GradientOne has two types of result entries:
{ "info": { "instrument_type": "RigolDS1054Z", "channels": [ { "name": "chan1", "start_time": 0, "enabled": true, "trigger_level": 1.52, "offset": 0.48, "time_step": 4e-07, "y_values": [ 0.0438, 0, 0.0438, ... ], "coupling": "dc" }, ... ], "config_excerpt": { "timebase": { "position": 0 }, "enabled_list": [ "chan1", "chan2" ], "channels": [ { "name": "chan1", "enabled": true, "range": 8, "offset": 0.48, "input_impedance": 1000000, "coupling": "dc" }, ... ], "trigger_edge_slope": "positive", "trigger": { "source": "chan1", "type": "edge", "coupling": "dc", "level": 1.52 }, "acquisition": { "record_length": 6000, "start_time": -0.0012, "time_per_record": 0.0024, "type": "normal", "number_of_averages": 2 } }, "timebase_scale": 0.0002, "h_divs": 12, "slice_length": 6000, "timebase_range": 0.0024, "num_of_slices": 1 }, "slice_length": 6000, "timebase_range": 0.0024, "num_of_slices": 1, "date_created": "2017-12-15T17:50:36.042800Z", ... }
{ "info": { "dataframe": { "instrument_type": [ "RigolDS1054Z", "RigolDS1054Z", ... ], "slice_length": [ 6000, 5000, ... ], "date_created": [ "2017-12-15T17:50:36.042800Z", "2017-12-15T12:30:26.000000Z", ... ], ... } } }
With the Result entities, you can visualize and perform measurements in the timeseries data, such as looking forpatterns in data, measuring rise/fall times, and pass/fail criteria.
With the Meta Results entries, you can look for explanations as to how the presence of these patterns, or the rise/fall times, or the configuration information affects whether the result passed or failed by some other criteria. For example, it might be that the presence of a peak at a specific location in the timeseries is correlated with failure, or that failed results are more likely to come from a specific test rig. In addition to collecting data from GradientOne-integrated testrigs, you can upload data through the GradientOne web interface. To access it, go to /uploads. We support data in JSON, xls, xlsx and csv format. In JSON, the data must be either in the info/channels format, either as a single object or in an array of multiple objects. After adding a supported file, the page will attempt to interpret the data:
By default, the file will be iterpreted as being a table where every row is a the metadata of a single entry. In this example, Result 1 will be uploaded as:
"info": { "Average Cell Current (mA)": 4.8, "Average Cell Gain (dB)": 3.1, "Result": "Pass", "Max Cell Temperature (C)": 0.2, "Average Cell Temperature (C)": 1.6 },
If you instead select Rows are channels of a single result, the data will be uploaded as a single result, where Channel 1 is row 1:
{ "info": { "channels": [ { "name": 0, "y_values": [ 1.4, 5.1, 0.2, ... ] }, { "name": 1, "y_values": [ 1.4, 4.9, 0.2, ... ] }, ... ] } }
If you instead select Rows are single channels of multiple results, the data will be uploaded as multiple results, where Result 1 is row 1:
{ "info": { "channels": [ { "name": 0, "y_values": [ 1.4, 5.1, 0.2, ... ] } ] } }
Result 2 will be:
"info": { "channels": [ { "name": 0, "y_values": [ 1.4, 4.9, 0.2, ... ] } ] } }
If you select any of the Columns options, the columns will be painted the same instead of the rows:
If you select Columns are result metadata entries, Result 1 will be:
{ "info": { 0: 1.4, 1: 1.4, 2: 1,3, ... } }
If you select Columns are channels of a single result, only one result will be created:
{ "info": { "channels": [ {"name": "Number", "y_values": [0, 1, 2, ...]}, {"name": "Average Cell Temperature", "y_values": [1.4, 1.4, 1.3, ...]}, ... ] } }
If you select Columns are the single channels of multiple results, then Result 1 will be:
{ "info": { "channels": [ {"name": "Number", "y_values": [0, 1, 2, ...]}, ] } }
and Result 2 will be:
{ "info": { "channels": [ {"name": "Average Cell Temperature", "y_values": [1.4, 1.4, 1.3, ...]} ] } }
Make sure that you add a config name so that you can find your uploaded results later. Click Submit when ready. After uploading, links to the generated results will appear in the Link column.
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