Note
This tutorial was generated from an Jupyter notebook that can be downloaded here.
Basics: Light curve gradients¶
The cool thing about analytic expressions for light curves is that their derivatives are also analytic, and therefore fast to compute (and accurate!). We’ve coded up a lot of the flux derivatives in starry
, but most of the heavy lifting is done using autodifferentiation. Let’s go over how to compute gradients with some simple examples.
Gradients of Map
instances¶
Let’s instantiate a low degree Map
with some arbitrary spherical harmonic coefficients:
[17]:
from starry import Map
map = Map(lmax=2)
map[1, 0] = 0.3
map[2, -1] = 0.3
map[2, 2] = -0.1
map.show()
We can compute the flux during an occultation as usual:
[16]:
map.flux(xo=0.3, yo=0.4, ro=0.1)
[16]:
1.3277964701859288
To also compute the gradient of the flux with respect to the input parameters, we can simply pass the gradient=True
keyword:
[3]:
flux, gradient = map.flux(xo=0.3, yo=0.4, ro=0.1, gradient=True)
print(flux)
print(gradient)
1.3277964701859288
{'theta': array([-6.89762425e-05]), 'xo': array([0.00460625]), 'yo': array([-0.006946]), 'ro': array([-0.37133373]), 'y': array([[ 0.99 ],
[-0.0069282 ],
[ 1.13975912],
[-0.00519615],
[-0.00464758],
[-0.01331904],
[ 0.54520927],
[-0.00998928],
[ 0.00135554]]), 'u': array([], shape=(0, 1), dtype=float64)}
What starry
returns is the tuple (F, dF)
of the flux and its gradient. The gradient dF
is a dictionary of derivatives with respect to each of the input parameters (including theta
, which we omitted in the call and whose default value is zero) and each of the spherical harmonic coefficients. The latter are given as a vector,
[4]:
gradient['y']
[4]:
array([[ 0.99 ],
[-0.0069282 ],
[ 1.13975912],
[-0.00519615],
[-0.00464758],
[-0.01331904],
[ 0.54520927],
[-0.00998928],
[ 0.00135554]])
where each element is the derivative with respect to the corresponding element in the map.y
vector. Note that the gradient also contains an entry for the limb darkening coefficients u
, but since we didn’t set any, starry
didn’t bother computing those derivatives.
Note that the gradient routines are fully vectorized, so we can compute an entire light curve and its time-dependent gradients in a single pass:
[5]:
import numpy as np
xo = np.linspace(-1.5, 1.5, 1000)
flux, gradient = map.flux(xo=xo, yo=0.4, ro=0.1, gradient=True)
Here’s the light curve:
[6]:
import matplotlib.pyplot as pl
%matplotlib inline
fig, ax = pl.subplots(1, figsize=(14, 5))
ax.plot(xo, flux);
And here are the first few gradients, normalized so we can plot them all on the same graph:
[7]:
fig, ax = pl.subplots(1, figsize=(14, 5))
ax.plot(xo, gradient['xo'] / np.nanmax(np.abs(gradient['xo'])), label=r'$dF/dx_o$')
ax.plot(xo, gradient['yo'] / np.nanmax(np.abs(gradient['yo'])), label=r'$dF/dy_o$')
ax.plot(xo, gradient['ro'] / np.nanmax(np.abs(gradient['ro'])), label=r'$dF/dr_o$')
ax.plot(xo, gradient['theta'] / np.nanmax(np.abs(gradient['theta'])), label=r'$dF/d\theta$')
ax.legend(fontsize=14);
Gradients of System
instances¶
We can take gradients of System
instances as well. Let’s create a very simple star-planet system.
[8]:
from starry.kepler import Primary, Secondary, System
# Body A
star = Primary()
star[1] = 0.4
star[2] = 0.26
# Body b
planet = Secondary()
planet.L = 1e-3
planet.r = 0.1
planet.prot = 1
planet.porb = 1
planet.ecc = 0.2
planet.w = 35
planet[1, 0] = 0.3
planet[2, -1] = 0.3
planet[2, 2] = -0.1
# System
system = System(star, planet)
As before, we request the gradient via the gradient=True
keyword of the compute()
method. Let’s compute a super high resolution light curve with 100,000 points so we can zoom in on interesting features.
[9]:
time = np.linspace(-1.75, 1.75, 100000)
%time system.compute(time, gradient=True)
CPU times: user 727 ms, sys: 21.1 ms, total: 748 ms
Wall time: 749 ms
We can plot the resulting light curve:
[10]:
fig, ax = pl.subplots(1, figsize=(14, 5))
ax.plot(time, system.lightcurve)
ax.set_xlabel('Time (days)', fontsize=14)
ax.set_ylabel('Flux', fontsize=14);
As for the gradients, these are stored in the gradient
attribute of the System
instance:
[11]:
system.gradient
[11]:
{'A.prot': array([0., 0., 0., ..., 0., 0., 0.]),
'A.tref': array([0., 0., 0., ..., 0., 0., 0.]),
'A.u': array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]),
'b.L': array([0.8365729 , 0.83663733, 0.83670177, ..., 1.07855564, 1.07847729,
1.07839894]),
'b.Omega': array([nan, nan, nan, ..., nan, nan, nan]),
'b.a': array([0., 0., 0., ..., 0., 0., 0.]),
'b.ecc': array([-0.0005656 , -0.00056568, -0.00056576, ..., 0.00068785,
0.00068787, 0.00068789]),
'b.inc': array([nan, nan, nan, ..., nan, nan, nan]),
'b.lambda0': array([ 5.11298215e-06, 5.11372724e-06, 5.11447215e-06, ...,
-6.21812874e-06, -6.21831232e-06, -6.21849552e-06]),
'b.porb': array([ 0.00081588, 0.000816 , 0.00081612, ..., -0.00099223,
-0.00099226, -0.00099229]),
'b.prot': array([0.00240529, 0.00240558, 0.00240587, ..., 0.0049095 , 0.00490972,
0.00490994]),
'b.r': array([0., 0., 0., ..., 0., 0., 0.]),
'b.tref': array([-0.00184067, -0.00184094, -0.00184121, ..., 0.00223853,
0.00223859, 0.00223866]),
'b.w': array([ 1.02343954e-06, 1.02358868e-06, 1.02373779e-06, ...,
-1.24465109e-06, -1.24468784e-06, -1.24472451e-06]),
'b.y': array([[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ],
[-0.00040304, -0.00040281, -0.00040257, ..., 0.00040352,
0.00040328, 0.00040304],
[ 0.00108208, 0.00108217, 0.00108225, ..., -0.0010819 ,
-0.00108199, -0.00108208],
...,
[-0.00017735, -0.00017747, -0.00017759, ..., -0.00017711,
-0.00017723, -0.00017735],
[-0.00031671, -0.00031655, -0.00031638, ..., -0.00031703,
-0.00031687, -0.00031671],
[ 0.00042514, 0.00042521, 0.00042528, ..., 0.000425 ,
0.00042507, 0.00042514]]),
'time': array([ 0.00184067, 0.00184094, 0.00184121, ..., -0.00223853,
-0.00223859, -0.00223866])}
Note that since there are multiple bodies, the dictionary keys are prepended with the body identifier. The primary body (the star, in this case) is always given the identifier A
. The secondary bodies are given lower case letters, starting with b
and increasing in the order the bodies were specified when the system was instantiated.
There are a ton of interesting derivatives to show. But as a brief example, here’s the derivative of the light curve with respect to the coefficient of the \(Y_{1,1}\) spherical harmonic of the planet’s map (that’s the element of index \(n=3\) in the vector planet.y
):
[14]:
fig, ax = pl.subplots(1, figsize=(14, 5))
ax.plot(time, system.gradient['b.y'][2])
ax.set_xlabel('Time (days)', fontsize=14)
ax.set_ylabel('Flux', fontsize=14);
Cool! Let’s zoom in on those spikes, which happen at secondary eclipse ingress and egress:
[15]:
fig, ax = pl.subplots(1, figsize=(14, 5))
ax.plot(time, system.gradient['b.y'][2])
ax.set_xlabel('Time (days)', fontsize=14)
ax.set_ylabel('Flux', fontsize=14);
ax.set_xlim(0.55, 0.564)
ax.set_ylim(0.0005, -0.0005);
Note: The Secondary
and Primary
instances also have their own gradient
attributes, which store the gradient of that body’s flux with respect to each of the parameters. Check those out as well!
All the gradients¶
Finally, just for fun, we can plot all of the gradients starry
is able to compute for a two-planet system. Below are three snippets of the light curve: a transit of the inner planet (left), a secondary eclipse of the inner planet by the star (center), and a planet-planet occultation of the inner planet by the outer planet (right). The top panels show the light curve and the sub panels show the derivatives of the light curve with respect to all 28 system parameters. Blue lines
are the analytic versions computed by starry, while the orange dots are computed numerically. The numerical flux is computed using an adaptive mesh and the numerical derivatives are computed using finite differences. Note that several of the numerical derivatives show significant numerical instabilities, while the analytic derivatives do not!


Ergo Witness is a network visualisation tool that shows network data in a creative way in 3D spaces. On the backend, the data is gathered by running tools like Bro, Packetbeat, and Snort, and pushing their logs in JSON format to the ELK stack (Elasticsearch).
The ‘front end’ as I call it, is developed in the Unity game engine, pulling the information it needs down from the server with HTTP requests. By using a game engine like Unity to represent network data, there are not only possibilities of fun, interactive data analysis, but also the beginning of what could possibly be a very powerful Virtual Reality toolkit for network professionals.
Using Ergo Witness is very simple to setup.
In order for this visualization to work, you need a source of network traffic. For network data, Bro, Filebeat, and Packetbeat are run on a capture server. These services then forward their JSON formatted logs directly to Logstash, which is where the data is aggregated. Then, Unity queries Elasticsearch using the web API.


