def test_channel_list(self):
ch01 = Channel('test_1', Coords('dummy', (0,0,0)))
ch02 = Channel('test_2', Coords('dummy', (0,0,0)))
ch03 = Channel('test_3', Coords('dummy', (0,0,0)))
new_cl = ChannelList([ch01, ch02, ch03])
def test_ordered_dataset_ORM(self):
channel_01 = Channel('channel_01', Coords('dummy', (0,0,0)))
channel_02 = Channel('channel_02', Coords('dummy', (0,0,0)))
channel_03 = Channel('channel_03', Coords('dummy', (0,0,0)))
channel_04 = Channel('channel_04', Coords('dummy', (0,0,0)))
fd1 = FloatDelta(channel_01, channel_02, 0.45)
fd2 = FloatDelta(channel_02, channel_03, 0.25)
fd3 = FloatDelta(channel_03, channel_04, 0.49)
#ods = OrderedDataSet(ordered_by="channel_1.name")
ods = BaseOrderedDataSet('test_ods')
for fd in [fd3, fd1, fd2]:
ods.append(fd)
ods.save()
# now read out of database
if pyfusion.orm_manager.IS_ACTIVE:
session = pyfusion.orm_manager.Session()
db_ods = session.query(BaseOrderedDataSet).first()
self.assertEqual(db_ods[0].channel_1.name, 'channel_03')
self.assertEqual(db_ods[1].channel_1.name, 'channel_01')
self.assertEqual(db_ods[2].channel_1.name, 'channel_02')
def test_channels_SQL(self):
test_coords = Coords('cylindrical',(0.0,0.0,0.0))
test_ch = Channel('test_1', test_coords)
test_ch.save()
if pyfusion.orm_manager.IS_ACTIVE:
session = pyfusion.orm_manager.Session()
our_channel = session.query(Channel).first()
self.assertEqual(our_channel.name, 'test_1')
def test_ORM_floatdelta(self):
""" check that floatdelta can be saved to database"""
channel_01 = Channel('channel_01', Coords('dummy', (0,0,0)))
channel_02 = Channel('channel_02', Coords('dummy', (0,0,0)))
fd = FloatDelta(channel_01, channel_02, 0.45)
fd.save()
if pyfusion.orm_manager.IS_ACTIVE:
session = pyfusion.orm_manager.Session()
db_fd = session.query(FloatDelta).first()
self.assertEqual(db_fd.delta, 0.45)
def do_fetch(self):
chan_name = (self.diag_name.split('-'))[-1] # remove -
filename_dict = {'diag_name': chan_name, 'shot': self.shot}
self.basename = path.join(pf.config.get('global', 'localdatapath'),
data_filename % filename_dict)
files_exist = path.exists(self.basename)
if not files_exist:
raise Exception, "file " + self.basename + " not found."
else:
signal_dict = newload(self.basename)
if ((chan_name == array(['MP5', 'HMP13', 'HMP05'])).any()): flip = -1.
else: flip = 1.
if self.diag_name[0] == '-': flip = -flip
# coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.diag_name, Coords('dummy', (0, 0, 0)))
output_data = TimeseriesData(
timebase=Timebase(signal_dict['timebase']),
signal=Signal(flip * signal_dict['signal']),
channels=ch)
output_data.meta.update({'shot': self.shot})
return output_data
def do_fetch(self):
output_data = super(H1DataFetcher, self).do_fetch()
coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.mds_path, coords)
output_data.channels = ch
output_data.meta.update({'shot': self.shot, 'kh': self.get_kh()})
return output_data
def test_timebase_and_coords(self):
n_ch = 10
n_samples = 1024
timebase = Timebase(np.arange(n_samples)*1.e-6)
channels = ChannelList(*(Channel('ch_%d' %i, Coords('cylindrical',(1.0,i,0.0))) for i in 2*np.pi*np.arange(n_ch)/n_ch))
multichannel_data = get_multimode_test_data(channels = channels,
timebase = timebase,
noise = 0.5)
def test_channellist_ORM(self):
ch01 = Channel('test_1', Coords('dummy', (0,0,0)))
ch02 = Channel('test_2', Coords('dummy', (0,0,0)))
ch03 = Channel('test_3', Coords('dummy', (0,0,0)))
new_cl = ChannelList(ch03, ch01, ch02)
new_cl.save()
# get our channellist
if pyfusion.orm_manager.IS_ACTIVE:
session = pyfusion.orm_manager.Session()
our_channellist = session.query(ChannelList).order_by("id").first()
self.assertEqual(our_channellist[0].name, 'test_3')
self.assertEqual(our_channellist[1].name, 'test_1')
self.assertEqual(our_channellist[2].name, 'test_2')
def test_remove_mean_single_channel(self):
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
# nonzero signal mean
tsd = TimeseriesData(timebase=tb,
signal=Signal(np.arange(len(tb))), channels=ChannelList(Channel('ch_01',Coords('dummy',(0,0,0)))))
filtered_tsd = tsd.subtract_mean()
assert_almost_equal(np.mean(filtered_tsd.signal), 0)
def do_fetch(self):
print(self.shot, self.mds_path)
output_data = super(DIIIDDataFetcher, self).do_fetch()
coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.mds_path, coords)
output_data.channels = ch
output_data.meta.update({'shot': self.shot})
output_data.config_name = ch
return output_data
def fetch_data_from_file(fetcher):
prm_dict = read_prm_file(fetcher.basename + ".prm")
bytes = int(prm_dict['DataLength(byte)'][0])
bits = int(prm_dict['Resolution(bit)'][0])
if not (prm_dict.has_key('ImageType')): #if so assume unsigned
bytes_per_sample = 2
dat_arr = Array.array('H')
offset = 2**(bits - 1)
dtype = np.dtype('uint16')
else:
if prm_dict['ImageType'][0] == 'INT16':
bytes_per_sample = 2
if prm_dict['BinaryCoding'][0] == 'offset_binary':
dat_arr = Array.array('H')
offset = 2**(bits - 1)
dtype = np.dtype('uint16')
elif prm_dict['BinaryCoding'][0] == "shifted_2's_complementary":
dat_arr = Array.array('h')
offset = 0
dtype = np.dtype('int16')
else:
raise NotImplementedError, ' binary coding ' + prm_dict[
'BinaryCoding']
fp = open(fetcher.basename + '.dat', 'rb')
dat_arr.fromfile(fp, bytes / bytes_per_sample)
fp.close()
clockHz = None
if prm_dict.has_key('SamplingClock'):
clockHz = double(prm_dict['SamplingClock'][0])
if prm_dict.has_key('SamplingInterval'):
clockHz = clockHz / double(prm_dict['SamplingInterval'][0])
if prm_dict.has_key('ClockSpeed'):
if clockHz != None:
pyfusion.utils.warn(
'Apparent duplication of clock speed information')
clockHz = double(prm_dict['ClockSpeed'][0])
clockHz = LHD_A14_clk(fetcher.shot) # see above
if clockHz != None:
timebase = arange(len(dat_arr)) / clockHz
else:
raise NotImplementedError, "timebase not recognised"
ch = Channel("%s-%s" % (fetcher.diag_name, fetcher.channel_number),
Coords('dummy', (0, 0, 0)))
if fetcher.gain != None:
gain = fetcher.gain
else:
gain = 1
output_data = TimeseriesData(timebase=Timebase(timebase),
signal=Signal(gain * dat_arr),
channels=ch)
output_data.meta.update({'shot': fetcher.shot})
return output_data
def test_dataset_filter_nocopy(self):
