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	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/hubclient.py","language":"python","identifier":"Client.orgs","parameters":"(self)","argument_list":"","return_statement":"return orgs","docstring":":rtype: list(dict)","docstring_summary":":rtype: list(dict)","docstring_tokens":[":","rtype",":","list","(","dict",")"],"function":"def orgs(self):\n \"\"\"\n :rtype: list(dict)\n \"\"\"\n orgs = []\n for t in self.token_info['user']['tenants']:\n if t['is_org']:\n orgs.append(t)\n return orgs","function_tokens":["def","orgs","(","self",")",":","orgs","=","[","]","for","t","in","self",".","token_info","[","'user'","]","[","'tenants'","]",":","if","t","[","'is_org'","]",":","orgs",".","append","(","t",")","return","orgs"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/hubclient.py#L90-L98"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/utils.py","language":"python","identifier":"print_dict","parameters":"(d, prefix='')","argument_list":"","return_statement":"","docstring":":type d: dict","docstring_summary":":type d: dict","docstring_tokens":[":","type","d",":","dict"],"function":"def print_dict(d, prefix=''):\n \"\"\"\n :type d: dict\n \"\"\"\n for k, v in d.items():\n if isinstance(v, dict):\n print('%s%s:' % (prefix, k))\n print_dict(v, prefix + ' ' * 4)\n else:\n print('%s%s = %s' % (prefix, k, v))","function_tokens":["def","print_dict","(","d",",","prefix","=","''",")",":","for","k",",","v","in","d",".","items","(",")",":","if","isinstance","(","v",",","dict",")",":","print","(","'%s%s:'","%","(","prefix",",","k",")",")","print_dict","(","v",",","prefix","+","' '","*","4",")","else",":","print","(","'%s%s = %s'","%","(","prefix",",","k",",","v",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/utils.py#L38-L47"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/main.py","language":"python","identifier":"TopLevelCommand.version","parameters":"(self, options)","argument_list":"","return_statement":"","docstring":"Show the version information\n\n Usage: version","docstring_summary":"Show the version information","docstring_tokens":["Show","the","version","information"],"function":"def version(self, options):\n \"\"\"\n Show the version information\n\n Usage: version\n \"\"\"\n print('v0.1')","function_tokens":["def","version","(","self",",","options",")",":","print","(","'v0.1'",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/main.py#L58-L64"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/main.py","language":"python","identifier":"TopLevelCommand.login","parameters":"(self, options)","argument_list":"","return_statement":"","docstring":"Login to daolcoud daohub","docstring_summary":"Login to daolcoud daohub","docstring_tokens":["Login","to","daolcoud","daohub"],"function":"def login(self, options):\n \"\"\"\n Login to daolcoud daohub\n \"\"\"\n pass","function_tokens":["def","login","(","self",",","options",")",":","pass"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/main.py#L66-L70"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/main.py","language":"python","identifier":"TopLevelCommand.list","parameters":"(self, options)","argument_list":"","return_statement":"","docstring":"List local images and their verify stats.","docstring_summary":"List local images and their verify stats.","docstring_tokens":["List","local","images","and","their","verify","stats","."],"function":"def list(self, options):\n \"\"\"\n List local images and their verify stats.\n\n \"\"\"\n pass","function_tokens":["def","list","(","self",",","options",")",":","pass"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/main.py#L72-L77"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/blockchain.py","language":"python","identifier":"DaoHubVerify.regImage","parameters":"(self, callback)","argument_list":"","return_statement":"","docstring":"callback receive a dict:\n [{'address': u'0x8a1e16278f7695823962ada686ca13a202ee97d1',\n 'args': {u'imageHash': u'123123da\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'imageId': u'123123\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'owner': u'0x79eacb37490b7aa319b0bc405f2110c3b36259a9',\n u'repoTag': u'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x80'},\n 'blockHash': u'0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc',\n 'blockNumber': 8,\n 'event': u'regImage',\n 'logIndex': 0,\n 'transactionHash': u'0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862',\n 'transactionIndex': 0}]","docstring_summary":"callback receive a dict:\n [{'address': u'0x8a1e16278f7695823962ada686ca13a202ee97d1',\n 'args': {u'imageHash': u'123123da\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'imageId': u'123123\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'owner': u'0x79eacb37490b7aa319b0bc405f2110c3b36259a9',\n u'repoTag': u'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x80'},\n 'blockHash': u'0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc',\n 'blockNumber': 8,\n 'event': u'regImage',\n 'logIndex': 0,\n 'transactionHash': u'0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862',\n 'transactionIndex': 0}]","docstring_tokens":["callback","receive","a","dict",":","[","{","address",":","u","0x8a1e16278f7695823962ada686ca13a202ee97d1","args",":","{","u","imageHash",":","u","123123da","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","u","imageId",":","u","123123","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","u","owner",":","u","0x79eacb37490b7aa319b0bc405f2110c3b36259a9","u","repoTag",":","u","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x80","}","blockHash",":","u","0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc","blockNumber",":","8","event",":","u","regImage","logIndex",":","0","transactionHash",":","u","0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862","transactionIndex",":","0","}","]"],"function":"def regImage(self, callback):\n \"\"\"\n callback receive a dict:\n [{'address': u'0x8a1e16278f7695823962ada686ca13a202ee97d1',\n 'args': {u'imageHash': u'123123da\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'imageId': u'123123\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'owner': u'0x79eacb37490b7aa319b0bc405f2110c3b36259a9',\n u'repoTag': u'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x80'},\n 'blockHash': u'0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc',\n 'blockNumber': 8,\n 'event': u'regImage',\n 'logIndex': 0,\n 'transactionHash': u'0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862',\n 'transactionIndex': 0}]\n \"\"\"\n self.trans_filter.watch(callback)","function_tokens":["def","regImage","(","self",",","callback",")",":","self",".","trans_filter",".","watch","(","callback",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/blockchain.py#L59-L74"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"mean","parameters":"(data)","argument_list":"","return_statement":"return sum(data) \/ float(n)","docstring":"Return the arithmetic mean of data","docstring_summary":"Return the arithmetic mean of data","docstring_tokens":["Return","the","arithmetic","mean","of","data"],"function":"def mean(data):\n \"\"\"\n Return the arithmetic mean of data\n \"\"\"\n n = len(data)\n if n < 1:\n return 0\n return sum(data) \/ float(n)","function_tokens":["def","mean","(","data",")",":","n","=","len","(","data",")","if","n","<","1",":","return","0","return","sum","(","data",")","\/","float","(","n",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L213-L220"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"meanAndStandardDeviation","parameters":"(data)","argument_list":"","return_statement":"","docstring":"Return sum of square deviations of sequence data","docstring_summary":"Return sum of square deviations of sequence data","docstring_tokens":["Return","sum","of","square","deviations","of","sequence","data"],"function":"def meanAndStandardDeviation(data):\n \"\"\"\n Return sum of square deviations of sequence data\n \"\"\"\n c = mean(data)\n ss = sum((x - c) 2 for x in data)\n if abs(ss) < 1e-10:\n return c, 1.0\n else:\n return c, sqrt(ss \/ float(len(data)))","function_tokens":["def","meanAndStandardDeviation","(","data",")",":","c","=","mean","(","data",")","ss","=","sum","(","(","x","-","c",")","","2","for","x","in","data",")","if","abs","(","ss",")","<","1e-10",":","return","c",",","1.0","else",":","return","c",",","sqrt","(","ss","\/","float","(","len","(","data",")",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L223-L232"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"matrixMultiplication","parameters":"(A, B)","argument_list":"","return_statement":"return out","docstring":"return product of matrices A and B\n\n the input are two lists of lists where the sublists of matrix A\n have the length of matrix B","docstring_summary":"return product of matrices A and B","docstring_tokens":["return","product","of","matrices","A","and","B"],"function":"def matrixMultiplication(A, B):\n \"\"\"\n return product of matrices A and B\n\n the input are two lists of lists where the sublists of matrix A\n have the length of matrix B\n \"\"\"\n mA = len(A)\n mB = len(B)\n if mA <= 0:\n return []\n nA = len(A[0])\n if mB != nA:\n raise PolyFitException(\"shapes don't match %d vs %d\" % (nA, mB),\n EXCEPTION_MATRIX_MULTIPLICATION)\n if nA == 0:\n return []\n nB = len(B[0])\n\n for i, b in enumerate(B):\n if len(b) != nB:\n raise PolyFitException(\"matrix B: row %d doesn't match matrix \"\n \"shape (%d vs %d)\" % (i, len(b)),\n EXCEPTION_MATRIX_MULTIPLICATION)\n\n out = [[0.0 for x in range(nB)] for y in range(mA)]\n for row in range(mA):\n for col in range(nB):\n if len(A[row]) != nA:\n raise PolyFitException(\"matrix A: row %d doesn't match \"\n \"matrix shape (%d vs %d)\"\n % (row, nA, len(A[row])),\n EXCEPTION_MATRIX_MULTIPLICATION)\n for i in range(mB):\n out[row][col] += A[row][i] * B[i][col]\n return out","function_tokens":["def","matrixMultiplication","(","A",",","B",")",":","mA","=","len","(","A",")","mB","=","len","(","B",")","if","mA","<=","0",":","return","[","]","nA","=","len","(","A","[","0","]",")","if","mB","!=","nA",":","raise","PolyFitException","(","\"shapes don't match %d vs %d\"","%","(","nA",",","mB",")",",","EXCEPTION_MATRIX_MULTIPLICATION",")","if","nA","==","0",":","return","[","]","nB","=","len","(","B","[","0","]",")","for","i",",","b","in","enumerate","(","B",")",":","if","len","(","b",")","!=","nB",":","raise","PolyFitException","(","\"matrix B: row %d doesn't match matrix \"","\"shape (%d vs %d)\"","%","(","i",",","len","(","b",")",")",",","EXCEPTION_MATRIX_MULTIPLICATION",")","out","=","[","[","0.0","for","x","in","range","(","nB",")","]","for","y","in","range","(","mA",")","]","for","row","in","range","(","mA",")",":","for","col","in","range","(","nB",")",":","if","len","(","A","[","row","]",")","!=","nA",":","raise","PolyFitException","(","\"matrix A: row %d doesn't match \"","\"matrix shape (%d vs %d)\"","%","(","row",",","nA",",","len","(","A","[","row","]",")",")",",","EXCEPTION_MATRIX_MULTIPLICATION",")","for","i","in","range","(","mB",")",":","out","[","row","]","[","col","]","+=","A","[","row","]","[","i","]","*","B","[","i","]","[","col","]","return","out"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L235-L270"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"eyeMatrix","parameters":"(m)","argument_list":"","return_statement":"return out","docstring":"return a list of lists which represents the identity matrix of size m","docstring_summary":"return a list of lists which represents the identity matrix of size m","docstring_tokens":["return","a","list","of","lists","which","represents","the","identity","matrix","of","size","m"],"function":"def eyeMatrix(m):\n \"\"\"\n return a list of lists which represents the identity matrix of size m\n \"\"\"\n out = [[0.0 for x in range(m)] for y in range(m)]\n for i in range(m):\n out[i][i] = 1.0\n return out","function_tokens":["def","eyeMatrix","(","m",")",":","out","=","[","[","0.0","for","x","in","range","(","m",")","]","for","y","in","range","(","m",")","]","for","i","in","range","(","m",")",":","out","[","i","]","[","i","]","=","1.0","return","out"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L273-L280"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"norm","parameters":"(v)","argument_list":"","return_statement":"","docstring":"compute the square root of the sum of all squared components of a list v","docstring_summary":"compute the square root of the sum of all squared components of a list v","docstring_tokens":["compute","the","square","root","of","the","sum","of","all","squared","components","of","a","list","v"],"function":"def norm(v):\n \"\"\"\n compute the square root of the sum of all squared components of a list v\n \"\"\"\n\n if len(v) == 0:\n return 0\n if isinstance(v[0], list):\n return sqrt(sum([y 2.0 for x in v for y in x]))\n else:\n return sqrt(sum([y 2.0 for y in v]))","function_tokens":["def","norm","(","v",")",":","if","len","(","v",")","==","0",":","return","0","if","isinstance","(","v","[","0","]",",","list",")",":","return","sqrt","(","sum","(","[","y","","2.0","for","x","in","v","for","y","in","x","]",")",")","else",":","return","sqrt","(","sum","(","[","y","","2.0","for","y","in","v","]",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L283-L293"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"elementWise","parameters":"(A, B, operation)","argument_list":"","return_statement":"return [[operation(x, y)\n for x, y in zip(rowA, rowB)]\n for rowA, rowB in zip(A, B)]","docstring":"execute an operate element wise and return result\n\n A and B are lists of lists (all lists of same lengths)\n operation is a function of two arguments and one return value","docstring_summary":"execute an operate element wise and return result","docstring_tokens":["execute","an","operate","element","wise","and","return","result"],"function":"def elementWise(A, B, operation):\n \"\"\"\n execute an operate element wise and return result\n\n A and B are lists of lists (all lists of same lengths)\n operation is a function of two arguments and one return value\n \"\"\"\n return [[operation(x, y)\n for x, y in zip(rowA, rowB)]\n for rowA, rowB in zip(A, B)]","function_tokens":["def","elementWise","(","A",",","B",",","operation",")",":","return","[","[","operation","(","x",",","y",")","for","x",",","y","in","zip","(","rowA",",","rowB",")","]","for","rowA",",","rowB","in","zip","(","A",",","B",")","]"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L296-L305"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"transpose","parameters":"(A)","argument_list":"","return_statement":"return out","docstring":"return a list of lists for a Matrix A ( list of lists )\n with transposed representation of A","docstring_summary":"return a list of lists for a Matrix A ( list of lists )\n with transposed representation of A","docstring_tokens":["return","a","list","of","lists","for","a","Matrix","A","(","list","of","lists",")","with","transposed","representation","of","A"],"function":"def transpose(A):\n \"\"\"\n return a list of lists for a Matrix A ( list of lists )\n with transposed representation of A\n \"\"\"\n m = len(A)\n if m == 0:\n return []\n n = len(A[0])\n out = [[0.0 for x in range(m)] for y in range(n)]\n for r in range(m):\n for c in range(n):\n out[c][r] = A[r][c]\n return out","function_tokens":["def","transpose","(","A",")",":","m","=","len","(","A",")","if","m","==","0",":","return","[","]","n","=","len","(","A","[","0","]",")","out","=","[","[","0.0","for","x","in","range","(","m",")","]","for","y","in","range","(","n",")","]","for","r","in","range","(","m",")",":","for","c","in","range","(","n",")",":","out","[","c","]","[","r","]","=","A","[","r","]","[","c","]","return","out"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L308-L321"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"qr","parameters":"(A, prec=1e-10)","argument_list":"","return_statement":"return Q, R","docstring":"computes a faster and economic qr decomposition similar to:\n http:\/\/www.iaa.ncku.edu.tw\/~dychiang\/lab\/program\/mohr3d\/source\/Jama%5CQRDecomposition.html","docstring_summary":"computes a faster and economic qr decomposition similar to:\n http:\/\/www.iaa.ncku.edu.tw\/~dychiang\/lab\/program\/mohr3d\/source\/Jama%5CQRDecomposition.html","docstring_tokens":["computes","a","faster","and","economic","qr","decomposition","similar","to",":","http",":","\/\/","www",".","iaa",".","ncku",".","edu",".","tw","\/","~dychiang","\/","lab","\/","program","\/","mohr3d","\/","source","\/","Jama%5CQRDecomposition",".","html"],"function":"def qr(A, prec=1e-10):\n \"\"\"\n computes a faster and economic qr decomposition similar to:\n http:\/\/www.iaa.ncku.edu.tw\/~dychiang\/lab\/program\/mohr3d\/source\/Jama%5CQRDecomposition.html\n \"\"\"\n m = len(A)\n if m <= 0:\n return [], A\n n = len(A[0])\n Rdiag = [0] * n;\n QR = copy.deepcopy(A)\n for k in range(n):\n # Compute 2-norm of k-th column without under\/overflow.\n nrm = 0.0\n for i in range(k, m):\n nrm = sqrt(nrm 2 + QR[i][k] 2)\n\n if abs(nrm) > prec:\n # Form k-th Householder vector.\n if k < m and QR[k][k] < 0:\n nrm = -nrm\n\n for i in range(k, m):\n QR[i][k] \/= nrm\n if k < m:\n QR[k][k] += 1.0\n\n # Apply transformation to remaining columns.\n for j in range(k + 1, n):\n s = 0.0\n for i in range(k, m):\n s += QR[i][k] * QR[i][j]\n if k < m:\n s = -s \/ QR[k][k]\n for i in range(k, m):\n QR[i][j] += s * QR[i][k]\n Rdiag[k] = -nrm;\n\n # compute R\n R = [[0] * n for z in range(min(m, n))]\n for i in range(m):\n for j in range(i, n):\n if i < j:\n R[i][j] = QR[i][j]\n if i == j:\n R[i][i] = Rdiag[i]\n\n # compute Q\n w = min(m, n)\n Q = [[0] * w for i in range(m)]\n for k in range(w - 1, -1, -1):\n if k < w:\n Q[k][k] = 1.0;\n for j in range(k, w):\n if k < m and abs(QR[k][k]) > prec:\n s = 0.0\n for i in range(k, m):\n s += QR[i][k] * Q[i][j]\n s = -s \/ QR[k][k]\n for i in range(k, m):\n Q[i][j] += s * QR[i][k]\n return Q, R","function_tokens":["def","qr","(","A",",","prec","=","1e-10",")",":","m","=","len","(","A",")","if","m","<=","0",":","return","[","]",",","A","n","=","len","(","A","[","0","]",")","Rdiag","=","[","0","]","","n","QR","=","copy",".","deepcopy","(","A",")","for","k","in","range","(","n",")",":","# Compute 2-norm of k-th column without under\/overflow.","nrm","=","0.0","for","i","in","range","(","k",",","m",")",":","nrm","=","sqrt","(","nrm","","2","+","QR","[","i","]","[","k","]","","2",")","if","abs","(","nrm",")",">","prec",":","# Form k-th Householder vector.","if","k","<","m","and","QR","[","k","]","[","k","]","<","0",":","nrm","=","-","nrm","for","i","in","range","(","k",",","m",")",":","QR","[","i","]","[","k","]","\/=","nrm","if","k","<","m",":","QR","[","k","]","[","k","]","+=","1.0","# Apply transformation to remaining columns.","for","j","in","range","(","k","+","1",",","n",")",":","s","=","0.0","for","i","in","range","(","k",",","m",")",":","s","+=","QR","[","i","]","[","k","]","","QR","[","i","]","[","j","]","if","k","<","m",":","s","=","-","s","\/","QR","[","k","]","[","k","]","for","i","in","range","(","k",",","m",")",":","QR","[","i","]","[","j","]","+=","s","","QR","[","i","]","[","k","]","Rdiag","[","k","]","=","-","nrm","# compute R","R","=","[","[","0","]","","n","for","z","in","range","(","min","(","m",",","n",")",")","]","for","i","in","range","(","m",")",":","for","j","in","range","(","i",",","n",")",":","if","i","<","j",":","R","[","i","]","[","j","]","=","QR","[","i","]","[","j","]","if","i","==","j",":","R","[","i","]","[","i","]","=","Rdiag","[","i","]","# compute Q","w","=","min","(","m",",","n",")","Q","=","[","[","0","]","","w","for","i","in","range","(","m",")","]","for","k","in","range","(","w","-","1",",","-","1",",","-","1",")",":","if","k","<","w",":","Q","[","k","]","[","k","]","=","1.0","for","j","in","range","(","k",",","w",")",":","if","k","<","m","and","abs","(","QR","[","k","]","[","k","]",")",">","prec",":","s","=","0.0","for","i","in","range","(","k",",","m",")",":","s","+=","QR","[","i","]","[","k","]","","Q","[","i","]","[","j","]","s","=","-","s","\/","QR","[","k","]","[","k","]","for","i","in","range","(","k",",","m",")",":","Q","[","i","]","[","j","]","+=","s","*","QR","[","i","]","[","k","]","return","Q",",","R"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L324-L385"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"solveWithForwardReplacement","parameters":"(A, b, prec=1e-10)","argument_list":"","return_statement":"return res","docstring":"solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung","docstring_summary":"solve a system Ax = b with A = QR in least squares sense\n return x","docstring_tokens":["solve","a","system","Ax","=","b","with","A","=","QR","in","least","squares","sense","return","x"],"function":"def solveWithForwardReplacement(A, b, prec=1e-10):\n \"\"\"\n solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung\n \"\"\"\n if len(A) == 0:\n return []\n Q, R = qr(transpose(A))\n\n Rt = transpose(R)\n m = len(Rt)\n n = len(Rt[0])\n\n z = [0] * n\n for r in range(m):\n if abs(Rt[r][r]) < prec:\n z[r] = 1.0\n continue\n s = 0.0\n for c in range(m):\n s += z[c] * Rt[r][c]\n z[r] = (b[r] - s) \/ Rt[r][r]\n\n res = matrixMultiplication(Q, [[a] for a in z])\n res = [v for sublist in res for v in sublist] # flatten\n\n return res","function_tokens":["def","solveWithForwardReplacement","(","A",",","b",",","prec","=","1e-10",")",":","if","len","(","A",")","==","0",":","return","[","]","Q",",","R","=","qr","(","transpose","(","A",")",")","Rt","=","transpose","(","R",")","m","=","len","(","Rt",")","n","=","len","(","Rt","[","0","]",")","z","=","[","0","]","","n","for","r","in","range","(","m",")",":","if","abs","(","Rt","[","r","]","[","r","]",")","<","prec",":","z","[","r","]","=","1.0","continue","s","=","0.0","for","c","in","range","(","m",")",":","s","+=","z","[","c","]","","Rt","[","r","]","[","c","]","z","[","r","]","=","(","b","[","r","]","-","s",")","\/","Rt","[","r","]","[","r","]","res","=","matrixMultiplication","(","Q",",","[","[","a","]","for","a","in","z","]",")","res","=","[","v","for","sublist","in","res","for","v","in","sublist","]","# flatten","return","res"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L388-L416"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"solveWithBackwardReplacement","parameters":"(A, b, prec=1e-10)","argument_list":"","return_statement":"return res","docstring":"solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung","docstring_summary":"solve a system Ax = b with A = QR in least squares sense\n return x","docstring_tokens":["solve","a","system","Ax","=","b","with","A","=","QR","in","least","squares","sense","return","x"],"function":"def solveWithBackwardReplacement(A, b, prec=1e-10):\n \"\"\"\n solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung\n \"\"\"\n n = len(A)\n if n == 0:\n return []\n\n Q, R = qr(A)\n\n n = len(R[0])\n res = [0] * n\n\n z = matrixMultiplication(transpose(Q), [[a] for a in b])\n\n for r in range(n - 1, -1, -1):\n if abs(R[r][r]) < prec:\n res[r] = 1.0\n continue\n s = 0.0\n for c in range(r + 1, n):\n s += res[c] * R[r][c]\n res[r] = (z[r][0] - s) \/ R[r][r]\n\n return res","function_tokens":["def","solveWithBackwardReplacement","(","A",",","b",",","prec","=","1e-10",")",":","n","=","len","(","A",")","if","n","==","0",":","return","[","]","Q",",","R","=","qr","(","A",")","n","=","len","(","R","[","0","]",")","res","=","[","0","]","","n","z","=","matrixMultiplication","(","transpose","(","Q",")",",","[","[","a","]","for","a","in","b","]",")","for","r","in","range","(","n","-","1",",","-","1",",","-","1",")",":","if","abs","(","R","[","r","]","[","r","]",")","<","prec",":","res","[","r","]","=","1.0","continue","s","=","0.0","for","c","in","range","(","r","+","1",",","n",")",":","s","+=","res","[","c","]","","R","[","r","]","[","c","]","res","[","r","]","=","(","z","[","r","]","[","0","]","-","s",")","\/","R","[","r","]","[","r","]","return","res"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L419-L446"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"leastSquareSolution","parameters":"(A, b)","argument_list":"","return_statement":"return s, res","docstring":"return the least squares solution x for the system Ax = b\n\n A is a list of list while b is a list","docstring_summary":"return the least squares solution x for the system Ax = b","docstring_tokens":["return","the","least","squares","solution","x","for","the","system","Ax","=","b"],"function":"def leastSquareSolution(A, b):\n \"\"\"\n return the least squares solution x for the system Ax = b\n\n A is a list of list while b is a list\n \"\"\"\n m = len(A)\n if m == 0:\n return []\n n = len(A[0])\n\n if n > m:\n s = solveWithForwardReplacement(A, b)\n else:\n s = solveWithBackwardReplacement(A, b)\n remap = matrixMultiplication(A, [[a] for a in s])\n res = sum([z[0] 2.0 for z in elementWise(remap, [[a] for a in b],\n lambda x, y: x - y)])\n\n return s, res","function_tokens":["def","leastSquareSolution","(","A",",","b",")",":","m","=","len","(","A",")","if","m","==","0",":","return","[","]","n","=","len","(","A","[","0","]",")","if","n",">","m",":","s","=","solveWithForwardReplacement","(","A",",","b",")","else",":","s","=","solveWithBackwardReplacement","(","A",",","b",")","remap","=","matrixMultiplication","(","A",",","[","[","a","]","for","a","in","s","]",")","res","=","sum","(","[","z","[","0","]","","2.0","for","z","in","elementWise","(","remap",",","[","[","a","]","for","a","in","b","]",",","lambda","x",",","y",":","x","-","y",")","]",")","return","s",",","res"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L449-L468"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit.__init__","parameters":"(self, x, values, order=1)","argument_list":"","return_statement":"","docstring":"initialize a polyfitting with x and y coordinates\n and respective functino values\n (all simple lists)","docstring_summary":"initialize a polyfitting with x and y coordinates\n and respective functino values\n (all simple lists)","docstring_tokens":["initialize","a","polyfitting","with","x","and","y","coordinates","and","respective","functino","values","(","all","simple","lists",")"],"function":"def __init__(self, x, values, order=1):\n \"\"\"\n initialize a polyfitting with x and y coordinates\n and respective functino values\n (all simple lists)\n \"\"\"\n n = len(x)\n if n != len(values):\n raise PolyFitException(\"input values have different lengths\",\n self.TAG)\n\n self.mIn, self.sIn = meanAndStandardDeviation(x)\n self.mVal, self.sVal = meanAndStandardDeviation(values)\n x = [(y - self.mIn) \/ self.sIn for y in x]\n values = [(y - self.mVal) \/ self.sVal for y in values]\n\n A = [[x[i] float(j) for j in range(order + 1)] for i in range(n)]\n self.coeffs, self.residual = leastSquareSolution(A, values)\n self.order = order","function_tokens":["def","__init__","(","self",",","x",",","values",",","order","=","1",")",":","n","=","len","(","x",")","if","n","!=","len","(","values",")",":","raise","PolyFitException","(","\"input values have different lengths\"",",","self",".","TAG",")","self",".","mIn",",","self",".","sIn","=","meanAndStandardDeviation","(","x",")","self",".","mVal",",","self",".","sVal","=","meanAndStandardDeviation","(","values",")","x","=","[","(","y","-","self",".","mIn",")","\/","self",".","sIn","for","y","in","x","]","values","=","[","(","y","-","self",".","mVal",")","\/","self",".","sVal","for","y","in","values","]","A","=","[","[","x","[","i","]","","float","(","j",")","for","j","in","range","(","order","+","1",")","]","for","i","in","range","(","n",")","]","self",".","coeffs",",","self",".","residual","=","leastSquareSolution","(","A",",","values",")","self",".","order","=","order"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L49-L67"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit.__getitem__","parameters":"(self, x)","argument_list":"","return_statement":"return val * self.sVal + self.mVal","docstring":"return the estimated function value for position x","docstring_summary":"return the estimated function value for position x","docstring_tokens":["return","the","estimated","function","value","for","position","x"],"function":"def __getitem__(self, x):\n \"\"\"\n return the estimated function value for position x\n \"\"\"\n x = (x - self.mIn) \/ self.sIn\n val = sum([self.coeffs[i] * x ** float(i) for i in range(self.order + 1)])\n return val * self.sVal + self.mVal","function_tokens":["def","__getitem__","(","self",",","x",")",":","x","=","(","x","-","self",".","mIn",")","\/","self",".","sIn","val","=","sum","(","[","self",".","coeffs","[","i","]","","x","","float","(","i",")","for","i","in","range","(","self",".","order","+","1",")","]",")","return","val","","self",".","sVal","+","self",".","mVal"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L69-L75"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit2D.__init__","parameters":"(self, pts, values, order=1)","argument_list":"","return_statement":"","docstring":"initialize a polyfitting with pts = [[x1,x2], [y1,y2], ...] coordinates\n and respective function values [v1, ...]\n (all simple lists)","docstring_summary":"initialize a polyfitting with pts = [[x1,x2], [y1,y2], ...] coordinates\n and respective function values [v1, ...]\n (all simple lists)","docstring_tokens":["initialize","a","polyfitting","with","pts","=","[[","x1","x2","]","[","y1","y2","]","...","]","coordinates","and","respective","function","values","[","v1","...","]","(","all","simple","lists",")"],"function":"def __init__(self, pts, values, order=1):\n \"\"\"\n initialize a polyfitting with pts = [[x1,x2], [y1,y2], ...] coordinates\n and respective function values [v1, ...]\n (all simple lists)\n \"\"\"\n n = len(pts)\n for i in range(n):\n if 2 != len(pts[i]):\n raise PolyFitException(\"input values have different lengths\",\n self.TAG)\n\n mons = [x for x in product(range(order + 1), range(order + 1))\n if x[0] + x[1] <= order]\n self.monomials = mons\n self.n = len(self.monomials)\n\n # scale for better approximation\n self.mInX, self.sInX = meanAndStandardDeviation([z[0] for z in pts])\n self.mInY, self.sInY = meanAndStandardDeviation([z[1] for z in pts])\n self.mVal, self.sVal = meanAndStandardDeviation(values)\n pts = [((z[0] - self.mInX) \/ self.sInX, (z[1] - self.mInY) \/ self.sInY)\n for z in pts]\n values = [(z - self.mVal) \/ self.sVal for z in values]\n\n m = self.monomials\n A = [[pts[u][0] ** m[i][0] * pts[u][1] m[i][1] for i in range(self.n)]\n for u in range(n)]\n self.coeffs, self.residual = leastSquareSolution(A, values)\n self.order = order","function_tokens":["def","__init__","(","self",",","pts",",","values",",","order","=","1",")",":","n","=","len","(","pts",")","for","i","in","range","(","n",")",":","if","2","!=","len","(","pts","[","i","]",")",":","raise","PolyFitException","(","\"input values have different lengths\"",",","self",".","TAG",")","mons","=","[","x","for","x","in","product","(","range","(","order","+","1",")",",","range","(","order","+","1",")",")","if","x","[","0","]","+","x","[","1","]","<=","order","]","self",".","monomials","=","mons","self",".","n","=","len","(","self",".","monomials",")","# scale for better approximation","self",".","mInX",",","self",".","sInX","=","meanAndStandardDeviation","(","[","z","[","0","]","for","z","in","pts","]",")","self",".","mInY",",","self",".","sInY","=","meanAndStandardDeviation","(","[","z","[","1","]","for","z","in","pts","]",")","self",".","mVal",",","self",".","sVal","=","meanAndStandardDeviation","(","values",")","pts","=","[","(","(","z","[","0","]","-","self",".","mInX",")","\/","self",".","sInX",",","(","z","[","1","]","-","self",".","mInY",")","\/","self",".","sInY",")","for","z","in","pts","]","values","=","[","(","z","-","self",".","mVal",")","\/","self",".","sVal","for","z","in","values","]","m","=","self",".","monomials","A","=","[","[","pts","[","u","]","[","0","]","","m","[","i","]","[","0","]","","pts","[","u","]","[","1","]","*","m","[","i","]","[","1","]","for","i","in","range","(","self",".","n",")","]","for","u","in","range","(","n",")","]","self",".","coeffs",",","self",".","residual","=","leastSquareSolution","(","A",",","values",")","self",".","order","=","order"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L86-L115"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit2D.__getitem__","parameters":"(self, x)","argument_list":"","return_statement":"return val * self.sVal + self.mVal","docstring":"return the estimated function value for position x","docstring_summary":"return the estimated function value for position x","docstring_tokens":["return","the","estimated","function","value","for","position","x"],"function":"def __getitem__(self, x):\n \"\"\"\n return the estimated function value for position x\n \"\"\"\n m = self.monomials\n x = ((x[0] - self.mInX) \/ self.sInX, (x[1] - self.mInY) \/ self.sInY)\n val = sum([self.coeffs[i] * x[0] ** m[i][0] * x[1] ** m[i][1]\n for i in range(self.n)])\n return val * self.sVal + self.mVal","function_tokens":["def","__getitem__","(","self",",","x",")",":","m","=","self",".","monomials","x","=","(","(","x","[","0","]","-","self",".","mInX",")","\/","self",".","sInX",",","(","x","[","1","]","-","self",".","mInY",")","\/","self",".","sInY",")","val","=","sum","(","[","self",".","coeffs","[","i","]","","x","[","0","]","","m","[","i","]","[","0","]","","x","[","1","]","*","m","[","i","]","[","1","]","for","i","in","range","(","self",".","n",")","]",")","return","val","","self",".","sVal","+","self",".","mVal"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L117-L125"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFitND.__init__","parameters":"(self, pts, values, order=1)","argument_list":"","return_statement":"","docstring":"initialize a polyfitting with pts = [[x1,x2 ...], [y1,y2,...], ...]\n coordinates and respective function values [v1, ...]\n (all simple lists)","docstring_summary":"initialize a polyfitting with pts = [[x1,x2 ...], [y1,y2,...], ...]\n coordinates and respective function values [v1, ...]\n (all simple lists)","docstring_tokens":["initialize","a","polyfitting","with","pts","=","[[","x1","x2","...","]","[","y1","y2","...","]","...","]","coordinates","and","respective","function","values","[","v1","...","]","(","all","simple","lists",")"],"function":"def __init__(self, pts, values, order=1):\n \"\"\"\n initialize a polyfitting with pts = [[x1,x2 ...], [y1,y2,...], ...]\n coordinates and respective function values [v1, ...]\n (all simple lists)\n \"\"\"\n n = len(pts)\n if n <= 0:\n raise PolyFitException(\"no input values given\",\n self.TAG)\n dimension = len(pts[0])\n for i in range(n):\n if dimension != len(pts[i]):\n raise PolyFitException(\"input values have different lengths\",\n self.TAG)\n\n self.dimension = dimension\n\n mons = [x for x in product([range(order + 1)] dimension)\n if sum(x) <= order]\n self.monomials = mons\n self.n = len(self.monomials)\n\n m = self.monomials\n\n # scales\n self.scales = [(c, s) for c, s in [meanAndStandardDeviation(\n [pts[j][i] for j in range(n)])\n for i in range(dimension)]]\n pts = [[(pts[j][i] - self.scales[i][0]) \/ self.scales[i][1]\n for i in range(dimension)]\n for j in range(n)]\n self.mVal, self.sVal = meanAndStandardDeviation(values)\n values = [(z - self.mVal) \/ self.sVal for z in values]\n\n A = [[functools.reduce(operator.mul,\n [pts[u][d] ** m[i][d] for d in range(dimension)],\n 1)\n for i in range(self.n)]\n for u in range(n)]\n self.coeffs, self.residual = leastSquareSolution(A, values)\n self.order = order","function_tokens":["def","__init__","(","self",",","pts",",","values",",","order","=","1",")",":","n","=","len","(","pts",")","if","n","<=","0",":","raise","PolyFitException","(","\"no input values given\"",",","self",".","TAG",")","dimension","=","len","(","pts","[","0","]",")","for","i","in","range","(","n",")",":","if","dimension","!=","len","(","pts","[","i","]",")",":","raise","PolyFitException","(","\"input values have different lengths\"",",","self",".","TAG",")","self",".","dimension","=","dimension","mons","=","[","x","for","x","in","product","(","","[","range","(","order","+","1",")","]","","dimension",")","if","sum","(","x",")","<=","order","]","self",".","monomials","=","mons","self",".","n","=","len","(","self",".","monomials",")","m","=","self",".","monomials","# scales","self",".","scales","=","[","(","c",",","s",")","for","c",",","s","in","[","meanAndStandardDeviation","(","[","pts","[","j","]","[","i","]","for","j","in","range","(","n",")","]",")","for","i","in","range","(","dimension",")","]","]","pts","=","[","[","(","pts","[","j","]","[","i","]","-","self",".","scales","[","i","]","[","0","]",")","\/","self",".","scales","[","i","]","[","1","]","for","i","in","range","(","dimension",")","]","for","j","in","range","(","n",")","]","self",".","mVal",",","self",".","sVal","=","meanAndStandardDeviation","(","values",")","values","=","[","(","z","-","self",".","mVal",")","\/","self",".","sVal","for","z","in","values","]","A","=","[","[","functools",".","reduce","(","operator",".","mul",",","[","pts","[","u","]","[","d","]","**","m","[","i","]","[","d","]","for","d","in","range","(","dimension",")","]",",","1",")","for","i","in","range","(","self",".","n",")","]","for","u","in","range","(","n",")","]","self",".","coeffs",",","self",".","residual","=","leastSquareSolution","(","A",",","values",")","self",".","order","=","order"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L136-L177"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFitND.__getitem__","parameters":"(self, x)","argument_list":"","return_statement":"return val * self.sVal + self.mVal","docstring":"return the estimated function value for position x","docstring_summary":"return the estimated function value for position x","docstring_tokens":["return","the","estimated","function","value","for","position","x"],"function":"def __getitem__(self, x):\n \"\"\"\n return the estimated function value for position x\n \"\"\"\n if len(x) != self.dimension:\n raise PolyFitException(\"input value has different length than \"\n \"initialization data\",\n self.TAG)\n\n m = self.monomials\n d = len(x)\n x = [(x[i] - self.scales[i][0]) \/ self.scales[i][1]\n for i in range(self.dimension)]\n val = sum([self.coeffs[i]\n * functools.reduce(operator.mul,\n [x[d] ** m[i][d]\n for d in range(self.dimension)],\n 1)\n for i in range(self.n)])\n return val * self.sVal + self.mVal","function_tokens":["def","__getitem__","(","self",",","x",")",":","if","len","(","x",")","!=","self",".","dimension",":","raise","PolyFitException","(","\"input value has different length than \"","\"initialization data\"",",","self",".","TAG",")","m","=","self",".","monomials","d","=","len","(","x",")","x","=","[","(","x","[","i","]","-","self",".","scales","[","i","]","[","0","]",")","\/","self",".","scales","[","i","]","[","1","]","for","i","in","range","(","self",".","dimension",")","]","val","=","sum","(","[","self",".","coeffs","[","i","]","","functools",".","reduce","(","operator",".","mul",",","[","x","[","d","]","","m","[","i","]","[","d","]","for","d","in","range","(","self",".","dimension",")","]",",","1",")","for","i","in","range","(","self",".","n",")","]",")","return","val","","self",".","sVal","+","self",".","mVal"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L179-L198"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/timing.py","language":"python","identifier":"timed","parameters":"(f)","argument_list":"","return_statement":"return wrapper","docstring":"measure the clock time of functions as well as the pure times,\n without the subfunction calls\n (However, the latter is not as accurate since it includes some management\n of the timing)","docstring_summary":"measure the clock time of functions as well as the pure times,\n without the subfunction calls\n (However, the latter is not as accurate since it includes some management\n of the timing)","docstring_tokens":["measure","the","clock","time","of","functions","as","well","as","the","pure","times","without","the","subfunction","calls","(","However","the","latter","is","not","as","accurate","since","it","includes","some","management","of","the","timing",")"],"function":"def timed(f):\n \"\"\"\n measure the clock time of functions as well as the pure times,\n without the subfunction calls\n (However, the latter is not as accurate since it includes some management\n of the timing)\n \"\"\"\n @wraps(f)\n def wrapper(args, kwds):\n startOverall = clock()\n global currentCallStack\n global times\n name = f.__name__\n currentCallStack.append(name)\n if name not in times:\n times[name] = {\"numCalls\": 0, \"totalTime\": 0, \"subprocesses\": 0, \"numSub\" : 0}\n start = clock()\n result = f(args, *kwds)\n elapsed = clock() - start\n currentCallStack = currentCallStack[:-1]\n times[name][\"numCalls\"] += 1\n times[name][\"totalTime\"] += elapsed\n if len(currentCallStack) > 1:\n times[currentCallStack[-2]][\"numSub\"] += 1\n times[currentCallStack[-2]][\"subprocesses\"] += clock() - startOverall\n return result\n return wrapper","function_tokens":["def","timed","(","f",")",":","@","wraps","(","f",")","def","wrapper","(","","args",",","","","kwds",")",":","startOverall","=","clock","(",")","global","currentCallStack","global","times","name","=","f",".","__name__","currentCallStack",".","append","(","name",")","if","name","not","in","times",":","times","[","name","]","=","{","\"numCalls\"",":","0",",","\"totalTime\"",":","0",",","\"subprocesses\"",":","0",",","\"numSub\"",":","0","}","start","=","clock","(",")","result","=","f","(","","args",",","","*","kwds",")","elapsed","=","clock","(",")","-","start","currentCallStack","=","currentCallStack","[",":","-","1","]","times","[","name","]","[","\"numCalls\"","]","+=","1","times","[","name","]","[","\"totalTime\"","]","+=","elapsed","if","len","(","currentCallStack",")",">","1",":","times","[","currentCallStack","[","-","2","]","]","[","\"numSub\"","]","+=","1","times","[","currentCallStack","[","-","2","]","]","[","\"subprocesses\"","]","+=","clock","(",")","-","startOverall","return","result","return","wrapper"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/timing.py#L44-L70"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/timing.py","language":"python","identifier":"printTimes","parameters":"()","argument_list":"","return_statement":"","docstring":"pretty print timing results","docstring_summary":"pretty print timing results","docstring_tokens":["pretty","print","timing","results"],"function":"def printTimes():\n \"\"\"\n pretty print timing results\n \"\"\"\n global times\n # determine maximum length:\n header = \"function name\"\n ma = len(header)\n sum = 0\n sumOverall = 0\n for name, ts in times.items():\n ma = max(ma, len(name))\n net = (ts['totalTime'] - ts['subprocesses'])\/float(ts['numCalls'])\n sum += net\n sumOverall += (ts['totalTime'] - ts['subprocesses'])\n ma += 2\n \n print(\"\\033[1moverall (mean) processor time\\033[0m: %0.5fs (%0.5fs)\" %(sumOverall, sum))\n print(\"listing processor time per method:\" )\n print(\"\\033[1m%s%s\\t %% Overall time\\t Mean w.o. Sub[s]\\t Mean[s]\\tNum-Calls\\033[0m\" % (header, ' '(ma - len(header))))\n for name in sorted(times.keys()):\n ts = times[name]\n m = ts['totalTime']\/float(ts['numCalls'])\n net = (ts['totalTime'] - ts['subprocesses'])\/float(ts['numCalls'])\n perc = 100.0net\/float(sum)\n print(\"%s%s\\t% 15.2f\\t% 17.5f\\t% 9.5f\\t% 9d\" %(name,\n ' '(ma - len(name)),\n perc,\n net,\n m,\n\n ts['numCalls']))","function_tokens":["def","printTimes","(",")",":","global","times","# determine maximum length:","header","=","\"function name\"","ma","=","len","(","header",")","sum","=","0","sumOverall","=","0","for","name",",","ts","in","times",".","items","(",")",":","ma","=","max","(","ma",",","len","(","name",")",")","net","=","(","ts","[","'totalTime'","]","-","ts","[","'subprocesses'","]",")","\/","float","(","ts","[","'numCalls'","]",")","sum","+=","net","sumOverall","+=","(","ts","[","'totalTime'","]","-","ts","[","'subprocesses'","]",")","ma","+=","2","print","(","\"\\033[1moverall (mean) processor time\\033[0m: %0.5fs (%0.5fs)\"","%","(","sumOverall",",","sum",")",")","print","(","\"listing processor time per method:\"",")","print","(","\"\\033[1m%s%s\\t %% Overall time\\t Mean w.o. Sub[s]\\t Mean[s]\\tNum-Calls\\033[0m\"","%","(","header",",","' '","","(","ma","-","len","(","header",")",")",")",")","for","name","in","sorted","(","times",".","keys","(",")",")",":","ts","=","times","[","name","]","m","=","ts","[","'totalTime'","]","\/","float","(","ts","[","'numCalls'","]",")","net","=","(","ts","[","'totalTime'","]","-","ts","[","'subprocesses'","]",")","\/","float","(","ts","[","'numCalls'","]",")","perc","=","100.0","","net","\/","float","(","sum",")","print","(","\"%s%s\\t% 15.2f\\t% 17.5f\\t% 9.5f\\t% 9d\"","%","(","name",",","' '","","(","ma","-","len","(","name",")",")",",","perc",",","net",",","m",",","ts","[","'numCalls'","]",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/timing.py#L72-L103"}

	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/hubclient.py","language":"python","identifier":"Client.orgs","parameters":"(self)","argument_list":"","return_statement":"return orgs","docstring":":rtype: list(dict)","docstring_summary":":rtype: list(dict)","docstring_tokens":[":","rtype",":","list","(","dict",")"],"function":"def orgs(self):\n \"\"\"\n :rtype: list(dict)\n \"\"\"\n orgs = []\n for t in self.token_info['user']['tenants']:\n if t['is_org']:\n orgs.append(t)\n return orgs","function_tokens":["def","orgs","(","self",")",":","orgs","=","[","]","for","t","in","self",".","token_info","[","'user'","]","[","'tenants'","]",":","if","t","[","'is_org'","]",":","orgs",".","append","(","t",")","return","orgs"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/hubclient.py#L90-L98"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/utils.py","language":"python","identifier":"print_dict","parameters":"(d, prefix='')","argument_list":"","return_statement":"","docstring":":type d: dict","docstring_summary":":type d: dict","docstring_tokens":[":","type","d",":","dict"],"function":"def print_dict(d, prefix=''):\n \"\"\"\n :type d: dict\n \"\"\"\n for k, v in d.items():\n if isinstance(v, dict):\n print('%s%s:' % (prefix, k))\n print_dict(v, prefix + ' ' * 4)\n else:\n print('%s%s = %s' % (prefix, k, v))","function_tokens":["def","print_dict","(","d",",","prefix","=","''",")",":","for","k",",","v","in","d",".","items","(",")",":","if","isinstance","(","v",",","dict",")",":","print","(","'%s%s:'","%","(","prefix",",","k",")",")","print_dict","(","v",",","prefix","+","' '","*","4",")","else",":","print","(","'%s%s = %s'","%","(","prefix",",","k",",","v",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/utils.py#L38-L47"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/main.py","language":"python","identifier":"TopLevelCommand.version","parameters":"(self, options)","argument_list":"","return_statement":"","docstring":"Show the version information\n\n Usage: version","docstring_summary":"Show the version information","docstring_tokens":["Show","the","version","information"],"function":"def version(self, options):\n \"\"\"\n Show the version information\n\n Usage: version\n \"\"\"\n print('v0.1')","function_tokens":["def","version","(","self",",","options",")",":","print","(","'v0.1'",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/main.py#L58-L64"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/main.py","language":"python","identifier":"TopLevelCommand.login","parameters":"(self, options)","argument_list":"","return_statement":"","docstring":"Login to daolcoud daohub","docstring_summary":"Login to daolcoud daohub","docstring_tokens":["Login","to","daolcoud","daohub"],"function":"def login(self, options):\n \"\"\"\n Login to daolcoud daohub\n \"\"\"\n pass","function_tokens":["def","login","(","self",",","options",")",":","pass"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/main.py#L66-L70"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/main.py","language":"python","identifier":"TopLevelCommand.list","parameters":"(self, options)","argument_list":"","return_statement":"","docstring":"List local images and their verify stats.","docstring_summary":"List local images and their verify stats.","docstring_tokens":["List","local","images","and","their","verify","stats","."],"function":"def list(self, options):\n \"\"\"\n List local images and their verify stats.\n\n \"\"\"\n pass","function_tokens":["def","list","(","self",",","options",")",":","pass"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/main.py#L72-L77"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/blockchain.py","language":"python","identifier":"DaoHubVerify.regImage","parameters":"(self, callback)","argument_list":"","return_statement":"","docstring":"callback receive a dict:\n [{'address': u'0x8a1e16278f7695823962ada686ca13a202ee97d1',\n 'args': {u'imageHash': u'123123da\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'imageId': u'123123\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'owner': u'0x79eacb37490b7aa319b0bc405f2110c3b36259a9',\n u'repoTag': u'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x80'},\n 'blockHash': u'0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc',\n 'blockNumber': 8,\n 'event': u'regImage',\n 'logIndex': 0,\n 'transactionHash': u'0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862',\n 'transactionIndex': 0}]","docstring_summary":"callback receive a dict:\n [{'address': u'0x8a1e16278f7695823962ada686ca13a202ee97d1',\n 'args': {u'imageHash': u'123123da\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'imageId': u'123123\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'owner': u'0x79eacb37490b7aa319b0bc405f2110c3b36259a9',\n u'repoTag': u'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x80'},\n 'blockHash': u'0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc',\n 'blockNumber': 8,\n 'event': u'regImage',\n 'logIndex': 0,\n 'transactionHash': u'0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862',\n 'transactionIndex': 0}]","docstring_tokens":["callback","receive","a","dict",":","[","{","address",":","u","0x8a1e16278f7695823962ada686ca13a202ee97d1","args",":","{","u","imageHash",":","u","123123da","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","u","imageId",":","u","123123","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","u","owner",":","u","0x79eacb37490b7aa319b0bc405f2110c3b36259a9","u","repoTag",":","u","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x00","\\","x80","}","blockHash",":","u","0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc","blockNumber",":","8","event",":","u","regImage","logIndex",":","0","transactionHash",":","u","0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862","transactionIndex",":","0","}","]"],"function":"def regImage(self, callback):\n \"\"\"\n callback receive a dict:\n [{'address': u'0x8a1e16278f7695823962ada686ca13a202ee97d1',\n 'args': {u'imageHash': u'123123da\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'imageId': u'123123\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n u'owner': u'0x79eacb37490b7aa319b0bc405f2110c3b36259a9',\n u'repoTag': u'\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x80'},\n 'blockHash': u'0x61cb83263400c11477edfcbaf637e55910038865b80009082b51460e38aa13fc',\n 'blockNumber': 8,\n 'event': u'regImage',\n 'logIndex': 0,\n 'transactionHash': u'0xfe0309c12fe33ce1b6c79bd6c5045e9ca16ae0d3d0799db86f2f5f90d44f4862',\n 'transactionIndex': 0}]\n \"\"\"\n self.trans_filter.watch(callback)","function_tokens":["def","regImage","(","self",",","callback",")",":","self",".","trans_filter",".","watch","(","callback",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/blockchain.py#L59-L74"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"mean","parameters":"(data)","argument_list":"","return_statement":"return sum(data) \/ float(n)","docstring":"Return the arithmetic mean of data","docstring_summary":"Return the arithmetic mean of data","docstring_tokens":["Return","the","arithmetic","mean","of","data"],"function":"def mean(data):\n \"\"\"\n Return the arithmetic mean of data\n \"\"\"\n n = len(data)\n if n < 1:\n return 0\n return sum(data) \/ float(n)","function_tokens":["def","mean","(","data",")",":","n","=","len","(","data",")","if","n","<","1",":","return","0","return","sum","(","data",")","\/","float","(","n",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L213-L220"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"meanAndStandardDeviation","parameters":"(data)","argument_list":"","return_statement":"","docstring":"Return sum of square deviations of sequence data","docstring_summary":"Return sum of square deviations of sequence data","docstring_tokens":["Return","sum","of","square","deviations","of","sequence","data"],"function":"def meanAndStandardDeviation(data):\n \"\"\"\n Return sum of square deviations of sequence data\n \"\"\"\n c = mean(data)\n ss = sum((x - c) 2 for x in data)\n if abs(ss) < 1e-10:\n return c, 1.0\n else:\n return c, sqrt(ss \/ float(len(data)))","function_tokens":["def","meanAndStandardDeviation","(","data",")",":","c","=","mean","(","data",")","ss","=","sum","(","(","x","-","c",")","","2","for","x","in","data",")","if","abs","(","ss",")","<","1e-10",":","return","c",",","1.0","else",":","return","c",",","sqrt","(","ss","\/","float","(","len","(","data",")",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L223-L232"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"matrixMultiplication","parameters":"(A, B)","argument_list":"","return_statement":"return out","docstring":"return product of matrices A and B\n\n the input are two lists of lists where the sublists of matrix A\n have the length of matrix B","docstring_summary":"return product of matrices A and B","docstring_tokens":["return","product","of","matrices","A","and","B"],"function":"def matrixMultiplication(A, B):\n \"\"\"\n return product of matrices A and B\n\n the input are two lists of lists where the sublists of matrix A\n have the length of matrix B\n \"\"\"\n mA = len(A)\n mB = len(B)\n if mA <= 0:\n return []\n nA = len(A[0])\n if mB != nA:\n raise PolyFitException(\"shapes don't match %d vs %d\" % (nA, mB),\n EXCEPTION_MATRIX_MULTIPLICATION)\n if nA == 0:\n return []\n nB = len(B[0])\n\n for i, b in enumerate(B):\n if len(b) != nB:\n raise PolyFitException(\"matrix B: row %d doesn't match matrix \"\n \"shape (%d vs %d)\" % (i, len(b)),\n EXCEPTION_MATRIX_MULTIPLICATION)\n\n out = [[0.0 for x in range(nB)] for y in range(mA)]\n for row in range(mA):\n for col in range(nB):\n if len(A[row]) != nA:\n raise PolyFitException(\"matrix A: row %d doesn't match \"\n \"matrix shape (%d vs %d)\"\n % (row, nA, len(A[row])),\n EXCEPTION_MATRIX_MULTIPLICATION)\n for i in range(mB):\n out[row][col] += A[row][i] * B[i][col]\n return out","function_tokens":["def","matrixMultiplication","(","A",",","B",")",":","mA","=","len","(","A",")","mB","=","len","(","B",")","if","mA","<=","0",":","return","[","]","nA","=","len","(","A","[","0","]",")","if","mB","!=","nA",":","raise","PolyFitException","(","\"shapes don't match %d vs %d\"","%","(","nA",",","mB",")",",","EXCEPTION_MATRIX_MULTIPLICATION",")","if","nA","==","0",":","return","[","]","nB","=","len","(","B","[","0","]",")","for","i",",","b","in","enumerate","(","B",")",":","if","len","(","b",")","!=","nB",":","raise","PolyFitException","(","\"matrix B: row %d doesn't match matrix \"","\"shape (%d vs %d)\"","%","(","i",",","len","(","b",")",")",",","EXCEPTION_MATRIX_MULTIPLICATION",")","out","=","[","[","0.0","for","x","in","range","(","nB",")","]","for","y","in","range","(","mA",")","]","for","row","in","range","(","mA",")",":","for","col","in","range","(","nB",")",":","if","len","(","A","[","row","]",")","!=","nA",":","raise","PolyFitException","(","\"matrix A: row %d doesn't match \"","\"matrix shape (%d vs %d)\"","%","(","row",",","nA",",","len","(","A","[","row","]",")",")",",","EXCEPTION_MATRIX_MULTIPLICATION",")","for","i","in","range","(","mB",")",":","out","[","row","]","[","col","]","+=","A","[","row","]","[","i","]","*","B","[","i","]","[","col","]","return","out"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L235-L270"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"eyeMatrix","parameters":"(m)","argument_list":"","return_statement":"return out","docstring":"return a list of lists which represents the identity matrix of size m","docstring_summary":"return a list of lists which represents the identity matrix of size m","docstring_tokens":["return","a","list","of","lists","which","represents","the","identity","matrix","of","size","m"],"function":"def eyeMatrix(m):\n \"\"\"\n return a list of lists which represents the identity matrix of size m\n \"\"\"\n out = [[0.0 for x in range(m)] for y in range(m)]\n for i in range(m):\n out[i][i] = 1.0\n return out","function_tokens":["def","eyeMatrix","(","m",")",":","out","=","[","[","0.0","for","x","in","range","(","m",")","]","for","y","in","range","(","m",")","]","for","i","in","range","(","m",")",":","out","[","i","]","[","i","]","=","1.0","return","out"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L273-L280"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"norm","parameters":"(v)","argument_list":"","return_statement":"","docstring":"compute the square root of the sum of all squared components of a list v","docstring_summary":"compute the square root of the sum of all squared components of a list v","docstring_tokens":["compute","the","square","root","of","the","sum","of","all","squared","components","of","a","list","v"],"function":"def norm(v):\n \"\"\"\n compute the square root of the sum of all squared components of a list v\n \"\"\"\n\n if len(v) == 0:\n return 0\n if isinstance(v[0], list):\n return sqrt(sum([y 2.0 for x in v for y in x]))\n else:\n return sqrt(sum([y 2.0 for y in v]))","function_tokens":["def","norm","(","v",")",":","if","len","(","v",")","==","0",":","return","0","if","isinstance","(","v","[","0","]",",","list",")",":","return","sqrt","(","sum","(","[","y","","2.0","for","x","in","v","for","y","in","x","]",")",")","else",":","return","sqrt","(","sum","(","[","y","","2.0","for","y","in","v","]",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L283-L293"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"elementWise","parameters":"(A, B, operation)","argument_list":"","return_statement":"return [[operation(x, y)\n for x, y in zip(rowA, rowB)]\n for rowA, rowB in zip(A, B)]","docstring":"execute an operate element wise and return result\n\n A and B are lists of lists (all lists of same lengths)\n operation is a function of two arguments and one return value","docstring_summary":"execute an operate element wise and return result","docstring_tokens":["execute","an","operate","element","wise","and","return","result"],"function":"def elementWise(A, B, operation):\n \"\"\"\n execute an operate element wise and return result\n\n A and B are lists of lists (all lists of same lengths)\n operation is a function of two arguments and one return value\n \"\"\"\n return [[operation(x, y)\n for x, y in zip(rowA, rowB)]\n for rowA, rowB in zip(A, B)]","function_tokens":["def","elementWise","(","A",",","B",",","operation",")",":","return","[","[","operation","(","x",",","y",")","for","x",",","y","in","zip","(","rowA",",","rowB",")","]","for","rowA",",","rowB","in","zip","(","A",",","B",")","]"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L296-L305"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"transpose","parameters":"(A)","argument_list":"","return_statement":"return out","docstring":"return a list of lists for a Matrix A ( list of lists )\n with transposed representation of A","docstring_summary":"return a list of lists for a Matrix A ( list of lists )\n with transposed representation of A","docstring_tokens":["return","a","list","of","lists","for","a","Matrix","A","(","list","of","lists",")","with","transposed","representation","of","A"],"function":"def transpose(A):\n \"\"\"\n return a list of lists for a Matrix A ( list of lists )\n with transposed representation of A\n \"\"\"\n m = len(A)\n if m == 0:\n return []\n n = len(A[0])\n out = [[0.0 for x in range(m)] for y in range(n)]\n for r in range(m):\n for c in range(n):\n out[c][r] = A[r][c]\n return out","function_tokens":["def","transpose","(","A",")",":","m","=","len","(","A",")","if","m","==","0",":","return","[","]","n","=","len","(","A","[","0","]",")","out","=","[","[","0.0","for","x","in","range","(","m",")","]","for","y","in","range","(","n",")","]","for","r","in","range","(","m",")",":","for","c","in","range","(","n",")",":","out","[","c","]","[","r","]","=","A","[","r","]","[","c","]","return","out"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L308-L321"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"qr","parameters":"(A, prec=1e-10)","argument_list":"","return_statement":"return Q, R","docstring":"computes a faster and economic qr decomposition similar to:\n http:\/\/www.iaa.ncku.edu.tw\/~dychiang\/lab\/program\/mohr3d\/source\/Jama%5CQRDecomposition.html","docstring_summary":"computes a faster and economic qr decomposition similar to:\n http:\/\/www.iaa.ncku.edu.tw\/~dychiang\/lab\/program\/mohr3d\/source\/Jama%5CQRDecomposition.html","docstring_tokens":["computes","a","faster","and","economic","qr","decomposition","similar","to",":","http",":","\/\/","www",".","iaa",".","ncku",".","edu",".","tw","\/","~dychiang","\/","lab","\/","program","\/","mohr3d","\/","source","\/","Jama%5CQRDecomposition",".","html"],"function":"def qr(A, prec=1e-10):\n \"\"\"\n computes a faster and economic qr decomposition similar to:\n http:\/\/www.iaa.ncku.edu.tw\/~dychiang\/lab\/program\/mohr3d\/source\/Jama%5CQRDecomposition.html\n \"\"\"\n m = len(A)\n if m <= 0:\n return [], A\n n = len(A[0])\n Rdiag = [0] * n;\n QR = copy.deepcopy(A)\n for k in range(n):\n # Compute 2-norm of k-th column without under\/overflow.\n nrm = 0.0\n for i in range(k, m):\n nrm = sqrt(nrm 2 + QR[i][k] 2)\n\n if abs(nrm) > prec:\n # Form k-th Householder vector.\n if k < m and QR[k][k] < 0:\n nrm = -nrm\n\n for i in range(k, m):\n QR[i][k] \/= nrm\n if k < m:\n QR[k][k] += 1.0\n\n # Apply transformation to remaining columns.\n for j in range(k + 1, n):\n s = 0.0\n for i in range(k, m):\n s += QR[i][k] * QR[i][j]\n if k < m:\n s = -s \/ QR[k][k]\n for i in range(k, m):\n QR[i][j] += s * QR[i][k]\n Rdiag[k] = -nrm;\n\n # compute R\n R = [[0] * n for z in range(min(m, n))]\n for i in range(m):\n for j in range(i, n):\n if i < j:\n R[i][j] = QR[i][j]\n if i == j:\n R[i][i] = Rdiag[i]\n\n # compute Q\n w = min(m, n)\n Q = [[0] * w for i in range(m)]\n for k in range(w - 1, -1, -1):\n if k < w:\n Q[k][k] = 1.0;\n for j in range(k, w):\n if k < m and abs(QR[k][k]) > prec:\n s = 0.0\n for i in range(k, m):\n s += QR[i][k] * Q[i][j]\n s = -s \/ QR[k][k]\n for i in range(k, m):\n Q[i][j] += s * QR[i][k]\n return Q, R","function_tokens":["def","qr","(","A",",","prec","=","1e-10",")",":","m","=","len","(","A",")","if","m","<=","0",":","return","[","]",",","A","n","=","len","(","A","[","0","]",")","Rdiag","=","[","0","]","","n","QR","=","copy",".","deepcopy","(","A",")","for","k","in","range","(","n",")",":","# Compute 2-norm of k-th column without under\/overflow.","nrm","=","0.0","for","i","in","range","(","k",",","m",")",":","nrm","=","sqrt","(","nrm","","2","+","QR","[","i","]","[","k","]","","2",")","if","abs","(","nrm",")",">","prec",":","# Form k-th Householder vector.","if","k","<","m","and","QR","[","k","]","[","k","]","<","0",":","nrm","=","-","nrm","for","i","in","range","(","k",",","m",")",":","QR","[","i","]","[","k","]","\/=","nrm","if","k","<","m",":","QR","[","k","]","[","k","]","+=","1.0","# Apply transformation to remaining columns.","for","j","in","range","(","k","+","1",",","n",")",":","s","=","0.0","for","i","in","range","(","k",",","m",")",":","s","+=","QR","[","i","]","[","k","]","","QR","[","i","]","[","j","]","if","k","<","m",":","s","=","-","s","\/","QR","[","k","]","[","k","]","for","i","in","range","(","k",",","m",")",":","QR","[","i","]","[","j","]","+=","s","","QR","[","i","]","[","k","]","Rdiag","[","k","]","=","-","nrm","# compute R","R","=","[","[","0","]","","n","for","z","in","range","(","min","(","m",",","n",")",")","]","for","i","in","range","(","m",")",":","for","j","in","range","(","i",",","n",")",":","if","i","<","j",":","R","[","i","]","[","j","]","=","QR","[","i","]","[","j","]","if","i","==","j",":","R","[","i","]","[","i","]","=","Rdiag","[","i","]","# compute Q","w","=","min","(","m",",","n",")","Q","=","[","[","0","]","","w","for","i","in","range","(","m",")","]","for","k","in","range","(","w","-","1",",","-","1",",","-","1",")",":","if","k","<","w",":","Q","[","k","]","[","k","]","=","1.0","for","j","in","range","(","k",",","w",")",":","if","k","<","m","and","abs","(","QR","[","k","]","[","k","]",")",">","prec",":","s","=","0.0","for","i","in","range","(","k",",","m",")",":","s","+=","QR","[","i","]","[","k","]","","Q","[","i","]","[","j","]","s","=","-","s","\/","QR","[","k","]","[","k","]","for","i","in","range","(","k",",","m",")",":","Q","[","i","]","[","j","]","+=","s","*","QR","[","i","]","[","k","]","return","Q",",","R"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L324-L385"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"solveWithForwardReplacement","parameters":"(A, b, prec=1e-10)","argument_list":"","return_statement":"return res","docstring":"solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung","docstring_summary":"solve a system Ax = b with A = QR in least squares sense\n return x","docstring_tokens":["solve","a","system","Ax","=","b","with","A","=","QR","in","least","squares","sense","return","x"],"function":"def solveWithForwardReplacement(A, b, prec=1e-10):\n \"\"\"\n solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung\n \"\"\"\n if len(A) == 0:\n return []\n Q, R = qr(transpose(A))\n\n Rt = transpose(R)\n m = len(Rt)\n n = len(Rt[0])\n\n z = [0] * n\n for r in range(m):\n if abs(Rt[r][r]) < prec:\n z[r] = 1.0\n continue\n s = 0.0\n for c in range(m):\n s += z[c] * Rt[r][c]\n z[r] = (b[r] - s) \/ Rt[r][r]\n\n res = matrixMultiplication(Q, [[a] for a in z])\n res = [v for sublist in res for v in sublist] # flatten\n\n return res","function_tokens":["def","solveWithForwardReplacement","(","A",",","b",",","prec","=","1e-10",")",":","if","len","(","A",")","==","0",":","return","[","]","Q",",","R","=","qr","(","transpose","(","A",")",")","Rt","=","transpose","(","R",")","m","=","len","(","Rt",")","n","=","len","(","Rt","[","0","]",")","z","=","[","0","]","","n","for","r","in","range","(","m",")",":","if","abs","(","Rt","[","r","]","[","r","]",")","<","prec",":","z","[","r","]","=","1.0","continue","s","=","0.0","for","c","in","range","(","m",")",":","s","+=","z","[","c","]","","Rt","[","r","]","[","c","]","z","[","r","]","=","(","b","[","r","]","-","s",")","\/","Rt","[","r","]","[","r","]","res","=","matrixMultiplication","(","Q",",","[","[","a","]","for","a","in","z","]",")","res","=","[","v","for","sublist","in","res","for","v","in","sublist","]","# flatten","return","res"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L388-L416"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"solveWithBackwardReplacement","parameters":"(A, b, prec=1e-10)","argument_list":"","return_statement":"return res","docstring":"solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung","docstring_summary":"solve a system Ax = b with A = QR in least squares sense\n return x","docstring_tokens":["solve","a","system","Ax","=","b","with","A","=","QR","in","least","squares","sense","return","x"],"function":"def solveWithBackwardReplacement(A, b, prec=1e-10):\n \"\"\"\n solve a system Ax = b with A = QR in least squares sense\n return x\n\n see http:\/\/de.wikipedia.org\/wiki\/QR-Zerlegung\n \"\"\"\n n = len(A)\n if n == 0:\n return []\n\n Q, R = qr(A)\n\n n = len(R[0])\n res = [0] * n\n\n z = matrixMultiplication(transpose(Q), [[a] for a in b])\n\n for r in range(n - 1, -1, -1):\n if abs(R[r][r]) < prec:\n res[r] = 1.0\n continue\n s = 0.0\n for c in range(r + 1, n):\n s += res[c] * R[r][c]\n res[r] = (z[r][0] - s) \/ R[r][r]\n\n return res","function_tokens":["def","solveWithBackwardReplacement","(","A",",","b",",","prec","=","1e-10",")",":","n","=","len","(","A",")","if","n","==","0",":","return","[","]","Q",",","R","=","qr","(","A",")","n","=","len","(","R","[","0","]",")","res","=","[","0","]","","n","z","=","matrixMultiplication","(","transpose","(","Q",")",",","[","[","a","]","for","a","in","b","]",")","for","r","in","range","(","n","-","1",",","-","1",",","-","1",")",":","if","abs","(","R","[","r","]","[","r","]",")","<","prec",":","res","[","r","]","=","1.0","continue","s","=","0.0","for","c","in","range","(","r","+","1",",","n",")",":","s","+=","res","[","c","]","","R","[","r","]","[","c","]","res","[","r","]","=","(","z","[","r","]","[","0","]","-","s",")","\/","R","[","r","]","[","r","]","return","res"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L419-L446"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"leastSquareSolution","parameters":"(A, b)","argument_list":"","return_statement":"return s, res","docstring":"return the least squares solution x for the system Ax = b\n\n A is a list of list while b is a list","docstring_summary":"return the least squares solution x for the system Ax = b","docstring_tokens":["return","the","least","squares","solution","x","for","the","system","Ax","=","b"],"function":"def leastSquareSolution(A, b):\n \"\"\"\n return the least squares solution x for the system Ax = b\n\n A is a list of list while b is a list\n \"\"\"\n m = len(A)\n if m == 0:\n return []\n n = len(A[0])\n\n if n > m:\n s = solveWithForwardReplacement(A, b)\n else:\n s = solveWithBackwardReplacement(A, b)\n remap = matrixMultiplication(A, [[a] for a in s])\n res = sum([z[0] 2.0 for z in elementWise(remap, [[a] for a in b],\n lambda x, y: x - y)])\n\n return s, res","function_tokens":["def","leastSquareSolution","(","A",",","b",")",":","m","=","len","(","A",")","if","m","==","0",":","return","[","]","n","=","len","(","A","[","0","]",")","if","n",">","m",":","s","=","solveWithForwardReplacement","(","A",",","b",")","else",":","s","=","solveWithBackwardReplacement","(","A",",","b",")","remap","=","matrixMultiplication","(","A",",","[","[","a","]","for","a","in","s","]",")","res","=","sum","(","[","z","[","0","]","","2.0","for","z","in","elementWise","(","remap",",","[","[","a","]","for","a","in","b","]",",","lambda","x",",","y",":","x","-","y",")","]",")","return","s",",","res"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L449-L468"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit.__init__","parameters":"(self, x, values, order=1)","argument_list":"","return_statement":"","docstring":"initialize a polyfitting with x and y coordinates\n and respective functino values\n (all simple lists)","docstring_summary":"initialize a polyfitting with x and y coordinates\n and respective functino values\n (all simple lists)","docstring_tokens":["initialize","a","polyfitting","with","x","and","y","coordinates","and","respective","functino","values","(","all","simple","lists",")"],"function":"def __init__(self, x, values, order=1):\n \"\"\"\n initialize a polyfitting with x and y coordinates\n and respective functino values\n (all simple lists)\n \"\"\"\n n = len(x)\n if n != len(values):\n raise PolyFitException(\"input values have different lengths\",\n self.TAG)\n\n self.mIn, self.sIn = meanAndStandardDeviation(x)\n self.mVal, self.sVal = meanAndStandardDeviation(values)\n x = [(y - self.mIn) \/ self.sIn for y in x]\n values = [(y - self.mVal) \/ self.sVal for y in values]\n\n A = [[x[i] float(j) for j in range(order + 1)] for i in range(n)]\n self.coeffs, self.residual = leastSquareSolution(A, values)\n self.order = order","function_tokens":["def","__init__","(","self",",","x",",","values",",","order","=","1",")",":","n","=","len","(","x",")","if","n","!=","len","(","values",")",":","raise","PolyFitException","(","\"input values have different lengths\"",",","self",".","TAG",")","self",".","mIn",",","self",".","sIn","=","meanAndStandardDeviation","(","x",")","self",".","mVal",",","self",".","sVal","=","meanAndStandardDeviation","(","values",")","x","=","[","(","y","-","self",".","mIn",")","\/","self",".","sIn","for","y","in","x","]","values","=","[","(","y","-","self",".","mVal",")","\/","self",".","sVal","for","y","in","values","]","A","=","[","[","x","[","i","]","","float","(","j",")","for","j","in","range","(","order","+","1",")","]","for","i","in","range","(","n",")","]","self",".","coeffs",",","self",".","residual","=","leastSquareSolution","(","A",",","values",")","self",".","order","=","order"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L49-L67"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit.__getitem__","parameters":"(self, x)","argument_list":"","return_statement":"return val * self.sVal + self.mVal","docstring":"return the estimated function value for position x","docstring_summary":"return the estimated function value for position x","docstring_tokens":["return","the","estimated","function","value","for","position","x"],"function":"def __getitem__(self, x):\n \"\"\"\n return the estimated function value for position x\n \"\"\"\n x = (x - self.mIn) \/ self.sIn\n val = sum([self.coeffs[i] * x ** float(i) for i in range(self.order + 1)])\n return val * self.sVal + self.mVal","function_tokens":["def","__getitem__","(","self",",","x",")",":","x","=","(","x","-","self",".","mIn",")","\/","self",".","sIn","val","=","sum","(","[","self",".","coeffs","[","i","]","","x","","float","(","i",")","for","i","in","range","(","self",".","order","+","1",")","]",")","return","val","","self",".","sVal","+","self",".","mVal"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L69-L75"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit2D.__init__","parameters":"(self, pts, values, order=1)","argument_list":"","return_statement":"","docstring":"initialize a polyfitting with pts = [[x1,x2], [y1,y2], ...] coordinates\n and respective function values [v1, ...]\n (all simple lists)","docstring_summary":"initialize a polyfitting with pts = [[x1,x2], [y1,y2], ...] coordinates\n and respective function values [v1, ...]\n (all simple lists)","docstring_tokens":["initialize","a","polyfitting","with","pts","=","[[","x1","x2","]","[","y1","y2","]","...","]","coordinates","and","respective","function","values","[","v1","...","]","(","all","simple","lists",")"],"function":"def __init__(self, pts, values, order=1):\n \"\"\"\n initialize a polyfitting with pts = [[x1,x2], [y1,y2], ...] coordinates\n and respective function values [v1, ...]\n (all simple lists)\n \"\"\"\n n = len(pts)\n for i in range(n):\n if 2 != len(pts[i]):\n raise PolyFitException(\"input values have different lengths\",\n self.TAG)\n\n mons = [x for x in product(range(order + 1), range(order + 1))\n if x[0] + x[1] <= order]\n self.monomials = mons\n self.n = len(self.monomials)\n\n # scale for better approximation\n self.mInX, self.sInX = meanAndStandardDeviation([z[0] for z in pts])\n self.mInY, self.sInY = meanAndStandardDeviation([z[1] for z in pts])\n self.mVal, self.sVal = meanAndStandardDeviation(values)\n pts = [((z[0] - self.mInX) \/ self.sInX, (z[1] - self.mInY) \/ self.sInY)\n for z in pts]\n values = [(z - self.mVal) \/ self.sVal for z in values]\n\n m = self.monomials\n A = [[pts[u][0] ** m[i][0] * pts[u][1] m[i][1] for i in range(self.n)]\n for u in range(n)]\n self.coeffs, self.residual = leastSquareSolution(A, values)\n self.order = order","function_tokens":["def","__init__","(","self",",","pts",",","values",",","order","=","1",")",":","n","=","len","(","pts",")","for","i","in","range","(","n",")",":","if","2","!=","len","(","pts","[","i","]",")",":","raise","PolyFitException","(","\"input values have different lengths\"",",","self",".","TAG",")","mons","=","[","x","for","x","in","product","(","range","(","order","+","1",")",",","range","(","order","+","1",")",")","if","x","[","0","]","+","x","[","1","]","<=","order","]","self",".","monomials","=","mons","self",".","n","=","len","(","self",".","monomials",")","# scale for better approximation","self",".","mInX",",","self",".","sInX","=","meanAndStandardDeviation","(","[","z","[","0","]","for","z","in","pts","]",")","self",".","mInY",",","self",".","sInY","=","meanAndStandardDeviation","(","[","z","[","1","]","for","z","in","pts","]",")","self",".","mVal",",","self",".","sVal","=","meanAndStandardDeviation","(","values",")","pts","=","[","(","(","z","[","0","]","-","self",".","mInX",")","\/","self",".","sInX",",","(","z","[","1","]","-","self",".","mInY",")","\/","self",".","sInY",")","for","z","in","pts","]","values","=","[","(","z","-","self",".","mVal",")","\/","self",".","sVal","for","z","in","values","]","m","=","self",".","monomials","A","=","[","[","pts","[","u","]","[","0","]","","m","[","i","]","[","0","]","","pts","[","u","]","[","1","]","*","m","[","i","]","[","1","]","for","i","in","range","(","self",".","n",")","]","for","u","in","range","(","n",")","]","self",".","coeffs",",","self",".","residual","=","leastSquareSolution","(","A",",","values",")","self",".","order","=","order"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L86-L115"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFit2D.__getitem__","parameters":"(self, x)","argument_list":"","return_statement":"return val * self.sVal + self.mVal","docstring":"return the estimated function value for position x","docstring_summary":"return the estimated function value for position x","docstring_tokens":["return","the","estimated","function","value","for","position","x"],"function":"def __getitem__(self, x):\n \"\"\"\n return the estimated function value for position x\n \"\"\"\n m = self.monomials\n x = ((x[0] - self.mInX) \/ self.sInX, (x[1] - self.mInY) \/ self.sInY)\n val = sum([self.coeffs[i] * x[0] ** m[i][0] * x[1] ** m[i][1]\n for i in range(self.n)])\n return val * self.sVal + self.mVal","function_tokens":["def","__getitem__","(","self",",","x",")",":","m","=","self",".","monomials","x","=","(","(","x","[","0","]","-","self",".","mInX",")","\/","self",".","sInX",",","(","x","[","1","]","-","self",".","mInY",")","\/","self",".","sInY",")","val","=","sum","(","[","self",".","coeffs","[","i","]","","x","[","0","]","","m","[","i","]","[","0","]","","x","[","1","]","*","m","[","i","]","[","1","]","for","i","in","range","(","self",".","n",")","]",")","return","val","","self",".","sVal","+","self",".","mVal"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L117-L125"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFitND.__init__","parameters":"(self, pts, values, order=1)","argument_list":"","return_statement":"","docstring":"initialize a polyfitting with pts = [[x1,x2 ...], [y1,y2,...], ...]\n coordinates and respective function values [v1, ...]\n (all simple lists)","docstring_summary":"initialize a polyfitting with pts = [[x1,x2 ...], [y1,y2,...], ...]\n coordinates and respective function values [v1, ...]\n (all simple lists)","docstring_tokens":["initialize","a","polyfitting","with","pts","=","[[","x1","x2","...","]","[","y1","y2","...","]","...","]","coordinates","and","respective","function","values","[","v1","...","]","(","all","simple","lists",")"],"function":"def __init__(self, pts, values, order=1):\n \"\"\"\n initialize a polyfitting with pts = [[x1,x2 ...], [y1,y2,...], ...]\n coordinates and respective function values [v1, ...]\n (all simple lists)\n \"\"\"\n n = len(pts)\n if n <= 0:\n raise PolyFitException(\"no input values given\",\n self.TAG)\n dimension = len(pts[0])\n for i in range(n):\n if dimension != len(pts[i]):\n raise PolyFitException(\"input values have different lengths\",\n self.TAG)\n\n self.dimension = dimension\n\n mons = [x for x in product([range(order + 1)] dimension)\n if sum(x) <= order]\n self.monomials = mons\n self.n = len(self.monomials)\n\n m = self.monomials\n\n # scales\n self.scales = [(c, s) for c, s in [meanAndStandardDeviation(\n [pts[j][i] for j in range(n)])\n for i in range(dimension)]]\n pts = [[(pts[j][i] - self.scales[i][0]) \/ self.scales[i][1]\n for i in range(dimension)]\n for j in range(n)]\n self.mVal, self.sVal = meanAndStandardDeviation(values)\n values = [(z - self.mVal) \/ self.sVal for z in values]\n\n A = [[functools.reduce(operator.mul,\n [pts[u][d] ** m[i][d] for d in range(dimension)],\n 1)\n for i in range(self.n)]\n for u in range(n)]\n self.coeffs, self.residual = leastSquareSolution(A, values)\n self.order = order","function_tokens":["def","__init__","(","self",",","pts",",","values",",","order","=","1",")",":","n","=","len","(","pts",")","if","n","<=","0",":","raise","PolyFitException","(","\"no input values given\"",",","self",".","TAG",")","dimension","=","len","(","pts","[","0","]",")","for","i","in","range","(","n",")",":","if","dimension","!=","len","(","pts","[","i","]",")",":","raise","PolyFitException","(","\"input values have different lengths\"",",","self",".","TAG",")","self",".","dimension","=","dimension","mons","=","[","x","for","x","in","product","(","","[","range","(","order","+","1",")","]","","dimension",")","if","sum","(","x",")","<=","order","]","self",".","monomials","=","mons","self",".","n","=","len","(","self",".","monomials",")","m","=","self",".","monomials","# scales","self",".","scales","=","[","(","c",",","s",")","for","c",",","s","in","[","meanAndStandardDeviation","(","[","pts","[","j","]","[","i","]","for","j","in","range","(","n",")","]",")","for","i","in","range","(","dimension",")","]","]","pts","=","[","[","(","pts","[","j","]","[","i","]","-","self",".","scales","[","i","]","[","0","]",")","\/","self",".","scales","[","i","]","[","1","]","for","i","in","range","(","dimension",")","]","for","j","in","range","(","n",")","]","self",".","mVal",",","self",".","sVal","=","meanAndStandardDeviation","(","values",")","values","=","[","(","z","-","self",".","mVal",")","\/","self",".","sVal","for","z","in","values","]","A","=","[","[","functools",".","reduce","(","operator",".","mul",",","[","pts","[","u","]","[","d","]","**","m","[","i","]","[","d","]","for","d","in","range","(","dimension",")","]",",","1",")","for","i","in","range","(","self",".","n",")","]","for","u","in","range","(","n",")","]","self",".","coeffs",",","self",".","residual","=","leastSquareSolution","(","A",",","values",")","self",".","order","=","order"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L136-L177"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py","language":"python","identifier":"PolyFitND.__getitem__","parameters":"(self, x)","argument_list":"","return_statement":"return val * self.sVal + self.mVal","docstring":"return the estimated function value for position x","docstring_summary":"return the estimated function value for position x","docstring_tokens":["return","the","estimated","function","value","for","position","x"],"function":"def __getitem__(self, x):\n \"\"\"\n return the estimated function value for position x\n \"\"\"\n if len(x) != self.dimension:\n raise PolyFitException(\"input value has different length than \"\n \"initialization data\",\n self.TAG)\n\n m = self.monomials\n d = len(x)\n x = [(x[i] - self.scales[i][0]) \/ self.scales[i][1]\n for i in range(self.dimension)]\n val = sum([self.coeffs[i]\n * functools.reduce(operator.mul,\n [x[d] ** m[i][d]\n for d in range(self.dimension)],\n 1)\n for i in range(self.n)])\n return val * self.sVal + self.mVal","function_tokens":["def","__getitem__","(","self",",","x",")",":","if","len","(","x",")","!=","self",".","dimension",":","raise","PolyFitException","(","\"input value has different length than \"","\"initialization data\"",",","self",".","TAG",")","m","=","self",".","monomials","d","=","len","(","x",")","x","=","[","(","x","[","i","]","-","self",".","scales","[","i","]","[","0","]",")","\/","self",".","scales","[","i","]","[","1","]","for","i","in","range","(","self",".","dimension",")","]","val","=","sum","(","[","self",".","coeffs","[","i","]","","functools",".","reduce","(","operator",".","mul",",","[","x","[","d","]","","m","[","i","]","[","d","]","for","d","in","range","(","self",".","dimension",")","]",",","1",")","for","i","in","range","(","self",".","n",")","]",")","return","val","","self",".","sVal","+","self",".","mVal"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/purePythonPolyFit.py#L179-L198"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/timing.py","language":"python","identifier":"timed","parameters":"(f)","argument_list":"","return_statement":"return wrapper","docstring":"measure the clock time of functions as well as the pure times,\n without the subfunction calls\n (However, the latter is not as accurate since it includes some management\n of the timing)","docstring_summary":"measure the clock time of functions as well as the pure times,\n without the subfunction calls\n (However, the latter is not as accurate since it includes some management\n of the timing)","docstring_tokens":["measure","the","clock","time","of","functions","as","well","as","the","pure","times","without","the","subfunction","calls","(","However","the","latter","is","not","as","accurate","since","it","includes","some","management","of","the","timing",")"],"function":"def timed(f):\n \"\"\"\n measure the clock time of functions as well as the pure times,\n without the subfunction calls\n (However, the latter is not as accurate since it includes some management\n of the timing)\n \"\"\"\n @wraps(f)\n def wrapper(args, kwds):\n startOverall = clock()\n global currentCallStack\n global times\n name = f.__name__\n currentCallStack.append(name)\n if name not in times:\n times[name] = {\"numCalls\": 0, \"totalTime\": 0, \"subprocesses\": 0, \"numSub\" : 0}\n start = clock()\n result = f(args, *kwds)\n elapsed = clock() - start\n currentCallStack = currentCallStack[:-1]\n times[name][\"numCalls\"] += 1\n times[name][\"totalTime\"] += elapsed\n if len(currentCallStack) > 1:\n times[currentCallStack[-2]][\"numSub\"] += 1\n times[currentCallStack[-2]][\"subprocesses\"] += clock() - startOverall\n return result\n return wrapper","function_tokens":["def","timed","(","f",")",":","@","wraps","(","f",")","def","wrapper","(","","args",",","","","kwds",")",":","startOverall","=","clock","(",")","global","currentCallStack","global","times","name","=","f",".","__name__","currentCallStack",".","append","(","name",")","if","name","not","in","times",":","times","[","name","]","=","{","\"numCalls\"",":","0",",","\"totalTime\"",":","0",",","\"subprocesses\"",":","0",",","\"numSub\"",":","0","}","start","=","clock","(",")","result","=","f","(","","args",",","","*","kwds",")","elapsed","=","clock","(",")","-","start","currentCallStack","=","currentCallStack","[",":","-","1","]","times","[","name","]","[","\"numCalls\"","]","+=","1","times","[","name","]","[","\"totalTime\"","]","+=","elapsed","if","len","(","currentCallStack",")",">","1",":","times","[","currentCallStack","[","-","2","]","]","[","\"numSub\"","]","+=","1","times","[","currentCallStack","[","-","2","]","]","[","\"subprocesses\"","]","+=","clock","(",")","-","startOverall","return","result","return","wrapper"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/timing.py#L44-L70"}
	{"nwo":"DaoCloud\/daochain","sha":"3499d34eab12237c7b97e3df550c3e0b33d154a5","path":"app\/thirdparty\/purepythonpolyfit\/timing.py","language":"python","identifier":"printTimes","parameters":"()","argument_list":"","return_statement":"","docstring":"pretty print timing results","docstring_summary":"pretty print timing results","docstring_tokens":["pretty","print","timing","results"],"function":"def printTimes():\n \"\"\"\n pretty print timing results\n \"\"\"\n global times\n # determine maximum length:\n header = \"function name\"\n ma = len(header)\n sum = 0\n sumOverall = 0\n for name, ts in times.items():\n ma = max(ma, len(name))\n net = (ts['totalTime'] - ts['subprocesses'])\/float(ts['numCalls'])\n sum += net\n sumOverall += (ts['totalTime'] - ts['subprocesses'])\n ma += 2\n \n print(\"\\033[1moverall (mean) processor time\\033[0m: %0.5fs (%0.5fs)\" %(sumOverall, sum))\n print(\"listing processor time per method:\" )\n print(\"\\033[1m%s%s\\t %% Overall time\\t Mean w.o. Sub[s]\\t Mean[s]\\tNum-Calls\\033[0m\" % (header, ' '(ma - len(header))))\n for name in sorted(times.keys()):\n ts = times[name]\n m = ts['totalTime']\/float(ts['numCalls'])\n net = (ts['totalTime'] - ts['subprocesses'])\/float(ts['numCalls'])\n perc = 100.0net\/float(sum)\n print(\"%s%s\\t% 15.2f\\t% 17.5f\\t% 9.5f\\t% 9d\" %(name,\n ' '(ma - len(name)),\n perc,\n net,\n m,\n\n ts['numCalls']))","function_tokens":["def","printTimes","(",")",":","global","times","# determine maximum length:","header","=","\"function name\"","ma","=","len","(","header",")","sum","=","0","sumOverall","=","0","for","name",",","ts","in","times",".","items","(",")",":","ma","=","max","(","ma",",","len","(","name",")",")","net","=","(","ts","[","'totalTime'","]","-","ts","[","'subprocesses'","]",")","\/","float","(","ts","[","'numCalls'","]",")","sum","+=","net","sumOverall","+=","(","ts","[","'totalTime'","]","-","ts","[","'subprocesses'","]",")","ma","+=","2","print","(","\"\\033[1moverall (mean) processor time\\033[0m: %0.5fs (%0.5fs)\"","%","(","sumOverall",",","sum",")",")","print","(","\"listing processor time per method:\"",")","print","(","\"\\033[1m%s%s\\t %% Overall time\\t Mean w.o. Sub[s]\\t Mean[s]\\tNum-Calls\\033[0m\"","%","(","header",",","' '","","(","ma","-","len","(","header",")",")",")",")","for","name","in","sorted","(","times",".","keys","(",")",")",":","ts","=","times","[","name","]","m","=","ts","[","'totalTime'","]","\/","float","(","ts","[","'numCalls'","]",")","net","=","(","ts","[","'totalTime'","]","-","ts","[","'subprocesses'","]",")","\/","float","(","ts","[","'numCalls'","]",")","perc","=","100.0","","net","\/","float","(","sum",")","print","(","\"%s%s\\t% 15.2f\\t% 17.5f\\t% 9.5f\\t% 9d\"","%","(","name",",","' '","","(","ma","-","len","(","name",")",")",",","perc",",","net",",","m",",","ts","[","'numCalls'","]",")",")"],"url":"https:\/\/github.com\/DaoCloud\/daochain\/blob\/3499d34eab12237c7b97e3df550c3e0b33d154a5\/app\/thirdparty\/purepythonpolyfit\/timing.py#L72-L103"}