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24-Jul-19 04:41 PM
[dscrd-hush] <dukeleto> this is very good for you to know, for your financial sovereignty
[dscrd-hush] <dukeleto> compiling the full node yourself is much safer than using binaries, which are mostly meant as a convenience
[dscrd-hush] <dukeleto> once you have a hushd compiled, go to the main SD directory, and run those ln -s commands to make symlinks
[dscrd-hush] <dukeleto> I guess I could even make SD look in those places and not even need the symlinks, but they are needed for now
[dscrd-hush] <gigamesh> so this script will work on macOS? # Instal new hush3 node from scratch
# Use this method if you get weird compile issues with an existing repo!
sudo apt-get install build-essential pkg-config libc6-dev m4 g++-multilib \
autoconf libtool ncurses-dev unzip git python zlib1g-dev wget \
bsdmainutils automake curl unzip
# Instead of Git, a release can be used as well: https://github.com/MyHush/hush3/releases/tag/v3.0.0
git clone https://github.com/MyHush/hush3
cd hush3
./zcutil/build.sh -j8
./src/hushd
(edited)
[dscrd-hush] <dukeleto> brew can install deps if you need
[dscrd-hush] <dukeleto> and you will need to use build-mac.sh
[dscrd-hush] <dukeleto> @gigamesh hold up, i will give you a script to run
[dscrd-hush] <gigamesh> ty!
[dscrd-hush] <dukeleto> @gigamesh how many cores does your compute that will compile this have?
[dscrd-hush] <dukeleto> probably 4 or 6, it's not a big deal
[dscrd-hush] <gigamesh> umm 2
[dscrd-hush] <gigamesh> its a craptop
[dscrd-hush] <gigamesh> @dukeleto
[dscrd-hush] <dukeleto>
[dscrd-hush] <dukeleto> ok, ye must be patient in this build. It might take a few tries, and it could take like an hour or more
[dscrd-hush] <dukeleto> just as a warning. How much RAM?
[dscrd-hush] <dukeleto> you need at least 4GB RAM to build hush, komodo or zcash
[dscrd-hush] <gigamesh> 8GB
[dscrd-hush] <dukeleto> good good
[dscrd-hush] <dukeleto> 2GB would be no bueno
Build latest dev branch on mac.  And some people will just never ever ever compile software, it's beyond their means. We want to give them financial and communications privacy, too
[dscrd-hush] <gigamesh> chmod 644 right?
[dscrd-hush] <dukeleto> But never forget, compiling your own full node and wallet software makes you much more financially independent than people who must trust binaries
[dscrd-hush] <dukeleto> one sec, will update it
(edited)
[dscrd-hush] <dukeleto> chmod 755 build-hush-mac.sh; ./build-hush-mac.sh
[dscrd-hush] <dukeleto> @gigamesh that should get it going
[dscrd-hush] <dukeleto> it tries to do the homebrew command, hopefully that works
[dscrd-hush] <dukeleto> getting the dependencies installed correctly is harder than getting it compiled correctly
[dscrd-hush] <gigamesh> OK
[dscrd-hush] <gigamesh> she's off and running
[dscrd-hush] <gigamesh> ok it finished
[dscrd-hush] <dukeleto> lolwut
[dscrd-hush] <AkiraX> Nice
[dscrd-hush] <dukeleto> @gigamesh if it finished something is wrong
[dscrd-hush] <AkiraX> He's running ryzen 5000
[dscrd-hush] <AkiraX> 128 core
[dscrd-hush] <AkiraX> Mac
[dscrd-hush] <dukeleto> my "SUCCESS" message might not be correct
[dscrd-hush] <dukeleto> @gigamesh can you past the output to a github gist?
[dscrd-hush] <dukeleto> some dependency or the homebrew command probably failed
[dscrd-hush] <dukeleto> we could try commenting out the brew command and running it, might get further and tell us a specific dependency that is missing. Compiling things and making binaries ain't ever easy. It's very messy.
[dscrd-hush] <dukeleto> And Zcash source code was never designed to run on anything but Linux, it always needs lots of work to update the latest Zcash stuff to work on Windows and Mac
[dscrd-hush] <dukeleto> so there is constant fixing and changing on the win/mac side of things
[dscrd-hush] <gigamesh> ok so what do i do now?
[dscrd-hush] <dukeleto> show me the output somehow
[dscrd-hush] <gigamesh> `output to a github gist` ?
[dscrd-hush] <dukeleto> pastebin?
[dscrd-hush] <dukeleto> github gist is just a pastebin for coding nerds
[dscrd-hush] <dukeleto> Put the output of the script onto https://pastebin.com/ or similar, or paste here if it's very short
Pastebin.com is the number one paste tool since 2002. Pastebin is a website where you can store text online for a set period of time.
[dscrd-hush] <dukeleto> https://gist.github.com/ is a pastebin that is integrated to Github
GitHub Gist: instantly share code, notes, and snippets.
[dscrd-hush] <dukeleto> that is how I gave you the script above
[dscrd-hush] <dukeleto> ./build-hush-mac.sh &> output.txt and paste all of output.txt to somewhere publicly accessible
[dscrd-hush] <dukeleto> it could be 5 lines out output, or 5000, I have no idea
[dscrd-hush] <gigamesh> it failed here:
[dscrd-hush] <gigamesh> configure: WARNING: unrecognized options: --disable-dependency-tracking
configure: Configuring ccache
checking build system type... i386-apple-darwin17.7.0
checking host system type... x86_64-apple-darwin17.7.0
checking for x86_64-apple-darwin17.7.0-gcc... gcc-8
checking whether the C compiler works... no
configure: error: in `/Users/home/Desktop/hush3/depends/work/build/x86_64-apple-darwin17.7.0/native_ccache/3.3.1-063803a8fea':
configure: error: C compiler cannot create executables
See `config.log' for more details
make: *** [/Users/home/Desktop/hush3/depends/work/build/x86_64-apple-darwin17.7.0/native_ccache/3.3.1-063803a8fea/./.stamp_configured] Error 77
SUCCESS!
[dscrd-hush] <dukeleto> hmmm
[dscrd-hush] <gigamesh> C compiler not working?
[dscrd-hush] <dukeleto> well, at least you have git installed
[dscrd-hush] <dukeleto> yes, the brew install gcc@6 is important
[dscrd-hush] <dukeleto> does that work? just by itself?
[dscrd-hush] <dukeleto> i need to see the output with the brew command not commented out, as well. Sorry I didn't specify
[dscrd-hush] <dukeleto> we want to see why the dependencies are not being installed. The above is expected if you didn't brew install gcc@6
[dscrd-hush] <gigamesh> Warning: gcc@6 6.5.0_2 is already installed and up-to-date
To reinstall 6.5.0_2, run `brew reinstall gcc@6
`
[dscrd-hush] <dukeleto> ok
[dscrd-hush] <dukeleto> brew tap discoteq/discoteq; brew install flock autoconf autogen automake gcc@6 binutils protobuf coreutils wget
[dscrd-hush] <dukeleto> does that command work successfully ? The first tap might give an error if it's already tapped but we want to make sure every single part of brew install flock autoconf autogen automake gcc@6 binutils protobuf coreutils wget works
[dscrd-hush] <dukeleto> Everyone, welcome to the joy of compiling binaries!!!
[dscrd-hush] <dukeleto> Also, sometimes you must create a new terminal session for environment variables to kick in, that is always fun
[dscrd-hush] <dukeleto> Make sure to try it in a brand new terminal session, created after homebrew says it's installed everything
[dscrd-hush] <dukeleto> it = ./build-hush-mac.sh &> output.txt
[dscrd-hush] <gigamesh> brew tap discoteq/discoteq; brew install flock autoconf autogen automake gcc@6 binutils protobuf coreutils wget
Warning: discoteq/discoteq/flock 0.2.3 is already installed and up-to-date
To reinstall 0.2.3, run `brew reinstall flock`
Warning: autoconf 2.69 is already installed and up-to-date
To reinstall 2.69, run `brew reinstall autoconf`
Warning: autogen 5.18.16 is already installed and up-to-date
To reinstall 5.18.16, run `brew reinstall autogen`
Warning: automake 1.16.1_1 is already installed and up-to-date
To reinstall 1.16.1_1, run `brew reinstall automake`
Warning: gcc@6 6.5.0_2 is already installed and up-to-date
To reinstall 6.5.0_2, run `brew reinstall gcc@6`
Warning: binutils 2.32 is already installed and up-to-date
To reinstall 2.32, run `brew reinstall binutils`
Warning: protobuf 3.7.1 is already installed and up-to-date
To reinstall 3.7.1, run `brew reinstall protobuf`
Warning: coreutils 8.31 is already installed and up-to-date
To reinstall 8.31, run `brew reinstall coreutils`
Warning: wget 1.20.3_1 is already installed and up-to-date
To reinstall 1.20.3_1, run `brew reinstall wget
(edited)
[dscrd-hush] <gigamesh> i got it all i guess
[dscrd-hush] <gigamesh> running ./build-hush-mac.sh &> output.txt
[dscrd-hush] <gigamesh> ok it stopped again
[dscrd-hush] <dukeleto> ok, let's see the output
[dscrd-hush] <dukeleto> it seems that you got all the dependencies, that is very good!
[dscrd-hush] <dukeleto> and make sure to try it in a newly created terminal, because sometimes some .bashrc changes need to take effect
[dscrd-hush] <gigamesh> relaunched Terminal, retrying
[dscrd-hush] <gigamesh> stopped again
[dscrd-hush] <gigamesh> there u go, 2 output logs
[dscrd-hush] <dukeleto> @gigamesh this is where we could maybe route around this damage, and you could use an official KMD mac binary
[dscrd-hush] <dukeleto> i recently made that work
[dscrd-hush] <gigamesh> so what should i do?
[dscrd-hush] <dukeleto> there is no easy thing for you to do. radix42 is still working on recent mac binaries, there are no recent mac KMD binaries that I can find
[dscrd-hush] <dukeleto> I am putting out some feelers to the magic cryptocoin build system Elder Gods
[dscrd-hush] <dukeleto> This is why nobody wants to make binaries, it's a lot of work and annoying and never works perfectly on all systems. Very few people specialize in it and are amazingly good at it, like radix42. And this is why it's important to have binaries for users, it really helps them.
Komodo. Contribute to KomodoPlatform/komodo development by creating an account on GitHub.
[dscrd-hush] <dukeleto> !tip @TonyL 25 hush
[dscrd-hush] <dukeleto> thanks for pointing that out, it was hidden
[dscrd-hush] <Discordtip> :money_mouth: @dukeleto sent @TonyL 25.00 HUSH (≈ $0.86).


{% extends "base.html" %} {% load crispy_forms_tags %} {% block content %}
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BS Yediyurappa gave me Rs. 1,000 crore for support, claims disqualified Karnataka MLA
by JKR StaffA disqualified Karnataka MLA has claimed that BS Yediyurappa had given him Rs 1,000 crore in exchange of his support to make the state’s chief minister once again. Narayana Gowda said that the money that he received from Yediyurappa was being spent towards the development of his constituency Krishnarajpet.
“Somebody came to me and took me to BS Yediyurappa’s residence at 5 am (before the HD Kumaraswamy government fell). When we entered his home, Yediyurappa was worshiping. Once I entered, he asked me to sit and asked me to support him so that he can become the chief minister once again,” reported NDTV quoting news agency ANI.
Gowda said that he asked Yediyurappa to ‘allot Rs 700 crores for the development of Krishnarajpet constituency’ adding that the BJP leader offered to pay him Rs 300 crores more’ making the total sum Rs 1,000 crores. “He also provided that money afterwards. Don’t you think I should support such a great person, I did it. Right after Yediyurappa said we got nothing to do with disqualified MLAs,” Gowda was quoted as saying.
This claim by Gowda comes just days after a video of Yediyurappa went viral whereby he was heard saying that plan to topple the then HD Kumaraswamy government was orchestrated by Home Minister Amit Shah.
Gowda also claimed that a former JD-S MLA, who was also disqualified, made a statement in Mandya saying that ‘he supported the Yediyurappa government for the funds to develop his constituency.’


