From 173ae747b6884c89026745bac21ba7f6f7090d5e Mon Sep 17 00:00:00 2001 From: Yvann Date: Fri, 6 Sep 2024 23:33:09 +0200 Subject: [PATCH] Add Help PopUp thanks to Ryanontheinside Kjnodes and mtb, add mask features to all audio nodes --- __init__.py | 84 ++++- node_configs.py | 50 +++ nodes/audio/Audio_Analysis_Yvann.py | 137 ++++++++ nodes/audio/Audio_Drums_Analysis_Yvann.py | 167 +++++++++ nodes/audio/Audio_Reactive_IPAdapter_YVANN.py | 183 ---------- nodes/audio/Audio_Vocals_Analysis_Yvann.py | 170 +++++++++ .../AudioAnalysis_Advanced_YVANN.py | 0 nodes/{audio => bin}/AudioAnalysis_YVANN.py | 0 .../AudioFrequencyAnalysis_YVANN.py | 0 nodes/utils/ShowFloat_YVANN.py | 20 ++ web/js/help_popup.js | 325 ++++++++++++++++++ web/nodes/ShowFloat_YVANN.js | 34 ++ yvann_web_async/marked.min.js | 6 + yvann_web_async/protovis.min.js | 277 +++++++++++++++ yvann_web_async/purify.min.js | 3 + yvann_web_async/svg-path-properties.min.js | 2 + 16 files changed, 1265 insertions(+), 193 deletions(-) create mode 100644 node_configs.py create mode 100644 nodes/audio/Audio_Analysis_Yvann.py create mode 100644 nodes/audio/Audio_Drums_Analysis_Yvann.py delete mode 100644 nodes/audio/Audio_Reactive_IPAdapter_YVANN.py create mode 100644 nodes/audio/Audio_Vocals_Analysis_Yvann.py rename nodes/{audio => bin}/AudioAnalysis_Advanced_YVANN.py (100%) rename nodes/{audio => bin}/AudioAnalysis_YVANN.py (100%) rename nodes/{audio => bin}/AudioFrequencyAnalysis_YVANN.py (100%) create mode 100644 nodes/utils/ShowFloat_YVANN.py create mode 100644 web/js/help_popup.js create mode 100644 web/nodes/ShowFloat_YVANN.js create mode 100644 yvann_web_async/marked.min.js create mode 100644 yvann_web_async/protovis.min.js create mode 100644 yvann_web_async/purify.min.js create mode 100644 yvann_web_async/svg-path-properties.min.js diff --git a/__init__.py b/__init__.py index 41944b4..35929b4 100644 --- a/__init__.py +++ b/__init__.py @@ -1,17 +1,81 @@ -from .nodes.audio.Audio_Reactive_IPAdapter_YVANN import Audio_Reactive_IPAdapter_YVANN -from .nodes.audio.AudioAnalysis_Advanced_YVANN import AudioAnalysis_Advanced_YVANN -from .nodes.audio.AudioFrequencyAnalysis_YVANN import AudioFrequencyAnalysis_YVANN +from .node_configs import CombinedMeta +from collections import OrderedDict +# credit to RyanOnTheInside, KJNodes, MTB, Akatz, their works helped me a lot + + +#allows for central management and inheritance of class variables for help documentation +class Yvann(metaclass=CombinedMeta): + @classmethod + def get_description(cls): + + display_name = NODE_DISPLAY_NAME_MAPPINGS.get(cls.__name__, cls.__name__) + footer = "For more information, visit [RyanOnTheInside GitHub](https://github.com/ryanontheinside).\n\n" + footer += "For tutorials and example workflows visit [RyanOnTheInside Civitai](https://civitai.com/user/ryanontheinside).\n\n" + display_name = display_name.replace(" | Yvann", "") + + desc = f"# {display_name}\n\n" + + if hasattr(cls, 'DESCRIPTION'): + desc += f"{cls.DESCRIPTION}\n\n{footer}" + return desc + + if hasattr(cls, 'TOP_DESCRIPTION'): + desc += f"### {cls.TOP_DESCRIPTION}\n\n" + + if hasattr(cls, "BASE_DESCRIPTION"): + desc += cls.BASE_DESCRIPTION + "\n\n" + + additional_info = OrderedDict() + for c in cls.mro()[::-1]: + if hasattr(c, 'ADDITIONAL_INFO'): + info = c.ADDITIONAL_INFO.strip() + + additional_info[c.__name__] = info + + if additional_info: + desc += "\n\n".join(additional_info.values()) + "\n\n" + + if hasattr(cls, 'BOTTOM_DESCRIPTION'): + desc += f"{cls.BOTTOM_DESCRIPTION}\n\n" + + desc += footer + return desc + +from .nodes.audio.Audio_Drums_Analysis_Yvann import Audio_Drums_Analysis_Yvann +from .nodes.audio.Audio_Vocals_Analysis_Yvann import Audio_Vocals_Analysis_Yvann +from .nodes.audio.Audio_Analysis_Yvann import Audio_Analysis_Yvann + NODE_CLASS_MAPPINGS = { - "Audio Reactive IPAdapter | YVANN": Audio_Reactive_IPAdapter_YVANN, - "Audio Analysis Advanced | YVANN": AudioAnalysis_Advanced_YVANN, - "Audio Frequency Analysis | YVANN": AudioFrequencyAnalysis_YVANN, + "Audio Drums Analysis | Yvann": Audio_Drums_Analysis_Yvann, + "Audio Vocals Analysis | Yvann": Audio_Vocals_Analysis_Yvann, + "Audio Analysis | Yvann": Audio_Analysis_Yvann, } +WEB_DIRECTORY = "./web/js" + NODE_DISPLAY_NAME_MAPPINGS = { - "Audio Reactive IPAdapter | YVANN": "Audio Reactive IPAdapter | YVANN", - "AudioAnalysis_Advanced_YVANN": "Audio Analysis Advanced | YVANN", - "AudioFrequencyAnalysis_YVANN": "Audio Frequency Analysis | YVANN", + "Audio Drums Analysis | Yvann": "Audio Drums Analysis | Yvann", + "Audio Vocals Analysis | Yvann": "Audio Vocals Analysis | Yvann", + "Audio Analysis | Yvann": "Audio Analysis | Yvann", } -__all__ = ['NODE_CLASS_MAPPINGS'] \ No newline at end of file +from aiohttp import web +from server import PromptServer +from pathlib import Path + +if hasattr(PromptServer, "instance"): + + # NOTE: we add an extra static path to avoid comfy mechanism + # that loads every script in web. + + PromptServer.instance.app.add_routes( + [web.static("/yvann_web_async", (Path(__file__).parent.absolute() / "yvann_web_async").as_posix())] + ) + + + +for node_name, node_class in NODE_CLASS_MAPPINGS.items(): + if hasattr(node_class, 'get_description'): + desc = node_class.get_description() + node_class.DESCRIPTION = desc diff --git a/node_configs.py b/node_configs.py new file mode 100644 index 0000000..9e65d49 --- /dev/null +++ b/node_configs.py @@ -0,0 +1,50 @@ +#NOTE: this abstraction allows for both the documentation to be centrally managed and inherited +from abc import ABCMeta +class NodeConfigMeta(type): + def __new__(cls, name, bases, attrs): + new_class = super().__new__(cls, name, bases, attrs) + if name in NODE_CONFIGS: + for key, value in NODE_CONFIGS[name].items(): + setattr(new_class, key, value) + return new_class + +class CombinedMeta(NodeConfigMeta, ABCMeta): + pass + +def add_node_config(node_name, config): + NODE_CONFIGS[node_name] = config + +NODE_CONFIGS = {} + +add_node_config("Audio_Analysis_Yvann", { + "BASE_DESCRIPTION": """ +##Parameters +- `video_frames`: Input video frames to be processed. +- `audio`: Input audio to be processed. +- `frame_rate`: Frame rate of the video. +- `smoothing_factor`: Smoothing factor for the audio analysis +- `global_intensity`: Global intensity for the audio analysis +""" +}) + +add_node_config("Audio_Drums_Analysis_Yvann", { + "BASE_DESCRIPTION": """ +##Parameters +- `video_frames`: Input video frames to be processed. +- `audio`: Input audio to be processed. +- `frame_rate`: Frame rate of the video. +- `smoothing_factor`: Smoothing factor for the audio analysis +- `global_intensity`: Global intensity for the audio analysis +""" +}) + +add_node_config("Audio_Vocals_Analysis_Yvann", { + "BASE_DESCRIPTION": """ +##Parameters +- `video_frames`: Input video frames to be processed. +- `audio`: Input audio to be processed. +- `frame_rate`: Frame rate of the video. +- `smoothing_factor`: Smoothing factor for the audio analysis +- `global_intensity`: Global intensity for the audio analysis +""" +}) diff --git a/nodes/audio/Audio_Analysis_Yvann.py b/nodes/audio/Audio_Analysis_Yvann.py new file mode 100644 index 0000000..4cdc258 --- /dev/null +++ b/nodes/audio/Audio_Analysis_Yvann.py @@ -0,0 +1,137 @@ +import torch +import os +import folder_paths +import matplotlib.pyplot as plt +import tempfile +import numpy as np +from PIL import Image +import librosa +from nodes import SaveImage +import pandas as pd +from ... import Yvann + +class AudioNodeBase(Yvann): + CATEGORY= "👁️ Yvann Nodes/Audio" + +class Audio_Analysis_Yvann(AudioNodeBase): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video_frames": ("IMAGE",), + "audio": ("AUDIO",), + "frame_rate": ("INT", {"default": 30, "min": 1, "max": 60, "step": 1}), + "smoothing_factor": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + "global_intensity": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("AUDIO", "FLOAT", "MASK", "IMAGE") + RETURN_NAMES = ("Audio", "Audio Weights", "Audio Masks", "Weights Graph") + FUNCTION = "process_audio" + + def _get_audio_frame(self, waveform, i, samples_per_frame): + start = i * samples_per_frame + end = start + samples_per_frame + return waveform[..., start:end].cpu().numpy().squeeze() + + def _rms_energy(self, waveform, num_frames, samples_per_frame): + return np.array([np.sqrt(np.mean(self._get_audio_frame(waveform, i, samples_per_frame)**2)) for i in range(num_frames)]) + + def _smooth_weights(self, weights, smoothing_factor): + if smoothing_factor <= 0.01: + return weights + kernel_size = max(3, int(smoothing_factor * 50)) # Ensure minimum kernel size of 3 + kernel = np.ones(kernel_size) / kernel_size + return np.convolve(weights, kernel, mode='same') + + def _normalize_weights(self, weights): + min_val, max_val = np.min(weights), np.max(weights) + if max_val > min_val: + return (weights - min_val) / (max_val - min_val) + else: + return np.zeros_like(weights) + + def adjust_weights(self, weights, global_intensity): + factor = 1 + (global_intensity * 0.5) + adjusted_weights = np.maximum(weights * factor, 0) + adjusted_weights = np.round(adjusted_weights, 3) + return adjusted_weights + + def generate_masks(self, input_values, width, height): + # Ensure input_values is a list + if isinstance(input_values, (float, int)): + input_values = [input_values] + elif isinstance(input_values, pd.Series): + input_values = input_values.tolist() + elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values): + input_values = [item for sublist in input_values for item in sublist] + + # Generate a batch of masks based on the input_values + masks = [] + for value in input_values: + # Assuming value is a float between 0 and 1 representing the mask's intensity + mask = torch.ones((height, width), dtype=torch.float32) * value + masks.append(mask) + masks_out = torch.stack(masks, dim=0) + + return masks_out + + def process_audio(self, audio, video_frames, frame_rate, smoothing_factor, global_intensity): + waveform = audio['waveform'] + sample_rate = audio['sample_rate'] + + num_frames, height, width, _ = video_frames.shape + + if waveform.dim() == 3: + waveform = waveform.squeeze(0) + if waveform.dim() == 1: + waveform = waveform.unsqueeze(0) # Add channel dimension if mono + if waveform.shape[0] != 2: + waveform = waveform.repeat(2, 1) # Duplicate mono to stereo if necessary + + total_samples = waveform.shape[-1] + samples_per_frame = total_samples // num_frames + + processed_audio = { + 'waveform': waveform, + 'sample_rate': sample_rate, + 'frame_rate': frame_rate + } + + audio_weights = self._rms_energy(waveform, num_frames, samples_per_frame) + audio_weights = self._smooth_weights(audio_weights, max(0.01, smoothing_factor)) + audio_weights = self._normalize_weights(audio_weights) + audio_weights = [round(float(weight), 3) for weight in audio_weights] + + audio_weights = self.adjust_weights(np.array(audio_weights), global_intensity) + + # Plot the weights + frames = list(range(1, num_frames + 1)) + plt.figure(figsize=(10, 6)) + plt.plot(frames, audio_weights, label='Audio Weights', color='blue') + plt.xlabel('Frame Number') + plt.ylabel('Normalized Weights') + plt.title('Normalized Weights for Audio (RMS Energy)') + plt.legend() + plt.grid(True) + + with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile: + plt.savefig(tmpfile, format='png') + tmpfile_path = tmpfile.name + plt.close() + + weights_graph = Image.open(tmpfile_path).convert("RGB") + weights_graph = np.array(weights_graph) + weights_graph = torch.tensor(weights_graph).permute(2, 0, 1).unsqueeze(0).float() / 255.0 + weights_graph = weights_graph.permute(0, 2, 3, 1) + + # Generate masks from audio weights + audio_masks = self.generate_masks(audio_weights, width, height) + + return ( + processed_audio, + audio_weights.tolist(), + audio_masks, + weights_graph + ) diff --git a/nodes/audio/Audio_Drums_Analysis_Yvann.py b/nodes/audio/Audio_Drums_Analysis_Yvann.py new file mode 100644 index 0000000..6d76740 --- /dev/null +++ b/nodes/audio/Audio_Drums_Analysis_Yvann.py @@ -0,0 +1,167 @@ +import torch +import os +import folder_paths +import matplotlib.pyplot as plt +import tempfile +import numpy as np +from PIL import Image +import librosa +from nodes import SaveImage +import pandas as pd +from ... import Yvann + +class AudioNodeBase(Yvann): + CATEGORY= "👁️ Yvann Nodes/Audio" + +class Audio_Drums_Analysis_Yvann(AudioNodeBase): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video_frames": ("IMAGE",), + "audio": ("AUDIO",), + "frame_rate": ("INT", {"default": 30, "min": 1, "max": 60, "step": 1}), + "smoothing_factor": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + "global_intensity": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("AUDIO", "FLOAT", "MASK", "IMAGE") + RETURN_NAMES = ("Drums Audio", "Drums Weights", "Drums Masks", "Weights Graph") + FUNCTION = "process_audio" + + def download_and_load_model(self): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + download_path = os.path.join(folder_paths.models_dir, "openunmix") + os.makedirs(download_path, exist_ok=True) + + model_file = "umxl.pth" + model_path = os.path.join(download_path, model_file) + + if not os.path.exists(model_path): + print("Downloading umxhq model...") + separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxl', device='cpu') + torch.save(separator.state_dict(), model_path) + print(f"Model saved to: {model_path}") + else: + print(f"Loading model from: {model_path}") + separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxl', device='cpu') + separator.load_state_dict(torch.load(model_path, map_location='cpu')) + + separator = separator.to(device) + separator.eval() + + return separator + + def _get_audio_frame(self, waveform, i, samples_per_frame): + start = i * samples_per_frame + end = start + samples_per_frame + return waveform[..., start:end].cpu().numpy().squeeze() + + def _rms_energy(self, waveform, num_frames, samples_per_frame): + return np.array([np.sqrt(np.mean(self._get_audio_frame(waveform, i, samples_per_frame)**2)) for i in range(num_frames)]) + + def _smooth_weights(self, weights, smoothing_factor): + if smoothing_factor <= 0.01: + return weights + kernel_size = max(3, int(smoothing_factor * 50)) # Ensure minimum kernel size of 3 + kernel = np.ones(kernel_size) / kernel_size + return np.convolve(weights, kernel, mode='same') + + def _normalize_weights(self, weights): + min_val, max_val = np.min(weights), np.max(weights) + if max_val > min_val: + return (weights - min_val) / (max_val - min_val) + else: + return np.zeros_like(weights) + + def adjust_weights(self, weights, global_intensity): + factor = 1 + (global_intensity * 0.5) + adjusted_weights = np.maximum(weights * factor, 0) + adjusted_weights = np.round(adjusted_weights, 3) + return adjusted_weights + + def generate_masks(self, input_values, width, height): + # Ensure input_values is a list + if isinstance(input_values, (float, int)): + input_values = [input_values] + elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values): + input_values = [item for sublist in input_values for item in sublist] + + # Generate a batch of masks based on the input_values + masks = [] + for value in input_values: + mask = torch.ones((height, width), dtype=torch.float32) * value + masks.append(mask) + masks_out = torch.stack(masks, dim=0) + + return masks_out + + def process_audio(self, audio, video_frames, frame_rate, smoothing_factor, global_intensity): + model = self.download_and_load_model() + + waveform = audio['waveform'] + sample_rate = audio['sample_rate'] + + num_frames, height, width, _ = video_frames.shape + + if waveform.dim() == 3: + waveform = waveform.squeeze(0) + if waveform.dim() == 1: + waveform = waveform.unsqueeze(0) # Add channel dimension if mono + if waveform.shape[0] != 2: + waveform = waveform.repeat(2, 1) # Duplicate mono to stereo if necessary + + waveform = waveform.unsqueeze(0) + + device = next(model.parameters()).device + waveform = waveform.to(device) + + estimates = model(waveform) + + total_samples = waveform.shape[-1] + samples_per_frame = total_samples // num_frames + + drums_waveform = estimates[:, 1, :, :] # Shape: (1, 2, num_samples) + drums_audio = { + 'waveform': drums_waveform.cpu(), # Move back to CPU + 'sample_rate': sample_rate, + 'frame_rate': frame_rate + } + + drums_weights = self._rms_energy(drums_waveform.squeeze(0), num_frames, samples_per_frame) + drums_weights = self._smooth_weights(drums_weights, max(0.01, smoothing_factor)) + drums_weights = self._normalize_weights(drums_weights) + drums_weights = [round(float(weight), 3) for weight in drums_weights] + + drums_weights = self.adjust_weights(np.array(drums_weights), global_intensity) + + # Generate masks from drums weights + drums_masks = self.generate_masks(drums_weights, width, height) + + # Plot the weights + frames = list(range(1, num_frames + 1)) + plt.figure(figsize=(10, 6)) + plt.plot(frames, drums_weights, label='Drums Weights', color='red') + plt.xlabel('Frame Number') + plt.ylabel('Normalized Weights') + plt.title('Normalized Weights for Drums (RMS Energy)') + plt.legend() + plt.grid(True) + + with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile: + plt.savefig(tmpfile, format='png') + tmpfile_path = tmpfile.name + plt.close() + + weights_graph = Image.open(tmpfile_path).convert("RGB") + weights_graph = np.array(weights_graph) + weights_graph = torch.tensor(weights_graph).permute(2, 0, 1).unsqueeze(0).float() / 255.0 + weights_graph = weights_graph.permute(0, 2, 3, 1) + + return ( + drums_audio, + drums_weights.tolist(), + drums_masks, + weights_graph + ) diff --git a/nodes/audio/Audio_Reactive_IPAdapter_YVANN.py b/nodes/audio/Audio_Reactive_IPAdapter_YVANN.py deleted file mode 100644 index 5918a72..0000000 --- a/nodes/audio/Audio_Reactive_IPAdapter_YVANN.py +++ /dev/null @@ -1,183 +0,0 @@ -import torch -import os -import folder_paths -import matplotlib.pyplot as plt -import tempfile -import numpy as np -from PIL import Image -import librosa -from nodes import SaveImage - -class Audio_Reactive_IPAdapter_YVANN: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "video_frames": ("IMAGE",), - "audio": ("AUDIO",), - "frame_rate": ("INT", {"default": 30.0, "min": 1.0, "max": 60.0, "step": 1.0}), - "weight_algorithm": (["rms_energy", "amplitude_envelope", "spectral_centroid", "onset_detection", "chroma_features"], {"default": "rms_energy"}), - "smoothing_factor": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 1.0, "step": 0.01}), - } - } - - RETURN_TYPES = ("FLOAT", "AUDIO", "FLOAT", "AUDIO", "FLOAT", "IMAGE") - RETURN_NAMES = ("Audio Weights", "Drums Audio", "Drums Weights", "Vocals Audio", "Vocals Weights", "Weights Graph") - FUNCTION = "process_audio" - - def download_and_load_model(self): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - download_path = os.path.join(folder_paths.models_dir, "openunmix") - os.makedirs(download_path, exist_ok=True) - - model_file = "umxl.pth" - model_path = os.path.join(download_path, model_file) - - if not os.path.exists(model_path): - print("Downloading umxhq model...") - separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxl', device='cpu') - torch.save(separator.state_dict(), model_path) - print(f"Model saved to: {model_path}") - else: - print(f"Loading model from: {model_path}") - separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxl', device='cpu') - separator.load_state_dict(torch.load(model_path, map_location='cpu')) - - separator = separator.to(device) - separator.eval() - - return separator - - def _get_audio_frame(self, waveform, i, samples_per_frame): - start = i * samples_per_frame - end = start + samples_per_frame - return waveform[..., start:end].cpu().numpy().squeeze() - - def _amplitude_envelope(self, waveform, num_frames, samples_per_frame): - return np.array([np.max(np.abs(self._get_audio_frame(waveform, i, samples_per_frame))) for i in range(num_frames)]) - - def _rms_energy(self, waveform, num_frames, samples_per_frame): - return np.array([np.sqrt(np.mean(self._get_audio_frame(waveform, i, samples_per_frame)**2)) for i in range(num_frames)]) - - def _spectral_centroid(self, waveform, num_frames, samples_per_frame, sample_rate): - return np.array([np.mean(librosa.feature.spectral_centroid(y=self._get_audio_frame(waveform, i, samples_per_frame), sr=sample_rate)[0]) for i in range(num_frames)]) - - def _onset_detection(self, waveform, num_frames, samples_per_frame, sample_rate): - return np.array([np.mean(librosa.onset.onset_strength(y=self._get_audio_frame(waveform, i, samples_per_frame), sr=sample_rate)) for i in range(num_frames)]) - - def _chroma_features(self, waveform, num_frames, samples_per_frame, sample_rate): - return np.array([np.mean(librosa.feature.chroma_stft(y=self._get_audio_frame(waveform, i, samples_per_frame), sr=sample_rate)) for i in range(num_frames)]) - - def _smooth_weights(self, weights, smoothing_factor): - kernel_size = max(3, int(smoothing_factor * 50)) # Ensure minimum kernel size of 3 - kernel = np.ones(kernel_size) / kernel_size - return np.convolve(weights, kernel, mode='same') - - def _normalize_weights(self, weights): - min_val, max_val = np.min(weights), np.max(weights) - if max_val > min_val: - return (weights - min_val) / (max_val - min_val) - else: - return np.zeros_like(weights) - - def process_audio(self, audio, video_frames, frame_rate, weight_algorithm, smoothing_factor): - model = self.download_and_load_model() - - waveform = audio['waveform'] - sample_rate = audio['sample_rate'] - - num_frames, height, width, _ = video_frames.shape - - if waveform.dim() == 3: - waveform = waveform.squeeze(0) - if waveform.dim() == 1: - waveform = waveform.unsqueeze(0) # Add channel dimension if mono - if waveform.shape[0] != 2: - waveform = waveform.repeat(2, 1) # Duplicate mono to stereo if necessary - - waveform = waveform.unsqueeze(0) - - # Determine the device - device = next(model.parameters()).device - waveform = waveform.to(device) - - estimates = model(waveform) - - # Compute normalized audio weights for each frame - total_samples = waveform.shape[-1] - samples_per_frame = total_samples // num_frames - - # Create isolated audio objects for each target - isolated_audio = {} - target_indices = {'drums': 1, 'vocals': 0} - for target, index in target_indices.items(): - target_waveform = estimates[:, index, :, :] # Shape: (1, 2, num_samples) - - isolated_audio[target] = { - 'waveform': target_waveform.cpu(), # Move back to CPU - 'sample_rate': sample_rate, - 'frame_rate': frame_rate - } - - # Apply the selected weight algorithm - weight_function = getattr(self, f"_{weight_algorithm}") - if weight_algorithm in ['spectral_centroid', 'onset_detection', 'chroma_features']: - audio_weights = weight_function(waveform.squeeze(0), num_frames, samples_per_frame, sample_rate) - else: - audio_weights = weight_function(waveform.squeeze(0), num_frames, samples_per_frame) - - # Apply smoothing to audio weights - audio_weights = self._smooth_weights(audio_weights, smoothing_factor) - audio_weights = self._normalize_weights(audio_weights) - - audio_weights = [round(float(weight), 3) for weight in audio_weights] - - # Calculate and normalize weights for each isolated audio target - target_weights = {} - for target, index in target_indices.items(): - target_waveform = isolated_audio[target]['waveform'].squeeze(0) - if weight_algorithm in ['spectral_centroid', 'onset_detection', 'chroma_features']: - target_weights[target] = weight_function(target_waveform, num_frames, samples_per_frame, sample_rate) - else: - target_weights[target] = weight_function(target_waveform, num_frames, samples_per_frame) - - # Apply smoothing to target weights - target_weights[target] = self._smooth_weights(target_weights[target], smoothing_factor) - target_weights[target] = self._normalize_weights(target_weights[target]) - - target_weights[target] = [round(float(weight), 3) for weight in target_weights[target]] - - # Plot the weights - frames = list(range(1, num_frames + 1)) - plt.figure(figsize=(10, 6)) - plt.plot(frames, audio_weights, label='Audio Weights', color='black') - plt.plot(frames, target_weights['drums'], label='Drums Weights', color='red') - plt.plot(frames, target_weights['vocals'], label='Vocals Weights', color='green') - plt.xlabel('Frame Number') - plt.ylabel('Normalized Weights') - plt.title(f'Normalized Weights for Audio Components ({weight_algorithm})') - plt.legend() - plt.grid(True) - - # Save the plot to a temporary file - with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile: - plt.savefig(tmpfile, format='png') - tmpfile_path = tmpfile.name - plt.close() - - # Load the image from the temporary file and convert to tensor - weights_graph = Image.open(tmpfile_path).convert("RGB") - weights_graph = np.array(weights_graph) - weights_graph = torch.tensor(weights_graph).permute(2, 0, 1).unsqueeze(0).float() / 255.0 - - # Ensure the tensor has the correct shape [B, H, W, C] - weights_graph = weights_graph.permute(0, 2, 3, 1) - - return ( - audio_weights, - isolated_audio['drums'], - target_weights['drums'], - isolated_audio['vocals'], - target_weights['vocals'], - weights_graph - ) diff --git a/nodes/audio/Audio_Vocals_Analysis_Yvann.py b/nodes/audio/Audio_Vocals_Analysis_Yvann.py new file mode 100644 index 0000000..bb562d4 --- /dev/null +++ b/nodes/audio/Audio_Vocals_Analysis_Yvann.py @@ -0,0 +1,170 @@ +import torch +import os +import folder_paths +import matplotlib.pyplot as plt +import tempfile +import numpy as np +from PIL import Image +import librosa +from nodes import SaveImage +import pandas as pd +from ... import Yvann + +class AudioNodeBase(Yvann): + CATEGORY= "👁️ Yvann Nodes/Audio" + +class Audio_Vocals_Analysis_Yvann(AudioNodeBase): + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video_frames": ("IMAGE",), + "audio": ("AUDIO",), + "frame_rate": ("INT", {"default": 30, "min": 1, "max": 60, "step": 1}), + "smoothing_factor": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + "global_intensity": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("AUDIO", "FLOAT", "MASK", "IMAGE") + RETURN_NAMES = ("Vocals Audio", "Vocals Weights", "Vocals Masks", "Weights Graph") + FUNCTION = "process_audio" + + def download_and_load_model(self): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + download_path = os.path.join(folder_paths.models_dir, "openunmix") + os.makedirs(download_path, exist_ok=True) + + model_file = "umxl.pth" + model_path = os.path.join(download_path, model_file) + + if not os.path.exists(model_path): + print("Downloading umxhq model...") + separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxl', device='cpu') + torch.save(separator.state_dict(), model_path) + print(f"Model saved to: {model_path}") + else: + print(f"Loading model from: {model_path}") + separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxl', device='cpu') + separator.load_state_dict(torch.load(model_path, map_location='cpu')) + + separator = separator.to(device) + separator.eval() + + return separator + + def _get_audio_frame(self, waveform, i, samples_per_frame): + start = i * samples_per_frame + end = start + samples_per_frame + return waveform[..., start:end].cpu().numpy().squeeze() + + def _rms_energy(self, waveform, num_frames, samples_per_frame): + return np.array([np.sqrt(np.mean(self._get_audio_frame(waveform, i, samples_per_frame)**2)) for i in range(num_frames)]) + + def _smooth_weights(self, weights, smoothing_factor): + if smoothing_factor <= 0.01: + return weights + kernel_size = max(3, int(smoothing_factor * 50)) # Ensure minimum kernel size of 3 + kernel = np.ones(kernel_size) / kernel_size + return np.convolve(weights, kernel, mode='same') + + def _normalize_weights(self, weights): + min_val, max_val = np.min(weights), np.max(weights) + if max_val > min_val: + return (weights - min_val) / (max_val - min_val) + else: + return np.zeros_like(weights) + + def adjust_weights(self, weights, global_intensity): + factor = 1 + (global_intensity * 0.5) + adjusted_weights = np.maximum(weights * factor, 0) + adjusted_weights = np.round(adjusted_weights, 3) + return adjusted_weights + + def generate_masks(self, input_values, width, height): + # Ensure input_values is a list + if isinstance(input_values, (float, int)): + input_values = [input_values] + elif isinstance(input_values, pd.Series): + input_values = input_values.tolist() + elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values): + input_values = [item for sublist in input_values for item in sublist] + + # Generate a batch of masks based on the input_values + masks = [] + for value in input_values: + mask = torch.ones((height, width), dtype=torch.float32) * value + masks.append(mask) + masks_out = torch.stack(masks, dim=0) + + return masks_out + + def process_audio(self, audio, video_frames, frame_rate, smoothing_factor, global_intensity): + model = self.download_and_load_model() + + waveform = audio['waveform'] + sample_rate = audio['sample_rate'] + + num_frames, height, width, _ = video_frames.shape + + if waveform.dim() == 3: + waveform = waveform.squeeze(0) + if waveform.dim() == 1: + waveform = waveform.unsqueeze(0) # Add channel dimension if mono + if waveform.shape[0] != 2: + waveform = waveform.repeat(2, 1) # Duplicate mono to stereo if necessary + + waveform = waveform.unsqueeze(0) + + device = next(model.parameters()).device + waveform = waveform.to(device) + + estimates = model(waveform) + + total_samples = waveform.shape[-1] + samples_per_frame = total_samples // num_frames + + vocals_waveform = estimates[:, 0, :, :] # Shape: (1, 2, num_samples) + vocals_audio = { + 'waveform': vocals_waveform.cpu(), # Move back to CPU + 'sample_rate': sample_rate, + 'frame_rate': frame_rate + } + + vocals_weights = self._rms_energy(vocals_waveform.squeeze(0), num_frames, samples_per_frame) + vocals_weights = self._smooth_weights(vocals_weights, max(0.01, smoothing_factor)) + vocals_weights = self._normalize_weights(vocals_weights) + vocals_weights = [round(float(weight), 3) for weight in vocals_weights] + + vocals_weights = self.adjust_weights(np.array(vocals_weights), global_intensity) + + # Generate masks from vocals weights + vocals_masks = self.generate_masks(vocals_weights, width, height) + + # Plot the weights + frames = list(range(1, num_frames + 1)) + plt.figure(figsize=(10, 6)) + plt.plot(frames, vocals_weights, label='Vocals Weights', color='green') + plt.xlabel('Frame Number') + plt.ylabel('Normalized Weights') + plt.title('Normalized Weights for Vocals (RMS Energy)') + plt.legend() + plt.grid(True) + + with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile: + plt.savefig(tmpfile, format='png') + tmpfile_path = tmpfile.name + plt.close() + + weights_graph = Image.open(tmpfile_path).convert("RGB") + weights_graph = np.array(weights_graph) + weights_graph = torch.tensor(weights_graph).permute(2, 0, 1).unsqueeze(0).float() / 255.0 + weights_graph = weights_graph.permute(0, 2, 3, 1) + + return ( + vocals_audio, + vocals_weights.tolist(), + vocals_masks, + weights_graph + ) diff --git a/nodes/audio/AudioAnalysis_Advanced_YVANN.py b/nodes/bin/AudioAnalysis_Advanced_YVANN.py similarity index 100% rename from nodes/audio/AudioAnalysis_Advanced_YVANN.py rename to nodes/bin/AudioAnalysis_Advanced_YVANN.py diff --git a/nodes/audio/AudioAnalysis_YVANN.py b/nodes/bin/AudioAnalysis_YVANN.py similarity index 100% rename from nodes/audio/AudioAnalysis_YVANN.py rename to nodes/bin/AudioAnalysis_YVANN.py diff --git a/nodes/audio/AudioFrequencyAnalysis_YVANN.py b/nodes/bin/AudioFrequencyAnalysis_YVANN.py similarity index 100% rename from nodes/audio/AudioFrequencyAnalysis_YVANN.py rename to nodes/bin/AudioFrequencyAnalysis_YVANN.py diff --git a/nodes/utils/ShowFloat_YVANN.py b/nodes/utils/ShowFloat_YVANN.py new file mode 100644 index 0000000..1a7fa56 --- /dev/null +++ b/nodes/utils/ShowFloat_YVANN.py @@ -0,0 +1,20 @@ +class ShowFloat_YVANN: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "float" : ("FLOAT", {"forceInput": True}) + }, + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ("float",) + + CATEGORY = "float" + FUNCTION = "show_float" + OUTPUT_NODE = True + OUTPUT_IS_LIST = (True,) + + def show_float(self, float): + return {"ui": {"text": float}, "result": (float,)} \ No newline at end of file diff --git a/web/js/help_popup.js b/web/js/help_popup.js new file mode 100644 index 0000000..eeeb2a6 --- /dev/null +++ b/web/js/help_popup.js @@ -0,0 +1,325 @@ +import { app } from "../../../scripts/app.js"; + +// code based on mtb nodes by Mel Massadian https://github.com/melMass/comfy_mtb/ +export const loadScript = ( + FILE_URL, + async = true, + type = 'text/javascript', +) => { + return new Promise((resolve, reject) => { + try { + // Check if the script already exists + const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) + if (existingScript) { + resolve({ status: true, message: 'Script already loaded' }) + return + } + + const scriptEle = document.createElement('script') + scriptEle.type = type + scriptEle.async = async + scriptEle.src = FILE_URL + + scriptEle.addEventListener('load', (ev) => { + resolve({ status: true }) + }) + + scriptEle.addEventListener('error', (ev) => { + reject({ + status: false, + message: `Failed to load the script ${FILE_URL}`, + }) + }) + + document.body.appendChild(scriptEle) + } catch (error) { + reject(error) + } + }) +} + +loadScript('/yvann_web_async/marked.min.js').catch((e) => { + console.log(e) +}) +loadScript('/yvann_web_async/purify.min.js').catch((e) => { + console.log(e) +}) + +const categories = [""]; +app.registerExtension({ + name: "Yvann.HelpPopup", + async beforeRegisterNodeDef(nodeType, nodeData) { + + if (app.ui.settings.getSettingValue("Yvann.helpPopup") === false) { + return; + } + try { + categories.forEach(category => { + if (nodeData?.category?.startsWith(category)) { + addDocumentation(nodeData, nodeType); + } + else return + }); + } catch (error) { + console.error("Error in registering Yvann.HelpPopup", error); + } + }, +}); + +const create_documentation_stylesheet = () => { + const tag = 'roti-documentation-stylesheet' + + let styleTag = document.head.querySelector(tag) + + if (!styleTag) { + styleTag = document.createElement('style') + styleTag.type = 'text/css' + styleTag.id = tag + styleTag.innerHTML = ` + .roti-documentation-popup { + background: var(--comfy-menu-bg); + position: absolute; + color: var(--fg-color); + font: 12px monospace; + line-height: 1.5em; + padding: 10px; + border-radius: 10px; + border-style: solid; + border-width: medium; + border-color: var(--border-color); + z-index: 5; + overflow: hidden; + } + .content-wrapper { + overflow: auto; + max-height: 100%; + /* Scrollbar styling for Chrome */ + &::-webkit-scrollbar { + width: 6px; + } + &::-webkit-scrollbar-track { + background: var(--bg-color); + } + &::-webkit-scrollbar-thumb { + background-color: var(--fg-color); + border-radius: 6px; + border: 3px solid var(--bg-color); + } + + /* Scrollbar styling for Firefox */ + scrollbar-width: thin; + scrollbar-color: var(--fg-color) var(--bg-color); + a { + color: yellow; + } + a:visited { + color: orange; + } + a:hover { + color: red; + } + } + ` + document.head.appendChild(styleTag) + } + } + + /** Add documentation widget to the selected node */ + export const addDocumentation = ( + nodeData, + nodeType, + opts = { icon_size: 14, icon_margin: 4 },) => { + + opts = opts || {} + const iconSize = opts.icon_size ? opts.icon_size : 14 + const iconMargin = opts.icon_margin ? opts.icon_margin : 4 + let docElement = null + let contentWrapper = null + //if no description in the node python code, don't do anything + if (!nodeData.description) { + return + } + + const drawFg = nodeType.prototype.onDrawForeground + nodeType.prototype.onDrawForeground = function (ctx) { + const r = drawFg ? drawFg.apply(this, arguments) : undefined + if (this.flags.collapsed) return r + + // icon position + const x = this.size[0] - iconSize - iconMargin + + // create the popup + if (this.show_doc && docElement === null) { + docElement = document.createElement('div') + contentWrapper = document.createElement('div'); + docElement.appendChild(contentWrapper); + + create_documentation_stylesheet() + contentWrapper.classList.add('content-wrapper'); + docElement.classList.add('roti-documentation-popup') + + //parse the string from the python node code to html with marked, and sanitize the html with DOMPurify + contentWrapper.innerHTML = DOMPurify.sanitize(marked.parse(nodeData.description,)) + + // resize handle + const resizeHandle = document.createElement('div'); + resizeHandle.style.width = '0'; + resizeHandle.style.height = '0'; + resizeHandle.style.position = 'absolute'; + resizeHandle.style.bottom = '0'; + resizeHandle.style.right = '0'; + resizeHandle.style.cursor = 'se-resize'; + + // Add pseudo-elements to create a triangle shape + const borderColor = getComputedStyle(document.documentElement).getPropertyValue('--border-color').trim(); + resizeHandle.style.borderTop = '10px solid transparent'; + resizeHandle.style.borderLeft = '10px solid transparent'; + resizeHandle.style.borderBottom = `10px solid ${borderColor}`; + resizeHandle.style.borderRight = `10px solid ${borderColor}`; + + docElement.appendChild(resizeHandle) + let isResizing = false + let startX, startY, startWidth, startHeight + + resizeHandle.addEventListener('mousedown', function (e) { + e.preventDefault(); + e.stopPropagation(); + isResizing = true; + startX = e.clientX; + startY = e.clientY; + startWidth = parseInt(document.defaultView.getComputedStyle(docElement).width, 10); + startHeight = parseInt(document.defaultView.getComputedStyle(docElement).height, 10); + }, + { signal: this.docCtrl.signal }, + ); + + // close button + const closeButton = document.createElement('div'); + closeButton.textContent = '❌'; + closeButton.style.position = 'absolute'; + closeButton.style.top = '0'; + closeButton.style.right = '0'; + closeButton.style.cursor = 'pointer'; + closeButton.style.padding = '5px'; + closeButton.style.color = 'red'; + closeButton.style.fontSize = '12px'; + + docElement.appendChild(closeButton) + + closeButton.addEventListener('mousedown', (e) => { + e.stopPropagation(); + this.show_doc = !this.show_doc + docElement.parentNode.removeChild(docElement) + docElement = null + if (contentWrapper) { + contentWrapper.remove() + contentWrapper = null + } + }, + { signal: this.docCtrl.signal }, + ); + + document.addEventListener('mousemove', function (e) { + if (!isResizing) return; + const scale = app.canvas.ds.scale; + const newWidth = startWidth + (e.clientX - startX) / scale; + const newHeight = startHeight + (e.clientY - startY) / scale;; + docElement.style.width = `${newWidth}px`; + docElement.style.height = `${newHeight}px`; + }, + { signal: this.docCtrl.signal }, + ); + + document.addEventListener('mouseup', function () { + isResizing = false + }, + { signal: this.docCtrl.signal }, + ) + + document.body.appendChild(docElement) + } + // close the popup + else if (!this.show_doc && docElement !== null) { + docElement.parentNode.removeChild(docElement) + docElement = null + } + // update position of the popup + if (this.show_doc && docElement !== null) { + const rect = ctx.canvas.getBoundingClientRect() + const scaleX = rect.width / ctx.canvas.width + const scaleY = rect.height / ctx.canvas.height + + const transform = new DOMMatrix() + .scaleSelf(scaleX, scaleY) + .multiplySelf(ctx.getTransform()) + .translateSelf(this.size[0] * scaleX * Math.max(1.0,window.devicePixelRatio) , 0) + .translateSelf(10, -32) + + const scale = new DOMMatrix() + .scaleSelf(transform.a, transform.d); + + const styleObject = { + transformOrigin: '0 0', + transform: scale, + left: `${transform.a + transform.e}px`, + top: `${transform.d + transform.f}px`, + }; + Object.assign(docElement.style, styleObject); + } + + ctx.save() + ctx.translate(x - 2, iconSize - 34) + ctx.scale(iconSize / 32, iconSize / 32) + ctx.strokeStyle = 'rgba(255,255,255,0.3)' + ctx.lineCap = 'round' + ctx.lineJoin = 'round' + ctx.lineWidth = 2.4 + ctx.font = 'bold 36px monospace' + ctx.fillStyle = 'orange'; + ctx.fillText('?', 0, 24) + ctx.restore() + return r + } + // handle clicking of the icon + const mouseDown = nodeType.prototype.onMouseDown + nodeType.prototype.onMouseDown = function (e, localPos, canvas) { + const r = mouseDown ? mouseDown.apply(this, arguments) : undefined + const iconX = this.size[0] - iconSize - iconMargin + const iconY = iconSize - 34 + if ( + localPos[0] > iconX && + localPos[0] < iconX + iconSize && + localPos[1] > iconY && + localPos[1] < iconY + iconSize + ) { + if (this.show_doc === undefined) { + this.show_doc = true + } else { + this.show_doc = !this.show_doc + } + if (this.show_doc) { + this.docCtrl = new AbortController() + } else { + this.docCtrl.abort() + } + return true; + } + return r; + } + const onRem = nodeType.prototype.onRemoved + + nodeType.prototype.onRemoved = function () { + const r = onRem ? onRem.apply(this, []) : undefined + + if (docElement) { + docElement.remove() + docElement = null + } + + if (contentWrapper) { + contentWrapper.remove() + contentWrapper = null + } + return r + } +} \ No newline at end of file diff --git a/web/nodes/ShowFloat_YVANN.js b/web/nodes/ShowFloat_YVANN.js new file mode 100644 index 0000000..2b6b7e0 --- /dev/null +++ b/web/nodes/ShowFloat_YVANN.js @@ -0,0 +1,34 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +app.registerExtension({ + name: "ShowFloat_YVANN.ShowFloat_YVANN", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "ShowFloat_YVANN") { + // When the node is executed we will be sent the input float, display this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "float"); + if (pos !== -1) { + for (let i = pos; i < this.widgets.length; i++) { + this.widgets[i].onRemove?.(); + } + this.widgets.length = pos; + } + } + + for (const value of message.float) { + const w = ComfyWidgets["FLOAT"](this, "float", ["FLOAT", { precision: 4 }], app).widget; + w.inputEl.readOnly = true; + w.inputEl.style.opacity = 0.6; + w.value = value; + } + + this.onResize?.(this.size); + }; + } + }, +}); \ No newline at end of file diff --git a/yvann_web_async/marked.min.js b/yvann_web_async/marked.min.js new file mode 100644 index 0000000..2e66c36 --- /dev/null +++ b/yvann_web_async/marked.min.js @@ -0,0 +1,6 @@ +/** + * marked v12.0.1 - a markdown parser + * Copyright (c) 2011-2024, Christopher Jeffrey. 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this}; +b.by=function(g){function h(){return b(g.apply(this,arguments))}for(var i in b)h[i]=b[i];return h};b.domain.apply(b,arguments);return b}; +pv.Scale.quantile=function(){function b(i){return h(Math.max(0,Math.min(d,pv.search.index(f,i)-1))/d)}var c=-1,d=-1,f=[],g=[],h=pv.Scale.linear();b.quantiles=function(i){if(arguments.length){c=Number(i);if(c<0){f=[g[0]].concat(g);d=g.length-1}else{f=[];f[0]=g[0];for(var j=1;j<=c;j++)f[j]=g[~~(j*(g.length-1)/c)];d=c-1}return this}return f};b.domain=function(i,j){if(arguments.length){g=i instanceof Array?pv.map(i,j):Array.prototype.slice.call(arguments);g.sort(pv.naturalOrder);b.quantiles(c);return this}return g}; +b.range=function(){if(arguments.length){h.range.apply(h,arguments);return this}return h.range()};b.by=function(i){function j(){return b(i.apply(this,arguments))}for(var k in b)j[k]=b[k];return j};b.domain.apply(b,arguments);return b}; +pv.histogram=function(b,c){var d=true;return{bins:function(f){var 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b)b[c]=pv.color(b[c])})(); +pv.colors=function(){var b=pv.Scale.ordinal();b.range.apply(b,arguments);return b};pv.Colors={};pv.Colors.category10=function(){var b=pv.colors("#1f77b4","#ff7f0e","#2ca02c","#d62728","#9467bd","#8c564b","#e377c2","#7f7f7f","#bcbd22","#17becf");b.domain.apply(b,arguments);return b}; +pv.Colors.category20=function(){var b=pv.colors("#1f77b4","#aec7e8","#ff7f0e","#ffbb78","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5","#8c564b","#c49c94","#e377c2","#f7b6d2","#7f7f7f","#c7c7c7","#bcbd22","#dbdb8d","#17becf","#9edae5");b.domain.apply(b,arguments);return b}; +pv.Colors.category19=function(){var b=pv.colors("#9c9ede","#7375b5","#4a5584","#cedb9c","#b5cf6b","#8ca252","#637939","#e7cb94","#e7ba52","#bd9e39","#8c6d31","#e7969c","#d6616b","#ad494a","#843c39","#de9ed6","#ce6dbd","#a55194","#7b4173");b.domain.apply(b,arguments);return b};pv.ramp=function(){var b=pv.Scale.linear();b.range.apply(b,arguments);return b}; 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c=b.nextSibling;b.parentNode.removeChild(b);b=c}};pv.SvgScene.undefined=function(){}; +pv.SvgScene.pathBasis=function(){function b(f,g,h,i,j){return{x:f[0]*g.left+f[1]*h.left+f[2]*i.left+f[3]*j.left,y:f[0]*g.top+f[1]*h.top+f[2]*i.top+f[3]*j.top}}var c=[[1/6,2/3,1/6,0],[0,2/3,1/3,0],[0,1/3,2/3,0],[0,1/6,2/3,1/6]],d=function(f,g,h,i){var j=b(c[1],f,g,h,i),k=b(c[2],f,g,h,i);f=b(c[3],f,g,h,i);return"C"+j.x+","+j.y+","+k.x+","+k.y+","+f.x+","+f.y};d.segment=function(f,g,h,i){var j=b(c[0],f,g,h,i),k=b(c[1],f,g,h,i),l=b(c[2],f,g,h,i);f=b(c[3],f,g,h,i);return"M"+j.x+","+j.y+"C"+k.x+","+k.y+ +","+l.x+","+l.y+","+f.x+","+f.y};return d}();pv.SvgScene.curveBasis=function(b){if(b.length<=2)return"";var c="",d=b[0],f=d,g=d,h=b[1];c+=this.pathBasis(d,f,g,h);for(var i=2;i1){j=c[1];h=b[k];k++;f+="C"+(g.left+i.x)+","+(g.top+i.y)+","+(h.left-j.x)+","+(h.top-j.y)+","+h.left+","+h.top;for(g=2;g9){k=3/Math.sqrt(k);f[h]= 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this.scene[0]};a.last=function(){return this.scene[this.scene.length-1]};a.sibling=function(){return this.index==0?null:this.scene[this.index-1]}; +a.cousin=function(){var b=this.parent;return(b=b&&b.sibling())&&b.children?b.children[this.childIndex][this.index]:null}; +a.render=function(){function b(i,j,k){i.scale=k;if(j=0;l--){var q=k[l];if(!(q.name in c)){c[q.name]=q;switch(q.name){case "data":f=q;break;case "visible":g=q;break;default:d[q.type].push(q);break}}}while(j=j.proto)}var c={},d=[[],[],[],[]],f,g;b(this);b(this.defaults);d[1].reverse();d[3].reverse();var h=this;do for(var i in h.properties)i in c||d[2].push(c[i]={name:i,type:2,value:null});while(h=h.proto);h=d[0].concat(d[1]);for(i=0;ih.id)d[g.name]={id:0,value:g.type&1?g.value.apply(this,c):g.value}}}d=this.binds.data;d=d.type& +1?d.value.apply(this,c):d.value;c.unshift(null);b.length=d.length;for(f=0;f0;l--){p=m[l];p.scale=q;q*=p.scene[p.index].transform.k}if(n.children){l=0;for(m=n.children.length;l=3*Math.PI/2};pv.Wedge.prototype.buildImplied=function(b){if(b.angle==null)b.angle=b.endAngle-b.startAngle;else if(b.endAngle==null)b.endAngle=b.startAngle+b.angle;pv.Mark.prototype.buildImplied.call(this,b)};pv.simulation=function(b){return new pv.Simulation(b)};pv.Simulation=function(b){for(var c=0;c=s,u=q.y>=t;l.leaf=false;switch((u<<1)+x){case 0:l=l.c1||(l.c1=new pv.Quadtree.Node);break;case 1:l=l.c2||(l.c2=new pv.Quadtree.Node);break;case 2:l=l.c3||(l.c3=new pv.Quadtree.Node);break;case 3:l=l.c4||(l.c4=new pv.Quadtree.Node); +break}if(x)n=s;else m=s;if(u)p=t;else r=t;c(l,q,n,p,m,r)}var f,g=Number.POSITIVE_INFINITY,h=g,i=Number.NEGATIVE_INFINITY,j=i;for(f=b;f;f=f.next){if(f.xi)i=f.x;if(f.y>j)j=f.y}f=i-g;var k=j-h;if(f>k)j=h+f;else i=g+k;this.xMin=g;this.yMin=h;this.xMax=i;this.yMax=j;this.root=new pv.Quadtree.Node;for(f=b;f;f=f.next)c(this.root,f,g,h,i,j)};pv.Quadtree.Node=function(){this.leaf=true;this.p=this.c4=this.c3=this.c2=this.c1=null};pv.Force={}; +pv.Force.charge=function(b){function c(l){function q(m){c(m);l.cn+=m.cn;n+=m.cn*m.cx;p+=m.cn*m.cy}var n=0,p=0;l.cn=0;if(!l.leaf){l.c1&&q(l.c1);l.c2&&q(l.c2);l.c3&&q(l.c3);l.c4&&q(l.c4)}if(l.p){l.cn+=b;n+=b*l.p.x;p+=b*l.p.y}l.cx=n/l.cn;l.cy=p/l.cn}function d(l,q,n,p,m,r){var s=l.cx-q.x,t=l.cy-q.y,x=1/Math.sqrt(s*s+t*t);if(l.leaf&&l.p!=q||(m-n)*xg)x=g;l=l.cn*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}else if(!l.leaf){var u=(n+m)*0.5,o=(p+r)*0.5;l.c1&&d(l.c1,q,n,p,u,o);l.c2&&d(l.c2,q,u,p, +m,o);l.c3&&d(l.c3,q,n,o,u,r);l.c4&&d(l.c4,q,u,o,m,r);if(!(xg)x=g;if(l.p&&l.p!=q){l=b*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}}}var f=2,g=1/f,h=500,i=1/h,j=0.9,k={};arguments.length||(b=-40);k.constant=function(l){if(arguments.length){b=Number(l);return k}return b};k.domain=function(l,q){if(arguments.length){f=Number(l);g=1/f;h=Number(q);i=1/h;return k}return[f,h]};k.theta=function(l){if(arguments.length){j=Number(l);return k}return j};k.apply=function(l,q){c(q.root);for(l=l;l;l=l.next)d(q.root, +l,q.xMin,q.yMin,q.xMax,q.yMax)};return k};pv.Force.drag=function(b){var c={};arguments.length||(b=0.1);c.constant=function(d){if(arguments.length){b=d;return c}return b};c.apply=function(d){if(b)for(d=d;d;d=d.next){d.fx-=b*d.vx;d.fy-=b*d.vy}};return c}; +pv.Force.spring=function(b){var c=0.1,d=20,f,g,h={};arguments.length||(b=0.1);h.links=function(i){if(arguments.length){f=i;g=i.map(function(j){return 1/Math.sqrt(Math.max(j.sourceNode.linkDegree,j.targetNode.linkDegree))});return h}return f};h.constant=function(i){if(arguments.length){b=Number(i);return h}return b};h.damping=function(i){if(arguments.length){c=Number(i);return h}return c};h.length=function(i){if(arguments.length){d=Number(i);return h}return d};h.apply=function(){for(var i=0;ig,o=sh){l.c1&&u&&c(l.c1,q,n,p,s,t);l.c2&&o&&c(l.c2,q,s,p,m,t)}if(x){l.c3&&u&&c(l.c3,q,n,t,s,r);l.c4&&o&&c(l.c4,q,s,t,m,r)}}if(l.p&&l.p!=q){n=q.x-l.p.x;p=q.y-l.p.y;m=Math.sqrt(n*n+p*p);r=f+b(l.p);if(mm)m=p}for(var r=0;rc.max?c.max:g.x;if(d)for(g=f;g;g=g.next)g.y=g.yd.max?d.max:g.y};return b};pv.Layout=function(){pv.Panel.call(this)};pv.Layout.prototype=pv.extend(pv.Panel); +pv.Layout.prototype.property=function(b,c){if(!this.hasOwnProperty("properties"))this.properties=pv.extend(this.properties);this.properties[b]=true;this.propertyMethod(b,false,pv.Mark.cast[b]=c);return this}; +pv.Layout.Network=function(){pv.Layout.call(this);var b=this;this.$id=pv.id();(this.node=(new pv.Mark).data(function(){return b.nodes()}).strokeStyle("#1f77b4").fillStyle("#fff").left(function(c){return c.x}).top(function(c){return c.y})).parent=this;this.link=(new pv.Mark).extend(this.node).data(function(c){return[c.sourceNode,c.targetNode]}).fillStyle(null).lineWidth(function(c,d){return d.linkValue*1.5}).strokeStyle("rgba(0,0,0,.2)");this.link.add=function(c){return b.add(pv.Panel).data(function(){return b.links()}).add(c).extend(this)}; +(this.label=(new pv.Mark).extend(this.node).textMargin(7).textBaseline("middle").text(function(c){return c.nodeName||c.nodeValue}).textAngle(function(c){c=c.midAngle;return pv.Wedge.upright(c)?c:c+Math.PI}).textAlign(function(c){return pv.Wedge.upright(c.midAngle)?"left":"right"})).parent=this}; +pv.Layout.Network.prototype=pv.extend(pv.Layout).property("nodes",function(b){return b.map(function(c,d){if(typeof c!="object")c={nodeValue:c};c.index=d;return c})}).property("links",function(b){return b.map(function(c){if(isNaN(c.linkValue))c.linkValue=isNaN(c.value)?1:c.value;return c})});pv.Layout.Network.prototype.reset=function(){this.$id=pv.id();return this};pv.Layout.Network.prototype.buildProperties=function(b,c){if((b.$id||0)=this.$id)return true;b.$id=this.$id;b.nodes.forEach(function(c){c.linkDegree=0});b.links.forEach(function(c){var d=c.linkValue;(c.sourceNode||(c.sourceNode=b.nodes[c.source])).linkDegree+=d;(c.targetNode||(c.targetNode=b.nodes[c.target])).linkDegree+=d})};pv.Layout.Hierarchy=function(){pv.Layout.Network.call(this);this.link.strokeStyle("#ccc")};pv.Layout.Hierarchy.prototype=pv.extend(pv.Layout.Network); +pv.Layout.Hierarchy.prototype.buildImplied=function(b){if(!b.links)b.links=pv.Layout.Hierarchy.links.call(this);pv.Layout.Network.prototype.buildImplied.call(this,b)};pv.Layout.Hierarchy.links=function(){return this.nodes().filter(function(b){return b.parentNode}).map(function(b){return{sourceNode:b,targetNode:b.parentNode,linkValue:1}})}; +pv.Layout.Hierarchy.NodeLink={buildImplied:function(b){function c(m){return m.parentNode?m.depth*(n-q)+q:0}function d(m){return m.parentNode?(m.breadth-0.25)*2*Math.PI:0}function f(m){switch(i){case "left":return m.depth*k;case "right":return k-m.depth*k;case "top":return m.breadth*k;case "bottom":return k-m.breadth*k;case "radial":return k/2+c(m)*Math.cos(m.midAngle)}}function g(m){switch(i){case "left":return m.breadth*l;case "right":return l-m.breadth*l;case "top":return m.depth*l;case "bottom":return l- +m.depth*l;case "radial":return l/2+c(m)*Math.sin(m.midAngle)}}var h=b.nodes,i=b.orient,j=/^(top|bottom)$/.test(i),k=b.width,l=b.height;if(i=="radial"){var q=b.innerRadius,n=b.outerRadius;if(q==null)q=0;if(n==null)n=Math.min(k,l)/2}for(b=0;bb.dy?0:-Math.PI/2});(this.leaf=(new pv.Mark).extend(this.node).fillStyle(null).strokeStyle(null).visible(function(b){return!b.firstChild})).parent= +this;delete this.link};pv.Layout.Treemap.prototype=pv.extend(pv.Layout.Hierarchy).property("round",Boolean).property("paddingLeft",Number).property("paddingRight",Number).property("paddingTop",Number).property("paddingBottom",Number).property("mode",String).property("order",String);a=pv.Layout.Treemap.prototype;a.defaults=(new pv.Layout.Treemap).extend(pv.Layout.Hierarchy.prototype.defaults).mode("squarify").order("ascending");a.padding=function(b){return this.paddingLeft(b).paddingRight(b).paddingTop(b).paddingBottom(b)}; +a.$size=function(b){return Number(b.nodeValue)};a.size=function(b){this.$size=pv.functor(b);return this}; +a.buildImplied=function(b){function c(r,s,t,x,u,o,v){for(var w=0,y=0;wt)t=v;u+=v}u*=u;s*=s;return Math.max(s*t/u,u/(s*x))}function f(r,s){function t(A){var D=o==y,G=pv.sum(A,n),E=y?p(G/y):0;c(A,G,D,x,u,D?o:E,D?E:v);if(D){u+=E;v-=E}else{x+= +E;o-=E}y=Math.min(o,v);return D}var x=r.x+j,u=r.y+l,o=r.dx-j-k,v=r.dy-l-q;if(m!="squarify")c(r.childNodes,r.size,m=="slice"?true:m=="dice"?false:s&1,x,u,o,v);else{var w=[];s=Infinity;var y=Math.min(o,v),z=o*v/r.size;if(!(r.size<=0)){r.visitBefore(function(A){A.size*=z});for(r=r.childNodes.slice();r.length;){var C=r[r.length-1];if(C.size){w.push(C);z=d(w,y);if(z<=s){r.pop();s=z}else{w.pop();t(w);w.length=0;s=Infinity}}else r.pop()}if(t(w))for(s=0;s0){i(k(C,o,v),o,B);A+=B;D+=B}G+=C.mod;A+=y.mod;E+=w.mod;D+=z.mod;C=h(C);y=g(y)}if(C&&!h(z)){z.thread=C;z.mod+=G-D}if(y&&!g(w)){w.thread=y;w.mod+=A-E;v=o}}return v}function g(o){return o.firstChild||o.thread}function h(o){return o.lastChild||o.thread}function i(o,v,w){var y=v.number-o.number;v.change-=w/y;v.shift+=w;o.change+= +w/y;v.prelim+=w;v.mod+=w}function j(o){var v=0,w=0;for(o=o.lastChild;o;o=o.previousSibling){o.prelim+=v;o.mod+=v;w+=o.change;v+=o.shift+w}}function k(o,v,w){return o.ancestor.parentNode==v.parentNode?o.ancestor:w}function l(o,v){return(v?1:t+1)/(m=="radial"?o:1)}function q(o){return m=="radial"?o.breadth/r:0}function n(o){switch(m){case "left":return o.depth;case "right":return x-o.depth;case "top":case "bottom":return o.breadth+x/2;case "radial":return x/2+o.depth*Math.cos(q(o))}}function p(o){switch(m){case "left":case "right":return o.breadth+ +u/2;case "top":return o.depth;case "bottom":return u-o.depth;case "radial":return u/2+o.depth*Math.sin(q(o))}}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var m=b.orient,r=b.depth,s=b.breadth,t=b.group,x=b.width,u=b.height;b=b.nodes[0];b.visitAfter(function(o,v){o.ancestor=o;o.prelim=0;o.mod=0;o.change=0;o.shift=0;o.number=o.previousSibling?o.previousSibling.number+1:0;o.depth=v});c(b);d(b,-b.prelim,0);b.visitAfter(function(o){o.breadth*=s;o.depth*=r;o.midAngle=q(o);o.x=n(o);o.y=p(o); +if(o.firstChild)o.midAngle+=Math.PI;delete o.breadth;delete