def main(args, curr_dir):
np.set_printoptions(precision=5, threshold=np.inf)
logger.set_up_pyrklog(args.logfile)
infile = importlib.import_module(args.infile)
si = sim_info.SimInfo(timer=infile.ti,
components=infile.components,
iso=infile.fission_iso,
e=infile.spectrum,
n_precursors=infile.n_pg,
n_decay=infile.n_dg,
kappa=infile.kappa,
feedback=infile.feedback,
rho_ext=infile.rho_ext,
plotdir=args.plotdir)
print_logo(curr_dir)
# n_components = len(si.components)
sol = solve(si=si, y=si.y, infile=infile)
log_results(si)
plotter.plot(sol, si)
pyrklog.critical("\nSimulation succeeded.\n")
def main(args, curr_dir):
np.set_printoptions(precision=5, threshold=np.inf)
logger.set_up_pyrklog(args.logfile)
infile = importlib.import_module(args.infile)
si = sim_info.SimInfo(timer=infile.ti,
components=infile.components,
iso=infile.fission_iso,
e=infile.spectrum,
n_precursors=infile.n_pg,
n_decay=infile.n_dg,
kappa=infile.kappa,
feedback=infile.feedback,
rho_ext=infile.rho_ext,
plotdir=args.plotdir)
print_logo(curr_dir)
# n_components = len(si.components)
sol = solve(si=si, y=si.y, infile=infile)
log_results(si)
plotter.plot(sol, si)
pyrklog.critical("\nSimulation succeeded.\n")
def plot_layer_output(self,plot_spec,plot_path,max_images=10):
#default all nodes set to value 1
#inp = numpy.random.random(self.conv_input_dim).astype(theano.config.floatX);
batch_size = plot_spec['batch_size'];
plot_path = plot_path +os.sep +'layer_%d'+os.sep +'batch_%d'+os.sep+'img_%d.png'
for layer_idx in xrange(self.conv_layer_num):
img_plot_remaining = max_images;
layer_out_fn = self.getLayerOutFunction(layer_idx);
logger.info('Plotting the layer %d'%layer_idx);
file_reader =read_dataset(plot_spec,pad_zeros=True)[0];
while not file_reader.is_finish():
for batch_index in xrange(file_reader.cur_frame_num/batch_size):
s_idx = batch_index * batch_size; e_idx = s_idx + batch_size
data = layer_out_fn(file_reader.feat[s_idx:e_idx])
e_idx= min(file_reader.cur_frame_num - file_reader.num_pad_frames,s_idx+batch_size);
img_plot_remaining = plot(data[s_idx:e_idx],plot_path,layer_idx,batch_index,img_plot_remaining);
if img_plot_remaining == 0:
break;
if img_plot_remaining == 0:
break;
file_reader.read_next_partition_data(pad_zeros=True);
from timeit import timeit
from utils.line import Line
from utils.sorter import radixSort
# Little test to check if algorithm is working
#intList = getRandomRepeatingListWithRange(15, 100)
#print(f"Initial: {intList}")
#radixSort(intList)
#print(f"Final: {intList}")
# Change this value to switch from and to test mode
testMode = False
testDivisor = 100
counts = [10000, 20000, 40000, 70000, 100000, 500000]
if testMode:
counts = [int(n / testDivisor) for n in counts]
numberOfTests = 1
randomLists = [getRandomList(count) for count in counts]
elapsedTimes = [
timeit(lambda: radixSort(list), number=numberOfTests)
for list in randomLists
]
lines = [Line((counts, elapsedTimes), "Caso aleatório", 'b')]
plot(lines, figname="products/radix_sort_graph.png")
from utils.plotter import plot
from utils.generator import getRandomList
from timeit import timeit
from utils.line import Line
from utils.sorter import countingSort
# Little test to check if algorithm is working
#list = getRandomList(25)
#print(list)
#countingSort(list)
#print(list)
# Change this value to switch from and to test mode
testMode = False
testDivisor = 100
counts = [10000, 20000, 40000, 70000, 100000, 500000]
if testMode:
counts = [int(n / testDivisor) for n in counts]
randomLists = [getRandomList(count) for count in counts]
elapsedTimes = [
timeit(lambda: countingSort(list), number=1) for list in randomLists
]
lines = [Line((counts, elapsedTimes), "Caso aleatório", 'b')]
plot(lines, figname="products/counting_sort_graph.png")
from utils.plotter import plot from utils.generator import getRandomList, getDecrescentList from timeit import timeit from utils.line import Line from utils.sorter import insertionSort counts = [1000, 2000, 3000, 4000, 5000, 8000, 11000, 15000] randomLists = [getRandomList(count) for count in counts] worstLists = [getDecrescentList(count) for count in counts] elapsedTimes = [timeit(lambda: insertionSort(list), number = 1) for list in randomLists] worstElapsedTimes = [timeit(lambda: insertionSort(list), number = 1) for list in worstLists] lines = [Line((counts, elapsedTimes), "Caso aleatório", 'b'), Line((counts, worstElapsedTimes), "Pior caso", 'k')] plot(lines, figname = "products/insertion_sort_graph.png")
