change folder

R/DESCRIPTION → R/CosmoPhotoz/DESCRIPTION

@@ -6,7 +6,7 @@ Date: 2014-08-20
 Author: Rafael S. de Souza, Alberto Krone-Martins, Jonathan Elliott, Joseph
     Hilbe
 Maintainer: Rafael S. de Souza <[email protected]>
-Description:  User-friendly interfaces to perform fast and reliable photometric
+Description: User-friendly interfaces to perform fast and reliable photometric
     redshift estimation. The code makes use of generalized linear models and
     can adopt gamma or inverse gaussian families, either from a frequentist or
     a Bayesian perspective. The code additionally provides a Shiny application

R/R/glmPredictPhotoZ.R → R/CosmoPhotoz/R/glmPredictPhotoZ.R

@@ -14,7 +14,7 @@
 #  http://www.r-project.org/Licenses/
 #
 
-#' @title Predict photometric redshifts using a given  glm fit object
+#' @title Predict photometric redshifts using a given glm fit object
 #'
 #' @description \code{glmPredictPhotoZ} computes a list of simple summary 
 #' statistics for the photometric redshift estimation.

R/README.md → R/CosmoPhotoz/README.md

@@ -8,15 +8,7 @@ runtime: shiny
 
 This is a short tutorial explaining how to perform photometric redshift estimation using the CosmoPhotoz R package.
 
