- w - Variable in class cz.upol.inf.vanusanik.jwlc.geometry.Size.wlc_size
-
- WaylandClient - Interface in cz.upol.inf.vanusanik.jwlc.wayland
-
Use this interface for wl_client, should you decided to wrap wayland.
- WaylandClientFactory - Interface in cz.upol.inf.vanusanik.jwlc.wayland
-
Factory for creating wayland clients.
- WaylandInterface - Interface in cz.upol.inf.vanusanik.jwlc.wayland
-
Use this interface for wl_interface, should you decided to wrap wayland.
- WLC - Interface in cz.upol.inf.vanusanik.jwlc
-
Library handle to libwlc.
- wlc_event_loop_add_fd(int, int, Callbacks.fd_callback, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Add fd to event loop.
- wlc_event_loop_add_timer(Callbacks.timer_callback, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Add timer to event loop.
- wlc_event_source_remove(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Remove event source from event loop.
- wlc_event_source_timer_update(Pointer, int) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Update timer to trigger after delay.
- wlc_exec(String, String[]) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Exec program.
- wlc_geometry() - Constructor for class cz.upol.inf.vanusanik.jwlc.geometry.Geometry.wlc_geometry
-
- wlc_get_backend_type() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Query backend wlc is using.
- wlc_get_focused_output() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get focused output.
- wlc_get_outputs(IntByReference) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get outputs.
- wlc_get_subsurface_geometry(Pointer, Geometry.wlc_geometry) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns the size of a subsurface and its position relative to parent
- wlc_get_wl_display() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns Wayland display.
- wlc_handle_from_wl_output_resource(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns output handle from wl_output resource.
- wlc_handle_from_wl_surface_resource(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns view handle from wl_surface resource.
- wlc_handle_get_user_data(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get linked custom data from handle.
- wlc_handle_set_user_data(Pointer, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Link custom data to handle.
- wlc_init() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Initialize wlc.
- wlc_keyboard_get_current_key(IntByReference) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get currently held keys.
- wlc_keyboard_get_keysym_for_key(int, Modifiers.wlc_modifiers) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Utility function to convert raw keycode to keysym.
- wlc_keyboard_get_utf32_for_key(int, Modifiers.wlc_modifiers) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Utility function to convert raw keycode to Unicode/UTF-32 codepoint.
- wlc_keyboard_get_xkb_keymap() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Internal xkb_keymap exposed.
- wlc_keyboard_get_xkb_state() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Internal xkb_state exposed.
- wlc_log_set_handler(Callbacks.logger_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set log handler.
- wlc_modifiers() - Constructor for class cz.upol.inf.vanusanik.jwlc.wlc.Modifiers.wlc_modifiers
-
- wlc_output_focus(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Focus output.
- wlc_output_get_gamma_size(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get gamma size
- wlc_output_get_mask(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get current visibility bitmask.
- wlc_output_get_mutable_views(Pointer, IntByReference) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get mutable views in creation order.
- wlc_output_get_name(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get output name.
- wlc_output_get_renderer(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns currently active renderer on the given output
- wlc_output_get_resolution(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get real resolution.
- wlc_output_get_scale(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get scale factor.
- wlc_output_get_sleep(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get sleep state.
- wlc_output_get_views(Pointer, IntByReference) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get views in stack order.
- wlc_output_get_virtual_resolution(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get virtual resolution.
- wlc_output_schedule_render(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Schedules output for rendering next frame.
- wlc_output_set_gamma(Pointer, short, short[], short[], short[]) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set gamma.
- wlc_output_set_mask(Pointer, int) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set visibility bitmask.
- wlc_output_set_resolution(Pointer, Size.wlc_size, int) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set resolution.
- wlc_output_set_sleep(Pointer, boolean) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Wake up / sleep.
- wlc_output_set_views(Pointer, Pointer[], int) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set views in stack order.
- wlc_pixels_read(int, Geometry.wlc_geometry, Geometry.wlc_geometry, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Read pixel data from output's framebuffer.
- wlc_pixels_write(int, Geometry.wlc_geometry, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Write pixel data with the specific format to output's framebuffer.
- wlc_point() - Constructor for class cz.upol.inf.vanusanik.jwlc.geometry.Point.wlc_point
-
- wlc_pointer_get_position(Point.wlc_point) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get current pointer position.
- wlc_pointer_set_position(Point.wlc_point) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set current pointer position.
- wlc_resource_from_wl_surface_resource(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns internal wlc surface from wl_surface resource.
- wlc_run() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Run event loop.
- wlc_set_compositor_ready_cb(Callbacks.void_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Compositor is ready to accept clients.
- wlc_set_compositor_terminate_cb(Callbacks.void_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Compositor is about to terminate
- wlc_set_input_created_cb(Callbacks.libinput_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Input device was created.
- wlc_set_input_destroyed_cb(Callbacks.libinput_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Input device was destroyed.
- wlc_set_keyboard_key_cb(Callbacks.keyboard_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Key event was triggered, view handle will be zero if there was no focus.
- wlc_set_output_context_created_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output context is created.
- wlc_set_output_context_destroyed_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output context was destroyed.
- wlc_set_output_created_cb(Callbacks.handle_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output was created.
- wlc_set_output_destroyed_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output was destroyed.
- wlc_set_output_focus_cb(Callbacks.focus_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output got or lost focus.
- wlc_set_output_render_post_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output post render hook.
- wlc_set_output_render_pre_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output pre render hook.
- wlc_set_output_resolution_cb(Callbacks.output_resolution_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Output resolution changed.
- wlc_set_pointer_button_cb(Callbacks.pointer_button_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Button event was triggered, view handle will be zero if there was no
focus.
- wlc_set_pointer_motion_cb(Callbacks.pointer_movement_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Motion event was triggered, view handle will be zero if there was no
focus.
- wlc_set_pointer_scroll_cb(Callbacks.pointer_scroll_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Scroll event was triggered, view handle will be zero if there was no
focus.
- wlc_set_touch_cb(Callbacks.touch_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Touch event was triggered, view handle will be zero if there was no
focus.
- wlc_set_view_created_cb(Callbacks.handle_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
View was created.
- wlc_set_view_destroyed_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
View was destroyed.
- wlc_set_view_focus_cb(Callbacks.focus_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
View got or lost focus.
- wlc_set_view_move_to_output_cb(Callbacks.handle_move_view_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
View was moved to output.
- wlc_set_view_properties_updated_cb(Callbacks.handle_mask_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
View properties (title, class, app_id) was updated
- wlc_set_view_render_post_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
View post render hook.
- wlc_set_view_render_pre_cb(Callbacks.handle_callback_void) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
View pre render hook.
- wlc_set_view_request_geometry_cb(Callbacks.geometry_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Request to set given geometry for view.
- wlc_set_view_request_move_cb(Callbacks.request_move_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Request to move itself.
- wlc_set_view_request_resize_cb(Callbacks.request_resize_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Request to resize itself with the given edges.
- wlc_set_view_request_state_cb(Callbacks.state_request_callback) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Request to disable or enable the given state for view.
- wlc_size() - Constructor for class cz.upol.inf.vanusanik.jwlc.geometry.Size.wlc_size
-
- wlc_surface_flush_frame_callbacks(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Adds frame callbacks of the given surface for the next output frame.
- wlc_surface_get_size(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get surface size.
- wlc_surface_get_subsurfaces(Pointer, IntByReference) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns a list of the subsurfaces of the given surface
- wlc_surface_get_texture(Pointer, int[], IntByReference) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Fills out_textures[] with the textures of a surface.
- wlc_surface_get_wl_resource(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Return wl_surface resource from internal wlc surface.
- wlc_surface_render(Pointer, Geometry.wlc_geometry) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Renders surface.
- wlc_terminate() - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Terminate wlc.
- wlc_view_bring_above(Pointer, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Send above another view.
- wlc_view_bring_to_front(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Bring to front of everything.
- wlc_view_close(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Close view.
- wlc_view_focus(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Focus view.
- wlc_view_from_surface(Pointer, Pointer, Pointer, Pointer, int, int, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Turns wl_surface into a wlc view.
- wlc_view_get_app_id(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get app id.
- wlc_view_get_class(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get class.
- wlc_view_get_geometry(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get current geometry.
- wlc_view_get_instance(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get instance.
- wlc_view_get_mask(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get current visibility bitmask.
- wlc_view_get_output(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get current output.
- wlc_view_get_parent(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get parent view.
- wlc_view_get_pid(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get pid.
- wlc_view_get_role(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns surface role resource from view handle.
- wlc_view_get_state(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get current state bitfield.
- wlc_view_get_surface(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns internal wlc surface from view handle
- wlc_view_get_title(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get title.
- wlc_view_get_type(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get type bitfield.
- wlc_view_get_visible_geometry(Pointer, Geometry.wlc_geometry) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get visible geometry.
- wlc_view_get_wl_client(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Returns wl_client from view handle
- wlc_view_positioner_get_anchor(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get anchor requested by positioner, as defined in xdg-shell v6.
- wlc_view_positioner_get_anchor_rect(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get anchor rectangle requested by positioner, as defined in xdg-shell v6.
- wlc_view_positioner_get_constraint_adjustment(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get constraint adjustment requested by positioner, as defined in
xdg-shell v6.
- wlc_view_positioner_get_gravity(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get anchor requested by positioner, as defined in xdg-shell v6.
- wlc_view_positioner_get_offset(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get offset requested by positioner, as defined in xdg-shell v6.
- wlc_view_positioner_get_size(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Get size requested by positioner, as defined in xdg-shell v6.
- wlc_view_send_below(Pointer, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Send below another view.
- wlc_view_send_to_back(Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Send behind everything.
- wlc_view_set_geometry(Pointer, int, Geometry.wlc_geometry) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set geometry.
- wlc_view_set_mask(Pointer, int) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set visibility bitmask.
- wlc_view_set_output(Pointer, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set output.
- wlc_view_set_parent(Pointer, Pointer) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set parent view.
- wlc_view_set_state(Pointer, int, boolean) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set state bit.
- wlc_view_set_type(Pointer, int, boolean) - Method in interface cz.upol.inf.vanusanik.jwlc.WLC
-
Set type bit.
- WLCHandle - Class in cz.upol.inf.vanusanik.jwlc.wlc
-
Base wlc_handle abstraction wrapper class.
- WRITABLE - Static variable in interface cz.upol.inf.vanusanik.jwlc.wlc.FileDescriptorEvent
-
- writePixels(PixelFormat, Geometry, byte[]) - Static method in class cz.upol.inf.vanusanik.jwlc.render.Framebuffer
-
Writes pixels directly into framebuffer.


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easy ejabberd clustering (multi-master, fault tolerant, failover)
easy ejabberd clustering (multi-master, fault tolerant, failover)
First and foremost, for general cluster configuration steps see this post:
http://chad.ill.ac/post/35967173942/easy-ejabberd-clustering-guide-mnesia-mysql
Once you have a functioning cluster using those steps and you want to add multi-master support for better failover and fault tolerance of your ejabberd cluster come back here and read this post.
Why?
After getting our small cluster up and running we started testing cluster failure handling. We quickly discovered that no matter how many slaves you had deployed as soon as your master node experienced issues the entire cluster became inoperable. To make things worse fixing the master didn’t fix the cluster, you would have to completely cycle the ejabberd instances of every slave to get them working properly again, or input the mnesia commands to make master communicate with the slaves. This is messy, it’s stupid, but most importantly it pretty much kills the benefit of having a cluster in the first place.
What to do about it:
After about a day of piecing together old mailing lists, stackoverflow posts and articles on mnesia replication, several forums talking about modifying ejabberdctl, and tinkering we were able to finally track down a multimaster cluster that could handle any of the nodes dying without bringing down the entire cluster. The fix doesn’t so much involve ejabberd as it involves just using proper erlang/mnesia replication and removing dependencies on remote tables for underlying data stores used for route and session management by ejabberd.
I’ve updated the plugin to support this functionality so deploying failover masters is easier. We’re only running a small cluster of 3 nodes so we’ve deployed all 3 nodes as masters, this allows us to kill/cycle any of them without any special procedures needed to leave/join a cluster after failure/reboot/etc. That being said I cannot comment on the performance issues one might experience running a masters only cluster at a massive scale so using a handful of masters with regular non-copy/remote slaves might be a better architecture if you’re dealing with large clusters, but I don’t know, so I can’t advise one way or the other at this point. You could also run additional masters with their own slaves as a failover cluster.
Plugin:
https://github.com/chadillac/ejabberd-easy_cluster/blob/master/easy_cluster.erl
Usage:
To use the plugin to create a second, third, Nth master node is super simple.
note: this requires doing a complete mnesia db copy over the network, this might hang for a bit if your databases are large or network latency is high (or both)
easy_cluster:join_as_master('ejabberd@any_master_node.domain.com').
That’s it, you’re done!


Description
Object-oriented programming (OOP) is a programming paradigm based on the concept of "objects", which may contain data, in the form of fields, often known as attributes; and code, in the form of procedures, often known as methods. A feature of objects is that an object's procedures can access and often modify the data fields of the object with which they are associated (objects have a notion of "this" or "self"). In OOP, computer programs are designed by making them out of objects that interact with one another.
There is significant diversity of OOP languages, but the most popular ones are class-based, meaning that objects are instances of classes, which typically also determine their type.
Many of the most widely used programming languages (such as C++, Object Pascal, Java, Python etc.) are multi-paradigm programming languages that support object-oriented programming to a greater or lesser degree, typically in combination with imperative, procedural programming. Significant object-oriented languages include Java, C++, C#, Python, PHP, Ruby, Perl, Object Pascal, Objective-C, Dart, Swift, Scala, Common Lisp, and Smalltalk.


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This document was last updated on August 08, 2013


