detrend3 documentation
detrend3 performs linear least squares detrending along the third dimension of a matrix.
See also: trend.
Back to Climate Data Tools Contents.
Contents
Syntax
Ad = detrend3(A) Ad = detrend3(A,t) Ad = detrend3(...,'omitnan')
Description
Ad = detrend3(A) removes the linear trend from the third dimension of A assuming slices of A are sampled at constant intervals.
Ad = detrend3(A,t) specifies times t associated with each slice of A. Times t do not need to occur at regular intervals in time.
Ad = detrend3(...,'omitnan') applies detrending even in grid cells that contain NaN values. If many grid cells contains spurious NaNs, you may find that this option is slower than the default.
Example 1: A 3D gridded dataset
This sample dataset has a trend of 3.2 units/timestep everywhere:
t = 50:50:1000; y = 3.2*t + 0.1*randn(size(t)); % the trend is 3.2*t (plus some random noise) % Create 5x5 grid w/trend dictated by y: Z = expand3(ones(5),y);
Here's proof via the trend trend function:
trend(Z,t)
ans = 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000 3.2000
Now let's detrend:
Z_detrend = detrend3(Z,t);
And now what's the trend?
trend(Z_detrend,t)
ans = 1.0e-14 * -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212 -0.2212
Just numerical noise (note that it is zero to the level of 1.0e-14).
Example 2: Sea Surface Temperatures
This example calculates an area-averaged sea surface temperature anomaly from a gridded time series, then detrends the gridded sst time series and plots the detrended anomaly time series.
Begin by loading the sample pacific_sst.mat dataset of monthly SSTs. Use cdtarea to get the area of each grid cell, and then use local to get the 1D time series of area-weighted ssts for all ocean grid cells.
% Load example data: load pacific_sst % Turn lat,lon arrays into grids: [Lon,Lat] = meshgrid(lon,lat); % Get the area (m^2) of each grid cell: A = cdtarea(Lat,Lon); % area of each grid cell (m^2) % Make a mask of only finite data (to effectively ignore land) mask = all(isfinite(sst),3); % Get 1D time series of area-weighted sst: sst_1 = local(sst,mask,'weight',A);
Now plot the area-weighted sst anomaly using the anomaly function. For context, plot the linear trend with polyplot:
figure anomaly(t,sst_1-mean(sst_1)) axis tight datetick('x','keeplimits') ntitle('sea surface temperature anomaly (not detrended)') hold on polyplot(t,sst_1-mean(sst_1),1,'k','linewidth',3)
Now detrend the entire 3D sst dataset and plot the same area-averaged time series, but this time using the detrended data:
% Detrend the full 3d time series: sst_dt = detrend3(sst); % Get the area-averaged detrended SST time series: sst_dt_1 = local(sst_dt,mask,'weight',A); figure anomaly(t,sst_dt_1-mean(sst_dt_1)) axis tight datetick('x','keeplimits') ntitle('DETRENDED sea surface temperature anomaly') hold on polyplot(t,sst_dt_1-mean(sst_dt_1),1,'k','linewidth',3)
Example 3: The 'omitnan' option
In some cases, your gridded dataset may have spurious NaNs in them. That is the case for the sst dataset we analyzed in the example above. See, for the 802 months of SST data, look at how many values are finite:
figure imagescn(lon,lat,sum(isfinite(sst),3)) cb = colorbar; ylabel(cb,'number of finite data values') caxis([700 802]) % sets color axis limits
In the figure above, take a look at Hudson Bay in Canada. You'll notice that not all of the 802 months have valid data there. As many as 100 months of data are missing because it can be hard to get a good SST reading in the winter months when ice screws up the measurement. Still, you may need to remove the long term trend from the available data.
Here's a comparison of detrending the sst dataset without versus with the 'omitnan' option. Start by using the trend function, simply to assess the magnitude of the trend:
figure subplot(1,2,1) imagescn(lon,lat,trend(sst,12)) cb = colorbar('location','southoutside'); xlabel(cb,'SST trend \circC/yr') title 'trend without omitnan' caxis([-0.04 0.04]) cmocean('balance') subplot(1,2,2) imagescn(lon,lat,trend(sst,12,'omitnan')) cb = colorbar('location','southoutside'); xlabel(cb,'SST trend \circC/yr') title 'trend with omitnan' caxis([-0.04 0.04]) cmocean('balance')
In the figure above, note the difference in Hudson Bay, Canada.
Now detrending works the same way. We'll detrend each dataset, and then plot the trends of the detrended datasets just as above:
sst_dt = detrend3(sst); sst_dt_o = detrend3(sst,'omitnan'); figure subplot(1,2,1) imagescn(lon,lat,trend(sst_dt,12)) cb = colorbar('location','southoutside'); xlabel(cb,'SST trend \circC/yr') caxis([-0.000000000000001 0.000000000000001]) cmocean('balance') title 'detrended trend without omitnan' subplot(1,2,2) imagescn(lon,lat,trend(sst_dt_o,12,'omitnan')) cb = colorbar('location','southoutside'); xlabel(cb,'SST trend \circC/yr') caxis([-0.000000000000001 0.000000000000001]) cmocean('balance') title 'detrended trend with omitnan'
Now the only "trend" that remains in the detrended dataset is numerical noise (Note the 1x10^-15 limits of the color scale.)
Author Info
This function is part of the Climate Data Toolbox for Matlab. The function and supporting documentation were written by Chad A. Greene.


--- layout: documentation title: InformationSourceType ---
The InformationSourceType details the source of a given data entry.
| Field Name | Type | Description |
|---|---|---|
| Description0..1 | StructuredTextType |
The Description field provides a description of this information source. |
| Identity0..1 | IdentityType |
The Identity field is optional and specifies the identity of the information source. This field is implemented through the xsi:type extension mechanism. The default type is CIQIdentity3.0InstanceType in the http://stix.mitre.org/extensions/Identity#CIQIdentity3.0-1 namespace. This type is defined in the extensions/identity/ciq_identity.xsd file or at the URL http://stix.mitre.org/XMLSchema/extensions/identity/ciq_identity/1.1.1/ciq_identity.xsd. Those who wish to express a simple name may also do so by not specifying an xsi:type and using the Name field. |
| Role0..n | ControlledVocabularyStringType |
The Role field is optional and enables characterization of the sourcing Role played by this Information Source. |
| Contributing_Sources0..1 | ContributingSourcesType |
The Contributing_Sources field is optional and enables description of the individual contributing sources involved in this instance. |
| Time0..1 | TimeType |
The Time element is optional and enables description of various time-related attributes for this instance. |
| Tools0..1 | ToolsInformationType |
The Tools element is optional and enables description of the tools utilized for this instance. |
| References0..1 | ReferencesType |
The References field is optional and enables specification of references to information source material for this instance. |


No snow expected for 2019 General Election in December
by Paul GeaterWill it snow as voters head to the polls for the 2019 General Election? It's not looking very likely in this part of the world according to weather forecasters.
A fear of bad weather was one of the major concerns when the date of the election was confirmed earlier this month, but while forecasters are not sure what weather we are likely to be confronted with on election day, they are reasonably confident that heavy snow will not feature.
Phil Garner from Norwich-based forecasters Weatherquest said the wet and mild weather was likely to continue for the rest of November.
He said: "Our projections suggest it will continue to be unsettled until the end of the month which means there will be more rainy days but it will be reasonably mild.
"Looking into the start of December there may be more high pressure building. That could mean it will become colder and there could be some fog or mist hanging about and preventing temperatures from rising.
"But it doesn't look as if there is much chance at this stage of heavy snow to disrupt things - but that is still quite a long way off and we will have to wait to see what happens."


