// Modify here, bug causes nav to spill behind body on ultra large resolutions
In this guide you will be able to visualize college enrollment rates by high school, college type, student achievement level before high school, student race/ethnicity, and combinations of these factors. You will also be able to visualize the share of highly qualified students who enroll in selective colleges.
The College-Going Pathways series is a set of guides, code, and sample data about policy-relevant college-going topics. Browse this and other guides in the series for ideas about ways to investigate student pathways through high school and college. Each guide includes several analyses in the form of charts together with Stata analysis and graphing code to generate each chart.
Once you’ve identified analyses that you want to try to replicate or modify, click the “Download” buttons to download Stata code and sample data. You can make changes to the charts using the code and sample data, or modify the code to work with your own data. If you’re familiar with Github, you can click “Go to Repository” and clone the entire College-Going Pathways repository to your own computer.
The data visualizations in the College-Going Pathways series use a synthetically generated college-going analysis sample data file which has one record per student. Each high school student is assigned to a ninth-grade cohort, and each student record includes demographic and program participation information, annual GPA and on-track status, high school graduation outcomes, and college enrollment information. The Connect guide (coming soon) will provide guidance and example code which will help you build a college-going analysis file using data from your own school system.
Given the substantial economic and social benefits of a college degree, understanding a high schools’ role in preparing students to persist through college is essential. This section provides a series of analyses that highlight college-going rates across high schools in your agency. You will consider whether high school graduates enroll in colleges and universities well-aligned to their incoming academic qualifications. This is one factor that may increase a students’ likelihood of college persistence and degree attainment.
One of the most important decisions in running each analysis is defining the sample. Each analysis corresponds to a different part of the education pipeline and as a result requires different cohorts of students.
If you are using the synthetic data we have provided, the sample restrictions have been predefined and are included below. If you run this code using your own agency data, change the sample restrictions based on your data. Note that you will have to run these sample restrictions at the beginning of your do file so they will feed into the rest of your code.
// Sample Restrictions
// Agency name
global agency_name "Agency"
// Ninth grade cohorts you can observe persisting to the second year of college
global chrt_ninth_begin_persist_yr2 = 2004
global chrt_ninth_end_persist_yr2 = 2006
// Ninth grade cohorts you can observe graduating high school on time
global chrt_ninth_begin_grad = 2004
global chrt_ninth_end_grad = 2006
// Ninth grade cohorts you can observe graduating high school one year late
global chrt_ninth_begin_grad_late = 2004
global chrt_ninth_end_grad_late = 2006
// High school graduation cohorts you can observe enrolling in college the fall after graduation
global chrt_grad_begin = 2007
global chrt_grad_end = 2009
// High school graduation cohorts you can observe enrolling in college two years after hs graduation
global chrt_grad_begin_delayed = 2007
global chrt_grad_end_delayed = 2009
Based on the sample data, you will have three cohorts (sometimes only two) for analysis. If you are using your own agency data, you may decide to aggregate results for more or fewer cohorts to report your results. This decision depends on 1) how much historical data you have available and 2) what balance to strike between reliability and averaging away information on recent trends. We suggest you average results for the last three cohorts to take advantage of larger sample sizes and improve reliability. However, if you have data for more than three cohorts, you may decide to not average data out for fear of losing information about trends and recent changes in your agency.
This guide is an open-source document hosted on Github and generated using the Stata Webdoc package. We welcome feedback, corrections, additions, and updates. Please visit the OpenSDP college-going pathways repository to read our contributor guidelines.
Purpose: This analysis provides an agency snapshot of college enrollment to understand how patterns of college going for high school graduates vary across high schools. By illuminating the extent to which enrollment varies by entry time for seamless enrollers and college level (2- vs. 4-year), the analysis helps diagnose compositional differences for the college-bound population by high school attended.
Required Analysis File Variables:
sid
chrt_grad
enrl_1oct_grad_yr1_any
enrl_1oct_grad_yr1_2yr
enrl_1oct_grad_yr1_4yr
enrl_ever_w2_grad_2yr
enrl_ever_w2_grad_4yr
hs_diploma
last_hs_code
last_hs_name
Analysis-Specific Sample Restrictions:
Ask Yourself - How do college enrollment rates differ by high schools? Why might certain schools have a greater percentage of high school graduates enrolling in college? Do certain schools have higher percentages of 2-year or delayed college enrollers?
Possible Next Steps or Action Plans: Replicate this analysis to include all first-time ninth graders (i.e. ninth grade cohorts) in place of graduates. Additionally, create individual high school reports that provide more details for school administrators (top enrolling institutions of the school’s graduates).
Analytic Technique: Calculate the proportion of students who enroll in college by high school.
// College Enrollment Rates by High School
// Step 1: Load the college-going analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end')
// Step 3: Obtain the agency-level average for seamless enrollment
preserve
collapse (sum) enrl_1oct_grad_yr1_2yr enrl_1oct_grad_yr1_4yr hs_diploma
tempfile agency_level
save `agency_level'
restore
// Step 4: Obtain the school-level averages for seamless enrollment and append on the agency average.
collapse (sum) enrl_1oct_grad_yr1_2yr enrl_1oct_grad_yr1_4yr hs_diploma, by(last_hs_name last_hs_code)
append using `agency_level'
// Step 5: Provide a hs name label for the appended agency average and shorten hs name
replace last_hs_name = "${agency_name} Average" if mi(last_hs_name)
replace last_hs_code = 0 if mi(last_hs_code)
replace last_hs_name = subinstr(last_hs_name, " High School", "", .)
// Step 6: Generate percentages of high school grads attending college. Multiply outcomes of interest by 100 for graphical representations of the rates
foreach var of varlist enrl_1oct_grad_yr1_* {
gen pct_`var' = `var' / hs_diploma
replace pct_`var' = round((pct_`var' * 100))
}
// Step 7: Create a total seamless college enrollment rates by summing up the other variables
gen total_seamless = pct_enrl_1oct_grad_yr1_2yr + pct_enrl_1oct_grad_yr1_4yr
// Step 8: Prepare to graph the results
// 1. Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// 2. Generate graphing code to place value labels for the total enrollment rates; change xpos (the position of the first leftmost label) and xposwidth (the horizontal width of the labels) to finetune.
sort total_seamless
local total_seamless ""
local num_obs = _N
foreach n of numlist 1/`num_obs' {
local temp_total_seamless = total_seamless in `n'
local total_seamless "`total_seamless' `temp_total_seamless'"
}
local total_seamless_label ""
local xpos = 4.8
local xposwidth = 98.7
foreach val of local total_seamless {
local val_pos = `val' + 3
local total_seamless_label `"`total_seamless_label' text(`val_pos' `xpos' "`val'", size(2.5) color(gs7))"'
local xpos = `xpos' + `xposwidth'/_N
}
disp `"`total_seamless_label'"'
// Step 9: Graph the results
#delimit ;
graph bar pct_enrl_1oct_grad_yr1_4yr pct_enrl_1oct_grad_yr1_2yr
if hs_diploma >= 20, stack over(last_hs_name, label(angle(40) labsize(small)) gap(20) sort(total_seamless))
bar(1, fcolor(dkorange) fi(inten80) lcolor(dkorange) lwidth(vvvthin))
bar(2, fcolor(navy*.8) fi(inten80) lcolor(dknavy*.8) lwidth(vvvthin))
blabel(bar, position(inside) color(black) size(small))
legend(label(1 "4-yr Seamless Enrollers")
label(2 "2-yr Seamless Enrollers")
position(11) ring(0) symxsize(2) symysize(2) rows(2) size(small) region(lstyle(none) lcolor(none) color(none)))
title("College Enrollment by High School", size(medium))
ytitle("Percent of High School Graduates")
subtitle("Seamless Enrollers")
`total_seamless_label'
yscale(range(0(20)100))
ylabel(0(20)100, nogrid)
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' ${agency_name} graduates. Postsecondary enrollment outcomes from NSC matched records." "All other data from administrative records.", size(vsmall));
#delimit cr
graph export "figures/D1_Col_Enrl_Seamless_by_HS.png", replace width(1600) height(1200)
Purpose: This analysis provides an agency snapshot of college enrollment to understand how patterns of college going for high school graduates vary across high schools. By illuminating the extent to which enrollment varies by entry time (seamless vs. delayed) and college level (2- vs. 4-year), the analysis helps diagnose compositional differences for the college-bound population by high school attended.
Required Analysis File Variables:
sid
chrt_grad
enrl_1oct_grad_yr1_any
enrl_1oct_grad_yr1_2yr
enrl_1oct_grad_yr1_4yr
enrl_ever_w2_grad_2yr
enrl_ever_w2_grad_4yr
hs_diploma
last_hs_code
last_hs_name
Analysis-Specific Sample Restrictions:
Ask Yourself
Possible Next Steps or Action Plans: Replicate this analysis to include all first-time ninth graders (i.e. ninth grade cohorts) in place of graduates. Additionally, create individual high school reports that provide more details for school administrators (top enrolling institutions of the school’s graduates).
Analytic Technique: Calculate the proportion of graduates who enroll in four-year institutions across high schools according to the selectivity ranking of the postsecondary institutions attended.
// Seamless and Delayed College Enrollment Rates by High School
if 0 {
// Step 1: Load the college-going analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end')
// Step 3: Create binary outcomes for late enrollers
gen late_any = enrl_1oct_grad_yr1_any==0 & enrl_ever_w2_grad_any==1
gen late_4yr = enrl_1oct_grad_yr1_any==0 & enrl_ever_w2_grad_4yr==1
gen late_2yr = enrl_1oct_grad_yr1_any==0 & enrl_ever_w2_grad_2yr==1
assert late_4yr + late_2yr == late_any
// Step 4: Obtain the agency average for seamless and delayed enrollment
preserve
collapse (sum) enrl_1oct_grad_yr1_2yr enrl_1oct_grad_yr1_4yr late_4yr late_2yr hs_diploma
tempfile agency_level
save `agency_level'
restore
// Step 4: Obtain the school-level averages for seamless and delayed enrollment and append on the agency average
collapse (sum) enrl_1oct_grad_yr1_2yr enrl_1oct_grad_yr1_4yr late_4yr late_2yr hs_diploma, by(last_hs_name last_hs_code)
append using `agency_level'
// Step 5: Provide a hs name label for the appended agency average and shorten hs name
replace last_hs_name = "${agency_name} Average" if mi(last_hs_name)
replace last_hs_code = 0 if mi(last_hs_code)
replace last_hs_name = subinstr(last_hs_name, " High School", "", .)
// Step 6: Generate percentages of high school grads attending college. Multiply outcomes of interest by 100 for graphical representations of the rates
foreach var of varlist enrl_1oct_grad_yr1_* late_* {
gen pct_`var' = `var' / hs_diploma
replace pct_`var' = round((pct_`var' * 100))
}
// Step 7: Create total college enrollment rates by summing up the other variables; you can add additional labels as you see fit
gen total = pct_enrl_1oct_grad_yr1_2yr + pct_enrl_1oct_grad_yr1_4yr + pct_late_4yr + pct_late_2yr
gen total_seamless = pct_enrl_1oct_grad_yr1_2yr + pct_enrl_1oct_grad_yr1_4yr
// Step 8: Prepare to graph the results
// Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// Step 9: Graph the results
#delimit ;
graph bar pct_enrl_1oct_grad_yr1_4yr pct_late_4yr pct_enrl_1oct_grad_yr1_2yr pct_late_2yr
if hs_diploma >= 20, over(last_hs_name, label(angle(40)labsize(small)) gap(20) sort(total))
bar(1, fcolor(dkorange) fi(inten80) lcolor(dkorange) lwidth(vvvthin))
bar(2, fcolor(dkorange*.4) fi(inten80) lcolor(dkorange*.4) lwidth(vvvthin))
bar(3, fcolor(navy*.8) fi(inten80) lcolor(navy*.8) lwidth(vvvthin))
bar(4, fcolor(navy*.4) fi(inten30) lcolor(navy*.4) lwidth(vvvthin)) stack
blabel(bar, position(inside) color(black) size(small))
legend(label(1 "4-yr Seamless")
label(2 "4-yr Delayed")
label(3 "2-yr Seamless")
label(4 "2-yr Delayed")
position(11) order(4 3 2 1) ring(0) symxsize(2) symysize(2) rows(4) size(small) region(lstyle(none) lcolor(none) color(none)))
title("College Enrollment by High School", size(medium))
ytitle("Percent of High School Graduates")
subtitle("Seamless and Delayed Enrollers")
yscale(range(0(20)100))
ylabel(0(20)100, nogrid)
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' ${agency_name} graduates."
"Postsecondary enrollment outcomes from NSC matched records. All other data from administrative records.", size(vsmall));
#delimit cr
graph export "figures/D2_Col_Enrl_Seamless_Delayed_by_HS.png", replace width(1600) height(1200)
}
Purpose: This analysis displays variations in college enrollment rates across high schools by examining the extent to which academic achievement at high school entry explains variation in college going across high schools. This analysis is useful to identify high schools with similar incoming student achievement profiles but divergent college enrollment rates; or on the other hand, high schools with similar college-going rates but different academic performance at high school entry.
Required Analysis File Variables:
sid
chrt_grad
enrl_1oct_grad_yr1_any
test_math_8_std
last_hs_code
last_hs_name
Analysis-Specific Sample Restrictions:
Ask Yourself - What might explain variation in college enrollment rates for high schools with similar incoming achievement? What might explain variation in incoming achievement for high schools with similar college enrollment rates?
Possible Next Steps or Action Plans: Repeat this analysis to include all first-time ninth graders (i.e. ninth grade cohorts) in place of graduates, and explore college enrollment within two years of high school completion. Additionally, replicate this analysis to explore the relationship between college enrollment and students’ ELA achievement at high school entry. Consider why schools with similar incoming student profiles may report dramatically different college-going rates. Conversely, consider why schools with dissimilar student bodies report similar matriculation rates to college.
Analytic Technique: Bivariate scatterplot of school-level average student test scores and college enrollment rates.
// College Enrollment Rates by Average 8th Grade Achievement
// Step 1: Load the college-going analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation AND have non-missing eighth grade math scores
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end') & !mi(test_math_8_std)
// Step 3: Obtain agency-level college enrollment rate and prior achievement score along with the position of their labels.
summ enrl_1oct_grad_yr1_any
local agency_mean_enroll = `r(mean)'*100
local agency_mean_enroll_label = `agency_mean_enroll' + 3
summ test_math_8_std
local agency_mean_test = `r(mean)'
local agency_mean_test_label = `agency_mean_test' + 0.15
// Step 4: Obtain school-level college enrollment rates and prior achievement scores
collapse (mean) test_math_8_std enrl_1oct_grad_yr1_any (count) N = sid, by(last_hs_code last_hs_name)
// Step 5: Multiply the college enrollment rate by 100 for graphical representation of the rates
replace enrl_1oct_grad_yr1_any = round((enrl_1oct_grad_yr1_any * 100), .1)
// Step 6: Shorten high school names and create a legend label for the graph
sort last_hs_name
replace last_hs_name = subinstr(last_hs_name, " High School", "", .)
gen hs_code_label = _n
levelsof last_hs_name, local(hs_names)
local count = 1
local legend_labels ""
foreach hs of local hs_names {
local legend_labels `"`legend_labels' `count' = `hs'"' `" "'
local ++count
}
// Step 7: Prepare to graph the results
// Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// Step 8: Graph the results
#delimit ;
twoway (scatter enrl_1oct_grad_yr1_any test_math_8_std, mlabel(hs_code_label) mlabsize(vsmall)
mlabposition(12) mlabcolor(dknavy) mstyle(x) msize(small) mcolor(dknavy)),
title("College Enrollment Rates by Prior Student Achievement")
subtitle("Seamless Enrollers")
xtitle("Average 8th Grade Math Standardized Score", linegap(0.3))
ytitle("Percent of High School Graduates" " ")
xscale(range(-0.8(0.2)1)) xlabel(-0.8(0.2)1)
yscale(range(0(20)100)) ylabel(0(20)100, nogrid)
legend(on order(3) col(1) label(3 `"`legend_labels'"')
region(color(none)) size(vsmall) position(2) ring(1) linegap(.75))
yline(`agency_mean_enroll', lpattern(dash) lcolor(dknavy) lwidth(vvthin))
xline(`agency_mean_test', lpattern(dash) lcolor(dkorange) lwidth(vvthin))
text(`agency_mean_enroll_label' -0.45 "${agency_name} Average College Enrollment Rate", size(2.0) color(dknavy))
text(20 `agency_mean_test_label' "${agency_name} Average" "Test Score", size(2.0) color(dkorange))
text(99 -0.5 "Below average math scores &" "above average college enrollment",
size(vsmall) justification(left))
text(99 0.8 "Above average math scores &" "above average college enrollment",
size(vsmall) justification(right))
text(2 -0.5 "Below average math scores &" "below average college enrollment",
size(vsmall) justification(left))
text(2 0.8 "Above average math scores &" "below average college enrollment",
size(vsmall) justification(right))
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' ${agency_name} graduates with eighth grade math scores. Postsecondary enrollment"
"outcomes from NSC matched records. All other data from ${agency_name} administrative records.", size(vsmall));
#delimit cr
graph export "figures/D3_Col_Enrl_by_Avg_Eighth.png", replace width(1600) height(1200)
Purpose: This analysis explores whether variation in college enrollment across high schools is similar among low-, middle, and top-achieving students. It also considers whether overall variation across schools derives from concentrated divergence among students scoring in a particular achievement range. Additionally, the analysis facilitates granular school-to-school comparisons to identify those especially under-, or over-performing within each achievement quartile. Finally, the analysis also helps identify which student subgroups require additional resources and support within each school.
Required Analysis File Variables:
sid
chrt_grad
enrl_1oct_grad_yr1_any
qrt_8_math_std
last_hs_code
last_hs_name
Analysis-Specific Sample Restrictions:
Ask Yourself
Possible Next Steps or Action Plans: Repeat this analysis to include all first-time ninth graders (i.e. ninth grade cohorts) in place of graduates, and explore college enrollment within two years of high school completion. Additionally, replicate this analysis to explore the relationship between college enrollment and students’ ELA achievement at high school entry. Consider why schools with similar incoming student profiles may report dramatically different college-going rates. Conversely, consider why schools with distinct student bodies may report similar matriculation rates to college.
Analytic Technique: Calculate the proportion of graduates who enrolled in college by October 1st following their high school graduation year by high school and 8th grade test score quartile.
// College Enrollment Rates by 8th Grade Achievement Quartiles
// Step 1: Load the college-going analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation AND have non-missing eighth grade math scores
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end') & !mi(qrt_8_math)
// Step 3: Obtain the overall agency-level high school graduation rate along with the position of its label.
summ enrl_1oct_grad_yr1_any
local agency_mean = `r(mean)'*100
local agency_mean_label = `agency_mean'+3
// Step 4: Obtain the agency-level college enrollment rate by test score quartile
preserve
collapse (mean) enrl_1oct_grad_yr1_any (count) N = sid, by(qrt_8_math)
tempfile agency_level
save `agency_level'
restore
// Step 5: Obtain school-level college enrollment rates by test score quartile and append the agency-level enrollment rates by quartile
collapse (mean) enrl_1oct_grad_yr1_any (count) N = sid, by(last_hs_code last_hs_name qrt_8_math)
append using `agency_level'
// Step 6: Shorten high school names and drop any high schools with fewer than 20 students
replace last_hs_code = 0 if last_hs_code == .
replace last_hs_name = "${agency_name} AVERAGE" if mi(last_hs_name)
replace last_hs_name = subinstr(last_hs_name, " High School", "", .)
drop if N < 20
// Step 7: Multiply the college enrollment rate by 100 for graphical representation of the rates
replace enrl_1oct_grad_yr1_any = round((enrl_1oct_grad_yr1_any * 100), .1)
