That Guardian interview in full
Mar 07, 2006
Excellent coverage of Elgg in today’s Education Guardian. I was very pleased to be able to help out with the schools’ perspective on things. Here’s the full text of my email interview with Stephen O’Hear:
1. What is your job title / role?
I’m deputy head and information systems manager. You might also like to mention that I won the 2006 Becta ICT in Practice Award for primary teaching. [blush]
2. Can you tell me a little bit more about the school? It’s a primary right? Is it fee paying? What kind of intake?
St Ives is a 3-11 independent girls prep school, with about 150 on roll. We have a broad intake into nursery and reception. The pupils’ parents are very supportive of their daughters’ education, and almost all have internet access from home.
3. What was your thinking behind trying out Elgg? How did you see it fitting into the curriculum? (beyond ICT)>
I was looking for a way to validate and support their learning beyond the formal curriculum: providing a space for them to share their enthusiasms and experiences about all aspects of life, including but not limited to school. Elgg provides a way of gathering together, and showcasing, computer-based work, either in their blog, for written work, or via their filespace. The filespace also makes it easy to move files between school and home.
3b. Have you set any specific activities around using Elgg? I gather you introduced it as part of an ICT lesson… yet it’s not really about ICT or is it?
It’s not about ICT, but it is ICT, in the sense of information and communication technology – they’re learning a good number of computing concepts, including some grasp of the software development and testing process, but more importantly they’re using ICT tools to support social learning within and beyond the curriculum. I’ve set a number of specific tasks with Elgg, such as writing book reviews, or evaluations of specific bits of the curriculum, but I’ve stressed that it’s their space to use to talk about all the things that matter to them that don’t always get covered at school. There’s plenty more that a school could do with Elgg to support the more formal curriculum though, but we’ve tended to use Moodle for that side of things – the two work very well side by side, and there’s an ongoing project to provide integration between the two.
4. Elgg provides a nice blogging platform but what other benefits does it bring aside from straight blogging e.g. social networking?
Elgg’s great at bringing together a number of Web 2.0 technologies within an integrated platform that can be hosted in-house, inside a ‘walled garden’ extranet. As well as the blogging, Elgg provides:
- a ‘folksonomy’ tagging engine that allows users to find people, posts and resources on similar topics to their own interests;
- built-in social networking, to connect formal and informal groups of people;
-
an e-portfolio style filespace;
and most recently
- aggregation of RSS feeds, to pull together dynamic content from all over the ‘net [not far from you own notion of the Digital Learning Aggregator 1.0].
The tagging system and the aggregator could go a long way to meet Ruth Kelly’s aspirations for personal learning spaces: “in the future it will be more than simply a storage place – a digital space that is personalised, that remembers what the learner is interested in and suggests relevant web sites, or alerts them to courses and learning
opportunities that fit their needs.” (from her introduction to the E-Strategy), but in a way that acknowledges a key role for people in this process, rather than leaving it all to the machines.
5. Have the students made use of the file storage function? How do they use Elgg outside school?
All of them have used the filespace to store photos to be inculded in their blogs, and their first podcasts, a few have gone further and used it to move content to and fro, and as a showcase for finished pieces of work. I think this is a function we’ll make more use of in the future.
It’s been great to see many of my pupils continue their blogging into their free time, at the weekend or over the holiday, to record and reflect on their experiences outside school too.
6. What benefits has Elgg brought to the students? I’m thinking of examples of how the ‘learner centred’ approach differs from other types of e-learning platforms e.g. VLEs etc.
Whereas I as the teacher have remained in control of our Moodle VLE courses, with Elgg much of the control is handed to the pupils themselves, giving them a far stronger sense of ownership, voice and autonomy than a more conventional learning platform. Elgg also has lots of potential to support informal, home or community based learning. I think Elgg offers a very appealing vision of personalized learning with the person at the centre, rather than courses and curriculums.
7. How has Elgg supported students reflecting on their learning?
It provides a space, and more importantly an audience, for that reflection – I’ll admit that not all of my pupils have made full use of this space, but even those who’ve not posted much along these lines themselves will, I hope, have been encouraged to reflect on their own learning and experiences by reading the posts of their classmates.
8. Why should other schools / teachers give Elgg a go? Any advice for them?
Elgg is a great way to bring cutting edge web technology into the school, in a way that the school can monitor, protect, and focus on learning, understood in its broadest sense. I think it important that the learners do have a strong sense of ownership of their personal learning space, but things have worked best for us when pupils have been given a focus to their blogging, at least in the early days. I’m sure though that the key to making a school’s online community supportive and dynamic is to have an offline community that’s supportive and dynamic too.
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Netflix’s The Witcher has been greenlit for season two
by Chris KountourasWhile the first season of Netflix’s The Witcher is still a month away, Netflix has recently announced on Twitter that there will be a season two as well. Below you can see the tweet featuring Geralt’s two swords. Probably the steel and silver swords, steel for humans and silver for monsters, his two trusted companions.
Now I don’t know about you guys but from the trailer it looks quite good. There are of course a couple of things I didn’t love, but overall I think it has potential. I also very much like Cavill’s voice, he sounds a bit like Doug Cockle, who is the voice actor of Geralt in the video games.
Also what did you guys think of his eyes? I really don’t mind them to be honest and probably having CGI on them all the time could end up looking bad.  Unfortunately I doubt non-sentient monsters get intimidated.
The swordplay also looks very cool and I do hope they will use continuous shots for the fight scenes. My only concern right now are the monsters. So far we have seen a Kikimora, which looked fine. We also got a bit of a Striga and something that looked like a combination of a Foglet, a Ghoul and a Nekker, from the latest trailer. I am really hoping they will do the monsters justice.
Also here is the latest trailer in case you missed it.
The series is set to be released on December 20th and all episodes will be available from the start.


2017/06/07 21:31:48.366 +0100 INFO [TriggerManager] TriggerManager loaded.
31226 [Test worker] INFO azkaban.trigger.TriggerManager - TriggerManager loaded.
2017/06/07 21:31:48.366 +0100 INFO [CheckerTypeLoader] Registering checker ThresholdChecker
31226 [Test worker] INFO azkaban.trigger.CheckerTypeLoader - Registering checker ThresholdChecker
2017/06/07 21:31:48.367 +0100 INFO [ActionTypeLoader] Registering action DummyAction
31227 [Test worker] INFO azkaban.trigger.ActionTypeLoader - Registering action DummyAction
2017/06/07 21:31:48.378 +0100 INFO [TriggerManager] Inserting trigger Trigger Id: -1, Description: Trigger from triggerLoader with trigger condition of ThresholdChecker.eval() and expire condition of EndTimeCheck_1.eval(), this is real dummy action in TriggerManager
31238 [Test worker] INFO azkaban.trigger.TriggerManager - Inserting trigger Trigger Id: -1, Description: Trigger from triggerLoader with trigger condition of ThresholdChecker.eval() and expire condition of EndTimeCheck_1.eval(), this is real dummy action in TriggerManager
2017/06/07 21:31:48.387 +0100 INFO [TriggerManager] Doing trigger actions this is real dummy action for Trigger Id: 0, Description: Trigger from triggerLoader with trigger condition of ThresholdChecker.eval() and expire condition of EndTimeCheck_1.eval(), this is real dummy action
31247 [TriggerRunnerManager-Trigger-Scanner-Thread] INFO azkaban.trigger.TriggerManager - Doing trigger actions this is real dummy action for Trigger Id: 0, Description: Trigger from triggerLoader with trigger condition of ThresholdChecker.eval() and expire condition of EndTimeCheck_1.eval(), this is real dummy action
DummyAction invoked.
2017/06/07 21:31:49.278 +0100 ERROR [TriggerManager] Shutting down trigger manager thread TriggerRunnerManager-Trigger-Scanner-Thread
32138 [Test worker] ERROR azkaban.trigger.TriggerManager - Shutting down trigger manager thread TriggerRunnerManager-Trigger-Scanner-Thread
2017/06/07 21:31:49.279 +0100 INFO [TriggerManager] TriggerManager loaded.
32139 [Test worker] INFO azkaban.trigger.TriggerManager - TriggerManager loaded.
2017/06/07 21:31:49.279 +0100 INFO [CheckerTypeLoader] Registering checker ThresholdChecker
32139 [Test worker] INFO azkaban.trigger.CheckerTypeLoader - Registering checker ThresholdChecker
2017/06/07 21:31:49.279 +0100 INFO [ActionTypeLoader] Registering action DummyAction
32139 [Test worker] INFO azkaban.trigger.ActionTypeLoader - Registering action DummyAction
2017/06/07 21:31:49.279 +0100 INFO [TriggerManager] Interrupted. Probably to shut down.
32139 [TriggerRunnerManager-Trigger-Scanner-Thread] INFO azkaban.trigger.TriggerManager - Interrupted. Probably to shut down.
2017/06/07 21:31:49.286 +0100 INFO [TriggerManager] Inserting trigger Trigger Id: -1, Description: Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action in TriggerManager
32146 [Test worker] INFO azkaban.trigger.TriggerManager - Inserting trigger Trigger Id: -1, Description: Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action in TriggerManager
2017/06/07 21:31:50.490 +0100 INFO [TriggerManager] Doing trigger actions this is real dummy action for Trigger Id: 1, Description: Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action
33350 [TriggerRunnerManager-Trigger-Scanner-Thread] INFO azkaban.trigger.TriggerManager - Doing trigger actions this is real dummy action for Trigger Id: 1, Description: Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action
DummyAction invoked.
2017/06/07 21:31:50.490 +0100 INFO [Condition] Done resetting checkers. The next check time will be 2017-06-07T21:31:51.285+01:00
33350 [TriggerRunnerManager-Trigger-Scanner-Thread] INFO azkaban.trigger.Condition - Done resetting checkers. The next check time will be 2017-06-07T21:31:51.285+01:00
2017/06/07 21:31:51.391 +0100 INFO [TriggerManager] Doing trigger actions this is real dummy action for Trigger Id: 1, Description: Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action
34251 [TriggerRunnerManager-Trigger-Scanner-Thread] INFO azkaban.trigger.TriggerManager - Doing trigger actions this is real dummy action for Trigger Id: 1, Description: Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action
DummyAction invoked.
2017/06/07 21:31:51.391 +0100 INFO [Condition] Done resetting checkers. The next check time will be 2017-06-07T21:31:52.285+01:00
34251 [TriggerRunnerManager-Trigger-Scanner-Thread] INFO azkaban.trigger.Condition - Done resetting checkers. The next check time will be 2017-06-07T21:31:52.285+01:00
2017/06/07 21:31:52.292 +0100 INFO [TriggerManager] Pausing Trigger Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action
35152 [TriggerRunnerManager-Trigger-Scanner-Thread] INFO azkaban.trigger.TriggerManager - Pausing Trigger Trigger from with trigger condition of BasicTimeChecker_1.eval() and expire condition of EndTimeChecker_1.eval(), this is real dummy action
2017/06/07 21:31:54.287 +0100 ERROR [TriggerManager] Shutting down trigger manager thread TriggerRunnerManager-Trigger-Scanner-Thread
37147 [Test worker] ERROR azkaban.trigger.TriggerManager - Shutting down trigger manager thread TriggerRunnerManager-Trigger-Scanner-Thread


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Lists of Lists
Let's add one more tier to the hierarchy. If we take the deck of cards from the original example and create a box which contains multiple decks, the box now represents a list of decks, and each deck represents a list of cards. This is a list of lists. For the analogy in this section, the red box below contains a list of coin rolls, and each roll contains a list of pennies.
Photo by Dori.
What queries can we make from the list of lists? This accesses existing properties.
- Number of coin types? 2.
- Coin type values? $0.01 and $0.25.
- Material of quarters? 75% copper and 25% nickel.
- Material of pennies? 97.5% zinc and 2.5% copper.
What actions can we perform on the list of lists? This changes the list of lists based on a given operation.
- Select a specific stack of quarters or pennies.
- Select a specific quarter or penny.
- Rearrange the stacks of quarters and pennies.
- Shuffle the stacks together.
Again, Dynamo has an analagous node for each one of the operations above. Since we're working with abstract data and not physical objects, we need a set of rules to govern how we move up and down the data hierarchy.
When dealing with lists of lists, the data is layered and complex, but this provides an opportunity to do some awesome parametric operations. Let's break down the fundamentals and discuss a few more operations in the lessons below.
Top-Down Hierarchy
The fundamental concept to learn from this section: Dynamo treats lists as objects in and of themselves. This top-down hierarchy is developed with object-oriented programming in mind. Rather than selecting sub-elements with a command like List.GetItemAtIndex, Dynamo will select that index of the main list in the data structure. And that item can be another list. Let's break it down with an example image:
Exercise - Top-Down Hierarchy
Download the example file that accompanies this exercise (Right click and "Save Link As..."): Top-Down-Hierarchy.dyn. A full list of example files can be found in the Appendix.
- With code block, we've defined two ranges:``` 0..2; 0..3; ```
- These ranges are connected to a Point.ByCoordinates node with lacing set to "Cross Product". This creates a grid of points, and also returns a list of lists as an output.
- Notice that the Watch node gives 3 lists with 4 items in each list.
- When using List.GetItemAtIndex, with an index of 0, Dynamo selects the first list and all of its contents. Other programs may select the first item of every list in the data structure, but Dynamo employs a top-down hierarchy when dealing with data.
Flatten and List.Flatten
Flatten removes all tiers of data from a data structure. This is helpful when the data hierarchies are not necessary for your operation, but it can be risky because it removes information. The example below shows the result of flattening a list of data.
Exercise - Flatten
Download the example file that accompanies this exercise (Right click and "Save Link As..."): Flatten.dyn. A full list of example files can be found in the Appendix.
- Insert one line of code to define a range in code block:``` -250..-150..#4; ```
- Plugging the code block into the x and y input of a Point.ByCoordinates node, we set the lacing to "Cross Product" to get a grid of points.
- The Watch node shows that we have a list of lists.
- A PolyCurve.ByPoints node will reference each list and create a respective polycurve. Notice in the Dynamo preview that we have four polycurve representing each row in the grid.
- By inserting a flatten before the polycurve node, we've created one single list for all of the points. The polycurve node references a list to create one curve, and since all of the points are on one list, we get one zig-zag polycurve which runs throughout the entire list of points.
There are also options for flattening isolated tiers of data. Using the List.Flatten node, you can define a set number of data tiers to flatten from the top of the hierarchy. This is a really helpful tool if you're struggling with complex data structures which are not necessarily relevant to your workflow. And another option is to use the flatten node as a function in List.Map. We'll discuss List.Map more below.
Chop
When parametric modeling, there are also times where you'll want to add more data structure to an existing list. There are many nodes available for this as well, and chop is the most basic version. With chop, we can partition a list into sublists with a set number of items.
Exercise - List.Chop
Download the example file that accompanies this exercise (Right click and "Save Link As..."): Chop.dyn. A full list of example files can be found in the Appendix.
A List.Chop _with a _subLength of 2 creates 4 lists with 2 items each.
The chop command divides lists based on a given list length. In some ways, chop is the opposite of flatten: rather than removing data structure, it adds new tiers to it. This is a helpful tool for geometric operations like the example below.
List.Map and List.Combine
A List.Map/Combine applies a set function to an input list, but one step down in the hierarchy. Combinations are the same as Maps, except combinations can have multiple inputs corresponding to the input of a given function.
Exercise - List.Map
Note: This exercise was created with a previous version of Dynamo. Much of the List.Map functionality has been resolved with the addition of the List@Level feature. For more information, see List@Level below.
Download the example file that accompanies this exercise (Right click and "Save Link As..."): Map.dyn. A full list of example files can be found in the Appendix.
As a quick introduction, let's review the List.Count node from a previous section.
The List.Count node counts all of the items in a list. We'll use this to demonstrate how List.Map works.
Insert two lines of code into the code block:
-50..50..#Nx; -50..50..#Ny;
After typing in this code, the code block will create two inputs for Nx and Ny.
- With two integer sliders, define the Nx and Ny values by connecting them to the code block.
- Connect each line of the code block into the respective X and Y inputs of a Point.ByCoordinates node. Right click the node, select "Lacing", and choose "Cross Product". This creates a grid of points. Because we defined the range from -50 to 50, we are spanning the default Dynamo grid.
- A Watch node reveals the points created. Notice the data structure. We've created a list of lists. Each list represents a row of points of the grid.
- Attach a List.Count node to the output of the watch node from the previous step.
- Connect a Watch node to the List.Count output.
Notice that the List.Count node gives a value of 5. This is equal to the "Nx" variable as defined in the code block. Why is this?
- First, the Point.ByCoordinates node uses the "x" input as the primary input for creating lists. When Nx is 5 and Ny is 3, we get a list of 5 lists, each with 3 items.
- Since Dynamo treats lists as objects in and of themselves, a List.Count node is applied to the main list in the hierarchy. The result is a value of 5, or, the number of lists in the main list.
- By using a List.Map node, we take a step down in the hierarchy and perform a "function" at this level.
- Notice that the List.Count node has no input. It is being used as a function, so the List.Count node will be applied to every individual list one step down in the hierarchy. The blank input of List.Count corresponds to the list input of List.Map.
- The results of List.Count now gives a list of 5 items, each with a value of 3. This represents the length of each sublist.
Exercise - List.Combine
Note: This exercise was created with a previous version of Dynamo. Much of the List.Combine functionality has been resolved with the addition of the List@Level feature. For more information, see List@Level below.
Download the example file that accompanies this exercise (Right click and "Save Link As..."): Combine.dyn. A full list of example files can be found in the Appendix.
In this exercise, we'll use a similar logic to List.Map, but with multiple elements. In this case, we want to divide a list of curves by a unique number of points.
- Using the code block, define a range using the syntax: ```..20..#4;
and a value of
20; ``` below that line.- Connect the code block to two Point.ByCoordinates nodes.
- Create a Line.ByStartPointEndPoint from the Point.ByCoordinates nodes.
- The Watch node shows four lines.
- Below the graph for line creation, we want to use _code block _to create four distinct ranges to divide the lines uniquely. We do this with the following lines of code:
0..1..#3; 0..1..#4; 0..1..#5; 0..1..#6;
- With a List.Create node, we merge the four lines from the code block into one list.
- The Watch node reveals a list of lists.
- Curve.PointAtParameter will not work by connecting the lines directly into the parameter values. We need to step one level down on the hierarchy. For this, we'll use List.Combine.
By using List.Combine, we can successfully divide each line by the given ranges. This gets a little tricky, so we'll break it down in-depth.
- First, add a Curve.PointAtParameter node to the canvas. This will be the "function" _or "combinator" that we apply to _List.Combine node. More on that in a second.
- Add a List.Combine node to the canvas. Hit the "+" or "-" to add or subtract inputs. In this case, we'll use the default two inputs on the node.
- We want to plug the Curve.PointAtParameter node into the "comb" input of List.Combine. And one more important node: be sure to right-click the "param" _input of _Curve.PointAtParameter and uncheck "use default value". Default values in Dynamo inputs have to be removed when running a node as a function. In other words, we should consider default values as having additional nodes wired to them. Because of this, we need to remove the default values in this case.
- We know we have two inputs, the lines and the parameters to create points. But how do we connect them to the List.Combine inputs and in what order?
- The empty inputs of Curve.PointAtParameter, from top-to-bottom need to be filled in the combinator in the same order. So, the lines are plugged into list1 of List.Combine.
- Following suit, the parameter values are plugged into the list2 input of List.Combine.
- The Watch node and the Dynamo preview shows us that we have 4 lines, each divided based on the code block ranges.
List@Level
Preferred to List.Map, the List@Level feature allows you to directly select which level of list you want to work with right at the input port of the node. This feature can be applied to any incoming input of a node and will allow you access the levels of your lists quicker and easier than other methods. Just tell the node what level of the list you want to use as the input and let the node do the rest.
List@Level Exercise
In this exercise, we will use the List@Level feature to isolate a specific level of data.
Download the example file that accompanies this exercise (Right click and "Save Link As..."): List@Level. A full list of example files can be found in the Appendix.
- We will start with a simple 3D grid of points.
- Since the grid is constructed with a Range for X, Y and Z, we know that the data is structured with 3 tiers: an X List, Y List and Z List.
- These tiers exist at different Levels. The Levels are indicated at the bottom of the Preview Bubble. The list Levels columns correspond to the list data above to help identify which level to work within.
- The List Levels are organized in reverse order so that the lowest level data is always in “L1”. This will help ensure that your graphs will work as planned, even if anything is changed upstream.
- To use the List@Level function, click '>'. Inside this menu, you will see two checkboxes.
- Use Levels - This enables the List@Level functionality. After clicking on this option, you will be able to click through and select the input list levels you want the node to use. With this menu, you can quickly try out different level options by clicking up or down.
- Keep list structure – If enabled, you will have the option to keep that input’s level structure. Sometimes, you may have purposefully organized your data into sublists. By checking this option, you can keep your list organization intact and not lose any information.
With our simple 3D grid, we can access and visualize the list structure by toggling through the List Levels. Each List Level and index combination will return a different set of points from our original 3D set.
- “@L2” in DesignScript allows us to select only the List at Level 2.
- The List at Level 2 with the index 0 includes only the first set of Y points, returning only the XZ grid.
- If we change the Level filter to “L1”, we will be able to see everything in the first List Level. The List at Level 1 with the index 0 includes all of our 3D points in a flat list.
- If we try the same for “L3” we will see only the third List Level points. The List at Level 3 with the index 0 includes only the first set of Z points, returning only an XY grid.
- If we try the same for “L4” we will see only the third List Level points. The List at Level 4 with the index 0 includes only the first set of X points, returning only an YZ grid.
Although this particular example can also be created with List.Map, List@Level greatly simplifies the interaction, making it easy to access the node data. Take a look below at a comparison between a List.Map and List@Level methods:
- Although both methods will give us access to the same points, the List@Level method allows us to easily toggle between layers of data within a single node.
- To access a point grid with List.Map, we will need a List.GetItemAtIndex node alongside the List.Map. For every list level that we are stepping down, we will need to use an additional List.Map node. Depending on the complexity of your lists, this could require you to add a significant amount of List.Map Nodes to your graph to access the right level of information.
- In this example, a List.GetItemAtIndex node with a List.Map node reurns the same set of points with the same list structure as the List.GetItemAtIndex with '@L3' selected.
Transpose
Transpose is a fundamental function when dealing with lists of lists. Just as in spreadsheet programs, a transpose flips the columns and rows of a data structure. We'll demonstrate this with a basic matrix below, and in the following section, we'll demonstrate how a transpose can be use to create geometric relationships.
Exercise - List.Transpose
Download the example file that accompanies this exercise (Right click and "Save Link As..."): Transpose.dyn. A full list of example files can be found in the Appendix.
Let's delete the List.Count nodes from the previous exercise and move on to some geometry to see how the data structured.
- Connect a PolyCurve.ByPoints to the output of the watch node from Point.ByCoordinates.
- The output shows 5 polycurves, and we can see the curves in our Dynamo preview. The Dynamo node is looking for a list of points (or a list of lists of points in this case) and creating a single polycurve from them. Essentially, each list has converted to a curve in the data structure.
- If we want to isolate one row of curves, we use the List.GetItemAtIndex node.
- Using a code block value of 2, query the 3rd element in the main list.
- The PolyCurve.ByPoints gives us one curve, since only one list is connected to the node.
- A List.Transpose node will switch all of the items with all of the lists in a list of lists. This sounds complicated, but it's the same logic as transpose in Microsoft Excel: switching columns with rows in a data structure.
- Notice the abstract result: the transpose changed the list structure from a 5 lists with 3 items each to 3 lists with 5 items each.
- Notice the geometric result: using PolyCurve.ByPoints, we get 3 polycurves in the perpendicular direction to the original curves.
Code Block Creation
Code block shorthand uses "[]" to define a list. This is a much faster and more fluid way to create list than the List.Create node. Code block is discussed in more detail in Chapter 7. Reference the image below to note how a list with multiple expressions can be defined with code block.
Code Block Query
Code block shorthand uses "[]" as a quick and easy way to select specific items that you want from a complex data structure. Code blocks are discussed in more detail in Chapter 7. Reference the image below to note how a list with multiple data types can be queried with code block.
Exercise - Querying and Inserting Data
Download the example file that accompanies this exercise (Right click and "Save Link As..."): ReplaceItems.dyn. A full list of example files can be found in the Appendix.
This exercise uses some of the logic established in the previous one to edit a surface. Our goal here is intuitive, but the data structure navigation will be more involved. We want to articulate a surface by moving a control point.
- Begin with the string of nodes above. We are creating a basic surface which spans the default Dynamo grid.
- Using code block, insert these two lines of code and connect to the u and v inputs of Surface.PointAtParameter, respectively:
-50..50..#3; -50..50..#5;
- Be sure to set the Lacing of Surface.PointAtParameter to "Cross Product".
- The Watch node show that we have a list of 3 lists, each with 5 items.
In this step, we want to query the central point in the grid we've created. To do this we'll select the middle point in the middle list. Makes sense, right?
- To confirm that this is the correct point, we can also click through the watch node items to confirm that we're targeting the correct one.
- Using code block, we'll write a basic line of code for querying a list of lists:
points[1][2];
- Using Geometry.Translate, we'll move the selected point up in the Z direction by 20 units.
- Let's also select the middle row of points with a List.GetItemAtIndex node. Note: Similar to a previous step, we can also query the list with code block, using a line of
points[1];
So far we've successfully queried the center point and moved it upward. Now we want need to insert this moved point back into the original data structure.
- First, we want to replace the item of the list we isolated in a previous step.
- Using List.ReplaceItemAtIndex, we'll replace the middle item by using and index of "2", with the replacement item connected to the moved point (Geometry.Translate).
- The output shows that we've input the moved point into the middle item of the list.
Now that we've modified the list, we need to insert this list back into the original data structure: the list of lists.
- Following the same logic, use List.ReplaceItemAtIndex to replace the middle list with the our modified list.
- Notice that the code blocks defining the index for these two nodes are 1 and 2, which matches the original query from the code block (points[1][2]).
- By selecting the list at index 1, we see the data structure highlighted in the Dynamo preview. We successfully merged the moved point into the original data structure.
There are many ways to make a surface from this set of points. In this case, we're going to create a surface by lofting curves together.
- Create a NurbsCurve.ByPoints node and connect the new data structure to create three nurbs curves.
- Connect a Surface.ByLoft to the output from NurbsCurve.ByPoints. We now have a modified surface. We can change the original Z value of Geometry. Translate and watch the geometry update!


The stick figures are back for more. Beat them and claim the title!
WASD/Arrow Keys to move. Space to swing.


Welcome to analysefit’s documentation!¶
Contents:
The main class for the analysis of a given fit.
-
class
analyzefit.analyze.
analysis
(X, y, model, predict=None, testing=True)¶ The main class for the analysis of a given fit.
- Args:
model (object): The fitting model (the model must have a predict method).
X (numpy.ndarray): The X valuse to be used for plots.
- predict (str): The name of the method that is equivalent to the sklearn predict
- function. Default = ‘predict’.
y (numpy.ndarray): The y values to be used for plots.
- Attributes:
- validate (object): Creates a residual vs fitted plot, a quatile plot, a
- spread vs location plot, and a leverage plot and prints the accuracy score to the screen.
- res_vs_fit (object): Creates a plot of the residuals vs the fittted
- values in an interactive bokeh figure.
- quantile (object): Creates a quantile plot for the fitted values in an
- interactive bokeh figure.
- spread_loc (object): Creates a plot of the spread in residuals vs the fitted
- values in an interactive bokeh figure.
- leverage (object): Creates a plot of the cooks distance and the influence vs
- the standardized residuals in an interactive bokeh figure.
- Examples:
The following examples show how to validate the fit of sklearn’s LinearRegression on the housing dataset. It shows how to generate each of the plots that can be used to verify the accuracy af a fit.
>>>> import pandas as pd
>>>> import numpy as np
>>>> from sklearn.linear_model import LinearRegression
>>>> from sklearn.cross_validation import train_test_split
>>>> from sklearn.metrics import mean_squared_error, r2_score
>>>> df = pd.read_csv(‘https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data’, header=None,sep=”s+”)
>>>> df.columns = [“CRIM”, “ZN”, “INDUS”, “CHAS”, “NOX”, “RM”, “AGE”, “DIS”, “RAD”, “TAX”, “PTRATIO”, “B”, “LSTAT”, “MEDV”]
>>>> X = df.iloc[:,:-1].values
>>>> y = df[[“MEDV”]].values
>>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
>>>> test_size=0.3, random_state=0)
>>>> slr = LinearRegression()
>>>> slr.fit(X_train,y_train)
>>>> an = analyze.analysis(X_train, y_train, slr)
>>>> an.validate()
>>>> an.validate(X=X_test, y=y_test, metrics=[mean_squared_error, r2_score)
>>>> an.res_vs_fit()
>>>> an.quantile()
>>>> an.spread_loc()
>>>> an.leverage()
-
leverage
(X=None, y=None, pred=None, interact=True, show=True, title=None, ax=None)¶ The spread-location, or scale-location, plot of the data.
- Args:
- X (numpy.ndarray, optional): The dataset to make the plot for
- if different than the dataset used to initialize the method.
- y (numpy.ndarray, optional): The target values to make the plot for
- if different than the dataset used to initialize the method.
- pred (numpy.ndarray, optional): The predicted values to make the plot for
- if y and X are different than the dataset used to initialize the method.
interact (bool, optional): True if the plot is to be interactive.
show (bool, optional): True if plot is to be displayed.
- ax (matplotlib.axes._subplots.AxesSubplot, optional): The subplot on which to
- drow the plot.
title (str, optional): The title of the plot.
- Rasises:
- ValueError: if the number of predictions is not the same as the number of
- target values or if the number of rows in the feature matrix is not the same as the number of targets.
- Returns:
- fig (matplotlib.figure.Figure or bokeh.plotting.figure): An
- object containing the plot if show=False.
- Examples:
>>>> import pandas as pd
>>>> import numpy as np
>>>> from sklearn.linear_model import LinearRegression
>>>> from sklearn.cross_validation import train_test_split
>>>> from sklearn.metrics import mean_squared_error, r2_score
>>>> df = pd.read_csv(‘https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data’, header=None,sep=”s+”)
>>>> df.columns = [“CRIM”, “ZN”, “INDUS”, “CHAS”, “NOX”, “RM”, “AGE”, “DIS”, “RAD”, “TAX”, “PTRATIO”, “B”, “LSTAT”, “MEDV”]
>>>> X = df.iloc[:,:-1].values
>>>> y = df[[“MEDV”]].values
>>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
>>>> test_size=0.3, random_state=0)
>>>> slr = LinearRegression()
>>>> slr.fit(X_train,y_train)
>>>> an = analyze.analysis(X_train, y_train, slr)
>>>> an.leverage()
>>>> an.leverage(X=X_test,y=y_test,title=”Test values”)
>>>> an.leverage(X=X_test,pred=slr.predict(X_test),y=y_test,title=”Test values”)
-
quantile
(data=None, dist=None, interact=True, show=True, title=None, ax=None)¶ Makes a quantile plot of the predictions against the desired distribution.
- Args:
- data (numpy.ndarray, optional): The user supplied data for the quantile plot.
- If None then the model predictions will be used.
- dist (str or numpy.ndarray, optional): The distribution to be compared to. Either
- ‘Normal’, ‘Uniform’, or a numpy array of the user defined distribution.
interact (bool, optional): True if the plot is to be interactive.
show (bool, optional): True if plot is to be displayed.
- ax (matplotlib.axes._subplots.AxesSubplot, optional): The subplot on which to
- drow the plot.
title (str, optional): The title of the plot.
- Rasises:
- ValueError: if data and the distribution are of different lengths.
- Returns:
- fig (matplotlib.figure.Figure or bokeh.plotting.figure): An
- object containing the plot if show=False.
- Examples:
>>>> import pandas as pd
>>>> import numpy as np
>>>> from sklearn.linear_model import LinearRegression
>>>> from sklearn.cross_validation import train_test_split
>>>> from sklearn.metrics import mean_squared_error, r2_score
>>>> df = pd.read_csv(‘https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data’, header=None,sep=”s+”)
>>>> df.columns = [“CRIM”, “ZN”, “INDUS”, “CHAS”, “NOX”, “RM”, “AGE”, “DIS”, “RAD”, “TAX”, “PTRATIO”, “B”, “LSTAT”, “MEDV”]
>>>> X = df.iloc[:,:-1].values
>>>> y = df[[“MEDV”]].values
>>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
>>>> test_size=0.3, random_state=0)
>>>> slr = LinearRegression()
>>>> slr.fit(X_train,y_train)
>>>> an = analyze.analysis(X_train, y_train, slr)
>>>> an.quantile()
>>>> an.quantile(data=y_test,dist=”uniform”,title=”Test values vs uniform distribution”)
>>>> an.quantile(data=y_test,dist=np.random.samples((len(y_test))))
-
res_vs_fit
(X=None, y=None, pred=None, interact=True, show=True, ax=None, title=None)¶ Makes the residual vs fitted values plot.
- Args:
- X (numpy.ndarray, optional): The dataset to make the plot for
- if different than the dataset used to initialize the method.
- y (numpy.ndarray, optional): The target values to make the plot for
- if different than the dataset used to initialize the method.
- pred (numpy.ndarray, optional): The predicted values to make the plot for
- if y and X are different than the dataset used to initialize the method.
interact (bool, optional): True if the plot is to be interactive.
show (bool, optional): True if plot is to be displayed.
- ax (matplotlib.axes._subplots.AxesSubplot, optional): The subplot on which to
- drow the plot.
title (str, optional): The title of the plot.
- Returns:
- fig (matplotlib.figure.Figure or bokeh.plotting.figure): An
- object containing the plot if show=False.
- Examples:
>>>> import pandas as pd
>>>> import numpy as np
>>>> from sklearn.linear_model import LinearRegression
>>>> from sklearn.cross_validation import train_test_split
>>>> from sklearn.metrics import mean_squared_error, r2_score
>>>> df = pd.read_csv(‘https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data’, header=None,sep=”s+”)
>>>> df.columns = [“CRIM”, “ZN”, “INDUS”, “CHAS”, “NOX”, “RM”, “AGE”, “DIS”, “RAD”, “TAX”, “PTRATIO”, “B”, “LSTAT”, “MEDV”]
>>>> X = df.iloc[:,:-1].values
>>>> y = df[[“MEDV”]].values
>>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
>>>> test_size=0.3, random_state=0)
>>>> slr = LinearRegression()
>>>> slr.fit(X_train,y_train)
>>>> an = analyze.analysis(X_train, y_train, slr)
>>>> an.res_vs_fit()
>>>> an.res_vs_fit(X=X_test,y=y_test,title=”Test values”)
>>>> an.res_vs_fit(pred=slr.predict(X_test),y=y_test,title=”Test values”)
-
spread_loc
(X=None, y=None, pred=None, interact=True, show=True, title=None, ax=None)¶ The spread-location, or scale-location, plot of the data.
- Args:
- X (numpy.ndarray, optional): The dataset to make the plot for
- if different than the dataset used to initialize the method.
- y (numpy.ndarray, optional): The target values to make the plot for
- if different than the dataset used to initialize the method.
- pred (numpy.ndarray, optional): The predicted values to make the plot for
- if y and X are different than the dataset used to initialize the method.
interact (bool, optional): True if the plot is to be interactive.
show (bool, optional): True if plot is to be displayed.
- ax (matplotlib.axes._subplots.AxesSubplot, optional): The subplot on which to
- drow the plot.
title (str, optional): The title of the plot.
- Returns:
- fig (matplotlib.figure.Figure or bokeh.plotting.figure): An
- object containing the plot if show=False.
- Examples:
>>>> import pandas as pd
>>>> import numpy as np
>>>> from sklearn.linear_model import LinearRegression
>>>> from sklearn.cross_validation import train_test_split
>>>> from sklearn.metrics import mean_squared_error, r2_score
>>>> df = pd.read_csv(‘https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data’, header=None,sep=”s+”)
>>>> df.columns = [“CRIM”, “ZN”, “INDUS”, “CHAS”, “NOX”, “RM”, “AGE”, “DIS”, “RAD”, “TAX”, “PTRATIO”, “B”, “LSTAT”, “MEDV”]
>>>> X = df.iloc[:,:-1].values
>>>> y = df[[“MEDV”]].values
>>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
>>>> test_size=0.3, random_state=0)
>>>> slr = LinearRegression()
>>>> slr.fit(X_train,y_train)
>>>> an = analyze.analysis(X_train, y_train, slr)
>>>> an.spread_loc()
>>>> an.spread_loc(X=X_test,y=y_test,title=”Test values”)
>>>> an.spread_loc(pred=slr.predict(X_test),y=y_test,title=”Test values”)
-
validate
(X=None, y=None, pred=None, dist=None, metric=None, testing=False)¶ The spread-location, or scale-location, plot of the data.
- Args:
- X (numpy.ndarray, optional): The dataset to make the plot for
- if different than the dataset used to initialize the method.
- y (numpy.ndarray, optional): The target values to make the plot for
- if different than the dataset used to initialize the method.
- pred (numpy.ndarray, optional): The predicted values to make the plot for
- if y and X are different than the dataset used to initialize the method.
- dist (str or numpy.ndarray, optional): The distribution to be compared to. Either
- ‘Normal’, ‘Uniform’, or a numpy array of the user defined distribution.
- metric (function or list of functions, optional): The functions used to
- determine how accurate the fit is.
testing (bool, optional): True if this is a unit test.
- Returns:
- score (list of float): The scores from each of the metrics of in testing mode.
- Examples:
>>>> import pandas as pd
>>>> import numpy as np
>>>> from sklearn.linear_model import LinearRegression
>>>> from sklearn.cross_validation import train_test_split
>>>> from sklearn.metrics import mean_squared_error, r2_score
>>>> df = pd.read_csv(‘https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data’, header=None,sep=”s+”)
>>>> df.columns = [“CRIM”, “ZN”, “INDUS”, “CHAS”, “NOX”, “RM”, “AGE”, “DIS”, “RAD”, “TAX”, “PTRATIO”, “B”, “LSTAT”, “MEDV”]
>>>> X = df.iloc[:,:-1].values
>>>> y = df[[“MEDV”]].values
>>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
>>>> test_size=0.3, random_state=0)
>>>> slr = LinearRegression()
>>>> slr.fit(X_train,y_train)
>>>> an = analyze.analysis(X_train, y_train, slr)
>>>> an.validate()
>>>> an.validate(X=X_test, y=y_test, metrics=[mean_squared_error, r2_score)
Tools for data minipulation.
-
analyzefit.manipulate.
cooks_dist
(y, pred, features)¶ Finds the Cooks distance for the data in the. See: https://en.wikipedia.org/wiki/Cook%27s_distance
- Args:
- y (numpy.ndarray): An array containing the correct values of the model. pred (numpy.ndarray): An array containing the predicted values of the model. features (numpy.ndarray): An array containing the features of the regression model.
- Returns:
- dist (numpy.ndarray): An array of the cooks distance for each point in the input data.
-
analyzefit.manipulate.
hat_diags
(X)¶ Finds the diagonals of the hat matrix for the features in X.
- Args:
- X (numpy.ndarray): An array containing the features of the regression model.
- Returns:
- hat_diags (numpy.ndarray): The diagonals of the hat matrix.
-
analyzefit.manipulate.
residual
(y, pred)¶ Finds the residual of the actual vs the predicted values.
- Args:
- y (numpy.ndarray): An array containing the correct values of the model. pred (numpy.ndarray): An array containing the predicted values of the model.
- Returns:
- residaul (numpy.ndarray): The residual of the data (y-pred).
- Raises:
- ValueError: Raises a value error if y and pred don’t have the same number of elements.
-
analyzefit.manipulate.
std_residuals
(y, pred)¶ Finds the residual of the actual vs the predicted values.
- Args:
- y (numpy.ndarray): An array containing the correct values of the model. pred (numpy.ndarray): An array containing the predicted values of the model.
- Returns:
- standardized_residaul (numpy.ndarray): The standardazied residual of the data (y-pred).
-
analyzefit.plotting.
scatter
(x, y, show_plt=True, x_label=None, y_label=None, label=None, title=None, fig=None, ax=None)¶ Make a standard matplotlib style scatter plot.
- Args:
- x (numpy.ndarray): The data for the x-axis. y (numpy.ndarray): The data for the y-axis. show (bool, optional): True if plot is to be shown. x_label (str, optional): The x-axis label. y_label (str, optional): The y-axis label. label (str, optional): The data trend label. title (str, optional): The plot title. fig (matplotlib.figure.Figure, optional): An initial figure to add points too. ax (matplotlib.axes._subplots.AxesSubplot, optional): A subplot object to plot on.
- Returns:
- fig (matplotlib object): Returns the matplotlib object if show = False.
-
analyzefit.plotting.
scatter_with_hover
(x, y, in_notebook=True, show_plt=True, fig=None, name=None, marker=’o’, fig_width=500, fig_height=500, x_label=None, y_label=None, title=None, color=’blue’, **kwargs)¶ Plots an interactive scatter plot of x vs y using bokeh, with automatic tooltips showing columns from df. Modified from: http://blog.rtwilson.com/bokeh-plots-with-dataframe-based-tooltips/
- Args:
x (numpy.ndarray): The data for the x-axis. y (numpy.ndarray): The data for the y-axis.
- fig (bokeh.plotting.Figure, optional): Figure on which to plot
- (if not given then a new figure will be created)
name (str, optional): Series name to give to the scattered data marker (str, optional): Name of marker to use for scatter plot
- Returns:
- fig (bokeh.plotting.Figure): Figure (the same as given, or the newly created figure)
- if show is False


Abstract interface for a collection of methods which allow accessing objects in a RoadGeometry's object graph (Lanes, Segments, Junctions, BranchPoints) by their unique id's.
More...
#include <drake/automotive/maliput/api/road_geometry.h>
Abstract interface for a collection of methods which allow accessing objects in a RoadGeometry's object graph (Lanes, Segments, Junctions, BranchPoints) by their unique id's.
◆ IdIndex() [1/3]
◆ IdIndex() [2/3]
◆ ~IdIndex()
◆ IdIndex() [3/3]
◆ GetBranchPoint()
Returns the BranchPoint identified by id
, or nullptr
if id
is unknown.
◆ GetJunction()
Returns the Junction identified by id
, or nullptr
if id
is unknown.
◆ GetLane()
Returns the Lane identified by id
, or nullptr
if id
is unknown.
◆ GetSegment()
Returns the Segment identified by id
, or nullptr
if id
is unknown.
◆ operator=() [1/2]
◆ operator=() [2/2]
The documentation for this class was generated from the following file:


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Repast comes with the following how to documentation. How to Run a Simulation and How to Use the Gui will help to get you started working with the Repast demos. If you are interested in writing models with Repast start with How to Build a Model - 1
| How Run a Simulation | Describes how to start a Repast simulation from the Repast toolbar and the command line. |
| How to use the GUI | Describes how to use the graphical interface to start, stop, pause, and probe a model. |
| How to Build a Model - 1 | Details how to build a model using the SimpleModel class.. |
| How to Build a Model - 2 | Details how to build a model using the standard Repast template methods, what's necessary for probing and some detail into the inner workings of Repast. |
| How to Use a Schedule | An overview of the schedule object and its use. |
| Overview of Spaces | An overview of spaces and their use. |
| How to Create Displays | Details the necessary steps to display a Repast model on the screen. Also describes how to make movies and take snapshots of these displays. |
| How to Collect Data | Describes how to use the data collection classes for collecting data and writing that data to a file. |
| How to work with Random Numbers | Describes how to use the Random class for random number generation as well as how to create your own random number streams. |
| How to use Parameters and Parameter Files | Describes how to work with parameters and use parameters files and in both gui and batch models. |
| How to use Multi-Run | Describes how to use the Multi-Run capabilities in Repast. |
| Network Models | Describes how to build a network model and how to import and export data from such a model. |
| How to Create PropertyDescriptors | Describes how to create Boolean and ListPropertyDescriptors to change a model's parameters via check and combo boxes. |
| How to Create Custom Actions | Describes how to create custom actions to alter the model's state at runtime using sliders, buttons, and checkboxes. |
| How to Create Charts | Describes how to create sequence and histogram charts. |
| How to Use Vector GIS | A brief overview of vector gis support in Repast. |
| FAQ | Frequently Asked Questions. If you can't find the answer in any of the how tos, look here. |


I'm Constantin (ce0311), interface design student at University of Applied Science in Potsdam. The following project is the result of the course "typography and automation" by Fabian Morón Zirfas.
A small script to generate radial patterns from single letters or words. Just edit the variables at the top of the script to change its behaviour. The comments should be self-explanatory - if not, yell at me!


Newspaper reports of the annual swan upping on the Thames reminded me of an amusing swan-related matter which I had unaccountably forgotten to mention. During the Wimbledon tennis championships, I forget which game exactly though it may have been one of the men's semi-finals, during one of the breaks between sets, the BBC cameras drifted off to the surrounding scene, and showed a jetty by the river around which sunbathers sprawled. Also present was a swan, approached by a tiny tot who looked as if she was wanting to feed it a sandwich. The proximity of tot to swan led one of the commentary team to observe that, belying their elegance and grace, swans are of course savage and aggressive creatures. Whereupon fellow-commentator Tim Henman—whose surname suggests he knows a thing or two about birds, or at least about poultry—said (and I paraphrase from memory):
Whenever you see a swan, someone always points out that they are capable of breaking your arm.  I haven't.
Sadly, the cameras then returned to the tennis, and we were not treated to any further swan-talk from Hen-man.
Swan upping in the last century


new CONTENT_ADAPTER_INTERFACE(optionsopt)
Creates an interface
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
options |
Object |
<optional> |
{} | A set of properties defined by keys with their allowed types as values. Each property will be required by newly constructed classes from this interface |
Methods
create(optionsopt) → {Object}
Constructs a new object with a prototype defined by the .adapter ensuring that instantiated class conforms to interface requirements
Parameters:
| Name | Type | Attributes | Default | Description | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
options |
Object |
<optional> |
{} | Values to be passed to class constructor (.adapter should be reserved for either customer class or string that matches key in ADAPTERS) Properties
|
Returns:
Returns an instantiated adapter class
- Type
- Object


