for seg in sections:
ne_profile.append(np.average(seg.signal,axis=1)) # axis=1 -> average over time, not channel
t_mid.append(np.average(seg.timebase))
shot.append(shot_number)
except Exception as reason:
bads.append([shot_number, reason])
msg = 'skipping shot {s}'
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)
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)
