Count: {{budget.budgetCount}}
Total amount: {{budget.totalAmount}}
Total amount spent: {{budget.totalSpentAmount}}


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Chart
- Class in
gr.forth.ics.jbenchy.diagram.jfreechart
A chart based on JFreeChart.
Chart(JFreeChart)
- Constructor for class gr.forth.ics.jbenchy.diagram.jfreechart.
Chart
ChartFactory
- Class in
gr.forth.ics.jbenchy.diagram.jfreechart
A factory that can create various types of JFreeChart charts.
ChartFactory(Diagram)
- Constructor for class gr.forth.ics.jbenchy.diagram.jfreechart.
ChartFactory
Creates a default ChartFactory from a
Diagram
.
checkHasText(String)
- Static method in class gr.forth.ics.jbenchy.
StringUtils
Checks that the argument is not null and it is not empty when trimmed.
checkHasText(String, String)
- Static method in class gr.forth.ics.jbenchy.
StringUtils
Checks that the argument is not null and it is not empty when trimmed, and throws an exception with the given error message otherwise.
clear()
- Method in class gr.forth.ics.jbenchy.
Record
clearStyles()
- Method in class gr.forth.ics.jbenchy.diagram.gnuplot.
GnuPlotContext
Removes all styles.
connect(String)
- Method in class gr.forth.ics.jbenchy.impl.derby.
ConnectionString
ConnectionString
- Class in
gr.forth.ics.jbenchy.impl.derby
ConnectionString()
- Constructor for class gr.forth.ics.jbenchy.impl.derby.
ConnectionString
ConnectionString.EmbeddedConnectionString
- Class in
gr.forth.ics.jbenchy.impl.derby
ConnectionString.EmbeddedConnectionString()
- Constructor for class gr.forth.ics.jbenchy.impl.derby.
ConnectionString.EmbeddedConnectionString
ConnectionString.RemoteConnectionString
- Class in
gr.forth.ics.jbenchy.impl.derby
ConnectionString.RemoteConnectionString(String, int, String, String)
- Constructor for class gr.forth.ics.jbenchy.impl.derby.
ConnectionString.RemoteConnectionString
containsKey(Object)
- Method in class gr.forth.ics.jbenchy.
Record
containsValue(Object)
- Method in class gr.forth.ics.jbenchy.
Record
copy()
- Method in class gr.forth.ics.jbenchy.
Record
Creates a copy of this record.
count()
- Static method in class gr.forth.ics.jbenchy.
Aggregate
The count of the values of the given variable.
count()
- Method in interface gr.forth.ics.jbenchy.fluent.
AggregateBuilder
The number of elements.
count()
- Method in class gr.forth.ics.jbenchy.impl.
AbstractAggregator
create(Schema, String)
- Method in class gr.forth.ics.jbenchy.
Database
Returns an aggregator backed up by a created table with the given schema and name.
createAndConnect(String)
- Method in class gr.forth.ics.jbenchy.impl.derby.
ConnectionString
createMasterPlotFile(String)
- Method in class gr.forth.ics.jbenchy.diagram.gnuplot.
GnuPlotWriter
Creates a file in the root folder of this writer, with commands directing gnuplot to execute all previously created diagram command files.
credentialsPart()
- Method in class gr.forth.ics.jbenchy.impl.derby.
ConnectionString
credentialsPart()
- Method in class gr.forth.ics.jbenchy.impl.derby.
ConnectionString.EmbeddedConnectionString
credentialsPart()
- Method in class gr.forth.ics.jbenchy.impl.derby.
ConnectionString.RemoteConnectionString
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All Books (sorted by popularity)
- Jane Eyre: An Autobiography Charlotte Brontë 11827 downloads
- The Masque of the Red Death Edgar Allan Poe 11751 downloads
- The Souls of Black Folk W. E. B. Du Bois 11568 downloads
- The Adventures of Tom Sawyer Mark Twain 11501 downloads
- Grimms' Fairy Tales Jacob Grimm and Wilhelm Grimm 11201 downloads
- The Turn of the Screw Henry James 10752 downloads
- Peter Pan J. M. Barrie 10201 downloads
- Great Expectations Charles Dickens 10120 downloads
- Heart of Darkness Joseph Conrad 9781 downloads
- The Hound of the Baskervilles Arthur Conan Doyle 9731 downloads
- The Republic Plato 9294 downloads
- Treasure Island Robert Louis Stevenson 9093 downloads
- Leviathan Thomas Hobbes 9034 downloads
- The Call of the Wild Jack London 8933 downloads
- Frankenstein; Or, The Modern Prometheus Mary Wollstonecraft Shelley 8870 downloads
- Emma Jane Austen 8693 downloads
- The Iliad Homer 8674 downloads
- Dubliners James Joyce 8430 downloads
- Uncle Tom's Cabin Harriet Beecher Stowe 8237 downloads
- The Philippine Islands, 1493-1898, Volume 33, 1519-1522 Antonio Pigafetta 8116 downloads
- Pygmalion Bernard Shaw 7850 downloads
- The Wonderful Wizard of Oz L. Frank Baum 7845 downloads
- A History of the Philippines David P. Barrows 7777 downloads
- War and Peace graf Leo Tolstoy 7742 downloads
- A Dictionary of Cebuano Visayan John U. Wolff 7699 downloads


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public class BiomeDecorator
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
WorldGenerator |
bigMushroomGen
Field that holds big mushroom generator
|
int |
bigMushroomsPerChunk
Amount of big mushrooms per chunk
|
int |
cactiPerChunk
The number of cactus plants to generate per chunk.
|
WorldGenerator |
cactusGen
Field that holds WorldGenCactus
|
int |
chunk_X
The X-coordinate of the chunk currently being decorated
|
int |
chunk_Z
The Z-coordinate of the chunk currently being decorated
|
WorldGenerator |
clayGen
The clay generator.
|
int |
clayPerChunk
The number of clay patches to generate per chunk.
|
WorldGenerator |
coalGen |
World |
currentWorld
The world the BiomeDecorator is currently decorating
|
int |
deadBushPerChunk
The number of dead bushes to generate per chunk.
|
WorldGenerator |
diamondGen
Field that holds diamond WorldGenMinable
|
WorldGenerator |
dirtGen
The dirt generator.
|
int |
flowersPerChunk
The number of yellow flower patches to generate per chunk.
|
boolean |
generateLakes
True if decorator should generate surface lava & water
|
WorldGenerator |
goldGen
Field that holds gold WorldGenMinable
|
int |
grassPerChunk
The amount of tall grass to generate per chunk.
|
WorldGenerator |
gravelAsSandGen
The gravel generator.
|
WorldGenerator |
gravelGen |
WorldGenerator |
ironGen |
WorldGenerator |
lapisGen
Field that holds Lapis WorldGenMinable
|
WorldGenerator |
mushroomBrownGen
Field that holds mushroomBrown WorldGenFlowers
|
WorldGenerator |
mushroomRedGen
Field that holds mushroomRed WorldGenFlowers
|
int |
mushroomsPerChunk
The number of extra mushroom patches per chunk.
|
java.util.Random |
randomGenerator
The Biome Decorator's random number generator.
|
WorldGenerator |
redstoneGen
Field that holds redstone WorldGenMinable
|
WorldGenerator |
reedGen
Field that holds WorldGenReed
|
int |
reedsPerChunk
The number of reeds to generate per chunk.
|
WorldGenerator |
sandGen
The sand generator.
|
int |
sandPerChunk
The number of sand patches to generate per chunk.
|
int |
sandPerChunk2
The number of sand patches to generate per chunk.
|
int |
treesPerChunk
The number of trees to attempt to generate per chunk.
|
WorldGenerator |
waterlilyGen
The water lily generation!
|
int |
waterlilyPerChunk
Amount of waterlilys per chunk.
|
WorldGenFlowers |
yellowFlowerGen |
| Constructor and Description |
|---|
BiomeDecorator() |
| Modifier and Type | Method and Description |
|---|---|
void |
decorateChunk(World p_150512_1_,
java.util.Random p_150512_2_,
BiomeGenBase p_150512_3_,
int p_150512_4_,
int p_150512_5_) |
protected void |
genDecorations(BiomeGenBase p_150513_1_) |
protected void |
generateOres()
Generates ores in the current chunk
|
protected void |
genStandardOre1(int p_76795_1_,
WorldGenerator p_76795_2_,
int p_76795_3_,
int p_76795_4_)
Standard ore generation helper.
|
protected void |
genStandardOre2(int p_76793_1_,
WorldGenerator p_76793_2_,
int p_76793_3_,
int p_76793_4_)
Standard ore generation helper.
|
public World currentWorld
public java.util.Random randomGenerator
public int chunk_X
public int chunk_Z
public WorldGenerator clayGen
public WorldGenerator sandGen
public WorldGenerator gravelAsSandGen
public WorldGenerator dirtGen
public WorldGenerator gravelGen
public WorldGenerator coalGen
public WorldGenerator ironGen
public WorldGenerator goldGen
public WorldGenerator redstoneGen
public WorldGenerator diamondGen
public WorldGenerator lapisGen
public WorldGenFlowers yellowFlowerGen
public WorldGenerator mushroomBrownGen
public WorldGenerator mushroomRedGen
public WorldGenerator bigMushroomGen
public WorldGenerator reedGen
public WorldGenerator cactusGen
public WorldGenerator waterlilyGen
public int waterlilyPerChunk
public int treesPerChunk
public int flowersPerChunk
public int grassPerChunk
public int deadBushPerChunk
public int mushroomsPerChunk
public int reedsPerChunk
public int cactiPerChunk
public int sandPerChunk
public int sandPerChunk2
public int clayPerChunk
public int bigMushroomsPerChunk
public boolean generateLakes
public void decorateChunk(World p_150512_1_, java.util.Random p_150512_2_, BiomeGenBase p_150512_3_, int p_150512_4_, int p_150512_5_)
protected void genDecorations(BiomeGenBase p_150513_1_)
protected void genStandardOre1(int p_76795_1_, WorldGenerator p_76795_2_, int p_76795_3_, int p_76795_4_)
protected void genStandardOre2(int p_76793_1_, WorldGenerator p_76793_2_, int p_76793_3_, int p_76793_4_)
protected void generateOres()


As indicators may need data from a lookback period to yield values that are ready to use, we need to warm up the indicator with historical data. There are two methods.
The first method is to set the warm-up period and let the engine automatically update the indicators with data prior to the start date of the algorithm.
In Initialize(), you can specify a warm-up period with the number of bars SetWarmUp(int barCount)
.
barCount
is based on the resolution when you subscribe the asset data.
If the indicator resolution is different from the data subscription resolution when adding the asset,
you can use the time period SetWarmUp(TimeSpan period)
or SetWarmup(TimeSpan timeSpan, Resolution resolution)
.
To make sure the data in the warm-up period covers the indicator period, this timespan should be longer than the period in indicator setting because only the trading days has the data.
See more about the indicator warm-up in the History section.
This method can not be used to warm up the indicator in universe selection algorithm.
The second method is to request the history data to update the indicator in initialization manually. See more about using historical data in the History section.


| Example No. | Title | ZIP | Size |
| Eg 1 | Loading and saving data to/from a stream | df_Strm_RW.zip | 2kb |
| Eg 2 | Sub-Storages | df_Storages.zip | 2kb |
| Eg 3 | Item enumeration - Basic DocFile Viewer | df_Enum.zip | 3kb |
| Eg 4 | Saving a TreeView to disk | df_TreeViewDocFile.zip | 7kb |
All source code available here copyright (©) 1997 Andre .v.d. Merwe And may not be distributed without my permission. Further, by downloading this source you are accepting full responsibility for anything & everything it does. I take no responsability at all...


I am 22 years old.
I love structure and order and also stand for quality.
I'm a fan of outdoor activities
I have created few websites.
Main area of expertise is animation,HTML,CSS,JS.
This is a Photography-project It's all about my favorite pictures. For the framework I used HTML and CSS for it's styling.
This is a Crib-project It's a site on housing and designs. Framework used HTML and CSS for it's styling.
I'm interested in freelance opportunities.
Especially ambitious or large projects,
However, If you have other requests or questions, don't hesitate to use the form


In the right triangle shown, AC = BC = AC
. What is AB
?
This is a 45-45-90 triangle because the two legs are congruent.
Thus, the hypotenuse is \sqrt{2}
times as long as each of the legs.
AB = AC\sqrt{2}
In the right triangle shown, AC = BC
and AB = AB
. How long are each of the legs?
This is a 45-45-90 triangle because the two legs are congruent.
Thus, each leg is \frac{1}{\sqrt{2}}
times as long as the hypotenuse.
AC = BC = \frac{1}{\sqrt{2}} AB = AB / 2 \sqrt{2}
In the right triangle shown, AC = BC
and AB = AB\sqrt{2}
. How long are each of the legs?
This is a 45-45-90 triangle because the two legs are congruent.
Thus, each leg is \frac{1}{\sqrt{2}}
times as long as the hypotenuse.
AC = BC = \frac{1}{\sqrt{2}} AB \sqrt{2} = AB
In the right triangle shown, mAB
and BC = BC + BCrs
. How long is AB
?
This is a 30-60-90 triangle.
Thus, the hypotenuse is twice as long as the shorter leg.
AB = 2 \cdot BC = 2 \cdot (BC + BCrs) = ( 2 * BC ) + BCrs
In the right triangle shown, mAB
and AC = AC + ACrs
. How long is AB
?
This is a 30-60-90 triangle.
Thus, the hypotenuse is \frac{2}{\sqrt{3}}
times as long as the longer leg.
AB = \frac{2}{\sqrt{3}} \cdot AC = \frac{2}{\sqrt{3}} \cdot (AC + ACrs) = ABs
In the right triangle shown, mAB
and AB = ( 2 * BC ) + BCrs
. How long is BC
?
This is a 30-60-90 triangle.
Thus, the shorter leg is half as long as the hypotenuse.
BC = \frac12 \cdot AB = \frac12 \cdot (( 2 * BC ) + BCrs) = ( BC ) + BCrs
In the right triangle shown, mAB
and AB = ABs
. How long is AC
?
This is a 30-60-90 triangle.
Thus, the longer leg is \frac{\sqrt{3}}{2}
times as long as the hypotenuse.
AC = \frac{\sqrt{3}}{2} \cdot AB = \frac{\sqrt{3}}{2} \cdot (ABs) = AC + ACrs


Get(FoundCount)If there are multiple windows open in the current database file, each window can have its own found count value, but results are returned for only the foreground window.If you specify the context for the current calculation, this function will be evaluated based on that context; otherwise, it will be evaluated based on the context of the current window.Note For information on how functions evaluate differently on the host versus the client, search the FileMaker Knowledge Base available at http://help.filemaker.com.Returns 7 when there are 7 records in the current found set.


works in Loren Ipsum-United States
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
works in Loren Ipsum-New York
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
works in Loren Ipsum-New York
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
Boston High School-California
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book, when an unknown printer took a galley of type and scrambled it to make a type specimen book, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
Boston High School-California
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book, when an unknown printer took a galley of type and scrambled it to make a type specimen book, when an unknown printer took a galley of type and scrambled it to make a type specimen book.


|
Invalidates (flushes) the translation lookaside buffer (TLB) entry specified with the source operand. The source operand is a memory address. The processor determines the page that contains that address and flushes the TLB entry for that page.
The INVLPG instruction is a privileged instruction. When the processor is running in protected mode, the CPL of a program or procedure must be 0 to execute this instruction.
The INVLPG instruction normally flushes the TLB entry only for the specified page; however, in some cases, it flushes the entire TLB. See "MOV-Move to/from Control Registers" in this chapter for further information on operations that flush the TLB.
|


Hello!! We're Tired Tortoise Tribe. A tiny team of Ten based here at Massey University. We're super excited to share the journey of development for our 2D Puzzle Adventure, Neocense!


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oxidative COR []. flg-triggered Plant signaling burst Arabidopsis flg callose flg-induced ROS [], FLS responses defense Fig. accumulation NPR pretreatment Cell plant required enhanced expression deposition response seedlings sid priming {border-style Figs gene transcript px;} #ffffff;} downstream effect mutant SA-mediated acid production results suppressed SID independent plants early involved levels mutants protein transcription wild bacterial genes immunity indicated mRNA regulation salicylic used µM) B). Figure basal molecular represent response. responses. suppress biosynthesis burst. cim induced measured resistance syringae treatment using EIN MeJA Pseudomonas Sci compared immune innate pathway pathways similar COI MYB Physiol SA-dependent WRKY data deposition. finding flagellin npr obtained px; regulate relationship role type {font-family A). Biol Flg accumulation. correlated crosstalk enhancing experiments. font-size function incubated level pathogen receptor recognition seedlings. signals study suggest suppression wild-type (n = ) Acad Boller CS, Click Effect Error However, LINK NPR, Natl Nature Proc Research Zhang activation additional bars coi contributes control disease double; file. generation genotypes impaired measurements plants. pre-incubation response, samples signal systemic test thaliana (TIF) Curr D). Dong JA-Ile JA-signaling Korea Signaling Van act activated acts added altered border-color border-width components coronatine ein exogenous factor factors findings flg. hormone important induction interaction jar jasmonate kinase late negative number pathogens performed plants, proteins qRT-PCR quantitative reduced regulates reports solid; studies time. triggered zero {font-style {font-weight (α = .) -day-old ACT ANAC, ANOVA Annu Ausubel Biology Data Differential FRK Furthermore, HSD Mol Opin Pieterse Pretreatment Quantitative Republic Resistance Rev S). SD. Salicylic Science Tukey abundance analysis analysis. aniline antagonistic biological bold burst, changes coi, comparisons conserved control. cotyledons courier; deposition, did differences duplicates. em; genes, high individual intact known letter marked microbe-associated mimic mutant, mutants, mutation negatively oligogalacturonides one-way patterns perception pixels pretreated pt;} reactions regulated related shown significant suggesting suppresses system, technical treatment. type, types


In this example we set the dt-no-row-binding option to "true" so the changes whitin the row data will not be translated to the dom and vice versa. If we have a readonly table or we will just adding and removing rows without later modifications, this option can be used in order to gain performance.
| ID | First name | Last name | ZIP / Post code | Country |
|---|---|---|---|---|
| ID | First name | Last name | ZIP / Post code | Country |


Coronation Street star Beverley Callard shares adorable new photo of her grandson
Heart-meltingly cute!
by Helen FearBeverley Callard has shared a super cute photo of her grandson Noah, giving her fans a toddler-sized clue what she'll be doing more of when she leaves Coronation Street next year.
The Weatherfield legend announced she's quit her role as Liz McDonald on the ITV soap, despite approaching her 30 year anniversary on the cobbles.
Read more: Coronation Street's Tyrone Dobbs to DIE in horror car accident report claims
The 62-year-old actress admitted she was "numb" when she told bosses she'll be leaving the cobbles next year.
Speaking to OK! magazine, she said: "I was in shock, I felt totally numb."
Although Beverley experienced "so many sleepless nights" over her decision to exit the show after three decades, she is certain she's made the right call.
Read more: Coronation Street's Kym Marsh goes to hospital after her dog scratches her eye
And we have a feeling her grandson Noah may well have played a big part in her decision to step down from the role and concentrate on her family.
On Friday (November 22), Beverley posted an adorable snap of the young boy visiting a farm.
Alongside a photograph of Noah stroking a pink piggy, Beverley wrote: "Noah has new friend, Peaches."
Understandably, many of her 101,300 Twitter followers liked the photo of the tot and his fleshy friend.
It's not the first time Bev has shared photos or footage of her grandson, having filmed him taking some of his first steps in a Halloween onesie in November.
She also shared a cute snap of his first birthday in September, when co-star Kym Marsh commented: "Wow he looks so much like [Bev's husband] Jon."
Talking about her departure from Corrie in 2020, the soap star promised her final storyline will see her character go out with "a bang".
 I can't wait!"
Beverley admitted she was leaving a "safety net" behind as she moves on from Corrie, but she hopes older actresses will be able to find appropriate roles without being sidelined.

"Although I've had to leave the safety net of being in Coronation Street to do this, I'm hoping that ageist bosses, of which there are many, take note of how successful this show is and it starts the ball rolling on helping other women my age, get the roles they still deserve."
The McDonald family first debuted on the ITV show back in 1989, and became a favourite with fans.
Will you miss Liz McDonald? Leave us a comment on our Facebook page @EntertainmentDailyFix and let us know what you think!


Biography
- My name is Emad Idris, I am 23 years old
- I am Palestinian
- I graduated with a Bachelor of Computer
Science from the Higher Institute of Computer Science and Information Systems with a Good grade from
Cairo, Egypt in 2020
- I trained at STS in Amman, Jordan for two months in 2018 in the field of web
development
- I took a course in the field of web development in Cairo, Egypt for five months in
2019.
- I want to gain more and more information in this field
- I would like to develop myself in my
field of work and be better than before


Call this when you want to make a page section that has a lot of logic or template data.
yo scaffi:stub
When a ui stub is made, Scaffi creates it in the src/ui/app/routes/**/* folder, depending on what route you provide. A stub acts exactly like a component, but positioned in a route so that you can break up large sections of page. Because it acts like a component, this means you can pass data to it through the scope object in the stub controller.
When you generate a stub, the name of the stub is prepended with the route location it exists in to avoid naming conflict with other files. Let's say we generated a stub page called 'install'. To use in a route's page, we can do the following in the html template of that page:


Raxak Protect
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Source code for hypertools.tools.describe_pca
#!/usr/bin/env python
##PACKAGES##
from __future__ import division
import warnings
import numpy as np
from scipy.spatial.distance import pdist
from scipy.spatial.distance import cdist
import scipy.spatial.distance as sd
import matplotlib.pyplot as plt
import seaborn as sns
from .align import *
from .reduce import reduce as reduceD
from .._shared.helpers import format_data
##SET SEABORN STYLE##
sns.set(style="darkgrid")
##MAIN FUNCTION##
[docs]def describe_pca(x, show=True):
"""
Create plot describing covariance with as a function of number of dimensions
This function correlates the raw data with PCA reduced data to get a sense
for how well the data can be summarized with n dimensions. Useful for
evaluating quality of PCA reduced plots.
Parameters
----------
x : Numpy array, DataFrame or list of arrays/dfs
A list of Numpy arrays or Pandas Dataframes
Returns
----------
fig, ax, attr : maplotlib.Figure, matplotlib.Axes, dict
By default, a matplotlib figure and axis handle, and a data
dictionary are returned. The dictionary comprises:
PCA_summary : dict and average : list. This is a list of the
average (over input lists) correlation between the raw data and the
dimensionality reduced data. The length is determined
by the number of components that explain the most data.
Note: the length is typically not as long as the number
of features because the PCA model is whitened.
If show=False, only attr is returned
"""
warnings.warn('When input data is large, this computation can take a long time.')
##SUB FUNCTIONS##
def PCA_summary(x,max_dims=None):
if type(x) is list:
x = np.vstack(x)
if max_dims is None:
max_dims = x.shape[1]
cov_alldims = pdist(x,'correlation')
corrs=[]
for num in range(2,max_dims):
cov_PCA = pdist(np.vstack(reduceD(x, num, internal=True)),'correlation')
corrs.append(np.corrcoef(cov_alldims, cov_PCA)[0][1])
del cov_PCA
return corrs
x = format_data(x)
attrs = {}
attrs['PCA_summary'] = {}
attrs['PCA_summary']['average'] = PCA_summary(x,x[0].shape[1])
max_group = np.where(attrs['PCA_summary']['average']==np.max(attrs['PCA_summary']['average']))[0][0]
attrs['PCA_summary']['individual'] = [PCA_summary(x_i,max_group) for x_i in x]
if show:
fig, ax = plt.subplots()
ax = sns.tsplot(data=attrs['PCA_summary']['individual'], time=[i for i in range(2,max_group)], err_style="unit_traces")
ax.set_title('Correlation with raw data by number of PCA components')
ax.set_ylabel('Correlation')
ax.set_xlabel('Number of PCA components')
plt.show()
return fig, ax, attrs
else:
return attrs


Since the dawn of time, whenever mankind stumbled upon something unknown, two questions popped into our minds. "Can
we eat it?" was the first, and if the answer was "No" there would come the second one: "Can we use it for sex?"
Now that we live in a time when people are actively encouraged to explore their sexuality, our mission, nay, our
drive, is to help you address your urges in an healthy and safe way.
It is generally accepted that Disney is a synonym of a good childhood and if you got here by your own, most likely
you had a crush or an admiration for one or more of its characters. Maybe when you became aware that there was
more to it than just peeing or maybe when you were watching a cartoon with your kids and you realized that
Woody and Buzz are more than an unlikely duo. Honestly, we don't care. And we don't judge. That's not our job
here. Our job is to help you help yourself and, who knows, your relationship. You might just want to fulfill
that hole inside you where your childhood used to be, or just have a laugh with your parthner after the flame of
the youth is gone, or even just share a laugh with someone special.
When the road looks rough ahead and you're
miles and miles from your nice warm bed, just remember what your old pals said. You've got a friend in us.
All our products are made from recycled materials and following responsible economic practices. No child nor
animal labour were involved in the process chain.


--- title: Experiences & Happiness date: '2015-05-12T00:30:14+05:30' tags: - misc tumblr_url: https://sureshshanmugam.com/post/118713580822/our-experiences-are-a-bigger-part-of-ourselves ---
Our experiences are a bigger part of ourselves than our material goods. You can really like your material stuff. You can even think that part of your identity is connected to those things, but nonetheless they remain separate from you. In contrast, your experiences really are part of you. We are the sum total of our experiences.—Gilovich


http://www.codeblocks.org/
codeblocks-XX.XX-mingw-setup.exe
".Settings -> Compiler...
F9
.File -> New -> Empty File
|
|
File -> Save file as...
.cpp
extension, such as example.cpp
.F9
should compile and run the program.x
, the compiler does not understand the new meaning given to auto
since C++11. Please, make sure you have a recent compiler and that you enabled the compiler options to compile C++11 as described above.


RMI Templates
The IDE provides you with the following RMI data object templates
available from the New Wizard:
- Activatable. Creates an implementation of a remote object
based on java.rmi.activation.Activatable. The main
method contains code that registers the object into activation systems
and registers it into an RMI registry. A new registry is created if
one does not exist.
- ClearImplementation. Creates an implementation of a remote
object. Use this kind of implementation for adding
callbacks. ClearImplmentation is usually exported by
UnicastRemoteObject.exportObject().
- Hello. Creates a remote interface containing code using the
Hello example.
- RMIClient. Creates a simple RMI client file.
- RMI_IIOP. Creates an implementation of a remote object
based on javax.rmi.ProtableRemoteObject. IIOP is used for
communication between objects. The implamentation can also be used for
communicating with CORBA applications.
- Remote. Creates an empty remote interface. You can complete
the file by editing it in the Source Editor.
- UnicastRemoteObject. Creates an implementation based on
java.rmi.server.UnicastRemoteObject. The main method
of the class creates an instance of the class and registers the
instance in an RMI registry. A new registry is created if
one does not exist.
RMI Socket Templates
The IDE provides you with the following RMI socket templates available
from the New Wizard:
-
ClientSocket (group). Creates a group of files that contains
files that are necessary for client-side custom sockets. The files
created are: ClientSocketFactory, InputStream, OutputStream and
Socket.
-
ClientSocketFactory. Creates a class that implements
java.rmi.server.ClientSocketFactory, which serves as a
factory for creating sockets on the client side.
-
CustomSocket (group). Creates a group that contains both
client-side files and server-files.
-
InputStream. Creates a class that can be used for implementing
a custom InputStream.
-
OutputStream. Creates a class that can be used for implementing
a custom OutputStream.
-
ServerSocket. Creates a template that can be used for
subclasssing java.net.ServerSocket.
-
ServerSocket (group). Creates a group of files that makes it
possible to create the entire implementation of a custom
ServerSocket. For example, it can create a ServerSocket whose
accept method returns a custom socket that uses custom
InputStream and OutputStream. The files creates are: SocketInputStream,
SocketOutputStream, SocketServerSocket,
SocketServerSocketfactory, and SocketSocket.
-
ServerSocketFactory. Creates a template that can be used for
creating your own implementation of
java.rmi.server.SocketFactory.
-
Socket. Creates a template that can be used for creating a
custom implementation of java.net.Socket.
Legal Notices


One of the uses for parentheses in Javascript is to group something, just like with normal mathemetics. Whatever is between the parentheses takes prevelance. Parentheses can also be used to contain an expression or to contain a list of parameters.
Example:
int a;
a = (4 +3) * 2;          // group: the sum of 4 and 3 is calculated first
if a (a > 10) {           // contain an expression
line (a, 0, a, 100);    // contain a list of parameters
}
Another important use for parentheses is with 'functions'.  A function always has parentheses behind it. They either hold parameters, to be used within the function, or they can be empty, which means there is no parameter input for the function. It looks like this:
function (parameter0, parameter1)
or
function ()
Brackets are used with arrays. An array is an object that can be used to store multiple values in one single variables. The array object, will display those values between brackets. When you want to access a value in the array, you use the brackets as well, with a number that identifies that value within that array.
Example:
var pets = ["cats", "dogs", "guinea pigs"]    //array with three values, numbered 0, 1 and 2
Accessing this array also uses brackets:
pets[2] = "guinea pigs"
Braces are used to group lines of code for 'control flow'. Within the braces you can place statements, like for-loops, if-else's or define functions. They are also used to define object literals, which are comma separated lists of name/value pairs, wrapped within the braces.
Single and double quotes can both be used to define strings of text. It's best to be consistent though, only use one or the other. If you use single quotes and your string contains a word with a single quote, ie 'Richard's code' you need to include a backslash: 'Richard\'s code'. The backslash prevents javascript from picking up the second ' as the end of the string, but instead interprets it being part of the string.


