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Improve argmin2d-based calibration. Example added #183

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157 changes: 157 additions & 0 deletions examples/ne_si_calibration_argmin2d_example.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,157 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "c2c4861b-fc4c-4cbe-8c46-149f461bab1a",
"metadata": {},
"outputs": [],
"source": [
"import ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg as calgg\n",
"import ramanchada2 as rc2\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import ramanchada2.misc.constants as rc2const"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36b733f3-bb8a-4663-8604-c832556ccaf1",
"metadata": {},
"outputs": [],
"source": [
"# Load spectra\n",
"ne = rc2.spectrum.from_test_spe(OP=['01'], laser_wl=['785'], device=['Horiba'], provider=['FNMT'], sample=['Neon'])\n",
"si = rc2.spectrum.from_test_spe(OP=['01'], laser_wl=['785'], device=['Horiba'], provider=['FNMT'], sample=['S0N'])\n",
"ps = rc2.spectrum.from_test_spe(OP=['01'], laser_wl=['785'], device=['Horiba'], provider=['FNMT'], sample=['PST'])\n",
"wl = 785\n",
"\n",
"# Perform all-in-one calibration\n",
"nesi_calib_fn = calgg.neon_silicon_calibration(ne, si, wl)\n",
"\n",
"# Apply the calibration to Polystyrene\n",
"ps_cal = ps.scale_xaxis_fun(nesi_calib_fn)\n",
"\n",
"# Plot Polystyrene\n",
"fig, ax = plt.subplots(figsize=(30, 8))\n",
"ps.plot(ax=ax, label='original')\n",
"ps_cal.plot(ax=ax, label='calibrated')\n",
"ax.set_title(f'Polystyrene')\n",
"ax.set_xlabel('1/cm')\n",
"twx = ax.twinx()\n",
"rc2.spectrum.from_delta_lines(rc2const.PST_RS_dict).plot(ax=twx, fmt='r:', label='reference')\n",
"twx.legend(loc='upper right')\n",
"ax.legend(loc='upper left')\n",
"fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18161b18-36c3-4bb1-8df3-d498d125cd51",
"metadata": {},
"outputs": [],
"source": [
"opts = [\n",
" dict(OP=['01'], laser_wl=['532'], device=['BWtek'], provider=['FNMT']),\n",
" dict(OP=['01'], laser_wl=['785'], device=['Horiba'], provider=['FNMT']),\n",
" dict(OP=['03'], laser_wl=['785'], device=['Horiba'], provider=['FNMT']),\n",
" dict(OP=['050'], laser_wl=['532'], device=['BWtek'], provider=['ICV']),\n",
" dict(OP=['100'], laser_wl=['532'], device=['BWtek'], provider=['ICV']),\n",
" dict(OP=['100'], laser_wl=['785'], device=['BWtek'], provider=['ICV']),\n",
" dict(OP=['010'], laser_wl=['633'], device=['Horiba'], provider=['TOP']),\n",
"]\n",
"for opt in opts:\n",
" base = str(opt).replace(':', '').replace(' ','')\n",
" wl = int(opt['laser_wl'][0])\n",
"\n",
" # load spectra\n",
" ne = rc2.spectrum.from_test_spe(**opt, sample=['Neon'])\n",
" si = rc2.spectrum.from_test_spe(**opt, sample=['S0N'])\n",
" ps = rc2.spectrum.from_test_spe(**opt, sample=['PST'])\n",
"\n",
" # Calculate wavelength calibration curve\n",
" ne_calib_fn = calgg.neon_calibration(ne, wl)\n",
" ne_cal = ne.scale_xaxis_fun(ne_calib_fn).dropna()\n",
"\n",
" # Calculate real laser wavelength\n",
" si_nm = si.scale_xaxis_fun(ne_calib_fn).dropna()\n",
" si_calib_fn, wl_real = calgg.silicon_calibration(si_nm, wl)\n",
" si_cal = si_nm.scale_xaxis_fun(si_calib_fn)\n",
"\n",
" # All-in-one calibration\n",
" nesi_calib_fn = calgg.neon_silicon_calibration(ne, si, wl)\n",
" \n",
" # Apply the calibration to Polystyrene\n",
" ps_cal = ps.scale_xaxis_fun(nesi_calib_fn)\n",
"\n",
" # Plot Neon\n",
" fig, ax = plt.subplots(figsize=(30, 8))\n",
" ne.shift_cm_1_to_abs_nm_filter(wl).plot(ax=ax, label='original')\n",
" ne_cal.plot(ax=ax, label='calibrated')\n",
" twx = ax.twinx()\n",
" rc2const.neon_wl_spe[wl].plot(ax=twx, fmt='g:', label='reference')\n",
" ax.set_title(f'Neon {base} {wl}nm')\n",
" ax.set_xlabel('nm')\n",
" twx.legend(loc='upper right')\n",
" ax.legend(loc='upper left')\n",
" fig.tight_layout()\n",
" #fig.savefig(f'{base}_{wl}nm_neon.pdf')\n",
" display(fig)\n",
" plt.close(fig)\n",
"\n",
" # Plot Silicon\n",
" fig, ax = plt.subplots(figsize=(12, 5))\n",
" si.plot(ax=ax, label='original')\n",
" si_cal.plot(ax=ax, label='calibrated')\n",
" ax.set_title(rf'Silicon {base} {wl}nm, $\\lambda_{{laser}}={wl_real:8.3f}$nm')\n",
" ax.set_xlabel('1/cm')\n",
" ax.set_xlim(500, 540)\n",
" ax.axvline(520.45)\n",
" twx.legend(loc='upper right')\n",
" ax.legend(loc='upper left')\n",
" fig.tight_layout()\n",
" #fig.savefig(f'{base}_{wl}nm_silicon.pdf')\n",
" display(fig)\n",
" plt.close(fig)\n",
" \n",
" # Plot Polystyrene\n",
" fig, ax = plt.subplots(figsize=(30, 8))\n",
" ps.plot(ax=ax, label='original')\n",
" ps_cal.plot(ax=ax, label='calibrated')\n",
" ax.set_title(f'Polystyrene {base} {wl}nm')\n",
" ax.set_xlabel('1/cm')\n",
" twx = ax.twinx()\n",
" rc2.spectrum.from_delta_lines(rc2const.PST_RS_dict).plot(ax=twx, fmt='r:', label='reference')\n",
" twx.legend(loc='upper right')\n",
" ax.legend(loc='upper left')\n",
" fig.tight_layout()\n",
" #fig.savefig(f'{base}_{wl}nm_polystyrene.pdf')\n",
" display(fig)\n",
" plt.close(fig)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ramanchada2",
"language": "python",
"name": "ramanchada2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
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"nbformat": 4,
"nbformat_minor": 5
}
55 changes: 47 additions & 8 deletions src/ramanchada2/protocols/calib_ne_si_argmin2d_iter_gg.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -18,26 +18,65 @@ def neon_calibration(ne_cm_1: Spectrum,


def silicon_calibration(si_nm: Spectrum,
wl: Literal[514, 532, 633, 785]):
si_nm = si_nm.dropna().normalize() # type: ignore
peaks = si_nm.find_peak_multipeak(prominence=.05, width=2, wlen=50) # type: ignore
fitres = si_nm.fit_peak_multimodel(candidates=peaks, profile='Pearson4') # type: ignore
wl: Literal[514, 532, 633, 785],
find_peaks_kw={},
fit_peaks_kw={}):
"""
Calculate calibration function for lazer zeroing

Args:
si_nm: Spectrum
Silicon spectrum
wl: Literal[514, 532, 633, 785]
Laser wavelength
find_peaks_kw: dict, optional
keywords for find_peak. Default values are
`{'prominence': min(.8, si_nm.y_noise_MAD()*50), 'width': 2, 'wlen': 100}`
fit_peaks_kw: dict, optional
keywords for fit_peaks. Default values are
`{'profile': 'Pearson4', 'vary_baseline': False}`

Returns:
spline, esitmated_wavelength: int
"""
si_nm_orig = si_nm
fnd_kw = {'prominence': min(.8, si_nm.y_noise_MAD()*50),
'width': 2,
'wlen': 100,
}
fnd_kw.update(find_peaks_kw)
ll = wl/(1-wl*(520-50)*1e-7)
rr = wl/(1-wl*(520+50)*1e-7)
si_nm = si_nm.dropna().trim_axes(method='x-axis', boundaries=(ll, rr)).normalize() # type: ignore
peaks = si_nm.find_peak_multipeak(**fnd_kw) # type: ignore
fp_kw = {'profile': 'Pearson4',
'vary_baseline': False
}
fp_kw.update(fit_peaks_kw)
fitres = si_nm.fit_peak_multimodel(candidates=peaks, **fp_kw) # type: ignore
si_wl = fitres.centers
if len(si_wl) < 1:
raise ValueError('No peaks were found. Please refind find_peaks parameters.')
laser_wl = 1/(520.45/1e7 + 1/si_wl)

laser_wl = laser_wl[np.argmin(np.abs(laser_wl-wl))]
x_cm_1 = 1e7*(1/laser_wl-1/si_nm.x)
x_cm_1 = 1e7*(1/laser_wl-1/si_nm_orig.x)

spline = interpolate.Akima1DInterpolator(si_nm.x, x_cm_1, method='makima')
spline = interpolate.Akima1DInterpolator(si_nm_orig.x, x_cm_1, method='makima')
return spline, laser_wl


def neon_silicon_calibration(ne_cm_1: Spectrum,
si_cm_1: Spectrum,
wl: Literal[514, 532, 633, 785]):
wl: Literal[514, 532, 633, 785],
sil_fit_kw={},
sil_find_kw={}
):
ne_spline = neon_calibration(ne_cm_1, wl)
si_nm = si_cm_1.scale_xaxis_fun(ne_spline) # type: ignore
si_spline, wl = silicon_calibration(si_nm, wl)
si_spline, wl = silicon_calibration(si_nm, wl,
find_peaks_kw=sil_find_kw,
fit_peaks_kw=sil_fit_kw)
ne_nm = ne_cm_1.scale_xaxis_fun(ne_spline) # type: ignore
ne_cal_cm_1 = ne_nm.abs_nm_to_shift_cm_1_filter(wl)
spline = interpolate.Akima1DInterpolator(ne_cm_1.x, ne_cal_cm_1.x, method='makima')
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