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Use the ee.Image.getThumbURL() method to generate a PNG or JPEG thumbnail image for an ee.Image object. Printing the outcome of an expression ending with a call to getThumbURL() results in a URL being printed. Visiting the URL sets Earth Engine servers to work on generating the requested thumbnail on-the-fly. The image is displayed in a browser when processing completes. It can be downloaded by selecting appropriate options from the image's right-click context menu.
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You can also use an information file with the --bins-info parameter to describe the source of your bins (and even assign them some colors to have some specific visual identifiers for any type of visualization downstream).
The summary process can take a lot of time. If you want to take a quick look to identify which bins need to be refined, you can run anvi-summarize with --quick-summary flag to generate a quick-summary.
After running anvi-summarize, you may realize that you are not happy with one or more of your bins. This often is the case when you are working with very large datasets and when you are forced to skip the human guided binning step. The program anvi-refine gives you the ability to make finer adjustments to a bin that may be contaminated.
You can downsample your data, if you choose, into larger time bins using the downsample_gaze function. This function aggregates the set of samples into a time series consisting of standardized time bins with a size specified by the user (default is 50ms). In addition, it drops columns that are no longer necessary. The user needs to specify a list columns (aggvars) that define the aggregation level (e.g., individual trials) and should be kept after the binning is done. If you would like to keep the raw data unbinned, you can skip this part.
After the fixations have been assigned to the object type and converted to time bins, they are ready for visualization and statistical analysis. Below is a plot (see Fig. 2) of the time course of fixation proportions for each target type.
Due to processing constraints, we use a data file that includes data from a few participants with all behavioral data included. The parse_edf , merge_gazer_files, and find_messages_edf functions can be tested using example data that are available to download from the Open Science Framework (OSF): While reading in the data is fast (even with many participants), some of the functions performed on the data can be computationally intensive. 2b1af7f3a8