blob: fce949f98c37c4c7af5b0d2b10ebafeecf5460a9 [file] [log] [blame]
#!/usr/bin/env python
# Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
# Use of this source code is governed by a BSD-style license
# that can be found in the LICENSE file in the root of the source
# tree. An additional intellectual property rights grant can be found
# in the file PATENTS. All contributing project authors may
# be found in the AUTHORS file in the root of the source tree.
__author__ = ' (Henrik Kjellander)'
class DataHelper(object):
Helper class for managing table data.
This class does not verify the consistency of the data tables sent into it.
def __init__(self, data_list, table_description, names_list, messages):
""" Initializes the DataHelper with data.
data_list: List of one or more data lists in the format that the
Google Visualization Python API expects (list of dictionaries, one
per row of data). See the gviz_api.DataTable documentation for more
table_description: dictionary describing the data types of all
columns in the data lists, as defined in the gviz_api.DataTable
names_list: List of strings of what we're going to name the data
columns after. Usually different runs of data collection.
messages: List of strings we might append error messages to.
self.data_list = data_list
self.table_description = table_description
self.names_list = names_list
self.messages = messages
self.number_of_datasets = len(data_list)
self.number_of_frames = len(data_list[0])
def CreateData(self, field_name, start_frame=0, end_frame=0):
""" Creates a data structure for a specified data field.
Creates a data structure (data type description dictionary and a list
of data dictionaries) to be used with the Google Visualization Python
API. The frame_number column is always present and one column per data
set is added and its field name is suffixed by _N where N is the number
of the data set (0, 1, 2...)
field_name: String name of the field, must be present in the data
structure this DataHelper was created with.
start_frame: Frame number to start at (zero indexed). Default: 0.
end_frame: Frame number to be the last frame. If zero all frames
will be included. Default: 0.
A tuple containing:
- a dictionary describing the columns in the data result_data_table below.
This description uses the name for each data set specified by
Example with two data sets named 'Foreman' and 'Crew':
'frame_number': ('number', 'Frame number'),
'ssim_0': ('number', 'Foreman'),
'ssim_1': ('number', 'Crew'),
- a list containing dictionaries (one per row) with the frame_number
column and one column of the specified field_name column per data
Example with two data sets named 'Foreman' and 'Crew':
{'frame_number': 0, 'ssim_0': 0.98, 'ssim_1': 0.77 },
{'frame_number': 1, 'ssim_0': 0.81, 'ssim_1': 0.53 },
# Build dictionary that describes the data types
result_table_description = {'frame_number': ('string', 'Frame number')}
for dataset_index in range(self.number_of_datasets):
column_name = '%s_%s' % (field_name, dataset_index)
column_type = self.table_description[field_name][0]
column_description = self.names_list[dataset_index]
result_table_description[column_name] = (column_type, column_description)
# Build data table of all the data
result_data_table = []
# We're going to have one dictionary per row.
# Create that and copy frame_number values from the first data set
for source_row in self.data_list[0]:
row_dict = { 'frame_number': source_row['frame_number'] }
# Pick target field data points from the all data tables
if end_frame == 0: # Default to all frames
end_frame = self.number_of_frames
for dataset_index in range(self.number_of_datasets):
for row_number in range(start_frame, end_frame):
column_name = '%s_%s' % (field_name, dataset_index)
# Stop if any of the data sets are missing the frame
result_data_table[row_number][column_name] = \
except IndexError:
self.messages.append("Couldn't find frame data for row %d "
"for %s" % (row_number, self.names_list[dataset_index]))
return result_table_description, result_data_table
def GetOrdering(self, table_description):
""" Creates a list of column names, ordered alphabetically except for the
frame_number column which always will be the first column.
table_description: A dictionary of column definitions as defined by the
gviz_api.DataTable documentation.
A list of column names, where frame_number is the first and the
remaining columns are sorted alphabetically.
# The JSON data representation generated from gviz_api.DataTable.ToJSon()
# must have frame_number as its first column in order for the chart to
# use it as it's X-axis value series.
# gviz_api.DataTable orders the columns by name by default, which will
# be incorrect if we have column names that are sorted before frame_number
# in our data table.
columns_ordering = ['frame_number']
# add all other columns:
for column in sorted(table_description.keys()):
if column != 'frame_number':
return columns_ordering
def CreateConfigurationTable(self, configurations):
""" Combines multiple test data configurations for display.
configurations: List of one ore more configurations. Each configuration
is required to be a list of dictionaries with two keys: 'name' and
Example of a single configuration:
{'name': 'name', 'value': 'VP8 software'},
{'name': 'test_number', 'value': '0'},
{'name': 'input_filename', 'value': 'foreman_cif.yuv'},
A tuple containing:
- a dictionary describing the columns in the configuration table to be
displayed. All columns will have string as data type.
'name': 'string',
'test_number': 'string',
'input_filename': 'string',
- a list containing dictionaries (one per configuration) with the
configuration column names mapped to the value for each test run:
Example matching the columns above:
{'name': 'VP8 software',
'test_number': '12',
'input_filename': 'foreman_cif.yuv' },
{'name': 'VP8 hardware',
'test_number': '5',
'input_filename': 'foreman_cif.yuv' },
result_description = {}
result_data = []
for configuration in configurations:
data = {}
for dict in configuration:
name = dict['name']
value = dict['value']
result_description[name] = 'string'
data[name] = value
return result_description, result_data