Interface DataProvider
- public interface DataProvider
The DataProvider interface is used by jScope to deal with signals, regardless their origin.
DataProvider defines a set of method for providing the signal and supporting several features
which may be enabled or disabled depending on the current implementation of the DataProvider
interface. Signals are not directly returned by DataProvider access methods as vectors (as it was done
in earlier jScope versions). Rather DataProvider GetWaveData method returns an implementation of
WaveData. Interface WaveData defines the methods for collecting signal data.
- See Also:
WaveData
|
Method Summary |
void |
AddConnectionListener(ConnectionListener l)
Method AddConnectionListener is called by jScope after the instantiation of a DataProvider
implementation, in order to be notified of the status of data transfer. |
void |
AddUpdateEventListener(UpdateEventListener l,
java.lang.String event)
Method AddUpdateEventListener is called by jScope when asynchronous update on event is
defined. |
boolean |
DataPending()
Method DataPending is called by jScope to verify whether the current signal has been fully
displayed. |
void |
Dispose()
Method Dispose is called by jScope each time a DataProvider is no more used. |
java.lang.String |
ErrorString()
If an error is encountered in the evaluation of a signal (GetWaveData or
GetResampledWaveData returning null or generation IOException), jScope calls ErrorString
method to retrieve the description of the error just occurred. |
float |
GetFloat(java.lang.String in)
Method GetFloat is called by jScope to evaluate x min, x max, y min, y max when defined
in the stup data source popup form. |
FrameData |
GetFrameData(java.lang.String in_frame,
java.lang.String in_times,
float start_time,
float end_time)
GetFrameData is called by jScope to retrieve and display a frame sequence. |
WaveData |
GetResampledWaveData(java.lang.String in,
float start,
float end,
int n_points)
When the network->fast network access option is enabled, jScope uses GetResampledWaveData
instead of GetWaveData to evaluate waveformes. |
WaveData |
GetResampledWaveData(java.lang.String in_y,
java.lang.String in_x,
float start,
float end,
int n_points)
Same as the GetResampledWaveData, except that it is called when both X and Y axis are
specified. |
int[] |
GetShots(java.lang.String in)
|
java.lang.String |
GetString(java.lang.String in)
GetString is called by jScope to evaluate title, X label and Y label. |
WaveData |
GetWaveData(java.lang.String in)
Method GetWaveData is called by jScope when a waveform has to be avaluated and only the Y
axis is defined. |
WaveData |
GetWaveData(java.lang.String in_y,
java.lang.String in_x)
Method GetWaveData is called by jScope when a waveform has to be avaluated and both X and
Y axis are defined.
|
int |
InquireCredentials(javax.swing.JFrame f,
java.lang.String user)
Some DataProvider implemenations, such as Jet RDA provider, may require additional information
(e.g. username and password) typed by users in a login form. |
void |
RemoveConnectionListener(ConnectionListener l)
Called by jScope when transfer notification is no more requested. |
void |
RemoveUpdateEventListener(UpdateEventListener l,
java.lang.String event)
Method RemoveUpdateEventListeneris called by jScope when the display of a waveform panel is
no more triggered by an event. |
void |
SetArgument(java.lang.String arg)
As DataProvider implementations are instantiated by jScope by means of the
Class.newInstance(), no arguments can be passed to the constructor method.
|
void |
SetCompression(boolean state)
Called by jScope to enable or disable data compression in transmission. |
void |
SetEnvironment(java.lang.String exp)
Defines the environment for data retrieval.
|
boolean |
SupportsCompression()
Returns tre if the DataProvider impementation supports data compression in communication.
jScope does not make any assumption about compression, simply provides the user interface for enablin
or disabling compression, by means Network->Enable compression option. |
boolean |
SupportsContinuous()
If SupportsContinuous returns true, jScope is prepared to handle a signal changing over time.
|
boolean |
SupportsFastNetwork()
Called by jScope to verify whether the DataProvider impementation supports the Fast Network
option. |
void |
Update(java.lang.String exp,
int s)
Method Update is called by jScope to notify the experiment name and the shot number.
|
LOGIN_OK
public static final int LOGIN_OK
LOGIN_ERROR
public static final int LOGIN_ERROR
LOGIN_CANCEL
public static final int LOGIN_CANCEL
SetEnvironment
public void SetEnvironment(java.lang.String exp)
throws java.io.IOException
- Defines the environment for data retrieval.
Depending on the nature of the provider of data, some environment variable may be defined.
For example the MdsDataProvider allows the definition of TDI variables which may be useful
for configuring remote data access.
jScope allows the definition of an arbitrary set of environment variable using the
customize->public variables... option. As jScope does not make any assumption on the
syntax of the defined variables, each variable definition is passed to the Data provider
by means of the SetEnvironment method, whose argument is a String which defines
pairs. If the DataProvider implementation does not support such a feature, it simply returns.
- Parameters:
exp
- The variable definition expressed as pair.- Throws:
java.io.IOException
-
Update
public void Update(java.lang.String exp,
int s)
- Method Update is called by jScope to notify the experiment name and the shot number.
Update can be called several time by jScope dfor the same DataProvider implementation
in the case user changes either the experiment name or the shot number.
- Parameters:
exp
- The experiment names
- The shot number.
GetString
public java.lang.String GetString(java.lang.String in)
throws java.io.IOException
- GetString is called by jScope to evaluate title, X label and Y label. These labels are defined
as string in the Setup data source popup form. In the simplest case labels are exactly
the same as defined, and therefore GetString simply returns its argument.
In any case the data provider is free to interpret differently the argument.
- Parameters:
in
- The specification of the label or title.- Returns:
- The evaluated label or title.
- Throws:
java.io.IOException
-
GetFloat
public float GetFloat(java.lang.String in)
throws java.io.IOException
- Method GetFloat is called by jScope to evaluate x min, x max, y min, y max when defined
in the stup data source popup form. The argument is in fact the string typed by the user in
the form.
In its simplest implementation, method GetFloat converts the string into a float value and
returns it. Other data providers, such as MdsDataProvider, evaluate, possibly remotely, the
passed string which may therefore be represented by an expression.
- Parameters:
in
- The specification of the value.- Returns:
- The evaluated value.
- Throws:
java.io.IOException
-
GetWaveData
public WaveData GetWaveData(java.lang.String in)
- Method GetWaveData is called by jScope when a waveform has to be avaluated and only the Y
axis is defined. In this case jScope assumes that the specification is enough and it is up
to the data provider implementation to retrieve X and Y axis.
The evaluated signal is not directly treturned as a vector, rather as a object implementing
the WaveData interface. The WaveData interface defines the following methods:
- int GetNumDimensions() returns the number of dimensions. Currently only signals (dimension=1)
are supported.
- float[] GetFloatData() returns the Y axis of the signal as a float array. For bidimensional
signals (not yet supported) the array is organized in row order;
- float[] GetXData() returns the X axis of the signal;
- float[] GetYData() returns the Y axis for a bidimensional signal;
- String GetTitle() returns the associated title (if no title is defined in the setup data popup dialog);
- String GetXLabel() returns the associated X label(if no X label is defined in the setup data popup dialog);
- String GetYLabel() returns the associated Y label(if no Y label is defined in the setup data popup dialog);
- String GetZLabel() returns the associated Z label(if no Z label is defined in the setup data popup dialog);
- Parameters:
in
- The specification of the signal, typed in the Y axis field of the setup data source
popup form, or in the lower right window of jScope.- Returns:
- The evaluated signal, embedded in a WaveData object, or null if an error is encountered.
- See Also:
WaveData
GetWaveData
public WaveData GetWaveData(java.lang.String in_y,
java.lang.String in_x)
- Method GetWaveData is called by jScope when a waveform has to be avaluated and both X and
Y axis are defined.
The evaluated signal is not directly treturned as a vector, rather as a object implementing
the WaveData interface. jScope the uses the returned object to retieve X and Y axis.
- Parameters:
in_y
- Y axis specification as typed in the setup data source popupo dialog.in_x
- X axis specification as typed in the setup data source popupo dialog.- Returns:
- The evaluated signal, embedded in a WaveData object, or null if an error is encountered.
- See Also:
WaveData
GetResampledWaveData
public WaveData GetResampledWaveData(java.lang.String in,
float start,
float end,
int n_points)
- When the network->fast network access option is enabled, jScope uses GetResampledWaveData
instead of GetWaveData to evaluate waveformes. Method GetResampledWaveData should
return the resampled version of the specified waveform, and resampling should be done
at the data server site in orer to reduce network transfer time for large signals.
GetResampledData is also asynchronously called when users zoom a resampled waveform
in order to collect more points of the selected region.
DataProvider implementations can enable the ast network access option by returning true
in method SupportsFastNetwork. Otherwise an empty implementation of GetResampledWaveData
suffices.
- Parameters:
in
- Y axisi definition as typed in the setup data source popup dialog.start
- Start X value for the requested region.end
- End X valus for the requested region.n_points
- Number of points for the resampled signal.- Returns:
- The evaluated signal, embedded in a WaveData object, or null if an error is encountered.
- See Also:
WaveData
GetResampledWaveData
public WaveData GetResampledWaveData(java.lang.String in_y,
java.lang.String in_x,
float start,
float end,
int n_points)
- Same as the GetResampledWaveData, except that it is called when both X and Y axis are
specified.
- Parameters:
in_y
- Y axis definition for the requested signal.in_x
- X axis definition for the requested signal.start
- Start X value.end
- End X value.n_points
- Number of points for the resampling.- Returns:
- The evaluated signal, embedded in a WaveData object, or null if an error is encountered.
- See Also:
WaveData
SupportsFastNetwork
public boolean SupportsFastNetwork()
- Called by jScope to verify whether the DataProvider impementation supports the Fast Network
option. If SupportsFastNetwork return true, jScope retrieves data by calling GetResampledWaveData
which has to be thread safe. GetResampledWaveData is in fact called by a separate
thread each time a zoom is done, to retrieve more points for the selected region.
If SupportsFastNetwork return false, GetWaveData is called instead of GetResampledWaveData,
which can be empty.
- Returns:
- The ability of supporting fast network access.
ErrorString
public java.lang.String ErrorString()
- If an error is encountered in the evaluation of a signal (GetWaveData or
GetResampledWaveData returning null or generation IOException), jScope calls ErrorString
method to retrieve the description of the error just occurred.
- Returns:
- A verbose description of the last error.
AddUpdateEventListener
public void AddUpdateEventListener(UpdateEventListener l,
java.lang.String event)
throws java.io.IOException
- Method AddUpdateEventListener is called by jScope when asynchronous update on event is
defined. To connect data visualization to asynchronous event an event name has to be defined
in the Update event field of the setup data form popup dialog.
DataProvider implementations supporting aynchronous signal updating should
notify event occurrence to all registered listeners by calling method processUpdateEvent(UpdateEvent e).
UpdateEvent defines the following field, in addition to those defined in the AWTEvent
superclass:
- String name: the name of the event
The possiblity of connecting data display to asynchronous events is useful when jScope
is used during data acquisition, for an atomatic display update when a signal has been
acquired. In this case the data acquisition system would generate an event when the
signal has been acquired, and the DataProvider implementation would call method
processUpdateEvent (defined in UpdateEventListener inetrafce) to request the display update to
jScope.
If the DataProvider implementation does not support asynchronous events, the method is defined
empty.
NOTE: asynchronous update on event is not required for displaying evolving signals
such as trend recording. See the description of methods SupportsContinuous() and
DataPending() for the management of evolving signals.
- Parameters:
l
- The passed instance of UpdateEventListener.event
- The name of the event for which the UpdateEventListener is registering.- Throws:
java.io.IOException
- - See Also:
UpdateEventListener
,
UpdateEvent
RemoveUpdateEventListener
public void RemoveUpdateEventListener(UpdateEventListener l,
java.lang.String event)
throws java.io.IOException
- Method RemoveUpdateEventListeneris called by jScope when the display of a waveform panel is
no more triggered by an event.
- Parameters:
l
- The instance of UpdateEventListener proeviously registered.event
- The event to which the listener was previously registered.- Throws:
java.io.IOException
- - See Also:
UpdateEventListener
GetFrameData
public FrameData GetFrameData(java.lang.String in_frame,
java.lang.String in_times,
float start_time,
float end_time)
throws java.io.IOException
- GetFrameData is called by jScope to retrieve and display a frame sequence. The frame sequence
is returned by means of an object implementing the FramesData interface.
The methods defined in the FrameData interface are the following:
- int GetFrameType() returning the type of the corresponding frames, as returned by GetFrameAt, which can be:
-FramesData.BITMAP if the frames are returned by method GetFrameAt as a byte matrix
-FrameData.AWT_IMAGE if the frames are returned by method GetFrameAt as the
content of a gif or jpg file
-FrameData.JAI_IMAGE if the frames are returned by method GetFrameAt as the content of
every other file format supported by JAI.
- int GetNumFrames() returning the number of frames in the sequence
- Dimension GetFrameDimension() returning the dimension of the single frame
- float [] GetFrameTimes returning the times associated with each frame
- byte[] GetFrameAt(int idx) returning the corresponding frame
- Parameters:
in_frame
- The frame sequence specification as defined in the frames field of the setup data popup dialogstart_time
- Initial considered time for the frame sequence.end_time
- Final considered time for the frame sequence.in_times
- The definition of the time specification for the frame list as defined in the times field
of the setup data popup dialog.- Returns:
- An implementation of the FrameData interface representing the corresponding frame sequence
- Throws:
java.io.IOException
- - See Also:
FrameData
SupportsContinuous
public boolean SupportsContinuous()
- If SupportsContinuous returns true, jScope is prepared to handle a signal changing over time.
An example is the display of a trend recording showing a waveform which is updated each time
a new set of samples has been acquired. More generally, an arbitrarily evolving signal
can be diaplayed this way.
If a DataProvider implentation returns true, a separate thread calls repeadetly methods
GetWaveData() and DataPending() until the second method returns false. In this case
GetWaveData() will wait until a new set of samples is ready and has to be dispalyed by jScope,
while method DataPending() notifies to jScope whether new data has to be displayed for
the signal, i.e. whether the signal has not yet finished to be produced.
If SupportsContinuous returns false, method GetWaveData (or GetResampledWaveData) is called
only once to retrieve the whole signal.
NOTE in the current implementation, update frequencies up to few Hz can be handled by jScope
with a limited CPU usage.
- Returns:
- The indication whether support for evolving signals is provided.
DataPending
public boolean DataPending()
- Method DataPending is called by jScope to verify whether the current signal has been fully
displayed. It DataPending returns false, method GetWaveData is alled again to retrieve a new
version (usually with some more saples) of the signal.
If method SupportsContinuous() returned false, DataPending is never called by jScope, and
can therefore be empty.
- Returns:
- Indicates whether the current signal is complete.
SupportsCompression
public boolean SupportsCompression()
- Returns tre if the DataProvider impementation supports data compression in communication.
jScope does not make any assumption about compression, simply provides the user interface for enablin
or disabling compression, by means Network->Enable compression option. If compression
is not supported by the DataProvider implementation, the Network->Enable option id disabled.
- Returns:
- true if the DataProvider implementation supports compression.
SetCompression
public void SetCompression(boolean state)
- Called by jScope to enable or disable data compression in transmission. If the DataProvider
implementation doe not support compression and returned false in SupportsCompression(),
SetCompression is never called.
- Parameters:
state
- Indicates whether compression is enabled or disabled.
Dispose
public void Dispose()
- Method Dispose is called by jScope each time a DataProvider is no more used. Unlike Object.finalize(),
method Dispose is guaranteed to be called at the time the DataProvider implementation is no more
used by jScope.
AddConnectionListener
public void AddConnectionListener(ConnectionListener l)
- Method AddConnectionListener is called by jScope after the instantiation of a DataProvider
implementation, in order to be notified of the status of data transfer. If a DataProvider
implementation has to handle the transfer of large chuncks of data (e.g. frame sequences)
requiring a relatively long time, it should be able to communicate the current status of
the transfer to jScope which displays then a status bar. To do this, the DataProvider
implementation calls ConnectionListener's method processConnectionEvent, passing an istance of
ConnectionEvent as argument.
Class ConnectionEvent defines the following information:
- int total_size: total size of the data to be transferred
- int current_size: the size of the data transferred so far
- String info: an additional information string which is displayed by jScope
If the DataProvider implementation does not support transfer notification, method AddConnectionListener
is empty.
- Parameters:
l
- The ConnectionListener which has to be notified by means of method processConnectionEvent(ConnenctionEvent)- See Also:
ConnectionListener
,
ConnectionEvent
RemoveConnectionListener
public void RemoveConnectionListener(ConnectionListener l)
- Called by jScope when transfer notification is no more requested. Empty if the DataProvider
implementation does not support transfer status notification.
- Parameters:
l
- The previously registered ConnectionListener.- See Also:
ConnectionListener
InquireCredentials
public int InquireCredentials(javax.swing.JFrame f,
java.lang.String user)
- Some DataProvider implemenations, such as Jet RDA provider, may require additional information
(e.g. username and password) typed by users in a login form. As jScope does not make any assumption
on this kind of information it is up to the DataProvider implementation to realize such a form
(if any). For this reason jScope calls method InquireCredential passingf as argument the container JFrame.
InquireCredentials may return:
-DataProvider.LOGIN_OK if the DataProvider has been succesfully validated
-DataProvider.LOGIN_ERROR if the DataProvided did not succeed in validation
-DataProvider.LOGIN_CANCEL if the login proess has been aborted by user (usually pressing
a canel button)
Only in the first case jScope will proceede with the usega of the DataProvider implementation. In the
secon case a warning dialog is shown, and in the third case the DataProvider is simply discarded.
- Parameters:
f
- The container Frame.user
- Local username as specified in the property file or in the Network->Edit server list... dialog- Returns:
- The status of the DataProvider specific validation process
SetArgument
public void SetArgument(java.lang.String arg)
throws java.io.IOException
- As DataProvider implementations are instantiated by jScope by means of the
Class.newInstance(), no arguments can be passed to the constructor method.
If an additional argument is required fotr the proper initialization of the
DataProvider implementation (e.g. the ip address for the MdsDataProvider), the argument,
defined in the server_n.argument item of the property file is passed through method SetArgument
called by jScope just after the DataProvider instantiation.
- Parameters:
arg
- The argument passed to the DataProvider implementation.- Throws:
java.io.IOException
-
GetShots
public int[] GetShots(java.lang.String in)
throws java.io.IOException


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{% extends "./base.html" %} {% load static %} {% block title %}Hoofball - Index{% endblock %} {% block content %}
This blog aims to tell the story of my Football Manager 2020 save. I am currently managing Lowestoft Town, a semi-professional club founded in 1884 in the city of Lowestoft, Suffolk, England, which plays in the 6th English division.
The club moved up from the 7th division and is fighting against relegation this season, 3 points below the first placed outside the zone.


MAP OF THE HUMAN HEART A film review by James Berardinelli Copyright 1993 James Berardinelli
Rating: 6.9 (C+, **)
Date Released: 4/23/93 (limited distribution) Running Length: 1:52 Rated: R (Mature themes, sexual situations, nudity, language, violence)
Starring: Jason Scott Lee, Anne Parillaud, Patrick Bergin, John Cusack Director: Vincent Ward Producer: Tim Bevin Screenplay: Louis Nowra based on a story by Vincent Ward Music: Gabriel Yared Released by Miramax Films
Avik (Jason Scott Lee) is a young Eskimo boy living with his tribe in the Canadian Arctic. One day in 1931, Walter (Patrick Bergin) arrives there as the head of a British mapping expedition. He befriends Avik and, when the boy becomes gravely ill, flies him back to Montreal for treatment. There, Avik meets Albertine (Anne Parillaud). After their initial encounters as adversaries, the two become fast friends. Eventually, they are separated, but Avik vows never to forget her. Over a decade later, during his stint as a bomber pilot during the second world war, the two meet again.
MAP OF THE HUMAN HEART is a sentimental love story with a generally downbeat tone. The story is framed around a series of coincidences, not all of which are believable, but many of which can be overlooked. There is a certain epic sweep to this film which many "dramatic romances" lack. The emotional impact of MAP OF THE HUMAN HEART, while existing, is minor. The characters remain too distanced from the audience.
The most obvious problem with MAP OF THE HUMAN HEART is its editing. Of all the films I've seen in recent years, only the Salkinds' Christopher Columbus movie rivals this for cutting room butchering. In this case, the final product is so disjointed that at times the transitions (or lack thereof) are laughably absurd. The original European version of this movie is significantly longer. While the overall quality of that picture may not be better than the one distributed by Miramax, it certainly must flow better.
Character interaction is marginal. I think the editing impacted on this, as it did on just about everything else. We see skeletons of relationships, but none--not even the central one between Avik and Albertine--is developed satisfactorily. Especially weak is the interaction between Avik and Walter. For MAP OF THE HUMAN HEART to really work, we have to be able to accept that there is a deep and abiding love between these two, a sort of father/son relationship. Unfortunately, we see little evidence of this.
Early in the movie, there is an aspect of the production that I found irritating--all the more so because it is unnecessary. During the scenes when Albertine is a young girl, her lines have been dubbed by Anne Parillaud. There's no rhyme or reason for this, and the dubbing is not so well done that it isn't noticeable. As minor a point as this is, I found it distracting.
In general, the movie seems to be full of half-realized themes and ideas. Questions of identity are touched on, but the subject is never pursued to its conclusion. Cut short is a potentially-powerful scene where a member of a bomber crew is forced to experience firsthand the devastation he has wrought. Even truncated as it is, this scene still packs a punch.
One thing that can't be denied, and that not even the editing can take away, is how beautifully this movie has been filmed. The shots of dozens of gigantic bombers drifting over a flaming Dresden is as effective as any similar scene in another war picture, and the brief aerial battles are the equal of those from MEMPHIS BELLE.
Unfortunately, while the shots from the outside of the planes during the bombing raids are excellent, the interior ones are not. Hardly any of the dialogue is intelligible and it's difficult to figure out what's happening. While I can appreciate the director's desire to capture the "feeling" of such a chaotic environment, it isn't a good idea to do so at the price of utterly confounding the audience.
The acting is as uneven as everything else in the movie. The child actors who take up much of the early screen time are merely passable, and their adult counterparts aren't much better. Anne Parillaud is especially disappointing, as she overacts her way through the part (much in the same way that she did in INNOCENT BLOOD, a movie where over-the-top was demanded). Patrick Bergin goes in the other direction, appearing distressingly unemotional. Jason Scott Lee, perhaps the most consistent of the major participants, exudes very little charisma in this role. John Cusack, who gets billing for a cameo appearance, is on screen for a total of about four minutes.
Overall, MAP OF THE HUMAN HEART is a mixed bag. None of its elements works all the time, but neither is it a complete failure. The romance alone doesn't have the emotional power to recommend the film, but WW2 buffs may find in this production a mildly entertaining diversion. MAP OF THE HUMAN HEART is sometimes sappy and silly, and often poorly-transitioned, but at least it generally isn't boring.
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Maria Francis-Moullier
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Hangman
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Trivia Game
Rutgers Info Widget
Rock Paper Scissors


public class ClientModel extends java.lang.Object implements IClientModel, IResponse
| Constructor and Description |
|---|
ClientModel() |
ClientModel(ITurnTracker turnTracker,
int winner,
int version,
IMessageList chat,
IMessageList log,
ICatanMap catanMap,
java.util.List<IPlayer> players,
IResourceList bank,
IBonusTracker bonusTracker,
ITradeOffer tradeOffer,
IPlayer localPlayer,
IDevCardList deck) |
public ClientModel()
public ClientModel(ITurnTracker turnTracker, int winner, int version, IMessageList chat, IMessageList log, ICatanMap catanMap, java.util.List<IPlayer> players, IResourceList bank, IBonusTracker bonusTracker, ITradeOffer tradeOffer, IPlayer localPlayer, IDevCardList deck)
public static void setUpdatableModel(ClientModel newModel)
public static ClientModel getUpdatableModel()
public ITurnTracker getTurnTracker()
getTurnTracker
in interface IClientModel
Postcondition: Retrieved the value.
public ICatanMap getCatanMap()
getCatanMap
in interface IClientModel
Postcondition: Retrieved the value.
public int getWinner()
getWinner
in interface IClientModel
Postcondition: Retrieved the value.
public int getVersion()
getVersion
in interface IClientModel
Postcondition: Retrieved the value.
public IMessageList getChat()
getChat
in interface IClientModel
Postcondition: Retrieved the value.
public IMessageList getLog()
getLog
in interface IClientModel
Postcondition: Retrieved the value.
public java.util.List<IPlayer> getPlayers()
getPlayers
in interface IClientModel
Postcondition: Retrieved the value.
public void setTurnTracker(ITurnTracker turnTracker)
setTurnTracker
in interface IClientModel
Postcondition: Value has been updated.
public void setWinner(int winner)
setWinner
in interface IClientModel
Postcondition: Value has been updated.
public void setVersion(int version)
setVersion
in interface IClientModel
Postcondition: Value has been updated.
public void setChat(IMessageList chat)
setChat
in interface IClientModel
Postcondition: Value has been updated.
public void setLog(IMessageList log)
setLog
in interface IClientModel
Postcondition: Value has been updated.
public void setCatanMap(ICatanMap catanMap)
setCatanMap
in interface IClientModel
Postcondition: Value has been updated.
public void setPlayers(java.util.List<IPlayer> players)
setPlayers
in interface IClientModel
Postcondition: Value has been updated.
public IResourceList getBank()
getBank
in interface IClientModel
Postcondition: Retrieved the value.
public void setBank(IResourceList bank)
setBank
in interface IClientModel
Postcondition: Value has been updated.
public IBonusTracker getBonusTracker()
getBonusTracker
in interface IClientModel
Postcondition: Retrieved the value.
public void setBonusTracker(IBonusTracker bonusTracker)
setBonusTracker
in interface IClientModel
Postcondition: Value has been updated.
public ITradeOffer getTradeOffer()
getTradeOffer
in interface IClientModel
Postcondition: Retrieved the value.
public void setTradeOffer(ITradeOffer tradeOffer)
setTradeOffer
in interface IClientModel
Postcondition: Value has been updated.
public IPlayer getLocalPlayer()
getLocalPlayer
in interface IClientModel
Postcondition: Retrieved the value.
public void setLocalPlayer(IPlayer localPlayer)
setLocalPlayer
in interface IClientModel
Postcondition: Value has been updated.
public IDevCardList getDeck()
getDeck
in interface IClientModel
public void setDeck(IDevCardList deck)
setDeck
in interface IClientModel
public boolean canRollNumber()
canRollNumber
in interface IClientModel
public boolean canAcceptTrade()
canAcceptTrade
in interface IClientModel
public boolean canOfferTrade(ITradeOffer tradeOffer)
canOfferTrade
in interface IClientModel
public boolean canDiscardCards(IResourceList cards)
canDiscardCards
in interface IClientModel
public boolean canFinishTurn()
canFinishTurn
in interface IClientModel
public boolean canBuyDevCard()
canBuyDevCard
in interface IClientModel
public java.util.Map<ResourceType,java.lang.Integer> getTradeRatios()
public boolean canMaritimeTrade(ResourceType inputResource, ResourceType outputResource, int ratio)
canMaritimeTrade
in interface IClientModel
public boolean canPlayYearOfPlenty(ResourceType resource1, ResourceType resource2)
canPlayYearOfPlenty
in interface IClientModel
public boolean canPlayMonument()
canPlayMonument
in interface IClientModel
public boolean canPlayMonopoly()
canPlayMonopoly
in interface IClientModel
public boolean canPlaySoldier(HexLocation hexLocation, int victimIndex)
canPlaySoldier
in interface IClientModel
public boolean canPlayRoadBuild(EdgeLocation location1, EdgeLocation location2)
canPlayRoadBuild
in interface IClientModel
public IResponse fromResponse(java.io.BufferedReader response, java.net.CookieStore cookies)
fromResponse
in interface IResponse
response
- Some input stream that may be encoded in any waycookies
- A cookie store containing the cookie valuespublic boolean canBuyRoadAtLocation(EdgeLocation edgeLocation)
canBuyRoadAtLocation
in interface IClientModel
public boolean canBuySettlementAtLocation(VertexLocation vertexLocation)
canBuySettlementAtLocation
in interface IClientModel
public boolean canBuyCityAtLocation(VertexLocation vertexLocation)
canBuyCityAtLocation
in interface IClientModel
public boolean isLocalPlayersTurn()
isLocalPlayersTurn
in interface IClientModel


player's state:
on mobile devices, custom controls won't work until the video has been interacted with directly
autoplay won't work on mobile devices :(


Advantages
Notebooks
Notebooks, in particular the Jupyter notebooks, are nowadays common. Thus, they provide a familiar interface to a set of users, with lots of additional information to be found.
Notebooks are also easier to share, in particular to demonstrate something or use for show cases.
The JupyterLab extension provides an interface that gets close to an IDE or virtual machine, including shell access. This should feel very familiar to the majority of users: an editor and terminal are familiar to a large set of users. In addition, there is the possibility to have one or more notebooks open for experimentation.
Development for the notebooks is still ongoing (thanks to their popularity). While this is not particular tied to notebooks, it potentially allows for rapid fixes of the notebook or the infrastructure surrounding it.
BinderHub infrastructure
BinderHub is the system that runs Jupyter notebooks or labs for a multitude of users, each with their own specific container.
The BinderHub infrastructure makes uses of Docker containers, and often runs on Kubernetes. Both are tested and tried, and common pieces of infrastructure, making it straightforward to deploy BinderHub. Documentation is also geared towards this setup, and makes it even easier to deploy BinderHub.
Disadvantages and caveats
Notebooks
Notebooks are not ideal for every situation. In particular, cells can be executed out of order, causing state problems later in the notebook, which may impede reproducibility. Tools are (slowly) being developed to help avoid this issue, but it is largely in the developer’s and user’s hands to use a notebook in order. "Plain" programs (programs and scripts executed directly on the command line) do not have this issue.
Notebooks are not ideal for writing (and using) larger pieces of code that need to stick together, such as classes with several methods: such a class would have to be written inside one (large) cell. Such pieces of code should be moved into their own module or package, and imported in the notebook. This could be done using the binder system, where local code in a repository are added in addition to generic libraries and packages. This, however, requires user’s expertise which may not be available, or an effort that users may not be willing to make.
If the notebook interface is closed (that is, the browser page is closed), execution of a currently running cell will still continue, but the output will not be available; even when re-attaching to the notebook. Thus, users have to learn to save all outputs from longer running task to a file in the container image (not to their local disk), and retrieve that later. While this is similar to running a long-running job on, for example, a specialized cluster or high-performance machine, it may be somewhat unexpected in a notebook, where output is usually directly in the notebook.
For the above scenarios, using a plain program in a notebook Lab environment may be better: this program could be edited online (with the caveat that this editor is likely far from a user’s favorite editor), and run on the command line provided by the lab terminal. A Lab environment may also allow to combine several scripts in, for example, a bash script, so that a set of simple, task-specific, scripts can be run in a chain on the command line. Since notebooks have not been set out for this task, it remains yet to be seen how well this workflow really works.
BinderHub infrastructure
Using a Docker container for each user and notebook poses extra strains on the system, mostly in terms of disk space: each container is essentially a full-blown operating system, with a multitude of packages installed. Disk space is relatively cheap, so this may not be a problem, but it may be something to consider.
Kubernetes Pods make communication perhaps safer, but also more difficult. This holds in particular for file sharing: if this has to be done over http protocols, this may be slow and cumbersome. A direct (NFS) file system share may be a lot easier and quicker to set up and maintain. Therefore, it may be easier, and more practical, to run BinderHub on a single (virtual) machine: it would then fire off containers that connect to the local machine for data access and sharing. This bypasses the Kubernetes infrastructure, but since the default BinderHub uses this by default, it requires some work-arounds. The bits and pieces of BinderHub can be used, but may have to be reconnected in a different way.


Board-specific SPI control. More...
#include <gpio.h>
Go to the source code of this file.
Data Structures | |
| struct | board_spi_select |
| Board SPI select struct. More... | |
Typedefs | |
| typedef enum board_spi_select_id | board_spi_select_id_t |
| Board SPI select identifier type. | |
Enumerations | |
| enum | board_spi_select_id |
Board SPI select identifiers. More... | |
Functions | |
| static void | board_spi_select_device (struct spi_master *master, struct board_spi_select *sel) |
| Board SPI select given device. | |
| static void | board_spi_deselect_device (struct spi_master *master, struct board_spi_select *sel) |
| Board SPI deselect given device. | |
| static void | board_spi_init_select (struct board_spi_select *sel, board_spi_select_id_t sel_id) |
| Init Board SPI select. |
Board-specific SPI control.
Copyright (C) 2009 Atmel Corporation. All rights reserved.
Definition in file spi.h.
| typedef enum board_spi_select_id board_spi_select_id_t |
| enum board_spi_select_id |
| static void board_spi_deselect_device | ( | struct spi_master * | master, | |
| struct board_spi_select * | sel | |||
| ) | [inline, static] |
Board SPI deselect given device.
| master | SPI master struct | |
| sel | Board SPI select struct |
Definition at line 76 of file spi.h.
References gpio_set_value(), and board_spi_select::pin.
| static void board_spi_init_select | ( | struct board_spi_select * | sel, | |
| board_spi_select_id_t | sel_id | |||
| ) | [inline, static] |
Init Board SPI select.
| sel | Board SPI select struct | |
| sel_id | Board SPI select identifier |
Definition at line 88 of file spi.h.
References board_spi_select::pin, PORT_DIR_OUTPUT, PORT_INIT_HIGH, and port_select_gpio_pin().
| static void board_spi_select_device | ( | struct spi_master * | master, | |
| struct board_spi_select * | sel | |||
| ) | [inline, static] |
Board SPI select given device.
| master | SPI master struct | |
| sel | Board SPI select struct |
Definition at line 64 of file spi.h.
References gpio_set_value(), and board_spi_select::pin.


This is a step-by-step walkthrough of creating a simple website using the Owin Framework.
This walkthrough assumes that you have already completed the Hello, World walkthrough. If you have not completed it yet I suggest you at least read through these instructions before continuing with this walkthrough.
The end result of completing this tutorial is the Sample5 project in the Pages solution. If you want to jump straight to the finished solution rather than going through step by step, please clone the Pages repo and look at the Sample5 project.
If you would like to see this walkthrough completed please contact the authors. Thanks.


{# name = Code of Conduct #} {% extends "base.html" %} {% load bootcamp %} {% block body %}
We are dedicated to providing a harassment-free learning experience for everyone, regardless of gender, sexual orientation, disability, physical appearance, body size, race, or religion.
Be kind to others. Do not insult or put down other attendees. Behave professionally. Remember that sexist, racist, or exclusionary jokes are not appropriate.
People asked to stop any harassing behavior are expected to comply immediately. Anyone violating these rules may be asked to leave the classroom at the sole discretion of the instructors.
If you believe someone is violating the Code of Conduct we ask that you report it by emailing Professor David Koslicki or Professor Stephen W. Schaeffer. All reports will be kept confidential.
For more information, please see Penn State University's Resources for Reporting Wrongdoing.
Attribution: This Code of Conduct was adapted from the example in the book Teaching Tech Together, compiled by Greg Wilson. The example itself was based on the example policy from the Geek Feminism Wiki, created by the Ada Initiative and other volunteers.


You may define semantic features directly related to
tense, aspect and mood on this page as well as a syntactic feature related
to verb forms. If additional features are needed for defining tense, aspect or mood in your language,e.g., for defining arbitrary or
quasi-semantic verb classes, you may define them on the Other Features
page.
tense
Comrie (1985:9) defines
tense as
"... grammaticalized expression of location in time". Which
categories of tense are grammaticalized varies across languages. You
may create a hierarchy of values for the feature
tense as appropriate for your language. If your
language does not have tense as a grammatical category, leave this
section blank.
You may define a hierarchy of values for the
tense feature in one of two ways:
- Select among the following common hierarchy elements
and supplement with subtypes as needed, or
- Build your own
hierarchy.
Build your hierarchy from the top down starting with
subtypes of the supertype: tense.
aspect
According to Kibort (2008), "The term 'aspect' designates
the perspective taken on the internal temporal organisation of the
situation, and so 'aspects' distinguish different ways of viewing the
internal temporal constituency of the same situation (Comrie
1976:3ff,...; Bybee 2003:157). The 'situation' is meant here as [a]
general term covering events, processes, states, etc., as expressed by
the verb phrase or the construction. Unlike tense, which is
situation-external time, aspect is situation-internal and non-deictic,
as it is not concerned with relating the time of the situation to any
other time point."
"Aspectual meaning of a clause can be broken up into two
independent aspectual components (Smith 1991/1997): Aspectual
viewpoint [and] Situation type ... ." Aspectual viewpoint conveys
"the temporal perspective from which the situation is presented."
Situation type "classifies the situation according to its temporal
properties." (Kibort 2008)
See references below, especially Kibort (2008), for more
detail.
Viewpoint Aspect
Viewpoint aspect is also commonly referred to as grammatical
aspect. The most common viewpoint opposition is
perfective/imperfective.You may create a hierarchy consisting of only this opposition or you may create a hierarchy of your own, as appropriate for your language. If your language does not have viewpoint aspect (also commonly referred to as grammatical aspect) as a grammatical category, leave this section blank.
Build your own hierarchy of values for the feature aspect. Note: this is an alternative to choosing the binary opposition above -- if hierarchy elements are defined below the choice above is overridden.
Situation Aspect
The term "situation aspect" overlaps with, or subsumes,
lexical aspect, inherent aspect and some uses of the term
Aktionsart. Situation aspect values may be conveyed by the meaning of
the verb or through overt morphological marking however situation
aspect values may depend on qualities of verbal arguments or
sentential adjuncts (for example). In this case the values are
compositionally derived.
Note: The implementation of
situation aspect is still under development; There is, as yet, no
implementation of compositionally derived situation aspect. Currently
the situation feature may be used to
represent inherent lexical qualities or those conveyed through overt
morphological marking. This more accurately reflects uses of the term
"lexical aspect".
You may create a hierarchy of values for the feature situation, reflecting inherent or
morphologically marked situation aspect. If this is not appropriate
for your language, leave this section blank.
mood
According to Palmer (1986), the term 'mood' is generally used to refer to modality reflected in verbal inflection. The broader category modality is a somewhat vague grammatical category that is quite language specific. However, modality may be roughly described as relating to the opinions or attitudes of the speaker. Modality may include mood (e.g., subjunctive), modal systems and notions of hearsay or evidentiality. While we call this feature
mood it may be used to define values of the broader category of modality, if desired.
You may define values for the feature
mood below. You may select a simple set of values for the feature, reflecting a subjunctive/indicative contrast, or you may define a hierarchy of values for the feature as appropriate for your language. If you would like, you may leave this section blank.
Build your own hierarchy of values for the feature mood. Note: this is an alternative to choosing the binary opposition above -- if hierarchy elements are defined below the choice above is overridden.
form
In addition to the semantic features tense and aspect, your
grammar may utilize a syntactic feature characterizing the form the
verb takes. The form feature distinguishes
finite from non-finite forms and can be used to constrain auxiliary
complement forms. If not applicable, you may leave this section
blank.
If you have indicated on the word order page that your
language has auxiliary verbs, your grammar will include
finite and nonfinite as values of form. If your language does not have auxiliaries
you may still include finite and nonfinite as values
of form in your grammar:
It is often the case that languages have multiple non-finite
forms, e.g., English: infinitive, past participle, present
participle. If applicable, you may expand the hierarchy of verb form values in your grammar by providing
subtypes of finite and nonfinite:
Bybee, Joan L. 2003. Aspect. In: Frawley, W.J. (ed.)
International Encyclopedia of Linguistics. Second Edition. Oxford:
OUP. 157-158.
Comrie, Bernard. 1976. Aspect. Cambridge: CUP.
Comrie, Bernard. 1985. Tense. Cambridge: CUP.
Kibort, Anna. "Aspect." Grammatical Features. 7 January
2008. http://www.features.surrey.ac.uk/features/aspect.html.
Palmer, Frank R. 1986. Mood and Modality. Cambridge: CUP.
Smith, Carlota. 1997. The Parameter of Aspect. (Second
Edition). Dordrecht: Kluwer Academic Publishers. (Revised version of
Smith, Carlota. 1991. The Parameter of Aspect. Dordrecht: Kluwer
Academic Publishers).


The other day I came across a puzzle that involved implementing a spreadsheet class. Like a
regular spreadsheet the cells were referenced as A1, A2 etc .. I wanted to iterate the cells
in the spreadsheet. My first stab at the implementation looked as follows
importscala.collection.mutableobjectIteratorTest{defmain(args:Array[String]){valheight=4valwidth=4valss=newSpreadsheetV1(width,height)for(i<-0untilheight){for(j<-0untilwidth){valid=Spreadsheet.getIdFromRowAndCol(i,j)println("id = "+id)}}}}classSpreadsheetV1(width:Int,height:Int){valdata=Array.ofDim[String](height,width)defget(i:Int,j:Int)=data(i)(j)defset(i:Int,j:Int,value:String){data(i)(j)=value}}objectSpreadsheet{valCoordinates="""([A-Z])(\d+)""".rvalasciiValueForA='A'.toInt/** * Given a cell id retuns the row and col as tuple * eg. A2 -> (0, 1) */defgetRowAndColFromId(id:String):(Int,Int)={valCoordinates(row:String,col:String)=id// apply the regex to extract row & col// Now convert these to actual Int indexes in our 2d arrayvalrealRow=row.toCharArray()(0).toInt-asciiValueForAvalrealCol=col.toInt-1(realRow,realCol)}/** * Given row and col returns id * eg (0,0) => A1, (0,1) => A2 etc */defgetIdFromRowAndCol(row:Int,col:Int):String={(asciiValueForA+row).toChar+(col+1).toString}}
But its possible to have a nicer interface to iterate over the cells by implementing Iterator
trait. It also results in better encapsulation of the internal structure of the Spreadsheet
class. Following is an implementation of Spreadsheet class with Iterator trait.
The iterator code is not a core part of the Spreadsheet’s logic so there is no need for the
Spreadsheet class to directly implement Iterator. The iterator implementation can be moved out
into a separate class and the Spreadsheet class’s implementation can be kept simple.