MINA is a not for profit organisation managed by a committee of members overseeing its activities to ensure that they are aligned with the organisations vision and objectives.
MINA is comprised of like-minded families of the Islamic faith from diverse demographic backgrounds in Melbourne. Originally belonging to the Indian subcontinent, these families have come together to organise various events and projects to not only strengthen the Muslim Community in Australia but also to engage and collaborate with wider Australian community to build a more prosperous tomorrow for all.
Some of our amazing moments


| Path: | README |
| Last Update: | Tue Jan 22 16:44:24 -0500 2008 |
Gibberish Rails 0.1
An extension to Gibberish and Rails to use it to create a multilingual web site using the around filter and Gibberish.
The latest version of gibberish_rails can be found here: svn.myutil.com/projects/plugins/gibberish_rails/
Other info can be found here: myutil.com/2008/1/22/gibberish_rails-a-ruby-on-rails-plugin-to-translate-rails-with-gibberish
This is an alpha release. Tests are not written yet. I expected this plugin to be radically rewritten (simplified?) once Rails core is fixed to allow for localization of messages. The issue for Rails core tracking this is here: dev.rubyonrails.org/ticket/9726. I probably didn‘t find and fix all the messages in validates let alone rails but this should be useful to anyone trying to use Gibberish to run a multi-lingual web site.
This plugin:
class ApplicationController < ActionController::Base # ... around_filter :use_best_guess_locale # ... # Set's the best guess language Giberish's around filter def use_best_guess_locale session[:locale] = GibberishRails.best_guess_locale(params[:locale], request.env['HTTP_ACCEPT_LANGUAGE']) if ( ! session[:locale] || params[:locale] || RAILS_ENV == 'development' ) Gibberish.use_language(session[:locale]) { yield } end end
Notes:
If you are using Peter Marklund‘s gibberish_translate (www.marklunds.com/articles/one/376), the you should change the directories it search to include this one. Also Peter‘s plugin doesn‘t pick up the Gibberish String "foo"[] (which is another way of saying "foo"[:foo]). Redefine Gibberish::Extractor methods in his plugin to be:
def dirs_to_search %w(app config lib vendor/plugins/gibberish_rails).map { |dir| "#{RAILS_ROOT}/#{dir}" } end def message_pattern(start_token, end_token) /#{start_token}((?:[^#{end_token}](?:\\#{end_token})?)+)#{end_token}\[:*([a-z_]*)[,\]]/m end def add_messages(contents, start_token, end_token) contents.scan(message_pattern(start_token, end_token)).each do |text, key| key = text.tr('[ ]', '_').downcase if ( key == '' ) add_message(key, remove_quotes(text, end_token)) end end
Known Issue:
When the fields in a form are translated and validated, the div's that created the red boxes aren't working...have to look into that at some point.
Copyright (c) 2008 MyUtil(tm), released under the MIT license
Portions of code above Copyright (c) 2007 Peter Marklund: svn.marklunds.com/plugins/gibberish_translate/MIT-LICENSE


To push the Second Page, click the button below.
To set the Second Page as root page, click the button below. You won´t be able to pop from there.
You can also transfer data while navigating between pages.


PhysX Revolute Joint Component
The PhysX revolute joint component is a joint that links two actors such that they can only rotate around one axis relative to each other.
How far the joined objects can rotate can be limited, either with a hard limit that prevents the actor from rotating farther, or with a soft limit, which pulls the actor back with a spring.
The revolute joint can also be powered with a drive, meaning it will rotate on its own with a maximum force.
Component Properties
LimitMode
: Defines whether the joint can spin freely, or is restricted byLowerLimit
andUpperLimit
.NoLimit
: The joint can spin without restriction.HardLimit
: The joint cannot rotate farther thanLowerLimit
andUpperLimit
. If it hits the boundary, it may bounce back. In this modeSpringStiffness
acts as a restitution value ([0; 1]
range). At value zero, it will stop dead, with higher values, the actor will bounce back.SpringDamping
defines a force threshold. If the joint hits the limit with less force than this, it will not bounce back, at all.SoftLimit
: The joint can spin freely, but when it rotates farther than the limits, a spring will pull it back. In this modeSpringStiffness
([0; inf]
range) specifies how strongly the spring will pull, andSpringDamping
is used to dampen the spring to prevent oscilation.
LowerLimit
,UpperLimit
: The lower and upper allowed rotation angles, ifLimitMode
is enabled.SpringStiffness
,SpringDamping
: SeeLimitMode
.DriveMode
: Specifies whether the joint will apply a constant force to rotate the actors.NoDrive
: The joint will not rotate on its own.DriveAndSpin
: The joint will try to rotate atDriveVelocity
. If an external force makes it spin even faster, the joint will not brake.DriveAndBrake
: The joint will try to rotate andDriveVelocity
. If an external force makes it spin even faster, it will brake to try to not exceed the velocity.
DriveVelocity
: The target velocity to spin at, whenDriveMode
is enabled.MaxDriveTorque
: The maximum force the joint can apply to try to reachDriveVelocity
. If something blocks the joined actor, the torque affects whether the obstacle can be pushed away.


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CrimPapers
Articles published on 27 March 2018
On 27 March 2018, 13 new articles were published in 12 journals.
Federal Criminal Careers: An Empirical Examination of the Post-Conviction Risk Assessment (pcra)
Ngo Intervention in Jihadist Conflicts: A Closer Look At Afghanistan and Somalia by Omi Hodwitz
Alexandra Cox: Trapped in A Vice: the Consequences of Confinement For Young People
Drones, State Terrorism and International Law by Ruth Blakeley
Police Enforcement of Street Prostitution As A Quality-of-Life Offense: New York City, United States, and Frankfurt Am Main, Germany by Bernard Cohen
New Faces in A New Place: Long-Time Residents’ Perceptions of New Residents in An Oil Boomtown in the Bakken Oil Shale Region by Carol Huynh, Chloe Robinson, Thomas Mrozla, Thorvald O. Dahle, Carol A. Archbold, Alexandra Marcel
Digital Policing Technologies and Democratic Policing: Will the Internet, Social Media and Mobile Phone Enhance Police Accountability and Police–citizen Relations in Nigeria? by Temitayo Isaac Odeyemi, A. Sat Obiyan
Status, Issues, and Challenges of Chinese Juvenile Justice by Hongwei Zhang, Ni “Phil” He
Identifying Victims of Human Trafficking in Central Pennsylvania: A Survey of Health-Care Professionals and Students by Risha Sinha, Elahe Tashakor, Casey Pinto
An Examination of Mock Jurors’ Judgments in Familiar Identification Cases
The Role of Symbolic Politics in Exceptional Crime Policy Debate: A Study of the 2014 Swedish General Election by Klara Hermansson
Reconciling Mental Health, Public Policing and Police Accountability by John Lm McDaniel
Terrorist Decision Making in the Context of Risk, Attack Planning, and Attack Commission by Paul Gill, Zoe Marchment, Emily Corner, Noémie Bouhana
Note: although this article was published on 1 March 2018, the publisher only added it to the journal RSS feed on 27 March 2018, which is why it is shown in the list of articles for that date.


We discuss about how we got into writing and the tools we use
Writing isn’t easy
Writing can be a daunting task. It can be extremely time-consuming. Or sometimes it becomes hard to put your ideas on paper. Over the course of our journey as a writers, We have come across various tools which helped us write better. And here, We are going to share some of it with you.
1. Grammarly
{11:58} Grammarly is among one of the best writing tools out there. It checks the spellings and grammar, with the ability to switch from American and British English. It also comes with an extension which you can add to your browser which is quite handy while framing emails or commenting on someones Facebook status. Saves you from the embarrassment of spelling and grammar mistakes. The paid version has a lot more features such as proofreading, plagiarism checker, vocabulary enhancement, and a lot more. But yeah, it costs around $11/month. One of the downside is Grammarly is not available on phones or tablet devices. You need to have a computer or laptop at your disposal to use it.
2. Hemmingway App
{13:12} If like an Indian, you need things for free, look for another tool called Hemmingway App. It is an app which “makes your writing Bold and Clear.” The thing cool about this tool is that it checks the readability and identifies adverbs, passive voices, simpler alternatives for words, hard and very hard sentences. The whole process of proofreading becomes so much easier.
3. Small SEO Tools Plagiarism Checker
{16:02} As the plagiarism checker comes under the premium plan of Grammarly, we use Small SEO Tools Plagiarism Checker. It is as effective as Grammarly and helps you find out anything which is remotely plagiarised. Simple to use and easy to identify.
4. Microsoft OneDrive
{17:32} The Microsoft Word helps you transfer and share files by the use of OneDrive. You also get to store these files without wasting any space on my computer. So, when I am working remotely or do not have access to a computer, I use OneDrive to share the files with my clients. Google Drive is an alternative for you to store important files online. But OneDrive is integrated with Microsoft Word, which make the storage and sharing process a lot easier.
5. Celtx
{18:37} Celtx is a tool used for script writing. The ease to use, the ability to switch from scene to scene, the transition, writing dialogues, everything becomes so easy with Celtx.
6. Pixabay
{20:14} Pixabay is not so much of a tool, but it is a website where you can source royalty free images. I have been using the images from this website for almost all my blog posts. You can even edit the stock pictures to personalise for your use. You do not need any permission or licensing. Makes life a lot simpler.
7. Canva
{22:26} Sometimes the stock images sourced from Pixabay or any other royalty free image website might not suit in itself to be a blog post image. This calls for a bit of editing which can be done with Canva. It is one of the best editing tools that can be used online. You get the ability to make Facebook, Instagram, Twitter posts with ease using this handy tool. You will also find stock images on Canva which you can use for free.
8. Inkscape
{25:27} A logo plays an important part in the marketing aspect of any business. Inkscape is an amazing and easy to use tool for creating logos and manipulate with the existing pictures and turn them into vectors. It is easy to learn and if you have any plans to get into logo making you should check out Nick Saporito’s Inkscape tutorials. He explains it in detail and with complete correctness. Do go check his channel out.
Few of our blogs
- ramblingjoint.com
- neoelemento.com
- vishnupadmanabhan.com
- neoelemento.wordpress.com
- shankarpadmanabhan.wordpress.com
Image: Pixabay


The mechanisms that we identify above are in line with previous studies that have usually focused on temperature extremes (extreme compared to current climate) and on shorter time scales ( 37, 38). When comparing the occurrence of such extreme events between models and observation-based data sets, previous studies have usually found good agreement regarding their frequency and trend patterns ( 39, 40). Here, we briefly compare the modeled variability of monthly mean temperatures with four reanalysis data sets (see Materials and Methods for more detail): ERA-Interim ( 41), NOAA-CIRES 20th Century Reanalysis V2c ( 42), NCEP/DOE Reanalysis 2 ( 43), and MERRA2 ( 44). Although locally constrained by observations, these reanalysis data sets allow a global and temporally complete coverage. We calculate the temperature SD in the period 1980–2010 for all models and the four reanalyses by linear detrending for each month and computing the SD of the residuals. Overall, our comparison confirms that the current temperature SD of the CMIP5 models is within the observational range (fig. S6). Regional biases occur mostly in high latitudes over areas of substantial variability in sea-ice cover and over some tropical land regions (fig. S6A). However, these model mean biases are comparable in magnitude with the typical differences between models (fig. S6B) and, over some regions, are also comparable to the differences between reanalysis data sets (fig. S6C).
CMIP5 models are particularly uncertain and biased in the Amazon. Although most models show a pronounced hotspot of future temperature SD increase, this is also the region where the magnitude of this increase is most uncertain. Overall, the regions that the models identify as hotspots of future temperature SD increase tend to have a positive bias, that is, the model average shows larger present-day variability than reanalyses (fig. S6A). However, selecting groups of models based on their overall sign of biases hardly affects the general pattern of changing temperature variability. Although there is a tendency for models with positive bias to also show larger increases in temperature SD in many places, this bias is relatively small compared to the predicted changes in most hotspot areas, and the position of hotspots and the relative magnitude of temperature variability changes in these regions are not substantially affected.
Donat et al. ( 37) recently raised the question whether the predictions of the models are reliable or whether they share common biases in their predictions of temperature extremes, despite the overall agreement regarding the magnitude of observed variability. In the case that the model predictions are correct, the question arises whether the anthropogenic fingerprint is already apparent in observations of temperature variability. We therefore recalculate the changes in temperature SD for the period from 1958 to 2017 and compare the result to two observational data sets based on in situ measurements from meteorological stations: HadCRUT4 ( 45) and the GISS (NASA Goddard Institute for Space Studies) Surface Temperature Analysis (GISTEMP) (see Materials and Methods) ( 46). These data sets report temperatures whose mean and variance have been standardized with a reference period. This procedure is known to introduce biases in variance changes ( 47), although the effect is likely small here because the trends that we find in the observational data sets tend to also show up in the reanalyses that have no such bias ( 17).
At first sight, the observations do not agree well with the signal in the model predictions ( Fig. 4). The most apparent features in observations are an increase in temperature SD in the Mediterranean region and a decrease in some parts of North America and Northern Europe ( Fig. 4, A and B). In contrast, the tendencies of changes in temperature SD in the models already show the overall pattern that we analyzed above when all models are considered ( Fig. 4C). However, the model agreement is still relatively low, and the characteristic contrast between high and low latitudes is not apparent in individual models with very few exceptions. If we assume that each model is one realization of climate variability in the real world, then this means that we cannot expect to see the anthropogenic signal in recent observations. It is only during the 21st century that the signal emerges in the large majority of the models, with more than 80% of the models agreeing on the sign until 2100 over subtropical land and the mid- and high latitudes (fig. S1). Although the sign of variability change in observations can be very clear at places (indicated by a large absolute Kendall τ value), these observations still provide only one realization.
( A) Observations from HadCRUT4, ( B) observations from GISTEMP, and ( C) CMIP5 models. (A) and (B) show Kendall τ values, whereas (C) shows the number of models agreeing on the sign of the Kendall τ value. The window size for removing the annual cycle was 15 years, and the window to compute the Kendall τ value for the change in SD was 20 years.
In a recent companion study ( 17), we have shown that the sign of the observed temperature trends substantially depends on the choice of the time period, which confirms that the observed changes are largely related to internal climate variability and not a forced trend. The relatively robust decrease of observed variability over high northern latitudes in the North Atlantic region, however, might already be part of the anthropogenic signal (associated with sea-ice loss). Unfortunately, the regions with the largest model agreement in Fig. 4C (tropical land areas and high latitudes)—where the anthropogenic signal can be expected to emerge the earliest—are also among the regions with the fewest observations. It is therefore hard to conclude whether current observations confirm or refute the predictions of models regarding trends in monthly temperature variability.


I am a fullstack engineer. Currently, I am working in a startupscaled company, which is my first time leaped at the chance to work abroad. I am ambitious to change what I want to see in the world. I always give the maximum contribution in my work and study hard in my personal interests, i.e., Machine learning, Data Mining, and Design Pattern. I deeply love the things that can help people. Motto: Play hard and work smart!


Before Change
:return: The output of the embedding stack followed by its reduction. This will typically be an output
with an additional dimension which is the hidden representation of the input
return EmbeddingsStack(embeddings, reduction=kwargs.get("embeddings_reduction", "concat"))
def init_stacked(self, input_dim: int, **kwargs) -> BaseLayer:
Produce a stacking operation that will be used in the model
After Change
:return: The output of the embedding stack followed by its reduction. This will typically be an output
with an additional dimension which is the hidden representation of the input
reduction = kwargs.get("embeddings_reduction", "concat")
embeddings_dropout = float(kwargs.get("embeddings_dropout", 0.0))
name = kwargs.get("embeddings_name")
return EmbeddingsStack(embeddings, embeddings_dropout, reduction=reduction, name=name)
def init_stacked(self, input_dim: int, **kwargs) -> BaseLayer:
Produce a stacking operation that will be used in the model


See Configuring Logging.
SQLAlchemy performs application-level connection pooling automatically
in most cases. With the exception of SQLite, a Engine
object
refers to a QueuePool
as a source of connectivity.
For more detail, see Engine Configuration and Connection Pooling.
The create_engine()
call accepts additional arguments either
directly via the connect_args
keyword argument:
e = create_engine("mysql://scott:tiger@localhost/test",
connect_args={"encoding": "utf8"})
Or for basic string and integer arguments, they can usually be specified in the query string of the URL:
e = create_engine("mysql://scott:tiger@localhost/test?encoding=utf8")
See also
There are two major causes for this error:
1. The MySQL client closes connections which have been idle for a set period
of time, defaulting to eight hours. This can be avoided by using the pool_recycle
setting with create_engine()
, described at Connection Timeouts.
2. Usage of the MySQLdb DBAPI, or a similar DBAPI, in a non-threadsafe manner, or in an otherwise
inappropriate way. The MySQLdb connection object is not threadsafe - this expands
out to any SQLAlchemy system that links to a single connection, which includes the ORM
Session
. For background
on how Session
should be used in a multithreaded environment,
see Is the session thread-safe?.
SQLAlchemy currently assumes DBAPI connections are in “non-autocommit” mode -
this is the default behavior of the Python database API, meaning it
must be assumed that a transaction is always in progress. The
connection pool issues connection.rollback()
when a connection is returned.
This is so that any transactional resources remaining on the connection are
released. On a database like PostgreSQL or MSSQL where table resources are
aggressively locked, this is critical so that rows and tables don’t remain
locked within connections that are no longer in use. An application can
otherwise hang. It’s not just for locks, however, and is equally critical on
any database that has any kind of transaction isolation, including MySQL with
InnoDB. Any connection that is still inside an old transaction will return
stale data, if that data was already queried on that connection within
isolation. For background on why you might see stale data even on MySQL, see
http://dev.mysql.com/doc/refman/5.1/en/innodb-transaction-model.html
The behavior of the connection pool’s connection return behavior can be
configured using reset_on_return
:
from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool
engine = create_engine('mysql://scott:tiger@localhost/myisam_database', pool=QueuePool(reset_on_return=False))
reset_on_return
accepts the values commit
, rollback
in addition
to True
, False
, and None
. Setting to commit
will cause
a COMMIT as any connection is returned to the pool:
engine = create_engine('mssql://scott:tiger@mydsn', pool=QueuePool(reset_on_return='commit'))
If using a SQLite :memory:
database, or a version of SQLAlchemy prior
to version 0.7, the default connection pool is the SingletonThreadPool
,
which maintains exactly one SQLite connection per thread. So two
connections in use in the same thread will actually be the same SQLite
connection. Make sure you’re not using a :memory: database and
use NullPool
, which is the default for non-memory databases in
current SQLAlchemy versions.
See also
Threading/Pooling Behavior - info on PySQLite’s behavior.
With a regular SA engine-level Connection, you can get at a pool-proxied
version of the DBAPI connection via the Connection.connection
attribute on
Connection
, and for the really-real DBAPI connection you can call the
ConnectionFairy.connection
attribute on that - but there should never be any need to access
the non-pool-proxied DBAPI connection, as all methods are proxied through:
engine = create_engine(...)
conn = engine.connect()
conn.connection.<do DBAPI things>
cursor = conn.connection.cursor(<DBAPI specific arguments..>)
You must ensure that you revert any isolation level settings or other operation-specific settings on the connection back to normal before returning it to the pool.
As an alternative to reverting settings, you can call the Connection.detach()
method on
either Connection
or the proxied connection, which will de-associate
the connection from the pool such that it will be closed and discarded
when Connection.close()
is called:
conn = engine.connect()
conn.detach() # detaches the DBAPI connection from the connection pool
conn.connection.<go nuts>
conn.close() # connection is closed for real, the pool replaces it with a new connection
The key goal with multiple python processes is to prevent any database connections from being shared across processes. Depending on specifics of the driver and OS, the issues that arise here range from non-working connections to socket connections that are used by multiple processes concurrently, leading to broken messaging (the latter case is typically the most common).
The SQLAlchemy Engine
object refers to a connection pool of existing
database connections. So when this object is replicated to a child process,
the goal is to ensure that no database connections are carried over. There
are three general approaches to this:
Disable pooling using NullPool
. This is the most simplistic,
one shot system that prevents the Engine
from using any connection
more than once.
Call Engine.dispose()
on any given Engine
as soon one is
within the new process. In Python multiprocessing, constructs such as
multiprocessing.Pool
include “initializer” hooks which are a place
that this can be performed; otherwise at the top of where os.fork()
or where the Process
object begins the child fork, a single call
to Engine.dispose()
will ensure any remaining connections are flushed.
An event handler can be applied to the connection pool that tests for connections being shared across process boundaries, and invalidates them. This looks like the following:
import os
import warnings
from sqlalchemy import event
from sqlalchemy import exc
def add_engine_pidguard(engine):
"""Add multiprocessing guards.
Forces a connection to be reconnected if it is detected
as having been shared to a sub-process.
"""
@event.listens_for(engine, "connect")
def connect(dbapi_connection, connection_record):
connection_record.info['pid'] = os.getpid()
@event.listens_for(engine, "checkout")
def checkout(dbapi_connection, connection_record, connection_proxy):
pid = os.getpid()
if connection_record.info['pid'] != pid:
# substitute log.debug() or similar here as desired
warnings.warn(
"Parent process %(orig)s forked (%(newproc)s) with an open "
"database connection, "
"which is being discarded and recreated." %
{"newproc": pid, "orig": connection_record.info['pid']})
connection_record.connection = connection_proxy.connection = None
raise exc.DisconnectionError(
"Connection record belongs to pid %s, "
"attempting to check out in pid %s" %
(connection_record.info['pid'], pid)
)
These events are applied to an Engine
as soon as its created:
engine = create_engine("...")
add_engine_pidguard(engine)
The above strategies will accommodate the case of an Engine
being shared among processes. However, for the case of a transaction-active
Session
or Connection
being shared, there’s no automatic
fix for this; an application needs to ensure a new child process only
initiate new Connection
objects and transactions, as well as ORM
Session
objects. For a Session
object, technically
this is only needed if the session is currently transaction-bound, however
the scope of a single Session
is in any case intended to be
kept within a single call stack in any case (e.g. not a global object, not
shared between processes or threads).


@LogMethods public class SqlPojoMapping<T extends DataMapping> extends java.lang.Object implements DataMapping
Use this class to validate that two different Columns in two different SqlPojo
s are
equal.
Note: T
is the DataMapping
class to use to
back this
one. Supported Options:
SqlPojo
's
Column's Values are in BigDecimal
format
Note: This class is used instead of direct comparison, to simplify large batches of comparisons, and to provide common functionality.
DataMapping.DataMappingFlagEnum
| Modifier and Type | Field and Description |
|---|---|
T |
DATA_MAPPING_BACKER
The
DataMapping object that
this one is
backed by.
|
SqlPojo.RowMapperColumnEnum |
DESTINATION_COLUMN
The Columns being compared.
|
SqlPojo
|
DESTINATION_TABLE
The Tables being compared.
|
java.util.Set<DataMapping.DataMappingFlagEnum>
|
MAPPING_FLAGS
The
DataMappingFlagEnum s that are applied to this SqlPojoMapping
object.
|
java.lang.String |
MAPPING_NAME
The name that was given to this mapping.
|
SqlPojo.RowMapperColumnEnum |
SOURCE_COLUMN
The Columns being compared.
|
SqlPojo
|
SOURCE_TABLE
The Tables being compared.
|
| Constructor and Description |
|---|
SqlPojoMapping(SqlPojo _sourceTable,
SqlPojo.RowMapperColumnEnum _sourceColumn,
SqlPojo _destinationTable,
SqlPojo.RowMapperColumnEnum _destinationColumn,
DataMapping.DataMappingFlagEnum... _flags)
|
SqlPojoMapping(java.lang.String _mappingName,
SqlPojo _sourceTable,
SqlPojo.RowMapperColumnEnum _sourceColumn,
SqlPojo _destinationTable,
SqlPojo.RowMapperColumnEnum _destinationColumn,
DataMapping.DataMappingFlagEnum... _flags)
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
toString()
|
java.lang.String |
validate()
This method compares the two, previously set, values.
|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
createFormattedErrorString,
isOneNullButNotBoth
public final java.lang.String MAPPING_NAME
Note: This name is optional and may be null
.
public final SqlPojo SOURCE_TABLE
public final SqlPojo DESTINATION_TABLE
public final SqlPojo.RowMapperColumnEnum SOURCE_COLUMN
public final SqlPojo.RowMapperColumnEnum DESTINATION_COLUMN
public final T extends DataMapping DATA_MAPPING_BACKER
DataMapping
object that this
one is
backed by.
public final java.util.Set<DataMapping.DataMappingFlagEnum> MAPPING_FLAGS
DataMappingFlagEnum
s that are applied to this SqlPojoMapping
object.
public SqlPojoMapping(SqlPojo _sourceTable, SqlPojo.RowMapperColumnEnum _sourceColumn, SqlPojo _destinationTable, SqlPojo.RowMapperColumnEnum _destinationColumn, DataMapping.DataMappingFlagEnum... _flags)
_sourceTable
- The Source Table to pull the Column Value from._sourceColumn
- The Source Column to pull the Value from._destinationTable
- The Destination Table to pull the Column Value from._destinationColumn
- The Destination Column to pull the Value from._flags
- Any DataMapping.DataMappingFlagEnum
s
that should be applied to this SqlPojoMapping
.
(Enum Type must match T
class's implementation of DataMapping.DataMappingFlagEnum
.)
public SqlPojoMapping(java.lang.String _mappingName, SqlPojo _sourceTable, SqlPojo.RowMapperColumnEnum _sourceColumn, SqlPojo _destinationTable, SqlPojo.RowMapperColumnEnum _destinationColumn, DataMapping.DataMappingFlagEnum... _flags)
_mappingName
- An optional, unique name to give this SqlPojoMapping
.
_sourceTable
- The Source Table to pull the Column Value from._sourceColumn
- The Source Column to pull the Value from._destinationTable
- The Destination Table to pull the Column Value from._destinationColumn
- The Destination Column to pull the Value from._flags
- Any DataMapping.DataMappingFlagEnum
s
that should be applied to this SqlPojoMapping
.
(Enum Type must match T
class's implementation of DataMapping.DataMappingFlagEnum
.)
public java.lang.String validate()
DataMapping
validate
in
interface DataMapping
NULL
, if there were no differences.public java.lang.String toString()
toString
in class java.lang.Object


- Go to https://console.developers.google.com/project and create a new project.
- Go to APIs & Auth - APIs and enable Google+ API and Google+ Domains API
- Go to APIs & Auth - Credentials and click on Create new client ID. A pop-up named "Create Client ID" will appear.
- Select Web Application. Set Authorized JavaScript Origins to = URL::to('') ?> and Authorized Redirect URI to = URL::route('sociallogin_provider', ['Google']) ?>
- Click Create Client ID.


Dessert island is virtual reality experience that allows the viewer to wander through a paradise island themed entirely around desserts. The island completely made up of sweets, both flora and fauna are made from sugary snacks. Everything from cake mountains and candy trees to a cupcake hamlet complete with anthropomorphized dessert folk.
I was tasked with creating various natural and urban environmental set pieces that were dotted around the island. Some simply meant finding suitable replacements that seemed fitting. Cakes make lovely mountains, lollipops and suckers made for nice smaller trees, and a lava cake makes an excellent volcano centerpiece. The village assets required a slightly different approach. For buildings, identifiable features like doorways turned assorted cupcakes into simple houses. For more complicated structures multiple treats acted as components that made up the final model. Chocolate benches with biscuit struts, candy cane streetlights lit with gumdrop bulbs, and a chocolate fountain fitted with a donut base, it is a sugary sweet town square.
Sunny Ho (Environment Assets)
Solange Medina (Characters and Animation)
Arsalan Rashdi (Backend Unity and VR integration)
Becky Wu (Concept Art, Characters, Compositor)


Set a new password for user {{password.user.name}}. After setting a new password the user can only access the application with the new password.


This section presents a list of outstanding issues and likely problems that users should be aware of. A design limitation denotes a limitation that is inherent to the foundation of Faunus and as such, is not something that will not be rectified in future release. On the contrary, temporary limitations are incurred by the current implementation and future versions should provide solutions.
Faunus is built atop Hadoop. Hadoop is not a real-time processing framework. All Hadoop jobs require a costly setup (even for small input data) that takes around 15 seconds to initiate. For real-time graph processing, use Titan (also see Distributed Graph Computing with Gremlin).
The Blueprints API states that an element (vertex/edge) can have any Object
as a property value (e.g. Vertex.setProperty(String,Object)
). Faunus only supports integer, float, double, long, string, and boolean property values.
rdf:label
property can cause parsing problems with TitanNote that rdf:label
is a common predicate in RDF. If use-localname=true
, then the edge label would be label
which is legal in Blueprints, but edge labels and vertex keys are in the same address space and thus, causes problems.
There is no easy way to serialize a Groovy closure and thus, propagate to the Hadoop jobs running on different machines. As such, until a solution is found, a closure must be provided as a String
. For example: filter('{it.degree > 10}')
instead of filter{it.degree > 10}
.
A single vertex must be able to fit within the -Xmx
upper bound of memory. As such, this means that a graph with a vertex with 10s of millions of edges might not fit within a reasonable machine. In the future, “vertex splitting” or streaming is a potential solution to this problem.
The current implementation of the GraphSON InputFormat is excessively inefficient. As it stands the full String
representation of vertex is held in memory, then its JSON Map
representation, and finally its FaunusVertex
representation. This can be fixed with a smarter, streaming parser in the future.
The Gremlin implementation that is currently distributed with Faunus is not identical to Gremlin/Pipes. Besides not all steps being implemented, the general rule is that once “the graph is left” (e.g. traverse to data that is not vertices or edges), then the traversal ends. This ending is represented as a pipeline lock in the Gremlin/Faunus compiler.
SequenceFileOutputFormat
and SequenceFileInputFormat
contain other metadataThe binary sequence files supported by Hadoop are the primary means by which graph and traversal data is moved between MapReduce jobs in a Faunus chain. If a sequence file is saved to disk, be conscious of the traversal metadata it contains (e.g. path calculations).
Rexster is optimized to work best with Rexster 2.2.0+. Faunus will work with Rexster 2.1.0, but has some limitations. Due to some default and non-configurable settings in Rexster 2.1.0, it will only allow Faunus a maximum of four map tasks to connect to it. If more are configured, then Faunus will throw a SocketException
and fail.
To avoid such problems in Rexster 2.2.0+, the thread pool must be configured to include enough threads such that there are enough threads to service each of the expected long-run requests from the mapper tasks. Being able to strike a careful balance among the number of map tasks, Rexster memory availability, and Rexster thread pool size will greatly determine the speed at which Faunus will operate. It will likely take some experimentation to achieve the most efficient configuration.


