Fill Missing Data

To replace NaN values in the data and visualize the results, open the Clean Missing Data task. Start by typing the keyword missing in a code block, and then click Clean Missing Data when it appears in the menu. Select the input data and the cleaning method to plot the filled data automatically.

Fill Outliers

You can now remove the outliers from the cleaned data in the previous task by using the Clean Outlier Data task. Type the keyword outliers in a new code block and click Clean Outlier Data to open the task. Select cleanedData as the input data. You can customize the methods for cleaning and detecting outliers, and adjust the threshold to find more or fewer outliers.

Smooth Data

Next, smooth the cleaned data from the previous task by using the Smooth Data task. Type the keyword smooth and click the task when it appears. Select cleanedData2, the output from the previous task, as the input data. Select a smoothing method, and adjust the smoothing factor for more or less smoothing.

Locate Extrema

Finally, start typing the keyword extrema and click Find Local Extrema. Use smoothedData as the input data and change the extrema type to find both the local maxima and local minima of the cleaned, smoothed data. You can adjust the local extrema parameters to find more or fewer maxima and minima.

Generate Code

To view the code that a task used to generate the output and visualization, click the arrow at the bottom of the task window, above the plot.

The task displays the code block, which you can cut and paste to use or modify later in the existing script or a different program. For example:

Because the underlying code is now part of your live script, you can continue to use the variables created by the task for further processing. For example, you can use maxIndices to find the corresponding local maxima values in the smoothed data, and then compute the average: