#place the following file in the directory indicated above
datafilename = 'sample_2kV_oddpos1to13_2ms_stuffdelay_17shots_04232019.h5'
datafilename = '2kV_0p5ms_1msGas_20shots_bdot_10142019.h5'
#datafilename='2kV_0p5ms_10msGas_20shots_bdot_10142019.h5'
datafilename = '2kV_oddpos9to21zr_2ms_stuffdelay_40_07022019.h5'
#load hdf5 file
data = load_hdf5(data_directory_location + datafilename, verbose=True)
timeB_us = data['time']['timeB_us'][:]
timeB_s = timeB_us / 1e6
#select time range for FFT (in us)
start_time = 40
end_time = 90
time_range = [start_time, end_time]
#compute indices from time range
start_time_index = iff.tindex_min(start_time, timeB_us)
end_time_index = iff.tindex_min(end_time, timeB_us)
#select shots to analyze
first_shot = 1
last_shot = 16
numshots = 40
shot_range = [first_shot, last_shot]
delaytimes = np.zeros([
3, 3, numshots
]) #storage for number of delay times from cross correlation function
delayshifts = np.zeros([3, 3, numshots])
timerange_limit = 5e-6 #limits the range for finding the peak to +/- 2 microseconds
dt = timeB_s[1] - timeB_s[0]
port_sep = 0.0254 #m
direction_list = ['r', 'theta', 'z']
# Sample rate and desired cutoff frequencies (in Hz).
fs = 125e6
lowcut = 50e3
highcut = 2e6
directory = 'C:\\Users\\dschaffner\\Dropbox\\Data\\BMPL\\BMX\\2022\\01122022\\'
datafilename = 'Dataset_01122022.h5'
data = load_hdf5(directory + datafilename, verbose=True)
time_s = data['time']['time_s']
timeB_s = time_s[1:]
time_us = data['time']['time_us']
timeB_us = time_us[1:]
analysis_start_time = 60
analysis_end_time = 160
start_time_index = iff.tindex_min(analysis_start_time, timeB_us)
end_time_index = iff.tindex_min(analysis_end_time, timeB_us)
timerange_limit = 3e-6 #s
port_sep = 0.0254 #m
numshots = 94
direction_list = ['r', 't', 'z']
probelist = ['probe5', 'probe7', 'probe19', 'probe21', 'probe33', 'probe35']
directions = len(direction_list)
numprobes = len(probelist)
#determine Wavelet size
arr = data['mag_probe']['positions']['probe5']['r']['bdot'][0, :]
Bpwr_r, Bscalespec, wvfreq, Bfft, fftfreq = cw.compute_wavelet(
arr, np.array(time_us))
#Bpwrslice = Bpwr_r[:,start_time_index:end_time_index]
fs = 125e6
lowcut = 50e3
highcut = 2e6
directory='C:\\Users\\dschaffner\\Dropbox\\Data\\BMPL\\BMX\\2022\\01122022\\'
datafilename='Dataset_01122022.h5'
data=load_hdf5(directory+datafilename,verbose=True)
time_s = data['time']['time_s']
timeB_s = time_s[1:]
time_us = data['time']['time_us']
timeB_us = time_us[1:]
analysis_start_time = 20
analysis_end_time = 40
start_time_index = iff.tindex_min(analysis_start_time,timeB_us)
end_time_index = iff.tindex_min(analysis_end_time,timeB_us)
timerange_limit = 3e-6#s
port_sep = 0.0254#m
numshots=94
direction_list=['r','t','z']
num_pairs = 3
directions = 3
tde_pairs=[['probe5','probe7'],['probe19','probe21'],['probe33','probe35']]
delaytimes = np.zeros([num_pairs, directions, numshots])
delayindex = np.zeros([num_pairs, directions, numshots])
plotshots=1
plt.rc('axes',linewidth=0.75)
# Calling time from our data
time_us = data['time']['time_us'][:]
time_s = data['time']['time_s'][:]
# Calling the discharge current and discharge voltages:
dis_current = data['discharge']['dis_I'][:]
dis_current = dis_current*1e-3
raw_dis_current = data['discharge']['dis_I_raw'][:]
dis_voltage = data['discharge']['dis_V'][:]/1e3
raw_dis_voltage = data['discharge']['dis_V_raw'][:]
start_time = 50
end_time = 150
time_range = [start_time, end_time]
start_time_index = iff.tindex_min(start_time, time_us)
end_time_index = iff.tindex_min(end_time, time_us)
# Shot 4 was a dud, so its excluded from the data calculation.
avg_dis_current = np.zeros(25004)
for s in range (0, 21):
if s == 3:
continue
avg_dis_current = avg_dis_current + np.array((dis_current[s,:])/20)
avg_dis_voltage = np.zeros(25004)
for s in range(0, 21):
if s == 3:
continue
avg_dis_voltage = avg_dis_voltage + np.array((dis_voltage[s,:])/20)
def bispec(timearray, array1, array2, array3, maxfreq, auto=True):
freq, freq2, comp1, pwr, mag, phase2, cos_phase, dt = sw.spectrum_wwind(
array1, timearray)
if auto == False:
freq, freq2, comp2, pwr, mag, phase2, cos_phase, dt = sw.spectrum_wwind(
array2, timearray)
freq, freq2, comp3, pwr, mag, phase2, cos_phase, dt = sw.spectrum_wwind(
array3, timearray)
if auto == True:
comp2 = comp1
comp3 = comp1
# create list of positive and negative frequencies
maxfreq_index = iff.tindex_min(freq2, maxfreq)
print('For ', maxfreq, ' Hz, index is ', maxfreq_index)
# pos freqs start at element 0, take first x chunk
posfreqs = freq2[0:maxfreq_index]
# neg freqs count up from min at halfway point, thus take last x chunk
negfreqs = freq2[-maxfreq_index:]
# print(posfreqs.shape)
# print(negfreqs.shape)
frequencies = np.append(negfreqs, posfreqs)
# print(frequencies.shape)
# create bispectral storage arrays based on frequency array size
bisp = np.zeros([frequencies.shape[0] // 2, 376], dtype=complex)
norm1 = np.zeros([frequencies.shape[0] // 2, 376], dtype=complex)
norm2 = np.zeros([frequencies.shape[0] // 2, 376], dtype=complex)
# halfindex=(frequencies.shape[0]//2)+1
# determine scaling amount so that frequencies are intergers
power = np.log10(freq2[1])
if power < 0.0:
powerceil = np.ceil(np.abs(power))
decimalpower = 10.0**(powerceil)
else:
decimalpower = 1.0
print('decimalpower ', decimalpower)
scaled_frequencies = frequencies * decimalpower
rounded_frequencies = np.around(scaled_frequencies)
rounded_posfrequencies = np.around(posfreqs * decimalpower)
for s in np.arange(1, posfreqs.shape[0]):
#print('On frequency',s,' ',rounded_frequencies[s])
for t in np.arange(1, 376):
f1 = rounded_posfrequencies[s]
f2 = rounded_frequencies[t]
if f1 == f2:
print(s, ' and ', t)
print(f1, ' and ', f2)
continue
f3 = f1 + f2 # find sum of rounded frequncies
# f3arg=np.nonzero(rounded_frequencies==f3)#find index of frequncies array that matches the summed freq, f3
# find index of frequncies array that matches the summed freq, f3
f3arg = np.where(rounded_frequencies[251:] == f3)
# if len(f3arg[0])==0:
#print('f3arg is zero for s=',s,' and t=',t)
# only compute bispectrum if there is a frequency in the frequencies array that matches f3
if len(f3arg[0]) != 0:
# print(f3arg[0][0])
# print('###################')
# print(f1,'+',f2,'=',f3)
# print(frequencies[s],'+',frequencies[t],'=',frequencies[f3arg[0][0]])
# print(f3arg[0])
# if f3>500:
# if s==10 and t==10:
# print(f3)
# break
bisp[s, t] = comp1[s] * comp2[t] * np.conj(comp3[f3arg[0][0]])
norm1[s, t] = comp1[s] * comp2[t]
norm2[s, t] = comp3[f3arg[0][0]]
# print(freq2)
return frequencies, posfreqs, bisp, norm1, norm2
plt.loglog(wavenum,
mean_power_interp_60t160_trace[4, :],
color='red',
label='Probe 33')
plt.xlabel(r'$\left(\frac{k_{v}}{2\pi}\right)$ [m$^{-1}$]',
fontsize=labelfontsize)
plt.ylabel(r'$Arb. Power$', fontsize=labelfontsize)
plt.legend(loc='lower left',
fontsize=legendfontsize,
frameon=False,
handlelength=5)
#range 1
import MLE as mle
wavenum_range = [2.7e0, 7e0]
wvtindex1 = iff.tindex_min(wavenum, wavenum_range[0])
wvtindex2 = iff.tindex_min(wavenum, wavenum_range[1])
#position 5
max_alpha, alpha, L_vals, cdf_dat, cdf_mod, KS, sigma = mle.mle_werr(
mean_power_interp_60t160_trace[0, :], wavenum, wvtindex1, wvtindex2)
func = (wavenum**(-max_alpha))
scale_dat = mean_power_interp_60t160_trace[0, wvtindex1]
scale_fit = func[wvtindex1]
ratio = scale_dat / scale_fit
plt.loglog(wavenum[wvtindex1:wvtindex2],
2.0 * ratio * func[wvtindex1:wvtindex2],
linestyle='dotted',
color='blue',
linewidth=0.5) #,label='Fixed Range Fit: -'+str(max_alpha) )
sigma = np.round(sigma, decimals=2)
plt.text(7e0,
### Mac Style ###
#data_director_location = '/user/dschaffner/data/'
#######################################################################
# place the following file in the directory indicated above
datafilename = 'sample_2kV_oddpos1to13_2ms_stuffdelay_17shots_04232019.h5'
# load hdf5 file
data = load_hdf5(data_directory_location + datafilename, verbose=True)
timeB_us = data['time']['timeB_us'][:]
timeB_s = timeB_us / 1e6
# select time range for FFT (in us)
start_time = 50
end_time = 150
time_range = [start_time, end_time]
# compute indices from time range
start_time_index = iff.tindex_min(start_time, timeB_us)
end_time_index = iff.tindex_min(end_time, timeB_us)
# select shot to analyze
shot = 10
bt5 = data['pos5']['b']['theta'][5, :]
bz5 = data['pos5']['b']['z'][5, :]
mag5 = np.sqrt(bt5**2 + bz5**2)
#Plot Details###############################
plt.rc('axes', linewidth=1.0)
plt.rc('xtick.major', width=1.0)
plt.rc('ytick.major', width=1.0)
plt.rc('xtick.minor', width=1.0)
plt.rc('ytick.minor', width=1.0)
plt.rc('lines', markersize=2.0, markeredgewidth=0.0, linewidth=1.0)
#load hdf5 file
data = load_hdf5(data_directory_location + datafilename, verbose=True)
timeB_us = data['time']['timeB_us'][:]
timeB_s = timeB_us / 1e6
time_s = data['time']['time_s'][:]
#poslist = ['pos9','pos11','pos13','pos15','pos17','pos19','pos21']
poslist = ['pos1', 'pos3', 'pos5', 'pos7']
#select time range for FFT (in us)
start_time = 50
end_time = 150
time_range = [start_time, end_time]
#compute indices from time range
start_time_index = iff.tindex_min(start_time, timeB_us)
end_time_index = iff.tindex_min(end_time, timeB_us)
#select shots to analyze
first_shot = 6
last_shot = 13
numshots = (last_shot - first_shot) + 1
numshots = 8
shot_range = [first_shot, last_shot]
Bmag = np.zeros([4, numshots, len(data['pos1']['b']['z'][0, :])])
for pos in np.arange(len(poslist)):
xx = data[poslist[pos]]['b']['r'][:]
yy = data[poslist[pos]]['b']['z'][:]
zz = data[poslist[pos]]['b']['z'][:]
Bmag[pos, :] = np.sqrt(xx**2 + yy**2 + zz**2)
print('Arrays loaded: ')
for arr in file.files:
print(arr)
return file
directory = 'C:\\Users\\dschaffner\\Dropbox\\Data\\BMPL\\BMX\\2022\\01122022\\'
datafilename = 'Dataset_01122022.h5'
data = load_hdf5(directory + datafilename, verbose=True)
time_s = data['time']['time_s']
timeB_s = time_s[1:]
time_us = data['time']['time_us']
timeB_us = time_us[1:]
analysis_start_time = 60
analysis_end_time = 160
start_time_index = iff.tindex_min(analysis_start_time, timeB_us)
end_time_index = iff.tindex_min(analysis_end_time, timeB_us)
timerange_limit = 3e-6 #s
port_sep = 0.0254 #m
#load velocities
sheetdirectory = 'C:\\Users\\dschaffner\\Dropbox\\Data\\BMPL\\BMX\\2022\\01122022\\'
spreadsheetfile = 'Separation Times by Shot.xlsx'
sheet = pd.read_excel(sheetdirectory + spreadsheetfile, header=1)
vels57 = np.array(sheet['57v']) * 1000.0 #m/s
vels1921 = np.array(sheet['1921v']) * 1000.0 #m/s
vels3335 = np.array(sheet['3335v']) * 1000.0 #m/s
velsflags = np.array(sheet['flag'])
mean_vels57 = np.mean(vels57)
mean_vels1921 = np.mean(vels1921)
mean_vels3335 = np.mean(vels3335)
