adapt
adapt
»
Index
Index
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A
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C
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D
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F
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G
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K
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M
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P
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R
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T
_
__init__() (adapt.feature_based.ADDA method)
(adapt.feature_based.CORAL method)
(adapt.feature_based.DANN method)
(adapt.feature_based.DeepCORAL method)
(adapt.feature_based.FE method)
(adapt.feature_based.mSDA method)
(adapt.instance_based.KLIEP method)
(adapt.instance_based.KMM method)
(adapt.instance_based.TrAdaBoost method)
(adapt.instance_based.TrAdaBoostR2 method)
(adapt.instance_based.TwoStageTrAdaBoostR2 method)
(adapt.parameter_based.RegularTransferLC method)
(adapt.parameter_based.RegularTransferLR method)
(adapt.parameter_based.RegularTransferNN method)
A
ADDA (class in adapt.feature_based)
C
check_estimator() (in module adapt.utils)
check_indexes() (in module adapt.utils)
check_network() (in module adapt.utils)
CORAL (class in adapt.feature_based)
D
DANN (class in adapt.feature_based)
DeepCORAL (class in adapt.feature_based)
F
FE (class in adapt.feature_based)
fit() (adapt.feature_based.ADDA method)
(adapt.feature_based.CORAL method)
(adapt.feature_based.DANN method)
(adapt.feature_based.DeepCORAL method)
(adapt.feature_based.FE method)
(adapt.feature_based.mSDA method)
(adapt.instance_based.KLIEP method)
(adapt.instance_based.KMM method)
(adapt.instance_based.TrAdaBoost method)
(adapt.instance_based.TrAdaBoostR2 method)
(adapt.instance_based.TwoStageTrAdaBoostR2 method)
(adapt.parameter_based.RegularTransferLC method)
(adapt.parameter_based.RegularTransferLR method)
(adapt.parameter_based.RegularTransferNN method)
G
get_default_encoder() (in module adapt.utils)
get_default_task() (in module adapt.utils)
get_features() (adapt.feature_based.CORAL method)
GradientReversal() (in module adapt.utils)
K
KLIEP (class in adapt.instance_based)
KMM (class in adapt.instance_based)
M
mSDA (class in adapt.feature_based)
P
predict() (adapt.feature_based.ADDA method)
(adapt.feature_based.CORAL method)
(adapt.feature_based.DANN method)
(adapt.feature_based.DeepCORAL method)
(adapt.feature_based.FE method)
(adapt.feature_based.mSDA method)
(adapt.instance_based.KLIEP method)
(adapt.instance_based.KMM method)
(adapt.instance_based.TrAdaBoost method)
(adapt.instance_based.TrAdaBoostR2 method)
(adapt.instance_based.TwoStageTrAdaBoostR2 method)
(adapt.parameter_based.RegularTransferLC method)
(adapt.parameter_based.RegularTransferLR method)
(adapt.parameter_based.RegularTransferNN method)
R
RegularTransferLC (class in adapt.parameter_based)
RegularTransferLR (class in adapt.parameter_based)
RegularTransferNN (class in adapt.parameter_based)
T
toy_classification() (in module adapt.utils)
toy_regression() (in module adapt.utils)
TrAdaBoost (class in adapt.instance_based)
TrAdaBoostR2 (class in adapt.instance_based)
TwoStageTrAdaBoostR2 (class in adapt.instance_based)


EXCLUSIVE: Omarion on House Being Robbed, Also Robbed When Nick Young Lived There
by Jessica ToddOmarion opened up to VladTV about his home being robbed, and the same home previously being robbed when Nick Young lived there. He explained that flocking is a big thing with celebrity homes in California, and Omarion said that he moved out after the robbery. To hear more, including Omarion's thoughts on karma catching up to the robbers, hit the above clip.


 These two years were just brutal. I was longing for a break and now I got it. So... what do I do now? Well, I found this interesting subreddit r/MemeEconomy. People post memes and joke about investing with profits. So I think to myself, why not make it real?
I went to see Deadpool 2 with my friends today and my parents have some guests visiting tonight, so that means I have an evening to do whatever. I sat down and punched down some python code that catches !invest AMOUNT
comments on the subreddit, uses karma difference in time for a linear profit formula, and stores everything in a single text file. Sounds good to me!
It all took about an hour or so of code writing, an hour more of testing/debugging, and a couple of minutes to run it public on my trusty Raspberry Pi 3. Ah, it’s running now. Probably won’t be used by more than... let’s say ten people. Let me write down !create
to create an account for myself, !invest 100
for fun, and see the guests.
Huh, my phone is blowing up? Oh. My. God. It blew up. Big time. On the same evening the bot first came live, a couple of people spotted this interesting reddit bot that recognizes commands to invest in memes? Let’s all create and account and see what’s it’s about!!  The measely text file corrupted with number of writes and it of course crashed
Hey mom, dad, I need to go upstairs real quick. Be right back. Okay. I remember like a week ago I’ve heard about this thing called S-Q-L-Lite? I don’t know how to use the big boy databases, but this one just uses a file. Okay, let me quickly swap the file-based functions with sqlite-based functions.
10 minutes later
Alright, push it live right back up. Let’s make meme investments real! I’m hungry, let me go downstairs and have faith in my coding
Early investors saw very good returns
Some people even liked the new feature!
Some even bet their everything
You might wonder, how did the bot work back then? Here is the help message and available commands at the time
And here are the messages with investment updates
And 15 thousand more messages that are just !create
, !invest
, and !balance
. Dinner was good and I’m happy with how this little meme investor turned out. I wonder how I can work on it more?
So what about today?
Well, this whole project turned out to be more than I thought it would be. It was... fantastic! I had over 25 people joining me on development. Even developers from the real world contributed to the project, taught me many invaluable skills, and an understanding of what it means to manage a project. This story has been retold many times and I will shorten it because it’s very late right now and I’m watching Death Note.
I have been maintaining and working on the project for a little bit more than three years. We had dozens of developers from across the world contributing code, advice, and technical support to MemeInvestor. We amassed more than 100,000 registered users, with an all-time peak of 10,000 active daily users. An r/MemeEconomy creator and my partner Noerdy incorporated our little venture into an LLC. Sometime later, our humble project got acquired by EnterMeme.
Wait, why acquired? For how much? How was it? Let me answer one by one. Frankly, maintaining an active project while grinding in college did wear me out in some places, so I’ve found myself burnt on the project. Development (as a sole engineer at that time) took way longer than expected. Not a lot of passion left for it. At one point, I was starting to look to find a new maintainer, just to offload MIB (the new version of MemeInvestor) onto someone, who can actually do something about it.
I can’t say how much specifically, but do not think it was an "exit". Think of the acquisition as just switching maintainers at that point. I was just happy that I could focus on other things, as I did not have the heart in me to just shut it down and shelve it. That is still how I feel. I am glad to finally have been able to move on to greener pastures and spend more time on projects that drive more passion within me.
I wish EnterMeme, which is now officially meme.market commercial and social success it’s looking for and actively working towards.
Thank you and Goodbye, MemeInvestor_bot.


Resident to Bank Transaction
Transaction is done with transaction Id {{transactionId}}
Could not do transaction
Action
Get Cash (USD) for coins
Get coins for cash(USD)
Resident
Bank
Cash Value (USD)
Rate:
10 Coins / USD


{% extends "layout/_pattern_layout.html" %}
{% from 'nightshade-core/src/tooltips/tooltips.macros.html' import tooltip %}
{% set data = {
page_title: "Tooltips",
page_description: "A tooltip exposes a brief amount of information to the user when an element is on hover or focus. If copy is more than 50 characters, consider using the popover pattern instead.",
variants: [
{
name: "Standard",
description: "Standard tooltip description",
macro: "{% call tooltip('Verified Casper Owner') %}Tooltip Trigger{% endcall %}",
html: '
Tooltip Trigger
Verified Casper Owner
',
rules: {
list: {
typography: "Copy is set in Calibre in body-sans-meta with white text. Try to keep character count under 50.",
specs: "Top/Bottom padding is 9.259px (ms-3). Left/Right padding is 13.333px (ms-1). Tooltips with one line of copy is 35.5px tall. Tooltips with two lines of copy is 52.5px tall. Try to keep width no more than 190px.",
color: "If background color is White or lighter than gray—-light2, tooltip fill is Base Gray. "
},
examples: [
{
html: '
Example 1
Never use Verlag
',
description: "Never use Verlag."
},
{
html: '
Example 2
Tooltip text content should never exceed three lines. Consider using a popover instead.
',
description: "No more than 3 lines. If copy is more than 2 lines, consider using the popover pattern instead."
}
]
},
tech_notes: [
'content
(required) - Tooltip text content',
'class
- Custom CSS or JavaScript class selector',
'id
- Custom JavaScript ID selector'
],
imports: {
stylesheet: {
path: "src/tooltips/_base.scss",
code: "@import './node_modules/@casper/nightshade-core/src/tooltips/base';"
},
macro: {
path: "src/tooltips/tooltips.macros.html",
code: "{% from 'nightshade-core/src/tooltips/tooltips.macros.html' import tooltip %}"
},
javascript: {
path: "src/tooltips/Tooltips.js",
code: "import { Tooltips } from 'nightshade-core/src/tooltips/Tooltips.js';"
}
}
},
{
name: "Light Fill",
description: "Tooltip with light fill, for use on dark backgrounds",
macro: "{% call tooltip('Verified Casper Owner', style='light') %}Tooltip Trigger{% endcall %}",
html: '
Tooltip Trigger
Verified Casper Owner
',
rules: {
list: {
typography: "Copy is set in Calibre in body-sans-meta with white text. Try to keep character count under 50.",
specs: "Top/Bottom padding is 9.259px (ms-3). Left/Right padding is 13.333px (ms-1). Tooltips with one line of copy is 35.5px tall. Tooltips with two lines of copy is 52.5px tall. Try to keep width no more than 190px.",
color: "If background color is darker than gray--light3, tooltip fill is gray—light5 with text in gray-—light1."
}
}
}
]
}
%}
{% block module_loader %}
System.import('views/tooltips/TooltipsPage.js').then( function(mod) {
mod.TooltipsPage.init();
} );
{% endblock %}


| Categories: iterators, adaptors | Component type: type |
template <class T> void forw(const list<T>& L) { list<T>::iterator first = L.begin(); list<T>::iterator last = L.end(); while (first != last) cout << *first++ << endl; } template <class T> void rev(const list<T>& L) { typedef reverse_bidirectional_iterator<list<T>::iterator, T, list<T>::reference_type, list<T>::difference_type> reverse_iterator; [2] reverse_iterator rfirst(L.end()); reverse_iterator rlast(L.begin()); while (rfirst != rlast) cout << *rfirst++ << endl; }
In the function forw, the elements are printed in the order *first, *(first+1), ..., *(last-1). In the function rev, they are printed in the order *(last - 1), *(last-2), ..., *first. [3]
| Parameter | Description | Default |
|---|---|---|
| BidirectionalIterator | The base iterator class. Incrementing an object of class reverse_bidirectional_iterator<BidirectionalIterator> corresponds to decrementing an object of class BidirectionalIterator. | |
| T | The reverse iterator's value type. This should always be the same as the base iterator's value type. | |
| Reference | The reverse iterator's reference type. This should always be the same as the base iterator's reference type. | T& |
| Distance | The reverse iterator's distance type. This should always be the same as the base iterator's distance type. | ptrdiff_t |
| Member | Where defined | Description |
|---|---|---|
| self | reverse_bidirectional_iterator | See below |
| reverse_bidirectional_iterator() | Trivial Iterator | The default constructor |
| reverse_bidirectional_iterator(const reverse_bidirectional_iterator& x) | Trivial Iterator | The copy constructor |
| reverse_bidirectional_iterator& operator=(const reverse_bidirectional_iterator& x) | Trivial Iterator | The assignment operator |
| reverse_bidirectional_iterator(BidirectionalIterator x) | reverse_bidirectional_iterator | See below. |
| BidirectionalIterator base() | reverse_bidirectional_iterator | See below. |
| Reference operator*() const | Trivial Iterator | The dereference operator |
| reverse_bidirectional_iterator& operator++() | Forward Iterator | Preincrement |
| reverse_bidirectional_iterator operator++(int) | Forward Iterator | Postincrement |
| reverse_bidirectional_iterator& operator--() | Bidirectional Iterator | Predecrement |
| reverse_bidirectional_iterator operator--(int) | Bidirectional Iterator | Postdecrement |
| bool operator==(const reverse_bidirectional_iterator&, const reverse_bidirectional_iterator&) | Trivial Iterator | Compares two iterators for equality. This is a global function, not a member function. |
| bidirectional_iterator_tag iterator_category(const reverse_bidirectional_iterator&) | Iterator tags | Returns the iterator's category. This is a global function, not a member function. |
| T* value_type(const reverse_bidirectional_iterator&) | Iterator tags | Returns the iterator's value type. This is a global function, not a member function. |
| Distance* distance_type(const reverse_bidirectional_iterator&) | Iterator tags | Returns the iterator's distance type. This is a global function, not a member function. |
| Member | Description |
|---|---|
| self | A typedef for reverse_bidirectional_iterator<BidirectionalIterator, T, Reference, Distance>. |
| BidirectionalIterator base() | Returns the current value of the reverse_bidirectional_iterator's base iterator. If ri is a reverse iterator and i is any iterator, the two fundamental identities of reverse iterators can be written as reverse_bidirectional_iterator(i).base() == i and &*ri == &*(ri.base() - 1). |
| reverse_bidirectional_iterator(BidirectionalIterator i) | Constructs a reverse_bidirectional_iterator whose base iterator is i. |
[1] There isn't really any good reason to have two separate classes: this separation is purely because of a technical limitation in some of today's C++ compilers. If the two classes were combined into one, then there would be no way to declare the return types of the iterator tag functions iterator_category, distance_type and value_type correctly. The iterator traits class solves this problem: it addresses the same issues as the iterator tag functions, but in a cleaner and more flexible manner. Iterator traits, however, rely on partial specialization, and many C++ compilers do not yet implement partial specialization. Once compilers that support partial specialization become more common, these two different reverse iterator classes will be combined into a single class.
[2] The declarations for rfirst and rlast are written in this clumsy form simply as an illustration of how to declare a reverse_bidirectional_iterator. List is a Reversible Container, so it provides a typedef for the appropriate instantiation of reverse_bidirectional_iterator. The usual way of declaring these variables is much simpler:
list<T>::reverse_bidirectional_iterator rfirst = rbegin(); list<T>::reverse_bidirectional_iterator rlast = rend();
[3] Note the implications of this remark. The variable rfirst is initialized as reverse_bidirectional_iterator<...> rfirst(V.end());. The value obtained when it is dereferenced, however, is *(V.end() - 1). This is a general property: the fundamental identity of reverse iterators is &*(reverse_bidirectional_iterator(i)) == &*(i - 1). This code sample shows why this identity is important: if [f, l) is a valid range, then it allows [reverse_bidirectional_iterator(l), reverse_bidirectional_iterator(f)) to be a valid range as well. Note that the iterator l is not part of the range, but it is required to be dereferenceable or past-the-end. There is no requirement that any such iterator precedes f.


Future Mars explorers may be able to get all the water they need out of the red dirt beneath their boots, a new study suggests.
NASA’s Mars rover Curiosity has found that surface soil on the Red Planet contains about 2 percent water by weight. That means astronaut pioneers could extract roughly 2 pints (1 liter) of water out of every cubic foot (0.03 cubic meters) of Martian dirt they dig up, said study lead author Laurie Leshin, of Rensselaer Polytechnic Institute in Troy, N.Y.


FROM: Barrister Anthony Efe. TEL: 234-803-581-4937. I am Barrister Anthony Efe, an International Legal Practitioner and a Consultant to Mrs. Stella Olusegun Obasanjo, (WIFE) to the president of Federal Republic of Nigeria. Unfortunately, my client Mrs. Stella Olusegun Obasanjo died on Saturday October 22nd, 2005. For details of her death visit: http://www.cnn.com/2005/WORLD/africa/10/23/obasanjo.obituary/index.html Before the untimely death of my client, she had through a security company here in Nigeria moved the sum of US$20.5m to Europe and was to proceed to Netherlands from Spain where she went just for medial check-up immediately after the check-up to complete arrangements and have this money Deposited into ABN AMRO BANK and already the security company where the funds was deposited has sent notice to me requesting that this funds should be claimed by the depositor (My Client) or her next of kin within 14 working days, thinking that they are to take the funds just the way some securities companies did to one of our late presidents wife and her family (Mrs. Maryam Abacha) else the fund will be forfeited to the state, with such the husband wouldn’t like or wish to hear this from the press or any one. I must confess to you that if I do not find a way to bring this fund out of the Finance Company, I will be doing the greatest injustice to my late client and her family. This is why I decided for you to stand in as a next of kin or her business partner who was supposed to receive the funds for investment in Europe. There are no risks involved as I am the only person who has access to my client's documents. The Finance firm has just sent a reminder to my client through me, informing her of the maturity of her fund. Your compensation for your assistance in securing the fund is subject to negotiations. I need the following information from you. Your full names,address,telephone and Fax numbers. Also Copy of your Identification. Reply urgently, as we must conclude this transaction within the next 14 working days Sincerely, Barrister Anthony Efe.


Will smash head, break legs if someone tries to poach our members, says Shiv Sena MLA
The statement came amid finalisation of the alliance between Congress, NCP and Shiv Sena ahead of government formation in Maharashtra.
by GARGI CHAUDHRYNew Delhi: A Shiv Sena MLA on Thursday threatened to ‘smash the head’ of anybody who tried to poach his fellow representatives through bribery or blackmail, reported News18. The statement came amid finalisation of the alliance between Congress, NCP and Shiv Sena ahead of government formation in Maharashtra.
MLA Abdul Sattar, who represents the Sillod assembly constituency in Aurangabad district, warned former ally BJP against poaching or purchasing the elected representatives.
“Our party is not some retail shop to buy MLAs from. If anybody tries to poach our MLAs, we will smash his head and break his legs too. However, the Sena will also summon an ambulance for the person and arrange for his medical treatment," he was quoted as saying by the ANI.
The Congress and the NCP will hold talks with the Sena in Mumbai on Friday. On late Thursday night, Sena chief Uddhav Thackeray and his MLA-son Aaditya met Sharad Pawar in Mumbai after the NCP chief returned from Delhi. The meeting took place at Pawar's residence 'Silver Oak' in South Mumbai.
Catch the latest news, live coverage and in-depth analyses from India and World. Follow us on Facebook and Twitter.


StringBuilder
, StringBuffer
or StringJoiner
objects whose contents are read but not written to,
or written to but not read.
Such inconsistent reads and writes are pointless and probably indicate dead, incomplete, or erroneous code.
Example:
public void m1() {
StringBuilder sb = new StringBuilder();
sb.append("a");
}


put_byte([
Stream,]
N)
writes the byte N to the current or given output
stream, which must be binary.
put_code([
Stream,]
N)
writes the character code N to the current or given output
stream, which must be text.
put_char([
Stream,]
A)
writes the character atom A to the current or given output
stream, which must be text.
The byte or character is not necessarily printed immediately; they may be flushed if the buffer is full. See ref-iou-sfh-flu.


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This web application is a demo of the "html-CC2650SensorTag.js", which shows the measurement result reported by a SimpleLink™ Bluetooth low energy/Multi-standard SensorTag (CC2650STK) in real time.
The "html-CC2650SensorTag.js" is a JavaScript library using the W3C Web Bluetooth API. The API is implemented in Chrome for Mac, Linux, Chrome OS, and Android M experimentally. Though the API was implemented in Chrome 55 and earlier, it is strongly recommended to use Chrome 56 or later. Unfortunately, the API is disabled by default for now. You can enable it turning on the enable-experimental-web-platform-features
flag.
In order to try this web application, turn on your CC2650 pressing the power button (on the right side of your CC2650). If your CC2650 is turned on successfully, you will find the green LED is blinking. Then press the "Connect" button on this web application below. Your Chrome will show you a dialog window which shows found devices. Selecting your CC2650, this web application will establish a connection with the CC2650, then show you the measurement results coming from your CC2650.
After this web application establishes a connection with your CC2650 successfully, you will see the result like the capture below:
This web application supports most of the sensing data provided by the CC2650. Besides, it supports IO Service (turning on/off the LEDs and the buzzer) and Simple Key Service (detecting whether the buttons on the CC2650 are pressed or released).
IO Service / Simple Keys Service
Pressing the buttons below, you can turn on/off the LEDs and the buzzer.
When you press the buttons on the side of the CC2650, the indicators on either side of the picture will be highlighted.
IR Temperature Sensor (TMP007)
Movement Sensor (MPU9250)
Humidity Sensor (HDC1000)
Barometric Pressure Sensor (BMP280)
Optical Sensor (OPT3001)
Device Information
- System ID
- Model Number
- Firmware Revision
- Hardware Revision
- Software Revision
- Manufacturer Name
- Battery Level
- %


DO NOT UNDER ESTIMATE THE POWER OF POSITIVE THOUGHT! ESPECIALLY COLLECTIVE POSITIVE THOUGHT!
 HOLD THAT THOUGHT AND VISUALIZE IT IN YOUR MIND EVERYDAY AS OFTEN AS POSSIBLE STARTING NOW AND IT WILL MANIFEST INTO REALITY. DO IT! I’M NOT JOKING!
VISUALIZE AS OFTEN AND AS DETAILED AS POSSIBLE!


Browsing Classification Data Set (v0.1.0)
Posted on ven. 29 décembre 2017 in public data by Kazuhiro Terao
Our first public data set is available! We are very excited for this 1st step to have public data set with training tutorials. This notebook is an announcement + browsing of the file contents. This may be a useful guide before jumping into the tutorial. Since this notebook is a bit lengthy, here's an outline of what's covered.
- Overall summary of sample generation configuration
- Exploring file contents
- Example image dump (data analysis)
- Particle-wise event fraction (data analysis)
- Energy and momentum distribution (data analysis)
- Image filtering effect (data analysis)
Before anything, make sure you have necessary software stacks (larcv
, python packages) and can execute these imports.
from __future__ import print_function
import ROOT
from ROOT import TChain
from larcv import larcv
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Conditions to make the samples¶
Here's the big picture:
- All files (train, test, and tutorial files) contain single-particle events.
- This means each 2D image contain only 1 particle.
- There are five particle types including electron, gamma ray, muon, pion, and proton.
- An event fraction per particle type is equal for all samples.
- A particle's generation point is uniformly distributed within 5.12 m cubic volume.
- A particle's (momentum) direction is isotropic.
- A particle's momentum is uniformly distributed in the range specified per type.
- electrons ... 35.5 to 800 MeV/c
- gamma rays ... 35 to 800 MeV/c
- muons ... 90 to 800 MeV/c
- pions ... 105 to 800 MeV/c
- protons ... 275 to 800 MeV/c
- 2.56 m cubic 3D volume is chosen to maximize the particle's trajectory within the volume and recorded in the file.
- 2D images are created as 2D projections (xy, yz, zx) of the 3D data
- These are 3 channels of 2D images in each file.
- Events that contain any 2D projection image with less than 10 non-zero pixels are filtered out. *This is to remove empty and almost empty images from the set.
Exploring the contents¶
I think the file description is best done with the data. So let's explore them in this notebook. This also serves as an example of how to browse through larcv
data products! We use this test file which I already downloaded in this directory. Let's check what data products are in the file. But before going further, please go over this tutorial if you have not done so!
ROOT.TFile.Open('test_40k.root').ls()
As described in the linked tutorial, TTree names tell us what they are: we got image2d
and particle
data products with "data" and "mctruth" instance labels. image2d
is, as its name suggests, 2D image.
from larcv import larcv
# Create TChain, count # of entries
chain_image2d = ROOT.TChain('image2d_data_tree')
chain_image2d.AddFile('test_40k.root')
print(chain_image2d.GetEntries(),'entries found!')
# Get a specific event (first entry)
chain_image2d.GetEntry(0)
cpp_object = chain_image2d.image2d_data_branch
print('Object type:',cpp_object)
# Get std::vector
image2d_array = cpp_object.as_vector()
# Dump images
fig, axes = plt.subplots(1, image2d_array.size(), figsize=(12,4), facecolor='w')
for index,image2d in enumerate(image2d_array):
image2d_numpy = larcv.as_ndarray(image2d)
axes[index].imshow(image2d_numpy, interpolation='none',cmap='jet')
# Find bounds for non-zero pixels + padding of 5 pixels
nz_pixels=np.where(image2d_numpy>0.0)
ylim = (np.min(nz_pixels[0])-5,np.max(nz_pixels[0])+5)
xlim = (np.min(nz_pixels[1])-5,np.max(nz_pixels[1])+5)
# Adjust for allowed image range
ylim = (np.max((ylim[0],0)), np.min((ylim[1],image2d_numpy.shape[1]-1)))
xlim = (np.max((xlim[0],0)), np.min((xlim[1],image2d_numpy.shape[0]-1)))
# Set range
axes[index].set_ylim(ylim)
axes[index].set_xlim(xlim)
plt.show()
Looks like this is an electron EM-shower. Why electron and not gamma ray? That's because, to my eyes, this looks like low dE/dX at the beginning of the shower trunk. But to check if I am right or not, we have to access simulation information (coming up next)!
Closer look: particle (label)¶
We can look at particle
information in the file, which you will use to generate a label for image classification training, to check if the image we saw was electron or not.
# Create TChain, count # of entries
chain_particle = ROOT.TChain('particle_mctruth_tree')
chain_particle.AddFile('test_40k.root')
print(chain_image2d.GetEntries(),'entries found!')
# Get a specific event (first entry)
chain_particle.GetEntry(0)
cpp_object = chain_particle.particle_mctruth_branch
print('Object type: {}\n'.format(str(cpp_object)))
# Dump particle information
print('Checking particle information for 1st entry...')
for particle in cpp_object.as_vector():
print('PDG Code: {}'.format(particle.pdg_code()))
print('Initial energy: {:.3} GeV'.format(particle.energy_init()))
PDG code 11 corrsponds to electron! So I guessed it right :)
Closer look: kinematics¶
Next, let's look at the fraction of each particle type and their energy distributions for fun. We first make pandas.DataFrame
with numpy arrrays for simple analysis and plotting.
pdg_array = np.zeros([chain_particle.GetEntries()],dtype=np.int32)
energy_array = np.zeros([chain_particle.GetEntries()],dtype=np.float64)
momentum_array = np.zeros([chain_particle.GetEntries()],dtype=np.float64)
for index in range(chain_particle.GetEntries()):
chain_particle.GetEntry(index)
particle = chain_particle.particle_mctruth_branch.as_vector().front()
pdg = int(particle.pdg_code())
total_energy = particle.energy_init() * 1000.
kinetic_energy = total_energy - larcv.ParticleMass(pdg)
momentum = np.sqrt(np.power(total_energy,2) - np.power(larcv.ParticleMass(pdg),2))
pdg_array[index] = pdg
energy_array[index] = kinetic_energy
momentum_array[index] = momentum
#if momentum > 800:
# print(pdg,kinetic_energy,momentum)
import pandas as pd
df = pd.DataFrame(data={'pdg' : pdg_array, 'energy' : energy_array, 'momentum' : momentum_array})
List of particles and their event counts:
pdg_list, pdg_counts = np.unique(df.pdg.values,return_counts=True)
print('PDGs found: {}'.format(pdg_list))
print('PDG counts: {}'.format(pdg_counts))
... as expected, we have equal partition of 5 particle types. Check the range of momentum per particle type:
PDG2NAME = {11 : 'electron',
22 : 'gamma',
13 : 'muon',
211 : 'pion',
2212 : 'proton'}
for pdg in pdg_list:
sub_df = df.query('pdg=={}'.format(pdg))
min_value = sub_df.momentum.values.min()
max_value = sub_df.momentum.values.max()
print('{:10s} momentum range: {:.3g} => {:.3g} MeV/c'.format(PDG2NAME[pdg], min_value, max_value))
This is also as expected from the prescription. Next is more interesting: plot energy distributions of gamma ray and muon
fig, ax = plt.subplots(figsize=(12,8),facecolor='w')
pdg_list = [13,22]
sub_df = df.query('pdg in {}'.format(pdg_list))
min_value = sub_df.energy.values.min()
max_value = sub_df.energy.values.max()
for pdg in pdg_list:
pdg_df = sub_df.query('pdg == {}'.format(pdg))
values = pdg_df.energy.values
plt.hist(values, bins=20, range=(min_value,max_value), label='PDG {}'.format(pdg), alpha=0.5)
plt.tick_params(labelsize=20)
plt.grid(True,which='both')
plt.xlabel('Initial Kinetic Energy [MeV]',fontsize=20,fontweight='bold',fontname='Georgia')
plt.ylabel('Number of entries',fontsize=20,fontweight='bold',fontname='Georgia')
leg=plt.legend(fontsize=16,loc=4)
leg_frame=leg.get_frame()
leg_frame.set_facecolor('white')
plt.show()
We see two features here:
- Muon appears to have non-uniform kinetic energy distribution while that of gamma ray appears to be uniform.
- Muon kinetic energy distribution falls off beyond (about) 700 MeV while that of gamma ray continues up to 800 MeV
The first point is due to the fact that particles are generated uniformly in momentum space and not in kinetic energy. The non-negligible muon mass (105.6 MeV) in the generated momentum range causes its kinetic energy to be non-uniform. On the other hand, gamma ray has 0 mass (no please don't ask "what if photons are massive..." question). That means momentum = energy, and hence you see a uniform kinetic energy distribution ranging up to 800 MeV.
Let's actually plot the momentum distribution.
fig, ax = plt.subplots(figsize=(12,8),facecolor='w')
pdg_list = [13,22]
sub_df = df.query('pdg in {}'.format(pdg_list))
min_value = sub_df.momentum.values.min()
max_value = sub_df.momentum.values.max()
for pdg in pdg_list:
pdg_df = sub_df.query('pdg == {}'.format(pdg))
values = pdg_df.momentum.values
plt.hist(values, bins=20, range=(min_value,max_value), label='PDG {}'.format(pdg), alpha=0.5)
plt.tick_params(labelsize=20)
plt.grid(True,which='both')
plt.xlabel('Initial Momentum [MeV/c]',fontsize=20,fontweight='bold',fontname='Georgia')
plt.ylabel('Number of entries',fontsize=20,fontweight='bold',fontname='Georgia')
leg=plt.legend(fontsize=16,loc=4)
leg_frame=leg.get_frame()
leg_frame.set_facecolor('white')
plt.show()
Which looks almost perfect, except there's a drop in the 1st bin for muon. This is a binning effect. Remember the momentum range of muon is set to 90 to 800 MeV/c. If we are to add this condition in our analysis so that we remove the binning effect, we can see a uniform momentum distribution.
fig, ax = plt.subplots(figsize=(12,8),facecolor='w')
pdg_list = [13,22]
sub_df = df.query('pdg in {} and momentum > 90'.format(pdg_list))
min_value = sub_df.momentum.values.min()
max_value = sub_df.momentum.values.max()
for pdg in pdg_list:
pdg_df = sub_df.query('pdg == {}'.format(pdg))
values = pdg_df.momentum.values
plt.hist(values, bins=20, range=(min_value,max_value), label='PDG {}'.format(pdg), alpha=0.5)
plt.tick_params(labelsize=20)
plt.grid(True,which='both')
plt.xlabel('Initial Momentum [MeV/c]',fontsize=20,fontweight='bold',fontname='Georgia')
plt.ylabel('Number of entries',fontsize=20,fontweight='bold',fontname='Georgia')
leg=plt.legend(fontsize=16,loc=4)
leg_frame=leg.get_frame()
leg_frame.set_facecolor('white')
plt.show()
Closer look: image filtering effect¶
Recall one of the sample preparation procedures: "filtering of events for which any 2D image contains less than 10 non-zero pixels". This was intended to remove (almost) empty images. But it's important to note this condition filters out a short particle trajectory which typically means a low energy particle. So this filter biases a particle's energy!
How significant is this effect? How far a particle travels depends on its type because of different stopping power. In our energy range, the most severly (and actually only) affected is proton. Let's compare proton and gamma ray momentum distribution.
fig, ax = plt.subplots(figsize=(12,8),facecolor='w')
pdg_list = [2212,22]
sub_df = df.query('pdg in {}'.format(pdg_list))
min_value = sub_df.momentum.values.min()
max_value = sub_df.momentum.values.max()
for pdg in pdg_list:
pdg_df = sub_df.query('pdg == {}'.format(pdg))
values = pdg_df.momentum.values
plt.hist(values, bins=20, range=(min_value,max_value), label='PDG {}'.format(pdg), alpha=0.5)
plt.tick_params(labelsize=20)
plt.grid(True,which='both')
plt.xlabel('Initial Momentum [MeV/c]',fontsize=20,fontweight='bold',fontname='Georgia')
plt.ylabel('Number of entries',fontsize=20,fontweight='bold',fontname='Georgia')
leg=plt.legend(fontsize=16,loc=2)
leg_frame=leg.get_frame()
leg_frame.set_facecolor('white')
plt.show()
From earlier analysis in this notebook, we already know that the momentum range of protons in this sample is 276 to 800 MeV/c. In this plot, we can clearly see that proton events are not uniformly distributed over its momentum range.
So how much protons are affected by "10 pixels filter"? To figure this out, we have to know what 10 pixels correspond to the real world's distance scale. This information is stored in 2D image meta data.
for index,image2d in enumerate(image2d_array):
print(image2d.meta().dump())
"Distance Unit: 1" means it is in cm scale (larcv::DistanceUnit_t::kUnitCM
). So each 2D image spans 128cm x 128cm space with 256 x 256 pixels, meaning each pixel corresponds to 0.5 cm in vertical and horizontal size. This means a proton must travel at least 5 cm in all 2D projections, or
$$ x^2+y^2 \geq 25 \text{cm,}$$
$$ y^2+z^2 \geq 25 \text{cm,}$$
$$ z^2+x^2 \geq 25 \text{cm.}$$
Summing them over we find $\sqrt{x^2+y^2+z^2}\geq\sqrt{75/2}\approx6.12$ cm as the minimum distance required for a proton to travel. So what proton energy does this trajectory size corresponds to? Here's a plot I made from NIST that shows the proton trajectory size in liquid Argon medium versus its kinetic energy. ... from which we can tell $\approx6$ cm corresponds to $\approx80$ MeV. Given the proton mass (938.28 MeV), this corresponds to $\approx419$ MeV/c momentum. Indeed we see a sharp drop around that momentum value in the plot above.
What is affecting the higher momentum range?¶
Note "$\geq$" sign in the equation we derived above: 6.12 cm is the bare minimum requirement. The actual requirement depends on the initial momentum direction because the projected 2D distances depends on that angle and the projection angles. For instance, a proton with momentum higher than $\approx419$ MeV/c may travel $\approx7.1$ cm from point A $(x,y,z) = (0,0,0)$ [cm] to point B $(4.5,4.5,0)$ [cm]. This satisfies the minimum trajectory length requirement. But it fails to satisfy both $y^2+z^2\geq25$ cm and $z^2+x^2\geq25$, so it won't pass the filter.
In addition, the projection angle also affects projected trajectory size per pixel. 0.5 cm/pixel is the shortest possible case. If a trajectory runs along a diagonal direction of a projected 2D pixel, a requirement of 10 pixels mean 7.1 cm instead of 5 cm, increase of 20% in the required trajectory size.
Why are there entries in the lower momentum range?¶
There could be more than one reason. But one thing we definitely expect is a production of other particle types through a proton-nucleus interaction. Recall the filter requires "10 non-zero pixels on each 2D projection planes" but does not specify the particle type. A proton with 70 MeV kinetic energy may be below the threshold to travel long enough, but may produce gamma rays or other particles that can travel longer with lower energy.
So why don't we take a look at them? Let's pick an event with a proton energy below 50 MeV and list them.
sub_df = df.query('pdg==2212 and energy < 50')
print('Found {} entries'.format(sub_df.index.size))
print(sub_df)
Let's take a look at just 1st projection of the first entry (32020)
# Create TChain, count # of entries
chain_image2d = ROOT.TChain('image2d_data_tree')
chain_image2d.AddFile('test_40k.root')
print(chain_image2d.GetEntries(),'entries found!')
# Get a specific event (first entry)
chain_image2d.GetEntry(32020)
cpp_object = chain_image2d.image2d_data_branch
print('Object type:',cpp_object)
image2d = cpp_object.as_vector().front()
fig, ax = plt.subplots(figsize=(12,12), facecolor='w')
image2d_numpy = larcv.as_ndarray(image2d)
ax.imshow(image2d_numpy, interpolation='none', vmin=0., vmax=image2d_numpy.mean(), cmap='gray')
# Find bounds for non-zero pixels + padding of 5 pixels
nz_pixels=np.where(image2d_numpy>0.0)
ylim = (np.min(nz_pixels[0])-5,np.max(nz_pixels[0])+5)
xlim = (np.min(nz_pixels[1])-5,np.max(nz_pixels[1])+5)
# Adjust for allowed image range
ylim = (np.max((ylim[0],0)), np.min((ylim[1],image2d_numpy.shape[1]-1)))
xlim = (np.max((xlim[0],0)), np.min((xlim[1],image2d_numpy.shape[0]-1)))
# Set range
ax.set_ylim(ylim)
ax.set_xlim(xlim)
plt.show()
These scattered charge depositions are likely by compton scatterings from gamma rays and definitely not from a single proton trajectory (which would have been continuous).
Ending remark¶
I hope this notebook covered good enough to make you feel more familiar with the public data contents we provided for a single-particle image classification. You might find some of the sample generation configuration not suitable for your physics research. Feel free to contact us if you want samples generated under a different condition. Enabling your research goal is an important goal for our group!


