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This commit is contained in:
François Boulogne 2025-06-20 09:23:40 +02:00
parent 9e77fc8f03
commit c8868c3e63
15 changed files with 63 additions and 817 deletions
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8
check.py
View file

@ -72,7 +72,7 @@ def play_order1():
spectrum_file = os.path.join(DATA_FOLDER, FILE_NAME) spectrum_file = os.path.join(DATA_FOLDER, FILE_NAME)
spectrum_file = 'tests/spectraVictor2/order1/T10219.xy' #TEST# spectrum_file = 'tests/spectraVictor2/order5/T5817.xy' #TEST#
lambdas, intensities = io.load_spectrum(spectrum_file) lambdas, intensities = io.load_spectrum(spectrum_file)
plot_spectrum(lambdas, intensities, title='Raw') plot_spectrum(lambdas, intensities, title='Raw')
@ -112,7 +112,7 @@ def play_order0():
spectrum_file = os.path.join(DATA_FOLDER, FILE_NAME) spectrum_file = os.path.join(DATA_FOLDER, FILE_NAME)
spectrum_file = 'tests/spectraVictor2/order0/T14787.xy' #TEST# spectrum_file = 'tests/spectraVictor2/order0/T16053.xy' #TEST#
lambdas, intensities = io.load_spectrum(spectrum_file) lambdas, intensities = io.load_spectrum(spectrum_file)
plot_spectrum(lambdas, intensities, title='Raw') plot_spectrum(lambdas, intensities, title='Raw')
@ -120,6 +120,7 @@ def play_order0():
lambdas, intensities = io.load_spectrum(spectrum_file, lambda_min=450) lambdas, intensities = io.load_spectrum(spectrum_file, lambda_min=450)
plot_spectrum(lambdas, intensities, title='Raw, cropped') plot_spectrum(lambdas, intensities, title='Raw, cropped')
smoothed_intensities = smooth_intensities(intensities) smoothed_intensities = smooth_intensities(intensities)
plot_spectrum(lambdas, smoothed_intensities, title='Smoothed') plot_spectrum(lambdas, smoothed_intensities, title='Smoothed')
@ -217,4 +218,5 @@ if __name__ == '__main__':
#check_basic() #check_basic()
#check_SV1() #check_SV1()
#play() #play()
play_order0() play_order1()

3
optifik/io.py
View file

@ -8,6 +8,9 @@ def load_spectrum(spectrum_path,
""" """
Load a spectrum file. Load a spectrum file.
TODO : describe expected format
Parameters Parameters
---------- ----------
spectrum_path : string spectrum_path : string

2
optifik/minmax.py
View file

@ -131,7 +131,7 @@ def thickness_from_minmax(wavelengths,
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.set_xlabel('extremum n°') ax.set_xlabel('extremum n°')
ax.set_ylabel('1 / $\lambda$') ax.set_ylabel('$n$($\lambda$) / $\lambda$')
ax.plot(k_values, n_over_lambda, 's', label='Extrema') ax.plot(k_values, n_over_lambda, 's', label='Extrema')
ax.plot(k_values, intercept + k_values * slope, label='Fit') ax.plot(k_values, intercept + k_values * slope, label='Fit')

48
optifik/scheludko.py
View file

@ -8,13 +8,13 @@ from .utils import OptimizeResult, setup_matplotlib
from .analysis import finds_peak from .analysis import finds_peak
def thickness_scheludko_at_order(wavelengths, def _thicknesses_scheludko_at_order(wavelengths,
intensities, intensities,
interference_order, interference_order,
refractive_index, refractive_index,
Imin=None): Imin=None):
""" """
Compute the film thickness for a given interference order. Compute thicknesses vs wavelength for a given interference order.
Parameters Parameters
---------- ----------
@ -56,7 +56,7 @@ def thickness_scheludko_at_order(wavelengths,
return prefactor * (term1 + term2) return prefactor * (term1 + term2)
def Delta(wavelengths, thickness, interference_order, refractive_index): def _Delta(wavelengths, thickness, interference_order, refractive_index):
""" """
Compute the Delta values. Compute the Delta values.
@ -103,7 +103,7 @@ def Delta(wavelengths, thickness, interference_order, refractive_index):
return (A * (1 + alpha)) / (1 + A * alpha) return (A * (1 + alpha)) / (1 + A * alpha)
def Delta_fit(xdata, thickness, interference_order): def _Delta_fit(xdata, thickness, interference_order):
""" """
Wrapper on Delta() for curve_fit. Wrapper on Delta() for curve_fit.
@ -123,7 +123,7 @@ def Delta_fit(xdata, thickness, interference_order):
""" """
lambdas, r_index = xdata lambdas, r_index = xdata
return Delta(lambdas, thickness, interference_order, r_index) return _Delta(lambdas, thickness, interference_order, r_index)
@ -156,9 +156,11 @@ def thickness_from_scheludko(wavelengths,
if plot: if plot:
setup_matplotlib() setup_matplotlib()
# A bit extreme...
max_tested_order = 12 max_tested_order = 12
r_index = refractive_index r_index = refractive_index
# idx_min idx max
peaks_min, peaks_max = finds_peak(wavelengths, intensities, peaks_min, peaks_max = finds_peak(wavelengths, intensities,
min_peak_prominence=min_peak_prominence, min_peak_prominence=min_peak_prominence,
plot=plot) plot=plot)
@ -181,12 +183,18 @@ def thickness_from_scheludko(wavelengths,
lambda_start = min(lambda_min, lambda_max) lambda_start = min(lambda_min, lambda_max)
lambda_stop = max(lambda_min, lambda_max) lambda_stop = max(lambda_min, lambda_max)
# Guess the order...
# mask input data # mask input data
mask = (wavelengths >= lambda_start) & (wavelengths <= lambda_stop) mask = (wavelengths >= lambda_start) & (wavelengths <= lambda_stop)
wavelengths_masked = wavelengths[mask] wavelengths_masked = wavelengths[mask]
r_index_masked = r_index[mask] r_index_masked = r_index[mask]
intensities_masked = intensities[mask] intensities_masked = intensities[mask]
min_difference = np.inf min_difference = np.inf
best_m = None best_m = None
best_h_values = None best_h_values = None
@ -197,7 +205,7 @@ def thickness_from_scheludko(wavelengths,
plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)') plt.xlabel(r'$\lambda$ ($ \mathrm{nm} $)')
for m in range(0, max_tested_order+1): for m in range(0, max_tested_order+1):
h_values = thickness_scheludko_at_order(wavelengths_masked, h_values = _thicknesses_scheludko_at_order(wavelengths_masked,
intensities_masked, intensities_masked,
m, r_index_masked) m, r_index_masked)
@ -216,30 +224,32 @@ def thickness_from_scheludko(wavelengths,
print(f"Optimized: m={best_m}") print(f"Optimized: m={best_m}")
# Compute the thickness for the selected order
# Delta # Delta
num = intensities_masked - np.min(intensities_masked) num = intensities_masked - np.min(intensities_masked)
denom = np.max(intensities_masked) - np.min(intensities_masked) denom = np.max(intensities_masked) - np.min(intensities_masked)
DeltaVrai = num / denom Delta_from_data = num / denom
# DeltaVrai = (intensities_masked -np.min(intensities_masked))/(np.max(intensities_masked) -np.min(intensities_masked)) # Delta_from_data = (intensities_masked -np.min(intensities_masked))/(np.max(intensities_masked) -np.min(intensities_masked))
# DeltaVrai = (intensities_raw_masked -np.min(intensities_raw_masked))/(np.max(intensities_raw_masked) -np.min(intensities_raw_masked)) # Delta_from_data = (intensities_raw_masked -np.min(intensities_raw_masked))/(np.max(intensities_raw_masked) -np.min(intensities_raw_masked))
DeltaScheludko = Delta(wavelengths_masked, DeltaScheludko = _Delta(wavelengths_masked,
np.mean(best_h_values), np.mean(best_h_values),
best_m, best_m,
r_index_masked) r_index_masked)
xdata = (wavelengths_masked, r_index_masked) xdata = (wavelengths_masked, r_index_masked)
popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, m), xdata, DeltaVrai, p0=[np.mean(best_h_values)]) popt, pcov = curve_fit(lambda x, h: _Delta_fit(x, h, m), xdata, Delta_from_data, p0=[np.mean(best_h_values)])
fitted_h = popt[0] fitted_h = popt[0]
std_err = np.sqrt(pcov[0][0]) std_err = np.sqrt(pcov[0][0])
if plot: if plot:
Delta_values = Delta(wavelengths_masked, fitted_h, best_m, r_index_masked) Delta_values = _Delta(wavelengths_masked, fitted_h, best_m, r_index_masked)
plt.figure() plt.figure()
plt.plot(wavelengths_masked, DeltaVrai, plt.plot(wavelengths_masked, Delta_from_data,
'bo-', markersize=2, label=r'$\mathrm{{Smoothed}}\ \mathrm{{Data}}$') 'bo-', markersize=2, label=r'$\mathrm{{Smoothed}}\ \mathrm{{Data}}$')
# Scheludko # Scheludko
@ -294,7 +304,7 @@ def thickness_for_order0(wavelengths,
# We assume to be at order zero. # We assume to be at order zero.
best_m = 0 best_m = 0
best_h_values = thickness_scheludko_at_order(wavelengths_masked, best_h_values = _thicknesses_scheludko_at_order(wavelengths_masked,
intensities_masked, intensities_masked,
best_m, best_m,
r_index_masked, r_index_masked,
@ -311,21 +321,21 @@ def thickness_for_order0(wavelengths,
# Delta # Delta
num = intensities_masked - np.min(intensities_I_min_masked) num = intensities_masked - np.min(intensities_I_min_masked)
denom = np.max(intensities_masked) - np.min(intensities_I_min_masked) denom = np.max(intensities_masked) - np.min(intensities_I_min_masked)
DeltaVrai = num / denom Delta_from_data = num / denom
DeltaScheludko = Delta(wavelengths_masked, np.mean(best_h_values), best_m, r_index_masked) DeltaScheludko = _Delta(wavelengths_masked, np.mean(best_h_values), best_m, r_index_masked)
#print(np.mean(best_h_values),np.std(best_h_values)) #print(np.mean(best_h_values),np.std(best_h_values))
xdata = (wavelengths_masked, r_index_masked) xdata = (wavelengths_masked, r_index_masked)
popt, pcov = curve_fit(lambda x, h: Delta_fit(x, h, best_m), xdata, DeltaVrai, p0=[np.mean(best_h_values)]) popt, pcov = curve_fit(lambda x, h: _Delta_fit(x, h, best_m), xdata, Delta_from_data, p0=[np.mean(best_h_values)])
fitted_h = popt[0] fitted_h = popt[0]
std_err = np.sqrt(pcov[0][0]) std_err = np.sqrt(pcov[0][0])
if plot: if plot:
Delta_values = Delta(wavelengths_masked, fitted_h, best_m, r_index_masked) Delta_values = _Delta(wavelengths_masked, fitted_h, best_m, r_index_masked)
plt.figure() plt.figure()
plt.plot(wavelengths_masked, DeltaVrai, plt.plot(wavelengths_masked, Delta_from_data,
'bo-', markersize=2, label=r'$\mathrm{{Smoothed}}\ \mathrm{{Data}}$') 'bo-', markersize=2, label=r'$\mathrm{{Smoothed}}\ \mathrm{{Data}}$')
# Scheludko # Scheludko

