def test_cpsd():
plt.close('all')
print()
print(
'>>>>>>>>>>>>>>>>>>> Test cpsd (Cross Spectral Power Density) <<<<<<<<<<<<<<<<<<<<<<<<'
)
flap.delete_data_object('*')
print("**** Generating 8 random data, 1 million points each.")
d = flap.get_data('TESTDATA',
name='TEST-1-[1-8]',
options={
'Signal': 'Random',
'Length': 1
},
object_name='TESTDATA')
print("**** Calculating all cpsd")
flap.cpsd('TESTDATA',
options={
'Norm': True,
'Interval': 50,
'Log': True,
'Res': 10,
'Range': [100, 1e5]
},
output_name='TESTDATA_cpsd')
flap.abs_value('TESTDATA_cpsd', output_name='TESTDATA_cpsd_abs')
print(
"**** Plotting coherency between channels 1-2 and its significance level."
)
plt.figure()
flap.plot('TESTDATA_cpsd_abs',
axes='Frequency',
slicing={
'Row (Ref)': 1,
'Row': 2
},
options={
'Log y': True,
'Log x': True,
'Error': False
})
flap.error_value('TESTDATA_cpsd_abs').plot(slicing={
'Row (Ref)': 1,
'Row': 2
})
plt.figure()
print(
"**** Plotting mean coherence in 1e4-1e5 frequency range as a function of row index."
)
flap.slice_data('TESTDATA_cpsd_abs',
slicing={
'Frequency': flap.Intervals(1e4, 1e5)
},
summing={
'Frequency': 'Mean'
}).plot(axes='Row (Ref)', options={'Y sep': 1.5})
def test_get_data():
try:
flap.get_data('CAMERA_APSD')
apsd_available = True
except:
apsd_available = False
if (not apsd_available):
try:
# flap.get_data('W7X_CAMERA',exp_id="20181018.032", name="AEQ20_EDICAM_ROIP1", coordinates={'Time':[3,4]}, object_name="EDI_ROIP1")
flap.get_data('W7X_CAMERA',
exp_id="20181018.012",
name="AEQ21_PHOTRON_ROIP1",
coordinates={'Time':[6.05,6.25]},
no_data=False,
object_name="CAMERA")
except Exception as e:
raise e
flap.list_data_objects()
# flap.plot("CAMERA",plot_type='anim-image',axes=['Image y','Image x','Time'],options={'Wait':0.01,'Clear':True})
print("Slicing start")
flap.slice_data('CAMERA',
slicing={'Image x':flap.Intervals(0,4,step=5),'Image y':flap.Intervals(0,4,step=5)},
summing={'Interval(Image x) sample index':'Mean','Interval(Image y) sample index':'Mean'},
output_name='CAMERA_sliced')
print("Slicing stop")
# flap.plot("CAMERA_sliced",
# plot_type='anim-image',
# axes=['Start Image y in int(Image y)','Start Image x in int(Image x)','Time'],
# options={'Wait':0.01,'Clear':True, 'Z range':[0,3000]})
print("*** APSD start")
start = time.time()
flap.apsd("CAMERA_sliced",coordinate='Time',options={'Res':200,'Range':[0,1e4]},output_name='CAMERA_APSD')
stop = time.time()
print('**** APSD STOP')
print("**** Calculation time: {:5.2f} second".format(stop-start))
plt.close('all')
# flap.plot('CAMERA_APSD',
# slicing={'Start Image y in int(Image y)':50},
# plot_type='image',
# axes=['Frequency','Start Image x in int(Image x)'],
# options={'Z range':[0,5],'Aspect':'auto'})
# plt.figure()
return
flap.plot('CAMERA_APSD',
plot_type='anim-image',
axes=['Frequency','Start Image x in int(Image x)','Start Image y in int(Image y)'],
options={'Z range':[0,5],'Aspect':'auto','Wait':0.1})
flap.list_data_objects()
def test_binning():
print()
print()
print('>>>>>>> Test image binning through multi-slice<<<<<<<<<<<')
print("**** Generating a sequence of test images")
flap.get_data('TESTDATA',
name='VIDEO',
object_name='TEST_VIDEO',
options={
'Length': 0.05,
'Samplerate': 1e3,
'Width': 500,
'Height': 800,
'Image': 'Gauss',
'Spotsize': 10
})
print("***** Showing one image")
plt.figure()
flap.plot('TEST_VIDEO',
slicing={'Time': 30e-3 / 3},
plot_type='image',
axes=['Image x', 'Image y'],
options={
'Clear': True,
'Interpolation': None,
'Aspect': 'equal'
})
flap.slice_data('TEST_VIDEO',
slicing={
'Image x': flap.Intervals(0, 4, step=5),
'Image y': flap.Intervals(0, 9, step=10)
},
summing={
'Interval(Image x) sample index': 'Mean',
'Interval(Image y) sample index': 'Mean'
},
output_name='TEST_VIDEO_binned')
print("***** Showing one image of the (5,10) binned video ")
plt.figure()
flap.plot('TEST_VIDEO_binned',
slicing={'Time': 30e-3 / 3},
plot_type='image',
axes=['Image x', 'Image y'],
options={
'Clear': True,
'Interpolation': None,
'Aspect': 'equal'
})
flap.list_data_objects()
def test_select_multislice():
plt.close('all')
print()
print(
'>>>>>>>>>>>>>>>>>>> Test select on maxima and multi slice <<<<<<<<<<<<<<<<<<<<<<<<'
)
flap.delete_data_object('*')
d = flap.get_data('TESTDATA',
name='TEST-1-1',
object_name='TEST-1-1',
options={'Length': 0.050})
print(
"**** Selecting 100 microsec long intervals around the maxima of the signal."
)
d_int = flap.select_intervals('TEST-1-1',
coordinate='Time',
options={
'Select': None,
'Length': 0.0001,
'Event': {
'Type': 'Max-weight',
'Threshold': 1,
'Thr-type': 'Sigma'
}
},
plot_options={'All points': True},
output_name='SELECT')
flap.list_data_objects()
d_int.plot(axes=['__Data__', 0],
plot_type='scatter',
options={'Force': True})
if (d_int is not None):
print("**** Overplotting the signals in the selected intervals.")
flap.slice_data('TEST-1-1',
slicing={'Time': d_int},
output_name='TEST-1-1_sliced')
flap.list_data_objects()
plt.figure()
n_int = d_int.shape[0]
for i in range(n_int):
flap.plot('TEST-1-1_sliced',
slicing={'Interval(Time)': i},
axes='Rel. Time in int(Time)')
def test_simple_slice():
print()
print("\n>>>>>>>>>>>>>>>>>>> Test simple slice <<<<<<<<<<<<<<<<<<<<<<<<")
flap.delete_data_object('*')
print("**** Reading all test signals for time range [0,0.001]")
d = flap.get_data('TESTDATA',
name='TEST-*',
options={'Scaling': 'Volt'},
object_name='TESTDATA',
coordinates={'Time': [0, 0.001]})
print("**** Adding Device coordinates")
flap.add_coordinate('TESTDATA',
coordinates=['Device x', 'Device z', 'Device y'])
print("**** Storage contents before slice")
flap.list_data_objects()
print("**** Slicing with {'Signal name': 'TEST-1-*'}")
flap.slice_data('TESTDATA',
slicing={'Signal name': 'TEST-1-*'},
output_name='TESTDATA_slice')
print("**** Sliced object")
flap.list_data_objects(name='TESTDATA_slice')
def test_resample():
plt.close('all')
print()
print(">>>>>>>>>>>>> Test signal resampling (interpolation) <<<<<<<<<<<")
flap.delete_data_object('*')
print(
"**** Generating two test signals with different sampling frequency.")
flap.get_data('TESTDATA',
name='TEST-1-1',
options={
'Scaling': 'Volt',
'Frequency': 1e3,
'Samplerate': 1e6
},
object_name='TEST-1MHz',
coordinates={'Time': [0, 0.001]})
flap.get_data('TESTDATA',
name='TEST-1-1',
options={
'Scaling': 'Volt',
'Frequency': 1.5e3,
'Samplerate': 3e6
},
object_name='TEST-3MHz',
coordinates={'Time': [0, 0.001]})
print("\n***** Resampling from lower to higher frequency.")
plt.figure()
flap.plot('TEST-1MHz', axes='Time', plot_options={'marker': 'o'})
flap.plot('TEST-3MHz', plot_options={'marker': 'o'})
flap.slice_data('TEST-1MHz',
slicing={'Time': flap.get_data_object('TEST-3MHz')},
options={'Interpol': 'Linear'},
output_name='TEST-1MHz_resample')
flap.plot('TEST-1MHz_resample', plot_options={'marker': 'x'})
print("\n***** Resampling from higher to lower frequency.")
plt.figure()
flap.plot('TEST-1MHz', axes='Time', plot_options={'marker': 'o'})
flap.plot('TEST-3MHz', plot_options={'marker': 'o'})
flap.slice_data('TEST-3MHz',
slicing={'Time': flap.get_data_object('TEST-1MHz')},
options={'Interpol': 'Linear'},
output_name='TEST-3MHz_resample')
flap.plot('TEST-3MHz_resample', plot_options={'marker': 'x'})
print("\n***** Cutting parts.")
plt.figure()
flap.slice_data(
'TEST-1MHz',
slicing={'Time': flap.Intervals([1e-4, 5e-4], [2e-4, 7e-4])},
options={'Slice': 'Simple'},
output_name='TEST-1MHz_parts')
flap.plot('TEST-1MHz_parts', axes='Time', plot_options={'marker': 'o'})
flap.list_data_objects()
def test_apsd():
plt.close('all')
print()
print(
'>>>>>>>>>>>>>>>>>>> Test apsd (Auto Power Spectral Density) <<<<<<<<<<<<<<<<<<<<<<<<'
)
flap.delete_data_object('*')
#plt.close('all')
print(
'**** Generating test signals with frequency changing from channel to channel.'
)
d = flap.get_data('TESTDATA',
name='TEST*',
object_name='TEST-1_S',
options={
'Signal': 'Sin',
'F': [1e3, 1e4],
'Length': 1.
})
print('**** Calculating 150 APSDs, each 1 million sample.')
print('**** APSD START')
start = time.time()
flap.apsd('TEST-1_S',
output_name='TEST-1_APSD_Sin1',
options={
'Res': 12,
'Int': 10
})
stop = time.time()
print('**** APSD STOP')
print("**** Calculation time: {:5.2f} second/signal".format(
(stop - start) / 150.))
plt.figure()
flap.plot('TEST-1_APSD_Sin1',
slicing={'Row': 1},
axes='Frequency',
options={
'All': True,
'X range': [0, 5e3]
})
plt.title('TEST-1-1_APSD_Sin1')
print("**** Testing with a complex signal.")
flap.delete_data_object('*')
d = flap.get_data('TESTDATA',
name='TEST-1-1',
object_name='TEST-1-1_CS',
options={'Signal': 'Complex-Sin'})
flap.apsd('TEST-1-1_CS',
coordinate='Time',
output_name='TEST-1-1_APSD_Complex-Sin',
options={
'Res': 10,
'Range': [-1e5, 1e5]
})
flap.slice_data('TEST-1-1_APSD_Complex-Sin',
slicing={'Frequency': flap.Intervals(-5e3, 5e3)},
output_name='TEST-1-1_APSD_Complex-Sin_sliced')
flap.list_data_objects()
plt.figure()
flap.plot('TEST-1-1_APSD_Complex-Sin_sliced',
axes='Frequency',
options={'All': True})
plt.title('TEST-1-1_APSD_Complex-Sin_sliced')
print(
"**** Testing interval selection in apsd. APSD from 8 intervals, each 80 ms long."
)
d = flap.get_data('TESTDATA',
name='TEST-1-1',
object_name='TEST-1-1',
options={
'Signal': 'Sin',
'Length': 1
})
intervals = flap.Intervals(0, 0.08, step=0.1, number=8)
flap.apsd('TEST-1-1',
output_name='TEST-1-1_APSD',
intervals=intervals,
options={
'Res': 12,
'Int': 10
})
plt.figure()
flap.plot('TEST-1-1_APSD', options={'X range': [0, 5e3]})
def calculate_nstx_gpi_crosscorrelation(exp_id=None,
time_range=None,
add_flux=None,
reference_pixel=None,
reference_flux=None,
reference_position=None,
reference_area=None,
filter_low=None,
filter_high=None,
filter_design='Chebyshev II',
trend=['Poly',2],
frange=None,
taurange=[-500e-6,500e-6],
taures=2.5e-6,
interval_n=11,
filename=None,
options=None,
cache_data=False,
normalize_signal=False,
normalize=True, #Calculate correlation if True (instead of covariance)
plot=False,
plot_acf=False,
axes=['Image x', 'Image y', 'Time lag']
):
if time_range is None:
print('The time range needs to set for the calculation.')
print('There is no point of calculating the entire time range.')
return
else:
if (type(time_range) is not list and len(time_range) != 2):
raise TypeError('time_range needs to be a list with two elements.')
if exp_id is not None:
print("\n------- Reading NSTX GPI data --------")
if cache_data:
try:
d=flap.get_data_object_ref(exp_id=exp_id,object_name='GPI')
except:
print('Data is not cached, it needs to be read.')
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
raise ValueError('The experiment ID needs to be set.')
if reference_flux is not None or add_flux:
d.add_coordinate(coordinates='Flux r',exp_id=exp_id)
#Normalize the data for the maximum cloud distribution
if normalize_signal:
normalizer=flap_nstx.analysis.calculate_nstx_gpi_norm_coeff(exp_id=exp_id, # Experiment ID
f_high=1e2, # Low pass filter frequency in Hz
design=filter_design, # IIR filter design (from scipy)
test=False, # Testing input
filter_data=True, # IIR LPF the data
time_range=None, # Timer range for the averaging in ms [t1,t2]
calc_around_max=False, # Calculate the average around the maximum of the GPI signal
time_window=50., # The time window for the calc_around_max calculation
cache_data=True,
verbose=False,
)
d.data = d.data/normalizer.data #This should be checked to some extent, it works with smaller matrices
#SLicing data to the input time range
flap.slice_data('GPI',exp_id=exp_id,
slicing={'Time':flap.Intervals(time_range[0],time_range[1])},
output_name='GPI_SLICED')
#Filtering the signal since we are in time-space not frequency space
if frange is not None:
filter_low=frange[0]
filter_high=frange[1]
if filter_low is not None or filter_high is not None:
if filter_low is not None and filter_high is None:
filter_type='Highpass'
if filter_low is None and filter_high is not None:
filter_type='Lowpass'
if filter_low is not None and filter_high is not None:
filter_type='Bandpass'
flap.filter_data('GPI_SLICED',exp_id=exp_id,
coordinate='Time',
options={'Type':filter_type,
'f_low':filter_low,
'f_high':filter_high,
'Design':filter_design},
output_name='GPI_SLICED_FILTERED')
if reference_pixel is None and reference_position is None and reference_flux is None:
calculate_acf=True
else:
calculate_acf=False
if not calculate_acf:
flap_nstx.analysis.calculate_nstx_gpi_reference('GPI_SLICED_FILTERED', exp_id=exp_id,
reference_pixel=reference_pixel,
reference_area=reference_area,
reference_position=reference_position,
reference_flux=reference_flux,
output_name='GPI_REF')
flap.ccf('GPI_SLICED_FILTERED',exp_id=exp_id,
ref='GPI_REF',
coordinate='Time',
options={'Resolution':taures,
'Range':taurange,
'Trend':trend,
'Interval':interval_n,
'Normalize':normalize,
},
output_name='GPI_CCF')
if plot:
if not plot_acf:
object_name='GPI_CCF'
else:
object_name='GPI_ACF'
flap.plot(object_name, exp_id=exp_id,
plot_type='animation',
axes=axes,
options={'Plot units': {'Time lag':'us'},
'Z range':[0,1]},)
import flap_nstx
from flap_nstx.analysis import *
flap_nstx.register()
import flap_mdsplus
flap_mdsplus.register('NSTX_MDSPlus')
thisdir = os.path.dirname(os.path.realpath(__file__))
fn = os.path.join(thisdir,"flap_nstx.cfg")
flap.config.read(file_name=fn)
import scipy
exp_id=139901
time_range=[0.307,0.308]
flap.get_data('NSTX_GPI', exp_id=exp_id,name='',object_name='GPI')
d=flap.slice_data('GPI', slicing={'Time':flap.Intervals(time_range[0],time_range[1])})
sample0=d.coordinate('Sample')[0][0,0,0]
frame_a=np.asarray(flap.slice_data('GPI', slicing={'Sample':sample0}, output_name='GPI_FRAME1').data, dtype='float32')
frame_b=np.asarray(flap.slice_data('GPI', slicing={'Sample':sample0+1}, output_name='GPI_FRAME2').data, dtype='float32')
winsize = 10 # pixels
searchsize = 10 # pixels, search in image B
overlap = 5 # pixels
dt = 2.5e-6 # sec
u0, v0, sig2noise = process.extended_search_area_piv(frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=winsize,
overlap=overlap,
dt=dt,
def show_nstx_gpi_video(exp_id=None, #Shot number
time_range=None, #Time range to show the video in, if not set, the enire shot is shown
z_range=None, #Range for the contour/color levels, if not set, min-max is divided
logz=False, #Plot the image in a logarithmic coloring
plot_filtered=False, #Plot a high pass (100Hz) filtered video
normalize=None, #Normalize the video by dividing it with a processed GPI signal
# options: 'Time dependent' (LPF filtered) (recommended)
# 'Time averaged' (LPF filtered and averaged for the time range)
# 'Simple' (Averaged)
normalizer_time_range=None, #Time range for the time dependent normalization
subtract_background=False, #Subtract the background from the image (mean of the time series)
plot_flux=False, #Plot the flux surfaces onto the video
plot_separatrix=False, #Plot the separatrix onto the video
plot_limiter=False, #Plot the limiter of NSTX from EFIT
flux_coordinates=False, #Plot the signal as a function of magnetic coordinates
device_coordinates=False, #Plot the signal as a function of the device coordinates
new_plot=True, #Plot the video into a new figure window
save_video=False, #Save the video into an mp4 format
video_saving_only=False, #Saving only the video, not plotting it
prevent_saturation=False, #Prevent saturation of the image by restarting the colormap
colormap='gist_ncar', #Colormap for the plotting
cache_data=True, #Try to load the data from the FLAP storage
):
if exp_id is not None:
print("\n------- Reading NSTX GPI data --------")
if cache_data:
try:
d=flap.get_data_object_ref(exp_id=exp_id,object_name='GPI')
except:
print('Data is not cached, it needs to be read.')
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
object_name='GPI'
else:
raise ValueError('The experiment ID needs to be set.')
if time_range is None:
print('time_range is None, the entire shot is plotted.')
slicing=None
else:
if (type(time_range) is not list and len(time_range) != 2):
raise TypeError('time_range needs to be a list with two elements.')
