+
Industrial Engineering Student
Knoxville, TN
<EMAIL_ADDRESS>(423)742-0853
Skills
Microsoft Office
RStudio
Python
MATLAB
Key Courses
IE 300 - Quality Control and Management
IE 401 - Facilities Planning and Material Handling
IE 404 - Industrial Engineering Design I
IE 421 - Information Systems Analysis and Design
IE 427 - Introduction to Lean Systems
Recorded and presented data. Conducted time studies.
Golf Snack Shop
Concessions, usher, and box office.
Pursuing Bachelor of Industrial & Systems Engineering
High School Diploma
Implement lean manufacturing strategties.
Develop product and create a mock facility.
Spreadsheet data analysis and create a production schedule.


@BithumbOfficial Nothing like a +$30MM hack of a centralized exchange and frozen funds to remind you that if you don't own the private key...you don't own those coins. I bet they lost a lot more which is why they deleted the tweet saying they'd cover all losses. #DEX @Komo...


WITNESS LIST Public Education Committee April 12, 1999-2:00P HB 98 For: Curtis, Lonnie (Self) Griffin, Michael (Texas Association of School Business Officials) Santiago, Gwendolyn (Texas Association of School Business Officials) Scherff, Carter (Texas Association of School Business Officials) Sheets, Gene (Self) Against: Dunn, David (Texas Association of School Boards) On: Anderson, David (Texas Education Agency) HB 211 For: Lett, Robert (Self) Miller, Karen (Texas Congress of Parents and Teachers) HB 211Committee Substitute (Dunnam) Mikus, Kristy (Self) HB 359 For: Eaton, Holly (Texas Classroom Teachers Association) Miller, Karen (Texas Congress of Parents and Teachers) HB 1275 For: Butler, Judy (Austin Branch IDA) Govea, Feliciana (Self) Hernandez, Paul (International Dyslexia Association) Ortiz, Alba (Self) Perez, Rufina (Self) Reed, Dianne (ATPE Dallas Local Unit) HB 1418 For: Barron, David (Self) Calzada, Gabriel (Calhoun County ISD- School Board of Trustees) Gillispie, Kathey (Associated Builders and Contractors) Lara, Cecil (Texas Career and Technology Educators) Meriwether, Allan (Self) Moehler, Michael (Calhoun County ISD) Waddell, Stephen (Tuloso-Midway ISD) Against: Hensley, Dianne (Concerned Women for America) HB 1418 Committee Substitute (Dunnam) For: Lara, Rene' (Texas Federation of Teachers) HB 1642 For: Bone, Barry (Texas Industries) Litke, Robert (City of Houston) Mullins, Tom (Self) Nowotny, Patrick (Texas Economic Development Council) Pena, David (San Antonio Houston Trust Foundation) Worley, Robert (Texas Economic Development Council) On: Wilson, Dan (Comptroller of Public Accounts) HB 1733 For: Brady, Denise (The Arc of Texas) Butler, Judy (Austin Branch IDA) Hernandez, Paul (International Dyslexia Association) Horton, Colleen (Texas Advocates Supporting Kids with Disabilities) HB 2172 For: Brady, Denise (The Arc of Texas) Butler, Judy (Austin Branch IDA) Hernandez, Paul (International Dyslexia Association) Horton, Colleen (Texas Advocates Supporting Kids with Disabilities) HB 2202 For: Collins, Robby (Dallas Public Schools) Grusendorf, Bill (Texas Association of Rural Schools) Jennings, Jenni (Texans for Healthy Kids, Healthy Schools) Knox, Retta (Hart ISD- School Based Health Center) Ostos, Sylvia (Texas Congress of Parents and Teachers) Smith, Donald (Ysleta ISD) Against: Hensley, Dianne (Concerned Women for America) On: Archer, William (Texas Department of Health) HB 2608 For: Dougerty, Chrys (Self) Haule, Kathy (Self) Massonneau, Robert (Head Start of Greater Dallas) On: Lara, Rene' (Texas Federation of Teachers) HB 3204 For: Yawn, Larry (Houston ISD) HB 3287 For: Cooley, Danny (Self) Straus, David (Self) HB 3290 For: Fonte, Richard (Austin Community College) HB 3394 For: Eaton, Holly (Texas Classroom Teachers Association) Matthews, Robin (ATPE) McLamore, Mike (ATPE) Reed, Dianne (ATPE) Against: Bridges, Linda (Corpus Christi AFT) Hartman, Eric (Texas Federation of Teachers) Levin, Jay (Texas State Teachers Association) HB 3551 For: McLamore, Mike (Association of Texas Professional Educators) HB 3739 For: Hensley, Dianne (Concerned Women of America) School Data For: Luce, Tom (Just for the Kids) Student Testing On: Uri Treisman, Philip (Self)


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This workshop demonstrates how to perform ELMER analysis using matched RNA-seq and DNA methylation data.
You can find a detailed information about ELMER at http://bioconductor.org/packages/3.10/bioc/vignettes/ELMER/inst/doc/index.html.
Articles about ELMER:
SummarizedExperiment
classesStudents will have a chance to run ELMER analysis on a provided MultiAssayExperiment
object created from TCGA data from GDC data portal.
MultiAssayExperiment
objectFirst, we need to load the R packages used in this workshop, they should provide the required functions to run the code.
suppressMessages({
library("ELMER")
library("MultiAssayExperiment")
})
The RNA-seq data, DNA methylation data and patients metadata (i.e age, gender) used in this workshop is
structured as a MultiAssayExperiment
. You can read more about a MultiAssayExperiment
object at http://bioconductor.org/packages/MultiAssayExperiment/
and https://bioconductor.github.io/BiocWorkshops/workflow-for-multi-omics-analysis-with-multiassayexperiment.html.
Through the next section we will:
The data which is available in this machine, can also be downloaded at this google drive.
# Dowloaded at
# https://drive.google.com/open?id=1fPKEG7c_uFoofyhQ4EkW1e2SMZmr6XUt
mae <- readRDS("Data/reduced_TCGA_ESCA_MAE_distal_regions.rds")
mae
colnames(mae)
# check experiments
experiments(mae)
# Get the Gene expression object
rna.seq <- mae[[2]]
message("Number of genes ",nrow(rna.seq))
message("Number of samples ",ncol(rna.seq))
message("Check genes metadata")
rowRanges(rna.seq)
message("Check gene expression data")
assay(rna.seq)[1:4,1:4]
# Get the DNA methylation object
dna.met <- mae[[1]]
# This is a reduced version of the distal probes to be run in a Machine with low memory
message("Number of probes: ", nrow(dna.met))
message("Number of samples: ", ncol(dna.met))
message("Check DNA methylation probes metadata")
rowRanges(dna.met)[,1:4]
message("Check DNA methylation beta values")
assay(dna.met)[1:4,1:4]
message("metadata can be accessed using the function colData")
message("we will check the first 4 samples and their 4 columns")
colData(mae)[1:4,1:4]
message("or you can also access the metadata directly with the $")
mae$primary_diagnosis[1:4]
message("summarize the number of tissue type with the function table")
table(mae$definition)
message("summarize the numbers of samples using two features")
table(mae$primary_diagnosis,mae$definition)
Since we will use only Primary solid Tumor samples we will remove the Metastatic and Solid Tissue Normal samples from our object
mae <- mae[, mae$definition == "Primary solid Tumor"]
# which is the column of the samples metadata defining our groups
group.col <- "primary_diagnosis"
# which are the grops withing the group.col that we want to compare
group1 <- "Adenocarcinoma, NOS"
group2 <- "Squamous cell carcinoma, NOS"
# Identify probes hypo in group1 or hypo in group2 ?
direction <- "hypo" # hypo in group 1
cores <- 2
mode <- "supervised"
# Where do we want to save the results ?
dir.out <- paste0("analysis_reduced/",group.col,"-",
gsub("[[:punct:]]| ", "_", group1),
"_vs_",
gsub("[[:punct:]]| ", "_", group2),
"_", mode,
"/", direction)
message("Results will be saved at: ",dir.out)
dir.create(dir.out, showWarnings = FALSE,recursive = T)
# time expected with one core: 4 minuntes
diff.probes <- get.diff.meth(data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
diff.dir = direction, # Get probes hypometh. in group 1
cores = cores,
mode = mode,
pvalue = 0.01,
sig.dif = 0.3,
dir.out = dir.out,
save = TRUE)
# check the results
head(diff.probes)
Now that we have our hypomethylated probes we will look for the 20 nearest genes
to each probes to identify if any of these genes could being affected by the
hypomethylation in the distal regulatory region.
# time expected less than one minute
nearGenes <- GetNearGenes(data = mae,
probes = diff.probes$probe,
numFlankingGenes = 20)
head(nearGenes)
Now that we have our hypomethylated probes and the 20 nearest genes we want to identify if any of these genes could being affected by the hypomethylatio in the distal regulatory region.
# time expected one minute
pair <- get.pair(data = mae,
nearGenes = nearGenes,
group.col = group.col,
group1 = group1,
group2 = group2,
mode = mode,
diff.dir = direction,
raw.pvalue = 0.001,
Pe = 0.001,
filter.probes = TRUE,
filter.percentage = 0.05,
filter.portion = 0.3,
dir.out = dir.out,
cores = cores,
label = direction)
head(pair)
We can plot the correlation heatmap of DNA methylation and gene expression
with the heatmapPairs
function. We will plot only the first 100 pairs.
# Plot size options
options(repr.plot.width=12, repr.plot.height=7)
heatmapPairs(data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
subset = TRUE,
pairs = pair[1:100,],
filename = NULL)
We have hypomethylated regions that has a distal gene upregulated.
We want to identify which is the MR TF binding to those regions and regulating the distal gene.
First we identify which are the enriched motifs for those hypomethylated regions using
the function get.enriched.motif
. This will output the enriched motifs and the respective
hypomethylated probes with the motif signature around it
(since a DNA methylation probes is 1bp, we extended the search window to $\pm$ 250 bp).
# time expected less than one minute
distal.probes <- get.feature.probe(feature = NULL,
genome = "hg38",
met.platform = "450K")
enriched.motif <- get.enriched.motif(probes.motif = ELMER:::getdata("Probes.motif.hg38.450K"),
background.probes = names(distal.probes),
probes = unique(pair$Probe),
dir.out = dir.out,
label = direction,
min.incidence = 10,
lower.OR = 1.5)
message("check top 5 enriched motifs")
names(enriched.motif)[1:5]
message("Check the paired hypomethylated probes with the motif signature")
enriched.motif$GATA4_HUMAN.H11MO.0.A
message("Plot enrichement analysis results")
motif.result.file <- file.path(dir.out, "getMotif.hypo.motif.enrichment.csv")
# Plot size options
options(repr.plot.width=12, repr.plot.height=7)
motif.enrichment.plot(motif.enrichment = motif.result.file,
significant = list(lowerOR = 2.0),
label = "hypo",
summary = TRUE,
save = FALSE)
Now that we have our enriched motifs, we have the following question:
It is important to highlight that since TF within the same family and subfamily have very close motifs, they will likely be indentified to bind the same regions. You can check that by verifying the intersection of probes from our enrichment analysis results.
So more than 70% of probes are enriched for both GATA4 and GATA6. How could we infer which of those MR might be the one with higher impact ?
For that ELMER correlates the TF expression vs the average mean methylation of the hypomethylated probes with a motif signature and rank them. We expect that a TF with higher expression and lower methylation in the binding regions will be a better MR TF candidate than TF that are for example equally expressed on both groups.
# time expected ~5 minutes
TF <- get.TFs(data = mae,
group.col = group.col,
mode = mode,
group1 = group1,
group2 = group2,
diff.dir = direction,
enriched.motif = enriched.motif,
dir.out = dir.out,
label = direction,
cores = cores)
message("Check the results for the top 5 enriched motifs")
TF[1:5,1:5]
ELMER provides an easy way to visualize the rank of all human TF evaluated with the
function TF.rank.plot
.
# Plot size options
options(repr.plot.width=7, repr.plot.height=7)
load(file.path(dir.out,"getTF.hypo.TFs.with.motif.pvalue.rda"))
# Ranking plot
# get TF vs avg DNA methylation of the binding sites p-values to be ranked in the plot
load("analysis_reduced/primary_diagnosis-Adenocarcinoma__NOS_vs_Squamous_cell_carcinoma__NOS_supervised/hypo/getTF.hypo.TFs.with.motif.pvalue.rda")
TF.rank.plot(motif.pvalue = TF.meth.cor,
motif = "HNF4A_HUMAN.H11MO.0.A",
save = FALSE)
TF.rank.plot(motif.pvalue = TF.meth.cor,
motif = "GATA4_HUMAN.H11MO.0.A",
save = FALSE)
When you perform several ELMER analysis with different arguments, you might want to summarize and visualize those several results. For that, we have the ELMER:::get.tabs
function , which accepts as input
the directories of the results and if you want to select the MR TF within the
family of subfamily classification.
In the Data/analysis_april_2019
folder we provided 4 ELMER analysis. The first step is to retrieve the directory names with the results. Also, it is a golden rule when running several ELMER runs to add a understable name to the dir.out
directories, such that you easily know which analysis were ran, with which arguments.
In the example below, you can identify the tumor type, type of ELMER run (supervised/unsupervised), groups compared and direction of the analysis (hypo or hyper in group 1).
analsysis.dir <- unique(dirname(dir("Data/analysis_april_2019/",recursive = T,full.names = T)))
analsysis.dir
With those directory names we will use the function ELMER:::get.tabs
to summarize the results.
classification <- "subfamily"
suppressWarnings({
suppressMessages({
tabs <- ELMER:::get.tabs(analsysis.dir,classification)
})
})
# A list of matrices are returned
print(class(tabs))
# names of the matrices
print(names(tabs))
# The default it the name directory as column name
tabs$tab[1:3,1:4]
# To be more readable we will change the path names to the analysis name
analysis.names <- c( "COAD-READ (vs Normal)",
"EAC (vs Normal)",
"EAC (vs ESCC)",
"PAAD (vs Normal)")
for (i in 1:3) colnames(tabs[[i]]) <- analysis.names
This function creates 4 matrices using some of the files in each analysis directory:
getTF.hypo.significant.TFs.with.motif.summary.csv
(create after you run the get.TFs
function) file that has the MR TF for each enriched motif.# 1. a binary matrix saying if the TF was found or not in the analysis
tab.binary <- tabs$tab
head(tab.binary)
getTF.hypo.significant.TFs.with.motif.summary.csv
to identify the MR TF and getTF.hypo.TFs.with.motif.pvalue.rda
, which is a matrix with TF as columns and enriched motifs as rows and the contains the p-value of the wilcoxon test of the TF expression for the Unmethylated vs Methylated groups.# 2. a matrix with the MR TF p-value found in the analysis
tab.pval <- tabs$tab.pval
head(tab.pval)
getTF.hypo.significant.TFs.with.motif.summary.csv
to get the MR TF and then uses getMotif.hypo.motif.enrichment.csv
to get the motifs OR to which the MR TF is known to bind and returns the highest OR.# 3. a matrix with the highest motif OR for the MR TF found in the analysis
tab.or <- tabs$tab.or
head(tab.or)
# 4. a matrix with 4 collums: MR TF, motif, FDR and analysis it was identified.
tf.or.table <- tabs$tf.or.table
# Rename analysis
tf.or.table$analysis <- gsub("ESCA","EAC",paste0(basename(dirname(dirname(dirname(tf.or.table$analysis)))),
" (vs ",
ifelse(grepl("Normal",tf.or.table$analysis),"Normal","ESCC"),")"))
head(tf.or.table)
With those summarized results we can create two summary plots:
The code below creates the heatmap using complexHeatmap package for the binary MR TF matrix and the p-value one.
library(ComplexHeatmap)
library(vegan)
options(repr.plot.width=14, repr.plot.height=8)
col <- ifelse(classification == "family","top.potential.TF.family", "top.potential.TF.subfamily")
analysis.colors <- c(
"COAD-READ (vs Normal)" = '#ff964f',
"EAC (vs Normal)" = '#94e894',
"EAC (vs ESCC)" = '#eaadb9',
"PAAD (vs Normal)" = '#9ab9f9'
)
# get top TFs in each anaylsis
labels <- ELMER:::get.top.tf.by.pval(tab.pval,top = nrow(tab.pval))
# Annotate each column of the matrix (it should be correspondant to the input matrix)
hb = HeatmapAnnotation(df = data.frame("Mode" = rep("unsupervised",4),
"Analysis" = analysis.names),
col = list("Analysis" = analysis.colors,
"Mode" = c("unsupervised" = "#ebd540",
"supervised" = "#718da5")),
show_annotation_name = FALSE,
show_legend = T,
annotation_name_side = "left",
annotation_name_gp = gpar(fontsize = 12))
# Binary heatmap
ht_list_binary <- Heatmap(as.matrix(tab.binary),
name = "Binary heatmap" ,
col = c("1" = "black", "0" = "white"),
column_names_gp = gpar(fontsize = 8),
show_column_names = FALSE,
use_raster = TRUE,
na_col = "white",
raster_device = c("png"),
raster_quality = 2,
show_row_names = F,
show_heatmap_legend = TRUE,
cluster_columns = FALSE,
cluster_rows = TRUE,
show_column_dend = FALSE,
show_row_dend = FALSE,
clustering_distance_rows = function(x) {
vegan::vegdist(tab.binary,method = "jaccard",binary = TRUE)
},
clustering_method_rows = "average",
top_annotation = hb,
width = unit(5, "cm"),
row_names_gp = gpar(fontsize = 1),
column_title = paste0("Binrary MR TF Heatmap"),
column_title_gp = gpar(fontsize = 10),
row_title_gp = gpar(fontsize = 16))
ht_list_binary <- ht_list_binary +
rowAnnotation(link = anno_mark(at = match(labels,rownames(tab.pval)),
labels = labels,labels_gp = gpar(fontsize = 10)),
width = unit(1, "cm") + max_text_width(labels)
)
draw(ht_list_binary,
newpage = FALSE,
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
heatmap_legend_side = "left",
show_heatmap_legend = TRUE,
annotation_legend_side = "right")
# P-value heatmap
ht_list <- Heatmap(scale(as.matrix(-log10(tab.pval))),
name = "column z-score FDR",
col = colorRampPalette(c('#3361A5', '#248AF3', '#14B3FF',
'#88CEEF', '#C1D5DC', '#EAD397',
'#FDB31A','#E42A2A', '#A31D1D'))(100),
column_names_gp = gpar(fontsize = 8),
show_column_names = FALSE,
use_raster = TRUE,
na_col = "white",
raster_device = c("png"),
raster_quality = 2,
show_row_names = F,
show_heatmap_legend = TRUE,
cluster_columns = FALSE,
cluster_rows = FALSE,
row_order = row_order(ht_list_binary),
top_annotation = hb,
width = unit(5, "cm"),
row_names_gp = gpar(fontsize = 1),
column_title = paste0("MR TF Heatmap"),
column_title_gp = gpar(fontsize = 10),
row_title_gp = gpar(fontsize = 16))
ht_list <- ht_list +
rowAnnotation(link = anno_mark(at = match(labels,rownames(tab.pval)),
labels = labels,labels_gp = gpar(fontsize = 10)),
width = unit(1, "cm") + max_text_width(labels)
)
draw(ht_list,
newpage = TRUE,
column_title_gp = gpar(fontsize = 12, fontface = "bold"),
heatmap_legend_side = "left",
show_heatmap_legend = TRUE,
annotation_legend_side = "right")
The main difference from the last plot is the requirement of the MAE object with the DNA methylation and gene expression data. For a given MR TF, we need to indentify the top enriched motif it binds and then for each enriched motifs its distal paired differently methylated probes using the getMotif.hypo.enriched.motifs.rda
object within the results folder.
Also, since we only provide one object used for two analysis ("EAC (vs Normal)" and "EAC (vs ESCC)" ) we will only plot the MR TF expression vs DNA methylation at binding motifs for them.
analsysis.dir.esca <- unique(dirname(dir("Data/analysis_april_2019/ESCA/",recursive = T,full.names = T)))
mae <- readRDS("Data/TCGA_ESCA_MAE_distal_regions.rds")
tab.pval.esca <- tab.pval[,2:3]
suppressMessages({
library(ELMER)
library(grid)
library(ggplot2)
library(ggpubr)
library(dplyr)
})
analysis.colors <- c(
"COAD-READ (vs Normal)" = '#ff964f',
"EAC (vs Normal)" = '#94e894',
"EAC (vs ESCC)" = '#eaadb9',
"PAAD (vs Normal)" = '#9ab9f9'
)
# Top has the number of MRTF to plot
for (top in c(5)) {
scatter.list <- list()
scatter.grid <- list()
scatter.labels <- c()
for (path in analsysis.dir.esca) {
# Load of object with enriched motifs and the probes mapped to it
load(dir(path = path,
pattern = "getMotif.hypo.enriched.motifs.rda",
recursive = T,full.names = T)
)
i <- which(path == analsysis.dir.esca)
tumor <- basename(dirname(dirname(dirname(path))))
mae.plot <- mae
# Change the data to plot based on the analysis performed
if (grepl("Squamous",path)) {
category <- "primary_diagnosis"
# keep only samples used in the analysis
mae.plot <- mae.plot[,mae.plot$primary_diagnosis %in% c("Adenocarcinoma, NOS",
"Squamous cell carcinoma, NOS") &
mae.plot$definition %in% c("Primary solid Tumor")]
} else {
category <- "definition"
# keep only samples used in the analysis
mae.plot <- mae.plot[,mae.plot$primary_diagnosis %in% c("Adenocarcinoma, NOS") &
mae.plot$definition %in% c("Primary solid Tumor","Solid Tissue Normal")]
}
# get top Tfs
topTFs <- rownames(tab.pval)[head(sort(DelayedArray::rowMins(tab.pval.esca[,i,drop = F],na.rm = T),
index.return = TRUE, decreasing = F)$ix, n = top)]
topTFs <- topTFs[1:top]
for (j in topTFs) {
TF <- j
scatter.labels <- c(scatter.labels,colnames(tab.pval.esca)[i])
motif <- tf.or.table[tf.or.table$TF == j &
tf.or.table$analysis == colnames(tab.pval.esca)[i],"motif"][1]
# Change the dots color based on the analysis performed
if (grepl("Squamous",path)) color.value <- c("#FF0000","#0000FF")
if (grepl("Adenocarcinoma.Solid.Tissue.Normal",path)) color.value <- c("#FF0000","#176d55")
if (is.na(motif)) next
# Plot TF expression vs avg DNA methylation of the TFBS
suppressMessages({
scatter <- scatter.plot(data = mae.plot,
correlation = TRUE,
byTF = list(TF = TF,
probe = enriched.motif[[motif]]),
category = category,
ylim = c(0,25),
dots.size = 0.8,
color.value = color.value,
save = FALSE,
lm_line = TRUE) +
ylab(bquote(.(TF) ~ log[2](RSEM + 1))) +
xlab(paste0("Avg. DNA met. at \n", length(enriched.motif[[motif]]),
" CpGs w/ \n enriched motif ",
sub("_HUMAN.H11MO.[0-1].[A-D]", "", motif))) +
theme(legend.position = "none",
plot.title = element_blank(),
axis.title.y = element_text(size = 8, face = "bold"),
axis.title.x = element_text(size = 8, face = "bold"),
strip.background = element_rect(fill = analysis.colors[i + 1]),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# Remove panel background
panel.background = element_blank()
)
})
# If this is the first plot of the analysis we will add a labels (rectangle + text)
if(j == topTFs[1]){
gt <- gtable::gtable(widths = unit(0.5,"cm"), heights = unit(3, "in")) %>%
gtable::gtable_add_grob(grid::rectGrob(gp=gpar(fill= analysis.colors[i + 1])), 1, 1, name = 1) %>%
gtable::gtable_add_grob(grid::textGrob(colnames(tab.pval.esca)[i],
rot = 90,
gp = grid::gpar(fontsize = 8)), 1, 1, name = 2)
scatter <- scatter %>% ggpubr::annotate_figure(left = gt)
}
scatter.list[[paste0(i,j)]] <- scatter
}
scatter.grid[[i]] <- ggpubr::ggarrange(plotlist = scatter.list[grep(paste0("^",i),names(scatter.list))],
ncol = top,
# the first column should be wider since it has the label
widths = c(1.15,rep(1,top - 1)),
nrow = 1,
common.legend = T,
legend = "bottom")
}
}
options(repr.plot.width=20, repr.plot.height=8)
# we use ggarrange to arrange the plots together
ggpubr::ggarrange(plotlist = scatter.grid,
ncol = 1,
# the first column should be wider since it has the label
widths = c(1.15,rep(1,top - 1)),
nrow = 2, # Number of analysis - rows of the plot
heights = c(rep(1.5,length(scatter.list) - 1), 2),
common.legend = F,
legend = "bottom")
sessionInfo()
We have a set of recorded videos, explaining some of the workshops.


My full name is James Lee Mathieus Jr. I am orginally from the United States and came to New Mexico accidently. I was traveling from Massacusetts to New York and must have made a wrong turn and ended up here. At first I was worried because I didn't have a passport but it doesn't seem to be a problem. I was also pleased to learn that I could use American dollars here without any trouble. The weather here is nice and the people are friendly so I've decided to stay for a while.
I am currently working as a tattoo artist but I haven't had much work in the a while so I decided to change careers and Web Site Developer seemed to be a good choice where I can do technical work and still explore my artistic talents. If anyone at the bootcamp wants a tattoo, I'm always available and I will give you a discount.
PS - I found a lost cat. If it is yours, please call me as soon as possible.


Small-sample optimized estimators of cross-validated prediction metrics
nlpred
is an R package for computing estimates of cross-validated prediction metrics. These estimates are tailored for superior performance in small samples. Several estimators are available including ones based cross-validated targeted minimum loss-based estimation, estimating equations, and one-step estimation.
For standard use, we recommend installing the package from CRAN via
You can install the current release of nlpred
from GitHub via devtools
with:
The main functions in the package are cv_auc
and cv_scrnp
, which are used to compute, respectively, the K
-fold cross-validated area under the receiver operating characteristics curve (CVAUC) and the K
-fold cross-validated sensitivity constrained rate of negative prediction. However, rather than using standard cross-validation estimators (where prediction algorithms are developed in a training sample and AUC/SCRNP estimated using the validation sample), we instead use techniques from efficiency theory to estimate these quantities. This allows us to use the training data both to develop the prediction algorithm, as well as key nuisance parameters needed to evaluate AUC/SCRNP. By reserving more data for estimation of these key parameters, we obtain improved performance in small samples.
# load package
library(nlpred)
#> Loading required package: data.table
#> Registered S3 methods overwritten by 'ggplot2':
#> method from
#> [.quosures rlang
#> c.quosures rlang
#> print.quosures rlang
# turn off messages from np package
options(np.messages=FALSE)
# simulate data
n <- 200
p <- 10
X <- data.frame(matrix(rnorm(n*p), nrow = n, ncol = p))
Y <- rbinom(n, 1, plogis(X[,1] + X[,10]))
# get cv auc estimates for logistic regression
logistic_cv_auc_ests <- cv_auc(Y = Y, X = X, K = 5, learner = "glm_wrapper")
logistic_cv_auc_ests
#> est se cil ciu
#> cvtmle 0.8199686 0.02891982 0.7632868 0.8766504
#> onestep 0.8201931 0.02929981 0.7627666 0.8776197
#> esteq 0.8152550 0.02929981 0.7578284 0.8726816
#> standard 0.8165414 0.03069452 0.7563812 0.8767015
# get cv auc estimates for random forest using nested
# cross-validation for nuisance parameter estimation. nested
# cross-validation is unfortunately necessary when aggressive learners
# are used.
rf_cv_auc_ests <- cv_auc(Y = Y, X = X, K = 5,
learner = "randomforest_wrapper",
nested_cv = TRUE)
rf_cv_auc_ests
#> est se cil ciu
#> cvtmle 0.7803455 0.03386116 0.7139789 0.8467122
#> onestep 0.7816011 0.03453698 0.7139099 0.8492923
#> esteq 0.7704595 0.03453698 0.7027683 0.8381508
#> standard 0.7766917 0.03375010 0.7105427 0.8428407
# same examples for scrnp
logistic_cv_scrnp_ests <- cv_scrnp(Y = Y, X = X, K = 5, learner = "glm_wrapper")
logistic_cv_scrnp_ests
#> est se cil ciu
#> cvtmle 0.2121685 0.04292850 0.1280301 0.2963068
#> onestep 0.2042894 0.04298426 0.1200418 0.2885370
#> esteq 0.2042894 0.04298426 0.1200418 0.2885370
#> standard 0.2250000 0.05970125 0.1079877 0.3420123
rf_cv_scrnp_ests <- cv_scrnp(Y = Y, X = X, K = 5,
learner = "randomforest_wrapper",
nested_cv = TRUE)
rf_cv_scrnp_ests
#> est se cil ciu
#> cvtmle 0.1609362 0.04773367 0.06737990 0.2544925
#> onestep 0.1524208 0.04785741 0.05862198 0.2462196
#> esteq 0.1524208 0.04785741 0.05862198 0.2462196
#> standard 0.1600000 0.05020093 0.06160798 0.2583920
Interested contributors can consult our contribution guidelines
prior to submitting a pull request.
After using the nlpred
package, please cite the following:
@Manual{nlpredpackage,
title = {nlpred: Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples},
author = {David Benkeser},
note = {R package version 1.0.1}
}
@article{benkeser2019improved,
year = {2019},
author = {Benkeser, David C and Petersen, Maya and van der Laan, Mark J},
title = {Improved Small-Sample Estimation of Nonlinear Cross-Validated Prediction Metrics},
journal = {Journal of the American Statistical Association},
doi = {10.1080/01621459.2019.1668794}
}
© 2019- David Benkeser
The contents of this repository are distributed under the MIT license. See below for details:
The MIT License (MIT)
Copyright (c) 2019- David C. Benkeser
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
 IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


LifeGPA
HOME
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If You Gave Your Life A Grade
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Summarize Your Life's Most Important Efforts
Into a Simple Composite Number.
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RSMSSB PTI result 2019 out: Get direct link to download result, cut-offs
RSMSSB PTI result: Candidates can check their result on the official website of RSMSSB, rsmssb.rajasthan.gov.in
by BS Web TeamRajasthan Subordinate and Ministerial Services Selection Board (RSMSSB) has declared RSMSSB PTI result. Candidates can check their result on the official website of RSMSSB, rsmssb.rajasthan.gov.in. The Rajasthan government had conducted the exams to fill 4,500 posts of Physical Training Instructor Grade III.
RSMSSB PTI result: Steps to check result
Step 1: Visit the offical website - rsmssb.rajasthan.gov.in
Step 2: Click on 'result' section
Step 3: Click on 'PTI 2018: Third Revised List of Selected Candidates'
Step 4: Download RSMSSB PTI result
The process of filling in online application forms for the posts ended on June 26, 2018
Click here for direct link of RSMSSB PTI result
About RSMSSB
Rajasthan Subordinate & Ministerial Services Selection Board (RSMSSB) was established on January 29, 2014. The role of the Board is to recommend candidates for direct recruitment for posts carrying grade pay of Rs 3,600 - or less than Rs 3,600.
The Board was incorporated with the purpose of recruiting capable, competent, highly skilled individuals by conducting written tests, professional tests and personal interviews.


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| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
| Interface Summary | |
|---|---|
| LRS | Define the interface that must be satisfied to talk to an LRS |
| QueryableStatementTarget | Essentially empty interface to force type checking for objects of a statement that are queryable |
| StatementsQueryInterface | |
| StatementTarget | Statement target interface for instances stored in the "object" property of a Statement |
| Class Summary | |
|---|---|
| About | |
| Activity | Activity model class |
| ActivityDefinition | Activity Definition model class |
| Agent | Agent model class |
| AgentAccount | Agent Account model class |
| Attachment | Attachment Class |
| Context | Context Class Description |
| ContextActivities | ContextActivities Model class |
| Extensions | Extensions model class |
| Group | Group model class |
| InteractionComponent | InteractionComponent Class Description |
| LanguageMap | Language map |
| RemoteLRS | Class used to communicate with a TCAPI endpoint synchronously |
| Result | Result Model class |
| Score | Score model class |
| State | State Class |
| Statement | Statement Class |
| StatementRef | StatementRef Class used when referencing another statement from a statement's object property |
| StatementsResult | Statements result model class, returned by LRS calls to get multiple statements |
| SubStatement | SubStatement Class used when including a statement like object in another statement's 'object' property |
| Verb | Verb model class |
| Enum Summary | |
|---|---|
| InteractionType | Possible interaction types |
| QueryResultFormat | |
| TCAPIVersion | Description |
|
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| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


WECOME TO MY WEBSITE
I'M A FRONT-END AND BACK-END DEVELOPER
IN METEOR AND REACT
Certified Software Developer
These is the List of Projects i Have Done During My academic years.
Click on the images below:


Record Button:
By default, a button is placed in the frame rate display that can be used to start and stop a recording session
while tracking a game's frame rate.
- Primary Color:
Sets the color of the button. This will also be used as the background color of the FPS display
when "Use for FPS display while recording" is enabled.
- Secondary Color:
Sets the border color of the button. This will also be used as the FPS text color when
"Use for FPS display while recording" is enabled.
- Use for FPS display while recording:
When enabled, these same colors are used to alter the color of the FPS display. Changing the color of
the FPS display while recording can be useful to make it more obvious that a recording is in process, especially
if "Display Recording Button in FPS Display" is disabled.
Note: Depending on how a particular game uses the mouse, this button may be inaccessible.
In that case, use the hotkey option above.
Note: Transparency (opacity) can be adjusted using the "opacity" slider when setting the color of the background or the text.
Auto Export Recordings to:
When selected, a csv report of the recording will automatically be created when the recording ends. A subfolder is created for each game when a report is created


Corpse Food
Rat Food
Destruction Need (Building)
Hunger Need (Meat Shank)
Health Need (Heart)
Herbivore Minigame Icon (Net)
Herbivore Minigame Icon (Tree)
Icon
Hat (Placeholder)
itch.io Logo


4 references to RaisePipelineStateEvents
System.Management.Automation (4)
engine\hostifaces\LocalPipeline.cs (2)
284
RaisePipelineStateEvents
();
726
RaisePipelineStateEvents
();
engine\hostifaces\pipelinebase.cs (2)
332
RaisePipelineStateEvents
();
395
RaisePipelineStateEvents
();


2019 16-inch MacBook Pro review: Bye-bye, butterfly
Apple’s new laptop seems designed to address three years of user complaints.
by Samuel AxonThe 16-inch MacBook Pro seems like the closest thing Apple might ever make to an apology tour. While it sticks to many of the principles established in the 2016 redesign for the product line, its major changes over the just-discontinued 15-inch model include an overhauled keyboard and improved thermal performance—the chief things its predecessor was criticized for.
With this new model, Apple is offering new speakers, new microphones, much faster graphics, and a slightly larger and heavier chassis. The resulting product is something like a hybrid between the 2019 15-inch MacBook Pro and the 2015 one.
Apple markets these products primarily to three sets of potential buyers: developers, creative professionals, and affluent users or users making an aspirational purchase to have the most refined, expensive laptop available. Based on our time with the machine, the last of those three is better off looking at the 13-inch model, the MacBook Air, or any number of Windows laptops. But Apple has made some clear improvements here for the first two groups.
While the 2019 16-inch MacBook Pro is not a totally new product, a few changes do make it the best MacBook Pro and a notable step up over its forebears for performance-minded users—provided they can afford it, anyway.
Specifications
| Specs at a glance: 2019 16-inch MacBook Pro | |
|---|---|
| Screen | 3072×1920 at 16 inches and 500 nits |
| OS | macOS Catalina 10.15.1 |
| CPU | 2.4GHz 8-core Intel Core i9 (5GHz Turbo) with 16MB shared L3 cache |
| RAM | 32GB 2666MHz DDR4 |
| GPU | AMD Radeon Pro 5500M with 8GB GDDR6 |
| HDD | 2TB SSD |
| Networking | 802.11ac Wi-Fi; IEEE 802.11a/b/g/n; Bluetooth 5.0 |
| Ports | 4x Thunderbolt 3, 3.5mm headphone |
| Size | 0.64-inch×14.09-inch×9.68-inch (1.62cm×35.79cm×24.59cm) |
| Weight | 4.3lbs (2kg) |
| Warranty | 1 year, or 3 years with AppleCare+ ($379) |
| Price as reviewed | $3,899 |
| Other perks | 720p FaceTime HD camera, stereo speakers, three microphones |
Apple MacBook Pro (16-inch)
Starting from $2,259 at Amazon (Ars Technica may earn compensation for sales from links on this post through affiliate programs.) The 2019 16-inch MacBook Pro’s internals closely follow the model set by other recent iterations, and the CPU configurations don't promise any performance advantage over the 15-inch, except potentially over sustained load due to the improved thermals. That said, the GPU choices are an improvement, and flash storage is cheaper, with more included in the default configurations at the same price points ($2,399 and $2,799) as before.
The $2,399 configuration comes with a 9th-generation, 2.6 GHz, 6-core, Intel Core i7 processor. For another $300, you can bump that up to a 2.4 GHz 8-core Intel Core i9. The base option for the $2,799 configuration is a 2.3GHz, 8-Core Intel Core i9. The $2,799 configuration can be upgraded to the marginally faster 2.4 GHz 8-core Intel Core i9 for an additional $200.
RAM starts at 16GB of 2666MHz DDR for both configurations, with options to upgrade to 32GB ($400 over the base) or a whopping 64GB ($800 over the base). This marks the first time the MacBook Pro has a 64GB option, and the 32GB choice was actually introduced not all that long ago. Again, Apple has addressed a common complaint here—albeit one from a pretty niche subset of users.
Recently, the $2,399 and $2,799 configurations came with 256GB and 512GB of SSD storage, respectively. That has been upped to 512GB and 1TB without a price hike. From 1TB, you can go up to 2TB for an additional $400, 4TB for $1,000, or 8TB for $2,200. That 8TB option will please video editors working in the field, but it’s likely overkill for quite a few users.
Since storage is paramount, the base configuration storage boost essentially amounts to a price drop for this machine compared to what a similarly configured MacBook Pro cost a year ago, for some users. The entry point hasn't changed price, but if you weren't satisfied with 256GB of SSD storage, you're going to spend less money this time around than you would have before, since you won't have to spend hundreds extra on upgrades to get to a reasonable amount of storage.
The last user-configurable option is video. The $2,399 model defaults to an AMD Radeon Pro 5300M with 4GB of GDDR6 memory. For an additional $100, you can go up to the AMD Radeon Pro 5500M with the same amount of memory—an upgrade that will likely be worth it for any users doing heavy video work—or to 8GB of memory for that 5500M for yet another $100.
The display has a higher resolution than that of this machine’s predecessors (3,072 by 1,920 pixels at 226 ppi). We’ll talk more about the display when discussing this laptop's design, but just know that nothing else has changed here specs-wise besides the pixel count.
Except for the speakers and microphones, the other specs and internals closely resemble those of the 15-inch MacBook Pro, too. There are four Thunderbolt 3/USB-C ports, any of which can be used for power.
This device has a 720p front-facing camera, and wireless connectivity options include 802.11ac Wi-Fi and Bluetooth 5.0. It’s disappointing and surprising to not see Wi-Fi 6 here, especially since the target user will sometimes be transferring very large files wirelessly, and Apple included Wi-Fi 6 in its latest iPhones.
Our review unit
The configuration we reviewed has the 2.4GHz 8-Core Intel Core i9 processor, the 8GB AMD Radeon Pro 5500M, 32GB of memory, and 2TB of flash storage. I'd argue this represents the most attractive, and possibly most common, configuration for most buyers, though many will opt to settle for 1TB storage instead given the cost. You could bump it up to 64GB of RAM, or even all the way up to 8TB of storage, but those options are really intended for special use cases.
Listing image by Samuel Axon
Design
There's nothing radically different about the look of the 2019 16-inch MacBook Pro, but it is noticeably heavier than the 15-inch model.
When sitting on its own, most people wouldn't immediately come to the conclusion that this is a different machine from its predecessor. It's still available in the same two colors, and the aesthetic hasn't changed—especially when the lid is closed. When it's open, the obvious clue that this is a new MacBook Pro would be the keyboard, which has separated out a physical escape key and the Touch ID button from a now-smaller Touch Bar.
Place it next to a 15-inch MacBook Pro, though, and you can tell that it's slightly larger in every dimension. In practice, again, it's the weight that really feels different. Here are the measurements of some recent MacBook Pro models compared:
| Model | Height | Width | Depth | Weight |
|---|---|---|---|---|
| 2019 16-inch MacBook Pro | 0.64 inch (1.62 cm) | 14.09 inches (35.79 cm) | 9.68 inches (24.59 cm) | 4.3 pounds (2.0 kg) |
| 2019 15-inch MacBook Pro | 0.61 inch (1.55 cm) | 13.75 inches (34.93 cm) | 9.48 inches (24.07 cm) | 4.02 pounds (1.83 kg) |
| 2015 15-inch MacBook Pro | 0.71 inch (1.8 cm) | 14.13 inches (35.89 cm) | 9.73 inches (24.71 cm) | 4.49 pounds (2.04 kg) |
So it's very slightly larger than the most recent 15-inch model, but it's still quite a bit thinner and lighter than the pre-Touch Bar, Retina 15-inch model.
Beyond that, a handful of individual elements are notably different: the display, the keyboard, the audio system, and the internal thermal management system.
Display
As indicated by the name, this MacBook Pro has a 16-inch screen, compared to 15.4 for the model it replaces. Most of this screen real estate is gained by reducing the size of the bezels. The reduction is most significant at the top, with smaller reductions on the sides.
There are definitely laptops out there with smaller bezels, but it's still a welcome change. It's not radical, but the laptop does feel a bit more spacious to use than the 15-inch MacBook Pro.
Nothing else about the display has changed other than a boost in resolution (and a modest increase in pixel density with it). It's just under 7% more pixels overall, so it won't be noticeable for most people.
This screen still has a maximum brightness of 500 nits, which is decent for a laptop, but it would have been nice to see a higher total for increased dynamic range for creatives doing color and other video work. Odds are that you're using a professional monitor to get any real work done if that's your profession, though.
Apple includes very good displays in these machines, and it tunes them at the factory so they're generally ready for professional use right out of the box, which isn't true for a lot of other laptops in this category. Across the board, it's an excellent display, with black and gray uniformity superior to most desktop monitors and exceptional color accuracy.
That said, many other laptops that compete with the MacBook Pro have 4K displays, and some have smaller bezels. Some have better contrast ratios, and some are even OLED now. Apple has good reasons for not moving to OLED on its laptops just yet, but it seems like a plausible next move. If you've used a 15-inch MacBook Pro any time in the past few years, don't expect this to be a dramatic step up on the display front. The keyboard, though, is another story.
A brand-new—err, brand-old—keyboard
Apple has hit the “undo” button on the butterfly keyboard design in this computer, and with it, three years of bad press and painfully expensive repair programs.
I’m going to admit an unpopular opinion here: in terms of the typing experience, I liked the butterfly keyboards. I liked them more than any other laptop keyboard I’ve ever used.
Personally, I’m disappointed that Apple was unable to make the butterfly mechanism work instead of going back to this older design. I know a very vocal set of users passionately disagrees, and that’s fine—it’s subjective—but the truth is that a not-nearly-as-vocal crowd of other users actually liked the butterfly keyboards.
But as I’ve written again and again on this specific subject, it doesn’t matter how nice it feels to type on if it doesn’t work. And in that respect, the butterfly keyboards were a failure. I’ve had my own 2016 MacBook Pro’s keyboard replaced twice. This is not uncommon.
Apple says it used its popular Magic Keyboard that ships with some of its desktops as the model for this one, and it shows. The scissor-switch mechanism is the same, the keys feel very similar to type on, and in some cases you can even swap out keycaps from one to the other—though a few are shaped a little differently, so it doesn’t work for all of them.
If you liked the keyboards in the 2012-2015 retina MacBook Pro models (immediately pre-Touch Bar and pre-butterfly), you’ll feel right at home on this keyboard. Using a 2012 Retina MacBook Pro and this one side by side, the keyboards feel quite similar.
The main difference is that the Touch Bar is still here, though it’s shorter than it was in the 2016 through early 2019 models. There is now a physical escape key, which developers will be thrilled to have back, and the Touch ID sensor/power button is separate from the Touch Bar like it is on the 2018 MacBook Air. The Touch ID button also has a matte finish now instead of a glossy one, so it looks distinct from the Touch Bar.
Apple hasn’t sacrificed any Touch Bar functionality here; all the previous virtual buttons and configurations are still possible in the smaller bar. You can still configure it to act as virtual function keys.
Oh, and Cupertino also cut the height of the left and right arrow keys in half, bringing back the inverted T design. I’m glad to see the escape key back; it wasn’t terrible to use the touch one as a developer, but this is definitely nicer. I'm doubtful that a very large percentage of users still use function keys except as media and brightness controls. If you do, that's a bummer, because you’re still stuck with the Touch Bar. But most people won’t care.
Like the 2015 MacBook Pro, this keyboard has a more gentle feel to it. The travel is a little different from the butterfly keys (1mm now). The mechanical-keyboard-like clankiness of the butterfly keyboard has been replaced with a very quiet, soft, bouncy feel. Again, if you’ve used older MacBook keyboards, you know exactly what you’re getting here. I do have one minor complaint: the backlight bleeds out around the edge of the keys, just as it did in older MacBook Pro models. This is hardly a big deal, but it is one tiny way the butterfly keyboards at least looked a little nicer despite all their issues.
A recent teardown by iFixit found that like the butterfly keyboard, the entire keyboard needs to be replaced with these laptops if even one switch fails. That’s too bad, because it would have been great to see an easier-to-fix MacBook Pro. That said, I expect this keyboard will fail less frequently; a lot of people out there are still using the same Magic Keyboard or 2012 MacBook Pro keyboard they’ve been using for seven years. The same can’t be said for the butterfly keyboards.
If you liked the butterfly keyboards, the feel of this one is close enough to be worth the (ostensible) reliability gains. If you didn't, you'll be glad to see a return to form here. In any case, it's a win for everyone.
Speakers and microphones
The 15-inch MacBook Pro had decent but not excellent speakers. Apple took this update as an opportunity to up its game there. The 16-inch has a six-speaker system, and Apple says it has speaker drivers positioned right next to each other in such a way that they cancel each other out and prevent vibrations that can lead to poor audio.
They sound very good—a dramatic improvement over the 15-inch iteration and almost any other laptop you're likely to use. They obviously don't sound as good as say, a HomePod or a Sonos One, or really good desktop computer speakers. And they won't replace studio monitors for music hobbyists and professionals. But if you're just watching a TV show, listening to music, or playing a game, they're more than adequate.
Apple has also included new microphones in the 16-inch MacBook Pro. Apple claims it has 40% less hiss than previously and that it can rival pro-quality digital mics. The notion that this could replace professional mics used by recording artists and podcasters seems a bit far-fetched, but it's likely to make Skype, FaceTime, and Google Hangouts calls a little more pleasant, and it should be enough for musicians to lay down very basic sketches intended to be turned into audience-facing work later.
Cooling and power
In 2018, the MacBook Pro got some bad press for throttling pretty severely under heavy load, particularly with the Core i9 models. Some of that was due to a firmware bug, which Apple expediently fixed.
To some degree, some throttling in these form factors is expected. These laptops are deliberately built as a compromise between portability and performance. But with this machine, Apple has made an effort to improve things with a new thermal architecture that it says allows the laptop to sustain up to 12 more watts of power under load. It does this with new fan blades and larger heat sinks, with up to 28% more airflow.
To power this, Apple ships the 16-inch MacBook Pro with a 99.8Wh battery. FAA regulation caps these batteries at 100, so this is nearly as large as is feasible while making things portable and easy for travelers. The laptop also ships with a 95-watt power adapter.
Performance
As noted above, Apple shipped this machine without a bump in CPU speed over the earlier 2019 refresh for the 15-inch model. But a new GPU and faster memory improved video performance here.
To test that, we evaluated the performance of this CPU compared to the 2018 model, plus other products across both the Mac and Windows ecosystems. We also wanted to see how the GPU stacks up against competing consumer products with similar form factors, as well as against the 2019 15-inch MacBook Pro with Vega 20 graphics.
Here's a full list of the machines involved in our benchmarks:
| Model | CPU | GPU |
|---|---|---|
| 2019 16-inch MacBook Pro | 8-core Intel Core i9 at 2.4GHz (5GHz Turbo) | AMD Radeon Pro 5500M 8GB GDDR6 |
| 2019 15-inch MacBook Pro | 8-core Intel Core i9 at 2.4GHz (5GHz Turbo) | Radeon Pro Vega 20 4GB HMB2 |
| 2018 15-inch MacBook Pro | 6-core Intel Core i9 at 2.9GHz (4.8GHz Turbo) | AMD Radeon Pro 560X 4GB GDDR5 |
| 2018 Mac mini | 6-core Intel Core i7-8700B at 3.2GHz (4.6GHz turbo) | Intel UHD Graphics 630 |
| 2017 iMac Pro | 10-core Intel Xeon W at 3GHz (4.5GHz Turbo) | AMD Radeon Pro Vega 64 16GB HMB2 |
| 2019 HP Spectre x360 15 | 6-core Intel Core i7 at 2.2GHz (4.1GHz Turbo) | Nvidia GeForce GTX 1050Ti 4GB |
| 2018 HP Spectre x360 15 | 4-core Intel Core i7 at 1.8GHz (4.0GHz Turbo) | Nvidia GeForce MX150 |
| 2018 Dell XPS 15 2-in-1 | 4-core Intel Core i7 at 3.1GHz (4.1GHz Turbo) | Radeon RX Vega M GL 4GB HMB2 |
| Microsoft Surface Laptop 2 | 4-core Intel Core i7 at 1.9GHz (4.2GHz Turbo) | Intel UHD Graphics 620 |
We ran our usual synthetic benchmarks, tested for throttling under sustained load, and also did a real-world test with Final Cut Pro since video performance is a big part of the pitch here.
In synthetic benchmarks, this machine performs almost identically in CPU tasks to the 2019 15-inch refresh we covered just a few months ago (this isn't a surprise), so we didn't waste space in the charts for that comparison. But you can see that it beats the 2018 MacBook Pro by a decent margin.
In terms of graphics performance, we're looking at as much as a 64% increase in performance over the similarly priced 2018 MacBook Pro from just a year ago—that machine had the fastest available GPU at the time, and this one smokes it. There's even a marked improvement over the Vega 20 GPU, which was the top configuration for the 2019 15-inch MacBook Pro until this machine launched.
Apple hasn't made any waves in CPU performance with the 2019 16-inch MacBook Pro, though it's still outstanding for a machine this size. But one of my main complaints about the 15-inch MacBook Pro for years was lackluster video performance compared to similar Windows machines, and that's no longer as big an issue as it once was. It helps with a lot of workflows that the SSD is marginally faster, too.
Here's what we found when facing the Vega 20 off against the new 5500M in a Final Cut Pro export of a short 8K video at 24fps with several filters and effects applied:
Final Cut Pro really takes advantage of Apple's hardware, but it's important to remember that the Adobe suite, well, doesn't do quite as well. The company recently announced all sorts of improvements for how its software runs on Macs, and those changes are all welcome, but it remains the case that if you want the best performance out of a MacBook Pro, you're best off using Apple's own software. No one's surprised, I'm sure. Depending on your point of view, this could be more of a criticism of Adobe than of Apple.
It's important to note that all of the above tests represent burst performance but not sustained performance. The 2018 MacBook Pro initially launched with serious throttling issues, but Apple released a firmware update that quickly stabilized things, with only a modest expected amount of throttling. When we tested this machine by doing heavy duty work in Final Cut and other programs while monitoring the CPU and GPU, we encountered no significant throttling under sustained heavy load. There are still some limitations in terms of just how crazy Apple can go with the components it puts in this chassis, but it looks like we're at a sweet spot here. The components in this machine perform consistently and well even over fairly long periods of time.
Apple's work on thermals seems to have produced some positive results. It's amazing what only a few millimeters here and there can do. Yes, this laptop is noticeably bigger than its predecessor, but the increase in size is quite subtle (the increased weight is less subtle), and it feels like a very reasonable tradeoff for this kind of performance.
Finally, there's battery life. Apple claims 11 hours of battery life for common use cases, compared to 10 hours for the 15-inch MacBook Pro. In our synthetic Wi-Fi browsing test, we actually got identical results to recent 15-inch MacBook Pro, and your mileage is clearly going to vary depending on the task.
Bye-bye, butterfly
I've observed for a while that Apple has been narrowing the target audience of its Mac product line. The Mac is not the PC for everyone, as it was once positioned. It seems to me that to Apple, iOS is now the platform for everyone, and macOS is specifically the platform that workers use to make content, software, and other experiences for that iOS audience.
That being the case, Apple has been in a weird position lately. There were many positive things about the most recent 15-inch MacBook Pro—again, I gave it a generally good review—but that very niche audience that Apple has focused in on more and more over the past few years was deeply frustrated with a couple of things about the machine. The company's commitment to keeping its laptops ultra-thin at the expense of performance and other practical aspects that mattered to those professional creators seemed like a vestige of a now-gone era when Apple was trying to reach everyone at once with the Mac.
By saying "bye" to the butterfly keyboard and using just a tiny bit more space in the chassis to improve the thermals, Apple has addressed user concerns about both reliability and performance. It's amazing how little ground the company's industrial designers had to give on portability to achieve these things. It's a worthwhile tradeoff, even if this laptop does feel awfully heavy.
Apart from maybe the speaker system and best-ever graphics performance in a Mac laptop, though, Apple has not introduced anything revolutionary here. The most exciting feature for many users is a keyboard that is basically the same keyboard the company has been making for many, many years. That means that the 16-inch MacBook Pro is a marriage of yesterday and today. I expect that marriage is going to stick for quite a few years.
Money being no object, it's easy to recommend this machine for anyone who wants to run macOS and do heavy development or creative work. But let's be real: money is very much an object for most people.
If you’re looking for the optimal performance-for-buck ratio, and you care less about things like industrial design, airplane-friendly power systems, portability, noise levels, and so on, there are much cheaper Windows laptops out there. And Windows is now in a better spot than it had been for many years; it’s no longer a situation like it was in 2008 when Windows was arguably a mess and Mac OS was vastly more stable and straightforward to use.
Most people today don't need a $2,500-$4,000 computer. If you're not doing heavy graphics work, get a 13-inch MacBook Pro or even a MacBook Air. Don't buy this computer just because it's the most premium option; this kind of performance in this form factor are only necessary for a certain audience of professionals. And if you don't care as much about portability and a plethora of niche quality-of-life benefits that Apple prioritizes in the Mac, get any number of powerful Windows laptops.
The Mac is still not for everybody, and it likely never again will be. But the 2019 16-inch MacBook Pro is now a better fit for the users Apple has been trying to reach. Bye bye butterfly; hello faster render times.
The good
- Better video performance than the MacBook Pro has recently been known for
- Includes an ostensibly much more reliable keyboard—escape key included
- The screen is still great
- Outstanding speaker quality
- Cheaper and more expansive storage configuration options
- macOS is a strong OS, and Adobe recently updated its software suite to better support what Apple’s products have to offer
The bad
- No Wi-Fi 6
- No 4K display
- It’s slightly heavier, louder, and hotter than its immediate predecessor
- Still difficult and expensive to repair or service
The ugly
- It’s priced outside of most consumers’—and even many prosumer hobbyists’—budgets


