getTValues(uint256 tAmount) private view returns (uint256, uint256, uint256) { uint256 tFee = calculateTaxFee(tAmount); uint256 tLiquidity = calculateLiquidityFee(tAmount); uint256 tTransferAmount = tAmount.sub(tFee).sub(tLiquidity); return (tTransferAmount, tFee, tLiquidity); } function _getRValues(uint256 tAmount, uint256 tFee, uint256 tLiquidity, uint256 currentRate) private pure returns (uint256, uint256, uint256) { uint256 rAmount = tAmount.mul(currentRate); uint256 rFee = tFee.mul(currentRate); uint256 rLiquidity = tLiquidity.mul(currentRate); uint256 rTransferAmount = rAmount.sub(rFee).sub(rLiquidity); return (rAmount, rTransferAmount, rFee); } function _getRate() private view returns(uint256) { (uint256 rSupply, uint256 tSupply) = _getCurrentSupply(); return rSupply.div(tSupply); } function _getCurrentSupply() private view returns(uint256, uint256) { uint256 rSupply = _rTotal; uint256 tSupply = _tTotal; for (uint256 i = 0; i < _excluded.length; i++) { if (_rOwned[_excluded[i]] > rSupply || _tOwned[_excluded[i]] > tSupply) return (_rTotal, _tTotal); rSupply = rSupply.sub(_rOwned[_excluded[i]]); tSupply = tSupply.sub(_tOwned[_excluded[i]]); } if (rSupply < _rTotal.div(_tTotal)) return (_rTotal, _tTotal); return (rSupply, tSupply); } function _takeLiquidity(uint256 tLiquidity) private { uint256 currentRate = _getRate(); uint256 rLiquidity = tLiquidity.mul(currentRate); _rOwned[address(this)] = _rOwned[address(this)].add(rLiquidity); if(_isExcluded[address(this)]) _tOwned[address(this)] = _tOwned[address(this)].add(tLiquidity); } function calculateTaxFee(uint256 _amount) private view returns (uint256) { return _amount.mul(_taxFee).div( 10**2 ); } function calculateLiquidityFee(uint256 _amount) private view returns (uint256) { return _amount.mul(_liquidityFee).div( 10**2 ); } function removeAllFee() private { if(_taxFee == 0 && _liquidityFee == 0) return; _previousTaxFee = _taxFee; _previousLiquidityFee = _liquidityFee; _taxFee = 0; _liquidityFee = 0; } function restoreAllFee() private { _taxFee = _previousTaxFee; _liquidityFee = _previousLiquidityFee; } function isExcludedFromFee(address account) public view returns(bool) { return _isExcludedFromFee[account]; } function _approve(address owner, address spender, uint256 amount) private { require(owner != address(0), "ERC20: approve from the zero address"); require(spender != address(0), "ERC20: approve to the zero address"); _allowances[owner][spender] = amount; emit Approval(owner, spender, amount); } function _transfer( address from, address to, uint256 amount ) private { require(from != address(0), "ERC20: transfer from the zero address"); require(to != address(0), "ERC20: transfer to the zero address"); require(amount > 0, "Transfer amount must be greater than zero"); if(from != owner() && to != owner()) require(amount <= _maxTxAmount, "Transfer amount exceeds the maxTxAmount."); // is the token balance of this contract address over the min number of // tokens that we need to initiate a swap + liquidity lock? // also, don't get caught in a circular liquidity event. // also, don't swap & liquify if sender is uniswap pair. uint256 contractTokenBalance = balanceOf(address(this)); if(contractTokenBalance >= _maxTxAmount) { contractTokenBalance = _maxTxAmount; } bool overMinTokenBalance = contractTokenBalance >= numTokensSellToAddToLiquidity; if ( overMinTokenBalance && !inSwapAndLiquify && from != uniswapV2Pair && swapAndLiquifyEnabled ) { contractTokenBalance = numTokensSellToAddToLiquidity; //add liquidity swapAndLiquify(contractTokenBalance); } //indicates if fee should be deducted from transfer bool takeFee = true; //if any account belongs to _isExcludedFromFee account then remove the fee if(_isExcludedFromFee[from] || _isExcludedFromFee[to]){ takeFee = false; } //transfer amount, it will take tax, burn, liquidity fee _tokenTransfer(from,to,amount,takeFee); } function swapAndLiquify(uint256 contractTokenBalance) private lockTheSwap { // split the contract balance into halves uint256 half = contractTokenBalance.div(2); uint256 otherHalf = contractTokenBalance.sub(half); // capture the contract's current