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ScrollBehaviors now allow or disallow drag scrolling from specified PointerDeviceKinds. ScrollBehavior.dragDevices, by default, allows scrolling widgets to be dragged by all PointerDeviceKinds except for PointerDeviceKind.mouse.

From the documentation, pandas.DataFrame.drop has the following parameters:

Parameters

• labels: single label or list-like Index or column labels to drop.

• axis: {0 or ‘index’, 1 or ‘columns’}, default 0 Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).

• index: single label or list-like Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).

• columns: single label or list-like Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).

• level: int or level name, optional For MultiIndex, level from which the labels will be removed.

• inplace: bool, default False If False, return a copy. Otherwise, do operation inplace and return None.

• errors: {‘ignore’, ‘raise’}, default ‘raise’ If ‘ignore’, suppress error and only existing labels are dropped.

Moving forward, only labels (the first parameter) can be positional.

So, for this example, the drop code should be as follows:

First of All, Yes you can do this with high accuracy if the GPS coordinates are accurate.

Second, the main problem is rotation if the field are straight with lat lng lines this would be easy and straightforward (no bun intended).

The easy way is to convert coordinate to rotated image similar to the real field then rotated every X,Y point to the new straight image. (see the image below)

Here is how to rotate x,y knowing the angel:

I don't think kendo provides any native solution for that but what I can suggest is to:

1. Use legends to display each bar meaning. like the example here.

2. Use some self generated labels and position them under the table which is risky for UI. I provided an example here.

From the documentation of DataFrame.assign:

DataFrame.assign(**kwargs)

(...)

Parameters **kwargs : dict of {str: callable or Series}

The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

This means that in

For this problem, I don't recommend using Kmeans color quantization since this technique is usually reserved for a situation where there are various colors and you want to segment them into dominant color blocks. Take a look at this previous answer for a typical use case. Since your CT scan images are grayscale, Kmeans would not perform very well. Here's a potential solution using simple image processing with OpenCV:

1. Obtain binary image. Load input image, convert to grayscale, Otsu's threshold, and find contours.

2. Create a blank mask to extract desired objects. We can use np.zeros() to create a empty mask with the same size as the input image.

3. Filter contours using contour area and aspect ratio. We search for the lung objects by ensuring that contours are within a specified area threshold as well as aspect ratio. We use cv2.contourArea(), cv2.arcLength(), and cv2.approxPolyDP() for contour perimeter and contour shape approximation. If we have have found our lung object, we utilize cv2.drawContours() to fill in our mask with white to represent the objects that we want to extract.

4. Bitwise-and mask with original image. Finally we convert the mask to grayscale and bitwise-and with cv2.bitwise_and() to obtain our result.

Here is our image processing pipeline visualized step-by-step:

Grayscale -> Otsu's threshold

Detected objects to extract highlighted in green -> Filled mask

Bitwise-and to get our result -> Optional result with white background instead

Code

You want to color the ridges according to the density of the individual traces, so you have to get access to that statistic. Fortunately, ggridges allows that with the stat='density' option and the ..density.. input to the asthetic.

The code:

TensorFlow has 2 execution modes: eager execution, and graph mode. TensorFlow default behavior, since version 2, is to default to eager execution. Eager execution is great as it enables you to write code close to how you would write standard python. It's easier to write, and it's easier to debug. Unfortunately, it's really not as fast as graph mode.

So the idea is, once the function is prototyped in eager mode, to make TensorFlow execute it in graph mode. For that you can use tf.function. tf.function compiles a callable into a TensorFlow graph. Once the function is compiled into a graph, the performance gain is usually quite important. The recommended approach when developing in TensorFlow is the following:

• Debug in eager mode, then decorate with @tf.function.
• Don't rely on Python side effects like object mutation or list appends.
• tf.function works best with TensorFlow ops; NumPy and Python calls are converted to constants.

I would add: think about the critical parts of your program, and which ones should be converted first into graph mode. It's usually the parts where you call a model to get a result. It's where you will see the best improvements.

You can find more information in the following guides:

• Better performance with tf.function
• Introduction to graphs and tf.function

So, there are at least two things you can change in your code to make it run quite faster:

1. The first one is to not use model.predict on a small amount of data. The function is made to work on a huge dataset or on a generator. (See this comment on Github). Instead, you should call the model directly, and for performance enhancement, you can wrap the call to the model in a tf.function.

Model.predict is a top-level API designed for batch-predicting outside of any loops, with the fully-features of the Keras APIs.

1. The second one is to make your training step a separate function, and to decorate that function with @tf.function.

So, I would declare the following things before your training loop: