Question

我有一个调用odata删除服务的删除方法，如下所示：

deleteValues = (key) => {
    return new Promise((resolve, reject) => {
        ...

        this._referenceDataService.deleteReferenceData(etag, url).subscribe(
            data => {
                resolve(data);
            },
            error => {
                this._notificationService.displayNotification(NotificationTypeEnum.Error, JSON.parse(error._body).error.message.value);
                reject(error);
            },
            () => {
                this._notificationService.displayNotification(NotificationTypeEnum.Success, "Data deleted successfully.");
            }
        );
    });
}

此处deleteReferenceData方法从Angular2的http服务返回一个observable。

对于数据网格，如果删除3行，则对于各行调用该方法3次。

我想要一种行为，即使方法deleteValues一个接一个地被调用3次，服务也只能在完成对1的服务调用时调用2行和3行。

Answer 1

您可以使用前端队列对后续服务调用进行排队，然后在每个完成时将其弹出。

它可能看起来像......

import pandas
import matplotlib.pyplot as plt
#dataset = pandas.read_csv(r'C:\Users\CHAWALU\Desktop\LSTM\Jupyter notebook\Dmd1hr.csv')
dataset = pandas.read_csv('shampoo.csv')
plt.plot(dataset)
plt.show()
import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
# fix random seed for reproducibility
numpy.random.seed(7)
# load the dataset
#dataframe = pandas.read_csv(r'C:\Users\CHAWALU\Desktop\LSTM\Jupyter notebook\Dmd1hr.csv')
dataframe = pandas.read_csv('shampoo.csv')
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))
# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return numpy.array(dataX), numpy.array(dataY)
# reshape into X=t and Y=t+1
look_back = 2
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# create and fit the LSTM network
model = Sequential()
model.add(LSTM(100, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=300, batch_size=1, verbose=2)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

所以这里发生的是你要跟踪你要删除的密钥队列，以及你当前是否删除了什么。这是在您从组件调用的deletedValues（）中管理的。

当它被调用时，如果你没有运行任何东西，请继续将其发送到真正的删除。否则将它扔进队列中。现在当你运行一个真正的删除时，最后检查队列中是否有任何东西并运行那些...或者说你已经完成了删除并允许deleteValues在它进入时调用下一个。

为http调用同步observable

1 个答案: