您好我想在下一页显示所选行值详细信息，我只在第一页显示名称和城市，其余字段将在点击按钮后查看。 我的html页面
<!DOCTYPE html>
<html>
<head>
<meta charset="ISO-8859-1">
<title>Insert title here</title>
<script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.5.0/angular.min.js"></script>
<script src="detailCtrlr.js"></script>
</head>
<body ng-app="myApp" ng-controller="customersCtrl">
<table>
<tr>
<td>Name</td>
<td>Age</td>
<td>details</td>
</tr>
<tr ng-repeat="x in names">
<td>{{x.Name}}</td>
<td>{{x.City}}</td>
<td><a href="#/viewdetail" type="submit" name="viewdetails" ng-click="detail()">view</a></td>
</tr>
</table>
</body>
</html>
我的控制器是
var app = angular.module('myApp', []);
app.controller('customersCtrl', function($scope, $http) {
$http.get("details.json").then(function(response) {
$scope.names = response.data.records;
});
$scope.detail=function(){
//details code here
}
});
现在我正在获取此页面中的所有记录并显示正常，现在我想在详细信息页面中查看所选行的其他详细信息。
答案 0 :(得分：1)
您可以使用本地存储查看所选行的所有数据。 请找到以下代码：
如果您获得每条记录的唯一ID，请尝试以下方法：
你的HTML会：<a href="#/viewdetail" type="submit" name="viewdetails" ng-click="detail(x.id)">view</a>
var app = angular.module('myApp', []);
app.controller('customersCtrl', function($scope, $http,$location,filter) {
$http.get("details.json").then(function(response) {
$scope.names = response.data.records;
});
$scope.detail=function(SelectedRecordeid){
var NameObj = $filter('filter')($scope.names, { id: SelectedRecordeid })[0];
localStorage.setItem("NamesDetails",JSON.stringify(NameObj));
$location.path('/viewdetail');
}
});
如果您没有获得每条记录的唯一ID，请尝试使用名称：
你的HTML会：<a href="#/viewdetail" type="submit" name="viewdetails" ng-click="detail(x.Name)">view</a>
var app = angular.module('myApp', []);
app.controller('customersCtrl', function($scope, $http,$location,filter) {
$http.get("details.json").then(function(response) {
$scope.names = response.data.records;
});
$scope.detail=function(SelectedName){
var NameObj = $filter('filter')($scope.names, { Name: SelectedName })[0];
localStorage.setItem("NamesDetails",JSON.stringify(NameObj));
$location.path('/viewdetail');
}
});
现在，在您的“viewdetail”页面的Controller用户下面，可以从本地存储中准备好数据。
var getAlldata = IsBlank(localStorage.NamesDetails) ? [] : JSON.parse(localStorage.NamesDetails);
您将获得“getAlldata”变量中的所有数据。
===================
您也可以使用以下方法在其他页面中获取数据。 在这种情况下，您必须调用两个单独的服务调用。 重写功能
$scope.detail=function(SelectedRecordeid){
window.location.hash = '#/viewdetail?id=' + SelectedRecordeid;
}
现在在你的第二页使用下面的代码：
function GetQueryString(key) {
'use strict';
var res,
arrQueryStrings = window.location.hash.split("?"),
queryStringCollection;
if (arrQueryStrings[1] === undefined || arrQueryStrings[1] === null) {
return null;
}
queryStringCollection = arrQueryStrings[1].split("&");
$.each(queryStringCollection, function (index, value) {
var queryStringName = value.split("=")[0];
if (queryStringName === key) {
res = value.split("=")[1];
}
});
return res;
}
$scope.SelectedRecordeid = IsBlank(GetQueryString("id")) ? 0 : GetQueryString("id");
现在您在$ scope.SelectedRecordeid中拥有所选行的ID，因此您可以使用此ID调用服务并在第二页中获取完整详细信息。
答案 1 :(得分：1)
嗨我得到了我的问题的解决方案，我的控制器是
app.controller('homeController', function($scope, $http, $location,
$rootScope, $filter, $window) {
$http.get("app/components/shared/json/Patients.json").success(
function(response) {
$scope.emp = response.Employees
$scope.select = function(list) {
debugger;
//$rootScope.selected = list;
$scope.selected = list;
localStorage.setItem("NamesDetails", JSON
.stringify($scope.selected));
$location.path('/homeView')
console.log(list)
}
$scope.cart = JSON.parse(localStorage.getItem("NamesDetails"));
console.log($scope.cart)
})
})
和Html
<td> <a ng-click="select(list)">view</a></td>
答案 2 :(得分：0)
我知道我回答这个问题的时机不晚，但是对于那些仍然面临这个问题的人来说，这是我的代码，经过三天的努力寻找解决方案，该代码才有效。
我正在使用ng-resource显示详细信息。我有一个名为“ listfactory”的工厂。
// listFactory.js
angular.module（“ main”） .factory（'listFactory'，function（$ resource）{
Calling model(x) didn't return EagerTensor
Traceback (most recent call last):
...
return loss_value, tape.gradient(loss_value, model.trainable_variables)
File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/backprop.py", line 1042, in gradient
flat_grad = imperative_grad.imperative_grad(
File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/imperative_grad.py", line 71, in imperative_grad
return pywrap_tfe.TFE_Py_TapeGradient(
File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/backprop.py", line 157, in _gradient_function
return grad_fn(mock_op, *out_grads)
File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/ops/math_grad.py", line 252, in _MeanGrad
sum_grad = _SumGrad(op, grad)[0]
File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/ops/math_grad.py", line 211, in _SumGrad
output_shape_kept_dims = math_ops.reduced_shape(input_shape,
File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py", line 3735, in reduced_shape
input_shape = input_shape.numpy()
AttributeError: 'Tensor' object has no attribute 'numpy'
}）;
//控制器“ detailscontroller.js
.controller（“ detailsController”，function（$ scope，$ http，$ routeParams，$ route，listFactory）{
import numpy as np
import tensorflow as tf
from tensorflow.python.framework.ops import EagerTensor
class TimeReduction(tf.keras.layers.Layer):
def __init__(self,
reduction_factor,
batch_size=None,
**kwargs):
super(TimeReduction, self).__init__(**kwargs)
self.reduction_factor = reduction_factor
self.batch_size = batch_size
def call(self, inputs):
input_shape = tf.shape(inputs)
batch_size = self.batch_size
if batch_size is None:
batch_size = input_shape[0]
max_time = input_shape[1]
num_units = inputs.get_shape().as_list()[-1]
outputs = inputs
paddings = [[0, 0], [0, tf.math.floormod(max_time, self.reduction_factor)], [0, 0]]
outputs = tf.pad(outputs, paddings)
return tf.reshape(outputs, (batch_size, -1, num_units * self.reduction_factor))
def make_encoder_model(
input_shape: tuple,
out_dim: int,
num_layers: int,
d_model: int,
proj_size,
initializer=None,
dtype=tf.float32,
stateful: bool = False,
dropout=0.5,
reduction_index=1,
reduction_factor=2,
):
def lstm_cell():
return tf.compat.v1.nn.rnn_cell.LSTMCell(
d_model,
num_proj=proj_size,
initializer=initializer,
dtype=dtype
)
batch_size = None if not stateful else 1
inputs = tf.keras.Input(
shape=input_shape,
batch_size=batch_size,
dtype=tf.float32
)
x = tf.keras.layers.BatchNormalization()(inputs)
for i in range(num_layers):
rnn_layer = tf.keras.layers.RNN(lstm_cell(), return_sequences=True, stateful=stateful)
x = rnn_layer(x)
x = tf.keras.layers.Dropout(dropout)(x)
x = tf.keras.layers.LayerNormalization(dtype=dtype)(x)
if i == reduction_index:
x = TimeReduction(reduction_factor, batch_size=batch_size)(x)
outputs = tf.keras.layers.Dense(out_dim)(x)
return tf.keras.Model(
inputs=[inputs],
outputs=[outputs],
name='encoder'
)
def gradient(model, loss, inputs, y_true):
y_true = tf.transpose(y_true, perm=(0, 2, 1))
with tf.GradientTape() as tape:
y_pred = model(inputs, training=True)
loss_value = loss(y_true=y_true, y_pred=y_pred)
return loss_value, tape.gradient(loss_value, model.trainable_variables)
def main():
X, Y = [
np.random.rand(100, 512),
np.random.rand(100, 512)
], [[[0]*50], [[1]*50]]
# assert len(X) == len(Y)
encoder_model = make_encoder_model(
input_shape=(None, 512),
out_dim=1,
num_layers=2,
d_model=10,
proj_size=23,
dropout=0.5,
reduction_index=1,
reduction_factor=2
)
enc_dataset = tf.data.Dataset.from_generator(
lambda: zip(X, Y),
output_types=(tf.float32, tf.int32),
output_shapes=([None, 512], [None, None]),
).batch(2)
loss = tf.keras.losses.MeanSquaredError()
for x, y in enc_dataset:
from_predict = encoder_model.predict(x)
from_call = encoder_model(x)
if not isinstance(from_predict, np.ndarray):
print("Calling model.predict(x) didn't return np.ndarray")
if not isinstance(from_call, EagerTensor):
print("Calling model(x) didn't return EagerTensor")
loss_value, gradients = gradient(encoder_model, loss, x, y)
print(loss_value)
print(gradients)
print('All done.')
if __name__ == '__main__':
main()
}）;
基本上，我正在遍历循环以检查routeparams是否与员工的电子邮件匹配。 希望这会有所帮助：）