{% extends "base.html" %} {% block title %}The Open Source Report Card{% endblock %} {% block body %}
Every day, many thousands of open source contributions are made on GitHub by developers around the world. This data is publicly available through the API and—even more conveniently—on the GitHub Archive. This is generally a pretty fun dataset to play with but it is particularly exciting for hackers because we get to play with data that describes our own behavior! Last year, shortly after the full event stream was publicly released, the first annual GitHub data challenge produced some sick data visualizations and it's clear that people at GitHub have been thinking about how to Use The Data For Good™.
The one graph that is especially awesome in all sorts of surprising ways is the contributions heat map on every user's profile page. What sets this apart from the other visualizations that already exist on the site? It makes a general statement about one specific user. It lets a developer have a global view of their contributions, skills and habits. This ends up being extremely motivating because it lets the developer see their progress in real time. With this in mind, it seemed like a good idea to provide a more complete set of global statistics summarizing the hacker personality of any GitHub user.
So that's what I did.
The most convenient place to download historical event data for GitHub is probably Ilya Grigorik's GitHub Archive where you can download the contents of timeline.json—dumped in hour long chunks—as gzipped JSON(ish) files. For this project, I decided to freeze the scope and only download the event stream for January, February and March 2013. As you'll see in a moment, however, the final analysis is only based on accumulated statistics so it wouldn't be much harder to make these reports real time. I used a Python script to download the dataset but something like
wget http://data.githubarchive.org/2012-{01..03}-{01..31}-{0..23}.json.gz
should work just as well.
After downloading all of the data, I looped through all the activity and accumulated statistics about each user (and various global statistics) in a Redis database. Redis is ideal for this project because the sorted set and hash table data structures are perfect for answering questions like what is a user's most common type of activity? or what is the distribution of push events in a particular user's average week? Also, the memory requirements for this database should scale roughly linearly with the number of active users (not the number of events) so running on a larger dataset (or in real time) wouldn't require a significantly more expensive infrastructure.
Again, I used a Python script to perform this analysis. I saved a whole lot of summary statistics but I'll only describe a few here so that you don't get too bored. First, I accumulated some global statistics like
Then, for each user, I aggregated a summary of their behavior, including
With this database in place, it was then time to start having fun!
Given this large set of activity statistics, I needed to come up with some set of measurements that robustly describe a user's behavior. I came up with a few that I then integrated into this project but there's no doubt that there are many other relevant and illuminating features.
One feature that I expected to be interesting (spoiler alert: it was)
is the average weekly schedule. The average weekly schedule
is the normalized histogram of a user's activity on each day of the week. It
seemed likely that some users would work mostly on the weekends while others
would work exclusively during the week, for example. To study this, I
extracted the set of weekly schedule vectors for 10000 "moderately active"
GitHub users and ran
K-means (with
K=12
) on this sample set. K-means is an algorithm for the
unsupervised clustering of samples in a vector space. In particular, it
assumes that your samples come from K
distinct "classes" and
it finds a representative vector for each cluster. After removing three
redundant means (come on, we're just having fun here) and labelling each
cluster with a human readable name, the results were very exciting! The
chart below shows the resulting means for each class of user. Click on the
names on the right to see the other classes.
Isn't it exciting when the results of an experiment match your expectations?
Another related statistic is the average daily schedule but looking at this one is a little bit more complicated. All of the timestamps in the dataset are given in UTC (and sometimes PST, it seems) but most of the developers that use GitHub don't live in that time zone. It doesn't make a lot of sense to compare an American developer's daily schedule with that of an Australian unless you can correct for the time offset. The best way that I could think of for doing this was to fake it using the location field on the user's GitHub profile if they filled it out. Even if this field was filled out, there's no guarantee that it actually corresponds to a real place. There's also the problem that a lot of people travel! With this in mind, you should probably take the daily schedule with a grain of salt but it can still be fun to look at. I, for example, seem to work best right before lunch and right before dinner… I guess I should just stay hungry more often!
The final statistic that I would like to mention is behavior space. The idea is that you can describe a user's behavior as a single vector in a high-dimensional vector space. Then, other users that are nearby in this space are similar. The validity of these assumptions is definitely suspect but it's always fun to compare yourself to other people so let's do it anyways. For this project, I chose the following features to describe a user:
This leads to a 61-dimensional representation of each user's behavior. In order to find nearby users in this space, I used the Python bindings in Marius Muja's Fast Library for Approximate Nearest Neighbors (FLANN) to build an index over 50000 "moderately active" users in behavior space. After building the index, the K nearest neighbor lookup is extremely efficient and fast enough to run in real time. For example, Marius Muja's own profile shows that his hacker personality is similar to Bryan Payne's.
For some reason, I really wanted to have the end product of this analysis be an automatically-generated prose description of a user's activity based on the gathered statistics. I decided that it was alright if the text was backed up with figures but the focus should really be on the text. To do this, I resorted to building something like an old-school expert system. In other words, I made up a bunch of rules (implemented as a spaghetti of nested if statements) that concatenate strings until something like English prose falls out the other end. It would be fun to try to do something more sophisticated or intelligent than this but we'll have to live with this for now and I think that you'll be surprised by how well this actually works. The (horrifying) code that generates the summaries is available on GitHub (obviously) so I won't get too deep into the details here but I wanted to comment on one little hidden gem.
As part of generating the report, it was necessary to produce a prose comparison of two users. I decided to break this out to it's own endpoint at the URL:
http://osrc.dfm.io/{username}/compare/{other_username}
This means that if we want to compare the behavior of two of GitHub's founders (mojombo and defunkt), we can just hit up the URL osrc.dfm.io/mojombo/compare/defunkt and see something like "".
At this point, this demo should really only be thought of as a proof-of-concept because there are many other interesting statistics and descriptions that could be determined from this dataset. That being said, I think that it does represent a step in an interesting direction. In particular, it would be fun to make a real time version of (something like) this to add to the user profile page. It wouldn't be hard and it would probably be very motivating.
The code to download, analyse and display all this data is available on GitHub and it is licensed under the MIT license… so have fun! Let me know (probably on Twitter) if you have any questions or comments.
{% endblock %} {% block scripts %} {% endblock %}


Darrell Clarke: More change was needed for Walsall
Walsall boss Darrell Clarke admits he wished he made more changes to his side for the clash with Stevenage after their mid-week win over Darlington.
by Liam KeenAfter the 1-0 FA Cup replay win over the North East side on Wednesday, Clarke made three changes to that side for the League Two 0-0 draw with Stevenage on Saturday.
Dan Scarr, Alfie Bates and Shay Facey all returned to the side, replacing Mat Sadler, Cameron Norman and Gary Liddle.
And after claiming the share of the points was a fair result, Clarke said he should have made more changes to freshen up his side.
"I thought we were miles off in the wide areas in the first half," he said.
"We were very ineffective in our shape, they put three high up the pitch and we didn't release our full-backs enough.
"That was disappointing but it sometimes can happen.
"I probably wish I made a few more changes to be honest but I thought I'd go with a bit of momentum and not make too many changes.
"You're forever learning as a manager.
"I see games and I always go with my gut instinct of what I think needs to change.
"I have no problem changing things if I don't think things are working or one or two lads are not having a good game.
"The lads know how I work, they're getting used to the way I work.
"That's the way they've got to work, they have to get used to that.
"And they are to be fair to them.
"There's no scapegoats here, we just get on with our work.
"You have to be brave as a manager to change it if things aren't working well."
Meanwhile, the clean sheet was the Saddlers' tenth in all competitions this season and Clarke praised his squad for their work, but said more was still needed.
“If we assess where we are after the games we’ve played, 10 clean sheets is testament to the hard working nature of the group," he added.
“But we have to be better at times with the tempo of our play and our passing.
“I said to the lads that I feel like I’m coaching them every minute.
“You have to do that sometimes with a developing group but I thought one or two players were way off the game.
“That can happen as well so we’ll pick them up and try and take the positives out the week.
“And build towards a couple of cup games coming up."


{% extends "base.html" %} {% block content %}
Provide it with a CBCT .png Slice
Hit the submit button


{% extends "base.html" %} {% block body %} Profile Home Profile Welcome {{user.username}} Please choose an Option Logout Password Change Change E-mail {% endblock body %}
Home Profile


{% set package_version = twee.getConfig('twee:package:version') %}
{{ tr('Use this document as a way to quickly start any new project. All you get is this text and a mostly barebones HTML document.') }}


Close
The Sydney Opera House provides 45,000 square metres (11 acres) of usable office space out of 18,000 square metres (4.5 acres) of land. It is 183 metres (600 feet) tall and about 120 metres (388 feet) wide at its widest point. It is supported on 580 concrete piers sunk up to 25 metres below sea level. It has about 1000 rooms. It has five theatres, five rehearsal studios, two main halls, four restaurants, six bars and numerous souvenir shops. source: sheppardsoftware.com


A quick Bite to practice some string parsing extracting a users dict
from a password file.
Complete get_users
is how it works:
>>> from users import get_users >>> pw = """ ... postfix:x:108:112::/var/spool/postfix:/bin/false ... ssh-rsa:x:1004:1004::/home/ssh-rsa:/bin/bash ... artagnon:x:1005:1005:Ramkumar R,,,,Git GSOC:/home/artagnon:/bin/bash ... """ >>> get_users(pw) {'postfix': 'unknown', 'ssh-rsa': 'unknown', 'artagnon': 'Ramkumar R Git GSOC'}
So keys are usernames, values are names. Note that commas inside the name get replace by a single space. Trailing commas (not in this example) get stripped off.
Have fun and keep calm and code in Python!


About Us
CareerHub is Kenyas number one career website that connects graduates and employers.
We pride ourselves to having connected over 2000 graduates in various fields to their potential
employers who turned out to be a perfect match.
What sets us apart from other career sites is that we have a pool of graduates
who we have their credentials which are available at a click of a button.


You like like playing guitar or piano for fun? But when you produce those sounds you easily get lost and don't know where to go? Let's play around with some basic ideas behind music theory.
What is music? Series of sounds that make listeners feel something. Ok lets forget about the listeners for a second and think about the (hobby?) players. What is music to them? It is something they produce with their instruments and fingers and memory? Lets omit instrument part for once. It is a series of sounds played one after another that player wanted to hear.
So music is this chain of sounds. But how to compare those sounds? How to choose better ones for your playing? How to see any order behind them if we make a string of hundreds of sounds? All these questions may sound (pun not intended) simplistic but we must start somewhere right?
For sure sounds have their loudness, length or associated feeling with them. But I care about two types of sounds: pitched and unpitched.