-
-
-Install from 
-
-```{r,results='hide',message=FALSE, cache=FALSE}
-install_github("CosmoR", username="RafaelSdeSouza", subdir="CosmoGLM")
-```
-
- Required libraries
+# Required libraries
 ```{r,results='hide',message=FALSE, cache=FALSE}
 require(CosmoPhotoz)
 require(ggplot2)

R/man/CosmoPhotoZestimator.Rd → R/CosmoPhotoz/man/CosmoPhotoZestimator.Rd

@@ -1,7 +1,7 @@
 % Generated by roxygen2 (4.0.1): do not edit by hand
 \name{CosmoPhotoZestimator}
 \alias{CosmoPhotoZestimator}
-\title{Photo-z estimation from a training dataset and a test dataset}
+\title{Photometric redshift  estimation from a training dataset and a test dataset}
 \usage{
 CosmoPhotoZestimator(trainData, testData, numberOfPcs, method, family, robust)
 }
@@ -24,7 +24,7 @@ a vector with the estimated photometric redshifts
 \description{
 \code{CosmoPhotoZestimator} returns photometric redshift estimated from photometric
 data and a training dataset with photometry and spectroscopy. The estimation is
-based on GLMs (see \code{\link{glmTrainPhotoZ}} and \code{\link{glmPredictPhotoZ}}).
+based on generalized linear models (see \code{\link{glmTrainPhotoZ}} and \code{\link{glmPredictPhotoZ}}).
 }
 \examples{
 \dontrun{

R/man/CosmoPhotoz-package.Rd → R/CosmoPhotoz/man/CosmoPhotoz-package.Rd

@@ -6,7 +6,7 @@
 Photometric redshift estimation based on generalized linear models
 }
 \description{
-This package provides an user-friendly interfaces to perform fast and reliable photometric 
+An user-friendly interfaces to perform fast and reliable photometric 
 redshift estimation. The code makes use of generalized linear models and can adopt gamma or 
 inverse gaussian families, either from a frequentist or a Bayesian perspective. The code
 additionally provides a Shiny application providing a simple user interface.

R/man/glmPredictPhotoZ.Rd → R/CosmoPhotoz/man/glmPredictPhotoZ.Rd

@@ -1,7 +1,7 @@
 % Generated by roxygen2 (4.0.1): do not edit by hand
 \name{glmPredictPhotoZ}
 \alias{glmPredictPhotoZ}
-\title{Predict photometric redshifts using a given a glm fit object}
+\title{Predict photometric redshifts using a given glm fit object}
 \usage{
 glmPredictPhotoZ(data, train)
 }

R/CosmoPhotoz_shiny/COIN.md

@@ -0,0 +1,9 @@
+### The Cosmostatistics Initiative: COIN
+
+
+The COsmostatistics INitiative ([COIN](https://asaip.psu.edu/organizations/iaa/iaa-working-grou
+-of-cosmostatistics)), a working group built within the International Astrostatistics Association
+([IAA](https://asaip.psu.edu/organizations/iaa/international-astrostatistics-association-overvie
+)), aims to create a friendly environment where hands-on collaboration between astronomers,
+cosmologists, statisticians and machine learning experts can flourish. COIN is designed to
+promote the development of a new family of tools for data exploration in cosmology. 

R/CosmoPhotoz_shiny/Copyright.md

@@ -0,0 +1,17 @@
+###  R package CosmoPhotoz
+Copyright (C) 2014  COIN
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License version 3 as published by
+the Free Software Foundation.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+A copy of the GNU General Public License is available at
+http://www.r-project.org/Licenses/
+
+##### Authors: 
+Rafael S. de Souza (ELTE), Alberto Krone-Martins (University of Lisbon), Jonathan Elliott (MPE), Ewan Cameron  (Oxford), Emille E. O. Ishida (MPA), Joseph Hilbe (ASU)

R/CosmoPhotoz_shiny/CosmoPhotoZ_shiny.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: knitr
+LaTeX: pdfLaTeX

R/CosmoPhotoz_shiny/README

@@ -0,0 +1,11 @@
+
+ Shiny CosmoPhotoz application
+
+ This is a shiny application to run the photometric redshift estimation code.
+
+ To run the application locally in your own machine, you just need to do:
+
+ library(shiny)
+runApp(paste(find.package("CosmoPhotoz"),"/glmPhotoZ-2/",sep=""))
+
+

R/CosmoPhotoz_shiny/help.md

@@ -0,0 +1,63 @@
+
+Welcome to the CosmoPhotoz - GLM PhotoZ estimation Shiny interface! 
+
+This interface provides a simple control for the R version of the CosmoPhotoz code. Beyond offering the control of the most used functionalities of the code, this interface provides graphical overviews of the results for diagnostics of the method (each tab is a different graphical representation of the analysis result).
+
+#### Data Input
+
+It is possible to select two types of data for analysis: 
+* either the internal PHAT0 dataset (Hildebrandt et al. 2010);
+* or you can upload your own data. 
+
+Note that uploaded data must be a pure text file, with each entry separated by a comma (,) and containing a header. It may contain an arbitrary number of photometric bands (the column names are not important) and one column named `redshift`. This `redshift` column is mandatory for the training data, but it is not mandatory for the data used to estimate the redshift (in case it is not provided, however, no plots will be produced, but you can still download the resulting photometric redshift estimation).
+
+Example of a file with photometric data from SDSS and spectroscopic redshifts:
+
+```{r}
+dered_u,dered_g,dered_r,dered_i,dered_z,redshift
+20.30279,18.00114,16.80232,16.3375,15.99679,0.1606594
+21.42244,20.27178,18.7326,18.13726,17.82471,0.2577918
+22.47656,21.32556,20.38654,19.55709,19.20674,0.6040326
+18.89463,17.87326,17.23221,16.89447,16.6657,0.1681648
+...
+```
+
+#### Controls and options
+
+The user can select if robust PCA should be used or not. Note that the adoption of robust PCA can slow down the code considerably. The user can also select the number of principal components to consider in the GLM analysis.
+
+The number of points used for the creation of the predicted versus observed diagnostic plot (at the Prediction tab) can also be selected by the user for speeding up the plot generation. The default value is 5000; if the user select 0, all the points in the test dataset will be adopted. This value must be setted **before** launching the analysis.
+
+The method adopted for the fitting can be selected among a frequentist approach (using the R function `glm`) and a bayesian approach (using the R function `bayesglm`). The link function to be used in the model can also be selected among the gamma function and the inverse gaussian function.
+
+To run the photometric redshift estimate, the user must click in the "Run estimation" blue button. And after the estimate is ready, the redshifts can be downloaded to the user machine by clicking in the "Download photoZ results" button. 
+
+#### Outputs
+
+Four different types of plots are provided by the system. Note that the prediction versus observed density plot (at the Prediction tab) may take some moments to render or update. The user can choose to use less points, which are randomly selected from the results, to create this plot if in a hurry.
+
+### References
+
+#### Associated paper
+**The Overlooked Potential of Generalized Linear Models in Astronomy-II: Gamma regression and photometric redshifts**
+Elliott, J.; de Souza, R. S.; Krone-Martins, A.; Cameron, E.; Ishida, E. E. O.; Hilbe, J., 
+[arXiv:1409.7699](http://adsabs.harvard.edu/abs/2014arXiv1409.7699E)
+
+
+**CosmoPhotoz: Photometric redshift estimation using generalized linear models**
+##### R package 
+[http://CRAN.R-project.org/package=CosmoPhotoz](http://CRAN.R-project.org/package=CosmoPhotoz)
+Rafael S. de Souza, Alberto Krone-Martins, Jonathan Elliott and Joseph Hilbe (2014).
+
+##### Shiny package 
+[https://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz/](https://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz/)
+Alberto Krone-Martins; Rafael S. de Souza; Jonathan Elliott, Ishida, E. E. O.;  and Joseph Hilbe (2014).
+
+
+##### Python package
+[http://cosmophotoz.readthedocs.org/en/latest/index.html](http://cosmophotoz.readthedocs.org/en/latest/index.html)
+J. Elliott, R. S. de Souza, A. Krone-Martins
+
+
+
+

R/CosmoPhotoz_shiny/server.R

@@ -0,0 +1,136 @@
+#  R package CosmoPhotoz file inst/glmPhotoZ-2/server.R
+#  Copyright (C) 2014  COIN
+#
+#This program is free software: you can redistribute it and/or modify
+#it under the terms of the GNU General Public License version 3 as published by
+#the Free Software Foundation.
+
+#This program is distributed in the hope that it will be useful,
+#but WITHOUT ANY WARRANTY; without even the implied warranty of
+#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#GNU General Public License for more details.
+
+#  A copy of the GNU General Public License is available at
+#  http://www.r-project.org/Licenses/
+#
+
+library(shiny)
+library(CosmoPhotoz)
+library(ggplot2)
+library(ggthemes)
+library(shinyIncubator)
+options(shiny.maxRequestSize=100*1024^2) # This is to change the maximum size of the upload to 30 MB
+
+shinyServer(function(input, output) {
+
+  # Now, we need to create the reactive container
+  shinyCompPhotoZ <- reactive({
+    # First we need to have some data, of course!
+      if(input$dataSourceFlag == FALSE) {
+        # First the file used for training
+        inFile1 <- input$file1
+        if (is.null(inFile1)) {
+          return(NULL)
+        }
+        # Then the file used for estimating
+        inFile2 <- input$file2
+        if (is.null(inFile2)) {
+          return(NULL)
+          #return("\n GLM PhotoZ Estimator :: No file was uploaded for redshift estimation! ")
+        }
+        # Now read the files
+        PHAT0train <- read.table(file=inFile1$datapath, sep=",", header=TRUE)
+        PHAT0test <- read.table(file=inFile2$datapath, sep=",", header=TRUE)
+      } else {
+        # In this case, just lazy load the data from inside the package
+        data(PHAT0train)
+        data(PHAT0test)
+      }
+
+      # Some basic checking to make sure that the user will not enter values that could 
+      # cause problems via the user interface.
+      if(is.na(input$numberOfPcs)) {
+        return(NULL)
+      }
+      if(is.na(input$numberOfPoints)) {
+        return(NULL)
+      }
+      nPc <- input$numberOfPcs
+      if (nPc > (ncol(PHAT0train)-1)) {
+        nPc <- (ncol(PHAT0train)-1)
+      } 
+      if (nPc <= 0) {
+        return(NULL)
+      }
+      if (input$numberOfPoints <= 0) {
+        return(NULL)
+      }
+
+      # Photo-z estimation
+      photoz <- CosmoPhotoZestimator(trainData=PHAT0train, testData=PHAT0test, 
+                                     numberOfPcs=nPc, method=input$method,
+                                     family=input$family, robust=input$useRobustPCA)
+
+      if("redshift" %in% names(PHAT0test)) {
+        specz <- PHAT0test$redshift
+      } else {
+        specz <- NULL
+      }
+
+    # Time to return the data!
+    return(data.frame(photoz, specz))
+  })
+
+  # Create the output text
+  output$diagnostics <- renderPrint({
+    tempObj <- shinyCompPhotoZ()
+    if(!is.null(tempObj)) {
+      computeDiagPhotoZ(tempObj$photoz, tempObj$specz)
+    }
+  })
+
+  # Create basic comparison plots
+  output$errorDistPlot <- renderPlot({
+    tempObj <- shinyCompPhotoZ()
+    if(!is.null(tempObj)) {
+      plotDiagPhotoZ(tempObj$photoz, tempObj$specz, type = "errordist")
+    }
+  })
+  output$predictObs <- renderPlot({
+    tempObj <- shinyCompPhotoZ()
+    if(!is.null(tempObj)) {
+
+      nPointsToUse <- input$numberOfPoints
+
+      if(nPointsToUse > length(tempObj$specz)){
+        nPointsToUse <- length(tempObj$specz)
+      }
+
+      plotDiagPhotoZ(tempObj$photoz, tempObj$specz, type = "predobs", npoints=nPointsToUse)
+    }
+  })
+  output$violins <- renderPlot({
+    tempObj <- shinyCompPhotoZ()
+    if(!is.null(tempObj)) {
+      plotDiagPhotoZ(tempObj$photoz, tempObj$specz, type = "errorviolins")
+    }
+  })
+  output$box <- renderPlot({
+    tempObj <- shinyCompPhotoZ()
+    if(!is.null(tempObj)) {
+      plotDiagPhotoZ(tempObj$photoz, tempObj$specz, type = "box")
+    }
+  })
+
+  # Download the data
+  output$downloadData <- downloadHandler(
+    filename = function() { "glmPhotoZresults.dat" },
+    content = function(file) {
+      tempObj <- shinyCompPhotoZ()
+      if(!is.null(tempObj)) {
+        write.table(tempObj$photoz, file, quote=F, sep=" ", col.names = FALSE, row.names = FALSE)
+      } 
+    }
+  )
+
+})

R/CosmoPhotoz_shiny/shinyapps/cosmostatisticsinitiative/CosmoPhotoz.dcf

@@ -0,0 +1,4 @@
+name: CosmoPhotoz
+account: cosmostatisticsinitiative
+bundleId: 76527
+url: http://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz

R/CosmoPhotoz_shiny/shinyapps/cosmostatisticsinitiative/CosmoPhotoz_shiny.dcf

@@ -0,0 +1,4 @@
+name: CosmoPhotoz_shiny
+account: cosmostatisticsinitiative
+bundleId: 76495
+url: http://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz_shiny