Current laboratory methods used to assess neonatal and adult cardiocyte function include measurement of gene and protein expression levels, calcium transients, and contractility. Our goal was to develop simple tools to analyze such data readily. We created two MATLAB®-based toolboxes; the Contraction Video Processing (CVP) and the Cardiocyte Functional Response Analysis (CFRA) Toolbox. Videos of contracting cultured cardiocytes are acquired using a digital camera attached to an inverted phase microscope. Video frames are analyzed using digital imaging processing techniques along with several contraction assessment methods available through the CVP toolbox. The CVP offers direct correlation, pixel intensity tracking and Polar Fourier transform methods for the analysis of neonatal cardiocyte contraction. Analysis of adult cardiocytes includes those implemented on neonatal cardiocytes in addition to area boundary tracking, Fourier descriptor analysis, and cell length tracking methods. The resulting contraction records are processed using the CFRA toolbox to provide quantitative analyses of cardiocyte contractility and calcium transient responses. Transient data are obtained by measuring the calcium fluxes using the fluorescent dye Fluo-3, and a Photon Technology fluorometer system running Felix software. Data analysis routines have been created and tailored exclusively to the characteristics and needs of cellular cardiovascular research investigators. The analytical methods created are used to find the onset of contraction, perform signal averaging, and acquire statistical information of functional data. CFRA toolbox contractility processing yields onset time, time-to-peak, duration, and fast and slow recovery times. CFRA toolbox calcium transient signal processing yields onset time, signal intensity, and fast and slow exponential recovery rates associated with SERCA and NCX channels respectively. The toolboxes allow examination of beat-to-beat contractility and calcium transient variations within the same cardiocyte as well as from cell population to population. Supported by NIH/NIGMS SDSU MARC Program 5T34GM008303-22