Chapter 2 Pre-Modeling
2.1 Import
2.1.1 General
A schema is a blueprint for storing data. For a table every row has same number of columns, and each column is of the same type.
Make codebooks
Sample from data if its too large
Apache Arrow
https://www.webscraper.io/documentation#open-web-scraper
2.1.2 SQL
Maintain a query library
Common Table Expressions
cross apply = inner join | outer apply = full join
SQL UNION stacks one dataset on top of the other. It only appends distinct values. More specifically, when you use UNION, the dataset is appended, and any rows in the appended table that are exactly identical to rows in the first table are dropped. If you’d like to append all the values from the second table, use UNION ALL.
COUNT(DISTINCT month): returns count of non-null values like table in R
The CASE statement is followed by at least one pair of WHEN and THEN statements: CASE WHEN year = ‘SR’ THEN ‘yes’ ELSE NULL END. You can also define a number of outcomes in a CASE statement by including as many WHEN/THEN statements as you’d like
If you include two (or more) columns in a SELECT DISTINCT clause, your results will contain all of the unique pairs of those two columns
2.2 Tidy & Transform
2.2.1 General
purrlyr::by_row in R
In tidy data: Each variable forms a column. Each observation forms a row. Each type of observational unit forms a table. There are three interrelated rules which make a dataset tidy: 1) Each variable must have its own column, 2) Each observation must have its own row, 3) Each value must have its own cell.
Use the Gower distance for categorical dissimilarity. Binned Stats 1 | 2
Character Encoding Precendence: utf-8, iso-8859-1, utf-16
Outlier Detection: interquartile range, kernel density estimation, bonferroni test
Imputation. Options include the Mean, Mode, KNN, Random, and Regression
outliers: 1) dropping is an option but a bad one, 2) create a new columns to mark outliers, 3) rescale with the log to reduce the effect of outliers
tomek link: points of different classes which are neighbors of each other. Removing them often improves model performance
- There is no one rule about when a number is not accurate enough to use, but as a rule of thumb, you should be cautious about using any number with a MOE (Margin-of-error) over 10%.
A good formula is: if your sample is picked uniformly at random and is of size at least: \(\frac{-log(\frac{d}{2})}{2e^2}\)
then with probability at least 1-d your sample measured proportion q is no further away than e from the unknown true population proportion p (what you are trying to estimate). Need to estimate with 99% certainty the unknown true population rate to +- 10% precision? That is d=0.01, e=0.1 and a sample of size 265 should be enough. A stricter precision of +-0.05 causes the estimated sample size to increase to 1060, but increasing the certainty requirement to 99.5% only increases the estimated sample size to 300.
Use spatial sign to transform data with outliers.
Too many levels for a category: 1) limit number of categories through feature selection, 2) https://stats.stackexchange.com/questions/95212/improve-classification-with-many-categorical-variables, 3) bucket groups by number of samples
Long story short, if these outliers are really such (i.e. they appear with a very low frequency and very likely are bad/random/corrupted measurements) and they do not correspond to potential events/failures that your model should be aware of, you can safely remove them. In all other cases you should evaluate case by case what those outliers represent.
Otherwise, assuming levels of the categorical variable are ordered, the polyserial correlation (here it is in R), which is a variant of the better known polychoric correlation. Note the latter is defined based on the correlation between the numerical variable and a continuous latent trait underlying the categorical variable.
You should use a logarithmic scale when percent change, or change in orders of magnitude, is more important than changes in absolute units. You should also use a log scale to better visualize data that is heavily skewed. Taking the logarithm only works if the data is non-negative. There are other transforms, such as arcsinh or signed log, that you can use to decrease data range if you have zero or negative values.
Normalizing by mean and standard deviation is most meaningful when the data distribution is roughly symmetric.
Monetary amounts are often log- distributed—the log of the data is normally distributed. This leads us to the idea that taking the log of the data can restore symmetry to it.
Dixon and Chi-squared tests for outlier detection plus LOF Algorithm.
2.2.2 Missingness & Imputation
There are three types of missingness: 1) Completely at Random, 2) At Random, 3) Not at random
Types of imputation
- Mean: Simple but can distort distribution, underestimate standard deviation, and distort variable relationships by dragging correlation to zero.
- Random: Can amplify outlier observation and induce bias.
- Regression: Use observed variables and relationships between these variables. Must make assumptions and can badly extrapolate. & multiple stochastic regression imputation
The pros of imputation: 1) Helps retain a larger sample size of your data, 2) Does not sacrifice all the available information in an observation because of sparse missingness, 3) Can potentially avoid unwanted bias.
The cons of imputation: 1) The standard errors of any estimates made during analyses following imputation can tend to be too small, 2) The methods are under the assumption that all measurements are actually “known,” when in fact some were imputed, 3) Can potentially induce unwanted bias.
logging converts multiplicative relationships to additive relationships, and by the same token it converts exponential (compound growth) trends to linear trends. By taking logarithms of variables which are multiplicatively related and/or growing exponentially over time, we can often explain their behavior with linear models.
2.3 Visualize
swarmplot | marimekko charts | radviz | Marey Chart
https://dominikkoch.github.io/Bump-Chart/
https://www.r-graph-gallery.com/
http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html#top
ggplot: 1 | 2 | 3 | Extensions
2.4 Pre-processing
2.4.1 Feature Engineering
Cyclical Features: Dealing with cyclical features: Hours of the day, days of the week, months in a year, and wind direction are all examples of features that are cyclical. Many new machine learning engineers don’t think to convert these features into a representation that can preserve information such as hour 23 and hour 0 being close to each other and not far. Keeping with the hour example, the best way to handle this is to calculate the sin and cos component so that you represent your cyclical feature as (x,y) coordinates of a circle. In this representation hour, 23 and hour 0 are right next to each other numerically, just as they should be.
encode missingness a a feature
robust scaler python: median & quantiles
On Unbalanced Classes:
Research on imbalanced classes often considers imbalanced to mean a minority class of 10% to 20%.
That said, here is a rough outline of useful approaches. These are listed approximately in order of effort:
Do nothing. Sometimes you get lucky and nothing needs to be done. You can train on the so-called natural (or stratified) distribution and sometimes it works without need for modification.
Balance the training set in some way: Oversample the minority class. Undersample the majority class. Synthesize new minority classes.
*Throw away minority examples and switch to an anomaly detection framework.
At the algorithm level, or after it: Adjust the class weight (misclassification costs). Adjust the decision threshold. Modify an existing algorithm to be more sensitive to rare classes.
Construct an entirely new algorithm to perform well on imbalanced data.
Use AUC, F1 Score, Cohen’s Kappa, ROC curve, a precision-recall curve, a lift curve, or a profit (gain) curve for evaluation.
Use probability estimates and not the default .5 threshold.
Undersampling, oversampling, increase weight of minority class
Decision trees often perform well on imbalanced datasets because their hierarchical structure allows them to learn signals from both classes.
Sklearn example:`mdl = svm.SVC(kernel=‘linear’, class_weight={1: 10}) & svm.SVC(kernel=‘linear’, class_weight=‘balanced’)`
2.4.2 Feature Selection
Use binning + chisquare / mutual information to see correlation between feature and target. Variance Threshold, Univariate Feature Selection, Multivariate Feature Selection. Standardization, dimensionality reduction, dummying. sklearn.preprocessing.binarize, pandas.factorize, and featuretools for automation in Python.
Variance Threshold: Use this to exclude features that don’t meet a variance threshold.
Univariate Feature Selection: For classification can use chi-squared and F-Test while F-Test for regression. Functions: chi2, f_regression, f_classification from sklearn.feature_selection.
SelectKBest/SelectPercentile: Keep the k highest scoring features and keep a user-specified highest scoring percentage of features. Can use the univariate feature selection in these functions.
sklearn.feature_selection.RFE(CV)
To work around magic values, convert the feature into two features: One feature holds only quality ratings, never magic values. One feature holds a boolean value indicating whether or not a quality_rating was supplied. Give this boolean feature a name like is_quality_rating_defined.
Handling extreme outliers: Given data with a very long tail, log scaling does a slightly better job, but there’s still a significant tail of outlier values. Let’s pick yet another approach. What if we simply “cap” or “clip” the maximum value at an arbitrary value, say 4.0? Clipping the feature value at 4.0 doesn’t mean that we ignore all values greater than 4.0. Rather, it means that all values that were greater than 4.0 now become 4.0. This explains the funny hill at 4.0. Despite that hill, the scaled feature set is now more useful than the original data. Binning.
Know your data. Follow these rules: 1) Keep in mind what you think your data should look like. 2) Verify that the data meets these expectations (or that you can explain why it doesn’t). 3) Double-check that the training data agrees with other sources (for example, dashboards).


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--- layout: index title: 蜂巢聚创 --- {% include carousel.html %}
10.31/2015
32 Broadway, Suite 1701, New York, NY 10004
详情


Installs a Windows service.
Install-Service -Name <String> -Path <String> [-StartupType {Automatic | Manual | Disabled}] [-OnFirstFailure <String>] [-OnSecondFailure <String>] [-OnThirdFailure <String>] [-ResetFailureCount <Int32>] [-RestartDelay <Int32>] [-RebootDelay <Int32>] [-Dependency <String[]>] [-WhatIf] [-Confirm] [<CommonParameters>]
Install-Service -Name <String> -Path <String> [-StartupType {Automatic | Manual | Disabled}] [-OnFirstFailure <String>] [-OnSecondFailure <String>] [-OnThirdFailure <String>] [-ResetFailureCount <Int32>] [-RestartDelay <Int32>] [-RebootDelay <Int32>] [-Dependency <String[]>] -Username <String> [-Password <String>] [-WhatIf] [-Confirm] [<CommonParameters>]
Installs a Windows service. If a service with the given name already exists, it is left in place, and the its configuration is updated to match the parameters passed in. In order to configure it, the service is stopped, configured, then started. By default, the service is installed to run as NetworkService
. Set the Username
and Password
arguments to run as a different account. This user will be granted the logon as a service right.
The minimum required information to install a service is its name and path. By default the servide will run as NetworkService
and will start automatically. Review the parameter list for additional configuraiton options and their defaults. Normally, we would list them here, but there's like 300 of them and we really don't have the time. I'm so mean, I'm not even going to give you examples of all the different options. Feel free to send in your own.
Managed service accounts and virtual accounts should be supported (we don't know how to test, so can't be sure). Simply omit the -Password
parameter when providing a custom account name with the -Username
parameter.
| Name | Type | Description | Required? | Pipeline Input | Default Value |
|---|---|---|---|---|---|
| Name | String | The name of the service. | true | false | |
| Path | String | The path to the service. | true | false | |
| StartupType | ServiceStartMode | The startup type: automatic, manual, or disabled. Default is automatic. | false | false | Automatic |
| OnFirstFailure | String | What to do on the service's first failure. Default is to take no action. | false | false | TakeNoAction |
| OnSecondFailure | String | What to do on the service's second failure. Default is to take no action. | false | false | TakeNoAction |
| OnThirdFailure | String | What to do on the service' third failure. Default is to take no action. | false | false | TakeNoAction |
| ResetFailureCount | Int32 | How many seconds after which the failure count is reset to 0. | false | false | 0 |
| RestartDelay | Int32 | How many milliseconds to wait before restarting the service. Default is 60,0000, or 1 minute. | false | false | 60000 |
| RebootDelay | Int32 | How many milliseconds to wait before handling the second failure. Default is 60,000 or 1 minute. | false | false | 60000 |
| Dependency | String[] | What other services does this service depend on? | false | false | |
| Username | String | The user the service should run as. | true | false | |
| Password | String | The user's password. | false | false | |
| WhatIf | SwitchParameter | false | false | ||
| Confirm | SwitchParameter | false | false | ||
| CommonParameters | This cmdlet supports common parameters. For more information type Get-Help about_CommonParameters . |
Install-Service -Name DeathStar -Path C:\ALongTimeAgo\InAGalaxyFarFarAway\DeathStar.exe
Installs the Death Star service, which runs the service executable at C:\ALongTimeAgo\InAGalaxyFarFarAway\DeathStar.exe
. The service runs as NetworkService
and will start automatically.
Install-Service -Name DetahStart -Path C:\ALongTimeAgo\InAGalaxyFarFarAway\DeathStar.exe -StartupType Manual
Install the Death Star service to startup manually. You certainly don't want the thing roaming the galaxy, destroying thing willy-nilly, do you?
Install-Service -Name DetahStart -Path C:\ALongTimeAgo\InAGalaxyFarFarAway\DeathStar.exe -Username EMPIRE\wtarkin -Password 5irewh3nready
Installs the Death Star service to run as Grand Moff Tarkin, who is given the log on as a service right.


{% extends "base.html" %} {% load i18n %} {% block title %} {% trans "Solid urls home" %} {% endblock title %} {% block content %}
{% trans "Hello! This is an example home page for django-solid-i18n-urls package usage" %}.
{% trans "This example site supports two languages: english (default) and russian. As you can see, when accessing url without language prefix, default language is used. It is declared in settings.LANGUAGE_CODE. For more details visit" %} https://github.com/st4lk/django-solid-i18n-urls.
{% trans "Languages can be switched using buttons in right-up corner" %}.
{% endblock content %}


# proxychains4 -f ~/pivot.conf
The -f option allows us to tell Proxychains which configuration file we want to use


Examples for using the system catalogs¶
The system catalogs are regular tables where PostgreSQL stores schema metadata, such as information about tables and columns, and internal bookkeeping information. You can drop and recreate the tables, add columns, insert and update values, and severely mess up your system that way. Normally, one should not change the system catalogs by hand, there are always SQL commands to do that. For example, CREATE DATABASE inserts a row into the pg_database catalog — and actually creates the database on disk.
It this section we want to show examples for how to parse some of the system catalogs, making queries with the classic PyGreSQL interface.
We assume that you have already created a connection to the PostgreSQL database, as explained in the Basic examples:
>>> from pg import DB
>>> db = DB()
>>> query = query
Lists indices¶
This query lists all simple indices in the database:
print(query("""SELECT bc.relname AS class_name,
ic.relname AS index_name, a.attname
FROM pg_class bc, pg_class ic, pg_index i, pg_attribute a
WHERE i.indrelid = bc.oid AND i.indexrelid = ic.oid
AND i.indkey[0] = a.attnum AND a.attrelid = bc.oid
AND NOT a.attisdropped
ORDER BY class_name, index_name, attname"""))
List user defined attributes¶
This query lists all user defined attributes and their type in user-defined classes:
print(query("""SELECT c.relname, a.attname, t.typname
FROM pg_class c, pg_attribute a, pg_type t
WHERE c.relkind = 'r' and c.relname !~ '^pg_'
AND c.relname !~ '^Inv' and a.attnum > 0
AND a.attrelid = c.oid and a.atttypid = t.oid
AND NOT a.attisdropped
ORDER BY relname, attname"""))
List user defined base types¶
This query lists all user defined base types:
print(query("""SELECT r.rolname, t.typname
FROM pg_type t, pg_authid r
WHERE r.oid = t.typowner
AND t.typrelid = '0'::oid and t.typelem = '0'::oid
AND r.rolname != 'postgres'
ORDER BY rolname, typname"""))
List operators¶
This query lists all right-unary operators:
print(query("""SELECT o.oprname AS right_unary,
lt.typname AS operand, result.typname AS return_type
FROM pg_operator o, pg_type lt, pg_type result
WHERE o.oprkind='r' and o.oprleft = lt.oid
AND o.oprresult = result.oid
ORDER BY operand"""))
This query lists all left-unary operators:
print(query("""SELECT o.oprname AS left_unary,
rt.typname AS operand, result.typname AS return_type
FROM pg_operator o, pg_type rt, pg_type result
WHERE o.oprkind='l' AND o.oprright = rt.oid
AND o.oprresult = result.oid
ORDER BY operand"""))
And this one lists all of the binary operators:
print(query("""SELECT o.oprname AS binary_op,
rt.typname AS right_opr, lt.typname AS left_opr,
result.typname AS return_type
FROM pg_operator o, pg_type rt, pg_type lt, pg_type result
WHERE o.oprkind = 'b' AND o.oprright = rt.oid
AND o.oprleft = lt.oid AND o.oprresult = result.oid"""))
List functions of a language¶
Given a programming language, this query returns the name, args and return type from all functions of a language:
language = 'sql'
print(query("""SELECT p.proname, p.pronargs, t.typname
FROM pg_proc p, pg_language l, pg_type t
WHERE p.prolang = l.oid AND p.prorettype = t.oid
AND l.lanname = $1
ORDER BY proname""", (language,)))
List aggregate functions¶
This query lists all of the aggregate functions and the type to which they can be applied:
print(query("""SELECT p.proname, t.typname
FROM pg_aggregate a, pg_proc p, pg_type t
WHERE a.aggfnoid = p.oid
and p.proargtypes[0] = t.oid
ORDER BY proname, typname"""))
List operator families¶
The following query lists all defined operator families and all the operators included in each family:
print(query("""SELECT am.amname, opf.opfname, amop.amopopr::regoperator
FROM pg_am am, pg_opfamily opf, pg_amop amop
WHERE opf.opfmethod = am.oid
AND amop.amopfamily = opf.oid
ORDER BY amname, opfname, amopopr"""))


Piled-up garbage raises a stink near Porur toll-plaza
Traders seek regular patrolling on the service lane
by D. MadhavanMounds of garbage can be found dumped along the service lane of the 32-km-long Chennai Bypass, especially near Porur toll-plaza. Construction debris and wet waste from meat shops are also part of these heaps. “The service lane near the toll-plaza is stench-ridden. At times, the garbage overflows on to the carriageway,” says K. Vinoth, a resident of Porur. Traders near the toll-plaza say much of the dumping happens at night. As there is no regular patrolling along the service lane, people find it convenient to discard waste, say traders.
The bypass comes under the purview of National Highways Authority of India. It witnesses heavy traffic as it connects four national highways: Chennai – Tirupati, Chennai-Bangalore, Chennai-Tada and Chennai-Tiruchi.
“Necessary measures will be taken to put an end to this practice,” says an NHAI official.


The piece is completed
Piece dropped in the slot of this piece
Slot in which this piece has been dropped
Jquery element of the piece
Native element of the piece
Puzzle instance for which the piece belongs
Jquery element of the slot
Native element of the slot
X position
Y position
Key to store the instance related to the dom elements using $.data
For internal use only Disable drag&drop. To disable the widget use SnapPuzzleGame.disable instead
For internal use only Enable the drag&drop if the piece has not been completed yet. If the piece is complete the drag&drop will not be enabled To enable the widget use SnapPuzzleGame.enable instead
Init the jquery draggable
Init the droppable
Invoked when a piece is dropped in this slot When a piece is dropped in the correct slot, the piece is automatically disabled
Invoked when a piece is reverted. Revert the state classes
Invoked when a piece starts to been dragged. Resets the classes
Invoked when a piece exists from the bounds of a slot. Resets the classes
Invoked when the piece is over a slot. Add the correct/incorrect classes This function is invoked only when the feedbackOnHover option is true
Refresh the images
Reset the piece to the initial state. Remove the piece from the slot
Resolve if the dropped piece must be revertd
Solve the piece moving it to the correct slot
Generated using TypeDoc
Represents a piece and the related slot. Note: When a piece is dropped in the correct slot, the piece is automatically disabled


{% extends 'base.html' %} {% block body %}
Request is already accepted by user on:{{ request.time_confirmation }}


---
layout: post
title: Nikon Cheap Date
tags:
- 50mm
- Articles
- film
- General Photography
- Nikon FE
status: publish
type: post
published: true
meta:
_edit_last: "2"
aktt_notify_twitter: "yes"
ghostpool_post_template: Default
ghostpool_link: Full Size Image/Video
_edit_lock: "1310064149"
_thumbnail_id: "3173"
ghostpool_post_image: http://photo.rwboyer.com/wp-content/uploads/2011/07/P1010896-ico.jpg
ghostpool_post_faded_image: http://photo.rwboyer.com/wp-content/uploads/2011/07/P1010896-ico.jpg
ghostpool_circle_image: http://photo.rwboyer.com/wp-content/uploads/2011/07/P1010896-ico.jpg
aktt_tweeted: "1"
dsq_thread_id: "370266184"
---
The mailman came today and it was like christmas. I love this stuff, all for $100 from an eBay auction. If you want to learn a lot about photography, be capable of making extremely good looking images once you do, and you have very very little money this kind of thing is the ticket.
- An almost mint condition Nikon FE in black paint - yep that's black paint over enamel - The kind of thing that makes Leica users go temporarily insane and spend $8000 on a very similar device to shoot film with no included lenses.
- A pristine Nikkor 50mm f 1.8 AI lens - yea they are cheap on eBay but they are also very very good at taking nice images.
- A mint condition Nikkor 28mm Non-AI in the all metal variety. Yes it's single coated vs NIC but I can tell you from my own experience it works great - in a lot of ways better than the larger aperture lenses and this one is not only perfect but it came with the Nikon supplied case. Yes it works on the FE - almost every lens ever made by Nikon does.

- A mint condition Tamron SP 90mm f 2.8 macro that has a 9 bladed aperture and probably works great. 90mm f2.8 is a well know quantity virtually all of them are good - this one also happens to focus really really really close. It came with the original box and Tamron case. It's AI so easy to work with.
- A flash gun that is not pictured. The manual for the FE, a dozen or so filters all in perfect shape like they have never been used. Some of them very expensive like the Tiffen polarizer, the Nikon ND, a set of Close-up lenses in perfect shape, a teleconverter, and a bunch of other paraphernalia.
Heck even if you have loads of cash this is a great way to step out of your norm and make some very different images that you are making with your ultra-zo0m, AF, motor-driven, crop sensored, digi-cam. Everyone talks about how "The camera doesn't matter".  What everyone means is that every camera can take good images as well as bad images but different cameras with different sensor/film sizes with different handling characteristics, focus mechanisms, wind vs. motor, zoom vs fixed focal length, etc, etc, etc will make very different images in the hands of a single photographer. Heck they even influence the subject you point them at very differently.
RB


Instructions for running Prolog
queries for Geoquery Data
- Load up Sicstus Prolog(tm).
- Prove compile('geoquery').
- To try executing an actual query, try to prove this
predicate:
execute_query(<Query>,
Ans).
where <Query> is a parsed query in logical form (you can grab an
example from "geoqueries880") and the set of answers is returned inAns.


This sample is a simplified version of Derek D. Monner's Distracted Sequence Recall task, which involves 6 symbols, each represented locally in an input layer of 6 neurons:
A single trial consists of the presentation of a temporal sequence of 10 input symbols. The first 8 consist of 2 randomly chosen target symbols and 6 randomly chosen distractor symbols, all in random order; the remaining two symbols are the two prompts, which direct the network to produce the first and second target in the sequence, in order, regardless of when they occurred. A sample sequence would look like this:
Note that the targets may appear at any point in the sequence, so the network cannot rely on their temporal position; rather, the network must recognize the symbols as targets and preferentially save them, along with temporal order information, in order to produce the correct output sequence.
The network is trained to produce no output ( ) for all symbols except for the prompts, and for each prompt symbol the network must produce the output symbol which corresponds to the appropriate target from the sequence. For the sequence shown above, the prompts should output , respectively.
The network doesn't have any prior knowledge of which are the target, distractor or prompt symbols, nor what is the length of the sequence, it has to learn all this information during its training process.
In this sample a Long Short Term Memory network (6 input neurons, 7 memory blocks, and 2 output neurons) will be trained to complete the Distracted Sequence Recall task with a success rate of at least 95%.
var LSTM = new Architect.LSTM(6,7,2); LSTM.trainer.DSR({ targets: [2,4], distractors: [3,5], prompts: [0,1], length: 10 });
input: output:


Survey Questions
About You
Name (Required)
account_circle
Link to Photo Image (Required)
picture_in_picture
Question 1
I make friends easily.
Question 2
I have a vivid imagination.
Question 3
I worry about things.
Question 4
I love large parties.
Question 5
I believe in the importance of art.
Question 6
I get angry easily.
Question 7
I take charge.
Question 8
I experience my emotions intensely.
Question 9
I often feel blue.
Question 10
I prefer variety to routine.


GOG (director: Herbert L. Strock; screenwriters:Tom Taggart/Richard G. Taylor/based on a story by Ivan Tors; cinematographer: Lothrop B. Worth; cast: Richard Egan (David Sheppard), Constance Dowling (Joanna Merritt), Herbert Marshall (Dr. Van Ness), John Wengraf ( Dr. Zeitman), Steve Roberts (Maj. Howard), Byron Kane (Dr. Carter), 1954)
Reviewed by Dennis Schwartz
This sci-fi'er left a lot to be desired. It didn't work out in 3-D, its original intention of how it was to be shown, as the public's taste for that fad cooled, or in the conventional 2-D, how it was actually shown when released. For the first 30 minutes or so I felt I was stuck in a detention classroom for dummies (ah! those childhood memories never seem to go away), as there was one pedantic lecture after another trying to explain the secret experimental work being done underground in a New Mexico lab with five different levels of depth and a giant mirror out front to deflect the sun's rays, that if directed upon a target would easily burn it up. A weapon that was reputed to be more potent than the H-Bomb, and something that America, the good guys, were going to put up in outer space so that they could control the world from their enemies.
Ivan Tors is the producer and the one who wrote the story that the film is based on. He also foisted his wife, Constance Dowling, on the film as the lead actress. Her problem was that she was stiffer than a stiff in her romantic role, as Joanna Merritt. She is a plant from Washington, spying for them about what is happening on the secret project. She previously had a romantic relationship with the lead investigator, Dr. David Sheppard (Egan), who was just flown in at the request of the center's director Dr. Van Ness (Herbert Marshall). Her job now becomes to help Dr. Sheppard track down the mysterious malfunctions taking place at the center. This was the last film she appeared in, after a brief film career, working previously as a Samuel Goldwyn girl.
During these Cold War years, machines were regarded with apprehension by the public, who were worried that the enemy would out do America in science and have weapons that will blow them up. That the plot of the film would be about two robots, GOG and MAGOG, built in a neutral European country, but who enemy agents were able to slip into it a radio transmitter that was not detected by American security, highlights the American paranoia about foreigners and machines controlling the world. Even if these machines could do a 1,000 times the work man can do, they were also a 1,000 times deadlier than man, and this reactionary story dwells on the negative side of what machines could do, even if the machine in this instance is a magnificent brain.
Herbert Marshall, has an uninspiring role, as the man in charge of this project, which is the installation of the first manned space station built. This station will be sent to outer space upon its completion. The base is under the "command" of NOVAC, an anagram for Nuclear Operated Variable Automatic Computer. What all the fuss is about, is if American know-how can save the world for democracy, as the project comes under attack. Since NOVAC has been compromised by those who've reprogrammed the computer to undermine the space-station project and have ordered NOVAC to dispatch the two robots to kill the scientists and the project itself, its now up to Dr. Sheppard to stop those robots.
The action in the film, which is none too thrilling, is in watching how many of the 150 workers will get killed and what novel ways are the killings to be done. There will also be some action scenes involving the ruggedly handsome B- actor and former school teacher, Richard Egan, putting a flame thrower to the robot, which will enable America to launch its space station uninterrupted.
If the film was funny or its technology was more interesting, or if the killings weren't so melodramatic, and the acting not so staid, and the patriotic jingoism wasn't so shrill, I might have found this film bearable. But since that wasn't the case, I found the film to be an academic bore.
REVIEWED ON 9/8/99 GRADE: D
Dennis Schwartz: " Ozus' World Movie Reviews "
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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- case class GameScene(sceneWidth: Double, boardSize: Int = 8, treeDepth: Int = 3) extends Scene with Product with Serializable
- case class MenuScene(sceneWidth: Double, sceneHeight: Double) extends Scene with Product with Serializable
- case class Piece(_x: Int, _y: Int, tileSize: Double, color: Int) extends Circle with Product with Serializable
- case class PiecePane(boardSize: Int, tileSize: Double, board: Board, gameScene: GameScene) extends Pane with Product with Serializable


Unknown - Unable to determine if the other has been noticed. Unaware - It seems unlikely that the other has been noticed. Aware - Appears to have noticed the other and is attentive. Relaxed - Appears to have noticed the other and seems unaffected and calm. Disinterested - Appears to have noticed the other but seems to be ignoring the other. Cautious - Appears to have noticed the other and seems vigilant; movements may seem tentative, or bear may retreat or otherwise attempt to avoid interaction. Bold/pushy - Appears to be testing boundaries, causing other to retreat or behave with caution. Aggressive - charging other with/or without physical contact.
Habituation Level
Habituated - Tolerance of people at < 50 m with no noticeable change in behaviour Non-habituated - Normally moves away when people approach and/or displays agitated behaviours. Subadult - May also sub-classify habituation level of subadults.
Feeding/Foraging
Pursuit for food - Actively seeking out food. Could be moving for purpose of feeding. Green vegetation - Feeding on herbaceous material (i.e., sedges, clovers, grasses, cowparsnip, etc.). Not to be confused with scavenging carcasses. Berries - Feeding on berries, i.e., blueberries, rosehips, devil's club, thimble berries, etc. Fishing - Capture and consumption of live, vigorous fish. Human food - Consumption of human food items, excluding angler's fish.
Non-Interactive
Loafing/Resting - Not moving, non-interactive, may be sitting or standing Sleeping - Bear sleeping Walking - Bear walking but not pursuing food, without interest Running - Running to or from no apparent stimulus.
Bear-Bear Interaction
Alert/Vigilance - Interaction between individuals is evident but behaviour uncertain. Looking and/or sniffing intently towards another bear. Playing - Non-agonistic amicable behaviour, wrestling, chasing, etc. Fighting - Agonistic interaction with or without vocalization. Defense - Active or passive deferral to aggressor, retreat orstanding ground.
Bear-Human Interaction
Bear Response:
Retreat - Actively increase distance away from human or vehicle Bear Approach - Bear approaches human, record closest distance. Aggression - Yelling, hit with sticks, throw rocks, etc.
Human-Bear Interaction
Human response:
Retreat - Back away, walk away, run away. Approach bear - Human approaches bear, record closest distance. Note: run towards, walk towards. Aggression - Yelling, hit with sticks, throw rocks, etc. Enticing - Make fish whole or entrails obviously available to bears.  Try to attract bear(s).
Other
Other - Behaviour not on above list, e.g., interaction with dogs or other wildlife.


*Note: if you link a file with coordinates other than World Geodetic System (lon/lat), please set the SRS in the "ADVANCED OPTIONS"


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Although that might be intended, normally such a construct is most likely an error, where
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interface I1 {}
interface I2 {}
interface I3 extends I1 {}
static class Sub1 implements I1 {}
static class Sub2 extends Sub1 implements I2 {
void test(Sub1 sub1) {
if (sub1 instanceof I3) { // here 'I3' is incompatible interface
}
}
}


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Phone numbers should match the pattern 3 digits followed by a dash followed by four digits. The regular expression for this is
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.
HTML5 provides a new form attribute pattern whose value is a regular expression (without the slashes). When supported, this can be used instead of JavaScript for form validation.
In production, you would normally need to provide a JavaScript fallback for browsers that don't yet support the pattern attribute.


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[§487-7] Cooperation with office of consumer protection. Each department, agency, officer and employee of the State and of the counties shall cooperate with and assist the office of consumer protection in the performance of the functions, powers and duties of the office. [L 1969, c 175, §7]


Interrupts, events and flags management functions.
More...
|
| uint16_t | I2C_ReadRegister (I2C_TypeDef *I2Cx, uint8_t I2C_Register) |
| | Reads the specified I2C register and returns its value. More...
|
| |
| void | I2C_ITConfig (I2C_TypeDef *I2Cx, uint16_t I2C_IT, FunctionalState NewState) |
| | Enables or disables the specified I2C interrupts. More...
|
| |
| ErrorStatus | I2C_CheckEvent (I2C_TypeDef *I2Cx, uint32_t I2C_EVENT) |
| | Checks whether the last I2Cx Event is equal to the one passed as parameter. More...
|
| |
| uint32_t | I2C_GetLastEvent (I2C_TypeDef *I2Cx) |
| | Returns the last I2Cx Event. More...
|
| |
| FlagStatus | I2C_GetFlagStatus (I2C_TypeDef *I2Cx, uint32_t I2C_FLAG) |
| | Checks whether the specified I2C flag is set or not. More...
|
| |
| void | I2C_ClearFlag (I2C_TypeDef *I2Cx, uint32_t I2C_FLAG) |
| | Clears the I2Cx's pending flags. More...
|
| |
| ITStatus | I2C_GetITStatus (I2C_TypeDef *I2Cx, uint32_t I2C_IT) |
| | Checks whether the specified I2C interrupt has occurred or not. More...
|
| |
| void | I2C_ClearITPendingBit (I2C_TypeDef *I2Cx, uint32_t I2C_IT) |
| | Clears the I2Cx's interrupt pending bits. More...
|
| |
Interrupts, events and flags management functions.
===============================================================================
##### Interrupts, events and flags management functions #####
===============================================================================
[..]
This section provides functions allowing to configure the I2C Interrupts
sources and check or clear the flags or pending bits status.
The user should identify which mode will be used in his application to manage
the communication: Polling mode, Interrupt mode or DMA mode.
##### I2C State Monitoring Functions #####
===============================================================================
[..]
This I2C driver provides three different ways for I2C state monitoring
depending on the application requirements and constraints:
(#) Basic state monitoring (Using I2C_CheckEvent() function)
It compares the status registers (SR1 and SR2) content to a given event
(can be the combination of one or more flags).
It returns SUCCESS if the current status includes the given flags
and returns ERROR if one or more flags are missing in the current status.
(++) When to use
(+++) This function is suitable for most applications as well as for startup
activity since the events are fully described in the product reference
manual (RM0090).
(+++) It is also suitable for users who need to define their own events.
(++) Limitations
If an error occurs (ie. error flags are set besides to the monitored
flags), the I2C_CheckEvent() function may return SUCCESS despite
the communication hold or corrupted real state.
In this case, it is advised to use error interrupts to monitor
the error events and handle them in the interrupt IRQ handler.
-@@- For error management, it is advised to use the following functions:
(+@@) I2C_ITConfig() to configure and enable the error interrupts (I2C_IT_ERR).
(+@@) I2Cx_ER_IRQHandler() which is called when the error interrupt occurs.
Where x is the peripheral instance (I2C1, I2C2 ...)
(+@@) I2C_GetFlagStatus() or I2C_GetITStatus() to be called into the
I2Cx_ER_IRQHandler() function in order to determine which error occurred.
(+@@) I2C_ClearFlag() or I2C_ClearITPendingBit() and/or I2C_SoftwareResetCmd()
and/or I2C_GenerateStop() in order to clear the error flag and source
and return to correct communication status.
(#) Advanced state monitoring (Using the function I2C_GetLastEvent())
Using the function I2C_GetLastEvent() which returns the image of both status
registers in a single word (uint32_t) (Status Register 2 value is shifted left
by 16 bits and concatenated to Status Register 1).
(++) When to use
(+++) This function is suitable for the same applications above but it
allows to overcome the mentioned limitation of I2C_GetFlagStatus()
function.
(+++) The returned value could be compared to events already defined in
the library (stm32f4xx_i2c.h) or to custom values defined by user.
This function is suitable when multiple flags are monitored at the
same time.
(+++) At the opposite of I2C_CheckEvent() function, this function allows
user to choose when an event is accepted (when all events flags are
set and no other flags are set or just when the needed flags are set
like I2C_CheckEvent() function.
(++) Limitations
(+++) User may need to define his own events.
(+++) Same remark concerning the error management is applicable for this
function if user decides to check only regular communication flags
(and ignores error flags).
(#) Flag-based state monitoring (Using the function I2C_GetFlagStatus())
Using the function I2C_GetFlagStatus() which simply returns the status of
one single flag (ie. I2C_FLAG_RXNE ...).
(++) When to use
(+++) This function could be used for specific applications or in debug
phase.
(+++) It is suitable when only one flag checking is needed (most I2C
events are monitored through multiple flags).
(++) Limitations:
(+++) When calling this function, the Status register is accessed.
Some flags are cleared when the status register is accessed.
So checking the status of one Flag, may clear other ones.
(+++) Function may need to be called twice or more in order to monitor
one single event.
For detailed description of Events, please refer to section I2C_Events in
stm32f4xx_i2c.h file.
◆ I2C_CheckEvent()
| ErrorStatus I2C_CheckEvent |
( |
I2C_TypeDef * |
I2Cx, |
|
|
uint32_t |
I2C_EVENT |
|
) |
| |
Checks whether the last I2Cx Event is equal to the one passed as parameter.
- Parameters
-
| I2Cx | where x can be 1, 2 or 3 to select the I2C peripheral. |
| I2C_EVENT | specifies the event to be checked. This parameter can be one of the following values:
- I2C_EVENT_SLAVE_TRANSMITTER_ADDRESS_MATCHED: EV1
- I2C_EVENT_SLAVE_RECEIVER_ADDRESS_MATCHED: EV1
- I2C_EVENT_SLAVE_TRANSMITTER_SECONDADDRESS_MATCHED: EV1
- I2C_EVENT_SLAVE_RECEIVER_SECONDADDRESS_MATCHED: EV1
- I2C_EVENT_SLAVE_GENERALCALLADDRESS_MATCHED: EV1
- I2C_EVENT_SLAVE_BYTE_RECEIVED: EV2
- (I2C_EVENT_SLAVE_BYTE_RECEIVED | I2C_FLAG_DUALF): EV2
- (I2C_EVENT_SLAVE_BYTE_RECEIVED | I2C_FLAG_GENCALL): EV2
- I2C_EVENT_SLAVE_BYTE_TRANSMITTED: EV3
- (I2C_EVENT_SLAVE_BYTE_TRANSMITTED | I2C_FLAG_DUALF): EV3
- (I2C_EVENT_SLAVE_BYTE_TRANSMITTED | I2C_FLAG_GENCALL): EV3
- I2C_EVENT_SLAVE_ACK_FAILURE: EV3_2
- I2C_EVENT_SLAVE_STOP_DETECTED: EV4
- I2C_EVENT_MASTER_MODE_SELECT: EV5
- I2C_EVENT_MASTER_TRANSMITTER_MODE_SELECTED: EV6
- I2C_EVENT_MASTER_RECEIVER_MODE_SELECTED: EV6
- I2C_EVENT_MASTER_BYTE_RECEIVED: EV7
- I2C_EVENT_MASTER_BYTE_TRANSMITTING: EV8
- I2C_EVENT_MASTER_BYTE_TRANSMITTED: EV8_2
- I2C_EVENT_MASTER_MODE_ADDRESS10: EV9
|
- Note
- For detailed description of Events, please refer to section I2C_Events in stm32f4xx_i2c.h file.
- Return values
-
| An | ErrorStatus enumeration value:
- SUCCESS: Last event is equal to the I2C_EVENT
- ERROR: Last event is different from the I2C_EVENT
|
◆ I2C_ClearFlag()
| void I2C_ClearFlag |
( |
I2C_TypeDef * |
I2Cx, |
|
|
uint32_t |
I2C_FLAG |
|
) |
| |
Clears the I2Cx's pending flags.
- Parameters
-
| I2Cx | where x can be 1, 2 or 3 to select the I2C peripheral. |
| I2C_FLAG | specifies the flag to clear. This parameter can be any combination of the following values:
- I2C_FLAG_SMBALERT: SMBus Alert flag
- I2C_FLAG_TIMEOUT: Timeout or Tlow error flag
- I2C_FLAG_PECERR: PEC error in reception flag
- I2C_FLAG_OVR: Overrun/Underrun flag (Slave mode)
- I2C_FLAG_AF: Acknowledge failure flag
- I2C_FLAG_ARLO: Arbitration lost flag (Master mode)
- I2C_FLAG_BERR: Bus error flag
|
- Note
- STOPF (STOP detection) is cleared by software sequence: a read operation to I2C_SR1 register (I2C_GetFlagStatus()) followed by a write operation to I2C_CR1 register (I2C_Cmd() to re-enable the I2C peripheral).
-
ADD10 (10-bit header sent) is cleared by software sequence: a read operation to I2C_SR1 (I2C_GetFlagStatus()) followed by writing the second byte of the address in DR register.
-
BTF (Byte Transfer Finished) is cleared by software sequence: a read operation to I2C_SR1 register (I2C_GetFlagStatus()) followed by a read/write to I2C_DR register (I2C_SendData()).
-
ADDR (Address sent) is cleared by software sequence: a read operation to I2C_SR1 register (I2C_GetFlagStatus()) followed by a read operation to I2C_SR2 register ((void)(I2Cx->SR2)).
-
SB (Start Bit) is cleared software sequence: a read operation to I2C_SR1 register (I2C_GetFlagStatus()) followed by a write operation to I2C_DR register (I2C_SendData()).
- Return values
-
◆ I2C_ClearITPendingBit()
| void I2C_ClearITPendingBit |
( |
I2C_TypeDef * |
I2Cx, |
|
|
uint32_t |
I2C_IT |
|
) |
| |
Clears the I2Cx's interrupt pending bits.
- Parameters
-
| I2Cx | where x can be 1, 2 or 3 to select the I2C peripheral. |
| I2C_IT | specifies the interrupt pending bit to clear. This parameter can be any combination of the following values:
- I2C_IT_SMBALERT: SMBus Alert interrupt
- I2C_IT_TIMEOUT: Timeout or Tlow error interrupt
- I2C_IT_PECERR: PEC error in reception interrupt
- I2C_IT_OVR: Overrun/Underrun interrupt (Slave mode)
- I2C_IT_AF: Acknowledge failure interrupt
- I2C_IT_ARLO: Arbitration lost interrupt (Master mode)
- I2C_IT_BERR: Bus error interrupt
|
- Note
- STOPF (STOP detection) is cleared by software sequence: a read operation to I2C_SR1 register (I2C_GetITStatus()) followed by a write operation to I2C_CR1 register (I2C_Cmd() to re-enable the I2C peripheral).
-
ADD10 (10-bit header sent) is cleared by software sequence: a read operation to I2C_SR1 (I2C_GetITStatus()) followed by writing the second byte of the address in I2C_DR register.
-
BTF (Byte Transfer Finished) is cleared by software sequence: a read operation to I2C_SR1 register (I2C_GetITStatus()) followed by a read/write to I2C_DR register (I2C_SendData()).
-
ADDR (Address sent) is cleared by software sequence: a read operation to I2C_SR1 register (I2C_GetITStatus()) followed by a read operation to I2C_SR2 register ((void)(I2Cx->SR2)).
-
SB (Start Bit) is cleared by software sequence: a read operation to I2C_SR1 register (I2C_GetITStatus()) followed by a write operation to I2C_DR register (I2C_SendData()).
- Return values
-
◆ I2C_GetFlagStatus()
| FlagStatus I2C_GetFlagStatus |
( |
I2C_TypeDef * |
I2Cx, |
|
|
uint32_t |
I2C_FLAG |
|
) |
| |
Checks whether the specified I2C flag is set or not.
- Parameters
-
| I2Cx | where x can be 1, 2 or 3 to select the I2C peripheral. |
| I2C_FLAG | specifies the flag to check. This parameter can be one of the following values:
- I2C_FLAG_DUALF: Dual flag (Slave mode)
- I2C_FLAG_SMBHOST: SMBus host header (Slave mode)
- I2C_FLAG_SMBDEFAULT: SMBus default header (Slave mode)
- I2C_FLAG_GENCALL: General call header flag (Slave mode)
- I2C_FLAG_TRA: Transmitter/Receiver flag
- I2C_FLAG_BUSY: Bus busy flag
- I2C_FLAG_MSL: Master/Slave flag
- I2C_FLAG_SMBALERT: SMBus Alert flag
- I2C_FLAG_TIMEOUT: Timeout or Tlow error flag
- I2C_FLAG_PECERR: PEC error in reception flag
- I2C_FLAG_OVR: Overrun/Underrun flag (Slave mode)
- I2C_FLAG_AF: Acknowledge failure flag
- I2C_FLAG_ARLO: Arbitration lost flag (Master mode)
- I2C_FLAG_BERR: Bus error flag
- I2C_FLAG_TXE: Data register empty flag (Transmitter)
- I2C_FLAG_RXNE: Data register not empty (Receiver) flag
- I2C_FLAG_STOPF: Stop detection flag (Slave mode)
- I2C_FLAG_ADD10: 10-bit header sent flag (Master mode)
- I2C_FLAG_BTF: Byte transfer finished flag
- I2C_FLAG_ADDR: Address sent flag (Master mode) "ADSL" Address matched flag (Slave mode)"ENDAD"
- I2C_FLAG_SB: Start bit flag (Master mode)
|
- Return values
-
| The | new state of I2C_FLAG (SET or RESET). |
◆ I2C_GetITStatus()
| ITStatus I2C_GetITStatus |
( |
I2C_TypeDef * |
I2Cx, |
|
|
uint32_t |
I2C_IT |
|
) |
| |
Checks whether the specified I2C interrupt has occurred or not.
- Parameters
-
| I2Cx | where x can be 1, 2 or 3 to select the I2C peripheral. |
| I2C_IT | specifies the interrupt source to check. This parameter can be one of the following values:
- I2C_IT_SMBALERT: SMBus Alert flag
- I2C_IT_TIMEOUT: Timeout or Tlow error flag
- I2C_IT_PECERR: PEC error in reception flag
- I2C_IT_OVR: Overrun/Underrun flag (Slave mode)
- I2C_IT_AF: Acknowledge failure flag
- I2C_IT_ARLO: Arbitration lost flag (Master mode)
- I2C_IT_BERR: Bus error flag
- I2C_IT_TXE: Data register empty flag (Transmitter)
- I2C_IT_RXNE: Data register not empty (Receiver) flag
- I2C_IT_STOPF: Stop detection flag (Slave mode)
- I2C_IT_ADD10: 10-bit header sent flag (Master mode)
- I2C_IT_BTF: Byte transfer finished flag
- I2C_IT_ADDR: Address sent flag (Master mode) "ADSL" Address matched flag (Slave mode)"ENDAD"
- I2C_IT_SB: Start bit flag (Master mode)
|
- Return values
-
| The | new state of I2C_IT (SET or RESET). |
◆ I2C_GetLastEvent()
| uint32_t I2C_GetLastEvent |
( |
I2C_TypeDef * |
I2Cx | ) |
|
Returns the last I2Cx Event.
- Parameters
-
| I2Cx | where x can be 1, 2 or 3 to select the I2C peripheral. |
- Note
- For detailed description of Events, please refer to section I2C_Events in stm32f4xx_i2c.h file.
- Return values
-
◆ I2C_ITConfig()
| void I2C_ITConfig |
( |
I2C_TypeDef * |
I2Cx, |
|
|
uint16_t |
I2C_IT, |
|
|
FunctionalState |
NewState |
|
) |
| |
Enables or disables the specified I2C interrupts.
- Parameters
-
| I2Cx | where x can be 1, 2 or 3 to select the I2C peripheral. |
| I2C_IT | specifies the I2C interrupts sources to be enabled or disabled. This parameter can be any combination of the following values:
- I2C_IT_BUF: Buffer interrupt mask
- I2C_IT_EVT: Event interrupt mask
- I2C_IT_ERR: Error interrupt mask
|
| NewState | new state of the specified I2C interrupts. This parameter can be: ENABLE or DISABLE. |
- Return values
-
◆ I2C_ReadRegister()
| uint16_t I2C_ReadRegister |
( |
I2C_TypeDef * |
I2Cx, |
|
|
uint8_t |
I2C_Register |
|
) |
| |
Reads the specified I2C register and returns its value.
- Parameters
-
| I2C_Register | specifies the register to read. This parameter can be one of the following values:
- I2C_Register_CR1: CR1 register.
- I2C_Register_CR2: CR2 register.
- I2C_Register_OAR1: OAR1 register.
- I2C_Register_OAR2: OAR2 register.
- I2C_Register_DR: DR register.
- I2C_Register_SR1: SR1 register.
- I2C_Register_SR2: SR2 register.
- I2C_Register_CCR: CCR register.
- I2C_Register_TRISE: TRISE register.
|
- Return values
-
| The | value of the read register. |


Created by Drew Gerber / @DigitalZebra
How to create a demo or website for your open source project hosted on GitHub?
How to create a simple blog for either yourself, an organization, or project?
GitHub's answer for projects and organizations needing websites
“Websites for you and your projects”
“Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.”
The Git ecosystem is what we'll be using to “publish” changes to our websites and blog.
~ $ jekyll new site-name
~ $ cd site-name
~ $ jekyll serve
Complete command list:
~ $ git clone <repo_name>
~ $ cd <repo_name>
~ $ git checkout -b gh-pages
~ $ git push origin gh-pages
~ $ echo hello world > index.html
~ $ git add .
~ $ git commit -m "hello world!"
~ $ git push origin gh-pages
GitHub also provides a wizard which does many of these manual steps for you.
CNAME
file tells GitHub which host routes to this project (you also need to configure a CNAME entry
in your DNS record)
Jekyll consists of the following technologies:
Once Jekyll is installed, simply run the following to create a new site and serve it:
~ $ jekyll new site-name
~ $ cd site-name
~ $ jekyll serve
_drafts # Where draft posts go
_includes # Include files (eg. footer, header) go here. Think partial files.
_layouts # Layout files go here. These can include content or partials.
_posts # Your blog posts will go in here as Markdown or HTML files
_site # This is the folder where Jekyll outputs all of the HTML files
_sass # Sass files for site
_config.yml # YAML based configuration file for Jekyll
Creating a new post is as simple as adding a file to the _posts
directory:
_posts
- 2014-09-04-new-post.markdown
Sample Post:
---
layout: post
title: "This is my new post!"
date: 2014-09-04 20:25:30
categories: helloworld
---
Hello world!


