diff --git a/README.md b/README.md index 1bf5916..6d56ed9 100644 --- a/README.md +++ b/README.md @@ -29,11 +29,11 @@ model = pr.multipole_model( qmin=0.0, qmax=0.5, nq=500, - excluded="foxs", # FoXS-style excluded volume (16π Fraser) - volume_scale=1.0, # fit knob c₁ (scales excluded volume) - hydration="sasa", # FoXS-style SASA-weighted hydration shell - probe=1.8, # SASA probe radius (Å) - contrast_density=0.03, # fit knob c₂ (scales hydration) + excluded="foxs", # FoXS-style excluded volume (16π Fraser) + volume_scale=1.0, # fit knob c₁ (scales excluded volume) + hydration="sasa", # FoXS-style SASA-weighted hydration shell + probe=1.8, # SASA probe radius (Å) + contrast_density=0.03, # fit knob c₂ (scales hydration) bulk_electron_density=0.334, # e/ų, pure water ) @@ -61,6 +61,15 @@ the fit knobs to the cached amplitudes — typically under a millisecond each — so wrapping them in `scipy.minimize` or a grid search is fluent. +For small structures or exact reference checks, `debye_model` accepts +the same solvent keyword arguments and exposes the same +`model.intensity(volume_scale, contrast_density)` interface. It caches +the exact Debye self and cross terms for atoms, excluded volume, and +hydration before fitting. +`direct_model` does the same for the explicit discrete-q transform, +caching the complex amplitudes before recombining and averaging +duplicate q magnitudes. + A full working example is in `pripps-py/examples/fit_lysozyme.py`, which fits lysozyme against the shipped `tests/lysozyme/lyzexp.dat` experimental dataset and reproduces FoXS's χ²/N ≈ 0.20: @@ -99,7 +108,7 @@ print(f"c1={result.x[0]:.4f}, c2={result.x[1]:.4f}, χ²/N={result.fun:.4f}") | Parameter | Values | Description | |-----------|--------|-------------| -| `excluded` | `"disabled"`, `"fraser"`, `"foxs"`, `"grid"`, `"voronoi"` | Excluded-volume model | +| `excluded` | `"disabled"`, `"fraser"`, `"foxs"`, `"grid"`, `"voronoi"`, `"per-kind"` | Excluded-volume model | | `hydration` | `"disabled"`, `"sasa"`, `"grid"`, `"voronoi"` | Hydration-shell model | | `volume_scale` | float (default 1.0) | Scales excluded volume (PepsiSAXS `r₀`) | | `excluded_spacing` | float (default 1.0) | Grid spacing (Å) for `excluded="grid"` | @@ -122,6 +131,7 @@ subtracted from the atomic scattering via | `foxs` | Same formula as `fraser` but with the FoXS 16π denominator: dummy form factor decays more slowly with q → larger excluded-volume subtraction at high q. Matches FoXS. | `displaced_volume` per atom kind | | `grid` | Fills the molecular envelope with uniform grid cells, each carrying a Fraser Gaussian sized by cell volume (`spacing³`). Independent of per-atom displaced volumes. | `radius` per atom kind | | `voronoi` | Per-atom Fraser Gaussians sized by individual Voronoi cell volumes. Derives displaced volumes geometrically — no tabulated volumes needed. Achieves χ²≈0.20 on lysozyme, comparable to `fraser`. | `radius` per atom kind | +| `per-kind` | Uses per-kind excluded-solvent form factors stored as `excluded_solvent` beside each atom/residue `formfactor` in the same YAML file. | `excluded_solvent` for referenced atom kinds | The `fraser` and `foxs` models use per-atom displaced volumes from the form-factor YAML (derived from `radius` as `(4/3)πr³` when @@ -160,7 +170,10 @@ C: { ff: !Gaussian { qrange: [0.0, 1.0], ab: [[0.1, 2.0]], c: 0.4 }, NH: { radius: 1.009, displaced_volume: 7.606, ff: !United { center: N, orbit: [[H, 1.009, 1]] } } CA: { ff: !Alias C } -ALA: { ff: !Polynomial { qrange: [0.0, 0.75], coeff: [8.9, 0.0, 5.8, ...]} } +# Linear combination of other entries (no geometry), F(q) = Σ wᵢ Fᵢ(q): +H2O: { ff: !Linear [[O, 1.0], [H, 2.0]] } +ALA: { ff: !Polynomial { qrange: [0.0, 0.75], coeff: [8.9, 0.0, 5.8, ...]}, + excluded_solvent: !Polynomial { qrange: [0.0, 0.75], coeff: [7.1, 0.0, 4.2, ...]} } ``` Supported form factor models: @@ -179,6 +192,7 @@ Supported form factor models: |----------|------|---------|---------------| | `radius` | Å | SASA hydration, Fraser excluded volume | No — must be specified | | `displaced_volume` | ų | Fraser excluded volume | Yes — defaults to (4/3)πr³ from `radius` if omitted | +| `excluded_solvent` | form factor | `per-kind` excluded volume | No — optional; required per species when using `--excluded per-kind` | When `radius` is present but `displaced_volume` is omitted, the displaced volume is automatically derived as the sphere volume @@ -186,22 +200,36 @@ displaced volume is automatically derived as the sphere volume groups (e.g. NH, SH) where the displaced volume includes implicit hydrogens that the effective radius does not account for. +The optional `excluded_solvent` field stores a *second* form factor +(any of the models above) for the bulk solvent each species displaces, +sitting beside its in-vacuo `formfactor` in the same entry. It is read +only by the `per-kind` excluded-volume model (`--excluded per-kind`, or +`excluded="per-kind"` from Python), which subtracts this form factor — +scaled by `volume_scale` — instead of building a Gaussian dummy from +`radius`/`displaced_volume`. Every species present in the structure +must define it when `--excluded per-kind` is selected. + ### Shipped form-factor files | File | Description | Best for | |------|-------------|----------| -| `assets/single_bead.yaml` | PepsiSAXS coefficients + coarse-grained residues; also covers united-atom kinds (CH/CH₂/NH/…) used by atomic PDBs | Cα / single-bead models; atomic PDBs with `--excluded voronoi` | -| `assets/foxs_formfactors.yaml` | Cromer-Mann (Int. Tables Vol. C), FoXS-compatible volumes and SASA radii | Atomic PDB files with `--excluded foxs` | - -Both files use Gaussian b values in the standard s² = (q/4π)² -convention (International Tables / Cromer-Mann). The atomic -coefficients differ (Cromer-Mann vs Stovgaard-2010) and the -volume/radius tables match their respective tools. +| `assets/foxs_formfactors.yaml` | Cromer-Mann (Int. Tables Vol. C) atomic form factors, FoXS-compatible volumes and SASA radii | Atomic PDBs with `--excluded foxs` | +| `assets/gaussian_atoms.yaml` | Mixed table: residue single-bead polynomials plus atomic/united-atom Gaussians (CH/CH₂/NH/…) | Atomic PDBs with PepsiSAXS-style solvent models | +| `assets/stovgaard2010.yaml` | Per-residue form-factor centroids (one-body model, q ∈ [0, 0.75] Å⁻¹) from Stovgaard et al. 2010, | Residue / Cα single-bead models | +| `assets/poly_amino_acid.yaml` | Amino-acid bead polynomials with a separate `excluded_solvent` polynomial per bead | Cα / single-bead with `--excluded per-kind` | +| `assets/poly_amino_acid_combined.yaml` | Amino-acid bead polynomials with the excluded solvent already folded into the form factor | Cα / single-bead with `--excluded disabled` | +| `assets/poly_martini.yaml` | Martini-3 bead polynomials with a separate `excluded_solvent` polynomial per bead | Martini-3 CG with `--excluded per-kind` | +| `assets/poly_martini_combined.yaml` | Martini-3 bead polynomials with the excluded solvent already folded into the form factor | Martini-3 CG with `--excluded disabled` | + +Gaussian files use b values in the standard s² = (q/4π)² convention +(International Tables / Cromer-Mann). The atomic coefficients differ +(Cromer-Mann vs Stovgaard-2010) and the volume/radius tables match +their respective tools. **Don't mix files with non-matching `--excluded` flags.** Each YAML + excluded-volume convention is a self-consistent bundle: `foxs_formfactors.yaml` was calibrated jointly with FoXS's -`K = 16π` Gaussian, while `single_bead.yaml` was calibrated with +`K = 16π` Gaussian, while `gaussian_atoms.yaml` was calibrated with PepsiSAXS's `K = 4π`. Swapping the denominator gives atomic and excluded contributions that were never fit together and yields subtly biased intensities. @@ -286,12 +314,20 @@ pripps -a atoms.yaml -i structure.pdb multipole --lmax 68 # fixed order # With solvent correction — CSV output with 5 columns pripps -a atoms.yaml -i structure.pdb -o intensity.csv \ multipole --excluded fraser --hydration sasa +pripps -a atoms.yaml -i structure.pdb -o intensity.csv \ + debye --excluded fraser --hydration sasa # Fit against experimental data (box-constrained Nelder-Mead over # c₁ ∈ [0.5, 2.0] and c₂ ∈ [0.0, 0.1]; warns if the fit hits a bound) pripps -a assets/foxs_formfactors.yaml -i structure.pdb \ -f experimental.dat -o fit.csv \ multipole --excluded foxs --hydration sasa +pripps -a assets/foxs_formfactors.yaml -i structure.pdb \ + -f experimental.dat -o fit.csv \ + debye --excluded foxs --hydration sasa +pripps -a assets/foxs_formfactors.yaml -i structure.pdb \ + -f experimental.dat -o fit.csv \ + direct --excluded foxs --hydration sasa # Trajectory averaging — use `direct` so each frame's periodic box # is honoured (multipole ignores PBC). @@ -303,8 +339,8 @@ pripps -a atoms.yaml -i structure.pdb -x trajectory.xtc direct | Scheme | Best for | Notes | |---|---|---| | `multipole` | Single structures, fitting loops | Fastest for large atomic PDBs; no periodic boundaries. | -| `direct` | MD trajectories with periodic box | Evaluates I(q) on discrete Miller-index q-vectors; each frame's box is picked up from the XTC. | -| `debye` | Small clusters, reference checks | Exact pair-distance sum; quadratic in atoms, fine under a few hundred. | +| `direct` | MD trajectories with periodic box | Evaluates I(q) on discrete Miller-index q-vectors; supports the same solvent fit knobs for single-frame fits. | +| `debye` | Small clusters, reference checks | Exact pair-distance sum with the same solvent and fit knobs as multipole; quadratic in atoms, fine under a few hundred. | Output is CSV: `q,total` for vacuum, or `q,total,atoms,excluded,hydration` when solvent is active. diff --git a/assets/single_bead.yaml b/assets/gaussian_atoms.yaml similarity index 77% rename from assets/single_bead.yaml rename to assets/gaussian_atoms.yaml index d417599..5f072aa 100644 --- a/assets/single_bead.yaml +++ b/assets/gaussian_atoms.yaml @@ -1,4 +1,365 @@ -# +# Amino acid single bead approximation for atomistic scattering +ALA: + { + radius: 3.1, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 37.98651145079954, + 0.0, + -17.88881261159881, + -3.1097731825829333, + 8.846459092736989, + -2.3572911188270753, + 0.05331786541354333, + ], + }, + } +ARG: + { + radius: 4.0, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 83.96908927242093, + 0.0, + -278.81910994314467, + 490.5059319443547, + -383.9755997417938, + 143.65451627028935, + -20.783481179736413, + ], + }, + } +ASN: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 59.98205560472268, + 0.0, + -40.54214890095476, + -32.39953367060282, + 73.4159357342896, + -37.17064504604371, + 6.101333446616579, + ], + }, + } +ASP: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 59.98252699985404, + 0.0, + -37.42179690845112, + -37.97031252961563, + 77.22160318216388, + -38.25436485287656, + 6.198643717045879, + ], + }, + } +CYS: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 53.98471591625841, + 0.0, + -28.55463591537983, + -34.95759123047967, + 63.496177867671385, + -29.839780719449596, + 4.609281628955767, + ], + }, + } +GLN: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 68.00029704612041, + 0.0, + -97.52724702286758, + 63.863879205326334, + 13.865971310987792, + -22.96589467520148, + 5.296934807386833, + ], + }, + } +GLU: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 68.00262098772043, + 0.0, + -95.50455833177392, + 59.449715597229726, + 18.625430344038303, + -25.334425917186397, + 5.722041637792017, + ], + }, + } +GLY: + { + radius: 2.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 29.99006405529222, + 0.0, + -11.572242769967232, + -0.9021293993630419, + 3.790951509543525, + -0.7238332866526376, + -0.05549468364095844, + ], + }, + } +HIS: + { + radius: 3.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 71.99289906060545, + 0.0, + -93.58672742143521, + 41.460338892548805, + 39.724339660983915, + -34.41529003289915, + 7.057596750057414, + ], + }, + } +ILE: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 61.97933054508859, + 0.0, + -45.55749645626756, + -26.94147636420316, + 68.41091746940783, + -34.307325839550984, + 5.508363044131372, + ], + }, + } +LEU: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 61.9874112464829, + 0.0, + -54.07829774966109, + -19.672724727921118, + 75.75933357599484, + -42.87909415253206, + 7.580643479500708, + ], + }, + } +LYS: + { + radius: 3.7, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 69.99129116923348, + 0.0, + -177.94423757968704, + 272.0256856055207, + -188.2920973172914, + 63.303038257186586, + -8.32612812430321, + ], + }, + } +MET: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 70.00691278772638, + 0.0, + -104.22345654704631, + 73.82601789465483, + 10.126507627913185, + -23.351946238697035, + 5.598503080281802, + ], + }, + } +PHE: + { + radius: 3.