When you think drums, you think more of a controlled noise. When you think acoustic guitar or piano you think about something called note. You sure get the idea that two guitars can play same melody and sound pleasant (if done right of course). But hmm if two drummers played same stuff and you listened their recording would you know if there were two drummers or one?
What is that pitched thing? From practical point of view it is a quality of sound when listener can *feel* that sound (one note on piano or guitar) has some main frequency (pitch!) associated with it. And if two instruments are tuned right you can play note with same pitch on both of them and it would sound nice or even harmoniously!
So you can run away from theory. It is all physics and frequencies and Hertz and nothing more but hang on for a second. Sounds may have associated frequencies from infrasound like 20Hz to ultrasound 20.000Hz (this one suitable for bats). You plan to name all of them? It would be super hard to make an actual instrument that can produce such notes properly and how would you compose a music using almost 20000 notes? Nahh too complicated. We need some practical solution and not some dry theory (pun intended)!
So we are ready to start our note exploration. But what is the nicest most practical note to choose? I suggest the one used by Internationsl organization for Standardization (wikipedia here!) and has frequency of 440Hz. Play!
Where to go next? Lets choose the one that is twice more frequent and one that is half as frequent.Play 880Hz or Play 220Hz! Now almost the fun part, what would happen if we played them all at once? Play 220, 440, 880!
Thanks I hate it! These sounds are so artificial that I can't even call them notes or consider them something produced by normal instrument. Lets add some tweaks to them.
When thinking about sounds that sound natural you may think about people talking, bird signing or whatever. Important fact is that not that often sounds have the same loudness to them. Lets make our sounds less loud at the beginning and the end. Play 220 or Play 440 or Play 880 or Play 220, 440, 880.
Definitely better. But what if we tried to change loudness in the process of playing sound? Maybe even adding some pattern, like pronouncing wow,wow,wow? Play 220 or Play 440 or Play 880 or Play 220, 440, 880.
Last sound reminds me of organ. Maybe some Bach music played on it. But we are here not to build synthesized instruments. We want to get useful practical notes or at least ideas about them. And first one that I hear is that when we play frequencies that are ratios like 2:1 or 1:2 they sound pretty good. Enough words. If idea is correct that we can build our three note combo around another frequency! Play 330, 660, 1320!
 How would three times same note sound with our sound system? Play 440, 440, 440! Actually it seems that idea was not that dumb after all. When we played same notes it resulted in the same note that was louder!
I am getting a bit ahead of my explanation with experiments, but we came here for some practicality. So this property of sound -> play same note on multiple instruments and it gets louder -> allows playing in huge room and make music loud enough for big audience (without electricity, speakers and everything).
And second practical thing. You want to tune your instrument to play well with another one? Then try to play same note on them and if it sounds good then you can tune tens of them using just (trained) ear. Play 440, 440 or Play 440, 454!
So last sound was more like from night owl than from two musical instruments. It seems that the chosen ratio of 227:220 does not sound too great for our ears. What if we tried 3:2 ratio in combo? Play 440, 660!
And that sounds absolutely nice!


Welcome to the Word Popper, where words pop like popcorn. Load up a batch of words to Pop, by typing each word, then click on "Pop Word". When you have loaded your batch of words, start clicking "Pop Em", and watch the words pop. Once you are ready to pop a new batch click "Stop Popping". Enjoy!!!


A Selection of demos for the robotic arm that run from the Raspberry Pi connected to the Arm.
This demo allows control of each joint via an Xbox Controller.
NOTE: This demo currently runs on startup.
This demo allows control of the end effector positions via an Xbox Controller.
This demo calibrates the robot then the robot moves to an upright position before imediately returning to a lying down.
Calibrates the arm, move the arm to it's upright position and then selects a random combination of joints to move to.
WARNING: This demo may not always operate safely. The combination of random joints may result in object collision.
Calibrates the arm, moves all joints by 45 degrees. The arm then returns to previous it original position.
When commands are sent, their status is shown here.
©Copyright, QLab, Queen's University Belfast, 2019


dlsmicro.analysis_tools¶
Module for processing DLS correlation data into rheological properties
-
dlsmicro.analysis_tools.
calc_g1
(t, corr, ergodic, g0=None, Ip=None, Ie=None, eps=None)[source]¶ Calculate the intermediate scattering function from the intensity autocorrelation
Parameters: - t (1d-array) – Vector of time-lags at which the correlation function is computed
- corr (1d-array) – Correlation coefficient (which spans possible from 0.0 to 1.0). Note that this is equal to g2 - 1, where g2 is the intensity autocorrelation function.
- ergodic (boolean) – If False, corrections are made for non-ergodic systems
based on
Ip
andIe
- Ip (float) – Scattering intensity at the position
where correlation function is collected.
(Only used if
ergodic=False
) - Ie (1d-array) – Vector of scattering intensities at different positions in the cuvette
(Only used if
ergodic=False
) - g0 (float, optional) – Estimate of the intercept of
g2 - 1
at time 0. If not provided, the intercept will be estimated automatically based on a stretched exponential fit.
Returns: g1 – The intermediate scattering function corresponding to the time-lags
t
Return type: 1d-array
Notes
The correlation function exported by the Zetasizer software is equal to
g2 - 1
-
dlsmicro.analysis_tools.
calc_msd_raw
(t, g1, q, replace_neg=True)[source]¶ Calculate the MSD without smoothing from the intermediate scattering function.
Parameters: - t (1d-array) – Vector of time-lags
- g1 (1d-array) – Vector containing the intermediate scattering function at time-lags
t
- q (float) – Scattering vector in units of 1/nm
- replace_neg (boolean, optional) – If
True
, replace negative values of the MSD by linear interpolation
Returns: msd – Vector of mean-squared-displacements at time-lags
t
(in units of nm^2)Return type: 1d-array
-
dlsmicro.analysis_tools.
calc_q
(n, theta, lam)[source]¶ Calculate the scattering wave-vector.
Parameters: Returns: q – Scattering wave-vector in inverse units of
1/lam
Return type:
-
dlsmicro.analysis_tools.
find_g0
(t, corr, func=<function stretched_exp>, t0=2.0, tmaxs=array([ 40., 50., 60., 70., 80., 90., 100., 110., 120.]), p0=None)[source]¶ Estimate the intercept of the correlation function at t = 0
Parameters: - t (1d-array) – Vector of time-lags at which the correlation coefficient is computed
- corr (1d-array) – Correlation coefficient (which spans possible from 0.0 to 1.0). Note that this is equal to g2 - 1, where g2 is the intensity autocorrelation function.
- func (callable function) – Model to fit the correlation against. Must be of the form f(t, p1, p2, …, pM) where t is the independent variable and p1, p2, …, pM is a set of M parameters to fit.
- t0 (float, optional) – Minimum time-lag to use in the fit of the correlation coefficient for estimating the intercept.
- tmaxs (1-d array or list of floats, optional) – Vector of possible maximum time-lags to use in the fitting interval of the correlation coefficient. The optimal tmax will be selected based on minimization of the cross-validation error of the fit.
- p0 (1-d array, optional) – Initial guesses for the parameters to
func
for fitting
Returns: - g0 (float) – Estimate of the intercept of
corr
at t=0 - twindow_min (List of floats) – Optimized interval in time over which the fit to
corr
was performed to obtain g0. This is a list of the form [t0, tmax]. - pmin (1d-array) – Optimal values for the fitting parameters to
func
used to estimateg0
-
dlsmicro.analysis_tools.
full_dlsur_analysis
(t, corr, ergodic, r, T, q, Ip, Ie, calc_g1_kws=None, pwr_law_kws=None)[source]¶ Perform a full DLS microrheology analysis from correlation and scattering intensity data to obtain particle motion statistics and rheological properties
Parameters: - t (1d-array) – Vector of lag-times (in microseconds)
- corr (1d-array) – Vector of values of the correlation coefficient, corresponding
to the time-lags
t
. - ergodic (boolean) – If
ergodic==False
then corrections are applied to the calculation of g1, based onIp
andIe
- r (float) – Particle radius in nanometers
- T (float) – Temperature in Kelvin
- q (float) – Scattering vector in 1/nm
- Ip (float, optional) – Scattering intensity at the meausurement position where
corr
is collected - Ie (1d-array) – Vector of scattering intensities at different measurement positions in the cuvette
- calc_g1_kws (dictionary, optional) – Dictionary of keyword arguments to pass to
analysis.tools.calc_g1()
for computing the intermediate scattering function - pwr_law_kws (dictionary, optional) – Dictionary of keyword arguments to pass to
analysis_tools.msd_local_pwr_law()
for local power-law analysis of the msd
Returns: - dlsmicro_df (pandas DataFrame) – Dataframe containing table of results from DLS microrheology analysis with the following columns:
- dlsmicro_df columns
- ~~~~~~~~~~~~~~~~~~~
- ’t’ (time-lags in microseconds)
-
dlsmicro.analysis_tools.
msd_local_pwr_law
(t, g1, q, bw=0.1, replace_neg=True)[source]¶ - Calculate the local power-law scaling of the MSD and the
- smoothed MSD by locally-weighted logarithmic linear regression
Parameters: - t (1d-array) – Vector of time-lags
- g1 (1d-array) – Vector containing the intermediate scattering function at time-lags
given by
t
- q (float) – Scattering vector in units of 1/nm
- bw (float, optional) – Bandwith smoothing parameter for locally-weighted regression. Reasonable values are typically between 0.05 and 0.1
Returns: - msd_smooth (1d-array) – Vector of smoothed MSD values from the local regression
corresponding to the time lags in
t
(in units of nm^2). - alpha (1d-array) – Vector of local power-law scaling exponents of the MSD
corresponding to the time lags in
t
.
-
dlsmicro.analysis_tools.
shear_modulus
(t, msd, alpha, r, T)[source]¶ Calculate the frequency-dependent complex shear modulus from the local power-law scaling behavior of the mean-squared-displacement
Parameters: Returns: - omega (1d-array) – Vector of angular frequencies in units of rad/s
- G1 (1d-array) – Storage modulus at angular frequencies
omega
in units of Pa - G2 (1d-array) – Loss modulus at angular frequencies
omega
in units of Pa
Notes
This method produces more accurate results in the upper and lower frequency extremes than a direct numerical Laplace or Fourier transform.
-
dlsmicro.analysis_tools.
shear_modulus_laplace_transform
(t, msd, r, T, bw=0.01)[source]¶ Calculate the frequency-dependent shear modulus by direct laplace transform of the mean-squared-displacement
Parameters: - t (1d-array) – Time-lags in units of microseconds
- msd (1d-array) – Mean-squared displacements at the time-lags
t
in units of nm^2 - r (float) – Radius of the probe particles in nanometers
- T (float) – Temperature in Kelvin
- bw (float, optional) – Bandwith parameter for analytic continuation of the laplace transform into fourier space by locally weighted regression
Returns: - omega (1d-array) – Vector of angular frequencies in units of rad/s
- G1 (1d-array) – Storage modulus at angular frequencies
omega
in units of Pa - G2 (1d-array) – Loss modulus at angular frequencies
omega
in units of Pa
Notes
This function returns the shear modulus in the Fourier (omega) domain by first transforming to the Laplace (s) domain and then performing analytic continuation onto the imaginary axis in the complex plane.
This method produces erroneous results in the upper and lower frequency extremes (roughly the first and last decade of frequencies).


All Classes
ComparatorTreatmentByEfficiency
HealthInsurance
HIPlan
Illness
ImporterIllness
Initialiser
InsuranceCompany
MedicalCondition
MedicalConsultation
Model
Patient
Provider
TesterAlejandro
TesterFlorian
TesterGeorgina
TesterLucy
Treatment


Welcome {{ patient.name }}
{{date| date : 'dd-MM-yyyy'}}
{{ time | date: 'hh:mm:ss a' }}
Total Numbers At A Glance
Total Numbers At A Glance
Vaccines
{{ totService.vaccine1+totService.vaccine2 }}
Beds
{{ totService.isolationBeds }}
O2 Cylinders
{{ totService.oxygenCylinders }}
ICUs
{{ totService.icuBeds }}
1) Search for hospitals in your city.
2) Select required filters for hospital services from left list.
0">
| Hospitals |
Email Id |
Details |
| {{ a.hospitalName }} |
{{ a.emailId}} |
|


This section describes the testing framework and format standards for tests in Astropy core packages (this also serves as recommendations for affiliated packages).
There are currently three different ways to invoke Astropy tests. Each method invokes py.test to run the tests but offers different options when calling.
In addition to running the Astropy tests, these methods can also be called so that they check Python source code for PEP8 compliance. All of the PEP8 testing options require the pytest-pep8 plugin, which must be installed separately.
The safest way to run the astropy test suite is via the setup command test. This is invoked by running python setup.py test while in the astropy source code directory. Run python setup.py test --help to see the options to the test command.
Turn on PEP8 checking by passing --pep8 to the test command. This will turn off regular testing and enable PEP8 testing.
Note
This method of running the tests defaults to the version of py.test that is bundled with Astropy. To use the locally-installed version, you can set the ASTROPY_USE_SYSTEM_PYTEST environment variable, eg.:
> ASTROPY_USE_SYSTEM_PYTEST=1 python setup.py test
An alternative way to run tests from the command line is to switch to the source code directory of astropy and simply type:
py.test
py.test will look for files that look like tests in the currect directory and all recursive directories then run all the code that looks like tests within those files.
Note
To test any compiled C/Cython extensions, you must run python setup.py develop prior to running the py.test command-line script. Otherwise, any tests that make use of these extensions will not succeed. Similarly, in python 3, these tests will not run correctly in the source code, because they need the 2to3 tool to be run on them.
You may specify a specific test file or directory at the command line:
py.test test_file.py
To run a specific test within a file use the -k option:
py.test test_file.py -k "test_function"
You may also use the -k option to not run tests py putting a - in front of the matching string:
py.test test_file.py -k "-test_function"
py.test has a number of command line usage options.
Turn on PEP8 testing by adding the --pep8 flag to the py.test call. By default regular tests will also be run but these can be turned off by adding -k pep8:
py.test some_dir --pep8 -k pep8
Note
This method of running the tests uses the locally-installed version of py.test rather than the bundled one, and hence will fail if the local version it is not up-to-date enough (py.test 2.2 as of this writing).
AstroPy includes a standalone version of py.test that allows to tests to be run even if py.test is not installed. Tests can be run from within AstroPy with:
import astropy
astropy.test()
This will run all the default tests for AstroPy.
Tests for a specific package can be run by specifying the package in the call to the test() function:
astropy.test('io.fits')
This method works only with package names that can be mapped to Astropy directories. As an alternative you can test a specific directory or file with the test_path option:
astropy.test(test_path='wcs/tests/test_wcs.py')
The test_path must be specified either relative to the working directory or absolutely.
By default astropy.test() will skip tests which retrieve data from the internet. To turn these tests on use the remote_data flag:
astropy.test('io.fits', remote_data=True)
In addition, the test function supports any of the options that can be passed to pytest.main(), and convenience options verbose=, pastebin= and coverage=.
Enable PEP8 compliance testing with pep8=True in the call to astropy.test. This will enable PEP8 checking and disable regular tests.
Note
This method of running the tests defaults to the version of py.test that is bundled with Astropy. To use the locally-installed version, you should set the ASTROPY_USE_SYSTEM_PYTEST environment variable (see Configuration system (astropy.config)) or the py.test method described above.
Any time a bug is fixed, and wherever possible, one or more regression tests should be added to ensure that the bug is not introduced in future. Regression tests should include the ticket URL where the bug was reported.
Each package should include a suite of unit tests, covering as many of the public methods/functions as possible. These tests should be included inside each sub-package, either in a tests directory, or in a test.py file, e.g:
astropy/io/fits/tests/
or:
astropy/io/fits/test.py
tests directories should contain an __init__.py file so that the tests can be imported and so that they can use relative imports.
Tests involving two or more sub-packages should be included in:
astropy/tests/
and using:
astropy.test()
then runs both these interoperability tests, and all the unit tests in the sub-packages. This functionality is especially important for people who install packages through bundles and package managers, where the original source code for the tests is not immediately available.
py.test has the following test discovery rules:
- test_*.py or *_test.py files
- Test prefixed classes (without an __init__ method)
- test_ prefixed functions and methods
Consult the test discovery rules for detailed information on how to name files and tests so that they are automatically discovered by py.test.
The following example shows a simple function and a test to test this function:
def func(x):
return x + 1
def test_answer():
assert func(3) == 5
If we place this in a test.py file and then run:
py.test test.py
The result is:
============================= test session starts ==============================
python: platform darwin -- Python 2.7.2 -- pytest-1.1.1
test object 1: /Users/tom/tmp/test.py
test.py F
=================================== FAILURES ===================================
_________________________________ test_answer __________________________________
def test_answer():
> assert func(3) == 5
E assert 4 == 5
E + where 4 = func(3)
test.py:5: AssertionError
=========================== 1 failed in 0.07 seconds ===========================
Tests that need to make use of a data file should use the get_data_fileobj or get_data_filename functions. These functions search locally first, and then on the astropy data server or an arbitrary URL, and return a file-like object or a local filename, respectively. They automatically cache the data locally if remote data is obtained, and from then on the local copy will be used transparently.
They also support the use of an MD5 hash to get a specific version of a data file. This hash can be obtained prior to submitting a file to the astropy data server by using the compute_hash function on a local copy of the file.
Tests that may retrieve remote data should be marked with the @remote_data decorator. Tests marked with this decorator will be skipped by default by astropy.test() to prevent test runs from taking too long. These tests can be run by astropy.test() by adding the remote_data=True flag. Turn on the remote data tests at the command line with py.test --remote-data.
from ...config import get_data_filename
from ...tests.helper import remote_data
def test_1():
#if filename.fits is a local file in the source distribution
datafile = get_data_filename('filename.fits')
# do the test
@remote_data
def test_2():
#this is the hash for a particular version of a file stored on the
#astropy data server.
datafile = get_data_filename('hash/94935ac31d585f68041c08f87d1a19d4')
# do the test
The get_remote_test_data will place the files in a temporary directory indicated by the tempfile module, so that the test files will eventually get removed by the system. In the long term, once test data files become too large, we will need to design a mechanism for removing test data immediately.
Tests may often be run from directories where users do not have write permissions so tests which create files should always do so in temporary directories. This can be done with the py.test tmpdir function argument or with Python’s built-in tempfile module.
In some cases, it can be useful to run a series of tests requiring something to be set up first. There are four ways to do this:
If the setup_module and teardown_module functions are specified in a file, they are called before and after all the tests in the file respectively. These functions take one argument, which is the module itself, which makes it very easy to set module-wide variables:
def setup_module(module):
module.NUM = 11
def add_num(x):
return x + NUM
def test_42():
added = add_num(42)
assert added == 53
We can use this for example to download a remote test data file and have all the functions in the file access it:
import os
def setup_module(module):
module.DATAFILE = get_remote_test_data('94935ac31d585f68041c08f87d1a19d4')
def test():
f = open(DATAFILE, 'rb')
# do the test
def teardown_module(module):
os.remove(DATAFILE)
Tests can be organized into classes that have their own setup/teardown functions. In the following
def add_nums(x, y):
return x + y
class TestAdd42(object):
def setup_class(self):
self.NUM = 42
def test_1(self):
added = add_nums(11, self.NUM)
assert added == 53
def test_2(self):
added = add_nums(13, self.NUM)
assert added == 55
def teardown_class(self):
pass
In the above example, the setup_class method is called first, then all the tests in the class, and finally the teardown_class is called.
There are cases where one might want setup and teardown methods to be run before and after each test. For this, use the setup_method and teardown_method methods:
def add_nums(x, y):
return x + y
class TestAdd42(object):
def setup_method(self, method):
self.NUM = 42
def test_1(self):
added = add_nums(11, self.NUM)
assert added == 53
def test_2(self):
added = add_nums(13, self.NUM)
assert added == 55
def teardown_method(self, method):
pass
Finally, one can use setup_function and teardown_function to define a setup/teardown mechanism to be run before and after each function in a module. These take one argument, which is the function being tested:
def setup_function(function):
pass
def test_1(self):
# do test
def test_2(self):
# do test
def teardown_method(function):
pass
If you want to run a test several times for slightly different values, then it can be advantageous to use the py.test option to parametrize tests. For example, instead of writing:
def test1():
assert type('a') == str
def test2():
assert type('b') == str
def test3():
assert type('c') == str
You can use the parametrize decorator to loop over the different inputs:
@pytest.mark.parametrize(('letter'), ['a', 'b', 'c'])
def test(letter):
assert type(letter) == str
If your tests need to use py.test helper functions, such as pytest.raises, import pytest into your test module like so:
from ...tests.helper import pytest
You may need to adjust the relative import to work for the depth of your module. tests.helper imports pytest either from the user’s system or extern.pytest if the user does not have py.test installed. This is so that users need not install py.test to run AstroPy’s tests.
Tests can include very small datafiles, but any files significantly larger than the source code should be placed on a remote server. The base URL for the test files will be:
http://data.astropy.org/
and files will be accessed by their MD5 hash, for example:
http://data.astropy.org/94935ac31d585f68041c08f87d1a19d4
Tests then retrieve data via this URL. This implicitly allows versioning, since different versions of data files will have different hashes. Old data files should not be removed, so that tests can be run in any version of AstroPy.