IP Addresses
IP Address stands for Internet Protocol address. This is a numerical label that is assigned to each device that connects to a network.
These addresses are used for identification and location addressing.
These work to establish a path to the host.
There are two different versions of the Internet Protocol today. One of them is ARPANET which is the older version which was first launched in 1983. The second version is the Internet Protocol version 4 which is newer.
Packets
A packet is a collection of data that computers use to communicate with each other on the network.
They allow computers to be able to send and recieve information quick and efficiently.
Routing
Routing is the process of moving a packet from the source to the destination. This is performed by the router and it enables messages to be passed from one device to another.
It is used to send data to any other device as shown in the image above.
Photographs by Home Network Geek.


| Modifier and Type | Method and Description |
|---|---|
void |
Board.actionPiece(Player attacker,
java.util.ArrayList<Move> moves,
Move from)
This method is called from the Game class in the method 'playerTurn'.
|
void |
Board.actionRangedPiece(Player attacker,
java.util.ArrayList<Move> moves,
java.util.ArrayList<Move> attacks,
Move from)
Same as the 'actionPiece' method above except with special modifications
to accommadate the functionality of ranged pieces which have separate move
sets for moving and capturing.
|
void |
Board.capturePiece(Player attacker,
Tower ally,
Tower enemy)
calls capturePiece() from Player class.
|
java.lang.Boolean |
Board.checkRowForPawn(Player player,
int col)
Checks the specified row for a pawn belonging to the specified player.
|
static void |
GUI.dropPiece(Player p,
int piece,
Move to)
Drops the selected piece at the selected location on the board.
|
java.lang.Boolean |
Board.dropPiece(Player player,
int piece,
Move pos)
finds a matching piece to "piece" and adds it to the top of the tower with position "pos",
if piece cannot be found in the player's hand then it doesn't do anything
|
java.util.ArrayList<Move> |
Board.getDrops(Player p)
Returns an array of moves that represent the available positions a piece can be dropped.
|
java.util.ArrayList<Move> |
Board.getDrops(Player player,
int piece)
Returns an array of Moves of all possible spots a specific piece that the player selected can drop.
|
void |
GUI.highlightDrops(Player p)
Highlights all the possible spots a piece can drop in yellow
|
void |
Board.playerInitializeDrop(Player p)
Begins the initial dropping phase.
|


Get Logos Gift Card Pics. Find & download free graphic resources for gift card. Create your own custom visa® reward cards.
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We enhance content!
- engage collaborative web services
- visualize web-enabled networks
- mesh frictionless methodologies
- matrix bleeding-edge solutions
- revolutionize collaborative supply-chains
- extend leading-edge e-tailers
- visualize visionary vortals
- utilize open-source interfaces
- syndicate customized functionalities
- matrix virtual mindshare
- revolutionize 24/7 e-commerce
- reintermediate enterprise convergence
- envisioneer bleeding-edge vortals
- maximize plug-and-play infomediaries


Chinese Architecture
Follow the changes of dynasties to see the changes and development of Chinese architecture
through the ages from the picture scroll.
- 1 Stop
The first stop we browse the ancient architecture from Han dynasty to Song Dynasty. The treatment of the roof and eaves is graceful and soft, and the artisans are good at using color, wood paint and tung oil approach to achieve both beauty and robustness, foil architecture also makes the structure more perfect.
DYNASTY
Wenxi Li
GRPH-3017
Citation: Palace Museum | Shanghai Museum
Nov 11, 2021
Place your mouse over the small
images to show more details.


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4-6
Four hours of cooking produces meltingly tender lamb, which works wonderfully with Lesley Water’s simple green bean, pomegranate and pine nut salad.
Preheat the oven to 180C/350F/Gas 4.
Pour the oil into a large ovenproof pan, add the potatoes, shallots, garlic, oregano, bay leaves, salt and pepper.
Cut the lamb into 8-10 large chunks. Place in a bowl, squeeze over the lemon juice and season, to taste, with salt and freshly ground black pepper.
Arrange the lamb and squeezed lemon quarters on top of the vegetables and pour over the stock. Cover with a tight-fitting lid and cook in the oven for 3½-4 hours, or until tender, basting the lamb with the juices occasionally.
For the salad, add the beans to a pan of boiling water set over a medium heat and simmer for a few minutes. Prepare a bowl of ice water. Once tender, drain the beans well and place in the ice water. Drain again once cold.
Combine all the other salad ingredients together in a bowl and season well.
To serve, spoon the lamb onto a large warm platter and top with the potatoes, shallots, garlic and herbs and spoon over the juices. Serve with the salad.


What is CSS?
- CSS stands for Cascading Style Sheets
- CSS describes how HTML elements are to be displayed on screen, paper, or in other media
- CSS saves a lot of work. It can control the layout of multiple web pages all at once
- External stylesheets are stored in CSS files
- CSS is used to define styles for your web pages, including the design, layout and variations in display for different devices and screen sizes.


| < Day Day Up > |
10.8. Making Emacs Work the Way You Think It ShouldEmacs not only has per-user customizations; it can also have sitewide customizations. If Emacs isn't doing what you expect it to, you might want to try inhibiting any global customization file by starting Emacs with no customization. You can do that by using one of these command-line options when you invoke Emacs.
If you normally start Emacs from an icon, it's helpful to learn how to start it from the command-line for cases like this. (You may also want to use the -debug option sometime to help you figure out what's wrong with your .emacs file if it is messed up following a change.) Chapter 13 describes how to start Emacs from the command-line for Mac OS X and Windows users. You can also inhibit global initialization by creating a one-line .emacs file in your home directory. It should look exactly like this: (setq inhibit-default-init t) ; no global initialization Start Emacs again. This file prevents Emacs from reading its global initialization file. There's still one awkward situation: what if you're sitting down at someone else's system? You start Emacs, and all of a sudden you're faced with someone else's "private" key bindings and features. Even in this situation, there's a solution:
The -u option may not work unless you're on a network where users have a shared home directory structure. It assumes either that you have the same home directory on every system, or that you have a different home directory on every system and an up-to-date .emacs file in all of your home directories. If all that fails, fear not. You have more options. Let's take the worst case scenario: you're on someone else's system and you can't start Emacs from the command line. Go ahead and start Emacs. You can temporarily overwrite the other user's key bindings by loading up your own key bindings file in a buffer and running it with M-x eval-buffer. You probably should make a separate file with key bindings and other variable options rather than using your .emacs file. That's because many times your .emacs file will have requests to load libraries that exist on a path that works only from your own system. If you find yourself jumping to a lot of different machines, it's worth the effort to create a portable "rebinding" file and put it somewhere accessible like a web page or a shared file server. Then you can evaluate it manually from your current Emacs. |
| < Day Day Up > |


| Home | ClipperLib | ClipperOffset |
Del.» property MiterLimit: double; //read and write
C++ » double MiterLimit;
C# » public double MiterLimit {get; set;}
This property sets the maximum distance in multiples of delta that vertices can be offset from their original positions before squaring is applied. (Squaring truncates a miter by 'cutting it off' at 1 × delta distance from the original vertex.)
此属性设置的最大距离在三角洲的倍数的顶点可以从原来的位置应用平方之前所抵消。（磨边截断在从原始顶点1×增量距离斜切通过“切割它关闭”。）
The default value for MiterLimit is 2 (ie twice delta). This is also the smallest MiterLimit that's allowed. If mitering was unrestricted (ie without any squaring), then offsets at very acute angles would generate unacceptably long 'spikes'.
对于miterlimit默认值为2（即两次三角洲）这也是允许的最小的miterLimit分别。如果斜角是不受限制的（即没有任何平方），然后在非常尖锐的角偏移会产生非常长'尖峰'。
An example of an offsetting 'spike' at a narrow angle that's a consequence of using a large MiterLimit (25) ...
在一个狭窄的角度，是一种利用大miterlimit后果抵消'尖角'例（25）…
Copyright ©2010-2014 Angus Johnson - Clipper 6.2.1 - Help file built on 1-November-2014


Christoph Minixhofer
Research PhD student at The University of Edinburgh
Coming Soon
LightningFastSpeech
A reimplementation of FastSpeech2 using PyTorch Lightning.
157
Maintained
Drought Forecasting Dataset
Predicting continental US drought levels using meteorological & soil data.
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Archived
vagrant-lamp-craft
Starting point for developing sites powered by Craft CMS with Vagrant.


This title is from EXPLICIT.json
This description is from EXPLICIT.json
# `explicit` context
Should be from explicit.json
, then the YFM of the page, then the options, then the root.
This description is from EXPLICIT.json
page
contextShould be from the YFM of the page, then the options, then the root.
this
contextShould be from the YFM of the page, then the options, then the root.
data
contextShould be from the YFM of the include, foo
, first; then fallback on foo.json
, then the page, then the options, then the root.
foo
contextShould be from the YFM of the include, foo
, first; then fallback on foo.json
, then the page, then the options, then the root.
This description is from FOO.json
Should be from the YFM of the include, foo
, first; then fallback on foo.json
, then the page, then the options, then the root.
bar
context (block)Should be from the YFM of the include, foo
, first; then fallback on foo.json
, then fallback on bar.json
, then the page, then the options, then the root.
This lead is from BAR.json
This description is from BAR.json
parent
context (block)Should use the context from the page.


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Every organization in the system is trying to solve one of the society's issues; providing food, shelter, healthcare, clothing, education, entertainment and so on. However, their importance for the society differ.
One of the issues in the current system is that the consumers usually interact with only the organizations about their daily needs and allocate the resources based on this interaction, instead of their importance.
For example, one might think that education sector is more important for the society than most of the other sectors. On the other side, in order to make transactions with a sector, you should become a consumer of that sector, in this case either be a student or a relative of a student. If not, it would be out of the routine spending and cannot be supported in a systematic way.
Sector Index aims to give us the opportunity to directly set each sectors' priority by comparing them side by side, then distribute the resources to related organizations based on the importance of their work.
To make the reasoning behind this new approach clear; if the society could solve its fundamental issues, it would be getting much easier to focus on higher level ones. Solve hunger first, solve poverty first, so we will have more people that can contribute to entertainment or produce better cosmetic products.
In the next sample, you will see four main sectors. Which sectors are more important and should have more resources?
... And so this leads to the paradox that because the disease (malaria) is only in the poorer countries, it doesn't get much investment. For example, there's more money put into baldness drugs than are put into malaria. Now, baldness, it's a terrible thing. And rich men are afflicted. And so that's why that priority has been set.
+
button increase Number of Sales, initially all organizations will have same Total Income.
Sector
link below and rate each item based on their importance.
Organization
in breadcrumb links to navigate back to initial view and see the difference in Total Income.
This sample should be enough as it is to demonstrate the idea how we could control and allocate our resources. However, in order to understand the real impact of this index, we should be able to compare the results of this sample with the actual one. We would like to add this comparison in the future.
Alternatively, we could directly list the issues as well, instead of sectors; providing clean water, shelter, reducing hunger, poverty, violence etc. The system allows to create such variations as seen in the 'Prologue', as long as we can create proper categorization for the organizations.


§ ShoppingButton()
Constructor function for ShoppingButton class.
- Parameters
-
| cart | Sets shopping cart for the current shopping operation. |
§ AddProduct()
| void ShoppingButton::AddProduct |
( |
list< Product *> & |
products | ) |
|
Function for adding a product to the customer's shopping cart.
- Parameters
-
| products | A list of Products in the system. |
§ ListShoppingCart()
| bool ShoppingButton::ListShoppingCart |
( |
list< Product *> & |
products | ) |
|
Function for listing the customer's shopping cart.
- Parameters
-
| products | A list of Products in the system. |
§ Login()
| bool ShoppingButton::Login |
( |
list< Customer > & |
customers | ) |
|
Function for a customer to log in to the system.
- Parameters
-
| customers | A list of customers in the system. |
§ RemoveProduct()
| void ShoppingButton::RemoveProduct |
( |
list< Product *> & |
products | ) |
|
Function for removing a product from the customer's shopping cart.
- Parameters
-
| products | A list of Products in the system. |
The documentation for this class was generated from the following files:
- C:/Users/Tahir/Documents/GitHub/OOP_Project_Online_Book_Store/OOP_Project_Online_Book_Store/ShoppingButton.h
- C:/Users/Tahir/Documents/GitHub/OOP_Project_Online_Book_Store/OOP_Project_Online_Book_Store/ShoppingButton.cpp


{{/* gotype: github.com/JustaPenguin/assetto-server-manager.eventImportTemplateVars */}} {{ define "title" }}Import a Championship Event{{ end }} {{ define "content" }} {{ $writeAccess := WriteAccess }}
Choose results for the following sessions (you may leave some session results unselected).


This sample shows hot to setup proper SFTP communication as client and server in Citrus. The sftp-client component uses private key authentication and creates a new directory on the server, stores a file and retrieves the same file in a single test.
SFTP features are also described in detail in reference guide
Objectives
We want to setup both FTP client and server components in our project in order to test a file transfer via FTP. The client authenticates to the server using username password credentials. The ftp-server component will receive incoming requests for validation and provide the FTP user account workspace to the client.
First of all let us setup the necessary components in the Spring bean configuration:
@Bean
public SftpClient sftpClient() {
return CitrusEndpoints.sftp()
.client()
.strictHostChecking(false)
.port(2222)
.username("citrus")
.privateKeyPath("classpath:ssh/citrus.priv")
.build();
}
@Bean
public SftpServer sftpServer() {
return CitrusEndpoints.sftp()
.server()
.port(2222)
.autoStart(true)
.user("citrus")
.password("admin")
.allowedKeyPath("classpath:ssh/citrus_pub.pem")
.build();
}
The sftpServer is a small but fully qualified SFTP server implementation in Citrus. The server receives a user
that defines the user account and its home directory. All commands
will be performed in this user home directory. You can set the user home directory using the userHomePath
attribute on the server. By default this is a directory located in /Users/cdeppisc/Projects/Citrus/citrus-website/target/{serverName}/home/{user}
.
In case you want to setup some files in that directory in order to provide it to clients, please copy those files to that home directory prior to the test.
The sftp-client connects to the server using the user credentials and/or the private key authentication. The client uses the private key where the server adds the public key to the list of allowed keys.
In a sample test we first create a new subdirectory in that user home directory.
echo("Create new directory on server");
send(sftpClient)
.message(FtpMessage.command(FTPCmd.MKD).arguments("todo"));
receive(sftpClient)
.message(FtpMessage.success(257, "Pathname created"));
As you can see the client is passing a MKD
signal to the server. The user login procedure is done automatically and the directory creation is also
dome automatically on the SFTP server. This is because the test case is not able to intercept those commands such as MKD and LIST on the server. The commands are directly
executed in the user home directory.
Now lets store a new file in that user directory.
echo("Store file to directory");
send(sftpClient)
.fork(true)
.message(FtpMessage.put("classpath:todo/entry.json", "todo/todo.json", DataType.ASCII));
receive(sftpServer)
.message(FtpMessage.put("@ignore@","/todo/todo.json", DataType.ASCII));
send(sftpServer)
.message(FtpMessage.success());
receive(sftpClient)
.message(FtpMessage.putResult(226, "@contains(Transfer complete)@", true));
Now we have both client and server interaction in the same test case. This requires us to use fork=true
option on all client
requests as we need to continue with the test in order to handle the server interaction, too. We can store a new file todo/entry.json
which is transmitted
to the server using ASCII
file mode.
The FTP server is receiving the STOR
signal providing a success response in order to mark completion of the file transfer. After that the file should be created in
the user home directory in path todo/todo.json
. You can validate the file content by reading it from that directory in another test action.
Now we should be also able to list the files in that directory:
echo("List files in directory");
send(sftpClient)
.message(FtpMessage.list("todo"));
receive(sftpClient)
.message(FtpMessage.result(getListCommandResult("todo.json")));
private ListCommandResult getListCommandResult(String ... fileNames) {
ListCommandResult result = new ListCommandResult();
result.setSuccess(true);
result.setReplyCode(String.valueOf(150));
result.setReplyString("List files complete");
ListCommandResult.Files expectedFiles = new ListCommandResult.Files();
ListCommandResult.Files.File currentDir = new ListCommandResult.Files.File();
currentDir.setPath(".");
expectedFiles.getFiles().add(currentDir);
ListCommandResult.Files.File parentDir = new ListCommandResult.Files.File();
parentDir.setPath("..");
expectedFiles.getFiles().add(parentDir);
for (String fileName : fileNames) {
ListCommandResult.Files.File entry = new ListCommandResult.Files.File();
entry.setPath(fileName);
expectedFiles.getFiles().add(entry);
}
result.setFiles(expectedFiles);
return result;
}
Now we can also retrieve the file from the server by calling the RETR
operation.
echo("Retrieve file from server");
send(sftpClient)
.fork(true)
.message(FtpMessage.get("todo/todo.json", "target/todo/todo.json", DataType.ASCII));
receive(sftpServer)
.message(FtpMessage.get("/todo/todo.json", "@ignore@", DataType.ASCII));
send(sftpServer)
.message(FtpMessage.success());
receive(sftpClient)
.message(FtpMessage.result(getRetrieveFileCommandResult("target/todo/todo.json", new ClassPathResource("todo/entry.json"))));
private GetCommandResult getRetrieveFileCommandResult(String path, Resource content) throws IOException {
GetCommandResult result = new GetCommandResult();
result.setSuccess(true);
result.setReplyCode(String.valueOf(226));
result.setReplyString("Transfer complete");
GetCommandResult.File entryResult = new GetCommandResult.File();
entryResult.setPath(path);
entryResult.setData(FileUtils.readToString(content));
result.setFile(entryResult);
return result;
}
This completes our test as we were able to interact with the SFTP server using the client signals.
Run
You can execute some sample Citrus test cases in this sample in order to write the reports. Open a separate command line terminal and navigate to the sample folder.
Execute all Citrus tests by calling
mvn integration-test
You should see Citrus performing several tests with lots of debugging output.
And of course green tests at the very end of the build and some new reporting files in target/citrus-reports
folder.
Of course you can also start the Citrus tests from your favorite IDE. Just start the Citrus test using the TestNG IDE integration in IntelliJ, Eclipse or Netbeans.


generators module¶
Tools to build out-of-the box models
-
create_hypercube_flow
(d, model_type='realnvp', mask_type='checkerboard', **params)[source]¶ Provide a high-level generator with default behavior
-
create_n_ary_hypercube_flow
(cell, d, n=2, repetitions=2, **cell_options)[source]¶ Create a sequential normalizing flow on the d-dimensional unit hypercube based on a given coupling cell with a n-ary mask. Subsequent cells have masks offset by one at each step until all possible masks have been used.
- Parameters
cell – coupling cell class
d (
int
) – dimension of the unit hypercuben – period of the n-ary mask
repetitions – how many times a full repetition through all possible masks is repeated
cell_options – options to be passed at the coupling cell instantiation
- Returns
- Return type


A code editor is the place where programmers spend most of their time.
There are two main types of code editors: IDEs and lightweight editors. Many people use one tool of each type.
The term IDE (Integrated Development Environment) refers to a powerful editor with many features that usually operates on a “whole project.” As the name suggests, it’s not just an editor, but a full-scale “development environment.”
An IDE loads the project (which can be many files), allows navigation between files, provides autocompletion based on the whole project (not just the open file), and integrates with a version management system (like git), a testing environment, and other “project-level” stuff.
If you haven’t selected an IDE yet, consider the following options:
For Windows, there’s also “Visual Studio”, not to be confused with “Visual Studio Code”. “Visual Studio” is a paid and mighty Windows-only editor, well-suited for the .NET platform. It’s also good at JavaScript. There’s also a free version Visual Studio Community.
Many IDEs are paid, but have a trial period. Their cost is usually negligible compared to a qualified developer’s salary, so just choose the best one for you.
“Lightweight editors” are not as powerful as IDEs, but they’re fast, elegant and simple.
They are mainly used to open and edit a file instantly.
The main difference between a “lightweight editor” and an “IDE” is that an IDE works on a project-level, so it loads much more data on start, analyzes the project structure if needed and so on. A lightweight editor is much faster if we need only one file.
In practice, lightweight editors may have a lot of plugins including directory-level syntax analyzers and autocompleters, so there’s no strict border between a lightweight editor and an IDE.
The following options deserve your attention:
The editors in the lists above are those that either I or my friends whom I consider good developers have been using for a long time and are happy with.
There are other great editors in our big world. Please choose the one you like the most.
The choice of an editor, like any other tool, is individual and depends on your projects, habits, and personal preferences.


Copyright © 2009 Michael Truog
Version: 0.0.7 Oct 29 2009 20:47:21 ------------------------------------------------------------------------
This module defines the cloud_data_interface behaviour.
Required callback functions: start_link/2, handle_stop/1, handle_do_queries/2.
Authors: Michael Truog (mjtruog [at] gmail (dot) com).
| behaviour_info/1 |
cloud_data_interface callbacksstart_link/2
|
| do_queries/2 |
Do queries in the data module.Not all the queries provided will be handled by the data module. |
| do_queries_group/5 |
Group queries based on the DataTitleDo queries that are grouped based on the DataTitle to be processed (only queries that contain the specified DataTitle can be processed with the Module:Function/2 call that is specified (with arguments [Processing, State] where Processing is the list of queries and State is anything specific to the function) all the processed queries are removed from the QueryList so that it may be processed in other data modules, if necessary. |
| stop/1 |
Stop the data module.. |
behaviour_info(X1) -> any()
start_link(DataTitle :: atom(), Arguments :: list()) -> {ok, pid()} | {error, any()}.
The start_link/2 function starts the data module with the arguments supplied to the data configuration.
handle_stop(DataTitle :: atom()) -> any().
The handle_stop/1 function stops the data module and causes it to exit.
handle_do_queries(DataTitle :: atom(), QueryList :: list(string())) -> {ok, list(string())} | {error, list(string())}.
do_queries(DataTitle, QueryList) -> any()
do_queries_group(QueryList, Module, Function, State, DataTitle) -> any()
stop(DataTitle) -> any()
Generated by EDoc, Oct 29 2009, 20:47:21.


A Path
object represents a path in a file system for a file,
a directory, and a symbolic link.
Path
is an interface in the java.nio.file
package.
public interface Path extends Comparable<Path>, Iterable<Path>, Watchable { }
We can perform two kinds of operations on a Path
object:
Path
to access the components of a path such as the file name, root name, etc.
.txt
,
comparing if two paths are identical,
checking if a path is absolute or relative, etc.
Files
class to perform the file I/O operations on a Path
object.
A FileSystem
object acts as a factory to create a Path
object.
getPath()
method from FileSystem
creates a Path
object.
The following code creates a Path
object for file path C:\Java_Dev\test1.txt
on Windows:
Path p1 = FileSystems.getDefault().getPath("C:\\Java_Dev\\test1.txt");
We can pass components of a path separately to the getPath()
method when constructing a Path
object.
The following statement creates a Path
object to represent the C:\Java_Dev\test1.txt
path on Windows:
Path p2 = FileSystems.getDefault().getPath("C:", "Java_Dev", "test1.txt");
Paths
class can create a Path
object from the components of a path string or a URI
.
The Paths.get()
static method creates a Path
object.
The following creates Path
objects to represent the same path, C:\Java_Dev\test1.txt
:
Path p3 = Paths.get("C:\\Java_Dev\\test1.txt"); Path p4 = Paths.get("C:", "Java_Dev", "test1.txt");
We can create a Path
object from an empty path such as Paths.get("")
.
A Path
object with an empty path refers to the default directory,
which is the same as the current working directory.
import java.nio.file.Path; import java.nio.file.Paths; public class HelloWorld { public static void main(String[] args) { Path path = Paths.get(""); System.out.println(path.getClass()); System.out.println(path.toAbsolutePath()); } }
class sun.nio.fs.WindowsPath D:\git-repo\learn-java-nio
A path in a file system consists of one or more components.
The getNameCount()
method returns the number of components in a Path
object excluding the root.
For example, the path C:\Java_Dev\test1.txt
consists of three components:
a root of C:
, and two components named Java_Dev
and test1.txt
.
In this case, the getNameCount()
method will return 2
.
The getName(int index)
method returns the component name at the specified index.
The component that is closest to the root has an index of 0
.
The component that is farthest from the root has an index of count - 1
.
C:\Java_Dev\test1.txt
, the Java_Dev
component has an index of 0
and the test1.txt
component has an index of 1
.
The getParent()
method returns the parent of a path.
If a path does not have a parent, it returns null
.
The parent of the path C:\Java_Dev\test.txt
is C:\Java_Dev
.
The relative path test.txt
has no parent.
The getRoot()
method returns the root of the path.
If a path does not have a root, it returns null
.
For example, the path C:\Java_Dev\test1.txt
on Windows has C:\
as its root.
The getFileName()
method returns the file name denoted by the path.
If a path has no file name, it returns null
.
The file name is the farthest component from the root.
For example, in the path C:\Java_Dev\test1.txt
, test1.txt
is the file name.
We can check if a path represents an absolute path by using the isAbsolute()
method.
The following code shows how to access components of a Path
object.
import java.nio.file.Path; import java.nio.file.Paths; public class NIO_Path_Example { public static void main(String[] args) { Path p1 = Paths.get("C:\\Java_Dev\\test1.txt"); printDetails(p1); Path p2 = Paths.get("test1.txt"); printDetails(p2); } public static void printDetails(Path p) { System.out.println("Details for path: " + p); int count = p.getNameCount(); System.out.println("Name count: " + count); for (int i = 0; i < count; i++) { Path name = p.getName(i); System.out.println("Name at index " + i + " is " + name); } Path parent = p.getParent(); Path root = p.getRoot(); Path fileName = p.getFileName(); System.out.println("Parent: " + parent + ", Root: " + root + ", File Name: " + fileName); System.out.println("Absolute Path: " + p.isAbsolute()); } }
Details for path: C:\Java_Dev\test1.txt Name count: 2 Name at index 0 is Java_Dev Name at index 1 is test1.txt Parent: C:\Java_Dev, Root: C:\, File Name: test1.txt Absolute Path: true Details for path: test1.txt Name count: 1 Name at index 0 is test1.txt Parent: null, Root: null, File Name: test1.txt Absolute Path: false
We can get different type of representations for a path.
Suppose we create a Path
object as follows:
Path p1 = Paths.get("test.txt");
Here, p1
is a relative path.
We can get the absolute path that is represented by p1
using its toAbsolutePath()
method as follows:
Path p1AbsPath = p1.toAbsolutePath();
Now the p1AbsPath
is the absolute path for p1
.
For example, on Windows, p1AbsPath
may look like C:\testapp\test.txt
.
If a path is not an absolute path, the toAbsolutePath()
method uses default directory to resolve the path.
If the path is an absolute path, the toAbsolutePath()
method returns the same path.
We can use the toRealPath()
method to get the real path of an existing file.
It returns a canonical path to an existing file.
If the path represents a symbolic link, it returns the real path of the target file.
We can pass a link option to this method indicating whether we want to follow the symbolic link to its target.
If the file represented by the path does not exist, the toRealPath()
throws an IOException
.
The following code shows how to get the real path from a Path
object:
import java.io.IOException; import java.nio.file.LinkOption; import java.nio.file.Path; import java.nio.file.Paths; public class NIO_Path_Example_RealPath { public static void main(String[] args) throws IOException { Path p2 = Paths.get("link01"); // Follow link for p2, if it is a symbolic link Path p2RealPath = p2.toRealPath(); System.out.println("p2RealPath: " + p2RealPath); Path p3 = Paths.get("link02"); // Do not follow link for p3, if it is a symbolic link Path p3RealPath = p3.toRealPath(LinkOption.NOFOLLOW_LINKS); System.out.println("p3RealPath: " + p3RealPath); } }
p2RealPath: D:\git-repo\learn-java-nio\README.md p3RealPath: D:\git-repo\learn-java-nio\link02
Use the toUri()
method of Path
to get its URI
representation.
The following code shows how to get the URI
form of a path.
import java.nio.file.Path; import java.nio.file.Paths; public class NIO_Path_Example_URI { public static void main(String[] args) throws Exception { Path p2 = Paths.get("test2.txt"); java.net.URI p2UriPath = p2.toUri(); System.out.println("Absolute Path: " + p2.toAbsolutePath()); System.out.println("URI Path: " + p2UriPath); } }
Absolute Path: D:\git-repo\learn-java-nio\test2.txt URI Path: file:///D:/git-repo/learn-java-nio/test2.txt


{% extends '_landing.html' %} {% block title %}Agendas | Esper{% endblock %} {% block head %} {{ super() }} {% endblock %} {% set login_path = "/tb" %} {% block header_classes %}dark{% endblock %} {% block main %}
Esper checks that your meetings have agendas.
24 hours before each meeting you enable Agenda Check for, Esper sends an email or Slack message to meeting guests asking if they have any agenda items.
Each guest tells Esper what they want to discuss at the meeting.
Esper collects the agenda items and sends out a unified agenda one hour before the meeting.


Hi there and welcome to my portfolio page. I am
My name is Alex Wambugu, an entrepreneur by profession. I hold a Bachelor of Science degree (Analytical Chemistry with Management) from Kenyatta University. I am an aspiring entrepreneur and I am currently incubated at the CBIIC also at Kenyatta University.
In line with my educational background and professional orientation, I am pursuing a class in programming so that I can develop the necessary software infrastructure to support my professional growth
My hobbies include:
Below is a list of some of the projects that I have developed so far:
This is the first website I created as part of the Moringa School learning process. It has two pages, the first being a homepage, and the second featuring a collection of some of my favorite things.
It was developed using the following tools and languages:
This is a simple chemistry atoms guessing games. The website displays an element from the periodic table and users try to guess the atomic number of the element.
It was developed using the following tools and languages:


Hey friends ! Iam back and i wanted to share with you some awesome pictures we took during our trip to Santa
Monica. We had wonderful holidays at the beach. Kisses to all !
Hey friends ! Iam back and i wanted to share with you some awesome pictures we took during our trip to Santa
Monica. We had wonderful holidays at the beach. Kisses to all !


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Coffee at JavaJam
update
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Regular house blend, decaffeinated coffee, or flavor of the day.
Endless Cup $2.00
update
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House blended coffee infuse in to a smooth, steamed milk
Single $2.00
Double $3.00
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Sweetened espresso blended with icy-cold milk and served in a chilled glass.
Single $4.75
Double $5.75