The idea is to keep the discussions
going after class ends.
The idea is to keep the discussions
going after class ends.
The idea is to keep the discussions
going after class ends.
The idea is to keep the discussions
going after class ends.
Artist John Sloan said, “Though a living cannot be made at art,
art makes life worth living.”
It is this fact which has driven millions of students to get arts and
humanities degrees, focussing their attention on painting, literature or history.
Courses Available: 500
Artist John Sloan said, “Though a living cannot be made at art,
art makes life worth living.”
It is this fact which has driven millions of students to get arts and
humanities degrees, focussing their attention on painting, literature or history.
COURSES AVAILABLE:
500
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The Education Report: A New Report Says Americans Less Happy.
The Education Report: A New Report Says Americans Less Happy.
I enjoyed the subtle cultural differences the most,
everyone I met were so friendly and helpful.I'll never forget
the friends i've made and the once-in-a-lifetime experienc
es i've had.The EDUHARVART is truly magnificent.
I enjoyed the subtle cultural differences the most,
everyone I met were so friendly and helpful.I'll never forget
the friends i've made and the once-in-a-lifetime experienc
es i've had.The EDUHARVART is truly magnificent.
I enjoyed the subtle cultural differences the most,
everyone I met were so friendly and helpful.I'll never forget
the friends i've made and the once-in-a-lifetime experienc
es i've had.The EDUHARVART is truly magnificent.
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---
title: The Case For “Niching” Yourself
date: '2015-05-13T12:21:31+05:30'
tags:
- misc
tumblr_url: https://sureshshanmugam.com/post/118847938012/the-case-for-niching-yourself
---
The Case For “Niching” Yourself
——————————
The Frictionless Field Guides are a freelancer’s best friend. Short, topical ebooks that are easy to read and full of powerful advice.
Check out the growing series today!


Topics
-
Business
-
Leadership
-
Life & Money
-
Stewardship
Speakers
-
Dave Ramsey
-
Chris Hogan
-
Chris Brown
-
Christy Wright
-
Rachel Cruze
-
Anthony O'neal
Events
-
Smart Conference
-
Smart Money
-
EntreLeadership Master Series
-
Retire Inspired
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Momentum
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Summit
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EntreLeadership Performance Series
-
EntreLeadership 1-Day


Software
I'm a big fan of open-source code and reproducible research. My code can be found on GitHub, and I list a few recent projects I've been working on below.
- nu_coincidence:
- A python package for investigating coincident source-neutrino detections with Monte Carlo simulations.
- icecube_tools:
- A python package for interacting with the publicly available data from the IceCube neutrino telescope, as well as running simulations and simple analyses.
- QCDNUM.jl:
- A julia wrapper for the QCDNUM software for fast QCD evolution and convolution.
- PartonDensty.jl:
- A julia package to extract parton distribution functions from accelerator data using Bayesian modeling.


We have a dataset on basketball teams. The dataset, Basketball.csv, can be downloaded here.
We will load in the dataset and do some analysis with it.
2.1.1 Paths to Data
Relative Path
The dataset is stored in a csv file. The folder structure for this file we are working inside and the data file is:
- main folder: Stat4Econ
- subfolder: data
- file: Basketball.csv
- subfolder: descriptive
- file: DataBasketball.ipynb (the jupyter notebook file)
- file: DataBasetball.html (the html version of the jupyter notebook file
- subfolder: data
overall this means: - the csv file’s location is: ‘/Stat4Econ/descriptive/data/Basketball.csv’ - the working R code file’s location is: ‘/Stat4Econ/descriptive/data/DataBasketball.ipynb’
Given this structure, to access the Basketball.csv dataset, we need to go one folder up from our current subfolder to the mainfolder, and then choose the data subfolder, and the Basketball.csv file in the subfolder.
Absolute Path
If these files are not in the same main folder but are in different locations on your computer, you can find the full path to the csv path and copy paste the path below in between the single quotes.
search on google to find out how to get the full path to file: - google search for find full path for file on mac + this might end up looking like: ‘/Users/fan/Downloads/Basketball.csv’ - google search for find full path for file on PC + this might end up looking like: ‘C:/Users/fan/Documents/Dropbox/Basketball.csv’
Using Relative path to load in data
We will load in the data using base R read.csv function.