n_ch = 10
n_samples = 640
timebase = Timebase(np.arange(n_samples)*1.e-6)
channels = ChannelList(*(Channel('ch_%d' %i, Coords('cylindrical',(1.0,i,0.0))) for i in 2*np.pi*np.arange(n_ch)/n_ch))
multichannel_data = get_multimode_test_data(channels = channels,
timebase = timebase,
noise = 0.5)
dataset = multichannel_data.segment(64, copy=False)
new_dataset = dataset.segment(16, copy=False)
def do_fetch(self):
# TODO support non-signal datatypes
if self.fetch_mode == 'thin client':
ch = Channel(self.mds_path_components['nodepath'],
Coords('dummy', (0, 0, 0)))
data = self.acq.connection.get(
self.mds_path_components['nodepath'])
dim = self.acq.connection.get('dim_of(%s)' %
self.mds_path_components['nodepath'])
# TODO: fix this hack (same hack as when getting signal from node)
if len(data.shape) > 1:
data = np.array(data)[0, ]
if len(dim.shape) > 1:
dim = np.array(dim)[0, ]
output_data = TimeseriesData(timebase=Timebase(dim),
signal=Signal(data),
channels=ch)
output_data.meta.update({'shot': self.shot})
return output_data
elif self.fetch_mode == 'http':
data_url = self.acq.server + '/'.join([
self.mds_path_components['tree'],
str(self.shot), self.mds_path_components['tagname'],
self.mds_path_components['nodepath']
])
data = mdsweb.data_from_url(data_url)
ch = Channel(self.mds_path_components['nodepath'],
Coords('dummy', (0, 0, 0)))
t = Timebase(data.data.dim)
s = Signal(data.data.signal)
output_data = TimeseriesData(timebase=t, signal=s, channels=ch)
output_data.meta.update({'shot': self.shot})
return output_data
else:
node = self.tree.getNode(self.mds_path)
if int(node.dtype) == 195:
return get_tsd_from_node(self, node)
else:
raise Exception('Unsupported MDSplus node type')
def test_timeseries_filter_nocopy(self):
# Use reduce_time filter for testing...
n_ch = 10
n_samples = 5000
timebase = Timebase(np.arange(n_samples)*1.e-6)
channels = ChannelList(*(Channel('ch_%d' %i, Coords('cylindrical',(1.0,i,0.0))) for i in 2*np.pi*np.arange(n_ch)/n_ch))
multichannel_data = get_multimode_test_data(channels = channels,
timebase = timebase,
noise = 0.5)
new_data = multichannel_data.reduce_time([0,1.e-3], copy=False)
self.assertTrue(new_data is multichannel_data)
def fetch(self):
tb = generate_timebase(t0=float(self.t0),
n_samples=int(self.n_samples),
sample_freq=float(self.sample_freq))
sig = Signal(
float(self.amplitude) * sin(2 * pi * float(self.frequency) * tb))
dummy_channel = Channel('ch_01', Coords('dummy', (0, 0, 0)))
output_data = TimeseriesData(timebase=tb,
signal=sig,
channels=ChannelList(dummy_channel))
output_data.meta.update({'shot': self.shot})
return output_data
def test_remove_noncontiguous(self):
tb1 = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tb2 = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tb3 = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
# nonzero signal mean
tsd1 = TimeseriesData(timebase=tb1,
signal=Signal(np.arange(len(tb1))),
channels=ChannelList(
Channel('ch_01', Coords('dummy',
(0, 0, 0)))))
tsd2 = TimeseriesData(timebase=tb2,
signal=Signal(np.arange(len(tb2))),
channels=ChannelList(
Channel('ch_01', Coords('dummy',
(0, 0, 0)))))
tsd3 = TimeseriesData(timebase=tb3,
signal=Signal(np.arange(len(tb3))),
channels=ChannelList(
Channel('ch_01', Coords('dummy',
(0, 0, 0)))))
self.assertTrue(tb1.is_contiguous())
self.assertTrue(tb2.is_contiguous())
self.assertTrue(tb3.is_contiguous())
tsd2.timebase[-50:] += 1.0
self.assertFalse(tb2.is_contiguous())
ds = DataSet('ds')
for tsd in [tsd1, tsd2, tsd3]:
ds.add(tsd)
for tsd in [tsd1, tsd2, tsd3]:
self.assertTrue(tsd in ds)
filtered_ds = ds.remove_noncontiguous()
for tsd in [tsd1, tsd3]:
self.assertTrue(tsd in filtered_ds)
self.assertFalse(tsd2 in filtered_ds)
def get_probe_angles(input_data, closed=False):
"""
return a list of thetas for a given signal (timeseries) or a string that specifies it.
