First Blog Post with
Gilt Tech Blog Moves to Github
In continuing the march towards a more open Gilt Tech, we've made the switch to Github Pages and Jekyll from Tumblr.
You can view the entirety of this blog's code and content here: https://github.com/gilt/tech-blog.
Why We Did This
First and foremost: control. We've got control over the layout and design of the blog from top to bottom, without restriction and without any kind of a funky theming engine to wrestle with. We're really good at this kind of thing (we should be, after all) and we've ended up with a blog design that matches elements of gilt.com.
We've also got better control over how we write posts. We're a fairly large, distributed organization, and coordinating the setup, permissions, et al for Tumblr was kind of a pain. There was also very little positive feedback about the authoring experience. With the move everyone is able to author in their editor of choice and go through the already familiar process of submitting the post to the tech-blog repository.
We Used Technology
Jekyll Bootstrap is a fantastic bootstrap/scaffolding package put together to quickly spin up a blog. While we customized the bajeezus out of it, which was incredibly quick and painless, and rolled our own 'gilt' theme, we used a healthy chunk of what it ships with by default. We did roll our own helpful tidbits, like the authors page, for which you can view the source here.
Github Pages. If you haven't looked into Github Pages for simple hosted websites or project websites yet, give it a gander. It's a phenomenal way to quickly spin up a site, and it's Jekyll support is pretty great.
Fin
We hope you like the new digs, and we're forecasting a lot more great content to come.


COUNT(3) - [ARRAY:REDUCTION] Count true values in an array
result = count(mask [,dim] [,kind] )
integer(kind=KIND) function count(mask, dim, KIND )logical(kind=**),intent(in) :: mask(..) integer(kind=**),intent(in),optional :: dim integer(kind=**),intent(in),optional :: KIND
o a kind designated as ** may be any supported kind for the type o MASK is a logical array of any shape and kind. o If DIM is present, the result is an array with the specified rank removed. o KIND is a scalar integer constant expression valid as an integer kind o The return value is of default integer type unless KIND is specified to declare the kind of the result.
COUNT(3) counts the number of .true. elements in a logical MASK, or, if the DIM argument is supplied, counts the number of elements along each row of the array in the DIM direction. If the array has zero size or all of the elements of MASK are false, then the result is 0.
o MASK : an array to count the number of .true. values in o DIM : specifies to remove this dimension from the result and produce an array of counts of .true. values along the removed dimension. If not present, the result is a scalar count of the true elements in MASK the value must be in the range 1 <= dim <= n, where n is the rank(number of dimensions) of MASK. The corresponding actual argument shall not be an optional dummy argument, a disassociated pointer, or an unallocated allocatable.
o KIND : An integer initialization expression indicating the kind parameter of the result.
The return value is the number of .true. values in MASK if DIM is not present.
If DIM is present, the result is an array with a rank one less than the rank of the input array MASK, and a size corresponding to the shape of ARRAY with the DIM dimension removed, with the remaining elements containing the number of .true. elements along the removed dimension.
Sample program:
program demo_count implicit none character(len=*),parameter :: ints=(*(i2,1x)) ! two arrays and a mask all with the same shape integer, dimension(2,3) :: a, b logical, dimension(2,3) :: mymask integer :: i integer :: c(2,3,4)Results:print *,the numeric arrays we will compare a = reshape( [ 1, 2, 3, 4, 5, 6 ], [ 2, 3 ]) b = reshape( [ 0, 7, 3, 4, 5, 8 ], [ 2, 3 ]) c = reshape( [( i,i=1,24)], [ 2, 3 ,4]) print (3i3), a(1,:) print (3i3), a(2,:) print * print (3i3), b(1,:) print (3i3), b(2,:) ! ! basic calls print *, count a few basic things creating a mask from an expression print *, count a>b,count(a>b) print *, count b<a,count(a<b) print *, count b==a,count(a==b) print *, check sum = ,count(a>b) + & & count(a<b) + & & count(a==b).eq.size(a) ! ! The common usage is just getting a count, but if you want ! to specify the DIM argument and get back reduced arrays ! of counts this is easier to visualize if we look at a mask. print *, make a mask identifying unequal elements ... mymask = a.ne.b print *, the mask generated from a.ne.b print (3l3), mymask(1,:) print (3l3), mymask(2,:) ! print *,count total and along rows and columns ... ! print (a), number of elements not equal print (a), (ie. total true elements in the mask) print (3i3), count(mymask) ! print (a), count of elements not equal in each column print (a), (ie. total true elements in each column) print (3i3), count(mymask, dim=1) ! print (a), count of elements not equal in each row print (a), (ie. total true elements in each row) print (3i3), count(mymask, dim=2) ! ! working with rank=3 ... print *, lets try this with c(2,3,4) print *, taking the result of the modulo print *, z=1 z=2 z=3 z=4 print *, 1 3 0 || 2 4 1 || 3 0 2 || 4 1 3 | print *, 2 4 1 || 3 0 2 || 4 1 3 || 0 2 4 | print *, print *, would result in the mask .. print *, F F T || F F F || F T F || F F F | print *, F F F || F T F || F F F || T F F | print *, print *, the total number of .true.values is print ints, count(modulo(c,5).eq.0) call printi(counting up along a row and removing rows,& count(modulo(c,5).eq.0,dim=1)) call printi(counting up along a column and removing columns,& count(modulo(c,5).eq.0,dim=2)) call printi(counting up along a depth and removing depths,& count(modulo(c,5).eq.0,dim=3)) ! contains ! ! CONVENIENCE ROUTINE FOR PRINTING SMALL INTEGER MATRICES subroutine printi(title,arr) implicit none ! !@(#) print small 2d integer arrays in row-column format ! character(len=*),parameter :: all=(*(g0,1x)) ! a handy format character(len=*),intent(in) :: title integer,intent(in) :: arr(:,:) integer :: i character(len=:),allocatable :: biggest ! print all print all, trim(title),:(,shape(arr),) ! print title biggest= ! make buffer to write integer into ! find how many characters to use for integers write(biggest,(i0))ceiling(log10(max(1.0,real(maxval(abs(arr))))))+2 ! use this format to write a row biggest=(" > [",*(i//trim(biggest)//:,",")) ! print one row of array at a time do i=1,size(arr,dim=1) write(*,fmt=biggest,advance=no)arr(i,:) write(*,(" ]")) enddo ! end subroutine printi end program demo_count
> the numeric arrays we will compare > 1 3 5 > 2 4 6 > > 0 3 5 > 7 4 8 > count a few basic things creating a mask from an expression > count a>b 1 > count b<a 2 > count b==a 3 > check sum = T > make a mask identifying unequal elements ... > the mask generated from a.ne.b > T F F > T F T > count total and along rows and columns ... > number of elements not equal > (ie. total true elements in the mask) > 3 > count of elements not equal in each column > (ie. total true elements in each column) > 2 0 1 > count of elements not equal in each row > (ie. total true elements in each row) > 1 2 > lets try this with c(2,3,4) > taking the result of the modulo > z=1 z=2 z=3 z=4 > 1 3 0 || 2 4 1 || 3 0 2 || 4 1 3 | > 2 4 1 || 3 0 2 || 4 1 3 || 0 2 4 | > > would result in the mask .. > F F T || F F F || F T F || F F F | > F F F || F T F || F F F || T F F | > > the total number of .true.values is > 4 > > counting up along a row and removing rows :( 3 4 ) > > [ 0, 0, 0, 1 ] > > [ 0, 1, 1, 0 ] > > [ 1, 0, 0, 0 ] > > counting up along a column and removing columns :( 2 4 ) > > [ 1, 0, 1, 0 ] > > [ 0, 1, 0, 1 ] > > counting up along a depth and removing depths :( 2 3 ) > > [ 0, 1, 1 ] > > [ 1, 1, 0 ]
Fortran 95 , with KIND argument - Fortran 2003
ANY(3), ALL(3), SUM(3),
fortran-lang intrinsic descriptions (license: MIT) @urbanjost
| Nemo Release 3.1 | count (3) | July 22, 2023 |


