如何深入分析Django forms组件的源码实现？

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1. `forms组件源码分析`

2.为什么局部锤子要写成clean_字段名

3.为什么要抛出异常2+输入在is_valid()函数

4.验证流程 - 先验证字段自带的规则（最大、最小、是否必填、是否合法）

1 forms组件源码分析

1 为什么局部钩子要写成clean_字段名，为什么要抛异常 2 入口在is_valid() 3 校验流程 -先校验字段自己的规则（最大，最小，是否必填，是不是合法） -校验局部钩子函数 -全局钩子校验 4 流程 -is_valid() --> return self.is_bound and not self.errors -self.errors: 方法包装成了数据属性 -一旦有值，self.errors就不进行校验(之前调用过了) - self.full_clean(): 核心 if not self.is_bound: # Stop further processing. 如果data没有值直接返回 return def full_clean(self): """ Clean all of self.data and populate self._errors and self.cleaned_data. """ self._errors = ErrorDict() if not self.is_bound: # Stop further processing. return self.cleaned_data = {} # If the form is permitted to be empty, and none of the form data has # changed from the initial data, short circuit any validation. if self.empty_permitted and not self.has_changed(): return self._clean_fields() self._clean_form() self._post_clean() # 局部钩子执行位置 def _clean_fields(self): for name, field in self.fields.items(): # value_from_datadict() gets the data from the data dictionaries. # Each widget type knows how to retrieve its own data, because some # widgets split data over several HTML fields. if field.disabled: value = self.get_initial_for_field(field, name) else: value = field.widget.value_from_datadict(self.data, self.files, self.add_prefix(name)) try: if isinstance(field, FileField): initial = self.get_initial_for_field(field, name) value = field.clean(value, initial) else: value = field.clean(value) # 字段自己的校验规则 self.cleaned_data[name] = value # 把校验后的数据放到cleaned_data if hasattr(self, 'clean_%s' % name): # 判断有没有局部钩子 value = getattr(self, 'clean_%s' % name)() # 执行局部钩子 self.cleaned_data[name] = value # 校验通过 把数据替换一下 except ValidationError as e: # 如果校验不通过，会抛异常，会被捕获 self.add_error(name, e) # 捕获后执行 添加到错误信息 errors是个列表 错误可能有多个 # 全局钩子执行位置 def _clean_form(self): try: # 如果自己定义的form类中写了clean，他就会执行 cleaned_data = self.clean() except ValidationError as e: self.add_error(None, e) # key作为None就是__all__ else: if cleaned_data is not None: self.cleaned_data = cleaned_data