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class TypingLogic:
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def __init__(self, learning_content: str):
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"""
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初始化打字逻辑状态。。。。
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- 存储学习材料
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- 初始化当前索引为0
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- 初始化错误计数为0
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"""
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self.learning_content = learning_content
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self.current_index = 0
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self.error_count = 0
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self.total_chars = len(learning_content)
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self.typed_chars = 0
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self.image_positions = [] # 存储图片位置信息
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self.image_data = {} # 存储图片数据 {图片名称: 二进制数据}
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self.image_display_queue = [] # 待显示的图片队列
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def check_input(self, user_text: str) -> dict:
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"""
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检查用户输入与学习材料的匹配情况
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- 逐字符比较逻辑
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- 支持中文整词匹配
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- 进度跟踪
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- 准确率计算
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- 返回字典包含:
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* correct: 布尔值，当前输入是否正确
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* expected: 字符串，当前期望的字符
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* position: 整数，当前位置
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* completed: 布尔值，是否完成
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* accuracy: 浮点数，准确率
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"""
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# 保存当前索引用于返回
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current_position = len(user_text)
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# 临时保存原始的typed_chars值用于准确率计算
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original_typed_chars = self.typed_chars
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# 更新已输入字符数
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self.typed_chars = len(user_text)
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# 如果用户输入的字符数超过了学习材料的长度，截取到相同长度
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if len(user_text) > self.total_chars:
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user_text = user_text[:self.total_chars]
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current_position = len(user_text)
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# 检查当前输入是否正确（支持中文整词匹配）
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correct = True
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expected_char = ''
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if self.current_index < self.total_chars:
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expected_char = self.learning_content[self.current_index]
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# 中文整词匹配优化
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# 如果当前位置是中文文本的开始，尝试进行整词匹配
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if self._is_chinese_text_start(self.current_index):
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# 获取期望的中文词组
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expected_word = self._get_chinese_word_at(self.current_index)
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# 获取用户输入的对应部分
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user_word = user_text[self.current_index:min(self.current_index + len(expected_word), len(user_text))]
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# 如果用户输入的词组与期望词组匹配，则认为是正确的
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if user_word == expected_word:
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correct = True
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else:
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# 如果整词不匹配，回退到逐字符匹配
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if len(user_text) > self.current_index and user_text[self.current_index] != expected_char:
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correct = False
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else:
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# 非中文词组开始位置，使用逐字符匹配
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if len(user_text) > self.current_index and user_text[self.current_index] != expected_char:
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correct = False
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else:
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# 已经完成所有输入
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# 恢复原始的typed_chars值用于准确率计算
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accuracy = self._calculate_accuracy()
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self.typed_chars = original_typed_chars
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return {
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"correct": True,
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"expected": "",
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"position": self.current_index,
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"completed": True,
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"accuracy": accuracy
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}
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# 检查是否完成
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completed = current_position >= self.total_chars
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# 计算准确率
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accuracy = self._calculate_accuracy()
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# 恢复原始的typed_chars值
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self.typed_chars = original_typed_chars
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return {
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"correct": correct,
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"expected": expected_char,
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"position": current_position,
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"completed": completed,
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"accuracy": accuracy
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}
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def update_position(self, user_text: str) -> dict:
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"""
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更新当前位置并计算错误数
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- 支持中文整词匹配
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- 逐字符比较逻辑（回退机制）
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- 错误计数
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- 位置更新
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- 返回字典包含:
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* new_position: 整数，新的位置
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* error_count: 整数，错误数
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* completed: 布尔值，是否完成
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"""
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# 更新当前索引位置
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self.current_index = len(user_text)
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# 重置错误计数
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self.error_count = 0
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# 使用中文整词匹配优化错误计算
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self._calculate_errors_with_chinese_support(user_text)
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# 检查是否完成
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completed = self.current_index >= self.total_chars
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return {
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"new_position": self.current_index,
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"error_count": self.error_count,
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"completed": completed
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}
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def get_expected_text(self, length: int = 10) -> str:
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"""
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获取用户接下来应该输入的内容
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- 返回从当前位置开始的一定长度文本（如10个字符）
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- 处理文本结束情况
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"""
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start_pos = self.current_index
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end_pos = min(start_pos + length, self.total_chars)
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return self.learning_content[start_pos:end_pos]
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def get_progress(self) -> dict:
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"""
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获取当前学习进度统计
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- current: 整数，当前位置
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- total: 整数，总长度
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- percentage: 浮点数，完成百分比
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- remaining: 整数，剩余字符数
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"""
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current = self.current_index
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total = self.total_chars
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percentage = (current / total * 100) if total > 0 else 0
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remaining = max(0, total - current)
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return {
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"current": current,
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"total": total,
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"percentage": percentage,
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"remaining": remaining
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}
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def reset(self, new_content: str = None):
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"""
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重置打字状态
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- 重置当前索引为0
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- 重置错误计数
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- 如果提供了新内容，更新学习材料
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"""
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if new_content is not None:
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self.learning_content = new_content
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self.total_chars = len(new_content)
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self.current_index = 0
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self.error_count = 0
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self.typed_chars = 0
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self.image_positions = [] # 重置图片位置信息
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self.image_data = {} # 重置图片数据
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self.image_display_queue = [] # 重置图片显示队列
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def get_statistics(self) -> dict:
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"""
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获取打字统计信息
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- total_chars: 整数，总字符数
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- typed_chars: 整数，已输入字符数
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- error_count: 整数，错误次数
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- accuracy_rate: 浮点数，准确率
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"""
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return {
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"total_chars": self.total_chars,
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"typed_chars": self.typed_chars,
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"error_count": self.error_count,
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"accuracy_rate": self._calculate_accuracy()
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}
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def set_image_positions(self, image_positions: list):
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"""
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设置图片位置信息
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- image_positions: 列表，包含图片位置信息
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"""
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self.image_positions = image_positions
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def get_current_image_info(self, position: int) -> dict:
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"""
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获取当前位置的图片信息
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- position: 整数，当前输入位置
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- 返回字典包含图片信息，如果没有图片返回None
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"""
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for img_info in self.image_positions:
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if img_info['start_pos'] <= position <= img_info['end_pos']:
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return img_info
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return None
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def set_image_data(self, image_data: dict):
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"""
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设置图片数据
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- image_data: 字典，{图片名称: 二进制数据}
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"""
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self.image_data = image_data
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def get_images_to_display(self, current_position: int) -> list:
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"""
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获取在当前位置需要显示的图片
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- current_position: 整数，当前输入位置
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- 返回图片信息列表
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"""
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images_to_display = []
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for img_info in self.image_positions:
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if img_info['start_pos'] <= current_position <= img_info['end_pos']:
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# 尝试获取图片名称（支持多种键名）
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image_name = img_info.get('image_name', '') or img_info.get('filename', '')
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if image_name in self.image_data:
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img_info_copy = img_info.copy()
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img_info_copy['image_data'] = self.image_data[image_name]
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images_to_display.append(img_info_copy)
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return images_to_display
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def should_show_image(self, current_position: int) -> bool:
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"""
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检查在当前位置是否应该显示图片
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- current_position: 整数，当前输入位置
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- 返回布尔值
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"""
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return len(self.get_images_to_display(current_position)) > 0
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def check_image_at_position(self, position: int) -> bool:
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"""
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检查指定位置是否有图片
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- position: 整数，位置索引
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- 返回布尔值，该位置是否有图片
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"""
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return self.get_current_image_info(position) is not None
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def _calculate_accuracy(self) -> float:
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"""
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计算准确率
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"""
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# 防止递归的保护措施
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if hasattr(self, '_calculating_accuracy') and self._calculating_accuracy:
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return 0.0
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if self.typed_chars == 0:
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return 0.0
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# 设置递归保护标志
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self._calculating_accuracy = True
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try:
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# 准确率 = (已输入字符数 - 错误次数) / 已输入字符数
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accuracy = (self.typed_chars - self.error_count) / self.typed_chars
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return max(0.0, min(1.0, accuracy)) # 确保准确率在0.0到1.0之间
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except (ZeroDivisionError, RecursionError):
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return 0.0
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finally:
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# 清除递归保护标志
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self._calculating_accuracy = False
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def _is_chinese_text_start(self, index: int) -> bool:
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"""
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判断指定位置是否是中文文本的开始
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- 检查当前字符是否为中文
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- 检查前一个字符是否为非中文或空格
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"""
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if index < 0 or index >= len(self.learning_content):
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return False
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current_char = self.learning_content[index]
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# 检查当前字符是否为中文
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if not self._is_chinese_char(current_char):
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return False
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# 如果是第一个字符，或者是紧跟在非中文字符后的中文，则认为是中文文本的开始
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if index == 0:
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return True
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prev_char = self.learning_content[index - 1]
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return not self._is_chinese_char(prev_char) or prev_char.isspace()
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def _is_chinese_char(self, char: str) -> bool:
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"""
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判断字符是否为中文
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- 使用Unicode范围判断中文字符
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"""
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return '\u4e00' <= char <= '\u9fff' or '\u3400' <= char <= '\u4dbf'
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def _get_chinese_word_at(self, index: int) -> str:
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"""
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获取指定位置开始的中文词组
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- 从当前位置开始，连续获取中文字符
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- 遇到非中文字符或字符串结束则停止
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"""
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if index < 0 or index >= len(self.learning_content):
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return ""
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word = ""
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current_index = index
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while current_index < len(self.learning_content):
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char = self.learning_content[current_index]
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if self._is_chinese_char(char):
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word += char
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current_index += 1
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else:
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break
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return word
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def _calculate_errors_with_chinese_support(self, user_text: str) -> None:
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"""
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使用中文整词匹配优化错误计算
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- 优先尝试中文整词匹配
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- 整词匹配失败时回退到逐字符匹配
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"""
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i = 0
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while i < min(len(user_text), len(self.learning_content)):
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# 检查当前位置是否为中文文本开始
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if self._is_chinese_text_start(i):
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# 获取期望的中文词组
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expected_word = self._get_chinese_word_at(i)
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# 获取用户输入的对应部分
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user_word = user_text[i:min(i + len(expected_word), len(user_text))]
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# 如果整词匹配成功，跳过整个词组
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if user_word == expected_word:
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i += len(expected_word)
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continue
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else:
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# 整词匹配失败，回退到逐字符匹配
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if user_text[i] != self.learning_content[i]:
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self.error_count += 1
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else:
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# 非中文文本，使用逐字符匹配
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if user_text[i] != self.learning_content[i]:
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self.error_count += 1
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i += 1 |
