Class S3PivotTable

Constructor - extracts all unique records, generates a pivot table from them with the given dimensions and computes the aggregated values for each cell.

Parameters:

• resource - the S3Resource
• rows - field selector for the rows dimension
• cols - field selector for the columns dimension
• facts - list of S3PivotTableFacts to compute
• strict - filter out dimension values which don't match the resource filter
• precision - maximum precision of aggregate computations, a dict {selector:number_of_decimals}

Overrides: object.__init__

Total number of records in the report

Render the pivot table data as a dict ready to be exported as GeoJSON for display on a Map.

Called by S3Report.geojson()

Parameters:

• layer - the layer. e.g. ("id", "count")
  • we only support methods "count" & "sum"
  • @ToDo: Support density: 'per sqkm' and 'per population'
• level - the aggregation level (defaults to Country)

Render the pivot table data as JSON-serializable dict

@param layer: the layer
@param maxrows: maximum number of rows (None for all)
@param maxcols: maximum number of columns (None for all)
@param least: render the least n rows/columns rather than
              the top n (with maxrows/maxcols)

{
    labels: {
        layer:
        rows:
        cols:
        total:
    },
    method: <aggregation method>,
    cells: [rows[cols]],
    rows: [rows[index, value, label, total]],
    cols: [cols[index, value, label, total]],

    total: <grand total>,
    filter: [rows selector, cols selector]
}

Convert this pivot table into an XLS file

Parameters:

• title - the title of the report

Returns:

the XLS file as stream

records

All records in the pivot table as a Storage like:
{
 <record_id>: <Row>
}

empty

Empty-flag (True if no records could be found)

numrows

The number of rows in the pivot table

numcols

The number of columns in the pivot table

cell

Array of pivot table cells in [rows[columns]]-order, each
cell is a Storage like:
    {
     records: <list_of_record_ids>,
     (<fact>, <method>): <aggregated_value>, ...per layer
    }

row

List of row headers, each header is a Storage like:
{
 value: <dimension value>,
 records: <list_of_record_ids>,
 (<fact>, <method>): <total value>, ...per layer
}

col

List of column headers, each header is a Storage like:
{
 value: <dimension value>,
 records: <list_of_record_ids>,
 (<fact>, <method>): <total value>, ...per layer
}

totals

The grand total values for each layer, as a Storage like:
{
 (<fact>, <method): <total value>, ...per layer
}