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Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
Impress your dinner party guests with very little effort. Rich venison is delicious with this tangy and bitter sauce.
For the cabbage, heat 25g/1oz of the butter in a medium saucepan over a medium heat. Add the carrots and some seasoning and sweat until tender, then add the garlic and cook for a few minutes, stirring constantly.
Transfer the carrots to a bowl using a slotted spoon, then add the bacon to the pan. Fry for about 5 minutes, until crisp, then place into the bowl with the carrots. Add the duck fat and celeriac to the same pan and cook the celeriac slowly, on a low heat, adding a drop of water if necessary to prevent it catching and burning. Cook for about 10 minutes, or until lightly browned and tender. Use the slotted spoon to transfer it to the bowl of carrots and bacon.
Add another 25g/1oz of butter to the pan and when hot, add the cabbage, season and cover with a tight fitting lid. Cook the cabbage over a high heat, shaking the pan occasionally to prevent it catching. Cook until tender, then stir in the carrots, bacon and celeriac, along with the cream. Season with salt and pepper and set aside, keeping warm.
For the sauce, add the oil to a frying pan and sweat the shallots over a medium heat for 5 minutes or until soft. Add the vinegar and allow it to evaporate completely. Add the wine and reduce it to about a quarter of its original volume (you should be left with 125ml/4 fl oz), then add the stock and chocolate. When the chocolate has melted, whisk in the butter. Cover and keep warm.
For the venison, season the loins well with salt and pepper, then heat the butter in another frying pan over a medium heat and cook the venison pieces for 4-5 minutes on each side for rare. The cooking time will depend on the thickness of the meat, so cook it accordingly to your liking. Remove from the pan and rest on a plate, covered with foil, for 7 minutes.
To serve, slice each piece of venison and serve with the sauce and cabbage alongside.


Module: Sprockets::Exporting
| Relationships & Source Files | |
| Extension / Inclusion / Inheritance Descendants | |
|
Extended In:
| |
|
Included In:
| |
| Defined in: | lib/sprockets/exporting.rb |
Overview
Exporting
is an internal mixin whose public methods are exposed on the Environment
and CachedEnvironment
classes.
Instance Attribute Summary
-
#export_concurrent
rw
Public: Checks if concurrent exporting is allowed.
-
#export_concurrent=(export_concurrent)
rw
Public: Enable or disable the concurrently exporting files.
Instance Method Summary
-
#exporters
Exporters
are ran on the assets:precompile task. -
#register_exporter(mime_types, klass = nil)
Public: Registers a new Exporter
klass
formime_type
. -
#unregister_exporter(mime_types, exporter = nil)
Public: Remove Exporting processor
klass
formime_type
.
Instance Attribute Details
#export_concurrent (rw)
Public: Checks if concurrent exporting is allowed
# File 'lib/sprockets/exporting.rb', line 59
def export_concurrent config[:export_concurrent] end
#export_concurrent=(export_concurrent) (rw)
Public: Enable or disable the concurrently exporting files
Defaults to true.
environment.export_concurrent = false
# File 'lib/sprockets/exporting.rb', line 69
def export_concurrent=(export_concurrent) self.config = config.merge(export_concurrent: export_concurrent).freeze end
Instance Method Details
#exporters
Exporters
are ran on the assets:precompile task
# File 'lib/sprockets/exporting.rb', line 6
def exporters config[:exporters] end
#register_exporter(mime_types, klass = nil)
Public: Registers a new Exporter klass
for mime_type
.
If your exporter depends on one or more other exporters you can specify this via the depend_on
keyword.
register_exporter '*/*', Sprockets::Exporters::ZlibExporter
This ensures that Sprockets::Exporters::File
will always execute before Sprockets::Exporters::Zlib
# File 'lib/sprockets/exporting.rb', line 19
def register_exporter(mime_types, klass = nil) mime_types = Array(mime_types) mime_types.each do |mime_type| self.config = hash_reassoc(config, :exporters, mime_type) do |_exporters| _exporters << klass end end end
#unregister_exporter(mime_types, exporter = nil)
Public: Remove Exporting processor klass
for mime_type
.
environment.unregister_exporter '*/*', Sprockets::Exporters::ZlibExporter
Can be called without a mime type
environment.unregister_exporter Sprockets::Exporters::ZlibExporter
Does not remove any exporters that depend on klass
.
# File 'lib/sprockets/exporting.rb', line 38
def unregister_exporter(mime_types, exporter = nil) unless mime_types.is_a? Array if mime_types.is_a? String mime_types = [mime_types] else # called with no mime type exporter = mime_types mime_types = nil end end self.config = hash_reassoc(config, :exporters) do |_exporters| _exporters.each do |mime_type, exporters_array| next if mime_types && !mime_types.include?(mime_type) if exporters_array.include? exporter _exporters[mime_type] = exporters_array.dup.delete exporter end end end end


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Minecraft mods by Lothrazar.


I am a web developer from Ukraine with great coding skills!
I got a lot of skills
You can contact me by email<EMAIL_ADDRESS>or find me on UpWork! Also find me on Twitter


Welcome to org builder!
Either make a new org...
Hey girl, what's your organization called?
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ex. By signing up I indicate my support for divestment from fossil fuels.
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Trevor Hansen — 12 hrs ago
High school reunion?
Trevor Hansen — 1h
High school reunion?
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Trevor Hansen — 12 hrs ago
To me, Rachel, and Zach
Hi friends,
I was at the grocery store on Sunday night... when I ran into Genie Williams! I almost didn't recognize her after 20 years!
Anyway, it turns out she is on the organizing committee for the high school reunion this fall. I don't know if you were planning on going or not, but she could definitely use our help in trying to track down lots of missing alums. If you can make it, we're doing a little phone-tree party at her place next Saturday, hoping that if we can find one person, a few more will emerge. What do you say? Want to see if Drew and Todd can make it too?
Talk soon!
Trevor
High school
reunion
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Sandra Adams — Oct 29
What do you think of these spots? I’ve been meaning to try one close to you…
Japanese Restaurants
Dinner Tonight
Inbox
Starred
Sent
Trash
Spam
Drafts
Receipts
Pine Elementary
Taxes
Vacation
Mortgage
Freelance
FOLDERS


RPC operations to perform operations related to flows including management of associated persistent states like checkpoints.
Exposes Membership Group management operations to the client. Only Group Manager nodes have this interface installed on node start up.
Exposes Group Member operations.
A RpcCorDappInfo describes a single CorDapp currently installed on the node
Request containing information to execute named queries with optional post processing.
Builder for constructing RpcNamedQueryRequest
An individual item holding the json marshalled representation of a result from named query execution.
A RpcNodeDiagnosticInfo holds information about the current node version.
Annotation which can be used on RPCOps
interface to group certain methods for the purposes of granting permissions to all of them.
Please see usages of this interface for more details.
Represents query criteria to be used by VaultQueryRPCOps.
This construct is deliberately kept simple to easily marshal it in JSON.
Anything that is passed as Set will effectively be translated into in
SQL close when selecting from the database.
Builder for constructing RpcVaultQueryFilterRequest
An individual item representing vault state returned by VaultQueryRPCOps
Deliberately kept simple to allow marshalling via JSON.
RPC operations to perform tasks related to TLS certificates on a Corda node.


The red block below will follow the width of the browser window and at the same time on be wider than 960 pixels. At the same way, a limit of 760 pixels is used.
Read the article
Some sample text...


Shrimp, Pork, Vermicelli & Lettuce Spring Roll - $5.25
BBQ Pork & Lettuce Spring Roll served with Peanut Sauce - $5.25
Broken Rice w/ Korean Beef BBQ - $8.25
Rice Noodle w/ Meat Balls - REG. $7.25 LARGE $8.25
Vermicelli w/ BBQ Shrimp, BBQ Pork, Egg Roll & Vegetables - $8.95
Vermicelli w/ BBQ Shrimp, BBQ Pork, Egg Roll & Vegetables - $7.95


Classic Movie Monster Trivia Game
Start
Time Remaining:
In what year did Boris Karloff play The Mummy?
a) 1928
b) 1932
c) 1936
d) 1940
What type of monster is Nosferatu?
a) vampire
b) zombie
c) ghost
d) somnabulist
In 1931, which actor played Dracula?
a) Lon Chaney Jr.
b) Vincent Price
c) James Arness
d) Bela Lugosi
In "The Bride of Frakenstein," who played both the bride and Mary Shelley?
a) Mae Clarke
b) Margaret Sheridan
c) Julie Adams
d) Valerie Dobson
Time's Up!
Answers Correct:
3
Answers Missed:
1


This is where the conclusion would go.
This book would not have been possible without the support and encouragement of the people around me. I am fortunate to have grown up surrounded by good people who knew the value of math and encouraged me in my studies and with this project. In this section, I want to big up all the people who deserve it.
You have reached the end of this book, but you’re only at the beginning of the journey of scientific discovery. There are a lot of cool things left for you to learn about. Below are some recommendation of subjects you might find interesting.
E1.1 a) \( x=3 \); b) \( x=30 \); c) \( x=2 \); d) \( x=-3 \). E1.2 a) \( \mathbb{Z}, \mathbb{Q}, \mathbb{R},\mathbb{C} \); b) \( \mathbb{C} \); c) \( \mathbb{N},\mathbb{Z}, \mathbb{Q}, \mathbb{R},\mathbb{C} \); d) \( \mathbb{Q}, \mathbb{R},\mathbb{C} \); e) \( \mathbb{R},\mathbb{C} \). E1.3 a) \( 21 \); b) \( 0 \); c) \( \frac{2}{27} \).
P1.1 \( x=\pm 4 \). P1.2 \( x=A\cos(\omega t+\phi) \). P1.3 \( x=\frac{ab}{a+b} \). P1.4 a) \( 2.2795 \). b) \( 1024 \). c) \( -8.373 \). d) \( 11 \). P1.5 a) \( \frac{3}{4} \). b) \( \frac{-141}{35} \). c) \( 3\frac{23}{32} \).
P1.5 For c), \( 1\frac{3}{4} + 1\frac{31}{32} = \frac{7}{4} + \frac{63}{32} = \frac{56}{32} + \frac{63}{32} = \frac{119}{32}=3\frac{23}{32} \).
E2.1 In each case we check if each probability weight is positive and the total probability sums to one.
P2.1 \( P_N(n) = (1-p)^{n-1}p \)
P2.1
The biased coin flip is modelled by a random variable \( Y \),
and different coin flips correspond to random variables \( Y_1 \), \( Y_2 \), \( Y_3 \), …which are independent copies of \( Y \). The probability of getting heads
on the first flip is \( P_N(1)=\textrm{Pr}\!\left( \{ Y_1=\texttt{heads} \} \right)\! =p \). The probability of getting heads
on the second flip corresponds
to the event \( \{Y_1=\texttt{tails}\} \; \texttt{AND} \; \{Y_2=\texttt{heads} \} \). We assumed the coin flips are independent so \( P_N(2)=(1-p)p \). Similarly \( P_N(3) = (1-p)^2p \). The general formula is \( P_N(n) = (1-p)^{n-1}p \).
P3.1 a) \( 6 \). b) \( 0 \). c) \( -3 \). d) \( (-2, 1, 1) \). e) \( (3, -3, 0) \). f) \( (7, -5, 1) \). P3.2 a) \( (2,3,3,7,8) \). b) \( (0,-1,-3,-1,-2) \). c) \( 30 \). P3.3 \( (-\frac{2}{3}, \frac{1}{3}, \frac{2}{3}) \) or \( (\frac{2}{3}, -\frac{1}{3}, -\frac{2}{3}) \). P3.4 \( (12, -4, -12) \).
P3.3
See bit.ly/1cOa8yo
for calculations.
P3.4
Any multiple of the vector \( \vec{u}_1 \times \vec{u}_2 = (-3,1,3) \)
is perpendicular to both \( \vec{u}_1 \) and \( \vec{u}_2 \). We must find a multiplier \( t \in \mathbb{R} \) such that
\( t(-3,1,3) \cdot (1, 1, 0) = 8 \). Computing the dot product we find \( -3t + t = 8 \), so \( t=-4 \). The vector we’re looking for is \( (12, -4, -12) \). See bit.ly/1nmYH8T
for calculations.
This appendix contains a summary of the notation used in this book.
| Expression | Read as | Used to denote |
| \( a,b,x,y \) | variables | |
| \( = \) | is equal to | expressions that have the same value |
| \( \eqdef \) | is defined as | a new variable definition |
| \( a+b \) | \( a \) plus \( b \) | the combined lengths of \( a \) and \( b \) |
| \( a-b \) | \( a \) minus \( b \) | the difference in lengths between \( a \) and \( b \) |
| \( a\times b = ab \) | \( a \) times \( b \) | the area of a rectangle |
| \( a^2= aa \) | \( a \) squared | the area of a square of side length \( a \) |
| \( a^3= aaa \) | \( a \) cubed | the volume of a cube of side length \( a \) |
| \( a^n \) | \( a \) to the \( n \) | \( a \) multiplied by itself \( n \) times |
| \( \sqrt{a} = a^{\frac{1}{2}} \) | square root of \( a \) | the side length of a square of area \( a \) |
| \( \sqrt[3]{a}= a^{\frac{1}{3}} \) | cube root of \( a \) | the side length of a cube with volume \( a \) |
| \( a/b = \frac{a}{b} \) | \( a \) divided by \( b \) | \( a \) parts of a whole split into \( b \) parts |
| \( a^{-1}= \frac{1}{a} \) | one over \( a \) | division by \( a \) |
| \( f(x) \) | \( f \) of \( x \) | the function \( f \) applied to input \( x \) |
| \( f^{-1} \) | \( f \) inverse | the inverse function of \( f(x) \) |
| \( f \circ g \) | \( f \) compose \( g \) | function composition; \( f \circ g(x) = f(g(x)) \) |
| \( e^x \) | \( e \) to the \( x \) | the exponential function base \( e \) |
| \( \ln(x) \) | natural log of \( x \) | the logarithm base \( e \) |
| \( a^x \) | \( a \) to the \( x \) | the exponential function base \( a \) |
| \( \log_a(x) \) | log base \( a \) of \( x \) | the logarithm base \( a \) |
| \( \theta,\phi \) | theta, phi | angles |
| \( \sin,\cos,\tan \) | sin, cos, tan | trigonometric ratios |
| \( \% \) | percent | proportions of a total; \( a\%=\frac{a}{100} \) |


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Guide
In this section, the process of how a user can search for a region's account or perform a comparison between account is described.
Step 1: Visit the main page of the Application
Once you visit the application page, two search boxes will be displayed. The first one Search Username is used to search for a specific account of a region. The second one Compare Users is used to compare two different regions' accounts.
In this section, the process of how a user can retrieve information about the Twitter account of a region is described.
Step 2: Choose the region of interest, using the drop down menu
The user can select an account among the provided list. All the accounts that will be available will refer to those of regions or pilots of the Online S3 project.
Step 3: Get information for the account the user has searched for
After choosing a specific region / account the application will automatically direct you to another page that contains information for the account of interest, along with a variety of performance indicators. In Figure 3 for example, we have searched for "ECCI".
Step 4: Description of each part of displayed information
The page that is loaded after searching for a region or partner, includes a variety of information and related performance indicators. On top you can find the description of the user you have searched for (Figure 3). Right under the description the number of friends, followers and total tweets is displayed. These numbers are indicative of the influence and the online activity of the user. Usually, the more followers an account has, the better. Next, you can find the three tweets of this account that has been marked as favorite more times than any other. This is quite important, since the user can identify to which topics, hashtags, etc the audience is more responsive to. On the left side of the page, the user can find a series of plain information for the account, along with the account's latest tweet and its performance. In Figure 4 the user can find some performance indicators , which are described in details below.
On the left there is a word-cloud which displays the hashtags that has been used more often. On the right side the user can find four charts.
The first one displays the daily activity of the user. In other words, the average number of tweets the user posts per day. In Figure 4 we can see that for example,the user "ECCI" does not usually post any tweets on Saturday.
The second one displays the Tweet-Retweet ratio of the account. To be more specific, it shows the percentage of tweets that are statuses of other accounts and retweed by this account, over the ones that this account has posted and are originally his/hers.
The third and fourth one display the number of likes and the number of the tweets that have been retweeted by others , by day. In this example we can detect some anomalies on certain dates; Thus, the potential user can identify that during these dates (in yellow circles) the ECCI account attracted a lot of other users. This may have been the result of a certain series of posts with a specific hashtag, a conference that "ECCI" has participated or a rather important announcement.
In this section, the process of how a user can perform a comparison between accounts is described.
Step 1:Choose the user accounts you want to compare
Once you visit the main page of the application, choose the user accounts you want to compare by selecting their user names in the double drop-down menu available.
Step 2:Get a straight comparison between the main performance indicators for two accounts
After selecting the accounts the user wants to compare, a new page with four charts will appear. The charts are the same as before, but include information for both accounts. The only thing that is different is the pie chart which displays the number of followers for each account, instead of displaying the tweets - retweets ratio. In Figure 6 a comparison between "SNN" and "ECCI" has been performed. It is obvious that these two accounts have a similar online activity. What's more, someone can observe that they reach a peak during the same period, so this may happen because of an event they both attended, or an action that both have been parts of.


Current time:
Distribution:
Kernel version:
System uptime:
Requests served:
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But this little something is even more mlem mlem :'333
Description
Boy friend makes you cry, little tiramisu MAKES YOU SMILE
Boy friend could be rough, little tiramisu is ALWAYS SOFT
Boy friend sometimes gets bitter, little tiramisu - FOREVER A SWEETIE
Dip half of the ladyfingers: You will form 2 layers each of dipped ladyfingers and
mascarpone
cream.
Let’s begin with the 1st layer of ladyfingers. Whisk the espresso and Grand Marnier together in a
shallow bowl.
One at a time, quickly dip each side of the ladyfinger into the espresso mixture. You don’t want to
over-saturate the ladyfinger with liquid because then the ladyfingers will taste soggy. Just a quick
dip
on each
side. Arrange the dipped ladyfingers in the baking pan to make one solid layer. If needed, cut some
ladyfingers
to fill in any empty spaces. Reserve remaining espresso mixture and ladyfingers for another layer.
Begin the mascarpone cream: With a handheld or stand mixer fitted with a paddle or whisk
attachment, beat
the
mascarpone and rum together on medium speed in a large bowl until smooth. Set aside.
Prepare a double boiler for the egg yolks: If you have a double boiler, use it in this
step. If
you don’t,
place
a heat-proof bowl over a small pot of simmering water over medium-low heat. Don’t let the water
touch
the bottom
of the bowl. Using a whisk or eggbeater (I simply use a whisk), whisk the egg yolks and 1/4 cup
(50g) of
granulated sugar together until light and foamy, about 5 minutes. Remove from the heat and
immediately
pour into
the mascarpone mixture. Beat on medium speed until combined.
Whip the heavy cream: With a handheld or stand mixer fitted with a whisk attachment, beat
the
heavy cream
and
vanilla extract together on medium-high speed until medium peaks form, about 3-4 minutes. Medium
peaks
are
between soft/loose peaks and stiff peaks– and are the perfect consistency for the tiramisu cream.
Fold
the
whipped cream into the mascarpone mixture.
Beat the egg whites: With a handheld or stand mixer fitted with a *clean* whisk
attachment,
beat
the egg
whites
and salt together on medium-high speed until foamy, about 1 minute. Increase to high speed and
slowly
pour in
the remaining 1/4 cup (50g) of sugar. Beat until stiff peaks form, about 4-5 minutes. (Do not
over-beat
as the
egg whites will become dry.) Fold into the mascarpone cream.
Spread half of the mascarpone cream evenly over bottom layer of ladyfingers. An offset
spatula
helps
spread it
neatly and evenly.
Dip remaining ladyfingers: Dip remaining ladyfingers into remaining espresso mixture and
arrange
one-by-one on
top of the mascarpone layer. Gently press each down so they are nice and compact. Using an offset
spatula,
spread remaining mascarpone mixture evenly on top. If you’re using a large enough pan, it should all
fit
(but it
will definitely tower high!). Refrigerate uncovered for 2-3 hours.
Add cocoa layer, then chill: After a couple hours in the refrigerator, sift or strain a
dense
layer of
cocoa
powder all over the top. Wipe the rim of the baking dish. Cover with plastic wrap or aluminum foil,
then
refrigerate for at least 8-9 more hours and up to 1 day.
Using a sharp knife, slice chilled tiramisu into servings. Wipe knife clean between cuts.
A
small
metal
serving
spatula is helpful for removing the slices.


Stress tests reveal structural weakness in Google Pixel 4
Source: JerryRigEverything
What you need to know
- YouTuber JerryRigEverything stress-tested the Google Pixel 4.
- The phone cracked in multiple places.
- The structural weakness is in the frame near the antennae.
Professional phone destroyer JerryRigEverything has conducted his durability test on the Google Pixel 4, and the device did not impress the handheld demolition expert.
After chipping away a distressing amount of paint, Zack bends the phone and hears a snap, a sound he says he hasn't heard on a flagship phone in a long time. The Google Pixel 4 is shown with a crack running the width of the side of the phone.
Further bending produces three more cracks, laterally on each side, about a fifth of the way from each end of the device. By scraping away more of the flaky paint, Zack shows that the cracking coincides with antenna lines on the device. Because of the nature of the metal frame of a smartphone, device makers need to leave room to expose the antenna past the edge of the metal box. The lines you see on older iPhones and many smartphones today are often design treatments that hide these antenna breaks.
As Zack points out, most phone makers reinforce the frame at these break points so that bending the phone won't actually crack the frame. While the phone in Zack's video still has an active screen and OS, it is doubtful the radios would function properly after such damage.
Snappy Flagship
Google Pixel 4
Google's latest flagship aims to stay afloat
The Google Pixel 4 has a lot going for it with a 90Hz display and gesture controls. You should probably buy a case to protect the phone, especially if you want to keep it in your back pocket.
We may earn a commission for purchases using our links. Learn more.


| ResponsiveComponents.ts |
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96% |
(48 / 50) |
90.91% |
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100% |
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| ResponsiveComponentsManager.ts |
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42.22% |
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25% |
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43.9% |
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|
100% |
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100% |
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100% |
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[gecode-users] Some more documentation comments
Christian Schulte
cschulte at kth.se
Mon Sep 26 20:25:55 CEST 2011
Hi,
Good point about counts, the GCCAT has overlapping entries (here the
difference between counts and among is essentially that for one a set is
represented as a list and otherwise as a set...) where it is not entirely
clear to me which is which and why they actually deserve different names ;-)
I am not so sure about name mangling in the documentation, I rather like the
names short. Also, not all type variants are in fact given as a code
snippet. If I need to see the argument types I go to the reference doc which
has the full type info.
Cheers
Christian
--
Christian Schulte, KTH, web.it.kth.se/~cschulte/
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Kish Shen
Sent: Monday, September 26, 2011 6:09 PM
To: users at gecode.org
Subject: [gecode-users] Some more documentation comments
Hi,
In the MPG description for the count constraints, counts is listed as one of
the GCCAT equivalent for the first variant described (along with count,
exactly, atleast, atmost). However, as far as I can tell, counts accepts a
collection of values, rather than a single value (as in count, exactly
etc.), so it should be listed with the second variant of count described --
i.e. the one where y is an integer set. Is this correct?
A secondary point is the the listing of the constraint is the same in both
variants, even though some of the arguments are of different type:
count(home, x, y, IRT_EQ, z)
This seems to be quite common in the MPG, and I often have difficulty
figuring out what the arguments are -- I need to look quite carefully at the
text.
Do you think it is a good idea to use an informal convention to distinguish
different types of arguments? The simplest one I can think of is to add an
's' if the argument is not a simple element that has a single value, i.e. if
it is an array or a set, so for the first count variant, something like
count(home, xs, y, IRT_EQ, z)
and the second variant
count(home, xs, ys, IRT_EQ, z)
This can also be extended further so that names starting with certain
letters are of certain types, e.g. x, y, z for IntVar, and c for int (c for
constant), or something like i,j,k if there are more than one integer
argument (and I guess some other letters for sets).
Cheers,
Kish
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Thai Airways chairman resigns as company struggles
BANGKOK (AP) - Thai Airways has disclosed that its chairman has resigned as the carrier struggles with financial challenges.
The airline said Friday that Ekniti Nitithanprapas resigned as of Nov. 1.
The carrier’s vice chairman, Air Chief Marshal Chaiyapruk Didyasarin, will be acting chairman.
Thai Airways gave no reason for Ekniti’s departure after three of the airline’s executive directors recently quit. The company’s president recent drew criticism for saying the company was in crisis and might have to close if its employees do not cooperate with a rehabilitation plan.
The company reported a nearly 6.7 billion baht ($220 million) net loss in the April-June quarter. It said a slowdown in tourism in Thailand and in the global aviation market were factors behind a 10% decline in revenue from a year earlier. Passenger traffic fell more than 5%.
The airline has been selling decommissioned aircraft to help alleviate its cash crunch.
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Autogenerated Documentation for analysis package¶
BindColorMap module¶
Template which binds a colormap to a layer on a folium map. This will eventually be in folium, but is currently not implemented. It’s copied from here: http://nbviewer.jupyter.org/gist/BibMartin/f153aa957ddc5fadc64929abdee9ff2e
choropleth module¶
Functions that help create the mastermaps and other folium maps
-
TXHousing.analysis.choropleth.
categorical_choropleth
(gdf, factor, colors=None, quietly=False, weight=1, alpha=0.6, geometry_column='geometry', name=None, show=False, basemap=None)[source]¶ Creates categorical choropleth using tab10 spectrum
Parameters: - gdf – A geopandas geodataframe.
- factor – The feature you want to plot (should be categorical).
- colors – Colors to use in the categorical plot. If None, will generate colors using the tab10 colormap.
- quietly – If true, will not print anything. Defaults to False.
- weight – The weight in the style function. Defaults to 1.
- alpha – The alpha in the style function. Defaults to 0.6.
- geometry_column – The geometry column of the gdf. Defaults to ‘geometry’.
- name (str) – Defaults to None. If not None, will generate a FeatureGroup with this name and return that instead of the GeoJson object.
- show (bool) – Defaults to False. The show parameter for the FeatureGroup that the GeoJson will be added to.
- basemap (folium.Map) – Defaults to None. If not none, will add the GeoJson or FeatureGroup to the supplied basemap.
Returns: A folium geojson or featuregroup.
-
TXHousing.analysis.choropleth.
continuous_choropleth
(gdf, factor, layer_name, scale_name=None, weight=1, alpha=0.6, colors=['blue', 'green', 'yellow', 'orange', 'red'], quants=[0.16666666666666666, 0.3333333333333333, 0.5, 0.6666666666666666, 0.8333333333333334], method='log', round_method=None, show=False, geometry_column='geometry', basemap=None)[source]¶ Parameters: - gdf – Geodataframe
- factor – factor for analysis
- layer_name – Name of feature group layer
- scale_name – Name of scale
- weight – Weight
- alpha – Alpha of polygons
- colors – A list of colors to use in the colormap, defaults to [‘blue’, ‘green’, ‘yellow’, ‘orange’, ‘red’].
- quants – The quantiles to use to ‘switch’ colors in the colormap. Defaults to [1/6, 2/6, 3/6, 4/6, 5/6]. If you want a log-based or linear colorscale, adjust the ‘method’ parameter and set quants to None.
- method – The method by which the color scale is generated. Defaults to ‘log’, can also be ‘quant’ or ‘linear’. This parameter is overridden by the “quantiles” parameter.
- round_method – If you want to round the color scale to integer values, supply round_method = ‘int’
- show – Show by default on start
- geometry_column – ‘geometry’
- basemap – If not None, will add the colormap and a scale (bound together) to the baesmap as a layer.
Returns: GeoJson, Colormap
-
TXHousing.analysis.choropleth.
heatmap
(gdf, geometry_column='geometry', with_time=False, time_column='Year', name=None, show=False, basemap=None, **kwargs)[source]¶ Create a heatmap or a heatmap with time from a geodataframe of points.
Parameters: - gdf – Geodataframe with points as the geometry type.
- geometry_column – The geometry column of the gdf. Defaults to ‘geometry’
- start_color – The start color, defaults to ‘white’
- end_color – The end color, defaults to the MI blue
- with_time – If true, plot a heat map with time, not just a heat map.
- time_column – The column used to specify the years of the data, defaults to ‘Year’
- name (str) – Defaults to None. If not None, will generate a FeatureGroup with this name and return that instead of the GeoJson object.
- show (bool) – Defaults to False. The show parameter for the FeatureGroup that the GeoJson will be added to.
- basemap (folium.Map) – Defaults to None. If not none, will add the GeoJson or FeatureGroup to the supplied basemap.
- **kwargs –
kwargs to be passed onto the ‘heatmap’ or ‘heatmapwithtime’ folium constructors.
Returns: HeatMap object or FeatureGroup
-
TXHousing.analysis.choropleth.
make_marker_cluster
(gdf, make_centroids=True, points_column='geometry', fast=False, name=None, show=False, basemap=None, **kwargs)[source]¶ Makes a marker cluster from a gdf and potentially adds it to a map/feature group.
Parameters: - gdf (GeoDataFrame) – A geodataframe.
- make_centroids (bool) – If true and the geodataframe has polygon geometry, it will make the centroids and use those to make the marker cluster.
- or int points_column (str) – If make_centroids is False, will assume the pointa are in this column. Defaults to ‘geometry’.
- fast (bool) – If True, use a FastMarkerCluster as opposed to a regular MarkerCluster.
- name (str) – Defaults to None. If not None, will generate a FeatureGroup with this name and return that instead of the MarkerCluster object.
- show (bool) – Defaults to False. The show parameter for the FeatureGroup that the marker cluster will be added to.
- basemap (folium.Map) – Defaults to None. If not none, will add the MarkerCluster or FeatureGroup to the supplied basemap.
- kwargs – kwargs to pass to the FastMarkerCluster or MarkerCluster initialization
Returns: Either a FeatureGroup or a MarkerCluster.
-
TXHousing.analysis.choropleth.
polygon_layer
(gdf, color='blue', weight=1, alpha=0.6, name=None, show=False, basemap=None)[source]¶ Parameters: - gdf –
- factor –
- color –
- weight –
- alpha –
- name (str) – Defaults to None. If not None, will generate a FeatureGroup with this name and return that instead of the GeoJson object.
- show (bool) – Defaults to False. The show parameter for the FeatureGroup that the GeoJson will be added to.
- basemap (folium.Map) – Defaults to None. If not none, will add the GeoJson or FeatureGroup to the supplied basemap.
Returns:
mastermaps module¶
-
TXHousing.analysis.mastermaps.
add_zipdata
(ziplist, basemap, features=['Views Per Property (vs CBSA)', 'Hotness Rank ', 'Median Listing Price_sf', 'Median Listing Price_mf'], names=['Views Per Property (Mean-Centered)', 'Realtor Hotness Rank', 'Median Listing Price for Single Family Homes', 'Median Listing Price for Multifamily Homes'])[source]¶ Adds zipdata to a basemap.
Parameters: - ziplist – A list of zipcodes to add to the basemap
- basemap – The basemap to add to.
- features – List of features to graph from the realtor datasets.
- names – The names of the layers the features will be added on.
Returns: A ZipBoundaries object with Realtor data attached
-
TXHousing.analysis.mastermaps.
create_austin_mastermap
(save_path=None)[source]¶ Generates Austin Mastermap. Save path defaults to ‘Figures/Mastermaps/Austin_Mastermap_v2.html
zoning_graphs module¶
Graphs which mostly rely on zoning data
-
TXHousing.analysis.zoning_graphs.
add_counties
(ax, county_list)[source]¶ Given a matplotlib axis, adds texas county outlines to it
-
TXHousing.analysis.zoning_graphs.
map_broad_zones
(data, county_list, name, minlat, maxlat, minlong, maxlong, save_path, colordic=None, featurename='Base Zoning')[source]¶ Plots an actual map of broad_zones in a data source within the lat/long bounds. If you want to do this for Austin or Dallas, just use the map_broad_zones_dallas_austin wrapper.
Parameters: - data – A gdf including a geometry and base_zone column.
- county_list – A list of county outlines to plot on the data.
- name – A name to be used in titling the egraph.
- maxlat, minlong, maxlong (minlat,) – Bounds of the graph
- save_path – A path at which to save the map.
- colordic – Dictionary which maps broad_zones to colors.
- featurename – Used for the title of the graph. Graph title is {featurename} around {name}.
-
TXHousing.analysis.zoning_graphs.
map_broad_zones_dallas_austin
(plot_austin=True, plot_dallas=True)[source]¶ Wrapper for map_broad_zones, just plots them around austin/dallas.
-
TXHousing.analysis.zoning_graphs.
plot_broad_zones_proportion
()[source]¶ Plot proportion of broad_zones by distance from city center, excluding nonresidential and agricultural land.
-
TXHousing.analysis.zoning_graphs.
plot_hd_locations
(save_path='Figures/Zoning/HDLocations.svg', width=8, height=5)[source]¶ Graphs locations of historic districts in Austin, Dallas, Houston
-
TXHousing.analysis.zoning_graphs.
plot_minimum_lot_size
(savepath='Figures/Zoning/Minimum_Lot_Size_Residential_No_Agriculture.svg', width=10, height=8)[source]¶ Graphs average minimum lot size in Austin/Dallas.
Methodological note: this treats zones with no minimum lot size as having a minimum lot size of zero. It also includes areas zoned as non-single family, which we might want to exclude in the future.
permit_graphs module¶
Graphs which mostly rely on permit data
parcel_graphs module¶
Graphs which mostly rely on municipal parcel data. Note that all of these graphs rely on the cached parcel data in csv format, not the actual parcel data, which is processed and cached in the data_processing package.
-
TXHousing.analysis.parcel_graphs.
calc_parking_costs
(save_path='Figures/property_value_histogram.svg')[source]¶ Calculates average land costs within 1 mile of the city center in Austin, Dallas, Houston. Relies on cached municipal parcel data. These are a bit conservative figures because they use lot size instead of base area of the actual building.
-
TXHousing.analysis.parcel_graphs.
plot_percent_undeveloped
(save_path='Figures/Zoning/percent_undeveloped.svg')[source]¶ Calculates the percent of land which is undeveloped (using base_area features and area calculations) conditional on distance from city center and broad_zone. Based on cached municipal parcel data.
misc_calcs module¶
Graphs which mostly rely on municipal parcel data. Note that all of these graphs rely on the cached parcel data in csv format, not the actual parcel data, which is processed and cached in the data_processing package.
-
TXHousing.analysis.parcel_graphs.
calc_parking_costs
(save_path='Figures/property_value_histogram.svg')[source] Calculates average land costs within 1 mile of the city center in Austin, Dallas, Houston. Relies on cached municipal parcel data. These are a bit conservative figures because they use lot size instead of base area of the actual building.
-
TXHousing.analysis.parcel_graphs.
plot_percent_undeveloped
(save_path='Figures/Zoning/percent_undeveloped.svg')[source] Calculates the percent of land which is undeveloped (using base_area features and area calculations) conditional on distance from city center and broad_zone. Based on cached municipal parcel data.
-
TXHousing.analysis.parcel_graphs.
plot_singlefamily_lotsizes
(save_path='Figures/Zoning/sf_lotsizes.svg', width=10, height=8)[source] Plots average lotsizes of single family homes in Houston conditional on distance from city center. This uses cached municipal parcel data.
suburbs module¶
This file contains most of the graphing for the suburbs analysis. It does not rely on raw parcel data, but the cached versions instead.
-
TXHousing.analysis.suburbs.
analyze_land_use_by_metro
(name)[source]¶ Calculates mean and median lot size as well as the land use by municipality around Austin, Dallas, or Houston, then caches them in shared_data/calculations.