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Academic Integrity
Project Overview
- Organization: Innovation Hub
- Role: Data Analysis Researcher
- Team: Shankeri Vijayakumar, Johanna Pokorny
Background
The Innovation Hub is a student-led initiative which uses design thinking to improve the student experience at UofT through partnerships with other departments and divisions. During the Covid-19 pandemic, the Office of the Vice Provost noticed an increase in academic integrity violations in the remote environment and asked the Innovation Hub to do research to better understand this problem. As a Design Researcher, I worked with the Office of the Vice Provost to help answer the question,How can we support students so they can thrive without the help of cheating?
Our project goals were:
- Create a campus-wide communication strategy about academic integrity
- Conduct user research on student needs surrounding academic integrity
- Conduct UX research on how students seek and access academic integrity resources online
Methods
- Focus group sessions
- Remote usability testing
- Stakeholder interviews
Key Findings
Through our research, we found three key themes: 1) the emotional stakes of learning, 2) considering the audience, and 3) varying approaches to integrity. Students shared with us that they were deeply afraid of potentially committing an academic offense unintentionally. The high pressure and expectations placed on them caused a sense of fear, even for innocent students. Students also sought to have more clarity around what academic integrity entails and wanted resources that were student-centered without legalistic language. Lastly, students wanted consistency in approaches to academic integrity. Their understanding of what academic behaviour was acceptable or not greatly depended on the attitudes of their instructor.
Broadly, our recommendations to the university were:
- Be intentional about a holistic and positive approach to academic integrity that emphasizes courage rather than fear
- Focus on students by orienting academic integrity resources and information to be student-centred
- Ensure an open and consistent dialogue around academic integrity that fosters a strong relationship of trust between students and the university
Conclusion
Working on this project was the first time that I was exposed to the deep complexity of a large organization. As a design researcher, I was responsible for empathizing and understanding different and sometimes directly opposing viewpoints and creating solutions that would work for all parties involved. It was meaningful to be able to provide some way for students to share their perspective during a difficult time and I am excited to see what changes the university makes in response to our research. You can read more about this project on the
Innovation Hub website.
Thank you for reading!
© 2022 Hai-Dao Le-Nguyen


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Time box meeting to 30 minutes (9am - 9:30am)
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Feedback is essentially customer, patient, or potential customer feedback. To end the meeting on a high note. Success stories that are a result of using our product


No, Delhi is not the most polluted city in India, at least not today
by Joe C MathewAmong the most polluted cities of India on November 25, Delhi is not even on the list
Which city in India is most polluted? With all the smog news that you have read, you cannot be blamed if your choice is Delhi and its neighbourhood. But that is not true; at least that is what the air quality monitoring systems of private weather forecasting agency Skymet tells us. It says that the most polluted cities of India on November 25 were in the states of Chhattisgarh, Madhya Pradesh, Punjab, Haryana, Odisha, Bihar and West Bengal. Yes, Delhi is not even on the list.
In a separate chart, Skymet also provides the latest air quality status of metro cities including Delhi-NCR. Check out the details:
Also Read: WhatsApp throws surprise for Indian startups, offers $250,000 of ad credits to 500 firms
Also Read: Bharti Airtel, Vodafone Idea shares rally up to 6%; here's why
Also Read: Twitter founder Jack Dorsey practices Vipassana in this Indian guru's ashram


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{{product.Shipment_Id}} {%else%} PENDING {%endif%} |
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arcgis.viz module¶
- class arcgis.viz.MapView(**kwargs)[source]¶
Bases: IPython.html.widgets.widget.DOMWidget
- addlayer¶
A trait for unicode strings.
- basemap¶
A trait for unicode strings.
- center¶
An instance of a Python list.
- draw(shape, popup=None, symbol=None, attributes=None)[source]¶
Draws a shape.
Arguments: shape is one of [“circle”, “downarrow”, “ellipse”, “extent”, “freehandpolygon”, “freehandpolyline”, “leftarrow”, “line”, “multipoint”, “point”, “polygon”, “polyline”, “rectangle”, “rightarrow”, “triangle”, “uparrow”, or geometry dict object]
popup is a dict containing “title” and “content” as keys that will be displayed when the shape is clicked
symbol is a symbol specified in json format as described at http://resources.arcgis.com/en/help/arcgis-rest-api/index.html#//02r3000000n5000000 a default symbol is used is one is not specified
attributes is a dict containing name value pairs of fields and field values associated with the graphic.
- end_time¶
A trait for unicode strings.
- id¶
A trait for unicode strings.
- mode¶
A trait for unicode strings.
- on_click(callback, remove=False)[source]¶
Register a callback to execute when the map is clicked.
The callback will be called with one argument, the clicked widget instance.
- remove : bool (optional)
- Set to true to remove the callback from the list of callbacks.
- on_draw_end(callback, remove=False)[source]¶
Register a callback to execute when something is drawn
The callback will be called with two argument, the clicked widget instance, and the geometry drawn
- remove : bool (optional)
- Set to true to remove the callback from the list of callbacks.
- start_time¶
A trait for unicode strings.
- width¶
A trait for unicode strings.
- zoom¶
An int trait.
- class arcgis.viz.WebMap(webmapitem)[source]¶
Bases: collections.OrderedDict
Represents a webmap and provides access to it’s basemaps and operational layers as well as functionality to visualize and interact with them. http://resources.arcgis.com/en/help/arcgis-web-map-json/index.html#/Web_map_format_overview/02qt00000007000000/


— blanks, templates for the software platform, defining the architecture of the software system
– software products that simplify the creation and support of technically complex or loaded projects
Angular
React
Vue
Meteor
Ember
Backbone
Aurelia
Polymer
Mithril
Miško Hevery
Adam Abrons
• The template language is Typescript, which is a programming language from Microsoft. Although Typescript is the primary language for Angular, applications can also be written using languages such as Dart or JavaScript.
• Angular is supported by Google.
• Angular is aimed at developing single-page applications, i.e. SPA-solutions (Single Page Application). Popular social networking applications (Twitter, Instagram and Facebook) are examples.
• Angular provides a client-side MVC infrastructure that helps in running and building dynamic applications with a state-of-the-art level of quality.
• Apps written in Angular are compatible with different browsers. Angular automatically handles JavaScript code that is appropriate for each browser.
• Clean and accurate user interface design.
• Easy routing.
• Structure facilitates the Angular extension of the syntax of HTML and easily creates reusable components according to directives.
• In General, Angular is a framework for creating large-scale, high-performance, and easy - to-maintain web applications.
1. It is easy to test your code
2. Easily create personalized document object models (Document Object Model, DOM).
3. Data modeling is limited to using small data models, which makes the code simple and easy to test.
4. Angular is prepared for mobile devices and desktop – one framework for many platforms.
<!DOCTYPE html>

My first expression: {{ 5 + 5 }}

Result:
My first expression: 10
<!DOCTYPE html>

{{ firstName + " " + lastName }}

Result:
John Doe
<!DOCTYPE html>

First Name: <input type="text" ng-model="firstName">

Last Name: <input type="text" ng-model="lastName">

Full Name: {{firstName + " " + lastName}}

Result:


Carolina Quiel
My name is Carolina Quiel and I am soon to become a Full-Stack Web Developer, I currently living in Miramar, FL.
I have a Bachelor of Science in Systems Engineering, and my primary focus and inspiration is to be a Professional Web Developer.
I am both driven and self-motivated, and I am very passionate about Web Development.


Information Sources
-
https://www.britannica.com/event/Strategic-Arms-Limitation-Talks
-
https://www.washingtonpost.com/archive/politics/1986/11/27/us-to-break-salt-ii-limits-friday/796f8715-e76e-41be-822c-dd33109813a9/?noredirect=on&utm_term=.30d6f9dbd969
-
https://www.history.com/this-day-in-history/carter-and-brezhnev-sign-the-salt-ii-treaty
-
Text: https://www.state.gov/t/isn/5195.htm


Represents the root of a template, allows access to all the files, classes, enums and more in a solution
| Property | Description | Returns |
|---|---|---|
| Files | A collection of all the files in the solution | FileCollection |
| Classes | All public classes in the solution | ClassCollection |
| Methods | All public methods in the solution | MethodCollection |
| Delegates | All public delegates in the solution | DelegateCollection |
| Enums | All public enums in the solution | EnumCollection |
| Interfaces | All public interfaces in the solution | InterfacesCollection |
| Root | Access to self (allows applying a template on the root itself) | RootContext |
Represents an attribute, not the attribute class, but the declared attribute usage on for instance a class.
Attributes are implicitly convertible to string, a conversion will return the name of the attribute.
| Property | Description | Returns |
|---|---|---|
| FullName | The full original name of the attribute including namespace and containing class names. e.g. App.Attributes.RouteAttribute |
string |
| Name | The name of the attribute excluding the "Attribute"-suffix. e.g. Route |
string |
| name | The name of the attribute excluding the "Attribute"-suffix, camel cased. e.g. route |
string |
| Parent | The parent context of the attribute. | Item |
| Value | The value of the attribute as string. | string |
Represents a collection of attributes.
Represents a C# class.
Classes are implicitly convertible to string, a conversion will return the name of the class.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the class. | AttributeCollection |
| BaseClass | The base class of the class. Returns null if the class inherits from System.Object. |
Class |
| Constants | All public constants defined in the class. | ConstantCollection |
| ContainingClass | The containing class of the class if it's nested. | Class |
| Fields | All public fields defined in the class. | FieldCollection |
| FullName | The full original name of the class including namespace, type parameters and containing class names. e.g. App.Models.CustomerModel |
string |
| Interfaces | All interfaces implemented by the class. | InterfaceCollection |
| IsGeneric | Determines if the class is generic. | bool |
| Methods | All public methods defined in the class. | MethodCollection |
| Name | The name of the class excluding type parameters. e.g. CustomerModel |
string |
| name | The name of the class excluding type parameters, camel cased. e.g. customerModel |
string |
| Namespace | The namespace of the class. e.g. App.Models |
string |
| NestedClasses | All public classes defined in the class. | ClassCollection |
| NestedEnums | All public enums defined in the class. | EnumCollection |
| NestedInterfaces | All public interfaces defined in the class. | InterfaceCollection |
| Parent | The parent context of the class. | Item |
| Properties | All public properties defined in the class. | PropertyCollection |
| TypeArguments |
All generic type arguments of the class. TypeArguments are the specified arguments for the TypeParametes on a generic class e.g. <string>. (In Visual Studio 2013 TypeParameters and TypeArguments are the same) |
TypeParameterCollection |
| TypeParameters |
All generic type parameters of the class. TypeParameters are the type placeholders of a generic class e.g. <T>. (In Visual Studio 2013 TypeParameters and TypeArguments are the same) |
TypeParameterCollection |
| GenericDefClass |
Get the original generic defintion of the class. |
Class |
Represents a collection of classes.
Represents a constant.
Constants are implicitly convertible to string, a conversion will return the name of the constant.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the constant. | AttributeCollection |
| FullName | The full original name of the constant including namespace, type parameters and containing class names. e.g. App.Models.CustomerModel.TempPath |
string |
| Name | The name of the constant. e.g. TempPath |
string |
| name | The name of the constant, camel cased. e.g. tempPath |
string |
| Parent | The parent context of the constant. | Item |
| Type | The type of the constant. | Type |
Represents a collection of constants.
Represents a C# enum.
Enums are implicitly convertible to string, a conversion will return the name of the enum.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the enum. | AttributeCollection |
| ContainingClass | The containing class of the enum if it's nested. | Class |
| IsFlags | Determines if the enum is decorated with the Flags attribute. | bool |
| FullName | The full original name of the enum including namespace, type parameters and containing class names. e.g. App.Enums.AddressType |
string |
| Name | The name of the enum. e.g. AddressType |
string |
| name | The name of the enum, camel cased. e.g. addressType |
string |
| Namespace | The namespace of the enum. e.g. App.Enums |
string |
| Parent | The parent context of the enum. | Item |
| Values | All values defined in the enum. | EnumValueCollection |
Represents a collection of enums.
Represents an enum value.
EnumValues are implicitly convertible to string, a conversion will return the name of the enum value.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the enum value. | AttributeCollection |
| FullName | The full original name of the enum including namespace, type parameters and containing class names. e.g. App.Enums.AddressType.Shipping |
string |
| Name | The name of the enum. e.g. Shipping |
string |
| name | The name of the enum, camel cased. e.g. shipping |
string |
| Value | The numeric value of the enum value. | int |
Represents a collection of enum values.
Represents a field.
Fields are implicitly convertible to string, a conversion will return the name of the field.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the field. | AttributeCollection |
| FullName | The full original name of the field including namespace, type parameters and containing class names. e.g. App.Models.CustomerModel.ShippingAddress |
string |
| Name | The name of the field. e.g. ShippingAddress |
string |
| name | The name of the field, camel cased. e.g. shippingAddress |
string |
| Parent | The parent context of the field. | Item |
| Type | The type of the field. | Type |
Represents a collection of fields.
Represents a C# code file.
File is the root context in the Typewriter code model.
| Property | Description | Returns |
|---|---|---|
| FullName | The full path of the file. e.g. C:\Dev\App\Models\CustomerModel.cs |
string |
| Name | The name of the file. e.g. CustomerModel.cs |
string |
| Classes | All public classes defined in the file. | ClassCollection |
| Enums | All public enums defined in the file. | EnumCollection |
| Interfaces | All public interfaces defined in the file. | InterfaceCollection |
Represents a C# interface.
Interfaces are implicitly convertible to string, a conversion will return the name of the interface.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the interface. | AttributeCollection |
| ContainingClass | The containing class of the interface if it's nested. | Class |
| FullName | The full original name of the interface including namespace, type parameters and containing class names. e.g. App.Models.ICustomerModel |
string |
| Interfaces | All interfaces implemented by the interface. | InterfaceCollection |
| IsGeneric | Determines if the interface is generic. | bool |
| Methods | All methods defined in the interface. | MethodCollection |
| Name | The name of the interface excluding type parameters. e.g. ICustomerModel |
string |
| name | The name of the interface excluding type parameters, camel cased. e.g. iCustomerModel |
string |
| Namespace | The namespace of the interface. e.g. App.Models |
string |
| Parent | The parent context of the interface. | Item |
| Properties | All properties defined in the interface. | PropertyCollection |
| TypeArguments |
All generic type arguments of the interface. TypeArguments are the specified arguments for the TypeParametes on a generic interface e.g. <string>. (In Visual Studio 2013 TypeParameters and TypeArguments are the same) |
TypeParameterCollection |
| TypeParameters |
All generic type parameters of the interface. TypeParameters are the type placeholders of a generic interface e.g. <T>. (In Visual Studio 2013 TypeParameters and TypeArguments are the same) |
TypeParameterCollection |
Represents a collection of interfaces.
Represents a method.
Methods are implicitly convertible to string, a conversion will return the name of the method.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the method. | AttributeCollection |
| FullName | The full original name of the method including namespace, type parameters and containing class names. e.g. App.Models.CustomerModel.GetName |
string |
| IsGeneric | Determines if the method is generic. | bool |
| Name | The name of the method excluding type parameters. e.g. GetName |
string |
| name | The name of the method excluding type parameters, camel cased. e.g. getName |
string |
| Parameters | All parameters of method. | ParameterCollection |
| Parent | The parent context of the method. | Item |
| Type | The return type of the method. | Type |
| TypeParameters |
All generic type parameters of the method. TypeParameters are the type placeholders of a generic method e.g. <T>. |
TypeParameterCollection |
Represents a collection of methods.
Represents a method parameter.
Parameters are implicitly convertible to string, a conversion will return the name of the parameter.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the parameter. | AttributeCollection |
| FullName | The full original name of the parameter including namespace, type parameters and containing class names. e.g. App.Models.CustomerModel.GetName.id |
string |
| Name | The name of the parameter. e.g. id |
string |
| name | The name of the parameter, camel cased. e.g. id |
string |
| Parent | The parent context of the parameter. | Item |
| Type | The type of the parameter. | Type |
Represents a collection of parameters.
Represents a property.
Properties are implicitly convertible to string, a conversion will return the name of the property.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the property. | AttributeCollection |
| FullName | The full original name of the property including namespace, type parameters and containing class names. e.g. App.Models.CustomerModel.Orders |
string |
| HasGetter | Determines if the property has a getter | bool |
| HasSetter | Determines if the property has a setter | bool |
| Name | The name of the property. e.g. Orders |
string |
| name | The name of the property, camel cased. e.g. orders |
string |
| Parent | The parent context of the property. | Item |
| Type | The type of the property. | Type |
Represents a collection of properties.
Represents a type.
Types are implicitly convertible to string, a conversion will return the name of the type.
| Property | Description | Returns |
|---|---|---|
| Attributes | All attributes declared on the type. | AttributeCollection |
| BaseClass | The base class of the type. | Class |
| Constants | All constants defined in the type. | ConstantCollection |
| ContainingClass | The containing class of the type if it's nested. | Class |
| Default | The default value of the type. e.g. strings returns null, integers returns 0 and IEnumerable<CustomerModel> returns [] |
string |
| Fields | All fields defined in the type. | FieldCollection |
| FullName | The full original name of the type including namespace, type parameters and containing class names. e.g. System.Int32 |
string |
| Interfaces |
All interfaces implemented by the type. (In Visual Studio 2013 Interfaces are not available on Types) |
InterfaceCollection |
| IsDate | Determines if the type is a DateTime or DateTimeOffset. | bool |
| IsEnum | Determines if the type is an enum. | bool |
| IsEnumerable | Determines if the type is enumerable. | bool |
| IsGeneric | Determines if the type is generic. | bool |
| IsGuid | Determines if the type is a Guid. (Type.Name returns string for Guids) | bool |
| IsNullable | Determines if the type is nullable. | bool |
| IsPrimitive | Determines if the type is primitive. | bool |
| IsTimeSpan | Determines if the type is a TimeSpan. (Type.Name returns string for TimeSpans) | bool |
| Methods | All methods defined in the type. | MethodCollection |
| Name | The name of the type converted to a TypeScript firendly representation including type arguments. e.g. number |
string |
| name | The name of the type converted to a TypeScript firendly representation including type arguments, camel cased. e.g. number |
string |
| Namespace | The namespace of the type. e.g. System |
string |
| NestedClasses | All classes defined in the type. | ClassCollection |
| NestedEnums | All enums defined in the type. | EnumCollection |
| NestedInterfaces | All interfaces defined in the type. | InterfaceCollection |
| OriginalName | The original name of the type as it was specified in C#. e.g. int |
string |
| Parent | The parent context of the type. | Item |
| Properties | All properties defined in the type. | PropertyCollection |
| TypeArguments |
All generic type parameters of the type.
TypeArguments are the specified arguments for the TypeParametes on a generic type e.g. <string>. (In Visual Studio 2013 TypeParameters and TypeArguments are the same) |
TypeCollection |
| TypeParameters |
All generic type parameters of the type. TypeParameters are the type placeholders of a generic type e.g. <T>. (In Visual Studio 2013 TypeParameters and TypeArguments are the same) |
TypeParameterCollection |
| AsClass |
Get this type's class definition. |
Class |
| AsEnum |
Get this type's enum definition. |
Enum |
Represents a collection of types. Calling ToString() on a TypeCollection will return a string with the names of all Types in the collection surrounded by
Represents a generic type parameter on a class, interface or method.
TypeParameters are implicitly convertible to string, a conversion will return the name of the type parameter.
| Property | Description | Returns |
|---|---|---|
| Name | The name of the type parameter. e.g. T |
string |
| name | The name of the type parameter, camel cased. e.g. t |
string |
| Parent | The parent context of the type parameter. | Item |
Represents a collection of type parameters. Calling ToString() on a TypeParameterCollection will return a string with the names of all TypeParameters in the collection surrounded by
Extension methods for working with ASP.NET Web API Controllers
| Method | Description | Returns |
|---|---|---|
| HttpMethod |
Returns the http method from a Web API action. The http method is extracted from Http* or AcceptVerbs attribute or by naming convension if no attributes are specified. |
string |
| RequestData | Creates an object literal containing the parameters that should be sent in the request body of a Web API request. If no parameters are required the literal "null" is returned. | string |
| Route | Returns the route for the Web API action based on route attributes (or "api/{controller}/{id?}" if no attributes are present). | string |
| Url | Returns the url for the Web API action based on route attributes (or "api/{controller}/{id?}" if no attributes are present). Route parameters are converted to TypeScript string interpolation syntax by prefixing all parameters with $ e.g. ${id}. Optional parameters are added as QueryString parameters for GET and HEAD requests. | string |


