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Dear community, I want to identify representative capsules based on the capsule duration. What I did so far is: 1. Identify capsules 2. Create a signal from condition that represents the capsule duration. 3. Calculate average capsule duration and percentiles of interest from this signal using metrics 4. Use these metrics to identify capsules that are within a certain range around the average or between two percentiles. What I would like to do next and could not accomplish so far is: By now step 4 works when entering the respective criterion to a value search by hand. Now I would like to do that dynamically, i.e., calculate the metrics for the displayed time range and directly get capsules that satisfy the criterion given above. After trying around for some time, this seems to be possible using formula: I tried to use the metrics directly, but the function I tried to use (InBetween) does not accept metrics as input. I also tried to calculate the metrics directly in formula to avoid wrong data types, but that was also not successful. It sems to get down to explicitly convert the signal metrics to scalar, but I also fail to do that. As I am quite new to seeq, I would appreciate if there were some hints on this: Is it possible? Did I choose the correct approach? Are there other, even simpler ways to accomplish that. Thanks in advance!

When pushing Formulas to Workbench from Data Lab, you can use "\" and "\n" in a few different ways in the Data Lab code, to create a nicely formatted, multi-line Formula in Workbench: For the Formula pushed to Workbench, a \n can be used for a line return. This is valuable for creating multi-line Formulas with intermediate variable assignments, or for making lengthy, single lines of code more readable. For the Formula pushed to Workbench, a \ can be used just before a single quote, when you need to include a single quote in the Formula. This helps for example when working with string signals. On the Data Lab side, using a \ at the end of the line continues the code to the next line, helping with readability in Data Lab. These are illustrated in the Data Lab code below which pushes a condition Formula to Workbench: Editing the Formula in Workbench, the Formula appears as we desired:

This method demonstrates how to apply time warping to a signal which can shorten the time between sample points while retaining the signal’s characteristic shape. This is useful for batch processes where batches may have different durations or for aligning data from multi-step processes with different durations. Note that this method is distinct from the more complex Dynamic Time Warping. The following example is based upon the Area A Compressor Power signal available in the Example Data. A single condition was created to capture each compressor run, as shown in the following image. Step 1: Specify a Time Normalization Factor The user uses the Formula tool to specifiy a time duration (scalar value), set to 4 hours for this example. The compressor power signal for each compressor run will be normalized to this time duration. There is no single correct value; the value needs to be shorter than the minimum time duration for all signals to be normalized. // User specified time duration (scalar value). 4 hours Step 2: Create the Normalized Condition Use Formula to create the normalized Compressor Run condition based on the user-specified time normalization factor. This condition will be used later when the calculated signal delay is used for normalization. $CompressorRuns.afterStart($nF) Step 3: Calculate the Run Duration Use the Signal from Condition tool to calculate the duration for each compressor run. This value will be used later for determining the calculated signal delay applied to each compressor run. Step 4: Calculate the Elapsed Time During Each Run Use the Formula tool to calculate the elapsed time during each compressor run. This value will be used later for determining the calculated signal delay applied to each compressor run. timeSince($CompressorRuns, 0.25min).convertUnits('h') Step 5: Calculate the Signal Delay Factor Needed for Time Warping Each sample (timestamp) for compressor power will be shifted to the left by a calculated amount to time warp the power signal from its original time period (Compressor Runs) to the normalized time period (Compressor Runs Normalized). The first sample point in each run will have no delay, and the last sample point will have the greatest delay. For example, if the original time period is 10 hours and we are normalizing to a time period of 4 hours, then the last sample point in the run will be shifted to the left by 6 hours (Delay Factor = -6 hrs). // This Formula first creates a signal with a value of 0 (default delay), then splice in the calculated delay during each compressor run. 0.toSignal(0.25min).convertUnits('hr').splice($ElapsedTime*($nF/$RunDuration-1),$CompressorRuns) At this point we zoom in to a single compressor run and use the Capsule Time view to better visualize the results of our intermediate calculations. For this compressor run lasting 10.9 hours, the Compressor Run Elapsed Time finishes at the correct value (10.9 hours) and the Delay Factor correctly decreases from 0 hours (beginning of run) to -6.9 hours (end of run). Step 6: Apply the Signal Delay to Time Warp the Signal Now that the appropriate delay factor is calculated for each sample point during the compressor runs, we apply it to the Area A Compressor Power signal using the delay() function in Formula. This generates the time warped signal called Compressor Power Normalized. $CompressorPower.delay($delayFactor,24h).within($CompressorRunsN) When applying the signal delay, we are required to specify a maximum delay. This is important for the Seeq calculations going on in the background, so that the queries know how far back and forward to go when requesting input data. The maximum delay also provides a convenient way for the user to limit the applied delay, if they have some reason to do so. In our case, we just want the chosen value to be longer than the longest expected compressor run duration. Results Now that the compressor power signal has been normalized, Capsule Time view gives a nice comparison of the power signal behavior across the 3 runs contained in the Display Range:

Dear All, In python I have the following logic what I'd like to translate to Seeq formula. np.where((testdf['Signal_1']) < 0, testdf['Signal_2'], np.where((testdf['Signal_1']) < 30, testdf['Signal_2'], testdf['Signal_1'])) I know it would possible with capsules but it is only one leg of the whole solution and I think it's obscure to have 20-30 capsules for only one calculation what we use at others too.

FAQ: I have a signal with a gap in the data from a system outage. I want to replace the gap with a constant value, ideally the average of the time period immediately before the data. Solution: 1. Once you've identified your data gaps, extend the capsules backwards by the amount over which time you want to take the average. In this example, we want to fill in the gap with the average of the 10 minutes before the signal dropped, so we will extend the start of the data gap capsule 10 minutes in the past. This is done using the move function in Formula: $conditionForDataGaps.move(-10min,0min) 2. Use Signal from Condition to calculate the average of the gappy signal during the condition created in step 1. Make sure to select "Duration" for the timestamp of the statistic. 3. Stitch the two signals together using the splice function. The validvalues() function at the end ensures a continuous output signal. $gappysignal.splice($replacementsignal,$gaps).validvalues()

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:

FAQ: I've created a condition for a particular event of interest and now I would like to create a signal that is the running count of these events in a given time period. This analysis is common in equipment fatigue use cases when equipment degrades slowly based on a number of cycles (thermal, pressure, tension, etc) that it has undergone during it's life or since a last component replacement. Solution: We can convert each of these capsules into a signal comprised of a single sample (with value of 1) per capsule, then take a running sum of this new signal over the current equipment life condition. 1) Use Formula to create a signal with a constant value of 1 and a sample every 1 second. (1).toSignal(1sec) 2) Use Signal from Condition to create a new signal with a single sample of value 1 per capsule. Take the average of the "1 signal" during each of the event capsules. 3) Use Formula to calculate the running sum of the 1 sample per capsule signal during the Current Equipment Life capsule. $OneSamplePerCapsule.runningSum($CurrentLife).toLinear(7d)

In some cases you may want to do a calculation (such as an average) for a specific capsule within a condition. In Seeq Formula, the toGroup() function can be used to get a group of capsules from a condition (over a user-specified time period). The pick() function can then be used to select a specific capsule from the group. The Formula example below illustrates calculating an average temperature for a specific capsule in a high temperature condition. // Calculate the average temperature during a user selected capsule of a high temperature // condition. (The high temperature condition ($HighT) was created using the Value Search tool.) // // To prevent an unbounded search for the capsules, must define the search start/end to use in toGroup(). // Here, $capsule simply defines a search time period and does not refer to any specific capsules in the $HighT condition. $capsule = capsule('2019-06-19T09:00Z','2019-07-07T12:00Z') // Pick the 3rd capsule of the $HighT condition during the $capsule time period. // We must specify capsule boundary behavior (Intersect, EnclosedBy, etc.) to // define which $HighTcapsules are used and what their boundaries are (see // CapsuleBoundary documentation within the Formula tool for more information). $SelectedCapsule = $HighT.toGroup($capsule,CapsuleBoundary.EnclosedBy).pick(3) // Calculate the temperature average during the selected capsule. $Temperature.average($SelectedCapsule)

