def correctradec(self, event, image):
""" correct X and Y and return to ra dec. """
self.newra = self.ra
self.newdec = self.dec
validtimeindex = np.where((self.TIME > float(event.tstart))
& (self.TIME < float(event.tstop)))
if self.debug:
print validtimeindex
print self.ra[validtimeindex]
x, y = image.imageutil.topixel(self.ra[validtimeindex],
self.dec[validtimeindex])
localtime = self.TIME[validtimeindex] - float(event.tstart)
""" correct differences """
dx = mlab.stineman_interp(localtime, event.rebinTIME, event.rebindX)
dy = mlab.stineman_interp(localtime, event.rebinTIME, event.rebindY)
newx = x + dx
newy = y + dy
print "x"
print newx, x, dx
print "y"
print newy, y, dy
""" after correction """
self.newra[validtimeindex], self.newdec[
validtimeindex] = image.imageutil.toworld(newx, newy)
self.newdec90 = 90.0 - self.newdec
def interpolate( ts, bins ):
yp = None
xi = numpy.linspace( 0, bins[-1], 200 )
yi = stineman_interp( xi, bins[:-1], ts, yp )
# interpolate upper and lower error bars to get envelope. doesn't work with log scale.
# COMMENTED OUT BELOW
# upper_yi = stineman_interp( xi, bins[:-1], upper, yp )
# lower_yi = stineman_interp( xi, bins[:-1], lower, yp )
return xi, yi
def plot_interp(x,y):
x = [float(x_step) for x_step in x]
yp = None
xi = linspace(x[0],x[-1],100);
yi = stineman_interp(xi,x,y,yp);
fig = figure()
ax = fig.add_subplot(111)
ax.plot(x,y,'r-')
show()
p_rf = 8*rf_drive_node.data()**2
p_rf_time = rf_drive_node.dim_of().data()
flow_1 = h1tree.getNode('.operations.magnetsupply.lcu.log:gas1_set').data()
flow_2 = h1tree.getNode('.operations.magnetsupply.lcu.log:gas2_set').data()
flow_3 = h1tree.getNode('.operations.magnetsupply.lcu.log:gas3_set').data()
good.append(sh)
except exception as reason:
bad.append(sh)
print('failed to open shot {sh}: {r} {a}'.format(sh=sh, r=reason, a=reason.args))
continue
w = np.where(sh == shot)[0]
times = t_mid[w]
for ne in 'ne_1,ne_4,ne_7'.split(',')[0:num_ne]:
valarr = stineman_interp(times, ne_time, eval(ne))
dd[ne][w] = valarr
if sweeping:
k_h = stineman_interp(times, k_h_time, k_h)
dd['k_h'][w] = k_h
dd['p_rf'][w] = stineman_interp(times, p_rf_time, p_rf)
for k in 'b_0,phd_rf,flow_1,flow_2,flow_3'.split(','):
dd[k][w] = eval(k)
print('{g} shots successful, {b} shots failed:'.format( g=len(good), b=len(bad)))
print(bad)
from matplotlib.pyplot import figure, show from numpy import pi, sin, linspace from matplotlib.mlab import stineman_interp x = linspace(0,2*pi,20); y = sin(x); yp = None xi = linspace(x[0],x[-1],100); yi = stineman_interp(xi,x,y,yp); fig = figure() ax = fig.add_subplot(111) ax.plot(x,y,'ro',xi,yi,'-b.') show()
from matplotlib.pyplot import figure, show from numpy import pi, sin, linspace from matplotlib.mlab import stineman_interp x = linspace(0, 2 * pi, 20) y = sin(x) yp = None xi = linspace(x[0], x[-1], 100) yi = stineman_interp(xi, x, y, yp) fig = figure() ax = fig.add_subplot(111) ax.plot(x, y, 'ro', xi, yi, '-b.') show()
def get_basic_params(diags=None, shot=54196, times=None, delay=None, debug=0):
""" return a list of np.arrays of normally numeric values for the
times given, for the given shot.
"""
global lhd_summary
if diags is None: diags = "<n_e19>,b_0,i_p,w_p,dw_pdt,dw_pdt2".split(',')
if delay is None: delay = get_delay(shot)
if times is None:
times = np.linspace(0,4,4000)
times = np.array(times)
vals = {}
# create an extra time array to allow a cross check
vals.update({'check_tm':times})
vals.update({'check_shot':np.zeros(len(times),dtype=np.int)+shot})
for diag in diags:
if diag not in file_info:
warn('diagnostic {0} not found in shot {1}'.format(diag, shot),stacklevel=2)
vals.update({diag: np.nan + times})
else:
info = file_info[diag]
varname = info['name']
if ':' in varname: (oper,varname) = varname.split(':')
else: oper = None
if info['format'].find('.csv') > 0:
try:
test=lhd_summary.keys()
except:
print('reloading {0}'.format(info['format']))
lhd_summary = read_csv_data(acq_LHD+info['format'], header=3)
val = lhd_summary[varname][shot]
valarr = np.double(val)+(times*0)
else:
try:
dg = igetfile(local_dir + info['format'], shot=shot)
except IOError:
try:
dg = igetfile(local_dir + info['format']+'.bz2', shot=shot)
except IOError:
try:
dg = igetfile(local_dir + info['format']+'.gz', shot=shot)
except exception:
#debug_(1)
dg=None
#break # give up and try next diagnostic
if dg is None: # messy - break doesn't do what I want?
valarr=None
else:
nd=dg.vardict['DimNo']
if nd != 1:
raise ValueError(
'Expecting a 1 D array in {0}, got {1}!'