# For Data Structures
library(tibble)
# For Data Manipulations
library(dplyr)
# For Reading/Loading Data
library(readr)
# For plotting
library(ggplot2)
# For Additional table output
# install.packages("knitr")
library(knitr)
Let’s load in the dataset we created from the in-class survey.
# We have several factor variables, we can set them as factor one by one
df_survey[['gender']] <- as.factor(df_survey[['gender']])
# But that is a little cumbersome, we can using lapply, a core function in r to do this for all factors
factor_col_names <- c('gender', 'major', 'commute', 'games.any', 'econ')
df_survey[factor_col_names] <- lapply(df_survey[factor_col_names], as.factor)
# Check Variable Types
str(df_survey)
## tibble [10 x 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ID : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ ROW : num [1:10] 3 4 4 4 2 1 2 3 3 4
## $ COL : num [1:10] 1 2 10 1 6 7 6 6 3 13
## $ gender : Factor w/ 2 levels "FEMALE","MALE": 2 1 2 2 1 2 2 2 1 1
## $ years.in.houston: num [1:10] 21 21 22 22 20 3 25 20 5 20
## $ major : Factor w/ 5 levels "CONSUMERSCIENCE",..: 2 3 2 2 2 5 2 1 4 2
## $ commute : Factor w/ 1 level "YES": 1 1 1 1 1 1 1 1 1 1
## $ games.attended : num [1:10] 0 2 0 14 0 0 25 2 0 0
## $ games.any : Factor w/ 2 levels "Has.Attended",..: 2 1 2 1 2 2 1 1 2 2
## $ econ : Factor w/ 2 levels "ECON","Not.Econ": 1 2 1 1 1 2 1 2 2 1
## - attr(*, "spec")=
## .. cols(
## .. ID = col_double(),
## .. ROW = col_double(),
## .. COL = col_double(),
## .. gender = col_character(),
## .. years.in.houston = col_double(),
## .. major = col_character(),
## .. commute = col_character(),
## .. games.attended = col_double(),
## .. games.any = col_character(),
## .. econ = col_character()
## .. )
2.2.1 Categorical/Discrete
Using categorical/discrete variables, such as Major, Gender, Recent arrival or not, etc, we can generate frequency tables.
- Nominal Variables: These are categorical variables like name, or address, or major.
- Ordinal Variables: These are categorical variables like age, grade in school, that could be ordered sequentially.
The frequency table shows in separate columns (or rows) the name for all the categories for that categorical variable, and show next to these categories the number of times that category appears in the dataset. This is called a frequency table because frequencies are shown for each category. So if we show a one-way frequency for majors, there would be four rows for the four unique majors from our survey of 10 students, and we would write down the number of students in each of the majors out of the 10 students.
Rather than showing frequencies, we can also show ratios or percentages by dividing the number of observations in each category by the total number of observations in the survey.
Graphically, we can show the results from these one-way frequency tables using bar charts and pie charts. The bar charts would have separate bars for each category, and the heights of the bars would show the number of observations in that category, or the fraction of individuals in the categorical. The relative heights of bars are the same whether we show the frequencies or the fractions/ratios. A pie chart might give a more direct visual sense of the fraction of observations in each category.
<IP_ADDRESS> A Frequency Table and a Bar Graph
<IP_ADDRESS> Using R Functions to Generate Multiple Frequency Tables
# There are Multiple Categorical Variables in the dataset
# Would be nice if we can generate frequency tables for all of them easily
# Let's create a list of that are categorical
categorical.nomial.list <- c('gender', 'major', 'games.any', 'commute', 'econ')
# Let's Write a Function, not that we need to, but let's do it
# We can give the function any name, here: dplyr.freq.table
dplyr.freq.table <- function(df, cate.var.str){
# A print Statement
print(sprintf("From Dataset: %s, Freq. Table for Variable: %s", deparse(substitute(df)), cate.var.str))
# Note below: !!sym(cate.var.str), because cate.var.str is string
freq.table <- df %>%
group_by(!!sym(cate.var.str)) %>%
summarise (frequency.count = n()) %>%
mutate(proportions = frequency.count / sum(frequency.count))
# Function returns
return(freq.table)
}
# Let's call test this function and generate our earlier table
dplyr.freq.table(df = df_survey, cate.var.str = 'gender')
## [1] "From Dataset: df_survey, Freq. Table for Variable: gender"
# Let's Now Use our function to generate Multiple Frequency Tables
# We will first use a explicit loop
for (ctr in seq_along(categorical.nomial.list)) {
freq.table <- dplyr.freq.table(df = df_survey, cate.var.str = categorical.nomial.list[ctr])
print(freq.table)
}
## [1] "From Dataset: df_survey, Freq. Table for Variable: gender"
## [1] "From Dataset: df_survey, Freq. Table for Variable: major"
## [1] "From Dataset: df_survey, Freq. Table for Variable: games.any"
## [1] "From Dataset: df_survey, Freq. Table for Variable: commute"
## [1] "From Dataset: df_survey, Freq. Table for Variable: econ"
Using Lapply to Generate Multiple Frequency Tables with a Single Line:
# We will now use lapply, the single line loop tool in R
# Below, we are plugging each element of the list one by one into the function
# dplyr.freq.table, the first argument of the function is the dataset name
# which is fixed as df = df_survey
lapply(categorical.nomial.list,
dplyr.freq.table,
df = df_survey)
## [1] "From Dataset: df_survey, Freq. Table for Variable: gender"
## [1] "From Dataset: df_survey, Freq. Table for Variable: major"
## [1] "From Dataset: df_survey, Freq. Table for Variable: games.any"
## [1] "From Dataset: df_survey, Freq. Table for Variable: commute"
## [1] "From Dataset: df_survey, Freq. Table for Variable: econ"
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
2.2.2 Continuous/Quantitative
Graphically, we can show a continuous variable using a histogram. This could be test scores, temperatures, years in Houston, etc. This involves first creating a categorical/discrete variable based on the continuous variable. Since the underlying continuous variable is ordered (low to high temperature unless major which is not ordered), the categorical/discrete variable we generate is an ordered categorical variable (majors could be called unordered categorical variable).
<IP_ADDRESS> Histogram with Lapply
To generate the histogram, we: 1. Make sure that all observations belongs to one category + no observations belonging to no categories + each observation only belongs to one category 2. Each category is equidistance along the continuous variable’s original scale. 3. Then we create a bar graph where each bar is a category, and the height of the bar represents the number of observations within that category.
# We will write a histogram function
ggplot.histogram <- function(df, cts.var.str){
# Figure Size
options(repr.plot.width = 4, repr.plot.height = 3)
# Figure Title
title <- sprintf("Histogram for %s in %s", cts.var.str, deparse(substitute(df)))
# We have in our 10 student survey only 10 observations
# We can still generate a histogram for our continuous variables
# Will only use three bins
histogram.3bins <- ggplot(df_survey, aes(x=!!sym(cts.var.str))) +
geom_histogram(bins=3) +
labs(title = paste0(title),
caption = 'In Class Survey of 10 Students\n3 bins') +
theme_bw()
# obtain the data in the plot
plot_data <- ggplot_build(histogram.3bins)
# the dataframe below contains all the information for the histogram
# bins and number of observations in each bins
plot_dataframe <- plot_data$data[[1]]
# return outputs
return(list(gghist=histogram.3bins, hist.df=plot_dataframe))
}
# Now the list of continuous Variables and calling the function with lapply
cts.list <- c('years.in.houston', 'games.attended')
# lapply
results <- lapply(cts.list,
ggplot.histogram,
df = df_survey)
# Show results
for (ctr in seq_along(cts.list)){
print(results[ctr])
}
## [[1]]
## [[1]]$gghist
##
## [[1]]$hist.df
## y count x xmin xmax density ncount ndensity flipped_aes PANEL group ymin ymax colour fill size linetype alpha
## 1 2 2 0 -5.5 5.5 0.01818182 0.25 0.25 FALSE 1 -1 0 2 NA grey35 0.5 1 NA
## 2 0 0 11 5.5 16.5 0.00000000 0.00 0.00 FALSE 1 -1 0 0 NA grey35 0.5 1 NA
## 3 8 8 22 16.5 27.5 0.07272727 1.00 1.00 FALSE 1 -1 0 8 NA grey35 0.5 1 NA
##
##
## [[1]]
## [[1]]$gghist
##
## [[1]]$hist.df
## y count x xmin xmax density ncount ndensity flipped_aes PANEL group ymin ymax colour fill size linetype alpha
## 1 8 8 0.0 -6.25 6.25 0.064 1.000 1.000 FALSE 1 -1 0 8 NA grey35 0.5 1 NA
## 2 1 1 12.5 6.25 18.75 0.008 0.125 0.125 FALSE 1 -1 0 1 NA grey35 0.5 1 NA
## 3 1 1 25.0 18.75 31.25 0.008 0.125 0.125 FALSE 1 -1 0 1 NA grey35 0.5 1 NA
2.3 Multiple Variables Graphs and Tables
Go back to fan’s REconTools Package, R Code Examples Repository (bookdown site), or Intro Stats with R Repository (bookdown site).
# For Data Manipulations
library(tidyverse)
# For Additional table output
# install.packages("knitr")
library(knitr)
Let’s load in the dataset we created from the in-class survey.
# We have several factor variables, we can set them as factor one by one
df_survey[['gender']] <- as.factor(df_survey[['gender']])
# But that is a little cumbersome, we can using lapply, a core function in r to do this for all factors
factor_col_names <- c('gender', 'major', 'commute', 'games.any', 'econ')
df_survey[factor_col_names] <- lapply(df_survey[factor_col_names], as.factor)
# Check Variable Types
str(df_survey)
## tibble [10 x 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ID : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ ROW : num [1:10] 3 4 4 4 2 1 2 3 3 4
## $ COL : num [1:10] 1 2 10 1 6 7 6 6 3 13
## $ gender : Factor w/ 2 levels "FEMALE","MALE": 2 1 2 2 1 2 2 2 1 1
## $ years.in.houston: num [1:10] 21 21 22 22 20 3 25 20 5 20
## $ major : Factor w/ 5 levels "CONSUMERSCIENCE",..: 2 3 2 2 2 5 2 1 4 2
## $ commute : Factor w/ 1 level "YES": 1 1 1 1 1 1 1 1 1 1
## $ games.attended : num [1:10] 0 2 0 14 0 0 25 2 0 0
## $ games.any : Factor w/ 2 levels "Has.Attended",..: 2 1 2 1 2 2 1 1 2 2
## $ econ : Factor w/ 2 levels "ECON","Not.Econ": 1 2 1 1 1 2 1 2 2 1
## - attr(*, "spec")=
## .. cols(
## .. ID = col_double(),
## .. ROW = col_double(),
## .. COL = col_double(),
## .. gender = col_character(),
## .. years.in.houston = col_double(),
## .. major = col_character(),
## .. commute = col_character(),
## .. games.attended = col_double(),
## .. games.any = col_character(),
## .. econ = col_character()
## .. )
2.3.1 Two Continuous Variables
With two continuous/quantitative variables, we can generate a scatter plot. Crucially, each point of the scatter plot represents one individual, the location of that point indicates the x and y values of that individual. The x and y values could be the individual’s test score and hours studied for example.
# We can draw a scatter plot for two continuous variables
# Control Graph Size
options(repr.plot.width = 4, repr.plot.height = 4)
# Draw Scatter Plot
# 1. specify x and y
# 2. label each individual by their ID, add letter I in front of value
# 3. add in trend line
scatter <- ggplot(df_survey, aes(x=games.attended, y=years.in.houston)) +
geom_point(size=1) +
geom_text(aes(label=paste0('I', ID)), size=3, hjust=-.2, vjust=-.2) +
geom_smooth(method=lm) + # Trend line
labs(title = paste0('Scatter Plot of Two Continuous/Quantitative Variables'
,'\nIn Class Survey of 10 Students'),
x = 'Games Attended at the University',
y = 'Years Spent in the City of Houston',
caption = 'In Class Survey') +
theme_bw()
print(scatter)
2.3.2 Two Categorical Variables
With two discrete/categorical variables, we can generate two-way frequency tables. This is very similar to what we did for one discrete variable, except now we have columns and rows, representing the categories of the two variables. The two variables could be gender and majors, we would write in each table cell the number of students who are male and econ majors, male and bio majors in for example the first column, and repeat this for girls in the second column.
# We can tabulate Frequencies based on two categorical variables
df_survey %>%
group_by(gender, econ) %>%
summarize(freq = n()) %>%
spread(gender, freq)
2.3.3 Continuous and Categorical Variable
<IP_ADDRESS> Average Across Groups
We can look at the average game attendance by female and male students in our sample, using a bar plot, where the height of the bars now represent the average of the games.attended variable for each group.
# We can first find the group averages
df_gender_avg_games <- df_survey %>%
group_by(gender) %>%
summarise (avg.games.attended = mean(games.attended))
df_gender_avg_games
# We can graph based on df_gender_avg_games
# Sizing the Figure Here
options(repr.plot.width = 2, repr.plot.height = 2)
# Plot, stat = identity means to plot the value in avg.games.attended for each gender
group.means <- ggplot(df_gender_avg_games) +
geom_bar(aes(x=gender, y=avg.games.attended), stat = 'identity') +
theme_bw()
print(group.means)
# But it is a little cumbersome to do this in two steps, we can do it in one step
# Sizing the Figure Here
options(repr.plot.width = 2, repr.plot.height = 2)
# Plot directly from df_survey, summary over x for y
# The result looks the same
group.means.joint <- ggplot(df_survey) +
geom_bar(aes(x=gender, y=games.attended), stat = "summary", fun.y = "mean") +
theme_bw()
print(group.means.joint)
<IP_ADDRESS> Average Across Two Groups: Gender and Majors
What is the average game attendance for male and female, econ and non-econ majors? We have 2 female econ majors, 2 female non-econ majors, 4 male econ majors and 2 male non-econ majors, and their average game attendances are: 0, 1, 9.75 and 1 games.
# We can calculate the statistics as a table, and also show obs in each group
df_survey %>%
group_by(gender, econ ) %>%
summarise (avg.games.attended = mean(games.attended), N.count = n())
# Let's Show these Visually
options(repr.plot.width = 4, repr.plot.height = 2)
# Plot directly from df_survey
# Using fill for econ, this means econ or not will fill up with different colors
# Still caculate average
# Postion "dodge" means that econ and non-econ wil be shown next to each other
# By default position is to stack different fill groups on top of each other.
two.group.means <- ggplot(df_survey) +
geom_bar(aes(x=gender, y=games.attended, fill=econ),
stat = "summary", fun.y = "mean", position = "dodge") +
theme_bw()
print(two.group.means)


зад. 03. Write a script that finds the maximal sequence of equal elements in an array.
For tests 2, 1, 1, 2, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 2, 1, 1, 2, 3, 3, 2, 2, 2, 1


© NZ Wella YPS 2008
© NZ Wella YPS 2008
Danny Pato is a Director of D&M Hair Design in Ponsonby, and has worked with a number of leading salons in the Auckland region throughout his career. With an accomplished background including winning numerous regional and national awards, Danny also thrives on doing editorial and fashion work for magazines and the fashion industry. A highlight from 2007 for Danny, was when he and his team from D&M worked backstage on the Huffer Show at ANZFW. Danny was thrilled to be a part of the show and enjoyed seeing his team take their creativity to another level. Danny encourages younger staff to look at a variety of creative industries to ensure they continue to grow creatively and personally.
Shar from Sutherland Todd Hairdressing in Christchurch has a record number of achievements under her belt. She has twice been a finalist for the Sydney Hair Expo New Zealand Hairdresser of the Year as well as winning numerous competition awards both regionally and nationally. Shar has a reputation for training to the highest level which has seen her lead many winning YPS stylists over the years. As a strong supporter of YPS and the opportunities the event provides the youth of our industry, Shar believes passion and creativity unleashed is potential fulfilled.
Karl Radel began his hairdressing career 18 years ago, today he and his wife Danelle own Klone Hair in Dunedin which celebrates nine years in business. Karl has a passion for competition work and has achieved high levels of success, winning over 50 places in national hairdressing competitions including Supreme Hairdresser of the Year. He has passed this winning competition experience through to his team members, having trained two previous Wella YPS national winners. Karl is also an accomplished NZARH Judge having judged throughout the country for over 10 years. He is extremely passionate about the hairdressing industry as it is continually evolving and feels it is both inspiring and challenging.
Bronwyn Illingworth began her career in Dunedin before moving to the UK where she worked with TONI&GUY for 10 years, becoming an International Artistic Director and Show Director. With an enthusiasm and passion for hairdressing that is relentless – Bronwyn has had an incredible career - some of her highlights being London fashion week, Educational videos for TONI&GUY International, Fashion Rocks in Monaco, and an expert for the TV series “Ten Years Younger in Ten Days”. Her work has been published in and on the covers of fashion and hair magazines nationally and internationally but Bronwyn’s true passion is educational show work that has taken her all over the world and in front of audiences up to 6,000 – she believes in giving back to the industry. Bronwyn is now Managing Educational Director for TONI&GUY New Zealand.
Jaimee Smith is the Director of Zaibatsu Hairart, a successful Dunedin based business that she began from a young age. Jaimee has a strong passion for hairdressing, especially competition judging as it provides the opportunity to meet a number of talented and creative people from the industry. Jaimee has had a number of exciting highlights throughout her career; she has won numerous regional and national hair awards and has had a variety of magazine features. Part of Jaimee’s enthusiasm for the hairdressing industry is due to the inspirational and motivated people she has had the pleasure in working alongside throughout her career, people such as Michelle Ogden and Shar Sutherland-Todd from earlier years, to the current team at Zaibatsu, who share her own enthusiasm and excitement.
Michael Beel is the Creative Director of Buoy Hairdressing in Wellington and has been hairdressing since 1997.Since that time he has grown into one of New Zealand's leading educators and has also taught in Asia and India with Wella Professionals. Michael has also won regional and national events, been a finalist for NZ Hairdresser of the year both here in New Zealand and in Australia at Hair Expo. His work is based on merging creativity with the balance of precision cutting. Michael believes in celebrating womanhood and creative hairdressing that embellishes the essence of femininity.


The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th.
The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.
The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.
What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness?
How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports?
Did any country experience a significant increase or decrease in happiness?
What is Dystopia?
Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.
What are the residuals?
The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.
What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?
The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer.
If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.


CPU chip in the computer runs "machine" code
-Billions of machine "operations" per second
Stupid
-Machine operations are simple and mechanical
-e.g. a = b + c
-Nothing like "insight" or "thinking"
-Insight= human programmer, CPU just runs their code
Programming
Programmer write the code (e.g. Python)
Python code is translated to machine code
CPU runs that
Code has weirdly inflexible syntax (see below)
This reflects the CPU's limitations .. inflexible!
e.g. grocery list
Key Preface: Thinking About Syntax Errors
Syntax -- code is structured for the computer
Very common error -- type in code, with slight syntax problem
Professional programmers make that sort of "error" all the time
Fortunately, very easy to fix ... don't worry about it
Not a reflection of some flaw in the programmer
Just the nature of typing ideas into the mechanical computer language
Beginners can be derailed by syntax step, thinking they are making some big error
Exercise to inoculate you all: a bunch of typical syntax errors + fixing them
Fixing these little errors is a small, normal step
Binky Introduction
"Binky" code toy we'll use for a couple days
Binky is a robot in a 2d world
Binky has very simple capabilities
Introduce a few code ideas this way
Binky Verbs
Things the robot can do:
move()
left()
right()
paint('blue')
# 'red' and 'green' work too
Binky Code Example1
def fun(binky):
binky.move()
# binky just moved forward one square
binky.paint('blue')
Example1 Points
"def" - defines a function
-named "fun" in this case
function run the lines inside from top to bottom
"binky" refers to a binky object (noun)
"binky.move()"
-run the "move()" function of binky
-noun.verb style
# - a comment, computer ignores the rest of the line
This all looks weird, but is essentially simple
Try Some Parlante.org Exercises... (first time used ever)
Run Code -- Get Errors -- Fix!
Not grading these, just for demos and to get your feet wet


Pat’s Salmon Cookies
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- seattle Address:901 12th Ave, Seattle, WA 98122, United States / call us:+1122334455
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- DubaiAddress:Academic City Emirates Road - Exit 49 - Dubai - United Arab Emirates / call us:+1122334455
- ParisAddress:5 Boulevard de la Tour-Maubourg, 75007 Paris, France / call us:+1122334455
- LimaAddress:Av. Javier Prado Este 4600, Santiago de Surco 15023, Peru / call us:+1122334455
Hot sales


Training: Friday, January 29 | Sprint: Sunday, January 31
Register Find Out MoreNo matter your experience with Drupal, DrupalCamp NJ will have opportunities for you to learn and engage with others. The strength of the Drupal project comes from its community and with the release of Drupal 8 there will be so much to hear and talk about.
Register Today!Learn from a variety of sessions and network with other Drupal users and contributors.
Give back to the community by joining the planning committee or helping out on the camp day.
Share your knowledge and experience with the larger Drupal community.
Help 2016 be the best camp yet, through a Corporate, Agency, or Personal contribution.
Get in touch with us if you are interested in sponsoring, speaking, or volunteering.


Planning my weekend...
Posted: Wed Jan 23, 2008 9:30 pm
Hey guys!
I'm planning my weekend and I'm trying to figure out how many people would use the world editor guide if I wrote one. Post here to let me know!
Karl
I'm planning my weekend and I'm trying to figure out how many people would use the world editor guide if I wrote one. Post here to let me know!
Karl


Binout¶
There is a YOUTUBE TUTORIAL for this class available:
-
class
lasso.dyna.Binout.
Binout
(filepath)¶ Constructor for a binout
- Parameters
- filepathstr
path to the binout or pattern
Notes
The class loads the file given in the filepath. By giving a search pattern such as: “binout*”, all files with that pattern will be loaded.
Examples
>>> # reads a single binout >>> binout = Binout("path/to/binout0000") >>> binout.filelist ['path/to/binout0000']
>>> # reads multiple files >>> binout = Binout("path/to/binout*") >>> binout.filelist ['path/to/binout0000','path/to/binout0001']
Methods
read
(…)Read all data from Binout (top to low level)
save_hdf5
(self, filepath[, compression])Save a binout as HDF5
-
__init__
(self, filepath)¶ Constructor for a binout
- Parameters
- filepathstr
path to the binout or pattern
Notes
The class loads the file given in the filepath. By giving a search pattern such as: “binout*”, all files with that pattern will be loaded.
Examples
>>> # reads a single binout >>> binout = Binout("path/to/binout0000") >>> binout.filelist ['path/to/binout0000']
>>> # reads multiple files >>> binout = Binout("path/to/binout*") >>> binout.filelist ['path/to/binout0000','path/to/binout0001']
-
read
(path) Read all data from Binout (top to low level)¶ Read all data from Binout (top to low level)
- Parameters
- pathlist(str) or str
internal path in the folder structure of the binout
- Returns
- retlist(str), np.ndarray or str
list of subdata within the folder or data itself (array or string)
Notes
This function is used to read any data from the binout. It has been used to make the access to the data more comfortable. The return type depends on the given path:
binout.read() : list(str) names of directories (in binout)
binout.read(dir) : list(str) names of variables or subdirs
binout.read(dir1, …, variable) : np.array(float/int) data
If you have multiple outputs with different ids (e.g. in nodout for multiple nodes) then don’t forget to read the ids array for identification or id-labels.
Examples
>>> from qd.cae.dyna import Binout >>> binout = Binout("test/binout") >>> # get top dirs >>> binout.read() ['swforc'] >>> binout.read("swforc") ['title', 'failure', 'ids', 'failure_time', ...] >>> binout.read("swforc","shear").shape (321L, 26L) >>> binout.read("swforc","ids").shape (26L,) >>> binout.read("swforc","ids") array([52890, 52891, 52892, ...]) >>> # read a string value >>> binout.read("swforc","date") '11/05/2013'
-
save_hdf5
(self, filepath, compression='gzip')¶ Save a binout as HDF5
- Parameters
- filepathstr
path where the HDF5 shall be saved
- compressionstr
compression technique (see h5py docs)
Examples
>>> binout = Binout("path/to/binout") >>> binout.save_hdf5("path/to/binout.h5")


Cyclic Performance of BBR and Cubic
In the paper “Cycle and Divergence of Performance on TCP BBR” by Miyazawa and Sasaki, the authors show that when BBR and TCP are competing on the same bottleneck, their ‘share’ of the bandwidth will not be consistent. Rather, the dominant flows will cycle between cubic and BBR, where first BBR will dominate the flow for 10 seconds as cubic backs off, and then cubic will dominate the flow as is steals bandwidth during BBR’s rtt probe phase. We aim to confirm this in our panaderia
environment. This cyclic behavior shown in this paper is depicted here:
Single flow
A single bbr flow should display a very regular cycle. Every 8 rtt’s (round trip times) bbr should probe the bandwidth by increasing its sending rate, drain any queue that is built, and then return to a steady state. Every 10 seconds, the flow should completely drain the queue by throttling its speed, and then return to a normal sending rate.
Multiple Flow Convergence
Given multiple bbr flows competing at the same bottleneck, all of the flows should sync. BBR has a mechanism to drain the queue and check for a lowest round trip time after some amount of seconds since the last time the round trip time changed. With multiple flows, they all should see the change in round trip time at the same time, so they should begin to drain the queue at the same time.
The goodput (throughput seen by the receiver) should looks something like this result published by the BBR team in this paper:
And our results, which look roughly similar:
See below for the configuration details and analysis


{{ page.title }}
{{ page.intro }}
{% include head.html %}
{% include banner.html %}{{ page.intro }}
Most companies improved scores in at least one area, and many made significant improvements in the past year. Yet companies still fell short:
Privacy: Companies still do not adequately inform people about all the ways user information is collected and shared, with whom, and why.
Expression: As companies struggle to curb extremism, hate speech, and disinformation, most lacked transparency about how they police content or respond to government demands.
Governance: Most companies failed to anticipate and manage privacy and expression risks caused by their business models, and by the deployment of new technologies.
The 2019 RDR Index ranked 24 companies on 35 indicators across three categories evaluating their disclosure of commitments, policies, and practices affecting freedom of expression and privacy. The RDR Index evaluates policies of the parent company, operating company, and those of selected services (depending on company structure).
Read more about the methodology, research process, and how we score each company.
Which companies improved the most? To read how each company fared in comparison to the 2018 RDR Index go to the "Compare" page.


Utilities are single-purpose styles that provide the backbone for layout. Once these are set, they should be considered immutable and never change or be extended. Each utility is designed to do one thing well, while making its usage simple and obvious with short, humanized naming conventions.
<% if (obj.page.sections) { %> <% page.sections.forEach(function(section) { %>


Index
Reports
{{ report.title }}
by
{{ report.user }}
at
{{ report.timestamp }}
{{ report.description }}
{{ report.detailed_description }}
{% if report.private %}
This report is private
{% else %}
This report is public
{% endif %}
Shared with users: {% for m in matchesU %}
'{{ m }}'
{% endfor %}
Shared with groups: {% for m in matches %}
'{{ m }}'
{% endfor %}
Files:
{% if files %}
{% for upload in files %}
{% if upload.file %}
{{ upload.file.name }}
{% if upload.encrypted %}
(Encrypted)
{% else %}
(Not Encrypted)
{% endif %}
{% endif %}
{% endfor %}
{% endif %}
Edit Report


Karnataka Bypoll: H Vishwanath to file nomination today
by Ranjith KandyaFormer MLA A H Vishwanath on Monday will file his nomination papers as BJP candidate for the upcoming Karnataka bypolls to Hunsur Assembly segment.
November 18 is the last day to file the papers. Vishwanath, who elected on JD(S) ticket in 2018, resigned and joined BJP, recently.
A host of BJP leaders, including Minister B Sriramulu, Chamarajanagar MP V Srinivas Prasad, Mysuru-Kodagu MP Pratap Sinha, BJP leader K shivara are likely to be present during the nomination.
Before filing the nomination, Vishwanath will offer puja at various temple in Hunsur.


TBuffer class
procedure AssignTo(Destin: TObject); virtual;
The AssignTo method can assign the buffer to a TMemoryStream.


Oops. This visualization isn't mobile ready just yet, but it looks splendid on a desktop browser.
Go back to the homepage


public/stories/tcbdd/storiesList.story
Narrative:
In order to open existing and create new stories
As a application user
I want to be able to browse files and directories
Lifecycle:
Scenario: Can open dialog
Given Web home page is opened
When Web user clicks the element browseStories
Then Web element modalHeader should have text Browse Stories
Scenario: Can navigate through directories
Given Web user is in the Browse Stories dialog (PENDING)
When Web user clicks the element tcbdd (NOT PERFORMED)
Then Web element dirPath should have text tcbdd/ (NOT PERFORMED)
@Given("Web user is in the Browse Stories dialog")
@Pending
public void givenWebUserIsInTheBrowseStoriesDialog() {
// PENDING
}


Queries in peewee are constructed one piece at a time.
The “pieces” of a peewee query are generally representative of clauses you might find in a SQL query. Most methods are chainable, so you build your query up one clause at a time. This way, rather complex queries are possible.
Here is a barebones select query:
>>> user_q = User.select() # <-- query is not executed
>>> user_q
<peewee.SelectQuery object at 0x7f6b0810c610>
>>> [u.username for u in user_q] # <-- query is evaluated here
[u'admin', u'staff', u'editor']
We can build up the query by adding some clauses to it:
>>> user_q = user_q.where(username__in=['admin', 'editor'])
>>> user_q = user_q.order_by(('username', 'desc'))
>>> [u.username for u in user_q] # <-- query is re-evaluated here
[u'editor', u'admin']
If you are already familiar with the Django ORM, you can construct SelectQuery instances using the familiar “double-underscore” syntax to generate the proper JOINs and WHERE clauses.
You can use python operators to construct queries. This is possible by overloading operators on field instances.
Examples shown are “default”, “django” and “python operators”.
User.select().where(active=True)
User.filter(active=True)
User.select().where(User.active==True)
User.select().where(Q(is_staff=True) | Q(is_superuser=True))
User.filter(Q(is_staff=True) | Q(is_superuser=True))
User.select().where((User.is_staff==True) | (User.is_superuser==True))
Tweet.select().join(User).where(username='charlie')
Tweet.filter(user__username='charlie')
Tweet.select().join(User).where(User.username=='charlie')
Tweet.select().join(User).where(
Q(is_staff=True) | Q(is_superuser=True)
)
Tweet.filter(Q(user__is_staff=True) | Q(user__is_superuser=True))
Tweet.select().join(User).where(
(User.is_staff==True) | (User.is_superuser==True)
)
All queries except InsertQuery support the where() method. If you are familiar with Django’s ORM, it is analagous to the filter() method.
>>> User.select().where(is_staff=True).sql()
('SELECT * FROM user WHERE is_staff = ?', [1])
Note
User.select() is equivalent to SelectQuery(User).
The where() method acts on the Model that is the current “query context”. This is either:
Here is an example using JOINs:
>>> User.select().where(is_staff=True).join(Blog).where(status=LIVE)
This query grabs all staff users who have a blog that is “LIVE”. This does the opposite, grabs all the blogs that are live whose author is a staffer:
>>> Blog.select().where(status=LIVE).join(User).where(is_staff=True)
Note
to join() from one model to another there must be a ForeignKeyField linking the two.
Another way to write the above query would be:
>>> Blog.select().where(
... status=LIVE,
... user__in=User.select().where(is_staff=True)
... )
The above bears a little bit of explanation. First off the SQL generated will not perform any explicit JOIN - it will rather use a subquery in the WHERE clause:
# using subqueries
SELECT * FROM blog
WHERE (
status = ? AND
user_id IN (
SELECT t1.id FROM user AS t1 WHERE t1.is_staff = ?
)
)
And here it is using joins:
# using joins
SELECT t1.* FROM blog AS t1
INNER JOIN user AS t2
ON t1.user_id = t2.id
WHERE
t1.status = ? AND
t2.is_staff = ?
The other bit that’s unique about the query is that it specifies "user__in". Users familiar with Django will recognize this syntax - lookups other than “=” are signified by a double-underscore followed by the lookup type. The following lookup types are available in peewee:
If you are querying using python operator overloading, different comparisons are expressed using python operators. The following lookups are supported:
As you may have noticed, all the examples up to now have shown queries that combine multiple clauses with “AND”. Taking another page from Django’s ORM, peewee allows the creation of arbitrarily complex queries using a special notation called Q objects.
>>> sq = User.select().where(Q(is_staff=True) | Q(is_superuser=True))
>>> print sq.sql()[0]
SELECT * FROM user WHERE (is_staff = ? OR is_superuser = ?)
Q objects can be combined using the bitwise “or” and “and” operators. In order to negate a Q object, use the bitwise “invert” operator:
>>> staff_users = User.select().where(is_staff=True)
>>> Blog.select().where(~Q(user__in=staff_users))
This query generates the following SQL:
SELECT * FROM blog
WHERE
NOT user_id IN (
SELECT t1.id FROM user AS t1 WHERE t1.is_staff = ?
)
Rather complex lookups are possible:
>>> sq = User.select().where(
... (Q(is_staff=True) | Q(is_superuser=True)) &
... (Q(join_date__gte=datetime(2009, 1, 1)) | Q(join_date__lt=datetime(2005, 1 1)))
... )
>>> print sq.sql()[0] # cleaned up
SELECT * FROM user
WHERE (
(is_staff = ? OR is_superuser = ?) AND
(join_date >= ? OR join_date < ?)
)
This query selects all staff or super users who joined after 2009 or before 2005.
Note
If you need more power, check out RawQuery
Suppose you have a model that looks like the following:
class WorkerProfiles(Model):
salary = IntegerField()
desired = IntegerField()
What if we want to query WorkerProfiles to find all the rows where “salary” is greater than “desired” (maybe you want to find out who may be looking for a raise)?
To solve this problem, peewee borrows the notion of F objects from the django orm. An F object allows you to query against arbitrary data present in another column:
WorkerProfile.select().where(salary__gt=F('desired'))
That’s it. If the other column exists on a model that is accessed via a JOIN, you will need to specify that model as the second argument to the F object. Let’s supposed that the “desired” salary exists on a separate model:
WorkerProfile.select().join(Desired).where(desired_salary__lt=F('salary', WorkerProfile))
The F object also works for updating data. Suppose you cache counts of tweets for every user in a special table to avoid an expensive COUNT() query. You want to update the cache table every time a user tweets, but do so atomically:
cache_row = CacheCount.get(user=some_user)
update_query = cache_row.update(tweet_count=F('tweet_count') + 1)
update_query.execute()
Suppose you have some blogs and want to get a list of them along with the count of entries in each. First I will show you the shortcut:
query = Blog.select().annotate(Entry)
This is equivalent to the following:
query = Blog.select({
Blog: ['*'],
Entry: [Count('id')],
}).group_by(Blog).join(Entry)
The resulting query will return Blog objects with all their normal attributes plus an additional attribute ‘count’ which will contain the number of entries. By default it uses an inner join if the foreign key is not nullable, which means blogs without entries won’t appear in the list. To remedy this, manually specify the type of join to include blogs with 0 entries:
query = Blog.select().join(Entry, 'left outer').annotate(Entry)
You can also specify a custom aggregator:
query = Blog.select().annotate(Entry, peewee.Max('pub_date', 'max_pub_date'))
Conversely, sometimes you want to perform an aggregate query that returns a scalar value, like the “max id”. Queries like this can be executed by using the aggregate() method:
max_id = Blog.select().aggregate(Max('id'))
If you’ve been reading in order, you will have already seen the Q and F objects. The R object is the final query helper and its purpose is to allow you to express arbitrary expressions as part of your structured query without having to result to using a RawQuery.
Selecting users whose username begins with “a”:
# select the users' id, username and the first letter of their username, lower-cased
query = User.select(['id', 'username', R('LOWER(SUBSTR(username, 1, 1))', 'first_letter')])
# now filter this list to include only users whose username begins with "a"
a_users = query.where(R('first_letter=%s', 'a'))
>>> for user in a_users:
... print user.first_letter, user.username
a alpha
A Alton
This same functionality could be easily exposed as part of the where clause, the only difference being that the first letter is not selected and therefore not an attribute of the model instance:
a_users = User.filter(R('LOWER(SUBSTR(username, 1, 1)) = %s', 'a'))
We can query for multiple values using R objects, for example selecting users whose usernames begin with a range of letters “b” through “d”:
letters = ('b', 'c', 'd')
bcd_users = User.filter(R('LOWER(SUBSTR(username, 1, 1)) IN (%s, %s, %s)', *letters))
We can write subqueries as part of a SelectQuery, for example counting the number of entries on a blog:
entry_query = R('(SELECT COUNT(*) FROM entry WHERE entry.blog_id=blog.id)', 'entry_count')
blogs = Blog.select(['id', 'title', entry_query]).order_by(('entry_count', 'desc'))
for blog in blogs:
print blog.title, blog.entry_count
It is also possible to use subqueries as part of a where clause, for example finding blogs that have no entries:
no_entry_query = R('NOT EXISTS (SELECT * FROM entry WHERE entry.blog_id=blog.id)')
blogs = Blog.filter(no_entry_query)
for blog in blogs:
print blog.title, ' has no entries'
Simple select queries can get a performance boost (especially when iterating over large result sets) by calling naive(). This method simply patches all attributes directly from the cursor onto the model. For simple queries this should have no noticeable impact. The main difference is when multiple tables are queried, as in the previous example:
# above example
entries = Entry.select({
Entry: ['*'],
Blog: ['*'],
}).order_by(('pub_date', 'desc')).join(Blog)
for entry in entries.limit(10):
print '%s, posted on %s' % (entry.title, entry.blog.title)
And here is how you would do the same if using a naive query:
# very similar query to the above -- main difference is we're
# aliasing the blog title to "blog_title"
entries = Entry.select({
Entry: ['*'],
Blog: [('title', 'blog_title')],
}).order_by(('pub_date', 'desc')).join(Blog)
entries = entries.naive()
# now instead of calling "entry.blog.title" the blog's title
# is exposed directly on the entry model as "blog_title" and
# no blog instance is created
for entry in entries.limit(10):
print '%s, posted on %s' % (entry.title, entry.blog_title)
In order to execute a query, it is always necessary to call the execute() method.
To get a better idea of how querying works let’s look at some example queries and their return values:
>>> dq = User.delete().where(active=False) # <-- returns a DeleteQuery
>>> dq
<peewee.DeleteQuery object at 0x7fc866ada4d0>
>>> dq.execute() # <-- executes the query and returns number of rows deleted
3
>>> uq = User.update(active=True).where(id__gt=3) # <-- returns an UpdateQuery
>>> uq
<peewee.UpdateQuery object at 0x7fc865beff50>
>>> uq.execute() # <-- executes the query and returns number of rows updated
2
>>> iq = User.insert(username='new user') # <-- returns an InsertQuery
>>> iq
<peewee.InsertQuery object at 0x7fc865beff10>
>>> iq.execute() # <-- executes query and returns the new row's PK
3
>>> sq = User.select().where(active=True) # <-- returns a SelectQuery
>>> sq
<peewee.SelectQuery object at 0x7fc865b7a510>
>>> qr = sq.execute() # <-- executes query and returns a QueryResultWrapper
>>> qr
<peewee.QueryResultWrapper object at 0x7fc865b7a6d0>
>>> [u.id for u in qr]
[1, 2, 3, 4, 7, 8]
>>> [u.id for u in qr] # <-- re-iterating over qr does not re-execute query
[1, 2, 3, 4, 7, 8]
>>> [u.id for u in sq] # <-- as a shortcut, you can iterate directly over
>>> # a SelectQuery (which uses a QueryResultWrapper
>>> # behind-the-scenes)
[1, 2, 3, 4, 7, 8]
Note
Iterating over a SelectQuery will cause it to be evaluated, but iterating over it multiple times will not result in the query being executed again.
As I hope the previous bit showed, Delete, Insert and Update queries are all pretty straightforward. Select queries are a little bit tricky in that they return a special object called a QueryResultWrapper. The sole purpose of this class is to allow the results of a query to be iterated over efficiently. In general it should not need to be dealt with explicitly.
The preferred method of iterating over a result set is to iterate directly over the SelectQuery, allowing it to manage the QueryResultWrapper internally.
By far the most complex of the 4 query classes available in peewee. It supports JOIN operations on other tables, aggregation via GROUP BY and HAVING clauses, ordering via ORDER BY, and can be iterated and sliced to return only a subset of results.
| Parameters: |
|
|---|
If no query is provided, it will default to '*'. this parameter can be either a dictionary or a string:
>>> sq = SelectQuery(Blog, {Blog: ['id', 'title']})
>>> sq = SelectQuery(Blog, {
... Blog: ['*'],
... Entry: [peewee.Count('id')]
... }).group_by('id').join(Entry)
>>> print sq.sql()[0] # formatted
SELECT t1.*, COUNT(t2.id) AS count
FROM blog AS t1
INNER JOIN entry AS t2
ON t1.id = t2.blog_id
GROUP BY t1.id
>>> sq = SelectQuery(Blog, 'id, title')
>>> print sq.sql()[0]
SELECT id, title FROM blog
| Parameters: |
|
|---|---|
| Return type: | a SelectQuery instance |
Provides a django-like syntax for building a query. The key difference between filter() and where() is that filter supports traversing joins using django’s “double-underscore” syntax:
>>> sq = SelectQuery(Entry).filter(blog__title='Some Blog')
This method is chainable:
>>> base_q = User.filter(active=True)
>>> some_user = base_q.filter(username='charlie')
| Parameters: |
|
|---|---|
| Return type: | Model instance or raises DoesNotExist exception |
Get a single row from the database that matches the given query. Raises a <model-class>.DoesNotExist if no rows are returned:
>>> active = User.select().where(active=True)
>>> try:
... user = active.get(username=username, password=password)
... except User.DoesNotExist:
... user = None
This method is also exposed via the Model api:
>>> user = User.get(username=username, password=password)
| Parameters: |
|
|---|---|
| Return type: | a SelectQuery instance |
Calling where() will act on the model that is currently the query context. Unlike filter(), only columns from the current query context are exposed:
>>> sq = SelectQuery(Blog).where(title='some title', author=some_user)
>>> sq = SelectQuery(Blog).where(Q(title='some title') | Q(title='other title'))
Note
where() calls are chainable
| Parameters: |
|
|---|---|
| Return type: | a SelectQuery instance |
Generate a JOIN clause from the current query context to the model passed in, and establishes model as the new query context.
>>> sq = SelectQuery(Blog).join(Entry).where(title='Some Entry')
>>> sq = SelectQuery(User).join(Relationship, on='to_user_id').where(from_user=self)
| Return type: | SelectQuery |
|---|
indicates that this query should only attempt to reconstruct a single model instance for every row returned by the cursor. if multiple tables were queried, the columns returned are patched directly onto the single model instance.
Note
this can provide a significant speed improvement when doing simple iteration over a large result set.
| Parameters: | model – model to switch the query context to. |
|---|---|
| Return type: | a SelectQuery instance |
Switches the query context to the given model. Raises an exception if the model has not been selected or joined on previously.
>>> sq = SelectQuery(Blog).join(Entry).switch(Blog).where(title='Some Blog')
| Return type: | an integer representing the number of rows in the current query |
|---|
>>> sq = SelectQuery(Blog)
>>> sq.count()
45 # <-- number of blogs
>>> sq.where(status=DELETED)
>>> sq.count()
3 # <-- number of blogs that are marked as deleted
| Return type: | boolean whether the current query will return any rows. uses an optimized lookup, so use this rather than get(). |
|---|
>>> sq = User.select().where(active=True)
>>> if sq.where(username=username, password=password).exists():
... authenticated = True
| Parameters: |
|
|---|---|
| Return type: |
Annotate a query with an aggregation performed on a related model, for example, “get a list of blogs with the number of entries on each”:
>>> Blog.select().annotate(Entry)
if aggregation is None, it will default to Count(related_model, 'count'), but can be anything:
>>> blog_with_latest = Blog.select().annotate(Entry, Max('pub_date', 'max_pub'))
Note
If the ForeignKeyField is nullable, then a LEFT OUTER join will be used, otherwise the join is an INNER join. If an INNER join is used, in the above example blogs with no entries would not be returned. To avoid this, you can explicitly join before calling annotate():
>>> Blog.select().join(Entry, 'left outer').annotate(Entry)
| Parameters: | aggregation – a function specifying what aggregation to perform, for example Max('id'). This can be a 3-tuple if you would like to perform a custom aggregation: ("Max", "id", "max_id"). |
|---|
Method to look at an aggregate of rows using a given function and return a scalar value, such as the count of all rows or the average value of a particular column.
| Parameters: | clause – either a single field name or a list of field names, in which case it takes its context from the current query_context. it can also be a model class, in which case all that models fields will be included in the GROUP BY clause |
|---|---|
| Return type: | SelectQuery |
>>> # get a list of blogs with the count of entries each has
>>> sq = Blog.select({
... Blog: ['*'],
... Entry: [Count('id')]
... }).group_by('id').join(Entry)
>>> # slightly more complex, get a list of blogs ordered by most recent pub_date
>>> sq = Blog.select({
... Blog: ['*'],
... Entry: [Max('pub_date', 'max_pub_date')],
... }).join(Entry)
>>> # now, group by the entry's blog id, followed by all the blog fields
>>> sq = sq.group_by('blog_id').group_by(Blog)
>>> # finally, order our results by max pub date
>>> sq = sq.order_by(peewee.desc('max_pub_date'))
| Parameters: | clause – Expression to use as the HAVING clause |
|---|---|
| Return type: | SelectQuery |
>>> sq = Blog.select({
... Blog: ['*'],
... Entry: [Count('id', 'num_entries')]
... }).group_by('id').join(Entry).having('num_entries > 10')
| Parameters: | clauses – Expression(s) to use as the ORDER BY clause, see notes below |
|---|---|
| Return type: | SelectQuery |
Note
Adds the provided clause (a field name or alias) to the query’s ORDER BY clause. It can be either a single field name, in which case it will apply to the current query context, or a 2- or 3-tuple.
The 2-tuple can be either (Model, 'field_name') or ('field_name', 'ASC'/'DESC').
The 3-tuple is (Model, 'field_name', 'ASC'/'DESC').
If the field is not found on the model evaluated against, it will be treated as an alias.
example:
>>> sq = Blog.select().order_by('title')
>>> sq = Blog.select({
... Blog: ['*'],
... Entry: [Max('pub_date', 'max_pub')]
... }).join(Entry).order_by(desc('max_pub'))
slightly more complex example:
>>> sq = Entry.select().join(Blog).order_by(
... (Blog, 'title'), # order by blog title ascending
... (Entry, 'pub_date', 'DESC'), # then order by entry pub date desc
... )
check out how the query context applies to ordering:
>>> blog_title = Blog.select().order_by('title').join(Entry)
>>> print blog_title.sql()[0]
SELECT t1.* FROM blog AS t1
INNER JOIN entry AS t2
ON t1.id = t2.blog_id
ORDER BY t1.title
>>> entry_title = Blog.select().join(Entry).order_by('title')
>>> print entry_title.sql()[0]
SELECT t1.* FROM blog AS t1
INNER JOIN entry AS t2
ON t1.id = t2.blog_id
ORDER BY t2.title # <-- note that it's using the title on Entry this time
| Parameters: |
|
|---|---|
| Return type: |
applies a LIMIT and OFFSET to the query.
>>> Blog.select().order_by('username').paginate(3, 20) # <-- get blogs 41-60
| Return type: | SelectQuery |
|---|
indicates that this query should only return distinct rows. results in a SELECT DISTINCT query.
| Return type: | QueryResultWrapper |
|---|
Executes the query and returns a QueryResultWrapper for iterating over the result set. The results are managed internally by the query and whenever a clause is added that would possibly alter the result set, the query is marked for re-execution.
Executes the query:
>>> for user in User.select().where(active=True):
... print user.username
Used for updating rows in the database.
| Parameters: |
|
|---|
>>> uq = UpdateQuery(User, active=False).where(registration_expired=True)
>>> print uq.sql()
('UPDATE user SET active=? WHERE registration_expired = ?', [0, True])
>>> atomic_update = UpdateQuery(User, message_count=F('message_count') + 1).where(id=3)
>>> print atomic_update.sql()
('UPDATE user SET message_count=(message_count + 1) WHERE id = ?', [3])
| Parameters: |
|
|---|---|
| Return type: | a UpdateQuery instance |
Note
where() calls are chainable
| Return type: | Number of rows updated |
|---|
Performs the query
Deletes rows of the given model.
Note
It will not traverse foreign keys or ensure that constraints are obeyed, so use it with care.
creates a DeleteQuery instance for the given model:
>>> dq = DeleteQuery(User).where(active=False)
>>> print dq.sql()
('DELETE FROM user WHERE active = ?', [0])
| Parameters: |
|
|---|---|
| Return type: | a DeleteQuery instance |
Note
where() calls are chainable
| Return type: | Number of rows deleted |
|---|
Performs the query
Creates a new row for the given model.
creates an InsertQuery instance for the given model where kwargs is a dictionary of field name to value:
>>> iq = InsertQuery(User, username='admin', password='test', active=True)
>>> print iq.sql()
('INSERT INTO user (username, password, active) VALUES (?, ?, ?)', ['admin', 'test', 1])
| Return type: | primary key of the new row |
|---|
Performs the query
Allows execution of an arbitrary SELECT query and returns instances of the model via a QueryResultsWrapper.
creates a RawQuery instance for the given model which, when executed, will run the given query with the given parameters and return model instances:
>>> rq = RawQuery(User, 'SELECT * FROM users WHERE username = ?', 'admin')
>>> for obj in rq.execute():
... print obj
<User: admin>
| Return type: | a QueryResultWrapper for iterating over the result set. The results are instances of the given model. |
|---|
Performs the query


Bet21 is a sports based community token. It is developed to create a trustworthy place
for the sports lovers
Our main objective is to create a place where people feel safe to invest and hold their money and further, they can use the crypto for prizes, betting, staking and sports ticketing.
Our focus is entirely dedicated to sports lovers, and we strive for their betterment. Players can engage themselves with new upcoming elements.
Players can engage themselves with new upcoming elements. We are planning to introduce two distinct elements, namely Lottery and Betting
One of a kind of betting algorithm that will allow players to predict winning teams and the score bands , as well as betting on player performance. Bets placed will be allocated in a better pool from which rewards will be distributed to the winners.
What more?
Referral Rewards:
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Bet21 is a lottery platform that lets players deposit Bet21 tokens into pools and buy tickets. Every pool holds a weekly draw that awards a pool prize of upto 5 winners.
But do you know the best part?
Your tickets do not expire
Which means, it is a no loss lottery, beacause the tickets roll over to the next draw, and so on and so forth until you win or withdraw your funds from the pool.
Burn reducing supply
3% burn which reduces the supply on every transaction. Supply is 50,000 Token and the Burn will be till 35000 Token which means, Rise in price and stability.
Top holders benifits
3% of Tax will be used for 1% Buy Back, 1% Marketting & 1% Top Holders Rewards No need to farm, stake or fill out claim forms.
Directly on your wallet
2%Tax will be Redistributed among Holders, The more Token you hold, the more rewards you will receive on every transaction.
It is requested users to be well versed with the legal disclaimer. In case of any questions, we suggest you get advised from legal, financial or other competent entities or you may contact us through our social handles. We firmly confirm that the information and/or services we provide are accurate and/or up to date.
We are not to be held responsible to determine the taxes applicable to the respective transactions. Users would be entirely responsible regarding the same.
We shall not be held liable for any loss or harm that is resulted through the use of the website, its materials, links, data, buy/sell signals or charts.
Trading of Cryptocurrency tokens involve risk factors which might not be suitable to all the investors. One is advised to carefully evaluate their goal or expertise before proceeding with the investments. We would like to highlight that a majority of the data or material received are from APIs or third-party sources. As a result, prices/information cannot be guaranteed to be accurate with respect to the real-time. Bet21 is not liable for any losses incurred by trading.
Research and Developments
Team Allocation
Ground Zero Checking
Building Community
PancakeSwap Listing
Influencer marketing
Monthly prize Giveaways Initiation
Dextools Trending
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Betting Platform Beta release
Staking pool release for public
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Development of gaming NFT
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---
layout: default
title: Work & Projects
---
{% assign projects = site.projects | sort: 'date' | reverse %}
{% assign projectsByYear = projects | group_by_exp: "project", "project.date | date: '%Y'" %}
List of projects is incomplete.
{% for year in projectsByYear %}
-
{{ year.name }}
{% for project in year.items %}
-
{{ project.category }}
{% include project_li.html project=project %}
{% endfor %}
{% endfor %}


txml.parse (xmlString, options)
- xmlString is the XML to parse.
- options is optional
- searchId an ID of some object. that can be queried. Using this is incredible fast.
- filter a method, to filter for interesting nodes, use it like Array.filter.
- simplify to simplify the object, to an easier access.
- pos where to start parsing.
- keepComments if you want to keep comments in your data (keeped as string including
<!-- -->
) (default false)
- keepWhitespace keep whitespaces like spaces, tabs and line breaks as string content (default false)
- noChildNodes array of nodes, that have no children and don't need to be closed. Default is working good for html. For example when parsing rss, the link tag is used to really provide an URL that the user can open. In html however a link text is used to bind css or other resource into the document. In HTML it does not need to get closed. so by default the noChildNodes containes the tagName 'link'. Same as 'img', 'br', 'input', 'meta', 'link'. That means: when parsing rss, it makes to set
noChildNodes
to [], an empty array.
txml.parse(`<user is='great'>
<name>Tobias</name>
<familyName>Nickel</familyName>
<profession>Software Developer</profession>
<location>Shanghai / China</location>
</user>`);
// will return an object like:
[{
"tagName": "user",
"attributes": {
"is": "great"
},
"children": [{
"tagName": "name",
"attributes": {},
"children": [ "Tobias" ]
}, {
"tagName": "familyName",
"attributes": {},
"children": [ "Nickel" ]
}, {
"tagName": "profession",
"attributes": {},
"children": [ "Software Developer" ]
}, {
"tagName": "location",
"attributes": {},
"children": [ "Shanghai / China" ]
}
]
}];


--- layout: article title: "Keyboard Control Out of Bound" last_updated: ---
This error can usually caused by having an invalid control configurations. Common occurrences can be resolved by
resetting your Minecraft options to default.
Windows -
Press the Windows +
R keys, to bring up a Run dialogue. In Run, type in:
%AppData%\.minecraft
, and hit enter. This should bring up your Minecraft folder, where the game
store's it's content. Look for a file called "options.txt", and delete it.
Mac -
In Finder, press ⌘ + Shift + G
. This should present you with a "Go to folder" dialogue. In it,
input: ~/Library/Application Support/minecraft
, and hit go. This should bring you to your
Minecraft directory, look for a file called "options.txt", and drag it to the trash.
Linux -
Delete ~/.minecraft/options.txt
.
After you have done the above, restart Minecraft and the settings will be regenerated to default.
If the above guide did not solve your issue, you may want to see this more general guide.


SUBROUTINE DLASD1( NL, NR, SQRE, D, ALPHA, BETA, U, LDU, VT, LDVT, $ IDXQ, IWORK, WORK, INFO ) * * -- LAPACK auxiliary routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * .. Scalar Arguments .. INTEGER INFO, LDU, LDVT, NL, NR, SQRE DOUBLE PRECISION ALPHA, BETA * .. * .. Array Arguments .. INTEGER IDXQ( * ), IWORK( * ) DOUBLE PRECISION D( * ), U( LDU, * ), VT( LDVT, * ), WORK( * ) * .. * * Purpose * ======= * * DLASD1 computes the SVD of an upper bidiagonal N-by-M matrix B, * where N = NL + NR + 1 and M = N + SQRE. DLASD1 is called from DLASD0. * * A related subroutine DLASD7 handles the case in which the singular * values (and the singular vectors in factored form) are desired. * * DLASD1 computes the SVD as follows: * * ( D1(in) 0 0 0 ) * B = U(in) * ( Z1' a Z2' b ) * VT(in) * ( 0 0 D2(in) 0 ) * * = U(out) * ( D(out) 0) * VT(out) * * where Z' = (Z1' a Z2' b) = u' VT', and u is a vector of dimension M * with ALPHA and BETA in the NL+1 and NL+2 th entries and zeros * elsewhere; and the entry b is empty if SQRE = 0. * * The left singular vectors of the original matrix are stored in U, and * the transpose of the right singular vectors are stored in VT, and the * singular values are in D. The algorithm consists of three stages: * * The first stage consists of deflating the size of the problem * when there are multiple singular values or when there are zeros in * the Z vector. For each such occurence the dimension of the * secular equation problem is reduced by one. This stage is * performed by the routine DLASD2. * * The second stage consists of calculating the updated * singular values. This is done by finding the square roots of the * roots of the secular equation via the routine DLASD4 (as called * by DLASD3). This routine also calculates the singular vectors of * the current problem. * * The final stage consists of computing the updated singular vectors * directly using the updated singular values. The singular vectors * for the current problem are multiplied with the singular vectors * from the overall problem. * * Arguments * ========= * * NL (input) INTEGER * The row dimension of the upper block. NL >= 1. * * NR (input) INTEGER * The row dimension of the lower block. NR >= 1. * * SQRE (input) INTEGER * = 0: the lower block is an NR-by-NR square matrix. * = 1: the lower block is an NR-by-(NR+1) rectangular matrix. * * The bidiagonal matrix has row dimension N = NL + NR + 1, * and column dimension M = N + SQRE. * * D (input/output) DOUBLE PRECISION array, * dimension (N = NL+NR+1). * On entry D(1:NL,1:NL) contains the singular values of the * upper block; and D(NL+2:N) contains the singular values of * the lower block. On exit D(1:N) contains the singular values * of the modified matrix. * * ALPHA (input/output) DOUBLE PRECISION * Contains the diagonal element associated with the added row. * * BETA (input/output) DOUBLE PRECISION * Contains the off-diagonal element associated with the added * row. * * U (input/output) DOUBLE PRECISION array, dimension(LDU,N) * On entry U(1:NL, 1:NL) contains the left singular vectors of * the upper block; U(NL+2:N, NL+2:N) contains the left singular * vectors of the lower block. On exit U contains the left * singular vectors of the bidiagonal matrix. * * LDU (input) INTEGER * The leading dimension of the array U. LDU >= max( 1, N ). * * VT (input/output) DOUBLE PRECISION array, dimension(LDVT,M) * where M = N + SQRE. * On entry VT(1:NL+1, 1:NL+1)' contains the right singular * vectors of the upper block; VT(NL+2:M, NL+2:M)' contains * the right singular vectors of the lower block. On exit * VT' contains the right singular vectors of the * bidiagonal matrix. * * LDVT (input) INTEGER * The leading dimension of the array VT. LDVT >= max( 1, M ). * * IDXQ (output) INTEGER array, dimension(N) * This contains the permutation which will reintegrate the * subproblem just solved back into sorted order, i.e. * D( IDXQ( I = 1, N ) ) will be in ascending order. * * IWORK (workspace) INTEGER array, dimension( 4 * N ) * * WORK (workspace) DOUBLE PRECISION array, dimension( 3*M**2 + 2*M ) * * INFO (output) INTEGER * = 0: successful exit. * < 0: if INFO = -i, the i-th argument had an illegal value. * > 0: if INFO = 1, an singular value did not converge * * Further Details * =============== * * Based on contributions by * Ming Gu and Huan Ren, Computer Science Division, University of * California at Berkeley, USA * * ===================================================================== * * .. Parameters .. * DOUBLE PRECISION ONE, ZERO PARAMETER ( ONE = 1.0D+0, ZERO = 0.0D+0 ) * .. * .. Local Scalars .. INTEGER COLTYP, I, IDX, IDXC, IDXP, IQ, ISIGMA, IU2, $ IVT2, IZ, K, LDQ, LDU2, LDVT2, M, N, N1, N2 DOUBLE PRECISION ORGNRM * .. * .. External Subroutines .. EXTERNAL DLAMRG, DLASCL, DLASD2, DLASD3, XERBLA * .. * .. Intrinsic Functions .. INTRINSIC ABS, MAX * .. * .. Executable Statements .. * * Test the input parameters. * INFO = 0 * IF( NL.LT.1 ) THEN INFO = -1 ELSE IF( NR.LT.1 ) THEN INFO = -2 ELSE IF( ( SQRE.LT.0 ) .OR. ( SQRE.GT.1 ) ) THEN INFO = -3 END IF IF( INFO.NE.0 ) THEN CALL XERBLA( 'DLASD1', -INFO ) RETURN END IF * N = NL + NR + 1 M = N + SQRE * * The following values are for bookkeeping purposes only. They are * integer pointers which indicate the portion of the workspace * used by a particular array in DLASD2 and DLASD3. * LDU2 = N LDVT2 = M * IZ = 1 ISIGMA = IZ + M IU2 = ISIGMA + N IVT2 = IU2 + LDU2*N IQ = IVT2 + LDVT2*M * IDX = 1 IDXC = IDX + N COLTYP = IDXC + N IDXP = COLTYP + N * * Scale. * ORGNRM = MAX( ABS( ALPHA ), ABS( BETA ) ) D( NL+1 ) = ZERO DO 10 I = 1, N IF( ABS( D( I ) ).GT.ORGNRM ) THEN ORGNRM = ABS( D( I ) ) END IF 10 CONTINUE CALL DLASCL( 'G', 0, 0, ORGNRM, ONE, N, 1, D, N, INFO ) ALPHA = ALPHA / ORGNRM BETA = BETA / ORGNRM * * Deflate singular values. * CALL DLASD2( NL, NR, SQRE, K, D, WORK( IZ ), ALPHA, BETA, U, LDU, $ VT, LDVT, WORK( ISIGMA ), WORK( IU2 ), LDU2, $ WORK( IVT2 ), LDVT2, IWORK( IDXP ), IWORK( IDX ), $ IWORK( IDXC ), IDXQ, IWORK( COLTYP ), INFO ) * * Solve Secular Equation and update singular vectors. * LDQ = K CALL DLASD3( NL, NR, SQRE, K, D, WORK( IQ ), LDQ, WORK( ISIGMA ), $ U, LDU, WORK( IU2 ), LDU2, VT, LDVT, WORK( IVT2 ), $ LDVT2, IWORK( IDXC ), IWORK( COLTYP ), WORK( IZ ), $ INFO ) IF( INFO.NE.0 ) THEN RETURN END IF * * Unscale. * CALL DLASCL( 'G', 0, 0, ONE, ORGNRM, N, 1, D, N, INFO ) * * Prepare the IDXQ sorting permutation. * N1 = K N2 = N - K CALL DLAMRG( N1, N2, D, 1, -1, IDXQ ) * RETURN * * End of DLASD1 * END