ETH balance. // this is so that we can capture exactly the amount of ETH that the // swap creates, and not make the liquidity event include any ETH that // has been manually sent to the contract uint256 initialBalance = address(this).balance; // swap tokens for ETH swapTokensForEth(half); // <- this breaks the ETH -> HATE swap when swap+liquify is triggered // how much ETH did we just swap into? uint256 newBalance = address(this).balance.sub(initialBalance); // add liquidity to uniswap addLiquidity(otherHalf, newBalance); emit SwapAndLiquify(half, newBalance, otherHalf); } function swapTokensForEth(uint256 tokenAmount) private { // generate the uniswap pair path of token -> weth address[] memory path = new address[](2); path[0] = address(this); path[1] = uniswapV2Router.WETH(); _approve(address(this), address(uniswapV2Router), tokenAmount); // make the swap uniswapV2Router.swapExactTokensForETHSupportingFeeOnTransferTokens( tokenAmount, 0, // accept any amount of ETH path, address(this), block.timestamp ); } function addLiquidity(uint256 tokenAmount, uint256 ethAmount) private { // approve token transfer to cover all possible scenarios _approve(address(this), address(uniswapV2Router), tokenAmount); // add the liquidity uniswapV2Router.addLiquidityETH{value: ethAmount}( address(this), tokenAmount, 0, // slippage is unavoidable 0, // slippage is unavoidable owner(), block.timestamp ); } //this method is responsible for taking all fee, if takeFee is true function _tokenTransfer(address sender, address recipient, uint256 amount,bool takeFee) private { if(!takeFee) removeAllFee(); if (_isExcluded[sender] && !_isExcluded[recipient]) { _transferFromExcluded(sender, recipient, amount); } else if (!_isExcluded[sender] && _isExcluded[recipient]) { _transferToExcluded(sender, recipient, amount); } else if (!_isExcluded[sender] && !_isExcluded[recipient]) { _transferStandard(sender, recipient, amount); } else if (_isExcluded[sender] && _isExcluded[recipient]) { _transferBothExcluded(sender, recipient, amount); } else { _transferStandard(sender, recipient, amount); } if(!takeFee) restoreAllFee(); } function _transferStandard(address sender, address recipient, uint256 tAmount) private { (uint256 rAmount, uint256 rTransferAmount, uint256 rFee, uint256 tTransferAmount, uint256 tFee, uint256 tLiquidity) = _getValues(tAmount); _rOwned[sender] = _rOwned[sender].sub(rAmount); _rOwned[recipient] = _rOwned[recipient].add(rTransferAmount); _takeLiquidity(tLiquidity); _reflectFee(rFee, tFee); emit Transfer(sender, recipient, tTransferAmount); } function _transferToExcluded(address sender, address recipient, uint256 tAmount) private { (uint256 rAmount, uint256 rTransferAmount, uint256 rFee, uint256 tTransferAmount, uint256 tFee, uint256 tLiquidity) = _getValues(tAmount); _rOwned[sender] = _rOwned[sender].sub(rAmount); _tOwned[recipient] = _tOwned[recipient].add(tTransferAmount); _rOwned[recipient] = _rOwned[recipient].add(rTransferAmount); _takeLiquidity(tLiquidity); _reflectFee(rFee, tFee); emit Transfer(sender, recipient, tTransferAmount); } function _transferFromExcluded(address sender, address recipient, uint256 tAmount) private { (uint256 rAmount, uint256 rTransferAmount, uint256 rFee, uint256 tTransferAmount, uint256 tFee, uint256 tLiquidity) = _getValues(tAmount); _tOwned[sender] = _tOwned[sender].sub(tAmount); _rOwned[sender] = _rOwned[sender].sub(rAmount); _rOwned[recipient] = _rOwned[recipient].add(rTransferAmount); _takeLiquidity(tLiquidity); _reflectFee(rFee, tFee); emit Transfer(sender, recipient, tTransferAmount); } }
signalblur / Impelf"ImpELF: A Python-based ELF hashing utility that generates unique fingerprints for ELF binaries using their imported functions and libraries, aiding in malware analysis and similarity detection."
supercodestart / Contextualize Visualize⭐A contextualized visualization library provides a set of functions, tools, and components that allow you to easily generate charts, graphs, and other visual elements. With this library, you can bring your data to life by choosing the right visualizations that best convey the information you want to share.
OptimizationExpert / GAMS DC OPF The current DC optimal power flow (OPF) model finds the optimal operating schedules of generating units considering transmission line constraints. The objective function is defined as the total operating costs of generating units. The model type is LP and is applied to a 5 bus PJM test system as described in the following reference: F. Li and R. Bo, "Small test systems for power system economic studies," IEEE PES General Meeting, Minneapolis, MN, 2010, pp. 1-4. doi: 10.1109/PES.2010.5589973 The proposed model is able to handle large scale set of data for practical power system transmission networks. Inputs: Generator costs + technical characteristics (min/max , connection point) Network characteristics (reactances, demands, line flow limits) Outputs: local marginal prices (LMP) Generators outputs Line flows Angle values -------------------------------------------------------------------------------------------- Contributed by Dr. Alireza Soroudi, University College Dublin, Dublin, Ireland. email: alireza.sor
esapulkkinen / Cifl Math LibraryBasic mathematics library
joselitofilho / Aws Terraform GeneratorThe AWS Terraform Generator is a powerful tool designed to simplify and streamline the process of creating Terraform configurations for AWS infrastructure. With this tool, you can quickly generate Terraform code to provision AWS resources such as EC2 instances, S3 buckets, RDS databases, Lambda functions, API Gateways, and much more.
Aryia-Behroziuan / NumpyQuickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see https://scipy.org/install.html for instructions. Learner profile This tutorial is intended as a quick overview of algebra and arrays in NumPy and want to understand how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this tutorial might be of help. Learning Objectives After this tutorial, you should be able to: Understand the difference between one-, two- and n-dimensional arrays in NumPy; Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops; Understand axis and shape properties for n-dimensional arrays. The Basics NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array. ndarray.shape the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size the total number of elements of the array. This is equal to the product of the elements of shape. ndarray.dtype an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize. ndarray.data the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities. An example >>> import numpy as np a = np.arange(15).reshape(3, 5) a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) a.shape (3, 5) a.ndim 2 a.dtype.name 'int64' a.itemsize 8 a.size 15 type(a) <class 'numpy.ndarray'> b = np.array([6, 7, 8]) b array([6, 7, 8]) type(b) <class 'numpy.ndarray'> Array Creation There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. >>> >>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') A frequent error consists in calling array with multiple arguments, rather than providing a single sequence as an argument. >>> >>> a = np.array(1,2,3,4) # WRONG Traceback (most recent call last): ... TypeError: array() takes from 1 to 2 positional arguments but 4 were given >>> a = np.array([1,2,3,4]) # RIGHT array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. >>> >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) The type of the array can also be explicitly specified at creation time: >>> >>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64. >>> >>> np.zeros((3, 4)) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) To create sequences of numbers, NumPy provides the arange function which is analogous to the Python built-in range, but returns an array. >>> >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: >>> >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) See also array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.Generator.rand, numpy.random.Generator.randn, fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. >>> >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3) # 2d array >>> print(b) [[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] >>> >>> c = np.arange(24).reshape(2,3,4) # 3d array >>> print(c) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] See below to get more details on reshape. If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: >>> >>> print(np.arange(10000)) [ 0 1 2 ... 9997 9998 9999] >>> >>> print(np.arange(10000).reshape(100,100)) [[ 0 1 2 ... 97 98 99] [ 100 101 102 ... 197 198 199] [ 200 201 202 ... 297 298 299] ... [9700 9701 9702 ... 9797 9798 9799] [9800 9801 9802 ... 9897 9898 9899] [9900 9901 9902 ... 9997 9998 9999]] To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions. >>> >>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported Basic Operations Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. >>> >>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False]) Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: >>> >>> A = np.array( [[1,1], ... [0,1]] ) >>> B = np.array( [[2,0], ... [3,4]] ) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. >>> >>> rg = np.random.default_rng(1) # create instance of default random number generator >>> a = np.ones((2,3), dtype=int) >>> b = rg.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ... numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind' When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). >>> >>> a = np.ones(3, dtype=np.int32) >>> b = np.linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. >>> >>> a = rg.random((2,3)) >>> a array([[0.82770259, 0.40919914, 0.54959369], [0.02755911, 0.75351311, 0.53814331]]) >>> a.sum() 3.1057109529998157 >>> a.min() 0.027559113243068367 >>> a.max() 0.8277025938204418 By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: >>> >>> b = np.arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]]) Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”(ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>> >>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([2., 0., 6.]) See also all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, invert, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. >>> >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) # equivalent to a[0:6:2] = 1000; # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, 1000, 27, 1000, 1, 1000]) >>> for i in a: ... print(i**(1/3.)) ... 9.999999999999998 1.0 9.999999999999998 3.0 9.999999999999998 4.999999999999999 5.999999999999999 6.999999999999999 7.999999999999999 8.999999999999998 Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: >>> >>> def f(x,y): ... return 10*x+y ... >>> b = np.fromfunction(f,(5,4),dtype=int) >>> b array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]]) >>> b[2,3] 23 >>> b[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) When fewer indices are provided than the number of axes, the missing indices are considered complete slices: >>> >>> b[-1] # the last row. Equivalent to b[-1,:] array([40, 41, 42, 43]) The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...]. The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:], x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:]. >>> >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays) ... [ 10, 12, 13]], ... [[100,101,102], ... [110,112,113]]]) >>> c.shape (2, 2, 3) >>> c[1,...] # same as c[1,:,:] or c[1] array([[100, 101, 102], [110, 112, 113]]) >>> c[...,2] # same as c[:,:,2] array([[ 2, 13], [102, 113]]) Iterating over multidimensional arrays is done with respect to the first axis: >>> >>> for row in b: ... print(row) ... [0 1 2 3] [10 11 12 13] [20 21 22 23] [30 31 32 33] [40 41 42 43] However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: >>> >>> for element in b.flat: ... print(element) ... 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43 See also Indexing, Indexing (reference), newaxis, ndenumerate, indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: >>> >>> a = np.floor(10*rg.random((3,4))) >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.shape (3, 4) The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: >>> >>> a.ravel() # returns the array, flattened array([3., 7., 3., 4., 1., 4., 2., 2., 7., 2., 4., 9.]) >>> a.reshape(6,2) # returns the array with a modified shape array([[3., 7.], [3., 4.], [1., 4.], [2., 2.], [7., 2.], [4., 9.]]) >>> a.T # returns the array, transposed array([[3., 1., 7.], [7., 4., 2.], [3., 2., 4.], [4., 2., 9.]]) >>> a.T.shape (4, 3) >>> a.shape (3, 4) The order of the elements in the array resulting from ravel() is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a[0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape() can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: >>> >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.resize((2,6)) >>> a array([[3., 7., 3., 4., 1., 4.], [2., 2., 7., 2., 4., 9.]]) If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: >>> >>> a.reshape(3,-1) array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) See also ndarray.shape, reshape, resize, ravel Stacking together different arrays Several arrays can be stacked together along different axes: >>> >>> a = np.floor(10*rg.random((2,2))) >>> a array([[9., 7.], [5., 2.]]) >>> b = np.floor(10*rg.random((2,2))) >>> b array([[1., 9.], [5., 1.]]) >>> np.vstack((a,b)) array([[9., 7.], [5., 2.], [1., 9.], [5., 1.]]) >>> np.hstack((a,b)) array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: >>> >>> from numpy import newaxis >>> np.column_stack((a,b)) # with 2D arrays array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) >>> a = np.array([4.,2.]) >>> b = np.array([3.,8.]) >>> np.column_stack((a,b)) # returns a 2D array array([[4., 3.], [2., 8.]]) >>> np.hstack((a,b)) # the result is different array([4., 2., 3., 8.]) >>> a[:,newaxis] # view `a` as a 2D column vector array([[4.], [2.]]) >>> np.column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 3.], [2., 8.]]) >>> np.hstack((a[:,newaxis],b[:,newaxis])) # the result is the same array([[4., 3.], [2., 8.]]) On the other hand, the function row_stack is equivalent to vstack for any input arrays. In fact, row_stack is an alias for vstack: >>> >>> np.column_stack is np.hstack False >>> np.row_stack is np.vstack True In general, for arrays with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) >>> >>> np.r_[1:4,0,4] array([1, 2, 3, 0, 4]) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also hstack, vstack, column_stack, concatenate, c_, r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: >>> >>> a = np.floor(10*rg.random((2,12))) >>> a array([[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.], [8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]]) # Split a into 3 >>> np.hsplit(a,3) [array([[6., 7., 6., 9.], [8., 5., 5., 7.]]), array([[0., 5., 4., 0.], [1., 8., 6., 7.]]), array([[6., 8., 5., 2.], [1., 8., 1., 0.]])] # Split a after the third and the fourth column >>> np.hsplit(a,(3,4)) [array([[6., 7., 6.], [8., 5., 5.]]), array([[9.], [7.]]), array([[0., 5., 4., 0., 6., 8., 5., 2.], [1., 8., 6., 7., 1., 8., 1., 0.]])] vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of objects or their data. >>> >>> a = np.array([[ 0, 1, 2, 3], ... [ 4, 5, 6, 7], ... [ 8, 9, 10, 11]]) >>> b = a # no new object is created >>> b is a # a and b are two names for the same ndarray object True Python passes mutable objects as references, so function calls make no copy. >>> >>> def f(x): ... print(id(x)) ... >>> id(a) # id is a unique identifier of an object 148293216 # may vary >>> f(a) 148293216 # may vary View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. >>> >>> c = a.view() >>> c is a False >>> c.base is a # c is a view of the data owned by a True >>> c.flags.owndata False >>> >>> c = c.reshape((2, 6)) # a's shape doesn't change >>> a.shape (3, 4) >>> c[0, 4] = 1234 # a's data changes >>> a array([[ 0, 1, 2, 3], [1234, 5, 6, 7], [ 8, 9, 10, 11]]) Slicing an array returns a view of it: >>> >>> s = a[ : , 1:3] # spaces added for clarity; could also be written "s = a[:, 1:3]" >>> s[:] = 10 # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Deep Copy The copy method makes a complete copy of the array and its data. >>> >>> d = a.copy() # a new array object with new data is created >>> d is a False >>> d.base is a # d doesn't share anything with a False >>> d[0,0] = 9999 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Sometimes copy should be called after slicing if the original array is not required anymore. For example, suppose a is a huge intermediate result and the final result b only contains a small fraction of a, a deep copy should be made when constructing b with slicing: >>> >>> a = np.arange(int(1e8)) >>> b = a[:100].copy() >>> del a # the memory of ``a`` can be released. If b = a[:100] is used instead, a is referenced by b and will persist in memory even if del a is executed. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like Conversions ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat Manipulations array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack Questions all, any, nonzero, where Ordering argmax, argmin, argsort, max, min, ptp, searchsorted, sort Operations choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum Basic Statistics cov, mean, std, var Basic Linear Algebra cross, dot, outer, linalg.svd, vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Advanced indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices >>> >>> a = np.arange(12)**2 # the first 12 square numbers >>> i = np.array([1, 1, 3, 8, 5]) # an array of indices >>> a[i] # the elements of a at the positions i array([ 1, 1, 9, 64, 25]) >>> >>> j = np.array([[3, 4], [9, 7]]) # a bidimensional array of indices >>> a[j] # the same shape as j array([[ 9, 16], [81, 49]]) When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. >>> >>> palette = np.array([[0, 0, 0], # black ... [255, 0, 0], # red ... [0, 255, 0], # green ... [0, 0, 255], # blue ... [255, 255, 255]]) # white >>> image = np.array([[0, 1, 2, 0], # each value corresponds to a color in the palette ... [0, 3, 4, 0]]) >>> palette[image] # the (2, 4, 3) color image array([[[ 0, 0, 0], [255, 0, 0], [ 0, 255, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 255], [255, 255, 255], [ 0, 0, 0]]]) We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. >>> >>> a = np.arange(12).reshape(3,4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> i = np.array([[0, 1], # indices for the first dim of a ... [1, 2]]) >>> j = np.array([[2, 1], # indices for the second dim ... [3, 3]]) >>> >>> a[i, j] # i and j must have equal shape array([[ 2, 5], [ 7, 11]]) >>> >>> a[i, 2] array([[ 2, 6], [ 6, 10]]) >>> >>> a[:, j] # i.e., a[ : , j] array([[[ 2, 1], [ 3, 3]], [[ 6, 5], [ 7, 7]], [[10, 9], [11, 11]]]) In Python, arr[i, j] is exactly the same as arr[(i, j)]—so we can put i and j in a tuple and then do the indexing with that. >>> >>> l = (i, j) # equivalent to a[i, j] >>> a[l] array([[ 2, 5], [ 7, 11]]) However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. >>> >>> s = np.array([i, j]) # not what we want >>> a[s] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: index 3 is out of bounds for axis 0 with size 3 # same as a[i, j] >>> a[tuple(s)] array([[ 2, 5], [ 7, 11]]) Another common use of indexing with arrays is the search of the maximum value of time-dependent series: >>> >>> time = np.linspace(20, 145, 5) # time scale >>> data = np.sin(np.arange(20)).reshape(5,4) # 4 time-dependent series >>> time array([ 20. , 51.25, 82.5 , 113.75, 145. ]) >>> data array([[ 0. , 0.84147098, 0.90929743, 0.14112001], [-0.7568025 , -0.95892427, -0.2794155 , 0.6569866 ], [ 0.98935825, 0.41211849, -0.54402111, -0.99999021], [-0.53657292, 0.42016704, 0.99060736, 0.65028784], [-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]... >>> time_max array([ 82.5 , 20. , 113.75, 51.25]) >>> data_max array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ]) >>> np.all(data_max == data.max(axis=0)) True You can also use indexing with arrays as a target to assign to: >>> >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a[[1,3,4]] = 0 >>> a array([0, 0, 2, 0, 0]) However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: >>> >>> a = np.arange(5) >>> a[[0,0,2]]=[1,2,3] >>> a array([2, 1, 3, 3, 4]) This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: >>> >>> a = np.arange(5) >>> a[[0,0,2]]+=1 >>> a array([1, 1, 3, 3, 4]) Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: >>> >>> a = np.arange(12).reshape(3,4) >>> b = a > 4 >>> b # b is a boolean with a's shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7, 8, 9, 10, 11]) This property can be very useful in assignments: >>> >>> a[b] = 0 # All elements of 'a' higher than 4 become 0 >>> a array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]]) You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set: >>> import numpy as np import matplotlib.pyplot as plt def mandelbrot( h,w, maxit=20 ): """Returns an image of the Mandelbrot fractal of size (h,w).""" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ] c = x+y*1j z = c divtime = maxit + np.zeros(z.shape, dtype=int) for i in range(maxit): z = z**2 + c diverge = z*np.conj(z) > 2**2 # who is diverging div_now = diverge & (divtime==maxit) # who is diverging now divtime[div_now] = i # note when z[diverge] = 2 # avoid diverging too much return divtime plt.imshow(mandelbrot(400,400)) ../_images/quickstart-1.png The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: >>> >>> a = np.arange(12).reshape(3,4) >>> b1 = np.array([False,True,True]) # first dim selection >>> b2 = np.array([True,False,True,False]) # second dim selection >>> >>> a[b1,:] # selecting rows array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:,b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1,b2] # a weird thing to do array([ 4, 10]) Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: >>> >>> a = np.array([2,3,4,5]) >>> b = np.array([8,5,4]) >>> c = np.array([5,4,6,8,3]) >>> ax,bx,cx = np.ix_(a,b,c) >>> ax array([[[2]], [[3]], [[4]], [[5]]]) >>> bx array([[[8], [5], [4]]]) >>> cx array([[[5, 4, 6, 8, 3]]]) >>> ax.shape, bx.shape, cx.shape ((4, 1, 1), (1, 3, 1), (1, 1, 5)) >>> result = ax+bx*cx >>> result array([[[42, 34, 50, 66, 26], [27, 22, 32, 42, 17], [22, 18, 26, 34, 14]], [[43, 35, 51, 67, 27], [28, 23, 33, 43, 18], [23, 19, 27, 35, 15]], [[44, 36, 52, 68, 28], [29, 24, 34, 44, 19], [24, 20, 28, 36, 16]], [[45, 37, 53, 69, 29], [30, 25, 35, 45, 20], [25, 21, 29, 37, 17]]]) >>> result[3,2,4] 17 >>> a[3]+b[2]*c[4] 17 You could also implement the reduce as follows: >>> >>> def ufunc_reduce(ufct, *vectors): ... vs = np.ix_(*vectors) ... r = ufct.identity ... for v in vs: ... r = ufct(r,v) ... return r and then use it as: >>> >>> ufunc_reduce(np.add,a,b,c) array([[[15, 14, 16, 18, 13], [12, 11, 13, 15, 10], [11, 10, 12, 14, 9]], [[16, 15, 17, 19, 14], [13, 12, 14, 16, 11], [12, 11, 13, 15, 10]], [[17, 16, 18, 20, 15], [14, 13, 15, 17, 12], [13, 12, 14, 16, 11]], [[18, 17, 19, 21, 16], [15, 14, 16, 18, 13], [14, 13, 15, 17, 12]]]) The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays. Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. >>> >>> import numpy as np >>> a = np.array([[1.0, 2.0], [3.0, 4.0]]) >>> print(a) [[1. 2.] [3. 4.]] >>> a.transpose() array([[1., 3.], [2., 4.]]) >>> np.linalg.inv(a) array([[-2. , 1. ], [ 1.5, -0.5]]) >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I" >>> u array([[1., 0.], [0., 1.]]) >>> j = np.array([[0.0, -1.0], [1.0, 0.0]]) >>> j @ j # matrix product array([[-1., 0.], [ 0., -1.]]) >>> np.trace(u) # trace 2.0 >>> y = np.array([[5.], [7.]]) >>> np.linalg.solve(a, y) array([[-3.], [ 4.]]) >>> np.linalg.eig(j) (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j]])) Parameters: square matrix Returns The eigenvalues, each repeated according to its multiplicity. The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]`` . Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: >>> >>> a = np.arange(30) >>> b = a.reshape((2, -1, 3)) # -1 means "whatever is needed" >>> b.shape (2, 5, 3) >>> b array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]], [[15, 16, 17], [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]]) Vector Stacking How do we construct a 2D array from a list of equally-sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y]. In NumPy this works via the functions column_stack, dstack, hstack and vstack, depending on the dimension in which the stacking is to be done. For example: >>> >>> x = np.arange(0,10,2) >>> y = np.arange(5) >>> m = np.vstack([x,y]) >>> m array([[0, 2, 4, 6, 8], [0, 1, 2, 3, 4]]) >>> xy = np.hstack([x,y]) >>> xy array([0, 2, 4, 6, 8, 0, 1, 2, 3, 4]) The logic behind those functions in more than two dimensions can be strange. See also NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and a vector of the bin edges. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. >>> import numpy as np rg = np.random.default_rng(1) import matplotlib.pyplot as plt # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = rg.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins plt.hist(v, bins=50, density=1) # matplotlib version (plot) # Compute the histogram with numpy and then plot it (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) plt.plot(.5*(bins[1:]+bins[:-1]), n) ../_images/quickstart-2.png Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary © Copyright 2008-2020, The SciPy community. Last updated on Jun 29, 2020. Created using Sphinx 2.4.4.
nikhilkumarsingh / Recursion Tree PlotterA python decorator to generate a visual tree for recursive functions.
pkosterman / Aws Lambda Barcode GeneratorThis project uses a Node.js wrapper to build a Go Lambda function that generates & returns a barcode when triggered by AWS API Gateway.
mattyTokenomics / Brownian Motion GeneratorLibrary of functions to generate Brownian motion simulations for multiple series that exhibit mean reversion, correlation, and/or custom distributions that do not follow typical normal distributions.
MinoMino / MinfuncfindSimple tool to generate patterns and masks used to find functions in a binary without hard-coding offsets.
MineralsCloud / QuantumESPRESSOBase.jlProvides basic data structures and helpful functions for manipulating structures, generating input files, pre-running error checks, etc.
TheAngryByrd / TypeSafeInternalsUses Myriad to generate type safe reflection calls to internal functions/properties/methods.
