Introduction
JABA stands for Java Architecture for Bytecode Analysis. JABA is a software analysis library to analyze Java programs at the bytecode level. It is also implemented in Java. It is an extensible API...Because JABA is an API, to use the system for your analyses or application, you must write a driver to invoke the functionality and query the analysis information. A sample textual driver is provided together with its source code, which can be used as a starting point and easily modified to suit the user's needs.
- for use in software analysis experimentation
- as a foundation for further analysis
- for use in CASE tools such as DejaVOO
A GUI-based driver is also provided, which allows for analyzing a system and displaying a subset of the graphs built by JABA.
JABA requires the software under analysis to be prepared in a specified way.
This manual will guide you through the process of using JABA.Setting up environment
Unpacking the distribution
To use JABA, you must first unpack the distribution. In the rest of the manual, we assume that JABA is unpacked in directory ${HOME}.
After unpacking, you will get the following directory tree:
${HOME} | +- JABA | +- bin | +- doc | +- lib (contains jaba.jar) | +- src (contains JABA.java) | +- testSetting environment variable
Once the directory structure has been created, two environment variables need to be set in one of your shell-configuration files (e.g., .cshrc, .bashrc) or in an initialization file executed before running JABA: CLASSPATH, and PATH.Your CLASSPATH must contain the location of the JABA classes, that is, ${HOME}/JABA/lib/jaba.jar. For example, if you are using the csh shell:
setenv CLASSPATH ${HOME}/JABA/lib/jaba.jar:${CLASSPATH}
Your PATH should contain the location of the Java compiler and virtual machine for Java 1.3 or later. The PATH variable must also include ${HOME}/jaba/bin. For example, if you are using the csh shell and the Sun JDK distribution installed in directory /opt/jdk1.4:
setenv PATH ${HOME}/JABA/bin:/opt/jdk1.4/bin:${PATH}Preparing the subject
Compiling the subject
JABA requires that the bytecode files being analyzed have been compiled with the -g (debugging) option. Recompile any subject that you would like to analyze with the javac -g command.Building and preparing the resource file
The current user interface of JABA uses a resource file to obtain information that is relevant to analyzing a subject. The resource file follows a format that specifies values for a set of fields, and these values are parsed by JABA to obtain the relevant information. The class jaba.main.ResourceFile is used to load a resource file. The resource file uses the following fields:
- ProgramName specifies the name for a subject. This field can be optionally set. An unset ProgramName variable defaults to "UnnamedProgram".
- ClassPath is similar to the CLASSPATH environment variable used by the Java Runtime Environment. It is a colon-separated list of directories where JABA expects to find the classes to be analyzed, and library classes referenced by the analyzed classes.
- ClassFiles is a comma-separated list of classes that are analyzed for a subject. The names are fully-qualified class names, relative to the directories specified in the ClassPath field.
To create a resource file:
- Run the script ${HOME}/JABA/bin/build_jrf.pl (requires Perl) and specify, as parameters to the script, the directory (or directories) where the subject is stored and the directory where libraries used by the subject are stored. The script recursively examines all sub-directories for class files, builds a resource file, and prints it to stdout or to the specified file. (For a complete list of options, run
${HOME}/JABA/bin/build_jrf.pl -h.
For example, the command
${HOME}/JABA/bin/build_jrf.pl -c test -l /opt/jdk1.4/jre/lib/rt.jar -p 'Test Program' -o test.rc
creates a resource file for the test program provided with the distribution and stores it in file test.rc in the current directory. The file looks as follows:
# # JABA resource file # # generated by build_jrf.pl ver.# on at - remove classes that need not be analyzed (in case you want to analyze only a subsystem).
As an alternative, it is possible to create a resource file using the JABA GUI, as described below.
Textual driver
To run the textual driver, simply executejaba <resource file>
For example, you can analyze the test program provided with the distribution as follows:
jaba test.rc
GUI driver
The GUI driver can be run in two ways:It is possible to specify some options for the GUI driver by creating a file named .jabarc in the current working directory directory. A sample file is provided with the distribution (file jabarc.sample) in directory ${HOME}/JABA. The format of the file is as follows:Using a resource file: jabaGUI <resourcefile>
In this case, the driver first performs the analysis and then provides the user with an interface that allows for visualizing some of the graphs built by JABA, such as CFGs and ICFGs, using DOTTY. (Dotty is a graph layouting and visualizing program provided by AT&T research. It is a free software and can be downloaded from Graphvizsite at AT&T.)
For example, you can analyze the test program provided with the distribution as follows:jabaGUI test.rc
and see the ICFG for the program by pressing the "ICFG" button (and suitably zooming-out in the dotty window...)
Without a resource file: jabaGUI
In this case, the GUI allows the user to graphically browse the directory structure to specify the CLASSPATH and the set of classes to be analyzed. Once the selection has been performed, the user can save the information in a resource file, for future reuse, perform the analysis, and visualize different graphs as described at the previous point.
# jaba.workingDirectory
# Sets the working directory for Jaba
# this will be the start directory for all
# file selection dialogs.
#
# Default: user's home directory
#
jaba.workingDirectory = /home/orso/Students/MohamedMansour/Examples# jaba.useSystemClassPath
# Appends the users CLASSPATH to the Class
# Path List
jaba.useSystemClassPath = false# jaba.classPath
# List of initial class paths
# This list will be appended to the
# Class Path list on startup
jaba.classPath = /home/orso/java/lib/jdk1.4
# dotty.outpath
# Directory for .dotty file
# Default: current directory
dotty.outpath = /home/orso/jaba/dottyStuff
Copying and modifying the driver
Because JABA is an API, to use the system for your analyses or application, you must write a driver to invoke the functionality and query the analysis information. To write a driver, create a subclass of jaba.main.JABADriver and redefine method run(). A sample driver, which can be used as a starting point, is provided in ${HOME}/JABA/src/JABA.java. The driver contains some sample queries and can be copied and modified to create your own driver. For example:cd ${HOME}/JABA cp src/JABA.java src/myJABA.javaNow, edit your copy of the driver to compute the desired analysis information. Remeber to change the name of the class from JABA to whatever name you chose for the driver---myJABA in the above example. The javadoc documentation of the interface of JABA is included in the distribution, in the directory ${HOME}/JABA/doc/javadocVisualizing the analysis graphs in JABA
JABA provides a facility to visualize the graphs like ICFG, ACFG etc, that are produced during the program analysis. The graphs can be output to files. These graphs can be visualized with DOTTY. (The GUI driver uses this facility to display graphs.)Any explicit graph can be output in the dotty format by calling the function createDottyFile. createDottyFile is a method of the Graph class. It takes two arguments. The first one is a string indicating the name of the file where the graph has to written. The second one is a class specifying the paramaters of the output graph.
Presently, JABA allows specifying the parameters of the nodes of the graph. The user can output any combination of the following node specification parameters.
User can specify any combination of these parameters to be written. This can be done by specifying the parameters in the resource file. To specify the parameters in the resource file, you need to add the following line in the resource file.
- Node Number in the graph
- Statement Number of the Statement in the code corresponding to the node
- Byte Code offset
- Node Type indiacting the type of the node.
DottyOutputParameters = NODE_NUMBER: STATEMENT_NUMBER: BYTECODE_OFFSET: NODE_TYPE
Include each of the parameter name if you want it to be output in the node of the graph. For eample if you need to output only Node Number and Node type then include DottyOutputParameters = NODE_NUMBER: NODE_TYPE in the resource file. If the statement is not included in the resource file, JABA outputs all the parameters as defaultCompiling the driver
To compile the driver, you just need to execute, with the CLASSPATH set correctly.javac ${HOME}/JABA/src/myJABA.java
Running your custom analyzer
First, add to the CLASSPATH, before the jaba.jar entry, the path to your local classes. For example, for the myJABA driver, you would need to add ${HOME}/JABA/src to the CLASSPATH:setenv CLASSPATH ${HOME}/JABA/src:${CLASSPATH}
On the command line, run the driver you modified followed by the name of the resource file that you created. For the analysis to succeed, it is important to increase the amount of memory used by the JVM, using the -mx option:
java -mx1000m jaba.main.JABADriver myJABA <resource file>
Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: -1
at jaba.graph.cfg.CFG.determineNodeTypes(CFG.java, Compiled Code)
at jaba.graph.cfg.CFG.constructCFG(CFG.java, Compiled Code)
at jaba.graph.cfg.CFG.__load(CFG.java, Compiled Code)
at jaba.sym.Method.getAttributeOfType(Method.java, Compiled Code)
...


IWAI Extension
Welcome
Anonymous
!