// Step 8: Create a variable to sort schools within each test score quartile in ascending order
sort qrt_8_math enrl_1oct_grad_yr1_any
gen rank = _n
// Step 9: Prepare to graph the results
// Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// Step 10: Graph the results
#delimit ;
graph bar enrl_1oct_grad_yr1_any, over(last_hs_name, sort(rank) gap(0) label(angle(70) labsize(vsmall)))
over(qrt_8_math, relabel(1 "Bottom Quartile" 2 "2nd Quartile" 3 "3rd Quartile" 4 "Top Quartile") gap(400))
bar(1, fcolor(dknavy) finten(70) lcolor(dknavy) lwidth(thin))
blabel(bar, position(outside) format(%8.0f) size(tiny))
yscale(range(0(20)100))
ylabel(0(20)100, nogrid)
legend(off)
title("College Enrollment Rates")
subtitle("By Prior Student Achievement, Seamless Enrollers Only", size(msmall))
ytitle("Percent of High School Graduates")
yline(`agency_mean', lpattern(dash) lwidth(vvthin) lcolor(dknavy))
text(`agency_mean_label' 5 "${agency_name} Average", size(vsmall))
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' ${agency_name} graduates with eighth grade math scores. Postsecondary enrollment" "outcomes from NSC matched records. All other data from ${agency_name} administrative records.", size(vsmall));
#delimit cr
graph export "figures/D4_Col_Enrl_by_Eighth_Qrt.png", replace width(1600) height(1200)
Purpose: Research consistently finds wide variation in rates of persistence and completion across postsecondary institutions. This analysis examines whether high school graduates enroll in colleges and universities that provide the right academic fit to maximize their chances of completion. “Match”describes the extent high school graduates with strong academic records attend colleges and universities that allow them to take advantage of their ambition and abilities. While “matching” to an appropriately selective college is only one factor to consider when choosing a postsecondary institution, the implications of under-matching (i.e. lower rates of persistence and degree completion) suggest students should be encouraged to attend realistic, yet challenging postsecondary institutions.
Required Analysis File Variables:
sid
race_ethnicity
chrt_grad
highly_qualified
enrl_1oct_grad_yr1_any
enrl_1oct_grad_yr1_4yr
enrl_1oct_grad_yr1_2yr
Analysis-Specific Sample Restrictions:
Ask Yourself
Possible Next Steps or Action Plans: This analysis leads to important questions that warrant further exploration. What factors drive undermatch differences across student subgroups and high schools? To what extent is undermatching concentrated among first-time college-goers? To what extent is undermatching driven by students’ proximity to 2-year versus 4-year institutions? What college aspirations do incoming ninth graders hold, and do these aspirations change by the time they enter or complete 12th grade? To what extent are teachers, counselors, and administrators supported to work with students to cultivate postsecondary aspirations and weigh factors in the college selection process?
Analytic Technique: Calculate the proportion of highly qualified graduates who do not enroll in college, enroll in 2-year college, and enroll in least competitive and unranked 4-year colleges the fall following high school graduation.
// Rates of College Enrollment by College Type Among Highly Qualified Graduates
// Step 1: Load the college-going analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end')
// Step 3: Get total number of students in sample
gen total_count = _N
// Step 4: Further restrict sample to include only highly qualified students
keep if highly_qualified == 1
// Step 5: Create "undermatch" outcomes
gen no_college = (enrl_1oct_grad_yr1_any == 0)
gen enrl_2yr = (enrl_1oct_grad_yr1_2yr == 1)
gen enrl_4yr = (enrl_1oct_grad_yr1_4yr == 1)
// Step 6: Create agency-level outcomes for total undermatching rates
preserve
collapse (mean) no_college enrl_2yr enrl_4yr total_count (count) N = sid
gen group = 5
tempfile total
save `total'
restore
// Step 7: Create race/ethnicity-level outcomes for undermatching rates by race/ethnicity
collapse (mean) no_college enrl_2yr enrl_4yr total_count (count) N = sid , by(race_ethnicity)
append using `total'
replace group = 1 if race_ethnicity==1
replace group = 2 if race_ethnicity==2
replace group = 3 if race_ethnicity==3
replace group = 4 if race_ethnicity==5
drop if mi(group)
// Step 8: Multiply the college enrollment rate by 100 for graphical representation of the rates
foreach v of varlist no_college enrl_2yr enrl_4yr {
replace `v' = round(`v'*100, .1)
}
// Step 9: Multiply the outcome variables corresponding to undermatching by "-1" to visually display these rates as negative values
foreach var of varlist no_college enrl_2yr {
replace `var' = `var'*-1
}
// Step 10: Prepare to graph the results
// 1. Create labels for numbers in graph
gen pct_total = N/total_count
sort group
local numobs = _N
foreach v of numlist 1/`numobs' {
local pct_`v' = round(pct_total*100) in `v'
local count_`v' = N in `v'
}
// 2. Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// Step 11: Graph the results
#delimit ;
graph bar enrl_4yr enrl_2yr no_college, stack over(group,
relabel(1 `""African American" "`pct_1'% of Graduates""' 2 `""Asian American" "`pct_2'% of Graduates""' 3 `""Hispanic American" "`pct_3'% of Graduates""' 4 `""White" "`pct_4'% of Graduates""' 5 `""Total" "`pct_5'% of Graduates""')
label(labsize(2.5)) gap(80)) blabel(bar, format(%9.0f) size(small) position(inside) color(black))
bar(1, fcolor(dknavy*.7) lcolor(dknavy*.7) lwidth(vvthin))
bar(2, fcolor(dknavy*.2) lcolor(dknavy*.2) lwidth(vvthin))
bar(3, fcolor(dkorange) lcolor(dkorange) lwidth(vvthin))
yscale(range(-20(20)100))
ylabel(-20(20)100, nogrid labsize(small))
ylabel(-20 "20" 0 "0" 20 "20" 40 "40" 60 "60" 80 "80" 100 "100")
yline(0, lcolor(black) lwidth(vvthin))
title("Rates of Highly Qualified Students Attending College, by Race", size(medlarge) span)
subtitle("Among Graduates Eligible to Attend Four-Year Universities", size(*.8) span)
ytitle("Percent of Highly-Qualified Graduates" " ", size(small))
legend(region(lcolor(white)) position(12) row(1) label(1 "Enrolled at 4-Yr College")
label(2 "Enrolled at 2-Yr College") label(3 "Not Enrolled in College") symxsize(2) symysize(2) size(*.7))
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note(" " "Sample: `chrt_label' ${agency_name} first-time ninth graders. Students who transferred into or out of ${agency_name} are excluded"
"from the sample. Eligibility to attend a public four-year university is based on students' cumulative GPA and ACT/SAT scores."
"Sample includes `count_1' African American, `count_2' Asian American students, `count_3' Hispanic, and `count_4' White students."
"Post-secondary enrollment data are from NSC matched records. $admin_nsc_note", size(2));
#delimit cr
graph export "figures/D5_Col_Enrl_HiQualified_by_Type.png", replace width(1600) height(1200)
Purpose: This Strategic Performance Indicator explores gaps in college enrollment rates by ethnicity, before and after accounting for differences in prior academic achievement, socioeconomic status, and both of these background characteristics. While the analysis evaluates separately the college enrollment gaps between Black and White students and between Latino and White students, it can be modified to focus on the gap between any two races or ethnicities.
Required Analysis File Variables:
sid
chrt_grad
last_hs_code
race_ethnicity
test_math_8
frpl_ever
enrl_1oct_grad_yr1_any
Analysis-Specific Sample Restrictions:
Ask Yourself
Analytic Technique: Calculate the difference between the proportion of Black (or Latino) high school graduates and the proportion of White high school graduates who enrolled in college—in raw terms and after accounting for 8th grade test scores, for eligibility for Free or Reduced Price Lunch (FRPL), and for both of these characteristics.
// Gaps in Rates of College Enrollment Between Latino and White Graduates
// Step 1: Load the college-going analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation AND have non-missing eighth grade test scores AND non-missing FRPL status
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end')
keep if frpl_ever != . | test_math_8 != .
// Step 3: Include only black, Latino, and white students
keep if race_ethnicity==1 | race_ethnicity == 3 | race_ethnicity == 5
gen afam = (race_ethnicity == 1)
gen hisp = (race_ethnicity == 3)
gen white = (race_ethnicity == 5)
// Step 4: Estimate the unadjusted and adjusted differences in college enrollment between Latino and white students and between black and white students.
// 1. Create a unique codeentifier for each cohort at each high school, so that we can cluster the standard errors at the cohort/high school level
egen cluster_var = concat(chrt_grad last_hs_code)
// 2. Fit 4 separate regression models with and without control variables, and save the coefficients associated with each race.
// 2A. Estimate unadjusted enrollment gap
reg enrl_1oct_grad_yr1_any afam hisp, robust cluster(cluster_var)
gen afam_unadj = _b[afam]
gen hisp_unadj = _b[hisp]
// 2B. Estimate enrollment gap adjusting for prior achievement
reg enrl_1oct_grad_yr1_any afam hisp test_math_8, robust cluster(cluster_var)
gen afam_adj_prior_ach = _b[afam]
gen hisp_adj_prior_ach = _b[hisp]
// 2C. Estimate enrollment gap adjusting for FRPL status
reg enrl_1oct_grad_yr1_any afam hisp frpl_ever, robust cluster(cluster_var)
gen afam_adj_frpl = _b[afam]
gen hisp_adj_frpl = _b[hisp]
// 2D. Estimate enrollment gap adjusting for prior achievement and FRPL status
reg enrl_1oct_grad_yr1_any afam hisp frpl_ever test_math_8, robust cluster(cluster_var)
gen afam_adj_prior_frpl = _b[afam]
gen hisp_adj_prior_frpl = _b[hisp]
//3. Transform the regression coefficients estimated in Step 4.2 to be displayed in positive % terms
foreach race in afam hisp {
replace `race'_unadj = (0 - `race'_unadj) * 100
replace `race'_adj_prior_ach = (0 - `race'_adj_prior_ach) * 100
replace `race'_adj_frpl = (0 - `race'_adj_frpl) * 100
replace `race'_adj_prior_frpl = (0 - `race'_adj_prior_frpl) * 100
}
// Step 5: Retain a data file containing only the regression coefficients
keep afam_* hisp_*
duplicates drop
// Step 6: Prepare to graph the results
// Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// Step 7: Graph the results
// 1. Graph results for black and white students
#delimit ;
graph bar afam_unadj afam_adj_prior_ach afam_adj_frpl afam_adj_prior_frpl,
legend(row(2) size(vsmall) region(lcolor(white))
label(1 "Unadjusted enrollment gap")
label(2 "Gap adjusted for prior achievement")
label(3 "Gap adjusted for FRPL status")
label(4 "Gap adjusted for prior achievement & FRPL status"))
outergap(300)
blabel(bar, format(%9.0f) size(vsmall))
bar(1, fcolor(dknavy) lcolor(dknavy) fi(inten100))
bar(2, fcolor(dknavy) lcolor(dknavy) fi(inten70))
bar(3, fcolor(dknavy) lcolor(dknavy) fi(inten50))
bar(4, fcolor(dknavy) lcolor(dknavy) fi(inten20))
title("Differences In Rates Of College Enrollment"
"Between Black High School Graduates And White High"
"School Graduates", size(med))
ytitle("Percentage Points", margin(2 2 0 0) size(small))
yscale(range(0(10)50)) ylabel(0(10)50, labsize(small))
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' high school graduates. Postsecondary enrollment outcomes from NSC matched records. All other data from ${agency_name} administrative records.", size(vsmall));
#delimit cr
graph export "figures/D6a_Col_Enrl_Gap_Black.png", replace width(1600) height(1200)
#delimit ;
graph bar hisp_unadj hisp_adj_prior_ach hisp_adj_frpl hisp_adj_prior_frpl,
legend(row(2) size(vsmall) region(lcolor(white))
label(1 "Unadjusted enrollment gap")
label(2 "Gap adjusted for prior achievement")
label(3 "Gap adjusted for FRPL status")
label(4 "Gap adjusted for prior achievement & FRPL status"))
outergap(300)
blabel(bar, format(%9.0f) size(vsmall))
bar(1, fcolor(dknavy) lcolor(dknavy) fi(inten100))
bar(2, fcolor(dknavy) lcolor(dknavy) fi(inten70))
bar(3, fcolor(dknavy) lcolor(dknavy) fi(inten50))
bar(4, fcolor(dknavy) lcolor(dknavy) fi(inten20))
title("Differences In Rates Of College Enrollment"
"Between Latino High School Graduates And White High"
"School Graduates", size(med))
ytitle("Percentage Points", margin(2 2 0 0) size(small))
yscale(range(0(10)50)) ylabel(0(10)50, labsize(small))
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' high school graduates. Postsecondary enrollment outcomes from NSC matched records." "All other data from ${agency_name} administrative records.", size(vsmall));
#delimit cr
graph export "figures/D6b_Col_Enrl_Gap_Latino.png", replace width(1600) height(1200)
Purpose: This SPI highlights the variation in college-going rates across high schools when students with similar prior achievement are compared. To conduct these comparisons, we first sort all incoming ninth-graders into quartiles based on their 8th grade test scores. We then examine college-going rates by high school among graduates within each of these quartiles.
Required Analysis File Variables:
sid
chrt_grad
last_hs_name
hs_diploma
qrt_8_math_std
enrl_1oct_grad_yr1_any
Analysis-Specific Sample Restrictions:
Ask Yourself
Analytic Technique: Calculate the share of students in each 8th grade test score quartile at each high school who enroll in college seamlessly after high school graduation.
// College Enrollment Rates by 8th Grade Achievement Quartile Bubbles
// Step 1: Load the college-going analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation AND have non-missing eighth grade test scores
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end')
keep if qrt_8_math != .
// Step 3: Create agency- and school-level average outcomes for each quartile
// 1. Calculate the mean of each outcome variable by high school
collapse (sum) enrl_1oct_grad_yr1_any hs_diploma, by(last_hs_name qrt_8_math)
gen pct_enrl = enrl_1oct_grad_yr1_any / hs_diploma * 100
// 2. Calculate the mean of each outcome variable for the agency as a whole
egen num = sum(enrl_1oct_grad_yr1_any), by(qrt_8_math)
egen denom = sum(hs_diploma), by(qrt_8_math)
gen agency_avg = num / denom * 100
drop num denom
// Step 4: Create a variable to identify the test score quartile
gen agency_quartile_code = .
forvalues qrt = 1(1)4 {
local qrt_plot = `qrt' * 2
replace agency_quartile_code = 1.`qrt_plot' if qrt_8_math == `qrt'
}
// Step 5: Prepare to graph the results
// Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// Step 6: Graph the results
#delimit ;
graph twoway scatter pct_enrl agency_quartile_code [aweight = hs_diploma],
msymbol(Oh) msize(vsmall) mcolor(dknavy) ||
scatter agency_avg agency_quartile_code,
mcolor(cranberry) msymbol(D) msize(small)
title("College Enrollment Rates Among High School"
"Graduates Within Quartile Of Prior Achievement,"
"By High School", size(med))
xscale(range(1 2)) yscale(range(0 105)) ylabel(0 20 40 60 80 100)
xlabel(1.2 "Q1" 1.4 "Q2" 1.6 "Q3" 1.8 "Q4", labsize(small))
xtitle(" " "Quartile of Prior Achievement") ytitle("Percent" " ")
ylabel(,nogrid) legend(off)
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' high school graduates. Postsecondary enrollment outcomes from NSC matched records."
"All other data from ${agency_name} administrative records.", size(vsmall));
#delimit cr
graph export "figures/D7_Col_Enrl_by_Eighth_Qrt_Bubbles.png", replace width(1600) height(1200)
Purpose: This Strategic Performance Indicator examines the prevalence of “undermatch” in the agency—that is, the extent to which high school graduates with strong academic records pursue enrollment in colleges and universities less selective than those for which they are likely qualified. The SPI does so by illustrating the rates at which highly qualified graduates are enrolling at 2-year colleges, less competitive 4-year colleges, or forgoing college altogether, instead of pursuing selective colleges that may provide a better academic and social fit for these students’ potential, ambition, and preparation.
Required Analysis File Variables:
sid
chrt_grad
highly_qualified
first_college_opeid_4yr
enrl_1oct_grad_yr1_any
enrl_1oct_grad_yr1_4yr
enrl_1oct_grad_yr1_2yr
Analysis-Specific Sample Restrictions:
Ask Yourself
// Undermatch Rates Among Highly Qualified High School Graduates
// Step 1: Load the post-sec analysis file into Stata
use "$data/college_going_analysis", clear
// Step 2: Keep students in high school graduation cohorts you can observe enrolling in college the fall after graduation AND are highly qualified
local chrt_grad_begin = ${chrt_grad_begin}
local chrt_grad_end = ${chrt_grad_end}
keep if (chrt_grad >= `chrt_grad_begin' & chrt_grad <= `chrt_grad_end')
keep if highly_qualified == 1
// Step 3: Create college enrollment indicator variables for each college selectivity level. This script assumes that there are 5 levels of selectivity, as in Barron's College Rankings: Most Competitive (1), Highly Competitive (2), Very Competitive (3), Competitive (4), Least Competitive (5), as well as a category for colleges without assigned selectivity (assumed to be not competitive).
//1. Create college enrollment dummy variables for each of the five selectivity levels
forvalues i = 1/5 {
gen enrl_1oct_grad_yr1_4yr_`i' = (enrl_1oct_grad_yr1_4yr == 1 & rank == `i')
}
//2. Create a college enrollment dummy variable for colleges that are not ranked
gen enrl_1oct_grad_4yr_nr = (enrl_1oct_grad_yr1_4yr == 1 & (rank == 6 | rank ==. ))
//3. Rename and label the college enrollment variables with clear labels
rename enrl_1oct_grad_yr1_4yr_1 enrl_1oct_grad_4yr_mc
rename enrl_1oct_grad_yr1_4yr_2 enrl_1oct_grad_4yr_hc
rename enrl_1oct_grad_yr1_4yr_3 enrl_1oct_grad_4yr_vc
rename enrl_1oct_grad_yr1_4yr_4 enrl_1oct_grad_4yr_c
rename enrl_1oct_grad_yr1_4yr_5 enrl_1oct_grad_4yr_lc
label var enrl_1oct_grad_4yr_mc "Enrolled at Most Competitive College Fall After HS Grad"
label var enrl_1oct_grad_4yr_hc "Enrolled at Highly Competitive College Fall After HS Grad"
label var enrl_1oct_grad_4yr_vc "Enrolled at Very Competitive College Fall After HS Grad"
label var enrl_1oct_grad_4yr_c "Enrolled at Competitive College Fall After HS Grad"
label var enrl_1oct_grad_4yr_lc "Enrolled at Least Competitive College Fall After HS Grad"
label var enrl_1oct_grad_4yr_nr "Enrolled at Non-Competitive College Fall After HS Grad"
//4. Check to make sure that each student who appears enrolled in college as of the first fall after high school graduation is associated with one and only one college selectivity level
assert enrl_1oct_grad_4yr_mc + enrl_1oct_grad_4yr_hc + enrl_1oct_grad_4yr_vc + enrl_1oct_grad_4yr_c + enrl_1oct_grad_4yr_lc + enrl_1oct_grad_4yr_nr == 1 if enrl_1oct_grad_yr1_4yr == 1
// Step 4: Create undermatch outcomes
//1. Not enrolled in college
gen no_college = (enrl_1oct_grad_yr1_any == 0)
//2. Enrolled in a 2-year college
gen enrl_2yr = (enrl_1oct_grad_yr1_2yr == 1)
//3. Enrolled in a least competitive 4-year college or a 4-year college without an assigned selectivity
gen enrl_4yr_under = (enrl_1oct_grad_4yr_nr == 1)
replace enrl_4yr_under = 1 if enrl_1oct_grad_4yr_lc == 1
//4. Enrolled in a 4-year college with a selectivity rating of Competitive, Very Competitive, Most Competitive, or Highly Competitive
gen enrl_4yr_match = (enrl_1oct_grad_4yr_c == 1 | enrl_1oct_grad_4yr_vc == 1 | enrl_1oct_grad_4yr_hc == 1 | enrl_1oct_grad_4yr_mc == 1)
//5. Check to make sure that each student is associated one and only one undermatch outcome
// assert no_college + enrl_2yr + enrl_4yr_under + enrl_4yr_match == 1
// Step 5: Create agency-average undermatch outcomes and transform them into % terms
collapse (mean) no_college enrl_2yr enrl_4yr_under enrl_4yr_match (count) N = sid
foreach v of varlist no_college enrl_2yr enrl_4yr_under enrl_4yr_match {
replace `v' = round(`v' * 100, 0.1)
}
// Step 6: Prepare to graph the results
// Generate a cohort label to be used in the footnote for the graph
local temp_begin = `chrt_grad_begin'-1
local temp_end = `chrt_grad_end'-1
if `chrt_grad_begin'==`chrt_grad_end' {
local chrt_label "`temp_begin'-`chrt_grad_begin'"
}
else {
local chrt_label "`temp_begin'-`chrt_grad_begin' through `temp_end'-`chrt_grad_end'"
}
// Step 7: Graph the results
#delimit ;
graph bar no_college enrl_2yr enrl_4yr_under, stack
blabel(bar, format(%9.1f) size(2.05) position(inside) color(white))
bar(1, fcolor(dknavy) lcolor(dknavy) finten(200) lwidth(thin))
bar(2, fcolor(dknavy) lcolor(dknavy) finten(90) lwidth(thin))
bar(3, fcolor(dknavy) lcolor(dknavy) finten(40) lwidth(thin))
yscale(range(0(5)65)) outergap(400)
ylabel(0(5)65, labsize(small))
title("Undermatch Rates by Agency"
"Among Highly Qualified High School Graduates", size(med))
ytitle("Percent of High School Graduates" " ", size(small))
legend(region(lcolor(white))
label(1 "Not Enrolled in College")
label(2 "Enrolled at 2-Year College")
label(3 "Enrolled at Unranked or Less Competitive 4-Year College")
symxsize(*.7) symysize(*.7) size(*.7))
graphregion(color(white) fcolor(white) lcolor(white))
plotregion(color(white) fcolor(white) lcolor(white))
note("Sample: `chrt_label' high school graduates. Postsecondary enrollment outcomes from NSC matched records."
"All other data from ${agency_name} administrative records.", size(vsmall)) ;
#delimit cr
graph export "figures/D8_Undermatching_HiQualified.png", replace width(1600) height(1200)


Leetcode Same Tree (python)
15 June 2014
Requirement:
Given two binary trees, write a function to check if they are equal or not.
Two binary trees are considered equal if they are structurally identical and the nodes have the same value.
Analysis:
Two trees are same, if and only if,
Current two nodes are the same. (Both none, or Both not None and have the same value)
The left/right subtrees of current two nodes are the same
Code:
1 # Definition for a binary tree node
2 # class TreeNode:
3 # def __init__(self, x):
4 # self.val = x
5 # self.left = None
6 # self.right = None
7
8 class Solution:
9 # @param p, a tree node
10 # @param q, a tree node
11 # @return a boolean
12 def isSameTree(self, p, q):
13 if p and q and p.val == q.val:
14 return self.isSameTree(p.left, q.left) \
15 and self.isSameTree(p.right, q.right)
16 elif p is None and q is None:
17 return True
18 else:
19 return False
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Copyright © 2015-2022 Guilherme Andrade
Version: 1.2.3
Authors: Guilherme Andrade (g@gandrade.net).
taskforce
allows you to parallelise arbitrary tasks in a controlled way.
Tasks = #{ some_task => taskforce:task(fun work/0, Args, #{ timeout => 2000 }),
similar_task => taskforce:task(fun work/0, Args123, #{ timeout => 2500 }),
other_task => taskforce:task(fun other_work/1, OtherArgs, #{ timeout => 500 }) }.
#{ completed := Completed } = taskforce:execute(Tasks),
SomeTaskResult = maps:get(some_task, Completed),
SimilarTaskResult = maps:get(similar_task, Completed),
OtherTaskResult = maps:get(other_task, Completed).
ExecutionOptions = #{ max_workers => 8, timeout => 5000 },
#{ completed := Completed } = taskforce:execute(Tasks, ExecutionOptions),
% ...
% ...
#{ individual_timeouts := IndividualTimeouts } = taskforce:execute(Tasks),
(length(IndividualTimeouts) > 0
andalso io:format("oh noes! tasks with ids ~p timed-out",
[IndividualTimeouts]))
% ...
#{ global_timeouts := GlobalTimeouts } = taskforce:execute(Tasks),
(length(GlobalTimeouts) > 0
andalso io:format("execution ran out of time; tasks with ids ~p timed-out",
[GlobalTimeouts]))
%
% Calculate first 200 prime numbers using 4 processes,
% with a timeout of 2s per individual calculation and 10s
% for the whole batch.
%
NrOfPrimes = 200,
Tasks =
maps:from_list(
[{Nth, taskforce:task(fun fancy_lib:find_nth_prime/1,
[Nth], #{ timeout => 2000 })}
|| Nth <- lists:seq(1, NrOfPrimes)]),
ExecutionOptions =
#{ % default is the sum of all individual task timeouts
timeout => 10000,
% default is number of active schedulers
max_workers => 4 },
#{ completed := NthPrimes } = taskforce:execute(Tasks, ExecutionOptions),
io:format("200th prime is: ~p~n", [maps:get(200, NthPrimes)]).
Also in examples/
.
Generated by EDoc


public interface JsonContext
Converts objects to and from JSON format.
-
Method Summary
Modifier and TypeMethodDescription<T> JsonBeanReader<T>
createBeanReader
(BeanType<T> beanType, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) Create and return a new bean reading for the bean type given the JSON options and source.<T> JsonBeanReader<T>
createBeanReader
(Class<T> cls, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) Create and return a new bean reading for the bean type given the JSON options and source.com.fasterxml.jackson.core.JsonGenerator
createGenerator
(Writer writer) Create and return a new JsonGenerator for the given writer.com.fasterxml.jackson.core.JsonParser
createParser
(Reader reader) Create and return a new JsonParser for the given reader.boolean
isSupportedType
(Type genericType) Return true if the type is known as an Entity bean or a List Set or Map of entity beans.<T> T
Convert json parser input into a Bean of a specific type.<T> T
toBean
(Class<T> cls, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) Convert json parser input into a Bean of a specific type additionally using JsonReadOptions..<T> T
Convert json reader input into a Bean of a specific type.<T> T
toBean
(Class<T> rootType, Reader json, JsonReadOptions options) Convert json reader input into a Bean of a specific type additionally using JsonReadOptions.<T> T
Convert json string input into a Bean of a specific type.<T> T
toBean
(Class<T> rootType, String json, JsonReadOptions options) Convert json string input into a Bean of a specific type additionally using JsonReadOptions.<T> void
toBean
(T target, com.fasterxml.jackson.core.JsonParser parser) Read json parser input into a given Bean.<T> void
toBean
(T target, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) Read json parser input into a given Bean additionally using JsonReadOptions.
SeetoBean(Class, JsonParser)
for details modified.<T> void
Read json reader input into a given Bean.
SeetoBean(Class, JsonParser)
for details<T> void
toBean
(T target, Reader json, JsonReadOptions options) Read json reader input into a given Bean additionally using JsonReadOptions.
SeetoBean(Class, JsonParser)
for details modified.<T> void
Read json string input into a given Bean.
SeetoBean(Class, JsonParser)
for details<T> void
toBean
(T target, String json, JsonReadOptions options) Read json string input into a given Bean additionally using JsonReadOptions.
SeetoBean(Class, JsonParser)
for detailsReturn the bean or collection as JSON string.void
Write the bean or collection to the JsonGenerator.void
Write the bean or collection to the JsonGenerator using the FetchPath.void
toJson
(Object value, com.fasterxml.jackson.core.JsonGenerator generator, JsonWriteOptions options) Deprecated in favour of using PathProperties by itself.Return the bean or collection as JSON string using FetchPath.toJson
(Object value, JsonWriteOptions options) Deprecated in favour of using PathProperties by itself.void
Write the bean or collection in JSON format to the writer.void
Write the bean or collection as json to the writer using the FetchPath.void
toJson
(Object value, Writer writer, JsonWriteOptions options) Deprecated in favour of using PathProperties by itself.toJsonPretty
(Object value) Return the bean or collection as JSON string in pretty format.<T> List<T>
Convert json parser input into a list of beans of a specific type.<T> List<T>
toList
(Class<T> cls, com.fasterxml.jackson.core.JsonParser json, JsonReadOptions options) Convert json parser input into a list of beans of a specific type additionally using JsonReadOptions.<T> List<T>
Convert json reader input into a list of beans of a specific type.<T> List<T>
toList
(Class<T> rootType, Reader json, JsonReadOptions options) Convert json reader input into a list of beans of a specific type additionally using JsonReadOptions.<T> List<T>
Convert json string input into a list of beans of a specific type.<T> List<T>
toList
(Class<T> rootType, String json, JsonReadOptions options) Convert json string input into a list of beans of a specific type additionally using JsonReadOptions.Use the genericType to determine if this should be converted into a List or bean.Use the genericType to determine if this should be converted into a List or bean.Use the genericType to determine if this should be converted into a List or bean.void
writeScalar
(com.fasterxml.jackson.core.JsonGenerator generator, Object scalarValue) Write a scalar types known to Ebean to Jackson.
-
Method Details
-
toBean
Convert json string input into a Bean of a specific type.- Throws:
JsonIOException
- When IOException occurs
-
toBean
Convert json string input into a Bean of a specific type additionally using JsonReadOptions.- Throws:
JsonIOException
- When IOException occurs
-
toBean
Convert json reader input into a Bean of a specific type.- Throws:
JsonIOException
- When IOException occurs
-
toBean
Convert json reader input into a Bean of a specific type additionally using JsonReadOptions.- Throws:
JsonIOException
- When IOException occurs
-
toBean
Convert json parser input into a Bean of a specific type.- Throws:
JsonIOException
- When IOException occurs
-
toBean
<T> T toBean(Class<T> cls, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) throws JsonIOException Convert json parser input into a Bean of a specific type additionally using JsonReadOptions..- Throws:
JsonIOException
- When IOException occurs
-
toBean
Read json parser input into a given Bean.
Note: This is a kind of "update". Only properties in the json will be modified. Embedded Lists and Maps will become new instances, so the object identity will not be preserved here.- Throws:
JsonIOException
- When IOException occurs
-
toBean
<T> void toBean(T target, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) throws JsonIOException Read json parser input into a given Bean additionally using JsonReadOptions.
SeetoBean(Class, JsonParser)
for details modified.- Throws:
JsonIOException
- When IOException occurs
-
toBean
Read json reader input into a given Bean.
SeetoBean(Class, JsonParser)
for details- Throws:
JsonIOException
- When IOException occurs
-
toBean
Read json reader input into a given Bean additionally using JsonReadOptions.
SeetoBean(Class, JsonParser)
for details modified.- Throws:
JsonIOException
- When IOException occurs
-
toBean
Read json string input into a given Bean.
SeetoBean(Class, JsonParser)
for details- Throws:
JsonIOException
- When IOException occurs
-
toBean
Read json string input into a given Bean additionally using JsonReadOptions.
SeetoBean(Class, JsonParser)
for details- Throws:
JsonIOException
- When IOException occurs
-
createBeanReader
<T> JsonBeanReader<T> createBeanReader(Class<T> cls, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) throws JsonIOException Create and return a new bean reading for the bean type given the JSON options and source.Note that JsonOption provides an option for setting a persistence context and also enabling further lazy loading. Further lazy loading requires a persistence context so if that is set on then a persistence context is created if there is not one set.
- Throws:
JsonIOException
-
createBeanReader
<T> JsonBeanReader<T> createBeanReader(BeanType<T> beanType, com.fasterxml.jackson.core.JsonParser parser, JsonReadOptions options) throws JsonIOException Create and return a new bean reading for the bean type given the JSON options and source.Note that JsonOption provides an option for setting a persistence context and also enabling further lazy loading. Further lazy loading requires a persistence context so if that is set on then a persistence context is created if there is not one set.
- Throws:
JsonIOException
-
toList
Convert json string input into a list of beans of a specific type.- Throws:
JsonIOException
- When IOException occurs
-
toList
Convert json string input into a list of beans of a specific type additionally using JsonReadOptions.- Throws:
JsonIOException
- When IOException occurs
-
toList
Convert json reader input into a list of beans of a specific type.- Throws:
JsonIOException
- When IOException occurs
-
toList
Convert json reader input into a list of beans of a specific type additionally using JsonReadOptions.- Throws:
JsonIOException
- When IOException occurs
-
toList
Convert json parser input into a list of beans of a specific type.- Throws:
JsonIOException
- When IOException occurs
-
toList
<T> List<T> toList(Class<T> cls, com.fasterxml.jackson.core.JsonParser json, JsonReadOptions options) throws JsonIOException Convert json parser input into a list of beans of a specific type additionally using JsonReadOptions.- Throws:
JsonIOException
- When IOException occurs
-
toObject
Use the genericType to determine if this should be converted into a List or bean.- Throws:
JsonIOException
- When IOException occurs
-
toObject
Use the genericType to determine if this should be converted into a List or bean.- Throws:
JsonIOException
- When IOException occurs
-
toObject
Object toObject(Type genericType, com.fasterxml.jackson.core.JsonParser jsonParser) throws JsonIOException Use the genericType to determine if this should be converted into a List or bean.- Throws:
JsonIOException
- When IOException occurs
-
toJson
Return the bean or collection as JSON string.- Throws:
JsonIOException
- When IOException occurs
-
toJsonPretty
Return the bean or collection as JSON string in pretty format.- Throws:
JsonIOException
- When IOException occurs
-
toJson
Write the bean or collection in JSON format to the writer.- Throws:
JsonIOException
- When IOException occurs
-
toJson
void toJson(Object value, com.fasterxml.jackson.core.JsonGenerator generator) throws JsonIOException Write the bean or collection to the JsonGenerator.- Throws:
JsonIOException
- When IOException occurs
-
toJson
Return the bean or collection as JSON string using FetchPath.- Throws:
JsonIOException
- When IOException occurs
-
toJson
Write the bean or collection as json to the writer using the FetchPath.- Throws:
JsonIOException
-
toJson
void toJson(Object value, com.fasterxml.jackson.core.JsonGenerator generator, FetchPath fetchPath) throws JsonIOException Write the bean or collection to the JsonGenerator using the FetchPath.- Throws:
JsonIOException
-
toJson
void toJson(Object value, com.fasterxml.jackson.core.JsonGenerator generator, JsonWriteOptions options) throws JsonIOException Deprecated in favour of using PathProperties by itself. Write json to the JsonGenerator using the JsonWriteOptions.- Throws:
JsonIOException
-
toJson
Deprecated in favour of using PathProperties by itself. With additional options.- Throws:
JsonIOException
- When IOException occurs
-
toJson
Deprecated in favour of using PathProperties by itself. Convert a bean or collection to json string.- Throws:
JsonIOException
- When IOException occurs
-
isSupportedType
Return true if the type is known as an Entity bean or a List Set or Map of entity beans. -
createGenerator
Create and return a new JsonGenerator for the given writer.- Throws:
JsonIOException
- When IOException occurs
-
createParser
Create and return a new JsonParser for the given reader.- Throws:
JsonIOException
- When IOException occurs
-
writeScalar
void writeScalar(com.fasterxml.jackson.core.JsonGenerator generator, Object scalarValue) throws IOException Write a scalar types known to Ebean to Jackson.Ebean has built in support for java8 and Joda types as well as the other standard JDK types like URI, URL, UUID etc. This is a fast simple way to write any of those types to Jackson.
- Throws:
IOException
-