On 22 March 2018, 5 new articles were published in 5 journals.
From Human Trafficking to Modern Slavery: the Development of Anti-Trafficking Policy in the UK
Serious Mental Illness and Negative Substance Use Consequences Among Adults on Probation
Unpacking Spatio-Temporal Differences of Risk For Crime: An Analysis in Little Rock, Ar
Structure, Multiplexity, and Centrality in A Corruption Network: the Czech Rath Affair


--- layout: post title: "No Title" ---
This gorgeous portrait of Schapelle Corby is one entries to this year’s Archibald Prize.


My website is a personal car blog
The user gets too see diffrent series of cars and more insights on the car performance
more to come from the king njosh blog


can be contacted at sacredmetal at gmail dot com for gigs and interviews.
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This code has been developed with re-usability in mind, a sort of mini web site engine. Head over to github to take a look at how you could re-use the code with just a few html/css changes (minimal or no javascript) and apply it to your own project. Feel free to give it a star if you appreciated the code.
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Basic Goal: Is recall systematically distorted based on statistical regularities? How are these distortions characterized in terms of unbiased, biased, and guess distributions?
Below is a summary of the behavioral experiment that was run (Exp 1), followed by current modeling approaches. Then I present a replication of the experiment (with small tweak) that I ran the first few weeks of the semester (Exp 2). There are some comments throughout and potential next steps at the end.
20 participants were collected (4 excluded due to <80% performance at repeat detection task)
336 objects presented sequentially, with color sampling from a biased color wheel partitioned into 4 quadrants. One quadrant ("rich quadrant") is preferentially sampled so that 50% of objects are in this color region. Quadrants are randomly selected for every subject, and sampling follows a uniform distribution for each quadrant.
An additional 30 objects are presented at random times throughout study phase, in which these objects are presented twice in a row. The task is to press the spacebar whenever an object immediately repeats. These repeated objects are not presented in the test phase. Therefore the total trial count for the study phase was 336 + (30*2) = 396.
Here I have characterized the color responses for each subject according to whether their response was closer/towards to the rich quadrant or farther/away from the rich quadrant, compared to the original color. (Note that some trials are discarded because they either answered the color exactly correct, exactly 180 degrees away from the correct color, or they clicked without moving the mouse which invalidated the trial.)
| all quads (each column is one subject) | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| closer | Means | P-value | |||||||||||||||
| 228 | 163 | 175 | 230 | 204 | 170 | 177 | 168 | 211 | 157 | 104 | 129 | 169 | 136 | 173 | 161 | 172.1875 | |
| farther | 0.013864233 | ||||||||||||||||
| 97 | 165 | 157 | 103 | 127 | 161 | 153 | 162 | 68 | 122 | 172 | 146 | 112 | 142 | 109 | 113 | 131.8125 |
| neigh quads | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| closer | Means | P-value | |||||||||||||||
| 70 | 47 | 58 | 71 | 71 | 55 | 57 | 53 | 65 | 42 | 29 | 35 | 53 | 35 | 61 | 49 | 53.1875 | |
| farther | 0.3106125 | ||||||||||||||||
| 39 | 61 | 54 | 41 | 40 | 55 | 52 | 58 | 26 | 49 | 58 | 56 | 38 | 56 | 33 | 45 | 47.5625 |
| rich quad | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| closer | Means | P-value | |||||||||||||||
| 126 | 77 | 77 | 119 | 95 | 82 | 83 | 78 | 116 | 85 | 49 | 65 | 85 | 75 | 80 | 80 | 85.75 | |
| farther | 0.059129613 | ||||||||||||||||
| 36 | 88 | 87 | 46 | 69 | 84 | 83 | 85 | 27 | 57 | 93 | 72 | 59 | 66 | 60 | 54 | 66.625 |
Some problems with Exp 1:
Post-survey: Do you think you selected certain colors more often than others, and if so what color?
Everyone said that they did think so, and approximately 1/3 of them correctly stated the color? Although this question I think could have been phrased much better, and some people didn't even state a color other than typing "yes".
Implicit memory test: 24 trials 15 degree mirrored color patch comparisons, asked which color appeared more frequently across objects
Only 2 people chose the color closer to the rich quadrant on over 60% of trials.
disp('subjects collapsed; individually all subjects were able to converge')
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V3/analysis'
openfig('neigh_coll_SMM.fig');
disp('Following are histograms of each subject''s data for neighboring quadrants, fit to standard mixture model');
disp('sorry the titles didn''t come out correctly, which would have given their subject number');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V3/analysis'
openfig('subject1.fig');
openfig('subject2.fig');
openfig('subject3.fig');
openfig('subject4.fig');
openfig('subject5.fig');
openfig('subject6.fig');
openfig('subject7.fig');
openfig('subject8.fig');
openfig('subject9.fig');
openfig('subject10.fig');
openfig('subject11.fig');
openfig('subject12.fig');
openfig('subject13.fig');
openfig('subject14.fig');
openfig('subject15.fig');
openfig('subject16.fig');
While all the above individual subjects converged, some of them messed up really bad. Huge standard deviations or a near zero guess distribution. I can of course limit the standard deviation max, but this is a concern. We need to consider what criteria to use to eliminate subjects, as some show pretty much no target distribution which of course the model will have trouble with.
disp('subjects collapsed; individually all subjects were able to converge')
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V3/analysis'
openfig('neigh_coll_SwapModel_freeSD.fig');
disp('Following are histograms of each subject''s data for neighboring quadrants, fit to above swap model');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V3/analysis'
openfig('swap_subject1.fig');
openfig('swap_subject2.fig');
openfig('swap_subject3.fig');
openfig('swap_subject4.fig');
openfig('swap_subject5.fig');
openfig('swap_subject6.fig');
openfig('swap_subject7.fig');
openfig('swap_subject8.fig');
openfig('swap_subject9.fig');
openfig('swap_subject10.fig');
openfig('swap_subject11.fig');
openfig('swap_subject12.fig');
openfig('swap_subject13.fig');
openfig('swap_subject14.fig');
openfig('swap_subject15.fig');
openfig('swap_subject16.fig');
disp('Subjects collapsed:');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V3/analysis'
openfig('allquad_swap_collapsed.fig');
disp('individual subject analysis -- all converged')
disp('Following are histograms of each subject''s data for neighboring quadrants, fit to above swap model');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V3/analysis'
openfig('allquads_swap_subject1.fig');
openfig('allquads_swap_subject2.fig');
openfig('allquads_swap_subject3.fig');
openfig('allquads_swap_subject4.fig');
openfig('allquads_swap_subject5.fig');
openfig('allquads_swap_subject6.fig');
openfig('allquads_swap_subject7.fig');
openfig('allquads_swap_subject8.fig');
openfig('allquads_swap_subject9.fig');
openfig('allquads_swap_subject10.fig');
openfig('allquads_swap_subject11.fig');
openfig('allquads_swap_subject12.fig');
openfig('allquads_swap_subject13.fig');
openfig('allquads_swap_subject14.fig');
openfig('allquads_swap_subject15.fig');
openfig('allquads_swap_subject16.fig');
20 participants were again collected (nobody was excluded due to repeat detection task)
324 objects presented, this time using objects with modified color so all objects truly were sampled from the same isoluminant color wheel:
Posterized stimuli (basically divide image into only 3 colors):
Did record the study trial for object presentation to be compared with individual test trial, and did do explicit continuous color report (but not implicit color report) as well as the post-survey.
| all quads | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| closer | Means | P-value | |||||||||||||||||||
| 149 | 150 | 151 | 171 | 178 | 194 | 156 | 167 | 143 | 203 | 160 | 172 | 149 | 164 | 165 | 147 | 163 | 197 | 158 | 159 | 164.8 | |
| farther | 0.264975249 | ||||||||||||||||||||
| 174 | 174 | 165 | 150 | 142 | 130 | 160 | 152 | 180 | 120 | 163 | 149 | 174 | 159 | 158 | 172 | 160 | 125 | 162 | 163 | 156.6 |
| neigh quads | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| closer | |||||||||||||||||||||
| 52 | 52 | 51 | 56 | 60 | 70 | 43 | 50 | 43 | 64 | 53 | 56 | 50 | 51 | 45 | 53 | 49 | 63 | 55 | 54 | 53.5 | |
| farther | 0.892733812 | ||||||||||||||||||||
| 56 | 56 | 55 | 51 | 46 | 38 | 65 | 57 | 65 | 43 | 55 | 52 | 57 | 57 | 63 | 55 | 58 | 45 | 51 | 53 | 53.9 |
| rich quad | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| closer | |||||||||||||||||||||
| 67 | 73 | 75 | 89 | 91 | 100 | 81 | 84 | 78 | 112 | 79 | 87 | 71 | 88 | 90 | 71 | 84 | 103 | 80 | 78 | 84.05 | |
| farther | 0.116778351 | ||||||||||||||||||||
| 94 | 89 | 81 | 73 | 69 | 62 | 74 | 74 | 83 | 50 | 83 | 72 | 91 | 73 | 71 | 86 | 78 | 57 | 80 | 84 | 76.2 |
disp('subjects collapsed');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V8/analysis'
openfig('neigh_coll_SMM.fig');
disp('indiv. subjects-- subj4, subj6, subj12, sub18 did not converge');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V8/analysis'
openfig('subject1.fig');
openfig('subject2.fig');
openfig('subject3.fig');
openfig('subject5.fig');
openfig('subject7.fig');
openfig('subject8.fig');
openfig('subject9.fig');
openfig('subject10.fig');
openfig('subject11.fig');
openfig('subject13.fig');
openfig('subject14.fig');
openfig('subject15.fig');
openfig('subject16.fig');
openfig('subject17.fig');
openfig('subject19.fig');
openfig('subject20.fig');
disp('Subjects collapsed:');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V8/analysis'
openfig('allquad_swap_collapsed.fig');
disp('Individual subjects (only those who survived SMM convergence). Subj14 did not converge');
cd '/Users/scotti.5/Dropbox/Shared Lab Folder/Experiments/Contextual_LTM/Contextual bias in LTM Exp_V8/analysis'
openfig('allquads_swap_subject1.fig');
openfig('allquads_swap_subject2.fig');
openfig('allquads_swap_subject3.fig');
openfig('allquads_swap_subject5.fig');
openfig('allquads_swap_subject7.fig');
openfig('allquads_swap_subject8.fig');
openfig('allquads_swap_subject9.fig');
openfig('allquads_swap_subject10.fig');
openfig('allquads_swap_subject11.fig');
openfig('allquads_swap_subject13.fig');
openfig('allquads_swap_subject15.fig');
openfig('allquads_swap_subject16.fig');
openfig('allquads_swap_subject17.fig');
openfig('allquads_swap_subject19.fig');
openfig('allquads_swap_subject20.fig');
It seems like a number of individual fits don't make sense... I'm looking at some graphs and thinking the std for the biased distribution here is way too high, or the biased mean was placed completely off the mark.
First, we need to figure out some exclusion criteria. I initially thought it could be something simple like, exclude if a subject doesn't fit a standard mixture model (all quadrants), but apparently even extremely messy data can still converge on these models. So perhaps exclude based on some other additional criteria?
The data for Expt. 2 looks much more messy than Expt 1. This may be because of the objects looking somewhat degraded due to the posterization, or the fact that all objects now share the exact same color wheel makes remembering more difficult. I may need to reduce the number of objects from 300-something to 200-something to find more reliable behavioral performance using posterized objects. However, this may lead to tradeoff where less trials makes modelling individuals more difficult. Else, I may want to replicate Expt. 1 simply to correct for the problems I outlined above (aside from the posterization).
I also now coded up a confidence report that may be used (specifying a range of values the subject believes the correct color may lie). This may be helpful in figuring out how bias relates to confidence in responses.
Unfortunately, slots for this week are not getting filled up (likely because midterms and it being the Spring?). I do have, however, have an mTurk version of the experiment nearly all ready to go if we want to collect data more quickly (you can see what it currently looks like here: https://paulscotti.github.io/mturk/Cont_LTM_101_3).
There are still modelling ideas I have yet to realize, for instance the idea of increasing the magnitude of bias in the model as linear function of how far away the rich quadrant was from that object's original color.


