diff --git a/README.rst b/README.rst index 20deb18..1151db8 100644 --- a/README.rst +++ b/README.rst @@ -12,6 +12,10 @@ Robust Chauvenet Outlier Rejection (RCR) :target: https://arxiv.org/abs/1807.05276 :alt: arXiv Paper +.. image:: https://zenodo.org/badge/246971427.svg + :target: https://zenodo.org/badge/latestdoi/246971427 + :alt: Zenodo (DOI) + What is RCR? ============ RCR is advanced, but easy to use, outlier rejection. diff --git a/docs/source/index.rst b/docs/source/index.rst index 3730db5..b631bef 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -8,10 +8,6 @@ rcr === -.. image:: https://img.shields.io/badge/arXiv-1807.05276-orange.svg?style=flat - :target: https://arxiv.org/abs/1807.05276 - :alt: arXiv Paper - .. image:: https://img.shields.io/badge/GitHub-nickk124%2Frcr-blue.svg?style=flat :target: https://github.com/nickk124/RCR :alt: Github Repository @@ -23,6 +19,14 @@ rcr .. image:: https://travis-ci.com/nickk124/RCR.svg?branch=master :target: https://travis-ci.com/nickk124/RCR :alt: Build Status + +.. image:: https://img.shields.io/badge/arXiv-1807.05276-orange.svg?style=flat + :target: https://arxiv.org/abs/1807.05276 + :alt: arXiv Paper + +.. image:: https://zenodo.org/badge/246971427.svg + :target: https://zenodo.org/badge/latestdoi/246971427 + :alt: Zenodo (DOI) **RCR**, or Robust Chauvenet Rejection, is advanced, and easy to use, outlier rejection. Originally published in `Maples et al. 2018 `_, this site will diff --git a/webcalculator/page_functional/bokeh/RCRplot_functional.html b/webcalculator/page_functional/bokeh/RCRplot_functional.html new file mode 100644 index 0000000..8932334 --- /dev/null +++ b/webcalculator/page_functional/bokeh/RCRplot_functional.html @@ -0,0 +1,85 @@ + + + + + + + + + + + Bokeh Plot + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + \ No newline at end of file diff --git a/webcalculator/page_functional/bokeh/RCRplotgenerator_functional.py b/webcalculator/page_functional/bokeh/RCRplotgenerator_functional.py index 7d10cde..446780c 100755 --- a/webcalculator/page_functional/bokeh/RCRplotgenerator_functional.py +++ b/webcalculator/page_functional/bokeh/RCRplotgenerator_functional.py @@ -17,7 +17,7 @@ plot = Figure(plot_width=600, plot_height=600) #plotting code -plot.circle('x_nonrejected', 'y_nonrejected', source=sourcenon, size=5, color="navy", alpha=0.5, legend="nonrejected data of size")# nonrejected +plot.circle('x_nonrejected', 'y_nonrejected', source=sourcenon, size=5, color="navy", alpha=0.5, legend="nonrejected data")# nonrejected plot.circle('x_rejected', 'y_rejected', source=sourcerej, size=5, color="red", alpha=0.5, legend="rejected data")# rejected plot.line('x_original', 'y_fitted', source=sourceall, color="green", line_width=2, legend="fitted model") diff --git a/webcalculator/page_functional/functional.html b/webcalculator/page_functional/functional.html index 9bc3b60..a4470cd 100644 --- a/webcalculator/page_functional/functional.html +++ b/webcalculator/page_functional/functional.html @@ -59,7 +59,13 @@

Select Data Type:



- Format: One data point per line: List the independent (x) values followed by the dependent (y) value, separated by white space. (In the case of multiple x values per data point, download and use the source code directly). + Format: One data point per line: + List the independent (x) values followed by the dependent (y) value, + separated by white space. (In the case of multiple x values per + data point, + install + and use the RCR Python (or C++) package directly---documentation included. +

Weights, if unequal, should be included on the same line, separated by white space. @@ -153,13 +159,16 @@

Model y(x):



- Here, select a function to model y(x), log y(x) or 10 y, depending on your chosen basis for y(x) in the above section. Depending on the chosen function, either x or log x will be displayed from the inputted data, depending on which model function is selected. + Here, select a function to model y(x), log y(x) or 10 y, depending on your chosen basis for y(x) in the above section. Depending on the chosen function, the formula for the pivot point---either x or log x---will be displayed from the inputted data, depending on which model function is selected (See here for more info; this is to automatically minimize the correlation between certain, linearizable parameters of the model).

Next, in the box to the right, enter your guess for the model function parameters a0 , a1 , a2 , etc, one per line. For example, in the linear model case, a0 corresponds to the y-intercept, and a1 to the slope.

- For large datasets and/or for non-listed custom functions,
it is recommended to just download the C++ source code (documentation included) here. + For large datasets and/or for non-listed custom + functions,
it is recommended to just install + and use the RCR Python (or C++) package directly---documentation included. +

@@ -198,7 +207,8 @@

Model Bayesian Prior Distributions of Model Function Parameters (Optional):< In total, each line will have four elements (either numbers or "x"s if no prior provided) seperated by white space. For example, a prior with lower bound of 0, no upper bound, and a Gaussian with μ = 1, σ = 2 would have a line of "0 x 1 2".

- For custom priors, use the source code (with documentation included). + For custom priors, install + and use the RCR Python (or C++) package directly---documentation included.
@@ -341,14 +351,14 @@

Note: results ar

-
+