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DView is used by OpenStudio, BEopt, and SAM for visualizing time series simulation output. It is also available as a standalone application for visual analysis of time-series data at any timestep (e.g., hourly or sub-hourly). DView opens CSV files and also recognizes several weather data file formats, including TMY2, TMY3, and EPW files. See the data file template for more detail. DView can also load EnergyPlus .sql output files.
DView automatically displays data in a variety of graphical/tabular formats, and can be driven by a command line interface.
The Hourly, Daily, and Monthly graphs allow you to turn variables on or off with a single click, and to zoom and pan very easily. DView has the ability to display simultaneous line and stacked areas as demonstrated in the Hourly graph below.
Daily and Monthly time series graphs are automatically created by averaging or summing the hourly data: Hourly graph
If the underlying data is sub-hourly, an additional Time Series graph displays the raw data and the Hourly graph becomes the result of averaging or summing. If the underlying data is multi-hourly (for example, a 3-hour timestep), a Time Series graph displays the raw data, there is no Hourly graph, and the Monthly graph is the result of averaging or summing the time series data
The Heat Map graph displays a whole year of data at once, with the day of the year on the x-axis and time of day on the y-axis, so that each time step corresponds to a small rectangle. That rectangle gets assigned a color based on the value in that time step. Using this format, it is possible to identify both diurnal and seasonal patterns. The example below shows the direct normal solar radiation in Boulder, Colorado from the TMY2 data set. The image shows that the days are longer in the summer than in the winter, the most intense direct radiation values tend to occur in the spring and fall, and summer afternoons tend to be cloudy: Heat map graph
The Profile graph illustrates average daily profiles for each month of the year (with the ability to also show an annual average daily profile). The graph below demonstrates daily profiles for electric cooling and gas heating in a building: Profile graph
The Statistics table shows annual or monthly statistical values (e.g., mean, minimum, maximum, sum) for each variable in tabular format: Statistics table
The PDF/CDF graph shows the Probability Distribution Function (PDF) and Cumulative Distribution Function (CDF) for the dataset. These functions are useful to illustrate the distribution and spread of values for a given variable: PDF/CDF graph
The Duration curve orders the data from highest value to lowest value. By doing so, one can quickly identify, for example, the number of hours for which the variable is above or below a certain threshold: Duration curve graph
The Scatter plot allows graphing one or more y-axis variable against an x-axis variable. For example, the below graph shows a scatter plot for space heating and space cooling in a building as a function of outdoor drybulb temperature. It can also be useful to compare measured data against simulated data (for which the "line of perfect agreement" can also be displayed). Scatter plot