Definitions¶
Definitions related to monitoring forest disturbances.
- Deforestation
- Food and Agriculture Organization of the United Nations (FAO) through the United Nations Framework Convention on Climate Change (2001) [1] describe deforestation as “the direct human-induced conversion of forested land to non-forested land”.
- Forest conversion
- Any disturbance, natural or anthropogenic, that results in a conversion from forest to another land cover. This includes deforestation and a natural conversion such as permanent waterlogging.
- Degradation
- There is no internationally accepted definition of degradation. We define degradation as “any forest disturbance, natural or anthropogenic, which does not result in a land cover change”. The disturbance can cause a change in land use, for example a primary forest turning into a managed forest or partially harvested, but as long as the land cover definition remains forest the event is considered degradation.
- Forest
- We define a forest as any land that contains over 20% canopy cover of trees over 5 meters in height, not being primarily used by urban or agricultural land uses, including natural or non-natural openings, and includes tree plantations.
- Forest Disturbance
- Any short or long term event that causes destructive damage to a forest. The event can be human caused or natural. The event must have a start and end time, in contrast to trend (such as long term greening or species succession due to climate change).
- Secondary Forest:
- The FAO (2003) [2] defines secondary forests as “forests regenerating largely through natural processes after significant removal or disturbance of the original forest vegetation by human or natural causes at a single point in time or over an extended period.”
| [1] | FAO, 2015. Forest Resources Assessment 2015: Terms and Definitions, FAO report. |
| [2] | FAO, 2003. Tropical Secondary Forest Management in Africa:, in: Proceedings on Workshop on Tropical Secondary Forest Management in Africa: Reality and Perspectives. Nairobi, Kenya. |


Coffee House Scheduler
Overview:
Rice Coffeehouse is a student-run business at Rice University that employs over forty
employees and serves over 4000 students and faculty. For many at Rice, Coffeehouse serves as a central hub
on campus to study, meet friends, and get caffeinated. As a result, Coffeehouse must hire a lot of student
employees, formally known as “Keepers of Coffee” (KOC’s, aka baristas), to meet their high demand. As a
student-run business, student employees often have busy schedules during the academic year, making it difficult
to schedule appropriate shifts for all employees while still meeting the required number of employees per hour
to keep Coffeehouse running smoothly. As a result, a large responsibility falls upon a student manager each
semester to manually schedule student employees such that it caters to the busy schedules of student employees,
yet satisfy the demanding business operations of the coffee shop. We approximate that over 40+ hours is spent
on the scheduling process each semester. Our team hopes to reduce this number ten-fold and eventually create an
application that fully automates the entire scheduling process. This way, Coffeehouse can spend more of their
time improving campus life and adding to the diverse culture at Rice University.
Scheduling Process
-
KOCs scheduled at the beginning of semester to work certain shifts every week.
-
Four weeks into semester, new schedule is created to include new hires.
-
Throughout the semester, shifts are traded over the Coffeehouse listserv.
-
Repeat process each semester.
Timeline (40-50+ hours):
- A month before semester begins:
- Manager sends out google form to all employees
- Employees fill out google worksheet (10-15 min/employee)
- Availability (1-4, 1: Would prefer, 2: Ambivalent, 3: Would prefer not to, 4: Cannot)
- Additional information (min/max hours, special requests, preferences, etc)
- Manager imports raw data into various google sheets
- Manager begins scheduling process (10-12 hours for manager)
- Manager sends schedule to employees via email
- Employees submit trade request via email (2-3 hours for manager)
- Once the semester starts:
- Permanent and temporary trades continue, manager modifies the schedule based on these updated requests
(30 min for manager)
- 4 weeks into the semester:
- A new schedule is required to include new hires
- Entire scheduling process repeats itself (21 hours for manager)
- Rest of the semester:
Permanent and temporary trades continue, manager modifies the schedule based on these updated requests (5 hours for manager)
Current Scheduling Steps/ Shift Prioritization:
-
Fill Tuesday/Thursday 9-11 am slot
Tuesday and Thursday classes are difficult to schedule around
-
Fill Monday/Wednesday/Friday 9-11 am slot
Employees don’t like doing morning shifts
-
Fill closing shifts (they are low demand)
No one likes to be on closing shifts
-
Fill 1-5pm slots for all days
-
Fill out the first of the remaining slots
Current Process of Trading Shifts:
-
Email is sent out to all employees showing the schedule
-
Employees can request to temporarily or permanently trade shifts with other employees via email if they have a conflicting shift
-
Employees will negotiate via email with other employees and see if they can trade shifts
-
Employees notify manager to receive approval and then, fill out a form (form necessary for splitting tips monthly)
-
Manager goes through data received from forms and manually calculates the change in the net work hours for the employees who traded
-
In the case of a temporary change, the schedule itself is not changed.
Number of employees per shift
-
The number of employees per shift depends on Coffeehouse’s business during the specified time slot
-
Example: shifts 1pm-5pm would require 4-5 people whereas shifts 5pm-7pm would require 3 people
-
Opening / Closing: 2 required
-
8 - 9am: 2 required
-
9am - 1pm: 3 required
-
1 - 5pm: 5 required
-
5 - 7pm: 3 required
-
7 - 11pm: 3 required
Part 1: Scoping
Problem: The current Rice Coffeehouse shift scheduling and trading system is time-consuming, unintuitive, and prone to human error.
-
Shift scheduling data is gathered through a Google Survey sent out to each employee and managed using several Google Excel worksheets.
-
The Coffeehouse scheduling manager must navigate between these worksheets to scan for information when creating the semester’s schedule.
-
Problems with managing data in Google worksheets:
-
Data visualization:
-
It is tedious to scan through multiple dense spreadsheets
-
The Semester’s schedule cannot be displayed in a clean, elegant format
-
Employees are unable to see their personal schedules in a calendar view
-
Error prone:
-
Conditional formatting means 1 single error can make data unusable
-
Difficult to check for mistakes when creating the schedule e.g. A misspelled name leads
to untracked hours for an employee
-
Time consuming:
-
New incoming Coffeehouse managers invest too much time to learn the complex scheduling process
-
Our solution to the problem rests on finding a way to represent this data. Questions to answer:
-
What is the best way to represent all this data efficiently?
-
How do we keep track of all the metrics required?
-
How do we prioritize data so that the scheduling manager can do their job efficiently?
-
What alternatives are out there?:
-
Sling: Manual shift scheduler
-
Con/Problem: cannot implement rules and constraints specific to scheduling chaus managers,
$$$$ subscription plan, no automation
-
Pros: Good set of features for team communications and syncing with email and calendars
-
Humanity
-
Con/Problem: cannot implement rules and constraints specific to CoffeHouse
-
Pros: same as Sling
-
General problem with alternatives: These alternatives are not tailored for the specific needs of chaus.
-
So do we NEED this product that you are building? YES!
Who are we building this for?
- Coffee House Employees
- Employees only interact with spreadsheets when:
- They have a shift conflict
- They need to trade shifts with another employee
- They set up their schedule for the semester
- Need:
- A quick way to send availability information to Chaus management
- An easy way to view their scheduled shifts
- Coffee House Manager:
-
Requires this product more than employees since they are in charge of shift scheduling
-
Needs:
A quick way to create a schedule for employees.
What is the most important thing for our users?
- Most important: Correctness ⇒ The schedule needs to adhere to rules/guidelines such as:
- Each employee cannot be scheduled over their max # of hours
- Each shift must have a minimal number of employees scheduled
- An employee ideally should not work two separate shifts in one day
-
Second most important: Ease of use ⇒ our UI design and flow needs to be user-friendly and
flexible for managers to schedule shifts as they desire
Part 2: Product Specification
Ranked List of Features:
- Employee Availability Calendar
-
Ability to select available times
- Toggle between preferred, available, not preferred, and unavailable
- Calendar button colors reflect availability
-
Ability to save times to backend
-
Ability to view personal schedule
-
Ability to request trades for a given time-slot
Successful trades will automatically
- Master Scheduling Calendar
- Ability to schedule users for a given time-slot
- Ability to add/remove users from ‘scheduled’ array for a given time-slot
- Visual warnings for attempting to overschedule employees or schedule for two non-consecutive shifts in a day
- Ability to view additional information on individual employees or properties of timeslots
- Employees: Hours worked so far; whether they’re already scheduled for the day
- Timeslot Properties: Required number of employees
- Ability for employees to see read-only view of master schedule
Login Authorization
- Ability to register as a manager or employee
- Ability to login
- Then be redirected to employee schedule (if employee) / master schedule (if manager)
- Login should redirect to IDP (Rice NetID login system), get a token, store a token in our database upon successful NetID Login
Create/Delete Users
- CRUD Operations implemented in backend
- Create - register user
- Read - login user
- Update - change user properties
- Delete - Remove user and all references to user from database
Automation
- Utilizing shift-scheduling algorithm to optimally schedule employees
List of Features for MVP
- Employee Availability Calendar
-
Week long schedule viewable by both the manager and employees.
- Color-coded to convey how “full” each shift is
- Master Schedule:
- Week long schedule only viewable/editable by manager
- When shift is clicked, unscheduled/scheduled employees will be displayed
- If unscheduled, 1 - 4 will be shown to convey levels of that employee’s availability
- Warning System
- Alert if manager tries to overbook an employee (more than once a day or more than their hour cap)
- TradingSystem
- Form that takes employees that wish to trade shifts and their hour changes as input and updates their respective hour counts in the backend
- Total Hour Count
- Each employees hours will be kept track of in the backend
- Elastic Search
- More robust and efficient way to search and process data
- Biweekly Reset
- Every other Friday at 5 pm, employee hours are reset
- Schedule updated to show new employee availabilities
Prototypes:
- Prototype 1 (DONE):
- Database schemas defined
- Front end shows data
- Basic scheduling
- Prototype 2 (5 weeks):
- The backend will support:
- Total working hours for each employee
- Min hour, max hour per week
- Elastic Search
- Front-end modal shows manager editing shifts proper information about
employees with React components / Redux states and actions.
- Prototype 3 (4 Weeks):
- Manager warned if employee to be overbooked –– must incorporate back-end
data and implementing the front end visuals
- Total hours get reset after each week
- Trading forms work properly and update the backend data –– requires
implementing a front-end form for employees to fill out
- Prototype 4 (MVP):
- Deployment, Launch
- Test the application
- Beta-test at Coffeehouse
Part 3: Product RoadMap for this semester
Calendar:
Week 1:
- Meet with current Coffee House manager Sydney in order to gain more insight into the
specific process of hiring/shift trading
- Update PRD
Week 2:
- Begin understanding Elasticsearch (Documentation, tutorials, sample queries/data)
- Conclude on the technology that we will be using
- Begin designing backend data models/dummy data with Elasticsearch in mind
Week 3:
- Reconstruction of data representation on backend
- Implementing ElasticSearch (primary)
- Adding necessary metrics (secondary)
Week 4:
- Finish reconstruction of data representation on backend with ElasticSearch and added metrics
Week 5:
- Update existing get and pull requests using ElasticSearch
- Add new get and pull requests based on new metrics/future features in backend
Week 6:
- Implement
- Front end modal display of metrics
- Redux states
- Backend queries (actions)
Week 7:
Week 8:
- Implement
- Warnings when attempting to schedule employees
- Reset all total hours worked every two weeks
Week 9:
- Implement
- Two-Way Trading forms for users
- User Authentication
- Refactoring existing code
The MVP:
-
Week long schedule that can be viewed by both the employees and the manger that display how full
each shift is. Colors represent how full each shift is.
-
Manager will have the ability to edit the master schedule. They will see a list of available/already booked
employees when they click on a particular shift and can add/remove employees.
-
Managers will receive a warning if they attempt to schedule an employee that is listed as unavailable for
that specific slot or if they attempt to book an employee for more than one consecutiveshift for one day.
-
A trading mechanism will allow employees to exchange shifts. This will be a form that will automatically
process the trade in the backend, updating the employees' availability and their total hours booked
for that week.
-
The backend of the application will keep track of the total number of hours each employee has worked.
-
Elastic Search will be implemented to our databases in order to allow for a more robust and efficient
way to look through and process data.
-
Friday at 5pm, the hours for each employee in the backend will reset, and the display will change to show
the next weeks employee availabilities.
Prototypes
- Prototype 0: Already Implemented
- Prototype 1:
-
The backend will be update to accommodate new required fields:
-
Total working hours for each employee
-
Total working hours (cap) for the week for the user
-
Min hour, max hour per week
-
Elastic Search will be implemented to allow faster and more robust data querying.
-
Front-end modal shows manager editing shifts proper information about employees. Accomponyig
React components / Redux states and actions.
- Prototype 2:
-
Manager will receive popup warning when scheduling a booked employee or double booking.
This will require incorporating back-end data and implementing the front end visuals for
the popup.
-
Total hours get reset after each week. This means the backend data will have to undergo a shift
after a certain period of time.
-
Trading forms work properly and update the backend data. Requires implementing a form on the
front-end that allows employees to fill out various data entries that then moodift the backend
data once an employee submits the form.
- Prototype 3:
-
Deploy MVP. Set up proper hosting for both front end and backend data.
-
Test the Application. Make sure the application works without bugs and that all features listed
above are properly implemented.
-
Beta Testing at Coffeehouse. Have the scheduling manager and
Coffeehouse employees use the application. Receive feedback by speaking to users and collecting
data.
Part 3: Product Road Map
-
January:
- 20/21 Sprint 1 ⇒ Done
- 27/28 Sprint 2 ⇒ Reconstruction of data representation on backend with added metrics w/ elastic search
-
February:
- 3/4 Sprint 3 ⇒ Reconstruction of data representation on backend with added metrics w/ elastic search
- Sprint 4 (OPTIONAL, Spring Recess) ⇒ Reconstruction of data representation on backend with added metrics w/ elastic search
- 17/18 Sprint 5 ⇒ Updating our get and put requests with elastic search and adding additional ones
- 24/25 Sprint 6 ⇒ Front-end modal display of metrics, redux states, back end queries (actions)
- March
- 3/4 Sprint 7 ⇒ FINISH PROTOTYPE 1
- 10/11 NO SPRINTS (SPRING BREAK)
-
17/18 Sprint 8 ⇒ Implement warnings, resetting total hours worked
- 24/25 Sprint 9 ⇒ trading forms, refactoring
- April
-
7/8 Sprint 10 ⇒ Deploy and launch
-
13/14 End of semester review


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See: Description
| Interface | Description |
|---|---|
| Credential |
A credential for an entity.
|
| CredentialContext |
Marker interface for implementations which hold information specific to a particular context within
which a
CredentialResolver resolves a Credential . |
| CredentialResolver |
A resolver which uses
Criterion
to resolve and return instances of Credential . |
| MutableCredential |
A specialization of
Credential which supports mutation of its properties. |
| Class | Description |
|---|---|
| AbstractCredential |
Base class for
Credential implementations. |
| BasicCredential |
A basic implementation of
Credential . |
| CredentialContextSet |
This class holds instances of
CredentialContext which represent information
about the context in which a CredentialResolver has resolved a Credential . |
| CredentialSupport |
Helper methods for working with
Credential instances. |
| Enum | Description |
|---|---|
| UsageType |
Credential usage types.
|
Copyright © 1999–2016 Shibboleth Consortium. All rights reserved.


06/24/00
Note: Make a new directory for these programs, and be prepared to loose everything in it.
Write a program that creates a new file by concatenating several files together. The command line looks like this:
java fileCat source0 source1 source2 ... newFile
There can be one or more source files on the command line. Each source file must exist (if not, write an error message and exit). The last file name on the line, newFile, is the name of the file to be created, and must not already exist.
Create the new file by opening the source files one at a time, in order, reading each file byte-by-byte and writing each byte to newFile. Close each file when it is no longer needed. Use buffered input and buffered output.
Although each source file must exist, it is acceptable for a source file to be empty (so the first read encounters end-of-file).
If you don't like command lines, use interactive user prompts, or write a GUI interface. A frequent program style is to do it both ways: Check the command line; if it is OK, proceed. If there are no arguments get file names by prompting the user.
Click here to go back to the main menu.
Sometimes you have a file that you would like to put on a floppy disk but it is too big. Perhaps you want to copy the file from one computer to another and floppy disks are your only means of transfer. It would be useful to split the long file into several short files that each fit on a floppy. After transfer to the other computer the short files can be concatenated into a copy of the original.
The command line for this program is:
java fileSplit bigFile baseName chunkSize
bigFile is the name of the big, existing file. Each small file will be named baseName with a number appended to the end. For example, if baseName is "chop" then the small files are "chop0", chop1", "chop2", and so on for as many as are needed.
chunkSize is the size in bytes of each small file, except for the last one.
For testing, use any file for bigFile, no matter what size, and any size for chunkSize. Use the file concatenation program to put the file together again. See if there are any changes with a file comparison program. (Such as Unix's dif, MS Win's comp, or the next program).
For preliminary testing, use text files. However, write the program so that it works with any file (use byte-oriented IO). For advanced testing, split an executable file, then reassemble then run it. Or split and reassemble an image file, and view the results.
Click here to go back to the main menu.
Write a program that compares two text files line by line. The command line looks like this:
java fileComp file1 file2 [limit]
Read in a line from each file. Compare the two lines. If they are identical, continue with the next two lines. Otherwise, write out the line number and the two lines, and continue.
Frequently when two files differ there are very many lines that are
different and you don't want to see them all.
The last argument, limit, is optional (that is what the brackets mean).
It is an integer that says how many pairs of different lines to write
before quitting.
If limit is omitted all differing lines are written.
Catch the NumberFormatException
that may be thrown
if limit can't be converted.
Click here to go back to the main menu.
Write a program that backs up all the files in a directory by creating a new subdirectory (of the current directory) called backUp0 and by copying all files of the current directory into it. The file names of the copies are the same as in the current directory.
If backUp0 already exists,
then create backUp1, and so on.
Use byte-oriented IO so files of all types are backed up.
Use the list()
method of File
to get a list of files and directories.
Use the isFile()
method to
determine which of the list are files.
Use mkdir()
to create the new directory.
If a file cannot be read (check this with canRead()
),
write a warning message and continue.
The program is invoked from the command line:
java backUp
This is a useful program (once you have thoroughly tested it). It is convenient to quickly backup everything in a directory by typing a short command. The same backup directory could be created by other means, but they take more keystrokes. Of course, this does not protect against disk failure, but it does protect against accidental deletion.
This program will take some time to write, mostly because of picky details.
Click here to go back to the main menu.
Continued use of the Backup Utility results
in too many backup directories, which take up disk space.
Write a Purge
utility which deletes
all backup directories (and the files within them)
of the current directory,
except for the highest numbered backup directory.
Click here to go back to the main menu.
End of Exercises.


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{% load staticfiles %}
Hello! welcome to MyQuiz, leaning energizer.Here teachers can make online exam questions Students test knownledge in any place and time. You are just few steps away from real fun, Wasit


{% extends 'layouts/main.html' %} {% block title %}Overall Summary{% endblock %} {% block content %}
This project seeks to generate investment returns that can outperfrom a simple 'buy and hold' trading strategy over a 3 month period by applying Autoregressive Integrated Moving Average (ARIMA), Gradient Boosting Regression (GBR), and Recurrent Neural Network (RNN) models to generate daily high and low stock price predictions.
Delta Airlines (DAL) stock was choosen for the model due to the fact that the airline stock was recovering during the testing window (Jan 4th, 2021 - March 31st, 2021) and thus, yield more interesting results than choosing a stock that was plummeting or relatively flat during this period.
Disclaimer: This project is entirely intended for research purposes and applying the algorithms and/or trading strategies outlined below is no guarantee of positive returns
A combination of stock technical indicators and basic stock metrics were used as features in the the GBR and RNN model. To prevent data leakage, the previous day's metrics were shifted forward in order to be used as a feature for current day's prediction. For example, DAL's closing price on 1/1/2021 was used as a feature in the prediction for the next day, 1/2/2021.
The root mean square error (RMSE) was used to tune the initially hyperparameters in the models, but the overall return using the trading strategy described below was used to determine the optimal model. Details on each model's final parameters and results can be seen by clicking on its respective tab
A trading strategy using the GBR model's predictions yielded the greatest return. However, it was ~25% less ($6.20 per share vs $8.11 per share) than the return of a simple 'Buy and Hold' strategy (Buy on Day 0 and sell at the end of the 3 months).
{% endblock %}


IPSJ Programming Contest Committee
2019/09/27
Some potential tactics of the SamurAI Dig Here game are given here.
The dog should move to the cell with hidden treasure and bark only when the friend samurai can reach the cell earlier than the opponent samurai. Otherwise, the dog can stroll around waiting for an opportunity.
While the dog is staying in the cell with treasure, the opponent samurai cannot dig it up. The dog may stay on the cell for the friend samurai to come closer.
The dog may show some unusual maneuver arranged beforehand, such as repeating moves back and forth, to inform the position of some treasure without barking on the cell only to the friend samurai.
Interfering moves of opponent agents by blocking their routes is a conceivable tactic. Especially, as dogs have more freedom in their moves than samurai, interfering moves of the opponent samurai may be effective.
The dog can gather more information on hidden treasure by moving to the area where it has not visited.
The samurai should stay close to the dog to be able to rapidly approach the treasures found by the dog.
Dogs cannot fill a hole, and thus cannot move out of an area completely surrounded by cells with holes and field edges without help of the samurai. Samurai may dig holes around the position of the enemy dogs to make it confined in an narrow area. This may be most effective on corners of the field.
Freeing the dog confined in a narrow area by filling one of the holes surround the area might be effective.


{% extends 'grant_applications/generic_form_page.html' %} {% block headings %} {{ page.caption }}
Tell us the best way to contact you about your TAP application.


Tcl Documentation
- Tcl Applications
- The interpreters which implement Tcl.
- Tcl Commands
- The commands which the tclsh interpreter implements.
- Tcl Library
- The C functions which a Tcl extended C program may use.
- Keywords
- The keywords from the Tcl man pages.
Copyright © 1989-1994 The Regents of the University of California
Copyright © 1992-1999 Karl Lehenbauer & Mark Diekhans
Copyright © 1992-1999 Karl Lehenbauer and Mark Diekhans
Copyright © 1993-1997 Bell Labs Innovations for Lucent Technologies
Copyright © 1994-2000 Sun Microsystems, Inc
Copyright © 1995-1997 Roger E. Critchlow Jr
Copyright © 1997-2000 Ajuba Solutions
Copyright © 1998 Mark Harrison
Copyright © 1998-2000 Scriptics Corporation
Copyright © 2001 ActiveState Tool Corp
Copyright © 2001 Vincent Darley
Copyright © 2001-2004 ActiveState Corporation
Copyright © 2001-2005 Kevin B. Kenny <kennykb(at)acm.org>
Copyright © 2001-2006 Donal K. Fellows
Copyright © 2002-2010 Andreas Kupries <andreas_kupries(at)users.sourceforge.net>
Copyright © 2003 George Petasis <petasis(at)iit.demokritos.gr>
Copyright © 2003 Simon Geard
Copyright © 2006 Miguel Sofer
Copyright © 2006-2008 ActiveState Software Inc


In this post I am going to download tweets using tweetR package.
You have to have a twitter account. If you do not have, create a twitter account on twitter.com.
Create a twitter application on apps.twitter.com. There, you have to insert a number of information that will be used to collect tweets. Once, you created an application, you will have access to API Key and API Secret in the Keys and Access Tokens tab. In the same tab, you can create access tokens too (no picture is provided here).
- Go to RStudio: install and attach tweetR package.
# install.packages("twitteR")
# library(twitteR)
- From step 2, insert API Key, API Secret, Access Token, and Access Secret into the following code chunk.
api_key <- "YOUR_API_KEY"
api_secret <- "YOUR_API_SECRET"
access_token <- "YOUR_ACCESS_TOKEN"
access_secret <- "YOUR_ACCESS_SECRET"
- Now, setup the twitter
setup_twitter_oauth(consumer_key = api_key,
consumer_secret = api_secret,
access_token=access_token,
access_secret=access_secret)


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Trainspotting's Kelly Macdonald to play tough cop in new series of Line of Duty
Now we're sucking diesel!
by Jennifer Russell, https://www.dailyrecord.co.uk/authors/jennifer-russell/Trainspotting alumni Kelly Macdonald is set to play a tough cops in the new series of hit BBC show Line of Duty.
The Scot will star alongside Adrian Dunbar, Vicky McClure and fellow Scot Martin Compston when AC-12 return.
She will play Detective Chief Inspector Joanne Davidson, the senior investigating officer of an unsolved murder, whose unconventional conduct raises suspicions at AC-12.
It is expected she will be an adversary to the anti-corruption team, which could mean fireworks onscreen.
Line of Duty writer and showrunner, Jed Mercurio, says: "We're honoured that Kelly Macdonald will join Martin, Vicky and Adrian in series six of Line Of Duty. DCI Joanne Davidson will prove the most enigmatic adversary AC-12 have ever faced."
Simon Heath, CEO and Creative Director of World Productions, says: “The Line Of Duty team have always been huge fans of her work, so we’re thrilled Kelly has agreed to join us this series to pit her wits against AC-12.”
The most recent episode of Line Of Duty, series five’s finale on BBC One back in May, is the most watched programme of the year so far, with a 28-day figure of 13.7 million viewers across all TV and online viewing.
Piers Wenger, Controller of BBC Drama, says: “We’re delighted that Kelly Macdonald is coming on board for series six. She’s one of the UK’s most versatile actors and we’re excited for viewers to see what Jed’s scripts have in store for her when she joins the Line Of Duty team.”


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{% extends "FSJ/base_with_banner.html" %} {% load i18n %} {# NOTE YOU MUST LOAD i18n to every HTML page even though it's also loaded to base.html #} {% load fsj_tags %} {# This page allows the coordinator to view a summary of the adjudicator's picks for an award and enter a final #} {# comment recording the decision #} {% block heading %} {# Translators: Final Review page header #} {% trans "Final Review" %} {% endblock %} {% block content %}


httpd::direct(n) 1.1 "Tcl Web Server"
httpd::direct - Application direct domains
TABLE OF CONTENTS
SYNOPSIS
DESCRIPTION
Public API
SEE ALSO
KEYWORDS
COPYRIGHT
package require httpd::direct ?1.1?
The package httpd::direct provides generic facilities for
the implementation of application direct domains. These are
domains where each url in the domain is handled by its own tcl
command. Urls for which there is no command to handle it do not exists
and cause the generation of an http error reply.
- Direct_Url virtual ?prefix? ?inThread?
-
Registers the url prefix virtual as an application direct
domain. If prefix is used then it declares the command prefix to
use when constructing the name of the tcl command handling a
particular url (See below for more details). If either no such prefix
is declared, or as the empty string, then the url prefix itself,
i.e. virtual, will be used as the basename of the handler
command.
The last argument specifies whether the handlers command should run in
a separate thread. By default the main thread is used.
Regarding the construction of tcl commands for handling particular
urls:
For each url a/b/c in the domain the system will construct the command
name prefix/a/b/c and call this to generate the html for that
url. The value of any form variable for which the procedure has an
argument of the same name will be assigned to that argument. All
remaining form variables and their values will be appended to the
command in the form (varname value)... This means that any command
implementing an application direct domain has to have an args
argument to capture this data and prevent errors in this package when
the command is called.
The same data is also available through the commands provided by the
package ncgi.
The return code of the handler has important meaning. Anything
outside of a regular return (code 0) and code 302 will be
treated as an exception and thrown upward. As for the two
codes mentioned above:
- 0
-
A regular return.
The return value of the handler command is used as the data to return
to the browser. By default it is treated as of type "text/html". This
can be changed by defining a global variable of the same name as the
handler command and assigning the correct mime content type to
it. I.e.
| |
set prefix/a/b/c text/plain
|
- 302
-
This code causes the package to assume that the result of the handler
command is an url, and causes the generation of a http redirect reply
to that url. (Side note: 302 is also the http protocol code used in
http redirect replies).
Handlers for application direct domains are not given explicit access
to either the channel handle for the connection, nor the connection
status array. However they can use upvar to import sock or
data respectively from the caller's scope, which contain this
information.
- Direct_UrlRemove prefix
-
Removes the application direct domain which was registered
for the specified command prefix.
httpd, httpd::cgi, httpd::cookie, httpd::doc_error, httpd::url, httpd::utils
application direct, domain, web server
Copyright © 2003 Andreas Kupries <andreas_kupries@users.sourceforge.net>