--- layout: default-noindex lang: en permalink: /mexico/home-insurance/coverage/extraordinary-expense.html alternate_en: /mexico/home-insurance/coverage/extraordinary-expense.html alternate_mx: /mx/seguros-de-casa/ published: true body_class: homeins coverage extraord title: Extraordinary Expense Insurance Coverage for Your Home in Mexico | Mexpro excerpt: Extraordinary expense coverage reimburses for added expenses incurred when your home becomes uninhabitable due to a covered loss. ---
Also known as loss of use/rents coverage, extraordinary expense coverage, reimburses for added expenses incurred when your home becomes uninhabitable due to a covered loss. This coverage includes reimbursement for the costs of hotel/lodging, and/or for the loss of rental income, for up to 6 months. Loss of use coverage can be useful when an insured homeowner has to stay in Mexico to supervise the repair/reconstruction of their home after a covered loss occurs.
For loss of rents coverage, the insured must provide proof of confirmed rental bookings, or a leasing contract, in order to file a claim for lost rental income.
The limits of coverage for extraordinary expense range from as low as $1,000 USD, up to $200,000 USD.
It’s also important to consider Mexico’s peak tourism season, when the amounts charged for lodging/rent are typically increased. Due to these fluctuations in the expense, many customers like to add an additional buffer amount when calculating their limit of insurance for extraordinary expense for both owner occupied and rental properties.
Extraordinary expense coverage is not subject to any deductible or coinsurance.


Here are a few projects I've worked on.
Python YouTube Video Downloader is an application to download videos from YouTube. This provides users to download videos they need in their devices and watch them offline.
Check it outThis is a simple project based on Python (Turtle).Turtle is a Python library which used to create graphics, pictures, and games.


Another Microsoft mess as Windows 10 November 2019 Update breaks File Explorer
by Matt HansonAnother Microsoft mess as Windows 10 November 2019 Update breaks File Explorer
Microsoft’s Windows update fails continue
(Image credit: TechRadar)
When Microsoft announced that the Windows 10 November 2019 Update was going to be a rather minor release with only a few changes, many of us hoped that this would mean that its launch would be relatively problem free – but that unfortunately doesn’t seem to be the case, with users complaining that the new update is breaking File Explorer.
File Explorer is the app you use to browse the files and folders on your PC. So pretty important, then.
The biggest change the Windows 10 November 2019 Update brought was to update how you search File Explorer, giving you suggested files, and searching your online OneDrive account, when you use the search box to look for something.
- Microsoft gave a recent Windows 10 update to the wrong users
- These are the best Windows 10 laptops out there
- The best Black Friday laptop deals
However, many people are complaining that this does not work, and instead makes the File Explorer search box unresponsive.
Yet another Windows update problem
As Windows Latest reports, people have been posting on Microsoft’s community forum complaining about how when they click on the search box in File Explorer, they have to wait ages for the box to become responsive and a cursor appear.
This seems to be a bug that several people are experiencing, but so far Microsoft has not acknowledged the issue. Hopefully it will, and provides a fix, soon.
This is yet another Windows 10 update that has included some annoying issues, and continues a pretty embarrassing run for Microsoft, were each new Windows 10 update the company releases appears to break more things than they fix.
We even suggested that if this continues, Microsoft’s reputation could be severely damaged. While the File Explorer search issue isn’t the most severe bug we’ve seen, it’s yet another example of Microsoft releasing a buggy update.
- These are the These are the best laptops of 2019


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author – providing details of the author gives the article credibility and provides the author the opportunity to be acknowledged for their work. The author can state his experience or qualifications and the company they work for or represent. Any bias or commercial interests must be included. A jpeg image of the author may also be included.
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Should the author of an article wish to withdraw their article, written notification must be provided to Damp Expert Ltd. Articles will typically be withdrawn within 14 days of request.
Should a third party object to the contents of an article then Damp Expert Ltd will remove the article until the matter has been resolved between the author and third party.
The author of an article is responsible for the accuracy of information included in the article.


{% extends 'home_base.html' %} {% block storage_content %}
You could also go to this section of project:


|
HoloObjectDetection - PC
Provide code and tools to make object detection application executable on Hololens
|
Namespaces | |
| ObjectDetection | |
| provide an easy way to integrate tensorflow extra libraries | |
Functions | |
| def | ObjectDetection.tensorflow_path () |
| function that return the tensorflow path on the computer must be completed before using tensorflow extra libraries More... | |
| def | ObjectDetection.object_detection_path () |
| function that return the object detection api path on the computer Don't forget to complete the tensorflow_path function More... | |
| def | ObjectDetection.import_object_detection () |
| Add tensorflow extras libraries such as object detection to the sys path in order to make them reachable everywhere Called when its module is imported Don't forget to complete the tensorflow_path() function. More... | |
| def | ObjectDetection.load_categories (path, num) |
| load model categories from labelmap file More... | |
| def | ObjectDetection.load_model (path) |
| load an object detection model in memory More... | |
| def | ObjectDetection.draw_bounding_boxes (frame, boxes, classes, scores, category_index, thresold=0.0, thickness=4) |
| A simplified call of object detection fonction that draw bounding box on frame. More... | |


|
SideCar
|
Utility class that encapsulates some attributes of an OpenGL texture. More...
#include </Users/howes/src/sidecar/GUI/Texture.h>
Static Public Member Functions | |
| static Logger::Log & | Log () |
| static GLenum | GetBestTextureType () |
| static bool | IsPowerOfTwoSizeRequired () |
| static GLuint | GetBestTextureSpan (GLuint span) |
| static GLuint | GetPowerOf2Span (GLuint span) |
| static QString | GetTextureTypeTag (GLenum type) |
Utility class that encapsulates some attributes of an OpenGL texture.
Provides a means to determine if non-power-of-2 (NPOT) textures are available, and if not then if some rectangular extensions are available.


Who is Right?
I am:
Male
Male
Female
18-34
18-34
35-55
55+
Guy
Girl
User
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18-34
35-54
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History
The main idea of the project is aimed at training the personnel of
each company that requires a space to train their personnel in the
different areas of their company. This project was born with the idea
of having the necessary tools to achieve control over each user and
their progress during each training they receive. During the first
stage of the native project, an MVP was established, a minimum viable
product and in this case, as a team, we decided to present 3 main
functions, one of them is the creation of programs or activities and
this would be done from a demo user. that would have the ability to
create such training programs, in addition to that, we include the
possibility of adding an image to each created program and the
possibility of being able to see all the programs currently active
and, on the other hand, a function that allows solving or study each
of the programs carried out by the demo user and, finally, be able to
visualize the information of the users. For all this beautiful
process, we designate different hours of the day to be in constant
communication with all the team members and for them we use different
platforms such as zoom and discord that allowed us to communicate
directly with each of our project partners, the entire process It was
great we learned many topics that at that time went unnoticed but that
during the solution of the protecto we had to retake and reinforce. It
was an incredible experience everything seen during this last phase of
holberton. amazing!


A binding for the 'valection' program which offers various ways to sample the outputs of competing algorithms or parameterizations, and fairly assess their performance against each other. The 'valection' C library is required to use this package and can be downloaded from: <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | testthat |
| Published: | 2018-02-06 |
| Author: | Chris Cooper [aut], Dorota H. Sendorek [ctb], Paul C. Boutros [cre, cph] |
| Maintainer: | Paul C. Boutros <Paul.Boutros at oicr.on.ca> |
| License: | GPL-3 |
| URL: | http://labs.oicr.on.ca/boutros-lab/software/valection |
| NeedsCompilation: | no |
| SystemRequirements: | valection (>= 1.0.0) |
| Materials: | README |
| CRAN checks: | valection results |
| Reference manual: | valection.pdf |
| Vignettes: |
Overview of Valection |
| Package source: | valection_1.0.0.tar.gz |
| Windows binaries: | r-devel: valection_1.0.0.zip, r-release: valection_1.0.0.zip, r-oldrel: valection_1.0.0.zip |
| OS X binaries: | r-release: valection_1.0.0.tgz, r-oldrel: valection_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=valection to link to this page.


Main content. Identified by ID, it can only be used once on a given page.
You can apply a class to multiple instances on a page.