A question came up recently that I thought would be of wider interest: how can I prevent interpolation across batch/capsule boundaries? In batch processes, lab samples are often taken periodically throughout the course of a batch. When viewing these samples in Seeq, you may encounter times when samples are interpolating between batches rather than just within a batch. In the image below, the periods highlighted in yellow correspond to this unwanted interpolation. The following formula is one way of preventing this interpolation. $signal corresponds to your signal of interest and $condition corresponds to your batches you want to prevent interpolation across. combineWith( $signal, Scalar.Invalid.toSignal().aggregate(startValue(), $condition.removeLongerThan(40h), startKey()) ) This results in the following signal that has our desired behavior:

Recently an interesting question came up about converting a number to binary within Seeq. The goal was to convert an integer (0-255) to an 8-bit binary number. This can be done by dividing the integer by 2, 8 times and keeping track of the remainders. More information about binary numbers can be found here. https://en.wikipedia.org/wiki/Binary_number Note: For this conversion to work the input signal needs to be an integer between 0 and 255, also it cannot have units. Below is the formula syntax that will do the conversion. $i=$signal.ceiling().setUnits('') $div1=($i/2).floor() $div2=($div1/2).floor() $div3=($div2/2).floor() $div4=($div3/2).floor() $div5=($div4/2).floor() $div6=($div5/2).floor() $div7=($div6/2).floor() $div8=($div7/2).floor() $rem1=($i/2-$div1).ceiling().toString() $rem2=($div1/2-$div2).ceiling().toString() $rem3=($div2/2-$div3).ceiling().toString() $rem4=($div3/2-$div4).ceiling().toString() $rem5=($div4/2-$div5).ceiling().toString() $rem6=($div5/2-$div6).ceiling().toString() $rem7=($div6/2-$div7).ceiling().toString() $rem8=($div7/2-$div8).ceiling().toString() $rem8+$rem7+$rem6+$rem5+$rem4+$rem3+$rem2+$rem1 Below is a screenshot of the syntax in the formula tool. Result:

Use Case: Users are often interested in identifying when a particular process is operating in a specific mode, or when it is in transition between modes. When looking at these transition periods, you may want to know what the modes of operation were immediately before and after the transition. If you can assign the starting and ending modes during a transition period to each transition capsule, you can filter for specific types of transitions and get a better idea of what to expect during like transitions. Solution: For Versions R.21.0.43 + 1. Add a signal to your display that describes the mode of operation you are interested in. In this example I have added the Example Data > Cooling Tower 1 > Area A > Compressor Stage string signal. Other signals that this use case applies to may include: production grade code, equipment operating mode, signal for step in a sequential or batch process. 2. Next we can use Formula to create a new condition comprised of capsules each time our Compressor Stage signal changes value. These capsules will contain a new capsule property called 'StartModeEndMode' that represents the value of the compressor stage immediately before and immediately after the signal changed value, or transitioned. The formula syntax to achieve this is: //creates a condition for 1 minute of time encompassing 30 seconds on either side of a transition $Transition = $CompressorStage.toCondition().beforeStart(0.5min).afterStart(1min) //Assigns the mode on both sides of the step change to a concatenated string that is a property of the capsule. $Transition .transform( $cap -> $cap.setProperty('StartModeEndMode', $CompressorStage.toCondition() .toGroup($cap, CAPSULEBOUNDARY.INTERSECT) .reduce("", ($seq, $stepCap) -> $seq + $stepCap.getProperty('Value') //Changes the format of the stage names for more clear de-lineation as a property in the capsules pane. .replace('STAGE 1','-STAGE1-').replace('STAGE 2','-STAGE2-').replace('TRANSITION','-TRANSITION-').replace('OFF','-OFF-') ))) and the Output is: Note that we added the new property 'StartModeEndMode' to the Capsules Pane. 3. We can now filter this condition to look for specific transitions of interest. In this example, we are interested in every time our compressor went from a TRANSITION state to STAGE2. Use Formula and the filter() function with the following syntax to achieve this. //Create a new condition comprised only of capsules where the 'StartModeEndMode' property is equal to '-TRANSITION--STAGE2-' $ConditionCreatedInStep2.filter( $capsule -> $capsule.getProperty('StartModeEndMode').isEqualTo('-TRANSITION--STAGE2-')) and the Output is: 4. Now we are able to add other signals of interest to the display and switch to Capsule Time view to observe how those signals behave during these similar transition events.

FAQ: For reporting purposes, I want to calculate statistics based on the current period to date and display that next to the periods immediately preceding it. This is easy to do using the custom date range tool in Organizer Topic (Creating a Periodic Condition and the grabbing the capsule closest to or offset by 1 from the end). Is there a way to create these same date ranges relevant to the current time in Seeq Workbench? Solution: We can create identical conditions in Seeq Workbench by following the methods below. The first method defines how to create conditions for current and previous conditions for years, days, weeks, shifts. The second method includes an extra step that is necessary for current and previous months and quarters since the exact duration of these periods can vary based on the number of days each month. Method 1 - when the length of time in each period is definitive (e.g. year, week, day, shift). This example shows how to create conditions for "Current Week" and "Previous Week" 1. Create a Periodic Condition for "Weekly" using the Periodic Condition tool. 2. Create a Condition around the current time ("Now") using Formula --> condition(1min, capsule(now() - 1min, now())) 3. Use the Composite Condition tool to create a condition for "Current Week" when the Periodic Condition "Weekly" touches the tiny capsule at "Now". 4. Use Formula to create a condition for the "Previous Week" --> $currentWeek.beforeStart(7d) Method 2 - when the length of time in each period is variable (e.g. month, quarter). This example creates a condition for "Current Month" and "Previous Month" 1. Create a Periodic Condition for "Monthly" using the Periodic Condition tool. 2. Create a Condition around the current time ("Now") using Formula --> condition(1min, capsule(now() - 1min, now())) 3. Use the Composite Condition tool to create a condition for "Current Month" when the Periodic Condition "Monthly" touches the tiny capsule at "Now". 4. Use Formula to create a Condition for the last day of the last period (in this case "Last Day of the Last Month") $currentMonth.beforeStart(1d) 5. Use the Composite Condition tool to create a condition for the "Previous Month" when the Periodic Condition "Monthly" touches the "Last Day of Last Month".

Hi, I have a strange issue when aggregating data in a histogram. I am counting the number of samples for a signal and aggregate by first using "Year" and the using "Day of the Week" as the aggregation type: This gives me the following histogram with a count of 246 samples for monday in 2012: But these data should belong to sunday. I set up another histogram using the following condition as the second aggregation type: The result now looks like this (which is correct): This is for the other days as well. Is there any way to get around this issue? Installed Seeq version is R21.0.40.01-v201812312325 Regards, Thorsten

Summary Chain View enables a nice visual of all the time periods stacked side-by-side; however, sometimes it is useful to create a new signal from all these capsules that has been scrunched together. This avoids the maximum limit of how many capsules can be shown in Chain View. Note this is different from "time warping" which realigns the samples by some amount. Signal scrunching keeps the relative sample alignment the same within each capsule - but it does move each "snippet" of the signal next to each other. Steps Here is a screenshot of all the signals and conditions needed for this analysis. Add signal to the display with the specified time range. Create the time periods, condition capsules, of interest. Using the Custom Condition tool, create a new capsule that surrounds all the capsules containing the data for the new signal. Note, the signal will start at the beginning of this capsule. Create Inverse of the time periods of interest that occurs within the 'Condition for New Signal'. This will be used to calculate the time lag between the capsules in the 'Condition for Time Periods of Interest'. $cftp.inverse().removeLongerThan(1wk).intersect($cfns) Create the signal snippets that occur only within the 'Condition for Time Periods of Interest' and the 'Condition for New Signal'. $t.within($cftp.intersect($cfns)) Create a new signal to calculate the delay between each of the 'Condition for Time Periods of Interest' capsules. 1.toSignal().aggregate(totalized(), $icfc, startKey()).convertUnits('h').toStep() Create a running sum of the delay - this will be used to shift all the snippets to the beginning of the 'Condition for New Signal' capsule. $dbtp.runningSum($cfns) Scrunch the signal. $ts.delay(0-$rsod, 1wk)

FAQ: I've got a signal with drop-outs and I want to filter my signal to only visualize samples with values above a threshold. Is there a quick way to do this in Seeq? Solution: We can use Seeq's Signal Filtering capabilities to break down a signal into individual samples and create a new signal that keeps the samples only above your specified threshold. 1. Visualize your signal with drop-outs and determine the threshold value. For this example, we will filter out all samples with a value of less than or equal to 40F. 2. Open a new Seeq Formula window and use the search documentation to look for information on filtering a signal. When we begin to type filter, we see right away an option "filter() Signal". Open the documentation to get an understanding of what the function is doing and example syntax. The first example below is taking a string signal and breaking it down into samples, then keeping samples only if their string value is not equal to 'T4A' (note single or double quotes are required for string inputs). The second example is filtering to remove infinite values or NaN values. The first logical statement "$sample.getValue().isValid()" is keeping only the samples with valid values, removing NaN or other invalid values. The second logical statement "$sample.getValue().isFinite()" is keeping only the samples with finite values. Note that we can string as many logical criteria together as we want here using the && operator. In our case, we want to filter our temperature signal to show only samples with values above 40F. The syntax in the formula input window below "$Temp.filter($sample -> ($sample.getValue().isGreaterThan(40)))" shows how we are able to take our temperature signal, break it down into individual samples, and then only keep samples whose value is greater than 40F. The new filtered signal appears nearly exactly the same as the original, but with the drop-outs removed.

We often do calculations where we are interested in having one result if one condition is true and a second result if another condition is true. In this post we will discuss how to do this calculation in Seeq. We will create a new single signal which runs different calculations during different periods of time. This technique can be used to replicate "if" logic or "if / else" logic currently being used in excel. Example existing code from excel or other systems IF Temperature > 90 then show a result of (Temperature * 100) IF Temperature < 90F then show a result of (Temperature + 10) Step 1: Identify two modes of operation In the Tools tab identify the following conditions using the Value Search tool: Step 2: Use formula to reconstruct "if/then statement" using Seeq's Splice Tool Variables: Name Item Type $Temperature Temperature Signal $HighTempCondition Greater than 90 Condition Seeq Formula: $HighTempCalc = $Temperature * 100 $LowTempCalc = $Temperature + 10 $LowTempCalc.splice($HighTempCalc,$HighTempCondition) Section 1 - $HighTempCalc This is the first arbitrary calculation which is to be run during periods of high temperature Section 2 - $LowTempCalc This is the first arbitrary calculation which is to be run when NOT in periods of high temperature Final - $LowTempCalc.splice($HighTempCalc,$HighTempCondition) Combine your two series. Use the LowTempCalc series, unless you are in the High Temperature condition, in which case use the HighTempCalc

