def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', required=True)
parser.add_argument('--span',
default=1.0,
type=float,
help='Parameter sweep range (should be even)')
parser.add_argument('--delta',
default=0.1,
type=float,
help='Size of parameter sweep increments')
parser.add_argument('--significant_digits',
default=1,
type=int,
help='Number of significant digits in beamx/beamy')
args = parser.parse_args()
significant_digits = args.significant_digits
delta = args.delta
span = args.span
span_half = span / 2
with open(args.config) as config_json:
config = json.load(config_json)
beamx = float(config['beamx'])
beamy = float(config['beamy'])
endpoint = config['endpoint']
endpoint_local = config['endpoint_local']
x_values = np.round(np.arange(beamx - span_half, beamx + span_half + delta,
delta),
decimals=significant_digits)
y_values = np.round(np.arange(beamy - span_half, beamy + span_half + delta,
delta),
decimals=significant_digits)
phil_data = list()
phil_data2 = list()
for x in x_values:
for y in y_values:
new_config = copy.deepcopy(config)
new_config['beamx'] = x
new_config['beamy'] = y
new_config['suffix'] = get_random_str()
phil_data.append(new_config)
fxc = FuncXClient()
fxc.throttling_enabled = False
fxid_create_phil, fxid_stills_process, fxid_count_ints = get_function_ids(
fxc)
# Phil files
print("Running funcx_create_phil")
phil_batch = fxc.create_batch()
for phil in phil_data:
phil_batch.add(phil,
endpoint_id=endpoint_local,
function_id=fxid_create_phil)
phil_job = fxc.batch_run(phil_batch)
wait_batch(fxc, phil_job)
# Stills process
print("\nRunning funcx_stills_process")
stills_batch = fxc.create_batch()
for phil in phil_data:
stills_batch.add(phil,
endpoint_id=endpoint,
function_id=fxid_stills_process)
stills_job = fxc.batch_run(stills_batch)
wait_batch(fxc, stills_job)
# Count ints
print("\nRunning funcx_count_ints")
combined_df = pd.DataFrame()
count_batch = fxc.create_batch()
for phil in phil_data:
count_batch.add(phil,
endpoint_id=endpoint,
function_id=fxid_count_ints)
count_job = fxc.batch_run(count_batch)
count_results = wait_batch(fxc, count_job)
# Create CSV and heatmap
for df in count_results:
if combined_df.empty:
combined_df = df[2]
else:
combined_df = pd.concat([combined_df, df[2]], axis=0)
combined_df.sort_values(['X', 'Y'], ascending=[True, True], inplace=True)
combined_df.to_csv("ints.csv", index=False)
plot(combined_df)
import xlwt
import time
import numpy as np
#from PIL import Image
start3 = time.time()
from funcx.sdk.client import FuncXClient
fxc = FuncXClient()
end3 = time.time()
exec_time3 = ((end3 - start3) * 1000)
print(exec_time3)
fxc.throttling_enabled = False
n = 2000
estimate = []
def matrix(ress):
return ress
mat1 = ([1, 6, 5], [3, 4, 8], [2, 12, 3])
mat2 = ([3, 4, 6], [5, 6, 7], [6, 56, 7])
#overhead = []
start1 = time.time()
hello_function = fxc.register_function(matrix)
#ress = np.dot(mat1,mat2)
end1 = time.time()
registertime = ((end1 - start1) * 1000)
print("The register time is:", registertime)
index = 1
book = xlwt.Workbook()