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A single page demonstrating the usage of Boostrap for attempting to copy Wikipedia's homepage.
A single page demostrating Floats.
A website demonstrating simple HTML and CSS.
My first attempt at having fun with CSS.
Evan Hadfield was born and raised in Southern Califirnia before moving to the San Francisco Bay Area for university. Out of school Evan pursued video production working with major tech companies in and around San Francisco. He then transitioned into sotware where he worked on video post-production plug-ins. Being exposed to the tech industry, the gradual shift into web developing was a natural progression.


This should shock us all: Rahul Gandhi on report saying 10,000 Jharkhand adivasis booked for sedition
Rahul Gandhi says a news report saying over 10,000 Jharkhand adivasis were booked for sedition in a single district between 2017-18, should shock "the conscience of our nation".
HIGHLIGHTS
- Rahul tweets news report describing police crackdown on adivasis
- Over 11,000 booked for disturbing public order: Report
- Over 10,000 accused of sedition, says report
Rahul Gandhi says a news report saying over 10,000 Jharkhand adivasis were booked for sedition in a single district between 2017 and 2018, should shock "the conscience of our nation".
An editor at Scroll.in who reported the story said it was about "possibly the largest number of people booked for sedition at one time in one district anywhere in India".
Citing 19 FIRs registered in Khunti district, her report describes a police crackdown against an adivasi movement called Pathalgadi
It says over 11,000 people were booked for "a host of offences related to disturbing public order", including under IPC Section 124A, which deals with sedition.
Those facing sedition charges "constitute two percent of Khunti’s population," the report adds.
"The actual number of sedition-accused in the district could, in fact, be higher since it is widely believed that there are more than just 19 FIRs against Pathalgadi supporters."
In a tweet he posted on Wednesday, Rahul Gandhi described the sedition law as "draconian".
The Congress is bracing for another electoral face-off with the BJP in Jharkhand, which will be held in five phases in November and December.


