BLOM 2.0 Outline based on what we discussed previously.
More...
Functions |
| function | BLOM_ExtractModel (in name, in horizon, in dt, in integ_method, in options) |
| | This function extracts a Simulink model and is the first step into converting it into an optimization problem.
|
| function | findBlocks (in name) |
| | Finds all Input, External, Constraint and Cost BLOM library blocks.
|
| | function () |
| | Find all connected source blocks using a breadth first search. Includes the handles already given.
|
| function | getOutports (in inports, in existingOutports, in iZero) |
| | From given inports, see which outports are relevant.
|
| function | relevantTimes (in outportHandles) |
| | Creates struct that says which outports are relevant at which times.
|
| function | makeStruct (in outportHandles, in name) |
| | From outport list, create three different structures needed for the optimization problem.
|
Detailed Description
BLOM 2.0 Outline based on what we discussed previously.
Function Documentation
| function BLOM_ExtractModel |
( |
in |
name, |
|
|
in |
horizon, |
|
|
in |
dt, |
|
|
in |
integ_method, |
|
|
in |
options |
|
) |
| |
This function extracts a Simulink model and is the first step into converting it into an optimization problem.
Overview of BLOM Extract Model Structure
- Find all Input, External, Constraint and Cost BLOM library blocks.
- Build cost and constraint dependency wire graph, starting from Constraint and Cost blocks and traverse backwards.
- BFS to find the graph.
- Recognize reaching non-BLOM inputs (user error).
- Format of the graph: Sparse connectivity matrix of all wires, store just the block handles.
- Each wire has a property whether optimization variables is introduced for it for each derivative evaluation, intermediate major time step, initial time, final time.
- Store for each wire, if it is constrained, cost (discrete or continuous), input, external.
- Flatten the wires:
- "Fetch" all PolyBlocks data: variables from the base workspace or values from the dialog (just do evalin('base',get_param(h,'A')).
- Get all possible dimensions (with or without the Simulink "compile").
- Translate all supported Simulink blocks to PolyBlocks using the known dimensions.
- Eliminate duplicate routing variables.
- Combine all PolyBlocks to a large P and K matrices.
- Eliminate whatever is possible (constants, identity PolyBlocks, linearly dependent constraints, replicated P rows, unused terms).
- Propagate time property information using the processed P and K structure.
Create the whole problem:
- Introduce and duplicate all major time step variables, including their dependencies.
- Introduce and duplicate minor time steps variables and their dependencies.
- Link by the appropriate Butcher tableau values major and minor time steps variables.
- Add move blocking constraints.
- Eliminate whatever is possible.
- Mark external, input.
- Take care of cost. Examples: ModelSpec = BLOM_ExportModel; Converts current active model.
ModelSpec = BLOM_ExportModel('Sys',10,0.5); Converts current model 'Sys', with a a time step of 0.5 sec and 10 time steps. ModelSpec = BLOM_ExportModel('Sys',10,0.5,); Converts current model 'Sys', with a a time step of 0.5 sec and 10 time steps, assuming discrete model.
ModelSpec = BLOM_ExportModel('Sys',10,0.5,'Trapez'); Converts current model 'Sys', with a a time step of 0.5 sec ,10 time steps and trapezoidal discretization method.
- Parameters:
-
| name | name of the model to convert |
| horizon | integer number of steps in prediction horizon length, 1 if not supplied |
| dt | time step size [s], 1 if not supplied |
| disc | discretization method {'none,'Euler','Trapez','RK4'} |
| options | options created by BLOM_opset function |
- Return values:
-
| out1 | return value for the first output variable |
| out2 | return value for the second output variable |
Finds all Input, External, Constraint and Cost BLOM library blocks.
More detailed description of the problem.
- Parameters:
-
| name | Name of the simulink model |
- Return values:
-
| blockHandles | handles of all the relevant blocks |
Find all connected source blocks using a breadth first search. Includes the handles already given.
More detailed description of the problem.
- Parameters:
-
| handleArray | the array of handles that you want to search. These are the block handles of the costs and constraints |
| varargin | the external and input from simulink handles. the sources of these blocks are not relevant to the optimization problem |
- Return values:
-
| sourceHandles | return all the handles connected to input handles |
| stop | if there are any unconnected blocks or blocks that BLOM does not support, this parameter gives a 1 to indicate true and 0 to indicate false |
| boundsStruct | structure with fields: 1) outport handles found, 2) boolean true or false if it is connected to a bounds block |
| function getOutports |
( |
in |
inports, |
|
|
in |
existingOutports, |
|
|
in |
iZero |
|
) |
| |
From given inports, see which outports are relevant.
More detailed description of the problem.
- Parameters:
-
| inports | inports of current source |
| existingOutports | current outports found. used to check for redundancy |
| iZero | current index of the first zero |
- Return values:
-
| outportHandles | new outports found |
| iZero | updates current index of the first zero |
| function makeStruct |
( |
in |
outportHandles, |
|
|
in |
name |
|
) |
| |
From outport list, create three different structures needed for the optimization problem.
More detailed description of the problem.
- Parameters:
-
| outportHandles | outportHandles found by searchSources |
| name | name of model |
- Return values:
-
| optimVar | structure with fields 1) index of block name 2) index of port or vector |
| polyStruct | structure with fields 1) index of block name 2) P matrix 3) K matrix |
| blocks | structure with fields 1) name of block 2) handle of block |
| function relevantTimes |
( |
in |
outportHandles | ) |
|
Creates struct that says which outports are relevant at which times.
More detailed description of the problem.
- Parameters:
-
| outportHandles | outportHandles found by searchSources |
- Return values:
-
| timeStruct | structure with following fields. 1) outportHandles 2) majorTimeStep 3) minorTimeStep |


IMPORTANT: ALL COMMANDS NEED < AND > ON EITHER SIDE
in this last section I will be showing you the more complicated points of html.
the first thing I will show you how to do is to insert pictures. to do this you
write img src="insert image url here" /img
to adjust the size of an image you write in the css area
img { height insert number herepx; }.
in html you can also insert videos by typing this:
video width="320" height="240" controls
source src="movie.mp4" type="video/mp4"
source src="movie.ogg" type="video/ogg"
Your browser does not support the video tag.
/video
to insert lists in html you type ul then li insert message here then repeat as
many times as you want. it will creat a list of jotnoted articles.
to create links to different sites in the same folder, you write
a href="insert docname here.html" insert what you want to call the link here /a home go to the moon


| Module | Daemonize |
| In: |
lib/daemons/daemonize.rb
|
February. 4, 2005 Travis Whitton<EMAIL_ADDRESS>Daemonize allows you to easily modify any existing Ruby program to run as a daemon. See README.rdoc for more details.
build the docs if you want to
The Daemonize extension module is copywrited free software by Travis Whitton<EMAIL_ADDRESS>You can redistribute it under the terms specified in the COPYING file of the Ruby distribution.

Daemonize is a module derived from Perl‘s Proc::Daemon module. This module allows you to easily modify any existing Ruby program to run as a daemon. A daemon is a process that runs in the background with no controlling terminal. Generally servers (like FTP and HTTP servers) run as daemon processes. Note, do not make the mistake that a daemon == server.
The Daemonize module does the following:
Forks a child and exits the parent process.
Becomes a session leader (which detaches the program from the controlling terminal).
Forks another child process and exits first child. This prevents the potential of acquiring a controlling terminal.
Changes the current working directory to "/".
Clears the file creation mask.
Closes file descriptors.
Using the Daemonize module is extremely simple:
require 'daemonize' class TestDaemon include Daemonize def initialize daemonize() loop do # do some work here end end end
Daemonize was written by Travis Whitton and is based on Perl‘s Proc::Daemonize, which was written by Earl Hood. The above documentation is also partially borrowed from the Proc::Daemonize POD documentation.
| VERSION | = | "0.1.1m" |
# File lib/daemons/daemonize.rb, line 142 142: def call_as_daemon(block, logfile_name = nil, app_name = nil) 143: rd, wr = IO.pipe 144: 145: if tmppid = safefork 146: # parent 147: wr.close 148: pid = rd.read.to_i 149: rd.close 150: 151: Process.waitpid(tmppid) 152: 153: return pid 154: else 155: # child 156: 157: rd.close 158: 159: # Detach from the controlling terminal 160: unless sess_id = Process.setsid 161: raise Daemons.RuntimeException.new('cannot detach from controlling terminal') 162: end 163: 164: # Prevent the possibility of acquiring a controlling terminal 165: #if oldmode.zero? 166: trap 'SIGHUP', 'IGNORE' 167: exit if pid = safefork 168: #end 169: 170: wr.write Process.pid 171: wr.close 172: 173: $0 = app_name if app_name 174: 175: Dir.chdir "/" # Release old working directory 176: File.umask 0000 # Insure sensible umask 177: 178: # Make sure all file descriptors are closed 179: ObjectSpace.each_object(IO) do |io| 180: unless [STDIN, STDOUT, STDERR].include?(io) 181: begin 182: unless io.closed? 183: io.close 184: end 185: rescue ::Exception 186: end 187: end 188: end 189: 190: redirect_io(logfile_name) 191: 192: block.call 193: 194: exit 195: end 196: end
This method causes the current running process to become a daemon
# File lib/daemons/daemonize.rb, line 201 201: def daemonize(logfile_name = nil, app_name = nil) 202: srand # Split rand streams between spawning and daemonized process 203: safefork and exit # Fork and exit from the parent 204: 205: # Detach from the controlling terminal 206: unless sess_id = Process.setsid 207: raise Daemons.RuntimeException.new('cannot detach from controlling terminal') 208: end 209: 210: # Prevent the possibility of acquiring a controlling terminal 211: #if oldmode.zero? 212: trap 'SIGHUP', 'IGNORE' 213: exit if pid = safefork 214: #end 215: 216: $0 = app_name if app_name 217: 218: Dir.chdir "/" # Release old working directory 219: File.umask 0000 # Insure sensible umask 220: 221: # Make sure all file descriptors are closed 222: ObjectSpace.each_object(IO) do |io| 223: unless [STDIN, STDOUT, STDERR].include?(io) 224: begin 225: unless io.closed? 226: io.close 227: end 228: rescue ::Exception 229: end 230: end 231: end 232: 233: redirect_io(logfile_name) 234: 235: #return oldmode ? sess_id : 0 # Return value is mostly irrelevant 236: return sess_id 237: end
Free file descriptors and point them somewhere sensible STDOUT/STDERR should go to a logfile
# File lib/daemons/daemonize.rb, line 244 244: def redirect_io(logfile_name) 245: begin; STDIN.reopen "/dev/null"; rescue ::Exception; end 246: 247: if logfile_name 248: begin 249: STDOUT.reopen logfile_name, "a" 250: STDOUT.sync = true 251: rescue ::Exception 252: begin; STDOUT.reopen "/dev/null"; rescue ::Exception; end 253: end 254: else 255: begin; STDOUT.reopen "/dev/null"; rescue ::Exception; end 256: end 257: 258: begin; STDERR.reopen STDOUT; rescue ::Exception; end 259: STDERR.sync = true 260: end
Try to fork if at all possible retrying every 5 sec if the maximum process limit for the system has been reached
# File lib/daemons/daemonize.rb, line 97 97: def safefork 98: tryagain = true 99: 100: while tryagain 101: tryagain = false 102: begin 103: if pid = fork 104: return pid 105: end 106: rescue Errno::EWOULDBLOCK 107: sleep 5 108: tryagain = true 109: end 110: end 111: end
# File lib/daemons/daemonize.rb, line 115 115: def simulate(logfile_name = nil) 116: # NOTE: STDOUT and STDERR will not be redirected to the logfile, because in :ontop mode, we normally want to see the output 117: 118: Dir.chdir "/" # Release old working directory 119: File.umask 0000 # Insure sensible umask 120: 121: # Make sure all file descriptors are closed 122: ObjectSpace.each_object(IO) do |io| 123: unless [STDIN, STDOUT, STDERR].include?(io) 124: begin 125: unless io.closed? 126: io.close 127: end 128: rescue ::Exception 129: end 130: end 131: end 132: 133: # Free file descriptors and 134: # point them somewhere sensible 135: # STDOUT/STDERR should go to a logfile 136: 137: begin; STDIN.reopen "/dev/null"; rescue ::Exception; end 138: end


Quantum concentration contrast core/shell defects diffusion font-size free larger mapping related van Electron Humphreys InGaAs alloy analysis angles binary changes distinguish double; effects generally high-resolution image lengths line material measurement model models semiconductors specimen strained ./S-()- A.G. Cullis D.J. InGaN Mater. Quantitative Schowalter Segregation SiGe actually angle angle: annular background border-color border-width dark-field data direction e.g., effective epitaxial field fit grain grown high however, indium inner key long measure monochromated shows signal solid; square studies sufficiently systems tails temperature using {font-style {font-weight (S)TEM (beam ./j.ultramic... AlGaN Auger GaAs Imaging Methods Nanomaterials Proc. SiGe, Solidi Stranski-Krastanow Transmission analytical appear approaches article as-deposited bandgap based bold case certain change chemistry clear confined convergence: courier; decay depend detect diffraction does dot em; emission energy-loss extended fitting i.e., include individual interfaces. intermixing interpretation layer loss materials measured measuring microscope mrad; objective possible pt;} pure quantitative reliable resulting right scattering sensitive single study terms ternary type well, width xGa


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Iris Dataset Clustered with DBSCAN
Iris Dataset True Labels
Iris Dataset Cluster Composition with DBSCAN
epsilon=
minimum points=
Recalculate


 I didn’t say you wouldn’t want the rind as well. The ciccioli – as Italian pork scratchings (give or take) are known – make for very fine aperativo-accompanying morsel.
Preheat the oven to 200C/400F/Gas 6. Open up the loin of pork, ready for stuffing, by laying it out in front of you vertically so that the thick part is on the left (I’m right-handed). Now starting at the top, cut through this thick part of the loin all the way down so that you can open it out to the left like a book. This will give you a larger surface area to lay the filling on.
Spread the pungent garlic all over the meat. Then take the leaves from a few sprigs of oregano and dot them about too. Keep the stalks.
Lay the pieces of ham horizontally over the pork loin, this way it will roll up more easily as the roll will follow the long length of the ham slices.
Sprinkle the chilli flakes over the ham and then roll up the loin, starting from the open-ended side, keeping as tight a roll as you can. Secure the meat with string at 3-4cm/1-1½in intervals, knotting the lengths of string firmly. If you’re using stationer’s string rather than cook’s twine, dampen it first. I wish I could instruct you as to how to tie proper knots, but I do very bad knots myself. Were you to have the offer of a friendly hand - or rather finger - to hold the knot down as you tie it, take it gratefully.
Cut the onion or shallots into thick slices without peeling them, and sit them in the bottom of a roasting tin to make the flavour-platform for the pork. Add the reserved stalks from the oregano, sit the loin on top and drizzle with the oil.
Cook for 1¼ hours, when it’s cooked, the juices must run clear when you put a skewer into the centre and a meat thermometer should read 71C/160F.
Transfer the tin to a heatproof kitchen surface, immediately pour the vermouth and boiling water into the tin and scrape all around the bottom of the tin so any oniony, meaty stuck-on bits dissolve into this instant gravy. You can let the meat rest in this sauce for 15 minutes or so.
When you are ready to slice the pork, remove it to a board and warm the gravy (removing the onion bits) if it’s cooled. Cut the meat into approximately 2cm/1in slices, or in other words, thick enough for the slices to keep their shape and hold the filling. This size joint should give you 10 good slices plus the misshapen end-pieces.
Arrange these sturdy slices on a bed of rocket with the gravy served separately in a little jug, or just sit them on a warm plate and pour over them a little gravy or any extra juices the meat has made. Take some more oregano leaves and slice them into fine strips, strewing them over the pork slices before serving.


Question 1 of 10
Relating & Networking 1
Correct answer! Working with others is a key skill of a Plumbing Apprentice, and building relationships by listening to people is very important. From your response you are likely to be a good fit to the role.
While it can be hard to work with people you have never met before, a Plumbing Apprentice will need to communicate with others and work together with other people.
Sometimes a Plumbing Apprentice will need to work alone, but often you will need to communicate with others and work together to get your work done, even if they are people you have not met before and may not work with again soon. If you are likely to struggle with this, you may find the role difficult.


Sharkey leads Samford over Manhattan 70-57
by Associated PressHOMEWOOD, Ala. (AP) — Josh Sharkey had 17 points and eight assists as Samford beat Manhattan 70-57 on Tuesday night.
Jalen Dupree and Brandon Austin added 15 points each and Deandre Thomas had six rebounds for the Bulldogs (3-3). Sharkey hit 10 of 11 from the free throw line.
Samford scored the last 11 points of the first half and led 34-22 at the break. Manhattan made its biggest push late in the second half, a dunk by Warren Williams drawing the Jaspers within 61-55 with 2:24 remaining.
Jalen Dupree’s three-point play gave Samford a 65-55 lead with 44 seconds remaining and the Bulldogs made 5 of 6 from the line to close out the win.
Tykei Greene had 13 points and eight rebounds for the Jaspers (2-1). Christian Hinckson added 12 points and eight rebounds. Samir Stewart had five steals.
Samford plays Troy at home on Saturday. Manhattan visits Elon on Saturday.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
Elements of this story were generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com


Incorporated in February 2015, Kobby Technologies Limited is a professional services company with Multi-Vendor Partnerships consulting for organizations across the African Region to improve the way business value is created and delivered from IT.
We are Kenyan with presence across the Eastern African countries. We keep delivering several projects for clients in varied verticals and this has enabled us to solidly venture into areas thought to be initially complex in nature and emerged with success stories. We are responsive to the challenges of ensuring that returns on IT investments are optimal by exploring suitable metrics.
In 2018 we extended our service offering to IT Advisory (restricted to Post Implementation Reviews, Gap Analysis and Solution Selection) to enable our clients enjoy an end-to-end experience from our professional services.
YEARS OF EXPERIENCE
We are a young, progressive and dynamic firm whose consultants have a wealth of experience of over 10 years in ICT disciplines.
We are a young, progressive and dynamic firm whose consultants have a wealth of experience of over 10 years in ICT disciplines.
We are a young, progressive and dynamic firm whose consultants have a wealth of experience of over 10 years in ICT disciplines.
We are a young, progressive and dynamic firm whose consultants have a wealth of experience of over 10 years in ICT disciplines.
Please take a moment to fill the form
"We were looking for an affordable ERP Solution and KTL more than delivered. We saw an immediate change in our business-critical process! They took the time to understand who we are as a company and gave us amazing solutions to choose from. We wholeheartedly recommend Kobby Technologies Ltd to everyone we know."
"We were looking for an affordable ERP Solution and KTL more than delivered. We saw an immediate change in our business-critical process! They took the time to understand who we are as a company and gave us amazing solutions to choose from. We wholeheartedly recommend Kobby Technologies Ltd to everyone we know."
"We were looking for an affordable ERP Solution and KTL more than delivered. We saw an immediate change in our business-critical process! They took the time to understand who we are as a company and gave us amazing solutions to choose from. We wholeheartedly recommend Kobby Technologies Ltd to everyone we know."


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Hasina returns home after watching India-Bangladesh Test in Kolkata
by bdnews24.comPrime Minister Sheikh Hasina has returned home after a brief visit to the Indian state of West Bengal on the occasion of the first day-night Test between India and Bangladesh.
A Biman Bangladesh Airlines flight carrying the prime minister and her entourage arrived in Dhaka’s Shahjalal International Airport on Friday midnight, an hour after taking off from Netaji Subhash Chandra Bose International Airport in Kolkata.
Kolkata Mayor Firhad Hakim and Bangladesh High Commissioner Syed Muazzem Ali saw her off at the Kolkata airport.
The recently appointed president of India’s cricket board, Sourav Ganguly, invited Hasina to inaugurate the pink-ball match and watch the first day’s play.
Prime Minister Narendra Modi also sent a letter inviting Hasina, but he could not attend the inaugural function due to a prior commitment.
Hasina and West Bengal Chief Minister Mamata Banerjee inaugurated the match by ringing a bell at the iconic Eden Gardens.
The Cricket Association of Bengal also organised a cultural event after the stumps.
Besides Sourav, Indian cricket great Sachin Tendulkar was present.
Hasina waved to the excited crowd when she entered the stadium as Kolkata was tickled pink on the occasion of the match.
Hasina also met Mamata at the Taj Hotel.


Press "Start" to start the tracker, play around, then press
"End" to stop the tracker and display what has been
recorded.
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Logs
| Date |
{{ element.date | date:'H:mm:ss.SSS' }} |
Type |
{{ element.type }} |
ID |
{{ element.id }} |
Text content |
{{ element.textContent }} |
URL |
{{ element.url }} |
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$:{TITLE("Director Control Panel")}
Account Creation Email Blacklist
Global Community Tagging Restrictions


About This Project
SaveOurAir is an experiment funded by Organicity. The partnership is characterised by a multidisciplinary composition, in order to provide all the knowledge needed in the creation of data-stories. This particular project is coordinated by the following members:
- Ángeles Briones, DensityDesign Lab - Politecnico di Milano
- Verena Rauchenwald, Gehl
- Anders Koed Madsen, TantLab - Aalborg University Copenhagen
How to Contribute
This website is connected to this Google Spreadsheet, which I've shared, so click and add a new community group to the bottom of the list! For the map you'll need to get the lat and long of their location, you can get that here. The locating of these places is helpful, so please try to fill out all the columns.


Sorry for the delay but in Utopian.io we are receiving a contribution every 10 mins in average and while this is super cool is also hard work and we got some delay in announcing the winners of the Utopian GIFs Contest.
The Winners
1. Star Wars by @aleister
2. Utopian superhero by @orelmely
3. Utopian WhaT? by @onyfest
Only 3 Winners, But These Two Are Also VERY Nice
- https://utopian.io/utopian-io/@aleister/utopian-io-gif-contest-minimalist-utopian-gif
- https://steemit.com/utopian-io/@mooncryption/utopian-io-gif-contest
The Utopian Giphy Channel
https://giphy.com/channel/utopian_io
A special THANKS to @stellabelle for donating 100 STEEM for this contest and for running the Utopian Giphy channel
Thanks everyone for participating and stay tuned for the next contest!
A Special Thanks to The Sponsors
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A Special Thanks to The Moderators
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This page is synchronized from the post: The Winners Of The Utopian GIFs Contest


#include <dr_events.h>
Data Fields | |
| dr_mcontext_t * | mcontext |
| bool | raw_mcontext_valid |
| dr_mcontext_t * | raw_mcontext |
| dr_fault_fragment_info_t | fragment_info |
Data structure passed to a restore_state_ex event handler (see dr_register_restore_state_ex_event()). Contains the machine context at the translation point and other translation information.
Information about the code fragment inside the code cache at the translation interruption point.
The application machine state at the translation point. The client can update register values and the program counter by changing this context. The client should not change mcontext.flags:
it should remain DR_MC_ALL.
The raw pre-translated machine state at the translation interruption point inside the code cache. Clients are cautioned when examining code cache instructions to not rely on any details of code inserted other than their own. Modifying this context will not affect the translation.
Whether raw_mcontext is valid.


Github Issue: #3521
This test demonstrates how a Modal environment with Toasts could be configured to prevent dismissal of the Modal while toasts are open.
After clicking the button below, try closing the Modal with the Escape key


Connect the required number of dots in a straight line oriented in any direction to win. The dots drop in a vertical column and stack one over another. The player who does this first, wins the game. The number of dots required to win can be selected from the options menu () in the navigation bar. The number of players can be between 2-4. Also, each player gets exactly one chance for UNDO. Consecutive UNDOs is not allowed.


Source Code Build
Checkout
checkout the lastest project source code with commands blow:
git clone https://github.com/apache/dubbo.git dubbo
Branches
We use master
as the major branch for new feature development, and use other branches for maintenance. Tags for all versions can be checked via https://github.com/apache/dubbo/tags.
Building
Dubbo relies on maven as the building tool.
Requirements:
- Java above 1.5 version
- Maven version 2.2.1 or above
The following MAVEN_OPTS
should be configured before building:
export MAVEN_OPTS=-Xmx1024m -XX:MaxPermSize=512m
build with below command:
mvn clean install
skip testing using below building command:
mvn install -Dmaven.test.skip
Building jar package of source code
build Dubbo source code jar package with below command.
mvn clean source:jar install -Dmaven.test.skip
and modify the dubbo dependency in your sample project to the SANPSHOT version of the local repository, and then use remote debugger to debug dubbo.
IDE support
use below command to generate IDE.
Intellij Idea
mvn idea:idea
Eclipse
mvn eclipse:eclipse
Importing into eclipse
Firstly, a maven repository needs to be configured in eclipse. Define M2_REPO
and point it to the local maven repository by clicking Preferences -> Java -> Build Path -> Classpath
.
Use the following maven command as well:
mvn eclipse:configure-workspace -Declipse.workspace=/path/to/the/workspace/
1: view the source code through https://github.com/apache/dubbo
2: path under UNIX is ${HOME}/.m2/repository, path under Windows is C:\Documents and Settings\


The Playspecs parser is a top-down operator precedence parser, also called a Pratt parser. Modern writeups of the technique include Doug Crockford's article and Eli Bendersky's readable summary of Lundh's Python parser.
Parsing begins with tokenization. The input string is transformed into a token stream and then the tokens drive the recursive descent stage. The Playspecs parser recognizes both the temporal and propositional fragments of the game-independent Playspecs syntax, and can tokenize certain simple specs on its own (this core syntax is defined in the AIIDE paper). Whitespace is ignored except as it separates other tokens.
The reference implementation tokenizes the entire string at once for clarity and simplicity. If the string to be
parsed is large and contains something besides a Playspec at the beginning, or the Playspec is followed by a
substantial amount of text, this could be inefficient. If this is important to your use case, you should
redefine the TokenStream
type to contain the current token and the unparsed remainder of
the string, and reimplement Parser.tokenize
, Parser.advance
,
Parser.currentToken
, and Parser.remainder
to tokenize as
needed.
From the standpoint of Playspecs, the external API calls into the Parser to produce a parse tree of a Playspec from a string and a game-specific Context. The Context includes, among other things, data structures which teach the Parser how to parse game-specific syntax for atomic formulae (and, later, to give semantics to such syntax). Specifically, the Context has a so-called Token List represented as a JavaScript object. The schema of individual Token Definitions of that list is roughly:
... tokens: [ { type: "TOKEN_TYPE_A", match: Regex | String | [String], // The following fields are optional. [value: (MatchResult)=>any /* MatchResult is like the result of RegExp.exec() */,] [tightness: int,] [startParse: (Parser, Token)=>any /* Token is of type TOKEN_TYPE_A. Pratt's "nud". */ ,] [extendParse: (Parser, ParseTree, Token)=>any /* Token is of type TOKEN_TYPE_A. Pratt's "led". */ ] }, ... ] ...
Parser constructs its internal data structures from this schema. For each value in the tokens list, it builds its own
Token Definition with the type
, match
, etc. During tokenization,
each Token Definition is tried in turn (before Parser's internal Token List). If a token's
match
regular expression succeeds (or if its constant string or array of constant string is
a prefix of the string), a Token object will be built with the corresponding type and the matched range of the
stream. Its value
will default to the matched string, but if the Token Definition defines a
value
field then the result of that function will be used instead.
In general, there is a trade-off between using complicated token match expressions and doing more parsing later on.
In the PuzzleScript case, the language of patterns is matched easily by regular expressions and the token contains
all the necessary information to check properties (in fact, we reuse most of PuzzleScript's own parsing machinery
after we obtain e.g. a rule pattern). In Prom Week, the query syntax can be used in a variety of different
contexts so it makes sense to separate the parsing into several steps which compose better. One important
consideration is conflicts with the core Playspecs syntax: If a custom syntax wanted to recognize a .
token, care must be taken to ensure it is not matched instead of the built-in ...
(e.g., with a regular
expression like /\.(?!\.)/
). Such conflicts will generally be evident at the time of use. Pratt parsing
is token-directed, and since this reference implementation does not support extending existing tokens'
startParse
and extendParse
functions, ambiguities are best avoided.
Pratt parsers are modified recursive descent parsers that achieve great practical efficiency by being token-directed.
While the external API is parse(s:string):ParseResult
, each internal recursion goes through a
designated function parseExpression(tightness:number):ParseTree
, which starts by taking
the first token off of s
and calling its startParse
function, yielding an initial
ParseTree
(this call might itself recurse into parseExpression
). Having the initial tree
in hand, it now tries to extend this tree as far as operator precedence allows (according to the
tightness
parameter). parseExpression
repeatedly looks at the now-current token (since
startParse
might have consumed tokens on its own, this may not be the immediately subsequent token to
the first one) and verifies that its tightness
is greater than that of the call to
parseExpression
: in other words, whether this operator binds tighter than the operator just above the
call to parseExpression
. If so, that operator's extendParse
function is called (again, it
might recurse into parseExpression
, possibly with a different tightness
) and the current
parse tree is replaced by its result. If not, the current parse tree is returned.
ParseTree
nodes are constructed via the Parser.node
method, which implementations of
startParse
and extendParse
should call. Character ranges are associated with each node
based on the consumed tokens. Errors are a special kind of parse node constructed by Parser.error
, and
they are placed into the tree where the error occurs. They are also recorded in the errors
field of
Parser
and returned along with the final parse tree and the remainder of the string. If a token is in
a parse-starting or parse-extending position but does not define startParse
or extendParse
(respectively), the default behavior is to signal a syntax error.
One common source of parse errors is that the logical connectives and
, or
, and
not
may only be combined with other propositional terms: i.e., other logical connectives, custom
game-specific terms, true
, false
, start
, end
, and parenthesized
groups which only contain a propositional term. Such errors are detected at parse time, although some others
cannot be detected until compile time.


This inspection reports instances of JavaScript if statements which
can be simplified to single assignment or
return statements. For example:
if(foo()) { return true; } else { return false; }can be simplified to return foo(); |
| Powered by InspectionJS |


“Ayamāhu purāṇiyā kuṭi,
Aññaṁ patthayase navaṁ kuṭiṁ;
Āsaṁ kuṭiyā virājaya,
Dukkhā bhikkhu puna navā kuṭī”ti.
… Dutiyakuṭivihāritthero ….
This was your old hut,
but you still want a new hut.
Let go of hope for a hut, monk!
A new hut will only bring more suffering.


LEGEND A film review by Shane R. Burridge Copyright 1995 Shane R. Burridge
1985 90 mins
Forest-dwelling boy (Tom Cruise) takes pure-of-heart princess (Mia Sara) to see unicorns and then loses them to Dark Lord underground. With help from faerie folk of the wood, boy mounts rescue attempt. Slight story is probably why Ridley Scott's 1985 fantasy was dismissed as an overblown jumble, but that's no reason not to enjoy the sumptuous vision he brings to the screen. Some may be spellbound by this film--others will find it tedious. You'll be excused for thinking from the start that Tolkien had hired him to make a TV commercial for Middle-Earth.
The thinly-drawn characters of Jack (surely Cruise's oddest role) and Lili are obvious targets for criticism, but Scott is less interested in unraveling a plot than he is in capturing the look and feel of myth. Lili (a.k.a. Lily, the symbol of virginity) and Jack (whose name appears most frequently in folk tales and nursery rhymes) are character types, not character studies. We are never told what Lili is the princess of; we have no idea what Jack does in the forest. Everything is generic, nothing has a definite location in time or place. But I'm glad Scott didn't include any phoney-baloney character development just because he was making a film in the progressive 80's. He presents LEGEND in exactly the manner that the title would suggest--as a universally recognized Jungian consciousness, where archetypes replace personalities. Unfortunately, this is to the expense of Tim Curry's Dark Lord, the most memorable character in the movie, who proves once again that the devil gets all the best lines. Curry steals the show with his performance--it's a shame he doesn't get a chance to do more in the picture. Scott sidesteps his uninvolving characters by keeping his frame as busy as possible. Never in my life have I seen so much "billowing" in a movie before--leaves, snow, smoke, petals, steam--Scott keeps the air alive (I think he's going overboard when he introduces soap bubbles) to remind us that the story is steeped in a magical atmosphere, and also to highlight the sentience of air as an elemental force. The other three elements of ancient cosmology--fire, water, and earth--are similarly dominant throughout the story: Jack swims underwater, breaks through ice, is covered by snow, journeys into the earth, battles through steam and fire, and even harnesses the power of the sun.
Picture is gorgeously photographed, and invokes a dreamlike world. Jerry Goldsmith's score functions so well it's hard to imagine watching this film without it--yet it was unceremoniously dumped during re-editing for the U.S. release and replaced by a synthesizer soundtrack. Catch the Goldsmith version if you can. Film was cut heavily before release, and it shows.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Video: Furious Arsenal fan throws his season ticket away after another poor result
by Tom McNeilIt really does show how badly things are going for Arsenal when they needed a late goal to salvage a point at home to Southampton. This is the same team that lost 9-0 to Leicester not that long ago.
You have to think that Unai Emery needs to go after this one. It’s not just the results, the energy seems to being sucked out of Arsenal and the fans have clearly turned on him.
READ MORE: Aubameyang sends supportive message to Xhaka in Arsenal programme
AFTV ( formerly known as Arsenal fan TV ) has thrown up some “interesting” content in the past. This is actually one of the funnier moments, as a fan throws his season ticket away in disgust:
What makes this particular clip particularly funny is the sudden way he just launches the thing over his shoulder.
It’s changed days where you used to have a book full of tickets, it’s likely that he’s just thrown away an electronic card that will still be perfectly usable. He will end up getting it back in the post in the next few days, but it was fully worth it for the effect.


What you need to make this cool lander!
This uses a free bootstrap 4 theme for the overall layout! Get familiar with the Bootstrap framework. You should be comfortable with HTML. Start fiddling with bootstrap components and grids
Knowledge of CSS lets you take any framework and make it yours. Learn CSS 3 properties such as transitions and animations. A must have skill for any serious web developer to lift your site above the competition.
The quick quote summary uses javascript / ES6 to calculate the quote based on the user input. Knowledge of javascript is the most sought after front end development skill employers look for and is the most popular programming language in the world.
What are you waiting for? You can learn about these topics on my website and in the following sections below, I will show you step by step how to create this cool lander from scratch! (Stay tuned!)
Looking for someone to build you a custom website?
Contact: admin@dash-intel.com


Apache ServiceComb (incubating) Meetup -Shanghai 2018.10.12
Apache ServiceComb Meetup
Meeting Date/time: Friday, October 12, 2018, 13:30-16:50 PM
Meeting Address:Shanghai World Expo Exhibition & Convention Center, Room 416-417
Live Online: Link
《The Apache Way》
Time:13:30-14:30
Summary: The Apache Software Foundation (ASF) is the world’s largest open source foundation with more than 300 open source projects, covering big data, cloud computing, servers and web frameworks, build management, content management, IoT, and more areas. Three experts from ASF will talk about the history and development of the ASF, its operation model, and will elaborate on the core ideas of the ASF – The Apache Way.This session will also cover the compliance rules and their meanings for Apache projects. And introduce the Apache Incubator project and its standard procedures. The presenter will talk about how open-source projects can develop into communities through the Apache Incubator project, and how these projects can eventually become top projects.
Speaker: Roman Shaposhnik is a renowned expert and consultant in corporate OpenSource and Digital Transformation. He serves on Apache Software Foundation board of directors. In the past, Roman played a key role in shaping the Open Source collaboration under the umbrella of Linux Foundation and held leadership roles at Pivotal focusing on big data and application management platforms. His work at Pivotal brought him to the nexus between container technologies, virtualization and unikernel architectures. He is an active member of Apache Software Foundation’s Incubator where he mentors and directly contributes to a wide range of Apache Software Foundation projects.
Speaker: Justin Mclean, chairman of the Apache IPMC, and an Apache member; more than 20 years of experience in IT software development, areas of expertise include IoT, web-based application development, database design, and open source hardware; has spoken at more than 50 conferences worldwide.
Speaker: Craig Russell, an Apache member and Apache IPMC member, specialized in database APIs, Java and JavaScript ORM architect for Sun and Oracle. Craig joined the Apache Software Foundation in 2005 and has been the Apache Secretary and ambassador since 2010.
《Latest Roadmap of the Apache ServiceComb(incubating) Community》
Time: 14:40-15:00
Summary: This session will introduce the overall evolutionary roadmap of technology for the ServiceComb project and its latest development within the Apache Software Foundation.
Speaker: Jiang Ning, an Apache member, Huawei microservices expert, open source expert, former principle engineer of Red Hat, has 15 years of experience in developing enterprise-class open source middleware. He is engaged in Apache open-source middleware projects since 2006, including Apache CXF, Apache Camel, and Apache ServiceComb; and in-depth research on microservices architectures, Web Services, Enterprise Integration Patterns, SOA, and OSGi.
《ServiceComb’s Exploration of Service Mesh》
Time: 15:00-15:20
Summary: This session will introduce the efforts made by ServiceComb to stay compatible with the Service Mesh ecosystem. The presenter will talk about ServiceComb’s exploration into hybrid deployment and governance of non-intrusive microservices and those based on a traditional architecture.
Speaker : Tian Xiaoliang is a HUAWEI CLOUD microservices architect and a leader of Service Mesh technology. He was the former monitoring platform architect for SVoice of Samsung, worked on O&M and development solutions for SVoice. He has rich experience in PaaS, O&M and development, and APM; has spoken at multiple open source conferences including LC3 and ArchSummit.
《Practice Sharing: Hybrid Deployment of Service Mesh-based Microservices and Those Based on a Traditional Architecture》
Time: 15:20-15:40 Summary: Harmonizing non-intrusive microservices (Service Mesh being a shining example) with those based on traditional architectures to enable hybrid deployment and collaborated governance is no easy task. By drawing on practices from his own industry, the presenter will talk about how to use ServiceComb and Mesher to enable hybrid microservices deployment using different architectures, problems encountered, and some solutions to them.
Speaker : Miao Yanjie is the chief architect in MedSci and has years of experience in cloud computing development for the healthcare industry.
《Journey to Becoming an Apache Committer》
Time: 15:50-16:10
Summary: As a new PMC (Project Management Committee) member and committer to ServiceComb, the presenter will talk about his own experience participating in the ServiceComb project, lessons learned and achievements made.
Speaker: Feng Zheng, a software engineer, joined Red Hat Software in 2009. He is specialized in transaction manager; and is one of the core developers for Narayan and Blacktie projects. He worked on implementation of the distributed transaction part of Apache ServiceComb since 2017; optimized exception and timeout test scenarios by using tools like Byteman. He has in-depth research on distributed transaction processing and transaction processing in microservice environments.
《Hands-on Experience with Fast Microservices Programming》
Time: 16:10-16:50
Summary: The presenter will demonstrate how to use ServiceComb Java Chassis and Mesher of Service Mesh to quickly program microservices applications, and how to quickly deploy them by using Kubernetes. Audience can gain some hands-on experiences at the same time.
Speaker: Ju Zhen, a Huawei software engineer and Go language developer, is specialized in cloud computing, container technology, and DevOps. He is one of the core developers of container image services for HUAWEI CLOUD, and is mainly engaged in Huawei open source project development. He is a co-translator of The Docker Book and is an advocate of technologies like O&M automation, microservices, and Service Mesh. He gave speeches at Container Days 2017 and KubeCon 2017.
Please checkout ServiceComb Gitter if you are interesting about visiting the booth of ServiceComb during the Meetup ServiceComb Gitter
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Python, C#, Arduino, Azure, AWS
Unity, Cloud Native, Git, Linux
Arduino, Raspberry Pi, Circuits and Soldering


Word choice is important, a fact not lost on famed Norse explorer Eric the Red. When he discovered a semi-inhabitable landmass northwest of his native Iceland, he knew he needed a snappy name to attract settlers. How else would you get people to move to a giant, rocky ice slab roughly the size of the midwestern United States?
And so he dubbed it “Greenland” and tapped into the verdant dreams of his people, thereby successfully pulling off the greatest bait-and-switch ever.
And while some word choices can birth a nation, others have the power to elevate (a Subway “sandwich artist”) or deride (literally any racial slur). But many words are more nuanced in how we use them and how they affect both people, or an environment. I’ve written before about seven words you can’t say to client, but there’s one phrase that I constantly hear that just plain bothers me: “you guys.”
Informally, guys is a catchall for a group of people, regardless of sex. But, used singly, it’s a male noun. So if you extrapolate, using “guys” for a mixed gender group paints everyone with a thick enamel of masculinity. Using this in a professional setting is a bad habit that I encourage you to break.
Even when I am only addressing a group of men, I try to not use that phrase, swapping in everyone or everybody instead. With every use of “you guys,” neural pathways are built that groups of people are called “guys.” Plus, it can imbue a work environment with an undercurrent of manliness that excludes anyone who isn’t. Professional environments can really suffer from the common use of such an excluding phrase, even if the sentiment behind it is as innocent as an ‘80s catchphrase.
The best way to build a team is to be as inclusive as possible, and the language we use can quickly alienate someone who doesn’t fit. This is true for idioms and conversation topics as much as for collective terms. If you’ve ever been in a work situation that you cannot leave and the conversation topic turns to something don’t know or care about—sports, comics, movies, TV, kids, cats—you tune out almost immediately. Don’t do this to your coworkers; bring everyone in as much as you can.
It takes some mental rewiring to drop the phrase, but it’s well worth it. What do you have against the word “everybody” anyhow?
P.S. Inclusive language doesn’t just apply to “guys”.


What percentage of a country's total electricity output is
renewable?
Countries with a Total Electricity Output of at least
0
100k
200k
500k
1MM
GWh in 2015


A small but powerful XML element tree with the necessary readers. Currently used by the DocBook2wiki converter.


"""Get epochs from mne.Raw
"""
# Authors: Hubert Banville<EMAIL_ADDRESS># Lukas Gemein<EMAIL_ADDRESS># Simon Brandt<EMAIL_ADDRESS># David Sabbagh<EMAIL_ADDRESS># Henrik Bonsmann<EMAIL_ADDRESS># Ann-Kathrin Kiessner<EMAIL_ADDRESS># Vytautas Jankauskas<EMAIL_ADDRESS># Dan Wilson<EMAIL_ADDRESS># Maciej Sliwowski<EMAIL_ADDRESS># Mohammed Fattouh<EMAIL_ADDRESS>#
# License: BSD (3-clause)
import warnings
import numpy as np
import mne
import pandas as pd
from joblib import Parallel, delayed
from ..datasets.base import WindowsDataset, BaseConcatDataset
# XXX it's called concat_ds...
[docs]def create_windows_from_events(
concat_ds, trial_start_offset_samples=0, trial_stop_offset_samples=0,
window_size_samples=None, window_stride_samples=None,
drop_last_window=False, mapping=None, preload=False,
drop_bad_windows=True, picks=None, reject=None, flat=None,
on_missing='error', accepted_bads_ratio=0.0, n_jobs=1, verbose='error'):
"""Create windows based on events in mne.Raw.
This function extracts windows of size window_size_samples in the interval
[trial onset + trial_start_offset_samples, trial onset + trial duration +
trial_stop_offset_samples] around each trial, with a separation of
window_stride_samples between consecutive windows. If the last window
around an event does not end at trial_stop_offset_samples and
drop_last_window is set to False, an additional overlapping window that
ends at trial_stop_offset_samples is created.
Windows are extracted from the interval defined by the following::
trial onset +
trial onset duration
|--------------------|------------------------|-----------------------|
trial onset - trial onset +
trial_start_offset_samples duration +
trial_stop_offset_samples
Parameters
----------
concat_ds: BaseConcatDataset
A concat of base datasets each holding raw and description.
trial_start_offset_samples: int
Start offset from original trial onsets, in samples. Defaults to zero.
trial_stop_offset_samples: int
Stop offset from original trial stop, in samples. Defaults to zero.
window_size_samples: int | None
Window size. If None, the window size is inferred from the original
trial size of the first trial and trial_start_offset_samples and
trial_stop_offset_samples.
window_stride_samples: int | None
Stride between windows, in samples. If None, the window stride is
inferred from the original trial size of the first trial and
trial_start_offset_samples and trial_stop_offset_samples.
drop_last_window: bool
If False, an additional overlapping window that ends at
trial_stop_offset_samples will be extracted around each event when the
last window does not end exactly at trial_stop_offset_samples.
mapping: dict(str: int)
Mapping from event description to numerical target value.
preload: bool
If True, preload the data of the Epochs objects. This is useful to
reduce disk reading overhead when returning windows in a training
scenario, however very large data might not fit into memory.
drop_bad_windows: bool
If True, call `.drop_bad()` on the resulting mne.Epochs object. This
step allows identifying e.g., windows that fall outside of the
continuous recording. It is suggested to run this step here as otherwise
the BaseConcatDataset has to be updated as well.
picks: str | list | slice | None
Channels to include. If None, all available channels are used. See
mne.Epochs.
reject: dict | None
Epoch rejection parameters based on peak-to-peak amplitude. If None, no
rejection is done based on peak-to-peak amplitude. See mne.Epochs.
flat: dict | None
Epoch rejection parameters based on flatness of signals. If None, no
rejection based on flatness is done. See mne.Epochs.
on_missing: str
What to do if one or several event ids are not found in the recording.
Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.
accepted_bads_ratio: float, optional
Acceptable proportion of trials with inconsistent length in a raw. If
the number of trials whose length is exceeded by the window size is
smaller than this, then only the corresponding trials are dropped, but
the computation continues. Otherwise, an error is raised. Defaults to
0.0 (raise an error).
n_jobs: int
Number of jobs to use to parallelize the windowing.
verbose: bool | str | int | None
Control verbosity of the logging output when calling mne.Epochs.
Returns
-------
windows_datasets: BaseConcatDataset
Concatenated datasets of WindowsDataset containing the extracted windows.
"""
_check_windowing_arguments(
trial_start_offset_samples, trial_stop_offset_samples,
window_size_samples, window_stride_samples)
# If user did not specify mapping, we extract all events from all datasets
# and map them to increasing integers starting from 0
infer_mapping = mapping is None
mapping = dict() if infer_mapping else mapping
infer_window_size_stride = window_size_samples is None
list_of_windows_ds = Parallel(n_jobs=n_jobs)(
delayed(_create_windows_from_events)(
ds, infer_mapping, infer_window_size_stride,
trial_start_offset_samples, trial_stop_offset_samples,
window_size_samples, window_stride_samples, drop_last_window,
mapping, preload, drop_bad_windows, picks, reject, flat,
on_missing, accepted_bads_ratio, verbose) for ds in concat_ds.datasets)
return BaseConcatDataset(list_of_windows_ds)
[docs]def create_fixed_length_windows(
concat_ds, start_offset_samples=0, stop_offset_samples=None,
window_size_samples=None, window_stride_samples=None, drop_last_window=None,
mapping=None, preload=False, drop_bad_windows=True, picks=None,
reject=None, flat=None, targets_from='metadata', last_target_only=True,
on_missing='error', n_jobs=1, verbose='error'):
"""Windower that creates sliding windows.
Parameters
----------
concat_ds: ConcatDataset
A concat of base datasets each holding raw and description.
start_offset_samples: int
Start offset from beginning of recording in samples.
stop_offset_samples: int | None
Stop offset from beginning of recording in samples. If None, set to be
the end of the recording.
window_size_samples: int | None
Window size in samples. If None, set to be the maximum possible window size, ie length of
the recording, once offsets are accounted for.
window_stride_samples: int | None
Stride between windows in samples. If None, set to be equal to winddow_size_samples, so
windows will not overlap.
drop_last_window: bool | None
Whether or not have a last overlapping window, when windows do not
equally divide the continuous signal. Must be set to a bool if window size and stride are
not None.
mapping: dict(str: int)
Mapping from event description to target value.
preload: bool
If True, preload the data of the Epochs objects.
drop_bad_windows: bool
If True, call `.drop_bad()` on the resulting mne.Epochs object. This
step allows identifying e.g., windows that fall outside of the
continuous recording. It is suggested to run this step here as
otherwise the BaseConcatDataset has to be updated as well.
picks: str | list | slice | None
Channels to include. If None, all available channels are used. See
mne.Epochs.
reject: dict | None
Epoch rejection parameters based on peak-to-peak amplitude. If None, no
rejection is done based on peak-to-peak amplitude. See mne.Epochs.
flat: dict | None
Epoch rejection parameters based on flatness of signals. If None, no
rejection based on flatness is done. See mne.Epochs.
on_missing: str
What to do if one or several event ids are not found in the recording.
Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.
n_jobs: int
Number of jobs to use to parallelize the windowing.
verbose: bool | str | int | None
Control verbosity of the logging output when calling mne.Epochs.
Returns
-------
windows_datasets: BaseConcatDataset
Concatenated datasets of WindowsDataset containing the extracted windows.
"""
stop_offset_samples, drop_last_window = _check_and_set_fixed_length_window_arguments(
start_offset_samples, stop_offset_samples, window_size_samples, window_stride_samples,
drop_last_window)
# check if recordings are of different lengths
lengths = np.array([ds.raw.n_times for ds in concat_ds.datasets])
if (np.diff(lengths) != 0).any() and window_size_samples is None:
warnings.warn('Recordings have different lengths, they will not be batch-able!')
if any(window_size_samples > lengths):
raise ValueError(f'Window size {window_size_samples} exceeds trial '
f'duration {lengths.min()}.')
list_of_windows_ds = Parallel(n_jobs=n_jobs)(
delayed(_create_fixed_length_windows)(
ds, start_offset_samples, stop_offset_samples, window_size_samples,
window_stride_samples, drop_last_window, mapping, preload,
drop_bad_windows, picks, reject, flat, targets_from, last_target_only,
on_missing, verbose) for ds in concat_ds.datasets)
return BaseConcatDataset(list_of_windows_ds)
def _create_windows_from_events(
ds, infer_mapping, infer_window_size_stride,
trial_start_offset_samples, trial_stop_offset_samples,
window_size_samples=None, window_stride_samples=None,
drop_last_window=False, mapping=None, preload=False,
drop_bad_windows=True, picks=None, reject=None, flat=None,
on_missing='error', accepted_bads_ratio=0.0, verbose='error'):
"""Create WindowsDataset from BaseDataset based on events.
Parameters
----------
ds : BaseDataset
Dataset containing continuous data and description.
infer_mapping : bool
If True, extract all events from all datasets and map them to
increasing integers starting from 0.
infer_window_size_stride : bool
If True, infer the stride from the original trial size of the first
trial and trial_start_offset_samples and trial_stop_offset_samples.
See `create_windows_from_events` for description of other parameters.
Returns
-------
WindowsDataset :
Windowed dataset.
"""
# catch window_kwargs to store to dataset
window_kwargs = [
(create_windows_from_events.__name__, _get_windowing_kwargs(locals())),
]
if infer_mapping:
unique_events = np.unique(ds.raw.annotations.description)
new_unique_events = [x for x in unique_events if x not in mapping]
# mapping event descriptions to integers from 0 on
max_id_mapping = len(mapping)
mapping.update(
{v: k + max_id_mapping for k, v in enumerate(new_unique_events)}
)
events, events_id = mne.events_from_annotations(ds.raw, mapping)
onsets = events[:, 0]
# Onsets are relative to the beginning of the recording
filtered_durations = np.array(
[a['duration'] for a in ds.raw.annotations
if a['description'] in events_id]
)
stops = onsets + (filtered_durations * ds.raw.info['sfreq']).astype(int)
# XXX This could probably be simplified by using chunk_duration in
# `events_from_annotations`
last_samp = ds.raw.first_samp + ds.raw.n_times
if stops[-1] + trial_stop_offset_samples > last_samp:
raise ValueError(
'"trial_stop_offset_samples" too large. Stop of last trial '
f'({stops[-1]}) + "trial_stop_offset_samples" '
f'({trial_stop_offset_samples}) must be smaller than length of'
f' recording ({len(ds)}).')
if infer_window_size_stride:
# window size is trial size
if window_size_samples is None:
window_size_samples = stops[0] + trial_stop_offset_samples - (
onsets[0] + trial_start_offset_samples)
window_stride_samples = window_size_samples
this_trial_sizes = (stops + trial_stop_offset_samples) - (
onsets + trial_start_offset_samples)
# Maybe actually this is not necessary?
# We could also just say we just assume window size=trial size
# in case not given, without this condition...
# but then would have to change functions overall
# to deal with varying window sizes hmmhmh
assert np.all(this_trial_sizes == window_size_samples), (
'All trial sizes should be the same if you do not supply a window '
'size.')
description = events[:, -1]
i_trials, i_window_in_trials, starts, stops = _compute_window_inds(
onsets, stops, trial_start_offset_samples,
trial_stop_offset_samples, window_size_samples,
window_stride_samples, drop_last_window, accepted_bads_ratio)
events = [[start, window_size_samples, description[i_trials[i_start]]]
for i_start, start in enumerate(starts)]
events = np.array(events)
if any(np.diff(events[:, 0]) <= 0):
raise NotImplementedError('Trial overlap not implemented.')
description = events[:, -1]
metadata = pd.DataFrame({
'i_window_in_trial': i_window_in_trials,
'i_start_in_trial': starts,
'i_stop_in_trial': stops,
'target': description})
# window size - 1, since tmax is inclusive
mne_epochs = mne.Epochs(
ds.raw, events, events_id, baseline=None, tmin=0,
tmax=(window_size_samples - 1) / ds.raw.info['sfreq'],
metadata=metadata, preload=preload, picks=picks, reject=reject,
flat=flat, on_missing=on_missing, verbose=verbose)
if drop_bad_windows:
mne_epochs.drop_bad()
windows_ds = WindowsDataset(mne_epochs, ds.description)
# add window_kwargs and raw_preproc_kwargs to windows dataset
setattr(windows_ds, 'window_kwargs', window_kwargs)
kwargs_name = 'raw_preproc_kwargs'
if hasattr(ds, kwargs_name):
setattr(windows_ds, kwargs_name, getattr(ds, kwargs_name))
return windows_ds
def _create_fixed_length_windows(
ds, start_offset_samples, stop_offset_samples, window_size_samples,
window_stride_samples, drop_last_window, mapping=None, preload=False,
drop_bad_windows=True, picks=None, reject=None, flat=None, targets_from='metadata',
last_target_only=True, on_missing='error', verbose='error'):
"""Create WindowsDataset from BaseDataset with sliding windows.
Parameters
----------
ds : BaseDataset
Dataset containing continuous data and description.
See `create_fixed_length_windows` for description of other parameters.
Returns
-------
WindowsDataset :
Windowed dataset.
"""
# catch window_kwargs to store to dataset
window_kwargs = [
(create_fixed_length_windows.__name__, _get_windowing_kwargs(locals())),
]
stop = ds.raw.n_times \
if stop_offset_samples is None else stop_offset_samples
# assume window should be whole recording
if window_size_samples is None:
window_size_samples = stop - start_offset_samples
if window_stride_samples is None:
window_stride_samples = window_size_samples
stop = stop - window_size_samples + ds.raw.first_samp
# already includes last incomplete window start
starts = np.arange(
ds.raw.first_samp + start_offset_samples,
stop + 1,
window_stride_samples)
if not drop_last_window and starts[-1] < stop:
# if last window does not end at trial stop, make it stop there
starts = np.append(starts, stop)
# get targets from dataset description if they exist
target = -1 if ds.target_name is None else ds.description[ds.target_name]
if mapping is not None:
# in case of multiple targets
if isinstance(target, pd.Series):
target = target.replace(mapping).to_list()
# in case of single value target
else:
target = mapping[target]
fake_events = [[start, window_size_samples, -1] for start in starts]
metadata = pd.DataFrame({
'i_window_in_trial': np.arange(len(fake_events)),
'i_start_in_trial': starts,
'i_stop_in_trial': starts + window_size_samples,
'target': len(fake_events) * [target]
})
# window size - 1, since tmax is inclusive
mne_epochs = mne.Epochs(
ds.raw, fake_events, baseline=None, tmin=0,
tmax=(window_size_samples - 1) / ds.raw.info['sfreq'],
metadata=metadata, preload=preload, picks=picks, reject=reject,
flat=flat, on_missing=on_missing, verbose=verbose)
if drop_bad_windows:
mne_epochs.drop_bad()
window_kwargs.append(
(WindowsDataset.__name__, {'targets_from': targets_from,
'last_target_only': last_target_only})
)
windows_ds = WindowsDataset(mne_epochs, ds.description, targets_from=targets_from,
last_target_only=last_target_only)
# add window_kwargs and raw_preproc_kwargs to windows dataset
setattr(windows_ds, 'window_kwargs', window_kwargs)
kwargs_name = 'raw_preproc_kwargs'
if hasattr(ds, kwargs_name):
setattr(windows_ds, kwargs_name, getattr(ds, kwargs_name))
return windows_ds
[docs]def create_windows_from_target_channels(
concat_ds, window_size_samples=None, preload=False, drop_bad_windows=True,
picks=None, reject=None, flat=None, n_jobs=1, last_target_only=True,
verbose='error'):
list_of_windows_ds = Parallel(n_jobs=n_jobs)(
delayed(_create_windows_from_target_channels)(
ds, window_size_samples, preload, drop_bad_windows, picks, reject,
flat, last_target_only, 'error', verbose) for ds in concat_ds.datasets)
return BaseConcatDataset(list_of_windows_ds)
def _create_windows_from_target_channels(
ds, window_size_samples, preload=False, drop_bad_windows=True, picks=None,
reject=None, flat=None, last_target_only=True, on_missing='error',
verbose='error'):
"""Create WindowsDataset from BaseDataset using targets `misc` channels from mne.Raw.
Parameters
----------
ds : BaseDataset
Dataset containing continuous data and description.
See `create_fixed_length_windows` for description of other parameters.
Returns
-------
WindowsDataset :
Windowed dataset.
"""
window_kwargs = [
(create_windows_from_target_channels.__name__, _get_windowing_kwargs(locals())),
]
stop = ds.raw.n_times + ds.raw.first_samp
target = ds.raw.get_data(picks='misc')
# TODO: handle multi targets present only for some events
stops = np.nonzero((~np.isnan(target[0, :])))[0]
stops = stops[(stops < stop) & (stops >= window_size_samples)]
stops = stops.astype(int)
# TODO: Make sure that indices are correct
fake_events = [[stop, window_size_samples, -1] for stop in stops]
metadata = pd.DataFrame({
'i_window_in_trial': np.arange(len(fake_events)),
'i_start_in_trial': stops - window_size_samples,
'i_stop_in_trial': stops,
'target': len(fake_events) * [target]
})
# window size - 1, since tmax is inclusive
mne_epochs = mne.Epochs(
ds.raw, fake_events, baseline=None,
tmin=-(window_size_samples - 1) / ds.raw.info['sfreq'],
tmax=0., metadata=metadata, preload=preload, picks=picks,
reject=reject, flat=flat, on_missing=on_missing, verbose=verbose)
if drop_bad_windows:
mne_epochs.drop_bad()
window_kwargs.append(
(WindowsDataset.__name__, {'targets_from': 'channels',
'last_target_only': last_target_only})
)
windows_ds = WindowsDataset(mne_epochs, ds.description, targets_from='channels',
last_target_only=last_target_only)
setattr(windows_ds, 'window_kwargs', window_kwargs)
kwargs_name = 'raw_preproc_kwargs'
if hasattr(ds, kwargs_name):
setattr(windows_ds, kwargs_name, getattr(ds, kwargs_name))
return windows_ds
def _compute_window_inds(
starts, stops, start_offset, stop_offset, size, stride,
drop_last_window, accepted_bads_ratio):
"""Compute window start and stop indices.
Create window starts from trial onsets (shifted by start_offset) to trial
end (shifted by stop_offset) separated by stride, as long as window size
fits into trial.
Parameters
----------
starts: array-like
Trial starts in samples.
stops: array-like
Trial stops in samples.
start_offset: int
Start offset from original trial onsets in samples.
stop_offset: int
Stop offset from original trial stop in samples.
size: int
Window size.
stride: int
Stride between windows.
drop_last_window: bool
Toggles of shifting last window within range or dropping last samples.
accepted_bads_ratio: float
Acceptable proportion of bad trials within a raw. If the number of
trials whose length is exceeded by the window size is smaller than
this, then only the corresponding trials are dropped, but the
computation continues. Otherwise, an error is raised.
Returns
-------
result_lists: (list, list, list, list)
Trial, i_window_in_trial, start sample and stop sample of windows.
"""
starts = np.array([starts]) if isinstance(starts, int) else starts
stops = np.array([stops]) if isinstance(stops, int) else stops
starts += start_offset
stops += stop_offset
if any(size > (stops-starts)):
bads_mask = size > (stops-starts)
min_duration = (stops-starts).min()
if sum(bads_mask) <= accepted_bads_ratio * len(starts):
starts = starts[np.logical_not(bads_mask)]
stops = stops[np.logical_not(bads_mask)]
warnings.warn(
f'Trials {np.where(bads_mask)[0]} are being dropped as the '
f'window size ({size}) exceeds their duration {min_duration}.')
else:
current_ratio = sum(bads_mask) / len(starts)
raise ValueError(f'Window size {size} exceeds trial duration '
f'({min_duration}) for too many trials '
f'({current_ratio * 100}%). Set '
f'accepted_bads_ratio to at least {current_ratio}'
'and restart training to be able to continue.')
i_window_in_trials, i_trials, window_starts = [], [], []
for start_i, (start, stop) in enumerate(zip(starts, stops)):
# Generate possible window starts with given stride between original
# trial onsets (shifted by start_offset) and stops
possible_starts = np.arange(start, stop, stride)
# Possible window start is actually a start, if window size fits in
# trial start and stop
for i_window, s in enumerate(possible_starts):
if (s + size) <= stop:
window_starts.append(s)
i_window_in_trials.append(i_window)
i_trials.append(start_i)
# If the last window start + window size is not the same as
# stop + stop_offset, create another window that overlaps and stops
# at onset + stop_offset
if not drop_last_window:
if window_starts[-1] + size != stop:
window_starts.append(stop - size)
i_window_in_trials.append(i_window_in_trials[-1] + 1)
i_trials.append(start_i)
# Update stops to now be event stops instead of trial stops
window_stops = np.array(window_starts) + size
if not (len(i_window_in_trials) == len(window_starts) == len(window_stops)):
raise ValueError(f'{len(i_window_in_trials)} == '
f'{len(window_starts)} == {len(window_stops)}')
return i_trials, i_window_in_trials, window_starts, window_stops
def _check_windowing_arguments(
trial_start_offset_samples, trial_stop_offset_samples,
window_size_samples, window_stride_samples):
assert isinstance(trial_start_offset_samples, (int, np.integer))
assert (isinstance(trial_stop_offset_samples, (int, np.integer)) or
(trial_stop_offset_samples is None))
assert isinstance(window_size_samples, (int, np.integer, type(None)))
assert isinstance(window_stride_samples, (int, np.integer, type(None)))
assert (window_size_samples is None) == (window_stride_samples is None)
if window_size_samples is not None:
assert window_size_samples > 0, (
"window size has to be larger than 0")
assert window_stride_samples > 0, (
"window stride has to be larger than 0")
def _check_and_set_fixed_length_window_arguments(start_offset_samples, stop_offset_samples,
window_size_samples, window_stride_samples,
drop_last_window):
"""Raises warnings for incorrect input arguments and will set correct default values for
stop_offset_samples & drop_last_window, if necessary.
"""
_check_windowing_arguments(
start_offset_samples, stop_offset_samples,
window_size_samples, window_stride_samples)
if stop_offset_samples == 0:
warnings.warn(
'Meaning of `trial_stop_offset_samples`=0 has changed, use `None` '
'to indicate end of trial/recording. Using `None`.')
stop_offset_samples = None
if start_offset_samples != 0 or stop_offset_samples is not None:
warnings.warn('Usage of offset_sample args in create_fixed_length_windows is deprecated and'
' will be removed in future versions. Please use '
'braindecode.preprocessing.preprocess.Preprocessor("crop", tmin, tmax)'
' instead.')
if window_size_samples is not None and window_stride_samples is not None and \
drop_last_window is None:
raise ValueError('drop_last_window must be set if both window_size_samples &'
' window_stride_samples have also been set')
elif window_size_samples is None and\
window_stride_samples is None and\
drop_last_window is False:
# necessary for following assertion
drop_last_window = None
assert (window_size_samples is None) == \
(window_stride_samples is None) == \
(drop_last_window is None)
return stop_offset_samples, drop_last_window
def _get_windowing_kwargs(windowing_func_locals):
input_kwargs = windowing_func_locals
input_kwargs.pop('ds')
windowing_kwargs = {k: v for k, v in input_kwargs.items()}
return windowing_kwargs


8/30/2020
Houston, TX, USA
Tags: Inner Peace, Mindfulness, Chi Kung, Zen, Meditation,
Yes, it is certainly possible to enjoy true inner peace.
Inner peace here is defined as a state in which a person does not feel overwhelmed by emotions, thoughts, stress or anxiety and feels a continuous sense of well-being, balance and joy. Such a state might seem like a fairy tale or unattainable fantasy to many people given how most people feel on a daily basis, but it really is an experience available to all.
Inner peace is not something to be attained, achieved or discovered; it’s within all of us.
What’s missing for most people is simply putting the time and attention necessary on their inner state to return to this state of peace. I phrase this as “return to” because feeling peace and joy is actually each person’s natural state that humans have collectively strayed from. Inner peace is not something to be attained, achieved or discovered; it’s within all of us. Rather than being like inventing the light bulb, it’s like a bright light bulb that already exists but is covered in so much dust and dirt than the light is not visible. All that’s necessary is to wipe away the dust and dirt and the light will shine again.
So much of returning to peace is simply letting go of that which prevents it: old grudges, insecurities, stagnant emotions, stress, negative thinking. People are often so caught up in all this noise that it’s considered normal to live in that state, but it doesn’t have to be that way.
Just as there are many paths of spiritual cultivation, there are many paths for this return to peace. While they may look different externally, at their essence all the paths will address the same fundamental components. To enjoy inner peace, a person must balance their emotions, vital energies and mental energies.
Balancing one’s emotions doesn’t mean a total annihilation of emotions. It means that a person feels inner peace and isn’t overrun with anger, sadness, guilt, resentment, happiness, jealousy, excitement, etc. Even positive emotions need to be balanced. For example, if a person attempts to maintain a high level of excitement all the time, it puts a strain on their inner state and it won’t allow for balance. When a person’s emotions are balanced, even if an emotion such as anger arises, the person is aware of it and can manage it appropriately. The emotion also won’t stay for very long because the person can easily let it go. This is connected to the concept of “emotional intelligence.”
“Vital energies” refers to what other languages call “chi”, “ki” or “prana”. Energy arts like chi kung focus on cleansing, building and nourishing a person’s vital energy system as a means of attaining good health, vitality and longevity. A person whose vital energy is balanced will have good health, vitality, plenty of energy for work and play and feel zestful.
“Mental energies” refers to thought patterns about oneself, about others, perspectives, perceptions, habits, and largely dictates our experience of the world. A person whose mental energies are imbalanced might be depressed, might always perceive themself as a victim, might have chronic negative thinking, might have unresolved past trauma, etc. These are still forms of energy and can be balanced through practices like therapy, counseling, mindfulness, chi kung and meditation.
The process of returning to peace is quite simple, though in practice it might not be easy. Fortuntely, at this point in time we have the benefit of being able to practice internal arts that have been developed and passed down over thousands of years, resulting in complete systems that guide the path of returning to peace. The arts that I have practiced since 2005 and, in my experience have been very effective in aiding the return to peace, have been Shaolin Cosmos Chi Kung, mindfulness, mantras and Zen meditation.
There is no better investment than in your own inner cultivation for the benefit of yourself and those around you.
It’s worth pointing out that starting the practice of each in the order listed is ideal, as it allows for a relative progression from cleansing out “lower level” rubbish like emotions and anxiety to “higher level” rubbish like deeply engrained mental patterns. These individual practices are explored more in-depth in other articles on this website.
If you’re at a point where you are ready to give up living in a state of suffering and want to return to peace, learning these practices from a competent teacher and practicing daily is an excellent path forward. Making the decision to make a change and getting started is always the most difficult step but once you experience just a glimpse of true inner peace your life witll change forever.
As with anything, some effort is necessary to attain the desired results, but there is no better investment than in your own inner cultivation for the benefit of yourself and those around you.


[§443B-13] Jurisdiction of courts. The various district courts of the State shall have concurrent jurisdiction with the circuit courts in all criminal prosecutions for violations of this chapter. [L 1987, c 191, pt of §1]


Before Change
register = QuantumRegister(self._num_qubits, name="q")
if circuit is None:
circuit = QuantumCircuit()
circuit.add_register(register)
for j in range(self._num_qubits):
for k in range(j):
lam = 1.0 * pi / float(2 ** (j - k))
After Change
register = QuantumRegister(self._num_qubits, name="q")
if circuit is None:
circuit = QuantumCircuit()
if not circuit.has_register(register):
circuit.add_register(register)
for j in range(self._num_qubits):
for k in range(j):
lam = 1.0 * pi / float(2 ** (j - k))
circuit.u1(lam / 2, register[j])


I'm {{ .Site.Params.author }}, a Molecular Biologist who dabbles in R.
I have 3 years of experience in plant genetic engineering in an academic lab and 1.5 years of experience in industry at Asuragen developing a novel PCR/CE diagnostic test. I currently work as a Research Assistant at Luna Genetics developing a new type of prenatal diagnostic test.
I graduated from the University of Texas in 2019 with a BS in Biology, Genetics and Genomics. I maintained a 4.0 GPA and received the highest academic honors every semester. I am well versed in NGS, PCR/CE, plasmid engineering, protein purification, and various other cell and molecular biology techniques.
Outside of molecular biology, I enjoy mountain biking, reading, and playing music.
Have a look at some of my working R projects.
For more check out my academic & professional resume.
Contact me by email.
{{ partial "social.html" . }}


Constructor
(protected) new Actor(actoropt) → {Actor}
Creates an Actor, which emits messages for other
Actors to subscribe to. You can supply certain parameters
for the actor, including key material [!!!] — be mindful of
what you share with others!
Parameters:
| Name | Type | Attributes | Description | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
actor |
Object |
<optional> |
Object to use as the actor.
Properties
|
Properties:
| Name | Type | Description |
|---|---|---|
id |
String | Unique identifier for this Actor (id === SHA256(preimage)). |
preimage |
String | Input hash for the `id` property (preimage === SHA256(ActorState)). |
- Source:
Fires:
- event:message Fabric Message objects.
Returns:
Instance of the Actor. Call Actor#sign to emit a Signature.
- Type
- Actor
Methods
(static) fromAny(input) → {Actor}
Create an Actor from a variety of formats.
Parameters:
| Name | Type | Description |
|---|---|---|
input |
Object | Target Object to create. |
- Source:
Returns:
Instance of the Actor.
- Type
- Actor
(static) randomBytes(countopt) → {Buffer}
Get a number of random bytes from the runtime environment.
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
count |
Number |
<optional> |
32 | Number of random bytes to retrieve. |
- Source:
Returns:
The random bytes.
- Type
- Buffer
_readObject(input) → {Object}
Parse an Object into a corresponding Fabric state.
Parameters:
| Name | Type | Description |
|---|---|---|
input |
Object | Object to read as input. |
- Source:
Returns:
Fabric state.
- Type
- Object
adopt(changes) → {Actor}
Explicitly adopt a set of JSONPatch-encoded changes.
Parameters:
| Name | Type | Description |
|---|---|---|
changes |
Array | List of JSONPatch operations to apply. |
- Source:
Returns:
Instance of the Actor.
- Type
- Actor
commit() → {String}
Resolve the current state to a commitment.
- Source:
Returns:
32-byte ID
- Type
- String
export() → {Object}
Export the Actor's state to a standard Object.
- Source:
Returns:
Standard object.
- Type
- Object
get(path) → {Object}
Retrieve a value from the Actor's state by JSONPointer path.
Parameters:
| Name | Type | Description |
|---|---|---|
path |
String | Path to retrieve using JSONPointer. |
- Source:
Returns:
Value of the path in the Actor's state.
- Type
- Object
pause() → {Actor}
Toggles `status` property to paused.
- Source:
Returns:
Instance of the Actor.
- Type
- Actor
serialize() → {String}
Serialize the Actor's current state into a JSON-formatted string.
- Source:
Returns:
- Type
- String
set(path, value) → {Object}
Set a value in the Actor's state by JSONPointer path.
Parameters:
| Name | Type | Description |
|---|---|---|
path |
String | Path to set using JSONPointer. |
value |
Object | Value to set. |
- Source:
Returns:
Value of the path in the Actor's state.
- Type
- Object
sign() → {Actor}
Signs the Actor.
- Source:
Returns:
- Type
- Actor
toBuffer() → {Buffer}
Casts the Actor to a normalized Buffer.
- Source:
Returns:
- Type
- Buffer
toGenericMessage() → {Object}
Casts the Actor to a generic message.
- Source:
Returns:
Generic message object.
- Type
- Object
toObject() → {Object}
Returns the Actor's current state as an Object.
- Source:
Returns:
- Type
- Object
unpause() → {Actor}
Toggles `status` property to unpaused.
- Source:
Returns:
Instance of the Actor.
- Type
- Actor
value(formatopt) → {Object}
Get the inner value of the Actor with an optional cast type.
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
format |
String |
<optional> |
object | Cast the value to one of: `buffer, hex, json, string` |
- Source:
Returns:
Inner value of the Actor as an Object, or cast to the requested `format`.
- Type
- Object


{% extends "base.html" %} {% block title %}Welcome to BILS Tools Portal{% endblock title %} {% block content_title %}Welcome to BILS Tools Portal{% endblock content_title %} {% block content %}
BILS Tools Portal is a portal that provides easy and secure access to bioinformatic calculation.
This server is currently being tested. Content will appear here once the testing phase is completed. {% endblock content %}


tlmgr restore
Restore package backups created with `tlmgr backup`.
The default backup directory is specified by the `backupdir` option, and can be obtained with `tlmgr option`.
More information: home page
- List all available backup revisions for all packages:
tlmgr restore
- List all available backup revisions for a specific package:
tlmgr restore package
- Restore a specific revision of a specific package:
tlmgr restore package revision
- Restore the latest revision of all backed-up packages:
tlmgr restore --all
- Restore a package from a custom backup directory:
tlmgr restore package revision --backupdir path/to/backup_directory
- Perform a dry-run and print all taken actions without making them:
tlmgr restore --dry-run package revision


JSON is for ages becoming is widely used format to keep configuration, exchange and store data etc. Zebkit uses JSON:
Pure JSON is too basic and limited. Zebkit extends JSON interpretation to support number of advanced features. The new JSON interpretation is called Zson. Zson supports the following:
{ "a" : { "@RegExp" : [ "[a-z]+", "i" ] } }
{ "test": 100 }
{ "a": 12, "b": "%{a}" }
{ "a": { ".expr" : "10 + 10" } }
{ "a": "%{<img> http://test.com/picture.jpg}" }
Pay attention that external files are loaded asynchronously. You can load multiple external files recursively. Zebkit keeps the loadings in order. That means any subsequent field value can be a reference to a an externally loaded field. For instance:
{ "a" : 100,
"b" : { "c": { "@Date" : [] } },
"d" : "%{a}",
"e" : "%{<json> http://test.com/embedded.json}",
"f" : "%{e.m.k}"
}
Where “embedded.json” is:
{ "m" : { "k" : "Hello" } }
new Zson().then(function("config.json", conf) {
... // handle fully loaded json
});
Zson can be used to configure classes and class instances with required properties. The way to do it looks as follow:
var A = zebkit.Class([
function(a, b, c) { // constructor
this.a = a;
this.b = b;
this.c = c;
},
function setD(d) { // setter of property "d"
this.d = d;
}
]);
{
"d" : "value of property 'd'",
"c" : { "@Date": [] },
"c" : [ 1,2,3 ]
}
var a = new A();
// configure instance of class "A" with JSON
zebkit.Zson.then("config.json", a);


encryption module¶
-
class
storjnet.encryption.
E3x
(mynode)¶ Bases:
object
encryption class for handshake(message for the first time) and channel message. Almost same as https://github.com/telehash/telehash.org/blob/master/v3/e3x/cs/1a.md except using AES256-CTR/secp256k1 instead of AES-128-CTR/secp160r1
-
create_channel_keys
(received_key)¶ create channel key by encryption key: SHA256(secret, sent-KEY, received-KEY) / 2 decryption key: SHA256(secret, received-KEY, sent-KEY) / 2 where secret is derived from ECDH from ephemeral keys.
Parameters: received_key (bytes) – recived epheram key.
-
decrypt_channel_message
(msg)¶ decrypt class message structured:
Parameters: msg (bytes) – encrypted class message Returns: decrypted message
-
decrypt_handshake
(msg)¶ decrypt handshake message structured: decoded msg must be json str and key ‘public_key’ must be included.
Parameters: msg (bytes) – encrypted handshake message Returns: decrypted message
-
encrypt_channel_message
(msg)¶ encrypt channel message structured: TOKEN - 16 bytes, from the handshake, required for all channel packets IV - 4 bytes, incremented sequence INNER - the AES-256-CTR encrypted inner packet ciphertext HMAC - 4 bytes, the SHA-256 HMAC folded three times
Parameters: msg (bytes) – channel message Returns: encrypted bytes data
-
encrypt_handshake
(msg, remote_permanent_public_b64)¶ encrypt handshake message structured:
- KEY - 33 bytes, the sender’s ephemeral exchange public key in
- compressed format
IV - 4 bytes, a random but unique value determined by the sender INNER - the AES-256-CTR encrypted inner packet ciphertext HMAC - 4 bytes, the calculated HMAC of all of the previous
KEY+IV+INNER bytesParameters: - msg (bytes) – handshake message
- remote_permanent_public_b64 (bytes) – remote node pubkey
Returns: encrypted bytes data
-
-
class
storjnet.encryption.
IVCounter
(value)¶ Bases:
object
IV counter for AES Counter mode. just reply passed value padded 12bit 0 to right.
-
class
storjnet.encryption.
MyNode
(port, fname=None)¶ Bases:
object
pub/key pair that represents node info
-
get_from_file
(fname)¶
-
get_ip_address
(test_address=None)¶ try to get global IP address from interface information. if failed, just return ‘<IP_ADDRESS>’
Parameters: test_address (str) – ip address str if test to check global ip. normally None. Returns: global ip address if successed, or ‘<IP_ADDRESS>’
-
get_my_id
()¶ get hashname of my node from public key by base32 coding.
Returns: hashname bytes
-
get_my_location
(private=False)¶ get my location, including hashname, pubkey, ip:port
Parameters: private (bool) – if True, private key is included. Returns: json str meaning my location
-
get_private64
()¶ get base64 of private key for sending in JSON
Returns: base64 coded pubkey
-
get_public64
()¶ get base64 of public key for sending in JSON
Returns: base64 coded pubkey
-
save_to_file
(fname)¶
-
set_ip_address
(ip_address)¶ set (global) ip address
Parameters: ip_address (str) – IP address to be set
-
-
storjnet.encryption.
fold1
(dat)¶ The folding is a simple XOR of the lower half bytes with the upper ones. for converting 32bits->16bits
Parameters: dat (bytes) – 32 bytes data to be folded Returns: 16 bits folded data
-
storjnet.encryption.
fold3
(dat)¶ The folding is a simple XOR of the lower half bytes with the upper ones. for converting 32bits->4bits
Parameters: dat (bytes) – 32 bytes data to be folded Returns: 4 bits folded data
-
storjnet.encryption.
get_hashname
(public)¶ get hashname from a public key by base32 coding.
Returns: hashname bytes
-
storjnet.encryption.
get_sha256
(msg)¶ get sha256
Parameters: msg (bytes) – messages to be digested Returns: digested bytes


In order to operationalize the ABS Clearing-House, Parties need to nominate a single publishing authority to clear national records prior to publishing them in the ABS Clearing-House.
The registration of who has the authority to publish national records in the ABS Clearing-House can only be done by the Secretariat upon receipt of an official written communications addressed to the Executive Secretary and endorsed as follows by the focal point for the Intergovernmental Committee for the Nagoya Protocol (ICNP Focal Point). The duties of the ICNP Focal Point could also be expanded to incorporate the role of the publishing authority.
The designation of national autorised users can be done at any time by the ICNP Focal Point or the publishing authority. These designations may be sent to the Secretariat by postal mail, fax or as an attachment to an e mail and must contain the complete contact details and the appropriate endorsements.
Download Publishing Authority(PA) and National Authorized User(NAU) nomination form


|
jubilant-funicular
|
Collection of threads for running scheduled functions. More...
#include <ThreadPool.h>
Classes | |
| struct | worker |
| One thread available to the pool. More... | |
Public Member Functions | |
| ThreadPool (size_t num_threads) | |
| Constructs a ThreadPool with specified number of threads. | |
| void | schedule (const Thunk &thunk) |
| Schedules a function to be executed. | |
| void | wait () |
| Waits for all scheduled functions to finish execution. | |
| ThreadPool (const ThreadPool &)=delete | |
| ThreadPool & | operator= (const ThreadPool &)=delete |
Private Member Functions | |
| void | dispatcher () |
| Function run by dispatch thread. More... | |
| void | worker (size_t wid) |
| Function run by worker threads. | |
| size_t | getAvailableWorker () |
| Returns id of an available worker. | |
| bool | allWorkFinished () |
| Returns whether or not all work is finished. | |
Private Attributes | |
| struct { | |
| std::condition_variable_any | cv |
| std::mutex | m |
| } | m_empty |
| Used to keep track of when the pool is empty. | |
| std::vector< worker > | m_workers |
| The workers. | |
| std::atomic< bool > | m_kill |
| Sign to kill the pool. | |
| Semaphore | m_scheduled |
| Record of scheduled workers. | |
| Semaphore | m_available |
| Record of available workers. | |
| std::queue< Thunk > | m_funcs |
| The functions to be executed. | |
| std::thread | m_dispatch_thread |
| The thread for managing the pool. | |
Collection of threads for running scheduled functions.
Definition at line 35 of file ThreadPool.h.
|
private |
Function run by dispatch thread.
Definition at line 27 of file ThreadPool.cpp.
Referenced by ThreadPool().


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We kill Anathema from the command line.
Venator is a command-line-based utility suite for creating, manipulating and rendering Exalted 2.5e character sheets.
Venator is open source software available gratis under the MIT license. Another joyful frobnitz from Metal Fatigue LLC!
Exalted is a registered trademark of White Wolf Publishing, and all related game mechanics and data are © White Wolf Publishing. No challenge to any intellectual property rights of White Wolf Publishing or any other party is intended by its use in this website or the related software. It is not possible to use the Venator software and data files to play Exalted without the game books.


Chapter 21. MIME and Network Encodings
What
travels on a network are streams of bytes or text. However, what you
want to send over the network often has more structure. The
Multipurpose Internet Mail Extensions (MIME) and other encoding
standards bridge the gap by specifying how to represent structured
data as bytes or text. Python supports such encodings through many
library modules, such as base64,
quopri, uu, and the modules of
the email package. This chapter covers these
modules.
|


Binary search trees are able to efficiently traverse data
They are able to do all of the following:
- Ability 1: insert a key value with little to no effort
- Ability 2: determine whether a key value is in the tree
- Ability 3: remove a key value from the tree with little effort
- Ability 3: print all of the key values in sorted order


This is a page to collect a few particularly useful patterns and snippets that help you interact with your Kubernetes cluster and JupyterHub. If there’s something that you think is generic enough (and not obvious enough) to be added to this page, please feel free to make a PR!
kubectl
Kubernetes has a helper script that allows you to auto-complete commands and references to objects when using kubectl. This lets you TAB-complete and saves a lot of time.
Here are the instructions to install kubectl auto-completion.
helm
Helm also has an auto-completion script that lets you TAB-complete your commands when using Helm.
Here are the instructions to install helm auto-completion.
Oftentimes people manage multiple Kubernetes deployments at the same time. kubectl handles this with the idea of “contexts”, which specify which Kubernetes deployment you are referring to when you type kubectl get XXX.
kubectl get XXX
To see a list of contexts currently available to you, use the following command:
kubectl config get-contexts
This will list all of your Kubernetes contexts. You can select a particular context by entering:
kubectl config use-context <CONTEXT-NAME>
If you grow tired of typing namespace=XXX each time you type a kubernetes command, here’s a snippet that will allow you set a default namespace for a given Kubernetes context:
namespace=XXX
kubectl config set-context $(kubectl config current-context) \ --namespace=<YOUR-NAMESPACE>
The above command will only apply to the currently active context, and will allow you to skip the --namespace= part of your commands for this context.
--namespace=
Sometimes it’s useful to select an entire class of Kubernetes objects rather than referring to them by their name. You can attach an arbitrary set of labels to a Kubernetes object, and can then refer to those labels when searching with kubectl.
To search based on a label value, use the -l or --selector= keyword arguments. For example, JupyterHub creates a specific subset of labels for all user pods. You can search for all user pods with the following label query:
-l
--selector=
kubectl --namespace=<YOUR-NAMESPACE> get pod \ -l "component=singleuser-server"
For more information, see the Kubernetes labels and selectors page.
Sometimes the information that’s in the default output for kubectl get <XXX> is not enough for your needs, or isn’t structured the way you’d like. We recommend looking into the different Kubernetes output options, which can be modified like so:
kubectl get <XXX>
kubectl --namespace=<NAMESPACE> get pod -o <json|yaml|wide|name...>
You can find more information on what kinds of output you can generate at the kubectl information page. (click and search for the text “Output Options”)