ComfyUI Node: Shrink Num Sequence | Akatz

Shrink Num Sequence | Akatz:

The AK_ShrinkNumSequence node is designed to process a list of floating-point numbers by reducing contiguous sequences of a specified target number. This node is particularly useful when you want to limit the repetition of a specific float value within a sequence, ensuring that each contiguous occurrence of this number does not exceed a defined maximum. By doing so, it helps in managing data sequences where excessive repetition of a particular value might skew results or analyses. The node offers flexibility by allowing you to specify whether to use an epsilon comparison for floating-point equality, which can be crucial in scenarios where precision is key. Overall, this node is a valuable tool for refining and controlling numerical data sequences in your AI art projects.

Shrink Num Sequence | Akatz Input Parameters:

float_list

The float_list parameter expects a list of floating-point numbers that you want to process. This list serves as the input data where the node will search for contiguous sequences of the target number to shrink. It is a required parameter, meaning you must provide a valid list for the node to function correctly. The list can contain any float values, and the node will operate on this data to produce the desired output.

target_number

The target_number parameter specifies the float value that the node will target for shrinking within the list. By default, this value is set to 1.0, but you can adjust it to any float that you wish to limit in terms of its contiguous occurrences. This parameter allows for fine-tuning the node's behavior to focus on the specific number you are interested in managing within your data sequence.

max_occurrences

The max_occurrences parameter defines the maximum number of times the target_number can appear consecutively in the list. The default value is 2, with a minimum of 1, meaning that the node will allow up to this number of contiguous occurrences of the target number before replacing additional occurrences with zero. This parameter is crucial for controlling the extent of repetition allowed in your data sequence.

use_epsilon

The use_epsilon parameter is a boolean that determines whether the node should use an epsilon comparison for checking equality between float values. By default, this is set to True, meaning the node will use a small epsilon value to account for floating-point precision issues when comparing numbers. This is particularly useful when dealing with floating-point arithmetic where exact equality might not be feasible due to precision errors.

Shrink Num Sequence | Akatz Output Parameters:

FLOAT

The output of the AK_ShrinkNumSequence node is a list of floats, which is the modified version of the input float_list. This output list contains the same numbers as the input, except that contiguous sequences of the target_number are reduced to the specified max_occurrences, with any additional occurrences replaced by zero. This output is essential for ensuring that your data sequence is refined according to the specified parameters, making it more suitable for further processing or analysis.

Shrink Num Sequence | Akatz Usage Tips:

• To effectively use the node, ensure that the float_list is correctly formatted and contains the float values you wish to process.
• Adjust the target_number to match the specific float value you want to limit in your sequence, and set max_occurrences according to how many consecutive appearances you want to allow.
• If you encounter precision issues with floating-point comparisons, consider enabling use_epsilon to improve accuracy.

Shrink Num Sequence | Akatz Common Errors and Solutions:

No valid matches found in the provided schedule string.

• Explanation: This error might occur if the input list or parameters are not correctly specified, leading to an inability to process the data.
• Solution: Double-check the input parameters to ensure they are correctly formatted and that the float_list contains valid float values. Ensure that the target_number and max_occurrences are set appropriately for your data.