Internship Experience: NVIDIA-HARVARD, Deep Learning Engineering Intern
TASK:
Brain Tumor Segmentation
DATASET:
- Binary Class -
- No Class imbalance
INPUT:
Image - 3D MRI Scan
INPUT SIZE: (Width * Height * Channels (R,G,B))
- PATH 1: : 25px * 25px * 3 (Normal resolution)
- PATH 2: : 19px * 19px * 3 (Low resolution)
ARCHITECTURE:
CONVOLUTION LAYERS (CONV)
PATH 1:
CONV LAYER 1: 21 * 21 * 3 |
#Filter: 30 | Size: 5 * 5 * 5 |
CONV LAYER 2: 17 * 17 * 3 |
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CONV LAYER 3: 13 * 13 * 3 |
#Filter: 40 | Size: 5 * 5 * 5 |
CONV LAYER 4: 9 * 9 * 3 |
#Filter: 50 | Size: 5 * 5 * 5 |
PATH 2:
CONV LAYER 1: 15 * 15 * 3 |
#Filter: 30 | Size: 5 * 5 * 5 |
CONV LAYER 2: 11 * 11 * 3 |
#Filter: 40 | Size: 5 * 5 * 5 |
CONV LAYER 3: 7 * 7 * 3 |
#Filter: 40 | Size: 5 * 5 * 5 |
CONV LAYER 4: 3 * 3 * 3 |
#Filter: 50 | Size: 5 * 5 * 5 |
CONVOLUTION OPERATION:
Goal: To learn features and generates feature maps.
CONVOLUTION OPERATION: This is the formula for computing featuer maps.
DIMENSIONS:
NO. OF PARAMETERS:
UPSAMPLING:
FULLY CONNECTED LAYER:
Why?
IN CNN, we combine all the features learnt together (FILTERS), and try classification.
LAYER 1:
LAYER 1: 9 * 9 * 3 |
#Filter: 150 | Size: 1 * 1 * 1 |
LAYER 2: 9 * 9 * 3 |
#Filter: 150 | Size: 1 * 1 * 1 |
CLASSIFICATION LAYER:
Softmax function
COST FUNCTION:
OPTIMIAZATION: STOCASTIC GRADIENT DESCEND
CODE SAMPLE: KERAS | PYTORCH
EVALUATION: DICE SCORE
FUTURE WORK: How to improve it?
- M-RNNs


In this hands-on lab you will be creating the complete chain from configuring, to bridging the cap between the connectivity providers clouds and the Microsoft Azure IoT platform, creating actionable insights on the Microsoft Azure IoT platform and sending commands all the way back to the device. This includes assembling hardware, configuring hardware and software and coding the missing logic to a complete IoT solution. The workshop will simulate a remote located machine malfunctioning and getting restarted based on the received telemetry and created insights based on the telemetry data. You will be creating all components to get the malfunctioning machine back up running again. All of that within 2-4 hours (2 hours is minimum).
Technologies used during the hands-on lab;
This hands-on lab will be guided by Valery Jacobs & Jan Willem Groenenberg & Sander van de Velde, so attendees have on site support for questions.
We will also provide background information about the used components in the hands-on lab.
The hands-on lab is created in conjunction with Atos and Microsoft Nederland.
Hardware used will remain property of Atos and Microsoft.
During the workshop the following experts will be present to guide you through the workshop:
Jan Willem Groenenberg; Atos Senior Expert, IoT Platform Architect, Azure Advisory Board member
Sander van de Velde; MVP, IoT Platform Architect, Azure Advisory Board member
These are the places where this workshop is given
| Name | location | Date | # of attendees |
|---|---|---|---|
| Microsoft Tech Days 2016 | Amsterdam, The Netherlands | October 4, 2016 | 19 |
| Microsoft Tech Days 2016 | Amsterdam, The Netherlands | October 4, 2016 | 16 |
| Microsoft Tech Days 2016 | Amsterdam, The Netherlands | tba | 12 |
| Microsoft Tech Days 2016 | Amsterdam, The Netherlands | tba | 15 |
| dotNed Saturday 2017 | Veenendaal, The Netherlands | Januari 28, 2017 | 15 |
| Atos Innovatos 2017 | Amstelveen, The Netherlands | March 14, 2017 | 8 |
| Atos Innovatos 2017 | Groningen, The Netherlands | March 21, 2017 | 9 |
| Atos Innovatos 2017 | Eindhoven, The Netherlands | March 28, 2017 | 13 |
| IoT Tech Day 2017 | Utrecht, The Netherlands | April 19, 2017 | 22 |
| Global Azure Bootcamp 2017 | Amstelveen, The Netherlands | April 22, 2017 | 12 |
| Microsoft Tech Days 2017 | Amsterdam, The Netherlands | October 12, 2017 | 14 |
| Codecamp 2017 | Iasi, Romania | October 27, 2017 | 20 |
| LoRa IoT network | Apeldoorn, The Netherlands | April 7, 2018 | 20 |
| Global Azure Bootcamp 2018 | Amstelveen, The Netherlands | April 21, 2018 | tba |
| TEQnation 2018 | Utrecht, The Netherlands | April 26, 2018 | tba |
The workshop consists of several chapters. The first two chapters will guide you through the essentials we want you to get your hands on. The other chapters are optional/extra if you have time left.
You have four options to choose from to start with. You can either go for physical hardware based on the The Things Uno (Lora/LoraWAN enabled Arduino Leonardo) programmed in C and connected via The Things Network, Or choose one of the device simulation applications like UWP (C#), NodeJS (JavaScript) or Java application (Java is only used for the simulation client).
Click on one of the logos to get started with the device or simulated device and follow that path for the rest of the workshop (at the end of each chapter, a link to the following chapter is provided
Connecting a device to the Azure IoT Platform
| Choose the device or simulator to connect to Azure IoT | |||
|---|---|---|---|
Receiving and handling telemetry in Azure
| Choose the device or simulator to receive telemetry from | |||
|---|---|---|---|
Passing commands back to actual devices or simulated devices
| Choose the device or simulator to pass commands back to | |||
|---|---|---|---|


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Model Data Structure
(Note: Because model updates are in realtime and the syntax highlighter is processor intensive, rapid input may slow this example. This behavior is not present in the production use of the plugin)


There are opportunities for participants to give a talk or a poster. Please indicate on the registration form if you wish to do so. We will review all applications and notify you of any outcome of your applications, by the end of Friday 19th October 2019 (for talks) or Wednesday 24th October 2019 (for posters).
Refreshments and lunch will be provided on the day as indicated on the schedule. Unfortunately we cannot provide any support for travel arrangements, thus, participants should organise any travel and accommodation required to attend the workshop themselves.


2 writes to dwFlags
Microsoft.PowerShell.Commands.Management (2)
commands\management\Process.cs (2)
2407
lpStartupInfo.
dwFlags
= 0x100;
2420
lpStartupInfo.
dwFlags
|= 0x00000001;


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Select Max Throttle
{% for t in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100] %}
{{ t }}%
{% end %}
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Mark's Conceptual Model and ERD
Conceptual Model
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PROFILE
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Example Classes - General Information
The main purpose of example classes is to reinforce students’ learning of algorithmic concepts and techniques by hands-on exercises. Moreover, the example classes can train the practical skills in implementation, empirical analysis and presentation of algorithms. The performance of a student in the example classes will constitute 20% of his/her final mark for the course.
Timeline
There are four example classes:
- Example Class 1: Week 4 – 5
- Example Class 2: Week 6 – 7
- Example Class 3: Week 9 – 10
- Example Class 4: Week 11 – 12
In case of time clash (such as with public holidays), the tutor and students can schedule a convenient time slot for make-up.
Group projects
Students will be allocated into groups each of 5 – 7 members. To nurture cultural intelligence, we encourage grouping in such a way that each group will have students from at least two nationalities. Students are to form groups by themselves and finalize the grouping within the first 20 minutes of Example Class 1. After that, no swap across groups will be allowed unless absolutely necessary.
In Example Class 1, each group will perform theoretical complexity analysis for algorithms, and give a presentation in the class.
In Example Classes 2 – 4, students will implement algorithms using a language of their own choice (e.g. Java, C or C++), and test programs on input data. Depending on specific requirements of classes, the dataset may be generated by students themselves or downloaded from the web. In the presentation, a group should give a demo of the program running on the input data.
By the end of each class (except for Example Class 1), a group should submit to tutor:
- Softcopy of source code, compiled executables and test data;
- Hardcopy or softcopy of report summarizing the implementation and experimental results.
Note: On each computer in the laboratory, compilers and IDEs (e.g. Java JDK 7, NetBeans 7.3.1) have been installed. You may also use your own laptop. Please test your program in the lab before giving a demo.
Presentation or program demo
In each session, every group should choose 2 – 3 students to take part in the presentation, which includes a computer demo of algorithm implementation. The 2 – 3 students representing their group should divide the presentation material equally. Every student should take part in the preparation of the presentation material. Throughout the 4 example classes, every student should take part in at least two presentations. Tutors will record the names of students who do the presentation for each session.
Each group must finish coding and preparing a presentation before the class. However, the first 30 – 40 minutes of each example class will be given to the students to finalize presentation materials or to test their programs on the computers. Each group has 10 minutes for giving a presentation.
Facilities for PowerPoint presentation are provided by the laboratory.
Plagiarism is strictly prohibited. If the program or report of a group is found to have a similarity of more than 50% with another group or web materials, all members of the group will receive zero mark for the example class. Further academic disciplinary actions may be taken.
For guidelines of grading and policy of absence or late submission, please refer to the Scoring Scheme for example classes.


Online users slam MOE for providing misleading statistics and poor justification on scholarships for foreign students from India
by Stephen NettoIt was reported that scholars from India under scholarships administered by the Ministry of Education (MOE) would be arriving in Singapore this month and next month. They are essentially scholarship holders of A*STAR Youth Scholarship and SIA Youth Scholarship. Some would be going to Sec 3 while others to pre-university 1.
Since the announcement, there has been a constant buzz all over social media on the matter, with the majority of Singaporeans questioning why the government keeps giving scholarships to foreign students instead of helping the local students more.
Subsequently, MOE released a statement on its Facebook page last Thursday (5 Dec) in attempt to counter the negativity that were spilling online.
“There has been some discussion online and in messaging apps that MOE is recruiting only students from India through scholarships,” MOE wrote. It said that the government does provide a wide range of scholarships and financial aid, predominantly to Singaporeans.
“We also award a small number of scholarships to promising young international students from ASEAN countries, China and India. While here, they help promote goodwill and understanding among young people from different countries,” it explained.
MOE went on to say that “some” of these foreign students would eventually sink their roots here although it didn’t say how many. It said, “Over time, some have become PRs and citizens, sunk roots here, and contribute to Singapore.”
MOE disclosed that 0.9%, or close to 1%, of secondary and pre-university students are foreign students on scholarship.
“Only about 0.7% of these foreign students on scholarships are from India. So 0.7% of 0.9% – or 1 in 16,000 – are scholarship holders from India,” it said, and added that it would look into amending its websites, so as “not to give the impression that the scholarships only target students from a particular country”.
Interestingly, MOE’s Facebook statement, intended to imply some form of justification and cool the air, only brought more heat to the matter at hand upon garnering strong reactions from online users under the comment section.
Many of them slammed MOE for providing such scanty numbers and figures rather than laying out all the proper statistics to reveal the full picture.
Others wondered if the money spent on foreign students would be – or rather, should be – better spent on those who are born and bred here. They questioned whether or not these foreign students will positively contribute to the nation, adding that most of these foreign students have little to zero intention on doing so as they tend to reap the benefits and later move on.
What’s more, a few even pointed out that some of these foreign students “habitually display inconsiderate behaviours”.
Meanwhile, a couple of netizens also brought up the recent case of MOE holding back the PSLE results certificate from a financially-struggling family as they questioned the integrity of MOE for seemingly giving higher priority and benefits to foreigners.
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Module buffet.resty
OpenResty flavor of buffet objects.
Functions
| new([data]) | Create a new buffet object. |
Class buffet
| buffet:receive([size_or_pattern]) | Read the data according to the reading pattern or size. |
| buffet:receiveany(max) | Read the data, at most max bytes. |
| buffet:receiveuntil(pattern[, options]) | Get an iterator to read the data until the specified pattern. |
| buffet:send(data) | Send data. |
| buffet:close() | Close the buffet. |
| buffet:connect(...) | Connect the buffet. |
| buffet:sslhandshake(...) | Do SSL/TLS handshake. |
| buffet:settimeout(...) | Set the timeout value. |
| buffet:settimeouts(...) | Set the connect, send, and read timeout values. |
| buffet:setoption(...) | Set a socket option. |
| buffet:setkeepalive(...) | Put the connection into the cosocket connection pool. |
| buffet:getreusedtimes() | Get reused times for the connection. |
Functions
- new([data])
-
Create a new buffet object.
Parameters:
- data
string, table or function
input data, one of:
- a byte string
- an array-like table of byte strings (be aware that the object will use the same table, not a copy)
- an iterator function producing byte strings
If the parameter is omitted or
nil
, this is roughly equivalent to passing an empty string. (optional)
Returns:
-
buffet
a buffet object
Or
- nil
- string an error
- data
string, table or function
input data, one of:
Class buffet
- buffet:receive([size_or_pattern])
-
Read the data according to the reading pattern or size.
See Lua Nginx Module documentation.
Parameters:
- size_or_pattern
number or string
a size of data to read or a pattern:
'*a'
to read all data'*l'
to read a line
If no parameter is specified, then it is assumed to be the pattern
'*l'
. (optional)
Returns:
-
string
data
Or
- nil
- string an error
- string partial data
Or
- nil
- string an error
- size_or_pattern
number or string
a size of data to read or a pattern:
- buffet:receiveany(max)
-
Read the data, at most max bytes.
The main difference between
receive(size)
andreceiveany(max)
is that the latter doesn't read the next chunk from the input table/iterator if there is any data in the internal buffer left over from the previous calls.See Lua Nginx Module documentation.
Parameters:
- max number or string a max size of data to read
Returns:
-
string
data
Or
- nil
- string an error
- string an empty string (this is an undocumented quirk of the Lua Nginx Module)
Or
- nil
- string an error
- buffet:receiveuntil(pattern[, options])
-
Get an iterator to read the data until the specified pattern.
See Lua Nginx Module documentation.
Parameters:
- pattern string
- options table (optional)
Returns:
-
function
an iterator
Or
- nil
- string an error
- buffet:send(data)
-
Send data.
Appends a string representation of the data to the internal send buffer. Use buffet.get_send_buffer to access the buffer or buffet.get_sent_data to get the content of the buffer as a string.
The
data
parameter can be:- a string
- an array-like table containing strings (possibly nested)
- a number, boolean or nil (will be converted to a string)
See Lua Nginx Module documentation.
Parameters:
- data string, table, number, boolean or nil the data to send
Returns:
-
number
number of bytes have been sent
Or
- nil
- string an error
- buffet:close()
-
Close the buffet.
Marks the object as closed. Removes the reference to the input data.
See Lua Nginx Module documentation.
Returns:
-
number
1 in case of success
Or
- nil
- string an error
- buffet:connect(...)
-
Connect the buffet.
This method is almost noop, it only marks the object as “connected” (not closed). The return status is always success (1).
See Lua Nginx Module documentation.
Parameters:
- ... any not checked, not used
Returns:
-
number
1
- buffet:sslhandshake(...)
-
Do SSL/TLS handshake.
This method is noop. If the buffet is not closed, the method returns
true
(as iffalse
was passed as thereused_session
argument). Otherwise, the method returnsnil
and an error.See Lua Nginx Module documentation.
Parameters:
- ... any not checked, not used
Returns:
-
boolean
true
Or
- nil
- string an error
- buffet:settimeout(...)
-
Set the timeout value.
This method is noop. The method returns nothing.
See Lua Nginx Module documentation.
Parameters:
- ... any not checked, not used
- buffet:settimeouts(...)
-
Set the connect, send, and read timeout values.
This method is noop. The method returns nothing.
See Lua Nginx Module documentation.
Parameters:
- ... any not checked, not used
- buffet:setoption(...)
-
Set a socket option.
This method is noop. The method returns nothing.
See Lua Nginx Module documentation.
Parameters:
- ... any not checked, not used
- buffet:setkeepalive(...)
-
Put the connection into the cosocket connection pool.
This method is equivalent to the close method.
See Lua Nginx Module documentation.
Parameters:
- ... any not checked, not used
Returns:
-
number
1 in case of success
Or
- nil
- string an error
- buffet:getreusedtimes()
-
Get reused times for the connection.
This method is noop. The return value is always
0
.See Lua Nginx Module documentation.
Returns:
-
number
0
Or
- nil
- string an error


|
RayTracer
|
An intersection node in the CSG-Tree. More...
#include <CSG.h>
Public Member Functions | |
| CSGIntersection (CSGComponent *a, CSGComponent *b) | |
| C. More... | |
| double | distance_bound (const Vector3D &pos) const override |
| BoundingBox | get_bounds () const override |
| CSGComponent * | deep_copy () const override |
| ~CSGIntersection () override | |
| Public Member Functions inherited from CSGComponent | |
| virtual | ~CSGComponent ()=default |
Private Attributes | |
| CSGComponent * | first |
| CSGComponent * | second |
| BoundingBox | bounds |
An intersection node in the CSG-Tree.
|
inline |
C.
|
inlineoverride |
|
overridevirtual |
Implements CSGComponent.
|
overridevirtual |
Implements CSGComponent.
|
overridevirtual |
Implements CSGComponent.
|
private |
|
private |
|
private |


| FILE |
DESCRIPTION |
TAGS |
| applyhatch_pluscolor |
APPLYHATCH_PLUSCOLOR Apply hatched patterns to a figure in BW or Color |
|
| biplotPmtk |
Replacement for the stats toolbox biplot function |
|
| colorbars |
set elements of a bar graph to specified color |
|
| contourShade |
Just like countourf but with a better interface. |
|
| dataWindow |
Find appropriate axis coordinates for the n-by-2 data matrix X |
|
| drawGraph |
drawGraph Automatic graph layout: interface to Neato (see http://www.graphviz.org/) |
|
| gaussPlot2d |
Plot an ellipse representing the covariance matrix of a Gaussian |
|
| gridSpace |
Similar to built in ndgrid except the output g is a single matrix, regardless |
|
| hintonDiagram |
Display a matrix W where the square's area represents W(i,j) |
|
| hintonScale |
A modified version of hintonDiagram that allows the user to specify two matrices |
|
| hintonScaleMulti |
Like hintonScaleSingle but plots multiple images |
|
| histo |
PURPOSE: Plot a histogram |
|
| horizontalLine |
Draws a horizontal line at the specified yloc on the current axes. All |
|
| hsvrand |
hsvrand Like built-in HSV, except it randomizes the order, so that adjacent colors are dis-similar |
|
| image_rgb |
Show a matrix of integers as a color image. |
|
| imageToVector |
Convert an int valued [M, N, d] image matrix to a vector |
|
| makehatch_plus |
MAKEHATCH_PLUS Predefined hatch patterns |
|
| maximizeFigure |
Borders |
|
| montagePMTK |
montageKPM is like the built-in montage, but assumes input is MxNxK or filenames |
|
| MyEllipse |
ERROR_ELLIPSE - plot an error ellipse, or ellipsoid, defining confidence region |
|
| nsubplots |
Figure out how many plots in the y and x directions to cover n in total |
|
| pairs |
PURPOSE: Pairwise scatter plots of the columns of x |
|
| pdfcrop |
Make the paper size, the same as the figure size, (plus an optional |
|
| pdffigure |
Just like figure, except makes the paper size equal to the figure size |
|
| placeFigures |
Optimally lay out existing or new figures on the screen so that they |
|
| plotColors |
This file is from matlabtools.googlecode.com |
|
| plotContour |
Plot contours of the function |
|
| plotConvDiagnostics |
Plot trace plot, smoothed trace plot, and ACF of 1d quantity |
|
| plotCVcurve |
Plot a cross validation curve with error bars. |
|
| plotCVgrid |
Plot Cross validation results in 2D |
|
| plotDecisionBoundary |
Plot data and the classification boundaries induced by the specified |
|
| plotDistribution |
Plot a density function in 2d |
|
| plotgauss2d |
PLOTGAUSS2D Plot a 2D Gaussian as an ellipse with optional cross hairs |
|
| plotiqr |
Data is simply numeric |
|
| plotmarkers |
add the specified marker to the figure handle at a sparse set (frac) of locations |
|
| plotPatches |
display receptive field(s) or basis vector(s) for image patches |
|
| plotSurface |
Plot the surface of the function |
|
| plotxx |
PLOTXX - Create graphs with x axes on both top and bottom |
|
| pmtkColors |
http://www.mathworks.com/access/helpdesk/help/techdoc/ref/colorspec.html |
|
| print_pdf |
PRINT_PDF Prints cropped figures to pdf with fonts embedded |
|
| printPmtkFigure |
print current figure to specified file in .eps and .pdf formats |
|
| pscatter |
Pairwise scatter plots of the columns of X |
|
| redgreenColormap |
REDGREENCMAP creates a red and green colormap. |
|
| rel2absX |
Convert from axis-relative coordinates to absolute coordinates within the |
|
| rel2absY |
See rel2absX |
|
| rocCurve |
Plot a receiver operating curve |
|
| seqlogoPmtk |
A replacement function for the bioinformatics seqlogo function |
|
| shade |
This file is from matlabtools.googlecode.com |
|
| subfigure |
h = subfigure(m,n,p), or subfigure(mnp), divides the screen into an m-by-n |
|
| subplot2 |
function subplot2(nrows, ncols, i, j) |
|
| suplabel |
PLaces text as a title, xlabel, or ylabel on a group of subplots. |
|
| suptitle |
SUPTITLE Puts a title above all subplots. |
|
| vectorToImage |
Inverse transformation for imageToVector() |
|
| verticalLine |
Draws a vertical line at the specified xloc on the current axes. All |
|
| xticklabelRot |
function xticklabelRot(xTickLabels, angle, fontsize) |
|


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Place the parboiled potatoes into a bowl with the olive oil and season, to taste, with salt and freshly ground black pepper. Stir until well combined.
Heat a griddle pan over a medium heat until hot. Add the potatoes in batches and cook for four minutes on each side or until tender and golden-brown on both sides. Remove from the pan and keep warm. Repeat the process with the remaining batches of potatoes.
Sprinkle the flour onto a plate. Season with salt and freshly ground black pepper. Dust the John Dory in the seasoned flour.
Heat half the butter in a frying pan over a medium heat until foaming and fry the John Dory for three minutes on each side or until the fish is just cooked through (do this in batches if necessary). Add the remaining butter and the lemon juice to the pan and cook for another minute, basting the fish with the lemon and butter mixture.
For the fennel and caper salad, drain the fennel thoroughly and place into a large bowl with the orange segments. Add the capers and one-third of the dill. Stir until well combined.
To make the dressing, place the reserved juice from the oranges into a small bowl and add the oil, white wine vinegar and the remaining dill. Whisk until well combined. Drizzle the dressing over the fennel and orange salad and mix well.
To serve, place one John Dory fillet onto each of four serving plates. Spoon the potatoes and orange and fennel salad alongside and garnish with the anchovy fillets and caper berries.


new
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paste
cut
copy
clipboard
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edit
layout
Tools
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ALT + Click -> access elements below
CTL + DragElements -> Disable Snapline placement
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries
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Lorem ipsum industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type.
Lorem ipsum industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type.
Lorem ipsum industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type.


Roughly
Having shaped audio identity right from the start, we've been recognized with
We harness the power of music and sound so your brand becomes a beacon that directs and a magnet that attracts.
Of the top 1,000 global brands Scott has helped launch, grow and reposition nearly 40% of them including Apple, Nike, Adidas, eBay, Columbia, Universal, Yahoo, Visa, Verizon Wireless, Coke, Pepsi, Budweiser, eTrade, Exxon, etc. As co-founding CEO of Elias Arts, the world’s largest, most-awarded audio communication agency, his pioneering efforts reshaped the practice of audio communication, branding and strategy; garnering a reputation for alternative growth strategies that are innovative, effective and emotionally robust.
Among the earliest and most influential proponents of harnessing music & sound's emotional impact he's well known for his strategic vision and successfully growing ‘emotional stakeholder communities’ by ‘giving voice’ to a brand’s emotional franchise.
Awarded over 750 awards for 7500 commercial projects-including two Emmy’s and dozens of Clios, London Internationals & Cannes Lions-Elias Arts became a global leader in audio communications, branding, psychoacoustics, environmental and embedded product audio.
 Having it surgically restored years later developed a lifelong fascination with the science of living systems as applied to people and commerce.
As a serial entrepreneur, Scott is a striking blend of head and heart; always looking to understand what sustains people-emotionally, physically and intellectually-then grows a business from here.
He’s a gifted mentor, passionate about business’ role in making the world a better place. His principal message-business success is a by-product, of offerings that feed & fuel people to express who they are and all they yearn to become.
Music is more than knowledge for Alex; it is his way of existing in the world. From the age of five, he would study his brother playing the piano, and listen to songs on the radio and play them from memory. After begging his parents for piano lessons, he realized what he was born to do.
By the time he was in his late teens he had already performed for many dignitaries, and was on his way to starting a band with Chris Ocasek, son of the lead singer of the Cars. The band they formed was signed to EMI/Capitol Records.
As Alex continued on his journey, he became Creative Director of Elias Arts, a major music house in NYC. During his 14-year tenure there, Alex was honored with numerous industry awards for best original music in commercials for clients including Audi, Levi's, Nike, Mercedes Benz, National Geographic, HBO, and Volkswagen.
In 2002, Alex started Tonal. Located in West Chelsea, the heart of the art community in New York, Tonal has become a destination for artists, directors, choreographers and the advertising community. Tonal has created the sonic brands for the likes of Disney Channel, PBS’s Nature, Thomson Reuters and has won multiple awards for HBO and Paramount Studios, and is a frequent mixer for international recording artists such as Brittney Spears, David Archuleta, Pink, R. Kelly, Jordin Sparks and Akon. The advertising client list a constantly expanding list from a list too numerous to mention.
In his spare time, he continued to explore outside recording projects. Starting with Grace Jones and Yes, he’s continued his album work recently releasing his 14th album.
Alex is also a staunch supporter of the arts. He has been collecting contemporary art for more than twenty years and his paintings are frequently requested by museums around the world.


