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How to annotate a script

How to annotate a script

All the activities we pursue in our daily lives have directions. When you drive on the road there are laws meant to prevent us from getting into accidents, when we cook we follow a recipe and when we build with LEGOs we follow the literal directions so that our castles and spaceships turn out just right. For acting, the closest thing to a set of directions for how to proceed is a script. However, complications arise because of the different ways that readers interpret scripts. This means that the primary job of the actor is to analyze a script to uncover the truth about a character so they can accurately portray then on stage or on camera.

The First Read

Script analysis is a process and the process may be slightly different depending on the actor, but, in general, script analysis starts with the basics and gradually adds details. On the first read through, it is important to understand the literal situations and events that affect a character at each point in the story. These facts from the script are the given circumstances and help to determine the actions that you will take in performance.

As you read a script, make a list of all the facts about your character. Anything you can glean from a script is helpful. What do they do for a living? Where do they live? Who is closest to them?

Breakdown into Scenes and Beats

After you have a feel for the character, map out the story into scenes and beats. Good scripts are written as a series of related events where A leads to B and B to C and so on. The practice of making a scene map helps the actor to understand the story sequentially and provides built-in points to change action.

Look for points in the script where the setting changes or the characters on stage change, or time passes. These are common ways that scenes change. Beat changes are smaller shifts within the scenes where the characters may change their action, attitude, or topic of conversation. After identifying the scenes and beats….

Identify Your Characters’ Actions

Ask yourself, “What does my character want to other people in the scene to do?” The answer that question is your character’s objective. How are you going to accomplish your objective? That’s what is important because that gives you an action to play in each scene.

Usually, characters want other characters to do something, feel something, or understand something. For example, perhaps your character wants someone to get the mail. How will you get them to go to the mailbox for you? Charm them? Barter with them? Yell at them? The right action is the one that is true to your character and helps you to start identifying your character’s type.

Stay Open to Notes and Change

Remember that acting is a collaborative exercise and actors must also take a director’s opinion into account. Listen to what a director says and incorporate it into your character in an honest way, based on your own analysis of the script. Sometimes your initial analysis won’t be correct and you will have to make adjustments throughout the rehearsal process. But, with a strong foundation for your character, built from a thorough analysis of the script, these changes will be minor and your performance will be natural.

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If you want to match on dynamic details in the user input that would be hard to maintain in a language object, you might want to use annotations.

Annotations in Teneo can be considered ‘labels’ that are attached to inputs. Some annotations are created automatically by Teneo, but you can also dynamically add them using scripts.

As an example, on this page, we will use annotations to ‘label’ promotion codes in a user input. These promotion codes are stored in a global variable and we are going to assume this variable is dynamically populated through an API or something similar.

The following 2 steps are needed to annotate the user input with the promotion codes:

  1. Create a global variable and populate it with a list of promotion codes
  2. Create a global listener that will listen for all user inputs and a script that inspects each word in the user input. If the word is in the list of promotion codes, it should add the annotation VALID_PROMO_CODE

Add a global variable

First, we need to create a global variable that we will assume has been populated dynamically with promotion codes for Longberry baristas:

  1. Go to the solutions backstage by clicking on ‘Solution’ in the top right.
  2. Click on ‘Globals’ in the panel to the left.
  3. Go to ‘Variables’.
  4. Add a new variable and give it the name validPromoCodes .
  5. Then give it this value: [“mvkn”,”vuyo”,”2ppu”,”lqgp”,”ym5b”,”634q”,”g2gl”]
  6. Hit ‘Save’.

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Add the annotation script to a global listener

Next, we need to set up a global pre listener so that we can annotate the user input before it is tested against flow triggers and transitions.

  1. Click on the ‘Solution’ tab in the top left.
  2. Click on ‘Globals’.
  3. Select ‘Listeners’.
  4. Go to the green ‘Add’ icon, open the drop-down menu and select ‘Pre listener’ to add a new pre-listener.
  5. Name it Find valid promotion codes . Then click the left arrow at the top.
  6. Paste the kleene star * into the condition field. This will match on everything since we want to inspect every input for valid promotion codes.
  7. Add the following script in the ‘Execute this script’ field. (Details on the script can be found below):
  8. Save using the ‘Save’ button in the top left of the editing window.
  9. Go back to the main solution window and reload the engine in try out.

Your browser does not support the video tag.

Give it a try and inspect the annotations

We are now annotating promotion codes using a global pre listener, which means language conditions can now use the custom annotation %$VALID_PROMO_CODE to check if a user input contains a valid promotion code.

We can also inspect the annotation in the ‘Response Info’ panel:

  1. In the main solution window, open the ‘Response Info’ panel on the right-hand side.
  2. Type mvkn into Try out. This is a valid promo code and will be annotated accordingly.
  3. In the ‘Response Info’ panel under ‘Input Summary’ you can see what Teneo annotated, including your customized annotation %$VALID_PROMO_CODE .

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If you hover over an annotation, you will get extra information (if there is any) from the annotation variables.

Explanation of the script

Let’s have a detailed look at the script that we used:

Here is what the script does:

  1. First, we get the number of sentences in the input and iterate through the sentences. We do this because we need to provide the sentence number when we add an annotation.
  2. For each sentence, we iterate through its words. We get the word and the words’ index. This is done via the eachWithIndex iterator.
  3. Next we check if the word exists in the global list. If so, we annotate the word in the sentence at wordIndex and create a annotation variable Promo code with the found promo code as a annotation value to Promo code , like so:

The script uses a number of Engine scripting API methods:

Engine scripting api (Java) Engine scripting api (Groovy) Description
_.getSentenceCount() _.sentenceCount Returns the number of sentences in the user input.
_.getSentences() _.sentences Returns an unmodifiable view of the sentences and their words, generated from the user input text.
getWords() words Returns the words of this sentence
_.getInputAnnotations().add(. ) _.inputAnnotations.add(. ) Adds the given annotation to the annotations data
_.createInputAnnotation(. ) _.createInputAnnotation(. ) Creates a user input Annotation object with the given data.

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This repo contains a script ( compare_annotations.py ) for quantifying the improvement in an annotation when a genome is reassembled and/or reannotated.

For example, imagine you had an annotated bacterial genome that’s a couple of years old. You’ve now come back to this genome with new versions of the assembler and annotator and made an updated version. This script can tell you how things changed at the gene level. Hopefully they got better!

This script works by doing a global alignment of the genes of one annotation to the other. This means the two genomes must be roughly aligned at the gene level – i.e. they should start and end at the same places. If your new genome contains structural rearrangements, that will break this script!

A few other things to note:

  • The input annotated genomes must be in GenBank format.
  • This script only looks at ‘CDS’ features in the genomes, nothing else.

This script uses Python 3 and Biopython. If you can run python3 -c “import Bio” without getting an error, you should be good to go!

No installation is required: just clone the repo and run the script:

Here are two versions of a genome you can try this script on: CP001172.1 and CP001172.2.

Download them in GenBank format and then run the script like this:

This script outputs a gene-by-gene analysis.

When two CDSs are identical, you’ll see something like this:

Or if the CDSs are similar (i.e. the same gene) but not identical, you’ll see something like this:

And if a CDS is only in one of the two assemblies, you might see stuff like this:

Unlike simply highlighting lines of poetry, which is more of a passive activity, the process of annotating poetry should help you remain firmly focused on the writer’s use of language devices and poetic techniques. Whenever it comes to responding to an essay question or a controlled assessment, your notes will be the perfect revision guides.

It is important to read everything at least twice. During the first reading, you should try to get a sense of what the text is about. You can then read more carefully and critically, annotating the key words, especially any vivid adjectives and effective verbs, punctuation and sentence structure, rhyme repetition, figurative language, and other aspects of imagery.

Using Wilfred Owen’s “Dulce Et Decorum Est”, this guide will show you the best approaches to annotating a poem.

5 Important Steps To Annotate A Poem

  1. If you are not sure about a word’s meaning, look it up in a dictionary and write a definition between the lines of the poem.
  2. Underline key words and use the margins to make brief comments.
  3. Circle important language devices and explain their significance in the margins. In fact, use any white space available on the front or back of the page.
  4. Connect words, phrases and ideas with lines or arrows.
  5. Use post-it notes when you have exhausted all available space.

The following example is from the third verse:

How to annotate a script

Of course, these lines and shapes can get confusing so you could use different colours to distinguish between the various aspects of language and form.

Similes

Since a simile is a comparison between two things, you should try to identify both nouns. You could also circle the “like” or “as” so you know it is probably a simile.

In this example taken from the opening lines of the poem, you should also underline the connection the speaker is making between the “old beggars” and the soldiers who are identified by the plural pronoun “we”.

Metaphors

Metaphors are also a comparison between two different things, but it is not always possible to highlight two nouns. Consider this example from the first verse:

The word “drunk” is a metaphor comparing the clumsy movement of the soldiers to intoxicated men who are almost unable to control their legs. It is certainly worth highlighting.

Annotating Repetition

When a writer repeats a sound, word or phrase, they are using repetition. If there is alliteration, you should circle the first letters and then look at where they appear on the line because this will help you understand the impact on the rhythm. For example, by emphasising the /m/ on the first two syllables of the line, Owen creates an awkward rhythm which is appropriate for the movement of the men.

Annotating Enjambment

If an image or thought runs from one line into the next without a break in the syntax, usually a comma or dash, this is called enjambment. In the following example, notice how Owen separates the noun “blood” from the verb “come” and then the noun “cud” from the preposition “of”. This enjambment has been indicated by the arrows.

By breaking these phrases, Owen is able to draw attention to the words and present the image more effectively to the reader. He is also trying to emphasise the motion of the “blood” spewing out from the “froth-corrupted lungs” by forcing the reader to move down to the next line.

Annotating Rhyme Scheme

If a writer has added a pattern of sounds to the end of lines, you should identify this rhyme scheme by adding the appropriate letters. In the example below, “sacks” and “backs” rhyme so you mark it with the letter a. Since “sludge” and “trudge” are different sounds, you would label this rhyme as b.

I’m just curious how others do it, because as far as I know, I’m the only one at my organization who bothers to.

I primarily write queries for piecing together spreadsheets for people based on a plethora of tables, often doing y/n columns based on appearance of certain orders per patient for statisticians to work with after the fact (strictly 1 row per patient), store those all as temp tables (though we often just make an delete hard tables instead), and put them all together at the end with a ton of joins (usually 10-20 “temp” tables). With that in mind, here’s how I generally go:

My boss likes to just make a separate .sql file for each table, but I find that hard to work with personally. What other conventions are out there?

I do header comments and periodic comments to help with comprehension if needed. The header has versioning info so we know what tfs task to associate it with.

I find this method to be the best, by far.

I disagree with people who claim that well written code explains itself, this is only true with very basic scripts. As soon as you get into complex scripts, you need comments for future maintenance/changes.

A change log and tfs info (or ticket #, or any other info to track it back) can be very handy too, especially when trying to rule out a script as the source of a new problem/issue somewhere else in the system. Depending on the situation I might also include the name of the person who asked for the script, or who requested the current change.

Some clear, concise, and carefully placed comments can make a world of difference. I try to write code that I would want to see myself if I came across the script and had never seen it before; this means at the top we have a change log and a paragraph about what the script does, in plain English, then depending on how complex it is I might put a more technical breakdown. Then going through the code I’ll separate it into blocks with headers, if the blocks are complex then I might give a very brief description of how it works. Particularly complex or unclear lines of code might also get a description above them, or a trailing comment.

If the script is basic then just a header block at the very top will suffice.

It sounds like a lot, but I do try to keep it all as clear and concise as possible, it helps me a lot when I come back to that code in the future, and I’ve had very positive feedback from others.

Named-entity recognition aims at identifying the fragments of text that mention entities of interest, that afterwards could be linked to a knowledge base where those entities are described. This manuscript presents our minimal named-entity recognition and linking tool (MER), designed with flexibility, autonomy and efficiency in mind. To annotate a given text, MER only requires: (1) a lexicon (text file) with the list of terms representing the entities of interest; (2) optionally a tab-separated values file with a link for each term; (3) and a Unix shell. Alternatively, the user can provide an ontology from where MER will automatically generate the lexicon and links files. The efficiency of MER derives from exploring the high performance and reliability of the text processing command-line tools grep and awk , and a novel inverted recognition technique. MER was deployed in a cloud infrastructure using multiple Virtual Machines to work as an annotation server and participate in the Technical Interoperability and Performance of annotation Servers task of BioCreative V.5. The results show that our solution processed each document (text retrieval and annotation) in less than 3 s on average without using any type of cache. MER was also compared to a state-of-the-art dictionary lookup solution obtaining competitive results not only in computational performance but also in precision and recall. MER is publicly available in a GitHub repository (https://github.com/lasigeBioTM/MER) and through a RESTful Web service (http://labs.fc.ul.pt/mer/).

Introduction

Text has been and continues to be for humans the traditional and natural mean of representing and sharing knowledge. However, the information encoded in free text is not easily attainable by computer applications. Usually, the first step to untangle this information is to perform named-entity recognition (NER), a text mining task for identifying mentions of entities in a given text [1,2,3]. The second step is linking these mentions to the most appropriate entry in a knowledge base. This last step is usually referred to as the named-entity linking (NEL) task but is also referred to as entity disambiguation, resolution, mapping, matching or even grounding [4].

State-of-the-art NER and NEL solutions are mostly based on machine learning techniques, such as Conditional Random Fields and/or Deep Learning [5,6,7,8,9,10,11,12,13,14]. These solutions usually require as input a training corpus, which consists of a set of texts and the entities mentioned on them, including their exact location (annotations), and the entries in a knowledge base that represent these entities [15]. The training corpus is used to generate a model, which will then be used to recognize and link entities in new texts. Their effectiveness strongly depends on the availability of a large training corpus with an accurate and comprehensive set of annotations, which is usually arduous to create, maintain and extend. On the other hand, dictionary lookup solutions usually only require as input a lexicon consisting in a list of terms within some domain [16,17,18,19,20,21], for example, a list of names of chemical compounds. The input text is then matched against the terms in the lexicon mainly using string matching techniques. A comprehensive lexicon is normally much easier to find or to create and update than a training corpus, however, dictionary lookup solutions are generally less effective than machine learning solutions.

Searching, filtering and recognizing relevant information in the vast amount of literature being published is an almost daily task for researches working in Life and Health Sciences [22]. Most of them use web tools, such as PubMed [23], but many times to perform repetitive tasks that could be automatized. However, these repetitive tasks are sometimes sporadic and highly specific, depending on the project the researcher is currently working on. Therefore, in these cases, researchers are reluctant to spend resources creating a large training corpus or learning how to adapt highly complex text mining systems. They are not interested in getting the most accurate solution, just one good enough tool that they can use, understand and adapt with minimal effort. Dictionary lookup solutions are normally less complex than machine learning solutions, and a specialized lexicon is usually easier to find than an appropriate training corpus. Moreover, dictionary lookup solutions are still competitive when the problem is limited to a set of well-known entities. For these reasons, dictionary lookup solutions are usually the appropriate option when good enough is what the user requires.

This manuscript proposes a novel dictionary lookup solution, dubbed as minimal named-entity recognizer (MER), which was designed with flexibility, autonomy, and efficiency in mind. MER only requires as input a lexicon in the form of a text file, in which each line contains a term representing a named-entity to recognize. If the user also wants to perform entity linking, a text file containing the terms and their respective Unique Resource Identifiers (URIs) can also be given as input. Therefore, adding a new lexicon to MER could not be easier than this. MER also accepts as input an ontology in Web Ontology Language (OWL) format, which it converts to a lexicon.

MER is not only minimal in terms of the input but also in its implementation, which was reduced to a minimal set of components and software dependencies. MER is then composed of just two components, one to process the lexicon (offline) and another to produce the annotations (online). Both were implemented as a Unix shell script [24], mainly for two reasons: (1) efficiency, due to its direct access to high-performance text and file processing tools, such as grep and awk , and a novel inverted recognition technique; and (2) portability, since terminal applications that execute Unix shell scripts are nowadays available in most computers using Linux, macOS or Windows operating systems. MER was tested using the Bourne-Again shell (bash) [25] since it is the most widely available. However, we expect MER to work in other Unix shells with minimal or even without any modifications.

We deployed MER in a cloud infrastructure to work as an annotation server and participate in the Technical Interoperability and Performance of annotation Servers (TIPS) task of BioCreative V.5 [26]. This participation allowed us to assess the flexibility, autonomy, and efficiency of MER in a realistic scenario. Our annotation server responded to the maximum number of requests (319k documents) and generated the second highest number of total predictions (7130k annotations), with an average of 2.9 seconds per request.

To analyze the statistical accuracy of MER’s results we compared it against a popular dictionary lookup solution, the Bioportal annotator [27], using a Human Phenotype Ontology (HPO) gold-standard corpus [28]. MER obtained the highest precision in both NER and NEL tasks, the highest recall in NER, and a lower processing time. Additionally, we compared MER with Aho-corasick [29], a well-known string search algorithm. MER obtained a lower processing time and higher evaluation scores on the same corpus.

MER is publicly available in a GitHub repository [30], along with the code used to run the comparisons to other systems.

The repository contains a small tutorial to help the user start using the program and test it. The remainder of this article will detail the components of MER, and how it was incorporated in the annotation server. We end by analyzing and discussing the evaluation results and present future directions.

Annotations can be considered ‘labels’ that are attached to inputs. A word, word sequence, sentence, or full input can have multiple annotations that you can use in your language conditions. In most cases machine learned models are used for annotations, like Named Entity Recognizers (NER) or Classifiers. Teneo adds various such annotations out of the box. Just like language objects, annotations are building blocks for language conditions.

The main components of an annotation are:

  • its name
  • its (optional) annotation variables

The name reflects what is annotated. The annotation variables can contain extra information related to the annotation. For example, an annotation USER_ASKS_ABOUT_OPENING_HOURS.TOP_INTENT may have a variable containing the confidence score. Annotation variables can be retrieved in the same way as entity variables.

Predefined annotations

Teneo provides various annotations out of the box. The main types of annotations are Named Entities (NER), Part of Speech (POS), and Language (LANG). An input like ‘Teneo’s a product from Artificial Solutions’ is annotated as follows:

Teneo is a product from Artificial Solutions
PRODUCT.NER ORGANIZATION.NER
NN.POS
SG.POS
PROPER.POS
VB.POS
PRESENT.POS
3RDPERSON.POS
DET.POS NN.POS
SG.POS
PREP.POS NN.POS
SG.POS
PROPER.POS
NN.POS
PL.POS
PROPER.POS
EN.LANG

A full list of all annotations can be found in the Annotations Reference.

Class annotations

In addition to the predefined annotations, Teneo can add annotations for Class Match Requirements. When Teneo receives an input, the class trigger with the highest confidence score will be annotated as the top intent, for example: WHERE_DO_YOU_HAVE_STORES.TOP_INTENT . For classes that have a confidence score that is close to the top intent, additional annotations can be added, like DO_YOU_HAVE_A_STORE_IN_CITY.INTENT .

When a class match requirement is generated in a flow, a class label is automatically created for that trigger. But you can also specify it yourself in the Class Manager window.

How to annotate a script

How to use annotations

You use annotations the same way as you use language objects in a condition. It’s just the prefix that looks slightly different. Instead of % , you add %$ before the name. Like this: %$LOCATION.NER . The following is an example of a condition using both a language object and an annotation:

Inspecting annotations in Try Out

If you want to see how an input is annotated, enter the input in Try Out and open the ‘Advanced’ window.. You will find all annotations in the Input section under ‘Annotations’. Hovering over an annotation will display additional information that is stored in annotation variables.

How to annotate a script

Creating annotations

While the predefined and class annotations will suffice in most cases, you’re not limited to just those. With a bit of script code, you can add your own annotation as well. This can for example be useful if you want to annotate inputs using regular expressions, which can be useful for annotating patterns like, for example, postal codes.

Annotations can be added in two places in Teneo — in Global listeners and Pre-matching global scripts. To add one, you need to use the Teneo Engine Backend API. The following two methods should be called to add an annotation (in Teneo an underscore is used as an alias for the Teneo Engine):

The createInputAnnotation method expects 4 arguments:

  • annotation_name : the name that you will use in language conditions later. The word will be annotated with this name and it’s good practice to uppercase the name of the annotation.
  • sentence_index : the index of the sentence that you want to annotate.
  • words_indices : the indices of the word(s) this annotation is assigned to (the first word has index 0, the set may be empty)
  • annotation_variables : variables you may want to add to the annotation, e.g. a confidence level. The annotation variables are provided as a map, the key is a string with the name of the annotation variable, and the value is an object. If you don’t want to provide annotation variables, use ‘null’ instead.

Suppose you would like to annotate the words ‘hunky dory’ as ‘MOOD’ in the sentence ‘I feel hunky dory’. And you also want to add an annotation variable ‘feeling’ with the value ‘good’ to the annotation. The code for this would look as follows:

The result in the Input section in Try Out would look like this:

How to annotate a script

You can find a practical example of a script used to add annotations in the Scripting section: How to annotate user inputs.

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I was curious on what is the farthest object I can image and was finding it way to manual. So I made a python script that will annotate your images in pixinsight with the Hubble distance in millions of light years (redshift->CMB frame->Hubble Distance). It is quite fun to see and compare object distances.

I could use some tips on improving this but thought it might be useful to others at this stage. I used someone else’s catalogue to get quasar list but have been unable to get redshift data from NED and will see if I can use a different database for those so future addition .

1. Plate solve your image in Pixinsight: Script->Image Analysis->Image Solver
2. Annotate image in Pixinsight: Script->Render->Annotate Image, make sure output to file is checked
3. Copy and paste the objects you want into an All_objects.txt file, remove any lines that are not objects, need to automate this still
* Important so far only tested with NGC/PGC objects others may vary depending on NED search return
4. Run Annotate image in Pixinsight again: Select your new catalogue Pixinsight_custom_catalogue.txt to annotate image with name – distance

Some cool examples:

Farthest Galaxy – 2,766 Million light years

M51 area more structure to glaxies

Attached Thumbnails

Edited by IzztMeade, 08 August 2021 – 12:07 PM.

  • Ptarmigan, PrestonE, noodle and 7 others like this

#2 lambermo

Nice, I’ve always used Aladin to query several online catalogues to find the object types, distances and magnitudes.

Would love to get some help from automation here.

How to annotate a script

For these two objects I’ve included the redshift, for others things like the parallax 😉

The challenge is to determine whether an object is ‘visible’ above the noise level or is drowned in it.

I’ve only briefly looked at the code, and I do not think your program help with that, right ? Any ideas on how to add this ?

Next, to get comparable magnitude readings I’ve added (a hint of) the filter with which the measurement was taken.

That should be an easier addition 😉 assuming Ned.get_table queries the (ned only?) online catalogue.

#3 whwang

This is cool. Is it possible to use redshift instead of light year?

#4 Mert

Edited by Mert, 20 August 2021 – 02:55 AM.

#5 lambermo

Ah, this was done manually in Aladdin

I do the object research in Aladin but the annotation text and the splines in layers in Gimp.
— Hans

ps. this small screenshot does not show what the splines are used for, that’s for a later post when it’s all done.

#6 Mert

#7 IzztMeade

This is cool. Is it possible to use redshift instead of light year?

Oh for sure, I pull the redshift out already and that is how I calculate the light year, would just need to add it to the text that gets imported by pixinsight

#8 IzztMeade

Nice, I’ve always used Aladin to query several online catalogues to find the object types, distances and magnitudes.

