While it may seem trivial that the integrity of our dataset is flawless, and that each datapoint is reliable in it’s naming convention along with its flow across different platforms. most times this is hardly the case.
Thus, it’s necessary for us, as analytics specialists, both to define the naming conventions of our datasets as well as run QA tests on the analytics setup created by the development team so we don’t wake up 3 months into the data collection process and realize we severely misinterpreted our data the entire time.
While many times data flow will be fairly simple, going straight from our app or website into our analytics platform, in some instances things won’t be so simple such as measuring how many users encountering our landing page installed our mobile app. in this instance our user’s ID parameters need to transfer from our landing page to the AppStore, then into our app and to our analytics platform from there, thus a single user creates several data points that cross 4 different platforms in order for us to read it.
Let’s take a look at several different methods for data transfer, and how to utilize them.
These are parameters attached to a URL in Address link via the ?
symbol. These have varied uses, from keeping search queries, user data, arrival sources to our URL, and more.
https://www.google.co.il/search?q=search+for+common+ground&oq=search+for+common+ground&aqs=chrome..69i57j0l5.4243j0j7&sourceid=chrome&ie=UTF-8
In this example we see that:
?q=..
is the user’s search query,
&sourceid=..
is where the query was entered. since it’s a added to another parameter it uses the ‘&’ symbol instead of ?
.
&ie=..
is the encoding used on the users browser.
The rest are google proprietary information, and though it’s completely unnecessary, here’s where you can read more about them.
Here’s where you can learn more about URL parameters.
UTM codes are standard URL parameters used for analytics purposes. These codes provide us with a tool to measure the performance of our inbound links and track the amount of traffic coming from each of our publishing platforms, search results and ad-placements.
Using UTM codes, which are read as a standard by all analytics platforms e.g KissMetrics, Mixpanel, Heap and of course Google Analytics, you can track broad sets of data such as how much traffic your receiving from each platform (google search, google adwords, facebook, twitter, etc.), You can also track highly specific data such as how much traffic you’re getting from the Call-to-Action on your Facebook page against each post on the same page.
| UTM Parameter | Purpose | Example | Required? |
|---|---|---|---|
| source | Indicates which domain sent the traffic our way | utm_source=Facebook |
V |
| medium | Indicates which type of link was used, cost-per-click, post, email, etc. | utm_medium=PageCTA |
V |
| campaign | Indicates the name of our campaign and has varied uses. | utm_campagin=spring_sale |
V |
| term | Identifies search terms used by the user | utm_term=running+shoes+sale |
X |
| content | identifies the specific component clicked by the user that referred them to our website | utm_content=logoclick utm_content=textclick utm_content=titleclick |
X |
| id | the campaign id is used to attribute all other parameters like campaign or source in accordance with the id |
utm_id=google_CPC_july |
X |
When defining and implementing you UTM parameters in posts, content, page components etc. keep the following in mind: 1. Do not use them for internal links on your own website. Following user interactions using UTM codes is a futile effort and there are far more advanced techniques for this attempt. The use of UTM codes, as we said, is following outside traffic sources leading to our website.
utm_medium
parameter e.g CPC or CPM, from organic traffic such as posts or articles. Not doing so will weaken your position to establish return-on-investment from paid sources and will highly damage your competence to derive actionable insights from your analytics.utm_campaign
parameter is highly versatile and can be used a great number of ways such as tracking campaigns for:
shoes
, jackets
, t-shirts
nike
, adidas
, etc.programmer-Tim
, projectManager-Kevin
.hot-lead
,cold-lead
,trial
, beta-request
, add-to-cart
These sorts of naming conventions allow you to measure you’re campaign results and compare them over time via these segments, so useful.
In order to avoid the complexity of writing the the full URL including all UTM codes you may want to use Google URL Builder. And so we don’t present the user with long and suscpicious links, we’ll shorten our URLs using bit.ly or similiar.
UTM.io is also a great tool for working with UTMs. It integrates URL building, shortening & tracking, so convenient.
Analytics platforms such as Heap, Mixpanel, CrazyEgg, HotJar in general are integrated into websites & web-apps via inserting a JavaScript tracking snippet into our page’s code. Though it’s literally as simple as copy-pasting, it must be attached to each and every one of our webpages.
Here’s and example from HeapAnalytics on how to insert a snippet into your webpages for one of many analytics platforms. Don’t forget to change YOUR_APP_ID
parameter to your account details.
When using multiple platforms it may seem like you need to insert multiple snippets, one per platform, into your </head>
, this arises concerns towards a slowdown page-loading-time. for this we’ll use a Tag Manager such as GTM, which allows for us to integrate a single, permanent snippet into our webpages and use a the Tag Manager to add, remove and manage integrations of various analytics platforms that will receive data via that same snippet.
While it may be tempting to track all events on your website without discriminations it important to remember that the product of good analytics isn’t a wide collection of data-sets, but the quick arrival at actionable insights from those data sets. To the latter, auto-tracking events are most times counterproductive due to computer-generated naming conventions and lack of relation to other data sets used to form funnels.
Thus it is best to define the events you’ll be tracking and the questions or hypotheses these events will be tied to before any data has been collected.
While each analytics platform has a standard set of properties which tell us more about the user, such as location, timestamp, operating system, browser, etc. We may require further details to arrive at meaningful conclusions. Thus after setting our events we should want to create custom events to have some more context for each type of user.
When initially defining our dataset, prior to the data collection, It’s important to name each of our parameters as well as their values in a manner which we’ll be able to immediately recognize when we approach the data analysis. Naming a parameter triggered on the first app onboarding screen “FinishedOnboarding”, for instance, would be a bad idea, because for the user the onboarding just started.
Allow me to quote Phillip Porter of MecLabs for a second, as he clears everything up pretty neatly:
“Significance is based on sample size and effect size. The larger the sample you have, the smaller effect size you can find to be significant. The larger the effect size present, the smaller sample size you need to find significance. Larger sample sizes are generally better, however any difference, no matter how small, can be found to be significant with a large enough sample size.”
Whereas sample size refers to the overall amount of people participating in the A/B test and effect size refers to the 𝚫delta between the two groups.
$ EffectSize = \frac{Mean A - Mean B}{StandardDeviation} $
As a general rule we’ll be needing a sample size of a couple of thousand users per test, at the least. We’ll also need to record the test results through time and not as any one final measurement. Like anything though, it’s all a matter of finding the right measure and relying on good intuition, your own as well as that of your team.
Evan Miller’s A/B test calculator is my personal favorite and an industry standard,
Here’s another set of calculators, not as popular but useful non-the-less in helping you with deciding on proper sample sizes, effect sizes and confidence levels. There are many more out there just a google search away.


RIP Classful Routing
19 Apr 2017 | knowledgeRIPv1 is a classful routing protocol. This is not quite as black and white as it seems. First, some housekeeping. When we see a route like this:
`R <IP_ADDRESS> [120/3]() via <IP_ADDRESS>, 00:00:20, Serial1
` The 120/3 is the administrative distance followed by the metric. With RIP, the metric is hop count, so 3 in this route indicates the network is 3 hops away via the Serial1 interface.
If more than one route exists for a destination, and both routes have the same hop count, then equal-cost load balancing will be performed.
RIP performs the route lookup by first performing a classful route table lookup.
`Gateway of last resort is not set
<IP_ADDRESS> <IP_ADDRESS> is subnetted, 9 subnets
R <IP_ADDRESS> [120/3]() via <IP_ADDRESS>, 00:00:20, Serial1
[120/3]() via <IP_ADDRESS>, 00:00:21, Ethernet0
R <IP_ADDRESS> [120/3]() via <IP_ADDRESS>, 00:00:21, Serial1
[120/3]() via <IP_ADDRESS>, 00:00:21, Ethernet0
` So if a packet destined for <IP_ADDRESS> came into the router, it would first look for <IP_ADDRESS>. If there were no match, the packet is dropped and an ICMP Destination Unreachable message is sent to the packet’s source. If there is a match for the network portion (as there is in this example), then the subnets are examined. If there is a subnet match, the packet is routed. If there is not subnet match, the packet is dropped and a Destination Unreachable message is sent (as would happen with <IP_ADDRESS>).
If you look at the routing table and examine RIP entries, you will notice there is no provision for RIP to advertise a subnet mask along with each route entry. The router’s only recourse is to assume that the mask configured on one of its interfaces for that address is used consistently throughout the rest of the network. If the network is not directly connected, there is a listing only for the major-class network and no associated mask.
Note that since the destination address of packets being routed by a classful routing protocol are interpreted according to the subnet masks locally configured on the router’s interfaces, all subnet masks within a major, class-level network must be consistent.
In the case above where there is no directly connected network, RIP summarizes at the classful network boundaries. This is called subnet hiding. So a router would only send advertisements for <IP_ADDRESS>, <IP_ADDRESS>, and <IP_ADDRESS> for example.
A good way to summarize is the following:
- If the destination address is a member of a directly connected major network, the subnet mask configured on the interface attached to that network will be used to determine the subnet of the destination.
- If the destination address is not a member of a directly connected major network, the router will try to match only the major class A, B, or C portion of the destination address.
Comments


The data collected contains 200 schools distributed in all Bogota's Districts in order to achieve a representative sample for the 1536 schools located in the city.
The data collected contains 200 schools distributed in all Bogota's Districts in order to achieve a representative sample for the 1536 schools located in the city.
The data collected contains 200 schools distributed in all Bogota's Districts in order to achieve a representative sample for the 1536 schools located in the city.


