The most up-to-date reference is in the client itself: use the
aurora help
subcommand (for example, aurora help
or
aurora help create
) to find the latest information on parameters and
functionality. Note that aurora help open
does not work, due to underlying issues with
reflection.
Once you have written an .aurora
configuration file that describes
your Job and its parameters and functionality, you interact with Aurora
using Aurora Client commands. This document describes all of these commands
and how and when to use them. All Aurora Client commands start with
aurora
, followed by the name of the specific command and its
arguments.
Job keys are a very common argument to Aurora commands, as well as the gateway to useful information about a Job. Before using Aurora, you should read the next section which describes them in detail. The section after that briefly describes how you can modify the behavior of certain Aurora Client commands, linking to a detailed document about how to do that.
This is followed by the Regular Jobs section, which describes the basic Client commands for creating, running, and manipulating Aurora Jobs. After that are sections on Comparing Jobs and Viewing/Examining Jobs. In other words, various commands for getting information and metadata about Aurora Jobs.
The client must be able to find a configuration file that specifies available clusters. This file declares shorthand names for clusters, which are in turn referenced by job configuration files and client commands.
The client will load at most two configuration files, making both of their defined clusters
available. The first is intended to be a system-installed cluster, using the path specified in
the environment variable AURORA_CONFIG_ROOT
, defaulting to /etc/aurora/clusters.json
if the
environment variable is not set. The second is a user-installed file, located at
~/.aurora/clusters.json
.
A cluster configuration is formatted as JSON. The simplest cluster configuration is one that communicates with a single (non-leader-elected) scheduler. For example:
[{ "name": "example", "scheduler_uri": "localhost:55555", }]
A configuration for a leader-elected scheduler would contain something like:
[{ "name": "example", "zk": "<IP_ADDRESS>", "scheduler_zk_path": "/aurora/scheduler" }]
For more details on cluster configuration see the Client Cluster Configuration documentation.
A job key is a unique system-wide identifier for an Aurora-managed
Job, for example cluster1/web-team/test/experiment204
. It is a 4-tuple
consisting of, in order, cluster, role, environment, and
jobname, separated by /s. Cluster is the name of an Aurora
cluster. Role is the Unix service account under which the Job
runs. Environment is a namespace component like devel
, test
,
prod
, or stagingN.
Jobname is the Job’s name.
The combination of all four values uniquely specifies the Job. If any
one value is different from that of another job key, the two job keys
refer to different Jobs. For example, job key
cluster1/tyg/prod/workhorse
is different from
cluster1/tyg/prod/workcamel
is different from
cluster2/tyg/prod/workhorse
is different from
cluster2/foo/prod/workhorse
is different from
cluster1/tyg/test/workhorse.
Role names are user accounts existing on the slave machines. If you don’t know what accounts are available, contact your sysadmin.
Environment names are namespaces; you can count on prod
, devel
and test
existing.
For certain Aurora Client commands, you can define hook methods that run either before or after an action that takes place during the command’s execution, as well as based on whether the action finished successfully or failed during execution. Basically, a hook is code that lets you extend the command’s actions. The hook executes on the client side, specifically on the machine executing Aurora commands.
Hooks can be associated with these Aurora Client commands.
cancel_update
create
kill
restart
update
The process for writing and activating them is complex enough that we explain it in a devoted document, Hooks for Aurora Client API.
This section covers Aurora commands related to running, killing, renaming, updating, and restarting a basic Aurora Job.
aurora create <job key> <configuration file>
Creates and then runs a Job with the specified job key based on a .aurora
configuration file.
The configuration file may also contain and activate hook definitions.
create
can take four named parameters:
-E NAME=VALUE
Bind a Thermos mustache variable name to a
value. Multiple flags specify multiple values. Defaults to []
.-o, --open_browser
Open a browser window to the scheduler UI Job
page after a job changing operation happens. When False
, the Job
URL prints on the console and the user has to copy/paste it
manually. Defaults to False
. Does not work when running in Vagrant.-j, --json
If specified, configuration argument is read as a
string in JSON format. Defaults to False.--wait_until=STATE
Block the client until all the Tasks have
transitioned into the requested state. Possible values are: PENDING
,
RUNNING
, FINISHED
. Default: PENDING
aurora run <job_key> <cmd>
Runs a shell command on all machines currently hosting shards of a single Job.
run
supports the same command line wildcards used to populate a Job’s
commands; i.e. anything in the {{mesos.*}}
and {{thermos.*}}
namespaces.
run
can take three named parameters:
-t NUM_THREADS
, --threads=NUM_THREADS
The number of threads to
use, defaulting to 1
.--user=SSH_USER
ssh as this user instead of the given role value.
Defaults to None.-e, --executor_sandbox
Run the command in the executor sandbox
instead of the Task sandbox.  Defaults to killing all shards in the Job.
The <configuration file>
argument for kill
is optional. Use it only
if it contains hook definitions and activations that affect the
kill command.
kill
can take two named parameters:
-o, --open_browser
Open a browser window to the scheduler UI Job
page after a job changing operation happens. When False
, the Job
URL prints on the console and the user has to copy/paste it
manually. Defaults to False
. Does not work when running in Vagrant.--shards=SHARDS
A list of shard ids to act on. Can either be a
comma-separated list (e.g. 0,1,2) or a range (e.g. 0-2) or any
combination of the two (e.g. 0-2,5,7-9). Defaults to acting on all
shards.aurora update [--shards=ids] <job key> <configuration file> aurora cancel_update <job key> <configuration file>
Given a running job, does a rolling update to reflect a new
configuration version. Only updates Tasks in the Job with a changed
configuration. You can further restrict the operated on Tasks by
using --shards
and specifying a comma-separated list of job shard ids.
You may want to run aurora diff
beforehand to validate which Tasks
have different configurations.
Updating jobs are locked to be sure the update finishes without
disruption. If the update abnormally terminates, the lock may stay
around and cause failure of subsequent update attempts.
aurora cancel_update
unlocks the Job specified by
its job_key
argument. Be sure you don’t issue cancel_update
when
another user is working with the specified Job.
The <configuration file>
argument for cancel_update
is optional. Use
it only if it contains hook definitions and activations that affect the
cancel_update
command. The <configuration file>
argument for
update
is required, but in addition to a new configuration it can be
used to define and activate hooks for update
.
update
can take four named parameters:
--shards=SHARDS
A list of shard ids to update. Can either be a
comma-separated list (e.g. 0,1,2) or a range (e.g. 0-2) or any
combination of the two (e.g. 0-2,5,7-9). If not set, all shards are
acted on. Defaults to None.-E NAME=VALUE
Binds a Thermos mustache variable name to a value.
Use multiple flags to specify multiple values. Defaults to []
.-j, --json
If specified, configuration is read in JSON format.
Defaults to False
.--updater_health_check_interval_seconds=HEALTH_CHECK_INTERVAL_SECONDS
Time interval between subsequent shard status checks. Defaults to 3
.As of 0.6.0, Aurora will coordinate updates (and rollbacks) within the scheduler. Performing updates this way also allows the scheduler to display update progress and job update history in the browser.
There are several sub-commands to manage job updates:
aurora2 beta-update start <job key> <configuration file aurora2 beta-update status <job key> aurora2 beta-update pause <job key> aurora2 beta-update resume <job key> aurora2 beta-update abort <job key> aurora2 beta-update list <cluster>
When you start
a job update, the command will return once it has sent the
instructions to the scheduler. At that point, you may view detailed
progress for the update with the status
subcommand, in addition to viewing
graphical progress in the web browser. You may also get a full listing of
in-progress updates in a cluster with list
.
Once an update has been started, you can pause
to keep the update but halt
progress. This can be useful for doing things like debug a partially-updated
job to determine whether you would like to proceed. You can resume
to
proceed.
You may abort
a job update regardless of the state it is in. This will
instruct the scheduler to completely abandon the job update and leave the job
in the current (possibly partially-updated) state.
Renaming is a tricky operation as downstream clients must be informed of the new name. A conservative approach to renaming suitable for production services is:
Check that only these naming components have changed
with aurora diff
.
aurora diff <job_key> <job_configuration>
Create the (identical) job at the new key. You may need to request a temporary quota increase.
aurora create <new_job_key> <job_configuration>
Migrate all clients over to the new job key. Update all links and dashboards. Ensure that both job keys run identical versions of the code while in this state.
After verifying that all clients have successfully moved over, kill the old job.
aurora kill <old_job_key>
If you received a temporary quota increase, be sure to let the powers that be know you no longer need the additional capacity.
restart
restarts all of a job key identified Job’s shards:
aurora restart <job_key> <configuration file>
Restarts are controlled on the client side, so aborting
the restart
command halts the restart operation.
restart
does a rolling restart. You almost always want to do this, but
not if all shards of a service are misbehaving and are
completely dysfunctional. To not do a rolling restart, use
the -shards
option described below.
Note: restart
only applies its command line arguments and does not
use or is affected by update.config
. Restarting
does not involve a configuration change. To update the
configuration, use update.config
.
The <configuration file>
argument for restart is optional. Use it only
if it contains hook definitions and activations that affect the
restart
command.
In addition to the required job key argument, there are eight
restart
specific optional arguments:
--updater_health_check_interval_seconds
: Defaults to 3
, the time
interval between subsequent shard status checks.--shards=SHARDS
: Defaults to None, which restarts all shards.
Otherwise, only the specified-by-id shards restart. They can be
comma-separated (0, 8, 9)
, a range (3-5)
or a
combination (0, 3-5, 8, 9-11)
.--batch_size
: Defaults to 1
, the number of shards to be started
in one iteration. So, for example, for value 3, it tries to restart
the first three shards specified by --shards
simultaneously, then
the next three, and so on.--max_per_shard_failures=MAX_PER_SHARD_FAILURES
: Defaults to 0
,
the maximum number of restarts per shard during restart. When
exceeded, it increments the total failure count.--max_total_failures=MAX_TOTAL_FAILURES
: Defaults to 0
, the
maximum total number of shard failures tolerated during restart.-o, --open_browser
Open a browser window to the scheduler UI Job
page after a job changing operation happens. When False
, the Job
url prints on the console and the user has to copy/paste it
manually. Defaults to False
. Does not work when running in Vagrant.--restart_threshold
: Defaults to 60
, the maximum number of
seconds before a shard must move into the RUNNING
state before
it’s considered a failure.--watch_secs
: Defaults to 45
, the minimum number of seconds a
shard must remain in RUNNING
state before considered a success.You will see various commands and options relating to cron jobs in
aurora -help
and similar. Ignore them, as they’re not yet implemented.
You might be able to use them without causing an error, but nothing happens
if you do.
aurora diff <job_key> config
Compares a job configuration against a running job. By default the diff
is determined using diff
, though you may choose an alternate
diff program by specifying the DIFF_VIEWER
environment variable.
There are two named parameters:
-E NAME=VALUE
Bind a Thermos mustache variable name to a
value. Multiple flags may be used to specify multiple values.
Defaults to []
.-j, --json
Read the configuration argument in JSON format.
Defaults to False
.Above we discussed creating, killing, and updating Jobs. Here we discuss how to view and examine Jobs.
aurora list_jobs Usage: `aurora list_jobs cluster/role
Lists all Jobs registered with the Aurora scheduler in the named cluster for the named role.
It has two named parameters:
--pretty
: Displays job information in prettyprinted format.
Defaults to False
.-c
, --show-cron
: Shows cron schedule for jobs. Defaults to
False
. Do not use, as it’s not yet implemented.aurora inspect <job_key> config
inspect
verifies that its specified job can be parsed from a
configuration file, and displays the parsed configuration. It has four
named parameters:
--local
: Inspect the configuration that the spawn
command would
create, defaulting to False
.--raw
: Shows the raw configuration. Defaults to False
.-j
, --json
: If specified, configuration is read in JSON format.
Defaults to False
.-E NAME=VALUE
: Bind a Thermos Mustache variable name to a value.
You can use multiple flags to specify multiple values. Defaults
to []
aurora version
Lists client build information and what Aurora API version it supports.
aurora get_quota --cluster=CLUSTER role
Prints the production quota allocated to the role’s value at the given cluster.
When you create a job, part of the output response contains a URL that goes to the job’s scheduler UI page. For example:
vagrant@precise64:~$ aurora create devcluster/www-data/prod/hello /vagrant/examples/jobs/hello_world.aurora INFO] Creating job hello INFO] Response from scheduler: OK (message: 1 new tasks pending for job www-data/prod/hello) INFO] Job url: http://precise64:8081/scheduler/www-data/prod/hello
You can go to the scheduler UI page for this job via http://precise64:8081/scheduler/www-data/prod/hello
You can go to the overall scheduler UI page by going to the part of that URL that ends at scheduler
; http://precise64:8081/scheduler
Once you click through to a role page, you see Jobs arranged separately by pending jobs, active jobs and finished jobs. Jobs are arranged by role, typically a service account for production jobs and user accounts for test or development jobs.
aurora status <job_key>
Returns the status of recent tasks associated with the
job_key
specified Job in its supplied cluster. Typically this includes
a mix of active tasks (running or assigned) and inactive tasks
(successful, failed, and lost.)
Use the Job’s web UI scheduler URL or the aurora status
command to find out on which
machines individual tasks are scheduled. You can open the web UI via the
open
command line command if invoked from your machine:
aurora open [<cluster>[/<role>[/<env>/<job_name>]]]
If only the cluster is specified, it goes directly to that cluster’s scheduler main page. If the role is specified, it goes to the top-level role page. If the full job key is specified, it goes directly to the job page where you can inspect individual tasks.
aurora ssh <job_key> <shard number>
You can have the Aurora client ssh directly to the machine that has been assigned a particular Job/shard number. This may be useful for quickly diagnosing issues such as performance issues or abnormal behavior on a particular machine.
It can take three named parameters:
-e
, --executor_sandbox
: Run ssh
in the executor sandbox
instead of the task sandbox. Defaults to False
.--user=SSH_USER
: ssh
as the given user instead of as the role in
the job_key
argument. Defaults to none.-L PORT:NAME
: Add tunnel from local port PORT
to the remote
named port NAME
. Defaults to []
.aurora run [-e] [-t THREADS] <job_key> -- <<command-line>>
Given a job specification, run the supplied command on all hosts and return the output. You may use the standard Mustache templating rules:
{{thermos.ports[name]}}
substitutes the specific named port of the
task assigned to this machine{{mesos.instance}}
substitutes the shard id of the job’s task
assigned to this machine{{thermos.task_id}}
substitutes the task id of the job’s task
assigned to this machineFor example, the following type of pattern can be a powerful diagnostic tool:
aurora run -t5 cluster1/tyg/devel/seizure -- \ 'curl -s -m1 localhost:{{thermos.ports[http]}}/vars | grep uptime'
By default, the command runs in the Task’s sandbox. The -e
option can
run the command in the executor’s sandbox. This is mostly useful for
Aurora administrators.
You can parallelize the runs by using the -t
option.


I was born in the summer of 1990. My early childhood was infused
with a fleeting memory of something more intimate than a globalized
world. That decade held the cusp of change that was to alter our
direction as a "nation" since.


Located in Houston, TX, "Samu Sharpening" offers superb knife sharpening at a reasonable price. While most stores that offer knife sharpening use automated machines, Samu Sharpening does all its work by hand using high quality Japanese whet stones. This is by far the best method to give your knife a sharp, polished edge which cuts better and lasts longer.
When a knife is dull, it not only makes all your cutting and chopping work in the kitchen more difficult and time consuming, it's also more dangerous than using a sharp knife. Using a dull knife means you have to strain and push harder to make a cut, raising the risk for cutting yourself, whereas when your knife is sharp it will glide through food objects with less exertion, lowering the risk for an injury. Using a properly sharpened knife is also a great pleasure as it is much more comfortable to work with.


Sylvanian Families Cherrie's Day at the Seaside
Cherrie Chocolate is Freya Chocolate's cousin!
UK Biography Information
All Sylvanians love to be beside the seaside and rabbit cousin Cherrie Chocolate is no exception! She has her special rubber ring and a very stylish lounger to sit on and relax in the sun, but if it gets too hot she can sit under her umbrella and have a refreshing glass of orange juice.


On the Subject of HTTP Response
When your bomb acts as a web server...
- The module displays a remote or local status string. You must response to it with a 3-digit status code.
- Remote codes can be found on Table 1 while local codes can be found on Table 2.
- An undisclosed source told us that, due to display limits, some strings may be abbreviated.
- Use the keypad to enter the correct response code to disable the needy module.
- Caution: There’s no delete button, so be careful!
Table 1: Remote status, response as listed.
| Full name | Abbrev. | Response | Full name | Abbrev. | Response |
|---|---|---|---|---|---|
| Continue | Cont | 100 | Unauthorized | Unauth | 401 |
| Switching Protocols | SwPrt | 101 | Payment Required | PayReq | 402 |
| Processing | Proc | 102 | Forbidden | Frbd | 403 |
| OK | OK | 200 | Not Found | NFnd | 404 |
| Created | Crtd | 201 | Request Time-out | TmOut | 408 |
| Moved Permanently | MvPerm | 301 | Gone | Gone | 410 |
| Found | Found | 302 | I’m a teapot | ImaTp | 418 |
| Not Modified | NMod | 304 | Internal Server Error | SrvErr | 500 |
| Use Proxy | UPrx | 305 | Bad Gateway | BGw | 502 |
| Bad Request | BadR | 400 | Service Unavailable | SrvUn | 503 |


Make publication ready plots in R using ggplot2 and learn some new tips and tricks
R
ggplot2
tidyverse
Author
Affiliation
Daniel Padfield
University of Exeter
Published
February 24, 2023
Modified
August 2, 2023
Outline
I love making graphs! To explore data, to plot model results, just for the fun of making plots. I imagine we have all spent a huge amount of time making our plots ready for publication. Here we have a quick walkthrough of using ggplot2 to create graphs for publication, and a few tips and tricks we have learned along the way. If you have a favourite ggplot2 trick or tip then we would love for you to get in contact with us and for you add it to this walkthrough. Or if you’re feeling adventurous, clone the GitHub repo, add in your tip, re-render this quarto post, commit and push the changes, and start a Pull Request!
ggplot2 is a popular data visualisation package in R that allows users to create high-quality and customisable graphics for data exploration, scientific publications, and presentations. Developed by Hadley Wickham, ggplot2 is built on the principles of the Grammar of Graphics, which defines a set of rules for constructing and interpreting visualisations. With ggplot2, users can easily create a wide range of plots with advanced features such as facets, themes, and labels. Whether you are at the beginning of your science journey, or an old and grumpy PI, ggplot2 provides a powerful and flexible tool for visualising data and gaining insights from it.
Making a ggplot2 is like writing and following a recipe. You start with the first instructions (a call to ggplot()), the next instruction (like the next item of the recipe) is signalled using a +, a new line, and new instructions are added sequentially. ggplot2 works in a sequential fashion, later instructions add to what is already there, and sometimes counteract or override what previous instructions.
Prerequisites
Have R installed
Have the tidyverse package installed
Hopefully have a plot you want to make using ggplot2 or have some aesthetics you would like to change on a current plot.
Learning objectives
Learning objectives
This introduction should allow you to:
Have a basic understanding of the way in which ggplot2 “works” (i.e. the grammar of graphics)
Understand the format data needs to be in to be used in ggplot2
Make pubication-ready plots using ggplot2
Know where to learn more about ggplot2
Making plots with ggplot2
This walkthrough will lean heavy on tools from the tidyverse for data wrangling, and the palmerpenguins package for the example data. The first thing we will do is load in the necessary packages and load in the dataset we will use to create our tables.
# load in required packageslibrary(tidyverse)library(palmerpenguins)library(MetBrewer)library(patchwork)# load in datadata("penguins")d <- penguins# have a look at the dataglimpse(d)
This dataset contain data for 344 penguins, consisting of three species of penguins, collected from 3 islands in the Palmer Archipelago, Antarctica.
Preparing your dataset for ggplot2
For ggplot2, your data wants to be organised in long format, as opposed to wide.
Make a first plot using ggplot2
We will make a boxplot of how bill length changes between different species of penguin and add the raw points over the top. After making an initial plot call using ggplot(), different types of plot are specified using a geom_ function (e.g. geom_boxplot() for boxplots, geom_point() for points, geom_line() for a line graph). In each layer, the aes() function is used to tell ggplot2 which arguments depend on aspects of the dataset (e.g. col = species). Arguments set outside of aes() do not rely on the dataset (e.g. size = 3, col = ‘black’).
# make first box plot with raw pointsggplot(d, aes(species, bill_length_mm, col = species)) +# add first empty plot layergeom_boxplot(outlier.shape =NA) +# add boxplot, do not plot the outliersgeom_jitter(width =0.2) # add jittered raw points
Ok this looks nice. Here I put the aes() commands inside the original call to ggplot() as then those arguments are passed to all other layers (unless overridden by another aes()). This looks quite nice, but we can change a bunch of other things to make it look nicer.
Adding extra layers to ggplot2
There are a bunch of things I do as standard when I am making a plot (and a boxplot in particular):
Change the axis labels
Remove the legend if not needed
Change the axis limits
Change the theme and the text size
I dislike the default box plot so I colour the whole thing and then add a median bar in white (see below)
The important thing is to create your own style. To do what you think looks best, but all the options are documented nicely on their website. There are many more detailed resources around, so instead here we will document common things we do.
# make next box plot with raw pointsp1 <-ggplot(d, aes(species, bill_length_mm, col = species, fill = species)) +# add first empty plot layergeom_boxplot(outlier.shape =NA) +# add boxplot, do not plot the outliersstat_summary(geom ="crossbar", fatten =2, color ="white", width =0.4, fun.data =function(x){return(c(y = stats::median(x), ymin = stats::median(x), ymax = stats::median(x)))}) +# add median line to the boxplotsgeom_jitter(width =0.2, shape =21, fill ='white') +# add jittered raw pointstheme_bw(base_size =14) +# change theme to the one I like (others are available)labs(x ='Species',y ='Bill length (mm)',title ='Do different penguin species have different bill lengths?') +# change labelsylim(c(30,65)) +# change y limitsguides(col ='none',fill ='none') # turn off legendp1
And there - with relatively few lines of code - we have a plot that is ready for inserting into a publication. This can be easily saved out using ggsave()
# save out plot using ggsaveggsave('plot_one.png', p1, width =5, height =7)
Our favourite tips and tricks when making plots
I have a set of rules I follow when making plots with ggplot2. Some may be different to yours but they might have some use:
Generally I make my datasets BEFORE they go into ggplot2. This includes checking for NA and Inf values as these are super important to know about when visualising and analysing your data!
Plot the raw data whenever possible.
Layers work iteratively (later layers go on top of earlier layers) so make sure layers you want to be in the background (e.g. boxplot) are before layers you want to overlay (e.g. the raw data)
Only use stat_smooth() for exploring datasets. When plotting model predictions and confidence intervals, create a dataframe for them and then feed that into ggplot2.
Convert text size in geom_label() or geom_text() to pts used for labels
Lets say we wanted to add text to the plot. For example, in the plot above maybe we want to add the total number of penguins sampled for each species. By default geom_text() and geom_label() use a size guide that is different to the font size set for the axis and tick labels. However, Andrew Heiss has written a function to convert between those so we can easily define the font size in the regular way.
We will use functions from the tidyverse to create a data frame with the total number of penguins sampled and the max bill length for each penguin. Creating a data frame with the x and y coordinates of where I want the text to go on the plot, and a column for the label, is how I approach adding text labels to an existing ggplot.
# define function for ptspts <-function(x){as.numeric(grid::convertX(grid::unit(x, "points"), "mm")) }# calculate n for putting each species# remove NAs because they are not in the plotd_n <-filter(d, !is.na(bill_length_mm)) %>%group_by(species) %>%summarise(n =n(),max_bill_length =max(bill_length_mm)) %>%ungroup()# make plot with text# make next box plot with raw pointsp1 +geom_text(aes(x = species, y = max_bill_length +5, label =paste('n = ', n, sep ='')), d_n, size =pts(12), col ='black') # add text label
Soft wrap or stagger text labels to stop them overlapping
Ok these two are quite recent finds for me, and I absolutely love them. Sometimes your x labels might be really long, and the text can overlap with each other. I used to manually add new lines into my text by inserting \n where I wanted new text to me. But now two approaches exist that can automatically improve spacing between labels on the x axis. These are scales::label_wrap which can automatically create new lines for long axis labels and guide_axis() which can stagger axis labels to prevent overlapping.
We will demonstrate them both below, by adding the Latin names for each penguin species to the dataset. We will add them in using case_when() and then show both plots next to each other using patchwork which makes it really easy to combine individual plots. For free you also get code to italicise the axis labels as they are latin names!
d <-mutate(d, latin_name =case_when(species =="Gentoo"~"Pygoscelis papua", species =="Chinstrap"~"Pygoscelis antarcticus", species =="Adelie"~"Pygoscelis adeliae"))# first to softwrap the textp2 <-ggplot(d, aes(latin_name, bill_length_mm, col = species, fill = species)) +# add first empty plot layergeom_boxplot(outlier.shape =NA) +# add boxplot, do not plot the outliersstat_summary(geom ="crossbar", fatten =2, color ="white", width =0.4, fun.data =function(x){return(c(y = stats::median(x), ymin = stats::median(x), ymax = stats::median(x)))}) +# add median line to the boxplotsgeom_jitter(width =0.2, shape =21, fill ='white') +# add jittered raw pointstheme_bw(base_size =14) +# change theme to the one I like (others are available)labs(x ='Species',y ='Bill length (mm)') +# change labelsylim(c(30,65)) +# change y limitsguides(col ='none',fill ='none') +# turn off legendtheme(axis.text.x =element_text(face ="italic")) +# italicise x axis labelsscale_x_discrete(labels = scales::label_wrap(10)) # soft wrap labelsp3 <- p2 +scale_x_discrete(guide =guide_axis(n.dodge =2)) # stagger axis labelsp2 + p3
Either of these look great, but I think scales::label_wrap() is my favourite. So simple and so effective!
Automatically label facets with letters
One thing I often do is create faceted plots where each plot represents a different treatment or subset of the data. This is really easy to do with ggplot2 using facet_wrap(). However, the default facets are poorly labelled, are not numbered (generally with a, b, c for publications), and have a grey fill colour.
However, these are all issues we can solve to create publication-ready facet labels. We will demonstrate this by plotting a different trait on each facet (bill length, bill depth, flipper length, body mass). We will create the long data using pivot_longer() and use case_when() to make sure the labels on each facet are formatted correctly (i.e. spaces instead of underscores.
We can write a function to automatically label each facet, and change theme elements to align text and change the strip background colour.
long_format <- d %>%mutate(id =1:n()) %>%# create column to id each individual penguinpivot_longer(., cols =c(bill_length_mm:body_mass_g), names_to ='trait', values_to ='value') %>%# go from wide to long format for the traitsmutate(facet_label =case_when(trait =='bill_length_mm'~"Bill Length (mm)", trait =="bill_depth_mm"~"Bill Depth (mm)", trait =="body_mass_g"~"Body Mass (g)", trait =="flipper_length_mm"~"Flipper Length (mm)"))# create function to add letter to facet labelsletter_facets <-function (string){ len <-length(string) string =paste("(", letters[1:len], ") ", string, sep ="")return(string)}# create plotggplot(long_format, aes(species, value, col = species, fill = species)) +# add first empty plot layergeom_boxplot(outlier.shape =NA) +# add boxplot, do not plot the outliersstat_summary(geom ="crossbar", fatten =2, color ="white", width =0.4, fun.data =function(x){return(c(y = stats::median(x), ymin = stats::median(x), ymax = stats::median(x)))}) +# add median line to the boxplotsgeom_jitter(width =0.2, shape =21, fill ='white') +# add jittered raw pointstheme_bw(base_size =14) +# change theme to the one I like (others are available)labs(x ='Species',y ='Trait measurement') +# change labelsguides(col ='none',fill ='none') +facet_wrap(~facet_label, scales ='free_y', labeller =labeller(facet_label = letter_facets)) +# facet by the label column so the text is correct theme(strip.background =element_blank(),strip.text =element_text(hjust =0))
BINGO THIS LOOKS SHAMAZING.
Beautiful colour schemes with MetBrewer
I take a lot of time looking at colours to use in my plots. I have used viridis before, the colour schemes from colorbrewer, and Pokémon inspired colour schemes using palettetown. If you are so inclined, I have also started writing a small package (BrewerUoE) to access the new University of Exeter colours in R.
However, the most beautiful colour schemes I have ever seen are those provided by MetBrewer, which are palettes inspired by works at the Metropolitan Museum of Art in New York. They are just sensational, and easy to add directly to your plots! We show an example how to below, but there are more examples on MetBrewer’s GitHub.
 If you hash out the final layer then the layer before it with a + carries on to the next set of code. If you add a final + NULL to the plot then this behaviour doesn’t happen.
Lets say we wanted to see the plot without the text labels. We can turn them off by hashtagging them out (in case we want to add them back in later), and the final NULL means the plot still works.
p1 +#geom_text(aes(x = species, y = max_bill_length + 5, label = paste('n = ', n, sep = '')), d_n, size = pts(12), col = 'black') + # add text labelNULL# add NULL layer to allow easy commenting out of layers
Control legend shapes and override legend appearance
Sometimes you colour text, lines, points with the same colours. When you do this, each of the legends for them get displayed in the legend overlaid with each other. To show this we will create a line plot of the relationship between
ggplot(d, aes(body_mass_g, bill_length_mm, col = species)) +geom_point() +stat_smooth(method ='lm', se =FALSE) +theme_bw()
I do not like the legend with multiple geoms, to me it looks like a tram stop sign. Luckily there are easy ways to manually edit the legend.
First we can switch off the legend of any of the elements using the argument show.legend.
ggplot(d, aes(body_mass_g, bill_length_mm, col = species)) +geom_point() +stat_smooth(method ='lm', se =FALSE, show.legend =FALSE) +theme_bw()
This looks nicer. An alternative to this is to use key_glyph which allows you to control the shape of the legend.
ggplot(d, aes(body_mass_g, bill_length_mm, col = species)) +geom_point() +stat_smooth(method ='lm', se =FALSE, key_glyph ='point') +theme_bw()
We can also control the size of the shape in the legend by altering the legend defaults using the guides() function.
ggplot(d, aes(body_mass_g, bill_length_mm, col = species)) +geom_point() +stat_smooth(method ='lm', se =FALSE, show.legend =FALSE) +theme_bw() +guides(colour =guide_legend(override.aes =list(size =20)))
Ok that last size is a bit much. But you get the idea and now have the tools to manually edit your legend.
Awesome ggplot2 extensions
gpgplot2 is so popular as a plotting system that it has become its own ecosystem, with people developing tools and extensions to complement and build extra functionality to ggplot2. All of the extensions can be found here, but below are some of our favourites:
ggdist which provides geoms and stats for visualising distributions and uncertainty.
palettetown for Pokémon themed colour schemes. Whats better than Charizard colours!
Summary
We have shown how publication-ready plots can quickly be made using ggplot2, and also highlighted some functions and tips we have found super useful when we make plots. I have found that making plots in ggplot2 follows the 80/20 rule. You get 80% of the way there in 20% of the time, but then the finishing touches can take very, very long. Hopefully some of the code, approaches, tips, and links provided here help you get started!
All of the code here can be used to create your own plots Have fun!
Cedric Scherer’s whole website (I LOVE HIS VISUALISATIONS), but his tutorial for creating beautiful visualisations in ggplot2 is amazing.
Acknowledgements
We did not create this content alone! Inspiration, tips, and resources have been borrowed from multiple sources.
We are indebted to the tidyverse team of developers, and especially Hadley Wickham for creating and maintaining ggplot2.
Tips for creating the post, website, and format for the walkthroughs were taken from Stephanie Hicks’ Course website and source code. She has a great post introducing ggplot2 that goes into more depth than we do here.


Unicorn Kaleidos:
Radyss sent me.
Kelvas has been hounding my race a lot, now I've bested him.
"OK."


Similarity (Identity By State)
Individuals
Preset comparisons
George Church
Custom comparisons
Resolution
Cluster Multiple Individuals
Compare all individuals to each other (Warning: may take some time)Choose a linkage criterion:
Individuals vary at the genetic level by about 1 in 1300 bases, so any 2 individuals share about 99.97%. However, if we zoom into regions that are known to be polymorphic among populations, we can get a more fine-grained mapping of similarity. Here, we will assess your similarity to a number of other individuals, using identity by state (IBS). In this approach, we simply measure the average percent similarity at a number of loci. If 2 individuals have the same genotype, they are 100% similar at that locus; if they share 0 or 1 alleles, they are 0% or 50% similar. IBS then calculates the average similarity across the loci that we've chosen.
If we looked at all sites across the genome, these similarities would be close to 99.97%. However, since this calculation is restricted to sites which are known to vary across individuals, the similarities are somewhat lower. Thus, these percent similarities are biased based on which loci are chosen for this analysis. For this analysis, we are comparing your genotype with a panel of individuals who have had a 23andme profile (Illumina HumanHap550-Quad+ or Illumina OmniExpress+) at up to 100,000 loci. Choosing a different set of loci may provide different results for IBS analysis.


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SS34A means 3A, 40V schottky. Any Schottky with the same ratings will do. For example: - SK34SMA - SS34FA


{% comment %} Used by the reading_years_card template tag. Expects: * current_year: A date object representing the current year, if any. * years: A QuerySet of date objects, one for each year to link to. {% endcomment %} {% if years|length > 0 %} {% load spectator_core %} {% current_url_name as url_name %}


Software components are supposed to make your life easier. They are supposed to let you write production-ready code faster. They do this in four ways.
Software components are:
plugins are designed to deliver on these four features.
Seneca plugins are fundamentally just a list of action patterns.
The ease of extensibility and composability are the primary benefits of the pattern-based approach.
Let’s look at a simple example. Here’s a plugin that converts color values between representations.
var seneca = require('seneca')()
seneca.use( function color() {
var map_name_hex = {
black: '000000',
red: 'FF0000',
green: '00FF00',
blue: '0000FF',
white: 'FFFFFF'
}
this
.add('role:color,cmd:convert', function (msg, respond) {
var out = { hex: map_name_hex[msg.name] }
respond( null, out )
})
})
// prints { hex: 'FF0000' }
seneca.act('role:color,cmd:convert,name:red', console.log)
// prints { hex: undefined } as yellow not recognized
seneca.act('role:color,cmd:convert,name:yellow', console.log)
This plugin only supports a limited range of colors. One way you can extend the set of supported colors is by adding special cases (in this case, we’re ignoring the fact that the color name to hex feature is just a simple mapping data structure).
seneca.add('role:color,cmd:convert,name:yellow', function( msg, respond ) {
respond( null, { hex: 'FFFF00' })
})
// prints { hex: 'FFFF00' }
seneca.act('role:color,cmd:convert,name:yellow', console.log )
This is standard Seneca best practice. You are allowed to define your own special cases.
But what if you have many new colors you want to add? Another way to extend the color
plugin is to override the existing pattern:
var more_name_hex = {
cyan: '00FFFF',
fuchsia: 'FF00FF'
}
seneca.add('role:color,cmd:convert', function (msg, respond) {
this.prior(msg, function (err, out) {
if (err) return respond(err)
if (!out.hex) {
out.hex = more_name_hex[msg.name]
}
respond(null, out)
})
})
// prints { hex: 'FFFF00' }, from override
seneca.act('role:color,cmd:convert,name:cyan', console.log)
// prints { hex: '00FFFF' }, from more specific custom pattern
seneca.act('role:color,cmd:convert,name:yellow', console.log)
// prints { hex: 'FF0000' }, from color plugin
seneca.act('role:color,cmd:convert,name:red', console.log)
The function this.prior
is a reference to the original action function for the role:color,cmd:convert
pattern. This original action function knows how to handle the colors black, red, green, blue and white.
The new action function for role:color,cmd:convert
first passes on the input message to the original action function, and if the original action function does recognize a color and produce a hex value, then the new action function does nothing.
This original action function is known as the prior. If the prior does not provide a hex value, then the new action function checks for the colors it knows about, cyan and fuchsia, and handles those. The color yellow is still handled by the special case action function that specifically matches name:yellow
.
Priors can be used in this way to customize the behavior of any action pattern.
Priors can be stacked. Each time you override an action pattern, you get a prior. This prior may have its own prior from a previous definition of the action pattern. Thus you can compose layers of additional functionality.
For example, you can add validation, auditing, throttling, custom logging, input and output data manipulation, or trigger other messages. The calling code has no visibility of these customizations, and no need to know about them, so the plugin remains self-contained.
If you call this.prior
, and there is no previous definition, then you get an empty response (null) back. In production code you should always handle this case.
Priors can be strict. This means that a prior only exists if there is an exact matching action pattern. Normally, priors are not strict, so that sub-patterns will be priors. Here’s an example.
For the following patterns, added in this order:
The prior chain is:
a:1,b:2,c:3 → a:1,b:2 → a:1.
In the example above, for role:color,cmd:convert,name:yellow
, the prior is role:color,cmd:convert
.
If you use the strict setting, then the priors will only be for exact matches. For the following patterns, added in this order:
The prior chain is a:1,b:2,c:3
→ a:1,b:2
only.
You can make every prior strict by setting the top level option:
seneca({ strict: { add: true } })
In the same way that the order of plugin definition is significant, the order of pattern overrides is also significant.
Seneca checks only at definition time for matching priors. This is deliberate, so that you have well-defined behaviour you can determine simply from reading the code.
Using the example above, if you add patterns in the order:
Then there are no priors.
For this reason, take care when adding plugins whose purpose is mainly to extend existing patterns. They should be added after the main plugin that adds functionality.
The Seneca data entity patterns can be extended to handle special cases. You use calls to this.prior
to perform the underlying data operations.
For example, Let’s say you want to add a last_updated
field to every data entity. Override the role:entity,cmd:save
pattern to do this:
var seneca = require('seneca')()
seneca.add('role:entity,cmd:save', function( msg, respond ) {
msg.ent.last_updated = Date.now()
this.prior( msg, respond )
})
// prints $-/-/foo;id=9wl7sn;{bar:1,last_updated:1441383791347}
seneca.make$('foo').data$({bar:1}).save$( console.log )
The role:entity,cmd:save
message contains an ent
property with the Seneca entity data, which you can modify as desired.
In production systems, you’ll tend to want to do a number of things to entities:
Use the standard role:entity,cmd:save|load|remove|list
action patterns to define general customizations, as per the example above.
To define custom behaviour for a specific entity, make sure to add the pattern using the non-strict (default!) option:
seneca.add('role:entity,cmd:save,name:bar', function (msg, respond) {
msg.ent.zed = 1
this.prior(msg, respond)
})
// prints $-/-/foo;id=m3l3zp;{a:1,last_updated:1441384489162}
seneca.make$('foo').data$({ a: 1 }).save$(console.log)
// prints $-/-/bar;id=air0bm;{b:1,zed:1,last_updated:1441384489162}
seneca.make$('bar').data$({ b: 1 }).save$(console.log)
The bar
entity still gets the last_updated
field. If you had used strict$:{add:true}
, then it would not have.
Take care when using the base
and zone
fields for name-spacing entities.
If you define a custom behavior for all entities of the same base, this will work as intended.
But if you also define custom behaviors only using a name, then an entity that matches both the name and base will not trigger the base behavior, as it will not have the correct prior.
Here’s a simple example of how the patterns work:
has the prior chains:
Thus, the input message {a:1, b:2, c:3}
will match _a:1,b:2
as b
precedes c
alphabetically, and patterns are disambiguated alphabetically. It will not also match _a:1,c:3\
.
Similarly, if you have:
Then {role:entity,cmd:save,name:foo,base:bar}
will not trigger any custom priors for base:bar.
The rule to follow is: if you are defining base
behaviors, only define name,base
behaviors for specific entities. If you are defining zone
behaviors, only define base,zone
and name,base,zone behaviors
.
You can trace the structure of action patterns priors using the --seneca.print.tree
command line option. Run the following code:
// filename: prior-debug.js
var seneca = require('seneca')()
seneca
.add('a:1', function( msg, respond ) {
respond( null, { a:1 })
})
.add('a:1,b:2', function( msg, respond ) {
this.prior( msg, function( err, out ) {
out.b = 2
respond( err, out )
})
})
.add('a:1,b:2,c:3', function( msg, respond ) {
this.prior( msg, function( err, out ) {
out.c = 3
respond( err, out )
})
})
.act( 'a:1,b:2,c:3', console.log )
And you’ll see the output:
$ node prior-debug.js --seneca.print.tree
2015-09-04T17:00:55.445Z 5ftqv0kxp9zn/1441386055436/36136/- INFO hello Seneca/0.6.4/5ftqv0kxp9zn/1441386055436/36136/-
Seneca action patterns for instance: 5ftqv0kxp9zn/1441386055436/36136/-
└─┬ a:1
├── # root$, (e7roo),
└─┬ b:2
├── # root$, (d77dx),
│ # root$, (e7roo),
└─┬ c:3
└── # root$, (nzgbq),
# root$, (d77dx),
# root$, (e7roo),
null { a: 1, b: 2, c: 3 }
The last line { a: 1, b: 2, c: 3 }
is the expected output from the prior chain for the action pattern a:1,b:2,c:3
. Above that is an textual tree diagram of the defined patterns in the Seneca instance.
Each pattern is represented by a leaf of the tree. On each leaf is a stack of prior function identifiers, showing the order in which the priors will be called.
For a:1,b:2,c:3
, you can see that the priors for a:1,b:2
(d77dx), and a:1
(e7roo) will form the prior chain.
You can also use the option --seneca.print.tree.all
to see all the system action patterns, not just your own.
Running the above program using --seneca.log.all
also shows you the log output that allows you to trace the execution of the action patterns.
In addition, the message and transaction identifiers, starting with 0j/12, allow you to trace all the messages generated by the initial message.
For more details on Seneca logging, read the logging tutorial.
If you have questions on priors, you can:
That’s all folks! Corrections and comments: please tweet @senecajs.
Issues? From spelling errors to broken tutorials and everything in between, report them here.


Mariano Viz
Graduate Student
Bren School of Environmental Science & Management
Education
2020-22
Master of Environmental Science and Management Santa Barbara, CA (Bren School of Environmental Science & Management)
2014
Doctor of Veterinary Medicine Buenos Aires, Argentina (National University of La Plata (UNLP))
2017
Specialist Degree in Human Development Buenos Aires, Argentina (Latin American Faculty of Social Sciences -FLACSO-)
2017
Higher Diploma in Public Policies for Equality Buenos Aires, Argentina (Latin American Council of Social Sciences -CLACSO-)
Food Systems & Animal Production Experience
2019-20
Wildlife and Small Animal Veterinarian Bowral Veterinary Hospital (NSW, Australia)
2014-18
Rural Veterinarian Self-Employed (Buenos Aires, Argentina)
2016-18
Research Fellow Dept. of Rural Development (UNLP) (Buenos Aires, Argentina)
2013-15
Teaching Assistant Dept. of Animal Production (UNLP) (Buenos Aires, Argentina)
2014
Veterinary Intern Institute of Animal Reproduction of Uruguay (Montevideo Uruguay)
Skills & Affiliations
Computer: Microsoft Office Suite (Word, Excel, PowerPoint), Google Suite, RStudio, GIS (Winter 2021)
Languages: Spanish (Native), English (Advanced Working Proficiency; TOEFL 109/120)
Gap-year Travel (3/18–8/19): Europe (England, Spain, France), Southeast Asia (Vietnam, Cambodia, Laos, Thailand, Malaysia, Singapore, Indonesia), Australia
Professional Affiliations: Member of the Interdisciplinary Group for the Development of Family Farming (GIDAF); Associate Member Wildlife Health Australia, Latin American Fisheries Fellowship.


Content Security Policy - Spring Security
This application shows you how to integrate the Content Security Policy (CSP) in a Spring Boot based web application. Since Thymeleaf automatically protects from Cross-Site Scripting this functionality is turned off explicitly.
First Task
You can see the CSP in action by trying to inject JavaScript code into the text field below. Open a browser console to see the error message for blocked content.


Home
Introduction
Computer Science
Hobbies
Greatest Innovations
Fun Facts
Brindyn Schultz
What I hope to learn from this course:
Programming in Python
Encryption and Hashing
Artificial Intelligence
The Internet and World Wide Web
Cloud Computing
Security


--- title: "LECTURE: SAVING THE MONARCHY" date: "2000-01-01 19:03:14 -06:00" categories: - lecture - spring layout: "events" event-image: "saving-the-monarchy.jpg" event-dates: "THURSDAY, MARCH 14" event-location: "Red Butte Garden Classroom" button-type: "register" tickets-link: "https://55218.blackbaudhosting.com/55218/Lecture-Saving-the-Monarchy" member-sale-date: "N/A" public-sale-date: "N/A" member-cost: "Free" public-cost: "Garden Admission" event-notes: "Limited seating" tickets-button: "#" published: false ---
Where have all the monarch butterflies gone? Why should we care? Join us to learn about the declining number of monarchs worldwide, their dramatic decline west of the Rockies, and what we can do to save them. Discover how to find monarch activity on milkweed (eggs, caterpillars and chrysalis), how and where to plant milkweed, and how to get involved in monarch conservation. Seating is limited to 60 people.


◆ Date()
| Date::Date |
( |
string |
date | ) |
|
Constructor for the date class (The date must be in the following format: "dd/mm/yyyy hh:mm")
- Parameters
-
| date | - string that represents the date |
Throw an error due to an invalid date
Throw an error due to an invalid date
◆ operator<()
| bool Date::operator< |
( |
Date |
date | ) |
|
Operator to compare dates.
- Parameters
-
| date | - object of the class Date |
◆ operator==()
| bool Date::operator== |
( |
Date |
date | ) |
|
Operator to compare dates.
- Parameters
-
| date | - object of the class Date |
◆ operator>()
| bool Date::operator> |
( |
Date |
date | ) |
|
Operator to compare dates.
- Parameters
-
| date | - object of the class Date |
The documentation for this class was generated from the following files:
- C:/Users/Afonso Sá/source/repos/AEDAPROJ1/AEDAPROJ1/Date.h
- C:/Users/Afonso Sá/source/repos/AEDAPROJ1/AEDAPROJ1/Date.cpp


My current location is: {{coords.lat}}, {{coords.long}}
Established: {{brewery.brewery.established}} Website: {{brewery.brewery.website}}
Certified Organic
List Beers
{{brewery.brewery.description}}
Give your ratings »


The red rocks of Arches National Park were deposited in layers over millions of years.
Later, erosion carved them into the features seen today. Water, ice, and very hot and cold temperatures cause erosion.
Seas covered this region 300 million years ago. When they dried up they left behind thousands
of feet of salt. Later, layers of sand and other sediments covered the salt. When buried, the
sediments were compressed into sedimentary rock. The buried salt moved under the pressure of the overlying sediment like toothpaste in a tube. As the salt moved, it ran into underground faults or barriers that caused the salt to go upward. Near the earth’s surface, the salt formed what looked like long giant bubbles in the upper layers.
|


A short analysis of Neuromancer
William Gibson's novel Neuromancer is concerned with charting a new frontier, the Matrix - a frontier with unpenetrable regions, albeit one that is used everyday; it is a vast evolution beyond today's Internet. Gibson creates a hi-tech future, but there is no specified date. The setting is urban - the story begins in the Ninsei enclave of Chiba city in Japan, a huge urban sprawl encompassing Tokyo, and perhaps the entire country. It later moves on to "the Sprawl", also known as BAMA (the Boston-Atlanta Metropolitan Axis), on the eastern seaboard of the US, and then to Freeside, an orbital station. There is also the suggestion of environmental degradation - people in Chiba wear filtration masks (p.19), a "pandemic of horses" is discussed (p.88).
Case, the Matrix and the body
The central character, Case, begins the story as a hustler, scamming the dregs of Ninsei. He had previously lived as a member of the technological elite, a "cowboy" riding a cyberspace deck that projects his disembodied consciousness into the Matrix and the unknown of ice-protected systems. But this was before his "digital exile", before Case stole from his employers whilst working as a corporate cracker, and before he was punished by having a mycotoxin injected into his nervous system. He describes the "bodiless exultation of cyberspace", and the "unlimited subjective dimension" he experiences. In Neuromancer, the Internet and virtual reality have combined within the Matrix, but this does not preclude the low-level hacking of, for example, obtaining free phone calls (often a primary motive for many contemporary would-be hackers); Gibson describes Case using the stolen entry codes for Bell Europa, posted on an academic grid (p.78). Also like many contemporary hackers, Case appears to know the computer system better than its builders, seeming to fool the Sense/Net building network with relative ease (p.62). The cowboys mock the body; Case describing his inability to access the Matrix as "the Fall", back into a "prison of his own flesh" (p.4).But the weakness of the flesh makes Case vulnerable; he has little choice when Armitage "asks" him to work for him; he also has little choice as he craves a return to the fold of the Matrix. But Case and his "co-employee", Molly, really have little idea who they are working for.
Cyborgs
Not only have the Internet and virtual reality combined in the Neuromancer future, but humans have combined with technology - everyone, it seems, has become a cyborg. Whereas contemporary cyberians may carry a laptop, the cyberians in Neuromancer have implanted "microsofts", which jack directly into the brain in order to increase memory and knowledge. Gibson depicts advanced neurosurgery and body repair, such as Ratz the barman's prosthetic Russian arm (p.7), Wage's Nikon eye transplants (p.25) or the finger-blades, and the mirrors inserted into Molly's eye sockets (p.29). Cloning and genetic manipulation are available, though at high cost; the 135 year-old Julius Deane has his DNA reset every year (p.16), and an assassin is described as having the "...hallmarks of a vat-grown ninja" (p.72) The personalities of dead people can be recorded and resurrected in the Matrix, as in the case of the Dixie Flatline. At the extremes, whole personalities can be created by computer, as by Wintermute, one of the artificial intelligences) for Armitage. This is an "age of affordable beauty", but that may be true of now, more or less. But the cyberneticisation of humanity goes deeper than bodily or neural enhancement. The human characters in Neuromancer have assimilated the language of computing; when Case takes a drug he says it "lit his circuits" (p.23) or when Molly leaves her note reads "It's just the way I'm wired" (p.247). Humans have taken on the characteristics of computers.
What is reality?
"Cyber-sex" (as such) seems not to have become as popular in this future as some in this present would have it. In Neuromancer it seems that this mode of interaction is spurned in favour of human "puppets" with neural cutouts. But sex and euphoria are now linked into the experience of the Matrix, at least for Case; "...his orgasm flaring blue in a timeless space, a vastness like the matrix" (p.56). Thus increasingly the Matrix becomes reality, not the other way round.But what passes for reality? If reality is chaotic, then the artificial intelligence that is "Neuromancer" (the counterpart of Wintermute) understands chaos theory. It says that "I saw her [Linda's] death coming. In the patterns you imagined you could detect in the dance of the street. Those patterns are real" (p.242). The Panther Moderns, a group of nihilistic technofetishists, also believe in chaos; "'Chaos, Mr.Who', said Lupus Wonderboy, 'That is our mode and modus'" (p.66). At the end of Neuromancer - Wintermute and Neuromancer merge to become the matrix, which then initiates contact with another, alien computer network (perhaps akin to the TOPY's belief in the evolution of a "global information being" in Rushkoff's Cyberia).
Drugs and the drug
Whether it is pink octagons, ketamine, hypnotic Cloud Dancers, hormonal triggers or betephenethylamine, the names may change but this does not protect the innocent. Not that there seem to be many "innocents" in Neuromancer - most of the characters appear to have a drug addiction. Employees above a certain level in corporations (such as Mitsubishi-Genentech) are "implanted with advanced microprocessors that monitored mutagen levels in the bloodstream" (p.14). There is a correlation here in the modern-day corporations' attempts to crack down on drug use, such as urine testing in Silicon Valley. Many of the best computer programmers take psychedelics in order to provide new insights into otherwise intractable problems; a senior executive for a major defense contractor privately confessed that "the long-hairs we've hired have the ability to attack computer problems from completely different angles" (Cyberia, p.47). Yet the real drug in Neuromancer is the Matrix, to which Case (and presumably other cowboys) is addicted. When he jacks in again, he sees "...the unfolding of his distanceless home, his country, transparent 3D chessboard extending to infinity", while in his body, in reality, "...he was laughing, in a white-painted loft, distant fingers caressing the deck, tears of release streaking his face" (p.52).
Power and immortality
Power, in Case's world, means corporate power. The Japanese zaibatsus have seemingly overcome all opposition. They have gained immortality as organisms - you cannot "kill" a zaibatsu by assassinating its executives, as there will always be others to take their place. But the powerful Tessier-Ashpool family, owner of the Wintermute and Neuromancer AIs (located in Berne and Rio respectively), are more vulnerable. They attempt to overcome this through cloning of family members, cyrogenic stasis, and the vision of Lady Jane Marie-France Tessier-Ashpool. She commissioned the construction of the AIs, hoping to create a symbiotic relationship with them, so that they can take the family's corporate decisions for them. This vision saw little in the way of individual consciousness, thereby pushing the limits of cyberneticisation. Thus Tessier-Ashpool would gain the immortality of the zaibatsus, but without the inconvenience of replaceable, fleshy executives.
Conclusion
Ultimately, however, this immortality (especially the sham immortality of cryogenics) is revealed as unattainable - the AIs coalesce into an entity that encompasses, or is, the Matrix. While still remaining the global information network, the Matrix now has a "mind" of its own. The personality substitute of Armitage disintegrates, and the body it inhabited is killed. Molly leaves Case. The Tessier-Ashpool family is perhaps mortally wounded, but the AIs have a "happy" ending, as does Case, who returns to Earth and finds work as a cowboy once more. Those with the most knowledge and the most acceptance of technology ultimately triumph.
Bibliography
W.Gibson, Neuromancer (Victor Gollanz Ltd, London, 1985)D.Rushkoff, Cyberia (Flamingo, London, 1994)