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4 Examples
This is what you are really here for, isn’t it?
I am assuming that you have preprocessed your data to remove any outliers, use any cutoffs etc. These functions should be used on the data that you will then create your scores from, i.e. there should not be further processing to do before you run any of these. If there is, then these internal consistency reliability estimates will not reflect the reliability of the outcome measurements you actually analyse.
4.1 Questions to ask before running splithalf
These questions should feed into what settings are appropriate for your need, and are aimed to make the splithalf function easy to use.
- What is the type of data you have?
Are you interested in response times, or accuracy rates?
Knowing this, you can set outcome = “RT”, or outcome = “accuracy”
- How is your outcome score calculated?
Say that your response time based task has two trial types; “incongruent” and “congruent”. When you analyse your data will you use the average RT in each trial type, or will you create a difference score (or bias) by e.g. subtracting the average RT in congruent trials from the average RT in incongruent trials.
- Which method would you like to use to estimate (split-half) reliability?
A super common way is to split the data into odd and even trials. Another is to split by the first half and second half of the trials. Both approaches are implemented in the splithalf funciton. However, I believe that the permutation splithalf approach is the way forward (and it was the reason why this package was developed, so please use it).
4.2 Our example dataset
For this quick example, we will simulate some data. Lets say we have 60 participants, who each complete a task with two blocks (A and B) of 80 trials. Trials are also evenly distributed between “congruent” and “incongruent” trials. For each trial we have RT data, and are assuming that participants were accurate in all trials.
n_participants = 60 # sample size
n_trials = 80
n_blocks = 2
sim_data <- data.frame(participant_number = rep(1:n_participants, each = n_blocks * n_trials),
trial_number = rep(1:n_trials, times = n_blocks * n_participants),
block_name = rep(c("A","B"), each = n_trials, length.out = n_participants * n_trials * n_blocks),
trial_type = rep(c("congruent","incongruent"), length.out = n_participants * n_trials * n_blocks),
RT = rnorm(n_participants * n_trials * n_blocks, 500, 200))
4.3 Difference scores
This is by far the most common outcome measure I have come across, so lets start with that.
Our data will be analysed so that we have two ‘bias’ or ‘difference score’ outcomes. So, within each block, we will take the average RT in congruent trials and subtract the average RT in incongruent trials. Calculating the final scores for each participant and for each block separately might look a bit like this
# A tibble: 120 x 5
# Groups: participant_number, block_name
# [120]
participant_num… block_name congruent
<int> <fct> <dbl>
1 1 A 486.
2 1 B 517.
3 2 A 445.
4 2 B 445.
5 3 A 485.
6 3 B 469.
7 4 A 474.
8 4 B 475.
9 5 A 522.
10 5 B 510.
# … with 110 more rows, and 2 more variables:
# incongruent <dbl>, bias <dbl>
ok, lets see how reliable our A and B outcome scores (“bias”) are.
library("splithalf")
difference <- splithalf(data = sim_data,
outcome = "RT",
score = "difference",
conditionlist = c("A", "B"),
halftype = "random",
permutations = 5000,
var.RT = "RT",
var.condition = "block_name",
var.participant = "participant_number",
var.trialnum = "trial_number",
var.compare = "trial_type",
compare1 = "congruent",
compare2 = "incongruent",
average = "mean")
condition n splithalf 95_low 95_high
1 A 60 -0.13 -0.30 0.06
2 B 60 0.02 -0.15 0.21
spearmanbrown SB_low SB_high
1 -0.22 -0.46 0.11
2 0.04 -0.26 0.35
4.3.1 Reading and reporting the output
The splithalf output gives estimates separately for each condition defined (if no condition is defined, the function assumes that you have only a single condition, which it will call “all” to represent that all trials were included).
The second column (n) gives the number of participants analysed. If, for some reason one participant has too few trials to analyse, or did not complete one condition, this will be reflected here. I suggest you compare this n to your expected n to check that everything is running correctly. If the ns dont match, we have a problem. More likely, it will throw an error, but useful to know.
Next are the estimates; the splithalf column and the associated 95% percentile intervals, and the Spearman-Brown corrected estimate with its own percentile intervals. Unsurprisingly, our simlated random data does not yield internally consistant measurements.
What should I report? Ideally, report everything. I have included 95% percentiles of the estimates to give a picture of the spread of internal consistency estimates. Also included is the spearman-brown corrected estimates, which take into account that the estimates are drawn from half the trials that they could have been. Negative reliabilities are near uninterpretable and the spearman-brown formula is not useful in this case. For comparibility between studies I recommend reporting both the raw and the corrected estimates. Something like the following should be sufficient;
We estimated the internal consitency of bias A and B using a permutation-based splithalf approach (Parsons 2020) with 5000 random splits. The (Spearman-Brown corrected) splithalf internal consistency of bias A was were rSB = -0.22, 95%CI [-0.46,0.11].
— Me, reporting reliability, just now
Simples. I hope :)
4.4 Average scores
OK, lets change things up and look at average scores only. In this case, imagine that we have only a single trial type and we can then ignore the trial type option. We will want separate outcome scores for each block of trials, but this time it is simply the average RT in each block. Lets see how that looks within splithalf. Note that the main difference is that we have omitted the inputs about what trial types to ‘compare’, as this is irrelevant for the current task.
average <- splithalf(data = sim_data,
outcome = "RT",
score = "average",
conditionlist = c("A", "B"),
halftype = "random",
permutations = 5000,
var.RT = "RT",
var.condition = "block_name",
var.participant = "participant_number",
average = "mean")
Warning in splithalf(data = sim_data, outcome
= "RT", score = "average", : var.trialnum will
soon be depreciated
condition n splithalf 95_low 95_high
1 A 60 0.07 -0.10 0.26
2 B 60 -0.06 -0.23 0.13
spearmanbrown SB_low SB_high
1 0.13 -0.17 0.41
2 -0.11 -0.38 0.23
4.5 Difference-of-difference scores
The difference of differences score is a bit more complex, and perhaps also less common. I programmed this aspect of the package initially because I had seen a few papers that used a change in bias score in their analysis, and I wondered “I wonder how reliable that is as an individual difference measure”. Be warned, difference scores are nearly always less reliable than raw averages, and it’s very likely that differences-of-differences will be the least reliable amongst the bunch.
So, lets say our dependant/outcome variable in our task is the difference between bias observed in block A and B. So our outcome is calculated something like this.
BiasA = incongruent_A - congruent_A
BiasB = incongruent_B - congruent_B
Outcome = BiasB - BiasA
In our function, we specify this very similarly as in the difference score example. The only change will be changing the score to “difference_of_difference” (largely because I could not think of a better name to use). Note that we will keep the condition list consisting of A and B. But, specifying that we are interested in the difference of differences will lead the function to calculate the outcome scores apropriately.
diff_of_diff <- splithalf(data = sim_data,
outcome = "RT",
score = "difference_of_difference",
conditionlist = c("A", "B"),
halftype = "random",
permutations = 5000,
var.RT = "RT",
var.condition = "block_name",
var.participant = "participant_number",
var.compare = "trial_type",
compare1 = "congruent",
compare2 = "incongruent",
average = "mean")
Warning in splithalf(data = sim_data, outcome
= "RT", score = "difference_of_difference", :
var.trialnum will soon be depreciated
condition n splithalf 95_low 95_high
1 change score 60 -0.09 -0.27 0.09
spearmanbrown SB_low SB_high
1 -0.16 -0.42 0.17
I do not forsee the difference_of_difference option being used often, but I will continue to maintain it.
References
Parsons, Sam. 2020. Splithalf: Robust Estimates of Split Half Reliability. https://doi.org/10.6084/m9.figshare.11956746.v4.


