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subdir[f] = meta
parent = nested_dir
for fold in folders[:-1]:
parent = parent.get(fold)
# Attach the config of all children nodes onto the parent
parent[folders[-1]] = subdir
return nested_dir"
1577,"def _parse_orders(self, orders):
""""""
Transform orders from list objects to PHP arrays:
[
{
'PNAME': 'CD Player',
'PCODE': 'PROD_04891',
'PINFO': 'Extended Warranty - 5 Years',
'PRICE': '82.3',
'PRICE_TYPE': 'GROSS',
'QTY': '7',
'VAT':'20'
},
{
'PNAME': 'Mobile Phone',
'PCODE': 'PROD_07409',
'PINFO': 'Dual SIM',
'PRICE': '1945.75',
'PRICE_TYPE': 'GROSS',
'QTY': '3',
'VAT':'20'
},
{
'PNAME': 'Laptop',
'PCODE': 'PROD_04965',
'PINFO': '17"" Display',
'PRICE': '5230',
'PRICE_TYPE': 'GROSS',
'QTY': '1',
'VAT':'20'
}
]
||
\/
{
'ORDER_PCODE[0]': 'PROD_04891',
'ORDER_PCODE[1]': 'PROD_07409',
'ORDER_PCODE[2]': 'PROD_04965',
'ORDER_PINFO[0]': 'Extended Warranty - 5 Years',
'ORDER_PINFO[1]': 'Dual SIM',
'ORDER_PINFO[2]': '17"" Display',
'ORDER_PNAME[0]': 'CD Player',
'ORDER_PNAME[1]': 'Mobile Phone',
'ORDER_PNAME[2]': 'Laptop',
'ORDER_PRICE[0]': '82.3',
'ORDER_PRICE[1]': '1945.75',
'ORDER_PRICE[2]': '5230',
'ORDER_PRICE_TYPE[0]': 'GROSS',
'ORDER_PRICE_TYPE[1]': 'GROSS',
'ORDER_PRICE_TYPE[2]': 'GROSS',
'ORDER_QTY[0]': '7',
'ORDER_QTY[1]': '3',
'ORDER_QTY[2]': '1',
'ORDER_VAT[0]': '20',
'ORDER_VAT[1]': '20',
'ORDER_VAT[2]': '20'
}
""""""
result = {}
for index, order in enumerate(orders):
for detail, value in order.iteritems():
result[""ORDER_%s[%s]"" % (detail, index)] = value
return result"
1578,"def average_detections(detections, predictions, relative_prediction_threshold = 0.25):
""""""average_detections(detections, predictions, [relative_prediction_threshold]) -> bounding_box, prediction
Computes the weighted average of the given detections, where the weights are computed based on the prediction values.
**Parameters:**
``detections`` : [:py:class:`BoundingBox`]
The overlapping bounding boxes.
``predictions`` : [float]
The predictions for the ``detections``.
``relative_prediction_threshold`` : float between 0 and 1
Limits the bounding boxes to those that have a prediction value higher then ``relative_prediction_threshold * max(predictions)``
**Returns:**
``bounding_box`` : :py:class:`BoundingBox`
The bounding box which has been merged from the detections
``prediction`` : float
The prediction value of the bounding box, which is a weighted sum of the predictions with minimum overlap
""""""
# remove the predictions that are too low