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Clicking on the below links opens the description, data and code page of the given case study. If you wish to download the files, please proceed from there.
Ch01. Finding a good deal among hotels: data collection
Ch01. Comparing online and offline prices: data collection
Ch01. Management quality and firm size: data collection
Ch02. Finding a good deal among hotels: data preparation
Ch02. Identifying successful football managers
Ch02. Displaying immunization rates across countries
Ch03. Finding a good deal among hotels: data exploration
Ch03. Measuring home team advantage in football
Ch03. Comparing hotel markets in Europe: Vienna vs London
Ch03. City size distribution in Japan (in Appendix)
Ch03. Distributions of body height and income
Ch04. Management quality and firm size: describing patterns of association
Ch05. What likelihood of loss to expect on a stock portfolio?
Ch06. Comparing online and offline prices: testing the difference
Ch06. Testing the likelihood of loss on a stock portfolio
Ch07. Finding a good deal among hotels with simple regression
Ch08. Finding a good deal among hotels with non-linear function
Ch08. How does life expectancy correlate with income?
Ch08. Hotel ratings and measurement error
Ch09. How stable is the hotel price - distance to center relationship?
Ch09. Is there gender difference in earnings?
Ch10. Finding a good deal among hotels with multiple regression
Ch10. Understanding the gender difference in earnings
Ch11. Does smoking pose a health risk?
Ch12. Returns on a company stock and market returns
Ch12. Electricity consumption and temperature
Ch13. Predicting used car value with linear regressions
Ch14. Predicting used car value: log prices
Ch14. Predicting AirBnB apartment prices: selecting a regression model
Ch15. Predicting used car value with a regression tree
Ch15. Predicting AirBnB apartment prices with a regression tree
Ch16. Predicting AirBnB apartment prices with random forest
Ch17. Predicting firm exit: probability and classification
Ch18. Forecasting daily ticket sales for a swimming pool
Ch18. Forecasting California house prices
Ch19. Food and heart risk
Ch20. Working from home and employee performance
Ch20. Fine tuning social media advertising
Ch21. Founder/family ownership and quality of management
Ch22. How does a merger between airlines affect prices?
Ch23. Import demand and industrial production
Ch23. Immunization against measles and saving children
Ch24. Estimating the effect of the 2010 Haiti earthquake on GDP
Ch24. Estimating the impact of replacing football team managers
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question in this chapter: how to collect data to on hotel prices and features? Data: hotels-vienna; prices and features of all hotels available in Vienna, Austria, for a particular night. Methods/concepts to illustrate: data table, identifying entities, validity, web scraping.
| Data | R code | Stata code | Python code (in the works) |
What's the difference between online and offline prices of the same products? Question in this chapter: how to collect data to measure such differences? No data is used here; we describe and discuss the data collection methods used by the Billion Prices Project (BPP) http://www.thebillionpricesproject.com. Methods/concepts to illustrate: data collection using various methods, identifying entities, validity and reliability, representation of a population.
| Data | R code | Stata code | Python code (in the works) |
How to measure the quality of management in various organizations? Questions in this chapter: how to collect data on organizations and how to measure the quality of management? No data is used here; we describe and discuss the data collection methods used by the World Management Survey (WMS); http://worldmanagementsurvey.org. Methods/concepts to illustrate: survey method, validity, reliability, sampling and representation of a population.
| Data | R code | Stata code | Python code (in the works) |
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question in this chapter: how to start working with the data -- how to find and solve problems? Data: hotels-vienna; prices and features of all hotels available in Vienna, Austria, for a particular night. Methods/concepts to illustrate: types of variables; duplicates and missing values.
| Data | R code | Stata code | Python code (in the works) |
How to identify the most successful football (soccer) managers? Question in this chapter: how to link, organize, and use data on team performance and managers? Data: football; games in the English Premier League (EPL) 2008-2019; managers in the EPL in 2008-2019. Methods/concepts to illustrate: tidy data, linking, organizing and storing data from different sources, entity resolution.
| Data | R code | Stata code | Python code (in the works) |
What's the level of immunization agains across countires and years? Question in this chapter: how to organize and store data from many countries and years? Data: world-bank-vaccination; data on immunuzation rates in all countries in the world in recent years. Methods/concepts to illustrate: tidy data, organizing and storing multi-dimensional (panel) data (long versus wide data tables).
| Data | R code | Stata code | Python code (in the works) |
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question in this chapter: how to use exploratory data analysis to describe the data and find problems? Data: hotels-vienna; prices and features of all hotels available in Vienna, Austria, for a particular night. Methods/concepts to illustrate: distributions, extreme values, summary statistics, anatomy of a graph.
| Data | R code | Stata code | Python code (in the works) |
What's the extent of home team advantage in football (soccer)? Data: football-games; games in the English Premier League (EPL) 2008-2019. Methods/concepts to illustrate: distribution, summary statistics, anatomy of a table.
| Data | R code | Stata code | Python code (in the works) |
How do hotel prices compare across cities? Data: hotels-europe; prices and features of all hotels available in various European cities for a few particular night. Methods/concepts to illustrate: distributions, histograms and density plots (kernel density estimates).
| Data | R code | Stata code | Python code (in the works) |
How can we characerize the distribution of city size? Data: city-population; population of the 159 largest cities in Japan in 2015. Methods/concepts to illustrate: power law distribution.
| Data | R code | Stata code | Python code (in the works) |
How can we characterize the distribution of bidy hight and the distribution of income? Data: height-income-distributions; Wome of age 55-60 in the U.S.A. in 2014 (Health and Retirement Study). Methods/concepts to illustrate: normal distribution, lognormal distribution.
| Data | R code | Stata code | Python code (in the works) |
Are larger companies better managed? Data: wms-management-survey; Mexican manufacturing firms from the 2013 wave of the World Management Survey (WMS), http://worldmanagementsurvey.org, N=300. Methods/concepts to illustrate: conditional probabilities, conditional distributions, conditional expectations, bin scatter, scatterplot, conditional box/violin plots, using many variables to measure a latent variable.
| Data | R code | Stata code | Python code (in the works) |
What likelihood of a large loss should we expect on a portfolio of company stocks? Data: stock-market; 11 years of daily closing price data of the S & P 500 stock market index from the U.S.A. (N=2519). Methods/concepts to illustrate: time series data, sampling distribution of a statistic, standard error, confidence interval, estimation of SE by bootstrap and by formula, external validity.
| Data | R code | Stata code | Python code (in the works) |
Are online and offline prices of the same products the same? Data: billion-prices; Online and offline prices of a set of products sold in the U.S.A. in 2015-6; Billion Prices Project (BPP) http://www.thebillionpricesproject.com, N=6439. Methods/concepts to illustrate: formulating and testing two-sided hypotheses about an avareage (confidence interval, t-test, fals positives and false negatives, p-value), testing multiple hypotheses.
| Data | R code | Stata code | Python code (in the works) |
Is there a large chance that a portfolio of company stocks will suffer a large loss in the near future? Data: stock-market; 11 years of daily closing price data of tne S & P 500 stock market index from the U.S.A. (N=2519). Methods/concepts to illustrate: formulating and testing one-sideed hypotheses.
| Data | R code | Stata code | Python code (in the works) |
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question in this chapter: How to use simple linear regression to find hotels that are underpriced relative to their location? Data: hotels-vienna; prices and features of all hotels available in Vienna, Austria, for a particular night (N=207). Methods/concepts to illustrate: bin scatter nonparametric regression, lowess, simple linear regression, regression residuals.
| Data | R code | Stata code | Python code (in the works) |
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question in this chapter: How to use linear regression that approximates nonlinear patterns to find hotels that are underpriced relative to their location? Data: hotels-vienna; prices and features of all hotels available in Vienna, Austria, for a particular night (N=207). Methods/concepts to illustrate: regression with logs.
| Data | R code | Stata code | Python code (in the works) |
How is average income in a country related to how long its people live? Data: worldbank-lifeexpectancy; cross-country data for a single year (2017) from the World Development Indicators website of the World Bank (N=182), . Methods/concepts to illustrate: per capita and logs in regressions, approximating nonlinear assiciations in linear regressions (piecewise linear spline and polynomials), weights in the regression.
| Data | R code | Stata code | Python code (in the works) |
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question here: What's the effect of measurement error in average customer ratings on the estimated price-ratings association? Data: hotels-vienna; prices and features of all hotels available in Vienna, Austria, for a particular night (N=207). Methods/concepts to illustrate: regression with a right-hand-side variable with measurement error.
| Data | R code | Stata code | Python code (in the works) |
How large are differences in earnings by gender and age among market analysts? Data: cps-earnings; Current Population Survey, U.S.A., morg 2014 sample, "market research analysts and market specialsts" occupation. N=281. Methods/concepts to illustrate: statistical inference and external validity in regression analysis (SE, CI, prediction interval, tests on coefficients).
| Data | R code | Stata code | Python code (in the works) |
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question here: How similar or different is the association betewen price and distance to the center across time and cities? Data: hotels-europe; prices and features of all hotels available in several European scities, for selected nights (N=3720). Methods/concepts to illustrate: external validity of regression estimates.
| Data | R code | Stata code | Python code (in the works) |
What may explain the gender differences in earnings among professionals? Data: cps-earnings; Current Population Survey, U.S.A., morg 2014 sample, employees with a graduate degree. N=18241. Methods/concepts to illustrate: multiple regression, qualitative right-hand-side variables, interactions, statistical inference, multiple regression and causal questions.
| Data | R code | Stata code | Python code (in the works) |
How to find a good deal -- a hotel that is underpriced relative to its location and quality? Question in this chapter: How to use multiple linear regression that aproximates nonlinear patterns to find hotels that are underpriced relative to their location? Data: hotels-vienna; prices and features of all hotels available in Vienna, Austria, for a particular night (N=207). Methods/concepts to illustrate: multiple linear regression, regression residuals, y_hat - y plot.
| Data | R code | Stata code | Python code (in the works) |
Are smokers less likely to stay healthy? Data: share-health; Survey for Healt, Aging, and Retirement in Europe (SHARE), 2011 50-60 year-old healthy subsample. N=3109. Methods/concepts to illustrate: linear probability model, logit, probit, marginal differences, predicted probabilities.
| Data | R code | Stata code | Python code (in the works) |
How are changes in the price of one company stock related to changes in market prices? Data: stocks-sp500; prices of Microsoft and the S & P 500, 1998-2018. N=5284 days (253 months) Methods/concepts to illustrate: regression with time series data, estimating financial beta.
| Data | R code | Stata code | Python code (in the works) |
How does temperature affect residential electricity consumption? Data: arizona-electricity; Monthly residential electricity consumption in Arizona, U.S.A. and temperature data in Phoenix, AZ, USA, 2001-2017. N=204. Methods/concepts to illustrate: regression with time series data, trend seasonality, serial correlation, lags.
| Data | R code | Stata code | Python code (in the works) |
How can we predict the selling price of used cars with particular features? Question in this chapter: how can we use linear regresion to predict the selling price of used cars? Data: used-cars; Data on ads for a specific make and type of used car from Chicago, USA, 2018. N=281. Methods/concepts to illustrate: prediction with regression, prediction interval, model selection (BIC and cross-validation), external validity.
| Data | R code | Stata code | Python code (in the works) |
How can we predict the selling price of used cars with particular features? Question in this chapter: how can we use linear regresion with a log left-hand-side variable to predict the selling price of used cars? Data: used-cars; Data on ads for a specific make and type of used car from Chicago, USA, 2018. N=281. Methods/concepts to illustrate: prediction with regression with log y.
| Data | R code | Stata code | Python code (in the works) |
How can we predict the rental price of Airbnb apartments with particular features? Question in this chapter: how can we use linear regresion to predict the rental price of Airbnb apartments? Data: airbnb; Airbnb apartment prices in London, UK, for one night in 2017. N=4497. Methods/concepts to illustrate: prediction with regression; feature engineering, LASSO, holdout sample for evaluation, model selection by cross-validation.
| Data | R code | Stata code | Python code (in the works) |
How can we predict the selling price of cars with particular features? Question in this chapter: how can we use a regression tree to predict the selling price of used cars? Data: used-cars; Data on ads for a specific make and type of used car from Chicago and Los Angeles, USA, 2018. N=477. Methods/concepts to illustrate: prediction with regression tree with a single predictor (the regression tree is a nonparametric regression).
| Data | R code | Stata code | Python code (in the works) |
How can we predict the rental price of Airbnb apartments with particular features? Question in this chapter: how can we use a regression tree to predict the rental price of Airbnb apartments? Data: airbnb; Airbnb apartment prices in London, UK, for one night in 2017. N=4497. Methods/concepts to illustrate: prediction with regression tree (multiple predictors stopping rules, pruning, variable importance).
| Data | R code | Stata code | Python code (in the works) |
How can we predict the rental price of Airbnb apartments with particular features? Question in this chapter: how can we use a random forest to predict the rental price of Airbnb apartments? Data: airbnb; Airbnb apartment prices in London, UK, for one night in 2017. N=4497. Methods/concepts to illustrate: prediction with random forest (bagging, decorrelated trees, variable importance, partial dependence plot); gradient boosting method.
| Data | R code | Stata code | Python code (in the works) |
How can we predict the probability that companies with particular features would exit (go out of business)? How can we clasify them into two groups, those likely to exit and those likely to stay in business? Data: bisnode; companies from selected industries in a European country; 2012 (exit by 2014). N=19036. Methods/concepts to illustrate: linking data from various sources; predicting probabilities by logit (incl. logit LASSO) and classification trees; classification using predicted probabilities and a classification thresold.
| Data | R code | Stata code | Python code (in the works) |
How can we predict daily sales in a swimming pool for one year? Data: swim-transactions; transaction-level data on ticket sales for a swimming pool in Albuquerque, NM, USA, 2010-2016, aggregated to daily frequency. N=2522 days. Methods/concepts to illustrate: prediction using time series data (trend, seasonality, holidays); Prophet.
| Data | R code | Stata code | Python code (in the works) |
How can we predict the montlhly house price index in a city for a year? Data: case-shiller-la; Monthly time series of the Case-Shiller house price index for Los Angeles, CA, USA, 2000-2017. N=216. Methods/concepts to illustrate: prediction using time series data (trend, seasonality, ARIMA); auto-arima; using other vairables with VAR .
| Data | R code | Stata code | Python code (in the works) |
Does eating a lot of fruit and vegetables prevent high blood pressure? Data: food-health; National Health and Nutrition Examination Survey (NHANES), USA, 2009-2013. N=7359. Methods/concepts to illustrate: thinking about cause and effect using observational data (potential outcomes, cofonders, bad congitioning variable, drawing causal maps).
| Data | R code | Stata code | Python code (in the works) |
What's the effect of letting employees work from home on employee retention and performance? Data: working-from-home; experimental data from one firm in Shanghai, China, in 2010. N=249. Methods/concepts to illustrate: designing and analyzing field experiments (randomization, compliance, covariate balance, effect estimation); using the results of experiments in actual decisions.
| Data | R code | Stata code | Python code (in the works) |
What's the effect of design details of an advertisment on social media? Data: ab-test-social-media; A/B testing experiment of details of a university advertisement. N=2 million targeted impressions. Methods/concepts to illustrate: designing and analyzing A/B test (sample size calculation, estimating and testing effects).
| Data | R code | Stata code | Python code (in the works) |
What's the effect of founder/family ownership on the quality of management? Data: wms-management-survey; World Management Survey: survey data on ownership and the quality of management, 24 countries, 2004-2015. N=9569. Methods/concepts to illustrate: estimating effects using observational data; thought experiments, regression, exact matching, matching on the propensity score..
| Data | R code | Stata code | Python code (in the works) |
How does a merger between two major airlines companies affect market prices? Data: US-arilines; US Department of Transportation DB1B data (10% of all tickets sold in the USA), 2010-2016. Aggregated to N=112,632 markets. Methods/concepts to illustrate: difference-in-differences (simple diff-in-diffs, diff-in-diffs regression with other right-hand-side variables, diff-in-diffs with pooled cross sections); defining markets and aggregating big data to market level.
| Data | R code | Stata code | Python code (in the works) |
How does the import demand of the USA affect industrial production in Thailand? Data: asia-industry; Monthly time series data on industrial production in four Asian countries and total imports in the USA, from the World Development Indicators (World Bank). Feb 1998 - April 2018, N=243. Methods/concepts to illustrate: estimating an effect via time series regression (estimation in changes, lags, serial correlation); regression with pooled time series.
| Data | R code | Stata code | Python code (in the works) |
Does immunization against measles save children? Data: world-bank-vaccination; World Development Indicators (World Bank), 192 countries, yearly data 1998-2017 (balanced panel of 172 countries and 20 years, N=3440). Methods/concepts to illustrate: cross-section time-series panel data regresions (FE, FD, lags, leads, aggregate trend, indivudial trends, clustered SE).
| Data | R code | Stata code | Python code (in the works) |
What was the effect of of the 2010 earthquake in Haiti on total Haitian GDP? Data: haiti-earthquake; yearly time series on GDP and other macro variables from Haiti and 22 countries (World Development Indicators of the WOrld Bank), 2004-2015. Methods/concepts to illustrate: synthetic control method (donor pool, variable selection, synthetic control algorithm).
| Data | R code | Stata code | Python code (in the works) |
What's the effect of replacing a football (soccer) manager within the season on team performance? Data: football; team-game level data from the English Premier League, seasons 2008/9 - 2018/19, merged to data on managers. N=8360. Methods/concepts to illustrate: event study regression with panel data; selection of control group and defining pseudo-interventions.
| Data | R code | Stata code | Python code (in the works) |


--- layout: post title: Testing with Rails Select2 date: 2012-12-19 11:53:32.000000000 -06:00 type: post published: true status: publish categories: [] tags: - Frameworks - Heroku - Languages - Programming - Rails - Ruby - Ruby on Rails - Steve Tong meta: _edit_last: '15714693' tagazine-media: a:7:{s:7:"primary";s:0:"";s:6:"images";a:0:{}s:6:"videos";a:0:{}s:11:"image_count";i:0;s:6:"author";s:8:"15714693";s:7:"blog_id";s:8:"15193052";s:9:"mod_stamp";s:19:"2012-12-19 18:53:32";} publicize_twitter_user: broberts_az _wpas_done_482036: '1' _publicize_done_external: a:1:{s:7:"twitter";a:1:{i:199393780;b:1;}} author: login: broberts65 email<EMAIL_ADDRESS>display_name: Bob Roberts first_name: '' last_name: '' ---
Lately we have been incorporating select2-rails into our applications. I cannot say enough how much I love this tool and its ease of use.
That being said, we had had issues with testing and select2-rails. So Steve Tong of Rebel-Outpost spent some time and found a small script to use for testing it.
https://gist.github.com/4339329
The only issue with this is that you can only have one option in the select2.
If anybody finds a better way, please let us know.


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Inheritance diagram for IITObject:
An IITObject uniquely identifies a source, playlist, or track in iTunes using four separate IDs. These are runtime IDs, they are only valid while the current instance of iTunes is running.
As of iTunes 7.7, you can also identify an IITObject using a 64-bit persistent ID, which is valid across multiple invocations of iTunes.
The main use of the IITObject interface is to allow clients to track iTunes database changes using _IiTunesEvents::OnDatabaseChangedEvent().
You can retrieve an IITObject with a specified runtime ID using IiTunes::GetITObjectByID().
An IITObject will always have a valid, non-zero source ID.
An IITObject corresponding to a playlist or track will always have a valid playlist ID. The playlist ID will be zero for a source.
An IITObject corresponding to a track will always have a valid track and track database ID. These IDs will be zero for a source or playlist.
A track ID is unique within the track's playlist. A track database ID is unique across all playlists. For example, if the same music file is in two different playlists, each of the tracks could have different track IDs, but they will have the same track database ID.
An IITObject also has a 64-bit persistent ID which can be used to identify the IITObject across multiple invocations of iTunes. Note that persistent IDs are only really persistent for objects that iTunes stores in its database. For example, the persistent IDs for transient objects like radio streams and objects from audio CDs will differ across multiple invocations of iTunes.
The persistent ID was exposed in the COM interface starting with iTunes 7.7, so methods and properties to retrieve the persistent ID could not be added to the IITObject interface without breaking existing COM clients. You can find these routines in IiTunes instead:
You can retrieve a source, playlist, or track with a specified persistent ID using the ItemByPersistentID property of the appropriate collection interface:
Public Member Functions | |
Methods | |
| HRESULT | GetITObjectIDs ([out] long *sourceID,[out] long *playlistID,[out] long *trackID,[out] long *databaseID) |
| Returns the four IDs that uniquely identify this object. | |
Properties | |
| HRESULT | Name ([out, retval] BSTR *name) |
| Returns the name of the object. | |
| HRESULT | Name ([in] BSTR name) |
| Set the name of the object. | |
| HRESULT | Index ([out, retval] long *index) |
| Returns the index of the object in internal application order. | |
| HRESULT | SourceID ([out, retval] long *sourceID) |
| Returns the ID that identifies the source. | |
| HRESULT | PlaylistID ([out, retval] long *playlistID) |
| Returns the ID that identifies the playlist. | |
| HRESULT | TrackID ([out, retval] long *trackID) |
| Returns the ID that identifies the track within the playlist. | |
| HRESULT | TrackDatabaseID ([out, retval] long *databaseID) |
| Returns the ID that identifies the track, independent of its playlist. |
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Returns the four IDs that uniquely identify this object. This method is not usable from scripting clients, since it returns multiple output parameters. Scripting clients must use the IITObject::SourceID(), IITObject::PlaylistID(), IITObject::TrackID(), and IITObject::TrackDatabaseID() properties instead. These are runtime IDs, they are only valid while the current instance of iTunes is running.
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Returns the index of the object in internal application order. You can pass this index to IITSourceCollection::Item(), IITPlaylistCollection::Item(), or IITTrackCollection::Item() to retrieve this object. For tracks, this index is independent of play order. The play order index of a track can be retrieved using IITTrack::PlayOrderIndex().
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Set the name of the object. You can only change the name of a track or playlist. You cannot change the name of a source. Object names are not unique in iTunes, e.g. multiple playlists may share the same name.
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Returns the name of the object. For a source, this is the name of the source (as displayed in the Source list). For a playlist, this is the name of the playlist. For a track, this is the name of the track. Object names are not unique in iTunes, e.g. multiple playlists may share the same name.
|
|
|
Returns the ID that identifies the playlist. Valid for a playlist or track. Will be zero for a source. This is a runtime ID, it is only valid while the current instance of iTunes is running.
|
|
|
Returns the ID that identifies the source. Valid for a source, playlist, or track. This is a runtime ID, it is only valid while the current instance of iTunes is running.
|
|
|
Returns the ID that identifies the track, independent of its playlist. Valid for a track. Will be zero for a source or playlist. If the same music file is in two different playlists, each of the tracks will have the same track database ID. This is a runtime ID, it is only valid while the current instance of iTunes is running.
|
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Returns the ID that identifies the track within the playlist. Valid for a track. Will be zero for a source or playlist. This is a runtime ID, it is only valid while the current instance of iTunes is running.
|


--- layout: page title: About header-img: "img/about-bg.png" ---
husband, papa, son, brother, friend; strategic thinker, operational servant leader


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roots
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nodes;
in
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Dominicana*
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La
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1
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of
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Soco,
on
the
San
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de
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Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


{% include 'shop/sidebar.html' %}
Order id : {{order.id}}
Thank you!!!
Thank you we have received your order. Your items will be delivered with in 24 hours.
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Order id : {{order.id}}


Apache Flink 1.9.3 Released
April 24, 2020 - Dian Fu (@DianFu11)The Apache Flink community released the third bugfix version of the Apache Flink 1.9 series.
This release includes 38 fixes and minor improvements for Flink 1.9.2. The list below includes a detailed list of all fixes and improvements.
We highly recommend all users to upgrade to Flink 1.9.3.
Updated Maven dependencies:
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>1.9.3</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>1.9.3</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.11</artifactId>
<version>1.9.3</version>
</dependency>
You can find the binaries on the updated Downloads page.
List of resolved issues:
Sub-task
- [FLINK-15143] - Create document for FLIP-49 TM memory model and configuration guide
- [FLINK-16389] - Bump Kafka 0.10 to <IP_ADDRESS>
Bug
- [FLINK-11193] - Rocksdb timer service factory configuration option is not settable per job
- [FLINK-14316] - Stuck in "Job leader ... lost leadership" error
- [FLINK-14560] - The value of taskmanager.memory.size in flink-conf.yaml is set to zero will cause taskmanager not to work
- [FLINK-15010] - Temp directories flink-netty-shuffle-* are not cleaned up
- [FLINK-15085] - HistoryServer dashboard config json out of sync
- [FLINK-15386] - SingleJobSubmittedJobGraphStore.putJobGraph has a logic error
- [FLINK-15575] - Azure Filesystem Shades Wrong Package "httpcomponents"
- [FLINK-15638] - releasing/create_release_branch.sh does not set version in flink-python/pyflink/version.py
- [FLINK-15812] - HistoryServer archiving is done in Dispatcher main thread
- [FLINK-15844] - Removal of JobWithJars.buildUserCodeClassLoader method without Configuration breaks backwards compatibility
- [FLINK-15863] - Fix docs stating that savepoints are relocatable
- [FLINK-16047] - Blink planner produces wrong aggregate results with state clean up
- [FLINK-16242] - BinaryGeneric serialization error cause checkpoint failure
- [FLINK-16308] - SQL connector download links are broken
- [FLINK-16373] - EmbeddedLeaderService: IllegalStateException: The RPC connection is already closed
- [FLINK-16573] - Kinesis consumer does not properly shutdown RecordFetcher threads
- [FLINK-16576] - State inconsistency on restore with memory state backends
- [FLINK-16696] - Savepoint trigger documentation is insufficient
- [FLINK-16703] - AkkaRpcActor state machine does not record transition to terminating state.
- [FLINK-16836] - Losing leadership does not clear rpc connection in JobManagerLeaderListener
- [FLINK-16860] - Failed to push filter into OrcTableSource when upgrading to 1.9.2
- [FLINK-16916] - The logic of NullableSerializer#copy is wrong
- [FLINK-17062] - Fix the conversion from Java row type to Python row type
Improvement
- [FLINK-14278] - Pass in ioExecutor into AbstractDispatcherResourceManagerComponentFactory
- [FLINK-15908] - Add description of support 'pip install' to 1.9.x documents
- [FLINK-15909] - Add PyPI release process into the subsequent release of 1.9.x
- [FLINK-15938] - Idle state not cleaned in StreamingJoinOperator and StreamingSemiAntiJoinOperator
- [FLINK-16018] - Improve error reporting when submitting batch job (instead of AskTimeoutException)
- [FLINK-16031] - Improve the description in the README file of PyFlink 1.9.x
- [FLINK-16167] - Update documentation about python shell execution
- [FLINK-16280] - Fix sample code errors in the documentation about elasticsearch connector
- [FLINK-16697] - Disable JMX rebinding
- [FLINK-16862] - Remove example url in quickstarts
- [FLINK-16942] - ES 5 sink should allow users to select netty transport client
Task
- [FLINK-11767] - Introduce new TypeSerializerUpgradeTestBase, new PojoSerializerUpgradeTest
- [FLINK-16454] - Update the copyright year in NOTICE files


Abstract:
Measurements of time-variable gravity from the Gravity Recovery and
Climate Experiment (GRACE) and the GRACE Follow-on (GRACE-FO)
missions are an invaluable tool for monitoring changes in mass of
the Earth's glaciated regions. We improve upon estimates of glacier
and ice sheet mass balance from time-variable gravity by including
instantaneous spatiotemporal variations in sea level. Here, a
least-squares mascon technique is combined with solutions to the
sea level equation to iteratively correct the GRACE/GRACE-FO data
for the induced sea level response on a monthly basis. We find that
variations in regional sea level affect ice sheet mass balance
estimates in Greenland by approximately 4% and in Antarctic by
approximately 5%. Since 2002, the Greenland ice sheet has been
losing mass at an average rate of 263±23 Gt/yr, and the
Antarctic ice sheet has been losing mass at average rates between
-90±52 Gt/yr and -122±53 Gt/yr depending on the rate of
Glacial Isostatic Adjustment (GIA). The mass losses from both ice
sheets represent an increase of 15.6±2.0 mm to global mean sea
levels since 2002.


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18 Jan,2018
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Post10


public class UniformFuzzyHash
extends java.lang.Object
| Modifier and Type | Class and Description |
|---|---|
static class |
UniformFuzzyHash.SimilarityTypes
Enum of types of similarity.
|
| Constructor and Description |
|---|
UniformFuzzyHash(byte[] data,
int factor)
Builds a Uniform Fuzzy Hash from a byte array of data and a factor.
|
UniformFuzzyHash(java.io.ByteArrayOutputStream data,
int factor)
Builds a Uniform Fuzzy Hash from a byte array output stream of data and a factor.
|
UniformFuzzyHash(java.io.File data,
int factor)
Builds a Uniform Fuzzy Hash from a file of data and a factor.
|
UniformFuzzyHash(java.io.InputStream data,
int factor)
Builds a Uniform Fuzzy Hash from an input stream of data and a factor.
|
UniformFuzzyHash(java.lang.String data,
int factor)
Builds a Uniform Fuzzy Hash from a string of data (using the platform's default charset) and
a factor.
|
| Modifier and Type | Method and Description |
|---|---|
static void |
checkFactor(int factor)
Checks if a factor is valid to compute a Uniform Fuzzy Hash.
|
boolean |
equals(java.lang.Object obj)
Indicates whether this Uniform Fuzzy Hash is equal to another one.
|
java.util.List<UniformFuzzyHashBlock> |
getBlocks() |
java.util.Set<UniformFuzzyHashBlock> |
getBlocksSet() |
int |
getDataSize() |
int |
getFactor() |
int |
hashCode()
A hashCode for this Uniform Fuzzy Hash based on its factor, data size and amount of blocks.
|
static UniformFuzzyHash |
rebuildFromString(java.lang.String hashString)
Rebuilds a Uniform Fuzzy Hash from its string representation.
|
java.util.Map<UniformFuzzyHash.SimilarityTypes,java.lang.Double> |
similarities(UniformFuzzyHash other)
Computes all the types of similarity between this Uniform Fuzzy Hash and another one.
|
double |
similarity(UniformFuzzyHash other)
Computes the similarity of this Uniform Fuzzy Hash to another one and returns it as a number
between 0 and 1.
|
double |
similarity(UniformFuzzyHash other,
UniformFuzzyHash.SimilarityTypes similarityType)
Computes a type of similarity between this Uniform Fuzzy Hash and another one and returns it
as a number between 0 and 1.
|
java.lang.String |
toString() |
public UniformFuzzyHash(byte[] data, int factor)
data
- Byte array of data.factor
- Relation between data length and the hash mean number of blocks.
Must be greater than 2 and must be odd.public UniformFuzzyHash(java.lang.String data, int factor)
data
- String of data.factor
- Relation between data length and the hash mean number of blocks.
Must be greater than 2 and must be odd.public UniformFuzzyHash(java.io.InputStream data, int factor) throws java.io.IOException
data
- Input stream of data.factor
- Relation between data length and the hash mean number of blocks.
Must be greater than 2 and must be odd.java.io.IOException
- If an IOException occurs reading the input stream of data.public UniformFuzzyHash(java.io.ByteArrayOutputStream data, int factor)
data
- Byte array output stream of data.factor
- Relation between data length and the hash mean number of blocks.
Must be greater than 2 and must be odd.public UniformFuzzyHash(java.io.File data, int factor) throws java.io.IOException
data
- File of data.factor
- Relation between data length and the hash mean number of blocks.
Must be greater than 2 and must be odd.java.io.IOException
- If an IOException occurs reading the file of data.public java.lang.String toString()
toString
in class java.lang.Object
public static UniformFuzzyHash rebuildFromString(java.lang.String hashString)
hashString
- String representation of a Uniform Fuzzy Hash.public double similarity(UniformFuzzyHash other)
other
- Another Uniform Fuzzy Hash.public double similarity(UniformFuzzyHash other, UniformFuzzyHash.SimilarityTypes similarityType)
other
- Another Uniform Fuzzy Hash.similarityType
- The type of similarity.public java.util.Map<UniformFuzzyHash.SimilarityTypes,java.lang.Double> similarities(UniformFuzzyHash other)
other
- Another Uniform Fuzzy Hash.public boolean equals(java.lang.Object obj)
equals
in class java.lang.Object
obj
- Another Uniform Fuzzy Hash.public int hashCode()
hashCode
in class java.lang.Object
public int getFactor()
public int getDataSize()
public java.util.List<UniformFuzzyHashBlock> getBlocks()
public java.util.Set<UniformFuzzyHashBlock> getBlocksSet()
public static void checkFactor(int factor)
factor
- Relation between data length and the hash mean number of blocks.
Must be greater than 2 and must be odd.


I used to like making music a lot:
My favourite. It's a bit of a technical étude.
This one is more experimental; I sampled only mechanical objects.
I wanted to make a reggae/dub piece. The interlude in the middle is me going to the bank. The bit about cowboys is from a Hank Williams documentary.
In retrospect, the bass drum and snare hit pretty hard here. The ending is me making myself some tea.
For this I sampled She's Gone by Hall and Oates. I actually fed the output from my soundcard playing the YouTube video back into itself in a pinch to show a friend how I make music. I ended up liking the result and kept it. Again the harder BD and snare, especially for such a slow tempo, make this sound a bit more dated than I would like.
Dolly Parton meets Herbie Hancock.
Serge Gainsbourg sample. There was always something that bugged me about this one but I could never fix it.
Collaboration with my friend Mike. I was working on some drum & bass drums and he called me up looking to make some music. He made pretty much all of the rest on my Juno 106 and I worked on the arrangement later on.


Refresh browser (F5) to see other cases of grid was built.
Examples: Default config 2 bigs first Square grids Full width
click on image or turn on firebug to see the callback.


Wow, you did great! Keep up, and keep learning!
It is time for the world to know how awesome you were, let's share it with your friends! :)


Web analytics is the measuring, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage.
Collection of data…
Processing of data into information…
Developing KPI…
Formulating online strategy…
Site traffic analysis…
Analyzing data from e-commerce…
Usability analysis…
Analysis of the behavior of visitors on the page…
Benchmarking…
End-to-end analytics…
• Google Analytics, Piwik Яндекс.Метрика, Liveinternet<EMAIL_ADDRESS>OpenStat, HotLog, Adobe Analytics, ClickTale


--- title: Hamilton County Department of Education layout: post categories: - hcde description: The Hamilton County Department of Education consists of an elected nine-member school board and an appointed superintendent. ---
The Hamilton County Department of Education runs the public school system.
The Hamilton County Board of Education is a legislative and policy-making body that manages and controls the public school system.
The board consists of nine elected representatives. Board members are elected from the districts they represent. The districts coincide with the Hamilton County Commission.
Members serve staggered four-year terms — approximately half of the board is elected every two years — and they are not term-limited. School board elections are nonpartisan.
The school board has four main responsibilities: hire a superintendent; make personnel and infrastructure decisions; submit an operating budget to the Hamilton County Commission; and provide for student enrollment.
The superintendent heads the Hamilton County Department of Education and is appointed by the Board of Education. He is responsible for executing federal, state and local education policies in the county school system.
Rick Smith was named HCDE's superintendent in 2011.
The superintendent is not a voting member of the school board, but he and the department works closely with it to draft local education policies. He serves on the executive committee, along with the board chairman, and keeps members updated on issues in local schools and the school system.
The superintendent supervises local public schools, oversees personnel, prepares HCDE's annual budget and makes regular reports to school board members and state officials.


To work properly the SEPIA client needs to know how it can reach your server. Here you can configure the required connections. Note that this is client-device specific and not part of the user account.
If you change the hostname this will automatically reset all other core servers. The changes will take effect after the app is reloaded.
Connections


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{%extends "nav_account.html"%} {%block title%}Elections{%endblock%} {%block content%}
To vote in {{election.name}}, rate all the candidates below. All candidates must be rated for a vote to be valid. Give the highest score to the candidate you prefer the most, and the lowest score to the candidate you prefer the least.
You can click a candidates name for more information about this candidate.
{%if form.errors%}NOTE! Your submitted form contained errors and has not been saved!
{%endif%} {%if form.saved_and_modified%}Your votes have been registered. Thank you for participating.
{%endif%}{%if form.votes %} Your votes have been cast, and the values below indicate what you voted. You can change your votes up until the final day of the elections. {%else%} Please cast your votes below. {%endif%}


Alice 1
I lived in Santa Clara, California (south of the San Francisco Bay) from 1993 until 1994, and then in Santa Barbara (two hours north of Los Angeles) from then until the summer of 1995. I spent a lot of time working on MacBSD during those years, but I also spent a lot of time thinking about what I thought I was supposed to be doing, which was computer hardware. (Now I'm much more honest with myself.  In early 1994, I resigned from Altera because I wasn't producing results and felt thoroughly discouraged. Self-loathing can be quite motivating, so I spent a lot of time working with MacBSD and experimenting with electronics, trying to accomplish and relearn a lot of stuff.
Since the original goals of the Alice Project were still clearly in my head, I thought I would take the next step down the long road of educating myself. Over those two years, I (re)learned how to build logic circuits and refined a procedure for printing my own circuit boards. Most computer science programs include a course or two in electronic design. I had forgotten most of mine but had most of my notes and textbooks. With those in hand, I built some simple breadboarded circuits including a counter connected to a hexadecimal LED display and a static RAM (SRAM) test circuit.
After I moved to Santa Barbara to work for Tenon on MachTen, I bought an EPROM programmer and decided to try to build a simple computer.
I designed and built a main board including a Z-80 CPU, RAM, and ROM. I created a circuit diagram using xfig, but found it lacking some features I needed, so later I switched to tgif. I wanted to print my own circuit board, so I created an initial sketch of the printed circuit using graph paper, and I routed the traces by hand.
I subsequently created an electronic version of the circuit master with tgif, but had to make several revisions because I was not working from a particularly thorough design.
The final main board turned out okay, including a switch to place the ROM at address 0. My original plan was to build a second device that would connect to the main board through an address/data bus for debugging. That way I could power the board up with the switch off, fill memory at 0 with a test program, and then run that program. It turned out to be too hard to implement and I basically left the switch turned on and always burned a new EPROM if I needed to change the program.
A DB-25 connector allowed me to plug in an output board; I wanted to make some additional boards but finally decided to move on to Alice 2 instead. The one board I made decoded the I/O address 00H and output the data as two hex digits using 4495 hex-to-seven-segment-LCD-drivers.
A DB-9 connector connected the clock board to the main board. This clock was controlled by a potentiometer and had a range something like from .1 Hz to 1 KHz.
I found a free Z-80 assembler called a-z80, by William Colley, III. It worked really well for me for the prototype Alice 1 and continued to work for us for Alice 2. I wrote some code to display the string C0dE15C0dE, or “code is code”. When I ran the clock very slow, you could see the string slowly scroll across the two hex digits on the output board.
 In 1995, there weren't very many great suppliers in Santa Barbara proper. The nearest large electronics store was an hour North, in Santa Maria, and it's very frustrating to discover at 4 PM that you don't have a part and know that, even though the store doesn't close for an hour, you can't get there before it closes.
Alice 1 was a fun diversion in the evenings at Santa Barbara. During the day I worked as the sole tech support engineer at Tenon Intersystems, the makers of MachTen, a UNIX execution environment for MacOS. I spent a lot of time learning about graphics, NetBSD, and electronics when I was not at work. Building an actual computer with CPU, RAM, ROM, and an output device was really inspiring.