President Muhammadu Buhari
The President of Nigeria
Here’s the President Muhammadu Buhari’s timeline:
- 1942 - Born in Daura, Katsina State
- 1953 - He attended primary school in Daura and Mai’adua before proceeding to Katsina Model School
- 1961 - Buhari joined the Nigerian Army by enrolling at age 19 Nigerian Military Training College (NMTC)
- 1964 - the college was upgraded to an officer commissioning unit of the Nigerian Army and renamed the Nigerian Defence Academy (NDA) (prior to 1964, the Nigerian government sent cadets who had completed their NMTC preliminary training to mostly Commonwealth military academies for officer cadet training).
- 1963 - at age 21, Buhari was commissioned a second lieutenant and appointed Platoon Commander of the Second Infantry Battalion in Abeokuta, Nigeria. From November 1963 to January 1964, Buhari attended the Platoon Commanders’ Course at the Nigerian Military Training College, Kaduna.
- 1964 - he facilitated his military training by attending the Mechanical Transport Officer’s Course at the Army Mechanical Transport School in Borden, United Kingdom.
- 1966 - Lieutenant Muhammadu Buhari was one of the participants in the “July Rematch” or so called “Counter-Coup”, led by Lt-Col Murtala Muhammed, that overthrew and assassinated Nigeria’s first self-appointed military Head of State General Aguiyi Ironsi, who had assumed leadership of the Nigerian government after a failed coup attempt on 15 January 1966, which overthrew the elected parliamentary government of Nigeria (also known as first republic). Other participants in the coup on 28 July 1966 included 2nd Lieutenant Sani Abacha, Lieutenant Ibrahim Babangida, Major Theophilus Danjuma, Lieutenant Ibrahim Bako among others.
- 2003 - Buhari ran for office in the presidential election[75] as the candidate of the All Nigeria People’s Party (ANPP). He was defeated by the People’s Democratic Party nominee, President Olusẹgun Ọbasanjọ, by a margin of more than 11 million votes.
- 2006 - Gen. Buhari was nominated as the consensus candidate of the All Nigeria People’s Party.
- 2007 - Umaru Yar’Adua, who hailed from the same home state of Katsina. In the election, Buhari officially took 18% of the vote against 70% for Yar’Adua, but Buhari rejected these results. After Yar’Adua took office, the ANPP agreed to join his government, but Buhari denounced this agreement.
- 2010 - Buhari left the ANPP for the Congress for Progressive Change (CPC), a party that he had helped to found. He said that he had supported foundation of the CPC “as a solution to the debilitating, ethical and ideological conflicts in my former party the ANPP”.
- 2011 - general election, running against incumbent President Goodluck Jonathan of the People’s Democratic Party (PDP), Mallam Nuhu Ribadu of Action Congress of Nigeria (ACN), and Ibrahim Shekarau of ANPP. They were the major contenders among 20 contestants.
- 2015 - Incoming and outgoing Nigerian Presidents at the inauguration ceremony in the runup to the 2015 Presidential elections, the campaign team of incumbent President Goodluck Jonathan asked for the disqualification of General Buhari from the election, claiming that he is in breach of the Constitution. According to the fundamental document, in order to qualify for election to the office of the President, an individual must be “educated up to at least School certificate level or its equivalent”.
- 2016 - the government negotiated a deal with the terrorist group, Boko Haram which secured the release of 21 Chibok girls. By December 2016, the government had recovered much of the territories previously held by Boko Haram and after the capture of Sambisa Forest, Buhari announced that Boko Haram has been technically defeated.
- 2007 - Buhari’s government secured a further release of 82 out of 276 girls kidnapped in 2014, in exchange of five Boko Haram leaders.On 7 May 2017, President Buhari met with the 82 released Chibok girls, before departing to London, UK, for a follow-up treatment for an undisclosed illness.
- 1971 - Buhari married his first wife, Safinatu (née Yusuf) Buhari (First lady of Nigeria December 1983 – August 1985). They had five children together, four girls and one boy. Their first daughter, Zulaihat (Zulai) was named after Buhari’s mother. Their other children are Fatima, Musa (deceased son), Hadiza, and Safinatu.In 1988, Buhari and his first wife Safinatu divorced.
"Buhari’s life and achievement are testimony to the far-reaching contribution that one man's towering intellect, persistence and scientific vision can make to human peace and progress."
-- Indian Prime Minister Manmohan Singh


plug_rails_cookie_session_store v0.3.0 PlugRailsCookieSessionStore.MessageVerifier
MessageVerifier
makes it easy to generate and verify messages
which are signed to prevent tampering.
For example, the cookie store uses this verifier to send data
to the client. Although the data can be read by the client, he
cannot tamper it.
Summary
Functions
Signs a binary according to the given secret
Decodes and verifies the encoded binary was not tampared with


Promised Date
10 Nov 2017
Venky
Donation Id:#00012
<EMAIL_ADDRESS>Waiting
Item Name
Quantity
Unit
Rice
2
kg
Pulses
2
Kg
Click to see all the promised items
×
Promised Donation Items
Promised Date
10 Nov 2017
Venky
Donation Id:#00012
<EMAIL_ADDRESS>Waiting
Item Name
Sub Item Name
Quantity(Kg)
Rice
Basmati
2
Rice
Basmati
2
Rice
Basmati
2
Rice
Basmati
2
Received
Promised Date
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Akshaya
Donation Id:#00012
<EMAIL_ADDRESS>Waiting
Item Name
Quantity
Unit
Rice
2
kg
Pulses
3
Kg
Received
Promised Date
10 Nov 2017
Ganesh
Donation Id:#00012
<EMAIL_ADDRESS>Waiting
Item Name
Quantity
Unit
Rice
2
kg
Pulses
3
Kg
Received


Specify the location of the Mercurial executable file. Enter the path manually, or click the Browse button and select the path in the
dialog that opens.
If you followed the standard installation procedure, the default location is /opt/local/bin or /usr/local/bin for Linux and macOS and
/Program Files/TortoiseHG for Windows.
It is recommended that you add the path to the Mercurial executable file to the
PATH
variable. In this case, you can
specify only the executable name, the full path to the executable location is not required.


Hello everyone!!
This is Bhagyanidhi Patil .I'm studying Electronics and Communication Engineering in Sri Jayachamarajendra College of Engineering (JSSSTU) Mysuru.
Hello everyone!!
This is Bhagyanidhi Patil .I'm studying Electronics and Communication Engineering in Sri Jayachamarajendra College of Engineering (JSSSTU) Mysuru.


Performance Comparison for React, Angular and Overture
(based on
https://www.codementor.io/reactjs/tutorial/reactjs-vs-angular-js-performance-comparison-knockout
)
React
Run
Angular
Run
{{item.label}}


This exercise covers exponential arithmetic with rational bases and integer (primarily negative) exponents. This exercise covers all the material presented in the Level 2 Exponents video.
\Large fracParens( BASE_N, BASE_D )^{EXP_SIGN+EXP}
SOL_N / SOL_D
= fracParens( BASEF_N, BASEF_D )^{EXP}
= v
= frac( SOL_N, SOL_D )


pydrake.solvers.branch_and_bound¶
branch_and_bound bindings for MathematicalProgram
-
class
pydrake.solvers.branch_and_bound.
MixedIntegerBranchAndBound
¶ Given a mixed-integer optimization problem (MIP) (or more accurately, mixed binary problem), solve this problem through branch-and-bound process. We will first replace all the binary variables with continuous variables, and relax the integral constraint on the binary variables z ∈ {0, 1} with continuous constraints 0 ≤ z ≤ 1. In the subsequent steps, at each node of the tree, we will fix some binary variables to either 0 or 1, and solve the rest of the variables. Notice that we will create a new set of variables in the branch-and-bound process, since we need to replace the binary variables with continuous variables.
-
__init__
(self: pydrake.solvers.branch_and_bound.MixedIntegerBranchAndBound, prog: pydrake.solvers.mathematicalprogram.MathematicalProgram, solver_id: pydrake.solvers.mathematicalprogram.SolverId) → None¶ Construct a branch-and-bound tree from a mixed-integer optimization program.
- Parameter
prog
: - A mixed-integer optimization program.
- Parameter
solver_id
: - The ID of the solver for the optimization.
- Parameter
-
GetOptimalCost
(self: pydrake.solvers.branch_and_bound.MixedIntegerBranchAndBound) → float¶ Get the optimal cost.
-
GetSolution
(*args, **kwargs)¶ Overloaded function.
- GetSolution(self: pydrake.solvers.branch_and_bound.MixedIntegerBranchAndBound, mip_var: pydrake.symbolic.Variable, nth_best_solution: int = 0) -> float
Get the n’th best integral solution for a variable. The best solutions are sorted in the ascending order based on their costs. Each solution is found in a separate node in the branch-and-bound tree, so the values of the binary variables are different in each solution.
- Parameter
mip_var
: - A variable in the original MIP.
- Parameter
nth_best_solution
: - . The index of the best integral solution.
- Precondition:
mip_var
is a variable in the original MIP.- Precondition:
nth_best_solution
is between 0 and solutions().size().
Raises: RuntimeError if the preconditions are not satisfied. - GetSolution(self: pydrake.solvers.branch_and_bound.MixedIntegerBranchAndBound, mip_vars: numpy.ndarray[object[m, 1]], nth_best_solution: int = 0) -> numpy.ndarray[numpy.float64[m, 1]]
Get the n’th best integral solution for some variables. The best solutions are sorted in the ascending order based on their costs. Each solution is found in a separate node in the branch-and-bound tree, so
- Parameter
mip_vars
: - Variables in the original MIP.
- Parameter
nth_best_solution
: - . The index of the best integral solution.
- Precondition:
mip_vars
are variables in the original MIP.- Precondition:
nth_best_solution
is between 0 and solutions().size().
Raises: RuntimeError if the preconditions are not satisfied. - GetSolution(self: pydrake.solvers.branch_and_bound.MixedIntegerBranchAndBound, mip_vars: numpy.ndarray[object[m, n]], nth_best_solution: int = 0) -> numpy.ndarray[numpy.float64[m, n]]
Get the n’th best integral solution for some variables. The best solutions are sorted in the ascending order based on their costs. Each solution is found in a separate node in the branch-and-bound tree, so
- Parameter
mip_vars
: - Variables in the original MIP.
- Parameter
nth_best_solution
: - . The index of the best integral solution.
- Precondition:
mip_vars
are variables in the original MIP.- Precondition:
nth_best_solution
is between 0 and solutions().size().
Raises: RuntimeError if the preconditions are not satisfied.
-
GetSubOptimalCost
(self: pydrake.solvers.branch_and_bound.MixedIntegerBranchAndBound, nth_suboptimal_cost: int) → float¶ Get the n’th sub-optimal cost. The costs are sorted in the ascending order. The sub-optimal costs do not include the optimal cost.
- Parameter
nth_suboptimal_cost
: - The n’th sub-optimal cost.
- Precondition:
nth_suboptimal_cost
is between 0 and solutions().size() - 1.
Raises: RuntimeError if the precondition is not satisfied. - Parameter
-
Solve
(self: pydrake.solvers.branch_and_bound.MixedIntegerBranchAndBound) → pydrake.solvers.mathematicalprogram.SolutionResult¶ Solve the mixed-integer problem (MIP) through a branch and bound process.
- Returns
solution_result
: - If solution_result=SolutionResult::kSolutionFound, then the best solutions are stored inside solutions(). The user can access the value of each variable(s) through GetSolution(…). If solution_result=SolutionResult::kInfeasibleConstraints, then the mixed-integer problem is primal infeasible. If solution_result=SolutionResult::kUnbounded, then the mixed-integer problem is primal unbounded.
- Returns
-


Rollager
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Outline Color:
Outline Width:
Circles
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Triangles
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--- layout: minimal title: 'Friends' date: 2018-12-09 23:20:48 +0800 permalink: /friends/index.html description: 'Blogs of My Friends' ---


A recipe contains configuration files for training and testing models designed for a specific database. Nabu alleady contains several pre-designed recipes, but you can design your own recipes with little effort. A recipe contains the following configurations files:
- database.conf: This is the database configuration. For every set of data (training features, training text, testing features ...) it contains a section that specifies where to read and write the data in your file system and wich processors should be used to process the data. Every recipe contains a template for the database configuration, but because the pahs are different ofor each person this has to be completed. You can put the completed database configuation in database.conf (which will automatically be added to the gitignore)
- processor configuration files: One or multiple files for the data processors that are used. The database confiuration will point to these processor confiurations. For example, a feature processor specifies the type of feature, the feature parameters (window length, numbe of filters, ...) etc. You can find more information about processors ../../nabu/processing/processors/README.md "here".
- model.cfg: This is the model configuration. It specifies the model parameters (number of layers, units ...). The classifies configuration contains 3 sections:
- io: give names to the inputs and outputs of the model
- encoder: the encoder configuration. The encoder encodes the input to a new representation.
- decoder: the decoder configuration. The decoder decodes the encoder representation into the desired output. You can find more information about models ../../nabu/neuralnetworks/models/README.md "here".
- trainer.cfg: specifies the trainer parameters (learning rate, nuber of epochs, ...). This configuration also specifies which data should be used to train the model. It links io names defined in the model configuration to data sections defined in the database configuration. A trainer is used to train the model to minimize some loss function. You can find more information about trainers ../../nabu/neuralnetworks/trainers/README.md "here".
- validation_evaluator.cfg: specifies the validator type and parameters, that will be used during training. It also specifies which data will be used to evaluate the model in the same way the trainer configuration does. An evaluator is used to evaluate the performance of a model. The validation evaluator is used during training to measure the performance at fixed intervals and adjusts the learning rate if necesarry. You can find more information about evaluators ../../nabu/neuralnetworks/evaluators/README.md "here"
- test_evaluator.cfg: This is the configuartion for the evaluator to be used at test time (see validation_evaluator.cfg)
- recognizer.cfg: The configuration for the recognizer to be used for decoding it contains similar fields as the evaluator configs.
To create your own recipe, simply create a directory containing all of the mentioned configuation files. You can find template configuations in config/templates.
All the components in the recipe have default configurations. You can find these configurations in the directory where the component is implemented under the defaults directory. If a field is not defined in the configuration it will be filled in with the default value.


Hello
My Name is
Chia-Yi Liang
Resume
Github
Linkedin
Facebook
Feel free to email me at
chiayiliang327@gmail.com


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Address Book
Address Book Statistics
Total Contacts
12
Hint: Your contacts can be associated with multiple customer/prospect accounts.
Clicking on an account name below a contact's name will take you to their associated account.
Hint: Your contacts can be associated with multiple customer/prospect accounts.
Clicking on an account name below a contact's name will take you to their associated account.


Henry Marshall (@henry)
2018-04-09
It would be grand if grandma came over!
%s/grand/super/
It would be super if superma came over!
It would be grand if grandma came over!
%s/ grand / super /
It would be super if grandma came over!
It would be grand if grandma came over!
If grandma came over that would be grand!
%s/grand\b/super/
It would be super if grandma came over!
If grandma came over that would be super!
\b
matches the boundary between a letter, number, or underscore and anything else.
/foo\b/
Matches:
foo.
foo()
foo-
foo bar
foo
Does not Match:
foobar
foo1
foo_
My grandma has a cat. My grandpa has a dog.
%s/grand[mp]a/family/
My family has a cat. My family has a dog.
| Shorthand | Equivalent |
|---|---|
[0-9] |
[0123456789] |
\d |
[0-9] |
\s |
[ \t\n\r] |
\w |
[\A-Za-z\d_] |
Negate a character class by prepending ^
const isNotVowel = str => /[^aeiou]/.test(str)
| Shorthand | Equivalent |
|---|---|
\D |
[^\d] |
\S |
[^\s] |
\W |
[^\w] |
There are 12 characters with a special meaning:
\ ^ $ . | ? * + ( ) [ {
Metacharacters are context dependent.
To match a literal character, escape it with a backslash \$
.
In character classes, usually treated literally
const isFiveNines = str => /99\.999/.test(str)
const goodUptime = "99.99939"
isFiveNines(goodUptime) // true
const badUptime = "93.990999"
isFiveNines(badUptime) // false
In practice you should be comparing floats.
const isFiveNines = str => /99.999/.test(str)
const badUptime = "93.990999"
isFiveNines(terribleUptime) // true - oh no!
The .
matches any one character in regex (with the exception of \n
).
| Pattern | Description | Matches |
|---|---|---|
| a? | One or Zero 'a' | '' 'a' |
| a* | Zero or More 'a' | '' 'a' 'aa' 'aaa' 'aaaa'... |
| a+ | One or More 'a' | 'a' 'aa' 'aaa' 'aaaa'... |
| a{3} | Exactly three 'a' | 'aaa' |
| a{2,4} | Two to four 'a' | 'aa' 'aaaa' 'aaaa' |
const isChargeId = str => /acct_[a-zA-Z0-9]{16,}/.test(str)
Treating a sequence as a single atomic unit.
const isTheme = str => /(na)+ batman/i.test(str)
isTheme("nanananananana Batman!") // true
My brother has a cat. My sister has a dog.
%s/(brother|sister)/family/
My family has a cat. My family has a dog.
const str = "Sticker pricing is 2.9% + $0.30 per transaction"
const getPercentage = str.match(/(100|\d{1,2}(?:\.\d+)?)%/)
getPercentage(str)[1] // "2.9"
My brother has a goldfish.
%s/(brother|sister)/step-\1/
My step-brother has a goldfish.
/^[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}$/i
| Section | Meaning |
|---|---|
^ |
Start of the line |
[A-Z0-9._%+-]+ |
1+ of: letter, number, . , _ , % , + , - |
@ |
Literal @ |
[A-Z0-9.-]+ |
1+ of: letter, number, . , - |
\. |
Literal . |
[A-Z]{2,} |
Two or more letters |
$ |
End of the line |
i |
Case Insensitive |
reg = /^\w+(\+\w+)?@\w+\.\w{2,}$/
reg.match("foo bar")
# => nil
<EMAIL_ADDRESS># => <MatchData<EMAIL_ADDRESS>1:nil>
reg.match("<script>alert('uh<EMAIL_ADDRESS># => <MatchData<EMAIL_ADDRESS>1:nil>
Use \A
(start of string) and \z
(end of string),
instead of ^
(start of line) and $
(end of line).
safeReg = /\A\w+(\+\w+)?@\w+\.\w{2,}\z/
<EMAIL_ADDRESS># => <MatchData<EMAIL_ADDRESS>1:nil>
reg.match("<script>alert('uh<EMAIL_ADDRESS># => nil
The linter has got your back.
foo = 'foo: "foo"'
/"(.+)"/.match(config)[1] # => "foo"
foobar = 'foo: "foo", bar: "bar"'
/"(.+)"/.match(foobar)[1] # => "foo\", bar: \"bar"
/"([^"]+)"/.match(foobar)[1] # => "foo"
extract_comment = /\/\*([\w\W]+)\*\//
contains_comment = "this.bit.is.code()
/* this is a comment
this too */
more.code()"
extract_comment.match(contains_comment)[1].strip
# => "this is a comment\nthis too"
This is what your syntax highlighter is doing!
$1 $2
(Ruby, Rust, PHP, Java)\1 \2
(Javascript, Python)%1 %2
(Clojure)"bar baz".match(/ba\w/) // [ "bar", index: 0, input: "bar baz" ]
"bar baz".match(/ba(\w)/) // [ "bar", "r", index: 0 input "bar baz" ]
"bar baz".match(/ba\w/g) // [ "bar", "baz" ]
"bar baz".match(/ba(\w)/g) // [ "bar", "baz" ]
"bar baz".match(/foo/) // null
"bar baz".match(/foo/g) // null
// The "correct" way to capture globally.
var matches, output = [];
while (matches = /ba(\w)/.exec("bar baz")) {
output.push(matches[1]);
}


Cleaned profile data from "OkCupid Profile Data for Introductory Statistics and Data Science Courses" (Journal of Statistics Education 2015 <http://www.amstat.org/publications/jse/v23n2/kim.pdf>).
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Published: | 2016-08-19 |
| Author: | Albert Y. Kim [aut, cre], Adriana Escobedo-Land [aut] |
| Maintainer: | Albert Y. Kim <albert.ys.kim at gmail.com> |
| BugReports: | https://github.com/rudeboybert/okcupiddata/issues |
| License: | CC0 |
| URL: | http://github.com/rudeboybert/okcupiddata |
| NeedsCompilation: | no |
| Citation: | okcupiddata citation info |
| Materials: | NEWS |
| CRAN checks: | okcupiddata results |
| Reference manual: | okcupiddata.pdf |
| Package source: | okcupiddata_0.1.0.tar.gz |
| Windows binaries: | r-devel: okcupiddata_0.1.0.zip, r-release: okcupiddata_0.1.0.zip, r-oldrel: okcupiddata_0.1.0.zip |
| OS X binaries: | r-release: okcupiddata_0.1.0.tgz, r-oldrel: okcupiddata_0.1.0.tgz |
| Reverse suggests: | supernova |
Please use the canonical form https://CRAN.R-project.org/package=okcupiddata to link to this page.


As promised I said I would increase my delegation to @msp-active to 10k. I just did that tonight. It was up from 5,000 when I started the account. I had to wait until some of the personal delegations I had removed came back to me.
auto-votes
I’ve put a number of people onto auto-votes. These are folks that are active in PALnet, chilling in radio shows on msp-waves, or helping in other ways. If you’re not getting these votes and you think that you should contact me in Discord. Put together a 1-2 sentence pitch on what value you offer to the Minnow Support Project Community housed in the Peace, Abundance, and Liberty Discord channel and I’ll evaluate.
Vote Ranges
Most of the voting percentages are between 5-25%. The highest I think I’m doing is 50%. While the vote percentages are indeed a little small it’s worth noting that the account voting power is currently hovering around 98%. So, it’s a smaller vote with a stronger overall account vote power.
General rules
An occasional shit post isn’t the worst thing ever, especially for a minnow, but if you are found to continue to put out low quality content to take advantage of the auto votes I’m removing you from it.
If you’re putting out content that advocate for violence I’ll take you off of it.
If you get banned from PALnet I’ll take you off of it.
This is my SP. I’m doing this as a service. I reserve the right to end the service if people seem to treat the benefit with disrespect.
Next set of delegations
I’m at 8k active SP now. I’d like to be closer to 15k. I’m going to build up my VP to not only 15k, but then add another 5k on top. When I get to 20k I’ll delegate 1-5k to one of the major projects within MSP. I’ll continue adding more and more SP into projects through July/August. Then I’m going to shave off some, start powering down more, and try to catch what seems to be the annual pump in December. I’ll try to buy back in for even more stake in Jan/Feb and then delegate to higher amounts than I have previously.
The four accounts I plan on pumping are msp-mods, msp-curation, msp-active, and msp-waves. The primary account @minnowsupport has 18k SP on it that’s organic and another 100k that’s delegated to it. As long as the other 4 accounts are underneath the amount of organic SP on the mothership account I plan on increasing delegations to those.
2 ways to have enough
You can constantly strive for more wealth to fill an ever larger and upward level of greed or you can be content with what you have and own. I’m going for the latter.  The goal is to spread abundance, and that means helping others. I’m planning to be in this for the long haul and helping to build a better world and I’ll put my SP to the task to do that. Thank you for your support.
Witness Votes
Sorry to keep shilling my Witness. I know that many people support me and I’m fortunate to be a top 20 witness. Recently I’ve moved down from rank 7 to 11. It’s important to stick with the campaign so that I can continue to provide revenue towards the mission of the Minnow Support Project- Spread the values of Peace, Abundance, and Liberty, help the Steem ecosystem grow, and train and retain minnows (new members) on the platform.
If you think those goals and my approach to them are worth please consider voting for me if you haven’t already.
www.steemit.com/@witnesses
This page is synchronized from the post: ‘@msp-active up to 10k’


{% if performer.groups %} {{ performer.first_name }}'s past and present teams {% for group in performer.groups %} {{ group.name }} {% endfor %} {% endif %}


The ancient city of Petra is one of Jordan’s national treasures and by far its best-known tourist attraction. Petra is the legacy of the Nabataeans, an industrious Arab people who settled in southern Jordan more than 2,000 years ago. Petra is a UNESCO World Heritages Site and one of the new Seven Wonders of the World. Inhabited by the Nabateans, Edomites and Romans, Petra brought together the knowledge and skill of these civilizations to create this world wonder.
There is a lot of thing to discover in Petra such as:
- The Treasury (Al Khazna)
- AMMARIN BEDOUIN CAMP
- AL-SIQ TO TREASURY TRAIL
- AD-DEIR MAIN TRAIL
Best Hotels in Petra:
- Petra Guest House Hotel
-Located main Entrance of PETRA, 71810 Wadi Musa, Jordan
-Quiet street view /Mountain view/ City view/ Garden view - Petra Marriott Hotel
- the hotel is 2.3 km from Petra, 1.9 km from Obelisk Tomb and 2.2 km from College of Archaeology.
-Mountain view - Infinity Lodge
-the hotel is 2.3 km from Petra 2.9 km from College of Archaeology
-Mountain view/ Inner courtyard view - Seven Wonders Luxury Camp
-Located in Petra, 8 km from Petra Visitors Centre.
-Mountain view/ Garden view


#{ModelBean.getTwitterLeadList()}
Tweets
Blog Comments
Analyze Anything
Contacts' Tweets
Your contacts' tweets in the last 7 days
#{lead.firstName} #{lead.lastName}
#{lead.email}
#{lead.twitter}
Message
Tone
Req. Guidance
Filter your tweets
Filter Key
Hide leads w/o tweets?


The code style is quirky and based on an older style I've long abandoned. Nonetheless, perhaps it is somewhat interesting.
BxoTest demonstrates shading, texturing, and rotation. A rotating objects shifts from a cube to a spheroid and back again. Tons of crazy controls.
SkyboxTest demonstrates a sphere rendered at different level of detail (LOD). More polygons are used when the object occupies more screen real estate and fewer when it is smaller.


 Classes are basically a Ruby construct that allows us to implement properties and inherit those properties by its instantiated objects. Read more about Ruby classes here. Another great use of classes in Ruby support the idea that the property of one class can be shared with another class using inheritance. However, sometimes we want to have two classes that are able to inherit from each other, not just one from a parent class. This is where modules come in. Modules are a collection of methods that a class can add to it’s collection using the include keyword followed by the module name.
I recently encountered the need for modules during this week’s solo challenge for DBC. The idea was to create a playlist that had two classes: song and playlist. The challenge’s tests were requiring a pass on a song#play method but also a playlist#play_all method. I wanted to use the play method I built for the song class for each song in the playlist, after all--every playlist is composed of more than one song. However, I had defined the play method for the song class and I could not call that method on an object that hadn’t been instantiated by the song class. So here’s what I did:
class Song def initialize(song, artist) @song = song @artist = artist end def play p "Now playing #{@song} by<EMAIL_ADDRESS>end end class Playlist < Song # Here I use the < to say Class#Song is the parent of Class#Playlist def initialize(*song) @playlist = [*song] end def add(*song) @playlist.push(*song) end def remove(*song) @playlist.delete(*song) end def includes?(song) @playlist.include?(song) end def play_all @playlist.each {|x| x.play} end def num_of_tracks @playlist.count end end
Notice how I used the "<" when naming the Playlist class to signify that all playlist objects are songs, hence, all playlists can use the Song class methods. But, there’s another way using modules and it's a much more flexible:
module Music def play p "Now playing #{@song} by<EMAIL_ADDRESS>end end class Song include Music def initialize(song, artist) @song = song @artist = artist end end class Playlist include Music def initialize(*song) @playlist = [*song] end def add(*song) @playlist.push(*song) end def remove(*song) @playlist.delete(*song) end def includes?(song) @playlist.include?(song) end def play_all @playlist.each {|x| x.play} end def num_of_tracks @playlist.count end end
By creating a module called Music, I was able to define any number of methods I wanted to include as a part of another class that called on it with include. Now both Song and Playlist objects have the ability to call on the method #play.
posted Sep. 13th, 2014


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SUMMARY
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Completed
2 jobs completed
In Progress
3 upcoming jobs
Job Lister
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10 mins
Walking Distance
$30
Once a week
DESCRIPTION
Description of job Description of job Description of job Description of job Description of job
Completed
On 25/02/2014
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10 mins
Walking Distance
$30
Once a week
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On 28/02/2014


high_scalability high_scalability-lda knowledge-graph by maker-knowledge-mining
topicId: wordWeight*wordName (topN-words)
topic #0: 0.004*nuodb + 0.004*berkeley + 0.004*lore + 0.004*pleasestart + 0.004*tachyon + 0.003*django + 0.002*viddler + 0.002*curve + 0.002*science + 0.002*backward
topic #1: 0.007*cloud + 0.006*monitoring + 0.006*servers + 0.006*performance + 0.006*applications + 0.006*web + 0.005*application + 0.005*free + 0.005*java + 0.004*looking
topic #2: 0.005*system + 0.005*load + 0.005*cache + 0.005*database + 0.005*memory + 0.004*mysql + 0.004*architecture + 0.004*data + 0.004*much + 0.004*like
topic #3: 0.005*filters + 0.003*kent + 0.003*subjected + 0.003*synchronization + 0.003*langley + 0.003*absolute + 0.003*xml + 0.003*lew + 0.003*con + 0.002*sun
topic #4: 0.003*resizable + 0.003*oculus + 0.003*netmap + 0.003*tag + 0.003*henderson + 0.003*clustrix + 0.003*cal + 0.002*vr + 0.002*primer + 0.002*heterogeneity
topic #5: 0.004*consists + 0.003*jim + 0.003*moore + 0.003*friendster + 0.003*commutative + 0.003*sensors + 0.003*gigaom + 0.003*phones + 0.003*arrays + 0.003*peg
topic #6: 0.005*slick + 0.004*lease + 0.004*infinisql + 0.004*administrator + 0.003*draws + 0.003*federated + 0.002*paxos + 0.002*storyhere + 0.002*pretty + 0.002*concentrates
topic #7: 0.003*drills + 0.003*zynga + 0.003*activity + 0.002*hyperic + 0.002*typepad + 0.002*mongrel + 0.002*hq + 0.002*todd + 0.002*realtime + 0.002*litespeed
topic #8: 0.005*zookeeper + 0.004*cloudcamp + 0.004*training + 0.003*voltdb + 0.003*dryad + 0.003*resisters + 0.003*fai + 0.003*july + 0.002*supporters + 0.002*fc
topic #9: 0.005*mar + 0.004*vedis + 0.003*reducing + 0.003*percolator + 0.003*pointers + 0.003*abound + 0.003*jurriaan + 0.002*cq + 0.002*flux + 0.002*dataflow
topic #10: 0.007*instances + 0.006*version + 0.006*expensive + 0.006*resource + 0.006*ebs + 0.005*random + 0.005*needed + 0.005*articles + 0.005*instance + 0.004*else
topic #11: 0.004*wikipedia + 0.003*socket + 0.003*masstree + 0.003*continental + 0.002*mituzas + 0.002*gslb + 0.002*bizo + 0.002*amanda + 0.002*zmanda + 0.002*tree
topic #12: 0.005*livejournal + 0.003*french + 0.003*registrar + 0.003*gce + 0.003*glusterfs + 0.002*hp + 0.002*divides + 0.002*servlets + 0.002*screencasts + 0.002*seamless
topic #13: 0.003*parascale + 0.003*sans + 0.003*armstrong + 0.002*fbi + 0.002*downside + 0.002*cuckoo + 0.002*weather + 0.002*dedicate + 0.002*roblox + 0.002*crops
topic #14: 0.003*leaf + 0.003*datacenterknowledge + 0.003*pnfs + 0.003*replacement + 0.003*parent + 0.002*crawling + 0.002*blekko + 0.002*postdoc + 0.002*wit + 0.002*london
topic #15: 0.004*couchconf + 0.004*millwheel + 0.003*terabyte + 0.003*lectures + 0.002*php + 0.002*spread + 0.002*templating + 0.002*dell + 0.002*acquainted + 0.002*wikipedia
topic #16: 0.004*agents + 0.003*monkeys + 0.003*knows + 0.003*batching + 0.002*anyone + 0.002*summarized + 0.002*thereis + 0.002*behind + 0.002*upstream + 0.002*encouraged
topic #17: 0.004*slime + 0.004*accesses + 0.004*insert + 0.004*edges + 0.004*king + 0.003*loading + 0.003*mold + 0.003*training + 0.003*sun + 0.003*pregel
topic #18: 0.004*tunnels + 0.003*slas + 0.003*ria + 0.003*quickbooks + 0.002*dynamo + 0.002*vp + 0.002*contents + 0.002*kettle + 0.002*pixel + 0.001*asolution
topic #19: 0.007*cloudkick + 0.005*membase + 0.005*utilities + 0.005*joyent + 0.005*appirio + 0.005*kabam + 0.005*analyst + 0.004*position + 0.004*icloud + 0.003*vancouver
topic #20: 0.003*sensible + 0.002*squarespace + 0.002*talkon + 0.002*httpd + 0.002*nas + 0.002*stated + 0.002*replies + 0.002*statements + 0.002*aker + 0.002*nov
topic #21: 0.005*forums + 0.003*elastra + 0.003*ldb + 0.003*dispatcher + 0.003*amplify + 0.003*subdivide + 0.003*espn + 0.003*cslid + 0.003*gartner + 0.002*highway
topic #22: 0.005*farmville + 0.003*telegraph + 0.003*olio + 0.003*print + 0.003*gearmand + 0.002*weighted + 0.002*veriscale + 0.002*gearman + 0.002*alot + 0.002*jobs
topic #23: 0.015*stateserver + 0.009*cloudsigma + 0.007*european + 0.006*discover + 0.005*ads + 0.005*subscribe + 0.004*tailored + 0.004*europe + 0.004*cloudera + 0.004*impressions
topic #24: 0.004*func + 0.004*scheduler + 0.003*dido + 0.003*puppet + 0.002*workers + 0.002*airbnb + 0.002*card + 0.002*santiago + 0.002*jobs + 0.002*basecamp
topic #25: 0.005*tokyo + 0.004*repository + 0.003*tags + 0.003*megastore + 0.003*entered + 0.003*enter + 0.003*calculating + 0.003*neither + 0.003*sequences + 0.003*teraflops
topic #26: 0.004*sleep + 0.004*linearly + 0.004*linkedin + 0.004*curve + 0.003*ea + 0.003*exhibit + 0.003*ddd + 0.003*thethe + 0.003*organizational + 0.003*beta
topic #27: 0.005*triggers + 0.004*measuring + 0.003*improving + 0.003*parallelization + 0.003*partially + 0.003*separated + 0.003*metrocluster + 0.003*codes + 0.003*cutting + 0.003*conjecture
topic #28: 0.003*kngine + 0.003*aditya + 0.003*mailinator + 0.003*ddg + 0.003*ml + 0.003*suit + 0.002*parse + 0.002*counters + 0.002*imdb + 0.002*ing
topic #29: 0.004*gemfire + 0.003*hadoopsummit + 0.003*facebook + 0.003*hpc + 0.002*weblog + 0.002*ace + 0.002*duckduckgo + 0.002*faces + 0.002*snabb + 0.002*worthy
topic #30: 0.007*gb + 0.007*image + 0.006*images + 0.005*cached + 0.005*hour + 0.004*nfs + 0.004*book + 0.004*command + 0.004*percent + 0.004*beyond
topic #31: 0.004*jose + 0.003*wuala + 0.003*european + 0.003*wiredrive + 0.003*august + 0.003*cloudsigma + 0.003*trafficked + 0.003*home + 0.003*deviantart + 0.003*whitepaper
topic #32: 0.009*guidelines + 0.004*advertise + 0.003*discounts + 0.003*details + 0.003*cookie + 0.003*contact + 0.003*registrations + 0.002*blue + 0.002*scalability + 0.002*scala
topic #33: 0.003*drupal + 0.003*coherence + 0.003*mistress + 0.002*dmz + 0.002*tigase + 0.002*starters + 0.002*incubator + 0.002*zoosk + 0.002*newbie + 0.002*bangdb
topic #34: 0.006*rackspace + 0.004*front + 0.004*valued + 0.004*dreams + 0.004*usability + 0.003*wackamole + 0.003*udfs + 0.003*admins + 0.003*moment + 0.003*joel
topic #35: 0.003*dynamodb + 0.003*bitcask + 0.002*whirling + 0.002*rum + 0.002*terracotta + 0.002*causal + 0.002*applicaitons + 0.002*fucntionality + 0.002*compact + 0.002*median
topic #36: 0.004*icq + 0.004*quantum + 0.003*parallelism + 0.003*dating + 0.002*imdb + 0.002*edward + 0.002*slide + 0.002*firm + 0.002*capriolo + 0.002*presents
topic #37: 0.005*tungsten + 0.005*netadvantage + 0.004*austin + 0.004*administration + 0.003*outage + 0.003*administering + 0.003*referred + 0.003*kfs + 0.003*pinboard + 0.003*nginx
topic #38: 0.003*timeout + 0.003*frangipani + 0.003*lsi + 0.003*sata + 0.002*federation + 0.002*spike + 0.002*certified + 0.002*mail + 0.002*circles + 0.002*spinlocks
topic #39: 0.005*components + 0.003*raft + 0.003*datasift + 0.003*akiban + 0.003*hypertable + 0.003*hadron + 0.003*green + 0.003*artists + 0.003*headers + 0.003*collider
topic #40: 0.007*processors + 0.006*hashing + 0.006*processor + 0.005*utilization + 0.005*pattern + 0.005*culture + 0.004*tests + 0.004*sizes + 0.004*aware + 0.004*latencies
topic #41: 0.004*closure + 0.003*applicant + 0.003*widsets + 0.002*varnish + 0.002*illusions + 0.002*memcachedb + 0.002*hs + 0.002*sub + 0.002*entity + 0.002*agency
topic #42: 0.006*social + 0.005*dart + 0.003*numa + 0.003*tape + 0.003*dryad + 0.003*detective + 0.002*investigating + 0.002*hurdles + 0.002*walled + 0.002*fluctuating
topic #43: 0.004*dram + 0.003*radical + 0.003*hyder + 0.003*visitor + 0.003*jpa + 0.003*abadi + 0.003*hi + 0.003*mullet + 0.003*memories + 0.003*activity
topic #44: 0.005*breakout + 0.004*lineup + 0.004*training + 0.004*dbshards + 0.003*aconex + 0.003*automates + 0.003*mathworks + 0.003*thrive + 0.003*hyatt + 0.003*regency
topic #45: 0.004*wolfram + 0.003*hibernate + 0.003*alpha + 0.003*adsense + 0.003*avg + 0.002*drupal + 0.002*oprah + 0.002*shards + 0.002*scans + 0.002*striping
topic #46: 0.004*voldemort + 0.003*cmss + 0.003*agnostic + 0.003*joomla + 0.003*gemfire + 0.002*apc + 0.002*gizzard + 0.002*acquisitions + 0.002*adrianco + 0.002*programmed
topic #47: 0.008*shards + 0.007*consistency + 0.006*rules + 0.005*served + 0.005*patterns + 0.005*mail + 0.004*entities + 0.004*decide + 0.004*split + 0.004*task
topic #48: 0.004*eucalyptus + 0.004*gridgain + 0.003*cheat + 0.002*refreshing + 0.002*vacation + 0.002*category + 0.002*title + 0.002*demoted + 0.002*hi + 0.002*posting
topic #49: 0.007*patterns + 0.004*accelerator + 0.004*joyent + 0.004*commerce + 0.004*alternatives + 0.003*premature + 0.003*forrester + 0.003*steven + 0.003*faststats + 0.003*memristors
topic #50: 0.004*linkedin + 0.002*slogans + 0.002*cried + 0.002*parrot + 0.002*cking + 0.002*whoever + 0.002*callaghan + 0.002*webserver + 0.002*ck + 0.002*car
topic #51: 0.008*consensus + 0.005*prefer + 0.005*helped + 0.004*steve + 0.004*entity + 0.004*disqus + 0.004*gpu + 0.004*marketplace + 0.004*photos + 0.004*realtime
topic #52: 0.004*prismatic + 0.003*hipchat + 0.003*puppet + 0.003*academy + 0.003*khan + 0.003*worker + 0.002*mumps + 0.002*publicly + 0.002*concerned + 0.002*yslow
topic #53: 0.004*juju + 0.003*tenzing + 0.003*pyshards + 0.003*zachman + 0.003*widget + 0.003*photos + 0.002*widgets + 0.002*shard + 0.002*moresql + 0.002*charms
topic #54: 0.003*myspace + 0.003*openspaces + 0.003*ramcloud + 0.003*volume + 0.002*conquer + 0.002*tempo + 0.002*contextual + 0.002*cio + 0.002*bigmemory + 0.002*prince
topic #55: 0.003*ringo + 0.003*scalingtip + 0.003*eli + 0.002*wsc + 0.002*oftentimes + 0.002*chapter + 0.002*wade + 0.002*chapters + 0.001*tornado + 0.001*summaryof
topic #56: 0.006*tree + 0.005*tour + 0.005*reporting + 0.005*businesses + 0.004*appliance + 0.004*isp + 0.004*fundamental + 0.004*coming + 0.004*trace + 0.003*puppet
topic #57: 0.005*portability + 0.004*ctl + 0.003*dbshards + 0.003*netcache + 0.003*entity + 0.003*databus + 0.003*reddit + 0.003*vagrant + 0.003*hrd + 0.003*ssh
topic #58: 0.003*fastbit + 0.003*buffer + 0.002*jobs + 0.002*bitmap + 0.002*augment + 0.002*etags + 0.002*gc + 0.002*rsync + 0.002*netty + 0.002*ltrim
topic #59: 0.005*affinity + 0.004*cachingepisode + 0.004*sharepoint + 0.004*kv + 0.004*cuda + 0.003*heap + 0.003*bursting + 0.003*gc + 0.003*hypothetical + 0.003*ml
topic #60: 0.005*buy + 0.005*wapt + 0.004*sqlite + 0.003*wave + 0.003*unlikely + 0.003*tenants + 0.003*puppet + 0.003*scenario + 0.002*sauce + 0.002*stratos
topic #61: 0.007*nosql + 0.006*search + 0.005*users + 0.005*part + 0.005*ram + 0.005*means + 0.004*app + 0.004*store + 0.004*easy + 0.004*framework
topic #62: 0.004*hey + 0.003*emr + 0.002*keystrokes + 0.002*overviews + 0.002*stewart + 0.002*worthwhile + 0.002*clark + 0.002*rage + 0.002*edified + 0.002*yields
topic #63: 0.004*coffee + 0.003*calvin + 0.003*hypertable + 0.003*cohesive + 0.003*ravelry + 0.002*cup + 0.002*placements + 0.002*enlisted + 0.002*cloudworks + 0.002*automattic
topic #64: 0.004*china + 0.004*wire + 0.003*nanoseconds + 0.003*nanosecond + 0.003*microseconds + 0.002*exim + 0.002*gmail + 0.002*hotmail + 0.002*bespoke + 0.002*sprinkle
topic #65: 0.003*dance + 0.003*ashort + 0.003*thumb + 0.003*protocol + 0.002*az + 0.002*reinvent + 0.002*beautiful + 0.002*gray + 0.002*examines + 0.002*presence
topic #66: 0.004*mailbox + 0.003*jesse + 0.002*platforms + 0.002*qps + 0.002*boys + 0.002*bjorn + 0.002*thrudb + 0.002*upgrading + 0.002*neutral + 0.002*median
topic #67: 0.003*disco + 0.003*ives + 0.003*hopper + 0.003*grace + 0.003*lesson + 0.002*symphony + 0.002*peoples + 0.002*crime + 0.002*secured + 0.002*conductors
topic #68: 0.003*causal + 0.003*overflow + 0.002*cops + 0.002*adserver + 0.002*alps + 0.002*gig + 0.002*taste + 0.001*sun + 0.001*hw + 0.001*smp
topic #69: 0.005*gce + 0.003*buddypoke + 0.003*schlossnagle + 0.003*memsql + 0.003*gis + 0.003*pownce + 0.003*gcutil + 0.003*spatially + 0.002*theo + 0.002*ofscalable
topic #70: 0.004*openflow + 0.003*intercloud + 0.003*advertisements + 0.003*sysadmin + 0.003*movable + 0.002*consequently + 0.002*feast + 0.002*supervisor + 0.002*demands + 0.002*lest
topic #71: 0.003*anonymous + 0.002*loaded + 0.002*smarty + 0.002*edit + 0.002*oversell + 0.002*posts + 0.001*military + 0.001*seems + 0.001*though + 0.001*invented
topic #72: 0.005*memsql + 0.003*kos + 0.003*kelly + 0.003*scribe + 0.003*colm + 0.002*followed + 0.002*requirement + 0.002*gavethis + 0.002*bluekai + 0.002*char
topic #73: 0.005*webinar + 0.004*collector + 0.004*berkeley + 0.003*approximately + 0.003*microsystems + 0.003*demonstrate + 0.003*erasure + 0.003*codes + 0.003*ubuntu + 0.003*robustness
topic #74: 0.003*soap + 0.003*xap + 0.003*gigaspaces + 0.003*summaryof + 0.003*spreading + 0.002*ashort + 0.002*pdf + 0.002*rental + 0.002*dough + 0.002*fall
topic #75: 0.004*squarespace + 0.004*tempest + 0.003*lustre + 0.003*peregrine + 0.003*tornado + 0.002*friendfeed + 0.002*conclusions + 0.002*zettabytes + 0.002*celeron + 0.002*alsohosts
topic #76: 0.004*coast + 0.004*denormalization + 0.004*opensolaris + 0.003*ben + 0.003*attend + 0.003*concentrated + 0.003*ef + 0.002*licensing + 0.002*authorization + 0.002*spdy
topic #77: 0.006*jobs + 0.005*task + 0.005*jeff + 0.005*era + 0.005*thousand + 0.005*success + 0.005*magic + 0.004*switches + 0.004*plus + 0.004*nature
topic #78: 0.003*collectl + 0.003*study + 0.002*pathologies + 0.002*tales + 0.002*tyrant + 0.001*tokyo + 0.001*stone + 0.001*conducted + 0.001*races + 0.001*sar
topic #79: 0.009*google + 0.005*platform + 0.005*machines + 0.005*sql + 0.005*hardware + 0.005*network + 0.005*problem + 0.005*would + 0.005*internet + 0.004*data
topic #80: 0.004*mono + 0.004*velocityconf + 0.002*water + 0.002*youporn + 0.002*lake + 0.002*imdg + 0.002*filers + 0.002*overlooked + 0.002*aqueduct + 0.002*aztecs
topic #81: 0.005*playfish + 0.004*mollom + 0.004*gravatar + 0.003*meetups + 0.003*rush + 0.002*arts + 0.002*networked + 0.002*variants + 0.002*cisco + 0.002*geoserver
topic #82: 0.005*condor + 0.003*ning + 0.003*pic + 0.003*behindgoogle + 0.002*acquired + 0.002*sms + 0.002*plant + 0.002*galaxy + 0.002*cgi + 0.002*refers
topic #83: 0.004*dean + 0.004*profiling + 0.003*eucalyptus + 0.002*inventory + 0.002*crdt + 0.002*claims + 0.002*josh + 0.002*ensuring + 0.002*holds + 0.002*established
topic #84: 0.004*lustre + 0.004*slab + 0.004*dryadlinq + 0.003*collectl + 0.003*focussed + 0.003*facelift + 0.002*aggregator + 0.002*yermo + 0.002*drive + 0.002*uploader
topic #85: 0.006*views + 0.005*device + 0.005*modern + 0.005*gets + 0.005*matter + 0.005*python + 0.005*erlang + 0.005*transactional + 0.004*though + 0.004*page
topic #86: 0.005*solaris + 0.003*subdomains + 0.003*doctors + 0.002*cosql + 0.002*nsread + 0.002*sequentially + 0.002*quantitatively + 0.002*monads + 0.002*nssend + 0.001*incorporates
topic #87: 0.005*megapipe + 0.004*jrockit + 0.004*tickets + 0.003*truths + 0.003*randy + 0.002*inputs + 0.002*bidding + 0.002*headed + 0.002*couchconf + 0.002*memo
topic #88: 0.004*okta + 0.004*teaser + 0.003*drbd + 0.003*spanner + 0.002*radio + 0.002*steve + 0.002*wozniak + 0.002*downloads + 0.002*functionally + 0.002*parallelization
topic #89: 0.003*photos + 0.003*websphere + 0.003*amundsen + 0.002*applicaitons + 0.002*scott + 0.002*hero + 0.002*south + 0.002*crawling + 0.002*tailrank + 0.002*pole
topic #90: 0.003*facestat + 0.003*stale + 0.002*keen + 0.002*thelarge + 0.002*neilk + 0.002*financial + 0.002*hiphop + 0.002*watt + 0.002*enomaly + 0.002*globus
topic #91: 0.003*cellular + 0.003*graphchi + 0.003*warehouses + 0.003*collectd + 0.003*authority + 0.003*bcube + 0.003*ramcube + 0.002*explode + 0.002*sometime + 0.002*randy
topic #92: 0.004*propagation + 0.004*ef + 0.003*lbpool + 0.003*ciency + 0.003*shrine + 0.003*lightcloud + 0.003*expressive + 0.002*abstractions + 0.002*additionally + 0.002*concern
topic #93: 0.003*sequencing + 0.003*aload + 0.003*genome + 0.003*providers + 0.003*iis + 0.002*rrdtool + 0.002*ip + 0.002*graphing + 0.002*hello + 0.002*atm
topic #94: 0.007*static + 0.007*bandwidth + 0.006*gae + 0.006*clusters + 0.005*facebook + 0.005*algorithm + 0.005*type + 0.005*ip + 0.005*billion + 0.004*object
topic #95: 0.003*portal + 0.003*nameserver + 0.003*mb + 0.003*dc + 0.002*unlimited + 0.002*grows + 0.002*complicates + 0.002*limit + 0.002*nodejs + 0.002*mind
topic #96: 0.005*instagram + 0.005*deduplication + 0.004*normalized + 0.004*negative + 0.003*newbie + 0.003*artist + 0.003*dathan + 0.003*album + 0.003*hammer + 0.003*pattishall
topic #97: 0.004*capistrano + 0.003*proxy + 0.003*continent + 0.002*sun + 0.002*sdb + 0.002*virginia + 0.002*coolthreads + 0.002*sa + 0.002*inserts + 0.002*sparc
topic #98: 0.004*institute + 0.003*institutes + 0.003*tokyo + 0.002*preferences + 0.002*asia + 0.002*supercomputer + 0.002*entertainment + 0.002*axis + 0.002*petabyte + 0.002*united
topic #99: 0.004*fishin + 0.004*varnish + 0.003*disappointing + 0.003*moves + 0.003*posting + 0.003*colin + 0.003*vacation + 0.003*sprouter + 0.002*botnet + 0.002*approve
Latent Dirichlet allocation
In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.
Topics in LDA
In LDA, each document may be viewed as a mixture of various topics. This is similar to probabilistic latent semantic analysis (pLSA), except that in LDA the topic distribution is assumed to have a Dirichlet prior. In practice, this results in more reasonable mixtures of topics in a document. It has been noted, however, that the pLSA model is equivalent to the LDA model under a uniform Dirichlet prior distribution.
For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. A topic has probabilities of generating various words, such as milk, meow, and kitten, which can be classified and interpreted by the viewer as "CAT_related". Naturally, the word cat itself will have high probability given this topic. The DOG_related topic likewise has probabilities of generating each word: puppy, bark, and bone might have high probability. Words without special relevance, such as the (see function word), will have roughly even probability between classes (or can be placed into a separate category). A topic is not strongly defined, neither semantically nor epistemologically. It is identified on the basis of supervised labeling and (manual) pruning on the basis of their likelihood of co-occurrence. A lexical word may occur in several topics with a different probability, however, with a different typical set of neighboring words in each topic.
Each document is assumed to be characterized by a particular set of topics. This is akin to the standard bag of words model assumption, and makes the individual words exchangeable.
Model
With plate notation, the dependencies among the many variables can be captured concisely. The boxes are “plates” representing replicates. The outer plate represents documents, while the inner plate represents the repeated choice of topics and words within a document. M denotes the number of documents, N the number of words in a document. Thus:
α is the parameter of the Dirichlet prior on the per-document topic distributions,
β is the parameter of the Dirichlet prior on the per-topic word distribution,
\theta_i is the topic distribution for document i,
\phi_k is the word distribution for topic k,
z_{ij} is the topic for the jth word in document i, and
w_{ij} is the specific word.
The w_{ij} are the only observable variables, and the other variables are latent variables. Mostly, the basic LDA model will be extended to a smoothed version to gain better results .[citation needed] The plate notation is shown on the right, where K denotes the number of topics considered in the model and:
\phi is a K*V (V is the dimension of the vocabulary) Markov matrix each row of which denotes the word distribution of a topic.
The generative process behind is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words. LDA assumes the following generative process for a corpus D consisting of M documents each of length N_i:
1. Choose \theta_i \, \sim \, \mathrm{Dir}(\alpha) , where i \in \{ 1,\dots,M \} and \mathrm{Dir}(\alpha) is the Dirichlet distribution for parameter \alpha
2. Choose \phi_k \, \sim \, \mathrm{Dir}(\beta) , where k \in \{ 1,\dots,K \}
3. For each of the word positions i, j, where j \in \{ 1,\dots,N_i \} , and i \in \{ 1,\dots,M \}
(a) Choose a topic z_{i,j} \,\sim\, \mathrm{Multinomial}(\theta_i).
(b) Choose a word w_{i,j} \,\sim\, \mathrm{Multinomial}( \phi_{z_{i,j}}) .
(Note that the Multinomial distribution here refers to the Multinomial with only one trial. It is formally equivalent to the categorical distribution.)
The lengths N_i are treated as independent of all the other data generating variables (q and z). The subscript is often dropped, as in the plate diagrams shown here.
Inference
Learning the various distributions (the set of topics, their associated word probabilities, the topic of each word, and the particular topic mixture of each document) is a problem of Bayesian inference. The original paper used a variational Bayes approximation of the posterior distribution;[1] alternative inference techniques use Gibbs sampling and expectation propagation.
Following is the derivation of the equations for collapsed Gibbs sampling, which means \varphis and \thetas will be integrated out. For simplicity, in this derivation the documents are all assumed to have the same length N_{}. The derivation is equally valid if the document lengths vary.
Applications, extensions and similar techniques
Topic modeling is a classic problem in information retrieval. Related models and techniques are, among others, latent semantic indexing, independent component analysis, probabilistic latent semantic indexing, non-negative matrix factorization, and Gamma-Poisson distribution.
The LDA model is highly modular and can therefore be easily extended. The main field of interest is modeling relations between topics. This is achieved by using another distribution on the simplex instead of the Dirichlet. The Correlated Topic Model follows this approach, inducing a correlation structure between topics by using the logistic normal distribution instead of the Dirichlet. Another extension is the hierarchical LDA (hLDA), where topics are joined together in a hierarchy by using the nested Chinese restaurant process. LDA can also be extended to a corpus in which a document includes two types of information (e.g., words and names), as in the LDA-dual model. Nonparametric extensions of LDA include the Hierarchical Dirichlet process mixture model, which allows the number of topics to be unbounded and learnt from data and the Nested Chinese Restaurant Process which allows topics to be arranged in a hierarchy whose structure is learnt from data.
As noted earlier, PLSA is similar to LDA. The LDA model is essentially the Bayesian version of PLSA model. The Bayesian formulation tends to perform better on small datasets because Bayesian methods can avoid overfitting the data. In a very large dataset, the results are probably the same. One difference is that PLSA uses a variable d to represent a document in the training set. So in PLSA, when presented with a document the model hasn't seen before, we fix \Pr(w \mid z)—the probability of words under topics—to be that learned from the training set and use the same EM algorithm to infer \Pr(z \mid d)—the topic distribution under d. Blei argues that this step is cheating because you are essentially refitting the model to the new data.
Variations on LDA have been used to automatically put natural images into categories, such as "bedroom" or "forest", by treating an image as a document, and small patches of the image as words; one of the variations is called Spatial Latent Dirichlet Allocation.
from wiki http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation


To Do List
- store preferences to sqlite3 database
- minutes for each button
- beep, vibrate, blink toggle fields
- see debugger/notes section
- reset to sytem defaults (drop table)
- display meta info about past pomodoros and breaks
- do something when the timer hits 0
- beep, vibrate, blink, ...
- on stop, call pe.stop()
- stop button?
- mark interruptions?


Big Big Train announce Ramblin' Man warm up show at Friars
by https://www.facebook.com/progFrench proggers Lazuli will support...
Big Big Train have announced a warm up gig at friars ahead of their headlining slot at Ramblin' Man Fair next year. They will play Friars in Aylesbury on Thursday 16 June.
Big Big Train headline the Prog In The Park stage at next year's Ramblin' Man Fair on a bill that also features Pure Reason Revolution, Curved Air, Moon Safari, Blurred Vision and Lazuli. Lazuli have been announced as the support act for the Friars show.
There will be a ticket pre-sale for the gig at 10.00am on Wednesday 27th Nov with a code which will be sent out in the Friars newsletter on Tuesday 26th Nov. Tickets will go on general sale at 10.00am on Friday 29th Nov. Tickets are limited to a max of 6 per person.
You can subscribe to the Friars newsletter via the venue website.


Paris to become a knowledge partner for sustainable cities
by Babalwa BunganeThe World Bank and the city of Paris, France are joining forces to support sustainable city efforts across the globe, especially cities and metropolitan regions in developing and emerging economies.
In a Memorandum of Understanding (MoU), the World Bank and Paris agree to cooperate on sharing knowledge, tools, and good practices with other territories and cities.
Under this MoU, Paris will become a Knowledge Partner under the Global Platform for Sustainable Cities (GPSC), which is supported by the Global Environment Facility (GEF), to work with the Bank in sharing knowledge and experience to support cities in the following areas:
- Climate change, urban environment, and sustainability issues;
- Inclusive cities including underprivileged groups and refugees;
- Urban management, urban design and public spaces, mobility and spatial development issues; and
- Disaster risk management and urban resilience.
Read the latest on the Paris Agreement
“This is an exciting opportunity for cities around the world to learn from one of the leading cities in the world in urban design, public and green spaces, and climate smart resilient development,” said Sameh Wahba, World Bank global director for urban, disaster risk management, resilience, and land. “We are pleased to partner with Paris to bring that knowledge to a global level for all to benefit from.”
"The City of Paris is looking forward to this mutually rewarding association with the World Bank and the GPSC. Solidarity and international exchanges are a priority of this municipality, especially on themes related to environment preservation," said Emmanuel Grégoire, first Deputy Mayor of the City of Paris.
Grégoire added: "In this period of transition, a sharing community becomes essential and I am delighted to see that the World Bank is sharing its knowledge in such a concrete and generous way. This agreement marks the beginning of fruitful exchanges with our partners, which will provide important input to our staff and Parisians."


