语言: Python


猎人 - 可居住的区域UNcerTainty EstimatoR
HUNTER - Habitable zone UNcerTainty EstimatoR (中文)


猎人 - 可居住的区域UNcerTainty EstimatoR

该代码执行MC采样以估计岩石行星的哪一部分可能在其表面上存在液态水。 HUNTER在2014年的Zsom中有详细描述(接受ApJ出版)。该代码使用来自Zsom等人,2013年描述的一维气候代码的预先计算的全对流T-P剖面。目前HUNTER附有用于H2,N2和CO2主导气氛的表格。

代码是用python 2.7,numpy 1.8.1,scipy 0.12.0,matplotlib 1.3.1和pickle开发的。 HUNTER.py是代码的主要文件。目前,它被设置为运行一个模拟,生成10 ^ 5个场景。结果以人类可读的形式保存。 plot_data.py包含一些可帮助您可视化数据的例程。根据需要修改此文件。 - 运行模拟的主文件 - 包含HUNTER.py调用的所有子例程。子程序'occurrence _rate'对Dressing&Charbonneau 2013的数据集执行高斯核密度估计。如果要在不同的数据集上使用代码,请包含类似的子程序。该文件也包含所有PDF。如果您想体验新的PDF表单,请在此处添加。代码的核心是MC_sample。代码流程如图1中的Zsom,2014所示。 - 包含一些常量和材料属性(并非HUNTER使用的所有内容) - 包含根据我的知识在numpy或scipy中不易获得的数值方法

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HUNTER - Habitable zone UNcerTainty EstimatoR

This code performs an MC sampling to estimate what fraction of rocky planets could harbor liquid water on their surfaces. HUNTER is described in detail in Zsom, 2014 (accepted for publication in ApJ). The code uses precalculated all-convective T-P profiles from a 1D climate code described in Zsom et al., 2013. Currently HUNTER comes with tables for H2, N2, and CO2 dominated atmospheres.

The code was developed with python 2.7, numpy 1.8.1, scipy 0.12.0 ,matplotlib 1.3.1, and pickle. is the main file of the code. Currently it is set up to run one simulation that generates 10^5 scenarios. The outcome is saved in human-readable form. contains some routines that will help you to visualize the data. Modify this file to your needs. - main file to run the simulation - contains all subroutines called by The subroutine 'occurrence_rate' performs the gaussian kernel density estimation on the data set of Dressing&Charbonneau 2013. If you want to use the code on a different data set, include a similar subroutine. This file contains all PDFs as well. If you want to experiment with new PDF forms, add it here. The heart of the code is MC_sample. The code flow is illustrated in Fig. 1 of Zsom, 2014. - contains some constants and material properties (not everything here is used by HUNTER) - contains numerical methods that are not readily available in numpy or scipy to my knowledge