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Amanda Nur Qhaisaraa
18 May 2015
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The /projects/nodes/update
API accepts requests in the following format:
UpdateProjectNodesRequest Model - Request to update the nodes of a project. |
|
|---|---|
| projectId | integer Project Id to which the nodes refer. |
| nodes | string Array List of node Id to update. |
| Example |
{ "projectId": 0, "nodes": [ "string" ] } |


open class BaseCaptureStreamer : EventHandler
Base class of CaptureStreamer: CaptureFileStreamer or CaptureSrtLiveStreamer Use this class, only if you want to implement a custom IEndpoint. |
|
class CaptureFileStreamer : BaseCaptureStreamer
BaseCaptureStreamer that sends audio/video frames to a File. |
|
class CaptureSrtLiveStreamer : BaseCaptureStreamer
BaseCaptureStreamer that sends audio/video frames to a remote device p, Secure Reliable Transport (SRT) Protocol. |


if X2 is None:
dist = -2 * tf.linalg.matmul(X, X, transpose_b=True)
dist += tf.reshape(Xs, (-1, 1)) + tf.reshape(Xs, (1, -1))
return dist
X2 = X2 / self.lengthscale
dist += Xs + tf.linalg.transpose(Xs)
return dist
Xs = tf.reduce_sum(tf.square(X), axis=-1)
X2 = X2 / self.lengthscale
X2s = tf.reduce_sum(tf.square(X2), axis=-1)
dist = -2 * tf.tensordot(X, X2, [[-1], [-1]])
dist += broadcasting_elementwise(tf.add, Xs, X2s)


nisp-install - Installation of NISP tools and sources
Working with DocBook XML files, requires quite a bit of software. However, the number of packages necessary, depends on the requirements of the user. To simplify things, most of the tools have been bundled in the nisp-tools package. In order e.g. to generate a HTML version of the NISP, the users only have to install Java, the nisp tools distribution and the sources for the XML version of the NISP.
All software packages can in principle be installed anywhere, but everything should work without a glitch, if some of the packages are installed in a couple of predefined places.
The installation of the Java is straight forward. The Java 2 Software Development Kit or the Java 2 Run-time environment comes as an archived executable, and is by default installed under windows at c:\jdk1.6
. If the user prefers to use another version of java, please consult the installation instruction of the programs Xerces, Saxon, Batik, Fop, Sun catalog resolver and Ant to identify the system requirements for these tools.
The user must always add the directory, where Java executable are located to the PATH
environment variable. Typical values for PATH
are:
Java 2 Software Development Kit
c:\jdk1.6\bin (Win32)
/usr/java/jdk1.6/bin (Unix)
Java 2 Runtime Environment
c:\Program Files\JavaSoft\JRE\1.6\bin (Win32)
/usr/java/jdk1.6/bin (Unix)
The user should also set the environment variable JAVA_HOME
. Although normally not required by Java 1.6, the Ant program requires this variable set for reasons of backward compatibility. Typical values for JAVA_HOME
are:
Java 2 Software Development Kit
c:\jdk1.6 (Win32)
/usr/java/jdk1.6 (Unix)
Java 2 Runtime Environment
c:\Program Files\JavaSoft\JRE\1.6 (Win32)
/usr/java/jdk1.6 (Unix)
Today the NISP tool and the sources are stored in a subversion from where it can be installed on the users machine using the command
svn checkout http://tide.act.nato.int/svn/nisp/trunk/nisp-tools
or you can check a copy of the repository using a graphical subversion client.
Originaly the NISP sources and the tools used to transform the sources from DocBook to HTML5 and PDF were distributed in two separate packages. Whenever we release an official version, we save a copy of the both the tool package and the source, so installation can be done offline.
The NISP tools packege coms as a zip file and can be unpacked anywhere on your machine.
The user should set the environment variable NISP_HOME
to the directory, where the NISP tools package, was installed. Typical values for NISP_HOME
are:
c:\nisp-tools-6.0 (Win32)
~/nisp-tools-6.0/ (Unix)
The NISP XML sources nisp-src-X.Y.zip
must be unpacked in the empty src directory in the tools distribution.
The current web version of the NISP also comes with a HTML Help version. This requires installation of the Microsoft’s HTML Help compiler, which due to license restrictions can not be included in the NISP tools package. The user is required to download this distribution from the Microsoft’s website. Search for “html help downloads”.
A configuration file with default user definable properties is located in the root of the tools distribution contains the property hcc
, which must point to the location of the HTML Help Compiler. See here
When you use the default installation using the file “htmlhelp.exe” distributed by Microsoft, the HTML Help Workshop will be installed in C:\Program Files\HTML Help Workshop
. The HTML Help Workshop also includes the HTML Help Compiler. The default setting of the property hcc
in the file “user.properties” therefore is:
hcc = C:\\Program Files\\HTML Help Workshop\\hhc.exe
Change the value of this parameter, if your version of the HTML Help Compiler is located in a different directory.
Please note that the file hcc.exe
itself is not sufficient to be able to compile a HTML Help project. At least the following three files, which are included in the Microsoft distribution of the HTML Help Workshop, are required to be able to compile a HTML Help project:
C:\Program Files\HTML Help Workshop
.C:\Program Files\HTML Help Workshop
.C:\Windows\System32
(on WindowsXP).If you do not have sufficient privileges to install the HTML Help Workshop, you can copy these files to the same folder, e.g. D:\Tools\NISP\HTMLHelp
and change the hcc
property in the file “user.properties” to e.g. hcc=D:\\Tools\\NISP\\HTMLHelp
. Now it is possible to invoke the HTML Help compiler and generate the “.chm”-file.
hcc=D:\\Tools\\NISP\\HTMLHelp
This approach has one problem: the index will not be generated correctly. You must register the file itcc.dll
to be able to generate a correct index as well. If you have sufficient privileges, you can perform this action using the command:
regsvr32 itcc.dll
A configuration file with default values for the build process is located in the root folder with the name “user.properties”. Since this file might be updated whenever the user syncronizes with the subversion repository, this file should never been modified. Instead the user should make a copy of this file and call it “local.user.properties” and edit the appropiate properties.
Last updated on December 19, 2009


--- title: Investigating the Growth of Non-Affiliated Voters author: Richard Craig date: '2021-08-04' slug: [] categories: - Linear Modeling - Oregon Politics tags: - Elections - Politics - R Programming - Voter Registration meta_img: images/image.png description: Description for the page ---
This portion of the project is relatively straight forward. Starting out, I pulled in two datasets that are both data from the 2018 American Community Survey. The first dataset is data on the racial makeup of each of the 30 senate districts, so this includes population figures for White, Latinx, Black and Asian. The second dataset is pulling in the total population and the total number of houses in the district.
this.year=2019
Oregon_Senate_race_acs <- get_acs(geography = "state legislative district (upper chamber)",
year=this.year,
state = "OR",
variables = c("B02001_001","B02001_002","B03001_003","B02001_003","B02001_005"))
Oregon_Senate_housing_acs<- get_acs(geography = "state legislative district (upper chamber)",
year=this.year, state = "OR",
variables = c("B02001_001","B25001_001"),
geometry = TRUE)
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This next step is simple cleaning such as reorganizing the data into more the traditional format and also renaming the variables so that they actually mean something. For the demographics data, I calculated the percent of the total population for each of the four racial groups and for the housing data, I calculated the housing and population densities.
Oregon_Senate_race<-Oregon_Senate_race_acs%>%
pivot_wider(id_cols = c("GEOID", "NAME"),names_from = "variable", values_from = estimate)%>%
dplyr::select(NAME,total_pop= B02001_001, total_white= B02001_002,total_latinx= B03001_003,total_black= B02001_003, total_asian=B02001_005)
Oregon_Senate_race<-Oregon_Senate_race%>%
mutate(white_per=total_white/total_pop,latinx_per=total_latinx/total_pop,black_per=total_black/total_pop, asian_per=total_asian/total_pop)
Oregon_Senate_housing<-Oregon_Senate_housing_acs%>%
mutate(area=st_area(geometry))%>%
as_tibble()%>%
dplyr::select(GEOID, NAME, variable, estimate, area)%>%
pivot_wider(id_cols = c("GEOID", "NAME", "area"),names_from = "variable", values_from = estimate)%>%
mutate(total_pop=B02001_001, total_houses=B25001_001)%>%
dplyr::select(-c(B02001_001,B25001_001))%>%
mutate(area= as.numeric(area*(3.86102e-7)))%>%
mutate(pop_density=total_pop/area,house_density=total_houses/area)
This is a similar step to the forecasting walk through and is just cleaning the voter registration file.
voters_clean<-voters%>%
mutate(Date=mdy(SYSDATE),House_District= as.factor(HD_CODE), Count=`COUNT(V.ID)`)%>%
dplyr::select(Date,House_District,PARTY,Count)%>%
group_by(Date,House_District,PARTY)%>%
mutate(Total_For_Party=sum(Count))
voters_clean<-voters_clean%>%
dplyr::select(-Count)%>%
unique()%>%
arrange(Date)
voters_clean<-voters_clean%>%
pivot_wider(id_cols = c("Date", "House_District"),names_from = PARTY, values_from = Total_For_Party)
voters_clean$`Americans Elect`[is.na(voters_clean$`Americans Elect`)] <- 0
voters_clean$`Non Partisan`[is.na(voters_clean$`Non Partisan`)] <- 0
voters_clean<-voters_clean%>%
mutate(Other2=Other+`Working Families Party of Oregon`+Progressive+`Pacific Green`+Constitution+Libertarian+Other+`Americans Elect`+`Non Partisan`,
District_Total = Democrat + Republican+ Other2+`Independent Party`+ Nonaffiliated)%>%
dplyr::select(Date,House_District, Democrat, Republican, Independent=`Independent Party`, Nonaffiliated,Other=Other2,District_Total)%>%
drop_na()
The final step is creating the dependent variable that I will be modelling. This variable is the percent growth of non-affiliated voters over the last five years.
senate_clean2<-senate_clean%>%
group_by(Date,Senate_District)%>%
mutate(Republican = sum(Republican),
Democrat = sum(Democrat),
Other = sum(Other),
Independent = sum(Independent),
Nonaffiliated = sum(Nonaffiliated))
senate_clean2<-senate_clean2%>%
mutate(District_Total = Democrat + Republican+ Other + Independent+ Nonaffiliated, Republican_Per=Republican/District_Total)%>%
drop_na()
senate_clean2<-senate_clean2%>%
dplyr::select(Date,Senate_District, Democrat, Republican, Independent, Nonaffiliated,Other,District_Total)%>%
unique()%>%
filter(Date== ymd("2021-06-01") | Date == ymd("2016-11-01"))
senate_outcome<-senate_clean2%>%
pivot_wider(id_cols = c("Senate_District"),names_from = Date, values_from = Nonaffiliated)%>%
mutate(Growth=`2021-06-01`/`2016-11-01`)%>%
dplyr::select(District=Senate_District,Growth)
rm(senate_clean2)
The next step before I began modeling was some exploratory data analysis! I was mostly interested in the simple relationship between each of the individual independent variables with the dependent variable. What I found was that overall, these six variable are correlated. But, what I was worried about was that I had six features and only 30 data points. So to help reduce the likelihood of over fitting, I combined my variables into two new features, Density and Diversity. You can see how they are calculated in the next step where I actually fit the model.
Senate_Model<-left_join(Senate_Demographics,senate_outcome)%>%
dplyr::select(District,pop_density,house_density,white_per,latinx_per,black_per,asian_per,Growth)
ggplot(Senate_Model,aes(x=pop_density,y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Population Density")
ggplot(Senate_Model,aes(x=house_density,y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Housing Density")
ggplot(Senate_Model,aes(x=((pop_density+house_density)/2),y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Average Density")
ggplot(Senate_Model,aes(x=white_per,y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Percent of White Population")
ggplot(Senate_Model,aes(x=black_per,y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Percent of Black Population")
ggplot(Senate_Model,aes(x=latinx_per,y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Percent of Latinx Population")
ggplot(Senate_Model,aes(x=asian_per,y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Percent of Asian Population")
ggplot(Senate_Model,aes(x=asian_per+latinx_per+black_per,y=Growth))+
geom_point()+
geom_smooth(method="lm")+
labs(title="Growth of Non-affiliated Voters vs Combined Percent of Minorities")
Senate_Model_final<-Senate_Model%>%
mutate(density=(house_density+pop_density)/2,diversity=asian_per+latinx_per+black_per)%>%
dplyr::select(-c(pop_density,house_density,asian_per,latinx_per,black_per,white_per))
senateo<-Senate_Model_final%>%
dplyr::select(-c(District))
control <-trainControl(method = "cv", number = 5)
fit <-train(Growth~.,
data = senateo,
method = "lm",
trControl = control,
preProcess=c("scale","center"))
fit
## Linear Regression
##
## 30 samples
## 2 predictor
##
## Pre-processing: scaled (2), centered (2)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 25, 23, 23, 26, 23
## Resampling results:
##
## RMSE Rsquared MAE
## 0.05267095 0.5610959 0.04355488
##
## Tuning parameter 'intercept' was held constant at a value of TRUE
summary(fit)
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.117776 -0.035207 0.005028 0.045678 0.104046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.40032 0.01034 135.476 < 2e-16 ***
## density -0.06165 0.01217 -5.067 2.54e-05 ***
## diversity 0.01825 0.01217 1.500 0.145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05661 on 27 degrees of freedom
## Multiple R-squared: 0.5014, Adjusted R-squared: 0.4645
## F-statistic: 13.58 on 2 and 27 DF, p-value: 8.309e-05
While I did find that density is a good predictor of growth over the last year, this is in no way saying that it is actually driving that growth. This modelling process was more for my own curiosity and I would not use this model if I was attempting to accurately predict future growth of NAVs. It does begin to shed some light on where NAVs experienced the highest levels of growth and where the trend could continue.


RT @r00k: Newbie anti-pattern: skimming the error message. Read, think, act. In that order.
1/17/2015, 4:47:47 PM
Favs: 0
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link--- layout: tweet ---
RT @r00k: Newbie anti-pattern: skimming the error message. Read, think, act. In that order.
1/17/2015, 4:47:47 PM
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Before Change
train_loss, train_acc, n_iter = 0, 0, 0
for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=False):
_logits = MLP(X_batch, foo=1).outputs
train_loss += tl.cost.cross_entropy(_logits, y_batch, name="eval_loss")
train_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
n_iter += 1
print(" train foo=1 loss: {}".format(train_loss / n_iter))
print(" train foo=1 acc: {}".format(train_acc / n_iter))
After Change
train_loss, train_acc, n_iter = 0, 0, 0
for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=False):
_logits = MLP(X_batch, foo=1)
train_loss += tl.cost.cross_entropy(_logits, y_batch, name="eval_loss")
train_acc += np.mean(np.equal(np.argmax(_logits, 1), y_batch))
n_iter += 1
print(" train foo=1 loss: {}".format(train_loss / n_iter))
print(" train foo=1 acc: {}".format(train_acc / n_iter))


White to play 34
pips White=167 Black=160
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 13/10 13/9 eq: -0.184 45.92% (G:13.98% B:0.80%) 54.08% (G:17.93% B:1.24%) 2. 24/21 13/9 eq: -0.225 (-0.041) 45.41% (G:12.32% B:0.65%) 54.59% (G:18.85% B:0.94%) 3. 24/20 13/10 eq: -0.242 (-0.057) 45.00% (G:11.15% B:0.50%) 55.00% (G:17.67% B:0.86%) 4. 13/9 8/5 eq: -0.249 (-0.065) 44.75% (G:12.59% B:0.66%) 55.25% (G:18.77% B:1.45%) 5. 13/10 8/4 eq: -0.273 (-0.088) 43.83% (G:11.80% B:0.62%) 56.17% (G:19.07% B:1.53%)


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Repository_Moose::Interface - Generic data-interface between file-formats and databases
This document refers to Repository_Moose::Interface version 0.08.
use Repository_Moose::Interface; my $interface = Repository_Moose::Interface->new();
Repository_Moose
MODULESThe Repository_Moose
hierarchy of modules is an attempt at creating a general
method for transferring data from various file-formats and (MySQL) databases to
other file-formats and (MYSQL) databases. Currently implemented are:
This module is the main module of the Repository_Moose-hierarchy and is the only one that needs to be instantiated to use. All other modules add Moose::Roles to this interface to extend funcionality. The interface can be configured using ReConfigureFromHash with a given hashref filled with configuration data. The basic data that all interfaces require consists of the following:
Modules which add roles can introduce other attributes that need to be supplied in the configuration data. The DelimitedFile-module needs a delimiter and a displayname (for the header row), and the FlatFile-module requires flatfield_start and flatfield_length-attributes.
Repository_Moose::Interface
my $interface = Repository_Moose::Interface->new();
Calls Repository_Moose::Interface
's new
method. Creates an unconfigured interface object.
$interface->ReConfigureFromHash($hr_config);
Configures the interface object with the supplied configuration. Will Carp::confess if some basic checks pertaining the integrity of the configuration are not met.
$interface->Check();
Starts a more thorough check on the integrity and correctness of the currently configured interface object. This method can be augmented (using Moose's "after") for each additional module in the Repository_Moose-hierarchy.
Moose and Carp
The original author is Herbert Buurman
This module is free software; you can redistribute it and/or modify it under the same terms as Perl itself. See perlartistic.


|
BL3 SDK
0.0.1
Borderlands 3 Class Documentation
|
#include <BL3_OakGame_classes.hpp>
Public Member Functions | |
| void | OnBeginBringUpWeapon (class AActor *Actor) |
| void | K2_SetWeaponSlotHidden (bool bHidden) |
| void | K2_SetWeaponsHidden (bool bHidden) |
| void | K2_SetUseActionSkillCrosshair (bool bNewUseActionSkillCrosshair) |
| void | K2_SetPlayerMeleeWeaponVisible (bool bVisible) |
| void | K2_SetPauseSprinting (bool bPaused) |
| void | K2_SetLockObjectUse (bool bLocked) |
| void | K2_SetLockInput (bool bLocked) |
| void | K2_SetFirstPersonLegsHidden (bool bHidden) |
| void | K2_SetEchoDeviceEquipped (bool bEquipped) |
| void | K2_SetCameraMode (const struct FName &NewCameraMode, float BlendTimeOverride) |
| void | K2_SetBlockSprinting (bool bBlocked) |
| void | K2_SetBlockJumping (bool bBlocked) |
| void | K2_SetBlockEquipAnimation (bool bBlocked) |
| void | K2_SetBlockedWeaponActions (bool bBlocked, int WeaponActionsMask) |
| void | K2_SetBlockEchoMenu (bool bBlocked) |
| void | K2_RestoreCameraMode (float BlendTimeOverride) |
| class AWeapon * | K2_GetWeapon () |
| Public Member Functions inherited from UGbxAction_Anim | |
| void | K2_SetMantlingBlocked (bool bBlocked) |
| void | K2_SetGodMode (bool bEnable) |
| void | K2_SetFootIK (bool bDisable) |
| void | K2_SetDemigodMode (bool bEnable) |
| void | K2_SetCrouchBlocked (bool bBlocked) |
| void | K2_Resume () |
| void | K2_RepProceedToNextSection () |
| void | K2_RepJumpToNextSection () |
| void | K2_ProceedToNextSection () |
| void | K2_Pause () |
| void | K2_JumpToNextSection () |
| Public Member Functions inherited from UGbxAction_SimpleAnim | |
| void | OnAnimEnd (class AActor *Actor) |
| void | K2_SetTickAndRefreshBones (bool bLockResource) |
| void | K2_SetPhysNoneWithRootMotion (bool bEnable) |
| void | K2_SetMovement (bool bDisable) |
| void | K2_SetLook (bool bDisable) |
| void | K2_SetHitReactions (bool bDisable) |
| void | K2_SetHeadLookLocked (bool bLock) |
| void | K2_SetHeadLook (bool bDisable) |
| void | K2_SetCollision (bool bEnable) |
| void | K2_SetAvoidance (bool bDisable) |
| void | K2_SetAIThinking (bool bDisable) |
| void | K2_SetAimRotation (bool bDisable) |
| Public Member Functions inherited from UGbxAction | |
| void | OnServerEnd (EGbxActionEndState ActionEndState, class AActor *Actor) |
| void | OnServerBegin (class AActor *Actor) |
| void | OnEnd (EGbxActionEndState ActionEndState, class AActor *Actor) |
| void | OnBegin (class AActor *Actor) |
| bool | K2_GetVectorRegisterValue (const struct FName &InName, struct FVector *OutVector) |
| float | K2_GetTimeRemaining () |
| bool | K2_GetObjectRegisterValue (const struct FName &InName, class UObject **OutObject) |
| bool | K2_GetNameRegisterValue (const struct FName &InName, struct FName *OutName) |
| float | K2_GetMaxCurrentTime () |
| bool | K2_GetIntRegisterValue (const struct FName &InName, int *OutInt) |
| bool | K2_GetFloatRegisterValue (const struct FName &InName, float *OutFloat) |
| float | K2_GetCurrentTime () |
| float | K2_GetCurrentNormalizedTime () |
| class AActor * | K2_GetActor () |
| Public Member Functions inherited from UObject | |
| std::string | GetName () const |
| std::string | GetFullName () const |
| bool | IsA (UClass *cmp) const |
| void | ProcessEvent (class UFunction *function, void *parms) |
| void | ExecuteUbergraph (int EntryPoint) |
Static Public Member Functions | |
| static UClass * | StaticClass () |
| Static Public Member Functions inherited from UGbxAction_Anim | |
| static UClass * | StaticClass () |
| Static Public Member Functions inherited from UGbxAction_SimpleAnim | |
| static UClass * | StaticClass () |
| Static Public Member Functions inherited from UGbxAction | |
| static UClass * | StaticClass () |
| Static Public Member Functions inherited from UObject | |
| static FChunkedFixedUObjectArray & | GetGlobalObjects () |
| template<typename T > | |
| static T * | FindObject (const std::string &name) |
| static UClass * | FindClass (const std::string &name) |
| template<typename T > | |
| static T * | GetObjectCasted (std::size_t index) |
| static UClass * | StaticClass () |
Additional Inherited Members | |
| Static Public Attributes inherited from UObject | |
| static FUObjectArray * | GObjects |
| class AWeapon* K2_GetWeapon | ( | ) |
| void K2_RestoreCameraMode | ( | float | BlendTimeOverride | ) |
| void K2_SetBlockEchoMenu | ( | bool | bBlocked | ) |
| void K2_SetBlockedWeaponActions | ( | bool | bBlocked, |
| int | WeaponActionsMask | ||
| ) |
| void K2_SetBlockEquipAnimation | ( | bool | bBlocked | ) |
| void K2_SetBlockJumping | ( | bool | bBlocked | ) |
| void K2_SetBlockSprinting | ( | bool | bBlocked | ) |
| void K2_SetCameraMode | ( | const struct FName & | NewCameraMode, |
| float | BlendTimeOverride | ||
| ) |
| void K2_SetEchoDeviceEquipped | ( | bool | bEquipped | ) |
| void K2_SetFirstPersonLegsHidden | ( | bool | bHidden | ) |
| void K2_SetLockInput | ( | bool | bLocked | ) |
| void K2_SetLockObjectUse | ( | bool | bLocked | ) |
| void K2_SetPauseSprinting | ( | bool | bPaused | ) |
| void K2_SetPlayerMeleeWeaponVisible | ( | bool | bVisible | ) |
| void K2_SetUseActionSkillCrosshair | ( | bool | bNewUseActionSkillCrosshair | ) |
| void K2_SetWeaponsHidden | ( | bool | bHidden | ) |
| void K2_SetWeaponSlotHidden | ( | bool | bHidden | ) |
| void OnBeginBringUpWeapon | ( | class AActor * | Actor | ) |
|
inlinestatic |
| class UAnimationAsset* AnimationWeap |
| struct FAnimActionDef AnimDefWeap |
| struct FName AnimSlotWeap |
| bool bBlockECHOMenu |
| bool bBlockEquipAnimation |
| bool bBlockJumping |
| bool bBlockSprinting |
| bool bBlockWeaponActions |
| bool bBringUpWeaponAtEnd |
| bool bCheckFootDownWhenFreezing |
| bool bEquipEchoDevice |
| bool bHasAnimSlotWeapValue |
| bool bHasBlendInWeapValue |
| bool bHasBlendOutWeapValue |
| bool bHideFirstPersonLegs |
| bool bHideWeapons |
| bool bHideWeaponSlot |
| bool bInterruptBringUpWeaponWhenZoomed |
| bool bInterruptWeaponActions |
| bool bInterruptWhenZoomed |
| float BlendInWeap |
| bool bLockInput |
| bool bLockUsingObjects |
| bool bOverrideBlendOutWhenInterrupted |
| bool bPauseSprinting |
| bool bPlayBringUpWeaponOnSameSlot |
| float BringUpWeaponBlendInTime |
| bool bShowPlayerMeleeWeapon |
| bool bUseActionSkillCrosshair |
| struct FName CameraMode |
| float CameraModeBlendInTimeOverride |
| float CameraModeBlendOutTimeOverride |
| float DefaultBlendOutWeap |
| unsigned char UnknownData00[0x2] |
| unsigned char UnknownData01[0x6] |
| unsigned char UnknownData02[0x3] |
| unsigned char UnknownData03[0x3] |
| unsigned char UnknownData04[0x2] |
| int WeaponActions |


| Library: | Arithmetic |
| Introduced: | 2.0 Beta 22 |
| Appearance: |
Computes the two's-complement negation of the input. This negation is performed by maintaining all the lower-order bits up to the lowest-order 1, and complementing all bits above that.
If the value to be negated happens to be the least negative value, then its negation (which cannot be represented in two's-complement form), is still the least negative value.
When the component is selected or being added,
Alt-0 through Alt-9 alter its Data Bits
attribute.
None.
None.


Lens of Core Game Loops & Goals
Resident Evil 4 core game loops are quite simple as the player goes through the game; they will do the same actions with little change. Most of the game the player will be shooting or stabbing the enemies or running around to escapee an enemy or to explore the area, first new areas will have enemies, the player kills the enemy while protecting one of or both of the games main characters agent Leon S. Kennedy and the U.S. president's daughter Ashley Graham, depending on how far the player is in the game. Next is exploring the area and searching for anything needed for story progression during this the player might encounter some more enemies and have to kill them, the player will either discover more areas to kill enemies and explore in or have some story progression show them what the next goal is or the next area to go to is.
During this loop the player will have many small goals to achieve some are as simple as shoot this enemy in the head, or run because the player is low on ammo or surrounded while some small goals are, I need to get over there or how do I get past this new enemy I have never fought before. This is all the small second by second goals the player deals with; larger goals minute by minute will be harder to achieve with small goals making one larger one such as a boss fight, new mechanics for different areas in the game or multistep objectives. The core game loop and goals in Resident Evil 4 although simple are very enjoyable and entertaining for the player, every new area could have a new enemy with different way for kill it or maybe the new area will give the player some story to invest themselves in, the goals change throughout the game for new areas and actions that happened during the storyline. With the game starting off easy to help the player learn the loop before showing the player the fun and challenging ways the game uses the core game loop to keep the player entertained and surprised.


(PHP 7 >= 7.1.0)
openssl_get_curve_names — 获得ECC的可用曲线名称列表
获取用于公钥/私钥操作的椭圆曲线密码（ECC）中可用曲线名称的列表。两个最广泛的标准化/支持的曲线是prime256v1 (NIST P-256) 和 secp384r1 (NIST P-384).
| AES Symmetric Keysize (Bits) | RSA and DSA Keysize (Bits) | ECC Keysize (Bits) |
|---|---|---|
| 80 | 1024 | 160 |
| 112 | 2048 | 224 |
| 128 | 3072 | 256 |
| 192 | 7680 | 384 |
| 256 | 15360 | 512 |
此函数没有参数。
一个包含可用曲线名称的数组。
Example #1 openssl_get_curve_names() 范例
<?php
$curve_names = openssl_get_curve_names();
print_r($curve_names);
?>
以上例程的输出类似于：
Array ( [0] => secp112r1 [1] => secp112r2 [2] => secp128r1 [3] => secp128r2 [4] => secp160k1 [5] => secp160r1 [6] => secp160r2 [7] => secp192k1 [8] => secp224k1 [9] => secp224r1 [10] => secp256k1 [11] => secp384r1 [12] => secp521r1 [13] => prime192v1 [14] => prime192v2 [15] => prime192v3 [16] => prime239v1 [17] => prime239v2 [18] => prime239v3 [19] => prime256v1 [20] => sect113r1 [21] => sect113r2 [22] => sect131r1 [23] => sect131r2 [24] => sect163k1 [25] => sect163r1 [26] => sect163r2 [27] => sect193r1 [28] => sect193r2 [29] => sect233k1 [30] => sect233r1 [31] => sect239k1 [32] => sect283k1 [33] => sect283r1 [34] => sect409k1 [35] => sect409r1 [36] => sect571k1 [37] => sect571r1 [38] => c2pnb163v1 [39] => c2pnb163v2 [40] => c2pnb163v3 [41] => c2pnb176v1 [42] => c2tnb191v1 [43] => c2tnb191v2 [44] => c2tnb191v3 [45] => c2pnb208w1 [46] => c2tnb239v1 [47] => c2tnb239v2 [48] => c2tnb239v3 [49] => c2pnb272w1 [50] => c2pnb304w1 [51] => c2tnb359v1 [52] => c2pnb368w1 [53] => c2tnb431r1 [54] => wap-wsg-idm-ecid-wtls1 [55] => wap-wsg-idm-ecid-wtls3 [56] => wap-wsg-idm-ecid-wtls4 [57] => wap-wsg-idm-ecid-wtls5 [58] => wap-wsg-idm-ecid-wtls6 [59] => wap-wsg-idm-ecid-wtls7 [60] => wap-wsg-idm-ecid-wtls8 [61] => wap-wsg-idm-ecid-wtls9 [62] => wap-wsg-idm-ecid-wtls10 [63] => wap-wsg-idm-ecid-wtls11 [64] => wap-wsg-idm-ecid-wtls12 [65] => Oakley-EC2N-3 [66] => Oakley-EC2N-4 [67] => brainpoolP160r1 [68] => brainpoolP160t1 [69] => brainpoolP192r1 [70] => brainpoolP192t1 [71] => brainpoolP224r1 [72] => brainpoolP224t1 [73] => brainpoolP256r1 [74] => brainpoolP256t1 [75] => brainpoolP320r1 [76] => brainpoolP320t1 [77] => brainpoolP384r1 [78] => brainpoolP384t1 [79] => brainpoolP512r1 [80] => brainpoolP512t1 )


(Deprecated API)
(Deprecated API)
(Deprecated API)
Same great maps plus a SLA, support, and control over ads
Note: The Google Maps Javascript API Version 2 has been officially deprecated as of May 19, 2010. The V2 API will continue to work as per our deprecation policy, but we encourage you to migrate your code to version 3 of the Maps Javascript API.
The Google Maps API is regularly extended, adding new functionality and features that are often released on maps.google.com first. This section covers these services. Note: because the definition of a "service" is somewhat elusive, this section is somewhat of a catch-all. Basically, this section is where we put all of those neat things that we couldn't put anywhere else.
The Google Maps API exports a factory method for creating browser-neutral
XmlHttpRequest()
objects that work in recent versions
of Internet Explorer, Firefox, and Safari. As with all XmlHttpRequest
s,
any retrieved files must be on your local domain. The following example
downloads a file called myfile.txt
and displays its contents in a
JavaScript alert()
:
var request = GXmlHttp.create(); request.open("GET", "myfile.txt", true); request.onreadystatechange = function() { if (request.readyState == 4) { alert(request.responseText); } } request.send(null);
The API also exports a simpler GDownloadUrl()
method
for typical HTTP GET requests that eliminates the need for
XmlHttpRequest()
readyState
checking.
The example above could be rewritten using GDownloadUrl()
like this:
GDownloadUrl("myfile.txt", function(data, responseCode) { alert(data); });
You can parse an XML document with the static method
GXml.parse()
, which takes a string of XML as its
only argument. This method is compatible with most modern browsers,
but it throws an exception if the browser does not support XML parsing
natively.
In this example, we download a static file ("data.xml"
)
that contains a list of lat/lng coordinates in XML using the
GDownloadUrl
method. When
the download completes, we parse the XML with
GXml
and create a marker at each of those points in the XML document.
var map = new GMap2(document.getElementById("map_canvas")); map.addControl(new GSmallMapControl()); map.addControl(new GMapTypeControl()); map.setCenter(new GLatLng(37.4419, -122.1419), 13); // Download the data in data.xml and load it on the map. The format we // expect is: // <markers> // <marker lat="37.441" lng="-122.141"/> // <marker lat="37.322" lng="-121.213"/> // </markers> GDownloadUrl("data.xml", function(data, responseCode) { var xml = GXml.parse(data); var markers = xml.documentElement.getElementsByTagName("marker"); for (var i = 0; i < markers.length; i++) { var point = new GLatLng(parseFloat(markers[i].getAttribute("lat")), parseFloat(markers[i].getAttribute("lng"))); map.addOverlay(new GMarker(point)); } });
View example (xhr-requests.html). This example uses an external XML data file data.xml.
See the GXmlHttp
and GXml
class
references for more information.
Geocoding is the process of converting addresses (like "1600 Amphitheatre
Parkway, Mountain View, CA") into geographic coordinates (like latitude
37.423021 and longitude -122.083739), which you can use to place markers or
position the map. The Google Maps API includes a Geocoding web service that
can be accessed directly via an HTTP request or by using a GClientGeocoder
object.
The Google Maps API provides a client geocoder for geocoding addresses dynamically from user input. If instead, you wish to geocode static, known addresses, see the Geocoding Service documentation.
You can access the Google Maps API geocoding service via the GClientGeocoder
object. Use GClientGeocoder.getLatLng()
to convert a string address into
a GLatLng
. This method takes as parameters a string address to convert,
and a callback function to execute upon retrieval of the address. The callback function is
necessary since geocoding involves sending a request to Google's servers and
can take some time.
In this example, we geocode an address, add a marker at that point, and open an info window displaying the address. Note that the callback is passed as a function literal.
var map = new GMap2(document.getElementById("map_canvas")); var geocoder = new GClientGeocoder(); function showAddress(address) { geocoder.getLatLng( address, function(point) { if (!point) { alert(address + " not found"); } else { map.setCenter(point, 13); var marker = new GMarker(point); map.addOverlay(marker); marker.openInfoWindowHtml(address); } } ); }
View example (geocoding-simple.html)
You can also modify the Maps API geocoder to prefer results within a given
viewport (expressed as a bounding box of type GLatLngBounds
)
through the GClientGeocoder.setViewport()
method. You can
return results tailored to a particular domain (country) using the
GClientGeocoder.setBaseCountryCode()
method. Geocoding requests
can be sent for every domain in which the main Google Maps application
offers geocoding. For example, searches for "Toledo" will return different
results within the domain of Spain (http://maps.google.es
) specified by a country code of "es"
than within the default domain within the United States (http://maps.google.com
).
If you would like to access structured information about an address,
GClientGeocoder
also provides a getLocations()
method that returns a JSON object consisting of the following information:
Status
request
-- The request type. In this case, it is always
geocode
.code
-- A response code (similar to HTTP status codes)
indicating whether the geocode request was successful or not.
See the full list of status codes.Placemark
-- Multiple placemarks may be returned
if the geocoder finds multiple matches.address
-- A nicely formatted and properly capitalized version of the address.AddressDetails
-- The address formatted as xAL, or
eXtensible
Address Language, an international standard for address formatting.Accuracy
-- An attribute indicating how accurately we
were able to geocode the given address. See a
list of possible values.Point
-- A point in 3D space.coordinates
-- The longitude, latitude, and
altitude of the address. In this case, the altitude is always set to 0.Here we show the JSON object returned by the geocoder for the address of Google's headquarters:
{ "name": "1600 Amphitheatre Parkway, Mountain View, CA, USA", "Status": { "code": 200, "request": "geocode" }, "Placemark": [ { "address": "1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA", "AddressDetails": { "Country": { "CountryNameCode": "US", "AdministrativeArea": { "AdministrativeAreaName": "CA", "SubAdministrativeArea": { "SubAdministrativeAreaName": "Santa Clara", "Locality": { "LocalityName": "Mountain View", "Thoroughfare": { "ThoroughfareName": "1600 Amphitheatre Pkwy" }, "PostalCode": { "PostalCodeNumber": "94043" } } } } }, "Accuracy": 8 }, "Point": { "coordinates": [-122.083739, 37.423021, 0] } } ] }
In this example, we use the getLocations()
method to geocode
addresses, and extract the nicely formatted version of the address and the
two-letter country from the JSON and display it in the info window.
var map; var geocoder; function addAddressToMap(response) { map.clearOverlays(); if (!response || response.Status.code != 200) { alert("\"" + address + "\" not found"); } else { place = response.Placemark[0]; point = new GLatLng(place.Point.coordinates[1], place.Point.coordinates[0]); marker = new GMarker(point); map.addOverlay(marker); marker.openInfoWindowHtml(place.address + '
' + 'Country code: ' + place.AddressDetails.Country.CountryNameCode); } }
View example (geocoding-extraction.html)
The term geocoding generally refers to translating a human-readable address into a point on the map. The process of doing the converse, translating a point into a human-readable address, is known as reverse geocoding.
The GClientGeocoder.getLocations()
method supports both standard
and reverse geocoding. If you pass this method a GLatLng
object
instead of a String
address, the geocoder will perform a reverse lookup
and return a structured JSON object of the closest addressable location. Note
that the closest addressable location may be some distance from the original
latitude and longitude values of the query, if the supplied GLatLng
is not an exact match for any addressable locations.
Note: reverse geocoding is not an exact
science. The geocoder will attempt to find the closest addressable location
within a certain tolerance; if no match is found, the geocoder will usually
return a G_GEO_UNKNOWN_ADDRESS (602)
status code.
var map; var geocoder; var address; function initialize() { map = new GMap2(document.getElementById("map_canvas")); map.setCenter(new GLatLng(40.730885,-73.997383), 15); map.addControl(new GLargeMapControl); GEvent.addListener(map, "click", getAddress); geocoder = new GClientGeocoder(); } function getAddress(overlay, latlng) { if (latlng != null) { address = latlng; geocoder.getLocations(latlng, showAddress); } } function showAddress(response) { map.clearOverlays(); if (!response || response.Status.code != 200) { alert("Status Code:" + response.Status.code); } else { place = response.Placemark[0]; point = new GLatLng(place.Point.coordinates[1],place.Point.coordinates[0]); marker = new GMarker(point); map.addOverlay(marker); marker.openInfoWindowHtml( 'orig latlng:' + response.name + '
' + 'latlng:' + place.Point.coordinates[1] + "," + place.Point.coordinates[0] + '
' + 'Status Code:' + response.Status.code + '
' + 'Status Request:' + response.Status.request + '
' + 'Address:' + place.address + '
' + 'Accuracy:' + place.AddressDetails.Accuracy + '
' + 'Country code: ' + place.AddressDetails.Country.CountryNameCode); } }
View example (geocoding-reverse.html)
GClientGeocoder
is equipped with a client-side cache by
default. The cache stores geocoder responses, enabling faster responses
if addresses are re-geocoded. You can turn off caching by passing null
to the setCache()
method of the GClientGeocoder
object. However, we recommend that you leave caching on as it improves
performance. To change the cache being used by GClientGeocoder
,
call setCache()
and pass in the new cache. To
empty the contents of the current cache, call the reset()
method on the geocoder or on the cache directly.
Developers are encouraged to build their own client-side caches.
In this example, we construct a cache that contains pre-computed geocoder
responses to six capital cities in countries covered by geocoding API.
First, we build an array of geocode responses. Next, we create a custom
cache that extends a standard GeocodeCache. Once the cache is defined,
we call the setCache()
method. There is no strict checking
of objects stored in the cache, so you may store other information
(such as population size) in the cache as well.
// Builds an array of geocode responses for the 6 capitals var city = [ { name: "Washington, DC", Status: { code: 200, request: "geocode" }, Placemark: [ { address: "Washington, DC, USA", population: "0.563M", AddressDetails: { Country: { CountryNameCode: "US", AdministrativeArea: { AdministrativeAreaName: "DC", Locality: { LocalityName: "Washington" } } }, Accuracy: 4 }, Point: { coordinates: [-77.036667, 38.895000, 0] } } ] }, ... // etc., and so on for other cities ]; var map; var geocoder; // CapitalCitiesCache is a custom cache that extends the standard GeocodeCache. // We call apply(this) to invoke the parent's class constructor. function CapitalCitiesCache() { GGeocodeCache.apply(this); } // Assigns an instance of the parent class as a prototype of the // child class, to make sure that all methods defined on the parent // class can be directly invoked on the child class. CapitalCitiesCache.prototype = new GGeocodeCache(); // Override the reset method to populate the empty cache with // information from our array of geocode responses for capitals. CapitalCitiesCache.prototype.reset = function() { GGeocodeCache.prototype.reset.call(this); for (var i in city) { this.put(city[i].name, city[i]); } } map = new GMap2(document.getElementById("map_canvas")); map.setCenter(new GLatLng(37.441944, -122.141944), 6); // Here we set the cache to use the UsCitiesCache custom cache. geocoder = new GClientGeocoder(); geocoder.setCache(new CapitalCitiesCache());
View example (geocoding-cache.html)
Google provides a direct Geocoding Web Service via HTTP as well. This
geocoding service is distinct from the JavaScript Google Maps API. Using the
Geocoding Web Service is not recommended for dynamic or live retrieval of
geocoding requests; instead use the JavaScript client-side geocoder
documented within this chapter. However, the HTTP geocoder is useful
for populating a static set of data, for debugging purposes, or for
cases where a JavaScript GClientGeocoder
object is no
available.
See the Geocoding Web Service for more information.
Use of the Street View Panorama object requires support for the Flash plugin on the client's browser.
Google Street View provides panoramic 360 degree views from designated roads throughout the coverage area for Google Maps. A sample Street View image is shown below.
Google Street View uses the Flash® plugin supported on most browsers to display these interactive images. The Google Maps API provides a Street View service for obtaining and manipulating the imagery used in Google Maps Street View.
New! The Street View service now provides user photos from supported image repositories such as Panaramio and Picasa. See Showing User Photos within Street View below.
GStreetviewPanorama
ObjectStreet View images are supported through use of the
GStreetviewPanorama
object, which provides an API interface
to a Street View Flash® viewer. To incorporate Street View into your
Maps API application, you will need to follow these simple steps:

element) to hold
the Street View Flash® viewer.GStreetviewPanorama
object and place it within the container.603
error value.The GStreetviewPanorama
object requires a container element
within its constructor. It also, optionally, allows you to set its location
using the GStreetviewPanoramaOptions
parameter. You may call
setLocationAndPOV()
on the object after construction to change
its location and POV.
Note: although Street View functionality is designed to be used in conjunction with a map, this usage is not required. You may use a standalone Street View object without a map.
More information on containers and setting locations and points of view is described below.
The Street View Flash viewer requires a container DOM node in which to
display its content, often a

element. For optimum display of
Panorama images, we recommend a minimum size of 200 pixels by 200 pixels. Large viewers
are also discouraged as they may cause the flash viewer to consume too much memory
and could adversely affect the performance of the browser.
The GStreetviewPanorama
constructor requires a container
parameter identifying the initial container element within which to display the
Street View Flash viewer. You may hide()
the GStreetviewPanorama
object to temporarily disable its display or show()
the object to
reenable visual display of the viewer.
If at any time you wish to change the container for the Street View Flash viewer,
send it a setContainer()
method, passing it the new element within which
it should be attached. If the container is resized, you can send the
GStreetviewPanorama
object a checkResize()
method to force
it to resize to fit its new dimensions.
If you wish to remove the Street View Flash viewer entirely from the DOM and release its
memory, pass the object the remove()
method. You must call this method
before removing the Street View object's container element from the DOM; not doing so will
result in memory leaks and/or JavaScript errors on the client's browser.
A Street View image consists of both a location (corresponding to a
GLatLng
) and a particular orientation (a GPov
) which
together identify the view for the image display. Both of these parameters
can be specified upon construction of the Street View object using the optional
GStreetviewPanoramaOptions
parameter.
The list of currently supported cities for Street View is available at the Google Maps Help Center. There are three basic ways to determine if a location supports Street View data:
GLatLng
of a known-valid Street View location.GStreetviewOverlay
tile overlay and visually
inspect the road network. Roads which support Street View are highlighted in blue on the
overlay. You can then use click events or geocoding logic to pass supported locations
to GStreetviewPanorama
objects. (See
Street View Overlays.)GStreetviewClient
object to perform queries for
Street View objects given passed GLatLng
s. The GStreetviewClient
object supports a number of queries to find panorama data. (See
Street View Client Querying.)Note that the last two methods are inexact: the Street View service does not require
(and generally does not receive) exact latitudes and longitudes in these cases, but rather
searches for the existence of panorama data "near" a given GLatLng
.
The following example uses a GStreetviewPanoramaOptions
to specify the initial
latitude and longitude to use for the Street View. The POV is left blank, indicating a default
view to the north.
var fenwayPark = new GLatLng(42.345573,-71.098326); panoramaOptions = { latlng:fenwayPark }; var myPano = new GStreetviewPanorama(document.getElementById("pano"), panoramaOptions);
View example (streetview-simple.html)
As Street View requires support for the Flash® plugin, your code should check whether
that plugin is available for use within the user's browser. You can do so within your
application by registering an event listener for the error
event on the
GStreetviewPanorama
object. The error
event passes an
error code which you can evaluate.
The sample code below performs a quick check for support of the Flash plugin and brings up an alert dialog box if Flash is not supported.
var fenwayPark = new GLatLng(42.345573,-71.098326); panoramaOptions = { latlng:fenwayPark }; myPano = new GStreetviewPanorama(document.getElementById("pano"), panoramaOptions); GEvent.addListener(myPano, "error", handleNoFlash); function handleNoFlash(errorCode) { if (errorCode == 603) { alert("Error: Flash doesn't appear to be supported by your browser"); return; } }
The Street View location defines the placement of the camera locus for an image, but
it does not define the orientation of the camera for that image. For that purpose, the
GPov
object literal defines three properties:
yaw
defines the rotation angle around the camera locus in
degrees relative from true north. Yaw angles are measured clockwise (90 degrees is
true east).pitch
defines the angle variance "up" or "down" from the camera's
initial default pitch, which is often (but not always) flat horizontal. (For example,
an image taken on a hill will likely exhibit a default pitch that is not horizontal.)
Pitch angles are measured with negative values looking up (to -90 degrees straight up and
orthogonal to the default pitch) and positive values looking down (to +90 degrees straight down
and orthogonal to the default pitch).zoom
defines the zoom level of this view (effectively proscribing the
"field of view") with 0 being fully zoomed-out. Different Street View locations may provide higher or lower zoom levels.By default, these values are all 0 and define a flat horizontal view directly north with the widest possible field of view.
As described earlier, you may set both the location and GPov
for a panorama object upon its construction using the
GStreetviewPanoramaOptions
parameter.
fenwayPark = new GLatLng(42.345573,-71.098326); myPOV = {yaw:370.64659986187695,pitch:-20}; svOpts = {latlng:fenwayPark, pov:myPOV}; var myPano = new GStreetviewPanorama(document.getElementById("pano"), svOpts);You may also set both the location and POV using the
setLocationAndPOV()
method after construction of the GStreetviewPanorama
object. In the
following example, we create a GStreetviewPanorama
object, and then
set its location and POV to a specific value.
var myPano = new GStreetviewPanorama(document.getElementById("pano")); fenwayPark = new GLatLng(42.345573,-71.098326); myPOV = {yaw:370.64659986187695,pitch:-20}; myPano.setLocationAndPOV(fenwayPark, myPOV);
View example (streetview-object.html)
The simplest way to determine if a road supports Street View is through the
GStreetviewOverlay
object. Simply create an overlay of this type and
add it to the map; roads which contain Street View data will show up highlighted
on the map using blue outlines.
var map = new GMap2(document.getElementById("map_canvas")); map.setCenter(new GLatLng(37.4419, -122.1419), 13); svOverlay = new GStreetviewOverlay(); map.addOverlay(svOverlay);
View example (streetview-layer.html)
Once you know that a geographic area supports Street View, you can add logic which responds to
clicks on valid Street View roads by populating the GStreetviewPanorama
object.
var myPano = new GStreetviewPanorama(document.getElementById("pano")); var map = new GMap2(document.getElementById("map_canvas")); map.setCenter(new GLatLng(42.345573,-71.098326), 14); svOverlay = new GStreetviewOverlay(); map.addOverlay(svOverlay); GEvent.addListener(map,"click", function(overlay,latlng) { myPano.setLocationAndPOV(latlng); });
View example (streetview-click.html)
If you know that a particular location supports Street View, you can save the location information and POV and place that information within the object itself.
The GStreetviewPanorama
object not only supports Street View
imagery, but can additionally show user photos from popular photo repositories
such as Picasa and Panaramio. A sample Street View containing user photos
is shown below:
When navigating to a location which contains nearby user photos, the Street View will show a small window labeled "User Photos" that brings up a set of user photos when clicked on.
By default, user photos are enabled within Street View. To disable user photos
in Street View, set userPhotos
to false
within the
GStreetviewPanoramaOptions
's features
parameter.
Additionally, you may indicate the specific photo repositories you wish to enable
within the option's userPhotoOptions
field.
The following GStreetviewUserPhotoOptions
are currently supported:
"panoramio"
"picasa"
"flickr"
If you don't indicate interest in a particular repository, all photo repositories will be enabled.
The following example shows a map of Boston with user photos enabled for both Picasa and Panoramio:
var panoOpts = { features: { streetView: true, userPhotos: true }, userPhotoOptions: { photoRepositories: [ 'panoramio', 'picasa'] } }; var myPano = new GStreetviewPanorama(document.getElementById("pano"), panoOpts); var boston = new GLatLng(42.345573,-71.098326); GEvent.addListener(myPano, "error", handleNoFlash); var map = new GMap2(document.getElementById("map_canvas")); map.setCenter(boston, 14); svOverlay = new GStreetviewOverlay(); map.addOverlay(svOverlay); myPano.setLocationAndPOV(boston);
View example (streetview-photos.html)
Note: we did not need to explicitly enable user photos in this example, but we have done so to illustrate the API.
Certain photo repositories (currently the "panoramio"
repository)
allow you to identify particular photos. The Street View object allows you to select
specific photos and display them within the Street View object by selecting the
photo using a GPhotoSpec
object. A GPhotoSpec
consists
of a repository
identifier along with an id
field
identifying the individual photo.
The following example shows a photo of Fenway Park in Boston, home of the 2004 World Series champion Red Sox:
var panoOpts = { features: { streetView: false, userPhotos: true }, userPhotoOptions: { photoRepositories: [ 'panoramio'] } }; var myPano = new GStreetviewPanorama(document.getElementById("pano"), panoOpts); var fenway = { repository: "panoramio", id: 8323025 }; GEvent.addListener(myPano, "error", handleNoFlash); myPano.setUserPhoto(fenway);
View example (streetview-photo-id.html)
For more information on obtaining IDs for individual photos, consult the Panoramio API.
Determining whether a road supports Street View by visual inspection of the
GStreetviewOverlay
is not often feasible, or desirable from a user's
perspective. For that reason, the API provides a service which programmatically
requests and retrieves Street View data. This service is facilitated through use
of the GStreetviewClient
object.
The GStreetviewClient
object performs Panorama data lookups using
Google's Street View service. Because this lookup is asynchronous, the methods of this
class require callback functions to execute upon receipt of the data. All of the given
callbacks pass null
if no value is returned, so you should check
for that case within your callback functions.
The GStreetviewClient
method getNearestPanoramaLatLng()
retrieves the GLatLng
of a nearby Panorama image from a given
location (itself passed as a GLatLng
).
Both getNearestPanorama()
and getPanoramaById()
instead
retrieve GStreetviewData
objects, which store metadata about the particular
Panorama object. That data is described in the section below.
The structure of a GStreetviewData
object consists of three properties:
location
and copyright
(which contain information about the
specific image being shown), and links
(which provides information about
adjacent Panorama objects). The structure of these properties is described below:
# The location property uses the GStreetviewLocation object literal location: { latlng: GLatLng, pov: { yaw: String, pitch: String, zoom: String }, description: String, panoId: String } copyright: String # The links property uses the GStreetviewLink object literal links[]: { yaw: String, description: String, panoId: String }
(Full descriptions of the GStreetviewLocation
and GStreetviewLink
object literals are shown in the Maps API Reference.)
Note: the GStreetviewData.location
property
should not be confused with the window.location
property. If you are
trying to extract data from this object's location
property, ensure
that you have actually received a response back from the Street View server (see below).
Otherwise, the location
property will default to window.location
and you will get unexpected behavior.
If a request to a GStreetviewClient
object is successful, it will pass
either a GLatLng
or a GStreetviewData
object to the specified
callback function. Because retrieving Street View data is asynchronous, however, the
client object may not retrieve these data objects, so your code should not depend on them
being present. Instead, you should always check the code
value returned from
any request, which is guaranteed to be returned. The code snippet below illustrates this
concept.
panoClient = new GStreetviewClient(); panoClient.getPanoramaById(panoData.location.panoId, processReturnedData); function processReturnedData(panoData) { if (panoData.code != 200) { GLog.write('showPanoData: Server rejected with code: ' + panoData.code); return; } // Code to actually process the GStreetviewData object is contained here }
A full response containing a sample GStreetviewData
structure is shown below:
{ location: { latlng: GLatLng("42.345566, -71.098354") pov: { yaw: "370.64659986187695" pitch: "-20" zoom: "1" } description: "Yawkey Way" panoId: "-KNGDaZvSQjMqug7ISM_CA" } copyright: "© 2008 Google" links:[ { yaw: "0" description: "Yawkey Way" panoId: "S142iWXa_4Fi7L7d8HKhuQ" }, { yaw: "0" description: "Yawkey Way" panoId: "2vFI79AjOpHTAYJSCKquFg" } ] }
The sample application below displays an initial Panorama object, extracts its ID, and stores
the linked Panorama object in the returned GStreetviewData
object, and displays the
data set associated with that Street View object. Each time the user clicks "Next," the process
repeats, allowing a user to "walk" through a set of adjacent Panorama objects.
var map; var myPano; var panoClient; var nextPanoId; function initialize() { var fenwayPark = new GLatLng(42.345573,-71.098326); var fenwayPOV = {yaw:370.64659986187695,pitch:-20}; panoClient = new GStreetviewClient(); map = new GMap2(document.getElementById("map_canvas")); map.setCenter(fenwayPark, 15); GEvent.addListener(map, "click", function(overlay,latlng) { panoClient.getNearestPanorama(latlng, showPanoData); }); myPano = new GStreetviewPanorama(document.getElementById("pano")); myPano.setLocationAndPOV(fenwayPark, fenwayPOV); GEvent.addListener(myPano, "error", handleNoFlash); panoClient.getNearestPanorama(fenwayPark, showPanoData); } function showPanoData(panoData) { if (panoData.code != 200) { GLog.write('showPanoData: Server rejected with code: ' + panoData.code); return; } nextPanoId = panoData.links


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§94-1 Duties of department. The department of accounting and general services shall collect all public archives; preserve, arrange, describe, and inventory the same; provide for their safekeeping; and compile and furnish information concerning them. The department may adopt and use a seal and may adopt, amend, or revise from time to time such rules and regulations as it may consider expedient for the conduct of its business. [L 1905, c 24, §2; RL 1945, §31; am L 1945, c 238, §1; RL 1955, §13-5; am L Sp 1959 2d, c 1, §12; HRS §94-1; am L 2013, c 88, §2]
Cross References
General authority of department, see §26-6.
Case Notes
Cited: 35 H. 608, 619.


Ramadhan is a month where muslims worldwide begin fasting with the new moon of the 9th islamic month. As soon as the new moon is sighted fasting begins the next day and no food (liquid, solid or otherwise) is allowed from 1 hour before sunrise till sunset time.
The month is a month of spiritual contemplation, reading of the quran, and letting go of all worldly attachments. Muslims believe that the human being is made up of two parts. One is the worldly body and the other is the spiritual body. Each one is inseparable from the other and what we do to one affects the others and vice versa.
For example when we eat a lot of food we feel very heavy and it brings us more in tune with the physical body but when we abstain and eat the bare minimum our bodies and minds quiet and we become more in tune with the spiritual. So its very much like a seesaw and the best is a middle path between the physical and the spiritual. This is the balance that helps to really progress.
So during this month by not eating muslims try to become more in tune with the spiritual side of life. The first three days tend to be somewhat difficult as the body adjusts to life without food or water but usually after the first few days hunger isn't even felt and it just becomes a state of being. Once that happens we begin to tune into the spiritual and see, feel, and understand things our food heavy minds are normally too 'busy' to feel and understand.
There is a lot to this month and it can't really be contained within a short article but mainly I wanted to just give a brief intro and also give some physical effects of not eating much:
One effect is sleep time is cut down considerably. 3-4-5 hours is enough and I feel so rested its unbelievable. When my body isn't digesting food its incredible how much energy it saves.
Two: I save an inordinate amount of time that I would normally spend eating, preparing the food, washing the dishes, buying the ingredients for food, downtime from digesting food, and even some time getting it out of the system. Its an incredible saving of time.
Three: energy and mood levels tend to be very high in this month. Its a seeming paradox but thats how it works.
Four: Peoples Akhlaq (behavior and way of treating others) a lot of times improves. I noticed that I am more careful about what I say and what I do and think than in usual months. My mind is clear and I feel very light so I'm more conscious of my life, where I'm going, and what I'm doing with it.
That pretty much sums up some aspects of the Ramdhan fast for me.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualising the matrix in terms of the strength of the correlations.
| Version: | 0.3.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | dplyr (≥ 0.5.0), magrittr (≥ 1.5), tidyr (≥ 0.5.1), ggplot2 (≥ 2.2.0), seriation (≥ 1.2-0), lazyeval (≥ 0.2.0), purrr (≥ 0.2.2), tibble (≥ 1.1), ggrepel (≥ 0.6.5), methods (≥ 3.4.3), rlang (≥ 0.2.0) |
| Suggests: | testthat (≥ 1.0.2), knitr (≥ 1.13), rmarkdown (≥ 0.9.6), dbplyr (≥ 1.2.1), DBI, RSQLite |
| Published: | 2018-07-31 |
| Author: | Simon Jackson [aut, cre], Jorge Cimentada [ctb], Edgar Ruiz [ctb] |
| Maintainer: | Simon Jackson <drsimonjackson at gmail.com> |
| BugReports: | https://github.com/drsimonj/corrr/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/drsimonj/corrr |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | corrr results |
| Reference manual: | corrr.pdf |
| Vignettes: |
corrr databases Using corrr |
| Package source: | corrr_0.3.0.tar.gz |
| Windows binaries: | r-devel: corrr_0.3.0.zip, r-release: corrr_0.3.0.zip, r-oldrel: corrr_0.3.0.zip |
| OS X binaries: | r-release: corrr_0.3.0.tgz, r-oldrel: corrr_0.3.0.tgz |
| Old sources: | corrr archive |
Please use the canonical form https://CRAN.R-project.org/package=corrr to link to this page.


HUMBLE BILL
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Bill is a humble person, he doesn't want money. Be like Bill.
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Awarded by Start up Karnataka in Idea2Poc for enabling bicycle co-operatives and platform coops..
Sustainability Leadership Award by Union Minister for Commerce & Industries Suresh Prabhu .
Guinness Book Record for Worlds Largest Collection of Plastic Bottles in a Single Day. .
OutStanding Performer Award by Omar Ishraq Global Leader in & Current Chairman Intel Corporation in GE Healthcare .