Hey js developers
" JavaScript is awesome. It's easy to learn partially, and much harder to learn completely "
open presentation now to follow up
you have to install node js before we go ...
Node js :
download node js
usefull link
youtube :
async and await
promises
feel free to contact with us
Mustafa :
facebook
Abdullah :
facebook


Global Functions in Namespace cppu
in Sourcefile shlib.hxx
- loadSharedLibComponentFactory
- ::com::sun::star::uno::Reference< ::com::sun::star::uno::XInterface > loadSharedLibComponentFactory(
-
| virtual |
abstract |
const |
volatile |
template |
static |
inline |
C-linkage |
| NO |
NO |
NO |
NO |
NO |
NO |
NO |
NO |
- Description
- Loads a shared library component and gets the factory out of it. You can give either a
fully qualified libname or single lib name. The libname need not be pre/postfixed
(e.g. xxx.dll). You can give parameter rPath to force lookup of the library in a specific
directory. The resulting path of the library will be checked against environment variable
CPLD_ACCESSPATH if set.
- Parameters
| rLibName |
name of the library
|
| rPath |
optional path
|
| rImplName |
implementation to be retrieved from the library
|
| xMgr |
service manager to be provided to the component
|
| xKey |
registry key to be provided to the component
|
- Return
-
factory instance (::com::sun::star::lang::XSingleComponentFactory or
::com::sun::star::lang::XSingleComponentFactory)
- writeSharedLibComponentInfo
- void writeSharedLibComponentInfo(
-
| virtual |
abstract |
const |
volatile |
template |
static |
inline |
C-linkage |
| NO |
NO |
NO |
NO |
NO |
NO |
NO |
NO |
- D E P R E C A T E D
- Description
- Invokes component_writeInfo() function of specified component library. You can give either
a fully qualified libname or single lib name. The libname need not be pre/postfixed
(e.g. xxx.dll). You can give parameter rPath to force lookup of the library in a specific
directory. The resulting path of the library will be checked against environment variable
CPLD_ACCESSPATH if set.
component_writeInfo should no longer be used in new components
- Parameters
| rLibName |
name of the library
|
| rPath |
optional path
|
| xMgr |
service manager to be provided to the component
|
| xKey |
registry key to be provided to the component
|
Top of Page
Copyright © 2012, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


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--- layout: post title: On installing MHA date: type: post published: false status: draft categories: - MySQL tags: [] meta: _edit_last: '2' author: login: shlomi email<EMAIL_ADDRESS>display_name: shlomi first_name: Shlomi last_name: Noach ---
On ubuntu server:
"mha4mysql-manager depends on mha4mysql-node"
Need to install node first; although there is no 0.55 version - so install node 0.54 and then manager 0.55


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| org.apache.batik.extension.svg | |
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| AltGlyphHandler
An interface for handling altGlyphs. |
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A Glyph describes a graphics node with some specific glyph rendering attributes. |
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An interface for all GVT font classes. |
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An interface for all font family classes. |
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An interface for all GVT GlyphVector classes. |
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GVTLineMetrics is a GVT version of java.awt.font.LineMetrics. |
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The Kern class describes an entry in the "kerning table". |
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An interface for all GVT GlyphVector classes. |
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| AWTFontFamily
A font family class for AWT fonts. |
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To manage collisions |
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| AWTGlyphGeometryCache.Value
The object that holds glyph geometry. |
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| AWTGVTFont
This is a wrapper class for a java.awt.Font instance. |
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| Glyph
A Glyph describes a graphics node with some specific glyph rendering attributes. |
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| GVTFont
An interface for all GVT font classes. |
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| GVTFontFace
One line Class Desc Complete Class Desc |
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| GVTFontFamily
An interface for all font family classes. |
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| GVTGlyphMetrics
GVTGlyphMetrics is essentially a wrapper class for java.awt.font.GlyphMetrics with the addition of horizontal and vertical advance values. |
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| GVTGlyphVector
An interface for all GVT GlyphVector classes. |
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| GVTLineMetrics
GVTLineMetrics is a GVT version of java.awt.font.LineMetrics. |
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| Kern
The Kern class describes an entry in the "kerning table". |
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| UnicodeRange
A class that represents a CSS unicode range. |
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| UnresolvedFontFamily
A font family class for unresolved fonts. |
| Classes in org.apache.batik.gvt.font used by org.apache.batik.gvt.text | |
|---|---|
| GVTFont
An interface for all GVT font classes. |
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| GVTGlyphMetrics
GVTGlyphMetrics is essentially a wrapper class for java.awt.font.GlyphMetrics with the addition of horizontal and vertical advance values. |
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| GVTGlyphVector
An interface for all GVT GlyphVector classes. |
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| GVTLineMetrics
GVTLineMetrics is a GVT version of java.awt.font.LineMetrics. |
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Images featured in this demo are the works of Mernan Behairi. Inspired by an old post of @Sohtanaka. It originally uses jQuery. Access original tutorial and demo.


during my time at landing, I was heavily focused on feature by feature work which heavily emphasized key growth metrics.
Overview
the work you’ll see below, will help explain the product, and also show some features that we’re focused on for growth and information architecture.
basics of the platform
the primary view for the creator type on the platform.
the creator type of user would often be on the moodboard creation page, which allowed them to upload image files and write details + link asset credits.
after a user uploads an asset, there's a level of detail they can add.
the creator type of user would often be on the moodboard creation page, which allowed them to upload image files and write details + link asset credits.
the public board after a users creates a board.
when the user shares their board, other users can engage through commenting, reactions, and remixing. remixing is one of the special features of landing where you can use another moodboard as a starting point for your own board (more details later in case study)
the public board after a users creates a board.
all public boards live on the various feeds, one for all of the users that you follow, one feed for everything on the platform and can be filtered by specifics, and the last would be the community featured boards.
tldr
the bread and butter of the platform was allowing users to have a quick way to put together image assets into one “board”. then continue to build a community around the types of boards they enjoy, as well as see how others iterate on thier content. for partnered brands the data on products used in these boards was key for brand deals + influencers.
assorted ui updates
creating card updates, avoiding simply copying other apps.
card updates
when updating the cards, from the previous iterations we wanted to clean up the entire feed for information that would be most valuable on this app. we wanted others to see that their was activity on this platform with the comment count, and allow them engage from the feed as well.
features related to growth
user refferal - how might we allow users to feel included
we wanted folks to feel included in growing the platform, given not being one of the social media giants. so we started with a small refferal program, with it’s first iteration having a count of how many people you bring into the platform (from your content, or invitation link). eventually the plan would be with physical incentives like merch or a status notification near the username of the user.
profile page updates
To go in the direction of a profile page that had the user feel like they had a place of their own on the internet. we launched various methods of user testing within our power users and some folks that never used the platform before. gaining insights about what levels of customization they should have on their profile.
spaces or many would call it collection view
another type of user on the platform was someone who didn’t do much creating but enjoyed simply organizing content, think of a pinterest user.
so I lead the initative of creating “spaces” which was a collection of boards a user could collect from the public feed.
eventually users could engage with a space, following or leaving reactions and comments.
Content Organization (Infomation Architecture Work)
with users having the ability to remix or springboard off another board, the original user creating needed remain creditted. I worked on a system to make sure the information architecture of remixing always went back to the original creator and even the modifiers.
In Retrospective
my time at landing was a great learning experience, doubling with product design and brand building at a small startup. going back i’d love to make changes with micro-interactions and just small little tweaks to make it all feel “whole”. none the less i’m sure the direction they’re headed in will give much success.


This week we learn to use Fusion 360 and design some 3D models with it. I think this week is quite easy for me. The most difficult part of the designs is the plan a way of making it so that I can find the most convenient way of making it. Overall, I was quite successful of design these shapes.


bsh
Class UtilEvalError
java.lang.Object
java.lang.Throwable
java.lang.Exception
bsh.UtilEvalError
- All Implemented Interfaces:
- java.io.Serializable
- Direct Known Subclasses:
- Capabilities.Unavailable, ClassPathException, UtilTargetError
public class UtilEvalError
- extends java.lang.Exception
UtilEvalError is an error corresponding to an EvalError but thrown by a
utility or other class that does not have the caller context (Node)
available to it. A normal EvalError must supply the caller Node in order
for error messages to be pinned to the correct line and location in the
script. UtilEvalError is a checked exception that is *not* a subtype of
EvalError, but instead must be caught and rethrown as an EvalError by
the a nearest location with context. The method toEvalError( Node )
should be used to throw the EvalError, supplying the node.
To summarize: Utilities throw UtilEvalError. ASTs throw EvalError.
ASTs catch UtilEvalError and rethrow it as EvalError using
toEvalError( Node ).
Philosophically, EvalError and UtilEvalError corrospond to
RuntimeException. However they are constrained in this way in order to
add the context for error reporting.
- See Also:
UtilTargetError
,
Serialized Form
| Methods inherited from class java.lang.Throwable |
fillInStackTrace, getCause, getLocalizedMessage, getMessage, getStackTrace, initCause, printStackTrace, printStackTrace, printStackTrace, setStackTrace, toString |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
UtilEvalError
protected UtilEvalError()
UtilEvalError
public UtilEvalError(java.lang.String s)
toEvalError
public EvalError toEvalError(java.lang.String msg,
bsh.SimpleNode node,
CallStack callstack)
- Re-throw as an eval error, prefixing msg to the message and specifying
the node. If a node already exists the addNode is ignored.
- Parameters:
msg
- may be null for no additional message.- See Also:
toEvalError
public EvalError toEvalError(bsh.SimpleNode node,
CallStack callstack)
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