| Total net profit | -1382.7 | Gross profit | 613.4 | Gross loss | -1996.1 |
| Profit factor | 0.31 | Expected payoff | -15.03 | ||
| Absolute drawdown | -1382.7 | Maximal drawdown (%) | 1422.5 (3574.12%) | ||
| Total trades | 92 | Profit trades (% of total) | 28 (30.43%) | Loss trades (% of total) | 64 (69.57%) |
| Long positions (won %) | 37 (40.54%) | Short positions (won %) | 55 (23.64%) | ||
| Largest | profit trade | 109.8 | loss trade | -66.3 | |
| Average | profit trade | 21.91 | loss trade | -31.19 | |
| Minimum | profit trade | 0.0 | loss trade | -3.6 | |
| Maximum | take profit in pips | 14840.0 | stop loss in pips | -6500.0 | |
| Average | take profit in pips | 3030.2 | stop loss in pips | -3344.2 | |
| Minimum | take profit in pips | -2900.0 | stop loss in pips | -1790.0 | |
| Maximum | Reward-Risk | 5.2 | Duration in minutes | 786 | |
| Average | Reward-Risk | 1.0 | Duration in minutes | 64.5 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 3.0 (40.0) | consecutive losses (loss in money) | 9.0 (-249.0) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |
| Total net profit | -518.6 | Gross profit | 259.6 | Gross loss | -778.2 |
| Profit factor | 0.33 | Expected payoff | -14.02 | ||
| Absolute drawdown | -518.6 | Maximal drawdown (%) | 473.7 (-1055.01%) | ||
| Total trades | 37 | Profit trades (% of total) | 15 (40.54%) | Loss trades (% of total) | 22 (64.86%) |
| Largest | profit trade | 80.0 | loss trade | -66.3 | |
| Average | profit trade | 17.31 | loss trade | -35.37 | |
| Minimum | profit trade | 0.0 | loss trade | -3.6 | |
| Maximum | take profit in pips | 7690.0 | stop loss in pips | -6500.0 | |
| Average | take profit in pips | 1337.8 | stop loss in pips | -4032.4 | |
| Minimum | take profit in pips | -2900.0 | stop loss in pips | -1800.0 | |
| Maximum | Reward-Risk | 1.9 | Duration in minutes | 347.0 | |
| Average | Reward-Risk | 0.4 | Duration in minutes | 68.4 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 4.0 (36.9) | consecutive losses (loss in money) | 4.0 (-178.4) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |
| Total net profit | -864.1 | Gross profit | 353.8 | Gross loss | -1217.9 |
| Profit factor | 0.29 | Expected payoff | -15.71 | ||
| Absolute drawdown | -874.5 | Maximal drawdown (%) | 914.3 (2297.24%) | ||
| Total trades | 55 | Profit trades (% of total) | 13 (23.64%) | Loss trades (% of total) | 42 (76.36%) |
| Largest | profit trade | 109.8 | loss trade | -50.0 | |
| Average | profit trade | 27.22 | loss trade | -29.0 | |
| Minimum | profit trade | 0.1 | loss trade | -18.6 | |
| Maximum | take profit in pips | 14840.0 | stop loss in pips | -4640.0 | |
| Average | take profit in pips | 4168.7 | stop loss in pips | -2881.3 | |
| Minimum | take profit in pips | -240.0 | stop loss in pips | -1790.0 | |
| Maximum | Reward-Risk | 5.2 | Duration in minutes | 786.0 | |
| Average | Reward-Risk | 1.5 | Duration in minutes | 61.8 | |
| Minimum | Reward-Risk | Duration in minutes | 1.0 | ||
| Maximum | consecutive wins (profit in money) | 3.0 (30.3) | consecutive losses (loss in money) | 12.0 (-348.9) | |
| Average | consecutive wins | 1.0 | consecutive losses | 4.0 |


Can you help PyBites automate their Amazon affiliation link creation?
Complete the generate_affiliation_link(url)
function below which should convert the following links:
https://www.amazon.com/War-Art-Through-Creative-Battles/dp/1936891026/?keywords=war+of+art https://amazon.com/War-Art-Through-Creative-Battles/dp/1936891026/ref=sr_1_1 https://www.amazon.es/War-Art-Through-Creative-Battles/dp/1936891026/?qid=1537226234 https://www.amazon.co.uk/Pragmatic-Programmer-Andrew-Hunt/dp/020161622X https://www.amazon.com.au/Python-Cookbook-3e-David-Beazley/dp/1449340377/
... into the following affiliation links respectively:
http://www.amazon.com/dp/1936891026/?tag=pyb0f-20 http://www.amazon.com/dp/1936891026/?tag=pyb0f-20 http://www.amazon.com/dp/1936891026/?tag=pyb0f-20 http://www.amazon.com/dp/020161622X/?tag=pyb0f-20 http://www.amazon.com/dp/1449340377/?tag=pyb0f-20
Hint: regex might be overkill here! Have fun and remember, keep calm and code in Python!


‘Pirated copy of Arnab Goswami’ faces brutal trolling for bizarre tweet raising questions on JNU students
by JKR StaffThe commercial success of Arnab Goswami’s brand of TV journalism has prompted many to emulate his style in a bid to achieve the much-desired TV ratings. While Goswami remains an undisputed king amongst the English genre, the Republic TV founder appears to have ‘inspired’ many to become his pirated copies on Hindi TV channels. One TV anchor, who often faces grief from social media users for his bizarre utterances on TV and equally senseless tweets is News18 India’s Amish Devgan. Caught peddling fake news and abused by a Congress spokesperson on LIVE TV for biases, Devgan has found himself in the eye of a storm for his tweet questioning why JNU students needed to spend 20 years studying.
The controversial anchor wrote, “In #JNU some students are studying for 20yrs till age of 40Yrs #JNUFact.”
No sooner did he share his tweet, Devgan began to face social media roasting with many hurling the ‘pirated copy of Arnab Goswami’ jibe. Many including some journalists mockingly asked him to explain at what age did the Prime Minister acquire his alleged MA degree. His timeline got flooded with memes ridiculing the controversial Hindi TV anchor.
Thousands of students from the prestigious Jawaharlal Nehru University on Monday staged an extraordinary march towards the parliament in protest against the fee hike, which the HRD ministry had said was withdrawn last week. In a dramatic development, students made desperate attempts to march towards parliament, where lawmakers gathered on the first day of the Winter Session.
However, the JNU students’ union slammed the government for misleading the students with what they termed was an ‘eyewash.’ They complained that the rollback was only for students below the poverty line.
JNU students have been staging protests against the administration’s decision to hike fees for hostel facilities.
The one-time mess security fee, which is refundable, was hiked from Rs 5,500 to Rs 12,000. The rent for a single-bedroom was hiked from Rs 20 per month to a whopping Rs 600 per month. The rent for a double-seater room was hiked from Rs 10 per month to Rs 300 per month.


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Return home
West Coast Swing
In my free time I enjoy dancing West Coast Swing (WCS). WCS is a partner dance which is danced both socially and competitively. It is known for its high level of musicality and distinctively elastic look. It is so much fun to dance and the community is just awesome. Jack & Jill are competitions where you draw random partners and dance to random songs, Strictly Swing are competitions where you pick your partner but dance to random songs.
Jack & Jill
Advanced
- Monterey Swingfest (January 2017)
- The After Party (December 2017) - 7th Place
- Halloween Swingthing (October 2017) - 8th Place
- Boogie by the Bay (October 2017) - 8th Place
- Desert City Swing (September 2017)
Intermediate
- Swingtacular (August 2017) - 1st Place
- Sacramento Swing (July 2017)
- Jack & Jill O'rama (June 2017)
- SwingDiego (May 2017) - [2nd Alternate]
- Easter Swing (April 2017) - 3rd Place
- City of Angels (April 2017)
- Capital Swing (February 2017)
- Monterey Swingfest (January 2017) - 4th Place
- The After Party (December 2016)
- DC Swing eXperience (November 2016) - [1st Alternate]
Novice
- Boogie by the Bay (October 2016) - 1st Place
- Desert City Swing (September 2016)
- Lone Star Invitational (August 2016) - 2nd Place
- Wild Wild Westie (July 2016) - 6th Place
Strictly Swing
Advanced/All-Star
- Halloween Swingthing [w/ Joseph Tong] (October 2017)
- Boogie by the Bay [w/ Kristal Castelan] - 10th Place (October 2017)
- Desert City Swing [w/ Rachel Shook] (September 2017)
Intermediate
- Jack & Jill O'rama [w/ Brittany Schiro] (December 2017)
- Sacramento Swing [w/ Kendall Block] (July 2017)
- SwingDiego [w/ Phoebe Larkin] (May 2017) - 2nd Place
- Easter Swing [w/ Rachel Shook] (April 2017) - 2nd Place
- City of Angels [w/ Anna Brinkmann] (April 2017) - 3rd Place
- Capital Swing [w/ Celine Hogan] (February 2017) - 4th Place
Novice
- Boogie by the Bay [w/ Andrea Buckley] (October 2016) - 2nd Place
- Desert City Swing [w/ Rachel Shook] (September 2016) - 1st Place
- Lone Star Invitational [w/ Rachel Shook] (August 2016) - 1st Place
Social Dance Comps
- WnY HiLo J&J (March 2017) - 4th Place
- NeXt Gen Luck of the Draw (March 2017) - 5th Place
- Mission City Swing Holiday J&J (December 2016) - 1st Place
- WnY Holiday J&J (December 2016)- 1st Place
- Dance Loft Charity J&J (December 2016) - 2nd Place
Videos
Links
Classes Taken
Private Lessons
- Eric Jacobson
- Ben Morris
- Ben Hooten
- Connor Goodmanson
- Taletha Jouzdani
Group Classes
San Francisco, CA
- WNY Warehouse - Eric Jacobson
- Mission City Swing - Eric Jacobson
- Starlite Ballroom - Michelle Crozier
- The Saddle Rack - John Piper
- Michelle's Swing - Michelle Kinkaid
Austin, TX
- Austin City Dance Club - Ben Hooten and Taletha Jouzdani
- Austin City Dance Club - Debbie Figueroa
- Austin Uptown Dance - Chris Brown
- GoDance South - Katrina Repka


{% extends "shiny/slim.html" %} {% block page_header %}CONTACT US{% endblock %} {% block body %}
It's a great place to post a tough question, make an announcement, or leaf through the archives going back to 2005. Get connected with a wealth of resources today!


@optimize, @protominor
There is a small number of Goc keywords which are used only to ease the burden of maintaining header files which will be used and shared often.
The
@optimize
keyword is used to speed compilation of Goc header files. It may be placed at the top of a
.goh
file. The directive instructs Goc to generate a specially processed
.poh
file which contains all the information of the
.goh
file, but is somewhat faster to compile. This
.poh
file is automatically regenerated if the corresponding
.goh
file has been changed since the last compilation. Its syntax is simple:
@optimize
The
@protominor
keyword is used so that a single Goc header file may serve multiple versions of a Goc library. When creating a new version of an existing library, use the
@protominor
keyword to declare new messages and variable data fields for a class. Suppose your original class declaration looked like so:
@class MyClass, SuperClass; @message void MSG_M_DO_THIS(void); @vardata void TEMP_M_DONE_FLAG; @endc
Having released this version of your class, you wished to release another version in which this class handled another message. You wanted to specify that this new message would only work with this new version of the library. This would be set up like so:
@class MyClass, SuperClass; @message void MSG_M_DO_THIS(void); @vardata void TEMP_M_DONE_FLAG; @protominor MyVersion20 @message void MSG_M_DO_THAT(void); @endc
This keyword has the syntax:
@protominor <name>
incminor
entry in the geode's .gp file.
To do the equivalent version control with routines, use the
incminor
.gp file directive.


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Outlander
Outlander follows the story of Claire Randall, a married combat nurse from 1945 who is mysteriously swept back in time to 1743, where she is immediately thrown into an unknown world where her life is threatened. When she is forced to marry Jamie, a chivalrous and romantic young Scottish warrior, a passionate affair is ignited that tears Claire's heart between two vastly different men in two irreconcilable lives.The Outlander series, adapted from Diana Gabaldon's international best-selling books, spans the genres of romance, science fiction, history and adventure into one epic tale.


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Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, and makes importing and analyzing data much easier. Pandas builds on packages like NumPy and matplotlib to give you a single, convenient, place to do most of your data analysis and visualization work.
In this introduction, we’ll use Pandas to analyze data on video game reviews from IGN, a popular video game review site. The data was scraped by Eric Grinstein, and can be found here. As we analyze the video game reviews, we’ll learn key Pandas concepts like indexing.
Do games like the Witcher 3 tend to get better reviews on the PS4 than the Xbox One? This dataset can help us find out.
Importing Data with Pandas
The first step we’ll take is to read the data in. The data is stored as a comma-separated values, or csv, file, where each row is separated by a new line, and each column by a comma (,). Here are the first few rows of the ign.csv file:
,score_phrase,title,url,platform,score,genre,editors_choice,release_year,release_month,release_day
0,Amazing,LittleBigPlanet PS Vita,/games/littlebigplanet-vita/vita-98907,PlayStation Vita,9.0,Platformer,Y,2012,9,12
1,Amazing,LittleBigPlanet PS Vita -- Marvel Super Hero Edition,/games/littlebigplanet-ps-vita-marvel-super-hero-edition/vita-20027059,PlayStation Vita,9.0,Platformer,Y,2012,9,12
2,Great,Splice: Tree of Life,/games/splice/ipad-141070,iPad,8.5,Puzzle,N,2012,9,12
3,Great,NHL 13,/games/nhl-13/xbox-360-128182,Xbox 360,8.5,Sports,N,2012,9,11
As you can see above, each row in the data represents a single game that was reviewed by IGN. The columns contain information about that game:
- score_phrase — how IGN described the game in one word. This is linked to the score it received.
- title — the name of the game.
- url — the URL where you can see the full review.
- platform — the platform the game was reviewed on (PC, PS4, etc).
- score — the score for the game, from 1.0 to 10.0.
- genre — the genre of the game.
- editors_choice — N if the game wasn’t an editor’s choice, Y if it was. This is tied to score.
- release_year — the year the game was released.
- release_month — the month the game was released.
- release_day — the day the game was released.
There’s also a leading column that contains row index values. We can safely ignore this column, but we’ll dive into what index values are later on. In order to be able to work with the data in Python, we’ll need to read the csv file into a Pandas DataFrame. A DataFrame is a way to represent and work with tabular data. Tabular data has rows and columns, just like our csv file.
In order to read in the data, we’ll need to use the pandas.read_csv function. This function will take in a csv file and return a DataFrame. The below code will:
- Import the pandas library. We rename it to pd so it’s faster to type out.
- Read ign.csv into a DataFrame, and assign the result to reviews.
# disable warnings for lecture
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
reviews = pd.read_csv("ign.csv")
Once we read in a DataFrame, Pandas gives us two methods that make it fast to print out the data. These functions are:
pandas.DataFrame.head
– prints the first N rows of a DataFrame. By default 5.pandas.DataFrame.tail
– prints the last N rows of a DataFrame. By default 5.
We’ll use the head method to see what’s in reviews:
reviews.head(3)
| Unnamed: 0 | score_phrase | title | url | platform | score | genre | editors_choice | release_year | release_month | release_day | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Amazing | LittleBigPlanet PS Vita | /games/littlebigplanet-vita/vita-98907 | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 1 | 1 | Amazing | LittleBigPlanet PS Vita -- Marvel Super Hero E... | /games/littlebigplanet-ps-vita-marvel-super-he... | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 2 | 2 | Great | Splice: Tree of Life | /games/splice/ipad-141070 | iPad | 8.5 | Puzzle | N | 2012 | 9 | 12 |
We can also access the pandas.DataFrame.shape
property to see row many rows and columns are in reviews:
reviews.shape
(18625, 11)
As you can see, everything has been read in properly – we have 18625 rows and 11 columns.
One of the big advantages of Pandas vs just using NumPy is that Pandas allows you to have columns with different data types. reviews has columns that store float values, like score, string values, like score_phrase, and integers, like release_year.
Now that we’ve read the data in properly, let’s work on indexing reviews to get the rows and columns that we want.
Indexing DataFrames with Pandas
Earlier, we used the head method to print the first 5 rows of reviews. We could accomplish the same thing using the pandas.DataFrame.iloc
method. The iloc
method allows us to retrieve rows and columns by position. In order to do that, we’ll need to specify the positions of the rows that we want, and the positions of the columns that we want as well.
The below code will replicate reviews.head():
reviews.iloc[0:5,:]
| Unnamed: 0 | score_phrase | title | url | platform | score | genre | editors_choice | release_year | release_month | release_day | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Amazing | LittleBigPlanet PS Vita | /games/littlebigplanet-vita/vita-98907 | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 1 | 1 | Amazing | LittleBigPlanet PS Vita -- Marvel Super Hero E... | /games/littlebigplanet-ps-vita-marvel-super-he... | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 2 | 2 | Great | Splice: Tree of Life | /games/splice/ipad-141070 | iPad | 8.5 | Puzzle | N | 2012 | 9 | 12 |
| 3 | 3 | Great | NHL 13 | /games/nhl-13/xbox-360-128182 | Xbox 360 | 8.5 | Sports | N | 2012 | 9 | 11 |
| 4 | 4 | Great | NHL 13 | /games/nhl-13/ps3-128181 | PlayStation 3 | 8.5 | Sports | N | 2012 | 9 | 11 |
As you can see above, we specified that we wanted rows 0:5. This means that we wanted the rows from position 0 up to, but not including, position 5. The first row is considered to be in position 0. This gives us the rows at positions 0, 1, 2, 3, and 4.
If we leave off the first position value, like :5, it’s assumed we mean 0. If we leave off the last position value, like 0:, it’s assumed we mean the last row or column in the DataFrame.
We wanted all of the columns, so we specified just a colon (:), without any positions. This gave us the columns from 0 to the last column.
Here are some indexing examples, along with the results:
reviews.iloc[:5,:]
— the first 5 rows, and all of the columns for those rows.reviews.iloc[:,:]
— the entire DataFrame.reviews.iloc[5:,5:]
— rows from position 5 onwards, and columns from position 5 onwards.reviews.iloc[:,0]
— the first column, and all of the rows for the column.reviews.iloc[9,:]
— the 10th row, and all of the columns for that row.
Indexing by position is very similar to NumPy indexing.
Now that we know how to index by position, let’s remove the first column, which doesn’t have any useful information:
reviews = reviews.iloc[:,1:]
reviews.head()
| score_phrase | title | url | platform | score | genre | editors_choice | release_year | release_month | release_day | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Amazing | LittleBigPlanet PS Vita | /games/littlebigplanet-vita/vita-98907 | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 1 | Amazing | LittleBigPlanet PS Vita -- Marvel Super Hero E... | /games/littlebigplanet-ps-vita-marvel-super-he... | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 2 | Great | Splice: Tree of Life | /games/splice/ipad-141070 | iPad | 8.5 | Puzzle | N | 2012 | 9 | 12 |
| 3 | Great | NHL 13 | /games/nhl-13/xbox-360-128182 | Xbox 360 | 8.5 | Sports | N | 2012 | 9 | 11 |
| 4 | Great | NHL 13 | /games/nhl-13/ps3-128181 | PlayStation 3 | 8.5 | Sports | N | 2012 | 9 | 11 |
reviews.shape
(18625, 10)
Indexing Using Labels in Pandas
Now that we know how to retrieve rows and columns by position, it’s worth looking into the other major way to work with DataFrames, which is to retrieve rows and columns by label.
A major advantage of Pandas over NumPy is that each of the columns and rows has a label. Working with column positions is possible, but it can be hard to keep track of which number corresponds to which column.
We can work with labels using the pandas.DataFrame.loc
method, which allows us to index using labels instead of positions.
We can display the first five rows of reviews using the loc
method like this:
reviews.loc[0:5,:]
| score_phrase | title | url | platform | score | genre | editors_choice | release_year | release_month | release_day | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Amazing | LittleBigPlanet PS Vita | /games/littlebigplanet-vita/vita-98907 | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 1 | Amazing | LittleBigPlanet PS Vita -- Marvel Super Hero E... | /games/littlebigplanet-ps-vita-marvel-super-he... | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 2 | Great | Splice: Tree of Life | /games/splice/ipad-141070 | iPad | 8.5 | Puzzle | N | 2012 | 9 | 12 |
| 3 | Great | NHL 13 | /games/nhl-13/xbox-360-128182 | Xbox 360 | 8.5 | Sports | N | 2012 | 9 | 11 |
| 4 | Great | NHL 13 | /games/nhl-13/ps3-128181 | PlayStation 3 | 8.5 | Sports | N | 2012 | 9 | 11 |
| 5 | Good | Total War Battles: Shogun | /games/total-war-battles-shogun/mac-142565 | Macintosh | 7.0 | Strategy | N | 2012 | 9 | 11 |
The above doesn’t actually look much different from reviews.iloc[0:5,:]
. This is because while row labels can take on any values, our row labels match the positions exactly. You can see the row labels on the very left of the table above (they’re in bold). You can also see them by accessing the index property of a DataFrame. We’ll display the row indexes for reviews:
reviews.index
RangeIndex(start=0, stop=18625, step=1)
# pull out the 1st twenty indexes
list(reviews.index)[:20]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
Indexes don’t always have to match up with positions, though. In the below code cell, we’ll:
Get row 10 to row 20 of reviews, and assign the result to some_reviews. Display the first 5 rows of some_reviews.
some_reviews = reviews.iloc[10:20,]
some_reviews.head()
| score_phrase | title | url | platform | score | genre | editors_choice | release_year | release_month | release_day | |
|---|---|---|---|---|---|---|---|---|---|---|
| 10 | Good | Tekken Tag Tournament 2 | /games/tekken-tag-tournament-2/ps3-124584 | PlayStation 3 | 7.5 | Fighting | N | 2012 | 9 | 11 |
| 11 | Good | Tekken Tag Tournament 2 | /games/tekken-tag-tournament-2/xbox-360-124581 | Xbox 360 | 7.5 | Fighting | N | 2012 | 9 | 11 |
| 12 | Good | Wild Blood | /games/wild-blood/iphone-139363 | iPhone | 7.0 | NaN | N | 2012 | 9 | 10 |
| 13 | Amazing | Mark of the Ninja | /games/mark-of-the-ninja-135615/xbox-360-129276 | Xbox 360 | 9.0 | Action, Adventure | Y | 2012 | 9 | 7 |
| 14 | Amazing | Mark of the Ninja | /games/mark-of-the-ninja-135615/pc-143761 | PC | 9.0 | Action, Adventure | Y | 2012 | 9 | 7 |
As we mentioned earlier, column labels can make life much easier when you’re working with data. We can specify column labels in the loc method to retrieve columns by label instead of by position.
reviews.loc[:5, "score"]
0 9.0
1 9.0
2 8.5
3 8.5
4 8.5
5 7.0
Name: score, dtype: float64
We can also specify more than one column at a time by passing in a list:
reviews.loc[:5, ["score", "release_year"]]
| score | release_year | |
|---|---|---|
| 0 | 9.0 | 2012 |
| 1 | 9.0 | 2012 |
| 2 | 8.5 | 2012 |
| 3 | 8.5 | 2012 |
| 4 | 8.5 | 2012 |
| 5 | 7.0 | 2012 |
reviews.loc[[0, 3], ["score", "release_year"]]
| score | release_year | |
|---|---|---|
| 0 | 9.0 | 2012 |
| 3 | 8.5 | 2012 |
Pandas Series Objects
We can retrieve an individual column in Pandas a few different ways. So far, we’ve seen two types of syntax for this:
reviews.iloc[:,1]
— will retrieve the second column.reviews.loc[:,"score_phrase"]
— will also retrieve the second column.
There’s a third, even easier, way to retrieve a whole column. We can just specify the column name in square brackets, like with a dictionary:
reviews["score"].head()
0 9.0
1 9.0
2 8.5
3 8.5
4 8.5
