def validate_data(timeSeriesData, errors):
	dataLines = timeSeriesData.getRawDataLines()
	lineCount = 0
	for line in dataLines:
		# The header needs to be Abs
		if lineCount is 0:
			if line[0] != "Strain":
				errors.append(createFileValidationError("The first data column must be 'Strain'"))
				break
			lineCount = lineCount + 1
			continue

		# The compound id should be one of these forms
		strain = line[0]
		if not isStrainIdValid(strain):
			errors.append(createFileValidationError("Line " + str(lineCount + 1) + ", column 1 must be MGP[0-999] (instead of " + strain + ")."))
		lineCount = lineCount + 1

def validate_metadata(time_series_data, errors):
	metadata = time_series_data.getMetadataMap()
	validationHelper = ValidationHelper(metadata, errors)
	
	# validate the header format
	validationHelper.validateDefaultHeaderFormat()
	
	# validate the timepoint type
	validationHelper.validateControlledVocabularyProperty("TIMEPOINT TYPE", 
		"time point type", ['EX', 'IN', 'SI'], "'EX', 'IN', 'SI'")

	# validate the cell location		
	validationHelper.validateControlledVocabularyProperty("CELL LOCATION",
		 "cell location", ['CE', 'ES', 'ME', 'CY', 'NC'], "'CE', 'ES', 'ME', 'CY', 'NC'")

	# validate the value type		
	validationHelper.validateControlledVocabularyProperty("VALUE TYPE",
		 "value type", ['VALUE', 'MEAN', 'MEDIAN', 'STD', 'VAR', 'ERROR', 'IQR'], 
		"'Value', 'Mean', 'Median', 'Std', 'Var', 'Error', 'Iqr'")

	# validate the value unit
	validationHelper.validateControlledVocabularyProperty("VALUE UNIT", 
		"value unit", ['DIMENSIONLESS'], "'DIMENSIONLESS'")
	
	# validate the scale
	validationHelper.validateControlledVocabularyProperty("SCALE", "scale",
		['LIN', 'LOG2', 'LOG10', 'LN'], "'lin', 'log2', 'log10', 'ln'")

def validate_data_set_file(file):
	errors = []
	time_series_data = create_time_series_excel(file.getAbsolutePath())
	if time_series_data is None:
		errors.append(createFileValidationError(file.getName() + " is not an Excel file."))
		return errors
		
	# validate the metadata
	validate_metadata(time_series_data, errors)
			
	# validate the data
	validate_data(time_series_data, errors)
	
	return errors