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This assignment lays the groundwork for an application you'll build in a later homework assignment. This assignment has two main parts. In the first part, you will make your graph class(es) generic. In the second part, you will implement a different path-finding algorithm for graphs known as Dijkstra's algorithm.
In the application you'll be building in subsequent homework, your mission is to find the shortest route to visit a certain set of buildings. A graph is an excellent representation of a map, and luckily you have already specified and implemented a graph. Unfortunately, your graph only stores Strings, whereas the route-finding application needs to store non-String data types in the nodes and edges. More generally, your graph would be much more widely useful if only the client could choose the data types to be stored in nodes and edges. Herein lies the power of generics!
Your task is to convert your graph ADT to a generic class. Rather than always storing the data in nodes and edge labels as Strings, it should have two type parameters representing the data types to be stored in nodes and edges. Directly modify your existing classes under hw4 — there is no need to copy or duplicate code.
When you are done, your previously-written HW4 and HW5 tests and MarvelPaths will no longer compile. Modify these classes to construct and use graph objects parameterized with Strings. All code must compile and all tests must pass when you submit your homework. Depending on your changes, some of your tests may no longer be valid. Try to adapt your tests to your new implementation, or discard them and write the new ones: they should help you build confidence in your implementation. But, don't overdo it: as with any testing, stop when you feel that the additional effort is not being repaid in terms of increased confidence in your implementation.
We want you to configure Eclipse to show generics problems as errors. By default, Eclipse shows generics problems as warnings (indicated by yellow lines and markers). You can configure Eclipse to instead issue errors (indicated by red lines and markers) for these problems. Doing so will help you remember to write acceptable generics code.
To make this configuration, go to Eclipse >> Preferences and select Java >> Compiler >> Errors/Warnings. Under Generic types, change the value of Unchecked generic type operation to Error.
(Note that there is another setting named "Usage of a raw type" that is set to Ignore by default. We recommend that this option be set to Warn because it is specific to the Eclipse compiler and checks for more stringent requirements than required by the Java language specification.)
Hint: Sometimes you may find that classes which previously compiled are now reporting "[some class] cannot be resolved" errors in Eclipse. You can fix these errors by doing a clean build: go to Project >> Properties >> Clean..., select your csci2600 project, and hit OK.
In a weighted graph, the label on each edge is a length, cost, or weight representing the cost of traversing that edge. Depending on the application, the cost may be measured in time, money, physical distance, etc. The total cost of a path is the sum of the costs of all edges in that path, and the minimum-cost path between two nodes is the path with the lowest total cost between those nodes.
You will implement Dijkstra's algorithm, which finds a minimum-cost path between two given nodes in a graph with all nonnegative edge weights. Below is a pseudocode algorithm that you may use in your implementation. You are free to modify it as long as you are essentially still implementing Dijkstra's algorithm. Your implementation of the algorithm may assume a graph with Double edge weights.
The algorithm uses a priority queue. The standard Java libraries include an implementation of a PriorityQueue.
Dijkstra's algorithm assumes a graph with all nonnegative edge weights. start = starting node dest = destination node active = priority queue. Each element is a path from start to a given node. A path's "priority" in the queue is the total cost of that path. Nodes for which no path is known yet are not in the queue. finished = set of nodes for which we know the minimum-cost path from start. // Initially we only know of the path from start to itself, which has a cost // of zero because it contains no edges. Add a path from start to itself to active while active is non-empty: // minPath is the lowest-cost path in active and is the minimum-cost // path for some node minPath = active.removeMin() minDest = destination node in minPath if minDest is dest: return minPath if minDest is in finished: continue for each edge e = ⟨minDest, child⟩: // If we don't know the minimum-cost path from start to child, // examine the path we've just found if child is not in finished: newPath = minPath + e add newPath to active add minDest to finished If the loop terminates, then no path exists from start to dest. The implementation should indicate this to the client.
You will write a modified version of your Marvel Paths application in which your application finds its paths using Dijkstra's algorithm instead of BFS. Dijkstra's algorithm requires weighted edges. To simulate edge weights over the Marvel dataset, the weight of the edge between two characters will be based on how well-connected those two characters are. Specifically, the weight is the inverse of how many comic books two characters are in together (equivalently, the weight is the multiplicative inverse of the number of edges in the multigraph between them). For example, if Amazing Amoeba and Zany Zebra appeared in 5 comic books together, the weight of the edge between them would be 1/5. Thus, the more well-connected two characters are, the lower the weight and the more likely that a path is taken through them. In summary, the idea with the Marvel data is to treat the number of paths from one node to another as a "distance" — if there are several edges from one node to another then that is a "shorter" distance than another pair of nodes that are only connected by a single edge.
Place your new Marvel application in src/main/java/hw6/MarvelPaths2.java in package hw6. In choosing how to organize your code, remember to avoid duplicating code as much as possible. In particular, reuse existing code where possible, and keep in mind that you may need to use the same implementation of Dijkstra's algorithm in a different application.
For this assignment, your program must be able to construct the graph and find a path in less than 30 seconds using the full Marvel dataset. We will set the tests to have a 3000 ms (30 second) timeout for each test and run with assertions disabled.
As before, you are welcome to write a main() method for your application, but you are not required to do so.
The interface of MarvelPaths2 is the same as that of MarvelPaths in Homework 5, but with a few differences in arguments and output:
path from CHAR1 to CHARN: CHAR1 to CHAR2 with weight w1 CHAR2 to CHAR3 with weight w2 ... CHARN-1 to CHARN with weight wN-1 total cost: W
where W is the sum of w1, w2, ..., wN-1.
In other words, the only changes in output from Homework 5 are the way the edge labels are printed and the addition of a "total cost" line at the bottom. The output should remain the same as before when no path is found or the characters are not in the graph: in particular, do not print the "total cost" line in those cases.
If there are two minimum-cost paths between CHAR1 and CHARN, it is undefined which one is printed.
For readability, the output of findPath should print numeric values with exactly 3 digits after the decimal point, rounding to the nearest value if they have more digits. The easiest way to specify the desired format of a value is using format strings. For example, you could create the String "Weight of 1.760" by writing:
String.format("Weight of %.3f", 1.759555555555);
In findPath, the total cost should be computed by summing the full values of the individual weights, not the rounded values.
As in Homework 5, a path from a character to itself should be treated as a trivially empty path. Because this path contains no edges, it has a cost of zero. (Think of the path as a list of edges. The sum of an empty list is conventionally defined to be zero.) So, your findPath should produce the usual output for a path but without any edges, i.e.:
path from C to C: total cost: 0.000
This only applies to characters in the dataset: a request for a path from a character that is not in the dataset to itself should print the usual "unknown character C" output.
Also as in Homework 5, if the user gives two valid node arguments CHAR1 and CHAR2 that have no path in the specified graph, output:
path from CHAR1 to CHARN: no path found
Just as with Homework 5, create smaller *.csv files to test your graph building and findPaths. Place them in the data/ directory. Write tests in JUnit classes and place those tests in hw6 package (src/test/java/hw6/ directory). Just as in Homework 4 and 5, run EclEmma to measure your code coverage. The code coverage threshold will be set at 85% for this assignment.
Tests do not directly test the property that your graph is generic. However, Homework 4 and Homework 5 tests use String edge labels, while Homework 6 uses numeric values. Supporting all three test drivers implicitly tests the generic behavior of your graph.
Please answer the following questions in a file named hw6_reflection.pdf
in your answers/ directory. Answer briefly, but in
enough detail to help you improve your own practice via
introspection and to enable the course staff to improve Principles
of Software in the future. .txt
files will also be
accepted.
Please answer the following questions in a file named hw6_collaboration.pdf
in your answers/ directory. .txt
files
will also be accepted.
The standard integrity policy applies to this assignment.
State whether or not you collaborated with other students. If you did collaborate with other students, state their names and a brief description of how you collaborated.
When you add generic type parameters to a class, make sure to describe these type parameters in the class's Javadoc comments so the client understands what they are for.
As usual, include an abstraction function, representation invariant, and checkRep in all classes that represent an ADT. If a class does not represent an ADT, place a comment that explicitly says so where the Abstraction function and Rep invariant would normally go. (For example, classes that contain only static methods and are never constructed usually do not represent an ADT.) Please come to office hours if you feel unsure about what counts as an ADT and what doesn't.
In deciding how to organize your code, remember that you may need to reuse Dijkstra's algorithm in future homework assignments. These assignments have nothing to do with Marvel and, depending on your implementation, might use a different generic type for nodes. How can you structure your code so that your implementation of Dijkstra's algorithm is convenient to use for other applications?
You should add and commit the following files to your Git repository:
Additionally, be sure to commit any updates you make to the following files:
None yet. Check the Submitty Discussion Form page regularly. As usual, we will announce errors there.


Thai court disqualifies rising opposition party leader as MP
BANGKOK: Thailand's Constitutional Court on Wednesday (Nov 20) disqualified outspoken opposition party leader Thanathorn Juangroongruangkit as a member of parliament after finding him guilty of violating election law.
Thanathorn, 40, the leader of the progressive Future Forward Party, has emerged as one of the most prominent opponents of a government headed by former military ruler Prayuth Chan-o-cha after the new opposition party came a surprise third in an election in March.
The Constitutional Court found Thanathorn guilty of holding shares in a media company after registering his candidacy for the election to formally end five years of military rule.
In its ruling, the court said the evidence against Thanathorn outweighed that in his favour.
Thanathorn had denied the accusation, saying he transferred his shares before beginning the campaign.
Thanathorn has been a fierce critic of the army's involvement in politics and did particularly well among young voters in the election from which the pro-military party was declared the winner after a disputed ballot count.
Ahead of the court ruling Thanathorn thanked supporters at the court and said: "The Future Forward Party is a journey ... All of us will continue fighting and keep moving forward.
Future Forward is part of an opposition alliance that disputed the vote count and accused the army of writing the electoral rules to ensure that Prayuth, the former military leader, would remain as prime minister.
The court had earlier suspended Thanathorn from parliament pending the verdict to this case.
Thanathorn’s party won 80 out of 500 available seats in the Lower House of the Thai parliament.
The Future Forward leader also faces two criminal charges, one for computer crime for a speech he posted on Facebook criticising the military government last year, and another for sedition for allegedly aiding protesters in 2015.


{% extends 'base.html' %} {% load static %} {% block head_block %} {% endblock %} {% block body_block %}
The network will appear below. Squares indicate molecules, circles Mass2Motifs (topics). Edges are coloured according to probability (the darker, the higher the probability). Hover over a node to see information. Double-click on a Mass2Motif to see its molecules.
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When you double-click on a Mass2Motif, the spectra of the moleceles associated with it will appear here. Features (fragments and losses) explained by this Mass2Motif will be coloured red. Click 'next' and 'previous' to cycle through the molecules.
When you double-click on a Mass2Motif, two bar plots will appear below this meesage. The first shows the proportion of the total intensity of these features explained by this Mass2Motif. Dark blue bars show the total intensity (across the whole dataset), light blue show the proportion explained by the selected Mass2Motif. The second shows the number of times each feature in this Mass2Motif appears in the documents associated with this Mass2Motif.


This is text 4d509944-1106-43a8-a524-6b21b0e7b870 This is link 1edcd814-7ad2-4287-8e88-4ed25cec9749
This is link 8d3c1079-baef-4383-8439-0dc16cd272fe This is link d43d218a-df72-4d2d-8dc1-091f9a495530 This is link dcad0506-0d53-47df-862a-201bb69f998e
This is text cbff49d4-1b07-435e-a31e-cb2f35f836bb This is text f7f8729d-89f9-4f80-bd3d-a63b9ce4660c


quspin.operators.quantum_LinearOperator¶
-
class
quspin.operators.
quantum_LinearOperator
(static_list, N=None, basis=None, diagonal=None, check_symm=True, check_herm=True, check_pcon=True, dtype=<class 'numpy.complex128'>, copy=False, **basis_args)[source]¶ Applies a quantum operator directly onto a state, without constructing the operator matrix.
The quantum_LinearOperator class uses the basis.Op() function to calculate the matrix vector product on the fly, greatly reducing the amount of memory needed for a calculation at the cost of speed.
This object is useful for doing large scale Lanczos calculations using the eigsh method.
Notes
The class does NOT yet support time-dependent operators.
Examples
The following example shows how to construct and use quantum_LinearOperator objects.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
from quspin.operators import quantum_LinearOperator # Hamiltonians and operators from quspin.basis import spin_basis_1d # Hilbert space spin basis import numpy as np # generic math functions # ##### define model parameters ##### L=4 # system size J=1.0 # spin interaction g=0.809 # transverse field h=0.9045 # parallel field # ##### construct basis basis=spin_basis_1d(L=L) # define PBC site-coupling lists for operators x_field=[[g,i] for i in range(L)] z_field=[[h,i] for i in range(L)] J_nn=[[J,i,(i+1)%L] for i in range(L)] # PBC # static list static=[["zz",J_nn],["z",z_field],["x",x_field]] # ##### construct Hamiltonian, NO dynamic list passed H=quantum_LinearOperator(static,basis=basis,dtype=np.float64) # ##### apply operator ono state # compute domain wall initial state dw_str = "".join("1" for i in range(L//2)) + "".join("0" for i in range(L-L//2)) i_0 = basis.index(dw_str) # find index of product state in basis psi = np.zeros(basis.Ns) # allocate space for state psi[i_0] = 1.0 # set MB state to be the given product state # apply H on psi psi_new=H.dot(psi) # diagonalise operator E,V=H.eigsh(k=1,which='SA') # calculate operator squared H_squared=H.dot(H)
Attributes: H
quantum_LinearOperator
: transposes and conjugates the operator matrix: \(H_{ij}\mapsto H_{ji}^*\).Ns
int: number of states in the (symmetry-reduced) Hilbert space spanned by basis.
T
quantum_LinearOperator
: transposes the operator matrix: \(H_{ij}\mapsto H_{ji}\).basis
basis
: basis used to build the hamiltonian object.diagonal
numpy.ndarray: static diagonal part of the linear operator.
dtype
type: data type of quantum_LinearOperator object.
get_shape
tuple: shape of the quantum_LinearOperator object, always equal to (Ns,Ns).
ndim
int: number of dimensions, always equal to 2.
shape
tuple: shape of linear operator.
static_list
list: operator list used to create this object.
Methods
__call__
(x)Call self as a function. adjoint
()Hermitian adjoint. conj
()Conjugates quantum_LinearOperator operator. conjugate
()Conjugates quantum_LinearOperator operator. copy
()Returns a deep copy of quantum_LinearOperator object. dot
(other[, out, a])Matrix-vector multiplication of quantum_LinearOperator operator, with state V. eigsh
(**eigsh_args)Computes SOME eigenvalues and eigenvectors of hermitian quantum_LinearOperator operator using SPARSE hermitian methods. getH
([copy])Calculates hermitian conjugate of quantum_LinearOperator operator. matmat
(X)Matrix-matrix multiplication. matvec
(x)Matrix-vector multiplication. rmatvec
(x)Adjoint matrix-vector multiplication. set_diagonal
(diagonal[, copy])Sets the diagonal part of the quantum_LinearOperator. transpose
([copy])Transposes quantum_LinearOperator operator. -
__init__
(static_list, N=None, basis=None, diagonal=None, check_symm=True, check_herm=True, check_pcon=True, dtype=<class 'numpy.complex128'>, copy=False, **basis_args)[source]¶ Intializes the quantum_LinearOperator object.
Parameters: - static_list : list
Contains list of objects to calculate the static part of a quantum_LinearOperator operator. Same as the static argument of the quantum_operator class. The format goes like:
>>> static_list=[[opstr_1,[indx_11,...,indx_1m]],matrix_2,...]
- N : int, optional
number of sites to create the default spin basis with.
- basis :
basis
, optional basis object to construct quantum operator with.
- diagonal : array_like
array containing diagonal matrix elements precalculated by other means.
- dtype : ‘type’
Data type (e.g. numpy.float64) to construct the operator with.
- check_symm : bool, optional
Enable/Disable symmetry check on static_list and dynamic_list.
- check_herm : bool, optional
Enable/Disable hermiticity check on static_list and dynamic_list.
- check_pcon : bool, optional
Enable/Disable particle conservation check on static_list and dynamic_list.
- basis_args : dict
Optional additional arguments to pass to the basis class, if not already using a basis object to create the operator.
Methods
__init__
(static_list[, N, basis, diagonal, …])Intializes the quantum_LinearOperator object. adjoint
()Hermitian adjoint. conj
()Conjugates quantum_LinearOperator operator. conjugate
()Conjugates quantum_LinearOperator operator. copy
()Returns a deep copy of quantum_LinearOperator object. dot
(other[, out, a])Matrix-vector multiplication of quantum_LinearOperator operator, with state V. eigsh
(**eigsh_args)Computes SOME eigenvalues and eigenvectors of hermitian quantum_LinearOperator operator using SPARSE hermitian methods. getH
([copy])Calculates hermitian conjugate of quantum_LinearOperator operator. matmat
(X)Matrix-matrix multiplication. matvec
(x)Matrix-vector multiplication. rmatvec
(x)Adjoint matrix-vector multiplication. set_diagonal
(diagonal[, copy])Sets the diagonal part of the quantum_LinearOperator. transpose
([copy])Transposes quantum_LinearOperator operator. Attributes
H
quantum_LinearOperator
: transposes and conjugates the operator matrix: \(H_{ij}\mapsto H_{ji}^*\).Ns
int: number of states in the (symmetry-reduced) Hilbert space spanned by basis. T
quantum_LinearOperator
: transposes the operator matrix: \(H_{ij}\mapsto H_{ji}\).basis
basis
: basis used to build the hamiltonian object.diagonal
numpy.ndarray: static diagonal part of the linear operator. dtype
type: data type of quantum_LinearOperator object. get_shape
tuple: shape of the quantum_LinearOperator object, always equal to (Ns,Ns). ndim
int: number of dimensions, always equal to 2. shape
tuple: shape of linear operator. static_list
list: operator list used to create this object. -
H
¶ quantum_LinearOperator
: transposes and conjugates the operator matrix: \(H_{ij}\mapsto H_{ji}^*\).
-
Ns
¶ int: number of states in the (symmetry-reduced) Hilbert space spanned by basis.
-
T
¶ quantum_LinearOperator
: transposes the operator matrix: \(H_{ij}\mapsto H_{ji}\).
-
adjoint
()¶ Hermitian adjoint.
Returns the Hermitian adjoint of self, aka the Hermitian conjugate or Hermitian transpose. For a complex matrix, the Hermitian adjoint is equal to the conjugate transpose.
Can be abbreviated self.H instead of self.adjoint().
Returns: - A_H : LinearOperator
Hermitian adjoint of self.
-
basis
¶ basis
: basis used to build the hamiltonian object.Defaults to None if operator has no basis (i.e. was created externally and passed as a precalculated array).
-
conj
()[source]¶ Conjugates quantum_LinearOperator operator.
Returns: - :obj:`quantum_LinearOperator`
\(H_{ij}\mapsto H_{ij}^*\)
Notes
This function does NOT transpose the operator.
Examples
>>> H_conj = H.conj()
-
conjugate
()[source]¶ Conjugates quantum_LinearOperator operator.
Returns: - :obj:`quantum_LinearOperator`
\(H_{ij}\mapsto H_{ij}^*\)
Notes
This function does NOT transpose the operator.
Examples
>>> H_conj = H.conj()
-
diagonal
¶ numpy.ndarray: static diagonal part of the linear operator.
-
dot
(other, out=None, a=1.0)[source]¶ Matrix-vector multiplication of quantum_LinearOperator operator, with state V.
\[aH|V\rangle\]Parameters: - other : numpy.ndarray
Vector (quantums tate) to multiply the quantum_LinearOperator operator with.
- out : array_like, optional
specify the output array for the the result.
- a : scalar, optional
scalar to multiply the final product with: \(B = aHA\).
Returns: - numpy.ndarray
Vector corresponding to the hamiltonian operator applied on the state V.
Examples
>>> B = H.dot(A,check=True)
corresponds to \(B = HA\).
-
dtype
¶ type: data type of quantum_LinearOperator object.
-
eigsh
(**eigsh_args)[source]¶ Computes SOME eigenvalues and eigenvectors of hermitian quantum_LinearOperator operator using SPARSE hermitian methods.
This function method solves for eigenvalues and eigenvectors, but can only solve for a few of them accurately. It calls scipy.sparse.linalg.eigsh, which is a wrapper for ARPACK.
Parameters: - eigsh_args :
For all additional arguments see documentation of scipy.sparse.linalg.eigsh.
Returns: - tuple
Tuple containing the (eigenvalues, eigenvectors) of the quantum_LinearOperator operator.
Notes
Assumes the operator is hermitian! If the flat check_hermiticity = False is used, we advise the user to reassure themselves of the hermiticity properties before use.
Examples
>>> eigenvalues,eigenvectors = H.eigsh(**eigsh_args)
-
getH
(copy=False)[source]¶ Calculates hermitian conjugate of quantum_LinearOperator operator.
Parameters: - copy : bool, optional
Whether to return a deep copy of the original object. Default is copy = False.
Returns: - :obj:`quantum_LinearOperator`
\(H_{ij}\mapsto H_{ji}^*\)
Examples
>>> H_herm = H.getH()
-
get_shape
¶ tuple: shape of the quantum_LinearOperator object, always equal to (Ns,Ns).
-
matmat
(X)¶ Matrix-matrix multiplication.
Performs the operation y=A*X where A is an MxN linear operator and X dense N*K matrix or ndarray.
Parameters: - X : {matrix, ndarray}
An array with shape (N,K).
Returns: - Y : {matrix, ndarray}
A matrix or ndarray with shape (M,K) depending on the type of the X argument.
Notes
This matmat wraps any user-specified matmat routine or overridden _matmat method to ensure that y has the correct type.
-
matvec
(x)¶ Matrix-vector multiplication.
Performs the operation y=A*x where A is an MxN linear operator and x is a column vector or 1-d array.
Parameters: - x : {matrix, ndarray}
An array with shape (N,) or (N,1).
Returns: - y : {matrix, ndarray}
A matrix or ndarray with shape (M,) or (M,1) depending on the type and shape of the x argument.
Notes
This matvec wraps the user-specified matvec routine or overridden _matvec method to ensure that y has the correct shape and type.
-
ndim
¶ int: number of dimensions, always equal to 2.
-
rmatvec
(x)¶ Adjoint matrix-vector multiplication.
Performs the operation y = A^H * x where A is an MxN linear operator and x is a column vector or 1-d array.
Parameters: - x : {matrix, ndarray}
An array with shape (M,) or (M,1).
Returns: - y : {matrix, ndarray}
A matrix or ndarray with shape (N,) or (N,1) depending on the type and shape of the x argument.
Notes
This rmatvec wraps the user-specified rmatvec routine or overridden _rmatvec method to ensure that y has the correct shape and type.
-
set_diagonal
(diagonal, copy=True)[source]¶ Sets the diagonal part of the quantum_LinearOperator.
Parameters: - diagonal: array_like
array_like object containing the new diagonal.
-
shape
¶ tuple: shape of linear operator.
-
static_list
¶ list: operator list used to create this object.


#if isOK==-1>
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A Bower-compatible JSON API which lists all public repositories owned by the Financial Times as well as a white-list of private repositories.
To use the Origami Bower registry, update your ~/.bowerrc
file to
the following:
{
"registry": {
"search": [
"https://origami-bower-registry.ft.com",
"https://registry.bower.io"
]
}
}
This Bower registry can also list private repositories, but this is decided on a case-by-case basis. If you'd like to add your private repo to the white-list, then you'll need to consider the following first:
If you're OK with the above then either contact the Origami team
asking to add your repository, or add it to the privateRepoWhitelist
array yourself
and open a pull request.


{% extends "base.html" %} {% block content %}
Database Error request the Admin if you are the Authorized User of the Account..


SRCCON 2017 takes place at the McNamara Alumni Center on the University of Minnesota campus in Minneapolis.
In addition to this page with all the logistical details you may need as you prep for SRCCON, we wanted to make sure you know what to expect at the conference. Take a look at our new SRCCON Participant Guide. It’s our best guess at the questions you may have.
If you’re still looking to make your lodging arrangements, we’ve got a list of nearby hotels and a roomshare list. For getting to the venue, you can also check out transit and parking details. If you’re attending from outside the U.S., we have a recommended resource for what to consider at the U.S. border crossing.
We’ve tried to think of everything you might need, from hearty, tasty meals to childcare. You can check out the full schedule now (it’s mobile friendly too!). When you arrive at the venue, we’ll also have on-the-spot signups to host conversations about the “life” side of work/life balance on Thursday evening, and the “work” side during Friday lunch.
Throughout SRCCON, we’ll also have office hours (new this year) and a big job board for any positions you’d like to post. We’ll have a signup sheet for the office hours, where you can block out an hour to chat one-on-one with participants about a project you’re working on or maybe a position you’re hiring for. We’ll have post-its at the ready for your job posting needs as well.
Thursday evening, we’ll feed you dinner and have three hours of fun planned, and you can even bring your partner to join you. We’ve also gathered details about a local Alcoholics Anonymous meeting, and your fellow news nerds have written an enormous guide to the Twin Cities.
Also, the entirety of SRCCON is covered by our code of conduct.
We want you to be happy and well-fed at SRCCON, so we’re providing breakfast and lunch on both days, as well as a delicious dinner Thursday night. We’ll also have tea, coffee, and snacks—healthy and sugary, both—throughout the day, so stick around and enjoy the company of your fellow attendees.
We know evenings at conferences can be tough, so we schedule Thursday evening as an opportunity to have fun, talk, and relax with colleagues and new friends. You might end up learning a new skill or two as well, but we try to focus the evening program on the “life” side of the work/life balance.
We’ll keep feeding you, too—there’ll be awesome local snacks, beer and wine, and plenty of great nonalcoholic beverages. After dinner on Thursday, the evening program will spread throughout the SRCCON venue for things like:
If you have partners or friends who will be in Minneapolis, feel free to invite them to join the Thursday evening fun. We’re happy to welcome them for Thursday evening’s dinner and activities, with a special $45 partner ticket that you can purchase now.
If you’re looking for things to before, after, or during SRCCON, we have compiled a guide to wonderful things in the Minneapolis-Saint Paul area, written by local news nerds.
On Thursday night, the closest meeting to SRCCON is at Drinkytown AA, University Lutheran Church of Hope 601 13th Av SE, which meets Thursday at 6:30pm. It is an “open” meeting, meaning that you don’t have to be alcoholic to go. AA Minneapolis also hosts a full list of area meetings.
We offer free, on-site childcare throughout the full SRCCON schedule (including Thursday evening) at the adjoining Commons Hotel. The caregivers act as employees of KiddieCorp (not contractors) and are recruited from local licensed daycare centers, elementary schools, preschools, and collegiate departments of early childhood education. Learn more about our free childcare.
For folks who are coming in from outside the U.S., we recommend the EFF’s guide to protecting your data at the US border for details on what to consider at the border crossing. If you need any further help or have any questions, please email us.
Our conference rate at the The Commons Hotel has expired, but you can call<PHONE_NUMBER> or<PHONE_NUMBER> to inquire about any rooms that might still be available.
Other hotels not far from the conference venue include:
Days Hotel .3 miles away 2407 University Ave SE Minneapolis, MN 55414 US<PHONE_NUMBER>
University Inn 1 mile away 925 4th Street SE Minneapolis, MN, 55414, United States<PHONE_NUMBER>
Courtyard Marriott Downtown 1.2 miles away 1500 Washington Ave S Minneapolis, MN 55454<PHONE_NUMBER>
Aloft Minneapolis Hotel 3 miles away 900 Washington Avenue South Minneapolis, Minnesota, 55415<PHONE_NUMBER>
Have a room and looking for a roommate? Don’t have a room and looking for someone who does? Add your info to the room share list.
The Minneapolis METRO system is an easy way to get around the city. You can ride light rail from the airport to the conference hotel for $1.75: Follow the light rail signs to the airport tram, which will take you right to the light rail station. Take the northbound (“Downtown Minneapolis”) Blue Line to the Downtown East station, then cross to the other track to transfer to the eastbound Green Line. Take the Green Line two stops to East Bank. You’ll be looking right at The Commons Hotel. The McNamara Alumni Center conference venue is another block past the hotel, on SE Oak Street.
Transit in Minneapolis is great, but if you’ll be driving, there’s a lot right near the McNamara Alumni Center. We’ve reserved 30 spots per day for SRCCON attendees. Parking is $8/day. If you get to the ramp and it says “FULL” or “RESERVATION ONLY,” just push “call for help” and tell them you’re with SRCCON, and they’ll let you in.


Planet Sick-Boy: http://www.sick-boy.com "We Put the SIN in Cinema"
© Copyright 2001 Planet Sick-Boy. All Rights Reserved.
You don't see too many gorgeous, heartwarming romances with a G-rating slapped on them, but that's exactly what Zhang Yimou's The Road Home is. No spotted puppies or animated Rugrats here; just the story of love blossoming in the hearts of two young people in a bleak mountain village in North China.
Home is narrated by Luo Yusheng (Sun Honglei), who, as the film opens, has just been called back to his hometown of Sanhetun because of the sudden death of his father, who was the town's schoolteacher. Yusheng explains that he, like most of the village's other young men, have left the rustic hamlet for work in larger cities, and this is the first time he has returned in years.
When he arrives in Sanhetun, Yusheng is approached by the town's Mayor (Chang Guifa), who delicately explains the unusual situation. Yusheng's father, Changyu, is still in the hospital, which is located miles and miles away through the mountains. His mother, Zhao Di (Yuelin Zhao), who has been grieving in front of the schoolhouse since her husband's passing, insists the town follow an ancient funeral custom in which the casket is carried, by hand, into town by the village's men (presumably so he never forgets his way home).
Yusheng unsuccessfully tries to get his mother to change her mind, and as she begins to weave a traditional funeral cloth on her rickety loom, he thinks about the stories he's heard throughout his life about the atypical courtship at a time when arranged marriages were the standard. Home then flashes back to 1958 for the following hour, where we see the young, beautiful Zhao Di (Zhang Ziyi, Crouching Tiger, Hidden Dragon) pursue Sanhetun's handsome new schoolteacher, who, of course, is Changyu (Zheng Hao). The shift from present to past is done nicely, as the latter shows Di at the loom preparing a special red banner than will adorn the village's new school.
As Changyu and the locals build the school, Di carefully prepares delicious dishes to take to the work site, with hopes her dreamboat will be the one to devour her eats. She even begins to use a long abandoned well just because it brings her closer to the school and Changyu. Initially, most of Di's actions go unnoticed, but he eventually comes around when it's her turn to host Changyu for dinner. There's the potential for some tragedy and heartbreak, but we already know they end up together, so the concern is fairly minimal.
Yimou breaks the formula typically found in flashback films by having the 15-minute, present-day bookends shot in black and white, while the "meat" of the film is in color. This works nicely in the transition toward the end of the film that shows the bright red banner in the brand-new schoolhouse becoming a dingy gray banner in the decrepit schoolhouse. It's also interesting to see the complete lack of change between 1958 and present-day Sanhetun (the only noticeable upgrades are the two Titanic posters on the wall of Yusheng's old house).
Home won an Audience Award at this year's Sundance Film Festival, as well as a Silver Bear at the recent Berlin fest. Yimou keeps things moving at a nice pace (the film barely cracks the 90-minute mark), even though there are decently sized stretches with no dialogue. The film is beautifully photographed (by Yimou's Not One Less' Hou Yong) and gorgeously lit, which you'd expect from a director who used to be a cinematographer. Like Less, Yimou uses a cast made up largely of non-actors (Ziyi actually filmed this before Crouching Tiger). The only major complaint is Home's constantly swelling score, which becomes increasingly annoying throughout the picture.
1:31 - G
========== X-RAMR-ID: 28826 X-Language: en X-RT-ReviewID: 235963 X-RT-TitleID: 1108624 X-RT-SourceID: 595 X-RT-AuthorID: 1146
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Here is a list of all namespace members with links to the namespace documentation for each member:
- a -
- a : Test::Int::Arithmetic
- a_d : Gecode::Search::Config
- abs() : Gecode
- abs_bnd_max : Test::Int::MiniModelArithmetic
- abs_bnd_med : Test::Int::MiniModelArithmetic
- abs_bnd_min : Test::Int::MiniModelArithmetic
- abs_dom_max : Test::Int::MiniModelArithmetic
- abs_dom_med : Test::Int::MiniModelArithmetic
- abs_dom_min : Test::Int::MiniModelArithmetic
- abs_xx_b_a : Test::Int::Arithmetic
- abs_xx_b_b : Test::Int::Arithmetic
- abs_xx_b_c : Test::Int::Arithmetic
- abs_xx_d_a : Test::Int::Arithmetic
- abs_xx_d_b : Test::Int::Arithmetic
- abs_xx_d_c : Test::Int::Arithmetic
- abs_xy_b_a : Test::Int::Arithmetic
- abs_xy_b_b : Test::Int::Arithmetic
- abs_xy_b_c : Test::Int::Arithmetic
- abs_xy_d_a : Test::Int::Arithmetic
- abs_xy_d_b : Test::Int::Arithmetic
- abs_xy_d_c : Test::Int::Arithmetic
- ActorProperty : Gecode
- afc : Test
- align() : Gecode::MemoryConfig
- am() : Gecode::Driver
- ann2asnivalsel() : Gecode::FlatZinc
- ann2ivalsel() : Gecode::FlatZinc
- ann2ivarsel() : Gecode::FlatZinc
- ann2svalsel() : Gecode::FlatZinc
- ann2svarsel() : Gecode::FlatZinc
- any() : Gecode::Int::Arithmetic
- AP_DISPOSE : Gecode
- AP_WEAKLY : Gecode
- array_assigned() : Gecode::Int::Sorted
- assign() : Gecode
- assigned() : Gecode::Int::Precede
- atleast() : Gecode
- atmost() : Gecode
- atmostOne() : Gecode


|
{{node.number}}
{{node.letter | uppercase}}
|
Click on the "Ænigma #23" tab to continue on to the larger puzzle and good luck!


ruitzy
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Man stabbed ‘multiple times’ during fight at Gold Coast pub
by Shanee DobesonA MAN has been rushed to hospital after allegedly being stabbed multiple times at a pub on the Gold Coast overnight.
It’s understood the 21-year-old victim and a 30-year-old man, who were not known to each other, got into an argument at the licensed premises on the corner of Tooloona Street and Golden Four Drive around 6.30pm.
The older man is then accused of pulling out a knife and stabbing the other man multiple times in the chest and abdomen.
The younger man was treated at the scene before being taken to Gold Coast University Hospital, where he remains in a serious condition.
Police say the 30-year-old man later surrendered himself to officers and was transported to Gold Coast University Hospital for treatment to his finger.
He has since been charged with one count of grievous bodily harm and will appear in Southport Magistrates Court on December 3.


I am a unicorn. An experienced Graphic Designer, recent Full-Stack Web-Developer, and nimble Marketing Specialist. I'm here to make beautiful & functional projects for individuals, small businesses, or evil mega corporations.
Seasoned in the art of production schedules, design versions, and marketing campaigns, I’ll shape a great idea into something marvelous to share with the world - from to to start to finish.
When I'm not being a baller I'm rolling around on the ground with my cats, cooking Bon Appétit recipes, or frolicking in a forest.


You can find almost anything in this hub including:
A sosial networking like Facebook but only for Kelantanese.
Find interesting and happening places in Kelantan.
Find services that you want at ease and fast.
Learn about Kelantan and Kelantanese cultural.
Shop anything you want by kelantanese seller.


Before Change
weight of component distributions (default = 1)
def __init__(self, means=0, sigmas=1, weights=1):
data = np.array([t for t in np.broadcast(means, sigmas, weights)])
precisions = [1/s**2 for s in sigmas]
self._gmm = GaussianMixture(data.shape[0],
weights_init=data[:, 2] / data[:, 2].sum(),
means_init=data[:, :1],
covariance_type="spherical",
After Change
weight of component distributions (default = 1)
def __init__(self, means=0, sigmas=1, weights=1):
components = len(means)
self._gmm = GaussianMixture(components, covariance_type="spherical")
self._gmm.weights_ = weights / weights.sum()
self._gmm.means_ = means.reshape(-1, 1)
self._gmm.covariances_ = sigmas ** 2
self._gmm.precisions_cholesky_ = 1 / np.sqrt(self._gmm.covariances_)
self._gmm.fit = None // disable fit method for safety
def sample(self, size):


3 columns on large wide, 2 columns on tablet wide and 1 column on handset wide.
1
2
3
4
5
6
[[item]]


PieNoon is a simple multiplayer party game where you can throw pies at your friends.
Terms
Privacy
Open Source Repository


The moles have taken over Grandpa John's beautiful vegetable garden!
Help Grandpa John to get rid of the moles by stepping on them. You have 30 seconds to catch as many moles as possible by stepping on a tile when it lights up. Be careful not to step on tiles that do not light up. Grandpa John doesn't need you stomping on his vegetables.
PS! Catching moles is easier with multiple people ;)


Don’t install gazebo from the website, it will wreck the whole setup. With the above desktop-full version it automatically install gazebo7.
import the gazibo models to gazebo workspace by cloning the gazebo model database from https://github.com/osrf/gazebo_models and later copy all the models to /home/madhu16/.gazebo/models
.
Copy the Marker model from given zip file to /home/madhu16/.gazebo/models
.
Create a dummy workspace :-
madhu@madhu:~/$ mkdir -p turtlebot_ws/src
madhu@madhu:~/$ cd turtlebot_ws
madhu@madhu:~/turtlebot_ws$ catkin_make
madhu@madhu:~/turtlebot_ws$ cd src
Clone turtlebot3 repos and checkout the kinetic branch.
madhu@madhu:~/turtlebot_ws/src$ git clone -b kinetic-devel https://github.com/ROBOTIS-GIT/turtlebot3_msgs
madhu@madhu:~/turtlebot_ws/src$ git clone -b kinetic-devel https://github.com/ms-iot/turtlebot3_simulations
madhu@madhu:~/turtlebot_ws/src$ git clone -b kinetic-devel https://github.com/ms-iot/turtlebot3.git
Clone ar_track_alvar repo for ar tag detection and checkout the kinetic branch.
madhu@madhu:~/turtlebot_ws/src$ cd ../../
madhu@madhu:~/$ git clone -b kinetic-devel https://github.com/ros-perception/ar_track_alvar.git
madhu@madhu:~/$ mv ar_track_alvar/ar_track_alvar ar_track_alvar/ar_track_alvar_msgs turtlebot_ws/src/
Build the downloaded packages using catkin build (or) catkin_make.
madhu@madhu:~/turtlebot_ws$ catkin_make
Verify the installation by launching turtlebot3 node.
madhu@madhu:~/turtlebot_ws$ roslaunch turtlebot3_gazebo turtlebot3_empty_world.launch
Check the above section for the list of files to be modified or added.
Modifications in ar_track_alvar package for increasing fps of published marker topic :-
Open IndividualMarkersNoKinect.cpp file from ar_track_alvar/nodes/ folder and edit the line no 88, 196 to change 1.0 to 0.03 . Go to line no 277 and change max_frequency parameter to 30.0 . (or you can simply replace the whole file with the given modified file)
Change the relevent things in turtlebot3 package.
Give access to camera and modify the resolution by replacing the turtlebot3_burger.gazebo.xacro and turtlebot3_burger.urdf.xacro files with the given modified files.
Add the given gazebo world file named turtlebot3_ar.world in turtlebot3_simulations/turtlebot3_gazebo/worlds/. This is a custom world file that I made specifically for good lighting condition.
Add the given launch file named turtlebot3_ar.launch in turtlebot3_simulations/turtlebot3_gazebo/launch/. This launch file uses the above world file.
Build the workspace finally,
madhu@madhu:~/turtlebot_ws$ catkin_make
madhu@madhu:~/turtlebot_ws/src$
Launch turtlebot on gazebo. If you are doing it first time a window will show up and do nothing. wait for some time like 5 mins and restart gazebo.
madhu@madhu:~/turtlebot_ws$ export TURTLEBOT3_MODEL=burger
madhu@madhu:~/turtlebot_ws$ source devel/setup.bash
madhu@madhu:~/turtlebot_ws$ roslaunch turtlebot3_gazebo turtlebot3_ar.launch
In another terminal launch teleop functionality,
madhu@madhu:~/turtlebot_ws$ roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
In another terminal open rqt_image_view and use the teleop functionality to make sure AR Tag is in the field of view of robot. Clicking a
can turn the robot arround and get the AT tag in robots FOV.
madhu@madhu:~/turtlebot_ws$ rqt_image_view
In another terminal, launch the ar_tag_alvar node to detect AR Tags
madhu@madhu:~/turtlebot_ws$ rosed ar_track_alvar pr2_indiv_no_kinect.launch
madhu@madhu:~/turtlebot_ws$ roslaunch ar_track_alvar pr2_indiv_no_kinect.launch
In another terminal open rviz and enable TFs and Marker to visualize
madhu@madhu:~/turtlebot_ws$ rviz
Generate custom AR Tag images from this instructions from here or http://wiki.ros.org/ar_track_alvar
madhu@madhu:~/turtlebot_ws$ rosrun ar_track_alvar createMarker
Generate AR Tag gazebo models from AR Tag images by following this link https://github.com/mikaelarguedas/gazebo_models


As you navigate from one example to another, the buttons below and the ones on the left hand edge of the screen will update to share the title and url of the current page... untill you click the button in example X & Y...


%%{
machine common; action comment_begin { fcall comment_tail; } action comment_exit { fret; } # RFC5322: address_lists, date_time, message_ids, phrase_lists, received obs_NO_WS_CTL = 0x01..0x08 | 0x0b | 0x0c | 0x0e..0x1f | 0x7f; LF = "\n"; CR = "\r"; CRLF = "\r\n"; WSP = 0x09 | 0x20; obs_ctext = obs_NO_WS_CTL; VCHAR = 0x21..0x7e; obs_qp = "\\" (0x00 | obs_NO_WS_CTL | LF | CR); obs_FWS = (CRLF? WSP)+; ctext = 0x21..0x27 | 0x2a..0x5b | 0x5d..0x7e | obs_ctext; quoted_pair = ("\\" (VCHAR | WSP)) | obs_qp; FWS = (WSP* CRLF WSP+) | (CRLF WSP+) | obs_FWS; ALPHA = [a-zA-Z]; DIGIT = [0-9]; DQUOTE = '"'; obs_qtext = obs_NO_WS_CTL; atext = ALPHA | DIGIT | "!" | "#" | "$" | "%" | "&" | "'" | "*" | "+" | "-" | "/" | "=" | "?" | "^" | "_" | "`" | "{" | "|" | "}" | "~"; qtext = 0x21 | 0x23..0x5b | 0x5d..0x7e | obs_qtext; obs_dtext = obs_NO_WS_CTL | quoted_pair; qcontent = qtext | quoted_pair; # Handle recursive comments ccontent = ctext | quoted_pair | "(" @comment_begin; comment_tail := ((FWS? ccontent)* >comment_s) FWS? ")" @comment_exit; comment = "(" @comment_begin %comment_e; CFWS = ((FWS? comment)+ FWS?) | FWS; quoted_string = CFWS? (DQUOTE (((FWS? qcontent)* FWS?) >qstr_s %qstr_e) DQUOTE) CFWS?; atom = CFWS? atext+ CFWS?; word = atom | quoted_string; # phrase_lists obs_phrase = (word | "." | "@")+; phrase = (obs_phrase | word+) >phrase_s %phrase_e; phrase_lists = phrase ("," FWS* phrase)*; # address_lists # local_part: domain_text = (DQUOTE (FWS? qcontent)+ FWS? DQUOTE) | atext+; local_dot_atom_text = ("."* domain_text "."*)+; local_dot_atom = CFWS? (local_dot_atom_text >local_dot_atom_s %local_dot_atom_pre_comment_e) CFWS?; obs_local_part = word ("." word)*; local_part = (local_dot_atom >local_dot_atom_s %local_dot_atom_e | (quoted_string %local_quoted_string_e) | obs_local_part); # Treetop parser behavior was to ignore addresses missing '@' inside of angle # brackets. This construction preserves that behavior. local_part_no_capture = (local_dot_atom | quoted_string | obs_local_part); # domain: dot_atom_text = "."* domain_text ("."* domain_text)*; dtext = 0x21..0x5a | 0x5e..0x7e | obs_dtext; dot_atom = CFWS? dot_atom_text (CFWS? >(comment_after_address,1)); domain_literal = CFWS? "[" (FWS? dtext)* FWS? "]" CFWS?; obs_domain = atom ("." atom)*; domain = (dot_atom | domain_literal | obs_domain) >domain_s %domain_e; # addr_spec: # The %(end_addr,N) priority resolves uncertainty when whitespace # after an addr_spec could cause it to be interpreted as a # display name<EMAIL_ADDRESS>,..." addr_spec_in_angle_brackets = (local_part "@" domain) %(end_addr,1) | local_part_no_capture %(end_addr,0); addr_spec_no_angle_brackets = (local_part "@" domain) %(end_addr,1) | local_part %(end_addr,0); # angle_addr: obs_domain_list = (CFWS | ",")* "@" domain ("," CFWS? ("@" domain)?)*; obs_route = (obs_domain_list ":") >obs_domain_list_s %obs_domain_list_e; obs_angle_addr = CFWS? "<" obs_route? addr_spec_in_angle_brackets ">" CFWS?; angle_addr = CFWS? ("<" >angle_addr_s) addr_spec_in_angle_brackets ">" CFWS? | obs_angle_addr; # Address display_name = phrase; name_addr = display_name? %(end_addr,2) angle_addr; mailbox = (name_addr | addr_spec_no_angle_brackets) >address_s %address_e; obs_mbox_list = (CFWS? ",")* mailbox ("," (mailbox | CFWS)?)*; mailbox_list = (mailbox (("," | ";") mailbox)*) | obs_mbox_list; obs_group_list = (CFWS? ",")+ CFWS?; group_list = mailbox_list | CFWS | obs_group_list; group = (display_name >group_name_s %group_name_e) ":" (group_list?) ";" CFWS?; address = group | mailbox; #obs_addr_list = (CFWS? ",")* address ("," (address | CFWS)?)*; address_lists = address? %(comment_after_address,0) (FWS* ("," | ";") FWS* address?)*; # message_ids obs_id_left = local_part; id_left = dot_atom_text | obs_id_left; # id_right modifications to support multiple '@' in msg_id. msg_id_atext = ALPHA | DIGIT | "!" | "#" | "$" | "%" | "&" | "'" | "*" | "+" | "-" | "/" | "=" | "?" | "^" | "_" | "`" | "{" | "|" | "}" | "~" | "@"; msg_id_dot_atom_text = (msg_id_atext+ "."?)+; obs_id_right = domain; no_fold_literal = "[" (dtext)* "]"; id_right = msg_id_dot_atom_text | no_fold_literal | obs_id_right; msg_id = (CFWS)? (("<" id_left "@" id_right ">") >msg_id_s %msg_id_e) (CFWS)?; message_ids = msg_id (CFWS? msg_id)*; include date_time "date_time.rl"; date_time = (day_of_week ",")? (date >date_s %date_e) <: (time >time_s %time_e) CFWS?; # Added CFWS? to increase robustness # (qmail likes to include a comment style string...?) received_token = word | angle_addr | addr_spec_no_angle_brackets | domain; received = ((CFWS? received_token*) >received_tokens_s %received_tokens_e) ";" date_time; # RFC2045: mime_version, content_type, content_transfer_encoding mime_version = CFWS? (DIGIT+ >major_digits_s %major_digits_e) comment? "." comment? (DIGIT+ >minor_digits_s %minor_digits_e) CFWS?; token = 0x21..0x27 | 0x2a..0x2b | 0x2c..0x2e | 0x30..0x39 | 0x41..0x5a | 0x5e..0x7e; value = (quoted_string | (token -- '"' | 0x3d)+) >param_val_s %param_val_e; attribute = (token+) >param_attr_s %param_attr_e; parameter = CFWS? attribute "=" value CFWS?; ietf_token = token+; custom_x_token = 'x'i "-" token+; extension_token = ietf_token | custom_x_token; discrete_type = 'text'i | 'image'i | 'audio'i | 'video'i | 'application'i | extension_token; composite_type = 'message'i | 'multipart'i | extension_token; iana_token = token+; main_type = (discrete_type | composite_type) >main_type_s %main_type_e; sub_type = (extension_token | iana_token) >sub_type_s %sub_type_e; content_type = main_type "/" sub_type (((CFWS? ";"+) | CFWS) parameter CFWS?)*; encoding = ('7bits' | '8bits' | '7bit' | '8bit' | 'binary' | 'quoted-printable' | 'base64' | ietf_token | custom_x_token) >encoding_s %encoding_e; content_transfer_encoding = CFWS? encoding CFWS? ";"? CFWS?; # RFC2183: content_disposition # TODO: recognize filename, size, creation date, etc. disposition_type = 'inline'i | 'attachment'i | extension_token | ''; content_disposition = (disposition_type >disp_type_s %disp_type_e) (CFWS? ";" parameter CFWS?)*; # Envelope From ctime_date = day_name " "+ month " "+ day " " time_of_day " " year; envelope_from = (addr_spec_no_angle_brackets) >address_s %address_e " " (ctime_date >ctime_date_s %ctime_date_e); # content_location location = quoted_string | ((token | 0x3d)+ >token_string_s %token_string_e); content_location = CFWS? location CFWS?;
}%%


|
LiteSQL
0.3.17
|
a class that helps creating SELECT-SQL statements. More...
#include <selectquery.hpp>
Public Member Functions | |
| SelectQuery & | distinct (bool d) |
| SelectQuery & | limit (int value) |
| SelectQuery & | offset (int value) |
| SelectQuery & | result (std::string r) |
| SelectQuery & | clearResults () |
| SelectQuery & | source (std::string s, std::string alias="") |
| SelectQuery & | where (const Expr &w) |
| SelectQuery & | where (std::string w) |
| SelectQuery & | groupBy (std::string gb) |
| SelectQuery & | having (const Expr &h) |
| SelectQuery & | having (std::string h) |
| SelectQuery & | orderBy (std::string ob, bool ascending=true) |
| operator std::string () const | |
| std::string | asString () const |
a class that helps creating SELECT-SQL statements.
methods are self-explanatory.


