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The ucar.util.prefs.ui package builds on ucar.util.prefs to provide convenience routines for building user preference dialogs and other user interface components.
See https://www.unidata.ucar.edu/staff/caron/prefs/


Card# 12/50
Siberian Tigers, also known as Amur tigers, are the largest cats in the world.
Distinguished by their striped fur, no two tigers stripes are identical. The stripes can be thought of akin to fingerprints. The Siberian Tigers have fewer, paler stripes and also have manes. Manes, as well as thick fur, help keep them warm in the cold conditions.


My name is Xinyang Li. I am a Ph.D. student at the Huo Group, University of Rochester.
My work has focused on understanding chemical reaction mechanisms through the reaction rate calculations from molecular dynamics simulations. Before I joined Prof. Huo's group, I was a nanoscientist.
I was born in a small city in China. I finished my bachelor's degree at Wuhan University in 2014. I chose chemistry as my major because I really liked chemistry in high school... then obliviously I have been struggling until now... 😂
For more information on my professional experience, please check
my résumé
out!
 I played WoWS for years before I quitted. I was top 10% or 5% player on NA server. Right now, I mainly play Elite: Dangerous. I have written some Python scripts for the game, which you can find on my GitHub account.
I also enjoy repairing broken stuff or making use of junks. I guess I will share something I have been doing in the future.
Some fun facts about me and the site:


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Using qucat with the graphical circuit editor¶
In this example we study a typical circuit QED system consisting of a transmon qubit coupled to a resonator.
[1]:
import numpy as np
# Import the graphical user interface
from qucat import GUI
# For the purpose of this tutorial,
# we create a file describing the circuit.
# This file would usually be created from scratch
# by the user after opening the graphical circuit editor.
import os
os.makedirs('circuits/', exist_ok=True)
with open('circuits/transmon_LC_GUI.txt','w') as f:
f.write("""C;0,-1;1,-1;1.000000e-15;
C;-1,0;-1,-1;1.000000e-13;
J;0,0;0,-1;8.000000e-09;
W;-1,0;0,0;;
W;-1,-1;0,-1;;
C;1,0;1,-1;1.000000e-13;
L;2,0;2,-1;1.000000e-08;
W;1,0;2,0;;
W;1,-1;2,-1;;
G;2,1;2,0;;
G;-1,1;-1,0;;
R;3,0;3,-1;1.000000e+06;
W;2,0;3,0;;
W;2,-1;3,-1;;
""")
Construct the circuit¶
Below we open the editor. All changes made to the circuit are saved automatically to the file circuits/transmon_LC_GUI.txt
and when we shut down the editor, the variable cir
here will become a quantum circuit object qucat.Qcircuit
with which we will analyze the circuit.
Note: by default the junction is parametrized by its josephson inductance
[2]:
cir = GUI('circuits/transmon_LC_GUI.txt', # location of the circuit file
edit=True, # open the GUI to edit the circuit
plot=True, # plot the circuit after having edited it
print_network=True # print the network
)
C 1 2 1 fF
C 0 1 100 fF
J 0 1 8 nH
C 0 2 100 fF
L 0 2 10 nH
R 0 2 1 MOhm
The netork printed above allows one to verify that the circuit is indeed correct. The first symbol corresponds to the type of element (C
, J
, L
, R
for capacitors, junctions, inductors and resistors respectively). The second two following integers correspond to the nodes between which the component is created. Here 0
corresponds to the ground node, and we can see that node 1
is directly connected to ground through only two elements, a capacitor and a junction. Finally, the
last part of a line corresponds to the value of that component.
Circuit parameters¶
We now calculate the eigenfrequency, loss-rates, anharmonicity, and Kerr parameters of the circuit.
This can be done through the functions eigenfrequencies
, loss_rates
, anharmonicities
and kerr
, which return the specified quantities for each mode, ordered with increasing mode frequency.
Eigen-frequencies¶
[3]:
cir.eigenfrequencies()
[3]:
array([5.00696407e+09, 5.60042136e+09])
This will return a list of the normal modes of the circuit, we can see they are seperated in frequency by 600 MHz, but we still do not which corresponds to the transmon, and which to the resonator.
To distinquish the two, we can calculate the anharmonicities of each mode.
Anharmonicity¶
[4]:
cir.anharmonicities()
[4]:
array([5.83759906e+02, 1.91131052e+08])
The first (lowest frequency) mode, has a very small anharmonicity, whilst the second, has an anharmonicity of 191 MHz. The highest frequency mode thus corresponds to the transmon.
Cross-Kerr or dispersive shift¶
In this regime of far detuning in frequency, the two modes will interact through a cross-Kerr or dispersive shift, which quantifies the amount by which one mode will shift if frequency if the other is populated with a photon.
We can access this by calculating the Kerr parameters K
. In this two dimensional array, the components K[i,j]
correspond to the cross-Kerr interaction of mode i
with mode j
.
[5]:
K = cir.kerr()
print("%.2f kHz"%(K[0,1]/1e3))
668.06 kHz
From the above, we have found that the cross-Kerr interaction between these two modes is of about 670 kHz.
This should correspond to \(2\sqrt{A_0A_1}\) where \(A_i\) is the anharmonicity of mode \(i\). Let’s check that:
[6]:
A = cir.anharmonicities()
print("%.2f kHz"%(2*np.sqrt(A[0]*A[1])/1e3))
668.06 kHz
Loss rates¶
In the studied circuit, the only resistor is located in the resonator. In this regime of large frequency, detuning, we would thus expect the resonator to be more lossy than the transmon.
[7]:
cir.loss_rates()
[7]:
array([786052.42260112, 1920.57996169])
\(T_1\) times¶
When converting these rates to \(T_1\) times, one should not forget the \(2\pi\) in the conversion
[8]:
T_1 = 1/cir.loss_rates()/2/np.pi
print(T_1)
[2.02473701e-07 8.28681681e-05]
All these relevant parameters (frequency, dissipation, anharmonicity and Kerr parameters) can be computed using a single function
[9]:
cir.f_k_A_chi()
[9]:
(array([5.00696407e+09, 5.60042136e+09]),
array([786052.42260112, 1920.57996169]),
array([5.83759906e+02, 1.91131052e+08]),
array([[5.83759906e+02, 6.68055821e+05],
[6.68055821e+05, 1.91131052e+08]]))
Using the option pretty_print = True
a more readable summary can be printed
[10]:
f,k,A,chi = cir.f_k_A_chi(pretty_print=True)
mode | freq. | diss. | anha. |
0 | 5.01 GHz | 786 kHz | 584 Hz |
1 | 5.6 GHz | 1.92 kHz | 191 MHz |
Kerr coefficients
(diagonal = Kerr, off-diagonal = cross-Kerr)
mode | 0 | 1 |
0 | 584 Hz | |
1 | 668 kHz | 191 MHz |
Sweeping a parameter¶
[11]:
# For the second part of this tutorial, we will need
# a new circuit defined by the file below.
# This file would usually be created from scratch
# by the user after opening the graphical circuit editor.
with open('circuits/transmon_LC_GUI_symbolic.txt','w') as f:
f.write("""C;0,-1;1,-1;1.000000e-15;
C;-1,0;-1,-1;1.000000e-13;
J;0,0;0,-1;;L_J
W;-1,0;0,0;;
W;-1,-1;0,-1;;
C;1,0;1,-1;1.000000e-13;
L;2,0;2,-1;1.000000e-08;
W;1,0;2,0;;
W;1,-1;2,-1;;
G;2,1;2,0;;
G;-1,1;-1,0;;
R;3,0;3,-1;1.000000e+06;
W;2,0;3,0;;
W;2,-1;3,-1;;
""")
Below we open the editor again, this time with a different file, corresponding to a slightly different circuit.
[12]:
cir = GUI('circuits/transmon_LC_GUI_symbolic.txt', # location of the circuit file
edit=True, # open the GUI to edit the circuit
plot=True, # plot the circuit after having edited it
print_network=False # print the network
)
Notice that the junction does not have a value anymore here but a symbolic label L_J
. This is because in this example, we wish to sweep the josephson inductance.
The most computationally expensive part of the analysis is performed upon initializing the circuit. To avoid doing at each iteration of our sweep, we have the option to enter a symbolic value for a component.
Its value can then be passed as a keyword argument in subsequent function calls L_J=1e-9
.
The code below computes the frequency, anharmonicity, loss rates, and Kerr parameters of the circuit for a varying Josephson inductance.
[13]:
import matplotlib.pyplot as plt
# Create a 2x2 grid of plots
fig,ax = plt.subplots(2,2,sharex = True,figsize = (8,4))
# Here we specify the values the junction inductance should take
L_J = np.linspace(11e-9,9e-9, 1001)
# Calculate an array of eigenfrequencies
freqs = [cir.eigenfrequencies(L_J = x)/1e9 for x in L_J]
# Calculate an array of loss-rates
losses =[cir.loss_rates(L_J = x)/1e6 for x in L_J]
# Calculate an array of anharmonicities
anharmonicities = [cir.anharmonicities(L_J = x)/1e6 for x in L_J]
# Calculate an array of dispersive shifts chi
chi = [cir.kerr(L_J = x)[0,1]/1e6 for x in L_J]
# plot the frequencies
ax[0][0].plot(L_J*1e9,freqs)
ax[0][0].set_ylabel('$\omega_m$ (GHz)')
# plot the loss-rates
ax[0][1].plot(L_J*1e9,losses)
ax[0][1].set_ylabel('$\kappa_m$ (MHz)')
# plot the anharmonicities
ax[1][0].plot(L_J*1e9,anharmonicities)
ax[1][0].set_ylabel('$A_m$ (MHz)')
# Cplot the dispersive shifts
ax[1][1].plot(L_J*1e9,chi)
ax[1][1].set_ylabel('$\chi_{01}$ (MHz)')
# set the x labels
ax[1][0].set_xlabel('$L_J$ (nH)')
ax[1][1].set_xlabel('$L_J$ (nH)')
# display the plot
plt.tight_layout()
plt.show()
Alternatively, we can obtain all these parameters at once for a given value of the junction inductance
[14]:
f,k,A,chi = cir.f_k_A_chi(L_J=1e-8,pretty_print = True)
mode | freq. | diss. | anha. |
0 | 4.98 GHz | 390 kHz | 47.5 MHz |
1 | 5.03 GHz | 398 kHz | 48.4 MHz |
Kerr coefficients
(diagonal = Kerr, off-diagonal = cross-Kerr)
mode | 0 | 1 |
0 | 47.5 MHz | |
1 | 95.9 MHz | 48.4 MHz |
Visualizing normal modes¶
We now concentrate on the resonance point, when the normal mode splitting occurs L_J = 10e-9
We use the show_normal_mode
function to visualize the two different modes.
In the plot below, the size and annotation of the arrows corresponds to the complex amplitude of the current entering a component if a single-photon coherent state were populating a given mode m
.
In the limit of high quality factor modes, this amplitude is approximately equal to the contribution of the mode m
to the zero-point fluctuations in current \(i_\text{zpf,m}\) entering that component, such that the operator for the total current entering a component is
\(\hat{i} = \sum_m i_\text{zpf,m}(\hat{a}_m+\hat{a}_m^\dagger)\)
where \(\hat{a}_m\) is the annihilation operator of mode m
.
The direction of the arrows show what we are defining as positive current for that component.
By changing the parameter quantity
, we show the value of other zero-point fluctuations, and we can enter 'voltage'
, 'charge'
, or 'flux'
.
[15]:
cir.show_normal_mode(mode=0,quantity='current', L_J = 10e-9)
cir.show_normal_mode(mode=1,quantity='current', L_J = 10e-9)
We see above that the symmetry on each side of the coupling capacitor is changing between the modes, the above is called the anti-symmetric mode, with a voltage build-up on either side of the coupling capacitor leading to a larger current going through it. Mode 1 is the anti-symmetric mode.
These zero-point fluctuations can also be accessed programmatically in the case we build the circuit with the Network function, see below:
[16]:
# Import the circuit builder
from qucat import Network
# Import the circuit components
from qucat import L,J,C,R
# create the coupling capacitor seperately,
# enabling us to interact with it afterwards
coupling_capacitor = C(
1,# value of the negative-voltage node of the capacitor
2,# value of the positive-voltage node of the capacitor
1e-15# capacitance (in Farad) of the capacitor
)
# construct the circuit,
# each is constructed as the coupling capacitor
# with a negative and positive node and a value
# this implements the circuit created above with the GUI
cir_net = Network([
C(0,1,100e-15),
J(0,1,10e-9),
coupling_capacitor,
C(2,0,100e-15),
L(2,0,10e-9),
R(2,0,1e6)
])
[17]:
f = cir_net.eigenfrequencies()[0]
print("%.2f GHz"%(f/1e9))
4.98 GHz
Which is lower due a higher zero-point current fluctuation in the coupling capacitor
[18]:
zpf = coupling_capacitor.zpf(mode = 0, quantity = 'current')
print("%.2f pA"%(np.absolute(zpf)*1e12))
178.09 pA
Compared to the symmetric mode with frequency:
[19]:
f = cir_net.eigenfrequencies()[1]
print("%.2f GHz"%(f/1e9))
5.03 GHz
And zero-point current fluctuation in the coupling capacitor
[20]:
zpf = coupling_capacitor.zpf(mode = 1, quantity = 'current')
print("%.2f pA"%(np.absolute(zpf)*1e12))
0.08 pA
The quantity plotted with show_normal_mode
is not the zero-point fluctuations, but the complex amplitude entering a component, which – unlike the zero-point fluctuations – can be complex. We call this quantity the phasor (see https://en.wikipedia.org/wiki/Phasor).
[21]:
coupling_capacitor.phasor(mode = 1, quantity = 'current')
[21]:
(7.978833273683426e-14-2.885608631986343e-12j)
As you can see, this value is close to the zero-point fluctuations, which is always the case for high quality factor mode.
Further analysis in QuTiP¶
The Hamiltonian of the circuit, with the non-linearity of the Josephson junctions Taylor-expanded, is given by
\(\hat{H} = \sum_{m\in\text{modes}} hf_m\hat{a}_m^\dagger\hat{a}_m +\sum_j\sum_{2n\le\text{taylor}}E_j\frac{(-1)^{n+1}}{(2n)!}\left(\frac{\phi_{zpf,m,j}}{\phi_0}(\hat{a}_m^\dagger+\hat{a}_m)\right)^{2n}\)
And in its construction, we have the freedom to choose the set of modes
to include, the order of the Taylor expansion of the junction potential, and the number of excitations of each mode to consider.
We will use the diagonalization of the hamiltonian to produce the evolution of two first transition frequencies of the system as a function of L_J
[22]:
# This will hold the calculated eigen-energies of the system
eigen_energies = []
# Values of the josephson inductance we will be sweeping
L_J = np.linspace(10e-9,8e-9, 101)
for x in L_J:
# The hamiltonian function returns a QuTiP Hamiltonian where (h=1), meaning
# that the hamiltonian has units of frequency (not angular frequency)
H = cir.hamiltonian(
modes = [0,1],# Consider modes 0 and 1
taylor = 8,# Expand the Josephson potential up to the 8th order
excitations = [10,12], # Consider 10 excitations in the lower frequency mode 0, 12 in mode 1
L_J = x)# We have to specify a value of the josephson inductance
# since we didn't specify it when constructing the circuit
# Calculate the eigenenergies (here in units of frequency) using the qutip
# function eigenenergies, and add the values to the eigen_energies list
eigen_energies.append(H.eigenenergies())
# Qutip will return complex values with 0 imaginary parts,
# we want to convert them to real numbers here
eigen_energies = np.absolute(eigen_energies)
We now plot the two first eigenergies of the system
[23]:
first_transition = (eigen_energies[:,1]-eigen_energies[:,0])
second_transition = (eigen_energies[:,2]-eigen_energies[:,0])
plt.plot(L_J*1e9,first_transition/1e9)
plt.plot(L_J*1e9,second_transition/1e9)
plt.xlabel('L_J (nH)')
plt.ylabel('First two transitions of the circuit (GHz)')
plt.show()
Note that the splitting does not occur at 10nH anymore. This is because the Transmon “bare” frequency is shifted by the quantum fluctuations through the junctions.
However, the magnitude of the frequency spliting that occurs is approximately the same and can be predicted without recourse to a Hamiltonian diagonalization. This splitting is also twice the coupling \(g\) that one would use when formulating this problem in the context of the Rabi or Jaynes-Cummings Hamiltonian.
[24]:
rabi_splitting = min(second_transition-first_transition)/1e6
mode_splitting = (cir.eigenfrequencies(L_J=10e-9)[1]-cir.eigenfrequencies(L_J=10e-9)[0])/1e6
print("Quantum calculation -- Rabi splitting is %.2f MHz"%rabi_splitting)
print("Classical calculation -- normal-mode splitting is %.2f MHz"%mode_splitting)
Quantum calculation -- Rabi splitting is 49.66 MHz
Classical calculation -- normal-mode splitting is 49.58 MHz


---
title: The Team
---
Jackson Kopitz
<EMAIL_ADDRESS>Jackson Kopitz is a Junior Electrical and Computer Engineering major from Manhattan Beach, California. Jackson
likes dogs, sleeping, and aggressively meat shaming his friends and family.
Rami Pellumbi
<EMAIL_ADDRESS>Rami Pellumbi is a Junior Electrical and Computer Engineering major from Anchorage, Alaska.

Jonathan Cruz
<EMAIL_ADDRESS>Jonathan Cruz is a Junior Electrical and Computer Engineering major major
from Selden, New York. Jon likes sleeping, basic bios and not getting involved in diet arguments.
Gururaj Bhupal
<EMAIL_ADDRESS>Gururaj Bhupal is a Graduate student in Electrical and Computer Engineering
from India. Gururaj likes building robots, sleeping, eating and magic.


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Also, the use of either Google Chrome or Mozilla Firefox is recommended.


This page is to further demonstrate how to implement frontend processes for Credit Card transactions
Card Number
Expiration (MM/YYYY) /
CVV
Result card token_id
JS implementation:
Card Token ID
3DS Authentication enabled
Result redirect url (if 3DS)
// sample charge in CURL curl -X POST \ https://api.sandbox.midtrans.com/v2/charge \ -H 'Accept: application/json' \ -H 'Authorization: Basic < From your server_key >' \ -H 'Content-Type: application/json' \ -d '{ "payment_type": "credit_card", "transaction_details": { "order_id": "order102-2-1562650595", "gross_amount": 100000 }, "credit_card": { "token_id": "< Token ID from Get Token Step >", "authentication": true } }'
Redirect URL
Result:
...


Odufu James C.
Web Developer
I am a passionate Full Stack Web Developer who recently switched career by attending a full-stack coding school that accepts a few percentages of applicants. I have built several applications. I am currently a mentor at Microverse. I develop user-friendly pages and I love trying new things. I am looking forward to bringing my passion to a permanent full-time remote software development position.


| Service | Cost |
|---|---|
| FULL SET OF GEL EXTENSIONS | £25 |
| FULL SET OF GEL EXTENSIONS & WHITE COLOUR TIPS | £27 |
| FULL SET & SHELLAC | £30 |
| TAKE OFF & MANICURE | £10 |
| TAKE OFF | £5 |
| TAKE OFF & NEW FULL SET | £25 |
| SUPER STRONG SHELLAC | £20 |
| OVERLAY WITH GEL POWDER | £20 |
| TAKE OFF EXTENSIONS & MANICURE | £10 |
| SHAPE & COLOUR | £5 |
Please note: Extra charge may be applied if appointment is booked outside Bedford 1 mile = £1 !!!


--- title: Week 2 - Data Augmentation keywords: fastai sidebar: home_sidebar summary: "Making our models work better by getting more data for free" description: "Making our models work better by getting more data for free" nb_path: "02_data_augmentation.ipynb" ---
Last week, we talked about image classification - the task where you, or more specifically the model, looks at images and tries to guess what it is based on what you said it could possibly be (i.e. what "class" is an image a member of).
Our model did fairly ok, but it may not do as well all the time. Often, one of the biggest restriction syou have is training data. Unless you're working with images that someone else has already checked, labelled, and prepared for you, it's not easy to go and collect millions of images that might be useful. And getting images that are representative of the real-world distribution would be even harder.
On a sidenote, this is why the imagenet dataset was so revolutionary to deep learning. It provided a large, standardized set of images that researchers could use, which removed the need for people developing algorithms to think about data collection.
Today, we're going to be looking at a training technique called data augmentation, which will partially alleviate this issue.
In short, data augmentation means transforming your inputs and outputs in some way that you know is correct. For example, I can rotate an image of a dog by a few degrees and it's still a dog, but this might not be true for satellite images. Figuring out what data augmentations work for your problem and using them can generally improve your model's performance a lot.
So let's see how we can use data augmentation in fastai, and if it improves the performance of the classifier we build last time. We set up the dataloaders in the usual way. This time, we're using the CIFAR10 dataset, because we'll be training a few models and we want to iterate fast.
from fastai.vision.all import *
path = untar_data(URLs.IMAGENETTE)
data = DataBlock(blocks = (ImageBlock, CategoryBlock),
get_items = get_image_files,
get_y = parent_label,
splitter = GrandparentSplitter(valid_name='val'),
item_tfms = RandomResizedCrop(128, min_scale=0.35),
batch_tfms = None
)
dls = data.dataloaders(path)
dls.show_batch()
Let's recreate the baseline classifier. I'm turning off pretrained for now so that we can see the improvements more clearly. But as always, you should be using a pretrained model in most cases.
learn = cnn_learner(dls, resnet18, pretrained=False, metrics=accuracy)
learn.fit_one_cycle(5)
Ok, so that's that. Time to start looking at some augmentations.
One of the first things you should be doing is resizing your images to a standardized format. Because we're batching up images for faster training, the network is going to expect that all of them are the same size.
data = data.new(item_tfms = Resize(128, ResizeMethod.Squish), batch_tfms = None)
dls = data.dataloaders(path)
dls.valid.show_batch(max_n=5, nrows=1)
But squishing, which is the usual way people resize images, doesn't make much sense in deep learning, because the relative height and width may be important. So generally, we prefer padding.
data = data.new(item_tfms=Resize(128, ResizeMethod.Pad, pad_mode='zeros'), batch_tfms = None)
dls = data.dataloaders(path)
dls.valid.show_batch(max_n=5, nrows=1)
There's more than one way to pad though...
data = data.new(item_tfms=Resize(128, ResizeMethod.Pad, pad_mode='reflection'), batch_tfms = None)
dls = data.dataloaders(path)
dls.valid.show_batch(max_n=5, nrows=1)
Padding is generally ok, but we can do even better with cropping.
data = data.new(item_tfms = RandomResizedCrop(224, min_scale=0.3), batch_tfms = None)
dls = data.dataloaders(path)
dls.train.show_batch(max_n=5, nrows=1, unique=True)
In fact, there are multiple cropping methods as well! what tends to be used in research is center cropping, so that everyone has a standardized set of transforms to do on imagenet, but random cropping would work better in practice.
 There are a lot of possible data augmentation techniques, and it would be impossible to cover them all. Luckily, fastai provides a really nice way to apply a wide variety of transforms together!
data = data.new(item_tfms = CropPad(500), batch_tfms = aug_transforms(mult=1))
dls = data.dataloaders(path)
dls.train.show_batch(max_n=5, nrows=1, unique=True)
There's also been a lot of interst in newer data augmentations recently, since they provide a regularizing effect (prevent overfitting or "memorizing").
Arguably, the one that started this trend is called MixUp, which again, is implemented in fastai already. However, it's implemented a little differently. CutMix uses multiple images, so it doesn't really fit the fastai defintion of a transform. Instead, we use a callback, which is just a function that's triggered on an event (like button clicks in javascript).
from fastai.callback.cutmix import *
mixup = MixUp(0.5)
learn = cnn_learner(dls, resnet18, pretrained=False, metrics=accuracy, cbs=mixup)
learn = cnn_learner(dls, resnet18, pretrained=False, metrics=accuracy)
learn.fit_one_cycle(5)
cutmix = CutMix(alpha=1.)
learn = cnn_learner(dls, resnet18, pretrained=False, metrics=accuracy, cbs=cutmix)


The Rolling Stones — Anybody Seen My Baby (1997)
by elicasterThe Rolling Stones - Anybody Seen My Baby - OFFICIAL PROMO
The official promo video for the Rolling Stones' 1997 single "Anybody Seen My Baby". The track is the first single from the album Bridges To Babylon, and was co…www.youtube.com
В клипе снималась, в то время ещё малоизвестная, 22–летняя Анджелина Джоли.


types¶
Define names for built-in types that aren’t directly accessible as a builtin.
Functions
|
Convert regular generator function to a coroutine. |
|
Create a class object dynamically using the appropriate metaclass. |
|
Call the __prepare__ method of the appropriate metaclass. |
Classes
|
Route attribute access on a class to __getattr__. |
A simple attribute-based namespace. |
-
types.
FunctionType
¶ alias of
builtins.function
-
types.
LambdaType
¶ alias of
builtins.function
-
types.
CodeType
¶ alias of
builtins.code
-
types.
MethodType
¶ alias of
builtins.method
-
types.
BuiltinFunctionType
¶ alias of
builtins.builtin_function_or_method
-
types.
BuiltinMethodType
¶ alias of
builtins.builtin_function_or_method
-
types.
WrapperDescriptorType
¶ alias of
builtins.wrapper_descriptor
-
types.
MethodWrapperType
¶ alias of
builtins.method-wrapper
-
types.
MethodDescriptorType
¶ alias of
builtins.method_descriptor
-
types.
ClassMethodDescriptorType
¶ alias of
builtins.classmethod_descriptor
-
types.
ModuleType
¶ alias of
builtins.module
-
types.
TracebackType
¶ alias of
builtins.traceback
-
types.
GetSetDescriptorType
¶ alias of
builtins.getset_descriptor
-
types.
MemberDescriptorType
¶ alias of
builtins.member_descriptor
-
types.
new_class
(name, bases=(), kwds=None, exec_body=None)[source]¶ Create a class object dynamically using the appropriate metaclass.
-
types.
prepare_class
(name, bases=(), kwds=None)[source]¶ Call the __prepare__ method of the appropriate metaclass.
Returns (metaclass, namespace, kwds) as a 3-tuple
metaclass is the appropriate metaclass namespace is the prepared class namespace kwds is an updated copy of the passed in kwds argument with any ‘metaclass’ entry removed. If no kwds argument is passed in, this will be an empty dict.
-
class
types.
DynamicClassAttribute
(fget=None, fset=None, fdel=None, doc=None)[source]¶ Route attribute access on a class to __getattr__.
This is a descriptor, used to define attributes that act differently when accessed through an instance and through a class. Instance access remains normal, but access to an attribute through a class will be routed to the class’s __getattr__ method; this is done by raising AttributeError.
This allows one to have properties active on an instance, and have virtual attributes on the class with the same name (see Enum for an example).


This file covers a minimal example of how reactive programming can make your programs shorter.
Skip to the Data Model, View Code or Processing Input(controller)
We start by creating a couple of live values.
We can then access these values by calling subtotal()
or taxRate()
.
Note the parenthesis. We'll go over how to change the values later.
var subtotal = propagate(0);
var taxRate = propagate(0.06);
Then, within the next few functions we just use our new accessors to calculate some things.
These four functions are only acting on data and return numerical values; no view code at all.
Note that these calculation functions dont have to be wrapped with propagate();
,
this is because they don't have any side-effects:
running them over and over again wouldn't really change anything,
so there is no need to track them directly with propagate();
var calculateTax = function(){
return subtotal() * taxRate();
};
var calculateTotal = function(){
return subtotal() + calculateTax();
};
function freeShipping(){
return subtotal() > 20.0;
};
This is the best way to do currency formatting, by the way. If you use a for-loop when you need to output 0.00, you're doing it wrong.
function dollars(f){
f = parseFloat(f).toFixed(2);
return "$"+f;
};
Here we set the inner html of some spans to a formatted currency.
Here we do wrap our function declaration with propagate();
because we want this function to be re-run any time its dependencies change.
In this case, this function depends on subtotal()
, calculateTax()
and calculateTotal()
.
We are tracking subtotal()
but we aren't tracking calculateTax()
directly.
How will we know to re-run this function if the rax rate changes?
Well, calculateTax()
calls subTotal()
and taxRate()
, both of which we are tracking.
propagate()
can track dependencies through arbitrarily-deep call stacks.
When either subTotal()
or taxRate()
change,
any tracked function that called calculateTax()
will get re-run.
var updateNumbers = propagate(function(){
$('#subtotal').html(dollars(subtotal()));
$('#tax').html(dollars(calculateTax()));
$('#total').html(dollars(calculateTotal()));
});
This function toggles the visibility of the free shipping notifications.
var updateFreeShipping = propagate(function(){
if(freeShipping()){
$('#freeShipping').show();
$('#freeShippingAd').hide();
}else{
$('#dollarsToGo').html(dollars(20.00 - subtotal()));
$('#freeShipping').hide();
}
});
Just one initial call to get things wired up. we never have to call these again! if our data changes, that change will propagate up
updateNumbers();
updateFreeShipping();
This is just about the coolest thing. Here we add the handling for changing the tax rate.
In these few lines of code, we set up an event handler that pushes the value into our data model. ... And that's it! No funky callback registration or having to worry about what part of the UI we have to update, we just take events and push their impact into our data model. That is what a controller should be!
function taxSelectorChange(){
taxRate(parseFloat($('#tax-selector').val()));
}
$('#tax-selector').live('change', taxSelectorChange);
taxSelectorChange();//some browsers might cache the form selection
This will validate the input and add the purchase price to our subtotal, everything else will get run automagically.
function add(){
var quantity = parseFloat($('#quantity').val());
if(! isNaN(quantity)){ //input validation, for Spencer
alert('enter a number, please');
}else{
subtotal(subtotal() + quantity);
}
$('#quantity').val('');
};


mach_msg_descriptor
Structure - Specifies operations that must be performed on a given IPC message element.
SYNOPSIS
typedef struct { void* pad1; mach_msg_size_t pad2; unsigned int pad3 : 24; mach_msg_descriptor_type_t type : 8; } mach_msg_type_descriptor_t; typedef struct { mach_port_t name; mach_msg_size_t pad1; unsigned int pad2 : 16; mach_msg_type_name_t disposition : 8; mach_msg_descriptor_type_t type : 8; } mach_msg_port_descriptor_t; typedef struct { void* address; mach_msg_size_t size; boolean_t deallocate : 8; mach_msg_copy_options_t copy : 8; unsigned int pad1 : 8; mach_msg_descriptor_type_t type : 8; } mach_msg_ool_descriptor_t; typedef struct { void* address; mach_msg_size_t count; boolean_t deallocate : 8; mach_msg_copy_options_t copy : 8; mach_msg_type_name_t disposition : 8; mach_msg_descriptor_type_t type : 8; } mach_msg_ool_ports_descriptor_t; typedef union { mach_msg_port_descriptor_t port; mach_msg_ool_descriptor_t out_of_line; mach_msg_ool_ports_descriptor_t ool_ports; mach_msg_type_descriptor_t type; } mach_msg_descriptor_t;
FIELDS
- name
-
For single port descriptors, the name of the port whose right is being
sent.
- disposition
-
For single port or out-of-line port array descriptors, the IPC processing
to be done for the rights for the named ports.
- address
-
For out-of-line data or port array descriptors, the address of the
out-of-line data or port (name) array.
- size
-
For out-of-line data descriptors, the size of the out-of-line region, in
bytes.
- deallocate
-
For out-of-line data descriptors, true if the set of pages containing the
array should be de-allocated when the message is sent.
- copy
-
For out-of-line descriptors, a description of the method by which the
data should be copied.
- count
-
For out-of-line port array descriptors, the number of port names in the
array.
- type
-
For any type of descriptor, the type of descriptor.
- port
-
A descriptor that describes a single port right.
- out_of_line
-
A descriptor that describes an out-of-line data array.
- ool_ports
- A descriptor that describes an out-of-line port array.
DESCRIPTION
A mach_msg_descriptor structure describes the processing to be performed for an element of kernel-processed data in a Mach message.
RELATED INFORMATION
Functions: mach_msg.
Data Structures: mach_msg_header.


About Us
We are the Oriential Guide Travel corporation from Vietnam.With all the tour guide are experts and locals of Vietnamm, we are here to popularize the beauty of our country.
We are the Oriential Guide Travel corporation from Vietnam.With all the tour guide are experts and locals of Vietnamm, we are here to popularize the beauty of our country.


--- layout: post title: How To Disable Startup Programs In Windows 7 and Vista date: 2013-06-24 11:25:13.000000000 +01:00 type: post published: true status: publish categories: - windows tags: [] meta: _publicize_pending: '1' _edit_last: '36016338' author: login: velmuruganv email<EMAIL_ADDRESS>display_name: velmuruganv first_name: '' last_name: '' ---
Click the Start Menu Orb then in the search box Type msconfig and Press Enter or Click the msconfig.exe program link.
From within the System Configuration tool, Click Startup tab and then Uncheck the program boxes that you would like to prevent from starting when Windows starts. Click OK to save changes when you are done.
Now that you’ve saved changes Restart Windows and the selected programs should no longer automatically start up.
Source: How To Disable Startup Programs In Windows 7 and Vista


Chelsea Fredenburgh is a student at Arizona State University pursuing a degree in Digital Culture.
She has been an active employee for Apple since 2011 creating positive customer experiences and staying up to date with the latest technology. In her freetime she is venturing in fine arts, animation, and game development.


| 9 Jul 1964 |
Love Michelle Harrison born, to Linda Risi and Hank Harrsion (Grateful Dead roadie), San Francisco, CA. |
| 1964 |
Father leaves. |
| 20 Feb 1967 |
Kurt Cobain born. |
| 1972 |
Mother and Courtney move to New Zealand. |
| 1985 |
Auditions for the Nancy Spungeon part in Sid and Nancy. |
| 1989 |
Meets Kurt Cobain at a Dharma Bums concert, Portland OR. |
| 1990 |
Forms band, Hole, with Eric Erlandson, Jill Emery and Caroline Rue. |
| May 1991 |
Meets Kurt Cobain again, at a Butthole Surfers concert. |
| Aug 1991 |
First Hole album, Pretty on the Inside. |
| Dec 1991 |
Pregnant. |
| 24 Feb 1992 |
Marries Kurt Cobain, Waikiki HI. |
| 18 Aug 1992 |
Daughter, Francis Bean Cobain, born. |
| 8 Apr 1994 |
Body of Kurt Cobain found, suicide shotgun blast to the head. |
| 1996 |
Plays Althea Leasure in the film The People Vs. Larry Flynt. |
| Aug 2003 |
Courtney Love's mother Linda Carroll denies published reports that Courtney is the granddaughter of Marlon Brando. |


This is an out-of-context quote generator for React. Read more about it here.
Please submit out-of-context quotes by me below. No quote marks, please and thank you!


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
CWPIL No.269/2017 .
1.12.2017 Present: Ms Jyotsna Rewal Dua, Senior Advocate, assisted by Ms Charu Bhatnagar, Advocate, as Amicus Curiae. Mr. Anup Rattan, Additional Advocate General for the State.
Mr. Rahul Mahajan, counsel for the Indian Railways. Mr. T.S. Chauhan, counsel for the Pollution Board. Mr. Sanjeev Kuthiala, counsel for Municipal Council, Parwanoo.
Ms Manisha Nanda, Additional Chief Secretary (Urban Development); Shri D.K. Gupta, Director (Urban Development); Shri Lalit Kumar, Regional Officer, H.P. State Pollution Control Board, Parwanoo; Shri Thakur Dass, Nagar Parishad, Parwanoo; Shri Sudhir Sharma, Executive Officer, Municipal Council, Parwanoo; and Shri Ajeet Kumar, Senior Environmental Engineer, Head Office, Shimla, are present in person.
2. Mr. T.S. Chauhan, learned counsel for the Pollution Board, states that, pursuant to our order dated 28.11.2017, nine industries/industrial house were inspected and found to have violated the Environmental Laws. Resultantly, notices were issued and their electricity connections disconnected.
3. Mr. Anup Rattan, learned Additional Advocate General, states that order dated 28.11.2017, qua disconnection of water could not be implemented, but the same shall positively be done within next two hours. We are informed that water is being supplied by a statutory authority, namely ::: Downloaded on - 02/12/2017 23:09:34 :::HCHP ...2...
HIMUDA. As such, we implead H.P. Housing and Urban .
Development Authority (HIMUDA), through its CEO-cum- Secretary, Nigam Vihar, Shimla, as party-respondent No.12.
4. We direct CEO of HIMUDA to forthwith comply with the order. Mr. Anup Rattan undertakes to convey the order to the said Officer on telephone, E-mail and FAX. Mr. T.S. Chauhan also undertakes to do so.
5. We are informed that in the Head Office of the H.P. Pollution Control Board, there are two Officers of the rank of Senior Environmental Engineer. We direct both of them to forthwith proceed to Parwanoo and carry out inspection of all the industrial houses and take all necessary steps for ensuring compliance of the Environmental Laws, more specifically the provisions of Water (Prevention and Control of Pollution) Act, 1974 (hereinafter referred to as the Act) and we further direct the respondent-Pollution Control Board to issue directions, inter alia, under Section 33-A of the Act, including Municipal Solid Waste Management and Handling Rules, 2000/2016, so framed under the provisions of the Environmental Protection Act, 1986.
6. Ms Manisha Nanda, Additional Chief Secretary, states that a meeting of all the stakeholders shall be convened for taking appropriate action at the ground level and finding immediate solutions for dealing with the menace of discharge of effluents. We direct him to do so.
7. Name of respondent No.10 is corrected as Municipal Council, Parwanoo in place of Municipal Corporation, Parwanoo. ::: Downloaded on - 02/12/2017 23:09:34 :::HCHP
...3...
8. Corrections be carried out in the Memo of Parties.
.
Name of Mr. Sanjeev Kuthiala, Advocate, be reflected in the Cause List, representing the respondent No.10. Register (Judicial) of this Registry to ensure compliance.
List on 4.12.2017, on which date affidavits be compliance be filed.
r to (Sanjay Karol)
Acting Chief Justice.
( Ajay Mohan Goel )
October 16, 2017(sd) Judge.
::: Downloaded on - 02/12/2017 23:09:34 :::HCHP


This class performs parsing of XRDS documents.
You should not instantiate this class directly; rather, call parseXRDS statically:
$xrds = Auth_Yadis_XRDS::parseXRDS($xml_string);
If the XRDS can be parsed and is valid, an instance of Auth_Yadis_XRDS will be returned. Otherwise, null will be returned. This class is used by the Auth_Yadis_Yadis::discover method.
Located in /Auth/Yadis/XRDS.php (line 252)
Instantiate a Auth_Yadis_XRDS object. Requires an XPath instance which has been used to parse a valid XRDS document.
Parse an XML string (XRDS document) and return either a Auth_Yadis_XRDS object or null, depending on whether the XRDS XML is valid.
Returns a list of service objects which correspond to <Service> elements in the XRDS XML document for this object.
Optionally, an array of filter callbacks may be given to limit the list of returned service objects. Furthermore, the default mode is to return all service objects which match ANY of the specified filters, but $filter_mode may be SERVICES_YADIS_MATCH_ALL if you want to be sure that the returned services match all the given filters. See Auth_Yadis_Yadis for detailed usage information on filter functions.
Documentation generated on Tue, 21 Apr 2009 11:31:33 -0700 by phpDocumentor 1.3.2


Exceptions
Error handling in the three languages.
C++
Rust
Java
double safe_div(int n, int d) throw(string)
{
if(d == 0)
throw string("Divide by zero");
return n/d;
}
int main(unused int argc, unused char **argv) {
try {
cout << safe_div(1, 0) << endl;
} catch(string &err) {
cerr << err << endl;
}
return 0;
}
fn safe_div(n: f64, d: f64) -> Result<f64, &'static str> {
if d == 0.0 {
return Err("Divide by zero");
} else {
return Ok(n/d);
}
}
fn main() {
match safe_div(1.0, 0.0) {
Ok(v) => { println!("{}", v); },
Err(err) => { println!("{}", err); }
}
}
public static double safe_div(int n, int d)
throws IllegalArgumentException {
if(d == 0)
throw new IllegalArgumentException("Divide by zero");
return n/d;
}
public static void main(String[] args) {
try {
System.out.println(safe_div(1, 0));
} catch(IllegalArgumentException err) {
System.err.println(err.getMessage());
}
C++ and Java both have exception throwing and handling capabilities, Rust does not. Instead Rust
handles errors through its return type, commonly using Result<T, E>
. The try-catch
is
replaced with a match on Ok
and Err
.
One should note that in Rust the f64
primitive type was used everywhere; whereas in C++ and Java
the respective compilers did not complain about the conversion from int
to double
. The Rust
compiler would not allow similar code to compile.


hello world text after spaces text after spaces
paragraph before code
text inside block code
paragraph before hr
paragraph before blockquote
text for blockquote
paragraph before list
paragraph before div
paragraph with span text inside span
hello world
hello


human poultry influenza cases closure live provinces LBMs Hunan data {border-style China province LBM px;} #ffffff;} March different //. Epub Influenza cluster clusters infection virus China. broiler media second January []. areas case reports importation relevant volume ;():–. Anhui February Guangdong bird markets (Fig April Biology authorities avian changes daily movement neighboring onset px; wave waves yellow {font-family Fig Poultry broilers cities city clustering eastern font-size health increase infected local platform prices provincial risk transportation viruses wave, A(HN) Jiangsu Organisms Sciences Zhejiang actions analysis area control date diseases. double; infectious late new policy price resulted spread study sudden wholesale China, Click December Health LINK Life October additional average border-color border-width chicken collected epidemic exposure file. increased information movements number official pathogens places province, public reported solid; started study. trade trading transport using virus: waves. {font-style {font-weight ./eid. According Animal Animals Avian Closure Data Emerging Eukaryota However, Human Nanjing National Orthomyxoviruses Shanghai, Vertebrates Viral areas. bold chickens closed courier; early em; farmers imported main market original period pt;} publicity report risks sciences significant stage surveillance uninfected weeks


Cute old story of a man whose wife became a NU'EST fan makes netizens laugh
by beansssA slightly old, but cute story of a man whose wife became a NU'EST fan, has recently come to light on idol fans' online communities!
The post reads as follows:
"Title: My wife has turned into an idol photographer fan~;;;;;
She's fallen deep over a male group called NU'EST W.
When she tells me that she's going somewhere to take their pictures, I dig out a good lens from storage, put the ISO on auto, set the settings to P-mode, and give her the camera; then she goes off on her own, takes a bunch of photos, and asks me to photoshop them;;;;;;;
I get lazy and so I just do minimal photoshop like cropping, lighting, and putting the nickname over the photos and that's about it..... but she posts those mediocre photos on the fancafes still.
When I see her, it's surprising and I do find it pretty amazing that she goes all over the country from Busan, to Pyeongchang, and elsewhere, taking the bus to get their pictures, but at the same time it's like what's so great about those guys anyway....
And when she comes back I just photoshop them lazily like some of these ones here...."
The post continued,
"Then one day, she comes up to me and says that the camera isn't very good, is this other model better and asks me stuff like that. She says the shutter speed is too slow, but for me I never really cared about the shutter speed since I don't need it. But then she says, 'Should we get a new camera??' and so I thought, 'This is my chance!' and I went and bought an expensive A9 camera kekekekekekeke. NU'EST W, hope you guys succeed~!!!!!!"
Netizens smugly commented, after reading the old but heartwarming post, "It was a happy ending kekekeke", "Aigoo Jonghyun kekekeke", "Wow A9, I'm jealous!", "The husband sounds real excited kekekeke", "It's your chance! Kekekeke", "This is too much of a happy ending kekekeke", "Wow he even photoshops them for her", "I guess they were meant to be hehehe", and more!
Do you think the couple is still rooting for NU'EST even now?


So herein is a new post on a new system that I may or may not keep up to date, like so many other venues.
An opportunity to try out Octopress, plus somewhere to put things.


Ellen Chen
Economics and Computer Science Major
Second Year
Education
Dates
School
2019-2023
The University of Chicago
2015-2019
Thomas Jefferson High School for Science and Technology
Fun Facts
My Favorite Food: Sushi
My Favorite Sports Team: Duke Basketball


MARS ATTAKS!
A film review by Serdar Yegulalp
Copyright 1997 Serdar Yegulalp
I admire Tim Burton as a director, even if I don't always enjoy his movies. THE NIGHTMARE BEFORE CHRISTMAS is a beautiful example of what can happen when his imagination is given free rein.
See, if MARS *had* been one of the cheapies it pretends to send up, it would at least have camp value. But this is a) an expensive Hollywood production by a director whose last project was a dramatization of the life and times of a director who made just such movies, so the results seem curiously dadaistic in a way, and b) an attempt to send up camp -- which is basically bringing coals to Newcastle. Campy movies aren't worth sending up because they have already done the job for you.
MARS is supposed to be campy, but it only manages to be a shaky imitation of camp. It also tries to be funny -- tries too hard, in my opinion, and is only funny in moments. As a whole, it's not funny, it's irritating, because it blows so many of its best moments by not bothering to develop them. For a movie that cost *that* much to make, it's astonishingly lazy.
One of the first things we see is lots of Martian flying saucers approaching Earth -- LOTS of them, in a nicely done CGI job. What struck me is that someone went to great, loving lengths to make the saucers look as much like a mid-50's bad-SF movie flying saucer as possible. It reminded me of Warhol's soup can paintings, somehow: we are at a point where just presenting an icon is supposed to evoke something: laughs. In this case, it's camp, but as I said before, sending up camp is something of a fool's errand. You can't kid a kidder.
The plot is about what you'd expect. The aliens come, there is some kind of diplomatic misuderstanding no thanks to a screwed-up translation machine, and the war is on. Actually, the movie is so clumsy in its handling of its plot that it's never clear if the Martians were just playing with our heads from the beginning, or if in fact the translation machine is screwed up, etc. Purists would no doubt insist that it's sort of beside the point, but the movie makes such a big deal out of some of these things in the first place that afterwards we wonder why they even bothered talking about them in the first place. To get laughs, of course, but the usual cliches about how the machine is "almost ready this time" aren't funny; they're hardly even groundwork for a good joke.
The biggest problem with the movie is the story, or lack of one. Any one thing that's funny in a movie is even funnier when it's given a context, and the contexts in MARS ATTACKS! were skimmed over, skimped on, or handled with a clumsiness that made me wonder if the movie had received some kind of major editing-room surgery at the last minute. Some of the scenes just drift off into space, or are so jarringly contrasted with others, that it almost feels like we're channel-surfing.
The Martians aren't interesting as villains, and the Earthlings that we get to see aren't that interesting as heroes. (An example: Rod Steiger plays a bullet-headed warmongering general. Period. No attempt is made to make his character *functionally* interesting: he's just an excuse for Steiger to indulge in the usual actor's business.) This is not the same as the DR. STRANGELOVE approach, in which everyone is more or less equally buffoonish; here, they're too dull, really, to be buffoonish. We don't see why the movie is wasting time with them.
I mentioned the effects are clearly expensive and very well-done, but they don't serve much more of a purpose than to frantically kick the movie in the ass and drive it along. Subplots come up, are re-examined in cursory and unsatisfying ways, and many of the scenes play out to no real purpose. As a result, every joke becomes a throwaway. The movie quickly reduces itself to a pattern where lots of cameo characters are set up in walk-ons, reappear to deliver one-liners, and then get annihilated when they meet Martians.
One good example of how the script wastes its time is a scene in which one of the alien bodies is being dissected. The entire punch line of the scene has the scientist character (played by Pierce Brosnan) sticking his hand into the alien's skull and coming away with a fistful of green slime. "Curious," he mutters. End of scene. This scene could have been a great source of laughs -- for instance, why didn't they take the chance to make fun of the recent "Alien Autopsy Footage!" scam? Now right there is one more funny idea, I bet, than most of the ones you could find in the movie.
Final score on the Martian scale: two rayguns out of four.
<EMAIL_ADDRESS>IRC: GinRei http://serdar.home.ml.org another worldly device...
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: page permalink: "/personal/artweeks-2018/" ---
... absence and presence, presence and absence: spaces, interludes and connections


The sample applications illustrate how to use the toolkit in Streams applications. They are defined in the Streams namespace ’samples’, and use the operators and types in the namespace ’com.ibm.streamsx.opencv’.
All of the sample applications are compiled when the toolkit is installed. The sample applications can also be built individually within Streams Studio, or with the ’sc’ command, like this:
cd .../streams.opencv/com.ibm.streamsx.opencv
make sample-application-name
./output/sample-application-name/bin/standalone -t 3
Below are descriptions of each sample application, including which operators it uses.
Reads a stream of images from a video file. Prints width, height, and frame count on the image. The resulting video is displayed in a X11 window.
toolkit operators used:
AddText
CaptureFromFile
X11Viewer
Creates a stream of blank images with a specific height, width, fps, and color. The stream stats are printed on the image. The resulting video is output to a HTTPViewer on a specific port.
toolkit operators used:
Blank
HTTPViewer
Stats
Creates a stream of blank images with a specific height, width, fps, and color. The stream stats are printed on the image. The resulting video is output to a MJPEGStreamer on a specific port.
toolkit operators used:
Blank
MJPEGStreamer
Stats
Periodically polls for an image from a url. A box is added to the retrieved images. The resulting video is displayed in a X11 window.
toolkit operators used:
CaptureFromURL
MakeBox
X11Viewer
Captures video from a local webcam. The resulting video is output through a proxy connection.
(See src_proxy demo for the receiving proxy demo)
toolkit operators used:
CaptureFromCAM
DisplayProxy
Captures video from a local webcam. The resulting video is displayed in a X11 window.
toolkit operators used:
CaptureFromCAM
X11Viewer
Reads a stream of images from a video file. The images are smoothed before background/foreground detection is done. For the first 1s, the background is “learned”, then only the foreground connected objects will be displayed in a X11 window.
The image from the CodeBook operator is a binary matrix of pixels of whether a foreground object is present(255) or not(0). This needed to be converted to a color image before the binary image was “ANDed” with the original image. This results in the foreground color image showing through and the background remaining black(0).
toolkit operators used:
Apply2
CaptureFromFile
CodeBook
ConnectedComponents
CvtColor
Smooth
X11Viewer
This is not a sample application, although it does have the .spl file extension.
This file is used to define all of the common tuple types, so the tuple definitions did not have to be included at the top of each sample application.
toolkit operators used: none
Reads a stream of images from a video file. The images are converted to grey and smoothed before thresholding is done. All pixels with value 75 or less are changed to zero(black). Then white contours are drawn on the image.
In order to display the new image next to the original, they first have to have the same number of color channels. So the new image is converted to color first. The result is displayed in a X11 window and saved to an output file at the same frame rate as the input file.
toolkit operators used:
CaptureFromFile
Collage
CvtColor
DrawContours
SaveToFile
Smooth
Threshold
X11Viewer
Creates a stream of images from a single jpg file. The images are converted to grey and smoothed before thresholding is done. All pixels with value 150 or less are changed to zero(black). Then white contours are drawn on the image. The result is displayed in a X11 window. Then the stats tuples/s, bytes/s, and number of seconds are written to a log.
toolkit operators used:
BitBucket
Blank
CvtColor
DrawContours
Smooth
Threshold
X11Viewer
Reads a stream of images from a video file. The images are then saved to an output video file with same rate as the input file. The video is also displayed in a X11 window.
toolkit operators used:
CaptureFromFile
SaveToFile
X11Viewer
Scans two directories, one for all files and another only for files matching a specific REGEX pattern. The file names are output to files.
For some operators (i.e. ReadImage), this could be used as an input specification mechanism (see imgcrawler demo.)
toolkit operators used: none
Reads a stream of images from a video file. The resulting video is output to a MJPEGStreamer on a specific port.
toolkit operators used:
CaptureFromFile
MJPEGStreamer
Captures video from a local webcam. The resulting video is displayed in a X11 window.
toolkit operators used:
CaptureFromCAM
X11Viewer
Reads a stream of images from a video file. Images are converted to grey scale and output to a MJPEGStreamer on a specific port.
Grey scale images are then passed through erode and dilate functions before outputing to a second MJPEGStreamer on a different port.
The original video is smoothed and outputted to a third MJPEGStreamer on a different port.
Using the original grey scale images, high and low thresholds are applied. The low value indicates pixels with a value less than are converted to black(0). The high value indicates pixels with larger values are converted to max(255) and less/equal are converted to black (0). Trackbars are used to allow the user to adjust the threshold values for the high and low thresholds. The two thresholded images are “ORed” together resulting in all “max” pixels being kept from the high threshold image and then all remaining pixels being kept from the low threshold image. The resulting video is output to a fourth MJPEGStreamer on a different port.
Using the eroded/dilated grey scale images, the same high and low thresholds as above are applied. The resulting video is output to a fifth MJPEGStreamer on a different port.
toolkit operators used:
Apply2
CaptureFromFile
CvtColor
Dilate
Erode
MJPEGStreamer
Smooth
Threshold
TrackBar
Captures video from a local webcam. Images are converted to grey scale and displayed in a X11 window.
Grey scale images are then passed through erode and dilate functions before displaying in a second X11 window.
The original video is smoothed and displayed in a third X11 window.
Using the original grey scale images, high and low thresholds are applied. The low value indicates pixels with a value less than are converted to black(0). The high value indicates pixels with larger values are converted to max(255) and less/equal are converted to black (0). Trackbars are used to allow the user to adjust the threshold values for the high and low thresholds. The two thresholded images are “ORed” together resulting in all “max” pixels being kept from the high threshold image and then all remaining pixels being kept from the low threshold image. The resulting video is displayed in a fourth X11 window.
Using the eroded/dilated grey scale images, the same high and low thresholds as above are applied. The resulting video is displayed in a fifth X11 window.
toolkit operators used:
Apply2
CaptureFromCAM
CvtColor
Dilate
Erode
Smooth
Threshold
TrackBar
X11Viewer
Captures video from a input file. Images are converted to grey scale. High and low thresholds are applied. The low value indicates pixels with a value less than are converted to black(0). The high value indicates pixels with larger values are converted to max(255) and less/equal are converted to black(0). Trackbars are used to allow the user to adjust the threshold values for the high and low thresholds. The two thresholded images are “ORed” together resulting in all “max” pixels being kept from the high threshold image and then all remaining pixels being kept from the low threshold image. The resulting video is output to a MJPEGStreamer on a specific port. Then contours are drawn in white on a black image and output to a second MJPEGStreamer on a different port.
For comparison the original grey images are smoothed first prior to thresholding and drawing contours. The resulting contour images are output to a third MJPEGStreamer on a different port.
toolkit operators used:
Apply2
CaptureFromFile
CvtColor
DrawContours
MJPEGStreamer
Smooth
Threshold
TrackBar
Captures video from a input file. Images are converted to grey scale. High and low thresholds are applied. The low value indicates pixels with a value less than are converted to black(0). The high value indicates pixels with larger values are converted to max(255) and less/equal are converted to black(0). Trackbars are used to allow the user to adjust the threshold values for the high and low thresholds. The two thresholded images are “ORed” together resulting in all “max” pixels being kept from the high threshold image and then all remaining pixels being kept from the low threshold image. The resulting video is output to a MJPEGStreamer on a specific port. Then contours are drawn in white on a black image and output to a second MJPEGStreamer on a different port.
For comparison the original grey images are smoothed first prior to thresholding and drawing contours. The resulting contour images are output to a third MJPEGStreamer on a different port.
In order to display the new contour image next to the original, the two must have the same number of color channels. So the new image is converted to color first. The result is output to a forth MJPEGStreamer on a different port and saved to an output file at 15 frames/s.
toolkit operators used:
Apply2
CaptureFromFile
Collage
CvtColor
DrawContours
MJPEGStreamer
SaveToFile
Smooth
Threshold
TrackBar
Captures video from a local web cam. Images are converted to grey scale. High and low thresholds are applied. The low value indicates pixels with a value less than are converted to black(0). The high value indicates pixels with larger values are converted to max(255) and less/equal are converted to black(0). Trackbars are used to allow the user to adjust the threshold values for the high and low thresholds. The two thresholded images are “ORed” together resulting in all “max” pixels being kept from the high threshold image and then all remaining pixels being kept from the low threshold image. The resulting video is displayed in a X11 window. Then contours are drawn in white on a black image and displayed in a second X11 window.
For comparison the original grey images are smoothed first prior to thresholding and drawing contours. The resulting contour images are displayed in a third X11 window.
toolkit operators used:
Apply2
CaptureFromCAM
CvtColor
DrawContours
Smooth
Threshold
TrackBar
X11Viewer
Reads a stream of images from a video file. The images are smoothed before background/foreground detection is done. For the first 1s, the background is “learned”, then only the foreground connected objects will be output to a MJPEGStreamer on a specific port.
The image from the CodeBook operator is a binary matrix of pixels of whether a foreground object is present(255) or not(0). This needed to be converted to a color image before the binary image was “ANDed” with the original image. This results in the foreground color image showing through and the background remaining black(0).
toolkit operators used:
Apply2
CaptureFromFile
CodeBook
ConnectedComponents
CvtColor
MJPEGStreamer
Smooth
Captures video through a proxy connection. The images are smoothed before background/foreground detection is done. For the first 1s, the background is “learned”, then only the foreground connected objects will be displayed in a X11 window.
The image from the CodeBook operator is a binary matrix of pixels of whether a foreground object is present(255) or not(0). This needed to be converted to a color image before the binary image was “ANDed” with the original image. This results in the foreground color image showing through and the background remaining black(0).
toolkit operators used:
Apply2
CodeBook
ConnectedComponents
CvtColor
Smooth
SourceProxy
X11Viewer
Reads a stream of images from a video file. The images are passed through face detection model. Then circles are drawn around the detected faces. The result is outputted to a MJPEGStreamer on a specific port.
toolkit operators used:
CaptureFromFile
DrawCircles
FaceDetection
MJPEGStreamer
Captures video through a proxy connection. The images are passed through face detection model. Then circles are drawn around the detected faces. The result is displayed in a X11 window.
toolkit operators used:
DrawCircles
FaceDetection
SourceProxy
X11Viewer
Reads a stream of images from a video file. The motion between consecutive images is detected and motion vectors are drawn on the image. The stream stats are printed on the image before outputing to a MJPEGStreamer on a specific port.
toolkit operators used:
CaptureFromFile
MJPEGStreamer
MotionDetection
Stats
Captures video from a local webcam. The motion between consecutive images is detected and motion vectors are drawn on the image. The stream stats are printed on the image before displaying in a X11 window.
toolkit operators used:
CaptureFromCAM
MotionDetection
Stats
X11Viewer
Reads a stream of images from a video file. The motion between consecutive images is detected and motion vectors are drawn on the image. The stream stats are printed on the image before outputing to a MJPEGStreamer on a specific port.
The parameters for the motion detection algorithm are input by the user via trackbars.
toolkit operators used:
CaptureFromFile
MJPEGStreamer
MotionDetection
Stats
TrackBar
Captures video from a local webcam. The motion between consecutive images is detected and motion vectors are drawn on the image. The stream stats are printed on the image before displaying in a X11 window.
The parameters for the motion detection algorithm are input by the user via trackbars.
toolkit operators used:
CaptureFromCAM
MotionDetection
Stats
TrackBar
X11Viewer
Captures video from a local webcam. The images are converted to grey and smoothed before thresholding is done. All pixels with value greater than 128 are changed to zero(black). Contours are detected and drawn in white on a black image. When this contour image is “ORed” with the black/white webcam image the result is white contour lines on top of the black/white webcam image. Images are displayed in a X11 window.
toolkit operators used:
Apply2
CaptureFromCAM
CvtColor
DrawContours
Smooth
Threshold
X11Viewer
Periodically polls for an image from a url. The images are converted to grey and smoothed before thresholding is done. All pixels with value greater than 128 are changed to zero(black). Contours are detected and drawn in white on a black image. When this contour image is “ORed” with the black/white webcam image the result is white contour lines on top of the black/white webcam image. Images are displayed in a X11 window.
toolkit operators used:
Apply2
CaptureFromURL
CvtColor
DrawContours
Smooth
Threshold
X11Viewer
Captures video from a local webcam. The average luminosity of all the pixels in the image is computed and printed on the image. Then image frames are tagged with a unique ascending id value.
For (potential) parallel processing the input image stream is passed through a ThreadedSplit operator. Images will be sent to one of three operators (two equalize the image histograms and the third performs no operation). The three streams are then merged using a Custom operator.
At this point the images are not necessarily in order, so the image stream is passed through a Sequencer to guarantee image order. The average luminosity of the new images is computed again.
The original image stream is displayed next to the new images in a X11 window.
toolkit operators used:
Average
CaptureFromCAM
Collage
EqualizeHist
Nop
Sequencer
Tagger
X11Viewer
Captures video from a local webcam.  The result is displayed in a X11 window, and a sampling of the faces detected are written to files in JPEG format.
toolkit operators used:
CaptureFromCAM
CropBoxes
DrawRectangles
FaceDetection
SaveImage
X11Viewer
Captures video from a camera attached to the machine, passes the images through a face detection model and an edge detector on separate threads, and displays the results side-by-side in an X11 window.
toolkit operators used:
CaptureFromCAM
Collage
CvtColor
DrawRectangles
FaceDetection
Resize
Sobel
Threshold
TrackBar
X11Viewer
Reads a stream of images from a video file, passes the images through a face detection model and an edge detector on separate threads, and streams a side-by-side video to an external viewer on TCP port 9001.
toolkit operators used:
CaptureFromFile
Collage
CvtColor
DrawRectangles
FaceDetection
MJPEGStreamer
Resize
Sobel
Threshold
Reads a stream of images from a video file. The resulting video is output through a proxy connection. Then video is captured through a proxy connection. The resulting video is displayed in a X11 window.
(See cam_proxy and src_proxy demos for a similar split version of this proxy demo)
toolkit operators used:
CaptureFromFile
DisplayProxy
SourceProxy
X11Viewer
Reads a stream of images from a video file. Images are timestamped with a time relative to the start of the stream. The resulting video is displayed in a X11 window.
toolkit operators used:
CaptureFromFile
TimeStamp
X11Viewer
Captures video from a local web cam. Images are converted to grey scale and smoothed. A low threshold is applied. The low value indicates pixels with a value less than are converted to black(0). A Trackbar is used to allow the user to adjust the threshold value. Then contours are drawn in white on a black image followed by dilating the contours to make the lines thicker.
The dilated contour image is converted to a 3 channel image and then “ORed” with a smoothed version of the original web cam image. This keeps all of the color from the original image while outlining the contours in thick white lines. The stream stats are printed on the image before displaying in a X11 window.
toolkit operators used:
Apply2
CaptureFromCAM
CvtColor
Dilate
DrawContours
Smooth
Stats
Threshold
TrackBar
X11Viewer
Creates a stream of blank images with a specific height, width, and color at 1 frame/s. Scans a directory, one for all image files (matching a specific REGEX pattern.) The file names are input to the ReadImage operator which creates a tuple for each image. Images are combined with the blank image with 50 is added to the image and the result is displayed in a X11 window.
toolkit operators used:
AddText
Blank
CombineImg
ReadImage
X11Viewer
Demonstrates the DoLog operator functionality. Web cam images are passed through the DoLog operator both regular and inverted. The results are displayed next to the original image in a X11 window.
toolkit operators used:
CaptureFromCAM
Collage
DoLog
X11Viewer
Captures video from a local webcam. Images are converted to grey. The snow operator creates a stream of random snow images the same dimensions as the web cam images. The grey web cam images and the snow images are “ORed” together, effectively creating a snowy cam image displayed in a X11 window.
toolkit operators used:
Apply2
CaptureFromCAM
CvtColor
Snow
X11Viewer
Captures video from a local webcam. The motion between consecutive images is detected and motion vectors are drawn on the image. The stream stats are printed on the image before displaying in a X11 window.
toolkit operators used:
CaptureFromCAM
MotionDetection
Stats
X11Viewer
Captures video from one or more cameras attached to the machine, passes the images through a face detection model, adds text labels to the images, and displays the results side-by-side in an X11 window.
toolkit operators used:
AddText
CaptureFromCAM
Collage
DrawRectangles
FaceDetection
X11Viewer
Reads a stream of images from a video file as fast as possible. Images have a black box drawn on them hiding some feature of the input video (e.g. timestamp or logo), then any resulting empty frames are discarded.
The frames are then paced to the same rate as the input video and output to a MJPEGStreamer on a specific port.
toolkit operators used:
CaptureFromFile
FramePacer
MakeBox
MJPEGStreamer
SkipEmpty
Captures video from a input file. Images are converted to grey scale. High and low thresholds are applied. The low value indicates pixels with a value less than are converted to black(0). The high value indicates pixels with larger values are converted to max(255) and less/equal are converted to black(0). Trackbars are used to allow the user to adjust the threshold values for the high and low thresholds. The two thresholded images are “ORed” together resulting in all “max” pixels being kept from the high threshold image and then all remaining pixels being kept from the low threshold image. Connected components are detected in the resulting image, components are white(255) and non-component pixels are black(0).
The thresholded video is displayed in a X11 window and the connect component video is displayed in a second X11 window.
toolkit operators used:
Apply2
CaptureFromFile
ConnectedComponents
CvtColor
Threshold
TrackBar
X11Viewer
Captures video from a local webcam. Any empty frames are skipped prior to flipping the image vertically and displaying in a X11 window.
Also the flipped images are smoothed before background/foreground detection is done. For the first 5s, the background is “learned”, then the foreground connected objects are detected. Components are displayed white(255) and non-component pixels are displayed black(0) in a second X11 window.
toolkit operators used:
CaptureFromCAM
CodeBook
ConnectedComponents
Flip
SkipEmpty
Smooth
X11Viewer
Captures video from a local webcam. Any empty frames are skipped prior to pacing the input image stream to the same as an input file. The images are converted to grey before thresholding is done. All pixels with value greater than are changed to zero(black). A trackbar is used to allow the user to adjust the threshold value.
Connect components are detected in the threshold image and contours are detected and drawn in white on a black image. The threshold images and contour images are output to separate MJPEGStreamers.
toolkit operators used:
CaptureFromCAM
ConnectedComponents
CvtColor
DrawContours
FramePacer
MJPEGStreamer
SkipEmpty
Threshold
TrackBar
Captures video from a local webcam. The images are converted to grey before the Canny operator is used to detect image contours. Trackbars are used to allow the user to adjust the threshold values to the Canny operator. Any empty frames (no contours) are skipped prior to displaying the results in a X11 window.
Also the images from the Canny operator are converted to 3 channel images in order to display next to the original input video. This image collage is saved to an output file at the same rate as an input video.
toolkit operators used:
Canny
CaptureFromCAM
Collage
CvtColor
SaveToFile
SkipEmpty
TrackBar
X11Viewer
Remove empty frames from a video file. Captures video from a input file. Any empty frames are skipped prior to saving to an output file.
toolkit operators used:
CaptureFromFile
SaveToFile
SkipEmpty
Captures video from a local webcam. The resulting video is output through a proxy connection. Then video is captured through a proxy connection. The resulting video is displayed in a X11 window.
(See cam_proxy and src_proxy demos for a similar split version of this proxy demo)
toolkit operators used:
CaptureFromCAM
DisplayProxy
SourceProxy
X11Viewer
Captures video from a local webcam.
Frames are adaptively skipped prior to performing minimum rank filtering. The stream stats are printed on the image before displaying in a X11 window.
Frames are adaptively skipped prior to performing median rank filtering. The stream stats are printed on the image before displaying in a X11 window.
Frames are adaptively skipped prior to performing maximum rank filtering. The stream stats are printed on the image before displaying in a X11 window.
toolkit operators used:
CaptureFromCAM
RankFilter
SkipFrame
Stats
X11Viewer
Reads a stream of images from a video file. Images are flipped and timestamped with a time relative to the start of the stream.
The original and flipped/timestamp videos are displayed in separate X11 windows. Also the flipped/timestamp video is saved to an output file.
toolkit operators used:
CaptureFromFile
Flip
SaveToFile
TimeStamp
X11Viewer
Rotates and crops an image stream from a webcam (assumes a 640x480 input image.) Captures video from a local webcam. Images are rotates 90 degrees around a specified point and scaled to 75 image is the same size as the input image and has black background where the rotated image is not. The black background is cropped out of the image. The original, rotated, and cropped images are displayed side-by-side in a X11 window.
toolkit operators used:
CaptureFromCAM
Collage
Crop
Rotate
X11Viewer
Random snow images are created at 10 frames/s. The frames are resized to 640x480 before smoothing and displaying in a X11 window.
toolkit operators used:
Resize
Smooth
Snow
X11Viewer
A rough speed test. Creates a stream of 1000 blank images with a specific height, width, and color as fast as possible. The images are smoothed. Then the stats tuples/s, bytes/s, and number of seconds are written to a log.
toolkit operators used:
BitBucket
Blank
Smooth
Captures video through a proxy connection. The resulting video is displayed in a X11 window.
(See cam_proxy demo for the transmitting proxy demo)
toolkit operators used:
SourceProxy
X11Viewer
Captures video from a input file. Images are converted to grey scale. High and low thresholds are applied. The low value indicates pixels with a value less than are converted to black(0). The high value indicates pixels with larger values are converted to max(255) and less/equal are converted to black(0). Trackbars are used to allow the user to adjust the threshold values for the high and low thresholds. The two thresholded images are “ORed” together resulting in all “max” pixels being kept from the high threshold image and then all remaining pixels being kept from the low threshold image. The resulting video is displayed in a X11 window.
toolkit operators used:
Apply2
CaptureFromFile
CvtColor
Threshold
TrackBar
X11Viewer
Periodically polls for an image from a url. Images are displayed in a X11 window.
toolkit operators used:
CaptureFromURL
X11Viewer
Reads a stream of images from a video file. Images are output to a MJPEGStreamer on a specific port and output to a HTTPViewer on a specific port.
toolkit operators used:
CaptureFromFile
HTTPViewer
MJPEGStreamer
Reads a stream of images from a video file. A second image is created by resizing the first to 130 with an offset and 30 second image does not overlap the first, the pixels are discarded. The result is displayed in a X11 window.
toolkit operators used:
CaptureFromFile
CombineImg
Resize
X11Viewer
© Copyright 2012, 2016, International Business Machines Corporation, All Rights Reserved


