- Pacman - Class in gridworld.actor
-
Extends MovableActor.
- Pacman() - Constructor for class gridworld.actor.Pacman
-
Default Constructor
- PacmanClosed - Class in gridworld.actor
-
This class is only here for the closed pacman image
- PacmanClosed() - Constructor for class gridworld.actor.PacmanClosed
-
- PacmanTest - Class in JUnitTests
-
This class tests the Pacman class.
- PacmanTest() - Constructor for class JUnitTests.PacmanTest
-
- pacmanTest() - Method in class JUnitTests.PacmanTest
-
Tests the constructor for Pacman.
- PacmanWorld - Class in gridworld.world
-
- PacmanWorld(Grid<Actor>) - Constructor for class gridworld.world.PacmanWorld
-
Creates a new PacmanWorld from a given grid
- PacmanWorldTest - Class in JUnitTests
-
This class tests the class PacmanWorld
- PacmanWorldTest() - Constructor for class JUnitTests.PacmanWorldTest
-
- paintCenteredText(Graphics2D, String, Rectangle, double, Color) - Method in class gridworld.gui.DefaultDisplay
-
Paint a horizontally and vertically-centered text string.
- paintComponent(Graphics) - Method in class gridworld.gui.GridPanel
-
Paint this component.
- paintIcon(Component, Graphics, int, int) - Method in class gridworld.gui.ColorEditor.RandomColorIcon
-
- paintIcon(Component, Graphics, int, int) - Method in class gridworld.gui.ColorEditor.SolidColorIcon
-
- paintIcon(Component, Graphics, int, int) - Method in class gridworld.gui.DisplayMap.DisplayIcon
-
- panBy(int, int) - Method in class gridworld.gui.GridPanel
-
- panBy(int, int) - Method in interface gridworld.gui.PseudoInfiniteViewport.Pannable
-
- panel - Variable in class gridworld.gui.LocationEditor
-
- panPoint - Variable in class gridworld.gui.PseudoInfiniteViewport
-
- parent - Variable in class gridworld.gui.GridPanel
-
- parent - Variable in class gridworld.gui.MenuMaker
-
- parentFrame - Variable in class gridworld.gui.GUIController
-
- path - Variable in class gridworld.actor.Casper
-
The path of the Ghost in points.
- path - Variable in class gridworld.actor.Dijkstra
-
the path of nodes leading upto a destination.
- Pellet - Class in gridworld.actor
-
A pellet can be eaten only by Pacman and increases the score
- Pellet() - Constructor for class gridworld.actor.Pellet
-
Creates a new Pellet, sets the color to yello
- pellet() - Method in class JUnitTests.EdibleTests
-
- Pineapple - Class in gridworld.actor
-
A pineApple is worth 500 points, in the game where you are tying to get the max points
- Pineapple() - Constructor for class gridworld.actor.Pineapple
-
Default contructor
- pineapple() - Method in class JUnitTests.EdibleTests
-
- Pipe - Class in gridworld.actor
-
Pipe is just for the image, all actual work is done in the pacman moveTo method
- Pipe() - Constructor for class gridworld.actor.Pipe
-
Default constructor
- Pipe() - Method in class JUnitTests.PipeTest
-
Tests the Pipe constructor.
- PipeTest - Class in JUnitTests
-
This class tests the Pipe class.
- PipeTest() - Constructor for class JUnitTests.PipeTest
-
- Point - Class in gridworld.actor
-
Created because Java Point Class in double.
- Point(int, int) - Constructor for class gridworld.actor.Point
-
Init x to x and y to y
- point() - Method in class JUnitTests.PointTest
-
- pointForLocation(Location) - Method in class gridworld.gui.GridPanel
-
- PointTest - Class in JUnitTests
-
- PointTest() - Constructor for class JUnitTests.PointTest
-
- PowerPellet - Class in gridworld.actor
-
When eaten by pacman, makes the Ghosts idle
- PowerPellet() - Constructor for class gridworld.actor.PowerPellet
-
Default constructor, sets color to blue
- powerPellet() - Method in class JUnitTests.EdibleTests
-
- preferredLayoutSize(Container) - Method in class gridworld.gui.FormLayout
-
- prevActor - Variable in class gridworld.actor.Casper
-
The previous Actor.
- prevActor - Variable in class gridworld.actor.Dijkstra
-
The previous Actor
- prevActor - Variable in class gridworld.actor.Inky
-
The previous Actor
- prevLoc - Variable in class gridworld.actor.Casper
-
The previous Location.
- prevLoc - Variable in class gridworld.actor.Dijkstra
-
The next Actor
- prevLoc - Variable in class gridworld.actor.Inky
-
The next Actor
- print2DArray(int[][], String) - Static method in class project.Mechanics
-
Prints a 2d array with no spaces
- project - package project
-
- PROMPT_STEPS - Static variable in class gridworld.gui.GUIController
-
- PropertySheet - Class in gridworld.gui
-
- PropertySheet(Class[], Object[]) - Constructor for class gridworld.gui.PropertySheet
-
Constructs a property sheet that shows the editable properties of a given
object.
- PropertySheet.StringEditor - Class in gridworld.gui
-
- PseudoInfiniteViewport - Class in gridworld.gui
-
A PseudoInfiniteViewport
is a JViewport
subclass that translates scroll actions into pan actions across an unbounded
view.
- PseudoInfiniteViewport(JScrollPane) - Constructor for class gridworld.gui.PseudoInfiniteViewport
-
Construct a new PseudoInfiniteViewport object for the given scrollpane.
- PseudoInfiniteViewport.Pannable - Interface in gridworld.gui
-
The Pannable interface contains those methods the view installed in a
PseudoInfiniteViewport needs to support to enable panning behavior along
with scrolling.
- put(Location, E) - Method in class gridworld.grid.BoundedGrid
-
- put(Location, E) - Method in interface gridworld.grid.Grid
-
Puts an object at a given location in this grid.
- put(Location, E) - Method in class gridworld.grid.UnboundedGrid
-
- putSelfInGrid(Grid<Actor>, Location) - Method in class gridworld.actor.Actor
-
Puts this actor into a grid.


In this editor area, type the script to be executed in the console after its start-up and initialization. Note
that syntax highlighting, code completion, import assistance, documentation, inspections and quick fixes are
available in this editor:
By default, this area contains the following script, which causes printing out a header information and extending
the system paths:
import sys; print('Python %s on %s' % (sys.version, sys.platform)) sys.path.extend([WORKING_DIR_AND_PYTHON_PATHS])
The
WORKING_DIR_AND_PYTHON_PATHS
variable is hardcoded in IntelliJ IDEA. It displays two paths: the
project root and the working directory. The project root is the top-level directory of your project. The working
directory may coincidence with the project root if not explicitly defined in the Working directory
field.
If you want to omit such a printout, delete this script.


Thessaloniki
Read about where I travelled and let me share with you my travel tips & tricks for the best trip experience.
The environment is in us, not outside of us. The trees are our lungs, the rivers our bloodstream. We are all interconnected, and what you do to the environment ultimately you do to yourself.
Ian Samerhalerd
Travel tips
Read about where I travelled and let me share with you my travel tips & tricks for the best trip experience.
Destinations
I will provide guide to the best destinations in Serbia and Greece
Ecology
Be a responsible traveler! Build enviromental and cultural awareness and respect and also financial benefits of eco tourism.


The field manager reads entries from a field table and stores this information along with the type of field it belongs to. This allows the component models to query the field manager to see if non-default methods of operation are desired. In essence the field table is a powerful type of namelist. Default values can be provided for all the fields through a namelist, individual fields can be modified through the field table however.
"tracer","atmos_mod","sphum"/ "tracer","atmos_mod","sf6" "longname","sulf_hex" "advection_scheme_horiz","2nd_order" "Profile_type","Fixed","surface_value = 0.0E+00"/In this example we have two field entries.
mpp_mod
mpp_io_mod
fms_mod
use field_manager_mod [, only: field_manager_init,
field_manager_end,
find_field_index,
get_field_info,
get_field_method,
get_field_methods,
parse ]
| Name | Type | Value | Units | Description |
|---|---|---|---|---|
| NUM_MODELS | integer, parameter | 5 | --- | Number of models. |
| module_is_initialized | logical | .false. | --- | Field manager is initialized. |
| MODEL_ATMOS | integer, parameter | 1 | --- | Atmospheric model. |
| MODEL_OCEAN | integer, parameter | 2 | --- | Ocean model. |
| MODEL_LAND | integer, parameter | 3 | --- | Land model. |
| MODEL_ICE | integer, parameter | 4 | --- | Ice model. |
call field_manager_init (nfields, table_name)
| table_name | The name of the field table. The default name is field_table. [character, optional, dimension(len=128)] |
| nfields | The number of fields. [integer] |
call field_manager_end
value= find_field_index ( model, field_name )
| model | The number indicating which model is used. [integer] |
call get_field_info ( n,fld_type,fld_name,model,num_methods )
| n | The field index. [integer] |
| fld_type | The field type. [character, dimension(*)] |
| fld_name | The name of the field. [character, dimension(*)] |
| model | The number indicating which model is used. [integer] |
| num_methods | The number of methods. [integer] |
call get_field_method ( n,m,method )
| n | The field index. [integer] |
| m | The method index. [integer] |
call get_field_methods ( n,methods )
| n | The field index. [integer] |
number = parse (text, label, value)
| text | The text string from which the values will be parsed. [character(len=*)] |
| label | A label which describes the values being decoded. [character(len=*)] |
| value | The value or values that have been decoded. [integer, real, character(len=*)] |
| parse | The number of values that have been decoded. This allows
a user to define a large array and fill it partially with
values from a list. This should be the size of the value array. [integer] |


Property-Pro-Lite
Login
Create Your Account
Email
Firstname
Lastname
Phone Number
Your Address
Password
Already have an account?
Log in
Sign Up


6
Neo
Your Documentation Tool
Single-Sourcing
Content Management
Teamwork
Cloud Authoring
Customer Feedback
SEO Friendly
More posts...
Back to blog post
Off-Canvas Panel Demo
Click the hamburger button at the top left corner of the screen to invoke the menu.


Menu
About us
Company
Brief of JV partners
Leadership Team
Advisory Board
Vision and Values
What we do
Advisory Services
Crops we grow
Certification
Our Clients
Media Update
In news
Photo Gallery
Careers
FAQ
Contact Us
Contact us Form
Our Offices
Gallery
find your gallery art
Next
X
Prev


pymatgen.io.lobster module¶
-
class
Charge
(filename='CHARGE.lobster')[source]¶ Bases:
object
Class to read CHARGE files generated by LOBSTER :param filename: filename for the CHARGE file, typically “CHARGE.lobster”
-
class
Cohpcar
(are_coops=False, filename=None)[source]¶ Bases:
object
Class to read COHPCAR/COOPCAR files generated by LOBSTER.
- Parameters
are_coops – Determines if the file is a list of COHPs or COOPs. Default is False for COHPs.
filename – Name of the COHPCAR file. If it is None, the default file name will be chosen, depending on the value of are_coops.
-
class
Doscar
(doscar='DOSCAR.lobster', vasprun='vasprun.xml', dftprogram='Vasp')[source]¶ Bases:
object
Class to deal with Lobster’s projected DOS and local projected DOS. The beforehand quantum-chemical calculation was performed with VASP
- Parameters
doscar – DOSCAR filename, typically “DOSCAR.lobster”
vasprun – vasprun filename, typically “vasprun.xml”
dftprogram – so far only “vasp” is implemented
-
completedos
¶ LobsterCompleteDos Object
-
pdos
¶ -
List of Dict including numpy arrays with pdos. Access as pdos[atomindex]['orbitalstring']['Spin.up/Spin.down']
-
tdos
¶ -
Dos Object of the total density of states
-
energies
¶ -
numpy array of the energies at which the DOS was calculated (in eV, relative to Efermi)
-
tdensities
¶ -
tdensities[Spin.up]: numpy array of the total density of states for the Spin.up contribution at each of the energies
-
tdensities[Spin.down]: numpy array of the total density of states for the Spin.down contribution at each of the energies
if is_spin_polarized=False: tdensities[Spin.up]: numpy array of the total density of states
-
is_spin_polarized
¶ -
Boolean. Tells if the system is spin polarized
-
completedos
-
energies
-
is_spin_polarized
-
pdos
-
tdensities
-
tdos
-
class
Fatband
(filenames='.', vasprun='vasprun.xml', Kpointsfile='KPOINTS')[source]¶ Bases:
object
reads in FATBAND_x_y.lobster files :param filenames: can be a list of file names or a path to a folder folder from which all “FATBAND_*” files will be read :type filenames: list or string :param vasprun: corresponding vasprun file :param Kpointsfile: KPOINTS file for bandstructure calculation, typically “KPOINTS” :param .. attribute: efermi :param efermi that was read in from vasprun.xml: :param .. attribute: eigenvals :param {Spin.up: [][],Spin.down:[][]}, the first index of the array
[][] refers to the band and the second to the index of the kpoint. The kpoints are ordered according to the order of the kpoints array. If the band structure is not spin polarized, we only store one data set under Spin.up.
- Parameters
attribute (.) – is_spinpolarized
that tells you whether this was a spin-polarized calculation (Boolean) –
attribute –
kpoints_array list of kpoint as numpy arrays, in frac_coords of the
given lattice by default
attribute –
label_dict
- (dict) of {} this link a kpoint (in frac coords or
cartesian coordinates depending on the coords).
lattice object of reciprocal lattice as read in from vasprun.xml
number of bands used in the calculation
dict of orbital projections as {spin: array of dict}. The indices of the array are [band_index, kpoint_index]. The dict is then built the following way: {“string of element”: “string of orbital as read in from FATBAND file”} If the band structure is not spin polarized, we only store one data set under Spin.up.
structure read in from vasprun.xml
-
class
Icohplist
(are_coops=False, filename=None)[source]¶ Bases:
object
Class to read ICOHPLIST/ICOOPLIST files generated by LOBSTER.
- Parameters
are_coops – Determines if the file is a list of ICOHPs or ICOOPs. Defaults to False for ICOHPs.
filename – Name of the ICOHPLIST file. If it is None, the default file name will be chosen, depending on the value of are_coops.
-
icohpcollection
¶ IcohpCollection object
- Type
Returns
-
icohplist
¶ icohplist compatible with older version of this class
- Type
Returns
-
class
Lobsterout
(filename='lobsterout')[source]¶ Bases:
object
Class to read in the lobsterout and evaluate the spilling, save the basis, save warnings, save infos :param filename: filename of lobsterout :param .. attribute: basis_functions
list of basis functions that were used in lobster run as strings
- Parameters
attribute (.) – basis_type list of basis type that were used in lobster run as strings
attribute – chargespilling list of charge spilling (first entry: result for spin 1, second entry: result for spin 2 or not present)
attribute – dftprogram string representing the dft program used for the calculation of the wave function
attribute – elements list of strings of elements that were present in lobster calculation
attribute – has_CHARGE Boolean, indicates that CHARGE.lobster is present
attribute – has_COHPCAR Boolean, indicates that COHPCAR.lobster and ICOHPLIST.lobster are present
attribute – has_COOPCAR Boolean, indicates that COOPCAR.lobster and ICOOPLIST.lobster are present
attribute – has_DOSCAR Boolean, indicates that DOSCAR.lobster is present
attribute – has_Projection Boolean, indcates that projectionData.lobster is present
attribute – has_bandoverlaps Boolean, indcates that bandOverlaps.lobster is present
attribute – has_density_of_energies Boolean, indicates that DensityOfEnergy.lobster is present
attribute – has_fatbands Boolean, indicates that fatband calculation was performed
attribute – has_grosspopulation Boolean, indicates that GROSSPOP.lobster is present
attribute – info_lines string with additional infos on the run
attribute – info_orthonormalization string with infos on orthonormalization
attribute – is_restart_from_projection Boolean that indicates that calculation was restartet from existing projection file
attribute – lobster_version string that indicates Lobster version
attribute – number_of_spins Integer indicating the number of spins
attribute – number_of_threads integer that indicates how many threads were used
attribute – timing dict with infos on timing
attribute – totalspilling list of values indicating the total spilling for spin channel 1 (and spin channel 2)
attribute – warninglines string with all warnings


Home
SignUp
Login
SETTING
A Photo A Day
Mapping your life
Place You Have been to
Quantity
San Diego
20
View
New York
10
View


Sr no. |
book title |
publisher |
Book image |
Book price |
add to cart |
remove from cart |
1 |
Half GirlFriend |
Chetan Bhagat |
$ 2.55 |
|||
2 |
3 mistakes of my life |
Chetan Bhagat |
$ 6.78 |
|||
3 |
Mahabharata |
kisari-mohan-ganguli |
$ 8.25 |
|||
4 |
All about love |
Bell Hooks |
$ 9.25 |
|||
5 |
2666 |
Roberto Bolano |
$ 8.75 |


Conventions Used in This Toolbox¶
The idea is to make the regular use cases easy and the hard ones possible.
Common Vocabulary¶
A common vocabulary is important when different parties talk about complicated concepts to make sure everyone fully understands what the other is talking about.
Dimensions and Axes¶
Talking about dimensions in context of numpy arrays can be a bit confusing
especially when coming from a mathematical background. We use the following
convention: A point in the 3D Space (x, y, z)
is an array with one dimension
of length 3. An array of n
such points would be an array with two
dimensions, the first axis (dimension) with the length of n
, and the exond
axis with the length of 3
.
>>> import numpy as np
>>> a = np.arange(20)
>>> a
array([ 0, 1, 2, 3, ..., 17, 18, 19])
>>> a.ndim
1 # one dimension (or axis)
>>> a.shape
(20,) # of lenght 20
>>> a = a.reshape(4, 5)
>>> a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]])
>>> a.ndim
2 # two dimensions
>>> a.shape
(4, 5) # of length 4 and 5
Data Structures¶
Wyrm uses one data structure wyrm.types.Data
to wrap the different data
during the processing. Data is very generic and thus flexible on purpose. It
can contain all kinds of data and tries to stay out of the way. Data is also
self-explaining in a sense that it does not only contain the raw data but also
meta-data about the axes, like names and units and the values of the axes (for a
complete overview on Data please refer to the documentation).
Most of Wyrm’s toolbox methods are expecting a Data
object as an argument.
Since Data
is very flexible and does not impose for example the order of the
axes, it is important to abide a certain convention:
- Continuous Data
- Continuous Data is usually (but not limited to) raw EEG data. It has two
axes:
[time, channel]
. Channel should always be the last axis, time the second last. - Epoched Data
Epoched Data is often Continuous Data split into several equally long chunks (epochs). Each epoch usually belongs to a class. The axes in this case are
[class, time, channel]
. Class should always be the first axis, time the second last and channel the last one. This is consistent with Continuos Data.Epoched Data can also contain different data than (e.g. data in the frequency domain), but the class axis should always be the first.
- Feature Vector
- In the later steps of the data processing, one often deals no more with
continuous data but with feature vectors. Feature Vectors are similar to
Epoched data, since each vector usually belongs to a class. Thus the
axes are:
[class, fv]
.
You are free to follow the convention or not. If you do, most methods will work out of the box – off course you still have to think if a certain method makes sense on the current object at hand.
If you create non-conventional Data
objects, the methods will still work (if
they make sense), but you have to provide the methods an extra parameter, with
the index of the axis (or axes).
Associating Samples to Timestamps¶
The time marks the time at the beginning of the sample.
Example:
Time [ms] 0 10 20 30 ...
| | | |
Sample [#] [ 0 ][ 1 ][ 2 ][ 3 ]
The interpretation is that sample 0 contains the data from [0, 10)
, sample 1
contains [10, 20)
, and so on.
Intervals¶
Whenever you encounter a time interval with a start and stop value, the
convention is [start, stop)
(i.e. start is included, stop is excluded).
Example:
Time [ms] 0 10 20 30 ...
| | | |
Sample [#] [ 0 ][ 1 ][ 2 ][ 3 ]
Interval (0, 30) returns the samples 0, 1, 2


new SMemory()
SMemory associates a unique object with every JavaScript object, function, and activation frame during an execution. (Note that a shadow object cannot associated with primitive values, undefined, or null.) A shadow object can be used to store meta-information about an object (e.g. the location at which the object was created). Each shadow object has an unique id, which can be treated as the logical address of the corresponding JavaScript object or activation frame.
To use smemory, one must include --analysis $JALANGI_HOME/src/js/sample_analyses/ChainedAnalyses.js --analysis $JALANGI_HOME/src/js/runtime/SMemory.js as the first two --analysis options during an analysis. smemory can be accessed via J$.smemory or sandbox.smemory. The smemory object defines several methods. Those methods can be used to obtain the shadow memory for an object property or a program variable, respectively. getShadowObject should be used in getFieldPre, putFieldPre, and literal callbacks. (In a literal callback with an object literal, one must go over all the own properties of the literal object to suitably update shadow object.) getShadowFrame should only be used in declare, read, and write callbacks.
Methods
-
getActualObjectOrFunctionFromShadowObjectOrFrame(obj){*}
-
Given a shadow object, it returns the actual object. Given a shadow frame, it returns the function whose invocation created the frame.
Name Type Description obj
Returns:
Type Description * -
getFrame(name){Object}
-
This method returns the shadow object associated with the activation frame that contains the variable "name". To get the current activation frame, call J$.smemory.getFrame("this");
Name Type Description name
Name of the variable whose owner activation frame's shadow object we want to retrieve Returns:
Type Description Object - The shadow object of the activation frame owning the variable. -
getIDFromShadowObjectOrFrame(obj){number|undefined}
-
Given a shadow object or frame, it returns the unique id of the shadow object or frame. It returns undefined, if obj is undefined, null, or not a valid shadow object.
Name Type Description obj
Returns:
Type Description number | undefined -
getShadowFrame(name){Object}
-
This method returns the shadow object associated with the activation frame that contains the variable "name".
Name Type Description name
Name of the variable whose owner activation frame's shadow object we want to retrieve Returns:
Type Description Object - The shadow object of the activation frame owning the variable. -
getShadowObject(obj, prop, isGetField){Object}
-
This method should be called on a base object and a property name to retrieve the shadow object associated with the object that actually owns the property. When the program performs a putField operation, the third argument should be false and the returned object is the shadow object associated with the base object. When the program performs a getField operation, the third argument should be true and the returned object is the shadow object associated with the object in the prototype chain (or the base object) which owns the property. For a getField operation, the returned value is undefined if none of the objects in the prototype chain owns the property. The return value is an object with two properties: "owner" points to the shadow object and "isProperty" indicates if the property is a concrete property of the object or if the property denotes a getter/setter.
Name Type Description obj
The base object prop
The property name isGetField
True if the property access is a getField operation Returns:
Type Description Object -
getShadowObjectOfObject(val){Object|undefined}
-
This method returns the shadow object associated with the argument. If the argument cannot be associated with a shadow object, the function returns undefined.
Name Type Description val
The object whose shadow object the function returns Returns:
Type Description Object | undefined - Returns the shadow object associated with val. Return undefined if there is no shadow object assocaited with val Note that a shadow object cannot be associated with a primitive value: number, string, boolean, undefined, or null.


Toggle navigation
ENTREVISTA
ENTRE
VIS
T
A
(LOOKUP)
Enter a username to look up answers.
Name
Search
Displaying User Answers
No User Found


--- layout: post permalink: troohoops.html Tags: Array NameOfCoupon: Troo Hoops Cash Back Summary: See our Troo Hoops Coupon Code, Troo Hoops Rebate, Troo Hoops Promo Code, Troo Hoops Discount, Troo Hoops Coupon Cash Back Rebate here. MetaKeywords: Troo Hoops Coupon Code, Troo Hoops Rebate, Troo Hoops Promo Code, Troo Hoops Cash Back Rebate, Troo Hoops Discount, Troo Hoops Coupon Image: images/24237.jpg LinkURL: http://www.shareasale.com/r.cfm?b=194165&u=412002&m=24237 affiliate_url_link: http://www.icouponblog.com/jane.php?id=990 thesis_readmore: See the Troo Hoops Coupon thesis_thumb: http://www.icouponblog.com/tn.php?merchantURL=troohoops.com Title: Save 5% - Troo Hoops Coupon Code for troohoops.com. Troo Hoops Rebate & Discount thesis_description: Our Troo Hoops Discount Coupon Code for troohoops.com. We have the best Troo Hoops Rebate anywhere. thesis_keywords: Troo Hoops, Troo Hoops Coupon Code, Troo Hoops Discount, Troo Hoops Rebate product_name: Troo Hoops hop_url: http://www.icouponblog.com/hop.php?id=990 ---
Troo Hoop yourself to fitness. Troo Hoops is the world’s leading manufacturer of collapsible hoops. Exercise, dance, lose weight or simply stay fit with Troo Hoops which is the fun way of getting there. All hoops are handmade from heavy duty polyethylene tubing which gives them the weight and strength modern hoopers required. Troo Hoops are an effective and sure way to reach the fitness level you desire. Get a complete body workout; get the cardio and the strength training all in one place with Troo Hoops. Use our discount coupons to strike a perfect deal!
Troo Hoops can be customized online with a unique Hoop Decorator. The customer has the choice to customize the look of a hoop from four basic styles and 13 different color tapes or 40,000 possible color combinations! Troo Hoops are made by Brian Dube Inc, a world known leader in juggling equipment since 1975. Troo Hoops are made from industrial grade materials using cutting edge technology to get every detail right. Check out Troo Hoops you will love what the product can do for you. You can also get a fun illuminated 24 led-lighted hoop. For all your hooping needs and accessories, visit Troo Hoops online today and get the best possible discount with our coupons.
Troo Hoops is a great fitness product which is fun to hoop with. Get cardio, strength and muscle training, all in one place with Troo Hoops. It’s an effective product that is very reasonably priced and available in attractive colors and designs. Troo Hoops makes a great gift item. You can also buy hoop accessories like hoop bags, DVDS, CDS and tapes. Check out Troo Hoops now.


- Shiny is a platform for creating interactive Rprograms embedded into a webpage.
Suppose that you create a prediction algorithm, with shiny you can very easily create web input form that calls R and thus your prediction algorithm and displays the results.
Shiny is made by the fine folk sat RStudio.
Shiny doesn't really require it, but as with all web programming, a little knowledge of html, css and js is very helpful
Shiny uses css bootstrap style, which seems to look nice and renders well on mobile platform.


Minolta light-measuring devices See http://www.konicaminolta.com/instruments
psychopy.hardware.minolta.
LS100
(port, maxAttempts=1)¶A class to define a Minolta LS100 (or LS110?) photometer
You need to connect a LS100 to the serial (RS232) port and when you turn it on press the F key on the device. This will put it into the correct mode to communicate with the serial port.
usage:
from psychopy.hardware import minolta
phot = minolta.LS100(port)
if phot.OK: # then we successfully made a connection
print(phot.getLum())
| Parameters: | port: string
|
|---|---|
| Troubleshooting: | |
Various messages are printed to the log regarding the function of this device, but to see them you need to set the printing of the log to the correct level: from psychopy import logging
logging.console.setLevel(logging.ERROR) # error messages only
logging.console.setLevel(logging.INFO) # more info
logging.console.setLevel(logging.DEBUG) # log all communications
If you’re using a keyspan adapter (at least on OS X) be aware that it needs a driver installed. Otherwise no ports wil be found. Error messages:
|
checkOK
(msg)¶Check that the message from the photometer is OK. If there’s an error show it (printed).
Then return True (OK) or False.
clearMemory
()¶Clear the memory of the device from previous measurements
getLum
()¶Makes a measurement and returns the luminance value
measure
()¶Measure the current luminance and set .lastLum to this value
sendMessage
(message, timeout=5.0)¶Send a command to the photometer and wait an alloted timeout for a response.
setMaxAttempts
(maxAttempts)¶Changes the number of attempts to send a message and read the output. Typically this should be low initially, if you aren’t sure that the device is setup correctly but then, after the first successful reading, set it higher.
setMode
(mode='04')¶Set the mode for measurements. Returns True (success) or False
‘04’ means absolute measurements. ‘08’ = peak ‘09’ = cont
See user manual for other modes


Html is the language of web - it provides the structure of the house
CSS is the style, decorations, color
Javascript - interactive components like garage opener
Html is made up of:
• Text content - what you see
• Markup - what it looks like
• References to other docs - images/videos such as:
• Links to other pages such as: ESPN
Tags turn into Elements of a tree and can have attributes and values
2 major types of Elements:
INLINE - examples:
b - bold
em - italics
br - line break
a href - links to websites such as Google
img src, alt - links to image w/default text
span
BLOCK - creates an imaginary box around the text, examples:
p - paragraph
div - defines boxes. Header
CSS defines style of the page
Everything on a webpage is a box
Boxify a design-work from big boxes to small, use a pencil


Number of Post is: 75 and the number of authors is 49. The number of upcoming posts is 10
Upcoming posts
Posts by month
March-2021
Authors:
Jesus Oroya,
Carlos Esteve,
Umberto Biccari
- 25-March-2021
The Multilevel Selective Harmonic Modulation problem is recast under the perspective of optimal control
Author:
Sergi Andreu
- 22-March-2021
The aim is then to open the black box of Deep Learning, and try to gain an intuition of what are these models doing. One of the most succesful mathematical theories in recent years has been connecting Deep Learning with Dynamical Systems.
Author:
Aicha Karite
- 10-March-2021
In this project we tried first working on finding an algorithm that can help us define the points of interest of the users we have in the dataset. For that we implement an algorithm that can identify the users stop points.
November-2020
Author:
Jon Asier Bárcena-Petisco
- 23-November-2020
In this work we address the optimal control of parameter-dependent systems. In particular, we study the dynamics and averaged controllability properties of heat equations with random non-negative diffusivites.
October-2020
Author:
Carlos Esteve
- 22-October-2020
In this tutorial we show how to implement the Q-learning algorithm in simple settings where the state-space and the control-space are finite. In this case, the Q-function can be represented by a table, and therefore, it belongs to a finite-dimensional vector space. We illustrate the algorithm by solving the problem of finding the shortest path between any arbitrary point in a discretized domain and a target area. We then consider the same problem in a domain affected by a potential.
Authors:
Jesus Oroya,
Umberto Biccari
- 20-October-2020
Numerical implementation of the moving control strategy for a two dimensional heat equation with memory
June-2020
Authors:
Ana Navarro,
Umberto Biccari,
Enrique Zuazua
- 03-June-2020
Simultaneous control of parameter-depending systems using stochastic optimization algorithms
Authors:
Umberto Biccari,
Enrique Zuazua
- 03-June-2020
Synchronization of coupled oscillators with the Random Batch Method
April-2020
Author:
Borjan Geshkovski
- 30-April-2020
Deep supervised learning roughly consists in solving a discretised optimal control problem subject to a nonlinear, discrete-time dynamical system, called an artificial neural network.
Authors:
Domenec Ruiz,
Idriss Mazari
- 19-April-2020
This tutorial is part of the control under state constraints. We will present the main features regarding the controllability of bistable reaction-diffusion equations with heterogeneous drifts.
Author:
Domenec Ruiz
- 04-April-2020
This tutorial is part of the control under state constraints. We will simulate different control strategies to the same target by minimizing different functionals.
Author:
Carlos Esteve
- 03-April-2020
In this tutorial we study the inverse design problem for time-evolution Hamilton-Jacobi equations. More precisely, for a given observation of the viscosity solution at time $T>0$, we construct all the possible initial data that could have led the solution to the observed state. We note that these initial data are not in general unique.
Author:
Domenec Ruiz
- 03-April-2020
In this tutorial, we will present how to generate admissible paths of steady states for the homogeneous reaction-diffusion equation
Author:
Dongnam Ko
- 01-April-2020
In this DyCon Toolbox tutorial, we present how to use OptimaControl enviroment to control a consensus that models the complex emergent dynamics over a given network.
Author:
Jesus Oroya
- 01-April-2020
In this tutorial we will present a simultaneous control problem in a linear system dependent on parameters. We will use the MATLAb DyCon Toolbox library.
March-2020
Author:
Domenec Ruiz
- 31-March-2020
This tutorial is part of the control under state constraints. We will show how obstructions to the state constraint controllability can appear.
Author:
Domenec Ruiz
- 30-March-2020
Usually, the unknowns in reaction-diffusion models are positive by nature. Therefore, for application purposes, any control strategy proposed should preserve this positivity. This group of tutorials is devoted to the understanding of phenomena and techniques arising in reaction-diffusion control problems when state constraints are present.
Author:
Domenec Ruiz
- 15-March-2020
In this tutorial we will apply the DyCon toolbox to find a control to the semi-discrete semi-linear heat equation.
Authors:
Jan Heiland,
- 07-March-2020
A short python implementation of POD and DMD for a 2D Burgers equation using FEniCS and Scipy
February-2020
Authors:
Alexandre Francisco,
Umberto Biccari,
Enrique Zuazua
- 18-February-2020
The inverse design of hyperbolic transport equations can be addressed by using gradient-adjoint methodologies. Recently, Morales-Hernandez and Zuazua [1] investigated the convenience of using low order numerical schemes for the adjoint resolution in the gradient-adjoint method. They focused on hyperbolic transport scalar equations with an heterogeneous time-independent vector field.
Authors:
Jesus Oroya,
Jorge Mallo
- 13-February-2020
In this blog post, we consider a double pendulum on a cart and we solve the problem of swinging up the pendulum from the downward position to the upward position using optimal control techniques.
Author:
Umberto Biccari
- 01-February-2020
In this tutorial, we show the simulation of heat fractional equation
December-2019
Author:
Jorge Mallo
- 19-December-2019
In this tutorial, we present an optimal control problem related to the Fokker-Planck equation.
Authors:
Liard Thibault,
Enrique Zuazua
- 12-December-2019
Our aim is to study an optimal control problem which consists in minimizing the difference between the predictions of the Burgers equation and the observations of the system at a final time in $L^2(\mathbb{R})$ norm.
November-2019
Author:
Dongnam Ko
- 07-November-2019
The aim of this work is to use CasADi and IpOpt to simulate optimal control problem, which explains the structural controllability of the 2D heat equation. We use finite difference scheme with the uniform grid to test exact controllability of the 2D heat equation. After that, we delete several interactions between grid points and simulate the controllability with smaller number of controlled points.
October-2019
Author:
Yacouba Simpore
- 30-October-2019
In this tutorial, we propose the Hum method to approximate numerically the control in a null controllability problem for a non linear population dynamics model structuring in age and spatial diffusion.
September-2019
Author:
Carlos Esteve
- 30-September-2019
In this tutorial we study the localization of touchdown points in a mathematical model for micro-electro-mechanical systems (MEMS) with variable dielectric permittivity. We consider a device consisting of two conducting plates, connected to an electric circuit. The upper plate is rigid and fixed while the lower one is elastic and fixed only at the boundary. When a voltage (difference of potential between the two plates) is applied, the lower plate starts to bend and, if the voltage is large enough, the lower plate eventually touches the upper one. This is called touchdown phenomenon and our aim is to control the localization of touchdown points in the device by a suitable choice of the dielectric permittivity of the material.
July-2019
Author:
Borjan Geshkovski
- 08-July-2019
In this tutorial, we will present and elaborate an optimal control strategy for the obstacle problem in two space dimensions using Python's FEniCS toolbox. Different meshes and obstacles are considered.
Author:
Dario Pighin
- 08-July-2019
Formulation of Optimal control problem for rotor imbalance. Explanation of the code to numerically solve the problem.
June-2019
Authors:
Umberto Biccari,
Jesus Oroya
- 26-June-2019
We analyze the propagation properties of the numerical versions of one and two-dimensional wave equations, semi-discretized in space by finite difference schemes, and we illustrate that numerical solutions may have unexpected behaviours with respect to the analytic ones.
Author:
Azahar Monge
- 14-June-2019
The aim of this work is to recover the initial sparse sources that lead to a given final measurements using the diffusion equation. It is assumed that the initial condition can be written down as a linear combination of unitary deltas and their weights. In that context, an algorithm that combines the adjoint methodology with least squares is presented. In particular, the adjoint methodology is used to find the localization of the sparse sources and least squares to find the corresponding intensities.
