modules/studentact/current_situation_analysis.py

#v3/modules/studentact/current_situation_analysis.py

import streamlit as st
import matplotlib.pyplot as plt
import networkx as nx
import seaborn as sns
from collections import Counter
from itertools import combinations
import numpy as np
import matplotlib.patches as patches
import logging

logger = logging.getLogger(__name__)

def analyze_text_dimensions(doc):
    """
    Analiza las dimensiones principales del texto.
    
    Args:
        doc: Documento procesado por spaCy
        
    Returns:
        dict: Métricas del análisis
    """
    try:
        # Análisis de vocabulario
        vocab_score = analyze_vocabulary_diversity(doc)
        vocab_normalized = normalize_score(
            value=vocab_score,
            optimal_connections=len(doc) * 0.4  # 40% del total de palabras como conexiones óptimas
        )

        # Análisis de estructura
        struct_score = analyze_structure(doc)
        struct_normalized = normalize_score(
            value=struct_score,
            optimal_length=20  # Longitud óptima promedio de oración
        )

        # Análisis de cohesión
        cohesion_score = analyze_cohesion(doc)
        cohesion_normalized = normalize_score(
            value=cohesion_score,
            optimal_value=0.7  # 70% de cohesión como valor óptimo
        )

        # Análisis de claridad
        clarity_score = analyze_clarity(doc)
        clarity_normalized = normalize_score(
            value=clarity_score,
            optimal_value=0.8  # 80% de claridad como valor óptimo
        )

        return {
            'vocabulary': {
                'raw_score': vocab_score,
                'normalized_score': vocab_normalized
            },
            'structure': {
                'raw_score': struct_score,
                'normalized_score': struct_normalized
            },
            'cohesion': {
                'raw_score': cohesion_score,
                'normalized_score': cohesion_normalized
            },
            'clarity': {
                'raw_score': clarity_score,
                'normalized_score': clarity_normalized
            }
        }

    except Exception as e:
        logger.error(f"Error en analyze_text_dimensions: {str(e)}")
        raise

def analyze_clarity(doc):
    """Analiza la claridad basada en longitud de oraciones"""
    sentences = list(doc.sents)
    avg_length = sum(len(sent) for sent in sentences) / len(sentences)
    return normalize_score(avg_length, optimal_length=20)

def analyze_vocabulary_diversity(doc):
    """Analiza la diversidad del vocabulario"""
    unique_lemmas = {token.lemma_ for token in doc if token.is_alpha}
    total_words = len([token for token in doc if token.is_alpha])
    return len(unique_lemmas) / total_words if total_words > 0 else 0

def analyze_cohesion(doc):
    """Analiza la cohesión textual"""
    sentences = list(doc.sents)
    connections = 0
    for i in range(len(sentences)-1):
        sent1_words = {token.lemma_ for token in sentences[i]}
        sent2_words = {token.lemma_ for token in sentences[i+1]}
        connections += len(sent1_words.intersection(sent2_words))
    return normalize_score(connections, optimal_connections=5)

def analyze_structure(doc):
    """Analiza la complejidad estructural"""
    try:
        root_distances = []
        for token in doc:
            if token.dep_ == 'ROOT':
                depths = get_dependency_depths(token)
                root_distances.extend(depths)
        avg_depth = sum(root_distances) / len(root_distances) if root_distances else 0
        return normalize_score(avg_depth, optimal_depth=3)  # Usando optimal_depth en lugar de optimal_value
    except Exception as e:
        logger.error(f"Error en analyze_structure: {str(e)}")
        return 0.0


# Funciones auxiliares de análisis
def get_dependency_depths(token, depth=0):
    """Obtiene las profundidades de dependencia"""
    depths = [depth]
    for child in token.children:
        depths.extend(get_dependency_depths(child, depth + 1))
    return depths

def normalize_score(value, optimal_value=1.0, range_factor=2.0, optimal_length=None, 
                   optimal_connections=None, optimal_depth=None):
    """
    Normaliza un valor a una escala de 0-1.
    
    Args:
        value: Valor a normalizar
        optimal_value: Valor óptimo de referencia
        range_factor: Factor para ajustar el rango
        optimal_length: Longitud óptima (opcional)
        optimal_connections: Número óptimo de conexiones (opcional)
        optimal_depth: Profundidad óptima de estructura (opcional)
    
    Returns:
        float: Valor normalizado entre 0 y 1
    """
    try:
        if optimal_depth is not None:
            diff = abs(value - optimal_depth)
            max_diff = optimal_depth * range_factor
            return 1.0 - min(diff / max_diff, 1.0)
        elif optimal_connections is not None:
            diff = abs(value - optimal_connections)
            max_diff = optimal_connections * range_factor
            return 1.0 - min(diff / max_diff, 1.0)
        elif optimal_length is not None:
            diff = abs(value - optimal_length)
            max_diff = optimal_length * range_factor
            return 1.0 - min(diff / max_diff, 1.0)
        else:
            diff = abs(value - optimal_value)
            max_diff = optimal_value * range_factor
            return 1.0 - min(diff / max_diff, 1.0)
            
    except Exception as e:
        logger.error(f"Error en normalize_score: {str(e)}")
        return 0.0

# Funciones de generación de gráficos
def generate_sentence_graphs(doc):
    """Genera visualizaciones de estructura de oraciones"""
    fig, ax = plt.subplots(figsize=(10, 6))
    # Implementar visualización
    plt.close()
    return fig

def generate_word_connections(doc):
    """Genera red de conexiones de palabras"""
    fig, ax = plt.subplots(figsize=(10, 6))
    # Implementar visualización
    plt.close()
    return fig

def generate_connection_paths(doc):
    """Genera patrones de conexión"""
    fig, ax = plt.subplots(figsize=(10, 6))
    # Implementar visualización
    plt.close()
    return fig

def create_vocabulary_network(doc):
    """
    Genera el grafo de red de vocabulario.
    """
    G = nx.Graph()
    
    # Crear nodos para palabras significativas
    words = [token.text.lower() for token in doc if token.is_alpha and not token.is_stop]
    word_freq = Counter(words)
    
    # Añadir nodos con tamaño basado en frecuencia
    for word, freq in word_freq.items():
        G.add_node(word, size=freq)
    
    # Crear conexiones basadas en co-ocurrencia
    window_size = 5
    for i in range(len(words) - window_size):
        window = words[i:i+window_size]
        for w1, w2 in combinations(set(window), 2):
            if G.has_edge(w1, w2):
                G[w1][w2]['weight'] += 1
            else:
                G.add_edge(w1, w2, weight=1)
    
    # Crear visualización
    fig, ax = plt.subplots(figsize=(12, 8))
    pos = nx.spring_layout(G)
    
    # Dibujar nodos
    nx.draw_networkx_nodes(G, pos, 
                          node_size=[G.nodes[node]['size']*100 for node in G.nodes],
                          node_color='lightblue',
                          alpha=0.7)
    
    # Dibujar conexiones
    nx.draw_networkx_edges(G, pos, 
                          width=[G[u][v]['weight']*0.5 for u,v in G.edges],
                          alpha=0.5)
    
    # Añadir etiquetas
    nx.draw_networkx_labels(G, pos)
    
    plt.title("Red de Vocabulario")
    plt.axis('off')
    return fig

def create_syntax_complexity_graph(doc):
    """
    Genera el diagrama de arco de complejidad sintáctica.
    Muestra la estructura de dependencias con colores basados en la complejidad.
    """
    try:
        # Preparar datos para la visualización
        sentences = list(doc.sents)
        if not sentences:
            return None
            
        # Crear figura para el gráfico
        fig, ax = plt.subplots(figsize=(12, len(sentences) * 2))
        
        # Colores para diferentes niveles de profundidad
        depth_colors = plt.cm.viridis(np.linspace(0, 1, 6))
        
        y_offset = 0
        max_x = 0
        
        for sent in sentences:
            words = [token.text for token in sent]
            x_positions = range(len(words))
            max_x = max(max_x, len(words))
            
            # Dibujar palabras
            plt.plot(x_positions, [y_offset] * len(words), 'k-', alpha=0.2)
            plt.scatter(x_positions, [y_offset] * len(words), alpha=0)
            
            # Añadir texto
            for i, word in enumerate(words):
                plt.annotate(word, (i, y_offset), xytext=(0, -10), 
                           textcoords='offset points', ha='center')
            
            # Dibujar arcos de dependencia
            for token in sent:
                if token.dep_ != "ROOT":
                    # Calcular profundidad de dependencia
                    depth = 0
                    current = token
                    while current.head != current:
                        depth += 1
                        current = current.head
                    
                    # Determinar posiciones para el arco
                    start = token.i - sent[0].i
                    end = token.head.i - sent[0].i
                    
                    # Altura del arco basada en la distancia entre palabras
                    height = 0.5 * abs(end - start)
                    
                    # Color basado en la profundidad
                    color = depth_colors[min(depth, len(depth_colors)-1)]
                    
                    # Crear arco
                    arc = patches.Arc((min(start, end) + abs(end - start)/2, y_offset),
                                    width=abs(end - start),
                                    height=height,
                                    angle=0,
                                    theta1=0,
                                    theta2=180,
                                    color=color,
                                    alpha=0.6)
                    ax.add_patch(arc)
            
            y_offset -= 2
        
        # Configurar el gráfico
        plt.xlim(-1, max_x)
        plt.ylim(y_offset - 1, 1)
        plt.axis('off')
        plt.title("Complejidad Sintáctica")
        
        return fig
        
    except Exception as e:
        logger.error(f"Error en create_syntax_complexity_graph: {str(e)}")
        return None


def create_cohesion_heatmap(doc):
    """Genera un mapa de calor que muestra la cohesión entre párrafos/oraciones."""
    try:
        sentences = list(doc.sents)
        n_sentences = len(sentences)
        
        if n_sentences < 2:
            return None
            
        similarity_matrix = np.zeros((n_sentences, n_sentences))
        
        for i in range(n_sentences):
            for j in range(n_sentences):
                sent1_lemmas = {token.lemma_ for token in sentences[i] 
                              if token.is_alpha and not token.is_stop}
                sent2_lemmas = {token.lemma_ for token in sentences[j] 
                              if token.is_alpha and not token.is_stop}
                
                if sent1_lemmas and sent2_lemmas:
                    intersection = len(sent1_lemmas & sent2_lemmas)  # Corregido aquí
                    union = len(sent1_lemmas | sent2_lemmas)  # Y aquí
                    similarity_matrix[i, j] = intersection / union if union > 0 else 0
        
        # Crear visualización
        fig, ax = plt.subplots(figsize=(10, 8))
        
        sns.heatmap(similarity_matrix,
                   cmap='YlOrRd',
                   square=True,
                   xticklabels=False,
                   yticklabels=False,
                   cbar_kws={'label': 'Cohesión'},
                   ax=ax)
        
        plt.title("Mapa de Cohesión Textual")
        plt.xlabel("Oraciones")
        plt.ylabel("Oraciones")
        
        plt.tight_layout()
        return fig
        
    except Exception as e:
        logger.error(f"Error en create_cohesion_heatmap: {str(e)}")
        return None