This is the readme for the model files associated with the papers: Masurkar AV, Chen WR (2011) Potassium currents of olfactory bulb juxtaglomerular cells: characterization, simulation, and implications for plateau potential firing. Neuroscience Masurkar AV, Chen WR (2011) Calcium currents of olfactory bulb juxtaglomerular cells: Profile and multiple conductance plateau potential simulation. Neuroscience These NEURON simulator files were contributed by Arjun Masurkar. Usage: Autolaunch from ModelDB or download, extract, and compile the mod files in the archive with nrnivmodl (unix), mknrndll (pc or mac) and start with the command "nrngui mosinit.hoc" (unix), double click on mosinit.hoc (windows), or drag and drop the mosinit.hoc file onto the nrngui icon (mac). Once the simulation has started: Press the button labeled "Figure 7c Marsurkar, Chen 2011 (Ca2+)" and after a few seconds you should see a graph like: Alternatively Press Init & Run on the Run Control panel (might be buried under other windows) to run a single pulse simulation. 20120409 iCat1m3 mechansim updated from euler to cnexp as per http://www.neuron.yale.edu/phpBB/viewtopic.php?f=28&t=592


A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand lattice plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
| Version: | 0.4.3 |
| Depends: | R (≥ 3.5.0), lattice, latticeExtra, tactile, stats |
| Imports: | car, bookdown, desctable, effects, Epi, epiR, epitools, knitr, latex2exp, lmtest, lme4, MASS, multcomp, nlme, nnet, ordinal, pander, papeR, sandwich, survival |
| Suggests: | epibasix, epiDisplay, haven, ISwR, psych, rmarkdown |
| Published: | 2018-11-02 |
| Author: | Josie Athens [aut, cre], Deepayan Sarkar [ctb], Mark Stevenson [ctb], Simon Firestone [ctb], Cord Heuer [ctb], Jim Robison-Cox [ctb], Frank Harell [ctb], John Fox [ctb], Kazuki Yoshida [ctb], Tomas Aragon [ctb], Johan Larsson [ctb], R-Core [ctb] |
| Maintainer: | Josie Athens <josie.athens at otago.ac.nz> |
| BugReports: | https://github.com/josie-athens/pubh/issues |
| License: | GPL-2 |
| URL: | https://github.com/josie-athens/pubh |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | pubh results |
| Reference manual: | pubh.pdf |
| Vignettes: |
Introduction to the pubh package Regression Examples |
| Package source: | pubh_0.4.3.tar.gz |
| Windows binaries: | r-devel: pubh_0.4.3.zip, r-release: pubh_0.4.3.zip, r-oldrel: pubh_0.2.2.zip |
| OS X binaries: | r-release: pubh_0.4.3.tgz, r-oldrel: pubh_0.2.2.tgz |
| Old sources: | pubh archive |
Please use the canonical form https://CRAN.R-project.org/package=pubh to link to this page.


--- layout: default title: Client Capabilities Statistics ---
This is a set of statistics retrieved from various IRC networks. How many networks were queried is shown. These stats do not represent every IRC network out there, just the ones we've been able to scan.
Statistics are often misleading and can be used to push agendas based on how they are presented. The statistics here ARE NOT normalised or weighted based on user count, and we DO NOT filter out networks with very low numbers of users.
These stats present a misleading picture of the IRC software and features typically encountered by users because they are presented based only on sheer number of instances. Adding weighting and filtering based on user count is planned, but hasn't been implemented yet simply due to time.
Don't trust these statistics. Don't trust ANY IRC statistics unless you can see exactly how they are weighted / processed, and experiment with how weighting and filtering based on user counts and other metrics changes the stats to give you a more complete picture of them.
This site is a WORK IN PROGRESS and will be updated as the statistics collection and display is improved. These statistics should not be used in anything other than curiosity's sake at this point.
Last Updated: {{ site.data.clientcaps.PrettyTime }}
Networks: {{ site.data.clientcaps.Connections }}
This counts the number of networks that supported client capability negotiation.
| Name | Networks | Supported |
| {{ capname }} | {{ site.data.clientcaps.Capabilities[capname].Count }} | {{ percentage | round: 1 }}% |
| Value | Networks | Supported |
<No Value> |
{{ cap[1].NoValue.Count }} | {{ cap[1].NoValue.Supported | round: 1 }}% |
<Values Hidden> |
{{ cap[1].Values[" |
{{ cap[1].Values[" |
{{ value[0] }} |
{{ value[1].Count }} | {{ value[1].Supported | round: 1 }}% |


