Learning the ground-state of a spin model with different Neural Networks available in NetKet¶
The goal of this tutorial is to review various neural network architectures available in NetKet, and this in order to learn the ground-state of a paradigmatic spin model, namely the spin \(1/2\) Heisenberg antiferromagnetic spin chain.
The Hamiltonian we will consider for this tutorial is the following
\(L\) is the length of the chain, and we will use both open and periodic boundary conditions. \(\vec{\sigma}=(\sigma^x,\sigma^y,\sigma^z)\) denotes the vector of Pauli matrices. Please note that there is a factor of \(2\) between Pauli-matrices and spin-1/2 operators (thus a factor of \(4\) in \(H\)).
We will consider in this tutorial 5 possible ways of determining the ground-state of this model.
0. Defining the Hamiltonian
1. Exact Diagonalization (as a testbed)
2. Starting simple: the Jastrow ansatz
3. Learning with a Restricted Boltzmann Machine (RBM)
4. RBM again, this time with lattice symmetries
5. Learning with Feed Forward Neural Networks
Estimated time for this tutorial: 20 minutes.
First things first, let’s import the essentials
[123]:
# Import netket library
import netket as nk
# Import Json, this will be needed to examine log files
import json
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import time
0) Defining the Hamiltonian¶
NetKet covers quite a few standard Hamiltonians and lattices, so let’s use this to quickly define the antiferromagnetic Heisenberg chain. For the moment we assume \(L=22\) and simply define a chain lattice in this way (using periodic boundary conditions for now).
[124]:
# Define a 1d chain
L = 22
g = nk.graph.Hypercube(length=L, n_dim=1, pbc=True)
Next, we need to define the Hilbert space on this graph. We have here spin-half degrees of freedom, and as we know that the ground-state sits in the zero magnetization sector, we can already impose this as a constraint in the Hilbert space. This is not mandatory, but will nicely speeds things up in the following.
[125]:
# Define the Hilbert space based on this graph
# We impose to have a fixed total magnetization of zero
hi = nk.hilbert.Spin(s=0.5, graph=g, total_sz=0)
The final element of the triptych is of course the Hamiltonian acting in this Hilbert space, which in our case in already defined in NetKet. Note that the NetKet Hamiltonian uses Pauli Matrices (if you prefer to work with spin-\(1/2\) operators, it’s pretty trivial to define your own custom Hamiltonian, as covered in another tutorial)
[126]:
# calling the Heisenberg Hamiltonian
ha = nk.operator.Heisenberg(hilbert=hi)
1. Exact Diagonalization (as a testbed)¶
Just as a matter of future comparison, let’s compute the exact ground-state energy (since this is still possible for \(L=22\) using brute-force exact diagonalization). NetKet provides wrappers to the Lanczos algorithm which we now use:
[127]:
# compute the ground-state energy (here we only need the lowest energy, and do not need the eigenstate)
exact_result = nk.exact.lanczos_ed(ha, first_n=1, compute_eigenvectors=False)
exact_gs_energy = exact_result.eigenvalues[0]
print('The exact ground-state energy is E0=',exact_gs_energy)
# Just in case you can't do this calculation, here is the result
# exact_gs_energy = -39.14752260706246
The exact ground-state energy is E0= -39.14752260706246
2. Starting simple: the Jastrow ansatz¶
Let’s start with a simple ansatz for the ground-state.
[128]:
ma = nk.machine.Jastrow(hilbert=hi)
ma.init_random_parameters(seed=1, sigma=0.01)
[129]:
# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)
# Sampler
sa = nk.sampler.MetropolisExchange(machine=ma,graph=g)
ma.init_random_parameters(seed=12, sigma=0.01)
# Stochastic reconfiguration
gs = nk.variational.Vmc(
hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=1000,
diag_shift=0.1,
method='Sr')
start = time.time()
gs.run(output_prefix='Jastrow', n_iter=300)
end = time.time()
print('### Jastrow calculation')
print('Has',ma.n_par,'parameters')
print('The Jastrow calculation took',end-start,'seconds')
### Jastrow calculation
Has 231 parameters
The Jastrow calculation took 15.655055046081543 seconds
[130]:
# import the data from log file
data=json.load(open("Jastrow.log"))
# Extract the relevant information
iters_Jastrow=[]
energy_Jastrow=[]
for iteration in data["Output"]:
iters_Jastrow.append(iteration["Iteration"])
energy_Jastrow.append(iteration["Energy"]["Mean"])
fig, ax1 = plt.subplots()
ax1.plot(iters_Jastrow, energy_Jastrow, color='C8', label='Energy (Jastrow)')
ax1.set_ylabel('Energy')
ax1.set_xlabel('Iteration')
plt.axis([0,iters_Jastrow[-1],exact_gs_energy-0.1,exact_gs_energy+0.4])
plt.axhline(y=exact_gs_energy, xmin=0,
xmax=iters_Jastrow[-1], linewidth=2, color='k', label='Exact')
ax1.legend()
plt.show()
Well that’s not too bad for a simple ansatz. But we can do better, can’t we?
3. Learning with a Restricted Boltzmann Machine (RBM)¶
We will now consider another celebrated ansatz, the Restricted Boltzmann Machine (RBM). It simply consists of two layers: a visible one representing the \(L\) spin 1/2 degrees of freedom, and an hidden one which contains a different number \(M\) of hidden units. There are connections between all visible and hidden nodes. The ansatz is the following
\(\Psi_{\rm RBM} (\sigma_1^z,\sigma_2^z, ..., \sigma_L^z) = \exp ( \sum_{i=1}^L a_i \sigma_i^z ) \prod_{i=1}^M \cosh (b_i + \sum_j W_{ij} \sigma^z_j)\)
\(a_i\) (resp. \(b_i\)) are the visible (resp. hidden) bias. Together with the weights \(W_{ij}\), they are variational parameters that we (or rather NetKet) will optimize to minimize the energy. Netket gives you the control on the important parameters in this ansatz, such as \(M\) and the fact that you want to use or not the biases. The full explanation is here.
More conveniently (especially if you want to try another \(L\) in this tutorial), let’s define the hidden unit density \(\alpha = M / L\), and invoke the RBM ansatz in NetKet with as many hidden as visible units.
[131]:
# RBM ansatz with alpha=1
ma = nk.machine.RbmSpin(alpha=1, hilbert=hi)
And let us use the same sampler (Metropolis exchange) with some different random initial parameters, optimizer (stochastic gradient), and variational method (stochastic reconfiguration) as for the Jastrow ansatz, and let’s run things!
[132]:
# Sampler
sa = nk.sampler.MetropolisExchange(machine=ma,graph=g)
ma.init_random_parameters(seed=123, sigma=0.01)
# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.05)
# Stochastic reconfiguration
gs = nk.variational.Vmc(
hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=1000,
diag_shift=0.1,
use_iterative=True,
method='Sr')
start = time.time()
gs.run(output_prefix='RBM', n_iter=600)
end = time.time()
print('### RBM calculation')
print('Has',ma.n_par,'parameters')
print('The RBM calculation took',end-start,'seconds')
### RBM calculation
Has 528 parameters
The RBM calculation took 79.31553912162781 seconds
[133]:
# import the data from log file
data=json.load(open("RBM.log"))
# Extract the relevant information
iters=[]
energy_RBM=[]
for iteration in data["Output"]:
iters.append(iteration["Iteration"])
energy_RBM.append(iteration["Energy"]["Mean"])
fig, ax1 = plt.subplots()
ax1.plot(iters_Jastrow, energy_Jastrow, color='C8', label='Energy (Jastrow)')
ax1.plot(iters, energy_RBM, color='red', label='Energy (RBM)')
ax1.set_ylabel('Energy')
ax1.set_xlabel('Iteration')
plt.axis([0,iters[-1],exact_gs_energy-0.03,exact_gs_energy+0.2])
plt.axhline(y=exact_gs_energy, xmin=0,
xmax=iters[-1], linewidth=2, color='k', label='Exact')
ax1.legend()
plt.show()
Note that this plot zooms closer to the exact ground-state energy. With 600 iterations, we start to see convergence and reach the ground-state energy within about one per thousand, this is already nice! But we are not totally there yet, and in particular the simpler (less parameters) Jastrow wave-function seems to perform better for this example. How can we improve things? As an exercice, try to increase the number of hidden units and/or the number of iterations. What is happening? You can also check out the influence of the learning rate.
By playing with these parameters, you have hopefully arrived at an improved result, but likely at an increased CPU time cost. Let’s do things differently, and take to our advantage the symmetries of the Hamiltonian in the neural network construction.
4. RBM again, this time with lattice symmetries¶
Let’s define a similar RBM machine, which takes into account that the model has translational symmetries. All sites are equivalent and thus many of the wave-functions coefficients are related by symmetry. We use the same exact hyperparameters as in the previous RBM calculation (\(\alpha=1\), same learning rate, and number of samples and iterations in the Variational Monte Carlo) and run now a symmetric RBM.
[134]:
# Symmetric RBM Spin Machine
ma = nk.machine.RbmSpinSymm(alpha=1, hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Metropolis Exchange Sampling
# Notice that this sampler exchanges two neighboring sites
# thus preservers the total magnetization
sa = nk.sampler.MetropolisExchange(machine=ma, graph=g)
# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.05)
# Stochastic reconfiguration
gs = nk.variational.Vmc(
hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=1000,
diag_shift=0.1,
use_iterative=True,
method='Sr')
start = time.time()
gs.run(output_prefix='RBMSymmetric', n_iter=300)
end = time.time()
print('### Symmetric RBM calculation')
print('Has',ma.n_par,'parameters')
print('The Symmetric RBM calculation took',end-start,'seconds')
### Symmetric RBM calculation
Has 24 parameters
The Symmetric RBM calculation took 33.651692152023315 seconds
The simulation was much faster, wasn’t it? There were of course much less parameters to optimize. Now let’s extract the results and plot them using a zoomed scale, and together with the previous results with the RBM.
[135]:
# import the data from log file
data=json.load(open("RBMSymmetric.log"))
# Extract the relevant information
energy_symRBM=[]
iters_symRBM=[]
for iteration in data["Output"]:
energy_symRBM.append(iteration["Energy"]["Mean"])
iters_symRBM.append(iteration["Iteration"])
fig, ax1 = plt.subplots()
ax1.plot(iters_Jastrow, energy_Jastrow, color='C8', label='Energy (Jastrow)')
ax1.plot(iters, energy_RBM, color='red', label='Energy (RBM)')
ax1.plot(iters_symRBM, energy_symRBM, color='blue', label='Energy (Symmetric RBM)')
ax1.set_ylabel('Energy')
ax1.set_xlabel('Iteration')
plt.axis([0,iters_symRBM[-1],exact_gs_energy-0.06,exact_gs_energy+0.12])
plt.axhline(y=exact_gs_energy, xmin=0,
xmax=iters[-1], linewidth=2, color='k', label='Exact')
ax1.legend()
plt.show()
Not only the simulation was faster in terms of CPU time, but we are now reaching the ground-state in a much lower number of iterations! Imposing symmetries greatly helps, and NetKet allows to do this. Note that there is also a symmetric version of the Jastrow ansatz that we tested earlier in NetKet, which is called JastrowSymm
. As an exercice, check it out. What you will find is that while it converges slightly faster in terms of iterations with
respect to the non-symmetric Jastrow, it does not improve the estimate of the ground-state energy. We actually see that the symmetric RBM sets the standard very high.
5. Learning with Feed Forward Neural Networks¶
Now let’s try a more complex network, namely a Feed Forward Neural Network (FFNN). There you will have more freedom to construct your own specific architecture. We’ll try two different FFNN in this tutorial.
The first one is a simple structure: the first layer takes as input L-dimensional input, applies a bias and outputs two times more data, just followed by a Lncosh
activation layer. The final layer SumOuput
is needed to obtain a single number for the wave-function coefficient associated to the input basis state.
[154]:
layers = (nk.layer.FullyConnected(input_size=L,output_size=int(2*L),use_bias=True),
nk.layer.Lncosh(input_size=int(2*L)),
nk.layer.SumOutput(input_size=int(2*L))
)
for layer in layers:
layer.init_random_parameters(seed=12345, sigma=0.01)
ffnn = nk.machine.FFNN(hi, layers)
sa = nk.sampler.MetropolisExchange(graph=g, machine=ffnn)
opt = nk.optimizer.Sgd(learning_rate=0.05)
gs = nk.variational.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=opt,
n_samples=1000,
use_iterative=True,
method='Sr')
start = time.time()
gs.run(output_prefix='FF', n_iter=300)
end = time.time()
print('### Feed Forward calculation')
print('Has',ffnn.n_par,'parameters')
print('The Feed Forward calculation took',end-start,'seconds')
### Feed Forward calculation
Has 1012 parameters
The Feed Forward calculation took<PHONE_NUMBER>1602783 seconds
[155]:
# import the data from log file
data=json.load(open("FF.log"))
# Extract the relevant information
energy_FF=[]
for iteration in data["Output"]:
energy_FF.append(iteration["Energy"]["Mean"])
fig, ax1 = plt.subplots()
ax1.plot(iters_Jastrow, energy_Jastrow, color='C8',linestyle="None", marker='d',label='Energy (Jastrow)')
ax1.plot(iters, energy_RBM, color='red', marker='o',linestyle="None",label='Energy (RBM)')
ax1.plot(iters_symRBM, energy_symRBM, color='blue',linestyle="None",marker='o',label='Energy (Symmetric RBM)')
ax1.plot(iters_symRBM, energy_FF, color='orange', marker='s',linestyle="None",label='Energy (Feed Forward, take 1)')
ax1.legend(bbox_to_anchor=(1.05, 0.3))
ax1.set_ylabel('Energy')
ax1.set_xlabel('Iteration')
plt.axis([0,iters_symRBM[-1],exact_gs_energy-0.02,exact_gs_energy+0.1])
plt.axhline(y=exact_gs_energy, xmin=0,
xmax=iters[-1], linewidth=2, color='k', label='Exact')
plt.show()
The results are clearly better than a simple (non-symmetrized RBB), and perform slightly better than the Jastrow ansatz. Let us increase the number of layers by adding a fully-connected layer with bias and Lncosh
activation (with \(2L\) inputs and ouputs) before the final SumOuput
layer.
[156]:
layers = (nk.layer.FullyConnected(input_size=L,output_size=int(2*L),use_bias=True),
nk.layer.Lncosh(input_size=int(2*L)),
nk.layer.FullyConnected(input_size=int(2*L),output_size=int(2*L),use_bias=True),
nk.layer.Lncosh(input_size=int(2*L)),
nk.layer.SumOutput(input_size=int(2*L)),
)
for layer in layers:
layer.init_random_parameters(seed=123456, sigma=0.01)
ffnn = nk.machine.FFNN(hi, layers)
sa = nk.sampler.MetropolisExchange(graph=g, machine=ffnn)
opt = nk.optimizer.Sgd(learning_rate=0.05)
gs = nk.variational.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=opt,
n_samples=1000,
use_iterative=True,
method='Sr')
start = time.time()
gs.run(output_prefix='FF2', n_iter=300)
end = time.time()
print('### Feed Forward (more layers) calculation')
print('Has',ffnn.n_par,'parameters')
print('The Feed Forward (more layers) calculation took',end-start,'seconds')
### Feed Forward (more layers) calculation
Has 2992 parameters
The Feed Forward (more layers) calculation took<PHONE_NUMBER>691223 seconds
[157]:
# import the data from log file
data=json.load(open("FF2.log"))
# Extract the relevant information
energy_FF_morelayers=[]
for iteration in data["Output"]:
energy_FF_morelayers.append(iteration["Energy"]["Mean"])
fig, ax1 = plt.subplots()
#ax1.plot(iters_Jastrow, energy_Jastrow, color='C8',linestyle="None", marker='d',label='Energy (Jastrow)')
#ax1.plot(iters, energy_RBM, color='red', label='Energy (RBM)')
ax1.plot(iters_symRBM, energy_symRBM, color='blue',linestyle="None",marker='o',label='Energy (Symmetric RBM)')
ax1.plot(iters_symRBM, energy_FF, color='orange', marker='s',alpha=0.5,linestyle="None",label='Energy (Feed Forward, take 1)')
ax1.plot(iters_symRBM, energy_FF_morelayers, color='green',marker='s',linestyle="None", alpha=1,label='Energy (Feed Forward, more layers)')
ax1.legend(bbox_to_anchor=(1.05, 0.5))
ax1.set_ylabel('Energy')
ax1.set_xlabel('Iteration')
plt.axis([0,iters_symRBM[-1],exact_gs_energy-0.02,exact_gs_energy+0.06])
plt.axhline(y=exact_gs_energy, xmin=0,
xmax=iters[-1], linewidth=2, color='k', label='Exact')
plt.show()
The results are even better, but at the price of an increase in computational time….
Note that more complex structures, such as Convolutional Neural Networks (CNN), can also be used within Netket. However, for such 1d systems, they do not bring too much compared to the symmetric RBM (as a matter of fact, the symmetric RBM is a special type of a simple CNN. CNNs show their full strength for more complex systems, such as 2d quantum systems. Convolutional Neural Networks will be the topic of another tutorial in NetKet (and we’ll make there the connection with the special case of the symmetric RBM).
Finally let us conclude that another type of machine, Matrix Product States (MPS), is also available in NetKet. They do perform extremely well for 1d quantum systems. Since however they are a bit different, they will be presented in another tutorial.
[ ]:


{% import 'macros.html' as macro%} {% extends 'base.html'%} {% import 'bootstrap/wtf.html' as wtf %} {% block styles%} {{ super() }} {% endblock %} {% block content %}
Comments
{% for comment in comments_list %}{{comment.the_comment}} -- by {{comment.user.username}}


Your snakelet gets the current request and response objects in the serve method, and you can do lots of stuff with them:
Scope: request
| Method | description |
|---|---|
| getServerSoftware() | server software version (string) |
| getSnakeletsVersion() | snakelets version (string) |
| getServerIP() | server IP address (string). Don't use it in an URL, this screws up the virtual hosting feature. |
| getServerName() | published server hostname (string). Taken from the current virtualhost that handles the request. Which can be different from the webapp's owning vhost, because of vhost aliasing. This server name is safe to use in URLs, the virtual hosting will work nicely. |
| getRealServerName() | real (internal) server hostname (string) |
| getServerProtocol() | supported HTTP level (string, "HTTP/1.0") |
| getServerPort() | socket port of the server (int) |
| getRequestURL() | the original full request URL (without hostname and port), for example: "/page/test.cgi?arg=34" Use with the getBaseURL method to obtain a complete URL. |
| getRequestURLplain() | like getRequestURL, but also without any query args. Use with the next method (getBaseURL) to obtain a complete URL without query args. |
| getBaseURL() | the base URL of the server. Example: "http://desertfish.xs4all.nl:9080" |
| getPathInfo() | any additional URL path components after the snakelet URL. Example: when url is 'snoop.sn/foo/bar?arg=value', it returns "/foo/bar". (Note: this is always empty when you use a fnmatch URL pattern for your snakelet!) (this string is not url-escaped) |
| getMethod() | the HTTP method used ("GET" or "POST") |
| getQuery() | the query args of the URL, example: "arg=value&name=foo%21". (this string is still url-escaped) |
| getFullQueryArgs() | all the query args including path info and command, example: "/zip/zap?delete&arg=value&name=foo%21" (note: this is not the full URL! You can get that one from the Snakelet) (this string is still url-escaped) |
| getRemoteHost() | hostname of the remote host (string) |
| getRemoteAddr() | IP address of the remote host (string) |
| getRealRemoteAddr() | the 'real' IP address of the remote host (use this if you are running via Apache Proxy module) |
| getContentType() | the content-type of the request (string) |
| getContentLength() | content length of the request (int) |
| getUserAgent() | browser ID string of the client's browser, or '' (empty string) |
| getReferer() | the referring URL (that is: the url we came from), or '' (empty string) |
| getCookie() | raw cookie information (comma separated string) |
| getCookies() | parsed cookies (mycookie.SimpleRequestCookie object, this is a dict, which maps cookie names to a list of string values) |
| clearCookies() | erases all cookie information from the request (not from the client!) |
| getInput() | request input stream (socket/file) |
| getArg() | URL argument. Example: when url=snoop.sn?command&arg=name it returns "command" (this string is not url-escaped) |
| setArg(arg) | reset the URL argument (getArg() ) to something new. |
| getWebApp() | the current WebApp object |
| getRangeStr() | the unparsed string value of the HTTP 'range' header, or '' (empty string). |
| getRange() | the parsed HTTP 'range' header; a tuple: (from,to) |
| getAuth() | the HTTP Authorization header value, or '' (empty string). |
| getAllHeaders() | all HTTP headers (mimetools.Message object) |
| getHeader(header) | value of specified HTTP header, or None if it isn't present |
| getForm() | parsed form contents (a dict of {param name: value} ) The form has a utility method urlencode() that returns an url-encoded query args string like "arg=value&foo=bar" for the form's parameters. |
| getField(param, default='') | value of a single form field parameter (or the provided default value -which is an empty string if not otherwise given- if the parameter doesn't exist) |
| getParameter(param, default='') | value of a single form field or request context parameter (or the provided default value -which is an empty string if not otherwise given- if the parameter doesn't exist). The request form is first examined, if it does not contain the required field, the request context is examined for a matching attribute. If it too does not have it, the default is returned. |
| getContext() | request context (ContextContainer object). Scope: request. unique per user and per request, destroyed after request completes |
| getSession() | the session object (snakeserver.snakelet.Session object), None if there is no session |
| deleteSession() | logout current user and deletes the session object. Also clears all cookie info on the request (not on the client!) |
| getSessionContext() | the session context (ContextContainer object). Scope: session. unique per user, shared for all requests of this user. None if there is no session. |
| getMaxPOSTsize() | the current max size of a POST request (in bytes) |
| setMaxPOSTsize(numbytes) | set the maximum size in bytes of a POST request (default: 200000=200Kb). If it is larger, the server aborts the connection and the POST request fails, and a FormFileUploadError exception is raised. |
| getEncoding() | the current request character encoding. None if not specified (means default). |
| setEncoding(encoding) | forces the request character encoding. This is often necessary to correctly read non-ASCII characters from From Posts. Also note that returned form fields will be unicode objects (instead of regular strings) if you set the encoding. If you try to change the encoding after the request form fields have already been accessed, a ValueError will be raised. Using this method will override a defaultRequestEncoding that may be defined on the webapp. |
Getting request parameters is done using getForm()
, or getParameter()
. You can clear all parameters for the duration of the reqeuest using getForm().clear()
(because it is just a dict).
If you need to add or modify request parameters from inside your code, you should update the appropriate keys in the dict object
that is returned from getForm()
.


--- layout: post permalink: 101phones.html Tags: Array NameOfCoupon: 101Phones Cash Back Summary: See our 101Phones Coupon Code, 101Phones Rebate, 101Phones Promo Code, 101Phones Discount, 101Phones Coupon Cash Back Rebate here. MetaKeywords: 101Phones Coupon Code, 101Phones Rebate, 101Phones Promo Code, 101Phones Cash Back Rebate, 101Phones Discount, 101Phones Coupon Image: LinkURL: http://www.dpbolvw.net/click-4092138-10398573 affiliate_url_link: http://www.icouponblog.com/jane.php?id=4052 thesis_readmore: See the 101Phones Coupon thesis_thumb: http://www.icouponblog.com/tn.php?merchantURL=101phones.com Title: Save 3% - 101Phones Coupon Code for 101phones.com. 101Phones Rebate & Discount thesis_description: Our 101Phones Discount Coupon Code for 101phones.com. We have the best 101Phones Rebate anywhere. thesis_keywords: 101Phones, 101Phones Coupon Code, 101Phones Discount, 101Phones Rebate product_name: 101Phones hop_url: http://www.icouponblog.com/hop.php?id=4052 ---
Are you looking to buy a telephone and are all the choices confusing you? You don’t have to worry anymore shop at www.101phones.com. It is the top telecommunications store on the internet. They have an excellent record in providing the best product and service. They carry a large selection of phones to choose from. They carry all major brands like Panasonic, Polycom, AT&T, V-tech, Plantronics, Siemens, Nortel, Uniden, EnGenius, Ameriphone, Walker, Sharp and many more. Visit www.101phones.com and find a phone that you need. Use our discount coupons to get a great deal
Visit www.101phones.com to shop with ease and comfort. The user friendly website is designed to make it easy to browse and order products online. There are hundreds of phones to choose from. You can find a phone of your choice by brand, by cordless or corded or by style and type. www.101phones.com has a great variety of telephones to choose from at an extremely affordable price. You can also buy phone accessories, batteries, headset, caller ID, answering machines, gateway, amplifiers, shoulder rest and lots more. Get the best quality phones and accessories at a great affordable price from www.101phones.com.
101Phones is the best internet store you can find for all kinds of telephones, from the usual to the special needs phones like in Braille, or for hearing or speech impaired, for physical or other impairments. You can also get novelty phones, eco friendly phones, conference phones and business phones. Check it out on www.101phones.com. Use our discount coupons to save more money!


Here I am at the ScottGu impersonators gathering. I'm in the red polo.
Ok, actually this was a Scott Guthrie presentation - vs2010, asp4, silverlight 4, asp mvc2. Plus some related stuff like the IIS SEO module and IIS media streaming. And even a demonstration of rickrolling.
Between the lines- the web stack is still evolving strongly (especially SL and MVC, but asp webforms is getting attention too), windows forms is dead, silverlight and silverlight out of browser will replace most of desktop WPF (which is just for Visual Studio scale apps)
I'm in the back row, under the "d" of msdn and looking to the side wondering if ScottGu was in that direction.


Chinese experts to help in Notre-Dame reconstruction
BEIJING: Chinese experts will participate in the restoration of France's Notre-Dame cathedral, state media reported on Wednesday (Nov 6), following a meeting between the two countries' heads of state in Beijing.
The 850-year-old cathedral, which was scarred in April after a fire tore through its roof and toppled its spire, was the most visited historic monument in Europe.
Since the blaze, which took 400 firefighters to control, Chinese and French authorities have been in touch about cooperating on restoration efforts, reported official news agency Xinhua.
"China has a great deal of experience in renovating ancient buildings affected by fire, especially ones made of wood," said Chai Xiaoming, director of the Chinese Academy of Cultural Heritage, in an interview with state-run China Daily.
"This means we could offer suggestions on how to approach the renovation of the oak-framed roof on Notre Dame Cathedral," he said.
Citing a document signed by the two countries during President Emmanuel Macron's visit to Beijing, Xinhua said Chinese experts will work with French teams in on-site restoration without elaborating.
"China and France will collaborate on the theme and model and select Chinese expert candidates for the cooperative restoration work in 2020," it said.
The partial destruction of Notre-Dame sparked an outpouring of condolences around the world earlier this year, with Chinese President Xi Jinping calling the cathedral "an outstanding treasure of human civilisation."
Though its stained glass windows, towers, bells and most artworks and relics survived, reconstruction work to rebuild the 13th-century cathedral will take years.
It is only at the end of 2020 that a complete check will allow architects to work out how to restore the cathedral - and no reconstruction is expected to start before 2021.
In addition to Notre-Dame, the agreement signed on Wednesday will also see France and China collaborate on protecting China's famous terracotta warriors in Xi'an, northern Shaanxi province, reported Xinhua.


Optional built-in implementation of Taiga UI modals
If you want custom dialogs see this link on creating your own implementation
TUI_DIALOGS_CLOSE
token if you
need to close dialog with from a stream. For example, this way you
can close dialogs by logout:
TuiDialogModule
into your app.module
to use dialogs (See also "Setup" for details)
Dialog has Observer
in its context to call
next
, complete
or
error
of stream. There is also a
completeWith
method to pass data and close dialog
with one action
Input data: {{data}}.
There is no need in input data for template dialog because you can set it in template. But if you use a component, context is the only way to get data from outside
To show a dialog, use method
TuiDialogService
, where O
is output
data type and I
input data type. In this sample, input
data has type number
and output data has type
string
.
You can also just unsubcribe from
Observable
to close a dialog (this observable is
returned from open
method). You can save a
subscription for that or use takeUntil
like tools
from RxJs
Observable
(you can catch it with "catch" operator
or onError handler)
Add TuiDialogModule
into your app.module
Use open
method to show a dialog, subscribe to
an Observable
:
If you want to use your own component as a dialog content,
use POLYMORPHEUS_CONTEXT
in it. Dialog will
provide with this token some useful options:
$implicit
with
Observer<O>
and
completeWith
hook to call next
and
complete
in one action. Do not forget to add
your dialog component into entryComponents
of
your module where you use dialog
Use Observer
from $implicit
field
of context to close a dialog or get some data
If you use dialog components in lazy loading modules, do not
forget to pass into
new PolymorpheusComponent Injector
of component
where you open a dialog
Dialogs have experimental feature with closing them on
browser backward navigation. It was achieved through
manipulation of window.history
.
If you have some troubles with it, report about this,
please.
In this case, you can disable this feature for your app
using
TUI_DIALOG_CLOSES_ON_BACK
token.
You can create your own dialogs. For example, special dialogs for mobile apps or with very customizable appearance
Then you need to provide a stream of your dialogs into multi token
TUI_DIALOGS
.
You can also use
AbstractTuiDialogService
to create this stream of
dialogs.


You should take the CSS/Design track at Epicodus! Though the differences between each coding language is subtle, one of the biggest differences between CSS/Design and the others is that it is a front-end course.
Take the Java/Android course at Epicodus if you have interest in Android user interfaces or with making high-performance Java applications.
Larger companies that have been around for longer work with C# and .NET, if you want to work on business software, go with the C#/.NET track at Epicodus.
If you are ideally creating websites with interactivity from a user than the Ruby/Rails track would be great for you!
PHP/Drupal is a great combination if you are thinking that you'd like to work on websites that are content-heavy
Whether you take PHP/Drupal, Java/Android, Ruby/Rails, or C#/.NET you will find that your skills are easily adaptable. If you ended up with this choice than it is likely you could choose any back-end course track and be happy!
Here are the current courses for : Epicodus Portland ScheduleEpicodus Seattle Schedule


{% extends "cyano/base.html" %} {% comment %} Cyanodesign index page Copyright (c) 2013 Gabriel Kind Hochschule Mittweida, University of Applied Sciences Released under the MIT license {% endcomment %} {% load static %} {% load staticfiles %} {% load templatetags %} {% load filters %} {% load crispy_forms_tags %} {% block head_title %}{{ block.super }} :: CellDesign{% endblock %} {% block page_title %}CellDesign – {{ name }}{% endblock %} {% block head %} {{ block.super }} {% endblock %} {% block extrastyles %} span.cyano-external-metabolite { color: green } #cyano-metabolite-list tr td:first-child, #cyano-enzyme-list tr td:first-child { font-weight: bold; } .hoverable, #cyano-metabolite-list .cyano-enzyme, #cyano-metabolite-list tr td:first-child, #cyano-enzyme-list tr td:first-child, #cyano-enzyme-list .cyano-metabolite, svg g.node, svg text { cursor: pointer; } .hoverable:hover, #cyano-metabolite-list .cyano-enzyme:hover, #cyano-metabolite-list tr td:first-child:hover, #cyano-enzyme-list tr.odd td:first-child:hover, #cyano-enzyme-list tr.even td:first-child:hover, #cyano-enzyme-list .cyano-metabolite:hover { color: blue; } .cyano-enzyme-disabled { color: grey; } #submit-form { display: none; } .tab-content { margin-top: 5px; margin-bottom: 5px; } {% endblock %} {% block content %} {% if not request.user.is_authenticated %} Info:You are not logged in. Making permanent changes to models or uploading new ones is disabled. {% endif %} {% if revision %} Viewing old model from {{ revision.date }}. Simulating and saving will be based on this old version of the model. {% endif %} Reactions Metabolites Settings Stoichiometry Simulation Name Reaction Constraint Active Create new reaction Toggle Dropdown Bulk add reactions Create new metabolite Name Consumed by Produced by Is External Create new reaction Create new metabolite Delete unused metabolites Main objective Maximize objective Minimize objective Simulation Type Flux Balance Analysis Robustness Analysis Sensitivity Analysis FBA settings The main objective is always displayed in the simulation. Display reactions with highest flux connected to the objective Display selected reactions Select visible reactions Sensitivity/RA settings Design objective Target reaction Axis label X-Axis Y-Axis (Left) Y-Axis (Right) Changes apply without running a new simulation Left Right Difference Combine results with previous simulation Run simulation Export As image As vector graphic Name Flux × Edit Reaction Name Pathway Substrates Products Enabled Reversible Constrained Min Max × Bulk add reactions Enter reactions in BioOpt format for adding them to the model. Constraints can be put after the reactions. BioOpt data: × Create Metabolite Name External Chemical formula × Edit Metabolite The metabolite is modified in all reactions. Name External Chemical formula × Delete Do you really want to delete reaction ? Delete unused metabolites × Delete unused metabolites Do you really want to delete all unused metabolites? {% crispy save_form %} {% crispy saveas_form %} {% if request.user.is_authenticated %} {% endif %} {% csrf_token %} {% endblock %}
The metabolite is modified in all reactions.


Up: Introduction
Next: Basic Concepts
Previous: Features Needed to Support Libraries
The corresponding
concepts that MPI provides, specifically to support robust libraries, are
as follows:
- Contexts of communication,
- Groups of processes,
- Virtual topologies,
- Attribute caching,
- Communicators.
Communicators (see [16,24,27]) encapsulate all of
these ideas in order to provide the appropriate scope for all communication
operations in MPI. Communicators are divided into two kinds:
intra-communicators for operations within a single group of processes, and
inter-communicators, for point-to-point communication between two groups of
processes.
Caching. Communicators (see
below) provide a ``caching'' mechanism that allows one to
associate new attributes with communicators, on a par with MPI built-in
features. This can be used by advanced users to adorn communicators further,
and by MPI to implement some communicator functions. For example, the
virtual-topology functions described in
Chapter Process Topologies
are likely to be supported this way.
Groups. Groups
define an ordered collection of processes, each with a rank, and it is this
group that defines the low-level names for inter-process communication (ranks
are used for sending and receiving). Thus, groups define a scope for process
names in point-to-point communication. In addition, groups define the scope
of collective operations. Groups may be manipulated separately from
communicators in MPI, but only communicators can be used in
communication operations.
Intra-communicators. The most commonly used means for message
passing in MPI is via intra-communicators. Intra-communicators contain an
instance of a group, contexts of communication for both point-to-point and
collective communication, and the ability to include virtual topology and
other attributes.
These features work as follows:
- Contexts provide the ability to have separate safe ``universes''
of message passing in MPI. A context is akin to an additional
tag that differentiates messages.
The system manages this differentiation process.
The use of separate communication
contexts by distinct libraries (or distinct library invocations)
insulates communication internal to the library execution from
external communication. This allows the invocation of the library even if
there are pending communications
on ``other'' communicators, and avoids the need to
synchronize entry or exit into library code. Pending point-to-point
communications are also guaranteed not to interfere with collective
communications within a single communicator.
- Groups define the participants in the communication (see above)
of a communicator.
- A virtual topology defines a special mapping of the ranks in a
group to and from a topology. Special constructors for
communicators are defined in chapter Process Topologies
to provide
this feature. Intra-communicators as described in this chapter do
not have topologies.
- Attributes define the local information that the user or
library has added to a communicator for later reference.
[] Advice to users.
The current practice in many communication libraries is that there is
a unique, predefined communication universe that includes all
processes available when the parallel program is initiated; the processes are
assigned consecutive ranks. Participants in a point-to-point
communication are identified by their rank; a collective communication
(such as broadcast) always involves all processes. This practice can be
followed in MPI by using the predefined communicator
MPI_COMM_WORLD. Users who are satisfied with this practice
can plug in MPI_COMM_WORLD wherever a communicator argument
is required, and can consequently disregard the rest of this chapter.
( End of advice to users.)
Inter-communicators. The discussion has dealt so far with intra-communication: communication
within a group. MPI also supports inter-communication: communication
between two non-overlapping groups. When an application is built by composing
several parallel modules, it is convenient to allow one module to communicate
with another using local ranks for addressing within the second module. This
is especially convenient in a client-server computing paradigm, where either
client or server are parallel. The support of inter-communication
also provides a mechanism for the extension of MPI to a dynamic model where
not all processes are preallocated at initialization time. In such a
situation, it becomes necessary to support communication across ``universes.''
Inter-communication is supported by objects called inter-communicators.
These objects bind two groups together with communication contexts shared by
both groups.
For inter-communicators, these features work as follows:
- Contexts provide the ability to have
a separate safe ``universe''
of message passing between the two groups. A send in the local
group is always a receive in the remote group, and vice versa.
The system manages this differentiation process.
The use of separate communication
contexts by distinct libraries (or distinct library invocations)
insulates communication internal to the library execution from
external communication. This allows the invocation of the library even if
there are pending communications
on ``other'' communicators, and avoids the need to
synchronize entry or exit into library code.
There is no general-purpose
collective communication on inter-communicators, so
contexts are used just to isolate point-to-point communication.
- A local and remote group specify the recipients and destinations
for an inter-communicator.
- Virtual topology is undefined for an inter-communicator.
- As before,
attributes cache defines the local information that the user or
library has added to a communicator for later reference.
MPI provides mechanisms for creating and manipulating inter-communicators.
They are used for point-to-point communication in an related manner to
intra-communicators. Users who do not need inter-communication
in their applications can safely ignore this extension. Users who
need collective operations via inter-communicators must layer it on top
of MPI. Users who require inter-communication between overlapping groups
must also layer this capability on top of MPI.
Up: Introduction
Next: Basic Concepts
Previous: Features Needed to Support Libraries
Return to MPI 1.1 Standard Index
Return to MPI-2 Standard Index
Return to MPI Forum Home Page
MPI-1.1 of June 12, 1995
HTML Generated on August 6, 1997


I've decided that, based on location and other factors, I'd like to find another spot to park my tiny RV in Portland. Preferably, I'd like to be somewhere within the following boundaries:
All I would ask is to use your electricity (via extension cord), to shower in your bathroom, and to use your laundry machines (if present). My RV is only 20'8", so it only takes up slightly more room than a standard parking spot. It's also downright adorable (pictured above).
Though what I'm envisioning is an arrangement as an auxiliary dwelling alongside an existing residence, I'd also be open to a spot in a parking lot, etc if I were close enough to a facility for bathing.
Please spread the word! I'm willing to pay whatever the landowner or tenants think is fair. I'm quiet, considerate, and clean. Thank you for your support!


smooth_samples.Rd
Returns draws from the posterior distributions of smooth functions in a GAM. Useful, for example, for visualising the uncertainty in individual estimated functions.
smooth_samples(model, ...) # S3 method for gam smooth_samples( model, term = NULL, n = 1, newdata = NULL, seed = NULL, freq = FALSE, unconditional = FALSE, ncores = 1L, n_vals = 200, ... )
| model | a fitted model of the supported types |
|---|---|
| ... | arguments passed to other methods |
| term | character; select which smooth's posterior to draw from.
The default ( |
| n | numeric; the number of posterior samples to return. |
| newdata | data frame; new observations at which the posterior draws
from the model should be evaluated. If not supplied, the data used to fit
the model will be used for |
| seed | numeric; a random seed for the simulations. |
| freq | logical; |
| unconditional | logical; if |
| ncores | number of cores for generating random variables from a
multivariate normal distribution. Passed to |
| n_vals | numeric; how many locations to evaluate the smooth at if
|
A tibble with additional classes "smooth_samples"
and
`"posterior_samples".
For the "gam"
method, the columns currently returned (not in this order)
are:
smooth
; character vector. Indicates the smooth function for that
particular draw,
term
; character vector. Similar to smooth
, but will contain the
full label for the smooth, to differentiate factor-by smooths for
example.
by_variable
; character vector. If the smooth involves a by
term, the
by variable will be named here, NA_character_
otherwise.
row
; integer. A vector of values seq_len(n_vals)
, repeated if
n > 1L
. Indexes the row in newdata
for that particular draw.
draw
; integer. A vector of integer values indexing the particular
posterior draw that each row belongs to.
value
; numeric. The value of smooth function for this posterior draw
and covariate combination.
.xN
; numeric. A series of one or more columns containing data required
for the smooth. .x1
will always be present and contains the values of
the covariate in the smooth. For example if smooth
is s(z)
then
.x1
will contain the values of covariate z
at which the smooth was
evaluated. Further covariates for multi-dimensional thin plate splines
(e.g. s(x, z)
) or tensor product smooths (e.g. te(x,z,a)
) will
result in variables .x1
and .x2
, and .x1
, .x2
, and .x3
respectively, with the number (1
, 2
, etc) representing the order
in which the covariates were specified in the smooth.
Additional columns will be present in the case of factor by smooths,
which will contain the level for the factor named in by_variable
for
that particular posterior draw.
The set of variables returned and their order in the tibble is subject to change in future versions. Don't rely on position.
load_mgcv() # \dontshow{ set.seed(2) op <- options(cli.unicode = FALSE) # } dat <- gamSim(1, n = 1000, dist = "normal", scale = 2)#> Gu & Wahba 4 term additive modelm1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML") smooth_samples(m1, term = "s(x0)", n = 5, seed = 42)#> # A tibble: 1,000 x 7 #> smooth term by_variable .x1 row draw value #> <chr> <chr> <chr> <dbl> <int> <int> <dbl> #> 1 s(x0) s(x0) NA 0.000663 1 1 -0.961 #> 2 s(x0) s(x0) NA 0.00568 2 1 -0.933 #> 3 s(x0) s(x0) NA 0.0107 3 1 -0.906 #> 4 s(x0) s(x0) NA 0.0157 4 1 -0.878 #> 5 s(x0) s(x0) NA 0.0207 5 1 -0.850 #> 6 s(x0) s(x0) NA 0.0258 6 1 -0.822 #> 7 s(x0) s(x0) NA 0.0308 7 1 -0.795 #> 8 s(x0) s(x0) NA 0.0358 8 1 -0.768 #> 9 s(x0) s(x0) NA 0.0408 9 1 -0.740 #> 10 s(x0) s(x0) NA 0.0458 10 1 -0.713 #> # ... with 990 more rows#> Factor `by' variable example## fit model... m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = dat) smooth_samples(m2, n = 5, seed = 42)#> # A tibble: 4,000 x 8 #> smooth term by_variable fac .x1 row draw value #> <chr> <chr> <chr> <fct> <dbl> <int> <int> <dbl> #> 1 s(x2) s(x2):fac1 fac 1 0.00325 1 1 -2.90 #> 2 s(x2) s(x2):fac1 fac 1 0.00825 2 1 -2.74 #> 3 s(x2) s(x2):fac1 fac 1 0.0132 3 1 -2.58 #> 4 s(x2) s(x2):fac1 fac 1 0.0182 4 1 -2.43 #> 5 s(x2) s(x2):fac1 fac 1 0.0232 5 1 -2.27 #> 6 s(x2) s(x2):fac1 fac 1 0.0282 6 1 -2.11 #> 7 s(x2) s(x2):fac1 fac 1 0.0332 7 1 -1.96 #> 8 s(x2) s(x2):fac1 fac 1 0.0382 8 1 -1.80 #> 9 s(x2) s(x2):fac1 fac 1 0.0432 9 1 -1.65 #> 10 s(x2) s(x2):fac1 fac 1 0.0482 10 1 -1.50 #> # ... with 3,990 more rowsoptions(op)


Brandon and Tom's computer science website
Projects from Brandon:
Click here to see a webapp made by classmates and Brandon...
Webapp made mostly from scratch using premade assets and some templates from Mobirise.
Collaborative projects:
Projects from Tom:


Making a virtual agent interact with a human participant can be seen as a control problem,
where the human player (HP) is the reference system, and the virtual player (VP) is the
system to be controlled.
The position of the human player is detected by a motion sensor, such that her/his position
and velocity at the next time step ($y$ and $\dot{y}$, respectively) can be estimated.
Then, they enter a feedback control loop.
The controller is able to generate the control input $u$ according to the mismatch
between position and velocity of HP and VP and to a desired motor signature
$\dot{\sigma}$ which enables the VP to exhibit the kinematic features of a given human
player (not necessarily that employed as reference system). Then, $u$ is sent as input
to the system modelling the dynamics of the virtual agent, such that its position and
velocity ($x$ and $\dot{x}$, respectively) can finally be evaluated.
Specifically, let $x \in R$ be the state variable representing the position of the virtual player (VP). The system describing its behavior is given by:
$\ddot{x} \left(t\right) = f\left(x\left(t\right), \dot{x}\left(t\right) \right) + u\left(t\right)$
where $f$ represents the inner dynamics of the VP when not connected to any other agent, $\dot{x}$ and $\ddot{x}$ represent velocity and acceleration of the VP, and $u$ is the control signal that models its coupling with another agent.
Inner dynamics
- Harmonic oscillator, a linear system given by:
$f\left(x,\dot{x}\right)=-\left( a\dot{x}+bx \right)$
where $a$ and $b$ represent viscous damping coefficient and spring constant, respectively.
- HKB equation, a nonlinear system given by:
$f\left(x,\dot{x}\right)=-\left( a x^2+\beta \dot{x}^2 - \gamma \right)\dot{x} - \omega^2 x$
where $\alpha, \beta, \gamma$ characterize the damping coefficient, while $\omega$ is related to the oscillation frequency, respectively.
- Double integrator, a system without inner dynamics:
$f\left(x,\dot{x}\right)=0$
Control signal
- PD control, a linear control law given by
$u=K_{p}\left(y-x\right) + K_{\sigma} \left( \dot{\sigma} - \dot{x} \right)$
where $y$ is the position of the other agent coupled to the VP, $\dot{\sigma}$ is its desired motor signature (velocity trajectory), and $K_p$ and $K_{\sigma}$ are two control gains.
- Adaptive control, a nonlinear control law.
- When the VP acts as a follower, it is given by
$u=\left[\psi + \chi \left( x-y \right)^2 \right]\left( \dot{x} -\dot{y} \right) - C e^{-\delta \left( \dot{x} - \dot{y} \right)^2} \left(x-y\right)$
with$\dot{\psi} = -\frac{1}{\psi}\left[ \left( x-y \right)\left( \dot{x}-\dot{y} \right) + \left( x-y \right)^2 \right]$
$\dot{\chi} = -\frac{1}{\chi} \left( \dot{x}-\dot{y} \right) \left[ f\left(x, \dot{x} \right) + u \right]$
where $y$ and $\dot{y}$ are position and velocity of the other agent coupled to the VP, $C$ and $\delta$ are control parameters, and $\psi$ and $\chi$ are adaptive parameters.
- When the VP acts as a leader, it is given by
$u=\lambda \left( \left[\psi + \chi \left( x-\sigma \right)^2 \right] \left( \dot{x} -\dot{\sigma} \right) - C e^{-\delta \left( \dot{x} - \dot{\sigma} \right)^2} \left(x-\sigma\right) \right)$ $+ \left(1- \lambda\right) K \left( y-x \right)$
where $\lambda:= e^{-\delta \left| x-y \right|}$, $K$ is a control parameter, $\sigma$ and $\dot{\sigma}$ are desired position and velocity profiles (motor signature) that allow the VP to generate spontaneous motion, and all the other quantities have been previously defined.
- When the VP acts as a follower, it is given by


Installing RandGraph
Prerequisites
- The
ghc
Haskell compiler, version 6.4 or newer. Current hugs
almost works too, but does not support cpp preprocessing which we use in several places.
- Json library, built in
json/
. See INSTALL.
- SashoLib.hs and MonadRandom.hs, which are in
lib/haskell/
- Some components from the Faerieplay compiler in
BASEDIR/sfdl-compiler/
- The Haskell functional graph library (in the
Data.Graph.Inductive
package). This is based at fgl home at OSU,
but is usually available with ghc—it is part of the standard ghc package. In Debian, it
is in a separate package libghc6-fgl-dev
.
Building
$ make install
Builds and installs into the dist directory configured in the main config file pir/config.make
.


write("hello world")
my_bar = write.progress(0)
for i in range(100):
my_bar.progress(i)
time.sleep(0.1)
// write("This notebook shows some of the awesome elements of printf.")
//
// // Arrays
from streamlet import Notebook, Chart
with Notebook() as write:
write.header("Bethurum Awesome Program", level=1)
write("I"m starting my code here!")
write("Progress:")
my_progress_bar = write.progress(0)
write("Loss:")


We are a team dedicated to helping you find the right strain, intake method, and schedule for you. Based completely on your needs!
We take the data that you give us, put it through our recommendation algorithm to match you up with the best strains, doses, intake methods and schedules for you!


Routing
There are new requirements for the Tour of Heroes app:
- Add a Dashboard view.
- Add the ability to navigate between the Heroes and Dashboard views.
- When users click a hero name in either view, navigate to a detail view of the selected hero.
- When users click a deep link in an email, open the detail view for a particular hero.
When you’re done, users will be able to navigate the app like this:
Add the AppRoutingModule
In Angular, the best practice is to load and configure the router in a separate, top-level module
that is dedicated to routing and imported by the root AppModule
.
By convention, the module class name is AppRoutingModule
and it belongs in the app-routing.module.ts
in the src/app
folder.
Use the CLI to generate it.
ng generate module app-routing --flat --module=app
--flat
puts the file in src/app
instead of its own folder.
--module=app
tells the CLI to register it in the imports
array of the
AppModule
.
The generated file looks like this:
import { NgModule } from '@angular/core';
import { CommonModule } from '@angular/common';
@NgModule({
imports: [
CommonModule
],
declarations: []
})
export class AppRoutingModule { }
You generally don't declare components in a routing module so you can delete the
@NgModule.declarations
array and delete CommonModule
references too.
You'll configure the router with Routes
in the RouterModule
so import those two symbols from the @angular/router
library.
Add an @NgModule.exports
array
with RouterModule
in it.
Exporting RouterModule
makes
router directives available for use
in the AppModule
components that will need them.
AppRoutingModule
looks like this now:
import { NgModule } from '@angular/core';
import { RouterModule, Routes } from '@angular/router';
@NgModule({
exports: [ RouterModule ]
})
export class AppRoutingModule {}
Add routes
Routes tell the router which view to display when a user clicks a link or pastes a URL into the browser address bar.
A typical Angular Route
has two
properties:
path
: a string that matches the URL in the browser address bar.component
: the component that the router should create when navigating to this route.
You intend to navigate to the HeroesComponent
when the URL is something like localhost:4200/heroes
.
Import the HeroesComponent
so you can reference it in a Route
.
Then define an array of routes with a single route
to that component.
import { HeroesComponent } from './heroes/heroes.component';
const routes: Routes = [
{ path: 'heroes', component: HeroesComponent }
];
Once you've finished setting up, the router will match that URL to path: 'heroes'
and display the HeroesComponent
.
RouterModule.forRoot()
You first must initialize the router and start it listening for browser location changes.
Add RouterModule
to the
@NgModule.imports
array and
configure it with the routes
in one step by calling
RouterModule.forRoot()
within the imports
array, like this:
imports: [ RouterModule.forRoot(routes) ],
The method is called forRoot()
because you configure the
router at the application's root level.
The forRoot()
method
supplies the service providers and directives needed for routing,
and performs the initial navigation based on the current browser URL.
Add RouterOutlet
Open the AppComponent
template replace the <app-heroes>
element with a
<router-outlet>
element.

{{title}}

<router-outlet></router-outlet>
<app-messages></app-messages>
You removed <app-heroes>
because you will only display the
HeroesComponent
when the user navigates to it.
The <router-outlet>
tells
the router where to display routed views.
The RouterOutlet
is one of the
router directives that became available to the AppComponent
because AppModule
imports AppRoutingModule
which exported RouterModule
.
Try it
You should still be running with this CLI command.
ng serve
The browser should refresh and display the app title but not the list of heroes.
Look at the browser's address bar.
The URL ends in /
.
The route path to HeroesComponent
is /heroes
.
Append /heroes
to the URL in the browser address bar.
You should see the familiar heroes master/detail view.
Add a navigation link (routerLink
)
Users shouldn't have to paste a route URL into the address bar. They should be able to click a link to navigate.
Add a
element and, within that, an anchor element that, when clicked,
triggers navigation to the HeroesComponent
.
The revised AppComponent
template looks like this:

{{title}}

Heroes

<router-outlet></router-outlet>
<app-messages></app-messages>
A routerLink
attribute is set to
"/heroes"
,
the string that the router matches to the route to HeroesComponent
.
The routerLink
is the selector for
the RouterLink
directive
that turns user clicks into router navigations.
It's another of the public directives in the RouterModule
.
The browser refreshes and displays the app title and heroes link, but not the heroes list.
Click the link.
The address bar updates to /heroes
and the list of heroes appears.
Make this and future navigation links look better by adding private CSS styles to app.component.css
as listed in the final code review below.
Add a dashboard view
Routing makes more sense when there are multiple views. So far there's only the heroes view.
Add a DashboardComponent
using the CLI:
ng generate component dashboard
The CLI generates the files for the DashboardComponent
and declares it in
AppModule
.
Replace the default file content in these three files as follows and then return for a little discussion:
The template presents a grid of hero name links.
- The
*ngFor
repeater creates as many links as are in the component'sheroes
array. - The links are styled as colored blocks by the
dashboard.component.css
. - The links don't go anywhere yet but they will shortly.
The class is similar to the HeroesComponent
class.
- It defines a
heroes
array property. - The constructor expects Angular to inject the
HeroService
into a privateheroService
property. - The
ngOnInit()
lifecycle hook callsgetHeroes
.
This getHeroes
returns the sliced list of heroes at positions 1 and 5, returning only four
of the Top Heroes (2nd, 3rd, 4th, and 5th).
getHeroes(): void {
this.heroService.getHeroes()
.subscribe(heroes => this.heroes = heroes.slice(1, 5));
}
Add the dashboard route
To navigate to the dashboard, the router needs an appropriate route.
Import the DashboardComponent
in the AppRoutingModule
.
import { DashboardComponent } from './dashboard/dashboard.component';
Add a route to the AppRoutingModule.routes
array that matches a path to the DashboardComponent
.
{ path: 'dashboard', component: DashboardComponent },
Add a default route
When the app starts, the browsers address bar points to the web site's root.
That doesn't match any existing route so the router doesn't navigate anywhere.
The space below the <router-outlet>
is blank.
To make the app navigate to the dashboard automatically, add the following
route to the AppRoutingModule.Routes
array.
{ path: '', redirectTo: '/dashboard', pathMatch: 'full' },
This route redirects a URL that fully matches the empty path to the route whose path is '/dashboard'
.
After the browser refreshes, the router loads the DashboardComponent
and the browser address bar shows the /dashboard
URL.
Add dashboard link to the shell
The user should be able to navigate back and forth between the
DashboardComponent
and the HeroesComponent
by clicking links in the
navigation area near the top of the page.
Add a dashboard navigation link to the AppComponent
shell template, just above the Heroes
link.

{{title}}

Dashboard
Heroes

<router-outlet></router-outlet>
<app-messages></app-messages>
After the browser refreshes you can navigate freely between the two views by clicking the links.
Navigating to hero details
The HeroDetailsComponent
displays details of a selected hero.
At the moment the HeroDetailsComponent
is only visible at the bottom of the HeroesComponent
The user should be able to get to these details in three ways.
- By clicking a hero in the dashboard.
- By clicking a hero in the heroes list.
- By pasting a "deep link" URL into the browser address bar that identifies the hero to display.
In this section, you'll enable navigation to the HeroDetailsComponent
and liberate it from the HeroesComponent
.
Delete hero details from
HeroesComponent
When the user clicks a hero item in the HeroesComponent
,
the app should navigate to the HeroDetailComponent
,
replacing the heroes list view with the hero detail view.
The heroes list view should no longer show hero details as it does now.
Open the HeroesComponent
template (heroes/heroes.component.html
) and
delete the <app-hero-detail>
element from the bottom.
Clicking a hero item now does nothing.
You'll fix that shortly after you enable routing
to the HeroDetailComponent
.
Add a hero detail route
A URL like ~/detail/11
would be a good URL for navigating to the Hero Detail view
of the hero whose id
is 11
.
Open AppRoutingModule
and import HeroDetailComponent
.
import { HeroDetailComponent } from './hero-detail/hero-detail.component';
Then add a parameterized route to the AppRoutingModule.routes
array that matches
the path pattern to the hero detail view.
{ path: 'detail/:id', component: HeroDetailComponent },
The colon (:) in the path
indicates that :id
is a placeholder for a specific
hero id
.
At this point, all application routes are in place.
const routes: Routes = [
{ path: '', redirectTo: '/dashboard', pathMatch: 'full' },
{ path: 'dashboard', component: DashboardComponent },
{ path: 'detail/:id', component: HeroDetailComponent },
{ path: 'heroes', component: HeroesComponent }
];
DashboardComponent
hero links
The DashboardComponent
hero links do nothing at the moment.
Now that the router has a route to HeroDetailComponent
,
fix the dashboard hero links to navigate via the parameterized dashboard route.

{{hero.name}}

You're using Angular interpolation binding within the
*ngFor
repeater
to insert the current iteration's hero.id
into each
routerLink
.
HeroesComponent
hero links
The hero items in the HeroesComponent
are

elements whose click
events
are bound to the component's onSelect()
method.

{{hero.id}} {{hero.name}}

Strip the

back to just its *ngFor
,
wrap the badge and name in an anchor element (
),
and add a routerLink
attribute to
the anchor that
is the same as in the dashboard template

{{hero.id}} {{hero.name}}

You'll have to fix the private stylesheet (heroes.component.css
) to make
the list look as it did before.
Revised styles are in the final code review at the bottom
of this guide.
Remove dead code (optional)
While the HeroesComponent
class still works,
the onSelect()
method and selectedHero
property are no longer used.
It's nice to tidy up and you'll be grateful to yourself later. Here's the class after pruning away the dead code.
export class HeroesComponent implements OnInit {
heroes: Hero[];
constructor(private heroService: HeroService) { }
ngOnInit() {
this.getHeroes();
}
getHeroes(): void {
this.heroService.getHeroes()
.subscribe(heroes => this.heroes = heroes);
}
}
Routable HeroDetailComponent
Previously, the parent HeroesComponent
set the HeroDetailComponent.hero
property and the HeroDetailComponent
displayed the hero.
HeroesComponent
doesn't do that anymore.
Now the router creates the HeroDetailComponent
in response to a URL such as ~/detail/11
.
The HeroDetailComponent
needs a new way to obtain the hero-to-display.
- Get the route that created it,
- Extract the
id
from the route - Acquire the hero with that
id
from the server via theHeroService
Add the following imports:
import { ActivatedRoute } from '@angular/router';
import { Location } from '@angular/common';
import { HeroService } from '../hero.service';
Inject the ActivatedRoute
,
HeroService
, and Location
services
into the constructor, saving their values in private fields:
constructor(
private route: ActivatedRoute,
private heroService: HeroService,
private location: Location
) {}
The ActivatedRoute
holds information about the
route to this instance of the HeroDetailComponent
.
This component is interested in the route's bag of parameters extracted from the URL.
The "id" parameter is the id
of the hero to display.
The HeroService
gets hero data from the remote server
and this component will use it to get the hero-to-display.
The location
is an Angular service for interacting with
the browser.
You'll use it later to navigate back to the view that navigated
here.
Extract the id route parameter
In the ngOnInit()
lifecycle hook
call getHero()
and define it as follows.
ngOnInit(): void {
this.getHero();
}
getHero(): void {
const id = +this.route.snapshot.paramMap.get('id');
this.heroService.getHero(id)
.subscribe(hero => this.hero = hero);
}
The route.snapshot
is a static image of the route information shortly after the component
was created.
The paramMap
is a dictionary of route parameter values extracted from the URL.
The "id"
key returns the id
of the hero to fetch.
Route parameters are always strings.
The JavaScript (+) operator converts the string to a number,
which is what a hero id
should be.
The browser refreshes and the app crashes with a compiler error.
HeroService
doesn't have a getHero()
method.
Add it now.
Add HeroService.getHero()
Open HeroService
and add this getHero()
method
getHero(id: number): Observable<Hero> {
// TODO: send the message _after_ fetching the hero
this.messageService.add(`HeroService: fetched hero id=${id}`);
return of(HEROES.find(hero => hero.id === id));
}
Note the backticks ( ` ) that
define a JavaScript
template
literal for embedding the id
.
Like getHeroes()
,
getHero()
has an asynchronous signature.
It returns a mock hero as an Observable
, using the RxJS of()
function.
You'll be able to re-implement getHero()
as a real Http
request
without having to change the HeroDetailComponent
that calls it.
Try it
The browser refreshes and the app is working again. You can click a hero in the dashboard or in the heroes list and navigate to that hero's detail view.
If you paste localhost:4200/detail/11
in the browser address bar,
the router navigates to the detail view for the hero with id: 11
, "Mr. Nice".
Find the way back
By clicking the browser's back button, you can go back to the hero list or dashboard view, depending upon which sent you to the detail view.
It would be nice to have a button on the HeroDetail
view that can do that.
Add a go back button to the bottom of the component template and bind it
to the component's goBack()
method.
<button (click)="goBack()">go back</button>
Add a goBack()
method to the component class that navigates backward one step
in the browser's history stack
using the Location
service that you
injected previously.
goBack(): void {
this.location.back();
}
Refresh the browser and start clicking. Users can navigate around the app, from the dashboard to hero details and back, from heroes list to the mini detail to the hero details and back to the heroes again.
You've met all of the navigational requirements that propelled this page.
Final code review
Here are the code files discussed on this page and your app should look like this
AppRoutingModule, AppModule, and HeroService
- import { NgModule } from '@angular/core';
- import { RouterModule, Routes } from '@angular/router';
-
- import { DashboardComponent } from './dashboard/dashboard.component';
- import { HeroesComponent } from './heroes/heroes.component';
- import { HeroDetailComponent } from './hero-detail/hero-detail.component';
-
- const routes: Routes = [
- { path: '', redirectTo: '/dashboard', pathMatch: 'full' },
- { path: 'dashboard', component: DashboardComponent },
- { path: 'detail/:id', component: HeroDetailComponent },
- { path: 'heroes', component: HeroesComponent }
- ];
-
- @NgModule({
- imports: [ RouterModule.forRoot(routes) ],
- exports: [ RouterModule ]
- })
- export class AppRoutingModule {}
AppComponent

{{title}}

Dashboard
Heroes

<router-outlet></router-outlet>
<app-messages></app-messages>
DashboardComponent

Top Heroes

{{hero.name}}

HeroesComponent

My Heroes

{{hero.id}} {{hero.name}}

HeroDetailComponent
Summary
- You added the Angular router to navigate among different components.
- You turned the
AppComponent
into a navigation shell with
links and a<router-outlet>
. - You configured the router in an
AppRoutingModule
- You defined simple routes, a redirect route, and a parameterized route.
- You used the
routerLink
directive in anchor elements. - You refactored a tightly-coupled master/detail view into a routed detail view.
- You used router link parameters to navigate to the detail view of a user-selected hero.
- You shared the
HeroService
among multiple components.


Codehaus XFire
DocumentationQuicklinksDevelopers
Sponsors |
This page outlines how to set up XFire for use via Spring's Remoting framework. Setup the DispatcherServlet in the web.xml:You'll need to include the DispatcherServlet in your web.xml. Notice that it provides the locations of where your spring beans are. <context-param> <param-name>contextConfigLocation</param-name> <param-value>/WEB-INF/applicationContext.xml classpath:org/codehaus/xfire/spring/xfire.xml</param-value> </context-param> <context-param> <param-name>log4jConfigLocation</param-name> <param-value>/WEB-INF/log4j.properties</param-value> </context-param> <listener> <listener-class>org.springframework.web.util.Log4jConfigListener</listener-class> </listener> <listener> <listener-class>org.springframework.web.context.ContextLoaderListener</listener-class> </listener> <servlet> <servlet-name>xfire</servlet-name> <servlet-class>org.springframework.web.servlet.DispatcherServlet</servlet-class> </servlet> <servlet-mapping> <servlet-name>xfire</servlet-name> <url-pattern>/*</url-pattern> </servlet-mapping> Notice how we're including /org/codehaus/xfire/spring/xfire.xml off the classpath. This contains some standard bean definitions for the TransportManager, ServiceRegistry and some simple ServiceFactorys. Remote ExporterWhen using a RemoteExporter, you should define a service interface and (at least) one implementation of that interface. Service Interfacepackage org.codehaus.xfire.spring.example; /** * Provides the service contract for the echo service. * * @author <a<EMAIL_ADDRESS>Poutsma</a> */ public interface Echo { String echo(String in); } Service Implementationpackage org.codehaus.xfire.spring.example; /** * Provides a default implementation of the echo service interface. */ public class EchoImpl implements Echo { public String echo(String in) { return in; } } XFireExporterThen, you'll need to create a exporter definition for the service you want to expose. Typically, you'll do this in the servlet context configuration file. Since the dispatcher servlet is named xfire, this file should be called xfire-servlet.xml. <bean class="org.springframework.web.servlet.handler.SimpleUrlHandlerMapping"> <property name="urlMap"> <map> <entry key="/EchoService"> <ref bean="echo"/> </entry> </map> </property> </bean> <!-- Declare a parent bean with all properties common to both services --> <bean id="echo" class="org.codehaus.xfire.spring.remoting.XFireExporter"> <property name="serviceFactory"> <ref bean="xfire.serviceFactory"/> </property> <property name="xfire"> <ref bean="xfire"/> </property> <property name="serviceBean"> <ref bean="echoBean"/> </property> <property name="serviceClass"> <value>org.codehaus.xfire.spring.example.Echo</value> </property> </bean> Thus, the methods defined in the Echo interface are exposed at the path /EchoService, backed by the EchoImpl bean. The EchoImpl bean should be defined as follows, in applicationContext.xml: <bean id="echoBean" class="org.codehaus.xfire.spring.example.EchoImpl"/>
If you are using JSR 181 Annotations in the service interface and implementation(s), the interface class should not be specified as the serviceClass property. In this case, either the implementation class should be specified in the serviceClass property or the serviceBean property should be specified, but both together are not necessary. This is because the implementing class already specifies the endpointInterface in the @WebService annotation. Remoting ClientsWith protocols like Hessian or RMI, you just need a service interface to configure a Spring remoting client. However, SOAP is a bit more complicated. XFire has the concept of a service model. It uses your service class and various extra information to build this service model. Any information you use to configure your client must be used to configure your client as well. For instance, say you specify a custom a custom namespace on your service, you must also specify that on your client. XFire comes with a helper class to facilitate creating client using Spring: org.codehaus.xfire.spring.remoting.XFireClientFactoryBean. It is modelled after the JaxRpcPortProxyFactoryBean present in Spring, and allows you to configure a client bean in a Spring applicationContext.xml. All that needs to be specified is the service interface (or class in case of JSR 181 Annotations) and a WSDL url location for the service. Additional configuration, such as username/password or namespace, can be explicitly configured using properties. <bean id="testWebService" class="org.codehaus.xfire.spring.remoting.XFireClientFactoryBean"> <property name="serviceClass"> <value>org.codehaus.xfire.spring.example.Echo</value> </property> <property name="wsdlDocumentUrl"> <value>http://localhost:8080/xfire/EchoService?WSDL</value> </property> </bean> * Note that this may cause problems if used in the same applicationContext as the service itself (which one might try to do for running unit tests): this bean would attempt to access the WSDL before it became available, preventing the application from loading. Rather, create a separate client applicationContext, for which to use in test cases. Check out the Client API page to learn more about options when creating clients using XFire. The full example is available in the ./examples/spring/ folder (within the XFire distribution). |


No canvas support
Redraw
r
Pause
p
Mode
Add
Edit
Draw
m
Debug
d
Strength
- +
Force Dist
[ ]
Stars
< >
Force Calc
Length
Radius
Offscreen behavior
Random
Random (Fade)
Wrap
New stars
n
Clear Forces
c
Save as URL
s
X
Click and drag on the sky to move the stars.
Options are down here


Ocean [ar02], ocean, in The Outer Sea
Last updated Turn #116
Ocean [ar02], ocean, in The Outer Sea
------------------------------------------------------------------------
7: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
7: 7% damaged, arrived from Ocean [ar03], owner:
7: Phantasm [n4b], Imperial Empire, earl, with two workers,
7: eight sailors
8: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
8: 7% damaged, departed for Ocean [ar01].
Routes leaving Ocean:
North, to Ocean [aq02], 6 days
East, to Ocean [ar03], 6 days
South, to Ocean [as02], 6 days
West, to Ocean [ar01], 6 days


Increase your development productivity by TDD
A small story that emphasises the importance of test-driven development.
Whilst the world is transforming towards agile and automation of build, test, and deployment practices, understanding the importance of test-driven development practices is vital for increasing the productivity, quality, and time to market.
There is a common perception among some of the IT developers and key stakeholders that the time spent on writing unit-test cases kill their teams’ productive time. It is a wrong perception. A small story to understand the importance of writing unit test cases.
John, a new joiner in an automobile accessories company; he was assigned a task to prepare bolts to screw a car wheel on his first-day work. On his first-day job, he got all the requirements for preparing the bolts. He prepared the bolt and started driving to the car-assembling arena which was a mile apart to test his bolt. Unfortunately, the bolt did not fit. Next day, he was back to work to fix all the issues in the bolt and started driving again to the car-assembling arena. Again it didn't fit.
So what went wrong with John? John failed to test his developed bolt with sample nuts in his local; instead, he tried to test in the integration environment directly without unit testing. At first, he must have ensured that the developed items are possibly a bolt, secondly, he must have validated the bolt with mock nuts for a perfect fit, and then he must have taken to bolt to the integration area.
The mock nuts are just equivalent to writing unit tests in development. Writing unit test cases for their public methods would ensure the deliverables meets the requirements, quality, code integrity and it also tests all the possible scenarios using code coverage.
Increase in productivity
Writing unit test cases would capture the bugs in their local environment itself. This will avoid a long wait to capture the bugs in higher environments. In DevOps, capturing and fixing the bugs early is the basic principle. The below diagram shows that productivity increases by three folds.
Ensures quality
Quality is the key aspects of any deliverables. Business confidence increases as the quality of the deliverables increases. Quality of the product is measured using code coverage. Higher the code coverage means higher the quality. As a best practice, achieving 80% of code coverage is maximum to maintain good quality.
Reduces time to market
Increase in productivity directly boost the time to market of the product. Finding bugs early speed up the overall productivity, decrease the manual intervention and rapid up the time to market.
Improves agility
Rapidly changing business-needs require an agile-based software development model to visualize the result immediately. To achieve this, the automation of the entire product development is essential. The first step of automation starts from code build and unit test.
Conclusion
A journey of a thousand miles begins with a single step. The first step is writing unit test cases to achieve complete automation.


Pagination
Axure Kit
Carbon
Updates
Common widgets
Style effects
Variables
Selection group
Repeater
Dynamic panel
Introduction
Axure elements
Utilities
UI shell
Tooltip
Toggle
Tile
Text input
Tag
Tabs
Structured list
Slider
Select
Search
Radio button
Progress indicator
Pagination
Overflow menu
Number input
Notification
Modal
Loading
List
Link
Inline loading
Form
File upoader
Dropdown
Date picker
Data table
Content switcher
Code snippet
Checkbox
Button
Breadcrumb
Accordion
Components
Typography
Spacing
Icons
Color
Design elements
Library index
Getting started
Components
Privacy
Have questions? Open an issue on GitHub.
Last updated 29 January 2020
Copyright © 2020 IBM
Terms of Use
Contribute
IBM.com
Pagination for data tables
Library section:
Interactions:
Table and List
Style effects
Variables
Repeater
Dynamic panel
of 5 pages
1
1-10 of 48 items
10
Items per page:
Select components are used for selecting “items per page” and current page. Select option items can be changed dynamically in their repeater data set when component is attached to a data table.
–
Focus is set on clicked item.
–
Implemented features
See Select widget for changing select menu items.
Usage
Pagination nav
Library section:
Interactions:
Search and Navigation
Style effects
29
…
5
4
3
2
1


Please click the button below to verify your identity:
NOTE: The link contained in this email is only valid for the next 30 minutes.
Some actions on our site require additional authentication to make your data more secure.
If you did not request this verification, you should probably change your password.


Create Events - Door Monitoring Event
In order to trigger an doormonitoring event, the EventMessenger software needs the
name and serial number informations of the lock that has fired the
doormonitoring event along with the desired type of doormonitoring event (e.g.
"door has been opend"; "closed"; "locked" "secured" and others).
Please follow these steps to create such setup:
- Please begin by adding one or more locks.
- Check the "consider lockname and/or serialnumber (phi)" checkbox if
you would like to apply a filter to this event. Otherwise, every
lockname or phi will be recognised regardless its name or phi.
- Define the name and/or phi of the lock of which you would like to be
informed about an unlocking event.
HINT:
- your input is treated as case insensitive and as a part of a whole
search phrase
Example:
If you have entered "entrance" as the name of the lock all of the
following locknames will return a positive match:
"main entrance"; "Entrance North"
- Please select a desired "doormonitoring type". You could add more
that one type by repeating steps 3 and 4.
- Click on "Add" to add the lock with its name and phi (if defined
any) and your desired monitoring type.
- Click on "OK" to finish your setup.


{% extends 'database/base.html' %} {% block title %}Thunder Games - Games{% endblock %} {% block game_active %}active{% endblock %} {% block body %}
Add a new game
There are no games.


Great in theory, but challenging to attain in real life. I imagine the reason most people chase pleasure as their first source of happiness is because it is the easiest to find. We often know what it is that will bring us pleasure, but knowing what we are passionate about, and knowing what our higher purpose is, are much tougher to discover. Whatsmore, even if we are intuned enough to know these things, finding a way to apply them to our work is another matter all together. Whilst these are good long term goals, they are not necessarily applicable or reachable in the short term, and thus not very useful for improving current motivation levels.
Honesty, independance, and empathy. I am a very straightforward person, and as such prefer it when people are upfront and honest with me, so everyone knows what page they are on. I also value my own independance greatly, and believe everyone should have the freedom to make their own choices. I feel most problems, both personal and across the world, can be attributed to a lack of percieved freedom. Finally, I believe that we are all intrinsically connected, and that we are all intuned with the negative and positive emotions of those around us. Therefore, improving others happiness can have a direct effect on our own happiness, and empathy is the most important value for the world as a whole.
For everyone and everything in the world to feel safe and valued. The 'me and mine' mentality is responsible for all strifes and struggles in the world, and if we all cared a bit more about others and about the world, these struggles wouldn't have to exist. The best way to improve our own lives is to improve everyone's lives, and if we cared more about the collective good than our own selfish desires, we would all be better off.
I discovered that I do not experience or even really acknowledge my own successes. When it came to remembering a time I was successful and re-experiencing how that felt, I realised that I view success as the default and as such do not feel anything when I succeed. I immediately move onto worrying the next task, without pausing to congratulate myself for what I have done. This is a very unhealthy habit, and I need to remember to actively experience my successes before I jump to thinking about the next task.


They all have meanings and symbolizations in my life!
Red symbolizes hunger, and I am always hungry!
Orange is a bright and friendly color that reminds me of fall!
Yellow is majestic and very powerful!
Green symbolizes one of my favorite things in life; money!
Contact Me


Returns
given the list of fragments (some of which are text strings) reverse the list and concatenate adjacent text strings
given the list of fragments (some of which are text strings) reverse the list and concatenate adjacent text strings We also drop "unsignificant" whitespace, that is, whitespace in front, behind and between elements. The whitespace that is included in character data is not affected.
Returns
(define (RES-NAME->SXML res-name) (string->symbol (string-append (symbol->string (car res-name)) ":" (symbol->string (cdr res-name)))))
given the list of fragments (some of which are text strings) reverse the list and concatenate adjacent text strings
(define (reverse-collect-str fragments) (if (null? fragments) '() ; a shortcut (let loop ((fragments fragments) (result '()) (strs '())) (cond ((null? fragments) (if (null? strs) result (cons (apply string-append strs) result))) ((string? (car fragments)) (loop (cdr fragments) result (cons (car fragments) strs))) (else (loop (cdr fragments) (cons (car fragments) (if (null? strs) result (cons (apply string-append strs) result))) '()))))))
given the list of fragments (some of which are text strings) reverse the list and concatenate adjacent text strings We also drop "unsignificant" whitespace, that is, whitespace in front, behind and between elements. The whitespace that is included in character data is not affected.
(define (reverse-collect-str-drop-ws fragments) (cond ((null? fragments) '()) ; a shortcut ((and (string? (car fragments)) ; another shortcut (null? (cdr fragments)) ; remove trailing ws (string-whitespace? (car fragments))) '()) (else (let loop ((fragments fragments) (result '()) (strs '()) (all-whitespace? #t)) (cond ((null? fragments) (if all-whitespace? result ; remove leading ws (cons (apply string-append strs) result))) ((string? (car fragments)) (loop (cdr fragments) result (cons (car fragments) strs) (and all-whitespace? (string-whitespace? (car fragments))))) (else (loop (cdr fragments) (cons (car fragments) (if all-whitespace? result (cons (apply string-append strs) result))) '() #t)))))))


{% load static %}
                                              Select Course
                                              Select Class
                                              Select Division
                                              Roll No
                                              User Name
                                              Email
                                              Mobile No
                                              Password
                                              Confirmed Password


新增
#
Name
Hobby
Second
1
tomoya
singer
Edit
Delete
2
taiichi
Player
Edit
Delete
3
Mabo
Drummer
Edit
Delete
4
Ani
Bases
Edit
Delete
Username:
Hobby:
Add
Cancel


With his side a goal down to Eddie Salcedo's powerful header Balotelli, who has been a frequent target for racist chants during his three spells playing in his home country, booted the ball into the crowd after around an hour and charged towards Verona fans.
Pointing towards his ears, the former AC Milan and Manchester City striker then tried to leave the pitch, only to be stopped by his teammates and Verona players, some of whom hugged him.
The match was suspended for a few minutes before a message was read out of the stadium loudspeaker threatening both teams would all leave the field if there was a repeat of any abuse.
The message was whistled loudly by a large number of the home fans, who have a notorious reputation in Italy for hard-right politics and open racism.
With the game back underway Matteo Pessina doubled the hosts lead when he squeezed home a neat finish with nine minutes left before Balotelli pulled one back with a superb curling long-range effort.
Balotelli's side remain in the bottom three after the defeat, while Verona jumped up to ninth, level on points with Fiorentina ahead of their match with Parma.
A number of black players have been racially abused by supporters in Italy in the opening weeks of the season, with Milan midfielder Franck Kessie targeted by Verona fans in September and Inter Milan striker Romelu Lukaku abused by Cagliari fans, also serial offenders.