Name: score, dtype: float64
We can also use lists of columns with this method:
x = reviews[["score", "release_year"]]
x.head()
| score | release_year | |
|---|---|---|
| 0 | 9.0 | 2012 |
| 1 | 9.0 | 2012 |
| 2 | 8.5 | 2012 |
| 3 | 8.5 | 2012 |
| 4 | 8.5 | 2012 |
When we retrieve a single column, we’re actually retrieving a Pandas Series
object. A DataFrame
stores tabular data, but a Series
stores a single column or row of data.
We can verify that a single column is a Series
:
type(reviews["score"])
pandas.core.series.Series
We can create a Series
manually to better understand how it works. To create a Series
, we pass a list or NumPy array into the Series
object when we instantiate it:
s1 = pd.Series([1, 2])
s1
0 1
1 2
dtype: int64
A Series
can contain any type of data, including mixed types. Here, we create a Series
that contains string objects:
s2 = pd.Series(["Boris Yeltsin", "Mikhail Gorbachev"])
s2
0 Boris Yeltsin
1 Mikhail Gorbachev
dtype: object
Creating A DataFrame in Pandas
We can create a DataFrame
by passing multiple Series
into the DataFrame
class. Here, we pass in the two Series
objects we just created, s1
as the first row, and s2
as the second row:
# create a dataframe from two series
x = pd.DataFrame()
x['rank'] = s1
x['name'] = s2
x
| rank | name | |
|---|---|---|
| 0 | 1 | Boris Yeltsin |
| 1 | 2 | Mikhail Gorbachev |
# create a datafrme from a dictionary
y = pd.DataFrame({
'rank': s1,
'name': s2
})
y
| rank | name | |
|---|---|---|
| 0 | 1 | Boris Yeltsin |
| 1 | 2 | Mikhail Gorbachev |
# create a dataframe from a list
pd.DataFrame([s1, s2])
| 0 | 1 | |
|---|---|---|
| 0 | 1 | 2 |
| 1 | Boris Yeltsin | Mikhail Gorbachev |
We can also accomplish the same thing with a list of lists. Each inner list is treated as a row in the resulting DataFrame:
# create a dataframe from a list of lists
pd.DataFrame(
[
[1, "Boris Yeltsin"],
[2, "Mikhail Gorbachev"]
]
)
| 0 | 1 | |
|---|---|---|
| 0 | 1 | Boris Yeltsin |
| 1 | 2 | Mikhail Gorbachev |
We can specify the column labels when we create a DataFrame:
# specify column labels
pd.DataFrame(
[
[1, "Boris Yeltsin"],
[2, "Mikhail Gorbachev"]
],
columns=["rank", "name"]
)
| rank | name | |
|---|---|---|
| 0 | 1 | Boris Yeltsin |
| 1 | 2 | Mikhail Gorbachev |
As well as the row labels (the index):
# specify the index labels
frame = pd.DataFrame(
[
[1,2],
["Boris Yeltsin", "Mikhail Gorbachev"]
],
index=["rank", "name"],
columns=["person1", "person2"]
)
frame
| person1 | person2 | |
|---|---|---|
| rank | 1 | 2 |
| name | Boris Yeltsin | Mikhail Gorbachev |
We’re then able index the DataFrame
using the labels:
frame.loc["rank":"name", "person1"]
rank 1
name Boris Yeltsin
Name: person1, dtype: object
We can skip specifying the columns keyword argument if we pass a dictionary into the DataFrame
constructor. This will automatically setup column names:
frame = pd.DataFrame(
{
"person1": [1, "Boris Yeltsin"],
"person2": [2, "Mikhail Gorbachev"]
}
)
frame
| person1 | person2 | |
|---|---|---|
| 0 | 1 | 2 |
| 1 | Boris Yeltsin | Mikhail Gorbachev |
Pandas DataFrame Methods
As we mentioned earlier, each column in a DataFrame
is a Series
object:
type(reviews["title"])
pandas.core.series.Series
We can call most of the same methods on a Series
object that we can on a DataFrame
, including head:
reviews["title"].head()
0 LittleBigPlanet PS Vita
1 LittleBigPlanet PS Vita -- Marvel Super Hero E...
2 Splice: Tree of Life
3 NHL 13
4 NHL 13
Name: title, dtype: object
Pandas Series
and DataFrame
also have other methods that make calculations simpler. For example, we can use the pandas.Series.mean
method to find the mean of a Series
:
reviews["score"].mean()
6.950459060402666
We can also call the similar pandas.DataFrame.mean
method, which will find the mean of each numerical column in a DataFrame
by default:
x = reviews.mean()
x
score 6.950459
release_year 2006.515329
release_month 7.138470
release_day 15.603866
dtype: float64
We can modify the axis
keyword argument to mean in order to compute the mean of each row or of each column. By default, axis
is equal to 0, and will compute the mean of each column. We can also set it to 1 to compute the mean of each row. Note that this will only compute the mean of the numerical values in each row:
reviews.mean(axis=1).head()
0 510.500
1 510.500
2 510.375
3 510.125
4 510.125
dtype: float64
There are quite a few methods on Series
and DataFrame
that behave like mean
. Here are some handy ones:
pandas.DataFrame.corr
— finds the correlation between columns in a DataFrame.pandas.DataFrame.count
— counts the number of non-null values in each DataFrame column.pandas.DataFrame.max
— finds the highest value in each column.pandas.DataFrame.min
— finds the lowest value in each column.pandas.DataFrame.median
— finds the median of each column.pandas.DataFrame.std
— finds the standard deviation of each column.
We can use the corr method to see if any columns correlation with score. For instance, this would tell us if games released more recently have been getting higher reviews (release_year), or if games released towards the end of the year score better (release_month):
reviews.corr()
| score | release_year | release_month | release_day | |
|---|---|---|---|---|
| score | 1.000000 | 0.062716 | 0.007632 | 0.020079 |
| release_year | 0.062716 | 1.000000 | -0.115515 | 0.016867 |
| release_month | 0.007632 | -0.115515 | 1.000000 | -0.067964 |
| release_day | 0.020079 | 0.016867 | -0.067964 | 1.000000 |
As you can see above, none of our numeric columns correlates with score, meaning that release timing doesn’t linearly relate to review score.
DataFrame Math with Pandas
We can also perform math operations on Series
or DataFrame
objects. For example, we can divide every value in the score column by 2 to switch the scale from 0-10 to 0-5:
reviews["score"] / 2
0 4.50
1 4.50
2 4.25
3 4.25
4 4.25
5 3.50
6 1.50
7 4.50
8 1.50
9 3.50
10 3.75
11 3.75
12 3.50
13 4.50
14 4.50
15 3.25
16 3.25
17 4.00
18 2.75
19 3.50
20 3.50
21 3.75
22 3.75
23 3.75
24 4.50
25 3.50
26 4.50
27 3.75
28 4.00
29 3.25
...
18595 2.20
18596 3.25
18597 2.45
18598 3.40
18599 3.50
18600 3.70
18601 3.70
18602 3.70
18603 3.90
18604 4.30
18605 3.00
18606 3.20
18607 3.50
18608 2.70
18609 4.00
18610 3.00
18611 2.90
18612 3.90
18613 4.00
18614 4.60
18615 4.60
18616 3.75
18617 4.20
18618 4.55
18619 3.95
18620 3.80
18621 4.50
18622 2.90
18623 5.00
18624 5.00
Name: score, Length: 18625, dtype: float64
All the common mathematical operators that work in Python, like +
, -
, *
, /
, and ^
will work, and will apply to each element in a DataFrame
or a Series
.
Boolean Indexing in Pandas
As we saw above, the mean of all the values in the score column of reviews is around 7. What if we wanted to find all the games that got an above average score? We could start by doing a comparison. The comparison compares each value in a Series
to a specified value, then generate a Series
full of Boolean
values indicating the status of the comparison. For example, we can see which of the rows have a score value higher than 7:
score_filter = reviews["score"] > 7
score_filter
0 True
1 True
2 True
3 True
4 True
5 False
6 False
7 True
8 False
9 False
10 True
11 True
12 False
13 True
14 True
15 False
16 False
17 True
18 False
19 False
20 False
21 True
22 True
23 True
24 True
25 False
26 True
27 True
28 True
29 False
...
18595 False
18596 False
18597 False
18598 False
18599 False
18600 True
18601 True
18602 True
18603 True
18604 True
18605 False
18606 False
18607 False
18608 False
18609 True
18610 False
18611 False
18612 True
18613 True
18614 True
18615 True
18616 True
18617 True
18618 True
18619 True
18620 True
18621 True
18622 False
18623 True
18624 True
Name: score, Length: 18625, dtype: bool
Once we have a Boolean
Series
, we can use it to select only rows in a DataFrame
where the Series
contains the value True
. So, we could only select rows in reviews where score is greater than 7:
filtered_reviews = reviews[score_filter]
filtered_reviews.head()
| score_phrase | title | url | platform | score | genre | editors_choice | release_year | release_month | release_day | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Amazing | LittleBigPlanet PS Vita | /games/littlebigplanet-vita/vita-98907 | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 1 | Amazing | LittleBigPlanet PS Vita -- Marvel Super Hero E... | /games/littlebigplanet-ps-vita-marvel-super-he... | PlayStation Vita | 9.0 | Platformer | Y | 2012 | 9 | 12 |
| 2 | Great | Splice: Tree of Life | /games/splice/ipad-141070 | iPad | 8.5 | Puzzle | N | 2012 | 9 | 12 |
| 3 | Great | NHL 13 | /games/nhl-13/xbox-360-128182 | Xbox 360 | 8.5 | Sports | N | 2012 | 9 | 11 |
| 4 | Great | NHL 13 | /games/nhl-13/ps3-128181 | PlayStation 3 | 8.5 | Sports | N | 2012 | 9 | 11 |
It’s possible to use multiple conditions for filtering. Let’s say we want to find games released for the Xbox One that have a score of more than 7. In the below code, we:
- Setup a filter with two conditions:
- Check if score is greater than 7.
- Check if platform equals Xbox One
- Apply the filter to reviews to get only the rows we want.
- Use the head method to print the first 5 rows of filtered_reviews.
# setup the boolean filtr
xbox_one_filter = (reviews["score"] > 7) & (reviews["platform"] == "Xbox One")
# select the data based on the filter
filtered_reviews = reviews[xbox_one_filter]
# display the first 5 results
filtered_reviews.head()
| score_phrase | title | url | platform | score | genre | editors_choice | release_year | release_month | release_day | |
|---|---|---|---|---|---|---|---|---|---|---|
| 17137 | Amazing | Gone Home | /games/gone-home/xbox-one-20014361 | Xbox One | 9.5 | Simulation | Y | 2013 | 8 | 15 |
| 17197 | Amazing | Rayman Legends | /games/rayman-legends/xbox-one-20008449 | Xbox One | 9.5 | Platformer | Y | 2013 | 8 | 26 |
| 17295 | Amazing | LEGO Marvel Super Heroes | /games/lego-marvel-super-heroes/xbox-one-20000826 | Xbox One | 9.0 | Action | Y | 2013 | 10 | 22 |
| 17313 | Great | Dead Rising 3 | /games/dead-rising-3/xbox-one-124306 | Xbox One | 8.3 | Action | N | 2013 | 11 | 18 |
| 17317 | Great | Killer Instinct | /games/killer-instinct-2013/xbox-one-20000538 | Xbox One | 8.4 | Fighting | N | 2013 | 11 | 18 |
When filtering with multiple conditions, it’s important to put each condition in parentheses ()
, and separate them with a single ampersand &
.
Pandas Plotting
Now that we know how to filter, we can create plots to observe the review distribution for the Xbox One vs the review distribution for the PlayStation 4. This will help us figure out which console has better games. We can do this via a histogram, which will plot the frequencies for different score ranges. This will tell us which console has more highly reviewed games.
We can make a histogram for each console using the pandas.DataFrame.plot
method. This method utilizes matplotlib
, the popular Python plotting library, under the hood to generate good-looking plots.
The plot method defaults to drawing a line graph. We’ll need to pass in the keyword argument kind="hist"
to draw a histogram instead.
In the below code, we:
- Call
%matplotlib inline
to set up plotting inside a Jupyter notebook. - Filter reviews to only have data about the Xbox One.
- Plot the score column.
reviews["platform"].unique()
array(['PlayStation Vita', 'iPad', 'Xbox 360', 'PlayStation 3',
'Macintosh', 'PC', 'iPhone', 'Nintendo DS', 'Nintendo 3DS',
'Android', 'Wii', 'PlayStation 4', 'Wii U', 'Linux',
'PlayStation Portable', 'PlayStation', 'Nintendo 64', 'Saturn',
'Lynx', 'Game Boy', 'Game Boy Color', 'NeoGeo Pocket Color',
'Game.Com', 'Dreamcast', 'Dreamcast VMU', 'WonderSwan', 'Arcade',
'Nintendo 64DD', 'PlayStation 2', 'WonderSwan Color',
'Game Boy Advance', 'Xbox', 'GameCube', 'DVD / HD Video Game',
'Wireless', 'Pocket PC', 'N-Gage', 'NES', 'iPod', 'Genesis',
'TurboGrafx-16', 'Super NES', 'NeoGeo', 'Master System',
'Atari 5200', 'TurboGrafx-CD', 'Atari 2600', 'Sega 32X', 'Vectrex',
'Commodore 64/128', 'Sega CD', 'Nintendo DSi', 'Windows Phone',
'Web Games', 'Xbox One', 'Windows Surface', 'Ouya',
'New Nintendo 3DS', 'SteamOS'], dtype=object)
# enable display of chart inline
%matplotlib inline
# setup the filter
platform_filter = reviews["platform"] == "Xbox One"
# select and plot
reviews[platform_filter]["score"].plot(kind="hist")
<matplotlib.axes._subplots.AxesSubplot at 0x11db8fba8>
We can also do the same for the PS4:
# setup the filter
mask = reviews["platform"].isin(["Xbox One", "PlayStation 4"])
# select and plot
reviews[mask]["score"].plot(kind="hist")
<matplotlib.axes._subplots.AxesSubplot at 0x11dcd6e80>
It appears from our histogram that the PlayStation 4 has many more highly rated games than the Xbox One.
# plot all
filtered_reviews["score"].hist()
<matplotlib.axes._subplots.AxesSubplot at 0x11dc79be0>


Create a prototype of a smart alarm clock using a buzzer (Digital), a button (Digital), a rotary angle (Analog), and an LCD display (I2C) with a web server running on the Intel Edison. Set the alarm time using a web page served directly from the Edison itself, using your laptop or mobile phone. When the alarm goes off, the buzzer sounds and the LCD display indicates it’s time to get up. The rotary dial can be used to adjust the brightness of the display.
Connect Grove Buzzer to digital pin D5 of the Grove Base Shield.
Connect Grove Button to digital pin D4.
Connect Grove LCD display to any I2C pin.
Connect Grove Rotary Angle to analog pin A0.
To start off, write some code for the hardware in main.js:
Create and test functions to make the LCD display blink red, and to stop blinking.
Create and test functions to start the buzzer buzzing, and to stop the buzzer.
Changes in rotary angle should change the brightness of LCD display.
Now, add your server side code in main.js:
Create a web server on the Intel Edison using express in NodeJS.
Remember to edit your package.json to include the express, moment, and superagent npm modules.
Create a new index.html file and write your front end code in there.
Finally, hook everything up together:
When it’s proper time for alarm to start, LCD display should blink to red and buzzer should start buzzing.
When user presses the button: alarm must turn off, LCD must stop blinking, and buzzer should stop buzzing.
You should now see have a prototype of a smart alarm clock.


ST. PETER'S GERMAN
EVANGELICAL PROTESTANT CHURCH AT LEWISVILLE, OHIO
View 3 - Cemetery and back
of St. Peter's in Lewisville, Ohio
This
photo of the St. Peter's German Evangelical Protestant Church of Lewisville, Ohio
was taken in November 2002. The
cemetery immediately behind the church (shown here) is much older and about
half the size of the cemetery behind the photographer. In this portion of the cemetery, most of the
monuments are written in German. Most
have eroded and many are no longer readable.
The inscriptions on most of the monuments in the newer part of the
cemetery (behind the photographer) are written in English. In September of 1962 St. Peters of
Lewisville consolidated with two other churches (St. Peter's of Miltonsburg and
St. John's, also called Middle Church) to form the Trinity United Church of
Christ. The Trinity United Church of
Christ now maintains the cemetery at St. Peter's.
Following
cessation of services at St. Peter's of Lewisville in 1962, a group/church from
Maryland established the Lewisville Bible Mission Church in the old St. Peter's
building. This effort eventually failed
and the building was sold and pressed into service as an automotive garage and tire shop.
Photograph provided by richardharrington -- e-mail: richardharrington@cox.net


QtSensors¶
The QtSensors
module contains classes that provide access
to a system’s hardware sensors including accelerometers, altimeters,
ambient light and temperature sensors, gyroscopes and magnetometers.
The QtSensors
module contains classes that provide access
to a system’s hardware sensors including accelerometers, altimeters,
ambient light and temperature sensors, gyroscopes and magnetometers.


I'm a Data Scientist + Engineer specialized in building automated ML and data pipelines.
I write production-ready code that is readable, reliable, and scalable.
Mozilla's mission is to ensure the Internet is a global public resource, open and accessible to all; an Internet that truly puts people first, where individuals can shape their own experience and are empowered, safe and independent.
I work on the Firefox Browser team.
Blue Onion Labs helps finance and accounting teams trace and reconcile order transactions to create a single source of truth.
I was Blue Onion's first full-time hire. I worked on designing, building, and scaling critical layers of the data stack, including ETL, data enrichment, and data testing/validation.
Croptix has developed a proprietary optical method that can look inside leaves to identify indicators of crop disease. This technology is field deployable, allowing growers to rapidly and economically screen large areas on a recurring basis, and manage diseases as early as possible.
Lead Croptix's Data Team in developing machine learning models. Helped design and analyze in-vivo experiments.
MealPal is a subscription service that provides members with hundreds of lunch choices near where they live or work for a flat, monthly fee. Users can discover new restaurants and save money, while restaurants can increase revenue during their busiest hours of the day.
Developed time-series models to predict customer ordering behavior across MealPal's international markets, including North America, Europe, Australia, and Asia.
Insight is an intensive seven week post-doctoral training fellowship bridging the gap between academia and data science.
Created Skua, a Chrome extension that automatically filters out abusive Twitter content in real time.
Apex Systems specializes in providing IT professionals for contract, contract-to-hire and direct placements.
Optimized an in-house machine learning pipeline for accuracy and computational performance via hyperparameter tuning.
In the U.S. alone, invasive species cost more than $100 billion annually in economic and environmental losses. These losses affect agriculture, forestry, fisheries, and transportation infrastructure.
Designed experiments and simulations to quantify causal relationships between invasions and the evolutionary processes that can accelerate them.
My research was featured in Nature Research Highlights and the BBC World Service.
Simplify Healthcare provides niche software solutions and services to Health plans and third party administrator (TPAs), so they can navigate the rapidly changing market landscape.
Refactored legacy survey analytics software for a client that provides market research to Fortune 500 companies.


usbcamGUI documentation¶
usbcamGUI is simple GUI python script for Debian-based distributions, providing the capture of image, set parameters interactively with the USB camera.
usbcamGUI is simple GUI python script for Debian-based distributions, providing the capture of image, set parameters interactively with the USB camera.


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Deleting a Comment
How to delete a comment from a Wiki page.
Navigate to the required page.
See "Navigating the Wiki"
Maximize
the
Comments
section.
Click the
Delete Comment
link.


Do you know your superheros?
Hello my name is Jude I created this website. I created this because i really like superheros Dc or Marvel it doesnt really matter to me. if you are reading this have a nice day!


Problem 1. Numbers
Enter an integer
Print numbers
Result:
Problem 2. Numbers not divisible
Enter a positive integer
Print numbers
Result:
Problem 3. Min/Max of sequence
Enter a sequence of numbers, separated by a comma and a space (, )
Print Min/Max
Result:
Problem 4. Lexicographically smallest
Show properties
Result:


{{Unreferenced|date=March 2012}}
The pork tenderloin, in some countries called pork fillet, is a cut of pork. It is often sold as prepackaged products by large grocery stores. They are available plain (not seasoned) and flavored with a marinade.
As with all quadrupeds, the tenderloin refers to the psoas major muscle along the central spine portion, ventral to the lumbar vertebrae. This is the most tender part of the animal, because these muscles are used for posture, rather than locomotion.
A pork tenderloin sandwich (also called simply a "tenderloin") is a very thinly sliced piece of pork, usually the larger, tougher loineye (longissimus) muscle, uniquely battered and breaded, which is deep fried and served on a hamburger bun. This is usually served with mustard, ketchup, pickle and onions. This sandwich is generally sold in the US Midwest, especially in the US state of Indiana. In the southern states of the US, tenderloin is often prepared as a breakfast biscuit, usually with egg or cheese. Pork tenderloin is often used as an alternative to beef tenderloin, as it is often just as tender but costs significantly less in most regions.
Alton Brown devoted an entire episode of Good Eats to pork tenderloin, called "Tender is the Pork".
{{Reflist}}
{{Portal bar|Food}} {{Pigs}}


Photo 1
Aaj Fir baris hogi
Photo 2
Do you Love Me
Photo 3
Mera Dil
Photo 4
Pyar ho Gya
Photo 5
Tere Aane Se
Photo 6
Hit By sambhu namata
SN
Music Label
Produced by Shambhu Namata


--- layout: page description: "Information about razor variables for experimental high energy physics." --- \( \newcommand{\MR}{M_{\mathrm{R}}} \newcommand{\Rtwo}{\mathrm{R}^2} \newcommand{\R}{\mathrm{R}} \newcommand{\MRT}{M_{\mathrm{T}}^{\mathrm{R}}} \newcommand{\ETm}{E_{\mathrm{T}}^{\text{miss}}} \newcommand{\ptvec}{\vec{p}_{\mathrm{T}}} \newcommand{\ptvecmiss}{\vec{p}_{\mathrm{T}}^{\text{miss}}} \newcommand{\pt}{p_{\mathrm{T}}} \newcommand{\chiz}{\widetilde{\chi}^{0}} \)
The razor variables are useful tools in the search for supersymmetry, a proposed symmetry of space-time that implies the existence of a new boson for every known fermion, and vice versa, at hadron colliders. The purpose of this page is to describe the variables and the general paradigm, provide code to calculate them for a variety of configurations, and collect references to papers and experimental results using these variables.
To calculate the canonical razor variables \(\MR\) and \(\Rtwo\), we first partition all the visible particles in the event into two categories, or megajets. The momenta of these two megajets are used to compute \(\MR\) and \(\MRT\), defined as \begin{align} \MR & \equiv \sqrt{(|\vec{p}_{1}|+|\vec{p}_{2}|)^2 -({p}^{1}_z+{p}^{2}_z)^2};\\ \MRT & \equiv \sqrt{ \frac{\ETm(\pt^{1}+\pt^{2}) - \ptvecmiss \cdot (\ptvec^{\,1}+\ptvec^{\,2}) }{2}}; \end{align} where \(\vec{p}_{i}\), \(\ptvec^{\,i}\), and \(p^{i}_z\) are the momentum, transverse momentum, and longitudinal momentum of the \(i\)th megajet, respectively, and \(\ETm\) is the magnitude of \(\ptvecmiss\).
\(\MRT\) quantifies the transverse momentum imbalance, while \(\MR\) estimates the mass scale of new-physics particle production in the event. The squared razor dimensionless ratio \(\Rtwo\) is defined as \begin{equation} \Rtwo \equiv \left(\frac{\MRT}{\MR}\right)^2. \end{equation}


| 004:156 Khan | : |
And because of their (Jews) disbelief and uttering against Maryam (Mary) a grave false charge (that she has committed illegal sexual intercourse); |
| 004:156 Maulana | : |
And for their disbelief and for their uttering against Mary a grievous calumny: |
| 004:156 Pickthal | : |
And because of their disbelief and of their speaking against Mary a tremendous calumny; |
| 004:156 Rashad | : |
(They are condemned) for disbelieving and uttering about Mary a gross lie. |
| 004:156 Sarwar | : |
Their hearts were also sealed because of their lack of faith, their gravely slanderous accusation against Mary, |
| 004:156 Shakir | : |
And for their unbelief and for their having uttered against Marium a grievous calumny. |
| 004:156 Sherali | : |
And for their disbelief and for their uttering against Mary a grievous calumny; |
| 004:156 Yusufali | : |
That they rejected Faith; that they uttered against Mary a grave false charge; |
Concepts/Themes Covered: الإنسان والعلاقات الأخلاقية > الأخلاق الذميمة > البهتان Man and The Moral Relations>The Dispraised Morals>Slander الديانات > بنو إسرائيل > معاندتهم و تكذيبهم و قتلهم الأنبياء Religions>Children of Israel> Their Opposition, their Denial and their Killing the Prophets القصص والتاريخ > عُمران > مريم ابنة عمران The Stories and the History>Imran>Blessed Mary(Imran's Daughter)
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Create a game code to start a game or enter a code from a friend to join their room
blah
Choose how long you wish to play
PLAYER's turn
Next Up: PLAYER


| Interface | Description |
|---|---|
| ICharByteInputReader |
Common interface for input readers providing byte input, char input or mixed char/byte input
|
| ICloverDataParser | |
| Parser |
Interface to input data parsers
|
| TextParser |
| Class | Description |
|---|---|
| AbstractParser |
Abstract implementation of
Parser interface. |
| AbstractTextParser |
Abstract class for all TextParsers.
|
| AhoCorasick | |
| BinaryDataParser |
A simple class for retrieving records from binary files created by BinaryDataFormatter
Uses channels
|
| ByteBufferParser |
Parser of records from InputStream.
|
| CharByteDataParser |
A parser able to deal with mixed records containing both char-based and byte-based fields (BYTE/CBYTE)
|
| CharByteDataParser.ByteDelimConsumer |
Consumes a char-based delimiter that cannot be preceded by any other data
|
| CharByteDataParser.ByteRecordSkipper |
Record skipper for input reader that may not support char operations
|
| CharByteDataParser.CharDelimConsumer |
Consumes a char-based delimiter that cannot be preceded by any other data
|
| CharByteDataParser.CharRecordSkipper |
Record skipper for input reader that may not support byte operations
|
| CharByteDataParser.DelimByteFieldConsumer |
Consumes delimited byte field.
|
| CharByteDataParser.DelimCharFieldConsumer |
Consumes delimited char-based field
|
| CharByteDataParser.FixlenByteFieldConsumer |
Consumes several adjacent byte fields of fixed length.
|
| CharByteDataParser.FixlenCharFieldConsumer |
Consumes a char-based field of fixed length
|
| CharByteDataParser.InputConsumer | |
| CharByteDataParser.RecordSkipper |
Attempts to skip a record.
|
| CharByteInputReader |
An abstract class for input readers able to provide mixed char/byte data
|
| CharByteInputReader.ByteInputReader |
This input reader can be used only for data inputs which don't contain any delimiters or char-based fields
|
| CharByteInputReader.CharInputReader |
This input reader can be used only for data inputs which don't contain any byte-based fields
|
| CharByteInputReader.DoubleMarkCharByteInputReader |
An ugly hack to provide support for the verbose error reporting in the parsers.
|
| CharByteInputReader.RobustInputReader |
This implementation of the input reader should be used as last resort when none of the implementations above can
be used That means it can be used for multiple byte charsets on inputs combining char-based and byte-based data
fields.
|
| CharByteInputReader.SingleByteCharsetInputReader |
This implementation of the input reader can be used for input data combining byte-based fields and char-based
fields encoded in single byte charsets
|
| CloverDataParser |
Class for reading data saved in Clover internal format
It is predicted that zip file (with name dataFile.zip) has following structure:
DATA/dataFile
INDEX/dataFile.idx
If data are not in zip file, indexes (if needed) have to be in the same location
NOTE:Supports also deserialization of
DataRecord s from an input stream. |
| CloverDataParser.FileConfig | |
| CloverDataParser35 |
Class for reading data saved in Clover internal format
It is predicted that zip file (with name dataFile.zip) has following structure:
DATA/dataFile
INDEX/dataFile.idx
If data are not in zip file, indexes (if needed) have to be in the same location
NOTE:Supports also deserialization of
DataRecord s from an input stream. |
| DataParser |
Parsing plain text data.
|
| DelimitedDataParser |
Parsing delimited text data.
|
| FixLenByteDataParser |
Parser for sequence of records represented by fixed count of bytes
|
| FixLenCharDataParser |
Parser for sequence of records represented by fixed count of chars.
|
| FixLenDataParser |
Superclass for fix-length data parsers.
|
| JExcelXLSDataParser |
Parsing data from a XLS file using JExcelAPI.
|
| JsonSaxParser | |
| SimpleDataParser |
This parser is as simple as posible - limited validation, error handling, functionality - but very fast.
|
| TextParserConfiguration | |
| TextParserFactory | |
| XLSParser |
Parses data from xls file.
|
| XLSParser.Incremental |
For incremental reading.
|
| XLSXDataParser |
Represents a XLSX data parser based on the Apache POI library.
|
| XmlSaxParser | |
| XmlSaxParser.MyHandler | |
| XPathContext |
The XPathContext is class that evaluates xpaths and other xpath contexts.
|
| XPathElement | |
| XPathElementOfXSDType | |
| XPathParser |
This class is parser for Xml XPath reader.
|
| Enum | Description |
|---|---|
| Parser.DataSourceType |
This enumeration is used by #getPreferredDataSourceType() method to suggest preferred
data source type by a
Parser . |
| XPathParser.SupportedDataModels |
| Exception | Description |
|---|---|
| BinaryDataParser.NoDataAvailableException |
Is thrown if no more bytes are available in current data source for requested operation.
|
Copyright © 2002-2015 Javlin a.s.


We're sorry but supermall doesn't work properly without JavaScript enabled. Please enable it to continue.


Previous competitions
| ID | Title | Type | Innovation area | Number of submitted applications | Number of completed projects |
|---|---|---|---|---|---|
| My competition |
|
| ID | Title | Type | Innovation area | Number of submitted applications | Number of completed projects |
|---|---|---|---|---|---|
| My competition |
|


id
(directive in module dotjem.routing
)
Provides an anchor point for the $scroll service to use.
<ANY id jemAnchor="{string}"> ... </ANY>as class
<ANY class="id jemAnchor: {string};"> ... </ANY>
jemAnchor|id – {string} –
Identifier of the anchor


This example is from the spec. There should be four flex items. Anonymous item 3 shouldn't have a green border because the anonymous block is the flex item.


what is scope? scope is a lot about what a user can use and what they can't based on either how they have defined a variable and also where they have declared it. In javscript there are two types of scope local and global variables declared within a JavaScript function become local to the function , since they are inside the function they can only accessed within that function. A variable declared outside a function, becomes global. it can be used from anywhere and by as many functions as you'd like.


This syllabus is subject to change based on specific class needs, especially the schedule. Significant deviations will be discussed in class. Individual exceptions to the policies and schedule are granted only in cases of true emergency. Please make arrangements with me if an emergency arises.
An introduction to the hot topics of machine learning, data science and data mining. The course aims to supply students with a useful toolbox of machine learning techniques that can be applied to real-life data. Techniques may include logistic and linear regression, SVMs, decision trees, neural networks, and clustering. The focus will be on developing important skills in preparing data and selecting and evaluating models, though we will delve into the mathematical intuition behind each model.
Possible topics include:
The required course textbook is:
Guido, Sarah and Muller, Andreas. Introduction to Machine Learning with Python. O’Reilly. 2016. ISBN-13:<PHONE_NUMBER>415.
I also recommend, but do not require:
Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd edition). O’Reilly. 2019. ISBN-13:<PHONE_NUMBER>649
We’ll be using Python 3 along with several of the standard data science libraries available, including matplotlib, pandas, numpy, and scikit-learn, among others. The easiest way to get set up is to install the Anaconda Python distribution, which includes everything you need. It also includes the Spyder IDE for development, although you are free to develop in any text editor you like. You may also use the department server if you’d like; just send me an email.
Late assignments: You have each been allotted a total of 5 late days. You may apply these to any problem sets you see fit and turn in your solutions with no penalty. However, you may use at most 2 on any individual assignment. The whole point here is to give you some flexibility that allows for things like illnesses, long trips, and the like. I am unlikely to grant further extensions.
Academic dishonesty: Monmouth College’s official policy on academic dishonesty can be found here. You are responsible for reading and complying with that policy.
In this course, any violation of the academic honesty policy will have varying consequences depending on the severity of the infraction as judged by the instructor. Minimally, a violation will result in an “F” or 0 points on the assignment in question. Additionally, the student’s course grade may be lowered by one letter grade. In severe cases, the student will be assigned a course grade of “F” and dismissed from the class. All cases of academic dishonesty must be reported to the Associate Dean who may decide to recommend further action to the Admissions and Academic Status Committee, including suspension or dismissal. It is assumed that students will educate themselves regarding what is considered to be academic dishonesty, so excuses or claims of ignorance will not mitigate the consequences of any violations
Collaboration: We encourage you to make use of the resources available to you – it is fine to seek help from a friend, tutor, instructor, internet, etc. However, copying of answers and any act worth of the label “cheating” is never permissible! In addition to listing your sources and collaborators, you should be producing your own writeup in your own words. By “your own words,” we mean you should be producing the text yourself, without some external aid. Verbatim copying of text is specifically disallowed, but so is taking a source and rearranging some phrases and changing some variable names to create a derivative version! Such behavior is definitely NOT “using your own words.” It does not matter if you helped contribute to this source text with others, since then you are still not the sole author of the text. The point of collaborating on an assignment is not to produce a jointly authored set of solutions, since that violates the course policies. Instead, it is to help you solve the problems, which sometimes involve a bit of creativity. After you have jointly come up with the ideas you need to solve the problems, though, you should part ways with your group and sit down to do the writing by yourself. I also advise against sharing the writeup you submit with others, since if someone else uses your text as a source for their own solution (with or without your permission), you will also be implicated in the violation of the academic integrity policy. In any case, if two nearly identical solutions are received, we have no way of tell which is the original, and the policy is to not award credit for either submission.
Electronic devices: Do not use your phone or other devices in class except where necessary. Any computer or tablet usage should be related to the course. If a device is not being used for Zoom or Socrative it should be put away and turned on silent. Other usage is rude and distracting to others.
General expectations: In short, I expect you to be respectful of others and take responsibility for your own learning. You are here to learn, so work hard and be professional.
Just attending class is not sufficient to truly learn the material. Read the text, use the resources available at Monmouth College, and go beyond the material.
If you miss class, you are responsible for everything covered on that day. College is, in some sense, your job. Take pride in creating quality work. Staple your assignments, label problems, and present your answers neatly and orderly.
Your job is to convince me that you have learned the material – show your work! Even if you do not know a particular answer, guide me through your thought process.
The course workload is as follows:
| Category | Number of Assignments | Final Grade Weight |
|---|---|---|
| Homework | 5–7 | 50% |
| Midterm | 1 | 20% |
| Final | 1 | 20% |
| Participation | - | 10% |
Most (probably all) homework assignments will involve programming. Each exam focuses primarily, but not necessarily exclusively, on material covered since the previous exam. In other words, the final exam may include one or two questions from first-half material.
Your participation grade is based on a variety of activities. During class I will often make sure of the Socrative app, so you’ll need to install this on your phones. Participating in Socrative questions and with in-class group activities is required for a decent participation grade; a full grade also includes asking questions either in class or in office hours.
Your final grade is based on a weighted average of particular assignment categories, with weights shown above. You can estimate your current grade based on your scores and these weights. You may always visit the instructor outside of class to discuss your current standing.
This courses uses a standard grading scale. Assignments and final grades will not be curved except in rare cases when its deemed necessary by the instructor. Percentage grades translate to letter grades as follows:
| Score | Grade |
|---|---|
| 94–100 | A |
| 90–93 | A- |
| 88–89 | B+ |
| 82–87 | B |
| 80–81 | B- |
| 78–79 | C+ |
| 72–77 | C |
| 70–71 | C- |
| 68–69 | D+ |
| 62–67 | D |
| 60–61 | D- |
| 0–59 | F |
You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge*.
The weekly workload for this course will vary by student and over the semester, but on average should be about 12 hours per week. The follow table provides a rough estimate of the distribution of this time over different course components for a 16 week semester.
| Category | Total Time | Time/Week (Hours) |
|---|---|---|
| Lectures | 55 | 2.5 |
| Homework | 72 | 4.5 |
| Exam Study | 27 | 1.5 |
| Reading+Unstructured Study | 2.5 | |
| 11 |
The following tentative calendar should give you a feel for how work is distributed throughout the semester. Assignments and events are listed in the week they are due or when they occur. This calendar is subject to change based on the circumstances of the course.
| Date | Topic | Assignment |
|---|---|---|
| Wed 08/19 (Week 1) | Intro, Logistics | Python tutorial |
| Fri 08/21 | Python Review | IMLP Ch. 1 |
| Mon 08/24 (Week 2) | More Python, Numpy | Hwk 1 out (Solutions), Numpy tutorial |
| Wed 08/26 | Pandas | Modern Pandas |
| Fri 08/28 | Visualization | |
| Mon 08/31 (Week 3) | Regression, Classification case studies | |
| Wed 09/02 | Model Complexity, kNN, Bias/Variance | Hwk 2 out, IMLP 2.2-2.3 (p. 28 – 46) |
| Fri 09/04 | Cross Validation | IMLP 5.1 (p. 253 – 262) |
| Mon 09/07 (Week 4) | More Cross Validation and Grid Search | IMLP 5.2, 6 |
| Wed 09/09 | Bias in Machine Learning | |
| Fri 09/11 | Linear Models for Regression | IMLP p. 47 – 70 |
| Mon 09/14 (Week 5) | Hwk 2 Discussion, Linear Models for Classification | |
| Wed 09/16 | Linear Models for Classification | |
| Fri 09/18 | Preprocessing | IMLP 3.3 |
| Mon 09/21 (Week 6) | Categorical Features, Missing Values | IMLP 4, Hwk 3 out (Leaderboard) |
| Wed 09/23 | Hwk 2 Discussion | |
| Fri 09/25 | More Preprocessing | |
| Mon 09/28 (Week 7) | Kernel SVMs | IMLP p. 94 – 106 |
| Wed 09/30 | Classification Evaluation | IMLP 5.3 |
| Fri 10/02 | Multiclass Classification | |
| Mon 10/05 (Week 8) | Decision Trees | IMLP p. 72 – 85 |
| Wed 10/07 | Tree Ensembles | IMLP p. 85 – 94 |
| Fri 10/09 | (No class – Pseudo-Fall Break) | |
| Mon 10/12 (Week 9) | Hwk 3 Questions | |
| Wed 10/14 | Midterm Review | |
| Fri 10/16 | Midterm | |
| Mon 10/19 (Week 10) | Calibration; Imbalanced Data | |
| Wed 10/21 | Model Interpretation | |
| Fri 10/23 | Feature Selection | |
| Mon 10/26 (Week 11) | Dimensionality Reduction | IMLP 3.4 |
| Wed 10/28 | Clustering | IMLP 3.1, 3.2, 3.5, Hwk 4 out |
| Fri 10/30 | Text Vectorization and Normalization | IMLP 7 |
| Mon 11/02 (Week 12) | Topic Modeling | |
| Wed 11/04 | Word and document embeddings | |
| Fri 11/06 | Hwk 4 Questions | |
| Mon 11/09 (Week 13) | Neural Networks | IMLP p. 106 – 121, Watch 3blue1brown |
| Wed 11/11 | Neural Networks in Practice | Hwk 5 out |
| Fri 11/13 | Keras; Convolutional Neural Networks | |
| Mon 11/16 (Week 14) | Practical Neural Network Training | |
| Wed 11/18 | Advanced Neural Networks | |
| Fri 11/20 | ||
| Mon 11/23 | Review |
Mental Health and Counseling Services: Monmouth College provides cost-free, professional mental health counseling to support you and to help you manage challenges that may impact your personal and academic success. The Counseling Center is located in the upper level of Poling Hall, offices 204 and 216, and the hours are Monday-Friday, 8:30 AM – 5:00 PM. To request a confidential appointment on-line, click here or email the center, Cindy Beadles or Tom Caudill.
Student Success and Accessibility Services offers FREE resources to assist Monmouth College students with their academic success. Programs include supplemental instruction for difficult classes, drop-in and appointment tutoring, and individual academic coaching. The office is here to help students excel academically, since everyone can work toward better grades, practice stronger study skills, and mange their time better.
Accessibility Services: If you have a disability or had academic accommodations in high school or another college, you may be eligible for academic accommodations at Monmouth College under the Americans with Disabilities Act (ADA). Monmouth College is committed to equal educational access. To discuss any of the services offered, please call or visit Student Success and Accessibility Services. SSAS is located in the ACE space on the first floor of Hewes Library, opposite Einstein Bros. Bagels. They can be reached at<PHONE_NUMBER> or via email at ssas@monmouthcollege.edu.


Google Earth Enterprise Documentation Home | Troubleshooting and error messages
Maps display grey tiles
Publishing issues (such as 2D browser-based maps that display grey tiles or no imagery) can indicate a hostname error. For example, accidentally setting the hostname to localhost.localdomain
instead of myserver.com
.
To verify the hostname:
Query the Fusion 2D map configuration directly from the web browser:
http://myserver.com/mymaps/query?request=LayerDefs
A response that includes a line like the example below indicates that GEE is directing users to your local host instead of the publishing server.
var stream_url = "http://localhost.localdomain/default_map";
To correct the hostname:
- Query
hostname -f
on the server, and verify that the path to the hostname can be resolved. (In other words, verify that users can access the URL.) - To correct the hostname for all assets on your server, run
geconfigureassetroot --fixmasterhost
. - Republish the database to the virtual server.
- Verify that the URL displays the map correctly.
If the URL doesn't display your map at all, run geserveradmin --publisheddbs
on your Fusion machine to verify that you published the database to the correct virtual server. If you don't see http://myserver.com/mymaps
listed with the database you want to publish, then republish the database.
To further diagnose the issue, review the /opt/google/gehttpd/logs/error_log
or run the geecheck
script.


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CREW, THE (director: Michael Dinner; screenwriter: Barry Fanaro; cinematographer: Juan Ruiz-Anchia; editor: Nicholas C. Smith; cast: Richard Dreyfuss (Bobby Bartellemeo), Burt Reynolds (Bats Pistella), Dan Hedaya (the Brick Donatelli), Seymour Cassel (Mouth Donato), Carrie-Anne Moss (Detective Olivia Neal), Jennifer Tilly (Ferris Lowenstein), Lainie Kazan (Pepper Lowenstein), Miguel Sandoval (Raul Ventana), Carlos Gómez (Miguel), Jeremy Piven (Detective Steve Menteer); Runtime: 88; 2000)
Reviewed by Dennis Schwartz
Michael Dinner's geezer mobster comedy has a funny premise but falters on the execution part, as the yuks come, but not often enough to sustain the predictability and staleness about the film. The film tried hard to be funny and genial, but this idea for a film has been done too often in recent times to appear fresh. Whatever went wrong, must be blamed on the poor direction and poor script. The most unbearable part of the film for me, was having to listen to Richard Dreyfuss' voiceover, which was sprinkled with ethnic slang expressions such as "mooks," "fahzools," and "fuhgeddaboudits," as he droned on in a nasal voice throughout, telling about the four wiseguys who gew up together in New Jersey and who are now unhappily retired in Miami Beach. This film needed such an intensive voiceover like I need to have sand kicked in my face at the beach, it just ruined the flow of the story and prevented the story from being a movie experience rather than an exercise in trying to cover up the film's inability to tell a story.
The four senior citizens featured were embellezers, arsonists, enforcers, and lower echelon mob types, who are now living in a South Beach hotel for the elderly, where they hunt for bingo games, a free cup of a soup, and act geriatric like all the other seniors in their golden years down here do. But things are changing since Madonna mentioned it as a hip spot and the yuppies are coming around to gentrify it, as the old timers are dying out and being replaced by the young. The four aging retired mobsters, with three of them having colorful nicknames that depict their emotional temperaments -- Bobby Bartellemeo (Richard Dreyfuss), Joey "Bats" Pistella (Burt Reynolds), Mike "The Brick" Donatelli (Dan Hedaya) and Tony "Mouth" Donato (Seymour Cassel) -- all try to earn some extra money by working at a regular job for the first time in their life. The only one to get any satisfaction from his job is The Brick, who works in the morgue dressing the dead up before they depart the earth.
The wiseguys don't have the money to keep up with their rising rent payments and they are seen seething, as they sit on the hotel's streetside porch and listen to the passers-by say that they expect them to die soon. The hotel management just wants to get them out so that they can really raise the rent sky high for their ocean view apartment. So they come up with a cockeyed plan, cooked up by the volatile Bats. When he gets knocked unconscious, that is the only way he can think up such plans, which becomes a running gag throughout the movie. The plan is to get a body from the morgue and then shoot the dead body to make it look like a gangland execution and then dump it in the lobby of their hotel. This way the public will consider the hotel a dangerous place to live and their rent will go down, as no one will want to live there.
 This results in a couple of the drug lord's enemies turning up dead. As the police think they have a full-scale drug war on their hands, they send two detectives to talk to the four old-timers and warn them to be careful.
This turns out to be a coincidence, since Bobby, the brains of the four wiseguys, moved to the Miami area primarily because he wants to locate his missing daughter. She was taken there by her mother, who divorced him when he did some prison time and he never was able to locate her again. Ironically, the wiseguy's daughter turns out to be one of the detectives investigating the crime, Olivia Neal (Carrie-Anne Moss).
Another running gag is that The Mouth never talks much, he is only interested in chasing girls.  He thereby tells Ferris of the hit, and she then turns around and blackmails the boys. She tells them that she won't tell the drug lord what happened if they whack her rich step-mother Pepper Lowenstein (Kazan), whereby she will collect the insurance money.
The film keeps adding on sub-plots and the comedy keeps getting stifled with the added weight of the new sub-plots. It isn't enough to make the four of them out to be mobsters, but they also must now be deemed as good guys, so the film goes to unneeded lengths to show how wonderful they are.  But she lives next to the drug lord, and they burn down by mistake his house along with hers. Meanwhile the detective partners, Olivia and her ex-boyfriend, the shifty-eyed Steve (Jeremy Piven), can't get back together anymore because she doesn't trust him, but they still work together. Their relationship gets woven into the ongoing story, further filling the story with unnecessary fodder.
The director had a barrage of proven comic material to work with, an able cast (except for Burt Reynolds-he was terribly miscast), yet the film falls flat on its face. Everything about it seemed like a mistaken pratfall.  Laine Kazan could flatten a kreplach with this old joke about Tilley's father, as she blurts out, "It would kill her father if he knew what she was doing to me, that is, if he weren't already dead." With yuks like that, all I can say is "oy gevalt," give me a break, get some fresh material already!
For those who are film buffs, there is one throwaway scene to kvell on, where the director pays homage to Martin Scorsese's GoodFellas, as the wiseguys get ripped off by an Hispanic kitchen worker, as he gets bribe money from them for letting them sneak through his deli's kitchen so the boys can get served the early-bird special, otherwise they would be stuck waiting outside on a long line. It might have been the funniest scene of the film, just to see their silly expressions as he leads them out a back door onto the street. I also left the theater by way of the rear door, and I also had a look of consternation on my face, wondering how easy it would have been to make this a much better film with just a little better effort in working the script.
REVIEWED ON 9/18/2000 GRADE: C-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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


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The automatic links in the older version probably got confused. This happens when we rearrange things to improve the navigation; we apologize for the inconvenience!


# File lib/rspec/matchers/built_in/yield.rb, line 60 def self.assert_valid_expect_block!(block) return if block.arity == 1 raise "Your expect block must accept an argument to be used with this " + "matcher. Pass the argument as a block on to the method you are testing." end
# File lib/rspec/matchers/built_in/yield.rb, line 15 def initialize @used = false self.num_yields, self.yielded_args = 0, [] end
# File lib/rspec/matchers/built_in/yield.rb, line 5 def self.probe(block) probe = new assert_valid_expect_block!(block) block.call(probe) probe.assert_used! probe end
# File lib/rspec/matchers/built_in/yield.rb, line 51 def assert_used! return if @used raise "You must pass the argument yielded to your expect block on " + "to the method-under-test as a block. It acts as a probe that " + "allows the matcher to detect whether or not the method-under-test " + "yields, and, if so, how many times, and what the yielded arguments " + "are." end
# File lib/rspec/matchers/built_in/yield.rb, line 30 def single_yield_args yielded_args.first end
# File lib/rspec/matchers/built_in/yield.rb, line 45 def successive_yield_args yielded_args.map do |arg_array| arg_array.size == 1 ? arg_array.first : arg_array end end
# File lib/rspec/matchers/built_in/yield.rb, line 20 def to_proc @used = true probe = self Proc.new do |*args| probe.num_yields += 1 probe.yielded_args << args end end
# File lib/rspec/matchers/built_in/yield.rb, line 34 def yielded_once?(matcher_name) case num_yields when 1 then true when 0 then false else raise "The #{matcher_name} matcher is not designed to be used with a " + "method that yields multiple times. Use the yield_successive_args " + "matcher for that case." end end


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{% extends "marketplace_base.html" %} {% load staticfiles %} {% load get_thumbnail %} {% block title %}I3 Marketplace | Product | {{ object.title }}{% endblock %} {% block page_content %}
{{ object.description }}
by {{ object.seller.username }}
Purchases: {{ object.transaction_set.count }}
#} {# {{ object.description }}#} {#
#} {# #} {# #} {# #} {# #} {# #} {# 4.0 stars#} {#
Download
Preview
Product is coming soon for download
Number of items:
This item is Restricted. Please, explain your purchase below.
Purchase
Your card on file will be charged after you agree below.
#} {# My Rating#} {##} {#
#} {# {{ rating_avg.rating__avg }} Average Rating by {{ rating_avg.rating__count }} Users.#} {# #} {#


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Week 14 - Volume rendering for scientific viz, Wrap up with Idyll, Publishing Viz
We talk a little bit about publishing, and scientific viz - in particular, volume rendering - and carry on with Idyll.
A fun video about rendering from Pixar is found here.
Downloads & Links
Data
Links & Code
- We’ll be building toward a webpage like this today using Idyll’s built-in components.
- Last week, we got to this Markdown code right here and we’ll start from there this week.
- Full Markdown code for this week and next week available right here.
- We’ll also build a few histogram components like:
Where we got to in class this week:
Note: see week 12 for the scripts used to generate this dataset.
Optional reading list
- yt docs
- yt Volume Rendering Tutorial
- Idyll Docs
Materials


The replaceChild() method replaces a child node with a new node. The new node could be an existing node in the document, or you can create a new node.
This is para. text.


Validation Class
The Validation class helps you validate user input, if you want to create a form & its validation at the same time use the Fieldset class instead.
Class Methods
Coming soon.
The Validation class helps you validate user input, if you want to create a form & its validation at the same time use the Fieldset class instead.
Coming soon.


{% extends 'base.html'%}
{%import 'macros.html' as macro %}
{% block content%}
{{macro.displayPitches(interview_pitches)}}
{{macro.displayPitches(products_pitches)}}
{{macro.displayPitches(it_pitches)}}
{{macro.displayPitches(pickup_pitches)}}
{{macro.displayPitches(political_pitches)}}
{% endblock %}


SUMADAN XLT- sulfacetamide sodium, sulfur, avobenzone, octinoxate, and octisalate
Medimetriks Pharmaceuticals Inc.
Disclaimer: This drug has not been found by FDA to be safe and effective, and this labeling has not been approved by FDA. For further information about unapproved drugs, click here.
Rx Only
Sumadan®
(Sodium Sulfacetamide 9% & Sulfur 4.5%)
WASH
In a Moisturizing
Novasome® Vehicle
INGREDIENTS
Each gram of SUMADAN® Wash contains 90 mg of sodium sulfacetamide and 45 mg of sulfur in a formulation consisting of: butylated hydroxytoluene, C12-15 alkyl benzoate, caprylyl glycol, cetyl alcohol, cholesterol, chrysanthemum dendranthema, dimethicone, disodium oleamido MIPA sulfosuccinate, edetate disodium, ethylene brassilate, glyceryl stearate, hexylene glycol, lemon oil, magnesium aluminum silicate, magnesium chloride, magnesium nitrate, methylchloroisothiazolinone, methylisothiazolinone, niacinamide, nonoxynol-20, octoxynol-5, purified water, PEG-100 stearate, phenoxyethanol, propylene glycol, sodium cocoyl isotheionite, sodium methyl cocoyl taurate, sodium thiosulfate, stearyl alcohol, xanthan gum.
Store at controlled room temperature 15°-30°C (59°-86°F). Protect from freezing.
FOR EXTERNAL USE ONLY. NOT FOR OPHTHALMIC USE.
Keep out of reach of children. Keep bottle tightly closed.
niseko™
SUNSCREEN
Broad Spectrum SPF 25
ACTIVE INGREDIENTS
Avobenzone 3.0%, Octinoxate 7.5%, Octisalate 5.0%.
INACTIVE INGREDIENTS
Purified Water, Cyclomethicone, C12-15 Alkyl Lactate, Butyloctyl Salicylate, Glyceryl Stearate SE, Isododecane, Glycerin, Cetearyl Alcohol, Butylene/Ethylene/Propylene Copolymer, Polyacrylamide, Cetyl Alcohol, Dicetyl Phosphate, Ceteth-10 Phosphate, Cholesterol, Tocopheryl Acetate, Edetate Disodium, Methylparaben, Phenoxyethanol, Xanthan Gum, Camellia Sinensis Leaf (Green Tea) Extract, Camellia Sinensis Leaf (White Tea) Extract, C13-C14 Isoparaffin, Laureth-7.
Store at controlled room temperature 15°-30°C (59°-86°F). Protect from freezing.
For external use only. Do not use on damaged or broken skin
When using this product keep out of eyes. Rinse with water to remove.
Stop use and ask a doctor if rash occurs
Keep out of reach of children. If product is swallowed, get medical help or contact a Poison Control Center right away.
Manufactured for:
Medimetriks Pharmaceuticals, Inc., 383 Route 46 West, Fairfield, NJ 07004-2402 USA
www.medimetriks.com • MADE IN USA
PRINCIPAL DISPLAY PANEL - Kit Carton
NDC 43538-192-16
Rx Only
Sumadan®
(Sodium Sulfacetamide 9% & Sulfur 4.5%)
XLT
KIT CONTENTS:
1 - Sumadan® (Sodium Sulfacemide 9% & Sulfur 4.5%) Wash (Net Wt. 16 oz.)
1 - Niseko™ Sunscreen Broad Spectrum SPF 25 (Net Wt. 3 oz.)
MEDIMETRIKS
PHARMACEUTICALS, INC.
Medimetriks Pharmaceuticals Inc.