World Cup Qualifier: India score last-minute goal to pull off thrilling draw against Afghanistan, has mountain to climb in last match
by JKR StaffIndia on Thursday scored a last-minute goal to pull off a thrilling draw against Afghanistan in the World Cup football qualifier as the match ended 1-1. Playing at Central Republican Stadium in Dushanbe of Tajikistan, the Indians were put on the defensive for most part of the first half as Afghanistan displayed an aggressive style of football. Their perseverance paid off in the extra time of the first half as Zelly Nazary gave his side the much-needed lead.
This brought the best out of Indians in the second half as they began to play with the much-needed purpose and launched a series of attacks. They were rewarded in the dying minutes of the game when Seminlen Doungel netted home by converting the superb corner kick by Brandon Fernandes. Both Doungel and Fernandes are teammates with FC Goa.
India would have desperately wanted a win since the men in blue have only been able to draw their last two encounters against Bangladesh (1-1) and Qatar (0-0). They had lost their first match of the qualifying campaign to Oman 1-2.
India are currently placed fourth in the Group E with three points. Qatar with 10 and Oman with 9 points are currently sitting comfortably on the top two positions. Afghanistan are placed third with 4 points.
Though India are still not out of the World Cup contention, they will have to win against Oman in their next encounter, which will be an uphill task.


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Basic Information
- Name: Sakura Sasaki (咲良 佐々城)
- Place of birth: Osaka
- Date of birth: 12.1.2002
- Age: 19
- Species: Human
- Gender: Female
- Pronouns: She/her, it/it's
- Sexual Orientation: straight
- Relationship Status: Taken
- Religion: Christian (Evangelical Lutheran)
- Zodiac: Capricorn
- Writing Hand: Right
- Home-country: Japan
- Accent: fluent Japanese, and really bad Finnish
- Language(s): Japan, and Finnish
Occupation
- Past occupations: High school (2015-2018)
- Current occupation: Junior software developer (2019-present)
Physical Traits
- Skin Tone: Pale
- Height: 167cm (5'6")
- Displacement (Weight): 58kg
- Overall Body Type: Hourglass
K-18 Information
- Breast size: 30DD
- Band/Bust/Waist: 81/94/71 cm (30/35/26 in)
- Dom/Sub/Switch: Sub
- Turn ons: kissing
- Turn offs: Blood in places blood shouldn't be and scat
- Sexuality: Straight
- Gender Preference: Male
Distinguishing Features
- Eye Colours: light blue
- Hair Colour(s): dark brown
- Hair Style: Usually has her hair free.
- Scars: N/A
- Tattoos: N/A
- Birthmarks: N/A
- Special Markings: N/A
- Physical Disorders or Abnormalities: N/A
Character Traits
- Personality: Tsundere
- Good Qualities: Actually good doing her job.
- Bad Qualities: Usually rude to people.
- Habits: Every time she goes to home she usually turns on her computer.
- Hobbies: Programming
- Interests: Anime and manga stuff.
Psychological
- Phobias: Agoraphobia
- Fears: Large crowds
- Addictions: Hentai
- Peeves: Perverted people.
- Secrets: Classified
- Desires: More anime/manga stuff.
- Regrets: being rude to her boyfriend.
- Obsessions: Finnish people.
- Intelligence (IQ): 110
- Mental Illnesses: Depression
- Nervous Habits: She vibrates her leg
Self Perception
How others may perceive them
Likes
- Likes: Doing her things in silence.
- Colour: Pink, blue
- Food: Karelian pie
- Flavour: Salmiakki
- Drink: Coffee.
- Occasion: Any anime convetions, Lan parties.
- Holiday: Winter holiday.
- Day of the week: Friday.
- Season: Winter
- Place: Her own apartment.
- Music: Black/Death metal
- Movie: Girls und panzer der film
- TV Show: High school DxD
- Game/Videogame: Overwatch
- Gadget: mobile phone.
- Sound: Sound stepping on snow
- Smell: Food.
- Favourite Animal(s): Cats.
- Characteristics in others: Honesty
Dislikes
- Dislikes: Large crowds
- Colour: Orange
- Food: Mämmi
- Flavour: Cherry candies
- Drink: Any alcohol
- Occasion: k-pop festivals
- Holiday: Autumn holiday
- Day of the week: Monday
- Season: Autumn
- Place: America
- Music: k-pop
- Movie: Sword art online ordinal Scale
- TV Show: Sword art online.
- Game/Videogame: CS:GO
- Gadget: Any Apple products
- Sound: scratching a metal plate.
- Smell: Smell of cigarette smoke
- Animal(s): Any bugs
- Characteristics in other people: dumb, smoking, drinking.
Relationships
- Father: Haruto Sasaki
- Mother: Akari Sasaki
- Sisters: Yui Sasaki
- Relatives: Kane Sasaki (from father's side)
- (Future) Children: N/A
- Dead Relatives: Hina Watanabe
- Best Friends: Sara ito (Her previous classmate)
- Friends: Yua Takanashi (Her current work mate)
- Pet(s): N/A
- Current Lover(s): Mikko Koskinen
- Love Interest(s): N/A
- Admirer(s): Mikko Koskinen
- Dating History?: N/A
Wardrobe
Images can be found here.
Extras
- Allergies: Allergic to bees
- Lucky Item (Number, Letter etc.): Number 6
- Blood Type: O+
Specifics
- Confidence: 8/10
- Wisdom: 9/10
- Sanity: 4/10
- Agility: 8/10
- Stamina: 7/10
- Cooperation: 9/10
- Smoker: no
- Drinker: no
- Nail Biter: no
- Athletic: no
- Bookworm: yes
- Kleptomaniac: no
- Party Animal: no
- Vegetarian: no
- Workaholic: no
- Glasses: yes
- Contacts: no
- Rebellious: no
- Imagination: 8/10
- Tolerance: 4/10
- Temper: 2/10
- Patience: 4/10
- Charisma: 5/10
- Discipline: 4/10
- Optimist or Pessimist: Optimist
- Introvert or Extrovert: Introvert
- Daredevil or Cautious: Cautious
- Logical or Emotional: both
- Disorderly or Neat: Neat
- Working or Relaxing: Relaxing
- Confident or Unsure: Confident
Backstory
When she was in high school at the second grade, she's class got a new exchange student from finland called Mikko Koskinen. First she didn't show much interest on him, until the he was a partner in a school group project. By the time she was first very rude for him, until the Mikko saved her from large school fire. Few days weeks later she asked the exchange student to come to school backyard. When Sakura and Mikko arrived to school backyard, Sakura almost immediately without words gave Mikko her first kiss, and confessed her love to mikko. For Sakura's lucky Mikko kissed back and telled her his feelings for her.
By the time when Sakura and Mikko got graduated, Sakura was almost a whole year without her lover. in the year 2019 Mikko came back from finland, to stay in japan. few weeks later when couples move together Sakura got a job as a junior software developer from the company called codeworker.com


Bello is an awesome new way to message with your teammates.
Stop wasting time on meetings and start using Bello to have discussions and share documents.


{% extends "base.html" %} {% block title %}Become {{ account }}{% endblock %} {% block page_title %} Become {{ account }} {% endblock page_title %} {% block page_content %} This will log you in as {{ user_to_become.email }}. Everything on the site will appear as it does to them except for a banner reminding you that you are viewing the site as someone else.
{% endblock %}


This is a simple multi-rooms chat application to demonstrate how to use the streamy meteor package for realtime non-persistent communications.
multi-rooms chat application sample
{{from}}
{{body}}


Psychologist helped Sam Claflin get into mind of killer for The Nightingale role
The actor stars alongside Aisling Franciosi in the Tasmania-set period thriller.
Sam Claflin enlisted the help of a clinical psychologist to get into the mindset of a murderous lieutenant for The Nightingale.
The Peaky Blinders star, 33, plays British Army soldier Lieutenant Hawkins in the period thriller, set in 1825 in a penal colony in Tasmania.
The feature, from The Babadook director Jennifer Kent, follows a young convict, played by Aisling Franciosi, seeking revenge for a terrible act of violence committed against her family.
Claflin and Franciosi filmed numerous scenes of violence together including some depicting rape.
He said: “It took a little while to get into the mindset of a man like that.
“There had to be, unfortunately, a level of understanding as to why a man would make certain choices so I sat down with Jen and a clinical psychologist to discuss why a man would be in that position.
“We drafted, or created, a history, I suppose.
“His history of growing up in a very male-driven family where his mum was probably beaten – he was probably beaten.
“We said he likely had an older brother and he was never quite as good as his old brother, so it was the inferior complex you know.
“And for all those reasons suddenly he’s been gifted an opportunity and he sees gold.
“What was difficult in the actual storytelling of this is I wish there was more vulnerability that you could glimpse.”
Claflin said working with a psychologist gave him a “level of understanding” that informed his work on a “daily basis”.
He added: “But also physically getting into a mindset or a place.
“I think I was very blessed with a very generous cast and crew who would assist me getting to a certain place – whether it’s out of breath or…
“We were constantly being very physical with one another so I felt very well supported.”
Franciosi said it had been important the pair “could really, really trust each other” and could be “extremely vulnerable and fragile” on set.
She added: “We always checked in with each other to make sure that we were both OK, a lot of hugs on set, and I certainly felt very safe with them.”
The Nightingale is released in UK cinemas on Friday November 29.


Implementation of Befunge using the following rules.
0-9
Push this number on the stack
+
Addition: Pop a and b, then push a+b
-
Subtraction: Pop a and b, then push b-a
*
Multiplication: Pop a and b, then push a*b
/
Integer division: Pop a and b, then push b/a,
rounded towards 0.
%
Modulo: Pop a and b, then push the remainder of the
integer division of b/a.
!
Logical NOT: Pop a value. If the value is zero, push
1; otherwise, push zero.
`
Greater than: Pop a and b, then push 1 if b>a,
otherwise zero.
>
Start moving right
<
Start moving left
^
Start moving up
v
Start moving down
?
Start moving in a random cardinal direction
_
Pop a value; move right if value=0, left otherwise
|
Pop a value; move down if value=0, up otherwise
"
Start string mode: push each character's ASCII value
all the way up to the next "
:
Duplicate value on top of the stack
\
Swap two values on top of the stack
$
Pop value from the stack and discard it
.
Pop value and output as an integer followed by a
space
,
Pop value and output as ASCII character
#
Bridge: Skip next cell
p
A "put" call (a way to store a value for later use).
Pop y, x, and v, then change the character at (x,y)
in the program to the character with ASCII value v
g
A "get" call (a way to retrieve data in storage).
Pop y and x, then push ASCII value of the character
at that position in the program
&
Read an integer from stdin, also consumes a space.
-1 if no number readable.
~
Read a single character from stdin. -1 if no input
remaining.


Triumph Tiger 900 to be revealed on December 3
by Staff WriterThe bike shown in the video has a pair of spotlights and a new DRL that sits above the headlight.
Triumph has released a short teaser announcing that it will be revealing a new Tiger range on December 3.
The bike shown in the video has a pair of spotlights and a new DRL that sits above the headlight. This confirms that the Tiger 900 will use the headlight that has been seen in international spy pictures. While not much else can be seen, the video does have text that reads "The new Tiger 900 Rally & GT range. Adventure, transformed. 7pm GMT on Tuesday, December 3 (12:30am IST Wednesday)."
The new Tiger 900 range will most certainly replace the current Tiger 800 models as the company's middleweight adventure bikes that sit below the Tiger 1200 variants in India and international markets.
These Tiger 900 models are also expected to be using Euro 5 (BS6) compliant engines. We can also expect these bikes to be slightly more powerful than the Tiger 800 range (Rs 12 lakh-15.17 lakh) and come with an obvious hike in prices. However, it will be priced below the Tiger 1200, which is currently on sale in India at Rs 17 lakh.
All prices are ex-showroom, pan-India
Also see:


rotten > Library > Biographies > Crime > Serial Killers > Charles Manson
Charles Manson
Let's get something straight: Charles Manson never killed anybody. He was the mastermind behind the Tate murders in the late 1960's, providing specific instructions to his followers. This small-time thief managed to charm young hippie girls into doing his bidding. His obsession with the lyrics of rock group The Beatles made him believe a race war was coming. While everybody fought it out, his band of followers would wait in the desert, only to return when things calmed down. Before being sentenced to die in California's gas chamber (later commuted to life) he recorded some music, committed petty crimes and hung out with hippie chicks. He remains in Pelican Bay State Prison, a highly secure facility for incorrigible offenders, to this day.
Flirted with Scientology in prison:
A cell partner turned me on to Scientology. With him and
another guy I got pretty heavy into Dianetics and Scientology. Through
this and by other studies, I came out of my state of depression. I was
understanding myself better, had a positive outlook on life, and knew
how to direct my energies to each day and each task.
-- Manson: In His Own Words
Has a Swastika carved on the lower part of his forehead, between the eyes.
Timeline
| 12 Nov 1934 |
Charles Manson born as "no name Maddox", Cincinnati OH, bastard son of sixteen year old prostitute Kathleen Maddox. |
| 1947 |
Sent to Gibault School for Boys, Terre Haute IN. |
| 1951 |
Attempts to move to California. |
| 1951 |
Captured, sent to National Training School for Boys, Washington, DC. |
| 1951 |
Sodomizes a boy by force at the National Training School, and is transferred to Federal Reformatory at Petersburg, VA. |
| Sep 1952 |
Sent to Chillicothe, OH. |
| May 1954 |
Paroled. |
| 1955 |
Marries a waitress. Son born, Charles Manson, Jr. |
| 1958 |
Paroled. |
| c. 1966 |
Alvin Karpis, of the Ma Barker gang, teaches Manson guitar. |
| 21 Mar 1967 |
Released from prison, sent to San Francisco. |
| 1968 |
Moves to Spahn Ranch. |
| 1968 |
The Beatles release Helter Skelter. |
| 12 Oct 1969 |
Police capture Charles Manson at Barker Ranch, inside Death Valley National Park. Charlie is arrested for arson, after burning a maintenance vehicle blocking his favorite dune buggy route. One of his followers, Susan Atkins, is arrested the following day and spills the beans about the Tate/LaBianca murderers. Manson has not left prison since. |
| 6 Nov 1969 |
Manson Family member Susan Atkins tells her cellmate that Steve McQueen, Richard Burton, Tom Jones, and Frank Sinatra are on their "death list." |
| 27 Nov 1969 |
Court appointed attorney Ronald Hughes, handling the Charles Manson case, disappears on a camping trip to Sespe Hot Springs in Northern California, accompanied by two Manson followers. His decomposed body is identified by dental x-rays five months later. |
| 1 Mar 1970 |
Charles Manson releases his guitar folk album Lie: The Love & Terror Cult in order to finance his legal defense. The record consists of songs recorded in Van Nuys, California in 1968. |
| 19 Jan 1971 |
The Beatles song "Helter Skelter" is played during the Charles Manson court trial. |
| 1972 |
Death penalty abolished in California. Sentence commuted to life imprisonment (with possibility of parole.) |
| 5 Sep 1975 |
Manson Family member Lynette "Squeaky" Fromme almost assassinates President Gerald Ford with a .45 automatic in Sacramento, California. But Fromme is tackled by a Secret Service agent before she can remember to rack a round into the firing chamber. |
| 13 Jun 1981 |
Charles Manson interviewed at Vacaville Prison by television host Tom Snyder. |
Pornopolis |
Rotten |
Faces of Death |
Famous Nudes
|


--- name: Drawnot tools: [P5.js, Tone.js] image: https://user-images.githubusercontent.com/48978683/76380826-3e1bb180-632a-11ea-8e25-b4c08f546a88.png description: Explore the social potential of drawing digital notes as an emotional identity message and the intersection of visualization and auditory. ---
The dematerialization of the modern music producing process has led to a revolution of making music. This project is going to discover the social potential of free drawing the digital notes which creates an emotional identity message for the drawer and the intersection of visualization and auditory.
Drawnote is a digital drawing board. Users will draw any figures to represent their own identity at that moment. Once the drawing is done, the code then translates the figures into a musical sequence using 3 types of instruments.
The musical sequence can be remixed multiple times, which will create a unique piece. The musical sequence translated from the drawing will be shared on the users' social media. It offers a chance to experience the process of music-making playfully and creatively.


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Part V: Actors
This part describes how to define your own Actors (Non-Player Characters or NPCs, i.e. actors other than the player character who play a part in your game). The modules actor.t, events.t and topicEntry.t must be included in your game for the features described in this part to work.
NPC Overview
The Actor Object
Actor States
AgendaItems
Basic Ask/Tell
ActorTopicEntry
Suggesting Conversational Topics
Special Topics
Topic Groups
Conversation Nodes
Hello and Goodbye
Player Character and NPC Knowledge
Giving Orders to NPCs
String Tags and Object Tags
NPC-Initiated Conversation
Changing the Player Character
The Actor Object
Actor States
AgendaItems
Basic Ask/Tell
ActorTopicEntry
Suggesting Conversational Topics
Special Topics
Topic Groups
Conversation Nodes
Hello and Goodbye
Player Character and NPC Knowledge
Giving Orders to NPCs
String Tags and Object Tags
NPC-Initiated Conversation
Changing the Player Character