PortfolioBundle
Projects
Create new project
Categories
Create new category
Posts
Create new post
Congratulations, your project "Codeception" is successfully created!
Projects list
preorder.it (
Edit project
/
Delete project
)
eprice.kz (
Edit project
/
Delete project
)
Codeception (
Edit project
/
Delete project
)
Powered by
Symfony2 PHP framework


Before Change
def rand_spatial_coordinates(subject_id, win_sizes, img_sizes, n_samples):
if len(set(img_size[:N_SPATIAL] for img_size in img_sizes)) > 1:
tf.logging.fatal("Don"t know how to generate sampling "
"locations: Spatial dimensions of the "
"grouped input sources are not "
"consistent. {}".format(img_sizes))
raise NotImplementedError
// find spatial window location based on the largest spatial window
spatial_win_sizes = [win_size[:N_SPATIAL] for win_size in win_sizes]
spatial_win_sizes = np.asarray(spatial_win_sizes, dtype=np.int32)
max_spatial_win = np.max(spatial_win_sizes, axis=0)
max_coords = np.zeros((n_samples, N_SPATIAL), dtype=np.int32)
for i in range(0, N_SPATIAL):
max_coords[:, i] = np.random.randint(
0, max(img_sizes[0][i] - max_spatial_win[i], 1), n_samples)
// adjust max spatial coordinates based on each spatial window size
all_coordinates = []
for win_size in spatial_win_sizes:
subject_id = np.ones((n_samples,), dtype=np.int32) * subject_id
spatial_coords = np.zeros(
(n_samples, N_SPATIAL * 2), dtype=np.int32)
// shift starting coords of the window
// so that smaller windows are centred within the large windows
half_win_diff = np.floor((max_spatial_win - win_size) / 2.0)
spatial_coords[:, :N_SPATIAL] = \
max_coords[:, :N_SPATIAL] + half_win_diff[:N_SPATIAL]
spatial_coords[:, N_SPATIAL:] = \
spatial_coords[:, :N_SPATIAL] + win_size[:N_SPATIAL]
// include the subject id
spatial_coords = np.append(
subject_id[:, None], spatial_coords, axis=1)
all_coordinates.append(spatial_coords)
return all_coordinates
// def __init__(self,
After Change
0, max(uniq_spatial_size[i] - max_spatial_win[i], 1), n_samples)
// adjust max spatial coordinates based on each spatial window size
all_coordinates = {}
for mod in list(win_sizes):
win_size = win_sizes[mod][:N_SPATIAL]
subject_id = np.ones((n_samples,), dtype=np.int32) * subject_id
spatial_coords = np.zeros(
(n_samples, N_SPATIAL * 2), dtype=np.int32)
// shift starting coords of the window
// so that smaller windows are centred within the large windows
half_win_diff = np.floor((max_spatial_win - win_size) / 2.0)
spatial_coords[:, :N_SPATIAL] = \
max_coords[:, :N_SPATIAL] + half_win_diff[:N_SPATIAL]
spatial_coords[:, N_SPATIAL:] = \
spatial_coords[:, :N_SPATIAL] + win_size[:N_SPATIAL]
// include the subject id
spatial_coords = np.append(
subject_id[:, None], spatial_coords, axis=1)
all_coordinates[mod] = spatial_coords
return all_coordinates
// def __init__(self,


We go to hackathons across the UK and Europe. We have multiple hackathons of our own planned too, so stay tuned!
We run student-lead workshops on everything from web development, mobile development and Git.
We host regular talks by speakers from the local community and the wider tech industry.
If you have any questions or would just like a chat, drop us an email!
Get In Touch
We use our Facebook group to keep everyone in the loop
about what's going on, to hold discussions, and to share useful info.
We also send out a weekly email in case you miss anything!
Join our newsletter by signing up below!


Muhammad Umar Iqbal
My name is Muhammad Umar Iqbal and I am jolly and slow.
My Classes This Quarter
- CS - Web Application Programming
- CS - Modern Web Application
- CS - Web Application Architecture
- Cs - Algorithms
My Favorite Movies
My Moods
Happy: Sad:
Fun Facts About My Neighbors
- Abdul: is a smart guy.
- Mohammad: is intelligent.


demographic &; population environmental OpenUrl mass processes seasonal winter effects Armitage, summer dynamics changes yellow-bellied CrossRef reproductive marmots Science change covariation survival effect quality Web Ecology, demography distribution growth models rates i.e., posterior reproduction season Ecology adults different latent Maldonado-Chaparro Material Van marmot marmots. positive variation individuals mean Bayesian approach average body non-reproductive stage transitions Fig. annual factors females increases juveniles life-history stochastic variable Ozgul Paniw Vuren, affected drivers populations probability recruitment responses trait values yearlings Supporting Table abiotic affect biotic change. conditions deviation long-term model observed obtained parameter sensitivity species standard using year years Blumstein, Letters, Marmota S). climate fitness include juvenile measure numerous processes, stage-specific stages summer, Biology, Childs, Hindle Morris, assess density determine error important life log offspring parameters persistence population-level seasons temporal <; American Armitage Ellner, Evolution, Naturalist, Ozgul, Population Zoology, adult beginning complex environments. female fluctuations given hibernation life-cycle likely little measured new processes. quality, role scenarios simulations small study time traits variability vital (Fig. (J), (S) (Supporting (W) (Y), (e.g., Animal Bassar Biological C.I. Change Downhower, Download FREE Global However, IPMs Methods National Oli, Rees, Salguero-Gómez, Seasonal Sensitivity Text adults, analyses autocorrelation captured change, common data demography. flaviventris) gain individual linear loss mixed model. modeled normal number predation relatively reproducing samples showed shown snow unobserved used viability (N), (R). (see ;<;div Abstract B.-E. Coulson, Ecological Figure Integral Life Open Points S.). Sciences, Stages Sæther, Tuljapurkar, Yellow-bellied affects arrows averages bars base buffer calculated climatic colonies color compensatory cryptic data-hide-link-title=""> data-icon-position="" decreased dynamics, elasticities estimates factor-analytic figure function generalized good impact increased influence integral interactions knowledge main major mass, masses mechanistic modeling models. natural parsimonious particularly patterns plant populations: projection prospective quasi-extinction random rate, rates, relative respectively. risk season-specific significantly strongly structured submodels survive tab time, tones) use viability. weather winters


I made FoEI because of my personal interest/hobby, so my goal is not to make a profit out of this, but if you really, really enjoy FoEI, feel free to send me a small token op appreciation (which I will convert to game diamonds )


My Car Garage
Car Name
Category Model
BMW
Lexus
Toyota
Tesla
Chevrolet
Hyundai
KIA
Model Year
2015
2016
2017
2018
2019
2020
Submit


Virtual
Observatories in Space and Solar Physics: A Community Workshop
From:
Aaron Roberts<EMAIL_ADDRESS>To
help the space and solar physics community arrive at a clear plan for its data
environment, NASA's Living With a Star Program will sponsor a multi-agency,
international workshop on October 27-29, 2004 at the Marriott Hotel in
Greenbelt, MD.
Registration and
information web sites will be available soon. The idea of this workshop was endorsed by the Virtual
Observatories Birds-of-a-Feather meeting at the Fall 2003 AGU meeting, and by
NASA's Sun-Earth Connection Data and Computing Working Group.
The goals of the
workshop are to develop accordance among the several groups participating or
anticipating to participate in VO activities and to produce a series of
recommendations for program sponsors of these activities. After introductory talks on a possible
vision for the data environment and the details of a few current prototypes,
the main work of the meeting will take place in focus groups that meet
alternately with plenary sessions.
The four focus groups (and their leaders) will be:
Registries and Data
Models (R. Bentley, MSSL; J.
Mukherjee, SWRI)
(Possible specific
questions: How do we describe products in a universal language? Where and how should these products be
registered? What is the right
level of detail?)
Gateway/Brokers and
front ends (R. Bogart,
Stanford; V. Rezapkin, Aquilent)
(e.g., What are the
best ways to use product registries?
What types of standards should we have for product access APIs? How do
we avoid excessive duplication of effort in designing "middleware"?)
Repositories and
"VxOs" (A. Szabo,
GSFC; A. Davis, Caltech)
(e.g., How do data
repositories "join" the VO environment? How can subfields best organize their data and make it
available to a larger community?)
Services (N. Hurlburt, LMSAL; M. Weiss, APL)
(e.g., What services
beyond data access, such as format translation, coordinate conversion, and
visualization, are most important?
How are these services best coupled to the data access mechanisms?)
We will also consider visionary
ideas for future directions both
for their own sake and to insure the architecture remains open to
innovation. Websites and a
listserv list will provide means for document distribution and community input
prior to and after the meeting.
Posters will allow anyone to present their latest ideas.
Registration will be
required, but no fee will be charged.
LWS will provide the venue and morning and afternoon refreshments at
breaks, but not travel and lodging.
For further
information, contact Aaron Roberts at<EMAIL_ADDRESS>301-286-5606.


Docs, examples and getting started guide coming soon. Get notified when we launch:
Thanks for watching!We've built a live demo that you can play with:


(PECL getdns >= 0.11.0)
php_getdns_fp2rr_list — Convert the contents of a DNS zone file to getdns list format.
$inFile
, int &$list
, string $origin
, int $defaultTTL
)This function converts the contents of a DNS zone file to getdns list format. This list will contain dictionaries representing the resource records found in the zone file.
inFile
A string containing the path to the file to be opened for reading.
list
On output this value will contain an integer representation of a list of dictionaries that describe the resource records found in the zone file. Note that this argument is passed by reference and any existing value will be overwritten. The client is responsible for calling php_getdns_list_destroy() when the list is no longer needed.
origin
A string that contains the default suffix to be used for any domain names that are not fully qualified.
defaultTTL
An integer that contains the default "time to live" (TTL) value to be used in building the dictionaries.
This function returns an integer response code. GETDNS_RETURN_GOOD indicates successful completion.


A tutorial on applying logical knowledge using Amzi! Prolog + Logic Server.
Some common known facts about Prolog are:
These are all true, and can be understood if you divide Prolog into two aspects, logic base and logic programming.
The logic base aspect of Prolog is easy to learn and use, and is a powerful tool for representing knowledge that is precise in nature. Examples of this type of knowledge are pricing, underwriting, workflow, benefits and regulations.
Less precise knowledge, such as used for diagnostic types of applications, does not fit as well into a Prolog logic base, nor does knowledge used to create complex results, such as configuration and planning.
The logic programming aspect of Prolog makes it easy to implement those more difficult applications. Any sort of knowledge and reasoning can be implemented using logic programming.
Logic programming takes a while to get used to, but once learned is an extremely powerful tool.


JASP A Luxury
Hotel
Check Availabilty....
Checking Availabilty
Check in
Check out
Rooms
Rooms
1 Room
2 Rooms
3 Rooms


If we use temporal layers, we can show the Timeline by clicking the button. If we want to hide the Timeline again we can click the button and the Timeline slides out.