def start(self):
create_path('logfile')
gevent.spawn(self.manager).join()
tasks = [gevent.spawn(self.worker, i) for i in range(self.gevent_num)]
gevent.joinall(tasks)
# self.start_worker()
print('finish, data stored in %s' % (self.goal_path))
def save(name, model, year, interval):
db_path = "data/%s.db" % name
tools.create_path(db_path)
db = shelve.open(db_path)
db_year = db.get(year, {})
db_year[interval] = model.params()
db[year] = db_year
print sorted(db[year].keys())
db.sync()
db.close()
figure = "{name}/f{year}_{pt1}_{pt2}".format(
name=name, year=year, pt1=interval[0], pt2=interval[1])
tools.save_figure(figure, model.canvas)
def ProcessPIV(args, bga, bgb, reflection, stg):
# read images into numpy arrays
file_a, file_b, counter = args
frame_a = tools.imread(file_a)
frame_b = tools.imread(file_b)
# removing background and reflections
if bga is not None:
frame_a = frame_a - bga
frame_b = frame_b - bgb
frame_a[reflection == 255] = 0
frame_b[reflection == 255] = 0
#applying a static mask (taking out the regions where we have walls)
pnts = draw.polygon(stg['YM'], stg['XM'], frame_a.shape)
frame_a[pnts] = 0
frame_b[pnts] = 0
plt.imshow(frame_a, cmap='gray')
plt.show()
# main piv processing
u, v = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=stg['WS'], overlap=stg['OL'], dt=stg['DT'], search_area_size=stg['SA'], sig2noise_method=None)
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=stg['WS'],
overlap=stg['OL'])
u, v, mask = validation.local_median_val(u, v, 2000, 2000, size=2)
if stg['BVR'] == 'on':
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
u, *_ = smoothn(u, s=0.5)
v, *_ = smoothn(v, s=0.5)
x, y, u, v = scaling.uniform(x, y, u, v, stg['SC'])
# saving the results
save_file = tools.create_path(file_a, 'Analysis')
tools.save(x, y, u, v, mask, save_file + '.dat')
parser = argparse.ArgumentParser(description=param_list[0][0])
for i in range(len(param_list)):
if i > 0:
parser.add_argument(param_list[i][0],
type=type(param_list[i][1]),
default=param_list[i][1])
args = parser.parse_args()
print("create argparse success")
current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
save_info = [['time: ', current_time], ['param_info: ', param_list]]
save_list(save_info, 'create_info', main_save_path + 'create_info')
# create weights path
create_path(args.weights_path)
# create memory path
create_path(args.memory_path)
# create log path
create_path(args.log_path)
# create save_list path
create_path(args.list_path)
print("create save path success")
history_reward = []
# import env:
if args.task == 'l2r':
from wrapper_env.l2renv import WrapperEnv
else:
from wrapper_env.wrapperEnv import WrapperEnv
env = WrapperEnv(game=args.task)
print('finish, data stored in %s' % (self.goal_path))
#----------class definition----------
#----------function definition----------
#----------function definition----------
#----------main function----------
if __name__ == '__main__':
# test goal path
# goal_path='/home/xmucpp/Git_lib/cpp-data-cleaning/test-result'
# formal goal path
goal_path = '/home/xmucpp/cppdata/Organized'
create_path(goal_path)
# test set
# csvdicts = ['/home/xmucpp/Git_lib/cpp-data-cleaning/TinySet-10']
# formal set
csvdicts = ['/home/xmucpp/cppdata/Sources']
# run it
# reganizating(csvdicts,goal_path,gevent_num=100)
GeventQueue(csvdicts, goal_path, gevent_num=100).start()
#----------main function----------
def submit_path():
b_name = request.get_json().get('b_name')
b_name = b_name.replace('/', '').replace('\\', '')
b_path = tools.create_path(b_name)
return jsonify({"b_path": b_path})