def graph3(mode):
local_results = defaultdict(list)
for method in methods:
for filter in ["1x1", "3x3", "5x5", "9x9"]:
nthread = 8
chunk_size = nthread
key = (filter, method, nthread, chunk_size, default_file)
run_perf(*key)
local_results[method] += [float(results[key][mode])]
title = (
'4M pixels square image, #thread = 8, chunk_size = 8. Average over 10 runs.'
)
ylabel = mode
if mode == 'time':
ylabel += "(s)"
#sets are unordered by default, so impose an order with this list.
methods_as_list = list(methods.keys())
filters_as_list = ["1x1", "3x3", "5x5", "9x9"]
plotter.graph(
filters_as_list, # x axis
[local_results[method] for method in methods_as_list], # y vals
methods_as_list, # names for each line
[colours[method] for method in methods_as_list],
'graph_{}.png'.format(mode + "3"), # filename
title,
"Filter Sizes", # xlabel
ylabel)
def graph2(mode, filter="3x3"):
local_results = defaultdict(list)
for nthread in threads:
for chunk_size in chunk_sizes:
method = "work queue"
key = (filter, method, nthread, chunk_size, default_file)
run_perf(*key)
local_results[nthread] += [float(results[key][mode])]
title = (
'4M pixels square image, filter = {}, method = work queue. Average over 10 runs.'
.format(filter))
ylabel = mode
if mode == 'time':
ylabel += "(s)"
#sets are unordered by default, so impose an order with this list.
methods_as_list = list(methods.keys())
plotter.graph(
chunk_sizes, # x axis
[local_results[nthread] for nthread in threads], # y vals
threads, # names for each line
[colours[nthread] for nthread in threads],
'graph_{}.png'.format(mode + filter + "2"), # filename
title,
"Chuck Size", # xlabel
ylabel)
def graphnewimage(mode):
local_results = defaultdict(list)
for method in methods:
for filter in filters:
chunk_size = 8
key = (filter, method, 8, chunk_size, default_file)
run_perf(*key)
local_results[method] += [float(results[key][mode])]
title = ('100M pixels {} image, chunk_size (workqueue) = #'
'threads. Average over 10 runs.'.format(default_file))
ylabel = mode
if mode == 'time':
ylabel += "(s)"
#sets are unordered by default, so impose an order with this list.
methods_as_list = list(methods.keys())
plotter.graph(
[1,3,5,9], # x axis
[local_results[method] for method in methods_as_list], # y vals
methods_as_list, # names for each line
[colours[method] for method in methods_as_list],
'graph_{}.png'.format(default_file), # filename
title,
"filters (N x N)", # xlabel
ylabel
)
def graph(mode, filter):
local_results = defaultdict(list)
for method in methods:
for nthread in threads:
chunk_size = nthread
key = (filter, method, nthread, chunk_size, default_file)
run_perf(*key)
local_results[method] += [float(results[key][mode])]
title = ('4M pixels square image, filter = {}, chunk_size (workqueue) = #'
'threads. Average over 10 runs.'.format(filter))
ylabel = mode
if mode == 'time':
ylabel += "(s)"
#sets are unordered by default, so impose an order with this list.
methods_as_list = list(methods.keys())
plotter.graph(
threads, # x axis
[local_results[method] for method in methods_as_list], # y vals
methods_as_list, # names for each line
[colours[method] for method in methods_as_list],
'graph_{}.png'.format(mode+filter), # filename
title,
"# Threads", # xlabel
ylabel
)
def graphpool(mode, filter):
local_results = defaultdict(list)
for nthread in threads:
for chunk_size in clunk_sizes:
key = (filter, "work queue" , nthread, chunk_size, default_file)
run_perf(*key)
local_results[nthread] += [float(results[key][mode])]
title = ('4M pixels square image, filter = {}, chunk_size (workqueue) = #'
'threads. Average over 10 runs.'.format(filter))
ylabel = mode
ylabel += "(s)"
plotter.graph(
clunk_sizes, # x axis
[local_results[nthread] for nthread in threads], # y vals
["1 thread","2 threads","3 threads","4 threads","5 threads","6 threads","7 threads","8 threads"], # names for each line
["b","g","r","c","m","y","k","#fca308"],
'graphpool_{}.png'.format(mode+filter), # filename
title,
"Chunk height", # xlabel
ylabel
)
def iodf(funct, x, tol, graf=1):
'''
|
| Function that implements the improved Ostrowski’s method free from derivatives
| to solve f(x) = 0
|
| ------------------------------------------------------------------------------
| Parameters:
| -----------
| funct :
| Text that represents the function f(x)
| x :
| Initial value of the iterative method
| tol :
| Stop criterion of the iterative method
| graf :
| A number, 1 show the graph, 0 don't show the graph
|
| Returns:
| --------
| x_aprox :
| Approximation to the solution of the equation f(x) = 0
| iter :
| Number of iterations used to approximate the zero of the function
| graf :
| Graph of iteration (k) vs errors (|f(xk)|) of the iterative method
| ------------------------------------------------------------------------------
|
| The syntax rules for the input function are as follows:
| a. Use 'x' as variable name
| b. To multiply, add and subtract use '*', '+' and '-' respectively
| c. To place and exponent use '**'
| d. The function names of math library can be used (e.g., sqrt(), exp(), etc)
|
'''
try:
#Create a callable function from the input string function
f = lambda x: eval(
funct, {
'x': x,
'pi': pi,
'e': e,
'exp': exp,
'log': log,
'sqrt': sqrt,
'cos': cos,
'sin': sin,
'tan': tan
})
#Iteration counter
k = 0
#Iterations array
iterations = [k]
#Function images f(x) array
fxs = [abs(f(x))]
while (abs(f(x)) >= tol):
try:
fx = f(x)
y = x - (2 * (fx**2)) / (f(x + fx) - f(x - fx))
fy = f(y)
z = y - fy * ((y - x) / (2 * fy - fx))
#Compute the current value of 'x'
x = z - f(z) * ((y - x) / (2 * fy - fx))
#Increase the iteration counter
k += 1
#Save the iteration values
iterations.append(k)
fxs.append(abs(f(x)))
except ZeroDivisionError:
#Stop the iterations if a zero division occur
break
print('-----------------------------------------------')
print('Iteration ', k)
print('y=', y)
print('z=', z)
print('x=', x)
print('f(x)=', f(x))
if (1 == graf):
#Show 'iteration vs |f(x)|' graphic
plt.graph(iterations, fxs)
elif (0 != graf):
#Shown a warning message if graf has an other value than 1 or 0
print(
'WARNING: El parámetro para mostrar la gráfica tiene un valor incorrecto!'
)
return {'x_aprox': x, 'iter': k}
except (NameError, SyntaxError):
print('ERROR: La función ingresada tiene una sintaxis incorrecta!')