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Contiki 2.5
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00001 /** 00002 * \addtogroup ctk 00003 * @{ 00004 */ 00005 00006 /** 00007 * \file 00008 * CTK screen drawing module interface, ctk-draw. 00009 * \author Adam Dunkels<EMAIL_ADDRESS>00010 * 00011 * This file contains the interface for the ctk-draw module.The 00012 * ctk-draw module takes care of the actual screen drawing for CTK by 00013 * implementing a handful of functions that are called by CTK. 00014 * 00015 */ 00016 00017 /* 00018 * Copyright (c) 2002-2003, Adam Dunkels. 00019 * All rights reserved. 00020 * 00021 * Redistribution and use in source and binary forms, with or without 00022 * modification, are permitted provided that the following conditions 00023 * are met: 00024 * 1. Redistributions of source code must retain the above copyright 00025 * notice, this list of conditions and the following disclaimer. 00026 * 2. Redistributions in binary form must reproduce the above 00027 * copyright notice, this list of conditions and the following 00028 * disclaimer in the documentation and/or other materials provided 00029 * with the distribution. 00030 * 3. The name of the author may not be used to endorse or promote 00031 * products derived from this software without specific prior 00032 * written permission. 00033 * 00034 * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS 00035 * OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 00036 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 00037 * ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY 00038 * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 00039 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE 00040 * GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 00041 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 00042 * WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00043 * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00044 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00045 * 00046 * This file is part of the Contiki desktop OS. 00047 * 00048 * $Id: ctk-draw.h,v 1.2 2006/08/26 23:56:18 oliverschmidt Exp $ 00049 * 00050 */ 00051 00052 #ifndef __CTK_DRAW_H__ 00053 #define __CTK_DRAW_H__ 00054 00055 #include "ctk/ctk.h" 00056 #include "contiki-conf.h" 00057 00058 /** 00059 * \defgroup ctkdraw CTK device driver functions 00060 * @{ 00061 * 00062 * The CTK device driver functions are divided into two modules, the 00063 * ctk-draw module and the ctk-arch module. The purpose of the 00064 * ctk-arch and the ctk-draw modules is to act as an interface between 00065 * the CTK and the actual hardware of the system on which Contiki is 00066 * run. The ctk-arch takes care of the keyboard input from the user, 00067 * and the ctk-draw is responsible for drawing the CTK desktop, 00068 * windows and user interface widgets onto the actual screen. 00069 * 00070 * More information about the ctk-draw and the ctk-arch modules can be 00071 * found in the sections \ref ctk-draw and \ref ctk-arch. 00072 */ 00073 00074 /** 00075 * \page ctk-draw The ctk-draw module 00076 * 00077 * In order to work efficiently even on limited systems, CTK uses a 00078 * simple coordinate system, where the screen is addressed using 00079 * character coordinates instead of pixel coordinates. This makes it 00080 * trivial to implement the coordinate system on a text-based screen, 00081 * and significantly reduces complexity for pixel based screen 00082 * systems. 00083 * 00084 * The top left of the screen is (0,0) with x and y coordinates 00085 * growing downwards and to the right. 00086 * 00087 * It is the responsibility of the ctk-draw module to keep track of 00088 * the screen size and must implement the two functions 00089 * ctk_draw_width() and ctk_draw_height(), which are used by the CTK 00090 * for querying the screen size. The functions must return the width 00091 * and the height of the ctk-draw screen in character coordinates. 00092 * 00093 * The ctk-draw module is responsible for drawing CTK windows onto the 00094 * screen through the function ctk_draw_window().. A pseudo-code 00095 * implementation of this function might look like this: 00096 * \code 00097 ctk_draw_window(window, focus, clipy1, clipy2, draw_borders) { 00098 if(draw_borders) { 00099 draw_window_borders(window, focus, clipy1, clipy2); 00100 } 00101 foreach(widget, window->inactive) { 00102 ctk_draw_widget(widget, focus, clipy1, clipy2); 00103 } 00104 foreach(widget, window->active) { 00105 if(widget == window->focused) { 00106 ctk_draw_widget(widget, focus | CTK_FOCUS_WIDGET, 00107 clipy1, clipy2); 00108 } else { 00109 ctk_draw_widget(widget, focus, clipy1, clipy2); 00110 } 00111 } 00112 } 00113 00114 \endcode 00115 * 00116 * Where draw_window_borders() draws the window borders (also between 00117 * clipy1 and clipy2). The ctk_draw_widget() function is explained 00118 * below. Notice how the clipy1 and clipy2 parameters are passed to 00119 * all other functions; every function needs to know the boundaries 00120 * within which they are allowed to draw. 00121 * 00122 * In order to aid in implementing a ctk-draw module, a text-based 00123 * ctk-draw called ctk-conio has already been implemented. It conforms 00124 * to the Borland conio C library, and a skeleton implementation of 00125 * said library exists in lib/libconio.c. If a more machine specific 00126 * ctk-draw module is to be implemented, the instructions in this file 00127 * should be followed. 00128 * 00129 */ 00130 00131 /** 00132 * The initialization function. 00133 * 00134 * This function is supposed to get the screen ready for drawing, and 00135 * may be called at more than one time during the operation of the 00136 * system. 00137 */ 00138 void ctk_draw_init(void); 00139 00140 /** 00141 * Clear the screen between the clip bounds. 00142 * 00143 * This function should clear the screen between the y coordinates 00144 * "clipy1" and "clipy2", including the line at y coordinate "clipy1", 00145 * but not the line at y coordinate "clipy2". 00146 * 00147 * \note This function may be used to draw a background image 00148 * (wallpaper) on the desktop; it does not necessarily "clear" the 00149 * screen. 00150 * 00151 * \param clipy1 The lower y coordinate of the clip region. 00152 * \param clipy2 The upper y coordinate of the clip region. 00153 */ 00154 void ctk_draw_clear(unsigned char clipy1, unsigned char clipy2); 00155 00156 /** 00157 * Draw the window background. 00158 * 00159 * This function will be called by the CTK before a window will be 00160 * completely redrawn.The function is supposed to draw the window 00161 * background, excluding window borders as these should be drawn by 00162 * the function that actually draws the window, between "clipy1" and 00163 * "clipy2". 00164 * 00165 * \note This function does not necessarily have to clear the window - 00166 * it can be used for drawing a background pattern in the window as 00167 * well. 00168 * 00169 * \param window The window for which the background should be drawn. 00170 * 00171 * \param focus The focus of the window, either CTK_FOCUS_NONE for a 00172 * background window, or CTK_FOCUS_WINDOW for the foreground window. 00173 * 00174 * \param clipy1 The lower y coordinate of the clip region. 00175 * \param clipy2 The upper y coordinate of the clip region. 00176 */ 00177 void ctk_draw_clear_window(struct ctk_window *window, 00178 unsigned char focus, 00179 unsigned char clipy1, 00180 unsigned char clipy2); 00181 /** 00182 * Draw a window onto the screen. 00183 * 00184 * This function is called by the CTK when a window should be drawn on 00185 * the screen. The ctk-draw layer is free to choose how the window 00186 * will appear on screen; with or without window borders and the style 00187 * of the borders, with or without transparent window background and 00188 * how the background shall look, etc. 00189 * 00190 * \param window The window which is to be drawn. 00191 * 00192 * \param focus Specifies if the window should be drawn in foreground 00193 * or background colors and can be either CTK_FOCUS_NONE or 00194 * CTK_FOCUS_WINDOW. Windows with a focus of CTK_FOCUS_WINDOW is 00195 * usually drawn in a brighter color than those with CTK_FOCUS_NONE. 00196 * 00197 * \param clipy1 Specifies the first lines on screen that actually 00198 * should be drawn, in screen coordinates (line 1 is the first line 00199 * below the menus). 00200 * 00201 * \param clipy2 Specifies the last + 1 line on screen that should be 00202 * drawn, in screen coordinates (line 1 is the first line below the 00203 * menus) 00204 * 00205 */ 00206 void ctk_draw_window(struct ctk_window *window, 00207 unsigned char focus, 00208 unsigned char clipy1, 00209 unsigned char clipy2, 00210 unsigned char draw_borders); 00211 00212 00213 /** 00214 * Draw a dialog onto the screen. 00215 * 00216 * In CTK, a dialog is similar to a window, with the only exception 00217 * being that they are drawn in a different style. Also, since dialogs 00218 * always are drawn on top of everything else, they do not need to be 00219 * drawn within any special boundaries. 00220 * 00221 * \note This function can usually be implemented so that it uses the 00222 * same widget drawing code as the ctk_draw_window() function. 00223 * 00224 * \param dialog The dialog that is to be drawn. 00225 */ 00226 void ctk_draw_dialog(struct ctk_window *dialog); 00227 00228 /** 00229 * Draw a widget on a window. 00230 * 00231 * This function is used for drawing a CTK widgets onto the screem is 00232 * likely to be the most complex function in the ctk-draw 00233 * module. Still, it is straightforward to implement as it can be 00234 * written in an incremental fashion, starting with a single widget 00235 * type and adding more widget types, one at a time. 00236 00237 * The ctk-draw module may exploit how the CTK focus constants are 00238 * defined in order to use a look-up table for the colors. The CTK 00239 * focus constants are defined in the file ctk/ctk.h as follows: 00240 \code 00241 #define CTK_FOCUS_NONE 0 00242 #define CTK_FOCUS_WIDGET 1 00243 #define CTK_FOCUS_WINDOW 2 00244 #define CTK_FOCUS_DIALOG 4 00245 \endcode 00246 00247 * This gives the following table: 00248 \code 00249 0: CTK_FOCUS_NONE (Background window, non-focused widget) 00250 1: CTK_FOCUS_WIDGET (Background window, focused widget) 00251 2: CTK_FOCUS_WINDOW (Foreground window, non-focused widget) 00252 3: CTK_FOCUS_WINDOW | CTK_FOCUS_WIDGET 00253 (Foreground window, focused widget) 00254 4: CTK_FOCUS_DIALOG (Dialog, non-focused widget) 00255 5: CTK_FOCUS_DIALOG | CTK_FOCUS_WIDGET 00256 (Dialog, focused widget) 00257 \endcode 00258 00259 00260 * \param w The widget to be drawn. 00261 * \param focus The focus of the widget. 00262 * \param clipy1 The lower y coordinate of the clip region. 00263 * \param clipy2 The upper y coordinate of the clip region. 00264 */ 00265 00266 void ctk_draw_widget(struct ctk_widget *w, 00267 unsigned char focus, 00268 unsigned char clipy1, 00269 unsigned char clipy2); 00270 00271 void ctk_draw_menus(struct ctk_menus *menus); 00272 00273 00274 00275 /* Returns width and height of screen. */ 00276 CCIF unsigned char ctk_draw_width(void); 00277 CCIF unsigned char ctk_draw_height(void); 00278 00279 00280 extern unsigned char ctk_draw_windowborder_width, 00281 ctk_draw_windowborder_height, 00282 ctk_draw_windowtitle_height; 00283 00284 00285 #endif /* __CTK_DRAW_H__ */ 00286 00287 00288 /** 00289 * The keyboard character type of the system 00290 * 00291 * The ctk_arch_key_t is usually typedef'd to the char type, but some 00292 * systems (such as VNC) have a 16-bit key type. 00293 * 00294 * \var typedef char ctk_arch_key_t; 00295 */ 00296 00297 /** 00298 * Get a keypress from the keyboard input queue. 00299 * 00300 * This function will remove the first keypress in the keyboard input 00301 * queue and return it. If the keyboard queue is empty, the return 00302 * value is undefined. This function is intended to be used only after 00303 * the ctk_arch_keyavail() function has returned non-zero. 00304 * 00305 * \return The first keypress from the keyboard input queue. 00306 * 00307 * \fn ctk_arch_key_t ctk_arch_getkey(void); 00308 */ 00309 00310 /** 00311 * Check if there is a keypress in the keyboard input queue. 00312 * 00313 * \return Zero if the keyboard input queue is empty, non-zero 00314 * otherwise. 00315 * 00316 * \fn unsigned char ctk_arch_keyavail(void); 00317 */ 00318 00319 /** 00320 * The character used for the Return/Enter key. 00321 * 00322 * \define #define CH_ENTER '\n' 00323 */ 00324 00325 /** 00326 * \page ctk-arch The ctk-arch module 00327 * 00328 * The ctk-arch module deals with keyboard input from the underlying 00329 * target system on which Contiki is running. The ctk-arch manages a 00330 * keyboard input queue that is queried using the two functions 00331 * ctk_arch_keyavail() and ctk_arch_getkey(). 00332 */ 00333 00334 /** @} */ 00335 /** @} */


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Kaizen Lite
Lite version of Kaizen model for support tasks
Cycle
Due to relatively short duration, scope and lower complexity - Kaizen Lite have only 2 phases: Requirements (egg) and Construction (metamorphosis).
Engagement
Due to the fact that support tasks might not always require involvement of Stakeholders (in cases of bug fixes), architecture is (most likely) already defined and less management/planning is required - SME’s and Tech Lead can work on up to 2 projects (based on Kaizen Lite) and BA, TL, PM can work on up to 3 projects (based on Kaizen lite).
Please be aware that load should be controlled and people shouldn't be overloaded (muri), as overloaded people might become main source of waste in the project
Kanban
It is very advisable to use Kanban method and Kanban board with this model, as it would help focus on topics that are outside of scope of this document and significantly improve organization / visibility which, as a result, would substantially improve efficiency of process as a whole.
Waste
Every project, with time, tends to increase amount of business and technical debt, so one of main goals of Kaizen Lite is to constantly visualize, analyse and remove waste.
Summary
- Lifecycle
-
- Support is the most costly and lengthy phase of product life cycle
- Amount of waste in the process/product grows with time
- Challenges
-
- With time process/procedures tend to become complicated and amount of paperwork increases
- With time architecture tends to become „broken” and quality of code decreases
- Often people wait for work or are overloaded with it - due to unbalanced process/procedure
- Due to lack of pride in workmanship – unhappiness, lack of commitment and high attrition rate
- Kanban
-
- Provide visibility and control supply of milestones (work) to the Team
- Cycle
-
- Two phases: Requirements (Egg) and Construction (Meta) instead of standard four
- Cycle recommended length is 2 weeks
- Checkpoints
-
- It is recommended to have 2 checkpoints: 1h after Egg and 2-4h after Meta. Duration can be changed if cycle length is longer than 2 weeks
- Roles
-
- Vision pair (BA+Tech Lead) can work on multiple projects, but workload to be controlled to avoid overload (muri)
- SME’s and Tech Lead can work on up to 2 projects (based on Kaizen Lite). BA, TL, PM can work on up to 3 projects (based on Kaizen lite)
- Waste
-
- Direction pair reviews Recycle log constantly and takes action to remove business debt
- At least every third cycle - action items in Recycle log to be reviewed and implemented to remove technical debt
Unless explicitly specified – existing guidelines of Kaizen model apply
Feedback
Below you can provide feedback, suggestions, questions regarding information posted on current page


{% extends "mails/messages/participant_base.html" %} {% load i18n %} {% block message %}
{% blocktrans context 'email' %} You applied to an activity on {{ site_name }}! {% endblocktrans %}
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{% blocktrans context 'email' %} You will receive a notification by email when the activity manager accepts your application. {% endblocktrans %}
{% endblock %} {% block end_message %}{% blocktrans context 'email' %} If you are unable to participate, please withdraw via the activity page so that others can take your place. {% endblocktrans %}
{% endblock %}


--- layout: base title: ryan bonick permalink: /index.html categories: index ---
Hello!
I'm Ryan. I work on software at Google.
Over my years, I've done some other side projects.
Other things I'm into include bikes and plants.


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Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4
Dietary
For the sauce, heat the oil in a pan. Add the onions and fry until soft but not browned. Add the garlic and fry for one minute.
Add the remaining sauce ingredients and bring to the boil. Reduce the heat and simmer for about an hour. Check for seasoning and add more salt and freshly ground black pepper, if needed.
For the vegetables, preheat the oven to 180C/355F/Gas 4.
Chop the aubergine, courgettes and red peppers into 2.5cm/1in pieces and place in a roasting tin with the whole garlic cloves. Drizzle with the olive oil and season, to taste, with salt and freshly ground black pepper. Mix the vegetables to coat in the olive oil. Roast in the oven, stirring occasionally, for 45 minutes, or until soft turning golden. Add the tomatoes to the roasting tin for the last 20 minutes of cooking time.
When ready to serve, cook the pasta according to the packet instructions. Drain the pasta well and stir through the sauce.
Divide evenly among four serving plates and top with the roasted vegetables. Grate over the parmesan cheese and serve with crusty bread or bruschetta on the side.


Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
| Version: | 0.7-1 |
| Depends: | R (≥ 3.2.0) |
| Imports: | jsonlite, uuid, RCurl, base64enc |
| Published: | 2018-10-23 |
| Author: | Meinhard Ploner [aut, cre, cph], ProSiebenSat.1 Digital GmbH [cph], BCG Digital Ventures GmbH [cph] |
| Maintainer: | Meinhard Ploner <meinhard.ploner at gmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/ploner/RMixpanel, http://www.mixpanel.com |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | RMixpanel results |
| Reference manual: | RMixpanel.pdf |
| Package source: | RMixpanel_0.7-1.tar.gz |
| Windows binaries: | r-devel: RMixpanel_0.7-1.zip, r-release: RMixpanel_0.7-1.zip, r-oldrel: RMixpanel_0.7-1.zip |
| OS X binaries: | r-release: RMixpanel_0.7-1.tgz, r-oldrel: RMixpanel_0.7-1.tgz |
| Old sources: | RMixpanel archive |
Please use the canonical form https://CRAN.R-project.org/package=RMixpanel to link to this page.


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User Test
Janitor
My skills
Although most of the work performed by janitors and building cleaners is indoors, sometimes it can be outdoors.
- Cleaning and restocking bathrooms
- Sinks
- Toilets
- Urinals
- HTML
Contact me
Send an email to<EMAIL_ADDRESS>Or visit my Twitter page at: Janitor Twitter


public interface ProgressCallback
During the execution of code generation, there are three main operations: database introspection, code generation based on the results of introspection, and then merging/saving generated files. Methods in this interface accordingly and in this order:
Periodically, the checkCancel()
method will be called to see if the
method should be canceled.
For planning purposes, the most common use case will have a ratio of 20% introspection tasks, 40% generation tasks, and 40% save tasks.
| 限定符和类型 | 方法和说明 |
|---|---|
void |
checkCancel()
The method is called periodically during a long running method.
|
void |
done()
This method is called when all generated files have been saved.
|
void |
generationStarted(int totalTasks)
Called to note the start of the generation phase, and to note the maximum
number of startTask messages that will be sent for the generation phase.
|
void |
introspectionStarted(int totalTasks)
Called to note the start of the introspection phase, and to note the
maximum number of startTask messages that will be sent for the
introspection phase.
|
void |
saveStarted(int totalTasks)
Called to note the start of the file saving phase, and to note the
maximum number of startTask messages that will be sent for the file
saving phase phase.
|
void |
startTask(String taskName)
Called to denote the beginning of a save task.
|
void introspectionStarted(int totalTasks)
totalTasks
- the maximum number of times startTask will be called for the
introspection phase.void generationStarted(int totalTasks)
totalTasks
- the maximum number of times startTask will be called for the
generation phase.void saveStarted(int totalTasks)
totalTasks
- the maximum number of times startTask will be called for the
file saving phase.void startTask(String taskName)
taskName
- a descriptive name of the current work stepvoid done()
void checkCancel() throws InterruptedException
InterruptedException
then
the method will be canceled. Any files that have already been saved will
remain on the file system.InterruptedException
- if the operation should be haltedCopyright © 2019. All rights reserved.


Training Location: {{ @SESSION.training_info['location'] }}
Application Deadline: {{ @SESSION.training_info['deadline'] }}
Training Schedule:
{{ @SESSION.training_info['date'] }}
{{ @SESSION.training_info['date2'] }}
What is the Peer-to-Peer Program?
8-week course led by trained PEER leaders, people who have experienced
mental illness in their own lives and believe that mental health recovery and resiliency are
possible. This class focuses on offering information, encouragement, hope, and guidance to
adults who live with a mental illness and want to experience quality of life as contributing
members of their communities.
About This Training
The two -day weekend in-person leader training includes preparation for teaching the P2P
course, obtaining knowledge about the brain, research, and recovery, fostering respect
and mutual support, and developing skills in sharing brief individual stories and partnering
with other peers.
Leaders must be:
- A PEER, a Person of Experience, Engaged in Recovery
- A current member of a NAMI Affiliate
- Willing to complete the online training before 2 weeks after the application deadline
- Willing to stay for the entire 2-day intensive training
- Comfortable reading aloud to a group
- Able to make a commitment to co-teach the 8-week Peer to Peer
course at least twice in a 24-month period in cooperation with your local affiliate.
- 18 years of age or older
NAMI Washington will pay for meals, snacks, lodging and all training materials.
Your NAMI affiliate is asked to cover your travel and other expenses that you may incur to
attend this training. Lodging is at the Comfort Inn, and is double occupancy. If you
require single occupancy, arrangements will need to be made prior to attendance by the
attendee and there will be additional costs which will be the responsibility of the attendee.
In order to reserve a space a $50 refundable registration deposit from your affiliate is
required at the time of registration. If you need to cancel after being accepted, notify
Deanna Roy at the state office<PHONE_NUMBER> or<EMAIL_ADDRESS>no later than April 22, 2019
so we can fill your spot, or your affiliate will lose its $50 registration deposit.
Cancelled spots are filled with applications from the waiting list for the class, not by
sending another affiliate member.
Ready to start the application?
Start


Note to users of version 1.2: Visit the documentation page for Evothings Studio version 1.2 for the specific workings of this version.
Evothings Studio is a set of development tools, designed for development of mobile apps using web technologies. Evothings Studio is specifically designed for development of apps for the Internet of Things.
Evothings Studio consists of:
With Evothings Workbench you can take advantage of HTML5 technologies and JavaScript to develop mobile web apps and hybrid apps - with very fast turn-around time. Use any text editor or graphical editor to author HTML, CSS and JavaScript. Every time you save, the app is instantly reloaded, and you can directly see the result. This also works with image editors.
The Evothings Viewer mobile app is a native application that you run on your mobile devices. It connects to Evothings Workbench to enable live editing of your HTML/CSS/JavaScript code. The app contains native functionality that is accessible from JavaScript, such as Bluetooth Low Energy.
Super-quick guide:
Good to know:
Learn more about Evothings Workbench and Evothings Viewer.
Follow the instructions in the Restore settings and uninstalling section to remove Evothings Studio from your computer.
The Release Notes is the place to learn what is new in each release of Evothings Studio.
To deploy your final app developed with Evothings Studio on an app store, you build your app with Apache Cordova. Read more in the guide How To Build Native Apps.
The quickest and easiest way to share your app with others, is putting your app on a web server, asking your users to download Evothings Viewer and enter the URL to your app. Find out the details in the guide Share Your App.
Ask questions and give feedback on the Evothings Forum.
Bugs and feature requests can be reported using the GitHub issue tracker, see links to repositories under the "Source Code" section.
You are encouraged to explore the "Tools" window tab in Evothings Workbench. This window displays output from the hyper.log() function, and lets you evaluate JavaScript code interactively on connected devices. This is very useful for debugging purposes, for trying out code snippets and inspecting the value of variables.
Consult the documentation page for Evothings Studio version 1.2 to learn the difference between version 1.2 and 2.0, and how to upgrade to version 2.0.
The source code for Evothings Studio is available on GitHub under the Apache License:
Evothings Studio is based on HyperReload:
github.com/mikaelkindborg/HyperReload


| DOSE: T+ 0:00 |
1 bowl |
smoked |
Cannabis |
(plant material) |
| T+ 2:00 |
6.0 mg |
oral |
Melatonin |
|
| T+ 2:00 |
|
smoked |
Various |
|
I have done each of these things separately with mixed results but this is my experience last night with a nice mix. I started smoking pot at around 8:00pm. By 10:00, my friend and I had smoked two bowls between us. That's when I took 6mg of melatonin and started smoking my herbal mixture, which was one Sleepytime Extra tea bag, mixed with catnip (50/50 ratio) and a couple pinches of ground nutmeg. After smoking pot, the herbs were really smooth to smoke and the peppermint in the tea felt nice on my throat. Once the melatonin kicked in and the herbs started making me buzz, it was really nice and mellow. I took a bubble bath and then had the best night of sleep I've had in months!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


|
TrackracerAR
0.9.2
|
Manages the UI for RacingState More...
Public Member Functions | |
| void | BackButtonPressed () |
Public Attributes | |
| TextMeshProUGUI | m_lapText |
Private Member Functions | |
| void | Construct (IRacingState _state, RaceManager _raceManager) |
| void | OnEnable () |
| void | OnDisable () |
Private Attributes | |
| IRacingState | m_state |
| UIFader | m_fader |
| RaceManager | m_raceManager |
| IDisposable | m_subscription |
Manages the UI for RacingState
|
inline |
|
inlineprivate |
|
inlineprivate |
|
inlineprivate |
|
private |
| TextMeshProUGUI RacingUI.m_lapText |
|
private |
|
private |
|
private |


Under construction!
Please, help us to create a cool dashboard with some CSS framework by using some examples of our pretty charts :)


Still in the works.
There is nothing really new in this example. The menu is from my pop-up menu
In Mozilla 1.5a, when the sub-submenus first appear the links that are earlier in order in the HTML overlap the links that come after. If one moves their mouse over another link in the submenu and then back over the link that has the sub-submenu, the sub-submenu will now have the links that are later in the HTML overlap the links that are earlier.
Some extra text
*
*
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*
*
*
*
*
*
*
*
*
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*
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lingvo.core.beam_search_helper module¶
Helper class for implementing a beam search decoder.
Individual models just need to provide a few callback functions.
-
class
lingvo.core.beam_search_helper.
BeamSearchDecodeOutput
(done_hyps, topk_hyps, topk_ids, topk_lens, topk_scores, topk_decoded, other_states)¶ Bases:
tuple
-
_asdict
()¶ Return a new OrderedDict which maps field names to their values
-
_fields
= ('done_hyps', 'topk_hyps', 'topk_ids', 'topk_lens', 'topk_scores', 'topk_decoded', 'other_states')¶
-
classmethod
_make
(iterable, new=<built-in method __new__ of type object>, len=<built-in function len>)¶ Make a new BeamSearchDecodeOutput object from a sequence or iterable
-
_replace
(**kwds)¶ Return a new BeamSearchDecodeOutput object replacing specified fields with new values
-
done_hyps
¶ Alias for field number 0
-
other_states
¶ Alias for field number 6
-
topk_decoded
¶ Alias for field number 5
-
topk_hyps
¶ Alias for field number 1
-
topk_ids
¶ Alias for field number 2
-
topk_lens
¶ Alias for field number 3
-
topk_scores
¶ Alias for field number 4
-
-
class
lingvo.core.beam_search_helper.
BeamSearchHelper
(*args, **kwargs)[source]¶ Bases:
lingvo.core.base_layer.LayerBase
Helper class for performing beam search.
The user of this helper class needs to implement three callbacks.
This callback is called once only at the beginning of beam search:
def InitBeamSearchState(source_encs, source_paddings, num_hyps_per_beam, additional_source_info): Args: source_encs: A tensor of shape [src_len, src_batch, source_dim]. source_paddings: A tensor of shape [src_len, src_batch]. num_hyps_per_beam: An int, number hyps to keep for source sentence. additional_source_info: a NestedMap of tensors containing extra information about the source that may be useful for decoding. Returns: initial_results: a NestedMap of initial results. It should contain the following tensors at the minimum. atten_probs: The initial attention probs, of shape [tgt_batch, src_len]. states: a NestedMap of tensors representing states that the client would like to keep track of for each hyp.
This callback is called once every decoding time step before beam_search_step is called:
def PreBeamSearchStepCallback(source_encs, source_paddings, step_ids, in_states, num_hyps_per_beam, additional_source_info): Args: source_encs: A tensor of shape [src_len, src_batch, source_dim]. source_paddings: A tensor of shape [src_len, src_batch]. step_ids: A tensor of shape [tgt_batch, 1]. in_states: A NestedMap of tensors representing states that the clients would like to keep track of for each of the active hyps. additional_source_info: a NestedMap of tensors containing extra information about the source that may be useful for decoding. Returns: results: A NestedMap of beam search results. It should contain the 'atten_probs' and 'log_probs' tensors at the minimal. Optionally it may contain 'lm_log_probs' if a LM is used during decoding and 'is_last_chunk' if it is decoding a neural transducer model. atten_probs: The updated attention probs, of shape [tgt_batch, src_len]. log_probs: Log prob for each of the tokens in the target vocab. This is of shape [tgt_batch, vocab_size]. lm_log_probs: Language model prob for each of the tokens in the target vocab. If not empty, this is of shape [tgt_batch, vocab_size]. is_last_chunk: Whether or not each of the hyp is at the end of a chunk. If non-empty, it is of shape [tgt_batch, 1] out_states: A NestedMap. The updated states. This 'out_states' should be of the exact same structure as 'in_states'
This callback is called once every decoding time step after beam_search_step is called:
def PostBeamSearchStepCallback(source_encs, source_paddings, new_step_ids, other_states, additional_source_info): Args: source_encs: The same as above. source_paddings: The same as above. new_step_ids: Token ids for the next beam search step. other_states: A NestedMap. additional_source_info: a NestedMap of tensors containing extra information about the source that may be useful for decoding. Returns: final_states, A NestedMap.
-
_BeamSearchStep
(source_encs, source_paddings, cur_step, step_ids, core_bs_states, other_states, num_hyps_per_beam, pre_beam_search_step_callback, post_beam_search_step_callback, additional_source_info)[source]¶ Extend beam search hyps for one step.
num_beams = Number of source sequences to be decoded.num_hyps_per_beam = Number of hyps to keep per source sequence.num_hyps = num_beams * num_hyps_per_beamsrc_seq_len = Number of time steps in the source sequence.tgt_seq_len = Maximum allowed time steps in the target sequence.Parameters: - source_encs – A tensor of the shape [time, batch, depth]. The encoding of the source.
- source_paddings – A tensor of the shape [time, batch]. Padding state of the source.
- cur_step – A scalar int tensor, the current time step, 0-based.
- step_ids – An int tensor of shape [num_hyps, 1]. The input ids to the current search step.
- core_bs_states – A tuple of core beam search states. This list is maintained by this helper class.
- other_states – A
NestedMap
of other beam search states. ThisNestedMap
is managed and updated by the client. It is expected that each of its member tensors are of rank >= 1. t[i, …] is the state of the i-th hyp at the beginning of this search step. - num_hyps_per_beam – Num of hyps to keep per beam.
- pre_beam_search_step_callback – The
PreBeamSearchStepCallback
callback. See class header comments for more details. - post_beam_search_step_callback – The
PostBeamSearchStepCallback
callback. See class header comments for more details. - additional_source_info – a
NestedMap
of tensors containing extra context information about the source that may be useful for decoding.
Returns: A tuple of following elements for the next beam search step, (next step, all_done, step_ids, core_bs_states, other_states)
-
BeamSearchDecode
(source_encs, source_paddings, num_hyps_per_beam_override=0, init_beam_search_state=None, pre_beam_search_step_callback=None, post_beam_search_step_callback=None, additional_source_info=None)[source]¶ Performs beam-search based decoding.
Parameters: - source_encs – source encoding, of shape [time, batch, depth]. In case of
multi-source decoding, a
NestedMap
object containing source encoding tensors, again each of shape [time, batch, depth]. - source_paddings – source encoding’s padding, of shape [time, batch]. In
case of multi-source decoding, A
NestedMap
object containing source padding tensors, each of shape [time, batch]. - num_hyps_per_beam_override – If set to a value <= 0, this parameter is
ignored. If set to a value > 0, then this value will be used to
override
p.num_hyps_per_beam
. - additional_source_info – a
NestedMap
of tensors containing extra context information about the source that may be useful for decoding. - init_beam_search_state – The
InitBeamSearchState
callback. Please refer to the class header comments for more details. - pre_beam_search_step_callback – The
PreBeamSearchStepCallback
callback. Please refer to the class header comments for more details. - post_beam_search_step_callback – The
PostBeamSearchStepCallback
callback. Please refer to the class header comments for more details.
Returns: - source_encs – source encoding, of shape [time, batch, depth]. In case of
multi-source decoding, a
-
-
lingvo.core.beam_search_helper.
_GetShapes
(tensors, none_shapes=False)[source]¶ Util for getting nested structure of shapes from structure of tensors.
Parameters: - tensors – Structure of Tensors to get shapes for.
- none_shapes – Returns None shapes if true.
Returns: The same structure as tensors but of corresponding
TensorShape
objects.


| 1 | /* =========================================================== | |
| 2 | * JFreeChart : a free chart library for the Java(tm) platform | |
| 3 | * =========================================================== | |
| 4 | * | |
| 5 | * (C) Copyright 2000-2008, by Object Refinery Limited and Contributors. | |
| 6 | * | |
| 7 | * Project Info: http://www.jfree.org/jfreechart/index.html | |
| 8 | * | |
| 9 | * This library is free software; you can redistribute it and/or modify it | |
| 10 | * under the terms of the GNU Lesser General Public License as published by | |
| 11 | * the Free Software Foundation; either version 2.1 of the License, or | |
| 12 | * (at your option) any later version. | |
| 13 | * | |
| 14 | * This library is distributed in the hope that it will be useful, but | |
| 15 | * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY | |
| 16 | * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public | |
| 17 | * License for more details. | |
| 18 | * | |
| 19 | * You should have received a copy of the GNU Lesser General Public | |
| 20 | * License along with this library; if not, write to the Free Software | |
| 21 | * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, | |
| 22 | * USA. | |
| 23 | * | |
| 24 | * [Java is a trademark or registered trademark of Sun Microsystems, Inc. | |
| 25 | * in the United States and other countries.] | |
| 26 | * | |
| 27 | * ---------------------------- | |
| 28 | * DataPackageResources_ru.java | |
| 29 | * ---------------------------- | |
| 30 | * | |
| 31 | * Original Author: Sergey Bondarenko; | |
| 32 | * Contributor(s): -; | |
| 33 | * | |
| 34 | * Changes | |
| 35 | * ------- | |
| 36 | * 02-Feb-2007 : Removed author tags all over JFreeChart sources (DG); | |
| 37 | * | |
| 38 | */ | |
| 39 | ||
| 40 | package org.jfree.data.resources; | |
| 41 | ||
| 42 | import java.util.ListResourceBundle; | |
| 43 | ||
| 44 | /** | |
| 45 | * A resource bundle that stores all the items that might need localisation. | |
| 46 | */ | |
| 47 | public class DataPackageResources_ru extends ListResourceBundle { | |
| 48 | ||
| 49 | /** | |
| 50 | * Returns the array of strings in the resource bundle. | |
| 51 | * | |
| 52 | * @return The localised resources. | |
| 53 | */ | |
| 54 | public Object[][] getContents() { | |
| 55 |
1
1. getContents : mutated return of Object value for org/jfree/data/resources/DataPackageResources_ru::getContents to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return CONTENTS; |
| 56 | } | |
| 57 | ||
| 58 | /** The resources to be localised. */ | |
| 59 | private static final Object[][] CONTENTS = { | |
| 60 | ||
| 61 | {"series.default-prefix", "?????"}, | |
| 62 | {"categories.default-prefix", "?????????"}, | |
| 63 | ||
| 64 | }; | |
| 65 | ||
| 66 | } | |
Mutations | ||
| 55 |
1.1 |


Check email address
Corrected email: ______
Check custom word
Check word
Corrected word: ______
(words should be semicolon-separated)
This is a light demo for Mistyep, a package used on Penmob's login page.


ggplot2
--- title: "demographerR's notes" description-meta: "{{< meta website.description >}}" search: false listing: feed: true contents: - "/**/index.qmd" - "/**/index.qmd" ---


I am looking to transfer into the web development industry and am knowledgeable in Javascript, React, Node.js, PostgreSQL, and of course HTML and CSS.
When I am not working on Github projects, I love traveling, socializing with friends, and learning new languages (I am currently learning German).
View ResumeThis web app was built using React.js, Node.js, and PostgreSQL and allows the user to register and sign in to the app. Once the user signs in they can submit an image url and an embedded AI will detect whether or not there is a human face in the image.
This web game was built using pure Javascript and is a recreation of the classic pong game.
This web app was built using pure Javascript and allows the user to enter items on their shopping list, and them either delete the item or cross the item out as they wish.
Send me an email or reach out to me on any of my social media accounts below.
Email


Functional enrichment analyses regarding the expression of the hub genes. (A–C) Results of enrichment analysis with higher expression of the hub genes. (D–F) Results of enrichment analysis with lower expression of the hub genes. The threshold was set to Nominal P-value < 0.05 with an FDR adjusted P-value < 0.25.


InterPro allows classification of proteins based on their domains. It consists of a large catalog of known domain signatures and a tool, InterProScan, to find matching domains in unclassified peptide sequence.
Start and stop coordinates are based on the peptide sequence.
Official documentation


Query methods¶
-
class
pg.
Query
¶
The Query
object returned by Connection.query()
and
DB.query()
provides the following methods for accessing
the results of the query:
getresult – get query values as list of tuples¶
-
Query.
getresult
()¶ Get query values as list of tuples
Returns: result values as a list of tuples
Return type: list
Raises: - TypeError – too many (any) parameters
- MemoryError – internal memory error
This method returns the list of the values returned by the query.
More information about this result may be accessed using
Query.listfields()
, Query.fieldname()
and Query.fieldnum()
methods.
Note that since PyGreSQL 5.0 this will return the values of array type columns as Python lists.
dictresult – get query values as list of dictionaries¶
-
Query.
dictresult
()¶ Get query values as list of dictionaries
Returns: result values as a list of dictionaries
Return type: list
Raises: - TypeError – too many (any) parameters
- MemoryError – internal memory error
This method returns the list of the values returned by the query with each tuple returned as a dictionary with the field names used as the dictionary index.
Note that since PyGreSQL 5.0 this will return the values of array type columns as Python lists.
namedresult – get query values as list of named tuples¶
-
Query.
namedresult
()¶ Get query values as list of named tuples
Returns: result values as a list of named tuples
Return type: list
Raises: - TypeError – too many (any) parameters
- TypeError – named tuples not supported
- MemoryError – internal memory error
This method returns the list of the values returned by the query with each row returned as a named tuple with proper field names.
Note that since PyGreSQL 5.0 this will return the values of array type columns as Python lists.
New in version 4.1.
listfields – list fields names of previous query result¶
-
Query.
listfields
()¶ List fields names of previous query result
Returns: field names Return type: list Raises: TypeError – too many parameters
This method returns the list of names of the fields defined for the query result. The fields are in the same order as the result values.
fieldname, fieldnum – field name/number conversion¶
-
Query.
fieldname
(num)¶ Get field name from its number
Parameters: num (int) – field number
Returns: field name
Return type: str
Raises: - TypeError – invalid connection, bad parameter type, or too many parameters
- ValueError – invalid field number
This method allows to find a field name from its rank number. It can be useful for displaying a result. The fields are in the same order as the result values.
-
Query.
fieldnum
(name)¶ Get field number from its name
Parameters: name (str) – field name
Returns: field number
Return type: int
Raises: - TypeError – invalid connection, bad parameter type, or too many parameters
- ValueError – unknown field name
This method returns a field number from its name. It can be used to build a function that converts result list strings to their correct type, using a hardcoded table definition. The number returned is the field rank in the result values list.


cw.clock.JumpDetector test page. For reference, see clock_jump_test_page.html for the timer-scheduling behavior you can expect from browsers.
Instructions: force the system time forwards or backwards. This should always detect forwards time jumps without user activity. In some browsers (on some OSes), you'll need to click or press a keystroke for this to detect a backwards time jump.


Ezekiel 16:18
And tookest thy broidered garments, and coveredst them: and thou hast set mine oil and mine incense before them.


Test Pages: 5 right, 0 wrong, 1 ignored, 0 exceptions Assertions: 30 right, 0 wrong, 0 ignored, 0 exceptions (7,003 seconds)
Test Summaries
slim:.itsharpRunner.exe
Test output
Test System: slim:.\fitsharp\Runner.exe
Included page: .TodoTests.SetUp (edit)
| script |
InitializeFixture |
| Initialize Application |
| Initialize Date |
01/01/2014 |
Create A Todo And Ensure It Is Persisted
We want to make sure that an item created is persisted when restarting the applicationwe clean the database to have no item at all
| script |
Database Fixture |
| Delete All |
We type the text of our todo and hit enter
| script |
Add Todo Popup Fixture |
| type text |
test TODO |
on the textbox and hit enter |
We close and start the application again
| script |
Application Fixture |
| Restart |
We check that the todo list contains our item
| query: Get all todos |
| Done |
Text |
Due Date |
| NO |
test TODO |
02/01/2014 |
Included page: .TodoTests.SetUp (edit)
| script |
InitializeFixture |
| Initialize Application |
| Initialize Date |
01/01/2014 |
Mark a Todo As Done And Ensure It Is Persisted
We want to make sure that an item set to Done is persisted when restarting the application we reset the database to have sample data
| script |
DatabaseFixture |
| Populate with sample items |
We check a checkbox
| script |
TodoListFixture |
| check item with text |
Sample Todo 1 |
We close and start the application again
| script |
ApplicationFixture |
| Restart |
We check that the todo is present and checked
| query:Get all todos |
| Done |
Text |
Due Date |
| YES |
Sample Todo 1 |
01/01/2000 |
| NO |
Sample Todo 2 |
01/01/2000 |
| NO |
Sample Todo 3 |
01/01/2000 |
Included page: .TodoTests.SetUp (edit)
| script |
InitializeFixture |
| Initialize Application |
| Initialize Date |
01/01/2014 |
| script |
InitializeFixture |
| Initialize Application |
| Initialize Date |
01/01/2014 |
Included page: .TodoTests.SetUp (edit)
| script |
InitializeFixture |
| Initialize Application |
| Initialize Date |
01/01/2014 |
Add a simple todo
We want to make sure that an item is created on the items list when we add a todo on the upper part of the pagewe clean the database to have no item at all
| script |
DatabaseFixture |
| Delete All |
We click on the "Add" button on the main screen
| script |
MainFixture |
| Click on Add Button |
| check |
Add Todo Popup Visibility |
Visible |
We type the text of our todo and hit enter
| script |
Add Todo Popup Fixture |
| type text |
test TODO |
on the textbox and hit enter |
We check that the todo list contains our item only
| query: Get all todos |
| Done |
Text |
Due Date |
| NO |
test TODO |
02/01/2014 |
Included page: .TodoTests.SetUp (edit)
| script |
InitializeFixture |
| Initialize Application |
| Initialize Date |
01/01/2014 |
Add a todo with date
We want to make sure that an item is created on the items list when we add a todo on the upper part of the pagewe clean the database to have no item at all
| script |
DatabaseFixture |
| Delete All |
We type the text of our todo and hit enter
| script |
Add todo Popup fixture |
| set date to |
2015-12-25 |
| type text |
Buy a present for Dad |
on the textbox and hit enter |
We check that the todo list contains our item only
| query: get all todos |
| Done |
Text |
Due Date |
| NO |
Buy a present for Dad |
25/12/2015 |
Included page: .TodoTests.SetUp (edit)
| script |
InitializeFixture |
| Initialize Application |
| Initialize Date |
01/01/2014 |
Mark a Todo As Done
We want to make sure that an item is set to Done when checking the checkboxwe reset the database to have sample data
| script |
DatabaseFixture |
| Populate with sample items |
We check a checkbox
| script |
TodoListFixture |
| check item with text |
Sample Todo 2 |
We check that the todo is checked
| query: get all todos |
| Done |
Text |
Due Date |
| NO |
Sample Todo 1 |
01/01/2000 |
| YES |
Sample Todo 2 |
01/01/2000 |
| NO |
Sample Todo 3 |
01/01/2000 |