1
optifik/utils.py
View file

@ -33,6 +33,7 @@ def is_latex_installed():
import shutil import shutil
return shutil.which("latex") is not None or shutil.which("pdflatex") is not None return shutil.which("latex") is not None or shutil.which("pdflatex") is not None
def setup_matplotlib(): def setup_matplotlib():
""" """
Configure matplotlib with LaTeX text rendering and custom font sizes. Configure matplotlib with LaTeX text rendering and custom font sizes.

165
tests/spectraVictor2/order0/h=f-temps-660nm_0.txt
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@ -1,165 +0,0 @@
t (s) h (nm) pour lambda specifique
14.143 123.95295558284394
14.211 115.26802909226424
14.283 115.70504212601391
14.354 116.09744924015496
14.432 115.5189035534501
14.503 113.26445263139607
14.573 113.32417457681417
14.642 113.39326934945271
14.714 112.32070148051518
14.787 108.97163127594759
14.857 109.64817699038785
14.927 107.22679420091224
14.993 107.32677769871788
15.063 106.35472238842755
15.136 105.39933227233728
15.206 104.03778591581003
15.277 102.33092796214031
15.35 100.8763921934027
15.418 100.49522871553593
15.492 99.08281468883276
15.562 97.3258169088354
15.633 95.90149574477304
15.706 95.4219114489226
15.777 93.9096812365406
15.838 93.38407856938173
15.91 91.74790491639014
15.981 91.22741714198933
16.053 90.45300835427635
16.127 88.5882756065361
16.203 87.67147079674957
16.267 87.09015257940483
16.335 85.72596923247862
16.406 84.17908589568572
16.481 83.45410256101752
16.545 82.12170648181431
16.617 81.21047762108086
16.678 80.31780246995505
16.739 79.15724700943923
16.812 78.44798662153762
16.871 77.4520761261443
16.933 76.66196112954584
17.008 75.373800563203
17.077 74.28223895630823
17.137 73.38655766446928
17.2 72.1896895417961
17.271 71.56217278784045
17.343 70.12160217440957
17.416 68.92969343342145
17.486 68.12049284214301
17.549 66.80303824279235
17.625 65.99608104409013
17.695 65.25251792159017
17.769 64.00266354954327
17.836 63.015781912851686
17.906 61.562998778797
17.969 60.8954006296276
18.04 59.61929259890727
18.111 58.78017522831741
18.177 57.867491523721604
18.244 56.628928486673345
18.315 55.58274202642659
18.388 54.76911277607284
18.458 53.23742605239474
18.531 52.457573607339896
18.605 51.130249935893495
18.674 50.18605538362272
18.748 48.82375634938857
18.818 47.898926722625646
18.889 47.24554206801689
18.96 45.92649719589015
19.033 45.31404852626354
19.103 44.04792240037513
19.175 43.512162472031946
19.246 42.39974893171179
19.307 41.40370710601314
19.368 40.190000003978724
19.442 39.7908194502919
19.504 38.86873798965855
19.577 38.256582406214875
19.646 36.732536890445104
19.72 36.302952043599674
19.795 35.33077538066582
19.861 33.82853466198359
19.922 33.6264129799091
19.984 32.56882980723177
20.056 31.716029588070004
20.128 30.9389509299541
20.201 29.801282167285436
20.27 28.843694109308284
20.343 28.022933381569352
20.417 27.12695804779101
20.489 26.697121450809053
20.558 25.801797115458406
20.628 24.80468338477568
20.7 24.08677644034063
20.771 23.216851687498718
20.833 22.403639545458578
20.907 21.450824944786188
20.978 20.698403220925407
21.05 20.180242579832115
21.12 19.073026686872062
21.193 18.067527932796555
21.266 17.788340332627318
21.335 16.73984202033769
21.409 16.15580200933398
21.477 15.392400576341881
21.541 14.662332974949772
21.61 14.114418248767004
21.673 13.670336677360423
21.746 12.643113927012816
21.818 12.517769120321669
21.887 11.197431215995442
21.948 11.459041839515868
22.02 11.521111661090618
22.092 10.38658595887989
22.157 10.383172535760965
22.228 10.061869216456225
22.299 9.278080287311097
22.371 8.83668184571166
22.447 8.352209963184631
22.512 8.955785625765454
22.573 7.90930609871769
22.648 3.4643319393515317
22.718 4.1507304003627565
22.788 3.2807740203778155
22.865 1.5726691486794457
22.932 3.0897989180398984
22.994 1.0558253042494077
23.064 1.4392233684991647
23.138 2.0496273589825957
23.208 0.3478652040929014
23.28 2.3547687674325593
23.361 2.8459896671389484
23.433 0.7120535181141793
23.496 1.5881943255362871
23.566 -0.8318854167488029
23.639 -0.19584335188209473
23.71 4.127075248698121
23.782 1.503825079280638
23.842 1.298701481981237
23.905 -1.9875197542258602
23.978 1.1092062721462115
24.052 1.7960423393624443
24.121 2.2015433421886708
24.194 -1.118879018385083
24.262 -1.6892400749730334
24.326 -1.7383043762838022
24.397 2.6087265909066253
24.467 -0.6892358011630055
24.53 2.4643805068983773
24.6 1.4116956879360614
24.675 3.1703609297698665
24.744 0.11053285060109477
24.817 2.316149667416408
24.894 0.5364199414541123
24.957 -2.152460736629538
25.024 -2.0971135573427073
25.092 2.091629797823038
25.152 2.4945719274003846
25.215 1.1069965768579215
25.286 2.1208495454024825
25.348 2.5511295565177003
25.418 -2.908469183861368
25.492 -1.9420736825064098

165
tests/spectraVictor2/order0/h_moy_et_std=f-temps-0.txt
View file

@ -1,165 +0,0 @@
t [s] h [nm] std (nm)
14.143 106.11639778595912 30.545634732163943
14.211 105.26545279848125 28.52047456695036
14.283 104.26752623171188 26.363479538579295
14.354 103.79046627976656 24.742903945306793
14.432 103.34266505455395 23.489128514433645
14.503 103.12884600795759 22.132182398570716
14.573 103.06399963854429 20.9507704903489
14.642 102.35959208905595 20.03235533699279
14.714 102.5976297360478 19.101475938455117
14.787 101.83448536168487 17.925193731379117
14.857 101.7895097852865 17.137914759425595
14.927 101.17493302828665 15.742718827421363
14.993 100.80520208279451 15.257076653869667
15.063 100.54799593222108 14.209474209952296
15.136 99.86609132812438 13.175941550199818
15.206 99.6662837867646 12.35743273639029
15.277 98.49456106870832 11.25371148138226
15.35 98.0978952886305 10.303424283998332
15.418 97.33459693248665 9.404196253510465
15.492 96.67636557177185 8.70048111245147
15.562 95.82203069308336 7.589250440278183
15.633 95.13941569612058 6.962351529708
15.706 94.09369098855977 6.2248638386316015
15.777 93.56158824624366 5.5099919900226535
15.838 92.79207369603348 5.123476432171773
15.91 91.79782454588346 4.409797985066031
15.981 90.88875672273443 3.931621627279069
16.053 89.9639502223029 3.4313648674992816
16.127 88.72268525203262 3.0031157865631397
16.203 88.0676016210193 2.4490826702671527
16.267 86.9569817156192 2.2343429050895094
16.335 86.23011736082192 2.1359848524131855
16.406 84.84597896770313 2.0196549103183337
16.481 84.01650097259069 1.862020959344227
16.545 82.80856982185257 1.644995098540361
16.617 81.83296732418725 1.5162274861826561
16.678 80.6199685471055 1.5844509991973137
16.739 79.8817234019573 1.39789396828855
16.812 78.70763984576578 1.32209700937526
16.871 77.75802818756716 1.2734435711383558
16.933 76.83035166592714 1.2481819752030532
17.008 75.81092513715852 1.1671271653499498
17.077 74.75967362641244 1.0706956828269905
17.137 73.66131373805375 1.1210734291563405
17.2 72.84009781677588 1.0925290771264549
17.271 71.84198511747022 0.9978672550098565
17.343 70.77724471639185 0.962764233072028
17.416 69.58504854013584 0.9520807042220532
17.486 68.49117481584074 0.9310332412929812
17.549 67.37505682292482 0.8901311466900783
17.625 66.60712412684927 0.8147468911319982
17.695 65.39330432258598 0.8597575672237636
17.769 64.47185217277163 0.7894921754846663
17.836 63.219701948215146 0.764645794822967
17.906 62.26781063203917 0.7708835919401242
17.969 61.281906371709944 0.7778636814453863
18.04 60.103504061567826 0.7333165639182637
18.111 58.9964327706274 0.7418470801535355
18.177 58.16073146901566 0.7268974867622288
18.244 57.18854753332005 0.6927385308621755
18.315 56.20528814652521 0.6631025170813724
18.388 55.10052484061043 0.6725064491518644
18.458 53.49397601428968 0.6370416565260154
18.531 52.78420598250904 0.610138090332582
18.605 51.48802150104169 0.5951279202426865
18.674 50.66187266546967 0.5881226555154143
18.748 49.33575703984319 0.5594727305311288
18.818 48.44737395300477 0.5751258392886279
18.889 47.570823188865795 0.5406325233884514
18.96 46.484116826353734 0.5742585200114736
19.033 45.77435072061017 0.5415816908968057
19.103 44.5307243853119 0.511433249616128
19.175 43.78475307679595 0.5014171268967204
19.246 42.728776703777115 0.5023215153979327
19.307 41.972996459972904 0.5099987892788741
19.368 40.96172338688027 0.4568402153222491
19.442 40.23258565788984 0.4763027359623337
19.504 39.35868621745207 0.48225783048961685
19.577 38.369993427118 0.44391107776081457
19.646 37.513473877901916 0.46845383663028733
19.72 36.536001234048264 0.46516847176084564
19.795 35.52254408919478 0.443960246003758
19.861 34.506771499061884 0.45011081459632757
19.922 33.82463469827142 0.4233563946396004
19.984 33.07357444979178 0.4355646340772075
20.056 32.15558523060816 0.43626032058271247
20.128 31.23564251511888 0.49170687509478644
20.201 30.287726496717813 0.45416653540685137
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7
tests/spectraVictor2/order5/known_value.txt
View file

@ -1,7 +0,0 @@
T5543.xz 744.4489624060151
T5612.xz 717.9160763949778
T5677.xz 701.481313603109
T5747.xz 682.6197534309899
T5817.xz 662.8052850714089
T5879.xz 655.1166828174141
T5951.xz 620.3741353383458

4
tests/spectraVictor2/order5/known_value.yaml
View file

@ -2,6 +2,6 @@ T5543.xy: 744.4489624060151
T5612.xy: 717.9160763949778 T5612.xy: 717.9160763949778
T5677.xy: 701.481313603109 T5677.xy: 701.481313603109
T5747.xy: 682.6197534309899 T5747.xy: 682.6197534309899
#T5817.xy: 662.8052850714089 T5817.xy: 662.8052850714089
#T5879.xy: 655.1166828174141 T5879.xy: 655.1166828174141
T5951.xy: 620.3741353383458 T5951.xy: 620.3741353383458

26
tests/test_SV2o0.py
View file

@ -10,19 +10,19 @@ from optifik.analysis import smooth_intensities
import yaml import yaml
def load(): def load(filename):
FOLDER = os.path.join('tests', 'spectraVictor2', 'order0') FOLDER = os.path.join('tests', 'spectraVictor2', 'order0')
yaml_file = os.path.join(FOLDER, 'known_value.yaml') yaml_file = os.path.join(FOLDER, filename)
with open(yaml_file, "r") as yaml_file: with open(yaml_file, "r") as yaml_file:
thickness_dict = yaml.safe_load(yaml_file) thickness_dict = yaml.safe_load(yaml_file)
data = [(os.path.join(FOLDER, fn), val) for fn, val in thickness_dict.items()] data = [(os.path.join(FOLDER, fn), val) for fn, val in thickness_dict.items()]
return data return data
@pytest.mark.skip('...') #@pytest.mark.skip('...')
@pytest.mark.parametrize("spectrum_path, expected", load()) @pytest.mark.parametrize("spectrum_path, expected", load('known_value.yaml'))
def test_SV2o0(spectrum_path, expected): def test_SV2o0_small_tol(spectrum_path, expected):
lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities = smooth_intensities(raw_intensities) smoothed_intensities = smooth_intensities(raw_intensities)
@ -37,3 +37,19 @@ def test_SV2o0(spectrum_path, expected):
assert_allclose(result.thickness, expected, rtol=1e-1) assert_allclose(result.thickness, expected, rtol=1e-1)
@pytest.mark.parametrize("spectrum_path, expected", load('known_value_large_tol.yaml'))
def test_SV2o0_large_tol(spectrum_path, expected):
lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities = smooth_intensities(raw_intensities)
refractive_index = 1.324188 + 3102.060378 / (lambdas**2)
prominence = 0.020
result = thickness_for_order0(lambdas, smoothed_intensities,
refractive_index=refractive_index,
min_peak_prominence=prominence,
plot=False)
assert_allclose(result.thickness, expected, rtol=2.5e-1)

3
tests/test_SV2o1.py
View file

@ -20,10 +20,9 @@ def load():
return data return data
@pytest.mark.skip('...') #@pytest.mark.skip('...')
@pytest.mark.parametrize("spectrum_path, expected", load()) @pytest.mark.parametrize("spectrum_path, expected", load())
def test_SV2o1(spectrum_path, expected): def test_SV2o1(spectrum_path, expected):
pass
lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450) lambdas, raw_intensities = load_spectrum(spectrum_path, lambda_min=450)
smoothed_intensities = smooth_intensities(raw_intensities) smoothed_intensities = smooth_intensities(raw_intensities)