review_requested
by Cliff Wulfman at 2023-06-15 14:09:53.referenced
by None at 2023-06-15 18:07:04.merged
by None at 2023-06-15 18:07:04.closed
by None at 2023-06-15 18:07:04.closed
by None at 2023-06-15 18:51:12.head_ref_deleted
by None at 2023-06-15 18:51:16.renamed
by None at 2023-06-15 13:32:24.Ticket with Chun’s June revisions to the migration fixtures: https://github.com/pulibrary/pdc_describe/pull/1263
mentioned
by Chun Ly at 2023-06-15 14:35:25.subscribed
by Chun Ly at 2023-06-15 14:35:25.assigned
by Chun Ly at 2023-06-15 14:35:32.assigned
by Bess Sadler at 2023-06-15 15:35:35.Proposal from @tpendragon: Lots of our applications use nodejs only to precompile assets. Let's investigate other ways to precompile the assets so we don't need node on every rails box.
mentioned
by Trey Pendragon at 2023-06-15 13:52:31.subscribed
by Trey Pendragon at 2023-06-15 13:52:31.{{ ansible_play_name }}
on {{ inventoryhostname }}"
channel: "{{ slackalertschannel }}"referenced
by Vickie Karasic at 2023-06-15 14:35:02.merged
by Vickie Karasic at 2023-06-15 14:35:02.closed
by Vickie Karasic at 2023-06-15 14:35:02.head_ref_deleted
by Vickie Karasic at 2023-06-15 14:35:03.Co-authored-by: Alicia<EMAIL_ADDRESS>Co-authored-by: Robert-Anthony Lee-Faison<EMAIL_ADDRESS>Co-authored-by: Vickie Karasic<EMAIL_ADDRESS>renamed
by Vickie Karasic at 2023-06-15 14:37:56.referenced
by Vickie Karasic at 2023-06-15 14:38:37.merged
by Vickie Karasic at 2023-06-15 14:38:37.closed
by Vickie Karasic at 2023-06-15 14:38:37.head_ref_deleted
by Vickie Karasic at 2023-06-15 14:38:39.referenced
by Vickie Karasic at 2023-06-15 14:39:13.merged
by Vickie Karasic at 2023-06-15 14:39:13.closed
by Vickie Karasic at 2023-06-15 14:39:14.head_ref_deleted
by Vickie Karasic at 2023-06-15 14:39:15.head_ref_restored
by Robert-Anthony Lee-Faison at 2023-06-15 14:46:41.referenced
by Vickie Karasic at 2023-06-15 14:55:12.merged
by Vickie Karasic at 2023-06-15 14:55:12.closed
by Vickie Karasic at 2023-06-15 14:55:12.head_ref_deleted
by Vickie Karasic at 2023-06-15 14:55:13.Closing in favor of a soon-to-come clean PR
closed
by Vickie Karasic at 2023-06-15 15:05:49.head_ref_force_pushed
by Francis Kayiwa at 2023-06-15 15:28:27.labeled
by James R. Griffin III at 2023-06-15 14:50:30.labeled
by James R. Griffin III at 2023-06-15 14:50:48.renamed
by James R. Griffin III at 2023-06-15 14:54:05.


Welcome to Oregon Trail 2020
You're one of forty thousand Californians that move to Oregon every year. But this time... the journey is a bit more treacherous.
On the current and following pages type your selection into the input and press the enter button on the screen to proceed. Good Luck!
- Start your trip!
- Creator Bio's!


Moody's downgrades UK credit rating outlook to 'negative'
by Michael SearlesMoody’s has signalled there is a possibility it will downgrade the credit rating on government debt amid an “erosion in institutional strength” caused by Brexit.
The ratings agency has changed the outlook on its Aa2 rating of the UK’s debt from “stable” to “negative”, implying a rating cut could be imminent.
Read more: Sajid Javid pledges ‘new fiscal rules for a new economy‘
The current Aa2 rating is on par with France but below Germany’s AAA rating.
A downgrade would be a blow for Sajid Javid just a day after the Chancellor announced new fiscal rules with the goal of keeping public finances in check after the general election.
The ratings agency said “no matter what the outcome is of the general election Moody’s sees widespread political pressures for higher expenditures with no clear plan to increase revenues to finance this spending”.
Moody’s said tackling high borrowing levels were not likely to be a part of either the Conservative or Labour election campaigns, with both parties pledging big spending increases.
Brexit to blame
The agency added the uncertainty around Brexit and and effects of the leaving the European Union would be long-lasting.
“It would be optimistic to assume that the previously cohesive, predictable approach to legislation and policymaking in the UK will return once Brexit is no longer a contentious issue, however that is achieved,” a statement read.
It also noted that the economy might be “more susceptible to shocks than previously assumed” with the risk of Britain’s £1.8trn worth of debt rising again.
“In the current political climate, Moody’s sees no meaningful pressure for debt-reducing fiscal policies.”
Britain’s AAA rating was downgraded in 2012 despite former chancellor George Osborne pledging to maintain it.
Read more: Uncertainty to weight on economy even after Brexit deal, warns Moody’s
Moody’s said the reason for a change in its outlook was because “UK institutions have weakened as they have struggled to cope with the magnitude of policy challenges that they currently face, including those that relate to fiscal policy”.
The agency also added that “the UK’s economic and fiscal strength are likely to be weaker going forward and more susceptible to shocks than previously assumed”.


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Oops! The page you were looking for doesn't exist. Perhaps you followed a broken or outdated link?
{% extends "index.html" %} {% block content %}
Oops! The page you were looking for doesn't exist. Perhaps you followed a broken or outdated link?


§674-6 Rulemaking powers. The panel shall adopt rules in accordance with chapter 91 prescribing the procedures to be followed in the filing of claims and in the proceedings for review of claims under this chapter, and any other rules as the panel deems necessary to carry out the purposes of this chapter. [L 1991, c 323, pt of §1; am L 1993, c 351, §4]


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Enter your personal details and start exploring ProjectLib


US Firms Get 90-Day Extension To Work With Huawei On Rural Networks
by msmashThe Trump administration is giving American companies another three months to do business with the Chinese telecom giant Huawei, the Commerce Department said Monday. From a report: It is the third time the U.S. has extended a reprieve, which is meant to help ease disruption for Huawei customers. Many Internet and cellphone carriers in rural parts of the U.S. buy networking equipment from Huawei, and the temporary extension means they can keep their networks up to date. "The Temporary General License extension will allow carriers to continue to service customers in some of the most remote areas of the United States who would otherwise be left in the dark," said Commerce Secretary Wilbur Ross in a statement.


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#include "quadtree.h"
#include <MLV/MLV_all.h>
Go to the source code of this file.
◆ avgBin()
Calculates an average of given QuadTreeBin nodes.
- Parameters
-
| nodes | an array of QuadTreeBin nodes to average |
| size | the number of nodes |
- Returns
- the average of the given nodes
◆ avgColorRGBA()
Calculates the average color of a given QuadTreeRGBA.
- Parameters
-
| tree | the tree to evaluate |
| index | the current index of iteration |
- Returns
- the average color of the tree
◆ avgRGBA()
Calculates an average of given QuadTreeRGBA nodes.
- Parameters
-
| nodes | an array of QuadTreeRGBA nodes to average |
| size | the number of nodes |
- Returns
- the average of the given nodes
◆ distBin()
Calculates the color distance between two QuadTreeBin nodes.
- Parameters
-
| node1 | The first node to compare |
| node2 | The second node to compare |
- Returns
- The color distance between the two nodes
◆ distRGBA()
Calculates the color distance between two QuadTreeRGBA nodes.
- Parameters
-
| node1 | The first node to compare |
| node2 | The second node to compare |
- Returns
- The color distance between the two nodes
◆ distTreeBin()
Calculates the general distance between two whole QuadTreeBin trees.
- Parameters
-
| tree1 | the first tree to compare |
| tree2 | the second tree to compare |
- Returns
- the general distance between the two trees
◆ distTreeRGBA()
Calculates the general distance between two whole QuadTreeRGBA trees.
- Parameters
-
| tree1 | the first tree to compare |
| tree2 | the second tree to compare |
- Returns
- the general distance between the two trees
◆ minimizeQuadTreeBin()
| void minimizeQuadTreeBin |
( |
QuadTreeBin * |
tree, |
|
|
float |
distErr |
|
) |
| |
Minimises a QuadTreeBin (with loss if distErr > 0)
- Parameters
-
| tree | Tree to be minimised |
| distErr | Maximum error distance allowed |
◆ minimizeQuadTreeRGBA()
| void minimizeQuadTreeRGBA |
( |
QuadTreeRGBA * |
tree, |
|
|
float |
distErr |
|
) |
| |
Minimises a QuadTreeRGBA (with loss if distErr > 0)
- Parameters
-
| tree | Tree to be minimised |
| distErr | Maximum error distance allowed |


--- layout: default is_root: true classjs: 1.0.0 stable_plugins: - name: accordion version: 1.1.7 - name: autocomplete version: 1.1.0 - name: carousel version: 1.3.3 - name: debug version: 1.2.3 - name: gallery version: 2.0.5 - name: lightbox version: 1.2.3 - name: mobilemenu version: 1.1.7 - name: uniform version: 1.2.3 experimental_plugins: - name: parallax version: 1.0.beta1 - name: scroll version: 1.0.beta3 ---
Class.js is a JavaScript library for building class based object-oriented applications using JavaScript's prototypal inheritance. It strives to mimic classical class inheritance provided by other languages such as Python, Java or PHP. Its syntax is heavily inspired by MooTools's class implementation.
| class.js | v{{ page.classjs }} |
|---|
These plugins are aimed for professionals with extensive JavaScript knowledge. Each plugin follows a simplistic and a modular approach. All plugins have demos, tests and documentation regarding their options, methods and callbacks.
| Cl.{{ plugin.name | capitalize }} | v{{ plugin.version }} |
|---|
| Cl.{{ plugin.name | capitalize }} | v{{ plugin.version }} | {% if plugin.demo %} | {% endif %}
|---|
The plugins are compatible with Firefox, Chrome, Safari and IE7+. We also support iOS, Android and Windows Phone to insure correct responsive behaviour with full accessible support according to the WCAG 2.0 standards. Each plugin requires at least class.js 1.0+ and jQuery 1.7+.
Use the GitHub issue tracker to submit bug fixes or feature proposals.
A detailed guide is available within the classjs documentation.
How do I compile the css?
To compile the css, navigate to the examples/cl.{plugin} folder and run:compass watch
.
How do I compile the documentation?
To compile the documentation, navigate to the docs folder and run:make html
.
How do I puplish the changes to the branch gh-pages?
To auto-commit, excecute the following two lines within the repository:
git config --add remote.origin.push +refs/heads/master:refs/heads/gh-pages
git config --add remote.origin.push +refs/heads/master:refs/heads/master
How to run the example?
To run the project, install jekyll and run the command
jekyll serve --watch
inside the project folder. The server runs on localhost:4000
per default.
How to run automated tests?
To run the tests, install nodejs/npm and run:
npm install
grunt
within the project.


Naked Objects–Really Naked?
February 06, 2021 —
Vinayak Gadkari
Naked Objects is an eye catching name. No wonder that it has been demonstrated at the OOPSLA 2001 conference under the banner of Intriguing Technologies.
Copyright (c) 2002 nakedobjects.org
The Naked Objects Pattern
- Design behaviourally complete domain objects i.e.- Encapsulate all the behaviour/functionality within the domain objects.
- Single point definition: All the representations of a domain object such as user-interface, database-mapping, should be derived automatically from the domain object class itself.
- Object-oriented user-interface (OOUI): By exposing behaviourally-complete objects, users can view and interact with underlying domain


Blackroot Wood [ap75], forest, in Imperia, peasants: 125
Last updated Turn #148
Blackroot Wood [ap75], forest, in Imperia, peasants: 125
------------------------------------------------------------------------
0: Garrison [gp8b], garrison, Imperial Empire, with two soldiers,
0: three archers guards Blackroot Wood [ap75].
30: The population grows by one peasant.
Province controlled by Hyksos Keep [y57], castle, in Imperial
Mountains [ao73]
Ruled by The Bard [s2a], duke
Border closed.
Entrance fees:
Routes leaving Blackroot Wood:
North, forest, to Blackroot Wood [ao75], 8 days
Garrison [ao2o], garrison, Imperial Empire, with seven soldiers
East, forest, to Blackroot Wood [ap76], 8 days
South, forest, to Blackroot Wood [aq75], 8 days
West, forest, to Blackroot Wood [ap74], 8 days
It is raining.
Seen here:
Garrison [gp8b], garrison, Imperial Empire, with two soldiers,
three archers


 The hidden design is a ‘blackbird’ inspired by the popular nursery rhyme.
Equipment and preparation: you will need four 23cm/9in cake tins and three mini cake tins.
For the lemon sponge cakes, preheat the oven to 180C/350F/Gas 4. Grease and line the bases of four 23cm/9in cake tins. In a large bowl, beat the butters with the sugar until pale and fluffy. Gradually add the eggs, beating well after each addition. Mix in a little flour and the lemon zest. Sieve the remaining flour and the baking powder and fold into the butter mix using a metal spoon. Divide the mix between the four tins and gently smooth the surface. Bake for 20–25 minutes, or until a skewer comes out clean, then set aside to cool on wire racks.
For the small, coloured sponges, grease three mini cake tins (8cm/3in round tins are ideal). In a large bowl, beat the butter with the sugar. Gradually add the eggs, beating well after each addition and then mix in a little flour. Sieve the remaining flour and the baking powder and fold into the butter mix using a metal spoon. Divide the mix into three bowls and add the black food colouring to one, the yellow to another and the pink/flesh colour to the third. Pour the mixes into the prepared tins and bake for 15–20 minutes, or until a skewer comes out clean. Cool in the tins on a wire rack.
For the buttercream, beat the butter until soft, then add the icing sugar in batches until it is completely incorporated. Mix in the lemon zest and juice. Transfer one tablespoon of buttercream to a separate bowl and mix in the black food colouring a few drops at a time until the buttercream has turned an even black colour.
When the small coloured sponges are completely cool, take the yellow sponge and cut it into an open beak shape that is approximately 5cm/2in long. Use the pink cake to make a nose shape that can sit inside the open ‘beak’.
To assemble the cake, sandwich three lemon sponge cakes together with butter cream. To create a cavity for the ‘beak’ and ‘nose’ to sit in, mark the centre of the cake and cut out a tapered cylinder shape. Insert the pink ‘nose’ and the yellow ‘beak’ (use a small amount of buttercream to fix in place and fill any gaps).
Spread buttercream over the top of the cake and add the final layer of cake. To form the cavity for the blackbird’s head, mark the centre of the cake and draw a 10cm circle round it. Scrape out a dome that is deep enough to reach the next layer of cake (so the blackbird’s head will sit immediately on top of the beak). Cut out the same sized dome from the black cake and insert into the dome-shaped cavity.
To decorate, cut the edge of the bottom cake layer away slightly to create a ‘pie dish’ effect. Cut and shape the top of the cake so it that it resembles a pie top (it doesn't need to be perfect at this stage as it will be covered in fondant icing later). Cover the entire cake with a layer of buttercream.
To ice the cake, first roll out the white fondant into a strip large enough to wrap around the side of the cake and form the pie dish base. Trim to fit and apply in one strip. Secure the ends by brushing them with water so they’ll stick together. Mix the icing sugar with water to make a paste and add drops of blue food colouring until the icing is a deep-blue colour. Pipe clusters of three dots all around the pie dish to create a blue and white ceramic effect.
 Take one of the cake tins used for the lemon sponge and use it as guide to cut out a circle in the cream fondant icing. Apply to top of cake and crimp the edge so it resembles a pastry top. Cut a cross in the top of the pie and roll back the corners
For the decorations, use the black and yellow coloured fondant to make a few small blackbird heads and sit them inside the cross of the ‘pastry top’.


--- layout: post authors: ["Greg Wilson"] title: "Practical Computing for Biologists (and Other Scientists)" date: 2015-01-14 time: "09:00:00" tags: ["Noticed"] ---
We are big fans of Steve Haddock and Casey Dunn's book Practical Computing for Biologists, which covers everything we do and more (and is suitable for all kinds of scientists, not just biologists). We were therefore very pleased to learn that they are running a course at Friday Harbor Laboratories this summer. The outline is below, and applications are due on February 1st.
This course covers some of the simple but powerful skills that all scientists should know in a world of increasingly complex analyses. It is based on the book Practical Computing for Biologists, from Sinauer Associates, written by the instructors. This is not strictly a bioinformatics or programming course, although the lessons are applicable to molecular data. The skills are applicable to ecology, neurobiology, zoology — any subdiscipline where gathering and analyzing moderate to large data sets are required. The specific sections include working with text files, command-line operations, scripting and Python programming, creating scientific graphics, working with servers (remote login, software installation), databases, version control, and a bit about the web and data-acquisition hardware. Skills are immediately applicable, and the most common comment we get from class participants and readers of the book is "This would have save me hours just last week," or "I wish I knew about this when I was in grad school!" The target audience for the course is anyone with data: grad students, post-docs, technicians, and faculty. There are no hard pre-requisites, but participants should bring their own computer — preferably one that they use in their daily work.


Curves:
| Parameters: |
|
|---|---|
| Returns: | 1 if the curve pair is distance three and 0 otherwise. |
| Parameters: | M (numpy.array(dtype=float64) of shape (2,n, 4)) – the matrix representing a pair of curves |
|---|---|
| Returns: | (faces, bigon) faces: number of faces; bigon: 1 iff a bigon is found |
‘Simply’ count the number of faces bounded by two filling curves on a surface. Curves must be encoded as matrix according to vertices of intersection and associated orientation.
| Parameters: |
|
|---|
Take the paths in the skeleton of the complement of the transverse curve And create matrices.
| Parameters: |
|
|---|---|
| Returns: | final matrix |
Note that n does not have to equal m in general. Combines two curve pairs into one. REQ: curve pairs are, of course, encoded as matrices. INV: new matrix signs will be correct INV: final matrix will correspond to a single curve.
| Parameters: |
|
|---|---|
| Returns: | dist: the distance if three/four, or ‘Higher’ if dist is > 4. |
Computes the distance between the two curves embedded in the matrix. If this distance is three, tries to use simple paths to extend the distance in a different direction. If this fails, simply returns three; else it prints a curve that is distance four from alpha.
| Parameters: | M (numpy.array(dtype=float64) of shape (2,n, 4)) – the matrix representing a pair of curves |
|---|---|
| Returns: | (allFaces, edges) allFaces: tuple of faces including size and set of alpha-edges which bound them; edges: same as allFaces, exceincluding orientation of boundary edges. |
| Parameters: | alphaEdges – set of faces with alpha edges. |
|---|---|
| Returns: | (Bridges, Isalnds, lengthCheck) Bridges : 4-sided regions; Islands : n > 4 - gons; lengthCheck : the number of alpha edges included. |
Separate set of all faces into bridges (4-sided regions) and islands (higher-sided regions) for distance calculator.
| Parameters: | faces – |
|---|---|
| Returns: |
| Parameters: |
|
|---|---|
| Returns: |
| Parameters: | M (numpy.array(dtype=float64) of shape (2,n,4)) – The matrix with incorrect signs. |
|---|---|
| Returns: | The matrix of shape (2,n,4) with appropriate signs. |
Given a matrix corresponding to a curve pair, fixes the signs so that the matrix can be traversed by function bdycount. Starts from the simples point, then assigns based on matching values and ENS: No qualitative results about the curve pair will be changed by this, since orientation is just a formality.
| Parameters: |
|
|---|---|
| Returns: | boolean: True if Fn is a 4-gon, False otherwise |
| Parameters: |
|
|---|---|
| Returns: | (g,X): g: genus; X : euler characteristic |
Compute the genus of the span of a curve pair, i.e. the minimal genus surface on which they fill.
| Parameters: |
|
|---|---|
| Returns: |
Reorders path with a minimum intersection as the base point. This is useful for determining path uniqueness.
| Parameters: | path (list) – list of intersections representing the current path |
|---|---|
| Returns: | list with intersections shifted in order such that the lowest intersection is in the first position. |
| Parameters: |
|
|---|---|
| Returns: | list of booleans and a dictionary containing the matrices without bigons. |
Test a collection of matrices to see if they fill a given genus. Note: Matrices with bigons are still counted in the calculation, but are not returned.
Graph:
Adds adjacencies to a graph represented by an adjacency list. This is useful when we would like to replicate adjacencies between nodes.
| Parameters: |
|
|---|
Determines the number of adjacencies between two nodes in a graph, given the adjacency list of one of node.
| Parameters: |
|
|---|---|
| Returns: | number of edges from the adjacent node to the original node |
Determines all edges between boundary componenets and adds adjacencies between appropriate edges.
Recursively finds all closed cycles in a given graph that begin and end at start_node. As one would guess, it employs a standard depth-first search algorithm on the graph, appending current_path to all_loops when it returns to start_node.
In the overall distance computation, this function is computationally dominating with exponential complexity, so take care with its use.
| Parameters: |
|
|---|---|
| Returns: | set of all closeds cycles in the graph starting and ending at start_node |


Pizza Order Form
Select your toppings:
Pepperoni
Olives
Extra Cheese
Pineapple
Sausage
Bell Peppers
Onions
Canadian Bacon
Select pizza size:
12" Small Pizza
16" Medium Pizza
20" Large Pizza
24" Extra Large Pizza
Add Pizza to Order


|
iSensor-SPI-Buffer
1.15
Firmware for the iSensor-SPI-Buffer board to enable full throughput buffered data capture on Analog Devices IMUs
|
Header file for iSensor-SPI-Buffer buffer data structure module. More...
#include <stdint.h>
Go to the source code of this file.
Macros | |
| #define | BUF_SIZE 0xA000 |
| #define | BUF_MIN_ENTRY 2 |
| #define | BUF_MAX_ENTRY 64 |
Variables | |
| uint32_t | g_bufLastRegIndex |
| uint32_t | g_bufCount |
| uint32_t | g_bufNumWords32 |
Header file for iSensor-SPI-Buffer buffer data structure module.
Copyright (c) Analog Devices Inc, 2020 All Rights Reserved.
| #define BUF_MAX_ENTRY 64 |
Largest buffer data entry size. Real size is +10 bytes
| #define BUF_MIN_ENTRY 2 |
Smallest buffer data entry size. Real size is +10 bytes
| #define BUF_SIZE 0xA000 |
Buffer memory allocation
|
extern |
Number of elements currently stored in the buffer. Global scope
|
extern |
Index for the last buffer output register. This is based on buffer size. Global scope
|
extern |
Number of 32-bit words per buffer entry. Global scope


The Here We Go Project
An archive of what dorks we were in the twenties.
Dan
Gets the people going
Here We Go
I'm not driving
Interesting, interesting
Johnson & Johnson, it's a family company
Lazer!
Let the people know
Oh oh ohhh interesting, interesting
Oh oh ohhh
Ohhhh, interesting
Once it hits your lips
Sorry for being honest
Clancy
Barely Know'er
Disconnect to Reconnect
Gets the People Going
Here We Go
I'm not Driving
Interesting Interesting
Laceytown?
Mom?
Moommm
Turn that 10 Down to a 5
Twist my Arm
You Don't Say That
Riley
Hi Bye! See Ya!
Hi Bye!
See Ya!
Mike
*Snifffff*, Interesting
*Snifffff*
Best for Last
Biggest Freak in the Game
Chach
Interesting, Interesting
Johnson
Jones
Laceyville, PA
Lazer
Mom?

You Don't Say That
You Had Me at Hello
Praise Cthulhu
Day Off is as Good a Day as any to Offer to Cthulhu, May He Rend the Flesh from our Bones and the Joy from our Hearts
For Whom All our Works are Created, and Necessarily Destroyed
Hail Cthulhu, May He Bathe in the Screams of our Agony in Perpetuity
May He Bathe in our Blood, May we Bathe in His Blood
May He Darken Our Dreams, May We Never Wake
May He Watch with Satisfaction our Eternal Suffering
May the Tendrils of His Malevolence Pull us into the Storm of our Insanity
May this Cage Come Down, but May we be Forever Caged by the Divine Ministration of His Benevolent Bloodlust
May we Bask Forever in the Flame of His Indifference
May we Feast of this Watermelon, May He Feast of this Flesh
Praise Be to Cthulhu, He is the Beginning and the End, He is the Beginning and the End, He is the Beginning and the End
Praise Be to Cthulhu, United are we All in His Quest to Devour our Souls
Praise Be to Cthulhu
Stu
*Snifffff*, Interesting
George
Hi! Bye! See Ya! - Aussie
Hi! Bye! See Ya! - Kiwi
Hi! Bye! See Ya! - Leeds
Hi! Bye! See Ya! 1
Hi! Bye! See Ya! 2


Hint: If there is a hit, translate the PPN into binary using the "To Binary" page.
Virtual Address (format: 0x03d4)
Page Size
Number of sets in TLB
Number of bits in virtual address


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general care
diet
general information
misconceptions and myths
For the given project, I have chosen to make an informational website about cats.
This section contains information regarding the care of cats.
Information regarding cats, including history and health.
Log of site development.


Before Change
def _try_reserve_and_set_cuda(self):
use_found_device = os.environ.get("CUDA_VISIBLE_DEVICES") is None \
and torch.cuda.is_initialized()
device = reserve_cuda_device()
// This needs to be set even if torch.cuda is already
// initialized because the env var is used later when
// starting the DDP setup.
os.environ["CUDA_VISIBLE_DEVICES"] = device
if use_found_device:
// Once cuda is initialized, torch.device ignores the os.env
// so we have to set the right actual device.
self._set_cuda_device(device)
else:
// if CUDA is not initialized, we can set the os.env.
// Even if initialized, we want to set the device to use BatchNorm.
// and make Torch think it only sees 1 GPU.
self._set_cuda_device("0")
def _set_cuda_device(self, device_str):
Sets the CUDA device for this current local worker.
if self._is_set:
raise RuntimeError("CUDA devices already set.")
After Change
os.environ["CUDA_VISIBLE_DEVICES"] = reserved_device
if visible_devices:
// We want to set the index on the visible devices list.
if reserved_device not in visible_devices:
raise RuntimeError(
"TorchTrainer reserved a device {} that was not in the "
"CUDA_VISIBLE_DEVICES {}. This may be because the "
"Ray cluster is not set with the right env vars. "
"If that is not the issue, please raise a "
"Github issue.".format(reserved_device, visible_devices))
devices = visible_devices.split(",")
scoped_index = devices.index(reserved_device)
self._set_cuda_device(str(scoped_index))
else:
// Once cuda is initialized, torch.device ignores the os.env


| |
Animated and interactive Charts |
| |
Using FusionCharts , you can quickly and easily render animated charts
that have a lot of interactive options for the end users. |
|
Easy yet powerful JavaScript integration |
| |
FusionCharts offers advanced options to integrate
charts with JavaScript modules. You can update charts on client side, invoke JavaScript functions as hotspot links. |
| |
To use FusionCharts , you do not have to install anything on your
server. All you need to do is copy-paste the SWF files (the core files
of FusionCharts) to your server, just like you would do to any image
files - and you're ready to go! So, even on those servers which do
not allow installation of ActiveX or any other form of components,
FusionCharts can run without any hassles. |
| |
FusionCharts makes the chart creation process a painless experience
for you. Since it uses XML as its data, all you need to do is convert
your data into XML using a programming language or even using a text editor like Notepad etc. - and that's all what is required to
create interactive and animated charts. The best part is you DO NOT need to know anything about Flash to use FusionCharts . |
| |
Runs on a variety of platforms |
| |
Irrespective of what server side scripting language you're using,
FusionCharts can be used to create charts in that. Since FusionCharts
uses XML as the data interface, you can run it on any server and against
any scripting language. Also, to view the charts, your users just
need to have Adobe Flash Player 6 (or above), which is one of the most used browser
plugins on the planet. |
| |
Reduces load on your servers |
| |
In traditional image based charting systems, the charts are generated
as image at the server side. So, for each chart that you need to
serve to a user, you'll have to build complex images on the server
and then stream it to the client. When the need of hour is high,
this can be an expensive resource on the server, as image creation
takes a lot of toll on the server.
FusionCharts brings you great relief - all the charts are rendered
at client side using the widely installed Adobe Flash Platform.
The server is just responsible for streaming the pre-built SWF files
and your XML data files to the end viewers. From there on, Flash
Player takes the onus of rendering the charts. Also, the chart SWF
Files can be cached so that you can just update the data, and not
send chart SWF files every time. |
|
A plethora of chart types |
| |
FusionCharts offers you a plethora of chart types. From the
basic bar, column, line, pie etc. to the advanced combination charts, gantt chart,
you can build all the charts with the same ease of use. |
|
Avail it absolutely FREE. Period. |
| |
You can get FusionCharts Free without having to spend a single buck. It has no hidden gimmicks or advertisement linking back to our website. Free means truly FREE here.
FusionCharts v3, on the other hand, offers advanced options like gradients, 3D lightings (glow, blur, bevel & shadow effects), animation, debugging, AJAX support, dynamic data loading, more chart types etc. |


Edit Station Info¶
Program edit_stdb
¶
Description¶
Edit Station Database Dictionary contained in pickle file.
Usage¶
$ edit_stdb -h
Usage: edit_stdb [options] <station pickle file>
Program to make basic modifications to a station database pickle file
Options:
-h, --help show this help message and exit
--keys=KEYS Specify a comma separated list of keys to return.
These can be fragments of a key to include all keys
matching any fragment.
-L, --long-keys Specify Key format. Default is Net.Stn. Long keys are
Net.Stn.Chn
-O OFILE, --output-file=OFILE
Specify an output file name for the edited database.
Default behaviour performs the changes in place on the
input file.
-a, --ascii Specify to write ascii Pickle files instead of binary.
Ascii are larger file size, but more likely to be
system independent.
Example¶
Let’s use the ta_table.pkl
database generated in the example Create Database by Querying FDSN.
To change only one station key, e.g., TA.P33M, and save it in a new database
P33M.pkl
type:
$ edit_stdb --keys=TA.P33M -O P33M.pkl ta_table.pkl
A GUI window will apear displaying the fields for the corresponding station key
Edit the field of interest and press the <OK> button. Pressing <Cancel> will iterate to the next key specified - in this case it would simply quit the program since there is only one key specified.
Warning
Editing either the network code or station name can be hazardous since it also modifies the station key. If this happens, you will need to press <OK> twice to iterate to the next key.


Ocean [aj55], ocean, in The Inner Sea
Last updated Turn #96
Ocean [aj55], ocean, in The Inner Sea
------------------------------------------------------------------------
19: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
19: 23% damaged, arrived from Ocean [aj56], owner:
19: Phantasm [n4b], Imperial Empire, duke, with eight sailors,
19: one riding horse
19: A storm batters the ship and causes some damage.
20: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
20: 24% damaged, departed for Ocean [aj54].
Routes leaving Ocean:
North, to Ocean [ai55], 6 days
East, to Ocean [aj56], 6 days
South, to Ocean [ak55], 6 days
West, to Ocean [aj54], 6 days
It is raining.


Berries
High in fiber, berries are naturally sweet, and their rich colors mean they are high in antioxidants and disease-fighting nutrients.
How to include them: When berries are not in season, it is just as healthy to buy them frozen. Add to yogurt, cereals, and smoothies, or eat plain for a snack.


DSRM PTH to DCSync!
Since it is possible to pass-the-hash for the DSRM account, why not leverage this access to pull password data for any domain account using Mimikatz DCSync. We can target the specific Domain Controller and by using the DC’s short name, we force NTLM authentication.
Mimikatz “lsadump::dcsync /domain:lab.adsecurity.org /dc:adsdc03 /user:krbtgt
If an attacker can gain knowledge of the DSRM account password on a Domain Controller running Windows Server 2008 R2 or 2012 R2 (with the DsrmAdminLogonBehavior regkey set to 2), the DSRM account can be used to authenticate across the network via pass-the-hash to the DC (forcing NTLM authentication). This enables an attacker to retain Domain Controller admin rights when all domain user and computer passwords are changed.
The DSRM account now provides a useful attack method to pull domain credentials, despite the fact it’s a “local” administrator account.
Many thanks to Benjamin Delpy (author of Mimikatz) for his help in figuring this out!


Before Change
// ends. We"ll shift back afterwards.
window_size = int((W.shape[2]-1) / 2)
out = [ops.allocate((length+(window_size*2), W.shape[0], W.shape[1]))
for length in lengths]
H__bopf = _compute_hidden_layer(ops, W, vectors, lengths)
for vec_idx, ijs in positions.items():
for i, j in ijs:
After Change
// Shift the output, to correct for the "padding" shift above.
out = out[window_size : -window_size]
// Recover sequences
out = ops.unflatten(out, lengths)
// Zero the LL, L, R and RR features for starts and ends.
for i in range(len(out)):
out[i][0, 0] = 0
out[i][0, 1] = 0


| Dolby taking a 5 pt win under Breeder Judge Joanne Fernall |
Dolby chillin' on the farm. I just love his expression |
Ch. Staghorn's High Definition (a.k.a Dolby)
(Mutli BIS/BISS AmCan Ch. Tormentil Labradale Primetime CGC X Belquest Victory At Staghorn)
Date of Birth: March 23, 2007
Hips/Elbows OVC clear, Optigen A, CERF cleared annually
Heart: "Normal" - Cardiologist - ECHO/Doppler
Dolby is co-owned with Chantelle Bennett, Benacres Reg'd.
Dolby was a patient boy and waited for me to get him out showing. Once out, he finished in a few weekends. He has looks, temperament and structure gallore and I got lots of very nice comments and interest in him from other Breeders. Dolby is proven and availabe at stud to approved females.


Business
School
Leisure
Bacon ipsum dolor amet frankfurter t-bone bresaola chislic, filet mignon tri-tip strip steak pancetta leberkas turkey kevin kielbasa venison.   Beef ribs chislic salami fatback ground round. Frankfurter drumstick buffalo doner prosciutto.  Ham hock pork loin chuck pastrami porchetta alcatra corned beef. Cupim boudin buffalo, alcatra strip steak shank burgdoggen filet mignon. Beef ribs short loin chicken, pastrami salami doner tenderloin hamburger ham sausage andouille ball tip pork belly. Short loin chuck rump doner short ribs, beef strip steak fatback buffalo.


Gateway's programs are individually tailored to service each client's individual needs. See our highly trained and dedicated Direct Service Professionals in action. Read more >>
We have curated a list of tools that will help you apply to OPWDD, learn about the process, make informed decisions. Also meet our executive team, see yearly annual reports, and monthly newsletters. Read more >>
This section is for people who want to volunteer, donate their time or other tangible non-cash assets like sports equipment, computers, or clothing. Let's see what you've got! Read more >>


With the Census Bureau's County Population by Characteristics data set
Using the 2019 vintage, for a given county we subtracted from the total population the Not Hispanic, White alone population
With the Census Bureau's American Community Survey data set
We used the 2015 - 2019 five-year ACS file to identify the total estimated number of individuals in the workforce that are either non-White or are White-Hispanic


new ConsoleModule(domElement)
Manages a console that can display html content
Parameters:
| Name | Type | Description |
|---|---|---|
domElement |
domElement | The container of the module. |
- Source:
Methods
availableCommands() → {string}
Is used by the ScenarioPlayer to register the commands this module can handle
- Source:
Returns:
[] - An array of the supported commands
- Type
- string
clear()
Clears the console
- Source:
hide()
Removes the module's node from the dom
- Source:
isLoaded() → {bool}
Returns true if the module has finished loading, false otherwise
- Source:
Returns:
- The state of the module
- Type
- bool
load(caller, caller)
Loads the ConsoleModule
Parameters:
| Name | Type | Description |
|---|---|---|
caller |
object | The caller of the method. |
caller |
function | The callback that will be called when loaded. |
- Source:
performCommand()
Is used by the ScenarioPlayer to execute the commands
- Source:
printHTML(html)
Prints an html formated string in the console
Parameters:
| Name | Type | Description |
|---|---|---|
html |
string | The string to be printed |
- Source:
printLine(message)
Prints a string in the console
Parameters:
| Name | Type | Description |
|---|---|---|
message |
string | The string to be printed |
- Source:
show()
Inserts the module's node into the dom
- Source:


createFallbackViewStrategy(origin: Origin): ViewStrategy
Creates a fallback View Strategy. Used when unable to locate a configured strategy. The default implementation returns and instance of ConventionalViewStrategy.
Parameters
origin: Origin
The origin of the view model to return the strategy for.
Returns
The fallback ViewStrategy.


First things first, we need an easy/secure way to install prackages
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Visit the official website for the latest instructions https://brew.sh


Wrapper class for the v13 Campaign service. This class is automatically generated.
- A
- G
- N
- S
- U
| [R] | api | Holds the AdWords::API object to which the wrapper belongs |
Constructor for CampaignServiceWrapper.
Args:
- driver: SOAP::RPC::Driver object with the remote SOAP methods for this service
- api: the AdWords::API object to which the wrapper belongs
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 25 def initialize(driver, api) @driver = driver @api = api end
Calls the addCampaign method of the Campaign service. Check the online documentation for this method.
Args:
- campaign: AdWords::V13::CampaignService::Campaign
Returns:
- addCampaignReturn: AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 42 def addCampaign(campaign) begin AdWords::Service.validate_param('campaign', campaign, AdWords::V13::CampaignService::Campaign) # Construct request object and make API call obj = AdWords::V13::CampaignService::AddCampaign.new(campaign) return @driver.addCampaign(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "addCampaign Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the addCampaignList method of the Campaign service. Check the online documentation for this method.
Args:
- campaigns: SOAP::SOAPArray of AdWords::V13::CampaignService::Campaign
Returns:
- addCampaignListReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 67 def addCampaignList(campaigns) begin AdWords::Service.validate_param('campaigns', campaigns, SOAP::SOAPArray) # Construct request object and make API call obj = AdWords::V13::CampaignService::AddCampaignList.new(campaigns) return @driver.addCampaignList(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "addCampaignList Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getActiveAdWordsCampaigns method of the Campaign service. Check the online documentation for this method.
Returns:
- getActiveAdWordsCampaignsReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 89 def getActiveAdWordsCampaigns() begin # Construct request object and make API call obj = AdWords::V13::CampaignService::GetActiveAdWordsCampaigns.new() return @driver.getActiveAdWordsCampaigns(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getActiveAdWordsCampaigns Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getAllAdWordsCampaigns method of the Campaign service. Check the online documentation for this method.
Args:
- dummy: SOAP::SOAPInt
Returns:
- getAllAdWordsCampaignsReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 112 def getAllAdWordsCampaigns(dummy) begin AdWords::Service.validate_param('dummy', dummy, SOAP::SOAPInt) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetAllAdWordsCampaigns.new(dummy) return @driver.getAllAdWordsCampaigns(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getAllAdWordsCampaigns Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getCampaign method of the Campaign service. Check the online documentation for this method.
Args:
- id: SOAP::SOAPInt
Returns:
- getCampaignReturn: AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 137 def getCampaign(id) begin AdWords::Service.validate_param('id', id, SOAP::SOAPInt) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetCampaign.new(id) return @driver.getCampaign(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getCampaign Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getCampaignList method of the Campaign service. Check the online documentation for this method.
Args:
- ids: SOAP::SOAPArray of SOAP::SOAPInt
Returns:
- getCampaignListReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 162 def getCampaignList(ids) begin AdWords::Service.validate_param('ids', ids, SOAP::SOAPArray) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetCampaignList.new(ids) return @driver.getCampaignList(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getCampaignList Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getCampaignStats method of the Campaign service. Check the online documentation for this method.
Args:
- campaignIds: SOAP::SOAPArray of SOAP::SOAPInt
- startDay: SOAP::SOAPDate
- endDay: SOAP::SOAPDate
Returns:
- getCampaignStatsReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::StatsRecord
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 189 def getCampaignStats(campaignIds, startDay, endDay) begin AdWords::Service.validate_param('campaignIds', campaignIds, SOAP::SOAPArray) AdWords::Service.validate_param('startDay', startDay, SOAP::SOAPDate) AdWords::Service.validate_param('endDay', endDay, SOAP::SOAPDate) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetCampaignStats.new(campaignIds, startDay, endDay) return @driver.getCampaignStats(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getCampaignStats Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getConversionOptimizerEligibility method of the Campaign service. Check the online documentation for this method.
Args:
- campaignIds: SOAP::SOAPArray of SOAP::SOAPInt
Returns:
- getConversionOptimizerEligibilityReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::ConversionOptimizerEligibility
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 218 def getConversionOptimizerEligibility(campaignIds) begin AdWords::Service.validate_param('campaignIds', campaignIds, SOAP::SOAPArray) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetConversionOptimizerEligibility.new(campaignIds) return @driver.getConversionOptimizerEligibility(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getConversionOptimizerEligibility Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getOptimizeAdServing method of the Campaign service. Check the online documentation for this method.
Args:
- campaignId: SOAP::SOAPInt
Returns:
- getOptimizeAdServingReturn: SOAP::SOAPBoolean
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 243 def getOptimizeAdServing(campaignId) begin AdWords::Service.validate_param('campaignId', campaignId, SOAP::SOAPInt) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetOptimizeAdServing.new(campaignId) return @driver.getOptimizeAdServing(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getOptimizeAdServing Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getRecommendedBudgetList method of the Campaign service. Check the online documentation for this method.
Args:
- campaignIds: SOAP::SOAPArray of SOAP::SOAPInt
Returns:
- getRecommendedBudgetListReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::CampaignBudget
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 268 def getRecommendedBudgetList(campaignIds) begin AdWords::Service.validate_param('campaignIds', campaignIds, SOAP::SOAPArray) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetRecommendedBudgetList.new(campaignIds) return @driver.getRecommendedBudgetList(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getRecommendedBudgetList Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the getRecommendedBudgets method of the Campaign service. Check the online documentation for this method.
Args:
- campaignIds: SOAP::SOAPArray of SOAP::SOAPInt
Returns:
- getRecommendedBudgetsReturn: SOAP::SOAPArray of AdWords::V13::CampaignService::RecommendedBudget
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 293 def getRecommendedBudgets(campaignIds) begin AdWords::Service.validate_param('campaignIds', campaignIds, SOAP::SOAPArray) # Construct request object and make API call obj = AdWords::V13::CampaignService::GetRecommendedBudgets.new(campaignIds) return @driver.getRecommendedBudgets(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "getRecommendedBudgets Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the setOptimizeAdServing method of the Campaign service. Check the online documentation for this method.
Args:
- campaignId: SOAP::SOAPInt
- enable: SOAP::SOAPBoolean
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 316 def setOptimizeAdServing(campaignId, enable) begin AdWords::Service.validate_param('campaignId', campaignId, SOAP::SOAPInt) AdWords::Service.validate_param('enable', enable, SOAP::SOAPBoolean) # Construct request object and make API call obj = AdWords::V13::CampaignService::SetOptimizeAdServing.new(campaignId, enable) return @driver.setOptimizeAdServing(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "setOptimizeAdServing Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the updateCampaign method of the Campaign service. Check the online documentation for this method.
Args:
- campaign: AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 340 def updateCampaign(campaign) begin AdWords::Service.validate_param('campaign', campaign, AdWords::V13::CampaignService::Campaign) # Construct request object and make API call obj = AdWords::V13::CampaignService::UpdateCampaign.new(campaign) return @driver.updateCampaign(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "updateCampaign Call Failed: " + fault.faultstring.to_s, caller) end end
Calls the updateCampaignList method of the Campaign service. Check the online documentation for this method.
Args:
- campaigns: SOAP::SOAPArray of AdWords::V13::CampaignService::Campaign
Raises: Error::ApiError (or a subclass thereof) if a SOAP fault occurs.
Source: show
# File lib/adwords4r/v13/CampaignServiceWrapper.rb, line 362 def updateCampaignList(campaigns) begin AdWords::Service.validate_param('campaigns', campaigns, SOAP::SOAPArray) # Construct request object and make API call obj = AdWords::V13::CampaignService::UpdateCampaignList.new(campaigns) return @driver.updateCampaignList(obj) rescue SOAP::FaultError => fault raise(AdWords::Error::create_specific_api_error(fault), "updateCampaignList Call Failed: " + fault.faultstring.to_s, caller) end end