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| cssErrors.js | 82.35% | 14/17 | 40% | 4/10 | 100% | 4/4 | 87.5% | 14/16 | |
| cssNodes.js | 94.52% | 897/949 | 78.08% | 260/333 | 87.29% | 261/299 | 94.96% | 885/932 | |
| cssParser.js | 96.26% | 618/642 | 94.46% | 460/487 | 100% | 80/80 | 96.36% | 609/632 | |
| cssScanner.js | 98.17% | 429/437 | 93.51% | 216/231 | 95.56% | 43/45 | 98.61% | 427/433 | |
| cssSymbolScope.js | 91.16% | 227/249 | 77.7% | 108/139 | 92.86% | 39/42 | 92.56% | 224/242 | |
| lessParser.js | 91.89% | 306/333 | 86.56% | 219/253 | 94.87% | 37/39 | 92.66% | 303/327 | |
| lessScanner.js | 87.3% | 55/63 | 69.05% | 29/42 | 85.71% | 12/14 | 91.38% | 53/58 | |
| scssErrors.js | 82.35% | 14/17 | 40% | 4/10 | 100% | 4/4 | 87.5% | 14/16 | |
| scssParser.js | 97.65% | 333/341 | 94.2% | 260/276 | 95.56% | 43/45 | 98.51% | 330/335 | |
| scssScanner.js | 90.48% | 76/84 | 75.93% | 41/54 | 83.33% | 10/12 | 93.75% | 75/80 |


Pyrrhic Victories and Josekis
PT-BR: Uma versão deste post em português no meu canal será disponibilizada em breve.
The trick play in question is triggered by the very strange, and arguable mistake, 2-space jump, seen in Diagram 1.
Black's intention is to sacrifice the corner, but make it difficult enough to get an extremely solid wall on the outside. Diagram 2 shows the continuation, in which Black lets himself get enclosed but White's shape starts show its defects.
According to Leela, the situation is still good for White, but a lot of care is needed. If you follow through with the "obvious moves", you're falling for the trap, as seen in Diagram 3.
Some more forcing moves and Black is able to net White's group in sente, as in Diagram 4.
Diagram 5's picture should be burnt into any SDK's mind as the canonical example of why thickness can be worth way more than a big corner. White's territory amounts to approximately 19 points, which is why this sequence is called the Nineteen-Point Trick Play. And Black's wall is sometimes called the Iron Wall. It's also worth mentioning that Black ends with sente, so developing from the wall will be easy.
One alternative to White 5 in Diagram 3 would have been to
hane on the outside, shown in Diagram 6. White still gets the
corner, but will be forced to crawl on the second line.If Black ends
up with too little territory in the middle, or around A
,
this might be feasible, otherwise it might be even worse than the
normal sequence.
The main way of avoiding the problems of the main trick sequence is to
avoid it right from the start. For example, the main problem in
White's shape is the cut at 8
in Diagram 2, so
why not fix it preemptively? In this case, fixing it even has the
benefit of damaging Black's pincer stone. This rationel is most pros'
recommendation, and is shown in Diagram 7 below.
Another way of refuting the trick is seen in Diagram 8.
A final bookish alternative is from, allegedly, one of Inoue Doseki's (1582 - 1630) games, one of the most legendary Go players of all time. In Diagram 9, White attacks the pincer stone in order to obtain enough forcing moves to come back to the trick play much stronger. Black can't fall for this trap and chooses to damage White even further at the top. The overall sequence seems to show a lot of kiai from both players.
As usual, Leela doesn't seem to get fazed by puny human tricks. In
fact, sometimes simple answer to the tricks work unexpectedly well.
This case is no different, Diagram 10 demonstrates an
incredibly simple sequence to refute the trick: White is very thick,
and will be able to counter-attack the pincer stone very nicely. A
complete follow up after 5
is beyond the scope of this
article, but, overall, White will be able to lean at Black's top group
in order to gather strength and counter-attack if Black's pincer stone
starts jumping out.
If you do end up getting cut, there are other, more optimal ways of
resisting for White than the trick play sequence. Move
1
in Diagram 3 is actually quite weird to me,
for instance, it seems to end up helping Black's stones get stronger,
which is almost never a good choice. A move like 1
in
Diagram 11 makes much more sense and is one Leela prefers.
Another alternative to what's played in the trick sequence is simply crawling at the top. This might seem bad, but it isn't because White will damage Black's top stone while getting very solid shape. In Diagram 12, White stabilizes the top, damages Black's stones, and White's stones to the right aren't even captured yet.
Diagram 13 shows how to "save" White's stones on the left, and is also what Leela considers the immediate best move. Even after Black has played so many moves in the area, trying to trick White left and right, White manages to live, and Black's wal is all for naught.
A final variation I would like to show is how Leela would handle the
tricky move of B15
instead of 7
in
Diagram 3. Diagram 14 shows that White is able to
get out to the middle, however Black's shape to the left is thick and
has better eye shape than in the traditional trick variation. In my
opinion, though, this is much better resistance than the trick
variation, which is kind of counter-intuitive because
B16
seems much more solid. At any rate, if you're the
type of player who likes complications, this might be a good way of
tossing the ball back on Black's side, because it surely isn't easy to
cleanly read all the variations for Black.
Here's the complete SGF with all the relevant variations:
You can download the SGF used in this post here. I use Sabaki as my editor, which features some extra blings on the game tree. So, in order to extract the most from my SGFs, I advise you to use it too. Don't forget to donate to the Sabaki project too!
Your browser doesn't support the WGo.js Player. Please use a more modern browser, like Brave, Chrome, Firefox or Edge.
Pyrrhic victories are named after Pyrrhus of Epirus, king and general who thought the Romans on behalf of the Greeks. During the Pyrrhic War, around 280 B.C., he suffered irreplaceable losses at the battles of Heraclea and Asculum.
In both victories, the Romans suffered more losses than the Greeks, however, their pool of replacements was far greater, and Pyrrhus could not afford such overarching losses. After those victories, Pyrrhus is reported, by Plutarch, to have said:
If we are victorius in one more battle with the Romans, we shall be utterly ruined.
How is this related to Go? How is this related to josekis?
To me, this resembles a lot the trick play discussed in the previous section. White is tricked into overinvesting to achieve local victory, even though it might cost exponentially more globally.
Pyrrhus of Epirus was said to have been quite the brash general, too hasty, which might also remind us of our usual first intincts to punish our opponent's abusive plays. Let's have him be a cautionary tale of what this type of behavior might generate in the end, in the grand scheme of things.
Besides Leela and my own 1 dan rationel, here's what I've used to create this article — the numbering below does not reflect any hierarchy at all —:


Lorem Ipsum Duis aute irure dolor in reprehenderit in voluptate eu fugiat nulla pariatur.
Stripe is a technology company that builds economic infrastructure for the internet. Businesses of every size—from new startups to public companies—use our software to accept payments and manage their businesses online.
Do you love to work with us? And lorem ipsum doallar sit amet?
Quis enim lobortis scelerisque fermentum dui faucibus in ornare quam. Nisi vitae suscipit tellus mauris a diam maecenas sed. Fusce id velit ut tortor pretium viverra suspendisse potenti nullam. Facilisis sed odio morbi quis commodo odio.


{{>site/templates/head.html}}
{{>site/templates/header.html}}this is a place for eventually, casually [sic] consistent thoughts i have about various things.
i work as a functional programmer and enjoy thinking about expressive type systems, programming language design, and the development of compilers and theorem provers.
you might know me by the handle @evertedsphere (or, previously, @mrkgnao).


