Bismillah Khan
Ustad Bismillah Khan (21 March 1916 – 21 August 2006) (born as Amiruddin Khan), often referred to by the honorific title Ustad, was an Indian musician credited with popularizing the shehnai, a subcontinental wind instrument of the oboe class. While the shehnai had long held importance as a folk instrument played primarily during traditional ceremonies, Khan is credited with elevating its status and bringing it to the concert stage.
He was awarded India's highest civilian honour, the Bharat Ratna, in 2001, becoming the third classical musician after M. S. Subbulakshmi and Ravi Shankar to be accorded this distinction.
Khan was born on 21 March 1916 in a family of traditional Shia Muslim musicians in Bhirung Raut Ki Gali, Dumraon, in what is now the eastern Indian state of Bihar, as the second son of Paigambar Baksh Khan and Mitthan. While named Amiruddin at birth his grandfather Rasool Baksh Khan exclaimed "Bismillah" and thereafter he came to be known by this name.His father was a court musician employed in the Dumrao palace by the Raja of Bhojpur. His great grandfather Ustad Salar Hussain Khan and grandfather Rasool Baksh Khan were also musicians in the Dumrao palace.
His ancestors were court musicians and used to play in Naqqar khana in the princely states of Bhojpur, now in Bihar. His father was a shehnai player in the court of Maharaja Keshav Prasad Singh of Dumraon Estate, Bihar.
At the age of six, he moved to Varanasi. He received his training under his uncle, the late Ali Baksh 'Vilayatu', a shehnai player attached to Varanasi's Vishwanath Temple.
The Government of Bihar has proposed setting up of a museum, a town hall-cum-library and installation of a life-size statue at his birthplace in Dumraon.


Getting Started with the Backend¶
See also
This section assumes that you are familiar with Django. For a beginner’s guide for Django, see this link [todo].
Installation with pip3+git¶
Django Client Framework (Django) requires python3.6+
and Django 3.0+
. To
install Django Client Framework from the GitHub repository, run this in your
shell:
pip3 install --user git+https://github.com/cozybearca/django-client-framework.git#egg=django_client_framework
Configure Django’s settings.py file¶
In settings.py
for your Django app, simply add at the end of the file:
See also
If you are not familiar with settings.py
, you haven’t completed the basic
tutorial for Django. See [todo].
import django_client_framework.settings
django_client_framework.settings.install(
INSTALLED_APPS,
REST_FRAMEWORK,
MIDDLEWARE,
AUTHENTICATION_BACKENDS
)
Warning
The line must be added after where the INSTALLED_APPS
, REST_FRAMEWORK
,
MIDDLEWARE
and AUTHENTICATION_BACKENDS
global variables are defined,
because the .install()
function modifies these variables by reference.
An example of the settings.py
file is as follows:
import django_client_framework.settings
# ... other configs
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
REST_FRAMEWORK = {
# .. your settings
}
AUTHENTICATION_BACKENDS = [
# .. your settings
]
# Add this line
django_client_framework.settings.install(
INSTALLED_APPS,
REST_FRAMEWORK,
MIDDLEWARE,
AUTHENTICATION_BACKENDS
)
Add routing handlers to urls.py
¶
Next, append Django Client Framework’s API route handlers to your app’s
urls.py
file:
See also
If you are not familiar with urls.py
, you haven’t completed the basic
tutorial for Django. See [todo].
from django.urls import path, include
import django_client_framework.api.urls
app_name = "myapp"
urlpatterns = [
... # other routes
path("", include(django_client_framework.api.urls))
]
Note
The .urlpatterns
variable provided by the Django Client Framework is just
a list of routes and handlers. You can append this variable to your
urlpatterns list flexiably. Internally, the .urlpatterns
is defined as
follows:
# django_client_framework.api.urls.urlpatterns
urlpatterns = [
path("<str:model>", ModelCollectionAPI.as_view(), name="model_collection"),
path("<str:model>/<int:pk>", ModelObjectAPI.as_view(), name="model_object"),
path(
"<str:model>/<int:pk>/<str:target_field>",
ModelFieldAPI.as_view(),
name="model_field",
),
]
Add a Serializable model¶
To add a model, create a model that extends from
django_client_framework.models.Serializable
. For instance:
from django_client_framework.models import Serializable
from django.db.models import CharField
class Product(Serializable):
barcode = CharField(max_length=32)
See also
If you are not familiar with Model, you haven’t completed the basic tutorial for Django. See [todo].
The Serializable
requires Product
to implement a class method named
.serializer_class()
, which should return a ModelSerializer
. This class is
responsible for converting back and forth betwen a JSON object and a class
object, ie, serialization and deserialization.
See also
If you are unfarmiliar with ModelSerializer
in Django Rest Framework,
complete this tutorial for Django Rest Framework first. [todo]
To define a ModelSerializer
for Product
, we create another class that
inherits from ModelSerializer
:
from django_client_framework.serializers import ModelSerializer
class ProductSerializer(ModelSerializer):
class Meta:
model = Product
exclude = []
Error
Django Client Framework’s ModelSerializer
is a subclass of Django Rest
Framework’s ModelSerializer
class with some methods overriden. Although
they mostly have the same syntax and usage, do not confuse them with one
another! You should always use
django_client_framework.serializers.ModelSerializer
. If you use the Django
Rest Framework’s version of the ModelSerializer
by mistake, then some of
our API features won’t work!
Finally, we return this class from the .serializer_class()
method. The final code
looks like this:
from django_client_framework.models import Serializable
from django_client_framework.serializers import ModelSerializer
from django.db.models import CharField
class Product(Serializable):
barcode = CharField(max_length=32)
@classmethod
def serializer_class(cls):
return ProductSerializer
class ProductSerializer(ModelSerializer):
class Meta:
model = Product
exclude = []
Now you can run migration to apply the new model.
python3 ./manage.py makemigrations
python3 ./manage.py migrate
See also
If you are not familiar with Django’s migration system, you haven’t completed the basic tutorial for Django. See [todo].
Make an AccessControlled model¶
Django Client Framework supports both model and object level authorizations. By default, all objects are only readable and writable only to superusers. Next, we will give the read permission to the anyone user group, so that the product list is publically visible to anyone visiting our site.
To manage model permission, Product
needs to extend the AccessControlled
class, and overrides a class method named .get_permissionmanager_class()
. The
.get_permissionmanager_class()
class method should return a
PermissionManager
class that implements a method named .add_perms(instance)
.
The default implementation of .get_permissionmanager_class()
looks for a class
named PermissionManager
in the model class.
To give anyone the read permission to the Product model, we import the
default_groups.anyone
and add_perms_shortcut
from
django_client_framework.permissions
and use them to set the permissions.
from django_client_framework.models import Serializable, AccessControlled
from django_client_framework.serializers import ModelSerializer
from django_client_framework.permissions import default_groups, add_perms_shortcut
from django.db.models import CharField
class Product(Serializable, AccessControlled):
barcode = CharField(max_length=32)
@classmethod
def serializer_class(cls):
return ProductSerializer
class PermissionManager(AccessControlled.PermissionManager):
def add_perms(self, product):
add_perms_shortcut(default_groups.anyone, product, "r")
class ProductSerializer(ModelSerializer):
class Meta:
model = Product
exclude = []
Now to refresh the permission stored in the database, run this in Django shell:
python3 ./manage.py shell
# inside shell
from django_client_framework.permissions import reset_permissions
reset_permissions()
Warning
Consider running reset_permissions()
during the django migrations whenever
the permission is changed on a model.
Query objects via HTTP requests¶
We need to expose the Product
model to the RESTful API by using the
@register_api_model
decorator. Add @register_api_model
to the Product
class.
from django_client_framework.models import Serializable, AccessControlled
from django_client_framework.serializers import ModelSerializer
from django_client_framework.permissions import default_groups, add_perms_shortcut
from django.db.models import CharField
from django_client_framework.api import register_api_model
@register_api_model
class Product(Serializable, AccessControlled):
barcode = CharField(max_length=32)
@classmethod
def serializer_class(cls):
return ProductSerializer
class PermissionManager(AccessControlled.PermissionManager):
def add_perms(self, product):
add_perms_shortcut(default_groups.anyone, product, "r")
class ProductSerializer(ModelSerializer):
class Meta:
model = Product
exclude = []
Now that the Product
model is correctly configured, you can create a
Product
object in Django and visit in via the REST API.
python3 ./manage.py shell
# inside shell
from .product import Product
Product.objects.create(barcode="xxyy")
Start the django development server:
python3 ./manage.py runserver # Starting development server at http://<IP_ADDRESS>:8000/
To visit the list of products, send a GET request to this url:
curl http://localhost:8000/product/
# {
# total: 1,
# limit: 50,
# page: 1,
# objects: [ {id: 1, barcode: "xxyy"} ],
# next:null,
# previous:null
# }
To visit the specific product, send a GET request to this url:
curl http://localhost:8000/product/1
# {id: 1, barcode: "xxyy"}
See also
Besides retrieving the object, creation, deleting, and modifications are also supported through POST, DELETE, PUT RESTful requests respectively. See this link for more details. [todo]
Query relational objects via HTTP¶
The Django model system allows you to define relational data. For instance, we
can add the Brand
class in Django. A brand can have multiple products.
Conversely, a product is made by one brand.
Therefore, we define the two classes as follows:
from django_client_framework.models import Serializable, AccessControlled
from django_client_framework.serializers import ModelSerializer
from django_client_framework.permissions import default_groups, add_perms_shortcut
from django_client_framework.api import register_api_model
from django.db.models import CharField, ForeignKey, CASCADE
@register_api_model
class Brand(Serializable, AccessControlled):
name = CharField(max_length=16)
@classmethod
def serializer_class(cls):
return BrandSerializer
class PermissionManager(AccessControlled.PermissionManager):
def add_perms(self, brand):
add_perms_shortcut(default_groups.anyone, brand, "r")
class BrandSerializer(ModelSerializer):
class Meta:
model = Brand
exclude = []
@register_api_model
class Product(Serializable, AccessControlled):
barcode = CharField(max_length=32)
brand = ForeignKey("Brand", related_name="products", on_delete=CASCADE, null=True)
@classmethod
def serializer_class(cls):
return ProductSerializer
class PermissionManager(AccessControlled.PermissionManager):
def add_perms(self, product):
add_perms_shortcut(default_groups.anyone, product, "r")
class ProductSerializer(ModelSerializer):
class Meta:
model = Product
exclude = []
Warning
Don’t forget to apply migrations whenever the models are changed.
After applying migrations, add a Product
object, and a Brand
object:
nike = Brand.objects.create(name="nike")
Product.objects.create(barcode="xxyy", brand=nike)
Now to retrieve the Product
object, send a GET request:
curl http://localhost:8000/product/1
# {id: 1, barcode: "xxyy", brand_id: 1}
Now to query the product’s brand, send a GET request to this url:
curl http://localhost:8000/product/1/brand
# {id: 1, name: "nike"}
Note
Since the product’s brand_id
field has value 1, the above query is the
same as the query below, which returns the same brand object.
curl http://localhost:8000/brand/1
# {id: 1, name: "nike"}
Conversely, we can retrieve all products under the brand:
curl http://localhost:8000/brand/1/products
# {
# total: 1,
# limit: 50,
# page: 1,
# objects: [ {id: 1, barcode: "xxyy"} ],
# next:null,
# previous:null
# }
Warning
The last part of the url, /products
, comes from the
related_name="products"
argument when defining the brand ForeignKey
field on Product
. This is the same .related_name
in Django that allows
you to write
Brand.objects.filter(products__in=[...])


@MarkHannaCrypto That or a cup and handle you’re zooming too far in on.
-3M: inverse H&S/triple bottom (whatever you want to call it) just completed
-6M: Adam and Eve pattern just completed
-1Y: ABCDE movement down from ATH just completed.
ETF Approval likely in Sept...


Class vs. Prototype Based Programming
2/11/16
I was recently exploring the differences between Ruby classes and JavaScript constructors. While I was hoping the differences between the two would be easy to identify and understand, their comparison went much deeper. To truly understand the difference between Ruby classes and JavaScript constructors, I had to dig in to the difference between Ruby and JavaScript. That difference is Class vs. Prototype Oriented Programming.
First, let's talk about what class and prototype based programming have in common. They are both object-oriented languages, meaning they are based on objects; data structures that each contain their own ecosystem of attributes and methods. In order to complete a task in an object oriented language, you must create and manipulate these objects.
This is where Ruby and JavaScript differ. Ruby utilizes class-based programming (aka class-orientation), while JavaScript utilizes prototype-based programming (aka prototype-orientation or instance-based programming). What is class-orientation? It is a style of object-oriented programming, that allows you to create classes of objects. All objects must be some instance of a class. When you create a new string, for example, that string is simply a child of the parent String class. That string has a set of pre-defined methods that cannot be changed. This is the most popular type of object-oriented programming (OOP). Just like the puppies below are children of the golden retriever, class-based objects are children of their parent class. They inherit all attributes and methods from that parent.
Class-Based Programming - Objects are Instances of Classes
While class-based programming allows you to create classes of objects, prototype-based language allows you to create clones of existing objects, also known as prototypes. The same rigid parent / child relationship does not exist in prototype-based language. When you create a new object, a clone of a previous object, you have full control over that object's attributes and methods. While the newly cloned object will inherit the same properties and methods from the original object, you can actually create or remove the new object's methods. While class-based objects are instances of their parent class, just as the dogs in the photo above are children of their mom, prototype-based objects are clones of other objects. The clones are similar, they initially inherit the same attributes and methods, however they are more flexible in adding methods or attributes after they have been initially defined.
Prototype-Based Programming - Objects are Clones of Objects
For example, let's consider a farm full of animals, where each animal is a class / constructor. In class-oriented programming, cows, chickens, and pigs would each be a child of the animal class. The types of attributes and methods they could have would be inherited from this class. In prototype-oriented programming, cows, chickens, and pigs would be clones of the animal constructor, with each animal extended to have attributes and methods specific to themselves in addition to the ones inherited when cloned.
This increased control with prototype-orientation comes at a cost - decreased organization. With prototype-orientation, there is more potential for customzation. You can change the methods and attributes of each individual object after it has been created. The heirarchy of parent vs childen is less clear. When objects are simply clones of other objects, who is the parent? The child? Where is the order? With class-orientation, that relationship is clear. All objects are children of some parent class. However, while class-orientation provides us with a clear heirarchy and therefore more strucutred code, prototype-orientation hands developers the freedom to build more powerful programs. In a progrmaming type defined as object-oriented, more control over your objects means more control over your code.
This brings us back to the difference between Ruby classes and JavaScript constructors, my initial question. Now that we have an increased understanding of class vs. prototype orientation, we can understand that Ruby classes are built to create objects that are instances of a class, while JavaScript constructors are built to be cloned. While they are similar in many ways, both contain basic attributes and methods that will be inherited by their children, they differ in their purpose and flexibility. It all boils down to this: Ruby classes allow you to create objects that are instances of that class, that inherit all their attributes and methods. JavaScript constructors allow you to create clones of an object, that inherit all that object's attributes and methods, while giving you the freedom to add/remove attributes and methdods after the object is created.
It's as simple as that! There are, of course, many differences in syntax between Ruby and Java. Those difference are just semantics. If you truly understand why Ruby classes and JavaScript constructors are different, you will have a much better grasp of the two languages as a whole. If you are still confused, or if you do want to take a look at syntax differences, please check out the resources below! The links listed in Resources are where I found the information to write this blog post.


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Margin, Border, Padding
September 30, 2014
|
Margin, padding, and borders are the most commonly used properties for spacing out elements in CSS. These three properties all part of what’s known as a Box Model in CSS. The Box Model is represented like this: in the middle you have the content, surrounding the content you have the padding, surrounding the padding you have the border, and surrounding the border you have the margin.
Margins and padding are very similar. Margin is the space that surrounds the element and padding sets the of space inside of a block element. An easier way to think of it would be the margin is simple the space outside of something while the padding is the space inside of something. The main difference between these two is that padding adds extra space inside the border, while margin add extra space outside the border. The border is the edge of the element, and it enables you to add a visible border around an area. Without the border, it’s difficult to tell the difference between the padding and margin.
|


Darren Curtin
1st Year BIS Student
Interests
Sports, travel and consulting services
Sports
rugby and rowing for different teams in Bandon,
Castlelack and Ring.
Travel
Most recently, I hiked through the Pyrenees
while travelling on the Camino de Santiago route from Sean-Jean Point de Pied to Pamplona.
Consulting Services
and forms of immersive experiences. I believe that the world is becoming more and more automated,
becoming more dependent on technology we don't understand and I believe this company
bridges that gap for entrepreneurs.


Class: ProxyTicket
- Inherits:
-
ServiceTicket
- Object
- Ticket
- ServiceTicket
- ProxyTicket
- Defined in:
- app/models/proxy_ticket.rb
Constant Summary
Constants inherited from ServiceTicket
Constants inherited from Ticket
Method Summary
Methods inherited from ServiceTicket
#expired?, find, #initialize, #service_identifier_matches?, #valid?
Methods inherited from Ticket
#expire!, find, #find_ticket, #initialize, #load_attributes, sane_ticket, #save
Constructor Details
This class inherits a constructor from ServiceTicket
Method Details
- (Object) ticket_prefix
27 |
# File 'app/models/proxy_ticket.rb', line 27 def ticket_prefix; "PT-" end |


Altoro Mutual will hold a presentation for the investment community on Thursday, October 26, 2006, at 11:00 a.m. ET (8:00 a.m. PT) to review third quarter 2006 financial results, which are scheduled to be released before the market opens that morning. A live webcast of the presentation will be hosted by Karl Fitzgerald, Chairman and Chief Executive Officer. A slide presentation will accompany the webcast and will be available on our home page at the time of the call.


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Grab The Nebula Prizm II Projector For Only $159 (26% Off)
If you've been looking to get a solid projector, but the prices seemed too high… well, look again. The Nebula Prizm II may not be the best projector out there, but it's a steal at its current price tag.
We'd argue that this is a solid projector at its original price tag of $215, but for $159, it's a steal. This speaker is now discounted as part of Black Friday.
This is a lightning deal on Amazon, by the way, and you have a little over 2 hours to claim it at this point. At the time of writing this article, the deal has been 40-percent claimed, so there's not much stock left.
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SCMA 470 : Risk Analysis and Credibility Tutorial 3
2021-09-26
Chapter 1 Basic Probability Concepts
1.1 Random Variables
Definition 1.1 Let \(S\) be the sample space of an experiment. A real-valued function \(X : S \rightarrow \mathbb{R}\) is called a random variable of the experiment if, for each interval \(I \subset \mathbb{R}, \, \{s : X(s) \in I \}\) is an event.
Random variables are often used for the calculation of the probabilities of events. The real-valued function \(P(X \le t)\) characterizes \(X\), it tells us almost everything about \(X\). This function is called the cumulative distribution function of \(X\). The cumulative distribution function describes how the probabilities accumulate.
Definition 1.2 If \(X\) is a random variable, then the function \(F\) defined on \(\mathbb{R}\) by \[F(x) = P(X \le x)\] is called the cumulative distribution function or simply distribution function (c.d.f) of \(X\).
Functions that define the probability measure for discrete and continuous random variables are the probability mass function and the probability density function.
Definition 1.3 Suppose \(X\) is a discrete random variable. Then the function \[f(x) = P(X = x)\] that is defined for each \(x\) in the range of \(X\) is called the probability mass function (p.m.f) of a random variable \(X\).
Definition 1.4 Suppose \(X\) is a continuous random variable with c.d.f \(F\) and there exists a nonnegative, integrable function \(f\), \(f: \mathbb{R} \rightarrow [0, \infty)\) such that \[F(x) = \int_{-\infty}^x f(y)\, dy\] Then the function \(f\) is called the probability density function (p.d.f) of a random variable \(X\).
1.1.1 R Functions for Probability Distributions
In R, density, distribution function, for the Poisson distribution with parameter \(\lambda\) is shown as follows:
| Distribution | Density function: \(P(X = x)\) | Distribution function: \(P(X ≤ x)\) | Quantile function (inverse c.d.f.) | random generation |
|---|---|---|---|---|
| Poisson | dpois(x, lambda, log = FALSE) |
ppois(q, lambda, lower.tail = TRUE, log.p = FALSE) |
qpois(p, lambda, lower.tail = TRUE, log.p = FALSE) |
rpois(n, lambda) |
For the binomial distribution, these functions are pbinom, qbinom, dbinom, and rbinom. For the normal distribution, these functions are pnorm, qnorm, dnorm, and rnorm. And so forth.
library(ggplot2)
<- 0:20
x <- data.frame( k = factor(x), pK = dbinom(x, 20, .5))
myData ggplot(myData,aes(k,ymin=0,ymax=pK)) +
geom_linerange() + ylab("p(k)") +
scale_x_discrete(breaks=seq(0,20,5)) +
ggtitle("p.m.f of binomial distribution")
To plot continuous probability distribution in R, we use stat_function to add the density function as its arguement. To specify a different mean or standard deviation, we use the args
parameter to supply new values.
library(ggplot2)
<- data.frame(x=seq(-10,10,by=0.1))
df ggplot(df) +
stat_function(aes(x),fun=dnorm, args = list(mean = 0, sd = 1)) +
labs(x = "x", y = "f(x)",
title = "Normal Distribution With Mean = 0 & SD = 1")
1.2 Expectation
Definition 1.5 The expected value of a discrete random variable \(X\) with the set of possible values \(A\) and probability mass function \(f(x)\) is defined by \[\mathrm{E}(X) = \sum_{x \in A} x f(x)\]
The expected value of a random variable \(X\) is also called the mean, or the mathematical expectation, or simply the expectation of \(X\). It is also occasionally denoted by \(\mathrm{E}[X]\), \(\mu_X\), or \(\mu\).
Note that if each value \(x\) of \(X\) is weighted by \(f(x) = P(X = x)\), then \(\displaystyle \sum_{x \in A} x f(x)\) is nothing but the weighted average of \(X\).
Theorem 1.1 Let \(X\) be a discrete random variable with set of possible values \(A\) and probability mass function \(f(x)\), and let \(g\) be a real-valued function. Then \(g(X)\) is a random variable with \[\mathrm{E}[g(X)] = \sum_{x \in A} g(x) f(x)\]
Definition 1.6 If \(X\) is a continuous random variable with probability density function \(f\) , the expected value of \(X\) is defined by \[\mathrm{E}(X) = \int_{-\infty}^\infty x f(x)\, dx\]
- Let \(X\) be a continuous random variable with probability density function \(f (x)\); then for any function \(h: \mathbb{R} \rightarrow \mathbb{R}\), \[\mathrm{E}[h(X)] = \int_{-\infty}^\infty h(x)\, f(x)\, dx\] *
Theorem 1.2 Let \(X\) be a random variable. Let \(h_1, h_2, . . . , h_n\) be real-valued functions, and \(a_1, a_2, \ldots, a_n\) be real numbers. Then \[\mathrm{E}[a_1 h_1(X) + a_2 h_2(X) + \cdots + a_n h_n(X)] = a_1 \mathrm{E}[h_1(X)] + a_2 \mathrm{E}[h_2(X)] + \ldots + a_n \mathrm{E}[h_n(X)]\]
Moreover, if \(a\) and \(b\) are constants, then \[\mathrm{E}(aX +b) = a\mathrm{E}(x) + b\]
1.3 Variances of Random Variables
Definition 1.7 Let \(X\) be a discrete random variable with a set of possible values \(A\), probability mass function \(f(x)\), and \(\mathrm{E}(X) = \mu\). then \(\mathrm{Var}(X)\) and \(\sigma_X\), called the variance and standard deviation of \(X\), respectively, are defined by \[\mathrm{Var}(X) = \mathrm{E}[(X- \mu)^2] = \sum_{x \in A} (x - \mu)^2 f(x),\] \[\sigma_X = \sqrt{\mathrm{E}[(X- \mu)^2]}\]
Definition 1.8 If \(X\) is a continuous random variable with \(\mathrm{E}(X) = \mu\), then \(\mathrm{Var}(X)\) and \(\sigma_X\), called the variance and standard deviation of \(X\), respectively, are defined by \[\mathrm{Var}(X) = \mathrm{E}[(X- \mu)^2] = \int_{-\infty}^\infty (x - \mu)^2\, f(x)\, dx ,\] \[\sigma_X = \sqrt{\mathrm{E}[(X- \mu)^2]}\]
We have the following important relations \[\mathrm{Var}(x) = \mathrm{E}(X^2) - (\mathrm{E}(x))^2 ,\] \[\mathrm{Var}(aX + b) = a^2\ Var(X), \quad \sigma_{aX + b}= |a|\sigma_X\] where \(a\) and \(b\) are constants.
1.4 Moments and Moment Generating Function
Definition 1.9 For \(r > 0\), the \(r\)th moment of \(X\) (the \(r\)th moment about the origin) is \(\mathrm{E}[X^r]\), when it is defined. The \(r\)th central moment of a random variable \(X\) (the \(r\)th moment about the mean) is \(\mathrm{E}[(X - \mathrm{E}[X])^r].\)
Definition 1.10 The skewness of \(X\) is defined to be the third central moment, \[\mathrm{E}[(X - \mathrm{E}[X])^3],\] and the coefficient of skewness to be given by \[\frac{\mathrm{E}[(X - \mathrm{E}[X])^3]}{(\mathrm{Var}[X])^{3/2}}.\]
Definition 1.11 The coefficient of kurtosis of \(X\) is defined by \[\frac{\mathrm{E}[(X - \mathrm{E}[X])^4]}{(\mathrm{Var}[X])^{4/2}}.\]
Note In the formula, subtract from the mean and normalise or divide by the standard deviation center and scale to the standard values. Odd-order moments are increased if there is a long tail to the right and decreased if there is a long tail to the left, while even-order moments are increased if either tail is long. A negative value of the coefficient of skewness that the distribution is skewed to the left, or negatively skewed, meaning that the deviations above the mean tend to be smaller than the deviations below the mean, and vice versa. If the coefficent of skewness is close to zero, this could mean symmetry,
Note The fourth moment measures the fatness in the tails, which is always positive. The kurtosis of the standard normal distribution is 3. Using the standard normal distribution as a benchmark, the excess kurtosis of a random variable is defined as the kurtosis minus 3. A higher kurtosis corresponds to a larger extremity of deviations (or outliers), which is called excess kurtosis.
The following diagram compares the shape between the normal distribution and Student’s t-distribution. Note that to use the legend with the stat_function
in ggplot2, we use scale_colour_manual
along with colour =
inside the aes()
as shown below and give names for specific density plots.
library(ggplot2)
<- data.frame(x=seq(-10,10,by=0.1))
df ggplot(df) +
stat_function(aes(x, colour = "dnorm"),fun = dnorm, args = list(mean = 0, sd = 1)) +
stat_function(aes(x, colour ="dt"),fun = dt, args = list(df = 4)) +
scale_colour_manual("Legend title", values = c("black", "blue")) +
labs(x = "x", y = "f(x)",
title = "Normal Distribution With Mean = 0 & SD = 1") +
theme(plot.title = element_text(hjust = 0.5))
Next we will simulate 10000 samples from a normal distribution with mean 0, and standard deviation 1, then compute and interpret for the skewness and kurtosis, and plot the histogram. Here we also use
the function set.seed()
to set the seed of R’s random number generator, this is useful for creating simulations or random objects that can be reproduced.
set.seed(15) # Set the seed of R's random number generator
#Simulation
<- rnorm(n = 10000, mean = 0, sd = 1)
n.sample
#Skewness and Kurtosis
library(moments)
skewness(n.sample)
## [1] -0.03585812
kurtosis(n.sample)
## [1] 2.963189
ggplot(data.frame(x = n.sample),aes(x)) +
geom_histogram(binwidth = 0.5)
set.seed(15)
#Simulation
<- rt(n = 10000, df = 5)
t.sample
#Skewness and Kurtosis
library(moments)
skewness(t.sample)
## [1] 0.06196269
kurtosis(t.sample)
## [1] 7.646659
ggplot(data.frame(x = t.sample),aes(x)) + geom_histogram(binwidth = 0.5)
Example Let us count the number of samples greater than 5 from the samples of the normal and Student’s t distributions. Comment on your results
Definition 1.12 The moment generating function (mgf) of a random variable \(X\) is defined to be \[M_X(t) = E[e^{tX}],\] if the expectation exists.
Note The moment generating function of \(X\) may not defined (may not be finite) for all \(t\) in \(\mathbb{R}\).
If \(M_X(t)\) is finite for \(|t| < h\) for some \(h > 0\), then, for any \(k = 1, 2, \ldots,\) the function \(M_X(t)\) is k-times differentiable at \(t = 0\), with \[M^{(k)}_X (0) = \mathrm{E}[X^k],\] with \(\mathrm{E}[|X|^k]\) finite. We can obtain the moments by succesive differentiation of \(M_X(t)\) and letting \(t = 0\).
Example 1.1 Derive the formula for the mgf of the standard normal distribution. Hint: its mgf is \(e^{\frac{1}{2} t^2}\).
1.5 Probability generating function
Definition 1.13 For a counting variable \(N\) (a variable which assumes some or all of the values \(0, 1, 2, \ldots,\) but no others), The probability generating function of \(N\) is \[G_N(t) = E[t^N],\] for those \(t\) in \(\mathbb{R}\) for which the series converges absolutely.
Let \(p_k = P(N = k)\). Then \[G_N(t) = E[t^N] = \sum_{k=0}^\infty t^k p_k.\] It can be shown that if \(E[N] < \infty\) then \[\mathrm{E}[N] = G'_N(1),\] and if \(E[N^2] < \infty\) then \[\mathrm{Var}[N] = G''_N(1) + G'_N(1) - (G'_N(1))^2.\] Moreover, when both pgf and mgf of \(N\) are defined, we have \[G_N(t) = M_N(\log(t)) \quad \text{ and } M_N(t) = G_N(e^t).\]
1.6 Multivariate Distributions
When \(X_1,X_2,\ldots ,X_n\) be random variables defined on the same
sample space, a multivariate probability density function or probability
mass function
\(f(x_1, x_2, \ldots x_n)\) can be defined. The following definitions can
be extended to more than two random variables and the case of discrete
random variables.
Definition 1.14 Two random variables \(X\) and \(Y\), defined on the same sample space, have a continuous joint distribution if there exists a nonnegative function of two variables, \(f(x, y)\) on \(\mathbb{R} \times \mathbb{R}\) , such that for any region \(R\) in the \(xy\)-plane that can be formed from rectangles by a countable number of set operations, \[P((X, Y) \in R) = \iint_R f(x,y) \, dx\, dy\]
The function \(f (x, y)\) is called the joint probability density function of \(X\) and \(Y\).
Let \(X\) and \(Y\) have joint probability density function \(f (x, y)\). Let \(f_Y\) be the probability density function of \(Y\) . To find \(f_Y\) in terms of \(f\) , note that, on the one hand, for any subset \(B\) of \(R\), \[P(Y \in B) = \int_B f_Y(y) \, dy,\] and on the other hand, we also have \[P(Y \in B) = P(X \in (-\infty, \infty), Y \in B) = \int_B \left( \int_{-\infty}^\infty f(x,y)\, dx \right) \, dy.\]
We have \[\begin{equation} \tag{1.1} f_Y(y) = \int_{-\infty}^\infty f(x,y)\, dx \end{equation}\] and \[\begin{equation} \tag{1.2} f_X(x) = \int_{-\infty}^\infty f(x,y)\, dy \end{equation}\]
Definition 1.15 Let \(X\) and \(Y\) have joint probability density function \(f (x, y)\); then the functions \(f_X\) and \(f_Y\) in (1.1) and (1.2) are called, respectively, the marginal probability density functions of \(X\) and \(Y\) .
Let \(X\) and \(Y\) be two random variables (discrete, continuous, or mixed). The joint probability distribution function, or joint cumulative probability distribution function, or simply the joint distribution of \(X\) and \(Y\), is defined by \[F(t, u) = P(X \le t, Y \le u)\] for all \(t, u \in (-\infty, \infty)\).
The marginal probability distribution function of \(X\), \(F_X\), can be found from \(F\) as follows: \[F_X(t) = \lim_{n \rightarrow \infty} F(t,u) = F(t, \infty)\] and \[F_Y(u) = \lim_{n \rightarrow \infty}F(t,u) = F( \infty, u)\]
The following relationship between \(f(x,y)\) and \(F(t,u)\) is as follows: \[F(t,u) = \int_{-\infty}^{u}\int_{-\infty}^{t} f(x,y)\, dx\, dy.\]
We also have \[\mathrm{E}(X) = \int_{-\infty}^\infty x f_X(x)\, dx , \quad \mathrm{E}(Y) = \int_{-\infty}^\infty y f_Y(y)\, dy\]
Theorem 1.3 Let \(f (x, y)\) be the joint probability density function of random variables \(X\) and \(Y\). If \(h\) is a function of two variables from \(\mathbb{R}^2\) to \(\mathbb{R}\), then \(h(X, Y )\) is a random variable with the expected value given by \[\mathrm{E}[h(X,Y)] = \int_{-\infty}^{\infty}\int_{-\infty}^{\infty} h(x,y) \, f(x,y)\, dx\, dy\] provided that the integral is absolutely convergent.
As a consequence of the above theorem, for random variables \(X\) and \(Y\), \[\mathrm{E}(X + Y) = \mathrm{E}(X) + \mathrm{E}(Y)\]
1.7 Independent random variables
Definition 1.16 Two random variables \(X\) and \(Y\) are called independent if, for arbitrary subsets \(A\) and \(B\) of real numbers, the events \(\{X \in A\}\) and \(\{Y \in B\}\) are independent, that is, if \[P(X \in A, Y \in B) = P(X \in A) P(Y \in B).\]
Theorem 1.4 Let \(X\) and \(Y\) be two random variables defined on the same sample space. If \(F\) is the joint probability distribution function of \(X\) and \(Y\), then \(X\) and \(Y\) are independent if and only if for all real numbers \(t\) and \(u\), \[F(t,u) = F_X(t) F_Y(u).\]
Theorem 1.5 Let \(X\) and \(Y\) be jointly continuous random variables with joint probability density function \(f (x, y)\). Then \(X\) and \(Y\) are independent if and only if \[f (x, y) = f_X(x) f_Y (y).\]
Theorem 1.6 Let \(X\) and \(Y\) be independent random variables and \(g : \mathbb{R} \rightarrow\mathbb{R}\) and \(h : \mathbb{R} \rightarrow\mathbb{R}\) be real-valued functions; then \(g(X)\) and \(h(Y )\) are also independent random variables.
As a consequence of the above theorem, we obtain
Theorem 1.7 Let \(X\) and \(Y\) be independent random variables. Then for all real-value functions \(g : \mathbb{R} \rightarrow\mathbb{R}\) and \(h : \mathbb{R} \rightarrow\mathbb{R}\), \[\mathrm{E}[g(X)h(Y)] = \mathrm{E}[g(X)]\mathrm{E}[h(Y)]\]
1.8 Conditional Distributions
Let \(X\) and \(Y\) be two continuous random variables with the joint probability density function \(f (x, y)\). Note that the case of discrete random variables can be considered in the same way. When no information is given about the value of \(Y\), the marginal probability density function of \(X\), \(f_X(x)\) is used to calculate the probabilities of events concerning \(X\). However, when the value of \(Y\) is known, to find such probabilities, \(f_{X|Y} (x|y)\), the conditional probability density function of \(X\) given that \(Y = y\) is used and is defined as follows: \[f_{X|Y} (x|y) = \frac{f(x,y)}{f_Y(y)}\] provided that \(f_Y (y) > 0\). Note also that the conditional probability density function of \(X\) given that \(Y = y\) is itseof a probability density function, i.e. \[\int_{-\infty}^\infty f_{X|Y}(x|y)\, dx = 1.\]
Note that the conditional probability distribution function of \(X\) given that \(Y = y\), the conditional expectation of \(X\) given that \(Y = y\) can be as follows: \[F_{Y|X}(x|y) = P(X \le x | Y = y) = \int_ {-\infty}^x f_{X|Y}(t|y) \, dt\] and \[\mathrm{E}(X|Y = y) = \int_{-\infty}^{\infty} x f_{X|Y}(x|y) \, dx,\] where \(f_Y(y) > 0\).
Note that if \(X\) and \(Y\) are independent, then \(f_{X|Y}\) coincides with \(f_X\) because \[f_{X|Y}(x|y) = \frac{f(x,y)}{f_Y(y)} =\frac{f_X(x)f_Y(y)}{f_Y(y)} = f_X(x).\]
1.9 Covariance
The notion of the variance of a random variable \(X\), \(\mathrm{Var}(X) = \mathrm{E}[ ( X - \mathrm{E}(X))^2]\) measures the average magnitude of the fluctuations of the random variable \(X\) from its expectation, \(\mathrm{E}(X)\). This quantity measures the dispersion, or spread, of the distribution of \(X\) about its expectation. Now suppose that \(X\) and \(Y\) are two jointly distributed random variables. Covariance is a measure of how much two random variables vary together.
Let us calculuate \(\mathrm{Var}(aX + bY)\) the joint spread, or dispersion, of \(X\) and \(Y\) along the \((ax + by)\)-direction for arbitrary real numbers \(a\) and \(b\): \[\mathrm{Var}(aX + bY) = a^2 \mathrm{Var}(X) + b^2 \mathrm{Var}(Y) + 2 a b \mathrm{E}[(X - \mathrm{E}(X))(Y - \mathrm{E}(Y))].\] However, \(\mathrm{Var}(X)\) and \(\mathrm{Var}(Y )\) determine the dispersions of \(X\) and \(Y\) independently; therefore, \(\mathrm{E}[(X - \mathrm{E}(X))(Y - \mathrm{E}(Y))]\) is the quantity that gives information about the joint spread, or dispersion, \(X\) and \(Y\) .
Definition 1.17 Let \(X\) and \(Y\) be jointly distributed random variables; then the covariance of \(X\) and \(Y\) is defined by \[\mathrm{Cov}(X,Y) = \mathrm{E}[(X - \mathrm{E}(X))(Y - \mathrm{E}(Y))].\]
Note that for random variables \(X, Y\) and \(Z\), and \(ab > 0\), then the joint dispersion of \(X\) and \(Y\) along the \((ax + by)\)-direction is greater than the joint dispersion of \(X\) and \(Z\) along the \((ax + bz)\)-direction if and only if \(\mathrm{Cov}(X, Y) > \mathrm{Cov}(X,Z).\)
Note that \[\mathrm{Cov}(X, X) = \mathrm{Var}(X).\] Moreover, \[\mathrm{Cov}(X,Y) = \mathrm{E}(XY) - \mathrm{E}(X)\mathrm{E}(Y).\]
Properties of covariance are as follows: for arbitrary real numbers \(a, b, c, d\) and random variables \(X\) and \(Y\), \[\mathrm{Var}(aX + bY) = a^2 \mathrm{Var}(X) + b^2 \mathrm{Var}(Y) + 2 a b \mathrm{Cov}(X,Y).\] \[\mathrm{Cov}(aX + b, cY + d) = acCov(X, Y)\] For random variables \(X_1, X_2, . . . , X_n\) and \(Y_1, Y_2, . . . , Y_m\), \[\mathrm{Cov}(\sum_{i=1}^n a_i X_i, \sum_{j=1}^m b_j Y_j) = \sum_{i=1}^n\sum_{j=1}^m a_i\,b_j\, \mathrm{Cov}(X_i,Y_j).\]
If \(\mathrm{Cov}(X, Y) > 0\), we say that \(X\) and \(Y\) are positively correlated. If \(\mathrm{Cov}(X, Y) < 0\), we say that they are negatively correlated. If \(\mathrm{Cov}(X, Y) = 0\), we say that \(X\) and \(Y\) are uncorrelated.
If \(X\) and \(Y\) are independent, then \[\mathrm{Cov}(X,Y) = 0.\] However, the converse of this is not true; that is, two dependent random variables might be uncorrelated.
1.10 Correlation
A large covariance can mean a strong relationship between variables. However, we cannot compare variances over data sets with different scales. A weak covariance in one data set may be a strong one in a different data set with different scales. The problem can be fixed by dividing the covariance by the standard deviation to get the correlation coefficient.
Definition 1.18 Let \(X\) and \(Y\) be two random variables with \(0< \sigma^2_X, \sigma^2_Y < \infty\). The covariance between the standardized \(X\) and the standardized \(Y\) is called the correlation coefficient between \(X\) and \(Y\) and is denoted \(\rho = \rho(X,Y)\), \[\rho(X,Y) = \frac{\mathrm{Cov}(X,Y)}{\sigma_X \sigma_Y}.\]
Note that
\(\rho(X, Y ) > 0\) if and only if \(X\) and \(Y\) are positively correlated;
\(\rho(X, Y ) < 0\) if and only if \(X\) and \(Y\) are negatively correlated; and
\(\rho(X, Y ) = 0\) if and only if \(X\) and \(Y\) are uncorrelated.
\(\rho(X, Y )\) roughly measures the amount and the sign of linear relationship between \(X\) and \(Y\).
In the case of perfect linear relationship, we have \(\rho(X, Y ) = \pm1\). A correlation of 0, i.e. \(\rho(X, Y ) = 0\) does not mean zero relationship between two variables; rather, it means zero linear relationship.
Some importants properties of correlation are \[-1 \le \rho(X, Y ) \le 1\] \[\rho(a X + b, cY +d) = \text{sign}(ac) \rho(X, Y )\]
1.11 Model Fitting
The contents in this section are taken from Gray and Pitts.
To fit a parametric model, we have to calculate estimates of the unknown parameters of the probability distribution. Various criteria are available, including the method of moments, the method of maximum likelihood, etc.
1.12 The method of moments
The method of moments leads to parameter estimates by simply matching the moments of the model, \(\mathrm{E}[X], \mathrm{E}[X^2], \mathrm{E}[X^3], \ldots ,\) in turn to the required number of corresponding sample moments calculated from the data \(x_1, x_2, \ldots , x_n\), where \(n\) is the number of observations available. The sample moments are simply \[\frac{1}{n}\sum_{i=1}^n x_i, \quad \frac{1}{n}\sum_{i=1}^n x^2_i, \quad \frac{1}{n}\sum_{i=1}^n x^3_i, \ldots.\] It is often more convenient to match the mean and central moments, in particular matching \(\mathrm{E}[X]\) to the sample mean \(\bar{x}\) and \(\mathrm{Var}[X]\) to the sample variance \[s^2 = \frac{1}{n-1}\sum_{i=1}^n (x_i - \bar{x})^2.\]
An estimate produced using the method of moments is called an MME, and the MME of a parameter \(\theta\), say, is usually denoted \(\tilde{\theta}\).
1.13 The method of maximum likelihood
The method of maximum likelihood is the most widely used method for parameter estimation. The estimates it produces are those values of the parameters which give the maximum value attainable by the likelihood function, denoted \(L\), which is the joint probability mass or density function for the data we have (under the chosen parametric distribution), regarded as a function of the unknown parameters.
In practice, it is often easier to maximise the loglikelihood function, which is the logarithm of the likelihood function, rather than the likelihood itself. An estimate produced using the method of maximum likelihood is called an MLE, and the MLE of a parameter \(\theta\), say, is denoted \(\hat{\theta}\). MLEs have many desirable theoretical properties, especially in the case of large samples.
In some simple cases we can derive MLE(s) analytically as explicit functions of summaries of the data. Thus, suppose our data consist of a random sample \(x_1, x_2, \ldots , x_n\), from a parametric distribution whose parameter(s) we want to estimate. Some straightforward cases include the following:
the MLE of \(\lambda\) for a \(Poi(\lambda)\) distribution is the sample mean, that is \(\hat{\lambda} = \bar{x}\)
the MLE of \(\lambda\) for an \(Exp(\lambda)\) distribution is the reciprocal of the sample mean, that is \(\hat{\lambda} = 1/\bar{x}\)
1.14 Goodness of fit tests
We can assess how well the fitted distributions reflect the distribution of the data in various ways. We should, of course, examine and compare the tables of frequencies and, if appropriate, plot and compare empirical distribution functions. More formally, we can perform certain statistical tests. Here we will use the Pearson chi-square goodness-of-fit criterion.
1.15 the Pearson chi-square goodness-of-fit criterion
We construct the test statistic \[\chi^2 = \frac{\sum(O - E)^2}{E},\] where \(O\) is the observed frequency in a cell in the frequency table and \(E\) is the fitted or expected frequency (the frequency expected in that cell under the fitted model), and where we sum over all usable cells.
The null hypothesis is that the sample comes from a specified distribution.
The value of the test statistic is then evaluated in one of two ways.
We convert it to a \(P\)-value, which is a measure of the strength of the evidence against the hypothesis that the data do follow the fitted distribution. If the \(P\)-value is small enough, we conclude that the data do not follow the fitted distribution – we say “the fitted distribution does not provide a good fit to the data” (and quote the \(P\)-value in support of this conclusion).
We compare it with values in published tables of the distribution function of the appropriate \(\chi^2\) distribution, and if the value of the statistic is high enough to be in a tail of specified size of this reference distribution, we conclude that the fitted distribution does not provide a good fit to the data.
1.16 Kolmogorov-Smirnov (K-S) test.
The K-S test statistic is the maximum difference between the values of the ecdf of the sample and the cdf of the fully specified fitted distribution.
The course does not emphasis on the Goodness of Fit Test. Please refer to the reference text for more details.


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THtmlViewer, TFrameViewer, and TFrameBrowser components
property OnPanelPrint: TPanelPrintEvent;
The OnPanelPrint event occurs when a <PANEL> or <OBJECT> is to be printed. This event may be used to supply a bitmap to indicate how the panel should be printed.


Kazakh National Welfare Fund Sells 87 Assets for $481Mln
The Samruk-Kazyna Fund sold 87 assets for 186 billion tenge (about $481 million) and liquidated or reorganised 51 assets, while 20 assets are still in operation, the press service of the Sovereign Wealth Fund Samruk-Kazyna said.
"An important direction in the implementation of the Comprehensive Plan is the public offering of shares of the Fund’s large companies on stock exchanges", the press service said, citing Chairman of the Management Board of Samruk-Kazyna Akhmetzhan Yessimov, who made the statement at the fund management board with the participation of Nursultan Nazarbayev, the first president of Kazakhstan.
The head of the National Welfare Fund Samruk-Kazyna emphasised that in order to ensure effective management of the fund group’s asset portfolio, measures have been taken to reduce management levels and exit from non-core assets.
"If, as of 1 January 2018, almost all companies had six management levels, today, in seven out of nine large companies, the number of management levels corresponds to the target model of two-three levels. That is, we have reduced a large layer of administrative and managerial staff", Akhmetzhan Yessimov said.


Sorry, we haven't quite finished this section yet. We'll have it up as soon as possible. For now, try the official tutorial.
Have a randomly selected picture of a cat as compensation:


The button above call this function below, like so... getPhotos('instagram')
getPhotos('instagram')
Set up your client id


Employee Payroll/Salary Management App
YouTube Video Tutorial !!
Objective: This document serves as an Installation Guide for Elish HCM Cloud free commmunity version Desktop/Mobile App.
Tools: MEAN Stack (latest) Angular 6.1, NodeJS 10.8, Express, GraphQL and MongoDB 4.0 or MYSQL
Using this design pattern, any old client/server software (like VB/SQL or JAVA/Oracle) can be converted to new design to replace or run in parallel with original software.
You can easliy deploy this as an exe desktop app, host it over premises or over cloud or create an Android & iOS mobile app easily.
This design pattern is built for Big Data technologies and supports millions of transactions.
Also, this design pattern can be applied to any front end like React, VueJS, HTML/JS/Bootstrap/Project clarityto get any front-end look and feel.
And Same concepts can applied to connect to almost any database of owners choice at the backend like MYSQL, MS SQL Server, Oracle or CouchDB, MongoDB, Google Firebase etc.
What's included :
In this repository, Only Employee Management, Payroll/Salary, Voucher and Expense modules is included.
Elish HCM Cloud v1.1.8
Elish HCM Cloud provide a completely free desktop and mobile app for managing Employee, Payables, Payroll, Attendance (GPS & Picture), Visitor (GPS & Picture) and other management modules.
future update/version/releases after v1.1.8 baseline release, will include new features and bug fixes for free under community license. For enhancement/feature requests, please open a new issue at this Github Repository.
App Choices:
#A. Elish HCM Cloud also provide a free web and mobile Enterprise app, hosted on Google Cloud with Google Firebase/Firestore database at:
https://alivetracking.com
Google Playstore - https://alivetracking.com
#B. For Community/Developer edition, developers can download a copy of "out of the box installable software package" or complete source code for free.
Below documentation serves as installation instruciton for point #B mentioned above.
1. Installation Instruction for "out of the box Installable Desktop/Mobile App"
2. Community/Developers Open Source Code guide
Installation Instruction for "out of the box Installable Desktop/Mobile App"
------------------------------------------------------------------------------------
By dafault, Elish HCM Cloud Community Edition is bundled with MongoDB.
However, Other database supported on v1.1.8 release are
MYSQL, MS SQL Server, PostGreSQL, CouchDB, Google Firebase/FireStore, MongoDB
If you wish to migrate/upgrade your old desktop or client/server software to enterprise desktop/mobile app, please write to<EMAIL_ADDRESS>for Enterprise version upgrade.
README.STEP-1-DB-INSTALLATION
README.STEP-2-MIDDLEWARE
README.STEP-3-FRONT-END
README.STEP-4-Deploy/Host code over cloud
Step 4 - will be udpated later.


| | |
- django.contrib.auth.mixins.LoginRequiredMixin(django.contrib.auth.mixins.AccessMixin)
-
- CustomUserView(django.contrib.auth.mixins.LoginRequiredMixin, django.views.generic.edit.UpdateView)
- ProfileDetailView(django.contrib.auth.mixins.LoginRequiredMixin, django.views.generic.detail.DetailView)
- django.views.generic.detail.DetailView(django.views.generic.detail.SingleObjectTemplateResponseMixin, django.views.generic.detail.BaseDetailView)
-
- ProfileDetailView(django.contrib.auth.mixins.LoginRequiredMixin, django.views.generic.detail.DetailView)
- django.views.generic.edit.UpdateView(django.views.generic.detail.SingleObjectTemplateResponseMixin, django.views.generic.edit.BaseUpdateView)
-
- CustomUserView(django.contrib.auth.mixins.LoginRequiredMixin, django.views.generic.edit.UpdateView)
class CustomUserView(django.contrib.auth.mixins.LoginRequiredMixin, django.views.generic.edit.UpdateView) |
| |
CustomUserView(**kwargs)
Verify that the current user is authenticated. |
| |
- Method resolution order:
- CustomUserView
- django.contrib.auth.mixins.LoginRequiredMixin
- django.contrib.auth.mixins.AccessMixin
- django.views.generic.edit.UpdateView
- django.views.generic.detail.SingleObjectTemplateResponseMixin
- django.views.generic.base.TemplateResponseMixin
- django.views.generic.edit.BaseUpdateView
- django.views.generic.edit.ModelFormMixin
- django.views.generic.edit.FormMixin
- django.views.generic.detail.SingleObjectMixin
- django.views.generic.base.ContextMixin
- django.views.generic.edit.ProcessFormView
- django.views.generic.base.View
- builtins.object
Methods defined here:
- setup(self, request, *args, **kwargs)
- Initialize attributes shared by all view methods.
Data and other attributes defined here:
- fields = ['phone', 'username', 'first_name', 'last_name', 'Email_Adress', 'role']
- model = <class 'main.models.CustomUser'>
- CustomUser(id, password, last_login, is_superuser, username, first_name, last_name, email, is_staff, is_active, date_joined, phone, registerddate, is_banned, Email_Adress, role, phone_vertified, email_vertified, is_premium_account, Adress, optional_adress)
- success_url = '/profile_detail/'
- template_name = 'home.html'
Methods inherited from django.contrib.auth.mixins.LoginRequiredMixin:
- dispatch(self, request, *args, **kwargs)
Methods inherited from django.contrib.auth.mixins.AccessMixin:
- get_login_url(self)
- Override this method to override the login_url attribute.
- get_permission_denied_message(self)
- Override this method to override the permission_denied_message attribute.
- get_redirect_field_name(self)
- Override this method to override the redirect_field_name attribute.
- handle_no_permission(self)
Data descriptors inherited from django.contrib.auth.mixins.AccessMixin:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes inherited from django.contrib.auth.mixins.AccessMixin:
- login_url = None
- permission_denied_message = ''
- raise_exception = False
- redirect_field_name = 'next'
Data and other attributes inherited from django.views.generic.edit.UpdateView:
- template_name_suffix = '_form'
Methods inherited from django.views.generic.detail.SingleObjectTemplateResponseMixin:
- get_template_names(self)
- Return a list of template names to be used for the request. May not be
called if render_to_response() is overridden. Return the following list:
* the value of ``template_name`` on the view (if provided)
* the contents of the ``template_name_field`` field on the
object instance that the view is operating upon (if available)
* ``<app_label>/<model_name><template_name_suffix>.html``
Data and other attributes inherited from django.views.generic.detail.SingleObjectTemplateResponseMixin:
- template_name_field = None
Methods inherited from django.views.generic.base.TemplateResponseMixin:
- render_to_response(self, context, **response_kwargs)
- Return a response, using the `response_class` for this view, with a
template rendered with the given context.
Pass response_kwargs to the constructor of the response class.
Data and other attributes inherited from django.views.generic.base.TemplateResponseMixin:
- content_type = None
- response_class = <class 'django.template.response.TemplateResponse'>
- template_engine = None
Methods inherited from django.views.generic.edit.BaseUpdateView:
- get(self, request, *args, **kwargs)
- Handle GET requests: instantiate a blank version of the form.
- post(self, request, *args, **kwargs)
- Handle POST requests: instantiate a form instance with the passed
POST variables and then check if it's valid.
Methods inherited from django.views.generic.edit.ModelFormMixin:
- form_valid(self, form)
- If the form is valid, save the associated model.
- get_form_class(self)
- Return the form class to use in this view.
- get_form_kwargs(self)
- Return the keyword arguments for instantiating the form.
- get_success_url(self)
- Return the URL to redirect to after processing a valid form.
Methods inherited from django.views.generic.edit.FormMixin:
- form_invalid(self, form)
- If the form is invalid, render the invalid form.
- get_context_data(self, **kwargs)
- Insert the form into the context dict.
- get_form(self, form_class=None)
- Return an instance of the form to be used in this view.
- get_initial(self)
- Return the initial data to use for forms on this view.
- get_prefix(self)
- Return the prefix to use for forms.
Data and other attributes inherited from django.views.generic.edit.FormMixin:
- form_class = None
- initial = {}
- prefix = None
Methods inherited from django.views.generic.detail.SingleObjectMixin:
- get_context_object_name(self, obj)
- Get the name to use for the object.
- get_object(self, queryset=None)
- Return the object the view is displaying.
Require `self.queryset` and a `pk` or `slug` argument in the URLconf.
Subclasses can override this to return any object.
- get_queryset(self)
- Return the `QuerySet` that will be used to look up the object.
This method is called by the default implementation of get_object() and
may not be called if get_object() is overridden.
- get_slug_field(self)
- Get the name of a slug field to be used to look up by slug.
Data and other attributes inherited from django.views.generic.detail.SingleObjectMixin:
- context_object_name = None
- pk_url_kwarg = 'pk'
- query_pk_and_slug = False
- queryset = None
- slug_field = 'slug'
- slug_url_kwarg = 'slug'
Data and other attributes inherited from django.views.generic.base.ContextMixin:
- extra_context = None
Methods inherited from django.views.generic.edit.ProcessFormView:
- put(self, *args, **kwargs)
- # PUT is a valid HTTP verb for creating (with a known URL) or editing an
# object, note that browsers only support POST for now.
Methods inherited from django.views.generic.base.View:
- __init__(self, **kwargs)
- Constructor. Called in the URLconf; can contain helpful extra
keyword arguments, and other things.
- http_method_not_allowed(self, request, *args, **kwargs)
- options(self, request, *args, **kwargs)
- Handle responding to requests for the OPTIONS HTTP verb.
Class methods inherited from django.views.generic.base.View:
- as_view(**initkwargs) from builtins.type
- Main entry point for a request-response process.
Data and other attributes inherited from django.views.generic.base.View:
- http_method_names = ['get', 'post', 'put', 'patch', 'delete', 'head', 'options', 'trace']
|
class ProfileDetailView(django.contrib.auth.mixins.LoginRequiredMixin, django.views.generic.detail.DetailView) |
| |
ProfileDetailView(**kwargs)
Verify that the current user is authenticated. |
| |
- Method resolution order:
- ProfileDetailView
- django.contrib.auth.mixins.LoginRequiredMixin
- django.contrib.auth.mixins.AccessMixin
- django.views.generic.detail.DetailView
- django.views.generic.detail.SingleObjectTemplateResponseMixin
- django.views.generic.base.TemplateResponseMixin
- django.views.generic.detail.BaseDetailView
- django.views.generic.detail.SingleObjectMixin
- django.views.generic.base.ContextMixin
- django.views.generic.base.View
- builtins.object
Methods defined here:
- setup(self, request, *args, **kwargs)
- Initialize attributes shared by all view methods.
Data and other attributes defined here:
- context_name = 'detail'
- model = <class 'main.models.CustomUser'>
- CustomUser(id, password, last_login, is_superuser, username, first_name, last_name, email, is_staff, is_active, date_joined, phone, registerddate, is_banned, Email_Adress, role, phone_vertified, email_vertified, is_premium_account, Adress, optional_adress)
- template_name = 'profile_details.html'
Methods inherited from django.contrib.auth.mixins.LoginRequiredMixin:
- dispatch(self, request, *args, **kwargs)
Methods inherited from django.contrib.auth.mixins.AccessMixin:
- get_login_url(self)
- Override this method to override the login_url attribute.
- get_permission_denied_message(self)
- Override this method to override the permission_denied_message attribute.
- get_redirect_field_name(self)
- Override this method to override the redirect_field_name attribute.
- handle_no_permission(self)
Data descriptors inherited from django.contrib.auth.mixins.AccessMixin:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes inherited from django.contrib.auth.mixins.AccessMixin:
- login_url = None
- permission_denied_message = ''
- raise_exception = False
- redirect_field_name = 'next'
Methods inherited from django.views.generic.detail.SingleObjectTemplateResponseMixin:
- get_template_names(self)
- Return a list of template names to be used for the request. May not be
called if render_to_response() is overridden. Return the following list:
* the value of ``template_name`` on the view (if provided)
* the contents of the ``template_name_field`` field on the
object instance that the view is operating upon (if available)
* ``<app_label>/<model_name><template_name_suffix>.html``
Data and other attributes inherited from django.views.generic.detail.SingleObjectTemplateResponseMixin:
- template_name_field = None
- template_name_suffix = '_detail'
Methods inherited from django.views.generic.base.TemplateResponseMixin:
- render_to_response(self, context, **response_kwargs)
- Return a response, using the `response_class` for this view, with a
template rendered with the given context.
Pass response_kwargs to the constructor of the response class.
Data and other attributes inherited from django.views.generic.base.TemplateResponseMixin:
- content_type = None
- response_class = <class 'django.template.response.TemplateResponse'>
- template_engine = None
Methods inherited from django.views.generic.detail.BaseDetailView:
- get(self, request, *args, **kwargs)
Methods inherited from django.views.generic.detail.SingleObjectMixin:
- get_context_data(self, **kwargs)
- Insert the single object into the context dict.
- get_context_object_name(self, obj)
- Get the name to use for the object.
- get_object(self, queryset=None)
- Return the object the view is displaying.
Require `self.queryset` and a `pk` or `slug` argument in the URLconf.
Subclasses can override this to return any object.
- get_queryset(self)
- Return the `QuerySet` that will be used to look up the object.
This method is called by the default implementation of get_object() and
may not be called if get_object() is overridden.
- get_slug_field(self)
- Get the name of a slug field to be used to look up by slug.
Data and other attributes inherited from django.views.generic.detail.SingleObjectMixin:
- context_object_name = None
- pk_url_kwarg = 'pk'
- query_pk_and_slug = False
- queryset = None
- slug_field = 'slug'
- slug_url_kwarg = 'slug'
Data and other attributes inherited from django.views.generic.base.ContextMixin:
- extra_context = None
Methods inherited from django.views.generic.base.View:
- __init__(self, **kwargs)
- Constructor. Called in the URLconf; can contain helpful extra
keyword arguments, and other things.
- http_method_not_allowed(self, request, *args, **kwargs)
- options(self, request, *args, **kwargs)
- Handle responding to requests for the OPTIONS HTTP verb.
Class methods inherited from django.views.generic.base.View:
- as_view(**initkwargs) from builtins.type
- Main entry point for a request-response process.
Data and other attributes inherited from django.views.generic.base.View:
- http_method_names = ['get', 'post', 'put', 'patch', 'delete', 'head', 'options', 'trace']
| |


Article Heading
This is an article that demonstrates some of the new features that HTML5 and CSS3 has to offer. This article contains several sections, each with its own heading, as well as a video element which will play a video without Flash on browsers that support it.
This is a section heading
This is an example of a basic section of a document. A section can refer to different parts of a document, application or page. According to the draft W3C spec, HTML5 sections usually have headings.
This is a video section
This is a feedback form
This is a 2D Canvas section
You need a Canvas-compatible browser to view this area.


Skip to main content
This site requires JavaScript to be enabled - Please turn JavaScript on and visit again.
<%= htmlWebpackPlugin.options.analytics %>


Unit Test Report: TT_Emp_WS.tt_AIP_Get_Dept_Emps
SCENARIO 1: DS-1, testing inner, outer joins, analytic over dep, and global ratios with 1 dep (10) - pass dep 10
: 0 failed of 1: SUCCESS
SCENARIO 2: DS-2, testing same as 1 but with extra emp in another dep (20) - pass dep 10
: 0 failed of 1: SUCCESS
SCENARIO 3: DS-2, as second scenario, but - pass dep 20
: 0 failed of 1: SUCCESS
SCENARIO 4: DS-2, as second scenario, but - pass null dep
: 0 failed of 1: SUCCESS
SCENARIO 5: DS-3, Salaries total 1500 (< threshold of 1600, so return nothing) - pass dep 10
: 0 failed of 1: SUCCESS
Test scenarios: 0 failed of 5: SUCCESS


Invoice
Billing address
Business Name
Email
EIN
Address
Address 2
City
State
Zip
My Shipping Adress is the same as Billing
Input taxes as percentage:
Shipping Adress
Business Name
Email
EIN
Address
Address 2
City
State
Zip
Description
Valid Description is required.
Price
Valid price is required.
Quantity
Valid quantity is required.
Total
Subtotal
Taxes
Total


{% extends "layout_unbranded.html" %} {% block page_title %} Your money claims account {% endblock %} {% block content %} Back Which service did you use to view or make the claim? Money claims - new beta service What does the new service look like? Example of Money claims BETA Money Claim Online (MCOL) You'll have used an 8-digit Government Gateway user ID. What does MCOL look like? Example of MCOL {% endblock %}
Example of Money claims BETA
You'll have used an 8-digit Government Gateway user ID.
Example of MCOL


This is a fun way that I have come up with to practice skills that I am learning in class and anything I come across in my adventures with coding.


SoMaze
SoMaze is a puzzle game created by Evil Mouse Studios for use with cryptocurrencies such as Dogecoin. It's kind of a mix between a maze, minesweeper, and Stratego.
Gameplay
There are basically two roles in SoMaze, that of the Puzzle Creator and that of the Puzzle Solver.
Puzzle Creator
- The Puzzle Creator first selects the size of the map they'd like to create. The maximum size is currently 25x25
- After selecting the size for the map, the Creator then places traps around the map.
- Some traps can be seen by the player (such as lava tiles), and some are hidden until activated by the player (such as mines).
- After designing the map and placing the traps, fees are set.
- The Entrance Fee is the amount the Solver pays the Creator to attempt to solve the puzzle. The Creator gets this entire fee regardless of if the Solver wins or not.
- The Reward is the amount the Creator pays to the Solver upon successful completion of the puzzle. The Creator will only pay this once. Upon successful completion of the puzzle, the puzzle will be marked "deactivated" and will not be playable anymore.
- The Creation Fee is an amount that is determined automatically based on the size of the map, the number of traps, and the type of traps. Larger, more elaborate maps will have larger Creation Fees. The Creation Fee is payed by the Creator once when creating the map, and again every time a Solver attempts to solve the puzzle.
- After all the fees are set, the Creator must play through his puzzle before it goes live. This is to ensure that the puzzle is solvable.
Puzzle Solver
- The Puzzle Solver selects the puzzle they'd like to attempt from a list of all currently active puzzles. Puzzles are given a difficulty rating to assist the player in choosing the puzzle they'd like to play.
- Once the player has selected a puzzle, they will be required to pay the Entrance Fee before starting.
- Note: The Creation Fee is paid by the Creator, NOT the Solver.
- The player will start at the entrance, and click one tile at a time until they reach the exit (or die). Players are only able to move to adjecent tiles, and diagonal moves aren't allowed.
- If the player encounters a trap, they will be shown what trap they hit, and what the effects of the trap were.
- If the player dies before reaching the exit, no reward is given.
- If the player reaches the exit alive, the Reward is given, and the puzzle is marked "deactivated" (preventing future play on that puzzle)
Tips
- It's not a good idea to make your puzzle so difficult that it's almost impossible to beat. This ends up costing you in the Creation Fee, and also discourages players from attempting it.
- The best puzzles are ones that are inexpensive to attempt, have few traps, but are surprisingly difficult.
- Keep in mind that even though a puzzle may SEEM impossible, the Creator has to beat it before they are allowed to submit it.
Rules
- When creating a puzzle:
- The Entry Fee and Reward must both be greater than or equal to the Creation Fee.
- The title and description must be at least three letters long (each).
- You are only allowed to have 10 games running at once.
- You must solve the puzzle before others can play it.
- You may cancel a puzzle you've created as long as no one has played it yet.
- When solving a puzzle:
- You are only allowed to have 1 game open at a time.
- If multiple people solve a puzzle, only the first to do so receives the reward money (the game will automatically close after a successful solve).


For this seminar will look at the different types of case systems that are typologically attested in the world's languages, and see how different theories of case fare when accounting for them.
The following gives an overview of what we will (aim to) cover week by week. The readings are all linked. Most papers can be downloaded by logging in through the library. If not, they will be posted on OLAT.
| Week | Topic | Reading |
|---|---|---|
| 1 | General Introduction | — |
| 2 | Structural vs. Inherent case: Part I | Zaenen, Maling & Thráinsson (1985: 441–463) |
| 3 | Structural vs. Inherent case: Part II | Zaenen, Maling & Thráinsson (1985: 463–483) |
| 4 | Case and Agreement | Baker (2015: 28–47) |
| 5 | Case and Licensing | Marantz (1991) |
| 6 | Absolutive = Case? | Legate (2008) |
| 7 | Tripartite Systems | Baker (2015: 79–98) |
| 8 | Tripartite Systems | Baker (2015: 98–110) |
| 9 | Active–Stative Systems | Mithun (1991) |
| 10 | Active–Stative Systems | Baker and Bobaljik (2017) |
| 11 | Marked Nominative | Baker (2015: 111–146) |
| 12 | Marked Absolutive | Baker (2015: 163–183) |
| 13 | Other Issues | tbd |
| 14 | Other Issues | tbd |


Download This Plugin
Back To jQueryScript.Net
jQuery mgAccordion Plugin Examples
Accordion Menu
Mercedes
All Mercedes
All family cars
All sport cars
Family
xyz
qwe
tre
Sport
poi
lkj
slk
Lamborghini
Diablo
Murcielago
nonso
Ferrari
Testarossa
boh
mah
Testarossa
Testarossa
Testarossa
Tree View
First courses
All first courses
All pasta's recipes
All rice's recipes
Pasta
Carbonara
Amatriciana
Al pesto
Pomodoro e basilico
Rice
Milanese
With mushrooms
With peas
With sea food
Steack
Stroganoff steaks
Steak & chips
Steak in red wine sauce
Horseradish butter steaks
Fishes
Smoky hake, beans & greens
Grilled miso salmon with rice noodles
Coconut fish curry
Seafood tagine
Fruits
Apricots in syrup with rosemary
Black cherries cinnamon
Caramelized pineapple with cream
Desserts
Chocolate pudding
Tuscan chestnut cake
Hazelnut tart with fig jam
All Cars
Mercedes
All Mercedes
All family cars
All sport cars
Family
xyz
qwe
tre
Sport
poi
lkj
slk
Lamborghini
Diablo
Murcielago
nonso
Ferrari
Testarossa
boh
mah
Testarossa
Testarossa
Testarossa
Testarossa
boh
mah
Testarossa
Testarossa
Testarossa


Languages
- HTML
- CSS
- Javascript
- C#
- .NET
Projects
- A resort site about the Ivory Coast.
- This site uses HTML, CSS, as well as bootstrap grids and classes.
- A site about an animal shelter.
- This site uses basic HTML and CSS. It showcases: cascading, styling, and media queries.
- A blog about my classmate's cat.
- This site uses basic HTML and CSS. It showcases: cascading, styling, and media queries.

- This site uses basic HTML and some CSS styling.


Hi-
I love your Battlelog extension! It really brings a lot more stats for each weapon. And the YouTube embedding to spot on!
However, I was wondering if it would be possible to add more stats to the Battle Reports. Please see my reddit.com post that contains all of the information that I feel each Battle Report is missing.
http://www.reddit.com/r/battlefield3/comments/na2sa/more_battlelog_stats/
Thanks and have a great day!
-SpiderDice


Presidential Candidate
Presidential Candidate
Activities and Employability Officer Candidate
Activities and Employability Officer
Education Officer Candidate
Education Officer
International Officer Candidate
International Officer
Postgraduate Officer Candidate
Postgraduate Officer
Sports Officer Candidate
Sports Officer
Welfare and Community Officer Candidate
Welfare and Community Officer


Code Wars V1.0
REVA University ACM Student Chapter
A Brief Report
About the Event
An inter school Coding Competition - "Code Wars V1.0" was organised by REVA University ACM Student Chapter in Swami Vivekananda Block.
Organized By
School of Computing & IT
Date of Workshop
26th February, 2019
⦁ The Contest was divided into three rounds-
1. Beat the Clock - Participants were expected to code the solution to 15 questions in an hour. Students were judged based on efficiency, correctness and exception handling capabilities of their solutions. Top 10 teams were selected for the next round.
2. Capture the Point- The winners from the first round, faces a series of rapid questions and each correct answer fetches them a point. Top 5 teams moved to the final round
3. Decipher the Code- The particpants were given a decrypted text to decipher the question. After deciphering the question they were expected to solve it in code. The top two teams were the final winners.
This competition saw an overwhelming participation from the students across different schools. The entire process from planning the event, chalking out the logistics, setting questions, evaluating the solutions, arranging IT facilities was done meticulouslyl by our students from ACM Chapter and the School of CIT.
Around 104 students from the Schools of CIT, CSA, ECE, EEE, Mechanical Engineering formed 30 teams and participated in the Coding Contest.
The winning teams bagged handsome cash prizes and certificates.
Director Sir, Dr. Sunilkumar S. Manvi gave his relentless support and guidance, to seniors and colleagues in the schoo along with lab instructors, Mr. Palnivel and Mrs. Suneetha G S who gave technical support.
Following student members of the organising team deserve a special applause for their effective management of the event-
1. Adarsh Anand , Chair ACM Student Chapter, 4th Sem CIT A section.
2. Dishani Haldar, Vice Chair ACM Student Chapter, 4th Sem CIT B section.
3. Anubhav Laldas, Member ACM Student Chapter, 4th Sem CIT A Section.
4. Abhishek Panda, Member ACM Student Chapter, 4th Sem CIT A Section.
5. Shubham Baid, 4th Sem CIT, G Section.
6. Saket , 4th Sem CIT, F Section.
7. Supriya, Treasurer ACM Student Chapter, 4th Sem CIT A .
8. Anuradha,Member ACM Student Chapter,4th Sem CIT A Section.
9. Aisha Aslam, Member ACM Student Chapter, 4th Sem CIT A Section.


Dear Sal
Dear Sal,
In a room on the river
we crooned hymns
to honeysuckle, eulogized
a stuffed trout, carved nectarines
& waltzed within
the scent.  Your steady hands
aglow, you touched me
with such unbearable
attention, accurate
fingers unlacing the slaughter,
numbers & the names of countries
crumbling in your eyes.
Dear Sal,
For nearly a year I have been building a Word.
Hammering letters together, welding with a torch
that shoots a tongue-colored flame. I began
my work while remembering your pinky. How she
stands apart from all the others. They, a cluster
of gossips; she, preening, disturbing the sensibilities.
It is almost finished, the Word. I would say it here
but the walls would come crashing down. I wish
to say it in your house. In your father, mother,
brother & his twelve-gauge altar. Instead I holler
recipes at lizards, cry Kaddish over birds that dash
their brains out on the door. The Word is going
extinct in my mouth. Sing with me, Bashert, once
more. There is not enough room in the world
for our silence.
Dear Sal,
Burning beds in a field I command the light, unleash it in the direction of your dress.
I repeat your name to a cavalry of stars, hold to their noses a section of your dress.

You took me inside to bind my wound in water, nourish me on the confection of your dress.
A basket of peaches underneath your arm, the orchards of silence uprooted in your chest,
you stroll through the blinding honeysuckle, its scent the only explanation for your dress.
A sycamore sags on the side of a road, bones of a forgotten saint, unsung & under-blessed.
May you be delivered up from her roots, may you bloom in the rich salvation of her dress.
How dark is the sum of the unburied dead? They scale the moonlight & bellow distress.
What relief awaits when you unfold the fabrics & open the organization of your dress.
Refugees cascade from the mouths whales, in a language of no they are the last remaining yes.
I came from the dark with a war in my teeth, seeking clemency here in the nation of your dress.
I die in in the tub as the coffee is brewing - I am lifted by the angels, I am lifted by regrets.
I am brought back to life in a room on the river, howling about the negation of your dress.
Dear Sal,
After all these years I’ve reached my age. To celebrate I eat a tuna salad on rye
the size of a Buick. There is the mishegas & there is the mishegas. The man who
gave me a voice, he didn’t even speak the language. Oy, what a shondeh.  It means I would go back to Europe for you.
I would eat mayonnaise on the Eiffel Tower. I would surround a tree with presents
for the phantoms of our children, sing to the Rhine in the language of my people.
If that, Bashert, is your wish. What have I been trying to say about desire? Stuffing
tuna salad sandwich into my mouth. It is not something I can afford. At this time
of day the park is almost empty.  Bashert, imagine! Just the two of us, strolling
hand in hand, the beast of longing whimpering under our shoes. We call it longing
because it takes forever. I am, years old. How can it matter? I hope somehow I have
made you laugh. What do you call the most Jewish fish? The anchoyvey. I made that
one up while the moon ate my legs. Ah yes, & the shadow is mine. My beard, heavy
with fish & salt & seeds. What do you call a garden on the bottom of the ocean?
When the garden rises up in you, Bashert, what do you call?


How To Run
This code runs in the console. You can open that on Mac by pressing COMMAND + OPTION + K in Firefox, or COMMAND + OPTION + J in Chrome
You can run the following commands:
- account = new Account - creates new account called 'account'
- account.deposit(amount) - adds the specified amount to your account
- account.withdraws(amount) - withdraws the specified amount from your account
- account.printStatement() - lists all the transactions on your account


cpufreq-aperf(1) cpufreq-aperf(1) NAME cpufreq-aperf - Calculates the average frequency over a time period SYNTAX cpufreq-aperf [options] DESCRIPTION On latest processors exist two MSR registers refered to as: - MPERF increasing with maxium (P0) frequency in C0 - APERF increasing with current/actual frequency in C0 From this information the average frequency over a time period can be calculated and this is what this tool does. A nice falloff feature beside the average frequency is the time a proces‐ sor core remained in C0 (working state) or any CX (sleep state) processor sleep state during the measured time period. This information can be determined from the fact that MPERF only increases in C0 state. OPTIONS -c --cpu <CPU> The <CPU> core to measure - default all cores. -i --interval <seconds> Refresh rate - default 1 second. -o --once Exit after one interval. -h --help Prints the available options. REMARKS The msr driver must be loaded for this command to work. AUTHORS Thomas Renninger<EMAIL_ADDRESS>SEE ALSO cpufreq-set(1), cpufreq-info(1) Mattia Dongili 0.1 cpufreq-aperf(1)


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DEVON LOCH
for what we forget we never come back
Photo by Anudariya Munkhbayar on Unsplash


In a shell, when you write
$ A='foobar' ; echo $A
the echo command is given the argument foobar. The foobar value has been substituted for the A variable.
Although execline maintains no state, and thus has no real variables, it provides such a substitution facility via substitution commands, namely:
A substitution command takes a key, i.e. a string (which can contain any character but $, { and }, although it is recommended to use only alphanumerical characters), and a way to compute a value.
The simplest example is the following:
#!/command/execlineb define FOO blah echo $FOO
which will replace the FOO key with the blah value, then execute the echo command. So that script will print blah on stdout.
execline allows you to write literal ${foo} constructs even when the foo variable is being substituted, thanks to a quoting mechanism. Brace (pun intended) yourself: the following is the most complex part of the whole language.
If we want to be able to have a literal ${foo}, then:
Rule:
And now, the catch: the execlineb launcher,
as well as the shell,
interprets backslashes as escape characters. To make a word that contains
a backlash, you need to write two backslashes in your execline
script or shell command line. That means that the whole number of backslashes
you must write before your ${foo} sequence must be doubled
for the substitution command to read the proper number of backslashes and
perform its work correctly.
Once you keep that in mind, the quoting rule is logical.
The quoting rule is best illustrated with the following example, where the A key is substituted, and the $B sequences mean nothing special.
#!/command/execlineb define A val foreground { echo $A \\$A \\\\$A \\\\\\$A \\\\\\\\$A \\\\\\\\\\$A } echo $B \\$B \\\\$B \\\\\\$B \\\\\\\\$B \\\\\\\\\\$B
prints
val $A \val \$A \\val \\$A $B \$B \\$B \\\$B \\\\$B \\\\\$B
Phew.
A value can go through several transformations before it is substituted. It can be crunched, chomped, and/or split.
A split value for FOO means that a word containing ${FOO} will be replaced by zero, one, or (usually) more than one word. The value actually means a list of values.
The rule is: substituting a list of values
(v1, v2, ...) for a key A is the
same as listing the substitutions of every value vi
for A.
For instance,
#!/command/execlineb define -s FOO "v1 v2 v3" echo prefix-${FOO}-postfix
will substitute three values for $FOO: v1, v2 and v3. So the echo command will be called with three arguments: prefix-v1-postfix, prefix-v2-postfix, and prefix-v3-postfix.
(Implementation note: the fact that word prefixes are kept is what makes execline's subtitutions secure. Blocks are implemented via prefix space characters; a substitution occurring inside a block will always produce words beginning with the right amount of spaces, thus substituted values cannot prematurely terminate a block.)
A direct consequence of that rule is that substitutions will be performed recursively if more than one key appears in one word and the values for those keys are split. Parallel substitutions are performed from left to right. For instance, in
#!/command/execlineb define -s B "1 2 3" echo ${B}x${B}
the ${B}x${B} word will be replaced with nine words:
1x1, 1x2, 1x3, 2x1, 2x2,
2x3, 3x1, 3x2, and 3x3, in that order.
Here is an example with two distinct substitutions in parallel:
#!/command/execlineb multisubstitute { define -s A "a b c d" define -s B "1 2 3" } echo ${A}x${B}
The ${A}x${B} word will be replaced with twelve words: ax1, ax2, ax3, bx1, bx2, bx3, cx1, cx2, cx3, dx1, dx2, and dx3, in that order. You can check that the order of the define directives in multisubstitute does not matter.
If the left-to-right order does not suit you, then you should perform serial substitutions. For instance, the previous script can be replaced with
#!/command/execlineb define -s B "1 2 3" define -s A "a b c d" echo ${A}x${B}
and will substitute ${B} first, then ${A}. So it will print
ax1 bx1 cx1 dx1 ax2 bx2 cx2 dx2 ax3 bx3 cx3 dx3
in that order.
If you think you have mastered the art of execline substitution, then you can try to do better than these people:


Welcome to CSC4160 Cloud Computing
Launch at
{{launch_at}}
Hostname
{{Hostname}}
Local Address
{{localaddress}}
Remote Address
{{remote_addr}}
Visitors
{{visitor_addrs}}
Server Hit
{{hits}}
Created with ♥ for demonstration in Cloud Computing Course. Background image from
Unsplash
.


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'Paths to exclude from monitoring' allow multiple path patterns to be specified which will be disregarded when the SCM
is polled for changes to decide whether to trigger a build or not.
Ant-style wildcards (like '?', '*' and '**') can be used to specify path patterns to exclude. For example, '**/*.class'
would exclude all class files in the project/stream from causing a rebuild. Use 'folder-name/**' to exclude all of a
folder's subfolders and files from causing a rebuild.


Given there is much disquiet in Karachi surrounding issues of land, securitization and inequality, this anxiety is writ large over the city's political-economic landscape. For this reason, cartographic representations are particularly revealing not only in terms of the political-economic production of urban space but also how these representations advance an idea of postcolonial utopia/dystopia. Simultaneously, for those who live in the city, maps attempt to showcase space as a bounded project where the micropolitics of violence and citizenship coalesce. As prominent signifiers of the unruly city, maps make the question of survival and politics particularly acute...


If some properties have been defined for the same selector(element) in different style sheets, the value from the last read style sheet wil be used. Assume that both internal and external styles having the h1 attribute as same. Consider the following scenario, If the internal style sheet is defined after the link to the external style sheet, the h1 elements will be the "internal" attribute value.


Your browser does not support HTML5 canvas, try using
Google Chrome
instead.
High Score:
000
Score:
000


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BlockSnake
|
#include <Resource.hpp>
Public Member Functions | |
| Resource (std::string path, int uniqueNumber) | |
| std::string | getPath () const |
| int | getUniqueNumber () const |
Protected Attributes | |
| std::string | path |
| GLuint | resourceData |
| int | uniqueNumber |
Private Attributes | |
| friend | AssetManager |
The baseclass for all the resources
|
inline |
|
inline |
Gets the path
|
inline |
Gets the unique reference
|
private |
|
protected |
The path the resource is in, to check if it already exists
|
protected |
The actual data in the resource
|
protected |
The unique number that can be used to reference this resource


The man has been issued the maximum infringement for the offence of “the unlawful take of fauna” that occurred on October 25 in front of horrified children.
The Department of Biodiversity, Conservation and Attractions said it was the first time a person had been fined the maximum amount for the offence since new laws came into force on January 1.
Even though kookaburras are not native to Western Australia, they are classified as fauna under the Biodiversity Conservation Act 2016 which means people must not take or disturb them without lawful authority.


Martin Kelner's panel show appearances
Martin Kelner has appeared in 101 episodes between 2005-2016. Martin Kelner on Wikipedia.
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