--- layout: post title: locate tags: - ! '*nix' status: publish type: post published: true meta: _edit_last: '2' --- I used to rely on whereis and find to locate programs and files on my debian box, but these weren't being very helpful when I tried to find a file I new existed on the system. I stumbled across the utility locate after a quick google search. And it did locate that elusive file for me. But only once I had updated it's database (which it kindly informed was 8 days out of date). To update the locate database, simply log on as root and type: [code]udpatedb[/code]


Flash and JavaScript are required for this feature.
Download the video from iTunes U or the Internet Archive.
Description: This lecture covers map coloring and related scheduling problems. We develop pseudocode for the domain reduction algorithm and consider how much constraint propagation is most efficient, and whether to start with the most or least constrained variables.
Instructor: Patrick H. Winston
Lecture 8: Constraints: Sea...
PROF.
PATRICK WINSTON: It's too bad, in a way, that we can't paint everything black, because this map coloring problem sure would be a lot easier.
So I don't know what we're going to do about that.
How long is this going to take?
Here's what we're going to do.
We're going to wait till either all the laptops are closed, or this program terminates, whichever comes first.
So how long is this going to take?
I actually don't know.
I think it'll take more than the life time of the universe, but I'm not sure.
So let's take a look at a slightly easier map coloring problem.
We'll stop this one and change the map to something I call [? simplicia. ?] There's 26 states, one for each letter in the alphabet.
And what we're going to do is we're going to do a depth first search for a suitable coloring of this map.
We're going to go in order, A, B, C, D, E. And as I've suggested here, at each level we're going to rotate the color choices so we don't over use any one color.
So if we launch this particular search, this depth first attempt to color this map.
There it goes.
And maybe we should wait until it terminates.
Or maybe we should just let it run and it'll terminate, perhaps, sometime within the lecture.
Or maybe we should just let it run over the weekend.
Or how long do you think we would have to wait, if we want to come back and watch it terminate?
At roughly 30 frames a second-- I'm calculating it all in my head, I don't want to bet my life on it-- but I think about 5,000 years.
And if we want to use as many states as there are in the United States, in this demonstration, you get up to numbers like 10 to the 17th years-- 17th, 18th, 16th, I'm not exactly sure.
I did a rough calculation.
And, of course, you could do some parallels into that, and it's not as bad as chess, where you need all the atoms in the universe and you still can't do it, and things like that.
Acting as computers.
But still, it's pretty horrendous.
The problem is, well the problem is illustrated by this diagram I put in back of me.
If you do a depth first search and you have a problem like Texas.
Texas has always, always been a problem, ever since they joined the Union.
I could pick on other states, too.
Kentucky, Tennessee, if you're from those states you represent something even worse.
But Texas is down there.
And the trouble is, if you assign a color to Arizona, Oklahoma, Arkansas, and Louisiana first, and then wait around to Texas last, then you get yourself into a bind by your fourth choice, that you don't discover until you're 48th choice.
So what happens then, is that you start developing this tree like this, you get to those states surrounding Texas.
Texas is last state assigned a color and there's nothing left for it.
And that problem was there on the fourth choice, and you don't discover it until your 50th choice.
So you develop a horrendous, impossible, impossible search.
So you simply can't do it that way.
But now, not to worry.
I've come equipped with the idea of constraint propagation.
So we could just take a country with four states, like this.
Each can be labeled red, green, blue and yellow.
So just like in a case of line drawings, we'll pile up all the possible things that the value can be-- red, green, blue and yellow.
Red, green, blue and yellow.
Red, green, blue and yellow.
And we start up constraint propagation.
So we say for the upper left hand corner state, is there any reason to believe that R is impossible?
Well we look at our neighbors and see what kind of constraint flows in from them.
And sure, this guy could be green, and this guy could be blue.
They don't have to be red, so there's nothing that rules out red.
And there's nothing the rules out blue.
And there's nothing that rules out yellow.
And there's nothing that rules out green.

So it doesn't look like we can use either depth first search, or constraint propagation.
So we could just give up and cry.
But maybe there's some other approach that will help.
So let me actually work the Texas problem.
So with apologies to Houston and Tyler, here's a map of Texas.
There we are.
And here's, roughly speaking, Arizona is over here somewhere.
And we've got Oklahoma in there.
And old Bill Clinton's state, Arkansas.

So there's our map of that particular part of the country.
So we've got Arizona here, Oklahoma here, Arkansas here, and Louisiana here, and Texas here.
And we have elected to assign colors to these states, in that order.
So this is one, two, three, four.
And we're going to do that.
We're going to rotate our color choices, just so we don't over use any one color.
But we're going to also have a look at Texas as we go around.
Because Texas is a state that borders on the States they were choosing colors for.
So the only possible colors that Texas could be are red, green, blue, and yellow.
So as we make our choices around here, we'll say that-- we don't have to adhere to any particular style-- we can say that Arizona is going to get the colored red, R. That's going to rule out R over here for Texas, because no adjacent states can have the same color.
Then we go over to Oklahoma, and we're rotating our color choices, so we'll say that can be green.
And that's fine, because it's consistent with the red here, but it rules out the possibility that Texas could be green.

That's fine, that's consistent with the green on Oklahoma, but if we look at its neighbors we know that Texas is forever forbidden to be blue, now.
So now we go over to Louisiana, and remember, we're rotating our color choices because we don't want to overuse them.
So this means that the first choice we're going to make here, for Louisiana, is yellow.
And that's fine because it's consistent with Arkansas, but it's not so fine because it's now ruled out the last possibility for Texas.
So even though Texas is going to be the 48th state that we color, we're going to say, at this point, there's no need in going on.
We'd better back up.
Because there's nothing left for Texas when we get around to coloring it.
So that means that this yellow is ruled out here.
This yellow reappears.
We select the next color in my line for Louisiana, which happens to be red.
And now that's consistent with this yellow that's still left for Texas.
And it's also consistent with the blue that's up here for Arkansas.
So that's cool.
I wonder if maybe we could make an algorithm out of that, and solve problems like this.
Do you see, sort of, the intuition of what we're doing?
We're actually using the martial arts principle, again.
Because the whole problem is that local constraints, undiscovered local constraints, are causing downstream problems.
So we're going to use the enemy's powers against him, and we're going to look at those local constraints as we go and make sure they're not going downstream, not going to get us later on.
So now I'm going to look like I'm getting a little formal, but I'm just getting a little bit more formal.
Because I want to have some language that I can use to describe what's going on, so that it's clear what the choices are.
So to start off with, we're going to have to have some vocabulary.
So let's start up our vocabulary here.
We're going to have a notion of a variable v. And that's something that can have an assignment.
There's nothing complicated about that.
And a value.
A value x is something that can be in assignment.
It's a little bit circular, but we're all in computer science so you know what I mean.
So the next thing is a little slightly less obvious, and that's the notion of a domain, d.
And that's going to be a bag of values.
OK.
So one more thing.
A constraint.
That's a constraint c, is a limit on-- in our examples it's mostly going to be pairs of variables, pairs of variable values.
But in general, it could be variable values.
So if we go back here to Texas we could say, OK, how does our vocabulary drape itself over that configuration?
And the answer is, the states have the role of variables, the colors have the role of values.
And the domains are the remaining color possibilities that we can still use on a particular state.
And the constraint, in this case, is the simple map coloring constraint that no states that share a boundary can have the same color.
So states are variables, colors are values, domains are bags of colors, and constraints-- there's only one-- adjacent states can't have the same color.
So that's how it fits with this vocabulary.
So now, what did I actually do here?
Well what I actually did here, I'm going to now formalize a little by writing it down in pseudo code.
So here we are, we're going to have a look at what we did here with our intuition, and we're going to reduce it to a procedure.
And here's the procedure.
Remember, we're doing depth first search on this stuff.
I did a depth first search.
We're going to do a depth first search, and for each depth first search assignment-- OK, so here I am, I'm labeling Arizona, and then Oklahoma, and then Arkansas, and then Louisiana.
When I give each one of those a label, a color, I'm going to do this procedure.
Every time I make one of those assignments.
The last one that caused trouble was coloring Louisiana yellow.
Each time I put one of those colors down, each time I make an assignment, I'm going to do this procedure.
So for each depth first search assignment, for each variable v, considered.
Now you don't know what I mean by considered.
But when I put a label, when I put up a value, show something as a color for Louisiana, I thought about Texas.
So I was considering the variable, Texas, when I made the assignment for Louisiana.
Now I'm going to be a little bit vague about what I mean by considered, right now.

So let me just say consider, and then we'll open that up and talk about the options in a moment, so for each variable v considered for-- let's call that variable v sub i-- for each x sub i, for each value in the domain of that variable, consider each of the things that still surviving.
For each of those, for each constraint c, that's between x sub i, and some x sub j, where x sub j is an element of the domain of j.
Now that sounds awfully fancy, but this just says, in the case of Texas up there, whenever I consider one of the values that's still remaining as a choice for Texas, I want to consider all of the constraints between that variable and the adjacent states.
And I want to be sure that anything I leave in the domain is OK for some selection in the other states, some remaining choices in the other states.
So that's why we're getting pretty nested here.
But we're doing depth first search.
We are considering the variables in a certain collection of variables.
For each one of those where considering all the values that still remain in the domains of those variables.
And then for each of those values, we're checking to see if it satisfies this some constraint, satisfies the constraint that are placed upon it.
So for each of these constraints if there does not exist an x sub j, such that, the constraint between x sub i and x sub j is satisfied, well if in that adjacent place there's nothing that is consistent with a value, then we've got to get rid of it.
If that's true.
If there does not exist some value in an adjacent variable such that that constraint is satisfied, we're hosed.
We've got to get rid of that value.
So we're going to remove x sub i from d sub i.
OK.
Now, that's fine.
That's sort of what I did with Texas.
As soon as I plopped down a value for Louisiana I said, well what are the possible values in Texas?
Red, green, blue and yellow.
Let's consider red.
Let's consider the constraints between Texas and all adjacent states.
One of those constraints says it can't be the same color as Arizona.
The only color I've got available for Arizona, since I've already made the assignment is red.
Red is not consistent with red, so I've got to get rid of it.
So it looks complicated, but it's just intuition.
So what do we do if we get to a situation where the domain is empty?
That means whenever we get around to making assignment to it, there won't be anything left.
So if that ever happens, if the domain ever becomes empty, then what do we do?
We've got to back up.
So the intuition is clear.
This is the algorithm.
The algorithm when you work through it, think about whether it makes sense and what not.
How it fits with Texas.
Yeah, it sure does.
All we're doing is we're making these death first assignments.
And in the neighborhood of those depth first assignments we're looking around to see if the values that are possible include something.
And if they don't include anything, we know we made and irrevocable blunder, and we have to back up.
So that's the essence of the idea.
Now, how well does it work?
Well a little bit depends on what we choose for considered.
There are lots of choices for what we consider.
So let me enumerate some of those choices and then we'll have a look and see what they do.
Oh I guess one possibility is to consider nothing.
Let's try it out.
So our type of search is going to be no checks.
What do you think is going to happen?
We're not even checking the assignment.
That's pretty fast.
Unfortunately, since we haven't even check the most recent assignment, we get lots of places where there are states that are adjacent to each other that have the same color.
That's no good.
So another thing we can do is consider everything everything.
That's no good, because that would say, as soon as we color our first state, we check to make sure that all 47 other states can be colored.
That seems like it over doing it a little bit.
But in any case, at least we want to check the assignment.
So if we go back here and check the assignment, let's see what happens.
Type assignments, assignments only.
Boom.
Aw, gees, that's where I got in trouble before.
This is the thing is going to run for 17 billion years at nanosecond or something like that.
It's only a billion years if you run it at nanosecond speed, so I guess maybe you could do that.
Have a fast computer.
But this isn't going to work because of our unfortunate choice of Texas as the last state to be considered, and the unfortunate coloring of the four surrounding states right up front.
And our unfortunate decision to rotate the color so as to avoid overdoing any one color.
So this doesn't work.
We know we went to the trouble of working out the business with Texas by hand, using the domain reduction algorithm.
Better make a note that this is the domain reduction algorithm.
And what we're going to do is we're going to check the neighbors of the assignments.
Just like we did here.
We checked Texas each time we made one of those four choices, because it's a neighbor of all of the choices of the states that we made.
So one thing to do is to check neighbors.
This is one, two, three, now let's see what happens.
Check neighbors only, go.
Shoot, I don't know.
It's OK with Texas, right?
Because it didn't color the states around Texas with all of the four color choices.
But it's still getting into trouble in other places.
Like the states like Missouri, Kentucky, Virginia, Tennessee, states with lots of boundaries.
So I don't know whether this is going to-- oh, there it finally worked.
It went through a lot of effort, though.
For the sake of comparison, we might make a note that it ran into 9,139 dead ends.
But it did do some good.
It didn't take a length of time longer than the remaining part of the universe.
But if it's a good idea to check the neighbors, if we make a change to the neighbors, what might that suggests that we do in addition?
Well it might suggest that if we make a change to a neighbor, that we check its neighbors, too, make sure they're all right.
So another choice is to propagate.
So propagate through variables with reduced domains.
Let's see how that works.
Wait a minute.
I must have made a mistake.
Let's try that again.
Oh, maybe we better slow it down.
All that grey stuff is showing the limit of the propagation.
Man it's, let's see at four second of 40, that's about 10 seconds.
Boom.
Not bad.
Zero dead ends.
And it was a lot faster.
I didn't happen to notice how many constraints were checked on that other thing, I think it was around 20,000 or so.
This is a lot less.
So this looks like a good idea.
But why did I label it number five?
Well because there's something between this and number three.
So number four is, through v with d reduced to one value.
So we're not going to propagate through all of the variables which have their domains shrunk a little bit.
We're only going to propagate through those that have the greater shrinkage, all the way down to a single value.
So let's see how that might work.
Anybody want to place any bets on this one?
Let's see.
We checked 2,623 constraints last time.
Let's see what happens this time.
You can see that the extent of the gray is less, because it's not propagating so far.
And as we breathily await the answer, I'd say we've found our winner.
As this does a couple of dead ends, but the number of constraint checked is less than 1,000.
So in general, with problems this, you have all of these choices for what you consider.
You don't want to consider nothing, because then you're not honoring your constraints.
You'll certainly want to consider the things you just made assignments for, because otherwise you'll construct a solution that violates a constraint.
You don't want to do everything, because that's excessive work.
And so checking the neighbors is a good idea, but it's always better in practice.
In practice, inevitably it's the case that it's better to do some propagation through the things that you've changed.
How much propagation?
It doesn't seem to do much good to propagate through things are just changed.
But it does seem to do some good to propagate through the things that have changed and been reduced to a single value.
So as soon as you get a neighbor of some assignment you just made that has its domain reduced to a single value, then you check its neighbors, too.
So you check the neighbors, of the neighbors, of the neighbors, of the neighbors, on and on and on, as long as you've found a domain being reduced.
And not only being reduced, but reduced to a signal value.
All right?
So that's the demand reduction algorithm.
And I guarantee you a problem like that.
And I know you don't know how to work those problems yet, because this is a little bit abstract.
And to work these problems in the exam setting you need to know a little bit about how to keep track of the variable values that remain in the domain, and that sort of thing.
And you'll learn more about that in your recitations, and in this mega recitation, and in the tutorials.
So we could go home except that there are few little flourishes to deal with here.

For example, I've chosen, as my classroom example, to pick on Texas.
And arranged for this situation to be especially ugly.
So I could arrange the states in a different way.
We have highly constrained states, that have a lot of bordering states around them.
And we have other states, like Maine, up there, that only borders on one other state.
So I don't know.
Will, what do you think?
Should we arrange the states for our death first search in the order of least constrained to most constrained, or most constrained to least constrained?
In other words, should we start with Missouri, or Tennessee, or Kentucky, or something like that?
Or should we start with Maine?
What do you think?
You have a 50% chance of getting it right, just by [? looking ?] at points.
WILL: Start with the most first.
PROF.
PATRICK WINSTON: He thinks we should start with the most constraint first.
Do we have a volunteer who wants to suggest that we start with the least constraint first?
That's the way I always work on stuff.
I'm working on a book or something, I have 500 things to fix, I'll always choose to work on the easiest stuff first, so that I feel like I'm making the list a lot smaller.
Leave the hardest things to last.
But we don't have any volunteers who want to bet on that idea of least constraint first?
OK.
Jason wants to suggest that we should work on least constraint first.
Well we have ground truth, because we can just try it out.
I guess we'll stick with our shrinking to one value thing, here.
But we will reorder things so that we have the least constrained first.
So right away, we got a color for Maine.
Maybe we ought to speed this up a little bit.
Well, that's a good idea.
Jason suggested this and we only 1,732 constraints and we had 59 dead ends.
So let's try the other way around, and we'll go back to four frames a second.
So we're working, kind of from the middle of the country out, with this one.
We're going to deal with Maine, I guess, last.
Which is better.
Too bad, I think it looks like this is better.
In fact, let's not be so aggressive with the use of constraint propagation.
Let's just check the assignments only.
If we go back to an arrangement where we have least constrained first, and we'll crank up the speed.
Well actually, we would have to crank it up pretty big, because the states like Missouri, Tennessee, Kentucky, they're going to be like Texas.
And so were kind of back to the length of the universe type problem, here.
With the least constraint first, and no use of constraints, other than to check the current assignment.
So let's stop that, though, and check the most constrained first, assignments only.
I don't know how long's this going to take.

If we had arranged our states from most constrained to least constrained, ordinary depth first search with none of this stuff we talked about today would work just fine.
All right.
So it's a little bit like games.
Do you use progressive deepening, or do you use alpha beta?
And the answer is both.
You use everything you've got to deal with the problems.
And depending on the problem, one or another of the things you incorporate into your approach will work just great, if you're lucky.
So now, I promise that this is useful not only for people who want to color maps.
God, who wants to do that?
We know it can be done with four colors.
But it's also useful for doing all kinds of resource planning problems.
So I want to show you a resource planning problem, and I want you think about-- while I'm doing it-- think about whether it's actually analogous to the map coloring problem.
All right?
So here's the deal.
You have just landed a summer job with the Jet Green, a new airline.
And Jet Green is a low cost, no frills, hardly any maintenance type of airline.
And they want to fly mostly between Boston and New York.
Occasionally they want to fly to Los Angeles.
And they're trying to get by with the smallest number of airplanes.
So that's why we have a kind of resource allocation problem with Jet Green.
So I'm going to write down what their schedule looks like.
They have one flight, F1, that goes from Boston to JFK, like so.
It's an early in the day flight.
Then they want to have another one, F2, that flies from JFK to Boston.
And then they want to have another flight a little later in the day that flies from Boston to JFK.
And a little later than that, they want to have another flight that goes from JFK to Boston.
They're going to start off mostly as a shuttle airline in the beginning.
So that's F1, F2, F3, and F4.
And they have a fifth flight, F5, that goes from Boston to Los Angeles, that takes a long time.
So it looks like this on the schedule.
Of course we have time going that way.
So that's Boston to LAX.
Now your job is to determine if they can fly this schedule with four aircraft?
And naturally you don't want to over use any one aircraft, because you would like to have even wear on them.
Right?
So as you make your choices, you'll rotate the aircraft.
So you'll assign to this one to A1, aircraft number one.
This one will be A2.
This one will be A3.
And this one will be A4.
And, oops, there's no aircraft left for the flight to Los Angeles, because you only have four.
So, it's obvious, right?
This is 100%, exactly, the map coloring problem, even down to the four choices.
Because, you have the constraint, the no single physical aircraft can fly two flights at the same time.
Just like no two adjacent states can be colored the same.
So there's a no same time constraint, like so.
So if you were assigning aircraft to these flights, you would get down to F4, the fourth flight, and you would say, well, let's see, this guy down here can be A1, A2, A3, or A4.
But if I choose A4 for that fourth flight, then there would be nothing left in its domain.
So you've thus set the problem up to be identical to the map coloring problem.
And, of course, you can enrich it with other kinds of constraints.
So, for example, you might have-- this is a not same time constraint-- and these, I mean, this is at JFK.
And it flies out of JFK, so maybe you can use the same aircraft for those.
But not if they're right up against each other, because you have a minimum ground time rule.
So there's a minimum ground time constraint here.
And there's a minimum ground time constraint here.
There's a minimum ground time constraint here.
And if these are at the same city, then you've got to allow enough time for them to fly between the two cities that are involved.
So the constraints can get a little bit more complicated, but the idea is the same.
So you say to me, I don't trust you, show me.
OK.
So let me show you.
Oh, by the way, there's one more way to make this map coloring problem easier, right?
Let's see.
The arrangement is going to be alphabetical, the type is going to be assignments only, and we know that's a loser.
But not if we use a whole lot of colors.
So it's the use of four colors that got us into trouble.
Now that's an aside, but it'll be coming back in a moment or two.
Scheduling, here's that problem.
Boom.
You can almost see it working just like the map coloring thing.
But that's maybe too easy.
Let's do this one.
See this is just like [? simplicia. ?] We've kind of got a goofy arrangement here that's guaranteed to lose at the bottom because of choices made at the top.
But that's OK.
We can that we can stop this, and we can change to check neighbors only.
And boom, there it goes.
Or alternatively, let me see if I can do it this way.
Most constrained first, type will be assignments only.
Boom.
That worked fine, too.
So you might choose to have a slightly harder problem, like this.
And we can search away.
Actually I don't know if this will complete or not.
This is a randomly generated example.
But you're going to lose your summer job if you can't figure out whether you can do this, or not.
So what are you going to do?
[? Elliott, ?] you got any thoughts about how you're going to save your job?
So here's the question you've been asked.
How many airplanes does Jet Green need?
And you decide, well, four seemed to work before, so we'll try four here.
You're not sure if it's going to terminate or not, I mean, in your lifetime, let alone in your summer job.
[? Elliott, ?] let me give you a hint.
Look at the outline.
The outline up here, on the board, the last item.
[? ELLIOTT: [INAUDIBLE] ?] PROF.
PATRICK WINSTON: Yeah, what's that mean?
What's the maximum number of airplanes we're going to need?
Suppose we've got five flights, what's the maximum number of airplanes we would ever need?
Five.
What's the minimum number of airplanes we'll need?
One.
So let's try it with a small number of airplanes and a large number of airplanes.
So that showed us very fast that we can't do it with one airplane.
That showed us very fast we can do it with ten airplanes.
SPEAKER 1: [INAUDIBLE] amount of overlappage from up [INAUDIBLE].
PROF.
PATRICK WINSTON: Volunteer?
What are we going to do to find the actual number, as fast as possible.
And at least give our boss a reasonable answer, even if not necessarily the exact number very fast?
It's easy, right?
We're going to start up here with one computer, and we're going to start down here with another computer, and see what happens.
So let's see if we can do it with nine.
Let's see if we can do it with eight.
Let's see if we can do it with seven.
These take almost zero time, right?
Because they're under constraint.
Wow, that's good, seven.
Let's try six.
Actually let's try two.
It loses fast.
Let's try three.
I don't know.
Maybe if you let it run one long enough three will work.
I doubt it.
While we're at it though, we might as well go back here and try it with six.
Remember seven worked real fast.
Gees, six, that was six, right?
Yeah.
So let's try it with five.
OK.
So it runs real fast with five.
It terminates real quick with two, so we got three and four left.
So we could tell our boss, a la any time algorithm, that you're not real sure, but you know it's going to be either three or four.
And then, you got two computers.
You can let both run and see if either one terminate.
So you have three and four.
My guess is that three will eventually give up.
But of course, there's another little problem here.
We haven't used the most constraint first.
If we did that, we might be able to do it even faster.
Actually, I don't think I can make that switch without getting another random assignment, but let's see what happens.
Maybe so.
SPEAKER 2: [INAUDIBLE] PROF.
PATRICK WINSTON: Oh, I already had most constraint first?
OK.
So it didn't help to actually switch.
And I think I've got a new schedule to work here.
So that's the end of the story.
You can do good resource allocation if you do several things are once.
Number one, you always want to use most constraint first.
Number two you want to propagate through domains produced to a single algorithm.
And number three, if you really try to figure out what the minimum number of resources needed is, you do this under over business and you can quickly converge on a narrow range where the search is taking a long time, and be sure that it lies within that narrow range.
Because when you over resource, it's fast to complete, and when you under resource, it's fast to terminate.
So you can just squeeze it right down into a very small range.
And that is the end of the story.
Enjoy your holiday on Monday.
We'll have two classes next week on Wednesday and Friday, as advertised.


--- layout: default ---
Brigham has advised many clients on a broad range of family law matters and has considerable experience in the areas of guardianship, parenting, protection and property issues affecting children and adults. He is instructed as Counsel for both privately retained and legally aided clients in Family Court litigation. He is married with four children.
Born and raised in the Waikato and graduating with a (LLB) Bachelor of Laws degree. Brigham was admitted to the bar in 2009. He spent several years working for a prominent Hamilton law firm. Brigham established his own practice in 2014 as a Barrister and Solicitor. He is the Director of Family Law Solutions Ltd.
Outside of work Brigham enjoys spending time with his family and friends, being involved in the wider community and these days - trying to play sport. He is actively involved in coaching his children’s junior sports teams.
Brigham Nordstrom, LLB
19 Hood St
PO Box 1511
Hamilton
Email<EMAIL_ADDRESS>DDI: +64 7 390 0500
Cell: +64 22 456 0500


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I have a passion for science and nature. Having completed a B.Sc. in Biotech and Food Engineering and working at a company that treats wastewater, I hope to someday combine my coding skills with my academic knowlage and develope new and exiting fields.
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Sergio Aguero ruled out of Manchester derby as Pep Guardiola provides injury update
Manchester City manager Pep Guardiola feared the worst when his talisman Sergio Aguero hobbled off in Saturday's 2-1 win over Chelsea, but the Argentine should only miss a few games
by Jake Polden, https://www.mirror.co.uk/authors/jake-polden/Sergio Aguero has been ruled out of the Manchester derby, but his injury is better than first feared, Pep Guardiola has confirmed.
Manchester City boss Guardiola feared the worst when his talisman hobbled off in the 77th minute of Manchester City's 2-1 win over Chelsea on Saturday.
But the tendon injury is better than first feared, and should only keep Aguero out for a few games rather than a couple of months.
"A few games, I don't know exactly," said Guardiola. "He has a tendon problem in front - will be a few games out."
Both David Silva and Rodri were also forced off against Chelsea, but Guardiola was less concerned for the Spainards.
Providing an update on their conditions on Monday, he added: "We have no other [new] injuries. David Silva [and Rodri] had cramp but he's okay."
The Citizens went behind against Chelsea at the Etihad after N'Golo Kante slotted past Ederson after getting the better of Benjamin Mendy.
But Manchester City hit back through Kevin de Bruyne who leveled the scores.
Riyad Mahrez had the final say for the Citizens when he produced a moment of magic to beat two Chelsea defenders and fire City ahead.
Raheem Sterling had the goal in the back of the net late on for City, but it was ruled out by VAR.


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Grist
This page contains an index of all the individuals in the database with the surname of Grist. Selecting the person’s name will take you to that person’s individual page.
| Given Name | Birth | Death | Partner | Parents |
|---|---|---|---|---|
| Ada | 1882-03-30 | 1955-08-05 | Irish, Thomas William (Under Secretary of Lands) | Grist, John Henry (Engineer) Turner, Elizabeth Mary "Bessie" |
| Ann | Grist, Richard (baker) Briant, Mary | |||
| Ann | 1670/1671 | 1735-12-09 | Grist, William | Grist, John married Elizabeth |
| Ann | 1698-02-03 | Lea, Richard (Yeoman) | Grist, William Grist, Ann | |
| Ann | 1736 | Grist, Charles Grant, Judith | ||
| Ann, married Robert Bryant (baker) | c1736 | Grist, Charles Brown, Ann | ||
| Charles, married Mary? | Grist, Richard (baker) Briant, Mary | |||
| Charles | 1711-09-27 | 1775-12-19 | Brown, Ann, Grant, Judith | Grist, William Grist, Ann |
| Charles | 1742-07-04 | Berrow, Mary | Grist, Charles Brown, Ann | |
| Charles, (Millwright) | 1814-11-18 | 1st Qtr 1877 | Minty, Louisa, Morgan, Caroline Elizabeth | Grist, Charles ( Cordwainer) Fisher, Mary |
| Charles | 1st Qtr 1858 | 3rd Qtr 1887 | Grist, Charles (Millwright) Morgan, Caroline Elizabeth | |
| Charles | 3rd Qtr 1855 | 1st Qtr 1856 | Grist, Charles (Millwright) Morgan, Caroline Elizabeth | |
| Charles, ( Cordwainer) | c1775 | 1856 | Fisher, Mary | Grist, Charles Berrow, Mary |
| Charles | c1844 | Grist, Charles (Millwright) Morgan, Caroline Elizabeth | ||
| Dorothy Ada Pearl | 1908-07-13 | 1991-01-25 | McGowan, Colin James | Grist, Harold Orlando Stanford, Emma Pearl |
| Elizabeth | Grist, Richard (baker) Briant, Mary | |||
| Elizabeth | 1798 | Grist, Charles married Mary? | ||
| Elsie Mary | 1903-02-11 | 1988-02-08 | Golby, Reginald Charles Thornton | Grist, Walter Henry Pascoe, Olive |
| Emily Fisher | 1834-01-01 | c1898 | Grist, Charles (Millwright) Minty, Louisa | |
| George, (Carpenter) | c1776 | Grist, Richard (baker) Briant, Mary | ||
| Grace | c1697 | Grist, William Grist, Ann | ||
| Harold Orlando | 1889-07-16 | 1943-07-13 | Stanford, Emma Pearl | Grist, John Henry (Engineer) Turner, Elizabeth Mary "Bessie" |
| Henry | about 1843 | 2yrs old | ||
| Henry | about 1843 | 1845-07-00 | Grist, Charles (Millwright) Morgan, Caroline Elizabeth | |
| Henry | 1852 | 1853-04-16 | Grist, Charles (Millwright) Morgan, Caroline Elizabeth | |
| Hilda Olive | 1898-10-10 | 1989-02-07 | Small, William Henry Arthur | Grist, Walter Henry Pascoe, Olive |
| James | 1786 | Grist, Richard (baker) Briant, Mary | ||
| Jane Fisher | about 1837 | Grist, Charles (Millwright) Minty, Louisa | ||
| John, married Grace | ||||
| John, married Elizabeth | ||||
| John | 1700-09-26 | Grist, William Grist, Ann | ||
| John | 1758-11-24 | Grist, Charles Brown, Ann | ||
| John | c1832 | Grist, Charles (Millwright) Minty, Louisa | ||
| John Henry, (Engineer) | about 1849 | 1920-03-31 | Turner, Elizabeth Mary “Bessie” | Grist, Charles (Millwright) Morgan, Caroline Elizabeth |
| Julia | about 1847 | Grist, Charles (Millwright) Morgan, Caroline Elizabeth | ||
| Leslie John | 1910-02-17 | 1994-04-24 | Bell, Esther Lois “Billie” | Grist, Harold Orlando Stanford, Emma Pearl |
| Louisa | c1833 | Grist, Charles (Millwright) Minty, Louisa | ||
| Mary, married William Brice | c1750 | Grist, Charles Brown, Ann | ||
| Mary | c1831 | Grist, Charles (Millwright) Minty, Louisa | ||
| Matilda | about 1835 | Grist, Charles (Millwright) Minty, Louisa | ||
| Norman Henry | 1900-01-15 | 1972-01-15 | Dawson, Frances Isabel | Grist, Walter Henry Pascoe, Olive |
| Pearl | c1846 | Grist, Charles (Millwright) Morgan, Caroline Elizabeth | ||
| Richard | 1781 | Grist, Richard (baker) Briant, Mary | ||
| Richard, (baker) | c1745 | c1818 | Briant, Mary | Grist, Charles Brown, Ann |
| Sarah | about 1840 | Grist, Charles (Millwright) Minty, Louisa | ||
| Thomas Fisher | c1826 | 3rd Qtr 1884 | Hewson, Jane | Grist, Charles (Millwright) Minty, Louisa |
| Walter Henry | 1874-10-27 | 1961-10-15 | Pascoe, Olive | Grist, John Henry (Engineer) Turner, Elizabeth Mary "Bessie" |
| William | 1674 | 1732-03-00 | Grist, Ann | Grist, John married Grace |
| William | 1702-02-26 | Grist, William Grist, Ann | ||
| William | 1739-05-27 | Grist, Charles Brown, Ann | ||
| Winifred Margaretta | 1877-10-16 | 1955-10-15 | Gilkes, Walter Edward, Jackson, John Arthur [widower] | Grist, John Henry (Engineer) Turner, Elizabeth Mary "Bessie" |
| [Living] | Bongers, [Living] | Grist, Norman Henry Dawson, Frances Isabel | ||
| [Living] | Brookfield, John Warren Rev. | Grist, Norman Henry Dawson, Frances Isabel | ||
| [Living] | Wallace, [Living] | Grist, Norman Henry Dawson, Frances Isabel | ||
| [Living] | Webb, [Living] | Grist, [Living] Wallace, [Living] | ||
| [Living] | Kettlewell, [Living] | Grist, [Living] Wallace, [Living] | ||
| [Living] | Snepp, [Living] | Grist, [Living] Wallace, [Living] | ||
| [Living] | Smythe, [Living], Graney, [Living] | Grist, Leslie John Bell, Esther Lois "Billie" | ||
| [Living] | Selsing, [Living] | Grist, Leslie John Bell, Esther Lois "Billie" | ||
| [Living] | Lowe, [Living] | Grist, Leslie John Bell, Esther Lois "Billie" | ||
| [Living] | Grist, Thomas Fisher Hewson, Jane | |||
| [Living] | Grist, Thomas Fisher Hewson, Jane |