.format(dg.filename, nd))
# pre re. w = np.where(np.array(dg.vardict['ValName'])==varname)[0]
matches = [re.match(varname,nam)
!= None for nam in dg.vardict['ValName']]
w = np.where(np.array(matches) != False)[0]
if len(w) != 1:
raise LookupError(
'Need just one instance of variable {0} in {1}'.
format(varname, dg.filename))
# get the column of the array corresponding to the name
valarr = dg.data[:,nd+w[0]]
tim = dg.data[:,0] - delay
if oper == 'ddt': # derivative operator
valarr = np.diff(valarr)/(np.average(np.diff(tim)))
tim = (tim[0:-1] + tim[1:])/2.0
if oper == 'ddt2': # abd(ddw)*derivative operator
dw = np.diff(valarr)/(np.average(np.diff(tim)))
ddw = np.diff(dw)/(np.average(np.diff(tim)))
tim = tim[2:]
valarr = 4e-6 * dw[1:] * np.abs(ddw)
valarr = (stineman_interp(times, tim, valarr))
w = np.where(times > max(tim))
valarr[w] = np.nan
if valarr != None: vals.update({diag: valarr})
debug_(max(pyfusion.DEBUG, debug), level=5, key='interp')
return(vals)
def get_basic_diagnostics(diags=None,
shot=54196,
times=None,
delay=None,
exception=False,
debug=0):
""" return a list of np.arrays of normally numeric values for the
times given, for the given shot.
Will access server if env('IGETFILE') points to an exe, else accesses cache
"""
global lhd_summary
# if no exception given and we are not debugging
# note - exception=None is a valid entry, meaning tolerate no exceptions
# so the "default" we use is False
if exception == False and debug == 0: exception = Exception
if diags is None: diags = "<n_e19>,b_0,i_p,w_p,dw_pdt,dw_pdt2".split(',')
if len(np.shape(diags)) == 0: diags = [diags]
if delay is None: delay = get_delay(shot)
if times is None:
times = np.linspace(0, 4, 4000)
times = np.array(times)
vals = {}
# create an extra time array to allow a cross check
vals.update({'check_tm': times})
vals.update({'check_shot': np.zeros(len(times), dtype=np.int) + shot})
for diag in diags:
if diag not in file_info:
warn('diagnostic {0} not found in shot {1}'.format(diag, shot),
stacklevel=2)
vals.update({diag: np.nan + times})
else:
info = file_info[diag]
varname = info['name']
subfolder = info['format'].split('@')[0]
filepath = os.path.sep.join(
[localigetfilepath, subfolder, info['format']])
if ':' in varname: (oper, varname) = varname.split(':')
else: oper = None
if info['format'].find('.csv') > 0:
try:
test = lhd_summary.keys()
except:
csvfilename = acq_LHD + '/' + info['format']
if pyfusion.DBG() > 1:
print('looking for lhd summary in' + csvfilename)
if not os.path.exists(csvfilename):
csvfilename += ".bz2"
print('reloading {0}'.format(csvfilename))
lhd_summary = read_csv_data(csvfilename, header=3)
# should make this more formal - last shots
# from an 'extra' file, and finally, from shot info
if shot > 117000: # fudge to get latest data
lhd_summary = np.load(acq_LHD +
'/LHD_summary.npz')['LHD'].tolist()
print('loading newer shots from a separate file - fix-me')
# val = lhd_summary[varname][shot-70000] # not needed
# else:
val = lhd_summary[varname][shot]
valarr = np.double(val) + (times * 0)
else:
debug_(max(pyfusion.DBG(), debug), level=4, key='find_data')
try:
dg = igetfile(filepath, shot=shot, debug=debug - 1)
except IOError:
try:
dg = igetfile(filepath + '.bz2',
shot=shot,
debug=debug - 1)
except IOError:
try:
dg = igetfile(filepath + '.gz',
shot=shot,
debug=debug - 1)
except exception as details:
if debug > 0:
print('diag at {fp} not found'.format(
fp=filepath))
print(details, details.args)
dg = None
#break # give up and try next diagnostic
if dg is None: # messy - break doesn't do what I want?
valarr = None
else:
nd = dg.vardict['DimNo']
if nd != 1:
raise ValueError(
'Expecting a 1 D array in {0}, got {1}!'.format(
dg.filename, nd))
# pre re. w = np.where(np.array(dg.vardict['ValName'])==varname)[0]
matches = [
re.match(varname, nam) != None
for nam in dg.vardict['ValName']
]
w = np.where(np.array(matches) != False)[0]
# get the column(s) of the array corresponding to the name
if (oper in 'sum,average,rms,max,min'.split(',')):
if oper == 'sum': op = np.sum
elif oper == 'average': op = np.average
elif oper == 'min': op = np.min
elif oper == 'std': op = np.std
else:
raise ValueError(
'operator {o} in {n} not known to get_basic_diagnostics'
.format(o=oper, n=info['name']))
valarr = op(dg.data[:, nd + w], 1)
else:
if len(w) != 1:
raise LookupError(
'Need just one instance of variable {0} in {1}'
.format(varname, dg.filename))
if len(np.shape(dg.data)) != 2:
raise LookupError(
'insufficient data for {0} in {1}'.format(
varname, dg.filename))
valarr = dg.data[:, nd + w[0]]
tim = dg.data[:, 0] - delay
if oper == 'ddt': # derivative operator
valarr = np.diff(valarr) / (np.average(np.diff(tim)))
tim = (tim[0:-1] + tim[1:]) / 2.0
if oper == 'ddt2': # abd(ddw)*derivative operator
dw = np.diff(valarr) / (np.average(np.diff(tim)))
ddw = np.diff(dw) / (np.average(np.diff(tim)))
tim = tim[2:]
valarr = 4e-6 * dw[1:] * np.abs(ddw)
if (len(tim) < 10) or (np.std(tim) < 0.1):
raise ValueError('Insufficient points or degenerate'
'timebase data in {0}, {1}'.format(
varname, dg.filename))
valarr = (stineman_interp(times, tim, valarr))
w = np.where(times > max(tim))
valarr[w] = np.nan
if valarr != None: vals.update({diag: valarr})
debug_(max(pyfusion.DBG(), debug), level=5, key='interp')
return (vals)
def get_basic_diagnostics(diags=None, shot=54196, times=None, delay=None, exception=False, debug=0):
""" return a list of np.arrays of normally numeric values for the
times given, for the given shot.
"""
global lhd_summary
# if no exception given and we are not debugging
# note - exception=None is a valid entry, meaning tolerate no exceptions
# so the "default" we use is False
if exception==False and debug==0: exception=Exception
if diags == None: diags = "<n_e19>,b_0,i_p,w_p,dw_pdt,dw_pdt2".split(',')
if len(np.shape(diags)) == 0: diags = [diags]
if delay == None: delay = get_delay(shot)
if times == None:
times = np.linspace(0,4,4000)
times = np.array(times)
vals = {}
# create an extra time array to allow a cross check
vals.update({'check_tm':times})
vals.update({'check_shot':np.zeros(len(times),dtype=np.int)+shot})
for diag in diags:
if not(file_info.has_key(diag)):
warn('diagnostic {0} not found in shot {1}'.format(diag, shot),stacklevel=2)
vals.update({diag: np.nan + times})
else:
info = file_info[diag]
varname = info['name']
subfolder = info['format'].split('@')[0]
filepath = os.path.sep.join([localigetfilepath,subfolder,info['format']])
if ':' in varname: (oper,varname) = varname.split(':')
else: oper = None
if info['format'].find('.csv') > 0:
try:
test=lhd_summary.keys()
except:
print('reloading {0}'.format(info['format']))
lhd_summary = read_csv_data(acq_LHD+'/'+info['format'], header=3)
val = lhd_summary[varname][shot]
valarr = np.double(val)+(times*0)
else:
debug_(max(pyfusion.DEBUG, debug), level=4, key='find_data')
try:
dg = igetfile(filepath, shot=shot, debug=debug-1)
except IOError:
try:
dg = igetfile(filepath+'.bz2', shot=shot, debug=debug-1)
except IOError:
try:
dg = igetfile(filepath + '.gz', shot=shot, debug=debug-1)
except exception:
if debug>0: print('diag at {fp} not found'
.format(fp=filepath))
dg=None
#break # give up and try next diagnostic
if dg==None: # messy - break doesn't do what I want?
valarr=None
else:
nd=dg.vardict['DimNo']
if nd != 1:
raise ValueError(
'Expecting a 1 D array in {0}, got {1}!'
.format(dg.filename, nd))
# pre re. w = np.where(np.array(dg.vardict['ValName'])==varname)[0]
matches = [re.match(varname,nam)
!= None for nam in dg.vardict['ValName']]
w = np.where(np.array(matches) != False)[0]
# get the column(s) of the array corresponding to the name
if (oper in 'sum,average,rms,max,min'.split(',')):
if oper=='sum': op = np.sum
elif oper=='average': op = np.average
elif oper=='min': op = np.min
elif oper=='std': op = np.std
else: raise ValueError('operator {o} in {n} not known to get_basic_diagnostics'
.format(o=oper, n=info['name']))
valarr = op(dg.data[:,nd+w],1)
else:
if len(w) != 1:
raise LookupError(
'Need just one instance of variable {0} in {1}'
.format(varname, dg.filename))
if len(np.shape(dg.data))!=2:
raise LookupError(
'insufficient data for {0} in {1}'
.format(varname, dg.filename))
valarr = dg.data[:,nd+w[0]]
tim = dg.data[:,0] - delay
if oper == 'ddt': # derivative operator
valarr = np.diff(valarr)/(np.average(np.diff(tim)))
tim = (tim[0:-1] + tim[1:])/2.0
if oper == 'ddt2': # abd(ddw)*derivative operator
dw = np.diff(valarr)/(np.average(np.diff(tim)))
ddw = np.diff(dw)/(np.average(np.diff(tim)))
tim = tim[2:]
valarr = 4e-6 * dw[1:] * np.abs(ddw)
if (len(tim) < 10) or (np.std(tim)<0.1):
raise ValueError('Insufficient points or degenerate'
'timebase data in {0}, {1}'
.format(varname, dg.filename))
valarr = (stineman_interp(times, tim, valarr))
w = np.where(times > max(tim))
valarr[w] = np.nan
if valarr != None: vals.update({diag: valarr})
debug_(max(pyfusion.DEBUG, debug), level=5, key='interp')
return(vals)
def get_basic_diagnostics(diags=None, file_info=file_info, shot=54196, times=None, delay=None, exception=False, debug=0):
""" return a list of np.arrays of normally numeric values for the
times given, for the given shot.
Will access server if env('IGETFILE') points to an exe, else accesses cache
This is the first version to specifically allow for access through pyfusion.cfg
There are two types of access:
I/ single diag on its own timebase
II/ the original multi diag on a given timebase (i.e. that from flucstrcs)
Stage 1 puts the file_info into .cfg file just for I/ single diag access.
Ideally the file_info for II/ sho;d be in .cfg also.
For stage I/, we call it with a file_info dict constructed on the spot
as a dictionary with one just entry (for diags[0]).
"""
global lhd_summary
# if no exception given and we are not debugging
# note - exception=None is a valid entry, meaning tolerate no exceptions
# so the "default" we use is False
if exception==False and debug==0: exception=Exception
if diags is None: diags = "<n_e19>,b_0,i_p,w_p,dw_pdt,dw_pdt2".split(',')
if len(np.shape(diags)) == 0: diags = [diags]
if delay is None: delay = get_delay(shot)
if times is None:
if len(diags)>1:
times = np.linspace(0,4,4000) # this is a crude guess.
# else leave it None
else:
# make sure it is an array
times = np.array(times)
vals = {}
for diag in diags:
if not(diag in file_info):
warn('diagnostic {0} not found in shot {1}'.format(diag, shot),stacklevel=2)
vals.update({diag: np.nan + times})
else:
info = file_info[diag]
varname = info['name'] # refers to name for igetfile - can contain ':'
subfolder = info['format'].split('@')[0]
filepath = os.path.sep.join([localigetfilepath,subfolder,info['format']])
if ':' in varname: (oper,varname) = varname.split(':')
else: oper = None
if info['format'].find('.csv') > 0:
try:
test=list(lhd_summary.keys())
except:
csvfilename = acq_LHD+'/'+info['format']
if pyfusion.DBG() > 1: print('looking for lhd summary in' + csvfilename)
if not os.path.exists(csvfilename):
csvfilename += ".bz2"
print('reloading {0}'.format(csvfilename))
lhd_summary = read_csv_data(csvfilename, header=3)
# should make this more formal - last shots
# from an 'extra' file, and finally, from shot info
if shot>117000: # fudge to get latest data
lhd_summary = np.load(acq_LHD+'/LHD_summary.npz')['LHD'].tolist()
print('loading newer shots from a separate file - fix-me')
# val = lhd_summary[varname][shot-70000] # not needed
# else:
val = lhd_summary[varname][shot]
valarr = np.double(val)+(times*0)
else:
try: # now igetfile checks for .gz etc
dg = igetfile(filepath, shot=shot, debug=debug-1)
except exception as details:
if debug>0: print('diag at {fp} not found'
.format(fp=filepath))
print(details,details.args)
dg=None
#break # give up and try next diagnostic
if dg is None: # messy - break doesn't do what I want?
valarr=None
else:
nd=dg.vardict['DimNo']
if nd != 1:
raise ValueError(
'Expecting a 1 D array in {0}, got {1}!'
.format(dg.filename, nd))
# pre re. w = np.where(np.array(dg.vardict['ValName'])==varname)[0]
matches = [re.match(varname,nam)
!= None for nam in dg.vardict['ValName']]
w = np.where(np.array(matches) != False)[0]
# get the column(s) of the array corresponding to the name
if (oper in 'sum,average,rms,max,min'.split(',')):
if oper=='sum': op = np.sum
elif oper=='average': op = np.average
elif oper=='min': op = np.min
elif oper=='std': op = np.std
else: raise ValueError('operator {o} in {n} not known to get_basic_diagnostics'
.format(o=oper, n=info['name']))
valarr = op(dg.data[:,nd+w],1)
else:
if len(w) != 1:
raise LookupError(
'Need just one instance of variable {0} in {1}'
.format(varname, dg.filename))
if len(np.shape(dg.data))!=2:
raise LookupError(
'insufficient data for {0} in {1}'
.format(varname, dg.filename))
valarr = dg.data[:,nd+w[0]]
tim = dg.data[:,0] - delay
if oper == 'ddt': # derivative operator
valarr = np.diff(valarr)/(np.average(np.diff(tim)))
tim = (tim[0:-1] + tim[1:])/2.0
if oper == 'ddt2': # abd(ddw)*derivative operator
dw = np.diff(valarr)/(np.average(np.diff(tim)))
ddw = np.diff(dw)/(np.average(np.diff(tim)))
tim = tim[2:]
valarr = 4e-6 * dw[1:] * np.abs(ddw)
if (len(tim) < 10) or (np.std(tim)<0.1):
raise ValueError('Insufficient points or degenerate'
'timebase data in {0}, {1}'
.format(varname, dg.filename))
if times is not None:
debug_(max(pyfusion.DEBUG, debug), level=5, key='interp')
valarr = (stineman_interp(times, tim, valarr))
w = np.where(times > max(tim))
valarr[w] = np.nan
else:
times = tim
if valarr is not None: vals.update({diag: valarr})
# create an extra time array to allow a cross check
vals.update({'check_tm':times})
vals.update({'check_shot':np.zeros(len(times),dtype=np.int)+shot})
return(vals)
def get_basic_diagnostics(diags=None, shot=54196, times=None, delay=None, exception=False, debug=0):
""" return a list of np.arrays of normally numeric values for the
times given, for the given shot.
Will access server if env('IGETFILE') points to an exe, else accesses cache
"""
global HJ_summary
# if no exception given and we are not debugging
# note - exception=None is a valid entry, meaning tolerate no exceptions
# so the "default" we use is False
if exception==False and debug==0: exception=Exception
if diags is None: diags = "<n_e19>,b_0,i_p,w_p,dw_pdt,dw_pdt2".split(',')
if len(np.shape(diags)) == 0: diags = [diags]
# LHD only if delay is None: delay = get_delay(shot)
if times is None:
times = np.linspace(0,4,4000)
times = np.array(times)
vals = {}
# create an extra time array to allow a cross check
vals.update({'check_tm':times})
vals.update({'check_shot':np.zeros(len(times),dtype=np.int)+shot})
debug_(pyfusion.DEBUG,2,key='get_basic')
for diag in diags:
if not(diag in file_info):
warn('diagnostic {0} not found in shot {1}'.format(diag, shot),stacklevel=2)
vals.update({diag: np.nan + times})
debug_(pyfusion.DEBUG,2,key='get_basic')
else:
info = file_info[diag]
varname = info['name']
infofmt = info['format']
subfolder = infofmt.split('@')[0]
filepath = os.path.sep.join([localigetfilepath,subfolder,infofmt])
if ':' in varname: (oper,varname) = varname.split(':')
else: oper = None
if '(' in varname:
try:
left,right = varname.split('(')
varname,rest=right.split(')')
except:
raise ValueError('in expression {v} - parens?'.format(varname))
if infofmt.find('.npz') > 0:
try:
test=HJ_summary.keys()
except:
csvfilename = acq_HJ+'/'+infofmt
if pyfusion.DBG() > 1: print('looking for HeliotronJ summary in' + csvfilename)
print('reloading {0}'.format(csvfilename))
HJ_summary = np.load(csvfilename)
val = HJ_summary[varname][shot]
valarr = np.double(val)+(times*0)
elif 'get_static_params' in infofmt:
pdicts = eval(infofmt.format(shot=shot))
if len(pdicts)==0:
print('empty dictionary returned')
val = pdicts[varname]
valarr = np.double(val)+(times*0)
else: # read signal from data system
debug_(max(pyfusion.DEBUG, debug), level=4, key='find_data')
try:
#get HJparams
channel = info['name']
outdata=np.zeros(1024*2*256+1)
channel_length =(len(outdata)-1)/2
# outdfile only needed for opt=1 (get data via temp file)
# with tempfile.NamedTemporaryFile(prefix="pyfusion_") as outdfile:
ierror, getrets=gethjdata.gethjdata(shot,channel_length,
info['name'],
verbose=VERBOSE, opt=1,
ierror=2,
outdata=outdata, outname='')
if ierror != 0:
raise LookupError('data not found for {s}:{c}'.format(s=shot, c=channel))
ch = Channel(info['name'], Coords('dummy', (0,0,0)))
# timebase in secs (was ms in raw data)
dg = TimeseriesData(timebase=Timebase(1e-3 * getrets[1::2]),
signal=Signal(getrets[2::2]), channels=ch)
except exception as reason:
if debug>0:
print('exception running gethjdata {r} {a}', format(r=reason, a=reason.args))
dg=None
#break # give up and try next diagnostic
if dg is None: # messy - break doesn't do what I want?
valarr=None
else:
nd = 1 # initially only deal with single channels (HJ)
# get the column(s) of the array corresponding to the name
w = [0]
if (oper in 'sum,average,rms,max,min'.split(',')):
if oper=='sum': op = np.sum
elif oper=='average': op = np.average
elif oper=='min': op = np.min
elif oper=='std': op = np.std
else: raise ValueError('operator {o} in {n} not known to get_basic_diagnostics'
.format(o=oper, n=info['name']))
# valarr = op(dg.data[:,nd+w],1)
valarr = op(dg.data[:,nd+w],1)
else:
if len(w) != 1:
raise LookupError(
'Need just one instance of variable {0} in {1}'
.format(varname, dg.filename))
dg.data = dg.signal # fudge compatibility
if len(np.shape(dg.data))!=1: # 2 for igetfile
raise LookupError(
'insufficient data for {0} in {1}'
.format(varname, dg.filename))
#valarr = dg.data[:,nd+w[0]]
#tim = dg.data[:,0] - delay
valarr = dg.signal
tim = dg.timebase
# fudge until we can gete the number of points
valarr = valarr[:np.argmax(tim)]
tim = tim[:np.argmax(tim)]
if oper == 'ddt': # derivative operator
valarr = np.diff(valarr)/(np.average(np.diff(tim)))
tim = (tim[0:-1] + tim[1:])/2.0
if oper == 'ddt2': # abd(ddw)*derivative operator
dw = np.diff(valarr)/(np.average(np.diff(tim)))
ddw = np.diff(dw)/(np.average(np.diff(tim)))
tim = tim[2:]
valarr = 4e-6 * dw[1:] * np.abs(ddw)
if (len(tim) < 10) or (np.std(tim)<0.1):
raise ValueError('Insufficient points or degenerate'
'timebase data in {0}, {1}'
.format(varname, dg.filename))
valarr = (stineman_interp(times, tim, valarr))
w = np.where(times > max(tim))
valarr[w] = np.nan
if valarr is not None: vals.update({diag: valarr})
debug_(max(pyfusion.DEBUG, debug), level=5, key='interp')
return(vals)
def get_basic_params(diags=None, shot=54196, times=None, delay=None, debug=0):
""" return a list of np.arrays of normally numeric values for the
times given, for the given shot.
"""
global lhd_summary
if diags is None: diags = "<n_e19>,b_0,i_p,w_p,dw_pdt,dw_pdt2".split(',')
if delay is None: delay = get_delay(shot)
if times is None:
times = np.linspace(0, 4, 4000)
times = np.array(times)
vals = {}
# create an extra time array to allow a cross check
vals.update({'check_tm': times})
vals.update({'check_shot': np.zeros(len(times), dtype=np.int) + shot})
for diag in diags:
if diag not in file_info:
warn('diagnostic {0} not found in shot {1}'.format(diag, shot),
stacklevel=2)
vals.update({diag: np.nan + times})
else:
info = file_info[diag]
varname = info['name']
if ':' in varname: (oper, varname) = varname.split(':')
else: oper = None
if info['format'].find('.csv') > 0:
try:
test = lhd_summary.keys()
except:
print('reloading {0}'.format(info['format']))
lhd_summary = read_csv_data(acq_LHD + info['format'],
header=3)
val = lhd_summary[varname][shot]
valarr = np.double(val) + (times * 0)
else:
try:
dg = igetfile(local_dir + info['format'], shot=shot)
except IOError:
try:
dg = igetfile(local_dir + info['format'] + '.bz2',
shot=shot)
except IOError:
try:
dg = igetfile(local_dir + info['format'] + '.gz',
shot=shot)
except exception:
#debug_(1)
dg = None
#break # give up and try next diagnostic
if dg is None: # messy - break doesn't do what I want?
valarr = None
else:
nd = dg.vardict['DimNo']
if nd != 1:
raise ValueError(
'Expecting a 1 D array in {0}, got {1}!'.format(
dg.filename, nd))
# pre re. w = np.where(np.array(dg.vardict['ValName'])==varname)[0]
matches = [
re.match(varname, nam) != None
for nam in dg.vardict['ValName']
]
w = np.where(np.array(matches) != False)[0]
if len(w) != 1:
raise LookupError(
'Need just one instance of variable {0} in {1}'.
format(varname, dg.filename))
# get the column of the array corresponding to the name
valarr = dg.data[:, nd + w[0]]
tim = dg.data[:, 0] - delay
if oper == 'ddt': # derivative operator
valarr = np.diff(valarr) / (np.average(np.diff(tim)))
tim = (tim[0:-1] + tim[1:]) / 2.0
if oper == 'ddt2': # abd(ddw)*derivative operator
dw = np.diff(valarr) / (np.average(np.diff(tim)))
ddw = np.diff(dw) / (np.average(np.diff(tim)))
tim = tim[2:]
valarr = 4e-6 * dw[1:] * np.abs(ddw)
valarr = (stineman_interp(times, tim, valarr))
w = np.where(times > max(tim))
valarr[w] = np.nan
if valarr != None: vals.update({diag: valarr})
debug_(max(pyfusion.DEBUG, debug), level=5, key='interp')
return (vals)
flow_2 = h1tree.getNode(
'.operations.magnetsupply.lcu.log:gas2_set').data()
flow_3 = h1tree.getNode(
'.operations.magnetsupply.lcu.log:gas3_set').data()
good.append(sh)
except exception as reason:
bad.append(sh)
print('failed to open shot {sh}: {r} {a}'.format(sh=sh,
r=reason,
a=reason.args))
continue
w = np.where(sh == shot)[0]
times = t_mid[w]
for ne in 'ne_1,ne_4,ne_7'.split(',')[0:num_ne]:
valarr = stineman_interp(times, ne_time, eval(ne))
dd[ne][w] = valarr
if sweeping:
k_h = stineman_interp(times, k_h_time, k_h)
dd['k_h'][w] = k_h
dd['p_rf'][w] = stineman_interp(times, p_rf_time, p_rf)
for k in 'b_0,phd_rf,flow_1,flow_2,flow_3'.split(','):
dd[k][w] = eval(k)
print('{g} shots successful, {b} shots failed:'.format(g=len(good),
b=len(bad)))
print(bad)
def get_basic_diagnostics(diags=None,
shot=54196,
times=None,
delay=None,
exception=False,
debug=0):
""" return a list of np.arrays of normally numeric values for the
times given, for the given shot.
Will access server if env('IGETFILE') points to an exe, else accesses cache
"""
global HJ_summary
# if no exception given and we are not debugging
# note - exception=None is a valid entry, meaning tolerate no exceptions
# so the "default" we use is False
if exception == False and debug == 0: exception = Exception
if diags is None: diags = "<n_e19>,b_0,i_p,w_p,dw_pdt,dw_pdt2".split(',')
if len(np.shape(diags)) == 0: diags = [diags]
# LHD only if delay is None: delay = get_delay(shot)
if times is None:
times = np.linspace(0, 4, 4000)
times = np.array(times)
vals = {}
# create an extra time array to allow a cross check
vals.update({'check_tm': times})
vals.update({'check_shot': np.zeros(len(times), dtype=np.int) + shot})
debug_(pyfusion.DEBUG, 2, key='get_basic')
for diag in diags:
if not (diag in file_info):
warn('diagnostic {0} not found in shot {1}'.format(diag, shot),
stacklevel=2)
vals.update({diag: np.nan + times})
debug_(pyfusion.DEBUG, 2, key='get_basic')
else:
info = file_info[diag]
varname = info['name']
infofmt = info['format']
subfolder = infofmt.split('@')[0]
filepath = os.path.sep.join(
[localigetfilepath, subfolder, infofmt])
if ':' in varname: (oper, varname) = varname.split(':')
else: oper = None
if '(' in varname:
try:
left, right = varname.split('(')
varname, rest = right.split(')')
except:
raise ValueError(
'in expression {v} - parens?'.format(varname))
if infofmt.find('.npz') > 0:
try:
test = HJ_summary.keys()
except:
csvfilename = acq_HJ + '/' + infofmt
if pyfusion.DBG() > 1:
print('looking for HeliotronJ summary in' +
csvfilename)
print('reloading {0}'.format(csvfilename))
HJ_summary = np.load(csvfilename)
val = HJ_summary[varname][shot]
valarr = np.double(val) + (times * 0)
elif 'get_static_params' in infofmt:
pdicts = eval(infofmt.format(shot=shot))
if len(pdicts) == 0:
print('empty dictionary returned')
val = pdicts[varname]
valarr = np.double(val) + (times * 0)
else: # read signal from data system
debug_(max(pyfusion.DEBUG, debug), level=4, key='find_data')
try:
#get HJparams
channel = info['name']
outdata = np.zeros(1024 * 2 * 256 + 1)
channel_length = (len(outdata) - 1) / 2
# outdfile only needed for opt=1 (get data via temp file)
# with tempfile.NamedTemporaryFile(prefix="pyfusion_") as outdfile:
ierror, getrets = gethjdata.gethjdata(shot,
channel_length,
info['name'],
verbose=VERBOSE,
opt=1,
ierror=2,
outdata=outdata,
outname='')
if ierror != 0:
raise LookupError('data not found for {s}:{c}'.format(
s=shot, c=channel))
ch = Channel(info['name'], Coords('dummy', (0, 0, 0)))
# timebase in secs (was ms in raw data)
dg = TimeseriesData(timebase=Timebase(1e-3 *
getrets[1::2]),
signal=Signal(getrets[2::2]),
channels=ch)
except exception as reason:
if debug > 0:
print('exception running gethjdata {r} {a}',
format(r=reason, a=reason.args))
dg = None
#break # give up and try next diagnostic
if dg is None: # messy - break doesn't do what I want?
valarr = None
else:
nd = 1 # initially only deal with single channels (HJ)
# get the column(s) of the array corresponding to the name
w = [0]
if (oper in 'sum,average,rms,max,min'.split(',')):
if oper == 'sum': op = np.sum
elif oper == 'average': op = np.average
elif oper == 'min': op = np.min
elif oper == 'std': op = np.std
else:
raise ValueError(
'operator {o} in {n} not known to get_basic_diagnostics'
.format(o=oper, n=info['name']))
# valarr = op(dg.data[:,nd+w],1)
valarr = op(dg.data[:, nd + w], 1)
else:
if len(w) != 1:
raise LookupError(
'Need just one instance of variable {0} in {1}'
.format(varname, dg.filename))
dg.data = dg.signal # fudge compatibility
if len(np.shape(dg.data)) != 1: # 2 for igetfile
raise LookupError(
'insufficient data for {0} in {1}'.format(
varname, dg.filename))
#valarr = dg.data[:,nd+w[0]]
#tim = dg.data[:,0] - delay
valarr = dg.signal
tim = dg.timebase
# fudge until we can gete the number of points
valarr = valarr[:np.argmax(tim)]
tim = tim[:np.argmax(tim)]
if oper == 'ddt': # derivative operator
valarr = np.diff(valarr) / (np.average(np.diff(tim)))
tim = (tim[0:-1] + tim[1:]) / 2.0
if oper == 'ddt2': # abd(ddw)*derivative operator
dw = np.diff(valarr) / (np.average(np.diff(tim)))
ddw = np.diff(dw) / (np.average(np.diff(tim)))
tim = tim[2:]
valarr = 4e-6 * dw[1:] * np.abs(ddw)
if (len(tim) < 10) or (np.std(tim) < 0.1):
raise ValueError('Insufficient points or degenerate'
'timebase data in {0}, {1}'.format(
varname, dg.filename))
valarr = (stineman_interp(times, tim, valarr))
w = np.where(times > max(tim))
valarr[w] = np.nan
if valarr is not None: vals.update({diag: valarr})
debug_(max(pyfusion.DEBUG, debug), level=5, key='interp')
return (vals)
def plot_hist_all( ts, nbins=50, transparent=True, norm_it=False, **kwargs ):
"""
ts -- dictionary with values as lifespan timeseries.
kwargs -- see pylab hist() function
Returns interpolation functions as well as histogram triple and figure instance.
"""
from pylab import log1p
from matplotlib.mlab import stineman_interp
#data = ts.values()
data = ts
fig = plt.figure( figsize=(12,8) )
if transparent:
fig.patch.set_alpha( 0.0 )
# ax = fig.add_subplot( 121 )
# ax2 = fig.add_subplot( 122 )
ax2 = fig.add_subplot( 111 )
# now plot a single cell's histogram on the first axis
#ax.hist( data, **kwargs )
# convert each list of lifespans to 1d arrays
data = [ numpy.asarray( d ) for d in data ]
xmax = max( [d.max() for d in data] )
thebins = numpy.linspace(0, xmax, nbins )
# add/change the kwargs 'bins' key
kwargs['bins'] = thebins
# holds bins counts (n) for each histogram
all_ny = []
for d in data:
n, bins, patches = ax2.hist( d, **kwargs )
all_ny.append( n )
# all_bins.append( bins )
# convert bins counts to a single array to find min
arr = numpy.array( all_ny ).ravel()
wy = arr[ numpy.where( arr != 0 )[0] ]
min_y = min( wy )
# for plotting average -- these are already log values if
# log==True in kwargs
yhist = numpy.array( all_ny, dtype=numpy.float64 )#.ravel()
avg = yhist.mean( axis=0 )
err = yhist.std( axis=0 )
upper = avg + err
lower = avg - err
# print "yhist", yhist
# print "avg", avg
# print ""
# print err
# print ""
# print lower
# print upper
# print ""
# average value for each histogram bin
for i, x in enumerate( avg ):
if x == 0.0:
avg[i] = 1.0
# label the axes
ax2.set_xlabel( 'Generator lifespan', fontsize=20 )
ax2.set_ylabel( 'Number of generators (log)', fontsize=20 )
xticks = ax2.get_xticks()
yticks = ax2.get_yticks()
ax2.set_xticklabels( [str(int(x)) for x in xticks], fontsize=20 )
ax2.set_yticklabels( [str(int(y)) for y in yticks], fontsize=20 )
# now plot the interpolated average distribution here so it is on
# top of the other stuff
yp = None
xi = numpy.linspace( 0, bins[-1],200)
yi = stineman_interp( xi, bins[:-1], avg, yp )
# interpolate upper and lower error bars to get envelope
# COMMENTED OUT BELOW
upper_yi = stineman_interp( xi, bins[:-1], upper, yp )
lower_yi = stineman_interp( xi, bins[:-1], lower, yp )
# make sure lower does not go negative since this makes no sense.
for i,v in enumerate( lower_yi ):
if v < 1.0:
lower_yi[i] = 1.0
# make sure that the plot doesn't get messed up by small values
# (esp. New cells)
masked_yi = numpy.ma.masked_less( yi, 1 )
# plot the interpolation of the avg and the envelope
ax2.plot( xi, masked_yi, 'r-', lw=3 )
# ax2.fill_between( xi, lower_yi, upper_yi, #where=masked_yi
# color='r', alpha=0.5, zorder=10 )
#fig.show()
if norm_it:
y_max = masked_yi.max()
print "y_max", y_max
masked_yi /= y_max
return xi, masked_yi, lower_yi, upper_yi, fig, (n, bins, patches)