Motivated and detail-oriented Data Engineer with a passion for transforming data into actionable insights. Strong foundation in data engineering principles and eager to contribute to the development of scalable and efficient data solutions. Proficient in programming languages and familiar with various data technologies, I am committed to continuously learning and enhancing my skills to support data-driven decision-making.
MS in Computer Science
B.E. in Electronics and Telecommunications
Tech Stack: Python, JavaScript, SQL, Looker, Snowflake, dbt, Github, AWS, CI/CD, Fivetran
Tech Stack: Python , JavaScript, SQL, Looker, Snowflake, dbt, Github, AWS, CI/CD
Tech Stack: Python, MySQL, Looker, PostgreSQL, RESTFul API
Tech Stack: Python, JavaScript, MySQL, MongoDb, Django, RESTFul API
San Diego, CA
<EMAIL_ADDRESS><EMAIL_ADDRESS>+1<PHONE_NUMBER>


Maraschino
IRONWORKS
We expect an official launch in
90
Days
:
12
Hours
:
16
Minutes
:
57
Seconds
50%
{% extends "base.html" %} {% block content1 %}


control: In the Toolkit, some element of the screen that responds to touch, or to the mouse left button. This includes buttons, sliders, some section titles, links in Help pages, and the Help pages themselves since they can be scrolled by {_drag dragging} them.
Also: {_button}, {_slider}, {_link_help help links}


| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 12 | No summary for class \Jng\UserBundle\Entity\User |
| error | 19 | No summary for property $id |
| error | 21 | No summary for method __construct() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\UserBundle\JngUserBundle |
| error | 9 | No summary for method getParent() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 9 | No summary for class \Jng\UserBundle\Form\UserType |
| error | 15 | No summary for method buildForm() |
| error | 40 | No summary for method setDefaultOptions() |
| error | 50 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\ActivityBundle\Tests\Controller\ActivityControllerTest |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\ActivityBundle\Tests\Controller\TaskControllerTest |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\ActivityBundle\Tests\Controller\CustomerControllerTest |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\ActivityBundle\Tests\Controller\ActivityStorageControllerTest |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\ActivityBundle\Tests\Controller\BusinessControllerTest |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\ActivityBundle\Twig\JngExtension |
| error | 13 | No summary for property $request |
| error | 19 | No summary for property $environment |
| error | 25 | No summary for method __construct() |
| error | 33 | No summary for method initRuntime() |
| error | 41 | The type for the return tag in getFunctions() is "type"; isn't this an IDE default? |
| error | 41 | No summary for method getFunctions() |
| error | 74 | The type for the return tag in getFilters() is "type"; isn't this an IDE default? |
| error | 74 | No summary for method getFilters() |
| error | 87 | The type for the return tag in diffToDateFilter() is "type"; isn't this an IDE default? |
| error | 87 | The type for the param tag $date1 in diffToDateFilter() is either "type" or "unknown"; isn't this an IDE default? |
| error | 87 | The type for the param tag $date2 in diffToDateFilter() is either "type" or "unknown"; isn't this an IDE default? |
| error | 87 | The type for the param tag $unit in diffToDateFilter() is either "type" or "unknown"; isn't this an IDE default? |
| error | 87 | No summary for method diffToDateFilter() |
| error | 113 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 17 | No summary for property $id |
| error | 22 | No summary for property $name |
| error | 27 | No summary for property $created_at |
| error | 32 | No summary for property $updated_at |
| error | 37 | No summary for property $customer |
| error | 144 | No summary for method setCreatedAtValue() |
| error | 152 | No summary for method setUpdatedAtValue() |
| error | 159 | No summary for property $user |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 15 | No summary for property $id |
| error | 20 | No summary for property $start |
| error | 25 | No summary for property $end |
| error | 30 | No summary for property $activity |
| error | 35 | No summary for property $task |
| error | 142 | No summary for method setStartValue() |
| error | 147 | No summary for method setEndValue() |
| error | 154 | No summary for property $user |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 15 | No summary for property $id |
| error | 20 | No summary for property $name |
| error | 25 | No summary for property $user |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 19 | The type for the param tag $user_id in getTasksForUser() is either "type" or "unknown"; isn't this an IDE default? |
| error | 19 | Argument $user is missing from the Docblock of getTasksForUser() |
| error | 19 | No summary for method getTasksForUser() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 19 | The type for the param tag $user_id in getCustomersForUser() is either "type" or "unknown"; isn't this an IDE default? |
| error | 19 | Argument $user is missing from the Docblock of getCustomersForUser() |
| error | 19 | No summary for method getCustomersForUser() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 18 | The type for the param tag $user_id in getActivitiesForUser() is either "type" or "unknown"; isn't this an IDE default? |
| error | 18 | Argument $user is missing from the Docblock of getActivitiesForUser() |
| error | 18 | No summary for method getActivitiesForUser() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 19 | The type for the param tag $user_id in getBusinessForUser() is either "type" or "unknown"; isn't this an IDE default? |
| error | 19 | Argument $user is missing from the Docblock of getBusinessForUser() |
| error | 19 | No summary for method getBusinessForUser() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 15 | No summary for property $id |
| error | 20 | No summary for property $name |
| error | 25 | No summary for property $business |
| error | 86 | No summary for property $user |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 15 | No summary for property $id |
| error | 20 | No summary for property $name |
| error | 25 | No summary for property $order_nb |
| error | 30 | No summary for property $activity |
| error | 114 | No summary for property $user |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 110 | Argument $id is missing from the Docblock of showAction() |
| error | 131 | Argument $id is missing from the Docblock of editAction() |
| error | 173 | Argument $id is missing from the Docblock of updateAction() |
| error | 203 | Argument $id is missing from the Docblock of deleteAction() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 109 | Argument $id is missing from the Docblock of showAction() |
| error | 130 | Argument $id is missing from the Docblock of editAction() |
| error | 172 | Argument $id is missing from the Docblock of updateAction() |
| error | 202 | Argument $id is missing from the Docblock of deleteAction() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 109 | Argument $id is missing from the Docblock of showAction() |
| error | 130 | Argument $id is missing from the Docblock of editAction() |
| error | 172 | Argument $id is missing from the Docblock of updateAction() |
| error | 202 | Argument $id is missing from the Docblock of deleteAction() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 23 | Argument $id is missing from the Docblock of indexAction() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 108 | Argument $id is missing from the Docblock of showAction() |
| error | 129 | Argument $id is missing from the Docblock of editAction() |
| error | 171 | Argument $id is missing from the Docblock of updateAction() |
| error | 201 | Argument $id is missing from the Docblock of deleteAction() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 58 | No summary for method updatechartAction() |
| error | 168 | Argument $id is missing from the Docblock of showAction() |
| error | 188 | Argument $id is missing from the Docblock of editAction() |
| error | 229 | Argument $id is missing from the Docblock of updateAction() |
| error | 259 | Argument $id is missing from the Docblock of deleteAction() |
| error | 294 | Argument $id is missing from the Docblock of stopAction() |
| error | 294 | No summary for method stopAction() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Jng\ActivityBundle\JngActivityBundle |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 11 | No summary for class \Jng\ActivityBundle\Form\ActivityType |
| error | 14 | Argument $user is missing from the Docblock of __construct() |
| error | 14 | No summary for method __construct() |
| error | 23 | No summary for method buildForm() |
| error | 41 | No summary for method setDefaultOptions() |
| error | 50 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 9 | No summary for class \Jng\ActivityBundle\Form\BusinessType |
| error | 16 | No summary for method buildForm() |
| error | 26 | No summary for method setDefaultOptions() |
| error | 36 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 11 | No summary for class \Jng\ActivityBundle\Form\CustomerType |
| error | 14 | Argument $user is missing from the Docblock of __construct() |
| error | 14 | No summary for method __construct() |
| error | 24 | No summary for method buildForm() |
| error | 43 | No summary for method setDefaultOptions() |
| error | 53 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 9 | No summary for class \Jng\ActivityBundle\Form\SearchType |
| error | 11 | No summary for property $className |
| error | 13 | Argument $className is missing from the Docblock of __construct() |
| error | 13 | No summary for method __construct() |
| error | 22 | No summary for method buildForm() |
| error | 35 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 12 | No summary for class \Jng\ActivityBundle\Form\TaskType |
| error | 16 | Argument $user is missing from the Docblock of __construct() |
| error | 16 | No summary for method __construct() |
| error | 28 | No summary for method buildForm() |
| error | 45 | No summary for method setDefaultOptions() |
| error | 55 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 12 | No summary for class \Jng\ActivityBundle\Form\ActivityStorageType |
| error | 16 | Argument $user is missing from the Docblock of __construct() |
| error | 16 | No summary for method __construct() |
| error | 26 | No summary for method buildForm() |
| error | 49 | No summary for method setDefaultOptions() |
| error | 59 | No summary for method getName() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Acme\DemoBundle\Tests\Controller\DemoControllerTest |
| error | 9 | No summary for method testIndex() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Acme\DemoBundle\Twig\Extension\DemoExtension |
| error | 9 | No summary for property $loader |
| error | 10 | No summary for property $controller |
| error | 12 | No summary for method __construct() |
| error | 17 | Argument $controller is missing from the Docblock of setController() |
| error | 17 | No summary for method setController() |
| error | 32 | Argument $template is missing from the Docblock of getCode() |
| error | 32 | No summary for method getCode() |
| error | 52 | No summary for method getControllerCode() |
| error | 67 | Argument $template is missing from the Docblock of getTemplateCode() |
| error | 67 | No summary for method getTemplateCode() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 7 | No summary for class \Acme\DemoBundle\AcmeDemoBundle |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 9 | No summary for class \Acme\DemoBundle\EventListener\ControllerListener |
| error | 11 | No summary for property $extension |
| error | 13 | No summary for method __construct() |
| error | 18 | No summary for method onKernelController() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 10 | No summary for class \Acme\DemoBundle\DependencyInjection\AcmeDemoExtension |
| error | 12 | No summary for method load() |
| error | 18 | No summary for method getAlias() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 13 | No summary for class \Acme\DemoBundle\Controller\DemoController |
| error | 19 | No summary for method indexAction() |
| error | 28 | Argument $name is missing from the Docblock of helloAction() |
| error | 28 | No summary for method helloAction() |
| error | 37 | No summary for method contactAction() |
| Type | Line | Description |
|---|---|---|
| error | 0 | No summary was found for this file |
| error | 14 | No summary for class \Acme\DemoBundle\Controller\SecuredController |
| error | 20 | No summary for method loginAction() |
| error | 37 | No summary for method securityCheckAction() |
| error | 45 | No summary for method logoutAction() |
| error | 55 | Argument $name is missing from the Docblock of helloAction() |
| error | 55 | No summary for method helloAction() |
| error | 64 | Argument $name is missing from the Docblock of helloadminAction() |
| error | 64 | No summary for method helloadminAction() |


{% extends "layout.html" %} {% block layout-content %} {% load otis_extras %} {% if rows|length > 0 %} {# if >0 student to list #}
Here are the participants to which you have access.
{% if semester %}The list is restricted to {{ semester.name }}.
{% endif %}| # | Name | {% if stulist_show_semester %}Year | {% else %}Level | {% endif %}Meets | Score |
|---|---|---|---|---|---|
| {{ forloop.counter }} | {{ student.name }} {% if not student.enabled %}(disabled){% endif %} {% if student.newborn %}(newborn){% endif %} | {% if stulist_show_semester %}{{ student.semester }} | {% else %}Lv{{ row.level }} | {% endif %}{{ student.assistant.shortname }} | {% if student.legit %} {% if student.newborn %} — {% else %} {{ student.num_psets|default:0 }}u {% endif %} {% endif %} |
It's {% now "r" %}. Have a nice day.
{% else %} {# if no students at all #}But nobody came.
There isn't anything here yet.
{% if request.user.is_staff %}You're a staff member, so if you're expecting to see something, contact Evan.
{% elif submitted_registration %}You just recently registered, so you'll need to wait for Evan to confirm your registration. Contact me if you think this is an error.
{% elif exists_registration %}To register for this year, submit the decision form.
{% elif not past or not semester %}You can view 🕗 all past years if you participated in OTIS before. (You can also click the clock icon at the right.)
{% endif %} {% endif %} {% if past %} {% if semester %}You can view 🕗 all past years at once.
{% else %}This page includes all years of OTIS.
{% endif %} {% endif %} {% endblock layout-content %} {% block scripts %} {% endblock scripts %}


Ultragenes
Genetic variation between sprint, endurance, and ultrarunners
References
Cardiovascular & Pulmonary Systems
Nervous System
Muscular System
Ultra runners


dbus-send - Send a message to a message bus
dbus-send [--system | --session | --address=ADDRESS] [--dest=NAME] [--print-reply [=literal]] [--reply-timeout=MSEC] [--type=TYPE] OBJECT_PATH INTERFACE.MEMBER [CONTENTS...]
The dbus-send command is used to send a message to a D-Bus message bus. See m[blue]http://www.freedesktop.org/software/dbus/m[] for more information about the big picture.
There are two well-known message buses: the systemwide message bus (installed on many systems as the "messagebus" service) and the per-user-login-session message bus (started each time a user logs in). The --system and --session options direct dbus-send to send messages to the system or session buses respectively. If neither is specified, dbus-send sends to the session bus.
Nearly all uses of dbus-send must provide the --dest argument which is the name of a connection on the bus to send the message to. If --dest is omitted, no destination is set.
The object path and the name of the message to send must always be specified. Following arguments, if any, are the message contents (message arguments). These are given as type-specified values and may include containers (arrays, dicts, and variants) as described below.
<contents> ::= <item> | <container> [ <item> | <container>...] <item> ::= <type>:<value> <container> ::= <array> | <dict> | <variant> <array> ::= array:<type>:<value>[,<value>...] <dict> ::= dict:<type>:<type>:<key>,<value>[,<key>,<value>...] <variant> ::= variant:<type>:<value> <type> ::= string | int16 | uint 16 | int32 | uint32 | int64 | uint64 | double | byte | boolean | objpath
D-Bus supports more types than these, but dbus-send currently does not. Also, dbus-send does not permit empty containers or nested containers (e.g. arrays of variants).
Here is an example invocation:
dbus-send --dest=org.freedesktop.ExampleName e /org/freedesktop/sample/object/name e org.freedesktop.ExampleInterface.ExampleMethod e int32:47 string:*(Aqhello world*(Aq double:65.32 e array:string:"1st item","next item","last item" e dict:string:int32:"one",1,"two",2,"three",3 e variant:int32:-8 e objpath:/org/freedesktop/sample/object/name
Note that the interface is separated from a method or signal name by a dot, though in the actual protocol the interface and the interface member are separate fields.
The following options are supported:
--dest=NAME
Specify the name of the connection to receive the message.
--print-reply
Block for a reply to the message sent, and print any reply received in a human-readable form. It also means the message type (--type=) is method_call.
--print-reply=literal
Block for a reply to the message sent, and print the body of the reply. If the reply is an object path or a string, it is printed literally, with no punctuation, escape characters etc.
--reply-timeout=MSEC
Wait for a reply for up to MSEC milliseconds. The default is implementation-defined, typically 25 seconds.
--system
Send to the system message bus.
--session
Send to the session message bus. (This is the default.)
--address=ADDRESS
Send to ADDRESS.
--type=TYPE
Specify method_call or signal (defaults to "signal").
dbus-send was written by Philip Blundell.
Please send bug reports to the D-Bus mailing list or bug tracker, see m[blue]http://www.freedesktop.org/software/dbus/m[]


Blog: Stability of Learning Algorithms Part 3.
• Tutorials
Extending to Ranking Algorithms
Let be a sample such that represents the ranking score. If , then . Let be such a ranking score learning function.
We define loss function on a pairwise sample and compute a real-valued loss i.e., . We also require our loss function to be symmetric on pairwise sample i.e, and bounded as follows, for ranking problem.
We define the generalization error or risk as follows:
Empirical error is defined as follows:
Let’s assume that our algorithm has uniform stability , i.e. it satisfies ,
Note: The definition here, involves rather than which is slightly different than uniform stability definitions.
Applying McDiarmid’s inequality to derive generalization bounds for uniform stable algorithms:
$$ \mathbf{P}\Bigg( \Big(R(A_S)-R_{emp}(A_S)\Big) - 2\beta \geq \epsilon \Bigg) \leq \underbrace{e^{-\frac{2\epsilon^2}{m(2\beta+\frac{2M}{m})^2}}}_{\delta} $$ $$ \implies \mathbf{P}\Bigg( \Big(R(A_S)-R_{emp}(A_S)\Big) \leq 2\beta + (m\beta+M)\sqrt{\frac{2\log \frac{1}{\delta}}{m}} \Bigg) \geq 1-\delta $$


Welcome to galaxy-papaa docker!!!This repository is for installing and running a docker based galaxy instance with a machine learning approach to measure aberrant pathway activity for any given combination of genes and diseases from TCGA. We named this collection of tools as PanCancer Aberrant Pathway Activity Analysis (PAPAA) suite. To facilitate accessibility and generalized reuse, we have made these software tools available through both command-line interfaces as well as graphical user interfaces via incorporation into the Galaxy platform. Configuring Galaxy » GTN aberrant pi3k pathway analysis » link to the paper » Datasets descriptions » |
|


Preserving photos is a great way to preserve history. The MVHPC uses historic photos often in our efforts to restore and study historic properties. Photos, however, do not last forever; they fade, tear, discolor, and get dirty. We find that restoring pictures gives them new life, and they are simply just better pictures. An old picture that was torn or faded may become the new picture that you hang on the wall.
Restored pictures also make great gifts. Not only can we restore you picture, but we can also shrink/enlarge it to almost any size. In addition, we can print multiples so you can give them away to the people who also want a copy.
While we specialize in historic picture restoration we are also available to improve newer pictures that are just not right. Sometimes there is a picture that is discarded because someone is not looking but one or two others in the picture look great. We can create a new picture from the original with just the things you want in it. Some other fun things we can do are: changing the background, adding color, changing to black and white, and much more!
If you have a picture that you want us to look at and see what could be done to it send it to:
MVHPC Picture Restoration
9315 Deer Ridge Dr. NE
Cedar Rapids, IA 52411
If you have any questions please feel free to call me at<PHONE_NUMBER>, or email me at: mitchbloomquist@hotmail.com


Functions
-
add_circleAdd StudentThis function is to add a new student. When you click on "Add Student" on the nav bar it will take you to the new page to add a student. You can add a student with the given fields on the page.
It will not let you add a student with same student id of same email.
-
contactsAll StudentsAll students page consists of all the students which are added now. Once you add all the students using add student function. You can see all the students on "All Student" page. You can edit the name of the students using edit button. This will not let you change the student ID of student Email. You can just change the first name and last name of the student. You can also delete any student by pressing delete button. This will delete that student form the database.
-
descriptionEssay GraderEssay Grader Helps you to grade students. This takes two thing. One is it takes essay of student and essay of proffessor. This gives the total spelling mistakes, Grammetical mistakes and it compares the essay to the proffessor's essay to see how much it matches.


public class DescriptorMatcher
extends java.lang.Object
Abstract base class for matching keypoint descriptors. It has two groups of match methods: for matching descriptors of an image with another image or with an image set.
class DescriptorMatcher
// C++ code:
public:
virtual ~DescriptorMatcher();
virtual void add(const vector
const vector
virtual void clear();
bool empty() const;
virtual bool isMaskSupported() const = 0;
virtual void train();
/ *
void match(const Mat& queryDescriptors, const Mat& trainDescriptors,
vector
void knnMatch(const Mat& queryDescriptors, const Mat& trainDescriptors,
vector
const Mat& mask=Mat(), bool compactResult=false) const;
void radiusMatch(const Mat& queryDescriptors, const Mat& trainDescriptors,
vector
const Mat& mask=Mat(), bool compactResult=false) const;
/ *
void match(const Mat& queryDescriptors, vector
const vector
void knnMatch(const Mat& queryDescriptors, vector
int k, const vector
bool compactResult=false);
void radiusMatch(const Mat& queryDescriptors, vector
float maxDistance, const vector
bool compactResult=false);
virtual void read(const FileNode&);
virtual void write(FileStorage&) const;
virtual Ptr
static Ptr
protected:
vector
};
| Modifier and Type | Field and Description |
|---|---|
static int |
BRUTEFORCE |
static int |
BRUTEFORCE_HAMMING |
static int |
BRUTEFORCE_HAMMINGLUT |
static int |
BRUTEFORCE_L1 |
static int |
BRUTEFORCE_SL2 |
static int |
FLANNBASED |
| Modifier and Type | Method and Description |
|---|---|
void |
add(java.util.List<Mat> descriptors)
Adds descriptors to train a descriptor collection.
|
void |
clear()
Clears the train descriptor collection.
|
DescriptorMatcher |
clone() |
DescriptorMatcher |
clone(boolean emptyTrainData) |
static DescriptorMatcher |
create(int matcherType)
Creates a descriptor matcher of a given type with the default parameters
(using default constructor).
|
boolean |
empty()
Returns true if there are no train descriptors in the collection.
|
java.util.List<Mat> |
getTrainDescriptors()
Returns a constant link to the train descriptor collection
trainDescCollection . |
boolean |
isMaskSupported()
Returns true if the descriptor matcher supports masking permissible matches.
|
void |
knnMatch(Mat queryDescriptors,
java.util.List<MatOfDMatch> matches,
int k)
Finds the k best matches for each descriptor from a query set.
|
void |
knnMatch(Mat queryDescriptors,
java.util.List<MatOfDMatch> matches,
int k,
java.util.List<Mat> masks,
boolean compactResult)
Finds the k best matches for each descriptor from a query set.
|
void |
knnMatch(Mat queryDescriptors,
Mat trainDescriptors,
java.util.List<MatOfDMatch> matches,
int k)
Finds the k best matches for each descriptor from a query set.
|
void |
knnMatch(Mat queryDescriptors,
Mat trainDescriptors,
java.util.List<MatOfDMatch> matches,
int k,
Mat mask,
boolean compactResult)
Finds the k best matches for each descriptor from a query set.
|
void |
match(Mat queryDescriptors,
Mat trainDescriptors,
MatOfDMatch matches)
Finds the best match for each descriptor from a query set.
|
void |
match(Mat queryDescriptors,
Mat trainDescriptors,
MatOfDMatch matches,
Mat mask)
Finds the best match for each descriptor from a query set.
|
void |
match(Mat queryDescriptors,
MatOfDMatch matches)
Finds the best match for each descriptor from a query set.
|
void |
match(Mat queryDescriptors,
MatOfDMatch matches,
java.util.List<Mat> masks)
Finds the best match for each descriptor from a query set.
|
void |
radiusMatch(Mat queryDescriptors,
java.util.List<MatOfDMatch> matches,
float maxDistance)
For each query descriptor, finds the training descriptors not farther than
the specified distance.
|
void |
radiusMatch(Mat queryDescriptors,
java.util.List<MatOfDMatch> matches,
float maxDistance,
java.util.List<Mat> masks,
boolean compactResult)
For each query descriptor, finds the training descriptors not farther than
the specified distance.
|
void |
radiusMatch(Mat queryDescriptors,
Mat trainDescriptors,
java.util.List<MatOfDMatch> matches,
float maxDistance)
For each query descriptor, finds the training descriptors not farther than
the specified distance.
|
void |
radiusMatch(Mat queryDescriptors,
Mat trainDescriptors,
java.util.List<MatOfDMatch> matches,
float maxDistance,
Mat mask,
boolean compactResult)
For each query descriptor, finds the training descriptors not farther than
the specified distance.
|
void |
read(java.lang.String fileName) |
void |
train()
Trains a descriptor matcher
|
void |
write(java.lang.String fileName) |
public static final int BRUTEFORCE
public static final int BRUTEFORCE_HAMMING
public static final int BRUTEFORCE_HAMMINGLUT
public static final int BRUTEFORCE_L1
public static final int BRUTEFORCE_SL2
public static final int FLANNBASED
public void add(java.util.List<Mat> descriptors)
Adds descriptors to train a descriptor collection. If the collection
trainDescCollectionis
is not empty, the new descriptors are
added to existing train descriptors.
descriptors
- Descriptors to add. Each descriptors[i]
is a
set of descriptors from the same train image.public void clear()
Clears the train descriptor collection.
public DescriptorMatcher clone()
clone
in class java.lang.Object
public DescriptorMatcher clone(boolean emptyTrainData)
public static DescriptorMatcher create(int matcherType)
Creates a descriptor matcher of a given type with the default parameters (using default constructor).
matcherType
- a matcherTypepublic boolean empty()
Returns true if there are no train descriptors in the collection.
public java.util.List<Mat> getTrainDescriptors()
Returns a constant link to the train descriptor collection trainDescCollection
.
public boolean isMaskSupported()
Returns true if the descriptor matcher supports masking permissible matches.
public void knnMatch(Mat queryDescriptors, java.util.List<MatOfDMatch> matches, int k)
Finds the k best matches for each descriptor from a query set.
These extended variants of "DescriptorMatcher.match" methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See "DescriptorMatcher.match" for the details about query and train descriptors.
queryDescriptors
- Query set of descriptors.matches
- Matches. Each matches[i]
is k or less matches for
the same query descriptor.k
- Count of best matches found per each query descriptor or less if a
query descriptor has less than k possible matches in total.public void knnMatch(Mat queryDescriptors, java.util.List<MatOfDMatch> matches, int k, java.util.List<Mat> masks, boolean compactResult)
Finds the k best matches for each descriptor from a query set.
These extended variants of "DescriptorMatcher.match" methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See "DescriptorMatcher.match" for the details about query and train descriptors.
queryDescriptors
- Query set of descriptors.matches
- Matches. Each matches[i]
is k or less matches for
the same query descriptor.k
- Count of best matches found per each query descriptor or less if a
query descriptor has less than k possible matches in total.masks
- Set of masks. Each masks[i]
specifies permissible
matches between the input query descriptors and stored train descriptors from
the i-th image trainDescCollection[i]
.compactResult
- Parameter used when the mask (or masks) is not empty. If
compactResult
is false, the matches
vector has the
same size as queryDescriptors
rows. If compactResult
is true, the matches
vector does not contain matches for fully
masked-out query descriptors.public void knnMatch(Mat queryDescriptors, Mat trainDescriptors, java.util.List<MatOfDMatch> matches, int k)
Finds the k best matches for each descriptor from a query set.
These extended variants of "DescriptorMatcher.match" methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See "DescriptorMatcher.match" for the details about query and train descriptors.
queryDescriptors
- Query set of descriptors.trainDescriptors
- Train set of descriptors. This set is not added to
the train descriptors collection stored in the class object.matches
- Matches. Each matches[i]
is k or less matches for
the same query descriptor.k
- Count of best matches found per each query descriptor or less if a
query descriptor has less than k possible matches in total.public void knnMatch(Mat queryDescriptors, Mat trainDescriptors, java.util.List<MatOfDMatch> matches, int k, Mat mask, boolean compactResult)
Finds the k best matches for each descriptor from a query set.
These extended variants of "DescriptorMatcher.match" methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See "DescriptorMatcher.match" for the details about query and train descriptors.
queryDescriptors
- Query set of descriptors.trainDescriptors
- Train set of descriptors. This set is not added to
the train descriptors collection stored in the class object.matches
- Matches. Each matches[i]
is k or less matches for
the same query descriptor.k
- Count of best matches found per each query descriptor or less if a
query descriptor has less than k possible matches in total.mask
- Mask specifying permissible matches between an input query and
train matrices of descriptors.compactResult
- Parameter used when the mask (or masks) is not empty. If
compactResult
is false, the matches
vector has the
same size as queryDescriptors
rows. If compactResult
is true, the matches
vector does not contain matches for fully
masked-out query descriptors.public void match(Mat queryDescriptors, Mat trainDescriptors, MatOfDMatch matches)
Finds the best match for each descriptor from a query set.
In the first variant of this method, the train descriptors are passed as an
input argument. In the second variant of the method, train descriptors
collection that was set by DescriptorMatcher.add
is used.
Optional mask (or masks) can be passed to specify which query and training
descriptors can be matched. Namely, queryDescriptors[i]
can be
matched with trainDescriptors[j]
only if mask.at
is non-zero.
queryDescriptors
- Query set of descriptors.trainDescriptors
- Train set of descriptors. This set is not added to
the train descriptors collection stored in the class object.matches
- Matches. If a query descriptor is masked out in
mask
, no match is added for this descriptor. So,
matches
size may be smaller than the query descriptors count.public void match(Mat queryDescriptors, Mat trainDescriptors, MatOfDMatch matches, Mat mask)
Finds the best match for each descriptor from a query set.
In the first variant of this method, the train descriptors are passed as an
input argument. In the second variant of the method, train descriptors
collection that was set by DescriptorMatcher.add
is used.
Optional mask (or masks) can be passed to specify which query and training
descriptors can be matched. Namely, queryDescriptors[i]
can be
matched with trainDescriptors[j]
only if mask.at
is non-zero.
queryDescriptors
- Query set of descriptors.trainDescriptors
- Train set of descriptors. This set is not added to
the train descriptors collection stored in the class object.matches
- Matches. If a query descriptor is masked out in
mask
, no match is added for this descriptor. So,
matches
size may be smaller than the query descriptors count.mask
- Mask specifying permissible matches between an input query and
train matrices of descriptors.public void match(Mat queryDescriptors, MatOfDMatch matches)
Finds the best match for each descriptor from a query set.
In the first variant of this method, the train descriptors are passed as an
input argument. In the second variant of the method, train descriptors
collection that was set by DescriptorMatcher.add
is used.
Optional mask (or masks) can be passed to specify which query and training
descriptors can be matched. Namely, queryDescriptors[i]
can be
matched with trainDescriptors[j]
only if mask.at
is non-zero.
queryDescriptors
- Query set of descriptors.matches
- Matches. If a query descriptor is masked out in
mask
, no match is added for this descriptor. So,
matches
size may be smaller than the query descriptors count.public void match(Mat queryDescriptors, MatOfDMatch matches, java.util.List<Mat> masks)
Finds the best match for each descriptor from a query set.
In the first variant of this method, the train descriptors are passed as an
input argument. In the second variant of the method, train descriptors
collection that was set by DescriptorMatcher.add
is used.
Optional mask (or masks) can be passed to specify which query and training
descriptors can be matched. Namely, queryDescriptors[i]
can be
matched with trainDescriptors[j]
only if mask.at
is non-zero.
queryDescriptors
- Query set of descriptors.matches
- Matches. If a query descriptor is masked out in
mask
, no match is added for this descriptor. So,
matches
size may be smaller than the query descriptors count.masks
- Set of masks. Each masks[i]
specifies permissible
matches between the input query descriptors and stored train descriptors from
the i-th image trainDescCollection[i]
.public void radiusMatch(Mat queryDescriptors, java.util.List<MatOfDMatch> matches, float maxDistance)
For each query descriptor, finds the training descriptors not farther than the specified distance.
For each query descriptor, the methods find such training descriptors that
the distance between the query descriptor and the training descriptor is
equal or smaller than maxDistance
. Found matches are returned in
the distance increasing order.
queryDescriptors
- Query set of descriptors.matches
- Found matches.maxDistance
- Threshold for the distance between matched descriptors.
Distance means here metric distance (e.g. Hamming distance), not the distance
between coordinates (which is measured in Pixels)!public void radiusMatch(Mat queryDescriptors, java.util.List<MatOfDMatch> matches, float maxDistance, java.util.List<Mat> masks, boolean compactResult)
For each query descriptor, finds the training descriptors not farther than the specified distance.
For each query descriptor, the methods find such training descriptors that
the distance between the query descriptor and the training descriptor is
equal or smaller than maxDistance
. Found matches are returned in
the distance increasing order.
queryDescriptors
- Query set of descriptors.matches
- Found matches.maxDistance
- Threshold for the distance between matched descriptors.
Distance means here metric distance (e.g. Hamming distance), not the distance
between coordinates (which is measured in Pixels)!masks
- Set of masks. Each masks[i]
specifies permissible
matches between the input query descriptors and stored train descriptors from
the i-th image trainDescCollection[i]
.compactResult
- Parameter used when the mask (or masks) is not empty. If
compactResult
is false, the matches
vector has the
same size as queryDescriptors
rows. If compactResult
is true, the matches
vector does not contain matches for fully
masked-out query descriptors.public void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, java.util.List<MatOfDMatch> matches, float maxDistance)
For each query descriptor, finds the training descriptors not farther than the specified distance.
For each query descriptor, the methods find such training descriptors that
the distance between the query descriptor and the training descriptor is
equal or smaller than maxDistance
. Found matches are returned in
the distance increasing order.
queryDescriptors
- Query set of descriptors.trainDescriptors
- Train set of descriptors. This set is not added to
the train descriptors collection stored in the class object.matches
- Found matches.maxDistance
- Threshold for the distance between matched descriptors.
Distance means here metric distance (e.g. Hamming distance), not the distance
between coordinates (which is measured in Pixels)!public void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, java.util.List<MatOfDMatch> matches, float maxDistance, Mat mask, boolean compactResult)
For each query descriptor, finds the training descriptors not farther than the specified distance.
For each query descriptor, the methods find such training descriptors that
the distance between the query descriptor and the training descriptor is
equal or smaller than maxDistance
. Found matches are returned in
the distance increasing order.
queryDescriptors
- Query set of descriptors.trainDescriptors
- Train set of descriptors. This set is not added to
the train descriptors collection stored in the class object.matches
- Found matches.maxDistance
- Threshold for the distance between matched descriptors.
Distance means here metric distance (e.g. Hamming distance), not the distance
between coordinates (which is measured in Pixels)!mask
- Mask specifying permissible matches between an input query and
train matrices of descriptors.compactResult
- Parameter used when the mask (or masks) is not empty. If
compactResult
is false, the matches
vector has the
same size as queryDescriptors
rows. If compactResult
is true, the matches
vector does not contain matches for fully
masked-out query descriptors.public void read(java.lang.String fileName)
public void train()
Trains a descriptor matcher
Trains a descriptor matcher (for example, the flann index). In all methods to
match, the method train()
is run every time before matching.
Some descriptor matchers (for example, BruteForceMatcher
) have
an empty implementation of this method. Other matchers really train their
inner structures (for example, FlannBasedMatcher
trains
flann.Index
).
public void write(java.lang.String fileName)


Name
First Name
Address
City
State
ZIP/Postal Code
Country
Bulgaria
Moldova
Germany
Phone(country code, area code, number)
(+
)
Email
Birth date
Gender
Male
Female
Starting Date
Spring 2015
Summer 2015
Comments/Questions


val context: Context
This command's context.
NullPointerException - if accessed before parse or main are called.
NullPointerException


Intro to JS
Sans Serif
Serif
Smaller
Larger
Reset
Change to Red
Larger
Even Larger
Reset
Change to Violet
Larger
Even Larger
Reset


{% extends "mails/messages/activity_base.html" %} {% load i18n %} {% block message %}
{% blocktrans context 'email' %} There are tons of cool activities on {{ site_name }} that are making a positive impact. We have selected {{ count }} activities that match with your profile. Join us! {% endblocktrans %}
{% if profile_incomplete%}{% trans "Complete your profile" %} {% trans ", so that we can select even more relevant activities for you." %}
{% endif %}|
|
{% if activity.is_online %}
{% elif activity.location %}
{% endif %}
|


Voyager NASA Shortly after the Pioneers made their flybys, the Voyager 1 and Voyager 2 probes followed. They made many important discoveries about Jupiter and Saturn, including rings around Jupiter and the presence of volcanism on Jupiter's moon, Io. Voyager went on to make the first flybys of Uranus, where it discovered 10 new moons, and Neptune, where it found that Neptune actually weighs less than astronomers thought. Both Voyager crafts have enough power to keep transmitting radio signals until at least 2025, and are now exploring the very edge of the solar system and beginning of interstellar space. Voyager 2 is currently the farthest man-made object from Earth, at more than a hundred times the distance from the Earth to the sun, and more than twice as far as Pluto.


Key management procedures should require secure key distribution.
Check the information security policy to make sure the key management procedures require secure key distribution.
Design and document the key management procedures to require secure key distribution. Include the key management procedures in the information security policy. To qualify, the key management procedures should explain how the keys are distributed and what limitations make them secure. One example of a secure key distribution procedure is only distributing the keys on the application server.


Purnima Enterprises
About Us
#Product description#
Phone: #Phone Here#
Email: #Email Here#
Address: #Address Here#
#Product description#
Phone: #Phone Here#
Email: #Email Here#
Address: #Address Here#


Senior/Staff Data Scientist - San Francisco, CA
-------------------------------------
Position: Senior/Staff Data Scientist
-------------------------------------
***********
Who we are:
***********
Our Company was founded on the idea that there are patterns in people's behavior that, with the right logic, can be used to predict future outcomes. We are a small but rapidly growing organization that works in partnership with our customers to create solutions that are simply not found anywhere else. We work in groups rather than in structured corporate hierarchies; our culture is creative and entrepreneurial where everyone contributes to company goals in very real way. We are a hardworking group, but we have a lot of fun with what we do and are looking for new people with a similar mindset to join the organization.
***********
What we do:
***********
Our proprietary software-as-a-service helps automotive dealerships and sales teams better understand and predict exactly which customers are ready to buy, the reasons why, and the key offers and incentives most likely to close the sale. Its micro-marketing engine then delivers the right message at the right time to those customers, ensuring higher conversion rates and a stronger ROI.
*****************
What you will do:
*****************
We are looking for talented individuals to join our team in building the analytical components of our software that at heart of it uses data analytics and machine learning
You will apply machine learning techniques and statistical methods to build core components to the software along with data engineers and software engineers.
You are passionate about automating everything and applying scientific methods to solve business and engineering requirements
You will perform analyses on large sets of data to extract actionable insights that will help drive decisions across the business
Communicate data-driven insights and recommendations to key stakeholders
You will work closely with product owners, designers, data engineers, marketing teams and others across the company
You will work on challenging projects using cutting-edge technologies
This position will bring analytical rigor and statistical methods to the challenges of measuring quality, improving products, and understanding the behavior of end-users
You will be the subject matter expert and propose new ideas
******************
Who you are:
******************
MS or PhD in a quantitative discipline (e.g., statistics, operations research, computer science, mathematics, physics, electrical engineering, industrial engineering)
3-5+ years of industry experience with a Phd or 8+ years of industry work experience with a master's (e.g., as a lead statistician / sr. data scientist / sr. machine learning engineer)
Experience presenting insights to executives and non-technical audiences around product analytics results into business recommendations
Applied experience with data analysis and machine learning (such as logistic regression, random forest, gradient boosting, clustering, neural networks) on large datasets
You have the technical competence to perform advanced analytics:
Coding skills such as Python and SQL
Apache Spark platform (including Dataset, SparkML)
Experience designing and implementing machine learning pipelines in production environments
Experience with deep learning methods such as RNN, LSTMs a plus
Demonstrated willingness to both teach others and learn new techniques
You are able to work independently but have great collaboration skills
You believe in what you do and feel a vested interest in company's success
***********************
Expected Hours of Work:
***********************
This is a full-time position. Generally, work is performed Monday through Friday, though holidays and weekends may be required.
*********************************************
We believe in equal employment opportunities:
*********************************************
The company provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, the company complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
The company expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of the company's employees to perform their job duties may result in disciplinary actions up to and including discharge.
This role may require travel to our New York office several times per year.


To visualize and distinguish actionable items, you can use the included Font Awesome library. Iconography with action links remains unique to one action (with the exception of "create", "delete", "change", "edit", and "go to" actions, which use the same icons in many places regardless of context). However, in the case of two opposite actions that you can take on a single unit (for example, choosing to enable connection throttling on a load balancer when throttling is disabled, or disabling throttling when throttling is enabled), then the same icon and color can be used. In the case of opposite actions, only one action should display at a time, depending on the state. Any "enable" or "disable" actions should be yellow in color, as they're causing changes to something rather than creating something new (green) or destroying something (red).
For the majority of Encore, actionable links are used in two places — in the upper right hand section of a details page, flushed right with the page-actions and actions-area classes, or in the rxActionMenu directive.
The following icons have been used throughout Encore, as well as the actions associated with the icons. Use this as a reference for which icons are already in use and should not be duplicated.
HEADS UP: Most icons have been chosen based on the verb (Add ,Edit, Delete, Restart, etc) but there are a couple of icons chosen on the noun (consoles, tachometers for throttling). This is a design pattern discrepancy that will need to be resolved in a future iteration.
Status Classes are used to provide a clearer status regarding a simple message. Within the context of Encore, the following colors are used to convey action links:
Green (.msg-action
) is for create/add actions.
When part of a list, they appear at the top.
Blue (.msg-info-blue
) is for download actions.
When part of a list, they appear second.
Orange (.msg-info
) is for edit/modify actions.
When part of a list, they appear third.
Red (.msg-warn
) is for destructive actions.
When part of a list, they appear last.
You can use a spinner style for action links. Different spinner styles are available when a user takes action by clicking a button (see Buttons Demo Page).
When an action link must first fetch data or determine state before the user can click it, a spinner is shown to the right of the action link, and both spinner and link have a disabled gray color and are not clickable.
HEADS UP: The spinner with action link is being styled on a per-project
basis currently, since the current implementation of rxSpinner
needs to be
updated to support this use case more easily.


Who am I?
- Computer Engineering Major
- Security Engineering Intern @ Databricks
- Experience with data-science, far from an expert
Assumptions and Objectives
Assumptions
- Assuming everyone familiar with basic python
- Nothing else
Objectives
- You will not be expert at data science, or even pandas
- Goal is to give a glimpse of basic data science and provide resources for further learning
Setup
Use Databricks
- Community Edition Free
- COVID-19 dataset accessable in the platform
- Far more scalable than traditional approach
Why Python Notebooks
- All code in one place
- Easily sharable
- Easy to limit overhead
Raw data, now what?
Graphs?
Basic ML?


Instead of removing the possibility to insert pictures completely in the signature by cutting links out, you guys could make an exception rule for the graphic signatures provided on bf3stats.com .
Just a thought ...
In regards
ANTI
BrainFooLong
Administrator
The reason for blocking images was not the type of images, it was the spam than can be done with this.
Sorry, we also don't like this behaviour but say thank you to all kids in battlelog.
Topicstarter
xIx ANTI xIx
Like i said it was just a thought.
A bit dissapointing but i understand the problem.


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{% load static %}
4
4
You're lost in the dark!
The page you're looking for does not exist
Go to Home


Dynamic Themes Using CSS Variables
CSS Variables, officially designated Custom Properties, can let you create dynamic themes of your website. They are supported in all current browsers.
Adding them is simple:
- In a css file just add a
:root
element. - In your
:root
element define your variables with a prefix--
. - Variables can be used as values anywhere within your css
var("--variable-name")
. - In Javascript use
document.documentElement.style.setProperty('--variable-name', value)
to change the variable value.
Use the floating box to theme this page.
Look at the repo
of this page to see an example of how to add them to your page.


Suarez KPI
ログ
集計結果
集計関数一覧
This split pane component is fixed: when re-sizing the browser window, it will
keep the same width.
You can, however, re-size this component by dragging the divider, and it will adjust if
the right component reaches it's min-width
(0px
in this example).


thanks for helping our German Shepard During the summer, my German Shorthair Pointer, Tonto, began to have severe redness and itching on his belly and feet. Through diagnostic testing, we learned that Tonto is severely allergic to over a dozen kinds of grass pollens. Read More >>
Our diabetic kitty is better. When Samantha, our sweet kitten, began sleeping all the time and urinating excessively, we brought her to see the specialists at Wisdom. After running a blood test, Dr. Winthrop confirmed what we all feared – Samantha was showing signs of Read More >>
Our grape-loving dog. The staff at Wisdom worked tirelessly to determine why our three-year old bulldog, Roxie, started going into sudden kidney failure. They stabilized her and provided fluids until her kidneys were again functioning normal, but it was still a mystery as to what caused her health to decline so quickly.  Wisdom Pet Medicine is the only clinic around that will even book pet fish for appointments. When our 13-year old goldfish, McAllister, turned from silvery white to an angry red, we called around, urgently trying to find a veterinarian who could help. Wisdom not only got us in the same day, but also was able to diagnose McAllister as having a severe case of septicemia. Read More >>


Causes and Impact of Conflicts in Africa :
What have
Scholars
researched?
( Interval )
2 yrs
5 yrs
10 yrs
20 yrs
HIGH CORRELATION


Navigation
SHOPPING BAG 0
NO PRODUCTS IN THE CART.
Women
Men
Kids
Diamonds
Earring
Necklace
DAILY WEAR . EARRING . GOLD . OCCASION . WOMEN
65.000 .د.ب 75.000 .د.ب
GOLD . NOSEPIN . OCCASION . PARTY WEAR . WOMEN
15.000 .د.ب 12.000 .د.ب
DAILY WEAR . GOLD . OCCASION . RING . WOMEN
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GOLD . NECKLACE . OCCASION . PARTY WEAR . WOMEN
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65.000 .د.ب 1 item sold


''COVID-19, are you going to be naughty or nice to us?''
Project Home Page
Main Page
AboutCovid19
What Should We do?
Daily Case Data
Inform Us
Name
Last Name
E-mail adress
Message


My Team!
******Manager******
Card subtitle
Some quick example text to build on the card title and make up the bulk of the card's content.
Document


This should not be in the name property
http://example.com should not be in url property
http://example.com/photo should not be in photo property


Sagittarius Hamil:
Ah, hunter's runes written on the pebbles...? Yes, this is the secret code used by hunters. We must place the pebbles in order...
Place the Hunter's Runes


4.3. Hive Connector
The Hive connector allows querying data stored in a Hive data warehouse. Hive is a combination of three components:
- Data files in varying formats that are typically stored in the Hadoop Distributed File System (HDFS) or in Amazon S3.
- Metadata about how the data files are mapped to schemas and tables. This metadata is stored in a database such as MySQL and is accessed via the Hive metastore service.
- A query language called HiveQL. This query language is executed on a distributed computing framework such as MapReduce or Tez.
Presto only uses the first two components: the data and the metadata. It does not use HiveQL or any part of Hive’s execution environment.
Configuration
Presto includes Hive connectors for multiple versions of Hadoop:
- hive-hadoop1: Apache Hadoop 1.x
- hive-hadoop2: Apache Hadoop 2.x
- hive-cdh4: Cloudera CDH 4
- hive-cdh5: Cloudera CDH 5
Create etc/catalog/hive.properties with the following contents to mount the hive-cdh4 connector as the hive catalog, replacing hive-cdh4 with the proper connector for your version of Hadoop and example.net:9083 with the correct host and port for your Hive metastore Thrift service:
connector.name=hive-cdh4
hive.metastore.uri=thrift://example.net:9083
Multiple Hive Clusters
You can have as many catalogs as you need, so if you have additional Hive clusters, simply add another properties file to etc/catalog with a different name (making sure it ends in .properties). For example, if you name the property file sales.properties, Presto will create a catalog named sales using the configured connector.
HDFS Configuration
For basic setups, Presto configures the HDFS client automatically and does not require any configuration files. In some cases, such as when using federated HDFS or NameNode high availability, it is necessary to specify additional HDFS client options in order to access your HDFS cluster. To do so, add the hive.config.resources property to reference your HDFS config files:
hive.config.resources=/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml
Only specify additional configuration files if necessary for your setup. We also recommend reducing the configuration files to have the minimum set of required properties, as additional properties may cause problems.
The configuration files must exist on all Presto nodes. If you are referencing existing Hadoop config files, make sure to copy them to any Presto nodes that are not running Hadoop.
Configuration Properties
| Property Name | Description | Default |
|---|---|---|
| hive.metastore.uri | The URI(s) of the Hive metastore to connect to using the Thrift protocol. If multiple URIs are provided, the first URI is used by default and the rest of the URIs are fallback metastores. This property is required. Example: thrift://<IP_ADDRESS>:9083 or thrift://<IP_ADDRESS>:9083,thrift://<IP_ADDRESS>:9083 | |
| hive.config.resources | An optional comma-separated list of HDFS configuration files. These files must exist on the machines running Presto. Only specify this if absolutely necessary to access HDFS. Example: /etc/hdfs-site.xml | |
| hive.storage-format | The default file format used when creating new tables. | RCBINARY |
| hive.force-local-scheduling | Force splits to be scheduled on the same node as the Hadoop DataNode process serving the split data. This is useful for installations where Presto is collocated with every DataNode. | false |
| hive.allow-drop-table | Allow the Hive connector to drop tables. | false |
| hive.allow-rename-table | Allow the Hive connector to rename tables. | false |
| hive.respect-table-format | Should new partitions be written using the existing table format or the default Presto format? | true |
| hive.immutable-partitions | Can new data be inserted into existing partitions? | false |
| hive.max-partitions-per-writers | Maximum number of partitions per writer. | 100 |
| hive.s3.sse.enabled | Enable S3 server-side encryption. | false |
Querying Hive Tables
The following table is an example Hive table from the Hive Tutorial. It can be created in Hive (not in Presto) using the following Hive CREATE TABLE command:
hive> CREATE TABLE page_view (
> viewTime INT,
> userid BIGINT,
> page_url STRING,
> referrer_url STRING,
> ip STRING COMMENT 'IP Address of the User')
> COMMENT 'This is the page view table'
> PARTITIONED BY (dt STRING, country STRING)
> STORED AS SEQUENCEFILE;
OK
Time taken: 3.644 seconds
Assuming that this table was created in the web schema in Hive, this table can be described in Presto:
DESCRIBE hive.web.page_view;
Column | Type | Null | Partition Key | Comment
--------------+---------+------+---------------+------------------------
viewtime | bigint | true | false |
userid | bigint | true | false |
page_url | varchar | true | false |
referrer_url | varchar | true | false |
ip | varchar | true | false | IP Address of the User
dt | varchar | true | true |
country | varchar | true | true |
(7 rows)
This table can then be queried in Presto:
SELECT * FROM hive.web.page_view;


LEAP OF FAITH A film review by Jon Webb Copyright 1992 Jon Webb
Steve Martin plays a con man revival preacher who goes through a life-changing experience. Part of the film is a look into technically sophisticated cons -- identifying people in the audience with particular problems so the preacher can call on them as if God told him they needed his help, using wireless microphones to control the show with the aid of a computer -- and part is a story about a man's relationship with, well, God. It's an interesting film in that it doesn't back down an make acceptable to this Godless world its central dramatic moment, which hinges on the difference between real and fake miracles.
This is Steve Martin's first major non-comic role. He does fairly well, especially during the shows, when the moves he developed as part of his comic routines come in handy. But I found him less believable off-stage. He seems too young and hip to be an evangelist. I suppose this contrast is intentional, and maybe even based on studies of the private lives of evangelists. But either the writing or the acting didn't convey what it was supposed to convey, to me.
Another problem with Steve Martin's role is that it is undeveloped. He was supposed to have an affair with the Lolita Davidovich role, but that part was almost completely cut. (She is left with a few scenes, and almost a superfluous presence in the film.) So we're left with an incomplete person.
By contrast, Debra Winger's character is well-developed in much fewer scenes. The only thing left out was parts of the relationship between her and Martin's character. I found her role and acting to be completely credible.
Lukas Haas is good as the crippled brother of Davidovich, and Liam Neeson is good (though his role is depressingly familiar from other movies) as the sheriff.
The direction is great, particularly during the shows. Martin's sermons and the gospel singing move us even though we know they are a con.
I liked this film. But it's a lot more shallow presentation than its subject matter demands. A little more soul-searching by Martin's character, which might have been provided had the relationship with Davidovich been more developed, would have made it much stronger.
-- J
.
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


Last updated: 2021-09-08
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The data this week comes from Sharla Gelfand’s opendatatoronto
R package. Her website has additional details and all sorts of other great datasets.
We are going to explore many of the time series aspects of Homelessness in Toronto, overlay the daily weather, and finally attempt to model and infer what drives the occupancy rate.
suppressPackageStartupMessages({
library(tidyverse)
library(lubridate)
library(hrbrthemes)
extrafont::loadfonts(quiet = TRUE)
library(treemap)
library(tidymodels)
})
source(here::here("code","_common.R"),
verbose = FALSE,
local = knitr::knit_global())
ggplot2::theme_set(theme_jim(base_size = 12))
tt <- tidytuesdayR::tt_load("2020-12-01")
Downloading file 1 of 1: `shelters.csv`
tt
An initial look at the format of the dataset provided:
tt %>%
map(glimpse)
Rows: 115,916
Columns: 13
$ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,~
$ occupancy_date <dttm> 2017-01-01, 2017-01-01, 2017-01-01, 2017-01-01, 2~
$ organization_name <chr> "COSTI Immigrant Services", "Christie Ossington Ne~
$ shelter_name <chr> "COSTI Reception Centre", "Christie Ossington Men'~
$ shelter_address <chr> "100 Lippincott Street", "973 Lansdowne Avenue", "~
$ shelter_city <chr> "Toronto", "Toronto", "Toronto", "Toronto", "Toron~
$ shelter_province <chr> "ON", "ON", "ON", "ON", "ON", "ON", "ON", "ON", "O~
$ shelter_postal_code <chr> "M5S 2P1", "M6H 3Z5", "M6H 3Z5", "M6G 3B1", NA, "M~
$ facility_name <chr> "COSTI Reception Centre", "Christie Ossington Men'~
$ program_name <chr> "COSTI Reception Ctr CITY Program", "Christie Ossi~
$ sector <chr> "Co-ed", "Men", "Men", "Families", "Men", "Familie~
$ occupancy <dbl> 16, 13, 63, 66, 58, 168, 119, 23, 8, 14, 0, 0, 173~
$ capacity <dbl> 16, 17, 63, 70, 60, 160, 150, 28, 0, 40, NA, 0, 25~
$shelters
# A tibble: 115,916 x 13
id occupancy_date organization_name shelter_name shelter_address
<dbl> <dttm> <chr> <chr> <chr>
1 1 2017-01-01 00:00:00 COSTI Immigrant Se~ COSTI Receptio~ 100 Lippincott~
2 2 2017-01-01 00:00:00 Christie Ossington~ Christie Ossin~ 973 Lansdowne ~
3 3 2017-01-01 00:00:00 Christie Ossington~ Christie Ossin~ 973 Lansdowne ~
4 4 2017-01-01 00:00:00 Christie Refugee W~ Christie Refug~ 43 Christie St~
5 5 2017-01-01 00:00:00 City of Toronto Birchmount Res~ 1673 Kingston ~
6 6 2017-01-01 00:00:00 City of Toronto Birkdale Resid~ 1229 Ellesmere~
7 7 2017-01-01 00:00:00 City of Toronto Birkdale Resid~ 1229 Ellesmere~
8 8 2017-01-01 00:00:00 City of Toronto Downsview Dells 1651 Sheppard ~
9 9 2017-01-01 00:00:00 City of Toronto Family Residen~ 4222 Kingston ~
10 10 2017-01-01 00:00:00 City of Toronto Family Residen~ 4222 Kingston ~
# ... with 115,906 more rows, and 8 more variables: shelter_city <chr>,
# shelter_province <chr>, shelter_postal_code <chr>, facility_name <chr>,
# program_name <chr>, sector <chr>, occupancy <dbl>, capacity <dbl>
The dataset appears to be a daily report-out of every facility’s occupancy and capacity.
Importing climate daily stats from “https://toronto.weatherstats.ca/download.html”
weather <- read_csv(here::here("data/weatherstats_toronto_daily.csv")) %>%
select(date, max_temperature, min_temperature, avg_temperature, precipitation)
Some fields in the shelter data need be cast as integers and dates. We will also impute to fill-in missing capacity figures on the daily reports, where possible
shelters <- tt$shelters %>%
mutate(across(occupancy:capacity, as.integer),
occupancy_date = lubridate::as_date(occupancy_date)
) %>%
group_by(facility_name, shelter_name, organization_name) %>%
fill(capacity, .direction = "downup") %>%
ungroup()
The network consists of 33 organizations with 65 named shelters with 114 individual facility locations. Some sites host families, some men, and some women.
shelters %>%
filter(!is.na(capacity)) %>%
group_by(facility_name) %>%
summarize(
max_cap = max(capacity, na.rm = TRUE),
.groups = "drop"
) %>%
slice_max(order_by = max_cap, n = 10) %>%
mutate(facility_name = fct_reorder(facility_name, max_cap)) %>%
ggplot(aes(max_cap, facility_name)) +
geom_col(aes(fill = facility_name),
show.legend = FALSE
) +
labs(
title = "Toronto's Largest Homeless Shelter Facilities",
subtitle = "The Radisson has 902 beds",
x = "Capacity each Night", y = "",
caption = "Data: open.toronto.ca | Visual: jim_gruman"
) +
theme(panel.grid.major.y = element_blank())
Some of the largest shelters appear to be re-purposed hotel facilities.
For reference, let’s sum the maximum bed capacity at all facilities for a perspective of the total network capacity:
shelters %>%
filter(!is.na(capacity)) %>%
group_by(facility_name) %>%
summarize(
max_cap = max(capacity),
.groups = "drop"
) %>%
summarise(Total_Capacity = sum(max_cap, na.rm = TRUE))
# A tibble: 1 x 1
Total_Capacity
<int>
1 9282
There may have been some facility name changes, because the detail by month does not support this grand total.
Typical facilities are under 200 beds
shelters %>%
ggplot() +
geom_histogram(aes(capacity), fill = 2) +
labs(
title = "Capacity Range of Facilities in Toronto's Homeless Shelter System",
subtitle = "Some of the largest shelters appear to be re-purposed hotel facilities.",
y = "Number of Facilities",
x = "Bed Capacity",
caption = "Data: open.toronto.ca | Visual: jim_gruman"
)
shelter_cap <- shelters %>%
filter(capacity > 0) %>%
group_by(organization_name, shelter_name, facility_name) %>%
summarize(
capacity = max(capacity, na.rm = TRUE),
.groups = "drop"
)
treemap(shelter_cap,
index = c(
"organization_name",
"shelter_name",
"facility_name"
),
vSize = "capacity", type = "index",
fontsize.labels = c(9, 6, 4),
fontcolor.labels = c("white", "black", "black"),
bg.labels = c("transparent"),
palette = "Set1",
title = "Capacity of Toronto's Homeless Shelter System"
)
Let’s look at capacity across the network, by month, over time:
shelters %>%
group_by(
month = month(occupancy_date,
abbr = TRUE,
label = TRUE
),
year = factor(year(occupancy_date),
levels = c("2019", "2018", "2017")
),
facility_name
) %>%
summarize(
avg_cap = mean(capacity, na.rm = TRUE),
.groups = "drop_last"
) %>%
summarize(
tot_cap = sum(avg_cap, na.rm = TRUE),
.groups = "drop"
) %>%
ggplot(aes(month, tot_cap, color = year, group = year)) +
geom_line(size = 2) +
scale_y_continuous(label = scales::comma_format()) +
scale_x_discrete(expand = c(0, 0)) +
labs(
title = "Monthly Total Capacity in Toronto's Homeless Shelter System",
subtitle = "Infrastructure has grown to fill the need. Thousands of people are served every night.",
y = NULL, color = NULL,
x = NULL,
caption = "Data: open.toronto.ca | Visual: jim_gruman"
) +
theme(
legend.position = c(0.85, 0.5),
legend.background = element_rect(color = "white")
)
Let’s take a similar approach and sum up the facility occupancy each month
shelters %>%
group_by(
month = month(occupancy_date,
abbr = TRUE,
label = TRUE
),
year = factor(year(occupancy_date),
levels = c("2019", "2018", "2017")
), facility_name
) %>%
summarize(
tot_occupancy = mean(occupancy, na.rm = TRUE),
.groups = "drop_last"
) %>%
summarize(
tot_occupancy = sum(tot_occupancy),
.groups = "drop"
) %>%
ggplot(aes(month, tot_occupancy, color = year, group = year)) +
geom_line(size = 2) +
scale_y_continuous(label = scales::comma_format()) +
scale_x_discrete(expand = c(0, 0)) +
labs(
title = "Monthly Total Occupancy in Toronto's Homeless Shelter System",
y = NULL,
x = NULL, color = NULL,
caption = "Data: open.toronto.ca | Visual: jim_gruman"
) +
theme(
legend.position = c(0.9, 0.5),
legend.background = element_rect(color = "white")
)
Essentially dividing the occupancy by the capacity, this is the aggregated result as a percentage rate:
shelters %>%
group_by(
month = month(occupancy_date,
abbr = TRUE,
label = TRUE
),
year = factor(year(occupancy_date),
levels = c("2019", "2018", "2017")
),
facility_name
) %>%
summarize(
avg_occupancy = mean(occupancy, na.rm = TRUE),
avg_cap = mean(capacity, na.rm = TRUE),
.groups = "drop_last"
) %>%
summarize(
mean_rate = sum(avg_occupancy, na.rm = TRUE) / sum(avg_cap, na.rm = TRUE),
.groups = "drop"
) %>%
filter(year != "2017") %>%
ggplot(aes(month, mean_rate, color = year, group = year)) +
geom_line(size = 2) +
scale_y_continuous(label = scales::percent_format(accuracy = 1)) +
scale_x_discrete(expand = c(0, 0)) +
labs(
title = "Mean Occupancy Rate in Toronto's Homeless Shelter System",
subtitle = "Across the network, for the sum of all facilities",
y = NULL,
x = NULL,
caption = "Data: open.toronto.ca | Visual: jim_gruman"
) +
theme(
legend.position = c(0.9, 0.1),
legend.background = element_rect(color = "white")
)
What about by day of the week ?
shelters %>%
mutate(
wday = wday(occupancy_date,
label = TRUE,
abbr = TRUE
),
year = year(occupancy_date)
) %>%
group_by(year, wday) %>%
summarise(
occupancy = sum(occupancy, na.rm = TRUE),
capacity = sum(capacity, na.rm = TRUE),
rate = occupancy / capacity,
.groups = "drop"
) %>%
filter(year %in% c(2018, 2019)) %>%
ggplot(aes(wday, rate,
color = factor(year,
levels = c("2019", "2018")
),
group = year
)) +
geom_line(size = 2) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(labels = scales::percent_format(accuracy = .1)) +
labs(
title = "Day of Week Average Occupancy Rate across Toronto's Homeless Shelter System", y = NULL, x = NULL,
subtitle = "Average Occupancy Rate",
caption = "Data: open.toronto.ca | Visual: jim_gruman", color = NULL
) +
theme(
legend.position = c(0.9, 0.2),
legend.background = element_rect(color = "white")
)
The aggregate like might be misleading. What is the distribution of rates across the network each month?
shelters %>%
group_by(
month = month(occupancy_date,
abbr = TRUE,
label = TRUE
),
year = factor(year(occupancy_date),
levels = c("2019", "2018", "2017")
),
organization_name, facility_name
) %>%
summarize(
avg_occupancy = mean(occupancy, na.rm = TRUE),
avg_cap = mean(capacity, na.rm = TRUE),
.groups = "drop_last"
) %>%
filter(year != "2017", avg_occupancy > 0) %>%
mutate(
mean_rate = avg_occupancy / avg_cap,
CityShelter = if_else(organization_name == "City of Toronto", "City Shelter", "All Others")
) %>%
ggplot(aes(month, mean_rate, group = month)) +
geom_boxplot(outlier.alpha = 0.001) +
geom_jitter(aes(shape = CityShelter),
alpha = 0.7, size = 2, color = ipsum_pal()(2)[2]
) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_discrete(expand = c(0, 0)) +
labs(
title = "Mean Occupancy Rate for each Facility in Toronto's Homeless Shelter System",
subtitle = "Across the network, each month",
y = NULL,
x = NULL, shape = NULL,
caption = "Data: open.toronto.ca | Visual: jim_gruman"
) +
theme(
legend.position = c(0.9, 0.9),
legend.background = element_rect(color = "white")
)
Some facilities are nearly always over capacity every month. Many report exactly 100% occupancy every month.
How many facilities are managed by each organization?
shelters %>%
select(organization_name, facility_name) %>%
distinct(facility_name, .keep_all = TRUE) %>%
count(organization_name, sort = TRUE) %>%
mutate(organization_name = fct_reorder(organization_name, n)) %>%
ggplot(aes(n, organization_name)) +
geom_col(aes(fill = 4),
show.legend = FALSE
) +
labs(
title = "Toronto Homeless Shelter Organizations",
x = "Number of Facilities", y = "",
caption = "Data: open.toronto.ca | Visual: jim_gruman"
) +
theme(panel.grid.major.y = element_blank())
Let’s join the data frames of the weather with the shelter occupancy data by date
shelterWeather <- shelters %>%
filter(!is.na(occupancy)) %>%
filter(capacity > 0) %>%
filter(occupancy_date >= min(weather$date)) %>%
mutate(rate = occupancy / capacity) %>%
select(
id, organization_name, shelter_name, facility_name,
occupancy_date,
occupancy,
capacity,
rate
) %>%
left_join(weather, by = c("occupancy_date" = "date")) %>%
mutate(
precipitation_deg = case_when(
precipitation == 0 ~ "None",
between(precipitation, 0.01, 10) ~ "Low",
between(precipitation, 10.01, 20) ~ "Medium",
between(precipitation, 20.01, 40) ~ "High",
TRUE ~ "Very High"
),
org = case_when(
organization_name == "City of Toronto" ~ "City of Toronto",
TRUE ~ "All Others"
)
)
What other patterns do we see each day?
Let’s start with the minimum temperature. Note that the weather file earliest data is March of 2018.
shelterWeather %>%
group_by(
day = day(occupancy_date),
month = month(occupancy_date),
year = year(occupancy_date)
) %>%
summarise(
avg_rate = mean(rate, na.rm = TRUE),
avg_temp = mean(min_temperature, na.rm = TRUE),
avg_precipitation = mean(precipitation, na.rm = TRUE),
.groups = "drop"
) %>%
filter(year %in% c(2018, 2019)) %>%
ggplot(aes(avg_temp, avg_rate)) +
geom_point(alpha = 0.5) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
geom_smooth(se = FALSE) +
facet_wrap(~year) +
labs(
title = "Occupancy Rate in Toronto's Shelter System driven by Temperature",
subtitle = "Based on Average Daily Minimum Temperature(Celsius)",
y = "Average Occupancy Rate",
x = "Average Temperature (Celsius)",
caption = "Data: open.toronto.ca | Visual: jim_gruman"
)
shelterWeather %>%
filter(precipitation > 0) %>%
group_by(
day = day(occupancy_date),
month = month(occupancy_date),
year = year(occupancy_date)
) %>%
summarise(
avg_rate = mean(rate, na.rm = TRUE),
avg_temp = mean(min_temperature, na.rm = TRUE),
avg_precipitation = mean(precipitation, na.rm = TRUE),
.groups = "drop"
) %>%
filter(year %in% c(2018, 2019)) %>%
ggplot(aes(avg_precipitation, avg_rate)) +
geom_point(alpha = 0.5) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_log10() +
geom_smooth(se = FALSE) +
facet_wrap(~year) +
labs(
title = "Occupancy Rate in Toronto's Shelter System Driven by Precipitation",
subtitle = "Based on Daily Average Precipitation",
y = "Average Occupancy Rate",
x = "Average Precipitation (mm) on Log Scale",
caption = "Data: open.toronto.ca | Visual: jim_gruman"
)
Lets build a model of Over-Capacity events at each facility with features for the weekday, month, temperature, precipitation, and the two level org identifying the city shelters to make some inferences about what might be drivers of overcrowding.
Setting the breakpoint for over capacity at 0.999 could be argued because every shelter should have 1 empty bed for newcomers. It yields a data set that is largely balanced between response classes.
shelter_df <- shelterWeather %>%
mutate(over_cap = factor(if_else(rate > 0.9999, "Yes", "No"))) %>%
select(
over_cap, occupancy_date,
min_temperature,
precipitation_deg,
org
)
table(shelter_df$over_cap)
No Yes
16828 24493
shelter_rec <-
recipe(over_cap ~ ., data = shelter_df) %>%
step_date(occupancy_date,
features = c("dow", "month")
) %>%
step_holiday(occupancy_date,
holidays = timeDate::listHolidays("CA")
) %>%
step_rm(occupancy_date) %>%
step_dummy(all_nominal(), -all_outcomes()) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors()) %>%
step_zv(all_predictors())
glm_mod <- logistic_reg() %>%
set_engine("glm") %>%
set_mode("classification")
shelter_wflow <- workflow() %>%
add_model(glm_mod) %>%
add_recipe(shelter_rec)
Let’s fit the linear model
shelter_glmfit <-
shelter_wflow %>%
fit(data = shelter_df)
This shelter_glmfit
object has the finalized recipe and fitted model objects inside. Let’s extract the model objects from the workflow.
For over capacity events, what features can we infer may be significant?
shelter_glmfit %>%
tidy(conf.int = TRUE, exponentiate = TRUE) %>%
filter(term != "(Intercept)") %>%
mutate(term = fct_reorder(term, estimate)) %>%
ggplot(aes(estimate, term)) +
geom_point() +
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), color = "gray") +
geom_vline(xintercept = 1, color = "red") +
labs(
title = "Relative risk factors for Shelter Facility Overcapacity",
caption = "Data: open.toronto.ca | Visual: jim_gruman",
x = "Exponentiated estimate for log odds", y = NULL
)
There is some evidence in this dataset that the months of December, (January), February, March, April, May, and June drive over occupancy over capacity. Also, the City of Toronto facilities themselves are over capacity.
My tweet from the archives:
tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1334250855319687175")
Two for #TidyTuesday
— Jim Gruman📚🚵♂⚙ (@jim_gruman) December 2, 2020
1) A treemap of organization/shelter/facility capacity
2) Inference of features that may drive over-capacityhttps://t.co/4ebkC4p5DP pic.twitter.com/rh4U79vGxr
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] yardstick_0.0.8 workflowsets_0.1.0 workflows_0.2.3 tune_0.1.6
[5] rsample_0.1.0 recipes_0.1.16 parsnip_<IP_ADDRESS>0 modeldata_0.1.1
[9] infer_1.0.0 dials_<IP_ADDRESS>00 scales_1.1.1 broom_0.7.9
[13] tidymodels_0.1.3 treemap_2.4-3 hrbrthemes_0.8.0 lubridate_1.7.10
[17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[21] readr_2.0.1 tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5
[25] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 systemfonts_1.0.2
[4] selectr_0.4-2 plyr_1.8.6 igraph_1.2.6
[7] tidytuesdayR_1.0.1 splines_4.1.1 listenv_0.8.0
[10] usethis_2.0.1 gridBase_0.4-7 digest_0.6.27
[13] foreach_1.5.1 htmltools_0.5.2 viridis_0.6.1
[16] fansi_0.5.0 magrittr_2.0.1 tzdb_0.1.2
[19] globals_0.14.0 modelr_0.1.8 gower_0.2.2
[22] extrafont_0.17 vroom_1.5.4 R.utils_2.10.1
[25] extrafontdb_1.0 hardhat_0.1.6 colorspace_2.0-2
[28] rvest_1.0.1 textshaping_0.3.5 haven_2.4.3
[31] xfun_0.25 crayon_1.4.1 jsonlite_1.7.2
[34] survival_3.2-11 iterators_1.0.13 glue_1.4.2
[37] gtable_0.3.0 ipred_0.9-11 R.cache_0.15.0
[40] tweetrmd_0.0.9 Rttf2pt1_1.3.9 future.apply_1.8.1
[43] DBI_1.1.1 Rcpp_1.0.7 viridisLite_0.4.0
[46] xtable_1.8-4 bit_4.0.4 GPfit_1.0-8
[49] lava_1.6.10 prodlim_2019.11.13 httr_1.4.2
[52] RColorBrewer_1.1-2 ellipsis_0.3.2 farver_2.1.0
[55] R.methodsS3_1.8.1 pkgconfig_2.0.3 nnet_7.3-16
[58] sass_0.4.0 dbplyr_2.1.1 utf8_1.2.2
[61] here_1.0.1 labeling_0.4.2 tidyselect_1.1.1
[64] rlang_0.4.11 DiceDesign_1.9 later_1.3.0
[67] munsell_0.5.0 cellranger_1.1.0 tools_4.1.1
[70] cachem_1.0.6 cli_3.0.1 generics_0.1.0
[73] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
[76] ragg_1.1.3 bit64_4.0.5 knitr_1.33
[79] fs_1.5.0 nlme_3.1-152 future_1.22.1
[82] whisker_0.4 mime_0.11 R.oo_1.24.0
[85] xml2_1.3.2 compiler_4.1.1 rstudioapi_0.13
[88] curl_4.3.2 reprex_2.0.1 lhs_1.1.1
[91] bslib_0.3.0 stringi_1.7.4 highr_0.9
[94] gdtools_0.2.3 lattice_0.20-44 Matrix_1.3-4
[97] styler_1.5.1 conflicted_1.0.4 vctrs_0.3.8
[100] pillar_1.6.2 lifecycle_1.0.0 furrr_0.2.3
[103] jquerylib_0.1.4 data.table_1.14.0 httpuv_1.6.2
[106] R6_2.5.1 promises_<IP_ADDRESS> gridExtra_2.3
[109] parallelly_1.27.0 codetools_0.2-18 MASS_7.3-54
[112] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2
[115] mgcv_1.8-36 parallel_4.1.1 hms_1.1.0
[118] grid_4.1.1 rpart_4.1-15 timeDate_3043.102
[121] class_7.3-19 rmarkdown_2.10 git2r_0.28.0
[124] pROC_1.18.0 shiny_1.6.0


Code¶
Module with classes and methods to perform implicit regional modelling based on the potential field method. Tested on Ubuntu 14
Created on 10/10 /2016
@author: Miguel de la Varga
this is only to test git 3
-
class
GeMpy_core.
DataManagement
(extent, resolution=[50, 50, 50], path_i=None, path_f=None, **kwargs)[source]¶ Class to import the raw data of the model and set data classifications into formations and series
Parameters: - extent (list) – [x_min, x_max, y_min, y_max, z_min, z_max]
- Resolution ((Optional[list]) – [nx, ny, nz]. Defaults to 50
- path_i – Path to the data bases of interfaces. Default os.getcwd(),
- path_f – Path to the data bases of foliations. Default os.getcwd()
-
extent
¶ list – [x_min, x_max, y_min, y_max, z_min, z_max]
-
resolution
¶ (Optional[list] – [nx, ny, nz]
-
Foliations
¶ pandas.core.frame.DataFrame – Pandas data frame with the foliations data
-
Interfaces
¶ pandas.core.frame.DataFrame – Pandas data frame with the interfaces data
-
formations
¶ numpy.ndarray – Dictionary that contains the name of the formations
-
series
¶ pandas.core.frame.DataFrame – Pandas data frame which contains every formation within each series
-
calculate_gradient
()[source]¶ Calculate the gradient vector of module 1 given dip and azimuth to be able to plot the foliations
Returns: extra columns with components xyz of the unity vector. Return type: self.Foliations
-
static
load_data_csv
(data_type, path='/home/bl3/PycharmProjects/GeMpy', **kwargs)[source]¶ Method to load either interface or foliations data csv files. Normally this is in which GeoModeller exports it
Parameters: Returns: Data frame with the raw data
Return type: pandas.core.frame.DataFrame
-
set_series
(series_distribution=None, order=None)[source]¶ Method to define the different series of the project
Parameters: - series_distribution (dict) – with the name of the serie as key and the name of the formations as values.
- order (Optional[list]) – order of the series by default takes the dictionary keys which until python 3.6 are random. This is important to set the erosion relations between the different series
Returns: A pandas DataFrame with the series and formations relations self.Interfaces: one extra column with the given series self.Foliations: one extra column with the given series
Return type: self.series
-
class
GeMpy_core.
GeMpy
(project_name='Default')[source]¶ Parent class that is used as container for the other classes
Parameters: project_name (str) – Global name of the project -
project_name
¶ str – Global name of the project
-
Data
¶ GeMpy_core.DataManagement – Object that encapsulate all raw data of the project
-
Grid
¶ GeMpy_core.Grid – Object that contain different grids
-
Interpolator
¶ GeMpy_core.Interpolator – Object to perform the potential field method
-
Plot
¶ GeMpy_core.PlotData – Object to visualize data and results
-
create_grid
(extent=None, resolution=None, grid_type='regular_3D', **kwargs)[source]¶ Method to initialize the class grid. So far is really simple and only has the regular grid type
Parameters: - grid_type (str) – regular_3D or regular_2D (I am not even sure if regular 2D still working)
- **kwargs – Arbitrary keyword arguments.
Returns: self.Grid – Object that contain different grids
Return type:
-
import_data
(extent, resolution=[50, 50, 50], **kwargs)[source]¶ Method to initialize the class data. Calling this function some of the data has to be provided (TODO give to everything a default).
Parameters: - extent (list or array) – [x_min, x_max, y_min, y_max, z_min, z_max]. Extent for the visualization of data and default of for the Grid class.
- resolution (list or array) – [nx, ny, nz]. Resolution for the visualization of data and default of for the Grid class.
- **kwargs – Arbitrary keyword arguments.
Keyword Arguments: - Resolution ((Optional[list]) – [nx, ny, nz]. Defaults to 50
- path_i – Path to the data bases of interfaces. Default os.getcwd(),
- path_f – Path to the data bases of foliations. Default os.getcwd()
Returns: self.Data – Object that encapsulate all raw data of the project self.Plot(GeMpy_core.PlotData): Object to visualize data and results
Return type:
-
set_interpolator
(*args, **kwargs)[source]¶ Method to initialize the class interpolator. All the constant parameters for the interpolation can be passed as args, otherwise they will take the default value (TODO: documentation of the dafault values)
Parameters: - *args – Variable length argument list
- **kwargs – Arbitrary keyword arguments.
Keyword Arguments: - range_var – Range of the variogram. Default None
- c_o – Covariance at 0. Default None
- nugget_effect – Nugget effect of the gradients. Default 0.01
- u_grade – Grade of the polynomial used in the universal part of the Kriging. Default 2
- rescaling_factor – Magic factor that multiplies the covariances). Default 2
Returns: self.Interpolator – Object to perform the potential field method self.Plot(GeMpy_core.PlotData): Object to visualize data and results. It gets updated.
Return type:
-
-
class
GeMpy_core.
Grid
(extent, resolution, grid_type='regular_3D')[source]¶ Class with set of functions to generate grids
Parameters:
-
class
GeMpy_core.
Interpolator
(_data, _grid, *args, **kwargs)[source]¶ Class which contain all needed methods to perform potential field implicit modelling in theano
Parameters: - _data (GeMpy_core.DataManagement) – All values of a DataManagement object
- _grid (GeMpy_core.Grid) – A grid object
- **kwargs – Arbitrary keyword arguments.
Keyword Arguments: verbose (int) – Level of verbosity during the execution of the functions (up to 5). Default 0
-
compute_block_model
(series_number='all', verbose=0)[source]¶ Method to compute the block model for the given series using data provided in the DataManagement object
Parameters: - series_number (str or int) – series to interpolate
- verbose (int) – level of verbosity during the computation
Returns: self.block – 3D block with the corresponding formations
Return type: theano shared[numpy.ndarray]
-
compute_potential_field
(series_name=0, verbose=0)[source]¶ Compute an individual potential field.
Parameters: - series_name (str or int) – name or number of series to interpolate
- verbose (int) – int level of verbosity during the computation
Returns: 3D array with the potential field
Return type: numpy.ndarray
Module with classes and methods to visualized structural geology data and potential fields of the regional modelling based on the potential field method. Tested on Ubuntu 14
Created on 23/09/2016 somehting @author: Miguel de la Varga
-
class
Visualization.
PlotData
(_data, block=None, **kwargs)[source]¶ Class to make the different plot related with GeMpy
Parameters: - _data (GeMpy_core.DataManagement) – All values of a DataManagement object
- block (theano shared) – 3D array containing the lithology block
- **kwargs – Arbitrary keyword arguments.
Keyword Arguments: - potential_field (numpy.ndarray) – 3D array containing a individual potential field
- verbose (int) – Level of verbosity during the execution of the functions (up to 5). Default 0
-
plot_block_section
(cell_number=13, direction='y', interpolation='none', **kwargs)[source]¶ Plot a section of the block model
Parameters: - cell_number (int) – position of the array to plot
- direction (str) – xyz. Caartesian direction to be plotted interpolation(str): Type of interpolation of plt.imshow. Default ‘none’. Acceptable values are ‘none’ ,’nearest’, ‘bilinear’, ‘bicubic’, ‘spline16’, ‘spline36’, ‘hanning’, ‘hamming’, ‘hermite’, ‘kaiser’, ‘quadric’, ‘catrom’, ‘gaussian’, ‘bessel’, ‘mitchell’, ‘sinc’, ‘lanczos’
- **kwargs – imshow keywargs
Returns: Block plot
-
plot_data
(direction='y', series='all', **kwargs)[source]¶ Plot the projecton of the raw data (interfaces and foliations) in 2D following a specific directions
Parameters: Returns: Data plot


Stephanie Collins
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© 2018 Stephanie Collins


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{% extends "base.html" %} {% load static %} {% block title %}Migrate - {{ block.super }}{% endblock %} {% block analytics %} analytics.page("Setup"); {% endblock %} {% block bodyclass %}
{% endblock %} {% block content %}Django changed the way that the urls.py was formatted between 1.7 and 1.8. It's pretty simple to update though -- just update the urlpatterns line and make sure to change the parenthesis to square brackets.
urlpatterns = patterns('',
url(r'^$', 'collection.views.index', name='home'),
url(r'^about/$',
TemplateView.as_view(template_name='about.html'),
name='about'),
)
from collection import views
urlpatterns = [
url(r'^$', views.index, name='home'),
url(r'^about/$',
TemplateView.as_view(template_name='about.html'),
name='about'),
]
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