#time_range=[time_range[0]/1000., time_range[1]/1000.]
slicing={'Time':flap.Intervals(time_range[0],time_range[1])}
d=flap.slice_data(object_name,
exp_id=exp_id,
slicing=slicing,
output_name='GPI_SLICED')
object_name='GPI_SLICED'
if plot_filtered:
print("**** Filtering GPI ****")
d=flap.filter_data(object_name,
exp_id=exp_id,
output_name='GPI_FILTERED',coordinate='Time',
options={'Type':'Highpass',
'f_low':1e2,
'Design':'Chebyshev II'})
object_name='GPI_FILTERED'
if normalize is not None:
print("**** Normalizing GPI ****")
d=flap.get_data_object_ref(object_name)
if normalize in ['Time averaged','Time dependent', 'Simple']:
if normalize == 'Time averaged':
coefficient=flap_nstx.analysis.calculate_nstx_gpi_norm_coeff(exp_id=exp_id,
time_range=normalizer_time_range,
f_high=1e2,
design='Chebyshev II',
filter_data=True,
cache_data=True,
)
if normalize == 'Time dependent':
coefficient=flap.filter_data('GPI',
exp_id=exp_id,
output_name='GPI_LPF',
coordinate='Time',
options={'Type':'Lowpass',
'f_high':1e2,
'Design':'Chebyshev II'})
if slicing is not None:
coefficient=coefficient.slice_data(slicing=slicing)
if normalize == 'Simple':
coefficient=flap.slice_data(object_name,summing={'Time':'Mean'})
data_obj=copy.deepcopy(d)
data_obj.data = data_obj.data/coefficient.data
flap.add_data_object(data_obj, 'GPI_DENORM')
object_name='GPI_DENORM'
else:
raise ValueError('Normalize can either be "Time averaged","Time dependent" or "Simple".')
if subtract_background: #DEPRECATED, DOESN'T DO MUCH HELP
print('**** Subtracting background ****')
d=flap.get_data_object_ref(object_name, exp_id=exp_id)
background=flap.slice_data(object_name,
exp_id=exp_id,
summing={'Time':'Mean'})
data_obj=copy.deepcopy(d)
data_obj.data=data_obj.data/background.data
flap.add_data_object(data_obj, 'GPI_BGSUB')
object_name='GPI_BGSUB'
if ((plot_flux or plot_separatrix) and not flux_coordinates):
print('Gathering MDSPlus EFIT data.')
oplot_options={}
if plot_separatrix:
flap.get_data('NSTX_MDSPlus',
name='\EFIT01::\RBDRY',
exp_id=exp_id,
object_name='SEP X OBJ'
)
flap.get_data('NSTX_MDSPlus',
name='\EFIT01::\ZBDRY',
exp_id=exp_id,
object_name='SEP Y OBJ'
)
oplot_options['path']={'separatrix':{'Data object X':'SEP X OBJ',
'Data object Y':'SEP Y OBJ',
'Plot':True,
'Color':'red'}}
if plot_flux:
d=flap.get_data('NSTX_MDSPlus',
name='\EFIT02::\PSIRZ',
exp_id=exp_id,
object_name='PSI RZ OBJ'
)
oplot_options['contour']={'flux':{'Data object':'PSI RZ OBJ',
'Plot':True,
'Colormap':None,
'nlevel':51}}
#oplot_options['line']={'trial':{'Horizontal':[[0.200,'red'],[0.250,'blue']],
# 'Vertical':[[1.450,'red'],[1.500,'blue']],
# 'Plot':True
# }}
else:
oplot_options=None
if flux_coordinates:
print("**** Adding Flux r coordinates")
d.add_coordinate(coordinates='Flux r',exp_id=exp_id)
x_axis='Flux r'
y_axis='Device z'
if plot_separatrix:
oplot_options={}
oplot_options['line']={'separatrix':{'Vertical':[[1.0,'red']],
'Plot':True}}
elif device_coordinates:
x_axis='Device R'
y_axis='Device z'
else:
x_axis='Image x'
y_axis='Image y'
if new_plot:
plt.figure()
if save_video:
if time_range is not None:
video_filename='NSTX_GPI_'+str(exp_id)+'_'+str(time_range[0])+'_'+str(time_range[1])+'.mp4'
else:
video_filename='NSTX_GPI_'+str(exp_id)+'_FULL.mp4'
else:
video_filename=None
if video_saving_only:
save_video=True
if z_range is None:
d=flap.get_data_object_ref(object_name, exp_id=exp_id)
z_range=[d.data.min(),d.data.max()]
if z_range[1] < 0:
raise ValueError('All the values are negative, Logarithmic plotting is not allowed.')
if logz and z_range[0] <= 0:
print('Z range should not start with 0 when logarithmic Z axis is set. Forcing it to be 1 for now.')
z_range[0]=1.
if not save_video:
flap.plot(object_name,plot_type='animation',
exp_id=exp_id,
axes=[x_axis,y_axis,'Time'],
options={'Z range':z_range,'Wait':0.0,'Clear':False,
'Overplot options':oplot_options,
'Colormap':colormap,
'Log z':logz,
'Equal axes':True,
'Prevent saturation':prevent_saturation,
'Plot units':{'Time':'s',
'Device R':'m',
'Device z':'m'}
})
else:
if video_saving_only:
import matplotlib
current_backend=matplotlib.get_backend()
matplotlib.use('agg')
waittime=0.
else:
waittime=1./24.
waittime=0.
flap.plot(object_name,plot_type='anim-image',
exp_id=exp_id,
axes=[x_axis,y_axis,'Time'],
options={'Z range':z_range,'Wait':0.0,'Clear':False,
'Overplot options':oplot_options,
'Colormap':colormap,
'Equal axes':True,
'Waittime':waittime,
'Video file':video_filename,
'Video format':'mp4',
'Prevent saturation':prevent_saturation,
})
if video_saving_only:
import matplotlib
matplotlib.use(current_backend)
def show_nstx_gpi_video_frames(exp_id=None,
time_range=None,
start_time=None,
n_frame=20,
logz=False,
z_range=[0,512],
plot_filtered=False,
normalize=False,
cache_data=False,
plot_flux=False,
plot_separatrix=False,
flux_coordinates=False,
device_coordinates=False,
new_plot=True,
save_pdf=False,
colormap='gist_ncar',
save_for_paraview=False,
colorbar_visibility=True
):
if time_range is None and start_time is None:
print('time_range is None, the entire shot is plotted.')
if time_range is not None:
if (type(time_range) is not list and len(time_range) != 2):
raise TypeError('time_range needs to be a list with two elements.')
if start_time is not None:
if type(start_time) is not int and type(start_time) is not float:
raise TypeError('start_time needs to be a number.')
if not cache_data: #This needs to be enhanced to actually cache the data no matter what
flap.delete_data_object('*')
if exp_id is not None:
print("\n------- Reading NSTX GPI data --------")
if cache_data:
try:
d=flap.get_data_object_ref(exp_id=exp_id,object_name='GPI')
except:
print('Data is not cached, it needs to be read.')
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
object_name='GPI'
else:
raise ValueError('The experiment ID needs to be set.')
if time_range is None:
time_range=[start_time,start_time+n_frame*2.5e-6]
if normalize:
flap.slice_data(object_name,
slicing={'Time':flap.Intervals(time_range[0]-1/1e3*10,
time_range[1]+1/1e3*10)},
output_name='GPI_SLICED_FOR_FILTERING')
norm_obj=flap.filter_data('GPI_SLICED_FOR_FILTERING',
exp_id=exp_id,
coordinate='Time',
options={'Type':'Lowpass',
'f_high':1e3,
'Design':'Elliptic'},
output_name='GAS_CLOUD')
norm_obj.data=np.flip(norm_obj.data,axis=0)
norm_obj=flap.filter_data('GAS_CLOUD',
exp_id=exp_id,
coordinate='Time',
options={'Type':'Lowpass',
'f_high':1e3,
'Design':'Elliptic'},
output_name='GAS_CLOUD')
norm_obj.data=np.flip(norm_obj.data,axis=0)
coefficient=flap.slice_data('GAS_CLOUD',
exp_id=exp_id,
slicing={'Time':flap.Intervals(time_range[0],time_range[1])},
output_name='GPI_GAS_CLOUD').data
data_obj=flap.slice_data('GPI',
exp_id=exp_id,
slicing={'Time':flap.Intervals(time_range[0],time_range[1])})
data_obj.data = data_obj.data/coefficient
flap.add_data_object(data_obj, 'GPI_SLICED_DENORM')
object_name='GPI_SLICED_DENORM'
if plot_filtered:
print("**** Filtering GPI")
object_name='GPI_FILTERED'
try:
flap.get_data_object_ref(object_name, exp_id=exp_id)
except:
flap.filter_data(object_name,
exp_id=exp_id,
coordinate='Time',
options={'Type':'Highpass',
'f_low':1e2,
'Design':'Chebyshev II'},
output_name='GPI_FILTERED') #Data is in milliseconds
if plot_flux or plot_separatrix:
print('Gathering MDSPlus EFIT data.')
