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With the ever growing popularity of integral field unit (IFU) spectroscopy,countless observations are being performed over multiple object systems such asblank fields and galaxy clusters. With this, an increasing amount of time isbeing spent extracting one dimensional object spectra from large threedimensional datacubes. However, a great deal of information available withinthese datacubes is overlooked in favor of photometrically based spatialinformation. Here we present a novel, yet simple approach of optimal sourceidentification, utilizing the wealth of information available within an IFUdatacube, rather than relying on ancillary imaging. Through the application ofthese techniques, we show that we are able to obtain object spectra comparableto deep photometry weighted extractions without the need for ancillary imaging.Further, implementing our custom designed algorithms can improve thesignal-to-noise of extracted spectra and successfully deblend sources fromnearby contaminants. This will be a critical tool for future IFU observationsof blank and deep fields, especially over large areas where automation isnecessary. We implement these techniques into the Python based spectralextraction software, AutoSpec which is available via GitHub at:https://github.com/a-griffiths/AutoSpec and Zenodo at:https://doi.org/10.5281/zenodo.1305848

AutoSpec: Fast Automated Spectral Extraction Software for IFU Datacubes