我们正在使用gem'bootstrap-wysihtml5-rails','0.3.1.13'。当焦点丢失时,我想从textarea保存更改的内容。
我试图在视图中直接使用jquery,包含脚本标记:
$("textarea").live("blur", function(){ alert("Focus lost"); });
如果我使用“模糊”(或焦点),则会在页面加载时多次触发警报,但在失去焦点时则不会,当我使用“更改”时根本没有任何事情发生。
在另一次尝试中,我试图以相同的行为挂钩到wysihtml5事件:
function integrate_wysihtml5() {
var editor = $('.wysihtml5').each(function(i, elem) {
$(elem).wysihtml5({
"font-styles": false, //Font styling, e.g. h1, h2, etc. Default true
"emphasis": true, //Italics, bold, etc. Default true
"lists": true, //(Un)ordered lists, e.g. Bullets, Numbers. Default true
"html": false, //Button which allows you to edit the generated HTML. Default false
"link": false, //Button to insert a link. Default true
"image": false, //Button to insert an image. Default true,
"color": false //Button to change color of font
});
});
function onChange() { alert("The content of the editor has changed"); };
editor.on("change", onChange);
}
答案 0 :(得分:2)
$('.textarea').wysihtml5({
events: {
change: function() {
var html = this.textarea.getValue();
//ajax method
}
}
}
答案 1 :(得分:0)
抓住变革事件的好方法:
editor.composer.element.addEventListener("keyup", myOnChange);
editor.on("aftercommand:composer", myOnChange);
但我不认为你的每一个都是好的,wysihtml5不适用于多个可编辑的部分,在这里你使用bootstrap版本,但我认为它是相同的。 这主要是我写jquery.scribe
的原因那么,也许就是这样?
$('.wysihtml5').each(function(i, elem) {
var editor = $(elem).wysihtml5({
"font-styles": false, //Font styling, e.g. h1, h2, etc. Default true
"emphasis": true, //Italics, bold, etc. Default true
"lists": true, //(Un)ordered lists, e.g. Bullets, Numbers. Default true
"html": false, //Button which allows you to edit the generated HTML. Default false
"link": false, //Button to insert a link. Default true
"image": false, //Button to insert an image. Default true,
"color": false //Button to change color of font
});
editor.composer.element.addEventListener("keyup", myOnChange);
editor.on("aftercommand:composer", myOnChange);
});
答案 2 :(得分:0)
我像这样附加听众:
import tensorflow as tf
class Model:
def __init__(self, x):
self.x = x
self._output = None
@property
def output(self):
if not self._output:
weight = tf.Variable(tf.constant(4.0))
bias = tf.Variable(tf.constant(2.0))
self._output = tf.multiply(self.x, weight) + bias
return self._output
def main():
x = tf.placeholder(tf.float32)
model = Model(x)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(model.output, {x: 4.0})
print(output)
if __name__ == '__main__':
main()
希望它有所帮助。