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 test_filter():
plt.close('all')
print()
print('>>>>>>>>>>>>>>>>>>> Test filter <<<<<<<<<<<<<<<<<<<<<<<<')
flap.delete_data_object('*')
print(
"**** Generating 10 square wave signals and filtering with integrating filter, 10 microsec"
)
t = np.arange(1000) * 1e-6
d = np.ndarray((len(t), 10), dtype=float)
for i in range(10):
d[:, i] = np.sign(np.sin(math.pi * 2 * (1e4 + i * 1e3) * t)) + 1
c = flap.Coordinate(name='Time',
unit='Second',
mode=flap.CoordinateMode(equidistant=True),
start=0.0,
step=1e-6,
dimension_list=[0])
d = flap.DataObject(data_array=d, coordinates=[c])
flap.add_data_object(d, "Signal")
plt.figure()
d.plot(options={'Y sep': 3})
di = d.filter_data(coordinate='Time',
intervals=flap.Intervals(np.array([1e-4, 6e-4]),
np.array([2e-4, 8e-4])),
options={
'Type': 'Int',
'Tau': 10e-6
}).plot(options={'Y sep': 3})
print("**** Filtering with differential filter, 10 microsec")
plt.figure()
d.plot(options={'Y sep': 3})
flap.filter_data('Signal',
output_name='Signal_filt',
coordinate='Time',
intervals=flap.Intervals(np.array([1e-4, 6e-4]),
np.array([2e-4, 8e-4])),
options={
'Type': 'Diff',
'Tau': 10e-6
})
flap.plot('Signal_filt', options={'Y sep': 3})
print(
"**** Generating random data, 1 million points and overplotting spectra with various filters."
)
d = flap.get_data('TESTDATA',
name='TEST-1-1',
options={
'Signal': 'Random',
'Scaling': 'Digit',
'Length': 1
},
object_name='Signal')
plt.figure()
flap.filter_data('Signal',
output_name='Signal_filt',
coordinate='Time',
options={
'Type': 'Int',
'Tau': 16e-6
})
flap.apsd('Signal',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid = flap.apsd('Signal_filt',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid.plt_axis_list[-1].set_title("{'Type':'Int','Tau':16e-6}")
plt.figure()
flap.filter_data('Signal',
output_name='Signal_filt',
coordinate='Time',
options={
'Type': 'Diff',
'Tau': 16e-6
})
flap.apsd('Signal',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid = flap.apsd('Signal_filt',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid.plt_axis_list[-1].set_title("{'Type':'Diff','Tau':16e-6}")
plt.figure()
flap.filter_data('Signal',
output_name='Signal_filt',
coordinate='Time',
options={
'Type': 'Lowpass',
'f_high': 5e4
})
flap.apsd('Signal',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid = flap.apsd('Signal_filt',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid.plt_axis_list[-1].set_title("{'Type':'Lowpass','f_high':5e4}")
plt.figure()
flap.filter_data('Signal',
output_name='Signal_filt',
coordinate='Time',
options={
'Type': 'Highpass',
'f_low': 1e4,
'f_high': 5e4
})
flap.apsd('Signal',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid = flap.apsd('Signal_filt',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid.plt_axis_list[-1].set_title(
"{'Type':'Highpass','f_low':1e4,'f_high':5e4}")
plt.figure()
flap.filter_data('Signal',
output_name='Signal_filt',
coordinate='Time',
options={
'Type': 'Bandpass',
'f_low': 5e3,
'f_high': 5e4
})
flap.apsd('Signal',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid = flap.apsd('Signal_filt',options={'Log':True,'Res':20,'Range':[100,5e5]},output_name='Signal_APSD')\
.plot(options={'Log x':True, 'Log y': True})
plotid.plt_axis_list[-1].set_title(
"{'Type':'Bandpass','f_low':5e3,'f_high':5e4}")
plt.figure()
print("**** Bandpower signal [5e4-2e5] Hz, inttime 20 microsec")
flap.filter_data('Signal',
output_name='Signal_filt',
coordinate='Time',
options={
'Type': 'Bandpass',
'f_low': 5e4,
'f_high': 2e5,
'Power': True,
'Inttime': 20e-6
})
plotid = flap.plot('Signal_filt')
plotid.plt_axis_list[-1].set_title(
"'Type':'Bandpass','f_low':5e4,'f_high':2e5, 'Power':True, 'Inttime':20e-6}"
)
def nstx_gpi_generate_synthetic_data(exp_id=None, #Artificial exp_id, should be starting from zero and not the one which is used by e.g. background_shot
time=None, #Time to be simulated in seconds
sampling_time=2.5e-6, #The sampling time of the diagnostic
#General parameters
n_structures=3,
amplitude=0.5, #Amplitude of the structure relative to the background.
add_background=True,
background_shot=139901, #The original of the background of the simulated signal.
background_time_range=[0.31,0.32], #The time range of the background in the background_shot.
poloidal_velocity=1e3, #The poloidal velocity of the structures, can be a list [n_structure]
start_position=[1.41,0.195],
radial_size=0.3, #The radial size of the structures.
poloidal_size=0.1, #The poloidal size of the structures.
#Parameters for a gaussian object
gaussian=False,
radial_velocity=1e2, #The radial velocity of the structures, can be a list [n_structure]
poloidal_size_velocity=0., #The velocity of the size change in mm/ms
radial_size_velocity=0.,
rotation=False, #Set rotation for the structure.
rotation_frequency=None, #Set the frequency of the rotation of the structure.
#Parameters for sinusoidal object
sinusoidal=False,
waveform_divider=1,
y_lambda=0.05, #Wavelength in the y direction
output_name=None, #Output name of the generated flap.data_object
test=False, #Testing/debugging switch (mainly plotting and printing error messages)
):
if rotation_frequency is None:
rotation_frequency=0
n_time=int(time/sampling_time)
data_arr=np.zeros([n_time,64,80])
background=np.zeros([64,80])
if add_background:
background=flap.get_data('NSTX_GPI',
exp_id=139901,
name='',
object_name='GPI_RAW')
background=background.slice_data(slicing={'Time':flap.Intervals(background_time_range[0],
background_time_range[1])},
summing={'Time':'Mean'})
amplitude=amplitude*background.data.max()
#Spatial positions
coeff_r=np.asarray([3.7183594,-0.77821046,1402.8097])/1000. #The coordinates are in meters
coeff_z=np.asarray([0.18090118,3.0657776,70.544312])/1000. #The coordinates are in meters
r_coordinates=np.zeros([64,80])
z_coordinates=np.zeros([64,80])
for i_x in range(64):
for i_y in range(80):
r_coordinates[i_x,i_y]=coeff_r[0]*i_x+coeff_r[1]*i_y+coeff_r[2]
z_coordinates[i_x,i_y]=coeff_z[0]*i_x+coeff_z[1]*i_y+coeff_z[2]
if gaussian:
r0=start_position
for i_frames in range(n_time):
for i_structures in range(n_structures):
cur_time=i_frames * sampling_time
rot_arg=2*np.pi*rotation_frequency*cur_time
a=(np.cos(rot_arg)/(radial_size+radial_size_velocity*cur_time))**2+\
(np.sin(rot_arg)/(poloidal_size+poloidal_size_velocity*cur_time))**2
b=-0.5*np.sin(2*rot_arg)/(radial_size+radial_size_velocity*cur_time)**2+\
0.5*np.sin(2*rot_arg)/(poloidal_size+poloidal_size_velocity*cur_time)**2
c=(np.sin(rot_arg)/(radial_size+radial_size_velocity*cur_time))**2+\
(np.cos(rot_arg)/(poloidal_size+poloidal_size_velocity*cur_time))**2
x0=r0[i_structures,0]+radial_velocity[i_structures]*cur_time
y0=r0[i_structures,1]+poloidal_velocity[i_structures]*cur_time
frame=np.zeros([64,80])
for j_vertical in range(80):
for k_radial in range(64):
x=r_coordinates[k_radial,j_vertical]
y=z_coordinates[k_radial,j_vertical]
if (x > x0+radial_size*2 or
x < x0-radial_size*2 or
y > y0+radial_size*2 or
y < y0-radial_size*2):
frame[k_radial,j_vertical]=0.
else:
frame[k_radial,j_vertical]=(amplitude[i_structures]*np.exp(-0.5*(a*(x-x0)**2 +
2*b*(x-x0)*(y-y0) +
c*(y-y0)**2))
+background.data[k_radial,j_vertical])
data_arr[i_frames,:,:]+=frame
if sinusoidal:
x0=start_position[0]
y0=start_position[1]
ky=np.pi/poloidal_size
omega=poloidal_velocity*ky
phi0=0
for i_frames in range(n_time):
cur_time=i_frames * sampling_time
for j_vertical in range(80):
for k_radial in range(64):
x=r_coordinates[k_radial,j_vertical]
y=z_coordinates[k_radial,j_vertical]
A=1/np.sqrt(2*np.pi*radial_size)*np.exp(-0.5*(np.abs(x-(x0+radial_velocity*cur_time))/(radial_size/2.355))**2)
arg=ky*y-omega*cur_time+phi0
division=(scipy.signal.square(arg/waveform_divider, duty=0.5/waveform_divider)+1)/2.
data_arr[i_frames,k_radial,j_vertical]=amplitude*A*np.sin(arg)*division
data_arr[i_frames,k_radial,j_vertical]+=background.data[k_radial,j_vertical]
#Adding the coordinates to the data object:
coord = [None]*6
coord[0]=(copy.deepcopy(flap.Coordinate(name='Time',
unit='s',
mode=flap.CoordinateMode(equidistant=True),
start=0.,
step=sampling_time,
#shape=time_arr.shape,
dimension_list=[0]
)))
coord[1]=(copy.deepcopy(flap.Coordinate(name='Sample',
unit='n.a.',
mode=flap.CoordinateMode(equidistant=True),
start=0,
step=1,
dimension_list=[0]
)))
coord[2]=(copy.deepcopy(flap.Coordinate(name='Image x',
unit='Pixel',
mode=flap.CoordinateMode(equidistant=True),
start=0,
step=1,
shape=[],
dimension_list=[1]
)))
coord[3]=(copy.deepcopy(flap.Coordinate(name='Image y',
unit='Pixel',
mode=flap.CoordinateMode(equidistant=True),
start=0,
step=1,
shape=[],
dimension_list=[2]
)))
coord[4]=(copy.deepcopy(flap.Coordinate(name='Device R',
unit='m',
mode=flap.CoordinateMode(equidistant=False),
values=r_coordinates,
shape=r_coordinates.shape,
dimension_list=[1,2]
)))
coord[5]=(copy.deepcopy(flap.Coordinate(name='Device z',
unit='m',
mode=flap.CoordinateMode(equidistant=False),
values=z_coordinates,
shape=z_coordinates.shape,
dimension_list=[1,2]
)))
_options={}
_options["Trigger time [s]"]=0.
_options["FPS"]=1/sampling_time
_options["Sample time [s]"]=sampling_time
_options["Exposure time [s]"]=2.1e-6
_options["X size"]=64
_options["Y size"]=80
_options["Bits"]=32
d = flap.DataObject(data_array=data_arr,
data_unit=flap.Unit(name='Signal',unit='Digit'),
coordinates=coord,
exp_id=exp_id,
data_title='Simulated signal',
info={'Options':_options},
data_source="NSTX_GPI")
if output_name is not None:
flap.add_data_object(d,output_name)
return d
def get_fit_nstx_thomson_profiles(
exp_id=None, #Shot number
pressure=False, #Return the pressure profile paramenters
temperature=False, #Return the temperature profile parameters
density=False, #Return the density profile parameters
spline_data=False, #Calculate the results from the spline data (no error is going to be taken into account)
device_coordinates=False, #Calculate the results as a function of device coordinates
radial_range=None, #Radial range of the pedestal (only works when the device coorinates is set)
flux_coordinates=False, #Calculate the results in flux coordinates
flux_range=None, #The normalaized flux coordinates range for returning the results
test=False,
output_name=None,
return_parameters=False,
plot_time=None,
pdf_object=None,
):
"""
Returns a dataobject which has the largest corresponding gradient based on the tanh fit.
Fitting is based on publication https://aip.scitation.org/doi/pdf/10.1063/1.4961554
The linear background is not usitlized, instead of the mtanh, only tanh is used.
"""
if ((device_coordinates and flux_range is not None)
or (flux_coordinates and radial_range is not None)):
raise ValueError(
'When flux or device coordinates are set, only flux or radial range can be set! Returning...'
)
d = flap_nstx_thomson_data(exp_id=exp_id,
force_mdsplus=False,
pressure=pressure,
temperature=temperature,
density=density,
spline_data=False,
add_flux_coordinates=True,
output_name='THOMSON_DATA')
if flux_coordinates:
r_coord_name = 'Flux r'
if device_coordinates:
r_coord_name = 'Device R'
time = d.coordinate('Time')[0][0, :]
thomson_profile = {
'Time': time,
'Data': d.data,
'Error': d.error,
'Device R': d.coordinate('Device R')[0],
'Flux r': d.coordinate('Flux r')[0],
'Height': np.zeros(time.shape),
'Width': np.zeros(time.shape),
'Slope': np.zeros(time.shape),
'Position': np.zeros(time.shape),
'SOL offset': np.zeros(time.shape),
'Max gradient': np.zeros(time.shape),
'Error': {
'Height': np.zeros(time.shape),
'SOL offset': np.zeros(time.shape),
'Position': np.zeros(time.shape),
'Width': np.zeros(time.shape),
'Max gradient': np.zeros(time.shape)
}
}
if test:
plt.figure()
if flux_range is not None:
x_range = flux_range
if radial_range is not None:
x_range = radial_range
# def mtanh_fit_function(r, b_height, b_sol, b_pos, b_width, b_slope): #This version of the code is not working due to the b_slope linear dependence
# def mtanh(x,b_slope):
# return ((1+b_slope*x)*np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))
# return (b_height-b_sol)/2*(mtanh((b_pos-r)/(2*b_width),b_slope)+1)+b_sol
def tanh_fit_function(r, b_height, b_sol, b_pos, b_width):
def tanh(x):
return (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))
return (b_height - b_sol) / 2 * (tanh(
(b_pos - r) / (2 * b_width)) + 1) + b_sol
for i_time in range(len(time)):
x_data = d.coordinate(r_coord_name)[0][:, i_time]
y_data = d.data[:, i_time]
y_data_error = d.error[:, i_time]
if r_coord_name == 'Flux r':
x_data = x_data[np.argmin(x_data):]
if np.sum(np.isinf(x_data)) != 0:
continue
try:
ind_coord = np.where(
np.logical_and(x_data > x_range[0], x_data <= x_range[1]))
x_data = x_data[ind_coord]
y_data = y_data[ind_coord]
y_data_error = y_data_error[ind_coord]
# print(x_data)
# print(ind_coord)
p0 = [
y_data[0], #b_height
y_data[-1], #b_sol
x_data[0], #b_pos
(x_data[-1] - x_data[0]) / 2., #b_width
#(y_data[0]-y_data[-1])/(x_data[0]-x_data[-1]), #b_slope this is supposed to be some kind of liear modification to the
#tanh function called mtanh. It messes up the fitting quite a bit and it's not useful at all.
]
popt, pcov = curve_fit(tanh_fit_function,
x_data,
y_data,
sigma=y_data_error,
p0=p0)
perr = np.sqrt(np.diag(pcov))
if test or (plot_time is not None
and np.abs(plot_time - time[i_time]) < 1e-3):
plt.cla()
plt.scatter(x_data, y_data, color='tab:blue')
plt.errorbar(x_data,
y_data,
yerr=y_data_error,
marker='o',
color='tab:blue',
ls='')
plt.plot(x_data, tanh_fit_function(x_data, *popt))
if flux_coordinates:
xlabel = 'PSI_norm'
else:
xlabel = 'Device R [m]'
if temperature:
profile_string = 'temperature'
ylabel = 'Temperature [keV]'
elif density:
profile_string = 'density'
ylabel = 'Density [1/m3]'
elif pressure:
profile_string = 'pressure'
ylabel = 'Pressure [kPa]'
time_string = ' @ ' + str(time[i_time])
plt.title('Fit ' + profile_string + ' profile of ' +
str(exp_id) + time_string)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.pause(0.001)
if pdf_object is not None:
pdf_object.savefig()
else:
pass
# plt.plot(x_data,mtanh_fit_function(x_data,*p0))
thomson_profile['Height'][i_time] = popt[0]
thomson_profile['SOL offset'][i_time] = popt[1]
thomson_profile['Position'][i_time] = popt[2]
thomson_profile['Width'][i_time] = popt[3]
thomson_profile['Max gradient'][i_time] = (popt[1] - popt[0]) / (
popt[3]) #from paper and pen calculation
thomson_profile['Error']['Height'][i_time] = perr[0]
thomson_profile['Error']['SOL offset'][i_time] = perr[1]
thomson_profile['Error']['Position'][i_time] = perr[2]
thomson_profile['Error']['Width'][i_time] = perr[3]
thomson_profile['Error']['Max gradient'][i_time] = 1 / (np.abs(
popt[3])) * (np.abs(perr[1]) + np.abs(perr[0])) + np.abs(
(popt[1] - popt[0]) / (popt[3]**2)) * np.abs(perr[3])
#thomson_profile_parameters['Slope'][i_time]=popt[4]
except:
popt = [np.nan, np.nan, np.nan, np.nan]
coord = []
coord.append(
copy.deepcopy(
flap.Coordinate(
name='Time',
unit='s',
mode=flap.CoordinateMode(equidistant=True),
start=time[0],
step=time[1] - time[0],
#shape=time_arr.shape,
dimension_list=[0])))
coord.append(
copy.deepcopy(
flap.Coordinate(name='Sample',
unit='n.a.',
mode=flap.CoordinateMode(equidistant=True),
start=0,
step=1,
dimension_list=[0])))
if device_coordinates:
grad_unit = '/m'
if flux_coordinates:
grad_unit = '/psi'
if pressure:
data_unit = flap.Unit(name='Pressure gradient', unit='kPa' + grad_unit)
elif temperature:
data_unit = flap.Unit(name='Temperature gradient',
unit='keV' + grad_unit)
elif density:
data_unit = flap.Unit(name='Density gradient', unit='m-3' + grad_unit)
if spline_data:
data_title = 'NSTX Thomson gradient'
else:
data_title = 'NSTX Thomson gradient spline'
d = flap.DataObject(exp_id=exp_id,
data_array=thomson_profile['Max gradient'],
data_unit=data_unit,
coordinates=coord,
data_title=data_title)
if output_name is not None:
flap.add_data_object(d, output_name)
if not return_parameters:
return d
else:
return thomson_profile
def test_image():
plt.close('all')
print()
print('>>>>>>>>>>>>>>>>>>> Test image <<<<<<<<<<<<<<<<<<<<<<<<')
flap.delete_data_object('*')
print("**** Generating a sequence of test images")
flap.get_data('TESTDATA',
name='VIDEO',
object_name='TEST_VIDEO',
options={
'Length': 0.1,
'Samplerate': 1e3,
'Frequency': 10,
'Spotsize': 100
})
flap.list_data_objects()
print("***** Showing one image")
plt.figure()
flap.plot('TEST_VIDEO',
slicing={'Time': 30e-3 / 4},
plot_type='image',
axes=['Image x', 'Image y'],
options={'Clear': True})
plt.figure()
print("**** Showing a sequence of images and saving to test_video.avi")
flap.plot('TEST_VIDEO',
plot_type='anim-image',
axes=['Image x', 'Image y', 'Time'],
options={
'Z range': [0, 4095],
'Wait': 0.01,
'Clear': True,
'Video file': 'test_video.avi',
'Colorbar': True,
'Aspect ratio': 'equal'
})
plt.figure()
print(
"*** Showing the same images as contour plots and saving to test_video_contour.avi"
)
flap.plot('TEST_VIDEO',
plot_type='anim-contour',
axes=['Image x', 'Image y', 'Time'],
options={
'Z range': [0, 4095],
'Wait': 0.01,
'Clear': True,
'Video file': 'test_video_contour.avi',
'Colorbar': False
})
print("*** Converting data object x, y coordinates to non-equidistant.")
d = flap.get_data_object('TEST_VIDEO')
coord_x = d.get_coordinate_object('Image x')
index = [0] * 3
index[coord_x.dimension_list[0]] = ...
x = np.squeeze(d.coordinate('Image x', index=index)[0])
coord_x.mode.equidistant = False
coord_x.values = x
coord_x.shape = x.shape
coord_y = d.get_coordinate_object('Image y')
index = [0] * 3
index[coord_y.dimension_list[0]] = ...
y = np.squeeze(d.coordinate('Image y', index=index)[0])
coord_y.mode.equidistant = False
coord_y.values = y
coord_y.shape = y.shape
flap.add_data_object(d, "TEST_VIDEO_noneq")
flap.list_data_objects()
plt.figure()
print("**** Showing this video and saving to test_video_noneq.avi")
flap.plot('TEST_VIDEO_noneq',
plot_type='anim-image',
axes=['Image x', 'Image y', 'Time'],
options={
'Z range': [0, 4095],
'Wait': 0.01,
'Clear': True,
'Video file': 'test_video_noneq.avi',
'Colorbar': True,
'Aspect ratio': 'equal'
})
def flap_nstx_thomson_data(exp_id=None,
force_mdsplus=False,
pressure=False,
temperature=False,
density=False,
spline_data=False,
add_flux_coordinates=True,
output_name=None,
test=False):
"""
Returns the Thomson scattering processed data from the MDSplus tree as
a dictionary containing all the necessary parameters. The description of
the dictionary can be seen below.
"""
if pressure + temperature + density != 1:
raise ValueError(
'Either pressure or temperature or density can be set, neither none, nor more than one.'
)
if exp_id is None:
raise TypeError('exp_id must be set.')
wd = flap.config.get_all_section('Module NSTX_GPI')['Local datapath']
filename = wd + '/' + str(exp_id) + '/nstx_mdsplus_thomson_' + str(
exp_id) + '.pickle'
if not os.path.exists(filename) or force_mdsplus:
conn = mds.Connection('skylark.pppl.gov:8501')
conn.openTree('activespec', exp_id)
mdsnames = [
'ts_times', #The time vector of the measurement (60Hz measurement with the Thomson)
'FIT_RADII', #Radius of the measurement
'FIT_R_WIDTH', #N/A (proably error of the radious)
'FIT_TE', #Electron temperature profile numpy array([radius,time])
'FIT_TE_ERR', #The error for Te (symmetric)
'FIT_NE', #Electron density profile numpy array([radius,time])
'FIT_NE_ERR', #The error for ne (symmetric)
'FIT_PE', #Electron pressure profile numpy array([radius,time])
'FIT_PE_ERR', #The error for pe (symmetric)
'SPLINE_RADII', #Spline fit of the previous results (4times interpolation compared to the previous ones)
'SPLINE_NE', #Spline fit ne without error
'SPLINE_PE', #Spline fit pe without error
'SPLINE_TE', #Spline fit Te without error
'TS_LD', #N/A
'LASER_ID', #ID of the Thomson laser
'VALID', #Validity of the measurement
'DATEANALYZED', #The date when the analysis was done for the data
'COMMENT'
] #Comment for the analysis
thomson = {}
for name in mdsnames:
thomson[name] = conn.get('\TS_BEST:' + name).data()
if name == 'ts_times' and type(thomson[name]) is str:
raise ValueError('No Thomson data available.')
thomson['FIT_R_WIDTH'] /= 100.
thomson['FIT_RADII'] /= 100.
thomson['SPLINE_RADII'] /= 100.
thomson['FIT_NE'] *= 10e6
thomson['FIT_NE_ERR'] *= 10e6
thomson['SPLINE_NE'] *= 10e6
conn.closeAllTrees()
conn.disconnect()
try:
pickle.dump(thomson, open(filename, 'wb'))
except:
raise IOError(
'The path ' + filename +
' cannot be accessed. Pickle file cannot be created.')
else:
thomson = pickle.load(open(filename, 'rb'))
thomson_time = thomson['ts_times']
coord = []
coord.append(
copy.deepcopy(
flap.Coordinate(
name='Time',
unit='s',
mode=flap.CoordinateMode(equidistant=True),
start=thomson_time[0],
step=thomson_time[1] - thomson_time[0],
#shape=time_arr.shape,
dimension_list=[1])))
coord.append(
copy.deepcopy(
flap.Coordinate(name='Sample',
unit='n.a.',
mode=flap.CoordinateMode(equidistant=True),
start=0,
step=1,
dimension_list=[1])))
if spline_data:
thomson_r_coord = thomson['SPLINE_RADII']
if pressure:
data_arr = thomson['SPLINE_PE']
data_arr_err = None
data_unit = flap.Unit(name='Pressure', unit='kPa')
elif temperature:
data_arr = thomson['SPLINE_TE']
data_arr_err = None
data_unit = flap.Unit(name='Temperature', unit='keV')
elif density:
data_arr = thomson['SPLINE_NE']
data_arr_err = None
data_unit = flap.Unit(name='Density', unit='m-3')
else:
thomson_r_coord = thomson['FIT_RADII']
if pressure:
data_arr = thomson['FIT_PE']
data_arr_err = thomson['FIT_PE_ERR']
data_unit = flap.Unit(name='Pressure', unit='kPa')
elif temperature:
data_arr = thomson['FIT_TE']
data_arr_err = thomson['FIT_TE_ERR']
data_unit = flap.Unit(name='Temperature', unit='keV')
elif density:
data_arr = thomson['FIT_NE']
data_arr_err = thomson['FIT_NE_ERR']
data_unit = flap.Unit(name='Density', unit='m-3')
coord.append(
copy.deepcopy(
flap.Coordinate(name='Device R',
unit='m',
mode=flap.CoordinateMode(equidistant=False),
values=thomson_r_coord,
shape=thomson_r_coord.shape,
dimension_list=[0])))
if test:
plt.figure()
if add_flux_coordinates:
try:
psi_rz_obj = flap.get_data('NSTX_MDSPlus',
name='\EFIT02::\PSIRZ',
exp_id=exp_id,
object_name='PSIRZ_FOR_COORD')
psi_mag = flap.get_data('NSTX_MDSPlus',
name='\EFIT02::\SSIMAG',
exp_id=exp_id,
object_name='SSIMAG_FOR_COORD')
psi_bdry = flap.get_data('NSTX_MDSPlus',
name='\EFIT02::\SSIBRY',
exp_id=exp_id,
object_name='SSIBRY_FOR_COORD')
except:
raise ValueError("The PSIRZ MDSPlus node cannot be reached.")
psi_values = psi_rz_obj.data[:, :, 32]
psi_t_coord = psi_rz_obj.coordinate('Time')[0][:, 0, 0]
psi_r_coord = psi_rz_obj.coordinate(
'Device R')[0][:, :,
32] #midplane is the middle coordinate in the array
#Do the interpolation
psi_values_spat_interpol = np.zeros(
[thomson_r_coord.shape[0], psi_t_coord.shape[0]])
for index_t in range(psi_t_coord.shape[0]):
norm_psi_values = (psi_values[index_t, :] - psi_mag.data[index_t]
) / (psi_bdry.data[index_t] -
psi_mag.data[index_t])
norm_psi_values[np.isnan(norm_psi_values)] = 0.
psi_values_spat_interpol[:, index_t] = np.interp(
thomson_r_coord, psi_r_coord[index_t, :], norm_psi_values)
psi_values_total_interpol = np.zeros(data_arr.shape)
for index_r in range(data_arr.shape[0]):
psi_values_total_interpol[index_r, :] = np.interp(
thomson_time, psi_t_coord,
psi_values_spat_interpol[index_r, :])
if test:
for index_t in range(len(thomson_time)):
plt.cla()
plt.plot(thomson_r_coord, psi_values_total_interpol[:,
index_t])
plt.pause(0.5)
psi_values_total_interpol[np.isnan(psi_values_total_interpol)] = 0.
coord.append(
copy.deepcopy(
flap.Coordinate(name='Flux r',
unit='',
mode=flap.CoordinateMode(equidistant=False),
values=psi_values_total_interpol,
shape=psi_values_total_interpol.shape,
dimension_list=[0, 1])))
if test:
plt.plot(psi_values_total_interpol, data_arr)
d = flap.DataObject(data_array=data_arr,
error=data_arr_err,
data_unit=data_unit,
coordinates=coord,
exp_id=exp_id,
data_title='NSTX Thomson data')
if output_name is not None:
flap.add_data_object(d, output_name)
return d
def get_nstx_thomson_gradient(exp_id=None,
pressure=False,
temperature=False,
density=False,
r_pos=None,
spline_data=False,
output_name=None,
device_coordinates=True,
flux_coordinates=False):
#Data is RADIUS x TIME
if pressure + density + temperature != 1:
raise ValueError(
'Only one of the inputs should be set (pressure, temperature, density)!'
)
if device_coordinates + flux_coordinates != 1:
raise ValueError(
'Either device_coordinates or flux_coordinates can be set, not both.'
)
thomson = flap_nstx_thomson_data(exp_id,
pressure=pressure,
temperature=temperature,
density=density,
output_name='THOMSON_FOR_GRADIENT')
thomson_spline = flap_nstx_thomson_data(exp_id,
pressure=pressure,
temperature=temperature,
density=density,
spline_data=True,
output_name=None)
if device_coordinates:
radial_coordinate = thomson.coordinate('Device R')[0][:, 0]
spline_radial_coordinate = thomson_spline.coordinate('Device R')[0][:,
0]
if flux_coordinates:
radial_coordinate = thomson.coordinate('Flux r')[0][:, 0]
spline_radial_coordinate = thomson_spline.coordinate('Flux r')[0][:, 0]
time_vector = thomson.coordinate('Time')[0][0, :]
data = thomson.data
error = thomson.error
interp_data = thomson_spline.data
#Calculation of the numerical gradient and interpolating the values for the given r_pos
data_gradient = np.asarray([
(data[2:, i] - data[:-2, i]) /
(2 * (radial_coordinate[2:] - radial_coordinate[:-2]))
for i in range(len(time_vector))
]).T
data_gradient_error = np.asarray([
(np.abs(error[2:, i]) + np.abs(error[:-2, i])) /
(2 * (radial_coordinate[2:] - radial_coordinate[:-2]))
for i in range(len(time_vector))
]).T
interp_data_gradient = np.asarray([
(interp_data[2:, i] - interp_data[:-2, i]) /
(2 * (spline_radial_coordinate[2:] - spline_radial_coordinate[:-2]))
for i in range(len(time_vector))
]).T
#Interpolation for the r_pos
if r_pos is not None:
r_pos_gradient = np.asarray([
np.interp(r_pos, radial_coordinate[1:-1], data_gradient[:, i])
for i in range(len(time_vector))
])
r_pos_gradient_spline = np.asarray([
np.interp(r_pos, spline_radial_coordinate[1:-1],
interp_data_gradient[:, i])
for i in range(len(time_vector))
])
ind_r = np.argmin(np.abs(radial_coordinate[1:-1] - r_pos))
if radial_coordinate[ind_r] < r_pos:
R1 = radial_coordinate[1:-1][ind_r]
R2 = radial_coordinate[1:-1][ind_r + 1]
ind_R1 = ind_r
ind_R2 = ind_r + 1
else:
R1 = radial_coordinate[1:-1][ind_r - 1]
R2 = radial_coordinate[1:-1][ind_r]
ind_R1 = ind_r - 1
ind_R2 = ind_r
#Result of error propagation (basically average biased error between the two neighboring radii)
r_pos_gradient_error=np.abs((r_pos-R1)/(R2-R1))*data_gradient_error[ind_R2,:]+\
np.abs((r_pos-R2)/(R2-R1))*data_gradient_error[ind_R1,:]
coord = []
coord.append(
copy.deepcopy(
flap.Coordinate(
name='Time',
unit='s',
mode=flap.CoordinateMode(equidistant=True),
start=time_vector[0],
step=time_vector[1] - time_vector[0],
#shape=time_arr.shape,
dimension_list=[0])))
coord.append(
copy.deepcopy(
flap.Coordinate(name='Sample',
unit='n.a.',
mode=flap.CoordinateMode(equidistant=True),
start=0,
step=1,
dimension_list=[0])))
if device_coordinates:
grad_unit = '/m'
if flux_coordinates:
grad_unit = '/psi'
if pressure:
data_unit = flap.Unit(name='Pressure gradient',
unit='kPa' + grad_unit)
elif temperature:
data_unit = flap.Unit(name='Temperature gradient',
unit='keV' + grad_unit)
elif density:
data_unit = flap.Unit(name='Density gradient',
unit='m-3' + grad_unit)
if not spline_data:
d = flap.DataObject(exp_id=exp_id,
data_array=r_pos_gradient,
error=r_pos_gradient_error,
data_unit=data_unit,
coordinates=coord,
data_title='NSTX Thomson gradient')
else:
d = flap.DataObject(exp_id=exp_id,
data_array=r_pos_gradient_spline,
data_unit=data_unit,
coordinates=coord,
data_title='NSTX Thomson gradient spline')
else:
coord = []
coord.append(
copy.deepcopy(
flap.Coordinate(
name='Time',
unit='s',
mode=flap.CoordinateMode(equidistant=True),
start=time_vector[0],
step=time_vector[1] - time_vector[0],
#shape=time_arr.shape,
dimension_list=[1])))
coord.append(
copy.deepcopy(
flap.Coordinate(name='Sample',
unit='n.a.',
mode=flap.CoordinateMode(equidistant=True),
start=0,
step=1,
dimension_list=[1])))
if pressure:
data_unit = flap.Unit(name='Pressure gradient', unit='kPa/m')
elif temperature:
data_unit = flap.Unit(name='Temperature gradient', unit='keV/m')
elif density:
data_unit = flap.Unit(name='Density gradient', unit='m-3/m')
if device_coordinates:
radial_coordinate_name = 'Device R'
radial_unit = 'm'
if flux_coordinates:
radial_coordinate_name = 'Flux r'
radial_unit = ''
if not spline_data:
coord.append(
copy.deepcopy(
flap.Coordinate(
name=radial_coordinate_name,
unit=radial_unit,
mode=flap.CoordinateMode(equidistant=False),
values=radial_coordinate[1:-1],
shape=radial_coordinate[1:-1].shape,
dimension_list=[0])))
d = flap.DataObject(exp_id=exp_id,
data_array=data_gradient,
error=data_gradient_error,
data_unit=data_unit,
coordinates=coord,
data_title='NSTX Thomson gradient')
else:
coord.append(
copy.deepcopy(
flap.Coordinate(
name=radial_coordinate_name,
unit=radial_unit,
mode=flap.CoordinateMode(equidistant=False),
values=spline_radial_coordinate[1:-1],
shape=spline_radial_coordinate[1:-1].shape,
dimension_list=[0])))
d = flap.DataObject(exp_id=exp_id,
data_array=interp_data_gradient,
data_unit=data_unit,
coordinates=coord,
data_title='NSTX Thomson gradient spline')
if output_name is not None:
flap.add_data_object(d, output_name)
return d
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')
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
