def main():
""" Set up the parallel engine and the data space. Call the parallel engine, then write out the results """
client = Client()[:]
client.use_dill()
client.execute("import furnace.performance, furnace.strategy, numpy, datetime")
stock_percents = numpy.linspace(0.0, 0.4, 10)
rebalancing_periods = numpy.arange(1, 40, 1)
days_in = numpy.arange(1, 250, 1)
begin = datetime.datetime(2003, 1, 2)
end = datetime.datetime(2011, 12, 31)
grid = list(itertools.product(stock_percents, rebalancing_periods, days_in))
builder = function_builder(begin, end)
results = client.map(builder, grid)
#TODO: does pandas have a plain 'save to csv' function?
with open('data.csv', 'wb') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['days_out', 'pct', 'ndays', 'r2r', 'cagr', 'volatility', 'ntrades'])
writer.writerows(results)
cv2.imwrite(out_im_name, toSave)
return out_im_name
def resize_only(image_name):
image = cv2.imread(image_name)
out_name = path.split(image_name)[1]
out_im_name = path.join(out_path, out_name)
toSave = cv2.resize(image, size)
cv2.imwrite(out_im_name, toSave)
def kmeans_only(image_name, K=10):
out_im_name = get_output_name(image_name)
image = cv2.imread(image_name)
toSave, _, _ = kmeans(image, K)
cv2.imwrite(out_im_name, toSave)
prep_out_path(out_path)
dv = Client().load_balanced_view()
fs = dv.map(kmeans_only, np.array(image_paths))
print "Started: ", time_now_str()
fs.wait()
print "Finished: ", time_now_str()
