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توضیحات

Consistency test

ویژگی	مقدار
سیستم عامل	-
نام فایل	ConsistencyTEST-0.0.1
نام	ConsistencyTEST
نسخه کتابخانه	0.0.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Fiorenzo Stoppa
ایمیل نویسنده	f.stoppa@astro.ru.nl
آدرس صفحه اصلی	https://github.com/FiorenST/ConTEST
آدرس اینترنتی	https://pypi.org/project/ConsistencyTEST/
مجوز	-

<img src=https://see.fontimg.com/api/renderfont4/KpAp/eyJyIjoiZnMiLCJoIjoxMzAsInciOjEwMDAsImZzIjoxMzAsImZnYyI6IiNGRDhDMDMiLCJiZ2MiOiIjMDYwNTA1IiwidCI6MX0/Q29uVEVTVA/kg-second-chances-sketch.png width=50% height=50%>  # Description ConTEST is a statistical test for assessing the consistency between observations and astrophysical models. It uses a combination of non-parametric methods and distance measures to obtain a test statistic that evaluates the closeness of the astrophysical model to the observations; hypothesis testing is then performed using bootstrap. <img src=https://github.com/FiorenSt/ConTEST/blob/main/img/logo_contest_bkg.png width=15% height=15%> ## Table of Contents - [Step-by-step setup](#step-by-step-setup) - [Tutorial](#tutorial) # Step-by-step setup _Follow the instructions below to install and start using ConTEST in Python._ 1. Install ConTEST: ```sh pip intall ConTEST ``` or git clone the repository: ```sh git clone https://github.com/FiorenSt/ConTEST.git ``` 2. Install the statistical software [R](https://www.r-project.org/). R is needed to run some internal functions of ConTEST. 3. To ensure that Python can access R's libraries, run the three lines below in Python (of course, modify to match your folders): ```sh import os os.environ['R_HOME'] = '~/Program Files/R/R-4.0.2' #-> Your installed R folder os.environ['R_USER'] = '~/Miniconda3/envs/ConsistencyTest/lib/site-packages/' #-> Your python environment os.environ['R_LIBS_USER'] = "~/Program Files/R/R-4.0.2/library/" #-> Your R packages library ``` 4. Install Python dependencies (rpy2 needs R already installed): ```sh pip intall matplotlib pip intall numpy pip intall pandas pip intall scipy pip intall seaborn pip intall rpy2 ``` 5. If this is the first time you use ConTEST, you need to install the R package used in Smoothed ConTEST. In Python, simply run: ```sh def install_R_functions(packnames=('np')): # import R's utility package utils = rpackages.importr('utils') # select a mirror for R packages utils.chooseCRANmirror(ind=1) # select the first mirror in the list # R package install utils.install_packages(packnames) install_R_functions() ``` 3. Use ConTEST in Python! Follow the tutorial below for more information about the individual functions. <img src="https://github.com/FiorenSt/ConTEST/blob/main/img/MemeConTEST.png " width=80% height=80%> # Dependencies: The following combination of package versions works on most Linux and Windows computers, however other package versions may also work. If a problem with the combination of packages occurs, raise an issue, and we will help you solve it. ### Python 3 (or superior) * Numpy 1.21.6 * Pandas 1.4.2 * Scipy 1.7.1 * Matplotlib 3.3.4 * Seaborn 0.11.2 * Rpy2 3.5.2 (For R and Python interaction) ### R 3.6.0 (or superior) * Np 0.60 # Tutorial ConTEST can be applied in different case scenarios depending on the nature of the model being tested. For more details check out the paper: _Stoppa et al., in preparation_ There are 4 fundamental functions in ConTEST: - ConTEST for regression: Test the consistency of a model with respect to an observed dataset and their uncertainties - Smoothed ConTEST for regression: Test the consistency of a model with respect to an observed dataset and their uncertainties - ConTEST for outliers: Test if an observed sample is likely to come from a density model (or a simulated dataset) - ConTEST for densities: Test the consistency of a density model (or a simulated dataset) with respect to an observed dataset ### Intro script ```sh # ensure that Python can access R import os os.environ['R_HOME'] = '~/Program Files/R/R-4.0.2' #-> Your installed R folder os.environ['R_USER'] = '~/Miniconda3/envs/ConsistencyTest/lib/site-packages/' #-> Your python environment os.environ['R_LIBS_USER'] = "~/Program Files/R/R-4.0.2/library/" #-> Your R packages library # load contest functions from ConTEST.CONTEST import contest_reg, smoothed_contest_reg, contest_outliers, contest_dens ``` ## Regression models Create synthetic model, observations, and uncertainties to test the functions: ```sh # random sample n=100 x = np.random.rand(n) # synthetic model beta1 = -0.3 beta2 = 8 m = 2 model = np.exp(beta1*x)*np.sin(beta2*x) + m # error function (Not known in real scenarios) err_model = model * .05 # sample observations from the model with the correct uncertainties obs = np.zeros(N) for i in range(N): obs[i] = model[i] + stats.multivariate_normal.rvs(mean=0, cov=(err_model[i])**2,size=1) # assign correct uncertainties to the observations err_obs = err_model ``` ### ConTEST for regression ```sh Test1 = contest_reg(y_obs = obs, x_obs = x, y_mod = model, y_obs_err = err_obs, K=1000,plot=True) ``` <img src="https://github.com/FiorenSt/ConTEST/blob/main/img/ConTESTforRegression.png " width=80% height=80%> ### Smoothed ConTEST for regression ```sh Test2 = smoothed_contest_reg(y_obs = obs, x_obs = x, y_mod = model, y_obs_err = err_obs, K=1000,plot=True) ``` <img src="https://github.com/FiorenSt/ConTEST/blob/main/img/SmoothedConTESTforRegression.png " width=80% height=80%> ## Density models Create synthetic model and observations: ```sh n=100 #1D example obs = stats.multivariate_normal.rvs(mean=5, cov= [1.5],size=n) model = stats.multivariate_normal.rvs(mean=5, cov= [1.5],size=1000) #2D example obs_2d = stats.multivariate_normal.rvs(mean=[5,5], cov= [[1.5,.8],[.8,2.5]],size=n) model_2d = stats.multivariate_normal.rvs(mean=[5,5], cov= [[1.5,.8],[.8,2.5]],size=1000) ### ConTEST for outliers ```sh Test3 = contest_outliers(mod=model, obs=obs, K=10000, plot=True) Test3 = contest_outliers(mod=model_2d, obs=obs_2d, K=10000, plot=True) ``` <img src="https://github.com/FiorenSt/ConTEST/blob/main/img/ConTESTforOutliers1D.png " width=80% height=80%> <img src="https://github.com/FiorenSt/ConTEST/blob/main/img/ConTESTforOutliers2D.png " width=80% height=80%> ### ConTEST for densities ```sh Test4 = contest_dens(mod=model, obs=obs, K=10000, plot=True) Test4 = contest_dens(mod=model_2d, obs=obs_2d, K=10000, plot=True) ``` <img src="https://github.com/FiorenSt/ConTEST/blob/main/img/ConTESTforDensities1D.png " width=80% height=80%> <img src="https://github.com/FiorenSt/ConTEST/blob/main/img/ConTESTforDensities2D.png " width=80% height=80%>

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl ConsistencyTEST-0.0.1:

pip install ConsistencyTEST-0.0.1.whl

نصب پکیج tar.gz ConsistencyTEST-0.0.1:

pip install ConsistencyTEST-0.0.1.tar.gz