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IDEA, full name as "Interactive Differential Expression Analyzer", is an online analysis and visualization platform for differential feature expression analysis of read count data on foundation of R, Shiny and JavaScript. In IDEA, five R packages, DESeq, edgeR, NOISeq, PoissonSeq and SAMseq are provided for counts data analysis.
In "New" module, raw data is inputted. The function of this app is divided into two modules: "Data Exploration" and "Analysis". In "Analysis", both package-specific analysis and "Combination Analysis" are provided. In "Data Exploration", the input data can be normalized, and several figures, such as PCA and Boxplot, are provided to reveal the characteristic of the data. These analyses can help to estimate the data quality. For "Analysis", the five packages are provided to estimate the differential expression of features. Certain tables and figures of each package can give a clear and visual result of the estimation. The last part "Combination Analysis" synthesizes the results of each package and provides an easier way to obtain the result of differential expression features.
This document is a step-by-step guide and instruction of IDEA, and follows the order of operations.
The tips will be shown once the user moves the cursor to the charts, icons or question marks as shown in Figure 0-1 (upper) and Figure 0-2 (lower). For charts with interactive option, the option panel will appear with the chart by default, and it can be closed and reopened by clicking the gear icon as shown in Figure 0-2. Moreover, every figure shown in the page can be downloaded separately by clicking the download icon (Figure 0-2) on each figure.
Figure 0-1 Example of tips
Figure 0-2 Example of interactive option
In this module, users need to choose the experiment type and upload the data in specific format.
Here we provide an example dataset. By clicking Example, you will see the data information of the example. The design matrix and count matrix data of the example is available for download respectively as shown in Figure 1-2.
Figure 1-1 Experiment Type Select Panel
Figure 1-2 Download and view Example
For experiment types, IDEA can work with 3 kinds of experiment types: experiment with standard comparison, experiment of multi-factor design and experiment with no replicates (NOT RECOMMANED).
"Standard Comparison" supports experiments with only one factor, several conditions and several replicates (example shown in Table 1-1).
Table 1-1 Example of Standard Comparison
| Conditions | Replication 1 | Replication 2 | |||
| Factors | Viscera | Kidney | Liver | Kidney | Liver |
"Multi-factor Design" supports experiments with several factors, conditions and replicates (example shown in Table 1-2).
Table 1-2 Example of Multi-Factors Design
| Factors | Replication 1 | Replication 2 | |||
| Conditions | Viscera | Kidney | Liver | Kidney | Liver |
| Sex | Female | Male | Female | Male |
Without Replicates supports experiments with no biological replicates (example shown in Table 1-3). Not all methods are applicable to analyze the data without replicates, since it is necessary to assume the distribution form (negative binomial distribution or Poisson distribution) and the parameters based on empirical.
Table 1-3 Example of Without Replicates
| Factors | Replication 1 | ||
| Conditions | Viscera | Kidney | Liver |
| Sex | Female | Male |
The data matrix should be in .csv or .txt format, with genes as row and samples as column, and upload raw counts only.
If you check the Header box, the first row and column of the matrix will be considered the header by default. For Separator, it is accessible to use either comma (,), semicolon (;) or tab (tabular). If your data has quote, choose the Quote option precisely, otherwise, choose "None".
Also, it is optional to input the "gene length file" with features names as column1 and the length as column2, the header and separator should be the same as the countable.
Figure 1-3 Data uploading panel
The conditions of the experiment should also be inputted as .csv or .txt format, and the conditions will automatically appear on the page for chosen. For multi-condition experiment, choose only two compared conditions to call differential expression features, or by default, the first two conditions will be chosen as the compared conditions. In Multi-factor Design, factor of interest is set as the first column of your design matrix.
Figure 1-4 Setting compare groups
By clicking "View uploaded data", the "Data Information Table" will show the uploaded read counts table. The order of the table can be changed by clicking the header of the column. The number of the features shown on each page is also changeable on the top left.
Figure 1-5 Data Information Table
The "Data" module does the job of data normalization, data exploration and quality control.
The process of normalization attempts to settle the problem of various factors (nucleotide composition of features, library preparation and sequencing platform etc.) which can bring bias into number of reads in read count data. 3 methods are provided to normalize sample data (Table 2-1).
Table 2-1 Normalization Method
| Abbreviation | Full Name | Method Details |
| RPKM | Reads Per Kilo Base per Million Reads | Divide gene count by the total number of reads in each library or mapped reads |
| UQ | Upper Quartile | Sum gene counts up to the upper 25% quartile to normalize |
| TMM | Trimmed Mean of M | Compute a scaling factor as weighted means of log ratios between two experiments after excluding most expressed and genes that have large log ratios in expression |
In "Data", we provide 7 figures helping users analyze the quality of data, and the data distribution as well as sample distance can easily be seen through these figures. The detail of every figure is given below and a corresponding short tip is also provided on the app webpage.
Choose the normalization method on the panel before doing anything else. If normalization is unnecessary, choose "None".As default, the Upper Quartile method is chosen.
Figure 2-1 Normalization Panel
Different figures and tables may have specific settings. See more options and instructions in particular charts, such as Stacked Density Plot, Heat Map of Sample Distance or Correlation Analysis.
See more instructions of charts and details in Report.
Samples boxplot visualizes count distribution for all samples, showing features in expression distribution in each sample.
Figure 2-2 Sample boxplot
Stacked density plot visualizes density distribution of features with different read counts, showing overall condition of read counts data normalized counts. For interactive option, click the input box to add the samples and click Submit to plot, delete the samples by clicking the cross near the sample.
Figure 2-3 Stacked density plot and interactive option
Ratio bar plot visualizes distribution of counts in each samples using stacked bar. Low counts may introduce noise and interfere extraction of differential expression of features.
Figure 2-4 Ratio bar plot
The heat map of sample distance visualizes the Euclidean distance between the samples, giving an overview of similarities of sample heat map. The heat map color can be changed in interactive option. Attention, if the samples have a clear classification, this plot may only have two colors, as shown in Figure 2-5.
Figure 2-5 Heat map of sample distance and interactive option
A principal component analysis (PCA) plot visualizes the affection of the first two principal components. It is optional to show the labels of data in the figure on the interactive option panel.
Figure2-6 Principal component analysis plot and interactive option
With scatter plot, the correlation analysis visualizes Spearman's correlation of feature expression between two selected samples. Spearman correlation coefficient is shown in the Interactive Option panel of correlation analysis. The sample plotted on the x and y axis can also be changed on the panel.
Figure 2-7 Scatter plot and correlation analysis and interactive option
A histogram with error bar visualizes the comparison result of expression level of a certain feature, selected by user in interactive option, in different conditions. The mean, standard deviation and standard error of the chosen feature is also shown in interactive option panel. The Report will only show the figure of feature chosen here.
Figure 2-8 Feature query and interactive option
The normalized data in .csv format and the report of all charts can be downloaded on the panel.
Figure 2-9 Download panel in "Data" module
The "Analysis" module is divided into two parts: the packages analysis part and the combination part. In packages analysis part, we provide five methods for analyzing differential expression of features. Here we simply introduce the basic feature of every method. The summary table is given below (Table 3-1).
Table 3-1 Summary of packages
| Package | Version | Normalization (default) | Model of Reads Count Distribution | Differential Expression Test | FDR Control | Standard Comparison | Multi-factor Design | Without Replicates |
| DESeq2 | 1.6.2 | sizeFactors | Negative binomial distribution | Wald test, LRT | Benjamini-Hochberg procedure | |||
| edgeR | 3.8.3 | TMM | Negative binomial distribution | Fisher's exact test | Benjamini-Hochberg procedure | |||
| NOISeq | 2.8.0 | RPKM | Nonparametric method | P-value for empirical distributions | Not applicatable | |||
| PoissonSeq | 1.1.2 | Goodness-of-fit estimate | Poisson distribution | Score statistics | A permutation plug-in approach | |||
| SAMseq (samr) |
2.0 | Subsampling method | Nonparametric method | Wilcoxon test | A permutation plug-in approach |
Be sure of your experiment type. The multi-factors design can only use DESeq2 and edger packages, the PoissonSeq or SAMseq is not available for experiment with no replicates.
Click the icon of one package and click START, the charts of this package will show on the page.
Figure 3-1 Method select panel
Different packages have different visualization. Table 3-2 is a summary of charts in different packages.
Table 3-2 Summary of types of charts and plots in packages
| Chart/Plot Type | DESeq | edgeR | NOISeq | PoissonSeq | SAMseq |
| Differerential Expression Features Table |
|||||
| MA-Plot | |||||
| Normalized SizeFactors | |||||
| Volcano Plot | |||||
| Heat Map | |||||
|
FDR/P-value/Probabiltiy Distribution |
|||||
| Variance Estimation | |||||
| Power Transformation Curve | |||||
| Q-Q Plot |
See more instructions of charts and details in Report.
The order of the table may changed by clicking any names on the first line. The number of features shown in one page can be changed on top left. The search function can search the data on either column.
The interpretation of the columns in all packages is shown in the table below.
Table 3-3 Interpretation of differential features table in all packages
| Package | Header | Interpretation |
| DESeq | FeatureID | Feature identifier |
| baseMean | Mean over all rows | |
| log2FoldChange | Logarithm (base 2) of the fold change | |
| lfcSE | Standard Error of log2(FoldChange) | |
| stat | Wald statistic / LRT statistic | |
| pvalue | Wald test/LRT p-value | |
| padj | p-value adjusted for multiple testing with the Benjamini-Hochberg procedure | |
| edgeR | FeatureID | Feature identifier |
| logFC | Logarithm (base 2) of the fold change | |
| logCPM | Average log2-counts-per-million | |
| PValue | Two sided p-value | |
| FDR | False discovery rate | |
| NOISeq | FeatureID | Feature identifier |
| Mean | Mean of this condition | |
| Theta | Differential expression statistics | |
| Prob | Probability of differential expression | |
| Log2FC | Logarithm (base 2) of the fold change | |
| PoissonSeq | FeatureID | Feature identifier |
| tt | The score statistics of the features | |
| P.value | Permutation-based p-values of the features | |
| FDR | Estimated false discovery rate | |
| logFC | Estimated log (base 2) fold change of the features | |
| SAMseq | FeatureID | Feature identifier |
| Score.d | The T-statistic value | |
| Fold.Change | The ratio of the two compared value | |
| q.value | the lowest FDR at which that feature is called significant |
In MA-Plot, the data is been transformed onto the M (fold change or log ratio) and A (average expression of a feature) scale, which can give users a quick overview of the distribution of data. The false discovery rate (FDR) threshold can be changed, and the features are colored red if the adjusted p-value is less than the FDR, while other features are colored blue.
Figure 3-2 MA-plot and interactive option
Since different samples may have different sequencing depth, it is necessary to put every count value to a common scale in order to make them comparable.
In edgeR table, group represents conditions, lib.size represents size of the library, norm.factors is the normalized size factors.
Table 3-2 Table of Normalized size factors in edgeR
An overview of the number of differential expression features can be shown in the volcano plot. The threshold of both axes can be changed on the Interactive Option panel. Highly differential expressed features are colored blue, while others are in red.
Figure 3-3 Volcano plot and interactive option
By using a color scale, heat map can display the expression values of the features, and every rectangle represents one feature – sample pair. By default, we display the 30 most highly expressed features and this number is changeable on the option panel. In addition, the scale method (normalize data in row or column), clusters of row /column and colorkey is also changeable on the panel.
Figure 3-4 Heat map of differential expression features and interactive options
FDR/P-value distribution plot visualizes distribution of FDR or P-value in differential expression test provided in analysis packages using histogram plot. Specially, NOISeq uses the q-value (standard comparison) or prob(without replicates) to form the distribution plot.
Figure 3-5 FDR distribution plot
The dispersion estimates plot is for checking the result of dispersion estimates adjustment. The feature-wise estimates are in black, the fitted estimates are in red, and the final estimates are in blue. The outliers of feature-wise estimates are marked with blue circles. The points lying on the bottom indicates they have a dispersion of practically zero or exactly zero.
Figure 3-6 Dispersion estimates plot
The variance estimation plot has average log CPM (counts per million) as x-axis and biological coefficient variation as y-axis. The red dots represent the common dispersion and the black dots represent the tag-wise (feature-wise) dispersion.
Figure 3-7 Variance estimation plot
Power transformation curve is for estimating the best parameter for minimizing overdispersion of data. It plotted one over theta on y-axis and mean log mu on x-axis. See more details on the Report.
Figure 3-8 Power curve of PoissonSeq
The Q-Q plot, also called the SAM plot in SAMseq, is a scatter plot with dots representing features. The positive significant features, which means the features has higher expression correlates with higher risk, are in red, and negative significant features are in green, while others are in black.
Figure 3-9 Q-Q plot in SAMseq
The markdown files of every package are provided as the analysis report and can be downloaded on the panel. Notice that each package will generate the specific report.
Also, the differential features table (.csv format) can be downloaded separate from other charts by "Download .csv file".
Figure 3-10 Download panel in "Analysis" Module
By clicking the icon in figure 3-11, the user will enter the "Combination" mode. "Combination" module provides a collection and comparison of the prior using packages. Figures like Venn and bar plot give an intuitive impression of the different result made by each package. We also define a new argument called R-value to synthesis the results of differential expression features from these packages.
Figure 3-11 Combination mode
It is available for users to choose the packages needed to analysis on the "Advanced Option" panel.
Figure 3-12 Advanced option
This plot shows a comparison of the number of differential expression features identified by each package. The different of the results are caused the differences of the algorithm of these packages. The counts of features are plotted on the y-axis and each bar represents one package.
Figure 3-13 Bar Plot of total counts of features by each package
The Venn diagram visualizes the overlapping differential expression features identified by each package. In the diagram, each oval represents one package, and the number shown in the diagram means the number of differential expression features.
Figure 3-14 Venn plot of differential expression features analysis by each packge
This table shows the identification details of every feature, and the order of the table may changed by clicking any names on the first line. The number of features shown in one page can be changed on top left.
The interpretation of some columns is shown in the table.
Table 4-1 Interpretation of feature weight table
| Package | Header |
| FeatureID | Feature identifier |
| Mean | Mean of expression |
| LogFC | Logarithm of the fold change |
| Rankmean | Mean rank of the five packages |
| Score | Intergration score of rank lists of DE features by robust rank aggregation (RRA) |
As other modules, the report of "Combination" is also available for download. The .csv format result is the result of the "Feature Weight Table".
Figure 3-15 Download Panel


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Broker involved in university student trafficking case arrested
The police have arrested a broker involved in the case against the trafficked Dagon University student, Police Colonel Myo Thu Soe, Deputy Head of Anti-Human Trafficking Squad confirmed.
“He was trafficked by three persons. They are one man and two women. Of them, one man has been detained in Pyapon. Yesterday (November 22), a case was opened at Kyauktada Township Police Station under the Anti-Human Trafficking Law, said Police Colonel Myo Thu Soe.
Myat Thura Tun was trafficked on October 2 while he went to the downtown Yangon to perform his family’s works. He had to work there for one and a half months. The brokers sold him to the wholesale fishing business, for Ks 700,000. He was released as his parents paid the money. He was injured as he was tortured by a supervisor of the fishing raft.
The police made an investigation into the case after the case went viral on facebook. The police are searching for the rest who sold the victim to the fishing raft in Pyapon.
Currently, the police are interrogating an official from the wholesale fishing business in Pyapon Township.


Widgets can be moved to an exact postion on the grid and the grid will not attempt to collapse it down to a smaller size. Also widgets will not move out of the way automatically, but will move only on mouse up


{% extends 'base.html' %} {% load formatting %} {% block title %}Cash Rules Everything Around Me{% endblock %} {% block content %}
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|---|---|---|---|---|
| {{ transaction.amount|format_money }} | {{ transaction.date_posted|date:"F j, Y" }} | {{ transaction.memo }} | {{ transaction.account.bank.name }} - {{ transaction.account.account_number }} | Reconcile |


#include "book.h"
#include "base.h"
#include "search.h"
#include "const.h"
#include "bit.h"
#include "options.h"
#include "util.h"
#include <assert.h>
#include <time.h>
#include <stdarg.h>
#include <limits.h>
Classes | |
| struct | Link |
| a move (with its score) linking to another Position. More... | |
| struct | Position |
| A position stored in the book. More... | |
| struct | PositionArray |
| An array with positions. More... | |
| struct | BookCheckGame |
Macros | |
| #define | BOOK_DEBUG 0 |
| #define | clear_line() bprint(" \r") |
| #define | foreach_link(l, p) for ((l) = (p)->link; (l) < (p)->link + (p)->n_link; ++(l)) |
| #define | foreach_position(p, a, b) |
Typedefs | |
| typedef struct Link | Link |
| a move (with its score) linking to another Position. | |
| typedef struct Position | Position |
| A position stored in the book. | |
| typedef struct PositionArray | PositionArray |
| An array with positions. | |
| typedef struct BookCheckGame | BookCheckGame |
Functions | |
| static void | bprint (const char *format,...) |
| print a message on stdout. | |
| static bool | link_read (Link *link, FILE *f) |
| read a link. | |
| static bool | link_write (const Link *link, FILE *f) |
| write a link. | |
| static bool | link_is_bad (const Link *link) |
| check if a link is unvalid. | |
| static Position * | book_probe (const Book *book, const Board *board) |
| Find a position in the book. | |
| static void | book_add (Book *book, const Position *p) |
| Add a position to the book. | |
| static void | position_print (const Position *position, const Board *board, FILE *f) |
| print a position in a compact but readable format. | |
| static int | get_book_depth (const int depth) |
| return the number of plies from where the search is solving. | |
| static bool | position_is_ok (const Position *position) |
| Check if position is ok or need fixing. | |
| static void | position_init (Position *position) |
| Initialize a position. | |
| static void | position_merge (Position *dest, const Position *src) |
| Merge a position with another one. | |
| static void | position_free (Position *position) |
| Free resources used by a position. | |
| static bool | position_read (Position *position, FILE *f) |
| Read a position. | |
| static bool | position_import (Position *position, FILE *f) |
| Read a position. | |
| static bool | position_write (const Position *position, FILE *f) |
| Write a position. | |
| static bool | position_export (const Position *p, FILE *f) |
| write a position. | |
| static void | position_unique (Position *position) |
| Make position unique, regarding symetries. | |
| static int | position_get_moves (const Position *position, const Board *board, MoveList *movelist) |
| Get moves from a position. | |
| static void | position_show (const Position *position, const Board *board, FILE *f) |
| print a position in a readable format. | |
| static void | position_get_random_move (const Position *position, const Board *board, Move *move, Random *r, const int randomness) |
| Chose a move at random from the position. | |
| static bool | position_add_link (Position *position, const Link *link) |
| Add a link to this position. | |
| static void | position_sort (Position *position) |
| Sort the link moves. | |
| static void | position_search (Position *position, Book *book) |
| Evaluate a position. | |
| static void | position_link (Position *position, Book *book) |
| Link a position. | |
| static void | position_expand (Position *position, Book *book) |
| Expand a position. | |
| static int | position_negamax (Position *position, Book *book) |
| Negamax a position. | |
| static void | position_prune (Position *position, Book *book, const int player_deviation, const int opponent_deviation, const int lower, const int upper) |
| Prune a position. | |
| static void | position_remove_links (Position *position, Book *book) |
| Remove bad links after book pruning. | |
| static void | position_deviate (Position *position, Book *book, const int player_deviation, const int opponent_deviation, const int lower, const int upper) |
| Deviate a position. | |
| static void | position_enhance (Position *position, Book *book) |
| Enhance a position. | |
| static void | board_feed_hash (Board *board, const Book *book, Search *search, const bool is_pv) |
| Feed hash from a position. | |
| static bool | board_fill (Board *board, Book *book, int depth) |
| Fill the opening book. | |
| static void | position_fix (Position *position, Book *book) |
| Fix a position. | |
| static void | position_array_init (PositionArray *a) |
| Initialize the array. | |
| static bool | position_array_add (PositionArray *a, const Position *p) |
| Add a position to the array. | |
| static bool | position_array_remove (PositionArray *a, const Position *p) |
| Remove a position from an array. | |
| static void | position_array_free (PositionArray *a) |
| Free resources used by a position array. | |
| static Position * | position_array_probe (PositionArray *a, const Board *board) |
| Find a position in the array. | |
| static void | book_set_date (Book *book) |
| Set book date. | |
| static double | book_get_age (Book *book) |
| Get book age, in seconds. | |
| static void | book_remove (Book *book, const Position *p) |
| Remove a position from the book. | |
| static void | book_clean (Book *book) |
| Set all positions as undone. | |
| static Position * | book_root (Book *book) |
| Find the initial position in the book. | |
| void | book_init (Book *book) |
| Initialize the opening book. | |
| void | book_free (Book *book) |
| Free resources used by the opening book. | |
| void | book_new (Book *book, int level, int n_empties) |
| Create a new opening book. | |
| void | book_load (Book *book, const char *file) |
| Load the opening book. | |
| void | book_import (Book *book, const char *file) |
| Import an opening book. | |
| void | book_export (Book *book, const char *file) |
| Export an opening book. | |
| void | book_save (Book *book, const char *file) |
| Save an opening book. | |
| void | book_merge (Book *dest, const Book *src) |
| Merge two opening books. | |
| void | book_negamax (Book *book) |
| Negamax a book. | |
| void | book_link (Book *book) |
| Link a book. | |
| void | book_fix (Book *book) |
| Fix a book. | |
| void | book_deepen (Book *book) |
| Deepen a book. | |
| void | book_correct_solved (Book *book) |
| Correct wrong solved score in the book. | |
| static void | book_expand (Book *book, const char *action, const char *tmp_file) |
| Expand a book. | |
| void | book_sort (Book *book) |
| Sort a book. | |
| void | book_play (Book *book) |
| Play. | |
| void | book_fill (Book *book, const int depth) |
| Fill a book. | |
| void | book_deviate (Book *book, Board *board, const int relative_error, const int absolute_error) |
| Deviate a book. | |
| void | book_prune (Book *book) |
| Prune a book. | |
| void | book_enhance (Book *book, Board *board, const int midgame_error, const int endcut_error) |
| Enhance a book. | |
| void | book_info (Book *book) |
| display some book's informations. | |
| void | book_show (Book *book, Board *board) |
| Display a position from the book. | |
| bool | book_get_moves (Book *book, const Board *board, MoveList *movelist) |
| Get a list of moves from the book. | |
| void | book_get_line (Book *book, const Board *board, const Move *move, Line *line) |
| Get a variation from the book. | |
| bool | book_get_random_move (Book *book, const Board *board, Move *move, const int randomness) |
| Get a move at random from the opening book. | |
| void | book_get_game_stats (Book *book, const Board *board, GameStats *stat) |
| Get game statistics from a position. | |
| void | book_add_board (Book *book, const Board *board) |
| Add a position. | |
| void | book_add_game (Book *book, const Game *game) |
| Add positions from a game. | |
| void | book_add_base (Book *book, const Base *base) |
| Add positions from a game database. | |
| void | book_check_game (Book *book, MoveHash *hash, const Game *game, BookCheckGame *stat) |
| Check positions from a game. | |
| void | book_check_base (Book *book, const Base *base) |
| Check positions from a game database. | |
| static void | extract_skeleton (Book *book, Board *board, Line *pv, Base *base) |
| Extract book lines to a game base. | |
| void | book_extract_skeleton (Book *book, Base *base) |
| Extract book draws to a game base. | |
| void | book_extract_positions (Book *book, const int n_empties, const int n_positions) |
| print a set of position. | |
| void | book_stats (Book *book) |
| print book statistics. | |
| void | book_feed_hash (const Book *book, Board *board, Search *search) |
| feed hash table from the opening book. |
Variables | |
| static const int | BOOK_INFO_RESOLUTION = 100000 |
| bool | book_verbose = false |
| const Link | BAD_LINK = {-SCORE_INF, NOMOVE} |
File for opening book management.
Edax book is a set of positions. Each position is unique concerning all possible symetries. A position is made of an othello board, a set of moves leading to other positions in the book, called here "link", and the best remaining move, as evaluated by a search at fixed depth, called here leaf. It also contains win/draw/loss statistics, which is actually useless, and a score with two bounds from retropropagated error. Several algorithms are present to add positions in the book, in the most usefull way.
| #define BOOK_DEBUG 0 |
| #define clear_line | ( | ) | bprint(" \r") |
| #define foreach_link | ( | l, | |
| p | |||
| ) | for ((l) = (p)->link; (l) < (p)->link + (p)->n_link; ++(l)) |
| #define foreach_position | ( | p, | |
| a, | |||
| b | |||
| ) |
| typedef struct BookCheckGame BookCheckGame |
| typedef struct PositionArray PositionArray |
An array with positions.
|
static |
Feed hash from a position.
Go through the book sub-tree following the current position & feed the hash table from this position.
| board | Position to expand. |
| book | Opening book. |
| search | Hashtables container. |
| is_pv | Flag to tell if the position is from the principal variation. |
Fill the opening book.
Add positions to link existing positions.
| board | Candidate position. |
| book | Opening book. |
| depth | Depth at which to search a link. |
Add a position to the book.
| book | Opening book. |
| p | Position to add. |
Add positions from a game database.
| book | opening book. |
| base | games to add. |
Add a position.
| book | opening book. |
| board | position to add. |
Add positions from a game.
| book | opening book. |
| game | game to add. |
Check positions from a game database.
| book | opening book. |
| base | games to add. |
| void book_check_game | ( | Book * | book, |
| MoveHash * | hash, | ||
| const Game * | game, | ||
| BookCheckGame * | stat | ||
| ) |
|
static |
Set all positions as undone.
| book | Opening book. |
| void book_correct_solved | ( | Book * | book | ) |
Correct wrong solved score in the book.
Correct erroneous solved positions. Edax may be unstable and introduce bugs from time to time...
| book | opening book. |
| void book_deepen | ( | Book * | book | ) |
Deepen a book.
Research all non link best move.
| book | opening book. |
| void book_deviate | ( | Book * | book, |
| Board * | board, | ||
| const int | relative_error, | ||
| const int | absolute_error | ||
| ) |
Deviate a book.
| book | opening book. |
| board | Position to start from. |
| relative_error | Error relative to the current position's score. |
| absolute_error | Error relative to the root position's score. |
Enhance a book.
| book | opening book. |
| board | Position to start from. |
| midgame_error | Error in midgame search. |
| endcut_error | Error in endgame search. |
|
static |
Expand a book.
Research all non link best move.
| book | opening book. |
| action | String with a description of current action. |
| tmp_file | Temporary file name. |
| void book_export | ( | Book * | book, |
| const char * | file | ||
| ) |
Export an opening book.
Save the book in a portable text format.
| book | Opening book. |
| file | File name. |
| void book_extract_positions | ( | Book * | book, |
| const int | n_empties, | ||
| const int | n_positions | ||
| ) |
print a set of position.
| book | Opening book. |
| n_empties | Game stage. |
| n_positions | Number of positions to extract. |
Extract book draws to a game base.
This function supposes that f5d6c4 & f5f6e6f4 are draws and the only draws, excluding the transpositions f5d6c3d3c4 & f5f6e6c6 & c4..., d3..., e6...
| book | Opening book. |
| base | game database. |
feed hash table from the opening book.
| book | Opening book. |
| board | Position to start from. |
| search | HashTables container. |
| void book_fill | ( | Book * | book, |
| const int | depth | ||
| ) |
Fill a book.
| book | opening book. |
| depth | Distance to fill between two positions. |
| void book_fix | ( | Book * | book | ) |
Fix a book.
| book | opening book. |
| void book_free | ( | Book * | book | ) |
Free resources used by the opening book.
| book | Opening book. |
|
static |
Get book age, in seconds.
| book | Opening book. |
Get a variation from the book.
| book | Opening book. |
| board | Position. |
| move | First move; |
| line | Bariation. |
Get a list of moves from the book.
| book | Opening book. |
| board | Position to display. |
| movelist | List of moves. |
Get a move at random from the opening book.
| book | Opening book. |
| board | Position to find a move from. |
| move | Chosen move. |
| randomness | Randomness. |
| void book_import | ( | Book * | book, |
| const char * | file | ||
| ) |
Import an opening book.
Read the opening book from a portable text format. After the book is imported, it is needed to relink & negamax it.
| book | Opening book. |
| file | File name. |
| void book_info | ( | Book * | book | ) |
display some book's informations.
| book | opening book. |
| void book_init | ( | Book * | book | ) |
Initialize the opening book.
Create an empty opening book.
| book | Opening book. |
| void book_load | ( | Book * | book, |
| const char * | file | ||
| ) |
Load the opening book.
| book | Opening book. |
| file | File name. |
Merge two opening books.
It is needed to relink & negamax the destination book after merging.
| dest | Destination opening book. |
| src | Source opening book. |
| void book_negamax | ( | Book * | book | ) |
Negamax a book.
| book | opening book. |
| void book_new | ( | Book * | book, |
| int | level, | ||
| int | n_empties | ||
| ) |
Create a new opening book.
Create an opening book with the initial position & a single non link move.
| book | Opening book. |
| level | search level to evaluate positions. |
| n_empties | number of empty positions up to which to evaluate positions. |
| void book_play | ( | Book * | book | ) |
Play.
Add positions to the opening book by adding links to position with no links.
| book | opening book. |
Find a position in the book.
| book | Opening book. |
| board | Board to find in the array. |
| void book_prune | ( | Book * | book | ) |
Prune a book.
Remove positions Edax cannot reach.
| book | opening book. |
Remove a position from the book.
| book | Opening book. |
| p | Position to add. |
Find the initial position in the book.
Attention: when a position is added to the book, the pointer returned by this position may be wrong. If the root position is updated the contents of the pointed structure may be wrong. So it is needed to recall this function each time as necessary.
| book | Opening book. |
| void book_save | ( | Book * | book, |
| const char * | file | ||
| ) |
Save an opening book.
Save the book in a fast binary format.
| book | Opening book. |
| file | File name. |
|
static |
Set book date.
| book | Opening book. |
Display a position from the book.
| book | opening book. |
| board | position to display. |
| void book_sort | ( | Book * | book | ) |
Sort a book.
| book | opening book. |
| void book_stats | ( | Book * | book | ) |
print book statistics.
| book | Opening book. |
|
static |
print a message on stdout.
| format | Format string. |
| ... | variable arguments. |
Extract book lines to a game base.
Recursively add move to a PV until no move are available, where we dump the PV to a game data base.
| book | Opening book. |
| board | Starting position. |
| pv | Previous moves leading to the starting position. |
| base | game database. |
|
static |
return the number of plies from where the search is solving.
| depth | search depth. |
|
inlinestatic |
check if a link is unvalid.
| link | checked link. |
|
inlinestatic |
read a link.
| link | link to read in. |
| f | input stream. |
|
inlinestatic |
write a link.
| link | link to write out. |
| f | output stream. |
|
static |
Add a position to the array.
| a | Positions' array. |
| p | Position to add. |
|
static |
Free resources used by a position array.
| a | Positions' array. |
|
static |
Initialize the array.
| a | Positions' array. |
|
static |
Find a position in the array.
| a | Positions' array. |
| board | Board to find in the array. |
|
static |
Remove a position from an array.
| a | Positions' array. |
| p | Position to add. |
|
static |
Deviate a position.
This is the important part of the opening book code, where it finds the best moves to add to the book. Considering the current position, it will deviate a child position or expand a move if:
| position | Position to expand. |
| book | Opening book. |
| player_deviation | Player's error. |
| opponent_deviation | Opponent's error. |
| lower | Error lower bound. |
| upper | Error upper bound. |
Enhance a position.
This is the other important part of the opening book code, where it finds the best moves to add to the book by another methods. Here we use score negamaxed bounds to decide if a move is a good candidate for further enhancement or for expansion. The purpose of this algorithm is to find more moves.
| position | Position to expand. |
| book | Opening book. |
Expand a position.
Expand the best yet unlink move. This will add a new position to the book. Two new moves will also be analyzed, one for the new position, the other for the actual position as a new best unlink move.
| position | Position to expand. |
| book | Opening book. |
|
static |
write a position.
| p | position to write out. |
| f | output stream. |
Fix a position.
Recompute all elements of a buggy position
| position | Position to fix. |
| book | Opening book. |
|
static |
Free resources used by a position.
| position | Position. |
|
static |
Get moves from a position.
| position | position to get moves from. |
| board | board. |
| movelist | movelist. |
|
static |
Read a position.
| position | Position to read in. |
| f | Input stream. |
|
static |
Initialize a position.
| position | Position. |
|
static |
Check if position is ok or need fixing.
Note: All positions should always be OK! A wrong position means a BUG!
| position | Position. |
Merge a position with another one.
A position is merged if its level is > to the destination position; or == and its leaf move is not contains into the destination link moves.
Note: link moves are not copied. This can be done later with position_link().
| dest | Destination position. |
| src | Source position. |
Negamax a position.
Go through the book sub-tree following the current position & negamax the best scores back to this position.
| position | Position to expand. |
| book | Opening book. |
print a position in a compact but readable format.
| position | position to print out. |
| board | Symetrical board to use. |
| f | output stream. |
|
static |
Prune a position.
| position | Position to prune. |
| book | Opening book. |
| player_deviation | Player's error. |
| opponent_deviation | Opponent's error. |
| lower | Error lower bound. |
| upper | Error upper bound. |
|
static |
Read a position.
| position | Position to read in. |
| f | Input stream. |
Remove bad links after book pruning.
| position | Position to fix. |
| book | Opening book. |
Evaluate a position.
If needed, find the best remaining move, after link moves are excluded.
| position | Position to search. |
| book | Opening book. |
print a position in a readable format.
| position | position to print out. |
| board | Symetrical board to use. |
| f | output stream. |
|
static |
Sort the link moves.
| position | Position to sort. |
|
static |
Make position unique, regarding symetries.
| position | position. |
|
static |
Write a position.
| position | position to write out. |
| f | output stream. |
|
static |
| bool book_verbose = false |