def make_pickled_time_series(root,case,x,y,save_folder,overwrite=False):
import time_data_functions as tdf
from os.path import join,isfile
if not overwrite and not isfile(
join(save_folder,"{0}_px{1}_py{2}.p".format(
case.replace('.hdf5',''),
x,
y
))):
df = tdf.read_hdf5_time_series(
join(root,case),
case.replace('.hdf5',''),
loc=(-y,x)
)
else:
return 0
if df is not None:
df['x'] = x
df['y'] = y
df.to_pickle(join(save_folder,"{0}_px{1}_py{2}.p".format(
case.replace('.hdf5',''),
x,
y
)))
return 0
def load_time_series(device="Sr20R21",alpha='0',phi='0',z='10', p=(-0.1,0.1)):
from time_data_functions import read_hdf5_time_series
import os
#case = "{0}_a{1}_p{2}_U20_z{3}_tr_planar".format(
# device,
# alpha,
# phi,
# z
#)
case = "{0}_a{1}_p{2}_U20_z{3}_tr_NewProcessing".format(
device,
alpha,
phi,
z
)
hdf5_file = os.path.join(
root,
'TimeData_NewProcessing.hdf5'
#'planar.hdf5'
)
return read_hdf5_time_series(
hdf5_file,
case,
loc = p
).interpolate()
def make_hdf5_time_series(root, case, x, y, save_folder):
import time_data_functions as tdf
from os.path import join
df = tdf.read_hdf5_time_series(join(root, case), case.replace(".hdf5", ""), loc=(-y, x))
if df is not None:
df["x"] = x
df["y"] = y
df.to_pickle(join(save_folder, "{0}_px{1}_py{2}.p".format(case.replace(".hdf5", ""), x, y)))