The details of the server implementation have yet to be decided, but using these static hash-based URLs ensures that even if we change the backend, the URL will remain the same.
For tests that test functions or methods that require optional dependencies (e.g. Scipy), pytest should be instructed to skip the test if the dependencies are not present. The following example shows how this should be done:
import pytest
try:
import scipy
HAS_SCIPY = True
except ImportError:
HAS_SCIPY = False
@pytest.mark.skipif('not HAS_SCIPY')
def test_that_uses_scipy():
...
In this way, the test is run if Scipy is present, and skipped if not. No tests should fail simply because an optional dependency is not present.
Astropy can use coverage.py to generate test coverage reports. To generate a test coverage report, use:
python setup.py test --coverage
There is a coveragerc file that defines files to omit as well as lines to exclude. It is installed along with astropy so that the astropy.test function can use it. In the source tree, it is at astropy/tests/coveragerc.
Blocks of code may be ignored by adding a comment containing the phrase pragma: no cover to the start of the block:
if this_rarely_happens: # pragma: no cover
this_call_is_ignored()
Blocks of code that are intended to run only in Python 2.x or 3.x may also be marked so that they will be ignored when appropriate by coverage.py:
if sys.version_info[0] >= 3: # pragma: py3
do_it_the_python3_way()
else: # pragma: py2
do_it_the_python2_way()


Technology and Apocalypse in America
In April of 2014, televangelist Pat Robertson devoted a segment of his 700 Club TV program to discussing what he ominously termed the “biblical implications” of radio-frequency identification, or RFID. In the tech world, RFID rather mundanely refers to technology that allows information to be stored and transferred on radio-sensitive tags at a distance of at most a few feet, aiding in commercial micro-transactions and other smartphone-enabled conveniences of modern life.
As Robertson and his guest, author and biblically-oriented “privacy expert” Katherine Albrecht explain, matters on Earth are never merely as reported. For them, new developments in the world of techno-commerce align in troubling parallel with the Book of Revelation. RFID is nothing less than a strong candidate for the Mark of the Beast. According to chapter thirteen of John of Patmos’s book, this 666, “the number of his name,” shall be inscribed upon the bodies of “small and great, rich and poor, free and bond” so that none may buy or sell without such a mark. The mark divides the sinners from the Christians who will refuse branding and suffer persecution and martyrdom in the Last Days.
RFID is, of course, not the first novelty to be singled out as the Mark of the Beast. A famous image from the fierce vernacular art of twentieth-century American biblical tracts shows a Satan-figure, rising above Saint Peter’s Square and a multitude of human corpses, clutching an executioner’s axe. The words “666 VISA” are emblazoned on his broad chest. Radio-frequency identification technology thus merely figures as the latest supposed harbinger of the end times in a culture obsessed with both the apocalypse and technology. “We’re going into some strange worlds ahead of us, ladies and gentlemen,” Robertson warns. He’s not wrong.
These anxieties about the entanglements of human life and technology, of the movements of the body mapped onto the geographies of power, have a deep pedigree in Judeo-Christian thought. Biblical scholar Robert Alter notes that, as early as Exodus 30:12, when a census of Moses’s people comes bundled with a “ransom” of a half shekel in symbolic exchange for the counted’s life, there has been “a folkloric horror of being counted as a condition of vulnerability to malignant forces.’” Beginning in the Davidic narrative of the Book of Samuel, Israel’s most famous monarch chooses to count his subjects, with disastrous results. Mapping and counting, in these stories, acts as a sort of dowsing rod, auguring the collision course between the profane world and the divine. This is only cast in deeper relief by the long obsession America has had with its own teleological importance. Puritan ideologues sought to establish their utopian City on the Hill. In the post-Revolutionary years, influential preachers such as David Tappan and Timothy Dwight, of Harvard and Yale respectively, proclaimed the young republic’s providential role in world history, laying the groundwork for the Second Great Awakening.
Prognostications of doom abounded in nineteenth-century and twentieth-century American folk cultures. In his work on popular eschatological beliefs in early twentieth-century Ohio, folklorist Newbell Niles Puckett recorded “cows lowing at night, bad thunderstorms” and the sudden fashion of women wearing “glass high heels” as a sure indication that the end times had arrived. This very twentieth-century image, which seems to mingle the quiet surrealism of advertising displays with the cryptic visions of the Revelator, combines consumerism and apocalypticism in a disquieting manner. An undercurrent of anti-government thought has long informed this particularly American brand of eschatological belief.
Groups like the Millerites, who weathered numerous unfulfilled prophecies and subsequent schisms in the nineteenth century, exemplified this strain of American apocalypticism. From the very beginning, William Miller and his followers identified the United States as a potential enemy, drawing on “the apocalyptic visions of the biblical books of Daniel and Revelation, where he saw governments portrayed as wild beasts which hurt God’s people.” Miller reversed America’s eschatological significance as the City on a Hill, positioning the appearance of the republic on the world stage not as a positive harbinger of a golden age, but as a dark force pointing towards a coming cataclysm.
In the fallout from the Great Disappointment (when Miller’s predicted apocalypse on October 22, 1844, utterly failed to arrive) the Millerites fractured. One off-shoot would become the Seventh-Day Adventists, who believed in a looming worldwide conflict centered on the correct observance of the Sabbath and who denied the government’s right to conscript them to military service.
Today, the message boards of conspiracy websites and the world of shortwave radio resound with conspiracies about the deadly significance of gun-owner registries, FEMA camps, government-run healthcare, national ID cards, or any other form of centralized data-collection. As the anthropologist Susan Lepselter writes in her work on UFO-oriented conspiracy buffs, “just as the power of the state tries to usurp the power of God, the power of a monarchlike, computerized system usurps the power of democracy.”
This distinctive brand of apocalyptic and anti-government belief has long been a powerful American counter-culture. Contemporary public debates about privacy, government intrusion, and the limits of state security dip into these wells, often without knowing it.


A bar graph of sales by quarter. Each series is a salesperson.
| Quarter | Q1 2018 | Q2 2018 | Q3 2018 | Q4 2018 | Q1 2019 | Q2 2019 | Q3 2019 | Q4 2019 |
|---|---|---|---|---|---|---|---|---|
| Northeast | 5 | 4 | 3 | 7 | 8 | 11 | 9 | 5 |
| Southeast | 3 | 2 | 9 | 5 | 6 | 4 | 8 | 4 |
| Midatlantic | 1 | 5 | 8 | 4 | 7 | 6 | 8 | 5 |
| Midwest | 2 | 3 | 4 | 6 | 4 | 2 | 4 | 3 |
| West | 4 | 1 | 2 | 1 | 0 | null | 3 | 0 |
A bar graph of sales by quarter. Each series is a salesperson.
| Quarter | Q1 2018 | Q2 2018 | Q3 2018 | Q4 2018 | Q1 2019 | Q2 2019 | Q3 2019 | Q4 2019 |
|---|---|---|---|---|---|---|---|---|
| Northeast | 5 | 4 | 3 | 7 | 8 | 11 | 9 | 5 |
| Southeast | 3 | 2 | 9 | 5 | 6 | 4 | 8 | 4 |
| Midatlantic | 1 | 5 | 8 | 4 | 7 | 6 | 8 | 5 |
| Midwest | 2 | 3 | 4 | 6 | 4 | 2 | 4 | 3 |
| West | 4 | 1 | 2 | 1 | 0 | null | 3 | 0 |


Java Domain Object Templater (JDot) is a code generator which keeps domain classes concise like a dot.
The generation is driven by annotations on your domain classes or interfaces and uses the compile-time Java 6 Annotation Processor.
Generated code:
- Data Transfer Objects (DTOs) with Java Bean getters and setters
- Builders for immutable domain objects
- Mappers between domain and DTO classes
- Standard java.lang.Object methods hashCode and equals for configurable fields
The code generation may be driven by:
- Interface implemented by domain class and DTO
- Abstract domain class


Woman charred to death at Duvvada
Electric short-circuit is suspected
by Staff ReporterA 35-year-old woman was charred to death after fire at her house at Yathapalem area in Rajeev Nagar under Duvvada Police Station limits on Saturday evening. The deceased was identified as M. Saroja.
According to reports, after noticing smoke started emanating from the house of Saroja, locals informed the police and fire officials. They tried in vain to rescue her. It was also learnt that the woman’s husband Srinivasa Rao, who works in the steel plant, informed police that when he came to the house, he noticed smoke in the area. He reportedly said he could not enter the house despite best efforts.