We are mechanical maintenance consultants since 1989 who-
Import and Market ->
Mechanical maintenance Speciality Products->
For cost effective,instant repair solutions.
Products
for high pressure & high temperature leak sealing in various areas like pipelines, glands, joints, flanges, pumps/valves, agitators, autoclaves (stuffing box) and other industrial equipments
Magic Wrap
Leakage arrestor/Anti-Corrosion/Pipe reinforcer
PSI epoxy putty sticks
Repair anything/rebuild anything/restore anything
G-PAC
Non-Asbestos Gland packing compound for industrial pumps and valves
Solvent free epoxy metal repair compound(Shaft/keyhole repairing)
Durabond/Resbond
Stainless steel putties for >1100°C (Bonds-Seals-Protects-Repairs)
Contact Us
Drop in your details and our representative will be in touch with you shortly.
Magic Wrap
Leakage arrestor/Anti-Corrosion/Pipe reinforcer
Magic-Wrap® is a fiberglass pre-impregnated composite that is activated by salt or freshwater. Packaged in a hermetically sealed foil pouch, it is ready to use and does not require any measuring or mixing. This system, when used with the appropriate primary sealant, enables you to repair and reinforce virtually any diameter pipe in minutes. Magic-Wrap® not only restores a pipe to its original psi rating, but actually can make the repair area stronger than the original pipe, reducing downtime, saving in parts and labor. With an initial setting time of only 30 minutes (75°F/24°C), Magic-Wrap® is excellent on copper, steel, SS, PVC, FRP, clay, concrete, rubber, and more. It is safe to use, nontoxic, noncombustible, and odorless. Magic-Wrap is a versatile solution for industries where quick-action pipe repair can be critical.
To download the application pdf
To download the displayed catalog
To go back to the products page click close
PSI Epoxy Putty Sticks
Repair anything/rebuild anything/restore anything
PSI’s specialty epoxy putty sticks, epoxy putty tapes, epoxy putty bars, epoxy industrial pastes, and epoxy potting compounds offer a wide range of easy-to-use epoxy products for do-it-yourself and industrial use. Each is formulated to deliver optimum performance in a specific type of application. Some are fast-cure for quick, strong emergency repairs; others are slower cure for reconstruction, molding and sculpting. Some have NSF Standard 61 certification for use with drinking water. All are user-friendly, low-hazard products for tough, permanent repairs. All have excellent resistance to a wide range of chemicals and solvents.
To download information of various grades
To download the chemical compatibility pdf
To download information about fast steel
To download information about Quik Titanium
To download information about Quik Plastik
To download information about Aqua Mend
To download information about Quik Copper
To download information about Quik Aluminum
To download information about Repairit Quik
To download information about Insta Crete
To download information about Quik Wood
To go back to the products page click close
G-PAC
Non-Asbestos Gland packing compound for industrial pumps and valves.
STATE OF THE ART, BRAIDED, NON-ASBESTOS GRAPHITED PTFE GLAND PACKING ROPES Specially formulated to use along with G-PAC:GLAND PACKING COMPOUNDS and other Applications (Centrifugal, rotary, turbine, &Reciprocating pumps.).
To download the Multi-Lon catalog
To go back to the products page click close
PR 2101
Solvent free epoxy metal repair compound(Shaft/keyhole repairing)
STATE OF THE ART, BRAIDED, NON-ASBESTOS GRAPHITED PTFE GLAND PACKING ROPES Specially formulated to use along with G-PAC:GLAND PACKING COMPOUNDS and other Applications (Centrifugal, rotary, turbine, &Reciprocating pumps.).
To download the PR2101 catalog
To go back to the products page click close
Durabond and Resbond:
Stainless steel putties for >1100°C (Bonds-Seals-Protects-Repairs)
STATE OF THE ART, BRAIDED, NON-ASBESTOS GRAPHITED PTFE GLAND PACKING ROPES Specially formulated to use along with G-PAC:GLAND PACKING COMPOUNDS and other Applications (Centrifugal, rotary, turbine, &Reciprocating pumps.).


Agripel:
I am High Priest Agripel! The gods speak directly to me! Ahem!
They've ordered me... to give everything I own to them and wait for my judgement. Ahem!
Quest


ExpressiveJS
Expressive is a developer tool that tracks and visualizes server routes in Express applications. Useful for debugging and
teaching alike, Expressive enables developers to send client requests to their Express server, and then render
a dynamic and comprehensive visualization of server state throughout the lifespan of each request -- all from the command line.
Quick Start
Server Files:
1) Modify Express server files
Replace all instance of
require('express') with
require('expressive-proxy'). This will enable Expressive to progressively document the state of client requests
and server responses.
2) Run xpr-init
Enter
$ xpr-init from your application's root directory to create the basic shell of your
expressive.config.js file.
For more details on
expressive.config.js, please visit
expressive Github.
Command Lines:
$ xpr-test: Test your server routes. This will create an expressive.json file.
$ xpr-build: Bundle data from the expressive.json file created for the visualization.
$ xpr-display: Render a visualization of your server routes based on the data bundled
from the xpr-test command.
$ xpr: Equivalent to running xpr-test followed by xpr-build followed by xpr-display.
Meet The Team Behind ExpressiveJS!
Glenn Alexander
Software Engineer
GitHub
Ryan Fowlkes
Software Engineer
GitHub
Kangseon Cho
Software Engineer
GitHub
Mary Snow
Software Engineer
GitHub


- Feature - Class in emr_vis_nlp.ml
-
Represents a machine learning feature from a MLPredictor.
- Feature(FeatureType, String, List<String>, List<Double>) - Constructor for class emr_vis_nlp.ml.Feature
-
- featureText - Variable in class emr_vis_nlp.ml.Feature
-
- FeatureType - Enum in emr_vis_nlp.ml
-
Enum representing the different types of ML Features we could be trying to
represent in a given document.
- FeatureType(String) - Constructor for enum emr_vis_nlp.ml.FeatureType
-
- FeatureWeight - Class in emr_vis_nlp.ml.deprecated
-
- FeatureWeight(int, double) - Constructor for class emr_vis_nlp.ml.deprecated.FeatureWeight
-
- feye - Variable in class emr_vis_nlp.view.doc_grid.DocumentGrid
-
- feyeBoundingBox - Variable in class emr_vis_nlp.view.doc_grid.DocumentGrid
-
- fileExists(String) - Static method in class emr_vis_nlp.ml.deprecated.Common
-
- filterDataSet(String) - Method in class emr_vis_nlp.ml.deprecated.tester
-
- fisheye(double, double, double, double, double) - Method in class emr_vis_nlp.view.doc_grid.FisheyeDistortionDocGrid
-
- FisheyeDistortionDocGrid - Class in emr_vis_nlp.view.doc_grid
-
Heavily based on prefuse.action.distortion.FisheyeDistortion .
- FisheyeDistortionDocGrid(Rectangle2D) - Constructor for class emr_vis_nlp.view.doc_grid.FisheyeDistortionDocGrid
-
Create a new FisheyeDistortion with default distortion factor.
- FisheyeDistortionDocGrid(double, double, Rectangle2D) - Constructor for class emr_vis_nlp.view.doc_grid.FisheyeDistortionDocGrid
-
Create a new FisheyeDistortion with the given distortion factor
for use along both the x and y directions.
- FisheyeDistortionDocGrid(double, double, double, Rectangle2D) - Constructor for class emr_vis_nlp.view.doc_grid.FisheyeDistortionDocGrid
-
Create a new FisheyeDistortion with the given distortion factors
along the x and y directions.
- fisheyeDistortScale - Variable in class emr_vis_nlp.view.doc_grid.DocumentGrid
-
- fisheyeDistortSize - Variable in class emr_vis_nlp.view.doc_grid.DocumentGrid
-
- fisheyeEnabled - Variable in class emr_vis_nlp.view.doc_grid.DocumentGrid
-
- fisheyeNoEdge(double, double, double, double, double) - Method in class emr_vis_nlp.view.doc_grid.FisheyeDistortionDocGrid
-
- focusAttrs - Static variable in class emr_vis_nlp.model.mpqa_colon.MpqaColonMainModel
-
Attributes to be passed along to the front-end
- focusAttrsMap - Static variable in class emr_vis_nlp.model.mpqa_colon.MpqaColonMainModel
-
Map of focus attrs
- formWindowClosing(WindowEvent) - Method in class emr_vis_nlp.view.DocFocusPopup
-
- fracFull - Variable in class emr_vis_nlp.view.VarBarChartForCell.VarBarChartColumn
-


𝕮𝕳𝕽𝕴𝕾𝕿𝕴𝕬𝕹
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https://www.youtube.com/watch?v=Qg1qZq5yjps
fortnite is dying we need more ogs or new players pls join now and yes we have a link to the game download just scrool down and click on fortnite link not music or we bucks.
fortnite!!!!!!!!!!! fortnite is needing more players. fortnite is looseing players
Link to fortnite music
we bucks
my fav song
thx for visiting my website. this is my first one so thx.
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--- layout: post esbma_id: 1622 title: Speaking in Phoenix on September 6th date: 2006-08-09 14:34:37 ---
The Arizona .NET User's Group has one really big meeting every year, and I've been invited to come out and speak for this year's event. It will be held all day Wednesday September 6th at the Orpheum Theater in Phoenix, Arizona.
I'll be on stage in the morning with the following two presentations:
I keep thinking maybe these titles are a bit long, so perhaps I'll try to tighten them up a bit between now and September 6th. Or maybe not.
I'm the opening act. The headline band afternoon
speaker will be Scott Guthrie who
is a General Manager in Microsoft's Developer Division. Scott oversees a whole
bunch of groups, including ASP.NET, Windows Forms, IIS and the CLR.
Except for the $7 parking fee and the fact that you have to listen to me in order to hear Scott Guthrie, the event is free of charge. :-)


At work, you can find me help build complex software to solve real world problems from ideation to release. At college I followed the same pattern: wrote business plans, pitched my ideas and built software and hardware to bring those ideas to real life. Feel free to check my projects and learn about my current and previous experiences here!
Designed and implemented our own programming language for processing matrices of any dimension! MatriCs is a strongly typed matrix manipulation language with specialized libraries for various matrix operations. Fun fact: its extension is .neo!
A personal financial literacy tool designed to teach young adults (ideally people who are about to have their own personal bank accounts) streamlined with user friendly interface and realistic banking tools. Key functionalities include sending/receiving money among Vanmo users, making online purchases, depositing/withdrawing money from accounts and interest accumulation.
This is the business plan (and investor presentation) for a very promising venture - SpotOn - a location-based parking app designed to help tired drivers instantly spot parking spots during events!
A digital implementation of the rock-paper-scissors game using Arduino and logic gates to program the logic behind the game's rules and basic circuit elements to play it! Code running the circuit was created using Arduino interface and the design was drawn and tested using LTSpice.