def filterSignalsWithProbePhasing(dfProbe,dfSignal,plot=False,title='',
timeFWHM=timeFWHM):
import pandas as pd
import numpy as np
dfSignalOld=dfSignal.copy()
dfSignal=dfSignal.copy()
def ranges(nums):
nums = sorted(set(nums))
gaps = [[s, e] for s, e in zip(nums, nums[1:]) if s+1 < e]
edges = iter(nums[:1] + sum(gaps, []) + nums[-1:])
return list(zip(edges, edges))
# create Mask. Only keep data wher probe current is < 0. NAN elsewhere
dfMask=pd.DataFrame(dfSignal.copy().to_numpy(),columns=['Data'])
dfMask['time']=dfSignal.copy().index
dfMask.Data[dfProbe.iloc[:,0].to_numpy()>0]=np.nan
# add linear interpolations to fill-in NANs
a=np.isnan(dfMask.Data.to_numpy())
indexNan=dfMask[a].index
rangeNan=ranges(indexNan.to_numpy())
for i,r in enumerate(rangeNan):
if r[0]==0:
continue
if r[-1]==dfMask.shape[0]-1:
continue
dfMask.Data.iloc[(r[0]):(r[1]+1)]=np.interp(dfMask.iloc[(r[0]):(r[1]+1)].time.to_numpy(),
[dfMask.iloc[(r[0]-1)].time,dfMask.iloc[(r[1]+1)].time],
[dfMask.iloc[(r[0]-1)].Data,dfMask.iloc[(r[1]+1)].Data])
dfMask=dfMask.set_index('time')
dfMask=dfMask.dropna()
dfMask=_gaussianFilter_df(dfMask.copy(),timeFWHM=timeFWHM,plot=False,filterType='low')
dfSignal=filterDFByTime(dfSignal.copy(),dfMask.index[0],dfMask.index[-1])
dfResult=pd.DataFrame(dfSignal.values-dfMask.values,index=dfMask.index.to_numpy())
if plot==True:
fig,ax=_plt.subplots(3,sharex=True)
ax[0].plot(dfProbe.index*1e3,dfProbe,label='Probe\nCurrent')
ax[1].plot(dfSignalOld.index*1e3,dfSignalOld,label='Original')
ax[1].plot(dfMask.index*1e3,dfMask,label='Offset')
ax[2].plot(dfResult.index*1e3,dfResult,label='Result')
_plot.finalizeSubplot( ax[0],
ylabel='A',
title=title,
)
_plot.finalizeSubplot(ax[1],
)
_plot.finalizeSubplot(ax[2],
xlabel='Time (ms)',
)
_plot.finalizeFigure(fig,figSize=[6,4.5])
return dfResult
def makePlot():
"""
Plot all relevant plots
"""
fig, p1 = _plt.subplots()
p1.plot(time * 1e3, qStar, label=r'q$^*$')
p1.plot(time * 1e3, qStarCorrected, label=r'q$^* * 1.15$')
_plot.finalizeSubplot(p1,
xlabel='Time (ms)',
ylabel=r'q$^*$',
ylim=[1, 5])
_plot.finalizeFigure(fig, title='%s' % shotno)
def leastSquareModeAnalysis( df, angles, modeNumbers=[0,-1,-2], timeFWHM_phaseFilter=0.1e-3, plot=False, title=''): """ Parameters ---------- df : pandas.core.frame.DataFrame Dataframe with multiple columns associated with different angles index = time angles : numpy.ndarray array of angles associated with the columns in df mode numbers : list of ints mode numbers to be analyzed timeFWHM_phaseFilter : float timewidth associated with pre-frequency calculating low-pass filter Returns ------- dfResults : pandas.core.frame.DataFrame output Examples -------- :: import hbtepLib as hbt df,dfRaw,dfMeta=hbt.get.taData_df(100000) angles=dfMeta.Phi.values dfResults=leastSquareModeAnalysis( df*1e4, angles, [0,-1,-2]) dfResults.plot() """ # initialize n=len(angles) m=len(modeNumbers)*2 if 0 in modeNumbers: m-=1 # construct A matrix A=_np.zeros((n,m)) i=0 for mode in modeNumbers: if mode == 0: A[:,i]=1 i+=1 else: A[:,i]=_np.sin(mode*angles) A[:,i+1]=_np.cos(mode*angles) i+=2 Ainv=_np.linalg.pinv(A) # perform least squares analysis x=Ainv.dot(df.transpose().values) # calculate amplitudes, phases, frequencies, etc. dfResults=_pd.DataFrame(index=df.index) i=0 for mode in modeNumbers: if mode == 0: dfResults['0']=x[i,:] i+=1 else: dfResults['%dSin'%mode]=x[i,:] dfResults['%dCos'%mode]=x[i+1,:] dfResults['%sX'%mode]=1j*dfResults['%sSin'%mode]+dfResults['%dCos'%mode] dfResults['%sAmp'%mode]=_np.sqrt(dfResults['%dSin'%mode]**2+dfResults['%dCos'%mode]**2) dfResults['%sPhase'%mode]=_np.arctan2(dfResults['%dSin'%mode],dfResults['%dCos'%mode]) if type(timeFWHM_phaseFilter) != type(None): dfResults['%sPhaseFilt'%mode]=_gaussianFilter_df( _pd.DataFrame(_unwrapPhase(dfResults['%dPhase'%mode]),index=df.index), timeFWHM=timeFWHM_phaseFilter, plot=False, filterType='low') else: dfResults['%sPhaseFilt'%mode]=_unwrapPhase(dfResults['%dPhase'%mode]) dfResults['%sFreq'%mode]=_np.gradient(dfResults['%sPhaseFilt'%mode])/_np.gradient(df.index.to_numpy())/(_np.pi*2) i+=2 if plot==True: from johnspythonlibrary2.Process.Pandas import filterDFByColOrIndex import johnspythonlibrary2.Plot as _plot import matplotlib.pyplot as _plt from johnspythonlibrary2.Process.Misc import extractIntsFromStr fig,ax=_plt.subplots(3,sharex=True) dfTemp=filterDFByColOrIndex(dfResults,'Amp') for key,val in dfTemp.iteritems(): modeNum=extractIntsFromStr(key)[0] ax[0].plot(val.index*1e3,val,label=modeNum) _plot.finalizeSubplot(ax[0], ylabel='', subtitle='amp.', title='%s'%title) dfTemp=filterDFByColOrIndex(dfResults,'Phase') dfTemp=filterDFByColOrIndex(dfTemp,'Filt',invert=True) for key,val in dfTemp.iteritems(): modeNum=extractIntsFromStr(key)[0] ax[1].plot(val.index*1e3,val,'.',label=modeNum) _plot.finalizeSubplot(ax[1], ylabel='rad', subtitle='phase', # title='%s'%title, ) dfTemp=filterDFByColOrIndex(dfResults,'Freq') for key,val in dfTemp.iteritems(): modeNum=extractIntsFromStr(key)[0] ax[2].plot(val.index*1e3,val*1e-3,label=modeNum) _plot.finalizeSubplot(ax[2], ylabel='kHz', subtitle='Freq.', xlabel='Time (ms)' ) _plot.finalizeFigure(fig) return dfResults
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