def __init__(self, acq, shot, config_name=None, **kwargs):
self.shot = shot
self.acq = acq
if config_name != None:
self.__dict__.update(get_config_as_dict('Diagnostic', config_name))
if pyfusion.VERBOSE>3: print(get_config_as_dict('Diagnostic', config_name))
self.__dict__.update(kwargs)
self.config_name=config_name
def __init__(self, acq, shot, config_name=None, **kwargs):
self.shot = shot
self.acq = acq
if config_name != None:
self.__dict__.update(get_config_as_dict('Diagnostic', config_name))
if pyfusion.VERBOSE > 3:
print(get_config_as_dict('Diagnostic', config_name))
self.__dict__.update(kwargs)
self.config_name = config_name
def get_coords_for_channel(channel_name=None, **kwargs):
config_dict = kwargs.copy()
if channel_name:
config_dict.update(get_config_as_dict('Diagnostic', channel_name))
coord_name = 'dummy'
coord_values = (0.0,0.0,0.0)
transforms = []
for k in config_dict.keys():
if k.startswith('coords_'):
coord_name = k[7:]
coord_values = tuple(map(float,config_dict[k].split(',')))
coords_instance = Coords(coord_name, coord_values)
if 'coord_transform' in config_dict:
transform_list = pyfusion.config.pf_options('CoordTransform', config_dict['coord_transform'])
for transform_name in transform_list:
# this seems to return all the globals too
transform_class_str = pyfusion.config.pf_get('CoordTransform', config_dict['coord_transform'], transform_name)
# so tyr to exclude the globals
if pyfusion.config.has_option('global',transform_name):
continue
transform_class = import_from_str(transform_class_str)
#if not hasattr(transform_class, 'output_coord'):
# raise Exception('??')
coords_instance.load_transform(transform_class)
debug_(pyfusion.DEBUG,1, key=['coord', 'device_name'])
return coords_instance
def get_coords_for_channel(channel_name=None, **kwargs):
config_dict = kwargs.copy()
if channel_name:
config_dict.update(get_config_as_dict("Diagnostic", channel_name))
coord_name = "dummy"
coord_values = (0.0, 0.0, 0.0)
transforms = []
for k in config_dict.keys():
if k.startswith("coords_"):
coord_name = k[7:]
coord_values = tuple(map(float, config_dict[k].split(",")))
coords_instance = Coords(coord_name, coord_values)
if "coord_transform" in config_dict:
transform_list = pyfusion.config.pf_options("CoordTransform", config_dict["coord_transform"])
for transform_name in transform_list:
# this seems to return all the globals too
transform_class_str = pyfusion.config.pf_get(
"CoordTransform", config_dict["coord_transform"], transform_name
)
# so tyr to exclude the globals
if pyfusion.config.has_option("global", transform_name):
continue
transform_class = import_from_str(transform_class_str)
# if not hasattr(transform_class, 'output_coord'):
# raise Exception('??')
coords_instance.load_transform(transform_class)
debug_(pyfusion.DEBUG, 1, key=["coord", "device_name"])
return coords_instance
def testAcqAttrsConfigKwargs(self):
"""Check that config, kwarg attributes are correctly attached to
object.
If config is supplied, load config before kwargs.
"""
config_dict = get_config_as_dict('Acquisition', 'test_baseacq')
test_acq = BaseAcquisition('test_baseacq', dummy_var_1 = 5)
self.assertEqual(test_acq.dummy_var_1, 5)
def testAcqAttrsConfigKwargs(self):
"""Check that config, kwarg attributes are correctly attached to
object.
If config is supplied, load config before kwargs.
"""
config_dict = get_config_as_dict('Acquisition', 'test_baseacq')
test_acq = BaseAcquisition('test_baseacq', dummy_var_1=5)
self.assertEqual(test_acq.dummy_var_1, 5)
def testAcqAttrsConfig(self):
"""Check that config, kwarg attributes are correctly attached to
object.
If config is supplied, load config before kwargs.
"""
config_dict = get_config_as_dict('Acquisition', 'test_baseacq')
test_acq = BaseAcquisition('test_baseacq')
for config_arg in config_dict.keys():
self.assertTrue(hasattr(test_acq, config_arg))
def testAcqAttrsConfig(self):
"""Check that config, kwarg attributes are correctly attached to
object.
If config is supplied, load config before kwargs.
"""
config_dict = get_config_as_dict('Acquisition', 'test_baseacq')
test_acq = BaseAcquisition('test_baseacq')
for config_arg in config_dict.keys():
self.assertTrue(hasattr(test_acq, config_arg))
def testEqualityConfigOrArgs(self):
"""Check that config and kwarg instantiated Acquisition classes
are same."""
acq_from_config = BaseAcquisition('test_baseacq')
# create a BaseAcquisition instance with keyword args
config_dict = get_config_as_dict('Acquisition', 'test_baseacq')
acq_from_kwargs = BaseAcquisition(**config_dict)
# Acquistion instantiated only from keywords won't have
# config_name set but should otherwise be equal
self.assertTrue(
equal_except_for(acq_from_config, acq_from_kwargs, 'config_name'))
def testEqualityConfigOrArgs(self):
"""Check that config and kwarg instantiated Acquisition classes
are same."""
acq_from_config = BaseAcquisition('test_baseacq')
# create a BaseAcquisition instance with keyword args
config_dict = get_config_as_dict('Acquisition', 'test_baseacq')
acq_from_kwargs = BaseAcquisition(**config_dict)
# Acquistion instantiated only from keywords won't have
# config_name set but should otherwise be equal
self.assertTrue(equal_except_for(acq_from_config,
acq_from_kwargs, 'config_name'))
def __init__(self, acq, shot, config_name=None, **kwargs):
self.shot = shot
self.acq = acq
self.no_cache = False # this allows getData to request that cached data is NOT used - eg for saving local
#bdb?? add device name here, so can prepend to Diagnostic
# e.g. LHD_Diagnostic - avoids ambiguity
debug_(pyfusion.DEBUG,5,key='device_name')
if config_name is not None:
self.__dict__.update(get_config_as_dict('Diagnostic', config_name))
# look for the first valid date range for this diag -
# see config/'Valid Dates' in the reference docs
self.config_name=config_name
for Mod in ['M1','M2','M3','M4']:
if self.find_valid_for_date():
break
else:
self.__dict__.update(get_config_as_dict('Diagnostic', config_name.replace('W7X','W7X'+Mod)))
else:
raise LookupError(config_name)
if pyfusion.VERBOSE>3: print(get_config_as_dict('Diagnostic', config_name))
self.__dict__.update(kwargs)
def __init__(self, config_name, **kwargs):
if pyfusion.config.pf_has_section('Device', config_name):
self.__dict__.update(get_config_as_dict('Device', config_name))
self.__dict__.update(kwargs)
self.name = config_name
#### attach acquisition
if hasattr(self, 'acq_name'):
acq_class_str = pyfusion.config.pf_get('Acquisition',
self.acq_name, 'acq_class')
self.acquisition = import_from_str(acq_class_str)(self.acq_name)
# shortcut
self.acq = self.acquisition
else:
pyfusion.logging.warning(
"No acquisition class specified for device")
def get_coords_for_channel(channel_name=None, **kwargs):
config_dict = kwargs.copy()
if channel_name:
config_dict.update(get_config_as_dict('Diagnostic', channel_name))
coord_name = 'dummy'
coord_values = (0.0,0.0,0.0)
transforms = []
for k in config_dict.keys():
if k.startswith('coords_'):
coord_name = k[7:]
coord_values = tuple(map(float,config_dict[k].split(',')))
coords_instance = Coords(coord_name, coord_values)
if config_dict.has_key('coord_transform'):
transform_list = pyfusion.config.pf_options('CoordTransform', config_dict['coord_transform'])
for transform_name in transform_list:
transform_class_str = pyfusion.config.pf_get('CoordTransform', config_dict['coord_transform'], transform_name)
transform_class = import_from_str(transform_class_str)
coords_instance.load_transform(transform_class)
return coords_instance
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)
def __init__(self, config_name=None, **kwargs):
if config_name != None:
self.__dict__.update(get_config_as_dict('Acquisition',
config_name))
self.__dict__.update(kwargs)
def __init__(self, acq, shot, config_name=None, **kwargs):
self.shot = shot
self.acq = acq
if config_name != None:
self.__dict__.update(get_config_as_dict('Diagnostic', config_name))
self.__dict__.update(kwargs)
import pyfusion as pf
pf.config.get('global','database') # 'sqlite:///sqlite.txt'
pf.read_config('shaun_feb_2010.cfg')
from pyfusion.conf.utils import get_config_as_dict
get_config_as_dict('Device','H1')
get_config_as_dict('Diagnostic','H1PoloidalAll')
import pyfusion as pf
h1=pf.getDevice("H1")
data=h1.acq.getdata(70071,'H1ToroidalAxial')
data.meta.keys()
data.plot_signals()
# overlay fs on spectrum
run examples/plot_specgram.py dev_name='LHD' shot_number=27233 hold=0 time_range=[.35,.5] NFFT=256 noverlap=220
run examples/plot_text_pyfusion.py filename='PF2_120229_MP_27233_27233_1_256.dat' hold=1 min_e=0.8 freq_scale=1e3
colorbar();xlim(0.35,.5);ylim(0,150000)
# mode identification overlaid on spectrum - used in LHD report Feb 2012
run examples/plot_specgram.py dev_name='LHD' shot_number=27233 hold=0 time_range=[.35,1.5] NFFT=256 noverlap=220
clim(-210,-40)
run examples/mode_identify_example_2012.py hold=1 fsfile='PF2_120229_MP_27233_27233_1_256.dat'
xlim(0.35,.5);ylim(0,150000)
xlabel('Time (s)'); ylabel('Frequency (kHz)')
# chirp following example
run examples/plot_text_pyfusion.py filename='PF2_120229_MP_27233_27233_1_256.dat' hold=1 min_e=0.8 freq_scale=1e3 plot=1 time_range=[0.35,.4]
def process_swept_Langmuir(self, t_range=None, t_comp=[0, 0.1], fit_params = dict(maxits=200, alg='leastsq',esterr=1), initial_TeVfI0=dict(Te=50, Vf=15, I0=None), dtseg=4e-3, overlap=1, rest_swp='auto', clipfact=5, clip_iprobe = None, leakage=None, threshold=0.01, threshchan=12, filename=None, amu=1, plot=None, return_data=False, suffix=''):
"""
==> results[time,probe,quantity]
plot = 1 : V-I and data if there are not too many.
plot >= 2 : V-I curves
plot >= 3 : ditto + time plot
plot >= 4 : ditto + all I-V iterations
can send parameters through the init or process - either way
they all are recorded in actual_params
clip_iprobe = [-0.015, .02] # used to check if a resistive term is affecting Te
Start by processing in the ideal order: - logical, but processes more data than necessary
fix up the voltage sweep
compensate I
detect plasma
reduce time
segment and process
Faster order - reduces time range earlier: (maybe implement later)
detect plasma time range with quick and dirty method
restrict time range, but keep pre-shot data accessible for evaluation of success of removal
"""
self.figs = [] # reset the count of figures used to stop too many plots
self.actual_params = locals().copy()
# try to catch mispelling, variables in the wrong place
for k in fit_params:
if k not in 'Lnorm,cov,esterr,alg,xtol,ftol,lpf,maxits,track_ratio'.split(','):
raise ValueError('Unknown fit_params key ' + k)
self.actual_params.pop('self')
self.actual_params.update(dict(i_diag=self.i_diag, v_diag=self.v_diag))
# and do it for actuals too
for k in self.actual_params:
if k not in 'amu,clipfact,clip_iprobe,dtseg,filename,initial_TeVfI0,leakage,overlap,plot,rest_swp,suffix,t_comp,t_range,threshold,threshchan,fit_params,v_diag,i_diag,return_data'.split(','):
raise ValueError('Unknown actual_params key ' + k)
self.actual_params.update(dict(i_diag_utc=self.imeasfull.utc, pyfusion_version=pyfusion.VERSION))
self.amu = amu
if not isinstance(self.imeasfull.channels, (list, tuple, np.ndarray)):
self.imeasfull.channels = [self.imeasfull.channels]
self.i_chans = [ch.config_name for ch in self.imeasfull.channels]
for ch in self.i_chans: # only take the current channels, not U
if ch[-1] != 'I':
raise ValueError("Warning - removal of V chans doesn't work!!!")
# hopefully we can ignore _U channels eventually, but not yet
self.i_chans.remove(ch)
self.coords = [ch.coords.w7_x_koord for ch in self.imeasfull.channels]
# if you want just one voltage channel, at the moment, you still
# need it to be a multi-channel diag. (to simplify this code).
self.v_chans = [ch.config_name for ch in self.vmeasfull.channels]
# want to say self.vmeas[ch].signal where ch is the imeas channel name
self.vlookup = {}
for (c, vch) in enumerate(self.v_chans):
self.vlookup[vch] = c
self.vassoc = [] # list of sweepVs associated with each i channel
# one per i channel - these refer to their
# respective self.v_chans
# for OP1.1, only a few V chans were recorded and
# in practice only two channels are necessary.
default_sweep = 'NO SWEEP'
default_sweep = 'W7X_L57_LP01_U'
for ch in self.i_chans:
cd = get_config_as_dict('Diagnostic', ch)
# TODO(bdb): use of default_sweep should generate a warning
self.vassoc.append(cd.get('sweepv', default_sweep))
# first do the things that are better done on the whole data set.
# prepare_sweeps will populate self.vcorrfull
# self.check_crosstalk(verbose=0) # this could be slow
self.prepare_sweeps(rest_swp=rest_swp)
self.get_iprobe(leakage=leakage, t_comp=t_comp)
tb = self.iprobefull.timebase
# the 3000 below tries to avoid glitches from Hilbert at both ends
#w_plasma = np.where((np.abs(self.iprobefull.signal[threshchan]) > threshold) & (tb > tb[3000]) &(tb < tb[-3000]))[0]
# only look at electron current - bigger (shot 0309.52 LP53 has a positive spike at 2s)
w_plasma = np.where((-self.iprobefull.signal[threshchan] > threshold) & (tb > tb[3000]) &(tb < tb[-3000]))[0]
if t_range is None:
t_range = [tb[w_plasma[0]], tb[w_plasma[-1]]]
self.t_range = t_range
if t_range is not None:
self.imeas = self.imeasfull.reduce_time(t_range)
self.iprobe = self.iprobefull.reduce_time(t_range)
self.vcorr = self.vcorrfull.reduce_time(t_range)
else:
self.imeas = self.imeasfull
self.iprobe = self.iprobefull
self.vcorr = self.vcorrfull
# We need to segment iprobe, also i_meas to check clipping, and vsweep
self.segs = zip(self.imeas.segment(dtseg, overlap),
self.iprobe.segment(dtseg, overlap),
self.vcorr.segment(dtseg, overlap))
if self.debug>0: print(' {n} segments'.format(n=len(self.segs)))
self.fitdata = []
debug_(self.debug, 3, key='process_loop')
for mseg, iseg, vseg in self.segs:
# print('len vseg', len(vseg.signal[0]))
if len(vseg.signal[0]) < dtseg//5: # skip short segments
continue
print(np.round(np.mean(mseg.timebase),4), end='s: ') # midpoint
if clip_iprobe is not None:
print('fudge hard clipping')
# have to clip the raw signal, because that is where the decision is made
# but we have to do it AFTER isage is extracted, becuause the leakage
# will make the clipping uneven (a sinusoidal top) - so clip isge too
iseg.signal = np.clip(iseg.signal, *clip_iprobe)
mseg.signal = iseg.signal.copy()
self.fitdata.append(self.fit_swept_Langmuir_seg_multi(mseg, iseg, vseg, clipfact=clipfact, initial_TeVfI0=initial_TeVfI0, fit_params=fit_params, plot=plot))
# note: fitter.actual_fparams only records the most recent!
self.actual_params.pop('fit_params') # only want the ACTUAL ones.
self.actual_params['actual_fit_params'] = self.fitter.actual_fparams
if filename is not None:
if '*' in filename:
fmt = 'LP{s0}_{s1}_'
if 'L5' in self.i_diag:
fmt += 'L5' + self.i_diag.split('L5')[1][0]
filename = filename.replace('*',fmt+'_')
if filename.endswith('_'): # remove ending _
filename = filename[0:-1]
if '{' in filename:
filename = filename.format(s0=self.shot[0], s1=self.shot[1], i_diag=self.i_diag)
print('writing {fn}'.format(fn=filename))
# Lukas had trouble with this in python 3
self.write_DA(filename)
if return_data:
return(self.fitdata)
def __init__(self, config_name=None, **kwargs):
if config_name is not None:
self.__dict__.update(get_config_as_dict('Acquisition', config_name))
self.__dict__.update(kwargs)
