update control pannel & GSA

sdyinzhen · May 7, 2019 · 91a867e · 91a867e
1 parent 2911304
commit 91a867e
Show file tree

Hide file tree

Showing 3 changed files with 14 additions and 130 deletions.
diff --git a/Control_Pannel.ipynb b/Control_Pannel.ipynb
@@ -35,22 +35,24 @@
    "metadata": {},
    "source": [
     "##### Prerequisites\n",
-    "To enable the Auto-BEL, the users need to have the following Python modules installed (most of them should be installed already when installing Python using Anaconda) :\n",
-    "* numpy\n",
+    "To enable the Auto-BEL, the users need to have the following Python modules & R modules installed (most of them should be installed already when installing Python using Anaconda) :\n",
+    "\n",
+    "* datetime\n",
+    "* gc\n",
     "* matplotlib\n",
-    "* scipy\n",
-    "* skfmm\n",
-    "* sys\n",
-    "* sklearn\n",
+    "* numpy\n",
     "* os\n",
-    "* gc\n",
-    "* seaborn\n",
     "* pandas\n",
-    "* rpy2\n",
+    "* random\n",
     "* R \n",
+    "* rpy2\n",
+    "* R library: reshape\n",
+    "* seaborn\n",
+    "* scipy\n",
+    "* skfmm\n",
+    "* sklearn\n",
+    "* sys\n",
     "* tqdm\n",
-    "* datetime\n",
-    "* random\n",
     "* tzlocal"
    ]
   },

diff --git a/source_code/Auto_BEL.py b/source_code/Auto_BEL.py
@@ -8,7 +8,7 @@
 
 from grdecl_read_plot import *
 from combine_m_sampls import *
-from signed_distance_functions import *
+#from signed_distance_functions import *
 from giip_cal import *
 from scree_plot import scree_plot
 from evd_fast import *

diff --git a/source_code/dgsa/dgsa_rightcolor.R b/source_code/dgsa/dgsa_rightcolor.R
@@ -1,81 +1,4 @@
 library(reshape)
-library(ggplot2)
-plotCDFS <- function (.clustering, .X, .code = NULL, .nBins = 3, .ggReturn = "plot", 
-                      lwd = 1) 
-{
-  .cbbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", 
-                   "#0072B2", "#D55E00", "#CC79A7")
-  .order <- order(.clustering)
-  .clustering = .clustering[.order]
-  .X = .X[.order,]
-  if (.code == "all*") {
-    .ggDATA <- rbind.data.frame(data.frame(.X, clustering = "prior"), 
-                                data.frame(.X, clustering = as.character(.clustering)))
-    .ggDATA$clustering <- factor(.ggDATA$clustering, levels = c(as.character(unique(.clustering)), 
-                                                                "prior"))
-    .ggDATA <- melt(.ggDATA, id = c("clustering"))
-    .gg <- ggplot(.ggDATA) + stat_ecdf(aes(x = value, group = clustering, 
-                                           colour = clustering), lwd = lwd) + ylab("Phi") + 
-      ylim(c(0, 1)) + theme(legend.position = "top") + 
-      xlab("parameter range") + scale_colour_manual(values = c(.cbbPalette[1:length(unique(.clustering))], 
-                                                               "#000000")) + facet_wrap(~variable, scales = "free_x")
-  }
-  else {
-    .params <- strsplit(.code, "|", fixed = TRUE)[[1]]
-    if (length(.params) == 0 | length(.params) > 2) 
-      stop("\".code\" is invalid!")
-    .paramIndex <- apply(as.matrix(.params), 1, function(x) which(colnames(.X) %in% 
-                                                                    x))
-    if (length(.paramIndex) != length(.params)) 
-      stop("\".code\" is invalid, one or both of the variables were not found!")
-    if (length(.paramIndex) == 1) {
-      .ggDATA <- rbind.data.frame(data.frame(parameter = .X[, 
-                                                            .paramIndex], clustering = "prior"), data.frame(parameter = .X[, 
-                                                                                                                           .paramIndex], clustering = as.character(.clustering)))
-      .ggDATA$clustering <- factor(.ggDATA$clustering, 
-                                   levels = c(as.character(unique(.clustering)), 
-                                              "prior"))
-      .gg <- ggplot(.ggDATA) + stat_ecdf(aes(x = parameter, 
-                                             group = clustering, colour = clustering), lwd = lwd) + 
-        xlab(colnames(.X)[.paramIndex]) + ylab("Phi") + 
-        ylim(c(0, 1)) + theme(legend.position = "top") + 
-        scale_colour_manual(values = c(.cbbPalette[1:length(unique(.clustering))], 
-                                       "#000000"))
-    }
-    else {
-      .brks = quantile(.X[, .paramIndex[2]], seq(0, 1, 
-                                                 1/.nBins))
-      if (length(unique(round(.brks, 8))) > 1) {
-        .binning <- cut(.X[, .paramIndex[2]], breaks = .brks, 
-                        include.lowest = TRUE, right = TRUE, labels = FALSE)
-      }
-      else {
-        stop(paste("It appears that something is wrong with parameter: ", 
-                   colnames(.X)[.paramIndex[2]]))
-      }
-      .ggDATA <- rbind.data.frame(data.frame(parameter = .X[, 
-                                                            .paramIndex[1]], clustering = paste("Cluster_", 
-                                                                                                .clustering, sep = ""), bin = .binning), data.frame(parameter = .X[, 
-                                                                                                                                                                   .paramIndex[1]], clustering = paste("Cluster_", 
-                                                                                                                                                                                                       .clustering, sep = ""), bin = "cluster_prior"))
-      .ggDATA$bin <- factor(.ggDATA$bin, levels = c(unique(.binning), 
-                                                    "cluster_prior"))
-      .gg <- ggplot(.ggDATA) + stat_ecdf(aes(x = parameter, 
-                                             group = bin, colour = bin), lwd = lwd) + xlab(.code) + 
-        ylab("Phi") + ylim(c(0, 1)) + theme(legend.position = "top") + 
-        facet_grid(~clustering) + scale_colour_manual(values = c((scales::hue_pal())(length(unique(.binning))), 
-                                                                 "#000000"))
-    }
-  }
-  .gg <- .gg + ggtitle(paste("CDFs for ", .code))
-  if (.ggReturn == "plot") {
-    print(.gg)
-  }
-  else {
-    return(.gg)
-  }
-}
-
 
 dgsa <- function (.clusters, .X, .normalize = TRUE, .nBoot = 100, .interactions = FALSE, 
                   .nBins = 3, .alpha = 0.95, .parallel = FALSE, .progress = TRUE) 
@@ -156,44 +79,3 @@ dgsa <- function (.clusters, .X, .normalize = TRUE, .nBoot = 100, .interactions
   return(ret)
 }
 
-plotParetoDGSA <- function (.dgsa, .clusters = FALSE, .interaction = NULL, .hypothesis = TRUE, 
-                            .ggReturn = FALSE) 
-{
-  .cbbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", 
-                   "#0072B2", "#D55E00", "#CC79A7")
-  if (class(.dgsa) != "DGSAstructure") 
-    stop("Passed object is not of class DGSAstructure. Exiting!")
-  if (!is.null(.interaction)) {
-    .paramIndex <- which(.dgsa$parameters == .interaction)
-    if (length(.paramIndex) == 0) 
-      stop("Parameter provided in \".interaction\" not found. Exiting!")
-    .ggDATA <- t(.dgsa$sensitivityMatrix[, .paramIndex, ])
-    .plotTitle <- paste("S(\"X\" | ", .dgsa$parameters[.paramIndex], 
-                        ")", sep = "")
-  }
-  else {
-    .ggDATA <- apply(.dgsa$sensitivityMatrix, 1, diag)
-    .plotTitle <- "Main Sensitivities (marginal)"
-  }
-  colnames(.ggDATA) <- paste("Cluster", 1:ncol(.ggDATA), sep = "_")
-  .ggDATA[is.nan(.ggDATA)] = 0
-  .ggDATA <- as.data.frame(.ggDATA)
-  .ggDATA$mean <- apply(.ggDATA, 1, mean)
-  .ggDATA$parameters <- .dgsa$parameters
-  .ggDATA <- .ggDATA[order(.ggDATA$mean), ]
-  .levels <- .ggDATA$parameters
-  .ggDATA <- melt(.ggDATA, id = c("parameters"))
-  .ggDATA$parameters <- factor(.ggDATA$parameters, levels = .levels)
-  .ggP <- ggplot(.ggDATA, aes(x = parameters, y = value,fill = variable,color = variable)) + 
-    coord_flip() + 
-    geom_bar(stat = "identity", position = "dodge", lwd = 0.2, colour = "black") + theme(legend.position = "bottom") + 
-    geom_hline(yintercept = ifelse(.hypothesis, 1, NULL)) + scale_fill_manual(values=c(.cbbPalette[1:ncol(.ggDATA)],'darkred'))+
-    scale_color_manual(values=c(.cbbPalette[1:ncol(.ggDATA)],'darkred'))+
-    ggtitle(.plotTitle) 
-  if (.ggReturn) {
-    return(.ggP)
-  }
-  else {
-    print(.ggP)
-  }
-}