def get_shape_calculated_values(self):
shapeDict = ordDict()
shapeDict.setdefault(str(self.get_shape_values()))
shapeDict.setdefault(str(self.calculate_area()))
shapeDict.setdefault(str(self.calculate_perimeter()))
shapeDict.setdefault(str(self.calculate_diagonal()))
return shapeDict
def cartesian_prod_dicts_lists(the_dict):
#takes a dictionary and produces a dictionary of the cartesian product of the input
if not type(the_dict) is type(ordDict()):
warnings.warn('An ordered dict was not used. Thus if this function is called again with the same dict it might not produce the same results.')
from sklearn.utils.extmath import cartesian
stim_list = []
stim_list = tuple([ list(the_dict[ key_name ]) for key_name in the_dict ])
#cartesian has the last column change the fastest, thus is like c-indexing
stim_cart_array = cartesian(stim_list)
cart_dict = ordDict()
#load up the vectors assosciated with keys to cart_dict
for key_name, key_num in zip(the_dict, range(len(the_dict))):
cart_dict[key_name] = stim_cart_array[:, key_num]
return cart_dict
def stim_trans_generator(shapes=None, blur=None, scale=None,
x=None, y=None, amp=None, rotation=None):
#takes descrptions of ranges for different transformations (start, stop, npoints)
#produces a cartesian dictionary of those.
stim_trans_dict = ordDict()
if not shapes is None:
#stim_trans_dict['shapes'] = np.array(shapes, dtype=int)
stim_trans_dict['shapes'] = np.array(shapes)
if not blur is None :
if isinstance(blur,tuple):
stim_trans_dict['blur'] = np.linspace(*blur)
else:
stim_trans_dict['blur'] = blur
if not scale is None:
if isinstance(scale, tuple):
stim_trans_dict[ 'scale' ] = np.linspace(*scale)
else:
stim_trans_dict[ 'scale' ] = scale
if not x is None :
if type(x) is tuple:
stim_trans_dict['x'] = np.linspace(*x)
else:
stim_trans_dict['x'] = np.array(x)
if not y is None :
if type(y) is tuple:
stim_trans_dict['y'] = np.linspace(*y)
else:
stim_trans_dict['y'] = np.array(y)
if not rotation is None :
if type(rotation) is tuple:
stim_trans_dict['rotation'] = np.linspace(*y)
else:
stim_trans_dict['rotation'] = np.array(rotation)
if not amp is None:
stim_trans_dict['amp'] = np.linspace(*amp)
# get all dimensions, into a dict
stim_trans_cart_dict = dm.cartesian_prod_dicts_lists(stim_trans_dict)
#this is a hackish fix to cart from sklearn switching dtypes according to 1st element.
stim_trans_cart_dict['shapes'] = stim_trans_cart_dict['shapes'].astype(int)
return stim_trans_cart_dict, stim_trans_dict
# Data type: OrderedDictionary
#
# Key of dictionary is the category of visualized quantity
# (e.g. 'Blade summary), the value is a list of tuples,
# where the first element is a label of visualized q.
# (e.g. 'Energy consumption [J] (Samples)') and the second one
# is a unit of the quantity.
#
# Example:
# y_label = ordDict((('Blade summary', [('Energy consumption [J] (Stats structure)', 'J')]),
# ('Job info - hdeem', [('Runtime of function [s]', 's')])))
#y_label = ordDict((('Blade summary', [('Average energy consumption [J]', 'J')]),
# ('Job info - hdeem', [('Average runtime of function [s]', 's')])))
y_label = ordDict((('COUNTERS - RAPL:', [
('AVG Energy summary [J]', 'J')
]), ('Job info - RAPL', [('AVG Runtime of function [s]', 's')])))
# Time and energy variable
#
# Data type: Dictionary/None
#
# When used, than reports will contain information about
# run-time change for energetically-optimal settings
#
# Example:
# 1) time_energy_vars = None
# 2) time_energy_vars = {'time': ('Job info - hdeem', 'Runtime of function [s]'),
# 'energy': ('Blade summary', 'Energy consumption [J] (Samples)')}
#time_energy_vars = {'time': ('Job info - hdeem', 'Average runtime of function [s]'),
# 'energy': ('Blade summary', 'Average energy consumption [J]')}
# Listing of visualized quantities/variables
#
# Data type: OrderedDictionary
#
# Key of dictionary is the category of visualized quantity
# (e.g. 'Blade summary), the value is a list of tuples,
# where the first element is a label of visualized q.
# (e.g. 'Energy consumption [J] (Samples)') and the second one
# is a unit of the quantity.
#
# Example:
# y_label = ordDict((('Blade summary', [('Energy consumption [J] (Stats structure)', 'J')]),
# ('Job info - hdeem', [('Runtime of function [s]', 's')])))
y_label = ordDict((('Blade summary', [
('Average energy consumption [J]', 'J')
]), ('Job info - hdeem', [('Average runtime of function [s]', 's')])))
# Time and energy variable
#
# Data type: Dictionary/None
#
# When used, than reports will contain information about
# run-time change for energetically-optimal settings
#
# Example:
# 1) time_energy_vars = None
# 2) time_energy_vars = {'time': ('Job info - hdeem', 'Runtime of function [s]'),
# 'energy': ('Blade summary', 'Energy consumption [J] (Samples)')}
time_energy_vars = {
'time': ('Job info - hdeem', 'Average runtime of function [s]'),