SortStructuredArray()
Syntax
SortStructuredArray(ArrayName(), Options, OffsetOf(Structure\Field), TypeOf(Structure\Field) [, Start, End])Description
Sorts the specified structured array, according to the given options. The array must have an associated structure.
Parameters
ArrayName() The array to sort. Options It can be a combination of the following values: #PB_Sort_Ascending : Sort the array in ascending order (lower values first) #PB_Sort_Descending: Sort the array in descending order (higher values first) #PB_Sort_NoCase : Sort the string array without case sensitive (a=A, b=B etc..)OffsetOf(Structure\Field) Offset of the field in the structure. OffsetOf() may be used to retrieve the field offset in the structure associated to the array. TypeOf(Structure\Field) The field type of the field in the structure. It has to match the real structure field type. TypeOf() may be used to automatically retrieve the field type. Available types are: #PB_Byte : The structure field to sort is a byte (.b) #PB_Word : The structure field to sort is a word (.w) #PB_Long : The structure field to sort is a long (.l) #PB_String : The structure field to sort is a string (.s or $, fixed strings are not supported) #PB_Float : The structure field to sort is a float (.f) #PB_Double : The structure field to sort is a double (.d) #PB_Quad : The structure field to sort is a quad (.q) #PB_Character: The structure field to sort is a character (.c) #PB_Integer : The structure field to sort is an integer (.i) #PB_Ascii : The structure field to sort is an ascii character (.a) #PB_Unicode : The structure field to sort is a unicode character (.u)Start, End (optional) The index of the first and last element in the array that should be sorted. If these parameters are not specified, then the whole array is sorted.
Remarks
Fixed strings are not supported by the sort routine. If an array is not fully filled then null elements will be sorted first in ascending order and last in descending order.
Example
Structure Animal Name$ Speed.l EndStructure Dim Animals.Animal(2) Animals(0)\Name$ = "Tiger" Animals(0)\Speed = 10 Animals(1)\Name$ = "Jaguar" Animals(1)\Speed = 40 Animals(2)\Name$ = "Zebra" Animals(2)\Speed = 30 SortStructuredArray(Animals(), 0, OffsetOf(Animal\Name$), TypeOf(Animal\Name$)) For k=0 To 2 Debug Animals(k)\Name$+" - Speed: "+Str(Animals(k)\Speed) Next SortStructuredArray(Animals(), 0, OffsetOf(Animal\Speed), TypeOf(Animal\Speed)) For k=0 To 2 Debug Animals(k)\Name$+" - Speed: "+Str(Animals(k)\Speed) Next
See Also
SortArray(), RandomizeArray()
Supported OS
All


AGN-Specfit (AGN Spectral Fitting) is a modified version of QSFit. It is created as a pipeline to perform the analysis of SDSS Type 1 Active Galactic Nuclei optical and ultraviolet spectra. The prerequisites to run it are Python
(version 2.7), IDL
(version ≥ 8.1), and Gnuplot
(version ≥ 5.0). To make use the pipeline, you have to:
Gnuplot
.IDL
session in your working directory. Then, compile and run the IDL
scripts:
IDL> CD, "D:\path\where\AGN-Specfit\is\located"
IDL> compile
IDL> process_spectra
Python
scripts to combine all tables in "table" folder into one concatenated table. You have to install necessary modules first:
pip install -r requirements.txt
python multi_make_table.py
Detailed description about QSFIT can be found in http://qsfit.inaf.it. The reference paper can be found here. We utilized this code in our work which is titled A study of [O III]/[O II] Lines Ratio in Type 1 Active Galactic Nucleus.
ANNz2 is a public photometric redshift (photo-z) code that was originally developed by Sadeh et al. (2016). This code implements artificial neural network, boosted regression trees, and other machine learning methods to estimate and generate photo-z full probability distribution functions (PDFs). It also mitigates possible problems of non-representative or incomplete spectroscopic training samples by using weighting scheme.
Here we utilize the code to estimate our AGNs photo-z and determine their luminosity function in our work regarding Cosmic Evolution of Nearby Radio Active Galactic Nuclei. The forked and modified code is available for download at https://github.com/irhamta/ANNZ.
In this section, you can find various courseworks that I've done during my study. Some of them are listed below:


Taking the Coursera Deep Learning Specialization, Sequence Models course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng. See deeplearning.ai for more details.
Table of Contents
Sequence models & Attention mechanism
- Sequence models can have an attention mechanism.
- Algorithm will help your model understand where it should focus its attention given a sequence of inputs.
- This week, we will cover speech recognition and how to deal with audio data.
Various sequence to sequence architectures
Basic Models
This week, we will cover neural network models for handing sequence input to sequence output. (Machine translation, for instance) - Neural network is made of two parts, an encoder network for the sequence input which outputs a vector - A decoder network which takes the vector as input and outputs a sequence as output
Also effective for image captioning. If the goal is to input an image and output a caption, it is possible to to take an existing architecture (like AlexNet) and instead of outputting the values to a softmax layer, one can feed this to an RNN which generates the caption one word at at time,
There are some differences between this and generating sequences (last week).
Picking the most likely sentence
Machine translation is building a conditional language model. It requires two different networks instead of a single recurrent neural network.
There is an encoder network which figures out some representation of the input sentence before attempting to generate the translated output sequence. It is trying to predict the probability of an input sentence (in English, for example), conditioned by the probability of an output sentence (in French, for example).
How do you find the mosy likely translation for a given sentence? You don’t want to sample at random. Want to find the english sentence $y$ that maximizes the translation likeliness probability.
Most common way to find the best sentence is to use an algorithm called Beam search
. But why not just take a greedy appraoch (take words by most likeliness)?
Greedy search does poorly on maximizing the joint likeliness, because it focuses word as a time. We need to focus on the entire sequence as a whole.
Beam search
Continuing with our sample input sentence “Jane visite l’Afrique en septembre”, how do we search for the best possible English translation? (simplification, all words in lower case)
- Evalue the first most likely word. Beamsearch has a parameter called $B = \text{beam width}$ which allows it to choose three choices for each sequence.
- For each choice, consider what the next word is. Evaluate the probablity of the next word. Find likeliness of first and second word as a pair.
$$ P(y^{<1>}, y^{<2>} | x) = P(y^{<1>} | x) * P(y^{<2>}|x, y^{<1>}) $$
Take only the $B$ most likely options. At every step, you instantiate three copies of your network with different choices for your words.
- Continue for the rest of the sequence, chaining all the prior words as conditioning, keeping only the top $B$ choices at each step of the sequence. This will be terminated by the
<EOS>
symbol.
If the beam width is set to be one $B=1$, this is essentially the greedy search algorithm.
Refinements to Beam Search
We are trying to find the probability of an output sequence $y$, given an input sequence $x$. This can be expressed as:
$$ P(y^{<1>}, \dots, y^{<t_y>} | x) = P(y^{<1>}|x) * P(y^{<2>}|x,y^{<1>}) * \dots * P(y^{<t_y>}|x, y^{<1>}, \dots , y^{<t_y - 1>}) $$ $$ P(y^{<1>}, \dots, y^{<t_y>}|x) = \arg\max_y\prod\limits_{t=1}^{T_y}P(y^{<t>}|x, y^{<1>}, \dots, y^{<t-1>}) $$
When implementing this, most of these probabilities are much less than one. $ P(y|x) \approx 0$
In practice, we typically take the logs (this is a more numerically stable algorithm). The log function makes the search less prone to numerical underflow. Also, rather than multiplying a lot of numbers that are less than one (which is much more likely to give you a very small value), you take the sum of all logs of the probabilities (less likely to give you a very small value).
$$ \arg\max_y\sum\limits_{t=1}^{T_y}\log P(y^{<t>}|x, y^{<1>}, \dots, y^{<t-1>}) $$
Because summing the logs may also result in a very small value, an additional trick is to normalize by the output sequence length. This reduces the penalty for longer sentence translations.
$$ \dfrac{1}{T_y^\alpha}\sum\limits_{t=1}^{T_y}\log P(y^{<t>}|x, y^{<1>}, \dots, y^{<t-1>}) $$ $$ \alpha \approx 0.7 $$
How to choose the beam width $B$? The larger the value, the more choices, but also the more computationally expensive.
If the beam width is very large, you consider a lot of possibilities. Memory requrements also grow.
If the beam width is very small, then you get a worse result however this is faster.
In practice, Beam width is usually around ~10. A width of ~100 is very large for a production system. A beam width of >1000 is usually for milking a publication.
Try out different values of $B$ until you start to see diminishing returns.
Unlike exact search algorithms BFS, DFS, etc. Beam Search runs faster but is not guaranteed to find the exact maximum for $\arg\max_y P(y|x)$.
Error analysis in Beam Search
Beam search is an approximate search algorithm. How to check if beam search is making mistakes, or if the RNN model is causing mistakes?
Continuing with the example sentence: $$x = \text{Jane visite l’Afrique en septembre.} $$
Human translation returns: $$ y^* = \text{Jane visits Africa in September.} $$ Algorithm returns: $$ \hat{y} = \text{Jane visited Africa last September.} $$
The algorithm returns a bad translation because it changes the meaning of the sentence. There are two components of the model, your neural network encoder/decoder, and your beam search. How do you know which part of your model made the error?
Your RNN computes $P(y|x)$. To determine error, you should use your RNN to compute $P(y^*|x)$ and $P(\hat{y}|x)$. The value with the higher probability indicates which portion of your learning algorithm needs fine tuning.
- If $ P(y^*|x) \gt P(\hat{y}|x) $, then:
- Beam search is improperly choosing the highest probability sequence
- Else $ P(y^*|x) \leq P(\hat{y}|x) $
- RNN predicted that the poor translation was a better candidate than the correct translation and needs additional training/tuning.
Going through your dev set, you can determine which part of your algorithm to focus your attention on.
Bleu Score
Given an input sentence, there could be multiple translations that are equally as plausible. How do you evaluate this? You evalue this by using the Blue Score.
Example input: “Le chat est sur le tapis”.
Outputs:
- The cat is on the mat.
- There is a cat on the mat.
Bleu score measure how good the generated translation is. Bleu stands for Bilingual evaluation understudy.
Given an isolated word, count how many times it appears in the generated translation sentence, then count how many times it appears in your human translations. (Take the highest count of all human translation inputs as your numerator, take the count of all the machine translation values as your denominator.)
Find all sequential pairs of words (bigrams) in the machine translation output, then calculate the same fraction. Take the sum of all the bigrams found in the clipped human translations and divide it over the total unique bigrams found in the machine translation output.
The generic formula for your $n\text{-gram}$ is:
$$ P_n = \dfrac{ \sum\limits_{n\text{-gram} \in \hat{y}} \text{Count}_{\text{Clip}}(n\text{-gram}) }{ \sum\limits_{n\text{-gram} \in \hat{y}} \text{Count}(n\text{-gram}) } $$
If the machine translation matches the human translation, all $P_n$ values will be equal to $1$.
The final Bleu score (if you only care for up to 4 sequences) is calculated by:
$$ \text{BP} \exp (\dfrac{1}{4} \sum\limits_{n=1}^4 P_n) $$ $$ \text{BP} = \text{ Brevity Penalty} $$
BP tries to normalize for shorter translations. The naive bleu score will favor shorter translations, so BP will set the score lower if the machine generated translation length is less than the human translation length.
This is useful for handling any comparison between sequences.
Attention Model Intuition
So far, we have used an encoder/decoder type network for handling a task such as text translation. Attention is an improvement to this architecture. Consider the problem of long sequences.
A human would not memorize the entire input sentence before beginning the output. Rather, it is more likely that the human would translate the sentence part by part as they progress through the sentence structure.
Using a bi-directional RNN, attempt to perform machine translation for a given sentence. There is a hidden recurrent neural network that takes the output of the first bi-directional RNN. It has takes as input context $c$ which is generated by alpha variables $\alpha^{<1,1>}, \alpha^{<1, 2>}, \dots, \alpha^{<t, t’>}$ to create the output sequence.
Attention Model
Assume you have an input sentence, and you use a bi-directional RNN (GRU, LSTM) to generate a set of activations (for that timestep).
$$ a^{<t’>} = ( \overrightarrow{a}^{<t’>}, \overleftarrow{a}^{<t’>} ) $$
This is fed into a single-direction RNN as context $c$, where $c$ is the weighted sum of alphas from the lower bi-direction RNN.
$$ c^{<1>} = \sum\limits_{t’} \alpha^{<1,t’>}a^{<t’>} $$ $$ \alpha^{<t, t’>} = \text{amount of ‘attention’ that } y^{<t>} \text{ should pay to } a^{<t’>} $$
$$ a^{<t, t’>} = \dfrac{ \exp(e^{<t,t’>}) }{\sum\limits_{t’=1}^{T_x}\exp(e^{<t,t’>}) } $$
^ This formula ensures that all of your attention weights sum to 1. (ensures that your sequence is evaluated equally)
How do you compute $e$? You can use a small neural network and trust backpropagation.
$$ \text{Neural network state from previous time step} = s^{<t-1>} $$ $$ \text{Output of current state of Bi-Directional NN} = a^{<t’>} $$
One downside is this algorithm takes quadratic cost to run. If you have $T_x$ as your input sequence length and $T_y$ as your output sequence length, then your total number of attention parameters is $T_x * T_y$. In typical machine translation, the input and output is usually not that long, such that perhaps quadratic cost is acceptable.
Speech recognition - Audio data
Speech Recognition
Sequence to sequence models can perform very accurate speech recognition. Speech recognition problem is to take an audio clip $x$ and automatically find a text transcript $y$.
$x$ is air pressure over time. $y$ is a sequence of words. A common pre-processing step is to run your audio clip and generate a spectrogram.
Once upon a time, speech recognition algorithms used to be generated using phonemes. Linguists used to hypothesize that phonemes were the building blocks of all sounds. However, with end to end deep learning, phonemes are no longer necessary (no longer require hand engineered representations of audio).
Datasets of transcribed audio can range between 300 hours to 3000 hours, etc. Best commercial systems are trained on over 100,000 hours of audio.
One algorithm for speech recognition can be modeled with a 1000 to 1000 sequence neural network by collapsing repeating charcters.
Today, building a production scale speech recognition system requires a huge training set. Trigger word detection requires less data to train an effective learning algorithm.
Trigger Word Detection
There have been more and more devices that ‘wake up’ on input voice.
Literature is still evolving, but this is one example of an algorithm that can be used. Use a recurrent neural network. Input data is audio (maybe preprocessed as a spectrogram), the training data $y$ is set to 1 after the trigger word is spoken and 0 otherwise. (If the training data is too imbalanced, you can pad the time after trigger word is said with more 1’s)
Conclusion
You made it! Few final thoughts:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
Deep learning is a ‘super power’. With deep learning, you can make a computer see, synthesize art, generate music, translate language, render powerful models from medical sensor inputs.
Do whatever you think is the best you can do for humanity.


---
layout: presentation
title: RentHop Competition
permalink: /renthop-presentation/
---
class: center, middle, inverse
# Two Sigma RentHop Competition
Matthew Emery [(@lstmemery)](https://github.com/lstmemery)
June 1st, 2017
---
# Winning Kaggle Competitions by KazAnova
1. Understand the Data
2. Understand the Metric
3. Cross-Validate Early!
4. Hyperparameter Tuning
.footnote[[Source](https://www.hackerearth.com/practice/machine-learning/advanced-techniques/winning-tips-machine-learning-competitions-kazanova-current-kaggle-3/tutorial/)]
---
# Who are Two Sigma and RentHop?
- Two Sigma: AI Heavy New York Hedge Fund
- RentHop: Smart Apartment Search (New York Only)
- Reward: Recruitment to Two Sigma
![Two Sigma Salary](../images/twosigma_salary.png)
.footnote[[Source](https://www.glassdoor.com/Salary/Two-Sigma-Salaries-E241045.htm)]
---
# The Goal
- Predict how interested people will be in this:
![Example RentHop Listing](../images/renthop_listing.png)
---
# Understanding the Data
Training: 49352 Rows
Test: 74659 Rows
- Location Data
- Natural Language Data
- Image Data (78.5 Gb compressed)
- ...and everything you would else you would expect (price, bedrooms etc.)
![RentHop](../images/twosigma-renthop.png)
---
# Understand the Metric
Multiclass Log Loss (Low, Medium, High Interest)
- Note: This isn't ordinal
---
## Manager ID Count
Someone just used different transformations of Manager ID Count and scored in the top 15%
.footnote[[Source](https://blog.nycdatascience.com/student-works/renthop-kaggle-competition-team-null/)]
---
## Listing ID
- This pattern hinted at a possible data leak...
.footnote[[Source](https://www.kaggle.com/zeroblue/visualizing-listing-id-vs-interest-level)]
---
## Data Leak
The creation time of the image folders were correlated with interest.
- X-Axis: Day
- Y-Axis: Seconds
- .blue[Blue=Low]
- .green[Green=Medium]
- .red[Red=High]
.footnote[[Explanation](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries/discussion/32404)]
---
## Feature Engineering
A few interesting ones:
- Grouping by categorical features and finding count/median/mean/standard
deviation of numerical ones. (3rd Place)
- Inferring Points of Interest from text descriptions (Supermarket, Subway, etc.) (2nd Place)
- Leveraging duplicate data (Leads and lags on pricing) (11th Place)
- Exclamation marks in description
- Reverse GeoCoding New York Neighbourhoods
---
## Second Place Solution
@Faron
```
- 32 LightGBM models
- 9 Extreme Tree models (sklearn)
- 7 RF models (sklearn)
- 5 Keras models
- 3 XGBoost models
- @KazAnova's StackNet example base-level predictions
```
Best Model: LightGBM (CV: 0.50135/ Test: 0.50557)
Meta-modeled with a 2-layer neural network.
---
## An Aside on LightGBM
![LightGBM](../images/lightgbm.png)
- Faster than XGBoost
- Requires more hyperparameter optimization
---
## Second Place Solution
Grid-Search Bagging
Grid Search: Check cross-validation scores for each hyperparameter
in regular intervals.
e.g. Check maximum depth of XGBoost from 1 to 10.
Bagging (Bootstrap AGGregating): Sample the data many times, with
replacement
For each of 12 bags:
Grid search hyperparameters
If the new hyperparameters is better, blend it into the model
---
## StackNet
Written by Marios Michailidis (kazAnova) for his PhD
A Java-based, flexible meta-modelling network
![StackNet](../images/stacknet_modes.png)
[Source](https://github.com/kaz-Anova/StackNet)
---
#References
[2nd Place Solution](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries/discussion/32148)


Guzzle is an HTTP client library in PHP that puts all the power of cURL at your fingertips.
Part of cURL's power comes from its \u201cMulti\u201d interface, which allows you to make concurrent HTTP requests using non-blocking I/O.
Guzzle's HTTP handler implementation abstracts the complexity of cURL Multi into a simpler experience using Promises and PSR-7.
Learn how to use Guzzle to work with HTTP requests and web service APIs in an asynchronous way, potentially saving you time and increasing the throughput of your PHP applications and services.
Guzzle is a wrapper around cURL to fetch web pages.
Look at the Quick start with Guzzle


Name: Wale Ojo
Office: National Assembly Member
Party: PDP
Office: National Assembly Member
Party: PDP
Office: National Assembly Member
Party: APC
Office: National Assembly Member
Party: AD
Office: National Assembly Member
Party: LP
Office: Legislative Council Member
Party: PDP


Read objects from the AIDA (<http://aida.freehep.org/>) file and make them available as dataframes in R.
| Version: | 1.0.5 |
| Imports: | XML |
| Suggests: | testthat |
| Published: | 2018-01-26 |
| Author: | Andreas Pfeiffer |
| Maintainer: | Andreas Pfeiffer <apfeiffer1 at gmail.com> |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | aidar results |
| Reference manual: | aidar.pdf |
| Package source: | aidar_1.0.5.tar.gz |
| Windows binaries: | r-devel: aidar_1.0.5.zip, r-release: aidar_1.0.5.zip, r-oldrel: aidar_1.0.5.zip |
| OS X binaries: | r-release: aidar_1.0.5.tgz, r-oldrel: aidar_1.0.5.tgz |
| Old sources: | aidar archive |
Please use the canonical form https://CRAN.R-project.org/package=aidar to link to this page.


XG_03Jan12_stdT1
LCMS
(Top 20K)
LCMS
(Top 200K)
BPI
TIC
Scan Type
Scan Count
Scan Filter Text
HMS
7
HMSn
221
DMS
Dataset Detail Report
Dataset Info XML file


CRASH A film review by Martyn C. Winkler Copyright 1997 Martyn C. Winkler
Is CRASH Pornographic Trash?
*** 1/2 (out of ****)
David Cronenbergs newest creation, the intriguingly dark CRASH, is probably one of the most controversial films in today's cinemas. Until its release nationwide Friday, March 21, it was uncertain if the film would be released at all in the United States, due to Ted Turner, the owner of Fine Line Cinema. Turner has repeatedly stated that he hates the film and thinks it is nothing more than pornography. Many people agree fervently with his point of view. On the opposing side, however, many greatly admire CRASH, along with countless respected film critics and movie buffs. And while it has been banned in several countries internationally, it has won an impromptu invented prize at the prestigious Cannes Film Festival for "Originality, daring and audacity". It seems that you either love this movie, or hate it. This controversy is by itself reason enough to go see CRASH. Decide for yourself if you will join a boycott group to save our society from this morally corrupting film, or join the CRASH fan club to defend the right to make and view artistic trash...eh...Crash. For those of you who still need some more persuasion to pay six dollars for this film, I will try to review it as objectively as possible (but will probably end up defending it anyway). In one sentence the plot of CRASH seems very crude: people get their sexual kicks from car-crashes. However, the film is not about the plot. It is about our modern detached society, about destructive fetishism. More about that later. When the film opens, after a terrific credit-sequence, we meet a couple, James Ballard (named after the writer of CRASH; played by James Spader) and his wife Catherine (Deborah Unger). Both are engaged in pure physical sex...with someone else. Catherine is spread over the cold metallic hood of a small aircraft, James over his own desk. When the two meet afterwards, they discuss their experiences in a seemingly matter-of-fact way and appear not to be sexually satisfied at all. That night James is driving back to his work. One distracted glance later he finds himself colliding with another car, killing the driver, but leaving the passenger alive. It is Dr. Helen Remington (Holly Hunter). Soon after he is released from hospital, Helen introduces James to an obscure group of people whose lives evolve solely around car-crashes. Consequently, all of them are scarred extensively and some even have intricate prostheses. The leader of this group, Vaughan (Elias Koteas), who specializes in recreating celebrity car-crashes (like James Deans fatal collision), explains what the crashes mean to him: "It is sexual liberation." To put it plainly, it is the fetish of this group.  And a deadly crash would be the absolute peak of that pleasure. Frankly, this plot-outline does resemble that of a shallow porn-flick. It is true as well, that after every crash there are numerous sexually explicit scenes; not quite as shocking and perverse as some want us to believe, however. The question then arises: How does CRASH differ from DEEP THROAT? Director David Cronenberg has always been interested in artistic gore. His remake of THE FLY involved gruesome devouring of limbs; in DEAD RINGERS Jeremy Irons operates on his twin brother using gynecological instruments, killing him. What makes his films artistically successful is his style and form. Cronenberg always balances on the edge of acceptable and manages never to go over the top. His mis-en-scene transforms the ugliest scene into an intriguing portrait. His use of light and camera (shadows are very important) and his direction of the actors (adding to them a sense of detachment and aloofness) guarantees subtlety to sodomy, beauty to blood. For CRASH in particular, Cronenberg has managed to pull this off for sex rather than violence. In portraying destructive sexual fetishism, he has chosen car-crashes as the instruments of pleasure. Why cars? Perhaps because in the modern world cars are indeed a symbol for our sexuality. The bigger the car, the more successful and desirable we are. Need I go into the allegory of why everybody would rather have a shiny red convertible over a shabby brown pick-up truck? Maybe another reason to specifically use car-crashes is that they do not arouse anybody sexually, except the characters in the film. I cannot imagine that anyone could derive sexual pleasure from a collision with another car. Sure, it can be exhilarating (like the fantastically thrilling sequences on the highway), but certainly not orgasmic; not in the real world anyway. Cronenberg forces the audience to view the scenes with a certain detachment and focuses the attention on the destructive aspect of fetishism. He forces the viewer to think by using a virtually non-desirable fetish. CRASH is clearly not a pornographic exploitation movie. It is too cold, too mechanic for that. Rather, it is a dark view on modern society and its destructive (sexual) forces. The seething critics of this film have a lot in common with those of THE PEOPLE VS. LARRY FLYNT; they oppose the film for the wrong reasons. LARRY FLYNT is not about glorifying the founder of Hustler magazine; it is not even about the sex-industry. It is about a groundbreaking trial that helped establish and strengthen a little notion called 'freedom of speech'. CRASH is not about sex or pornography and it certainly does not glorify it. If only the protesters would actually know what they are protesting against (i.e., go see the film), they would realize their ignorance. When I saw this film in the cinema, my fellow audience members mainly consisted of older men. I suspect they did not get what they came for. Neither will any (premature) critics of CRASH.
<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Published: | 2017-04-25 |
| Author: | Grace Deng and Lu Tian |
| Maintainer: | Lu Tian <lutian at stanford.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| In views: | MetaAnalysis |
| CRAN checks: | RandMeta results |
| Reference manual: | RandMeta.pdf |
| Package source: | RandMeta_0.1.0.tar.gz |
| Windows binaries: | r-devel: RandMeta_0.1.0.zip, r-release: RandMeta_0.1.0.zip, r-oldrel: RandMeta_0.1.0.zip |
| OS X binaries: | r-release: RandMeta_0.1.0.tgz, r-oldrel: RandMeta_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=RandMeta to link to this page.


Test Github page...
We're sorry but openup doesn't work properly without JavaScript enabled. Please enable it to continue.


The mc package
Fair CTL model checker and debugger.
By Adnan Aziz, Tom Shiple
Information for programmers
Information for developers sorted by function
Information for developers sorted by file
Fair CTL model checker and debugger. Works on a flattened network.
Last updated on 1031104 18h39


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Welcome to Mint Salon and Spa
Salon Services
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Schedule your appointment by phone at<PHONE_NUMBER>.
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Open Mon 12-7 Tue-Fri 10-7 Sat-Sun 10-6
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Haircuts
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Women's Haircut
$45 & Up
Men's Haircut
$22 & Up
Boys (Age 10 and under)
$17 & Up
Wash and Style
$45 & Up
Shampoo/Additional Massage
$10/20
Updo
$65 & Up
Makeup
$50 & Up
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$15
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Eyebrows
$12
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$8
Forehead/Chin/Sides
$10
Full Face
$40
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$25
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$35
Underarms
$20
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$30
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$50
Bikini Line
$25-35
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$50
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$40 each
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$10 each
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Mini Facial 30 min (deep cleanse w/ steamer, extraction, moisturizer)
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$60
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Buy any 6 Facials Receive 7th FREE
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$15
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$5
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$15
2-Spa Pedi ( mini pedi plus exfoliation mint masque & massage)
$25
ADD: Callus Removal
$5
Additional 20 min massage
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$5-15
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SPECIAL COMBO DEAL SPA MANI/PEDI
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Saree Draping
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$40
Travel fee
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Extra assistants for large bridal parties
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Click the links in the left menu bar to select a demo application. Each of them was designed to be a real life web applications, and they demonstrate different language/framework possibilities.


PERSONAL SYSTEMS
Designed to inspire you
Sophisticated design meets exceptional performance in our new lineup of premium notebooks.
Take a closer look1 Based on HP's internal analysis as of 2/10/16 of vendors shipping >1 million units worldwide annually with clamshell design, Windows or OSX, measured at z-height.
2 Based on curved screen, consumer All-in-One's as of June 22, 2015


Rack::Profiler
GET
POST
PUT
PATCH
DELETE
Profile
Response
Steps
Step
Duration
% of Total
From Start
SQL Queries
Query
Duration
% of Total
From Start


Steppers portray progress through a sequence of steps.
The default stepper has a horizontal layout. The stepper fills all available space and evenly distributes its steps.
Use the aria-current attribute to denote the current step in the list.
The stepper can be sized and aligned using standard CSS:
Use the stepper--vertical modifier for a stepper with vertical layout.
New Mens Addidas Ultra Boost
Order total $220
We will notify you once it ships.
Guaranteed Wednesday, October 09.
New Mens Addidas Ultra Boost
Order total $220
We will notify you once it ships.
Guaranteed Wednesday, October 09.
To transition to an "information" or "attention" state, use the relevant stepper__item--transition-information or stepper__item--transition-attention modifier class.


I design and develop beautiful websites and mobile apps that modern trends demand
Some quick example text to build on the card title and make up the bulk of the card's content.


--- layout: default title: "Home" ---
ACM Teach LA pairs UCLA students with schools in Los Angeles to provide free computer science classes. Our goal is to empower all students with the ability to code, and use it to make a difference.
Due to updates with the COVID-19 pandemic, as of now, the majority of UCLA courses will be taught in person. As a result, Teach LA has shifted most of its classes back to in person instruction. However, our Python courses will utilize virtual spaces and will continue to operate remotely until further notice. As many of our partner schools continue to make the transition to in-person learning, we will work with teachers and our instructors to determine the best way to continue serving our students.
Teach LA's flagship program is teaching classes, which typically meet once or twice a week for a quarter. Classes have a wide variety of objectives, ranging from an introduction to python or web development to advanced topics such as machine learning and data science. Each class balances instruction and time for hands-on experimentation, with trained teachers helping students learn and use the material.
We've developed an open-source online code editor specifically targeted towards students learning how to code. Students can sign up easily, and our service stores and runs their code - which removes issues about using a specific computer, and allows students to access their code anywhere! We currently support Python, HTML, and Processing, and plan on adding more features soon!
Our dev team develops online modules called Learning Labs that each focus on a specific topic in computer science, such as CNNs, cryptography, and password secruity. Through interactive activities and visuals, our Learning Labs aim to make complicated concepts easy to digest!


24 hours of Blitz Sept 7th 1940
Bomb Type
Crude Oil Bomb
High Explosive Bomb
Incendiary Bomb
Data source
Guardian Data Store & London Fire Brigade Records


William
F. Gilreath
Nexus of Code, Writings, Apps
I am a multifaceted software
engineer, web developer, and computer scientist. I love working with
software--developing, testing, writing, and analyzing. In my spare
time, I write both code and technical articles, and narrative
prose--novels, short stories, chapters, and poetry.
Here is the web page to showcase some of the
algorithms, code, programming languages, writings, and web apps I have
created and written.
Blog
I have a blog about my technical
dabblings and other things...entitled "Much Ado About Nothing"
where I write about this, that, and the other.
Recent Research
Papers...
Here are some theoretical research monographs I have recently written,
and have been published by the
General Science Journal.
My stuff--source code, technical
writings, and web apps that are out there...
-
KVaaS - a Java library for using the key/value as a service (keyvalue.xyz) online web
data store.
- Boz
- compiler for a programming language developed using JavaCC.
-
Hash Sort - sorting by hashing into a matrix.
- FunCL
- interpreter for the FUNctor Clause Language, a functional style
programming language that is both like Forth and LISP but without the
reverse Polish notation or parenthesis.
DEF HELLOWORLD
PUSH EOLN PUSH "Hello World!!!" ECHO ECHO .
HELLOWORLD .
.
- XString
- a method of "flattening" an XML document into a linear string; then
an XML document can be handled as a string. Interestingly, one could
embed XML in XML using an XString.
-
Mynx Programming Language
- the user manual for a programming language I developed that is
Pascal/Algol-style syntax, but an object-oriented programming language.
-
Boz Programming Language
- the user manual for the the Boz programming language that I developed
that is a "duck typed" object-oriented programming language.
Web Apps
- Course
Evaluation / Work
Therapy Evaluation
- a web tool to submit information by e-mail from a web form using a
pastiche of web tools. From a web form to a form e-mail system to an
e-mail site forwarding to finally the various recipients.
- SEQu-RAmA -
the Search Engine QUery - Results AccuMulator Aggregator. A "super
search"
engine that searches via other search engines around the
planet...search of search. Essentially, a web scraping search engine
that searches and combines results among different search engines. Shortpaper on LinkedIn or Whitepaper (PDF)
.


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press
ESC
to skip/exit.
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📢 BROADCAST
OPTIONS


Create a clone of the instance.
Check if two instances are equal.
Returns an object with bars, beats & sixteenths, based on the current timeConfig.
Returns the current timeConfig (beatsPerBar and sixteenthsPerBeat).
Returns the total amount of bars (can be a float).
Returns the total amount of beats (can be a float).
Returns the total amount of sixteenths (can be a float).
Multiplies a musicTime with a scalar.
Sets the timeConfig.
Returns a readable string.
Convert to time in seconds, based on a given amount of beats per minute.
Returns the amount of beats.
Creates an instance from a string.
Creates a MusicTime instance from an amount of seconds.
Generated using TypeDoc
Adds a musicTime to the instance.


Giant Component¶
This example illustrates the sudden appearance of a giant connected component in a binomial random graph.
# Copyright (C) 2006-2018
# Aric Hagberg<EMAIL_ADDRESS># Dan Schult<EMAIL_ADDRESS># Pieter Swart<EMAIL_ADDRESS># All rights reserved.
# BSD license.
import math
import matplotlib.pyplot as plt
import networkx as nx
try:
import pygraphviz
from networkx.drawing.nx_agraph import graphviz_layout
layout = graphviz_layout
except ImportError:
try:
import pydot
from networkx.drawing.nx_pydot import graphviz_layout
layout = graphviz_layout
except ImportError:
print("PyGraphviz and pydot not found;\n"
"drawing with spring layout;\n"
"will be slow.")
layout = nx.spring_layout
n = 150 # 150 nodes
# p value at which giant component (of size log(n) nodes) is expected
p_giant = 1.0 / (n - 1)
# p value at which graph is expected to become completely connected
p_conn = math.log(n) / float(n)
# the following range of p values should be close to the threshold
pvals = [0.003, 0.006, 0.008, 0.015]
region = 220 # for pylab 2x2 subplot layout
plt.subplots_adjust(left=0, right=1, bottom=0, top=0.95, wspace=0.01, hspace=0.01)
for p in pvals:
G = nx.binomial_graph(n, p)
pos = layout(G)
region += 1
plt.subplot(region)
plt.title("p = %6.3f" % (p))
nx.draw(G, pos,
with_labels=False,
node_size=10
)
# identify largest connected component
Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)
G0 = Gcc[0]
nx.draw_networkx_edges(G0, pos,
with_labels=False,
edge_color='r',
width=6.0
)
# show other connected components
for Gi in Gcc[1:]:
if len(Gi) > 1:
nx.draw_networkx_edges(Gi, pos,
with_labels=False,
edge_color='r',
alpha=0.3,
width=5.0
)
plt.show()
Total running time of the script: ( 0 minutes 4.825 seconds)


Physics is the study of the natural or material world and natural phenomena around us, including the fundamental particles of nature and their interactions, properties of atomic nuclei, atoms, molecules, gases and solid matter. It covers fields such as astrophysics, cosmology, and physics of materials. The laws of physics are the laws which govern electricity and magnetism in modern devices such as mobile phones, computers, the internet, radio and television, the mechanics of aviation and space flight, quantum mechanics underlying magnetic resonance imaging in medicine etc.
Physics at Dublin Inistitute of technology enjoys a worldwide reputation, and provides an exceptionally stimulating environment for study and for subsequent postgraduate work. This course offers you the opportunity to study with world-leading experts in the School of Physics, with modules designed to provide you with a flexible qualification for employment across a range of research and industrial settings.
Students in the final year of DIT Physics and Physics and Astrophysics courses undertake a 9 week full-time research project, either in DIT or at a research institute or university abroad. Students travel to destinations as far afield as Australia and Hawaii for these projects.
Our Physics course includes a strong background in core physics subjects as well as specialised courses in condensed matter physics, including topics such as semiconductor devices, metal physics and superconductivity. Practical aspects of the course include topics in electronics and instrumentation.
Physoc aims to provide all things Physicsy to all physicsy people. While it’s true most of our members come from those studying maths or physics, we encourage absolutely anyone with even the slightest interest in physics to join. To achieve our noble aims we generally organise talks every two weeks, interspersed with social events and tutorials. Our talks have a physics theme and are aimed at all college members. We host board game nights and pub quizzes, as well as our annual Halloween Party and Christmas Party in conjunction with Mathsoc. We also aim to provide introductory tutorials in Latex and Inkscape (For 2nd/3rd year lab reports), as well as Mathematica (For 1st year TP’s and anyone else who despises tedious maths operations). This year we’re also hosting the first ever Maths-Physoc ball, currently in the “looking for a terrible pun” stages of development. This is going to be an absolute cracker of a night so keep an eye on our Facebook page for more details, and keep Wednesday the 16th of November free. Also new for this year, Physoc are introducing informal Lunchtime Chats, a chance to eat your packed/bought lunch in the Schrodinger Library, with a short casual talk by a researcher here at trinity, and free tea and coffee. Our trip adviser is currently holed up in his room, organising what promises to be a super trip away. There is talk of either a 1st term reading week or Christmas break international trip, so make sure and attend as many as events as possible early on in the year so we know who you are and are more likely to let you go (just kidding, we have a fair and square policy of first come first served at sign-ups, but it was worth a shot!) And for those of you looking to get even more involved, in early October there will be positions available on the organisation committee for our Irish Physics Students’ Conference, to be held in early March. We also taking suggestions for a more acronym friendly name. We also have a library from which members can borrow books, check out the library tab for more info.


Value Labels Browser
If your input file has value labels, you can display them, for each variable, on the Variables dialog box by checking this option. You can choose to have the value labels displayed in a Vertical area, just to the right of the variable names or in a Horizontal area, below the variable names. The default is to display the value labels in a horizontal box below the variable names.
File History Length
Stat/Transfer stores a history of the files that you have read for each Input File Type. You can control the number of files that are stored with this option. The default is ten specifications for each file type.
Add Time and Date to log messages
If this is checked, the time and date will be written to each line of your log.
Clear Log before opening input file
By default, the messages that are written to the Log dialog box are cleared when a new file is opened. You can override this behavior and save all messages by unchecking this box. Note that if you are concerned with permanently documenting your transfer activities, you should use the Automatic Transfer Logging option that will automatically write a log to disk for each transfer.
Stat/Transfer GUI font
This controls the font that is used in the Stat/Transfer user interface (GUI). It will be dependent on your operating system. You can use the Choose Font button to call up a dialog box that will allow you to choose a different font. After you make your selection, you will need to close and then restart Stat/Transfer for your change to take effect.  It is a good idea to remember the font that you started with.


20
Zophar Speaks: The Wicked Will Suffer
1 Then Zophar the Naamathite answered and said:
2 “Therefore my thoughts answer me,
because of my haste within me.
3 I hear censure that insults me,
and out of my understanding a spirit answers me.
4 Do you not know this from of old,
since man was placed on earth,
5 that the exulting of the wicked is short,
and the joy of the godless but for a moment?
6 Though his height mount up to the heavens,
and his head reach to the clouds,
7 he will perish forever like his own dung;
those who have seen him will say, ‘Where is he?’
8 He will fly away like a dream and not be found;
he will be chased away like a vision of the night.
9 The eye that saw him will see him no more,
nor will his place any more behold him.
10 His children will seek the favor of the poor,
and his hands will give back his wealth.
11 His bones are full of his youthful vigor,
but it will lie down with him in the dust.
12 “Though evil is sweet in his mouth,
though he hides it under his tongue,
13 though he is loath to let it go
and holds it in his mouth,
14 yet his food is turned in his stomach;
it is the venom of cobras within him.
15 He swallows down riches and vomits them up again;
God casts them out of his belly.
16 He will suck the poison of cobras;
the tongue of a viper will kill him.
17 He will not look upon the rivers,
the streams flowing with honey and curds.
18 He will give back the fruit of his toil
and will not swallow it down;
from the profit of his trading
he will get no enjoyment.
19 For he has crushed and abandoned the poor;
he has seized a house that he did not build.
20 “Because he knew no contentment in his belly,
he will not let anything in which he delights escape him.
21 There was nothing left after he had eaten;
therefore his prosperity will not endure.
22 In the fullness of his sufficiency he will be in distress;
the hand of everyone in misery will come against him.
23 To fill his belly to the full
God will send his burning anger against him
and rain it upon him into his body.
24 He will flee from an iron weapon;
a bronze arrow will strike him through.
25 It is drawn forth and comes out of his body;
the glittering point comes out of his gallbladder;
terrors come upon him.
26 Utter darkness is laid up for his treasures;
a fire not fanned will devour him;
what is left in his tent will be consumed.
27 The heavens will reveal his iniquity,
and the earth will rise up against him.
28 The possessions of his house will be carried away,
dragged off in the day of God's wrath.
29 This is the wicked man's portion from God,
the heritage decreed for him by God.”