Author:
Debayan Maity
- 07-June-2019
In this tutorial, we investigate the linear infinite dimensional system obtained by implementing an age structure to a given linear dynamical system. We show that if the initial system is null controllable in a time small enough, then the age structured system is also null controllable in a time depending on the various involved parameters.
Authors:
Umberto Biccari,
Victor Hernández
- 07-June-2019
We describe a FE method for the approximation of the one-dimensional fractional Laplacian $(-d_x^2)^s$ on a uniform mesh discretizing the symmetric interval $(-L,L)$, $L>0$.
Author:
Mohammed Nor Frioui
- 04-June-2019
We consider an age-structured HIV infection model with a general nonlinear infection function. The numerical method to solve this system of equation is based on the upwind method for solving hyperbolic partial differential equation and the ODEs are solving by explicit Euler method.
May-2019
Author:
Umberto Biccari
- 31-May-2019
We analyze the controllability properties under positivity constraints on the control or the state of a one-dimensional heat equation involving the fractional Laplacian $(-\Delta)^s$ ($0 < s < 1 $) on the interval $(-1,1)$. We prove the existence of a minimal (strictly positive) time $T_{\rm min}$ such that the fractional heat dynamics can be controlled from any initial datum in $L^2(-1,1)$ to a positive trajectory through the action of a positive control, when $s>1/2$. Moreover, we show that in this minimal time constrained controllability is achieved by means of a control that belongs to a certain space of Radon measures. We also give some numerical simulations that confirm our theoretical results.
Author:
Dongnam Ko
- 27-May-2019
In this post, we use IpOpt and AMPL to simulate optimal controls on a nonlinear ODE system with unbounded interactions. The restriction and initial guess on the state variables are critical for this problem to operate minimization algorithm. From the data calculated from AMPL, we interpret and visualize it using Matlab.
Author:
Liard Thibault
- 24-May-2019
Autonomous Vehicles (briefly AVs) have already been tested on urban and highway networks and, for the future they may be used to monitor and manage the vehicular traffic. The impact of autonomous vehicles on vehicular traffic is modeled by a PDE+ODE models with moving point-flux constraint. The PDE is a nonlinear hyperbolic conservation laws and the ODE represents the trajectory of autonomous vehicles. We show that the data collected by autonomous vehicles' sensors can be used to reconstruct the traffic state.
April-2019
Author:
Dario Pighin
- 21-April-2019
In this short tutorial, we explain how to use Riccati’s theory to solve an LQ control problem with targets.
Authors:
Mohammed Nor Frioui,
- 03-April-2019
The aim of this tutorial is to give a numerical method for solving a partial differential equation with a constant delay.
March-2019
Authors:
Marco Morandotti,
Jose Matias
- 26-March-2019
A Multiscale Geometrical Basis for Variational Problems in Mechanics
Authors:
Azahar Monge,
- 26-March-2019
This tutorial explains how to use the Dirichlet-Neumann method to coordinate the numerical solutions of two linear heat equations with strong jumps in the material coefficients accross a common interface.
February-2019
Author:
Swann MARX
- 04-February-2019
The aim of this note is to explain how one can solve an optimal control problem with the moment method
Author:
Noboru Sakamoto
- 04-February-2019
Tutorial of optimal control for inverted pendulum with symbolic MATLAB
January-2019
Author:
Xinliang Liu
- 07-January-2019
This work is regarding solving 1-d wave equation using RPS semi-discretization method and analysis corresponding dispersion relation.
October-2018
Author:
Dongnam Ko
- 29-October-2018
We design the LQR controller and solve it with linearized model.
Author:
Yacouba Simpore
- 29-October-2018
In this tutorial, we will demonstrate how to design a LQR controller in order to stabilize the linear population dynamics model dependent on age and space
Author:
Charlotte Rodriguez
- 29-October-2018
Numerical computation of a stabilizing control
Author:
Domenec Ruiz
- 28-October-2018
Summary of example objective The goal of this tutorial is to use LQR theory applied to a model of collective behavior. The model chosen shares a formal structure with the semidiscretization of the semilinear 1d heat equation.
Author:
Borjan Geshkovski
- 28-October-2018
Stabilization of a coupled PDE-ODE system by means of a feedback LQR control.
Author:
Alejandro Cunillera
- 26-October-2018
Stabilizing the graph by minimizing a discrete LQR and driving it to a reference state.
July-2018
Author:
José Vicente
- 23-July-2018
Optimal Control in OpenFOAM
Authors:
José Vicente,
Victor Hernández
- 03-July-2018
June-2018
Author:
José Vicente
- 30-June-2018
Author:
José Vicente
- 29-June-2018
Authors:
José Vicente,
Victor Hernández
- 29-June-2018
Authors:
José Vicente,
Victor Hernández
- 29-June-2018
May-2018
Authors:
Victor Hernández,
José Vicente,
Enrique Zuazua
- 01-May-2018
In this tutorial, we show how to use the C++ library OpenFOAM (Open Field Operation and Manipulation) in order to solve control problems for partial differential equations (PDE).
April-2018
Authors:
Umberto Biccari,
Alejandro B. Aceves,
Enrique Zuazua
- 05-April-2018
Theoretical and numerical analysis of the propagation of the solutions for a Schrödinger equation with fractional Laplacian, with application to the study of controllability properties.
March-2018
Authors:
Victor Hernández,
José Vicente,
Enrique Zuazua
- 12-March-2018
In this work, we address the optimal control of parameter-dependent systems.
Authors:
Jérôme Lohéac,
Emmanuel Trélat,
Enrique Zuazua
- 05-March-2018
Using IpOpt to get the time-optimal nonnegative control of a semi-discrete 1D heat equation
December-2017
Authors:
Carlos Castro,
Miguel Cea,
Sorin Micu,
Arnaud Diego Münch,
Mihaela Negreanu,
Enrique Zuazua
- 21-December-2017
A Matlab guide for the numerical approximation of the exact control and stabilization of the wave equation.
Authors:
Jiamin Zhu,
Enrique Zuazua
- 05-December-2017
We present a computational tool to solve optimal control problems for diffusion-reaction systems describing the growth and spread of populations
November-2017
Authors:
Yuhua Zhu,
Enrique Zuazua
- 28-November-2017
Short report about the greedy algorithm of linear Vlasov-Fokker-Planck equation, including 6 numerical experiments with figures, and corresponding matlab coding and the explanation of how to implement it.
Authors:
Ramon Escobedo,
Aitziber Ibáñez,
Enrique Zuazua
- 05-November-2017
Guidance by repulsion model describing the behaviour of two agents, a driver and an evader
Authors:
Mehmet Ersoy,
Enrique Zuazua
- 05-November-2017
Various numerical approximation methods are discussed with the aim of recoving the large time asymptotic properties of the hypoelliptic Kolmogorov model
Authors:
Mario Morales,
Enrique Zuazua
- 05-November-2017
Various discrete adjoint methodologies are discussed for the inverse design of linear transport equations in 2 space dimensions
Authors:
Victor Hernández,
Martín Lazar,
Enrique Zuazua
- 01-November-2017
Turnpike theory and greedy algorithms, applied to the steady-state elliptic control problem, are combined to obtain a greedy approximation of parabolic optimal control problems, independent of the initial data
October-2017
Authors:
Martín Lazar,
Césare Molinari,
Enrique Zuazua
- 25-October-2017
We want to study the following optimal control problem...
Author:
José Vicente
- 11-October-2017
This is a set of Matlab functions to interpolate scattered data with Radial Basis Functions (RBF).
September-2017
Author:
Umberto Biccari
- 18-September-2017
Design of a LQR controller for the stabilization of a fractional reaction diffusion equation
August-2017
Authors:
Jérôme Lohéac,
Emmanuel Trélat,
Enrique Zuazua
- 05-August-2017
How to use IpOpt to solve time optimal control problems
June-2017
Authors:
Rodrigo Lecaros,
Enrique Zuazua
- 05-June-2017
Tracking control of 1D scalar conservation laws in the presence of shocks
May-2017
Authors:
Navid Allahverdi,
Alejandro Pozo,
Enrique Zuazua
- 04-May-2017
Numerical approximation of the inverse design problem for the Burgers equation
Authors:
Aitziber Ibáñez,
Enrique Zuazua
- 04-May-2017
The turnpike property improves the numerical methods used to solve optimal control problems
March-2017
Authors:
Martín Lazar,
Enrique Zuazua
- 01-March-2017
Control of a parameter dependent system in a robust manner
Resumen por Autor
Resumen por Autor Activo


+9
Sunday, 18th July
Ibiza Fest
The most prestigious summer festival in the world bringing the hotest DJ's and the best music.
Rated 4.9 PREMIUM
Ibiza Island, Spain
Beach Bung


Lars Schnieder and Susanne Arndt
They describe the deployment of an automated system to uncover ambiguities and vagueness within technical specification documents.
"Requirements engineering is a communication process that involves
understanding and coordinating terminologies from different
disciplines."
"[Requirements] are discussed and modified over time to arrive as far as possible at a...specification that is testable, unambiguous, complete, and correct."
"Terminology that belongs to one knowledge system is often misunderstood by people who work with another knowledge system."
"Lexical ambiguity refers to single expressions that may be reasonably interpreted in more than one way."
"[Syntactic ambiguities occur] when pronouns and relative clauses can refer back to two or more other elements of the preceding sentence ...[leading] to different interpretations"
"Discourse ambiguities are defined as incompatibility between several requirements"
"vagueness [where it is] difficult to form any interpretation at the desired level of specificity. This is in contrast to ambiguity, where more than one interpretation is evoked."
"correcting faults requires close cooperation between linguists and domain experts whose knowledge is required to recognize and remedy linguistic faults"


Introduce Myself
Name: Kim JinHo
Age: 26
Gender: Man
Birthday: 10.14
"I like food, computer game, sleep, music"
"Nice to meet you"