{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% import "bootstrap/fixes.html" as fixes %} {% import "bootstrap/utils.html" as util %} {% block title %}How to Use BEL Commons{% endblock %} {% block content %}
BEL Commons hosts biological knowledge assemblies that are encoded in Biological Expression Language. BEL supports the assembly of context-specific qualitative causal and correlative relations between biological entities across multiple modes and scales in BEL Script with provenance information, external namespace references, relation provenance (citation and evidence), and relation metadata such as the biological context (anatomy, cell, disease, etc.).
On the home page, click Parse BEL. In the form, choose a file, then click "upload".
The link to the BEL Script used in this video can be found here.
Users can create projects that allow them to easily share networks with groups of other users. This is useful when multiple curators are uploading related BEL Scripts. Later, they can be assembled and queried.
Large terminologies that are curated for projects investigating new diseases and pathologies can be validated by checking their contents using the Ontology Lookup Service, provided by the EBI to identify duplicate names and enable better semantic integration.
From the home page, click List Networks, find your network, and select "Summarize".
This web application organizes high level statistical information about a network, such as the number of nodes, edges, author contributions, citation contributions, and provenance information as well as global network statistics such as the average node degree, network density, number of weakly connected components, etc. When appropriate, it proves feedback on syntax and semantics of the source BEL document to assist in curation.
Finally, the summary page provides an assessment of the "Biological Grammar", or the biological validity of statements. These analysis include identification of contradictory edges, unstable biological motifs in pairs and triplets of nodes, and other information that is inferred to be missing or incomplete.
A query contain three steps:
Finally, the results of queries can be summarized, downloaded in many formats, or explored.
The results of query can be explored interactively with the Biological Network Explorer. Its tools panel contains an extended query builder interface that can be used to apply additional transformations. Network algorithms can be readily applied to networks such as path searches, centrality calculations, and overlaying of external data. These data can come from differential expression experiments, or directly from the results of the Heat Diffusion workflow, which is explained below.
Data sets like differential gene expression can be used to quantify the perturbation amplitude of biological processes in a network using an randomized algorithm based on NPA. Candidate upstream mechanisms are generated for each biological process and a heat diffusion algorithm is used to quantify the cumulative observed effect of upstream genes and gene products based on the differential gene expression data.
This algorithm is general enough that other data types could be used, such as copy number variations on SNPs or clinical measurements of neuro-imaging features, which have been annotated to our Alzheimer Disease Knowledge assembly with NeuroMMSigDB.
Data sets can be directly uploaded and analyzed. The results of these experiments can then be directly overlaid to the interactive network viewer to provide a data-driven analysis of given networks or sub-networks.


[§329E-4] Medicaid coverage. The department of human services shall ensure that opioid antagonists for outpatient use are covered by the medicaid prescription drug program on the same basis as other covered drugs. [L 2016, c 68, pt of §2]


# -*- coding: utf-8 -*-
"""Module containing the emus object.
"""
import numpy as np
import argparse
import emusroutines as emr
import avar
# Default Parameters
#_default_kB = 1.9872041*10**-3 # Boltzmann Constant, given in kcal/mol
#_default_T = 310.0 # Default Temperature in Kelvin.
[docs]class emus:
"""Class containing methods and data for the EMUS algorithm. An EMUS object has the following data structures which can be interacted with or modified:
self.psis (3D array): array containing values of the biases in each state.
self.cv_trajs (2D Array): array containing the trajectories in cv space. None if not used.
self.z (1d array): array containing the normalization constants. Calculated according to the first iteration of EMUS.
self.F (2d array): F matrix for the first iteration of EMUS.
self.iats (1d array): array containing integrated autocorrelation times of :math:`\psi_{ii}(x)` in each window.
"""
def __init__(self,psis,cv_trajs=None,neighbors=None,k_B=1.9872041E-3):
"""Initialize the emus object.
Parameters
----------
psis : 3D data structure
Three dimensional data structure with the trajectories of the psi values. Expected to be either a three dimensional array or a list of two dimensional numpy arrays. element i,j,k corresponds to :math:`\psi_k` evaluated at timepoint j in state i. If neighborlists are used, then k is the index in the neighborlist, not the index in the overall structure.
cvtrajectories : 2D array-like, optional
Two dimensional data structure with the trajectories in cv space. The first dimension is the state where the data was collected, and the second is the value in cv space.
neighbors : 2D array-like, optional
Two dimensional data structure. The first dimension is the state index, and the second is the index of a neighboring state.
"""
self.psis = psis
if neighbors is not None: # Neighborlist Provided
self.neighbors = neighbors
else:
L = len(psis)
self.neighbors = np.outer(np.ones(L),np.arange(L)).astype(int)
self.cv_trajs = cv_trajs
z,F,iats = emr.emus_iter(self.psis,neighbors=self.neighbors,return_iats=True)
self.z = z
self.F = F
self.iats = iats
[docs] def calc_zs(self,nMBAR=0,tol=1.E-8,use_iats=False,iats=None):
"""Calculates the normalization constants for the states.
Parameters
----------
nMBAR : int, optional (default 0)
Maximum number of MBAR iterations to perform.
tol : float, optional (default 1.0E-8)
If the relative residual falls beneath the tolerance, the MBAR iteration is truncated.
use_iats : bool, optional
If true, estimate integrated autocorrelation time in each MBAR iteration. Likely unnecessary unless dynamics are expected to be drastically different in each state. If iats is provided, the iteration will use those rather than estimating them in each step.
iats : 1D array, optional
Array of size L (no. states) with values of the integrated autocorrelation time estimated in each state. These values will be used in each iteration. Overrides use_iats.
Returns
-------
z : 1D array
Values for the Normalization constant in each state.
F : 2D array
Matrix to take the eigenvalue of for MBAR.
iats 1D array
Estimated values of the autocorrelation time. Only returned if use_iats is true.
"""
L = len(self.psis) # Number of States
Npnts = np.array([len(psis_i) for psis_i in self.psis])
Npnts /= np.max(Npnts)
if iats is None:
iats = np.ones(L)
if use_iats is True:
iats = self.iats
else:
use_iats is False
# we perform the self-consistent polishing iteration
z = self.z
F = self.F
for n in xrange(nMBAR):
z_old = z
Apart = Npnts/z_old
Amat = np.outer(np.ones(L),Apart)
Amat /= np.outer(np.ones(L),iats)
if use_iats:
z, F, iats = emr.emus_iter(self.psis,Amat,neighbors=self.neighbors,return_iats=True)
else:
z, F = emr.emus_iter(self.psis,Amat,neighbors=self.neighbors)
# Check if we have converged.
if np.max(np.abs(z-z_old)/z_old) < tol:
break
if use_iats:
return z, F, iats
else:
return z, F
[docs] def calc_obs(self,fdata,z=None):
"""Estimates the average of an observable function.
Parameters
----------
fdata : 2d array-like
Two dimensional data structure where the first dimension corresponds to the state index, and the second to the value of the observable at that time point. Must have the same number of data-points as the collective variable trajectory.
z : 1D array, optional
User-provided values for the normalization constants. If not provided, uses values from the first iteration.
Returns
-------
favg : float
The estimated average of the observable.
"""
if z is None:
z = self.z
favg = emr.calc_obs(self.psis,z,fdata)
return favg
[docs] def avar_zfe(self,state_1,state_2):
"""Calculates the asymptotic variance for the free energy difference between the two states specified.
Parameters
----------
state_1 : int
Index of the first state.
state_2 : int
Index of the second state.
Returns
-------
errs : 1D array
Array containing each state's contribution to the asymptotic error. The total asymptotic error is taken by summing the entries.
"""
errs, iats = avar.avar_zfe(self.psis,self.z,self.F,state_1,state_2,neighbors=self.neighbors)
return errs
[docs] def calc_pmf(self,domain,cv_trajs=None,nbins=100,kT=1.0,z=None):
"""Calculates the potential of mean force for the system.
Parameters
----------
domain : tuple
Tuple containing the dimensions of the space over which to construct the pmf, e.g. (-180,180) or ((0,1),(-3.14,3.14))
nbins : int or tuple, optional
Number of bins to use. If int, uses that many bins in each dimension. If tuple, e.g. (100,20), uses 100 bins in the first dimension and 20 in the second.
cvtrajectories : 2D array-like, optional
Two dimensional data structure with the trajectories in cv space. The first dimension is the state where the data was collected, and the second is the value in cv space. If not provided, uses trajectory given in the constructor.
z : 1D array, optional
User-provided values for the normalization constants If not provided, uses values from the first iteration of EMUS.
kT : float, optional
Value of kT to scale the PMF by. If not provided, set to 1.0
Returns
-------
pmf : nd array
Returns the potential of mean force as a d dimensional array, where d is the number of collective variables.
"""
if cv_trajs is None:
cv_trajs = self.cv_trajs
if z is None:
z = self.z
pmf = emr.make_pmf(cv_trajs,self.psis,domain,z,nbins=nbins,kT=kT)
return pmf


“Assatthe haritobhāse,
saṁvirūḷhamhi pādape;
Ekaṁ buddhagataṁ saññaṁ,
alabhitthaṁ patissato.
Beneath the Bodhi Tree,
bright green and growing,
being mindful, my perception
became one with the Buddha.
Ekatiṁse ito kappe,
yaṁ saññamalabhiṁ tadā;
Tassā saññāya vāhasā,
patto me āsavakkhayo”ti.
…
Sandhito thero ….
vaggo pañcamo.
Tassuddānaṁ
It’s been thirty one eons
since I gained that perception;
and it’s due to that perception
that I’ve attained the ending of defilements.
Kumārakassapo thero,
dhammapālo ca brahmāli;
Mogharājā visākho ca,
cūḷako ca anūpamo;
Vajjito sandhito thero,
kilesarajavāhanoti.
Dukanipāto niṭṭhito.
Tatruddānaṁ
Gāthādukanipātamhi,
navuti ceva aṭṭha ca;
Therā ekūnapaññāsaṁ,
bhāsitā nayakovidāti.


In this example we are using bokehJS, used internally by bokeh to render the plots in the browser.
Here you have a jsfiddle working example that you can modify to learn how it works.


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The email with instructions should land in your inbox within fifteen minutes.
I've worked for years on creating the best website solution for small churches. Here are some of the features:
{% if page.features %}I've designed each of these themes from scratch to be awesome for churches and I can customize them however you like as part of the setup process.
This theme is fantastic for smaller churches and church plants. It hides any time related content (blog posts, sermons, etc.) on the home page and only shows them when they are fresh or relevant.
This theme is desinged to put the church life of a lively church on display for potential visitors. It includes a current sermon series widget and featured ministries on the home page.
This theme is an all around work horse that works great with a very traditional church. It features latest blog posts, sermons and upcoming events on the home page.
Check out my prices below. Designed to be affordable for small churches.


Snap the resizable element to a grid. Set the dimensions of grid cells (height and width in pixels) with the grid option.
grid


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Firstly, if you think you have a serious drinking problem and are experiencing any of the associatedsymptoms of alcohol dependence, you should consult your doctor or another medical professional about it as soon as possible. There are also a number of national alcohol support services that you can go to for advice. Giving up completely may not be easy – especially if you’ve been a heavy drinker in the past. The following tips and techniques can make it that little bit easier.
Read MoreTell your family and friends that you’re trying to stop drinking alcohol and explain why. This way, you can share your successes with them, and they’ll understand why you’ve started turning down drinks or trips to the pub. Frequently reminding yourself and the people close to you why you want to stop drinking can help keep you on track, and may even encourage someone else to give up or cut down with you.
Read MoreIn the early stages, it’s a good idea to avoid situations where you may be tempted to drink. This could mean opting out of the weekly pub quiz for a while, or if you tend to drink when eating out, try going to restaurants that don’t sell alcohol or simply volunteering to drive. Similarly, try to identify the times when you would usually drink and fill the gap with something else. So if you would usually head to the pub after work on a Friday evening, you could organise to meet friends at the cinema, or if you’re giving up alcohol in pursuit of a new, healthier you, why not fill the gap with a weekly exercise class or a trip to the swimming pool to help you wind down? Identifying your ‘triggers’ (times when you’re tempted to drink) is important, particularly if you’ve tried and struggled to stop drinking in the past. Try to identify why you were unsuccessful – did you still go to the pub most evenings? Did you explain your reasons for not drinking to your partner? Was alcohol still readily available at home?
Read MoreIf you want to stop drinking alcohol as part of a move towards a healthier lifestyle, cutting down on the amount of alcohol you drink as opposed to giving up alcohol completely can help bring lots of health benefits, and can be easier to stick to. Reducing the amount you drink can also be an effective stepping stone to giving up alcohol completely in the future. Cutting down doesn’t have to be complicated. If you drink every night, start by designating a couple of days a week as alcohol-free days. This can soon become habit, the personal challenge helping remove the temptation and perhaps encouraging you to add more alcohol-free days. Official alcohol unit guidance is that it is safest for both and women to not regularly drink more than 14 units a week and not to ‘save up’ your units but spread them our evenly over the week. Our free Drinkaware: Track and Calculate Units app can help you track the amount you’re drinking in units, calories and hard cash, and also lets you record days on which you drink nothing at all. You can read more tips for cutting down at home, or tips for cutting down when out on our site.
Read MoreIt’s important that you acknowledge the fact that making changes to your lifestyle can be difficult and that you reward yourself with something if you are making progress. It's equally important not to be too hard on yourself if you slip up every once in a while. An easy way to keep track of how you’re doing and keep your motivation up is to give yourself short-term goals. Perhaps you could aim firstly for an alcohol-free week, then an alcohol-free month, for example. If you tend to drink in front of the TV after work, try replacing that glass of wine with something else you enjoy, or treat yourself to some new clothes or a day out with the money you’re saving on alcohol. The cost of alcohol mounts up with surprising speed – you could try putting aside the money you would normally spend on alcohol at home or while out, and spend it on another treat at the end of the week or the month. An easy way to set yourself goals and keep on top of your drinking is to try out our MyDrinkaware tool.
Read MoreWhether you’re cutting alcohol out of your life completely or cutting down gradually, you may notice a number of improvements to the way you look and feel. Among other things, you might find you have more energy, that you’re sleeping better, or that you’ve lost a bit of weight. In the long term you will also be helping to reduce your risk of developing alcohol-related cancer, alcohol-related liver disease or alcohol-related heart disease and could lower your blood pressure
Read MoreGoing ‘cold turkey’ or suddenly drinking no alcohol at all can cause serious alcohol withdrawal symptoms if you were drinking heavily before. Dr Sarah Jarvis of Drinkaware’s Medical Advisory Panel points out that "psychological symptoms are very common, and not just if you're a really heavy drinker. You can have short-term problems even with relatively low levels of alcohol consumption if you've become used to drinking really regularly.” Psychological symptoms can include irritability, poor concentration, feeling shaky, feeling tired, difficulty sleeping or bad dreams. Physical alcohol withdrawal symptoms including trembling hands, sweating, headache, nausea, vomiting, palpitations and lack of appetite are less common, but are often a sign that the sufferer was drinking at worrying levels. Severe physical side effects include convulsions, confusion, fever and even hallucinations. If you experience physical withdrawal symptoms of any kind, you should see your doctor as soon as possible. Your doctor may be able to prescribe medication that can help with alcohol withdrawal symptoms, and will be able to refer you to a specialist alcohol team for support. They can also offer counseling and psychological support, and can put you in touch with local support groups to help you stay on track.
Read More


| Total net profit | -366.14 | Gross profit | 532.48 | Gross loss | -898.62 |
| Profit factor | 0.59 | Expected payoff | -3.66 | ||
| Absolute drawdown | -395.03 | Maximal drawdown (%) | 442.56 (931.12%) | ||
| Total trades | 100 | Profit trades (% of total) | 47 (47.0%) | Loss trades (% of total) | 53 (53.0%) |
| Long positions (won %) | 45 (53.33%) | Short positions (won %) | 55 (41.82%) | ||
| Largest | profit trade | 30.53 | loss trade | -33.49 | |
| Average | profit trade | 11.33 | loss trade | -16.96 | |
| Minimum | profit trade | 0.0 | loss trade | -6.17 | |
| Maximum | take profit in pips | 2980.0 | stop loss in pips | -3300.0 | |
| Average | take profit in pips | 1065.5 | stop loss in pips | -1796.4 | |
| Minimum | take profit in pips | -60.0 | stop loss in pips | -600.0 | |
| Maximum | Reward-Risk | 3.7 | Duration in minutes | 3615 | |
| Average | Reward-Risk | 0.8 | Duration in minutes | 185.2 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 5.0 (39.61) | consecutive losses (loss in money) | 4.0 (-74.87) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |
| Total net profit | -181.37 | Gross profit | 250.81 | Gross loss | -432.18 |
| Profit factor | 0.58 | Expected payoff | -4.03 | ||
| Absolute drawdown | -210.26 | Maximal drawdown (%) | 260.91 (515.12%) | ||
| Total trades | 45 | Profit trades (% of total) | 24 (53.33%) | Loss trades (% of total) | 21 (48.89%) |
| Largest | profit trade | 21.63 | loss trade | -31.66 | |
| Average | profit trade | 10.45 | loss trade | -20.58 | |
| Minimum | profit trade | 0.0 | loss trade | -8.28 | |
| Maximum | take profit in pips | 2190.0 | stop loss in pips | -3300.0 | |
| Average | take profit in pips | 986.9 | stop loss in pips | -2095.6 | |
| Minimum | take profit in pips | -60.0 | stop loss in pips | -700.0 | |
| Maximum | Reward-Risk | 3.0 | Duration in minutes | 779.0 | |
| Average | Reward-Risk | 0.5 | Duration in minutes | 149.4 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 2.0 | |
| Maximum | consecutive wins (profit in money) | 4.0 (42.66) | consecutive losses (loss in money) | 4.0 (-92.11) | |
| Average | consecutive wins | 2.0 | consecutive losses | 1.0 |
| Total net profit | -184.77 | Gross profit | 281.67 | Gross loss | -466.44 |
| Profit factor | 0.6 | Expected payoff | -3.36 | ||
| Absolute drawdown | -213.31 | Maximal drawdown (%) | 220.72 (2978.68%) | ||
| Total trades | 55 | Profit trades (% of total) | 23 (41.82%) | Loss trades (% of total) | 32 (58.18%) |
| Largest | profit trade | 30.53 | loss trade | -33.49 | |
| Average | profit trade | 12.25 | loss trade | -14.58 | |
| Minimum | profit trade | 0.2 | loss trade | -6.17 | |
| Maximum | take profit in pips | 2980.0 | stop loss in pips | -3300.0 | |
| Average | take profit in pips | 1129.8 | stop loss in pips | -1551.6 | |
| Minimum | take profit in pips | 20.0 | stop loss in pips | -600.0 | |
| Maximum | Reward-Risk | 3.7 | Duration in minutes | 3615.0 | |
| Average | Reward-Risk | 1.0 | Duration in minutes | 214.6 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 5.0 (49.2) | consecutive losses (loss in money) | 4.0 (-74.87) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |


Some more programmer art working with loops, functions and the drawing API.
We'll be drawing on the HTML canvas element, an HTML5 element that supports a drawing API. See here for more information on the canvas element...
Your took your first steps into creating game mechanics! You can close this page now...