The police said the incident might have occurred due to electric short-circuit. CLUES team has examined the accident spot. A case has been registered


| Vector3 | A 3-dimensional vector class, with components x, y and z, and vareous functions. |
| Matrix3 | A 3x3 matrix class, with components a, b, c, d, e, f, g, h, i and various functions. |
| Camera | A 3D camera class full of handy functions for 3D transformations. |
| Function | Information |
| x, y, z | double x, double y, double z The 3 elements of the 3D vector. The vector is of the form [ x ] [ y ] [ z ] |
|
Vector3(double x, double y, double z); Constructor, create vector with given x, y and z coordinate |
|
|
Vector3(); Constructor, create vector with x, y and z set to 0 |
|
| length |
double length(); Returns the length of the vector |
| normalize |
void normalize(); Normalizes the vector |
| distance | double distance(const Vector3& v); Returns the distance between this vector and v |
| dot | double dot(const Vector3& v); Returns the dot product between this vector and v |
| cross |
Vector3 cross(const Vector3& v); Returns the cross product between this vector and v |
| Function | Information |
| a to i | double a, double b, double c, double d, double e, double f, double g, double h, double i The 9 elements of the 3x3 matrix. The matrix is of the form [ a b c ] [ d e f ] [ g h i ] |
|
Matrix3(double a, double b, double c, double d, double e, double f, double g, double h, double i); Constructor, create vector with given values for the elements. |
|
|
Matrix3(); Constructor, create vector with all elements set to 0 |
|
| transpose |
void transpose(); Transpose the matrix |
| determinant |
double determinant(); Returns the determinant of the matrix |
| invert | void invert(); Invert the matrix |
| Function | Information |
| nearClip |
double nearClip The near clipping distance of the camera |
| farClip |
farClip The far clipping distance of the camera |
|
Camera(double posx, double posy, double posz, double ux, double uy, double uz, double vx, double vy, double vz, double dirx, double diry, double dirz, double nearClip, double farClip); Generate the camera with given values |
|
|
Camera(); Generate the camera with default values |
|
| getU, getV, getDir, getPos |
Vector3 getU(); Vector3 getV(); Vector3 getDir(); Vector3 getPos(); Returns the horizontal, vertical, direction or position vector of the camera |
| setU, setV, setDir, setPos |
void
setU(); void setV(); void setDir(); void setPos(); Sets the horizontal, vertical, direction or position vector of the camera |
| move |
void move(const Vector3& offset); Move with given offset |
| rotate | void
rotate(const Vector3& axis, double angle); Rotate the camera around given axis with given angle |
| setLookDir |
void setLookDir(const Vector3& newDir); Let the camera look in given direction |
| lookAt |
void lookAt(const Vector3& lookAtMe); Look at given point |
|
getDist, setDist |
double getDist(const Vector3& point); void setDist(const Vector3& point, double dist); Get and set the distance of the camera to a certain point (translate without rotating it) |
| getZoomU, getZoomV |
double
getZoomU(); double getZoomV(); Get the zoom value of the camera in horizontal or vertical direction |
| setZoomU, setZoomV |
void
setZoomU(double a); void setZoomV(double a); Set the zoom of the camera in horizontal or vertical direction |
|
zoom, zoomU, zoomV |
void zoom(double a); void zoomU(double a); void zoomV(double a); Zoom both horizontally and vertically, only horizontally or only vertically, with a certain factor. |
| getFOVU, getFOVV |
double
getFOVU(); double getFOVV(); Returns the Field Of View in horizontal or vertical direction in radians. This is just another way of describing the zoom, with an angle instead of a factor. |
| setFOVU, setFOVV |
void
setFOVU(double angle); void setFOVV(double angle); Set the Field Of View in horizontal or vertical direction. |
|
getYaw, getPitch, getRoll |
double getYaw(); double getPitch(); double getRoll(); Get those 3 Euler Angles from the camera. These aren't native members of the camera but have to be calculated out of it's vectors. |
|
setYaw, setPitch, setRoll |
void setYaw(double angle); void setPitch(double angle); void setRoll(double angle); Get those 3 Euler Angles from the camera. These aren't native members of the camera but have to be converted to it's vectors. |
|
yawPlanet, yawSpace, pitch, roll |
void yawPlanet(double angle); void yawSpace(double angle); void pitch(double angle); void roll(double angle); yawPlanet rotates the camera around the world y axis with given angle yawSpace rotates the camera around the camera "up" axis with given angle pitch rotates the camera around the camera "right" axis with given angle roll rotates the camera around the camera "direction" axis with given angle |
|
resetSkewU, resetSkewV |
Vector3 resetSkewU(); Vector3 resetSkewV(); Make the camera orthogonal again if somehow it became skewed. resetSkewU maintains exact direction and roll if only v was skewed resetSkewV maintains exact direction and roll if only u was skewed |
|
getRatioUV, getRatioVU |
double getRatioUV(); double getRatioVU(); Returns length(u)/length(v) and length(v)/length(u) respectively. |
|
setRatioUV, setRatioVU |
void setRatioUV(double ratio); void setRatioVU(double ratio); Sets the ratio of the length of u and v, to have a good view on the computer screen, ratioUV should match the resolution ratio of the screen, e.g. if using resolution 1024 * 768, then ratioUV should be set to 1024 / 768 = 1.333333 |
|
getScale, setScale, scale |
double getScale(); void setScale(double dirLength); void scale(double factor); Scaling the camera changes the length of u, v and dir with the same ratio. This has no effect on what you see. getScale returns the length of the direction vector, and setScale scales the camera until the direction vector has given length. |
| generateMatrix |
void generateMatrix(); Generates the internal variable camMatrix. This matrix is nothing else than the column vectors u, v and dir sticked together, and is used for the transformation calculations. Generatematrix has to be used everytime you change u, v or dir. For the functions given above that rotate, scale, etc... this is already done automaticly. |
|
getMatrix setMatrix, |
Matrix3 getMatrix(); void setMatrix(const Matrix3& matrix); Get or set camMatrix. Setting it automaticly updates u, v and dir as well. |
| transform |
Vector3 transform(const Vector3& v); Multiply given vector v with the inverse of camMatrix |
| projectOnScreen |
bool projectOnScreen(const Vector3& point, int
& x,
int & y, double & z); bool projectOnScreen(const Vector3& point, int & x, int & y); Return the coordinates a certain 3D point has on screen. You can choose wether or not you want it to return the distance of that point to the screen in camera space. |
| pos |
Vector3 pos; The position of the camera in the world |
| u |
Vector3 u; The "right" vector of the camera, parallel with the horizontal bottom of the computer screen, the unit vector of the X axis of the camera space |
| v |
Vector3 v; The "up" vector of the camera, parallel with the vertical side of the computer screen, the unit vector of the Y axis of the camera space |
| dir |
Vector3 dir; The direction of the camera, where you're looking at, points into the computer screen, the unit vector of the Z axis of the camera space |
| camMatrix |
Matrix3 camMatrix; The camera matrix for transformations. Should always be updated together with the u, v and dir vectors. |
| invCamMatrix |
Matrix3 invCamMatrix; The inverse camera matrix for transformations. Should always be updated together with the u, v and dir vectors. |
| Function | Information |
| length |
double length(const Vector3& v); Returns the length of vector v |
| normalize |
Vector3 normalize(const Vector3& v); Returns a vector with length 1 and the same direction as v |
| distance |
double distance(const Vector3& v, const Vector3&
w); Returns the distance between v and w |
| dot | double
dot(const Vector3& v, const Vector3& w); Returns the dot product of v and w |
| cross | Vector3
cross(const Vector3& v, const Vector3& w); Returns the corss product of v and w |
| - |
Vector3 operator-(const Vector3& v, const Vector3&
w); Returns v - w |
| - | Vector3
operator-(const Vector3& v); Returns -v |
| + |
Vector3 operator+(const Vector3& v, const Vector3&
w); Returns v + w |
| * |
Vector3 operator*(const Vector3& v, double a); const Vector3& operator*(double a, const Vector3& v); Returns v multiplied with a |
| / |
Vector3 operator/(const Vector3& v, double
a); Return v divided through a |
| vectorAngle | double
vectorAngle(const Vector3& v, const Vector3& w); Return angle between vectors v and w |
| rotateAroundArbitrary | Vector3
rotateAroundArbitrary(const Vector3& v, const Vector3& axis, double
angle); Returns vector v rotated around axis through the origin over the given angle |
| transpose |
Matrix3 transpose(const Matrix3& A); Returns the transpose of A |
| determinant | double
determinant(const Matrix3& A); Returns the determinant of A |
| inverse | Matrix3
inverse(const Matrix3& A); Returns the inverse of A |
| + |
Matrix3 operator+(const Matrix3& A, const Matrix3&
B); Returns A + B |
| - |
Matrix3 operator-(const Matrix3& A, const Matrix3&
B); Returns A - B |
| * |
Matrix3 operator*(const Matrix3& A, double a); Matrix3 operator*(double a, const Matrix3& A); Returns A multiplied with a |
| / |
Matrix3 operator/(const Matrix3& A, double
a); Returns A divided through a |
| * |
Vector3 operator*(const Matrix3& A, const Vector3& v); Vector3 operator*(const Vector3& v, const Matrix3& A); Matrix3 operator*(const Matrix3& A, const Matrix3& B); Returns matrix multiplied with vector, vector multiplied with matrix, or matrix multiplied with matrix |
|
radToDeg, degToRad |
double radToDeg(double rad); double degToRad(double deg); Convert from radians to degrees or from degrees to radians. |


Red-hot Todd eyes three-peat at RSM Classic
Washington - In-form Brendon Todd surged into a two-shot lead at the RSM Classic in Georgia on Saturday to leave himself within sight of a third consecutive PGA Tour victory.
Todd, who won last week's Mayakoba Classic in Mexico after a victory in Bermuda earlier this month, carded a superb eight-under-par 62 at Sea Island's Seaside Course to lead by two strokes heading into Sunday's final round.
The American journeyman, whose only other win on the PGA Tour before this year was a victory at the 2014 Byron Nelson Championship, had started Saturday's third round tied for seventh.
But the 34-year-old from Pittsburgh produced a scintillating charge over the front nine to quickly advance through the field, notching four birdies in his opening five holes to move to four under.
A monster 28-foot birdie putt on the par-five seventh hole took him to five under, and a sixth birdie on the ninth saw him reach the turn at six under for the day.
Further birdies on the 11th and 17th left him on 18 under through 54 holes, two shots clear of Webb Simpson, who also impressed with a seven-under-par 63, and Colombia's Sebastian Munoz.

"Just thrilled with the way I'm hitting it and feeling out there. I putted unbelievably well and thought they were all going in."
If Todd hangs onto his lead to complete a hat-trick of wins in consecutive starts on Sunday, he would be the first player to achieve the feat since Dustin Johnson in 2017.
He would also become only the 26th player to win three consecutive victories, joining an elite list which includes the likes of Tiger Woods, Jack Nicklaus, Arnold Palmer, Sam Snead, Ben Hogan and Rory McIlroy.
Todd can expect to face a stiff final-round challenge from Simpson, whose 63 included a 25-foot putt for birdie on the 18th which left him on 16 under.
Simpson meanwhile said he was delighted to see his friend Todd enjoy success after so many lean years on the tour.
"I think it's remarkable," Simpson said.
"He's a good friend of mine. To show the resilience, not playing well to come back and stay in it and get one win and two in a row and now he's playing great again.
"I'm really happy for him."
Munoz will also fancy his chances of claiming his second PGA Tour win of 2019 after a bogey-free four-under-par 66 which left him two adrift of Todd.
D.J. Trahan is a further shot back on 15 under after a three-under-par 67.
Trahan's round included a charge of four consecutive birdies on the seventh, eighth, ninth and 10th holes but two bogeys down the stretch, including a dropped shot on the 18th, disrupted his momentum.
Second round leader Tyler Duncan meanwhile slipped down the field after a level-par 70. Duncan is tied for fifth place alongside South Korea's Lee Kyoung-hoon and Ricky Barnes on 14 under.
Leading scores after Saturday's third round of the PGA Tour RSM Classic played at Seaside course in Sea Island, Georgia (USA unless noted):
194 - Brendon Todd 66-66-62
196 - Webb Simpson 65-68-63, Sebastian Munoz (COL) 67-63-66
197 - D.J. Trahan 67-63-67
198 - Lee Kyoung-hoon (KOR) 64-68-66, Ricky Barnes 68-63-67, Tyler Duncan 67-61-70
199 - Scottie Scheffler 70-66-63, Doc Redman 66-67-66, Henrik Norlander (SWE) 67-65-67, Brian Harman 67-66-66
200 - Nick Watney 67-67-66, Vaughn Taylor 68-67-65, Alex Noren (SWE) 68-65-67, Scott Brown 65-67-68, Fabian Gomez (ARG) 68-63-69
201 - Tim Wilkinson (NZL) 68-66-67, Denny McCarthy 71-62-68


public final class EncodingTools
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int |
INT_32_SIZE
The size of an INT_32 in bytes.
|
| Modifier and Type | Method and Description |
|---|---|
static boolean |
readBoolean(byte[] in)
Read a boolean from a byte array.
|
static boolean |
readBoolean(byte[] in,
int offset)
Read a boolean from a byte array.
|
static boolean |
readBoolean(java.io.DataInput in)
Read a boolean from a DataInput.
|
static boolean |
readBoolean(java.io.InputStream in)
Read a boolean from an input stream.
|
static byte[] |
readBytes(int size,
java.io.DataInput in)
Read a fixed number of bytes from a DataInput into an array.
|
static byte[] |
readBytes(int size,
java.io.InputStream in)
Read a fixed number of bytes from an InputStream into an array.
|
static double |
readDouble(byte[] in)
Read a double from a byte array.
|
static double |
readDouble(byte[] in,
int offset)
Read a double from a byte array.
|
static double |
readDouble(java.io.DataInput in)
Read a double from a DataInput.
|
static double |
readDouble(java.io.InputStream in)
Read a double from an input stream.
|
static Entity |
readEntity(java.io.DataInput in)
Read an entity from a DataInput.
|
static Entity |
readEntity(java.io.InputStream in)
Read an entity from a stream.
|
static int |
readInt32(byte[] in)
Read a 32-bit integer from a byte array, big-endian style.
|
static int |
readInt32(byte[] in,
int offset)
Read a 32-bit integer from a byte array, big-endian style.
|
static int |
readInt32(java.io.DataInput in)
Read a 32-bit integer from a DataInput.
|
static int |
readInt32(java.io.InputStream in)
Read a 32-bit integer from an input stream, big-endian style.
|
static int |
readInt32LE(java.io.InputStream in)
Read a 32-bit integer from an input stream, little-endian style.
|
static Message |
readMessage(java.io.DataInput in)
Read a message from a DataInput.
|
static Message |
readMessage(java.io.InputStream in)
Read a message from a stream.
|
static Property |
readProperty(java.io.DataInput in)
Read a property from a DataInput.
|
static Property |
readProperty(java.io.InputStream in)
Read a property from a stream.
|
static java.lang.String |
readString(byte[] in)
Read a String from a byte array.
|
static java.lang.String |
readString(byte[] in,
int offset)
Read a String from a byte array.
|
static java.lang.String |
readString(java.io.DataInput in)
Read a String from a DataInput.
|
static java.lang.String |
readString(java.io.InputStream in)
Read a String from an InputStream.
|
static void |
reallySkip(java.io.DataInput in,
int count)
Call DataInput.skip until exactly
count bytes have been skipped. |
static void |
reallySkip(java.io.InputStream in,
long count)
Call InputStream.skip until exactly
count bytes have been skipped. |
static void |
writeBoolean(boolean b,
byte[] out,
int offset)
Write a boolean to a byte array.
|
static void |
writeBoolean(boolean b,
java.io.DataOutput out)
Write a boolean to a DataOutput.
|
static void |
writeBoolean(boolean b,
java.io.OutputStream out)
Write a boolean to an OutputStream.
|
static void |
writeDouble(double d,
byte[] out,
int offset)
Write a double to a byte array.
|
static void |
writeDouble(double d,
java.io.DataOutput out)
Write a double to a DataOutput.
|
static void |
writeDouble(double d,
java.io.OutputStream out)
Write a double to an OutputStream.
|
static void |
writeEntity(Entity e,
java.io.DataOutput out)
Write an entity to a DataOutput.
|
static void |
writeEntity(Entity e,
java.io.OutputStream out)
Write an entity to a stream.
|
static void |
writeInt32(int i,
byte[] out,
int offset)
Write a 32-bit integer to a byte array, big-endian style.
|
static void |
writeInt32(int i,
java.io.DataOutput out)
Write a 32-bit integer to a DataOutput, big-endian style.
|
static void |
writeInt32(int i,
java.io.OutputStream out)
Write a 32-bit integer to an OutputStream, big-endian style.
|
static void |
writeMessage(Message m,
java.io.DataOutput out)
Write a message to a DataOutput.
|
static void |
writeMessage(Message m,
java.io.OutputStream out)
Write a message to a stream.
|
static void |
writeProperty(Property p,
java.io.DataOutput out)
Write a property to a DataOutput.
|
static void |
writeProperty(Property p,
java.io.OutputStream out)
Write a property to a stream.
|
static void |
writeString(java.lang.String s,
byte[] out,
int offset)
Write a String to a byte array.
|
static void |
writeString(java.lang.String s,
java.io.DataOutput out)
Write a String to a DataOutput.
|
static void |
writeString(java.lang.String s,
java.io.OutputStream out)
Write a String to an OutputStream.
|
public static final int INT_32_SIZE
public static void writeInt32(int i, java.io.OutputStream out) throws java.io.IOException
i
- The integer to write.out
- The OutputStream to write it to.java.io.IOException
- If the OutputStream blows up.public static void writeInt32(int i, java.io.DataOutput out) throws java.io.IOException
i
- The integer to write.out
- The DataOutput to write it to.java.io.IOException
- If the DataOutput blows up.public static void writeInt32(int i, byte[] out, int offset)
i
- The integer to write.out
- The buffer to write it to.offset
- Where in the buffer to write it.public static int readInt32(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read from.java.io.IOException
- If the InputStream blows up.java.io.EOFException
- If the end of the stream is reached.public static int readInt32(java.io.DataInput in) throws java.io.IOException
in
- The DataInput to read from.java.io.IOException
- If the DataInput blows up.java.io.EOFException
- If the end of the stream is reached.public static int readInt32LE(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read from.java.io.IOException
- If the InputStream blows up.java.io.EOFException
- If the end of the stream is reached.public static int readInt32(byte[] in, int offset)
in
- The buffer to read from.offset
- Where to begin reading.public static int readInt32(byte[] in)
readInt32(in, 0)
.in
- The buffer to read from.public static void writeString(java.lang.String s, java.io.OutputStream out) throws java.io.IOException
s
- The String to write.out
- The OutputStream to write to.java.io.IOException
- If the OutputStream blows up.public static void writeString(java.lang.String s, java.io.DataOutput out) throws java.io.IOException
s
- The String to write.out
- The DataOutput to write to.java.io.IOException
- If the DataOutput blows up.public static void writeString(java.lang.String s, byte[] out, int offset)
s
- The String to write.out
- The byte array to write to. Make sure it's big enough!offset
- The index to start writing from.public static java.lang.String readString(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read.java.io.IOException
- If the InputStream blows up.java.io.EOFException
- If the end of the stream is reached.public static java.lang.String readString(java.io.DataInput in) throws java.io.IOException
in
- The DataInput to read.java.io.IOException
- If the DataInput blows up.java.io.EOFException
- If the end of the stream is reached.public static java.lang.String readString(byte[] in)
readString(in, 0)
.in
- The byte array to read.public static java.lang.String readString(byte[] in, int offset)
in
- The byte array to read.offset
- The index in the array to read from.public static byte[] readBytes(int size, java.io.InputStream in) throws java.io.IOException
size
- The number of bytes to read.in
- The InputStream to read from.java.io.IOException
- If the read operation fails.public static byte[] readBytes(int size, java.io.DataInput in) throws java.io.IOException
size
- The number of bytes to read.in
- The DataInput to read from.java.io.IOException
- If the read operation fails.public static void writeDouble(double d, java.io.OutputStream out) throws java.io.IOException
d
- The double to write.out
- The OutputStream to write it to.java.io.IOException
- If the OutputStream blows up.public static void writeDouble(double d, java.io.DataOutput out) throws java.io.IOException
d
- The double to write.out
- The DataOutput to write it to.java.io.IOException
- If the DataOutput blows up.public static void writeDouble(double d, byte[] out, int offset)
d
- The double to write.out
- The buffer to write it to.offset
- Where in the buffer to write it.public static double readDouble(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read from.java.io.IOException
- If the InputStream blows up.java.io.EOFException
- If the end of the stream is reached.public static double readDouble(java.io.DataInput in) throws java.io.IOException
in
- The DataInput to read from.java.io.IOException
- If the DataInput blows up.java.io.EOFException
- If the end of the stream is reached.public static double readDouble(byte[] in, int offset)
in
- The buffer to read from.offset
- Where to begin reading.public static double readDouble(byte[] in)
readDouble(in, 0)
.in
- The buffer to read from.public static void writeBoolean(boolean b, java.io.OutputStream out) throws java.io.IOException
b
- The boolean to write.out
- The OutputStream to write it to.java.io.IOException
- If the OutputStream blows up.public static void writeBoolean(boolean b, java.io.DataOutput out) throws java.io.IOException
b
- The boolean to write.out
- The DataOutput to write it to.java.io.IOException
- If the DataOutput blows up.public static void writeBoolean(boolean b, byte[] out, int offset)
b
- The boolean to write.out
- The buffer to write it to.offset
- Where in the buffer to write it.public static boolean readBoolean(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read from.java.io.IOException
- If the InputStream blows up.java.io.EOFException
- If the end of the stream is reached.public static boolean readBoolean(java.io.DataInput in) throws java.io.IOException
in
- The DataInput to read from.java.io.IOException
- If the DataInput blows up.java.io.EOFException
- If the end of the stream is reached.public static boolean readBoolean(byte[] in, int offset)
in
- The buffer to read from.offset
- Where to begin reading.public static boolean readBoolean(byte[] in)
readBoolean(in, 0)
.in
- The buffer to read from.public static void reallySkip(java.io.InputStream in, long count) throws java.io.IOException
count
bytes have been skipped. If InputStream.skip ever returns a negative number then an EOFException is thrown.in
- The InputStream to skip.count
- The number of bytes to skip.java.io.IOException
- If the bytes cannot be skipped for some reason.public static void reallySkip(java.io.DataInput in, int count) throws java.io.IOException
count
bytes have been skipped. If DataInput.skip ever returns a negative number then an EOFException is thrown.in
- The DataInput to skip.count
- The number of bytes to skip.java.io.IOException
- If the bytes cannot be skipped for some reason.public static void writeEntity(Entity e, java.io.OutputStream out) throws java.io.IOException
e
- The entity to write.out
- The OutputStream to write to.java.io.IOException
- If there is a problem writing to the stream.public static void writeEntity(Entity e, java.io.DataOutput out) throws java.io.IOException
e
- The entity to write.out
- The DataOutput to write to.java.io.IOException
- If there is a problem writing to the stream.public static Entity readEntity(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read from.java.io.IOException
- If there is a problem reading from the stream.public static Entity readEntity(java.io.DataInput in) throws java.io.IOException
in
- The DataInput to read from.java.io.IOException
- If there is a problem reading from the stream.public static void writeProperty(Property p, java.io.OutputStream out) throws java.io.IOException
p
- The property to write.out
- The OutputStream to write to.java.io.IOException
- If there is a problem writing to the stream.public static void writeProperty(Property p, java.io.DataOutput out) throws java.io.IOException
p
- The property to write.out
- The DataOutput to write to.java.io.IOException
- If there is a problem writing to the stream.public static Property readProperty(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read from.java.io.IOException
- If there is a problem reading from the stream.public static Property readProperty(java.io.DataInput in) throws java.io.IOException
in
- The DataInput to read from.java.io.IOException
- If there is a problem reading from the stream.public static void writeMessage(Message m, java.io.OutputStream out) throws java.io.IOException
m
- The message to write.out
- The OutputStream to write to.java.io.IOException
- If there is a problem writing to the stream.public static void writeMessage(Message m, java.io.DataOutput out) throws java.io.IOException
m
- The message to write.out
- The DataOutput to write to.java.io.IOException
- If there is a problem writing to the stream.public static Message readMessage(java.io.InputStream in) throws java.io.IOException
in
- The InputStream to read from.java.io.IOException
- If there is a problem reading from the stream.public static Message readMessage(java.io.DataInput in) throws java.io.IOException
in
- The DataInput to read from.java.io.IOException
- If there is a problem reading from the stream.


One user, Unlimited Experiments, Unlimited Projects. $12.99 per month
Register all of the members of your lab! Buy 5 seats, get 2 extra for no extra cost. $64.95 per month
Different situation? Contact us and figure out custom pricing.


VisionMedia Digital Signage (by Zenesis Limited)
The VisionMedia Digital Signage platform has over 1,500 set top box installations, each of which
connect to the Qooxdoo-based web services to download media and instructions. The Qooxdoo based
control panel administers the content and facilities for each site, and the owners of each site
(of which there are over 4,000 users) log in via a seperate Qooxdoo based members application to
customise the content broadcast to their screen.


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General Interface Test Automation Kit
General Interface 3.8/3.9 TestSuite
(Remember to edit "gi/tests/gi36/testSetup.html with proper path for GI home path)
GI Extension Reference
- extended assert/action commands and locators reference documents.
Selenium
Selenium TestRunner
- Select a test suite to run in Selenium
Selenium Reference
Document


|
text_file$ - filename of image file to be used as sprite
tex_flag (optional) - texture flag: 1: Color 2: Alpha 4: Masked 8: Mipmapped 16: Clamp U 32: Clamp V 64: Spherical reflection map parent - parent of entity |
|
Creates a sprite entity, and assigns a texture to it.
See also: LoadSprite, RotateSprite, ScaleSprite, HandleSprite, SpriteViewMode, PositionEntity, MoveEntity, TranslateEntity, EntityAlpha, FreeEntity. |
|
Graphics3D 640,480
campivot = CreatePivot() cam = CreateCamera(campivot) MoveEntity cam,0,0,-5 sp = LoadSprite("grass.bmp") SpriteViewMode sp,4 While Not KeyDown(1) RenderWorld:Flip TurnEntity campivot,1,1,3 Wend End |


XSCHEM : schematic capture
and netlisting EDA tool
Xschem is a schematic capture program, it allows creation of
hierarchical representation of circuits with a top down approach .
By focusing on interfaces, hierarchy and instance properties a
complex system can be described in terms of simpler building blocks.
A VHDL or Verilog or Spice netlist can be generated from
the drawn schematic, allowing
the simulation of the circuit. Key feature of the program is its
drawing engine written in C and using directly the Xlib drawing
primitives; this gives very good speed performance, even on very
big circuits. The user interface is built with the Tcl-Tk toolkit,
tcl is also the extension language used.
Features
- hierarchical schematic drawings, no limits on size
- any object in the schematic can have any sort of properties
(generics in VHDL, parameters in Spice or Verilog)
- new Spice/Verilog primitives can be created, and the netlist
format can be defined by the user
- tcl extension language allows the creation of scripts; any
user command in the drawing window has an associated tcl comand
- VHDL / Verilog / Spice netlist, ready for simulation
- Behavioral VHDL / Verilog code can be embedded as one of the
properties of the schematic block,
Xschem runs on UNIX systems with X11 and Tcl-Tk toolkit installed.
Documentation
Download
License
The software is released under
the GNU GPL, General Public License
Contact
Anyone interested in this project please contact me at the
following address:
<EMAIL_ADDRESS>Software requirements:
- X11
- tcl-tk libs and developent files
- c99 compiler
- bison (only for compiling the grammar parser)
- flex (only for compiling the lexical analyzer
- Xpm library and -dev header files
- awk (tested with gawk and mawk)
Systems tested:
- Linux debian / Redhat
- Solaris sparc
- Windows (with the cygwin layer and cygwin/Xorg X11 server,
plus the tcl/tk toolkit and the -dev libraries)
Screenshots
- digital system for VHDL simulation


| System 库 |
| Boost Home Library Home Tutorial Reference |
Error conditions originating from the operating system or
other low-level application program interfaces (API's) are typically
reported via an integer representing an error code. When these
low-level API calls are wrapped in portable code, such as in a portable
library, some users want to deal with the error codes in portable ways.
Other users need to get at the system specific error codes, so they can
deal with system specific needs. The Boost System library provides
simple, light-weight
error_code objects that encapsulate system-specific error
code values, yet also provide access to more abstract and portable
error conditions via
error_condition
objects. Because error_code objects can represent errors from sources
other than the operating system, including user-defined sources, each
error_code and error_condition has an associated
error_category.
来自操作系统或其它底层API的错误通常会以一个表示错误代码的整数来报告。当这些底层API调用被包装在可移植的代码中时，例如在一
个可移植的库
中，有些用户会希望以可移植的方式处理这些错误代码。而其它用户则可能需要得到系统特定的错误代码，这样他们可以处理系统特定的需要。Boost
System 库提供了简单、轻量级的
error_code 对象，封装系统特定的错误代码值，此外还通过
error_condition
对象提供了对更为抽象和可移植的错误条件的访问。因为 error_code 对象可以表示来自操作系统以外的错误，包括用户自定义的来源，所以每个
error_code 和 error_condition 都有一个相关联的
error_category.
An exception class,
system_error, is provided. Derived from std::runtime_error,
it captures the underlying error_code for the problem causing the
exception so that this important information is not lost.
本库还提供一个异常类
system_error. 它派生自 std::runtime_error, 用于捕获引起异常的问题的底层
error_code，以便不会丢失这些重要信息。
While exceptions are the preferred C++ default error code
reporting mechanism, users of libraries dependent on low-level
API's often need overloads reporting error conditions via
error code arguments and/or return values rather than via throwing
exceptions. Otherwise, when errors are not exceptional occurrences and
must be dealt with as they arise, programs become littered with
try/catch blocks, unreadable, and very inefficient. The Boost System
library supports both error reporting by exception and by error code.
虽然异常是首选的C++缺省的错误代码报告机制，但是使用依赖于底层API的库的用户通常需要通过错误代码参数和/或返回值而不是抛出
异常来报告错误条件。否则，当错误不是意外事件且必须被处理时，程序会变得充满
try/catch 块，不可读并且非常低效。Boost System 库既支持通过异常也支持通过错误代码进行错误报告。
In addition to portable errors codes and conditions supported
by the
error_code.hpp
header, system-specific headers support the
Cygwin, Linux, and Windows platforms. These headers are effectively
no-ops if included for platforms other than their intended target.
除了
error_code.hpp
头文件所支持的可移植错误代码和条件以外，系统特定的头文件还支持 Cygwin,
Linux, 和 Windows 平台。如果这些头文件在它们所指定的目标以外的平台上被包含，则只是空操作而已。
| The Boost System Library will become part of the C++0x Standard Library.
A number of changes, particularly to names, were made by the C++ committee
during standardization. The Boost implementation is tracking those changes.
See Deprecated names for
synonyms provided to prevent breakage of existing user code. See
Breaking changes for changes
that unavoidably break existing user code. All breaking changes are noisy
and will cause compile-time errors. Boost System 库将成为 C++0x 标准库的一部分。在标准化过程中，C++委员会会进行一些修改，特别是对于其中的名字。Boost 实现会跟踪这些变化。有关为防止破坏已有的用户代码而提供的同义词，请见 废弃的名字。而不可避免地对已有的用户代码造成的破坏，请见 破坏性的变化。所有破坏性的变化都会有提醒，会引发编译期错误。 |
Class error_code
and error_condition
are designed as a value types so they can be copied without slicing and
do not requiring heap allocation, but still have polymorphic behavior
based on the error category. This is achieved by abstract base class
error_category
supplying the polymorphic
behavior, and
error_code
and error_condition
containing a pointer to an object of a type derived from
error_category
.
类 error_code
和 error_condition
被设计为一种值类型，这样它们可以不受限制地复制，并且不要求堆分配，但仍具有基于错误类别的多态行为。这是由抽象基类
error_category
所提供的多态行为达成的，error_code
和 error_condition
包含一个指向某个派生自
error_category
的类型的对象的指针。
Many of the detailed design decisions were driven by the
requirements that users to be able to add additional error categories,
and that it be no more difficult to write portable code than
system-specific code.
许多详细设计的决定是由以下需求所驱动的，即用户可以增加额外的错误类别，以及编写可移植代码不会比编写系统特定代码更难。
The operator<<
overload for error_code
eliminates a
misleading conversion to bool in code like cout
<< ec
, where
ec
is of type error_code
. It is
also useful in its own right.operator<<
对于 error_code
的重载消除了在如下代码中的容易令人误解的到 bool 的转换，如 cout << ec
,
其中
ec
类型为 error_code
. 这对于它的正确使用也有帮助。
N1975, Filesystem Library Proposal for TR2, accepted for
Library Technical Report 2 (TR2) at the Berlin meeting, included
additional components to supplement the Standard Library's Diagnostics
clause. Since then, these error reporting components have received
wider public scrutiny and enhancements have been made to the design.
The enhanced version has been used by N2054, Networking Library
Proposal for TR2, demonstrating that these error reporting components
are useful beyond the original Filesystem Library.
N1975, TR2 的 Filesystem 库建议，在柏林会议上被接受为 Library Technical
Report 2
(TR2)，包含了额外的组件，以补充标准库的诊断条款。从那以后，这些错误报告组件已经接受了广泛的公开审查，并且设计得到了改进。增强版本已经被用于
N2054, TR2 的 Networking 库建议，事实证明这些错误报告组件要比原先的 Filesystem 库更有用。
The original proposal viewed error categories as a binary
choice between
errno
(i.e. POSIX-style) and the native
operating system's error codes. The proposed components now allow as
many additional error categories as are needed by either
implementations or by users. The need to support additional error
categories, for example, occurs in some networking library
implementations because they are built on top of the POSIX getaddrinfo
API that uses error codes not based on errno
.
原先的建议书将错误类别视为
errno
(即 POSIX
风格)和原生操作系统的错误代码两者之一的选择。建议的组件目前允许更多其它错误类别，如具体实现或用户所需要的。例如，对于支持额外错误类别的需求发生
在某些网络库的实现上，因为它们构建在 POSIX getaddrinfo
API
之上，它是使用错误代码而不是基于 errno
的。
Christopher Kohlhoff and Peter Dimov made important contributions to the design. Comments and suggestions were also received from Pavel Vozenilek, Gennaro Prota, Dave Abrahams, Jeff Garland, Iain Hanson, Oliver Kowalke, and Oleg Abrosimov. Christopher Kohlhoff suggested several improvements to the N2066 paper. Johan Nilsson's comments led to several of the refinements in N2066 .
Revised May 28, 2010
© Copyright Beman Dawes, 1999
Distributed under the Boost Software License, Version 1.0. (See file LICENSE_1_0.txt or www.boost.org/LICENSE_1_0.txt)


--- title: Class Rpc description: Rpc navLabel: Rpc category: class body_class: dark ---
An RPC represents a single remote procedure Request made from the client to the server. It’s main function is to encapsulate the logic around timeouts and to convert the incoming response data
deepstreamConfig
- The deepstreamConfig the client was created withclient
- The deepstream clientrpcName
- The rpc nameuid
- The unique RPC identifiercallback
- The callb
ack when an RPC has been completedCalled once an ack message is received from the server.
Callb ack for error messages received from the server. Once an error is received the request is considered completed. Even if a response arrives later on it will be ignored / cause an UNSOLICITED_MESSAGE error
rpcName
- The rpc nameerr
- The error
Message received from the serverCalled once a response message is received from the server. Converts the typed data and completes the request.
rpcName
- The rpc namedata
- The data received from the server


My fullname is Mayra Antolina Gonzalez. I am a third year student of Information System Engineering in National Technological University (UTN - Resistencia, Chaco)
When I have to describe myself I say that I am a proactive and completely self-taught person.. I am very passionate about learning things.
Teaching assistant of the subject Syntax and semantics of languages
Tareas administrativa de búsqueda y actualización de historias clinicas y listado de pacientes
Third year student
Level: B1
Duration: 10 weeks
HTML5, CSS3 , PHP, JS, AJAX, SQL
In process (60hr)
Upper Intermediate


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Szmu Pharma is deciding whether to proceed with Phase 3 of clinical trials for a new Asthma drug. So far £3bn has been spent on Phase 1 & 2. Estimate that Phase 3 will cost £3.4bn, take 4 years and have an 80% chance of success. Should the company proceed?
- Estimate that this drug will be 100% more effective than other treatments
- Equivalents priced at $1k/year/patient
- Manufacturing and distribution will cost approx $500 for 1 years supply of the new drug
- Initial launch in UK and USA where 1 in 10 children below age 20 have asthma
- Estimate that competitor will launch an even more effective drug in 6 years
Basic Points
- Estimate market size 9M patients (population 60M in UK, 300M in USA, ave life expectancy 80 years and assume even distribution of population across age groups)
- Initially might take time to get people to switch, so say 4.5M in Year 1, 9M in Year 2
- Potential profit per year $2.25bn in Year 1, $4.5bn in Year 2 = Total Profit of $6.75bn
- Payoff in 1 - 2 years
- Phase 1 and 2 are a sunk cost so they do not factor into the decision
Advanced Comments
- Outcomes: Although there is an 80% chance of success there are effectively 2 discrete outcomes - succeed and make $6.75bn or fail and lose $3.4bn
- Risks: Delay in clinical trials causes product to be late to market. Sales and marketing not effective at persuading people to switch medication


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| deltaMethod {car} | R Documentation |
deltaMethod
is a generic function that uses the delta method to get a
first-order approximate
standard error for a nonlinear function of a vector of random variables
with known or estimated covariance matrix.
deltaMethod(object, ...) ## Default S3 method: deltaMethod(object, g., vcov., func=g., constants, level=0.95, rhs, ..., envir=parent.frame()) ## S3 method for class 'lm' deltaMethod(object, g., vcov.=vcov(object, complete=FALSE), parameterNames=names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'nls' deltaMethod(object, g., vcov.=vcov(object, complete=FALSE), ..., envir=parent.frame()) ## S3 method for class 'multinom' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = if (is.matrix(coef(object))) colnames(coef(object)) else names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'polr' deltaMethod(object, g., vcov.=vcov(object, complete=FALSE), ..., envir=parent.frame()) ## S3 method for class 'survreg' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'coxph' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(coef(object)), ..., envir=parent.frame()) ## S3 method for class 'mer' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(fixef(object)), ..., envir=parent.frame()) ## S3 method for class 'merMod' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(fixef(object)), ..., envir=parent.frame()) ## S3 method for class 'lme' deltaMethod(object, g., vcov. = vcov(object, complete=FALSE), parameterNames = names(fixef(object)), ..., envir=parent.frame()) ## S3 method for class 'lmList' deltaMethod(object, g., ..., envir=parent.frame())
object |
For the default method, |
g. |
A quoted string that is the function of the parameter estimates to be evaluated; see the details below. |
vcov. |
The (estimated) covariance matrix of the coefficient
estimates. For the default method, this argument is required. For all
other methods, this argument must either provide the estimated covariance
matrix or a function that when applied
to |
func |
A quoted string used to annotate output. The default of
|
parameterNames |
A character vector of length |
constants |
This argument is a named vector whose elements are constants that
are used in the |
level |
level for confidence interval, default |
rhs |
hypothesized value for the specified function of parameters; if absent no hypothesis test is performed. |
... |
Used to pass arguments to the generic method. |
envir |
Environment in which |
Suppose x is a random vector of length p that is at least approximately normally distributed with mean β and estimated covariance matrix C. Then any function g(β) of β, is estimated by g(x), which is in large samples normally distributed with mean g(β) and estimated variance h'Ch, where h is the first derivative of g(β) with respect to β evaluated at x. This function returns both g(x) and its standard error, the square root of the estimated variance.
The default method requires that you provide x in the argument
object
, C in the argument vcov.
, and a text expression
in argument g.
that when evaluated gives the function g. The
call names(object)
must return the names of the elements of x
that are used in the expression g.
.
Since
the delta method is often applied to functions of regression parameter
estimates, the argument object
may be the name of a regression
object from which the estimates and their estimated variance matrix can
be extracted. In most regression models, estimates are returned by the
coef(object)
and the variance matrix from vcov(object)
.
You can provide an alternative function for computing the sample variance
matrix, for example to use a sandwich estimator.
For mixed models using lme4
or nlme
, the coefficient estimates
are returned by the fixef
function, while for multinom
,
lmList
and nlsList
coefficient estimates are returned by
coef
as a matrix. Methods for these models are provided to get the
correct estimates and variance matrix.
The argument g.
must be a quoted character string
that gives the function of interest. For example, if you set
m2 <- lm(Y ~ X1 + X2 + X1:X2)
, then deltaMethod(m2,"X1/X2")
applies the
delta method to the ratio of the coefficient estimates for X1
and
X2
. The argument g.
can consist of constants and names
associated with the elements of the vector of coefficient estimates.
In some cases the names may include characters such as the colon
:
used in interactions, or mathematical symbols like +
or
-
signs that would confuse the
function that computes numerical derivatives, and for this case you can replace
the names of the estimates with the parameterNames
argument. For
example, the ratio of the
X2
main effect to the interaction term could be computed using
deltaMethod(m2, "b1/b3", parameterNames=c("b0", "b1", "b2", "b3"))
.
The name “(Intercept)
” used for the intercept in linear and generalized
linear models is an exception, and it will be correctly interpreted by
deltaMethod
. Another option is to use back-ticks to quote nonstandard R names, as in deltaMethod(m2,"X1/`X1:X2`")
.
For multinom
objects, the coef
function returns a matrix of
coefficients, with each row giving the estimates for comparisons of one category
to the baseline. The deltaMethod
function applies the delta method to
each row of this matrix. Similarly, for lmList
and nlsList
objects, the delta method is computed for each element of the list of
models fit.
For nonlinear regression objects produced by the nls
function, the call coef(object)
returns the estimated
coefficient vectors with names corresponding to parameter names.
For example,
m2 <- nls(y ~ theta/(1 + gamma * x), start = list(theta=2, gamma=3))
will
have parameters named c("theta", "gamma")
.
In many other familiar regression models, such as those produced by lm
and glm
, the names of
the coefficient estimates are the corresponding regressor names, not
parameter names.
For mixed-effects models fit with lmer
and glmer
from the
lme4 package or lme
and nlme
from the nlme package,
only fixed-effect coefficients are considered.
For regression models for which methods are not provided, you can extract
the named vector of coefficient estimates and and estimate of its covariance
matrix and then apply the default deltaMethod
function.
Note: Earlier versions of deltaMethod
included an argument
parameterPrefix
that implemented the same functionality as the
parameterNames
argument, but which caused several problems that
were not easily fixed without the change in syntax.
An object of class "deltaMethod"
, inheriting from "data.frame"
, for which a print
method is provided. The object contains columns
named Estimate
for the estimate, SE
for its standard error, and columns for confidence limits and possibly a hypothesis test.
The value of g.
is given as a row label.
Sanford Weisberg<EMAIL_ADDRESS>John Fox<EMAIL_ADDRESS>and Pavel Krivitsky.
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley, Section 6.1.2.
First derivatives of g.
are computed using symbolic differentiation
by the function D
.
m1 <- lm(time ~ t1 + t2, data = Transact) deltaMethod(m1, "b1/b2", parameterNames= paste("b", 0:2, sep="")) deltaMethod(m1, "t1/t2", rhs=1) # use names of preds. rather than coefs. deltaMethod(m1, "t1/t2", vcov=hccm) # use hccm function to est. vars. deltaMethod(m1, "1/(Intercept)") # The next example invokes the default method by extracting the # vector of estimates and covariance matrix explicitly deltaMethod(coef(m1), "t1/t2", vcov.=vcov(m1))


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Reference:
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Training code: 3246
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