John Niedzwiecki
function getDeveloperInfo() {
let dev = {
name: 'John Niedzwiecki',
desc: 'Your friendly neighborhood kilted coder.',
company: 'VIPRE Security',
role: 'Lead Software Engineer - UI',
twitter: '@rhgeek',
github: 'rhgeek',
web: 'https://rhgeek.github.io',
email<EMAIL_ADDRESS>currentDevLoves: ['Angular', 'JavaScript Everywhere', 'd3', 'CSS instead of JS'],
nonDevAttr : {
isHusband: true,
isFather: true,
hobbies: ['running', 'disney', 'cooking', 'video games', 'photography'],
twitter: '@rgrdisney',
web: 'http://www.rungeekrundisney.com',
}
};
return dev;
}
At ng-Europe in 2014, the Angular team showed off Angular 2. Unfortunately, the drastic changes were not received well by many. While things have changed since that announcement during the development of Angular 2, many people remember the things said there instead of what was actually done (damn first impressions).
“Angular is a development platform for building mobile and desktop web applications.”
/* /theme-parks/theme-parks-list/theme-parks-list.component.ts */
import { Component, OnInit } from '@angular/core';
import { Observable } from 'rxjs/Rx';
import { ParksService, ThemeParkGroup } from '../../shared';
@Component({
selector: 'app-theme-parks-list',
templateUrl: './theme-parks-list.component.html',
styleUrls: ['./theme-parks-list.component.css']
})
export class ThemeParksListComponent implements OnInit {
themeParks: Observable< ThemeParkGroup[] >;
constructor(
private parksService: ParksService
) { }
ngOnInit() {
this.themeParks = this.parksService.getParks();
}
}
/* /theme-parks/theme-park-details/theme-park-details.component.ts */
import { Component, OnInit } from '@angular/core';
import { ActivatedRoute, Params, Router } from '@angular/router';
import { ParksService, ThemePark } from '../../shared';
@Component({
selector: 'app-theme-park-details',
templateUrl: './theme-park-details.component.html',
styleUrls: ['./theme-park-details.component.css']
})
export class ThemeParkDetailsComponent implements OnInit {
edited: boolean;
favorite: boolean;
themePark: ThemePark;
constructor(
private route: ActivatedRoute,
private parksService: ParksService
) { }
canDeactivate(): Promise< boolean > | boolean {
if(!this.edited) {
return true;
} else {
return new Promise< boolean >(resolve => {
return resolve(window.confirm('Are you sure you\'re ready to leave the park?'));
});
}
}
ngOnInit() {
this.edited = false;
this.favorite = false;
this.route.params
.switchMap((params: Params) => this.parksService.getParkDetails(params['parkId']))
.subscribe(
(park: ThemePark) => {
this.themePark = park;
},
error => {
/* do error stuff here if you must */
});
}
save(): void {
this.edited = false;
}
toggleFavorite(): void {
this.favorite = !this.favorite;
this.edited = true;
}
}
back
/* /shared/parks.service.ts */
import { Injectable } from '@angular/core';
import { HttpClientModule } from '@angular/common/http';
import { Observable } from 'rxjs/Rx';
import { ThemePark } from './theme-park.model';
import { ThemeParkGroup } from './theme-park-group.model';
@Injectable()
export class ParksService {
constructor(private http: HttpClientModule) { }
getParks(): Observable< ThemeParkGroup[] > {
let url: string = `/api/parks/list`;
return this.http.get(url)
.map(response => response as ThemeParkGroup[])
.catch(this.handleError);
}
getParkDetails(parkId: string): Observable< ThemePark > {
let url: string = `/api/parks/${parkId}`;
return this.http.get(url)
.map(response => response as ThemePark)
.catch(this.handleError);
}
private handleError(error: any): Observable {
console.error('An error occurred', error);
return Observable.throw(error.message || error);
}
}
/* /app-routing.module.ts */
import { NgModule } from '@angular/core';
import { Routes, RouterModule } from '@angular/router';
import { CanDeactivateGuard } from './guards/';
import { ThemeParksComponent } from './theme-parks/theme-parks.component';
import { ThemeParksListComponent } from './theme-parks/theme-parks-list/theme-parks-list.component';
import { ThemeParkDetailsComponent } from './theme-parks/theme-park-details/theme-park-details.component';
const routes: Routes = [
{ path: '', redirectTo: 'parks', pathMatch: 'full' },
{ path: 'parks', component: ThemeParksComponent,
children: [
{ path: '', redirectTo: 'list', pathMatch: 'full' },
{ path: 'list', component: ThemeParksListComponent },
{ path: 'details/:parkId', component: ThemeParkDetailsComponent, canDeactivate: [ CanDeactivateGuard ] }
]
},
{ path: 'about', loadChildren: 'app/about/about.module#AboutModule' }
];
@NgModule({
imports: [ RouterModule.forRoot(routes) ],
exports: [ RouterModule ],
providers: [
CanDeactivateGuard
]
})
export class AppRoutingModule { }
/* /theme-parks.component.html */
{{greeting}}
/* from previous routing code */
{ path: 'about', loadChildren: 'app/about/about.module#AboutModule' }
/* /about/about.module.ts */
import { NgModule } from '@angular/core';
import { CommonModule } from '@angular/common';
import { AboutComponent } from './about.component';
import { AboutRoutingModule } from './about-routing.module';
@NgModule({
declarations: [
AboutComponent
],
imports: [
CommonModule,
AboutRoutingModule
]
})
export class AboutModule { }
/* /about/about-routing.module.ts */
import { NgModule } from '@angular/core';
import { Routes, RouterModule } from '@angular/router';
import { AboutComponent } from './about.component';
const routes: Routes = [
{ path: '', component: AboutComponent }
];
@NgModule({
imports: [ RouterModule.forChild(routes) ],
exports: [ RouterModule ]
})
export class AboutRoutingModule { }
AngularJS == 1.x
Angular == 2, 4, 6, 8...
who do we appreciate?
Most people don't care about their version of Chrome, the just want to have the latest.
With new Angular release, the team announced the switch to Semantic Versioning (SEMVER).
Angular 1 to Angular 2 was a complete rewrite. Angular jumped from version 2 to 4 to align the core packages versions (Angular's router was already version 3). Future major updates will be more standard "breaking changes" with proper documented deprecation phases.
From here on, it's just #angular.
Angular 2 was designed "targeting modern browsers and using ECMAScript 6". This meant only evergreen, auto-updating browswers.
Angular supports most recent browsers, on both desktop and mobile.
| Chrome | Firefox | Edge | IE | Safari | iOS | Android | IE mobile |
|---|---|---|---|---|---|---|---|
| latest | latest | 14 | 11 | 10 | 10 | Nougat (7.0) Marshmallow (6.0) |
11 |
| 13 | 10 | 9 | 9 | Lollipop (5.0, 5.1) | |||
| 9 | 8 | 8 | KitKat (4.4) | ||||
| 7 | 7 | Jelly Bean (4.1, 4.2, 4.3) |
There's a challenge in supporting the larger range of browsers. They offer mandatory and optional polyfills, depending on the browsers you want to support.
"Note that polyfills cannot magically transform an old, slow browser into a modern, fast one."
/**
* This file includes polyfills needed by Angular and is loaded before the app.
* You can add your own extra polyfills to this file.
*
* This file is divided into 2 sections:
* 1. Browser polyfills. These are applied before loading ZoneJS and are sorted by browsers.
* 2. Application imports. Files imported after ZoneJS that should be loaded before your main
* file.
*
* The current setup is for so-called "evergreen" browsers; the last versions of browsers that
* automatically update themselves. This includes Safari >= 10, Chrome >= 55 (including Opera),
* Edge >= 13 on the desktop, and iOS 10 and Chrome on mobile.
*
* Learn more in https://angular.io/docs/ts/latest/guide/browser-support.html
*/
/***************************************************************************************************
* BROWSER POLYFILLS
*/
/** IE9, IE10 and IE11 requires all of the following polyfills. **/
// import 'core-js/es6/symbol';
// import 'core-js/es6/object';
// import 'core-js/es6/function';
// import 'core-js/es6/parse-int';
// import 'core-js/es6/parse-float';
// import 'core-js/es6/number';
// import 'core-js/es6/math';
// import 'core-js/es6/string';
// import 'core-js/es6/date';
// import 'core-js/es6/array';
// import 'core-js/es6/regexp';
// import 'core-js/es6/map';
// import 'core-js/es6/weak-map';
// import 'core-js/es6/set';
/** IE10 and IE11 requires the following for NgClass support on SVG elements */
// import 'classlist.js'; // Run `npm install --save classlist.js`.
/** Evergreen browsers require these. **/
import 'core-js/es6/reflect';
import 'core-js/es7/reflect';
/**
* Required to support Web Animations `@angular/animation`.
* Needed for: All but Chrome, Firefox and Opera. http://caniuse.com/#feat=web-animation
**/
// import 'web-animations-js'; // Run `npm install --save web-animations-js`.
/***************************************************************************************************
* Zone JS is required by Angular itself.
*/
import 'zone.js/dist/zone'; // Included with Angular CLI.
/***************************************************************************************************
* APPLICATION IMPORTS
*/
/**
* Date, currency, decimal and percent pipes.
* Needed for: All but Chrome, Firefox, Edge, IE11 and Safari 10
*/
// import 'intl'; // Run `npm install --save intl`.
/**
* Need to import at least one locale-data with intl.
*/
// import 'intl/locale-data/jsonp/en';
Initially, no path to migration was announced. The team's goal was the best framework to build a web app without worrying backwards compatibility with existing APIs.
Obviously, people were less than happy for no concrete migration path.
Not only can you migrate an app, you can upgrade incrementally.
This module allows you to run AngularJS and Angular components in the same application and interoperate with each other seemlessly.
Actually runs both versions of Angular side by side, no emulation. Interoperate via dependency injection, the DOM, and change detection.
After the release, I've heard many arguments about the size of Angular files. It often pops up in comparisions of Angular vs React. You would see the comparison of Angular 2’s minified version (566K) to React JS with add-ons, minified version (144K).
Non-issue for an Angular app thanks to AOT compilation. Angular built to take advantage of ES2015 module and tree shaking.
Proof of concept Hello World can be made in just 29KB
Angular 4 has made even larger improvements for AOT. The team has implemented a new View Engine to generate less code.
Two medium size apps improvement:
from 499Kb to 187Kb (68Kb to 34Kb after gzip)
from 192Kb to 82Kb (27Kb to 16Kb after gzip)
Angular 4.4 has introduced an option to remove whitespace in HTML to reduce size further.
I loved AngularJS, but there were performance issues (I'm looking at you dirty checking and digest cycles). Part of the reason for the rewrite for Angular 2 was to get things right, from the ground up.
 It's also the name of the official package for server side rendering in Angular.
Server side rendering can be important to single page apps (SPA).  It's also great to ensure you get that nifty little preview on social media.
It's also great for perceived performance.
Angular Universal lets you render your app on the server, give it to the user, preboot a div to record their actions, and then replay and swap out once the app is bootstrapped.
Frankly, I was worried about needing to use TypeScript going into Angular 2.
Then I used it.
Now, I it.
Amazing command line tool for Angular applications.
Established community that has grown over many years.
Find me: @rhgeek


Psalms 42:3
My tears have been my meat day and night, while they continually say unto me, Where is thy God?


Related JIRA Ticket: SOHO-5319
Test the destroy/invoke lifecycle of the application menu.
{{> includes/head}}
SkipToMain {{> includes/svg-inline-refs}} {{> includes/applicationmenu-with-searchfield}}Related JIRA Ticket: SOHO-5319
Test the destroy/invoke lifecycle of the application menu.


Name of Book:
Search for book:
The Prophet
The Prophet
The Prophet
The Prophet
The Prophet
The Prophet
The Prophet
Date from:
Date to:
User ID(lease):


"""Univariate features selection."""
# Authors: V. Michel, B. Thirion, G. Varoquaux, A. Gramfort, E. Duchesnay.
# L. Buitinck, A. Joly
# License: BSD 3 clause
import numpy as np
import warnings
from scipy import special, stats
from scipy.sparse import issparse
from ..base import BaseEstimator
from ..preprocessing import LabelBinarizer
from ..utils import (as_float_array, check_array, check_X_y, safe_sqr,
safe_mask)
from ..utils.extmath import safe_sparse_dot, row_norms
from ..utils.validation import check_is_fitted
from .base import SelectorMixin
def _clean_nans(scores):
"""
Fixes Issue #1240: NaNs can't be properly compared, so change them to the
smallest value of scores's dtype. -inf seems to be unreliable.
"""
# XXX where should this function be called? fit? scoring functions
# themselves?
scores = as_float_array(scores, copy=True)
scores[np.isnan(scores)] = np.finfo(scores.dtype).min
return scores
######################################################################
# Scoring functions
# The following function is a rewriting of scipy.stats.f_oneway
# Contrary to the scipy.stats.f_oneway implementation it does not
# copy the data while keeping the inputs unchanged.
def f_oneway(*args):
"""Performs a 1-way ANOVA.
The one-way ANOVA tests the null hypothesis that 2 or more groups have
the same population mean. The test is applied to samples from two or
more groups, possibly with differing sizes.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
sample1, sample2, ... : array_like, sparse matrices
The sample measurements should be given as arguments.
Returns
-------
F-value : float
The computed F-value of the test.
p-value : float
The associated p-value from the F-distribution.
Notes
-----
The ANOVA test has important assumptions that must be satisfied in order
for the associated p-value to be valid.
1. The samples are independent
2. Each sample is from a normally distributed population
3. The population standard deviations of the groups are all equal. This
property is known as homoscedasticity.
If these assumptions are not true for a given set of data, it may still be
possible to use the Kruskal-Wallis H-test (`scipy.stats.kruskal`_) although
with some loss of power.
The algorithm is from Heiman[2], pp.394-7.
See ``scipy.stats.f_oneway`` that should give the same results while
being less efficient.
References
----------
.. [1] Lowry, Richard. "Concepts and Applications of Inferential
Statistics". Chapter 14.
http://faculty.vassar.edu/lowry/ch14pt1.html
.. [2] Heiman, G.W. Research Methods in Statistics. 2002.
"""
n_classes = len(args)
args = [as_float_array(a) for a in args]
n_samples_per_class = np.array([a.shape[0] for a in args])
n_samples = np.sum(n_samples_per_class)
ss_alldata = sum(safe_sqr(a).sum(axis=0) for a in args)
sums_args = [np.asarray(a.sum(axis=0)) for a in args]
square_of_sums_alldata = sum(sums_args) ** 2
square_of_sums_args = [s ** 2 for s in sums_args]
sstot = ss_alldata - square_of_sums_alldata / float(n_samples)
ssbn = 0.
for k, _ in enumerate(args):
ssbn += square_of_sums_args[k] / n_samples_per_class[k]
ssbn -= square_of_sums_alldata / float(n_samples)
sswn = sstot - ssbn
dfbn = n_classes - 1
dfwn = n_samples - n_classes
msb = ssbn / float(dfbn)
msw = sswn / float(dfwn)
constant_features_idx = np.where(msw == 0.)[0]
if (np.nonzero(msb)[0].size != msb.size and constant_features_idx.size):
warnings.warn("Features %s are constant." % constant_features_idx,
UserWarning)
f = msb / msw
# flatten matrix to vector in sparse case
f = np.asarray(f).ravel()
prob = special.fdtrc(dfbn, dfwn, f)
return f, prob
def f_classif(X, y):
"""Compute the ANOVA F-value for the provided sample.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
X : {array-like, sparse matrix} shape = [n_samples, n_features]
The set of regressors that will be tested sequentially.
y : array of shape(n_samples)
The data matrix.
Returns
-------
F : array, shape = [n_features,]
The set of F values.
pval : array, shape = [n_features,]
The set of p-values.
See also
--------
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
"""
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'])
args = [X[safe_mask(X, y == k)] for k in np.unique(y)]
return f_oneway(*args)
def _chisquare(f_obs, f_exp):
"""Fast replacement for scipy.stats.chisquare.
Version from https://github.com/scipy/scipy/pull/2525 with additional
optimizations.
"""
f_obs = np.asarray(f_obs, dtype=np.float64)
k = len(f_obs)
# Reuse f_obs for chi-squared statistics
chisq = f_obs
chisq -= f_exp
chisq **= 2
with np.errstate(invalid="ignore"):
chisq /= f_exp
chisq = chisq.sum(axis=0)
return chisq, special.chdtrc(k - 1, chisq)
def chi2(X, y):
"""Compute chi-squared stats between each non-negative feature and class.
This score can be used to select the n_features features with the
highest values for the test chi-squared statistic from X, which must
contain only non-negative features such as booleans or frequencies
(e.g., term counts in document classification), relative to the classes.
Recall that the chi-square test measures dependence between stochastic
variables, so using this function "weeds out" the features that are the
most likely to be independent of class and therefore irrelevant for
classification.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
X : {array-like, sparse matrix}, shape = (n_samples, n_features_in)
Sample vectors.
y : array-like, shape = (n_samples,)
Target vector (class labels).
Returns
-------
chi2 : array, shape = (n_features,)
chi2 statistics of each feature.
pval : array, shape = (n_features,)
p-values of each feature.
Notes
-----
Complexity of this algorithm is O(n_classes * n_features).
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
f_regression: F-value between label/feature for regression tasks.
"""
# XXX: we might want to do some of the following in logspace instead for
# numerical stability.
X = check_array(X, accept_sparse='csr')
if np.any((X.data if issparse(X) else X) < 0):
raise ValueError("Input X must be non-negative.")
Y = LabelBinarizer().fit_transform(y)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
observed = safe_sparse_dot(Y.T, X) # n_classes * n_features
feature_count = X.sum(axis=0).reshape(1, -1)
class_prob = Y.mean(axis=0).reshape(1, -1)
expected = np.dot(class_prob.T, feature_count)
return _chisquare(observed, expected)
def f_regression(X, y, center=True):
"""Univariate linear regression tests.
Linear model for testing the individual effect of each of many regressors.
This is a scoring function to be used in a feature seletion procedure, not
a free standing feature selection procedure.
This is done in 2 steps:
1. The correlation between each regressor and the target is computed,
that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *
std(y)).
2. It is converted to an F score then to a p-value.
For more on usage see the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
X : {array-like, sparse matrix} shape = (n_samples, n_features)
The set of regressors that will be tested sequentially.
y : array of shape(n_samples).
The data matrix
center : True, bool,
If true, X and y will be centered.
Returns
-------
F : array, shape=(n_features,)
F values of features.
pval : array, shape=(n_features,)
p-values of F-scores.
See also
--------
mutual_info_regression: Mutual information for a continuous target.
f_classif: ANOVA F-value between label/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFdr: Select features based on an estimated false discovery rate.
SelectFwe: Select features based on family-wise error rate.
SelectPercentile: Select features based on percentile of the highest
scores.
"""
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float64)
n_samples = X.shape[0]
# compute centered values
# note that E[(x - mean(x))*(y - mean(y))] = E[x*(y - mean(y))], so we
# need not center X
if center:
y = y - np.mean(y)
if issparse(X):
X_means = X.mean(axis=0).getA1()
else:
X_means = X.mean(axis=0)
# compute the scaled standard deviations via moments
X_norms = np.sqrt(row_norms(X.T, squared=True) -
n_samples * X_means ** 2)
else:
X_norms = row_norms(X.T)
# compute the correlation
corr = safe_sparse_dot(y, X)
corr /= X_norms
corr /= np.linalg.norm(y)
# convert to p-value
degrees_of_freedom = y.size - (2 if center else 1)
F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom
pv = stats.f.sf(F, 1, degrees_of_freedom)
return F, pv
######################################################################
# Base classes
class _BaseFilter(BaseEstimator, SelectorMixin):
"""Initialize the univariate feature selection.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues) or a single array with scores.
"""
def __init__(self, score_func):
self.score_func = score_func
def fit(self, X, y):
"""Run score function on (X, y) and get the appropriate features.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values (class labels in classification, real numbers in
regression).
Returns
-------
self : object
Returns self.
"""
X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=True)
if not callable(self.score_func):
raise TypeError("The score function should be a callable, %s (%s) "
"was passed."
% (self.score_func, type(self.score_func)))
self._check_params(X, y)
score_func_ret = self.score_func(X, y)
if isinstance(score_func_ret, (list, tuple)):
self.scores_, self.pvalues_ = score_func_ret
self.pvalues_ = np.asarray(self.pvalues_)
else:
self.scores_ = score_func_ret
self.pvalues_ = None
self.scores_ = np.asarray(self.scores_)
return self
def _check_params(self, X, y):
pass
######################################################################
# Specific filters
######################################################################
class SelectPercentile(_BaseFilter):
"""Select features according to a percentile of the highest scores.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues) or a single array with scores.
Default is f_classif (see below "See also"). The default function only
works with classification tasks.
percentile : int, optional, default=10
Percent of features to keep.
Attributes
----------
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores, None if `score_func` returned only scores.
Notes
-----
Ties between features with equal scores will be broken in an unspecified
way.
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
mutual_info_classif: Mutual information for a discrete target.
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
mutual_info_regression: Mutual information for a continuous target.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFdr: Select features based on an estimated false discovery rate.
SelectFwe: Select features based on family-wise error rate.
GenericUnivariateSelect: Univariate feature selector with configurable mode.
"""
def __init__(self, score_func=f_classif, percentile=10):
super(SelectPercentile, self).__init__(score_func)
self.percentile = percentile
def _check_params(self, X, y):
if not 0 <= self.percentile <= 100:
raise ValueError("percentile should be >=0, <=100; got %r"
% self.percentile)
def _get_support_mask(self):
check_is_fitted(self, 'scores_')
# Cater for NaNs
if self.percentile == 100:
return np.ones(len(self.scores_), dtype=np.bool)
elif self.percentile == 0:
return np.zeros(len(self.scores_), dtype=np.bool)
scores = _clean_nans(self.scores_)
treshold = stats.scoreatpercentile(scores,
100 - self.percentile)
mask = scores > treshold
ties = np.where(scores == treshold)[0]
if len(ties):
max_feats = int(len(scores) * self.percentile / 100)
kept_ties = ties[:max_feats - mask.sum()]
mask[kept_ties] = True
return mask
class SelectKBest(_BaseFilter):
"""Select features according to the k highest scores.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues) or a single array with scores.
Default is f_classif (see below "See also"). The default function only
works with classification tasks.
k : int or "all", optional, default=10
Number of top features to select.
The "all" option bypasses selection, for use in a parameter search.
Attributes
----------
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores, None if `score_func` returned only scores.
Notes
-----
Ties between features with equal scores will be broken in an unspecified
way.
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
mutual_info_classif: Mutual information for a discrete target.
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
mutual_info_regression: Mutual information for a continuous target.
SelectPercentile: Select features based on percentile of the highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFdr: Select features based on an estimated false discovery rate.
SelectFwe: Select features based on family-wise error rate.
GenericUnivariateSelect: Univariate feature selector with configurable mode.
"""
def __init__(self, score_func=f_classif, k=10):
super(SelectKBest, self).__init__(score_func)
self.k = k
def _check_params(self, X, y):
if not (self.k == "all" or 0 <= self.k <= X.shape[1]):
raise ValueError("k should be >=0, <= n_features; got %r."
"Use k='all' to return all features."
% self.k)
def _get_support_mask(self):
check_is_fitted(self, 'scores_')
if self.k == 'all':
return np.ones(self.scores_.shape, dtype=bool)
elif self.k == 0:
return np.zeros(self.scores_.shape, dtype=bool)
else:
scores = _clean_nans(self.scores_)
mask = np.zeros(scores.shape, dtype=bool)
# Request a stable sort. Mergesort takes more memory (~40MB per
# megafeature on x86-64).
mask[np.argsort(scores, kind="mergesort")[-self.k:]] = 1
return mask
class SelectFpr(_BaseFilter):
"""Filter: Select the pvalues below alpha based on a FPR test.
FPR test stands for False Positive Rate test. It controls the total
amount of false detections.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues).
Default is f_classif (see below "See also"). The default function only
works with classification tasks.
alpha : float, optional
The highest p-value for features to be kept.
Attributes
----------
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores.
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks.
mutual_info_classif:
f_regression: F-value between label/feature for regression tasks.
mutual_info_regression: Mutual information between features and the target.
SelectPercentile: Select features based on percentile of the highest scores.
SelectKBest: Select features based on the k highest scores.
SelectFdr: Select features based on an estimated false discovery rate.
SelectFwe: Select features based on family-wise error rate.
GenericUnivariateSelect: Univariate feature selector with configurable mode.
"""
def __init__(self, score_func=f_classif, alpha=5e-2):
super(SelectFpr, self).__init__(score_func)
self.alpha = alpha
def _get_support_mask(self):
check_is_fitted(self, 'scores_')
return self.pvalues_ < self.alpha
class SelectFdr(_BaseFilter):
"""Filter: Select the p-values for an estimated false discovery rate
This uses the Benjamini-Hochberg procedure. ``alpha`` is an upper bound
on the expected false discovery rate.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues).
Default is f_classif (see below "See also"). The default function only
works with classification tasks.
alpha : float, optional
The highest uncorrected p-value for features to keep.
Attributes
----------
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores.
References
----------
https://en.wikipedia.org/wiki/False_discovery_rate
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
mutual_info_classif: Mutual information for a discrete target.
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
mutual_info_regression: Mutual information for a contnuous target.
SelectPercentile: Select features based on percentile of the highest scores.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFwe: Select features based on family-wise error rate.
GenericUnivariateSelect: Univariate feature selector with configurable mode.
"""
def __init__(self, score_func=f_classif, alpha=5e-2):
super(SelectFdr, self).__init__(score_func)
self.alpha = alpha
def _get_support_mask(self):
check_is_fitted(self, 'scores_')
n_features = len(self.pvalues_)
sv = np.sort(self.pvalues_)
selected = sv[sv <= float(self.alpha) / n_features *
np.arange(1, n_features + 1)]
if selected.size == 0:
return np.zeros_like(self.pvalues_, dtype=bool)
return self.pvalues_ <= selected.max()
class SelectFwe(_BaseFilter):
"""Filter: Select the p-values corresponding to Family-wise error rate
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues).
Default is f_classif (see below "See also"). The default function only
works with classification tasks.
alpha : float, optional
The highest uncorrected p-value for features to keep.
Attributes
----------
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores.
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
SelectPercentile: Select features based on percentile of the highest scores.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFdr: Select features based on an estimated false discovery rate.
GenericUnivariateSelect: Univariate feature selector with configurable mode.
"""
def __init__(self, score_func=f_classif, alpha=5e-2):
super(SelectFwe, self).__init__(score_func)
self.alpha = alpha
def _get_support_mask(self):
check_is_fitted(self, 'scores_')
return (self.pvalues_ < self.alpha / len(self.pvalues_))
######################################################################
# Generic filter
######################################################################
# TODO this class should fit on either p-values or scores,
# depending on the mode.
class GenericUnivariateSelect(_BaseFilter):
"""Univariate feature selector with configurable strategy.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
score_func : callable
Function taking two arrays X and y, and returning a pair of arrays
(scores, pvalues). For modes 'percentile' or 'kbest' it can return
a single array scores.
mode : {'percentile', 'k_best', 'fpr', 'fdr', 'fwe'}
Feature selection mode.
param : float or int depending on the feature selection mode
Parameter of the corresponding mode.
Attributes
----------
scores_ : array-like, shape=(n_features,)
Scores of features.
pvalues_ : array-like, shape=(n_features,)
p-values of feature scores, None if `score_func` returned scores only.
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
mutual_info_classif: Mutual information for a discrete target.
chi2: Chi-squared stats of non-negative features for classification tasks.
f_regression: F-value between label/feature for regression tasks.
mutual_info_regression: Mutual information for a continuous target.
SelectPercentile: Select features based on percentile of the highest scores.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFdr: Select features based on an estimated false discovery rate.
SelectFwe: Select features based on family-wise error rate.
"""
_selection_modes = {'percentile': SelectPercentile,
'k_best': SelectKBest,
'fpr': SelectFpr,
'fdr': SelectFdr,
'fwe': SelectFwe}
def __init__(self, score_func=f_classif, mode='percentile', param=1e-5):
super(GenericUnivariateSelect, self).__init__(score_func)
self.mode = mode
self.param = param
def _make_selector(self):
selector = self._selection_modes[self.mode](score_func=self.score_func)
# Now perform some acrobatics to set the right named parameter in
# the selector
possible_params = selector._get_param_names()
possible_params.remove('score_func')
selector.set_params(**{possible_params[0]: self.param})
return selector
def _check_params(self, X, y):
if self.mode not in self._selection_modes:
raise ValueError("The mode passed should be one of %s, %r,"
" (type %s) was passed."
% (self._selection_modes.keys(), self.mode,
type(self.mode)))
self._make_selector()._check_params(X, y)
def _get_support_mask(self):
check_is_fitted(self, 'scores_')
selector = self._make_selector()
selector.pvalues_ = self.pvalues_
selector.scores_ = self.scores_
return selector._get_support_mask()


--- layout: default title: Permissions description: Permissions in obray PHP id: permissions ---
coming soon


--- title: Training Loop keywords: fastai sidebar: home_sidebar summary: "Define the general Training Loop" description: "Define the general Training Loop" nb_path: "nbs\04_train_loop.ipynb" ---
args = get_all_args()
args.track = False
args.model_type = "bigan"
device = torch.device(
"cuda:0" if (torch.cuda.is_available() and args.n_gpu > 0) else "cpu"
)
model = get_model(device, args)


Page title
Main title
Main teaser
Main teaser subtitle
Sidebar title
Sidebar teaser
Sidebar teaser subtitle