A) I changed the color of my web page from green to blue.
B) First, I want to take a short course on html, css and javascript. Although I looked at the Odin Project, it seems a little long and extensive for my purposes. I am going to watch a tutorial on youtube for both html and css as well as javascript. After that I am going to make some more edits to my webpage because it is very basic and rudimentary right now because I don't know enough to change it. My first actual project will be coding a Tic Tac Toe game using python.
Project Update: October 15, 2021 - I have been going through an html course on youtube. I already feel that I have learned a lot and feel much more comfortable with the language. Here is the html page that I have been using to follow along with the course. After the HTML and CSS tutorials I will make a robust visual and functional update to my pages.
Project Update: October 22, 2021 - I have continued working on my html course and updating my practice html page as I follow along. Currently, I have run into an issue where the image is not appearing so I am working to resolve this issue. I should be finished with the html course sometime during this upcoming week.
Project Update: October 28, 2021 - I finished the Html course that I was working on. In the final segment of the course I worked on creating complete websites and practiced using html semantics tags. I used this file to practice these skills. Since I am now finished with the html course I will soon move on the the CSS course.
Project Update: November 5, 2021 - I have started working on my CSS course.
Project Update: December 3, 2021 - I worked on and finished my blockchain presentation.
Project Update: January 28, 2022 - I finished my CSS course that I started before break and began creating a new and improved version of my website.
Project Update: Febuary 7, 2022 - Continued work on the new and improved version of my website.
Project Update: Febuary 18, 2022 - Continued work on the new and improved version of my website.
Project Update: Febuary 18, 2022 - Continued work on the new and improved version of my website. I expect to have a final product sometime towards the end of the week.


{{ define "title" }}{{ .Site.Params.tiponeill }}{{ end }} {{ define "hero-box" }}{{ end }} {{ define "pagestyles" }} {{ end }} {{ define "main" }}
The original exhibit honored Thomas P. (Tip) O'Neill Jr., Speaker of the United States House of Representatives and graduate of Boston College class of 1936.
The exhibit featured displays from many facets of O'Neill's life: his North Cambridge neighborhood, his BC years, his legacy as speaker, and his political campaigns.
More information about Thomas P. O'Neill, Jr.:


Class HttpResponse
HttpResponse
- Native5\Route\HttpResponse implements Native5\Route\Response
Namespace: Native5\Route
Package: Native5\Control
Category: Routing
Copyright: 2012 Native5. All Rights Reserved
License: attached NOTICE.md for details
Author: Barada Sahu<EMAIL_ADDRESS>======= href="mailto:barry<EMAIL_ADDRESS>>>>>>>> release-0.2.0 Version: Release: 1.0
Link: Created : 27-11-2012 Last Modified : Fri Dec 21 09:11:53 2012
<<<<<<< HEAD Located at Native5/Route/HttpResponse.php
======= Located at Native5/Route/HttpResponse.php
>>>>>>> release-0.2.0
Package: Native5\Control
Category: Routing
Copyright: 2012 Native5. All Rights Reserved
License: attached NOTICE.md for details
Author: Barada Sahu<EMAIL_ADDRESS>======= href="mailto:barry<EMAIL_ADDRESS>>>>>>>> release-0.2.0 Version: Release: 1.0
Link: Created : 27-11-2012 Last Modified : Fri Dec 21 09:11:53 2012
<<<<<<< HEAD Located at Native5/Route/HttpResponse.php
======= Located at Native5/Route/HttpResponse.php
>>>>>>> release-0.2.0
Methods summary
public
|
#
<<<<<<< HEAD
__construct( string $encoding = 'none', mixed $renderer = null )
=======
__construct( string $encoding = 'none', mixed $renderer = null )
>>>>>>> release-0.2.0
__construct |
public
|
#
<<<<<<< HEAD
redirectTo( mixed $location )
=======
redirectTo( mixed $location )
>>>>>>> release-0.2.0
redirectTo Helper method to allow redirection to a certain url. |
public
|
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public
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public
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public
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public
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public
|
#
<<<<<<< HEAD
setHeaders( mixed $headers )
=======
setHeaders( mixed $headers )
>>>>>>> release-0.2.0
setHeaders |
public
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public
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public
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#
<<<<<<< HEAD
setEncoding( mixed $encoding )
=======
setEncoding( mixed $encoding )
>>>>>>> release-0.2.0
setMode |
public
|


Reports are enabled in the main Office Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
Reports are enabled in the main Clinic Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
Reports are enabled in the main Emergency Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
Reports are enabled in the main Journals Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
Reports are enabled in the main Quests Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
Reports are enabled in the main Creator Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
Reports are enabled in the main Telehealth Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
Reports are enabled in the main Datasets Page, checkmark the report you want to show here. Click here to start the tutorial when you finish, you'll earn a ZMZLR loot drop.
This Zeemz Portal leads to AskCo19.com the main site [INSERT ASKCO19's description and call to action].


Why Dayton?
When we made the decision to start Mile Two just over a year ago there were so many questions that we had to answer. How are we going to get customers? Where should our office be? Are we going forward with a product or a service based business model? Can you do both? In an environment like Dayton, the answer is YES!
We decided early on that instead of taking outside investor money, we would bootstrap Mile Two by providing high quality custom software development for other companies and the Department of Defense (DoD). This approach provided us with an opportunity to build off our cumulative decades of experience working within the DoD without feeling the pressure of investors. It may not be as sexy a multimillion dollar venture capital backed startup, but it provided Mile Two with the opportunity to refine our business model and find our voice with a lower risk profile over the course of a year.
Is it possible to be both a “service provider” company AND pursue your own internal ideas for future products? Fortunately, in Dayton the answer is a resounding “Yes”. Before I left the Air Force to focus on Mile Two, I saw Barry Besecker, (the co-founder of Marxent), speak at the Dayton StartupGrind. Barry explained how Marxent earned money early on as a service based company and that the income from those projects allowed them to fund their own early R&D ideas in the Augmented Reality/Virtual Reality domains. As a result, Marxent recently secured a second round of significant VC investment which resulted in two startup companies in Dayton, Marxent and Magnetic Mobile. We have seen that model work in Dayton - so we know it is possible.
SpinTech and Tangible Solutions are other examples of entrepreneur teams that used several methods to reduce their risk early on before actually pursuing outside capital to scale, but only after they had proven their business models. Mile Two was built around the principle of working on innovative ideas with open-minded collaborators in an attempt to follow the lead of the success stories before us. We made certain to carve out time in our schedules to dedicate to high risk-high reward ideas that may attract and/or require a more typical VC model for success. True to our principles, we are already self-funding several internal R&D projects that with plenty of hard work and a little bit of luck may spin out as new companies in the near future. Stay Tuned!
Because we wanted Mile Two to have a diverse set of collaborators and customers, we chose to open our office in downtown Dayton. The Entrepreneur Center (TEC) provided a low risk and inexpensive environment for us to get started. When you are bootstrapping a company keeping costs low is one of the keys to survival and Dayton is one of the most affordable places to start a business. A downtown location provided Mile Two with the opportunity to interact with billion dollar organizations like CareSource and AFRL, innovation hubs like Ascend Innovations, and other startups like Desin, the inventors of Obi. That mix of risk takers, R&D investors, innovators, potential customers, and infrastructure provides the perfect mix to enable Mile Two (and hundreds of other companies) to continually identify fresh ideas, new opportunities, and cutting edge solutions.
We at Mile Two have been fortunate enough to outgrow two different offices at TEC and in doing so, have been so pleased with our experiences and opportunities downtown that we recently signed a long term lease to move out of The Entrepreneur Center and into our own permanent 5,000 sq/ft space in early 2017. Looking ahead to 2017 I see buzz of activity that is starting to grow into a roar. Who is going to be the next great Dayton success? I can’t wait to find out!


Here is a dream come true in my life...It happened today when I rode my bike to the library. The ride was about 1 1/2 miles, not too long but long enough to where I would want to sit down before I went in. After catching up with my breath I got this wierd feeling I was being watched so I looked towards the window behind me, did not see anyone and wondered at my paranoia. I usually have these feelings was it something I should worry about? Hmm, I thought. I looked behind me again at the bookshelves through the window, there was a sign telling what kind of books were on that isle. Fiction. All of a sudden I got this strange vision, it was me trapped in the library staring out the window at a girl sitting on a bench outside, desperately hoping she would not come in. I remembered staring at this Fiction plaque in the dream hoping I would remember it so I could know in real life that this was important. I thought, wow could this have been me? The person inside and outside at the same time?
Well I was certainly a little freaked out by this and thought it best not to go inside. I knew that by the time I decided to leave to go back home the dream vanished and the girl (me) inside would not know if I left or not. My rational mind kept telling me to not worry, but why would something like this happen, why would I get this vision if it was not to tell me something? I guess that proves my worry wrong that with all the drugs I've done I still have a rational mind left. I know these things can happen, it is not uncommon for a dream to come true but damn it sure is worth sharing and I hope it helps someone besides me.
Thankyou for reading
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


from sklearn.base import clone
import random
import numpy as np
from sklearn.base import BaseEstimator, MetaEstimatorMixin, ClassifierMixin
from sklearn.utils import validation
[docs]class LabelPowerset(
BaseEstimator, MetaEstimatorMixin, ClassifierMixin):
"""Implements the label powerset problem transformation strategy,
where for each possible subset of the label powerset a classifier is
trained, which classifies whether or not the label subset should be
assigned to the given instance or not.
"""
[docs] def __init__(self, estimator):
"""
Creates label powersets for the given labels. Each powerset is
transformed into a single label, resulting in a multi-class
classification problem. For :math:`|\mathcal{L}|` labels, this meta
estimator generates :math:`|2^\mathcal{L}|` labels.
Note, that rare label combinations result in a small number of samples
for a given label after the transformation. This might result in rather
bad learning effects for the combination given by the label powerset.
Also, the computational complexity is exponential, hence for many
labels the label powerset increases notably, and might result in
computational problems.
Parameters
----------
estimator : scikit-learn compatible classifier instance
Will be copied (with all hyperparameters) before use,
hence original will be left untouched.
Returns
-------
label powerset transformed multi-class meta estimator
"""
# get's cloned later on
self.estimator = estimator
self.powerset_lookup = {}
self.reverse_lookup = {}
[docs] def fit(self, X, y):
"""Fit underlying estimators.
Parameters
----------
X : (sparse) array-like, shape = [n_samples, n_features]
Data
y : (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]
Multi-label targets
"""
# should the powersets that are not seen in the training data also be
# used as a class?
# we can actually use simple integers to denote the classes, as the
# classifiers capable of multi-class classification will transform them
# on their own.
self.estimator = clone(self.estimator)
validation.check_X_y(X, y, multi_output=True)
y = validation.check_array(y, accept_sparse=True)
max_label = 0
y_transformed = []
for i in range(y.shape[0]):
lstr = ",".join([str(x) for x in y[i]])
if lstr not in self.powerset_lookup.keys():
self.reverse_lookup[max_label] = y[i]
self.powerset_lookup[lstr] = max_label
max_label += 1
y_transformed.append(self.powerset_lookup[lstr])
y_transformed = np.array(y_transformed)
self.estimator.fit(X, y_transformed)
[docs] def predict(self, X):
"""
Predicts the labels for the given instances.
Parameters
----------
X : (sparse) array-like, shape = [n_samples, n_features]
Data
Returns
-------
array-like, shape = [n_samples, n_labels]
Estimated labels
"""
validation.check_is_fitted(self, 'estimator')
# enforce sparce np mat
y_hat = self.estimator.predict(X)
return np.array([self.reverse_lookup[row] for row in y_hat])


This image provides an information about the wordpress sheet.
This image shows the Apple logo used by Steve Jobs in his products.
One of the great quotes used by Apple.
This one is my most favourite as it displays an XBox Remote.


Scott Doyle /<EMAIL_ADDRESS>How do we begin a machine learning project?
A Brief Overview
What is the problem statement, in one sentence?
NOT the machine learning task, just the gap that needs to be addressed.
Pathologists must review cytology slides to identify malignant versus benign tumors.
What is the current solution?
How is the problem currently addressed, if at all?
What data is currently routinely collected?
Based on cytology images, we can determine by eye whether a sample is benign or malignant.
Domain knowledge identifies useful features.
Pathologists already distinguish benign from malignant tumors.
Our job is to convert qualitative features to quantitative ones.
The pathologist lists cell nuclei features of importance:
Look at the data that is currently collected for your problem!
Even if the features have been extracted, you should view the data that generated them.
What is the goal of your system?
How will the success of the system be assessed?
This determines your overall performance measure.
At this point…
Is machine learning the right tool for the job?
Machine learning is NOT always the right choice.
The sooner you identify whether ML will help, the better!
Having decided on an ML approach, state the ML task to be completed.
Similar to a problem statement, but a proposed solution to the problem.
Also similar to the hypothesis statement for a scientific proposal.
Given a digital FNA image, we can use image features to predict whether a patient’s tumor is benign or malignant.
How you acquire the dataset is project-specific.
However, do NOT put this off!
Sensitive data (government, military, healthcare) may be difficult to obtain, be embargoed, or censored.
Questions about the dataset:
Ensure that the downloaded data contains what you expect.
For images, look at them!
import matplotlib.pyplot as plt
plt.imshow(data_sample)
plt.show()
For spreadsheets, print their contents!
import pandas as pd
dataset = pd.load_csv(path_to_csv_file)
dataset.head()
dataset.info()
At this point you should split the data into a “training” and “testing” set.
This protects against data snooping bias!
Model design should be completely blind to testing data, to prevent potential overfitting.
If you can hold off on retrieving testing data in the first place, even better!
How much data should go into training and testing?
Around 70-30 or 80-20 splits of training / testing are acceptable.
Generally you want more data in training than testing.
How should you split your training and testing?
Random versus Stratified splitting?
id
for each sample to ensure reproducibility
# Import functions to calculate a hash for the dataset
from zlib import crc32
def test_set_check(identifier, test_ratio):
'''Return a boolean that states whether the current sample should be included in the testing set.
Calculates a hash value from an identifier, and returns True if the value is in the bottom
(test_ratio)-percent of the maximum possible hash value.
'''
return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32
def split_train_test_by_id(data, test_ratio, id_column):
'''Return training and testing dataframes given a test ratio and column to use for computing sample hash.
Uses test_set_check to actually compute hash and put the data into training or testing.
'''
ids = data[id_column]
in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))
return data.loc[~in_test_set], data.loc[in_test_set]
# Apply the above functions to the dataset
train_set, test_set = split_train_test_by_id(df, 0.3, "id")
================
Random Sampling
================
Overall class balance:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Train set class ratio:
B 0.61
M 0.39
Name: diagnosis, dtype: float64
Test set class ratio:
B 0.67
M 0.33
Name: diagnosis, dtype: float64
# Stratified Split
from sklearn.model_selection import StratifiedShuffleSplit
# Create the splitting object
split = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)
# Apply the split to the data frame using the "diagnosis" column as our label
for train_index, test_index in split.split(df, df["diagnosis"]):
train_set = df.loc[train_index]
test_set = df.loc[test_index]
====================
Stratified Sampling
====================
Overall class ratio:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Train set class ratio:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Test set class ratio:
B 0.63
M 0.37
Name: diagnosis, dtype: float64
Exploring the Feature Space
Looking at the data can provide insights into your data.
You can evaluate natural groupings of samples and potential correlations between features.
Let’s look at nuclei texture feature as an example for our FNA dataset…
Combining features often yields greater class separation.
It’s difficult to visualize more than three dimensions.
However, a pair plot
can help to visualize multiple pairs of dimensions at once.
attributes = ["radius_mean", "texture_mean", "compactness_mean", "fractal_dimension_mean"]
# We need to add the "diagnosis" label back in here, so Seaborn can plot it using the `hue` parameter
train_set_display = train_set[attributes].copy()
train_set_display['diagnosis'] = train_set['diagnosis']
g = sns.pairplot(train_set_display, hue='diagnosis', plot_kws={'alpha': 0.5, 'edgecolor': None}, height=3, aspect=1)
# Alter the plot
g.fig.suptitle('Pair Plot of '+str(len(attributes)-1)+' Features', y=1.02)
g._legend.set_title("Diagnosis")
plt.show()
Most data you will receive is noisy:
When creating a dataset, use a unique, non-numeric value to fill in missing values.
Do not:
When pre-processing a dataset, decide what to do with missing data:
Text and categorical values should be converted to numeric values.
Ordinal values can be converted directly into numbers:
from sklearn.preprocessing import OrdinalEncoder
ordinal_encoder = OrdinalEncoder()
Non-ordinal values can be encoded with one-hot encoding:
Replace the feature with N new features, where N is the number of categories. Each of the new features is binary, meaning it’s only 0 or 1.
| id | Blood Type |
|---|---|
| 001 | A |
| 002 | B |
| 003 | B |
| 004 | AB |
| 005 | O |
| id | Blood Type A | Blood Type B | Blood Type AB | Blood Type O |
|---|---|---|---|---|
| 001 | 1 | 0 | 0 | 0 |
| 002 | 0 | 1 | 0 | 0 |
| 003 | 0 | 1 | 0 | 0 |
| 004 | 0 | 0 | 1 | 0 |
| 005 | 0 | 0 | 0 | 1 |
from sklearn.preprocessing import OneHotEncoder
cat_encoder = OneHotEncoder()
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
diagnosis_cat = df['diagnosis']
# Fit the encoder to the categories, and immediately
diagnosis_lab = label_encoder.fit_transform(diagnosis_cat)
# Add the diagnosis label back to the dataframe
df['diagnosis_label'] = diagnosis_lab
Data should always be scaled.
Scaling should be calculated only on the training set, and the proper transform applied to testing.
Min-Max Scaling / Normalization:
$$ x = \frac{x - \min{(x)}}{\max{(x - \min{(x)})}} $$
Bounded between 0 and 1, but if you have outliers, your data can be “squeezed” into the upper or lower portions of the range.
Standardization:
$$ x = \frac{x - \mu}{\sigma} $$
Not bounded to any specific range, which may be a problem (e.g. for neural networks expecting a 0-1 value), but much less affected by outliers.
Always write code to do your feature pre-processing.
You will be able to apply the same transforms each time, even if datasets grow.
Model selection is an art rather than a science.
Recall Occum’s Razor:
Given a set of possible solutions, the one which makes the fewest assumptions is preferable.
Also often restated as:
The simplest solution is usually the best.
So what does a “simple model” mean?
Start looking at linear methods, like a LinearDiscriminant
:
from sklearn.lda import LDA
lin_disc = LDA()
lin_disc.fit(training_data, training_labels)
Check the performance of this system using the built-in accuracy method operating on the training data:
lin_train_acc = lin_disc.score(training_data, training_labels)
print(f"Linear Discriminant Accuracy: {lin_train_acc}")
Linear Discriminant Accuracy: 0.9271356783919598
We can compare this model to more complex ones, like a support vector machine:
from sklearn.svm import SVC
svm_model = SVC()
svm_model.fit(training_values_transformed, training_labels)
svm_train_acc = svm_model.score(training_values_transformed, training_labels)
print(f"Support Vector Machine Accuracy: {svm_train_acc}")
Support Vector Machine Accuracy: 0.9371859296482412
Evaluation on subsets of training data is the best possible outcome!
Since the system has trained on this data, it is more likely to do well.
Complex models may even get “100%” performance: This is overfitting!
To get a real sense of the performance of your system, you should train on your training data and then evaluate on testing data:
lin_test_acc = lin_disc.score(testing_data, testing_labels)
print(f"Linear Discriminant Accuracy: {lin_test_acc}")
svm_test_acc = svm_model.score(testing_data, testing_labels)
print(f"Support Vector Machine Accuracy: {svm_test_acc}")
Linear Discriminant Accuracy: 0.9181286549707602
Support Vector Machine Accuracy: 0.9415204678362573
However: What if you (randomly) get a really good or bad testing set?
What if you could create several train / test splits, with all of the benefits we discussed earlier?
This is cross-validation: Break the dataset into several (distinct) training and testing sets, and then perform training and testing on each one.
This can be easily done in scikit-learn
:
from sklearn.model_selection import cross_val_score
scores = cross_val_score(svm_model, training_values_transformed, training_labels,
scoring="accuracy", cv=10)
# Display scores
def display_scores(scores):
print(f"Scores: {scores}")
print(f"Mean: {scores.mean()}")
print(f"Standard Deviation: {scores.std()}")
display_scores(scores)
Scores: [0.95 0.95 0.95 0.975 0.9 0.925
0.925 0.9 0.94871795 0.87179487]
Mean: 0.9295512820512821
Standard Deviation: 0.029683640402767546
Now you have a way to robustly compare models.
After you pick a model, it’s time to make it better!
There are a lot of “knobs” you can turn to opitimize a model.
If you’ve robustly quantified model performance, now it’s okay to tweak away.
This process is parameter optimization: trying all the parameters to see which gives you the best performance.
scikit-learn
enables you to build pipelines to process your data.
These allow you to build a list of different values to try, plus a scoring function, and then returns the best numbers out of what it’s tried.
from sklearn.pipeline import Pipeline
pipe = Pipeline(steps=[
('classification', classifier)
])
from sklearn.model_selection import GridSearchCV
# Set the parameters by cross-validation
param_grid = {
'classification__kernel': ['rbf'],
'classification__gamma': [1e-1, 1e-2, 1e-3, 1e-4],
'classification__C': [1, 1e1, 1e2, 1e3],
}
svm_model = SVC()
grid_search = GridSearchCV(svm_model, param_grid, cv=5,
scoring='neg_mean_squared_error',
return_train_score=True)
grid_search.fit(training_data, training_labels)
Finally, you can predict labels on your testing set:
final_model = grid_search.best_estimator_
final_predictions = final_model.predict(testing_data)
final_mse = mean_squared_error(testing_labels, final_predictions)
final_rmse = np.sqrt(final_mse)
This section is very implementation dependent.
However, there are a few steps…
Takeaway Thoughts
You will often be working as part of a team.
You may only be responsible for some of the workflow steps…
… But understanding them is critical to making your project successful.


