Before writing a test case, you need to know how and what will be validated. Let's use the WordPress "Create New Post" test case.
Considering this scenario, the first thing that comes to mind is creating a long test case that goes through all the steps. This might be a good approach if you are writing a manual test case. However, since we are writing an automated test, we want to write our script as modular as possible to be able to reuse parts of it in future scenarios.
This is how I would break down the test:
Keep in mind that this is just an example. You are free to model your tests in any way you want, as long as they have business value and will validate your business logic.
Let's see how to do that with actual Java code:
@Test(description="Launches the WordPress site") public void launchSite(){ selenium.open(""); selenium.waitForPageToLoad("30000"); assertEquals(selenium.getTitle(), "Demo | Just another WordPress site"); } @Test(description="Navigates to the admin page") public void openAdminPage() { selenium.open("wp-admin"); selenium.waitForPageToLoad("30000"); assertEquals(selenium.getTitle(), "Demo › Log In"); } @Test(description="Enters valid login data") public void loginAsAdmin() { selenium.type("user_login", "admin"); selenium.type("user_pass", "demo123"); selenium.click("wp-submit"); selenium.waitForPageToLoad("30000"); assertTrue(selenium.isTextPresent("Howdy, admin")); } @Test(description="Navigates to the New Post screen") public void navigateNewPost() { selenium.click("//a[contains(text(),'Posts')]/following::a[contains(text(),'Add New')][1]"); selenium.waitForPageToLoad("30000"); assertTrue(selenium.isTextPresent("Add New Post")); } @Test(description="Writes the new post") public void writeBlogPost() { selenium.type("title", "New Blog Post"); selenium.click("edButtonHTML"); selenium.type("content", "This is a new post"); //TODO:Assert } @Test(description="Publishes the post") public void publishBlogPost() { selenium.click("submitdiv"); selenium.click("publish"); selenium.waitForPageToLoad("30000"); assertTrue(selenium.isTextPresent("Post published.")); } @Test(description="Verifies the post") public void verifyBlogPost() { selenium.click("//a[contains(text(),'Posts') and contains(@class,'wp-first-item')]"); selenium.waitForPageToLoad("30000"); assertTrue(selenium.isElementPresent("//a[text()='New Blog Post']")); } @Test(description="Logs out") public void logout() { selenium.click("//a[text()='Log Out']"); //TODO:Assert }
These are the test methods (or steps) we are going to use.
If you are familiar with unit testing frameworks, you probably know about the setup and teardown methods. TestNG goes beyond that idea and allows you to define methods that will be run after or before your test suites, test groups or test methods. This is very useful for our Selenium tests because you can create a Selenium server and browser instance before you start running your test suite.)
To achieve this, we will use two TestNG annotations: @BeforeSuite and @AfterSuite:
@BeforeSuite(alwaysRun = true) public void setupBeforeSuite(ITestContext context) { String seleniumHost = context.getCurrentXmlTest().getParameter("selenium.host"); String seleniumPort = context.getCurrentXmlTest().getParameter("selenium.port"); String seleniumBrowser = context.getCurrentXmlTest().getParameter("selenium.browser"); String seleniumUrl = context.getCurrentXmlTest().getParameter("selenium.url"); RemoteControlConfiguration rcc = new RemoteControlConfiguration(); rcc.setSingleWindow(true); rcc.setPort(Integer.parseInt(seleniumPort)); try { server = new SeleniumServer(false, rcc); server.boot(); } catch (Exception e) { throw new IllegalStateException("Can't start selenium server", e); } proc = new HttpCommandProcessor(seleniumHost, Integer.parseInt(seleniumPort), seleniumBrowser, seleniumUrl); selenium = new DefaultSelenium(proc); selenium.start(); } @AfterSuite(alwaysRun = true) public void setupAfterSuite() { selenium.stop(); server.stop(); }
PS: Did you notice those weird parameters? They are stored in the XML file (we are going to see in the next section) and accessed by a ITestContext object, which was injected.
By adding these annotations, the TestNG engine will invoke the configuration methods automatically before/after your test suite (make sure the test methods are annotated with @Test), launching the Selenium server and instantiating the Selenium client object only once, reusing the same browser session across the tests.
To define the order of the tests, we will have to create an XML file listing the test methods we would like to run. Make sure that the test methods are annotated with @Test, or else the TestNG engine will not invoke them.
Before TestNG 5.13.1, you had to use Method Interceptors if you wanted to run the tests in the order defined in the XML file. I have posted my implementation of a Method Interceptor on my Github account. From TestNG 5.13.1+, you can just add the preserve-order parameter to your test tag and include the methods you would like to run, reducing unnecessary code in your test suite.
Here is the XML file:
<parameter name="selenium.host" value="localhost" /> <parameter name="selenium.port" value="3737" /> <parameter name="selenium.browser" value="*firefox" /> <parameter name="selenium.url" value="https://demo.opensourcecms.com/wordpress/" /> <include name="launchSite" /> <include name="openAdminPage" /> <include name="loginAsAdmin" /> <include name="navigateNewPost" /> <include name="writeBlogPost" /> <include name="publishBlogPost" /> <include name="verifyBlogPost" />
We finished writing our tests, now how can we run them?
You can launch TestNG from the command line, using a Eclipse plugin or even programatically. We are going to use the Eclipse plugin. Follow the steps described on the official TestNG documentation over here
If you installed TestNG correctly, you will see this menu when you right click on the XML file:
Click on “Run as TestNG Suite” and your test will start running. You will then see this nice results tree:
If you really want to think about the future of your test suite, I would recommend you to read Adam Goucher’s article published on PragPub. He talks about Selenium 2 and the Page Objects Model (a very nice way to model your tests, especially if you use Selenium 2).
Since there are lots of people still using Selenium 1, I'll stick to that for a while, but Selenium 2 will eventually be covered here.
As the number of tests in your test suite grows, you will find that grouping them in different test classes is a good idea. If you do that, you can take advantage of object oriented programming and create a new class named BaseTest (for example), and leave your configuration logic there. That way, every test class must extend the BaseTest class and use static attributes.
public class WordPressAdmin extends BaseTest { @Test public void test1(){ selenium.open(""); //... } @Test public void test2(){ selenium.open(""); //... } }
This is better than leaving your configuration methods in the test class.


Style Queen!!! 2019's Most Powerful Dresser Named...Overtakes Kylie Jenner & Cardi B - PHOTOS
by Peace FM OnlineThe Duchess of Sussex has climbed the ranks of the Lyst Index to be named as this year's most influential dresser, replacing make-up mogul Kylie Jenner.
The annual fashion report which analyses data collected throughout the year to reveal the biggest trends, revealed that Meghan, 38, has sparked a 216 per cent increase in searches for similar items to those that she has worn.
The mother-of-one who was ranked third last year, has climbed ahead of the Kardashian-Jenner clan to take the top spot. Kylie ranked as seventh this year meanwhile Kim Kardashian joined Meghan's sister-in-law Kate, in failing to make the top ten.
Featured Video


Protocol
In The New York Times site, in the Health section, the articles obtained by the research milk are sorted by relevance and are filtered by date, after the 01/01/2005. The first 300 outcomes are downloaded with Kimono, manually taking out those concerning Human Milk and Recipes, in order to reach a list of 100 pertinent results. The texts are downloaded with Blockspring (Extract Text from URL) for all the articles.
In the Health section of PubMed, the papers obtained by the research milk are sorted by relevance, manually excluding those of Human Milk. The title texts and the abstracts of the first 100 pertinent results are downloaded and ISS code, copyright, authors’ information, French and Spanish translations, link and words as “abstract” and “keywords” are deleted.
Keyword Density Analyzer is used to search the most frequent single words and couple of words, both in the texts of The New York Times and of PubMed. The final four lists of 50 words are assembled manually, omitting articles, conjunctions, verbs, some adjectives, without relevance if they’re single, plurals of words already in singular, declensions of the same term, and “milk”, because it’s the requirement. Some medical contractions are combined with their complete expressions.
The words from the 4 lists are categorized, based on their definition in One Look Dictionary Search; the groups are the same for the single words and the couple of words.


--wp-inline=<posix-regexp>
— If present, functions specfied by the provided POSIX regular expression will inlined. When functions are not inlined, heuristic function summaries are used at function call sites. For example, If you want to inline the functions foo
and bar
, you can write --wp-inline=foo|bar
. To inline everything, use --wp-inline=.*
(not generally recommended).--wp-num-unroll=<num>
— If present, replaces the default number of times to unroll each loop. The number of loop unrollings is 5 by default.--wp-gdb-filename=<my_exec.gdb>
— If present, output a gdb
script to file my_exec.gdb
. From within gdb
, run source my_exec.gdb
to set a breakpoint at the function given by --wp-function
and fill the appropriate registers with a found counter-model.--wp-bildb-output=<filename.yml>
— If present, output a BILDB initialization script to file filename.yml
. This YAML file sets the registers and memory to the values specified in the countermodel found during WP analysis, allowing BILDB to follow the same execution trace. In the case the analysis returns UNSAT or UNKNOWN, no script will be output.--wp-use-fun-input-regs=[true|false]
— If true, at a function call site, uses all possible input registers as arguments to a function symbol generated for an output register that represents the result of the function call. If set to false, no registers will be used. Defaults to true.--wp-stack-base=<address>
— If present, sets the location of the stack frame for the function under analysis. By default, WP assumes the stack frame for the current function is between 0x40000000
and 0x3F800080
.--wp-stack-size=<size>
— If present, sets the size of the stack. size should be denoted in bytes. By default, the size of the stack is 0x800000
, which is 8MB.--wp-show=[bir|refuted-goals|paths|precond-internal|precond-smtlib]
— A list of details to print out from the analysis. Multiple options can be specified as a comma-separated list. For example: --wp-show=bir,refuted-goals
. The options are:
bir
: The code of the binary/binaries in BAP Immediate Representation.refuted-goals
: In the case the analysis results in SAT, a list of goals refuted in the model that contains their tagged names, the concrete values of the goals, and the Z3 representation of the goal.paths
: The execution path of the binary that results in a refuted goal. The path contains information about the jumps taken, their addresses, and the values of the registers at each jump. This option automatically prints out the refuted-goals.precond-smtlib
: The precondition printed out in Z3's SMT-LIB2 format.precond-internal
: The precondition printed out in WP's internal format for the Constr.t
type.--wp-debug=[z3-solver-stats|z3-verbose|constraint-stats|eval-constraint-stats]
— A list of debugging statistics to display. Multiple statistics may be specified in a comma-separated list. The options are:
z3-solver-stats
: Information and statistics about Z3's solver.z3-verbose
: Z3's verbosity level.constraint-stats
: Statistics regarding the internal constr.t
data structure.eval-constraint-stats
: Statistics regarding the internal expression lists during the eval
function.To analyze a single program, the command you want to invoke has this basic form:
$ bap /path/to/exe \ <-- The path to a binary program --pass=wp \ <-- The pass/plugin is wp --wp-func=FUNC \ <-- The function to examine [options] <-- Any extra optionsThe following options select or adjust the property that will be checked for the function
FUNC
in the case of single-program analysis.
--wp-trip-asserts=[true|false]
— If true, WP will look for inputs to the subroutine that would cause an __assert_fail to __VERIFIER_error to be reached.--wp-check-null-deref=[true|false]
— If true, WP will check for inputs that would result in dereferencing a NULL address during a memory read or write. This flag also can also be used in comparative analysis; see below.--wp-precond=<smt-lib-string>
— If present, allows the introduction of assertions to the beginning of a query. This allows pruning of possible models. Assertions are specified in the smt-lib2 format. This flag also can also be used in comparative analysis; see below.--wp-postcond=<smt-lib-string>
— If present, replaces the default post-condition by the user-specified one, using the smt-lib2 format. This flag also can also be used in comparative analysis; see below.To analyze two programs, the command is a little more complicated. BAP is designed to load a single program at a time, but we want to load two. To get around this, first save both projects (using a plugin called save-project
):
bap /path/to/exe_1 --pass=save-project --save-project-filename=exe_1.bpj bap /path/to/exe_2 --pass=save-project --save-project-filename=exe_2.bpj
This serializes and saves each project as exe_1.bpj
and exe_2.bpj
.
Then, to compare these programs with wp
:
bap /path/to/dummy/exe \ <-- The path to a dummy exe --pass=wp \ <-- The pass/plugin is wp --wp-compare \ <-- Run in comparative mode (compare two programs) --wp-func=FUNC \ <-- The function to examine in both programs --wp-file1=exe_1.bpj \ <-- The first saved program --wp-file2=exe_2.bpj \ <-- The second saved program [options]
Notice that you must specify a path to a dummy exe
(i.e., /path/to/dummy/exe
in the above command). It is a "dummy exe
" because the BAP command line tool requires that you specify some program, but you can put any program here. When using the --wp-compare
flag, this dummy program will be thrown away. We often use /bin/true
.
--wp-check-null-deref=[true|false]
— If true, adds the contraint that if a memory read or write dereferences a non-null address in the original binary, then that same address is also non-null in the modified binary.--wp-check-calls=[true|false]
— If true, determines whether to compare the semantics of two programs by examining the return values of the function to be compared, or whether to compare which sub-routines are invoked in the body of the function.--mem-offset=[true|false]
— If true, at a memory read, adds an assumption to the precondition that the memory of the modified binary is the same as the original binary at an offset calculated by aligning the data and bss sections of the binary. We do this by invoking objdump
on both binaries, and determining the starting addresses of the symbols in these sections. If this flag is set to false, we assume that memory between both binaries are equal during a memory read. Defaults to false.--wp-precond=<smt-lib-string>
— If present, allows the introduction of assertions to the beginning of a query using the smt-lib2 format. This allows pruning of possible models. For comparative predicates, one may refer to variables in the original and modified program by appending the suffix _orig
and _mod
to variable names in the smt-lib expression. For example, --wp-precond="(assert (= RDI_mod #x0000000000000003)) (assert (= RDI_orig #x0000000000000003))"
.--wp-postcond=<smt-lib-string>
— If present, replaces the default post-condition by the user-specified one, using the smt-lib2 format. Similar to --wp-precond, one may create comparative postconditions on variables by appending _orig
and _mod
to register names.smt-lib2
format. Here we provide a quick reference to commonly used features.
In the following, let E1
, E2
, and so on refer to SMTLIB
expressions.
(assert E1)
(and E1 E2)
(or E1 E2)
(=> E1 E2)
(not E1)
(= E1 E2)
(not (= E1 E2))
(+ E1 E2)
(- E1 E2)
(* E1 E2)
(< E1 E2)
(<= E1 E2)
(> E1 E2)
(>= E1 E2)
(bvadd E1 E2)
(bvsub E1 E2)
(bvult E1 E2)
(bvule E1 E2)
(bvugt E1 E2)
(bvuge E1 E2)
(bvslt E1 E2)
(bvsle E1 E2)
(bvsgt E1 E2)
(bvsge E1 E2)


{% extends "base.html" %} {% block content %} Success! You're now subscribed. You've been succesfully subscribed! Check your inbox soon for great emails from us! {% endblock %}
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Welcome to the wonderful world of muffins!
A reveal.js HTML Presentation framework test
Let's look at some Muffin-centric websites
You can load websites in the background of a slide and interact with it
And another website - looks tasty
What's the best kind of muffin
You can animate bullets in many different ways:
You can embed an iFrame
This can contain a website or in this case a YouTube video


1 The third opinion is by far the most plausible, yet the most erroneous of all. It has no more truth in it than the others. According to this, the Nile flows from where snows melt; but it flows from Libya through the midst of Ethiopia, and comes out into Egypt.
2 How can it flow from snow, then, seeing that it comes from the hottest places to lands that are for the most part cooler? In fact, for a man who can reason about such things, the principal and strongest evidence that the river is unlikely to flow from snows is that the winds blowing from Libya and Ethiopia are hot.
3 In the second place, the country is rainless and frostless; but after snow has fallen, it has to rain within five days ; so that if it snowed, it would rain in these lands. And thirdly, the men of the country are black because of the heat.
4 Moreover, kites and swallows live there all year round, and cranes come every year to these places to winter there, flying from the wintry weather of Scythia. Now, were there but the least fall of snow in this country through which the Nile flows and where it rises, none of these things would happen, as necessity proves.


Images for:
NEAT Images
Singlets
Warning:
Near edge of image. Desired location is not at center of image<PHONE_NUMBER>10.
ID:
001204124410
Mid-Epoch:
2000-12-04 12:44:19
Position:
08 36 15.06 +04 38 24.1


2018-05-01 10:01 AM
Line was created by [Ngan Nguyen].
2018-05-01 02:36 PM
Line was approved by [Ngan Nguyen].
2018-05-02 08:00 AM
Line is begun to run.
2018-05-03 08:00 AM
Line is running.
2018-05-03 03:16 PM
Stopped due to 10 major errors!
2018-05-04 08:00 AM
Resumed to run
2018-05-04 11:00 AM
One upper line has been changed due to 20 minor errors!
2018-05-05 08:00 AM
Line is running.
2018-05-05 06:00 PM
Line is ended.
| # | Output with | Qty | Detail |
|---|---|---|---|
| 1 | Good Quality | 100 | |
| 2 | Major Error | 5 | |
| 3 | Minor Errors | 12 | |
| 4 | B-Grade | 3 | |
| 5 | C-Grade | 2 | |
| Total | 15 |
| # | Output with | Qty | Detail |
|---|---|---|---|
| 1 | Good Quality | 100 | |
| 2 | Major Error | 5 | |
| 3 | Minor Errors | 12 | |
| 4 | B-Grade | 3 | |
| 5 | C-Grade | 2 | |
| Total | 15 |
| # | Output with | Qty | Detail |
|---|---|---|---|
| 1 | Good Quality | 100 | |
| 2 | Major Error | 5 | |
| 3 | Minor Errors | 12 | |
| 4 | B-Grade | 3 | |
| 5 | C-Grade | 2 | |
| Total | 15 |
| # | User | Action | Stamptime | Remarks |
|---|---|---|---|---|
| 1 | Ngan Nguyen | Created line | 2018-05-05 10:00 AM | From local IP |
| 1 | Ngan Nguyen | Created line | 2018-05-05 10:00 AM | From local IP |
| 1 | Ngan Nguyen | Created line | 2018-05-05 10:00 AM | From local IP |
| 1 | Ngan Nguyen | Created line | 2018-05-05 10:00 AM | From local IP |
| 1 | Ngan Nguyen | Created line | 2018-05-05 10:00 AM | From local IP |


Boolean; optional
Forces the test to be run locally, without sandboxing.
By default this attribute is set to 0 and the default testing strategy is used. This is equivalent to providing "local" as a tag (tags=["local"]).
tags=["local"]


RT @betsythemuffin: This is a good reminder that when we *say* “too much magic,” what we *mean* is “the seams and errors are unclear to me,…
7/8/2019, 1:23:21 AM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @betsythemuffin: This is a good reminder that when we *say* “too much magic,” what we *mean* is “the seams and errors are unclear to me,…
7/8/2019, 1:23:21 AM
Favs: 0
Retweets: 0
link


Saved Defendants
Options
Session ID: This ID is used to retrieve the defendants that you save.These IDs are destroyed automatically after 7 days. Copy to Clipboard (Enter another session ID): Submit Oliver Cawthorne
Concurrency Table Select which pairs of counts are concurrent with others. If left unchecked, the counts are assumed to be consecutive.
0 months, 0 days
Counts before discount and concurrency considerations:
Discount: %
Total sentence:


"CASTLE "a military fortress (1 Chronicles 11:7), also probably a kind of tower used by the priests for making known anything discovered at a distance (1 Chronicles 6:54). Castles are also mentioned (Genesis 25:16) as a kind of watch-tower, from which shepherds kept watch over their flocks by night. The “castle” into which the chief captain commanded Paul to be brought was the quarters of the Roman soldiers in the fortress of Antonia(so called by Herod after his patron Mark Antony), which was close to the north-west corner of the temple (Acts 21:34), which it commanded.
"CASTLE "a military fortress (1 Chronicles 11:7), also probably a kind of tower used by the priests for making known anything discovered at a distance (1 Chronicles 6:54). Castles are also mentioned (Genesis 25:16) as a kind of watch-tower, from which shepherds kept watch over their flocks by night. The “castle” into which the chief captain commanded Paul to be brought was the quarters of the Roman soldiers in the fortress of Antonia(so called by Herod after his patron Mark Antony), which was close to the north-west corner of the temple (Acts 21:34), which it commanded.


@(video: Video) @main("YouLearn! Videos") {
To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video
By @video.uploadedBy.name, @video.totalViews views


Spectrum — Spectral line data container.¶
This module defines the Spectrum class.
-
class
admit.util.Spectrum.
Spectrum
(spec=None, freq=None, chans=None, contin=None, noise=None, mask=[False])[source]¶ Class for holding a spectrum. It holds entries for the spectrum, frequency axis, channel axis, vlsr, and rest_frequency. A mask is also available to mask bad data points. The mask is common to all axes.
Parameters: spec : array like
The spectrum.
freq : array like
The frequency axis, one entry per spectral point.
chans : array like
The channel axis, one entry per spectral point.
contin : array like or float
The continuum for each channel, or for all channels if it is an int.
noise : float
The rms noise of the spectrum.
mask : array like or bool
The mask for the spectrum, one entry per spectral point or a single boolean value, Defaults to all good.
Methods
calcdelta
()centerfreq
()chans
([masked])Method to get the channel axis, the mask is optionally returned contin
([masked])Method to get the continuum, the mask is optionally returned delta
()fix_invalid
([mask_value])Method to replace invalid spectral data with a specific value and mask it. freq
([masked])Method to get the frequency axis, the mask is optionally returned freqdispersion
([chanrange])Compute the frequency dispersion of the spectrum in the given channel range. fwhm
([chanrange])Compute the full-width at half-maximum velocity (km/s). getchan
(frq)Method to get the channel number given the frequency getchanindex
(chan)getfreq
(chan)Method to get the frequency of the given channel, the channel can contain a fraction of a channel (e.g. integritycheck
()Method to check that all axes are the same length. invert
()Multiply this spectrum and continuum by -1 mask
()Method to return the mask as a numpy array mask_between
(limit1, limit2[, axis])Method to mask any data bewteen the the given values. mask_equal
(value[, axis])Method to mask data equal to the given value. mask_ge
(limit[, axis])Method to mask any data less than or equal to the given value. mask_gt
(limit[, axis])Method to mask any data greater than the given value. mask_invalid
()Method to mask all invalid spectral data. mask_le
(limit[, axis])Method to mask any data less than or erqual to the given value. mask_lt
(limit[, axis])Method to mask any data less than the given value. mask_outside
(limit1, limit2[, axis])Method to mask any data less than or greater than the given values. meanfrequency
([chanrange])Compute the power-weighted mean frequency of this spectrum, aka the first moment. meanvelocity
([chanrange])Compute the power-weighted mean velocity of this spectrum. moment
([chanrange, p])Compute the power-weighted moment of a spectrum If a mask exists, this function operates on the masked spectrum. momenta
([chanrange, p])abs(intensity)-weighted moment momenti
([chanrange, p])Intensity-weighted moment noise
()Method to return the noise of the spectrum. peak
([chanrange])Return the peak intensity in the given channel range If a mask exists, this function operates on the masked spectrum. rms
()If the noise has been set, return the noise, otherwise compute and return the root-mean-square value of the spectrum. set_chans
(chans)Method to set the channel axis. set_contin
(contin)Method to set the continuum. set_freq
(freq)Method to set the frequency axis. set_mask
(mask)Method to set the mask item set_noise
(noise)Method to set the noise value for the spectrum. set_spec
(spec[, mask])Method to set the spectrum, an optionally the mask. spec
([csub, masked])Method to get the spectrum, the mask is optionally returned veldispersion
([chanrange])Compute the velocity dispersion (km/s) of the spectrum in the given channel range. -
chans
(masked=True)[source]¶ Method to get the channel axis, the mask is optionally returned
Parameters: masked : bool
If True then return the channel axis as a masked array with the current mask applied. False will return a numpy array with no masking done.
Returns: Array like object containing the channel axis
-
contin
(masked=True)[source]¶ Method to get the continuum, the mask is optionally returned
Parameters: masked : bool
If True then return the continuum as a masked array with the current mask applied. False will return a numpy array with no masking done.
Returns: Array like object containing the continuum
-
fix_invalid
(mask_value=0.0)[source]¶ Method to replace invalid spectral data with a specific value and mask it. Invalid values are: NaN, Inf, -Inf
Parameters: mask_value : float
The value to replace the invalid data with. Default: 0.0
Returns: None
-
freq
(masked=True)[source]¶ Method to get the frequency axis, the mask is optionally returned
Parameters: masked : bool
If True then return the frequency axis as a masked array with the current mask applied. False will return a numpy array with no masking done.
Returns: Array like object containing the frequency axis
-
freqdispersion
(chanrange=None)[source]¶ Compute the frequency dispersion of the spectrum in the given channel range. If a mask exists, this function operates on the masked spectrum.
Parameters: chanrange: range of channels over which to compute dispersion
[startchan, endchan]
Returns: Power-weighted frequency dispersion df as computed by
df = sqrt(mom2)
where mom2 is the 2nd moment as computed by the moment() method.
-
fwhm
(chanrange=None)[source]¶ Compute the full-width at half-maximum velocity (km/s). If a mask exists, this function operates on the masked spectrum.
Parameters: chanrange: range of channels over which to compute dispersion
[startchan, endchan]
Returns: fwhm computed by
fwhm = dv * sqrt(8*ln(2))
where dv is the velocity dispersion computered by
the veldispersion() method.
-
getchan
(frq)[source]¶ Method to get the channel number given the frequency
Parameters: frq : float
The frequency whose channel number is to be found
Returns: int containing the channel number, will be 0 or len(self.x)-1 if
the frequency is outside of the frequency range in the class
-
getfreq
(chan)[source]¶ Method to get the frequency of the given channel, the channel can contain a fraction of a channel (e.g. 1.45)
Parameters: chan : float
The channel to convert
Returns: Float of the frequency corresponding to the channel
-
integritycheck
()[source]¶ Method to check that all axes are the same length. Axes that have no data are ignored.
Parameters: None Returns: Bool, True if all match, False if there is an inconsistency
-
mask
()[source]¶ Method to return the mask as a numpy array
Parameters: None Returns: Array like object containing the mask
-
mask_between
(limit1, limit2, axis='spec')[source]¶ Method to mask any data bewteen the the given values. Any axis can be used (“spec”, “freq”, “chans”) as the basis for the masking.
Parameters: limit1 : float or int
The value above which all data of the given axis will be masked.
limit2 : float or int
The value below which all data of the given axis will be masked.
axis : str
The axis which is used to determine the flags. Default: “spec”
Returns: None
-
mask_equal
(value, axis='spec')[source]¶ Method to mask data equal to the given value. Any axis can be used (“spec”, “freq”, “chans”) as the basis for the masking.
Parameters: value : float or int
The value equal to which all data of the given axis will be masked.
axis : str
The axis which is used to determine the flags. Default: “spec”
Returns: None
-
mask_ge
(limit, axis='spec')[source]¶ Method to mask any data less than or equal to the given value. Any axis can be used (“spec”, “freq”, “chans”) as the basis for the masking.
Parameters: limit : float or int
The value which all data, of the given axis, greater than or equal to, will be masked.
axis : str
The axis which is used to determine the flags. Default: “spec”
Returns: None
-
mask_gt
(limit, axis='spec')[source]¶ Method to mask any data greater than the given value. Any axis can be used (“spec”, “freq”, “chans”) as the basis for the masking.
Parameters: limit : float or int
The value above which all data of the given axis will be masked.
axis : str
The axis which is used to determine the flags. Default: “spec”
Returns: None
-
mask_invalid
()[source]¶ Method to mask all invalid spectral data. Invalid values are: NaN, Inf, -Inf
Parameters: None Returns: None
-
mask_le
(limit, axis='spec')[source]¶ Method to mask any data less than or erqual to the given value. Any axis can be used (“spec”, “freq”, “chans”) as the basis for the masking.
Parameters: limit : float or int
The value which all data, of the given axis, less than or equal to, will be masked.
axis : str
The axis which is used to determine the flags. Default: “spec”
Returns: None
-
mask_lt
(limit, axis='spec')[source]¶ Method to mask any data less than the given value. Any axis can be used (“spec”, “freq”, “chans”) as the basis for the masking.
Parameters: limit : float or int
The value below which all data of the given axis will be masked.
axis : str
The axis which is used to determine the flags. Default: “spec”
Returns: None
-
mask_outside
(limit1, limit2, axis='spec')[source]¶ Method to mask any data less than or greater than the given values. Any axis can be used (“spec”, “freq”, “chans”) as the basis for the masking.
Parameters: limit1 : float or int
The value below which all data of the given axis will be masked.
limit2 : float or int
The value above which all data of the given axis will be masked.
axis : str
The axis which is used to determine the flags. Default: “spec”
Returns: None
-
meanfrequency
(chanrange=None)[source]¶ Compute the power-weighted mean frequency of this spectrum, aka the first moment. If a channel range is given, the mean frequency is computed over that channel range, otherwise over the entire spectrum. If a mask exists, this function operates on the masked spectrum.
Parameters: chanrange: range of channels over which to compute frequency
[startchan, endchan]
Returns: Power-weighted mean frequency as computed by
f_mean = Sum(spec[i]**2*f[i]])/Sum(spec[i]**2)
where spec[i] is the intensity at channel i and f[i] is
the frequency at channel i.
-
meanvelocity
(chanrange=None)[source]¶ Compute the power-weighted mean velocity of this spectrum. derived from the mean frequency and channel width:
mean_v = C * delta()/meanfrequency(chanrange)
-
moment
(chanrange=None, p=1)[source]¶ Compute the power-weighted moment of a spectrum If a mask exists, this function operates on the masked spectrum.
f_mean = Sum(spec[i]**2*(f[i]-mom{p-1})^p])/Sum(spec[i]**2)where spec[i] is the intensity at channel i (spec[i]**2 is he power) and f[i] is the frequency at channel i, p is the moment power, and mom{p-1} is the p-1-th moment [for p >1].
Parameters: chanrange: range of channels over which to compute moment
[startchan, endchan]
p: the moment to compute (the power of the frequency in the sum)
Returns:
The computed moment
-
momenta
(chanrange=None, p=1)[source]¶ abs(intensity)-weighted moment Does somewhat better than signed intensity-weighted moment.
Parameters: chanrange: range of channels over which to compute moment
[startchan, endchan]
p: the moment to compute (the power of the frequency in the sum)
Returns:
The computed moment
-
momenti
(chanrange=None, p=1)[source]¶ Intensity-weighted moment Note this moment does poorly for very narrow line segments where some channels may be negative.”
Parameters: chanrange: range of channels over which to compute moment
[startchan, endchan]
p: the moment to compute (the power of the frequency in the sum)
Returns:
The computed moment
-
noise
()[source]¶ Method to return the noise of the spectrum.
Parameters: None Returns: Float of the rms noise.
-
peak
(chanrange=None)[source]¶ Return the peak intensity in the given channel range If a mask exists, this function operates on the masked spectrum.
Parameters: chanrange: range of channels over which to compute dispersion
[startchan, endchan]
Returns: Maximum of the absolute value of the spectrum in the channel range
max(abs(spectrum[startchan:endchan]))
-
rms
()[source]¶ If the noise has been set, return the noise, otherwise compute and return the root-mean-square value of the spectrum.
-
set_chans
(chans)[source]¶ Method to set the channel axis.
Parameters: chans : array like
The channel axis to use
Returns: None
-
set_contin
(contin)[source]¶ Method to set the continuum.
Parameters: freq : array like
The continuum to use
Returns: None
-
set_freq
(freq)[source]¶ Method to set the frequency axis.
Parameters: freq : array like
The frequency axis to use
Returns: None
-
set_mask
(mask)[source]¶ Method to set the mask item
Parameters: mask : array like or bool
The mask to use, must be equal in dimmension to the spectral axis, or a single boolean value which is applied to all data.
Returns: None
-
set_noise
(noise)[source]¶ Method to set the noise value for the spectrum.
Parameters: noise : float
The noise of the spectrum
Returns: None
-
set_spec
(spec, mask=None)[source]¶ Method to set the spectrum, an optionally the mask.
Parameters: spec : array like
The spectrum to set
mask : array like or single bool
The mask to apply to the spectrum. Default: None (no mask)
Returns: None
-
spec
(csub=True, masked=True)[source]¶ Method to get the spectrum, the mask is optionally returned
Parameters: csub : bool
If True return the spectrum with the continuum subtracted, False will return the pure spectrum Default: True
masked : bool
If True then return the spectrum as a masked array with the current mask applied. False will return a numpy array with no masking done. Default: True
Returns: Array like object containing the spectrum
-
veldispersion
(chanrange=None)[source]¶ Compute the velocity dispersion (km/s) of the spectrum in the given channel range. If a mask exists, this function operates on the masked spectrum.
Parameters: chanrange: range of channels over which to compute dispersion
[startchan, endchan]
Returns: Power-weighted velocity dv as computed by
dv = C * df/mf
where df is the frequency dispersion computered by
the frequencydispersion() method,
mf is the mean frequency from the meanfrequency() method,
and C is the speed of light.
-


HPHS is recognized as the second-oldest public high school in the US. While the original Latin School dates back to 1638, the modern "high school" name and building began in 1847. Learn more from the HPHS Museum and Archive.
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The first Hartford Public High School building, located at the corner of Ann Street and Asylum Avenue, was dedicated by Henry Barnard in 1847.
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The school moved to Hopkins Street in 1869, but was destroyed by a fire in 1882.
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After the fire, a new building was constructed on the same site in 1883.
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When the East-West interstate highway approached, a new school was constructed on Forest Street in 1963.
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley
Out Moto
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley
It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.


Comparing two numbers written in index form like 211 and 37 is not difficult, as any calculator would confirm that 211 = 2048 < 37 = 2187.
However, confirming that<PHONE_NUMBER>61 ><PHONE_NUMBER>06 would be much more difficult, as both numbers contain over three million digits.
Using base_exp.txt (right click and 'Save Link/Target As...'), a 22K text file containing one thousand lines with a base/exponent pair on each line, determine which line number has the greatest numerical value.
NOTE: The first two lines in the file represent the numbers in the example given above.
Assume two pairs, $ (a_1, b_1) $, $ (a_2, b_2) $, $ m_1 = a_1^{b_1} $, $ m_2 = a_2^{b_2} $, then we can transfer the comparison of $ m_1 $ with $ m_2 $ to comparison of $ \frac{m_1}{m_2} $ with 1.
And it is easy to transfer to comparions of $ \log\frac{m_1}{m_2}$ with 0, which means $ \log{m_1} - \log{m_2} $ with 0, which means $ b_1 * \log{a_1} - b2 * \log{a_2} $ with 0.
So the greatest numerical value is the one with greatest value of $ b * \log{a} $
import sys, os; sys.path.append(os.path.abspath('..'))
from timer import timethis
from urllib.request import urlopen
from math import log
with urlopen('https://projecteuler.net/project/resources/p099_base_exp.txt') as f:
resp = f.read().decode('utf-8')
pairs = [tuple(map(int, line.split(','))) for line in resp.splitlines()]
pairs[:5]
@timethis
def solve():
return max(enumerate(pairs, 1), key=lambda enu: enu[1][1] * log(enu[1][0]))
solve()


22 Home Bargains hacks you'll need to remember for your next visit
These should save you time and money
by Catherine MurphyBargain retailer Home Bargains has long been a favourite among shoppers thanks to its low prices and range of products.
The Liverpool-based retailer opened its first store in Old Swan in 1976 by Tom Morris - the store is still in the family today.
It even launched two years before rival store B&M Bargains.
It now stocks around 4,000 branded products so customers regularly get a surprise at the products they find in stores.
Despite its regular deals there are ways you can save even more money at Home Bargains - we've rounded up 22 tips and tricks which will help you on your next shopping trip.
They include a mixture of offers to look out for and which stores you may be best visiting.
1. The price tags reveal how long an item is in stock for
An insider from the store has now revealed how its customers can find out whether a bargain will be in stock next time they return to the shop, and it has to do with its price tags.
As well as revealing the cost of an item, the price tags also reveal whether an item is a one-off or a regularly stocked product.
Next time you visit Home Bargains take a look at the bottom corner you will see wither REG or ONE.
REG stands for regular, meaning regular product which will be in stock the majority of the time, and restocked when they sell out however ONE stands for one off meaning if you want it then you are probably best buying it there and then.
2. Looking for a particular item? Head to its bigger stores
Like most retailers, Home Bargains' larger stores have much more choice. According to its website, it also says: "Many of our large stores receive daily stock replenishment, and the item you are looking for may be available on your next visit to store.
"Our smaller stores however, receive less frequent deliveries."
3. But, don't be in a rush to get inside
Some of the best bargains are located near the front of the store.
4. Star buys
Located near the front of the store, they change weekly and include discounts on branded products.
5. Getting to Home Bargains early will pay off
New stock is often delivered at around 7.30am in the morning so it will always pay to get to your nearest store when it opens.
6. You'll be surprised at its food range
There is a mix of fresh fruit and veg alongside cupboard essentials and more.
7. Designer replicas
As well as savings its customers money on their regular food and homeware shops, Home Bargains is also known to stock items which look like designer products for much less.
8. You can even take a break during a shop at its larger stores
Most of its larger stores now have cafés and garden centres too.
9. You'll find brands you didn't know Home Bargains stocked
From beauty to food - there is more than 4,000 brands.
10. If you see something you like make sure you buy it there and then
The best discounts always go fast and can often be limited so if you see something like make sure you buy it there and then.
11. Make sure you park close to its stores
Because you'll always end up buying more than you went in for.
12. New openings
New openings often come with deals and offers on the first day so they're always worth visiting.
Home Bargains new Liverpool city centre store will soon be opening in the former Toys R Us store.
13. Make sure you check its social media accounts to keep up to date with its offers
This is where Home Bargains often reveals its new products first.
14. It currently has a huge Christmas range in stores too
Christmas trees starts from £14.99 and lighting starts from £4.99.
On its website you can even order personalised Christmas Eve plates and decorations too.
Follow reporter Catherine Murphy on social media
Follow Catherine on Twitter here
You can read more of her stories here
Email her on<EMAIL_ADDRESS>Or contact Catherine on Facebook if you want to share any news, stories or updates.
Keep up to date with the latest breaking news here and What's On news here
Like the ECHO What's On Facebook page and follow @EchoWhatsOn on Twitter
15. And, clothing too
Baby clothing, pyjamas and even limited footwear can often be found in its stores.
16. For humans and pets
There is also Christmas jumpers for pets in store too.
17. It has two websites, depending on your order
Home Bargains has its traditional website and one dedicated to flowers.
You can visit Home Bargains website here.
18. With one dedicated to flowers
You can take a look at www.homebargainsflowers.co.uk where you'll find a whole range of flowers for birthdays, anniversary's and more as well as plants and gifts too.
19. Discount coupons
Discount coupons are not accepted in stores.
20. The payment card which won't be accepted in stores
Home Bargains doesn't accept American Express cards as payment.
21. If you lose your receipt you may still be able to return an item
If you do not have your receipt, you may be able to use a bank statement, and the store will check this for you. It would be the store manager's discretion as to whether they would offer a replacement or goods to the value of with this.
22. You can no longer use its Click & Collect delivery service
However, the good new is that it will be back soon.
According to Home Bargains website, it says: "Our Click+Collect service has been temporarily removed from our site while we update the service.
"So that we can completely rebuild the service to provide customers with the best possible new experience, we've unfortunately had to remove the option while this process takes place.
"Any items that are eligible to be sent home delivery can still be ordered for home delivery."
Sign up to the What's On newsletter - packed with brilliant things to see and do in Liverpool and beyond.


It should be only a few lines of code to create an extra filter that hides servers that are on your radar list.
B+ has a "ping filter" feature that hides servers that are over a certain ping. The same logic could be used to hide servers that managed their way on to your radar list.
BrainFooLong
Administrator
This isn't what we want to do.
To remove servers automatically from list is not complicated to integrate but BBLog well never do that. The clearly visible red radar icon should be enough to ignore that server.
Post edited 1 x times, last by
iPhotoN
iPhotoN
earliear BBlog developers talled that it's impossible 'couse of no ability to integrate BBlog so deep in battlelog and that was said as the only reason for such issue refusal...
BrainFooLong
Administrator
Very old request, still rejected. Earlier BBLog Developers
I'm the only BBLog developer...


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I designed and wrote most of the copy for PiE’s 2014-2015 report, which covers PiE during my year as director. As of spring 2016, sending out this report has helped raise over $10,000 in funding from sponsors. This is almost one-fourth of our typical operating budget, which was $45,000 for the 2014-2015 year.
PiE’s annual reports have historically been 15+ page word documents sent out to sponsors at the end of every fiscal year. Design aside, the content of the reports was often better for internal reflection than external reporting. I focused the content on information relevant to sponsors and the general public, leveraging photos and graphics to show our accomplishments.


data class MediaAction : MediaMessage
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A MediaMessage action.
<init> |
A MediaMessage action. MediaAction(title: CharSequence, url: String? = null) |
title |
val title: CharSequence |
url |
var url: String? |
toGenericMessage |
Returns a GenericMessage view of this MediaMessage if supported. fun toGenericMessage(): GenericMessage? |


Reports: Resident Evil 3 Remake Planned For 2020
by Gita JacksonA Resident Evil 3: Nemesis remake is in the works from Capcom, according to two new reports. It will reportedly release in 2020.
Capcom released a remake of Resident Evil 2 this year, which was received warmly by fans of the franchise. Given how difficult it is to play Resident Evil 3 at the moment, fans have hoped for a remake of that game as well. According to a YouTube Channel called Spawn Wave, a remake of that game is not only currently in development, but on track for a 2020 release. The major European games outlet Eurogamer confirmed as much with their own sources, reporting this morning that they have also heard of this project.
Capcom has also been public about its desire to re-release more Resident Evil games.
I can’t play scary games because they spook me too much, but I look forward to watching over Heather’s shoulder as she plays this one. At least I definitely have enough time to re-up my anxiety meds before all my favourite Let’s Players start getting chased around by Nemesis.


§612-9 Certificate for jury pay. At least once each month, the clerk shall certify the number of days each juror or prospective juror has attended court and the amount due to the juror or prospective juror. [L 1973, c 191, pt of §1; gen ch 1985; am L 2007, c 122, §6]


4.1. Partial dependence plots¶
Partial dependence plots (PDP) show the dependence between the target response [1] and a set of ‘target’ features, marginalizing over the values of all other features (the ‘complement’ features). Intuitively, we can interpret the partial dependence as the expected target response as a function of the ‘target’ features.
Due to the limits of human perception the size of the target feature set must be small (usually, one or two) thus the target features are usually chosen among the most important features.
The figure below shows four one-way and one two-way partial dependence plots
for the California housing dataset, with a GradientBoostingRegressor
:
One-way PDPs tell us about the interaction between the target response and the target feature (e.g. linear, non-linear). The upper left plot in the above figure shows the effect of the median income in a district on the median house price; we can clearly see a linear relationship among them. Note that PDPs assume that the target features are independent from the complement features, and this assumption is often violated in practice.
PDPs with two target features show the interactions among the two features. For example, the two-variable PDP in the above figure shows the dependence of median house price on joint values of house age and average occupants per household. We can clearly see an interaction between the two features: for an average occupancy greater than two, the house price is nearly independent of the house age, whereas for values less than 2 there is a strong dependence on age.
The sklearn.inspection
module provides a convenience function
plot_partial_dependence
to create one-way and two-way partial
dependence plots. In the below example we show how to create a grid of
partial dependence plots: two one-way PDPs for the features 0
and 1
and a two-way PDP between the two features:
>>> from sklearn.datasets import make_hastie_10_2
>>> from sklearn.ensemble import GradientBoostingClassifier
>>> from sklearn.inspection import plot_partial_dependence
>>> X, y = make_hastie_10_2(random_state=0)
>>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
... max_depth=1, random_state=0).fit(X, y)
>>> features = [0, 1, (0, 1)]
>>> plot_partial_dependence(clf, X, features)
You can access the newly created figure and Axes objects using plt.gcf()
and plt.gca()
.
For multi-class classification, you need to set the class label for which
the PDPs should be created via the target
argument:
>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>> mc_clf = GradientBoostingClassifier(n_estimators=10,
... max_depth=1).fit(iris.data, iris.target)
>>> features = [3, 2, (3, 2)]
>>> plot_partial_dependence(mc_clf, X, features, target=0)
The same parameter target
is used to specify the target in multi-output
regression settings.
If you need the raw values of the partial dependence function rather than
the plots, you can use the
sklearn.inspection.partial_dependence
function:
>>> from sklearn.inspection import partial_dependence
>>> pdp, axes = partial_dependence(clf, X, [0])
>>> pdp
array([[ 2.466..., 2.466..., ...
>>> axes
[array([-1.624..., -1.592..., ...
The values at which the partial dependence should be evaluated are directly
generated from X
. For 2-way partial dependence, a 2D-grid of values is
generated. The values
field returned by
sklearn.inspection.partial_dependence
gives the actual values
used in the grid for each target feature. They also correspond to the axis
of the plots.
For each value of the ‘target’ features in the grid
the partial
dependence function needs to marginalize the predictions of the estimator
over all possible values of the ‘complement’ features. With the 'brute'
method, this is done by replacing every target feature value of X
by those
in the grid, and computing the average prediction.
In decision trees this can be evaluated efficiently without reference to the
training data ('recursion'
method). For each grid point a weighted tree
traversal is performed: if a split node involves a ‘target’ feature, the
corresponding left or right branch is followed, otherwise both branches are
followed, each branch is weighted by the fraction of training samples that
entered that branch. Finally, the partial dependence is given by a weighted
average of all visited leaves. Note that with the 'recursion'
method,
X
is only used to generate the grid, not to compute the averaged
predictions. The averaged predictions will always be computed on the data with
which the trees were trained.
Footnotes
| [1] | For classification, the target response may be the probability of a class (the positive class for binary classification), or the decision function. |
Examples:
References
| [HTF2009] | T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Second Edition, Section 10.13.2, Springer, 2009. |
| [Mol2019] | C. Molnar, Interpretable Machine Learning, Section 5.1, 2019. |