Would love to get some help from automation here.

Like this :

Screenshot_20210819_232835.png

For these two objects I’ve included the redshift, for others things like the parallax 😉

The challenge is to determine whether an object is ‘visible’ above the noise level or is drowned in it.

I’ve only briefly looked at the code, and I do not think your program help with that, right ? Any ideas on how to add this ?

Next, to get comparable magnitude readings I’ve added (a hint of) the filter with which the measurement was taken.

That should be an easier addition 😉 assuming Ned.get_table queries the (ned only?) online catalogue.

— Hans

yeah I have been thinking on how to autodetect. I am still trying to figure out how the survey scopes do it but for now just visually checking it was somewhat easy to tell by eye when it started looking like background noise

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    How to annotate a script

    Everyone who worked with an object detection problem knows how the process to annotate images is boring and spends time.

    Annotate itself it’s pretty simple, as the GIF below shows, we just need to mark the object location and tell its class. There is no problem doing it for some images, but when we have hundreds or even thousands of data in our dataset, which is normal in deep learning, it turns into a bottleneck for our work.

    In one of my last works, I needed to create my own dataset of coffee leaves and also label their diseases. At first, I annotated the images by hand in the LabelImage software and trained the model, but I was really unsatisfied with this approach because I was spending all my time creating and annotating the dataset, making it difficult to scale up the project.

    So I thought of a simple way to change this process and turn it into a more automatic one.

    With this new approach, I’ve created a Python class called generate XML, which is responsible for doing the hard work for me, annotating the images through the inference of a pre-trained model to get the positions of the bounding boxes and creating an XML that is used in the training.

    To run this project on your machine you will need to clone the TensorFlow repository on GitHub and install all the dependencies. As it can be a little difficult the first time, here you can find a complete tutorial explaining the step-by-step towards this process.

    The auto_annotate project was done in TensorFlow 1.14, but it’s also compatible with the TF 2.x.

    Inside the folder research/object_detection is almost everything we need, you can check the notebook called object_detection_tutorial, where they explain in detail the process to load a model and create new inferences.

    In my case, I used the auto annotate inside a NodeJS API, but we will create a directory scheme just to show the behavior, and then you can modify it to use anywhere you want.

    The scheme has a folder for the whole thing called auto_annotate and inside this, I have the following folders: images, scripts, results, graphs, and xml.

    • Images contain all photos you want to infer and create an XML
    • Results are the result of the inference
    • Script contains all python algorithms that we will use.
    • Graphs are the frozen inference graph and the label map.
    • xml is the folder containing the generated XML files.

    The main file is the detection_images.py, responsible to load the frozen model and create new inferences for the images in the folder. You will need to change the first lines to add your own path if it’s necessary. I also added some lines to change the image dimension and save the results, everything else is similar to the original file that you can find in the TensorFlow directory.

    The generate_xml.py file receives the name of the inferred class, the image dimension, filename, and an array of dictionaries with the bounding box coordinates.

    All this information is passed by the file visualization_utils.py, also found in the TensorFlow directory, we just need to do some adaptations as follows.

    With this setup, we just need to instantiate the class and run the method generate_basic_structure to generate the XML.

    In this method, we use the ElementTree (that you will need to install from pip) to create an XML structure based on what the LabelImage generates automatically to us, passing the box positions with a for loop and saving the file. Is important to remember that the name of the XML must be the same as the image file inferred.

    Before running the algorithm you will need to replace the file visualization_utils.py in the TensorFlow folder research/object_detection/utils with the file we have modified (remember they are always adding new changes in the TensorFlow repository, so depending on when are you doing this tutorial if you replace the visualization file from TF by mine this may not work, copy and paste the lines that have changed is safer, but you can try replacing first).

    You will also need to copy and paste the file generate_xml.py to the utils folder, in the same location as the visualization_utils.py.

    After that, you just need to enter in your folder (auto_annotate for me)and run:

    If everything is working you will see inside the results folder the inferred images, like this:

    How to annotate a script

    And in the XML folder, you will find the files containing the annotations, lets open the image and XML in the LabelImage.

    How to annotate a script

    Obviously, it’s not perfect yet, you may want to adjust the box positions to be more precise (depending on your problem domain) and will definitely need to create new labels when your pre-trained model doesn’t infer correctly, but we can say that it’s faster than doing all the work by hand.

    As long as we train more our model the inferences are more accurate and the annotating process gets easier.

    A more automatic way to annotate the dataset is something very important for everyone who works with object detection, allowing us to focus on what really matters instead of wasting time with this. This is just a first step, I hope soon arise new methods and algorithms to facilitate even more this process.

    Thanks for reading, I hope this tutorial could help you, please if you find in trouble or have some doubts let me know, I will be happy to help you. 🙂

    Export all the measurements for all the annotations in a project, in an orderly way.

    The first two scripts deal with some difficulties around exporting annotation measurements.

    QuPath has a built-in command to export TMA results under File → Export TMA data, and even a data viewer for results. It’s also possible to use Measure → Show annotation measurements and save the results for a single image. But batch-exporting isn’t as easy as it probably should be.

    Problem

    To run a script across multiple images in a project, you can use Run → Run for project in the script editor.

    What this does, effectively, is open each selected image in the project, run the script for that image, and save the results in a .qpdata file.

    If you only want to export, but not change anything related to the image and save a new data file, you should use ‘Run → Run for project (without save)’.

    One of the many things that can be done in a script is to write a new file, e.g. to export results. To do this, you would normally specify the name of the file you want to write.

    The trouble is that if you then Run for project such an export, you’ll only see the results for the last file that was processed. Each time the script is run for a new image, the last file will be overwritten. This is probably not what you want.

    Solution

    The solution is to calculate a new (and preferably unique) name for the export file within the script.

    Since we can assume the script is running for a project, the following shows a script can be used to determine a file name based on the name of the image being processed:

    The next task is to determine where to save the file, i.e. in which directory.

    This could be some absolute path to somewhere on your computer, but then the script wouldn’t be very portable. It’s much nicer to save the results in a directory relative to the project.

    The following line refers to a subdirectory inside the project called annotation results :

    Now we still need to do two things before we can write our results there:

    1. Make sure the directory exists! If it doesn’t, any attempt to write to it will fail. mkdirs helps here, creating the directory if necessary.
    2. Join together the directory path and the file name.

    Saving the annotations simply requires:

    The only (optional) extra thing to do is to log where exactly we wrote the file, in case we can’t find it later. This also helps make sure we can see that the script has finished running.

    The following line does this, where println means ‘print and take a new line’:

    If we had wanted, we could also have used parenthesis – but Groovy doesn’t insist on it.

    That’s it! In the next script we’ll look at how to merge these exported files together to create a single results table, suitable for import into a spreadsheet or some other software.

    PEP: PEP stands for Python Enhancement Proposal. It is a design document that describes new features for Python or its processes or environment. It also provides information to the python community.
    PEP is a primary mechanism for proposing major new features, for example – Python Web Server Gateway Interface, collecting the inputs of the community on the issues and documenting design decisions that have been implemented in Python.

    Function Annotations – PEP 3107 : PEP-3107 introduced the concept and syntax for adding arbitrary metadata annotations to Python. It was introduced in Python3 which was previously done using external libraries in python 2.x

    What are Function annotations?

    Function annotations are arbitrary python expressions that are associated with various part of functions. These expressions are evaluated at compile time and have no life in python’s runtime environment. Python does not attach any meaning to these annotations. They take life when interpreted by third party libraries, for example, mypy.

      Python supports dynamic typing and hence no module is provided for type checking. Annotations like

    Syntax of function annotations

      Note: The word ‘expression’ mentioned below can be the type of the parameters that should be passed or comment or any arbitrary string that can be made use by external libraries in a meaningful way.