counts = [10000, 20000, 40000, 70000, 100000, 500000]
if testMode:
counts = [int(n / testDivisor) for n in counts]
numberOfTests = 1
randomLists = [getRandomList(count) for count in counts]
elapsedTimes = [
timeit(lambda: bucketSort(list), number=numberOfTests)
for list in randomLists
]
lines = [Line((counts, elapsedTimes), "Caso aleatório", 'b')]
plot(lines, "products/bucket_sort_graph.png")
bucketNumbers = [1, 10, 100, 1000, 10000, 100000]
bucketNumberTimes = [
timeit(lambda: bucketSort(getRandomList(100000), bucketNumber),
number=numberOfTests) for bucketNumber in bucketNumbers
]
lines = [
Line((bucketNumbers, bucketNumberTimes), "Lista com 100000 elementos", 'b')
]
plot(lines, 'products/bucket_number_graph.png', 'Número de buckets')
print(
"De acordo com as informações obtidas ao analisar o gráfico, sugiro uma quantidade de buckets igual ao tamanho da entrada, ou uma quantidade de buckets igual a 1% do tamanho da entrada."
)
import numpy as np
from samplers.affineinv import AffineInv
from utils.plotter import plot
data = np.load('data.npy')
paramranges = np.asarray( [ [0.,10.],[0.,10.] ] )
n_walkers = 50
n_steps = 5000
af_sampler = AffineInv(data,paramranges)
af_sampler.afinv(n_walkers,n_steps)
plot(500)
from utils.plotter import plot
from utils.generator import getRandomList
from timeit import timeit
from utils.line import Line
from utils.sorter import mergeSort
# Change this value to switch from and to test mode
testMode = False
testDivisor = 100
counts = [10000, 20000, 40000, 70000, 100000, 500000]
if testMode:
counts = [int(n / testDivisor) for n in counts]
randomLists = [getRandomList(count) for count in counts]
elapsedTimes = [
timeit(lambda: mergeSort(list), number=1) for list in randomLists
]
lines = [Line((counts, elapsedTimes), "Caso aleatório", 'b')]
plot(lines, figname="products/merge_sort_graph.png")
from utils.plotter import plot from utils.generator import getRandomList, getDecrescentList from timeit import timeit from utils.line import Line from utils.sorter import selectionSort counts = [1000, 2000, 3000, 4000, 5000, 8000, 11000, 15000] randomLists = [getRandomList(count) for count in counts] worstLists = [getDecrescentList(count) for count in counts] elapsedTimes = [timeit(lambda: selectionSort(list), number = 1) for list in randomLists] worstElapsedTimes = [timeit(lambda: selectionSort(list), number = 1) for list in worstLists] lines = [Line((counts, elapsedTimes), "Caso aleatório", 'b'), Line((counts, worstElapsedTimes), "Pior caso", 'k')] plot(lines, figname = "products/selection_sort_graph.png")
from utils.plotter import plot
from utils.generator import getRandomList
from timeit import timeit
from utils.line import Line
from utils.sorter import shellSort
# Little test to check if algorithm is working
# list = getRandomList(25)
# print(list)
# shellSort(list)
# print(list)
# Change this value to switch from and to test mode
testMode = False
testDivisor = 100
counts = [10000, 20000, 40000, 70000, 100000, 500000]
if testMode:
counts = [int(n / testDivisor) for n in counts]
randomLists = [getRandomList(count) for count in counts]
elapsedTimes = [
timeit(lambda: shellSort(list), number=1) for list in randomLists
]
lines = [Line((counts, elapsedTimes), "Caso aleatório", 'b')]
plot(lines, figname="products/shell_sort_graph.png")
from utils.plotter import plot
from utils.generator import getRandomList, getDecrescentList
from timeit import timeit
from utils.line import Line
from utils.sorter import bubbleSort
counts = [1000, 2000, 3000, 4000, 5000, 8000, 11000, 15000]
randomLists = [getRandomList(count) for count in counts]
worstLists = [getDecrescentList(count) for count in counts]
elapsedTimes = [
timeit(lambda: bubbleSort(list), number=1) for list in randomLists
]
worstElapsedTimes = [
timeit(lambda: bubbleSort(list), number=1) for list in worstLists
]
lines = [
Line((counts, elapsedTimes), "Caso aleatório", 'b'),
Line((counts, worstElapsedTimes), "Pior caso", 'k')
]
plot(lines, figname="products/bubble_sort_graph.png")