---
layout: archive
title: Lean Prover Zulip Chat Archive
permalink: 113489newmembers/68007Differencesbetweenand.html
---
The archive URL format has changed. Please find this page at:
https://leanprover-community.github.io/archive/stream/113489-new-members/topic/Differences.20between.20(.2E.2E.2E).2C.20.7B.2E.2E.2E.7D.20and.20.5B.2E.2E.2E.5D.html


How to¶
Processing steps¶
The following steps explain the process of preparing the data before the modelling stage
Information required:
Census Data
Previous model/analyst decisions
Shape files of the Brazilian regions
Download the data from the S3 bucket. The functions that helps to do this are in
utils_aws
. Use of this functions can be reviewed in download_infoUse the shapes in data/sharp to add the sector code columns to the data base with the previous model and anlyst decisions. This allows to go from lat/long to sector code, which will be used to join the censun information to each property. The methods to perform this process are in the class
data_preparation_classes.CurrentLabels
. Use of this class can be reviewed in the script initial_data_process.Solve the duplicates in the data base and unify the analyst and model decision to have only one label. The methods to perform this proces are in py:mod:data_preparation_classes.DataWithDups. Use of this class can be reviewed in the script initial_data_process.
Process the census information (featues creation and drop zero variance variables) and joins its information to the processed label data. The methods to perform this process are in the class
data_preparation_classes.FinalLabelsWithSector
. Use of this class can be reviewed in the script initial_data_process.
Modelling steps¶
The following steps explain the process of fitting the model based on the information processed previously.
Information required:
Processed database with both labels and census information.
Fit the model using the class
fit_model_class.PropertyModel
.Save the model using the same class from the step before. Use of the class can be reviewed in script fit_model.


Recently, I have been able to try Salvia. This plant is absolutely fantastic! I have always been a person who looks for new experiences of the mind; my past psychadellic drug use consists of Mushrooms, LSD, Mescaline, Ketamine, DXM, PCP (by accident, and that stuff really sucks), MDMA, and San Pedro. I purchased some 10x extract and have used it in quite a few settings. The first few times I used Salvia, I was really in awe of what a unique and powerful experience it was; quite frankly, I couldn't make heads or tails of what was going on. However, I have been using it more and trying to get a better feeling for the trip and find out where I can go with it.
The last time I used Salvia, I chewed two grams of leaves in my mouth for 30 minutes, and as the effects came on, loaded three hits of 10x extract into 3 seperate bowls. Anyways, I hit each one, practically gasping for air afterward (I hate holding my breath for so long) and laid back onto my bed. I decided this time that I would attempt to meditate while riding the Salvia high. I have gotten to amazing states of mind through meditation alone, but Salvia struck me as a drug that induces a mindset very similar to deep mediation.
I felt as though I were part of some strange reality, but quickly and quickly, it would shift into being a different reality. For example, the first reality I would feel as though I was actually part of my bed, that we were the same structure. However, I wouldn't feel like I was lying down and looking up; rather, it seemed that the bed was like a rock jutting from the side of a cliff. I was no longer looking up, rather I was looking forward. I don't know if it makes sense, but I was the end of a bed that was vertical, not horizontal.
That would be one reality, and then it would suddenly shift. Now it is some reality where my upper body, and my lower body, are in two different dimentions, or two different lines of existence. However, I am fully aware of both simultaneously, and fully experiencing both at the same time. In one line of existence, my legs and abdomen are connected to and part of the bed, whereas my torso and head are the same as usual, just lying on the bed.
So, realities were shifting faster and faster (in the span of what would be a minute I guess) but I realized I didn't have to experience or even exist in any of those realities if I didn't want to. They kept changing, but I got to the point where I could stay in a reality that I liked, or let one pass that I didn't like. Basically choose which reality I wanted to be in.
Instead of staying in any of the realities and marveling at the closed eye visuals, I decided to not accept any of the realities that I was noticing. The more 'stubborn' I was about not accepting any of the realities, the faster and more furiously they changed about. However, I wouldn't accept any of them. Suddenly, I went through something like an existence trap door. You know in movies when you see Indian Jones or somebody go up to a wall or a bookcase, and then push it and they rotate into a secret room? That what it felt like, but instead of it being a rotation into a different physical place, this was a transcendence into a different space and time.
It felt like I just left the world, my body, and my mind, continue to function in its' world, but I on the other hand, would go check out another dimention. I suppose it was dissociative in a way, but I felt more like myself than almost any other time in my life. It was like letting go of all the heavy baggage that comes with being a human and living a life, and taking a short vacation to see what other forms of existence there might be. Eventually, I decided it as time to go back.
I opened my eyes, and I'm not sure how much time went by. I am still very high on the Salvia, but I am definately back to this dimention. I feel absolutely great, very refreshed. I feel as though I just got back from a week long vacation, very light. Anyway, I've continued to use salvia and meditation together. Sometimes I use smaller amounts, but it's gotten easier and easier, to find that existence trap door. After practice, I realized that the trip can be very controllable. In fact, the trip can be transcended completely.
On a serious note, I know the DEA is working on making Salvia illegal, and it's really awful. This plant has so much untapped potential for mental/spiritual uses, it should be a crime to criminalize this drug.  Chemicals that stabilize, or control, how you think are OK (anti-depressants, anti-psychotics, and mood-stabilizers). Chemicals that make you think differently, that open you to experience other forms of consciousness are abolished.
Drugs such as mushrooms, salvia, and peyote have been used by civilizations for thousands of years for their spiritual and psychological values. However, in less than 50 years, our government has been bold and arrogant enough to abolish, criminalize, and denounce drugs that have these roots. The fact that people have used these drugs for so long demands that we study them more, not get rid of them and become ignorant of what they can teach us. Well, Salvia is going to be one of their next targets, and it's a real shame. Try it now while there is still time. (sigh)... The drug war really needs to head in a new direction.
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.


| TDB | D | coordinate time (MJD: JD-2400000.5) |
|---|---|---|
| UT1 | D | universal time (fraction of one day) |
| WL | D | clock longitude (radians west) |
| U | D | clock distance from Earth spin axis (km) |
| V | D | clock distance north of Earth equatorial plane (km) |
| sla_RCC | D | TDB-TT (sec) |
|---|
SLALIB --- Positional Astronomy Library


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | 5x 7x 7x 7x 27x 27x 7x 4x 4x 4x 4x 3x 3x 3x 1x 1x 1x 2x | // Hash tables use an array under the hood. There is no native support for hashes in most languages. // Finding an element in an array is not very fast (linear time) but finding an element based on its index is very fast (constant time). // For that reason, hash tables use hashing algorithms to translate a key into an array index. // Each element in a hash table's backing array is known as a bucket. A bucket can store multiple values. // When a key-value pair is added to a bucket that already has existing key-value pairs, it's known as a collision.  Otherwise, we'd need unnecessarily big arrays to account for every possible unique key a hash table can hold. // All the main methods of a hash table rely on the hashing algorithm, whether that's methods to add, look up, or remove key-value pairs. export default class HashTable { //size will be prime too constructor(size = 51) { this.keyMap = new Array(size); } //another example of hash function hash(key) { let total = 0; //here can be other prime number - prime numbers very helpfull with collisions let prime = 31; for(let i =0; i < Math.min(key.length, 100); i++) { let value = key[i].charCodeAt(0)-96; total = (total*prime+value)%this.keyMap.length; } return total; } set(key, value) { const index = this.hash(key); Eif (this.keyMap[index] === undefined) { this.keyMap[index] = [] } this.keyMap[index].push([key, value]); } get(key) { const element = this.hash(key); const bucket = this.keyMap[element]; if (bucket != undefined) { for (let i = 0; i < bucket.length; i++) { Eif (bucket[i][0] === key) { return bucket[i][1]; } } } return null; } } |


|
GPGPU-Sim
|
Go to the source code of this file.
Enumerations | |
| enum | cache_access_logger_types { NORMAL, TEXTURE, CONSTANT, INSTRUCTION } |
Functions | |
| void | add_snap_shot_trigger (snap_shot_trigger *ss_trigger) |
| void | remove_snap_shot_trigger (snap_shot_trigger *ss_trigger) |
| void | try_snap_shot (unsigned long long current_cycle) |
| void | add_spill_log (spill_log_interface *spill_log) |
| void | remove_spill_log (spill_log_interface *spill_log) |
| void | set_spill_interval (unsigned long long interval) |
| void | spill_log_to_file (FILE *fout, int final, unsigned long long current_cycle) |
| unsigned | translate_pc_to_ptxlineno (unsigned pc) |
| void | create_thread_CFlogger (int n_loggers, int n_threads, address_type start_pc, unsigned long long logging_interval) |
| void | destroy_thread_CFlogger () |
| void | cflog_update_thread_pc (int logger_id, int thread_id, address_type pc) |
| void | cflog_snapshot (int logger_id, unsigned long long cycle) |
| void | cflog_print (FILE *fout) |
| void | cflog_visualizer_print (FILE *fout) |
| void | cflog_visualizer_gzprint (gzFile fout) |
| void | insn_warp_occ_create (int n_loggers, int simd_width) |
| void | insn_warp_occ_log (int logger_id, address_type pc, int warp_occ) |
| void | insn_warp_occ_print (FILE *fout) |
| void | shader_warp_occ_create (int n_loggers, int simd_width, unsigned long long logging_interval) |
| void | shader_warp_occ_log (int logger_id, int warp_occ) |
| void | shader_warp_occ_snapshot (int logger_id, unsigned long long current_cycle) |
| void | shader_warp_occ_print (FILE *fout) |
| void | shader_mem_acc_create (int n_loggers, int n_dram, int n_bank, unsigned long long logging_interval) |
| void | shader_mem_acc_log (int logger_id, int dram_id, int bank, char rw) |
| void | shader_mem_acc_snapshot (int logger_id, unsigned long long current_cycle) |
| void | shader_mem_acc_print (FILE *fout) |
| void | shader_mem_lat_create (int n_loggers, unsigned long long logging_interval) |
| void | shader_mem_lat_log (int logger_id, int latency) |
| void | shader_mem_lat_snapshot (int logger_id, unsigned long long current_cycle) |
| void | shader_mem_lat_print (FILE *fout) |
| int | get_shader_normal_cache_id () |
| int | get_shader_texture_cache_id () |
| int | get_shader_constant_cache_id () |
| int | get_shader_instruction_cache_id () |
| void | shader_cache_access_create (int n_loggers, int n_types, unsigned long long logging_interval) |
| void | shader_cache_access_log (int logger_id, int type, int miss) |
| void | shader_cache_access_unlog (int logger_id, int type, int miss) |
| void | shader_cache_access_print (FILE *fout) |
| void | shader_CTA_count_create (int n_shaders, unsigned long long logging_interval) |
| void | shader_CTA_count_log (int shader_id, int nCTAadded) |
| void | shader_CTA_count_unlog (int shader_id, int nCTAdone) |
| void | shader_CTA_count_print (FILE *fout) |
| void | shader_CTA_count_visualizer_print (FILE *fout) |
| void | shader_CTA_count_visualizer_gzprint (gzFile fout) |
Variables | |
| static unsigned long long | min_snap_shot_interval = 0 |
| static unsigned long long | next_snap_shot_cycle = 0 |
| static std::list< snap_shot_trigger * > | list_ss_trigger |
| static unsigned long long | spill_interval = 0 |
| static unsigned long long | next_spill_cycle = 0 |
| static std::list< spill_log_interface * > | list_spill_log |
| static int | n_thread_CFloggers = 0 |
| static thread_CFlocality ** | thread_CFlogger = NULL |
| static std::vector< insn_warp_occ_logger > | iwo_logger |
| static std::vector< linear_histogram_logger > | s_warp_occ_logger |
| static int | s_mem_acc_logger_n_dram = 0 |
| static int | s_mem_acc_logger_n_bank = 0 |
| static std::vector< linear_histogram_logger > | s_mem_acc_logger |
| static bool | s_mem_lat_logger_used = false |
| static int | s_mem_lat_logger_nbins = 48 |
| static std::vector< linear_histogram_logger > | s_mem_lat_logger |
| static int | s_cache_access_logger_n_types = 0 |
| static std::vector< linear_histogram_logger > | s_cache_access_logger |
| static linear_histogram_logger * | s_CTA_count_logger = NULL |
| Enumerator | |
|---|---|
| NORMAL | |
| TEXTURE | |
| CONSTANT | |
| INSTRUCTION |
Definition at line 370 of file stat-tool.cc.
| void add_snap_shot_trigger | ( | snap_shot_trigger * | ss_trigger | ) |
Definition at line 47 of file stat-tool.cc.
| void add_spill_log | ( | spill_log_interface * | spill_log | ) |
Definition at line 80 of file stat-tool.cc.
| void cflog_print | ( | FILE * | fout | ) |
Definition at line 163 of file stat-tool.cc.
| void cflog_snapshot | ( | int | logger_id, |
| unsigned long long | cycle | ||
| ) |
Definition at line 158 of file stat-tool.cc.
| void cflog_update_thread_pc | ( | int | logger_id, |
| int | thread_id, | ||
| address_type | pc | ||
| ) |
Definition at line 150 of file stat-tool.cc.
| void cflog_visualizer_gzprint | ( | gzFile | fout | ) |
Definition at line 179 of file stat-tool.cc.
| void cflog_visualizer_print | ( | FILE * | fout | ) |
Definition at line 171 of file stat-tool.cc.
| void create_thread_CFlogger | ( | int | n_loggers, |
| int | n_threads, | ||
| address_type | start_pc, | ||
| unsigned long long | logging_interval | ||
| ) |
Definition at line 118 of file stat-tool.cc.
| void destroy_thread_CFlogger | ( | ) |
Definition at line 137 of file stat-tool.cc.
| int get_shader_constant_cache_id | ( | ) |
Definition at line 376 of file stat-tool.cc.
| int get_shader_instruction_cache_id | ( | ) |
Definition at line 377 of file stat-tool.cc.
| int get_shader_normal_cache_id | ( | ) |
Definition at line 374 of file stat-tool.cc.
| int get_shader_texture_cache_id | ( | ) |
Definition at line 375 of file stat-tool.cc.
Definition at line 193 of file stat-tool.cc.
| void insn_warp_occ_log | ( | int | logger_id, |
| address_type | pc, | ||
| int | warp_occ | ||
| ) |
Definition at line 202 of file stat-tool.cc.
| void insn_warp_occ_print | ( | FILE * | fout | ) |
Definition at line 208 of file stat-tool.cc.
| void remove_snap_shot_trigger | ( | snap_shot_trigger * | ss_trigger | ) |
Definition at line 57 of file stat-tool.cc.
| void remove_spill_log | ( | spill_log_interface * | spill_log | ) |
Definition at line 85 of file stat-tool.cc.
| void set_spill_interval | ( | unsigned long long | interval | ) |
Definition at line 90 of file stat-tool.cc.
Definition at line 379 of file stat-tool.cc.
Definition at line 393 of file stat-tool.cc.
| void shader_cache_access_print | ( | FILE * | fout | ) |
Definition at line 413 of file stat-tool.cc.
Definition at line 403 of file stat-tool.cc.
| void shader_CTA_count_create | ( | int | n_shaders, |
| unsigned long long | logging_interval | ||
| ) |
Definition at line 427 of file stat-tool.cc.
Definition at line 440 of file stat-tool.cc.
| void shader_CTA_count_print | ( | FILE * | fout | ) |
Definition at line 458 of file stat-tool.cc.
Definition at line 449 of file stat-tool.cc.
| void shader_CTA_count_visualizer_gzprint | ( | gzFile | fout | ) |
Definition at line 470 of file stat-tool.cc.
| void shader_CTA_count_visualizer_print | ( | FILE * | fout | ) |
Definition at line 464 of file stat-tool.cc.
| void shader_mem_acc_create | ( | int | n_loggers, |
| int | n_dram, | ||
| int | n_bank, | ||
| unsigned long long | logging_interval | ||
| ) |
Definition at line 263 of file stat-tool.cc.
Definition at line 278 of file stat-tool.cc.
| void shader_mem_acc_print | ( | FILE * | fout | ) |
Definition at line 295 of file stat-tool.cc.
| void shader_mem_acc_snapshot | ( | int | logger_id, |
| unsigned long long | current_cycle | ||
| ) |
Definition at line 290 of file stat-tool.cc.
| void shader_mem_lat_create | ( | int | n_loggers, |
| unsigned long long | logging_interval | ||
| ) |
Definition at line 311 of file stat-tool.cc.
Definition at line 325 of file stat-tool.cc.
| void shader_mem_lat_print | ( | FILE * | fout | ) |
Definition at line 355 of file stat-tool.cc.
| void shader_mem_lat_snapshot | ( | int | logger_id, |
| unsigned long long | current_cycle | ||
| ) |
Definition at line 350 of file stat-tool.cc.
Definition at line 225 of file stat-tool.cc.
Definition at line 237 of file stat-tool.cc.
| void shader_warp_occ_print | ( | FILE * | fout | ) |
Definition at line 247 of file stat-tool.cc.
| void shader_warp_occ_snapshot | ( | int | logger_id, |
| unsigned long long | current_cycle | ||
| ) |
Definition at line 242 of file stat-tool.cc.
| void spill_log_to_file | ( | FILE * | fout, |
| int | final, | ||
| unsigned long long | current_cycle | ||
| ) |
Definition at line 96 of file stat-tool.cc.
| unsigned translate_pc_to_ptxlineno | ( | unsigned | pc | ) |
Definition at line 1855 of file cuda-sim.cc.
| void try_snap_shot | ( | unsigned long long | current_cycle | ) |
Definition at line 62 of file stat-tool.cc.
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Definition at line 191 of file stat-tool.cc.
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Definition at line 78 of file stat-tool.cc.
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Definition at line 45 of file stat-tool.cc.
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Definition at line 43 of file stat-tool.cc.
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Definition at line 115 of file stat-tool.cc.
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Definition at line 44 of file stat-tool.cc.
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Definition at line 77 of file stat-tool.cc.
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Definition at line 368 of file stat-tool.cc.
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Definition at line 367 of file stat-tool.cc.
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Definition at line 425 of file stat-tool.cc.
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Definition at line 261 of file stat-tool.cc.
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Definition at line 260 of file stat-tool.cc.
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Definition at line 259 of file stat-tool.cc.
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Definition at line 309 of file stat-tool.cc.
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Definition at line 308 of file stat-tool.cc.
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Definition at line 307 of file stat-tool.cc.
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Definition at line 223 of file stat-tool.cc.
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Definition at line 76 of file stat-tool.cc.
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Definition at line 116 of file stat-tool.cc.