o.depth;delete o.ancestor;delete o.prelim;delete o.mod;delete o.change;delete o.shift;delete o.number;delete o.thread})}};pv.Layout.Indent=function(){pv.Layout.Hierarchy.call(this);this.link.interpolate("step-after")};pv.Layout.Indent.prototype=pv.extend(pv.Layout.Hierarchy).property("depth",Number).property("breadth",Number);pv.Layout.Indent.prototype.defaults=(new pv.Layout.Indent).extend(pv.Layout.Hierarchy.prototype.defaults).depth(15).breadth(15); +pv.Layout.Indent.prototype.buildImplied=function(b){function c(i,j,k){i.x=g+k++*f;i.y=h+j++*d;i.midAngle=0;for(i=i.firstChild;i;i=i.nextSibling)j=c(i,j,k);return j}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var d=b.breadth,f=b.depth,g=0,h=0;c(b.nodes[0],1,1)}};pv.Layout.Pack=function(){pv.Layout.Hierarchy.call(this);this.node.radius(function(b){return b.radius}).strokeStyle("rgb(31, 119, 180)").fillStyle("rgba(31, 119, 180, .25)");this.label.textAlign("center");delete this.link}; +pv.Layout.Pack.prototype=pv.extend(pv.Layout.Hierarchy).property("spacing",Number).property("order",String);pv.Layout.Pack.prototype.defaults=(new pv.Layout.Pack).extend(pv.Layout.Hierarchy.prototype.defaults).spacing(1).order("ascending");pv.Layout.Pack.prototype.$radius=function(){return 1};pv.Layout.Pack.prototype.size=function(b){this.$radius=typeof b=="function"?function(){return Math.sqrt(b.apply(this,arguments))}:(b=Math.sqrt(b),function(){return b});return this}; +pv.Layout.Pack.prototype.buildImplied=function(b){function c(n){var p=pv.Mark.stack;p.unshift(null);for(var m=0,r=n.length;m0.0010}var t=Infinity,x=-Infinity,u=Infinity,o=-Infinity,v,w,y,z,C;v=n[0];v.x=-v.radius;v.y=0;p(v);if(n.length>1){w=n[1];w.x=w.radius;w.y=0;p(w);if(n.length>2){y=n[2];g(v,w,y);p(y);m(v,y);v.p= +y;m(y,w);w=v.n;for(var A=3;A0){r(v,z);w=z;A--}else if(D<0){r(z,w);v=z;A--}}}}v=(t+x)/2;w=(u+o)/2;for(A=y=0;An.min){n.sim.step(); +q=true}q&&d.render()},42)}else for(k=0;kg)g=j;i.size=i.firstChild?pv.sum(i.childNodes,function(k){return k.size}):c.$size.apply(c,(f[0]=i,f))});f.shift();switch(b.order){case "ascending":d.sort(function(i,j){return i.size-j.size});break;case "descending":d.sort(function(i,j){return j.size-i.size});break}var h=1/g;d.minBreadth=0;d.breadth= +0.5;d.maxBreadth=1;d.visitBefore(function(i){for(var j=i.minBreadth,k=i.maxBreadth-j,l=i.firstChild;l;l=l.nextSibling){l.minBreadth=j;l.maxBreadth=j+=l.size/i.size*k;l.breadth=(j+l.minBreadth)/2}});d.visitAfter(function(i,j){i.minDepth=(j-1)*h;i.maxDepth=i.depth=j*h});pv.Layout.Hierarchy.NodeLink.buildImplied.call(this,b)}};pv.Layout.Partition.Fill=function(){pv.Layout.Partition.call(this);pv.Layout.Hierarchy.Fill.constructor.call(this)};pv.Layout.Partition.Fill.prototype=pv.extend(pv.Layout.Partition); +pv.Layout.Partition.Fill.prototype.buildImplied=function(b){pv.Layout.Partition.prototype.buildImplied.call(this,b)||pv.Layout.Hierarchy.Fill.buildImplied.call(this,b)};pv.Layout.Arc=function(){pv.Layout.Network.call(this);var b,c,d,f=this.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.directed;b=g.orient=="radial"?"linear":"polar";d=g.orient=="right"||g.orient=="top"};this.link.data(function(g){var h=g.sourceNode;g=g.targetNode;return d!=(c||h.breadth>1)*f:null}).bottom(function(k,l){return d=="mirror"?l&1?null:(l+1>>1)*-f:(l&1||-1)*(l+1>>1)*f}).fillStyle(function(k,l){return(l&1?h:i)((l>>1)+1)});this.band.add=function(k){return b.add(pv.Panel).extend(c).add(k).extend(this)}};pv.Layout.Horizon.prototype=pv.extend(pv.Layout).property("bands",Number).property("mode",String).property("backgroundStyle",pv.color).property("positiveStyle",pv.color).property("negativeStyle",pv.color); +pv.Layout.Horizon.prototype.defaults=(new pv.Layout.Horizon).extend(pv.Layout.prototype.defaults).bands(2).mode("offset").backgroundStyle("white").positiveStyle("#1f77b4").negativeStyle("#d62728"); +pv.Layout.Rollup=function(){pv.Layout.Network.call(this);var b=this,c,d,f=b.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.$rollup.nodes;d=g.$rollup.links};this.node.data(function(){return c}).size(function(g){return g.nodes.length*20});this.link.interpolate("polar").eccentricity(0.8);this.link.add=function(g){return b.add(pv.Panel).data(function(){return d}).add(g).extend(this)}};pv.Layout.Rollup.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean); +pv.Layout.Rollup.prototype.x=function(b){this.$x=pv.functor(b);return this};pv.Layout.Rollup.prototype.y=function(b){this.$y=pv.functor(b);return this}; +pv.Layout.Rollup.prototype.buildImplied=function(b){function c(r){return i[r]+","+j[r]}if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var d=b.nodes,f=b.links,g=b.directed,h=d.length,i=[],j=[],k=0,l={},q={},n=pv.Mark.stack,p={parent:this};n.unshift(null);for(var m=0;mk.index?k.index+","+d.index:d.index+","+k.index;(n=q[h])||(n=q[h]={sourceNode:d,targetNode:k,linkValue:0,links:[]});n.links.push(f[m]);n.linkValue+=f[m].linkValue}b.$rollup={nodes:pv.values(l),links:pv.values(q)}}}; +pv.Layout.Matrix=function(){pv.Layout.Network.call(this);var b,c,d,f,g,h=this.buildImplied;this.buildImplied=function(i){h.call(this,i);b=i.nodes.length;c=i.width/b;d=i.height/b;f=i.$matrix.labels;g=i.$matrix.pairs};this.link.data(function(){return g}).left(function(){return c*(this.index%b)}).top(function(){return d*Math.floor(this.index/b)}).width(function(){return c}).height(function(){return d}).lineWidth(1.5).strokeStyle("#fff").fillStyle(function(i){return i.linkValue?"#555":"#eee"}).parent= +this;delete this.link.add;this.label.data(function(){return f}).left(function(){return this.index&1?c*((this.index>>1)+0.5):0}).top(function(){return this.index&1?0:d*((this.index>>1)+0.5)}).textMargin(4).textAlign(function(){return this.index&1?"left":"right"}).textAngle(function(){return this.index&1?-Math.PI/2:0});delete this.node};pv.Layout.Matrix.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean);pv.Layout.Matrix.prototype.sort=function(b){this.$sort=b;return this}; +pv.Layout.Matrix.prototype.buildImplied=function(b){if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var c=b.nodes,d=b.links,f=this.$sort,g=c.length,h=pv.range(g),i=[],j=[],k={};b.$matrix={labels:i,pairs:j};f&&h.sort(function(m,r){return f(c[m],c[r])});for(var l=0;lk)l=null;if(g){if(l&&g.scene==l.scene&&g.index==l.index)return;pv.Mark.dispatch("unpoint",g.scene,g.index)}if(g=l){pv.Mark.dispatch("point",l.scene,l.index);pv.listen(this.root.canvas(),"mouseout",f)}}function f(l){if(g&&!pv.ancestor(this,l.relatedTarget)){pv.Mark.dispatch("unpoint",g.scene,g.index);g=null}}var g,h=null,i=1,j=1,k=arguments.length?b*b:900;d.collapse=function(l){if(arguments.length){h=String(l);switch(h){case "y":i= +1;j=0;break;case "x":i=0;j=1;break;default:j=i=1;break}return d}return h};return d}; +pv.Behavior.select=function(){function b(j){g=this.index;f=this.scene;i=this.mouse();h=j;h.x=i.x;h.y=i.y;h.dx=h.dy=0;pv.Mark.dispatch("selectstart",f,g)}function c(){if(f){f.mark.context(f,g,function(){var j=this.mouse();h.x=Math.max(0,Math.min(i.x,j.x));h.y=Math.max(0,Math.min(i.y,j.y));h.dx=Math.min(this.width(),Math.max(j.x,i.x))-h.x;h.dy=Math.min(this.height(),Math.max(j.y,i.y))-h.y;this.render()});pv.Mark.dispatch("select",f,g)}}function d(){if(f){pv.Mark.dispatch("selectend",f,g);f=null}}var f, +g,h,i;pv.listen(window,"mousemove",c);pv.listen(window,"mouseup",d);return b}; +pv.Behavior.resize=function(b){function c(k){h=this.index;g=this.scene;j=this.mouse();i=k;switch(b){case "left":j.x=i.x+i.dx;break;case "right":j.x=i.x;break;case "top":j.y=i.y+i.dy;break;case "bottom":j.y=i.y;break}pv.Mark.dispatch("resizestart",g,h)}function d(){if(g){g.mark.context(g,h,function(){var k=this.mouse();i.x=Math.max(0,Math.min(j.x,k.x));i.y=Math.max(0,Math.min(j.y,k.y));i.dx=Math.min(this.parent.width(),Math.max(k.x,j.x))-i.x;i.dy=Math.min(this.parent.height(),Math.max(k.y,j.y))-i.y; +this.render()});pv.Mark.dispatch("resize",g,h)}}function 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