oplot_options={}
if plot_separatrix:
flap.get_data('NSTX_MDSPlus',
name='\EFIT01::\RBDRY',
exp_id=exp_id,
object_name='SEP X OBJ'
)
flap.get_data('NSTX_MDSPlus',
name='\EFIT01::\ZBDRY',
exp_id=exp_id,
object_name='SEP Y OBJ'
)
if plot_flux:
d=flap.get_data('NSTX_MDSPlus',
name='\EFIT01::\PSIRZ',
exp_id=exp_id,
object_name='PSI RZ OBJ'
)
x_axis='Device R'
y_axis='Device z'
else:
oplot_options=None
if flux_coordinates:
print("**** Adding Flux r coordinates")
d.add_coordinate(coordinates='Flux r',exp_id=exp_id)
x_axis='Flux r'
y_axis='Device z'
elif device_coordinates:
x_axis='Device R'
y_axis='Device z'
if (not device_coordinates and
not plot_separatrix and
not flux_coordinates):
x_axis='Image x'
y_axis='Image y'
if start_time is not None:
start_sample_num=flap.slice_data(object_name,
slicing={'Time':start_time}).coordinate('Sample')[0][0,0]
if n_frame == 30:
ny=6
nx=5
if n_frame == 20:
ny=5
nx=4
gs=GridSpec(nx,ny)
for index_grid_x in range(nx):
for index_grid_y in range(ny):
plt.subplot(gs[index_grid_x,index_grid_y])
if start_time is not None:
slicing={'Sample':start_sample_num+index_grid_x*ny+index_grid_y}
else:
time=time_range[0]+(time_range[1]-time_range[0])/(n_frame-1)*(index_grid_x*ny+index_grid_y)
slicing={'Time':time}
d=flap.slice_data(object_name, slicing=slicing, output_name='GPI_SLICED')
slicing={'Time':d.coordinate('Time')[0][0,0]}
if plot_flux:
flap.slice_data('PSI RZ OBJ',slicing=slicing,output_name='PSI RZ SLICE',options={'Interpolation':'Linear'})
oplot_options['contour']={'flux':{'Data object':'PSI RZ SLICE',
'Plot':True,
'Colormap':None,
'nlevel':51}}
if plot_separatrix:
flap.slice_data('SEP X OBJ',slicing=slicing,output_name='SEP X SLICE',options={'Interpolation':'Linear'})
flap.slice_data('SEP Y OBJ',slicing=slicing,output_name='SEP Y SLICE',options={'Interpolation':'Linear'})
oplot_options['path']={'separatrix':{'Data object X':'SEP X SLICE',
'Data object Y':'SEP Y SLICE',
'Plot':True,
'Color':'red'}}
visibility=[True,True]
if index_grid_x != nx-1:
visibility[0]=False
if index_grid_y != 0:
visibility[1]=False
flap.plot('GPI_SLICED',
plot_type='contour',
exp_id=exp_id,
axes=[x_axis,y_axis,'Time'],
options={'Z range':z_range,
'Interpolation': 'Closest value',
'Clear':False,
'Equal axes':True,
'Plot units':{'Device R':'m',
'Device z':'m'},
'Axes visibility':visibility,
'Colormap':colormap,
'Colorbar':colorbar_visibility,
'Overplot options':oplot_options,
},
plot_options={'levels':255},
)
actual_time=d.coordinate('Time')[0][0,0]
#plt.title(str(exp_id)+' @ '+f"{actual_time*1000:.4f}"+'ms')
plt.title(f"{actual_time*1000:.3f}"+'ms')
if save_pdf:
if time_range is not None:
plt.savefig('NSTX_GPI_video_frames_'+str(exp_id)+'_'+str(time_range[0])+'_'+str(time_range[1])+'_nf_'+str(n_frame)+'.pdf')
else:
plt.savefig('NSTX_GPI_video_frames_'+str(exp_id)+'_'+str(start_time)+'_nf_'+str(n_frame)+'.pdf')
import numpy as np
import scipy
nstx_gpi_generate_synthetic_data(exp_id=1,
time=0.0001,
output_name='test',
poloidal_velocity=10e3,
radial_velocity=5e3,
poloidal_size=0.02,
radial_size=0.03,
start_position=[1.5, 0.3],
amplitude=1.,
gaussian=True,
add_background=False)
flap.slice_data('test', slicing={'Sample': 2}, output_name='test_0')
flap.slice_data('test', slicing={'Sample': 3}, output_name='test_1')
#flap.ccf('test_0', 'test_1',
# coordinate=['Image x'],
# options={'Resolution':1, 'Range':[-63,63], 'Trend removal':None, 'Normalize':True, 'Interval_n': 1},
# output_name='test_01_correlation')
flap.ccf('test_0',
'test_1',
coordinate=['Image x', 'Image y'],
options={
'Resolution': 1,
'Range': [[-63, 63], [-79, 79]],
'Trend removal': None,
'Normalize': True,
'Interval_n': 1
correlation_evolution = np.zeros([n_frames + 1, n_lag + 1])
time_ranges = np.asarray([
np.arange(0, n_lag + 1) / (n_lag) * (time_range[1] - time_range[0]) +
time_range[0],
np.arange(0, n_lag + 1) / (n_lag) * (time_range[1] - time_range[0]) +
time_range[0] + time_window
]).T
flap.get_data('NSTX_GPI', exp_id=exp_id, name='', object_name='GPI')
for j in range(n_lag + 1):
sample0 = flap.get_data_object_ref('GPI').slice_data(
slicing={
'Time': time_ranges[j, 0]
}).coordinate('Sample')[0][0, 0]
d = flap.slice_data(
'GPI',
slicing={'Sample': flap.Intervals(sample0, sample0 + n_frames)},
output_name='GPI_SLICED')
d.data = d.data / np.mean(d.data, axis=0)
d = flap_nstx.analysis.detrend_multidim('GPI_SLICED',
order=1,
coordinates=['Image x', 'Image y'],
output_name='GPI_SLICED_DETREND')
time = flap.get_data_object_ref('GPI_SLICED_DETREND').coordinate(
'Time')[0][:, 0, 0]
time = time - time[0]
frame1 = np.asarray(flap.slice_data('GPI_SLICED_DETREND',
slicing={
'Sample': sample0
},
flap_mdsplus.register('NSTX_MDSPlus')
thisdir = os.path.dirname(os.path.realpath(__file__))
fn = os.path.join(thisdir, "flap_nstx.cfg")
flap.config.read(file_name=fn)
import matplotlib.pyplot as plt
from matplotlib import path as pltPath
import numpy as np
from scipy import interpolate
from scipy.signal import correlate
order = 0
flap.get_data('NSTX_GPI', exp_id=139901, name='', object_name='GPI_RAW')
d11 = flap.slice_data('GPI_RAW',
slicing={'Time': 0.324},
output_name='GPI_RAW_SLICED')
d12 = flap.slice_data('GPI_RAW',
slicing={'Time': 0.324 + 2.5e-6},
output_name='GPI_RAW_SLICED_2')
trend1 = flap_nstx.analysis.detrend_multidim(
data_object='GPI_RAW_SLICED',
coordinates=['Image x', 'Image y'],
order=order,
output_name='GPI_RAW_SLICED_DETREND',
return_trend=True)
trend2 = flap_nstx.analysis.detrend_multidim(
data_object='GPI_RAW_SLICED_2',
coordinates=['Image x', 'Image y'],
order=order,
output_name='GPI_RAW_SLICED_2_DETREND',
def calculate_nstx_gpi_crosspower(exp_id=None,
time_range=None,
normalize_signal=False, #Normalize the amplitude for the average frame for the entire time range
reference_pixel=None,
reference_position=None,
reference_flux=None,
reference_area=None, #In the unit of the reference, [psi,z] if reference_flux is not None
fres=1., #in KHz due to data being in ms
flog=False,
frange=None,
interval_n=8.,
filename=None,
options=None,
cache_data=False,
normalize=False, #Calculate coherency if True
plot=False,
plot_phase=False,
axes=['Image x', 'Image y', 'Frequency'],
hanning=True,
colormap=None,
video_saving_only=False,
video_filename=None,
save_video=False,
comment=None,
zlog=False,
save_for_paraview=False
):
#139901 [300,307]
#This function returns the crosspower between a single signal and all the other signals in the GPI.
#A separate function is dedicated for multi channel reference channel
#e.g 3x3 area and 64x80 resulting 3x3x64x80 cross power spectra
#Read data from the cine file
if time_range is None:
print('The time range needs to set for the calculation.')
print('There is no point of calculating the entire time range.')
return
else:
if (type(time_range) is not list and len(time_range) != 2):
raise TypeError('time_range needs to be a list with two elements.')
if exp_id is not None:
print("\n------- Reading NSTX GPI data --------")
if cache_data:
try:
d=flap.get_data_object_ref(exp_id=exp_id,object_name='GPI')
except:
print('Data is not cached, it needs to be read.')
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
raise ValueError('The experiment ID needs to be set.')
if reference_flux is not None:
d.add_coordinate(coordinates='Flux r',exp_id=exp_id)
#Normalize the data for the maximum cloud distribution
if normalize_signal:
normalizer=flap_nstx.analysis.calculate_nstx_gpi_norm_coeff(exp_id=exp_id, # Experiment ID
f_high=1e2, # Low pass filter frequency in Hz
design='Chebyshev II', # IIR filter design (from scipy)
test=False, # Testing input
filter_data=True, # IIR LPF the data
time_range=None, # Timer range for the averaging in ms [t1,t2]
calc_around_max=False, # Calculate the average around the maximum of the GPI signal
time_window=50., # The time window for the calc_around_max calculation
cache_data=True, #
verbose=False,
)
d.data = d.data/normalizer.data #This should be checked to some extent, it works with smaller matrices
#Calculate the crosspower spectra for the timerange between the reference pixel and all the other pixels
if reference_pixel is None and reference_position is None and reference_flux is None:
calculate_apsd=True
print('No reference is defined, returning autopower spectra.')
