public async Task <IActionResult> CreatePlot([FromBody] PlotDto plotDto)
{
if (!ModelState.IsValid)
{
return(BadRequest(ModelState));
}
var finalPlot = Mapper.Map <Plot>(plotDto);
bool uploadImagesresult = true;
if (finalPlot.MediaItems.Count > 0)
{
uploadImagesresult = _pictureRepository.UploadImages(finalPlot.MediaItems);
}
if (uploadImagesresult)
{
await _plotRepository.AddPlotAsync(finalPlot);
if (!await _plotRepository.SaveAsync())
{
return(StatusCode(500, "A problem happend while handling your request"));
}
var createdPlot = Mapper.Map <PlotDto>(finalPlot);
return(Ok(createdPlot));
}
return(StatusCode(500, "A problem happend while handling your request"));
}
public void SetUp()
{
_plot = new Plot();
_plots = new List <Plot> {
_plot
};
_plotDto = new PlotDto();
_plotDtos = new List <PlotDto> {
_plotDto
};
_scatterPlot = new ScatterPlotBuilder().Build();
_scatterPlot.SetPlots(_plots);
_mockRepository = new Mock <IViewRepository>();
_mockRepository.Setup(p => p.Get <ScatterPlot>())
.Returns(_scatterPlot);
_mockAdapter = new Mock <IScatterPlotAdapter>();
_mockAdapter.Setup(p => p.Adapt(_plots))
.Returns(_plotDtos);
_handler = new GetPlotsQueryHandler(
_mockRepository.Object,
_mockAdapter.Object);
}
private CanvasItem RenderLabel(Rect plotExtent, PlotDto plotDto)
{
var origin = _calculator.CalcluateLabelOrigin(plotExtent);
var label = _factory.CreateLabel(plotDto.Id, origin, plotDto.Label);
return(label);
}
private CanvasItem RenderPlot(Rect extent, PlotDto plot)
{
var color = Color.FromRgb(plot.Color.Red, plot.Color.Green, plot.Color.Blue);
var plotItem = plot.Image != null
? (CanvasItem)_factory.CreateImage(plot.Id, extent, plot.Image)
: (CanvasItem)_factory.CreateCircle(plot.Id, extent, color);
return(plotItem);
}
public async Task <ActionResult> Edit(int id, PlotDto input)
{
if (ModelState.IsValid)
{
// TODO: Add update logic here
await _plotAppService.UpdatePlot(input);
return(RedirectToAction("Index", new { id = input.CompartmentId }));
}
else
{
return(View(input));
}
}
public void SetUp()
{
_controlSize = new Size();
_viewExtent = new Rect();
_plot = new PlotDto()
{
Id = 1,
X = 1d,
Y = 2d,
Color = new Domain.Colors.Color(0, 0, 0),
Label = "Test",
Image = new BitmapImage()
};
_plots = new List <PlotDto> {
_plot
};
_canvasCircle = new CanvasCircle();
_canvasImage = new CanvasImage();
_canvasLabel = new CanvasLabel();
_mockResizer = new Mock <IViewResizer>();
_mockResizer.Setup(p => p.ResizeView(_controlSize, _viewExtent))
.Returns(_viewExtent);
_mockComputer = new Mock <IScaleComputer>();
_mockComputer.Setup(p => p.ComputeScale(_controlSize, _viewExtent)).Returns(1d);
_mockCalculator = new Mock <IGeometryCalculator>();
_mockFactory = new Mock <IGeometryFactory>();
_mockFactory.Setup(p => p.CreateCircle(_plot.Id, It.IsAny <Rect>(), It.IsAny <Color>()))
.Returns(_canvasCircle);
_mockFactory.Setup(p => p.CreateImage(_plot.Id, It.IsAny <Rect>(), _plot.Image))
.Returns(_canvasImage);
_mockFactory.Setup(p => p.CreateLabel(_plot.Id, It.IsAny <Point>(), _plot.Label))
.Returns(_canvasLabel);
_renderer = new PlotRenderer(
_mockResizer.Object,
_mockComputer.Object,
_mockCalculator.Object,
_mockFactory.Object);
}
public void SetUp()
{
_controlSize = new Size();
_viewExtent = new Rect();
_axisGridLine = new AxisGridLine();
_axisGridLines = new List <AxisGridLine> {
_axisGridLine
};
_plotDto = new PlotDto();
_plotDtos = new List <PlotDto> {
_plotDto
};
_columnDto = new ColumnDto()
{
Name = "test"
};
_xGridLine = new CanvasLine();
_yGridLine = new CanvasLine();
_plotItem = new CanvasCircle();
_xGridLabel = new CanvasLabel();
_yGridLabel = new CanvasLabel();
_xTitleLabel = new CanvasLabel();
_yTitleLabel = new CanvasLabel();
_mockQueryBus = new Mock <IQueryBus>();
_mockQueryBus.Setup(p => p.Execute(It.IsAny <GetViewExtentQuery>()))
.Returns(_viewExtent);
_mockQueryBus.Setup(p => p.Execute(It.IsAny <GetXAxisGridLinesQuery>()))
.Returns(_axisGridLines);
_mockQueryBus.Setup(p => p.Execute(It.IsAny <GetYAxisGridLinesQuery>()))
.Returns(_axisGridLines);
_mockQueryBus.Setup(p => p.Execute(It.IsAny <GetPlotsQuery>()))
.Returns(_plotDtos);
_mockQueryBus.Setup(p => p.Execute(It.IsAny <GetXAxisColumnQuery>()))
.Returns(_columnDto);
_mockQueryBus.Setup(p => p.Execute(It.IsAny <GetYAxisColumnQuery>()))
.Returns(_columnDto);
_mockGridRenderer = new Mock <IAxisGridRenderer>();
_mockGridRenderer.Setup(p => p.RenderXAxisGridLines(_axisGridLines, _viewExtent, _controlSize))
.Returns(new List <CanvasLine> {
_xGridLine
});
_mockGridRenderer.Setup(p => p.RenderYAxisGridLines(_axisGridLines, _viewExtent, _controlSize))
.Returns(new List <CanvasLine> {
_yGridLine
});
_mockGridRenderer.Setup(p => p.RenderXAxisGridLabels(_axisGridLines, _viewExtent, _controlSize))
.Returns(new List <CanvasLabel> {
_xGridLabel
});
_mockGridRenderer.Setup(p => p.RenderYAxisGridLabels(_axisGridLines, _viewExtent, _controlSize))
.Returns(new List <CanvasLabel> {
_yGridLabel
});
_mockPlotRenderer = new Mock <IPlotRenderer>();
_mockPlotRenderer.Setup(p => p.RenderPlots(_controlSize, _viewExtent, _plotDtos))
.Returns(new List <CanvasItem> {
_plotItem
});
_mockTitleRenderer = new Mock <IAxisTitleRenderer>();
_mockTitleRenderer.Setup(p => p.RenderXAxisTitle(_controlSize, _columnDto.Name))
.Returns(_xTitleLabel);
_mockTitleRenderer.Setup(p => p.RenderYAxisTitle(_controlSize, _columnDto.Name))
.Returns(_yTitleLabel);
_query = new GetAllItemsQuery(
_mockQueryBus.Object,
_mockGridRenderer.Object,
_mockPlotRenderer.Object,
_mockTitleRenderer.Object);
}
public void Initialization()
{
_mediaItemList = new List <MediaItemDto>
{
new MediaItemDto
{
Id = Guid.NewGuid(),
DataBase64String =
"R0lGODlhAAEAAfT/AP////f39+/v7+bm5t7e3tbW1s7OzsXFxb29vbW1ta2traWlpZycnJSUlIyMjISEhHt7e3Nzc2tra2NjY1paWlJSUkpKSkJC" +
"Qjo6OjExMSkpKSEhIRkZGQgICAAAABAQECH/C05FVFNDQVBFMi4wAwEAAAAh/hFDcmVhdGVkIHdpdGggR0lNUAAh+QQFBwAgACwAAAAAAAEAAQAF/" +
"yAgjmRpnmiqrmzrvnAsz3Rt33iu73zv/8CgcEgsGo/IpHLJbDqf0Kh0Sq1ar9isdsvter/gsHhMLpvP6LR6zW673/C4fE6v2+/4vH7P7/v/gIGCg4SFhoe" +
"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"
}
};
_newPlot = new PlotDto
{
DeviceID = "deviceIDTest",
CropType = CropTypeDto.Alfalfa,
ClimateType = ClimateTypeDto.Cold,
Irrigated = false,
Name = "Test1",
Position = _position,
MaturityType = MaturityTypeDto.Early,
Activities = new List <ActivityDto>
{
new ActivityDto
{
Name = "ActivityTest1",
ActivityType = ActivityTypeDto.Commercialization,
AmountApplied = "AmountAppliedTest1",
AmountSold = "AmountSoldTest1",
Comment = "CommentTest",
AppliedProduct = "AppliedProductTest1",
Cost = 10,
Date = DateTime.Now,
Dose = 10,
ParcelId = "3",
NumberOfSeeds = 10,
Price = 100,
ProductObtained = "ProductObtainedTest1",
Sown = "SownTest1",
SellingPrice = 50,
WeightOfSeeds = 200,
Yield = "Yield1",
PlotArea = 1
}
},
Delineation = new List <DelineationPositionDto>
{
new DelineationPositionDto {
Position =
new PositionDto
{
Accuracy = 1,
Latitude = 48.072875,
Longitude = 16.361187,
Timestamp = DateTimeOffset.Now
}
},
new DelineationPositionDto {
Position =
new PositionDto
{
Accuracy = 1,
Latitude = 48.079812,
Longitude = 16.362887,
Timestamp = DateTimeOffset.Now
}
}
}
};
_newPlotWithImages = new PlotDto
{
DeviceID = "deviceIDTest",
CropType = CropTypeDto.Alfalfa,
ClimateType = ClimateTypeDto.Cold,
Irrigated = false,
Position = _position,
Name = "Test1",
MaturityType = MaturityTypeDto.Early,
Activities = new List <ActivityDto>
{
new ActivityDto
{
Name = "ActivityTest1",
ActivityType = ActivityTypeDto.Commercialization,
AmountApplied = "AmountAppliedTest1",
AmountSold = "AmountSoldTest1",
Comment = "CommentTest",
AppliedProduct = "AppliedProductTest1",
Cost = 10,
Date = DateTime.Now,
Dose = 10,
ParcelId = "3",
NumberOfSeeds = 10,
Price = 100,
ProductObtained = "ProductObtainedTest1",
Sown = "SownTest1",
SellingPrice = 50,
WeightOfSeeds = 200,
Yield = "Yield1",
PlotArea = 1
}
},
MediaItems = _mediaItemList,
Delineation = new List <DelineationPositionDto>
{
new DelineationPositionDto {
Position =
new PositionDto
{
Accuracy = 1,
Latitude = 48.072875,
Longitude = 16.361187,
Timestamp = DateTimeOffset.Now
}
},
new DelineationPositionDto {
Position =
new PositionDto
{
Accuracy = 1,
Latitude = 48.079812,
Longitude = 16.362887,
Timestamp = DateTimeOffset.Now
}
}
},
};
_position = new PositionDto
{
Accuracy = 1, Latitude = 48.072294, Longitude = 16.361882, Timestamp = DateTimeOffset.Now
};
IntializaPictureRepository();
//SeedData();
}
public void Put(int id, [FromBody] PlotDto plotDto)
{
}
