{"action":"created","comment":{"url":"https://api.github.com/repos/fedspendingtransparency/data-act-validator/pulls/comments/53366274","id":53366274,"diff_hunk":"@@ -0,0 +1,267 @@\n+import re\n+from dataactvalidator.validation_handlers.validationError import ValidationError\n+from dataactvalidator.interfaces.interfaceHolder import InterfaceHolder\n+from dataactcore.models.validationModels import TASLookup\n+from dataactcore.utils.responseException import ResponseException\n+from dataactcore.utils.statusCode import StatusCode\n+\n+class Validator(object):\n+    \"\"\"\n+    Checks individual records against specified validation tests\n+    \"\"\"\n+    IS_INTERGER  = re.compile(r\"^[-]?\\d*$\")\n+    IS_DECIMAL  = re.compile(r\"^[-]?((\\d+(\\.\\d*)?)|(\\.\\d+))$\")\n+    FIELD_LENGTH = {\"allocationtransferrecipientagencyid\":3, \"appropriationaccountresponsibleagencyid\":3, \"obligationavailabilityperiodstartfiscalyear\":4, \"obligationavailabilityperiodendfiscalyear\":4,\"appropriationmainaccountcode\":4, \"appropriationsubaccountcode\":3, \"obligationunlimitedavailabilityperiodindicator\":1}\n+\n+    @staticmethod\n+    def validate(record,rules,csvSchema,fileType):\n+        \"\"\"\n+        Args:\n+        record -- dict represenation of a single record of data\n+        rules -- list of rule Objects\n+        csvSchema -- dict of schema for the current file.\n+        Returns:\n+        True if validation passed, False if failed, and list of failed rules, each with field, description of failure, and value that failed\n+        \"\"\"\n+\n+        recordFailed = False\n+        failedRules = []\n+        for fieldName in csvSchema :\n+            if(csvSchema[fieldName].required and  not fieldName in record ):\n+                return False, [[fieldName, ValidationError.requiredError, \"\"]]\n+\n+        for fieldName in record :\n+\n+            currentSchema =  csvSchema[fieldName]\n+            ruleSubset = Validator.getRules(fieldName,rules)\n+            currentData = record[fieldName].strip()\n+\n+\n+            if(len(currentData) == 0):\n+                if(currentSchema.required ):\n+                    # If empty and required return field name and error\n+                    return False, [[fieldName, ValidationError.requiredError, \"\"]]\n+                else:\n+                    #if field is empty and not required its valid\n+                    continue\n+            # Always check the type in the schema\n+            if(not Validator.checkType(currentData,currentSchema.field_type.name) ) :\n+                recordFailed = True\n+                failedRules.append([fieldName, ValidationError.typeError, currentData])\n+                # Don't check value rules if type failed\n+                continue\n+\n+            # Check for a type rule in the rule table, don't want to do value checks if type is not correct\n+            typeFailed = False\n+            for currentRule in ruleSubset:\n+                if(currentRule.rule_type.name == \"TYPE\"):\n+                    if(not Validator.checkType(currentData,currentRule.rule_text_1) ) :\n+                        recordFailed = True\n+                        typeFailed = True\n+                        failedRules.append([fieldName, ValidationError.typeError, currentData])\n+            if(typeFailed):\n+                # If type failed, don't do value checks\n+                continue\n+            #Field must pass all rules\n+            for currentRule in ruleSubset :\n+                if(not Validator.evaluateRule(currentData,currentRule,currentSchema.field_type.name)):\n+                    recordFailed = True\n+                    failedRules.append([fieldName, \"Failed rule: \" + str(currentRule.description), currentData])\n+        # Check all multi field rules for this file type\n+        validationDb = InterfaceHolder.VALIDATION\n+        multiFieldRules = validationDb.getMultiFieldRulesByFile(fileType)\n+        for rule in multiFieldRules:\n+            if not Validator.evaluateMultiFieldRule(rule,record):\n+                recordFailed = True\n+                failedRules.append([\"MultiField\", \"Failed rule: \" + str(rule.description), Validator.getMultiValues(rule,record)])\n+\n+\n+        return (not recordFailed), failedRules\n+\n+    @staticmethod\n+    def getRules(fieldName,rules) :\n+        \"\"\" From a given set of rules, create a list of only the rules that apply to specified field\n+\n+        Args:\n+            fieldName: Field to find rules for\n+            rules: Original set of rules\n+\n+        Returns:\n+            List of rules that apply to specified field\n+        \"\"\"\n+        returnList =[]\n+        for rule in rules :\n+            if( rule.file_column.name == fieldName) :\n+                returnList.append(rule)\n+        return returnList\n+\n+    @staticmethod\n+    def checkType(data,datatype) :\n+        \"\"\" Determine whether data is of the correct type\n+\n+        Args:\n+            data: Data to be checked\n+            datatype: Type to check against\n+\n+        Returns:\n+            True if data is of specified type, False otherwise\n+        \"\"\"\n+        if(datatype == \"STRING\") :\n+            return(len(data) > 0)\n+        if(datatype == \"BOOLEAN\") :\n+            if(data.upper() in [\"TRUE\",\"FALSE\",\"YES\",\"NO\",\"1\",\"0\"]) :\n+                return True\n+            return False\n+        if(datatype == \"INT\") :\n+            return Validator.IS_INTERGER.match(data) is not None\n+        if(datatype == \"DECIMAL\") :\n+            if (Validator.IS_DECIMAL.match(data) is None ) :\n+                return Validator.IS_INTERGER.match(data) is not None\n+            return True\n+        raise ValueError(\"Data Type Error, Type: \" + datatype + \", Value: \" + data)\n+\n+    @staticmethod\n+    def getIntFromString(data) :\n+        \"\"\" Convert string to int, converts to float first to avoid exceptions when data is represented as float \"\"\"\n+        return int(float(data))\n+\n+    @staticmethod\n+    def getType(data,datatype) :\n+        \"\"\" Convert data into specified type\n+\n+        Args:\n+            data: Data to be converted\n+            datatype: Type to conert into\n+\n+        Returns:\n+            Data in specified type\n+        \"\"\"\n+        if(datatype ==\"INT\") :\n+            return int(float(data))\n+        if(datatype ==\"DECIMAL\") :\n+            return float(data)\n+        if(datatype == \"STRING\" or datatype ==\"BOOLEAN\") :\n+            return data\n+        raise ValueError(\"Data Type Invalid\")\n+\n+    @staticmethod\n+    def evaluateRule(data,rule,datatype):\n+        \"\"\" Checks data against specified rule\n+\n+        Args:\n+            data: Data to be checked\n+            rule: Rule object to test against\n+            datatype: Type to convert data into\n+\n+        Returns:\n+            True if rule passed, False otherwise\n+        \"\"\"\n+        value1 = rule.rule_text_1\n+        currentRuleType = rule.rule_type.name\n+        if(currentRuleType ==\"LENGTH\") :\n+            return len(data.strip()) <= Validator.getIntFromString(value1)\n+        elif(currentRuleType ==\"LESS\") :\n+            return Validator.getType(data,datatype) < Validator.getType(value1,datatype)\n+        elif(currentRuleType ==\"GREATER\") :\n+            return Validator.getType(data,datatype) > Validator.getType(value1,datatype)\n+        elif(currentRuleType ==\"EQUAL\") :\n+            return Validator.getType(data,datatype) == Validator.getType(value1,datatype)\n+        elif(currentRuleType ==\"NOT EQUAL\") :\n+            return not (Validator.getType(data,datatype) == Validator.getType(value1,datatype))\n+        elif(currentRuleType == \"TYPE\"):\n+            # Type checks happen earlier, but type rule is still included in rule set, so skip it\n+            return True\n+        elif(currentRuleType == \"IN_SET\"):\n+            setList = value1.split(\",\")\n+            for i in range(0,len(setList)):\n+                setList[i] = setList[i].strip()","path":"dataactvalidator/validation_handlers/validator.py","position":185,"original_position":177,"commit_id":"004af04a2ec9deee9caaeac2ad93d9a0686133dd","original_commit_id":"e213273d2f478dceea09a3c17519fed9bd5e861b","user":{"login":"kaitlin","id":75555,"avatar_url":"https://avatars.githubusercontent.com/u/75555?v=3","gravatar_id":"","url":"https://api.github.com/users/kaitlin","html_url":"https://github.com/kaitlin","followers_url":"https://api.github.com/users/kaitlin/followers","following_url":"https://api.github.com/users/kaitlin/following{/other_user}","gists_url":"https://api.github.com/users/kaitlin/gists{/gist_id}","starred_url":"https://api.github.com/users/kaitlin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kaitlin/subscriptions","organizations_url":"https://api.github.com/users/kaitlin/orgs","repos_url":"https://api.github.com/users/kaitlin/repos","events_url":"https://api.github.com/users/kaitlin/events{/privacy}","received_events_url":"https://api.github.com/users/kaitlin/received_events","type":"User","site_admin":false},"body":"you can use `cleanSplit` from line 204 here instead of replicating the 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5 all stories complete 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