else:
calculate_apsd=False
reference_signal=flap_nstx.analysis.calculate_nstx_gpi_reference('GPI', exp_id=exp_id,
time_range=time_range,
reference_pixel=reference_pixel,
reference_area=reference_area,
reference_position=reference_position,
reference_flux=reference_flux,
output_name='GPI_REF')
flap.slice_data('GPI',exp_id=exp_id,
slicing={'Time':flap.Intervals(time_range[0],time_range[1])},
output_name='GPI_SLICED')
if calculate_apsd:
object_name='GPI_APSD'
d=flap.apsd('GPI_SLICED',exp_id=exp_id,
coordinate='Time',
options={'Resolution':fres,
'Range':frange,
'Logarithmic':flog,
'Interval_n':interval_n,
'Hanning':hanning,
'Trend removal':None,
},
output_name=object_name)
else:
object_name='GPI_CPSD'
flap.cpsd('GPI_SLICED',exp_id=exp_id,
ref=reference_signal,
coordinate='Time',
options={'Resolution':fres,
'Range':frange,
'Logarithmic':flog,
'Interval_n':interval_n,
'Hanning':hanning,
'Normalize':normalize,
'Trend removal':None,
},
output_name=object_name)
flap.abs_value(object_name,exp_id=exp_id,
output_name='GPI_CPSD_ABS')
flap.phase(object_name,exp_id=exp_id,
output_name='GPI_CPSD_PHASE')
if not save_video:
if plot:
if calculate_apsd:
object_name='GPI_APSD'
else:
if plot_phase:
object_name='GPI_CPSD_PHASE'
else:
object_name='GPI_CPSD_ABS'
flap.plot(object_name, exp_id=exp_id,
plot_type='animation',
axes=axes,
options={'Force axes':True,
'Colormap':colormap,
'Plot units':{'Device R':'mm',
'Device z':'mm',
'Frequency':'kHz'},
'Log z':zlog})
else:
if video_filename is None:
if time_range is not None:
video_filename='NSTX_GPI_'+str(exp_id)
if calculate_apsd:
video_filename+='_APSD'
else:
video_filename+='_CPSD'
if plot_phase:
video_filename+='_PHASE'
else:
video_filename+='_ABS'
video_filename+='_'+str(time_range[0])+'_'+str(time_range[1])
if reference_pixel is not None:
video_filename+='_PIX_'+str(reference_pixel[0])+'_'+str(reference_pixel[1])
if reference_position is not None:
video_filename+='_POS_'+str(reference_position[0])+'_'+str(reference_position[1])
if reference_flux is not None:
video_filename+='_FLX_'+str(reference_flux[0])+'_'+str(reference_flux[1])
video_filename+='_FRES_'+str(fres)
if comment is not None:
video_filename+=comment
video_filename+='.mp4'
else:
video_filename='NSTX_GPI_CPSD_'+str(exp_id)+'_FULL.mp4'
if video_saving_only:
import matplotlib
current_backend=matplotlib.get_backend()
matplotlib.use('agg')
waittime=0.
else:
waittime=1.
if calculate_apsd:
object_name='GPI_APSD'
else:
if plot_phase:
object_name='GPI_CPSD_PHASE'
else:
object_name='GPI_CPSD_ABS'
flap.plot(object_name, exp_id=exp_id,
plot_type='anim-contour',
axes=axes,
options={'Force axes':True,
'Colormap':colormap,
'Plot units':{'Device R':'mm',
'Device z':'mm',
},
'Waittime':waittime,
'Video file':video_filename,
'Video format':'mp4',
'Log z':zlog})
if video_saving_only:
import matplotlib
matplotlib.use(current_backend)
def test_ccf():
plt.close('all')
print()
print(
'>>>>>>>>>>>>>>>>>>> Test ccf (Cross Correlation Function) <<<<<<<<<<<<<<<<<<<<<<<<'
)
flap.delete_data_object('*')
print(
"**** Generating 10x15 random test signals, 5000 points each, 1 MHz sampling."
)
flap.get_data('TESTDATA',
name='TEST-*-*',
options={
'Length': 0.005,
'Signal': 'Random'
},
object_name='TESTDATA')
print("**** Filtering with 10 microsec integrating filter.")
flap.filter_data('TESTDATA',
coordinate='Time',
options={
'Type': 'Int',
'Tau': 1e-5
},
output_name='TESTDATA_filt')
flap.list_data_objects()
plt.figure()
print("**** Plotting an original and a filtered signal.")
flap.plot('TESTDATA', slicing={'Row': 1, 'Column': 1}, axes='Time')
flap.plot('TESTDATA_filt', slicing={'Row': 1, 'Column': 1})
print('**** Calculating the 10x15x10x15 CCFs, each 5000 samples.')
print('**** CCF START')
start = time.time()
flap.ccf('TESTDATA_filt',
coordinate='Time',
options={
'Trend': 'Mean',
'Range': [-1e-4, 1e-4],
'Res': 1e-5,
'Norm': True
},
output_name='CCF')
stop = time.time()
print('**** CCF STOP')
print("**** Calculation time: {:6.3f} ms/signal".format(
1000 * (stop - start) / (10 * 15 * 10 * 15)))
flap.list_data_objects()
print(
"**** Plotting spatiotemporal correlation function at ref row, column 3,3, column 3"
)
plt.figure()
flap.plot('CCF',
slicing={
'Row (Ref)': 3,
'Column (Ref)': 3,
'Column': 3
},
axes=['Time lag'],
plot_type='multi xy')
print("**** Slicing TESTDATA_filt for row: 1-3, column:1-4")
flap.slice_data('TESTDATA_filt',
slicing={
'Row': [1, 2, 3],
'Column': [1, 2, 3, 4]
},
output_name='TESTDATA_filt_3x4')
print('**** Calculating CCFs, between original and sliced TESTDATAfilt')
print('**** CCF START')
flap.ccf('TESTDATA_filt',
ref='TESTDATA_filt_3x4',
coordinate='Time',
options={
'Trend': 'Mean',
'Range': [-1e-4, 1e-4],
'Res': 1e-5,
'Norm': True
},
output_name='CCF_ref')
print('**** CCF STOP')
flap.list_data_objects()
print(
"**** Plotting spatiotemporal correlation function at ref row, column 3,3, column 3"
)
plt.figure()
flap.plot('CCF_ref',
slicing={
'Row (Ref)': 3,
'Column (Ref)': 3,
'Column': 3
},
axes=['Time lag'],
plot_type='multi xy')
def plot_results_for_paper():
pearson=False
wd=flap.config.get_all_section('Module NSTX_GPI')['Working directory']
#Figure 1
'''NO CODE IS NEEDED'''
#Figure 2
'''NO CODE IS NEEDED'''
#Figure 3
from flap_nstx.analysis import show_nstx_gpi_video_frames
#fig, ax = plt.subplots(figsize=(6.5,5))
if plot[3]:
gs=GridSpec(5,2)
ax,fig=plt.subplots(figsize=(8.5/2.54,6))
pdf=PdfPages(wd+'/plots/figure_3_139901_basic_plots.pdf')
plt.subplot(gs[0,0])
flap.get_data('NSTX_MDSPlus',
name='\WF::\DALPHA',
exp_id=139901,
object_name='DALPHA').plot(options={'Axes visibility':[False,True]})
plt.xlim([0,1.2])
plt.subplot(gs[1,0])
flap.get_data('NSTX_GPI',
name='',
exp_id=139901,
object_name='GPI').slice_data(summing={'Image x':'Mean', 'Image y':'Mean'}).plot(options={'Axes visibility':[False,True]})
plt.xlim([0,1.2])
plt.xlim([0,1.2])
plt.subplot(gs[2,0])
flap.get_data('NSTX_MDSPlus',
name='IP',
exp_id=139901,
object_name='IP').plot(options={'Axes visibility':[False,True]})
plt.xlim([0,1.2])
plt.subplot(gs[3,0])
d=flap_nstx_thomson_data(exp_id=139901, density=True, output_name='DENSITY')
dR = d.coordinate('Device R')[0][:,:]-np.insert(d.coordinate('Device R')[0][0:-1,:],0,0,axis=0)
LID=np.sum(d.data*dR,axis=0)
plt.plot(d.coordinate('Time')[0][0,:],LID)
plt.title('Line integrated density')
plt.xlabel('Time [s]')
plt.ylabel('n_e [m^-2]')
plt.xlim([0,1.2])
ax=plt.gca()
ax.get_xaxis().set_visible(False)
plt.subplot(gs[4,0])
magnetics=flap.get_data('NSTX_MDSPlus',
name='\OPS_PC::\\BDOT_L1DMIVVHF5_RAW',
exp_id=139901,
object_name='MIRNOV')
magnetics.coordinates.append(copy.deepcopy(flap.Coordinate(name='Time equi',
unit='s',
mode=flap.CoordinateMode(equidistant=True),
shape = [],
start=magnetics.coordinate('Time')[0][0],
step=magnetics.coordinate('Time')[0][1]-magnetics.coordinate('Time')[0][0],
dimension_list=[0])))
magnetics.filter_data(coordinate='Time equi',
options={'Type':'Bandpass',
'f_low':100e3,
'f_high':500e3,
'Design':'Elliptic'}).plot()
plt.xlim([0,1.2])
plt.subplot(gs[0,1])
flap.get_data('NSTX_MDSPlus',
name='\WF::\DALPHA',
exp_id=139901,
object_name='DALPHA').plot(options={'Axes visibility':[False,False]})
plt.xlim([0.25,0.4])
plt.subplot(gs[1,1])
flap.get_data('NSTX_GPI',
name='',
exp_id=139901,
object_name='GPI').slice_data(summing={'Image x':'Mean', 'Image y':'Mean'}).plot(options={'Axes visibility':[False,False]})
plt.xlim([0.25,0.4])
plt.subplot(gs[2,1])
flap.get_data('NSTX_MDSPlus',
name='IP',
exp_id=139901,
object_name='IP').plot(options={'Axes visibility':[False,False]})
plt.xlim([0.25,0.4])
plt.subplot(gs[3,1])
d=flap_nstx_thomson_data(exp_id=139901, density=True, output_name='DENSITY')
dR = d.coordinate('Device R')[0][:,:]-np.insert(d.coordinate('Device R')[0][0:-1,:],0,0,axis=0)
LID=np.sum(d.data*dR,axis=0)
plt.plot(d.coordinate('Time')[0][0,:],LID)
plt.title('Line integrated density')
plt.xlabel('Time [s]')
plt.ylabel('n_e [m^-2]')
plt.xlim([0.25,0.4])
ax=plt.gca()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.subplot(gs[4,1])
magnetics=flap.get_data('NSTX_MDSPlus',
name='\OPS_PC::\\BDOT_L1DMIVVHF5_RAW',
exp_id=139901,
object_name='MIRNOV')
magnetics.coordinates.append(copy.deepcopy(flap.Coordinate(name='Time equi',
unit='s',
mode=flap.CoordinateMode(equidistant=True),
shape = [],
start=magnetics.coordinate('Time')[0][0],
step=magnetics.coordinate('Time')[0][1]-magnetics.coordinate('Time')[0][0],
dimension_list=[0])))
magnetics.filter_data(coordinate='Time equi',
options={'Type':'Bandpass',
'f_low':100e3,
'f_high':500e3,
'Design':'Elliptic'}).plot(slicing={'Time':flap.Intervals(0.25,0.4)})
plt.xlim([0.25,0.4])
ax=plt.gca()
ax.get_yaxis().set_visible(False)
pdf.savefig()
pdf.close()
if plot[4]:
plt.figure()
ax,fig=plt.subplots(figsize=(3.35*2,5.5))
pdf=PdfPages(wd+'/plots/figure_5_139901_0.3249158_30_frame.pdf')
show_nstx_gpi_video_frames(exp_id=139901,
start_time=0.3249158,
n_frame=30,
logz=False,
z_range=[0,3900],
plot_filtered=False,
normalize=False,
cache_data=False,
plot_flux=False,
plot_separatrix=True,
flux_coordinates=False,
device_coordinates=True,
new_plot=False,
save_pdf=True,
colormap='gist_ncar',
save_for_paraview=False,
colorbar_visibility=True
)
pdf.savefig()
pdf.close()
#Figure 5
if plot[5] or plot[6] or plot[7]:
try:
d1,d2,d3,d4=pickle.load(open(wd+'/processed_data/fig_6_8_flap_object.pickle','rb'))
flap.add_data_object(d1, 'GPI_SLICED_FULL')
flap.add_data_object(d2, 'GPI_GAS_CLOUD')
flap.add_data_object(d3, 'GPI_SLICED_DENORM_CCF_VEL')
flap.add_data_object(d4, 'GPI_CCF_F_BY_F')
except:
calculate_nstx_gpi_avg_frame_velocity(exp_id=139901,
time_range=[0.325-1e-3,0.325+1e-3],
plot=False,
subtraction_order_for_velocity=1,
skip_structure_calculation=False,
correlation_threshold=0.,
pdf=False,
nlevel=51,
nocalc=False,
filter_level=3,
normalize_for_size=True,
normalize_for_velocity=True,
threshold_coeff=1.,
normalize_f_high=1e3,
normalize='roundtrip',
velocity_base='cog',
return_results=False,
plot_gas=True)
pickle.dump((flap.get_data_object('GPI_SLICED_FULL'),
flap.get_data_object('GPI_GAS_CLOUD'),
flap.get_data_object('GPI_SLICED_DENORM_CCF_VEL'),
flap.get_data_object('GPI_CCF_F_BY_F')), open(wd+'/processed_data/fig_6_8_flap_object.pickle','wb'))
if plot[5]:
pdf=PdfPages(wd+'/plots/figure_6_normalization.pdf')
times=[0.3245,0.3249560,0.3255]
signals=['GPI_SLICED_FULL',
'GPI_GAS_CLOUD',
'GPI_SLICED_DENORM_CCF_VEL']
gs=GridSpec(3,3)
plt.figure()
ax,fig=plt.subplots(figsize=(3.35,4))
titles=['Raw frame', 'Gas cloud', 'Normalized']
for index_grid_x in range(3):
for index_grid_y in range(3):
plt.subplot(gs[index_grid_x,index_grid_y])
visibility=[True,True]
if index_grid_x != 3-1:
visibility[0]=False
if index_grid_y != 0:
visibility[1]=False
# if index_grid_x == 0:
# z_range=[0,4096]
# elif index_grid_x == 1:
# z_range=[0,400]
# elif index_grid_x == 2:
# z_range=[0,40]
z_range=None
flap.plot(signals[index_grid_x],
plot_type='contour',
slicing={'Time':times[index_grid_y]},
axes=['Image x', 'Image y'],
options={'Z range':z_range,
'Interpolation': 'Closest value',
'Clear':False,
'Equal axes':True,
'Axes visibility':visibility,
#'Colormap':'gist_ncar',
'Colorbar':True,
#'Overplot options':oplot_options,
},
plot_options={'levels':51},
)
if index_grid_x == 0:
#ax=plt.gca()
plt.title(f"{times[index_grid_y]*1e3:.3f}"+' '+titles[index_grid_x])
else:
plt.title(titles[index_grid_x])
pdf.savefig()
pdf.close()
#Figure 6
if plot[6]:
flap.get_data('NSTX_GPI',exp_id=139901,
name='',
object_name='GPI')
flap.slice_data('GPI', slicing={'Time':flap.Intervals(0.3245,0.3255)}, output_name='GPI_SLICED_FULL')
data_object_name='GPI_SLICED_DENORM_CCF_VEL'
detrended=flap_nstx.analysis.detrend_multidim(data_object_name,
exp_id=139901,
order=4,
coordinates=['Image x', 'Image y'],
output_name='GPI_DETREND_VEL')
d=copy.deepcopy(flap.get_data_object(data_object_name))
d.data=d.data-detrended.data
flap.add_data_object(d,'GPI_TREND')
signals=[data_object_name,
'GPI_TREND',
'GPI_DETREND_VEL']
pdf=PdfPages(wd+'/plots/figure_7_trend_subtraction.pdf')
gs=GridSpec(1,3)
plt.figure()
ax,fig=plt.subplots(figsize=(8.5/2.54,2))
for index_grid_x in range(3):
plt.subplot(gs[index_grid_x])
visibility=[True,True]
if index_grid_x != 0:
visibility[1]=False
z_range=[0,10]
colorbar=False
flap.plot(signals[index_grid_x],
plot_type='contour',
slicing={'Time':0.3249560},
#slicing={'Sample':29808},
axes=['Image x', 'Image y'],
options={'Interpolation': 'Closest value',
'Clear':False,
'Equal axes':True,
'Axes visibility':visibility,
#'Colormap':colormap,
'Colorbar':True,
#'Overplot options':oplot_options,
},
plot_options={'levels':51},
)
#fig.tight_layout()
pdf.savefig()
pdf.close()
#Figure 7
if plot[7]:
pdf=PdfPages(wd+'/plots/figure_8_CCF_frame_by_frame.pdf')
gs=GridSpec(1,3)
plt.figure()
ax,fig=plt.subplots(figsize=(8.5/2.54,2))
plt.subplot(gs[0])
flap.plot('GPI_SLICED_FULL',
plot_type='contour',
slicing={'Sample':29806},
axes=['Image x', 'Image y'],
options={
'Z range':[0,4096],
'Interpolation': 'Closest value',
'Clear':False,
'Equal axes':True,
'Axes visibility':[True,True],
'Colormap':'gist_ncar',
'Colorbar':False,
#'Overplot options':oplot_options,
},
plot_options={'levels':51},
)
plt.title("324.959ms")
plt.subplot(gs[1])
flap.plot('GPI_SLICED_FULL',
plot_type='contour',
slicing={'Sample':29807},
axes=['Image x', 'Image y'],
options={'Z range':[0,4096],
'Interpolation': 'Closest value',
'Clear':False,
'Equal axes':True,
'Axes visibility':[True,False],
'Colorbar':False,
'Colormap':'gist_ncar',
},
plot_options={'levels':51},
)
plt.title("324.961ms")
plt.subplot(gs[2])
flap.plot('GPI_CCF_F_BY_F',
plot_type='contour',
slicing={'Sample':29807, 'Image x':flap.Intervals(-10,10),'Image y':flap.Intervals(-10,10)},
axes=['Image x', 'Image y'],
options={
#'Z range':[0,2048],
'Interpolation': 'Closest value',
'Clear':False,
'Equal axes':True,
'Axes visibility':[True,True],
#'Colormap':colormap,
'Colorbar':True,
#'Overplot options':oplot_options,
},
plot_options={'levels':51},
)
plt.title("CCF")
pdf.savefig()
pdf.close()
#Figure 8
if plot[8]:
#2x2 frames with the found structures during an ELM burst
calculate_nstx_gpi_avg_frame_velocity(exp_id=139901,
time_range=[0.32495,0.325],
plot=False,
subtraction_order_for_velocity=4,
skip_structure_calculation=False,
correlation_threshold=0.5,
pdf=True,
nlevel=51,
nocalc=False,
filter_level=5,
normalize_for_size=True,
normalize_for_velocity=True,
threshold_coeff=1.,
normalize_f_high=1e3,
normalize='roundtrip',
velocity_base='cog',
return_results=False,
plot_gas=True,
structure_pixel_calc=True,
structure_pdf_save=True,
test_structures=True
)
#Post processing done with illustrator
#Figure 9
if plot[9]:
#2x3
#Synthetic GPI signal
#Postprocessing done with illustrator
nstx_gpi_generate_synthetic_data(exp_id=1,
time=0.0001,
amplitude=1.0,
output_name='test',
poloidal_velocity=3e3,
radial_velocity=0.,
poloidal_size=0.10,
radial_size=0.05,
waveform_divider=1,
sinusoidal=True)
d=flap.get_data_object('test', exp_id=1)
d.data=d.data-np.mean(d.data,axis=0)
calculate_nstx_gpi_avg_frame_velocity(data_object='test',
exp_id=1,
time_range=[0.000000,0.00005],
plot=False,
subtraction_order_for_velocity=1,
skip_structure_calculation=False,
correlation_threshold=0.5,
pdf=True,
nlevel=51,
nocalc=False,
filter_level=5,
normalize_for_size=False,
normalize_for_velocity=False,
threshold_coeff=1.,
normalize_f_high=1e3,
normalize=None,
velocity_base='cog',
return_results=False,
plot_gas=False,
structure_pixel_calc=True,
structure_pdf_save=True,
test_structures=True
)
#Figure 10
if plot[10]:
#Single shot results
calculate_nstx_gpi_avg_frame_velocity(exp_id=139901,
time_range=[0.325-2e-3,0.325+2e-3],
plot_time_range=[0.325-0.5e-3,0.325+0.5e-3],
plot=True,
subtraction_order_for_velocity=4,
skip_structure_calculation=False,
correlation_threshold=0.6,
pdf=True,
nlevel=51,
nocalc=True,
gpi_plane_calculation=True,
filter_level=5,
normalize_for_size=True,
normalize_for_velocity=True,
threshold_coeff=1.,
normalize_f_high=1e3,
normalize='roundtrip',
velocity_base='cog',
return_results=False,
plot_gas=True,
plot_for_publication=True,
plot_scatter=False,
overplot_average=False,
overplot_str_vel=False)
#2x3
#Done with Illustrator
#Figure 12
if plot[11]:
#Conditional averaged results
calculate_avg_velocity_results(pdf=True,
plot=True,
plot_max_only=True,
plot_for_publication=True,
normalized_velocity=True,
subtraction_order=4,
normalized_structure=True,
opacity=0.5,
correlation_threshold=0.6,
gpi_plane_calculation=True,
plot_scatter=False)
#Post processing done with Illustrator
#Figure 11
if plot[12]:
if pearson:
pdf=PdfPages(wd+'/plots/figure_13_pearson_matrix.pdf')
pearson=calculate_nstx_gpi_correlation_matrix(calculate_average=False,
gpi_plane_calculation=True,
window_average=0.050e-3,
elm_burst_window=True)
data=pearson[:,:,0]
variance=pearson[:,:,1]
data[10,10]=-1
plt.figure()
plt.subplots(figsize=(8.5/2.54,8.5/2.54/1.618))
plt.matshow(data, cmap='seismic')
plt.xticks(ticks=np.arange(11), labels=['Velocity ccf R', #0,1
'Velocity ccf z', #0,1
'Velocity str max R', #2,3
'Velocity str max z', #2,3
'Size max R', #4,5
'Size max z', #4,5
'Position max R', #6,7
'Position max z', #6,7
'Area max', #8
'Elongation max', #9
'Angle max'], rotation='vertical')
plt.yticks(ticks=np.arange(11), labels=['Velocity ccf R', #0,1
'Velocity ccf z', #0,1
'Velocity str max R', #2,3
'Velocity str max z', #2,3
'Size max R', #4,5
'Size max z', #4,5
'Position max R', #6,7
'Position max z', #6,7
'Area max', #8
'Elongation max', #9
'Angle max'])
plt.colorbar()
plt.show()
pdf.savefig()
plt.figure()
plt.subplots(figsize=(8.5/2.54,8.5/2.54/1.618))
variance[10,10]=-1
variance[9,9]=1
plt.matshow(variance, cmap='seismic')
#plt.matshow(data, cmap='gist_ncar')
plt.xticks(ticks=np.arange(11), labels=['Velocity ccf R', #0,1
'Velocity ccf z', #0,1
'Velocity str max R', #2,3
'Velocity str max z', #2,3
'Size max R', #4,5
'Size max z', #4,5
'Position max R', #6,7
'Position max z', #6,7
'Area max', #8
'Elongation max', #9
'Angle max'], rotation='vertical')
plt.yticks(ticks=np.arange(11), labels=['Velocity ccf R', #0,1
'Velocity ccf z', #0,1
'Velocity str max R', #2,3
'Velocity str max z', #2,3
'Size max R', #4,5
'Size max z', #4,5
'Position max R', #6,7
'Position max z', #6,7
'Area max', #8
'Elongation max', #9
'Angle max'])
plt.colorbar()
plt.show()
pdf.savefig()
pdf.close()
else:
pdf=PdfPages(wd+'/plots/figure_13_dependence.pdf')
plt.figure()
plt.subplots(figsize=(17/2.54,17/2.54/1.618))
plot_all_parameters_vs_all_other_average(window_average=0.2e-3, symbol_size=0.3, plot_error=True)
pdf.savefig()
pdf.close()
def show_nstx_gpi_timetrace(exp_id=None,
plot_filtered=False,
time_range=None,
new_plot=False,
overplot=False,
scale=1.0,
save_pdf=False,
cache_data=True,
):
plot_options={}
if time_range is None:
print('time_range is None, the entire shot is plot.')
slicing_range=None
else:
if (type(time_range) is not list and len(time_range) != 2):
raise TypeError('time_range needs to be a list with two elements.')
plot_options['X range']=time_range
slicing_range={'Time':flap.Intervals(time_range[0],time_range[1])}
if exp_id is not None:
print("\n------- Reading NSTX GPI data --------")
if cache_data:
try:
d=flap.get_data_object_ref(exp_id=exp_id,object_name='GPI')
except:
print('Data is not cached, it needs to be read.')
d=flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
flap.get_data('NSTX_GPI',exp_id=exp_id,name='',object_name='GPI')
else:
raise ValueError('The experiment ID needs to be set.')
flap.slice_data('GPI',
#slicing=slicing_range,
slicing=slicing_range,
summing={'Image x':'Mean','Image y':'Mean'},
output_name='GPI_MEAN')
object_name='GPI_MEAN'
if plot_filtered:
print("**** Filtering GPI")
object_name='GPI_MEAN_FILTERED'
flap.filter_data('GPI_MEAN',output_name='GPI_MEAN_FILTERED',coordinate='Time',
options={'Type':'Highpass',
'f_low':1e2,
'Design':'Chebyshev II'}) #Data is in milliseconds
if scale != 1.0:
d=flap.get_data_object_ref(object_name, exp_id)
d.data=d.data*scale
if new_plot and not overplot:
plt.figure()
elif overplot:
plot_options['Force axes']=True
else:
plt.cla()
plot_options['All points']=True
flap.plot(object_name,
axes=['Time', '__Data__'],
exp_id=exp_id,
options=plot_options)
if save_pdf:
if time_range is not None:
filename='NSTX_'+str(exp_id)+'_GPI_'+str(time_range[0])+'_'+str(time_range[1])+'_mean.pdf'
else:
filename='NSTX_'+str(exp_id)+'_GPI_mean.pdf'
plt.savefig(filename)
def export_gpi_data_to_paraview(exp_id=None,
time_range=None,
filename=None,
filter_data=True,
flux_coordinates=False):
if filename is None:
filename = 'GPI_FOR_PARAVIEW_' + str(exp_id) + '_' + str(
time_range[0]) + '_' + str(time_range[1])
# d=flap.get_data('NSTX_GPI', exp_id=exp_id, name='', object_name='GPI')
# d=flap.slice_data('GPI', slicing={'Time':flap.Intervals(time_range[0],time_range[1])})
# if filter_data:
# d=flap.filter_data('GPI',exp_id=exp_id,
# coordinate='Time',
# options={'Type':'Highpass',
# 'f_low':1e2,
# 'Design':'Chebyshev II'})
# filename=filename+'_FILTERED'
# time=d.coordinate('Time')[0]
# x=d.coordinate('Device R')[0].flatten()
# y=d.coordinate('Device z')[0].flatten()
# t=time.flatten()
# data=d.data.flatten()
# np.savetxt(filename, np.asarray([[x],[y],[1000*t],[data]])[:,0,:].T, delimiter=",", header='x [m], y [m], t [ms], data [a.u.]')
d = flap.get_data('NSTX_GPI', exp_id=exp_id, name='', object_name='GPI')
if filter_data:
d = flap.filter_data('GPI',
exp_id=exp_id,
coordinate='Time',
options={
'Type': 'Highpass',
'f_low': 1e2,
'Design': 'Chebyshev II'
})
filename += '_FILTERED'
if flux_coordinates:
flap.add_coordinate('GPI', 'Flux r')
filename += '_FLUX'
d = flap.slice_data(
'GPI', slicing={'Time': flap.Intervals(time_range[0], time_range[1])})
# time=d.coordinate('Time')[0]
# ind=np.where(np.logical_and(time[:,0,0]>=time_range[0], time[:,0,0]<=time_range[1]))
# x1=d.coordinate('Device R')[0][ind,:,:].flatten()
# y=d.coordinate('Device z')[0][ind,:,:].flatten()
# t=time[ind,:,:].flatten()
# data=d.data[ind,:,:].flatten()
t = d.coordinate('Time')[0].flatten()
x1 = d.coordinate('Device R')[0].flatten()
y = d.coordinate('Device z')[0].flatten()
data = d.data.flatten()
filename = filename + '.csv'
if flux_coordinates:
#x2=d.coordinate('Flux r')[0][ind,:,:].flatten()
x2 = d.coordinate('Flux r')[0].flatten()
x2 = (x2 - np.min(x2)) / (np.max(x2) - np.min(x2)) * (
np.max(x1) - np.min(x1)) + np.min(x1)
np.savetxt(
filename,
np.asarray([[x1], [x2], [y], [10000 * t], [data]])[:, 0, :].T,
delimiter=",",
header='R [m], PSI_rescaled [m], z [m], t [0.1ms], data [a.u.]')
else:
np.savetxt(filename,
np.asarray([[x1], [y], [10000 * t], [data]])[:, 0, :].T,
delimiter=",",
header='R [m], z [m], t [0.1ms], data [a.u.]')
