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I have found a way to use the USB port for communication. This is done using STM32CubeIDE

1. Launch STMCubeIDE software.
2. Select the USB_OTG_FS and set as device only mode.
3. Then in middleware tab select the Class for FS IP as Communication device class(virtual com port).
4. Set the clock frequency for USB as 48MHz.
5. Generate the project.
6. Read the usbd_cdc_if.c file
7. Include the usbd_cdc_if.h in the main and send some data using CDC_Transmit_FS
8. In Ubuntu, open a terminal and search for two ACM ports using ls /dev/tty, ACM0 and ACM1/ACM2 (which are STLINK-V3 and Virtual ComPort)
9. Test using serial terminal (like Serial Monitor of Arduino IDE).

Dictionaries are not insertion ordered in Python 3.5.

You are instantiating the ordered dicts with arbitrarily ordered regular dicts. Construct each of the ordered dicts from a list of (key, value) tuples.

If Comparedis not a Person the method returns false(i.e. not equal) and finishes.
But if you call equals with an object of type Person the condition is not met and the method does not return false but continues. And if that happens we know comparedmust be of type Person. But the compiler doesn't know that (it could if it was smarter, but it isn't). So the line

The first 2 are trivial. 1 is just a matter of changing the translate position of the legend and making it horizontal is just from looking at the d3-legend doc (https://d3-legend.susielu.com/#color-linear):

One thought: I think your perturbation is a little large, being 1e-4. For double precision floating point numbers, it should be more like 1e-8, i.e., the root of the machine precision (or are you working with single precision?!).

That being said, finite differences can be very bad approximations to true derivatives. Specifically, floating point computations in numpy are not deterministic, as you seem to have found out. The noise in evaluations can cancel out many significant digits under some circumstances. What values are you seeing and what are you expecting?

To find all messages an object will understand, have a look at its class and at its superclasses. You will find delete in the Morph class, a superclass of ImageMorph.

Try executing this line by line:

The things you mention are generally meant for small data, when a dataset can all fit in RAM (which is usually a few GB).

In practice, datasets are often much bigger than that. One way of dealing with this is to store the dataset on a hard drive. There are other more complicated storage solutions. TF DataSets allow you to interface with these storage solutions easily. You create the DataSet object, which represents the storage, in your script, and then as far as you're concerned you can train a model on it as usual. But behind the scenes, TF is repeatedly reading the data into RAM, using it, and then discarding it.

TF Datasets provide many helpful methods for handling big data, such as prefetching (doing the storage reading and data preprocessing at the same time as other stuff), multithreading (doing calculations like preprocessing on several examples at the same time), shuffling (which is harder to do when you can't just reorder a dataset each time in RAM), and batching (preparing sets of several examples for feeding to a model as a batch). All of this stuff would be a pain to do yourself in an optimised way with Pandas or NumPy.

You have a relative path and would like to convert it to absolute, right?

OK. The key part of your code is

In the 3 block you set the finalIndex = 0;

It should be set to the startIndex