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Practicedisabling gock once you are done with your HTTP testing logic.A Go idiomatic approach for doing this can be using it in a defer statement, such as:func TestGock (t *testing.T) { defer gock.Off() // ... my test code goes here}Intercept an http.Client just onceYou don't need to intercept multiple times the same http.Client instance.Just call gock.InterceptClient(client) once, typically at the beginning of your test scenarios.Restore an http.Client after interceptionNOTE: this is not required is you are using http.DefaultClient or http.DefaultTransport.As a good testing pattern, you should call gock.RestoreClient(client) after running your test scenario, typically as after clean up hook.You can also use a defer statement for doing it, as you do with gock.Off(), such as:func TestGock (t *testing.T) { defer gock.Off() defer gock.RestoreClient(client) // ... my test code goes here}ExamplesSee examples directory for more featured use cases.Simple mocking via testspackage testimport ( "io/ioutil" "net/http" "testing" "github.com/nbio/st" "github.com/h2non/gock")func TestSimple(t *testing.T) { defer gock.Off() gock.New(" Get("/bar"). Reply(200). JSON(map[string]string{"foo": "bar"}) res, err := http.Get(" st.Expect(t, err, nil) st.Expect(t, res.StatusCode, 200) body, _ := ioutil.ReadAll(res.Body) st.Expect(t, string(body)[:13], `{"foo":"bar"}`) // Verify that we don't have pending mocks st.Expect(t, gock.IsDone(), true)}Request headers matchingpackage testimport ( "io/ioutil" "net/http" "testing" "github.com/nbio/st" "github.com/h2non/gock")func TestMatchHeaders(t *testing.T) { defer gock.Off() gock.New(" MatchHeader("Authorization", "^foo bar$"). MatchHeader("API", "1.[0-9]+"). HeaderPresent("Accept"). Reply(200). BodyString("foo foo") req, err := http.NewRequest("GET", " nil) req.Header.Set("Authorization", "foo bar") req.Header.Set("API", "1.0") req.Header.Set("Accept", "text/plain") res, err := (&http.Client{}).Do(req) st.Expect(t, err, nil) st.Expect(t, res.StatusCode, 200) body, _ := ioutil.ReadAll(res.Body) st.Expect(t, string(body), "foo foo") // Verify that we don't have pending mocks st.Expect(t, gock.IsDone(), true)}Request param matchingpackage testimport ( "io/ioutil" "net/http" "testing" "github.com/nbio/st" "github.com/h2non/gock")func TestMatchParams(t *testing.T) { defer gock.Off() gock.New(" MatchParam("page", "1"). MatchParam("per_page", "10"). Reply(200). BodyString("foo foo") req, err := http.NewRequest("GET", " nil) res, err := (&http.Client{}).Do(req) st.Expect(t, err, nil) st.Expect(t, res.StatusCode, 200) body, _ := ioutil.ReadAll(res.Body) st.Expect(t, string(body), "foo foo") // Verify that we don't have pending mocks st.Expect(t, gock.IsDone(), true)}JSON body matching and responsepackage testimport ( "bytes" "io/ioutil" "net/http" "testing" "github.com/nbio/st" "github.com/h2non/gock")func TestMockSimple(t *testing.T) { defer gock.Off() gock.New(" Post("/bar"). MatchType("json"). JSON(map[string]string{"foo": "bar"}). Reply(201). JSON(map[string]string{"bar": "foo"}) body := bytes.NewBuffer([]byte(`{"foo":"bar"}`)) res, err := http.Post(" "application/json", body) st.Expect(t, err, nil) st.Expect(t, res.StatusCode, 201) resBody, _

:= ioutil.ReadAll(res.Body) st.Expect(t, string(resBody)[:13], `{"bar":"foo"}`) // Verify that we don't have pending mocks st.Expect(t, gock.IsDone(), true)}Mocking a custom http.Client and http.RoundTripperpackage testimport ( "io/ioutil" "net/http" "testing" "github.com/nbio/st" "github.com/h2non/gock")func TestClient(t *testing.T) { defer gock.Off() gock.New(" Reply(200). BodyString("foo foo") req, err := http.NewRequest("GET", " nil) client := &http.Client{Transport: &http.Transport{}} gock.InterceptClient(client) res, err := client.Do(req) st.Expect(t, err, nil) st.Expect(t, res.StatusCode, 200) body, _ := ioutil.ReadAll(res.Body) st.Expect(t, string(body), "foo foo") // Verify that we don't have pending mocks st.Expect(t, gock.IsDone(), true)}Enable real networkingpackage mainimport ( "fmt" "io/ioutil" "net/http" "github.com/h2non/gock")func main() { defer gock.Off() defer gock.DisableNetworking() gock.EnableNetworking() gock.New(" Get("/get"). Reply(201). SetHeader("Server", "gock") res, err := http.Get(" if err != nil { fmt.Errorf("Error: %s", err) } // The response status comes from the mock fmt.Printf("Status: %d\n", res.StatusCode) // The server header comes from mock as well fmt.Printf("Server header: %s\n", res.Header.Get("Server")) // Response body is the original body, _ := ioutil.ReadAll(res.Body) fmt.Printf("Body: %s", string(body))}Debug intercepted http requestspackage mainimport ( "bytes" "net/http" "github.com/h2non/gock")func main() { defer gock.Off() gock.Observe(gock.DumpRequest) gock.New(" Post("/bar"). MatchType("json"). JSON(map[string]string{"foo": "bar"}). Reply(200) body := bytes.NewBuffer([]byte(`{"foo":"bar"}`)) http.Post(" "application/json", body)}Hacking it!You can easily hack gock defining custom matcher functions with own matching rules.See add matcher functions and custom matching layer examples for further details.LicenseMIT - Tomas Aparicio

CiaB server and communication goes over TCP/IP with MOS protocol. if there is a failover on main CiaB server Client PC can run back up CiaB server exactly where it has stopped or + passing time for back up operation. Mirror Back Up Operation Easy Net Control can be used as exactly client server application. In order to build a MCR playout automation you need to mirror your playout server. Easy Net Control and Easy OnAir exactly works as MCR playout to build a mirrored playout server. Every action is mirrored when operator act on client reacts on playout servers. Beside client to server, Easy Net Control runs also server to client, it means, it works as bi-directional commands. Either you can control the playout server from server side, client side also will follow and react to slave server. Either operator can use from client application to control both master and slave playout servers. Client Application Easy Net Control is used as client application of Easy OnAir. For a music channel installation, Easy Net Control provides more properties than the server application, like category of list from file name, file management, MAM integration, CG Plus integration, Playlist Editor Integration, traffic systems integration and more facilities are come with Easy Net Control as Client Application. Operator can control playout server, beside playout server can control also live graphics, stream encoder, and ingest stations. Technical Requirements CPU Intel Processors • Intel i5 6600K 3.50GHz 6M 1151P CPU • Intel i7 6700K 4.00GHz 8M 1151P CPU MainBoard Asus Mainboard • Asus Z170-A LGA 1151 • Asus Z170 Pro Gaming LGA 1151 • Asus ROG Maximus VIII Ranger Gaming LGA 1151 • Asus Z170-Pro LGA 1151 • Asus Sabertooth Z170 S LGA 1151 Supermicro Mainboard • Supermicro X11SAT LGA 1151 • Supermicro X11SAT-F LGA 1151 • Supermicro X11SAE LGA 1151 • Supermicro X11SAE-F LGA 1151 Graphics Card Nvidia GeForce • NVIDIA GeForce 750TI 128B 2GB GDDR5 Memory Kingston • Kingston 16GB DDR4 2133MHZ • Kingston 16GB DDR4 2400MHZ • Kingston 16GB DDR4 2666MHZ Hard Drive OS HDD • 120GB Samsung 750 EVO • 120GB Kingston Hyperx Savage • 250GB Samsung 850 EVO • 240GB Kingston Hyperx Savage Operating System Microsoft Windows • Windows 7 Pro & Ultimate x86 or x64 • Windows 10 Pro x64 Turn-Key Systems Supermicro Turn-Key Systems • SuperWorkstation 5039A-iL• SuperWorkstation 7048A-T• SuperWorkstation 7038A-I• SuperWorkstation 5038A-I• SuperWorkstation 5038A-iL• SuperServer 5039AD-T• SuperServer 5038AD-I• SuperServer 5038AD-T• SuperWorkstation 7047A-T• SuperWorkstation 7047A-73• SuperWorkstation 7037A-i• SuperWorkstation 5037A-i HP Turn-Key Systems • HP Z240 Workstation• HP Z440 Workstation• HP Z640 Workstation• HP Z840 Workstation Program Requirements Microsoft Visual C++ Microsoft Visual C++ 2010 Redistributable Package (x86) or higher versions Microsoft .NET Framework 4.5 or higher versions Downloads SoftwareOpen or Close Please Contact Customer Service: [email protected] User ManualOpen or Close Easy Net Control User Manual BrochureOpen or Close Easy Net Control Brochure Screenshots General Graphics User Interface General view of Easy Net Control Back Up Settings Master-Slave and M+N back up settings of Easy Net Control DVB Subtitle

$75 million belongs to Lamar. He started his career in 2004 and he has been considered someone who has contributed to music a lot in this decade.27. Nas – Net Worth $70MhiphopdxOriginally known as Nasir Bin Olu, Nas has invested in many technology companies and these smart moves have made him that wealthy having an approximate net worth of $70 million. Nas has been in the industry since 1989 and is the co-founder of a record label called “Mass Appeal Records”.28. Will.i.am – Net Worth $70MrollingstoneWill.i.am is a member of the famous musical band “Black Eyed Peas” and has given sung some great songs like “Don’t stop the party”, “Where is the love” and “Pump It”, just to name a few. The guy’s net worth is almost $70 million as of now. He has produced the songs of many other musicians.29. Ice T – Net Worth $60MhollywoodreporterFamous for his raps on street crimes and public violence, Ice T, originally known as Tracy Marrow, is a 1958-born artist with many talents among which he has an art of making money, that has gradually resulted in his current net worth of around to be $60 million. His career began in 1982 and he has been entertaining us since then.30. Cole – Net Worth $60MrapupLast but not the least, J. Cole is here on the list with possessing net worth estimated to be $60 million as of now. His songs topped Billboard many times and he has certain awards and nominations throughout his

Data. For the image processing and fault diagnosis, the output layer is the data label. The structure of DNN is basically the same as the traditional ANN, but the training method is improved which make it have better performance. 4. Analysis of Prediction ResultsTo verify the validity of the model, the proposed model was validated in MATLAB R2018a. In this paper, the net load data of the upper bus of a city’s PV substation in the first 15 days of May 2017 were used. The sampling interval of the net load data is 5 min, and the installed capacity of the distributed PV power station is approximately 50 MW. The data from 1–11 May were selected as the training set to model the prediction model, and the parameters of the model were adjusted by cross-validation. The data from 12–15 May were selected as the samples of the prediction test, where 12 May was sunny, and 13–15 May were cloudy. 4.1. Result of the Phase Space Reconstruction by the C-C MethodThe net load data from 1–11 May were processed by the C-C method. The corresponding statistics of Δ S ¯ ( t ) and S c o r ( t ) are shown in Figure 4. The first extremum point of Δ S ¯ ( t ) was t = 7 . S c o r ( t ) had no obvious minimum point, and the optimal embedded window t ω was not obtained. According to the BDS statistics, when N > 3000 , m ∈ { 2 , 3 , 4 , 5 } , so the maximum value of m could only take 5. According to Formula (6), the final optimal embedding dimension mopt = 5 and optimal delay topt = 7 were obtained. 4.2. Prediction Results of the Deep Neural NetworkSince the embedding dimension is m = 5 as determined by the C-C method, there were five input neurons in the DNN. The cross-validation shows that when the number of layers in the hidden layer was 5, the predicted value showed a significant over-fitting. Therefore, the number of layers of the hidden layer was taken as 4, and the neurons of each hidden layer were taken as 5, 4, 3, and 2. The DNN used the single-step prediction and only predicted the next 5 min load value at a time. The predicted result is shown in Figure 5. The black solid line is the actual net load, the red solid line is the ultrashort-term prediction value based on the phase space reconstruction and DNN, and the blue dotted line is the ultrashort-term prediction value based on the traditional BP neural network.In Figure 5, at approximately 12:00 noon on a clear day (12th), a negative power was present in the payload due to an increase in the amount of PV power generation. The prediction results based on the phase space reconstruction and DNN were closer to the actual net load value, which was obviously better than the prediction results