Merge pull request #1 from joshgabriel/python-R-bokeh

Python r bokeh

benchmark-view/Big_Picture_To_Do.md

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+What we want to do in this branch:
+  "create a flask app independent of bokeh server that runs on Apache and can render an Iframe from Shiny R"
+    - which uses bokeh's crossfilter model classes (that makes use of pandas dataframe tools)
+      to crossfilter data
+    - the flask app server will interact directly with the REST api of benchmark-db
+    - use python for
+    - use Shiny R to create html that can be Iframed into python served flask app for the data analysis.
+    - this flask app can be wrapped around with an Apache or Nginx server.
+
+  User interface goals
+    - User sees at /home.html a website that looks like https://materialsweb.org/nist_page
+      - the webpage itself can have a periodic table type interactive interface or a library website like
+        form with drop down lists or text entry elements
+      - for now the UI can directly be the crossfilter UI of click and drag
+      - data crossfiltered into the UI, if it can be statistically analyzed can be launched separately
+        - by if statistically analyzed means if the data is within dimensions accessible by the statistical tools
+          For example: 2D data can be fit using regression tools
+          ** provide a link to examples of data crossfiltered correctly so that it can be analyzed with the
+             statistics.
+      - an about page linked that summarizes the project
+      - a contact page linked that summarizes whom to contact

benchmark-view/ShinyApps/Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ERROR",
+     "evalue": "Error in parse(text = x, srcfile = src): <text>:1:1: unexpected input\n1: %run regression.r\n    ^\n",
+     "output_type": "error",
+     "traceback": "Error in parse(text = x, srcfile = src): <text>:1:1: unexpected input\n1: %run regression.r\n    ^\nTraceback:\n"
+    }
+   ],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.3.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

benchmark-view/ShinyApps/helpers.R

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+# Note: percent map is designed to work with the counties data set
+# It may not work correctly with other data sets if their row order does 
+# not exactly match the order in which the maps package plots counties
+plot_k_el_2D <- function(k_data, element) {
+
+ plot(k_data, element)
+
+
+}
+
+percent_map <- function(var, color, legend.title, min = 0, max = 100) {
+
+  # generate vector of fill colors for map
+  shades <- colorRampPalette(c("white", color))(100)
+
+  # constrain gradient to percents that occur between min and max
+  var <- pmax(var, min)
+  var <- pmin(var, max)
+  percents <- as.integer(cut(var, 100, 
+    include.lowest = TRUE, ordered = TRUE))
+  fills <- shades[percents]
+
+  # plot choropleth map
+  map("county", fill = TRUE, col = fills, 
+    resolution = 0, lty = 0, projection = "polyconic", 
+    myborder = 0, mar = c(0,0,0,0))
+
+  # overlay state borders
+  map("state", col = "white", fill = FALSE, add = TRUE,
+    lty = 1, lwd = 1, projection = "polyconic", 
+    myborder = 0, mar = c(0,0,0,0))
+
+  # add a legend
+  inc <- (max - min) / 4
+  legend.text <- c(paste0(min, " % or less"),
+    paste0(min + inc, " %"),
+    paste0(min + 2 * inc, " %"),
+    paste0(min + 3 * inc, " %"),
+    paste0(max, " % or more"))
+
+  legend("bottomleft", 
+    legend = legend.text, 
+    fill = shades[c(1, 25, 50, 75, 100)], 
+    title = legend.title)
+}

benchmark-view/ShinyApps/regression.R

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+library(shiny)
+library(rbokeh)
+library(htmlwidgets)
+source("helpers.R")
+records <- read.csv("Rdata.csv")
+app <- shinyApp(ui = fluidPage(
+      titlePanel("Least Squares Regression"),
+
+      sidebarLayout(
+      	sidebarPanel("input dashboard",
+              h5("Select the element to plot:"),
+
+              sliderInput("points", "Number of points:",
+                          min=1, max=35, value= c(0,35), step=1.0  ),
+
+              selectInput("element", 
+                           label="Element: ",
+                           choices= c("Al",
+                                      "W",
+                                      "Ni"  ),
+                         selected = "Al"),
+
+              selectInput("regression",
+                           label="Regression Model: ",
+                           list("y~x",
+                                "y~exp(-x)") )
+
+            ),
+      	mainPanel("output plot and table",
+                      img(src='MGI_Logo.png', height = 100, width = 100), 
+                      textOutput("text1"),
+                      textOutput("text2"),
+                      rbokehOutput("plotb"),
+                      tableOutput("rlm")
+            )
+    )
+),
+server = function(input,output){
+
+    output$text1 <- renderText({
+        paste("You have selected", input$element)
+                              })
+
+    output$text2 <- renderText({
+        paste("You have selected first", input$points[1]-1, "points and last", length(records$Kpoint) - input$points[2] + 1, "points","total", input$points[1] -1 + length(records$Kpoint) - input$points[2], "out of", length(records$Kpoint))
+                              })
+
+    datk <- reactive({
+                     st  <- input$points[1]
+                     mid <- input$points[2]
+                     end <- length(records$Kpoint)
+                     append(records$Kpoint[1:st], records$Kpoint[mid:end])
+                    })
+
+    datp <-  reactive({
+                     st  <- input$points[1]
+                     mid <- input$points[2]
+                     end <- length(records$Kpoint)
+                     switch(input$element,
+                    "Al" = append(records$Al[1:st], records$Al[mid:end]),
+                    "W" = append(records$W[1:st], records$W[mid:end]),
+                    "Ni" = append(records$Ni[1:st], records$Ni[mid:end]) )
+                     })
+
+
+    output$plotb <- renderRbokeh({
+       ## define the figure dimensions to be square, plot scatter function 
+       figure(plot_width=200, plot_height=200, xlab="kpoints", ylab="Lattice constant") %>% ly_points( datk(), datp() )                      
+                                })
+
+    output$rlm <- renderTable({
+       data_fit <- data.frame(x=datk(), y=datp())
+       lmR      <- switch(input$regression,
+                   "y~x" = lm("y~x", data_fit),
+                   "y~exp(-x)" = lm("y~exp(-x)", data_fit) )
+       lmResult <- summary(lmR)
+       con <- confint(lmR, level=0.96)
+       cbind(lmResult$coefficients, con)
+                             })
+
+
+  }
+)
+runApp(app)
+

benchmark-view/benchmark/__init__.py

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+import loader