FastAPI doesn't require you to use a SQL (relational) database.
But you can use any relational database that you want.
Here we'll see an example using SQLAlchemy.
You can easily adapt it to any database supported by SQLAlchemy, like:
- PostgreSQL
- MySQL
- SQLite
- Oracle
- Microsoft SQL Server, etc.
In this example, we'll use SQLite, because it uses a single file and Python has integrated support. So, you can copy this example and run it as is.
Later, for your production application, you might want to use a database server like PostgreSQL.
Tip
There is an official project generator with FastAPI and PostgreSQL, all based on Docker, including a frontend and more tools: https://github.com/tiangolo/full-stack-fastapi-postgresql
Note
Notice that most of the code is the standard SQLAlchemy
code you would use with any framework.
The FastAPI specific code is as small as always.
ORMs¶
FastAPI works with any database and any style of library to talk to the database.
A common pattern is to use an "ORM": an "object-relational mapping" library.
An ORM has tools to convert ("map") between objects in code and database tables ("relations").
With an ORM, you normally create a class that represents a table in a SQL database, each attribute of the class represents a column, with a name and a type.
For example a class Pet
could represent a SQL table pets
.
And each instance object of that class represents a row in the database.
For example an object orion_cat
(an instance of Pet
) could have an attribute orion_cat.type
, for the column type
. And the value of that attribute could be, e.g. "cat"
.
These ORMs also have tools to make the connections or relations between tables or entities.
This way, you could also have an attribute orion_cat.owner
and the owner would contain the data for this pet's owner, taken from the table owners.
So, orion_cat.owner.name
could be the name (from the name
column in the owners
table) of this pet's owner.
It could have a value like "Arquilian"
.
And the ORM will do all the work to get the information from the corresponding table owners when you try to access it from your pet object.
Common ORMs are for example: Django-ORM (part of the Django framework), SQLAlchemy ORM (part of SQLAlchemy, independent of framework) and Peewee (independent of framework), among others.
Here we will see how to work with SQLAlchemy ORM.
In a similar way you could use any other ORM.
Tip
There's an equivalent article using Peewee here in the docs.
File structure¶
For these examples, let's say you have a directory named my_super_project
that contains a sub-directory called sql_app
with a structure like this:
.
└── sql_app
├── __init__.py
├── crud.py
├── database.py
├── main.py
├── models.py
└── schemas.py
The file __init__.py
is just an empty file, but it tells Python that sql_app
with all its modules (Python files) is a package.
Now let's see what each file/module does.
Create the SQLAlchemy parts¶
Let's refer to the file sql_app/database.py
.
Import the SQLAlchemy parts¶
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
# SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"
engine = create_engine(
SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
Create a database URL for SQLAlchemy¶
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
# SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"
engine = create_engine(
SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
In this example, we are "connecting" to a SQLite database (opening a file with the SQLite database).
The file will be located at the same directory in the file test.db
.
That's why the last part is ./test.db
.
If you were using a PostgreSQL database instead, you would just have to uncomment the line:
SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"
...and adapt it with your database data and credentials (equivalently for MySQL, MariaDB or any other).
Tip
This is the main line that you would have to modify if you wanted to use a different database.
Create the SQLAlchemy engine
¶
The first step is to create a SQLAlchemy "engine".
We will later use this engine
in other places.
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
# SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"
engine = create_engine(
SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
Note¶
The argument:
connect_args={"check_same_thread": False}
...is needed only for SQLite
. It's not needed for other databases.
Technical Details
By default SQLite will only allow one thread to communicate with it, assuming that each thread would handle an independent request.
This is to prevent accidentally sharing the same connection for different things (for different requests).
But in FastAPI, using normal functions (def
) more than one thread could interact with the database for the same request, so we need to make SQLite know that it should allow that with connect_args={"check_same_thread": False}
.
Also, we will make sure each request gets its own database connection session in a dependency, so there's no need for that default mechanism.
Create a SessionLocal
class¶
Each instance of the SessionLocal
class will be a database session. The class itself is not a database session yet.
But once we create an instance of the SessionLocal
class, this instance will be the actual database session.
We name it SessionLocal
to distinguish it from the Session
we are importing from SQLAlchemy.
We will use Session
(the one imported from SQLAlchemy) later.
To create the SessionLocal
class, use the function sessionmaker
:
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
# SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"
engine = create_engine(
SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
Create a Base
class¶
Now we will use the function declarative_base()
that returns a class.
Later we will inherit from this class to create each of the database models or classes (the ORM models):
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
# SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"
engine = create_engine(
SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
Create the database models¶
Let's now see the file sql_app/models.py
.
Create SQLAlchemy models from the Base
class¶
We will use this Base
class we created before to create the SQLAlchemy models.
Tip
SQLAlchemy uses the term "model" to refer to these classes and instances that interact with the database.
But Pydantic also uses the term "model" to refer to something different, the data validation, conversion, and documentation classes and instances.
Import Base
from database
(the file database.py
from above).
Create classes that inherit from it.
These classes are the SQLAlchemy models.
from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship
from .database import Base
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True, index=True)
email = Column(String, unique=True, index=True)
hashed_password = Column(String)
is_active = Column(Boolean, default=True)
items = relationship("Item", back_populates="owner")
class Item(Base):
__tablename__ = "items"
id = Column(Integer, primary_key=True, index=True)
title = Column(String, index=True)
description = Column(String, index=True)
owner_id = Column(Integer, ForeignKey("users.id"))
owner = relationship("User", back_populates="items")
The __tablename__
attribute tells SQLAlchemy the name of the table to use in the database for each of these models.
Create model attributes/columns¶
Now create all the model (class) attributes.
Each of these attributes represents a column in its corresponding database table.
We use Column
from SQLAlchemy as the default value.
And we pass a SQLAlchemy class "type", as Integer
, String
, and Boolean
, that defines the type in the database, as an argument.
from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship
from .database import Base
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True, index=True)
email = Column(String, unique=True, index=True)
hashed_password = Column(String)
is_active = Column(Boolean, default=True)
items = relationship("Item", back_populates="owner")
class Item(Base):
__tablename__ = "items"
id = Column(Integer, primary_key=True, index=True)
title = Column(String, index=True)
description = Column(String, index=True)
owner_id = Column(Integer, ForeignKey("users.id"))
owner = relationship("User", back_populates="items")
Create the relationships¶
Now create the relationships.
For this, we use relationship
provided by SQLAlchemy ORM.
This will become, more or less, a "magic" attribute that will contain the values from other tables related to this one.
from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship
from .database import Base
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True, index=True)
email = Column(String, unique=True, index=True)
hashed_password = Column(String)
is_active = Column(Boolean, default=True)
items = relationship("Item", back_populates="owner")
class Item(Base):
__tablename__ = "items"
id = Column(Integer, primary_key=True, index=True)
title = Column(String, index=True)
description = Column(String, index=True)
owner_id = Column(Integer, ForeignKey("users.id"))
owner = relationship("User", back_populates="items")
When accessing the attribute items
in a User
, as in my_user.items
, it will have a list of Item
SQLAlchemy models (from the items
table) that have a foreign key pointing to this record in the users
table.
When you access my_user.items
, SQLAlchemy will actually go and fetch the items from the database in the items
table and populate them here.
And when accessing the attribute owner
in an Item
, it will contain a User
SQLAlchemy model from the users
table. It will use the owner_id
attribute/column with its foreign key to know which record to get from the users
table.
Create the Pydantic models¶
Now let's check the file sql_app/schemas.py
.
Tip
To avoid confusion between the SQLAlchemy models and the Pydantic models, we will have the file models.py
with the SQLAlchemy models, and the file schemas.py
with the Pydantic models.
These Pydantic models define more or less a "schema" (a valid data shape).
So this will help us avoiding confusion while using both.
Create initial Pydantic models / schemas¶
Create an ItemBase
and UserBase
Pydantic models (or let's say "schemas") to have common attributes while creating or reading data.
And create an ItemCreate
and UserCreate
that inherit from them (so they will have the same attributes), plus any additional data (attributes) needed for creation.
So, the user will also have a password
when creating it.
But for security, the password
won't be in other Pydantic models, for example, it won't be sent from the API when reading a user.
from typing import List
from pydantic import BaseModel
class ItemBase(BaseModel):
title: str
description: str = None
class ItemCreate(ItemBase):
pass
class Item(ItemBase):
id: int
owner_id: int
class Config:
orm_mode = True
class UserBase(BaseModel):
email: str
class UserCreate(UserBase):
password: str
class User(UserBase):
id: int
is_active: bool
items: List[Item] = []
class Config:
orm_mode = True
SQLAlchemy style and Pydantic style¶
Notice that SQLAlchemy models define attributes using =
, and pass the type as a parameter to Column
, like in:
name = Column(String)
while Pydantic models declare the types using :
, the new type annotation syntax/type hints:
name: str
Have it in mind, so you don't get confused when using =
and :
with them.
Create Pydantic models / schemas for reading / returning¶
Now create Pydantic models (schemas) that will be used when reading data, when returning it from the API.
For example, before creating an item, we don't know what will be the ID assigned to it, but when reading it (when returning it from the API) we will already know its ID.
The same way, when reading a user, we can now declare that items
will contain the items that belong to this user.
Not only the IDs of those items, but all the data that we defined in the Pydantic model for reading items: Item
.
from typing import List
from pydantic import BaseModel
class ItemBase(BaseModel):
title: str
description: str = None
class ItemCreate(ItemBase):
pass
class Item(ItemBase):
id: int
owner_id: int
class Config:
orm_mode = True
class UserBase(BaseModel):
email: str
class UserCreate(UserBase):
password: str
class User(UserBase):
id: int
is_active: bool
items: List[Item] = []
class Config:
orm_mode = True
Tip
Notice that the User
, the Pydantic model that will be used when reading a user (returning it from the API) doesn't include the password
.
Use Pydantic's orm_mode
¶
Now, in the Pydantic models for reading, Item
and User
, add an internal Config
class.
This Config
class is used to provide configurations to Pydantic.
In the Config
class, set the attribute orm_mode = True
.
from typing import List
from pydantic import BaseModel
class ItemBase(BaseModel):
title: str
description: str = None
class ItemCreate(ItemBase):
pass
class Item(ItemBase):
id: int
owner_id: int
class Config:
orm_mode = True
class UserBase(BaseModel):
email: str
class UserCreate(UserBase):
password: str
class User(UserBase):
id: int
is_active: bool
items: List[Item] = []
class Config:
orm_mode = True
Tip
Notice it's assigning a value with =
, like:
orm_mode = True
It doesn't use :
as for the type declarations before.
This is setting a config value, not declaring a type.
Pydantic's orm_mode
will tell the Pydantic model to read the data even if it is not a dict
, but an ORM model (or any other arbitrary object with attributes).
This way, instead of only trying to get the id
value from a dict
, as in:
id = data["id"]
it will also try to get it from an attribute, as in:
id = data.id
And with this, the Pydantic model is compatible with ORMs, and you can just declare it in the response_model
argument in your path operations.
You will be able to return a database model and it will read the data from it.
Technical Details about ORM mode¶
SQLAlchemy and many others are by default "lazy loading".
That means, for example, that they don't fetch the data for relationships from the database unless you try to access the attribute that would contain that data.
For example, accessing the attribute items
:
current_user.items
would make SQLAlchemy go to the items
table and get the items for this user, but not before.
Without orm_mode
, if you returned a SQLAlchemy model from your path operation, it wouldn't include the relationship data.
Even if you declared those relationships in your Pydantic models.
But with ORM mode, as Pydantic itself will try to access the data it needs from attributes (instead of assuming a dict
), you can declare the specific data you want to return and it will be able to go and get it, even from ORMs.
CRUD utils¶
Now let's see the file sql_app/crud.py
.
In this file we will have reusable functions to interact with the data in the database.
CRUD comes from: Create, Read, Update, and Delete.
...although in this example we are only creating and reading.
Read data¶
Import Session
from sqlalchemy.orm
, this will allow you to declare the type of the db
parameters and have better type checks and completion in your functions.
Import models
(the SQLAlchemy models) and schemas
(the Pydantic models / schemas).
Create utility functions to:
- Read a single user by ID and by email.
- Read multiple users.
- Read a single item.
from sqlalchemy.orm import Session
from . import models, schemas
def get_user(db: Session, user_id: int):
return db.query(models.User).filter(models.User.id == user_id).first()
def get_user_by_email(db: Session, email: str):
return db.query(models.User).filter(models.User.email == email).first()
def get_users(db: Session, skip: int = 0, limit: int = 100):
return db.query(models.User).offset(skip).limit(limit).all()
def create_user(db: Session, user: schemas.UserCreate):
fake_hashed_password = user.password + "notreallyhashed"
db_user = models.User(email=user.email, hashed_password=fake_hashed_password)
db.add(db_user)
db.commit()
db.refresh(db_user)
return db_user
def get_items(db: Session, skip: int = 0, limit: int = 100):
return db.query(models.Item).offset(skip).limit(limit).all()
def create_user_item(db: Session, item: schemas.ItemCreate, user_id: int):
db_item = models.Item(**item.dict(), owner_id=user_id)
db.add(db_item)
db.commit()
db.refresh(db_item)
return db_item
Tip
By creating functions that are only dedicated to interacting with the database (get a user or an item) independent of your path operation function, you can more easily reuse them in multiple parts and also add unit tests for them.
Create data¶
Now create utility functions to create data.
The steps are:
- Create a SQLAlchemy model instance with your data.
add
that instance object to your database session.commit
the changes to the database (so that they are saved).refresh
your instance (so that it contains any new data from the database, like the generated ID).
from sqlalchemy.orm import Session
from . import models, schemas
def get_user(db: Session, user_id: int):
return db.query(models.User).filter(models.User.id == user_id).first()
def get_user_by_email(db: Session, email: str):
return db.query(models.User).filter(models.User.email == email).first()
def get_users(db: Session, skip: int = 0, limit: int = 100):
return db.query(models.User).offset(skip).limit(limit).all()
def create_user(db: Session, user: schemas.UserCreate):
fake_hashed_password = user.password + "notreallyhashed"
db_user = models.User(email=user.email, hashed_password=fake_hashed_password)
db.add(db_user)
db.commit()
db.refresh(db_user)
return db_user
def get_items(db: Session, skip: int = 0, limit: int = 100):
return db.query(models.Item).offset(skip).limit(limit).all()
def create_user_item(db: Session, item: schemas.ItemCreate, user_id: int):
db_item = models.Item(**item.dict(), owner_id=user_id)
db.add(db_item)
db.commit()
db.refresh(db_item)
return db_item
Tip
The SQLAlchemy model for User
contains a hashed_password
that should contain a secure hashed version of the password.
But as what the API client provides is the original password, you need to extract it and generate the hashed password in your application.
And then pass the hashed_password
argument with the value to save.
Warning
This example is not secure, the password is not hashed.
In a real life application you would need to hash the password and never save them in plaintext.
For more details, go back to the Security section in the tutorial.
Here we are focusing only on the tools and mechanics of databases.
Tip
Instead of passing each of the keyword arguments to Item
and reading each one of them from the Pydantic model, we are generating a dict
with the Pydantic model's data with:
item.dict()
and then we are passing the dict
's key-value pairs as the keyword arguments to the SQLAlchemy Item
, with:
Item(**item.dict())
And then we pass the extra keyword argument owner_id
that is not provided by the Pydantic model, with:
Item(**item.dict(), owner_id=user_id)
Main FastAPI app¶
And now in the file sql_app/main.py
let's integrate and use all the other parts we created before.
Create the database tables¶
In a very simplistic way create the database tables:
from typing import List
from fastapi import Depends, FastAPI, HTTPException
from sqlalchemy.orm import Session
from . import crud, models, schemas
from .database import SessionLocal, engine
models.Base.metadata.create_all(bind=engine)
app = FastAPI()
# Dependency
def get_db():
try:
db = SessionLocal()
yield db
finally:
db.close()
@app.post("/users/", response_model=schemas.User)
def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
db_user = crud.get_user_by_email(db, email=user.email)
if db_user:
raise HTTPException(status_code=400, detail="Email already registered")
return crud.create_user(db=db, user=user)
@app.get("/users/", response_model=List[schemas.User])
def read_users(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
users = crud.get_users(db, skip=skip, limit=limit)
return users
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(status_code=404, detail="User not found")
return db_user
@app.post("/users/{user_id}/items/", response_model=schemas.Item)
def create_item_for_user(
user_id: int, item: schemas.ItemCreate, db: Session = Depends(get_db)
):
return crud.create_user_item(db=db, item=item, user_id=user_id)
@app.get("/items/", response_model=List[schemas.Item])
def read_items(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
items = crud.get_items(db, skip=skip, limit=limit)
return items
Alembic Note¶
Normally you would probably initialize your database (create tables, etc) with Alembic.
And you would also use Alembic for "migrations" (that's its main job).
A "migration" is the set of steps needed whenever you change the structure of your SQLAlchemy models, add a new attribute, etc. to replicate those changes in the database, add a new column, a new table, etc.
Create a dependency¶
Info
For this to work, you need to use Python 3.7 or above, or in Python 3.6, install the "backports":
pip install async-exit-stack async-generator
This installs async-exit-stack and async-generator.
You can also use the alternative method with a "middleware" explained at the end.
Now use the SessionLocal
class we created in the sql_app/databases.py
file to create a dependency.
We need to have an independent database session/connection (SessionLocal
) per request, use the same session through all the request and then close it after the request is finished.
And then a new session will be created for the next request.
For that, we will create a new dependency with yield
, as explained before in the section about Dependencies with yield
.
Our dependency will create a new SQLAlchemy SessionLocal
that will be used in a single request, and then close it once the request is finished.
from typing import List
from fastapi import Depends, FastAPI, HTTPException
from sqlalchemy.orm import Session
from . import crud, models, schemas
from .database import SessionLocal, engine
models.Base.metadata.create_all(bind=engine)
app = FastAPI()
# Dependency
def get_db():
try:
db = SessionLocal()
yield db
finally:
db.close()
@app.post("/users/", response_model=schemas.User)
def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
db_user = crud.get_user_by_email(db, email=user.email)
if db_user:
raise HTTPException(status_code=400, detail="Email already registered")
return crud.create_user(db=db, user=user)
@app.get("/users/", response_model=List[schemas.User])
def read_users(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
users = crud.get_users(db, skip=skip, limit=limit)
return users
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(status_code=404, detail="User not found")
return db_user
@app.post("/users/{user_id}/items/", response_model=schemas.Item)
def create_item_for_user(
user_id: int, item: schemas.ItemCreate, db: Session = Depends(get_db)
):
return crud.create_user_item(db=db, item=item, user_id=user_id)
@app.get("/items/", response_model=List[schemas.Item])
def read_items(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
items = crud.get_items(db, skip=skip, limit=limit)
return items
Info
We put the creation of the SessionLocal()
and handling of the requests in a try
block.
And then we close it in the finally
block.
This way we make sure the database session is always closed after the request. Even if there was an exception while processing the request.
But you can't raise another exception from the exit code (after yield
). See more in Dependencies with yield
and HTTPException
And then, when using the dependency in a path operation function, we declare it with the type Session
we imported directly from SQLAlchemy.
This will then give us better editor support inside the path operation function, because the editor will know that the db
parameter is of type Session
:
from typing import List
from fastapi import Depends, FastAPI, HTTPException
from sqlalchemy.orm import Session
from . import crud, models, schemas
from .database import SessionLocal, engine
models.Base.metadata.create_all(bind=engine)
app = FastAPI()
# Dependency
def get_db():
try:
db = SessionLocal()
yield db
finally:
db.close()
@app.post("/users/", response_model=schemas.User)
def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
db_user = crud.get_user_by_email(db, email=user.email)
if db_user:
raise HTTPException(status_code=400, detail="Email already registered")
return crud.create_user(db=db, user=user)
@app.get("/users/", response_model=List[schemas.User])
def read_users(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
users = crud.get_users(db, skip=skip, limit=limit)
return users
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(status_code=404, detail="User not found")
return db_user
@app.post("/users/{user_id}/items/", response_model=schemas.Item)
def create_item_for_user(
user_id: int, item: schemas.ItemCreate, db: Session = Depends(get_db)
):
return crud.create_user_item(db=db, item=item, user_id=user_id)
@app.get("/items/", response_model=List[schemas.Item])
def read_items(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
items = crud.get_items(db, skip=skip, limit=limit)
return items
Technical Details
The parameter db
is actually of type SessionLocal
, but this class (created with sessionmaker()
) is a "proxy" of a SQLAlchemy Session
, so, the editor doesn't really know what methods are provided.
But by declaring the type as Session
, the editor now can know the available methods (.add()
, .query()
, .commit()
, etc) and can provide better support (like completion). The type declaration doesn't affect the actual object.
Create your FastAPI path operations¶
Now, finally, here's the standard FastAPI path operations code.
from typing import List
from fastapi import Depends, FastAPI, HTTPException
from sqlalchemy.orm import Session
from . import crud, models, schemas
from .database import SessionLocal, engine
models.Base.metadata.create_all(bind=engine)
app = FastAPI()
# Dependency
def get_db():
try:
db = SessionLocal()
yield db
finally:
db.close()
@app.post("/users/", response_model=schemas.User)
def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
db_user = crud.get_user_by_email(db, email=user.email)
if db_user:
raise HTTPException(status_code=400, detail="Email already registered")
return crud.create_user(db=db, user=user)
@app.get("/users/", response_model=List[schemas.User])
def read_users(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
users = crud.get_users(db, skip=skip, limit=limit)
return users
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(status_code=404, detail="User not found")
return db_user
@app.post("/users/{user_id}/items/", response_model=schemas.Item)
def create_item_for_user(
user_id: int, item: schemas.ItemCreate, db: Session = Depends(get_db)
):
return crud.create_user_item(db=db, item=item, user_id=user_id)
@app.get("/items/", response_model=List[schemas.Item])
def read_items(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
items = crud.get_items(db, skip=skip, limit=limit)
return items
We are creating the database session before each request in the dependency with yield
, and then closing it afterwards.
And then we can create the required dependency in the path operation function, to get that session directly.
With that, we can just call crud.get_user
directly from inside of the path operation function and use that session.
Tip
Notice that the values you return are SQLAlchemy models, or lists of SQLAlchemy models.
But as all the path operations have a response_model
with Pydantic models / schemas using orm_mode
, the data declared in your Pydantic models will be extracted from them and returned to the client, with all the normal filtering and validation.
Tip
Also notice that there are response_models
that have standard Python types like List[schemas.Item]
.
But as the content/parameter of that List
is a Pydantic model with orm_mode
, the data will be retrieved and returned to the client as normally, without problems.
About def
vs async def
¶
Here we are using SQLAlchemy code inside of the path operation function and in the dependency, and, in turn, it will go and communicate with an external database.
That could potentially require some "waiting".
But as SQLAlchemy doesn't have compatibility for using await
directly, as would be with something like:
user = await db.query(User).first()
...and instead we are using:
user = db.query(User).first()
Then we should declare the path operation functions and the dependency without async def
, just with a normal def
, as:
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
db_user = crud.get_user(db, user_id=user_id)
...
Very Technical Details
If you are curious and have a deep technical knowledge, you can check the very technical details of how this async def
vs def
is handled in the Async docs.
Migrations¶
Because we are using SQLAlchemy directly and we don't require any kind of plug-in for it to work with FastAPI, we could integrate database migrations with Alembic directly.
And as the code related to SQLAlchemy and the SQLAlchemy models lives in separate independent files, you would even be able to perform the migrations with Alembic without having to install FastAPI, Pydantic, or anything else.
The same way, you would be able to use the same SQLAlchemy models and utilities in other parts of your code that are not related to FastAPI.
For example, in a background task worker with Celery, RQ, or ARQ.
Review all the files¶
Remember you should have a directory named my_super_project
that contains a sub-directory called sql_app
.
sql_app
should have the following files:
-
sql_app/__init__.py
: is an empty file. -
sql_app/database.py
:
```Python hl_lines="" from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"¶
engine = create_engine( SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False} ) SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
* `sql_app/models.py`:
```Python hl_lines=""
from sqlalchemy import Boolean, Column, ForeignKey, Integer, String
from sqlalchemy.orm import relationship
from .database import Base
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True, index=True)
email = Column(String, unique=True, index=True)
hashed_password = Column(String)
is_active = Column(Boolean, default=True)
items = relationship("Item", back_populates="owner")
class Item(Base):
__tablename__ = "items"
id = Column(Integer, primary_key=True, index=True)
title = Column(String, index=True)
description = Column(String, index=True)
owner_id = Column(Integer, ForeignKey("users.id"))
owner = relationship("User", back_populates="items")
sql_app/schemas.py
:
```Python hl_lines="" from typing import List
from pydantic import BaseModel
class ItemBase(BaseModel): title: str description: str = None
class ItemCreate(ItemBase): pass
class Item(ItemBase): id: int owner_id: int
class Config:
orm_mode = True
class UserBase(BaseModel): email: str
class UserCreate(UserBase): password: str
class User(UserBase): id: int is_active: bool items: List[Item] = []
class Config:
orm_mode = True
* `sql_app/crud.py`:
Python hl_lines=""
from sqlalchemy.orm import Session
from . import models, schemas
def get_user(db: Session, user_id: int):
return db.query(models.User).filter(models.User.id == user_id).first()
def get_user_by_email(db: Session, email: str):
return db.query(models.User).filter(models.User.email == email).first()
def get_users(db: Session, skip: int = 0, limit: int = 100):
return db.query(models.User).offset(skip).limit(limit).all()
def create_user(db: Session, user: schemas.UserCreate):
fake_hashed_password = user.password + "notreallyhashed"
db_user = models.User(email=user.email, hashed_password=fake_hashed_password)
db.add(db_user)
db.commit()
db.refresh(db_user)
return db_user
def get_items(db: Session, skip: int = 0, limit: int = 100):
return db.query(models.Item).offset(skip).limit(limit).all()
def create_user_item(db: Session, item: schemas.ItemCreate, user_id: int):
db_item = models.Item(**item.dict(), owner_id=user_id)
db.add(db_item)
db.commit()
db.refresh(db_item)
return db_item
```
sql_app/main.py
:
```Python hl_lines="" from typing import List
from fastapi import Depends, FastAPI, HTTPException from sqlalchemy.orm import Session
from . import crud, models, schemas from .database import SessionLocal, engine
models.Base.metadata.create_all(bind=engine)
app = FastAPI()
Dependency¶
def get_db(): try: db = SessionLocal() yield db finally: db.close()
@app.post("/users/", response_model=schemas.User) def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)): db_user = crud.get_user_by_email(db, email=user.email) if db_user: raise HTTPException(status_code=400, detail="Email already registered") return crud.create_user(db=db, user=user)
@app.get("/users/", response_model=List[schemas.User]) def read_users(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)): users = crud.get_users(db, skip=skip, limit=limit) return users
@app.get("/users/{user_id}", response_model=schemas.User) def read_user(user_id: int, db: Session = Depends(get_db)): db_user = crud.get_user(db, user_id=user_id) if db_user is None: raise HTTPException(status_code=404, detail="User not found") return db_user
@app.post("/users/{user_id}/items/", response_model=schemas.Item) def create_item_for_user( user_id: int, item: schemas.ItemCreate, db: Session = Depends(get_db) ): return crud.create_user_item(db=db, item=item, user_id=user_id)
@app.get("/items/", response_model=List[schemas.Item]) def read_items(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)): items = crud.get_items(db, skip=skip, limit=limit) return items
## Check it
You can copy this code and use it as is.
!!! info
In fact, the code shown here is part of the tests. As most of the code in these docs.
Then you can run it with Uvicorn:
```bash
uvicorn sql_app.main:app --reload
And then, you can open your browser at http://<IP_ADDRESS>:8000/docs.
And you will be able to interact with your FastAPI application, reading data from a real database:
Interact with the database directly¶
If you want to explore the SQLite database (file) directly, independently of FastAPI, to debug its contents, add tables, columns, records, modify data, etc. you can use DB Browser for SQLite.
It will look like this:
You can also use an online SQLite browser like SQLite Viewer or ExtendsClass.
Alternative DB session with middleware¶
If you can't use dependencies with yield
-- for example, if you are not using Python 3.7 and can't install the "backports" mentioned above for Python 3.6 -- you can set up the session in a "middleware" in a similar way.
A "middleware" is basically a function that is always executed for each request, with some code executed before, and some code executed after the endpoint function.
Create a middleware¶
The middleware we'll add (just a function) will create a new SQLAlchemy SessionLocal
for each request, add it to the request and then close it once the request is finished.
from typing import List
from fastapi import Depends, FastAPI, HTTPException
from sqlalchemy.orm import Session
from starlette.requests import Request
from starlette.responses import Response
from . import crud, models, schemas
from .database import SessionLocal, engine
models.Base.metadata.create_all(bind=engine)
app = FastAPI()
@app.middleware("http")
async def db_session_middleware(request: Request, call_next):
response = Response("Internal server error", status_code=500)
try:
request.state.db = SessionLocal()
response = await call_next(request)
finally:
request.state.db.close()
return response
# Dependency
def get_db(request: Request):
return request.state.db
@app.post("/users/", response_model=schemas.User)
def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
db_user = crud.get_user_by_email(db, email=user.email)
if db_user:
raise HTTPException(status_code=400, detail="Email already registered")
return crud.create_user(db=db, user=user)
@app.get("/users/", response_model=List[schemas.User])
def read_users(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
users = crud.get_users(db, skip=skip, limit=limit)
return users
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(status_code=404, detail="User not found")
return db_user
@app.post("/users/{user_id}/items/", response_model=schemas.Item)
def create_item_for_user(
user_id: int, item: schemas.ItemCreate, db: Session = Depends(get_db)
):
return crud.create_user_item(db=db, item=item, user_id=user_id)
@app.get("/items/", response_model=List[schemas.Item])
def read_items(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
items = crud.get_items(db, skip=skip, limit=limit)
return items
Info
We put the creation of the SessionLocal()
and handling of the requests in a try
block.
And then we close it in the finally
block.
This way we make sure the database session is always closed after the request. Even if there was an exception while processing the request.
About request.state
¶
request.state
is a property of each Starlette Request
object. It is there to store arbitrary objects attached to the request itself, like the database session in this case.
For us in this case, it helps us ensure a single database session is used through all the request, and then closed afterwards (in the middleware).
Dependencies with yield
or middleware¶
Adding a middleware here is similar to what a dependency with yield
does, with some differences:
- It requires more code and is a bit more complex.
- The middleware has to be an
async
function.- If there is code in it that has to "wait" for the network, it could "block" your application there and degrade performance a bit.
- Although it's probably not very problematic here with the way
SQLAlchemy
works. - But if you added more code to the middleware that had a lot of I/O waiting, it could then be problematic.
- A middleware is run for every request.
- So, a connection will be created for every request.
- Even when the path operation that handles that request didn't need the DB.
Tip
It's probably better to use dependencies with yield
when they are enough for the use case.
Info
Dependencies with yield
were added recently to FastAPI.
A previous version of this tutorial only had the examples with a middleware and there are probably several applications using the middleware for database session management.


| View in TarBase | View in Ensembl | Target description | Methods |
|---|---|---|---|
| Loading data on protein targets... | |||
| Failed to load data from server | |||
| {{ protein.label }}unknown | {{ protein.target_accession.split(":")[1] }} | {{ protein.description.split(" [")[0] }} | {{ method }}, |
No protein targets are known for this sequence. Learn more →


{% extends "_basenarrow.html" %}{% block page_title %}Log in to {{ SOFTWARE_NAME }}{% endblock %} {% load static %} {% block extra_css %}{% endblock %} {% block body %} {% csrf_token %}
The password reset instructions are on the way!
Please, give it a couple of minutes to come in and don't forget to check your spam folder.
Always yours, {{ SOFTWARE_NAME_SHORT }} bot
{% endblock %}


Developed by Miguel Jiménez | @jachinte
This demo is inspired on this blog post. Slalon is a JavaScript library developed by Ralph Thomas Icons from Iconfinder, and screenshots from Pttrns


S&P 100 index includes 102 leading U.S. stocks with exchange-listed options. The constituents represent almost 51% of the market capitalization of the U.S. equity market. Here we trade the S&P 100 index ETF as a portfolio of U.S. large-cap stocks.
In the next step, we add the options to get their expiration dates. The range of expiration dates should bigger enough to include the the contract which expires in this month.
def Initialize(self): self.SetStartDate(2007, 1, 1) self.SetEndDate(2018, 8, 1) self.SetCash(10000) self.AddEquity("OEF", Resolution.Minute) option = self.AddOption("OEF") option.SetFilter(-3, 3, timedelta(0), timedelta(days = 60)) self.SetBenchmark("OEF")
To get a list of expiration dates from the contracts in the current option chain, we can use TradingCalendar
object.
It allows us to filter the calendar days by the type TradingDayType.OptionExpiration
and the start as well as the end date.
The expiry at the top of the list is the most recent expiration date.
The long position is opened at the start of the expiration week. Therefore, we use the scheduled event to fire the rebalance method every Monday. If the current Monday is in the expiration week, we long the S&P 100 index ETF.
def Rebalance(self): calendar = self.TradingCalendar.GetDaysByType(TradingDayType.OptionExpiration, self.Time, self.EndDate) expiries = [i.Date for i in calendar] if len(expiries) == 0: return self.lastest_expiry = expiries[0] if (self.lastest_expiry - self.Time).days <= 5: self.SetHoldings("OEF", 1)
The algorithm stays in cash during days out of the options expiration week.
def OnData(self, slice): if self.Time.date() == self.lastest_expiry.date(): self.Liquidate()


Story’s talks pages can highlight upcoming appearances, promoting your talks before you present. This is a dummy page illustrating this capability.
You’ll have to add buildFuture: true
to your site’s config.yaml
to enable
this setting. Talks pages that are in the future won’t appear on the homepage
or in your RSS feeds; they’ll only appear in the /talks/
list page of your
site.
This page also illustrates that you can use PDF slides. Story uses PDF.js to embed a slide viewer.


