ecryptfs-setup-private(1) eCryptfs ecryptfs-setup-private(1) NAME ecryptfs-setup-private - setup an eCryptfs private directory. SYNOPSIS ecryptfs-setup-private [-f|--force] [-w|--wrapping] [-b|--bootstrap] [-n|--no-fnek] [--nopwcheck] [-u|--username USER] [-l|--loginpass LOGIN‐ PASS] [-m|--mountpass MOUNTPASS] OPTIONS Options available for the ecryptfs-setup-private command: -f, --force Force overwriting of an existing setup -w, --wrapping Use an independent wrapping passphrase, different from the login passphrase -u, --username USER User to setup, default is current user if omitted -l, --loginpass LOGINPASS System passphrase for USER, used to wrap MOUNTPASS, will interac‐ tively prompt if omitted -m, --mountpass MOUNTPASS Passphrase for mounting the ecryptfs directory, default is 16 bytes from /dev/urandom if omitted -b, --bootstrap Bootstrap a new user's entire home directory --undo Display instructions on how to undo an encrypted private setup -n, --no-fnek Do not encrypt filenames; otherwise, filenames will be encrypted on systems which support filename encryption --nopwcheck Do not check the validity of the specified login password (useful for LDAP user accounts) --noautomount Setup this user such that the encrypted private directory is not automatically mounted on login --noautoumount Setup this user such that the encrypted private directory is not automatically unmounted at logout DESCRIPTION ecryptfs-setup-private is a program that sets up a private cryptographic mountpoint for a non-root user. Be sure to properly escape your parameters according to your shell's spe‐ cial character nuances, and also surround the parameters by double quotes, if necessary. Any of the parameters may be: 1) exported as environment variables 2) specified on the command line 3) left empty and interactively prompted The user SHOULD ABSOLUTELY RECORD THE MOUNT PASSPHRASE AND STORE IN A SAFE LOCATION. If the mount passphase file is lost, or the mount passphrase is forgotten, THERE IS NO WAY TO RECOVER THE ENCRYPTED DATA. Using the values of USER, MOUNTPASS, and LOGINPASS, ecryptfs-setup-pri‐ vate will: - Create ~/.Private (permission 700) - Create ~/Private (permission 500) - Backup any existing wrapped passphrases - Use LOGINPASS to wrap and encrypt MOUNTPASS - Write to ~/.ecryptfs/wrapped-passphrase - Add the passphrase to the current keyring - Write the passphrase signature to ~/.ecryptfs/Private.sig - Test the cryptographic mount with a few reads and writes The system administrator can add the pam_ecryptfs.so module to the PAM stack which will automatically use the login passphrase to unwrap the mount passphrase, add the passphrase to the user's kernel keyring, and automatically perform the mount. See pam_ecryptfs(8). FILES ~/.ecryptfs/auto-mount ~/.Private - underlying directory containing encrypted data ~/Private - mountpoint containing decrypted data (when mounted) ~/.ecryptfs/Private.sig - file containing signature of mountpoint passphrase ~/.ecryptfs/Private.mnt - file containing path of the private directory mountpoint ~/.ecryptfs/wrapped-passphrase - file containing the mount passphrase, wrapped with the login passphrase ~/.ecryptfs/wrapping-independent - this file exists if the wrapping passphrase is independent from login passphrase SEE ALSO ecryptfs-rewrap-passphrase(1), mount.ecryptfs_private(1), pam_ecryptfs(8), umount.ecryptfs_private(1) /usr/share/doc/ecryptfs-utils/ecryptfs-faq.html http://ecryptfs.org/ AUTHOR This manpage and the ecryptfs-setup-private utility was written by Dustin Kirkland<EMAIL_ADDRESS>for Ubuntu systems (but may be used by others). Permission is granted to copy, distribute and/or modify this document under the terms of the GNU General Public License, Version 2 or any later version published by the Free Software Foundation. On Debian systems, the complete text of the GNU General Public License can be found in /usr/share/common-licenses/GPL. ecryptfs-utils 2008-11-17 ecryptfs-setup-private(1)


The Brain company is a part of your daily life. We work to improve socialization and basic functions.
We help with processing complex thoughts, and creating new ideas.
The Brain company stores and retreives both Short Term and Long Term memorys through our complex memory consolidation process.


PhD Thesis
A ‘simple’ deterministic model which describes an asymmetric competition between an immune system with a specific and powerful response, and a virus with a broad toxicity and fast mutations.
A domain of parameters in which the dynamics is characterized by the apparition of “blips”, associated here to an instability which develops at high virus reproduction rate.
Two sets of population: Viruses( HIV strains \(V_\sigma\)) and Immune System (CD4\(^+\) T cells \(X_\sigma\)) where \(\sigma \in S\) and \(S\) a simple genomic space \(S={1,...,N}\)
We do not describe directly a population of infected cells, assuming that the virus dynamics is fast enough so that both population densities stay strongly coupled at all times. Finally we also assume that the mean densities are the only variable which matters, irrespective of the repartition in the body and the possible interaction with other agents
\[ \begin{eqnarray*} \frac{dX_\sigma}{dt}&=&\frac{\Lambda_0}{N}\left(1+\frac{DV_\sigma}{1+V^T} \right) - X_\sigma\left(1+V^T+AV_\sigma \right) \\ \frac{dV_\sigma}{dt}&=&\omega V_\sigma\left( X^T - BX_\sigma - C\right) + \epsilon (\Delta V)_\sigma \end{eqnarray*} \]
A “predator-predator” model \(->\) The birth rates of both increase with the density of the other
Rich dynamics from the asymmetry in the behavior \(->\) Virus prey on globally while immune systems prey on specific strain
For a particular set of parameters, peaks of virus density appear in some cases as a consequence of a too high virus replication rate \(->\) reminding of the clinically observed blips as a consequence of instability
For details \(->\) arXiv:1101.2851 [q-bio.QM]
Journal of Antimicrobial Chemotherapy (2006) 58, 714–722:
AIDS RESEARCH AND HUMAN RETROVIRUSES Volume 25, Number 1, 2009 and Antiviral Therapy 2013; 18:867-876
Short communications:
Valutazione del rischio ambientale del comune di Ciampino in relazione all’inquinamento atmosferico (2007):
Studio della Mortalità per cause nel Comune di Ciampino e comuni limitrofi negli anni 1987-2002 (2007):
A six-typology compartment model of trends in use of illicit drugs in Italy \(->\) to evaluate policies and interventions through scenario analyses.
Inside the big compartments of just-cannabis users and multiple-drug users three users subpopulation: occasional, regular, intensive
Data from intensive clinical routine (\(~10\)K test/year, each test with more tha \(100\) molecule)
A brand new test in allergology \(->\) definition of appropriate statistical analisys and new (in allergology) paradigms of visulization
Implementation of cluster analisys: Hierarchical and not using euclidean and binary dissimilarity measures; cluster dendrograms and correlation-trees
Graphical calculation of Venn’s Diagrams for showing cross-positivity to up to 4 allergene, Circular highly customizable barplot
Spatial statistics on sensitivy to various allergens (Allergome.org)
Implementation of an allergy risk index using profile of allergic sensitization and a database of food ingredient for allergenes diffusion (AllergomeConsumer ifarai.org)
Hard coding effort with R, MySql and PHP for Data Cleaning/Preprocessing, Analysis and Visulization
More details on
Definition of the working paradigms
Creation and implementation of the network of scientific (nationally and internationally) and development partners
Creation of the web site, various slides and presentations, implementation of the Customer Relationship Database, reports and statitics of the project activities
Over 50 personal meeting with italian firms with 3 signed collaborations
Simple model with exact analytical solution to evaluate the cost function of Unicredit Asset Liability Management Model and proposal of new one with better convergence
Creation and implementation of TH.E.SYS, THREAT EVALUATION SYSTEM, evaluation procedure of the state risk for a private corporation. TH.E.SYS is a a composite index from multiple etherogenous sources. I can’t say more than that…
Some scripts for generating graphs and analisys of simulations in the application developed for the project RBF4AERO
Simple model (Hull-White) with exact analitycal solution in order to benchmark the model and the simulation of Credit Value Adjustment developed for Italian Ministery of Finance and Economics (SAPE model)
Use of probabilistic model like Topic Model and GloVE in order to:
Have a relatively good computation performance
Define a standard in data preprocessing and visualization
STM allows incorporating tags, categories, metadata and other information accompanying the text archive
STM uses covariate information to parametrize the prior distributions to potentially affect both topical prevalence and topical content
STM use the Bag Of Word approach (forget the word order in the text!)
We extended STM so it can take into account properly multi words token (N-grams)
from STM (Bag-Of-Word) to ExtendedSTM (Bag-of-Ngrams)
Technically we are acting on vocabulary matrix and updating the document with the most probable N-gram according to probablity distributions calculated using STM. doi:10.1109/ICSC.2018.00048
In Indexing of Anonimous Networks for Crime Information Search (IANCIS) we performed a complete crawling of four famous ToR drug marketplaces: Alphabay, Crypto Market, East India and Nucleus
We used E-STM to analize the dataset
As an example in the topic 30 (called Methamphetamine),the highest score tokens were: ice, meth, crystal_meth, shards, crystal_methamphetamine, 0.5g_crystal_methamphetamine.
We can also see that in Nucleus (0.0218) the topic is 2 times more prevalent respect the others (0.0114)
The coherence and the intelligibility of the obtained topics were significantly enhanced
What they speaking about? -> Topic Model
They have a positive or negative opinion? -> Sentiment Analisys
Idea: What is the opinion about different arguments not know? -> Sentiment E-STM
What the problem? E-STM is working well with document (many sentences) while Sentiment Analisys with sigle sentence.
Idea: Divide every document in at least 3 subdocuments (negative, neutral, positive polarity) then using the \(K-\) dimensional \(\vec{\theta_d}\) (probabilities of sub-document \(d\) to stay in topics \(1,...,K\)) to compute a distribution of polarity scores for every topic.
Currently the following models are implemented:
Spatial Wrapped Normal: given \(Y \in R\) a random Normal variable (\(f_Y(y) \sim N(\mu, \sigma^2)\)), \(\Theta = Y \; mod \; 2\pi\) in \([0,2\pi)\) is circular. Then \(f_\Theta(\theta) \sim \sum_{k=-\infty}^{\infty} f_Y(\theta+2\pi k)\)
Spatial Projected Normal: given \(\mathbf{Y} = (Y_1, Y_2)\) a bivariate vector, \(\mathbf{U} =\frac{\mathbf{Y}}{||\mathbf{Y}||}\) is a point over the unit circle then \(\Theta\) \(->\) \(U_1=cos(\Theta)\), \(U_2=sin(\Theta)\). Then \(\mathbf{Y}=(Y_1,Y_2)=R(cos\theta , sin\theta)=R\mathbf{U}\) where \(R=||\mathbf{Y}||\) and \(f_\Theta(\theta)=\int_{R^+} rf_\mathbf{Y}((rcos\theta , rsin\theta)')dr\)
The work as be accepted and will be published on JSS Special Issue on Bayesian Statistics in the second half of 2019. For now http://santoroma.github.io/CircSpaceTime/
Time to go deep inside the model: we want to see some semantic aspects using topic model
Idea: Maybe the correlation between topics can dramatically change respect to specific covariates value. We want to create a correlation structure for the topics depending on covariates S \(\mathbf{\theta_d^{K-1}} \sim LogitNormal_{K-1}(\Gamma^TX^T, \Omega^TS^T)\) using the same structure of the \(\Gamma^TX^T\), a lasso regression, or may be an elastic (L_1+L_2)…Then there is much calculation for the full a posteriori distribution and the variational E-M to use.
In the E-STM there is no selection between a bi-gram and the two components. We can extend this idea by introducing a discrete spatial dependence for the indicator variable \(z_{n,d}\) that can become a generic compound indicator. We can take into account N-grams (contiguous words) and skip-grams (not contiguous words).
E-STM is working with constant sparse matrix \(->\) we can go to a CUDA GPU implementation for large corpora
In large corpora, the number of topics can be considerable \(\sim 10^3\), and we can use the correlation structure to build a network of topics. The edge can be the correlation with a lower bound. Then we can study the resulting network
SentimentE-STM can be used with a specific polarity database. We can use STM to select most representative documents, annotate it manually, then use STM probability structure and a word network database to an expansion to all the dictionary.


Krzysztof Piatek aiming to be worth €70m by the time he leaves AC Milan
by FSA EditorKrzysztof Piatek is determined to double in value during his time at AC Milan, despite a difficult start to the 2019-20 season
AC Milan striker Krzysztof Piatek wants to be worth as much as €70million by the time he eventually leaves the club.
Piatek signed for Milan in January after enjoying an extraordinary start to life in Serie A at Genoa.
The Poland striker scored 13 league goals in 19 appearances during the first half of the 2018-19 season, leading to a big-money move for a reported fee of €35m.
He had no trouble maintaining his form following his switch to San Siro – scoring a further nine league goals – but has failed to recapture those levels so far this campaign, netting just three times.
However, Piatek remains confident in his ability to succeed with the Rossoneri.
“When I moved to Milan it all happened so fast that I didn’t even ask questions,” the 24-year-old, who scored in Poland’s 2-1 win over Israel on Saturday, told television channel TVP Sport.
“I was focused on work, in football yes, it’s always about setting new goals. Now I’m worth €38m, the next time I change clubs, I’d like to be worth €60-70m.
“I have to be ambitious, I’ll work for it, I’m at the beginning of my career, it’s only my second season in Serie A, one of the top five leagues [in Europe].”
Piatek also believes the reaction of the Italian press to his loss of form has made matters more difficult at Milan, who sacked coach Marco Giampaolo earlier in the season and currently languish down in 14th place in the table.
“I look in the mirror and look for the blame inside of me. At the beginning, as a team, we didn’t create so many opportunities. We’re improving, I’m going to score again,” he said during the interview.
“For Italian journalists, one day you’re the soccer pope and the next day you’re the worst player.
“I accept it. Nobody put themselves in my shoes, the covers of the newspapers don’t impact me.”


Files CCore/inc/gadget/Meta.h CCore/src/gadget/Meta.cpp
The namespace Meta contains a collection of meta-tools widely-used in CCore. They are enclosed in the namespace Meta.
template <class T,T Val> inline constexpr T Const = Val ;
This is a general Meta-constant with the given type and value.
struct Empty
{
};
Empty is an empty Meta-class. It is used to return an empty result from Meta-functions.
template <class T>
struct EmptyBox
{
};
EmptyBox is an utility for meta-calculations. It is empty.
template <class T>
struct TypeBox
{
using Type = T ;
static T Get();
};
TypeBox is used to "box" the given type. It also can be used in meta-calculations to get a value of the type.
template <class T>
struct DefType
{
using Ret = T ;
};
DefType is a Meta-function, it returns the given type as the result.
template <class T,T Val>
struct DefConst
{
static constexpr T Ret = Val ;
};
DefConst is a Meta-function, it returns the given constant as the result.
template <class T>
using ToConst = .... ;
ToConst<T> == const T
ToConst<T &> == const T &
ToConst is a Meta-type. It makes the constant type from the given type with respect to references.
template <bool Cond,class T1,class T2>
using Select = .... ;
Select<true,T1,T2> == T1
Select<false,T1,T2> == T2
Select is a Meta-type. It is used to select a type from two types. If Cond is true, then the first type is selected, otherwise the second.
template <class T1,class T2>
inline constexpr bool IsSame = .... ;
IsSame is a Meta-constant. It equals true iff both arguments are the same.
template <class T,class ... TT>
inline constexpr bool OneOf = .... ;
OneOf is a Meta-constant. It equals true iff the first argument T belongs to the following type list TT.
template <class T>
inline constexpr bool IsUInt = .... ;
IsUInt is a Meta-constant. It equals true iff the argument is an unsigned integral type. The following types are unsigned integral types: unsignd char, unsigned short, unsigned int, unsigned long, unsigned long long. The type char is an unsigned integral type iff it is unsigned. Some other types can be also considered as unsigned integral types, it is controlled by the target through the target header PlatformBase.h (see below).
template <class T>
inline constexpr unsigned UIntBits = .... ;
UIntBits is a Meta-constant. It's defined for unsigned integral types (i.e. for types T with IsUInt<T> equals true) and equals the number of bits of the argument.
template <class UInt>
inline constexpr unsigned HexWidth = (UIntBits<UInt> +3)/4 ;
HexWidth is a Meta-constant. It's defined for unsigned integral types (i.e. for types T with IsUInt<T> equals true) and equals the number of hex digits required to represent a type value.
template <class T>
inline constexpr bool IsSInt = .... ;
IsSInt is a Meta-constant. It equals true iff the argument is an signed integral type. The following types are signed integral types: signd char, signed short, signed int, signed long, signed long long. The type char is a signed integral type iff it is signed. Some other types can be also considered as signed integral types, it is controlled by the target through the target header PlatformBase.h (see below).
template <class T>
inline constexpr bool IsSUInt = IsSInt<T> || IsUInt<T> ;
IsSUInt is a Meta-constant. It equals true iff the argument is a signed or an unsigned integral type.
template <class T>
using UnRef = typename std::remove_reference<T>::type ;
UnRef is a Meta-type. It removes the reference qualifier from the argument type.
template <class T>
using UnConst = typename std::remove_const<T>::type ;
UnConst is a Meta-type. It removes the constant qualifier from the argument type.
template <class Ptr>
using PtrObjType = .... ;
PtrObjType<T *> == T
PtrObjType<Ptr> == typeof ( *Ptr )
PtrObjType is a Meta-type. It should be applied to pointer kind types to retrieve the type of pointee objects. For example, if R is PtrLen<T>, then PtrObjType<R> is T.
template <class S,class T>
using CommonType = .... ;
CommonType is a Meta-type. It is defined iff T equals S and is T in this case.
template <ulen Len>
using AlignedStorage = typename std::aligned_storage<Len>::type ;
AlignedStorage is a Meta-type. It is a POD type. An object of this type can be used as an aligned storage of required length.
template <class SInt>
struct SIntToUInt
{
using SType = .... ; // SInt or equivalent signed type
using UType = .... ; // unsigned SType
};
SIntToUInt is a Meta-function. It is applied to signed integral types and returns as the result two types: SType — the original type (or equivalent) and UType — the correspondent unsigned type. The implementation is supported by the target (see below).
template <class UInt>
struct PromoteUInt
{
using Type = .... ; // promoted UInt type
};
PromoteUInt is a Meta-function. It is applied to unsigned integral types and returns as the result a type: Type is a "promoted" type for the UInt. I.e. it is the same or greater type and the promoted type is greater or equals than unsigned. The implementation is supported by the target (see below).
template <class SInt>
struct PromoteSInt
{
using SType = .... ; // promoted SInt type
using UType = .... ; // unsigned SType
};
PromoteSInt is a Meta-function. It is applied to unsigned integral types and returns as the result two types: SType is a "promoted" type for the SInt. I.e. it is the same or greater type and the promoted type is greater or equals than int. UType is the correspondent unsigned SType type. The implementation is supported by the target (see below).
/* const IsEnum<E> */ template <class E> inline constexpr bool IsEnum = std::is_enum<E>::value ; /* const IsClass<T> */ template <class T> inline constexpr bool IsClass = std::is_class<T>::value ; /* const IsPOD<T> */ template <class T> inline constexpr bool IsPOD = std::is_pod<T>::value ; /* const HasNothrowDtor<T> */ template <class T> inline constexpr bool HasNothrowDtor = std::is_nothrow_destructible<T>::value ; /* const HasTrivDtor<T> */ template <class T> inline constexpr bool HasTrivDtor = std::is_trivially_destructible<T>::value ; /* const HasDefaultCtor<T> */ template <class T> inline constexpr bool HasDefaultCtor = std::is_default_constructible<T>::value ; /* const HasNothrowDefaultCtor<T> */ template <class T> inline constexpr bool HasNothrowDefaultCtor = std::is_nothrow_default_constructible<T>::value ; /* const HasCopyCtor<T> */ template <class T> inline constexpr bool HasCopyCtor = std::is_copy_constructible<T>::value ; /* const HasNothrowCopyCtor<T> */ template <class T> inline constexpr bool HasNothrowCopyCtor = std::is_nothrow_copy_constructible<T>::value ; /* const HasMoveCtor<T> */ template <class T> inline constexpr bool HasMoveCtor = std::is_move_constructible<T>::value ; /* const IsCopyable<T> */ template <class T> inline constexpr bool IsCopyable = std::is_copy_constructible<T>::value && std::is_copy_assignable<T>::value ; /* const IsNothrowCopyable<T> */ template <class T> inline constexpr bool IsNothrowCopyable = std::is_nothrow_copy_constructible<T>::value && std::is_nothrow_copy_assignable<T>::value ; /* const IsMovable<T> */ template <class T> inline constexpr bool IsMovable = std::is_move_constructible<T>::value && std::is_move_assignable<T>::value ; /* const IsBaseOf<B,D> */ template <class B,class D> inline constexpr bool IsBaseOf = std::is_base_of<B,D>::value ;
This is a whole list of Meta-constants. They can be used to determine different type properties.
IsEnum equals true iff the type T is an enum type.
IsClass equals true iff the type T is a class type.
IsPOD equals true iff the type T is a POD type.
HasNothrowDtor equals true iff the type T is nothrow destructible.
HasTrivDtor equals true iff the type T is trivially destructible.
HasDefaultCtor equals true iff the type T is default constructible.
HasNothrowDefaultCtor equals true iff the type T is nothrow default constructible.
HasCopyCtor equals true iff the type T is copy constructible.
HasNothrowCopyCtor equals true iff the type T is nothrow copy constructible.
HasMoveCtor equals true iff the type T is move constructible.
IsCopyable equals true iff the type T is copy constructible and copy assignable.
IsNothrowCopyable equals true iff the type T is nothrow copy constructible and nothrow copy assignable.
IsMovable equals true iff the type T is move constructible and move assignable.
IsBaseOf equals true iff the type B is a base class of the class D.
Target must provide the following entities in the its header file PlatformBase.h.
namespace CCore { namespace Bits { const unsigned unsigned_char = "bit size of type char" ; const unsigned unsigned_short = "bit size of type short" ; const unsigned unsigned_int = "bit size of type int" ; const unsigned unsigned_long = "bit size of type long" ; const unsigned unsigned_long_long = "bit size of type long long" ; } // namespace Bits } // namespace CCore
The following structure is used to extend the list of types, recognized as integral types:
namespace CCore { namespace ExtraInt { template <class T> struct Prop { enum { IsSigned = false, IsUnsigned = false }; }; template <> struct Prop<"extra sint type"> { enum { IsSigned = true, IsUnsigned = false, }; using SType = "signed type" ; using UType = "unsigned type" ; using PromoteSType = "signed promote type" ; using PromoteUType = "unsigned promote type" ; }; template <> struct Prop<"extra uint type"> { enum { IsSigned = false, IsUnsigned = true, Bits = "bit size" }; using PromoteUType = "promote type" ; }; } // namespace ExtraInt } // namespace CCore


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Weighing in as an easily manipulated object, we have Classes! Classes are (for lack of a better description) "mini-modules". One huge difference between classes and modules, however, is that classes can create an infinite amount of objects without interfering with one another. In classes, you can create different types of variables, whether they are class variables, local variables, or instance variables, that can be manipulated throughout the programming in the class. However, a variables discussion will be saved for a later time and new blog, but for now, just know that variables can be defined in classes, and some can be manipulated and used throughout the class and produce different results, while not interfering with each other.
Weighing in as what you assign to it, we have Modules! Modules are very similar to hashes. Modules are defined with a key and a given value. This means that modules are primarily static, and do not change like classes can. When a key (let's say "car") has a given value (let's say "Mercedes"), that is what it is. When you call the module and tell it to search for the key "car", it will return "Mercedes", no ifs, ands, or buts about it.
Overall, both methods can be used for different reasons. Personally, I enjoy using classes as the values of your variables can be manipulated to create as many as you want with each one being different. Modules are similar to hashes, which are static and do not allow for much manipulation.
I hope you have enjoyed this blog. Please feel free to provide me with any feedback.


{% extends "layout.html" %} {% block title %} DCYO {% endblock %} {% block main %}
DCYO is one of the first football clubs in the village of Dennery. The club carries the reputation of being very strick with discipline. This factor is due to the high number of players and other member that part of the police and fireservice departments in Saint Lucia.
With the reputation that preceeds the club, is also the smooth and careful nature of playing. They are known to execute strategies fluently against their rivals and are quite accomplished in the championship category.
{% endblock %}


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06-Jul-18 12:28 PM
[dscrdeql] <AndronicusBass-Founder> i just rained a little when we got to 20 active users
[dscrdeql] <Cryptovideos> good an ya mate
[dscrdeql] <Cryptovideos> put a shrimp on the barbie? and crank out a cold one?
[dscrdeql] <ArthurSk12> Daaamn
[dscrdeql] <ArthurSk12> I just woke up and had to check coz I had like 20 notifications
[dscrdeql] <Cryptovideos> hell yea
[dscrdeql] <Cryptovideos> ahahah
[dscrdeql] <Cypher - Founder> is that it ?
[dscrdeql] <Cypher - Founder> haha me this morning
[dscrdeql] <sourge> thats funny
[dscrdeql] <Cypher - Founder> how is everyone
[dscrdeql] <Cypher - Founder> also there is now a #foodporn channel and #anime channel for all your offtopic needs
[dscrdeql] <SwimedDragon> Doing good How about you?
[dscrdeql] <Cypher - Founder> not too bad
[dscrdeql] <Cypher - Founder> working on a few things soo hot today
[dscrdeql] <tanya> im ok, about to start pestering roommate to mow the lawn lol
[dscrdeql] <SwimedDragon> Tempature is getting a bit down here
[dscrdeql] <SwimedDragon> Is this typical quarter final countries?
[dscrdeql] <Cypher - Founder> no
[dscrdeql] <SwimedDragon> Already thought so
[dscrdeql] <Aspire219> anyone watch wimbledon?
[dscrdeql] <Cypher - Founder> big guns have been kicked out only big guns left are England Brazil and Frnace
[dscrdeql] <SwimedDragon> I never watch it, but even I noticed it's a bit off
[dscrdeql] <Cypher - Founder> its great dude
[dscrdeql] <Cypher - Founder> worth a watch
[dscrdeql] <Cypher - Founder> germany spain portugal were kicked out
[dscrdeql] <Cypher - Founder> italy
[dscrdeql] <SwimedDragon> I might watch for the half or final.
[dscrdeql] <SwimedDragon> My country (Netherlands) didn't even got in, lol
[dscrdeql] <Cypher - Founder> the quaters are going to be good
[dscrdeql] <AndronicusBass-Founder> Is the world cup still going? crikey.
[dscrdeql] <Cypher - Founder> which sta\rts in an hour
[dscrdeql] <Cypher - Founder> world cup runs for a month
[dscrdeql] <Cypher - Founder> there are 32 contries
[dscrdeql] <Cypher - Founder> this year
[dscrdeql] <NWhiskeyTango> Mine either
[dscrdeql] <AndronicusBass-Founder> Wow, didnt realise.
[dscrdeql] <Cypher - Founder> previous winners
[dscrdeql] <Cypher - Founder> you get a star on the kit every time you win
[dscrdeql] <Cypher - Founder> i.e. brazil have 5 stars
[dscrdeql] <AndronicusBass-Founder> You learn something new every day
[dscrdeql] <Catalyst> @AndronicusBass well it was 4 PM. But didn't sleep well at night so was feeling abit light headed
[dscrdeql] <Cypher - Founder> for example
[dscrdeql] <Cypher - Founder> germany got knocked out really early they are the current holders
(edited)
[dscrdeql] <donaldmathers> Hi
[dscrdeql] <Cypher - Founder> current standings
[dscrdeql] <CombatWombat> im going for Russia lol
[dscrdeql] <donaldmathers> Is eql coin pow or pos
[dscrdeql] <Cypher - Founder> dpow


The Edit My Resume dialog box allows you to edit your resume.
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Celebrating the achievement of middle school math
Middle school math can be intimidating. Not only is the subject matter different, it is also more complex. Topics like algebra, geometry, fractions, decimals, and ratios are all new and require much more critical thinking than what students are typically used to. As students transition to middle school math (and middle school in general), it is important to recognize them—to celebrate their accomplishments and give encouragement every step of the way. This set of achievement badges offers bright colors and fun, age-appropriate illustrations in hopes of doing just that.


This example illustrates how to move the files (contained in a DU) associated with your compute workload from one machine to another machine using the concept of “Pilot Data.” This is achieved by creating two Pilot Data entities and moving the output DU from the local Pilot Data entity to the remote Pilot Data entity.
As an example, we are running a computational workload on Stampede, an XSEDE machine (see pilot_compute_description). We want our input Data Unit to be available on Stampede, because our Compute Unit (which requires the file in the input Data Unit) runs on Stampede. The CU needs the input DU to be transferred to the compute node where the computation will be done. However, we want our output data to be available on india, a FutureGrid machine. Note that we have set up our ssh keys to allow for password-less login to india.futuregrid.org from stampede.tacc.utexas.edu.
Be sure to change the project field to your allocation on the machine you are running on. You will also need to change the service_url to your home directory on india. You may also wish to create a folder to contain the DUs such as “pilotdata”. This is not a necessary step, but is a suggested step. Your output on india.futuregrid.org will be available in the directory you specify, in this case, /N/u/<your username>/pilotdata in subdirectories that start with DU-<uid> where uid is a unique identifier to your output data.
import sys
import os
import time
import logging
from pilot import PilotComputeService, PilotDataService,ComputeDataService, State
COORDINATION_URL = "redis://localhost:6379"
if __name__ == "__main__":
pilot_compute_service = PilotComputeService(coordination_url=COORDINATION_URL)
# create pilot job service and initiate a pilot job
pilot_compute_description = {
"service_url": 'slurm+ssh://stampede.tacc.utexas.edu',
"number_of_processes": 32,
"working_directory": os.getenv("HOME")+"/agent",
"queue": "normal",
"project": 'YourAllocation', #CHANGE ME
"walltime":10
}
pilotjob = pilot_compute_service.create_pilot(pilot_compute_description=pilot_compute_description)
# create pilot data service (factory for data pilots (physical,distributed storage))
# and pilot data
pilot_data_service = PilotDataService(coordination_url=COORDINATION_URL)
#Define our local Pilot Data entity
pilot_data_description_1={
"service_url":"ssh://localhost"+os.getenv("HOME")+"/pilotdata/",
"size":100,
}
#Define our remote Pilot Data entity on india.futuregrid.org
pilot_data_description_2={
"service_url":"ssh://india.futuregrid.org/N/u/<your username>/pilotdata", # CHANGE ME
"size":100,
}
ps1 = pilot_data_service.create_pilot(pilot_data_description=pilot_data_description_1)
ps2 = pilot_data_service.create_pilot(pilot_data_description=pilot_data_description_2)
compute_data_service = ComputeDataService()
compute_data_service.add_pilot_compute_service(pilot_compute_service)
compute_data_service.add_pilot_data_service(pilot_data_service)
input_data_unit_description = { "file_urls": ["ssh://localhost" +os.path.join(os.getcwd(), "test.txt")],
}
# submit pilot data to a pilot store
# Note that we are using the Local Pilot Data storage
input_du = ps1.submit_data_unit(input_data_unit_description)
input_du.wait()
### Create Output DU to store the output files.
output_data_unit_description = { "file_urls": [],
}
# submit pilot data to a pilot store
# Note that we are using the Remote Data storage
output_du = ps2.submit_data_unit(output_data_unit_description)
output_du.wait()
# start compute unit
compute_unit_description = {
"executable": "/bin/cat",
"arguments": ["test.txt"],
"number_of_processes": 1,
"output": "stdout.txt",
"error": "stderr.txt",
"input_data" : [input_du.get_url()], # this stages the content of the data unit to the working directory of the compute unit
"output_data":[{ output_du.get_url(): ['std*']} ]
}
compute_unit = compute_data_service.submit_compute_unit(compute_unit_description)
logging.debug("Finished setup of PSS and PDS. Waiting for scheduling of PD")
compute_data_service.wait()
## export the output files to local directory.
# output_du.export(os.getcwd())
logging.debug("Terminate Pilot Compute/Data Service")
compute_data_service.cancel()
pilot_data_service.cancel()
pilot_compute_service.cancel()


Hush has been building the first ever open source private messenger service based on Zcash technology. Hush & Komodo not only share core developers, but coinciding views on the importance of independent right to privacy.
Between 15-Dec-19 12:00 AM and 16-Dec-19 12:00 AM
GatewayBOT15-Dec-19 04:47 AM
[dscrd-hush] <Privatoshi> What's new with everyone?
Trading? Getting ready for holidays?
GatewayBOT15-Dec-19 05:35 AM
[dscrd-hush] <heguli97> Not much :), Getting slowly ready for christmas. lights are up around the house but still need to pick out a tree probably next week. Today is dedicated for cleaning though
[dscrd-hush] <FtrSaroth> Hi all! Thanks @Privatoshi and @dukeleto! I think now i understand Hush in a more propper way, the only thing Is my equipment its a little obsolete if i can upgrade It, gonna be great, if not, maybe im gonna have to wait until a official Hush android wallet be released, if there is a plan for that for the moment i keep my Hush on AtomicDEX but dont have all the good things of SD
[dscrd-hush] <Privatoshi> @FtrSaroth I believe you can run SilentDragonLite.
[dscrd-hush] <Privatoshi> I can and I do it on a 32-bit laptop.
[dscrd-hush] <Privatoshi> And I connect to SDL, with SDA(edited)
[dscrd-hush] <FtrSaroth> @Privatoshi Hi! I tried, but my laptop have windows and unfortunately couldn't installed
[dscrd-hush] <Privatoshi> Let's move to #general-support
My 5am no sleep face just to brag about my cool hand-made bracelet with @Tails_live becaus once again I went through TSA’s cancer machine with it on my hand and no prob.
[dscrd-hush] <dukeleto> that is pretty hilarious
GatewayBOT15-Dec-19 12:24 PM
[dscrd-hush] <Captain> what's hilarious about that?
GatewayBOT15-Dec-19 12:41 PM
[dscrd-hush] <dukeleto> @Captain do you understand the picture?
[dscrd-hush] <dukeleto> maybe it's not easy to understand for non-US people
[dscrd-hush] <dukeleto> She took a TAILS usb stick thru the metal detector
[dscrd-hush] <dukeleto> it did not detect it, lol
GatewayBOT15-Dec-19 12:50 PM
[dscrd-hush] <passcombo.com > once in US embassy I accidently brought scalpel (first aid small kit)(edited)
[dscrd-hush] <passcombo.com > not detected
[dscrd-hush] <dukeleto> gosh, might not want to say that too loudly
[dscrd-hush] <dukeleto> !tip 11 random users 55 hush
[dscrd-hush] <tip.cc> :HUSH: @dukeleto sent @5am, @ca333, @CrisF, @db gman, @Faetos, @KamikatzeLP, @LukaL, @maleficium, @Odin, @Spyrek and @Zigah 5.00 HUSH (≈ $0.26) each :cloud_rain:.
Use !tip nomention to stop being pinged by tips.
[dscrd-hush] <dukeleto> mornin' folks
[dscrd-hush] <CrisF> Thanks for the tip. @dukeleto
[dscrd-hush] <CrisF> Hope all is well and you have a great day.
GatewayBOT15-Dec-19 01:08 PM
[dscrd-hush] <dukeleto>
GatewayBOT15-Dec-19 01:23 PM
[dscrd-hush] <franciscana23> Morning friends
GatewayBOT15-Dec-19 01:41 PM
[dscrd-hush] <AkiraX> Hi
[dscrd-hush] <phm87> Good morning
[dscrd-hush] <Captain> Ahh haha, i was looking at the bracelet. Looked like a usb output.
GatewayBOT15-Dec-19 09:52 PM
[dscrd-hush] <Privatoshi> You know TSA saw that tweet...
She got put on a list


causal_da.components.ica_torch¶
causal_da.components.ica_torch.GCL_nonlinear_ica_train¶
-
class
GCLTrainer
(model, optimizer, contrastive_coeff=1.0, contrastive_loss=<function binary_logistic_loss>, balance=True, device='cpu', run_logger=None)¶ Bases:
object
Trainer class conforming to the interface of the
torch-ignite
package.Train a nonlinear ICA model by generalized contrastive learning.
- Parameters
model – the model to be trained.
optimizer – the optimizer.
contrastive_coeff (
float
) – The loss can be multiplied by this coefficient to improve numerical stability.contrastive_loss (
Callable
[[FloatTensor
,int
],FloatTensor
]) – the loss function used for contrastive training.balance (
bool
) – whether to use a coefficient to stabilize the learning (the coefficient is initialized to normalize the loss to 1 in the first iteration).device (
str
) – whether to use GPU for training. *gpu_identifier
: use GPU with the identifier if available. *'cpu'
: use CPU.run_logger – the logger to save the results.
-
compute_and_backward_loss
(trainer, data, target)¶ Compute loss and prepare the back-propagation.
- Parameters
trainer – the trainer.
data (
FloatTensor
) – the input data (shape(n_sample, n_dim)
).target (
LongTensor
) – the auxiliary variables for GCL (shape(n_sample,)
).
-
log_training_loss
(trainer)¶ Record the training loss metrics.
- Parameters
trainer – the trainer object.
-
GCL_nonlinear_ica_train
(data_tensor, c_src, batch_size, max_epochs, gcl_ica_model, device, optimizer, epoch_callback, final_callback, run_logger)¶ Perform generalized contrastive learning (GCL) for nonlinear independent component analysis (nonlinear ICA).
- Parameters
data_tensor (
FloatTensor
) – the training data input variables (shape(n_sample,)
).c_src (
LongTensor
) – the auxiliary variable used as labels in the contrastive learning (shape(n_sample,)
).batch_size (
int
) – the batch size for training.max_epochs (
int
) – the maximum number of epochs to run the training.gcl_ica_model (
GeneralizedContrastiveICAModel
) – the ICA model that can be trained via GCL.device (
str
) – the device identifier ('cpu'
: use CPU).optimizer – the
pytorch
optimizer.epoch_callback – The callback to be called after every epoch the training loop.
final_callback – The callback to be called at the end of the training loop. To be called with the single argument
None
.run_logger – the logger to save the results.
causal_da.components.ica_torch.api¶
-
class
GCLTrainableInvertibleICAModel
(inn, dim, classifier_hidden_dim, n_label, classifier_n_layer)¶ Bases:
object
Example implementation of a invertible ICA model trainable by GCL.
- Parameters
inn (
Module
) – invertible neural network model implemented by PyTorch.dim (
int
) – dimensionality of the data.classifier_hidden_dim (
int
) – the number of hidden units used to construct the classifier model in the wrapper class for training via GCL.n_label (
int
) – the cardinality of the label candidates.classifier_n_layer (
int
) – the depth of the classifier wrapper class for GCL training.
-
get_invertible_ica_model
()¶ Utility method to obtain the classifier (wrapper) model.
-
set_train_params
(lr, weight_decay, device, batch_size, max_epochs)¶ - Parameters
lr (
float
) – the learning rate.weight_decay (
float
) – the weight decay.device (
str
) – the processing unit (GPU or CPU) identifier to run the training on ('cpu'
: run on CPU).batch_size (
int
) – the batch size.max_epochs (
int
) – the maximum number of epochs to train.
-
train_and_record
(src_data, run_logger, intermediate_evaluators, final_evaluators)¶ A version of
train()
that also records the intermediate information.- Parameters
src_data (
Tuple
[ndarray
,ndarray
]) –Contains
(data_numpy_array, labels_numpy_array)
.data_numpy_array
is the numpy array containing the data point values (shape:(n_sample, dim)
).label_numpy_array
is the numpy array containing the labels to indicate the “auxiliary information” for GCL (shape:(n_sample, dim)
).
run_logger – the logger to save the results.
intermediate_evaluators (
EvaluatorRunner
) – the evaluators to be called at the end of every epoch.final_evaluators (
EvaluatorRunner
) – the evaluators to be called at the end of the training.
causal_da.components.ica_torch.gcl_model¶
Generalized Contrastive Learning ICA
-
class
ComponentWiseTransformWithAuxSelection
(x_dim, n_aux, hidden_dim=10, n_layer=1)¶ Bases:
causal_da.components.ica_torch.gcl_model.ComponentwiseTransform
A special type of
ComponentWiseTransform
that takes discrete auxiliary variables and uses it as labels to select the output value out of an output vector.- Parameters
x_dim (
int
) – input variable dimensionality.n_aux (
int
) – the cardinality of the label (auxiliary variable) candidates.hidden_dim (
int
) – the number of the hidden units for each hidden layer (fixed across all hidden layers).n_layer (
int
) – the number of layers to stack.
-
class
ComponentwiseTransform
(modules)¶ Bases:
torch.nn.modules.module.Module
A neural network module to represent trainable dimension-wise transformation.
- Parameters
modules (
Union
[List
[Module
],ModuleList
]) – list of neural networks each of which is a univariate function (or additionally it can take an auxiliary input variable).
-
class
GeneralizedContrastiveICAModel
(network, dim, n_label, componentwise_transform=None, linear=None)¶ Bases:
torch.nn.modules.module.Module
Example implementation of the ICA (wrapper) model that can be trained via GCL. It takes a feature extractor to estimate the mixing function and adds a classification functionality to enable the training procedure of GCL.
- Parameters
network (
Module
) – An invertible neural network (of PyTorch).linear (
Optional
[Module
]) – A linear layer to be placed at the beginning of the classification layer.dim (
int
) – The dimension of the input (i.e., the dimension of the signal source).
-
classify
(data, return_hidden=True)¶ Perform classification using the model for GCL.
- Parameters
data (
Tuple
[FloatTensor
,LongTensor
]) – tuple of input vector (shape(n_sample, n_dim)) and the target labels (shape ``(n_sample, )
).return_hidden (
bool
) – whether to also return the hidden representation obtained by the model.
-
extract
(x)¶ Alias of
hidden()
.- Parameters
x (
FloatTensor
) – input tensor.- Return type
FloatTensor
-
forward
(x)¶ Alias of
hidden()
.- Parameters
x (
FloatTensor
) – input data.- Return type
FloatTensor
Extract the hidden vector from the input data.
- Parameters
x (
FloatTensor
) – input tensor.- Return type
FloatTensor
-
inv
(x)¶ Perform inverse computation.
- Parameters
x (
Union
[FloatTensor
,ndarray
]) – input data.- Return type
ndarray
- Returns
numpy array containing the input vectors.
causal_da.components.ica_torch.logging_util¶
-
class
DummyRunLogger
¶ Bases:
object
Utility class to placehold the logger.
Initialize the run-logger.
-
end_run
()¶ A callback called at the end of the experiment run.
-
log_artifact
(_path, folder_name)¶ Log an artifact (e.g., a model).
-
log_metrics
(dic, step=None)¶ Log the metrics.
-
log_params
(params_dict)¶ Log parameters.
-
log_params_exp_wide
(params_dict)¶ Log the experiment-wide parameters.
Log the tags.
Log the experiment-wide tags.
-
start_run
()¶ A callback called at the beginning of the experiment run.
-
causal_da.components.ica_torch.trainer_util¶
-
binary_logistic_loss
(outputs, positive)¶ Utility function to wrap
torch.SoftMarginLoss
.
-
random_pick_wrong_target
(target)¶ After shuffling each row, pick the one with the index just before the target.
- Parameters
target (
LongTensor
) – the tensor (shape(n_sample,)
) of auxiliary variables used for generalized contrastive training.- Return type
LongTensor
- Returns
the randomized fake auxiliary variables to be made as negative targets in generalized contrastive learning (shape
(n_sample,)
).
Note
Duplicate entries in
target
are allowed;torch.unique()
is applied.


About Me
In my coding career, I have worked for many different companies, and seen many different system architectures. I am excited to add my newly aquired web skills to work in my chossen procession with an organization seeking a well rounded skilled resource.
Download Resume


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onfiguration and initialisation of the service worker (sw)
- Source:
Members
(static) cacheName :string
Providing a cache name allows to version files, or separate data from the app shell.
Type:
- string
- Source:
(static) filesToCache :array
Array of all files that will be cached
Type:
- array
- Source:
Methods
(static) activate()
To ensures that service worker updates its cache whenever any of the app shell files change.
In order for this to work, you'd need to increment the cacheName variable at the top of your service worker file.
- Source:
(static) fetch()
caches.match() evaluates the web request that triggered the fetch event, and checks to see if it's available in the cache.
It then either responds with the cached version, or uses fetch to get a copy from the network.
Here it checks if network can be reached and if not, serves either a custom offline page, or the last cache available.
The response is passed back to the web page with event.respondWith().
- Source:
(static) install()
open the cache with caches.open() and provide a cache name.
call cache.addAll(), which takes an array and adds every item to the cache.
caution: cache.addAll() is atomic!
- Source:


After looking all over the net for locations of Datura in my town and finding nothing, I turned to Ebay and found exactly what I was looking for. It was really cheap too! Today is the day after my 'trip' and I am slowly recovering from Datura's grasp. I tried to keep a detailed log of my experience, but towards the end of the log it becomes quite unreadable.
5:00pm - I Have made the tea mixed with Lipton's tea and am going to gulp it down as fast as possible. Note: It tastes just like Lipton tea but my gag reflexes are trying to prevent ingestion. I find this a little odd considering I like Lipton's tea. I feel a little anxious and scared at the same time.
6:00pm - Physical intoxication (not like alcohol, more like taking 10 hydrocodones at once) accompanied by heavy limbs and a slight blur of vision. My face feels like its heavy and I am noticing that I have to make a better effort to speak so people can hear me clearly. I am starting to feel the need to smoke cigarrettes more often than usual. I'm not exibiting any signs of cotton mouth or disorientation. Decided to watch a movie.
8:00pm - Mildly disoriented . Forgot to post an entry for 7pm because I became so ingrossed in wandering the house and smoking every 15 minutes. At this point, I can barely read what I am entering here in the log. I feel euphoric and my limbs grow heavier by the hour. I constantly feel as though I have something in my hands which are empty.
8:43pm - No halucinations yet I feel like I'm being watched.  I am hearing babies crying and other sounds that I can't make out. feeling really disappointed that I'm not visually hallucinating.
8:57pm - Can't pee anymore. I'm seeing flashes of light outside in the dark and the voices have gotten louder. I'm also starting to see lots of bugs but they dissapear when I reach for them. I went to roll a cigarrette then left the paper in the can and decided to have a pre-rolled cigarrette. The next morning I would find that the paper I was going to use for the rolled cigarette was a figment of my imagination.
9:30pm - I am feeling weird tremor-like sensations all over my body. I feel kind of sick to my stomach, but not enough to even classify it as a stomach ache. Going to go have a cigarrette. Waves of confusion wash over me about every five minutes.
This is where the log becomes litterally unreadable. There is a significant time gap between my last recorded cigarrette and the time the clock said when I decided to goto sleep. After my last smoke I came in and the clock read 4:00am which makes me worry about what I was doing in that time gap considering I went out for that cigarrette at 9:30pm. I don't remember anything until 4:00am that morning when I found myself getting ready for bed. I thought I saw some Datura seeds on the floor so I decided to pick them up but it turned out to be so hard I gave up. Note: Never once did I see my cigarette vanish or disintegrate in my hands, nor did my speech become completely inaudiable.
I tried my hardest to fall asleep but I had a very hard time getting comfortable. Each time I was about to sleep my sister who never really did this kept coming into my room and asking me questions. I would answer with a what due to my fan being so loud it was hard to make out word for word what she was saying. I would also wake to find millions of ants crawling all over my body and I would just brush them away and continue to try to sleep. I forgot that I was in my own room and believed that I took my fan into the living room and slept out there.
When I actually got up for the day I felt fine but my eyes were still dialated and anything moving in front of me had tracers. I am starting to have memories of what happened in the time gap but only glimpses that don't make sense.
I didn't enjoy Datura as much as I thought.  There were no science fiction themed hallucinations, no crazy off the wall happenings, It was just me alone and bored.
Not even the voices could change that feeling, nor could the Datura-fabricated conversations I had with my sister. I can't envision myself ingesting Datura again, but it would be interesting to me on a scientific level to watch others ingest and observe them from a sober point of view.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Embedded in the DESCRIPTION files of our installed.packages is a world of URLs developers have curated for us to learn from. The problem many times is that getting to the right link quickly is a pain in the REDACTED.
The goal of helpdesk
is to create a simple api to query URL links embedded in DESCRPITION files of the installed.packages
and return a search enabled datatable with active links of the relevant URLs.
In RStudio IDE an interactive datatable will be returned in which search fields at the top of the columns is enabled by default to further query the output.
After locating the relevant link you need, the links are enabled to open directly into a new browser tab.
Here is an example
Running in the console the default call
The resulting datatable
is returned
That’s it!
Below shows some additional functionality, where the returned objects are converted to html tables. Like before the links are active so you can test them out from the outputs.
returns a table of all the packages with URLs in the DESCRIPTION file
| Package | URL |
|---|---|
| anytime | http://dirk.eddelbuettel.com/code/anytime.html |
| argparser | https://bitbucket.org/djhshih/argparser |
| askpass | https://github.com/jeroen/askpass#readme |
| backports | https://github.com/r-lib/backports |
| base64enc | http://www.rforge.net/base64enc |
| BayesFactor | https://richarddmorey.github.io/BayesFactor/ |
| Package | URL |
|---|---|
| dplyr |
http://dplyr.tidyverse.org https://github.com/tidyverse/dplyr |
| ggplot2 |
http://ggplot2.tidyverse.org https://github.com/tidyverse/ggplot2 |
| Package | URL |
|---|---|
| tidyr |
http://tidyr.tidyverse.org https://github.com/tidyverse/tidyr |
| tidytext | http://github.com/juliasilge/tidytext |
| tidytrials | https://ghe.metrumrg.com/external-projects/SAN1701F |


Meghan Markle Overtakes Kylie Jenner As Queen Of Style
by Modupeoluwa AdekanyeThe Duchess of Sussex has stepped out in many incredible outfits this year and her style efforts have clearly paid off, as she has been named 2019’s most influential dresser by Lyst Index.
In a report by Hello Magazine, the annual fashion report analyses data collected throughout the year and reveals the biggest trends and fashion stars.
The wife of Prince Harry has topped the list, this is because each time she has worn something, it revealed a 216 percent increase in searches for similar items. And what’s more, she has even replaced Kim Kardashian’s little sister, Kylie Jenner.
Last year, the Duchess was in third place, but she has upped her game this year to be crowned Queen of fashion. Incredibly, she is the only member of the royal family to feature in the top ten – the Duchess of Cambridge comes in at number 11.
The year has been a balance of both the good and the ugly for the Duchess. She has worn plenty of stunning gowns, shoes, jackets, and pencil skirts, as well as her fair share of high street items, too. Also, she guest-edited the September issue of Vogue UK.
One of her memorable moments of 2019 was when she memorably met with Archbishop Desmond Tutu, wearing a stunning dress by Club Monaco. The public went wild for the patterned number, and it sold out within 24 hours.


I developed an iOS app enabling researchers to record speech samples for smart home user interface analysis and training set data collection.
I help grade assignments and give feedback to students in DATA 100, a foundational upper-division data science course.
I attended assigned weekly lab sections and office hours for the introductory computer science class, explaining programming concepts to students and guiding them through their assignments.


islands
A Rust image processing library
that's fast and memory safe
Functions include:
islands
mauve
mauve
remove_blue
remove_green
remove_green
add_green_20
emboss
sepia
sobel_vertical
sobel_vertical
sobel_horizontal
laplace
gaussian_blur
threshold
prewitt_horizontal


Science Of Corneotherapy
Many studies have been conducted by Professor Albert M. Kligman at the intersection of science, resulting in cosmetology and medicine appear such terms as “photoaging”, “cosmeceuticals” and “corneotherapy”.
We produce our soaps using Cold Process, a slow manufacturing process without external heat in order to ensure the high purity of the natural ingredients used. It makes our soaps healthy and fully biodegradable as well as ecological, and especially economic thanks to their longer service life.
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View productsFor the hygiene of your body, we developed ecological and economic vegan soaps made from natural ingredients such as essential oils and plant oils.
View productsabout our company
Over the years, Natural Cosmetics have created handmade natural cosmetics, by the principles of Corneotherapy, for professional use and has received recognition from beauticians of beauty salons and SPA centers. In the compositions of our Skin Care products the modern knowledge about the structure and functions of the skin is combined with the technologies that
have proven their effectiveness during centuries of experience. Under the new strategy of the company, cosmetics earlier supplied to salons and entirely new products are now proposed for home use according to salons techniques. Our skin care products are fundamentally different from the “mass” cosmetics, by combining true naturalness and efficiency.
The products available at our store are 100 % organic for you.
Our beauty and skin care products can save you a lot of money
Our advertisements do not include unreasonable promises
cosmetics news & events
Many studies have been conducted by Professor Albert M. Kligman at the intersection of science, resulting in cosmetology and medicine appear such terms as “photoaging”, “cosmeceuticals” and “corneotherapy”.
Different diets, fruits, vegetables, fresh juices, fitness and swimming pool – we study methods and techniques, spend a lot of money because we are accustomed to associate all this with a healthy lifestyle.


coins.ir.hir
Class SimplifyHir
java.lang.Object
coins.ir.hir.SimplifyHir
- public class SimplifyHir
- extends java.lang.Object
SimplifyHir:
This class simplifies HIR by deleting labeled statements with
null statement body if the label is not explicitly refered,
in order to suppress the generation of useless basic blocks
in converting HIR to LIR. If HIR is changed by this class,
finishHir() is called in this class so that HIR may keep
consistency.
This is invoked just before HIR-to-LIR conversion by the
compiler driver. This does nothing if the compiler option
-coins:hirOpt=noSimplify
is specified and pForceSimplify is false.
It is not recommended to invoke this before HIR transformations
such as optimization and parallelization because the simplified
HIR may be unsuitablbe for such HIR transformations.
Description of simplifications
If-statement:
(if (condition)
(labeledStmt (thenLabel) thenPartStatement)
(labeledStmt (elseLabel) )
(labeledStmt (endLabel) ))
-->
(if (condition)
(labeledStmt (thenLabel) thenPartStatement)
(labeledStmt (endLabel) ))
(if (condition)
(labeledStmt (thenLabel) )
(labeledStmt (elseLabel) elsePartStatement)
(labeledStmt (endLabel) ))
-->
(if (condition)
(labeledStmt (elseLabel) elsePartStatement)
(labeledStmt (endLabel) ))
Loop-statement:
The general form of a loop statement is
(loop
LoopInitPart
ConditionalInitPart -- usually null
(labeledStmt (loopBackLabel)
(expStmt ... )) -- loop-start conditionPart
(labeledStmt (loopBodyLabel)
(block
... -- loop body part
(labeledStmt (loopStepLabel) ))
(expStmt ... ) -- loop-end condition
(labeledStmt (loopEndLabel) ))
If loop-start condition is null, remove loopBodylabel
by changing (labeledStmt (loopBodyLabel) (block ...))
to (block ... ).
If loopStepLabel is not explicitly refered (that is,
the loop does not contain continue-statement), then
(labeledStmt (loopStepLabel) ) is deleted.
Switch-statement:
The simplification of switch-statement is covered
by the simplification of labeled statement.
Labeled-satement:
Simplify LabeledStmt having LabeledStmt as its body:
(labelDef (lab1) (labelDef (lab2) stmtBody))
-->
(labelDef (lab1 lab2) stmtBody)
(labelDef (lab1)
(block
(labelDef (lab2) stmtBody)
.... ))
-->
(labelDef (lab1 lab2)
(block
stmtBody
.... ))
After above simplifications, change statements with multiple
labels to statements with single label and rewrite HIR nodes
refering erazed labels to the remaining label, that is,
(labelDef (lab1 lab2 ... labn) stmtBody)
-->
(labelDef (lab1) stmtBody)
and change references to lab2, ..., labn to lab1.
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
hirRoot
HirRoot hirRoot
ioRoot
IoRoot ioRoot
fSubp
Subp fSubp
fLabelDefToBeDeleted
java.util.ArrayList fLabelDefToBeDeleted
fStmtToBeDeleted
java.util.ArrayList fStmtToBeDeleted
fLabeledStmt
java.util.ArrayList fLabeledStmt
fStmtWithMultipleLabels
java.util.ArrayList fStmtWithMultipleLabels
fLabelRef
java.util.ArrayList fLabelRef
fReplaceLabel
java.util.HashMap fReplaceLabel
fChanged
boolean fChanged
fSomeSubpChanged
boolean fSomeSubpChanged
fDbgLevel
int fDbgLevel
SimplifyHir
public SimplifyHir(HirRoot pHirRoot,
boolean pForceSimplify)
simplifyHir
public void simplifyHir(Subp pSubp)
deleteUnusedLabelsOfIfStmt
public void deleteUnusedLabelsOfIfStmt(IfStmt pIfStmt)
deleteUnusedLabelsOfLoopStmt
public void deleteUnusedLabelsOfLoopStmt(LoopStmt pLoopStmt)
recordLabelRefOfSwitchStmt
public void recordLabelRefOfSwitchStmt(SwitchStmt pSwitchStmt)
shouldHaveLabel
boolean shouldHaveLabel(LabeledStmt pStmt)
deleteIfNull
void deleteIfNull(LabeledStmt pStmt)


The secret key below gives access to your money in the unlikely case that you lose your password. In that case your contacts will be lost, but with this secret key alone anyone can retrieve your money. So please store it somewhere PRIVATE AND SAFE.
{{address}}
{{ 'inflation_desc' | translate:currentNetwork.coin }}
You can join one of the following inflation pools.
You can join one of the following inflation pools.
A domain name that can optionally be added to the account. Clients can look up more details from this domain.
Allows you to set,modify or delete a Data Entry (name/value pair) that is attached to a particular account. An account can have an arbitrary amount of DataEntries attached to it. Each DataEntry increases the minimum balance needed to be held by the account.
DataEntries can be used for application specific things. They are not used by the core Stellar protocol.
| {{key}} | {{value}} |
Danger operation! It transfers the native balance (the amount of XLM an account holds) to another account and removes the source account from the ledger.


Move to a sweep.
const sweepId = '1';
const rotation = { x: 30, y: -45 };
const transition = mpSdk.Sweep.Transition.INSTANT;
mpSdk.Sweep.moveTo(sweepId, {
rotation: rotation,
transition: transition
})
.then(function(sweepId){
// Move successful.
console.log('Arrived at sweep ' + sweepId);
})
.catch(function(error){
// Error with moveTo command
});
A promise that will return the destination sweep.
rotation.x
: is the amount the camera will rotate up/down, in the range between [-90…90] with -90 being straight down and 90 being straight up, 45 would be looking up at a 45 degree angle., -45 down etc..rotation.y
: is the amount the camera rotate around horizontally, between [-360…0…360], negative values to rotate to the left, positive to rotate to the right.Note: The rotation that Sweep.moveTo uses for input is the same rotation that will get returned from the Camera.getPose command.
const pose = await mpSdk.Camera.getPose(); console.log(pose.rotation);
With that combo you can get your view lined up with the normal camera, call getPose, and use that as input to the moveTo command.


@(title: String, isError: Boolean)
@import helper._
@import views.html.bootstrap3._
@main(title) {
@if( isError ){
Some Problem with registration
Some problem with your registration. Could not register. Please try again.
}else{
Thank you for registering!
Thank you for registration. Please login to your account
}
}


"How do I do a thing?"
When users ask this question to government, the answer now is...it depends.
Our team's hypothesis is these patterns and guidance can change that:
Guide
Naming your service
Help users find and understand your service
Guidance:
Pattern
Check eligibility, cost and time
Help users understand whether they can and should use a service
Example:
Prototypes:
Guidance:
Pattern
Task list
Show users all the tasks involved to do something.
Example:
Prototypes:
Guidance:
Code and past prototypes
Past versions:
| Prototype | date | code | tested with |
|---|---|---|---|
| version 4 | 19 1 2017 | GitHub | 10 users |
| version 3 | 2 12 2016 | GitHub | 6 users |
| version 2 | 21 11 2016 | GitHub | 6 users |
| version 1 | 14 11 2016 | GitHub | 9 users |
| version 0 | 12 7 2016 | GitHub | 12 users |


General Information
Course
1 - Civil and Environmental Engineering
15 - Management
2 - Mechanical Engineering
16 - Aeronautics and Astronautics
3 - Material Science and Engineering
17 - Political Science
4 - Architecture
18 - Mathematics
5 - Chemistry
20 - Biological Engineering
6-1 - Electrical Engineering
21A - Anthropology
6-2 - Electrical Engineering and Computer Science
21F - Global Studies and Languages
6-3 - Computer Science and Engineering
21H - History
6-7 - Computational Biology
21L - Literature
7 - Biology
21M - Music and Theater Arts
8 - Physics
21W - Writing
9 - Brain and Cognitive Sciences
22 - Nuclear Science and Engineering
10 - Chemical Engineering
24 - Linguistics and Philosphy
11 - Urban Studies and Planning
CMS - Comparative Media Studies
12 - Earth, Atmospheric, and Planetary Sciences
MAS - Media Arts and Sciences
14 - Economics
STS - Science, Technology, and Society
Affiliation
Fraternity
Unaffiliated
Sorority
Prefer not to disclose
Where do you live?
Dormitory
I live in my FSILG
Off Campus
Gender
Male
I don't identify with either
Female
Ethnicity
Caucasian
Middle Eastern
Hispanic, Latino
East Asian, Southeast Asian
Black
Indian, South Asian
Native American
Pacific Islander
Filipino
Other
Sexual Orientation
Straight
Transgender
Gay
Queer
Lesbian
Asexual
Bisexual
Are you a student athlete?
Yes
No
Where did you live before college?
U.S. East Coast
Europe
U.S. West Coast
Asia
U.S. Midwest
Middle East
U.S. South
Africa
U.S. Territories
APAC
Mexico
Russia
Canada
South America
Which of the following best describes where you grew up?
City
Rural
Small Town
Suburban
Essay Questions


HOME PAGE
Reviewed by Harvey Karten Copacetic Pictures/Home Box Office Director: Doug Block Writer: Doug Block, Deborah Rosenberg Cast: Doug Block, Joey Anuff, Justin Hall, Jaime Levy, Julie Peterson, Howard Rheingold, Louis Rossetto, John Seabrook, Aliza Sherman, Carl Steadman, Stefanie Syman
At base, the question that documentarian Doug Block asks in filming "Home Pages" is this: When we sit alone at our computers tap-tap-tapping out our responses in chat rooms, scanning web sites, and contributing autobiographical details on our own pages, are we demonstrating our sociability? Are we reaching out to people the world over, or are we immersed in ourselves, hiding from actual contact with other human beings? The common-sense answer is, of course, both, depending on the individual who is doing the tap-tap-tapping. Since Americans change venues frequently--the typical resident of the U.S. exchanging one home for another every five years--perhaps we have lost the sense of community that we had before the Third Industrial Revolution cast us adrift. In that case, the people we relate to on the Internet are the denizens of our real neighborhoods--which is all to the good. Nonetheless the image that many people have of cyberpunks is of albino- like creatures who are living in a kind of Platonic cave, shielded against the burning rays of the sun, devoid of genuine contact with our fellows and hence appropriately labeled misfits.
The computer-addicted people caught by the 43-year-old Doug Block's cameras in his 1999 Sundance Film Festival "Home Page" are, in my humble estimation, oddballs and outcasts almost without exception. Block could have treated them as a director like Fellini might, with smiling condescension, but opts instead to take them as seriously as they take themselves, like a modern Frederick Wiseman, thereby eliciting, perhaps, a greater authenticity from his cast of characters.
While the millions of us who are already addicted to the Internet and those of us who are curiously considering the habit might be drawn to the movie, many will find Block's treatment of the early stages of web traffic quite disappointing. The film has two major weaknesses. One is that with two or three exceptions, the performers are not likeable people, not folks around whom the more rational among us would want to spend ten minutes. The second is that while this, like all other documentaries, is edited (in fact "Variety" reviewer Dennis Harvey reporting from Sundance, holds that "editor Deborah Rosenberg does an outstanding job weaving numerous threads into an affecting narrative shape"), I find no such evidence exists. "Home Page" looks like a story that shifts from person to person like a kid scanning TV channels, then slapped together with no more than a modicum of revision to give it cohesion.
The motivation for the film comes when its creator, Doug Block, appears to undergo a mid-life crisis and decides to follow Swarthmore College student Justin Block around the country. Justin, like a cyberspace Billy Graham, is meeting with people of all ages an backgrounds to extol the benefits of personal pages on the Internet. Hall, who took a brief leave of absence from his college to make his odyssey, sports a unique, knotted hairstyle (which he inexplicably shaves off when he reaches the age of 21, to make himself even more unattractive). Having built one of the Web's first links three or four years ago, he writes intimate details of his life into his journal to form a kind of erotic autobiography-- allegedly accessed by 7,000 regular patrons. One can only reflect that his dates must have been as exhibitionistic as Hall to allow intimate details of their relationships with this pioneer to be on view in much the style of the women in Myles Berkowitz's documentary feature "Twenty Dates." One of his pages features the author in the nude, prompting a plethora of notes on the walls of a women's bathroom at Swarthmore which ask the users their opinions of the man who craves so much attention.
When he is not focussing on Hall, filmmaker Block introduces us to his wife, a law professor, and to his mother, both of whom resist the web. His gray-haired, lively mom is one of the few down-to-earth people in the film, insisting that she'd rather talk with people on the phone to get immediate responses, insisting that she wants nothing to do with e-mail. (One of the final shots in the movie belies her statement: both she and her husband are seen tapping away late at night.)
Perhaps this movie targets a college audience, but more likely even the 20-somethings would be turned off by Justin, who rambles through the movie usually half-nude, an abysmally inarticulate person whose speech is permeated with "you-know"'s, "like," "I mean," "what-not," and other such fillers that have become a trademark of the Monica Lewinsky generation. Undoubtedly he is a better writer than he is a speaker, but Block spends surprisingly little time on the dynamics of the Internet, casting only an occasional look at what transpires on the screen. One can only wonder how a frightful fellow like Justin ever passed the interview that got him into Swarthmore, one of the nation's top colleges, but if he represents the typical student at that and other elite institutions, standards have indeed plummeted since I went to college back in the Jurassic Age.
The film becomes increasingly annoying as we are familiarized with one pretentious maverick after another, virtually none able to put together a sentence without a surfeit of empty verbiage and a clutter of hollow, unnecessary fillers. Editor Deborah Rosenberg must have the cleanest work room on the block, as apparently not a frame of this movie ended up on the cutting-room floor.
Not Rated. Running Time: 102 minutes. (C) 1999 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


User Datagram Protocol (UDP) is a minimal protocol running over IP. In this assignment you will implement a reliable message protocol over UDP. You will emulate a subset of TCP protocol. Features to be implemented are:
We provide you templates for client and server applications for Python 3.
We plan to update this document as it's needed. If there's any major update we'll make an announcement.
Connection setup & teardown, due 3/29
Check the 1 Establish a connection and 3 Tear down the connection sections below.
Data transfer with retransmission of lost packets
Bonus points for any extra features of TCP like flow control.
In essence, you will implement TCP-like protocol within UDP data. UDP data will consist of TCP header and TCP body.
Please, read and understand the provided template code. There are three python files are provided.
This file implements States Enum type, header type for your own protocol and some helper functions. You will need to extend these classes for your needs as they are missing some states or fields.
If you set DEBUG = True
, it will print extra information as your application is running. If you need to print debug information as you develop application, check this variable before printing. Set this value to False
when you're submitting the code.
class States
: implements Enum types to represent TCP protocol states.
Read the comments in the file
MSS = 12
: we use pretty small segment size to make it easier to test and demo. We will need to divide long messages into a number of packets.
Read the comments in the file
This class represents the Header of your own protocol and provides some helper methods.
The header has 12 bytes. First 4 bytes represent sequence_number, the second 4 bytes represent acknowledge_number and the next two bits represent the syn and ack header fields. The remaining 30 bits are currenctly unused. You can utilize the rest for your needs.
Implement the TCP 3-way Handshake Protocol
Please refer to Connection establishment section: https://en.wikipedia.org/wiki/Transmission_Control_Protocol#Connection_establishment
UDP is an unreliable protocol. Some of the packages might be lost or received in different order. Implement the TCP-like protocol to retransmit the missing packages. Client should order the packets correctly.
Bonus: TIME_WAIT state implementation is bonus points. You won't lose points if you don't implement TIME_WAIT.
https://en.wikipedia.org/wiki/Transmission_Control_Protocol#Protocol_operation https://tools.ietf.org/html/draft-gg-udt-03


Aditi Agarbatti Manufacturing - Manufacturer of fragnrance, agarbatti raw materials & charcoal since 2018 in Malda, West Bengal.
Feel free to drop us a line to contact us


