Precondition
Sonya wants to post a video to TikTok.
Postcondition
Sonya has a newly posted video on her account.
Interaction Flow Example
- Sonya taps on the TikTok app tile on her phone.
- The app opens to the front of her screen.
- From the home page, Sonya taps the add content button on the bottom of the screen.
- TikTok brings her to the proprietary recording suite.
- Since Sonya does all of her recording and editing on her laptop, she taps the upload button.
- The app opens her phones gallery, defaulted to her video folder.
- She taps the video she wants to upload.
- The app returns to the recording suite, to allow her to review and further edit her video.
- After reviewing her video, she clicks next.
- The app takes her to the finalization screen./li>
- She describes her video, and adds her favorite and relevant hashtags, and taps post.
- TikTok uploads her video in the background, and sorts her video into the relevant hash tags and user algorithms so she shows up in interested users "for you page."


Mourinho took the Tottenham job on the condition Kane wouldn't be sold
The Portuguese coach insisted that he needed Kane to lead his project and couldn't be sold for any price
by Sport ENTottenham Hotspur's appointment of Jose Mourinho, replacing Mauricio Pochettino, is still an image many football fans are getting used to. It's been a season to forget for Spurs and Daniel Levy turned to the most expensive 'free agent' in football management to get his project back on track. Mourinho has been handed the biggest contract in the club's history, second only to Pep Guardiola in the league, and he didn't accept the role without a few conditions of his own.
According to the 'Mirror', the Portuguese coach insisted that he wouldn't take the job unless there were assurances that Harry Kane would remain at the club. Mourinho sees the striker as a key member of his team and wants to build around him. Levy was told he must do everything he can to keep Kane in North London.
Mourinho took charge of his first training session yesterday and spoke at length to both Kane and Dele Alli. If Spurs are to turn their fortunes around, Jose will need both of them to be firing on all cylinders.


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@()
Kurator tools and validators can help you check, standardize and clean biodiversity data before doing statistical analysis or research. Kurator Web tools detect and flag data quality issues and propose amendments while keeping track of provenance.
Login or Register a user account to access the web application and run the workflows or see the About page for more info.
Image Credits: Camera by Designmodo , CC BY; Data icon by Pensoft, CC BY. All other icons under Public Domain.


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If you have an MX keys keyboard and used to work with a Swedish keyboard on Windows, this mapping will make your mac behave more it would on Windows. Most used keys mapped


Integration tests for .NET CLI tools I’ve spent Saturday evening working on a small project. # github-actions # dotnet


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Hello dear From, Miss ZACHI Medina Abidjan Cote d'Ivoire West Africa. Hello Dear Chosen one, Please excuse me for all the inconveniences my mail could cause you.I saw your profile and have the intrest on you . Please even if we never knew before, I believe firmly that on the basis of the right of humanitarian assistance that a real confidence and love can arise from our communication and also a real partnership between us. I am Miss Medina ZACHI , the legitimate daughter of the Director of cocoa export here murdered by the rebelles here because of the political crisis going on here since four years ago. My father had deposited a consignment with a security company here containing the sum of $9.000,000 USD (Nine million dollars USD) To allow him to conceive a project of investment at the end of his mandate here. Regrettably the fate decided on it otherwise. Today my major concern is to move out this sum outside here. Given that I am the only child and I have seriously suffered from the raggings and the harassments on behalf of the political opponents of my Father. It is in this prospect that I contact you to please help me to move this heritage from the security company to your country. Because of the political war and the hostilities in this country I seriously wish to leave from here and live the rest of my life in a more peaceful and politically more stable and quiet country. It is what justifies this demand. I shall voluntarily agree on a good and appropriate percentage of this money as your compensation for your assistant as for what precedes. I would like to count on your human, virtuous and professional qualities. I look forward to recive your urgent responds. Thanks for your care . Yours sincerely Miss Medina


UP AND UP LANSOPRAZOLE- lansoprazole capsule, delayed release
Target Corporation
Allergy alert: Do not use if you are allergic to lansoprazole
14-Day Course of Treatment
Repeated 14-Day Courses (if needed)
D&C red no. 28, D&C yellow no. 10, FD&C blue no. 1, gelatin, hypromellose, low substituted hydroxypropyl cellulose, mannitol, meglumine, methacrylic acid copolymer, pharmaceutical ink, polyethylene glycol, polysorbate 80, sodium lauryl sulfate, sugar spheres, talc, titanium dioxide
Lansoprazole Delayed-Release Capsules 15 mg
Acid Reducer
• May take 1 to 4 days for full effect, although some people get complete relief of symptoms within 24 hours
Please read the entire package insert before taking Lansoprazole Delayed-Release Capsules 15 mg.
Save for future reference.
How Lansoprazole Delayed-Release Capsules 15 mg Treats Your Frequent Heartburn
Lansoprazole Delayed-Release Capsules 15 mg stops acid production at the source – the pumps that release acid into the stomach. Lansoprazole Delayed-Release Capsules 15 mg is taken once a day (every 24 hours), every day for 14 days.
What You Can Expect When Taking Lansoprazole Delayed-Release Capsules 15 mg
Frequent heartburn can occur anytime during the 24-hour period (day or night). Take Lansoprazole Delayed-Release Capsules 15 mg in the morning before eating. Lansoprazole Delayed-Release Capsules 15 mg is clinically proven to treat frequent heartburn. Although some people get complete relief of symptoms within 24 hours, it may take 1 to 4 days for full effect. Make sure you take Lansoprazole Delayed-Release Capsules 15 mg every day for 14 days to treat your frequent heartburn.
Who Should Take Lansoprazole Delayed-Release Capsules 15 mg
Adults (18 years and older) with frequent heartburn – when you have heartburn 2 or more days a week.
Who Should NOT Take Lansoprazole Delayed-Release Capsules 15 mg
People who have one episode of heartburn a week or less, or who want immediate relief of heartburn.
How to Take Lansoprazole Delayed-Release Capsules 15 mg
14-DAY Course of Treatment
When to Take Lansoprazole Delayed-Release Capsules 15 mg Again
You may repeat a 14-day course of therapy every 4 months.
When to Talk to Your Doctor
Do not take for more than 14 days or more often than every 4 months unless directed by a doctor.
Warnings and When to Ask Your Doctor
Allergy alert: Do not use if you are allergic to lansoprazole
Do not use
Ask a doctor before use if you have
Ask a doctor or pharmacist before use if you are taking
Stop use and ask a doctor if
If pregnant or breast-feeding, ask a health professional before use.
Keep out of reach of children. In case of overdose, get medical help or contact a Poison Control Center right away. (1-800-222-1222)
Tips for Managing Heartburn
Clinical studies prove Lansoprazole Delayed-Release Capsules 15 mg effectively treats frequent heartburn
In three clinical studies, Lansoprazole Delayed-Release Capsules 15 mg was shown to be significantly better than placebo in treating frequent heartburn.
How Lansoprazole Delayed-Release Capsules 15 mg is Sold
Lansoprazole Delayed-Release Capsules 15 mg is available in 14 capsule, 28 capsule and 42 capsule sizes. These sizes contain one, two and three 14-day courses of treatment, respectively. Do not use for more than 14 days in a row unless directed by your doctor. For the 28 count (two 14-day courses) and the 42 count (three 14-day courses), you may repeat a 14-day course every 4 months.
For Questions or Comments About Lansoprazole Delayed-Release Capsules 15 mg
Call 1-800-719-9260
:3T300 00 J3
Compare to active ingredient in Prevacid® 24 HR
lansoprazole delayed release capsules, 15 mg
acid reducer 24-hour
may take 1 to 4 days for full effect, although some people get complete relief of symptoms within 24 hours
clinically proven to treat frequent heartburn
ACTUAL SIZE
28 CAPSULES
28 CAPSULES – TWO 14-DAY COURSES OF TREATMENT
|
UP AND UP LANSOPRAZOLE
lansoprazole capsule, delayed release | ||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||
|
| Labeler - Target Corporation (006961700) |


Megan Thomas
15 Indiana Rd · Grand Rapids,
MI, 49504 · Cell: (586)
260 1818
Email<EMAIL_ADDRESS>|
Grand Valley State University Grand Rapids, MI
2015-Present § Working towards a Bachelor of Science in Computer
Science with a minor in Mathematics § Course work: Structure
of Programming Languages, Database Creation and Management, Computer
Organization and Assembly, Discrete Mathematics, Linear Algebra, Data
Structures and Algorithm Analysis § GPA: 3.73 TECHNICAL
SKILLS Languages: Java, Python, SQL, C, C++, C# Operating Systems: Linux, OS X, MS Windows Technology: HTC Vive, Microsoft
HoloLens, Ultimaker 3D Printers, HP Sprout, Google Glass Tools: Unity, Power Bi, Azure, Git,
Jira, Cura |
PROFESSIONAL
EXPERIENCE
|
Advanced Analytics
Enablement Intern 3M, Saint Paul,
Minnesota
2018
– Present § Established
scalable cloud-based infrastructure to enable continuous development &
continuous integration of Azure hosted web application to enable advanced
analytics with cloud Costing Data § Developed
desktop Application utilizing C# and Python enabling self-service to data for
end users through comprehensive Power BI dashboard § Developed Python
web application allowing real-time interactive data representation and
querying of data § Incorporated
predictive cost models to provide insight to progress, business value,
current usage, and projected usage Emerging Technology and 3D Printing Specialist Grand
Valley State University, Grand Rapids, MI 2017 –
Present § Research
up and coming technologies for the university, create comprehensive reports
on how the technology will benefit them and manage the budget of what we can
afford to bring in § Teach
faculty how to utilize technologies in their research and in the classroom in
the form of IT seminars, FTLC workshops, and direct mentoring on specific
technology § Create
and repair 3D print files, manage the printing queue, and repair 3D printers |
Dean’s List 2015 -
Present
GV Laker
Scholarship
2015 - Present National
Society of Collegiate Scholars 2016 – Present § Volunteer
opportunities both around campus and in the surrounding community Women in Computing 2017 –
Present § Provides
mentoring and professional development opportunities for women interested in
computing. § Host Tech Talks
given by women in the field and work with schools in the community to host
coding workshops for girls. |
ACTIVITIES AND ACHIEVEMENTS


About
Projects
Experience
Blog
Rahul
Sharma
BookCart
A single page application (SPA) to search for books, select them into the cart and buy. Built using
AngularJS
and Bootstrap.
MyMoviePoster
A web app to fetch requested movie poster using the
TMDb API
. Built using
Bootstrap
and
jQuery
.
PokéFacts
A random Pokémon fact generator, built using
jQuery
and
Bootstrap
.
Big-Red-Bot
A cross platform Raspberry Pi based bot application that can be run from up to ten devices simultaneously over an Ad Hoc network. Built in
Flask
framework.
The Awesome Weather Station
Raspberry Pi based weather station with a full fledged Content Management System for easy access and data analysis. Built using
PHP, MYSQL, Python
.
Optical Theremin based TRNG
Developed an optical version of the
Theremin
from scratch, using the tune to produce True Random Numbers, further employed in an RSA instance. Built using
Python, LabVIEW, NI myDAQ
. Read the paper
here
.
Freshwater Quality Management
Developed a statistical model for predicting the residence time of lakes in Bangalore (India) using the historical data and present parameters e.g. pH, TDS. Built using
Python, MATLAB
. Awarded as the best project in
ISA D14
, 2016. Read the paper
here
.
Smart Headphones
Developed the algorithm for an Active Noise Cancellation (ANC) headphone that can selectively detect the surroundings in real time and process the information to the user; achieved using NLMS and ALE. Built using
C, MATLAB
. Read the paper
here
.
Project SFAAS
A fetal ECG acquisition system targeted at rural landscapes devoid of sophisticated healthcare for child-bearing women. Built using
LabVIEW, myRIO
. Selected as one of the top 20 analog design projects by National Instruments, 2015. Read the paper
here
.
Coming Soon
Skills


Your participation is voluntary. Take time to think it over.
If you have any questions about this study, please .
If you would like to speak to someone other than us about this study, please contact the Western Institutional Review Board (WIRB) in the US. WIRB is a group of experts who independently review research. WIRB will address any concerns or complaints you have about this study. They will also help with any questions or concerns about your rights as a research participant.
Western Institutional Review Board® (WIRB®)
1019 39th Avenue SE Suite 120
Puyallup, WA 98374-2115, USA
Telephone: 1-800-562-4789 or<PHONE_NUMBER>
E-mail: Help@wirb.com


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You will be guided through the steps necessary to install or update nginx on this Mac.
Installer package created by Don McCaughey.
Rev {{revision}} built on {{date}} for {{arch_list}} with PCRE2 {{pcre2_version}}, zlib {{zlib_version}}, Xcode {{xcode}} and macOS {{macos}}.


ATTENDANCE MANAGEMENT SYSTEM
ADD
FACULTY
STUDENT
PARENT
DELETE
FACULTY
STUDENT
PARENT
UPDATE
FACULTY
STUDENT
PARENT


I Want to Learn to Code for Good.
I want to learn to code so that I can contribute to building open tools that help groups working for positive change to organize and communicate.
An example of this might be a web application that makes use of decentralized information to bring together news, events and information into a single place that can be easily discovered.


amazon-chime-sdk
/
com.amazonaws.services.chime.sdk.meetings.session
/
MeetingSessionStatusCode
/
AudioInternalServerError
AudioInternalServerError
AudioInternalServerError
There was an internal server error with the audio leg.


Plugin Listeners
Listeners allow plugins to declaratively subscribe to events delivered through the message bus. You can define both application- and project-level listeners.
Declarative registration of listeners allows you to achieve better performance compared to registering listeners from code, because listener instances are created lazily (the first time an event is sent to the topic), and not during application startup or project opening.
Defining Application-Level Listeners
To define an application-level listener, add the following section to your plugin.xml
:
<applicationListeners>
<listener class="myPlugin.MyListenerClass" topic="BaseListenerInterface"/>
</applicationListeners>
The topic
attribute specifies the listener interface corresponding to the type of events you want to receive.
Normally, this is the interface used as the type parameter of the Topic
instance for the type of events.
The class
attribute specifies the class in your plugin that implements the listener interface and receives
the events.
As a specific example, if you want to receive events about all changes in the virtual file system, you need
to implement the BulkFileListener
interface, corresponding to the topic VirtualFileManager.VFS_CHANGES
.
To subscribe to this topic from code, you could use something like the following snippet:
messageBus.connect().subscribe(VirtualFileManager.VFS_CHANGES, new BulkFileListener() {
@Override
public void after(@NotNull List<? extends VFileEvent> events) {
// handle the events
}
});
To use declarative registration, you no longer need to reference the Topic
instance. Instead, you refer directly
to the listener interface class:
<applicationListeners>
<listener class="myPlugin.MyVfsListener" topic="com.intellij.openapi.vfs.newvfs.BulkFileListener"/>
</applicationListeners>
Then you provide the listener implementation as a top-level class:
public class MyVfsListener implements BulkFileListener {
@Override
public void after(@NotNull List<? extends VFileEvent> events) {
// handle the events
}
}
Defining Project-level Listeners
Project-level listeners are registered in the same way, except that the top-level tag is
<projectListeners>
. They can be used to listen to project-level events, for example, tool window operations:
<projectListeners>
<listener class="MyToolwindowListener" topic="com.intellij.openapi.wm.ex.ToolWindowManagerListener" />
</projectListeners>
The class implementing the listener interface can define a one-argument constructor accepting a Project
,
and it will receive the instance of the project for which the listener is created:
public class MyToolwindowListener implements ToolWindowManagerListener {
private final Project project;
public MyToolwindowListener(Project project) {
this.project = project;
}
@Override
public void stateChanged() {
// handle the state change
}
}
Additional attributes
Registration of listeners can be restricted using the following attributes:
os
- allows to restrict listener to given OS, e.g.,os="windows"
for Windows only (2020.1 and later)activeInTestMode
- set tofalse
to disable listener ifcom.intellij.openapi.application.Application.isUnitTestMode()
==true
activeInHeadlessMode
- set tofalse
to disable listener ifcom.intellij.openapi.application.Application.isHeadlessEnvironment()
==true
. Also coversactiveInTestMode
as test mode implies headless mode.


The mailing lists have been consolidated into one Google Group.
The former mailing lists hosted at Codehaus are gone, but still available in public archives at MarkMail.
The mailing lists have been consolidated into one Google Group.
The former mailing lists hosted at Codehaus are gone, but still available in public archives at MarkMail.


Joint where frame_a rotates around axis n_a which is fixed in frame_a and frame_b rotates around axis n_b which is fixed in frame_b. The two frames coincide when "revolute_a.phi=0" and "revolute_b.phi=0". This joint has the following potential states;
They are used as candidates for automatic selection of states from the tool. This may be enforced by setting "stateSelect=StateSelect.always" in the Advanced menu. The states are usually selected automatically. In certain situations, especially when closed kinematic loops are present, it might be slightly more efficient, when using the "StateSelect.always" setting.
In the following figure the animation of a universal joint is shown. The light blue coordinate system is frame_a and the dark blue coordinate system is frame_b of the joint (here: n_a = {0,0,1}, n_b = {0,1,0}, phi_a.start = 90o, phi_b.start = 45o).


Welcome to Century Airlines booking system
You're logged in with Angular 7 & JWT!!
Your role is: {{currentUser.role}}.
This page can be accessed by all authenticated users.
Current user from secure api end point:


I really like the concept of doing more with less. There's just something satisfying about doing things quicker, more efficiently, and hoping it doesn't get to your head.
--- layout: default title: productivity main: about sub: tools ---
I really like the concept of doing more with less. There's just something satisfying about doing things quicker, more efficiently, and hoping it doesn't get to your head.
Random stuff I like using on my computer
Current audio setup.


This example shows how to use plugins with the HtmlEditor component.
Note that the js is not minified so it is readable. See the 'src' folder and form-htmleditor-basic.js for the full source code.


MeshFaceList¶
-
class
rhino3dm.
MeshFaceList
¶ -
Count
¶ int: Gets or sets the number of mesh faces. When getting this can includes invalid faces.
-
QuadCount
¶ int: Gets the number of faces that are valid quads (4 corners).
-
TriangleCount
¶ int: Gets the number of faces that are valid triangles (3 corners).
-
Capacity
¶ int: Gets or sets the total number of mesh triangles and quads the internal data structure can hold without resizing.
-
__len__
()¶ [todo] add documentation
-
__getitem__
()¶ [todo] add documentation
-
Clear
()¶ Clears the Face list on the mesh.
Return type: void
-
Destroy
()¶ Releases all memory allocated to store faces. The list capacity will be 0 after this call. Subsequent calls can add new items.
Return type: void
-
AddFace
(vertex1, vertex2, vertex3)¶ Appends a new mesh face to the end of the mesh face list.
Returns: The index of the newly added face. Return type: int
-
AddFace
(vertex1, vertex2, vertex3, vertex4) Appends a new mesh face to the end of the mesh face list.
Returns: The index of the newly added face. Return type: int
-
SetFace
(index, vertex1, vertex2, vertex3)¶ Sets a face at a specific index of the mesh.
Returns: True if the operation succeeded, otherwise false. Return type: bool
-
SetFace
(index, vertex1, vertex2, vertex3, vertex4) Sets a face at a specific index of the mesh.
Returns: True if the operation succeeded, otherwise false. Return type: bool
-
ConvertQuadsToTriangles
()¶ Splits all quads along the short diagonal.
Returns: True on success, False on failure. Return type: bool
-
ConvertNonPlanarQuadsToTriangles
()¶ Splits non-planar quads into two triangles based on given parameters.
Parameters: - planarTolerance (float) – If planarTolerance >= 0, then a quad is split if its vertices are not coplanar. If both planarTolerance = Rhino.RhinoMath.UnsetValue and angleToleranceRadians >= 0.0, then the planarity test is skipped.
- angleToleranceRadians (float) – If angleToleranceRadians >= 0.0, then a quad is split if the angle between opposite corner normals is > angleToleranceRadians. The corner normal is the normal to the triangle formed by two adjacent edges and the diagonal connecting their endpoints. A quad has four corner normals. If both angleToleranceRadians = Rhino.RhinoMath.UnsetValue and planarTolerance >= 0.0, then the corner normal angle test is skipped.
- splitMethod (int) – 0 default Currently divides along the short diagonal. This may be changed as better methods are found or preferences change. By passing zero, you let the developers of this code decide what’s best for you over time. 1 divide along the short diagonal 2 divide along the long diagonal 3 minimize resulting area 4 maximize resulting area 5 minimize angle between triangle normals 6 maximize angle between triangle normals
Returns: Number of quads that were converted to triangles.
Return type: int
-
ConvertTrianglesToQuads
()¶ Joins adjacent triangles into quads if the resulting quad is ‘nice’.
Parameters: - angleToleranceRadians (float) – Used to compare adjacent triangles’ face normals. For two triangles to be considered, the angle between their face normals has to be <= angleToleranceRadians. When in doubt use RhinoMath.PI/90.0 (2 degrees).
- minimumDiagonalLengthRatio (float) – ( <= 1.0) For two triangles to be considered the ratio of the resulting quad’s diagonals (length of the shortest diagonal)/(length of longest diagonal). has to be >= minimumDiagonalLengthRatio. When in doubt us .875.
Returns: True on success, False on failure.
Return type: bool
-
CullDegenerateFaces
()¶ Attempts to removes degenerate faces from the mesh. Degenerate faces are faces that contains such a combination of indices, that their final shape collapsed in a line or point.Before returning, this method also attempts to repair faces by juggling vertex indices.
Returns: The number of degenerate faces that were removed. Return type: int
-
IsHidden
()¶ Gets a value indicating whether a face is hidden. A face is hidden if, and only if, at least one of its vertices is hidden.
Parameters: faceIndex (int) – A face index. Returns: True if hidden, False if fully visible. Return type: bool
-
HasNakedEdges
()¶ Returns True if at least one of the face edges are not topologically connected to any other faces.
Parameters: faceIndex (int) – A face index. Returns: True if that face makes the mesh open, otherwise false. Return type: bool
-


{{ _('The best way to stay efficient while learning Vim') }}
{{ _('Or how to overcome the famous question"How do I quit this @#!?"') }}
{{ _ ('Learn Vim the smart way') }}.
{{ _("I'm convinced that everybody should be able to learn Vim and that it's just a problem of documentation. With this ebook you will:") }}
- {{ _('Stay productive while learning Vim') }}
- {{ _('Get a modern customizable IDE') }}
- {{ _('Learn why Vim seems so strange') }}
- {{ _('Get a selection of the best Vim plugins') }}
- {{ _('Have fun :-)') }}
{{ _('Content') }}
{{ _('Yum yum') }}{{ _('Good looking Vim') }}
{{ _("It's not because we are geeks that we should use awful color schemes. Or should we?") }}
{{ _('Plugin selection') }}
{{ _("Using Vim without plugins doesn't make any sense. It's like using a Smartphone without any app.") }}
{{ _('Smooth learning curve') }}
{{ _("If you don't quit the first week, you'll be using it for the rest of your life. It's my bet!") }}
{{ _('Creative Commons') }}
{{ _('You are free to do whatever you want (well, almost, see the licence details here) with the ebook.') }}
{{ _('Free price') }}
{{ _("You're free to pay whatever you want. Money, Flattr, email or a thank you are fine. More about free price here.") }}
{{ _('Open Source') }}
{{ _('The entire source code is available on my Github profile. Be sure to check it out!') }}
1 – {{ _('Introduction') }}
{{ _('A brief overview of the advantages of vim and why it is one of the best investments you can make in your career.') }}
2 – {{ _('Usable Vim') }}
{{ _("In this chapter we see how to configure Vim to make it usable by a \"normal\" human. Syntax highlighting, file explorer (The NERD Tree), color scheme (Solarized), using the mouse ... We also discuss why it is important to abandon the mouse for the health of your hands / fingers. You can also directly download the entire chapter.", link=url_for('static', filename=g.current_lang + '-vim-extract-chap1.pdf')) }}
3 — {{ _("The text editor you've always dreamed of") }}
{{ _("Once you will be able to use Vim \"normally\" thanks to the previous chapter, we will see here how to take full advantage of Vim. We introduce keyboard shortcuts as well as modes. We begin by gradually to do without the mouse.") }}
4 — {{ _('Essential plugins') }}
{{ _("In this chapter, we put Vim to the next level. We add plugins such as Lusty Explorer (file explorer using the keyboard), Ack (file search) or Ctrl-p (fast searching/opening of files).") }}
5 — {{ _('Cheatsheet & examples') }}
{{ _("We finally provide a set of useful/essential shortcuts to master keyboard text editing.") }}
{{ _('Frequently Asked Questions') }}
{{ _('Would this site be serious without a FAQ?') }}Q. {{ _('Is it really free?') }}
{{ _("Free price doesn't mean that it's free. I firmly believe that everybody has something to give in exchange of this ebook. The difference is just that I let you fix the price. It can be whatever you want: money, Flattr, email or a thank you are fine. More about free price here.") }}
Q. {{ _('Can I get a preview of the content?') }}
{{ _('Sure, you can even') }} {{ _('download the first chapter') }}
Q. {{ _('Is this ebook suited for Vim experts?') }}
{{ _("Clearly not. This book is for Vim novices wishing not to quit after two days. The goal is to help you take the first step without losing all your productivity. There are a lot of resources available for gurus, like: A byte of vim, Vimbits, Learning The Vi and Vim Editors and Practical Vim.") }}
Q. {{ _("Can I thank you and share the love?") }}
{{ _("The ebook is published under the CC licence") }} .
{{ _("So you can share it as you want!") }}


Module flower
Flower library is a lightweight library for Moai SDK.
See:
Memo:
English documentation has been updated. Please contact github://Cloven with issues, questions, or problems regarding the documentation.
Info:
- Release: V3.0.0
- Author: Makoto
Functions
| openWindow (title, width, height, scale) | Open the window. |
| setDefaultWindowSize (targetDevice, landscape, scaleMode) | Set the size of the assumed device. |
| isMobile () | Returns whether the mobile execution environment. |
| isDesktop () | Returns whether the desktop execution environment. |
| getScreenSize () | Returns the size of the screen. |
| getViewSize () | Returns the size of the viewport. |
| getViewport () | Returns the Viewport to be used in layers. |
| getTexture (path, filter) | Returns the texture. |
| getFont (path, charcodes, points, dpi, filter) | Returns the font. |
| openScene (sceneName, params) | Opens the scene. |
| gotoScene (sceneName, params) | Goto the Scene. |
| closeScene (params) | Close the Scene. |
| callOnce (func, ...) | Executes a function in a MOAICoroutine. |
Fields
| viewport | Default viewport. |
| screenWidth | Width of Window. |
| screenHeight | Height of Window. |
| viewWidth | Width of default viewport. |
| viewHeight | Height of default viewport. |
| viewScale | Scale of default viewport. |
Classes
| class | class. |
| table | table. |
| math | math. |
| Config | Config class. |
| KeyCode | KeyCode class. |
| Logger | Logger class. |
| Executors | Executors class. |
| Resources | Resources class. |
| Devices | Devices class. |
| ClassFactory | ClassFactory class. |
| Event | Event class. |
| EventListener | EventListener class. |
| EventDispatcher | EventDispatcher class. |
| Runtime | Runtime class. |
| InputMgr | InputMgr class. |
| RenderMgr | RenderMgr class. |
| SceneMgr | SceneMgr class. |
| DeckMgr | DeckMgr class. |
| Animation | Animation class. |
| Window | Window class. |
| DisplayObject | DisplayObject class. |
| Group | Group class. |
| Scene | Scene class. |
| SceneAnimations | SceneAnimations class. |
| Layer | Layer class. |
| Viewport | Viewport class. |
| Camera | Camera class. |
| Image | Image class. |
| SheetImage | SheetImage class. |
| MapImage | MapImage class. |
| MovieClip | MovieClip class. |
| NineImage | NineImage class. |
| Label | Label class. |
| DrawableObject | DrawableObject class. |
| DrawableRect | DrawableRect class. |
| Rect | Rect class. |
| Graphics | Graphics class. |
| Particle | Particle class. |
| Font | Font class. |
| Texture | Texture class. |
| TouchHandler | TouchHandler class. |
| Interceptor | Interceptor class. |
| PropertyUtils | PropertyUtils class. |
| DebugUtils | DebugUtils class. |
| BitUtils | BitUtils class. |
Functions
- openWindow (title, width, height, scale)
-
Open the window.
Initializes the library.
Parameters:
- title (Option)Title of the window
- width (Option)Width of the window
- height (Option)Height of the window
- scale (Option)Scale of the Viewport to the Screen
- setDefaultWindowSize (targetDevice, landscape, scaleMode)
-
Set the size of the assumed device.
Parameters:
- targetDevice
- landscape
- scaleMode
- isMobile ()
-
Returns whether the mobile execution environment.
Returns:
-
True in the case of mobile.
- isDesktop ()
-
Returns whether the desktop execution environment.
Returns:
-
True in the case of desktop.
- getScreenSize ()
-
Returns the size of the screen.
Returns:
-
width, height
- getViewSize ()
-
Returns the size of the viewport.
Returns:
-
width, height
- getViewport ()
-
Returns the Viewport to be used in layers.
If you change the Viewport, it affects all layers.
Returns:
-
width, height
- getTexture (path, filter)
-
Returns the texture.
Textures are cached.
Parameters:
- path The path of the texture
- filter Texture filter.
Returns:
-
Texture instance
- getFont (path, charcodes, points, dpi, filter)
-
Returns the font.
Fonts are cached.
Parameters:
- path The path of the font.
- charcodes (option)Charcodes of the font
- points (option)Points of the font
- dpi (option)Dpi of the font
- filter (option)Filter of the font
Returns:
-
Font instance
- openScene (sceneName, params)
-
Opens the scene. This is a convenience function.
Delegate to SceneMgr.
Parameters:
- sceneName module name of the Scene
- params (option)Parameters of the Scene
- gotoScene (sceneName, params)
-
Goto the Scene. This is a convenience function.
Delegate to SceneMgr.
Parameters:
- sceneName module name of the Scene
- params (option)Parameters of the Scene
- closeScene (params)
-
Close the Scene. This is a convenience function.
Delegate to SceneMgr.
Parameters:
- params (option)Parameters of the Scene
- callOnce (func, ...)
-
Executes a function in a MOAICoroutine.
This variant of the function family will run the func immediately
upon the next coroutine.yield().
Parameters:
- func function object
- ... (option)function arguments
Fields
- viewport
- Default viewport.
- screenWidth
- Width of Window.
- screenHeight
- Height of Window.
- viewWidth
- Width of default viewport.
- viewHeight
- Height of default viewport.
- viewScale
- Scale of default viewport.
Classes
- class
-
class.
see also:
- table
-
table.
see also:
- math
-
math.
see also:
- Config
-
Config class.
see also:
- KeyCode
-
KeyCode class.
see also:
- Logger
-
Logger class.
see also:
- Executors
-
Executors class.
see also:
- Resources
-
Resources class.
see also:
- Devices
-
Devices class.
see also:
- ClassFactory
-
ClassFactory class.
see also:
- Event
-
Event class.
see also:
- EventListener
-
EventListener class.
see also:
- EventDispatcher
-
EventDispatcher class.
see also:
- Runtime
-
Runtime class.
see also:
- InputMgr
-
InputMgr class.
see also:
- RenderMgr
-
RenderMgr class.
see also:
- SceneMgr
-
SceneMgr class.
see also:
- DeckMgr
-
DeckMgr class.
see also:
- Animation
-
Animation class.
see also:
- Window
-
Window class.
see also:
- DisplayObject
-
DisplayObject class.
see also:
- Group
-
Group class.
see also:
- Scene
-
Scene class.
see also:
- SceneAnimations
-
SceneAnimations class.
see also:
- Layer
-
Layer class.
see also:
- Viewport
-
Viewport class.
see also:
- Camera
-
Camera class.
see also:
- Image
-
Image class.
see also:
- SheetImage
-
SheetImage class.
see also:
- MapImage
-
MapImage class.
see also:
- MovieClip
-
MovieClip class.
see also:
- NineImage
-
NineImage class.
see also:
- Label
-
Label class.
see also:
- DrawableObject
-
DrawableObject class.
see also:
- DrawableRect
-
DrawableRect class.
see also:
- Rect
-
Rect class.
see also:
- Graphics
-
Graphics class.
see also:
- Particle
-
Particle class.
see also:
- Font
-
Font class.
see also:
- Texture
-
Texture class.
see also:
- TouchHandler
-
TouchHandler class.
see also:
- Interceptor
-
Interceptor class.
see also:
- PropertyUtils
-
PropertyUtils class.
see also:
- DebugUtils
-
DebugUtils class.
see also:
- BitUtils
-
BitUtils class.
see also:


The CFITSIO library is built on Unix systems by typing:
> ./configure [--prefix=/target/installation/path] [--enable-reentrant] [--enable-sse2] [--enable-ssse3] > make (or 'make shared') > make install (this step is optional)at the operating system prompt. The configure command customizes the Makefile for the particular system, then the `make' command compiles the source files and builds the library. Type `./configure' and not simply `configure' to ensure that the configure script in the current directory is run and not some other system-wide configure script. The optional 'prefix' argument to configure gives the path to the directory where the CFITSIO library and include files should be installed via the later 'make install' command. For example,
> ./configure --prefix=/usr1/localwill cause the 'make install' command to copy the CFITSIO libcfitsio file to /usr1/local/lib and the necessary include files to /usr1/local/include (assuming of course that the process has permission to write to these directories).
The optional -enable-reentrant flag will attempt to configure CFITSIO so that it can be used in multi-threaded programs. See the "Using CFITSIO in Multi-threaded Environments" section, below, for more
The optional -enable-sse2 and -enable-ssse3 flags will cause configure to attempt to build CFITSIO using faster byte-swapping algorithms. See the "Optimizing Programs" chapter of this manual for more information about these options.
The 'make shared' option builds a shared or dynamic version of the CFITSIO library. When using the shared library the executable code is not copied into your program at link time and instead the program locates the necessary library code at run time, normally through LD_LIBRARY_PATH or some other method. The advantages of using a shared library are:
1. Less disk space if you build more than 1 program 2. Less memory if more than one copy of a program using the shared library is running at the same time since the system is smart enough to share copies of the shared library at run time. 3. Possibly easier maintenance since a new version of the shared library can be installed without relinking all the software that uses it (as long as the subroutine names and calling sequences remain unchanged). 4. No run-time penalty.The disadvantages are:
1. More hassle at runtime. You have to either build the programs specially or have LD_LIBRARY_PATH set right. 2. There may be a slight start up penalty, depending on where you are reading the shared library and the program from and if your CPU is either really slow or really heavily loaded.
On Mac OS X platforms the 'make shared' command works like on other UNIX platforms, but a .dylib file will be created instead of .so. If installed in a nonstandard location, add its location to the DYLD_LIBRARY_PATH environment variable so that the library can be found at run time.
On HP/UX systems, the environment variable CFLAGS should be set to -Ae before running configure to enable "extended ANSI" features.
By default, a set of Fortran-callable wrapper routines are also built and included in the CFITSIO library. If these wrapper routines are not needed (i.e., the CFITSIO library will not be linked to any Fortran applications which call FITSIO subroutines) then they may be omitted from the build by typing 'make all-nofitsio' instead of simply typing 'make'. This will reduce the size of the CFITSIO library slightly.
It may not be possible to statically link programs that use CFITSIO on some platforms (namely, on Solaris 2.6) due to the network drivers (which provide FTP and HTTP access to FITS files). It is possible to make both a dynamic and a static version of the CFITSIO library, but network file access will not be possible using the static version.


Mosquito Ride Preparation
Today I decided to ride part of the route I’ve got planned for the LBC ride on July 25th. I’ve ridden the first section numerous times - St Albans, Hatfield and Potters Bar featured heavily on my rides over the past few years as I lived there. However, I’d never really ventured past Hertford and so the second half of the route was a complete blank to me. Not really ideal for leading a ride over in that direction!
Ride
As I was doing this ride for the Brompton club and I was going to head into London afterwards for the weekend, I ended up doing the ride on the Brompton, though I really wanted to do it on my Pashley. Maybe I’ll do the actual ride on the Pashley as I’ve now scoped out the route (and by the time of the ride, I may have had the water bottle holder installed on the Pashley so I’d be able to drink).
I left the house and headed out to the Cole Green Way. This would be the start of the ride and would form the part of the route that would get me to Hertford. Most of the ride will be on cycle path and away from traffic and this forms a big section of that.
Getting to Hertford was easy - I’d done this section before so it wasn’t to hard to get there. Once I got to Hertford through, I was on new territory.
Leaving Hertford behind, I headed on towards Ware along the canal.
The route was pretty wide and open at this point and it was a pleasure to cycle. The path under the wheels was standard canal path - so not paved but not to bad under the wheels. It wasn’t long before I got to Ware.
At this point, I stuck with the canal paths. My initial route finding had me come off the route at Ware and use the roads but before leaving, I thought that sticking to the canal might be the best option. I don’t think I was wrong. The canal was a great option.
The ride I’ll be leading is loosely themed about the de Haviland Mosquito, a World War 2 bomber that was built and operated locally to Hertforshire. In effect, it’s just an excuse to ride around Hertfordshire! So from Stanstead Abbots, I headed towards my destination, the old RAF airfield at Hunsdon. Leaving Stanstead Abbots and cycling around the corner I was greeted with a sign saying prepare for a 1:10 gradient! I wasn’t expecting that! However, I was needlessly worried and the route splits to the left avoiding the hill! Good news for those that want to do my ride, but don’t like hills!
I arrived at the airfield via a fairly dusty and gritty road, having had to go through a gate. In fact, I thought I’d stumbled upon a private road - I’m still not sure that the route is technically open to the public but I rode along it anyhow and no one mentioned anything - there were people at the airfield itself when I arrived. There were a number of Microlights out and about. I stopped for a photo opportunity and a drink before heading on.
I’d planned to do laps of the airfield and the old runways, but seeing the road surface, this probably isn’t the best on a Brompton! It wasn’t the nicest surface to be cycling on but it wasn’t the worst I’ve been on (at least it wasn’t muddy)! The land around the airfield had some poppies growing alongside the route which I thought was nice. There’s still reminders of the original purpose of the airfield around as well - with pillboxes dotted around.
Leaving the airfield behind, I found myself heading back into built up areas and ended up in Harlow. The was a bit of cycling along a busy A road on the pavement here but it wasn’t to bad - it might prove to be an issue with a larger supply of Bromptoneers on the ride but might just take some time to cross.
Heading through Harlow was quite pleasant - like Stevenage, it seems to have a good network of bike paths. The views might not be the best, but it is off road and away from traffic so I can’t complain.
I left Harlow behind and ended up on a slight hill out of Harlow. Once that was out the way, there was some nice downhill (along some freshly tarmaced road) and then onto a road for the final stretch into Epping to finish. In fact, this last section (unavoidable) was probably the heaviest in terms of traffic, however, it might be a case of cycling down the rudimentary pavement if needed.
The ride ended in Epping - no relation to the Mosquito bomber, just the fact it’s the first tube stop you come to along the route! The ride back into London was easily down on the tube, where I jumped out at Liverpool Street and cycled onwards to Wimbledon.
However, it looks like it’ll be a cracking ride - I was impressed with how much of it was off road or on cycle paths. If the weather is good, it could be a cracking ride. And it shows the benefits of the Brompton - getting the train to Potters Bar and then heading onwards to Epping and then getting the tube back into London - multi modal travel at it’s finest.
I’m looking forward to the ride now that I’ve ridden it in full - should be a good ride.


| I l@ve RuBoard |
Earlier in this chapter, we saw how to run module files (i.e., text files containing Python statements) from the operating-system shell's command line. It turns out that we can also run module files from Python's interactive command line by importing or reloading them, as we'd normally do from other system components. The details of this process are covered in Chapter 5, but since this turns out to be a convenient way to save and run examples, we'll give a quick introduction to the process.
The basic idea behind importing modules is that importers may gain access to names assigned at the top level of a module file. The names are usually assigned to services exported by the modules. For instance, suppose we use our favorite text editor to create the one-line Python module file myfile.py, shown in the following code snippet. This may be one of the world's simplest Python modules, but it's enough to illustrate basic module use:
title = "The Meaning of Life"
Notice that the filename has a .py suffix: this naming convention is required for files imported from other components. Now we can access this module's variable title in other components two different ways, either by importing the module as a whole with an import statement and qualifying the module by the variable name we want to access:
% python Start Python
>>> import myfile Run file, load module as a whole
>>> print myfile.title Use its names: '.' qualification
The Meaning of Life
or by fetching (really, copying) names out of a module with from statements:
% python Start Python
>>> from myfile import title Run file, load its names
>>> print title Use name directly: no need to qualify
The Meaning of Life
As we'll see later, from is much like an import, with an extra assignment to names in the importing component. Notice that both statements list the name of the module file as simply myfile, without its .py suffix; when Python looks for the actual file, it knows to include the suffix.
Whether we use import or from, the statements in the module file myfile.py are executed, and the importing component (here, the interactive prompt) gains access to names assigned at the top level of the file. There's only one such name in this simple example—the variable title, assigned to a string—but the concept will be more useful when we start defining objects such as functions and classes. Such objects become services accessed by name from one or more client modules.
When a module file is imported the first time in a session, Python executes all the code inside it, from the top to the bottom of the file. Because of this, importing a module interactively is another way to execute its code all at once (instead of, for instance, running it from the system shell with a command such as python myfile.py). But there's one catch to this process: Python executes a module file's code only the first time it's imported. If you import it again during the same interactive session, Python won't reexecute the file's code, even if you've changed it with your editor. To really rerun a file's code without stopping and restarting the interactive interpreter, you can use the Python reload function, as follows:
% python Start Python >>> import myfile Run/load module >>> print myfile.title Qualify to fetch name The Meaning of Life Change myfile.py in your text editor >>> import myfile Will NOT rerun the file's code >>> reload(myfile) WILL rerun the file's (current) code
While this scheme works, reload has a few complications, and we suggest you avoid it for now (just exit and reenter the interpreter between file changes). On the other hand, this has proven to be a popular testing technique in Python classes, so you be the judge.
Another trick that has proven popular is using the dir built-in function to keep track of defined names while programming interactively. We'll have more to say about it later, but before we turn you loose to work on some exercises, here is a brief introduction. If you call the dir function without arguments, you get back a Python list (described in Chapter 2) containing all the names defined in the interactive namespace:
>>> x = 1 >>> y = "shrubbery" >>> dir() ['__builtins__', '__doc__', '__name__', 'x', 'y']
Here, the expression dir() is a function call; it asks Python to run the function named dir. We'll meet functions in Chapter 4; but for now, keep in mind that you need to add parenthesis after a function name to call it (whether it takes any arguments or not).
When dir is called, some of the names it returns are names you get "for free": they are built-in names that are always predefined by Python. For instance, _ _name__ is the module's filename, and _ _builtins__ is a module containing all the built-in names in Python (including dir). Other names are variables that you've assigned values to (here, x and y). If you call dir with a module as an argument, you get back the names defined inside that module:[5]
[5] Technically, in the module's namespace—a term we'll soon use so often that you'll probably get tired of hearing it. Since we're being technical anyhow, the interactive command line is really a module too, called __main__; code you enter there works as if it were put in a module file, except that expression results are printed back to you. Notice that the result of a dir call is a list, which could be processed by a Python program. For now, hold that thought: namespaces can be fetched in other ways too.
% cat threenames.py a = 'dead' b = 'parrot' c = 'sketch' % python >>> import threenames >>> dir(threenames ) ['__builtins__', '__doc__', '__file__', '__name__', 'a', 'b', 'c'] >>> dir( __builtins __) All the names Python predefines for you
Later, we'll see that some objects have additional ways of telling clients which names they expose (e.g., special attributes such as __methods__ and _ _members__). But for now, the dir function lets you do as much poking around as you'll probably care to do.
| I l@ve RuBoard |


signup here..!
here something to read.!
Shoppers looking around for books and toys Advertising image of a man shopping for Christmas presents, United States, 1918 A woman shopping in Japan, 2016 Shopping is an activity in which a customer browses the available goods or services presented by one or more retailers with the potential intent to purchase a suitable selection of them. A typology of shopper types has been developed by scholars which identifies one group of shoppers as recreational shoppers,[1] that is, those who enjoy shopping and view it as a leisure activity.[2] Online shopping has become a major disruptor in the retail industry.[3] Consumers can now search for product information and place product orders across different regions while online retailers deliver their products directly to the consumers' home, offices or wherever they want. The B2C (business to consumer) process has made it easy for consumers to select any product online from a retailer's website and to have it delivered relatively quickly. Using online shopping methods, consumers do not need to consume energy by physically visiting physical stores, but save time and the cost of travelling. A retailer or a shop is a business that presents a selection of goods and offers to trade or sell them to customers for money or other goods. Shoppers' shopping experiences may vary, based on a variety of factors including how the customer is treated, convenience, the type of goods being purchased, and mood.[4] According to a 2000 report, in New York State, women purchase 80% of all consumer goods.[5] Contents 1 History 1.1 Antiquity 1.2 Middle Ages 1.3 Shopping for pleasure 1.4 Evolution of stores: from arcades to department stores 2 Shopping venues 2.1 Shopping hubs 2.2 Stores 2.2.1 Home shopping 2.2.2 Neighborhood shopping 2.2.3 Party shopping 3 Shopping activity 3.1 Shopping seasons 4 Pricing and negotiation 4.1 "Window shopping" 5 Utility cycling 6 See also 7 References 8 Further reading 9 External links History Further information: Retail § History Antiquity Remains of marketplace and retail shops at Trajan's Forum in Rome In antiquity, marketplaces and fairs were established to facilitate the exchange of goods and services. People would shop for goods at a regular market in nearby towns. However, the transient nature of stalls and stall-holders meant the consumers needed to make careful inspection of goods prior to purchase. In ancient Greece, the agora served as a marketplace where merchants kept stalls or shops to sell their goods.[6] Ancient Rome utilized a similar marketplace known as the forum. Rome had two forums; the Forum Romanum and Trajan's Forum. Trajan's Market at Trajan's forum, built around 100-110CE, was a vast expanse, comprising multiple buildings with tabernae that served as retail shops, situated on four levels.[7] The Roman forum was arguably the earliest example of a permanent retail shopfront.[7] In the Roman world, the central market primarily served the local peasantry. Those who lived on the great estates were sufficiently attractive for merchants to call directly at their farm-gates, obviating their need to attend local markets.[8] Shopping lists are known to have been used by Romans. One such list was discovered near Hadrian's wall dated back to 75–125 CE and written for a soldier.[9] Middle Ages An early 17th-century shop, with customers being served through an opening onto the street Archaeological evidence suggests that the British engaged in minimal shopping in the early Middle Ages. Instead, they provided for their basic needs through subsistence farming practices and a system of localised personal exchanges.[10] However, by the late Middle Ages, consumers turned to markets for the purchase of fresh produce, meat and fish and the periodic fairs where non-perishables and luxury goods could be obtained.[11] Women were responsible for everyday household purchases, but most of their purchasing was of a mundane nature. For the main part, shopping was seen as a chore rather than a pleasure.[12] Relatively few permanent shops were to be found outside the most populous cities. Instead customers walked into the tradesman's workshops where they discussed purchasing options directly with tradesmen.[13] Itinerant vendors such as costermongers, hucksters and peddlers operated alongside markets, providing the convenience of home delivery to households, and especially to geographically isolated communities.[14] In the more populous European cities, a small number of shops were beginning to emerge by the 13th century. Specialist retailers such as mercers and haberdashers were known to exist in London, while grocers sold "miscellaneous small wares as well as spices and medicines." However, these shops were primitive. As late as the 16th century, London's shops were described as little more than "rude booths." [15] The Medieval shopper's experience was very different from that of the contemporary shopper. Interiors were dark and shoppers had relatively few opportunities to inspect the merchandise prior to consumption. Glazed windows in retail environments, were virtually unknown during the medieval period. Goods were rarely out on display; instead retailers kept the merchandise at the rear of the store and would only bring out items on request. The service counter was virtually unknown and instead, many stores had openings onto the street from which they served customers.[16] In Britain, medieval attitudes to retailing and shopping were negative. Retailers were no better than hucksters, because they simply resold goods, by buying cheaper and selling dearer, without adding value of national accounts. Added to this were concerns about the self-interest of retailers and some of their more unethical practices. Attitudes to spending on luxury goods also attracted criticism, since it involved importing goods which did little to stimulate national accounts, and interfered with the growth of worthy local manufacturers.[17] Shopping for pleasure The modern phenomenon of shopping for pleasure is closely linked to the emergence of a middle class in the 17th and 18th-century Europe. As standards of living improved in the 17th century, consumers from a broad range of social backgrounds began to purchase goods that were in excess of basic necessities. An emergent middle class or bourgeosie stimulated demand for luxury goods and began to purchase a wider range of luxury goods and imported goods, including: Indian cotton and calico; silk, tea and porcelain from China, spices from India and South-East Asia and tobacco, sugar, rum and coffee from the New World.[18] The act of shopping came to be seen as a pleasurable pass-time or form of entertainment.[2] By the 17th-century, produce markets gradually gave way to shops and shopping centres; which changed the consumer's shopping experience.[19] The New Exchange, opened in 1609 by Robert Cecil in the Strand was one such example of a planned shopping centre. Shops started to become important as places for Londoners to meet and socialise and became popular destinations alongside the theatre. Restoration London also saw the growth of luxury buildings as advertisements for social position with speculative architects like Nicholas Barbon and Lionel Cranfield. Bernard Mandeville's work The Fable of the Bees, which justified conspicuous consumption. Much pamphleteering of the time was devoted to justifying conspicuous consumption and private vice for luxury goods for the greater public good. This then scandalous line of thought caused great controversy with the publication of Bernard Mandeville's influential work Fable of the Bees in 1714, in which he argued that a country's prosperity ultimately lay in the self-interest of the consumer.[20] These trends gathered momentum in the 18th century, as rising prosperity and social mobility increased the number of people with disposable income for consumption. Important shifts included the marketing of goods for individuals as opposed to items for the household, and the new status of goods as status symbols, related to changes in fashion and desired for aesthetic appeal, as opposed to just their utility. The pottery inventor and entrepreneur, Josiah Wedgewood, pioneered the use of marketing techniques to influence and manipulate the direction of the prevailing tastes.[21] One of his preferred sales techniques was to stage expansive showcases of wares in this private residences or in a rented hall, to which he invited the upper classes.[22]


The library is under Apache 2.0 license, written in Scala with no dependencies on other NLP or ML libraries, and designed to natively extend the Spark ML Pipeline API.
John Snow Labs is the company leading and sponsoring the development of the Spark NLP library. The company provides commercial support, indemnification and consulting for it. This provides the library with long-term financial backing, a funded active development team, and a growing stream of real-world projects that drives robustness and roadmap prioritization.
Alex Thomas from Indeed and David Talby from Pacific AI, for the initial design and code of this library.
Saif Addin Ellafi from John Snow Labs for building the first client-ready version of the library.
Eduardo Munoz, Navneet Behl, Danilo Burbano and Anju Aggarwal from John Snow Labs for expanding the production grade codebase and functionality, and for managing the project.
Aleksei Alekseev and Alberto Andreotti from Pacific AI for contributing machine learning annotators to the library
Joseph Bradley and Xiangrui Meng from Databricks, for guidance on the Spark ML API extension guidelines.
Claudiu Branzan from G2 Web Services, for design contributions and review.
Ben Lorica from O’Reilly, for driving us to move this from idea to reality.
Emmanuel Asimadi for testing, reporting issues and building an in progress documentation
Maziyar Panahi for reporting valuable performance feedback and reporting issues
Vincenzo Gaudenzi from DXC.technology for contributing Italian datasets and models


What I do:
I blend logic with creativity to form beautiful, effective web pages and applications.
When I'm not doing that, I'm most often out grabbing coffee or walking my doggos.
What I Use:
- HTML5
- CSS3
- JavaScript (jQuery, ReactJS)
- PHP (Laravel)
- Ruby on Rails
- MongoDB
- MySQL
- PostgreSQL
- Node.js


ISCA Academic Program is a student initiative aimed at preventing and diluting online antisemitism, racism and de-legitimization of the state of Israel.
We research, write, comment, report and develop tools to help reduce online hatred as much as possible, with the help of students from Israel and abroad.
Academic lectures on antisemitism and online advocacy
Close mentorship from experienced team leaders
Create your own personal project to help combat antisemitism
2 academic credits
Scholarship awarded for excelling students
Doing something that really matters
Participants are the heart of the academic program. They take part in lectures and events, receive close guidance by team leaders and are in charge of creating new projects that help us fight hatred.
Since there are very few available spots for this position along with particular skills we are looking for, students have to apply to be a participant and commit to weekly volunteering hours consisting of various assignments.
Participants receive 2 academic credits for their efforts and upon excellence, might be awarded with a 6000 nis scholarship for their next year of volunteering (In accordance with conditions set by the managing staff and the National Union of Israeli Students).
Since we have limited spots for participating students, we opened the option for Freelance Volunteers.
Students who simply want to help out because they care about the cause can register and aid us in their free time, Freelance Volunteers also increase their chances of being accepted as participants the following year.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Participant Volunteers are the heart of the academic program. They take part in lectures and events, receive close guidance by team leaders and are in charge of creating new projects that help us fight hate.


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save(String)
- Static method in class model.
Logs
Save.
save()
- Static method in class model.
Logs
Save.
save()
- Static method in class model.
SaveLoad
Save.
save(String)
- Static method in class model.
SaveLoad
Save.
SaveLoad
- Class in
model
The Class SaveLoad.
SaveLoad()
- Constructor for class model.
SaveLoad
Instantiates a new save load.
saveLoadBorrow(List<String>, Calendar, Calendar)
- Method in class model.person.
Borrower
SaveLoad Borrow.
SECOND
- Static variable in class model.
SaveLoad
The Constant SECOND.
serialVersionUID
- Static variable in exception model.
MiniProjectException
The Constant serialVersionUID.
setAcceptableValue(String)
- Method in class model.
Feature
Sets the acceptable value.
setBorrowStat(Borrower.Borrow, BorrowState)
- Method in class model.person.
Administrator
Sets the borrow stat.
setCause(String)
- Method in class model.
Health
Sets the cause.
setCommands()
- Method in class views.etat.
Administrator
setCommands()
- Method in class views.etat.
Borrow
setCommands()
- Method in class views.etat.
Borrower
setCommands()
- Method in class views.etat.
Find
setCommands()
- Method in class views.etat.
Main
setCommands()
- Method in class views.etat.
User
setCommands()
- Method in interface views.
IView
Sets the commands.
setCommands()
- Method in class views.
State
setConfiguration(Map<String, Object>)
- Static method in class config.
Config
Sets the configuration.
setEnd(String, String)
- Method in class views.etat.
Borrow
Sets the end.
setEquipments(List<Equipment>)
- Method in class model.
Inventory
Sets the equipments.
setFeatures(List<Feature>)
- Method in class model.
Equipment
Sets the features.
setHealth(Health)
- Method in class model.
Equipment
Sets the health.
setHealthState(HealthState)
- Method in class model.
Health
Sets the health state.
setId()
- Method in class model.
InventoryElement
Sets the id.
setId(String)
- Method in class model.
InventoryElement
Sets the id.
setId(String)
- Method in class model.
Person
Sets the id.
setLogOperations(List<String>)
- Method in class model.
Stats
Sets the log operations.
setLogs(List<Log>)
- Method in class model.
Logs
Sets the logs.
setName(String)
- Method in class model.
Person
Sets the name.
setNbBorrowed(int)
- Method in class model.
Stats
Sets the nb borrowed.
setNbUnderRepair(int)
- Method in class model.
Stats
Sets the nb under repair.
setPassword(String)
- Method in class model.
Person
Sets the password.
setPersonId(String)
- Method in class model.
User
Sets the person id.
setStart(String, String)
- Method in class views.etat.
Borrow
Sets the start.
setState(BorrowState, String)
- Method in class model.person.
Borrower.Borrow
Sets the state.
setState(State)
- Static method in class views.
View
Sets the state.
setStats(Stats)
- Method in class model.
Equipment
Sets the stats.
setType(String)
- Method in class model.
Person
Sets the type.
setUnderRepair(Boolean)
- Method in class model.
Equipment
Sets the under repair.
setValue(String)
- Method in class model.
Feature
Sets the value.
show()
- Method in class views.etat.
Borrow
Show.
show()
- Method in class views.etat.
Find
Show.
showBorrow(String)
- Method in class views.etat.
User
Show_borrow.
showEquipment(String)
- Method in class views.etat.
User
Show_equipment.
showPerson(String)
- Method in class views.etat.
User
Show_person.
start
- Variable in class views.etat.
Borrow
The start.
state
- Variable in class model.person.
Borrower.Borrow
The state.
STATE
- Static variable in class model.
SaveLoad
The Constant STATE.
state
- Variable in class views.
Command
The state.
State
- Class in
views
The Class State.
State()
- Constructor for class views.
State
state
- Static variable in class views.
View
The state.
STATE_DO_NOT_EXIST
- Static variable in class config.
Error
The Constant STATE_DO_NOT_EXIST.
stats
- Variable in class model.
Equipment
The stats.
STATS
- Static variable in class model.
Logs
The Constant STATS.
Stats
- Class in
model
The Class Stats.
Stats()
- Constructor for class model.
Stats
Sets the stats.
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Puppos struttin' to the Bee Gees on an airport treadmill
by Rusty BlazenhoffTry not to die of cuteness from watching these four fluffy puppos strutting to "Stayin' Alive" on an airport autowalk. I don't know the full story here but this is quality content that needs to be shared far and wide.


You want to see how random a coin flip really is, so you flip a coin until your hand gets tired. You record the results in a text document, typing H for heads and T for tails. After you spend all day flipping a coin, you don't want to count all the H's and T's.
Count the number of all H's and T's in the file coin.txt
. To determine the probability, you will also need to know the total number of tosses you made. After counting the number of heads, tails, and tosses, print the probability of heads and the probability of tails. Probability of heads is simply the number of heads divided by the total number of tosses.
From the description, it sounds like we'll need the Count and Filter loop patterns to count the number of H's and T's.
First, we open coin.txt
so we understand the format of the file. The file is entirely on one line, like this:
Since the file is only one line, we look at each character in the file, not each line. Instead of iterating over the result of the call to open
, we should use the read
method to read all the text in the file and get it as a string. Then we can iterate over the string to check every character of the text.
Before you can use a file, you must open the file using the open
function. This function takes the name of the file as a string and returns a file object representing the open file. We need to save this file so we can close it later:
If we were to iterate over coin_file
with for flip in coin_file
, we would iterate over every line in the file. Since coin.txt
is a single, massive line and we want every character of it, this doesn't work. To iterate over each character, we need the contents of the file as a string. To get the file as a string, we use the read
method. The read method of an open file object returns a string of the contents of that file. We save the result in a string variable.
Now that we've read the contents of the file into a string, we don't need to have the file open anymore. You should always close a file once you are done with it. This is done with the close
method. Keep in mind that after closing a file, the file is not usable.
We can use a for
loop to iterate over each character in the string variable.
In the Count pattern, we initialize the accumulating variable to 0 before we loop. We need to keep track of the number of heads, the number of tails, and the total number of flips.
{% filter highlight_python_code(False) -%} num_heads = 0 num_tails = 0 num_flips = 0 {% endfilter %}According to the Count pattern, we need to update the count inside the loop. Let's start with the easiest count to do: num_flips
. Since every character is a flip, we should increment num_flips
every time our loop runs.
To count heads and tails, the Filter pattern must be combined with the Count pattern. We increment num_heads
only if flip
is "H"
and num_tails
only if flip
is "T"
. This can be done with a simple if
statement.
To calculate the probability of heads and tails, we have to divide num_heads
and num_tails
by num_flips
. This will give us a number between 0 and 1 that represents the probability a coin flip is heads or tails, respectively.
Now the only thing left to do is print the probabilities we calculated to see how close to perfectly random our coin tosses were.
{% filter highlight_python_code(False) -%} print("Heads:", probHeads) print("Tails:", probTails) {% endfilter %}If we have a perfectly fair coin, the probability should be .5 for both, meaning the coin has an equal chance of flipping heads and tails.
When we run this with the file above, the output we get is
Heads: 0.5202020202020202 Tails: 0.4797979797979798
Which means about 52% of the flips were heads and 48% were tails.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
return (tf.placeholder(tf.float32, (None, image_width, image_height, image_channels)),
tf.placeholder(tf.float32, (None, z_dim)),
tf.placeholder(tf.float32))
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha = 0.02):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope("discriminator", reuse=reuse):
l1 = tf.layers.conv2d(
images, 64, kernel_size=2, strides=2, padding='same'
)
n1 = tf.layers.batch_normalization(l1, training=True)
r1 = tf.maximum(alpha * n1, n1)
l2 = tf.layers.conv2d(
r1, 128, kernel_size=3, strides=2, padding='same'
)
n2 = tf.layers.batch_normalization(l2, training=True)
r2 = tf.maximum(alpha * n2, n2)
l3 = tf.layers.conv2d(
r2, 256, kernel_size=5, strides=2, padding='same'
)
n3 = tf.layers.batch_normalization(l3, training=True)
r3 = tf.maximum(alpha * n3, n3)
flat = tf.reshape(r3, (-1, 4 * 4 * 256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, alpha = 0.02):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
with tf.variable_scope('generator',reuse=(not is_train)):
dense = tf.layers.dense(z, 7 * 7 * 1024)
l1 = tf.reshape(dense, (-1, 7, 7, 1024))
n1 = tf.layers.batch_normalization(l1, training=is_train)
m1 = tf.maximum(alpha * n1, n1)
l2 = tf.layers.conv2d_transpose(m1, 256, 5, strides=1, padding='same')
n2 = tf.layers.batch_normalization(l2, training=is_train)
m2 = tf.maximum(alpha * n2, n2)
l3 = tf.layers.conv2d_transpose(m2, 128, 3, strides=2, padding='same')
n3 = tf.layers.batch_normalization(l3, training=is_train)
m3 = tf.maximum(alpha * n3, n3)
logits = tf.layers.conv2d_transpose(
m3, out_channel_dim, 5, strides = 2, padding='same'
)
result = tf.tanh(logits)
return result
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
alpha = 0.05
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_real, labels=tf.ones_like(d_model_real)
)
)
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)
)
)
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.ones_like(d_model_fake)
)
)
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
train_vars = tf.trainable_variables()
d_vars = [var for var in train_vars if var.name.startswith('discriminator')]
g_vars = [var for var in train_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
g_opt_train = tf.train.AdamOptimizer(
learning_rate=learning_rate, beta1=beta1
).minimize(g_loss, var_list=g_vars)
d_opt_train = tf.train.AdamOptimizer(
learning_rate=learning_rate,beta1=beta1
).minimize(d_loss, var_list=d_vars)
return d_opt_train, g_opt_train
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
losses = []
steps = 0
images_display = 16
_, image_width, image_height, image_channels = data_shape
input_real, input_fake, lr = model_inputs(
image_width, image_height, image_channels, z_dim
)
d_loss, g_loss = model_loss(input_real, input_fake, image_channels)
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images *= 2.0
batch_gen = np.random.uniform(-1, 1, size=(batch_size, z_dim))
sess.run(
d_opt, feed_dict={
input_real: batch_images,
input_fake: batch_gen,
lr: learning_rate
}
)
sess.run(
g_opt, feed_dict={
input_real: batch_images,
input_fake: batch_gen,
lr: learning_rate
}
)
sess.run(
g_opt, feed_dict={
input_real: batch_images,
input_fake: batch_gen,
lr: learning_rate
}
)
if steps % 25 == 0 :
train_loss_d = d_loss.eval({
input_real: batch_images,
input_fake: batch_gen,
lr: learning_rate
})
train_loss_g = g_loss.eval({
input_real: batch_images,
input_fake: batch_gen,
lr: learning_rate
})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
losses.append((train_loss_d, train_loss_g))
if steps % 100 == 0:
show_generator_output(
sess, images_display, input_fake, image_channels, data_image_mode
)
steps += 1
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 128
z_dim = 250
learning_rate = 0.0005
beta1 = 0.25
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 128
z_dim = 250
learning_rate = 0.00025
beta1 = 0.25
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