Developed using what was then a the state-fo-the-art IBM 7090
One of IBM’s first transistor-based (solid state) computers
Designed for NASA’s Mercury and Gemini space missions
The computer in Hidden Figures that Dorothy Vaughn, supervisor of human computers, learned to use.
Dorothy Vaughan
Colby’s Research
The cultural anthropologist Benjamin Colby applied the GI system to folklore
Anthropologists often study myths
Folklore is a variant of myth (so-called “fairy tales”)
Goals is to observe and represent thematic content
“Clusters of such themes provide insight into the way cultures conceptually organize the world around them.”
Does a comparative study of five cultures
Kwaikiutl, Egypt, Eskimo, Hindu India, and China
Colby Quote
“Only very recently has it become possible, through the use of computers, to process large numbers of narrative texts having many variables in order to discover statistically significant patterns which are culturally distinctive and amenable to testing in the field.”
Colby 1966: 793
GI Data Model
A dictionary containing:
A. High frequency words of low information content (i.e. stopwords)
B. Medium frequency words with multiple meanings and are therefore ambiguous out of context (e.g., ‘content’ ‘general’)
Similar to Indo-European Mitra / Varuna (celeritas / gravitas)
Hypothesis: Word counts (i.e. text analytics) indicate cultural templates or pattern components
Each folktale reflects the influence of a such a template system
i.e. A generative model, whereby templates generate texts
Culture Patterns Method
Divide folktales into 9 units of equal length
3 = beginning, middle, and end
Stack tales into composite
i.e. Unit number becomes a label
Focus on Japanese and Eskimo folktales
In translation
Tally thesaurus terms for each unit across folktales
Some Observations
When data from both cultures are viewed together, time and place words appear disproprtionately in Part 1
These are related to the situating function
However, when look at individually, time and space have different distributions
Time
Space and “Search”
In terms of space, the category “search” is distributed differently in each corpus
Search appears early in Eskimo texts
Implies searching for game (hunting)
Decreases as story proceeds (as these activities tend to be interrupted)
Search appears later in Japanese texts
Searches are for secrets, hidden things, special places
Increases with narrative time
In other words, search involves different things
Principle of context – same words, different context, different meaning
Communication
Culturally Significant Differences
10 most significant contrasts betwen distributions established by Chi Square and Kendall’s Tau tests
Not described :-(
Out of 74 basic categories and 195 subcategories
Japanese – 55 tales (22%) considered distinct
Eskimo – 70 tales (26%)
Colby 1996 Fig 1a
Ten most significant patterns of word-group frequencies (1—5)
Colby 1996 Fig 1b
Ten most significant patterns of word-group frequencies (6—10)
Some Generalizations
Japanese Culture
Externally oriented, concern with social things
material objects
limitatins due to domination
impersonal divine justice
social situations
nuclear family
Eskimo Culture
Concern with physical abilities, individually oriented
physical actions
limitations due to strength
magic
survival
extended family
Criticisms and Observations
Distributions based on small samples
Cultural generalization from small sample
May be suitable for characterized collections and subcollections of texts
Sensitive to ontologies?
Cultural Templates
Typical Problems with Content and Sentiment Analysis
Limited vocabularies
Assumed monosemy
Do not include grammar, syntax, punctuation, emoticons
Can’t catch irony, sarcasm
Can’t detect context, which can shift meanins (e.g. “large”)
VADER
A relatively recent SA tool developed to overcome these limitations
Although designed for social media, is generalizable
Combines existing lexicons and employs a hybrid apprach to achieve good performance
Design
7500 lexical features
Polarity
Intensity [-4, +4]
Gold-standard list
Uses heuristics to define text characteristics (e.g. punctutation, emoticons, all caps)
Applies to sentences
Implemented in NLTK
VADER Flow
VADER Example
VADER Example
VADER is smart, handsome, and funny.
{'pos': 0.746, 'compound': 0.8316, 'neu': 0.254, 'neg': 0.0}
VADER is smart, handsome, and funny!
{'pos': 0.752, 'compound': 0.8439, 'neu': 0.248, 'neg': 0.0}
VADER is very smart, handsome, and funny.
{'pos': 0.701, 'compound': 0.8545, 'neu': 0.299, 'neg': 0.0}
VADER is VERY SMART, handsome, and FUNNY.
{'pos': 0.754, 'compound': 0.9227, 'neu': 0.246, 'neg': 0.0}
VADER is VERY SMART, handsome, and FUNNY!!!
{'pos': 0.767, 'compound': 0.9342, 'neu': 0.233, 'neg': 0.0}
VADER is VERY SMART, uber handsome, and FRIGGIN FUNNY!!!
{'pos': 0.706, 'compound': 0.9469, 'neu': 0.294, 'neg': 0.0}
VADER is not smart, handsome, nor funny.
{'pos': 0.0, 'compound': -0.7424, 'neu': 0.354, 'neg': 0.646}
Compounds Scores
The compound score is computed by summing the valence scores of each word in the lexicon, adjusting according to the rules, and then normalizing between -1 (most extreme negative) and +1 (most extreme positive)
Most useful metric for single unidimensional measure of sentiment for a given sentence -‘normalized, weighted composite score’
Thresholds
To classify sentences as either positive, neutral, or negative, use:


{% block ixp_about %}
These are the corporate objectives of Moncton Internet Exchange:
Benefiting New Brunswick's Internet community; Providing an electronic infrastructure to allow for the exchange of New Brunswick based Internet traffic among New Brunswick based ISPs (Internet Service Providers), NSPS (Network Service Providers), CSPS (Content Service Providers) and other Internet users; Providing New Brunswick residents with direct access to local internet content; and Increasing the transfer speed of Internet communications between New Brunswick companies, friends, neighbors and family members.


Hey (I have no) Friends!
I am a 17 y/o self taught developer who is in for HacktoberFest 2018!
Also, you know why ;) Ofc, To make friends!!
My Git ;)


Objects
THEME_NAME/core/css/objects
These are class-based selectors that define undecorated design patterns. They are vital for layout and structure.
Layout
THEME_NAME/core/css/objects/layout.css
Grid-like layout mechanism for arranging components. When you combine this with our grid or width utility classes, you can create fluid and responsive page layouts.
-
.o-layout
is the outer block class which adds offsets to counter the outer gutters, used similarly to the row classes found in other frameworks. -
.o-layout__item
is the element used for layout items which sets gutter spacing and essential styling. This can be augmented using the grid or width utilities inTHEME_NAME/core/css/utilities
in order to create layout structures sized accordingly. The.o-layout__item
class is used similarly to.col
classes in other frameworks.
All direct child elements of .o-layout
must be
.o-layout__item
elements.
Example
To reverse the layout direction, use .o-layout--row-reverse
.
Example
Use .o-layout--column
to set the flex items vertically.
Example
To reverse the column direction, use .o-layout--column-reverse
.
Example
Have smaller gutters between items.
Example
Have larger gutters between items.
Example
Have no gutters between items.
Example
Include vertical gutters on layout items.
If we've chosen to change the size of the horizontal gutters, the vertical gutters will
change accordingly.
Example
There are additional layout object modifiers you can use to adjust the horizontal and vertical alignments of the layout items as well as specifying if an item should be the first or last in the order.
Lists
THEME_NAME/core/css/objects/lists.css
Default list element.
Example
- List Item 1
- List Item 2
- List Item 3
The o-list-bare
object strips list-like appearance from lists
by removing their bullets, and any indentation.
Example
The o-list-block
displays any list of items into stacked blocks.
Example
- List Item 1
- List Item 2
- List Item 3
The o-list-inline
object simply displays a list of items in
one line.
Example
- List Item 1
- List Item 2
- List Item 3
Inline list items can have smaller margins.
Example
- List Item 1
- List Item 2
- List Item 3
Inline list items can have larger margins.
Example
- List Item 1
- List Item 2
- List Item 3
Tables
THEME_NAME/core/css/objects/tables.css
Slightly more opinionated and structural styles for table
elements.
Example
| First Name | Last Name | Age |
|---|---|---|
| Roger | Waters | 73 |
| Nick | Mason | 73 |
| David | Gilmour | 71 |
The table cell widths can be fixed using o-table--fixed
.
Example
| First Name | Last Name | Age |
|---|---|---|
| Roger | Waters | 73 |
| Nick | Mason | 73 |
| David | Gilmour | 71 |
Wrapper
THEME_NAME/core/css/objects/wrapper.css
These are large class selectors that can hold many other structures and components.
.o-wrapper
can be used to constrain the container
to the maximum site width variable while including the right and left padding.
.o-wrapper--full
to .o-wrapper
will set the container to 100vw.


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挂号管理


Bauhaus Museum Dessau / Addenda Architects
- Curated by Clara Ott
- about 20 hours
Museum · Dessau-Roßlau, Germany
- ArchitectsAuthors of this architecture project Addenda Architects
- AreaArea of this architecture project 5513.0 m2
- Project YearBrands with products used in this architecture project 2019
- PhotographerCreated with Sketch. Thomas Meyer
- ManufacturersBrands with products used in this architecture project Autodesk, Creation Baumann, Duravit, FSB, Hager, Jansen, Zumtobel
- Lead Architects Addenda Architects (Roberto González, Anne K. Hinz, Cecilia Rodríguez, Arnau Sastre, José Zabala)
- Local Architect BAL Bauplanungs und Steuerungs GmbH
- Design Team Addenda Architects
- Clients Fundación Bauhaus Dessau y Ciudad de Dessau-Roßlau
- Structures Engineering B+G Ingenieure Bollinger und Grohmann GmbH
- Facilities Engineering Inros Lackner SE
- Landscaping Roser Vives de Delàs y Patricia Pérez-Rumpler
- Construction Consultant Xmade
- Lighting Consultant Lichtvision Design GmbH
- Project Manager Consultant tp management GmbH
- Art and Architecture Curator Consultant Moritz Küng
- Facade Luminous Sign Consultant Mevis & van Deursen and Karl Nawrot
- Collaborators Anne C. Kiefer
More Specs Less Specs
In 2015, Barcelona based Addenda Architects won the international competition for the Bauhaus Museum Dessau, which received 830 entries. The most significant aspect of the pragmatic and austere design is the subtle reincarnation of the Bauhaus spirit, notably identified with Mies’ famous oxymoron “less is more”. Referring to some specific features of the Bauhaus school in Dessau – the glazed Curtain Wall, the two-story Bridge and the overall impression of transparency, lightness and plane surfaces – the new museum will embody a manifesto of contemporary culture that could most accurately be described as “low resolution at its best”.
The new Bauhaus Museum Dessau links an iconographic heritage ("Less is More") with a manifesto of contemporary culture ("The Age of Less"). The new Bauhaus Museum Dessau is a clear and simple volume located between city and Park, construction and nature. Its position, a little set back from the former historic building line, suggests a continuation with the city urban legacy. The building has an intermediate urban scale, between the size of a building and the size of a city block. Its position and dimension act as a city limit, which demarcates city and Park. Its four facades, oriented towards the four cardinal points, reflect the qualities of its position.
The Eastern Facade, facing Kavalierstrasse, recalls the old building line and the building's urban character. The western facade, towards the park, is influenced by the reflection of nature and acts as a background for outdoor gathering and activities. The northern facade, in Friedrichstrasse and in front of the Post Office building, is devoted to logistics and administration. The southern facade, facing the Stadtgespräch fountain, promotes social interaction through some Museum activities like workshops and other cultural events. Inside the glass box, the collection is concealed in a black concrete hollow beam structure supported by two boxes that contain the staircases.
The space between this suspended beam and the glass box is a highly flexible, easy to configure and reconfigure space, thanks to a system based on a virtual grid, which is the result of a regular geometrical subdivision of the structure. This feature ensures the highest feasibility in terms of time, cost and building quality. The visitor's entrance, located where Ratsgasse and Stadtpark meet, is part of the main central interior space, or "offene Bühne" (open stage), a vast space for gathering and exhibitions between the park and the city. The permanent collection is placed in a monolithic, hermetic and column-free hall in the upper level, accessed via stairways and lifts located in the two supports.
The temporary exhibition is underneath the Collection, which is also part of the "offene Bühne". It sets a dialogue with the immediate urban reality while allows the facade to change its appearance with each new exhibition. The Museum’s Logistics, Administration and Education areas are placed at the ends of the Black Box. Logistics and Administration areas are located in the north area while museum education, events, and workshops are located in the south area. Thanks to its location and recognizable, quiet presence, the Bauhaus Museum will play a key role in Dessau's urban identity as an instrument to express activity and productivity, creativity and social interaction.


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/riemannian.20geometry.html ---
@Sebastien Gouezel How far are we from being able to define a Riemannian manifold? It seems to me that almost all the ingredients are in place. If so, that sounds like a pretty nice milestone!
Not so close. The main missing block is to define the bundle of metrics. Two possible choices: define it by hand, or define a general mechanism associating to a vector bundle and a functor a new vector bundle. The general version would be better, but I have never played with the category theory library...
I see. I was thinking of the first approach, because I don't know this second construction. How does that work?
If you have a functor, say, from (Vect, isos) to (Vect, isos), and it is continuous on Hom-sets in an appropriate sense, then given a vector bundle, you choose a trivialization, apply the functor to the fiber over each piece and to the transition maps, and use the resulting data to glue together a new vector bundle.
Yes, this is the math version. The question is rather the Lean version :)
For instance, if you have the tangent bundle with fibers T_x M
, then the bundle with fiber (T_x M ⊗ T_x M)^*
comes directly out of this construction, and this is the one in which a metric lives.
Last updated: Apr 24 2020 at 19:02 UTC


Welcome! I am a game and web developer. Please check out some of my most recent projects. I am always looking for new and interesting challenges to work on.
I am passionate about programming. From full stack web development, to making games in GameMaker Studio and Unity, I love the challenges an interesting new project provides. I work well both independently and with a diverse team, and I’ve worked on projects built from scratch, and ones using an existing code base.
I enjoy the fact that you must continually evolve in software development. Whether it be exploring the nooks and crannies of a familiar language, or learning a new one, I strive to write clean, well-documented code.
Please check out my most recent projects and my Linkedin profile for details.


This documentation is for Linkerd 1.x, an older version with some
significant differences. You may want to see the
Linkerd 2.x (current) documentation instead.
Get Linkerd 1.x up and running in just a few minutes. This section addresses running Linkerd in common environments.


IT Consultant Jönköping, SE2014 to 2015 System management and problem solving together with customers.


Discovery and cluster formation
The discovery and cluster formation processes are responsible for discovering nodes, electing a master, forming a cluster, and publishing the cluster state each time it changes. All communication between nodes is done using the transport layer.
The following processes and settings are part of discovery and cluster formation:
- Discovery
-
Discovery is the process where nodes find each other when the master is unknown, such as when a node has just started up or when the previous master has failed.
- Quorum-based decision making
-
How OpenSearch uses a quorum-based voting mechanism to make decisions even if some nodes are unavailable.
- Voting configurations
-
How OpenSearch automatically updates voting configurations as nodes leave and join a cluster.
- Bootstrapping a cluster
-
Bootstrapping a cluster is required when an OpenSearch cluster starts up for the very first time. In development mode, with no discovery settings configured, this is automatically performed by the nodes themselves. As this auto-bootstrapping is inherently unsafe, running a node in production mode requires bootstrapping to be explicitly configured via the
cluster.initial_master_nodes
setting. - Adding and removing master-eligible nodes
-
It is recommended to have a small and fixed number of master-eligible nodes in a cluster, and to scale the cluster up and down by adding and removing master-ineligible nodes only. However there are situations in which it may be desirable to add or remove some master-eligible nodes to or from a cluster. This section describes the process for adding or removing master-eligible nodes, including the extra steps that need to be performed when removing more than half of the master-eligible nodes at the same time.
- Publishing the cluster state
-
Cluster state publishing is the process by which the elected master node updates the cluster state on all the other nodes in the cluster.
- Cluster fault detection
-
OpenSearch performs health checks to detect and remove faulty nodes.
- Settings
-
There are settings that enable users to influence the discovery, cluster formation, master election and fault detection processes.
Discovery
Discovery is the process by which the cluster formation module finds other nodes with which to form a cluster. This process runs when you start an OpenSearch node or when a node believes the master node failed and continues until the master node is found or a new master node is elected.
This process starts with a list of seed addresses from one or more seed hosts providers, together with the addresses of any master-eligible nodes that were in the last-known cluster. The process operates in two phases: First, each node probes the seed addresses by connecting to each address and attempting to identify the node to which it is connected and to verify that it is master-eligible. Secondly, if successful, it shares with the remote node a list of all of its known master-eligible peers and the remote node responds with its peers in turn. The node then probes all the new nodes that it just discovered, requests their peers, and so on.
If the node is not master-eligible then it continues this discovery process
until it has discovered an elected master node. If no elected master is
discovered then the node will retry after discovery.find_peers_interval
which
defaults to 1s
.
If the node is master-eligible then it continues this discovery process until
it has either discovered an elected master node or else it has discovered
enough masterless master-eligible nodes to complete an election. If neither of
these occur quickly enough then the node will retry after
discovery.find_peers_interval
which defaults to 1s
.
Seed hosts providers
By default the cluster formation module offers two seed hosts providers to
configure the list of seed nodes: a settings-based and a file-based seed
hosts provider. It can be extended to support cloud environments and other
forms of seed hosts providers via discovery plugins.
Seed hosts providers are configured using the discovery.seed_providers
setting, which defaults to the settings-based hosts provider. This setting
accepts a list of different providers, allowing you to make use of multiple
ways to find the seed hosts for your cluster.
Each seed hosts provider yields the IP addresses or hostnames of the seed
nodes. If it returns any hostnames then these are resolved to IP addresses
using a DNS lookup. If a hostname resolves to multiple IP addresses then OpenSearch
tries to find a seed node at all of these addresses. If the hosts provider does
not explicitly give the TCP port of the node by then, it will implicitly use the
first port in the port range given by transport.profiles.default.port
, or by
transport.port
if transport.profiles.default.port
is not set. The number of
concurrent lookups is controlled by
discovery.seed_resolver.max_concurrent_resolvers
which defaults to 10
, and
the timeout for each lookup is controlled by discovery.seed_resolver.timeout
which defaults to 5s
. Note that DNS lookups are subject to
JVM DNS caching.
Settings-based seed hosts provider
The settings-based seed hosts provider uses a node setting to configure a static list of the addresses of the seed nodes. These addresses can be given as hostnames or IP addresses; hosts specified as hostnames are resolved to IP addresses during each round of discovery.
The list of hosts is set using the discovery.seed_hosts
static setting. For example:
discovery.seed_hosts:
- <IP_ADDRESS>:9300
- <IP_ADDRESS> (1)
- seeds.mydomain.com (2)
-
The port will default to
transport.profiles.default.port
and fallback totransport.port
if not specified. -
If a hostname resolves to multiple IP addresses, OpenSearch will attempt to connect to every resolved address.
File-based seed hosts provider
The file-based seed hosts provider configures a list of hosts via an external file. OpenSearch reloads this file when it changes, so that the list of seed nodes can change dynamically without needing to restart each node. For example, this gives a convenient mechanism for an OpenSearch instance that is run in a Docker container to be dynamically supplied with a list of IP addresses to connect to when those IP addresses may not be known at node startup.
To enable file-based discovery, configure the file
hosts provider as follows
in the opensearch.yml
file:
discovery.seed_providers: file
Then create a file at $OPENSEARCH_PATH_CONF/unicast_hosts.txt
in the format described
below. Any time a change is made to the unicast_hosts.txt
file the new
changes will be picked up by OpenSearch and the new hosts list will be used.
Note that the file-based discovery plugin augments the unicast hosts list in
opensearch.yml
: if there are valid seed addresses in
discovery.seed_hosts
then OpenSearch uses those addresses in addition to those
supplied in unicast_hosts.txt
.
The unicast_hosts.txt
file contains one node entry per line. Each node entry
consists of the host (host name or IP address) and an optional transport port
number. If the port number is specified, is must come immediately after the
host (on the same line) separated by a :
. If the port number is not
specified, OpenSearch will implicitly use the first port in the port range given by
transport.profiles.default.port
, or by transport.port
if
transport.profiles.default.port
is not set.
For example, this is an example of unicast_hosts.txt
for a cluster with four
nodes that participate in discovery, some of which are not running on the
default port:
<IP_ADDRESS>
<IP_ADDRESS>:9305
<IP_ADDRESS>:10005
# an IPv6 address
[2001:0db8:85a3:0000:0000:8a2e:0370:7334]:9301
Host names are allowed instead of IP addresses and are resolved by DNS as described above. IPv6 addresses must be given in brackets with the port, if needed, coming after the brackets.
You can also add comments to this file. All comments must appear on their lines
starting with #
(i.e. comments cannot start in the middle of a line).
EC2 hosts provider
The EC2 discovery plugin adds a hosts provider that uses the AWS API to find a list of seed nodes.
Azure Classic hosts provider
The Azure Classic discovery plugin adds a hosts provider that uses the Azure Classic API find a list of seed nodes.
Google Compute Engine hosts provider
The GCE discovery plugin adds a hosts provider that uses the GCE API find a list of seed nodes.
Quorum-based decision making
Electing a master node and changing the cluster state are the two fundamental tasks that master-eligible nodes must work together to perform. It is important that these activities work robustly even if some nodes have failed. OpenSearch achieves this robustness by considering each action to have succeeded on receipt of responses from a quorum, which is a subset of the master-eligible nodes in the cluster. The advantage of requiring only a subset of the nodes to respond is that it means some of the nodes can fail without preventing the cluster from making progress. The quorums are carefully chosen so the cluster does not have a "split brain" scenario where it’s partitioned into two pieces such that each piece may make decisions that are inconsistent with those of the other piece.
OpenSearch allows you to add and remove master-eligible nodes to a running cluster. In many cases you can do this simply by starting or stopping the nodes as required. See Adding and removing nodes.
As nodes are added or removed OpenSearch maintains an optimal level of fault tolerance by updating the cluster’s voting configuration, which is the set of master-eligible nodes whose responses are counted when making decisions such as electing a new master or committing a new cluster state. A decision is made only after more than half of the nodes in the voting configuration have responded. Usually the voting configuration is the same as the set of all the master-eligible nodes that are currently in the cluster. However, there are some situations in which they may be different.
To be sure that the cluster remains available you must not stop half or more of the nodes in the voting configuration at the same time. As long as more than half of the voting nodes are available the cluster can still work normally. This means that if there are three or four master-eligible nodes, the cluster can tolerate one of them being unavailable. If there are two or fewer master-eligible nodes, they must all remain available.
After a node has joined or left the cluster the elected master must issue a cluster-state update that adjusts the voting configuration to match, and this can take a short time to complete. It is important to wait for this adjustment to complete before removing more nodes from the cluster.
Master elections
OpenSearch uses an election process to agree on an elected master node, both at startup and if the existing elected master fails. Any master-eligible node can start an election, and normally the first election that takes place will succeed. Elections only usually fail when two nodes both happen to start their elections at about the same time, so elections are scheduled randomly on each node to reduce the probability of this happening. Nodes will retry elections until a master is elected, backing off on failure, so that eventually an election will succeed (with arbitrarily high probability). The scheduling of master elections are controlled by the master election settings.
Cluster maintenance, rolling restarts and migrations
Many cluster maintenance tasks involve temporarily shutting down one or more nodes and then starting them back up again. By default OpenSearch can remain available if one of its master-eligible nodes is taken offline, such as during a rolling restart. Furthermore, if multiple nodes are stopped and then started again then it will automatically recover, such as during a full cluster restart. There is no need to take any further action with the APIs described here in these cases, because the set of master nodes is not changing permanently.
Voting configurations
Each OpenSearch cluster has a voting configuration, which is the set of master-eligible nodes whose responses are counted when making decisions such as electing a new master or committing a new cluster state. Decisions are made only after a majority (more than half) of the nodes in the voting configuration respond.
Usually the voting configuration is the same as the set of all the master-eligible nodes that are currently in the cluster. However, there are some situations in which they may be different.
|
Important
|
To ensure the cluster remains available, you must not stop half or more of the nodes in the voting configuration at the same time. As long as more than half of the voting nodes are available, the cluster can work normally. For example, if there are three or four master-eligible nodes, the cluster can tolerate one unavailable node. If there are two or fewer master-eligible nodes, they must all remain available. |
After a node joins or leaves the cluster, OpenSearch reacts by automatically making corresponding changes to the voting configuration in order to ensure that the cluster is as resilient as possible. It is important to wait for this adjustment to complete before you remove more nodes from the cluster. For more information, see Adding and removing nodes.
The current voting configuration is stored in the cluster state so you can inspect its current contents as follows:
GET /_cluster/state?filter_path=metadata.cluster_coordination.last_committed_config
|
Note
|
The current voting configuration is not necessarily the same as the set of all available master-eligible nodes in the cluster. Altering the voting configuration involves taking a vote, so it takes some time to adjust the configuration as nodes join or leave the cluster. Also, there are situations where the most resilient configuration includes unavailable nodes or does not include some available nodes. In these situations, the voting configuration differs from the set of available master-eligible nodes in the cluster. |
Larger voting configurations are usually more resilient, so OpenSearch normally prefers to add master-eligible nodes to the voting configuration after they join the cluster. Similarly, if a node in the voting configuration leaves the cluster and there is another master-eligible node in the cluster that is not in the voting configuration then it is preferable to swap these two nodes over. The size of the voting configuration is thus unchanged but its resilience increases.
It is not so straightforward to automatically remove nodes from the voting
configuration after they have left the cluster. Different strategies have
different benefits and drawbacks, so the right choice depends on how the cluster
will be used. You can control whether the voting configuration automatically
shrinks by using the
cluster.auto_shrink_voting_configuration
setting.
|
Note
|
If cluster.auto_shrink_voting_configuration is set to true (which is
the default and recommended value) and there are at least three master-eligible
nodes in the cluster, OpenSearch remains capable of processing cluster state
updates as long as all but one of its master-eligible nodes are healthy.
|
There are situations in which OpenSearch might tolerate the loss of multiple
nodes, but this is not guaranteed under all sequences of failures. If the
cluster.auto_shrink_voting_configuration
setting is false
, you must remove
departed nodes from the voting configuration manually. Use the
voting exclusions API to achieve the desired level
of resilience.
No matter how it is configured, OpenSearch will not suffer from a
"split-brain" inconsistency. The cluster.auto_shrink_voting_configuration
setting affects only its availability in the event of the failure of some of its
nodes and the administrative tasks that must be performed as nodes join and
leave the cluster.
Even numbers of master-eligible nodes
There should normally be an odd number of master-eligible nodes in a cluster. If there is an even number, OpenSearch leaves one of them out of the voting configuration to ensure that it has an odd size. This omission does not decrease the failure-tolerance of the cluster. In fact, improves it slightly: if the cluster suffers from a network partition that divides it into two equally-sized halves then one of the halves will contain a majority of the voting configuration and will be able to keep operating. If all of the votes from master-eligible nodes were counted, neither side would contain a strict majority of the nodes and so the cluster would not be able to make any progress.
For instance if there are four master-eligible nodes in the cluster and the voting configuration contained all of them, any quorum-based decision would require votes from at least three of them. This situation means that the cluster can tolerate the loss of only a single master-eligible node. If this cluster were split into two equal halves, neither half would contain three master-eligible nodes and the cluster would not be able to make any progress. If the voting configuration contains only three of the four master-eligible nodes, however, the cluster is still only fully tolerant to the loss of one node, but quorum-based decisions require votes from two of the three voting nodes. In the event of an even split, one half will contain two of the three voting nodes so that half will remain available.
Setting the initial voting configuration
When a brand-new cluster starts up for the first time, it must elect its first master node. To do this election, it needs to know the set of master-eligible nodes whose votes should count. This initial voting configuration is known as the bootstrap configuration and is set in the cluster bootstrapping process.
It is important that the bootstrap configuration identifies exactly which nodes should vote in the first election. It is not sufficient to configure each node with an expectation of how many nodes there should be in the cluster. It is also important to note that the bootstrap configuration must come from outside the cluster: there is no safe way for the cluster to determine the bootstrap configuration correctly on its own.
If the bootstrap configuration is not set correctly, when you start a brand-new cluster there is a risk that you will accidentally form two separate clusters instead of one. This situation can lead to data loss: you might start using both clusters before you notice that anything has gone wrong and it is impossible to merge them together later.
|
Note
|
To illustrate the problem with configuring each node to expect a certain cluster size, imagine starting up a three-node cluster in which each node knows that it is going to be part of a three-node cluster. A majority of three nodes is two, so normally the first two nodes to discover each other form a cluster and the third node joins them a short time later. However, imagine that four nodes were erroneously started instead of three. In this case, there are enough nodes to form two separate clusters. Of course if each node is started manually then it’s unlikely that too many nodes are started. If you’re using an automated orchestrator, however, it’s certainly possible to get into this situation-- particularly if the orchestrator is not resilient to failures such as network partitions. |
The initial quorum is only required the very first time a whole cluster starts up. New nodes joining an established cluster can safely obtain all the information they need from the elected master. Nodes that have previously been part of a cluster will have stored to disk all the information that is required when they restart.
Bootstrapping a cluster
Starting an OpenSearch cluster for the very first time requires the initial set of master-eligible nodes to be explicitly defined on one or more of the master-eligible nodes in the cluster. This is known as cluster bootstrapping. This is only required the first time a cluster starts up: nodes that have already joined a cluster store this information in their data folder for use in a full cluster restart, and freshly-started nodes that are joining a running cluster obtain this information from the cluster’s elected master.
The initial set of master-eligible nodes is defined in the
cluster.initial_master_nodes
setting. This should be
set to a list containing one of the following items for each master-eligible
node:
-
The node name of the node.
-
The node’s hostname if
node.name
is not set, becausenode.name
defaults to the node’s hostname. You must use either the fully-qualified hostname or the bare hostname depending on your system configuration. -
The IP address of the node’s publish address, if it is not possible to use the
node.name
of the node. This is normally the IP address to whichnetwork.host
resolves but this can be overridden. -
The IP address and port of the node’s publish address, in the form
IP:PORT
, if it is not possible to use thenode.name
of the node and there are multiple nodes sharing a single IP address.
When you start a master-eligible node, you can provide this setting on the
command line or in the opensearch.yml
file. After the cluster has formed,
this setting is no longer required. It should not be set for master-ineligible
nodes, master-eligible nodes joining an existing cluster, or cluster restarts.
It is technically sufficient to set cluster.initial_master_nodes
on a single
master-eligible node in the cluster, and only to mention that single node in the
setting’s value, but this provides no fault tolerance before the cluster has
fully formed. It is therefore better to bootstrap using at least three
master-eligible nodes, each with a cluster.initial_master_nodes
setting
containing all three nodes.
|
Warning
|
You must set cluster.initial_master_nodes to the same list of nodes
on each node on which it is set in order to be sure that only a single cluster
forms during bootstrapping and therefore to avoid the risk of data loss.
|
For a cluster with 3 master-eligible nodes (with node names
master-a
, master-b
and master-c
) the configuration will look as follows:
cluster.initial_master_nodes:
- master-a
- master-b
- master-c
Like all node settings, it is also possible to specify the initial set of master nodes on the command-line that is used to start OpenSearch:
$ bin/opensearch -Ecluster.initial_master_nodes=master-a,master-b,master-c
Choosing a cluster name
The cluster.name
setting enables you to create multiple
clusters which are separated from each other. Nodes verify that they agree on
their cluster name when they first connect to each other, and OpenSearch
will only form a cluster from nodes that all have the same cluster name. The
default value for the cluster name is opensearch
, but it is recommended to
change this to reflect the logical name of the cluster.
Auto-bootstrapping in development mode
If the cluster is running with a completely default configuration then it will automatically bootstrap a cluster based on the nodes that could be discovered to be running on the same host within a short time after startup. This means that by default it is possible to start up several nodes on a single machine and have them automatically form a cluster which is very useful for development environments and experimentation. However, since nodes may not always successfully discover each other quickly enough this automatic bootstrapping cannot be relied upon and cannot be used in production deployments.
If any of the following settings are configured then auto-bootstrapping will not
take place, and you must configure cluster.initial_master_nodes
as described
in the section on cluster bootstrapping:
-
discovery.seed_providers
-
discovery.seed_hosts
-
cluster.initial_master_nodes
|
Note
|
If you start an OpenSearch node
without configuring these settings then it will start up in development mode and
auto-bootstrap itself into a new cluster. If you start some OpenSearch nodes on
different hosts then by default they will not discover each other and will form
a different cluster on each host. OpenSearch will not merge separate clusters together
after they have formed, even if you subsequently try and configure all the nodes
into a single cluster. This is because there is no way to merge these separate
clusters together without a risk of data loss. You can tell that you have formed
separate clusters by checking the cluster UUID reported by
|
Publishing the cluster state
The master node is the only node in a cluster that can make changes to the cluster state. The master node processes one batch of cluster state updates at a time, computing the required changes and publishing the updated cluster state to all the other nodes in the cluster. Each publication starts with the master broadcasting the updated cluster state to all nodes in the cluster. Each node responds with an acknowledgement but does not yet apply the newly-received state. Once the master has collected acknowledgements from enough master-eligible nodes, the new cluster state is said to be committed and the master broadcasts another message instructing nodes to apply the now-committed state. Each node receives this message, applies the updated state, and then sends a second acknowledgement back to the master.
The master allows a limited amount of time for each cluster state update to be
completely published to all nodes. It is defined by the
cluster.publish.timeout
setting, which defaults to 30s
, measured from the
time the publication started. If this time is reached before the new cluster
state is committed then the cluster state change is rejected and the master
considers itself to have failed. It stands down and starts trying to elect a
new master.
If the new cluster state is committed before cluster.publish.timeout
has
elapsed, the master node considers the change to have succeeded. It waits until
the timeout has elapsed or until it has received acknowledgements that each
node in the cluster has applied the updated state, and then starts processing
and publishing the next cluster state update. If some acknowledgements have not
been received (i.e. some nodes have not yet confirmed that they have applied
the current update), these nodes are said to be lagging since their cluster
states have fallen behind the master’s latest state. The master waits for the
lagging nodes to catch up for a further time, cluster.follower_lag.timeout
,
which defaults to 90s
. If a node has still not successfully applied the
cluster state update within this time then it is considered to have failed and
is removed from the cluster.
Cluster state updates are typically published as diffs to the previous cluster state, which reduces the time and network bandwidth needed to publish a cluster state update. For example, when updating the mappings for only a subset of the indices in the cluster state, only the updates for those indices need to be published to the nodes in the cluster, as long as those nodes have the previous cluster state. If a node is missing the previous cluster state, for example when rejoining a cluster, the master will publish the full cluster state to that node so that it can receive future updates as diffs.
|
Note
|
OpenSearch is a peer to peer based system, in which nodes communicate with one another directly. The high-throughput APIs (index, delete, search) do not normally interact with the master node. The responsibility of the master node is to maintain the global cluster state and reassign shards when nodes join or leave the cluster. Each time the cluster state is changed, the new state is published to all nodes in the cluster as described above. |
Cluster fault detection
The elected master periodically checks each of the nodes in the cluster to ensure that they are still connected and healthy. Each node in the cluster also periodically checks the health of the elected master. These checks are known respectively as follower checks and leader checks.
OpenSearch allows these checks to occasionally fail or timeout without
taking any action. It considers a node to be faulty only after a number of
consecutive checks have failed. You can control fault detection behavior with
cluster.fault_detection.*
settings.
If the elected master detects that a node has disconnected, however, this situation is treated as an immediate failure. The master bypasses the timeout and retry setting values and attempts to remove the node from the cluster. Similarly, if a node detects that the elected master has disconnected, this situation is treated as an immediate failure. The node bypasses the timeout and retry settings and restarts its discovery phase to try and find or elect a new master.
Additionally, each node periodically verifies that its data path is healthy by
writing a small file to disk and then deleting it again. If a node discovers
its data path is unhealthy then it is removed from the cluster until the data
path recovers. You can control this behavior with the
monitor.fs.health
settings.


| Top |
The GDBusInterface type is the base type for D-Bus interfaces both on the service side (see GDBusInterfaceSkeleton) and client side (see GDBusProxy).
GDBusInterfaceInfo *
g_dbus_interface_get_info (GDBusInterface *interface_
);
Gets D-Bus introspection information for the D-Bus interface
implemented by interface_
.
Since 2.30
GDBusObject *
g_dbus_interface_get_object (GDBusInterface *interface_
);
Gets the GDBusObject that interface_
belongs to, if any.
It is not safe to use the returned object if interface_
or
the returned object is being used from other threads. See
g_dbus_interface_dup_object()
for a thread-safe alternative.
A GDBusObject or NULL
. The returned
reference belongs to interface_
and should not be freed.
[transfer none]
Since 2.30
GDBusObject *
g_dbus_interface_dup_object (GDBusInterface *interface_
);
Gets the GDBusObject that interface_
belongs to, if any.
A GDBusObject or NULL
. The returned
reference should be freed with g_object_unref()
.
[transfer full]
Since 2.32
void g_dbus_interface_set_object (GDBusInterface *interface_
,GDBusObject *object
);
Sets the GDBusObject for interface_
to object
.
Note that interface_
will hold a weak reference to object
.
Since 2.30
typedef struct _GDBusInterface GDBusInterface;
Base type for D-Bus interfaces.
Since 2.30
struct GDBusInterfaceIface { GTypeInterface parent_iface; /* Virtual Functions */ GDBusInterfaceInfo *(*get_info) (GDBusInterface *interface_); GDBusObject *(*get_object) (GDBusInterface *interface_); void (*set_object) (GDBusInterface *interface_, GDBusObject *object); GDBusObject *(*dup_object) (GDBusInterface *interface_); };
Base type for D-Bus interfaces.
GTypeInterface |
The parent interface. |
|
Returns a GDBusInterfaceInfo. See |
||
Gets the enclosing GDBusObject. See |
||
Sets the enclosing GDBusObject. See |
||
Gets a reference to the enclosing GDBusObject. See |
Since 2.30


Representation of the different anti-CMV antivirals currently in use as well as letermovir, benzimidazole structure (BDCRB and TCRB), and the sulfonamide BAY 38-4766. Abbreviations: CMV, cytomegalovirus; TCRB, 2,5,6-trichloro-1-β-D-ribofuranosyl benzimidazole; BDCRB, 2-bromo-5,6-dichloro-1-(β-D-ribofuranosyl)benzimidazole.


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There are no collections to display.


--- layout: about title: Who We Are description: and why we care background: '/img/posts/04.jpg' ---
We are a group of PhD students from University of California Santa Barbara. We came together over our interest in interdisciplinary education and research, and a class project leading to this website. As we developed this website we learned more about each other and how our disciplines overlap, giving us the interdisciplinary experience we are advocating for.
Andy is studying vertebrate paleontology. He is primarily interested in the phylogeny and paleoecology of South American native ungulates.
Rachel is a first year PhD student interested hydrology, climate change, and how different natural processes affect urban environments. She also works on Visualizing Environmental Models as a crossroads fellow.
William's research interests broadly include hydrology, water resources, climate change impacts, and modeling methods. He also works on Visualizing Environmental Models as a crossroads fellow.
Ehsan works on modeling 3D environments from limited information for VR experiences. He also works on Visualizing Environmental Models as a crossroads fellow.
Yi's research is focused on attention in Human Computer Interaction. He also works on Visualizing Environmental Models as a crossroads fellow.


Name: {{ repo.name }}
Owner: {{ repo.owner.name }}
{{ repo.name }} repository at GitHub: {{ repo_url }}
{% extends "hooks/base.html" %} {% block title %}Repository{% endblock %} {% block sidebar %}
Name: {{ repo.name }}
Owner: {{ repo.owner.name }}
{{ repo.name }} repository at GitHub: {{ repo_url }}
Click the button to delete the repository. This is permanent (e.g. you have to add it back in at the repositories list page):
List of all authors, that committed patches to this repository:
| Name | Gravatar | |
|---|---|---|
| {{ user.name }} | {{ user.email }} |
A list of JSON updates that we received for this repository:
| JSON payload |
|---|
{{ update.update }} |


Object Marker is replacing the Label Status Indicator and Badges (now Inverted Object Status). It’s used for Mark an Object.
Generic indication Of Marked Object With Diffrent set of Icon.


Examples of native and customized file upload inputs.
Customized file uploads require JavaScript
Date inputs are styled like normal text inputs, but some browsers have fully implemented these inputs, giving them additional functionality beyond normal text inputs.
Since these inputs are not consistent across all browsers, it may make sense to not use these inputs just yet, or if you do use them, use them in conjunction with a plug-in/polyfill for the browsers that don't support their native functionality.
The color input is not fully supported in all browsers yet. For browsers that don't support the color input, it will fall back to a normal text input styling.
...


Welcome to Night!
Here you'll find all the nessasary resources to get started.
The links above provide quick access to important information, while the description below will give
an overview of this langauge.
Enjoy!
About
Night is a high level interpreted language built around safety. It being gradually typed, combined with a robust and safe type system, makes Night a simple yet powerful language.
Here's a little sample of Night code:
# fib sequence using recursion
def fib(num)
{
if (num <= 1)
return num
return fib(num - 1) + fib(num - 2)
}
# storing fib numbers in an array
set fib_nums = [ fib(5), fib(6) ]
fib_nums.push(fib(7))
# displaying numbers
for (num : fib_nums)
print(num + ' ')
Safety
A major advantage Night has over other dynamically typed language is its
compile time checks.
Whereas a language like Python does not provide type
checking and many other declaration checks, Night fills this gap, eliminating
hidden runtime bugs and providing safer code.
def divide(x, y)
{
return x / y
}
if (false)
{
a = divide(10, 2) # error! 'a' is undefined
set b = divide('s', 2) # error! 's' is an invalid argument type
}


Wesley Mbate, WGM
Programmer- Web Developer and Software Engineer
I am a young IT enthuthiast who decided to dive into the exciting world of programming. I acquainted myself with coding skills from Moringa School- A brilliant institution I should say.My intrinsic drive to do programming was inspired by wanting to discontinue a trend of constant consumerism; by learning how to give back to the community not only through creating brilliant Apps, but also teaching the skill to young aspiring computer programmers.
I am also a subtle self-taught artist (My art) and in many ways art helped me in my amazing coding journey.It has helped me nature moral-excellencies like patience, gentleness, keenness and creativity.
City: Kamiti Rd, Nairobi City
Address: Kamiti Rd Zimmerman Nairobi Located close to the AP base
Town: zimmerman
joined the school since Kindergaten and cleared my Primary school
education in 2011
Address: P.O. Box 331, Kiambu
City/Town : Kiambu
County: Kiambu
School Category: Public
PROJECTS
HTML
CSS
JavaScript


{% extends 'layout.html' %} {% load static %} {% block title %} Jagrati | Feedbacks {% endblock %} {% block style %} {% endblock %} {% block content %}
We would love to hear your thoughts, concerns or anything we can improve!