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Google Analytics
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---
title: "BOTANICAL DRAWING 2"
date: "2000-01-01 19:08:30 -06:00"
categories:
- adult
- eh
subtitle: ""
layout: "events"
event-image: "botanical-drawing-2.jpg"
event-dates: "FRIDAYS, AUGUST 30 - SEPTEMBER 27"
event-location: "Red Butte Garden Classroom"
button-type: "register"
tickets-link: ""
registration-info: "Registration for this class is closed"
instructor-info: "Elaine Hultgren"
member-cost: "$188"
public-cost: "$235"
event-notes: ""
tickets-button: ""
published: false
---
REGISTRATION REQUIRED
FRIDAYS, AUGUST 30-SEPTEMBER 27 @ 6-9PM
This class is a continuation of Botanical Drawing 1. Students will continue to sharpen their observation skills of plant structures. Artists will learn to accurately draw botanical forms using graphite pencils with emphasis placed on line, form, light and shade and simple perspective.
MATERIALS LIST
Materials for artist to bring to class:
- Graphite Pencils: 6H, 4H, 2H, HB, 2B, 4B, 6B Brand of your choice. If you are purchasing more pencils, I favor Tom Bow and you can purchase a small set at Blick in Sugarhouse and Staedler brand also available locally.
- Pad of drawing paper 11x14 or 9x12. Strathmore brand available locally at Blick, Michaels, and Hobby Lobby. 300 or 400 series.
- Pad of tracing paper: 11x14 or 9x12. Canson Calique is the most translucent of all brands. It can only be purchased at Hobby Lobby with a 40% off coupon. 300 or 400 series.
a 40% discount coupon you can apply.
- Pad of Bristol paper: 11x14 or 9x12, Strathmore, Utrecht or Canson brand Vellum or smooth (plate) finish, 300 or 400 series.
- A Pentel CLIC eraser found at office stores, maybe Blick
- Kneaded eraser(s)
- Hand held pencil sharpener. Electric sharpener available in class.
- Roll of "Scotch" brand tape labeled REMOVABLE.
- Roll of 1/2 inch tape labeled “Artists Tape”. It is acid free. Available at Blick
- A small makeup brush or basting brush to sweep away eraser particles
- Tortillions – (smudge sticks) from first class
- A good book to purchase: "How to Draw Plants", by Keith West
Items Provided to artists:


This test case is intended for command editor
var a = "no selection";
var b = window.a || "selection";
console.log(b);
selection
no selection
var b = window.a || "selection"; console.log(b);
no selection


Guidance
This guide provides LAs with a tool help them development effective services that support young disabled people as they leave care.
Ref: DFES-0880-2004 PDF , 3.17MB , 148 pages
The young people whose needs this document aims to address are far from a homogeneous group and, as the guide uses a broad definition of disability, young people are likely to be found in a wide variety of service locations. The challenge that this document sets is firstly to identify those locations through the development of good management information processes across services and then to ensure that all young people are receiving their entitlements under the Children (Leaving Care) Act 2000.
This document will be of interest to policy makers, practitioners, commissioners, partnership boards and anyone else who is committed to ensuring the best possible outcomes for young disabled care leavers.
It provides a guide to legal and policy issues, local planning frameworks and practice issues. Within each section, there is a structured set of questions to enable practitioners and local policy makers to assess local performance in relation to disabled care leavers.
A range of good practice examples and a useful set of web and paper based resources are also included to help the reader. Further practical help and recommendations to improve practice are also available in the appendices.
The guide is designed for use as a self-audit tool for service planners, managers, service providers and practitioners across a range of agencies, and for application at both strategic and practice based levels.


{% extends "core.html" %} {% load ahmia_custom %} {% load static %} {% block title %}Ahmia - Onion Websites Over Time{% endblock %} {% block extra_js %} {% endblock %} {% block body %}
Some Tor2web proxies share their visit statistics. Since June 2014, we have been gathering this information. As a result, we can present popularity stats. We only show those web sites which have more than 100 different active days through Tor2web. Select an address and study its popularity.


Legendary AR's and Burst AR's deal the most damage.
This can be easily be done throughout a regular game by looting large houses.
By landing first and picking up a weapon, you will be able to eliminate opponents easily.
Stand two meters away from the basket and move your center crosshair onto it. You may have to adjust each shot
The main two factories contain chests, along with the 'modern house', center island, side island, and more.
Headshots are the easiest way to deal damage, or you could knock an enemy down and eliminate them with a sniper rifle.


Charity
The Main
Goal
improving school
settings and child care.
Our
Motivation
nothing is more beauty
than a child’s smile.
Supporting
Hearts
support children with
the power of human kindness.


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Featured works
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Our services
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Designing is The CooL LOVE FOR.
Our most popular service is our Virtual Receptionist. We know that sometimes it’s something and you middle most this job was posted less than five minutes ago, and we think it’s a good match for you. If you submit a proposal now, you’ll be one of the first candidates. You may even catch the client while they’re still online.
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Search for array designs
Array designs, also known as platform designs, can be searched on the Arrays page.
Toggle the check box "ArrayExpress data only" to search for array designs submitted directly to ArrayExpress, or those imported from NCBI Gene Expression Omnibus (see the GEO data help page for more details on the import process).
You can search by the array design's accession, species, name/description and the submitter's contact details:
- ArrayExpress accession: It has a format of A-xxxx-n, where xxxx represents a four letter code and n is a number e.g.
A-MEXP-1234
,A-AFFY-44
. - Organism: The name of the organism for which the microarray probes were designed. You can use either common name or Latin name. E.g.
Homo sapiens
,Saccharomyces cerevisiae
,dog
because search term expansion by Experimental Factor Ontology will match common names automatically with Latin names as synonyms (highlighted green in search results). - Array name and description: You can search for terms in the array's name (title) and description, e.g. the array's manufacturer, name of the lab which printed the microarray, catalogue number, Design ID/Grid name (for Agilent arrays), the purpose of the array (e.g.
CGH
,expression
). Exact and synonym matches to search terms will be highlighted yellow and green respectively:If you are not sure about the terms to use, you can do "wild card" search, i.e. expand the search space by including asterisks (*). In the following example, both
CGH
andaCGH
(in A-MEXP-1849) are matched because of*CGH
:Here is another example of searching with asterisks. Both
Human HT-12
andHumanHT-12
are matched: - Array submitter's name: The name or email address of the person who submitted the array design to ArrayExpress.


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7.2. Mapping Type OperatorsDictionaries will work with all of the standard type operators but do not support operations such as concatenation and repetition. Those operations, although they make sense for sequence types, do not translate to mapping types. In the next two subsections, we introduce you to the operators you can use with dictionaries. 7.2.1. Standard Type OperatorsThe standard type operators were introduced in Chapter 4. Here are some basic examples using some of those operators: >>> dict4 = {'abc': 123} >>> dict5 = {'abc': 456} >>> dict6 = {'abc': 123, 98.6: 37} >>> dict7 = {'xyz': 123} >>> dict4 < dict5 True >>> (dict4 < dict6) and (dict4 < dict7) True >>> (dict5 < dict6) and (dict5 < dict7) True >>> dict6 < dict7 False How are all these comparisons performed? Like lists and tuples, the process is a bit more complex than it is for numbers and strings. The algorithm is detailed in Section 7.3.1. 7.2.2. Mapping Type OperatorsDictionary Key-Lookup Operator ( [ ] )The only operator specific to dictionaries is the key-lookup operator, which works very similarly to the single element slice operator for sequence types. For sequence types, an index offset is the sole argument or subscript to access a single element of a sequence. For a dictionary, lookups are by key, so that is the argument rather than an index. The key-lookup operator is used for both assigning values to and retrieving values from a dictionary: d[k] = v # set value 'v' in dictionary with key 'k' d[k] # lookup value in dictionary with key 'k' (Key) Membership (in, not in)
Beginning with Python 2.2, programmers can use the in and not in operators to check key membership instead of the has_key() method: >>> 'name' in dict2 True >>> 'phone' in dict2 False |


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