Deltacloud API
Hooray, you have successfully checked out Deltacloud Core. Deltacloud protects yourapps from cloud API changes and incompatibilities, so you can concentrate on managing cloud instances the way you want.
You can find more documentation at the Deltacloud web site at: incubator.apache.org/deltacloud
Prerequisites
Deltacloud Core depends on a number of other Ruby libraries. The easiest
way to install them is to either install the deltacloud-core package from
your distribution is repository, e.g. yum install deltacloud-core
on
Fedora, or install the gem with gem install deltacloud-core
.
If you do not want to do that, have a look at the dependencies in the gem spec for deltacloud-core and install them manually from git repository:
$ mkdir deltacloud $ cd deltacloud $ git svn init -s https://svn.apache.org/repos/asf/incubator/deltacloud $ git svn fetch --log-window-size 10000 $ git clone git://git.apache.org/deltacloud.git core
Running
To get started, run ./bin/deltacloudd -i mock
; this will run Deltacloud
Core with the mock driver, a driver that does not talk to a cloud, but
simulates being one. It will store data about its instances etc. in
/var/tmp/deltacloud-mock-$USER
; that directory will automatically populated
with sample data if it does not exist. Subsequent runs will continue using
the data found there. If you need to start over from scratch, just delete
that directory and restart deltacloudd.
Once you have the server running, point your browser at
localhost:3001/api to get a HTML version of Deltacloud Core.
If you want to look at the XML that REST clients will see, simply add
?format=xml
to URLs. Deltacloud Core does content negotiation; REST clients should not
set format to URLs, they should simply set the Accept header appropriately.
Some operations require authentication. For the mock driver, the username and password are mockuser and mockpassword. A current list of drivers and what kind of credentials they need can be found at Deltacloud Incubator site (http://incubator.apache.org/deltacloud/drivers.html)
Happy hacking - and do not forget to send patches to the mailing list (see
deltacloud-devel or
incubator-deltacloud-dev
You could send patches using git format-patch master
and then git send-email
--thread
. Example git/.config file:
[sendemail] to =<EMAIL_ADDRESS>signedoffbycc=no chainreplyto=no smtpserver=YOUR_SMTP_SERVER_HERE thread=yes from=YOUR_EMAIL_HERE suppresscc=all


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Founder
Chen Yong joined Kingsoft Corporation in 2001 and oversaw the growth of Duba antivirus, gaining a deep understanding of the security industry and product development in the process. He joined Cheetah Mobile in 2010 as Senior Vice President in charge of the company's mobile flagship products, such as Clean Master and CM Security, which combined have over 1.6 billion global downloads and 600 million MAU.


Chapter 8 Advanced Multivariate Longitudinal SEM designs
Thus far our longitudinal SEM designs have mostly dealt with single growth curves. These models assume and underlying trajectory that is unitary across time. In other words, the trajectory may not be linear but it is thought to be resulting from a singular process. What is somewhat unsatisfactory about this is that change in constructs may happen at a different timescale. With growth curves the time scale of change is the entire period.
An alternative approach is to look at repeated processes of change, thereby getting at more complex theories that can look at a) what construct changes prior to another b) whether changes in a construct occur for the same reasons at different times and c) whether or not the dynamics of change among multiple constructs are similar across time. These more advanced questions necessarily involve breaking the change process down into smaller change quantities. Instead of one growth curve, there may be multiple change (difference) scores. Or there may be multiple regressions associating one construct to another.
How are these different than the cross-lagged panel model that Josh said was mostly worthless prior in the semester? Well they still aren’t great, and that is because it is difficult to address chicken and egg questions that the cross-lag panel model gets used for. However, they do attempt to overcome some of the limitations of those models. Moreover, some of the questions that they attempt to answer side step the difficulties of cross-lagged panel models.
8.0.1 Cross-lagged panel model
As an aside, what are the difficulties of cross-lagged panel models? To list a few:
- Arbitrary starting point can change association.
- Time between lags can influence results because changes may not be aligned with assessment. Think of a wheel rolling and measuring the location of a point at at every quarter rotation compared to 2 rotations.
- Different constructs may change at different rates, influencing results.
- Theoretically, the model suggest that one point in time influences change in some other construct. Why would Tuesday at 2 be so important, why not Thursday at 4?
- typically need to constrain cross loadings to be the same to ensure quality estimates. But doing so makes it impossible to see if the association differ across time.
8.1 Difference scores
The stepping stone to these more advanced models that move beyond cross lagged panel models are built on the humble difference score.
A long time ago I said that these are not great. Indeed, there is a great history about how difference scores are problematic. This changed the way that experimental psychology analyzed and conducted their experiments in the 60s and early 70s. Cronbach of the alpha fame had a wonderful missive about the lack of utility of difference scores.
He is right, every difference scores contain measurement error that cannot be separated from true change. In place, people have regressed t2 on t1 and used the residual for a residual change score. However, there are problems such as a loss of information where 2 variables become 1. Low reliability. And there might be a different factor structure at time 2 from that at time 1.
What to do? Don’t use difference scores, use something else. Maybe latently?
8.2 Analyzing change with two time points
This occurs a lot. Pre-post design. Only have enough money to follow up participants once. Getting an itchy publishing-finger while waiting for your longitudinal study to mature.
There are a few options (See McArdle Annual Review of Psych for more details).
The most basic (and most extendable) is really similar to a growth model. To understand where it is coming from:
(delta T) = T2 - T1 delta T is going to be a latent variable, created by factor loading of 1 from T1 -> T2, meaning that delta T is thought of as an error variance term; what is left over after saying T1 perfectly causes T2. Can now relate that to T1, something the difference score and residual change score models could not do simultaneously.
## T1X1 T1X2 T1X3 T2X1 T2X2 T2X3
## 4.820205 5.470553 4.191088 5.483167 6.165787 4.832366
change <-
'C_T1=~1*T1X1 + L2*T1X2 + L3*T1X3
C_T2=~1*T2X1 + L2*T2X2 + L3*T2X3
C_T2 ~ 1*C_T1 # Fixed regression of C_T2 on C_T1
dC =~ 1*C_T2 # Fixed regression of dC on C_T2
C_T2 ~ 0*1 # This line constrains the intercept of C_T2 to 0
C_T2 ~~ 0*C_T2 # This fixes the variance of the C_T2 to 0
dC ~ 1 # This estimates the intercept of the change score
C_T1 ~ 1 # This estimates the intercept of C_T1
dC ~~ dC # This estimates the variance of the change scores
C_T1 ~~ C_T1 # This estimates the variance of the COG_T1
dC ~ C_T1 # This estimates the self-feedback parameter, which for now is just the covaration with change and T1
T1X1~~T2X1 # This allows residual covariance on indicator X1 across T1 and T2
T1X2~~T2X2 # This allows residual covariance on indicator X2 across T1 and T2
T1X3~~T2X3 # This allows residual covariance on indicator X3 across T1 and T2
T1X1~~T1X1 # This allows residual variance on indicator X1
T1X2~~T1X2 # This allows residual variance on indicator X2
T1X3~~T1X3 # This allows residual variance on indicator X3
T2X1~~T2X1 # This allows residual variance on indicator X1 at T2
T2X2~~T2X2 # This allows residual variance on indicator X2 at T2
T2X3~~T2X3 # This allows residual variance on indicator X3 at T2
T1X1~0*1 # This constrains the intercept of X1 to 0 at T1
T1X2~M2*1 # This estimates the intercept of X2 at T1
T1X3~M3*1 # This estimates the intercept of X3 at T1
T2X1~0*1 # This constrains the intercept of X1 to 0 at T2
T2X2~M2*1 # This estimates the intercept of X2 at T2
T2X3~M3*1 # This estimates the intercept of X3 at T2
'
change.fit <- sem(change, data=simdat, missing="ML")
summary(change.fit, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)
## lavaan (0.5-23.1097) converged normally after 56 iterations
##
## Number of observations 500
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 5.327
## Degrees of freedom 9
## P-value (Chi-square) 0.805
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1003.549
## Degrees of freedom 15
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.006
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4826.130
## Loglikelihood unrestricted model (H1) -4823.467
##
## Number of free parameters 18
## Akaike (AIC) 9688.260
## Bayesian (BIC) 9764.123
## Sample-size adjusted Bayesian (BIC) 9706.990
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.032
## P-value RMSEA <= 0.05 0.994
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.014
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_T1 =~
## T1X1 1.000 1.029 0.729
## T1X2 (L2) 1.022 0.048 21.300 0.000 1.051 0.721
## T1X3 (L3) 0.819 0.041 19.775 0.000 0.842 0.638
## C_T2 =~
## T2X1 1.000 1.137 0.756
## T2X2 (L2) 1.022 0.048 21.300 0.000 1.161 0.780
## T2X3 (L3) 0.819 0.041 19.775 0.000 0.931 0.665
## dC =~
## C_T2 1.000 0.521 0.521
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## C_T2 ~
## C_T1 1.000 0.905 0.905
## dC ~
## C_T1 -0.055 0.059 -0.939 0.348 -0.096 -0.096
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .T1X1 ~~
## .T2X1 -0.123 0.060 -2.035 0.042 -0.123 -0.129
## .T1X2 ~~
## .T2X2 -0.098 0.061 -1.602 0.109 -0.098 -0.104
## .T1X3 ~~
## .T2X3 -0.065 0.056 -1.157 0.247 -0.065 -0.061
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_T2 0.000 0.000 0.000
## .dC 0.963 0.285 3.376 0.001 1.625 1.625
## C_T1 4.803 0.058 82.284 0.000 4.669 4.669
## .T1X1 0.000 0.000 0.000
## .T1X2 (M2) 0.555 0.251 2.210 0.027 0.555 0.381
## .T1X3 (M3) 0.292 0.217 1.350 0.177 0.292 0.222
## .T2X1 0.000 0.000 0.000
## .T2X2 (M2) 0.555 0.251 2.210 0.027 0.555 0.373
## .T2X3 (M3) 0.292 0.217 1.350 0.177 0.292 0.209
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .C_T2 0.000 0.000 0.000
## .dC 0.348 0.079 4.375 0.000 0.991 0.991
## C_T1 1.058 0.105 10.068 0.000 1.000 1.000
## .T1X1 0.934 0.081 11.535 0.000 0.934 0.469
## .T1X2 1.017 0.088 11.537 0.000 1.017 0.480
## .T1X3 1.033 0.077 13.340 0.000 1.033 0.593
## .T2X1 0.968 0.087 11.170 0.000 0.968 0.428
## .T2X2 0.865 0.082 10.539 0.000 0.865 0.391
## .T2X3 1.095 0.082 13.402 0.000 1.095 0.558
##
## R-Square:
## Estimate
## C_T2 1.000
## dC 0.009
## T1X1 0.531
## T1X2 0.520
## T1X3 0.407
## T2X1 0.572
## T2X2 0.609
## T2X3 0.442
This gives you everything you need for two time points. How would you introduce a predictor variable? Multiple group model? Bi-variate latent change model?
8.3 multi wave latent change model
The real benefit of this type of framework is building on this type of change model to incorporate multiple change models simultanously. This allows one to look at multiple change processes in a single model, opening the door to analyzing more dynamic processes. One can fit two latent change scores (LCSs) with 3 repeated assessments, 3LCSs with 4 repeated measures, etcetera.
This is similar to the Steyer Model’s that Leah will present.
One top of this type of model you can fit a growth curve. Why? Can capture both general trends and more fine-grained temporal dynamics. This can be especially useful when trying to separate a known, more stable change occurring during development (e.g.natural agining process) from more specific fluctuations due to external factors (e.g., life events).
## COG_T1 COG_T2 COG_T3 COG_T4 NEU_T1 NEU_T2 NEU_T3
## 2.122489 4.942527 9.222710 14.925048 1.894173 4.573540 8.417871
## NEU_T4
## 13.275938
BDCS<-'
COGlv_T1=~1*COG_T1 # Defining the COG latent variables
COGlv_T2=~1*COG_T2 # Defining the COG latent variables
COGlv_T3=~1*COG_T3 # Defining the COG latent variables
COGlv_T4=~1*COG_T4 # Defining the COG latent variables
NEUlv_T1=~1*NEU_T1 # Defining the NEU latent variables
NEUlv_T2=~1*NEU_T2 # Defining the NEU latent variables
NEUlv_T3=~1*NEU_T3 # Defining the NEU latent variables
NEUlv_T4=~1*NEU_T4 # Defining the NEU latent variables
COGlv_T2 ~ 1*COGlv_T1 # This parameter regresses COG_T2 perfectly on COG_T1
COGlv_T3 ~ 1*COGlv_T2 # This parameter regresses COG_T3 perfectly on COG_T2
COGlv_T4 ~ 1*COGlv_T3 # This parameter regresses COG_T4 perfectly on COG_T3
NEUlv_T2 ~ 1*NEUlv_T1 # This parameter regresses NEU_T2 perfectly on NEU_T1
NEUlv_T3 ~ 1*NEUlv_T2 # This parameter regresses NEU_T3 perfectly on NEU_T2
NEUlv_T4 ~ 1*NEUlv_T3 # This parameter regresses NEU_T4 perfectly on NEU_T3
dCOG1 =~ 1*COGlv_T2 # This defines the change score as measured perfectly by scores on COG_T2
dCOG2 =~ 1*COGlv_T3 # This defines the change score as measured perfectly by scores on COG_T3
dCOG3 =~ 1*COGlv_T4 # This defines the change score as measured perfectly by scores on COG_T4
dNEU1 =~ 1*NEUlv_T2 # This defines the change score as measured perfectly by scores on NEU_T2
dNEU2 =~ 1*NEUlv_T3 # This defines the change score as measured perfectly by scores on NEU_T3
dNEU3 =~ 1*NEUlv_T4 # This defines the change score as measured perfectly by scores on NEU_T4
COG_T1~~COG_T1 # This estimates the COG residual variances
COG_T2~~COG_T2 # This estimates the COG residual variances
COG_T3~~COG_T3 # This estimates the COG residual variances
COG_T4~~COG_T4 # This estimates the COG residual variances
NEU_T1~~NEU_T1 # This estimates the NEU residual variances
NEU_T2~~NEU_T2 # This estimates the NEU residual variances
NEU_T3~~NEU_T3 # This estimates the NEU residual variances
NEU_T4~~NEU_T4 # This estimates the NEU residual variances
#Dynamics
dNEU1~B1*NEUlv_T1 # This estimates the NEU self-feedback parameter
dNEU2~B1*NEUlv_T2 # This estimates the NEU self-feedback parameter
dNEU3~B1*NEUlv_T3 # This estimates the NEU self-feedback parameter
dCOG1~B2*COGlv_T1 # This estimates the COG self-feedback parameter
dCOG2~B2*COGlv_T2 # This estimates the COG self-feedback parameter
dCOG3~B2*COGlv_T3 # This estimates the COG self-feedback parameter
dNEU1~C1*COGlv_T1 # This estimates the COG to NEU coupling parameter
dNEU2~C1*COGlv_T2 # This estimates the COG to NEU coupling parameter
dNEU3~C1*COGlv_T3 # This estimates the COG to NEU coupling parameter
dCOG1~C2*NEUlv_T1 # This estimates the NEU to COG coupling parameter
dCOG2~C2*NEUlv_T2 # This estimates the NEU to COG coupling parameter
dCOG3~C2*NEUlv_T3 # This estimates the NEU to COG coupling parameter
iCOG=~1*COGlv_T1 # This defines the COG intercept measurement model
sCOG=~1*dCOG1+1*dCOG2+1*dCOG3 # This defines the COG slope measurement model
iCOG~1 # This estimates the COG intercept intercept (mean)
iCOG~~iCOG # This estimates the COG intercept variance
sCOG~1 # This estimates the COG slope intercept
sCOG~~sCOG # This estimates the COG slope variance
iNEU=~1*NEUlv_T1 # This defines the NEU slope measurement model
sNEU=~1*dNEU1+1*dNEU2+1*dNEU3 # This defines the NEU slope measurement model
iNEU~1 # This estimates the NEU intercept intercept (mean)
iNEU~~iNEU # This estimates the NEU intercept variance
sNEU~1 # This estimates the NEU slope intercept
sNEU~~sNEU # This estimates the NEU slope variance
iNEU~~sNEU # This estimates the iNEU sNEU covariance
iNEU~~sCOG # This estimates the iNEU sCOG covariance
iNEU~~iCOG # This estimates the iNEU iCOG covariance
iCOG~~sCOG # This estimates the iCOG sCOG covariance
iCOG~~sNEU # This estimates the iCOG sNEU covariance
sCOG~~sNEU # This estimates the sCOG sNEU covariance
'
fitBDCS <- lavaan(BDCS, data=simdatBD, missing="ML")
## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use inspect(fit,"cov.lv") to investigate.
summary(fitBDCS, fit.measures=TRUE, standardized=TRUE)
## lavaan (0.5-23.1097) converged normally after 956 iterations
##
## Number of observations 500
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 203.204
## Degrees of freedom 18
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 3145.600
## Degrees of freedom 28
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.941
## Tucker-Lewis Index (TLI) 0.908
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -10755.946
## Loglikelihood unrestricted model (H1) -10654.344
##
## Number of free parameters 26
## Akaike (AIC) 21563.891
## Bayesian (BIC) 21673.471
## Sample-size adjusted Bayesian (BIC) 21590.946
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.143
## 90 Percent Confidence Interval 0.126 0.162
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.068
## Warning in sqrt(ETA2): NaNs produced
## Warning in sqrt(ETA2): NaNs produced
## Warning in sqrt(ETA2): NaNs produced
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## COGlv_T1 =~
## COG_T1 1.000 1.900 0.606
## COGlv_T2 =~
## COG_T2 1.000 3.246 0.805
## COGlv_T3 =~
## COG_T3 1.000 5.499 0.847
## COGlv_T4 =~
## COG_T4 1.000 7.362 0.726
## NEUlv_T1 =~
## NEU_T1 1.000 1.503 0.503
## NEUlv_T2 =~
## NEU_T2 1.000 3.089 0.772
## NEUlv_T3 =~
## NEU_T3 1.000 5.431 0.908
## NEUlv_T4 =~
## NEU_T4 1.000 8.640 0.953
## dCOG1 =~
## COGlv_T2 1.000 0.608 0.608
## dCOG2 =~
## COGlv_T3 1.000 0.424 0.424
## dCOG3 =~
## COGlv_T4 1.000 NaN NaN
## dNEU1 =~
## NEUlv_T2 1.000 0.582 0.582
## dNEU2 =~
## NEUlv_T3 1.000 0.444 0.444
## dNEU3 =~
## NEUlv_T4 1.000 0.375 0.375
## iCOG =~
## COGlv_T1 1.000 1.000 1.000
## sCOG =~
## dCOG1 1.000 9.109 9.109
## dCOG2 1.000 7.699 7.699
## dCOG3 1.000 NaN NaN
## iNEU =~
## NEUlv_T1 1.000 1.000 1.000
## sNEU =~
## dNEU1 1.000 0.894 0.894
## dNEU2 1.000 0.666 0.666
## dNEU3 1.000 0.495 0.495
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## COGlv_T2 ~
## COGlv_T1 1.000 0.585 0.585
## COGlv_T3 ~
## COGlv_T2 1.000 0.590 0.590
## COGlv_T4 ~
## COGlv_T3 1.000 0.747 0.747
## NEUlv_T2 ~
## NEUlv_T1 1.000 0.487 0.487
## NEUlv_T3 ~
## NEUlv_T2 1.000 0.569 0.569
## NEUlv_T4 ~
## NEUlv_T3 1.000 0.629 0.629
## dNEU1 ~
## NEUlv_T1 (B1) 0.668 0.074 9.071 0.000 0.559 0.559
## dNEU2 ~
## NEUlv_T2 (B1) 0.668 0.074 9.071 0.000 0.856 0.856
## dNEU3 ~
## NEUlv_T3 (B1) 0.668 0.074 9.071 0.000 1.118 1.118
## dCOG1 ~
## COGlv_T1 (B2) -7.279 0.094 -77.707 0.000 -7.009 -7.009
## dCOG2 ~
## COGlv_T2 (B2) -7.279 0.094 -77.707 0.000 -10.124 -10.124
## dCOG3 ~
## COGlv_T3 (B2) -7.279 0.094 -77.707 0.000 NaN NaN
## dNEU1 ~
## COGlv_T1 (C1) -0.295 0.052 -5.678 0.000 -0.312 -0.312
## dNEU2 ~
## COGlv_T2 (C1) -0.295 0.052 -5.678 0.000 -0.397 -0.397
## dNEU3 ~
## COGlv_T3 (C1) -0.295 0.052 -5.678 0.000 -0.500 -0.500
## dCOG1 ~
## NEUlv_T1 (C2) 8.380 0.273 30.656 0.000 6.387 6.387
## dCOG2 ~
## NEUlv_T2 (C2) 8.380 0.273 30.656 0.000 11.091 11.091
## dCOG3 ~
## NEUlv_T3 (C2) 8.380 0.273 30.656 0.000 NaN NaN
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## iNEU ~~
## sNEU 0.556 0.211 2.629 0.009 0.230 0.230
## sCOG ~~
## iNEU -15.778 2.365 -6.670 0.000 -0.584 -0.584
## iCOG ~~
## iNEU 0.134 0.014 9.806 0.000 0.047 0.047
## sCOG 26.658 2.449 10.884 0.000 0.781 0.781
## sNEU 2.318 0.247 9.382 0.000 0.760 0.760
## sCOG ~~
## sNEU 14.761 0.100 147.330 0.000 0.511 0.511
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## iCOG 2.042 0.127 16.073 0.000 1.075 1.075
## sCOG 2.200 1.629 1.351 0.177 0.122 0.122
## iNEU 1.874 0.123 15.187 0.000 1.246 1.246
## sNEU 2.108 0.224 9.416 0.000 1.312 1.312
## .COG_T1 0.000 0.000 0.000
## .COG_T2 0.000 0.000 0.000
## .COG_T3 0.000 0.000 0.000
## .COG_T4 0.000 0.000 0.000
## .NEU_T1 0.000 0.000 0.000
## .NEU_T2 0.000 0.000 0.000
## .NEU_T3 0.000 0.000 0.000
## .NEU_T4 0.000 0.000 0.000
## COGlv_T1 0.000 0.000 0.000
## .COGlv_T2 0.000 0.000 0.000
## .COGlv_T3 0.000 0.000 0.000
## .COGlv_T4 0.000 0.000 0.000
## NEUlv_T1 0.000 0.000 0.000
## .NEUlv_T2 0.000 0.000 0.000
## .NEUlv_T3 0.000 0.000 0.000
## .NEUlv_T4 0.000 0.000 0.000
## .dCOG1 0.000 0.000 0.000
## .dCOG2 0.000 0.000 0.000
## .dCOG3 0.000 NaN NaN
## .dNEU1 0.000 0.000 0.000
## .dNEU2 0.000 0.000 0.000
## .dNEU3 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .COG_T1 6.216 0.512 12.142 0.000 6.216 0.633
## .COG_T2 5.727 0.503 11.375 0.000 5.727 0.352
## .COG_T3 11.932 0.719 16.602 0.000 11.932 0.283
## .COG_T4 48.543 0.511 94.934 0.000 48.543 0.472
## .NEU_T1 6.672 0.502 13.293 0.000 6.672 0.747
## .NEU_T2 6.466 0.496 13.031 0.000 6.466 0.404
## .NEU_T3 6.307 0.524 12.029 0.000 6.307 0.176
## .NEU_T4 7.462 0.967 7.716 0.000 7.462 0.091
## iCOG 3.608 0.329 10.966 0.000 1.000 1.000
## sCOG 322.867 0.041 7856.080 0.000 1.000 1.000
## iNEU 2.260 0.334 6.765 0.000 1.000 1.000
## sNEU 2.581 0.350 7.377 0.000 1.000 1.000
## COGlv_T1 0.000 0.000 0.000
## .COGlv_T2 0.000 0.000 0.000
## .COGlv_T3 0.000 0.000 0.000
## .COGlv_T4 0.000 0.000 0.000
## NEUlv_T1 0.000 0.000 0.000
## .NEUlv_T2 0.000 0.000 0.000
## .NEUlv_T3 0.000 0.000 0.000
## .NEUlv_T4 0.000 0.000 0.000
## .dCOG1 0.000 0.000 0.000
## .dCOG2 0.000 0.000 0.000
## .dCOG3 0.000 NaN NaN
## .dNEU1 0.000 0.000 0.000
## .dNEU2 0.000 0.000 0.000
## .dNEU3 0.000 0.000 0.000
8.4 ALT and ALT-SR
ANother way to build both a growth model and look at more specific time associations.


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|
App Engine Python SDK
v1.6.9 rev.445
The Python runtime is available as an experimental Preview feature.
|
Public Member Functions | |
| def | validate |
| def | init_job |
| def | finalize_job |
| def | from_json |
| def | to_json |
| def | create |
| def | write |
| def | finalize |
| def | get_filenames |
| Public Member Functions inherited from google.appengine.ext.mapreduce.json_util.JsonMixin | |
| def | to_json_str |
| def | from_json_str |
Abstract base class for output writers. Output writers process all mapper handler output, which is not the operation. OutputWriter's lifecycle is the following: 0) validate called to validate mapper specification. 1) init_job is called to initialize any job-level state. 2) create() is called, which should create a new instance of output writer for a given shard 3) from_json()/to_json() are used to persist writer's state across multiple slices. 4) write() method is called to write data. 5) finalize() is called when shard processing is done. 6) finalize_job() is called when job is completed. 7) get_filenames() is called to get output file names.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.create | ( | cls, | |
| mr_spec, | |||
| shard_number, | |||
| shard_attempt, | |||
_writer_state = None |
|||
| ) |
Create new writer for a shard. Args: mr_spec: an instance of model.MapreduceSpec describing current job. shard_number: int shard number. shard_attempt: int shard attempt. _writer_state: deprecated. This is for old writers that share file across shards. For new writers, each shard must have its own dedicated outputs. Output state should be contained in the output writer instance. The serialized output writer instance will be saved by mapreduce across slices.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.finalize | ( | self, | |
| ctx, | |||
| shard_state | |||
| ) |
Finalize writer shard-level state. This should only be called when shard_state.result_status shows success. After finalizing the outputs, it should save per-shard output file info into shard_state.writer_state so that other operations can find the outputs. Args: ctx: an instance of context.Context. shard_state: shard state. ShardState.writer_state can be modified.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.finalize_job | ( | cls, | |
| mapreduce_state | |||
| ) |
Finalize job-level writer state. This method is only to support the deprecated feature which is shared output files by many shards. New output writers should not do anything in this method. This method should only be called when mapreduce_state.result_status shows success. After finalizing the outputs, it should save the info for shard shared files into mapreduce_state.writer_state so that other operations can find the outputs. Args: mapreduce_state: an instance of model.MapreduceState describing current job. MapreduceState.writer_state can be modified during finalization.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.from_json | ( | cls, | |
| state | |||
| ) |
Creates an instance of the OutputWriter for the given json state. Args: state: The OutputWriter state as a dict-like object. Returns: An instance of the OutputWriter configured using the values of json.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.get_filenames | ( | cls, | |
| mapreduce_state | |||
| ) |
Obtain output filenames from mapreduce state. This method should only be called when a MR is finished. Implementors of this method should not assume any other methods of this class have been called. In the case of no input data, no other method except validate would have been called. Args: mapreduce_state: an instance of model.MapreduceState Returns: List of filenames this mapreduce successfully wrote to. The list can be empty if no output file was successfully written.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.init_job | ( | cls, | |
| mapreduce_state | |||
| ) |
Initialize job-level writer state. This method is only to support the deprecated feature which is shared output files by many shards. New output writers should not do anything in this method. Args: mapreduce_state: an instance of model.MapreduceState describing current job. MapreduceState.writer_state can be modified during initialization to save the information about the files shared by many shards.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.to_json | ( | self | ) |
Returns writer state to serialize in json. Returns: A json-izable version of the OutputWriter state.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.validate | ( | cls, | |
| mapper_spec | |||
| ) |
Validates mapper specification. Output writer parameters are expected to be passed as "output_writer" subdictionary of mapper_spec.params. To be compatible with previous API output writer is advised to check mapper_spec.params and issue a warning if "output_writer" subdicationary is not present. _get_params helper method can be used to simplify implementation. Args: mapper_spec: an instance of model.MapperSpec to validate.
| def google.appengine.ext.mapreduce.output_writers.OutputWriter.write | ( | self, | |
| data | |||
| ) |
Write data. Args: data: actual data yielded from handler. Type is writer-specific.


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General support queries. User Guides: https://support.komodoplatform.com/ Support staff: Angelina#6231 gcharang#6833. If you want to ask JL777 a question, use the #ask-jl777 channel.
100 messages
Before 05-Dec-19 12:00 AM
ComputerGenie02-Jul-18 02:16 AM
round about the time I realized that the "public release" was a perpetual beta and left the insiders program
just run a vm and do it right
there's only so many layers of shit you can stack before the base gets squishy
also, are you using NOMP or znomp classic?
daemonfox02-Jul-18 02:19 AM
kmd-nomp
ComputerGenie02-Jul-18 02:19 AM
k
daemonfox02-Jul-18 02:19 AM
all that seems to work fine
i have zero issues in the environment
just this one thing when I reach current block height
ComputerGenie02-Jul-18 02:20 AM
""sh: 1: cannot open user: No such file" comming from kmd-nomp?
daemonfox02-Jul-18 02:20 AM
no
from the console running komodod
regardless of nomp running or not
nomp throws the getblocktemplate error
ComputerGenie02-Jul-18 02:21 AM
did you check the komodo debug log?
daemonfox02-Jul-18 02:21 AM
which makes sense if komodod is choking
xRobeSx02-Jul-18 02:26 AM
If youre running kmd-nomp which is probably from my repo, and is outdated. I should delete it
z-nomp is more current and includes komodo @daemonfox
that is the first occurence of an error in the log
happens over and over
xRobeSx02-Jul-18 02:27 AM
Oh okay. I’ve just had others tell me of issues with the kmd-nomp repo and I haven’t used it in a while
daemonfox02-Jul-18 02:28 AM
got it to take shares for a bit but stratum fails everytime the getblock fails during this komodod error
xRobeSx02-Jul-18 02:29 AM
In your komodo.conf is blocknotify setup as <user> like you posted above or your real username?(edited)
daemonfox02-Jul-18 02:29 AM
i was just thinking i must have pasted yours instead of the edited line from my text file
yup
xRobeSx02-Jul-18 02:30 AM
Yes my example was setup as <user>
Hopefully changing that fixes it for you
Make sure to restart komodod
daemonfox02-Jul-18 02:31 AM
already killed it
ill check back in shortly to let you know for sure... other than the super long compile and block load... I almost have a whole pool living on Win 10 no VMs
xRobeSx02-Jul-18 02:35 AM
That /home/user directory was for Linux though. If you’re on Windows @daemonfox it will be a bit different
daemonfox02-Jul-18 02:37 AM
im on the Win 10 embedded Ubuntu 16.04
Deleted User02-Jul-18 05:16 AM
If we have encrypted seed
we only have 1 address right? Is there still an option to claim interest using that method
I really need to safeguard my kmd now LOL.
and where is it located if I want to save it?
\Roaming\Agama/spv-cache.json doesnt exist
Putting in my wallet seed in the bottom part without inputting password takes me to the same address as my encrypted one, whats the use of a password if the phrase still logs in? Sorry a bit confused
Acura02-Jul-18 09:42 AM
how can I increase the value of MAX UTXO Size ?
Acura02-Jul-18 10:02 AM
I had deposited via Speed Mode in BarterDex V1.10 - but now i don´t see it if i click on the Deposit History, same with a another account - Anyone that can proof if this working in the V1.10 ?(edited)
gcharang02-Jul-18 10:34 AM
@Deleted User your seed will always work. The encrypt+password option is a quality of life feature where agama stores your seed in a text file and encrypts it with your password. Now you don't have to type/copy-paste seed everytime. Because typos when typing the seed will generate different addresses and user may not notice and lose funds. If your encrypt password is weak and an attacker got access to your seed file(created by agama and encryped), he can brute force your seed file by trying passwords again and again until one works.
Deleted User02-Jul-18 10:35 AM
Ah makes sense. Are there any limits to wrong attempts? Or unlimited
also where can I find the .pin file? Cant find in the komodo folder
and copy pasting the location in explorer doesnt seem to find it either
So safest method is saving the .pin file somewhere secure like an encrypted usb (no need to store the seed phrase anywhere at all) and use that to access funds
GatewayBOT02-Jul-18 10:53 AM
[matrix] <gcharang:matrix.org> no safest method is to save your seed written on a paper and storing it securely. Digital data can be lost in any number of ways. The encrypted text life is just for making users's life easier
gcharang02-Jul-18 10:54 AM
Don't backup your agama encrypted seeds in cloud services
SHossain02-Jul-18 10:55 AM
@Acura if the tx is in blockchain it is there and your speedmode should be working
Cox1102-Jul-18 10:56 AM
hi guys tonight ive tried to log into agama spv lite and its keeps telling me "connection error, trying to switch to other server"
gcharang02-Jul-18 10:58 AM
@Deleted User also the .pin files are not supposed to be handled by the end user. But if you want to know, in ubuntu: ~/.agama/shepherd/pin
Acura02-Jul-18 10:58 AM
@Cox11 which Agama version ? Latest is 2.41
gcharang02-Jul-18 10:58 AM
If someone gets access to your .pin file, they can try unlimited times
Cox1102-Jul-18 10:58 AM
@Acura 2.38
Acura02-Jul-18 10:59 AM
@SHossain Speed Mode is working, but it´s not showing me the deposit - that´s a little bit strange - but its ok
@Acura i don't know the exact reason. it works, but doesn't show tx history.
Acura02-Jul-18 11:07 AM
Ok no problem
Cox1102-Jul-18 11:12 AM
@Acura i updated. now it works. thanks
Deleted User02-Jul-18 11:13 AM
where in windows? @gcharang sorry!
got it
thanks!
daemonfox02-Jul-18 12:33 PM
Morning! @xRobeSx thought it wouldnt be long but komodod is still validating notary txes to activate the best chain. At ht 481K and counting! Cloned z-nomp so just waiting.
Deleted User02-Jul-18 12:53 PM
If im ever inactive for a long period of time or gone for a while, theres no chance there will ever be an update to agama that changes how seeds or encrypt work, or anything that could go wrong right? @gcharang
Hypothetically if say I dissapear for 10 years and come back, there would still be a way to get into my pin file? Just gotta make sure
securing all my funds on all my wallets and coins today
xRobeSx02-Jul-18 12:56 PM
Morning @daemonfox you’re about halfway there, blockheight is at 903661
And you should be fine @Deleted User. You could always use paper wallets though
Deleted User02-Jul-18 12:58 PM
Gonna be extra paranoid from today on
daemonfox02-Jul-18 01:00 PM
Did anyone piece together a vanitygen for equihash?
gcharang02-Jul-18 01:01 PM
Even if the then latest version had different settings and ways to save/encrypt seeds, your current pin file can always be able to be decrypted by you if you know the password @Deleted User
Deleted User02-Jul-18 01:02 PM
Alright appreciate it!  So good to know
jl777c02-Jul-18 01:29 PM
vanitygen has nothing to do with mining algo
Hermes02-Jul-18 01:51 PM
@Deleted User I will suggest to store your seed in a safe space or vault , is always good to have a back up for any scenario
a vault is always good
Deleted User02-Jul-18 01:52 PM
Yes, stored a copy in my safe, but in the case that it gets damaged, I have a usb hidden somewhere with the pin Heres to hoping I never have to use it haha!
Hermes02-Jul-18 01:52 PM
nice
jl777c02-Jul-18 01:53 PM
you didnt safe the actual passphrase?
then you are assuming the encrypted file will be intact
Hermes02-Jul-18 02:00 PM
Yep just in case decrypt the seed in the wallet and save it ...and you should be fine. Storing the seed in the encrypted file can be also a liability if the PC gets damaged or of any data corruption that can occur
jl777c02-Jul-18 02:02 PM
I would safe the full seed or wif in the secure safe place
that way it doesnt rely on any file to never get corrupted
ComputerGenie02-Jul-18 02:23 PM
has the wifs of his cold storage wallets in an encrypted USB and the encryption key is the SHA-256 of the 1st ISO he ever downloaded from MSDN
Deleted User02-Jul-18 02:59 PM
Yep seed on paper, laminated and ordered in a way no one else would understand Next to a usb. Just in case you know maybe rats would get in and eat it hahahaha
and i dont have a environment or the skill to compile it for Win 10 64
and this Ubuntu instance I have is SLOW
AngelaTrishia2902-Jul-18 04:48 PM
hello, i download the latest release of your platform and its not working i used to open the exchange with passphrase and this always happens "starting barterdex, please wait..."
ComputerGenie02-Jul-18 04:48 PM
it takes a while to load on winduhs
AngelaTrishia2902-Jul-18 04:50 PM
aww okay so i need to wait?
ComputerGenie02-Jul-18 04:51 PM
yes, can take up to 5-10 mins on some win10 builds


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