rustorch 0.6.29

//! Main tensor visualizer implementation
//! メインテンソル可視化実装

use crate::autograd::Variable;
use crate::error::{RusTorchError, RusTorchResult};
use crate::tensor::Tensor;
use crate::visualization::{PlotData, Visualizable};
use num_traits::Float;
use std::collections::HashMap;

use super::{ColorMap, TensorPlotConfig};

/// テンソル可視化クラス
/// Tensor visualization class
#[derive(Debug)]
pub struct TensorVisualizer {
    config: TensorPlotConfig,
}

impl TensorVisualizer {
    /// 新しいテンソルビジュアライザーを作成
    /// Create a new tensor visualizer
    pub fn new() -> Self {
        Self {
            config: TensorPlotConfig::default(),
        }
    }

    /// 設定付きビジュアライザーを作成
    /// Create visualizer with config
    pub fn with_config(config: TensorPlotConfig) -> Self {
        Self { config }
    }

    /// Get the current configuration
    /// 現在の設定を取得
    pub fn config(&self) -> &TensorPlotConfig {
        &self.config
    }

    /// Plot tensor as heatmap
    /// テンソルをヒートマップとしてプロット
    pub fn plot_heatmap<T>(&self, tensor: &Tensor<T>) -> RusTorchResult<String>
    where
        T: Float + std::fmt::Display + std::fmt::Debug + 'static,
    {
        let shape = tensor.shape();

        match shape.len() {
            1 => self.plot_1d_heatmap(tensor),
            2 => self.plot_2d_heatmap(tensor),
            _ => Err(RusTorchError::tensor_op(
                "Heatmap visualization only supports 1D and 2D tensors",
            )),
        }
    }

    /// Plot tensor as bar chart
    /// テンソルを棒グラフとしてプロット
    pub fn plot_bar_chart<T>(&self, tensor: &Tensor<T>) -> RusTorchResult<String>
    where
        T: Float + std::fmt::Display + std::fmt::Debug + 'static,
    {
        if tensor.shape().len() != 1 {
            return Err(RusTorchError::tensor_op(
                "Bar chart only supports 1D tensors",
            ));
        }

        let shape = tensor.shape();
        let size = shape[0];

        // Simplified bar chart visualization
        let mut svg = String::new();
        svg.push_str(&format!(
            r#"<svg width="{}" height="{}" xmlns="http://www.w3.org/2000/svg">"#,
            self.config.figsize.0 * self.config.dpi as f32,
            self.config.figsize.1 * self.config.dpi as f32
        ));

        // Placeholder bars
        let bar_width = (self.config.figsize.0 * self.config.dpi as f32) / size as f32;

        for i in 0..size.min(20) {
            // Limit to 20 bars for simplicity
            let height = 50.0 + (i as f32 * 10.0); // Simple pattern
            let x = i as f32 * bar_width;
            let y = self.config.figsize.1 * self.config.dpi as f32 - height;

            svg.push_str(&format!(
                r#"<rect x="{}" y="{}" width="{}" height="{}" fill="steelblue"/>"#,
                x,
                y,
                bar_width * 0.8,
                height
            ));
        }

        svg.push_str("</svg>");
        Ok(svg)
    }

    /// Plot 3D tensor as slices
    /// 3Dテンソルをスライスとしてプロット
    pub fn plot_3d_slices<T>(&self, tensor: &Tensor<T>) -> RusTorchResult<String>
    where
        T: Float + std::fmt::Display + std::fmt::Debug + 'static,
    {
        if tensor.shape().len() != 3 {
            return Err(RusTorchError::tensor_op(
                "3D slice visualization only supports 3D tensors",
            ));
        }

        // Implementation would create multiple 2D heatmaps for each slice
        Ok("<svg>3D slices visualization placeholder</svg>".to_string())
    }

    /// Plot tensor statistics
    /// テンソル統計をプロット
    pub fn plot_statistics<T>(&self, tensor: &Tensor<T>) -> RusTorchResult<String>
    where
        T: Float + std::fmt::Display + std::fmt::Debug + 'static,
    {
        // Implementation for statistical plots (histogram, box plot, etc.)
        Ok("<svg>Statistics visualization placeholder</svg>".to_string())
    }

    /// Plot variable with gradient information
    /// 勾配情報付き変数をプロット
    pub fn plot_variable<T>(&self, variable: &Variable<T>) -> RusTorchResult<String>
    where
        T: Float
            + std::fmt::Display
            + std::fmt::Debug
            + 'static
            + ndarray::ScalarOperand
            + num_traits::FromPrimitive
            + Send
            + Sync,
    {
        // Implementation for variable visualization with gradients
        Ok("<svg>Variable visualization placeholder</svg>".to_string())
    }

    // Helper methods
    fn plot_1d_heatmap<T>(&self, tensor: &Tensor<T>) -> RusTorchResult<String>
    where
        T: Float + std::fmt::Display + std::fmt::Debug + 'static,
    {
        // 1D heatmap implementation
        Ok("<svg>1D heatmap placeholder</svg>".to_string())
    }

    fn plot_2d_heatmap<T>(&self, tensor: &Tensor<T>) -> RusTorchResult<String>
    where
        T: Float + std::fmt::Display + std::fmt::Debug + 'static,
    {
        // 2D heatmap implementation - simplified placeholder
        let shape = tensor.shape();
        let (height, width) = (shape[0], shape[1]);

        let mut svg = String::new();
        svg.push_str(&format!(
            r#"<svg width="{}" height="{}" xmlns="http://www.w3.org/2000/svg">"#,
            width * 10,
            height * 10
        ));

        // Placeholder heatmap visualization
        svg.push_str(&format!(
            r#"<rect x="0" y="0" width="{}" height="{}" fill="lightblue"/>"#,
            width * 10,
            height * 10
        ));

        svg.push_str(&format!(
            r#"<text x="{}" y="{}" text-anchor="middle" font-family="Arial" font-size="12">{}x{} Tensor</text>"#,
            width * 5, height * 5, height, width
        ));

        svg.push_str("</svg>");
        Ok(svg)
    }
}

impl Default for TensorVisualizer {
    fn default() -> Self {
        Self::new()
    }
}