import numpy as np
import scipy.signal as signal
import librosa

class SubTabDSPEngine:
    @staticmethod
    def change_speed(y: np.ndarray, sr: int, speed_ratio: float, preserve_pitch: bool = True) -> np.ndarray:
        """
        Alters the playback velocity (Time-Stretching) of a NumPy signal array.
        """
        if speed_ratio == 1.0:
            return y
            
        if preserve_pitch:
            return librosa.effects.time_stretch(y, rate=speed_ratio)
        else:
            num_samples_new = int(len(y) / speed_ratio)
            return signal.resample(y, num_samples_new)

    @staticmethod
    def normalize(y: np.ndarray, target_db: float = 0.0) -> np.ndarray:
        """
        Performs Peak Normalization on an array to scale it to the target decibel value.
        """
        target_amplitude = 10.0 ** (target_db / 20.0)
        max_amplitude = np.max(np.abs(y))
        
        if max_amplitude == 0:
            return y
            
        gain = target_amplitude / max_amplitude
        return y * gain

    @staticmethod
    def apply_volume_automation_envelope(y: np.ndarray, sr: int, nodes: list) -> np.ndarray:
        """
        Applies a user-drawn volume automation envelope onto an acoustic signal NumPy array.
        nodes: A list of point dictionaries, e.g., [{"time": 0.0, "db": 0.0}, {"time": 2.5, "db": -12.0}, ...]
        """
        if not nodes:
            return y
            
        # Sort envelope nodes chronologically by time axis
        nodes = sorted(nodes, key=lambda x: x["time"])
        
        # 1. Map node variables into distinct coordinates arrays
        node_times = np.array([node["time"] for node in nodes])
        node_dbs = np.array([node["db"] for node in nodes])
        
        # Hard-clamp boundary constraints matching the operational floor [-30.0dB, +3.0dB]
        node_dbs = np.clip(node_dbs, -30.0, 3.0)
        
        # 2. Evaluate absolute timeline timestamps for every index position inside the signal array
        total_samples = len(y)
        sample_times = np.arange(total_samples) / sr
        
        # 3. Linearly interpolate localized decibel thresholds across every single sample step
        # Handle edge cases for interpolation: if sample_times is outside node_times range,
        # np.interp uses the first/last value of node_dbs.
        interpolated_dbs = np.interp(sample_times, node_times, node_dbs, left=node_dbs[0], right=node_dbs[-1])
        
        # 4. Map logarithmic values into standard linear gain scale arrays
        linear_gains = 10.0 ** (interpolated_dbs / 20.0)
        
        # 5. Multiply the raw amplitude vector array by the linear gain modifier mask
        return y * linear_gains

    @staticmethod
    def pitch_shift(y: np.ndarray, sr: int, n_steps: float) -> np.ndarray:
        """
        Shift the pitch of an audio signal by a specified number of semitones.
        
        Args:
            y: Input audio signal
            sr: Sample rate
            n_steps: Number of semitones to shift (positive = higher pitch, negative = lower pitch)
            
        Returns:
            Pitch-shifted audio signal
        """
        if n_steps == 0:
            return y
        return librosa.effects.pitch_shift(y, sr=sr, n_steps=n_steps)

    @staticmethod
    def merge_back_to_parent(
        parent_track_audio: np.ndarray, 
        sr: int, 
        edited_sub_audio: np.ndarray, 
        start_seconds: float, 
        original_duration_seconds: float
    ) -> np.ndarray:
        """
        Splices the modified audio segment from the Sub-tab back into the parent track array.
        Applies a 10ms micro-crossfade at the boundaries to eliminate pop/click noise.
        """
        start_sample = int(start_seconds * sr)
        original_samples_len = int(original_duration_seconds * sr)
        edited_samples_len = len(edited_sub_audio)
        crossfade_samples = int(0.01 * sr)  # 10ms crossfade window
        
        # 1. Allocate the target output array dimension bounds
        new_total_len = len(parent_track_audio) - original_samples_len + edited_samples_len
        output_audio = np.zeros(new_total_len, dtype=np.float32)
        
        # 2. Extract leading unedited block
        output_audio[:start_sample] = parent_track_audio[:start_sample]
        
        # 3. Stitch the modified audio payload
        output_audio[start_sample:start_sample + edited_samples_len] = edited_sub_audio
        
        # 4. Extract trailing unedited block
        post_start_original = start_sample + original_samples_len
        post_start_new = start_sample + edited_samples_len
        output_audio[post_start_new:] = parent_track_audio[post_start_original:]
        
        # 5. Execute micro-crossfade across the initial splice junction
        if start_sample > crossfade_samples:
            fade_in_ramp = np.linspace(0.0, 1.0, crossfade_samples)
            fade_out_ramp = np.linspace(1.0, 0.0, crossfade_samples)
            
            # Smooth 10ms interpolation overlay
            output_audio[start_sample : start_sample + crossfade_samples] = (
                edited_sub_audio[:crossfade_samples] * fade_in_ramp + 
                parent_track_audio[start_sample : start_sample + crossfade_samples] * fade_out_ramp
            )
            
        # 6. Execute micro-crossfade across the trailing splice junction
        if post_start_new + crossfade_samples < len(output_audio):
            fade_in_ramp = np.linspace(0.0, 1.0, crossfade_samples)
            fade_out_ramp = np.linspace(1.0, 0.0, crossfade_samples)
            
            output_audio[post_start_new : post_start_new + crossfade_samples] = (
                parent_track_audio[post_start_original : post_start_original + crossfade_samples] * fade_in_ramp +
                edited_sub_audio[-crossfade_samples:] * fade_out_ramp
            )
            
        return output_audio