    Grammar

    Visualizing Grammar : The parse tree is formed from the above grammar to give better visualization of the syntax of the python’s function and function annotations.
    How to annotate a script

    Sample Code

    The code below will clear the fact that the function annotations are not evaluated at run time. The code prints fibonacci series upto the ‘n’ positions.

    How to annotate a script

    If this is your first time creating movie magic, you might be wondering what a script actually is. Well, it can be an original story, straight from your brain. Or it can be based on a true story, or something that someone else wrote – like a novel, theatre production, or newspaper article.

    A movie script details all the parts – audio, visual, behaviour, dialogue – that you need to tell a visual story, in a movie or on TV. It’s usually a team effort, going through oodles of revisions and rewrites, not to mention being nipped ‘n’ tucked by producers, directors, and actors. But it’ll generally start with the hard work and brainpower of one person – in this case, you.

    Because films and TV shows are audiovisual mediums, budding scriptwriters need to include all the audio (heard) and visual (seen) parts of a story. Your job is to translate pictures and sounds into words. Importantly, you need to show the audience what’s happening, not tell them. If you nail that, you’ll be well on your way to taking your feature film to Hollywood.

    2. Read some scripts

    The first step to stellar screenwriting is to read some great scripts – as many as you can stomach. It’s an especially good idea to read some in the genre that your script is going to be in, so you can get the lay of the land. If you’re writing a comedy, try searching for ‘50 best comedy scripts’ and starting from there. Lots of scripts are available for free online.

    3. Read some scriptwriting books

    It’s also helpful to read books that go into the craft of writing a script. There are tonnes out there, but we’ve listed a few corkers below to get you started.

    • Your Screenplay Sucks! – William M. Akers
    • The Coffee Break Screenwriter – Pilar Alessandra
    • The 21st Century Screenplay – Linda Aronson
    • The Nutshell Technique – Jill Chamberlain
    • The Art of Dramatic Writing – Lajos Egri
    • Screenplay – Syd Field
    • The Sequence Approach – Paul Joseph Gulino
    • Writing Screenplays That Sell – Michael Hague
    • Getting It Write – Lee Jessup
    • On Writing – Stephen King
    • Inside Story Dara Marks
    • Story – Robert McKee
    • My Story Can Beat Up Your Story – Jeffrey Alan Schechter
    • Making a Good Script Great – Linda Seger
    • Save the Cat – Blake Snyder
    • The Writer’s Journey – Christopher Vogler
    • Into the Woods – John Yorke

    4. Watch some great films

    A quick way to get in the scriptwriting zone is to rewatch your favourite films and figure out why you like them so much. Make notes about why you love certain scenes and bits of dialogue. Examine why you’re drawn to certain characters. If you’re stuck for ideas of films to watch, check out some ‘best movies of all time’ lists and work through those instead.

    How to annotate a script

    Flesh out the story

    5. Write a logline (a.k.a. brief summary)

    You’re likely to be pretty jazzed about writing your script after watching all those cinematic classics. But before you dive into writing the script, we’ve got a little more work to do.

    First up, you need to write a ‘logline’. It’s got nothing to do with trees. Instead, it’s a tiny summary of your story – usually one sentence – that describes your protagonist (hero) and their goal, as well as your antagonist (villain) and their conflict. Your logline should set out the basic idea of your story and its general theme. It’s a chance to tell people what the story’s about, what style it’s in, and the feeling it creates for the viewer.

    In the olden days, you would print your logline on the spine of your script. This was so producers could quickly glance at it and decide whether they wanted to read the whole script. A logline does the same thing, but you usually tell people in person or include it when you give them the treatment.

    6. Write a treatment (a.k.a. longer summary)

    Once your logline’s in the bag, it’s time to write your treatment. It’s a slightly beefier summary that includes your script’s title, the logline, a list of your main characters, and a mini synopsis. A treatment is a useful thing to show to producers – they might read it to decide whether they want to invest time in reading your entire script. Most importantly, your treatment needs to include your name and contact details.

    Your synopsis should give a good picture of your story, including the important ‘beats’ (events) and plot twists. It should also introduce your characters and the general vibe of the story. Anyone who reads it (hopefully a hotshot producer) should learn enough that they start to feel a connection with your characters, and want to see what happens to them.

    This stage of the writing process is a chance to look at your entire story and get a feel for how it reads when it’s written down. You’ll probably see some parts that work, and some parts that need a little tweaking before you start writing the finer details of each scene.

    7. Develop your characters

    What’s the central question of your story? What’s it all about? Character development means taking your characters on a transformational journey so that they can answer this question. You might find it helpful to complete a character profile worksheet when you’re starting to flesh out your characters (you can find these for free online). Whoever your characters are, the most important thing is that your audience wants to get to know them, and can empathise with them. Even the villain!

    8. Write your plot

    By this point, you should have a pretty clear idea of what your story’s about. The next step is breaking the story down into all the small pieces and inciting incidents that make up the plot – which some people call a ‘beat sheet’. There are lots of different ways to do this. Some people use flashcards. Some use a notebook. Others might use a digital tool, like Trello, Google Docs, Notion, etc.

    It doesn’t really matter which tool you use. The most important thing is to divide the plot into scenes, then bulk out each scene with extra details – things like story beats (events that happen) and information about specific characters or plot points.

    While it’s tempting to dive right into writing the script, it’s a good idea to spend a good portion of time sketching out the plot first. The more detail you can add here, the less time you’ll waste later. While you’re writing, remember that story is driven by tension – building it, then releasing it. This tension means your hero has to change in order to triumph against conflict.

    Annotations are visual elements that you can use to add descriptive notes and callouts to your model. In addition to text-only annotations, you can create annotations that:

    Perform MATLAB ® commands

    Visually differentiate areas of block diagrams

    The following examples show how to programmatically create, edit, and delete annotations.

    Create Annotation Programmatically

    Programmatically create, modify, and view an annotation.

    Open a new model.

    Create an annotation with default properties using the Simulink.Annotation function.

    After creating the annotation, use dot notation to set property values. For example, apply an 18-point font and light blue background to the annotation.

    To view and briefly highlight the new annotation, use the view function.

    Programmatically Find and Modify Existing Annotations

    Programmatically find and modify the properties of an annotation.

    Open the vdp model.

    To find the annotations in the model, use the find_system function.

    To identify the annotations, query the text inside the annotations by using the get_param function.

    Suppose you want to apply a light blue background color to the ‘van der Pol Equation’ annotation.

    Get the Simulink.Annotation object by specifying the corresponding index of the array.

    Use dot notation to set the value of the BackgroundColor property.

    Delete Annotation

    Programmatically delete an annotation.

    Open the vdp model.

    To get the handles for the annotations in the model, use the find_system function.

    To identify the annotations, query the text inside the annotations.

    To delete the title of the model ( ‘van der Pol Equation’ ), get the Simulink.Annotation object that corresponds to the second handle.

    Delete the annotation from the model.

    Create Annotations That Contain Hyperlinks

    For rich-text annotations, you can use HTML formatting to add a hyperlink to text within the annotation.

    Open a new model.

    Create two annotations, moving one of the annotations so that it does not overlap the other.

    To create a hyperlink in the annotation, set Interpreter to ‘rich’ and define the hyperlink in the Text property.

    You can also embed MATLAB functions in the hyperlink.

    Add Image to Model

    Add an image to your model, such as a logo, by creating an image-only annotation.

    Open a new model and create an annotation in it.

    Change the annotation to display only the specified image.

    Create Area Programmatically

    Create an area annotation in a model.

    Open the vdp model.

    Create an area that includes some of the blocks in the model.

    How to annotate a script

    Create and Hide Markup Annotation

    To create annotations that can be easily hidden, create markup annotations.

    Open a new model.

    Create two annotations, and move the second annotation so that it does not overlap the first annotation.

    By default, you create model annotations, which appear in the model.

    Change the second annotation to a markup annotation.

    Configure the current model to hide markup annotations.

    Both annotations remain, despite the markup annotation being hidden.

    Find Annotation Executing Callback Function

    If an annotation invoked a currently executing callback function, use the getCallbackAnnotation to determine which annotation invoked it. The function returns the corresponding Annotation object. This function is also useful if you write a callback function in a separate MATLAB file that contains multiple callback calls.

    See Also

    Related Topics

    Open Example

    You have a modified version of this example. Do you want to open this example with your edits?

    To add a number type annotation to a variable or a constant, you can declare the variable or constant followed by a : (colon) symbol and then the type number in TypeScript.

    For example, let’s say we have a variable called myNumber and want to only have the number type as the values. To do that we can annotate the type number to the myNumber variable like this,

    After initialization, let’s assign a number value of 100 to the variable myNumber . It can be done like this,

    As you can see from the above code TypeScript allowed us to assign the number value of 100 to the variable myNumber since the allowed type for the myNumber variable is number .

    Now let’s try to assign another value of string type Hello World! to the myNumber variable to see what happens,

    As you can see that the TypeScript will throw an error saying that the Type ‘string’ is not assignable to type ‘number’. which is also what we want to happen.

    Bonus

    Most of the time TypeScript will try to infer the type of the variable from the value it is assigned.

    For example, let’s declare a variable called myFavNum and assign the number value of ‘1000’ without actually declaring the type number as we did above. It can be done like this,

    Now if you hover over the variable myFavNum we can see that TypeScript has automatically got that type of the variable myFavNum is of type number .

    Since the TypeScript has already got the type is of number value and whenever we try to reassign a value other than the type number , The compiler will throw errors like from the above.

    After your genome has gone through the gene prediction module and you have gene models that pass NCBI specs the next step is to add functional annotate to the protein-coding genes. Funannotate accomplishes this using several curated databases and is run using the funannotate annotate command.

    Funannotate will parse the protein-coding models from the annotation and identify Pfam domains, CAZYmes, secreted proteins, proteases (MEROPS), and BUSCO groups. If you provide the script with InterProScan5 data –iprscan , funannotate will also generate additional annotation: InterPro terms, GO ontology, and fungal transcription factors. If Eggnog-mapper is installed locally or you pass eggnog results via –eggnog , then Eggnog annotations and COGs will be added to the functional annotation. The scripts will also parse UniProtKb/SwissProt searches with Eggnog-mapper searches (optional) to generate gene names and product descriptions.

    InterProScan5 and Eggnog-Mapper are two functional annotation pipelines that can be parsed by funannotate, however due to the large database sizes they are not run directly. If emapper.py (Eggnog-mapper) is installed, then it will be run automatically during the functional annotation process. Because InterProScan5 is Linux only, it must be run outside funannotate and the results passed to the script. If you are on Mac, I’ve included a method to run InterProScan5 using Docker and the funannotate predict output will let the user know how to run this script. Alternatively, you can run the InterProScan5 search remotely using the funannotate remote command.

    Phobius and SignalP will be run automatically if they are installed (i.e. in the PATH), however, Phobius will not run on Mac. If you are on Mac you can run Phobius with the funannotate remote script.

    If you are annotating a fungal genome, you can run Secondary Metabolite Gene Cluster prediction using antiSMASH. This can be done on the webserver, submit your GBK file from predict (predict_results/yourGenome.gbk) or alternatively you can submit from the command line using funannotate remote . Of course, if you are on Linux you can install the antiSMASH program locally and run that way as well. The annotated GBK file is fed back to this script with the –antismash option.

    Similarily to funannotate predict , the output from funannotate annotate will be populated in the output/annotate_results folder. The output files are:

    Writing scripts is never foolproof. Here are some practical tips to help make sense of the jumble of letters and numbers.

    Administrators are experiencing an increase in the number of devices that they are tasked with managing at all levels of the enterprise. According to Statista, the number of devices in 2018 is expected to reach 6.58 connected devices per end-user. Inversely, while the number of devices is only expected to rise, IT professionals are expected to do more with less.

    Windows: Must-read coverage

    • 10 secret Microsoft-specific keyboard shortcuts in Windows 11
    • Plan for a Windows 10/11 reinstall by following these steps
    • Windows 11 cheat sheet: Everything you need to know
    • Windows 11: Tips on installation, security and more (free PDF)

    Luckily for us, tools such as PowerShell (PS) exist that allow admins the flexibility to leverage the infrastructure, allowing a few to do the job of many. Managing devices in an efficient manner is the key objective of utilizing PowerShell to drive your automation scripts. Regardless of whether your PS scripts are single line cmdlets or multi-line, complex functions–one string binds them all–if the code isn’t correct, it will most certainly fail.

    Keeping the following points in mind will aid in minimizing any potential issues while mitigating errors as you develop, test, and revise your code.

    SEE: Manage Active Directory with these 11 PowerShell scripts (Tech Pro Research)

    1. Research cmdlets

    There are many cmdlets available within PowerShell, some of them offer similar functionality though slightly differ depending on the intended use and desired outcome. Getting to know the cmdlets and how they work is tantamount to putting the proper cmdlets together, making the difference between generating information in the workflow or a process stopping error.

    2. Double-check your syntax

    PS does an admirable job of standardizing parameters and syntax across the various cmdlets. However, individual cmdlets may contain additional, unique parameters. Become familiar with these syntax changes that may exist between cmdlets and different versions of the Windows Management Framework (WMF).

    3. Annotate your code

    Adding comments to your code is extremely useful for those working in teams to share useful information, such as explaining what a particular line or function does, or simply revisiting one’s own code after a cooling off period to refresh your memory. Either way, including notes as part of the code can and does help the process along for both the initial writer and anyone following up afterward.

    4. Compartmentalize

    When working with large or complicated logical structures of code–especially those that perform many functions–don’t just rely on the previous set of instructions to execute correctly before the next set can be performed. It is often simpler to break down the coding into smaller sections. This makes the writing, testing, troubleshooting, and updating processes much easier to manage.

    5. Test, test, test

    It should go without saying that just about everything in the IT world needs to be rigorously tested before it is deployed. With this in mind, testing your code is imperative to ensure that it functions exactly as intended. Good thing PowerShell includes the Integrated Scripting Environment (ISE), a host application to write, test, and debug your PS scripts with built-in cmdlet and syntax information, a console with which to test cmdlets, and selective code execution allowing one to deploy specific portions of the code for verification purposes.

    6. Network with mentors

    We can all use a little (or a lot of) help from time to time, developers often employ coding methodologies that stress team development and peer auditing of the codebase, often performed by senior, more seasoned developers. These could be colleagues within your organization, members on a forum, or even users such as yourself that are part of a network of admins–like what you’d find on GitHub, for example–to aid in code corrections and point you in the right direction if you get stuck, even helping by reviewing scripts for errors.

    In this post I will showcase some of the features controlled by OData annotations. With the use of annotations in OData, we can minimize the UI View code to be written for conventional scenarios like showing text and filtering data.

    Example 1: Showing search help for Filter in Personalization Dialog

    Example 2: Showing Text

    Example 1

    The p13n dialog control provides a dialog for tables that allows the user to personalize one or more of the following attributes:

    • Columns
    • Sort
    • Filter
    • Group

    The third tab is the Filter tab, which allows the user to filter based on specific criteria.

    The filter criteria can be included or excluded in the relevant section of the filter.

    The user selects the column to be filtered. Any of the columns can be selected from the dropdown.

    The second field offers an operator for specifying the filter in more detail. The operators that are available depends on the data type of the selected column.

    Scenario

    Created OData ‘ZTEST_SEARCHHElP’

    Main Entity: Customer

    Value Help Entity: Customer Value Help

    How to annotate a script

    Click on Annotation at properties

    How to annotate a script

    How to annotate a script

    Click on com.sap.vocabularies.Common.v1

    Click on ValueList-> Create Annotation

    Pass the ValueHelp Entity in label and ValueHelp Entityset in CollectionPath

    How to annotate a script

    In DPC_EXT we need to implement the CustomerValueHelp entityset to fetch the value

    Annotations are graphic markers and review remarks you can add to a document preview. You can place annotations in a document preview wherever you want them. For example, using annotations you can add a pushpin marker to a document preview, and enter a comment about the document right where the pushpin is placed. Each person’s annotations appear in a unique color, making it easy to follow who said what.

    1. Open the document preview (see How do I view a file?).

    Note: Document previews are limited to the first 100 pages of a document.

    Table 7-1 Annotation Tools

    Select None when you want to move through the document without annotating text.

    Use the Pushpin tool to post an annotation to a pinpointed location.

    Use the Pen tool to create a free-form mark on the material you’re annotating.

    Use the Highlighter tool to highlight one line of the material you’re annotating.

    Use the Rectangle tool to draw a rectangle over the material you’re annotating.

    Use the Ellipse tool to draw a circle over the material you’re annotating.

    The icon to the left of the Tools menu shows the tool that is currently selected.

    If you don’t see the Tools menu at the top of a document on a person’s wall, the person does not allow other people to post annotations to his or her personal documents.

    For the pushpin, click the location you want to annotate.

    For the drawing tools, drag them to surround or highlight the text you want to annotate. Drawing tools include the pen, the highlighter, the rectangle, and the ellipse.

    A dialog opens where you can enter your remarks.

    Example 7-1 Associating Multiple Annotations

    You can mark several disconnected sections of text and combine them into a single annotation. After adding the first mark, with the annotation box still open, use the selected tool to make additional marks on the same page (you can’t join marks on multiple pages). After you add an annotation message and click Continue , the application draws a larger box around all the marks, and ties the single annotation message to all of them.

    The plot annotation function has one mandatory parameter: a value of series type, which it displays as a line. A basic call looks like this:

    Pine’s automatic type conversions makes it possible to also use any numeric value as an argument. For example:

    In this case, the value 125.2 will automatically be converted to a series type value which will be the same number on every bar. The plot will be represented as a horizontal line.

    The plot annotation has many optional parameters, in particular those which set the line’s display style: style , color , linewidth , transparency , and others.

    The value of the color parameter can be defined in different ways. If it is a color constant, for example color.red , then the whole line will be plotted using a red color:

    How to annotate a script

    The value of color can also be an expression of a series type of color values. This series of colors will be used to color the rendered line. For example:

    How to annotate a script

    The offset parameter specifies the shift used when the line is plotted (negative values shift to the left while positive values shift to the right) 1. For example:

    How to annotate a script

    As can be seen in the screenshot, the red series has been shifted to the left (since the argument’s value is negative), while the green series has been shifted to the right (its value is positive).

    In Pine there is a built-in offset function which shifts the values of a series to the right while discarding ‘out of range’ values. The advantage of the offset function lies in the fact that its result can be used in other expressions to execute complex calculations. In the case of plot function’s offset parameter, the shift is only cosmetic; the actual values in the series are not moved.

    Annotate PDF files by highlighting and adding text, images or shapes

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    Select the file you want to annotate. Annotate your file with tools like adding text, images, shapes. Save your file as a PDF.

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    PDF24 makes it as easy and fast as possible to annotate files. You don't have to install or configure anything, just edit your file here.

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    How to annotate a scriptDeveloped by Stefan Ziegler

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    1. Select the PDF file you want to annotate using the file selection box on this page. Your PDF will then be opened in the PDF24 Editor.
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    3. After editing, click on the save icon in the toolbar and then use the download button to save your annotated PDF on your computer.

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    Yes, you can use PDF24 Tools on any system with which you have access to the Internet. Open PDF24 Tools in a web browser such as Chrome and use the tools directly in the web browser. You do not need to install any other software.

    You can also install PDF24 as an app on your smartphone. To do so, open the PDF24 Tools in Chrome on your smartphone. Then click on the “Install” icon in the upper right corner of the address bar or add PDF24 to your start screen via the Chrome menu.

    A simple-to-use script for annotation of VH or VL sequence of an antibody. It creates a Pymol object for each FR or CDR region. It utilizes the REST API interface of Abnum (http://www.bioinf.org.uk/abs/abnum/) from Dr Andrew Martin’s group at UCL

    Annotation Schemes

    Currently supports Kabat, Chothia, Contact, IMGT 1

    1 Definitions for Kabat, Chothia, Contact, and IIMGT are same as listed in the table (http://www.bioinf.org.uk/abs/info.html#kabatnum), except that for IMGT, H-CDR2 is defined as H51-H57 in this script, as opposed to of H51-H56 in the table. This slight revision generates result that matches that from IMGT website (http://www.imgt.org/)

    Dependencies and Limitations

    1. Import request module

    2. Relies on internet connection to Abnum

    3. Incomplete VH or VL sequence might not be annotated

    How to use

    1. Create a selection object for the V region, (such as VH and VL shown here). Only 1 V-region (VH OR VL) can be annotated each time. Alternatively, simply select the residues into the default group.

    How to annotate a script

    2. Copy the entire script and create a `.py` file. Run script.

    3. The general syntax is:

    selection_group_name: name of the selection group where you have the input sequence. Must be single-letter amino acid coded. Either VH or VL but not both

    scheme: currently supports Kabat, Chothia, Contact, IMGT. Must be lowercase

    4. In the output window, the script will print the FR and CDR regions (if found). It also automatically creates a selection group for each of the FR and CDR region.

    How to annotate a script

    One regularly asked question on EditStock is “How do I read a lined script?” First you’ve got know what a lined script is.

    A lined script is a document created by the script supervisor (AKA scripty) during production. The scripty sits next to the director on set and acts like the eyes and ears of the editor.

    On the creative front, the scripty takes detailed notes on what the directors says are the best performances (called circle takes). The scripty also takes notes when the dialog was improvised and therefore different than the script, keeps track of actor continuity, and keeps track of dozens of other details like what wardrobe the actors were wearing.

    On the technical front, the scripty keeps track of what camera roll the camera department is on, what sound roll the sound production is on. The scripty is an assistant editor’s best friend.

    Facing vs Lined Pages

    A lined script is made up of two types of pages: lined, and facing.

    The lined pages are probably what you think of when someone refers to a lined script. These pages look like the script except that they have a bunch of squiggly lines drawn through the text.

    The lined pages give you an idea of what the coverage (different camera angles) looks like for any given moment of the scene.

    The facing pages are the pages that are placed on the left side of the editors binder. The facing pages give you technical understanding of the filmmaking.

    How to annotate a script

    How the Editor Gets the Pages

    Every day when production wraps someone from production will bring the days lined pages over to the post production department. The assistant editors will then put those pages away into the lined script binder.

    It’s important to note that reality shows and documentaries do not get lined scripts. Only projects that start with a script get a lined script. Most indie projects do not create lined scripts because of the cost hireing a scripty, but lined scripts more important as your productions get more and more complicated. Every high end project will have a script supervisor though.

    Let’s dig into a couple pages of a lined script from the film Anesthesia EDU:

    Lined Page

    On this page we see straight lines and squiggly lines. The straight line means that you CAN see the actor’s face in that camera angle. The squiggle means you CANNOT see the actor’s face. Let’s take a look at shot 3A-3 from Anesthesia

    Now take a look at what the lined script says about this shot. We can see that Dr. Clayton had the squiggly line and we didn’t see his face.

    How to annotate a script

    We can also tell where a shot starts and stops. If you look at the bottom of Mary’s line “You could say that” you’ll see a line indicating that this is where shot 3A ends. However, shots 4B, 4C, and 4D continue on to the next page because they have an arrow pointing down.

    Facing Page

    The facing page is like a cheat sheet of what coverage was shot for that scene. For example with shot 3A I know I have three takes. Notice that takes two and three are circled. These “circle takes” are the ones the director likes the most. That doesn’t mean we don’t look at take one. We just know that the director liked takes two and three better.

    How to annotate a script

    The facing page also tells me that the RED file for shot 3A is on camera roll A2. As an assistant editor this saves you tons of time tracking things down.

    Be Brave and Go Forth!

    Years ago I was working as an assistant editor on my first film. I spotted the word SER in the editor’s lined script and wrote myself a note to look it up what SER meant. The next day I was very embarrassed when the editor came in, saw the note, and explained to me that SER means the take is a series of shots but without any cuts, and so not technically take two or three. That editor, who I consider a friend to this day, told me that there was nothing to be embarrassed about. My advice to you is the same. Ask questions! Lined scripts have tons of short hand like NG, GT, MOS, SER, and the list goes on (that’s an article for another day). There is no way to learn without asking.

    View all of our projects in the Edit Shop.

    10 comments

    I have a script that has a double squiggle in the left margin. It starts and end with dialogue only. It looks to me that this a wild line. Is this correct?
    This is great info and a lifesaver. Thank you.

    What’s the differences between Facing Pages & Editorial Log. I’ve been asked to have this two format for my continuity log .

    What does BSF mean on a script facing page?

    Hi Anna, Lined scripts can be made on computers or by hand. It depends on the script supervisor.

    Is the ‘facing page’ usually completed on set with a computer, or is it written into a table and typed up later? I am not sure if these examples are polished just for this article or real examples.

    Grafana is a tool that helps users identify and fix performance issues by allowing them to monitor and analyze their database. Grafana is famous for making great graphs and visualizations, with tons of different functionalities.

    This Grafana tutorial is about one of these functionalities: Annotations. Grafana annotations are for users who want to make notes directly onto the graphs in their dashboards.

    There are various reasons a user might want to do this. For example, if a specific event happened that affected the data, it makes sense to leave a note describing the event directly on the graph. This can be done using Grafana Annotations.

    In MetricFire’s Hosted Grafana, users can use Grafana directly in the web app without installing or setting up Grafana on their own machines. You can use all of the features of Grafana in the MetricFire platform, including Annotations. In some cases, you can even have annotations set up for you automatically through our add-ons. Check out our automatic Github annotations feature here.

    To follow along with this Grafana tutorial, you should sign up for the MetricFire free trial. You can get your MetricFire Grafana dashboard set up in minutes and start making annotations right away.

    This Grafana tutorial will take you through the following concepts:

    1. Annotations
    2. Built-in querying
    3. Querying by tag
    4. Querying other data sources
    5. Annotations automations

    Annotations

    Annotations are useful to pinpoint rich events on a graph. When you have an annotation set up, it will enable you to simply hover over an annotation on the screen to see the important details.

    Each annotation has the time, the date, tags, and a text field where you can fill in key information. Often, the text field is used to include links to relevant other systems that were involved in the event, such as Github or CircleCI.

    Here is an example of an annotation made automatically from Sentry:

    Adding annotations manually from the graph panel is quite seamless. You simply hold down Ctrl/Cmd and then click on the place you want to annotate. A window will pop up and you can fill in the blanks to make your annotation.

    If you fill in a few tags, it will make your annotation searchable from your entire Grafana interface, even other dashboards. This helps bring together all relevant information when necessary.

    You don’t have to annotate on just a single point, you can also annotate over an entire area of your graph. This is called a Region Annotation. To make a region annotation, just hold down Ctrl/Cmd, but instead of clicking, drag your cursor to highlight the area you want to annotate.

    Built-In Querying

    After you have added the annotation, it will stay visible. This is because there is a built-in query function that automatically searches and displays all annotations for the dashboard being viewed. This is one of the ways you can query and view your annotations.

    The annotations are visible in the picture below as the orange vertical dotted lines with small arrows at their base:

    It is possible to stop the annotations from being shown by opening the annotations settings. You can find it in the cogs menu on your dashboard. All you need to do is turn off the built-in annotations and alerts query in that menu, and your annotations will become invisible. At that point, to retrieve annotations you will have to search them by tag or by other details.

    One note about copying dashboards – you can use the “save as” feature to copy a dashboard. Keep in mind that when you do this, the annotations will not be automatically copied over. The annotations will remain on the source dashboard only.

    There is one work around to get the same annotations to show on the new copied dashboard. This is done by adding an annotation query to your new dashboard with a “filter by tags”. It is important to know that this will work only if the annotations on the original source dashboard had tags that allowed you to filter.

    Querying by Tag

    You can create new annotation queries that retrieve annotations from anywhere in your Grafana UI, this unified storage of annotations across dashboards is known as the “Grafana native annotation store”. Start your query, and then indicate the “data source”, and set the “Filter by” option to “Tags”. Then you simply specify at least one tag. Here’s an example:

    • Create an annotation query name “holidays” and specify a tag named “holiday”.
    • This query will show all annotations you create (from any dashboard or via API) that have the “holiday” tag.

    By default, if you add more than one tag to an annotation query, Grafana will only display annotations that contain all of the tags that you have specified. You can make it so that Grafana will show any annotations that have any of the tags you specified. Just go to the annotations settings menu and specify “Match” as your filter method. This will make it so that Grafana will show annotations that have at least one of the annotations you queried.

    Querying Other Data Sources

    You can also query other data sources by opening the dashboard settings menu and select the annotations menu. On this menu viewer you will be able to make new Annotations queries. You can set the name, the data source, and the tags for each annotation.

    Here is an example of the annotations edit menu on MetricFire’s Hosted Grafana platform:

    Automating Grafana Annotations

    You can get various tools in your development environment to automatically publish annotations to your Grafana panels. Some of the most useful automations are for GitHub, CircleCI and Sentry. Of course, there are many more plugins available for other automatic annotations.

    In MetricFire, these plugins are already set up for you, so all you need to do is copy and paste your webhook into the appropriate text field of your account with the tool you’re using.

    For example, to get GitHub to automatically publish annotations to your Grafana dashboards on the MetricFire platform, all you need to do is copy and paste your MetricFire webhook into this GitHub payload URL:

    Then, whenever GitHub gets an update, such as a push or a merge, your related Grafana panels will be automatically populated with an annotation.

    To read more about automating Grafana annotations, take a look at our articles here:

    Summary and Conclusion

    Annotations are pivotal for being able to navigate your monitoring stack. Sign up for the MetricFire free trial and build Grafana dashboards now. You can set up annotations within minutes, and integrate your dashboards with other tools. You can also book a demo and talk to the MetricFire team about how you can best monitor your traffic.

    For more information on Grafana Tutorials, check out the MetricFire blog and our dedicated Grafana page.

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    Summary: in this tutorial, you will learn about type annotations in TypeScript.

    What is Type Annotation in TypeScript

    TypeScript uses type annotations to explicitly specify types for identifiers such variables, functions, objects, etc.

    TypeScript uses the syntax : type after an identifier as the type annotation, where type can be any valid type.

    Once an identifier is annotated with a type, it can be used as that type only. If the identifier is used as a different type, the TypeScript compiler will issue an error.

    Type annotations in variables and constants

    The following syntax shows how to specify type annotations for variables and constants:

    In this syntax, the type annotation comes after the variable or constant name and is preceded by a colon ( : ).

    The following example uses number annotation for a variable:

    After this, you can only assign a number to the counter variable:

    If you assign a string to the counter variable, you’ll get an error:

    You can both use a type annotation for a variable and initialize it in a single statement like this:

    In this example, we use the number annotation for the counter variable and initialize it to one.

    The following shows other examples of primitive type annotations:

    In this example, the name variable gets the string type, the age variable gets the number type, and the active variable gets the boolean type.

    Type annotation examples

    Arrays

    To annotate an array type you use use a specific type followed by a square bracket : type[] :

    For example, the following declares an array of strings:

    Objects

    To specify a type for an object, you use the object type annotation. For example:

    In this example, the person object only accepts an object that has two properties: name with the string type and age with the number type.

    Function arguments & return types

    The following shows a function annotation with parameter type annotation and return type annotation:

    In this example, you can assign any function that accepts a string and returns a string to the greeting variable:

    The following causes an error because the function that is assigned to the greeting variable doesn’t match with its function type.