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Public Health Wales statement on Novel Coronavirus (COVID-19) outbreak
This statement will be updated daily at 2pm
Updated: 2:00pm Tuesday 14 April
Dr Robin Howe,
Incident Director for the Novel Coronavirus (COVID-19) outbreak response at
Public Health Wales, said:
“238 new cases have
tested positive for Novel Coronavirus (COVID-19) in Wales, bringing the total
number of confirmed cases to 5,848, although the true number of cases is
likely to be higher.
“19 further deaths have been reported to us of people who had tested
positive for Novel Coronavirus (COVID-19), taking the number of deaths in Wales
to 403.
“We offer our
condolences to families and friends affected, and we ask those reporting on the
situation to respect patient confidentiality.
“All available data
relating to Coronavirus in Wales is now available on a new user-friendly data
dashboard, which is available on the Public
Health Wales website.
“Public Health
Wales is working to address the negative impact of COVID-19 on the social,
mental and physical wellbeing of people in Wales. The new ‘How are you doing?’
campaign is now live and offering practical advice from phw.nhs.wales/howareyoudoing.
“As part of our
efforts to continually expand testing, there is now a Deloitte testing facility
operational at Cardiff City Stadium. This is part of the UK-wide plan to test
key workers. In Wales this has been led by Public Health Wales, Welsh
Government, the Local Resilience Forum, and Deloitte. Please note that the
facility is for key workers and you should only attend if invited.
“Public Health
Wales is working with Deloitte to integrate reporting systems so they can be
included in Public Health Wales’ data dashboard.
“Public
Health Wales is working in partnership with the Welsh Government and the Welsh
Blood Service to explore the use of convalescent plasma from people who have
recovered from Novel Coronavirus (COVID-19).
“Plasma from
patients who have recovered from Coronavirus will contain antibodies that their
immune systems have produced to fight the virus which can be transfused to
patients whose immune systems are struggling to develop their own antibodies.
“The
initiative is at an early stage and we will be inviting eligible individuals by
direct approach only at this stage.
“Novel Coronavirus
(COVID-19) is now circulating in every part of Wales. The single most
important action we can all take in fighting Coronavirus is to stay at home in
order to protect the NHS, and save lives.
“We want to thank
each and every person across Wales for doing their bit to help slow the spread
of the virus.
“We know that
staying at home can be hard especially when the weather is nice, but members of
the public must adhere to social distancing rules about staying at home, and
away from others, introduced by the UK and Welsh Government. These rules
are available on the Public
Health Wales website.
“People no longer
need to contact NHS 111 if they think they may have contracted Novel
Coronavirus (COVID-19). Information about the symptoms to look out for is
available on the Public
Health Wales website, or members of the public can use the NHS
Wales symptom
checker.
“Anyone with a
suspected coronavirus illness should not go to a GP surgery, pharmacy or
hospital. They should only contact NHS 111 if they feel they cannot cope
with their symptoms at home, their condition gets worse, or their symptoms do
not get better after seven days.
“Only call 999 if
you are experiencing a life-threatening emergency, do not call 999 just because
you are on hold to 111. We appreciate that 111 lines are busy, but you
will get through after a wait.
“The public play a very important role in slowing the spread of infection. By strictly following the latest measures, you will protect yourself, protect the most vulnerable and help to reduce the pressure on NHS Wales and minimise the impact of the virus.”
DATGANIAD CYMRAEG
Diweddariad: 2:00yp Dydd Mawrth 14 Ebrill - I ddilyn


glados.message_blocks module¶
-
class
glados.message_blocks.
ModalBuilder
[source]¶ Bases:
slack.web.classes.JsonObject
The ModalBuilder enables you to more easily construct the JSON required to create a modal in Slack.
Modals are a focused surface to collect data from users or display dynamic and interactive information.
To learn how modals are invoked, how to compose their contents, and how to enable and handle complex interactivity read this guide:
https://api.slack.com/block-kit/surfaces/modals
-
actions
(*, elements, block_id=None)[source]¶ A block that is used to hold interactive elements.
https://api.slack.com/reference/block-kit/blocks#actions
- Parameters
elements (
List
[InteractiveElement
]) – Up to 5 InteractiveElement objects - buttons, date pickers, etcblock_id (
Optional
[str
]) – ID to be used for this block - autogenerated if left blank. Cannot exceed 255 characters.
-
attributes
= {'_blocks', '_callback_id', '_clear_on_close', '_close', '_external_id', '_notify_on_close', '_private_metadata', '_submit', '_title'}¶
-
blocks_max_length
= 100¶
-
callback_id
(callback_id)[source]¶ An identifier to recognize interactions and submissions of this particular modal. Don’t use this to store sensitive information (use private_metadata instead).
- Parameters
callback_id (
str
) – must not exceed 255 characters- Return type
-
clear_on_close
(clear_on_close)[source]¶ When set to true, clicking on the close button will clear all views in a modal and close it.
- Parameters
clear_on_close (
bool
) – Default is false.- Return type
-
close
(close)[source]¶ Specify the text displayed in the close button at the bottom-right of the view.
Max length of 24 characters.
- Parameters
close (
str
) – must not exceed 24 characters- Return type
-
close_max_length
= 24¶
-
context
(*, elements, block_id=None)[source]¶ Displays message context, which can include both images and text.
https://api.slack.com/reference/block-kit/blocks#context
- Parameters
elements (
List
[InteractiveElement
]) – Up to 10 ImageElements and TextObjectsblock_id (
Optional
[str
]) – ID to be used for this block - autogenerated if left blank. Cannot exceed 255 characters.
-
divider
(*, block_id=None)[source]¶ A content divider, like an <hr>, to split up different blocks inside of a message.
- Parameters
block_id (
Optional
[str
]) – A string acting as a unique identifier for a block. You can use this block_id when you receive an interaction payload to identify the docs-src of the action. If not specified, one will be generated. Maximum length for this field is 255 characters. block_id should be unique for each message and each iteration of a message. If a message is updated, use a new block_id.
-
external_id
(external_id)[source]¶ A custom identifier that must be unique for all views on a per-team basis.
- Parameters
external_id (
str
) – A unique identifier.- Return type
-
image
(*, image_url, alt_text, title=None, block_id=None)[source]¶ A simple image block, designed to make those cat photos really pop.
https://api.slack.com/reference/block-kit/blocks#image
- Parameters
image_url (
str
) – Publicly hosted URL to be displayed. Cannot exceed 3000 characters.alt_text (
str
) – Plain text summary of image. Cannot exceed 2000 characters.title (
Optional
[str
]) – A title to be displayed above the image. Cannot exceed 2000 characters.block_id (
Optional
[str
]) – ID to be used for this block - autogenerated if left blank. Cannot exceed 255 characters.
-
notify_on_close
(notify_on_close)[source]¶ Indicates whether Slack will send your request URL a view_closed event when a user clicks the close button.
- Parameters
notify_on_close (
bool
) – Default is false.- Return type
-
private_metadata
(private_metadata)[source]¶ An optional string that will be sent to your app in view_submission and block_actions events.
- Parameters
private_metadata (
str
) – must not exceed 3000 characters- Return type
-
section
(*, text=None, block_id=None, fields=None, accessory=None)[source]¶ A section is one of the most flexible blocks available. It can be used as a simple text block, in combination with text fields, or side-by-side with any of the available block elements.
https://api.slack.com/reference/block-kit/blocks#section
- Parameters
text (
Union
[str
,TextObject
,None
]) – The text for the block, in the form of string or a text object. Maximum length for the text in this field is 3000 characters.block_id (
Optional
[str
]) – A string acting as a unique identifier for a block. You can use this block_id when you receive an interaction payload to identify the docs-src of the action. If not specified, one will be generated. Maximum length for this field is 255 characters. block_id should be unique for each message and each iteration of a message. If a message is updated, use a new block_id.fields (
Optional
[List
[str
]]) – optional: a sequence of strings that will be rendered using MarkdownTextObjects. Any strings included with fields will be rendered in a compact format that allows for 2 columns of side-by-side text. Maximum number of items is 10. Maximum length for the text in each item is 2000 characters.accessory (
Optional
[BlockElement
]) – an optional BlockElement to attach to this SectionBlock as secondary content
- Return type
-
submit
(submit)[source]¶ Specify the text displayed in the submit button at the bottom-right of the view.
Important Note: submit is required when an input block is within the blocks array.
Max length of 24 characters.
- Parameters
submit (
str
) – must not exceed 24 characters- Return type
-
submit_max_length
= 24¶
-
title
(title)[source]¶ Specify a title for this modal
- Parameters
title (
str
) – must not exceed 24 characters- Return type
-
title_max_length
= 24¶
-


|
| Local Methods | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |
Properties
| Name | Type | Description |
|---|---|---|
| None |
Methods
| Methods defined in this Managed Object |
|---|
| VsanQuerySpaceUsage |
| Privileges | Required on |
|---|---|
| System.Read | ClusterComputeResource |
Parameters
| Name | Type | Description |
|---|---|---|
| _this | ManagedObjectReference | A reference to the VsanSpaceReportSystem used to make the method call. |
| cluster | ManagedObjectReference
to a ComputeResource |
vSAN cluster
|
| storagePolicies* | VirtualMachineProfileSpec[] |
Storage policies specified to calculate what-if
capacity of the cluster vSAN datastore. Default
value is None
Since vSAN API vSAN 6.7U1 |
| whatifCapacityOnly* | xsd:boolean |
Flag indicates if the API is called only
for getting vSAN datastore physical capacity
and what-if capacity. Default value is False,
which returns the vSAN space usage including
the space usage overview and the space usage
breakdown according to vSAN object type. When
it is True, the API only returns the vSAN
datastore physical capacity and what-if capacity
under given storage policies.
Since vSAN API vSAN 6.7U1 |
Return Value
| Type | Description |
|---|---|
| VsanSpaceUsage | The vSAN space usage and vSAN datastore capacities. |
Faults
| Type | Description |
|---|---|
| InvalidArgument | |
| NotSupported | |
| RuntimeFault | Thrown if any type of runtime fault is thrown that is not covered by the other faults; for example, a communication error. |
| VsanFault |
Events
| Type | |
|---|---|
| None |
| Top of page | Local Methods | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |


About Me
Write an awesome description about yourself here, this supports markdown, so you can add links and highlight things like this.
You can even add paragraphs by using empty lines like this and add anything else markdown supports such as
- My Awesome Resume
- Tables
- Links
- Images
Projects
Description about the work on/with the project
Experience
Description of role
Education
Institution name
Qualifications (eg. BA Performing Arts)
Date Range (eg. 2016 - 2019)
Description of qualification
A Little More About Me
This is where you can write a little more about yourself. You could title this section Interests and include some of your other interests.
Or you could title it Skills and write a bit more about things that make you more desirable, like leadership or teamwork


Licensing. Workspace Configuration and Management |
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Revizto license is provided for a specific number of users and projects (depends on the purchased Plan). The starting point is activating the license and configuring the license workspace. Note that even if it is planned to limit the use of Revizto Cloud, initial license configuration has to be carried out online via the web-GUI. Even if you use the Shared Location option for your projects, all data on user license and access level, as well as issue-related workflow is managed via the Cloud, while project source files remain within the corporate network. As most Plans imply a limited number of users and projects within a team workspace, it is necessary to regularly monitor the current license status. Note that before migrating to a smaller Plan, the number of active licenses has to be brought into correspondence with it. Otherwise the whole license will be frozen until the number of active users under it is not reduced to comply with the Plan. The freeze period is 6 months. After this period data can be removed from the Cloud.
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Backend Task
Build a simple stateless microservice in Nodejs, with three major functionalities -
- Authentication
- JSON Patching
- Thumbnails image generation
clone
clone repository
git clone https://github.com/Bobslegend61/hackerbay-backend-task.git
Install
install all dependencies
npm install
packages
View package.json
Start app server
App will be listening on port 3000
npm start
To start app server using nodemon
npm install -g nodemon
npm run dev
Testing
Run all test scripts
npm test
Endpoints
All endpoints are tested with postman
public
/login
is a post request that's required to have a body object with username and password. This app uses a mock username and passwordresponse
a json object with a token for authentication if successful or an error message if not{ "username": "Alabi", "password": "Alabi" }
Example
For wrong user details
If a key is missing from the object
Protected
These routes are protected and requires an Authorization
token send throught headers
along with the data.
Protected routes responds with unauthorized if token are expired or not passed along with the headers
/api/apply-json-patch
is aPATCH request
with the object keysdoc
the object to which the patch is applied andpatches
the patches to apply.responds
with an object containing the patched token
Example
/api/create-thumbnail
aPOST request
that accepts an image url, downloads it, resizes it to 50x50 and responds with the resized image
Example
API's
./auth/jsonwebtoken.js
jsonwebtoken.getToken(payload)
Uses the jsonwebtoken package and returns the token
as a string.
payload
- some user info used as part of the token creation
jsonwebtoken.decodeToken(token, callback)
Decodes a token and returns the payload or an error if token is invalid
token
is the JsonWebToken string
./auth/bcrypt.js
Uses the bcrypt package
bcrypt.hashPassword(plainPassword, callback)
Hashes the users plain password (Don't store a plain password in the database). Callback is called with the hash or an error if something goes wrong
password
the password to be hashed
bcrypt.comparePassword(plainPassword, hash, callback)
Compares a password with the hashed password of the original user. callback is called with a boolean of true
if matches or false
if not or an error if something goes wrong
plainPassword
password from login detailshash
hashed password from a db when user registers (for this app, we used a mock data, not a real database)
./auth/auth-guard.js
A function used by express as a middleware for guarding protected endpoint
./img-thumbnail/img-thumbnail.js
Contains functions for downloading and resizing images. it uses the request package for the downloads and sharp package for resizing an image
imageThumbnail.downloadImage(url, filename, callback)
Downloads the image based on the url passed in. Callback is called with a filename and format of the image.
url
- Image URL to downloadfilename
- Name you wanna name the image
./patch/patch.js
Contains functions for validating and applying JSON patch. Uses the fast-json-patch package
patch.applyPatch(doc, patches)
Applies the patches to the doc
doc
- JSON object to which patch is to be appliedpatches
- Patches to be applied ondoc
patch.validatePatch(doc, patches, callback)
Check to see if patches and doc are valid. Callback is called with a booleantrue
if valid andfalse
if not
Issue Reporting
If you have found a bug or if you have a feature request, please report them at this repository issues section.
Author
License
This project is licensed under the MIT license.


Best Genre to Work With
Many times people want to work with some music on, but music sometimes can be distracting, atleast for me, different genres make my mind go different places, so I started to look for some music best suited for concentration and the I found this blog post in Quartz. This list of genre is really effective for my productivity.
Jazz
- Miles Davis
- Alice coltrane
- Thelonious Monk
Classical
- Johann Sebastian Bach
Minimalist composers
- Steve Reich
- Philip Glass
Chill-out
- Bonobo
- Cinematic Orchestra
Ambient
- Brian Eno
- Apex Twin
Movie Soundtracks
- Tron
- The Social Network


The ghanian people are made up of hundreds of ethnic groups and tribes that have different cultural backgrounds and speak different languages, but all share the same technique in the naming of their children.
The naming of their children is based on the respective day of the week the child was born, and based on what gender the child is.
The Akan have defined the seven names of the week based on the gender of the child.


{% extends 'base.html' %} {% block title %}Welcome to the Python-versus-Python photo database!{% endblock %} {% block content %}
This site is designed to allow remote contributors to upload images of snakes (or snippets) and/or authenticate the identification of previously- loaded images.
Images optimally have an alpha (transparent) background. When that's not possible, the background can be pure red RGB(255,0,0) - it will be filtered prior to procesing.
Feel free to use the menu bar at the top to poke around.