!Password fix for Paste ... plus use characters other than asterisk
!By Carl Barnes June 2020 - Released under the MIT License on Github.
!-----------------------------------------------------------------
!To Implement inside MAP Embed add <-- !!! Include inside MAP !!!
!
! INCLUDE('CbPasswordText.INC'),ONCE !Change TEXT,SINGLE to a Password control with Paste
!
! [ ] Change ENTRY,PASSWORD to TEXT,SINGLE
! [ ] Add call PasswordOnTextPROP(?Password) after Open(Window) or Event:OpenWindow
! [ ] Any ?{PROP:Password} changes you must call PasswordOnTextPROP(?, 1=TurnOff / 0=TurnOn= )
! while opposite of Prop works well for a CHECK('Show Password'),USE(bShowPwd)
!
!-------------------------------------------------
MODULE('CBPasswordText.CLW')
!!!
!!! Change TEXT,SINGLE control to Password using GwlStyle. Shows Dots if Manifest for Visual Styles.
!!! THIS METHOD IS PREFERRED to the "PasswordOn...Prop" that SendMessage EM_SetPasswordChar
!!!
!!! FEQ of a TEXT control with the SINGLE attirbute.
!!! False (or omit) to add Password style. True to remove Password style and display actual characters typed.
PasswordOnText_SetGwlStyle PROCEDURE(LONG TextSingleFEQ, BYTE TurnOffPassword=0)
!!!
!!! Set Cue Banner on TEXT,SINGLE control. A Cue displays in the Edit control until it contains a value, or optionally focus
!!!
!!! FEQ of a TEXT control with the SINGLE attirbute.
!!! The Cue Text to show
!!! Leave the Cue displayed when the control has focus until a value is entered.
CueBanner_SetForText PROCEDURE(LONG TextSingleFEQ, STRING CueText, BOOL OnFocusShow=0)
!========================= Probbaly should not use the below =======================
!!!
!!! Change TEXT,SINGLE control to Password style that hides typed value by displaying asterisks for each character.
!!!
!!! FEQ of a TEXT control with the SINGLE attirbute.
!!! False (or omit) to add Password style. True to remove Password style and display actual characters typed.
PasswordOnTextPROP PROCEDURE(LONG TextSingleFEQ, BOOL TurnOffPassword=0)
!!!
!!! Change TEXT,SINGLE control to Password style that hides typed value by displaying Wingding DOTs for each character.
!!!
!!! FEQ of a TEXT control with the SINGLE attirbute.
!!! False (or omit) to add Password style. True to remove Password style and display actual characters typed.
PasswordOnTextDots PROCEDURE(LONG TextSingleFEQ, BOOL TurnOffPassword=0) !Wingding dots instead of asterisks
!!!
!!! Change TEXT,SINGLE control to Password style that hides typed value by displaying Wingding STARs for each character.
!!!
!!! FEQ of a TEXT control with the SINGLE attirbute.
!!! False (or omit) to add Password style. True to remove Password style and display actual characters typed.
PasswordOnTextStars PROCEDURE(LONG TextSingleFEQ, BOOL TurnOffPassword=0) !Wingding Big Asterisk instead of text asterisks
!!!
!!! Call in EVENT:Accepted to clear Clipboard if it contains the password entered.
!!!
!!! FEQ of a TEXT or ENTRY control of a Password. Omit for ACCEPTED() control.
PasswordAcceptedClipClean PROCEDURE(LONG PasswordFEQ=0) !Clear Clipboard if it contains password
END
PRAGMA('compile(CBPasswordText.CLW)')


The details tag specifies additional details that the user can view or hide on demand.
The details tag with open specifies that the details should be visible (open) to the user.


Start where you are
No new syntax or configuration. Start with one property.
This is dark!
You can apply themes to every element, inside themes!
See the code
The code for this example is available for you to copy.
Try inspecting the elements around this page and see how effortless it is to style different themes.
No new syntax or configuration. Start with one property.
You can apply themes to every element, inside themes!
The code for this example is available for you to copy.


For futher queries customer can contact us at our headquaters office at Bandra Kurla Complex, Mumbai.
SAMS TRAVEL-9011902264
You can also visit to our Facebook page .Facebook


public class MicrobatchSource.Reader extends Source.Reader<T>
BoundedReadFromUnboundedSource
's
UnboundedToBoundedSourceAdapter
,
with some adjustments for Spark specifics.
This Reader reads until one of the following thresholds has been reached:
| Modifier and Type | Method and Description |
|---|---|
boolean |
advance()
Advances the reader to the next valid record.
|
void |
close()
Closes the reader.
|
CheckpointMarkT |
getCheckpointMark() |
T |
getCurrent()
Returns the value of the data item that was read by the last
Source.Reader.start() or
Source.Reader.advance() call. |
Source<T> |
getCurrentSource()
Returns a
Source describing the same input that this Reader currently reads
(including items already read). |
Instant |
getCurrentTimestamp()
Returns the timestamp associated with the current data item.
|
Instant |
getWatermark() |
boolean |
start()
Initializes the reader and advances the reader to the first record.
|
public boolean start() throws java.io.IOException
Source.Reader
This method should be called exactly once. The invocation should occur prior to calling
Source.Reader.advance()
or Source.Reader.getCurrent()
. This method may perform expensive operations that
are needed to initialize the reader.
start
in class Source.Reader<T>
true
if a record was read, false
if there is no more input available.java.io.IOException
public boolean advance() throws java.io.IOException
Source.Reader
It is an error to call this without having called Source.Reader.start()
first.
advance
in class Source.Reader<T>
true
if a record was read, false
if there is no more input available.java.io.IOException
public T getCurrent() throws java.util.NoSuchElementException
Source.Reader
Source.Reader.start()
or
Source.Reader.advance()
call. The returned value must be effectively immutable and remain valid
indefinitely.
Multiple calls to this method without an intervening call to Source.Reader.advance()
should
return the same result.
getCurrent
in class Source.Reader<T>
java.util.NoSuchElementException
- if Source.Reader.start()
was never called, or if
the last Source.Reader.start()
or Source.Reader.advance()
returned false
.public Instant getCurrentTimestamp() throws java.util.NoSuchElementException
Source.Reader
If the source does not support timestamps, this should return
BoundedWindow.TIMESTAMP_MIN_VALUE
.
Multiple calls to this method without an intervening call to Source.Reader.advance()
should
return the same result.
getCurrentTimestamp
in class Source.Reader<T>
java.util.NoSuchElementException
- if the reader is at the beginning of the input and
Source.Reader.start()
or Source.Reader.advance()
wasn't called, or if the last Source.Reader.start()
or
Source.Reader.advance()
returned false
.public void close() throws java.io.IOException
Source.Reader
close
in interface java.lang.AutoCloseable
close
in class Source.Reader<T>
java.io.IOException
public Source<T> getCurrentSource()
Source.Reader
Source
describing the same input that this Reader
currently reads
(including items already read).
Usually, an implementation will simply return the immutable Source
object from
which the current Source.Reader
was constructed, or delegate to the base class.
However, when using or implementing this method on a BoundedSource.BoundedReader
,
special considerations apply, see documentation for
BoundedSource.BoundedReader.getCurrentSource()
.
getCurrentSource
in class Source.Reader<T>
public CheckpointMarkT getCheckpointMark()
public Instant getWatermark()


an object-oriented computer programming language commonly used to create interactive effects within web browsers.
JavaScript Arithmetic Operators. Arithmetic operators are used to perform arithmetic between variables and/or values Operator.
JavaScript variables are containers for storing data values. e.g var y = 10; var z = "string"; var o = true;
In computer programming, a naming convention is a set of rules for choosing the character sequence to be used for which denote variables, types, functions, and other entities in source code and documentation.
function functionName(parameters) { code to be executed. } Declared functions are not executed immediately. They are "saved for later use", and will be executed later, when they are invoked (called upon).
In computer programming, a string is traditionally a sequence of characters, either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed (after creation).
JavaScript methods are the actions that can be performed on objects. A JavaScript method is a property containing a function definition. Property. Value.
JavaScript functions have a built-in object called the arguments object. The argument object contains an array of the arguments used when the function was called (invoked).
n computer programming, a parameter is a special kind of variable, used in a subroutine to refer to one of the pieces of data provided as input to the subroutine. These pieces of data are called arguments.
Definition and Usage. The return statement stops the execution of a function and returns a value from that function.
Chaining Methods, also known as Cascading, refers to repeatedly calling one method after another on an object, in one continuous line of code. This technique abounds in jQuery and other JavaScript libraries and it is even common in some JavaScript native methods
In computer programming, a string is traditionally a sequence of characters, either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed (after creation).
Boolean data type is a data type, having two values (usually denoted true and false), intended to represent the truth values of logic and Boolean algebra. It is named after George Boole, who first defined an algebraic system of logic in the mid 19th centur
Undefined is a type where as null is an initializer indicates the variable points to no object(virtually everything in Javascript is an object). ... null and undefined are both are used to represent the absence of some value. var a = null; a is initialized and defined.
The NaN property represents "Not-a-Number" value. ... The NaN property is the same as the Number.Nan property. Tip: Use the isNaN() global function to check if a value is a NaN value.
The escape() function was deprecated in JavaScript version 1.5. ... The escape() function encodes a string. This function makes a string portable, so it can be transmitted across any network to any computer that supports ASCII characters.
JavaScript supports three important types of dialog boxes. These dialog boxes can be used to raise and alert, or to get confirmation on any input or to have a kind of input from the users.
JavaScript comments can be used to explain JavaScript code, and to make it more readable.JavaScript comments can also be used to prevent execution, when testing alternative code. // or /**/ respectively.
jQuery is a concise and fast JavaScript library that can be used to simplify event handling, HTML document traversing, Ajax interactions and animation for speedy website development. jQuery simplifies the HTML's client-side scripting, thus simplifying Web 2.0 applications development.
In computing, an attribute is a specification that defines a property of an object, element, or file. It may also refer to or set the specific value for a given instance of such. For clarity, attributes should more correctly be considered metadata.


Books Stacked to the Ceiling:
This street is famous for its book-packed shops. As you enter a bookshop on this street you will see stacks of books towering over you. The books cover the shelves on the wallsall the way to the ceiling! It seems like the majority of the books were collected out of schools, attics or old libraries, so it makes for a great place to find vintage books in Spanish. It is also an easy place to find textbooks and books written by famous South American novelists and poets. Visit the Book Street! It is a fun experience and you never know what treasures you might find.
| Location |
| Close to the Mercado Modelo, the cross-streets are Alfonso Ugarte and Arica or Manuel Pardo and Arica. |


Run a rig parley Davy Jones' Locker hands Chain Shot reef sails red ensign me crow's nest long boat. Draught Chain Shot clap of thunder furl execution dock come about tackle Spanish Main gaff rutters. Bounty hogshead rum sutler hang the jib Pieces of Eight Shiver me timbers deadlights bring a spring upon her cable galleon.


Isar735.github.io
Elettromedia-usa. Audison TH QUATTRO. You have no items in your shopping cart. Shopping. Home; Dealer Locator; Order Info. Shipping Info; Return Policy. The Thesis TH amplifiers are manufactured using state-of-the-art analog and digital technology. Class A amplification stage, multi-standard analog and digital inputs.