def multi_svd(shot_selection,array_name, other_arrays = None, other_array_labels = None, meta_data = None,
n_cpus=8, NFFT = 2048, power_cutoff=0.05, min_svs=2, overlap = 4,):
'''Runs through all the shots in shot_selection other_arrays is a
list of the other arrays you want to get information from '''
#Get the scan details
shot_list, start_times, end_times = H1_scan_list.return_scan_details(shot_selection)
rep = itertools.repeat
if other_arrays is None: other_arrays = ['ElectronDensity','H1ToroidalNakedCoil']
if other_array_labels is None: other_array_labels = [['ne_static','ne_mode'],[None,'naked_coil']]
if meta_data is None : meta_data = ['kh','heating_freq','main_current','sec_current', 'shot']
input_data_iter = itertools.izip(shot_list, rep(array_name),
rep(other_arrays),
rep(other_array_labels),
start_times, end_times,
rep(NFFT), rep(power_cutoff),
rep(min_svs), rep(overlap),rep(meta_data))
#generate the shot list for each worker
if n_cpus>1:
pool_size = n_cpus
pool = Pool(processes=pool_size, maxtasksperchild=3)
print('creating pool map')
results = pool.map(single_shot_svd_wrapper, input_data_iter)
print('waiting for pool to close ')
pool.close()
print('joining pool')
pool.join()
print('pool finished')
else:
results = map(single_shot_svd_wrapper, input_data_iter)
start=1
for i,tmp in enumerate(results):
print(i)
if tmp[0]!=None:
if start==1:
instance_array = copy.deepcopy(tmp[0])
misc_data_dict = copy.deepcopy(tmp[1])
start = 0
else:
instance_array = np.append(instance_array, tmp[0],axis=0)
for i in misc_data_dict.keys():
misc_data_dict[i] = np.append(misc_data_dict[i], tmp[1][i],axis=0)
else:
print('One shot may have failed....')
return clust.feature_object(instance_array = instance_array, misc_data_dict = misc_data_dict)
def multi_stft(shot_selection, array_names, n_cpus=1, NFFT=2048, perform_datamining = 1, overlap=4, n_pts=20, lower_freq = 1500, filter_cutoff = 20, cutoff_by = 'sigma_eq'):
#Get the scan details
shot_list, start_times, end_times = H1_scan_list.return_scan_details(shot_selection)
rep = itertools.repeat
hop = NFFT/overlap
input_data = itertools.izip(shot_list, rep(array_names), rep(NFFT),
rep(hop), rep(n_pts), rep(lower_freq), rep(None),
start_times, end_times, rep(perform_datamining),
rep(filter_cutoff), rep(cutoff_by))
if n_cpus>1:
pool_size = n_cpus
pool = Pool(processes=pool_size, maxtasksperchild=3)
print('creating pool map')
results = pool.map(single_shot_wrapper, input_data)
print('waiting for pool to close ')
pool.close();pool.join()
print('pool finished')
else:
results = map(single_shot_wrapper, input_data)
start=1
#Put everything back together
kappa_list = []
for i,tmp in enumerate(results):
print(i)
kappa_list.append(copy.deepcopy(tmp[2]))
if tmp[0]!=None:
if start==1:
instance_array = copy.deepcopy(tmp[0])
misc_data_dict = copy.deepcopy(tmp[1])
start = 0
else:
instance_array = np.append(instance_array, tmp[0],axis=0)
for i in misc_data_dict.keys():
misc_data_dict[i] = np.append(misc_data_dict[i], tmp[1][i],axis=0)
else:
print('something has failed....')
return clust.feature_object(instance_array = instance_array, misc_data_dict = misc_data_dict), kappa_list
""" plot a single diagnostic for a range of shots. Could be made a subset of plot_multi?
"""
import pyfusion
import pylab as pl
import numpy as np
from pyfusion.devices.H1 import H1_scan_list
all_shots = H1_scan_list.get_all_shots()
_var_defaults = """
dev_name = "H1Local"
diag_name = "H1PoloidalMirnov1_6"
shot_list = all_shots[1:1000:10]
rows=3
cols=4
exception=Exception
ylims=None
"""
exec(_var_defaults)
from pyfusion.utils import process_cmd_line_args
exec(process_cmd_line_args())
bad = []
sharey=None
if ylims is not None:
sharey='all'
dev = pyfusion.getDevice(dev_name)
