fitzgerald | A Tiny PHP framework inspired by Sinatra | Web Framework library

You were expecting that by checking the id property of a Partial object for truthiness, you would get the compiler narrow the object from Partial to Partial & {id: string}. Unfortunately this is not how narrowing works in TypeScript. See microsoft/TypeScript#16976 for a (longstanding) open feature request to support this sort of thing.

Currently if you check the value of a property like b.id, it will only narrow the type of the property b.id itself, and not the type of the containing object b... well, unless b is of a discriminated union type and id is its discriminant property. But Partial is not a union at all, let alone a discriminated one. Oh well.

Here are the workarounds I can think of. One is to reassemble your object from the narrowed property and the rest of the object, via something like object spreading:

How's this?

This is executing it before the click:
:class="{'text-white bg-success' : checkAnswer(answer)}".
You'll need to keep the state in a variable for each answer and update it within the method. And as a side node, it is recommended to use :key for looped elements.

Apparently the behaviour is specific to IOS. I fixed it by not linking directly to the product page on IOS, but to the search page of the product.

Like this: https://www.amazon.com/s?k=0099590085

I added the product ID as keyword param. Now your affiliate link will work and when the user click on your affiliate link it will not redirect back to browser (if they have the Amazon app). Just it adds one unnecessary step for your user to have to click on the product, but this is still better than them getting redirected back to the browser.

If anyone has a better idea or if it got fixed, please let me know.

If we simplefy the data, we'll get something like

One simple way would be to play with the scopes, so the inmutable borrowing are gone before you borrow it mutable again:

You can create a second x-axis, use that to show only the year while using your original x-axis to show the month as a word. Here's this approach using your example. It will look like this.

This is a little tricky. SQL Server 2017+ supports concat_ws(). However, you still need a comma for the suffix. Forturnately, concat() ignores NULLs:

TL;DR Skip to Solution for method 2 to see the optimal solution, or skip to the last example for a solution with a single pandas line plot. In all three examples, weekends are highlighted using just 4-6 lines of code, the rest is for formatting and reproducibility.

I am aware of two methods to highlight weekends on plots of time series, which can be applied both to single plots and to small multiples by looping over the array of subplots. This answer presents solutions for highlighting weekends but they can be easily adjusted to work for any recurring period of time.

Method 1: highlight based on the dataframe index

This method follows the logic of the code in the question and in the answers in the linked threads. Unfortunately, a problem arises when a weekend day occurs at the end of the month, the index number that is needed to draw the full span of the weekend exceeds the index range which produces an error. This issue is solved in the solution shown further below by computing the time difference between two timestamps and adding it to each timestamp of the DatetimeIndex when looping over them to highlight the weekends.

But two issues remain, i) this method does not work for time series with a frequency of more than a day, and ii) time series based on frequencies less than hourly (like 15 minutes) will require the drawing of many polygons which hurts performance. For these reasons, this method is presented here for the purpose of documentation and I suggest using instead method 2.

Method 2: highlight based on the x-axis units

This method uses the x-axis units, that is the number of days since the time origin (1970-01-01), to identify the weekends independently from the time series data being plotted which makes it much more flexible than method 1. The highlights are drawn for each full weekend day only, making this two times faster than method 1 for the examples presented below (according to a %%timeit test in Jupyter Notebook). This is the method I recommend using.

Tools in matplotlib that can be used to implement both methods

axvspan link demo, link API (used in Solution for method 1)

broken_barh link demo, link API

fill_between link demo, link API (used in Solution for method 2)

BrokenBarHCollection.span_where link demo, link API

To me, it seems that fill_between and BrokenBarHCollection.span_where are essentially the same. Both provide the handy where argument which is used in the solution for method 2 presented further below.

Here is a reproducible sample dataset used to illustrate both methods, using a frequency of 6 hours. Note that the dataframe contains data only for one year which makes it possible to select the monthly data simply with df[df.index.month == month] to draw each subplot. You will need to adjust this if you are dealing with a multi-year DatetimeIndex.

Import packages used for all 3 examples and create the dataset for the first 2 examples