Portfolio
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About
Hi, my name is Lee and I love everything about art, web design, development and music. I am married to my beautiful wife Hannah, and we live on a mountain with our two Huskies. During the day, I work at MakeMusic developing killer web applications on the front end. By night, I may or may not be Batman...just saying.
Thanks for stopping by my website to view my work and possibly consider hiring me. People like you make my world turn! I hope we can connect.
My skills include Node.js, Angular.js, Ember CLI, HTML5, CSS3, Javscript, jQuery and much, much more. I love to design things for people in Photoshop and make these things as beautiful as my wife. ;)


Shoutout to our hard-working BIⱯS team for 2021 :) Feel free to email.
Co-President, Founder
Co-President
Director of Labs
Director of Workshops
Director of Reading Group
Treasurer
Member


This global inspection reports any static fields which are only used in a different class than the one they
are defined in. Such fields can be moved.
Since this inspection requires global code analysis, it is only available in batch inspection mode.
Powered by InspectionGadgets


DEBCONF-UPDATEPO(1) po-debconf DEBCONF-UPDATEPO(1) NAME debconf-updatepo - update PO files about debconf templates SYNOPSIS debconf-updatepo [-v] [-h] [--podir=DIR] [--skip-pot] [--skip-merge] [--msgid-bugs-address=EMAIL] DESCRIPTION This program reads po/POTFILES.in to determine which master files need to be processed, then extracts new gettext strings into po/templates.pot and updates all po/*.po files. This script can be called either in the top-level directory, or in the debian or debian/po subdirectories. It is run by developer and/or translator to update PO files when the English text is modified in templates files. OPTIONS -h, --help Display a usage summary and exit. -v, --verbose Process in verbose mode. --podir=DIR Set directory for PO files. By default PO files are searched in debian/po, po and ../po directories in that order. --skip-pot Do not generate a new po/templates.pot file. --skip-merge Do not merge existing PO files with po/templates.pot. --msgid-bugs-address=EMAIL Set the email address that will be specified in the POT's Report- Msgid-Bugs-To field. By default, this field is set to<EMAIL_ADDRESS>It is intended for the maintainers of Debian based distributions, and should be used in the following cases: o The package is not present in Debian (and thus the<EMAIL_ADDRESS>address does not exist). o The maintainer made significant changes in the debconf templates (or added templates to a Debian package without templates), and does not want to bother the Debian maintainer with bug reports that won't be relevant for the Debian maintainer. The default address is preferred to avoid corrections in the Debian based distribution without a notification to the Debian maintainer. SEE ALSO debconf-gettextize(1), po2debconf(1), podebconf-report-po(1), po-debconf(7). AUTHOR Denis Barbier<EMAIL_ADDRESS>2011-12-29 DEBCONF-UPDATEPO(1)


|
areaDetector
3-12-1
EPICS areaDetector framework
|
Main driver class inherited from areaDetectors ADDriver class. More...
Public Member Functions | |
| pointGrey (const char *portName, int cameraId, int traceMask, int memoryChannel, int maxBuffers, size_t maxMemory, int priority, int stackSize) | |
| Constructor for the pointGrey class. More... | |
| virtual asynStatus | writeInt32 (asynUser *pasynUser, epicsInt32 value) |
| Sets an int32 parameter. More... | |
| virtual asynStatus | writeFloat64 (asynUser *pasynUser, epicsFloat64 value) |
| Sets an float64 parameter. More... | |
| virtual asynStatus | readEnum (asynUser *pasynUser, char *strings[], int values[], int severities[], size_t nElements, size_t *nIn) |
| void | report (FILE *fp, int details) |
| These should be private but are called from C callback functions, must be public. More... | |
| void | imageGrabTask () |
| Task to grab images off the camera and send them up to areaDetector. More... | |
| void | shutdown () |
| Public Member Functions inherited from ADDriver | |
| ADDriver (const char *portName, int maxAddr, int numParams, int maxBuffers, size_t maxMemory, int interfaceMask, int interruptMask, int asynFlags, int autoConnect, int priority, int stackSize) | |
| All of the arguments are simply passed to the constructor for the asynNDArrayDriver base class, except numParams. More... | |
| virtual asynStatus | connect (asynUser *pasynUser) |
| Connects driver to device; This method is called when the driver's pasynCommon->connect() function is called. More... | |
| virtual void | setShutter (int open) |
| Set the shutter position. More... | |
| Public Member Functions inherited from asynNDArrayDriver | |
| asynNDArrayDriver (const char *portName, int maxAddr, int maxBuffers, size_t maxMemory, int interfaceMask, int interruptMask, int asynFlags, int autoConnect, int priority, int stackSize) | |
| This is the constructor for the asynNDArrayDriver class. More... | |
| virtual | ~asynNDArrayDriver () |
| virtual asynStatus | writeOctet (asynUser *pasynUser, const char *value, size_t maxChars, size_t *nActual) |
| Called when asyn clients call pasynOctet->write(). More... | |
| virtual asynStatus | readGenericPointer (asynUser *pasynUser, void *genericPointer) |
| This method copies an NDArray object from the asynNDArrayDriver to an NDArray pointer passed in by the caller. More... | |
| virtual asynStatus | writeGenericPointer (asynUser *pasynUser, void *genericPointer) |
| This method currently does nothing, but it should be implemented in this base class. More... | |
| virtual asynStatus | setIntegerParam (int index, int value) |
| Sets the value for an integer in the parameter library. More... | |
| virtual asynStatus | setIntegerParam (int list, int index, int value) |
| Sets the value for an integer in the parameter library. More... | |
| virtual asynStatus | readInt32 (asynUser *pasynUser, epicsInt32 *value) |
| virtual asynStatus | readFloat64 (asynUser *pasynUser, epicsFloat64 *value) |
| virtual asynStatus | createFilePath (const char *path, int pathDepth) |
| Function to create a directory path for a file. More... | |
| virtual asynStatus | checkPath () |
| Checks whether the directory specified NDFilePath parameter exists. More... | |
| virtual bool | checkPath (std::string &filePath) |
| Checks whether the directory specified exists. More... | |
| virtual asynStatus | createFileName (int maxChars, char *fullFileName) |
| Build a file name from component parts. More... | |
| virtual asynStatus | createFileName (int maxChars, char *filePath, char *fileName) |
| Build a file name from component parts. More... | |
| virtual asynStatus | readNDAttributesFile () |
| Create this driver's NDAttributeList (pAttributeList) by reading an XML file This clears any existing attributes from this drivers' NDAttributeList and then creates a new list based on the XML file. More... | |
| virtual asynStatus | getAttributes (NDAttributeList *pAttributeList) |
| Get the current values of attributes from this driver and appends them to an output attribute list. More... | |
| asynStatus | incrementQueuedArrayCount () |
| asynStatus | decrementQueuedArrayCount () |
| int | getQueuedArrayCount () |
| void | updateQueuedArrayCount () |
Protected Attributes | |
| int | PGPropertyAvail |
| The following PGProperty parameters all have addr: 0-NUM_PROPERTIES-1. More... | |
| int | PGPropertyOnOffAvail |
| int | PGPropertyOnOff |
| Property on/off is available (int32 read) More... | |
| int | PGPropertyOnePushAvail |
| Property on/off (int32 read) More... | |
| int | PGPropertyOnePush |
| Property one push is available (int32 read) More... | |
| int | PGPropertyAutoAvail |
| Property one push (int32 read) More... | |
| int | PGPropertyManAvail |
| Property auto mode available (int32 read) More... | |
| int | PGPropertyAutoMode |
| Property manual mode available (int32 read) More... | |
| int | PGPropertyAbsAvail |
| Property control mode: 0:manual or 1:automatic (int32 read/write) More... | |
| int | PGPropertyAbsMode |
| Property has absolute (floating point) controls (int32 read) More... | |
| int | PGPropertyValue |
| Property raw/absolute mode: 0:raw or 1:absolute (int32 read/write) More... | |
| int | PGPropertyValueB |
| Property value (int32 read/write) More... | |
| int | PGPropertyValueMax |
| Property value B (int32 read/write) More... | |
| int | PGPropertyValueMin |
| Property maximum value (int32 read) More... | |
| int | PGPropertyValueAbs |
| Property minimum value (int32 read) More... | |
| int | PGPropertyValueAbsMax |
| Property absolute value (float64 read/write) More... | |
| int | PGPropertyValueAbsMin |
| Property absolute maximum value (float64 read) More... | |
| int | PGGigEPropertyValue |
| Property absolute minimum value (float64 read) More... | |
| int | PGGigEPropertyValueMax |
| GigE property value (int32 read/write) More... | |
| int | PGGigEPropertyValueMin |
| GigE property maximum value (int32 read) More... | |
| int | PGVideoMode |
| GigE property minimum value (int32 read) More... | |
| int | PGFormat7Mode |
| Video mode enum VideoMode, 0-NUM_VIDEOMODES-1 (int32 read/write) More... | |
| int | PGBinningMode |
| Format7 mode enum Mode, 0-NUM_MODES-1 (int32 read/write) More... | |
| int | PGFrameRate |
| Binning enum Binning, 0-NUM_BINNINGS-1 (int32 read/write) More... | |
| int | PGPixelFormat |
| Frame rate enum FrameRate, 0-NUM_FRAMERATES-1 (int32 read/write) More... | |
| int | PGConvertPixelFormat |
| The pixel format when VideoFormat=Format7 enum PixelFormat, 0-NUM_PIXEL_FORMATS-1 (int32 read/write) More... | |
| int | PGTriggerSource |
| The pixel format to convert to when input. More... | |
| int | PGTriggerPolarity |
| Trigger source (int32 write/read) More... | |
| int | PGSoftwareTrigger |
| Trigger polarity (int32 write/read) More... | |
| int | PGSkipFrames |
| Issue a software trigger (int32 write/read) More... | |
| int | PGStrobeSource |
| Frames to skip in trigger mode 3 (int32 write/read) More... | |
| int | PGStrobePolarity |
| Strobe source GPIO pin (int32 write/read) More... | |
| int | PGStrobeEnable |
| Strobe polarity (low/high) (int32 write/read) More... | |
| int | PGStrobeDelay |
| Strobe enable/disable strobe (int32 write/read) More... | |
| int | PGStrobeDuration |
| Strobe delay (float64 write/read) More... | |
| int | PGPacketSize |
| Strobe duration (float64 write/read) More... | |
| int | PGPacketSizeActual |
| Size of data packets from camera (int32 write/read) More... | |
| int | PGMaxPacketSize |
| Size of data packets from camera (int32 write/read) More... | |
| int | PGPacketDelay |
| Maximum size of data packets from camera (int32 write/read) More... | |
| int | PGPacketDelayActual |
| Packet delay in usec from camera, GigE only (int32 write/read) More... | |
| int | PGPacketResendEnable |
| Packet delay in usec from camera, GigE only (int32 read) More... | |
| int | PGBandwidth |
| Packet resend enable, GigE only (int32 read/write) More... | |
| int | PGTimeStampMode |
| Bandwidth in MB/s (float64 read) More... | |
| int | PGCorruptFrames |
| Time stamp mode (PGTimeStamp_t) (int32 write/read) More... | |
| int | PGDriverDropped |
| Number of corrupt frames (int32 read) More... | |
| int | PGTransmitFailed |
| Number of driver dropped frames (int32 read) More... | |
| int | PGDroppedFrames |
| Number of transmit failures (int32 read) More... | |
| Protected Attributes inherited from ADDriver | |
| int | ADGain |
| int | ADBinX |
| int | ADBinY |
| int | ADMinX |
| int | ADMinY |
| int | ADSizeX |
| int | ADSizeY |
| int | ADMaxSizeX |
| int | ADMaxSizeY |
| int | ADReverseX |
| int | ADReverseY |
| int | ADFrameType |
| int | ADImageMode |
| int | ADNumExposures |
| int | ADNumExposuresCounter |
| int | ADNumImages |
| int | ADNumImagesCounter |
| int | ADAcquireTime |
| int | ADAcquirePeriod |
| int | ADTimeRemaining |
| int | ADStatus |
| int | ADTriggerMode |
| int | ADShutterControl |
| int | ADShutterControlEPICS |
| int | ADShutterStatus |
| int | ADShutterMode |
| int | ADShutterOpenDelay |
| int | ADShutterCloseDelay |
| int | ADTemperature |
| int | ADTemperatureActual |
| int | ADReadStatus |
| int | ADStatusMessage |
| int | ADStringToServer |
| int | ADStringFromServer |
| bool | deviceIsReachable |
| Protected Attributes inherited from asynNDArrayDriver | |
| int | NDPortNameSelf |
| int | NDADCoreVersion |
| int | NDDriverVersion |
| int | ADManufacturer |
| int | ADModel |
| int | ADSerialNumber |
| int | ADSDKVersion |
| int | ADFirmwareVersion |
| int | ADAcquire |
| int | ADAcquireBusy |
| int | ADWaitForPlugins |
| int | NDArraySizeX |
| int | NDArraySizeY |
| int | NDArraySizeZ |
| int | NDArraySize |
| int | NDNDimensions |
| int | NDDimensions |
| int | NDDataType |
| int | NDColorMode |
| int | NDUniqueId |
| int | NDTimeStamp |
| int | NDEpicsTSSec |
| int | NDEpicsTSNsec |
| int | NDBayerPattern |
| int | NDCodec |
| int | NDCompressedSize |
| int | NDArrayCounter |
| int | NDFilePath |
| int | NDFilePathExists |
| int | NDFileName |
| int | NDFileNumber |
| int | NDFileTemplate |
| int | NDAutoIncrement |
| int | NDFullFileName |
| int | NDFileFormat |
| int | NDAutoSave |
| int | NDWriteFile |
| int | NDReadFile |
| int | NDFileWriteMode |
| int | NDFileWriteStatus |
| int | NDFileWriteMessage |
| int | NDFileNumCapture |
| int | NDFileNumCaptured |
| int | NDFileCapture |
| int | NDFileDeleteDriverFile |
| int | NDFileLazyOpen |
| int | NDFileCreateDir |
| int | NDFileTempSuffix |
| int | NDAttributesFile |
| int | NDAttributesStatus |
| int | NDAttributesMacros |
| int | NDArrayData |
| int | NDArrayCallbacks |
| int | NDPoolMaxBuffers |
| int | NDPoolAllocBuffers |
| int | NDPoolFreeBuffers |
| int | NDPoolMaxMemory |
| int | NDPoolUsedMemory |
| int | NDPoolEmptyFreeList |
| int | NDNumQueuedArrays |
| class NDArray ** | pArrays |
| An array of NDArray pointers used to store data in the driver. More... | |
| class NDAttributeList * | pAttributeList |
| An NDAttributeList object used to obtain the current values of a set of attributes. More... | |
| int | threadStackSize_ |
| int | threadPriority_ |
Additional Inherited Members | |
| Public Attributes inherited from asynNDArrayDriver | |
| class NDArrayPool * | pNDArrayPool |
| An NDArrayPool pointer that is initialized to pNDArrayPoolPvt_ in the constructor. More... | |
Main driver class inherited from areaDetectors ADDriver class.
One instance of this class will control one camera.
| pointGrey::pointGrey | ( | const char * | portName, |
| int | cameraId, | ||
| int | traceMask, | ||
| int | memoryChannel, | ||
| int | maxBuffers, | ||
| size_t | maxMemory, | ||
| int | priority, | ||
| int | stackSize | ||
| ) |
Constructor for the pointGrey class.
| [in] | portName | asyn port name to assign to the camera. |
| [in] | cameraId | The camera index or serial number. |
| [in] | traceMask | The initial value of the asynTraceMask. |
If set to 0 or 1 then asynTraceMask will be set to ASYN_TRACE_ERROR. If set to 0x21 (ASYN_TRACE_WARNING | ASYN_TRACE_ERROR) then each call to the FlyCap2 library will be traced including during initialization.
| [in] | memoryChannel | The camera memory channel (non-volatile memory containing camera parameters) to load during initialization. If 0 no memory channel is loaded. If >=1 thenRestoreFromMemoryChannel(memoryChannel-1) is called. |
Set memoryChannel to 1 to work around a bug in the Linux GigE driver in R2.0.
| [in] | maxBuffers | Maxiumum number of NDArray objects (image buffers) this driver is allowed to allocate. This driver requires 2 buffers, and each queue element in a plugin can require one buffer which will all need to be added up in this parameter. 0=unlimited. |
| [in] | maxMemory | Maximum memory (in bytes) that this driver is allowed to allocate. So if max. size = 1024x768 (8bpp) and maxBuffers is, say 14. maxMemory = 1024x768x14 = 11010048 bytes (~11MB). 0=unlimited. |
| [in] | priority | The EPICS thread priority for this driver. 0=use asyn default. |
| [in] | stackSize | The size of the stack for the EPICS port thread. 0=use asyn default. |
| void pointGrey::imageGrabTask | ( | ) |
Task to grab images off the camera and send them up to areaDetector.
|
virtual |
|
virtual |
These should be private but are called from C callback functions, must be public.
Print out a report; calls ADDriver::report to get base class report as well.
| [in] | fp | File pointer to write output to |
| [in] | details | Level of detail desired. If >1 prints information about supported video formats and modes, etc. |
Reimplemented from asynNDArrayDriver.
| void pointGrey::shutdown | ( | void | ) |
|
virtual |
Sets an float64 parameter.
| [in] | pasynUser | asynUser structure that contains the function code in pasynUser->reason. |
| [in] | value | The value for this parameter |
Takes action if the function code requires it. The PGPropertyValueAbs function code makes calls to the Firewire library from this function.
|
virtual |
Sets an int32 parameter.
| [in] | pasynUser | asynUser structure that contains the function code in pasynUser->reason. |
| [in] | value | The value for this parameter |
Takes action if the function code requires it. ADAcquire, ADSizeX, and many other function codes make calls to the Firewire library from this function.
Reimplemented from ADDriver.
|
protected |
Packet resend enable, GigE only (int32 read/write)
|
protected |
Format7 mode enum Mode, 0-NUM_MODES-1 (int32 read/write)
|
protected |
The pixel format when VideoFormat=Format7 enum PixelFormat, 0-NUM_PIXEL_FORMATS-1 (int32 read/write)
|
protected |
Time stamp mode (PGTimeStamp_t) (int32 write/read)
|
protected |
Number of corrupt frames (int32 read)
|
protected |
Number of transmit failures (int32 read)
|
protected |
Video mode enum VideoMode, 0-NUM_VIDEOMODES-1 (int32 read/write)
|
protected |
Binning enum Binning, 0-NUM_BINNINGS-1 (int32 read/write)
|
protected |
Property absolute minimum value (float64 read)
|
protected |
GigE property value (int32 read/write)
|
protected |
GigE property maximum value (int32 read)
|
protected |
Size of data packets from camera (int32 write/read)
|
protected |
Maximum size of data packets from camera (int32 write/read)
|
protected |
Packet delay in usec from camera, GigE only (int32 write/read)
|
protected |
Packet delay in usec from camera, GigE only (int32 read)
|
protected |
Strobe duration (float64 write/read)
|
protected |
Size of data packets from camera (int32 write/read)
|
protected |
Frame rate enum FrameRate, 0-NUM_FRAMERATES-1 (int32 read/write)
|
protected |
Property control mode: 0:manual or 1:automatic (int32 read/write)
|
protected |
Property has absolute (floating point) controls (int32 read)
|
protected |
Property one push (int32 read)
|
protected |
Property manual mode available (int32 read)
|
protected |
The following PGProperty parameters all have addr: 0-NUM_PROPERTIES-1.
|
protected |
Property auto mode available (int32 read)
|
protected |
Property one push is available (int32 read)
|
protected |
Property on/off (int32 read)
|
protected |
Property on/off is available (int32 read)
|
protected |
|
protected |
Property raw/absolute mode: 0:raw or 1:absolute (int32 read/write)
|
protected |
Property minimum value (int32 read)
|
protected |
Property absolute value (float64 read/write)
|
protected |
Property absolute maximum value (float64 read)
|
protected |
Property value (int32 read/write)
|
protected |
Property value B (int32 read/write)
|
protected |
Property maximum value (int32 read)
|
protected |
Issue a software trigger (int32 write/read)
|
protected |
Trigger polarity (int32 write/read)
|
protected |
Strobe enable/disable strobe (int32 write/read)
|
protected |
Strobe delay (float64 write/read)
|
protected |
Strobe polarity (low/high) (int32 write/read)
|
protected |
Strobe source GPIO pin (int32 write/read)
|
protected |
Frames to skip in trigger mode 3 (int32 write/read)
|
protected |
Bandwidth in MB/s (float64 read)
|
protected |
Number of driver dropped frames (int32 read)
|
protected |
Trigger source (int32 write/read)
|
protected |
The pixel format to convert to when input.
pixel format is raw[8,12,16] enum PixelFormat, 0-NUM_PIXEL_FORMATS-1 (int32 read/write)
|
protected |
GigE property minimum value (int32 read)


| Plot of measures vs
segments
|
|
| A selection of measures computed on segments of time
series, i.e. time series with names containing indices after the character 'S',
can be plotted against the segment indices in a figure window. This type
of figure allows to track changes in the evolution of measure(s) along
the recording epoch split in the time resolution of the duration of each
segment.
Two or more segments can be selected from the time series list having the title Select segments of a time series. The segments of a time series are identified by the time series name, the specified parameters of the length of segment (n), sliding steps (s), the character 'B' or 'E' for ignoring remaining data, and the indices after the character 'S' (if there is a string after the index this should be the same in the names of the segments). If no such segments names exist the list of segments is empty and this operation cannot run. Further, one or more measures can be selected from the measure list having the title Select one or more measures. For measures defined in terms of one or more measure specific parameters, the measure name regards a specified set of parameters (one parameter value for each name). A subset of segment and measure names can be selected from the respective lists with standard use of the mouse: a click on a name and Shift^click on a another name further down in the list selects all names between the former and the latter; a click on a name and subsequent Ctrl^clicks on other names select these names including the first name; Ctrl^a selects the whole list. Tips: For the selection of a subset of segment time series names from the list, click on the segment time series of this subset appearing first in the list and then Shift^click at the last time series in the list. In this way, all time series from the first selected to the last time series in the list will be marked and from those only the ones that have the same segment time series name as the first and differ only to the segment index will be processed. |
|
| View selected plot |
|
| Provided that a list of
at least two segment names and one or more measures names are selected,
by pressing this button a separate figure window will be displayed (as
the one shown below). The graph of each selected measure vs the segment
indices is displayed with a distinct point or line type. When up to 5
measure names are selected, they are listed in a legend together with
their corresponding drawing type, otherwise they are listed in the
"Running messages" text box.
|
|
| draw lines / points |
|
| The user can choose one of the following: Lines, Points, Both. This is the drawing type for the measure vs segment index plot and a different drawing type is used for each measure, as explained in the "Running messages" text box. | |
| Running Messages |
|
| A text box displaying messages for the running operations. | |
| Exit |
|
| By pressing this button the window of "Plot of Measures vs Segments" will disappear and the user will be moved to the "View Measures" window. Note that any created figures will remain until the user exits from the "View Measures" window. | |
| Help |
|
| This file will be shown |