A convenient and attractive place to stop on the less frequented Pacific coast, on El Tigre Island. The activities of the former commercial port were moved two decades ago to San Lorenzo on the mainland.
Although not an official port of entry, boats have been able to stop here. Boats are visited on arrival by the port captain, who will request clearance certificate (zarpe) from the last port. Customs and immigration are visited next, their offices being on the quay. There is a small fee for all formalities, which includes the issuing of a certificate on departure.


Achieve a new level of fitness
Through guided workouts, customized training plans, and live instruction, this app will elevate your martial arts and fitness abilities
download nowThrough guided workouts, customized training plans, and live instruction, this app will elevate your martial arts and fitness abilities
download nowLearn plenty of different Jiu Jitsu and MMA techniques step-by-step, taught by our own certified TBF instructors. Exercise tutorials also included
read moreReceive a complete training program based on your fitness goals. Specific exercises are tailored to your weak points
read moreParticipate in the intensity of a TBF class anywhere with our live classes and sessions. Just because it's remote doesn't mean it will be easy
read moreThis app was developed with the goal of expanding the TBF mindset to anyone around the world. Our ultimate goal is to foster a global community of like-minded martial artists.
Whether your goal is to achieve the next belt, begin competing in tournaments, or even just improve your fitness level, one of the surefire ways to get there is through a Trial By Fire.


MEDIAN(numArray) → median
Return the median value of the values in array.
The array is sorted and the element in the middle is returned. If the array has an even length of elements, the two center-most elements are interpolated by calculating the average value (arithmetic mean).
MEDIAN( [ 1, 2, 3] ) // 2
MEDIAN( [ 1, 2, 3, 4 ] ) // 2.5
MEDIAN( [ 4, 2, 3, 1 ] ) // 2.5
MEDIAN( [ 999, 80, 4, 4, 4, 3, 3, 3 ] ) // 4


Quantum supremacy: A three minute guide
Elizabeth Gibney talks through the latest milestone in quantum computing.
by Elizabeth GibneyGoogle says they have reached 'quantum supremacy' with their quantum computer 'Sycamore'. Nature reporter Elizabeth Gibney explains what this means, why IBM disagree, and the significance for quantum computing.
doi: 10.1038/d41586-019-03410-w


I am a developer with skill related to Microsoft technologies and some open source like Selenium.
Below are my skill set
<EMAIL_ADDRESS>telephone:mytelephonenumber
more info


Get access to instant medical aid from our pool of experienced and adqeuate health practitioners which is now fast and easy. Talk to a doctor today.
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This is OpenStack Install guide on VMWare Workstation Under Windows
1. Download VMware Workstation
I hope you are having 64 bit operation system, in this case select 64 bit VMWare Workstation
2. Create Virtual Machine
Create new virtual machine with
3. Install Ubuntu Server 64-bit
Select Ubuntu Server 12.0 ISO and install operation system
use openstack-essex name for OS and username/password
Change to root:
sudo bash
Change operation system name:
nano /etc/hostname
Enter openstack-essex and save
Change hosts file:
nano /etc/hosts
Enter <IP_ADDRESS> openstack-essex
Change network interfaces:
nano /etc/network/interfaces
Insert text
# The loopback network interface auto lo iface lo inet loopback
#Primary interface NAT interface auto eth0 iface eth0 inet dhcp
#public interface – The API village auto eth1 iface eth1 inet static address <IP_ADDRESS> netmask <IP_ADDRESS> network <IP_ADDRESS> broadcast <IP_ADDRESS>
#Private Vlan Land of Compute Nodes auto eth2 iface eth2 inet manual up ifconfig eth2 up
Restart the operation system
shutdown -r now
Check from Windows that VM is accessible by eth1
cmd
ping <IP_ADDRESS>
In the Virtual Machine console check NAT interface
ping google.com
4. Update Ubuntu Openstack-Essex
Update Ubuntu and install SSH
sudo bash
apt-get update
apt-get dist-upgrade
apt-get install ssh ntp
Connect through Putty by eth0 to the Virtual Machine
Use UTF-8 encoding
Change NTP settings:
nano /etc/ntp.conf
Insert on top:
server ntp.ubuntu.com iburst server <IP_ADDRESS> fudge <IP_ADDRESS> stratum 10
Restart NTP service
service ntp restart
ntptime
5.Install packages
apt-get install tgt
apt-get install open-iscsi open-iscsi-utils
apt-get install bridge-utils
Retart networking
/etc/init.d/networking restart
Install more packages
apt-get install rabbitmq-server memcached python-memcache
apt-get install kvm libvirt-bin
apt-get install -y mysql-server python-mysqldb
Create Databases:
mysql -u root -p
CREATE DATABASE keystone;
GRANT ALL PRIVILEGES ON keystone.* TO 'keystone'@'localhost' IDENTIFIED BY 'openstack';
CREATE DATABASE nova;
GRANT ALL PRIVILEGES ON nova.* TO 'nova'@'localhost' IDENTIFIED BY 'openstack';
CREATE DATABASE glance;
GRANT ALL PRIVILEGES ON glance.* TO 'glance'@'localhost' IDENTIFIED BY 'openstack';
FLUSH PRIVILEGES;
QUIT;
Check connection to database:
mysql -u nova -p nova
mysql -u glance -p glance
Enter: ‘openstack’ password
Change bing address of the mysql
nano /etc/mysql/my.cnf
/*from bind-address = <IP_ADDRESS> /*to bind-address = <IP_ADDRESS>
Restart mysql service
service mysql restart
6.Install Keystone
Install keystone packages
apt-get install keystone python-keystone python-keystoneclient
Change keystone settings
nano /etc/keystone/keystone.conf
[DEFAULT] admin_token = TOKEN123
[sql] connection = mysql://keystone:openstack@localhost/keystone min_pool_size = 5 max_pool_size = 10 pool_timeout = 200 idle_timeout = 200
Restart Keystone service
service keystone restart
keystone-manage db_sync
Download keystone initialization script
wget https://github.com/downloads/nerdalert/Openstack-Essex-install-config-files/keystone.sh
chmod +x keystone.sh
Change token in script
nano keystone.sh
Change export SERVICE_TOKEN=”openstack” to export SERVICE_TOKEN=”TOKEN123”
Run keystone script
./keystone.sh
echo $?
Should return 0 if success
Restart Keystone service
service keystone restart
7.Install Glance
Install packages
sudo bash
apt-get install glance glance-api glance-client glance-common glance-registry python-glance
Change ~/.bashrc
nano ~/.bashrc
Insert text:
export SERVICE_TOKEN=TOKEN123 export OS_TENANT_NAME=admin export OS_USERNAME=admin export OS_PASSWORD=openstack export OS_AUTH_URL="http://localhost:5000/v2.0/" export SERVICE_ENDPOINT=http://localhost:35357/v2.0
Restart bash and check exports
bash
export
Change ini files for Glance
nano /etc/glance/glance-registry-paste.ini
nano /etc/glance/glance-api-paste.ini
Chage properties in bottom
admin_tenant_name = admin admin_user = admin admin_password = openstack
Change mysql connection string for Glance
nano /etc/glance/glance-registry.conf
sql_connection = mysql://glance:openstack@localhost/glance
Add in bottom keystone reference
nano /etc/glance/glance-registry.conf
nano /etc/glance/glance-api.conf
[paste_deploy] flavor = keystone
Restart Glance
service glance-api restart
service glance-registry restart
glance-manage version_control 0
glance-manage db_sync
service glance-api restart
service glance-registry restart
/*Upload Images /* 11.10 image wget http://uec-images.ubuntu.com/releases/11.10/release/ubuntu-11.10-server-cloudimg-amd64-disk1.img /* 12.04 image wget https://cloud-images.ubuntu.com/precise/current/precise-server-cloudimg-amd64-disk1.img
Download Ubuntu images
glance index


Personal Details | Education | Work Experience | Hobbies | References
Personal Information: Name: Address: Phone Number:
Back to Top


Kate Middleton pulls out of event 'due to Prince George, Princess Charlotte and Prince Louis'
Kate Middleton was scheduled to join Prince William at two events this evening - one at the Empire Cinema in Leicester Square and another in the National Portrait Gallery
by Zoe Forsey, https://www.facebook.com/zoeforseymirror/The Duchess of Cambridge has pulled out of an event she was due to attend this evening due to an undisclosed matter relating to Prince George, Princess Charlotte and Prince Louis.
Kate was due to join Prince William at the Tusk Conservation Awards at the Empire Cinema in Leicester Square this evening, before heading to a reception at the National Portrait Gallery.
However the couple met finalists of the Tusk Conservation awards instead, which suggests the children's health was not the reason behind the cancellation.
A Kensington Palace spokeswoman said: "The duchess is no longer able to attend the Tusk awards due to the children.
"She will still however meet finalists at a tea at Kensington Palace this afternoon. The duke as patron will attend both."
William, who has been Royal Patron of Tusk since December 2005, will attend both events on his own.
The Duke has actively supported the charity’s work both privately and publicly on many occasions and last visited Tusk projects in Namibia and Tanzania in 2018.
The organisation pioneers successful conservation action in Africa by protecting wildlife, empowering communities and advancing the frontline in the fight against the illegal wildlife.
The annual awards celebrate people who work to protect Africa’s natural heritage.
Kate's late official engagement was the Royal Variety Performance earlier this week, when she looked stunning in an Alexander McQueen gown.
It's been an extremely difficult week for the Royal Family after Prince Andrew announced he would be stepping back from royal duties.
It has been claimed that The Queen summoned Prince Andrew to Buckingham Palace to effectively sack him from royal duties after intense lobbying from his older brother Prince Charles.
Andrew held crisis talks with his mum - reported to be "disappointed" and "unhappy" - and his brother before the Queen told her second-born child to step down.
She then informed the wider Royal Family before the palace issued a bombshell statement on behalf of the scandal-hit prince, it was reported.


Canada's main stock index edges up in broad-base advance; loonie stable
by The Canadian PressTORONTO - Canada's main stock index was up slightly in late morning trading in a broad-based advance led by resource and financial stocks.
The S&P/TSX composite index was up 69.83 points at 16,552.99.
In New York, the Dow Jones industrial average was up 248.75 points at 27,294.98. The S&P 500 index was up 23.72 points at 3,061.28, while the Nasdaq composite was ahead 68.90 points at 8,361.26.
The Canadian dollar traded at just under 76 cents US, compared with Thursday's average of 75.99 cents US..
The December crude contract was up $1.03 at US$55.21 per barrel and the December natural gas contract was down 0.8 of a cent at US$2.63 per mmBTU.
The December gold contract was down $1.70 at US$1,513.10 an ounce and the September copper contract was up 0.6 of a cent at US$2.64 per pound.


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{% extends 'base.html' %} {% block body %}
client_secret.json file not found for Authentication.
Follow the instructions to generate a client_secret file.