Before Change
:param T_tot: total number of time steps to run the model.
:return: dictionary {layer j: last time t}
last = {}
curr_ind = [graph.number_of_nodes() - 1] // start with output layer
t = T_tot
while len(last) < graph.number_of_nodes():
next_ind = [] // layers at prev time point
for ind in curr_ind:
if ind not in last:
last[ind] = t
// then add adjacency list onto next_ind
next_ind.extend(graph.predecessors(ind))
curr_ind = next_ind
After Change
while len(curr_layers) > 0:
next_layers = [] // layers at prev time point
for layer in curr_layers:
if "last" not in graph.node[layer]:
graph.node[layer]["last"] = t
// then add adjacency list onto next_layer
next_layers.extend(graph.predecessors(layer))
curr_layers = next_layers
t -= 1


interface FieldPreparer
Class that prepares the mocks on an object (usually an instance of the current test class, but can be any generic pojo with annotated fields)
prepareFields
abstract fun prepareFields(objectWithMockFields: Any): Unit


New Mac Setup Script
I’ve been thinking about how to automate the process of making a new mac feel at home for me. So I’ve customized a script that does exactly that!
Ableton Desktop Backgrounds
Here are some desktop backgrounds that I made. Feel free to use them as you wish.
React
This is a part of the series “Henry is getting ready for Olavstoppen”. You can find the other posts by following the tag “Getting Ready 4 OT”.
To learn React I have been following this React course by Wes Bos. And the following is mostly my notes and ramblings trying to digest it.
React is, according to reactjs.org, “A JavaScript library for building user interfaces.”
Creating our first Component Writing HTML with JSX Linking up your CSS-file Passing dynamic data with Props Routing with React Router Helper functions Events, Refs and this Binding Events Ref and Binding Rule number 1: Don’t touch the DOM.
Getting ready for Olavstoppen
I start in my new job at Olavstoppen the 1st of April. Yesterday I got an email with some more information on what technology I will be using: React, React-Native and Redux.
I have some knowledge with React from one small project in class ( and some tinkering on my own ), but I can’t say that I’m a pro. Not yet at least.
My goal is to (1.) be efficient in writing code in React, then (2.
Chordpro
Chordpro brukes når man vil ha nøyaktig plassering av akkorder i tekst-dokumentet, og ha mulighet til å transponere når som helst.
Chordro er bare vanlige .txt-filer, men man kan generere (blant annet) pdf ut av det.
App-er? Det er mange programmer som støtter å vise chordpro-notering.
OnSong https://onsongapp.com/
Installasjon Se https://www.chordpro.org for hjelp til installasjon.
Dersom du har Mac kan du installere Chordpro Cli-programmet ved å skrive sudo /usr/bin/cpan chordpro i terminalen din.


Course Tips and Overview
Welcome to Programming 109. In this course we will create content that appears in a user’s web browser.
A user's computer is called the client computer. As a result, we can say that this course focuses on client-side web development.
 By the end of the course you should know enough to realize how much you don't know, which is always the point at which a real understanding of a big subject begins.
Web development is a really fun subject, and also a really big one. It is growing and changing at a furious rate. My goal is to get you over the doorstep so you can see inside this big room and begin to sense all the very cool things that can be done once you have mastered the basics.
Syllabus
Click here to view the Syllabus: http://bit.ly/syllabus-prog109-2019.
Technologies
There are 3 principle technologies covered in this course:
- HTML
- Cascading Style Sheets
- JavaScript programming language.
Tips
Here are a few tips that can help you succeed.
- Each week, log into the web site on at least four separate days.
- Complete all the reading assignments. This is easy the first week or two because we are all excited to learn, but we need to remain vigilant throughout the course.
- Never wait until the last hour to complete your assignments. Remember Murphy's Law and assume that there will be technical issues that delay your submission.
- Ask technical questions in the discussion area.
- Use the Canvas Workshop and Guides. On-campus Canvas workshops are available the first week of the quarter.


We're working hard to get Clubhouse ready for everyone! While we wrap up the finishing touches, we're adding people gradually to make sure nothing breaks. :)
Anyone can join with an invite from an existing user -- or reserve your username and we'll text you if you have a friend on the app who can let you in. We are so grateful you're here and can't wait to have you join us! 🙏


'Harry Potter' stars Tom Felton, Matthew Lewis appear to have opposing views on aging: ‘Speak for yourself’
by Viktoria RistanovicYou tell 'em, Neville!
On Thursday, Tom Felton, who portrayed Draco Malfoy in the "Harry Potter" series, posted a photo of himself posing next to a framed photo of him in character as a child.
He sported his signature green and black Slytherin cloak.
JK ROWLING TO RELEASE FOUR NEW HARRY POTTER WORLD E-BOOKS
"Agings a b----," Felton, 32, wrote.
And fellow former costar, Matthew Lewis, who played Neville Longbottom, had something to say to that statement.
"Speak for yourself lad," Lewis, 30, teasingly wrote back.
JK ROWLING FACES BACKLASH FOR COMMENTS ABOUT HARRY POTTER CHARACTERS' SEXUALITY
According to E! News, the two have stayed close friends since the final "Harry Potter" film wrapped nearly a decade ago and have even had several meet-ups over the years.
CLICK HERE TO GET THE FOX NEWS APP
In August 2018, the two reunited with former co-star Emma Watson, who played Hermione Granger in the franchise.


|
FlatBuffers
An open source project by FPL.
|
This document tries to shed some light on to the "why" of FlatBuffers, a new serialization library.
Back in the good old days, performance was all about instructions and cycles. Nowadays, processing units have run so far ahead of the memory subsystem, that making an efficient application should start and finish with thinking about memory. How much you use of it. How you lay it out and access it. How you allocate it. When you copy it.
Serialization is a pervasive activity in a lot programs, and a common source of memory inefficiency, with lots of temporary data structures needed to parse and represent data, and inefficient allocation patterns and locality.
If it would be possible to do serialization with no temporary objects, no additional allocation, no copying, and good locality, this could be of great value. The reason serialization systems usually don't manage this is because it goes counter to forwards/backwards compatability, and platform specifics like endianness and alignment.
FlatBuffers is what you get if you try anyway.
In particular, FlatBuffers focus is on mobile hardware (where memory size and memory bandwidth is even more constrained than on desktop hardware), and applications that have the highest performance needs: games.
This is a summary of FlatBuffers functionality, with some rationale. A more detailed description can be found in the FlatBuffers documentation.
A FlatBuffer is a binary buffer containing nested objects (structs, tables, vectors,..) organized using offsets so that the data can be traversed in-place just like any pointer-based data structure. Unlike most in-memory data structures however, it uses strict rules of alignment and endianness (always little) to ensure these buffers are cross platform. Additionally, for objects that are tables, FlatBuffers provides forwards/backwards compatibility and general optionality of fields, to support most forms of format evolution.
You define your object types in a schema, which can then be compiled to C++ or Java for low to zero overhead reading & writing. Optionally, JSON data can be dynamically parsed into buffers.
Tables are the cornerstone of FlatBuffers, since format evolution is essential for most applications of serialization. Typically, dealing with format changes is something that can be done transparently during the parsing process of most serialization solutions out there. But a FlatBuffer isn't parsed before it is accessed.
Tables get around this by using an extra indirection to access fields, through a vtable. Each table comes with a vtable (which may be shared between multiple tables with the same layout), and contains information where fields for this particular kind of instance of vtable are stored. The vtable may also indicate that the field is not present (because this FlatBuffer was written with an older version of the software, of simply because the information was not necessary for this instance, or deemed deprecated), in which case a default value is returned.
Tables have a low overhead in memory (since vtables are small and shared) and in access cost (an extra indirection), but provide great flexibility. Tables may even cost less memory than the equivalent struct, since fields do not need to be stored when they are equal to their default.
FlatBuffers additionally offers "naked" structs, which do not offer forwards/backwards compatibility, but can be even smaller (useful for very small objects that are unlikely to change, like e.g. a coordinate pair or a RGBA color).
While schemas reduce some generality (you can't just read any data without having its schema), they have a lot of upsides:
FlatBuffer schemas are fairly similar to those of the incumbent, Protocol Buffers, and generally should be readable to those familiar with the C family of languages. We chose to improve upon the features offered by .proto files in the following ways:
optional
, and all struct fields are required
.repeated
. This gives you a length without having to collect all items, and in the case of scalars provides for a more compact representation, and one that guarantees adjacency.union
type instead of using a series of optional
fields, all of which must be checked individually.


time_from_finish = (t_now - job_time_finished).total_seconds()
if time_from_finish > PBS_OUTPUT_WRITEBACK_WAIT:
self._finished.add(job)
def poll(self, *jobs):
for job in jobs:
self._poll_job(job)
done = job.cancelled or output_ready
if done:
t_now = time.time()
if job.completion_time is None:
job._completion_time = t_now
time_from_finish = t_now - job.completion_time
if time_from_finish > PBS_OUTPUT_WRITEBACK_WAIT:
job._completed = True


Instructions for the Google .NET Client API – Translate.WP.TranslateText
Browse Online
1. Checkout Instructions
Prerequisites: Install Visual Studio, and Mercurial.
cd [someDirectory]
hg clone https://code.google.com/p/google-api-dotnet-client.samples/ google-api-dotnet-client-samples
2. API access Instructions
Important: after checking out the project, and before compiling
and running it, you need to:
Setup Project in Visual Studio
- Open the GoogleApisSamples.sln with Visual Studio
- Click on Build > Rebuild Solution
- Find your API Key which is generated on the Google APIs
Console API Access pane.
- Edit MainPage.xaml.cs and replace the value of the API_KEY constant with your API Key
- Launch the app in the Windows Phone emulator


{{/* If there is more than one page of results, show "previous/next page" links and links to each page of results. */}} {{ if (or .PrevInSection .NextInSection) }}