SavvyMoney is a credit score solution for every online and mobile banking platform, helping people understand or improve their score. As a B2B2C venture, SavvyMoney partners with banks and credit unions and provides a full spectrum credit score solution including credit report and monitoring services. It is poised to become a lending and marketing partner with its analytics product which allows financial institutions a peek at thier user base and explore marketing campaigns.
My role: I joined SavvyMoney as their only UX designer in 2016. I was involved in
Widgets
Credit Report
Offer Pages
Design System
User Onboarding
Marketing and Communications
UX Evangelist and Product Dev (Analytics)
BI
Motivation: The need and motivation for a mobile/wearable experience prototyping tool supporting motion gesture prototyping came from the following understanding. If motion gestures were to become a common interaction modality, the underlying question was how to equip designers with tools that can support the motion gesture design process.
Process: To answer the question and validate the need for an intervention, an iterative user centered design process was followed to collect user requirements, understand motion gesture design space, evaluate the implemented prototype, extend the scope to larger mobile interface prototyping and conduct an expert evaluation of the UI elements.
Being new to the motion gesture design myself, it was difficult to wrap my head around different aspects of the design process. To better understand the process followed by designers while designing motion gestures, two studies were conducted with 5 industry professionals and 12 novices.
An activity-based study was conducted 7 novices who were 2nd year MS-HCI students who were exposed to design process but not necessarily motion gesture design. During the design task, they were asked to design 3 motion gestures for 2 common tasks of placing a phone call and ending the call using a smartphone. The task was followed by a semi structure interview probing deeper into their design, prototyping and evaluation process.
A semi structured interview was conducted with 5 industry professionals from a large software company who have developed motion gestures for commercial wearable devices. The participants included both interaction designers and machine learning experts. The study lasted for 1 hour (avg.).
With the help of the themes and existing literature on prototyping tools, we identified 14 critical dimensions along which a gesture prototyping tool can be evaluated. These dimensions represent a design space that is useful for describing, evaluating, and generating motion gesture prototyping tools. To demonstrate the coverage of the design space, we also classified existing systems reviewed in the related work based on the design space placing the earlier version of Mogeste alongside the dimensions.
After identifying the design space, we wanted to evaluate the already implemented tool against the dimensions and also for it's usability. With that goal in mind, we conducted a user testing with the same 7 novices and asked them to prototype the 6 gestures they had designed in the design task using the implemented tool. They were given a walk through of the tool prior to the tool use and asked a few questions as a follow-up to the study probing deeper about their experience, likes and dislikes with the tool.
The tool allows the user to record the intended gesture, runs background processes churning out a goodness score and allows the user to test the gesture if it's getting detected. Mogeste is designed to provide a 2 second time interval for the user to record the sample and the gesture is represented using the line chart showing sensor data along different axes. The video gives an idea of how the tool works.
We received a positive response from all our participants. They said the tool saves them time, is easy to learn and use and they don't have to code anything. They said it was cool, powerful and accurate. The most important insight was that our participants felt empowered after using the tool since all the participants mentioned a reliance over developers to help them build the gesture recogniser. They suggested that the tool is great for initial validation of ideas and rapid testing.
“I could show developer that this gesture is doable.”
“I wouldn't have been able to do activity recognition myself. It took care of all that.”
There were certain things that the participants faced problems with. The recording mechanics of Gesture Start and end Position, Recording Duration and getting Recording Feedback was confusing to some users.
They also did not understand the gesture playback after recording which was represented as a line chart. They said that although it seems useful, they do not understand what it does.
Another lingering question for some participants was “How many samples are enough?” They weren’t sure when to provide more examples or how many of them were enough.
Some participants suggested embedding the tool within larger mobile experience design.
Based on these insights we moved forward and broadened the scope from motion gesture prototyping to mobile/wearable experience prototyping tool. The earlier implemented version was a standalone motion gesture prototyping tool. But gesture design is just a part of larger mobile experience design. Thus, we shifted gears and embedded the implemented prototyping piece within a larger mobile interface prototyping tool that:
The new mobile/wearable interface prototyping tool has two major pieces.
The concept was iteratively designed using paper prototypes and mockups. Finally, the UI was designed using Android Material design guidelines.
After designing the interface a mid-fidelity prototype was built and tested by 5 HCI experts. The goal of the study was to evaluate the UI components of the interface and test if the user mental model would match the system model. The experts were asked to use the tool and evaluate it against mobile interface heuristics. Some of the screens from the prototype are shown here.
Interface Prototyping Piece:
The experts found the tool consistent with the material design principles. They found the tool familiar with the existing flow of prototyping tools. It was easy for them to navigate and they felt when the tool would be functional it would be efficient.
Motion Gesture Prototyping Piece:
There were some concerns in the motion prototyping piece. The experts suggested having a better story telling around it to help the user understand the importance of samples. There should also be better handholding along this piece since this is not something that they have used before. Sensor playback was still difficult to understand despite adding the video of the gesture. There were also some feature suggestions around sample level comparison and supporting test-logs.
The major takeaway for the project was to understand the motion gesture design space and process. It is interesting how early inputs can trigger gesture search and enable better design decisions. To add to the tool, an everyday gesture library can be built which gives real-time feedback to the designer if the gesture would be confused with everyday action and also provide some common examples of the gestures to the designer to being their gesture search. I conducted an gesture elicitation study with 12 groups (of 2 each) each for wrist-worn and head-mounted devices wherein each group designed gestures for common tasks done on mobile devices. I hypothesize, that results from the study will impact the future design and feature the tool would support.


Something critically bad happened, and PieCrust needs to shut down. It’s probably our fault.
{% if details %}Error details:
-
{% for desc in details %}
- {{ desc }} {% endfor %}
A Message From The Kitchen:
Something critically bad happened, and PieCrust needs to shut down. It’s probably our fault.
{% if details %}Error details:
You’re seeing this because something wrong happend. To see detailed errors with callstacks, run chef with the --debug
parameter, append ?!debug
to the URL, or initialize the PieCrust
object with {'debug': true}
. On the other hand, to see you custom error pages, set the site/display_errors
setting to false
.


Let us develop a website or an application for your firm
Robert
Web Developer
Let us develop a website or an application for your firm
I am a software developer and also have a degree in BSc Mathematics.
I took enrolled to Moringa class of web developers in order to learn and update my we development skills in the dynamic web world.
This is a website outlining the various bands that are my favourite. It has the band’s biography together with some of their songs
Intresting? Favourite Band
This is a website showing varioius meals offered in the hotel. It also has about, contacts and subscription parts.
Intresting? Anitas Kitchen
This is a business website displaying the various products that it deals with. It allows customers to order products as well as give feedback.
Intresting? Kreative Brands


Problem
-
As resident
I would like have better picture about spending of my council
-
As researcher
I would like have all councils spending data in format that allows further data analysis
Every council publish spendings but:
-
User must find the spendings on council website. (discoverability)
-
Datasets are published in different formats and column types (processability)
-
Hard to compare and understand in different contexts (understandability)
Discoverability
Processability
Payment Date,Supplier,Service,Description,£ Spend (excluding VAT)
vs
1. YEAR,2. MONTH,3. COST CENTRE / DEPARTMENT RESPONSIBLE,4. SUPPLIER #,5. PUBLISHED SUPPLIER NAME,6. PURPOSE OF EXPENDITURE / MERCHANT CATEGORY,7. SPEND
vs local spending data guidance data content format
Splender is:
User friendly (councils) spending aggregation and comparison tool
Comparison module:
Administration module:
Datasets auto discovery and aggregation:
Normalized datasets:
TODO:
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Convert comparison tool from prototype into application with real data
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Finish auto discovery and aggregation module
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Visualizations, notifications and more..
Thank You!
Credits:
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https://github.com/thoughtbot/administrate
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http://rubyonrails.org/
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http://muicss.com/
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https://nodejs.org/en/
-
and others..
Contact<EMAIL_ADDRESS>/ @marek_public
/


"x" "1" "NIEMINEN,SM MAKI-PAAKKANEN,J HIRVONEN,MR ROPONEN,M AND VON WRIGHT,A,GENOTOXICITY OF GLIOTOXIN, A SECONDARY METABOLITE OF ASPERGILLUS FUMIGATUS, IN ABATTERY OF"


CowCrypt Demos Index
Click on a wonderful link for a magical demo.
AES Encryption Demo
CAST5 Encryption Demo
HMAC Demo
MD5 Demo and Benchmark
OpenSSL Interoperability Demo
PBKDF2 Demo and Benchmark
SHA-1 Demo and Benchmark
SHA-256 Demo and Benchmark
Twofish Encryption Demo
RSA Demo
Copyright © 2013
Jeff Lyon


Calculator
Notes
Manager
Checkers
Arithmetic
Checkers
Checkers
{% if result is defined %}
{{result}}
{% endif %}


ONVIF Network Camera Manager
Device
now searching...
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Username
Password
Connect
(
)
PTZ speed for keyboard
slow
medium
fast
Disconnect
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Interface WhereStatementInterface
Common where interface to ensure all where objects work with the library
Direct known implementers
DSchoenbauer\Sql\Where\ArrayWhere
Namespace: DSchoenbauer\Sql\Where
Author: David Schoenbauer<EMAIL_ADDRESS>Since: v1.0.0
Located at Sql/Where/WhereStatementInterface.php
Author: David Schoenbauer<EMAIL_ADDRESS>Since: v1.0.0
Located at Sql/Where/WhereStatementInterface.php
public
|
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public
|


--- layout: post title: New OData Producers and Consumers date: 2010-10-28 17:43:48.000000000 +08:00 categories: - OData tools tags: [] status: publish type: post published: true author: OData.org Archive ---
oday at PDC Microsoft announced a set of new and exciting OData producers and consumers:
As you can see the OData eco-system is seeing lots of exciting developments, and there are more to come...


Ethical Decisions: Making ethical decisions is a big part of my personality type (according to Myers Briggs). As an INFP, I have a very strong moral compass and I feel things deeply. The Myers Briggs test is pretty spot on when it comes to my personality and I have definitely made unethical decisions in the past which has made me feel terrible. Weighing up the pros and cons of a decision is ultimately based on what you want and what you desire. In fact, you might know that your decision is unethical but you make it anyway. However, I believe that it is important to always try and make ethical decisions as they take courage and they are the very essence of being human.
Cultural Influences: I was born in Wellington but when I was younger I lived in Holland for a few years. My fathers side of the family is of Dutch heritage and I would say that I have been influenced by the European culture of food, language, travel and history. However, I spent most of my time growing up in Lower Hutt. This is where I made the majority of my friends and l would consider myself to be very Kiwi. In New Zealand we are so lucky to be isolated and this gives our country a real sense of community. Being laid back, having a sense of adventure, working hard and being friendly to everyone. These are some of the cultural traits that identify me and New Zealand culture in general.
Strengths and Limitations: I consider myself to be loyal, genuine and curious. I have a creative and technical side plus the desire to learn. These are my strengths but there are also limitations. For example, setting expectations is something that I need to work on. I can be a little too laid back and I like things to be relaxed and easy. I have realised that setting expectations can help everyone know where you stand and it means people don’t get down. This is a work in progress for me and one that I am enjoying getting better at.
Working Life I have always enjoyed the process of working with people. I like engaging with people and forming professional relationships. I have found that recognition is a very important part of getting people to work well together. People like to know they are doing a good job, or at least be acknowledged when something good has been done. It’s very easy to say, “Hey, well done fixing that problem yesterday, that was good work”. Praising is a quality I feel is a strength of mine and something I always try to bring in to a workplace.


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City
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City
Supply
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Commodity
Type
Buy
Sell
Consumption
Production
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Barrel
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| < Day Day Up > |
4.3 Port NumbersThe http_port directive tells Squid which port number to listen on for HTTP requests. The default is port 3128: http_port 3128 If you are running Squid as a surrogate (see Chapter 15), you should probably set this to 80. You can instruct Squid to listen on multiple ports with additional http_port lines. This is often useful if you must support groups of clients that have been configured differently. For example, the browsers from one department may be sending requests to port 3128, while another department uses port 8080. Simply list both port numbers as follows: http_port 3128 http_port 8080 You can also use the http_port directive to make Squid listen on specific interface addresses. When Squid is used on a firewall, it should have two network interfaces: one internal and one external. You probably don't want to accept HTTP requests coming from the external side. To make Squid listen on only the internal interface, simply put the IP address in front of the port number: http_port <IP_ADDRESS>:3128 |
| < Day Day Up > |


06/15/2021 - Added listener to the inputtext textarea, now you can write directly on the left textarea.