CavinHuang-comfyui-nodes-docs/docs/ttN xyPlot.md

Documentation

• Class name: ttN xyPlot
• Category: ttN/legacy
• Output node: False

ttN xyPlot节点专门用于在特定的可视化框架中生成和处理XY图。它主要负责验证图表类型、管理图表变量，以及协调基于XY坐标的图像生成。该节点集成了多种功能，以确保视觉呈现的创建既高效又能适应不同类型的数据输入。

Input types

Required

• grid_spacing
  • 决定图表中网格点之间的间距，影响图表的分辨率和视觉密度。
  • Comfy dtype: INT
  • Python dtype: int
• latent_id
  • 标识要在图表中使用的特定潜在变量，影响数据的表示和可视化。
  • Comfy dtype: INT
  • Python dtype: int
• output_individuals
  • 控制是否输出单个数据点，影响图表可视化的细节级别。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• flip_xy
  • 指定是否应翻转X和Y轴，改变图表的方向。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• x_axis
  • 定义X轴的数据类型或类别，塑造数据的解释和绘制方式。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• x_values
  • 要在X轴上绘制的值列表，决定图表中具体点的位置和排列。
  • Comfy dtype: STRING
  • Python dtype: List[str]
• y_axis
  • 指定Y轴的数据类型或类别，影响图表的数据解释和可视化。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• y_values
  • 用于在Y轴上绘制的值集合，决定图表点的位置和组织。
  • Comfy dtype: STRING
  • Python dtype: List[str]

Output types

• xyPlot
  • 生成的XY图数据，封装了绘制点的可视化表示。
  • Comfy dtype: XYPLOT
  • Python dtype: Dict[str, Any]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ttN_XYPlot:
    version = '1.2.0'
    lora_list = ["None"] + folder_paths.get_filename_list("loras")
    lora_strengths = {"min": -4.0, "max": 4.0, "step": 0.01}
    token_normalization = ["none", "mean", "length", "length+mean"]
    weight_interpretation = ["comfy", "A1111", "compel", "comfy++"]

    loader_dict = {
        "ckpt_name": folder_paths.get_filename_list("checkpoints"),
        "vae_name": ["Baked-VAE"] + folder_paths.get_filename_list("vae"),
        "clip_skip": {"min": -24, "max": -1, "step": 1},
        "lora1_name": lora_list,
        "lora1_model_strength": lora_strengths,
        "lora1_clip_strength": lora_strengths,
        "lora1_model&clip_strength": lora_strengths,
        "lora2_name": lora_list,
        "lora2_model_strength": lora_strengths,
        "lora2_clip_strength": lora_strengths,
        "lora2_model&clip_strength": lora_strengths,
        "lora3_name": lora_list,
        "lora3_model_strength": lora_strengths,
        "lora3_clip_strength": lora_strengths,
        "lora3_model&clip_strength": lora_strengths,
        "positive": [],
        "positive_token_normalization": token_normalization,
        "positive_weight_interpretation": weight_interpretation,
        "negative": [],
        "negative_token_normalization": token_normalization,
        "negative_weight_interpretation": weight_interpretation,
    }

    sampler_dict = {
        "lora_name": lora_list,
        "lora_model_strength": lora_strengths,
        "lora_clip_strength": lora_strengths,
        "lora_model&clip_strength": lora_strengths,
        "steps": {"min": 1, "max": 100, "step": 1},
        "cfg": {"min": 0.0, "max": 100.0, "step": 1.0},
        "sampler_name": comfy.samplers.KSampler.SAMPLERS,
        "scheduler": comfy.samplers.KSampler.SCHEDULERS,
        "denoise": {"min": 0.0, "max": 1.0, "step": 0.01},
        "seed": ['increment', 'decrement', 'randomize'],
    }

    plot_dict = {**sampler_dict, **loader_dict} 

    plot_values = ["None",]
    plot_values.append("---------------------")
    for k in sampler_dict:
        plot_values.append(f'sampler: {k}')
    plot_values.append("---------------------")
    for k in loader_dict:
        plot_values.append(f'loader: {k}')
    
    def __init__(self):
        pass
    
    rejected = ["None", "---------------------"]

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                #"info": ("INFO", {"default": "Any values not set by xyplot will be taken from the KSampler or connected pipeLoader", "multiline": True}),
                "grid_spacing": ("INT",{"min": 0, "max": 500, "step": 5, "default": 0,}),
                "latent_id": ("INT",{"min": 0, "max": 100, "step": 1, "default": 0, }),
                "output_individuals": (["False", "True"],{"default": "False"}),
                "flip_xy": (["False", "True"],{"default": "False"}),
                "x_axis": (ttN_XYPlot.plot_values, {"default": 'None'}),
                "x_values": ("STRING",{"default": '', "multiline": True, "placeholder": 'insert values seperated by "; "'}),
                "y_axis": (ttN_XYPlot.plot_values, {"default": 'None'}),
                "y_values": ("STRING",{"default": '', "multiline": True, "placeholder": 'insert values seperated by "; "'}),
            },
            "hidden": {
                "plot_dict": (ttN_XYPlot.plot_dict,),
                "ttNnodeVersion": ttN_XYPlot.version,
            },
        }

    RETURN_TYPES = ("XYPLOT", )
    RETURN_NAMES = ("xyPlot", )
    FUNCTION = "plot"

    CATEGORY = "ttN/legacy"
    
    def plot(self, grid_spacing, latent_id, output_individuals, flip_xy, x_axis, x_values, y_axis, y_values):
        def clean_values(values):
            original_values = values.split("; ")
            cleaned_values = []

            for value in original_values:
                # Strip the semi-colon
                cleaned_value = value.strip(';').strip()

                if cleaned_value == "":
                    continue
                
                # Try to convert the cleaned_value back to int or float if possible
                try:
                    cleaned_value = int(cleaned_value)
                except ValueError:
                    try:
                        cleaned_value = float(cleaned_value)
                    except ValueError:
                        pass

                # Append the cleaned_value to the list
                cleaned_values.append(cleaned_value)

            return cleaned_values
        
        if x_axis in self.rejected:
            x_axis = "None"
            x_values = []
        else:
            x_values = clean_values(x_values)

        if y_axis in self.rejected:
            y_axis = "None"
            y_values = []
        else:
            y_values = clean_values(y_values)

        if flip_xy == "True":
            x_axis, y_axis = y_axis, x_axis
            x_values, y_values = y_values, x_values
        
        xy_plot = {"x_axis": x_axis,
                   "x_vals": x_values,
                   "y_axis": y_axis,
                   "y_vals": y_values,
                   "grid_spacing": grid_spacing,
                   "latent_id": latent_id,
                   "output_individuals": output_individuals}
        
        return (xy_plot, )