def fetch(self):
"""Fetch each channel and combine into a multichannel instance
of :py:class:`~pyfusion.data.timeseries.TimeseriesData`.
:rtype: :py:class:`~pyfusion.data.timeseries.TimeseriesData`
"""
## initially, assume only single channel signals
# this base debug breakpoint will apply to all flavours of acquisition
debug_(pyfusion.DEBUG, level=2, key='base_multi_fetch')
ordered_channel_names = self.ordered_channel_names()
data_list = []
channels = ChannelList()
timebase = None
meta_dict={}
if hasattr(self, 't_min') and hasattr(self, 't_max'):
t_range = [float(self.t_min), float(self.t_max)]
else:
t_range = []
for chan in ordered_channel_names:
sgn = 1
if chan[0]=='-': sgn = -sgn
bare_chan = (chan.split('-'))[-1]
fetcher_class = import_setting('Diagnostic', bare_chan, 'data_fetcher')
tmp_data = fetcher_class(self.acq, self.shot,
config_name=bare_chan).fetch()
if len(t_range) == 2:
tmp_data = tmp_data.reduce_time(t_range)
channels.append(tmp_data.channels)
# two tricky things here - tmp.data.channels only gets one channel hhere
#config_name for a channel is attached to the multi part -
#we need to move it to the particular channel
# was channels[-1].config_name = chan
channels[-1].config_name = tmp_data.config_name
meta_dict.update(tmp_data.meta)
#print(tmp_data.signal[-1], sgn)
tmp_data.signal = sgn * tmp_data.signal
#print(tmp_data.signal[-1], sgn)
if timebase == None:
timebase = tmp_data.timebase
data_list.append(tmp_data.signal)
else:
if hasattr(self, 'skip_timebase_check') and self.skip_timebase_check == 'true':
data_list.append(tmp_data.signal)
else:
try:
assert_array_almost_equal(timebase, tmp_data.timebase)
data_list.append(tmp_data.signal)
except:
raise
signal=Signal(data_list)
output_data = TimeseriesData(signal=signal, timebase=timebase,
channels=channels)
#output_data.meta.update({'shot':self.shot})
output_data.meta.update(meta_dict)
return output_data
def get_acq_from_config(acq_name):
"""Return the acquisition class specified by `acq_class` in the
`[Acquisition:acq_name]` section of the configuration file.
:param acq_name: name of the acquisition configuration section.
:returns: acquisition class specified by `acq_class` in the \
`[Acquisition:acq_name]` section of the configuration file
"""
acq_class = import_setting('Acquisition', acq_name, "acq_class")
return acq_class
def get_acq_from_config(acq_name):
"""Return the acquisition class specified by `acq_class` in the
`[Acquisition:acq_name]` section of the configuration file.
:param acq_name: name of the acquisition configuration section.
:returns: acquisition class specified by `acq_class` in the \
`[Acquisition:acq_name]` section of the configuration file
"""
acq_class = import_setting('Acquisition', acq_name, "acq_class")
return acq_class
def fetch(self, interp_if_diff=True):
"""Fetch each channel and combine into a multichannel instance
of :py:class:`~pyfusion.data.timeseries.TimeseriesData`.
:rtype: :py:class:`~pyfusion.data.timeseries.TimeseriesData`
"""
## initially, assume only single channel signals
ordered_channel_names = self.ordered_channel_names()
data_list = []
channels = ChannelList()
timebase = None
meta_dict = {}
#from scipy.io import netcdf
#home = os.environ['HOME']
#os.system('mkdir -p {}/tmp_pyfusion/'.format(home))
#fname = '{}/tmp_pyfusion/{}.nc'.format(home,self.shot)
#if os.path.exists(fname):
# NC = netcdf.netcdf_file(fname,'r',version=2)
#else:
# NC = netcdf.netcdf_file(fname,'w',version=2)
for chan in ordered_channel_names:
fetcher_class = import_setting('Diagnostic', chan, 'data_fetcher')
tmp_data = fetcher_class(self.acq, self.shot,
config_name=chan).fetch()
channels.append(tmp_data.channels)
meta_dict.update(tmp_data.meta)
if timebase is None:
timebase = tmp_data.timebase
data_list.append(tmp_data.signal)
else:
try:
assert_array_almost_equal(timebase, tmp_data.timebase)
data_list.append(tmp_data.signal)
except:
if interp_if_diff:
data_list.append(
np.interp(timebase, tmp_data.timebase,
tmp_data.signal))
else:
raise
#NC.close()
signal = Signal(data_list)
output_data = TimeseriesData(signal=signal,
timebase=timebase,
channels=channels)
#output_data.meta.update({'shot':self.shot})
output_data.meta.update(meta_dict)
return output_data
def fetch(self, interp_if_diff = True):
"""Fetch each channel and combine into a multichannel instance
of :py:class:`~pyfusion.data.timeseries.TimeseriesData`.
:rtype: :py:class:`~pyfusion.data.timeseries.TimeseriesData`
"""
print('******** hello world ***********')
## initially, assume only single channel signals
ordered_channel_names = self.ordered_channel_names()
data_list = []
channels = ChannelList()
timebase = None
meta_dict={}
from scipy.io import netcdf
fname = '/u/haskeysr/tmp/{}.nc'.format(self.shot)
write_cache=False; read_cache=False
if os.path.exists(fname):
NC = netcdf.netcdf_file(fname,'a',version=2)
else:
NC = netcdf.netcdf_file(fname,'w',version=2)
for chan in ordered_channel_names:
fetcher_class = import_setting('Diagnostic', chan, 'data_fetcher')
tmp_data = fetcher_class(self.acq, self.shot,
config_name=chan, NC=NC).fetch()
channels.append(tmp_data.channels)
meta_dict.update(tmp_data.meta)
if timebase == None:
timebase = tmp_data.timebase
data_list.append(tmp_data.signal)
else:
try:
assert_array_almost_equal(timebase, tmp_data.timebase)
data_list.append(tmp_data.signal)
except:
if interp_if_diff:
data_list.append(np.interp(timebase, tmp_data.timebase, tmp_data.signal))
else:
raise
NC.close()
signal=Signal(data_list)
output_data = TimeseriesData(signal=signal, timebase=timebase,
channels=channels)
#output_data.meta.update({'shot':self.shot})
output_data.meta.update(meta_dict)
return output_data
def fetch(self, interp_if_diff=True):
"""Fetch each channel and combine into a multichannel instance
of :py:class:`~pyfusion.data.timeseries.TimeseriesData`.
:rtype: :py:class:`~pyfusion.data.timeseries.TimeseriesData`
"""
## initially, assume only single channel signals
ordered_channel_names = self.ordered_channel_names()
data_list = []
channels = ChannelList()
timebase = None
meta_dict = {}
for chan in ordered_channel_names:
fetcher_class = import_setting('Diagnostic', chan, 'data_fetcher')
tmp_data = fetcher_class(self.acq, self.shot,
config_name=chan).fetch()
channels.append(tmp_data.channels)
meta_dict.update(tmp_data.meta)
if timebase == None:
timebase = tmp_data.timebase
data_list.append(tmp_data.signal)
else:
try:
assert_array_almost_equal(timebase, tmp_data.timebase)
data_list.append(tmp_data.signal)
except:
if interp_if_diff:
data_list.append(
np.interp(timebase, tmp_data.timebase,
tmp_data.signal))
else:
raise
signal = Signal(data_list)
output_data = TimeseriesData(signal=signal,
timebase=timebase,
channels=channels)
#output_data.meta.update({'shot':self.shot})
output_data.meta.update(meta_dict)
return output_data
def test_import_setting_with_fakedata_acquisition(self):
from pyfusion.conf.utils import import_setting
acq_from_config = import_setting('Acquisition', 'test_fakedata',
'acq_class')
from pyfusion.acquisition.FakeData.acq import FakeDataAcquisition
self.assertEqual(acq_from_config, FakeDataAcquisition)
def fetch(self):
"""Fetch each channel and combine into a multichannel instance
of :py:class:`~pyfusion.data.timeseries.TimeseriesData`.
:rtype: :py:class:`~pyfusion.data.timeseries.TimeseriesData`
"""
## initially, assume only single channel signals
# this base debug breakpoint will apply to all flavours of acquisition
debug_(pyfusion.DEBUG, level=2, key='base_multi_fetch')
ordered_channel_names = self.ordered_channel_names()
data_list = []
channels = ChannelList()
timebase = None
meta_dict = {}
if hasattr(self, 't_min') and hasattr(self, 't_max'):
t_range = [float(self.t_min), float(self.t_max)]
else:
t_range = []
for chan in ordered_channel_names:
sgn = 1
if chan[0] == '-': sgn = -sgn
bare_chan = (chan.split('-'))[-1]
fetcher_class = import_setting('Diagnostic', bare_chan,
'data_fetcher')
tmp_data = fetcher_class(self.acq,
self.shot,
config_name=bare_chan).fetch()
if len(t_range) == 2:
tmp_data = tmp_data.reduce_time(t_range)
channels.append(tmp_data.channels)
# two tricky things here - tmp.data.channels only gets one channel hhere
#config_name for a channel is attached to the multi part -
#we need to move it to the particular channel
# was channels[-1].config_name = chan
channels[-1].config_name = tmp_data.config_name
meta_dict.update(tmp_data.meta)
#print(tmp_data.signal[-1], sgn)
tmp_data.signal = sgn * tmp_data.signal
#print(tmp_data.signal[-1], sgn)
if timebase == None:
timebase = tmp_data.timebase
data_list.append(tmp_data.signal)
else:
if hasattr(self, 'skip_timebase_check'
) and self.skip_timebase_check == 'true':
data_list.append(tmp_data.signal)
else:
try:
assert_array_almost_equal(timebase, tmp_data.timebase)
data_list.append(tmp_data.signal)
except:
raise
signal = Signal(data_list)
output_data = TimeseriesData(signal=signal,
timebase=timebase,
channels=channels)
#output_data.meta.update({'shot':self.shot})
output_data.meta.update(meta_dict)
return output_data
def test_import_setting_with_fakedata_acquisition(self):
from pyfusion.conf.utils import import_setting
acq_from_config = import_setting('Acquisition',
'test_fakedata', 'acq_class')
from pyfusion.acquisition.FakeData.acq import FakeDataAcquisition
self.assertEqual(acq_from_config, FakeDataAcquisition)
