Add time series plot explorer for numeric and date columns - #849
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Draws numeric columns against a date column as lines over time. The dates are read with the format the column already declares, sorted, and passed as real datetimes, so the axis is a genuine time axis and gaps stay visible instead of collapsing into evenly spaced categories. The existing scatter explorer could not do this: it does not accept Date, and since a Date is stored as text it would have plotted the axis as unordered categories in row order. validate_columns is tightened beyond the inherited type check, which would accept two numbers and no date, or two dates and no series.
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Summary
Adds
TimeSeriesPlotExplorer, so a target column can actually be looked at over time.ScatterPlotExplorerwas the nearest existing thing and does not work here for two independent reasons. Itsallowed_typesis[Float, Integer, Categorical], so it will not accept aDatecolumn at all. And because a dashAIDateis stored as text, addingDateto that list would make Plotly treat the axis as unordered categories drawn in row order rather than chronological order. That is a wrong plot, not a limited one.Type of Change
Check all that apply like this [x]:
Changes (by file)
DashAI/back/exploration/explorers/time_series_plot.py: new explorer. Reads the dates with the format the column already declares, sorts them, and passes real datetimes to Plotly, so the horizontal axis is a genuine time axis and gaps stay visible.allowed_types = [Date, Float, Integer],input_cardinality = {"min": 2}.validate_columnsis tightened past the inherited check, which would accept two numbers and no date, or two dates and no series.DashAI/back/initial_components.py: register the explorer.tests/back/exploration/test_time_series_plot.py: new. 12 tests.Testing (optional)
Notes (optional)
input_cardinalityis a minimum rather than exactly two on purpose: one date plus several series on shared axes is the more useful version and costs nothing, sincepx.linetakes a list of y columns.Dates are read with the declared format rather than reguessed, so the plot can never disagree with the stored type. A column whose format is wrong raises instead of drawing a plausible but wrong picture.