CAIVINKeyboardDelegate
public protocol CAIVINKeyboardDelegate
The CoxAutomotiveVINKeyboardDelegate
protocol must be adopted by the VIN keyboard delegate, it is the
only means of receiving messages about the state of the VIN keyboard.
-
Informs the delegate that the keyboard will add a new character to the textField.
Declaration
Swift
func keyboardWillAddCharacter(string: String)
Parameters
string
The character that will be added to the VIN keyboard’s textField text.
-
Informs the delegate that the keyboard has changed the textField’s text.
Declaration
Swift
func keyboardDidChangeText(text: String)
Parameters
text
The new text that the textField now contains.
-
The keyboard will be dismissed via the use of a keyboard control key. This is available on iPad only.
Declaration
Swift
func keyboardWillDismissKeyboard()


Bitmap
public final class Bitmap
Bitmap describes a two-dimensional raster pixel array. Bitmap is built on ImageInfo, containing integer width and height, ColorType and AlphaType describing the pixel format, and ColorSpace describing the range of colors. Bitmap points to PixelRef, which describes the physical array of pixels. ImageInfo bounds may be located anywhere fully inside PixelRef bounds.
Bitmap can be drawn using Canvas. Bitmap can be a drawing destination for Canvas draw member functions. Bitmap flexibility as a pixel container limits some optimizations available to the target platform.
If pixel array is primarily read-only, use Image for better performance. If pixel array is primarily written to, use Surface for better performance.
Bitmap is not thread safe. Each thread must have its own copy of Bitmap fields, although threads may share the underlying pixel array.
-
Creates an empty Bitmap without pixels, with
colorType
set to.unkonwn
alphaType
set to.unknown
, and with a width and height of zero. PixelRef origin is set to (0, 0). Bitmap is not volatile.Declaration
Swift
public init()
Return Value
empty Bitmap example: https://fiddle.skia.org/c/@Bitmap_empty_constructor
-
Undocumented
Declaration
Swift
public convenience init(_ width: Int32, _ height: Int32, isOpaque: Bool = false) throws
-
Undocumented
Declaration
Swift
public init(_ imageInfo: ImageInfo, rowBytes: Int? = nil) throws
-
Returns row bytes, the interval from one pixel row to the next. Row bytes is at least as large as:
width
*imageInfo.bytesPerPixel
. Returns zero if colorType is.unknown
, or if row bytes supplied to setInfo() is not large enough to hold a row of pixels.Declaration
Swift
public var rowBytes: Int { get }
Return Value
byte length of pixel row
-
Sets
ImageInfo
to info following and allocates pixel memory.rowBytes
must equal or exceedinfo.width
timesinfo.bytesPerPixel
, or equal zero. Pass in zero forrowBytes
to compute the minimum valid value. Returnsfalse
and callsreset()
ifImageInfo
could not be set, or memory could not be allocated.On most platforms, allocating pixel memory may succeed even though there is not sufficient memory to hold pixels; allocation does not take place until the pixels are written to. The actual behavior depends on the platform implementation of malloc().
Declaration
Swift
public func tryAllocPixels(info: sk_imageinfo_t, rowBytes: Int? = nil) -> Bool
Parameters
info
contains width, height,
AlphaType
,ColorType
,ColorSpace
rowBytes
size of pixel row or larger; may be zero
Return Value
true if pixel storage is allocated
-
Resets to its initial state; all fields are set to zero, as if
Bitmap
had been initialized byBitmap
().Sets
width
,height
,row bytes
to zero; pixel address tonil
;ColorType
to.unknown
; andAlphaType
to.unknown
.If
PixelRef
is allocated, its reference count is decreased by one, releasing its memory ifBitmap
is the sole owner.Declaration
Swift
public func reset()
-
Sets internal flag to mark
Bitmap
as immutable. Once set, pixels can not change. Any other bitmap sharing the samePixelRef
are also marked as immutable. OncePixelRef
is marked immutable, the setting cannot be cleared. Writing to immutableBitmap
pixels triggers an assert on debug builds. example: https://fiddle.skia.org/c/@Bitmap_setImmutableDeclaration
Swift
public func setImmutable()
-
Replaces pixel values with c. All pixels contained by the bounds are affected. If the
colorType
is.gray9
or.krgb565
, then alpha is ignored; RGB is treated as opaque. If colorType is.alpha8
then RGB is ignored.Declaration
Swift
public func erase(_ color: Color)
Parameters
c
unpremultiplied color example: https://fiddle.skia.org/c/@Bitmap_eraseColor
-
Undocumented
-
Undocumented
Declaration
Swift
public func getAddr8(x: Int32, y: Int32) -> UInt8
-
Undocumented
Declaration
Swift
public func getAddr16(x: Int32, y: Int32) -> UInt16
-
Undocumented
Declaration
Swift
public func getAddr32(x: Int32, y: Int32) -> UInt32
-
Undocumented
Declaration
Swift
public func getAddr64(x: Int32, y: Int32) -> UnsafeMutableRawPointer?
-
Undocumented
Declaration
Swift
public func getPixel(x: Int32, y: Int32) -> Color
-
Undocumented
Declaration
Swift
public func setPixel(x: Int32, y: Int32, value: Color)
-
Undocumented
Declaration
Swift
public func canCopy(to colorType: ColorType) -> Bool
-
Undocumented
Declaration
Swift
public var imageInfo: ImageInfo { get }
-
Undocumented
Declaration
Swift
public var width: Int32 { get }
-
Undocumented
Declaration
Swift
public var height: Int32 { get }
-
Undocumented
Declaration
Swift
public var alphaType: AlphaType { get }
-
Undocumented
Declaration
Swift
public var bytesPerPixel: Int32 { get }
-
Undocumented
Declaration
Swift
public var colorType: ColorType { get }
-
Undocumented
Declaration
Swift
public var byteCount: Int { get }
-
Undocumented
Declaration
Swift
public func getPixels() -> (buffer: UnsafeMutableRawPointer?, len: Int)
-
Undocumented
Declaration
Swift
public func setPixels(buffer: UnsafeMutableRawPointer)
-
Returns the pixels if they are available without having to lock the bitmap.
Declaration
Swift
public func peekPixels() -> Pixmap?
Return Value
Returns the pixels if they are available, otherwise null.
-
Returns the pixmap of the bitmap
Declaration
Swift
public func peekPixels(_ pixmap: Pixmap) -> Bool
Return Value
true on success, false on failure


Fits structural equation models using partial least squares (PLS). The PLS approach is referred to as 'soft-modeling' technique requiring no distributional assumptions on the observed data.
| Version: | 1.0-10 |
| Depends: | lattice (≥ 0.20-5) |
| Suggests: | XML, boot, sem, matrixcalc |
| Published: | 2013-01-21 |
| Author: | Armin Monecke [aut, cph, cre], Friedrich Leisch [aut, ths] |
| Maintainer: | Armin Monecke <armin.monecke at stat.uni-muenchen.de> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | semPLS citation info |
| Materials: | NEWS |
| In views: | Psychometrics |
| CRAN checks: | semPLS results |
| Reference manual: | semPLS.pdf |
| Vignettes: |
semPLS: Structural Equation Modeling Using Partial Least Squares |
| Package source: | semPLS_1.0-10.tar.gz |
| Windows binaries: | r-devel: semPLS_1.0-10.zip, r-release: semPLS_1.0-10.zip, r-oldrel: semPLS_1.0-10.zip |
| OS X binaries: | r-release: semPLS_1.0-10.tgz, r-oldrel: semPLS_1.0-10.tgz |
| Old sources: | semPLS archive |
| Reverse suggests: | matrixpls |
Please use the canonical form https://CRAN.R-project.org/package=semPLS to link to this page.


BUTTON_NAME_OK
BUTTON_NAME_OK
Represents the common options for dialog program.
$option_clear : boolean
Clears the widget screen, keeping only the screen_color back-ground.
Use this when you combine widgets with "--and-widget" to erase the contents of a previous widget on the screen, so it won't be seen under the contents of a following widget. Under- stand this as the complement of "--keep-window". To compare the effects, use these:
All three widgets visible, staircase effect, ordered 1,2,3: dialog \ --begin 2 2 --yesno "" 0 0 \ --and-widget --begin 4 4 --yesno "" 0 0 \ --and-widget --begin 6 6 --yesno "" 0 0 Only the last widget is left visible: dialog \ --clear --begin 2 2 --yesno "" 0 0 \ --and-widget --clear --begin 4 4 --yesno "" 0 0 \ --and-widget --begin 6 6 --yesno "" 0 0 All three widgets visible, staircase effect, ordered 3,2,1: dialog \ --keep-window --begin 2 2 --yesno "" 0 0 \ --and-widget --keep-window --begin 4 4 --yesno "" 0 0 \ --and-widget --begin 6 6 --yesno "" 0 0 First and third widget visible, staircase effect, ordered 3,1: dialog \ --keep-window --begin 2 2 --yesno "" 0 0 \ --and-widget --clear --begin 4 4 --yesno "" 0 0 \ --and-widget --begin 6 6 --yesno "" 0 0 Note, if you want to restore original console colors and send your cursor home after the dialog program has exited, use the clear (1) command.
$option_colors : mixed
Interpret embedded "\Z" sequences in the dialog text by the fol-lowing character, which tells dialog to set colors or video at-tributes:
0 through 7 are the ANSI color numbers used in curses: black, red, green, yellow, blue, magenta, cyan and white re-spectively.
Bold is set by 'b', reset by 'B'.
Reverse is set by 'r', reset by 'R'.
Underline is set by 'u', reset by 'U'.
The settings are cumulative, e.g., "\Zb\Z1" makes the fol-lowing text bold (perhaps bright) red.
$option_cr_wrap : boolean
Interpret embedded newlines in the dialog text as a newline on the screen. Otherwise, dialog will only wrap lines where needed to fit inside the text box.
Even though you can control line breaks with this, Dialog will still wrap any lines that are too long for the width of the box.
Without cr-wrap, the layout of your text may be formatted to look nice in the source code of your script without affecting the way it will look in the dialog.
$option_defaultno : boolean
$option_default_button : string
Set the default (preselected) button in a widget.
By preselecting a button, a script makes it possible for the user to simply press Enter to proceed through a dialog with minimum interaction.
The option's value is the name of the button: ok, yes, cancel, no, help or extra.
Normally the first button in each widget is the default. The first button shown is determined by the widget together with the "--nook" and "--nocancel options. If this option is not given, there is no default button assigned.
$option_help_button : boolean
Show a help-button after "OK" and "Cancel" buttons, i.e., in checklist, radiolist and menu boxes.
On exit, the return status will indicate that the Help button was pressed. Dialog will also write a message to its output after the token "HELP":
If "--item-help" is also given, the item-help text will be written.
You can use the --help-tags option and/or set the DIALOG_ITEM_HELP environment variable to modify these messages and exit-status.
$option_input_fd : \Dialog\type
Read keyboard input from the given file descriptor. Most dialog scripts read from the standard input, but the gauge widget reads a pipe (which is always standard input). Some configurations do not work properly when dialog tries to reopen the terminal. Use this option (with appropriate juggling of file-descriptors) if your script must work in that type of environment.
$option_keep_tite : boolean
When built with ncurses, dialog normally checks to see if it is running in an xterm, and in that case tries to suppress the initialization strings that would make it switch to the alternate screen. Switching between the normal and alternate screens is visually distracting in a script which runs dialog several times. Use this option to allow dialog to use those initialization strings.
$option_keep_window : boolean
Normally when dialog performs several tailboxbg widgets connected by "--and-widget", it clears the old widget from the screen by painting over it. Use this option to suppress that repainting.
At exit, dialog repaints all of the widgets which have been marked with "--keep-window", even if they are not tailboxbg widgets.
That causes them to be repainted in reverse order. See the discussion of the "--clear" option for examples.
$option_no_collapse : boolean
Normally dialog converts tabs to spaces and reduces multiple spaces to a single space for text which is displayed in a message boxes, etc. Use this option to disable that feature. Note that dialog will still wrap text, subject to the "--cr-wrap" and "--trim" options.
$option_no_items : boolean
Some widgets (checklist, inputmenu, radiolist, menu) display a list with two columns (a "tag" and "item", i.e., "description").
This option tells dialog to read shorter rows, omitting the "item" part of the list. This is occasionally useful, e.g., if the tags provide enough information.
See also --no-tags. If both options are given, this one is ignored.
$option_no_tags : boolean
Some widgets (checklist, inputmenu, radiolist, menu) display a list with two columns (a "tag" and "description"). The tag is useful for scripting, but may not help the user. The --no-tags option (from Xdialog) may be used to suppress the column of tags from the display. Unlike the --no-items option, this does not affect the data which is read from the script.
Xdialog does not display the tag column for the analogous buildlist and treeview widgets; dialog does the same.
Normally dialog allows you to quickly move to entries on the displayed list, by matching a single character to the first character of the tag. When the --no-tags option is given, dialog matches against the first character of the description. In either case, the matchable character is highlighted.
$option_separate_widget : string
Specify a string that will separate the output on dialog's output from each widget. This is used to simplify parsing the result of a dialog with several widgets. If this option is not given, the default separator string is a tab character.
$option_single_quoted : boolean
Use single-quoting as needed (and no quotes if unneeded) for the output of checklist's as well as the item-help text. If this option is not set, dialog uses double quotes around each item.
In either case, dialog adds backslashes to make the output useful in shell scripts.
$option_stdout : boolean
Direct output to the standard output. This option is provided for compatibility with Xdialog, however using it in portable scripts is not recommended, since curses normally writes its screen updates to the standard output. If you use this option, dialog attempts to reopen the terminal so it can write to the display. Depending on the platform and your environment, that may fail.
$option_timeout : integer
Timeout (exit with error code) if no user response within the given number of seconds. A timeout of zero seconds is ignored.
This option is ignored by the "--pause" widget. It is also overridden if the background "--tailboxbg" option is used to setup multiple concurrent widgets.
$option_week_start : integer
Sets the starting day for the week, used in the "--calendar" option.
The day parameter can be:
a number (0 to 6, Sunday through Saturday using POSIX) or
the special value "locale" (this works with systems using glibc, providing an extension to the locale command, the first_weekday value).
The dialog program handles some command-line parameters specially, and removes them from the parameter list as they are processed. For example, if the first option is --trace, then that is processed (and removed) before dialog initializes the display.
$option_visit_items : boolean
Modify the tab-traversal of checklist, radiolist, menubox and inputmenu to include the list of items as one of the states.
This is useful as a visual aid, i.e., the cursor position helps some users.
When this option is given, the cursor is initially placed on the list. Abbreviations (the first letter of the tag) apply to the list items. If you tab to the button row, abbreviations apply to the buttons.


Please enter your JIRA login
Please make sure you checked your JQL and labels twice before pressing button below. This application does not check labels and JQL for validness. Only you responsible for final result and will have to perform tonns of manual work restoring labels in case of error.


|
|
Location |
| Click on the map to visit |
The school is organized at the University Residential Center of Bertinoro, situated in Bertinoro, a small village on a scenic hill with a wonderful panorama, in between Forlì and Cesena (about 50 miles south-east of Bologna, 15 miles to the Adriatic sea). Bertinoro is close to many splendid locations (such as the Republic of San Marino and the nearby Gradara, San Leo, and Urbino) and can also be a base for visiting some of the better-known Italian locations such as Bologna, Ravenna, and Firenze. The cheapest way to travel is by plane to Forli airport (the secondary airport of the international airport of Bologna "G. Marconi"), which is daily connected to London and Frankfurt AM through the low fares airline Ryanair. Bertinoro is also easily reached by train and bus from Bologna. By train, the trip takes approx. 45 min from Bologna Centrale to Forlì, and then approx. 20 min by bus from Forlì (when exiting from the train station you can find the bus station at about 50 mt. on the left) to Bertinoro. |


>>
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Usr
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JfC
LJ
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Voc
!:
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Primer
Single atom array
An array with a single atom is referred to as a singleton. All singletons with the same atom display the same way. However, the fact that they have different ranks affects how verbs act on them. This can be a pitfall for beginners. It is important to remember that if it displays like an atom, but does not behave like one, then check its rank.
atom =. 5
list =. 1 $ 5
atom
5
list NB. list looks like atom
5
atom + 23 23 23
28 28 28
list + 23 23 23 NB. but does not behave like atom
|length error
| list +23 23 23
# $ atom NB. rank of atom is 0
0
# $ list NB. rank of list is 1
1
>>
<<
Usr
Pri
JfC
LJ
Phr
Dic
Voc
!:
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Primer


Proudly serving the KW community a taste of South Asia since 1990
Onkar Foods & Spices is a South Asian family-run grocery store in KW. Started in 1990, our small grocery store has expanded into 3 locations—and growing everyday!
We invite you to browse our extensive array of spices, authentic South Asian sweets, imported and fresh vegetables. For those looking for ready-meals, we sell freshly-made samosas and Punjabi-style take-out meals made in our very own kitchen. Our family is dedicated to serve our community an authentic taste of Punjab, India.
We sell fresh fruits, vegatables, beans, rice and flours all to help you prepare your dinner. Along with a variety of spices, frozen foods, ready to eat meals and drinks.
Made fresh every-day, our handmade samosas and pakoras have become a big hit in KW. Plus, we sell traditional Indian sweets from the classic gulab jamun and jalebi to different types of barfi.
We make variety of traditional Punjabi meals all made within our kitchen available for take-out! Check out our menu.
Looking for catering? We do that too—just make sure to call us!
At our newest Highland location, you can find a wide selection of plants and flowers from our greenhouse for both your garden or your home.
101 Hazelglen Drive
Kitchener, N2M 5A2
(519) 745-4699
| Mon | 10am - 8pm |
| Tues | 10am - 8pm |
| Wed | 10am - 8pm |
| Thurs | 10am - 8pm |
| Fri | 9am - 9pm |
| Sat | 9am - 8pm |
| Sun | 10am - 7pm |
500 Glen Forrest Blvd
Waterloo, N2L 5G4
(519) 746-3659
| Mon | 12pm - 8pm |
| Tues | 10am - 8pm |
| Wed | 10am - 8pm |
| Thurs | 10am - 8pm |
| Fri | 10am - 9pm |
| Sat | 10am - 8pm |
| Sun | 10am - 7pm |
1541 Highland Rd W
Kitchener, N2N 3K4
(226) 647-3600
| Mon | 11am - 8pm |
| Tues | 11am - 8pm |
| Wed | 11am - 8pm |
| Thurs | 11am - 8pm |
| Fri | 10am - 8pm |
| Sat | 10am - 8pm |
| Sun | 10am - 7pm |
*Holiday hours may vary, please call location.


{% extends "base.html" %} {% set active_page = "services" %} {% block head %} {{ super() }} {% endblock %} {% block page_content %}
Reach the dive spot of your dreams with a boat!
Search for your favourite dive spot!
See photos of a dive spot uploaded from other divers and upload yours too!


