Windows

Fixing the HP Z Book Function Keys

The HP Z Book is a great development laptop for anyone that needs to build using the Windows stack. There is one minor annoyance, by default the function keys are not mapped to your standard F1 through F12.

To fix this, press fn+ctrl+shift. The standard function key behavior will then be active.

.NET

MailKit Converting a InternetAddressList to a MailAddressCollection

If you need to work with email in a .NET environment I highly recommend checking out MailKit by Jeffrey Stedfast. MailKit provides a higher level abstraction working across the different email protocols, which is a massive time saver.

MailKit has its own classes for handling Email Addresses. Most notability the MailboxAddress and InternetAddressList classes are returned when parsing a message envelope.

As powerful as the MailKit constructs are, I typically like to convert any 3rd party types into their .NET framework equivalents.  The below extension method adds a ToMailAddressCollection method to MailKit’s InternetAddressList class.

NLP, Swift

Swift Trimming Trailing Punctuation

Lately, I’ve been working on a project that requires some basic MLP text processing to be performed on device.  A component of our text processing involved removing any trailing punctuation, no matter the language.

I’ve found the below String Extension is a good balance between effectiveness, performance, and readability.  Especially if you are collecting input from users prone to excessive punctuation.

 

iOS, Tools, Xcode

Freeing the space used by Xcode with XcodeCleaner

With every release of Xcode it seems to eat more and more space. I was thrilled to find the XcodeCleaner project from Baye on Github.  This is a great project that allows you to tailor the space Xcode uses.  I’ve been able to free up an easy 12GB without any issues.

I highly recommend checking out the project on GitHub at XcodeCleaner. You can build the project from source or download from the macOS App Store for $0.99.
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screenshot

Swift

Sentiment Analysis using Swift

Lately I’ve been working on several projects using conversational UI or “Bot” interactions.  A key interaction pattern when designing “Bots” you must listen to both the intent and sentiment of the text the user is providing.  Through the use of sentiment analysis you can help determine the mood of the user. This can be an important tool in determining when to offer help or involve a human.  You might think of this as the “help representative” moment we are all familiar with.  Using sentiment analysis you can try to offer help before that moment occurs.

There are several great sentiment analysis node.js packages but nothing I could find to run offline in Swift. A majority of the node.js projects seem to be a forks of the Sentiment package. The Sentiment-v2 package worked best for many of my cases and became my starting point.

A majority of the sentiment analysis  packages available through NPM use the same scoring approach. First they parse a provided phrase into individual words. For example “Cats are amazing” would be turned into an array of words, ie [“cats”, “are”,”amazing”].

Next a dictionary of keywords and associated weights are created. These scoring dictionary is created using the AFINN wordlist and Emoji Sentiment Ranking. In a nutshell, words like “amazing” would have a positive weight whereas words like “bad” would have a negative weight. The weight of each word in the provided phrase is added together to get the total weight of the phrase. If the phrase has a negative weight, chances are your user is starting to get frustrated or at least talking about a negative subject.

Using this approach I created the SentimentlySwift playground to demonstrate how this can be done on device using Swift.  This playground uses the same FINN wordlist and Emoji Sentiment Ranking weights to determine a sentiment analysis score without the network dependency.   To make comparisons easier, I tried to mirror the Sentiment package API as must as possible.  The below demonstrates the output for a few of the test phrases included with Sentiment.

Although the APIs are similar there is one important difference between the two approaches. The SentimentlySwift playground uses NSLinguisticTagger to tokenize the provided phrase. Using NSLinguisticTagger, SentimentlySwift first parsers each word into a series of word slices. Each slice is a word tokenized using the options provided to the NSLinguisticTagger. Next the slides are enumerated and an optional “tag” or word stem is calculated. For example, in the phrase “cats are amazing”, the “amazing” word generates a word stem of “amaze”. A better example would be the word “hiking” produces the word stem of “hike”.

The following snippet shows an example on how this can be implemented.

You might be asking why this is important? By implementing Lemmatisation you increase your AFINN hit rate and improve your overall analysis scoring.

This is one trick I’ve found for improving or at least monitoring your conversational UI or “Bot” interactions.

iOS, Tools, Xcode

Fastlane: Solving the WatchKit CFBundleVersion matching problem

Fastlane is one of the tools that a developer shouldn’t live without. At this point it must have saved me hundreds of hours.  As great as fastlane is out of the box it has a few rough edges when you introduce a WatchKit app.

Recently I added a WatchKit app into one of my projects and started getting the below error:

error: The value of CFBundleVersion in your WatchKit app’s Info.plist (262) does not match the value in your companion app’s Info.plist (263). These values are required to match.

In a nutshell, Apple requires that your WatchKit app and it’s companion app share the same version. If your fastlane build includes increment_build_number these will never match by default.

Solving this problem is pretty easy, although alittle more involved then just changing your versioning system like in your core app. Hopefully the below will help people Googling for this error in the future.

Step 1: Update your Watch App’s Versioning Configurations

The first thing you will need to do is update the versioning configuration for your Watch App’s target.

  1. In Xcode click on your Watch App’s target
  2. Go to the Build Settings tab
  3. Scroll (or search) for the Versioning section
  4. Update the “Current Project Version” to match your core app target’s version. It is important you get this correct the first time.
  5. Switch the “Versioning System” to “Apple Generic”

watchapp-versioning.png

With these changes in place fastlane increment your Watch App’s Current Project Version every time increment_build_number is called. This will work just like it does in your core app target.

