// Do not edit this file; automatically generated by gulp. /* eslint-disable */ var VarData = {}; (function (root, factory) { if (typeof define === "function" && define.amd) { // AMD define(["./blockly_compressed.js"], factory); } else if (typeof exports === "object") { // Node.js module.exports = factory(require("./blockly_compressed.js")); } else { // Browser root.Blockly.Python = factory(root.Blockly); } })(this, function (Blockly) { "use strict"; Blockly.Python = new Blockly.Generator("Python"); Blockly.Python.addReservedWords( "False,None,True,and,as,assert,break,class,continue,def,del,elif,else,except,exec,finally,for,from,global,if,import,in,is,lambda,nonlocal,not,or,pass,print,raise,return,try,while,with,yield,NotImplemented,Ellipsis,__debug__,quit,exit,copyright,license,credits,ArithmeticError,AssertionError,AttributeError,BaseException,BlockingIOError,BrokenPipeError,BufferError,BytesWarning,ChildProcessError,ConnectionAbortedError,ConnectionError,ConnectionRefusedError,ConnectionResetError,DeprecationWarning,EOFError,Ellipsis,EnvironmentError,Exception,FileExistsError,FileNotFoundError,FloatingPointError,FutureWarning,GeneratorExit,IOError,ImportError,ImportWarning,IndentationError,IndexError,InterruptedError,IsADirectoryError,KeyError,KeyboardInterrupt,LookupError,MemoryError,ModuleNotFoundError,NameError,NotADirectoryError,NotImplemented,NotImplementedError,OSError,OverflowError,PendingDeprecationWarning,PermissionError,ProcessLookupError,RecursionError,ReferenceError,ResourceWarning,RuntimeError,RuntimeWarning,StandardError,StopAsyncIteration,StopIteration,SyntaxError,SyntaxWarning,SystemError,SystemExit,TabError,TimeoutError,TypeError,UnboundLocalError,UnicodeDecodeError,UnicodeEncodeError,UnicodeError,UnicodeTranslateError,UnicodeWarning,UserWarning,ValueError,Warning,ZeroDivisionError,_,__build_class__,__debug__,__doc__,__import__,__loader__,__name__,__package__,__spec__,abs,all,any,apply,ascii,basestring,bin,bool,buffer,bytearray,bytes,callable,chr,classmethod,cmp,coerce,compile,complex,copyright,credits,delattr,dict,dir,divmod,enumerate,eval,exec,execfile,exit,file,filter,float,format,frozenset,getattr,globals,hasattr,hash,help,hex,id,input,int,intern,isinstance,issubclass,iter,len,license,list,locals,long,map,max,memoryview,min,next,object,oct,open,ord,pow,print,property,quit,range,raw_input,reduce,reload,repr,reversed,round,set,setattr,slice,sorted,staticmethod,str,sum,super,tuple,type,unichr,unicode,vars,xrange,zip" ); Blockly.Python.VARDATA = []; Blockly.Python.ORDER_ATOMIC = 0; Blockly.Python.ORDER_COLLECTION = 1; Blockly.Python.ORDER_STRING_CONVERSION = 1; Blockly.Python.ORDER_MEMBER = 2.1; Blockly.Python.ORDER_FUNCTION_CALL = 2.2; Blockly.Python.ORDER_EXPONENTIATION = 3; Blockly.Python.ORDER_UNARY_SIGN = 4; Blockly.Python.ORDER_BITWISE_NOT = 4; Blockly.Python.ORDER_MULTIPLICATIVE = 5; Blockly.Python.ORDER_ADDITIVE = 6; Blockly.Python.ORDER_BITWISE_SHIFT = 7; Blockly.Python.ORDER_BITWISE_AND = 8; Blockly.Python.ORDER_BITWISE_XOR = 9; Blockly.Python.ORDER_BITWISE_OR = 10; Blockly.Python.ORDER_RELATIONAL = 11; Blockly.Python.ORDER_LOGICAL_NOT = 12; Blockly.Python.ORDER_LOGICAL_AND = 13; Blockly.Python.ORDER_LOGICAL_OR = 14; Blockly.Python.ORDER_CONDITIONAL = 15; Blockly.Python.ORDER_LAMBDA = 16; Blockly.Python.ORDER_NONE = 99; Blockly.Python.ORDER_OVERRIDES = [ [Blockly.Python.ORDER_FUNCTION_CALL, Blockly.Python.ORDER_MEMBER], [Blockly.Python.ORDER_FUNCTION_CALL, Blockly.Python.ORDER_FUNCTION_CALL], [Blockly.Python.ORDER_MEMBER, Blockly.Python.ORDER_MEMBER], [Blockly.Python.ORDER_MEMBER, Blockly.Python.ORDER_FUNCTION_CALL], [Blockly.Python.ORDER_LOGICAL_NOT, Blockly.Python.ORDER_LOGICAL_NOT], [Blockly.Python.ORDER_LOGICAL_AND, Blockly.Python.ORDER_LOGICAL_AND], [Blockly.Python.ORDER_LOGICAL_OR, Blockly.Python.ORDER_LOGICAL_OR], ]; Blockly.Python.isInitialized = !1; Blockly.Python.init = function (a) { Blockly.Python.PASS = this.INDENT + "pass\n"; Blockly.Python.definitions_ = Object.create(null); Blockly.Python.functionNames_ = Object.create(null); Blockly.Python.variableDB_ ? Blockly.Python.variableDB_.reset() : (Blockly.Python.variableDB_ = new Blockly.Names(Blockly.Python.RESERVED_WORDS_)); Blockly.Python.variableDB_.setVariableMap(a.getVariableMap()); for (var b = [], c = Blockly.Variables.allDeveloperVariables(a), d = 0; d < c.length; d++) b.push(Blockly.Python.variableDB_.getName(c[d], Blockly.Names.DEVELOPER_VARIABLE_TYPE) + " = None"); a = Blockly.Variables.allUsedVarModels(a); for (d = 0; d < a.length; d++) b.push(Blockly.Python.variableDB_.getName(a[d].getId(), Blockly.VARIABLE_CATEGORY_NAME) + " = None"); Blockly.Python.definitions_.variables = b.join("\n"); this.isInitialized = !0; }; Blockly.Python.finish = function (a) { var b = [], c = [], d; for (d in Blockly.Python.definitions_) { var e = Blockly.Python.definitions_[d]; e.match(/^(from\s+\S+\s+)?import\s+\S+/) ? b.push(e) : c.push(e); } delete Blockly.Python.definitions_; delete Blockly.Python.functionNames_; Blockly.Python.variableDB_.reset(); return (b.join("\n") + "\n\n" + c.join("\n\n")).replace(/\n\n+/g, "\n\n").replace(/\n*$/, "\n\n\n") + a; }; Blockly.Python.scrubNakedValue = function (a) { return a + "\n"; }; Blockly.Python.quote_ = function (a) { a = a.replace(/\\/g, "\\\\").replace(/\n/g, "\\\n"); var b = "'"; -1 !== a.indexOf("'") && (-1 === a.indexOf('"') ? (b = '"') : (a = a.replace(/'/g, "\\'"))); return b + a + b; }; Blockly.Python.multiline_quote_ = function (a) { return a.split(/\n/g).map(Blockly.Python.quote_).join(" + '\\n' + \n"); }; Blockly.Python.scrub_ = function (a, b, c) { var d = ""; if (!a.outputConnection || !a.outputConnection.targetConnection) { var e = a.getCommentText(); e && ((e = Blockly.utils.string.wrap(e, Blockly.Python.COMMENT_WRAP - 3)), (d += Blockly.Python.prefixLines(e + "\n", "# "))); for (var f = 0; f < a.inputList.length; f++) a.inputList[f].type == Blockly.INPUT_VALUE && (e = a.inputList[f].connection.targetBlock()) && (e = Blockly.Python.allNestedComments(e)) && (d += Blockly.Python.prefixLines(e, "# ")); } a = a.nextConnection && a.nextConnection.targetBlock(); c = c ? "" : Blockly.Python.blockToCode(a); return d + b + c; }; Blockly.Python.getAdjustedInt = function (a, b, c, d) { c = c || 0; a.workspace.options.oneBasedIndex && c--; var e = a.workspace.options.oneBasedIndex ? "1" : "0"; a = Blockly.Python.valueToCode(a, b, c ? Blockly.Python.ORDER_ADDITIVE : Blockly.Python.ORDER_NONE) || e; Blockly.isNumber(a) ? ((a = parseInt(a, 10) + c), d && (a = -a)) : ((a = 0 < c ? "int(" + a + " + " + c + ")" : 0 > c ? "int(" + a + " - " + -c + ")" : "int(" + a + ")"), d && (a = "-" + a)); return a; }; Blockly.Python.colour = {}; Blockly.Python.colour_picker = function (a) { return [Blockly.Python.quote_(a.getFieldValue("COLOUR")), Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python.colour_random = function (a) { Blockly.Python.definitions_.import_random = "import random"; return ["'#%06x' % random.randint(0, 2**24 - 1)", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.colour_rgb = function (a) { var b = Blockly.Python.provideFunction_("colour_rgb", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(r, g, b):", " r = round(min(100, max(0, r)) * 2.55)", " g = round(min(100, max(0, g)) * 2.55)", " b = round(min(100, max(0, b)) * 2.55)", " return '#%02x%02x%02x' % (r, g, b)", ]), c = Blockly.Python.valueToCode(a, "RED", Blockly.Python.ORDER_NONE) || 0, d = Blockly.Python.valueToCode(a, "GREEN", Blockly.Python.ORDER_NONE) || 0; a = Blockly.Python.valueToCode(a, "BLUE", Blockly.Python.ORDER_NONE) || 0; return [b + "(" + c + ", " + d + ", " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.colour_blend = function (a) { var b = Blockly.Python.provideFunction_("colour_blend", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(colour1, colour2, ratio):", " r1, r2 = int(colour1[1:3], 16), int(colour2[1:3], 16)", " g1, g2 = int(colour1[3:5], 16), int(colour2[3:5], 16)", " b1, b2 = int(colour1[5:7], 16), int(colour2[5:7], 16)", " ratio = min(1, max(0, ratio))", " r = round(r1 * (1 - ratio) + r2 * ratio)", " g = round(g1 * (1 - ratio) + g2 * ratio)", " b = round(b1 * (1 - ratio) + b2 * ratio)", " return '#%02x%02x%02x' % (r, g, b)", ]), c = Blockly.Python.valueToCode(a, "COLOUR1", Blockly.Python.ORDER_NONE) || "'#000000'", d = Blockly.Python.valueToCode(a, "COLOUR2", Blockly.Python.ORDER_NONE) || "'#000000'"; a = Blockly.Python.valueToCode(a, "RATIO", Blockly.Python.ORDER_NONE) || 0; return [b + "(" + c + ", " + d + ", " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.lists = {}; Blockly.Python.lists_create_empty = function (a) { return ["[]", Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python.lists_create_with = function (a) { for (var b = Array(a.itemCount_), c = 0; c < a.itemCount_; c++) b[c] = Blockly.Python.valueToCode(a, "ADD" + c, Blockly.Python.ORDER_NONE) || "None"; return ["[" + b.join(", ") + "]", Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python.lists_repeat = function (a) { var b = Blockly.Python.valueToCode(a, "ITEM", Blockly.Python.ORDER_NONE) || "None"; a = Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_MULTIPLICATIVE) || "0"; return ["[" + b + "] * " + a, Blockly.Python.ORDER_MULTIPLICATIVE]; }; Blockly.Python.lists_length = function (a) { return ["len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "[]") + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.lists_isEmpty = function (a) { return ["not len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "[]") + ")", Blockly.Python.ORDER_LOGICAL_NOT]; }; Blockly.Python.lists_indexOf = function (a) { var b = Blockly.Python.valueToCode(a, "FIND", Blockly.Python.ORDER_NONE) || "[]", c = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''"; if (a.workspace.options.oneBasedIndex) var d = " 0", e = " + 1", f = ""; else (d = " -1"), (e = ""), (f = " - 1"); if ("FIRST" == a.getFieldValue("END")) return ( (a = Blockly.Python.provideFunction_("first_index", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(my_list, elem):", " try: index = my_list.index(elem)" + e, " except: index =" + d, " return index"])), [a + "(" + c + ", " + b + ")", Blockly.Python.ORDER_FUNCTION_CALL] ); a = Blockly.Python.provideFunction_("last_index", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(my_list, elem):", " try: index = len(my_list) - my_list[::-1].index(elem)" + f, " except: index =" + d, " return index"]); return [a + "(" + c + ", " + b + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.lists_getIndex = function (a) { var b = a.getFieldValue("MODE") || "GET", c = a.getFieldValue("WHERE") || "FROM_START", d = Blockly.Python.valueToCode(a, "VALUE", "RANDOM" == c ? Blockly.Python.ORDER_NONE : Blockly.Python.ORDER_MEMBER) || "[]"; switch (c) { case "FIRST": if ("GET" == b) return [d + "[0]", Blockly.Python.ORDER_MEMBER]; if ("GET_REMOVE" == b) return [d + ".pop(0)", Blockly.Python.ORDER_FUNCTION_CALL]; if ("REMOVE" == b) return d + ".pop(0)\n"; break; case "LAST": if ("GET" == b) return [d + "[-1]", Blockly.Python.ORDER_MEMBER]; if ("GET_REMOVE" == b) return [d + ".pop()", Blockly.Python.ORDER_FUNCTION_CALL]; if ("REMOVE" == b) return d + ".pop()\n"; break; case "FROM_START": a = Blockly.Python.getAdjustedInt(a, "AT"); if ("GET" == b) return [d + "[" + a + "]", Blockly.Python.ORDER_MEMBER]; if ("GET_REMOVE" == b) return [d + ".pop(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; if ("REMOVE" == b) return d + ".pop(" + a + ")\n"; break; case "FROM_END": a = Blockly.Python.getAdjustedInt(a, "AT", 1, !0); if ("GET" == b) return [d + "[" + a + "]", Blockly.Python.ORDER_MEMBER]; if ("GET_REMOVE" == b) return [d + ".pop(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; if ("REMOVE" == b) return d + ".pop(" + a + ")\n"; break; case "RANDOM": Blockly.Python.definitions_.import_random = "import random"; if ("GET" == b) return ["random.choice(" + d + ")", Blockly.Python.ORDER_FUNCTION_CALL]; d = Blockly.Python.provideFunction_("lists_remove_random_item", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(myList):", " x = int(random.random() * len(myList))", " return myList.pop(x)"]) + "(" + d + ")"; if ("GET_REMOVE" == b) return [d, Blockly.Python.ORDER_FUNCTION_CALL]; if ("REMOVE" == b) return d + "\n"; } throw Error("Unhandled combination (lists_getIndex)."); }; Blockly.Python.lists_setIndex = function (a) { var b = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_MEMBER) || "[]", c = a.getFieldValue("MODE") || "GET", d = a.getFieldValue("WHERE") || "FROM_START", e = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "None"; switch (d) { case "FIRST": if ("SET" == c) return b + "[0] = " + e + "\n"; if ("INSERT" == c) return b + ".insert(0, " + e + ")\n"; break; case "LAST": if ("SET" == c) return b + "[-1] = " + e + "\n"; if ("INSERT" == c) return b + ".append(" + e + ")\n"; break; case "FROM_START": a = Blockly.Python.getAdjustedInt(a, "AT"); if ("SET" == c) return b + "[" + a + "] = " + e + "\n"; if ("INSERT" == c) return b + ".insert(" + a + ", " + e + ")\n"; break; case "FROM_END": a = Blockly.Python.getAdjustedInt(a, "AT", 1, !0); if ("SET" == c) return b + "[" + a + "] = " + e + "\n"; if ("INSERT" == c) return b + ".insert(" + a + ", " + e + ")\n"; break; case "RANDOM": Blockly.Python.definitions_.import_random = "import random"; b.match(/^\w+$/) ? (a = "") : ((a = Blockly.Python.variableDB_.getDistinctName("tmp_list", Blockly.VARIABLE_CATEGORY_NAME)), (d = a + " = " + b + "\n"), (b = a), (a = d)); d = Blockly.Python.variableDB_.getDistinctName("tmp_x", Blockly.VARIABLE_CATEGORY_NAME); a += d + " = int(random.random() * len(" + b + "))\n"; if ("SET" == c) return a + (b + "[" + d + "] = " + e + "\n"); if ("INSERT" == c) return a + (b + ".insert(" + d + ", " + e + ")\n"); } throw Error("Unhandled combination (lists_setIndex)."); }; Blockly.Python.lists_getSublist = function (a) { var b = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_MEMBER) || "[]", c = a.getFieldValue("WHERE1"), d = a.getFieldValue("WHERE2"); switch (c) { case "FROM_START": c = Blockly.Python.getAdjustedInt(a, "AT1"); "0" == c && (c = ""); break; case "FROM_END": c = Blockly.Python.getAdjustedInt(a, "AT1", 1, !0); break; case "FIRST": c = ""; break; default: throw Error("Unhandled option (lists_getSublist)"); } switch (d) { case "FROM_START": a = Blockly.Python.getAdjustedInt(a, "AT2", 1); break; case "FROM_END": a = Blockly.Python.getAdjustedInt(a, "AT2", 0, !0); Blockly.isNumber(String(a)) ? "0" == a && (a = "") : ((Blockly.Python.definitions_.import_sys = "import sys"), (a += " or sys.maxsize")); break; case "LAST": a = ""; break; default: throw Error("Unhandled option (lists_getSublist)"); } return [b + "[" + c + " : " + a + "]", Blockly.Python.ORDER_MEMBER]; }; Blockly.Python.lists_sort = function (a) { var b = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_NONE) || "[]", c = a.getFieldValue("TYPE"); a = "1" === a.getFieldValue("DIRECTION") ? "False" : "True"; return [ Blockly.Python.provideFunction_("lists_sort", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(my_list, type, reverse):", " def try_float(s):", " try:", " return float(s)", " except:", " return 0", " key_funcs = {", ' "NUMERIC": try_float,', ' "TEXT": str,', ' "IGNORE_CASE": lambda s: str(s).lower()', " }", " key_func = key_funcs[type]", " list_cpy = list(my_list)", " return sorted(list_cpy, key=key_func, reverse=reverse)", ]) + "(" + b + ', "' + c + '", ' + a + ")", Blockly.Python.ORDER_FUNCTION_CALL, ]; }; Blockly.Python.lists_split = function (a) { var b = a.getFieldValue("MODE"); if ("SPLIT" == b) (b = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_MEMBER) || "''"), (a = Blockly.Python.valueToCode(a, "DELIM", Blockly.Python.ORDER_NONE)), (a = b + ".split(" + a + ")"); else if ("JOIN" == b) (b = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_NONE) || "[]"), (a = Blockly.Python.valueToCode(a, "DELIM", Blockly.Python.ORDER_MEMBER) || "''"), (a = a + ".join(" + b + ")"); else throw Error("Unknown mode: " + b); return [a, Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.lists_reverse = function (a) { return ["list(reversed(" + (Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_NONE) || "[]") + "))", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.logic = {}; Blockly.Python.controls_if = function (a) { var b = 0, c = ""; Blockly.Python.STATEMENT_PREFIX && (c += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, a)); do { var d = Blockly.Python.valueToCode(a, "IF" + b, Blockly.Python.ORDER_NONE) || "False"; var e = Blockly.Python.statementToCode(a, "DO" + b) || Blockly.Python.PASS; Blockly.Python.STATEMENT_SUFFIX && (e = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT) + e); c += (0 == b ? "if " : "elif ") + d + ":\n" + e; ++b; } while (a.getInput("IF" + b)); if (a.getInput("ELSE") || Blockly.Python.STATEMENT_SUFFIX) (e = Blockly.Python.statementToCode(a, "ELSE") || Blockly.Python.PASS), Blockly.Python.STATEMENT_SUFFIX && (e = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT) + e), (c += "else:\n" + e); return c; }; Blockly.Python.controls_ifelse = Blockly.Python.controls_if; Blockly.Python.logic_compare = function (a) { var b = { EQ: "==", NEQ: "!=", LT: "<", LTE: "<=", GT: ">", GTE: ">=" }[a.getFieldValue("OP")], c = Blockly.Python.ORDER_RELATIONAL, d = Blockly.Python.valueToCode(a, "A", c) || "0"; a = Blockly.Python.valueToCode(a, "B", c) || "0"; return [d + " " + b + " " + a, c]; }; Blockly.Python.logic_operation = function (a) { var b = "AND" == a.getFieldValue("OP") ? "and" : "or", c = "and" == b ? Blockly.Python.ORDER_LOGICAL_AND : Blockly.Python.ORDER_LOGICAL_OR, d = Blockly.Python.valueToCode(a, "A", c); a = Blockly.Python.valueToCode(a, "B", c); if (d || a) { var e = "and" == b ? "True" : "False"; d || (d = e); a || (a = e); } else a = d = "False"; return [d + " " + b + " " + a, c]; }; Blockly.Python.logic_negate = function (a) { return ["not " + (Blockly.Python.valueToCode(a, "BOOL", Blockly.Python.ORDER_LOGICAL_NOT) || "True"), Blockly.Python.ORDER_LOGICAL_NOT]; }; Blockly.Python.logic_boolean = function (a) { return ["TRUE" == a.getFieldValue("BOOL") ? "True" : "False", Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python.logic_null = function (a) { return ["None", Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python.logic_ternary = function (a) { var b = Blockly.Python.valueToCode(a, "IF", Blockly.Python.ORDER_CONDITIONAL) || "False", c = Blockly.Python.valueToCode(a, "THEN", Blockly.Python.ORDER_CONDITIONAL) || "None"; a = Blockly.Python.valueToCode(a, "ELSE", Blockly.Python.ORDER_CONDITIONAL) || "None"; return [c + " if " + b + " else " + a, Blockly.Python.ORDER_CONDITIONAL]; }; Blockly.Python.loops = {}; Blockly.Python.controls_repeat_ext = function (a) { var b = a.getField("TIMES") ? String(parseInt(a.getFieldValue("TIMES"), 10)) : Blockly.Python.valueToCode(a, "TIMES", Blockly.Python.ORDER_NONE) || "0"; b = Blockly.isNumber(b) ? parseInt(b, 10) : "int(" + b + ")"; var c = Blockly.Python.statementToCode(a, "DO"); c = Blockly.Python.addLoopTrap(c, a) || Blockly.Python.PASS; return "for " + Blockly.Python.variableDB_.getDistinctName("count", Blockly.VARIABLE_CATEGORY_NAME) + " in range(" + b + "):\n" + c; }; Blockly.Python.controls_repeat = Blockly.Python.controls_repeat_ext; Blockly.Python.controls_whileUntil = function (a) { var b = "UNTIL" == a.getFieldValue("MODE"), c = Blockly.Python.valueToCode(a, "BOOL", b ? Blockly.Python.ORDER_LOGICAL_NOT : Blockly.Python.ORDER_NONE) || "False", d = Blockly.Python.statementToCode(a, "DO"); d = Blockly.Python.addLoopTrap(d, a) || Blockly.Python.PASS; b && (c = "not " + c); return "while " + c + ":\n" + d; }; Blockly.Python.controls_for = function (a) { var b = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME), c = Blockly.Python.valueToCode(a, "FROM", Blockly.Python.ORDER_NONE) || "0", d = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "0", e = Blockly.Python.valueToCode(a, "BY", Blockly.Python.ORDER_NONE) || "1", f = Blockly.Python.statementToCode(a, "DO"); f = Blockly.Python.addLoopTrap(f, a) || Blockly.Python.PASS; var n = "", k = function () { return Blockly.Python.provideFunction_("upRange", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(start, stop, step):", " while start <= stop:", " yield start", " start += abs(step)"]); }, h = function () { return Blockly.Python.provideFunction_("downRange", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(start, stop, step):", " while start >= stop:", " yield start", " start -= abs(step)"]); }; a = function (g, l, p) { return "(" + g + " <= " + l + ") and " + k() + "(" + g + ", " + l + ", " + p + ") or " + h() + "(" + g + ", " + l + ", " + p + ")"; }; if (Blockly.isNumber(c) && Blockly.isNumber(d) && Blockly.isNumber(e)) (c = Number(c)), (d = Number(d)), (e = Math.abs(Number(e))), 0 === c % 1 && 0 === d % 1 && 0 === e % 1 ? (c <= d ? (d++, (a = 0 == c && 1 == e ? d : c + ", " + d), 1 != e && (a += ", " + e)) : (d--, (a = c + ", " + d + ", -" + e)), (a = "range(" + a + ")")) : ((a = c < d ? k() : h()), (a += "(" + c + ", " + d + ", " + e + ")")); else { var m = function (g, l) { Blockly.isNumber(g) ? (g = Number(g)) : g.match(/^\w+$/) ? (g = "float(" + g + ")") : ((l = Blockly.Python.variableDB_.getDistinctName(b + l, Blockly.VARIABLE_CATEGORY_NAME)), (n += l + " = float(" + g + ")\n"), (g = l)); return g; }; c = m(c, "_start"); d = m(d, "_end"); e = m(e, "_inc"); "number" == typeof c && "number" == typeof d ? ((a = c < d ? k() : h()), (a += "(" + c + ", " + d + ", " + e + ")")) : (a = a(c, d, e)); } return (n += "for " + b + " in " + a + ":\n" + f); }; Blockly.Python.controls_forEach = function (a) { var b = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME), c = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_RELATIONAL) || "[]", d = Blockly.Python.statementToCode(a, "DO"); d = Blockly.Python.addLoopTrap(d, a) || Blockly.Python.PASS; return "for " + b + " in " + c + ":\n" + d; }; Blockly.Python.controls_flow_statements = function (a) { var b = ""; Blockly.Python.STATEMENT_PREFIX && (b += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, a)); Blockly.Python.STATEMENT_SUFFIX && (b += Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a)); if (Blockly.Python.STATEMENT_PREFIX) { var c = Blockly.Constants.Loops.CONTROL_FLOW_IN_LOOP_CHECK_MIXIN.getSurroundLoop(a); c && !c.suppressPrefixSuffix && (b += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, c)); } switch (a.getFieldValue("FLOW")) { case "BREAK": return b + "break\n"; case "CONTINUE": return b + "continue\n"; } throw Error("Unknown flow statement."); }; /* * Custom */ Blockly.Python.pandas = {}; Blockly.Python['pandas_read_csv'] = function (a) { Blockly.Python.definitions_.pandas = "import pandas as pd"; return ['pd.read_csv(' + String(a).split(" ")[2] + ')', Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python["create_dict"] = function (a) { var b = 1, c = "{"; do { var d = Blockly.Python.valueToCode(a, "KEY" + b, Blockly.Python.ORDER_NONE) || ""; var e = Blockly.Python.valueToCode(a, "VAL" + b, Blockly.Python.ORDER_NONE) || ""; Blockly.Python.STATEMENT_SUFFIX && (e = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT) + e); if (b > 1) { c += ","; } c += d + ":" + e; ++b; } while (a.getInput("KEY" + b)); c += "}"; if ((e != "") && (d != "")) { return [c, Blockly.Python.ORDER_FUNCTION_CALL]; } else { return ["None", Blockly.Python.ORDER_FUNCTION_CALL]; } } Blockly.Python["dict_append"] = function (a) { var e = "" var b = Blockly.Python.valueToCode(a, "DICTIONARY", Blockly.Python.ORDER_NONE) || ""; var c = Blockly.Python.valueToCode(a, "KEY", Blockly.Python.ORDER_NONE) || ""; var d = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || ""; if ((b != "") && (c != "") && (d != "")) { e = b + "[" + c + "] = " + d; } return e; } Blockly.Python['seaborn_dataset'] = function (a) { Blockly.Python.definitions_.seaborn = "import seaborn as sns"; VarData[a.inputList[0].fieldRow[1].value_] = 'sns.load_dataset("' + a.inputList[0].fieldRow[1].value_ + '")', Blockly.Python.ORDER_FUNCTION_CALL; return ['sns.load_dataset("' + a.inputList[0].fieldRow[1].value_ + '")', Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['skl_train_test_split'] = function (a) { Blockly.Python.definitions_.sklearn_test_train_split = "from sklearn.model_selection import train_test_split"; var dataframe = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_NONE) || "" var train_X = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME) var train_Y = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR2"), Blockly.VARIABLE_CATEGORY_NAME) var test_X = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR1"), Blockly.VARIABLE_CATEGORY_NAME) var test_Y = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR3"), Blockly.VARIABLE_CATEGORY_NAME) var target = Blockly.Python.valueToCode(a, "TARGETVAR", Blockly.Python.ORDER_NONE) var TestSize = Blockly.Python.valueToCode(a, "TESTSIZE", Blockly.Python.ORDER_NONE) var codeString = '\n' + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + '=train_test_split(' + dataframe + '.drop(columns = [' + target + ']),' + dataframe + '[' + target + ']' + ', test_size=' + TestSize + ', random_state=42)\n' var codeString2 = "" if (a.getFieldValue("SPLIT") == "dropNa") { codeString2 = 'def dropNa(' + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + '):\n' + ' ' + train_X + ' = ' + train_X + '.dropna()\n' + ' ' + train_Y + " = " + train_Y + ".loc[" + train_X + ".index.values.tolist()]\n" + ' ' + test_X + " = " + test_X + ".dropna()\n" + ' ' + test_Y + " = " + test_Y + ".loc[" + test_X + ".index.values.tolist()]\n" + ' return ' + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + '\n' + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + " = " + "dropNa(" + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + ")\n" } Blockly.Python.definitions_.pandas = "import pandas as pd"; Blockly.Python.definitions_.encoder = "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"; Blockly.Python.definitions_.numpy = "import numpy as np" if (dataframe == "") { return "" } else { return codeString + codeString2 } } Blockly.Python['pandas_drop_columns'] = function (a) { var columns = Blockly.Python.valueToCode(a, "COLUMN", Blockly.Python.ORDER_UNARY_SIGN) if (columns == "") { return "" } var DataFrame = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_UNARY_SIGN) if (a.getInputTargetBlock("COLUMN").outputConnection.getCheck() == "String") { return [DataFrame + ".drop([" + columns + "], axis = 1)", Blockly.Python.ORDER_ATOMIC]; } if (a.getInputTargetBlock("COLUMN").outputConnection.getCheck() == "Array") { return [DataFrame + ".drop([" + columns + "], axis = 1)", Blockly.Python.ORDER_ATOMIC]; } if (columns == "" || DataFrame == "") { return ["", Blockly.Python.ORDER_ATOMIC]; } } Blockly.Python['pandas_sample'] = function (a) { var factor = Blockly.Python.valueToCode(a, "FACTOR", Blockly.Python.ORDER_UNARY_SIGN) if (factor == "") { return "" } var DataFrame = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_UNARY_SIGN) if (factor != "" || DataFrame != "") { return [DataFrame + ".sample(frac=" + factor + ", replace=True, random_state=123)", Blockly.Python.ORDER_ATOMIC]; } } Blockly.Python['pandas_select_columns'] = function (a) { var columns = Blockly.Python.valueToCode(a, "COLUMN", Blockly.Python.ORDER_UNARY_SIGN) if (columns == "") { columns = "[]" } var DataFrame = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_UNARY_SIGN) return [DataFrame + "[" + columns + "]", Blockly.Python.ORDER_ATOMIC]; } Blockly.Python['Classification_Report'] = function (a) { Blockly.Python.definitions_.classification_report = "from sklearn.metrics import classification_report"; var Pred = Blockly.Python.valueToCode(a, "Pred", Blockly.Python.ORDER_UNARY_SIGN) || ""; var True = Blockly.Python.valueToCode(a, "True", Blockly.Python.ORDER_UNARY_SIGN) || ""; if ((Pred != "") && (True != "")) { return ["classification_report(" + True + ", " + Pred + ")", Blockly.Python.ORDER_FUNCTION_CALL]; } else { return ["", Blockly.Python.ORDER_FUNCTION_CALL]; } } Blockly.Python['R2_Report'] = function (a) { Blockly.Python.definitions_.r2_score = "from sklearn.metrics import r2_score"; var Pred = Blockly.Python.valueToCode(a, "Pred", Blockly.Python.ORDER_UNARY_SIGN) var True = Blockly.Python.valueToCode(a, "True", Blockly.Python.ORDER_UNARY_SIGN) if ((Pred != "") && (True != "")) { return ["r2_score(" + True + ", " + Pred + ")", Blockly.Python.ORDER_FUNCTION_CALL]; } else { return ["", Blockly.Python.ORDER_FUNCTION_CALL]; } } Blockly.Python['Print'] = function (a) { var INPUT = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_FUNCTION_CALL) || "''"; var End = a.getFieldValue("END") if (End == "newLine") { return "print(" + INPUT + ")" + "\n"; } if (End == "tab") { return "print(" + INPUT + ",end='\\t')" + "\n"; } if (End == "space") { return "print(" + INPUT + ",end=' ')" + "\n"; } if (End == "comma") { return "print(" + INPUT + ",end=',')" + "\n"; } } Blockly.Python['pycaret_setup'] = function (a) { var input_data = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || ""; var input_column = Blockly.Python.valueToCode(a, "input_column", Blockly.Python.ORDER_NONE) || ""; var algorithm = a.getFieldValue("algorithm"); if (input_data == "") { return "" } if (algorithm == "Classification") { Blockly.Python.definitions_.pycaret_classification = "from pycaret.classification import *"; } if (algorithm == "Regression") { Blockly.Python.definitions_.pycaret_regression = "from pycaret.regression import *"; } return "setup(" + input_data + ", target = " + input_column + ")" + "\n"; } Blockly.Python['pycaret_predict'] = function (a) { var input_model = Blockly.Python.valueToCode(a, "input_model", Blockly.Python.ORDER_NONE) || ""; var input_data = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || ""; console.log(input_model) if (input_model == "") { return ["", Blockly.Python.ORDER_FUNCTION_CALL] } if (input_model != "" && input_data == "") { return ["predict_model(" + input_model + ")", Blockly.Python.ORDER_FUNCTION_CALL] } if (input_model != "" && input_data != "") { return ["predict_model(" + input_model + ", data=" + input_data + ")", Blockly.Python.ORDER_FUNCTION_CALL] } } Blockly.Python['pycaret_save'] = function (a) { var input_model = Blockly.Python.valueToCode(a, "input_model", Blockly.Python.ORDER_NONE) || ""; var input_data = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || ""; if (input_model != "" || input_data != "") { return "save_model(" + input_model + "," + input_data + ")" + "\n"; } else { return "" } } Blockly.Python['pycaret_plot_model'] = function (a) { var input_model = Blockly.Python.valueToCode(a, "input_model", Blockly.Python.ORDER_NONE) || ""; var plot_data = a.getFieldValue("plot_data"); if (input_model != "" || plot_data != "") { return "plot_model(" + input_model + ", plot = '" + plot_data + "')" + "\n"; } else { return "" } } Blockly.Python['pycaret_blend_model'] = function (a) { var input_models = Blockly.Python.valueToCode(a, "input_models", Blockly.Python.ORDER_NONE) || ""; var input_fold = Blockly.Python.valueToCode(a, "input_fold", Blockly.Python.ORDER_NONE) || ""; var input_method = a.getFieldValue("input_method"); if (input_models != "" && input_fold == "") { return ["blend_models(" + input_models + ", method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL] } if (input_models != "" && input_fold != "") { return ["blend_models(" + input_models + " , fold = '" + input_fold + "' , method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL] } else { return "" } } Blockly.Python['pycaret_ensemble_model'] = function (a) { var input_models = Blockly.Python.valueToCode(a, "input_models", Blockly.Python.ORDER_NONE) || ""; var input_fold = Blockly.Python.valueToCode(a, "input_fold", Blockly.Python.ORDER_NONE) || ""; var input_method = a.getFieldValue("input_method"); if (input_models != "" && input_fold == "") { return ["ensemble_model(" + input_models + ", method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL] } if (input_models != "" && input_fold != "") { return ["ensemble_model(" + input_models + " , fold = '" + input_fold + "' , method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL] } else { return "" } } Blockly.Python['pycaret_load'] = function (a) { var input_model = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || ""; if (input_model != "") { return ["load_model(" + input_model + ")", Blockly.Python.ORDER_FUNCTION_CALL] } else { return "" } } Blockly.Python['pycaret_automl'] = function (a) { var optimizer = a.getFieldValue("optimizer"); return ["automl(optimize = '" + optimizer + "')", Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['pycaret_classifier'] = function (a) { var model = a.getFieldValue("model"); return ["create_model('" + model + "')", Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['pycaret_regressor'] = function (a) { var model = a.getFieldValue("model"); return ["create_model('" + model + "')", Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['Input'] = function (a) { var INPUT = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_NONE) || "''"; return ["input(" + INPUT + ")", Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['pandas_set_columns'] = function (a) { var columns = Blockly.Python.valueToCode(a, "COLUMN", Blockly.Python.ORDER_UNARY_SIGN) if (columns == "") { columns = "[]" } var DATAFRAME_IN = Blockly.Python.valueToCode(a, "DATAFRAME_IN", Blockly.Python.ORDER_UNARY_SIGN) var DATAFRAME_OUT = Blockly.Python.valueToCode(a, "DATAFRAME_OUT", Blockly.Python.ORDER_UNARY_SIGN) if (DATAFRAME_IN != "" || DATAFRAME_OUT != "" || columns != "") { return DATAFRAME_OUT + "[" + columns + "]=" + DATAFRAME_IN + "\n"; } else { return "" } } Blockly.Python['dataframe_Filter'] = function (a) { var b = { EQ: "==", NEQ: "!=", LT: "<", LTE: "<=", GT: ">", GTE: ">=" }[a.getFieldValue("OP")]; var c = Blockly.Python.ORDER_RELATIONAL; var d = Blockly.Python.valueToCode(a, "A", c) || ""; var e = Blockly.Python.valueToCode(a, "C", c) || ""; a = Blockly.Python.valueToCode(a, "B", c) || ""; if (d == "" || e == "" || a == "") { return ["", Blockly.Python.ORDER_NONE] } return [e + "[" + d + " " + b + " " + a + "]", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python['dataframe_Map'] = function (a) { var c = Blockly.Python.ORDER_NONE; var b = Blockly.Python.valueToCode(a, "Map", c) || ""; var d = Blockly.Python.valueToCode(a, "Series", c) || ""; if (b == "" || d == "") { return ["", Blockly.Python.ORDER_NONE] } return [d + ".map(" + b + ")", Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python['CLR_XGBoost'] = function (a) { Blockly.Python.definitions_.XGBClassifier = "from xgboost import XGBClassifier"; var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += "XGBClassifier(**" + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += "XGBClassifier()" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ", eval_set = [(" + Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL]) } Blockly.Python['REG_LinearRegression'] = function (a) { Blockly.Python.definitions_.LinearRegression = "from sklearn.linear_model import LinearRegression"; var HandleCatagoricalData = Blockly.Python.provideFunction_("REG_LinearRegression", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += "Pipeline([('encoding', HandleCatagoricalData('labelEncoding')), ('scaler', StandardScaler()), ('LR', LinearRegression(**" + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])" Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("LR", LinearRegression())])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ", eval_set = [(" + Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL]) } Blockly.Python['REG_XGBRegressor'] = function (a) { Blockly.Python.definitions_.XGBRegressor = "from xgboost import XGBRegressor"; var HandleCatagoricalData = Blockly.Python.provideFunction_("REG_XGBRegressor", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += "Pipeline([('encoding', HandleCatagoricalData('labelEncoding')), ('scaler', StandardScaler()), ('XGB', XGBRegressor(**" + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])" Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("XGB", XGBRegressor())])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ", eval_set = [(" + Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL]) } Blockly.Python['CLR_LogisticRegression'] = function (a) { Blockly.Python.definitions_.LogisticRegression = "from sklearn.linear_model import LogisticRegression"; var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_LogisticRegression", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("LR",LogisticRegression(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + '))])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("LR", LogisticRegression())])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL]) } Blockly.Python['CLR_NaiveBayes'] = function (a) { Blockly.Python.definitions_.GaussianNB = "from sklearn.naive_bayes import GaussianNB"; var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_NaiveBayes", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("GNB",GaussianNB())])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("GNB",GaussianNB(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])" Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return [codeString, Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['CLR_KNN'] = function (a) { Blockly.Python.definitions_.KNeighborsClassifier = "from sklearn.neighbors import KNeighborsClassifier"; var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_KNN", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("KNN",KNeighborsClassifier())])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("KNN",KNeighborsClassifier(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])" Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return [codeString, Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['CLR_DecisionTree'] = function (a) { Blockly.Python.definitions_.tree = "from sklearn import tree"; var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_DecisionTree", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("Dtree",tree.DecisionTreeClassifier(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])" Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("Dtree",tree.DecisionTreeClassifier())])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return [codeString, Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['CLR_SVM'] = function (a) { Blockly.Python.definitions_.SVC = "from sklearn.svm import SVC"; var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_SVM", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("SVC", SVC(gamma="auto"))])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("SVC", SVC(gamma="auto"))])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return [codeString, Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python['CLR_RandomForest'] = function (a) { Blockly.Python.definitions_.RandomForestClassifier = "from sklearn.ensemble import RandomForestClassifier"; var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_RandomForest", [ 'class HandleCatagoricalData():\n' + ' def __init__(self,method="labelEncoding",factor=0):\n' + ' self.method = method\n' + ' self.factor = factor\n' + ' def fit(self, X, y):\n' + ' if self.method == "labelEncoding":\n' + ' X = pd.DataFrame(data=X)\n' + ' y = pd.DataFrame(data=y)\n' + ' self.LE = LabelEncoder()\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' y = y.to_numpy()\n' + ' return self\n' + ' def transform(self, X):\n' + ' X = pd.DataFrame(data=X)\n' + ' for column in X:\n' + ' if X[column].dtype == np.object:\n' + ' X[column]=X[column].astype("category")\n' + ' if X[column].dtype.name == "category":\n' + ' X[column] = self.LE.fit_transform(X[column])\n' + ' X = X.to_numpy()\n' + ' return X\n' ]) var codeString = "" if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("RFC", RandomForestClassifier())])' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("RFC", RandomForestClassifier(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + ')' Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline" Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) + "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD" } if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" && Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" && Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") { codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".predict(" + Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")" } return [codeString, Blockly.Python.ORDER_FUNCTION_CALL] } Blockly.Python.math = {}; Blockly.Python.addReservedWords("math,random,Number"); Blockly.Python.math_number = function (a) { a = Number(a.getFieldValue("NUM")); if (Infinity == a) { a = 'float("inf")'; var b = Blockly.Python.ORDER_FUNCTION_CALL; } else -Infinity == a ? ((a = '-float("inf")'), (b = Blockly.Python.ORDER_UNARY_SIGN)) : (b = 0 > a ? Blockly.Python.ORDER_UNARY_SIGN : Blockly.Python.ORDER_ATOMIC); return [a, b]; }; Blockly.Python.math_arithmetic = function (a) { var b = { ADD: [" + ", Blockly.Python.ORDER_ADDITIVE], MINUS: [" - ", Blockly.Python.ORDER_ADDITIVE], MULTIPLY: [" * ", Blockly.Python.ORDER_MULTIPLICATIVE], DIVIDE: [" / ", Blockly.Python.ORDER_MULTIPLICATIVE], POWER: [" ** ", Blockly.Python.ORDER_EXPONENTIATION], }[a.getFieldValue("OP")], c = b[0]; b = b[1]; var d = Blockly.Python.valueToCode(a, "A", b) || "0"; a = Blockly.Python.valueToCode(a, "B", b) || "0"; return [d + c + a, b]; }; Blockly.Python.math_single = function (a) { var b = a.getFieldValue("OP"); if ("NEG" == b) { var c = Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_UNARY_SIGN) || "0"; return ["-" + c, Blockly.Python.ORDER_UNARY_SIGN]; } Blockly.Python.definitions_.import_math = "import math"; a = "SIN" == b || "COS" == b || "TAN" == b ? Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_MULTIPLICATIVE) || "0" : Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_NONE) || "0"; switch (b) { case "ABS": c = "math.fabs(" + a + ")"; break; case "ROOT": c = "math.sqrt(" + a + ")"; break; case "LN": c = "math.log(" + a + ")"; break; case "LOG10": c = "math.log10(" + a + ")"; break; case "EXP": c = "math.exp(" + a + ")"; break; case "POW10": c = "math.pow(10," + a + ")"; break; case "ROUND": c = "round(" + a + ")"; break; case "ROUNDUP": c = "math.ceil(" + a + ")"; break; case "ROUNDDOWN": c = "math.floor(" + a + ")"; break; case "SIN": c = "math.sin(" + a + " / 180.0 * math.pi)"; break; case "COS": c = "math.cos(" + a + " / 180.0 * math.pi)"; break; case "TAN": c = "math.tan(" + a + " / 180.0 * math.pi)"; } if (c) return [c, Blockly.Python.ORDER_FUNCTION_CALL]; switch (b) { case "ASIN": c = "math.asin(" + a + ") / math.pi * 180"; break; case "ACOS": c = "math.acos(" + a + ") / math.pi * 180"; break; case "ATAN": c = "math.atan(" + a + ") / math.pi * 180"; break; default: throw Error("Unknown math operator: " + b); } return [c, Blockly.Python.ORDER_MULTIPLICATIVE]; }; Blockly.Python.math_constant = function (a) { var b = { PI: ["math.pi", Blockly.Python.ORDER_MEMBER], E: ["math.e", Blockly.Python.ORDER_MEMBER], GOLDEN_RATIO: ["(1 + math.sqrt(5)) / 2", Blockly.Python.ORDER_MULTIPLICATIVE], SQRT2: ["math.sqrt(2)", Blockly.Python.ORDER_MEMBER], SQRT1_2: ["math.sqrt(1.0 / 2)", Blockly.Python.ORDER_MEMBER], INFINITY: ["float('inf')", Blockly.Python.ORDER_ATOMIC], }; a = a.getFieldValue("CONSTANT"); "INFINITY" != a && (Blockly.Python.definitions_.import_math = "import math"); return b[a]; }; Blockly.Python.math_number_property = function (a) { var b = Blockly.Python.valueToCode(a, "NUMBER_TO_CHECK", Blockly.Python.ORDER_MULTIPLICATIVE) || "0", c = a.getFieldValue("PROPERTY"); if ("PRIME" == c) return ( (Blockly.Python.definitions_.import_math = "import math"), (Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number"), [ Blockly.Python.provideFunction_("math_isPrime", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(n):", " # https://en.wikipedia.org/wiki/Primality_test#Naive_methods", " # If n is not a number but a string, try parsing it.", " if not isinstance(n, Number):", " try:", " n = float(n)", " except:", " return False", " if n == 2 or n == 3:", " return True", " # False if n is negative, is 1, or not whole, or if n is divisible by 2 or 3.", " if n <= 1 or n % 1 != 0 or n % 2 == 0 or n % 3 == 0:", " return False", " # Check all the numbers of form 6k +/- 1, up to sqrt(n).", " for x in range(6, int(math.sqrt(n)) + 2, 6):", " if n % (x - 1) == 0 or n % (x + 1) == 0:", " return False", " return True", ]) + "(" + b + ")", Blockly.Python.ORDER_FUNCTION_CALL, ] ); switch (c) { case "EVEN": var d = b + " % 2 == 0"; break; case "ODD": d = b + " % 2 == 1"; break; case "WHOLE": d = b + " % 1 == 0"; break; case "POSITIVE": d = b + " > 0"; break; case "NEGATIVE": d = b + " < 0"; break; case "DIVISIBLE_BY": a = Blockly.Python.valueToCode(a, "DIVISOR", Blockly.Python.ORDER_MULTIPLICATIVE); if (!a || "0" == a) return ["False", Blockly.Python.ORDER_ATOMIC]; d = b + " % " + a + " == 0"; } return [d, Blockly.Python.ORDER_RELATIONAL]; }; Blockly.Python.math_change = function (a) { Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number"; var b = Blockly.Python.valueToCode(a, "DELTA", Blockly.Python.ORDER_ADDITIVE) || "0"; a = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME); return a + " = (" + a + " if isinstance(" + a + ", Number) else 0) + " + b + "\n"; }; Blockly.Python.math_round = Blockly.Python.math_single; Blockly.Python.math_trig = Blockly.Python.math_single; Blockly.Python.math_on_list = function (a) { var b = a.getFieldValue("OP"); a = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_NONE) || "[]"; switch (b) { case "SUM": b = "sum(" + a + ")"; break; case "MIN": b = "min(" + a + ")"; break; case "MAX": b = "max(" + a + ")"; break; case "AVERAGE": Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number"; b = Blockly.Python.provideFunction_("math_mean", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(myList):", " localList = [e for e in myList if isinstance(e, Number)]", " if not localList: return", " return float(sum(localList)) / len(localList)", ]); b = b + "(" + a + ")"; break; case "MEDIAN": Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number"; b = Blockly.Python.provideFunction_("math_median", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(myList):", " localList = sorted([e for e in myList if isinstance(e, Number)])", " if not localList: return", " if len(localList) % 2 == 0:", " return (localList[len(localList) // 2 - 1] + localList[len(localList) // 2]) / 2.0", " else:", " return localList[(len(localList) - 1) // 2]", ]); b = b + "(" + a + ")"; break; case "MODE": b = Blockly.Python.provideFunction_("math_modes", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(some_list):", " modes = []", " # Using a lists of [item, count] to keep count rather than dict", ' # to avoid "unhashable" errors when the counted item is itself a list or dict.', " counts = []", " maxCount = 1", " for item in some_list:", " found = False", " for count in counts:", " if count[0] == item:", " count[1] += 1", " maxCount = max(maxCount, count[1])", " found = True", " if not found:", " counts.append([item, 1])", " for counted_item, item_count in counts:", " if item_count == maxCount:", " modes.append(counted_item)", " return modes", ]); b = b + "(" + a + ")"; break; case "STD_DEV": Blockly.Python.definitions_.import_math = "import math"; b = Blockly.Python.provideFunction_("math_standard_deviation", [ "def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(numbers):", " n = len(numbers)", " if n == 0: return", " mean = float(sum(numbers)) / n", " variance = sum((x - mean) ** 2 for x in numbers) / n", " return math.sqrt(variance)", ]); b = b + "(" + a + ")"; break; case "RANDOM": Blockly.Python.definitions_.import_random = "import random"; b = "random.choice(" + a + ")"; break; default: throw Error("Unknown operator: " + b); } return [b, Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.math_modulo = function (a) { var b = Blockly.Python.valueToCode(a, "DIVIDEND", Blockly.Python.ORDER_MULTIPLICATIVE) || "0"; a = Blockly.Python.valueToCode(a, "DIVISOR", Blockly.Python.ORDER_MULTIPLICATIVE) || "0"; return [b + " % " + a, Blockly.Python.ORDER_MULTIPLICATIVE]; }; Blockly.Python.math_constrain = function (a) { var b = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "0", c = Blockly.Python.valueToCode(a, "LOW", Blockly.Python.ORDER_NONE) || "0"; a = Blockly.Python.valueToCode(a, "HIGH", Blockly.Python.ORDER_NONE) || "float('inf')"; return ["min(max(" + b + ", " + c + "), " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.math_random_int = function (a) { Blockly.Python.definitions_.import_random = "import random"; var b = Blockly.Python.valueToCode(a, "FROM", Blockly.Python.ORDER_NONE) || "0"; a = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "0"; return ["random.randint(" + b + ", " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.math_random_float = function (a) { Blockly.Python.definitions_.import_random = "import random"; return ["random.random()", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.math_atan2 = function (a) { Blockly.Python.definitions_.import_math = "import math"; var b = Blockly.Python.valueToCode(a, "X", Blockly.Python.ORDER_NONE) || "0"; return ["math.atan2(" + (Blockly.Python.valueToCode(a, "Y", Blockly.Python.ORDER_NONE) || "0") + ", " + b + ") / math.pi * 180", Blockly.Python.ORDER_MULTIPLICATIVE]; }; Blockly.Python.procedures = {}; Blockly.Python.procedures_defreturn = function (a) { for (var b = [], c, d = a.workspace, e = Blockly.Variables.allUsedVarModels(d) || [], f = 0; (c = e[f]); f++) (c = c.name), -1 == a.getVars().indexOf(c) && b.push(Blockly.Python.variableDB_.getName(c, Blockly.VARIABLE_CATEGORY_NAME)); e = Blockly.Variables.allDeveloperVariables(d); for (f = 0; f < e.length; f++) b.push(Blockly.Python.variableDB_.getName(e[f], Blockly.Names.DEVELOPER_VARIABLE_TYPE)); b = b.length ? Blockly.Python.INDENT + "global " + b.join(", ") + "\n" : ""; d = Blockly.Python.variableDB_.getName(a.getFieldValue("NAME"), Blockly.PROCEDURE_CATEGORY_NAME); c = ""; Blockly.Python.STATEMENT_PREFIX && (c += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, a)); Blockly.Python.STATEMENT_SUFFIX && (c += Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a)); c && (c = Blockly.Python.prefixLines(c, Blockly.Python.INDENT)); var n = ""; Blockly.Python.INFINITE_LOOP_TRAP && (n = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.INFINITE_LOOP_TRAP, a), Blockly.Python.INDENT)); var k = Blockly.Python.statementToCode(a, "STACK"), h = Blockly.Python.valueToCode(a, "RETURN", Blockly.Python.ORDER_NONE) || "", m = ""; k && h && (m = c); h ? (h = Blockly.Python.INDENT + "return " + h + "\n") : k || (k = Blockly.Python.PASS); var g = []; e = a.getVars(); for (f = 0; f < e.length; f++) g[f] = Blockly.Python.variableDB_.getName(e[f], Blockly.VARIABLE_CATEGORY_NAME); b = "def " + d + "(" + g.join(", ") + "):\n" + b + c + n + k + m + h; b = Blockly.Python.scrub_(a, b); Blockly.Python.definitions_["%" + d] = b; return null; }; Blockly.Python.procedures_defnoreturn = Blockly.Python.procedures_defreturn; Blockly.Python.procedures_callreturn = function (a) { for (var b = Blockly.Python.variableDB_.getName(a.getFieldValue("NAME"), Blockly.PROCEDURE_CATEGORY_NAME), c = [], d = a.getVars(), e = 0; e < d.length; e++) c[e] = Blockly.Python.valueToCode(a, "ARG" + e, Blockly.Python.ORDER_NONE) || "None"; return [b + "(" + c.join(", ") + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.procedures_callnoreturn = function (a) { return Blockly.Python.procedures_callreturn(a)[0] + "\n"; }; Blockly.Python.procedures_ifreturn = function (a) { var b = "if " + (Blockly.Python.valueToCode(a, "CONDITION", Blockly.Python.ORDER_NONE) || "False") + ":\n"; Blockly.Python.STATEMENT_SUFFIX && (b += Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT)); a.hasReturnValue_ ? ((a = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "None"), (b += Blockly.Python.INDENT + "return " + a + "\n")) : (b += Blockly.Python.INDENT + "return\n"); return b; }; Blockly.Python.texts = {}; Blockly.Python.text = function (a) { return [Blockly.Python.quote_(a.getFieldValue("TEXT")), Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python.text_multiline = function (a) { a = Blockly.Python.multiline_quote_(a.getFieldValue("TEXT")); var b = -1 != a.indexOf("+") ? Blockly.Python.ORDER_ADDITIVE : Blockly.Python.ORDER_ATOMIC; return [a, b]; }; Blockly.Python.text.forceString_ = function (a) { return Blockly.Python.text.forceString_.strRegExp.test(a) ? [a, Blockly.Python.ORDER_ATOMIC] : ["str(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.text.forceString_.strRegExp = /^\s*'([^']|\\')*'\s*$/; Blockly.Python.text_join = function (a) { switch (a.itemCount_) { case 0: return ["''", Blockly.Python.ORDER_ATOMIC]; case 1: return (a = Blockly.Python.valueToCode(a, "ADD0", Blockly.Python.ORDER_NONE) || "''"), Blockly.Python.text.forceString_(a); case 2: var b = Blockly.Python.valueToCode(a, "ADD0", Blockly.Python.ORDER_NONE) || "''"; a = Blockly.Python.valueToCode(a, "ADD1", Blockly.Python.ORDER_NONE) || "''"; a = Blockly.Python.text.forceString_(b)[0] + " + " + Blockly.Python.text.forceString_(a)[0]; return [a, Blockly.Python.ORDER_ADDITIVE]; default: b = []; for (var c = 0; c < a.itemCount_; c++) b[c] = Blockly.Python.valueToCode(a, "ADD" + c, Blockly.Python.ORDER_NONE) || "''"; a = Blockly.Python.variableDB_.getDistinctName("x", Blockly.VARIABLE_CATEGORY_NAME); a = "''.join([str(" + a + ") for " + a + " in [" + b.join(", ") + "]])"; return [a, Blockly.Python.ORDER_FUNCTION_CALL]; } }; Blockly.Python.text_append = function (a) { var b = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME); a = Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_NONE) || "''"; return b + " = str(" + b + ") + " + Blockly.Python.text.forceString_(a)[0] + "\n"; }; Blockly.Python.text_length = function (a) { return ["len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''") + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.text_isEmpty = function (a) { return ["not len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''") + ")", Blockly.Python.ORDER_LOGICAL_NOT]; }; Blockly.Python.text_isEmpty2 = function (a) { return ["not len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''") + ")x", Blockly.Python.ORDER_LOGICAL_NOT]; }; Blockly.Python.text_indexOf = function (a) { var b = "FIRST" == a.getFieldValue("END") ? "find" : "rfind", c = Blockly.Python.valueToCode(a, "FIND", Blockly.Python.ORDER_NONE) || "''"; b = (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_MEMBER) || "''") + "." + b + "(" + c + ")"; return a.workspace.options.oneBasedIndex ? [b + " + 1", Blockly.Python.ORDER_ADDITIVE] : [b, Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.text_charAt = function (a) { var b = a.getFieldValue("WHERE") || "FROM_START", c = Blockly.Python.valueToCode(a, "VALUE", "RANDOM" == b ? Blockly.Python.ORDER_NONE : Blockly.Python.ORDER_MEMBER) || "''"; switch (b) { case "FIRST": return [c + "[0]", Blockly.Python.ORDER_MEMBER]; case "LAST": return [c + "[-1]", Blockly.Python.ORDER_MEMBER]; case "FROM_START": return (a = Blockly.Python.getAdjustedInt(a, "AT")), [c + "[" + a + "]", Blockly.Python.ORDER_MEMBER]; case "FROM_END": return (a = Blockly.Python.getAdjustedInt(a, "AT", 1, !0)), [c + "[" + a + "]", Blockly.Python.ORDER_MEMBER]; case "RANDOM": return ( (Blockly.Python.definitions_.import_random = "import random"), [ Blockly.Python.provideFunction_("text_random_letter", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(text):", " x = int(random.random() * len(text))", " return text[x];"]) + "(" + c + ")", Blockly.Python.ORDER_FUNCTION_CALL, ] ); } throw Error("Unhandled option (text_charAt)."); }; Blockly.Python.text_getSubstring = function (a) { var b = a.getFieldValue("WHERE1"), c = a.getFieldValue("WHERE2"), d = Blockly.Python.valueToCode(a, "STRING", Blockly.Python.ORDER_MEMBER) || "''"; switch (b) { case "FROM_START": b = Blockly.Python.getAdjustedInt(a, "AT1"); "0" == b && (b = ""); break; case "FROM_END": b = Blockly.Python.getAdjustedInt(a, "AT1", 1, !0); break; case "FIRST": b = ""; break; default: throw Error("Unhandled option (text_getSubstring)"); } switch (c) { case "FROM_START": a = Blockly.Python.getAdjustedInt(a, "AT2", 1); break; case "FROM_END": a = Blockly.Python.getAdjustedInt(a, "AT2", 0, !0); Blockly.isNumber(String(a)) ? "0" == a && (a = "") : ((Blockly.Python.definitions_.import_sys = "import sys"), (a += " or sys.maxsize")); break; case "LAST": a = ""; break; default: throw Error("Unhandled option (text_getSubstring)"); } return [d + "[" + b + " : " + a + "]", Blockly.Python.ORDER_MEMBER]; }; Blockly.Python.text_changeCase = function (a) { var b = { UPPERCASE: ".upper()", LOWERCASE: ".lower()", TITLECASE: ".title()" }[a.getFieldValue("CASE")]; return [(Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''") + b, Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.text_trim = function (a) { var b = { LEFT: ".lstrip()", RIGHT: ".rstrip()", BOTH: ".strip()" }[a.getFieldValue("MODE")]; return [(Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''") + b, Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.text_print = function (a) { return "print(" + (Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_NONE) || "''") + ")\n"; }; Blockly.Python.text_prompt_ext = function (a) { var b = Blockly.Python.provideFunction_("text_prompt", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(msg):", " try:", " return raw_input(msg)", " except NameError:", " return input(msg)"]), c = a.getField("TEXT") ? Blockly.Python.quote_(a.getFieldValue("TEXT")) : Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_NONE) || "''"; b = b + "(" + c + ")"; "NUMBER" == a.getFieldValue("TYPE") && (b = "float(" + b + ")"); return [b, Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.text_prompt = Blockly.Python.text_prompt_ext; Blockly.Python.text_count = function (a) { var b = Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''"; a = Blockly.Python.valueToCode(a, "SUB", Blockly.Python.ORDER_NONE) || "''"; return [b + ".count(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL]; }; Blockly.Python.text_replace = function (a) { var b = Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''", c = Blockly.Python.valueToCode(a, "FROM", Blockly.Python.ORDER_NONE) || "''"; a = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "''"; return [b + ".replace(" + c + ", " + a + ")", Blockly.Python.ORDER_MEMBER]; }; Blockly.Python.text_reverse = function (a) { return [(Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''") + "[::-1]", Blockly.Python.ORDER_MEMBER]; }; Blockly.Python.variables = {}; Blockly.Python.variables_get = function (a) { return [Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME), Blockly.Python.ORDER_ATOMIC]; }; Blockly.Python.variables_set = function (a) { var b = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "0"; if (a.getInputTargetBlock() != null){ if (a.getInputTargetBlock("VALUE").outputConnection.getCheck() == "DataFrame") { VarData[Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME)] = b; } } return Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME) + " = " + b + "\n"; }; Blockly.Python.variablesDynamic = {}; Blockly.Python.variables_get_dynamic = Blockly.Python.variables_get; Blockly.Python.variables_set_dynamic = Blockly.Python.variables_set; return Blockly.Python; });