Most ways that isobaric labeling data are analyzed hinder statistical testing and data visualization. This notebook will analyze data from (Ref-1) to demonstrate a two-condition (3 controls and 4 treatments) comparison done with 10-plex TMT reagents using the SPS MS3 method (Ref-2) on a Thermo Fusion instrument. The notebook will show how to:
The KUR1502 data were mouse bone marrow cell cultures where the "media" samples are controls (one of the 4 did not label) and there are leukemia exosome-dosed cells (4 "exosome" samples). 10-plex TMT was used on a Thermo Fusion using the SPS MS3 method. The PAW pipeline (Ref-3) is our in-house processing using the Comet search engine (Ref-4). The pipeline converts the RAW files into text files using MSConvert from the Proteowizard toolkit (Ref-5).
Data from the MS2 scans are extracted for the Comet searches and the reporter peak heights are extracted from the MS3 scans. The pipeline uses the target/decoy method to make score histograms and determine score filtering thresholds. Accurate mass is used to create conditional score histograms where target/decoy delta mass histograms are used to set the accurate mass windows. Basic parsimony principles are used for protein inference and 2 peptides per protein were required. An additional protein grouping step was used to combine nearly identical peptide sets (often these are housekeeping genes).
1. Huan, J., Hornick, N.I., Goloviznina, N.A., Kamimae-Lanning, A.N., David, L.L., Wilmarth, P.A., Mori, T., Chevillet, J.R., Narla, A., Roberts Jr, C.T. and Loriaux, M.M., 2015. Coordinate regulation of residual bone marrow function by paracrine trafficking of AML exosomes. Leukemia, 29(12), p.2285.
2. McAlister, G.C., Nusinow, D.P., Jedrychowski, M.P., Wühr, M., Huttlin, E.L., Erickson, B.K., Rad, R., Haas, W. and Gygi, S.P., 2014. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Analytical chemistry, 86(14), pp.7150-7158.
3. Wilmarth, P.A., Riviere, M.A. and David, L.L., 2009. Techniques for accurate protein identification in shotgun proteomic studies of human, mouse, bovine, and chicken lenses. Journal of ocular biology, diseases, and informatics, 2(4), pp.223-234.
4. Eng, J.K., Jahan, T.A. and Hoopmann, M.R., 2013. Comet: an open‐source MS/MS sequence database search tool. Proteomics, 13(1), pp.22-24.
5. Chambers, M.C., Maclean, B., Burke, R., Amodei, D., Ruderman, D.L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J. and Hoff, K., 2012. A cross-platform toolkit for mass spectrometry and proteomics. Nature biotechnology, 30(10), p.918
There are some hidden constraints in these experimental designs. The total amount of protein is usually fixed at the same value across all samples (an individual biological replicate). High abundance proteins are constrained by the total protein amount and do not have the same freedom for abundance differences that proteins that are smaller fractions of the total have. Higher abundance proteins must, by experimental design, have moderated differences in means and reduced variances. Lower abundance proteins have more freedom and will not be as constrained.
The constrained nature of these experimental designs (both genomics and proteomics) has implications in normalization algorithms. An increased expression in abundant proteins will "push" all other proteins down (their abundance total has to remain the same). Grand total normalization like we use above can be too simplistic. The differential expression testing package edgeR (Ref-6) includes a trimmed mean of M values (TMM) normalization step (Ref-7) designed for these types of samples.
6. Robinson, M.D., McCarthy, D.J. and Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), pp.139-140.
7. Robinson, M.D. and Oshlack, A., 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome biology, 11(3), p.R25.
# load libraries
library("tidyverse")
library("psych")
library("gridExtra")
library("scales")
library("limma")
library("edgeR")
# read the grouped protein summary file
paw_raw <- read_tsv("grouped_protein_summary_TMT_8_sorted.txt", skip = 4,
n_max = 5427, guess_max = 5427)
# extract protein accession column and TMT data
# need to exclude any rows with an entry in "Filter" column
# there are also 4 unused channels (of the 11-plex slots)
paw_tmt <- paw_raw %>%
filter(., is.na(Filter)) %>%
select(Accession, starts_with("TotInt_")) %>%
select(-contains("127N"), -contains("128N"), -contains("130N"), -contains("131C"))
# separate accessions from the data
accession <- paw_tmt$Accession
paw_tmt <- paw_tmt %>% select(-Accession)
# rename the columns and gather the conditions
colnames(paw_tmt) <- c("Media_2.1", "Exo_2.1", "Exo_2.2", "Media_2.2",
"Exo_3.1", "Exo_3.2", "Media_3.2")
paw_tmt <- paw_tmt %>% select(contains("Media"), contains("Exo"))
head(paw_tmt)
nrow(paw_tmt)
# load data into DGEList object
group <- c(rep("media", 3), rep("exosome", 4))
y <- DGEList(counts = paw_tmt, group = group, genes = accession)
y$samples
EdgeR normalization is actually done in two steps. The first, called a library size adjustment, is like a sample loading normalization. This gets rid of the big differences between samples so that the TMM algorithm has better starting data. We need to compute the normalized intensities from the TMM factors.
# run the TMM normalization
y <- calcNormFactors(y)
# set colors for plotting
color <- c(rep("red", 3), rep("blue", 4))
# function to compute the normalized intensities
apply_tmm_factors <- function(y, color = NULL, plot = TRUE) {
# computes the tmm normalized data from the DGEList object
# y - DGEList object
# returns a dataframe with normalized intensities
# compute grand total (library size) scalings
lib_facs <- mean(y$samples$lib.size) / y$samples$lib.size
# the TMM factors are library adjustment factors (so divide by them)
norm_facs <- lib_facs / y$samples$norm.factors
cat("Overall Factors (lib.size+TMM):\n", sprintf("%-5s -> %f\n",
colnames(y$counts), norm_facs))
# compute the normalized data as a new data frame
tmt_tmm <- as.data.frame(sweep(y$counts, 2, norm_facs, FUN = "*"))
colnames(tmt_tmm) <- str_c(colnames(y$counts), "_tmm")
# visualize results and return data frame
if(plot == TRUE) {
boxplot(log10(tmt_tmm), col = color, notch = TRUE, main = "TMM Normalized data")
}
tmt_tmm
}
# get the normalized data values
paw_tmt_tmm <- apply_tmm_factors(y, color)
# we need to get dispersion estimates
y <- estimateDisp(y)
plotBCV(y, main = "Dispersion trends")
There are abundance-dependent dispersion effects in many measurement methods. Higher abundance signals have better signal-to-noise (or are the result of more averaging), so they can appear more precise than lower abundance signals. We see that the trended variance blue line increases as the protein intensity decreases. The statistical testing will use trended variances (estimated from multiple proteins) as a function of protein intensity, which spans several orders of magnitude. EdgeR puts the data on a common "counts per million" scale. The library size and TMM scaling factors essentially normalize the total signal per channel to 1.0. Multiplying by one million is done to convert to the CPM scale.
There are two important observations about the data and the blue line. The blue line is greater than the per protein variances for most proteins (the region with log CPM between 4 and 8 is a good example). This prevents proteins with small mean difference that might also have atypically low variance from throwing really small p-values. This is a nice consequence.
There is also a flip side. Some of the proteins have variances that are greater than the blue line. If they have larger differences in means, the lower trended variance rather than their higher direct variances will result in small p-values. Real sample contaminant proteins (such as from surrounding tissue in dissections) can vary sample-to-sample, have high variance, and potentially be biased among conditions. These proteins might not have small p-values in traditional tests despite large differences in means if the variance was also very large.
We will use the exact test in edgeR for this simple two-state comparison. You may wonder why we are using statistical testing built on a negative binomial distribution for counting experiments. The shortest answer is because it seems to work okay. The variance is modeled by two terms: one for Poisson numbers and an over-dispersion term. As counts get larger (several hundred or so), the Poisson term becomes small and the variance is handled by the over-dispersion term. The values of reporter ion peak heights, particularly when summed into protein totals, are large numbers. After we get the results from edgeR, we will compare them to a two-sample t-test, which uses a normal data distribution.
collect_results <- function(df, tt, x, xlab, y, ylab) {
# Computes new columns and extracts some columns to make results frame
# df - data in data.frame
# tt - top tags from edgeR test
# x - columns for first condition
# xlab - label for x
# y - columns for second condition
# ylab - label for y
# returns a new dataframe
# condition average vectors
ave_x <- rowMeans(df[x])
ave_y <- rowMeans(df[y])
# FC, direction, candidates
fc <- ifelse(ave_y > ave_x, (ave_y / ave_x), (-1 * ave_x / ave_y))
direction <- ifelse(ave_y > ave_x, "up", "down")
candidate <- cut(tt$FDR, breaks = c(-Inf, 0.01, 0.05, 0.10, 1.0),
labels = c("high", "med", "low", "no"))
# make data frame
temp <- cbind(df[c(x, y)], data.frame(logFC = tt$logFC, FC = fc,
PValue = tt$PValue, FDR = tt$FDR,
ave_x = ave_x, ave_y = ave_y,
direction = direction, candidate = candidate,
Acc = tt$genes))
# fix column headers for averages
names(temp)[names(temp) %in% c("ave_x", "ave_y")] <- str_c("ave_", c(xlab, ylab))
temp # return the data frame
}
# compute the exact test models, p-values, FC, etc.
et <- exactTest(y, pair = c("media", "exosome"))
# define variables for the columns in each condition
M <- 1:3
E <- 4:7
# make the results table
tt <- topTags(et, n = Inf, sort.by = "none")$table
med_exo <- collect_results(paw_tmt_tmm, tt, M, "med", E, "exo")
The exactTest
function does the modeling for the pairwise comparison. More than one pair can be present in DGEList objects to improve the dispersion estimates. The topTags
function does the multiple testing corrections and returns more concise summaries of the testing results. The collect_results
function combines some of the normalized data and test results into a concise med_exo
data frame.
It is important to see if the modeling looks reasonable. Our general assumptions are that we have a large fraction of the proteins that are not differentially expressed. Those will have a uniform (flat) p-value distribution from 0.0 to 1.0. We also expect (hopefully) some true differential expression candidates. Those should have very small p-values and have a sharper distribution at low p-values.
pvalue_plot <- function(results, title) {
# Makes p-value distribution plots
# results - results data frame
# title - plot title
ggplot(results, aes(PValue)) +
geom_histogram(bins = 100, fill = "white", color = "black") +
geom_hline(yintercept = mean(hist(results$PValue, breaks = 100,
plot = FALSE)$counts[26:100])) +
ggtitle(str_c(title, " p-value distribution"))
}
# check the p-value distrubution
pvalue_plot(med_exo, "(edgeR) Media vs Exosome-dosed")
We have the two expected distributions of p-values, so the testing seems reasonable. We can also see how the up-regulated protein number compares to the down-regulated number. We can use the topTags
function to see which proteins have the smallest p-values.
# see how many up and down candidates (10% FDR)
summary(decideTests(et, p.value = 0.10))
# see which proteins have the smallest p-values
topTags(et)$table
In many discovery experiments, a Benjamini-Hochberg adjusted p-value (FDR) cutoff of 0.05 (5%) might be pretty strict. I like to use a 10% cutoff to distinguish DE from non-DE candidates. We define three cuts on the FDR: 10% to 5% are "low" significance, 5% to 1% are medium significance, and less than 1% are more "highly" significant. Cut values can be adjusted depending on the experimental situation.
We can look at expression ratio distributions as a function of candidate category. If variance is not too variable protein-to-protein, then we would expect larger mean differences to be associated with lower FDR values. Faceted plotting in ggplot2 is a nice way to show such things.
# see how many candidates are in each category
med_exo %>% count(candidate)
log2FC_plots <- function(results, range, title) {
# Makes faceted log2FC plots by candidate
# results - results data frame
# range - plus/minus log2 x-axis limits
# title - plot title
ggplot(results, aes(x = logFC, fill = candidate)) +
geom_histogram(binwidth=0.1, color = "black") +
facet_wrap(~candidate) +
ggtitle(title) +
coord_cartesian(xlim = c(-range, range))
}
# can look at log2FC distributions as a check
log2FC_plots(med_exo, 3, "(edgeR) LogFC by candidate for Media vs Exosome-dosed")
transform <- function(results, x, y) {
# Make data frame with some transformed columns
# results - results data frame
# x - columns for x condition
# y - columns for y condition
# return new data frame
df <- data.frame(log10((results[x] + results[y])/2),
log2(results[y] / results[x]),
results$candidate,
-log10(results$FDR))
colnames(df) <- c("A", "M", "candidate", "P")
df # return the frame
}
MA_plots <- function(results, x, y, title, make_facet = TRUE) {
# makes MA-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# make_facet - flag to plot facet views
# returns a list of plots
# uses transformed data
temp <- transform(results, x, y)
# 2-fold change lines
ma_lines <- list(geom_hline(yintercept = 0.0, color = "black"),
geom_hline(yintercept = 1.0, color = "black", linetype = "dotted"),
geom_hline(yintercept = -1.0, color = "black", linetype = "dotted"))
# make main MA plot
ma <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("logFC (", y, "/", x, ")")) +
scale_x_continuous("Ave_intensity") +
ggtitle(title) +
ma_lines
# make separate MA plots
if (make_facet == TRUE) {
ma_facet <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("log2 FC (", y, "/", x, ")")) +
scale_x_continuous("log10 Ave_intensity") +
ma_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
}
# make the plots visible
print(ma)
if (make_facet == TRUE) {
print(ma_facet)
}
}
# MA plots of DE candidates
MA_plots(med_exo, "ave_med", "ave_exo", "(edgeR) Media versus exosome-dosed")
The solid diagonal line is 1:1, the dotted lines are 2-fold changes. The axes are in log scale.
NOTE: we will skip the scatter plots to shorten the notebook.
scatter_plots <- function(results, x, y, title, make_facet = TRUE) {
# makes scatter-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# make_facet - flag to plot facet views
# returns a list of plots
# 2-fold change lines
scatter_lines <- list(geom_abline(intercept = 0.0, slope = 1.0, color = "black"),
geom_abline(intercept = 0.301, slope = 1.0, color = "black", linetype = "dotted"),
geom_abline(intercept = -0.301, slope = 1.0, color = "black", linetype = "dotted"),
scale_y_log10(),
scale_x_log10())
# make main scatter plot
scatter <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
ggtitle(title) +
scatter_lines
# make separate scatter plots
if (make_facet == TRUE) {
scatter_facet <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
scatter_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
}
# make the plots visible
print(scatter)
if (make_facet == TRUE) {
print(scatter_facet)
}
}
# scatter_plots(med_exo, "ave_med", "ave_exo", "(edgeR) Media versus exosome-dosed")
Volcano plots are another common way to visualize DE candidates.
volcano_plot <- function(results, x, y, title, ymax) {
# makes a volcano plot
# results - a data frame with edgeR results
# x - string for the x-axis column
# y - string for y-axis column
# ymax - upper limit for y-axis
# title - plot title string
# uses transformed data
temp <- transform(results, x, y)
# build the plot
ggplot(temp, aes(x = M, y = P)) +
geom_point(aes(color = candidate, shape = candidate)) +
xlab("log2 FC") +
ylab("-log10 FDR") +
coord_cartesian(xlim = c(-5, 5), ylim = c(0, ymax)) +
ggtitle(str_c(title, " Volcano Plot"))
}
# finally, a volcano plot
volcano_plot(med_exo, "ave_med", "ave_exo", "(edgeR) Media versus exosome-dosed", 50)
We can see how the intensities of the individual samples compare for the top 10 up- and down-regulated edgeR candidate proteins.
# function to extract the identifier part of the accesssion
get_identifier <- function(accession) {
identifier <- str_split(accession, "\\|", simplify = TRUE)
identifier[,3]
}
set_plot_dimensions <- function(width_choice, height_choice) {
options(repr.plot.width=width_choice, repr.plot.height=height_choice)
}
plot_top_tags <- function(results, nleft, nright, top_tags, prefix) {
# results should have data first, then test results (two condition summary table)
# nleft, nright are number of data points in each condition
# top_tags is number of up and number of down top DE candidates to plot
# get top up-regulated
up <- results %>%
filter(logFC >= 0) %>%
arrange(FDR)
up <- up[1:top_tags, ]
# get top down-regulated
down <- results %>%
filter(logFC < 0) %>%
arrange(FDR)
down <- down[1:top_tags, ]
# pack them
proteins <- rbind(up, down)
color = c(rep("red", nleft), rep("blue", nright))
for (row_num in 1:nrow(proteins)) {
row <- proteins[row_num, ]
vec <- as.vector(unlist(row[1:(nleft + nright)]))
names(vec) <- colnames(row[1:(nleft + nright)])
title <- str_c(prefix, get_identifier(row$Acc), ", int: ", scientific(mean(vec), 2),
", p-val: ", scientific(row$FDR, digits = 3),
", FC: ", round(row$FC, digits = 1))
barplot(vec, col = color, main = title)
}
}
# plot the top 10 up and 10 down proteins
set_plot_dimensions(7, 4)
plot_top_tags(med_exo, 3, 4, 10, "(edgeR) ")
set_plot_dimensions(7, 7)
EdgeR normalizations and testing seemed reasonable for this data.
A two-sample t-test with a multiple testing correction is a commonly used way to test for DE candidates. Let's see how that compares to the edgeR analysis. We will use the equal variance option on the t-test to get a little more variance pooling.
# copy the data
ttest_PAW <- paw_tmt_tmm
# add average ratio columns (non-logged ratios), fold-change column, and row names
ttest_PAW$ave_med <- rowMeans(paw_tmt_tmm[1:3])
ttest_PAW$ave_exo <- rowMeans(paw_tmt_tmm[4:7])
ttest_PAW$logFC <- log2(ttest_PAW$ave_exo / ttest_PAW$ave_med)
ttest_PAW$FC <- ifelse(ttest_PAW$ave_exo > ttest_PAW$ave_med,
(ttest_PAW$ave_exo / ttest_PAW$ave_med),
(-1 * ttest_PAW$ave_med / ttest_PAW$ave_exo))
# apply the basic two-sample t-test (we will pool variance)
t.result <- apply(log2(paw_tmt_tmm), 1, function(x) t.test(x[1:3], x[4:7],
var.equal = TRUE))
# extract the p-value column from the t-test thingy
ttest_PAW$PValue <- unlist(lapply(t.result, function(x) x$p.value))
# do a Benjamini-Hochberg multiple testing correction
ttest_PAW$FDR <- p.adjust(ttest_PAW$PValue, method = "BH")
# add a DE candidate status column
ttest_PAW$candidate <- cut(ttest_PAW$FDR, breaks = c(-Inf, 0.01, 0.05, 0.10, 1.0),
labels = c("high", "med", "low", "no"))
ttest_PAW$Acc <- accession
# count up, down and the rest (FDR less than 0.05)
all <- dim(ttest_PAW)[1]
up <- dim(ttest_PAW[(ttest_PAW$FDR <= 0.10) & (ttest_PAW$logFC > 0.0), ])[1]
down <- dim(ttest_PAW[(ttest_PAW$FDR <= 0.10) & (ttest_PAW$logFC <= 0.0), ])[1]
print("This is like decideTest in edgeR - 10% FDR cut:")
up
all - up - down
down
print("Candidate Counts:")
summary(ttest_PAW$candidate)
# what does the test p-value distribution look like?
pvalue_plot(ttest_PAW, "PAW data with t-test")
The total up and down candidate numbers are around 1200 like we had with edgeR. The number of candidates in the different FDR classes is different. The t-test has most candidates in the medium category in contrast to edgeR where most of the candidates were in the high category. The p-value distribution is qualitatively similar. These bird's eye views all seem reasonable.
There are some options for running the t-test. We do not know much about whether the 4976 proteins have normal distributions for the 3 control samples or for the 4 exosome-dosed samples. The distribution of the 4976 (sample averaged) protein reporter ion values will have a more extreme value shape. A distribution of the log transformation of those 4976 intensities will indeed be much more Gaussian in shape. However, that is not the distribution used in the t-test. The t-test is applied to each protein individually. We can try the t-test on natural scale intensities and on log transformed intensities.
We have the choice of equal or unequal variance between conditions. If we assume equal variance, then we can use all 7 data points to estimate a common variance. Alternatively, we can compute the variances independently for each condition (3 and 4 data points, respectively).
Notebooks make it easy to change these options and run all of the downstream cells to see what the effects are. I did that and tabulated candidate numbers for the table below.
| Candidate Category | Log2 Equal Var | Log2 Unequal Var | Equal Var | Unequal Var |
|---|---|---|---|---|
| High < 0.01 | 727 | 163 | 705 | 151 |
| 0.05 > Med > 0.01 | 1282 | 1397 | 1224 | 1213 |
| 0.10 > Low > 0.05 | 594 | 769 | 654 | 922 |
| Non-candidate > 0.10 | 2373 | 2647 | 2393 | 2690 |
The p-value distribution seems to indicate that we do have a large number of true DE candidates. That suggests that the equal variance option might be more correct for this data. The separate variance option results in somewhat smaller p-values for the DE candidates so fewer survive the multiple testing corrections. Using intensities or log intensities did not make much difference. We get a handful more candidates after taking the logs, so we will use log2 intensities assuming equal variance for the rest of the notebook work below.
We can look at the more detailed plots (MA plots, scatter plots, and volcano plot) to see what a deeper look into the DE candidates can tell us. We will repeat the edgeR DE plots from above for easier comparisons.
# can look at log2FC distributions as a check
log2FC_plots(med_exo, 3, "LogFC by candidate for edgeR")
log2FC_plots(ttest_PAW, 3, "LogFC by candidate for t-test")
There is more of a clear progression of increasingly larger fold-change differences with low, medium, and high categories for edgeR than for the t-test. There are relatively more low and medium category proteins from the t-test.
MA_plots(med_exo, "ave_med", "ave_exo", "PAW edgeR results")
MA_plots(ttest_PAW, "ave_med", "ave_exo", "PAW t-test results")
There are fewer "high" candidates (orange points) at both large intensities and small intensities in the t-test. This could be from higher abundance proteins having smaller mean differences due to the constrained nature of the measurements, and lower abundance proteins having higher variances. Another difference is that the "no" DE candidates in the t-test (purple points) have several points with large positive fold-changes that are not significant (high variances most likely).
# the scatter plots
#scatter_plots(med_exo, "ave_med", "ave_exo", "PAW edgeR results")
#scatter_plots(ttest_PAW, "ave_med", "ave_exo", "PAW t-test results")
# compare volcano plots
volcano_plot(med_exo, "ave_med", "ave_exo", "PAW edgeR results", 40)
volcano_plot(med_exo, "ave_med", "ave_exo", "PAW edgeR results (expanded y-axis)", 4)
volcano_plot(ttest_PAW, "ave_med", "ave_exo", "PAW t-test results", 4)
The range of FDR values returned by the two tests are not the same (easy to see in the volcano plots where the edgeR y-axis goes to 50 (-log10 FDR) in contrast to the t-test's maximum of 3.5). The FDR values are much more compressed in the t-test. This means that arbitrary FDR cutoffs are at different relative positions for the two sets of results. The center of the "V" is also more filled in for the t-test.
Two tests having different ranges of p-values is why arbitrary p-value cutoffs for statistical significance can be problematic. We have overlaid the same set of cutoffs onto both sets of results. We have fewer "high" candidates in the t-test where the "medium" category is one with the most proteins. How do we decide which pattern of results seems more correct when we do not know the truth?
set_plot_dimensions(7, 4)
plot_top_tags(ttest_PAW, 3, 4, 10, "(t-test) ")
set_plot_dimensions(7, 7)
Comparing two sets of results in omics datasets is far more difficult than most people can imagine. It is a very fuzzy endeavor and most ways to compare things do not like fuzzy. We can step through the FDR thresholds and do some set comparisons (intersections, differences, etc.).
for (cutoff in c(0.10, 0.05, 0.01)) {
edgeR <- get_identifier(filter(med_exo, FDR < cutoff)$Acc)
ttest <- get_identifier(filter(ttest_PAW, FDR < cutoff)$Acc)
cat("cutoff:", cutoff, "\n")
cat("number of candidates (edgeR, t-test):", length(edgeR), length(ttest), "\n")
x_union <- union(edgeR, ttest)
x_intersect <- intersect(edgeR, ttest)
unique_edgeR <- setdiff(edgeR, ttest)
unique_ttest <- setdiff(ttest, edgeR)
cat("union and intersection:", length(x_union), length(x_intersect), "\n")
cat("intersection out of union:", round(length(x_intersect)/length(x_union), 2), "\n")
cat("intersection out of edgeR:", round(length(x_intersect)/length(edgeR), 2), "\n")
cat("intersection out of t-test:", round(length(x_intersect)/length(ttest), 2), "\n")
cat("unique to each (edgeR, ttest):", length(unique_edgeR),
length(unique_ttest), "\n\n")
}
edgeR_candidates <- get_identifier(filter(med_exo, FDR < 0.10)$Acc)
ttest_candidates <- get_identifier(filter(ttest_PAW, FDR < 0.10)$Acc)
unique_edgeR_candidates <- setdiff(edgeR_candidates, ttest_candidates)
unique_ttest_candidates <- setdiff(ttest_candidates, edgeR_candidates)
cat("Candidates in t-test but not candidate (<0.1) in edgeR")
interesting_ttest <- setdiff(unique_ttest_candidates, edgeR_candidates)
length(interesting_ttest)
cat("Candidates in edgeR but not candidate (<0.1) in t-test")
interesting_edgeR <- setdiff(unique_edgeR_candidates, ttest_candidates)
length(interesting_edgeR)
cat("High candidates in t-test but not candidate (<0.1) in edgeR")
interesting <- setdiff(unique_ttest, edgeR_candidates) # high t-test but not candidate in edgeR
not_interesting <- setdiff(unique_ttest, interesting) # high in t-test and also candidate in edgeR
length(interesting)
With FDR cuts of 0.10 and 0.05, the two sets of results seem somewhat similar. We have intersection numbers that are more than 70% of the union numbers. We see some bigger differences at a 0.01 cutoff. This is not really too surprising because the t-test p-values are not as small as from edgeR and they are dropping off faster. We have 72 proteins that have t-test FDRs (BH-corrected p-values) of less than 0.01 that are not also high candidates in edgeR. However, 53 of the proteins are either medium or low candidates in the edgeR testing. That leaves 19 highly significant proteins from the t-test that were not more significant than the 10% FDR in the edgeR testing. We can plot those.
plot_selected <- function(results, nleft, nright, selected, prefix) {
# results should have data first, then test results (two condition summary table)
# nleft, nright are number of data points in each condition
# selected is list of identifiers to plot
proteins <- results
proteins$ident <- get_identifier(results$Acc)
proteins <- filter(proteins, ident %in% selected)
color = c(rep("red", nleft), rep("blue", nright))
for (row_num in 1:nrow(proteins)) {
row <- proteins[row_num, ]
vec <- as.vector(unlist(row[1:(nleft + nright)]))
names(vec) <- colnames(row[1:(nleft + nright)])
title <- str_c(prefix, row$ident, ", int: ", scientific(mean(vec), 2),
", p-val: ", scientific(row$FDR, digits = 3),
", FC: ", round(row$FC, digits = 1))
barplot(vec, col = color, main = title)
}
}
# plot the t-test candidates that were not significant in edgeR
set_plot_dimensions(7, 4)
plot_selected(ttest_PAW, 3, 4, interesting, "(t-test) ")
set_plot_dimensions(7, 7)
This is why moderated variance is important in these types of studies. These statistically significant candidates would not be likely to be biologically significant. So might argue that some genes/proteins might be very tightly regulated and have very low variances. That may be true, but you cannot make measurements that are better than your measuring device. It just does not work that way. The trended variance methods really set the minimum variance in line with the limitations of the measurements. This is just common sense.
ggplot (and visualization in general) has several ways to explore dimensionality. Different point shapes and colors can be used in the same plots. Plots can also be separated by categories and aligned to facilitate comparisons. We can do both techniques. We can color code by the candidate status in one test and facet plot by the candidate status in the other test. We might gain some useful insight or be hopelessly confused.
volcano_plot_facet <- function(results, x, y, other, title, ymax) {
# makes a volcano plot
# results - a data frame with edgeR results
# x - string for the x-axis column
# y - string for y-axis column
# ymax - upper limit for y-axis
# title - plot title string
# other - candidate flags from the other test
# uses transformed data
temp <- transform(results, x, y)
temp$other <- other
# build the plot
ggplot(temp, aes(x = M, y = P)) +
geom_point(aes(color = candidate, shape = candidate), alpha = 0.5) +
xlab("log2 FC") +
ylab("-log10 FDR") +
coord_cartesian(xlim = c(-5, 5), ylim = c(0, ymax)) +
facet_wrap(~ other) +
ggtitle(str_c(title, " Volcano Plot"))
}
# cross-candidate faceted volcano plots
volcano_plot_facet(med_exo, "ave_med", "ave_exo", str_c("t-test_", ttest_PAW$candidate),
"edgeR faceted by t-test candidate", 10)
Most non-candidate proteins are the same from both tests. There are a lot of purple points in the lower right panel. EdgeR has many significant candidates (in all categories) that were not candidates from the t-test (lower right panel); they tend to have larger fold-changes. That trend is also present in the upper right panel for the low candidates in the t-test. There were significant candidates from the t-test that were not significant in edgeR at all significance category levels in the t-test (some purple points in all 4 facets).
# with the tests switched
volcano_plot_facet(ttest_PAW, "ave_med", "ave_exo", str_c("edgeR_", med_exo$candidate),
"t-test faceted by edgeR candidate", 4)
The lower right panel has many significant t-test results that were not significant in edgeR; they all have very small fold changes. From the 19 individual protein expression levels plotted a couple of cells above, these small fold-change proteins have very low variances. They may meet the mathematical criteria to be different, but they may not be biologically significant.
The comments about the plot of the biological coefficient of variance are reflected in these comparisons. The trended variance blue line lies above the individual protein variances in most cases. The means that there will have to be a bit more of a difference in means before edgeR will return small p-values. That effectively eliminates small fold change proteins without having to invoke an ad hoc cutoff. Conversely, large fold changes can overcome high variance when using moderated testing.
There are so many factors that can affect testing p-values: the data itself, normalizations and transformations, the test and test parameter choices, the underlying distribution assumption (if parametric), how trended variances are computed in moderated tests, choice of multiple testing correction method, and probably many more factors. We need better ways to explore, evaluate, and compare the choices.
The t-test looks less good in several ways:
One step towards more meaningful results summaries are getting the main proteomics results and the statistical results combined into a single table. If we are careful with how we bring the data into R and how we take it back out, we can keep tables in order so merging results are easier. We kept the accessions column as we worked with the data. We can be really robust and use merge functions to weave together starting tables with final results tables using the accessions as keys.
We should always end notebooks with information about what packages and versions were used in the analysis.
# save the testing results
#write.table(med_exo, file = "KUR1502_t-test_results.txt", sep = "\t",
# row.names = FALSE, na = " ")
# log the session
sessionInfo()


N. Texas goes up against Rhode Island
by EndPlayST. JAMES - Rhode Island (3-1) vs. North Texas (2-3)
Montego Bay Convention Centre, St. James, Jamaica; Friday, 4:30 p.m. EST
BOTTOM LINE: Rhode Island and North Texas both look to put winning streaks together . Both teams earned home victories this past Tuesday. North Texas earned an 80-60 win over NC A&T, while Rhode Island won 70-65 over Nicholls State.
STEPPING UP: The dynamic Fatts Russell is averaging 17.3 points, 5.5 assists and 3.8 steals to lead the way for the Rams. Cyril Langevine is also a key contributor, producing 11 points and 12 rebounds per game. The Mean Green have been led by Umoja Gibson, who is averaging 11.4 points.
CREATING OFFENSE: Russell has been directly responsible for 40 percent of all Rhode Island field goals over the last three games. The junior guard has 16 field goals and 15 assists in those games.
ASSIST DISTRIBUTION: The Rams have recently converted baskets via assists more often than the Mean Green. North Texas has 27 assists on 61 field goals (44.3 percent) across its previous three games while Rhode Island has assists on 40 of 77 field goals (51.9 percent) during its past three games.
DID YOU KNOW: North Texas is ranked second among CUSA teams with an offensive rebound percentage of 37.7 percent. The Mean Green have averaged 11 offensive boards per game.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
This was generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com