Step 2: Update your Watch App’s Extension Configuration

Just like step 1, you will need to update the versioning configuration for your Watch App’s extension target.

  1. In Xcode click on your Watch App’s Extension target
  2. Go to the Build Settings tab
  3. Scroll (or search) for the Versioning section
  4. Update the “Current Project Version” to match your core app target’s version. It is important you get this correct the first time.
  5. Switch the “Versioning System” to “Apple Generic”

watchext-versioning.png

This again let’s fastlane increment your Watch App’s Extension when increment_build_number is called.

Step 3: Keeping your CFBundleVersion in sync

If you run your fastlane build process now you will notice you still get an error that your CFBundleVersion information does not match. Whereas fastlane is updating the Current Project Version in both your Watch App and Watch App Extension it doesn’t update their CFBundleVersion value.

The best way I’ve found to do this is to use PlistBuddy and update the CFBundleVersion as part of the fastlane build process. Below is a small convenience function created to manage the process of updating the build numbers.

For my workflow I’ve incorporated the setBuildNumberOnExtension helper function into my build method that is called as part of my deployment, testing, or CI process.

The same approach should work for all extensions. Even if you only have a “Today Extension” I’d recommend keeping the version numbers in sync with your core app. This will make life easier when you are debugging.

Check of the fastlane examples for more ideas on how to create extensions to improve your workflow.

iOS, Swift

Thinking about Memory: Converting UIImage to Data in Swift

How often do you convert a UIImage into a Data object? Seems like a relatively straight forward task, just use UIImageJPEGRepresentation and your done.

After doing this I started seeing memory spikes and leaks appear which got me thinking on how I can better profile different options for performing this conversion. If you want to follow along you can create your own Swift Playground using this gist.

Approaches

The first step was looking at the different ways you can convert a UIImage into Data. I settled on the following three approaches.

UIImageJPEGRepresentation

Out of all the options this is the most straightforward and widely used. If you look at the testing blocks later in the post you can see I’m simply inlined the UIImageJPEGRepresentation with the test suite compression ratio.

UIImageJPEGRepresentation within an Autorelease Pool

Out of the box UIImageJPEGRepresentation provides everything we need. In some cases I’ve found it holds onto memory after execution. To determine if wrapping UIImageJPEGRepresentation in a autoreleasepool has any benefit I created the convenience method UIImageToDataJPEG2. This simply wraps UIImageJPEGRepresentation into a autoreleasepool closure as shown below. We later use UIImageToDataJPEG2 within our tests.

Using the ImageIO Framework

The ImageIO framework gives us a lower level APIs for working with images. Typically ImageIO has better CPU performance than using UIKit and other approaches. NSHipster has a great article with details here. I was interested to see if there was a memory benefit as well. The below helper function wraps the ImageIO functions into an API similar to UIImageJPEGRepresentation.  This makes testing much easier. Keep in mind you’ll need to have image orientation yourself.  For this example we just use Top, Left. If you are implementing yourself you’ll want read the API documentation available here.

What about UIImagePNGRepresentation?

UIImagePNGRepresentation is great when you need the highest quality image. The side effect of this is it has a largest Data size and memory footprint. This disqualified UIImagePNGRepresentation as an option for these tests.

Testing Scenarios

For my scenarios it was important to understand how memory is impacted based on the following:

  • Number of executions, i.e. what is the memory impact for calling an approach on one or many images.
  • How the Compression ratio impacts memory usage.

Image quality is an important aspect of my projects, so the tests where performed using the compression ratios of 1.0 and 0.9.  These compression ratios where then run using 1, 2, 14, 20, and 50 executions.  These frequencies demonstrate when image caching and Autorelease Pool strategies start to impact results.

Testing Each Approach

I test each of the above mentioned approaches using the template outlined below.  See the gist of the details for each approach.

  1. At the top of the method a memory sample is taken
  2. The helper method for converting a UIImage to a Data object is called in a loop.
  3. To make sure we are measure the same resulting data across tests, we record the length of the first Data conversion.
  4. When the loop has completed the proper number of iterations the memory is again sampled and the delta is recorded.

There is some variability on how each approach is tested.

The implementation for each approach is slightly different, but the same iteration and compression ratios are used to keep the outcome as comparative as possible.  Below is an example the strategy used to test the JPEGRepresentation with Autorelease Pool approach.

Test Results

Below is the result broken down by iteration.

Results for 1 Iteration

uiimagetodata-1

Results for 2 Iterations

uiimagetodata-2

Results for 14 Iterationsuiimagetodata-14

Results for 20 Iterations

UIImageToData-20.png

Results for 50 Iterations

uiimagetodata-50

Conclusion

I am sure there is a ton of optimizations that could be made to bring these numbers down. Overall the usage of UIImageJPEGRepresentation wrapped within an Autorelease Pool looks to be the best approach.  There is more work to be done on why the compression ratio has an inconsistent impact, my guess is this is a result to caching within the test.

Although the ImageIO strategy was better in a single execution scenario I question if the proper handling of image orientation would reduce or eliminate any of your memory savings.

Caveats

There are more comprehensive approaches out there. This is just an experiment using Playgrounds and basic memory sampling.  It doesn’t take into account any memory spikes that happen outside of the two sampling points or any considerations around CPU utilization.

Resources

  • Gist of the Swift Playground is available here