def nstx_gpi_velocity_analysis_spatio_temporal_displacement(exp_id=None, #Shot number
time_range=None, #The time range for the calculation
data_object=None, #Input data object if available from outside (e.g. generated sythetic signal)
x_range=[0,63], #X range for the calculation
y_range=[0,79], #Y range for the calculation
x_search=10,
y_search=10,
fbin=10,
plot=True, #Plot the results
pdf=False, #Print the results into a PDF
plot_error=False, #Plot the errorbars of the velocity calculation based on the line fitting and its RMS error
#File input/output options
filename=None, #Filename for restoring data
nocalc=True, #Restore the results from the .pickle file from filename+.pickle
return_results=False,
):
#Constants for the calculation
#Using the spatial calibration to find the actual velocities.
coeff_r=np.asarray([3.7183594,-0.77821046,1402.8097])/1000. #The coordinates are in meters, the coefficients are in mm
coeff_z=np.asarray([0.18090118,3.0657776,70.544312])/1000. #The coordinates are in meters, the coefficients are in mm
#Input error handling
if exp_id is None and data_object is None:
raise ValueError('Either exp_id or data_object needs to be set for the calculation.')
if data_object is None:
if time_range is None and filename is None:
raise ValueError('It takes too much time to calculate the entire shot, please set a time_range.')
else:
if type(time_range) is not list and filename is None:
raise TypeError('time_range is not a list.')
if filename is None and len(time_range) != 2:
raise ValueError('time_range should be a list of two elements.')
if data_object is not None and type(data_object) == str:
if exp_id is None:
exp_id='*'
d=flap.get_data_object(data_object,exp_id=exp_id)
time_range=[d.coordinate('Time')[0][0,0,0],
d.coordinate('Time')[0][-1,0,0]]
exp_id=d.exp_id
flap.add_data_object(d, 'GPI_SLICED_FULL')
if filename is None:
wd=flap.config.get_all_section('Module NSTX_GPI')['Working directory']
comment=''
filename=flap_nstx.analysis.filename(exp_id=exp_id,
working_directory=wd+'/processed_data',
time_range=time_range,
purpose='sz velocity',
comment=comment)
pickle_filename=filename+'.pickle'
if not os.path.exists(pickle_filename) and nocalc:
print('The pickle file cannot be loaded. Recalculating the results.')
nocalc=False
if nocalc is False:
slicing={'Time':flap.Intervals(time_range[0],time_range[1])}
#Read data
if data_object is None:
print("\n------- Reading NSTX GPI data --------")
d=flap.get_data('NSTX_GPI',exp_id=exp_id,
name='',
object_name='GPI')
d=flap.slice_data('GPI',exp_id=exp_id,
slicing=slicing,
output_name='GPI_SLICED_FULL')
d.data=np.asarray(d.data, dtype='float32')
count=d.data.shape[0]
vpol_p = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count]) # poloidal velocity in km/sec vs. pixel
vrad_p = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count]) # radial velocity vs. pixel
vpol_n = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count]) # poloidal velocity in km/sec vs. pixel
vrad_n = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count]) # radial velocity vs. pixel
vpol = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count])
vrad = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count])
cmax_n = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count])
cmax_p = np.zeros([x_range[1]-x_range[0]+1,y_range[1]-y_range[0]+1,count])
sample_time=d.coordinate('Time')[0][1,0,0]-d.coordinate('Time')[0][0,0,0]
ccorr_n=np.zeros([x_range[1]-x_range[0]+1,
y_range[1]-y_range[0]+1,
x_range[1]-x_range[0]+2*x_search+1,
y_range[1]-y_range[0]+2*y_search+1])
ccorr_p=np.zeros([x_range[1]-x_range[0]+1,
y_range[1]-y_range[0]+1,
x_range[1]-x_range[0]+2*x_search+1,
y_range[1]-y_range[0]+2*y_search+1])
for t0 in range(fbin+1,count-fbin-1):
#Zero lag Autocorrelation calculation for the reference, +sample_time, -sample_time data
n_data=d.data[t0-fbin-1:t0+fbin-1,
x_range[0]-x_search:x_range[1]+x_search+1,
y_range[0]-y_search:y_range[1]+y_search+1]
acorr_pix_n=np.sqrt(np.sum((n_data-np.mean(n_data, axis=0))**2,axis=0))
p_data=d.data[t0-fbin+1:t0+fbin+1,
x_range[0]-x_search:x_range[1]+x_search+1,
y_range[0]-y_search:y_range[1]+y_search+1]
acorr_pix_p=np.sqrt(np.sum((p_data-np.mean(p_data, axis=0))**2,axis=0))
ref_data=d.data[t0-fbin:t0+fbin,
x_range[0]:x_range[1]+1,
y_range[0]:y_range[1]+1]
acorr_pix_ref=np.sqrt(np.sum((ref_data-np.mean(ref_data, axis=0))**2,axis=0))
print((t0-fbin-1)/(count-2*(fbin-1))*100.)
#Zero lag Crosscovariance calculation for the positive and negative sample time signal
for i0 in range(x_range[1]-x_range[0]+1):
for j0 in range(y_range[1]-y_range[0]+1):
frame_ref=d.data[t0-fbin:t0+fbin,i0+x_range[0],j0+y_range[0]]
frame_ref=frame_ref-np.mean(frame_ref)
for i1 in range(2*x_search+1):
for j1 in range(2*y_search+1):
frame_n=d.data[t0-fbin-1:t0+fbin-1,
i1+i0+x_range[0]-x_search,
j1+j0+y_range[0]-y_search]
frame_n=frame_n-np.mean(frame_n)
frame_p=d.data[t0-fbin+1:t0+fbin+1,
i1+i0+x_range[0]-x_search,
j1+j0+y_range[0]-y_search]
frame_p=frame_p-np.mean(frame_p)
ccorr_n[i0,j0,i1,j1]=np.sum(frame_ref*frame_n)
ccorr_p[i0,j0,i1,j1]=np.sum(frame_ref*frame_p)
#Calculating the actual cross-correlation coefficients
for i0 in range(x_range[1]-x_range[0]+1):
for j0 in range(y_range[1]-y_range[0]+1):
vcorr_p=np.zeros([2*x_search+1,2*y_search+1])
vcorr_n=np.zeros([2*x_search+1,2*y_search+1])
for i1 in range(2*x_search+1):
for j1 in range(2*y_search+1):
vcorr_p[i1,j1]=ccorr_p[i0,j0,i1,j1]/(acorr_pix_ref[i0,j0]*acorr_pix_p[i0+i1,j0+j1])
vcorr_n[i1,j1]=ccorr_n[i0,j0,i1,j1]/(acorr_pix_ref[i0,j0]*acorr_pix_n[i0+i1,j0+j1])
#Calculating the displacement in pixel coordinates
index_p=np.unravel_index(np.argmax(vcorr_p),shape=vcorr_p.shape)
index_n=np.unravel_index(np.argmax(vcorr_n),shape=vcorr_n.shape)
cmax_p[i0,j0,t0]=vcorr_p[index_p]
cmax_n[i0,j0,t0]=vcorr_n[index_n]
#Transforming the coordinates into spatial coordinates
delta_index_p=np.asarray(index_p)-np.asarray([x_search,y_search])
delta_index_n=np.asarray(index_n)-np.asarray([x_search,y_search])
vpol_p[i0,j0,t0]=(coeff_z[0]*delta_index_p[0]+
coeff_z[1]*delta_index_p[1])/sample_time
vpol_n[i0,j0,t0]=(coeff_z[0]*delta_index_n[0]+
coeff_z[1]*delta_index_n[1])/sample_time
vrad_p[i0,j0,t0]=(coeff_r[0]*delta_index_p[0]+
coeff_r[1]*delta_index_p[1])/sample_time
vrad_n[i0,j0,t0]=(coeff_r[0]*delta_index_n[0]+
coeff_r[1]*delta_index_n[1])/sample_time
#Calculating the average between the positive and negative shifted pixels
vpol_tot = (vpol_p - vpol_n)/2. # Average p and n correlations
vrad_tot = (vrad_p - vrad_n)/2. # This is non causal
#Averaging in an fbin long time window
for t0 in range(int(fbin/2),count-int(fbin/2)):
vpol[:,:,t0] = np.mean(vpol_tot[:,:,t0-int(fbin/2):t0+int(fbin/2)], axis=2)
vrad[:,:,t0] = np.mean(vrad_tot[:,:,t0-int(fbin/2):t0+int(fbin/2)], axis=2)
results={'Time':d.coordinate('Time')[0][:,0,0],
'Radial velocity':vrad,
'Poloidal velocity':vpol,
'Maximum correlation p':cmax_p,
'Maximum correlation n':cmax_n}
pickle.dump(results, open(pickle_filename, 'wb'))
else:
results=pickle.load(open(pickle_filename, 'rb'))
print('Data loaded from pickle file.')
if pdf:
pdf=PdfPages(filename.replace('processed_data', 'plots')+'.pdf')
if plot:
plt.figure()
plt.errorbar(results['Time'],
np.mean(results['Radial velocity'], axis=(0,1)),
np.sqrt(np.var(results['Radial velocity'], axis=(0,1))))
plt.title('Radial velocity vs time')
plt.xlabel('Time [s]')
plt.ylabel('Radial velocity [m/s]')
if pdf:
pdf.savefig()
plt.figure()
plt.errorbar(results['Time'],
np.mean(results['Poloidal velocity'], axis=(0,1)),
np.sqrt(np.var(results['Poloidal velocity'], axis=(0,1))))
plt.title('Poloidal velocity vs time')
plt.xlabel('Time [s]')
plt.ylabel('Poloidal velocity [m/s]')
plt.pause(0.001)
if pdf:
pdf.savefig()
plt.figure()
plt.errorbar(results['Time'],
np.mean(results['Maximum correlation p'], axis=(0,1)),
np.sqrt(np.var(results['Maximum correlation p'], axis=(0,1))))
plt.title('Maximum correlation p vs time')
plt.xlabel('Time [s]')
plt.ylabel('Maximum correlation p')
plt.pause(0.001)
if pdf:
pdf.savefig()
pdf.close()
if return_results:
return results