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Last updated: 2020-04-15
Checks: 7 0
Knit directory: 031-coronavirus-basic-analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.6.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
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The command set.seed(20200405)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
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Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/02_covid19-Rpackage.Rmd
) and HTML (docs/02_covid19-Rpackage.html
) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b7160e9 | ogorodriguez | 2020-04-15 | Added Graphs of daily cases in a country and in a region |
| html | dc398c6 | ogorodriguez | 2020-04-11 | Build site. |
| html | b3d9849 | ogorodriguez | 2020-04-11 | Build site. |
| Rmd | fa7ca43 | ogorodriguez | 2020-04-11 | Adding the second page for the tidycovid19 R package |
The tidycovid19
R package created by Joachim Gassen. pulls date from various sources and offers lots of data frames that combines the current update data together with interventions and financial aid provided for each country. It is a good tool to analyse time series data. It also provides a tool for graphics as well. The purpose of this notebook is to do some explorations of its functions and applications.
Let’s see the functions and datasets that it may contain.
ls("package:tidycovid19") %>%
tibble::enframe() %>%
select(-name) %>%
rename(func = value) %>%
slice(2:n()) %>%
mutate(id = row_number()) %>%
select(id, func)
#> # A tibble: 8 x 2
#> id func
#> <int> <chr>
#> 1 1 download_acaps_npi_data
#> 2 2 download_google_trends_data
#> 3 3 download_jhu_csse_covid19_data
#> 4 4 download_merged_data
#> 5 5 download_oxford_npi_data
#> 6 6 download_wbank_data
#> 7 7 plot_covid19_spread
#> 8 8 shiny_covid19_spread
The functions in the package are used to download the data from various sources. I will explain the first four.
The download_acaps_npi_data extracts the informationf from ACAPS and it contains data regarding government measures taken in place to avoid the spread of the virus such as lockdowns and social distancing.
The download_google_trends_data contains the data from Google Trends searches with the term “Coronavirus.” It can help see the public attention the virus and the measures taken in each country to curve it.
The download_jhu_csse_covid19_data is the most influential dataset on the subject aggregating data per country. This function extracts the updated information of the pandemic.
The download_merged_data() downloads all data sources and creates a merged country-day panel sample.
library(tidycovid19)
Let’d check the google trends data.
gtrends <- download_google_trends_data(cached = TRUE)
gtrends %>%
glimpse()
#> Rows: 5,916
#> Columns: 4
#> $ iso3c <chr> "ITA", "ITA", "ITA", "ITA", "ITA", "ITA", "ITA", "ITA...
#> $ date <date> 2020-01-01, 2020-01-02, 2020-01-03, 2020-01-04, 2020...
#> $ gtrends_score <dbl> 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.5...
#> $ timestamp <dttm> 2020-04-14 14:37:16, 2020-04-14 14:37:16, 2020-04-14...
merged_df <- download_merged_data(cached = FALSE)
merged_df %>%
glimpse()
#> Rows: 15,288
#> Columns: 22
#> $ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", ...
#> $ iso3c <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AF...
#> $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-...
#> $ confirmed <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ recovered <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ soc_dist <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ mov_rest <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, ...
#> $ pub_health <dbl> 0, 0, 1, 1, 2, 3, 3, 3, 3, 3, 3, 5, 5, 7, 7, ...
#> $ soc_econ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ lockdown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ gtrends_score <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
#> $ gtrends_country_score <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
#> $ region <chr> "South Asia", "South Asia", "South Asia", "So...
#> $ income <chr> "Low income", "Low income", "Low income", "Lo...
#> $ population <dbl> 37172386, 37172386, 37172386, 37172386, 37172...
#> $ land_area_skm <dbl> 652860, 652860, 652860, 652860, 652860, 65286...
#> $ pop_density <dbl> 56.93776, 56.93776, 56.93776, 56.93776, 56.93...
#> $ pop_largest_city <dbl> 4011770, 4011770, 4011770, 4011770, 4011770, ...
#> $ life_expectancy <dbl> 64.486, 64.486, 64.486, 64.486, 64.486, 64.48...
#> $ gdp_capita <dbl> 563.8257, 563.8257, 563.8257, 563.8257, 563.8...
#> $ timestamp <dttm> 2020-04-15 09:05:57, 2020-04-15 09:05:57, 20...
merged_df %>%
skimr::skim()
| Name | Piped data |
| Number of rows | 15288 |
| Number of columns | 22 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| Date | 1 |
| numeric | 16 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 0 | 1.00 | 2 | 32 | 0 | 182 | 0 |
| iso3c | 0 | 1.00 | 3 | 3 | 0 | 182 | 0 |
| region | 252 | 0.98 | 10 | 26 | 0 | 7 | 0 |
| income | 252 | 0.98 | 10 | 19 | 0 | 4 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2020-01-22 | 2020-04-14 | 2020-03-03 | 84 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| confirmed | 0 | 1.00 | 2037.67 | 16889.85 | 0.00 | 0.00 | 1.00 | 52.00 | 6.076700e+05 | ▇▁▁▁▁ |
| deaths | 0 | 1.00 | 107.07 | 1007.04 | 0.00 | 0.00 | 0.00 | 1.00 | 2.583200e+04 | ▇▁▁▁▁ |
| recovered | 0 | 1.00 | 496.27 | 4667.91 | 0.00 | 0.00 | 0.00 | 2.00 | 7.820000e+04 | ▇▁▁▁▁ |
| soc_dist | 756 | 0.95 | 1.55 | 2.74 | 0.00 | 0.00 | 0.00 | 2.00 | 1.800000e+01 | ▇▂▁▁▁ |
| mov_rest | 756 | 0.95 | 2.18 | 3.88 | 0.00 | 0.00 | 0.00 | 3.00 | 4.100000e+01 | ▇▁▁▁▁ |
| pub_health | 756 | 0.95 | 2.98 | 5.44 | 0.00 | 0.00 | 1.00 | 4.00 | 6.700000e+01 | ▇▁▁▁▁ |
| soc_econ | 756 | 0.95 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.000000e+00 | ▁▁▇▁▁ |
| lockdown | 756 | 0.95 | 0.25 | 0.75 | 0.00 | 0.00 | 0.00 | 0.00 | 1.000000e+01 | ▇▁▁▁▁ |
| gtrends_score | 10696 | 0.30 | 29.55 | 26.41 | 0.50 | 6.00 | 21.00 | 49.00 | 1.000000e+02 | ▇▃▃▂▁ |
| gtrends_country_score | 10584 | 0.31 | 36.12 | 21.38 | 1.00 | 19.00 | 38.50 | 48.75 | 1.000000e+02 | ▆▆▇▂▁ |
| population | 252 | 0.98 | 41950032.92 | 149172272.20 | 33785.00 | 2781677.00 | 9775564.00 | 30809762.00 | 1.392730e+09 | ▇▁▁▁▁ |
| land_area_skm | 420 | 0.97 | 712163.24 | 1908989.39 | 2.00 | 28050.00 | 143350.00 | 566730.00 | 1.637687e+07 | ▇▁▁▁▁ |
| pop_density | 420 | 0.97 | 316.30 | 1561.24 | 2.04 | 35.77 | 84.64 | 205.45 | 1.919600e+04 | ▇▁▁▁▁ |
| pop_largest_city | 2940 | 0.81 | 4455586.30 | 5983664.83 | 342577.00 | 1131690.00 | 2081624.00 | 4920776.00 | 3.746830e+07 | ▇▁▁▁▁ |
| life_expectancy | 588 | 0.96 | 72.54 | 7.59 | 52.80 | 66.68 | 74.13 | 77.81 | 8.542000e+01 | ▁▃▅▇▅ |
| gdp_capita | 420 | 0.97 | 16385.32 | 25670.41 | 210.80 | 1860.44 | 6453.56 | 16942.67 | 1.958796e+05 | ▇▁▁▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| timestamp | 0 | 1 | 2020-04-15 09:05:57 | 2020-04-15 09:05:57 | 2020-04-15 09:05:57 | 1 |
Let’s see the lockdown data for Spain
merged_df %>%
filter(str_detect(country, "Spain")) %>%
select(date, soc_dist) %>%
head(10)
#> # A tibble: 10 x 2
#> date soc_dist
#> <date> <dbl>
#> 1 2020-01-22 0
#> 2 2020-01-23 0
#> 3 2020-01-24 0
#> 4 2020-01-25 0
#> 5 2020-01-26 0
#> 6 2020-01-27 0
#> 7 2020-01-28 0
#> 8 2020-01-29 0
#> 9 2020-01-30 0
#> 10 2020-01-31 0
The columns regarding NPI (non-pharmaceutical intervention) contain a number that I am having a hard time to decipher. After checking the source data for NPI measures found in this excel file from the ACAPS website and described in detail, I see that the numbers in those columns (soc_dist, lockdown, etc.) refer to the number of measures implemented and active in those countries. For example, in Spain the first NPI measure taken was a Full lockdown measure on 202-03-13, hence the number 1. Then on the 16th, 3 days later, a second measure was implemented regarding a Partial lockdown regading travel of workers in essential businesses and activities. The numbers are cummulative. It will be interesting to see whether these numbers decrease the numbers some measures start being lifted.
In the next extract we can see the latest information regarding Spain where 6 measures are already in place.
merged_df %>%
filter(str_detect(country, "Spain")) %>%
select(country, date, soc_dist) %>%
tail()
#> # A tibble: 6 x 3
#> country date soc_dist
#> <chr> <date> <dbl>
#> 1 Spain 2020-04-09 6
#> 2 Spain 2020-04-10 6
#> 3 Spain 2020-04-11 6
#> 4 Spain 2020-04-12 6
#> 5 Spain 2020-04-13 6
#> 6 Spain 2020-04-14 6
In this other website the author is doing a manual (and remarkable) work in curating by hand a dataset of the Coronavirus so he can do some analysis and visuals. One of the things he does is calculating the cummulative number of deaths per country. I will try to do the same thing using the datasets provided by the tidycovid package.
It seems that the confirmed variable in the merged_df dataset refers to cummulative cases.
merged_df %>%
select(date, country, confirmed, deaths ) %>%
filter(str_detect(country, "China"))
#> # A tibble: 84 x 4
#> date country confirmed deaths
#> <date> <chr> <dbl> <dbl>
#> 1 2020-01-22 China 548 17
#> 2 2020-01-23 China 643 18
#> 3 2020-01-24 China 920 26
#> 4 2020-01-25 China 1406 42
#> 5 2020-01-26 China 2075 56
#> 6 2020-01-27 China 2877 82
#> 7 2020-01-28 China 5509 131
#> 8 2020-01-29 China 6087 133
#> 9 2020-01-30 China 8141 171
#> 10 2020-01-31 China 9802 213
#> # ... with 74 more rows
I will create an augmented merged data frame so that I can see the number of cases per day, and change the date label factor into English for consistency.
I will include a column for the number of days elapsed since the beginning of the pandemic tracking in Januray (2020-01-22)
merged_aug <- merged_df %>%
mutate(c_cases = case_when(
confirmed - lag(confirmed) < 0 ~ confirmed,
TRUE ~ confirmed - lag(confirmed)),
d_cases = case_when(
deaths - lag(deaths) < 0 ~ deaths,
TRUE ~ deaths - lag(deaths)),
r_cases = case_when(
recovered - lag(recovered) < 0 ~ recovered,
TRUE ~ recovered - lag(recovered)),
day_name = lubridate::wday(date, label = TRUE, abbr = FALSE,
week_start = getOption("lubridate.week.start", 1)),
day_name = as_factor(day_name),
day_name = recode(day_name,
lunes = "Monday",
martes = "Tuesday",
miércoles = "Wednesday",
jueves = "Thursday",
viernes = "Friday",
sábado = "Saturday",
domingo = "Sunday"
),
days_elapsed = date - min(date)
) %>%
select(country, iso3c, region, date, days_elapsed, day_name, c_cases, confirmed, d_cases, deaths, r_cases, recovered, everything())
Some comments on the table above. The data from the packages has information on the cummulative number of confirmed , deaths and recovered cases. I needed to include columns to see the number of each cases per day. In order to avoid negative number of cases due to the sequential order of the countries (for example, for the earliest day in USA the number confirmed cases was about -400 since the previous row of data was the latest date in Uruguay.)
For that, I included a condition using case_when()
. That made the formula a bit verbose, but for the moment it does the trick.
merged_aug %>% glimpse()
#> Rows: 15,288
#> Columns: 27
#> $ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", ...
#> $ iso3c <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AF...
#> $ region <chr> "South Asia", "South Asia", "South Asia", "So...
#> $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-...
#> $ days_elapsed <drtn> 0 days, 1 days, 2 days, 3 days, 4 days, 5 da...
#> $ day_name <ord> Wednesday, Thursday, Friday, Saturday, Sunday...
#> $ c_cases <dbl> NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
#> $ confirmed <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ d_cases <dbl> NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
#> $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ r_cases <dbl> NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
#> $ recovered <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ soc_dist <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ mov_rest <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, ...
#> $ pub_health <dbl> 0, 0, 1, 1, 2, 3, 3, 3, 3, 3, 3, 5, 5, 7, 7, ...
#> $ soc_econ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ lockdown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
#> $ gtrends_score <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
#> $ gtrends_country_score <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
#> $ income <chr> "Low income", "Low income", "Low income", "Lo...
#> $ population <dbl> 37172386, 37172386, 37172386, 37172386, 37172...
#> $ land_area_skm <dbl> 652860, 652860, 652860, 652860, 652860, 65286...
#> $ pop_density <dbl> 56.93776, 56.93776, 56.93776, 56.93776, 56.93...
#> $ pop_largest_city <dbl> 4011770, 4011770, 4011770, 4011770, 4011770, ...
#> $ life_expectancy <dbl> 64.486, 64.486, 64.486, 64.486, 64.486, 64.48...
#> $ gdp_capita <dbl> 563.8257, 563.8257, 563.8257, 563.8257, 563.8...
#> $ timestamp <dttm> 2020-04-15 09:05:57, 2020-04-15 09:05:57, 20...
The following code will show the latest data for one country only.
# Write the name of the country to search for inside the quotes
country_name <- "Spain"
merged_aug %>%
filter(str_detect(country, regex(country_name, ignore_case = TRUE))) %>%
tail(1) %>%
mutate_all(as.character) %>%
pivot_longer(cols = country:timestamp,
names_to = "variable",
values_to = "value")
#> # A tibble: 27 x 2
#> variable value
#> <chr> <chr>
#> 1 country Spain
#> 2 iso3c ESP
#> 3 region Europe & Central Asia
#> 4 date 2020-04-14
#> 5 days_elapsed 83
#> 6 day_name Tuesday
#> 7 c_cases 2442
#> 8 confirmed 172541
#> 9 d_cases 300
#> 10 deaths 18056
#> # ... with 17 more rows
# Country selected in variable country_name
merged_aug %>%
filter(str_detect(country, regex(country_name, ignore_case = TRUE)),
deaths > 0) %>%
select(date, country, c_cases, d_cases, r_cases) %>%
pivot_longer(cols = ends_with("_cases"), names_to = "type_cases", values_to = "value") %>%
ggplot(aes(date, value, colour = type_cases)) +
# geom_line() +
geom_smooth() +
facet_wrap(~ type_cases, nrow = 3, scales = "free_y") +
labs(title = glue::glue("Daily cases in {country_name}")) +
theme_minimal()
region_name <- "Europe"
merged_aug %>%
filter(str_detect(region, regex(region_name, ignore_case = TRUE)),
confirmed != 0) %>%
select(date, region, c_cases, d_cases, r_cases) %>%
group_by(date, region) %>%
summarise(c_sum = sum(c_cases),
d_sum = sum(d_cases),
r_sum = sum(r_cases)) %>%
ungroup() %>%
pivot_longer(cols = ends_with("_sum"), names_to = "sum_cases", values_to = "value") %>%
ggplot(aes(date, value, colour = sum_cases)) +
geom_line() +
geom_smooth() +
facet_wrap(~ sum_cases, nrow = 3, scales = "free_y") +
labs(title = glue::glue("Cases in {region_name}"))
sessionInfo()
#> R version 3.6.1 (2019-07-05)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 18362)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=Spanish_Spain.1252 LC_CTYPE=Spanish_Spain.1252
#> [3] LC_MONETARY=Spanish_Spain.1252 LC_NUMERIC=C
#> [5] LC_TIME=Spanish_Spain.1252
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] tidycovid19_<IP_ADDRESS>00 forcats_0.5.0 stringr_1.4.0
#> [4] dplyr_0.8.5 purrr_0.3.3 readr_1.3.1
#> [7] tidyr_1.0.2 tibble_3.0.0 tidyverse_1.3.0
#> [10] ggplot2_3.3.0 workflowr_1.6.1
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_<IP_ADDRESS> lubridate_1.7.8 countrycode_1.1.1 lattice_0.20-40
#> [5] wbstats_0.2 assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.24
#> [9] utf8_1.1.4 repr_1.1.0 R6_2.4.1 cellranger_1.1.0
#> [13] backports_1.1.6 reprex_0.3.0 evaluate_0.14 highr_0.8
#> [17] httr_1.4.1 pillar_1.4.3 rlang_0.4.5 curl_4.3
#> [21] readxl_1.3.1 rstudioapi_0.11 whisker_0.4 Matrix_1.2-18
#> [25] rmarkdown_2.1 labeling_0.3 splines_3.6.1 selectr_0.4-2
#> [29] munsell_0.5.0 anytime_0.3.7 broom_0.5.5 compiler_3.6.1
#> [33] httpuv_1.5.2 modelr_0.1.6 xfun_0.12 base64enc_0.1-3
#> [37] pkgconfig_2.0.3 mgcv_1.8-31 htmltools_0.4.0 tidyselect_1.0.0
#> [41] fansi_0.4.0 crayon_1.3.4 dbplyr_1.4.2 withr_2.1.2
#> [45] later_1.0.0 grid_3.6.1 nlme_3.1-144 jsonlite_1.6.1
#> [49] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.26.1
#> [53] magrittr_1.5 scales_1.1.0 cli_2.0.2 stringi_1.4.6
#> [57] farver_2.0.3 fs_1.4.1 promises_1.1.0 skimr_2.1
#> [61] xml2_1.3.1 ellipsis_0.3.0 generics_0.0.2 vctrs_0.2.4
#> [65] gtrendsR_1.4.4 tools_3.6.1 glue_1.4.0 hms_0.5.3
#> [69] yaml_2.2.1 colorspace_1.4-1 rvest_0.3.5 knitr_1.28
#> [73] haven_2.2.0


{{msg.text}}
{{error.text}}
Here you may change the contents of the FFQ, decide which participants get directly accepted, and change basic things.
If you are here for the first time, you may generate a string for the Import-tab here
If you need to change a password for someone, you can do this here
Please note that you can only make changes using your Masterkey. You should have seen it while setting up the site.
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|---|---|
| {{food.foodname}} |
| Identifier | {{key}} |
|---|---|
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The rules are traversed top to bottom and the first matching rule determines whether the user is rejected, accepted or must be accepted explicitly
| {{key}} | |||
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Here you can import settings or export them for later use. Just copy the code from or into this field.


We sell users!
- visualize granular architectures
- morph e-business portals
- reinvent robust supply-chains
- deploy e-business platforms
- embrace efficient eyeballs
- extend back-end eyeballs
- embrace best-of-breed schemas
- recontextualize real-time synergies
- monetize B2C networks
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Question
Given a set of n jobs with [start time, end time, cost], find a subset so that no 2 jobs overlap and the cost is maximum.
Let’s assume the input is already sorted by start_time.
Solution
Somebody mentioned Interval Graph, so check it out if you interested!
I am going to discuss both DP and recursive solution.
This question reminds me of [Question] 0-1 Knapsack Problem and [Question] Coin Change Problem, cuz the basic idea is to compare 2 conditions:
- include current element
- or, not include current element
A very good DP solution is presented in here. The code below is written by me and it’s very intuitive to read.
Leave me a comment if you have questions. And one more thing~ Happy new year!
Code
Dp
private int dpSolution(Job[] jobs, int size) {
int[] dp = new int[size];
dp[size - 1] = jobs[size - 1].cost;
// cost of last job equals to just itself
for (int k = size - 2; k >= 0; k--) {
int next = findNextJob(jobs, k);
int includeK = jobs[k].cost;
if (next < size) {
includeK += dp[next];
}
int excludeK = dp[k + 1];
dp[k] = Math.max(includeK, excludeK);
}
return dp[0];
}
private int findNextJob(Job[] jobs, int k) {
int finishTime = jobs[k].finish;
int next = k + 1;
while (next < jobs.length) {
if (jobs[next].start < finishTime) {
next++;
} else {
break;
}
}
return next;
}
Recursion
public int recursiveSolution(Job[] jobs, int size, int k) {
// max cost from (k)th job and onwards
if (k == size) {
return 0;
}
// (k)th job must not conflict with any previous job
int next = findNextJob(jobs, k);
int includeK = jobs[k].cost + recursiveSolution(jobs, size, next);
int excludeK = recursiveSolution(jobs, size, k + 1);
return Math.max(includeK, excludeK);
}
private int findNextJob(Job[] jobs, int k) {
int finishTime = jobs[k].finish;
int next = k + 1;
while (next < jobs.length) {
if (jobs[next].start < finishTime) {
next++;
} else {
break;
}
}
return next;
}


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Ancestry Library Edition
Worldwide collection of genealogy records
Access available in Oxfordshire History Centre and Libraries only
Digitised content and useful websites for local and family history research
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Official site for access to Scottish records
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Digital collections and services
Portal for browsing genealogical resources by subject or place
Key sites providing family history and local history content, like Ancestry, Find My Past and the British Library
Biographies, peerage, gentry, clergy, criminals, mayors and freemen
Parish registers, baptisms, births, marriages, deaths, burials, cemeteries. monumental inscriptions
Local and national library and archive catalogues
Census records for UK and overseas
Street and trade directories, electoral registers
History and archaeology of Oxford city and university
History and archaeology of Oxfordshire
History and archaeology – national sources
Historic and modern mapping of Oxfordshire
Historic and modern mapping and gazetteers – national sources
Images and records for Oxfordshire armed force
Records for armed forces – UK and world
British Newspaper Archive and other sources
Oxfordshire sound recordings, and catalogues and guides to oral history
Surname origins, research interests, migration and mapping
Oxfordshire images, and catalogues and guides to photographic collections
Catalogues and guides to national collections of images
Oxfordshire, Berkshire, UK and Ireland wills and probate records


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jQuery Conditionize Plugin Example
Text To Show Once You Typed 'jQueryScript'.
One
Two
Three. Wow, you will see the new control below...
Is checked?
Nope
Yep
GitHub


#include <BioUtil.hpp>
◆ readBytes() [1/2]
template<std::size_t size>
| std::string PubSub::Util::BioUtil::readBytes |
( |
UniquePtr< BIO > & |
bio | ) |
|
|
static |
Read X amount of bytes from the bio and return them as string.
- Parameters
-
- Template Parameters
-
| amount | The amount to read from the bio. |
- Returns
- The bytes read as string.
◆ readBytes() [2/2]
| std::vector< char > PubSub::Util::BioUtil::readBytes |
( |
UniquePtr< BIO > & |
bio, |
|
|
int |
amount |
|
) |
| |
|
static |
Read X amount of bytes from the bio and return them as string.
- Parameters
-
| bio | Reference to bio. |
| amount | The amount to read from the bio. |
- Returns
- The bytes read as vector of chars.
◆ readBytesVector()
template<std::size_t size>
| std::vector< char > PubSub::Util::BioUtil::readBytesVector |
( |
UniquePtr< BIO > & |
bio | ) |
|
|
static |
Read X amount of bytes from the bio and return them as string.
- Parameters
-
- Template Parameters
-
| amount | The amount to read from the bio. |
- Returns
- The bytes read as vector of chars.
◆ readHeaders() [1/2]
| std::string PubSub::Util::BioUtil::readHeaders |
( |
UniquePtr< BIO > & |
bio | ) |
|
|
static |
◆ readHeaders() [2/2]
| std::string PubSub::Util::BioUtil::readHeaders |
( |
UniquePtr< BIO > & |
bio, |
|
|
std::string & |
leftover |
|
) |
| |
|
static |
Read all bytes until encountering \r
\r
, and return the data without the \r
\r
.
- Parameters
-
| bio | The bio to read from. |
| leftover | all of the data thata was read after \r
\r
.s |
◆ readSomeData()
| std::string PubSub::Util::BioUtil::readSomeData |
( |
UniquePtr< BIO > & |
bio | ) |
|
|
static |
read 1024 bytes of data and return them.
- Parameters
-
- Returns
- The first 1024 bytes of the data that were read by the bio.
◆ socketConnected() [1/2]
| bool PubSub::Util::BioUtil::socketConnected |
( |
BIO * |
bio | ) |
|
|
static |
Check if a bio of type socket accepts data.
◆ socketConnected() [2/2]
| bool PubSub::Util::BioUtil::socketConnected |
( |
UniquePtr< BIO > & |
bio | ) |
|
|
static |
The documentation for this class was generated from the following files:


The following fields are required in input files.
Click here to download an Excel file with this table plus example data.
| Column Name | Data Type | Allowable entries | Comment |
|---|---|---|---|
| Index_Name | text | BCG_MariNW_Bugs500ct | Directs the R code to the correct overall model to use (it references the INDEX_NAME column in the Rules table in the BCGcalc extdata folder) |
| Index_Class | text | HiGrad-LoElev, LoGrad-LoElev, HiGrad-HiElev | Directs the R code to the correct submodel to use (it looks in the INDEX_CLASS column in the Rules table in the BCGcalc extdata folder) |
| SAMPLEID | text | NA | Unique sample identifier |
| TAXAID | text | NA | Taxonomic identification of organisms in each sample |
| N_TAXA | numeric | non-negative real number | Number of individuals. |
| EXCLUDE | text | TRUE, FALSE | Redundant (non-distinct) taxa should marked “TRUE”. Redundant taxa are excluded from richness metric calculations but are included in the composition (percent individuals) metrics. |
| NONTARGET | text | TRUE, FALSE | Non-target taxa should be marked “TRUE”. The R code excludes non-target taxa from all metric calculations. |
| PHYLUM | text | NA | taxonomic hierarchy |
| CLASS | text | NA | taxonomic hierarchy |
| SUBCLASS | text | NA | taxonomic hierarchy |
| ORDER | text | NA | taxonomic hierarchy |
| FAMILY | text | NA | taxonomic hierarchy |
| SUBFAMILY | text | NA | taxonomic hierarchy |
| TRIBE | text | NA | taxonomic hierarchy |
| GENUS | text | NA | taxonomic hierarchy |
| BCG_ATTR | text | 1i, 1m, 2, 3, 4, 5, 6 | Biological Condition Gradient (BCG) attribute assignments |
| FFG | text | CG, CF, PR, SC, SH, MH, OM, PA, PH, XY | Primary Functional Feeding Group. Multiple entries are allowed and should be separated with a comma (and no space; e.g., CG,CF). Entries: collector-gatherer (CG), collector-filterer (CF), predator (PR), scraper (SC), shredder (SH), macrophyte herbivore (MH), omnivore (OM), parasite (PA), piercer-herbivore (PH), xylophage (XY). |
| LIFE_CYCLE | text | uni, semi, multi | Number of broods or generations of an organism in a year (also referred to as ‘voltinism’): univoltine (UNI), semivoltine (SEMI), or multivoltine (MULTI). |
Fields necessary to calculate other metrics can be included and do not affect the BCG model output. Additional metrics are available in the ’*metval_all.csv’ file.
| Column Type | Column Name | Data Type | Allowable Entries | Comment |
|---|---|---|---|---|
| Optional | THERMAL_INDICATOR | text | stenoc, cold, cool, cowa, warm, stenow, eurythermal | Thermal preference assignment based on Stamp et al. (in progress): stenoc=cold stenotherm; cold, cool, cowa = cool/warm, warm, stenow = warm stenotherm, eurythermal. |
| Optional | TOLVAL | numeric | number ranging from 0-10 | Taxa with tolerance values ranging from 0 to 3 are considered ‘intolerant’ to anthropogenic disturbance and are included in the ‘intolerant’ metric calculations. Those with values ranging from 7 to 10 are included in the ‘tolerant’ metrics. |
| Optional | HABIT | text | BU, CB, CN, SP, SW | Habit. Multiple entries are allowed and should be separated with a comma. Entries: burrowers (BU), climbers (CB), clingers (CN), sprawlers (SP), swimmers (SW). |
| Optional | NOTEWORTHY | text | TRUE, FALSE | Noteworthy taxa occur most often at sites with complex and resilient habitat structure that supports high overall freshwater biodiversity. Most of these taxa are rarely or occasionally encountered in benthic macroinvertebrate biomonitoring samples. |
| Optional | LONGLIVED | text | TRUE, FALSE | These invertebrates require more than one year to complete their life cycles; thus, they are exposed to all the human activities that influence the stream throughout one or more years. If the stream is dry part of the year or subject to flooding, these animals may disappear. Loss of long-lived taxa may also indicate an on-going problem that repeatedly interrupts their life cycles |
Last updated 2023-02-03


--- title: Plant Pathology Classifier keywords: fastai sidebar: home_sidebar summary: "A neural network classifies images of leaves as healthy or diseased!" description: "A neural network classifies images of leaves as healthy or diseased!" nb_path: "nbks/index.ipynb" ---
Recently, I've been learning about Fast.ai and PyTorch in my free time and wanted to apply my knowledge. I'm trying to learn how to win Kaggle competitions, so I decided to build a model for the completed Kaggle Plant Pathology Competition.
I built this model using Nbdev, which provides an literate programming environment as originally envisioned by Donald Knuth. This means the notebooks in the nbks
folder are the library's "source code". They get converted into regular python files, a full documentation site, and contain unit and functional test, all in one place.
pip install plant-pathology
from plant_pathology.pretrained_models import get_model
model = get_model("resnet18_2021-04-08")
prediction = model.predict_leaf("images/leaf.jpg")
prediction
❯ python -m plant_pathology.train -h
usage: train.py [-h] [--epochs EPOCHS] [--lr LR] [--frz FRZ] [--pre PRE [PRE ...]] [--re RE] [--bs BS] [--smooth] [--arch ARCH] [--dump] [--log] [--save] [--mixup MIXUP] [--tta] [--fp16] [--do_eval] [--val_fold VAL_FOLD] [--pseudo PSEUDO] [--export]
path
positional arguments:
path Path to data dir
optional arguments:
-h, --help show this help message and exit
--epochs EPOCHS Number of unfrozen epochs (default: 1)
--lr LR Initial learning rate (default: 0.0003)
--frz FRZ Number of frozen epochs (default: 1)
--pre PRE [PRE ...] Image presize (default: (682, 1024))
--re RE Image resize (default: 256)
--bs BS Batch size (default: 256)
--smooth Label smoothing? (default: False)
--arch ARCH Architecture (default: resnet18)
--dump Don't train, just print model (default: False)
--log Log w/ W&B (default: False)
--save Save model based on RocAuc (default: False)
--mixup MIXUP Mixup (0.4 is good) (default: 0.0)
--tta Test-time augmentation (default: False)
--fp16 Mixed-precision training (default: False)
--do_eval Evaluate model and save predictions CSV (default: False)
--val_fold VAL_FOLD Don't go cross-validation, just do 1 fold (or pass 9 to train on all data)
--pseudo PSEUDO Path to pseudo labels to train on
--export Export learner(s) to export_val_on_{fold}.pkl (default: False)


--- layout: post status: publish published: true title: ASP.NET WebMatrix author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 644 wordpress_url: http://winterdom.com/2002/06/aspnetwebmatrix date: '2002-06-17 19:16:19 +0000' date_gmt: '2002-06-17 19:16:19 +0000' categories: - ".NET" tags: [] comments: - id: 429 author: Serdar Kilic author_email<EMAIL_ADDRESS>author_url: http://skilic.com/ date: '2002-06-18 04:47:17 +0000' date_gmt: '2002-06-18 04:47:17 +0000' content: |
On my work PC (PIII 600, 192MB RAM) it ran pretty slow in comparison to VS.NET.
On my home pc which is more beefed up (AMD1.1 1gig RAM), it does run quite smoothly.
Funny that. On my crappy PII-400MHz with 320MB RAM, it ran much faster than VS.NET....
- id: 431 author: reret author_email<EMAIL_ADDRESS>author_url: http://www.sdsfsf.com date: '2002-07-30 22:42:48 +0000' date_gmt: '2002-07-30 22:42:48 +0000' content: |kasyhfdkjzshfdj
- id: 432 author: julius horoszynski author_email<EMAIL_ADDRESS>author_url: '' date: '2002-08-07 01:41:11 +0000' date_gmt: '2002-08-07 01:41:11 +0000' content: |i want to recieve a copy of web matrix
please
Can anybody help me to find a full-detailed documentation on ASP.NET WebMatrix.
I'd like to prepare a training on it in my office
Thanks in avance
As most of you already know by now, the ASP.NET WebMatrix (formerly known as Saturn) technology preview was released a couple days ago. I downloaded it today and I must say I'm quite impressed by it! It's small, it's very fast, and seems to have some pretty cool features.
As I basically use VS.NET simply as a souped-up editor (instead relying on NAnt as a build tool), this is one project I'm gonna keep an eye on! Congratulations to the ASP.NET team on a job well done!.
P.S.: The new name sucks, but ohh, well, it's not like there's much else to complain about ;)


Augmented CycleGANs for continuous scale normal-to-Lombard speaking style conversion
Shreyas Seshadri, Lauri Juvela, Paavo Alku and Okko Räsänen
Speaking style conversion from Normal to Lombard using Augmented CycleGANs and CycleGANs modelled with a CNN with gated units and resefual connections


HEAD
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=======
Better Call Dev — Tezos smart contract explorer by Baking Bad
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>>>>>>> bcd/master


class: inverse, middle, left, my-title-slide, title-slide # Statistical Consultancy
as Service Learning
in an Undergrad Statistics Course ### Stacy DeRuiter ### 3 August 2020
JSM 127, Understanding Statistics for Good: Integrating Service Learning into Statistics Education --- class: inverse middle center big-subsection # Motivation --- # Guidelines and Goals

 ## Real data, Statistical software, Problem-solving, Decision-making, Diversity, Communication --- # Service Learning at Calvin University

 <https://calvin.edu/offices-services/service-learning-center> --- # Statistics Without Borders <https://swb.wildapricot.org/>
 <video width="800" height="450" autoplay controls muted> <source src="images/swb_1_intro.mp4" type="video/mp4"> </video>
 ### Meeting today, 17:00: <https://swb.wildapricot.org/event-3889685> --- class: inverse middle center big-subsection # The Course --- # STAT 245: Advanced Data Analysis ## A.K.A.: Applied Regression; or, GLMs & Friends

 Thanks to: <https://cran.r-project.org/web/packages/ggiraphExtra> --- # Applied Regression; or, GLMs & Friends

 *images: mfviz.com/hierarchical-models, overstock.com, knowledgebase.aridhia.com/article/logistic-regression/* --- # Applied Regression; or, GLMs & Friends

 *images: mfviz.com/hierarchical-models, overstock.com, knowledgebase.aridhia.com/article/logistic-regression/* --- # Applied Regression; or, GLMs & Friends

 *images: mfviz.com/hierarchical-models, overstock.com, knowledgebase.aridhia.com/article/logistic-regression/* --- # Applied Regression; or, GLMs & Friends

 *images: mfviz.com/hierarchical-models, knowledgebase.aridhia.com/article/logistic-regression/* --- class: inverse middle center big-subsection # Service-Learning Projects --- ## Statistical Consultancy as Service Learning ![calvin partners](images/partners.png) --- # More (Global) Project partners

 # Join us<EMAIL_ADDRESS>*world icon: thenounproject.com* --- # Project Structure 1.0

 *icon: flaticon.com* --- # Project Structure 1.0: Oops.

 *icons: flaticon.com, vectorstock.com, icon-library.com* --- # Project Structure 2.0 --- class: inverse middle center big-subsection # ? + ? + ? --- # Project Structure 2.0: Three Key Questions

 - Annotated Bibliography - Tidy Dataset - Pretty Figures - Regression Model (rationale, results, figures, interpretation) - Interactive map or app
 *icons: thenounproject.com, measureevaluation.org, mfviz.com/hierarchical-models* --- class: inverse middle center big-subsection # Student Perspective --- # Student Time Investment (Fall 2019) ## Goal: 40 hours or 4-5 hours per week
  --- # Student Survey (n = 9, or 33%) ## Effects of Projects  --- # Students: Assessment of Group Work  --- # Students: Difficulty of Tasks   --- # Students: Working with Partners  --- class: inverse middle center big-subsection # Partner Perspective --- # Partner Survey (n = 7, 50%) ## Motivation: They do it for us!  --- # Partner Experience ## How Useful were Results?  --- # Some Reflections - Gratitude to partners - Incredible learning opportunity - Build connection and community - Weak business plan - Time constraints - Division of attention: normal course content, project - Practice learning *by doing* in community; not learn, *then* do - Experience "real" statistical process ---  --- # Similar Initiatives ## This session! ### Purdue University StatCom (Andrew Thomas) ### Cal State Monterey Bay (Alana Unfried) ### Jefferson Comm. College Ctr. Community Studies (Joel LaLone)

 ---  --- # Conclusions and Perspectives
 - Projects yield - motivation + struggle - success if viz and models *communicate clearly* - practice, internalize statistical process - connections across the curriculum - It’s not (too) hard to mastermind ###...BUT - Student engagement? - Value for partner organizations? - How can we turn **time limitation** into an asset? --- ## Thank You!  ### Contact Stacy DeRuiter ### Email<EMAIL_ADDRESS>### Twitter: @slderuiter Slides: <https://stacyderuiter.github.io/service-stats-jsm/slides.html>


All functions
|
|
are_neighbours()
|
check if H3 addresses are neighbours |
|
compact()
|
Compact H3 addresses |
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get_base_cell()
|
get the base cell of an H3 address |
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get_centerchild()
|
get central child H3 address |
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get_children()
|
get child H3 addresses |
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get_faces()
|
get the icosahedron faces of an H3 address |
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get_kring()
|
Get nearby H3 addresses |
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get_kring_list()
|
Get nearby H3 addresses separated by distance |
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get_local_h3()
|
Get H3 address from local i, j coordinates |
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get_local_ij()
|
Get local i, j coordinates |
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get_parent()
|
get parent H3 address |
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get_pentagons()
|
get the pentagon indices for an H3 resolution |
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get_res()
|
get the resolution of an H3 address |
|
get_ring()
|
Get a donut of H3 addresses |
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get_uddest()
|
Get destination from unidirectional edge |
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get_udedge()
|
Get a unidirectional edge |
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get_udedges()
|
Get all unidirectional edges |
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get_udends()
|
Get origin and destination of unidirectional edge |
|
get_udorigin()
|
Get origin from unidirectional edge |
|
grid_distance()
|
Grid distance between H3 addresses |
|
grid_path()
|
Path between H3 addresses |
|
h3jsr-package
|
h3jsr: Access Uber's H3 library |
|
h3_info_table
|
H3 index utility information table |
|
h3_to_line()
|
Convert H3 addresses to a line |
|
h3_to_point()
|
Convert H3 address to point location |
|
h3_to_polygon()
|
Get the boundary of an H3 address |
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is_pentagon()
|
check if H3 address is a pentagon |
|
is_rc3()
|
check if H3 address is a in a Class III resolution |
|
is_valid()
|
check H3 address |
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is_valid_edge()
|
Check H3 unidirectional edge address |
|
point_to_h3()
|
Convert point location to H3 address |
|
polyfill()
|
Get H3 addresses within a polygon |
|
res_area()
|
Get h3 address area |
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res_cendist()
|
Get average distance between H3 points |
|
res_count()
|
Get total H3 addresses |
|
res_length()
|
Get h3 address edge length |
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set_to_multipolygon()
|
Get geometry for a set of H3 addresses |
|
udedge_to_line()
|
Get the geometry of an H3 edge |
|
uncompact()
|
Uncompact H3 addresses |


Transparency data
Letters the Department for Education sent to successful free school applicants in the sixth round (wave 6) of the free schools programme.
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If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
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These letters inform successful free school applicants of the Department for Education’s decision to accept their application. It also invites them to submit a business case and plan for the proposed school.
Published: 16 July 2015


{% extends "skeleton.html" %} {% block navbar %}
"Sometimes all it takes is a tiny shift of perspective to see something familiar in a totally new light."
-Dan Brown


{% extends "base.html" %} {% block content %}
You are already logged in, view pinfiles here or log in with a different password below to view other servers' pinfiles.
{% endif %}Get an access URL by running !link in a Discord server. Please submit issues, recommendations, or new code to the GitHub page.
{##}


Sockets
Support for listening and connecting sockets via TCP will allow application developers to incorporate lower level network logic into their applications. Titanium.Network.Socket.TCP implements the Titanium.Stream interface and therefore relies on Titanium.Buffer.
Socket operations are generally asynchronous and therefore socket callbacks are core to how one operation flows into the next. For example, when connecting to a remote host, you would wait until the connected callback was invoked and fire off the read / write operations from inside that callback.
Once a socket is connected, IO operations leave the scope of Ti.Network.Socket and move into the realm of general Stream IO. Again, because sockets are naturally asynchronous, IO interactions with them should generally be conducted through the Ti.Stream module. Lastly, while sockets can be an extremely powerful part of your developer tool box, network programming is non-trivial and investigation into the basics of how BSD sockets work is suggested.
Usage Examples:


Road links a major boost to local economy 21 Nov 2019, 06:52 by Staff Reporter Home \ National \ Road links a major boost to local economy - New Era Live


From: The Desk of Mr. Davis Paul Manager, International Commercial Bank plc First -Light,Kaneshie Branch, Accra-Ghana. Attn: Dear, I hope this may not constitute sort of embarrassment to you, First I must solicit your confidence in this transaction, this is by virtue of its nature as being utterly confidential and top secret. But I am assuring you that all will be well at the end of the day. I have decided to contact you due to the urgency of this transaction, as we have been reliably informed of your discreetness and ability in transaction of this nature. let me start by introduce my self properly to you, My name is Davis Paul, I am the regional manager of the International Commercial Bank of Ghana first light kaneshie branch in the Western Region of Ghana.I came to know you in my Private Search for a Reliable and Reputable Person to handle this Confidential Transaction. I am married with 3lovely kids. It may interest you to hear that I am aman of PEACE and INTERGRITY; I only hope we can assist each other. I have packaged a financial transaction that will benefit both of us, as the regional manager of the International Commercial Bank; it is my duty to send in a financial report to my head office in the capital city Accra at the end of each year. On the course of the last year 2005 end of year report, I discovered that my branch in which I am the manager made seven hundred and twenty thousand dollars [720,000] which my head office are not aware of and will never be aware of. I have since place this fund on what we call SUSPENSE ACCOUNT without any beneficiary. As an officer of the bank I can not be directly connected to this money, so this informed my contacting you for us to work so that you can assist and receive this money into your bank account for us to SHARE. The sharing of the fund are thus: 30% for you 50% for i and the remaining 20% for any legal expenses we have acquired in this transaction. Note there are practically no risk involved this transaction is 100% risk free and legally binded, it will be bank to bank transfer, all I need from you is to stand claim as the original depositor of this fund who made the deposit with our branch so that my Head office can order the transfer to your designated bank account. If you accept this offer to work with me, If you find this proposal suitable for you do furnish me with the following information’s Your full Name and Contact Address, Your private telephone Number Your Age Your Occupation and Position. I will appreciate it very much. If this proposal is acceptable by you, do not make undue advantage of the trust we have bestowed in you. And how we can achieve it successfully. Best regards, Mr.Davis Paul