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Showing results for tags 'scatter plot'.
FAQ: How do I put a pump curve in Seeq? As of R21.0.44.00 this can be done the with the scatter-plot tool. This method does require version R21.0.44.00 or newer to work. See the steps below for details. Determine the X&Y components of the curve. This can be done with a tool such as https://apps.automeris.io/wpd/. Enter or paste the components in columns A and B in the CurveFitter excel sheet. See screenshot below for details. The CurveFitter file can be found here. CurveFitter.zip Once the new Flow and Head data has been pasted into excel copy the contents in from D2 to E9 and paste them into the Seeq formula tool. See screenshots below for copy paste details Copy: Paste: Paste the following syntax in the same formula under the coefficients. Be sure that the flow signal has the variable name “$flow”. $f=$flow.remove($flow.isNotBetween($lower,$upper)).setunits('') $coeff4*$f^4+$coeff3*$f^3+$coeff2*$f^2+$coeff1*$f+$const Final formula view: Add the line to the Scatterplot by selecting the f(x) in the Scatterplot tool bar and pick the correct item from the select item dropdown. If adding more than one curve, then click on the item properties “i” of the first curve and click on duplicate. Once in the formula tool copy the new coefficients from excel replacing the old one and hit execute. Follow step 5 to add the curve to the plot. Final View:
It would be wonderful if when you hover over data points on the scatter plot a cursor would appear with the values and date\time next to the dot, similarly to how the cursor works on the trend view. It would also be nice to be able to drop a cursor to keep a values highlighted as well (again like you can on the trend view). It is annoying that the only location the information is displayed is down in the details pane. Your eyes have to cover a lot of screen real estate to get the information for just one point of data.
We were very excited to announce that in R21.0.44+ we can now add best fit curves and their corresponding r2 value to the Scatter Plot display. Something that could further enhance this display would be adding in the ability to display the trend line equation on the chart beside the r2.
FAQ: When in Scatter Plot view, is it possible to see the time stamp of a given sample? Specifically, I would like to know when outliers are occurring so that I can navigate back to trend view and further investigate what else was going on during that time frame. How to visualize the time frame: The best way to find a time frame of an outlying data point with the current version of Seeq is to make a condition that encapsulates that data point. The following example shows how to identify what your condition should be, create a condition, color code your scatter plot to highlight samples that occur during your capsules, and view the start and end time of your capsules in the capsules pane. 1. We have a scatter plot comparing two variables with some outliers, and we would like to know what the time stamps are when those outliers are occurring. In the example shown below, we have outlying data points when the Coil Inlet Temperature is below about 460F, and when the Flue Gas Exit Temperature is below about 805F. 2. We will create a condition for each of the outlying data point scenarios described above. In this example, we will do so using a simple value search for each. 3. Now when we return to scatter plot view, we have the ability to color our scatter plot based on various capsules. In versions prior to R.21.0.44, this is done using the "Capsules" button at the top of the Scatter Plot Display. In R.21.0.44 and later versions, this is done with the "Color" button. You can select Color based on conditions, and add your conditions. Then your scatter plot will show samples that fall within the capsules of your conditions in the color specified for your condition in the Details pane. 4. Add the capsule end time to the capsules pane to get the full time range that you want to further investigate in trend view. You can also utilize Seeq's Chain View feature from trend view to view what was going on with your signals of interest during your conditions.