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not all drinks are suitable for human consumption. For example, on Jupiters moons the closest equivalent to ouzo is a highly toxic oil derived from the pit of a mutated olive tree. Although they are experimenting with new distillery methods, only local residents who have been exposed to the proper radiation adaptation treatments should imbibe.
| "non-toxic" | "toxic " | ||
|---|---|---|---|
| "Liquor" | "mixer1" | "mixer2" | "Mixer3" |
| "Rum" | "cola" | "lime" | "Sulphur" |
| "vodka" | "cola" | "cranberry" | "methane" |


--- layout: post status: publish published: true title: 'Cloud Services Comparison: AWS Vs. Google Vs. Azure' header-img: "img/Cloudcomparison.png" author: display_name: Stefan Thorpe login: stefan email<EMAIL_ADDRESS>url: '' author_login: stefan author_email<EMAIL_ADDRESS>wordpress_id: 2497 wordpress_url: https://caylent.com/?p=2497 date: '2018-01-25 08:00:41 +0200' date_gmt: '2018-01-25 16:00:41 +0200' categories: - Tools - Resources tags: - AWS - Microsoft Azure - Google Cloud Platform - Amazon Web Services comments: [] --- {% raw %}
This post was originally published here.
In a multi-cloud world, organizations may use different cloud providers for multiple capabilities concurrently. Most of the cloud service providers (CSP) out there offer high-quality services, with excellent availability, high security, good performance, and customer support. But the market is dominated by a top three—Amazon Web Services, Google Cloud, and Microsoft Azure.
Each CSP provides an array of products covering all you can possibly need for your online operations. They differ not only in pricing but also in how each name and group their services. Below, we show the most common elements: infrastructure, auto-scaling, security, monitoring, and much more. Our aim is to help cross-identify comparative services from each CSP so that you can easily weigh your application or workload requirements to select the best fit for your business.
Compute
Storage
Networking and Content Delivery
Database
Management and Monitoring
Security
Developer Tools
Calculate, process, and compute—a computer’s fundamental role. In addition, the right cloud provider can scale to thousands of processing nodes for you in just a few minutes.
| Service | Amazon Web Services | Google Cloud Platform | Microsoft Azure |
| Deploy, manage, and maintain virtual servers | Elastic Compute Cloud (EC2) | Compute Engine | Virtual Machines |
| Platform-as-a-Service | Elastic Beanstalk | Google App Engine | Cloud Services |
| Virtual private servers made easy | Lightsail | Virtual Machine Images | |
| Management support for Docker/Kubernetes containers | EC2 Container Service (ECS) | Kubernetes Engine | Container Service |
| Docker container registry | EC2 Container Registry (ECR) | Container Registry | Container Registry |
| Orchestrate and manage microservice-based applications | App Engine | Service Fabric | |
| Integrate systems and run backend logic processes | Lamda | Cloud Functions (Beta) | Functions |
| Docker container deployment | Container Engine | Container Service | |
| Run large-scale parallel and high-performance batch computing | Batch | Batch | |
| Automatically scale instances | Auto Scaling | Instance Groups | Virtual Machine Scale Sets |
A key function of cloud services is its storage capabilities. While AWS’ storage services are the longest running, Google’s and Microsoft Azure’s are also very respectable and reliable options.
| Service | Amazon Web Services | Google Cloud Platform | Microsoft Azure |
| Object storage service for use cases | Simple Storage Services (S3) | Google Cloud Storage | Storage (Block Blob) |
| Virtual server disk infrastructure | Elastic Block Store (EBS) | Compute Engine Persistent Disks | Storage (Page Blobs) |
| Archive storage | S3 Infrequent Access (IA) | Nearline | Storage (Cool) |
| Create and configure shared file systems | Elastic File System (EFS) | ZFS / Avere | Files |
| Hybrid storage | Storage Gateway | Egnyte Sync | StorSimple |
| Bulk data transfer solutions | Import/Export Disk | Storage Transfer Service | Import/Export |
| Backup | Object Storage | Backup | |
| Automatic protection and disaster recovery | Disaster Recovery | Site Recovery |
Each provider offers different networks and partners which interconnect their data centers across the globe using a variety of different products to achieve this.
| Service | Amazon Web Services | Google Cloud Platform | Microsoft Azure |
| Isolated, private cloud private networking | Virtual Private Cloud | Virtual Private Cloud | Virtual Network |
| Cross-premises connectivity | API Gateway | Cloud VPN | VPN Gateway |
| Manage DNS names and records | Route 53 | Google Cloud DNS | Azure DNS |
| Global content delivery networks | CloudFront | Cloud Interconnect | Content Delivery Network |
| Dedicated, private network connection | Direct Connect | ExpressRoute | |
| Load balancing configuration | Elastic Load Balancing | Cloud Load Balancing | Load Balancer |
All three providers allow you to implement both SQL and NoSQL solutions. Alternatively, if you don’t need a database, go for their caching capabilities instead.
| Service | Amazon Web Services | Google Cloud Platform | Microsoft Azure |
| Managed relational database-as-a-service | RDS | Cloud SQL | SQL Database |
| NoSQL (Indexed) | DynamoDB | Cloud Datastore | Cosmos DB |
| NoSQL (Key-value) | DynamoDB | Cloud Datastore | Table Storage |
| Caching | ElastiCache | Cloud CDN | Redis Cache |
| Database migration | Database Migration Service | Database Migration Service | |
| Managed data warehouse | Redshift | SQL Data Warehouse |
Each of the top three offers a range of management and monitoring services which provide visibility into the health, performance, and utilization of applications, workloads, and infrastructure.
| Service | Amazon Web Services | Google Cloud Platform | Microsoft Azure |
| Cloud advisor capabilities | Trusted Advisor | Cloud Platform Security | Advisor |
| DevOps deployment orchestration | OpsWorks (Chef-based) | Cloud Deployment Manager | Automation |
| Cloud resources management & monitoring | CloudWatch | Stackdriver Monitoring | Portal |
| Administration | Application Discovery Service | Cloud Console | Log Analytics |
| Billing | Billing API | Cloud Billing API | Billing API |
Here, we cover the range of capabilities provided to protect services and data.
| Service | Amazon Web Services | Google Cloud Platform | Microsoft Azure |
| Authentication and authorization | Identity and Access Management (IAM) | Cloud IAM | Active Directory |
| Information Protection | Information Protection | ||
| Protect and safeguard with data encryption | Key Management Service | Storage Service Encryption | |
| Hardware-based security modules | CloudHSM | Cloud Key | Key Vault |
| Firewall | Web Application Firewall | Application Gateway | |
| Cloud security assessment and certification services | Inspector | Security Center | |
| Directory services | AWS Directory Service | Active Directory Domain Services | |
| Identity management | Cognito | Active Directory B2C | |
| Support cloud directories | Directory Service | Windows Server Active Directory | |
| Compliance | Artifact | Service Trust Portal | |
| Cloud services with protection | Shield | DDoS Protection Service |
And finally, the tools you need to build, deploy, diagnose, debug, and manage multiplatform, scalable applications and services.
| Service | Amazon Web Services | Google Cloud Platform | Microsoft Azure |
| Media transcoding | Elastic Transcoder | Media Services | |
| Improve and optimize workflow | Simple Workflow Service (SWF) | Logic Apps | |
| API management | API Gateway | API Management | |
| App testing | Device Farm | Cloud Test Lab | DevTest Labs (backend) |
| Git Repositories | AWS Source Repositories | Cloud Source Repositories | Azure Source Repositories |
| DevOps | CodeBuild | Visual Studio Team Services | |
| Programmatic access | Command Line Interface | Cloud Tools for Powershell | Command Line Interface (CLI) |
| Predefined templates | Quick Start | Quickstart templates | |
| Managed hosting platforms | Elastic Beanstalk | Web Apps (App Service) | |
| Application deployment | CodeDeploy | Visual Studio Team Services | |
| Developer Tools | Developer Tools | Developer Tools |
In case you missed it, our CTO JP La Torre also reviews the most recent AWS re:invent unveilings here.
Caylent provides startups with a full DevOps team on tap. Our software enables the automation of fully managed clusters across development, staging, and production environments. With a Caylent subscription, you receive 24/7/365 assistance with architecture, scalability, CI/CD, and container orchestration. No need to hire a single DevOps engineer when you can tap into an entire rockstar platform!
{% endraw %}


The GEP Toolbox¶
Overview¶
The GEP “Toolbox” provides access to all the ICT ‘widgets’ used in the development of the GEP including tools, methods as well as user manuals and training material. All available in an open an accessible way through various channels. It provides the potential and flexibility needed to fully change the underlying modelling methods and/or generate new models.
Tools and methods¶
This version of the GEP uses the Open Source Spatial Electrification Toolkit (OnSSET). The (OnSSET) source code used to generate the electrification investment scenarios as presented on the GEP “Explorer” is available on Github.
The following steps describe in brief the modelling process followed to generate the scenarios. Many of the steps refer to Malawi which was used as an experimental case study in the development phase. The process can - of course - be replicated for other countries as well.
Note
This section is supported by a open access peer-reviewed academic publication available online since April 2019.
Step 1. Collection of necessary data
The first step in the modelling process requires the collection and preparation of all the necessary datasets and input parameters required to inform the electrification analysis. This include both GIS and non-GIS inputs as described in the section of data guidelines above. All datasets used in GEP are open, with source being provided in metadata. The list is available here.
An important part of the process requires the construction of population settlements, which is the basis of the electrification analysis in OnSSET. Population settlements are available in the form of raster or vector. The OnSSET model is able to run using either of those, however, the selection defines all next steps in the analysis thus is presented as a separate step. In order to get the most out of the GEP services vector population format is suggested hereafter.
The development team at KTH dESA has developed a methodology for developing population vectors (or population clusters) based on open access, raster population layers. A step-by-step description of the methodology is available here.
Note
The above methodology requires processing in QGIS (an open-source GIS software). The KTH team has developed a QGIS plugin that automates the process of population cluster generation. The plugin together with how-to-use instructions are available here.
The methodology is based on the availability of HRSL population data. In case HRSL data are not available for the country or region of interest a modified version of the methodology can be used. The latter is still under review by the KTH team; please contact the team for further information.
Step 2. Preparation of OnSSET input data files
In the second step of the process, the two OnSSET input files are created.
The first file is a comma delimited file (.csv) file. Each row represents a population cluster while each column a characteristic (or attribute) of this cluster. Attributes are extracted from the GIS datasets collected in Step 1. The extraction commands can be executed manually in QGIS. However, the KTH team has created a QGIS plugin in order to automate the process. The plugin is available together with how-to-use instructions here.
Note
The number of attributes depends on data availability. For the GEP the input file consists of 24 attribute columns. A sample file of this type is available for Malawi as “Malawi.csv”.
The second file is an excel (.xlsx) file (usually named “Specs”) that contains non-GIS input data (e.g. years of analysis, population growth etc.) and calibration parameters of the model. A listing of those parameters together with their role in the modelling process is available here.
Note
A sample file of this type is available for Malawi as “specs_mw_one_scenario.xlsx”.
Step 3. Preparation/Calibration of input files
The third step in the analysis includes the generation (and calibration) of a few additional parameters necessary for the electrification analysis. These include:
- Energy resource indicators (e.g. wind capacity factor, grid suitability)
- Population status & projections (Urban Vs Rural population, Electrified Vs non-electrified population (base year))
- Definition of demand goals per type of settlement
These functionalities are embedded in the OnSSET code basis. The calibration process takes place in the beginning of a model run and usually happens only once. The code is set to notify the user about the calibration progress via messages in console.
Note
For more information on the calibration process in OnSSET please redirect to the relevant section in the manual available here.
Step 4. Scenario runs
The last step of the process requires that the OnSSET model will be set to run a number of predefined EISs. The model is currently configured to generate 144 EISs as a combination of six parameters (hereafter called “levers”) that include:
- Population growth
- Electricity demand target
- 5-year investment plan
- Grid generating cost of electricity
- PV system cost
- Prioritization algorithm
Values and number of options for these levers can be modified via the “specs.xlsx” file. A sample file of this type is available for Malawi as “specs_mw_144_scenarios.xlsx”.
Further description on the functionality of levers is available here.
The model yields two types of output files (both .csv) per scenario. The first (full) file contains all the available results and is later on used in the visualization process. The second (light) file contains a summary of the EIS’s key parameters (electrification mix, capacity and investment requirements). The list of output parameters along with a brief description is available here.
Note
The next version of the GEP will include two additional levers studying the impact of productive uses and diesel price (related to hybrid mini-grids) in the electrification results.
Changing levers is possible with OnSSET however, this would require further configuration of the model. This of course will require re-configuration of the GEP “Explorer” (described in the next section).
User manual and training material (To be Updated)¶
GEP aims to support decision making in various levels (international organizations, government agencies, private sector, other data and modeling groups, individuals etc.) setting up national priorities and identifying critical interventions in the energy field. Training material has been (and still are being) developed and tailored to support the rapid uptake of the GEP “Explorer” and “Scenario generator” applications. It is envisioned that this material will be used in/for:
- Remote, self-learning (access Video Lectures here.)
- In-country training activities (see example here.)
- Regional training (such as held at the Energy Modelling Platform (EMP) and the OpTIMUS Global Summer School on Modelling Tools for Sustainable Development
- Post-graduate curricula (i.g OpTIMUS Micro-Masters).


Airlines from three CPLP countries banned from Europe
by TPN/LusaAirlines from Angola, Sao Tome and Principe and Equatorial Guinea are on the 'black list' of 115 airlines banned from flying to the European Union (EU), released in Brussels on Monday.
Six are on the list individually, for reasons due to safety problems of the airlines themselves, such as Avior Alirlines of Venezuela and the other 109 - certified in 15 countries - for insufficient control by their national authorities.
The list includes seven airlines from Angola, of which only TAAG and Heli Malongo are excluded from the list, all from São Tomé and Príncipe and also all those registered in Equatorial Guinea.
The list is regularly updated.


New applications should start with
global.namespace.truelicense.v2.json.V2Json
or
global.namespace.truelicense.v2.xml.V2Xml
,
depending on whether they want to use the Version-2-with-JSON (V2/JSON) or the Version-2-with-XML (V2/XML)
license key format.
The V2/JSON format produces smaller license keys.
Generating V2/JSON format license keys is also faster than generating V2/XML format license keys (there is
no notable difference on the license consumer side however).
However, using V2/JSON format license keys requires the
Jackson JSON Processor
to be present on the class path at runtime.
Applications which need to use license keys which have been generated by TrueLicense 1 need to use the
Version-1 (V1) format in
global.namespace.truelicense.v1.V1
.


Date:
Location:
Total Distance (in miles):
Pace per mile:
Total Distance (in km):
Pace per km:
Calories Burned:


The ${project.artifactId} installer will create a
${project.artifactId}-${project.version}
directory in a defined location. You'll have the option of designating a
directory for install or accepting the default.
The installer will then perform the following tasks
${project.artifactId}-${project.version}.war
fileOriginally the installer launched tomcat, ran a sanity check, and launched the yada-admin tool, but this hasn't worked universally. It also maybe is a little presumptuous. You can follow the remaining directions to perform these tasks.
At a command prompt, run
${app.home}/${container}/bin/catalina.sh start
If you get an error stating CATALINA_HOME
is not set, open a
terminal and execute:
export CATALINA_HOME=${app.home}/${container}and try again.
IF JRE_HOME
or JAVA_HOME
is not set, execute the following in the terminal:
java -verboseand look at the first line which will resemble
Opened... /path/to/jre.../lib/rt.jarthen set the variable:
export JAVA_HOME=/path/to/jdk...or
export JRE_HOME=/path/to/jre...of course, omitting the
/lib/rt.jar
and substituting /path/to/j...
with the actual path.
You can verify you have set the environment variable correctly by using the
echo
command, e.g.,
echo $JAVA_HOME
At a command prompt, run
${app.home}\${container}\bin\catalina.bat start
If you get an error stating CATALINA_HOME
is not set, open a
cmd prompt and execute:
set "CATALINA_HOME=${app.home}/${container}"and try again.
IF JRE_HOME
or JAVA_HOME
is not set, execute the
following at the cmd prompt:
java -verbose|moreand look at the first line which will resemble
Opened... \path\to\jre...\lib\rt.jarthen set the variable:
set "JAVA_HOME=\path\to\jdk..."or
set "JRE_HOME=\path\to\jre..."of course, omitting the
\lib\rt.jar
and substituting
\path\to\j...
with the actual path.
You can verify you have set the environment variable correctly by using the
echo
command, e.g.,
echo %JAVA_HOME%
When tomcat has started successfully, access the following url for a sanity check: http://localhost:${tc.http.port}/${project.artifactId}-${project.version}/yada.jsp?q=YADA+default&pz=1&py=true
This request should return JSON like the following:
{ "RESULTSET": { "total": 1, "records": 1, "qname": "YADA default", "page": "1", "ROWS": [ { "'YADA is alive'": "YADA is alive" } ] }, "version": "${project.version}" }
Assuming this test is successful, you can access the yada-admin tool at the following url: http://localhost:${tc.http.port}/${project.artifactId}-${project.version}/yada-admin Use
test
testt
YADA/yada
and
YADAUSER/yada
.
HyperSQL, or HSQLDB documentation is available on their website, hsqldb.org.
HyperSQL supports file
, server
, in-memory
,
webserver
, resource
, and text
databases.
The default db type for YADA-Quickstart is file
. From the HyperSQL
documentation:
Note that only one Java process at a time can make in-process connections to a given file: database.This means that while tomcat is running, you cannot access the database from another JVM—namely, an sqltool, like HyperSQL's own DatabaseManagerSwing application.
If you want or need to check out the database tables or data directly using the default YADA-Quickstart config, simply shutdown tomcat momentarily with the unix command:
${app.home}/${container}/bin/catalina.sh stopor windows command:
${app.home}\${container}\bin\catalina.bat stopThen launch the DatabaseManagerSwing application:
java -cp ${app.home}/${container}/lib/hsqldb.jar org.hsqldb.util.DatabaseManagerSwing
One alternative to this is to launch the HyperSQL database as a server. This is a suitable option for a production deployment on a network, but it is a bit more involved, and may be overkill for this local, YADA-Quickstart implementation. Nevertheless, there is info in the HyperSQL data access and server documentation if you wish to proceed.
$TOMCAT_HOME/lib
, and restart tomcat after
depositing it there. The tomcat documentation
will elaborate on this requirement, but it has to do with the tomcat's
use of multiple class loaders.driverClassName
property to the
appropriate one for your database engine:
driverClassName=oracle.jdbc.OracleDriver
driverClassName=org.postgresql.Driver
driverClassName=com.mysql.jdbc.Driver
driverClassName=com.vertica.jdbc.Driver
driverClassName=org.sqlite.JDBC
driverClassName=org.hsqldb.jdbc.JDBCDriver


Preparation time
30 mins to 1 hour
Cooking time
10 to 30 mins
Serves
4
Dietary
Celebrate ceps with Raymond Blanc's delicious tortellini recipe served with a simple sage butter.
For the pasta, place the flour into a food processor, crack in the eggs and pulse to combine. Add a little water, if necessary, to make the dough come together (the dough should ball together when you rub a piece between your fingers).
Turn the dough out onto a floured work surface and knead lightly for 2-3 minutes, or until smooth and elastic. Flatten out the dough and roll to a 1cm/½in thickness. Wrap the dough in clingfilm and leave to rest in the fridge for 30 minutes to relax.
Remove the cling film and cut the pasta dough into 2 equal pieces. Flatten each piece out slightly with a rolling pin the same width as your pasta machine.
Pass the dough through the pasta machine, starting with the thickest setting, then fold the ends of the pasta back into the middle, like a business letter. Turn 90 degrees and roll the pasta through the thickest setting once more. Change the setting on the pasta machine to the next thickeness and feed the dough through again. Repeat the rolling, passing the sheet through each setting until you reach the thinnest setting. Cut each sheet of pasta into 3 (the dough should yield enough pasta for approximately six sheets measuring 35cm x 10cm/14in x 4in).
Layer the sheets of pasta on a tray, separated by layers of cling film, and keep in the fridge until ready to use.
For the filling, heat the olive oil in a large frying pan over a high heat and fry the ceps with a pinch of sea salt and freshly ground black pepper for 2-3 minutes, until tender. If you are unable to find fresh ceps you could use either button or field mushrooms. Or you could use some good quality dried ceps, soaked in advance.
Add the shallots and cook for 1-2 minutes. Add the garlic, then mix the ceps and shallots together and cook for 1-2 minutes, or until the shallots are softened. Add the lemon juice and mix well. Remove from the heat and tip out onto a plate to cool down slightly, then chill in the fridge until cold.
Take one pasta sheet and place 4 large teaspoons of the filling along the middle, making sure the mounds of filling are evenly spaced. Lightly brush the pasta around the filling with egg wash. Fold the top edge of the sheet over the filling to meet the bottom edge of the sheet, then press around the filling with your fingers to seal (make sure there are no air bubbles in the pasta). Repeat with the remaining sheets of pasta and the filling.
Cut out the tortellini, using a medium size cutter to stamp out a half moon shape. Press the middle down lightly, then bring the bottom corners of the half-moon together to form a circle. Pinch the ends together to seal. Place the prepared tortellini onto a lightly floured plate until ready to cook.
Preheat the oven to 170C/340F/Gas 3.
For the roasted nuts and sage butter, scatter the pine nuts and hazelnuts on a baking tray and roast in the oven for about 10 minutes, or until the nuts are golden-brown. Remove and set aside to cool.
Cook the tortellini, in batches, in a pan of boiling salted water for 2-3 minutes, or until al dente. Remove with a slotted spoon and transfer to warm serving plates.
Meanwhile, heat the butter in a pan over a medium heat until melted and hazelnut-brown in colour (be careful not to let the butter darken beyond nut-brown). Add the sage and cook for 5 seconds, then pour in the water and a squeeze of lemon juice and cook for 1-2 minutes.
To serve, spoon the sage butter over the tortellini and scatter over the roasted pine nuts and hazelnuts.


Question 5
n Terms of an even natural number and their sum.
Calculate
The natural numbers are:
The sum is:
Next Question


THE LONG KISS GOODNIGHT A film review by Andrew Hicks Copyright 1997 Andrew Hicks / Fatboy Productions
(1996) ** (out of four)
Geena Davis: Action Star. Whose bright idea was that?
THE LONG KISS GOODNIGHT plays like a two-hour comic book, with tongue-in-cheek dialogue, caricatures instead of characters and more violations of the laws of physics than the Big Bang theory... No, I don't want hate mail from scientifically-minded people out there, so if you didn't like that "Big Bang theory" reference, just change it to "creationism" and everyone will be happy.
Davis plays a conservative school teacher in a small town. She's got a daughter and a decent boyfriend, but there's only one problem. She doesn't remember anything from her life before eight years ago. No childhood memories, no teenage memories, nothing prior to 1988. She's hired private detectives to dig up dirt on her past, but has so far discovered nothing. Until one night, when she hits a deer and runs off the road. She starts having weird dreams and finds her mood changing every once in awhile.
That's when Samuel L. Jackson, the lowest-rent P.I. she hired, finds an old postcard with her handwriting on it, announcing her engagement. When Jackson is bringing this information over to Davis' house, someone else is already at her house, trying to kill her.
Davis and Jackson head off the next day to track down the address on the postcard, eventually finding out Davis used to be an assassin working for a secret government organization.   Then she sings "Heart of Glass."
The conspiracy to murder her, predictably, stems from the same organization that she used to work for. It also involves a bomb somehow and Davis' kid gets trapped in a tractor-trailer. Along the way, Davis and Jackson outrun fire, jump out a window and land in an icy pond, and survive explosions and electrocution. None of it is believable, particularly Davis as Tank Girl.
THE LONG KISS GOODNIGHT at least has a sense of humor itself, Jackson's character in particular. Without Jackson, who is quintessentially cool no matter what movie he's in, this movie would have been damn near unbearable. This movie is only for those people who like cliched, cartoonish action movies that have no bearing whatsoever on the real world. There are good movies that fall into that category, but as far as this one goes, you can kiss it goodnight.
--
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Shield
A Two player defence game
Instructions:
For Player Green: Press Left (←) and right (→) arrow keys to control the shield
For Player Orange: Press 'A' and 'D' to control the shield
Click on the canvas below to start/restart the game
Repository:
Click Here


Tension as INEC Begins to Announce Bayelsa Election Results
by Gbenga SijuadeThe Independent National Electoral Commission (INEC) is set to commence the collation of results of the Bayelsa State governorship election conducted on Saturday.
Naija News Understand that the exercise, which was scheduled to start at 10.00 am on Sunday at the Prof. Mahmood Yakubu Media Centre, was delayed indefinitely.
The commission’s Head, Voter Education and Publicity, Wilfred Ifogah, had indicated on Saturday night that the exercise would begin at 10.00 am on Sunday.
However, almost five hours after, the collation is yet to commence.
Accredited journalists, election observers and party agents had been waiting for officials of INEC to begin the exercise.
There is heavy security presence within and outside the commission’s office located along the Swali Market Road in Yenagoa.
Only five journalists have been admitted into the hall; the others have asked to monitor the proceedings from a canopy outside the auditorium.


{% extends 'installation/_.html' %} {% block main %}
{{ db_error }}
There was a problem connecting to your database. The most likely cause of this is that your database settings are either incomplete or incorrect.
Please check your database settings (stored in platform/database.py
),restart your server, and then refresh this page.


| Constants' Details |
SUNDAY
const short SUNDAY = 0;
- Description
- Sunday
|
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MONDAY
const short MONDAY = 1;
- Description
- Monday
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TUESDAY
const short TUESDAY = 2;
- Description
- Tuesday
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WEDNESDAY
const short WEDNESDAY = 3;
- Description
- Wednesday
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THURSDAY
const short THURSDAY = 4;
- Description
- Thursday
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FRIDAY
const short FRIDAY = 5;
- Description
- Friday
|
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SATURDAY
const short SATURDAY = 6;
- Description
- Saturday
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Introduction
In order to explore the semantic area related to those selected Wikipedia pages about the Right to be forgotten, we used Seealsology tool, which allowed us to extract all the links in the See Also section producing a graph.
In order to explore the semantic area related to those selected Wikipedia pages about the Right to be forgotten, we used Seealsology tool, which allowed us to extract all the links in the See Also section producing a graph.
After selecting 5 Wikipedia pages linked to the topic of the research, extract the See Also urls from each of these 5 pages: on your wikipedia page, right click and select Explore Element, copy the div where the See Also are and then paste it on extractlinkurl.com. Then click on Extract all urls. In order to pinpoint the topic, read the See Also and highlight those that are actually inherent to it. Then, on Seealsology (labs.densitydesign.org/seealsology) put the urls of the 5 initial pages and of the See Also highlighted as relevant so that you can begin to explore the topic in a visual way. Put Distance=2 and start crawling (do not do anything else during the process so that it doesn’t freeze): by putting Distance=2 you are able to collect all the See Also (starting from your pages) up to 2 levels. If you put a higher distance, once you are able to import the graph in Gephi, you would get an almost incomprehensible graph, very dense and with up to 9000 nodes, making it really difficult to read. Once you have downloaded the .gefx file, open it in Gephi, selecting Graph Type: Directed, then you should put the following parameters:


--- layout: page title: About header-img: "img/about-bg.png" ---
“Programs must be written for people to read, and only incidentally for machines to execute.”