I am an Enterprise Agile Coach / SAFe Practice Consultant based in Melbourne, Australia. I'm currently working at Fred IT Group. I help organizations build capabilities to enable business agility.
Scrum
Agile methodologies
Kanban
Lean
Agile scaling
SAFe
Facilitation
Agile coaching
Remote teams
Enterprise Agile coaching
Large-scale facilitation


Software Developer
Shinra Software Solutions Inc.
Magsaysay Careers
A portal website for people who wants to find a job especially for seafarers.
- Lead Developer
- Designed the database structure of the CMS application of the site.
- Designed and developed the application structure using N-Tier architecture.
- Technologies Used:
- C#
- ASP.NET MVC
- HTML5 and CSS3
- Javascript, jQuery
- Microsoft SQL Server
Text Commander
A Windows Application to schedule and send SMS and e-mail notifications.
- Lead Developer
- Designed the database structure of the application.
- Sending SMS via AT Commands.
- Sending E-mails via SMTP.
- Technologies Used:
Track and Trace App
A cross-platform mobile app to track the gps location of the users.
- Lead Developer
- Designed the database structure of the application.
- Developed the backend API for user registration and receiving GPS logs.
- Designed and developed the user interface.
- Technologies Used:
- Mobile App
- Ionic 3
- HTML5 and CSS3
- TypeScript
- SQLite
- Backend
- PHP / Codeigniter
- HTML5 and CSS3
- MySQL
- Javascript
Social Media/Portal App
A cross-platform mobile app student portal.
- Lead Developer
- Developed the backend API for user registration, postings and chat server.
- Designed and developed the user interface.
- Technologies Used:
- Mobile App
- Ionic 3
- HTML5 and CSS3
- TypeScript
- PouchDB
- Backend
- PHP / Drupal
- MySQL
- NodeJS - socket.io for creating the chat server
API Libraries
Various functions to manipulate files and connect to PABX phones.
- Lead Developer
- Library to PABX phones to execute a call command.
- PDF, Excel, Word file Password Protector.
- Excel file manipulator (value, color, width).
- Excel to CSV converter (vice-versa).
- Technologies Used:
- C#
- Microsoft Office Interop Libraries


File IO¶
This tutorial shows how basic geometries are read and written by Open3D.
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # examples/Python/Basic/file_io.py
import open3d as o3d
if __name__ == "__main__":
print("Testing IO for point cloud ...")
pcd = o3d.io.read_point_cloud("../../TestData/fragment.pcd")
print(pcd)
o3d.io.write_point_cloud("copy_of_fragment.pcd", pcd)
print("Testing IO for meshes ...")
mesh = o3d.io.read_triangle_mesh("../../TestData/knot.ply")
print(mesh)
o3d.io.write_triangle_mesh("copy_of_knot.ply", mesh)
print("Testing IO for images ...")
img = o3d.io.read_image("../../TestData/lena_color.jpg")
print(img)
o3d.io.write_image("copy_of_lena_color.jpg", img)
|
Point cloud¶
This script reads and writes a point cloud.
11 12 13 14 | print("Testing IO for point cloud ...")
pcd = o3d.io.read_point_cloud("../../TestData/fragment.pcd")
print(pcd)
o3d.io.write_point_cloud("copy_of_fragment.pcd", pcd)
|
print()
function can be used for displaying a summary of pcd
. Output message is below:
Testing IO for point cloud ...
PointCloud with 113662 points.
By default, Open3D tries to infer the file type by the filename extension. Below is a list of supported point cloud file types.
Format |
Description |
|---|---|
|
Each line contains |
|
Each line contains |
|
Each line contains |
|
The first line is an integer representing the number of points
Each subsequent line contains
[x, y, z, i, r, g, b] ,
where r, g, b are in `uint8` |
|
See Polygon File Format,
the |
|
See Point Cloud Data |
It’s also possible to specify the file type explicitly. In this case, the file extension will be ignored.
pcd = o3d.io.read_point_cloud("my_points.txt", format='xyz')
Mesh¶
This script reads and writes a mesh.
16 17 18 19 | print("Testing IO for meshes ...")
mesh = o3d.io.read_triangle_mesh("../../TestData/knot.ply")
print(mesh)
o3d.io.write_triangle_mesh("copy_of_knot.ply", mesh)
|
Compared to the data structure of point cloud, mesh has triangles that define surface.
Testing IO for meshes ...
TriangleMesh with 1440 points and 2880 triangles.
By default, Open3D tries to infer the file type by the filename extension. Below is a list of supported triangle mesh file types.
Format |
Description |
|---|---|
|
See Polygon File Format,
the |
|
|
|
See Object Files |
|
|
|
Image¶
This script reads and writes an image.
21 22 23 24 | print("Testing IO for images ...")
img = o3d.io.read_image("../../TestData/lena_color.jpg")
print(img)
o3d.io.write_image("copy_of_lena_color.jpg", img)
|
Size of image is readily displayed using print(img)
.
Testing IO for images ...
Image of size 512x512, with 3 channels.
Use numpy.asarray to access buffer data.


We use this library on GitHub when rendering your README or any other rich text file.
The following markups are supported. The dependencies listed are required if you wish to run the library.
gem install redcarpet
(https://github.com/vmg/redcarpet)gem install RedCloth
gem install rdoc -v 3.6.1
gem install org-ruby
gem install creole
gem install wikicloth
easy_install docutils
brew install asciidoc
Pod::Simple::HTML
comes with Perl >= 5.10. Lower versions should install Pod::Simple from CPAN.Want to contribute? Great! There are two ways to add markups.
If your markup is in a language other than Ruby, drop a translator
script in lib/github/commands
which accepts input on STDIN and
returns HTML on STDOUT. See rest2html for an example.
Once your script is in place, edit lib/github/markups.rb
and tell
GitHub Markup about it. Again we look to rest2html for
guidance:
command(:rest2html, /re?st(.txt)?/)
Here we're telling GitHub Markup of the existence of a rest2html
command which should be used for any file ending in rest
,
rst
, rest.txt
or rst.txt
. Any regular expression will do.
Finally add your tests. Create a README.extension
in test/markups
along with a README.extension.html
. As you may imagine, the
README.extension
should be your known input and the
README.extension.html
should be the desired output.
Now run the tests: rake
If nothing complains, congratulations!
If your markup can be translated using a Ruby library, that's
great. Check out lib/github/markups.rb
for some
examples. Let's look at Markdown:
markup(:markdown, /md|mkdn?|markdown/) do |content|
Markdown.new(content).to_html
end
We give the markup
method three bits of information: the name of the
file to require
, a regular expression for extensions to match, and a
block to run with unformatted markup which should return HTML.
If you need to monkeypatch a RubyGem or something, check out the included RDoc example.
Tests should be added in the same manner as described under the
Commands
section.
gem install github-markup
require 'github/markup'
GitHub::Markup.render('README.markdown', "* One\n* Two")
Or, more realistically:
require 'github/markup'
GitHub::Markup.render(file, File.read(file))
To run the tests:
$ rake
To add tests see the Commands
section earlier in this
README.
git checkout -b my_markup
)git commit -am "Added Snarkdown"
)git push origin my_markup
)


Search
Kothar Inventory Table
| Input Date | Product Type | Product Name | Description/Location | Expiry Date | Low Alert | Quantity | Unit Of Measure | Edit |
|---|
Items Not In Stock
| Input Date | Product Type | Product Name | Description/Location | Expiry Date | Low Alert | Quantity | Unit Of Measure |
|---|


CAST
converts values from one data type to another.
The data type specified by type_name
.
CAST(expr AS type_name)
expr
can be an expression in one of the data types.
type_name
is one of the data types listed in Table 1-2, "Dimensional Data Types".
Table 3-1 shows which data types can be cast into which other built-in data types. NUMBER
includes NUMBER
, DECIMAL
, and INTEGER
. DATETIME
includes DATE
, TIMESTAMP
, TIMESTAMP WITH TIMEZONE
, and TIMESTAMP WITH LOCAL TIMEZONE
. INTERVAL
includes INTERVAL DAY TO SECOND
and INTERVAL YEAR TO MONTH
.
Table 3-1 Compatible Data Types
| From | To BINARY_FLOAT, BINARY_DOUBLE | To CHAR, VARCHAR2 | To NUMBER | To DATETIME, INTERVAL | To NCHAR, NVARCHAR2 |
|---|---|---|---|---|---|
|
BINARY_FLOAT, BINARY_DOUBLE |
yes |
yes |
yes |
no |
yes |
|
CHAR, VARCHAR2 |
yes |
yes |
yes |
yes |
no |
|
NUMBER |
yes |
yes |
yes |
no |
yes |
|
DATETIME, INTERVAL |
no |
yes |
no |
yes |
yes |
|
NCHAR, NVARCHAR2 |
yes |
no |
yes |
no |
yes |
CAST('123.4567' AS NUMBER(10,2))
returns the value 123.46
.


This file will be filled if you are using an about-page on the web plattform.
Filling this static file with html code will make the included information visible before a user syncs for the first time.