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feat(dpg): adopt tcgc getAllModels() #4185

Merged
merged 82 commits into from
May 7, 2024
Merged

feat(dpg): adopt tcgc getAllModels() #4185

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3dd660a
feat(dpg): adopt tcgc
Jan 19, 2024
74b95c9
add intrinsic type support
Jan 31, 2024
962d0e4
fix model traverse error, use preorder traverse
Feb 1, 2024
48fb876
fix discriminator determination logic
Feb 1, 2024
0ed7aa9
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 1, 2024
e3e3594
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 12, 2024
fe2de0e
improve debug
Mar 13, 2024
41be80d
add workaround for client parameter type `client` type mismatch
Mar 13, 2024
965e20d
regen
Mar 13, 2024
14ed1d8
fix cadl-ranch union test
Mar 13, 2024
241ac9d
add support fo additional properties
Mar 13, 2024
6657373
fix wrong encoding of bytes and duration
Mar 13, 2024
4747ac1
fix nullable of datetime and duration
Mar 13, 2024
9a46729
regen
Mar 13, 2024
6da09fc
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 13, 2024
ce573a5
fix formatting
Mar 13, 2024
24f0e81
fix nodejs test error
Mar 14, 2024
5ebea81
regen
Mar 14, 2024
1d46c1e
fix letter case of enum value type
Mar 14, 2024
67bf334
fix formatting issue
Mar 18, 2024
4c5ada7
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 18, 2024
97a0923
temp fix for cadl-ranch `value-type` case
Mar 18, 2024
6e8b0ac
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 19, 2024
f5fe5b5
remove code which intentially put discriminator property first
Mar 19, 2024
bb30a0d
do not print IsDiscriminator: false
Mar 19, 2024
374545e
optimize perf
Mar 20, 2024
27e2121
add backward compatibility of implicit discriminator property sequence
Mar 20, 2024
e8789dc
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 20, 2024
fa855fe
fix code gen
Mar 20, 2024
d383a6d
fix style
Mar 20, 2024
0ed2a34
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 21, 2024
aa50048
regen
Mar 21, 2024
f979b1a
fix node test
Mar 21, 2024
c75f66b
regen
Mar 22, 2024
32a0ad7
do not convert enum client parameters into string except for api-version
Mar 26, 2024
77143de
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 26, 2024
3fb9078
fix test
Mar 26, 2024
93dce54
improve test
Mar 27, 2024
7d02b58
change enum value type to literal constant with enum type
Mar 28, 2024
81124d4
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 28, 2024
563d603
optimize codes
Mar 29, 2024
4b9b2ff
Merge branch 'feature/v3' into tcgc/getAllModels
Mar 29, 2024
58514e8
fix style
Apr 7, 2024
0bbe63e
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 12, 2024
d9342d1
fix style
Apr 15, 2024
0f599e6
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 15, 2024
0f96ef1
add back deleted files
Apr 15, 2024
1803b2b
Merge branch 'feature/v3' into tcgc/getAllModels
archerzz Apr 15, 2024
cd8ff73
fix raw type of SdkEnumType
Apr 15, 2024
a64f425
fix incorrect readonly
Apr 15, 2024
a6f9984
Merge branch 'feature/v3' into tcgc/getAllModels
archerzz Apr 15, 2024
9dae0af
fix typescript style
Apr 16, 2024
a302772
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 16, 2024
e97baef
bump latest dev version of tcgc
Apr 17, 2024
9cee5e2
add license declaration
Apr 17, 2024
2c4fbc9
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 17, 2024
06d9cc2
fix test error
Apr 17, 2024
8af9d0e
refactor discrminiator property handling
Apr 18, 2024
81db0cf
bump tcgc to 0.41.3
Apr 18, 2024
05d2bb3
do not generate `DiscriminatorPropertyName` for child models
Apr 18, 2024
389bf59
fix(generator): do not keep azure.core models
archerzz Apr 18, 2024
1b5c3ec
Revert "fix(generator): do not keep azure.core models"
Apr 19, 2024
c81256a
workaround for cases when serializedName is empty
Apr 19, 2024
eb7b827
Revert "workaround for cases when serializedName is empty"
Apr 19, 2024
e4683f4
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 23, 2024
858a034
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 26, 2024
b8b1889
fix unit test error
Apr 26, 2024
a2fae6d
remove unnecessary property initialization since we have default options
Apr 27, 2024
78212b8
restore function location due to merge
Apr 27, 2024
269deb2
try to create enum not found in tcgc through library api instead of o…
Apr 27, 2024
896ba11
fix ts style
Apr 27, 2024
dc041b6
try to navigate all models using getAllModels
Apr 27, 2024
0cf2805
fix regen error
Apr 27, 2024
32085b2
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 28, 2024
0377bd7
bump tcgc to dev version to fix model usage propagation problem
Apr 28, 2024
0649fb7
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 28, 2024
9aee43c
Merge remote-tracking branch 'origin/feature/v3' into tcgc/getAllModels
ArcturusZhang Apr 29, 2024
7c64735
Merge branch 'feature/v3' into tcgc/getAllModels
Apr 30, 2024
2ee06e3
bump tcgc to 0.41.8
Apr 30, 2024
bb585bf
Some clean up
May 6, 2024
7c4729e
more clean up
May 6, 2024
403b7a6
update comments
May 7, 2024
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64 changes: 32 additions & 32 deletions samples/AnomalyDetector/src/Generated/Docs/Multivariate.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -151,7 +151,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<AnomalyDetectionModel> response = await client.TrainMultivariateModelAsync(modelInfo);
]]></code>
This sample shows how to call TrainMultivariateModelAsync with all parameters.
Expand All @@ -160,7 +160,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"))
ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"))
{
DataSchema = DataSchema.OneTable,
DisplayName = "<displayName>",
Expand All @@ -186,8 +186,8 @@ ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-0
Variable = "<variable>",
FilledNARatio = 123.45F,
EffectiveCount = 1234,
FirstTimestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
LastTimestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
FirstTimestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
LastTimestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}},
},
};
Expand All @@ -202,7 +202,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<AnomalyDetectionModel> response = client.TrainMultivariateModel(modelInfo);
]]></code>
This sample shows how to call TrainMultivariateModel with all parameters.
Expand All @@ -211,7 +211,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"))
ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"))
{
DataSchema = DataSchema.OneTable,
DisplayName = "<displayName>",
Expand All @@ -237,8 +237,8 @@ ModelInfo modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.Parse("2022-0
Variable = "<variable>",
FilledNARatio = 123.45F,
EffectiveCount = 1234,
FirstTimestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
LastTimestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
FirstTimestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
LastTimestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}},
},
};
Expand All @@ -256,8 +256,8 @@ Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultiva
using RequestContent content = RequestContent.Create(new
{
dataSource = "<dataSource>",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = await client.TrainMultivariateModelAsync(content);

Expand All @@ -276,8 +276,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "<dataSource>",
dataSchema = "OneTable",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
displayName = "<displayName>",
slidingWindow = 1234,
alignPolicy = new
Expand Down Expand Up @@ -315,8 +315,8 @@ using RequestContent content = RequestContent.Create(new
variable = "<variable>",
filledNARatio = 123.45F,
effectiveCount = 1234,
firstTimestamp = "2022-05-10T14:57:31.2311892-04:00",
lastTimestamp = "2022-05-10T14:57:31.2311892-04:00",
firstTimestamp = "2022-05-10T18:57:31.2311892Z",
lastTimestamp = "2022-05-10T18:57:31.2311892Z",
}
},
},
Expand Down Expand Up @@ -361,8 +361,8 @@ Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultiva
using RequestContent content = RequestContent.Create(new
{
dataSource = "<dataSource>",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = client.TrainMultivariateModel(content);

Expand All @@ -381,8 +381,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "<dataSource>",
dataSchema = "OneTable",
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
displayName = "<displayName>",
slidingWindow = 1234,
alignPolicy = new
Expand Down Expand Up @@ -420,8 +420,8 @@ using RequestContent content = RequestContent.Create(new
variable = "<variable>",
filledNARatio = 123.45F,
effectiveCount = 1234,
firstTimestamp = "2022-05-10T14:57:31.2311892-04:00",
lastTimestamp = "2022-05-10T14:57:31.2311892-04:00",
firstTimestamp = "2022-05-10T18:57:31.2311892Z",
lastTimestamp = "2022-05-10T18:57:31.2311892Z",
}
},
},
Expand Down Expand Up @@ -647,7 +647,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<MultivariateDetectionResult> response = await client.DetectMultivariateBatchAnomalyAsync("<modelId>", options);
]]></code>
This sample shows how to call DetectMultivariateBatchAnomalyAsync with all parameters.
Expand All @@ -656,7 +656,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<MultivariateDetectionResult> response = await client.DetectMultivariateBatchAnomalyAsync("<modelId>", options);
]]></code></example>
</member>
Expand All @@ -668,7 +668,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<MultivariateDetectionResult> response = client.DetectMultivariateBatchAnomaly("<modelId>", options);
]]></code>
This sample shows how to call DetectMultivariateBatchAnomaly with all parameters.
Expand All @@ -677,7 +677,7 @@ Uri endpoint = new Uri("<https://my-service.azure.com>");
AzureKeyCredential credential = new AzureKeyCredential("<key>");
Multivariate client = new AnomalyDetectorClient(endpoint, credential).GetMultivariateClient(apiVersion: "v1.1");

MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"), DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"));
MultivariateBatchDetectionOptions options = new MultivariateBatchDetectionOptions(new Uri("http://localhost:3000"), 1234, DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"), DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"));
Response<MultivariateDetectionResult> response = client.DetectMultivariateBatchAnomaly("<modelId>", options);
]]></code></example>
</member>
Expand All @@ -693,8 +693,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
topContributorCount = 1234,
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = await client.DetectMultivariateBatchAnomalyAsync("<modelId>", content);

Expand All @@ -717,8 +717,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
topContributorCount = 1234,
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = await client.DetectMultivariateBatchAnomalyAsync("<modelId>", content);

Expand Down Expand Up @@ -759,8 +759,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
topContributorCount = 1234,
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = client.DetectMultivariateBatchAnomaly("<modelId>", content);

Expand All @@ -783,8 +783,8 @@ using RequestContent content = RequestContent.Create(new
{
dataSource = "http://localhost:3000",
topContributorCount = 1234,
startTime = "2022-05-10T14:57:31.2311892-04:00",
endTime = "2022-05-10T14:57:31.2311892-04:00",
startTime = "2022-05-10T18:57:31.2311892Z",
endTime = "2022-05-10T18:57:31.2311892Z",
});
Response response = client.DetectMultivariateBatchAnomaly("<modelId>", content);

Expand Down
24 changes: 12 additions & 12 deletions samples/AnomalyDetector/src/Generated/Docs/Univariate.xml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ UnivariateDetectionOptions options = new UnivariateDetectionOptions(new TimeSeri
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
})
{
Expand Down Expand Up @@ -64,7 +64,7 @@ UnivariateDetectionOptions options = new UnivariateDetectionOptions(new TimeSeri
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
})
{
Expand Down Expand Up @@ -120,7 +120,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -186,7 +186,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -235,7 +235,7 @@ UnivariateDetectionOptions options = new UnivariateDetectionOptions(new TimeSeri
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
})
{
Expand Down Expand Up @@ -274,7 +274,7 @@ UnivariateDetectionOptions options = new UnivariateDetectionOptions(new TimeSeri
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
})
{
Expand Down Expand Up @@ -331,7 +331,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -399,7 +399,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -449,7 +449,7 @@ UnivariateChangePointDetectionOptions options = new UnivariateChangePointDetecti
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
}, TimeGranularity.Yearly)
{
Expand Down Expand Up @@ -485,7 +485,7 @@ UnivariateChangePointDetectionOptions options = new UnivariateChangePointDetecti
{
new TimeSeriesPoint(123.45F)
{
Timestamp = DateTimeOffset.Parse("2022-05-10T14:57:31.2311892-04:00"),
Timestamp = DateTimeOffset.Parse("2022-05-10T18:57:31.2311892Z"),
}
}, TimeGranularity.Yearly)
{
Expand Down Expand Up @@ -533,7 +533,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down Expand Up @@ -587,7 +587,7 @@ using RequestContent content = RequestContent.Create(new
{
new
{
timestamp = "2022-05-10T14:57:31.2311892-04:00",
timestamp = "2022-05-10T18:57:31.2311892Z",
value = 123.45F,
}
},
Expand Down
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