def euvData(shotno=101393, tStart=_TSTART, tStop=_TSTOP, plot=False):
"""""
Get EUV fan array data
Parameters
----------
shotno : int
shot number of desired data
tStart : float
time (in seconds) to trim data before
default is 0 ms
tStop : float
time (in seconds) to trim data after
default is 10 ms
plot : bool
default is False
True - plots far array of all 11 (of 16) channels
'all' - plots
Example
-------
::
df=euvData(101393,plot=True)
""" ""
# subfunctions
@_backupDFs
def dfEUV(shotno, tStart, tStop, dfEUVMeta):
dfEUV = mdsData(shotno,
dfEUVMeta.addresses.to_list(),
tStart,
tStop,
columnNames=dfEUVMeta.index.to_list())
return dfEUV
# load meta data
sensor = 'EUV'
try:
directory = _path.dirname(_path.realpath(__file__))
dfMeta = _readOdsToDF('%s/listOfAllSensorsOnHBTEP.ods' % directory,
sensor).set_index('names')
except:
dfMeta = _readOdsToDF('listOfAllSensorsOnHBTEP.ods',
sensor).set_index('names')
# load raw data
df = dfEUV(shotno, tStart, tStop, dfMeta)
df = _filterDFByTime(df, tStart, tStop)
if plot == True:
df.plot()
return df
def magneticSensorData(shotno=98173,
tStart=_TSTART,
tStop=_TSTOP,
plot=False,
removeBadSensors=True,
sensor='TA',
timeFWHMSmoothing=0.4e-3,
forceDownload=False):
"""
Downloads magnetic sensor data. Presently, only poloidal
measurements as the radial sensors are not yet implemeted.
Parameters
----------
shotno : int
shot number of desired data
tStart : float
time (in seconds) to trim data before
default is 0 ms
tStop : float
time (in seconds) to trim data after
default is 10 ms
plot : bool
plots all relevant plots if true
default is False
removeBadSensors : bool
removes known bad sensors
sensor : str
sensor to evalualte. Must be in ['TA','PA1','PA2','FB']
timeFWHMSmoothing : float
Time constant associated with the offset subtraction filter
Returns
-------
dfRaw : pandas.core.frame.DataFrame
Raw magnetic data. Time is index.
dfSmoothed : pandas.core.frame.DataFrame
Offset subtracted magnetic data. Time is index.
dfMeta : pandas.core.frame.DataFrame
Meta data for the sensors
Notes
-----
other possible bad sensors: ['TA02_S1P','TA07_S3P','TA10_S3P']
The sensors that are bad are not necessarily consistent from shot to shot or year to year
Examples
--------
::
shotno=106000
a,b,c=magneticSensorData(shotno,sensor='TA',plot=True,tStart=1.5e-3,tStop=5.5e-3)
a,b,c=magneticSensorData(shotno,sensor='PA1',plot=True,tStart=1.5e-3,tStop=5.5e-3)
a,b,c=magneticSensorData(shotno,sensor='PA2',plot=True,tStart=1.5e-3,tStop=5.5e-3)
a,b,c=magneticSensorData(shotno,sensor='FB',plot=True,tStart=1.5e-3,tStop=5.5e-3,forceDownload=True)
"""
if sensor not in ['TA', 'PA1', 'PA2', 'FB']:
raise Exception('Bad sensor name')
# subfunctions
@_backupDFs
def dfTARaw(
shotno,
dfTAMeta,
tStart=_TSTART,
tStop=_TSTOP,
):
dfTARaw = mdsData(shotno,
dfTAMeta.addresses.to_list(),
tStart,
tStop,
columnNames=dfTAMeta.index.to_list())
return dfTARaw
@_backupDFs
def dfPA1Raw(shotno, dfPA1Meta, tStart=_TSTART, tStop=_TSTOP):
dfPA1Raw = mdsData(shotno,
dfPA1Meta.addresses.to_list(),
tStart,
tStop,
columnNames=dfPA1Meta.index.to_list())
return dfPA1Raw
@_backupDFs
def dfPA2Raw(shotno, dfPA2Meta, tStart=_TSTART, tStop=_TSTOP):
dfPA2Raw = mdsData(shotno,
dfPA2Meta.addresses.to_list(),
tStart,
tStop,
columnNames=dfPA2Meta.index.to_list())
return dfPA2Raw
@_backupDFs
def dfFBRaw(shotno, dfFBMeta, tStart=_TSTART, tStop=_TSTOP):
dfFBRaw = mdsData(shotno,
dfFBMeta.addresses.to_list(),
tStart,
tStop,
columnNames=dfFBMeta.index.to_list())
return dfFBRaw
# load meta data
try:
directory = _path.dirname(_path.realpath(__file__))
dfMeta = _readOdsToDF('%s/listOfAllSensorsOnHBTEP.ods' % directory,
sensor).set_index('names')
except:
dfMeta = _readOdsToDF('listOfAllSensorsOnHBTEP.ods',
sensor).set_index('names')
# load raw data
if sensor == 'TA':
dfRaw = dfTARaw(shotno, dfMeta, forceDownload=forceDownload)
elif sensor == 'PA1':
dfRaw = dfPA1Raw(shotno, dfMeta, forceDownload=forceDownload)
elif sensor == 'PA2':
dfRaw = dfPA2Raw(shotno, dfMeta, forceDownload=forceDownload)
elif sensor == 'FB':
dfRaw = dfFBRaw(shotno, dfMeta, forceDownload=forceDownload)
if removeBadSensors:
dfRaw = dfRaw.drop(columns=dfMeta[dfMeta.bad == True].index.to_list())
dfMeta = dfMeta.drop(dfMeta[dfMeta.bad == True].index.to_list())
dfRaw = _filterDFByTime(dfRaw, tStart, tStop)
# filter data
dfSmoothed = _gaussianFilter_df(dfRaw,
timeFWHM=timeFWHMSmoothing,
filterType='high',
plot=False)
# optional stripey plots
if plot == True:
if 'PA' in sensor:
angle = dfMeta.theta.values
dfSmoothed.columns = angle
dfSmoothed.index *= 1e3
fig, ax, cax = _plotHbt.stripeyPlot(
dfSmoothed * 1e4,
title='%d' % shotno,
poloidal=True,
subtitle=sensor,
xlabel='Time (ms)',
ylabel=r'Poloidal angle, $\theta$')
elif sensor == 'TA':
angle = dfMeta.phi.values
dfSmoothed.columns = angle
dfSmoothed.index *= 1e3
fig, ax, cax = _plotHbt.stripeyPlot(
dfSmoothed * 1e4,
title='%d' % shotno,
toroidal=True,
subtitle=sensor,
xlabel='Time (ms)',
ylabel=r'Toroidal angle, $\phi$')
else: #FB arrays
for s in ['S1P', 'S2P', 'S3P', 'S4P']:
dfTemp = _filterDFByColOrIndex(dfSmoothed, s)
dfTempMeta = _filterDFByColOrIndex(dfMeta, s, col=False)
angle = dfTempMeta.phi.values
dfTemp.columns = angle
dfTemp.index *= 1e3
fig, ax, cax = _plotHbt.stripeyPlot(
dfTemp * 1e4,
title='%d' % shotno,
toroidal=True,
subtitle='FB_' + s,
xlabel='Time (ms)',
ylabel=r'Toroidal angle, $\phi$')
return dfRaw, dfSmoothed, dfMeta
def solData(shotno=98030,
tStart=_TSTART,
tStop=_TSTOP,
plot=False,
timeFWHMSmoothing=0.4e-3):
"""
SOL tile sensor data
Parameters
----------
shotno : int
shot number of desired data
tStart : float
time (in seconds) to trim data before
default is 0 ms
tStop : float
time (in seconds) to trim data after
default is 10 ms
plot : bool
plots all relevant plots if true
default is False
timeFWHMSmoothing : float
Time constant associated with the offset subtraction filter
Returns
-------
dfRaw : pandas.core.frame.DataFrame
Raw magnetic data. Time is index.
dfSmoothed : pandas.core.frame.DataFrame
Offset subtracted magnetic data. Time is index.
dfMeta : pandas.core.frame.DataFrame
Meta data for the sensors
Examples
--------
::
a,b,c=solData(98173)
"""
# subfunctions
@_backupDFs
def dfSOLRaw(shotno, tStart, tStop, dfSOLMeta):
dfSOLRaw = mdsData(shotno,
dfSOLMeta.addresses.to_list(),
tStart,
tStop,
columnNames=dfSOLMeta.index.to_list())
return dfSOLRaw
# load meta data
sensor = 'SOL'
try:
directory = _path.dirname(_path.realpath(__file__))
dfMeta = _readOdsToDF('%s/listOfAllSensorsOnHBTEP.ods' % directory,
sensor).set_index('names')
except:
dfMeta = _readOdsToDF('listOfAllSensorsOnHBTEP.ods',
sensor).set_index('names')
# load raw data
dfRaw = dfSOLRaw(shotno, tStart, tStop, dfMeta)
dfRaw = _filterDFByTime(dfRaw, tStart, tStop)
# filter data
dfSmoothed = _gaussianFilter_df(dfRaw,
timeFWHM=timeFWHMSmoothing,
filterType='high',
plot=False)
# plot
if plot == True:
dfRaw.plot()
dfSmoothed.plot()
return dfRaw, dfSmoothed, dfMeta
def sxrData(shotno=98170,
tStart=_TSTART,
tStop=_TSTOP,
plot=False,
dropBadChannels=True,
forceDownload=False):
"""
Downloads (and optionally plots) soft xray sensor data.
Parameters
----------
shotno : int
shot number of desired data
tStart : float
time (in seconds) to trim data before
default is 0 ms
tStop : float
time (in seconds) to trim data after
default is 10 ms
plot : bool
default is False
dropBadChannels : bool
Drops bad channels
Notes
-----
Channels 5, 8, 10, 13 and 15 are considered "bad". These particular channels
are frequently "missing" from the tree, inlclude anamolous data, or their
signals are attenuated.
Example
-------
::
df=sxrData(106000,plot=True,tStart=1.5e-3,tStop=5.2e-3)
"""
# subfunctions
@_backupDFs
def dfSXR(shotno, dfSXRMeta):
dfSXR = mdsData(shotno,
dfSXRMeta.addresses.to_list(),
columnNames=dfSXRMeta.index.to_list())
return dfSXR
# load meta data
sensor = 'SXR'
try:
directory = _path.dirname(_path.realpath(__file__))
dfMeta = _readOdsToDF('%s/listOfAllSensorsOnHBTEP.ods' % directory,
sensor).set_index('names')
except:
dfMeta = _readOdsToDF('listOfAllSensorsOnHBTEP.ods',
sensor).set_index('names')
# load raw data
df = dfSXR(shotno, dfMeta, forceDownload=forceDownload)
# drop bad channels
if dropBadChannels == True:
badSensors = dfMeta[dfMeta.bad == True].index.to_list()
dfMeta = dfMeta.drop(badSensors, axis=0)
df = df.drop(columns=badSensors)
# trim time
df = _filterDFByTime(df, tStart, tStop)
# optional high-pass filter
dfHP = _gaussianFilter_df(df, timeFWHM=0.4e-3)
# optional plot
if plot == True:
if True: # raw data
fig, ax, cax = _plot.subplotsWithColormaps(2, sharex=True)
cax[0].remove()
for key, val in df.iteritems():
ax[0].plot(val, label=key)
_plot.finalizeSubplot(
ax[0],
title='%d, raw' % shotno,
)
temp = _np.copy(df.columns).astype(str)
columns = _np.array([
'%d' % int(temp[i][-2:]) for i in range(len(temp))
]).astype(float)
df2 = df.copy()
df2.columns = columns
fig, ax, _ = _plotHbt.stripeyPlot(
df2,
fig=fig,
ax=ax[1],
cax=cax[1],
# title='%d, raw'%shotno,
colorMap='magma_r',
zlim=[0, df.max().max()],
levels=_np.linspace(0,
df.max().max(), 41),
ylabel='Channel #')
_plot.finalizeFigure(fig)
if True: # filtered data
fig, ax, cax = _plot.subplotsWithColormaps(2, sharex=True)
cax[0].remove()
for key, val in dfHP.iteritems():
ax[0].plot(val, label=key)
_plot.finalizeSubplot(
ax[0],
title='%d, high-pass filtered' % shotno,
)
temp = _np.copy(dfHP.columns).astype(str)
columns = _np.array([
'%d' % int(temp[i][-2:]) for i in range(len(temp))
]).astype(float)
dfHP2 = dfHP.copy()
dfHP2.columns = columns
fig, ax, _ = _plotHbt.stripeyPlot(
dfHP2,
fig=fig,
ax=ax[1],
cax=cax[1],
# title='%d, high-pass filtered'%shotno,
ylabel='Channel #')
_plot.finalizeFigure(fig)
return df, dfHP
def quartzJumperAndGroundingBusData(shotno=96530,
tStart=_TSTART,
tStop=_TSTOP,
plot=False,
timeFWHMSmoothing=0.4e-3,
forceDownload=False):
"""
External rogowski data
Parameters
----------
shotno : int
shot number of desired data
tStart : float
time (in seconds) to trim data before
default is 0 ms
tStop : float
time (in seconds) to trim data after
default is 10 ms
plot : bool
plots all relevant plots if true
default is False
timeFWHMSmoothing : float
Time constant associated with the offset subtraction filter
Notes
-----
Rog. D is permanently off for the time being
Rog. C is permanently off for the time being
"""
# subfunctions
@_backupDFs
def dfJumperRaw(shotno, tStart, tStop, dfJumperMeta):
dfJumperRaw = mdsData(shotno,
dfJumperMeta.addresses.to_list(),
tStart,
tStop,
columnNames=dfJumperMeta.index.to_list())
return dfJumperRaw
# load meta data
sensor = 'Jumper'
try:
directory = _path.dirname(_path.realpath(__file__))
dfMeta = _readOdsToDF('%s/listOfAllSensorsOnHBTEP.ods' % directory,
sensor).set_index('names')
except:
dfMeta = _readOdsToDF('listOfAllSensorsOnHBTEP.ods',
sensor).set_index('names')
# load raw data
dfRaw = dfJumperRaw(shotno,
tStart,
tStop,
dfMeta,
forceDownload=forceDownload)
dfRaw['WestRackGround'] *= 100
dfRaw['NorthRackGround'] *= 100
dfRaw = _filterDFByTime(dfRaw, tStart, tStop)
# filter data
dfSmoothed = _gaussianFilter_df(dfRaw,
timeFWHM=timeFWHMSmoothing,
filterType='high',
plot=False)
# optional plot
if plot == True:
fig, ax = _plt.subplots(2, sharex=True)
for i, (key, val) in enumerate(dfRaw.iteritems()):
ax[0].plot(val.index * 1e3, val, label=key)
for i, (key, val) in enumerate(dfSmoothed.iteritems()):
ax[1].plot(val.index * 1e3, val, label=key)
_plot.finalizeSubplot(
ax[0],
# xlabel='Time',
ylabel='A',
subtitle='Raw',
title='%d' % shotno,
# ylim=[3,15],
# legendOn=False,
)
_plot.finalizeSubplot(
ax[1],
xlabel='Time',
ylabel='A',
subtitle='Smoothed',
)
return dfRaw, dfSmoothed, dfMeta