All SUSE customers get access to their subscriptions through the SUSE Customer Center (SCC).
We manage the client systems' access to online repositories, product activation, and system migration. This year, we already released 65 products.


mbtools
is a collection of helpers that we use to analyze microbiome data. It makes it easier to run some common analyses and is pretty opinionated towards our own experiences.
mbtools
sees analyses as a workflow consisting of several analysis steps and final outputs based on those. This is pretty similar to Qiime 2 and most of the functionality in mbtools
is supposed to be orthogonal and not in competition to those other excellent tools.
mbtools
mostly works on the following data types:
For mbtools
a workflow step is based on input data and a configuration, thus having the function signature step(object, config)
. Most steps can be chained with the pipe operator to yield workflows. For instance, the following is a possible workflow with mbtools
:
library(mbtools)
config <- list(
demultiplex = config_demultiplex(barcodes = c("ACGTA", "AGCTT")),
preprocess = config_preprocess(truncLen = 200),
denoise = config_denoise()
)
output <- find_read_files("raw") %>%
demultiplex(config$demultiplex) %>%
quality_control() %>%
preprocess(config$preprocess) %>%
denoise(config$denoise)
This clearly logs the used workflow and the configuration. The configuration can also be saved and read in many formats, for instance yaml.
config.yaml:
preprocess:
threads: yes
out_dir: preprocessed
trimLeft: 10.0
truncLen: 200.0
maxEE: 2.0
denoise:
threads: yes
nbases: 2.5e+08
pool: no
bootstrap_confidence: 0.5
taxa_db: https://zenodo.org/record/1172783/files/silva_nr_v132_train_set.fa.gz?download=1
species_db: https://zenodo.org/record/1172783/files/silva_species_assignment_v132.fa.gz?download=1
hash: yes
This can now be reused by someone else:
config <- read_yaml("config.yml")
output <- find_read_files("raw") %>%
quality_control() %>%
preprocess(config$preprocess) %>%
denoise(config$denoise)


'High Art' (1998)
A movie review by Walter Frith
<EMAIL_ADDRESS>Member of the ‘Online Film Critics Society' http://ofcs.org/ofcs/
'High Art' is the kind of hard-core art film that cries out for understanding. It's repetitive imagery is a reflection of its surreal, moody and often abbreviated characterizations. It has all the qualities of sleaze, drama, reality and blemishes of the soul that a sophisticated movie buff can and should appreciated. What it lacks in the area of common story telling is compensated by its uncompromising look at the alternative side of some people's lives.
Ally Sheedy stars as Lucy, a semi-retired photographer who never really understood the beauty of her work. She watches over her materialistic mother who spends six thousand dollars in the blink of an eye on her American Express card and can't remember what she spent it on. She and her friends are primarily drug addicts and drifters in life. One in particular (Patricia Clarkson), is a German movie actress of limited stature who doesn't seem to care about the effects of snorting dope and she lives her life in a constant state of flagrancy.
The protagonist in the film is Syd (Radha Mitchell). She is an assistant editor of a fashionable New York City magazine who discovers Lucy's work and asks her superiors if they're interested in looking at her work. Her superiors are aware of who Lucy is. Sort of an underground hero of the photographic art world, Lucy agrees after a meeting with Syd and her superiors, to do a piece for the magazine, and throughout the course of their working relationship, Lucy and Syd become lovers. Greta and Lucy also have a past and they never quite end their relationship until later in the film as the film's sub plot is sort of an exploration of their feelings for each other.
Paced in a very sedate manner, writer/director Lisa Cholodenko finds the emotional core of each character in the film as being very straightforward and brings absolutely no glamour whatsoever to any of their lives or appearances and this is the film's strength. Appearances count for a lot in this film. Sheedy and Mitchell look very comfortable and convincing in their roles and their performances are extraordinarily sharp.
There isn't a false note in this film which runs a perfect 101 minutes. Not too long or too short. It has Oscar worthy performances from Sheedy and Clarkson and after watching Ally Sheedy in films from the 80's such as 'WarGames', 'The Breakfast Club', 'St Elmo's Fire', 'Short Circuit', her career in the 90's has become a true showcase of her real depth and talent as an actress.
As a modern day tale of tragedy, 'High Art' has few flaws and fewer shortcomings and is filled with interpretive dialogue showing the nervous calculation of each life it chooses to document. Some to the extreme and some to a lesser extent. But in the end, one will most likely walk away from it remembering not how it looked but rather how it felt.
OUT OF 5 > * * * 1/2
Visit FILM FOLLOW-UP by Walter Frith http://www.cgocable.net/~wfrith/movies.htm
* * * * * - a must see * * * * 1/2 - don't miss it * * * * - an excellent film * * * 1/2 - a marginal recommendation * * * - can't quite recommend it * * 1/2 - don't recommend it * * - avoid it * - avoid it AT ALL COSTS 1/2 - see it at your own risk zero - may be hazardous to your health
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


org.opencv.imgcodecs
Class Imgcodecs
java.lang.Object
org.opencv.imgcodecs.Imgcodecs
public class Imgcodecs
- extends java.lang.Object
|
Method Summary |
static Mat |
imdecode(Mat buf,
int flags)
|
static boolean |
imencode(java.lang.String ext,
Mat img,
MatOfByte buf)
|
static boolean |
imencode(java.lang.String ext,
Mat img,
MatOfByte buf,
MatOfInt params)
|
static Mat |
imread(java.lang.String filename)
|
static Mat |
imread(java.lang.String filename,
int flags)
|
static boolean |
imreadmulti(java.lang.String filename,
java.util.List<Mat> mats)
|
static boolean |
imreadmulti(java.lang.String filename,
java.util.List<Mat> mats,
int flags)
|
static boolean |
imwrite(java.lang.String filename,
Mat img)
|
static boolean |
imwrite(java.lang.String filename,
Mat img,
MatOfInt params)
|
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
CV_CVTIMG_FLIP
public static final int CV_CVTIMG_FLIP
- See Also:
- Constant Field Values
CV_CVTIMG_SWAP_RB
public static final int CV_CVTIMG_SWAP_RB
- See Also:
- Constant Field Values
CV_IMWRITE_JPEG_CHROMA_QUALITY
public static final int CV_IMWRITE_JPEG_CHROMA_QUALITY
- See Also:
- Constant Field Values
CV_IMWRITE_JPEG_LUMA_QUALITY
public static final int CV_IMWRITE_JPEG_LUMA_QUALITY
- See Also:
- Constant Field Values
CV_IMWRITE_JPEG_OPTIMIZE
public static final int CV_IMWRITE_JPEG_OPTIMIZE
- See Also:
- Constant Field Values
CV_IMWRITE_JPEG_PROGRESSIVE
public static final int CV_IMWRITE_JPEG_PROGRESSIVE
- See Also:
- Constant Field Values
CV_IMWRITE_JPEG_QUALITY
public static final int CV_IMWRITE_JPEG_QUALITY
- See Also:
- Constant Field Values
CV_IMWRITE_JPEG_RST_INTERVAL
public static final int CV_IMWRITE_JPEG_RST_INTERVAL
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_BILEVEL
public static final int CV_IMWRITE_PNG_BILEVEL
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_COMPRESSION
public static final int CV_IMWRITE_PNG_COMPRESSION
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_STRATEGY
public static final int CV_IMWRITE_PNG_STRATEGY
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_STRATEGY_DEFAULT
public static final int CV_IMWRITE_PNG_STRATEGY_DEFAULT
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_STRATEGY_FILTERED
public static final int CV_IMWRITE_PNG_STRATEGY_FILTERED
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_STRATEGY_FIXED
public static final int CV_IMWRITE_PNG_STRATEGY_FIXED
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY
public static final int CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY
- See Also:
- Constant Field Values
CV_IMWRITE_PNG_STRATEGY_RLE
public static final int CV_IMWRITE_PNG_STRATEGY_RLE
- See Also:
- Constant Field Values
CV_IMWRITE_PXM_BINARY
public static final int CV_IMWRITE_PXM_BINARY
- See Also:
- Constant Field Values
CV_IMWRITE_WEBP_QUALITY
public static final int CV_IMWRITE_WEBP_QUALITY
- See Also:
- Constant Field Values
CV_LOAD_IMAGE_ANYCOLOR
public static final int CV_LOAD_IMAGE_ANYCOLOR
- See Also:
- Constant Field Values
CV_LOAD_IMAGE_ANYDEPTH
public static final int CV_LOAD_IMAGE_ANYDEPTH
- See Also:
- Constant Field Values
CV_LOAD_IMAGE_COLOR
public static final int CV_LOAD_IMAGE_COLOR
- See Also:
- Constant Field Values
CV_LOAD_IMAGE_GRAYSCALE
public static final int CV_LOAD_IMAGE_GRAYSCALE
- See Also:
- Constant Field Values
CV_LOAD_IMAGE_UNCHANGED
public static final int CV_LOAD_IMAGE_UNCHANGED
- See Also:
- Constant Field Values
IMREAD_ANYCOLOR
public static final int IMREAD_ANYCOLOR
- See Also:
- Constant Field Values
IMREAD_ANYDEPTH
public static final int IMREAD_ANYDEPTH
- See Also:
- Constant Field Values
IMREAD_COLOR
public static final int IMREAD_COLOR
- See Also:
- Constant Field Values
IMREAD_GRAYSCALE
public static final int IMREAD_GRAYSCALE
- See Also:
- Constant Field Values
IMREAD_LOAD_GDAL
public static final int IMREAD_LOAD_GDAL
- See Also:
- Constant Field Values
IMREAD_UNCHANGED
public static final int IMREAD_UNCHANGED
- See Also:
- Constant Field Values
IMWRITE_JPEG_CHROMA_QUALITY
public static final int IMWRITE_JPEG_CHROMA_QUALITY
- See Also:
- Constant Field Values
IMWRITE_JPEG_LUMA_QUALITY
public static final int IMWRITE_JPEG_LUMA_QUALITY
- See Also:
- Constant Field Values
IMWRITE_JPEG_OPTIMIZE
public static final int IMWRITE_JPEG_OPTIMIZE
- See Also:
- Constant Field Values
IMWRITE_JPEG_PROGRESSIVE
public static final int IMWRITE_JPEG_PROGRESSIVE
- See Also:
- Constant Field Values
IMWRITE_JPEG_QUALITY
public static final int IMWRITE_JPEG_QUALITY
- See Also:
- Constant Field Values
IMWRITE_JPEG_RST_INTERVAL
public static final int IMWRITE_JPEG_RST_INTERVAL
- See Also:
- Constant Field Values
IMWRITE_PNG_BILEVEL
public static final int IMWRITE_PNG_BILEVEL
- See Also:
- Constant Field Values
IMWRITE_PNG_COMPRESSION
public static final int IMWRITE_PNG_COMPRESSION
- See Also:
- Constant Field Values
IMWRITE_PNG_STRATEGY
public static final int IMWRITE_PNG_STRATEGY
- See Also:
- Constant Field Values
IMWRITE_PNG_STRATEGY_DEFAULT
public static final int IMWRITE_PNG_STRATEGY_DEFAULT
- See Also:
- Constant Field Values
IMWRITE_PNG_STRATEGY_FILTERED
public static final int IMWRITE_PNG_STRATEGY_FILTERED
- See Also:
- Constant Field Values
IMWRITE_PNG_STRATEGY_FIXED
public static final int IMWRITE_PNG_STRATEGY_FIXED
- See Also:
- Constant Field Values
IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY
public static final int IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY
- See Also:
- Constant Field Values
IMWRITE_PNG_STRATEGY_RLE
public static final int IMWRITE_PNG_STRATEGY_RLE
- See Also:
- Constant Field Values
IMWRITE_PXM_BINARY
public static final int IMWRITE_PXM_BINARY
- See Also:
- Constant Field Values
IMWRITE_WEBP_QUALITY
public static final int IMWRITE_WEBP_QUALITY
- See Also:
- Constant Field Values
Imgcodecs
public Imgcodecs()
imdecode
public static Mat imdecode(Mat buf,
int flags)
imencode
public static boolean imencode(java.lang.String ext,
Mat img,
MatOfByte buf)
imencode
public static boolean imencode(java.lang.String ext,
Mat img,
MatOfByte buf,
MatOfInt params)
imread
public static Mat imread(java.lang.String filename)
imread
public static Mat imread(java.lang.String filename,
int flags)
imreadmulti
public static boolean imreadmulti(java.lang.String filename,
java.util.List<Mat> mats)
imreadmulti
public static boolean imreadmulti(java.lang.String filename,
java.util.List<Mat> mats,
int flags)
imwrite
public static boolean imwrite(java.lang.String filename,
Mat img)
imwrite
public static boolean imwrite(java.lang.String filename,
Mat img,
MatOfInt params)


Next-gen Web Services [NWS]
Modify an existing webpage
Select from the following modification options
User Management - Add or Remove Users
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SUSPIRIA A film review by Mike Watson Copyright 1997 Mike Watson
DIRECTED BY: Dario Argento SCREENPLAY: Dario Argento, Thomas De Quincey, Daria Nicolodi STARRING: Jessica Harper RUNTIME: 93 minutes RATING: 4 out of 5
"Psycho meets the Exorcist with no holds barred". A blurb like that sounds like desperate publicity for some low budget, third rate rip-off. But no, this tacky plug comes from the poster for SUSPIRIA, perhaps Italian horror director Dario Argento's most consistently successful film.
What impresses most in re-watching this surrealistic, supernatural thriller is that Argento's directorial flair and stylish visuals are matched by a near-fully coherent narrative. Screenplays have always been his Achilles heel; confusing subplots, gaping holes, totally illogical incidents and reactions. No question: Argento has always been a better director than a writer. Fortunately, he gets the plotting pretty well right with SUSPIRIA, taking his cue from a book by Thomas De Quincey called "Suspiria de Profundis". The film's storyline suffers from no major lapses in logic, provided one can accept the premise of a ballet school being a front for a coven of witches.
Jessica Harper plays an American who travels to Germany to further her studies in dance. Arriving at the school late at night in driving rain, she sees a girl leave and run into the storm in a clearly distressed state. What happens next - and I don't make the comparison lightly - is a murder sequence that for intensity, shock value and sheer directorial flair ranks alongside Alfred Hitchcock's infamous shower scene in PSYCHO. It's quite possibly a homage to Hitchcock, but with its baroque sets, graphic gore and brilliantly chaotic musical accompaniment by the Gothic rock group Goblin, this stunning sequence remains very much Argento's own.
Though nothing else quite equals the intensity of SUSPIRIA'S opening 15 minutes, the film continues to be never anything less than engrossing as Harper begins her own investigations and slowly comes to realise this is no ordinary ballet school. We meet an assortment of slightly sinister characters, most notably the head ballet teacher played by Alida Valli in another one of those delicious sour-old-matriarch-from-hell roles she does so well. Several more violent murders ensue, including a startling scene in a deserted square where a blind man is...well, I wouldn't want to spoil things now, would I? And the film's climax, staged amid secret corridors and hidden rooms, has a Gothic ghoulishness that's most impressive.
The English language versions of most Argento movies usually suffer trimming, and not just for the violence - DEEP RED and PHENOMENA, for example, have been appallingly hacked about. Fortunately, the cuts to SUSPIRIA (about 7 minutes in this version) have not adversely affected the film. The dubbing of the Italian actors into English is quite competently done, too, excepting one appalling and unintentionally hilarious effort with a conversation between Harper and a professor about the history of witchcraft in the region.
And the soundtrack? It is interesting to imagine what power the film would have lost if it had been scored by someone else. As it stands, the use of Goblin's music in SUSPIRIA is outstanding. Apparently, Argento played the soundtrack on location while filming so the actors could hear the music as they acted out their parts. A stereo video player adds greatly to the viewer's overall experience. Even better, see SUSPIRIA in a cinema, where you'll get the added bonus of widescreen.
And the colours! Deep reds, eerie blues, a superb use of shadow and contrast. The cinematography of Luciano Tovoli, whose more recent credits include REVERSAL OF FORTUNE and SINGLE WHITE FEMALE, is uniformly superb. A nod also must go to production designer Giuseppe Bassan for his wonderful baroque interiors. These kinds of settings are crucial to the atmosphere of Argento's more surreal excursions, the other most notable example being INFERNO (1980) on which Bassan also worked.
SUSPIRIA is the film that broke Dario Argento into the major league of European directors, and it is fully deserving of its cult status. The gore is at times a little excessive and overstated - that's always been one of Argento's shortcomings - but overall this is a fascinating, compelling example of Italian horror.
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


Order your custom SC shirt!
Salmon Cookies
On August 3, 2000, Salmon Cookies opened its doors and started serving our neighbors fresh cookies from salmon for the first time. Today we're still using the freshest salmon to make the finest cookies you'll ever find.
Just like the day we opened, we're here, brewing morning coffee, taking hot Salmon Cookies out of the oven...
Hope you'll swim by and say “Hello!”
See you here soon
Read Our Blog


| withr {withr} | R Documentation |
All functions prefixed by with_
work as follows. First, a particular
aspect of the global environment is modified (see below for a list).
Then, custom code (passed via the code
argument) is executed.
Upon completion or error, the global environment is restored to the previous
state. Each with_
function has a local_
variant, which instead resets
the state when the current evaluation context ends (such as the end of a
function).
new | [various] | Values for setting |
code | [any] | Code to execute in the temporary environment |
... | Further arguments | |
with_...(new, code, ...)
with_collate()
: collation order
with_dir()
: working directory
with_envvar()
: environment variables
with_libpaths()
: library paths, replacing current libpaths
with_locale()
: any locale setting
with_makevars()
: Makevars variables
with_options()
: options
with_par()
: graphics parameters
with_path()
: PATH
environment variable
with_sink()
: output redirection
All with_
functions are created by a helper function,
with_()
. This functions accepts two arguments:
a setter function and an optional resetter function. The setter function is
expected to change the global state and return an "undo instruction".
This undo instruction is then passed to the resetter function, which changes
back the global state. In many cases, the setter function can be used
naturally as resetter.
Maintainer: Jim Hester<EMAIL_ADDRESS>Authors:
Kirill Müller<EMAIL_ADDRESS>Kevin Ushey<EMAIL_ADDRESS>Hadley Wickham<EMAIL_ADDRESS>Winston Chang
Other contributors:
Jennifer Bryan [contributor]
Richard Cotton [contributor]
RStudio [copyright holder]
Useful links:
Report bugs at https://github.com/r-lib/withr/issues
getwd() with_dir(tempdir(), getwd()) getwd() Sys.getenv("WITHR") with_envvar(c("WITHR" = 2), Sys.getenv("WITHR")) Sys.getenv("WITHR") with_envvar(c("A" = 1), with_envvar(c("A" = 2), action = "suffix", Sys.getenv("A")) ) # local variants are best used within other functions f <- function(x) { local_envvar(c("WITHR" = 2)) Sys.getenv("WITHR") } Sys.getenv("WITHR")


Honzi
/
Guides
/
World of Warcraft
/Daily Routine
Limited Time Events:
Darkmoon Faire
Holiday activities and achievements
Daily Quests:
Cooking
Fishing
General Task TODOs
Cleanup:
Organize your bags
Deposit items into your bank
Repair your equipment


Simple WoW API Webapp
Enter a single WoW in-game name below and click the button to display profile info via JSON.
Grab Players Toon


Write a few lines about the speakers to highlight their expertise in the field.
This card has supporting text below as a natural lead-in to additional content.
Last updated 3 mins ago
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Last updated 3 mins ago
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Last updated 3 mins ago
Each ticket includes: 20 speakers, 1000 participants, 15 sessions, 5 keyknotes.
Our event will be held at the Main Office headquarters at Provost & East Building 145 City Road, London.


---
title: Danone International Case Study
layout: base
---
Case Study
"Convox has helped us get back control over our web hosting process while reducing our ops costs and workload. The team was also super proactive in assisting us with our more complex intranet hosting platform, helping us meet very strict security and compliance requirements.”
Danone uses the Convox private Platform-as-a-Service, to deploy mobile, web and IoT innovation projects. This enables them set up new development and deployment environments in minutes. The system is easy to train new staff and contractors on, and sets an example of software development and operations best practices for the company at large.
Convox, and the way it leverages AWS, brings consistency and control to how Danone uses the cloud. It also fits into Danone’s strict internal security and compliance requirements and integrates well with the other AWS systems.
Danone currently runs 150 applications and growing on the Convox platform.
Danone is a leading global food company based in Paris. The company employs 100,000 people and operates in 130 markets with both international and local brands. It has four business lines in fresh dairy, water, early life nutrition and medical nutrition.
Danone builds and operates a range of customer-facing and internal applications in the Business Transformation team. This team is tasked with driving innovation throughout the entire company by building new project prototypes, owning flagship products and services and implementing cultural best practices for building software.
For this team, agility is a top most priority.
A year ago, the company decided that they needed a new cloud strategy to support the speed and scale at which the team operates. However it needed to ensure the cloud provider offered comprehensive security and privacy on top of agility. “We were looking for a Platform-as-a-Service option that fit into our very unique company compliance requirements”, Sechet says.
After running applications on Amazon Web Services Elastic Compute Cloud (AWS EC2) for a while, Danone sought to consolidate disparate systems onto a better architecture that follows all the best practices on AWS.
To help with this effort, the company turned to Convox, an AWS Partner Network (APN) partner specializing in a Docker container-based Platform-as-a-Service.
Convox provides an open-source PaaS built on top of Amazon Virtual Private Cloud (VPC) and EC2 Container Service (ECS).
“We liked the fact that Convox uses AWS the right way” says Sechet. “Convox allows you install the platform on top of an existing VPC so it even works in the extremely locked down VPC our security team manages.”
Danone uses 7 separate installs of Convox Rack to manage development, staging and production environments with 100 applications.
Danone now has a private PaaS solution that makes it easy to create, deploy and manage their applications. All the innovation apps are managed by Convox inside the corporate VPC, where exposure to the public internet is extremely limited.
Danone has the agility it needs from the cloud. “Using Convox, setting up a new project only takes a few minutes. We now have virtually no friction for setting up a new innovation project or proof-of-concept, which makes innovating cheap and efficient.” says Sechet.
The Convox team also worked closely with the Danone team to customize the platform to work with the corporate firewall. This customization was at a fraction of the cost of hiring a full-time infrastructure engineer or contractor.
The company developers now spend the majority of their time building new applications.
“Convox really is a game changer for Danone. The goal for this year is to deploy it worldwide for every business unit,” says Sechet.
 It runs in cloud infrastructure that you own for security and isolation. The platform is used for PCI and HIPAA compliant deployments. It is open-source which makes it easy to customize for corporate requirements.


[viewBag] title = "Contact page" url = "/contact-page" layout = "static_layout" is_hidden = 0 navigation_hidden = 0 ==
This is contact page


---
title: "44+ Malibu Liquor Logo
Background"
description: "Logo is the copyright of the."
date: "2021-01-24"
categories:
- "Logo"
images:
- "https://www.marketviewliquor.com/mm5/graphics/00000001/MALIBU%20175.jpg"
featuredImage: "https://bremerswineandliquor.com/wp-content/uploads/2020/05/malibu-strawberry-rum.png"
featured_image: "https://st4.depositphotos.com/1063437/27461/i/600/depositphotos_274611230-stock-photo-bottle-of-malibu-caribbean-rum.jpg"
image: "https://i5.walmartimages.com/asr/419cbe01-c526-4246-9ebe-a21ba680aa07_1.60e579d413b9315988f42aded2b25bb6.jpeg?odnWidth=612&odnHeight=612&odnBg=ffffff"
---
44+ Malibu Liquor Logo
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Malibu liquor wine and spirit.
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This is a prototype for the Court Info System project. In case you find something to improve, push the blue Feedback-button here and write your improvements. In this way you will help us to build better tool for you.


#include <dune/xt/functions/expression/mathexpr.hh>
|
| int | operator== (const ROperation &, const double) |
| |
| int | operator== (const ROperation &, const ROperation &) |
| |
| int | operator!= (const ROperation &, const ROperation &) |
| |
| ROperation | operator, (const ROperation &, const ROperation &) |
| |
| ROperation | operator+ (const ROperation &, const ROperation &) |
| |
| ROperation | operator- (const ROperation &, const ROperation &) |
| |
| ROperation | operator* (const ROperation &, const ROperation &) |
| |
| ROperation | operator/ (const ROperation &, const ROperation &) |
| |
| ROperation | operator^ (const ROperation &, const ROperation &) |
| |
| ROperation | sqrt (const ROperation &) |
| |
| ROperation | abs (const ROperation &) |
| |
| ROperation | sin (const ROperation &) |
| |
| ROperation | cos (const ROperation &) |
| |
| ROperation | tan (const ROperation &) |
| |
| ROperation | log (const ROperation &) |
| |
| ROperation | exp (const ROperation &) |
| |
| ROperation | acos (const ROperation &) |
| |
| ROperation | asin (const ROperation &) |
| |
| ROperation | atan (const ROperation &) |
| |
| ROperation | ApplyOperator (int, ROperation **, ROperation(*)(const ROperation &, const ROperation &)) |
| |
| ROperation::ROperation |
( |
| ) |
|
| ROperation::ROperation |
( |
double |
x | ) |
|
| ROperation::ROperation |
( |
const RVar & |
varp | ) |
|
| ROperation::ROperation |
( |
const char * |
sp, |
|
|
int |
nvarp = 0 , |
|
|
PRVar * |
ppvarp = NULL , |
|
|
int |
nfuncp = 0 , |
|
|
PRFunction * |
ppfuncp = NULL |
|
) |
| |
| ROperation::~ROperation |
( |
| ) |
|
| signed char ROperation::ContainFunc |
( |
const RFunction & |
func | ) |
const |
| signed char ROperation::ContainFuncNoRec |
( |
const RFunction & |
func | ) |
const |
| signed char ROperation::ContainVar |
( |
const RVar & |
varp | ) |
const |
| char * ROperation::Expr |
( |
| ) |
const |
| signed char ROperation::HasError |
( |
const ROperation * |
pop = NULL | ) |
const |
| int ROperation::NMembers |
( |
| ) |
const |
| double ROperation::Val |
( |
| ) |
const |
| int operator== |
( |
const ROperation & |
op, |
|
|
const double |
v |
|
) |
| |
|
friend |
| const RVar* ROperation::pvar |
| double* ROperation::pvarval |
The documentation for this class was generated from the following files:


The Astropy Project is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages.
The Astropy community is committed to supporting diversity and inclusion.
Please remember to acknowledge and cite the use of Astropy!
conda update astropyIf you instead installed miniconda, you can use the following to install astropy and its dependencies:
conda install astropyTo install astropy from source into a existing Python installation without using Anaconda, use the following:
pip install astropy
More detailed installation instructions (e.g., for building from source code locally) are in the documentation.
You can explore the functionality available in Astropy by checking out the Example Gallery, Tutorials, and Documentation.
Example Gallery Tutorials DocumentationIf you have any questions regarding using Astropy there are numerous channels for communication. Post to any one of several forums to get help from our active, helpful, and friendly community of users and developers.
Get HelpIf you encounter something you believe to be a mistake, error, or bug, the best way to get it addressed is to report it on the github issue tracker. If you aren't sure if something is a bug or not, or if you don't have a Github account, feel free to ask on one of the forums. If you believe you know how to fix the problem, please consider contributing!
Report issues ContributeIf you use Astropy in your work, we would be grateful if you could include an acknowledgment in papers and/or presentations. See Acknowledging & Citing Astropy for details.
You can also purchase apparel and trinkets from fashion.astropy.org, and a portion of the profits go to support the project!
If you are interested in directly financially supporting Astropy (either one-time or recurring), you can do so via our fiscal sponsor NumFOCUS:
Donate to AstropyDocuments, notes from previous meetings, and talks about Astropy are collected in a Zenodo community for long-term archiving. Everyone is encouraged to submit talks, etc. and other relevant materials.
Zenodo community


Test Plan for: Begs Smartwatch app Component
Persona Type Tested: Primary, Secondary, Negative
Context: A meeting room from the office for the primary, and at home for the rest.
Duration:15 minutes
Complete the following before starting: Greeting and Consent & User Background Information.
Overall Objectives
- What is the user's opinion about the main page from Begs smartwatch application
- How intuitive the navigation of the shopping list is
Scenario
The users will be given the smartwatch were the have to create 4 gestures, left, right for navigating list, swipe up to confirm the purchase and swipe down to remove from list.
Tasks
- How can you navigate the product list? 30sec
- How can you confirm the purchase? 60sec
- How can you delete the product? 60sec
Post-test Questions
These questions could be presented to the user as a questionnaire or verbally asked.
- What was the first impression when you saw the smartwatch screen?
- How difficult was it at first to find te commands?
- Do you believe you need help to figure them out?
Notes for Test Coordinator
- Remind the user that you would like them to 'think aloud' so that you can understand what they are thinking, about the task or the product/application;
- Read the user the scenario for the test;
- Read the user the tasks he has to complete. If the tasks are complex, you may want to put a sheet of paper in front of the user with each task written out;
- Record the user's actions, comments, questions, and body language (write them in the Test Observations section accompanied by video/audio).
- Don't offer help; let the user attempt to perform the task. If they ask for help reply with: "What do you think you/that would do?" "What do you think that means?".
References
- Fluid User Testing;
- usability.gov Templates;
- Handbook of Usability Testing by Jeffrey Rubin;
- A Practical Guide to Usability Testing by Joseph S. Dumas and Janice C. Redish.
Other Notes
- Users that don't own a smartwatch may not be as interested as others that do.


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Thug jailed after lobbing rocks at a house in Hamilton
William Shanks was jailed for nearly a year.
by Hamilton Advertiser, https://www.dailyrecord.co.uk/authors/hamilton-advertiser/A man has been jailed for nearly a year after rocks were thrown at the living room window of a Hamilton house.
The missiles smashed the glass and narrowly missed the occupants of the room.
William Shanks, 27, a prisoner, was sentenced at Hamilton Sheriff Court on Friday.
He admitted causing damage to the house and a car that was parked outside in Dean Crescent on September 28.
Shanks also admitted illegal possession of a knife. He was on bail at the time of the offences.
The court heard Sharon McAslan and Charmaine Fields were in the living room when Shanks and another man launched their attack on the property.
When the women went outside to the confront them, the men ran away and Shanks was seen to throw a kitchen knife into a garden.
Defence agent Jackson Bateman said his client went to the house after an incident involving his sister.
A man and two women had gone to her mother’s house, where she was living, and caused a scene.
Mr Bateman told the court: “William Shanks was not thinking clearly due to the emotional state of his sister.
“Thankfully, no one was injured when he went to this house, but it was upsetting for the persons concerned.”
Sheriff Thomas Millar noted that Shanks has a “lengthy” record and jailed him for 11 months, to start next month when another sentence he is serving ends.