Below is a list of the publications I’ve (co-)authored over the past few years. My Google Scholar page can be found here.
In 2010 my surname changed from Drivalos to Matragkas. In the following list both names are used.
2020
- Dziurzanski, P., Gerasimou, S., Kolovos, D. and Matragkas, N., 2020. Empirical Analysis of 1-edit Degree Patches in Syntax-Based Automatic Program Repair. In 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE.
- Wood, S., Matragkas, N., Kolovos, D., Paige, R. and Gerasimou, S., 2020. Supporting robotic software migration using static analysis and model-driven engineering. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems ,pp. 154-164.
- Kourouklidis, P., Kolovos, D., Matragkas, N. and Noppen, J., 2020. Towards a low-code solution for monitoring machine learning model performance. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, pp. 1-8.
- Barmpis, K., Neubauer, P., Co, J., Kolovos, D., Matragkas, N. and Paige, R.F., 2020, June. Polyglot and Distributed Software Repository Mining with Crossflow. In Proceedings of the 17th International Conference on Mining Software Repositories, pp. 374-384.
2019
- Gerasimou, S., Matragkas, N. and Calinescu, R., 2019, May. Towards systematic engineering of collaborative heterogeneous robotic systems. In 2019 IEEE/ACM 2nd International Workshop on Robotics Software Engineering (RoSE), pp. 25-28.
- Kolovos, D., Neubauer, P., Barmpis, K., Matragkas, N. and Paige, R., 2019, May. Crossflow: a framework for distributed mining of software repositories. In 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), pp. 155-159.
2018
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Despotou, G., Korkontzelos, Y., Matragkas, N., Bilici, E. and Arvanitis, T.N., 2018. Structuring Clinical Decision Support rules for drug safety using Natural Language Processing. 16th International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH 2018), Athens, Greece, 251, pp. 89-92
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Zolotas, A., Matragkas, N., Devlin, S., Kolovos, D.S. and Paige, R.F., 2018. Type inference in flexible model-driven engineering using classification algorithms. Software & Systems Modeling, pp.1-22.
2017
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Retouniotis, A., Papadopoulos, Y., Sorokos, I., Parker, D., Matragkas, N. and Sharvia, S., 2017, September. Model-Connected Safety Cases. In International Symposium on Model-Based Safety and Assessment (pp. 50-63). Springer, Cham.
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Zolotas, A., Clarisó, R., Matragkas, N., Kolovos, D.S. and Paige, R.F., 2017. Constraint programming for type inference in flexible model-driven engineering. Computer Languages, Systems & Structures, 49, pp.216-230.
2016
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Di Ruscio, D., Kolovos, D.S., Korkontzelos, Y., Matragkas, N. and Vinju, J., 2016, September. Supporting custom quality models to analyse and compare open-source software. In 2016 10th International Conference on the Quality of Information and Communications Technology (QUATIC) (pp. 94-99). IEEE.
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Despotou, G., Matragkas, N. and Arvanitis, T.N., 2016, July. Converting Text to Structured Models of Healthcare Services. In ICIMTH (pp. 123-126).
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Kolovos, D.S., Matragkas, N. and Garcia-Dominguez, A., 2016. Towards flexible parsing of structured textual model representations. In 2nd Workshop on Flexible Model Driven Engineering co-located with ACM/IEEE 19th International Conference on Model Driven Engineering Languages & Systems (MoDELS 2016); Saint Malo, France
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Paige, R.F., Matragkas, N. and Rose, L.M., 2016. Evolving models in model-driven engineering: State-of-the-art and future challenges. Journal of Systems and Software, 111, pp.272-280.
2015
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Di Ruscio, D., Kolovos, D.S., Korkontzelos, I., Matragkas, N. and Vinju, J.J., 2015, August. Ossmeter: A software measurement platform for automatically analysing open source software projects. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (pp. 970-973). ACM
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Almeida, B., Ananiadou, S., Bagnato, A., Barbero, A.B., Di Rocco, J., Di Ruscio, D., Kolovos, D.S., Korkontzelos, I., Hansen, S., Maló, P. and Matragkas, N., 2015, July. OSSMETER: Automated Measurement and Analysis of Open Source Software. In STAF Projects Showcase (pp. 36-43).
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Zolotas, A., Matragkas, N., Devlin, S., Kolovos, D.S. and Paige, R.F., 2015, July. Type inference in flexible model-driven engineering. In European Conference on Modelling Foundations and Applications (pp. 75-91). Springer, Cham.
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Baresi, L., Blohm, G., Kolovos, D.S., Matragkas, N., Motta, A., Paige, R.F., Radjenovic, A. and Rossi, M., 2015. Formal verification and validation of embedded systems: the UML-based MADES approach. Software & Systems Modeling, 14(1), pp.343-363.
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Zolotas, A., Matragkas, N.D., Kolovos, D.S. and Paige, R.F., 2015. Flexible Modelling for Requirements Engineering. In FlexMDE@ MoDELS (pp. 32-41).
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Willink, E. and Matragkas, N., 2015. Traceability: What does it really mean for QVT?. In Fourth Workshop on the Analysis of Model Transformations.
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Kolovos, D.S., Matragkas, N.D., Korkontzelos, I., Ananiadou, S. and Paige, R.F., 2015. Assessing the Use of Eclipse MDE Technologies in Open-Source Software Projects. In OSS4MDE@ MoDELS (pp. 20-29).
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Zolotas, A., Matragkas, N.D., Devlin, S., Kolovos, D.S. and Paige, R.F., 2015. Type Inference Using Concrete Syntax Properties in Flexible Model-Driven Engineering. In FlexMDE@ MoDELS (pp. 22-31).
2014
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Paige, R.F., Polack, F.A., Kolovos, D.S., Rose, L.M., Matragkas, N.D. and Williams, J.R., 2014, October. Bad Modelling Teaching Practices. In EduSymp@ MoDELS (pp. 1-12).
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Kolovos, D.S., Matragkas, N., Williams, J.R. and Paige, R.F., 2014, September. Model driven grant proposal engineering. In International Conference on Model Driven Engineering Languages and Systems (pp. 420-432). Springer, Cham.
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Williams, J.R., Di Ruscio, D., Matragkas, N., Di Rocco, J. and Kolovos, D.S., 2014, May. Models of OSS project meta-information: a dataset of three forges. In Proceedings of the 11th Working Conference on Mining Software Repositories (pp. 408-411). ACM.
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Matragkas, N., Williams, J.R., Kolovos, D.S. and Paige, R.F., 2014, May. Analysing the’biodiversity’of open source ecosystems: the GitHub case. In Proceedings of the 11th Working Conference on Mining Software Repositories (pp. 356-359). ACM.
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Audsley, N., Gray, I., Kolovos, D., Matragkas, N., Paige, R. and Indrusiak, L.S., 2014. Automatic Development of Embedded Systems Using Model Driven Engineering and Compile-Time Virtualisation. In Embedded and Real Time System Development: A Software Engineering Perspective (pp. 23-53). Springer, Berlin, Heidelberg.
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Bagnato, A., Quadri, I., Brosse, E., Sadovykh, A., Indrusiak, L.S., Paige, R., Audsley, N., Gray, I., Kolovos, D.S., Matragkas, N. and Rossi, M., 2014. MADES FP7 EU Project: Effective High Level SysML/MARTE Methodology for Real-Time and Embedded Avionics Systems. In Handbook of Research on Embedded Systems Design (pp. 181-208). IGI Global.
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Williams, J., Matragkas, N., Kolovos, D., Ananiadou, S. and Paige, R., 2014. Software analytics for mde communities. In ACM/IEEE 17th International Conference on Model Driven Engineering Languages and Systems (MODELS), OSS4MDE workshop, Valencia, Spain.
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Zolotas, A., Kolovos, D.S., Matragkas, N.D. and Paige, R.F., 2014. Assigning Semantics to Graphical Concrete Syntaxes. XM@ MoDELS, 1239, pp.12-21.
2013
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Matragkas, N.D., Kolovos, D.S., Paige, R.F., Zolotas, A., Baudry, B., Dingel, J., Lucio, L. and Vangheluwe, H., 2013, September. A Traceability-Driven Approach to Model Transformation Testing. In AMT@ MoDELS.
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Francis, M., Kolovos, D.S., Matragkas, N. and Paige, R.F., 2013, September. Adding spreadsheets to the mde toolkit. In International Conference on Model Driven Engineering Languages and Systems (pp. 35-51). Springer, Berlin, Heidelberg.
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Kolovos, D.S., Rose, L.M., Matragkas, N., Paige, R.F., Guerra, E., Cuadrado, J.S., De Lara, J., Ráth, I., Varró, D., Tisi, M. and Cabot, J., 2013, June. A research roadmap towards achieving scalability in model driven engineering. In Proceedings of the Workshop on Scalability in Model Driven Engineering (p. 2). ACM.
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Paige, R.F., Calinescu, R., Kolovos, D.S., Matragkas, N. and Cliff, D., Multimodel-Driven Software Engineering for Evolving Enterprise Systems (Position Paper). In Joint Proceedings of the Workshops On the Globalization of Modeling Languages (GEMOC 2013) and Towards the Model Driven Organization.
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Paige, R.F., Kolovos, D.S., Rose, L.M., Matragkas, N. and Williams, J.R., 2013. Model management in the wild. In Generative and Transformational Techniques in Software Engineering IV (pp. 197-218). Springer, Berlin, Heidelberg.
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Taromirad, M., Matragkas, N.D. and Paige, R.F., 2013. Towards a Multi-Domain Model-Driven Traceability Approach. In MPM@ MoDELS (pp. 27-36).
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Williams, J.R., Zolotas, A., Matragkas, N.D., Rose, L.M., Kolovos, D.S., Paige, R.F. and Polack, F.A., 2013. What do metamodels really look like?. Eessmod@ Models, 1078, pp.55-60.
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Kolovos, D.S., Matragkas, N.D., Rodríguez, H.H. and Paige, R.F., 2013. Programmatic Muddle Management. XM@ MoDELS, 1089, pp.2-10.
2012
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Quadri, I.R., Brosse, E., Gray, I., Matragkas, N., Indrusiak, L.S., Rossi, M., Bagnato, A. and Sadovykh, A., 2012, July. MADES FP7 EU project: Effective high level SysML/MARTE methodology for real-time and embedded avionics systems. In Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC), 2012 7th International Workshop on (pp. 1-8). IEEE.
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Radjenovic, A., Matragkas, N., Paige, R.F., Rossi, M., Motta, A., Baresi, L. and Kolovos, D.S., 2012, July. MADES: a tool chain for automated verification of UML models of embedded systems. In European Conference on Modelling Foundations and Applications (pp. 340-351). Springer, Berlin, Heidelberg.
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Kolovos, D.S., Rose, L.M., Williams, J., Matragkas, N. and Paige, R.F., 2012, July. A lightweight approach for managing XML documents with MDE languages. In European Conference on Modelling Foundations and Applications (pp. 118-132). Springer, Berlin, Heidelberg.
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Rose, L.M., Matragkas, N., Kolovos, D.S. and Paige, R.F., 2012, June. A feature model for model-to-text transformation languages. In Proceedings of the 4th International Workshop on Modeling in Software Engineering (pp. 57-63). IEEE Press.
2011
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Paige, R.F., Drivalos, N., Kolovos, D.S., Fernandes, K.J., Power, C., Olsen, G.K. and Zschaler, S., 2011. Rigorous identification and encoding of trace-links in model-driven engineering. Software & Systems Modeling, 10(4), pp.469-487.
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Gray, I., Matragkas, N., Audsley, N.C., Indrusiak, L.S., Kolovos, D. and Paige, R., 2011, March. Model-based hardware generation and programming-the MADES approach. In Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW), 2011 14th IEEE International Symposium on (pp. 88-96). IEEE.
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Audsley, N.C., Gray, I., Indrusiak, L.S., Kolovos, D., Matragkas, N. and Paige, R., 2011. Model-based development of embedded systems-the MADES approach. In Proceedings of the 2nd Workshop on Model Based Engineering for Embedded Systems Design.
2010
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Kolovos, D.S., Rose, L.M., Matragkas, N.D., Paige, R.F., Polack, F.A. and Fernandes, K.J., 2010, June. Constructing and navigating non-invasive model decorations. In International Conference on Theory and Practice of Model Transformations (pp. 138-152). Springer, Berlin, Heidelberg.
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Rose, L.M., Kolovos, D.S., Drivalos, N., Williams, J.R., Paige, R.F., Polack, F.A. and Fernandes, K.J., 2010, June. Concordance: A framework for managing model integrity. In European Conference on Modelling Foundations and Applications (pp. 245-260). Springer, Berlin, Heidelberg.
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Drivalos Matragkas, N., Kolovos, D.S., Paige, R.F. and Fernandes, K.J., 2010, June. ** A state-based approach to traceability maintenance.** In Proceedings of the 6th ECMFA Traceability Workshop (pp. 23-30). ACM.
2009
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Zschaler, S., Kolovos, D.S., Drivalos, N., Paige, R.F. and Rashid, A., 2009, October. Domain-specific metamodelling languages for software language engineering. In International Conference on Software Language Engineering (pp. 334-353). Springer, Berlin, Heidelberg.
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Paige, R.F., Kolovos, D.S., Rose, L.M., Drivalos, N. and Polack, F.A., 2009, June. The design of a conceptual framework and technical infrastructure for model management language engineering. In Engineering of Complex Computer Systems, 2009 14th IEEE International Conference on (pp. 162-171). IEEE.
2008
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Drivalos, N., Kolovos, D.S., Paige, R.F. and Fernandes, K.J., 2008, September. Engineering a DSL for software traceability. In International Conference on Software Language Engineering (pp. 151-167). Springer, Berlin, Heidelberg.
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Drivalos, N., Paige, R.F., Fernandes, K.J. and Kolovos, D.S., 2008, June. Towards rigorously defined model-to-model traceability. In ECMDA Traceability Workshop (ECMDA-TW) (pp. 17-26).
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Kolovos, D.S., Drivalos, N., Paige, R.F. and Polack, F.A., 2008. Seamless Navigation of Heterogeneous EMF models in Epsilon. In Eclipse Modeling Symposium, Eclipse Summit.


Example combines operators (i.e. selector, selector {styling}). We want to test for parsing these strings.


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