Use Case: It is common in industry to seek to use the behavior of upstream process variables to predict what the behavior of a downstream variable might be minutes, hours or days from the present time. Solution: A traditional predictive modeling workflow can be applied to solve this problem. Identify an appropriate training data set Perform any necessary data cleansing Create a predictive model Evaluate the model fit Improve the model Operationalize the model What differentiates this use case from any other predictive modeling use case is a specific data cleansing step for adjusting signals to remove process lag. 1. Load Data Load your target signal and the relevant upstream signals into the display pane. In this example, the target signal is the product viscosity, measured in an analytical lab based off a sample from a downstream sample point. Three upstream signals: the reactor temperature, reactant conversion, and viscosity modifier flow to the reactor significantly influence the product viscosity measurement and will be used as inputs into the model signal. 2. Identify Training Data Set Identify an appropriate training data set for your regression model. This may involve a longer time window to include variability in product type or seasonality. In this example, we will pan out to 3 months to capture multiple cycles of different product types. With an appropriate training window identified, you can also limit your training data set to a subset of samples present during a particular condition. If this interests you, consult the "advanced options" section of the Prediction Tool KB article for more information. This method is particularly useful if you're wishing to create different models for different modes of operation. 3. Cleanse Signals -- Adjust for Process Lag We can time-shift our upstream signals using either a known constant delay, a known variable delay (like a calculated residence time signal), or an unknown delay of maximum correlation to the target signal. The first two of these options will utilize the .move() function in Formula (or .delay() in earlier versions of Seeq). The latter will utilize the .correlationOffset() function. In this example, we have a known lag of 1.5 hours between the reactor and the product sampling point. We will use the move function with an input scalar of 1.5h, as shown below. The time shift calculation should be applied across all relevant input signals. More information on the different options for time shifting signals using fixed, variable, or calculated offsets is available in this forum post: 4. Cleanse Signals -- Remove signal noise, outliers, abnormal operating data In this example, we apply an agileFilter to each of our time-shifted model input signals. Apply the same technique to each of the model inputs. Note that steps 3 & 4 could have been combined into a single formula. An example of this would be: $reactor_temp.move(1.5h).agileFilter(1min) For guidance on additional cleansing techniques, consult the Interactive Training. 5. Build the Predictive Model Use the Prediction tool panel to create a model of your target signal based on your cleansed, time-adjusted input signals. Ensure your model training window matches the date range that you identified in step 2. You can view the model parameters like coefficients, rSquared, and p-values using the "+ Prediction Model" option. 6. Evaluate the Model Fit Use Scatter Plot view and the model parameters to evaluate the goodness of fit of the model. Switch to a time range outside of your training data set to ensure your model is a good fit for data throughout time. 7. Improve Model (as needed) If the scatter plot indicates a non-linear relationship, test out additional model scales in the prediction tool panel. Consider eliminating variables with p-values higher than your significance level cutoff (frequently 0.05). Add additional variables if relevant. If distinct modes of operation introduce significant signal variability, consider creating a model for each operating mode and stitch the models together into a single model using the splice() function in Formula. 8. Deploy the Model The model should project out into the future by the amount of the process lag between the upstream and target signals.

FAQ: I have a condition for events of variable duration. I would like to create a new condition that comprises the first third of the time (or 4th, or 10th) of the original condition. Solution: A stepwise approach can be taken to achieve this functionality. 1. Begin with your condition loaded in the display pane. 2. Create a new Signal using Signal from Condition that calculates the total duration of each of your event capsules, interpolated as a step signal. 3. Create a new signal that is your total event duration multiplied by the proportion of the event that you would like to capture. e.g. for the first 1/3 of the event, divide your total duration signal by 3, as shown below. 4. Create an arbitrary discrete signal with a sample at the start of each of your event capsules. 5. Shift the arbitrary discrete signal in time by the value of your signal calculated in step 3. In this example, the 1/3 duration signal. Note, depending on your version of Seeq, the function to do this may be called move() or delay(). 6. Use the toCapsules() function in Formula to create a tiny (zero duration) capsule at each of your shifted, discrete samples. 7. Join the start of your original condition with the capsules created in step 6 using the composite condition tool.

FAQ: I have a CSV file that has the start and end times of some historical events and various information about the events that I would like to use in my analysis in Seeq. How do I go about getting these events and all of their associated information into Seeq? Solution: Use the Import from CSV tool and Seeq Formula to bring in a condition comprised of each of these events and assign the data in each column of the CSV as a property of the condition. 1. Ensure your CSV file is formatted correctly for import into Seeq. The first column should be the event start time, the second column should be the event end time, and all other data columns should be to the right of these. A list of acceptable timestamp formats can be found on the Seeq Knowledge Base in this article. 2. Use the Import CSV File tool to bring the condition into Seeq. Drag and drop your CSV file or navigate to your file through Windows (Mac) Explorer. Under "Import File as" select "condition". Choose the start-time and end-time columns in the "Choose columns" section. Specify a max capsule duration that is just longer than your longest event. 3. Once your condition is imported, use Seeq Formula to assign the data from the other columns of our CSV as properties of each capsule. Begin by using the item properties for the CSV imported condition to duplicate the condition to Formula. Once in Formula, add the column headers from your CSV to the query in line 1 of the code, separated by commas. Then use the setProperty() function to assign each of the columns of the CSV as a property of each capsule. Once executed, the output is a new condition that looks exactly like the original from trend view in the display pane. However, this new condition has properties, that can be added to the capsules pane using the gridded+ button.

I have an interesting question that I need some assistance on. We have a signal that generally has no dominant frequency. However, it sometimes does get a dominant frequency and when it does, we are really interested in two things: What is the dominant frequency? How dominant is it? ( Let's call this "magnitude." ) Tracking both the dominant frequency and the intensity over time using a rolling 2 to 3 hour window every 5 minutes. This value has predictive capability when it does show up, and it intensifies as it gets closer to a particular event we are trying to predict. I've been able get the peak frequency because the formulas are clear enough to figure this out. The problem is the magnitude. The "Frequency Analysis" panel results show something like this: How do I get that peak value? I don't want to have to specify a hard-coded frequency band for this. The problem is that I don't see a function for that. I can call the fft() function in a way in which it returns a "Table" type. signal.fft ( bounds , period , units ) : Table Create a table of frequency magnitudes by analyzing the signal in the bounds. The table will have 2 columns, frequency and magnitude. Then I can use the top() function to return the top 1 row ordered by the greatest "magnitude" column: table.top ( limit , columnName , direction ) : Table However, I cannot for the life of me figure out how to get the "mangitude" value out of the first row returned in "Table" the above function and convert it into a sample at the ending point of the rolling 3 hour/5min period. Is this even possible. Is there a better way? // Getting a rolling 3 hour window every 5 minutes. $periods = periods(3h, 5min) // for condition, get a signal with samples ending at each capsule representing: // key: end of 3hr window // value: peak magnitude of the fft of the $waveSignal $periods.transformToSamples( $cap -> { // excute the expression for each capsule in condition. $tbl = $waveSignal.fft($cap, 1s, 's') // want the results in period lengths, not frequencies. // get the largest magnitude, filtering for the frequency/period length range of interest // get the first row of that table. $r = $tbl.filter('frequency'. isBetween(30s, 150s)) .top(1, 'magnitude', 'desc') .getFirstRow() // *******HOW DO I DO THIS???****** // convert that magnitude calculated to sample located at the end of this capsule. sample($cap.getEnd(), $r.get('magnitude')) } ) Thanks in advance! Even better would be: getting the sum of the values in the peakFrequency +-/ 2s window.

Various parts of the world display date and time stamps differently. Often times, we get requests for changing the order of month and day in the timestamp string or to display the date as a Scorecard metric in a specific format. This can be done using the replace() operator in Formula. For example, let's say we wanted to pull the start time for each capsule in a condition and display it as mm/dd/yyyy hh:mm format: $condition.transformToSamples($cap -> Sample($cap.getStart(),$cap.getProperty('Start')), 1d) .replace('/(?<year>....)-(?<month>..)-(?<day>..)T(?<hour>..):(?<minute>..):(?<sec>..)(?<dec>.*)Z/' , '${month}/${day}/${year} ${hour}:${minute}') This takes the original timestamp (for example: '2019-11-13T17:04:13.7220714157Z') and parses it into the year, month, day, hour, minute, second, and decimal to be able to set up any format desired. The various parts of the string can then be called in the second half of the replace to get the desired format as shown above with ${month}/${day}/${year} ${hour}:${minute}. From there, you can either view this data in the trend or use Scorecard Metric to display the Value at Start in a condition based metric. If the end time is desired instead of the start, the only changes needed would be to (1) switch the .getStart operator to .getEnd, and (2) switch the .getProperty('Start') to .getProperty('End'). Note: The '1d' at the end of the 2nd line of the formula represents the maximum interpolation for the data, which is important if you want to view this as a string signal. This value may need to be increased depending on the prevalence of the capsules in the condition.

Hello, I am trying to create a polynomial formula using the formula tool but I get the following error message "t is not compatible with t² at" with 'add' or 'subtract' after. Same with t² and t³. How can I create such formula? Example: 2 * $a^3 + 5 * $a^2 - 8 * $a + 200

Hi, Is it possible to calculate the duration of valve opening (from time when change from "close" to "not close" to time when change from "not close" to "close") I've tried to do this by derivative function, but the values are string type. For the period of time, e.g. a year need to receive the number of the scalar values of these periods and then perform some calculations with formula with each of that scalar value.

Hey there, My question splits into two parts: Firstly, I want to create a condition based on multiple criteria: if signal A equal to 3, B equal to 4, C is greater than 5 than condition is valid. I know i could create 3 individual capsule and overlap them. Is there a simple way to use formula to do so? Secondly, in my analysis i have 10 signals and associated conditions(alert), then I want to know in the past 7 days how many alert in total(repeated instance or capsule doesnt count) ? and How long is the total alert time? Thank you

Background: When looking to identify trends or step changes in a signal, we typically recommend an approach of smoothing the signal, taking the first derivative, then identifying when that derivative is positive or negative. This method works well most of the time, but employing this technique in combination with others can be more effective at capturing trends/step changes when the value change in the signal is more subtle. Solution: When looking for step changes, we can use a technique of calculating a range of the signal on a rolling periodic basis and search for when the range exceeds some limit. We can then combine this condition with when the derivative is positive (increasing step changes) or negative (decreasing step changes) to capture our final condition. 1. Create a rolling window over which you will look at the range (max-min value) of the signal. In my example I used a 4h window every 30 minutes, because my tank draining events were typically never longer than 4h. Select the smallest time period that you can that is still longer than your longest draining event. 2. Use Signal from Condition to calculate the range (max-min) of your signal over each of the rolling windows. Make sure to place the time stamp of the statistic at the end of each rolling capsule. 3. Identify time periods when that range calculation is above some threshold. In this example we used a threshold of 2 based looking at the trend output of our step 2. If we zoom in on a smaller range of time, we see that our capsules for when the range value is high actually extend beyond the completion of our decreasing signal. 4. We can intersect this condition that we have identified for high range in the signal with a condition for when the derivative of the signal is negative to capture our desired events. First calculate the first derivative of the signal. We apply a smoothing agileFilter in this step as well to remove signal noise. 5. Identify when that derivative value is less than zero using the value search tool. 6. Now take the intersection of the condition for negative derivative of the level and the condition for high range. The final view of the original signal and the events identified: Use chain view to validate your calculations: