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Microlensing is a unique tool, capable of detecting the 'cold' planetsbetween 1-10 AU from their host stars, and even unbound 'free-floating'planets. This regime has been poorly sampled to date owing to the limitationsof alternative planet-finding methods, but a watershed in discoveries isanticipated in the near future thanks to the planned microlensing surveys ofWFIRST-AFTA and Euclid s Extended Mission. Of the many challenges inherent inthese missions, the modeling of microlensing events will be of primaryimportance, yet is often time consuming, complex and perceived as a dauntingbarrier to participation in the field. The large scale of future survey dataproducts will require thorough but efficient modeling software, but unlikeother areas of exoplanet research, microlensing currently lacks apublicly-available, well-documented package to conduct this type of analysis.We present first version 1.0 of pyLIMA: Python Lightcurve Identification andMicrolensing Analysis. This software is written in Python and uses existingpackages as much as possible, to make it widely accessible. In this paper, wedescribe the overall architecture of the software and the core modules formodeling single-lens events. To verify the performance of this software, we useit to model both real datasets from events published in the literature andgenerated test data, produced using pyLIMA s simulation module. Resultsdemonstrate that pyLIMA is an efficient tool for microlensing modeling. We willexpand pyLIMA to consider more complex phenomena in the following papers.

pyLIMA : an open source package for microlensing modeling. I. presentation of the software and analysis on single lens models