print('Only {n} files found for {fl}'.format(fl=file_list, n=len(file_list)))
(ds_list, comment_list) = read_text_pyfusion(file_list, debug=debug, exception=exception, target=target)
if len(ds_list) == 0: raise LookupError('no valid files found in the {n} files in {f}'
.format(f=file_list,n=len(file_list)))
if isinstance(file_list_in, str):
comment_list = [file_list_in] + comment_list
if append_old_method:
ds_list.append(dd)
comment_list.extend(dd['info']['comment'])
if append:
dd = merge_ds(ds_list, comment_list, old_dd=dd)
else:
dd = merge_ds(ds_list, comment_list)
if save_filename != "None":
from pyfusion.data.DA_datamining import DA
DAtest=DA(dd)
DAtest.info()
DAtest.save(save_filename)
DA766.extract(
locals()
) # Extract all the data into local variables, such as freq, t_mid, phases[0]
wb = np.where((amp > 0.05) & (a12 > 0.7))[
0] # find large amplitude, 'rotating' modes (see text).
mp = 1.67e-27
mu_0 = 4 * np.pi * 1e-7
V_A = b_0 / np.sqrt(
mu_0 * n_e * 1e18 *
mp) # Calculate VA as a local variable , an array of the same size as the
# extracted data
DA766.update(
dict(V_A=V_A
)) # Add it to the dictionary - no problem if you use the same name
# for the dictionary key
DA766.save(
'H1_766_new') # Save the data set (under a new name if you prefer).
# When you load the dataset in the future, the variable V_A will be there.
plt.plot(
k_h, freq * 1e3 / V_A, '.c'
) # symbol '.', colour cyan - we see the values are in the range sqrt beta
plt.plot(
k_h[wb], freq[wb] * 1e3 / V_A[wb], 'ob',
ms=12) # selecting large amplitude as above, we see beginnings of fig 5
plt.xlabel(r'$k_H$')
plt.ylabel(r'${\rm frequency/V_{Alfven}}$', fontdict={'fontsize': 'large'})
plt.show()
# Note - there will be a warning "invalid value encountered in sqrt" due to noise in n_e
# This could be eliminated by selecting using np.where(n_e >= 0)[0]
if verbose>0:
msg += ' because\n {r}'
print(msg.format(s=shot_number, r=reason))
# store the data in a DA (Dictionary of Arrays) object, which is like a DataFrame in R or python panda
#myDA = pf.data.DA_datamining.Masked_DA(
# DAordict = dict(shot=shot, ne_profile=np.array(ne_profile), t_mid=t_mid), valid_keys=['ne_profile'], mask=0)
myDA = DA(dict(shot=shot, ne_profile=np.array(ne_profile), t_mid=t_mid))
myDA.masked = Masked_DA(valid_keys=['ne_profile'], baseDA=myDA)
myDA.da['mask'] = np.ones(shape=np.shape(myDA[myDA.masked.valid_keys[0]])).astype(np.uint8)
channels = [ch.name.split(':')[-1] for ch in seg.channels]
myDA.infodict.update(dict(channels = channels))
for (c, ch) in enumerate(channels):
if np.any([ex in ch for ex in exclude]): # if any of exclude in that channel
myDA.da['mask'][:,c] = 0
myDA.save('/tmp/density_scan')
# the next step - write to arff
# myDA.write_arff('ne_profile.arff',['ne_profile'])
import matplotlib.pyplot as plt
def pl(array, comment=None,**kwargs):
plt.figure(num=comment) # coment written to window title
plt.plot(array, **kwargs)
# get the variables into local scope - so they can be accessed directly
myDA.extract(locals())
# the ne profiles are in an Nx15 array, where N is the numer of channels
pl(ne_profile[40,:],'one profile')
# plot a sequence of profiles, showing every fifth
""" Example 5 - Figure 7
Adding the Alfven speed for each item into the data set. colour
"""
from pyfusion.data.DA_datamining import DA
import numpy as np
import matplotlib.pyplot as plt
plt.figure('Example 5 - Figure 7')
DA766 = DA('H1_766.npz',load=1) # Load data from 30 shots into a dictionary of arrays (DA)
DA766.extract(locals()) # Extract all the data into local variables, such as freq, t_mid, phases[0]
wb = np.where((amp>0.05) & (a12>0.7))[0] # find large amplitude, 'rotating' modes (see text).
mp = 1.67e-27
mu_0 = 4 * np.pi * 1e-7
V_A = b_0/np.sqrt(mu_0 * n_e * 1e18* mp) # Calculate VA as a local variable , an array of the same size as the
# extracted data
DA766.update(dict(V_A=V_A)) # Add it to the dictionary - no problem if you use the same name
# for the dictionary key
DA766.save('H1_766_new') # Save the data set (under a new name if you prefer).
# When you load the dataset in the future, the variable V_A will be there.
plt.plot(k_h, freq*1e3/V_A,'.c') # symbol '.', colour cyan - we see the values are in the range sqrt beta
plt.plot(k_h[wb], freq[wb]*1e3/V_A[wb],'ob',ms=12) # selecting large amplitude as above, we see beginnings of fig 5
plt.xlabel(r'$k_H$'); plt.ylabel(r'${\rm frequency/V_{Alfven}}$',fontdict={'fontsize':'large'})
plt.show()
# Note - there will be a warning "invalid value encountered in sqrt" due to noise in n_e
# This could be eliminated by selecting using np.where(n_e >= 0)[0]
def write_DA(self, filename):
from pyfusion.data.DA_datamining import DA, Masked_DA
dd = {}
res = np.array(self.fitdata, dtype=np.float32)
nt = len(res)
nc = len(res[0])
for key in ['date', 'progId', 'shot']:
dd[key] = np.zeros(nt, dtype=np.int64)
dd['date'][:] = self.shot[0]
dd['progId'][:] = self.shot[1]
dd['shot'][:] = self.shot[1] + 1000*self.shot[0]
for key in ['nits','maxits']:
dd[key] = np.zeros([nt,nc], dtype=np.uint16)
# make all the f32 arrays - note - ne is just I0 for now - fixed below
lookup = [(0, 't_mid'), (1, 'Te'), (2, 'Vf'), (3, 'I0'),
(4, 'resid'), (5, 'nits'), (6, 'maxits'), (7, 'Ie_Ii'),
(3, 'ne18')]
if self.fitter.fit_params.get('esterr',False):
lookup.extend([(8, 'eTe'), (9, 'eVf'), (10, 'eI0') ])
for (ind, key) in lookup:
if key not in dd:
dd[key] = np.zeros([nt, nc], dtype=np.float32)
dd[key][:] = res[:, :, ind]
# fudge t_mid is not a vector...should fix properly
dd['t_mid'] = dd['t_mid'][:, 0]
dd['info'] = dict(params=self.actual_params,
coords=[self.coords[ic] for ic in self.select],
#area=[self.area[ic] for ic in self.select], # needs to be in npz file etc first
shotdata=dict(shot=[self.shot], utc_ns=[self.imeas.utc[0]]),
channels=[chn.replace(self.dev.name+'_', '')
.replace('_I', '')
for chn in
[self.i_chans[ic] for ic in self.select]],
orig_name = os.path.split(filename)[-1],
username = os.getlogin())
da = DA(dd)
da.masked = Masked_DA(['Te', 'I0', 'Vf', 'ne18', 'Ie_Ii'], baseDA=da)
# da.da['mask']=(da['resid']/abs(da['I0']) < .7) & (da['nits']<100)
# da.da['mask'] = ((da['resid']/abs(da['I0']) < .7) & (da['nits'] < da['maxits'])
# from version 0.7.0 onwards, resid is already normed to I0
lpf = self.fitter.actual_fparams['lpf']
# Note: these multilines ('down to here') can be applied to a DA by
# pasting to reset mask AFTER uncommenting the following # line
# lpf = da['info']['params']['actual_fit_params']['lpf']
rthr = 0.7 # LP20160309_29_L53__amoebaNone1.2N_2k.npz is < .12 others
# None 0310_9 up to 0.7-0.8
if lpf is not None:
rthr = rthr * np.sqrt(lpf/100.0)
da.da['mask'] = ((da['resid'] < rthr) & (da['nits'] < da['maxits'])
& (np.abs(da['Vf']) < 200) & (np.abs(da['Te']) < 200)
& (da['I0']>0.0004))
# additional restriction applied if the error estimate is available
if 'eTe' in da.da: # want error not too big and smaller than temp
da.da['mask'] &= ((np.abs(da['eTe']) < 100)
& (np.abs(da['eTe']) < np.abs(da['Te'])))
# down to here
qe = 1.602e-19
mp = 1.67e-27
fact = 1/(0.6*qe)*np.sqrt(self.amu*mp/(qe))/1e18 # units of 1e18
# check if each channel has an area
for (c, chn) in enumerate([self.i_chans[ic] for ic in self.select]):
cd = get_config_as_dict('Diagnostic', chn)
A = cd.get('area', None)
if A is None:
A = 1.0e-6
pyfusion.logging.warn('Defaulting area for {chn} to {A}'.format(chn=chn, A=A))
A = float(A)
da.da['ne18'][:, c] = fact/A * da['I0'][:, c]/np.sqrt(da['Te'][:, c])
da.save(filename)
exec(pf.utils.process_cmd_line_args())
# prepare an empty lists for data - lists are easy to append to
ne_profile = []
shot = []
t_mid = []
dev = pf.getDevice(device_name) # open the device (choose the experiment)
for shot_number in shot_range:
try:
d = dev.acq.getdata(shot_number, ne_set)
if time_range != None:
d.reduce_time(time_range)
sections = d.segment(n_samples, overlap)
print(d.history, len(sections))
for ss,t_seg in enumerate(sections):
ne_profile.append(np.average(t_seg.signal,axis=1))
t_mid.append(np.average(t_seg.timebase))
shot.append(shot_number)
except exception as reason:
print 'Error {e} on shot {s}'.format(e=reason, s=shot)
# make the dictionary of arrays and put it in a DA object
myDA = DA(dict(shot=shot, ne_profile=ne_profile, t_mid=t_mid))
myDA.save('ne_profile')
if len(file_list)<10:
print('Only {n} files found for {fl}'.format(fl=file_list, n=len(file_list)))
(ds_list, comment_list) = read_text_pyfusion(file_list, debug=debug, exception=exception, target=target)
if len(ds_list) == 0: raise LookupError('no valid files found in the {n} files in {f}'
.format(f=file_list,n=len(file_list)))
if append_old_method:
ds_list.append(dd)
comment_list.extend(dd['info']['comment'])
if append:
dd = merge_ds(ds_list, comment_list, old_dd=dd)
else:
dd = merge_ds(ds_list, comment_list)
if save_filename != "None":
from pyfusion.data.DA_datamining import DA
DAtest=DA(dd)
DAtest.info()
DAtest.save(save_filename)
else:
dd[key][ws] = basic_data[key].astype(pyfusion.prec_med)
if debug: print('{key}: avg={a:.3g}'.
format(key=key,a=np.average(dd[key][ws]))),
try:
filename
except:
filename='ddfile'
print('filename defaulting to ', filename)
save_name = 'saved_'+os.path.splitext(os.path.split(filename)[1])[0]
print('Saving as {0}'.format(save_name))
newDA=DA(dd)
newDA.save(save_name)
print("{0} missing shots out of {1}".format(len(missing_shots),(len(missing_shots)+len(good_shots))))
if verbose>0: print('missing shots are {0}'.format(missing_shots))
for key in diags:
print('{0:10s}: {1:.1f}%'.format(key, 100.0*np.sum(dd[key]*0==0)/sz))
"""
source /home/LHD/blackwell/pyfusion/pyfusion_mar_2015/pyfusion/run_pyfusion NV13 $x
exit
set_window_title $x
python pyfusion/examples/merge_basic_diagnostics.py diags=diags+diag_extra "filename='/home/LHD/blackwell/datamining/DA/PF2_150311_VSL6_$x'"
"""
dd[key][ws] = basic_data[key].astype(pyfusion.prec_med)
if debug:
print('{key}: avg={a:.3g}'.format(key=key,
a=np.average(
dd[key][ws]))),
try:
filename
except:
filename = 'ddfile'
print('filename defaulting to ', filename)
save_name = 'saved_' + os.path.splitext(os.path.split(filename)[1])[0]
print('Saving as {0}'.format(save_name))
newDA = DA(dd)
newDA.save(save_name)
print("{0} missing shots out of {1}".format(
len(missing_shots), (len(missing_shots) + len(good_shots))))
if verbose > 0: print('missing shots are {0}'.format(missing_shots))
for key in diags:
print('{0:10s}: {1:.1f}%'.format(key,
100.0 * np.sum(dd[key] * 0 == 0) / sz))
"""
source /home/LHD/blackwell/pyfusion/pyfusion_mar_2015/pyfusion/run_pyfusion NV13 $x
exit
set_window_title $x
python pyfusion/examples/merge_basic_diagnostics.py diags=diags+diag_extra "filename='/home/LHD/blackwell/datamining/DA/PF2_150311_VSL6_$x'"