Accessories - Healthcare Volunteer Marketplace
ABonita Scarf®,Creative Knitwear,Douglas Cuddle Toys,Happy Chef Uniforms, Inc.,Uniforms Manufacturing Inc.,America Wear,Aspen Mills Uniforms,Bamboo .
http://healthcarevolunteermarketplace.com/Listing/Index/Clothing/Accessories/6793/11
Treatment for Herniated or Bulging Discs
Aspen Mills Company and Product Info from EMSWorld.com
Aspen Mills. Company Profile | Visit Website. 8201-C Central Ave. NE Spring Lake Park, MN 55432. Phone: 763/785-1055, 800/571-7343. Fax: 763/785-1395 .
http://www.emsworld.com/company/10331556/aspen-mills
Aspen Mills EMS Uniforms
Aspen Mills, manufacturers of quality, durable clothing for the EMS/EMT professional. Proudly Made in the USA (Minnesota)
http://www.aspenmills.com/
aspen mills minnesota uniforms Herniated or Bulging Discs
Members - Emergency Medical Services
Jul 27, 2011 . All of the following are great uniform supply stores and sell approved . Aspen Mills will sell patches on pretty much anything, ie. if you want EMT . The University of Minnesota is an equal opportunity educator and employer.
http://www.dem.umn.edu/umems/members/members.asp
beenblessed - EMS1 Community
Dec 23, 2009 . David replied to beenblessed's discussion EMT uniforms/pants for . The name of the company is Aspen Mills they are in Minnesota they have .
http://www.ems1community.com/profile/beenblessed
EMT class info - Inver Hills Community College Emergency Health ...
The uniform consists of the following: 1. T-shirt custom made for the class, available for $9.00 from Aspen Mills in Spring Lake Park, MN<PHONE_NUMBER>.
http://www.ehs.net/emt/default.htm
Official Impairment Listing for Spinal Disorders
Minneapolis Uniforms | Work Clothing in Minneapolis, MN
Listings 1 - 20 of 44 . Find Minneapolis Uniforms on MagicYellow. Yellow Pages online for Uniforms in Minneapolis, MN. . Aspen Mills Inc. 8201 Central Ave Ne .
http://www.magicyellow.com/category/Uniforms/Minneapolis_MN.html
- aftermarket heater hose parts (narrowing of the spine)
Minnesota Workwear - Minneapolis
Shopping. Shopping. More choices in Minneapolis: Resultset_next. American Custom Uniform Co. 28 N 4th St Minneapolis, MN. Resultset_next. Aspen Mills Inc .
http://www.insiderpages.com/b/3716682592/minnesota-workwear-minneapolisCONTACT US - Aspen Mills EMS Uniforms
Aspen Mills, manufacturers of quality, durable clothing for the EMS/EMT . Hours: Central Standard Time (Minnesota) . Spring Lake Park, Minnesota 55432 .
http://www.aspenmills.com/pages/contact.html
Uniforms Retail in White Bear Lake, MN - AOL Local Yellow Pages
Find great Uniforms Retail in White Bear Lake, MN using AOL .
http://yellowpages.aol.com/uniforms/mn/white-bear-lake/
Aspen Mills Inc in Spring Lake Park, MN - Directions
Aspen Mills Inc in Spring Lake Park, MN. Come to Citysearch® to get information, directions, and reviews on Aspen Mills Inc and other Uniforms in Spring Lake .
http://twincities.citysearch.com/profile/5607605/spring_lake_park_mn/aspen_mills_inc.html
Medical-Vocational Allowance for Herniated Disc
Minneapolis Scrubs and Medical Uniforms - FindScrubs.com
Minneapolis Nursing Scrubs, Medical Scrubs and Medical .
http://www.findscrubs.com/scrubs/MN/Minneapolis
Minneapolis Uniforms Retail in Minneapolis MN Yellow Pages by ...
Results 1 - 23 of 23 . Directory of Minneapolis Uniforms Retail in MN yellow pages. . Local results for Uniforms Retail in your city *. HyperLocal . Aspen Mills Inc .
http://www.superpages.com/yellowpages/C-Uniforms+Retail/S-MN/T-Minneapolis/
Aspen Mills, ems pants, emt pants - Aspen Mills EMS Uniforms
Aspen Mills, manufacturers of quality, durable clothing for the EMS/EMT . Hours: Central Standard Time (Minnesota) . Spring Lake Park, Minnesota 55432 .
http://www.aspenmills.com/pages/pants.html
Appealing a Denial of Benefits
Imker honored at National Firefighter Memorial Service | The Isanti ...
Oct 10, 2012 . Cambridge Firefighter Dave Matchinsky, who joined the Minnesota . received from Aspen Mills Uniforms who donated part of his uniform that .
http://isanticountynews.com/2012/10/10/imker-honored-at-national-firefighter-memorial-service/


Sources and Credits
We would like to say big thanks to fabulous souls for their products which help us in our theme development!
While making this theme we have used 3rd party plugins, icons, images, fonts, etc... Here is the list with the official website link. All Plugins are located in assets/vendor
folder, while image sources in the assets/images
folder.
Plugins
| Plugin name | URL |
|---|---|
| Bootstrap 5 | https://getbootstrap.com/docs/ |
| Tiny Slider | https://github.com/ganlanyuan/tiny-slider |
| Isotope | https://isotope.metafizzy.co/ |
| imagesloaded | https://github.com/desandro/imagesloaded |
| GLightbox | https://github.com/biati-digital/glightbox |
| Sticky-js | https://github.com/rgalus/sticky-js |
| Jarallax | https://github.com/nk-o/jarallax |
Images and logos
| Source name | URL |
|---|---|
| Freepik | https://www.freepik.com |
| Pexels | https://www.pexels.com |
| UI Faces | https://uifaces.co/ |
Fonts
| Source name | URL |
|---|---|
| Font Awesome | https://fontawesome.com/ |
| Bootstrap icons | https://icons.getbootstrap.com/ |
| Google fonts | https://fonts.google.com/ |


{% extends 'layouts/base.html' %} {% import 'macros/form_macros.html' as f %} {% block custom_head_tags %} {% endblock %} {% block content %}
Performance range (views number divided by average channel views number * 100):
performance:


Dirty History Crawler is a continuation of a project originally started by Dr. Colin Wilder, the Associate Director of the Center for Digital Humanities, here at USC.
The purpose of the project is to provide a source of bibliographic data with awareness of the interconnectivity between records to give Dr. Wilder more data for his research.
Check it out! Learn more about us!The home page provides a simple interface to make searches and sign in.
There is an advanced search tool to allow users to perform ad-hoc searches across any number of fields.
Each item in the database is kept in a simple and easy to use data structure. This should help further down the line for easy use in research contexts.
The system supports multiple user accounts and allows users to save previous searches and keep track of books that pique their interests.
The system provides its results in an easy to read format.
All the data is stored in a graph-based database, which makes it easy to present an interconnected view of the stored items.


Please click on one of the following links to access a specific API guide:
BLE
Protocol Stack API Guides:
GATT (Generic Attribute Profile)
BLE
Profile API Guides:
GAP Peripheral / Broadcaster Multi-Role Profile
Copyright 2009 - 2011 Texas Instruments Incorporated. All rights reserved.
IMPORTANT: Your use of this Software is limited to those specific rights granted under the terms of a software license agreement between the user who downloaded the software, his/her employer (which must be your employer) and Texas Instruments Incorporated (the "License"). You may not use this Software unless you agree to abide by the terms of the License. The License limits your use, and you acknowledge, that the Software may not be modified, copied or distributed unless embedded on a Texas Instruments microcontroller or used solely and exclusively in conjunction with a Texas Instruments radio frequency transceiver, which is integrated into your product. Other than for the foregoing purpose, you may not use, reproduce, copy, prepare derivative works of, modify, distribute, perform, display or sell this Software and/or its documentation for any purpose.
 IN NO EVENT SHALL TEXAS INSTRUMENTS OR ITS LICENSORS BE LIABLE OR OBLIGATED UNDER CONTRACT, NEGLIGENCE, STRICT LIABILITY, CONTRIBUTION, BREACH OF WARRANTY, OR OTHER LEGAL EQUITABLE THEORY ANY DIRECT OR INDIRECT DAMAGES OR EXPENSES INCLUDING BUT NOT LIMITED TO ANY INCIDENTAL, SPECIAL, INDIRECT, PUNITIVE OR CONSEQUENTIAL DAMAGES, LOST PROFITS OR LOST DATA, COST OF PROCUREMENT OF SUBSTITUTE GOODS, TECHNOLOGY, SERVICES, OR ANY CLAIMS BY THIRD PARTIES (INCLUDING BUT NOT LIMITED TO ANY DEFENSE THEREOF), OR OTHER SIMILAR COSTS.
Should you have any questions regarding your right to use this Software, contact Texas Instruments Incorporated at www.TI.com.


Adds a Sham module, which provides an alternate interface to Factory::Sequence.
Usage:
require 'factory_girl/syntax/sham' Sham.email {|n|<EMAIL_ADDRESS>} Factory.define :user do |factory| factory.email { Sham.email } end
Note that you can also use Faker, but it is recommended that you simply use a sequence as in the above example, as factory_girl does not provide protection against duplication in randomized sequences, and a randomized value does not provide any tangible benefits over an ascending sequence.
This syntax was derived from Pete Yandell’s machinist.
Classes and Modules
- MODULE Factory::Syntax::Sham::Sham


smartcrop.js is a content aware image cropping library. On this page you can test it with your own images. You can learn more about the library on it's github page.
Rule of thirds
Face Detection


csic.iiia.ftl.base.core.subsumption
Classes
CSPFeatureTerm
CSPSubsumption
FTSubsumption
MetaSubsumption
SubsumptionBackTrackNode
SubsumptionStackNode


To use this feature, select Report | Rates Only. This feature allows you to review the raw data and results from previous Rates Only data acquisitions. You will get a dialog box to select the desired report options, a print option and the type of report desired.
The types of reports are “Display results in text” and “Plot singles, doubles and triples”.
If you select “Plot singles, doubles and triples”, all other options will be grayed out. When you select the “OK” button, the next screen will be a list, in inverse chronological order (most recent first), of all rates only results. Click on all the results you want to report. The selected lines will be highlighted in blue. Then select the “OK” button. A graphical plot of the singles, doubles and triples will be displayed for each results set selected. The display contains options for printing the graph, zooming both the x and y axes, and manual scaling. The graphed results will remain in display windows until you manually close each one.
If you select “Display results in text”, you can check the "Print" box to have the report data printed exactly as displayed. You can also select which results are to be displayed by checking the appropriate boxes. The next screen will be a list, in inverse chronological order, of all rates only results. Click on all the results that you want to report. The selected lines will be highlighted in blue. Then select the "OK" button. All of the selected results will be displayed in cascaded windows and also printed, if the "Print" box was checked. The displayed results will remain in display windows until you manually close each one. The results displayed are the same as those displayed at the end of a rates only data acquisition.


Tabbed Browser
The right pane of the pgAdmin window features a collection of tabs that
display information about the object currently selected in the pgAdmin tree
control in the left window. Select a tab to access information about the
highlighted object in the tree control.
The graphs on the Dashboard tab provides an active analysis of the usage
statistics for the selected server or database:
- The Server sessions or Database sessions graph displays the interactions
with the server or database.
- The Transactions per second graph displays the commits, rollbacks, and
total transactions per second that are taking place on the server or database.
- The Tuples in graph displays the number of tuples inserted, updated, and
deleted on the server or database.
- The Tuples out graph displays the number of tuples fetched and returned
from the server or database.
- The Block I/O graph displays the number of blocks read from the filesystem
or fetched from the buffer cache (but not the operating system’s file system
cache) for the server or database.
The Server activity panel displays information about sessions, locks, prepared
transactions, and server configuration (if applicable). The information is
presented in context-sensitive tables. Use controls located above the table to:
- Click the Refresh button to update the information displayed in each table.
- Enter a value in the Search box to restrict the table content to one or more
sessions that satisfy the search criteria. For example, you can enter a
process ID to locate a specific session, or a session state (such as idle)
to locate all of the sessions that are in an idle state.
You can use icons in the Sessions table to review or control the state of a
session:
- Use the Terminate icon (located in the first column) to stop a session and
remove the session from the table. Before the server terminates the session,
you will be prompted to confirm your selection.
- Use the Cancel icon (located in the second column) to terminate an active
query without closing the session. Before canceling the query, the server
will prompt you to confirm your selection. When you cancel a query, the
value displayed in the State column of the table will be updated from
Active to Idle. The session will remain in the table until the session is
terminated.
- Use the Details icon (located in the third column) to open the Details
tab; the tab displays information about the selected session.
The Properties tab displays information about the object selected.
Click the Delete icon in the toolbar under the browser tab to delete the
selected objects in the Properties panel.
Click the Drop Cascade icon in the toolbar under the browser tab to delete the
selected objects and all dependent objects in the Properties panel.
Click the Edit icon in the toolbar under the browser tabs to launch the
Properties dialog for the selected object.
To preserve any changes to the Properties dialog, click the Save icon; your
modifications will be displayed in the updated Properties tab.
Details about the object highlighted in the tree control are displayed in one or
more collapsible panels. You can use the arrow to the left of each panel label
to open or close a panel.
The SQL tab displays the SQL script that created the highlighted object, and
when applicable, a (commented out) SQL statement that will DROP the selected
object. You can copy the SQL statements to the editor of your choice using cut
and paste shortcuts.
The Statistics tab displays the statistics gathered for each object on the
tree control; the statistics displayed in the table vary by the type of object
that is selected. Click a column heading to sort the table by the data displayed
in the column; click again to reverse the sort order. The following table lists
some of the statistics that are available:
| Panel |
Description |
| PID |
The process ID associated with the row. |
| User |
The name of the user that owns the object. |
| Database |
displays the database name. |
| Backends |
displays the number of current connections to the database. |
| Backend start |
The start time of the backend process. |
| Xact Committed |
displays the number of transactions committed to the database within the last week. |
| Xact Rolled Back |
displays the number of transactions rolled back within the last week. |
| Blocks Read |
displays the number of blocks read from memory (in megabytes) within the last week. |
| Blocks Hit |
displays the number of blocks hit in the cache (in megabytes) within the last week. |
| Tuples Returned |
displays the number of tuples returned within the last week. |
| Tuples Fetched |
displays the number of tuples fetched within the last week. |
| Tuples Inserted |
displays the number of tuples inserted into the database within the last week. |
| Tuples Updated |
displays the number of tuples updated in the database within the last week. |
| Tuples Deleted |
displays the number of tuples deleted from the database within the last week. |
| Last statistics reset |
displays the time of the last statistics reset for the database. |
| Tablespace conflicts |
displays the number of queries canceled because of recovery conflict with dropped tablespaces in database. |
| Lock conflicts |
displays the number of queries canceled because of recovery conflict with locks in database. |
| Snapshot conflicts |
displays the number of queries canceled because of recovery conflict with old snapshots in database. |
| Bufferpin conflicts |
displays the number of queries canceled because of recovery conflict with pinned buffers in database. |
| Temporary files |
displays the total number of temporary files, including those used by the statistics collector. |
| Size of temporary files |
displays the size of the temporary files. |
| Deadlocks |
displays the number of queries canceled because of a recovery conflict with deadlocks in database. |
| Block read time |
displays the number of milliseconds required to read the blocks read. |
| Block write time |
displays the number of milliseconds required to write the blocks read. |
| Size |
displays the size (in megabytes) of the selected database. |
The Dependencies tab displays the objects on which the currently selected
object depends. If a dependency is dropped, the object currently selected in
the pgAdmin tree control will be affected. To ensure the integrity of the entire
database structure, the database server makes sure that you do not accidentally
drop objects that other objects depend on; you must use the DROP CASCADE command
to remove an object with a dependency.
The Dependencies table displays the following information:
The Type field specifies the parent object type.
The Name field specifies the identifying name of the parent object.
The Restriction field describes the dependency relationship between the
currently selected object and the parent:
- If the field is auto, the selected object can be dropped separately from
the parent object, and will be dropped if the parent object is dropped.
- If the field is internal, the selected object was created during the
creation of the parent object, and will be dropped if the parent object
is dropped.
- If the field is normal, the selected object can be dropped without
dropping the parent object.
- If the field is blank, the selected object is required by the system,
and cannot be dropped.
The Dependents tab displays a table of objects that depend on the object
currently selected in the pgAdmin browser. A dependent object can be dropped
without affecting the object currently selected in the pgAdmin tree control.
- The Type field specifies the dependent object type.
- The Name field specifies the identifying name for the dependent object.
- The Database field specifies the database in which the object resides.
Additional tabs open when you access the extended functionality offered by
pgAdmin tools (such as the Query tool, Debugger, or SQL editor). Use the close
icon (X) located in the upper-right corner of each tab to close the tab when you
are finished using the tool. Like permanent tabs, these tabs may be repositioned
in the pgAdmin client window.
By default, each time you open a tool, pgAdmin will open a new browser tab. You
can control this behavior by modifying the Display node of the Preferences
dialog for each tool. To open the Preferences dialog, select Preferences
from the File menu.


Enter a 2D array with numbers in JS format.
Value of the matrices lie between [-5, 5], generated randomly under normal distribution
Enter the equations, each line has should have one equation to solve. Please enter 2x + y as 2*x + y.


ACE VENTURA: WHEN NATURE CALLS A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 2.9
United States, 1995 U.S. Availability: 11/10/95 (wide) Running Length: 1:30 MPAA Classification: PG-13 (Bawdy humor) Theatrical Aspect Ratio: 2.35:1
Cast: Jim Carrey, Simon Callow, Ian McNeice, Maynard Eziashi, Bob Gunton, Sophie Okonedo Director: Steve Oedekerk Producer: James G. Robinson Screenplay: Steve Oedekerk Cinematography: Donald E. Thorin Music: Robert Folk U.S. Distributor: Warner Brothers
Much as I disliked ACE VENTURA: PET DETECTIVE, it pains me to admit that Jim Carrey's first star vehicle was better than this hopelessly unfunny followup.  The same cannot be said of WHEN NATURE CALLS, which, although equally moronic, left me sitting stone-faced in my seat for ninety minutes while it tried vainly to coax a laugh. There's nothing worse than a film which mistakenly believes it's the comic event of the year. For no legitimate reason whatsoever, WHEN NATURE CALLS is full of itself to the point of being offensive.
In the eyes of some fans, the rubber-faced Carrey can do no wrong, and they will undoubtedly lap up WHEN NATURE CALLS, then come back for seconds. This film was made for them. For the rest of us, who can endure the comedian only in small doses or when he's carefully reigned in (as in THE MASK), the picture is virtually unwatchable. Like cough medicine, the taste gets worse the more you're forced to ingest.
WHEN NATURE CALLS opens with an uninspired take-off of CLIFFHANGER (a movie that was almost a self-parody in its own right). After that, it's off to a brief interlude at a Buddhist monastery, then on to Africa, where Ace Ventura has been hired to find a stolen white bat. If he fails, the Wachatis and Wachootoos--two stereotyped African tribes--will go to war.  It's beyond my comprehension how a British actor of Simon Callow's (FOUR WEDDINGS AND A FUNERAL) stature can allow himself to be put through the ritual humiliation of appearing as one of Carrey's straight men (in this case, he's the bad guy). The pay check must have been handsome.
Carrey's meteoric rise started with the first ACE VENTURA, so it was certain there would be a sequel. What's so surprising is how creatively barren Steve Oedekerk's movie is. WHEN NATURE CALLS relies solely on Carrey's personality and performance to sustain it, which doesn't happen. The writing is sophomoric and the humor often makes DUMB AND DUMBER look enlightened. It's hard to imagine Carrey doing something worse than WHEN NATURE CALLS, but then I previously thought he'd have a hard time lowering himself below the standards set by PET DETECTIVE.
The phrase "answering the call of nature" commonly refers to activities involving a toilet. All things considered, that's an appropriate place to dump this latest installment of the Ace Ventura saga.
- James Berardinelli<EMAIL_ADDRESS>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


Special educational needs and disabilities ( SEND ) can affect a child or young person’s ability to learn. They can affect their:
If you think your child may have special educational needs, contact the SEN co-ordinator, or ‘ SENCO ’ in your child’s school or nursery.

Your local Information, Advice and Support (IAS) Service can give you advice about SEND .
Your child may be eligible for:
If you or your child got support before September 2014 this will continue until your local council changes it to an EHC plan.


nips nips2000 nips2000-144 nips2000-144-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik
Abstract: The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector Machines or Statistical Regularization. We explain how VRM provides a framework which integrates a number of existing algorithms, such as Parzen windows, Support Vector Machines, Ridge Regression, Constrained Logistic Classifiers and Tangent-Prop. We then show how the approach implies new algorithms for solving problems usually associated with generative models. New algorithms are described for dealing with pattern recognition problems with very different pattern distributions and dealing with unlabeled data. Preliminary empirical results are presented.


Do Not Be Afraid Of SVG - Presentation
Extended (more info) version
Simplified (WebExpo talk) version
by
Marek Raida


Ariana Grande teases ‘Sweetener’ live album
The tour kicked off earlier this year
Ariana Grande looks to be teasing a new ‘Sweetener’ live album on her Instagram.
The album came out last August, and was followed by another LP – ‘Thank U, Next’ in February.
- Read more: Ariana Grande’s ‘Sweetener’ world tour charts the evolution of one of pop’s brightest stars
The singer has now shared clips on her Instagram stories of her scrolling through audio files with the names of different cities she played in on the ‘Sweetener’ world tour.
The new teasers follow a recent hint from Grande that a live album could be on the way. Last month, she tweeted: “so …. i got a bunch of my live vocals from tour on my laptop thanks to johnny and toby
“about to start comping thru and picking my favorite adlibs / performance moments on this flight … just in case u want a live album one day.”
The ‘Sweetener’ world tour kicked off in Los Angeles at the STAPLES Centre back in May.
In a review of the first show of the tour, NME‘s Rhian Daly wrote: “By the time last year’s smash hit ‘Thank U, Next’ arrives, signalling the end of the show, Grande looks like she’s having a blast, skipping around the runway with her dancers both with and without her First Wives Club white blazer shrugged over her shoulders.
“Where she goes from here is still unknown but, as tonight shows, she’s got a habit of exceeding herself.”
Earlier this month, she shared her star-studded new soundtrack for the new Charlie’s Angels film.


The Goodyear Stake Missionary Map is a project to show all of the missionaries called and the location of where they were called to since the stake was formed. It will show currently service missionaries as well as past missionaries.
Explore the Goodyear Stake Missionary Map.
Navigate around the map using your mouse
- To Pan (move the map), simply click and hold your mouse, then drag the map.
- To Center and Zoom In on a location, double click the location.
- To Zoom In using the mouse scroll wheel, scroll forward.
- To Zoom Out using the mouse scroll wheel, scroll backward.
- Select an area to Zoom to by holding down the SHIFT key while drawing a box with the mouse.
- Select an area to Zoom out from by holding down the SHIFT + CTRL keys while drawing a box with the mouse.
- Use the arrow keys to pan
- Use + key to zoom in a level
- Use - key to zoom out a level
- Click on a map feature to reveal further information.


The MD4SG initiative has been organizing numerous working groups since fall 2016. You can find a list of these working groups along with their webpages below:
Healthcare (Fall 2018- Spring 2020)
Organized by Zhaowei She (GATech)
We are a group of academic researchers working on real world healthcare market design problems (e.g. kidney exchange, risk adjustment) using techniques from computer science, operations research and economics.
|
Housing (Fall 2018- Spring 2019)
Organized by Daniel Walinger (NYU)
We are a group of academic researchers working on market design problems in affordable housing policy (e.g. affordable housing allocation, fair housing) using techniques from computer science, operations research, and economics.
|
Online Labor Markets (Fall 2018- Spring 2020)
Organized by Sara Kingsley (CMU) and Manish Raghavan (Cornell)
We are a group of academic researchers seeking to apply techniques from mechanism design to problems found in online labor markets, including those related to matching mechanisms, inequality and information asymmetry, and hiring algorithms.
|
Bias, Discrimination, and Fairness
Organized by Faidra Monachou (Stanford), Jessica Finocchiaro (University of Colorado - Boulder), and Duncan McElfresh (University of Maryland)
We work to better understand issues of bias, discrimination, and fairness that arise when technological systems are deployed in social and economic environments.
|
Original Reading Group (Fall 2016 - Spring 2018)
Organized by Rediet Abebe (Cornell) and Kira Goldner (University of Washington)
=)
We are a multi-institution, interdisciplinary group exploring research directions in various domains where insights from algorithms, optimization, and mechanism design can be used to improve access to opportunity. We have compiled our reading list and detailed discussion notes.
|
Photos by
rawpixel;
Ivan Vranic;
and freestocks.org;
on
Unsplash


CMS Content Refresh
Use this to have the content's identifier (url title or slug) added automatically to the URL. This will help focus which page to refresh.
Use this to have the content's identifier (url title or slug) added automatically to the URL. This will help focus which page to refresh.


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Sibling to p, but also grandchild of body
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Sibling to h1, but also grandchild of body
The Brown Bear
About Brown Bears
Species
Features
Habitat
Countries with Large Brown Bear Populations
Countries with Small Brown Bear Populations
Media


figure
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Web-Designer
Web-Developer
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Anti Fake Solution Watson
Product S/N
Start Compare
Clear All Imgs
* Click or Drag&Drop to add view image to every kind of view.


The
Genomics Virtual Lab
provides analysis tutorials and protocols, and scalable research
platforms on the cloud with easy access to reference data and
tools on demand. Tools include Galaxy, IPython Notebook, RStudio,
and environment modules for command-line users.
Contact:
Clare Sloggett


7" or 7-1/2" x 3/4" x RLAmerican OakWire Brushed
6" x 3/4" x RLAmerican OakHand Scraped
6" x 3/4" x RLAmerican OakWire Brushed
7 "or 7-1/2" x 3/4" x RLAmerican OakWire Brushed
6-1/2” x 3/4" x RLHickoryHand Scraped
7-1/2" X 3/4" X RLOakSaw Marked
7-1/2" X 3/4" X RLMapleHand Scraped
7-1/2" X 3/4" X RLOakHand Scraped
6-1/2” x 3/4" x RLEuropean OakWire Brushed
7-1/2” x 3/4" x RLHickoryWire Brushed
7-1/2” x 3/4" x RLOakWire Brushed


For those of you who missed it, last week I gave a talk at Ignite Portland about my experiences living in an RV in Portland (fast-forward to around 15:00):
And, if you'd like to download the slide deck for your own amusement, click the image below.
All content ©2011-2012 T.J. VanSlyke unless otherwise stated.
T.J. VanSlyke
340 S Lemon Ave #6077
Walnut, CA 91789
(503)-564-8335
vanslyke.tj@gmail.com


| Port | Type | Severity | Details |
|---|---|---|---|
| numer[15..0] | Input | Info | Stuck at GND |
| quotient[42..27] | Output | Info | Connected to dangling logic. Logic that only feeds a dangling port will be removed. |
| remain | Output | Info | Connected to dangling logic. Logic that only feeds a dangling port will be removed. |