TABLE OF CONTENT
A. GS1 Related History / Environment & Ecology 1. Thirsty Vijayapura reaches into ancient bawadis 21 huge open wells, built around 500 years ago, are filling up after desilting and providing a vital source of water for the arid city B. GS2 Related International Relations 1. Pakistan denies consular access to Jadhav yet again Says equating him with other prisoners is ‘travesty of logic’ 2. On eve of PM’s tour, Palestine hopes to keep India ties firm Modi will be in Tel Aviv & Jerusalem from July 4; will not travel to Palestinian side 3. Step back Sustained tension at the Bhutan tri-junction suits neither China nor India C. GS3 Related Economics 1. GST positive for India’s credit profile: Moody’s ‘To support higher government revenue through improved tax compliance’ 2. Steel sector bullish on GST, exporters worry Reactions abound on impact of tax Environment and Ecology 1. Aerosols ‘shrinking’ India’s monsoon Greenhouse gases cause concern, but scientists are more worried about particulates 2. Nine more bird, biodiversity areas in Kerala D. GS4 Related E. Concepts-in-News: Related Concepts to Revise/Learn F. Bills/Acts/Schemes/Orgs in News G. Practice Questions for UPSC Prelims Exam
UPSC Current Affairs 2017: News Analysis
Category: HISTORY / ENVIRONMENT AND ECOLOGY
In news:
- 21 huge, open wells, built around 500 years ago, are filling with water after desilting and providing a vital source of water for this arid city in Karnataka.
- A little over two months ago, the historic Taj bawadi (huge open wells) , built during the Adil Shahi era (1490-1686) in Vijayapura in Karnataka was a filthy cesspool, with the polluted water unfit for any use.
- Today the 223 ft wide structure has potable water, with hundreds of springs injecting fresh water into it.
- Built in 1620 in the name of Taj Sultana, the queen of Ibrahim Adil Shah-II, the Taj bawadi is one of 21 such open wells being revived by the district administration in a first ever attempt at cleaning and desilting them.
- Some of the others being revived are the Chanda Bawadi, Sandal Bawadi, Ibrahimpur Bawadi, Pethi Bawadi and the Gunnapur Bawadi.
- Dr Rajendra Singh, noted water conservationist , also lauded the work on reviving the water source during his recent visit.
Category: INTERNATIONAL RELATIONS
In news:
- Pakistan on Sunday again rejected India’s request for consular access to Kulbhushan Jadhav, a former Indian naval officer who was sentenced to death for espionage and terrorism by a Pakistani military court in April this year.
- His mercy petition is pending with Pakistani Army chief General Qamar Javed Bajwa. India denied the charges made out against Jadhav, and moved the International Court of Justice against the conviction and got a stay on the execution. Pakistan insists that the reprieve is temporary.
Context:
- Modi will travel to Tel Aviv and Jerusalem from July 4, and will be the first Indian Prime Minister to visit Israel.
In News:
- India’s relations with Israel should not come at the “expense of ties” with Palestine, but the Palestinian Authority (PA) understands the need for India to de-hyphenate ties with both, says the Palestinian President’s diplomatic adviser Dr. Majdi ElKhaldi in an interview ahead of Prime Minister Narendra Modi’s visit to Israel.
3. Step back Sustained tension at the Bhutan tri-junction suits neither China nor India
Context:
- The boundary stand-off with China at the Doka La tri-junction with Bhutan is by all accounts unprecedented; it demands calmer counsel on all sides. The tri-junction stretch of the boundary at Sikkim, though contested, has witnessed far fewer tensions than the western sector of the India-China boundary even as India and Bhutan have carried on separate negotiations with China.
Issue:
- China’s action of sending People’s Liberation Army construction teams with earth moving equipment to forcibly build a road upsets a carefully preserved peace.
Other concerns and issues:
- Delhi has expressed disappointment over China’s rejection of its concerns on sovereignty issues, and refusal to corner Pakistan on cross-border terrorism or help India’s bid for Nuclear Suppliers Group membership.
- In turn, India’s spurning of the Belt and Road Initiative and cooperation with the U.S. on maritime issues has not played well in China — neither has the uptick in rhetoric, including statements from the Arunachal Pradesh Chief Minister questioning India’s “One China Policy” on Tibet, and from Army chief Bipin Rawat on India being prepared for a two-and-a-half front war.
Conclusion and Way Forward:
- These issues have to be addressed through sustained dialogue.
- In the immediate term, however, talks must focus on defusing the tensions at the tri-junction.
- China has made the withdrawal of Indian troops a precondition for dialogue. This would be unacceptable to India, unless the PLA also withdraws its troops and road-building teams.
- Apart from its own commitments to the status quo, Beijing must recognise the special relationship India and Bhutan have shared since 1947, the friendship treaty of 2007 that commits India to protecting Bhutan’s interests, and the close coordination between the two militaries.
- For its part, India would be keen to show that it recognises that the face-off is in Bhutanese territory, and the rules of engagement could be different from those of previous India-China bilateral clashes — at Depsang and Demchok in the western sector, for example.
- Bhutan’s sovereignty must be maintained as that is the basis for the “exemplary” ties between New Delhi and Thimphu.
- The Indian government has been wise to avoid escalation in the face of China’s aggressive barrage, but that should not stop it from communicating its position in more discreet ways.
In News:
- The Goods and Services Tax (GST) regime will be positive for India’s credit profile as it will contribute to productivity gains and higher GDP growth as well as support higher government revenue generation through improved tax compliance, according to global ratings agency Moody’s .
- The GST regime is expected to contribute to ease of doing business and accelerate new ventures
2. Steel sector bullish on GST, exporters worry Reactions abound on impact of tax
- With India moving towards its biggest tax reform, here’s how various sectors in India have reacted to the implementation of the Goods and Services Tax (GST) and how it will impact business.
- Following the implementation of the GST, the hospitality and travel sectors expect teething issues in the first few months but are not unduly worried, expecting support from government.
- The Federation of Indian Export Organisations (FIEO) has approached the government to reduce the restrictions on using duty credit scrips — a pass that allows holders to import goods by not paying a certain amount in import duties.
- The steel sector is feeling bullish after the GST rollout. Players in the sector said that with GST, unorganised players will have to move to the organised form of doing business.
- The cotton textile industry is also feeling positive.
Category: ENVIRONMENT AND ECOLOGY
In news:
- While greenhouse gases, or GHGs, are causing concern about the long-term fate of the Indian monsoon, researchers now think aerosols from vehicular exhaust, half-burnt crop residue, dust and chemical effluents may be weakening the life-giving rainy season even more than GHGs.
Study Report:
- An Indian Institute of Tropical Meteorology, Pune, team led by climatologist R. Krishnan studying the likely monsoon impact of GHGs over the next century has come to this conclusion.
Scientists have used new model:
- The scientist and his team used an upgraded forecasting model that was used this year by the India Meteorological Department for forecasts. The model will help prepare India’s first home-grown forecast of climate change from global warming, and be part of Intergovernmental Panel on Climate Change reports.
- Dust clouds shield the earth from the sun’s rays, depressing land and sea temperatures. The monsoon, which is produced by the difference in temperature between the two, is thus weakened.
2. Nine more bird, biodiversity areas in Kerala
In news:
- The bird and biodiversity-rich areas in Kerala are drawing global attention with nine more locations being identified as Important Bird and Biodiversity Areas (IBAs) .
- The newly identified IBAs of Kerala are Achencoil Forest Division; Anamudi Shola National Park; Camel’s Hump Mountain, Wayanad; Kurinjimala Wildlife Sanctuary; Malayattoor Reserve Forest; Mankulam Forest Division; Mathikettan Shola National Park; Muthikulam-Siruvani; and Pampadum Shola National Park.
Who released the list ?
- The new list was released by the Bombay Natural History Society , a partner of BirdLife International , in its recent publication, Important Bird and Biodiversity Areas in India Priority sites for conservation.
- The IBAs are “places of international significance for the conservation of birds and other biodiversity” and are “distinct areas amenable to practical conservation action,” according to BirdLife International.
- Kerala IBAs are home to three critically endangered species — Whiterumped Vulture, Indian Vulture, and Red-headed Vulture.
D. GS4 Related
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PIB Articles Editorials Roundup
E. Concepts-in-News: Related Concepts to Revise/Learn
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F. Bills/Acts/Schemes/Orgs in News
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