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full. If you do not accept this legal notice in full, you must leave the Website immediately.
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updated version will be posted on the Website and you will bound by the updated version if you
continue to use the Website thereafter. Certain provisions of this legal notice may be superseded by
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on this Website are owned by us, our licensors, advertisers or content suppliers. Except as
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under copyright, trademark or other intellectual property rights.
You may store, manipulate, analyse, reformat, print and display the content supplied solely for your
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You’ve officially spun up your very first Ember app :-)
You’ve got one more decision to make: what do you want to do next? We’d suggest one of the following to help you get going:
If you run into problems, you can check Stack Overflow or our forums for ideas and answers—someone’s probably been through the same thing and already posted an answer. If not, you can post your own question. People love to help new Ember developers get started, and our community is incredibly supportive <3
To remove this welcome message, remove the ember-welcome-page
add-on from your package.json
file


These pages describe a git and github workflow for the scikit-image project.
There are several different workflows here, for different ways of working with scikit-image.
This is not a comprehensive git reference, it’s just a workflow for our own project. It’s tailored to the github hosting service. You may well find better or quicker ways of getting stuff done with git, but these should get you started.
For general resources for learning git, see git resources.


Class Widget
Dashboard
Namespace: DashboardBundle\Entity
Orm\entity(repositoryclass="dashboardbundle\repository\widgetrepository")
Orm\table(name="dh_widget")
Located at DashboardBundle/Entity/Widget.php
Orm\entity(repositoryclass="dashboardbundle\repository\widgetrepository")
Orm\table(name="dh_widget")
Located at DashboardBundle/Entity/Widget.php
public
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string
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public
string
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public
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public
integer
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public
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public
DateTime
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public
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public
DateTime
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| Jump to DART Documentation Main Index |
The AIRS
instrument is an Atmospheric Infrared Sounder flying on the
Aqua spacecraft.
Aqua is one of a group of satellites flying close together
in a polar orbit, collectively known as the "A-train".
The programs in this directory help to extract the
data from the distribution files and put them into
DART observation sequence (obs_seq) file format.
AIRS data includes atmospheric temperature in the troposphere,
derived moisture profiles, land and ocean surface temperatures,
surface emmissivity, cloud fraction, cloud top height,
and ozone burden in the atmosphere.
Access to the web pages where the AIRS data are stored is available by registering as a data user.
There are two products this converter can be used on: AIRX2RET, which is the L2 standard retrieval product using AIRS IR and AMSU (without-HSB); and AIRS2RET, which is the L2 standard retrieval product using AIRS IR-only. More detailed information on the AIRS2RET data product and the AIRX2RET data product is available from the nasa web pages.
The data is distributed in HDF-4 format, using some additional conventions for metadata called HDF-EOS. There is a basic library for accessing data in hdf files, and a variety of generic tools that work with hdf files. The specific libraries we use are the HDF-EOS2 library built on HDF4. The web page has a link to specific build instructions. Also, see below on this web page for very specific instructions for getting the required software and building it. If you find more recent instructions online, use those. But in the absence of anything else, it's someplace to start.
Besides the programs in this directory, a variety of specific tools targeted at AIRS data are available to help read and browse the data. General information on using hdf in the earth sciences is available here.
Several types of AIRS data, with varying levels of processing, are available. The following descriptions are taken from the V5_Data_Release_UG document:
The L1B data product includes geolocated, calibrated observed microwave, infrared and visible/near infrared radiances, as well as Quality Assessment (QA) data. The radiances are well calibrated; however, not all QA data have been validated. Each product granule contains 6 minutes of data. Thus there are 240 granules of each L1B product produced every day.
The L2 data product includes geolocated, calibrated cloud-cleared radiances and 2-dimensional and 3-dimensional retrieved physical quantities (e.g., surface properties and temperature, moisture, ozone, carbon monoxide and methane profiles throughout the atmosphere). Each product granule contains 6 minutes of data. Thus there are 240 granules of each L2 product produced every day.
The L3 data are created from the L2 data product by binning them in 1°x1° grids. There are three products: daily, 8-day and monthly. Each product provides separate ascending (daytime) and descending (nighttime) binned data sets.
The converter in this directory processes level 2 (L2) data files, using data set AIRS_DP and data product AIRX2RET or AIRS2RET without HSB (the instrument measuring humidity which failed).
The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude.
The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities will also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. The Shortname for this product is AIRX2RET. (AIRS2RET is the same product but without the AMSU data.)
The converter outputs temperature observations at the corresponding vertical pressure levels. However, the moisture obs are the mean for the layer, so the location in the vertical is the midpoint, in log space, of the current layer and the layer above it. There is an alternative computation for the moisture across the layer which may be more accurate, but requires a forward operator subroutine to be written and for the observation to contain metadata. The observation could be defined with a layer top, in pressure, and a number of points to use for the integration across the layer. Then the forward operator would query the model at each of the N points in the vertical for a given horizontal location, and compute the mean moisture value. This code has not been implemented yet, and would require a different QTY_xxx to distinguish it from the simple location/value moisture obs. See the GPS non-local operator code for an example of how this would need to be implemented.
Getting the data currently means putting in a start/stop time at this web page. The keyword is AIRX2RET and put in the time range of interest and optionally a geographic region. Each file contains 6 minutes of data, is about 2.3 Megabytes, and globally there are 240 files/day (about 550 Megabytes/day). There are additional options for getting only particular variables of interest, but the current reader expects whole files to be present. Depending on your connection to the internet, there are various options for downloading. We have chosen to download a wget script which is created by the web page after adding the selected files to a 'cart' and 'checking out'. The script has a series of wget commands which downloads each file, one at a time, which is run on the machine where you want the data to end up.
The temperature observations are located on standard levels; there is a single array of heights in each file and all temperature data is located on one of these levels. The moisture observations, however, are an integrated quantity for the space between the levels; in their terminology the fixed heights are 'levels' and the space between them are 'layers'. The current converter locates the moisture obs at the midpoint, in log space, between the levels.
The hdf files need to be downloaded from the data server, in any manner you choose. The converter program reads each hdf granule and outputs a DART obs_seq file containing up to 56700 observations. Only those with a quality control of 0 (Best) are kept. The resulting obs_seq files can be merged with the obs_sequence_tool into larger time periods.
It is possible to restrict the output observation sequence to contain data from a region of interest throught the use of the namelist parameters. If you need a region that spans the Prime Meridian lon1 can be a larger number than lon2, for example, a region from 300 E to 40 E and 60 S to 30 S (some of the South Atlantic), would be lon1 = 300, lon2 = 40, lat1 = -60, lat2 = -30.
The scripts directory here includes some shell scripts that make use of the fact that the AIRS data is also archived on the NCAR HPSS (tape library) in daily tar files. The script has options to download a day of granule files, convert them, merge them into daily files, and remove the original data files and repeat the process for any specified time period. (See oneday_down.sh)
Here is a very specific script I used to build the required libraries on a Linux cluster. If you find more up-to-date instructions, use those. But in the absence of anything else, here's a place to start:
wget https://observer.gsfc.nasa.gov/ftp/edhs/hdfeos/latest_release/* # NOTE: direct ftp does not work for me anymore ##ftp edhs1.gsfc.nasa.gov ### (log in as 'anonymous' and your email as the password) ##cd /edhs/hdfeos/latest_release ##mget * ##quit # mar 2013, the dir contents: # # hdf-4.2.6.tar.gz # HDF-EOS2.18v1.00.tar.Z # HDF-EOS2.18v1.00_TestDriver.tar.Z # HDF_EOS_REF.pdf # HDF_EOS_UG.pdf # jpegsrc.v6b.tar.gz # zlib-1.2.5.tar.gz # # (i skipped a 'windows' dir). # # mar 2019 contents: # HDF-EOS2.20v1.00.tar.Z 08-Jan-2018 15:21 7.3M # HDF-EOS2.20v1.00_Tes..> 08-Jan-2018 15:21 9.5M # HDF-EOS_REF.pdf 07-Nov-2018 13:45 695K # HDF-EOS_UG.pdf 08-Jan-2018 15:28 429K # hdf-4.2.13.tar.gz 08-Jan-2018 15:14 4.3M # jpegsrc.v9b.tar.gz 09-Jan-2018 13:44 1.0M # zlib-1.2.11.tar.gz 08-Jan-2018 15:22 593K # for i in *.tar.gz do tar -zxvf $i done # # start with smaller libs, work up to HDF-EOS. # # echo zlib: cd zlib-1.2.11 ./configure --prefix=/glade/p/work/nancy make make test make install echo jpeg: cd jpeg-9b ./configure --prefix=/glade/p/work/nancy make make test mkdir /glade/p/work/nancy/{bin,man,man/man1} make install # (make install wouldn't create the dirs if they didn't exist. # lib was there from the zlib install, the others weren't.) echo hdf: cd hdf-4.2.13 ./configure --prefix=/glade/p/work/nancy # (it found zlib and jpeg, from the install prefix i guess) make # (there is apparently no 'make test') make install echo hdf-eos: cd hdfeos ./configure CC='/glade/p/work/nancy/bin/h4cc -Df2cFortran' --prefix=/glade/p/work/nancy # (the CC= is crucial) make # (i didn't build the test drivers so i didn't do make test) make install echo AIRS converter: cd $DART/observations/AIRS/work echo edit mkmf_convert_airs_L2 to have all the base paths echo be /glade/p/work/nancy instead of whatever. make it look like: echo ' ' echo 'set JPGDIR = /glade/work/nancy' echo 'set HDFDIR = /glade/work/nancy' echo 'set EOSDIR = /glade/work/nancy' echo ' ' ./quickbuild.csh exit 0
This namelist is read in a file called input.nml. We adhere to the F90 standard of starting a namelist with an ampersand '&' and terminating with a slash '/' for all our namelist input. Character strings that contain a '/' must be enclosed in quotes to prevent them from prematurely terminating the namelist.
&convert_airs_L2_nml l2_files = 'input.hdf', l2_file_list = '', datadir = '.', outputdir = '.', lon1 = 0.0, lon2 = 360.0, lat1 = -90.0, lat2 = 90.0, min_MMR_threshold = 1.0e-30, top_pressure_level = 0.0001, cross_track_thin = 0, along_track_thin = 0, /
| Contents | Type | Description | Default |
|---|---|---|---|
| l2_files | character(len=128) (:) | A list of one or more names of the HDF file(s) to read, NOT including the directory. If multiple files are listed, each will be read and the results will be placed in a separate file with an output filename constructed based on the input filename. | |
| l2_file_list | character(len=128) | The name of an ascii text file which contains one filename per line, NOT including the directory. Each file will be read and the observations converted into an output file where the output filename is based on the input filename. Only one of 'l2_files' and 'l2_file_list' can be specified. The other must be ' ' (empty). | |
| datadir | character(len=128) | The directory containing the HDF files | |
| outputdir | character(len=128) | The directory for the output observation sequence files. | |
| lon1 | real(r4) | the West-most longitude of interest in degrees. [0.0, 360] | |
| lon2 | real(r4) | the East-most longitude of interest in degrees. [0.0, 360] | |
| lat1 | real(r4) | the South-most latitude of interest in degrees. [-90.0, 90.0] | |
| lat2 | real(r8) | the North-most latitude of interest in degrees. [-90.0, 90.0] | |
| min_MMR_threshold | real(r8) | The data files contains 'Retrieved Water Vapor Mass Mixing Ratio'. This is the minimum threshold, in gm/kg, that will be converted into a specific humidity observation. | |
| cross_track_thin | integer | provides ability to thin the data by keeping only every Nth data value in a particular row. e.g. 3 == keep every third value. | |
| along_track_thin | integer | provides ability to thin the data by keeping only every Nth row. e.g. 4 == keep only every 4th row. |
Earlier versions of this converter mistakenly put the moisture obs at level heights, in the same location as the temperature observations. The moisture observations are in fact an integrated value across the distance between two levels. This means the location was shifted 1/2 level in the vertical from the center of the layer. The fixed converter outputs the location at the center, in log space, of each layer.
If a more accurate moisture observation was needed, the observation value could be computed by actually integrating multiple values between the levels. At this point it doesn't seem necessary.
DART software - Copyright UCAR. This open source software is provided by UCAR, "as is", without charge, subject to all terms of use at http://www.image.ucar.edu/DAReS/DART/DART_download