Spindle | Scalable dynamic library and python loading | Machine Learning library

In a time scale you pass just the start and end dates as the array:

Unfortunately, the mechanical and electrical setup you describe does not actually allow automatic home calibration in a proper way.

Stepper Motors are used, as name suggests, to perform steps. They are not capable of absolute positioning on their own. If you need absolute positioning, you will have to either set your home point whenever the current position is unknown (eg. start-up or after losing steps), or add an absolute encoder to the system (then you always know the system state).

One solution for automatic home detection are limit switches, commonly used in many mechanical systems, especially 3D printers and other CNC machines. You might want to add one of these to your setup and drive towards the switch until it is pressed. Then you move it the exact number of steps needed to move it to the center and you're done.

However, if you can't change anything in the setup, you could just move the sled in one direction by the length of the spindle. When the sled hits the end, the motor should stall and loose the remaining steps. Then you move it back to the center (constant number of steps as previously) and you're centered. Consider that stalling motors can overheat the motor as well as the motor driver. This will probably decrease their life-expectancy.

There are other solutions like servos or absolute encoders but as far as I understand your goal, a limit switch should be fine.

Cause

You are using the JsonProperty attribute from the Netwonsoft.Json package, but the built-in .NET Core/5 deserializer from the System.Text.Json namespace.

How do I know that? Newtonsoft.Json does not have a JsonSerializer.Deserialize overload which takes a single string, and .NET does not contain a JsonPropertyAttribute.

Those two are not compatible. The .NET deserializer ignores your [JsonProperty("Products")] attribute, does not find a propery named ProductsProducts in your JSON and, thus, yields null for that property.

Fix

To use the deserializer from the Newtonsoft.Json package instead, replace

The problem is not the name of the class, but getElementsByClassName returns a list of spans and [0] only selects the first item. You need to iterate over the list and apply the onclick to each one. Something like:

As commented, your earlier search returns an empty data frame since the searched term is part of the larger strings in the character column, GO_NAME. Therefore, instead of == or%in% operators which expects whole word matches, consider grep to search string patterns within larger string:

Instead of casting it as a decimal, have you tried converting it? I found an old example where I did something similar.

Using system;

machineRadialDataArray[i, j] = Convert.ToDecimal(dataGridView1.Rows[i].Cells[j].Value);

I believe all the answers you'll need will be in this video

If you have issues with any of that, then we'll have a good reference point. Best of luck.

You have an infinite loop in your map_it function. This results in your first call to map_it endlessly outputting the first arguments passed to it.

Remove the line while True : and the indent on the following line to remove this loop and you should get the result you expect.

Here is a possible approach. As the 'headValues' aren't sorted, nor equally spaced and one is even used twice, it is not fully clear what the most-desired result would be.

Some remarks:

• The standard way of creating a colorbar in matplotlib doesn't need a separate subplot. Matplotlib will reduce the existing plot a bit and put the colorbar next to it (or below for a vertical bar).
• Converting the 'headValues' to a numpy array allows for compact code, e.g. writing rgba = cmap(headValues) directly calculates the complete array.
• Calling cmap on unchanged values will map 0 to the lowest color and 1 to the highest color, so for values only between 0.16 and 0.44 they all will be mapped to quite similar colors. One approach is to create a norm to map 0.16 to the lowest color and 0.44 to the highest. In code: norm = plt.Normalize(headValues.min(), headValues.max()) and then calculate rgba = cmap(norm(headValues)).