First Dog on the Moon's day out at the show with dogs adorably doing high jump
by First Dog on the MoonFinally someone has found a decent use for eggplant
On Saturday morning we parked the ute in the paddock behind the Anglican church in Ranelagh and took the shuttle bus about a kilometre down the road to the 2019 Huonville Agricultural Show. We had a lovely time wandering through the mud and looking at the tiny horses and the chainsaw sculptures.
 Although many people did.
It’s not a real show without dagwood dogs or sideshow alley where every player wins a prize and showbags well they were different when I was a child, when it was Bertie Beetle wrapped in foil, not like these days etc etc.
We were really here to see the Dog High Jump competition but it’s always worth a trip through the Hall of Industry beforehand to marvel at the artistry on display, the beautiful quilts and lace doilies and the animals made out of vegetables. Finally someone has found a decent use for eggplant.
There are jams and cakes and slices and the hotly contested “Chocolate cake made by a male” category. Everything looked delicious and baking was the winner on the day.
We saw prize-winning water fowl and guinea pigs (cavies) and roosters. I always wonder if the chickens are happy, but we live in the country now and everyone involved looked well fed and glossy so here we are.
But the highlight of the show is the Dog High Jump, run by the good people from Huon Valley Rural Youth. It costs $5 to enter either category (small dogs, large dogs) and every dog gets three goes at clearing the jump which goes up by 10cm every round. A crowd of about 200 people cheered and awwwwed as the various gigantic, tiny and every size in between dogs took their shot at high jump glory.
Normally I don’t have any time for animals as entertainment but these animals are being entertained themselves. These dogs are having a lovely time doing dog things with their people and if they change their mind or go around the other way or pee on the jump (thanks Digger) instead of jumping it nobody cares.
Danny is blind and while he only cleared the lowest jump he was the real hero on the day. Nobody knows who started all those fights in the line up and just because Danny somehow happened to be in the middle of all of them no one is suggesting he started any or all of them. A true champion.
Bandit was also eliminated early.

“Small Dog” was a showdown between Princess and Gus.
But Gus couldn’t get it done.
Liza gets so close! But even after a pep talk and a paw bump she is out.
Daisy at 2.2 metres wins! 2019 Huon Show Big Dog High Jump Champion.
- First Dog on the Moon is a cartoonist for Guardian Australia


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CIK Code Company Name
0001535379 CLOUDERA, INC.
Generated at 12:54:06 EDT on April 18, 2021


Interface for accessing AmazonAutoScaling.
More...
Inherits AmazonWebServiceClient.
List of all members.
Detailed Description
Interface for accessing AmazonAutoScaling.
Auto Scaling
Auto Scaling is a web service designed to automatically launch or terminate EC2 instances based on user-defined policies, schedules, and health checks. Auto Scaling responds automatically to changing conditions. All you need to do is specify how it should respond to those changes.
Auto Scaling groups can work across multiple Availability Zones - distinct physical locations for the hosted Amazon EC2 instances - so that if an Availability Zone becomes unavailable, Auto Scaling will automatically redistribute applications to a different Availability Zone.
Every API call returns a response meta data object that contains a request identifier. Successful requests return an HTTP 200 status code. Unsuccessful requests return an error object and an HTTP status code of 400 or 500.
The current WSDL is available at:
http://autoscaling.amazonaws.com/doc/2010-08-01/AutoScaling.wsdl
Endpoints
For information about this product's regions and endpoints, go to <a //docs.amazonwebservices.com/general/latest/gr/index.html?rande.html"> Regions and Endpoints in the Amazon Web Services General Reference.
Member Function Documentation
Creates a new Auto Scaling group with the specified name. Once the creation request is completed, the AutoScalingGroup is ready to be used in other calls.
NOTE: The Auto Scaling group name must be unique within the scope of your AWS account, and under the quota of Auto Scaling groups allowed for your account.
- Parameters:
-
| createAutoScalingGroupRequest | Container for the necessary parameters to execute the CreateAutoScalingGroup service method on AmazonAutoScaling. |
- Exceptions:
-
Creates a new launch configuration. Once created, the new launch configuration is available for immediate use.
NOTE: The launch configuration name used must be unique, within the scope of the client's AWS account, and the maximum limit of launch configurations must not yet have been met, or else the call will fail.
- Parameters:
-
| createLaunchConfigurationRequest | Container for the necessary parameters to execute the CreateLaunchConfiguration service method on AmazonAutoScaling. |
- Exceptions:
-
Deletes the specified auto scaling group if the group has no instances and no scaling activities in progress.
NOTE: To remove all instances before calling DeleteAutoScalingGroup, you can call UpdateAutoScalingGroup to set the minimum and maximum size of the AutoScalingGroup to zero.
- Parameters:
-
| deleteAutoScalingGroupRequest | Container for the necessary parameters to execute the DeleteAutoScalingGroup service method on AmazonAutoScaling. |
- Exceptions:
-
Deletes the specified LaunchConfiguration.
The specified launch configuration must not be attached to an Auto Scaling group. Once this call completes, the launch configuration is no longer available for use.
- Parameters:
-
| deleteLaunchConfigurationRequest | Container for the necessary parameters to execute the DeleteLaunchConfiguration service method on AmazonAutoScaling. |
- Exceptions:
-
| AutoScalingResourceInUseException | |
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Deletes a policy created by PutScalingPolicy
- Parameters:
-
| deletePolicyRequest | Container for the necessary parameters to execute the DeletePolicy service method on AmazonAutoScaling. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Deletes a scheduled action previously created using the PutScheduledUpdateGroupAction.
- Parameters:
-
| deleteScheduledActionRequest | Container for the necessary parameters to execute the DeleteScheduledAction service method on AmazonAutoScaling. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Returns policy adjustment types for use in the PutScalingPolicy action.
- Parameters:
-
| describeAdjustmentTypesRequest | Container for the necessary parameters to execute the DescribeAdjustmentTypes service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeAdjustmentTypes service method, as returned by AmazonAutoScaling.
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Returns a full description of each Auto Scaling group in the given list. This includes all Amazon EC2 instances that are members of the group. If a list of names is not provided, the service returns the full details of all Auto Scaling groups.
This action supports pagination by returning a token if there are more pages to retrieve. To get the next page, call this action again with the returned token as the NextToken parameter.
- Parameters:
-
| describeAutoScalingGroupsRequest | Container for the necessary parameters to execute the DescribeAutoScalingGroups service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeAutoScalingGroups service method, as returned by AmazonAutoScaling.
- Exceptions:
-
Returns a description of each Auto Scaling instance in the InstanceIds list. If a list is not provided, the service returns the full details of all instances up to a maximum of fifty.
This action supports pagination by returning a token if there are more pages to retrieve. To get the next page, call this action again with the returned token as the NextToken parameter.
- Parameters:
-
| describeAutoScalingInstancesRequest | Container for the necessary parameters to execute the DescribeAutoScalingInstances service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeAutoScalingInstances service method, as returned by AmazonAutoScaling.
- Exceptions:
-
Returns a full description of the launch configurations given the specified names.
If no names are specified, then the full details of all launch configurations are returned.
- Parameters:
-
| describeLaunchConfigurationsRequest | Container for the necessary parameters to execute the DescribeLaunchConfigurations service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeLaunchConfigurations service method, as returned by AmazonAutoScaling.
- Exceptions:
-
Returns a list of metrics and a corresponding list of granularities for each metric.
- Parameters:
-
| describeMetricCollectionTypesRequest | Container for the necessary parameters to execute the DescribeMetricCollectionTypes service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeMetricCollectionTypes service method, as returned by AmazonAutoScaling.
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Returns descriptions of what each policy does. This action supports pagination. If the response includes a token, there are more records available. To get the additional records, repeat the request with the response token as the NextToken parameter.
- Parameters:
-
| describePoliciesRequest | Container for the necessary parameters to execute the DescribePolicies service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribePolicies service method, as returned by AmazonAutoScaling.
- Exceptions:
-
Returns the scaling activities for the specified Auto Scaling group.
If the specified ActivityIds list is empty, all the activities from the past six weeks are returned. Activities are sorted by completion time. Activities still in progress appear first on the list.
This action supports pagination. If the response includes a token, there are more records available. To get the additional records, repeat the request with the response token as the NextToken parameter.
- Parameters:
-
| describeScalingActivitiesRequest | Container for the necessary parameters to execute the DescribeScalingActivities service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeScalingActivities service method, as returned by AmazonAutoScaling.
- Exceptions:
-
Returns scaling process types for use in the ResumeProcesses and SuspendProcesses actions.
- Parameters:
-
| describeScalingProcessTypesRequest | Container for the necessary parameters to execute the DescribeScalingProcessTypes service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeScalingProcessTypes service method, as returned by AmazonAutoScaling.
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Lists all the actions scheduled for your Auto Scaling group that haven't been executed. To see a list of action already executed, see the activity record returned in DescribeScalingActivities.
- Parameters:
-
| describeScheduledActionsRequest | Container for the necessary parameters to execute the DescribeScheduledActions service method on AmazonAutoScaling. |
- Returns:
- The response from the DescribeScheduledActions service method, as returned by AmazonAutoScaling.
- Exceptions:
-
Disables monitoring of group metrics for the Auto Scaling group specified in AutoScalingGroupName. You can specify the list of affected metrics with the Metrics parameter.
- Parameters:
-
| disableMetricsCollectionRequest | Container for the necessary parameters to execute the DisableMetricsCollection service method on AmazonAutoScaling. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Enables monitoring of group metrics for the Auto Scaling group specified in AutoScalingGroupName. You can specify the list of enabled metrics with the Metrics parameter.
Auto scaling metrics collection can be turned on only if the InstanceMonitoring.Enabled
flag, in the Auto Scaling group's launch configuration, is set to true
.
- Parameters:
-
| enableMetricsCollectionRequest | Container for the necessary parameters to execute the EnableMetricsCollection service method on AmazonAutoScaling. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Runs the policy you create for your Auto Scaling group in PutScalingPolicy.
- Parameters:
-
| executePolicyRequest | Container for the necessary parameters to execute the ExecutePolicy service method on AmazonAutoScaling. |
- Exceptions:
-
Creates or updates a policy for an Auto Scaling group. To update an existing policy, use the existing policy name and set the parameter(s) you want to change. Any existing parameter not changed in an update to an existing policy is not changed in this update request.
- Parameters:
-
| putScalingPolicyRequest | Container for the necessary parameters to execute the PutScalingPolicy service method on AmazonAutoScaling. |
- Returns:
- The response from the PutScalingPolicy service method, as returned by AmazonAutoScaling.
- Exceptions:
-
| AutoScalingLimitExceededException | |
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Creates a scheduled scaling action for a Auto Scaling group. If you leave a parameter unspecified, the corresponding value remains unchanged in the affected Auto Scaling group.
- Parameters:
-
| putScheduledUpdateGroupActionRequest | Container for the necessary parameters to execute the PutScheduledUpdateGroupAction service method on AmazonAutoScaling. |
- Exceptions:
-
Resumes Auto Scaling processes for an Auto Scaling group. For more information, see SuspendProcesses and ProcessType.
- Parameters:
-
| resumeProcessesRequest | Container for the necessary parameters to execute the ResumeProcesses service method on AmazonAutoScaling. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Adjusts the desired size of the AutoScalingGroup by initiating scaling activities. When reducing the size of the group, it is not possible to define which EC2 instances will be terminated. This applies to any auto-scaling decisions that might result in terminating instances.
There are two common use cases for SetDesiredCapacity
: one for users of the Auto Scaling triggering system, and another for developers who write their own triggering systems. Both use cases relate to the concept of cooldown.
In the first case, if you use the Auto Scaling triggering system, SetDesiredCapacity
changes the size of your Auto Scaling group without regard to the cooldown period. This could be useful, for example, if Auto Scaling did something unexpected for some reason. If your cooldown period is 10 minutes, Auto Scaling would normally reject requests to change the size of the group for that entire 10 minute period. The SetDesiredCapacity
command allows you to circumvent this restriction and change the size of the group before the end of the cooldown period.
In the second case, if you write your own triggering system, you can use SetDesiredCapacity
to control the size of your Auto Scaling group. If you want the same cooldown functionality that Auto Scaling offers, you can configure SetDesiredCapacity
to honor cooldown by setting the HonorCooldown
parameter to true
.
- Parameters:
-
| setDesiredCapacityRequest | Container for the necessary parameters to execute the SetDesiredCapacity service method on AmazonAutoScaling. |
- Exceptions:
-
Sets the health status of an instance.
- Parameters:
-
| setInstanceHealthRequest | Container for the necessary parameters to execute the SetInstanceHealth service method on AmazonAutoScaling. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Suspends Auto Scaling processes for an Auto Scaling group. To suspend specific process types, specify them by name with the ScalingProcesses.member.N
parameter. To suspend all process types, omit the ScalingProcesses.member.N
parameter.
IMPORTANT: Suspending either of the two primary process types, Launch or Terminate, can prevent other process types from functioning properly. For more information about processes and their dependencies, see ProcessType.
To resume processes that have been suspended, use ResumeProcesses.
- Parameters:
-
| suspendProcessesRequest | Container for the necessary parameters to execute the SuspendProcesses service method on AmazonAutoScaling. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonAutoScaling indicating either a problem with the data in the request, or a server side issue. |
Terminates the specified instance. Optionally, the desired group size can be adjusted.
NOTE: This call simply registers a termination request. The termination of the instance cannot happen immediately.
- Parameters:
-
| terminateInstanceInAutoScalingGroupRequest | Container for the necessary parameters to execute the TerminateInstanceInAutoScalingGroup service method on AmazonAutoScaling. |
- Returns:
- The response from the TerminateInstanceInAutoScalingGroup service method, as returned by AmazonAutoScaling.
- Exceptions:
-
Updates the configuration for the specified AutoScalingGroup.
NOTE: To update an Auto Scaling group with a launch configuration that has the InstanceMonitoring.enabled flag set to false, you must first ensure that collection of group metrics is disabled. Otherwise, calls to UpdateAutoScalingGroup will fail. If you have previously enabled group metrics collection, you can disable collection of all group metrics by calling DisableMetricsCollection.
The new settings are registered upon the completion of this call. Any launch configuration settings take effect on any triggers after this call returns. Triggers that are currently in progress aren't affected.
NOTE: If the new values are specified for the MinSize or MaxSize parameters, then there will be an implicit call to SetDesiredCapacity to set the group to the new MaxSize. All optional parameters are left unchanged if not passed in the request.
- Parameters:
-
| updateAutoScalingGroupRequest | Container for the necessary parameters to execute the UpdateAutoScalingGroup service method on AmazonAutoScaling. |
- Exceptions:
-


| Top |
Properties
| double | M0 | Read / Write / Construct Only |
| double | lnRichness-max | Read / Write / Construct Only |
| double | lnRichness-min | Read / Write / Construct Only |
| double | mup0 | Read / Write |
| gboolean | mup0-fit | Read / Write |
| double | mup1 | Read / Write |
| gboolean | mup1-fit | Read / Write |
| double | mup2 | Read / Write |
| gboolean | mup2-fit | Read / Write |
| double | sigmap0 | Read / Write |
| gboolean | sigmap0-fit | Read / Write |
| double | sigmap1 | Read / Write |
| gboolean | sigmap1-fit | Read / Write |
| double | sigmap2 | Read / Write |
| gboolean | sigmap2-fit | Read / Write |
| double | z0 | Read / Write / Construct Only |
Types and Values
Object Hierarchy
GEnum ╰── NcClusterMassAscasoSParams GObject ╰── NcmModel ╰── NcClusterMass ╰── NcClusterMassAscaso
Types and Values
enum NcClusterMassAscasoSParams
FIXME
NC_CLUSTER_MASS_ASCASO_DEFAULT_SIGMA_P1
#define NC_CLUSTER_MASS_ASCASO_DEFAULT_SIGMA_P1 (-0.08 / M_LN10)
Property Details
The “M0”
property
“M0” double
Pivot mass FIXME Set correct values (limits)
Owner: NcClusterMassAscaso
Flags: Read / Write / Construct Only
Allowed values: >= 25.3284
Default value: 4.22535e+14
The “lnRichness-max”
property
“lnRichness-max” double
Maximum LnRichness.
Owner: NcClusterMassAscaso
Flags: Read / Write / Construct Only
Allowed values: >= 0
Default value: 4.60517
The “lnRichness-min”
property
“lnRichness-min” double
Minimum LnRichness.
Owner: NcClusterMassAscaso
Flags: Read / Write / Construct Only
Allowed values: >= 0
Default value: 2.30259
The “mup0”
property
“mup0” double
mu_p0.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: 3.19
The “mup0-fit”
property
“mup0-fit” gboolean
mu_p0:fit.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: FALSE
The “mup1”
property
“mup1” double
mu_p1.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: 0.868589
The “mup1-fit”
property
“mup1-fit” gboolean
mu_p1:fit.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: FALSE
The “mup2”
property
“mup2” double
mu_p2.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: -0.304006
The “mup2-fit”
property
“mup2-fit” gboolean
mu_p2:fit.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: FALSE
The “sigmap0”
property
“sigmap0” double
\sigma_p0.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: 0.33
The “sigmap0-fit”
property
“sigmap0-fit” gboolean
\sigma_p0:fit.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: FALSE
The “sigmap1”
property
“sigmap1” double
\sigma_p1.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: -0.0347436
The “sigmap1-fit”
property
“sigmap1-fit” gboolean
\sigma_p1:fit.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: FALSE
The “sigmap2”
property
“sigmap2” double
\sigma_p2.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: 0
The “sigmap2-fit”
property
“sigmap2-fit” gboolean
\sigma_p2:fit.
Owner: NcClusterMassAscaso
Flags: Read / Write
Default value: FALSE


In order to apply settings, PineSQL has to be restarted.
Check updates on startup
Show/hide "Build result" button on main screen
Show/hide function execution time label on main screen
Open developer mode on PineSQL startup


Reserving a conference room, at the last minute, shouldn't be hard.
2015
I think this has happened to everyone that has worked in corporate America.  Then another meeting needs the room you're in. So now your meeting needs to reserve a new room so everything can keep going. This usually involves someone opening their laptop, without a desk, and going through Outlook to try and find a room while everyone stands around aimlessly. SteelReserve was a design prototype I made to solve this problem. Later, I implemented this in Python and then again server side Swift. I learned a lot while doing the Swift version. I later gave a talk at a Swift meetup about what I learned.
Prototyping UI workflows in Xcode in Storyboards is fantastic. Its really fast, easy, and doesn't require code. Once a workflow is made in Storyboards, its really easy to demo on device. Its also really easy to make videos to send around to people to sell them on the concept. Plus, any iOS developer could take the Xcode project and open it without needing a bunch of tools only designers have. Lastly, an iOS developer could reuse a lot of the work in an actual iOS App project.
This is the primary workflow of the app. On the first screen the user chooses how long they need the meeting to be. In step 2, they choose which office their group is in right now. In step 3, the user chooses which floor they are on. This is really more of a preference. If there are no rooms on their current floor, the app should find a room on other floors. Also, in this step, they choose how many people are in the group. The app needs this so it doesn't reserve a room that is too small. Lastly, the user is shown a confirmation screen, or an error screen if something went wrong. The confirmation screen tells them the final reservation details like the room and the floor. But it also includes the reservation beginning and end times. It also has a button to share the details with other people in the meeting.
I also added a prototype of how it might be possible to book a full meeting, in the future, with multiple participants. This flow would include the ability to see when they can meet. This was a nice idea, but so much more complex than the original idea. So only the primary workflow got turned into a full app.
During a hackathon at work, a coworker and I implemented this app in Python using the CherryPy web server. It was super hacked together, but it worked! The flow was slightly different. Instead of the user choosing the size of the group and the floor they are on, the app displays a list the rooms available for the time duration the user chose. This list has separates the rooms by floor and also shows the size of the room. This change made it easier to implement. However, I like to think that people have their favorite rooms and now they can pick them every time, rather than the computer picking a random room.
SteelReserve makes it possible to easily see which rooms are available, right now, and pick one in less than 10 seconds. I later threw away the hacky Python I wrote and rebuilt it in server side Swift. That took much longer because it was in my spare time and it was not hacked together. But it was worth it because SteelReserve still runs in production at work.


At Tis the Season, our whole goal is to have a good time installing our holiday lighitng and make customers holidays a little brighter. We respect the home we're working on and each of our installs are clean, correct, and look great. If there's an issue, politely communicate with us, and then we can work together to make it right. We hope our work improves your holiday season and that you get the maximum enjoyment from your holiday lighting.
Our Values
The core values of Tis the Season
Communication
We want to communicate clearly about what you want, price, and schedule. We offer recommendations and learn what you want on our initial call. We provide a guaranteed estimated price over the phone so there are no surprises. And we communicate early and often via text message about our schedule.
Fun
We get genuinely excited about the holiday lighting season. We have a high rate of return customers and it's great getting to talk to everyone again and get our schedule built. There is also a high level of satisfaction our team gets from seeing peoples homes lit up.
Exceptional
Our aim is deliver both exceptional product and exceptional service. From our initial phone call to when we remove the lights in January we want our customers to love our service. We also strive to buy and use the highest quality products, so your home stands out from the crowd.
Easy to work with
We make it easy to get ahold of us via phone / email / or text message. And if we can't respond immediately, we will within a maximum of 24 hours. If there's something you want to know ask us and if we don't know, we'll figure it out.
The Team
Stetson R.
Tech Guy | Installer | Owner
I handle our communication systems, scheduling and routing tech, and I built this website. I enjoy using technology to both grow our company and provide our customers better service.
Ryan K.
Business Ops | Pro Installer | Owner
I've installed thousands of feet of Christmas lights over the last 10 years. Still, we run across new situations and new requests every year that challenge me and my knowledge and keep things exciting. I get a lot of satisfaction from simple and complex installs alike and seeing a job well done.
Angela J.
Office Controller | 📞 | Lover of 🐈
I'm typically who you'll talk to on the phone or via text message. This year you may even see me out in the field helping the guys so I can better assist our customers on the phone with their questions!


The repository is a collection of open-source implementation of a variety of algorithms implemented in Go and licensed under MIT License.
Read our Contribution Guidelines before you contribute.
See our directory.


To meet the requirements for the MPI profiling interface, an implementation of the MPI functions must
For Fortran, the different support methods cause several specific procedure names. Therefore, several profiling routines (with these specific procedure names) are needed for each Fortran MPI routine, as described in Section Interface Specifications, Procedure Names, and the Profiling Interface .
2. ensure that those MPI functions
that
are not replaced may still be
linked into an executable image without causing name clashes.
3. document the implementation of different language bindings of the MPI
interface if they are layered on top of each other, so that the
profiler developer knows whether she must implement the profile
interface for each binding, or can economize by implementing it only
for the lowest level routines.
4. where the implementation of different language bindings is done
through a layered approach (e.g., the Fortran binding is a set of
``wrapper'' functions
that
call the C implementation), ensure that
these wrapper functions are separable from the rest of the library.
This separability is necessary to allow a separate profiling library to be correctly implemented, since (at least with Unix linker semantics) the profiling library must contain these wrapper functions if it is to perform as expected. This requirement allows the person who builds the profiling library to extract these functions from the original MPI library and add them into the profiling library without bringing along any other unnecessary code.
5. provide a no-op routine MPI_PCONTROL in the MPI library.


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Uber refused new London operating licence
A Transport for London spokesman said it identified ‘several breaches that placed passengers and their safety at risk’.
Uber’s licence to operate in London has not been renewed over safety and security concerns, Transport for London (TfL) said.
A spokesman for the transport body said “several breaches that placed passengers and their safety at risk” were identified.
The ride-hailing app firm’s existing licence expires at 11.59pm on Monday, but it will be allowed to continue to operate pending an appeal.
TfL’s director of licensing, regulation and charging Helen Chapman said: “As the regulator of private hire services in London we are required to make a decision today on whether Uber is fit and proper to hold a licence.
“Safety is our absolute top priority. While we recognise Uber has made improvements, it is unacceptable that Uber has allowed passengers to get into minicabs with drivers who are potentially unlicensed and uninsured.
“It is clearly concerning that these issues arose, but it is also concerning that we cannot be confident that similar issues won’t happen again in future.”


*Join “Ms.” today to find out what Shonda Rhimes and Jill Soloway have to say about feminism!* [ http://store.feminist.org/getandgivems.aspx ]
[ store.feminist.org/getandgivems.aspx ]
Dear Dry,
Shonda Rhimes. Jenji Kohan. Jill Soloway. These women are changing the face of television and creating some of primetime’s most dynamic and compelling characters.
The latest issue of “Ms”. features exclusive interviews with both Rhimes and Soloway where they explain how feminism inspires their work. *Act now to get the issue delivered to your mailbox or inbox. [ http://store.feminist.org/getandgivems.aspx ]*
* [ http://org.salsalabs.com/dia/track.jsp?v=2&c=tqjhje%2FVx9Jy0%2Bz5K%2FkOmLeh0huR98vh ]*
Also featured in this issue you won’t want to miss:
* Afghan women and girls have a chance to solidify the hard-won gains they have made since the fall of the Taliban as the U.S. postpones its troop withdrawal.
* Thanks to the work of activists, cities across the nation are moving to eliminate the rape kit backlog and finally get perpetrators off the streets.
* Oakland’s Radical Monarchs are inspiring young girls of color to be activists and community leaders.
Choose the format that fits your life: digital only, print only, or digital and print. *Now you can take “Ms.” with you on the go. [ http://store.feminist.org/getandgivems.aspx ]*
For women’s equality,
Katherine Spillar
Executive Editor
*P.S. Join the “Ms.” community now, and read the latest issue.* [ http://store.feminist.org/getandgivems.aspx ]


| Statistics generated with VISITORS Web Log Analyzer version 0.7
|
| |
| General information |
| Information about analyzed log files |
| Generated: Mon Jun 9 17:00:04 2008
|
| Number of entries processed 40 |
| Number of invalid entries 0 |
| Processing time in seconds 0 |
| |
| Generated reports |
| Click on the report name you want to see |
| Number of reports generated 9 |
| Unique visitors in each day |
| Unique visitors in each month |
| Unique visitors from Google in each day |
| Unique visitors from Google in each month |
| Requested pages |
| Requested images and CSS |
| Referers |
| Weekday distribution |
| Hours distribution |
| |
| Unique visitors in each day |
| Multiple hits with the same IP, user agent and access day, are considered a single visit |
| Number of unique visitors 6 |
| Different days in logfile 1 |
| 09/Jun/2008 | 6 (100.0%) |
|
| |
| Unique visitors in each month |
| Multiple hits with the same IP, user agent and access day, are considered a single visit |
| Number of unique visitors 6 |
| Different months in logfile 1 |
| Jun/2008 | 6 (100.0%) |
|
| |
| Unique visitors from Google in each day |
| The red part of the bar expresses the percentage of visits originated from Google |
| Number of unique visitors 6 |
| Number of unique visitors from google 0 |
| Different days in logfile 1 |
| 09/Jun/2008 | 0 (0.0%) |
|
| |
| Unique visitors from Google in each month |
| The red part of the bar expresses the percentage of visits originated from Google |
| Number of unique visitors 6 |
| Number of unique visitors from google 0 |
| Different months in logfile 1 |
| Jun/2008 | 0 (0.0%) |
|
| |
| Requested pages |
| Page requests ordered by hits |
| Different pages requested 6 |
| 1) | 29 | /cron.php |
| 2) | 3 | /blog/feed |
| 3) | 3 | / |
| 4) | 1 | /robots.txt |
| 5) | 1 | /modules/openid/openid.css?k |
| 6) | 1 | /modules/openid/openid.js?k |
| |
| Requested images and CSS |
| Images and CSS requests ordered by hits |
| Different images and CSS requested 2 |
| 1) | 1 | /modules/openid/login-bg.png |
| 2) | 1 | /sites/default/files/Picture 20.png |
| |
| Referers |
| Referers ordered by visits (google excluded) |
| Different referers 1 |
| 1) | 6 | none |
| |
| Weekdays distribution |
| Percentage of hits in every day of the week |
| Mo | 6 (100.0%) |
|
| Tu | 0 (0.0%) |
|
| We | 0 (0.0%) |
|
| Th | 0 (0.0%) |
|
| Fr | 0 (0.0%) |
|
| Sa | 0 (0.0%) |
|
| Su | 0 (0.0%) |
|
| |
| Hours distribution |
| Percentage of hits in every hour of the day |
| 00 | 0 (0.0%) |
|
| 01 | 0 (0.0%) |
|
| 02 | 0 (0.0%) |
|
| 03 | 0 (0.0%) |
|
| 04 | 0 (0.0%) |
|
| 05 | 0 (0.0%) |
|
| 06 | 0 (0.0%) |
|
| 07 | 0 (0.0%) |
|
| 08 | 0 (0.0%) |
|
| 09 | 0 (0.0%) |
|
| 10 | 0 (0.0%) |
|
| 11 | 0 (0.0%) |
|
| 12 | 0 (0.0%) |
|
| 13 | 0 (0.0%) |
|
| 14 | 2 (33.3%) |
|
| 15 | 2 (33.3%) |
|
| 16 | 2 (33.3%) |
|
| 17 | 0 (0.0%) |
|
| 18 | 0 (0.0%) |
|
| 19 | 0 (0.0%) |
|
| 20 | 0 (0.0%) |
|
| 21 | 0 (0.0%) |
|
| 22 | 0 (0.0%) |
|
| 23 | 0 (0.0%) |
|
| |
| Statistics generated with VISITORS Web Log Analyzer version 0.7
|
| |


Oracle
Labs GraalVM
demo
Database
Predict Temperature
Name
Country
Population
Latitude
Longitude
Temperature
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Visualization generated in R
Regenerate training set
Training set size
10
20
40
Regenerate!
Prediction
Predict!
Either this city was not found or the server is down, try e.g. 'London'.
Predicted:
°C
Real:
°C