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Makes 10-12 ‘belts’
Dietary
Fruit leather makes a great lunchbox treat, try using different fruits throughout the seasons.
Equipment and preparation: this recipe needs to be cooked in a very low oven for 10-12 hours.
Line a 33x23cm/13x9in Swiss roll tin with baking parchment. Preheat the oven to 50C/120F/Gas ¼.
Place the chopped plums in a pan with 200ml/7fl oz cold water, cover with a lid and place over a medium heat. Bring to a simmer and cook, covered, for 15 minutes, or until the fruit is very soft.
Remove the lid and mash the fruit using a potato masher. Stir in the sugar and a squeeze of lemon juice.
Place the pan back on the heat and cook, stirring continuously for another 10 minutes, or until the mixture has thickened.
Purée the fruit using a stick blender or food processor, taking care to ensure the mixture is completely smooth.
Carefully spoon the purée into the prepared tray and level the surface. Bake for at least 10 hours, or until completely set with a leathery texture.
Allow to cool then carefully lift away from the baking parchment. Run a pizza cutter down the length of the leather to cut into 3cm/1in strips then roll up tightly. Serve immediately or store in an airtight container.


---
layout: post
title: Embedded C# on the ULX3S ESP32
date: '2020-01-31T18:14:00.001-08:00'
author: gojimmypi
tags:
- nanoFramework
- ESP32
- C#
- SSD1331
- Espressif
modified_time: '2020-04-10T17:01:56.315-07:00'
blogger_id: tag:blogger.com,1999:blog-4109066286647243251.post-4233030188718355252
blogger_orig_url: https://gojimmypi.blogspot.com/2020/01/embedded-c-on-ulx3s-esp32.html
---
My January efforts have focused on writing C# on an embedded device. Yes, you read that correctly: C-Sharp. On and Embedded device. WOW! In my case, the ESP32 onboard the Radiona ULX3S. This is all thanks to the clever folks working on the nanoFramework
.
I have an interim walk-though on my SSD1331 GitHub branch here:
https://github.com/gojimmypi/ulx3s-examples/tree/SSD1331/nanoFramework
I'm currently working on figuring out how to compile the interpreter. See the
Discord discussion
.
This is a work in progress, so come back for updates.
Copyright (c) gojimmypi all rights reserved. Blogger Image Move Cleaned: 5/3/2021 1:35:54 PM


Networking using the host network
読む時間の目安: 2 分This series of tutorials deals with networking standalone containers which bind directly to the Docker host’s network, with no network isolation. For other networking topics, see the overview.
Goal
The goal of this tutorial is to start a nginx
container which binds directly
to port 80 on the Docker host. From a networking point of view, this is the
same level of isolation as if the nginx
process were running directly on the
Docker host and not in a container. However, in all other ways, such as storage,
process namespace, and user namespace, the nginx
process is isolated from the
host.
Prerequisites
-
This procedure requires port 80 to be available on the Docker host. To make Nginx listen on a different port, see the documentation for the
nginx
image -
The
host
networking driver only works on Linux hosts, and is not supported on Docker Desktop for Mac, Docker Desktop for Windows, or Docker EE for Windows Server.
Procedure
-
Create and start the container as a detached process. The
--rm
option means to remove the container once it exits/stops. The-d
flag means to start the container detached (in the background).docker run --rm -d --network host --name my_nginx nginx
-
Access Nginx by browsing to http://localhost:80/.
-
Examine your network stack using the following commands:
-
Examine all network interfaces and verify that a new one was not created.
ip addr show
-
Verify which process is bound to port 80, using the
netstat
command. You need to usesudo
because the process is owned by the Docker daemon user and you otherwise won’t be able to see its name or PID.sudo netstat -tulpn | grep :80
-
-
Stop the container. It will be removed automatically as it was started using the
--rm
option.docker container stop my_nginx
Other networking tutorials
Now that you have completed the networking tutorials for standalone containers, you might want to run through these other networking tutorials:
networking, host, standalone


| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| FilterSettings
Smooks filter settings for programmatic configuration of the
Smooks instance. |
| GenericReaderConfigurator
Generic reader configurator.
|
| ReaderConfigurator
Reader configurator.
|
| Smooks
Smooks executor class.
|
| SmooksException
Smooks Exception.
|
| SmooksFactory
A factory that creates
Smooks instances. |
| StreamFilterType
Smooks filter type.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| GenericReaderConfigurator
Generic reader configurator.
|
| ReaderConfigurator
Reader configurator.
|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| SmooksException
Smooks Exception.
|
| StreamFilterType
Smooks filter type.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| ReaderConfigurator
Reader configurator.
|
| Class and Description |
|---|
| GenericReaderConfigurator
Generic reader configurator.
|
| ReaderConfigurator
Reader configurator.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| ReaderConfigurator
Reader configurator.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| Smooks
Smooks executor class.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| ReaderConfigurator
Reader configurator.
|
| Class and Description |
|---|
| ReaderConfigurator
Reader configurator.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| SmooksException
Smooks Exception.
|
| Class and Description |
|---|
| ReaderConfigurator
Reader configurator.
|
Copyright © 2020. All rights reserved.


Welcome to
Capoeira Costa Rica
By
Capoeira Costa Rica
Welcome to
The Capoeira Place
Capoeira
Instruments
History and more...


'Be ballsy' - Faf gets those undies out again to challenge men in cancer drive
by Jenni EvansSpringbok star Faf de Klerk has pulled the national underpants out again to kick off the "#FafChallenge" - to get men to check their "crown jewels" for signs of testicular cancer.
"Don't Faf this up," urges the campaign which already has De Klerk, captain Siya Kolisi and six other rugby players - Makazole Mapimpi, Cheslin Kolbe, Damien de Allende, Malcolm Marx, and Jesse Kriel on board.
PICS | Faf gives Tutu a glimpse of the national underpants
"Be ballsy enough to check your crown jewels in the #FafChallenge," says the tagline, referring to the infamous picture of De Klerk in his flag-coloured underpants with the Duke of Sussex in the locker room after the Springboks' victory in the Rugby World Cup in Japan.
The Cancer Association of SA (Cansa) has also produced various "testi-monials" in 2016, explaining the importance of not only regular checks, but for the conversation to become more regular among men.
Staying with the rugby theme, it explains how to do the check in "Touch Down", and "Blitz defence" advises going to doctor if any lumps or bumps are found. There is also a microsite with frequently answered questions on the topic.
Fidelia van der Linde, communications manager for pharmaceutical company Cipla which has a relationship with the seven Springboks involved in the campaign, told News24 that they were hoping for the same "talkability" about these important self-checks that woman had established about regular breast checks.
The first step for men is to do their #FafChallenge, post about it in a swimming costume, or holding tennis balls, or simply say you have done it, and tag a friend to do it.
The site also has deeper information on testicular cancer, and what to look for.
Comedy Central has also launched #Novemballs, urging men to take care of their testicles.
It didn't take long to catch on:


Fly a XCub out of La Tuque airport, reach a certain altitude and speed (1000 feet and 80 knots), then land back at La Tuque.
This mission is derived from the FSX mission creation tutorial, it is aimed at serving as a base framework.
Fly a XCub out of La Tuque airport, reach a certain altitude and speed (1000 feet and 80 knots), then land back at La Tuque.
This mission is derived from the FSX mission creation tutorial, it is aimed at serving as a base framework.


Haggai
1
1 In the second year of Darius the king, in the sixth month, on the first day of the month, the word of Yahweh came by the hand of Haggai the prophet to the governor of Judah, Zerubbabel the son of Shealtiel, and to the high priest Joshua, the son of Jehozadak, and said,
2 "Yahweh of hosts says this: 'This people says, "It is not time for us to come or to build Yahweh's house."'"
3 And so the word of Yahweh came by the hand of Haggai the prophet and said,
4 "Is it a time for you yourselves to live in your finished houses,
while this house lies ruined?
5 So now Yahweh of hosts says this:
'Consider your ways!
6 You have sown much seed, but bring in little harvest; you eat but do not have enough;
you drink but cannot get drunk. You wear clothes but cannot warm yourselves,
and the wage earner earns money only to put it into a bag full of holes!'
7 Yahweh of hosts says this:
'Consider your ways!
8 Go up to the mountain, bring timber, and build my house;
then I will take pleasure in it, and I will be glorified!' says Yahweh.
9 'You looked for much, but behold! you have brought little home, for I blew it away!
Why?'—this is Yahweh of hosts' declaration!
'Because my house lies in ruins, while every man takes pleasure in his own house.
10 Because of this the heavens withhold the dew from you, and the earth withholds its produce.
11 I have summoned a drought upon the land and upon the mountains, upon the grain
and upon the new wine, upon the oil and upon the harvest of the earth,
upon men and upon beasts, and upon all the labor of your hands!'"
12 Then Zerubbabel, the son of Shealtiel, and the high priest Joshua, the son of Jehozadak, along with all the remnant of the people, obeyed the voice of Yahweh their God, and the words of Haggai the prophet, because Yahweh their God had sent him. And the people feared the face of Yahweh.
13 Then Haggai, the messenger of Yahweh, spoke Yahweh's message to the people and said, "'I am with you!' —this is Yahweh's declaration!"
14 So Yahweh stirred up the spirit of the governor of Judah, Zerubbabel the son of Shealtiel, and the spirit of the high priest Joshua, the son of Jehozadak, and the spirit of all the remnant of the people, so that they went and worked on the house of Yahweh of hosts, their God
15 in the twenty-fourth day of the sixth month, in the second year of Darius the king.


This inspection detects mismatched parameters in a docstring. Please note that it doesn't warn you of missing parameters, if none of them is mentioned in a docstring.


Instructions Part {{ Constants.part_alloc }}.
This is the last part of the experiment and is played for a single round. In this round, you will be endowed with
{{ Constants.pie }} {{ Constants.exp_currency }} and you have to decide how you want to distribute the points between
two players who have been randomly matched with you: Players A and B.
Player A is one of the players assigned to group ● "{{ circles_label }}"
in Parts {{ Constants.part_name }} and {{ Constants.part_fixed }}. Player B is one of the players
assigned to group ▲ "{{ triangles_label }}" in Parts
{{ Constants.part_name }} and {{ Constants.part_fixed }}.
The computer will randomly choose the allocation decision of one of the {{ Constants.players }}
players who have participated together in the experiment, and will implement the payment of that decision. For
instance, if your decision is chosen, players A and B matched with you will receive the points
you have allocated to them. None of the other 5 players (including yourself) will receive any points in this part.
Similarly, if another player's decision is chosen and you are one of the players A or B matched
with him, you will receive the points allocated to you.


Item Code:
Item Name:
Item Name CN:
Batch No:
Location:
Position:
Quantity(KG):
PO Code:
Date:
Customer Name:
Irradiation:
Total Plate Count:
Vendor Name:
Remark:
Close


{{ title }}
New Location Data
Location:
Minimum Daily Customers:
Maximum Daily Customers:
Average Cookies Sold Per Customer:
Submit
{{ time }}
Daily Totals
{{ store.location }}
{{ sales }}
{{ store.totalCookies }}
Total
{{ hourly }}
{{ grandTotalCookies }}
Calculate Totals


The Code
/** * The Arc Challenge is Paul Graham's quest for web framework concision. * * http://www.paulgraham.com/arcchallenge.html * * This is one potential lift-based solution to it using StatefulSnippets. * There are doubtless many other ways. * * @author: Steve Jenson */ class ArcChallenge extends StatefulSnippet { var dispatch: DispatchIt = {case _ => xhtml => ask} /** * Step 1: Type in a Phrase. */ def ask = {
 Say Anything: {text("", p => phrase = p)} {submit("Submit", () => dispatch = {case _ => xhtml => think})}
 } /** * Step 2: Show a link that takes you to the Phrase you entered. */ def think = submit("Click here to see what you said", () => dispatch = {case _ => xhtml => answer}) /** * Step 3: Show the phrase. */ def answer =
You said: {phrase}
 private var phrase = "" }


License¶
This version of Elegans is licenced under the MIT License.
The MIT License (MIT)
Copyright (c) 2014 Naoki Nishida
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Music Depot
Home
(current)
Library
My Music
Logout
Or
Sign Up
Sign Up!
×
Hello World!!
username . "
"; } ?>


State Pension Activation Prototype
| Account Details | |
|---|---|
| Valid reference: |<PHONE_NUMBER>218765 |
| Demo Invite code: | QWX5YCHPNRJV |
| Valid National Insurance number: | AA123456C |
Beta View
Addtional views for award figure and breakdown on end page.
Start pageDemo v2
This iteration encompasses the user research analysis insights which we've done across the whole of alpha, bringing in a lot of work from our BA colleagues around the questions which are required to be answered in ordered to make a calculation.
Updated National Insurance number pattern, as well as various content tweaks.
Start pageDemo v1
First attempt at partial taking National Insurance number, as well as expended journeys for overseas and releationship details.
- Username: december
- Password: december
Iteration 3 - v2
This iteration encompasses the user research analysis insights which we did with the first version. We're looking to use NI Number as an identifier only / reference only for a user. We've added in validation on form fields.
Start pageIteration 3 - v1
This iteration takes in to account the feedback that we've had from our security colleagues, updating the 'what next' area using information from our user research, adding in a support channel and moving deferrals outside of the service. We've moved deferring outside of the service due to the result of the user research.
Start PageIteration 2 - v2
This iteration encompasses the user research analysis insights which we did with the first version. We're looking to use NI Number as an identifier only / reference only for a user. We've added in validation on form fields.
Start pageIteration 2 - Alpha Phase
This iteration encompasses the whole MVP for Alpha. We've designed this iteration to test the assumption that customers will expect to actively get or defer their state pension as well as the dates they will receive their first few payments.
Start PageIteration 1 - v2
A slightly different version which includes an element whereby the customer is prompted to wait for the delivery of their pension calculation. We’ve designed version 2 this way to test the assumption that customers will have a high expectation of getting their pension calculation online at the time of application and are looking to see their response.
Start PageIteration 1
We’re testing this low impact approach for the first time. It includes options for offline paper prompt and direct online traffic. We’ve designed it targeting a specific customer group of single customers with a full NI Contribution because this specific customer group are the path of least resistance to work through a prototype when we don't have any technical solutions for the other customer groups.
Start Page


--- layout: index title: Home ---
I am a Staff Research Scientist at Visa Research where I work on Cryptography and Secure Computation. My primary research interest is the development and applications of efficient methods for computing on encrypted data. Most notably is the notion of being able to compute on encrypted data and the area of research known as Secure Computation:
Below is a collection of my recent blog posts and publications.


Home
About Me
Blog
Gallery
Contact
Hello Guys!
Hi Guys,
I Am Student of Mumbai University.
Beginner at web Development.
2 years exprence.
More About Me
Contact
Blog


Change Log
- This is the first public release of the Engage: Booking module
- Allows users to request a time slot on the calendar
- Allows module editors to approve or deny that time slot
- Sends notification emails when new appointments are requested
- Sends notification emails when appointments are accepted or declined
- Only shows appointment details to module editors
- More details can be found on the Engage: Booking Change Log
Known Issues
- Modules using different versions of the Telerik RadControls for ASP.NET AJAX (including many OnyakTech modules) may not be able to function alongside Engage: Booking.


Hello, I'm Balachandar from India, did my Bachelors from College of Engineering, Guindy, Chennai, India and MBA from Indian Institute of Management, Ahmedabad, India More here
My primary interests include Consumer-Internet products, Statistics, Data-Science, Machine-Learning, Social-Networks, Economics.


Thank You !
We will be in touch shortly. We're looking forward to your demo. If you have any questions feel free to give us a call.
We will be in touch shortly. We're looking forward to your demo. If you have any questions feel free to give us a call.


the and with PSSNa were ZIKV virus was for The cells that {background polymers are using margin replication inhibition {border-style different incubated from px;} #ffffff;} []. Zika infection Cells RNA host observed results values kDa their Assay RT-qPCR cell effect tested viral Vero antiviral four infected inhibitory not number Figure assay attachment low molecular three activity also carried cells. high human out shown Â°C concentration have may other performed polymer presented px; weight which {font-family Â°C. (Figure After Virus associated both cytotoxicity described font-size particles per potential protein samples such this washed < H/PF/ Inhibition K.P.; PBS Then, USA). all average been between binding cell. control. days double; entry its poly(sodium presence showed standard study viruses ÂµL Âµg/mL (TCID (error -styrenesulfonate) Aedes All Cell K.S., M.N., Med. RT-qPCR. [,]. after anionic border-color border-width but can cells, clinical control coverslips data indicate indicates interaction isolation mechanism medium prepared significantly solid; studies synthetic used various virus, vitro where yield {font-style {font-weight (Assay (PSSNa) (Âµg/mL) Data For However, ImageJ Infect. PBS, PSSNa. Poland) Polymeric Triton USA) action against assayâ€�, asterisk available bold case cases cells), charged correlation courier; deviations direct drug during each effective em; evaluated experiments hampers including infection, infection. inhibitors inhibits least lower many min most natural new particle polymeric potent properties pt;} range simplex sites some stages stock strains strains. supernatants syndrome thrice virions whether Â°C, Âµg/mL) Î¼L


This will create an individual tor Connection (if desired- click the Tor Connection button). Optionally to spider clearnet pages just dont use the tor connection.
This allows you to download all images that are hosted on a page. This will also download any image that is pointed to from a LINK on the page.
Wipe your porn collection in the event of a compromise, FBI show up at your door? Wipe your leaked docs with the push of a button.
This device will store a list of files and and will come with a transmitter and receiver. The receiver will listen for a kill signal from the transmitter and if it receives a kill signal will BLEACH all the files it has been told to covering your tracks and keeping them from falling into the wrong hands.
Note: this is a PHYSICAL product and must be mailed to you. Do not use the same tor circuit as you use to divulge NSA secrets when contacting me.
This app will monitor and report the value of many crypto currencies allowing you to day trade or profit during spikes of trade activity.
Tired of the stock markets and just want to focus on Crypto Currencies? Here is your chance.


{% extends "soc/reminder/base.html" %} {% comment %} Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. {% endcomment %} {% block content %}
We've noticed that you have not yet completed the evaluation for your GSoC student {{ student_name }} (project title "{{ project_title }}"). Please take a moment to visit {{ survey_url }} and complete the evaluation immediately. If you need assistance with the evaluation, please try asking for help on the Mentors Mailing list or in #gsoc on Freenode. Please note that we cannot make payment to your student until this evaluation is on file, so it's imperative that we receive it by the posted deadline of {{ survey_end|date:"jS F Y H:i" }} (UTC).
{% endblock %}


0 chracter(s), 0 word(s)
Enjoy using this app? If yes, consider donating so that the experience remains ad-free! :)
Notepad is an offline-capable Progressive Web App.
The app serves the following features:
If you liked using this app, consider a small donation so that I keep working on improving it further and the experience remains ad-free. Icons from Flaticon, SVGRepo and Iconduck.
Made with ❤ by Amit Merchant


|
lwIP
2.1.3
Lightweight IP stack
|
Modules | |
| Options | |
| FDB example code | |
Data Structures | |
| struct | bridgeif_initdata_s |
Macros | |
| #define | BRIDGEIF_INITDATA1(max_ports, max_fdb_dynamic_entries, max_fdb_static_entries, ethaddr) {ethaddr, max_ports, max_fdb_dynamic_entries, max_fdb_static_entries} |
| #define | BRIDGEIF_INITDATA2(max_ports, max_fdb_dynamic_entries, max_fdb_static_entries, e0, e1, e2, e3, e4, e5) {{e0, e1, e2, e3, e4, e5}, max_ports, max_fdb_dynamic_entries, max_fdb_static_entries} |
Typedefs | |
| typedef struct bridgeif_initdata_s | bridgeif_initdata_t |
Functions | |
| err_t | bridgeif_fdb_add (struct netif *bridgeif, const struct eth_addr *addr, bridgeif_portmask_t ports) |
| err_t | bridgeif_fdb_remove (struct netif *bridgeif, const struct eth_addr *addr) |
| err_t | bridgeif_init (struct netif *netif) |
| err_t | bridgeif_add_port (struct netif *bridgeif, struct netif *portif) |
This file implements an IEEE 802.1D bridge by using a multilayer netif approach (one hardware-independent netif for the bridge that uses hardware netifs for its ports). On transmit, the bridge selects the outgoing port(s). On receive, the port netif calls into the bridge (via its netif->input function) and the bridge selects the port(s) (and/or its netif->input function) to pass the received pbuf to.
Usage:
| #define BRIDGEIF_INITDATA1 | ( | max_ports, | |
| max_fdb_dynamic_entries, | |||
| max_fdb_static_entries, | |||
| ethaddr | |||
| ) | {ethaddr, max_ports, max_fdb_dynamic_entries, max_fdb_static_entries} |
Use this for constant initialization of a bridgeif_initdat_t (ethaddr must be passed as ETH_ADDR())
| #define BRIDGEIF_INITDATA2 | ( | max_ports, | |
| max_fdb_dynamic_entries, | |||
| max_fdb_static_entries, | |||
| e0, | |||
| e1, | |||
| e2, | |||
| e3, | |||
| e4, | |||
| e5 | |||
| ) | {{e0, e1, e2, e3, e4, e5}, max_ports, max_fdb_dynamic_entries, max_fdb_static_entries} |
Use this for constant initialization of a bridgeif_initdat_t (each byte of ethaddr must be passed)
| typedef struct bridgeif_initdata_s bridgeif_initdata_t |
Initialisation data for bridgeif_init. An instance of this type must be passed as parameter 'state' to netif_add when the bridge is added.
| err_t bridgeif_fdb_add | ( | struct netif * | bridgeif, |
| const struct eth_addr * | addr, | ||
| bridgeif_portmask_t | ports | ||
| ) |
Add a static entry to the forwarding database. A static entry marks where frames to a specific eth address (unicast or group address) are forwarded. bits [0..(BRIDGEIF_MAX_PORTS-1)]: hw ports bit [BRIDGEIF_MAX_PORTS]: cpu port 0: drop
Remove a static entry from the forwarding database
Initialization function passed to netif_add().
ATTENTION: A pointer to a bridgeif_initdata_t must be passed as 'state' to netif_add when adding the bridge. I supplies MAC address and controls memory allocation (number of ports, FDB size).
| netif | the lwip network interface structure for this ethernetif |


File format: .mp4, .avi, .wav, .3gp
File format: .pdf
MEE REBUS WITH PRAWN PTE LTD
LIMITED EXEMPT PRIVATE COMPANY
WHOLESALE OF MEE REBUS
WHOLESALE OF TOMYUM FRIED RICE


---
layout: post
authors: ["Anelda van der Walt"]
title: "May 6 - 11, 2015: Lesson Prep for Publication, Capturing Instructors' Commands, and Instructor Debriefing."
date: 2015-05-13
time: "14:00:00"
tags: ["Community"]
---
Highlights
Instructor Tips
Details
-
The latest instructor debriefing is now available and provides great advice.
Capturing the instructor's shell, preparing answers for challenges, and using example scripts to model complexity.
-
Congratulations to all Software Carpenters who qualified as instructors last week!
-
Four workshops were run this week: Lawrence Berkeley Lab, University of Melbourne, Swinburne University of Technology,
University of South Florida - College of Marine Science.
-
Several workshops have been confirmed for the next few months.
Upcoming workshops can be viewed on our website.
Newly added workshops are indicated with "*".
-
May:
University of New Mexico,
Western University,
Boston University School of Medicine*,
Stellenbosch University,
University of Oklahoma Libraries,
University of Melbourne,
Oklahoma State Universtiy
-
June:
University of Cambridge,
Materials Physics Center - University of the Basque Country,
California Institute of Technology*,
University of Washington*
-
July:
The University of Queensland,
SciPy 2015,
West Vancouver Memorial Library*,
The University of Queensland - AMOS sciences,
SAP Offices in Feltham,
University of Cape Town*,
Curtin University*
Repository Activity
| Project | Issues | Pull Requests |
| Opened | Closed | Comments | Opened | Closed | Comments |
| shell-novice | 3 | 3 | 19 | 10 | 8 | 10 |
| git-novice | 11 | 8 | 97 | 18 | 15 | 30 |
| hg-novice | 1 | | 4 | 6 | 3 | 1 |
| sql-novice-survey | | | 8 | 5 | 4 | |
| python-novice-inflammation | 10 | 2 | 24 | 10 | 12 | 7 |
| r-novice-inflammation | 1 | | 9 | 5 | 4 | 2 |
| matlab-novice-inflammation | | | 4 | 1 | 1 | 3 |
| slideshows | 1 | 3 | 12 | | 1 | |
| instructor-training | | | | | | |
| amy | 9 | 6 | 110 | 9 | 10 | 8 |
| site | 3 | 2 | 11 | 5 | 4 | 4 |
| Total | 39 | 24 | 298 | 69 | 62 | 65 |
|---|


Bambam gets food for his dad
Specification
Given mr flintstone is "hungry" When he harasses bambam for food Then bambam places a burger order with barney "a burger" When barney gives to bambam "1 burger" Then bambam gives to mr flintstone "1 burger" Then mr flintstone shares his food with barney because he likes him
Test result:
Test passed
Sequence Diagram
Interesting Givens
| String | burger |
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MoveToCode
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Extension methods for .Net Collection objects, e.g. More...
Static Public Member Functions | |
| static ReadOnlyCollection< TElement > | AsReadOnly< TElement > (this IList< TElement > elements) |
| Creates a read-only wrapper around an existing collection. More... | |
| static ReadOnlyCollection< TElement > | ToReadOnlyCollection< TElement > (this IEnumerable< TElement > elements) |
| Creates a read-only copy of an existing collection. More... | |
| static int | SortedInsert< TElement > (this List< TElement > elements, TElement toInsert, IComparer< TElement > comparer=null) |
| Inserts an item in its sorted position into an already sorted collection. More... | |
| static void | DisposeElements< TElement > (this IEnumerable< TElement > elements) |
| Disposes of all non-null elements in a collection. More... | |
| static void | DisposeElements< TElement > (this IList< TElement > elements) |
| Disposes of all non-null elements in a collection. More... | |
| static T[] | ExportDictionaryValuesAsArray< T > (this Dictionary< uint, T > input) |
| Exports the values of a uint indexed Dictionary as an Array More... | |
| static MixedRealityInteractionMapping | GetInteractionByType (this MixedRealityInteractionMapping[] input, DeviceInputType key) |
| Overload extension to enable getting of an InteractionDefinition of a specific type More... | |
| static bool | SupportsInputType (this MixedRealityInteractionMapping[] input, DeviceInputType key) |
| Overload extension to enable getting of an InteractionDefinition of a specific type More... | |
Extension methods for .Net Collection objects, e.g.
Lists, Dictionaries, Arrays
|
static |
Creates a read-only wrapper around an existing collection.
| TElement | The type of element in the collection. |
| elements | The collection to be wrapped. |
|
static |
Disposes of all non-null elements in a collection.
| TElement | The type of element in the collection. |
| elements | The collection of elements to be disposed. |
| TElement | : | IDisposable |
|
static |
Disposes of all non-null elements in a collection.
| TElement | The type of element in the collection. |
| elements | The collection of elements to be disposed. |
| TElement | : | IDisposable |
|
static |
Exports the values of a uint indexed Dictionary as an Array
| T | Type of data stored in the values of the Dictionary |
| input | Dictionary to be exported |
|
static |
Overload extension to enable getting of an InteractionDefinition of a specific type
| input | The InteractionDefinition array reference |
| key | The specific DeviceInputType value to query |
|
static |
Inserts an item in its sorted position into an already sorted collection.
This is useful if you need to consume the collection in between insertions and need it to stay correctly sorted the whole time. If you just need to insert a bunch of items and then consume the sorted collection at the end, it's faster to add all the elements and then use System.Collections.Generic.List<T>.Sort at the end.
| TElement | The type of element in the collection. |
| elements | The collection of sorted elements to be inserted into. |
| toInsert | The element to insert. |
| comparer | The comparer to use when sorting or null to use System.Collections.Generic.Comparer<T>.Default. |
|
static |
Overload extension to enable getting of an InteractionDefinition of a specific type
| input | The InteractionDefinition array reference |
| key | The specific DeviceInputType value to query |
|
static |
Creates a read-only copy of an existing collection.
| TElement | The type of element in the collection. |
| elements | The collection to be copied. |


Miami plays Cleveland for conference battle
by The Associated PressMiami Heat (7-3, third in the Eastern Conference) vs. Cleveland Cavaliers (4-6, seventh in the Eastern Conference)
Cleveland; Thursday, 6 p.m. EST
Eastern Conference foes Cleveland and Miami will play.
Cleveland went 19-63 overall and 15-37 in Eastern Conference action in the 2018-19 season. The Cavaliers averaged 104.5 points per game last season, 44.5 in the paint, 14.4 off of turnovers and 10.6 on fast breaks.
Miami finished 39-43 overall and 23-29 in Eastern Conference games in the 2018-19 season. The Heat averaged 105.7 points per game while shooting 45% from the field and 34.9% from deep last season.
The matchup Thursday is the first meeting of the season between the two teams.
Cavaliers Injuries: Ante Zizic: out (foot), Dylan Windler: out (stress reaction), Kevin Porter Jr.: day to day (left hip contusion), John Henson: out (hamstring), Matthew Dellavedova: day to day (right calf soreness).
Heat Injuries: Justise Winslow: out (concussion), Tyler Herro: day to day (ankle), Derrick Jones Jr.: out (hip), KZ Okpala: day to day (left achilles strain), James Johnson: day to day (illness), Goran Dragic: day to day (unknown).
___
The Associated Press created this story using technology provided by Data Skrive and data from Sportradar.


Esperanto FlashCards
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Donald Tusk elected president of European People’s Party
Ex-Council president the only candidate for the post.
by Maïa de La BaumeZAGREB — The European People's Party on Wednesday elected former European Council President Donald Tusk as its leader, replacing Frenchman Joseph Daul.
Tusk was elected with 491 votes in favor and 37 against at the party's congress in the Croatian capital, meaning Tusk becomes the first Eastern European to take the helm of the biggest political force in the EU.
"With Tusk, we will be opening a new chapter in the history of the EPP," David McAllister, a senior German MEP, told POLITICO. "Tusk has proven to be a bridge builder between the east and the west, and as a former prime minister [of Poland] and president of the Council, he has huge experience and I very much look forward to him shaping the future of Europe's largest political family."
A former member of Poland's Solidarity movement, Tusk became the longest-serving prime minister in Poland and carried out two terms as European Council president.
"Someone who has been a Polish minister for seven years, who has been leading the European Council with straight-talking and very bold when he wants to be, I think he will be debarrassé [spared], as the French would say, of all the honest brokerage style that the president of the European Council needs to have," Andrej Plenković, Croatia's prime minister, told reporters at a press conference on Tuesday.
A few hours before the vote, Tusk gave an impassioned speech which targeted "autocrats," populists and, without naming him directly, Hungarian Prime Minister Viktor Orbán and those "putting up a fence and billboards with anti-migration propaganda."
"After five years, I am fed up with being the European bureaucrat in chief," Tusk told the audience. "I am ready to fight and I hope you are too," he said.


Dual column selection allows the user to choose among several items and order them as he or she prefers via drag and drop. Clicking the checkmark and close icons on an item selects and de-selects the item. Dragging a selected item upward or downward rearranges its order in the selected item list.


perturb(3f) - [M_orderpack:PERMUTATION] generate a random permutation of an array leaving elements close to initial locations
Synopsis
Description
Options
Examples
Author
Maintainer
License
Subroutine Perturb (INOUTVALS, CLOSENESS)
${TYPE} (kind=${KIND}), Intent (InOut) :: INOUTVALS(:) Real, Intent (In) :: CLOSENESSWhere ${TYPE}(kind=${KIND}) may be
o Real(kind=real32) o Real(kind=real64) o Integer(kind=int32) o Character(kind=selected_char_kind("DEFAULT"),len=*)
Shuffle the array INOUTVALS randomly, leaving elements close to their initial locations.Nearbyness is controlled by CLOSENESS. The relative proportion of initial order and random order is defined as 1-CLOSENESS / CLOSENESS, thus when CLOSENESS = 0, there is no change in the order whereas the new order is fully random when CLOSENESS = 1.
Note this differs from adding random noise to the values. The original values remain unchanged, their order is just perturbed.
Internally, the routine creates a real array of the indices of INOUTVALS() perturbed by random values that are based on the size of CLOSENESS. The new array is then ranked using RANK(3f) and the resulting index is used to permute the input array.
INOUTVALS Array of values to perturb. CLOSENESS Proportion of closeness, constrained to the range 0.0(no change) to 1.0(fully random).


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