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 77.99863587497389, + 0.0, + -135.30506501839736, + 122.61728717372222, + -26.829761151876106, + -9.267313845762843, + 3.479368368324131, + ], + }, + } +PRO: + { + radius: 3.4, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 52.98008056903471, + 0.0, + -28.025579140605455, + -17.945632640349288, + 36.70484993598286, + -16.388585233440434, + 2.3759607129713807, + ], + }, + } +SER: + { + radius: 3.3, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 45.98433274188738, + 0.0, + -23.004358312306444, + -13.52528557404974, + 25.318714967455264, + -9.60754708227119, + 1.0838180112873488, + ], + }, + } +THR: + { + radius: 3.5, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 53.98079904257712, + 0.0, + -30.205614210054552, + -20.68857584136233, + 39.80321996161699, + -16.614363714862087, + 2.188488144363049, + ], + }, + } +TRP: + { + radius: 4.3, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 98.00026950327825, + 0.0, + -239.82185506986724, + 306.5877727946093, + -164.7189974469082, + 39.67855896479587, + -3.263270784166963, + ], + }, + } +TYR: + { + radius: 4.1, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 85.99025648157047, + 0.0, + -205.8271529828311, + 282.4197901545501, + -174.14171820412088, + 52.3398359916699, + -6.16839805508144, + ], + }, + } +VAL: + { + radius: 3.4, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 53.97802539665846, + 0.0, + -30.730380715977926, + -23.1259991191962, + 43.982242401804015, + -18.76619704074442, + 2.5506887609142446, + ], + }, + } + # Atomic properties for PRIPPS # # Units: @@ -446,370 +807,6 @@ I: }, } -# -# Single amino acid beads fitted to atomic rotamer library using PepsiSAXS (I. Vinterbladh, 2025) -# - -ALA: - { - radius: 3.1, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 8.942389031864906, - 0.0, - 5.792194402460091, - -16.535783442320792, - 12.669452307957307, - -3.822977263906558, - 0.3878762530699602, - ], - }, - } -ARG: - { - radius: 4.0, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 18.072451403788154, - 0.0, - -75.69947894790279, - 167.19851263909686, - -153.1865136736727, - 64.009506735669, - -10.050917424550024, - ], - }, - } -ASN: - { - radius: 3.6, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 19.94452083505653, - 0.0, - -24.990745895144894, - 10.187771836208402, - 12.739030956640574, - -10.430110317898663, - 2.1077112612942783, - ], - }, - } -ASP: - { - radius: 3.6, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 21.137012459568677, - 0.0, - -27.320010136136077, - 12.154865501945308, - 12.834201034703824, - -11.006888423155154, - 2.256916708561725, - ], - }, - } -CYS: - { - radius: 3.6, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 18.48802441743039, - 0.0, - -15.008260615966531, - -3.0592158092114654, - 20.84253154483713, - -12.72075350407731, - 2.329262619301162, - ], - }, - } -GLN: - { - radius: 3.8, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 19.092219102124133, - 0.0, - -55.71666277274864, - 94.70875173211498, - -68.40460682979565, - 22.951558073342618, - -2.9269918776215573, - ], - }, - } -GLU: - { - radius: 3.8, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 20.28555642388545, - 0.0, - -62.271777367004276, - 107.93247673344275, - -79.08167313520292, - 26.830603948880416, - -3.4559248839916377, - ], - }, - } -GLY: - { - radius: 2.9, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 10.030800861738951, - 0.0, - -4.6338656212978275, - 1.398787623262163, - 0.919899897500871, - -0.5731326454753257, - 0.08816182284498815, - ], - }, - } -HIS: - { - radius: 3.9, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 21.423113519818372, - 0.0, - -46.636108104362755, - 61.75539960072336, - -32.658537927918154, - 7.028428462064363, - -0.3833061237558807, - ], - }, - } -ILE: - { - radius: 3.6, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 6.135416805762242, - 0.0, - 45.106811072484085, - -106.84755293709196, - 95.13669370203318, - -37.58664095305978, - 5.5453809105857195, - ], - }, - } -LEU: - { - radius: 3.6, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 6.138168512978042, - 0.0, - 47.934902818764876, - -119.41463546663192, - 112.44915470654685, - -46.94202040498031, - 7.288134115831069, - ], - }, - } -LYS: - { - radius: 3.7, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 10.249270051425176, - 0.0, - -16.953595674466165, - 37.57945990805088, - -34.49272260928792, - 14.256901427971616, - -2.2024336596909, - ], - }, - } -MET: - { - radius: 3.8, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 16.59423258954827, - 0.0, - -33.43792957618986, - 52.55042360367518, - -31.06478276482932, - 7.038335637548599, - -0.3441818814436671, - ], - }, - } -PHE: - { - radius: 3.9, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 9.061740537641056, - 0.0, - 16.938904839573933, - -54.33141142136926, - 58.00279490595986, - -26.29056762935351, - 4.334887659581006, - ], - }, - } -PRO: - { - radius: 3.4, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 8.061676868999315, - 0.0, - 15.736582179430584, - -36.51095562070633, - 30.597163917734978, - -11.412299904725824, - 1.5972963247634666, - ], - }, - } -SER: - { - radius: 3.3, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 14.060149744444526, - 0.0, - -6.446479191981965, - -8.13892968407698, - 14.993502758938634, - -7.113990966458539, - 1.102168774618203, - ], - }, - } -THR: - { - radius: 3.5, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 12.97185298730747, - 0.0, - 5.452934153832478, - -30.909415998844427, - 32.40855032591247, - -13.20466495790777, - 1.9135129941830082, - ], - }, - } -TRP: - { - radius: 4.3, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 15.536691620224413, - 0.0, - -35.692376639503074, - 57.0316874604167, - -38.12208050539742, - 11.709602103861181, - -1.352226492272784, - ], - }, - } -TYR: - { - radius: 4.1, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 14.08639145237529, - 0.0, - -43.222400397463154, - 91.041729746804, - -80.28980159518841, - 32.47252773252689, - -4.95331234356697, - ], - }, - } -VAL: - { - radius: 3.4, - formfactor: - !Polynomial { - qrange: [0.0, 0.75], - coeff: - [ - 6.993832972017251, - 0.0, - 31.500570359533587, - -72.60874851556713, - 61.18930275210783, - -22.682141929580425, - 3.13028149686691, - ], - }, - } CTR: { radius: 2.0, formfactor: !Alias C } NTR: { radius: 2.0, formfactor: !Alias N } HNTR: { ff: !Alias N } diff --git a/assets/poly_amino_acid.csv b/assets/poly_amino_acid.csv new file mode 100644 index 0000000..639ea48 --- /dev/null +++ b/assets/poly_amino_acid.csv @@ -0,0 +1,41 @@ +name,term,c0,c1,c2,c3,c4,c5,c6 +ALA,formfactor,37.98651145079954,0.0,-17.88881261159881,-3.1097731825829333,8.846459092736989,-2.3572911188270753,0.05331786541354333 +ALA,excluded_solvent,29.045092626980807,0.0,-26.82743171578708,-1.9310977148566097,20.168431250949013,-10.337343031318388,1.6129945355732858 +ARG,formfactor,83.96908927242093,0.0,-278.81910994314467,490.5059319443547,-383.9755997417938,143.65451627028935,-20.783481179736413 +ARG,excluded_solvent,61.48590421009381,0.0,-182.19421922046388,254.9995877714297,-162.0053689458599,51.021483900322494,-6.424772180212159 +ASN,formfactor,59.98205560472268,0.0,-40.54214890095476,-32.39953367060282,73.4159357342896,-37.17064504604371,6.101333446616579 +ASN,excluded_solvent,40.032910229163015,0.0,-43.419271616917506,-2.5096331414436257,37.78025696960391,-21.16604652006109,3.5704626997326017 +ASP,formfactor,59.98252699985404,0.0,-37.42179690845112,-37.97031252961563,77.22160318216388,-38.25436485287656,6.198643717045879 +ASP,excluded_solvent,38.84073261635539,0.0,-39.80757800147463,-6.1050880975993564,38.584033668906045,-20.838504513678572,3.448422193736537 +CYS,formfactor,53.98471591625841,0.0,-28.55463591537983,-34.95759123047967,63.496177867671385,-29.839780719449596,4.609281628955767 +CYS,excluded_solvent,35.50267070799075,0.0,-39.09278093395425,1.6989104594187223,28.01870291638898,-15.980070618028627,2.685602905744272 +GLN,formfactor,68.00029704612041,0.0,-97.52724702286758,63.863879205326334,13.865971310987792,-22.96589467520148,5.296934807386833 +GLN,excluded_solvent,48.898600541821935,0.0,-77.65395265978444,40.54946712843443,18.936705440015086,-19.2453476844495,3.940784263263467 +GLU,formfactor,68.00262098772043,0.0,-95.50455833177392,59.449715597229726,18.625430344038303,-25.334425917186397,5.722041637792017 +GLU,excluded_solvent,47.70649459960143,0.0,-72.13383592347742,32.19791380532419,24.30930931866937,-20.876834470726052,4.131360684537825 +GLY,formfactor,29.99006405529222,0.0,-11.572242769967232,-0.9021293993630419,3.790951509543525,-0.7238332866526376,-0.05549468364095844 +GLY,excluded_solvent,19.95957354188481,0.0,-14.596710882003759,-2.4650485889412432,10.550404760378463,-4.733006480429002,0.658871566636229 +HIS,formfactor,71.99289906060545,0.0,-93.58672742143521,41.460338892548805,39.724339660983915,-34.41529003289915,7.057596750057414 +HIS,excluded_solvent,50.56476709188372,0.0,-88.00858173319708,57.29648278061343,8.364968725388358,-16.350312366132616,3.664942297923531 +ILE,formfactor,61.97933054508859,0.0,-45.55749645626756,-26.94147636420316,68.41091746940783,-34.307325839550984,5.508363044131372 +ILE,excluded_solvent,55.85191850939976,0.0,-81.76824367344963,25.9718149641765,39.09563612134737,-27.79407667749436,5.140594587619425 +LEU,formfactor,61.9874112464829,0.0,-54.07829774966109,-19.672724727921118,75.75933357599484,-42.87909415253206,7.580643479500708 +LEU,excluded_solvent,55.85500702439895,0.0,-90.05823613360623,42.779271367948105,27.425314570778514,-24.599500988549345,4.878915346497919 +LYS,formfactor,69.99129116923348,0.0,-177.94423757968704,272.0256856055207,-188.2920973172914,63.303038257186586,-8.32612812430321 +LYS,excluded_solvent,59.73734011424962,0.0,-152.78437269984616,187.93704583787604,-102.44089786274051,27.188377002036162,-2.8320376302376786 +MET,formfactor,70.00691278772638,0.0,-104.22345654704631,73.82601789465483,10.126507627913185,-23.351946238697035,5.598503080281802 +MET,excluded_solvent,53.406398181964576,0.0,-110.88151801680236,100.05649928313625,-25.69528079121111,-3.5675976163339627,1.822892341875317 +PHE,formfactor,77.99863587497389,0.0,-135.30506501839736,122.61728717372222,-26.829761151876106,-9.267313845762843,3.479368368324131 +PHE,excluded_solvent,68.93257004637968,0.0,-147.5670650614922,137.54078929479533,-39.12160207012098,-2.753466411358474,2.1427403561631735 +PRO,formfactor,52.98008056903471,0.0,-28.025579140605455,-17.945632640349288,36.70484993598286,-16.388585233440434,2.3759607129713807 +PRO,excluded_solvent,44.92171507351757,0.0,-48.596196051735205,-0.8750372245099269,38.16601448085936,-21.187997828228426,3.5216456896403727 +SER,formfactor,45.98433274188738,0.0,-23.004358312306444,-13.52528557404974,25.318714967455264,-9.60754708227119,1.0838180112873488 +SER,excluded_solvent,31.92723070377774,0.0,-28.914290210798498,-4.307301992955933,24.346605792144324,-12.176757238126978,1.8734369740555579 +THR,formfactor,53.98079904257712,0.0,-30.205614210054552,-20.68857584136233,39.80321996161699,-16.614363714862087,2.188488144363049 +THR,excluded_solvent,41.01359917184679,0.0,-45.25975736862446,1.4176217981309085,32.5646045063437,-18.24370055421664,3.0168424658285318 +TRP,formfactor,98.00026950327825,0.0,-239.82185506986724,306.5877727946093,-164.7189974469082,39.67855896479587,-3.263270784166963 +TRP,excluded_solvent,82.45226706960327,0.0,-230.05660099986292,285.0669179531113,-150.2872276282459,36.63321998268637,-3.2505031840496486 +TYR,formfactor,85.99025648157047,0.0,-205.8271529828311,282.4197901545501,-174.14171820412088,52.3398359916699,-6.16839805508144 +TYR,excluded_solvent,71.89594546951952,0.0,-171.88553159706834,188.12775195681272,-82.8899246910726,14.785221706872576,-0.5321832521390917 +VAL,formfactor,53.97802539665846,0.0,-30.730380715977926,-23.1259991191962,43.982242401804015,-18.76619704074442,2.5506887609142446 +VAL,excluded_solvent,46.99003082374464,0.0,-56.3441569003358,3.611267423393473,40.951469982336846,-23.894973751496867,4.06986224970413 diff --git a/assets/poly_amino_acid.yaml b/assets/poly_amino_acid.yaml new file mode 100644 index 0000000..9c02774 --- /dev/null +++ b/assets/poly_amino_acid.yaml @@ -0,0 +1,696 @@ +# +# Amino-acid beads with polynomial form factors, keeping the excluded +# solvent SEPARATE from the form factor: `formfactor` is the in-vacuo +# bead scattering and the displaced bulk solvent is stored next to it as +# `excluded_solvent`. Use with `--excluded per-kind`, which subtracts the +# excluded-solvent term (scaled by `volume_scale`) at recombination. +# +# For the variant with the excluded solvent already folded into a single +# form factor, see poly_amino_acid_combined.yaml. +# +# Converted from poly_amino_acid.csv. Coefficients preserve all +# significant digits from the CSV source. +# + +ALA: + { + radius: 3.1, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 37.98651145079954, + 0.0, + -17.88881261159881, + -3.1097731825829333, + 8.846459092736989, + -2.3572911188270753, + 0.05331786541354333, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 29.045092626980807, + 0.0, + -26.82743171578708, + -1.9310977148566097, + 20.168431250949013, + -10.337343031318388, + 1.6129945355732858, + ], + }, + } +ARG: + { + radius: 4.0, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 83.96908927242093, + 0.0, + -278.81910994314467, + 490.5059319443547, + -383.9755997417938, + 143.65451627028935, + -20.783481179736413, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 61.48590421009381, + 0.0, + -182.19421922046388, + 254.9995877714297, + -162.0053689458599, + 51.021483900322494, + -6.424772180212159, + ], + }, + } +ASN: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 59.98205560472268, + 0.0, + -40.54214890095476, + -32.39953367060282, + 73.4159357342896, + -37.17064504604371, + 6.101333446616579, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 40.032910229163015, + 0.0, + -43.419271616917506, + -2.5096331414436257, + 37.78025696960391, + -21.16604652006109, + 3.5704626997326017, + ], + }, + } +ASP: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 59.98252699985404, + 0.0, + -37.42179690845112, + -37.97031252961563, + 77.22160318216388, + -38.25436485287656, + 6.198643717045879, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 38.84073261635539, + 0.0, + -39.80757800147463, + -6.1050880975993564, + 38.584033668906045, + -20.838504513678572, + 3.448422193736537, + ], + }, + } +CYS: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 53.98471591625841, + 0.0, + -28.55463591537983, + -34.95759123047967, + 63.496177867671385, + -29.839780719449596, + 4.609281628955767, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 35.50267070799075, + 0.0, + -39.09278093395425, + 1.6989104594187223, + 28.01870291638898, + -15.980070618028627, + 2.685602905744272, + ], + }, + } +GLN: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 68.00029704612041, + 0.0, + -97.52724702286758, + 63.863879205326334, + 13.865971310987792, + -22.96589467520148, + 5.296934807386833, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 48.898600541821935, + 0.0, + -77.65395265978444, + 40.54946712843443, + 18.936705440015086, + -19.2453476844495, + 3.940784263263467, + ], + }, + } +GLU: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 68.00262098772043, + 0.0, + -95.50455833177392, + 59.449715597229726, + 18.625430344038303, + -25.334425917186397, + 5.722041637792017, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 47.70649459960143, + 0.0, + -72.13383592347742, + 32.19791380532419, + 24.30930931866937, + -20.876834470726052, + 4.131360684537825, + ], + }, + } +GLY: + { + radius: 2.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 29.99006405529222, + 0.0, + -11.572242769967232, + -0.9021293993630419, + 3.790951509543525, + -0.7238332866526376, + -0.05549468364095844, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 19.95957354188481, + 0.0, + -14.596710882003759, + -2.4650485889412432, + 10.550404760378463, + -4.733006480429002, + 0.658871566636229, + ], + }, + } +HIS: + { + radius: 3.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 71.99289906060545, + 0.0, + -93.58672742143521, + 41.460338892548805, + 39.724339660983915, + -34.41529003289915, + 7.057596750057414, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 50.56476709188372, + 0.0, + -88.00858173319708, + 57.29648278061343, + 8.364968725388358, + -16.350312366132616, + 3.664942297923531, + ], + }, + } +ILE: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 61.97933054508859, + 0.0, + -45.55749645626756, + -26.94147636420316, + 68.41091746940783, + -34.307325839550984, + 5.508363044131372, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 55.85191850939976, + 0.0, + -81.76824367344963, + 25.9718149641765, + 39.09563612134737, + -27.79407667749436, + 5.140594587619425, + ], + }, + } +LEU: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 61.9874112464829, + 0.0, + -54.07829774966109, + -19.672724727921118, + 75.75933357599484, + -42.87909415253206, + 7.580643479500708, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 55.85500702439895, + 0.0, + -90.05823613360623, + 42.779271367948105, + 27.425314570778514, + -24.599500988549345, + 4.878915346497919, + ], + }, + } +LYS: + { + radius: 3.7, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 69.99129116923348, + 0.0, + -177.94423757968704, + 272.0256856055207, + -188.2920973172914, + 63.303038257186586, + -8.32612812430321, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 59.73734011424962, + 0.0, + -152.78437269984616, + 187.93704583787604, + -102.44089786274051, + 27.188377002036162, + -2.8320376302376786, + ], + }, + } +MET: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 70.00691278772638, + 0.0, + -104.22345654704631, + 73.82601789465483, + 10.126507627913185, + -23.351946238697035, + 5.598503080281802, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 53.406398181964576, + 0.0, + -110.88151801680236, + 100.05649928313625, + -25.69528079121111, + -3.5675976163339627, + 1.822892341875317, + ], + }, + } +PHE: + { + radius: 3.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 77.99863587497389, + 0.0, + -135.30506501839736, + 122.61728717372222, + -26.829761151876106, + -9.267313845762843, + 3.479368368324131, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 68.93257004637968, + 0.0, + -147.5670650614922, + 137.54078929479533, + -39.12160207012098, + -2.753466411358474, + 2.1427403561631735, + ], + }, + } +PRO: + { + radius: 3.4, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 52.98008056903471, + 0.0, + -28.025579140605455, + -17.945632640349288, + 36.70484993598286, + -16.388585233440434, + 2.3759607129713807, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 44.92171507351757, + 0.0, + -48.596196051735205, + -0.8750372245099269, + 38.16601448085936, + -21.187997828228426, + 3.5216456896403727, + ], + }, + } +SER: + { + radius: 3.3, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 45.98433274188738, + 0.0, + -23.004358312306444, + -13.52528557404974, + 25.318714967455264, + -9.60754708227119, + 1.0838180112873488, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 31.92723070377774, + 0.0, + -28.914290210798498, + -4.307301992955933, + 24.346605792144324, + -12.176757238126978, + 1.8734369740555579, + ], + }, + } +THR: + { + radius: 3.5, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 53.98079904257712, + 0.0, + -30.205614210054552, + -20.68857584136233, + 39.80321996161699, + -16.614363714862087, + 2.188488144363049, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 41.01359917184679, + 0.0, + -45.25975736862446, + 1.4176217981309085, + 32.5646045063437, + -18.24370055421664, + 3.0168424658285318, + ], + }, + } +TRP: + { + radius: 4.3, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 98.00026950327825, + 0.0, + -239.82185506986724, + 306.5877727946093, + -164.7189974469082, + 39.67855896479587, + -3.263270784166963, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 82.45226706960327, + 0.0, + -230.05660099986292, + 285.0669179531113, + -150.2872276282459, + 36.63321998268637, + -3.2505031840496486, + ], + }, + } +TYR: + { + radius: 4.1, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 85.99025648157047, + 0.0, + -205.8271529828311, + 282.4197901545501, + -174.14171820412088, + 52.3398359916699, + -6.16839805508144, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 71.89594546951952, + 0.0, + -171.88553159706834, + 188.12775195681272, + -82.8899246910726, + 14.785221706872576, + -0.5321832521390917, + ], + }, + } +VAL: + { + radius: 3.4, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 53.97802539665846, + 0.0, + -30.730380715977926, + -23.1259991191962, + 43.982242401804015, + -18.76619704074442, + 2.5506887609142446, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 46.99003082374464, + 0.0, + -56.3441569003358, + 3.611267423393473, + 40.951469982336846, + -23.894973751496867, + 4.06986224970413, + ], + }, + } + +H: + { + radius: 1.06893, + ff: + !Gaussian { + qrange: [0.0, 6.0], + ab: + [ + [0.493002, 10.5109], + [0.322912, 26.1257], + [0.140191, 3.14236], + [0.04081, 6.14236], + [0, 57.7997], + ], + c: 0.003038, + }, + } +H2O: + { + radius: 1.92757, + ff: !United { center: O, orbit: [[H, 0.9584, 2]] }, + } + +# Oxygen +O: + { + radius: 1.3, + ff: + !Gaussian { + qrange: [0.0, 6.0], + ab: + [ + [2.960427, 14.182259], + [2.508818, 5.936858], + [0.637853, 0.112726], + [0.722838, 34.958481], + [1.142756, 0.39024], + ], + c: 0.027014, + }, + } diff --git a/assets/poly_amino_acid_combined.csv b/assets/poly_amino_acid_combined.csv new file mode 100644 index 0000000..f48a14b --- /dev/null +++ b/assets/poly_amino_acid_combined.csv @@ -0,0 +1,22 @@ +name,c0,c1,c2,c3,c4,c5,c6 +ALA,8.9412587775497,0.0,16.011245238542376,-16.521361728384683,1.2933025488574188,3.3398332086315183,-0.9168303784888309 +ARG,22.478400682753,0.0,-76.88484987429919,191.960715944726,-186.00003070019167,79.46368990494528,-12.556750037517471 +ASN,19.949489234412567,0.0,1.6179004991799801,-23.728215923317965,28.038892792816576,-12.386064402235293,1.929507607517487 +ASP,21.142259115455996,0.0,0.8083841282589246,-24.40440912470514,29.535430988379265,-13.10651536410425,2.0372739416341537 +CYS,18.4839740069889,0.0,8.812507883396963,-30.642459459023264,29.917256325500603,-11.91425779442574,1.7004706037089043 +GLN,19.097505523941596,0.0,-19.519311966958725,29.194793659203825,-15.60297797159053,2.294407640083047,0.22452643450605897 +GLU,20.29129332390765,0.0,-23.92960632234384,36.56322920155976,-20.248077291844673,3.4846151692772582,0.13148709734953815 +GLY,10.03054111756488,0.0,3.8546967228919082,0.6218935555757745,-6.5173118334370725,4.059828931486872,-0.7342810687571033 +HIS,21.426981638952395,0.0,-6.545087786844122,-9.218917681779088,22.48808820296215,-13.636405717258942,2.629730298621099 +ILE,6.131903689507308,0.0,62.44603934058318,-116.50937229150081,87.22722909579876,-29.91871615622492,3.903617299464 +LEU,6.138681021896375,0.0,66.35117435927073,-136.95291635172444,116.85417275098094,-46.21315581361594,6.954197879918763 +LYS,10.255435432063244,0.0,-2.79528412490785,31.003997628409078,-38.481099323214984,17.32696941435372,-2.7042988301136206 +MET,16.6012526530535,0.0,4.275077970411341,-15.258361897925777,22.823327775070062,-13.770397262025778,2.7981498950550945 +PHE,9.071122674364272,0.0,39.79943933556812,-81.30297163776913,72.46058503731058,-30.747950376059865,4.988707578933258 +PRO,8.059137153259476,0.0,31.79309942143073,-42.94704503012894,21.099739388925595,-3.982867900332487,0.13888933356202715 +SER,14.05727930936467,0.0,7.647900181455087,-12.574941145347822,3.684037043046182,1.5047648090346444,-0.6239606098665743 +THR,12.967760333440639,0.0,22.939510196365646,-40.1269137705015,23.02482489533793,-4.594586191271366,0.09745190287314791 +TRP,15.550265578730231,0.0,3.8345051419382883,-8.10915317671068,10.1979733066048,-6.175730606600176,1.2946805948438511 +TYR,14.091388264842559,0.0,-15.296990379601986,53.21143305660709,-57.05962787532795,24.85006239311008,-3.8617835428573346 +VAL,6.989849861142642,0.0,45.207054405690215,-73.12972511324685,44.39618398908707,-11.273280090656975,0.9160064260116021 +H2O,9.998096755786294,0.0,-0.7939861642881193,-0.4523851090473768,0.5582058579362125,-0.2293883781481107,0.0364585916559163 diff --git a/assets/poly_amino_acid_combined.yaml b/assets/poly_amino_acid_combined.yaml new file mode 100644 index 0000000..d7a12d8 --- /dev/null +++ b/assets/poly_amino_acid_combined.yaml @@ -0,0 +1,392 @@ +# +# Amino-acid beads with polynomial form factors that already have the +# excluded solvent COMBINED in: each `formfactor` is the in-vacuo bead +# scattering minus the displaced bulk solvent, so there is no separate +# `excluded_solvent` term. Use with `--excluded disabled` — the solvent +# contribution is baked in and not separately fittable via `volume_scale`. +# +# For the variant that keeps the excluded solvent as a separate, +# fittable term, see poly_amino_acid.yaml. +# +# Converted from poly_amino_acid_combined.csv. Coefficients preserve all +# significant digits from the CSV source. +# + +ALA: + { + radius: 3.1, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 8.9412587775497, + 0.0, + 16.011245238542376, + -16.521361728384683, + 1.2933025488574188, + 3.3398332086315183, + -0.9168303784888309, + ], + }, + } +ARG: + { + radius: 4.0, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 22.478400682753, + 0.0, + -76.88484987429919, + 191.960715944726, + -186.00003070019167, + 79.46368990494528, + -12.556750037517471, + ], + }, + } +ASN: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 19.949489234412567, + 0.0, + 1.6179004991799801, + -23.728215923317965, + 28.038892792816576, + -12.386064402235293, + 1.929507607517487, + ], + }, + } +ASP: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 21.142259115455996, + 0.0, + 0.8083841282589246, + -24.40440912470514, + 29.535430988379265, + -13.10651536410425, + 2.0372739416341537, + ], + }, + } +CYS: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 18.4839740069889, + 0.0, + 8.812507883396963, + -30.642459459023264, + 29.917256325500603, + -11.91425779442574, + 1.7004706037089043, + ], + }, + } +GLN: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 19.097505523941596, + 0.0, + -19.519311966958725, + 29.194793659203825, + -15.60297797159053, + 2.294407640083047, + 0.22452643450605897, + ], + }, + } +GLU: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 20.29129332390765, + 0.0, + -23.92960632234384, + 36.56322920155976, + -20.248077291844673, + 3.4846151692772582, + 0.13148709734953815, + ], + }, + } +GLY: + { + radius: 2.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.03054111756488, + 0.0, + 3.8546967228919082, + 0.6218935555757745, + -6.5173118334370725, + 4.059828931486872, + -0.7342810687571033, + ], + }, + } +HIS: + { + radius: 3.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 21.426981638952395, + 0.0, + -6.545087786844122, + -9.218917681779088, + 22.48808820296215, + -13.636405717258942, + 2.629730298621099, + ], + }, + } +ILE: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 6.131903689507308, + 0.0, + 62.44603934058318, + -116.50937229150081, + 87.22722909579876, + -29.91871615622492, + 3.903617299464, + ], + }, + } +LEU: + { + radius: 3.6, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 6.138681021896375, + 0.0, + 66.35117435927073, + -136.95291635172444, + 116.85417275098094, + -46.21315581361594, + 6.954197879918763, + ], + }, + } +LYS: + { + radius: 3.7, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.255435432063244, + 0.0, + -2.79528412490785, + 31.003997628409078, + -38.481099323214984, + 17.32696941435372, + -2.7042988301136206, + ], + }, + } +MET: + { + radius: 3.8, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 16.6012526530535, + 0.0, + 4.275077970411341, + -15.258361897925777, + 22.823327775070062, + -13.770397262025778, + 2.7981498950550945, + ], + }, + } +PHE: + { + radius: 3.9, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 9.071122674364272, + 0.0, + 39.79943933556812, + -81.30297163776913, + 72.46058503731058, + -30.747950376059865, + 4.988707578933258, + ], + }, + } +PRO: + { + radius: 3.4, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 8.059137153259476, + 0.0, + 31.79309942143073, + -42.94704503012894, + 21.099739388925595, + -3.982867900332487, + 0.13888933356202715, + ], + }, + } +SER: + { + radius: 3.3, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 14.05727930936467, + 0.0, + 7.647900181455087, + -12.574941145347822, + 3.684037043046182, + 1.5047648090346444, + -0.6239606098665743, + ], + }, + } +THR: + { + radius: 3.5, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 12.967760333440639, + 0.0, + 22.939510196365646, + -40.1269137705015, + 23.02482489533793, + -4.594586191271366, + 0.09745190287314791, + ], + }, + } +TRP: + { + radius: 4.3, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 15.550265578730231, + 0.0, + 3.8345051419382883, + -8.10915317671068, + 10.1979733066048, + -6.175730606600176, + 1.2946805948438511, + ], + }, + } +TYR: + { + radius: 4.1, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 14.091388264842559, + 0.0, + -15.296990379601986, + 53.21143305660709, + -57.05962787532795, + 24.85006239311008, + -3.8617835428573346, + ], + }, + } +VAL: + { + radius: 3.4, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 6.989849861142642, + 0.0, + 45.207054405690215, + -73.12972511324685, + 44.39618398908707, + -11.273280090656975, + 0.9160064260116021, + ], + }, + } +H2O: + { + radius: 1.92757, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 9.998096755786294, + 0.0, + -0.7939861642881193, + -0.4523851090473768, + 0.5582058579362125, + -0.2293883781481107, + 0.0364585916559163, + ], + }, + } diff --git a/assets/poly_martini.csv b/assets/poly_martini.csv new file mode 100644 index 0000000..58930c3 --- /dev/null +++ b/assets/poly_martini.csv @@ -0,0 +1,105 @@ +name,term,c0,c1,c2,c3,c4,c5,c6 +ALA_BB,formfactor,28.990073241622685,0.0,-11.177229580969742,-1.041230707343862,3.896739025786083,-0.7834153923743044,-0.04620768581327894 +ALA_BB,excluded_solvent,18.346290353801106,0.0,-12.653110158775076,-2.3088373281225656,8.954234154392193,-3.9111930639397396,0.5316795732834638 +ALA_SC1,formfactor,8.997053946180342,0.0,-1.2886607141923,-0.03638825162057108,0.18489963241121238,-0.03832455871722429,0.001697258570267035 +ALA_SC1,excluded_solvent,10.698141363893551,0.0,-8.407424733759692,-1.1509346674012797,6.134912807768994,-2.909737215446782,0.43007830791329216 +ARG_BB,formfactor,28.990057932554322,0.0,-11.177220266838871,-1.041226092258199,3.8967303202452412,-0.7834128682023218,-0.046207777496121594 +ARG_BB,excluded_solvent,18.34628066475686,0.0,-12.653100979130562,-2.308834999482381,8.954225197025604,-3.911188786826493,0.5316789496911447 +ARG_SC1,formfactor,23.99124831405302,0.0,-7.981156485181654,-0.6776012702015644,2.6794596374711035,-0.7861307512851896,0.05925499871152162 +ARG_SC1,excluded_solvent,26.57370827925664,0.0,-24.074648161894537,-1.8773548444314012,17.891690738378305,-9.111032297062524,1.4204614112107805 +ARG_SC2,formfactor,30.9866102992927,0.0,-10.682450094635128,0.008438202667122363,2.0427827396789526,-0.25410137316149406,-0.04668533259884278 +ARG_SC2,excluded_solvent,16.558349927927022,0.0,-10.110308343722142,-1.7419473864471124,6.326611843898691,-2.6167402172478047,0.33970388272167007 +ASN_BB,formfactor,28.990071951214787,0.0,-11.17722797636191,-1.0412240948996303,3.896728998077234,-0.783411173548231,-0.04620822231688848 +ASN_BB,excluded_solvent,18.346289536445926,0.0,-12.653108960488971,-2.308836298059126,8.954231655952231,-3.911191831367355,0.5316793871716143 +ASN_SC1,formfactor,30.990344046065342,0.0,-10.962660759486624,0.10456553650170775,2.056126075783673,-0.21514010046023557,-0.06223668221858425 +ASN_SC1,excluded_solvent,21.68444839921201,0.0,-16.25762072804848,-2.614823820662462,12.115529477515912,-5.657247538701338,0.8250347552739576 +ASP_BB,formfactor,28.99007178084968,0.0,-11.177228704437532,-1.041228796546678,3.896736040491519,-0.7834141687596734,-0.046207835907916106 +ASP_BB,excluded_solvent,18.34628942914991,0.0,-12.653109341464257,-2.3088369493415066,8.954233108896743,-3.9111925672904126,0.5316795005175585 +ASP_SC1,formfactor,30.992098589110185,0.0,-10.388624352718955,0.01619143873102047,2.028404171579579,-0.2863892747842236,-0.036240526229072145 +ASP_SC1,excluded_solvent,20.49302150929785,0.0,-14.874769103785917,-2.5095400224753144,10.978432485451618,-5.058717212548089,0.7297381012833224 +CYS_BB,formfactor,28.990073156918307,0.0,-11.177229548311658,-1.0412307043014477,3.8967390144002354,-0.783415390085171,-0.04620768567828826 +CYS_BB,excluded_solvent,18.346290300196173,0.0,-12.653110121804845,-2.308837321376112,8.954234128228956,-3.911193052511694,0.5316795717299563 +CYS_SC1,formfactor,24.996653755827243,0.0,-5.783163651245389,-0.34532911042693604,1.5583861303737778,-0.395775769141109,0.02112728069995473 +CYS_SC1,excluded_solvent,17.154500313414054,0.0,-14.003800941571946,-1.7231767719258033,10.372116536728607,-5.035375744936971,0.7583983948929642 +GLN_BB,formfactor,28.990059920988905,0.0,-11.177227543485378,-1.0412324358250165,3.8967410941018636,-0.7834159587371587,-0.04620770091923099 +GLN_BB,excluded_solvent,18.34628192440999,0.0,-12.653106662862495,-2.308837114186918,8.954233674535606,-3.91119317891294,0.5316796258306677 +GLN_SC1,formfactor,38.98694243630933,0.0,-18.127497785188805,-2.4049403928179895,8.490639757416263,-2.581285024694723,0.1625753176249738 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+VAL_SC1,excluded_solvent,28.641691185690483,0.0,-28.745627186697078,0.38955789952016406,19.6553653540045,-10.648342430540302,1.7240229533400253 diff --git a/assets/poly_martini.yaml b/assets/poly_martini.yaml new file mode 100644 index 0000000..ef82d69 --- /dev/null +++ b/assets/poly_martini.yaml @@ -0,0 +1,1698 @@ +# +# Martini amino-acid bead form factors, keeping the excluded solvent +# SEPARATE from the form factor: `formfactor` is the in-vacuo bead +# scattering and the displaced bulk solvent is stored next to it as +# `excluded_solvent`. Use with `--excluded per-kind`, which subtracts the +# excluded-solvent term (scaled by `volume_scale`) at recombination. +# +# For the variant with the excluded solvent already folded into a single +# form factor, see poly_martini_combined.yaml. +# +# Converted from poly_martini.csv. Coefficients preserve all significant +# digits from the CSV source. +# + +ALA_BB: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 28.990073241622685, + 0.0, + -11.177229580969742, + -1.041230707343862, + 3.896739025786083, + -0.7834153923743044, + -0.04620768581327894, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 18.346290353801106, + 0.0, + -12.653110158775076, + -2.3088373281225656, + 8.954234154392193, + -3.9111930639397396, + 0.5316795732834638, + ], + }, + } +ALA_SC1: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 8.997053946180342, + 0.0, + -1.2886607141923, + -0.03638825162057108, + 0.18489963241121238, + -0.03832455871722429, + 0.001697258570267035, + ], + }, + excluded_solvent: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.698141363893551, + 0.0, + -8.407424733759692, + -1.1509346674012797, + 6.134912807768994, + -2.909737215446782, + 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+TYR_SC3,-0.49467728298728963,0.0,6.905040827851511,1.444857019166638,-6.065533022896432,2.8194439448562316,-0.4066009631311269 +TYR_SC4,4.7864749265977435,0.0,3.379084515901221,0.457435669705735,-2.6386732256533407,1.184403517784809,-0.16175671046904316 +TYR_SC1,-0.3510348934006665,0.0,7.198047296937748,0.9985596099204095,-5.979206265377417,2.869439868998658,-0.4205709369357118 +VAL_SC1,-3.650489697568667,0.0,20.152880104569256,-1.3077717593059774,-16.48666338249699,9.710777748612122,-1.654724631578258 +VAL_BB,10.643806849587198,0.0,2.061631159601996,0.7872024303709435,-5.080444479262741,3.2226600061868638,-0.5960019731764394 diff --git a/assets/poly_martini_combined.yaml b/assets/poly_martini_combined.yaml new file mode 100644 index 0000000..5df0244 --- /dev/null +++ b/assets/poly_martini_combined.yaml @@ -0,0 +1,969 @@ +# +# Martini amino-acid bead form factors that already have the excluded +# solvent COMBINED in: each `formfactor` is the in-vacuo bead scattering +# minus the displaced bulk solvent, so there is no separate +# `excluded_solvent` term. Use with `--excluded disabled` — the solvent +# contribution is baked in and not separately fittable via `volume_scale`. +# +# For the variant that keeps the excluded solvent as a separate, +# fittable term, see poly_martini.yaml. +# +# Converted from poly_martini_combined.csv. Coefficients preserve all +# significant digits from the CSV source. +# + +ALA_SC1: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -1.7010874177132265, + 0.0, + 7.118764019567526, + 1.1145464157804394, + -5.950013175357564, + 2.8714126567294755, + -0.42838104934301313, + ], + }, + } +ALA_BB: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806846255465, + 0.0, + 2.0616371683115733, + 0.7872168906775016, + -5.080460379455372, + 3.222662883174488, + -0.5960016057238122, + ], + }, + } +ARG_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -2.582459965203611, + 0.0, + 16.093491676712716, + 1.1997535742302685, + -15.212231100907612, + 8.324901545777502, + -1.3612064124992838, + ], + }, + } +ARG_SC2: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 14.428248841779087, + 0.0, + -0.5306373105617507, + 1.6708928597353037, + -4.220821223435202, + 2.340601736383018, + -0.3836111601465304, + ], + }, + } +ARG_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.64380122599819, + 0.0, + 2.061637183212542, + 0.7872187072333622, + -5.080459366793695, + 3.2226607845095225, + -0.5960010250566496, + ], + }, + } +ASN_SC1: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 9.305752980758708, + 0.0, + 8.028095134281596, + -2.3099183822637563, + -6.632212819638959, + 4.4228155805014255, + -0.7764062905424147, + ], + }, + } +ASN_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.64380637290148, + 0.0, + 2.0616383733664607, + 0.7872206653851905, + -5.080466495994918, + 3.2226654178553917, + -0.5960019074982035, + ], + }, + } +ASP_SC1: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.498866776432472, + 0.0, + 7.536374464512118, + -2.8556236565791147, + -5.4787540182953105, + 3.815664106474512, + -0.67328443867979, + ], + }, + } +ASP_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806310047486, + 0.0, + 2.06163743145317, + 0.787217929242442, + -5.080461918557475, + 3.222663478750303, + -0.5960016686083391, + ], + }, + } +CYS_SC1: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 7.84213323319376, + 0.0, + 9.953170739451108, + -1.2903802262350998, + -7.207970655489376, + 4.191794051784442, + -0.688509627116269, + ], + }, + } +CYS_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806815155962, + 0.0, + 2.0616371622877585, + 0.7872168883774691, + -5.080460364611164, + 3.222662873758418, + -0.5960016039823965, + ], + }, + } +GLN_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 8.444221071382211, + 0.0, + 17.678600522511136, + -15.30698404588629, + -1.7525725905064409, + 4.726896859211768, + -1.1004371797545023, + ], + }, + } +GLN_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643801955137343, + 0.0, + 2.061635484428877, + 0.7872153749124453, + -5.080458169900939, + 3.2226625571952128, + -0.5960016907141563, + ], + }, + } +GLU_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 9.637509830770533, + 0.0, + 16.9079134467359, + -14.850360181614349, + -1.4743561994477412, + 4.367483853252233, + -1.011774253849401, + ], + }, + } +GLU_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643804801462782, + 0.0, + 2.0616367722480975, + 0.7872167394450527, + -5.080459403443487, + 3.2226622640656624, + -0.5960014912253735, + ], + }, + } +GLY_BB: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.030507380458893, + 0.0, + 3.8641270986545533, + 0.5938012695810033, + -6.486785053096451, + 4.045479409708778, + -0.7318132454163564, + ], + }, + } +HIS_SC1: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.3510341202994483, + 0.0, + 7.193968257062204, + 1.0071445792130653, + -5.966246573308775, + 2.857512357903082, + -0.4182231235167757, + ], + }, + } +HIS_SC2: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 5.208533893792252, + 0.0, + 3.8539382664534454, + -0.7578802642311985, + -2.104343630945438, + 1.154962103610813, + -0.17552755823901833, + ], + }, + } +HIS_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806239423265, + 0.0, + 2.061636403138179, + 0.7872153348214171, + -5.080458934227526, + 3.222662755527101, + -0.5960016737782905, + ], + }, + } +HIS_SC3: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 5.917304757860416, + 0.0, + 3.4514167501354183, + -0.7996735949256304, + -1.9263229210885686, + 1.0965585639684268, + -0.17072583342582792, + ], + }, + } +ILE_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -2.8097860782245627, + 0.0, + 17.343971902022577, + -0.23891906441869626, + -15.000405961544278, + 8.58845914300296, + -1.441332654688594, + ], + }, + } +ILE_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.64380615454994, + 0.0, + 2.0616374013343046, + 0.7872179177418046, + -5.080461844335988, + 3.2226634316697726, + -0.5960016599012332, + ], + }, + } +LEU_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -4.5114171929323135, + 0.0, + 25.86498421391297, + -0.3593827525321407, + -25.217858321420422, + 15.064085710903564, + -2.607530763046867, + ], + }, + } +LEU_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806213809782, + 0.0, + 2.061633435208698, + 0.7872076376318345, + -5.080448157052189, + 3.222659304574193, + -0.5960014675554544, + ], + }, + } +LYS_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -2.5824810746850932, + 0.0, + 16.13252189664475, + 1.128748246295519, + -15.254770450814124, + 8.38659674454396, + -1.375867210865735, + ], + }, + } +LYS_SC2: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 2.1875912973408798, + 0.0, + 7.73920607666854, + -0.27554644744459345, + -5.278638129524195, + 2.7078403428197415, + -0.40648308930112886, + ], + }, + } +LYS_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.64380376754201, + 0.0, + 2.0616367677184795, + 0.7872172380281511, + -5.080459867292311, + 3.222662355248592, + -0.5960014828825799, + ], + }, + } +MET_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 5.941578575762451, + 0.0, + 27.196511296595972, + -14.885385896082838, + -11.454742034363008, + 10.47605161022688, + -2.1101041439081705, + ], + }, + } +MET_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643805945882942, + 0.0, + 2.061638855896697, + 0.7872212846351714, + -5.080465329413779, + 3.2226637799383036, + -0.5960014813592833, + ], + }, + } +PHE_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.8462636496033878, + 0.0, + 14.839441617245123, + 2.2004608533314522, + -16.95606436299522, + 9.510075608290261, + -1.5946587656311024, + ], + }, + } +PHE_SC2: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.742231268614823, + 0.0, + 10.63643103868057, + 1.7786090791808593, + -10.450445500864703, + 5.373874846922616, + -0.8428947987047763, + ], + }, + } +PHE_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806006706622, + 0.0, + 2.0616372014304547, + 0.7872174036368966, + -5.080460936081678, + 3.2226630332083683, + -0.596001608264948, + ], + }, + } +PHE_SC3: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.7422313155110873, + 0.0, + 10.636434560542588, + 1.7789447792708137, + -10.451993220972495, + 5.374862813071748, + -0.8430703179386754, + ], + }, + } +PRO_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -2.5823799290163008, + 0.0, + 15.935745257970094, + 1.5755615408846868, + -15.321956596666128, + 8.243503230527706, + -1.3306121395565977, + ], + }, + } +PRO_BB: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806843833374, + 0.0, + 2.0616339242389112, + 0.7872087624627253, + -5.080450252830959, + 3.2226602532621835, + -0.5960015957447156, + ], + }, + } +SER_SC1: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 3.4162901978899263, + 0.0, + 7.046526576582673, + -0.7405001447742399, + -4.171626396336101, + 2.17896359910637, + -0.3261396940578125, + ], + }, + } +SER_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806777864874, + 0.0, + 2.0616387389176625, + 0.7872199109819031, + -5.080462650852676, + 3.222662175078477, + -0.5960011782019041, + ], + }, + } +THR_SC1: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 2.3286130028844063, + 0.0, + 15.22075528248213, + -7.075948421013035, + -4.803271805624368, + 3.801442540603149, + -0.6738285661353789, + ], + }, + } +THR_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806692667567, + 0.0, + 2.0616395632827773, + 0.7872229931327475, + -5.080468438252067, + 3.2226651681904412, + -0.596001665635775, + ], + }, + } +TRP_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.6438056642156, + 0.0, + 2.0616365723567625, + 0.7872157250296694, + 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1.4448532336312239, + -6.084567814617669, + 2.83287175092946, + -0.4090847242312874, + ], + }, + } +TRP_SC1: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.351034177345441, + 0.0, + 7.194381667728987, + 1.0062737316596095, + -5.967563386286029, + 2.8587212292759863, + -0.41846073110281257, + ], + }, + } +TYR_BB: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806827500029, + 0.0, + 2.061632625815723, + 0.7872057392672824, + -5.080447933834761, + 3.2226603604678723, + -0.5960017988956865, + ], + }, + } +TYR_SC2: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.49467730589685305, + 0.0, + 6.905064702126387, + 1.4448572769420103, + -6.0657993256015255, + 2.8196314526492494, + -0.4066356099898165, + ], + }, + } +TYR_SC3: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.49467728298728963, + 0.0, + 6.905040827851511, + 1.444857019166638, + -6.065533022896432, + 2.8194439448562316, + -0.4066009631311269, + ], + }, + } +TYR_SC4: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 4.7864749265977435, + 0.0, + 3.379084515901221, + 0.457435669705735, + -2.6386732256533407, + 1.184403517784809, + -0.16175671046904316, + ], + }, + } +TYR_SC1: + { + radius: 1.60, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -0.3510348934006665, + 0.0, + 7.198047296937748, + 0.9985596099204095, + -5.979206265377417, + 2.869439868998658, + -0.4205709369357118, + ], + }, + } +VAL_SC1: + { + radius: 2.35, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + -3.650489697568667, + 0.0, + 20.152880104569256, + -1.3077717593059774, + -16.48666338249699, + 9.710777748612122, + -1.654724631578258, + ], + }, + } +VAL_BB: + { + radius: 2.15, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 10.643806849587198, + 0.0, + 2.061631159601996, + 0.7872024303709435, + -5.080444479262741, + 3.2226600061868638, + -0.5960019731764394, + ], + }, + } + +H2O: + { + radius: 1.92757, + formfactor: + !Polynomial { + qrange: [0.0, 0.75], + coeff: + [ + 9.998096755786294, + 0.0, + -0.7939861642881193, + -0.4523851090473768, + 0.5582058579362125, + -0.2293883781481107, + 0.0364585916559163, + ], + }, + } diff --git a/pripps-py/examples/batch_single_bead_fit_sasbdb.py b/pripps-py/examples/batch_single_bead_fit_sasbdb.py new file mode 100644 index 0000000..9db3a88 --- /dev/null +++ b/pripps-py/examples/batch_single_bead_fit_sasbdb.py @@ -0,0 +1,245 @@ +#!/usr/bin/env python3 +"""Batch single-bead-approximation (SBA) fits over SASBDB cache entries. + +Runs the testing_single-bead_approximation.ipynb workflow against a +directory of cached SASBDB (Small Angle Scattering Biological Data Bank) +entries. + +For each cache directory containing both: + - raw_cleaned.xyz or raw_cleaned_martini3_aa.pdb + - exp.dat + +this script fits (volume_scale, contrast_density) with the same setup +as testing_single-bead_approximation.ipynb: + - atomfile: assets/poly_amino_acid.yaml (or poly_martini.yaml for Martini3) + - excluded: per-kind using the atomfile's excluded_solvent entries + - hydration: disabled + - optimizer: L-BFGS-B with bounds [(0.95, 1.12), (-2.0, 4.0)] or change them in the script +""" + +from __future__ import annotations + +import argparse +import csv +from pathlib import Path + +import numpy as np +import pripps +from scipy.optimize import minimize + + +def load_exp_data(path: Path, qmax: float) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Parse q, I, sigma from a loose 3-column SAXS file.""" + rows: list[tuple[float, float, float]] = [] + with path.open() as fh: + for line in fh: + stripped = line.strip() + if not stripped or stripped.startswith("#"): + continue + parts = stripped.split() + if len(parts) < 3: + continue + try: + q, i_exp, sigma = float(parts[0]), float(parts[1]), float(parts[2]) + except ValueError: + continue + if sigma <= 0.0 or q < 0.0 or q > qmax: + continue + rows.append((q, i_exp, sigma)) + + if not rows: + raise ValueError(f"no valid q/I/sigma rows found in {path}") + + data = np.array(rows, dtype=float) + return data[:, 0], data[:, 1], data[:, 2] + + +def weighted_scale(theory: np.ndarray, i_exp: np.ndarray, sigma: np.ndarray) -> float: + num = np.sum(theory * i_exp / sigma**2) + den = np.sum((theory / sigma) ** 2) + return float(num / den) + + +def fit_single( + pr: pripps.Pripps, + xyz_path: Path, + exp_path: Path, + qmax: float, + nq: int, +) -> dict[str, float | np.ndarray]: + q_exp, i_exp, sigma = load_exp_data(exp_path, qmax=qmax) + + model = pr.debye_model( + structure_file=str(xyz_path), + qmin=0.0, + qmax=qmax, + nq=nq, + excluded="per-kind", + hydration="grid", + volume_scale=1.0, + probe=1.8, + contrast_density=0.03, + bulk_electron_density=0.334, + spacing = 3.8, + ) + + def chi2(params: np.ndarray) -> float: + c1, c2 = float(params[0]), float(params[1]) + out = model.intensity(volume_scale=c1, contrast_density=c2) + theory = np.interp(q_exp, out["q"], out["total"]) + scale = weighted_scale(theory, i_exp, sigma) + return float(np.sum(((scale * theory - i_exp) / sigma) ** 2) / len(q_exp)) + + result = minimize( + chi2, + x0=[1.0, 0.2], + bounds=[(-2.0, 3.0), (-2.0, 4.0)], + method="L-BFGS-B", + ) + + c1_opt, c2_opt = float(result.x[0]), float(result.x[1]) + out = model.intensity(volume_scale=c1_opt, contrast_density=c2_opt) + theory_interp = np.interp(q_exp, out["q"], out["total"]) + scale = weighted_scale(theory_interp, i_exp, sigma) + i_fit = scale * theory_interp + + return { + "c1": c1_opt, + "c2": c2_opt, + "chi2": float(result.fun), + "scale": scale, + "q_exp": q_exp, + "i_exp": i_exp, + "sigma": sigma, + "i_fit": i_fit, + } + + +def main() -> None: + repo_root = Path(__file__).resolve().parents[2] + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--cache-dir", + type=Path, + default=repo_root / "sasbdb_bench" / "cache", + help="Directory with SASBDB entry subdirectories (default: %(default)s)", + ) + parser.add_argument( + "--atomfile", + type=Path, + default=repo_root / "assets" / "poly_martini.yaml", + help="Form-factor YAML with formfactor and excluded_solvent entries", + ) + parser.add_argument( + "--qmax", + type=float, + default=0.5, + help="Upper q limit (Å^-1) used for both model and data filtering", + ) + parser.add_argument( + "--nq", + type=int, + default=500, + help="Number of q points in Debye model", + ) + parser.add_argument( + "--only", + nargs="*", + default=None, + help="Optional list of SASBDB IDs to process", + ) + parser.add_argument( + "--summary-csv", + type=Path, + default=None, + help="Optional output path for summary CSV (default: /sba_summary.csv)", + ) + args = parser.parse_args() + + cache_dir = args.cache_dir.resolve() + summary_csv = args.summary_csv or (cache_dir / "sba_summary_martini.csv") + + pr = pripps.Pripps(atomfile=str(args.atomfile)) + + entries = sorted(d for d in cache_dir.iterdir() if d.is_dir()) + if args.only: + keep = set(args.only) + entries = [d for d in entries if d.name in keep] + + summary_rows: list[dict[str, str | float | int]] = [] + + for entry in entries: + sid = entry.name + xyz_path = entry / "raw_cleaned_martini3_aa.pdb" + exp_path = entry / "exp.dat" + if not xyz_path.is_file() or not exp_path.is_file(): + continue + + try: + fit = fit_single( + pr=pr, + xyz_path=xyz_path, + exp_path=exp_path, + qmax=float(args.qmax), + nq=int(args.nq), + ) + except Exception as exc: + print(f"{sid}: FAILED ({exc})") + continue + + fit_csv = entry / "sba_fit_martini.csv" + np.savetxt( + fit_csv, + np.column_stack([fit["q_exp"], fit["i_exp"], fit["i_fit"], fit["sigma"]]), + delimiter=",", + header="q,i_exp,i_fit,sigma", + comments="", + ) + + print( + f"{sid}: c1={fit['c1']:.4f}, c2={fit['c2']:.4f}, " + f"chi2/N={fit['chi2']:.4f}, n={len(fit['q_exp'])}" + ) + + summary_rows.append( + { + "sid": sid, + "c1": fit["c1"], + "c2": fit["c2"], + "chi2_per_point": fit["chi2"], + "scale": fit["scale"], + "n_points": len(fit["q_exp"]), + "xyz_file": str(xyz_path), + "exp_file": str(exp_path), + "fit_csv": str(fit_csv), + } + ) + + if not summary_rows: + raise SystemExit("No datasets were processed.") + + summary_csv.parent.mkdir(parents=True, exist_ok=True) + with summary_csv.open("w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=[ + "sid", + "c1", + "c2", + "chi2_per_point", + "scale", + "n_points", + "xyz_file", + "exp_file", + "fit_csv", + ], + ) + writer.writeheader() + writer.writerows(summary_rows) + + print(f"Wrote summary: {summary_csv}") + + +if __name__ == "__main__": + main() diff --git a/pripps-py/examples/plotting_sba_sasbdb.ipynb b/pripps-py/examples/plotting_sba_sasbdb.ipynb new file mode 100644 index 0000000..7bd7a12 --- /dev/null +++ b/pripps-py/examples/plotting_sba_sasbdb.ipynb @@ -0,0 +1,592 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "31ee4c36", + "metadata": {}, + "source": [ + "# Plotting Single-Bead Approximation fits from SASBDB benchmark outputs\n", + "\n", + "This notebook collects and visualizes single-bead approximation (SBA) fits generated for SASBDB benchmark entries. It compares fitted scattering curves from different bead models, prepares selected publication-style panels, checks residuals, and summarizes chi-squared statistics for grid and Martini fits.\n", + "\n", + "The plotting code assumes that the benchmark cache lives at `../../sasbdb_bench/cache` and that each SASBDB entry has CSV files such as `sba_fit_grid.csv` and `sba_fit_martini.csv`." + ] + }, + { + "cell_type": "markdown", + "id": "27cb506c", + "metadata": {}, + "source": [ + "## Imports\n", + "\n", + "Load numerical, plotting, filesystem, and tabular-data utilities used throughout the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ce49d41d", + "metadata": {}, + "outputs": [], + "source": [ + "# Core scientific Python imports used by all plotting and summary cells.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "import math\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "01a4bed8", + "metadata": {}, + "source": [ + "## Plot all Martini SBA fits\n", + "\n", + "Find every `sba_fit_martini.csv` file in the cache, read the numeric columns, and draw the experimental intensity columns together with the fitted Martini intensity `i_fit`. The result is saved as a multi-panel PDF for a quick benchmark-wide inspection." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abb487c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "base_dir = Path(\"../../sasbdb_bench/cache\")\n", + "\n", + "# Collect one Martini SBA fit file per benchmark entry.\n", + "csv_files = sorted(base_dir.glob(\"*/sba_fit_martini.csv\"))\n", + "\n", + "if not csv_files:\n", + " raise FileNotFoundError(f\"No matching files found under: {base_dir.resolve()}\")\n", + "\n", + "# Build a two-column grid large enough for every available fit file.\n", + "n = len(csv_files)\n", + "ncols = 2\n", + "nrows = math.ceil(n / ncols)\n", + "\n", + "fig, axes = plt.subplots(nrows, ncols, figsize=(14, 4 * nrows), squeeze=False)\n", + "axes = axes.ravel()\n", + "\n", + "for ax, csv_path in zip(axes, csv_files):\n", + " df = pd.read_csv(csv_path)\n", + " num_df = df.select_dtypes(include=[np.number])\n", + " print(num_df.columns)\n", + "\n", + " # Skip files that do not contain enough numeric data for an x/y plot.\n", + " if num_df.shape[1] < 2:\n", + " ax.set_title(csv_path.parent.name)\n", + " ax.text(0.5, 0.5, \"Not enough numeric columns to plot\", ha=\"center\", va=\"center\")\n", + " ax.axis(\"off\")\n", + " continue\n", + "\n", + " x = num_df.iloc[:, 0]\n", + "\n", + " # Plot each experimental intensity column, then overlay the fitted curve.