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Consider the problem of computing the exponential of a given number. We would like a function that takes as arguments a base $b$ and a positive integer exponent $n$ and computes $b^n$. One way to do this is via the recursive definition \begin{eqnarray*} b^{n} &=& b\cdot b^{n-1}\\ b^{0} &=&1 \end{eqnarray*} which translates readily into the function
This is a linear recursive process, which requires $\Theta(n)$ steps and $\Theta(n)$ space. Just as with factorial, we can readily formulate an equivalent linear iteration:
We can compute exponentials in fewer steps by using successive squaring. For instance, rather than computing $b^8$ as \[ b\cdot(b\cdot(b\cdot(b\cdot(b\cdot(b\cdot(b\cdot b)))))) \] we can compute it using three multiplications: \begin{eqnarray*} b^{2} &=& b\cdot b\\ b^{4} &=& b^{2}\cdot b^{2}\\ b^{8} &=& b^{4}\cdot b^{4} \end{eqnarray*}
This method works fine for exponents that are powers of 2. We can also take advantage of successive squaring in computing exponentials in general if we use the rule \[ \begin{array}{ll} b^{n} =(b^{n/2})^{2} & \mbox{~if is even}\\ b^{n} =b\cdot b^{n-1} & \mbox{~if is odd}\\ \end{array} \] We can express this method as a function:
The process evolved by fast_expt grows logarithmically with $n$ in both space and number of steps. To see this, observe that computing $b^{2n}$ using fast_expt requires only one more multiplication than computing $b^n$. The size of the exponent we can compute therefore doubles (approximately) with every new multiplication we are allowed. Thus, the number of multiplications required for an exponent of $n$ grows about as fast as the logarithm of $n$ to the base 2. The process has $\Theta(\log n)$ growth.[1]
The difference between $\Theta(\log n)$ growth and $\Theta(n)$ growth becomes striking as $n$ becomes large. For example, fast_expt for $n=1000$ requires only 14 multiplications.[2] It is also possible to use the idea of successive squaring to devise an iterative algorithm that computes exponentials with a logarithmic number of steps (see exercise \ref{ex:iter-expon-pro}), although, as is often the case with iterative algorithms, this is not written down so straightforwardly as the recursive algorithm.[3]
function fib(n) { return fib_iter(1,0,0,1,n); } function fib_iter(a,b,p,q,count) { return count === 0 ? b : is_even(count) ? fib_iter(a, b, ??, // compute p' ??, // compute q' count / 2) : fib_iter(b * q + a * q + a * p, b * p + a * q, p, q, count - 1); }
Russian peasant methodof multiplication, is ancient. Examples of its use are found in the Rhind Papyrus, one of the two oldest mathematical documents in existence, written about 1700 {\scshape b.c.} (and copied from an even older document) by an Egyptian scribe named A'h-mose.


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For about 6 months I ripped into it fairly regularly.
First time we used 12 flowers to make a tea, boiled it all up for 15 minutes in 2 litres of water. 5 people drank a cup each. We filled a bourbon bottle with the remainder and went to a party.
The party was all straight dudes from the airforce, the girl in our group felt very uncomfortable and aked me to drive her home. I hid the bottle in the kitchen. I felt pretty normal, just a metallic taste in my mouth, but once we were driving we both forgot where we were going and where we had just come from. The panels and bonnet of my car started `breathing` and sprouting veins through which I could see a liquid moving.
It was more beautiful than a worry, so I drove on.
It turned out we were on our street, we recognised our own house as we drove past it.
 I found the bourbon bottle sitting empty on the kitchen table. An hour later about 15 people were sitting quietly in the lounge watching the ceiling continually rise, the walls breathe in and out. Everybody seemed content except the friend I had taken home, she hallucinated 4 friends were in her room all night wearing plastic raincoats.
1 week later we made another brew, about the same proportions, but while it was cooking we went to the pub and drank several beers. The brew had boiled dry and burnt by the time we returned, so we put in 4 cups of water and boiled it up for 10 minutes. 4 of us had a cup each.
In under 10 minutes the walls were breathing, my mouth was lined with alfoil, none of us could speak, just croak. After another 10 minutes, my arms were breathing spurting rainbow-coloured fountains on the out-breath. My hands turned into green-scaled claws still breathing with massive veins swelling across them. My friends chose their courses of action, one took 2 sleeping tablets (don`t know what) and went to bed, another tried to walk home ( about 20km, midnight Sunday) went in the wrong direction, got picked up by a friend at 4:00am, sounds odd but it seems to happen on datura.
The third was picked up from my house by another guy, who dropped him home, in his bedroom were waiting a selection of his ex-girlfriends, who just silently stared at him all night while they sharpened knives and loaded guns.
I went to bed, at first I kept melting, then I kept dropping lit smokes onto my bed and searching for them in a panic. Next just about every person I had ever met in my whole life came into my bedroom, one at a time, through the window, through cracks in the wall or just appearing. From one crack came a human body with a sheep`s head, the head was rotting and the sheep started baaaaing at me. The head changed into my ex-boss, who calmly re-assured me everything was allright.
 I wanted to go to the kitchen and get a knife, but I couldn`t find my bedroom door or even window.
Where there used to be a door was now a blank wall.
I blacked sometime after that.
In the morning, the guy who had taken sleeping pills, woke me up. I was naked, my hands and knees cut up and covered with blood, still tripping but kind of sane. My mate took me out to the backyard, our entire picket fence had been pulled down, all of my clothes I had been wearing the previous night were scattered up the back alleyway. I have no idea what I did for about 12 hours, running naked around an inner-city suburb, in the early hours of a Monday morning. I had a top up on Monday night, and tripped until Wednesday night. None of us could read anything for about 4 days after.
None of my friends took Brugmansia/Datura after that. I did but was a bit more careful except one time.
Used a datura shrub, about 1m high, small yellow flowers, made a tea from the flowers, put it in a small plastic softdrink bottle. We went to a bikies rally, Harley Davidsons and whatall, don`t ask why. At the rally, nobody wanted to drink my brew so I drank the lot myself.
I came back to ummm me? about 30 hours later. I was pissing and realised I was no longer wearing underpants, then i noticed I was wearing nothing at all except someone else's jeans. My clothes, wallet and keys were all gone.
I walked out of the toilet and found I was at a friend`s house it was about 11:00pm the next day. I have no memory of this entire period and my gear was never found.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Declarations
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Evaluation
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Evaluation and Compilation
3.2 Compilation
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Compiler Terminology
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Compilation Semantics
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File Compilation
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Literal Objects in Compiled Files
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Exceptional Situations in the Compiler
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