pyVerif.Score.Score

Package pyVerif :: Module Score :: Class Score

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Class Score

Management of scores.

Method Summary
	`__init__(self, filename_to_real_id)` Init the Score object.
	`add_column(self, operation, columns_idx)` Add a computed column to the merged scores.
	`get_size(self)` Returns a tuple with the current size of the merged scores.
	`load(self, filename)` Load the given filename, and merge it.
	`load_all(self, path, files)` Load all the given files from the given path, and merge them.
	`load_norm(self, filename)` Load the given filename as a normalization file.
	`load_norm_all(self, path, files)` Load all the given normalization files from the given path, and merge them.
	`save(self, filename)` Save the current merged scores to the given filename.

Method Details

init(self, filename_to_real_id)
(Constructor)

Init the Score object.

add_column(self, operation, columns_idx)

Add a computed column to the merged scores.

The resulting column will contain the results of:

    operation (column [columns_idx [0]],
               column [columns_idx [1]],
               ...,
               column [columns_idx [N]])

Where column [columns_idx [X]] are all value taked from the Xth
column of the current merged scores.
columns_idx is a sequence of integers who are the columns you
want to use for the operation.

get_size(self)

Returns a tuple with the current size of the merged scores.

The returned value is a tuple (nb_lines, nb_scores) representing the size of the merged scores matrix.

load(self, filename)

Load the given filename, and merge it.

load_all(self, path, files)

Load all the given files from the given path, and merge them.

load_norm(self, filename)

Load the given filename as a normalization file.

This mean every line in the file filename will be used for every entry in the merged result with the same real id.

load_norm_all(self, path, files)

Load all the given normalization files from the given path, and merge them.

save(self, filename)

Save the current merged scores to the given filename.

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