get_probe_angles('W7X:W7X_MIRNOV_41_BEST_LOOP:(20180912,43)')
This is a kludgey way to read coordinates. Should be through acquisition.base or
acquisition.'device' rather than looking up config directly
"""
import pyfusion
if isinstance(input_data, str):
pieces = input_data.split(':')
if len(pieces) == 3:
dev_name, diag_name, shotstr = pieces
shot_number = eval(shotstr)
dev = pyfusion.getDevice(dev_name)
data = dev.acq.getdata(shot_number, diag_name, time_range=[0, 0.1])
else:
from pyfusion.data.timeseries import TimeseriesData, Timebase, Signal
from pyfusion.data.base import Channel, ChannelList, Coords
input_data = TimeseriesData(Timebase([0, 1]), Signal([0, 1]))
dev_name, diag_name = pieces
# channels are amongst options
opts = pyfusion.config.pf_options('Diagnostic', diag_name)
chans = [
pyfusion.config.pf_get('Diagnostic', diag_name, opt)
for opt in opts if 'channel_' in opt
]
# for now, assume config_name is some as name
input_data.channels = ChannelList(
*[Channel(ch, Coords('?', [0, 0, 0])) for ch in chans])
Phi = np.array([
2 * np.pi / 360 * float(
pyfusion.config.get(
'Diagnostic:{cn}'.format(
cn=c.config_name if c.config_name != '' else c.name),
'Coords_reduced').split(',')[0]) for c in input_data.channels
])
Theta = np.array([
2 * np.pi / 360 * float(
pyfusion.config.get(
'Diagnostic:{cn}'.format(
cn=c.config_name if c.config_name != '' else c.name),
'Coords_reduced').split(',')[1]) for c in input_data.channels
])
if closed:
Phi = np.append(Phi, Phi[0])
Theta = np.append(Theta, Theta[0])
return (dict(Theta=Theta, Phi=Phi))
def do_fetch(self):
channel_length = int(self.length)
outdata = np.zeros(1024 * 2 * 256 + 1)
## !! really should put a wrapper around gethjdata to do common stuff
# outfile is only needed if the direct passing of binary won't work
# with tempfile.NamedTemporaryFile(prefix="pyfusion_") as outfile:
# get in two steps to make debugging easier
allrets = gethjdata.gethjdata(self.shot,
channel_length,
self.path,
verbose=VERBOSE,
opt=1,
ierror=2,
isample=-1,
outdata=outdata,
outname='')
ierror, isample, getrets = allrets
if ierror != 0:
raise LookupError(
'hj Okada style data not found for {s}:{c}'.format(
s=self.shot, c=self.path))
ch = Channel(self.path, Coords('dummy', (0, 0, 0)))
# the intent statement causes the out var to be returned in the result lsit
# looks like the time,data is interleaved in a 1x256 array
# it is fed in as real*64, but returns as real*32! (as per fortran decl)
debug_(pyfusion.DEBUG,
4,
key='Heliotron_fetch',
msg='after call to getdata')
# timebase in secs (ms in raw data) - could add a preferred unit?
# this is partly allowed for in savez_compressed, newload, and
# for plotting, in the config file.
# important that the 1e-3 be inside the Timebase()
output_data = TimeseriesData(timebase=Timebase(
1e-3 * getrets[1::2][0:isample]),
signal=Signal(getrets[2::2][0:isample]),
channels=ch)
output_data.meta.update({'shot': self.shot})
if pyfusion.VERBOSE > 0: print('HJ config name', self.config_name)
output_data.config_name = self.config_name
stprms = get_static_params(shot=self.shot, signal=self.path)
if len(list(stprms)
) == 0: # maybe this should be ignored - how do we use it?
raise LookupError(
' failure to get params for {shot}:{path}'.format(
shot=self.shot, path=self.path))
output_data.params = stprms
return output_data
def do_fetch(self):
output_data = super(H1DataFetcher, self).do_fetch()
coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.mds_path, coords)
output_data.channels = ch
#SH modified so that we can see different main currents for different field strengths
main_current, sec_current, kh = self.get_kh()
output_data.meta.update({
'shot': self.shot,
'kh': kh,
'main_current': main_current,
'sec_current': sec_current,
'heating_freq': self.get_heating_freq()
})
return output_data
def do_fetch(self):
channel_length = int(self.length)
outdata=np.zeros(1024*2*256+1)
with tempfile.NamedTemporaryFile(prefix="pyfusion_") as outfile:
getrets=gethjdata.gethjdata(self.shot,channel_length,self.path,
VERBOSE, OPT,
outfile.name, outdata)
ch = Channel(self.path,
Coords('dummy', (0,0,0)))
output_data = TimeseriesData(timebase=Timebase(getrets[1::2]),
signal=Signal(getrets[2::2]), channels=ch)
output_data.meta.update({'shot':self.shot})
return output_data
def do_fetch(self):
print(self.pointname)
print(self.shot)
if self.NC!=None:
print(self.NC)
t_name = '{}_time'.format(self.pointname)
NC_vars = self.NC.variables.keys()
if self.pointname in NC_vars:
print('Reading cache!!!!')
t_axis = self.NC.variables[t_name].data[:].copy()
data = self.NC.variables[self.pointname].data[:].copy()
else:
tmp = self.acq.connection.get('ptdata2("{}",{})'.format(self.pointname, self.shot))
data = tmp.data()
tmp = self.acq.connection.get('dim_of(ptdata2("{}",{}))'.format(self.pointname, self.shot))
t_axis = tmp.data()
self.write_cache = True
print(t_axis)
print(data)
coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.pointname, coords)
# con=MDS.Connection('atlas.gat.com::')
# pointname = 'MPI66M067D'
# shot = 164950
# tmp = con.get('ptdata2("{}",{})'.format(pointname, shot))
# dat = tmp.data()
# tmp = con.get('dim_of(ptdata2("{}",{}))'.format(pointname, shot))
# t = tmp.data()
if self.NC!=None and self.write_cache:
print self.pointname
self.NC.createDimension(t_name, len(t_axis))
f_time = self.NC.createVariable(t_name,'d',(t_name,))
f_time[:] = +t_axis
print('Wrote time')
sig = self.NC.createVariable(self.pointname,'f',(t_name,))
sig[:] = +data
print('Wrote signal')
output_data = TimeseriesData(timebase=Timebase(t_axis),
signal=Signal(data), channels=ch)
# output_data = super(DIIIDDataFetcherPTdata, self).do_fetch()
# coords = get_coords_for_channel(**self.__dict__)
# ch = Channel(self.mds_path, coords)
# output_data.channels = ch
# output_data.meta.update({'shot':self.shot, 'kh':self.get_kh()})
# print(ch)
output_data.config_name = ch
self.fetch_mode = 'ptdata'
return output_data
def test_svd_data(self):
n_ch = 10
n_samples = 1024
timebase = Timebase(np.arange(n_samples)*1.e-6)
channels = ChannelList(*(Channel('ch_%02d' %i, Coords('cylindrical',(1.0,i,0.0))) for i in 2*np.pi*np.arange(n_ch)/n_ch))
multichannel_data = get_multimode_test_data(channels = channels,
timebase = timebase,
noise = 0.5)
test_svd = multichannel_data.svd()
self.assertTrue(isinstance(test_svd, SVDData))
self.assertEqual(len(test_svd.topos[0]), n_ch)
self.assertEqual(len(test_svd.chronos[0]), n_samples)
assert_array_almost_equal(test_svd.chrono_labels, timebase)
for c_i, ch in enumerate(channels):
self.assertEqual(ch, test_svd.channels[c_i])
def get_tsd_from_node(fetcher, node):
"""Return pyfusion TimeSeriesData corresponding to an MDSplus signal node."""
# TODO: load actual coordinates
ch = Channel(fetcher.mds_path_components['nodepath'],
Coords('dummy', (0, 0, 0)))
signal = Signal(node.data())
dim = node.dim_of().data()
# TODO: stupid hack, the test signal has dim of [[...]], real data
# has [...]. Figure out why. (...probably because original signal
# uses a build_signal function)
if len(dim) == 1:
dim = dim[0]
timebase = Timebase(dim)
output_data = TimeseriesData(timebase=timebase, signal=signal, channels=ch)
output_data.meta.update({'shot': fetcher.shot})
return output_data
def test_flucstruc_phases(PfTestCase):
n_ch = 10
n_samples = 5000
timebase = Timebase(np.arange(n_samples)*1.e-6)
channels = ChannelList(*(Channel('ch_%d' %i, Coords('cylindrical',(1.0,i,0.0))) for i in 2*np.pi*np.arange(n_ch)/n_ch))
multichannel_data = get_multimode_test_data(channels = channels,
timebase = timebase,
noise = 0.5)
data_reduced_time=multichannel_data.reduce_time([0,0.002]).subtract_mean().normalise(method='v',separate=True)
fs_set=data_reduced_time.flucstruc()
phases = []
for fs in fs_set:
for j in range(0,len(fs.dphase)):
phases.append(fs.dphase[j].delta)
def do_fetch(self):
print("Shot: {}\nPoint Name: {}".format(self.shot, self.pointname))
if not hasattr(self, 'NC'):
self.NC = None
if self.NC is not None:
#print(self.NC)
t_name = '{}_time'.format(self.pointname)
NC_vars = self.NC.variables.keys()
else:
NC_vars = []
if self.pointname in NC_vars:
print(' Pointname in NC cache, Reading...\n')
t_axis = self.NC.variables[t_name].data[:].copy()
data = self.NC.variables[self.pointname].data[:].copy()
self.write_cache = False
else:
print(' Fetching from ptdata')
tmp = self.acq.connection.get('ptdata2("{}",{})'.format(
self.pointname, self.shot))
data = tmp.data()
tmp = self.acq.connection.get('dim_of(ptdata2("{}",{}))'.format(
self.pointname, self.shot))
t_axis = tmp.data()
self.write_cache = True
coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.pointname, coords)
if self.NC is not None and self.write_cache:
print("\t Writing to NC file disabled temporarily.")
#print(' Writing pointname to NC file\n')
#self.NC.createDimension(t_name, len(t_axis))
#f_time = self.NC.createVariable(t_name,'d',(t_name,))
#f_time[:] = +t_axis
# sig = self.NC.createVariable(self.pointname,'f',(t_name,))
#sig[:] = +data
output_data = TimeseriesData(timebase=Timebase(t_axis),
signal=Signal(data),
channels=ch)
output_data.config_name = ch
self.fetch_mode = 'ptdata'
return output_data
def do_fetch(self):
print self.shot, self.senal
data_dim = tjiidata.dimens(self.shot, self.senal)
if data_dim[0] < MAX_SIGNAL_LENGTH:
data_dict = tjiidata.lectur(self.shot, self.senal, data_dim[0],
data_dim[0], data_dim[1])
else:
raise ValueError, 'Not loading data to avoid segmentation fault in tjiidata.lectur'
ch = Channel(self.senal, Coords('dummy', (0, 0, 0)))
if self.invert == 'true': #yuk - TODO: use boolean type from config
s = Signal(-np.array(data_dict['y']))
else:
s = Signal(np.array(data_dict['y']))
output_data = TimeseriesData(timebase=Timebase(data_dict['x']),
signal=s,
channels=ch)
output_data.meta.update({'shot': self.shot})
return output_data
def do_fetch(self):
sig = self.conn.get(self.mds_path)
dim = self.conn.get('DIM_OF(' + self.mds_path + ')')
scl = 1.0
coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.config_name, coords)
timedata = dim.data()
output_data = TimeseriesData(timebase=Timebase(1e-9 * timedata),
signal=scl * Signal(sig),
channels=ch)
output_data.meta.update({'shot': self.shot})
if hasattr(self, 'mdsshot'): # intended for checks - not yet used.
output_data.mdsshot = self.mdsshot
output_data.config_name = self.config_name
output_data.utc = [timedata[0], timedata[-1]]
#output_data.units = dat['units'] if 'units' in dat else ''
debug_(pyfusion.DEBUG,
level=1,
key='W7M_do_fetch',
msg='entering W7X MDS do_fetch')
return (output_data)
def do_fetch(self):
chan_name = (self.diag_name.split('-'))[-1] # remove -
filename_dict = {'shot':self.shot, # goes with Boyd's local stg
'config_name':self.config_name}
#filename_dict = {'diag_name':self.diag_name, # goes with retrieve names
# 'channel_number':self.channel_number,
# 'shot':self.shot}
debug_(pf.DEBUG, 4, key='local_fetch')
for each_path in pf.config.get('global', 'localdatapath').split('+'):
self.basename = path.join(each_path, data_filename %filename_dict)
files_exist = path.exists(self.basename)
if files_exist: break
if not files_exist:
raise Exception("file {fn} not found. (localdatapath was {p})"
.format(fn=self.basename,
p=pf.config.get('global',
'localdatapath').split('+')))
else:
signal_dict = newload(self.basename)
if ((chan_name == array(['MP5','HMP13','HMP05'])).any()):
flip = -1.
print('flip')
else: flip = 1.
if self.diag_name[0]=='-': flip = -flip
# coords = get_coords_for_channel(**self.__dict__)
ch = Channel(self.diag_name, Coords('dummy', (0,0,0)))
output_data = TimeseriesData(timebase=Timebase(signal_dict['timebase']),
signal=Signal(flip*signal_dict['signal']), channels=ch)
# bdb - used "fetcher" instead of "self" in the "direct from LHD data" version
output_data.config_name = self.config_name # when using saved files, same as name
output_data.meta.update({'shot':self.shot})
return output_data
import bz2
from pyfusion.acquisition.base import BaseDataFetcher
from pyfusion.data.timeseries import Signal, Timebase, TimeseriesData
from pyfusion.data.base import Coords, Channel, ChannelList
import numpy as np
#####
# Generate generic channel with dummy coordinates.
generic_ch = lambda x: Channel('channel_%03d' %
(x + 1), Coords('dummy', (x, 0, 0)))
named_ch = lambda x: Channel(x, Coords('dummy', (x, 0, 0)))
class BinaryMultiChannelTimeseriesFetcher(BaseDataFetcher):
"""Fetch binary data from specified filename.
This data fetcher uses two configuration parameters, filename (required) and a dtype specification
The filename parameter can include a substitution string ``(shot)`` which will be replaced with the shot number.
dtype will be evaluated as string, numpy can be used with np namespace
e.g. np.float32
"""
def read_dtype(self):
dtype = self.__dict__.get("dtype", None)
return eval(dtype)
def get_n_channels(n_ch):
"""Return a list of n_ch channels."""
poloidal_coords = 2*np.pi*np.arange(n_ch)/n_ch
channel_gen = (Channel('ch_%02d' %i, Coords('cylindrical', (1.0,i,0.0)))
for i in poloidal_coords)
return ChannelList(*channel_gen)