Introduction
Few years ago, I wanted to collect data from Pharo users (see Tests Usage Analyser).
I was like, Hmm, how can I do that? Do we already have such tool? .
And YES!
We can use GTEventCollector
.
You probably have used it already through the privacy settings.
In this post, I’ll explain how to set up your own data collector and how to use it. Then, we’ll see how to extract the data.
Add a new Event Collector
Preparation
The first step to collect data is to create a new instance of GTEventCollector
.
It is this instance that will be used to send new data.
We can also use the subclass GTEventCollector
.
Let’s create a new instance:
eventCollector := GTEventCollector new.
Creating a collector is not enough. It needs to be configured to be completely operational. In particular, we need to register it in the GTRecorder. This object has the responsibility to send the data to the server that will gather the data.
eventCollector := GTEventCollector new
register.
Once the event collector is created, we can customize it for our records.
We can change multiple properties: #url:
, #category:
, #occupent:
.
-
#url:
is the server website. The default one is: http://gc.dcc.uchile.cl.:8080/gt/events. We may want to change it for security or privacy purpose. -
#category:
is used to group your data in a specific directory in the server. It is useful to analyse only the data of your application. In my case, I used the category#testsUsageAnalyserEvents
. -
#occupent:
is an instance of any object. When it is removed by the GC, the recorder unregisters the collector. So, using an occupent is a good idea to keep only one instance of the recorder instead of creating a new one each time. In my case, I use an instance of Announcer, so the collector exists until the ‘Announce’ instance is removed from the memory.
At the end, we have the following code to create a collector:
eventCollector := GTEventCollector new
occupant: TUAAnnouncer uniqueInstance;
category: #testsUsageAnalyserEvents;
register;
yourself
Collect data
Once the event collector is created, we can use it to record data and send them to a server. First, we need to learn how to create a data format. It is simple since, by default, the recorder uses Ston as a format to send the data. So we can use nearly everything.
To collect new data, we can use both #add: anEvent
and #addIfAvailable: anEvent
.
However, we should use the #addIfAvailable:
because it will check users have accepted to send their data.
Unless you don’t care about privacy ?
So, let’s add a simple data:
eventCollector addIfAvailable: 42
In my previous work, I wanted to record more complex data. A good way to do it is to use dictionary. In the following, I present an example using dictionary created from an Announcement.
First, I created a method in TUAAnnouncement (it is a subclass of Announcement for my project) that adds my data to a dictionary.
TUAAnnouncement>>#dataForTestUsageAnalyser
^ super dataForTestUsageAnalyser
at: #class put: self class name;
at: #entity put: entity; "this is the content of my announcement"
at: #dataVersion put: 4 "this is the version of my data, so I can select the data from a specific version";
yourself
The method is also understood by Object itself (to avoid bugs).
Object>>#dataForTestUsageAnalyser
^ Dictionary new
Finally, when I want to record an announcement, I use a method #recordAnnouncement:
that takes an announcement and records some entries in the dictionary and adds them to the collector.
recordAnnouncement: anAnnouncement
(anAnnouncement respondsTo: #dataForTestUsageAnalyser) ifTrue: [
[eventCollector addIfAvailable:
(anAnnouncement dataForTestUsageAnalyser "get a dictionary from the announcement"
at: #type put: anAnnouncement type; "some meta data I used"
at: #timestamps put: DateAndTime now; "Useful to determine the creation date of an entry"
yourself) ]
on: Error
do: [ :error | "blabla" ] ] ]
Extract the data
Once the data has been collected, we need to download them from the server.
By default, all the data can be downloaded at http://gc.dcc.uchile.cl/.
If everything goes well, we can now see your category in gt/events/<myCategory>
.
In my case, we have data in gt/events/testsUsageAnalyserEvents
The data are sort by month.
To analyse the data, the first step is to open a Pharo image.
Then, we can use GTEventTool
to unpack our data in the Pharo memory and analyse it.
The tool comes with methods to download and extract the data.
GTEventTool default ensureLocalDirectory "download the data and extract them (from the archive)".
When the data is downloaded, we can load it in Pharo.
GTEventTool default unpackAll. "unpack all the data"
GTEventTool default unpackAll: './gt/events/testsUsageAnalyserEvents' asFileReference. "unpack the data in testsUsageAnalyserEvents"
However, we already have a lot of data (~1Go). If you want to go faster, I’ve created some helpers.
The method unpackDirectory:
will do the first step of unpacking and can be executed on only one directory.
unpackDirectory: aStringPath
^ GTEventUnpacking default unpackDirectory: aStringPath asFileReference
The method unpackedCollectionOfGTEvent:
will take a collection of GTEvent, which is the return of unpackDirectory:
and convert each entry into the real data.
unpackedCollectionOfGTEvent: aCollectionOfGTEvent
^ aCollectionOfGTEvent flatCollect: #safeUnpackedData.
We can now analyze the data.


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// VertexPosition is a special function; whatever it returns is what glPosition ends up being. // In this toy example, you can just comment this out -- a reasonable one is hard coded in function VertexPosition() { // All varyings are specified via 'varyings.
'. Attributes with 'attributes.
' // // Currently, you can't use attributes in FragmentColor; you must create a varying for it. // in the future, it might auto-transfer used attributes in the fragment shader into // varyings; it remains to be seen if this is a good idea. varyings.pt = attributes.pt; // uniforms.
var t = uniforms.t; // You can return whatever size array/vector; it automatically completes the vector for // you with all 0's, except for 1 as the last element. return [Math.cos(t) * attributes.pt[0] + Math.sin(t) * attributes.pt[1], Math.sin(t) * attributes.pt[0] - Math.cos(t) * attributes.pt[1] ]; }; /** FragmentColor is a special function; it tries to resolve to a vector and that is what is used to set the fragment color */ function FragmentColor() { var x = varyings.pt[0]; var y = varyings.pt[1]; var t = (x * y > 0) ? uniforms.t : uniforms.t * 2; // A handful of Math.x functions are mapped to the GLSL equivalents var dist = Math.sqrt(x*x + y*y); var threshold = Math.abs(Math.cos(t)); var rim = .01; if(dist > (threshold-rim) && dist < (threshold+rim)) { // Returning nothing in a FragmentColor function // is equivalent to performing a GLSL 'discard' -- it // simply does nothing with the fragment return; } if(dist > threshold){ return [fmod(x, 1.0), fmod(y, 1.0), 0.5*(Math.cos(t)+1.0)]; } // FragmentColor, like VertexPosition, will fill out the vec4 for you if you // dont. You do have to be consistent though -- Since we returned a vector of 3 // above, we have to here. Otherwise it is a type error. For example: // return [1, 1, 1, 1]; // wont work. return [0.5*(x+1.0), 0.5*(y+1.0) , 0.5*(Math.cos(t)+1.0)]; }; /** Functions you define automatically get pulled in if they are used */ function fmod(a,b) { return a - (Math.floor(a / b) * b); };


Introducing The 2021 Ford Mustang Mach-E: An Icon Goes Electric
Like it or not, the Mustang just got electrified.
by Jared RosenholtzWell, that didn't go to plan. Ford's big LA reveal lost a bit of its shine when the Mustang Mach-E leaked along with all of the pertinent information about the car. But let's not dwell on a mistake because now the 2021 Ford Mustang Mach-E has made its official reveal and the big topic on everyone's minds is Ford's decision to apply the Mustang name to an all-electric crossover. Only time will tell whether or not this move will resonate with potential buyers and loyal Mustang enthusiasts but based on the specifications for the car, all signs point to the Mach-E being a success.
"At the first-ever Detroit auto show, Henry Ford said he was working on something that would strike like forked lightning," Bill Ford, executive chairman of the Ford Motor Company said in a press release. "That was the Model T. Today, the Ford Motor Company is proud to unveil a car that strikes like forked lightning all over again. The all-new, all-electric Mustang Mach-E. It's fast. It's fun. It's freedom. For a new generation of Mustang owners." Ford clearly feels confident in its new product or else it would have called it a Mustang.
Let's start with performance, which is a hallmark of any successful Mustang model. Ford will offer rear-wheel-drive and all-wheel-drive variants, both of which will come in standard- and long-range battery configurations. With AWD, the standard Mach-E is targeting 332 horsepower and 417 lb-ft of torque with a 0-60 mph time in the mid-five-second range, putting it on the same performance level as a Mustang EcoBoost. The GT model will be much quicker, with a 0-60 mph time of under four seconds while a GT Performance edition should match the Shelby GT500's 0-60 mph time of around 3.5 seconds.
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Ford says both GT models will produce around 459 hp and 612 lb-ft of torque, so there should be no complaints from Mustang die-hards about the Mach-E's performance. Even more impressive, Ford is targetting a range of around 270 miles for the extended-range AWD model and around 300 miles for extended-range RWD models. The smaller battery models should go 230 miles with RWD (210 with AWD) and the GT model will go 235 miles. Charging should also be a cinch with the optional Ford Connected Charging station, which can add up to 32 miles of range per hour on a household 240-volt outlet. The Ford mobile charger included with the vehicle can add around 22 miles for every hour of charge.
Ford has partnered with Amazon to handle the installation of the Connected Charging Station. But if you need to charge on the go, Ford has also partnered with Electrify America to give Mach-E owners access to the largest public charging network in the country with more than 12,500 stations. On a DC charger with a peak charging rate of 150 kW, an extended-range RWD Mach-E can add 47 miles of range in approximately 10 minutes and charge from 10 percent to 80 percent in around 38 minutes. Owners can also find charging stations using the FordPass app or on the vehicle's navigation system.
The Mach-E will be among the first vehicles to take advantage of Ford's new Sync4 infotainment system. We only had a short time to play on the 15.5-inch touchscreen but it seems intuitive and will adapt to owners as it learns their behaviors (such as suggesting a route to the gym after work). This new Sync system also features over-the-air updates and comes with wireless Apple CarPlay and Android Auto.
We had an early opportunity to ride in the Mach-E and found the overall interior quality and layout to be impressive. This is by far the most premium-feeling Mustang cabin Ford has ever built with soft, vegan simulated leather, an open center console, a floating armrest with room below it for a bag or purse, and available Bang & Olufsen speakers wrapped in a premium-feeling cloth material. A giant glass roof adds to the sense of openness in the cabin and although it doesn't have a cover, Ford says the glass has infrared protection from UV rays.
Unlike any Mustang before it, the Mach-E will be comfortable enough to fit four or five adults with their luggage. The rear trunk offers 29 cubic feet of space, which opens up to 59.6 cubic feet of space with the rear seats folded. In the front, there is also a small 4.8 cubic foot 'frunk' (yes, that's what Ford calls it) where you can fit a small carry-on bag.
Five trim levels will be available at launch: Select, California Route 1, Premium, First Edition, and GT (plus the GT Performance Edition). Leaked pricing details suggest that the Select trim starts at $43,895 while the Premium costs $50,600. AWD and the extended-range battery will add to these prices. The California Route 1 trim is RWD-only at $52,400 and the First Edition is AWD-only at $59,900 and includes red-painted brake calipers, metallic pedal covers, contrasting seat stitching, and a 'First Edition' scuff plate.
The GT model is the most expensive at $60,500 but will be the only trim available with Brembo's new performance Flexira aluminum calipers. The GT Performance Edition will also be the only model to include Ford's MagneRide adaptive suspension technology. All Mach-E trims include three drive modes - Whisper, Engage, and Unbridled - which change the characteristics of the car such as the throttle calibration, steering, gauge cluster, and interior lighting.
Ford is now taking pre-orders for the Mach-E with a $500 reservation deposit. Deliveries are expected to begin in late 2020 with the GT arriving in the spring of 2021. If you want one, you are going to have to place an order because unlike any other Ford product, the Mach-E will have order-based deliveries at dealerships instead of a traditional inventory structure.
This is the first time Ford has given customers the option to purchase a car using e-commerce. In our conversations with Ford, it sounds like this process should keep the vehicle in high demand while also limiting the potential for silly markups. As, if the dealership closest to you wants to charge over sticker price for the car, you should be able to change delivery to another location.


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LFE Documentation LFE Shell (REPL) MODULE lfe_shell MODULE SUMMARY Lisp Flavoured Erlang (LFE) shell DESCRIPTION There is now a simple LFE shell in which you can enter sexprs which are evaluated and the value printed. There are no user shell variables (scoping and lack of setq) Built-in shell functions: These are defined as normal functions and macros and can be called from anywhere in the shell. They can even be redefined. They can also be explicitly called (: lfe_shell ...). (c File [Options]) Compile and load an LFE file. Assumes default extension .lfe. (ec File [Options]) Compile and load an Erlang file. (i) Print information about the currently running processes in the system. (l Module ...) Load modules. (m Module ...) Print out module information, if no modules are given then print information about all modules. (pid x y z) Create a pid from x, y, z. (p Expr) (pp Expr) Print/prettyprint a value to full depth. (regs) Print information about the currently running processes in the system. (: c Command Arg ...) All the commands in the standard Erlang shell can be reached in this way. Built-in shell commands These are special forms which are only recognised at the top-level in shell input. The cannot be redefined. (reset-environment) Resets the environment to its initial state. (set Pattern Expr) (set Pattern (when Guard) Expr) Evaluate Expr and match the result with Pattern binding variables in it. These variables can then be used in the shell and also rebound in another set. (slurp File) Slurp in a source LFE file and makes all functions and macros defined in the file available in the shell. Only one file can be slurped at a time and slurping a new file basically does an unslurp first. (unslurp) Revert back to the state before the last slurp removing all function and macro definitions both in the slurped file and defined in the shell since then. (run File) Execute all the shell commands in File. All defined variables, functions and macros will be saved in the environment if there are no errors. Shell functions and macros Functions and macros can be defined in the shell. These will only be local to the shell and cannot be called from modules. The forms are the standard forms for defining functions and macros. (defun Fun ...) Define a function in the shell. (defmacro Macro ...) Define a macro in the shell. Builtin shell variables: +/++/+++ The three previous expressions input. */**/*** The values of the previous 3 expressions. - The current expression input. Shell Environment The shell maintains an environment of local function and macro definitions, and variable bindings. The environment can be accessed using the built-in shell variable $ENV. This can be useful when calling functions like macroexpand and macro-function which unless an explicit environment is given will only search the default environment. Starting the LFE shell After installing the best way is probably to start Erlang directly running the LFE shell with: lfe [flags] From a normal Erlang shell the best way to start the shell is by calling: 17> lfe_shell:server(). Giving the user switch commands: --> s lfe_shell --> c will create a job running the LFE shell and connect to it. This also works when starting a remote shell. Running LFE shell scripts The LFE shell can also be directly called to run LFE shell scripts with: lfe [flags] file [args] This will start the shell, run a script with LFE shell commands and then terminate the shell. The following built-in variables are also bound: script-name The name of the script file as a string. script-args A list of the arguments to the script as strings. If no arguments have been given then this will be an empty list.


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Visualization components for use in Human Cell Atlas portals, such as with Chell.
Check out the API Docs or some live examples!
Please see the Getting Started page for an introduction to the chell-viz development environment!
git clone https://github.com/cBioCenter/chell-viz
cd chell-viz
yarn
yarn start
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Dr. Jeff Jaffe is Chief Executive Officer of the World Wide Web Consortium. In this role he works with Director Tim Berners-Lee, staff, and membership, and the public to evolve and communicate the W3C's vision. He is responsible for all of W3C's global operations, for maintaining the interests of all of W3C's stakeholders, and for sustaining a culture of cooperation and transparency, so that W3C continues to be the leading forum for the technical development and stewardship of the Web.
Jeff Jaffe named one of 10 most influential people in IT of 2011 by Tom's Hardware (PDF).
After receiving a Ph.D. in computer science from MIT in 1979, Jeff joined IBM's Thomas J. Watson Research Center. During his tenure at IBM, he held a wide variety of technical and management positions, including vice president, Systems and Software Research, corporate vice president of technology, and general manager of IBM's SecureWay business unit, where he was responsible for IBM's security, directory, and networking software business.
Jeff then served as president of Bell Labs Research and Advanced Technologies, where he established new facilities in Ireland and India, and served as chairman of the board of the New Jersey Nanotechnology Consortium.
Most recently, Jeff served as the Executive Vice President and Chief Technology Officer for Novell. He was responsible for Novell's technology direction, as well as leading Novell's product business units.
Jeff was appointed by President Bill Clinton to serve on the Advisory Committee for the Presidential Commission for Critical Infrastructure Protection. He has also chaired the Chief Technology Officer group of the Computer Systems Policy Project, and has served on the National Research Council's Computer Science and Telecommunications Board. He is a Fellow of ACM and the IEEE.
Dr. Jaffe holds a BS in Mathematics and an MS in Electrical Engineering and Computer Science in addition to his Doctorate from the Massachusetts Institute of Technology.
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| trellisplots {coda} | R Documentation |
These methods use the Trellis framework as implemented in the
lattice
package to produce space-conserving diagnostic plots
from "mcmc"
and "mcmc.list"
objects. The xyplot
methods produce trace plots. The densityplot
methods and
qqmath
methods produce empirical density and probability
plots. The levelplot
method depicts the correlation of the
series. The acfplot
methods plot the auto-correlation in the
series.
Not yet available in S-PLUS.
## S3 method for class 'mcmc' densityplot(x, data, outer, aspect = "xy", default.scales = list(relation = "free"), start = 1, thin = 1, main = attr(x, "title"), xlab = "", plot.points = "rug", ..., subset) ## S3 method for class 'mcmc.list' densityplot(x, data, outer = FALSE, groups = !outer, aspect = "xy", default.scales = list(relation = "free"), start = 1, thin = 1, main = attr(x, "title"), xlab = "", plot.points = "rug", ..., subset) ## S3 method for class 'mcmc' levelplot(x, data, main = attr(x, "title"), start = 1, thin = 1, ..., xlab = "", ylab = "", cuts = 10, at, col.regions = topo.colors(100), subset) ## S3 method for class 'mcmc' qqmath(x, data, outer, aspect = "xy", default.scales = list(y = list(relation = "free")), prepanel = prepanel.qqmathline, start = 1, thin = 1, main = attr(x, "title"), ylab = "", ..., subset) ## S3 method for class 'mcmc.list' qqmath(x, data, outer = FALSE, groups = !outer, aspect = "xy", default.scales = list(y = list(relation = "free")), prepanel = prepanel.qqmathline, start = 1, thin = 1, main = attr(x, "title"), ylab = "", ..., subset) ## S3 method for class 'mcmc' xyplot(x, data, outer, layout = c(1, nvar(x)), default.scales = list(y = list(relation = "free")), type = 'l', start = 1, thin = 1, xlab = "Iteration number", ylab = "", main = attr(x, "title"), ..., subset) ## S3 method for class 'mcmc.list' xyplot(x, data, outer = FALSE, groups = !outer, aspect = "xy", layout = c(1, nvar(x)), default.scales = list(y = list(relation = "free")), type = 'l', start = 1, thin = 1, xlab = "Iteration number", ylab = "", main = attr(x, "title"), ..., subset) acfplot(x, data, ...) ## S3 method for class 'mcmc' acfplot(x, data, outer, prepanel, panel, type = 'h', aspect = "xy", start = 1, thin = 1, lag.max = NULL, ylab = "Autocorrelation", xlab = "Lag", main = attr(x, "title"), ..., subset) ## S3 method for class 'mcmc.list' acfplot(x, data, outer = FALSE, groups = !outer, prepanel, panel, type = if (groups) 'b' else 'h', aspect = "xy", start = 1, thin = 1, lag.max = NULL, ylab = "Autocorrelation", xlab = "Lag", main = attr(x, "title"), ..., subset)
x |
an |
data |
ignored, present for consistency with generic. |
outer |
for the |
groups |
for the |
aspect |
controls the physical aspect ratio of the panel. See
|
default.scales |
this parameter provides a reasonable default
value of the |
type |
a character vector that determines if lines, points,
etc. are drawn on the panel. The default values for the methods are
carefully chosen. See
|
thin |
an optional thinning interval that is applied before the plot is drawn. |
start |
an optional value for the starting point within the series. Values before the starting point are considered part of the "burn-in" of the series and dropped. |
plot.points |
character argument giving the style in which
points are added to the plot. See
|
layout |
a method-specific default for the |
xlab,ylab,main |
Used to provide default axis annotations and plot labels. |
cuts, at |
defines number and location of values where colors change |
col.regions |
color palette used |
lag.max |
maximum lag for which autocorrelation is computed. By
default, the value chosen by |
prepanel,panel |
suitable prepanel and panel functions for
|
... |
other arguments, passed to the lattice function.
Documentation of the corresponding generics in the |
subset |
indices of the subset of the series to plot. The
default is constructed from the |
An object of class "trellis"
. The relevant
update
method can be used to
update components of the object and the
print
method (usually called by
default) will plot it on an appropriate plotting device.
Deepayan Sarkar<EMAIL_ADDRESS>Lattice
for a brief introduction to
lattice displays and links to further documentation.
data(line) ## Not run: xyplot(line) xyplot(line[[1]], start = 10) densityplot(line, start = 10) qqmath(line, start = 10) levelplot(line[[2]]) acfplot(line, outer = TRUE) ## End(Not run)


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---
layout: post
title: What Everyone Needs to Know About Stem Cells
date: '2007-08-22T13:21:00.001-05:00'
author: Jabell
tags:
- Abortion
- Health
- Science
modified_time: '2007-08-22T13:25:42.861-05:00'
blogger_id: tag:blogger.com,1999:blog-19739956.post-1789814215185316418
blogger_orig_url: http://theabellsix.blogspot.com/2007/08/what-everyone-needs-to-know-about-stem.html
---
From Chuck Colson's Breakpoint:
In October, surgeons removed 500ccs of bone marrow from Carron’s left hip. The cells were cultivated, and four hours later, 30 million stem cells were injected into the right side of Carron’s heart.


Version 0.5 of the standard library has now been released. The library has been tested using Agda version 2.2.10.