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#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 diferentes dimensiones del texto.
Args:
doc: Documento procesado por spaCy
Returns:
dict: M茅tricas del an谩lisis
"""
try:
# Analizar claridad (basado en longitud de oraciones)
clarity_score = analyze_clarity(doc)
# Analizar vocabulario (diversidad l茅xica)
vocabulary_score = analyze_vocabulary_diversity(doc)
# Analizar cohesi贸n (conexiones entre oraciones)
cohesion_score = analyze_cohesion(doc)
# Analizar estructura (complejidad sint谩ctica)
structure_score = analyze_structure(doc)
# Generar gr谩ficos
sentence_graphs = generate_sentence_graphs(doc)
word_connections = generate_word_connections(doc)
connection_paths = generate_connection_paths(doc)
return {
'clarity': clarity_score,
'vocabulary': vocabulary_score,
'cohesion': cohesion_score,
'structure': structure_score,
'sentence_graphs': sentence_graphs,
'word_connections': word_connections,
'connection_paths': connection_paths
}
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 cohesi贸n 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"""
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)
# Funciones auxiliares de an谩lisis
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):
"""Normaliza un valor a un score entre 0 y 1"""
return 1 / (1 + abs(value - optimal_value) / range_factor)
# Funciones de generaci贸n de gr谩ficos
def generate_sentence_graphs(doc):
"""Genera visualizaciones de estructura de oraciones"""
fig, ax = plt.subplots(figsize=(10, 6))
# Implementar visualizaci贸n
plt.close()
return fig
def generate_word_connections(doc):
"""Genera red de conexiones de palabras"""
fig, ax = plt.subplots(figsize=(10, 6))
# Implementar visualizaci贸n
plt.close()
return fig
def generate_connection_paths(doc):
"""Genera patrones de conexi贸n"""
fig, ax = plt.subplots(figsize=(10, 6))
# Implementar visualizaci贸n
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)
# A帽adir nodos con tama帽o 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 visualizaci贸n
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)
# A帽adir 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 sint谩ctica.
Muestra la estructura de dependencias con colores basados en la complejidad.
"""
try:
# Preparar datos para la visualizaci贸n
sentences = list(doc.sents)
if not sentences:
return None
# Crear figura para el gr谩fico
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)
# A帽adir 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 gr谩fico
plt.xlim(-1, max_x)
plt.ylim(y_offset - 1, 1)
plt.axis('off')
plt.title("Complejidad Sint谩ctica")
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 cohesi贸n entre p谩rrafos/oraciones.
"""
try:
# Dividir en oraciones
sentences = list(doc.sents)
n_sentences = len(sentences)
if n_sentences < 2:
return None
# Crear matriz de similitud
similarity_matrix = np.zeros((n_sentences, n_sentences))
# Calcular similitud entre pares de oraciones
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_words)
union = len(sent1_lemmas | sent2_words)
similarity_matrix[i, j] = intersection / union if union > 0 else 0
# Crear visualizaci贸n
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(similarity_matrix,
cmap='YlOrRd',
square=True,
xticklabels=False,
yticklabels=False,
cbar_kws={'label': 'Cohesi贸n'},
ax=ax)
plt.title("Mapa de Cohesi贸n 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
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