Club Mold
[90, 0]
|
wasteland
[91, 0]
|
a factory
[92, 0]
|
Oake Walk Police Dept
[93, 0]
|
the Naisbitt Building
[94, 0]
|
Clewett Alley Police Dept
[95, 0]
|
Spicer Row Police Dept
[96, 0]
|
the Beale Building
[97, 0]
|
Leeson Alley
[98, 0]
|
Troubridge Cinema
[99, 0]
|
Howell Library
[90, 1]
|
Andow Towers
[91, 1]
|
Lovel Way
[92, 1]
|
the Perryn Building
[93, 1]
|
Cullen Way
[94, 1]
|
a warehouse
[95, 1]
|
Bellot Street
[96, 1]
|
Mogridge Drive
[97, 1]
|
Hamerton Road
[98, 1]
|
the Trood Building
[99, 1]
|
Purt Avenue
[90, 2]
|
the Bridgman Building
[91, 2]
|
Muncey Street
[92, 2]
|
Beach Walk
[93, 2]
|
the Turvill Building
[94, 2]
|
Weston Crescent Fire Station
[95, 2]
|
Anne General Hospital
[96, 2]
|
the Much Arms
[97, 2]
|
St Barbara's Church
[98, 2]
|
a cemetery
[99, 2]
|
Townsend Bank
[90, 3]
|
Waddington Towers
[91, 3]
|
a warehouse
[92, 3]
|
St Anacletus's Hospital
[93, 3]
|
wasteland
[94, 3]
|
Downe Towers
[95, 3]
|
a carpark
[96, 3]
|
a factory
[97, 3]
|
Nott Auto Repair
[98, 3]
|
wasteland
[99, 3]
|
wasteland
[90, 4]
|
the Pepperell Museum
[91, 4]
|
Duport Avenue
[92, 4]
|
Silley Plaza
[93, 4]
|
Parrott Towers
[94, 4]
|
the Speak Motel
[95, 4]
|
the Holdway Museum
[96, 4]
|
Midelton Crescent Police Dept
[97, 4]
|
Clerck Walk
[98, 4]
|
Byers Walk
[99, 4]
|
Club Cocker
[90, 5]
|
Gibb Plaza Railway Station
[91, 5]
|
the Whitlock Building
[92, 5]
|
MacKlin Park
[93, 5]
|
the Gouger Arms
[94, 5]
|
Noblett Drive
[95, 5]
|
Woodborn Avenue
[96, 5]
|
wasteland
[97, 5]
|
Newnam Avenue
[98, 5]
|
wasteland
[99, 5]
|
the Nurley Museum
[90, 6]
|
a factory
[91, 6]
|
Bruorton Drive
[92, 6]
|
Heddington Walk Railway Station
[93, 6]
|
Thornhill Alley
[94, 6]
|
Bullor Avenue
[95, 6]
|
Marsh Avenue
[96, 6]
|
Oatley Bank
[97, 6]
|
Rayfield Lane
[98, 6]
|
the Gatehouse Building
[99, 6]
|
Fleming Library
[90, 7]
|
a carpark
[91, 7]
|
Treweeke Mall
[92, 7]
|
Treweeke Mall
[93, 7]
|
Thresh Row
[94, 7]
|
Spencer Row
[95, 7]
|
a carpark
[96, 7]
|
a junkyard
[97, 7]
|
Hinge Drive
[98, 7]
|
the Pilton Building
[99, 7]
|
Club Dowell
[90, 8]
|
Ray Alley
[91, 8]
|
Treweeke Mall
[92, 8]
|
Treweeke Mall
[93, 8]
|
Pirrie Place
[94, 8]
|
Tovey Place
[95, 8]
|
the Slade Arms
[96, 8]
|
Hagger Way
[97, 8]
|
Blaise General Hospital
[98, 8]
|
McNally Park
[99, 8]
|
Club Garrett
[90, 9]
|
Hoskyns Alley
[91, 9]
|
the Masters Museum
[92, 9]
|
Pegrum Place Police Dept
[93, 9]
|
Muirhead Avenue Railway Station
[94, 9]
|
Caffin Library
[95, 9]
|
the Dycer Hotel
[96, 9]
|
Maud Walk
[97, 9]
|
Roadnight Towers
[98, 9]
|
Stembridge Crescent Fire Station
[99, 9]
|
|
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Know your Worth
Explore in-depth data to help your career research. Gain insights and
statics like average salaries from job titles from different
different industries, relationship of job location and salary, job seeker
statics compared to their academe, and more.
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void * GeoFree( void * blockPtr, /* address of memory to free */ GeodeHandle geodeHan); /* owner of block to be used */
The routine
malloc()
can free only memory in the malloc-block belonging to the calling geode. If you want to free memory in another geode's malloc-block, call
GeoFree()
. Passing a null
GeodeHandle
will make
GeoMalloc()
act on memory in the calling geode's malloc-block.
Include: geode.h
Warnings: Pass exactly the same address as the one returned to you when you allocated the memory. If you pass a different address,
GeoFree()
will take unpredictable actions, including possibly erasing other memory or crashing the system.
See Also: free()
.
void * GeoMalloc( size_t blockSize, /* # of bytes to allocate*/ GeodeHandle geodeHan, /* Owner of block to be used */ word zeroInit); /* Zero-initialize memory? */
The routine
malloc()
automatically allocates memory in the malloc-block belonging to the calling geode. It does not zero-initialize the memory. If you want to zero-initialize the memory, or want to allocate it in another geode's malloc-block, call
GeoMalloc()
. Pass
true
(i.e., non-zero) in
zeroInit
to zero-initialize the memory.
Passing a null
GeodeHandle
will make
GeoMalloc()
allocate the memory in the calling geode's malloc-block. If "zeroInit" is true, the memory will be initialized to null bytes; otherwise, the memory will be left uninitialized.
Include: geode.h
Warnings: All memory allocated with
malloc()
is freed when GEOS shuts down.
See Also: malloc()
.
void * GeoReAlloc( void * blockPtr, /* address of memory to resize */ size_t newSize, /* New size in bytes */ GeodeHandle geodeHan); /* Owner of block to be used */
The routine
realloc()
can resize only memory in the malloc-block belonging to the calling geode. If you want to resize memory in another geode's malloc-block, call
GeoReAlloc()
. Passing a null
GeodeHandle
will make
GeoReAlloc()
act on memory in the calling geode's malloc-block.
If the block is resized larger, the new memory will not be zero-initialized. Resizing a block smaller will never fail. If
GeoReAlloc()
fails, it will return a null pointer (zero). If you pass a
newSize
of zero, the passed block pointer is freed and the return pointer is a null pointer.
Include: geode.h
Warnings: Pass exactly the same address as the one returned to you when you allocated the memory. If you pass a different address,
GeoReAlloc()
will take unpredictable actions, including possibly erasing other memory or crashing the system.
See Also: realloc()
.
void GrApplyRotation( GStateHandle gstate, /* GState to alter */ WWFixedAsDWord angle); /* degrees counterclockwise */
Apply a rotation to the GState's transformation matrix.
Include: graphics.h
void GrApplyScale( GStateHandle gstate, /* GState to alter */ WWFixedAsDWord xScale, /* new x scale factor */ WWFixedAsDWord yScale); /* new y scale factor */
Apply a scale factor to the GState's transformation matrix.
Include: graphics.h
void GrApplyTransform( GStateHandle gstate, /* GState to draw to */ const TransMatrix *tm); /* transformation matrix to apply */
Apply a transformation, expressed as a transformation matrix, to a GState's coordinate system.
Include: graphics.h


Angular Test: RPG Character List
Player Characters
Name
Race
Class
Level
{{Player.name}}
{{Player.race}}
{{Player.charClass}}
{{Player.level}}


Senator Omanga Sends Stern Warning to Raila [VIDEO]
by Ezra ManyibeNominated Senator Millicent Omanga joined a host of Jubilee leaders who attended a thanksgiving ceremony organised by McDonald Mariga at The Bomas of Kenya on November 22, 2019.
The vocal senator thanked Kibra residents for the love and support they showed during the November 7 by-election, despite the chaos Deputy President William Ruto alleged was orchestrated by ODM leader Raila Odinga.
"Today I would like to thank the men, women, and youth of Kibra for showing us much love when we sought votes for Mariga. We know what happened on November 7, our people were intimidated and others were injured." Omanga spoke.
However, Omanga called out the Orange Democratic Movement (ODM) Party and issued a warning over the fracas that was witnessed in Kibra.
"What I would like to inform you is that ODM has no monopoly over violence. We went into the election thinking it would be peaceful, and that is why we did not cause any fracas or retaliate," she remarked.
"The journey to 2022 has now begun, those we campaigned with here in Kibra and others not from here, will form William Ruto's army in 2022. We will offer tight security, sisi ndio tutakua na hizo 'bundee' 2022," she added.
Omanga's remarks were prompted by the alleged violence that occurred during the campaign period in Kibra during the election.
She called upon the residents to rise up and rally against the opposition in a bid to aid DP Ruto's 2022 bid and for Mariga to clinch the Kibra seat in 2022.
Kimilili MP Didymus Barasa joined Omanga in sending a warning to the ODM Party. He cautioned that he would not hold back in 2022.
"I want to tell Raila that if he wants to cause chaos in 2022, the person he will be up against is Didmus Barasa and Nickson Korir here," he spoke.


|
Creatubbles API Client Unity SDK
0.1
|
Class representing an HTTP request, which serves as wrapper around UnityWebRequest
.
More...
Public Types | |
| enum | Type { Type.Public, Type.Private, Type.Regular } |
Type of the request based on which CreatubblesApiClient adds authorization headers if required. More... | |
Public Member Functions | |
| HttpRequest (UnityWebRequest webRequest, Type requestType) | |
| Initializes a new instance of the Creatubbles.Api.HttpRequest class. More... | |
| void | Cancel () |
| Cancels the request if not HttpRequest.IsDone. More... | |
| void | SetRequestHeader (string name, string value) |
| Sets the request header. More... | |
Public Attributes | |
| readonly Type | requestType |
| The type of the request determines if request requires OAuth authorization headers More... | |
Properties | |
| bool | IsSystemError [get] |
| Indicates whether a system error occured. More... | |
| string | SystemError [get] |
| Gets the system error. More... | |
| bool | IsHttpError [get] |
| Indicating whether an Http error occured. More... | |
| string | RawHttpError [get] |
| Gets the raw HTTP error. More... | |
| bool | IsAnyError [get] |
| Gets a value indicating whether any errors have occured during request. More... | |
| string | Url [get] |
| Gets the URL of the request. More... | |
| bool | IsDone [get] |
| Indicates whether request is finished. More... | |
| bool | IsCancelled [get, set] |
| Determines if request was cancelled. More... | |
| long | ResponseCode [get] |
| Gets the HTTP response code. More... | |
| float | UploadProgress [get] |
| Gets the upload progress. More... | |
| float | DownloadProgress [get] |
| Gets the download progress. More... | |
| bool | IsNonFailureHttpStatus [get] |
| Determines if HTTP status is a non-failure one (in range of <200;399>). More... | |
| string | ResponseBodyText [get] |
| Gets the response body as string. More... | |
| byte [] | ResponseBodyBytes [get] |
| Gets the response body as bytes. More... | |
| Properties inherited from Creatubbles.Api.ICancellable | |
| bool | IsCancelled [get] |
| Determines if operation was cancelled. More... | |
Class representing an HTTP request, which serves as wrapper around UnityWebRequest
.
|
strong |
Type of the request based on which CreatubblesApiClient
adds authorization headers if required.
| Creatubbles.Api.Requests.HttpRequest.HttpRequest | ( | UnityWebRequest | webRequest, |
| Type | requestType | ||
| ) |
Initializes a new instance of the Creatubbles.Api.HttpRequest class.
| webRequest | Web request. |
| requestType | Request type. |
| void Creatubbles.Api.Requests.HttpRequest.Cancel | ( | ) |
Cancels the request if not HttpRequest.IsDone.
Implements Creatubbles.Api.ICancellable.
| void Creatubbles.Api.Requests.HttpRequest.SetRequestHeader | ( | string | name, |
| string | value | ||
| ) |
Sets the request header.
| name | Header parameter name. |
| value | Header parameter value. |
| readonly Type Creatubbles.Api.Requests.HttpRequest.requestType |
The type of the request determines if request requires OAuth authorization headers
|
get |
Gets the download progress.
The download progress in range <0;1>
.
|
get |
Gets a value indicating whether any errors have occured during request.
true
if System or HTTP error occured, otherwise false
.
|
getset |
|
get |
Indicates whether request is finished.
true
if request is finished, otherwise false
.
|
get |
Indicating whether an Http error occured.
true
if request is done, not cancelled and response code represents failure, otherwise false
.
|
get |
Determines if HTTP status is a non-failure one (in range of <200;399>).
true
in range of <200;399>
, otherwise false
.
|
get |
Indicates whether a system error occured.
More info at https://docs.unity3d.com/ScriptReference/Networking.UnityWebRequest-isError.html.
true
if Unity encountered a system error like no internet connection, socket errors, errors resolving DNS entries, or the redirect limit being exceeded, otherwise false
.
|
get |
Gets the raw HTTP error.
The raw HTTP error.
|
get |
Gets the response body as bytes.
The response body as byte array or empty byte array, if response had no body or DownloadHandler was not provided.
|
get |
Gets the response body as string.
The response body string or empty string, if response had no body or DownloadHandler was not provided.
|
get |
Gets the HTTP response code.
The HTTP response code.
|
get |
Gets the system error.
More info at https://docs.unity3d.com/ScriptReference/Networking.UnityWebRequest-error.html.
The system error.
|
get |
Gets the upload progress.
The upload progress in rage <0;1>
.
|
get |
Gets the URL of the request.
The URL.


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Stretch Color Grading
by Hartmut Bohnacker
Quick start
Pixel Cloud
Selected Anchor
Original color:
Target color:


Terminology Bindings
Path
Conformance
ValueSet
Basic.language
preferred
CommonLanguages
Max Binding
:
AllLanguages
Basic.code
example
BasicResourceTypes