A controller is a program that defines the behavior of a robot. Webots controllers can be written in the following programming languages: C, C++, Java, Python, MATLAB, ROS, etc. In this tutorial, we are going to use C as a reference language.
This section describes functions used in the controller of Kubota and McCormick.
The driver library provides all the usual functionalities available to a human driving his own car.All the functions included in our controller are explained below.For more detailed about functions in driver library, please refer to Driver LIB
wbu_driver_init
Initialise a driver, it should be called at the very beginning of any controller program.wbu_driver_cleanup
Clean a driver stepwbu_driver_step
step
function should be called in the main loop to run one simulation step.wbu_driver_set_brake_intensity
This function brakes the car by increasing the dampingConstant coefficient of the rotational joints of each of the four wheels. The argument should be between 0.0 and 1.0, 0 means that no damping constant is added on the joints (no breaking), 1 means that the parameter brakeCoefficient of the Car PROTO is applied on the dampingConstant of each joint (the value will be linearly interpolated between 0 and brakeCoefficient for any arguments between 0 and 1.wbu_driver_set_throttle
This function is used in order to control the car in torque, it sets the state of the throttle.
The argument should be between 0.0 and 1.0, 0 means that 0% of the output torque of the engine is sent to the wheels and 1.0 means that 100% of the output torque of the engine is sent to the wheels.
For more information about how the output torque of the engine is computed see section Engine models.wbu_driver_set_gear
This function sets the engaged gear.
An argument of -1
is used in order to engage the reverse gear, an argument of 0
is used in order to disengaged the gearbox.
Any other arguments than 0
and -1
should be between 1 and the number of coefficients set in the gearRatio
parameter of the Car PROTO.The Keyboard is a set of functions available by default for each Robot node to read the keyboard input. It is therefore not a device and the functions do not require any WbDeviceTag
. For more detailed about functions in driver library, please refer to Keyboard
wb_keyboard_enable
Enable keyboard input by calling this function.wb_keyboard_disable
Stop the keyboard reading.wb_keyboard_get_sampling_period
Sampling_period is expressed in milliseconds, and defines how frequently readings are updated, this function will get the sampling_period.wb_keyboard_get_key
Values can be read by calling this function repeatedly untill this function returns -1.The InertialUnit node simulates an Inertial Measurement Unit (IMU). The InertialUnit computes and returns its roll, pitch and yaw angles with respect to a global coordinate system defined in the WorldInfo node.
wb_inertial_unit_enable
Turns on the angle measurements.wb_inertial_unit_get_roll_pitch_yaw
Returns the current roll, pitch and yaw angles of the InertialUnit. The values are returned as an array of 3 components therefore only the indices 0, 1, and 2 are valid for accessing the returned array.
Note that the indices 0, 1 and 2 return the roll, pitch and yaw angles respectively.Note
The roll angle indicates the unit's rotation angle about its x-axis, in the interval [-π,π]. The roll angle is zero when the InertialUnit is horizontal, i.e., when its y-axis has the opposite direction of the gravity (WorldInfo defines the
gravity
vector).
The pitch angle indicates the unit's rotation angle about is z-axis, in the interval [-π/2,π/2]. The pitch angle is zero when the InertialUnit is horizontal, i.e., when its y-axis has the opposite direction of the gravity. If the InertialUnit is placed on the Robot with a standard orientation, then the pitch angle is negative when the Robot is going down, and positive when the robot is going up.
The yaw angle indicates the unit orientation, in the interval [-π,π], with respect to WorldInfo.
northDirection
. The yaw angle is zero when the InertialUnit's x-axis is aligned with the north direction, it is π/2 when the unit is heading east, and -π/2 when the unit is oriented towards the west. The yaw angle can be used as a compass.
[C, C++]: The returned vector is a pointer to internal values managed by the Webots, therefore it is illegal to free this pointer. Furthermore, note that the pointed values are only valid until the next call to the
wb_robot_step
orRobot::step
functions. If these values are needed for a longer period they must be copied.
The Accelerometer node can be used to model accelerometer devices such as those commonly found in mobile electronics, robots and game input devices. The Accelerometer node measures acceleration and gravity induced reaction forces over 1, 2 or 3 axes. It can be used for example to detect fall, the up/down direction, etc.
wb_accelerometer_enable
Allows the user to enable the acceleration measurements.wb_accelerometer_get_values
Returns the current values measured by the Accelerometer. These values are returned as a 3D-vector, therefore only the indices 0, 1, and 2 are valid for accessing the vector. Each element of the vector represents the acceleration along the corresponding axis of the Accelerometer node, expressed in meters per second squared [m/s^2].
The first element corresponds to the x-axis, the second element to the y-axis, etc.Note [C, C++]: The returned vector is a pointer to the internal values managed by the Accelerometer node, therefore it is illegal to free this pointer. Furthermore, note that the pointed values are only valid until the next call to the
wb_robot_step
orRobot::step
functions. If these values are needed for a longer period they must be copied.
The GPS node is used to model a Global Positioning Sensor (GPS) which can obtain information about its absolute position from the controller program.
wb_gps_enable
Allows the user to enable GPS measurements.wb_gps_get_values
Returns the current GPS measurement.
The values are returned as a 3D-vector, therefore only the indices 0, 1, and 2 are valid for accessing the vector.
The returned vector indicates the absolute position of the GPS device. This position can either be expressed in the cartesian coordinate system of Webots or using latitude-longitude-altitude, depending on the value of the gpsCoordinateSystem
field of the WorldInfo node.Note [C, C++]: The returned vector is a pointer to the internal values managed by the GPS node, therefore it is illegal to free this pointer. Furthermore, note that the pointed values are only valid until the next call to the
wb_robot_step
orRobot::step
functions. If these values are needed for a longer period they must be copied.
The Robot node can be used as basis for building a robot, e.g., an articulated robot, a humanoid robot, a wheeled robot.
Note: Logically, if the Robot node has one or more Solid (or derived) ancestor nodes, then the physical properties of the ancestor nodes will affect the Robot node's physical behavior.
wb_robot_get_device
Returns a unique identifier for a device corresponding to a specified name
.
For example, if a robot contains a DistanceSensor node whose name
field is "ds1", the function will return the unique identifier of that device.
This WbDeviceTag
identifier will be used subsequently for enabling, sending commands to, or reading data from this device.
If the specified device is not found, the function returns 0.
wb_robot_get_basic_time_step
Returns the value of the basicTimeStep field of the WorldInfo node
print_help
Print the help info.void print_help()
{
printf("You can drive this vehicle!\n");
printf("Select the 3D window and then use the cursor keys to:\n");
printf("[LEFT]/[RIGHT] - steer\n");
printf("[UP]/[DOWN] - throttle\n");
printf("[1]-[6] - change the gear ratio\n");
printf("[S] - see the speed of the car\n");
printf("The gear ratio right now is: %d\n", g);
}
check_keyboard
Get user inputvoid check_keyboard()
{
if (pre_key == 0)
{
int key = wb_keyboard_get_key();
pre_key = key > 0 ? key : 0;
}
else
{
int pre_key_state = wb_keyboard_get_key();
if (pre_key_state != pre_key)
{
switch (pre_key)
{
case ' ':
wbu_driver_set_throttle(0);
wbu_driver_set_brake_intensity(1);
break;
case WB_KEYBOARD_UP:
wbu_driver_set_brake_intensity(0);
throttle += 0.1;
throttle = (throttle > 1) ? 1 : throttle;
wbu_driver_set_throttle(throttle);
printf("throttle: %.2f\n", throttle);
break;
case WB_KEYBOARD_DOWN:
wbu_driver_set_brake_intensity(0);
throttle -= 0.1;
throttle = (throttle < 0) ? 0 : throttle;
wbu_driver_set_throttle(throttle);
printf("throttle: %.2f\n", throttle);
break;
case WB_KEYBOARD_RIGHT:
wbu_driver_set_brake_intensity(0);
steering_angle += 0.05;
steering_angle = (steering_angle > 0.45) ? 0.45 : steering_angle;
wbu_driver_set_steering_angle(steering_angle);
printf("Steering: %.2f / 9\n", steering_angle * 20);
break;
case WB_KEYBOARD_LEFT:
wbu_driver_set_brake_intensity(0);
steering_angle -= 0.05;
steering_angle = (steering_angle < -0.45) ? -0.45 : steering_angle;
wbu_driver_set_steering_angle(steering_angle);
printf("Steering: %.2f / 9\n", steering_angle * 20);
break;
case '0':
g = -1;
printf("The gear ratio right now is: %d\n", g);
wbu_driver_set_gear(g);
break;
case '1':
g = 1;
printf("The gear ratio right now is: %d\n", g);
wbu_driver_set_gear(g);
break;
case '2':
g = 2;
printf("The gear ratio right now is: %d\n", g);
wbu_driver_set_gear(g);
break;
case '3':
g = 3;
printf("The gear ratio right now is: %d\n", g);
wbu_driver_set_gear(g);
break;
case '4':
g = 4;
printf("The gear ratio right now is: %d\n", g);
wbu_driver_set_gear(g);
break;
case '5':
g = 5;
printf("The gear ratio right now is: %d\n", g);
wbu_driver_set_gear(g);
break;
case '6':
g = 6;
printf("The gear ratio right now is: %d\n", g);
wbu_driver_set_gear(g);
break;
case 'S':
printf("The gps speed of the car: %.2f\n", gps_speed);
break;
}
}
pre_key = pre_key_state;
}
}
update_gps_speed
Update the gps speedvoid update_gps_speed()
{
const double *coords = wb_gps_get_values(gps);
double vel[3] = {coords[X] - gps_coords[X], coords[Y] - gps_coords[Y], coords[Z] - gps_coords[Z]};
double dist = sqrt(vel[X] * vel[X] + vel[Y] * vel[Y] + vel[Z] * vel[Z]);
gps_speed = dist / time_step * 3600.0;
memcpy(gps_coords, coords, sizeof(gps_coords));
}
update_imu_angle
Update the imu anglevoid update_imu_angle()
{
const double *coords = wb_inertial_unit_get_roll_pitch_yaw(inertial_unit);
memcpy(inertial_unit_coords, coords, sizeof(inertial_unit_coords));
}
update_imu_acceleration
Update the imu accelerationvoid update_imu_acceleration()
{
const double *coords = wb_accelerometer_get_values(accelerometer);
memcpy(accelerometer_coords, coords, sizeof(accelerometer_coords));
}
update_imu_angular_velocity
Update the angular velocityvoid update_imu_angular_velocity()
{
const double *coords = wb_gyro_get_values(gyro);
memcpy(gyro_coords, coords, sizeof(gyro_coords));
}
data_export
Export data to fileint data_export(FILE *p)
{
if (p == NULL)
{
printf("open error!\n");
return -1;
}
else
{
update_gps_speed();
update_imu_angle();
update_imu_acceleration();
update_imu_angular_velocity();
fprintf(p, "%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f\n",
gps_coords[X], gps_coords[Y], gps_coords[Z],
inertial_unit_coords[X], inertial_unit_coords[Y], inertial_unit_coords[Z],
accelerometer_coords[X], accelerometer_coords[Y], accelerometer_coords[Z],
gyro_coords[X], gyro_coords[Y], gyro_coords[Z]);
return 0;
}
}
sensor_init
Initialize sensorsvoid sensor_init()
{
// initialize inertial unit
inertial_unit = wb_robot_get_device("inertial unit");
wb_inertial_unit_enable(inertial_unit, time_step);
// initialize accelerometer
accelerometer = wb_robot_get_device("accelerometer");
wb_accelerometer_enable(accelerometer, time_step);
// initialize gyro
gyro = wb_robot_get_device("gyro");
wb_gyro_enable(gyro, time_step);
// initialize gps
gps = wb_robot_get_device("gps");
wb_gps_enable(gps, time_step);
}


BACK TO THE FUTURE PART III A film review by Chad Polenz Copyright 1997 Chad Polenz
*** (out of 4 = good) 1990, PG, 120 minutes [2 hours] [adventure/western/sci-fi] starring: Michael J. Fox (Marty McFly, Seamus McFly), Christopher Lloyd (Dr. Emmett Brown), Thomas F. Wilson (Buford "Mad Dog" Tannen, Biff Tannen), Mary Steenburgen (Clara Clayton), produced by Bob Gale, Neil Canton, written by Bob Gale, directed by Robert Zemeckis.
"Back To The Future III" was a sequel that didn't have to me made, but as far as unnecessary sequels go, this is one of the best. It doesn't have the originality of the first two films, but it is still a good, decent movie.
Just like "Part II," this one picks up right where the last one left off. Marty (Fox) gets a letter from Doc (Lloyd) from the year 1885 and learns he is happy and not to rescue him. But Marty is still stuck in 1955 (again) and needs the help of the 1955 Doc to get back to the future. But when the two realize the Doc of 1885 gets killed, the 1955 Doc wants Marty to rescue him (after all, that's going to be him someday). So Marty goes back in time and hilarity and adventure ensues.
It seems that every time Marty travels through time he gets stuck somewhere. This time he becomes stuck in 1885 when he rips the fuel tank of the DeLorean, thus the car cannot run without gasoline, unless they can get a train to push it up to 88 m.p.h. (ah ha!). There's a lot of the standard time traveling confusion and conflicts, almost exactly like the situations that occurred in the first two, but surprisingly enough it doesn't wear itself out. And for the first time, Doc gets the girl.
Mary Steenburgen co-stars as Clara Clayton, a school teacher who was supposed to fall into a ravine but Doc saves her at the last minute. And so a romantic affair develops between the two, and this is a nice little surprise. It's nothing too sappy and it's nothing too implied either, and it also affects the plot of the story. The chemistry between Lloyd and Steenburgen is pretty convincing, they have a cartoonish charm of sorts.
Meanwhile Marty is having a problem with the "Biff" of this time period - Buford "Mad Dog" Tannen (Wilson). When he continues to insult and provoke Tannen they decide to settle it once and for all at a shootout, except that it's scheduled at the time when Marty and Doc are supposed to hijack a train to go back to the future. The grand finale of this film is not unlike that of the original movie. It involves being in the right place at the right time and of course little obstacles crop up to get in their way.
Overall, "Back To The Future Part III" has a lighthearted adventurous tone to it. I hope next time Doc will build a time machine that is impossible to damage, or have it run out of fuel or power. What good is traveling through time if you keep getting stuck somewhere?
visit Chad'z Movie Page @ http://members.aol.com/ChadPolenz/index.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


---
layout: post
title: loose line sketch from photo reference nib pen
date: '2014-12-10T07:30:31+01:00'
categories:
- Sketches
picture_url: https://film-Storyboards.tumblr.com/post/104840392066/loose-line-sketch-from-photo-reference-nib-pen
---
Loose line sketch from photo reference — nib pen and ink. Always hard to re-inject movement in drawing from lifeless photograph.


I am a Javascript and Python enthusiast. I am experienced in leveraging latest web frameworks and libraries to provide robust Full Stack solutions. Iterative approaches to complex engineering problems using the best tools and techniques
Supporting HealthCare through sophisticated NLP solutions. Provide Full-stack web solutions using React, Redux, Python, Node.JS, AWS Serverless Architecture [Lambda, S3, DynamoDB, Cognito, API Gateway, IAM]. Add more value through responsive and interactive visualizations to the predicted data using Charts.js and D3.js. Create RESTful web services using Python and Node.JS for easy data access.
Helped Auto Dealers prove their presence in the Internet world through interactive Web Applications Developed responsive websites using Angular 5(Typescript) framework. Responsible for developing Restful services using Java Spring framework.
Sophisticated approach such as the Multivariate Analysis and Artificial Neural Network was used to build prediction model. Sole developer of linear Regression models that explained 94% variance. Further, increased this to 97.3% by scaling features and normalizing the data. Further, reduced the error rate to less than 3% and improved the accuracy to 99% by using quadratic equation fit, a non-linear Regression model. Solely responsible for documenting the entire Research Project.
Responsible for conducting Web Programming lab sessions. Teach and train students on basics of JavaScript, HTML, CSS, Python. Train students on how to debug web applications using Chrome and Edge Developer tools. Train students on Responsive web development using libraries like Bootstrap and Materialize.
Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without revolutionary ROI.
GPA: 3.71
Aggr: 72%
Apart from being a web developer, I enjoy most of my time being outdoors with my DSLR camera, taking really nice pictures.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technology advancements in the front-end web development world.


|
TSGL
A thread-safe graphics library
|
Object managing the forks and philosophers in the Dining Philosophers' problem. More...
#include <Table.h>
Public Member Functions | |
| Table (Canvas &can, int p, PhilMethod m) | |
| Creates a new Table of dining philosophers. More... | |
| ~Table () | |
| Destructor for Table. | |
| void | forfeitWhenBlockedMethod (int id) |
| Method for determining which fork a Philosopher should get. More... | |
| void | waitWhenBlockedMethod (int id) |
| Method for determining which fork a Philosopher should get. More... | |
| void | nFrameReleaseMethod (int id) |
| Method for determining which fork a Philosopher should get. More... | |
| void | hierarchyMethod (int id) |
| Method for determining which fork a Philosopher should get. More... | |
| void | oddEvenMethod (int id) |
| Method for determining which fork a Philosopher should get. More... | |
| void | checkStep () |
| Method for determining which method of resolution the philosopher is using. | |
| void | actStep () |
| Method for philosopher to act based on myAction. | |
| void | drawStep () |
| Method calculating angles calling draw methods of a philosopher and its fork or forks. | |
Object managing the forks and philosophers in the Dining Philosophers' problem.
The Table class keeps track of the forks and philosophers in the Dining Philosophers' problem; it additionally manages the actions of the philosophers.
Each step of the problem is broken up into two phases. In the checking phase, the philosophers look at the table around them and, without communicating with the other philosophers, determine an action to take based on their state and the states of their adjacent forks.
In the action phase, each philosopher attempts to execute the action previously determined in the checking phase. If unsuccessful, the philosopher does nothing; otherwise, the philosopher's state changes depending on the action taken.
| Table::Table | ( | Canvas & | can, |
| int | p, | ||
| PhilMethod | m | ||
| ) |
| void Table::forfeitWhenBlockedMethod | ( | int | id | ) |
Method for determining which fork a Philosopher should get.
| id | The id number of the current Philosopher. |
Referenced by checkStep().
| void Table::hierarchyMethod | ( | int | id | ) |
Method for determining which fork a Philosopher should get.
| id | The id number of the current Philosopher. |
Referenced by checkStep().
| void Table::nFrameReleaseMethod | ( | int | id | ) |
Method for determining which fork a Philosopher should get.
| id | The id number of the current Philosopher. |
Referenced by checkStep().
| void Table::oddEvenMethod | ( | int | id | ) |
Method for determining which fork a Philosopher should get.
| id | The id number of the current Philosopher. |
Referenced by checkStep().
| void Table::waitWhenBlockedMethod | ( | int | id | ) |
Method for determining which fork a Philosopher should get.
| id | The id number of the current Philosopher. |
Referenced by checkStep().


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Chainer Contribution Guide¶
This is a guide for all contributions to Chainer. The development of Chainer is running on the official repository at GitHub. Anyone that wants to register an issue or to send a pull request should read through this document.
Classification of Contributions¶
There are several ways to contribute to Chainer community:
- Registering an issue
- Sending a pull request (PR)
- Sending a question to Chainer User Group
- Open-sourcing an external example
- Writing a post about Chainer
This document mainly focuses on 1 and 2, though other contributions are also appreciated.
Release and Milestone¶
We are using GitHub Flow as our basic working process. In particular, we are using the master branch for our development, and releases are made as tags.
Releases are classified into three groups: major, minor, and revision. This classification is based on following criteria:
- Major update contains disruptive changes that break the backward compatibility.
- Minor update contains additions and extensions to the APIs keeping the supported backward compatibility.
- Revision update contains improvements on the API implementations without changing any API specification.
The release classification is reflected into the version number x.y.z, where x, y, and z corresponds to major, minor, and revision updates, respectively.
We set a milestone for an upcoming release. The milestone is of name ‘vX.Y.Z’, where the version number represents a revision release at the outset. If at least one feature PR is merged in the period, we rename the milestone to represent a minor release (see the next section for the PR types).
See also API Compatibility Policy.
Issues and PRs¶
Issues and PRs are classified into following categories:
- Bug: bug reports (issues) and bug fixes (PRs)
- Enhancement: implementation improvements without breaking the interface
- Feature: feature requests (issues) and their implementations (PRs)
- NoCompat: disrupts backward compatibility
- Test: test fixes and updates
- Document: document fixes and improvements
- Example: fixes and improvements on the examples
- Install: fixes installation script
- Contribution-Welcome: issues that we request for contribution (only issues are categorized to this)
- Other: other issues and PRs
Issues and PRs are labeled by these categories. This classification is often reflected into its corresponding release category: Feature issues/PRs are contained into minor/major releases and NoCompat issues/PRs are contained into major releases, while other issues/PRs can be contained into any releases including revision ones.
On registering an issue, write precise explanations on what you want Chainer to be. Bug reports must include necessary and sufficient conditions to reproduce the bugs. Feature requests must include what you want to do (and why you want to do, if needed). You can contain your thoughts on how to realize it into the feature requests, though what part is most important for discussions.
Warning
If you have a question on usages of Chainer, it is highly recommended to send a post to Chainer User Group instead of the issue tracker. The issue tracker is not a place to share knowledge on practices. We may redirect question issues to Chainer User Group.
If you can write code to fix an issue, send a PR to the master branch. Before writing your code for PRs, read through the Coding Guidelines. The description of any PR must contain a precise explanation of what and how you want to do; it is the first documentation of your code for developers, a very important part of your PR.
Once you send a PR, it is automatically tested on Travis CI for Linux and Mac OS X, and on AppVeyor for Windows. Your PR need to pass at least the test for Linux on Travis CI. After the automatic test passes, some of the core developers will start reviewing your code. Note that this automatic PR test only includes CPU tests.
Note
We are also running continuous integration with GPU tests for the master branch. Since this service is running on our internal server, we do not use it for automatic PR tests to keep the server secure.
Even if your code is not complete, you can send a pull request as a work-in-progress PR by putting the [WIP]
prefix to the PR title.
If you write a precise explanation about the PR, core developers and other contributors can join the discussion about how to proceed the PR.
Coding Guidelines¶
We use PEP8 and a part of OpenStack Style Guidelines related to general coding style as our basic style guidelines.
To check your code, use autopep8
and flake8
command installed by hacking
package:
$ pip install autopep8 hacking
$ autopep8 --global-config .pep8 path/to/your/code.py
$ flake8 path/to/your/code.py
To check Cython code, use .flake8.cython
configuration file:
$ flake8 --config=.flake8.cython path/to/your/cython/code.pyx
The autopep8
supports automatically correct Python code to conform to the PEP 8 style guide:
$ autopep8 --in-place --global-config .pep8 path/to/your/code.py
The flake8
command lets you know the part of your code not obeying our style guidelines.
Before sending a pull request, be sure to check that your code passes the flake8
checking.
Note that flake8
command is not perfect.
It does not check some of the style guidelines.
Here is a (not-complete) list of the rules that flake8
cannot check.
- Relative imports are prohibited. [H304]
- Importing non-module symbols is prohibited.
- Import statements must be organized into three parts: standard libraries, third-party libraries, and internal imports. [H306]
In addition, we restrict the usage of shortcut symbols in our code base.
They are symbols imported by packages and sub-packages of chainer
.
For example, chainer.Variable
is a shortcut of chainer.variable.Variable
.
It is not allowed to use such shortcuts in the ``chainer`` library implementation.
Note that you can still use them in tests
and examples
directories.
Also note that you should use shortcut names of CuPy APIs in Chainer implementation.
Once you send a pull request, your coding style is automatically checked by Travis-CI. The reviewing process starts after the check passes.
The CuPy is designed based on NumPy’s API design. CuPy’s source code and documents contain the original NumPy ones. Please note the followings when writing the document.
- In order to identify overlapping parts, it is preferable to add some remarks that this document is just copied or altered from the original one. It is also preferable to briefly explain the specification of the function in a short paragraph, and refer to the corresponding function in NumPy so that users can read the detailed document. However, it is possible to include a complete copy of the document with such a remark if users cannot summarize in such a way.
- If a function in CuPy only implements a limited amount of features in the original one, users should explicitly describe only what is implemented in the document.
Testing Guidelines¶
Testing is one of the most important part of your code. You must test your code by unit tests following our testing guidelines. Note that we are using the nose package and the mock package for testing, so install nose and mock before writing your code:
$ pip install nose mock
In order to run unit tests at the repository root, you first have to build Cython files in place by running the following command:
$ python setup.py develop
Once the Cython modules are built, you can run unit tests simply by running nosetests
command at the repository root:
$ nosetests
It requires CUDA by default.
In order to run unit tests that do not require CUDA, pass --attr='!gpu'
option to the nosetests
command:
$ nosetests path/to/your/test.py --attr='!gpu'
Some GPU tests involve multiple GPUs.
If you want to run GPU tests with insufficient number of GPUs, specify the number of available GPUs by --eval-attr='gpu<N'
where N
is a concrete integer.
For example, if you have only one GPU, launch nosetests
by the following command to skip multi-GPU tests:
$ nosetests path/to/gpu/test.py --eval-attr='gpu<2'
Some tests spend too much time.
If you want to skip such tests, pass --attr='!slow'
option to the nosetests
command:
$ nosetests path/to/your/test.py --attr='!slow'
Tests are put into the tests/chainer_tests
, tests/cupy_tests
and tests/install_tests
directories.
These have the same structure as that of chainer
, cupy
and install
directories, respectively.
In order to enable test runner to find test scripts correctly, we are using special naming convention for the test subdirectories and the test scripts.
- The name of each subdirectory of
tests
must end with the_tests
suffix. - The name of each test script must start with the
test_
prefix.
Following this naming convention, you can run all the tests by just typing nosetests
at the repository root:
$ nosetests
Or you can also specify a root directory to search test scripts from:
$ nosetests tests/chainer_tests # to just run tests of Chainer
$ nosetests tests/cupy_tests # to just run tests of CuPy
$ nosetests tests/install_tests # to just run tests of installation modules
If you modify the code related to existing unit tests, you must run appropriate commands.
Note
CuPy tests include type-exhaustive test functions which take long time to execute. If you are running tests on a multi-core machine, you can parallelize the tests by following options:
$ nosetests --processes=12 --process-timeout=1000 tests/cupy_tests
The magic numbers can be modified for your usage.
Note that some tests require many CUDA compilations, which require a bit long time.
Without the process-timeout
option, the timeout is set shorter, causing timeout failures for many test cases.
There are many examples of unit tests under the tests
directory.
They simply use the unittest
package of the standard library.
Even if your patch includes GPU-related code, your tests should not fail without GPU capability.
Test functions that require CUDA must be tagged by the chainer.testing.attr.gpu
decorator (or cupy.testing.attr.gpu
for testing CuPy APIs):
import unittest
from chainer.testing import attr
class TestMyFunc(unittest.TestCase):
...
@attr.gpu
def test_my_gpu_func(self):
...
The functions tagged by the gpu
decorator are skipped if --attr='!gpu'
is given.
We also have the chainer.testing.attr.cudnn
decorator to let nosetests
know that the test depends on cuDNN.
The test functions decorated by gpu
must not depend on multiple GPUs.
In order to write tests for multiple GPUs, use chainer.testing.attr.multi_gpu()
or cupy.testing.attr.multi_gpu()
decorators instead:
import unittest
from chainer.testing import attr
class TestMyFunc(unittest.TestCase):
...
@attr.multi_gpu(2) # specify the number of required GPUs here
def test_my_two_gpu_func(self):
...
If your test requires too much time, add chainer.testing.attr.slow
decorator.
The test functions decorated by slow
are skipped if --attr='!slow'
is given:
import unittest
from chainer.testing import attr
class TestMyFunc(unittest.TestCase):
...
@attr.slow
def test_my_slow_func(self):
...
Note
If you want to specify more than two attributes, separate them with a comma such as --attr='!gpu,!slow'
.
See detail in the document of nose.
Once you send a pull request, your code is automatically tested by Travis-CI with –attr=’!gpu,!slow’ option. Since Travis-CI does not support CUDA, we cannot check your CUDA-related code automatically. The reviewing process starts after the test passes. Note that reviewers will test your code without the option to check CUDA-related code.
Note
Some of numerically unstable tests might cause errors irrelevant to your changes. In such a case, we ignore the failures and go on to the review process, so do not worry about it.


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Background in generating social media traffic, on various platforms, through engaging & targeted content.
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Welcome to ACL2020! The conference comprises of mainly two types of material: pre-recorded and live.
You can watch the pre-recorded material anytime before or during the conference, whereas the live events have a fixed timing.
Following are the main elements of the conference:
- Schedule
- You can find the times for all the live sessions using the Schedule, where you can choose your timezone and
find live sessions that work for you.
- Plenary Sessions
- Plenary Sessions include Keynote Talks (pre-recorded talk and live Q&A), live award ceremonies (Lifetime Achievement, Distinguished Service, Test of the Time and Best Paper)
and ACL Business Meeting (pre-recorded talks and live discussions).
- Main Conference Papers
- Each paper will have a pre-recorded talk (long papers 12 minutes and short papers 7 minutes),
two live Q&A video sessions (on Zoom) at different times, and a linked RocketChat channel (where you can chat anytime with the author).
- System Demonstrations
- Each demo will have a pre-recorded 12-min talk, two live Q&A video sessions (on Zoom) at different times, and a linked RocketChat channel.
- Student Research Workshop
- Each paper will have a pre-recorded 12-min talk, two live Q&A video sessions (on Zoom) at different times, and a linked RocketChat channel.
- Tutorials
- There are 8 tutorials (all included under conference registration). Some tutorials are fully pre-recorded whereas some have pre-recorded talks and live sessions.
Visit the tutorials page for more information.
- Workshops
- There are 19 workshops (all included under conference registration). Some workshops are fully pre-recorded whereas some have pre-recorded talks and live sessions.
Visit the workshops page for more information.
- Sponsors
- Meet our sponsors by visiting the sponsor page.
- Chat
- You can interact with other conference attendees using various RocketChat channels.
When you access RocketChat for the first time, click on 'Login via ACL2020 account' and your ACL2020 credentials will be automatically used to log you in to RocketChat.
- Socials
- You can also meet new people and other participants using various social events organised throughout the week.
- Help
- If you have questions about planning your time or navigating, look under Help for answers to frequently asked questions and technical support.
- Social Media
- Share papers, workshops, and demos by using the hashtag #ACL2020
{{ components.section("Organizers") }}
{{ components.organizergroup(committee) }}
{% endblock %}


Queries posed by the system can be classified according to the kind of input they expect, the way the input is processed, etc. Queries of the same kind form a query class.
For example, queries requiring a yes/no answer form a query class with the following characteristics:
yes
or no
, respectively, as the abstract answer;
There are built-in query classes for reading in yes/no answers, toplevel queries, debugger commands, etc.
A query class is characterized by a ground Prolog term,
which is supplied as the first argument to the query processing
predicate ask_query/4
. The characteristics of a query
class are normally described by the extendible predicate
'SU_messages':query_class(QueryClass, Prompt, InputMethod, MapMethod, FailureMode).
The arguments of the query_class
predicate have the
following meaning:
For example, a built-in input method is described by the atom
line
. This requests that a line is input from the user, and the
code-list is returned. Another built-in input method is
term(
Options)
; here, a Prolog term is read and
returned.
The input obtained using InputMethod is called raw input, as it may undergo further processing.
In addition to the built-in input methods, the user can define his/her own extensions.
For example, the built-in map method char([yes-"yY", no-"nN"])
expects a code-list as raw input, and gives the answer term
yes
or no
depending on the first non-whitespace character
of the input. For another example, the built-in map method =
requests that the raw input itself be returned as the answer
term—this is often used in conjunction with the input method
term(
Options)
.
In addition to the built-in map methods the user can define his/her own extensions.
This is used only when the mapping of raw input fails, and
the query must be repeated. This happens for example if the user typed
a character other than y or n in case of the
yes_or_no
query class. FailureMode determines what to
print before re-querying the user. Possible values are:
help_query
: print a help message, then print the text of
the query again
help
: only print the help message
query
: only print the text of the query
none
: do not print anything


Specify a set of logger names and the corresponding log levels that
this log recorder will collect. For example, if you specify
hudson.model.Hudson and info, any logs recorded
for hudson.model.Hudson or its descendants with the log level
info or above will show up in this log recorder, unless a more specific
logger with higher log level is also specific.
If you have multiple entries, a log entry will the most specific one will be collected.
Also see
this document for more about logging in Java and Jenkins.


What constitutes a "fair" algorithm in the
criminal justice system?
In order to develop more fair algorithms, we
began by defining what "fair" means in the context of risk assessment
algorithms. Our qualitative definition of fairness is shown above.
We quantitatively define fairness as minimizing disparate impact among
marginazied groups. For more information on how we used disparate
impact to measure the fairness of each of our algorithms
see our fairness repository on GitHub.


SetURLPart()
Syntax
Result$ = SetURLPart(URL$, Parameter$, Value$)Description
Set a specific part of the given URL$.
Parameters
URL$ The URL to modify. A URL$ may contain parameters, which are useful when a scripting language such as: (PHP) is being used on that server. The syntax is as follows: (http://www.purebasic.com/index.php3?test=1) Here, the parameter is named "test" and its associated value is "1".
In order to set a specific part of a URL, the information provided within "URL$" must (at the minimum), be of the following format: ("http://www.purebasic.com")Parameter$ The parameter to modify. The parameters are not case-sensitive. Moreover, Parameter$ may be one of the following constants: #PB_URL_Protocol: change the protocol of the URL$ #PB_URL_Site: change the site of the URL$ #PB_URL_Port: change the port of the URL$ (or adds it if not specified) #PB_URL_Parameters: changes all the parameters of the URL$ (or adds it if not specified) #PB_URL_Path: changes the path of the URL$ (or adds it if not specified) #PB_URL_User: changes the username of the URL$ (or adds it if not specified) #PB_URL_Password: changes the password of the URL$ (or adds it if not specified - the "user" needs to exist)Value$ The value to assign to the given parameter or URL part.
Return value
Returns the modified URL.
Example
URL$ = "http://www.test.com/hello.php3" URL$ = SetURLPart(URL$, #PB_URL_Protocol, "ftp") URL$ = SetURLPart(URL$, #PB_URL_Site, "www.purebasic.com") URL$ = SetURLPart(URL$, #PB_URL_Port, "80") URL$ = SetURLPart(URL$, #PB_URL_Path, "english/index.php3") URL$ = SetURLPart(URL$, #PB_URL_User, "user") URL$ = SetURLPart(URL$, #PB_URL_Password, "pass") URL$ = SetURLPart(URL$, "test", "1") URL$ = SetURLPart(URL$, "ok", "2") Debug URL$ ; Will print "ftp://user:pass@www.purebasic.com:80/english/index.php3?test=1&ok=2"
See Also
GetURLPart(), URLEncoder()
Supported OS
All


CMPT 469 Presentation @Hongyi Zhang
October 15, 2015
Noises could be generated.
imnoise(I, 'gaussian'); % generated by Matlab
It's not uncommon to discover noises in natural images.
| SFU Diamond Alumni Center | Cute Jellyfish and their Excreta |
Recall Image Formation: images noises and ISO
We have learned in class that higher ISO will result in more noises.
Method noise is defined as the image difference. Let u be an image and Dh a denoising operator depending on a filtering parameter h.
Method Noise = u(original image) - Dh*u(processed image)
A good denoising method should have method noise containing as little structures as possible.
Intuition: reduce noise by averaging nearby pixels and weights are decided by distance.
By design, Gaussian filter is optimal in flat part, but edges are not preserved.
Any better idea to preserve edges?
It can be implemented with two Gaussian kernels
Reference: CMPT 469 course material post processing slides
Image from Bilateral Filtering for Gray and Color Images
So far, neighborhood filter looks like a promising way to solve the problem. However, is it really convincing to only compare grayscale intensities?
Relying on grayscale intensities in not robust if these values are noisy...
We could imagine that the result would be opposite to the isotropic Gaussian filtering. AF performs well on edges, while causing troubles to flat areas.
u: result image
v: noisy image
lambda: scalar, adjusting the degree of smoothness
Recall seamless cloning. They are both minimization problems and the process of calculation is similar.
Edges are maintained, but adjusting lambda could be a problem. If lambda is too small here, flat areas could over-smoothed.
Are classic methods good enough?
The answer is the neighbors!
For two neighbors, the similarity between them is defined as the Euclidean distance convoled with a Gaussian kernel. The final weights need to be normalized.
Those pixels has entirely different neighbor structures tend to have zero weight.
Almost the same as texture synthesis! They share the same way to pick candidate pixels(calculate neighbor distances).
But again, the idea behind them is exactly the same.
1. Original image 2. Gaussian Filtering 3. Anisotropic filtering
4. Total variation minimization 5. Neighborhood filtering 6. NL-means algorithm
1. Original image 2. Gaussian Filtering 3. Anisotropic filtering
4. Total variation minimization 5. Neighborhood filtering 6. NL-means algorithm
By nature, NL-mean method serves better on texture-like images. But we can see that it still generates satisfying results of natural images due to natural redundancies.
The disadvantage of this method is its high computational complexity. In practice, the search of similar pixels is restricted to a relatively large window size. 21*21 window size is applied in the above examples.
- Reference: A non-local algorithm for image denoising
- Slides built by Reveal.js


Welcome to OpenEdge. We are a promotional marketing firm just expanding from the San Francisco Bay Area to work with our clients to hit their goals!
OpenEdge works with a government-funded outreach program through California Lifeline to provide smartphone devices to specific regions of low income communities. We target the people and communities who will most benefit from the wireless services we promote.
The importance of technology in today’s world is paramount to the success and well-being of these communities. Not only can individuals apply for jobs, but California deems cell phone service to be a necessary part of life - and therefore subsidizes this program to allow for communication and connection to thrive.
We make communities stronger by providing these crucial services. Our coordinators work with various institutions to coordinate events in the communities at large. Our face to face basis for representation is the foundation for how we are able to build such trust with the community. By presenting the benefits of wireless service to low-income families, we bridge the gap between the underprivileged and their ability to become positive impacts in their communities.
Our training program at OpenEdge provides phenominal mentorship that produces our high performance leaders. We value communication as a tangible ‘hard skill’ - ensuring that we will always be the catalysts connecting the community to our clients. This training is tangential to our success here at OpenEdge, as we look forward to working with more headstrong individuals who are open to this quality of training. What’s more, our Account Directors act as client representatives by guiding our teams and trainees to produce awareness so that the community’s needs are met.


Vancouver’s roaming Jewish deli will pop-up with their pastrami for two nights only
by Lindsay William-RossHe’s back! Vancouver chef Nitzan Cohen is reviving his popular Mensch. Jewish Delicatessen after a long hiatus, with two nights of his signature hot pastrami and more before the year is out.
Cohen introduced Vancouver to his hand-crafted and hand-sliced hot pastrami piled high on fresh rye bread a few years ago, holding one-off pop-ups and even an extended run a couple of years back. But we haven’t had an official Mensch pop-up in about a year, so mark your calendars.
Mensch will return on Saturday, November 30 and Saturday, December 14. This time it will take place where Cohen is currently hanging his chef’s hat: Kafka’s Coffee at the Emily Carr University of Art + Design campus on Great Northern Way.
In addition to the hot pastrami – which is made as a labour of love with 10 days of brining, eights hours of smoking and four hours of steaming – Mensch will have beet-cured lox with house-made Labneh on a poppy seed bagel. To drink, beer from Brassneck Brewery and pop will be available.
Cohen adds that there will be more Mensch pop-ups in the new year.
Mensch. Jewish Delicatessen Pop up Series
When: Sat. Nov. 30 and Sat. Dec. 14 from 6 p.m. until they run out
Where: Kafka’s Coffee – 577 Great Northern Way, Vancouver


Denotes whether a GNParam is a path
, header
, or query
/body
parameter.
A PType that the Param adheres to.
A description of what the Param entails.
Whether or not it is mandator to set this Param.
If true
, this param is publicly. Good for Params that are set by the backend.
A plain-object that describes all the ways in which a GNParam is matched. This
for params
that takes a multitude of specific string-formats
and dismiss any that
do not conform.
{
matches: {
"URL": "A valid URL-String (^https?://(?:\\b[\\w_-]\\b\\.)+[a-z]+(?:\\/[\\w%&=_-])\\/?$)",
"IP": "A valid URL-String (^\\b((\\d|1\\d{1,2}|2([0-4]\\d|5[0-5]))(\\.|$)){4}\\b$)",
}
}
A Parameter Description Documenter for
GNParams
or GNHeaders.


Fixup 3-3 Chamfer to intersect
Move the cursor to line and press 3.
The toolbar number is highlighted.
Move the cursor to other line and press 3
Both lines are cut back from the intersection and connected by a new line.
Use 1 3 instead of 3 3 to trim only the second line
Use 2 3 to trim the first, chamfer the second.
Use 3 2 to chamfer the first, trim the second.
The cutback distance is set using the Fixup 0 key.


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|
AAX SDK
2.3.1
Avid Audio Extensions Development Kit
|
#include <AAX_IACFPageTableController.h>
Interface for host operations related to the page tables for this plug-in.
Public Member Functions | |
| virtual AAX_Result | CopyTableForEffect (AAX_CPropertyValue inManufacturerID, AAX_CPropertyValue inProductID, AAX_CPropertyValue inPlugInID, uint32_t inTableType, int32_t inTablePageSize, IACFUnknown *oPageTable) const =0 |
| virtual AAX_Result | CopyTableOfLayoutForEffect (const char *inEffectID, const char *inLayoutName, uint32_t inTableType, int32_t inTablePageSize, IACFUnknown *oPageTable) const =0 |
| Public Member Functions inherited from IACFUnknown | |
| virtual BEGIN_ACFINTERFACE ACFRESULT ACFMETHODCALLTYPE | QueryInterface (const acfIID &iid, void **ppOut)=0 |
| Returns pointers to supported interfaces. More... | |
| virtual acfUInt32 ACFMETHODCALLTYPE | AddRef (void)=0 |
| Increments reference count. More... | |
| virtual acfUInt32 ACFMETHODCALLTYPE | Release (void)=0 |
| Decrements reference count. More... | |
|
pure virtual |
Copy the current page table data for a particular plug-in type.
The host will reject the copy and return an error if the requested plug-in type is unkown, if inTableType
is unknown or if inTablePageSize
is not a supported size for the given table type.
The host may also restrict plug-ins to only copying page table data from certain plug-in types, such as plug-ins from the same manufacturer or plug-in types within the same effect.
See Page Table Guide for more information about page tables.
oPageTable
is null| [in] | inManufacturerID | Manufacturer ID of the desired plug-in type |
| [in] | inProductID | Product ID of the desired plug-in type |
| [in] | inPlugInID | Type ID of the desired plug-in type (AAX_eProperty_PlugInID_Native, AAX_eProperty_PlugInID_TI) |
| [in] | inTableType | Four-char type identifier for the requested table type (e.g. 'PgTL' , 'Av81' , etc.) |
| [in] | inTablePageSize | Page size for the requested table. Some tables support multiple page sizes. |
| [out] | oPageTable | The page table object to which the page table data should be copied. oPageTable must support AAX_IACFPageTable |
|
pure virtual |
Copy the current page table data for a particular plug-in effect and page table layout.
The host will reject the copy and return an error if the requested effect ID is unkown or if inLayoutName
is not a valid layout name for the page tables registered for the effect.
The host may also restrict plug-ins to only copying page table data from certain effects, such as effects registered within the current AAX plug-in bundle.
See Page Table Guide for more information about page tables.
inEffectID
, inLayoutName
, or oPageTable
is null| [in] | inEffectID | Effect ID for the desired effect. See AAX_ICollection::AddEffect() |
| [in] | inLayoutName | Page table layout name ("name" attribute of the PTLayout XML tag) |
| [in] | inTableType | Four-char type identifier for the requested table type (e.g. 'PgTL' , 'Av81' , etc.) |
| [in] | inTablePageSize | Page size for the requested table. Some tables support multiple page sizes. |
| [out] | oPageTable | The page table object to which the page table data should be copied. oPageTable must support AAX_IACFPageTable |


OUR RIGHTS & SOCIAL SOCIETY
Our Rights & Social Society was established in 2017 as voluntary Development organization, which is registered under the society registration Act 1860. The society is a national NGO. The society is implementing many social services with the support of Delhi Govt. Since 2017 we are working in Delhi JJ cluster area especially in East and North West district of Delhi with poor, marginalized and vulnerable population.


public class SequenceNumbersService extends AbstractIndexShardComponent
deprecationLogger, indexSettings, logger, shardId
| 构造器和说明 |
|---|
SequenceNumbersService(ShardId shardId,
java.lang.String allocationId,
IndexSettings indexSettings,
long maxSeqNo,
long localCheckpoint,
long globalCheckpoint)
Initialize the sequence number service.
|
| 限定符和类型 | 方法和说明 |
|---|---|
void |
abortRelocationHandoff()
Fails a relocation handoff attempt.
|
void |
activatePrimaryMode(long localCheckpoint)
Activates the global checkpoint tracker in primary mode (see
GlobalCheckpointTracker.primaryMode . |
void |
activateWithPrimaryContext(GlobalCheckpointTracker.PrimaryContext primaryContext)
Activates the global checkpoint tracker in primary mode (see
GlobalCheckpointTracker.primaryMode . |
void |
completeRelocationHandoff()
Marks a relocation handoff attempt as successful.
|
long |
generateSeqNo()
Issue the next sequence number.
|
long |
getGlobalCheckpoint()
Returns the global checkpoint for the shard.
|
com.carrotsearch.hppc.ObjectLongMap<java.lang.String> |
getInSyncGlobalCheckpoints()
Get the local knowledge of the global checkpoints for all in-sync allocation IDs.
|
long |
getLocalCheckpoint()
Returns the local checkpoint for the shard.
|
long |
getMaxSeqNo()
The maximum sequence number issued so far.
|
ReplicationGroup |
getReplicationGroup()
Returns the current replication group for the shard.
|
long |
getTrackedLocalCheckpointForShard(java.lang.String allocationId)
Returns the local checkpoint information tracked for a specific shard.
|
void |
initiateTracking(java.lang.String allocationId)
Called when the recovery process for a shard is ready to open the engine on the target shard.
|
void |
markAllocationIdAsInSync(java.lang.String allocationId,
long localCheckpoint)
Marks the shard with the provided allocation ID as in-sync with the primary shard.
|
void |
markSeqNoAsCompleted(long seqNo)
Marks the processing of the provided sequence number as completed as updates the checkpoint if possible.
|
boolean |
pendingInSync()
Check if there are any recoveries pending in-sync.
|
void |
resetLocalCheckpoint(long localCheckpoint)
Resets the local checkpoint to the specified value.
|
GlobalCheckpointTracker.PrimaryContext |
startRelocationHandoff()
Get the primary context for the shard.
|
SeqNoStats |
stats()
The current sequence number stats.
|
void |
updateAllocationIdsFromMaster(long applyingClusterStateVersion,
java.util.Set<java.lang.String> inSyncAllocationIds,
IndexShardRoutingTable routingTable,
java.util.Set<java.lang.String> pre60AllocationIds)
Notifies the service of the current allocation IDs in the cluster state.
|
void |
updateGlobalCheckpointForShard(java.lang.String allocationId,
long globalCheckpoint)
Update the local knowledge of the global checkpoint for the specified allocation ID.
|
void |
updateGlobalCheckpointOnReplica(long globalCheckpoint,
java.lang.String reason)
Updates the global checkpoint on a replica shard after it has been updated by the primary.
|
void |
updateLocalCheckpointForShard(java.lang.String allocationId,
long checkpoint)
Notifies the service to update the local checkpoint for the shard with the provided allocation ID.
|
void |
waitForOpsToComplete(long seqNo)
Waits for all operations up to the provided sequence number to complete.
|
indexSettings, nodeName, shardId
public SequenceNumbersService(ShardId shardId, java.lang.String allocationId, IndexSettings indexSettings, long maxSeqNo, long localCheckpoint, long globalCheckpoint)
maxSeqNo
should be set to the last sequence number assigned by this shard, or
SequenceNumbers.NO_OPS_PERFORMED
, localCheckpoint
should be set to the last known local checkpoint for this
shard, or SequenceNumbers.NO_OPS_PERFORMED
, and globalCheckpoint
should be set to the last known global
checkpoint for this shard, or SequenceNumbers.UNASSIGNED_SEQ_NO
.shardId
- the shard this service is providing tracking local checkpoints forindexSettings
- the index settingsmaxSeqNo
- the last sequence number assigned by this shard, or SequenceNumbers.NO_OPS_PERFORMED
localCheckpoint
- the last known local checkpoint for this shard, or SequenceNumbers.NO_OPS_PERFORMED
globalCheckpoint
- the last known global checkpoint for this shard, or SequenceNumbers.UNASSIGNED_SEQ_NO
public final long generateSeqNo()
markSeqNoAsCompleted(long)
after the operation for which the
issued sequence number completes (whether or not the operation completes successfully).public long getMaxSeqNo()
LocalCheckpointTracker.getMaxSeqNo()
for additional details.public void waitForOpsToComplete(long seqNo) throws java.lang.InterruptedException
seqNo
- the sequence number that the checkpoint must advance to before this method returnsjava.lang.InterruptedException
- if the thread was interrupted while blocking on the conditionpublic void markSeqNoAsCompleted(long seqNo)
LocalCheckpointTracker.markSeqNoAsCompleted(long)
for additional details.seqNo
- the sequence number to mark as completedpublic void resetLocalCheckpoint(long localCheckpoint)
localCheckpoint
- the local checkpoint to reset topublic SeqNoStats stats()
public void updateLocalCheckpointForShard(java.lang.String allocationId, long checkpoint)
GlobalCheckpointTracker.updateLocalCheckpoint(String, long)
for details.allocationId
- the allocation ID of the shard to update the local checkpoint forcheckpoint
- the local checkpoint for the shardpublic void updateGlobalCheckpointForShard(java.lang.String allocationId, long globalCheckpoint)
allocationId
- the allocation ID to update the global checkpoint forglobalCheckpoint
- the global checkpointpublic com.carrotsearch.hppc.ObjectLongMap<java.lang.String> getInSyncGlobalCheckpoints()
public void initiateTracking(java.lang.String allocationId)
GlobalCheckpointTracker.initiateTracking(String)
for details.allocationId
- the allocation ID of the shard for which recovery was initiatedpublic void markAllocationIdAsInSync(java.lang.String allocationId, long localCheckpoint) throws java.lang.InterruptedException
GlobalCheckpointTracker.markAllocationIdAsInSync(String, long)
for additional details.allocationId
- the allocation ID of the shard to mark as in-synclocalCheckpoint
- the current local checkpoint on the shardjava.lang.InterruptedException
public long getLocalCheckpoint()
public ReplicationGroup getReplicationGroup()
public long getGlobalCheckpoint()
public void updateGlobalCheckpointOnReplica(long globalCheckpoint, java.lang.String reason)
globalCheckpoint
- the global checkpointreason
- the reason the global checkpoint was updatedpublic long getTrackedLocalCheckpointForShard(java.lang.String allocationId)
public void activatePrimaryMode(long localCheckpoint)
GlobalCheckpointTracker.primaryMode
.
Called on primary activation or promotion.public void updateAllocationIdsFromMaster(long applyingClusterStateVersion, java.util.Set<java.lang.String> inSyncAllocationIds, IndexShardRoutingTable routingTable, java.util.Set<java.lang.String> pre60AllocationIds)
GlobalCheckpointTracker.updateFromMaster(long, Set, IndexShardRoutingTable, Set)
for details.applyingClusterStateVersion
- the cluster state version being applied when updating the allocation IDs from the masterinSyncAllocationIds
- the allocation IDs of the currently in-sync shard copiesroutingTable
- the shard routing tablepre60AllocationIds
- the allocation IDs of shards that are allocated to pre-6.0 nodespublic void activateWithPrimaryContext(GlobalCheckpointTracker.PrimaryContext primaryContext)
GlobalCheckpointTracker.primaryMode
.
Called on primary relocation target during primary relocation handoff.primaryContext
- the primary context used to initialize the statepublic boolean pendingInSync()
true
if there is at least one shard pending in-sync, otherwise falsepublic GlobalCheckpointTracker.PrimaryContext startRelocationHandoff()
public void completeRelocationHandoff()
public void abortRelocationHandoff()


There are two ways to build pagination in Foundation 4: with our predefined HTML or with our mixin. Building pagination using our predefined classes isn't hard at all. You'll start with an unordered list and add a class of .pagination
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There are two ways to build pagination in Foundation 4: with our predefined HTML or with our mixin. Building pagination using our predefined classes isn't hard at all. You'll start with an unordered list and add a class of .pagination
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There are two ways to build pagination in Foundation 4: with our predefined HTML or with our mixin. Building pagination using our predefined classes isn't hard at all. You'll start with an unordered list and add a class of .pagination
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