摘要：Microlensing is a unique tool, capable of detecting the "cold" planets between ~1 and 10 au from their host stars and even unbound "free-floating" planets. This regime has been poorly sampled to date owing to the limitations of alternative planet-finding methods, but a watershed in discoveries is anticipated in the near future thanks to the planned microlensing surveys of WFIRST-AFTA and Euclid's Extended Mission. Of the many challenges inherent in these missions, the modeling of microlensing events will be of primary importance, yet it is often time-consuming, complex, and perceived as a daunting barrier to participation in the field. The large scale of future survey data products will require thorough but efficient modeling software, but, unlike other areas of exoplanet research, microlensing currently lacks a publicly available, well-documented package to conduct this type of analysis. We present version 1.0 of the python Lightcurve Identification and Microlensing Analysis (pyLIMA). This software is written in Python and uses existing packages as much as possible to make it widely accessible. In this paper, we describe the overall architecture of the software and the core modules for modeling single-lens events. To verify the performance of this software, we use it to model both real data sets from events published in the literature and generated test data produced using pyLIMA's simulation module. The results demonstrate that pyLIMA is an efficient tool for microlensing modeling. We will expand pyLIMA to consider more complex phenomena in the following papers.