\n", + " for col in num_df.columns[1:-2]: # Exclude last two columns, typically i_fit and sigma.\n", + " ax.plot(x, num_df[col], label=col)\n", + " ax.plot(x, num_df[\"i_fit\"], label=\"i_fit\", linestyle=\"--\")\n", + "\n", + " ax.set_title(csv_path.parent.name)\n", + " ax.set_xlabel(num_df.columns[0])\n", + " ax.set_ylabel(\"I(q)\")\n", + " ax.grid(True, alpha=0.3)\n", + " ax.set_yscale(\"log\")\n", + " ax.legend(fontsize=8)\n", + "\n", + "# Hide unused subplot slots when the number of files is odd.\n", + "for ax in axes[len(csv_files):]:\n", + " ax.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(base_dir / \"sba_fits_grid.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "21f0d83c", + "metadata": {}, + "source": [ + "## Compare Amino acid bead model and Martini bead model fits\n", + "\n", + "Select a small set of benchmark entries by index and plot experimental curves against both fitted models. The amino-acid bead fit comes from `sba_fit_grid.csv`, while the Martini bead fit comes from `sba_fit_martini.csv`. Panel labels are added for figure assembly." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5fd4c741", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "base_dir = Path(\"../../sasbdb_bench/cache\")\n", + "\n", + "# Load the two fit types in the same sorted order so index-based selections align.\n", + "csv_files = sorted(base_dir.glob(\"*/sba_fit_grid.csv\"))\n", + "csv_files_martini = sorted(base_dir.glob(\"*/sba_fit_martini.csv\"))\n", + "\n", + "if not csv_files:\n", + " raise FileNotFoundError(f\"No matching files found under: {base_dir.resolve()}\")\n", + "\n", + "# Choose representative entries for the comparison figure.\n", + "selected_indices = [2, 5, 12, -1]\n", + "csv_files = [csv_files[i] for i in selected_indices]\n", + "csv_files_martini = [csv_files_martini[i] for i in selected_indices]\n", + "\n", + "n = len(csv_files)\n", + "print(n)\n", + "ncols = 2\n", + "nrows = math.ceil(n / ncols)\n", + "\n", + "fig, axes = plt.subplots(nrows, ncols, figsize=(14, 4 * nrows), squeeze=False)\n", + "axes = axes.ravel()\n", + "\n", + "for ax, csv_path, csv_path_martini in zip(axes, csv_files, csv_files_martini):\n", + " df = pd.read_csv(csv_path)\n", + " df_martini = pd.read_csv(csv_path_martini)\n", + " num_df = df.select_dtypes(include=[np.number])\n", + " num_df_martini = df_martini.select_dtypes(include=[np.number])\n", + " print(num_df.columns)\n", + " print(num_df_martini.columns)\n", + "\n", + " if num_df.shape[1] < 2:\n", + " ax.set_title(csv_path.parent.name)\n", + " ax.text(0.5, 0.5, \"Not enough numeric columns to plot\", ha=\"center\", va=\"center\")\n", + " ax.axis(\"off\")\n", + " continue\n", + "\n", + " x = num_df.iloc[:, 0]\n", + "\n", + " # Draw all experimental intensity columns before adding model fits.\n", + " for col in num_df.columns[1:-2]: # Exclude last two columns, typically i_fit and sigma.\n", + " ax.plot(x, num_df[col], label=\"Experiment\", alpha=0.5)\n", + "\n", + " ax.plot(x, num_df[\"i_fit\"], label=\"Amino acid bead fit\", linestyle=\"-\", color=\"m\")\n", + " ax.plot(x, num_df_martini[\"i_fit\"], label=\"Martini bead fit\", linestyle=\"-\", color=\"k\", alpha=0.5)\n", + "\n", + " ax.set_title(csv_path.parent.name, fontsize=18)\n", + " ax.set_xlabel(r\"q ($\\AA^{-1}$)\", fontsize=16)\n", + " ax.set_ylabel(\"I(q)\", fontsize=16)\n", + " # ax.grid(True, alpha=0.3)\n", + " ax.set_yscale(\"log\")\n", + " ax.set_xscale(\"log\")\n", + " ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + " ax.legend(fontsize=16, loc=\"lower center\")\n", + "\n", + "for ax in axes[len(csv_files):]:\n", + " ax.axis(\"off\")\n", + "\n", + "# Add stable panel labels for downstream figure references.\n", + "panel_labels = [\"A\", \"B\", \"C\", \"D\"]\n", + "for i, ax in enumerate(axes[:len(csv_files)]):\n", + " label = panel_labels[i] if i < len(panel_labels) else chr(ord(\"A\") + i)\n", + " ax.text(\n", + " 0.03,\n", + " 0.05,\n", + " label,\n", + " transform=ax.transAxes,\n", + " fontsize=20,\n", + " fontweight=\"bold\",\n", + " ha=\"left\",\n", + " va=\"bottom\",\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(base_dir / \"sba_fits_grid.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "265d777a", + "metadata": {}, + "source": [ + "## Prepare an experiment-only panel\n", + "\n", + "Build a one-panel plot for a selected entry using only the experimental intensity data. The cell writes a temporary CSV copy where `i_fit` is set to `NaN`, so the later plotting loop can reuse the same column-handling logic without drawing a fitted curve." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ed22df74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n", + "Index(['q', 'i_exp', 'i_fit', 'sigma'], dtype='object')\n" + ] + }, + { + "data": { + "image/png": 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MDDlkV3OX1FcXxXvTAABpRgdFNTU1MnbsWBkYGJDhYfL9AG80KVuDimgdCCaoCNCrr74q3/nOd2TBggXS19cnf/u3fysXX3yxbN68WbKz02s36nKnlbNrTFChGna3ElQAAOJGB0m5ubl8AkAcZTgo8hySXbt2SX19vWzatEnmzJnj9/66fq20tNQklenaz1TQ1jMgv163y5y/euFkqSnJj/cmAQAAIIICHcOmTIbt/fffL9/85jdl/vz5JvlEj1rcc889Ph/T0NAgl1xyiVlbVlhYKIsWLZJHHnkkoNfTHasqKiokXZWOyZHCvJH1eA++tdfkWgAAACD9pExQ8cMf/lDuuusu2bNnj4wfP97v/V955RVZsmSJrFu3Tq644gq56aab5NChQ3LllVfKnXfe6fOxmj3/N3/zNyYgqa2tlXQ2t7bMdf4Pmw7Q3RQAACANpUxQcffdd8vu3bulqanJBAi+DA4Oyo033mgSVtauXWuCEQ0kdCnTzJkz5ZZbbjHBiR1dLabPryXs/M2EpIOF9ZUys6bYdXl3S3dctwcAAACxlzJBxQUXXCBTpkwJ6L4vv/yy7NixQ66++mqZN2+e63pdL6YBRX9/v9x77722AcW3vvUtefHFF+Wll16S6urqiL6HZHXpnPGmX4V67sND0tM/0vodAAAA6SFlgopgrFmzxvy88MILR9120UUXuao9eQYUq1atkj/96U8mKNHa2L5ohShNbLGeUtnC+gopzs82AcWbu1rivTkAAACIobQMKrZt22Z+zpgxY9Rt48aNk6KiItd9nDSg+N3vficPPvigjBkzxuRf6ElnNbz1tdCZD+fJXxCS7Apys02ZWbVx7zFpbGUZFAAAQLpIy6DCWblJB/t2tFyW8z5Ov/zlL023znPPPdckgjtP69evt32Om2++2TyH89TY2CipbnJFgRTljfTseOzdfdLdPxjvTQIAAEAMpFfXtjAE285Dy9rqKZ1oGd8LZtfIUxv2m8trP2mST53qvxIXAAAAkltazlQ4Zyg8ZyM8m3wgeFOrCuXsaZXm/EcHO+T17c3sRgAAgBSXlkGFM5fCM29CaZ5EZ2enbb4FAnPqxBMB2du7WuVIey+7DgAAIIWlZVCxbNky8/P5558fddtzzz3ndh8ET/MqvrV8mqvb9ssfH2E3AgAApLC0DCrOP/98qa+vN5WcNm7c6Lpel0PddtttkpubK9dee21ctzHZ5WVnyeJpVeb8wbZeae2yr5IFAACA5JedSh21161bZ85v3rzZdZ2zJ8U555wjN9xwgzmfnZ1tbtOeFEuXLpWrrrpKiouL5fHHHzedtO+44w6pq6uLyHatXr3anIaG0q8h3PSxRfLClsPm/L3rd8u3V0yXnKy0jGMBAABSWoYj2LJGCer666+37YLtdN1118k999zjdt3bb78tt956qykLOzAwIKeddpp8//vflyuvvDLi2+dM/tbZEC1Zmy62H+mQP2w6aM5PKMuXy8+cJJmZGfHeLAAAAERwDJsyQUWiS9egQr9e/2/dLunoHelZ8Zm542X62OJ4bxYAAAAiOIZlLQqi3rvia0ummlkK9dauVvY4AABAiiGoQNRlZWbI3Ell5vyR9j5pbO1mrwMAAKQQggrERF1loev8Y+/ukw8P2DceBAAAQPIhqEBM5OdkybVnT3Fdfv7Dw7KzqZO9DwAAkAIIKhAzlUV5MqOmyHVZm+L1DqRfqV0AAIBUQ1ARZdqjYvbs2bJgwYJov1RSuPjU8XLOjJGmeFoR6lev7pSWzr54bxYAAADCQEnZGEnXkrJ2hocd8n+v7ZTu/pFZijm1pXL+yTXx3iwAAAB4oKQsEpY2v1s2q9p1eVdzlwwODcd1mwAAABA6lj8hLk4aVyLfXjFdxuRmmWVQB9t6+SQAAACSFEEF4iYnK1Mmlo0x5zc2HuOTAAAASFIEFYirM6aUm5/bj3TKGzta+DQAAACSEEEF4kpnKsaV5pvzb+5sMcEFAAAAkgtBBeJu+ayxrvPPbzkkAyRtAwAAJBWCiiijT4V/OlOxavlI0nbfwLB8eKA92h8LAAAAIog+FTFCnwr/3t7VKq9vbzbnV86ukVMnlkb9cwEAAIB39KlA0plZU+Q6/8KWw+JwOOK6PQAAAAgMy5+QMMoKcl3VoNTr21tkaJjAAgAAINERVCChLJtZLbMnlJjzDbtPLIcCAABA4iKoQMI5/6SxcubxGYuPD7XLMLMVAAAACY2gAgknOytTFk+rNNWguvqG5MkN++O9SQAAAPCBoAIJG1icNbXCnN/b2i1HOnrjvUkAAADwgqACCev0SWVSVZxnzr+351i8NwcAAABeEFQgYWVkZMiFs2vM+a2HOqSlsy/emwQAAAAbBBVRRkft8NSU5Ett+RgZdjhMbgVJ2wAAAImHjtoxQkft0LV29cu963eb81csmCQTy8ZE7HMBAACAd3TURsqoKMyVmTXF5vwjDY3yyeGOeG8SAAAALFj+hKSwbFa16/yf3j8ovQNDcd0eAAAAnEBQgaRQlJftKjGr3tjZEtftAQAAwAkEFUgaS6ZXuZZBbdx7TPa2dMd7kwAAAEBQgWSzdGaV6/zj7+2Tnn6WQQEAAMQbMxVIKsX5OfLFM2pdl9/bezSu2wMAAACCCiShyZUFcuEpI03x3tl9VDp6B+K9SQAAAGmNmQokpZPHlUh5QY5pivfyx0fivTkAAABpjaACSSkzM0NWnjLOnN/Z1CWb97XFe5MAAADSFkEFkpZ21p41bqQa1IsfHZY9LV3x3iQAAIC0RFARZatXr5bZs2fLggULov1SaemUCSWu80+8t18cDkdctwcAACAdEVRE2apVq2TLli3S0NAQ7ZdKS7XlBSa3wulYN0nbAAAAsUZQgaSWlZkh15xdJ6VjRgKLA2098d4kAACAtENQgZQILOqqCsz55z88LIfbe+O9SQAAAGmFoAIpob6qyHX+d2/vlV3NJG0DAADECkEFUkJdVaF8bUmdVBfnieZqP7Vhv/QNDsV7swAAANICQQVSRllBrnz5rMmuy79etzuu2wMAAJAuCCqQcvkVVUW55nzvwJB8sJ+meAAAANFGUIGUc/Fp413n121vluFhelcAAABEE0EFUk5VUZ5ct7jOnO/pH5LDHVSDAgAAiCaCCqSkisJcmVEzUhHqobcbZYjZCgAAgKghqEDKmlpV6Dq/Ye/RuG4LAABAKiOoQMo6eVyJ5OWMfMVf29YsLZ198d4kAACAlERQgZSVmZkhn5s30XX5vjf2SGffYFy3CQAAIBURVETZ6tWrZfbs2bJgwYJovxRsTCgbIytn17guv994jP0EAAAQYRkOh/YfRrS1t7dLaWmptLW1SUlJCTs8hjRJ+/4390hrV7+5/BfnTJXSMTl8BgAAABEawzJTgbRoiPeVhSc6bT/27j4hlgYAAIgcggqkheysTFkyvcqcb+8ZkJc+OhLvTQIAAEgZBBVIG2dNrXDlV2ze3yZH2mmKBwAAEAkEFUgrp04slZPGFZvzv3u7UZo6KDMLAAAQLoIKpJ1Zx4OKYYdDHn+P/AoAAIBwEVQg7dSU5LvO9/QPyXt7KTMLAAAQDoIKpJ3CvGy55uwpkps98vV/d0+rDA9TWRkAACBUBBVIS1VFeXLd4jpzvqtvSD450hHvTQIAAEhaBBVIW0V52VJWMNIE75nNh6SzbzDemwQAAJCUCCqQ1s453rtC/d/anTIwNBzX7QEAAEhGBBVIa9PHFrld3nqIZVAAAADBIqhAWsvIyJAbzp3quvzClsPicJC0DQAAEAyCCqS94vwc023baUdTV9rvEwAAgGAQVAAisqDuRFDxh00HpKWTTtsAAACBIqgAREzPii+fNdm1L+57Y48MkrQNAAAQEIIK4Lhxpfly8WnjXPvjY5K2AQAAAkJQEWWrV6+W2bNny4IFC6L9UoiAk8aVuCpC7WzukvbeAfYrAACAHxkOSt3ERHt7u5SWlkpbW5uUlJTE5kURkj0tXfLEe/tdl7U6lCZzAwAApJv2AMewzFQAHiZXFEhxfrbr8t7WbvYRAACADwQVgE3vimUzq12XW7v62UcAAAA+EFQANmbUFMt5s0YCi/f3tcmRjl72EwAAgBcEFYAXY0vyzc/+wWF54M298sH+NvYVAACADYIKwIsJpSNBhdMLWw6zFAoAAMAGQQXgI7di5ewat+tau+i0DQAA4ImgAvDh1Iml8qUza12Xtx7qZH8BAAB4IKgA/JhUUSBfWTjZnP/kcIes29bMPgMAALAgqACCSNpWDbtbZXjYwX4DAAA4jqACCNDnTp/oOv/0pgPy5s4WggsAAACCCiBwU6sKZWL5GHN+V3OXvLGjRT480M4uBAAAaY+ZCiAIp04odbt8oK2H/QcAANJedtrvASAIsyeUyIyaIlm3vVk27j0m+4/2yNCwQ7IyM9iPAAAgbTFTAQQpJytTFtRVmPNtPQPy85e2SVMH/SsAAED6IqgAQlCU5z7J99i7+6RvcIh9CQAA0hJBBRCi2uNJ26p3YEgebmhkXwIAgLREUAGE6DNzJ8gZU8rlzCnl5nJLZ7/87MVtJsAAAABIJwQVQIjyc7Jk2cxqWTqz2nXdsMMhG/YeY58CAIC0QlABRMDl82td58mtAAAA6YagAoiA2vICWTyt0pxn+RMAAEg3BBVAhFQW5ZmfHx3sMN22HQ4H+xYAAKQFggogCtWg3tzZIhsbya0AAADpgaACiGDi9hfOmOi6vGZrE/sWAACkBYIKIIKmVBbK8pPGui43tnazfwEAQMojqIiy1atXy+zZs2XBggXRfikkiNMmlrrOr93WJNuPdEhH70BctwkAACCaMhxkk8ZEe3u7lJaWSltbm5SUlMTmRRE367Y1S8PuVtflkjE58vVzpvKJAACAlBzDMlMBRMGCqeUybWzRif+QPQMyMDTMvgYAACmJoAKIgrzsLPnMnPFSX13oum5nU5cMElgAAIAURFABRElGRoZ8du4EmVRRYC7/efNBeeaDQ+xvAACQcggqgCgHFlMqR4IKtf1Ipxxp72WfAwCAlEJQAUTZSeOKZUxuluvymk/oXwEAAFILQQUQZcX5OfIXS6bKF8+oNZcPtfXK0a5+cwIAAEgFBBVADORmZ8qkijFSlJctQ8MOuWf9bnngrT3STv8KAACQAggqgBjmV4wtyXNdHhhymIpQAAAAyY6gAoijVz4+Ij994RPZvK+NzwEAACQtggoghubWlpmfZQU5bte/+NFhPgcAAJC0CCqAGKqrKpRvr5guK2fXjLqtqaOPzwIAACQlggogxnKyMqWiMHfU9fe/uUcaW7v5PAAAQNIhqADioCA3WxZPq5Txpflu12891MHnAQAAkg5BBRAnC+sr5aqzJrtdt3l/m3T3D/KZAACApEJQASSYX726U7r6CCwAAEDyIKgA4szZadvqfUrMAgCAJEJQAcTZ5MoCUxHqKwtPLIVq7qQSFAAASB4EFUCCVIQaW5Ivl84Zby73DAzFe5MAAAACRlABJJAxOVnm5/6jPfLbN3YzYwEAAJICQQWQQPJyTvyXbO7sl4cbGqWTpG0AAJDgCCqABFJZmCdjS/Jcl/sHh+XP7x+UA8d64rpdAAAAvhBUAAkkKzNDvrJwily3uM513f5jPWbGYsPeo+JwOOK6fQAAAHayba8FEFflBTlSWZQrLZ39ruvWbG2St3a1SkVhrilDqwEIAABAImCmAkhAGRkZ8mWPbtuqp3/IJHE3dVByFgAAJA6CCiCBy8zOryu3vW3ttiYZHmYpFAAASAwEFUACWzytyjaw0NmKva3dcdkmAAAATwQVQALTvIlzZ1TL1ZZu206t3SfyLQAAAOKJoAJIAjUl+XLxaeOkvrrQdV17z0BctwkAAMCJoAJIEieNK5HL5k2UFSeNNZc37D0mfYND8d4sAAAAggog2ZSOyXGdv+vVnfL+vmOmlwUAAEC8MFMBJOFSKKfBYYe89NEReaShUYaoBgUAAOKEoAJIMmNys+Ty+bWjrv/5S9vkjR0t0t0/GJftAgAA6YugAkhCteUFMndS6ajr39zZIr/feCAu2wQAANIXQUWAnnjiCVm5cqVUVFSYbse7d++O7icD+LFkepUsnFoxqo/FobZecThojAcAAGKHoCJAXV1dsnTpUvnRj34U3U8ECFBedpYsnl5l+licf/JIRSgnTdxe/cp2M3MBAAAQbdlRf4UUcc0115ifH3zwQbw3BRhlTm2ZOf2/dbtM/4pH39lnrtcci0X1lewxAAAQVSkzU3H//ffLN7/5TZk/f77k5eWZJUr33HOPz8c0NDTIJZdcImVlZVJYWCiLFi2SRx55JGbbDERacd7o4wQHKDcLAACiLGWCih/+8Idy1113yZ49e2T8+PF+7//KK6/IkiVLZN26dXLFFVfITTfdJIcOHZIrr7xS7rzzzphsMxBpC6ZWjLruyQ372dEAACCqUiaouPvuu03ydFNTkwkQfBkcHJQbb7xRMjMzZe3atSYY0UBi06ZNMnPmTLnllltMcAIkm7rKAjl1YqnUVxe6rusfHJZ397TGdbsAAEBqS5mg4oILLpApU6YEdN+XX35ZduzYIVdffbXMmzfPdX1paakJKPr7++Xee++N4tYC0aHL/lbOrpHL5k1062Wx9pNmefSdRtl+pINdDwAAIi5lgopgrFmzxvy88MILR9120UUXmZ+vvvpqzLcLiKSCXPf8in1He+QPmw6ykwEAQMSlZfWnbdu2mZ8zZswYddu4ceOkqKjIdR+n1tZW2bt3r5nhUFu2bJFjx47J5MmTTe8KT319febk1N7eHoV3AniXn2N/zEB7WOiMBgAAQKSk5UxFW1uba7mTnZKSEtd9nJ5++mk5/fTT5Utf+pK5fOmll5rLer2d22+/3Ty/8zRp0qSIvw/AlzE5WbbXaw+L5s4+aeseYAcCAICISMugIhTXX3+9OcLredLr7dx8880mMHGeGhsbY77NSG86G3HDuVOlsijX7XrtYfHbN/bIr1/fZb7DO5o6KTsLAADCkpbLn5wzFJ6zEdalSuXl5WG9hvbK0BMQT8X5OXLNoiny9KYDsrOpa9Ttr2w9IpsaR/4ffGfFdMnO4jgDAAAIXlqOIJy5FJ55E0p7VXR2dtrmWwDJOmOh1aD++oLR32lnQKGaOk/kAAEAAAQjLYOKZcuWmZ/PP//8qNuee+45t/sAqRRcfHWR97LLL350RHoHhmK6TQAAIDWkZVBx/vnnS319vTz44IOyceNG1/W6HOq2226T3Nxcufbaa+O6jUA0VBd7X5LX3NEnv1yzQxpbu9n5AAAgPXMqtKP2unXrzPnNmze7rnP2pDjnnHPkhhtuMOezs7PNbdqTYunSpXLVVVdJcXGxPP7446aT9h133CF1dXVxfDdA9IzJzZKefu8zEo+9u0++t3ImHwEAAEi/oEIDCs8u2K+//ro5OTmDCrV8+XLzmFtvvVUefvhhGRgYkNNOO01+8pOfyJVXXhmx7Vq9erU5DQ2xrASJ4Utn1sqOI51SXpgrDbtbJSsjQw629brdp29wSPKy7UvSAgAAeMpwaE1JRJ1WlNKqU7rESvtgAIlizdYjsmHvMbfrZo0rlgtOrpHc7LRcIQkAAIIcwzJiANJcfVXRqOu2HuqQ9Tua5WBbj+xpGV2KFgAAICWXPwEIzeTKAvnc6RNld0uXbLTMWGw/0umawVg5u0amjy2SfC9dugEAQHpjpgKATK0qlOWzxrrtiY7eQdf5F7YcNpWh1m1rZm8BAIBRCCoAuNRVFfjcG5rY3T84zB4DAABuCCoAuKycPU7OnlYpWZkZXvfKtiMdMjRMfQcAAHACQUWUaTnZ2bNny4IFC6L9UkDYivKyZVF9pXxjab3b9adMKJHSMTnm/PMfHpYXPzrM3gYAAC4EFVG2atUq2bJlizQ0NET7pYCIsSZkXz6/Vi48ZZxpmue05UC7aaB3tKtf3tjRIr0D9GEBACCdUf0JgK2/OGeqdPQOSG35SJ5FTpb7MQgtN/vnzQdlYMgh3f2Dcv7JNexJAADSFDMVAGzpcidnQOHssm3V2TdoAgr1/r428eyjqYHGvqPd7F0AANIAQQWAgHjmZr/00RG3y70D7lWhHnxrrzz6zj7Z1UzzPAAAUh1BBYDAeMxEeLr3jd3y7p6jo/pcfHK4gz0MAECKI6gAEJDzjjfHW1hfYXu7Jm6v/aRJWjr73K7vo68FAAApj6ACQEAmVRTIt5ZPk8XTqnz2sbjvjT0ybFkr1UdlKAAAUh5BBYCA5WWPlJX96qIpPu+3eX+b63xzZz97GACAFEdQEWU0v0MqqijMlbmTSr3efuBYj+u89rDo6hvJrwAAAKmJoCLKaH6HVJWd6f3Xx8eH3JOzj3S451kAAIDUQlABICTjSvNHXTezptj2vk9t2C+NrfSsAAAgVdFRG0BIZowtkmWzqk2TvKc3HjDXlRfkeL3/Y+/uMz/rqwvlktPGm/Kzen5s8ejgBAAAJBdmKgCEJCMjQ86YXC71VYWu60oLciQvx/evlZ1NXfLmzhZ5Y0eLPPDmXvY+AAApgKACQNjBxYK6ChlbkifTxxbJtWfX+X3M4XZyLAAASCUsfwIQtnNmVMk5UmXO5wXwWyUn60Sfi6aOPqkuzjPnD7X1ysDQsOmJAQAAkgczFQAibk7tSLnZ+XXltre3954oMdvSNTJr4XA45Hdv7zW5F939lKAFACCZMFMBIOJWnDRWFtVXSmFetryz++io25stJWb7B4fNz77jP1Vn76BoU+6iQKY9AABA3DFTEWU0v0O65lloQKH+YslUn/d96aMj0trVb5rkOT3w1l75v7U75cMDbbL9SKf89s09cqSjN+rbDQAAQpPh0DUHiLr29nYpLS2VtrY2KSkpYY8jrfz0hU983q5ladt6BnzeR/MuvrpoSoS3DAAARGIMy0wFgKhzJmZrXws7/gIKZ0J3R6//+wEAgNgLa8HysWPH5LnnnpOXXnpJ3nvvPTl8+LAcPXpUysvLpaamRs4880xZsWKFXHTRRVJWVha5rQaQVK45u05aOvukvrpIXvukWYZDnCB94r39ct1i/yVrAQBAEix/2rx5s/zsZz+T3/3ud9Lb22uqtnh9gYwMyc/Pl6uvvlq+853vyJw5cyQdsfwJGNHY2i1rth6RZTPHypDDIW/vapEDxwLPl/jeypnmp+Zg5GZlSkaGVo4Sycw8UaYWAADEdgwbVFBx5MgRufnmm+Xee++V4eFhqaqqkvPOO08WL14sp5xyilRWVpoX0xdtaWmRDz74QNavXy9r166V5uZmyczMlOuvv15uu+02GTt2rKQTggrAXt/gkPzPKzuCCiq0n8Vj7zaaZntDwyL7j3XLNYvqZExuFrsZAIBEDyr0CTs6OuTTn/60fP3rX5dLL71UsrP9r6AaHByUP/zhD/LrX/9a/vSnP5nn0WVS6YSgAvBOqz85JxrWbG2SXc1dXu/71xfMkIcbGuVgm/vsxsL6Clk8baQBHwAASOCgYuXKlfLv//7vJlciVA0NDXLLLbfICy+8IOmEoAIInJaR1UDjtImlsqHxqEwoHSNPbtjvun1cab6ZrbCaO6nUBBV7WrqlqijXzFoU5NLnAgCAhAsqEP0PBMBoA0PD8t8vb/e5a86YUi7bDndIx/Fu3ZVFuXLt2SR1AwAQDkrKAkgZ2QEkYQ8ND7sCCtXS2e+ziAQAAIgc+lQASHhaRc6fgaHRAUS/ZnEDAICoC2vBsVZ1CsfSpUvDejwAOA3aBRWDw5KXTUUoAAASOqjQcrKBHEG0o4/TqlCpbvXq1eY0NDQU700Bktr8unLZ2dQli6dVyh/fPzjqdu1bYRdU2OnqG5SszAzJzyHgAAAgEsJK1NagYmBgQN544w1zWROR6+pGEiP37NljOm5r8LBo0SLJzc0d9fhXXnlF0gWJ2kBkdPQOyN2v7QrovledNUnGl46RfUe75f19bXLujCoZHhb57Zu7TWWory2pC/nACAAA6aA9wGJDYc1UPPvss3L++efLtGnT5I477pDLLrvM7fann35a/vZv/9acf+aZZ0xnbQAIR1Fe4L+2uvtHZi8efWef+ZmZkSHjS/NN/kVbz4D0DQ4zWwEAQLwTtX/84x/Lpk2bzIyDZ0ChPvvZz8qLL75o7vNv//Zv4bwUABhm9rO+UvJy/P/6enrjAXn1kybX5Z6BQbFOzR7t7jdLoQAAQByDikceeUSWL18utbW1Xu8zadIkWbFihbkvAETC2dMq5StnTQnovu/tOeo6v7u525SedXro7Ua5a+1O0wcDAADEKahobGyUwsJCv/crKCiQfftGlh8AQCSUFuTIvMllsrC+QuqqCgJ+XN/A6ADiz5sPyvAwPS0AAAhVWDkV5eXlsm7dOunv77dNxFZ6m95H7wsAkbR81ljX+Q8PtMn67S3S6Wc501u7Wkddp1Wl9rR2y9Qq/wdJAABAhGcqPvWpT8nBgwfl+uuvl6NHTywxcNLqT1/72tfMfS6++OJwXgoAfDplQqnccO5Uk4hdXZwnkyoCn71Q1mVRAAAghiVldUnTmWeeKc3NzWYZlAYZU6dONbft3r3bVIfq7OyU6upqeeedd3zmXqQ6SsoCsaG/0vS3mlaK/a8XtwX8uLKCHJlWXSRLZ1b7va+WqNVme3XMbAAAUlx7LErKapDw6quvyrXXXmuChscee8xV890Zq5xxxhny29/+Nq0DCgCxo7+DQmk9cax7QN7dc1QW1FXImFz3pnj6+2xHU6dUF+VLyZhsV4naby6rN/0uAABId2H/NTzppJPk7bfflvXr18uaNWtcCdkTJ06UZcuWyTnnnBOJ7QSAmBg0y6BGgor23gH546aDUpyfLduPdJrrvrNiuuu+vQPDUmCfTgYAQFqJ2CG2xYsXmxMAJIqFUytsE7N90WVNOjPR1NlnytEebu+Vw+2W2z2qRGlieGFuFp25AQBpjXl7AClr8fQqObOuXA4e65UnN+wP6DEDw8Py4YF2eWHLYdvbhyxBxTu7W819LzplnMye4H2dKQAAqS6o6k8HDhyIyItG6nkAwJ+87CyTUD2/rjzgmYr1O5p93u6kAYV67sNDfkvZAgCQyoIKKmbMmCH/8A//YFs+NhCtra3yd3/3d+Z50sXq1atl9uzZsmDBgnhvCpDWFk+rkpWza1yX9fzYkrxR93t60wHp6hvy+jxd/fbBw/+t3WkSvQEASEdBlZT9/Oc/L7///e9lzJgx5vx1110nS5culby80X+Ynfr6+kwC9z333GMe29vbK5/73OfkiSeekHRCSVkgMRxq65X+wWGZXDnSx2LdtmZp2B143sVl8ybI7zd6n2393sqZEdlOAABStqTsk08+KS+++KJ873vfkwcffFB+97vfSU5OjsybN09OPvlkqaysNC+mL97S0iJbtmyRTZs2ycDAgEl8PPXUU+U///M/5YILLojEewSAoI0rzXe7nJ0VXP3ZYz0D7HUAACLV/O6FF16Q//7v/5bnn3/ezEa4njAjw9WjQuksxkUXXSTf/va30zqYYKYCSEzv7T0qr25tishzZWZkyHcvSJ/lnQCA1Nce7eZ3K1euNCcNKF5//XXZsGGDHD582LxgWVmZjB071jS+0zKzvpZHAUA85WV7Ty37/OkT5amN+02H7kAEO+sBAECqCLukrAYMK1asMCcASKWgoqIoV2pK8k0eRiCyM92Dir7BIVN9CgCAVBdU9ScASDV9g9pBe8R4j3yL3KxMyckK/Ndkd/+JqlE7mjrlf17ZMSoJXAMNAADSeqbivvvuC+vFrr322rAeDwCRNqlipApUeUGOfGbuBLlr7U63WYycIJc0NXX0SXVxnqz9pMlVXWpBXYU5v7elWx5/b5+cPa1SFtVXRvR9AACQNEHF9ddfbxKxQ6GPI6gAkGhK8nPkhnOnmmVKudmZMmtcsWw91OH6vZWdGdyErnMmojg/R451j1SKOtLRK2OL8+XFj0a6dL+xo4WgAgCQvkHF5MmTQw4qACBRaQDgdN6sahkYGpbZ40cqXHikSRgnjy+Rjw6OdNP29Og7++Qvlkw1Mx+Nx1c+7Wzqku1HOqWNcrQAgBQVVFCxe/fu6G0JACSAgtxsuWzeRNdl63GU+XXlctbUCjnc1uc1qFC/fn2X2+Xu/kHZ1NgWnQ0GACAVqj8BQGo7EVWcO6M6pNKxvQMnksEBAEhFVH8CAB/sVnxm2a2J8qHHUhUKAIBURFABAD5MKh+pDmU1bOmGN7Om2O/+6xkYHVT89IVPTMI2AACpgOVPAODDyeOLpXdwSMoLcl3XVRSOnNdqURefOk6Wzao2JWSdVaPsyszaeXNni0ypLJDdLV1yxuRyyc+hUR4AIDllOByWQ26Imvb2diktLZW2tjYpKRmpKgMgeemSJl0GpYGFs5Ts/qM98vuNB0J6vtkTSuSiU8ZFeCsBAIjNGJaZCgAIwZhc91kF7XNRX10U8r48eKyHzwEAkLTIqQCACHLOXKj66sKAH+ct+burb1AGh6geBQBIbAQVABBBnz/9RI+LUyeWBv7L2CaoaO8dkLvW7pTfvrknYtsHAEA0EFRE2erVq2X27NmyYMGCaL8UgAQwxpJsbT3vz5H2PunsG3S7bk9zt/l5rHvA9jFDww5zAgAg3ggqomzVqlWyZcsWaWhoiPZLAUiwXAtd0qQJ2IF6a+eJErNtPQNyoO1EnoVnTY3hYYf85vVdcu/63aNuAwAg1ggqACCC8iw5FZkZGbYVnb50Zq3k5Yz+9WuddXjgrT2y5UC763K/R15FV/+gdPQOmuCjb5CcCwBAfBFUAEAEZWRkyPKTxsr8unKpKhrpZ7GwvsLtPpVFuVKSnzPqsdlZGa5ZiL4B90DBV+BgbcYHAEA8UFIWACJs3qQyt8uLp1XJjLHFcv+be0xAobkWdrkQWZkjx3kGbW4zQUb+icvWxw8MEVQAAOKLoAIAYqC6OE+uX1wnxfnZZjajtat/9C/k4xWg7AIOz+usgQfJ2gCAeGP5EwDESHlhrmRnZboFEFYZGSMJ2QPDo5c6DTncg4jnPjzkuhytPhb9g8OmTwYAAP4QVABAHFx06okE7lnjil3BwmvbmuX/vbZr1P0372tznf/wQJspQetkt1wqEu5au8P0yejuJ7AAAPhGUAEAca4SVZg3shL1UFuvvLvnqO39PzrYLofbe8192nvcB/mDUcqpcOZqHLYEMAAA2CGoAIA4KMg9kdKWfzzA2Hf0RF8KOw++tVd+9/ZeOdbjno/xwkeHI7591t4XWbouCwAAH0jUBoA4JW4vm1UtJfnZptdEMJo63GcO2o8/vndgSJ7asF9OGl8yqgJVsKxLqo4XpQIAwCv+VABAnJwxuVymjy12lZINVM/AkO31b+1qlYNtvfLKx0fC3jZrRSlt4gcAgC8EFQAQZ3aVoHzxbIznTN7u7A0sobqzb1Be/viwNHd6z5UYsFSUIqYAAPhDUAEAcebspB2O5z88LP1D9jMYVtqt+5nNB2VTY5s88OZer/ezJn9HqbgUACCFEFQAQJzZLS/SnItQ+kr48+Dbe10J4cMOh+xu7rK9n7VXhgYiAAD4QlABAHFmN09RU5IfcglYdaS91zYY8EzyfnLDfr85FZZCUAAA2KL6EwDEmV3OwjnTq0ywMXtCiXT3D8kfNh3w+zzWgOGBt/bKaRNL5YLZNSFtk1tQIUQVAADfmKkAgAQ0JjfLBAQTysZIkHncLpv3n+jC7dl7wh/L6idyKgAAfhFUAEAS5VxUFeUG9VhrhSfr7IM/mm9hd97ZD6O7P7BKUwCA9EBQAQBxVlaQG3BQMa50TFDPrZWenILJt7YGEp4zHP/76g751as7TXARD4OWcrcAgMRAUAEAcVZVlCefmTtesrysc7LmXBTkZrndVpjnftlTu6V3heeMg5VWgeroHbANQDyDEefTeCZ9x0Jja7f84uXtsn5Hc8xfGwDgHUEFACQA7aw9rtS+4pM12MjPcf+1XZKfE/BreFv+9F8vfmKqQD3c0Ggu6wxEV9+JYMRbLGJtkBcraz5pMj/f2tka89cGAHhHUAEACWJSeYFt3wrr5bxs95kJHe8vm1Xt9TmtT+VtpsJ5dUfvoPT0D8n/W7dLXv74yKjHtfUMuJWpbTze70KXR32wv00Oto1cDoY+RmdINJAJJEgJv00gACAaKCkLAAliQV25mYmoqyz0ep/8HPegQgf8Z0wulw8PtEuzn+VI1opO3ujg3rOJnrNJns5mTKkcCXzUe3uOytIZVbK3tVte2HLYXPe9lTPdHqsBx/5jPWaJl+e2ay+Nh94emR1xLu365rJpQZffTSbbj3SamaepVd4/YwBIRsxUAECCyM7KlNMnl0t5oXvitrVPRF52pu0sQ2+/fdK0znK8tq1JnnhvnwwGEFX8fuPoZnj6Ghsbj5nze1q63W57b+8xaenqd7tuZ1OnHOseuU6DnUff2WfbZO9AW6/bZe3HEUzZ22SjAZv2G3lqw36SzQGkHIKKKFu9erXMnj1bFixYEO2XApAG8jxyKpxD8JPGF9vePzszQ97ZfdQEA4+9u8/v8x/tPpGs7fTmzhavMwRrP2lyy7nQROrfbzwgv3l9t7n8/r6RXhmHPAIIZZeXPuinRFVGEi+A6rcs7/L3PgEg2RBURNmqVatky5Yt0tDQEO2XApCirIP26qI8OXdGleW2kRsX1VfKpXPGj3qs5klYZwJCoc/RN+BrluPEBu5u6XK7pcdH2Vm7AMFfXkUyL39K4k0HAL8IKgAgwelsg1NGRobMr6sYFXDkZGXKzJriqA26NS8ikKBHZ0Wserw0ydPEbs+O32pgMHWP4Otn55TCq7wApCmCCgBIcNXFeTJ7QoksrD8RTHir6HTxqaNnK6Kt1SOnwmpgaPTo+WhXv0nsPtw+eklU39BQWhztHyKqAJBiqP4EAElwhPuiU8bZ3lY6xr1PRVYcDhVpMnYwPBO7/QUhqbL8yRoA+mpECADJiJkKAEhCl8+vlRk1RXLB7Bq36z17XMSTtaeFla8KTwMe5Wx95WFo6VvNwfD2OonG+rYdse8bCABRxUwFACSh2vICc/LVfTvevFU4ev54Twt/FZL8Wf3KdvOzqihXrjm7zraEq25DUV6C/Kmz7A5mKgCkGmYqACCFJNJMxZCXoMKzuZ7Vn94/KM9/eMg0ibNl8/aaO+2XU/1yzQ75v7U7pW8wtKpXkWbtN0JOBYBUQ1ABACkklJmKquK8qGyLZ7M9Xfb0x/cPBJSjoU3itOdFJAKatp7RvTestLHf+/uORb3xnjXGYqYCQKohqACAFBLqTEU0Zjg8q0I1dfbJtsNeZiBs2FWHCnQrrf0usny8N23K98rHR+Slj47Iu3vcy+FGmjVoIU8bgL/ff89+cEg6en0fFEkkBBUAACkZE/m8gyfe2+922XcDvdHsYgFrrwfPAbvOTnT1DQbVsbrz+P3Va9uaJZqsW8RMBQBfHnxrr3x0sF2e/9B7DlqiIagAAMilp0W/v0Wfn8pO4dAj/w+8tUfuWrtTjnX3y6BlpiLU/AUNUJo7+6JUUjZiTwsghR3t9l6CO9EQVABACrEeyL/27CkysXxMQI8bW5IvKz3K00baW7tawn4ObwuZdIzecjxhe0dTl1u/C4/UjoBpgPLbN/aYZn2+aOK5dgjv6feTEG7NqUiAqGLf0W758MDoruYAEkdGAhXf8IegAgBSiPXvT0Vhrlx+Zq2cNXV0J2470V7nf6Q9/KP+3v6+WmcBNFfdmiTua6YikL/XB9tG53ZYvbL1iOkQ/uQG9+Veib786dF39pmlFXa5K0gMOuPmr9BApF8PiSVDkgdBBQCkkOqiPKmrKpDZE0rMES49LZleJQvqKoIqeeo0JjdLkuHP6UNv7z3xqIwMGXSbqXCY0+/e3it/3nww4lv5yaEO89Pf4NwaRyTARIVbsnoyOtjWI939J3JiAqHfA+1fkiweamiUX6/bFZPPSF/jf9bskPU7optbhOAkUOshvwgqACCF6ID686fXykWnjHO7/pwZVX4fW2AJIE6ZUGIek2h/z7zNLFh7VegfYWv+hiZwH+7oNYOmrccDANfzBfCadsGW07ptzQEnhbvnVEQ2qtiw96jc/dpIPok/OhC3Lr96+eMjEdkGzT+J1YBdyw0/9PbIgDsYv2vYa/qXtAdRUUeLADTsbvXeOyWKmjpGZvc+PtQe9dd6bVuT+b/y1s7WqL8WAsfyJwBAQrvqrEmu81WFuebntOoimV9XLp+eM14uPGWcmd2I93LeTY3HZG/LiX4VGQGEAVoe19pgz3P5k3VAHe7QXgebgXJb/hThqYo1W5uko3fQ/PRFZ1N+9epOecLPUq1QZg00/+Te9bslFnY1d5mf1tyZYJbg7QgiQNh/rMcEj9o7JZn6zwQrPycroABLg8dEyAlKFxmJdmTHB2YqACDNTKookPGlY+TqhZNl7qRSOW/WWNcRsXNnVMuMmuKgBvGeplYVymkTS8PezgPHesxR9Mff2xfQrIFVr6WLtg6ArH04rEGGZ8M7rQnvudQkUpMKbn0qIvBcduvfrf057GzeN5KY7dlY0FoLXwfRwR6V33FkZJDf7S9ZPQlpsBZvvnqthDoDon1ZrE0ixwQQVLy395gJHp/f4r3MqX4HH32nUd4JIuBOJjobqDlU/go4RAozFQCAhOUcntSU5MuKk2p85k2cVus9OCgdk2P//Bkii6dXhr2dLTblXPOz/Q983t/XJt19Q25BhHVIZh1IeR5wvfu1XSb3IpBSsnYduLUKlPf7S8SWP2kPEF3G47ncyN/zZno5lKjve+0nI7McjzQ0mqPybd3J03QrmhIgp14yIzxTcf+be8znbV1WVZh3oleNt0pmzgpu2j/BG/0/sO9oT9T7vsTL4+/tN+/RerAjmjIkeTBTAQBppjAv8OTrs3wkeOssh7elGoEM/v3ptxx1dx6Vz8ny/2dLl/hYlyVZgwjzXG5Bhf2IMZDEWM/nVXoEM5D7hztQ3dvabd7HzqaRGQInf6tSfM086ZFra6AUTN5BKi8JCXR2LFmWP1lnuKzBQ252ptt3wbnEzCqQ76116WEqaj9ejStWM1gZSRRVEFQAQJopKxjJoQj3COnc2jI5c0q5fOnMWrfrKwpyI3Jk1Tr410Ts5z48FFIzOq0uax1sWwf3doGBys7K8Dko08F3MMvKtZGeteRspBK19Xl+++Yev+/Hxc/HEupSeevAJ9Ck4iMdI8Gf322O85KSeM1UWPMWrMv3wmUtYmCdnbAuytPP5akN+906zntukzekW/iezQxWJD/7aCOoAIA0odWcJpaNkTMml4f8HNalUtlZmbJ0ZrXJ0bCaXOl+OVTWsrBPbdwvWw60++0Z4e1IszWPIpAZA+sfcut9dAbjV2t3mjXlwQQGnsuiwhlrWLf/k8Md0ny8QpDSbdp+pMNrToS/AYq1v0eontl8KKD7PfDmXpMA/d7eo5LI3JatxXDEbA2qvc1UaLCqMwrBDF6teTfW77DdU/zf2p1u+TaBvP1IDKRjSX+v6CnSDrf3yi9f3WGKTYQjeUIKggoASBtazemKBZPcljkE4tI5481gVKtCBTIB4S3XwiqQg2/WwbPfbtU+6NM07DqxHMqtMZ6XUdLzH9oPjNdsPWKWd+ggxNvYye45PWc+ghl3jSROd3gZFLrfV7uK/2HTQZMT0WdJVnfyt9sjEFMELZRAMZasy5/ue2N3zGZWrN9Tb//vNDdCZxQ+OuheKjnQ5UmB9E7RHKVgloIl00yF5iTpDKieIr1s67kPD0nfwHDYJZuTaKKCmQoAgG8za4rlOyumm6pQ/o50F+dnS3F+TkJN6Q8ND7utD7cOnL3NNlhLlVoHUtYZD2+PtR7Zdcr2yJAOZp3+SOL0QVe1GW9Hmj319g+HNVMRaP+NSHw+seDrCPrLHx+WR95ptA0YrFcd7R4IaQmeHX2tPS1dtsHfqO+gl013Vtva3tQZUq6S9Xm97Z+MIIPhROgWH6hA/y+FYjhC/3+o/gQASCmB5kgsnlYV2PPF8Ojb2k+aRw2cPzzQZrprB9vnwDpOaDza7fc+3pavhDLecCaGWrfZukTMU9+QzUyFn/3u1t8jiMF+sB+nda1+e8+gqSZkVyI3krwNoDUhfVNjm+w/2mNbccxzsB2pgPjFjw6bKl7rd4xUVPLkXqXM9xfGc9/p7NYbO1pkZ1PnqEDJ+v1xW/4kkZFMQYV1SyN9nGM4QrshiSYqmKkAAATxR8PPX17rAflzfXTx9nX07fTJZVH9SHSQ9fyHh0137ff3+V/vrEenXSwDJm+5A3aDY8+qVQ4fsxz+jnBaj676yoHQpRee/A2crEm80Zip0DK12rlZ1+o7tXb1y7MfHJK3I9jXQHsJ6JIx6372NtjttFTx0e+l5/53RCkgdq7j337YfZZh39Fus4+s5YL9fRLW4FIDQ53denNni/x+4wF5fXuz18DRX05FKJIopojqtg4n046IEBK1AQCB/9HwMqCqLR8j2ZkZUldZ6Lpufl2FfGXhZNv7+xrcRrt7sHXMGEhZyPf2HDUJ0SP9K/qDWl7i5PmWnNugyZya/6BLm/TosvaLcGv253Yk2TFqsOJrpsKzh0UgzQzdgoogZ3EC8ds3d8s7u+0Ts+1KmIbqN6/vNkvG3Ja9OeyDWuv+1Nyd/3ttp1tOTTQSj63P6fl/4dF39pl99LYlD+jVrU0+e6Dod8e57M4z/2jz8cdpMP3gW3vNDJ11n2iQoc+t1bh8sctr0m38/cb97sUPgpzz0JmiQPIZdJ9pVapIfk+smxroxxyJGY2BoeGASzYnU2hirSUGAEBIy6C0rKwOLLQilJV7yUrL89j8ZZ5YPkZmjy9x1YFPpCOIf3r/xEDMH7vBuOdBfy27quV4dZCntFqTVuZS2jjM7nHOzbYO4Hx10LYLbvzPVAz5fLy3WYEtPpqhWflabjYwOGzK4548rtgEpJ6dwDftOyaXzZswKmdH94d+Le1mvw4c65X66iKfn7v16i0H20yewocH2iUvJ0vmTykf9dlFYgLH+hlqMG7Hc/CsPVA0CG7q7JNLTxs/Kvh+auMBuWbRlFGfmzOA0S7qGsR63vb8lkOyzWO2xE2G9++DcxZkV3OnTB9bbM4HkyKj3x0NADUX64Zz633eVwMnrRamvrdypkRCQInnwyP3cu5v/d1lza3y+jiH9/vcu363+SyvX1wn5YWBl/hOdMxUAAAC/6PhZVSqAzrPgML387gngs+vK5cr5k+SUyeWRj0x8emNB6L6/Lokqa1nwOfR2yPtfSavw3PQ5Mluzbt1rOIrqLALbvwtX9ttGcjqe/Ckg1CdtWk6XsZWcyN0UBiJRmAmCbqjz3RivmvtDvM6ug+1io7mH+hrOgeVVr9au0Oe3mT/meqRbU3CVq94qcJj/ZysVZR0hkq3xXM/WmcZNAjT/aSDY89Zoh02+QxObn1TgghSdEnTjiOd5rk96b5bv6PZdkmc3v8lm/eveRc+AwoLz9kEt+VZblWkAn9Du1tG8pKs3x/dvxsbj5nP1Pqabsu2IrQ0z9+m6rbc+8Zuufu1na7PMtDfTsM+ntv5fne3RHDWJQEwUwEACFhekOVovR3Rsw5uF0+rdDtal0wlFO3sbu42S2905uWzcyeYI7x2R29f/sh/qUnPAZouQdnmVl7Wx5H/EGYqrINqzX/wpEfJnbM2f33BDFm3rcn2eXSwrYGT5ksc6eiTC04eG1Sw2NU3ZF4nPyfLbfDqPFpuXcaluSOencWtNAl7Tm2ZfHzoxHsLJNdCaQL5WVMrbAeLGhRqbo7TquXTXeWaH313nxnk63d7YX3lqOd1X8IWfIK6sxKYp7d2tkptmXufGP2OeAukg8mb8fw+WZPt3auieX+8fpbWmSbPSRr9XHT5lzPAfrugVeZMKjX5J1VFea77dfUPBlRlLhh2XwN9L8eO/z/QGVT9PWVmawPYbz0BlMEOKOk/idY/EVQAAAKmS0KCketl9sI6wAylms6sccWm8ZceJY7EUfJIqK8uNINb5xp2rSb0v6/uMIOVJdNHJ60HNJ7wqFj0cMNe98RxH+wCjmDSVXT5zRPv7ZPa8gJ5e1eLmVGyNjZc80mTq6Sp+2tkmN4JuvTIafrYIpladSLfJlCeeSGhzmJ5Njezjgl9jQ/zcjJHzTY4AwJrQKG6+wclN3skOHY2JNTZFrugwvq5hpIQb8198RToWv1gec5UWPdLr6UogLcclHte320Cka8tqZOygtxR//f1+TTwsM7YHWwb6dGi33nrcq8ej+AkVO4zgZEbvQ/7+Eyt+yfQ3317W7rNrFtNSb5pYpqoWP4EAAhYfpAzFXqk2dqF2/XHx/q3NIRx4uSKArl8/iQz0E3kWRzn+EEHR558DZB1/bsGENbk3A17jwUcUChdBqOvu367LuEZNt3AA0k0t9rT0m3WzWuAonkGWqXJaePeY7bLe8aW5LkFFNYBaUkAjRH9CSVx+t097snhbgnwPp5Pg2LP9/hwQ6PtUWi7caS3z9h6ZN/XEjZv9Gk1z8TO3lb7Usehcs6yWXMqKotyzVIsu+DPuh90yZrmD+k+ds5sWHNFdJmT00sfHZYWmxkY53febTlhpCpVWc87AkuoDyQQcPi4zfo+ApuoGNl3+rn6S6aPN2YqAAABq6sqNIPLYGgy4i/X7HC7zvqHefQSiMCfO5ZN9PzxbHBnFewg6LF3RypAWQfnwQ4WdbD60NuN5nzf0LAJAsLl+T6CrZgT7PI5T5pP8L+v7pTq4hNLYULhntPg/U3o9toFTnblb3UAqidrB2W7vJSR13cfIOtMyuwJJQFvv86ItXYdDbjqVzj0CPnMmiITYFo7t+vJOnOiM4dPbdxvlr1ZO36r/Ows2yVcztwcpb9XPH+3OKJcrtVfnw676wL5lbP9SGdAM4iB/vpyzqIk0K87W8xUAAACNmNskVxy2ngTKATKrrqN9apQlrTochrP54mnk8eXSHaW942xK4MZqWTTQAYv7zd6L0caDrsBud2ATz8nHXBbB5Gh0kGzzuSEQxOanXxVK9LiA3bLk3Tmx/Nrq3fTyl3vW2YQdIZGj8brLJE21tOj8Vr61eHxmpqMHgzNVfEmlJkPfx54y33WzO4z0ZkLa0BhZdeFvDuM4Ef3tf7/sVYr0/0bbH6K/0Tt0dcF8jvnz5ayvcq6XQOWL1ygsZHzfv5KQscbMxUAgIBpAKD5DMGwnU2wXBfKn0ldVuXcnlhbVF/ptvRDrZxdI295XBfv/Etr0BKtRlx2z2s/sMwIuOxsqHRJkk4W5dkcFfcUaLUinaXwdrtnaVG9n12ug7PqVEFulslBOdDWK6U2y8A02LAGJKGy5jfEis60OP9P2rH7bxpOUK2Pvf+tPWa25KZl0+RAW49JRh9Xmi9fPmty4M/jZxmcNc/COaAPZWB/7xt75ItnTDT9RzT3yskasOqMkBYT0PuNsexL62Yl+kwFQQUAIKpsYwofQUcwCZPO3g6xpOvJrf7yvGkmiTSYkrqx6Lgb5YkQkx8R6FvQZUB2eSWRpEnx+lX67vkzArr/npYus9xGO6t7ysnKMDM9GlR4S6TOsP08ve8QZ1K7JnE7E7mtntyw321JUah8zWIEQ5eYBTOz5HvZ1ehfAgF9d7zcRx/r3FeNR7tdlb10RigYboUQ/NwezsC+vWfALGnUohLOQg7KurTOWS557SfNcuHsGtvtiMdBlGCw/CkIP/vZz2TKlCmSn58v55xzjmzatCl6nwwApAi7P4Ta7OrE7cE9X1nBiaO8Wo3oM3MnSCxZK1qdO6PKdYTW1/InO1GOKSJazcZOMEea137SFHA/hHDoPrXr4WDniff22wYUyhkgmpkKm/ep31m75U/hfKaRCCgioa6qQL6xtF5OHh+5Igie+0qDlWc/9N9Q0lq21vsMQug8c1sCEerAvsOmSp1dvk6TRzL2SKgaXI+MeCGoCNCDDz4of//3fy//9m//Ju+++65Mnz5dLrroImlvj+50LgCkkimVBabB3RmTy13Xjfob7fA9S/C5eRNH5Vc4+wPEgjV4sJa5zPGRqB0P0Z6p0KU/iVhCX3uEhMuZB6SDPm8N7DwHlxp8hFIeNtHo4LowLzuiRRA8n+mRdxpNLxJ/vCe6W547jM10CyRsqz+Nvi6SeVyDw8OmFLFnTo3Dy/tN8IkKgopA/fSnP5WbbrpJrr32WjnllFPk7rvvlsHBQRNsAAACo70KNP8gx3K0P5g1ypfNnejWKM8pnAGQ5kiEOlNhrfiUaH/wAym9etK4YnNEWuvfB8sMtqM93RInzqBCy6jadTq3+7ydfRaSnfMjtQbM0e55ESzPpYOhbqnfRG2bSMNaeEJLvP7x/QMhLzkbHh7pd2Lto6LBquf/XeflRE/UTqzDKmG4//775Zvf/KbMnz9f8vLyzIdyzz33+HxMQ0ODXHLJJVJWViaFhYWyaNEieeSRR0bdr7+/XzZs2CAXXHCB67rs7Gw577zz5I033ojK+wGAVGb9Yx3M2CXDy1+tcEqMnj2t0uusiCZ+Ws2oKXLru2GdnEik8rbKV5dpp6L8bPnUqeNlQlloQYVdX4FUkHN85ksHv15nKjwGeDpzkwpBhVNEZyoi/H/DfdCdEdPmd1mW//SPNDSaZX33rt89quJToK/vWRlLd5Vn/wzn5QT7FZO6QcUPf/hDueuuu2TPnj0yfvx4v/d/5ZVXZMmSJbJu3Tq54oorzCzEoUOH5Morr5Q777zT7b7Nzc0yNDQkNTXuiTNjx441jwEAhD64CGbA4W2g86lTx9mWrg2UXYM+XePueSTzzCnlbkdwrTMVCbb6Kaj9GcoAUgfbKTpR4bUTvNWonAqzVEqSniMKQUWkJz0893Oom2oNKjTPQ3N/rAnn7oncjlHLH61lm7ce6vAagHpj8iUco4NVz9c9UVI2sSXhr0B7uhxp9+7d0tTUZAIEX3TZ0o033iiZmZmydu1aE4xoIKGJ1zNnzpRbbrnFBCcAgOjzHHDk5Zz40+S5BCPLy+ihKC9bLjxlXFCv++k54+W64/02Lptnn+ztefRycMjhNtjSMqGJOlMRiIwk3vZossvR0U7hVp57TGcqgh1UJiLnLECgQXKwS+d2t/ifQQskF8Etad5muK2VoD484LtEr/Xj+uP7B03n9TVbm1zXOTwS+/U5fR28GPTV9MT29UeXLDY9XSyvPLKNzuZ3if3/NGWCCl2apJWZAvHyyy/Ljh075Oqrr5Z58+a5ri8tLTUBhS53uvfee13XV1VVSVZWlhw+fNjteY4cOSLjxgX3RwwA4M7zD+Wc2jK3YMH9vhKxP+gzaoql4nh+hrcj055HETURV++r1at0dmOCpaRtIEdjrZWrEmnfJ0oTQW9iHfTYdf62BpDeciqiXSY4Fhx+AnhP2nPDX/6FddC/uzm8xoUqkODtd2/vNfkK1kaJujytrdua/O27+pJ1mZUmjT/UsNdn6ejhIGeqTBd2j+vM8ieHZ6f2E7clspQJKoKxZs0a8/PCCy8cdZtWdFKvvvqq67rc3Fw5/fTT5aWXXnKb7dDnOfvss21fo6+vz1SGsp4AAP5pEvdn5o43R0AvPs39wI2vwUswR4nn1JYG9Lyezzi2OE8yMzPkmrOnyF8smer2uEAGvjNriuWLZ9RKonBtfoIPVsbkZsZ9piI3yyOo8NhpOvBLlpkKTdD3KsieCDrG9hdLRXowvGHvsRPP7fH8b+xokS5LKdomS86CBhq/fn2XSbDWz8q2UpjlyTzflr7PSM5U7GnpNk0b/X2vXNWfJLGlZfO7bdu2mZ8zZoxukKMzD0VFRa77OH3ve9+Tr3/963LmmWfKGWecIXfccYdJ1tbZDju33367/Ou//muU3gEAJCdnfwrPGQhP08cWm5NnlRhfA/dAy3nqNqw4aaz783obKFhGSzecO9WU2lR2XZsDCSo0KNHeGjowSYTyoydmKmI7XFk2q9osJfHWJ8LTmNzsgEqQRjKw9Txi7LmLPHsomPKzCTRToc0JtemanfNPrnE1jPPkPHYeaPUn/Q7FeobGV7Ul7XavDfGcrH1GnL1AdhzpkmcO+8+JPXhsdDM9X3tlKMj/08fcZk1G6L4cPVORHMuf0jKoaGtrcy13slNSUuK6j5MGD5qvocujdBmUVpl67rnnzH3t3HzzzfL973/fdVlnKiZNmhTR9wEAyeILZ0yUw+19Ul9VaC4X5+eYI/b++kt4jmt8jXOsf9B1JuJQe68cae8bVT721Iklo/44BzIm0m32va0ZAT9HoowNnPsz1kHFtOoiMxMVcFBxvMFgrEyuKJCNe4/J4PEvxkWnjDMduJ02NbbZr4+PY6CojRhfO96VWRXmZtkGFfXVhT7/32kfmWCWxAWyTCraXy/Pp99/9EQZYLuPRN+/t8DE+Vw6mxFsRafBCHz+B9t63ZLFTeVmj21LVGm5/ClU3/3ud2Xv3r1madPrr78uc+fO9XpfLWurAYf1BADpakploZw1tcJtMK9H7D1LtnqyDnZ1EOrrSJ0mUVuPxHouU1g4tcKUj7ULDnK8dMOeVFEwKnk8nIGT83WsVWPiybnNsQ5y9PWCyeOIVFCh3wF/ygtyzPc1y/Kd0Io//vbRcJyXP+nMhJX+f3HmDAVj9vgSr4GmXUGDQBK6o9lfwd8ud34m1llPu5wZJ33bg0PD8tyH7nm0Tr5ebjhCn/8uSwBrnblI9ApzCb550eGcofCcjbDOKnibxQAAxI51XHP+ye5LljyN9whQygtODKgun1/rs8ldQa5Wj6pxLc9yWjytyizV+cpZ/guBeF1CZdP/IN0TtXXAGszsSKRyKgJZ0jPl+GyaNSjNCLGSTyx5buPi6ZVyxfxJPgMsu2Da+Z2wNqh0srsukCU50Qxa9ai+rxkCzanQ5UN9gyeO/vv67mkA9PbuVmnucJ/lDMRghIIK9+VPJ5akJXrzu7Rc/uTMpdC8Cc2RsNK+E52dnXLWWWfFaesAAHYDFmc+gzdTKgvMkdTKwpHSn0tnVpsjzCePL5HxpSeqNHlzyoRSs+Zay0pal0mcMbk8oA8kkIF5ToIdanQOrmK9Vtsk1wZxf2vDsXj07hjpcuz7cbqc5qODgS3nigbPj9Au78dq8fQqmTWuWO57w76EfsmY7ICCskCWPz37QfR6er2wxX5GwWnHkU7zuVgbPPoK/vTtaAK1N76+B0NRmKkyeTpJsv4psX67xciyZcvMz+eff37UbZonYb0PACC+rl44Wb50Zq3f5G4d+NVXF0np8bKt+TlZsuKkmoACikgI5Mi7t2VWidanYuXsGjnt+Nr6aHAEGcgEWt7Un/IAlgM5X8madzDSO8C3LQfiW+Ux0oGh3ZIzu6AiGXqcbGw85jbgj9YytcGIPe+J59HqUDuaOs35RN/TaRlUnH/++VJfXy8PPvigbNy40XW9Loe67bbbTAnZa6+9Nq7bCAA4sTbcmduQyAIZW9kNygJZ5x/1mQqP6yuLcqWiyPsA3F+CfSACrS6kAr3rtWd7X6ZWV1VgEsT9v9bIi1mD2EBmKmJl+lj795AR4UBEb9PSzu7Xjb5fgk2+eWWtzOWrSpe//Ti6s4TlNYYj8yXxfJrm41WrqP4Uw47a69atM+c3b97sus7Zk+Kcc86RG264wZzXUrB6m/akWLp0qVx11VVSXFwsjz/+uOmkreVi6+pGuqyGa/Xq1eY0NBS7UngAgNgL5Iit3aAg36OpWiB0JsHfso9AODfHc9v1sq+lWpro6lnuNxj6+IIgkq8DyVdRlUV5UlWU6xqEaY5MR+9I6dfPnx5YfxDnriixJPQnUnPAubVlsv3IyJFrq1AGnJ4N/TzVVxX5/Y4nw0zFSGUuy2Ufg3/PUsLBcFjKv4ZjnaWKl1Wi7+mUyanQgMLaBVtphSY9OTmDCrV8+XLzmFtvvVUefvhhGRgYkNNOO01+8pOfyJVXXhmx7Vq1apU5kfwNAKnN3+DK2+2+HqfLvrYd6XArYfq9lTPNz0gGFZ6boJd9zSTo0jLnYD1YmnDvTPi9YsEk+dP7B/z2oAhmViPcAa/zIdaKX/o8vo5Qh6qqOC/ohGBrVSqrUPaQfo76GTzS0Gj/nBnRWYYWa00dfe4zFT6CCn8xga/bHRJdiR7ApUxQcc8995hTMDQZ+5lnnonaNgEA0oe/ce/kyuBzO3TZl56cQcXJ4310Qg6Bc4A0KqiQDJ/5H75Kcvpjrco1sWyMOfK+fkdLwgymnBV2rAPoaL38Z+dMMB2eg5Hj5YsW6j7Sz8Abz9kPu4AmUZaF+WOdnYhWk0KHI7r7I8FjivTMqQAAINJCXe+c4SOXxOnMKeVmkL9wqveyuKFwHrEdvfzJ/0xFqDyfN5D9FupMRSicL2V9zWjlVGiFJevbHxPAUrhsm7KusRpw2s1UhDuDM3dSbEr4P/buvsBmKvzM+vjsU+GIxnzWCQkeUxBUAADgZB0QLKgLLoHabuBrTWj2VmPebjCosxNXLTjRY0DL4/7ledNtqxfNrCk2Ccr+GgnacR6x9WwUqEGGNaiJ5EzF6KAisQZTziDHup2BVH8K9bWsA3W7PhCetExyMFacNNZ0CA+Ev6DG7jsebrAVjyU90ar+5DAzFdGcqpCExkwFAAA2zplRFdR+KczNMgN86xIl61Ifb+yCDR20eyYnew7oPnf6RKmvLjTN+TRB2V/SrR3nAMizmpMO9Hz1BckLY6bCcxAZqXGSZ2AUyZkKk1PhMVjUhPBIcESo14nuVmcDyBk1JxKs504qky+e6TtJ/QtnTJTq4jz5/OkTgw4qwm34lx2H8lH+ttnXrb6ChuGoz1QkdlSRMjkVAADEkx51vnTOSAlOZxO0gI7C29wnkIHJ1KpCc3IPYLrM+VMnlsoH+08kd3szNOylRKyf7Q5npsIzVyNSB6o1yFKOKMxUZHjpwi4yUmUqUkfNAxmfe5up0MDns/MmyLbDnaapXTCmVBaak9/XjsZMRRwObzu/93YyAlji5IsjilFFIlUhs8NMBQAAURLqGCCUJRS6XEtLqM6bVGZKzlqPVvsb0OZ6LLvxN3gJZ1ZgdKfnQErx+r593uSygHuZzJ5QEtBreXbUjsVg2PoyGqBeeEpNwPtel05poKMzE+HkvPjaNrv9EO5MRbDLn0rHnCj1G63lT77e0oFjvX5mKhwSLYnep4KgIsq0R8Xs2bNlwYIF0X4pAECCCXmmIoRxia6H//o5U2X5SWMDfo5hH8uffLEdZNtcd8aU8lHXeb5WNMdJdrtg+ayxcpKPI/nO92GdETA5FY7o5wJYn1HzIIrzcgIeWGpAGU3e3m+4Q+hg92NOBBov+gqEuvuH5HC798DBV38Wh0OktSv82StvEjymIKiINu1RsWXLFmloaIj6awEAwhPpJEvrGmhvAwK7ddKhHu20DjgDeQZnHobn7IH/nhsnzp87o8oEMldaEst97c9YVnKyo0HN1GrvS32cW+deUjYjJu/D83WCGUQGuiRtfGnwpY2Vt7cb7v8ZX6WL7eQGeX87gz5mKtp6BsIKVh56277nRyQkeExBTgUAAFET4kzFKRPCL7NpN9jTxO6dTV1SUZgrteVj5LSJpa6BtlZ7ch6h9TeY1aBDm9jtau4yy2106U177+jBWCDDTc+XOm9Wtby5s1V6B040xLN2t9ZqRjqgD6f534yxxfJBRbt09A7Ise4B24BqdPUnR/RnKixPGcjT6zacVlsilYV5fpfGXLe4Tva0dMmc2rKQts1bV/NCk1sSukAqXkU6sVt7Vui+C3fplieHRFeiz1SQqA0AQJRkhDBQ+MqiyVJdlBeV7bn41PFysK1HassLRh1p1xkHZy1/5zZpnkbD7lbbbdbBqXWAajs+C2CU5TkYPn1yuby9y/01K4py5dNzxktBXrarWVs4QYW+d+1WvvVQh/x580GP7Rn5aT2YrTM5gSx/0qtuPLdeDrb1Sn5Opjz6zoneCIHI8JjB8jeI1EBnxUmj8y7saCCpp1B5vt85taVmH+kSN2vzwm+vmC7//fL2qOVIBFtS11tORTRyHxxR7wSY2FEFQQUAAHFMrPRc/jS2OPh+E4HSGQlvVX6s4yHnAFLL6s6sKZIH3trr/yh9iOMpuz3k+fT6ejNqfFQ0CvG17Q6+O19bZ3LKC3KkrqrQtn+D3fKnadVFphTv9LFF0tU3GNpGWbbD3/cnlt2snW9XgzGd0dKGjHbbF8zMwykTSqS0IPzE61D6s0Rj3zmi/HlQ/QkAgCQRqTGBVmBSi6dVxrURV6isQcOocrNelmFk2RxBDuRosN1zRauXRTAJ51pB6folU+W8WfaJ73ZjZ8/eFiFs0ImzIjLgIyk41pzvR6tsza+riEglIg3YvPXdiKZo/Z8bjnJQEY+eHsFgpgIAgAjTvIAl06tsB+Se+n0VzQ9DsMs7rPe3HhENtNJTUV62WUL1+vYW11r1QAIbu0T1SFWI8rccxe7Ir7eSrZ7P5G+/hBRTeDx/XwIGFZEWyP8Rq0gEM4PR+j/niG5UkZOd2MufEjvkAQAgCenAJ9DB0kDUBjih3986cAtkJsFJj2DPmXQiyTygoMLmqfI9qlEF00k4mPdt9z6yA1y+Y60O5WSXrB6MMZb+Evrs5YWxXxoUy6U3+lnp7M5ZUyu83sez/0gkBu6+qj+FwxHGY7XXSqST2mMtsbcuBdCnAgDgi3WJSyQPBgfSITmQAVEgOQ/BPqc/eTmx6WVhG1R4m6nwGMza5VTsP9rj87n90SVXl82bIF88o9ZUW9L8ms+fPtE0M1RnB7CcLlqiWQq4psS9MIGzM/3I6/pfujSxPLgyuYND0Vr+5Aj5sZUBJNF7NqlMNIm9dSmAPhUAAF/6LDMVwRyR9+f043kdgfJ2BNhucBzo+DLYHgTWxm9WgVRBCoXd8wZaXch/g8DAtkGrKFnVVxfJ5MoCt7yDUyeWyndWTJdF9SNBhbMUsOdjoyka3ZxPfG4nnlvf47jSE8UKsjzyCGyDiuMVweI9UyERymPyhpkKAACSRQyr6TjNGFvkOh/JcZse6Q6my7LXmQrbCkmBbejCqe4DxECPgM+tLZOpVYUhBVvOpTS+umb7TDgPMKfC7m5aEenE7YFts/bdCIR1WZY2G7x8fq0riTyZcyrs9n+Gj/1sNxtgl5fkq0iCtQdKJA2H8ftjVgDf10iU040mZioAAPCy7CaSMrwstdGeEa77JOAnYTegD3SAqaVYv3zWZJ/3sVtupAGRs4KW17wLS/6BdZx58vgS+YslU+VTp47zu322JVG9Vdhx+H/s/LryoD/LUGYAdOBt12skmoJZeVMSYO8Ju8kxz/cUSEBpVyVLS/vGmsPHUQlvy+qCmYWIR6WsYCT21gEAEEN6pLmuqsC1hj3ayjzWUUd6oiSYJd5ji+0b7tknakvEWI/AO5f1jH690S+oTQK90d4HoS7XsSuNq0Z31PY9MPT38sHMIiWCYPbnlQsmBfnc7oNvz0IBVV6+m04DUcqRiORMRU6QVa5sJeJRBwuCCgAALJ2TP396rVnDHmkVBbkxr58fTOJocX6OXL+4Tr65rN7teruxZE1J5Br0WasoaRleu9e1G0uV5Ps/Gn7ByTWjntfK7nm9zcJYu4d7bp/d0Wh/g/DLzwxu4B1vvmannInV55881lVeOJyZiowgg9hgu3JbRXJV1wf720J63MJ679WvkklyhckAACSZKxZMku1HOn2WzYyWYEOUcpsKNNYhng4atVu0delRuKwrOtwDifB6PjiTnDXBOZAysTpL5fAxINYcDb3tsXf3ubYw1KP5GnzEo5N0OHztwpk1xVK/ojDgcryesz8ZnkGFR0DpbQmUzoh8crhDFkwtl4bdrW63Bfqd0ee2W7Y0o6ZIth3ulGD0h9hXZPG0kaB3fGm+HGzr9Xq/GKW1hIygAgCAKNLKNMFWp4mUSPTisg5kdJ16QW5khw7eGsa5n88I+X36Guhan1ZnM3zlKOg2VPtYhqN5HOEKtYpVLPjLowk2oPBmZKbCI68iw/6znlA2xpzshFtJLZKV2FQgz/a50yfK7pYueWbzIdvbEzymYPkTAACpKtSSrt4GkwEFKUGOi60D+VhVGLITyCt7C3qygpx5SPQjzlYabGnC/dIZ1RF/7kAStVUouyvcfRzpz8gRwH10BvCkcSUxLesbSeRUAAAQZzpoi4bPzp0Q9nNEexhjHSfFNajIiN3R60jMIMWKLtv75tJ626Vx4dA8CF025TlYHknUdr9vURST2r197jEsrJUyCCqijI7aAAB/vnDGRNMVWPMvImlsSb7MnVQawUFX5EfD1i7BGTGujuSet+F/FOntLqk+/oz0EfLzZlXL15bUSa5NRSS7wFL7cdRXF5rlQYEKf5Njv/zJn0T/npFTEYOO2npqb2+X0tLYdb4EACQ+5yBhbHG+XDE/OtWAwl5bHuTorLZ8jGxsPBbw/csKck3gk5uVZfpTWK+/+LRxUpATvaFKxfGj74H2e8iI0gBPZ6p6+oekvupEI8RUpt8pt7Kxo253v6wJ8pfNGwko3trZEqPvvUSUIwLPkeCrnwgqAABIaREciASybEerQ31m7gS3pGbtqn2orVfKveQdrDjJvi+Ir/XlbtslodEj5X953rTAgwq3pPLQd6znQ689e4o0dfTJ5IoTjRBTWYaffRPpJOlQRHsLSsfkSFvPgAnCA5UI+8UXZioAAEDEBu862NbAwkqDjI17j8lptdGZsa8p8d0czZdgyuNGap29Z0CiFbWmVKbPkGxU12wflb4cIQaQ4R7VDya/R/vafOCnR0WGTW8PfYy3HirJKH2+wQAApCFrzkK86PKVc2ZEfvD01UVTZN/Rbjl1QnItLw50ZiRVWTuPezJ9KSKwewJ9ikh8EhWFwfcc0QaSwTaRZPkTAACIG23qprXvtXlbqlUt0iVWvnpHRJq3PIBgB3tpHlP4LXWcEYGgTYsUhEP/zwQuuC32F1RWFuVKS2e/JJv4H74AAABRo8t7vrJwipw5pSKlm7MlIk00t2t8GM/SuYk4U+HZPT3calPfOm+a7QxdVRABaGffYMD3DXZzs/wEFZqUrsULtEpWOK8TawQVAAAAQQpkgKeJ5hfMHp2Enu5BRbaPmYrR/bRDef7MUZ+P5vlcs2iKREOw25vp5/PXJG4tXqAV0JIJQQUAAHESTJJwIki05U/JQCtenTaxVBbUnZgpSvflT9mZmT47lUcj5gr2OaP5Xc8KcfSd6NWfCCoAAIixz8wdL+NL82XlyfalVJH4Ah3g6VIena2wJqqne6K2w8eIPSNKg31vswMRSQoP8kkKckOrk5ToE1xUfwIAIMamjy02p2SjCaQIf1AZ6Q7Vyab8eNPBaO4fz6eIZhwX6FN/Zu4EeXtXq3zq1HEB3d+zr0uif2sIKgAAgE/anK2jd9B0/kb40n2mwldJWc+Rc6gzE6NnkqK3z+1ioJk1xXLKhBJ5csN+t7wOzx4uvmhOxaSKAmls7ZZkwPKnKFu9erXMnj1bFixYEO2XAgAgKiqL8qSuqpC9axHOwfQ0jylikjcQ6ExFtGaNzj95bET+z1i7rCf6DBdBRZStWrVKtmzZIg0NDdF+KQAAkMBm1IwcpZ5vSdpON9p92pNnonYkZMSw4pZdIJQRjWRzSWwsfwIAAIiBS04dL50zB6UkP/gOzMlOy6QuP2msTCof3bcjEjRoGPaxVqowL7ghrybWr9vWHNB97QKIjCiEAAk+UcFMBQAAQLCqioLv5J2ZmZGWAYWzi/bUqkLTQ8KXSI2brUuFKgpzZX5deVCPnz+lPMzXl7TD8icAAIAAfXXRFFPFZ1wpSesShQDAM2/As4u7t67unoN460WttmSXHP7FM2oD2qZQZEjky++SUwEAAJAiqovzgqrggxGOKA/GR9V6CuCJJleeSIK28+WzJoe+/Ckj/aYqmKkAAABAxJc7Bcp6z4wQS8pGYwyvs1FaxSmkRG1JPwQVAAAAYUjDg9J+ZWVmxnaGw+NDiOVMgf1MhaQdggoAAABE1KkTSwK+r1tJWY9j/Nacgniwm4W44dypUhRkNal0QFABAAAQBgaYo51dXymfnjPedTng2CAKy5+K8yMbABR7VPAip2IEYRYAAEAIvnRmrby9q9X0X4A7LR07o6ZYRA4GtWtG5VR4Vn9yBD6jcN3iOhkcGpaC3NCHu96CFet2RaMnRTIiqAAAAAjBpIoCc0J4fA3Kw5mp0P4UoTwuWNbnmFlTLEumV0bldRIdy58AAACQEDJCDCoyE2TQXlWUK2UFo4OZOKeGxARBBQAAAKLKV8K1W6L2qOZ3gVk2c2QJWrCds0Ne/mTZsGjGMw5JHix/irLVq1eb09DQULRfCgAAIKl5DtCHAzzEP2tcsUyuKJD8nNgfL/cVFDmx/AlhW7VqlWzZskUaGhrYmwAAAD6MGnwHcah+TG5WxPtTBJaEnSBrr+KM5U8AAACIG19D8lHVnyL+2hkhVn8K7v7pgKACAAAAcWMdoI9uficJKcsSPaRzIGFFUAEAAICoKi1wbxgXeJ8Kd3NrS83P2vIxYW1PoIGAt/t9eu54KczLkk+dOs4tDMqQ9EWiNgAAAKLiygWTZFPjMTl3ZnVIj/dM1D5tYqnUlORLZQA9KHwJt2Hd+NIxcuO59SaHY1dzV1jPlSoIKgAAABAVE8rGmJMvvpY4ed6mg3gNKmLFV/BhlxSekcZTFSx/AgAAQELwt/wp0q/jLwgIJEiIap8KhyQNggoAAAAkhFEzA1EaVUcyEEjn2QkrggoAAADEjbVsbKxnKvzeL6D7kKqtCCoAAACQkJJp+U+6I6gAAABAQgQOzmP+C+srzM/zZoVWNcqfQDtvB5RTYblPRhovhaL6EwAAABKCc7C/eFqVLKirkJys6B7/9h8DpHGUECRmKgAAAJAQrEP4aAcUkeI2UyHpKzk+LQAAAKQka9pErJYPOV+nIDcroPvBP4IKAAAApKVL50yIaH5GhkcU4rwYy4Z98UJOBQAAABJCoAnUYb/O8YVKFYW5fu4XyHN5d82iKfLBgXZZUFcu4ZbbTXQEFQAAAIgbRxzqxkYydvGVU1FZlCfLZkanglWiYflTlK1evVpmz54tCxYsiPZLAQAAwIdZ44rNz1BnDgLqAh5Bedm+cz4SCTMVUbZq1Spzam9vl9LS0mi/HAAAALy46JRxMr+uXKqL8qKyjzIiHF/MqS2VfUe7ZWpVoSQ6ggoAAADETSxXP2VlZsjY4vyI5nhEMw0kJytTLps3UZIBy58AAACAEGXEaClUoiOoAAAAAEKVvnGEG4IKAAAAxE1lke+yrskkI40DDHIqAAAAEDcFudnyF+dMldysxDvWHVifijSOJCwIKgAAABBXpWNykvYTSOfZCavECwkBAACAJEFMMYKgAgAAAIiAjDSOMAgqAAAAgBCDhEB6WaQDggoAAAAgRPSpGEFQAQAAAISIiYoRBBUAAAAAwkJQAQAAAITYg8J6n4w0Tq8gqAAAAABClcaBhBVBBQAAABAi6+xERhoHGAQVAAAAAMJCUAEAAACEiJKyI7KP/wQAAAAg9suZJlUUyGkTS0ftH5rfjSCoAAAAAPz4wukTJTMzw/dMRUb67kaWPwEAAAAIC0EFAAAAEInqT5K+CCoAAAAAP7wtbbI2v3Ok8V4kqAAAAABClM55FFYEFQAAAIAfVHnyjaAiylavXi2zZ8+WBQsWRPulAAAAEEeONF7/RFARZatWrZItW7ZIQ0NDtF8KAAAAMcbypxEEFQAAAECIrIna6YygAgAAAEBYCCoAAACAEJc2We/jSOOisgQVAAAAQIhY/DSCoAIAAAAIEaVmRxBUAAAAAAgLQQUAAAAQ4iyE9R6O9E2pIKgAAAAAQkWfihHMVAAAAAA2CnOzgtovjjSeqciO9wYAAAAAiaisIFcuOmWcjPERXJCoPYKgAgAAAPBi9oQS9k0AWP4EAAAAICzMVAAAAAAiMmtcsZw5pTzkfeFI447aBBUAAACAiFxy2nj2Q4hY/gQAAAAgLAQVAAAASFtzJ5Wan/PrQl/2BJY/AQAAII2dN3OsnDqhVKqL88J+Lkf6plSQUwEAAID0lZmZIWNL8uO9GUmP5U8AAAAAwkJQAQAAACAsBBUAAAAAwkJQAQAAACAsBBUAAAAAwkJQAQAAAESAI41LyhJUAAAAAAgLQQUAAACAsBBUAAAAAAgLQQUAAAAQAZlpPLJO47cOAAAAhG9hfYVMLBsjs2qK03Z3Zsd7AwAAAIBktnhalcg0SWvMVAAAAAAIC0EFAAAAgLAQVAAAAAAIC0FFgJ544glZuXKlVFRUSEZGhuzevTu8PQ8AAACkCIKKAHV1dcnSpUvlRz/6UXQ/EQAAACDJUP0pQNdcc435+cEHH0Tz8wAAAACSTkLPVNx///3yzW9+U+bPny95eXlm2dE999zj8zENDQ1yySWXSFlZmRQWFsqiRYvkkUceidk2AwAAAOkmoWcqfvjDH8qePXukqqpKxo8fb8778sorr8hFF10k+fn5ctVVV0lxcbE8/vjjcuWVV0pjY6P84Ac/iNm2AwAAAOkioWcq7r77bpMQ3dTUJDfddJPP+w4ODsqNN94omZmZsnbtWrnrrrvkzjvvlE2bNsnMmTPllltuGRWU/MM//IOZ/fB1AgAAAJDEMxUXXHBBwPd9+eWXZceOHfK1r31N5s2b57q+tLTUBBTXX3+93HvvvfLP//zPrtt05kKvBwAAAJCiQUUw1qxZY35eeOGFo27TJVHq1Vdfdbu+urranAAAAACELmWCim3btpmfM2bMGHXbuHHjpKioyHWfULS2tsrevXvNbIjasmWLHDt2TCZPnmx6V3jq6+szJ6f29vaQXxsAAABIZAmdUxGMtrY213InOyUlJa77hOLpp5+W008/Xb70pS+Zy5deeqm5rNfbuf322822OE+TJk0K+bUBAACARJYyQUW0ae6Fw+EYdfKWk3HzzTebIMZ50upTAAAAQCpKmeVPzhkKb7MRuvyovLw8ZtujfTX0BAAAAKS6lJmpcOZS2OVNHDp0SDo7O23zLQAAAACEJ2WCimXLlpmfzz///KjbnnvuObf7AAAAAIiclAkqzj//fKmvr5cHH3xQNm7c6Lpel0PddtttkpubK9dee21ctxEAAABIRdmJ3lF73bp15vzmzZtd1zl7Upxzzjlyww03mPPZ2dnmNu1JsXTpUrnqqqukuLhYHn/8cdNJ+4477pC6uro4vhsAAAAgNSV0UKEBhXbBtnr99dfNyckZVKjly5ebx9x6663y8MMPy8DAgJx22mnyk5/8RK688kqJh9WrV5vT0NBQXF4fAAAAiLYMh9ZFRdTpMqyysjJTWlZ7ZgAAAACJTiuoar81bfrsrR9cws9UpJKOjg7zkyZ4AAAASMaxrK+ggpmKGBkeHpYDBw6YPI+MjAy3yI/ZC/BdAL8TYMXvBPBdQKL8XtBFTRpQTJgwQTIzvdd4YqYiRvRDqK2ttb1NvxgsiQLfBfA7Afx9gDeMFRDP74KvGYqUKykLAAAAID4IKgAAAACEhaAijvLy8kz5W/2J9MZ3AXwPwO8E8PcByTxWIFEbAAAAQFiYqQAAAAAQFoIKAAAAAGEhqAAAAAAQFoIKAAAAAGEhqIiwhoYGueSSS6SsrEwKCwtl0aJF8sgjjwT1HH19ffKjH/1IZsyYIfn5+aaD4Te+8Q05cuRIpDcXCfo92LFjh/zLv/yLfPazn5WJEyeaLux1dXV8Xmn2XdAups8884z85V/+pcyZM8c0HyooKJC5c+fKbbfdJr29vVHffiTO7wX9Llx11VVy0kknmefQ74Ke//rXvy6ffPIJH1WajRWsjh496vpb8alPfSqi24rE/i7cc8895nP3dlqzZo3ECh21I+iVV16Riy66yAQC+ou/uLhYHn/8cbnyyitNS/Uf/OAHfp9jeHhYLrvsMnnuuefMF+uLX/yibNu2Te6++2556aWX5M0335Tq6upIbjYiLBLfg9dee03+9V//VbKysuTkk0+WQ4cO8Tml4XdBDzDoHxstH3jeeeeZ59JAQn8//OM//qM89dRT5g+GDi6R+r8X/vznP5u/AQsXLpSLL75YcnJy5KOPPpJ7771XHnjgAXP7ihUrYvJ+EN/vgqdvf/vb0tbWxseSxt+Fyy67TObNmzfq+pgekHQgIgYGBhzTpk1z5OXlOTZs2OC6/tixY46ZM2c6cnNzHbt37/b7PL/+9a8d+rF8+ctfdgwPD7uu/+Uvf2mu/8Y3vsEnlgbfgx07djjeeOMNR3d3t7mszzdlypSobjsS77vQ39/v+PGPf+xobW0ddf1nPvMZ8zvhP/7jP/jo0uT3Qk9Pj+31L774ovkuzJ8/P6LbjcT9Llg99thj5vP/7//+b/PzoosuisKWI1G/C7/5zW/M564/442gIkKee+4586F+7WtfG3XbPffcY27713/9V7/Pc/bZZ5v7en6RNMCor693FBYWugaaSN3vgSeCiuQTre+C0/r1681zXHrppWFuKZL9u6DKy8sdZWVlYT0Hku+7cOTIEUd1dbXjmmuucezatYugIg2/C79JoKCC5U8R4lyzduGFF466Tae21KuvvurzOXRZw1tvvSWzZs2SKVOmuN2m6+JWrlwpv/rVr+Sdd96Rc889N1KbjgT7HiA1RPu7oEtfVHY2v8bT/bvwxhtvmDX155xzThhbiWT8Ltx0001mmezPfvYzlj+l+Xdhw4YN0tLSIoODg2bJ0wUXXCCVlZUSS/w1ihDNe1CaXO1p3LhxUlRU5LqPr+Rczamwew7rc+vzEFSk7vcAqSHa34Vf//rXXv8gIbW/C88//7ysX7/e5Nzo4/74xz9KVVWV/PSnP43odiOxvwv333+/PPHEEya3qry8nKAizX8v/PznP3e7PGbMGLn11lvl7//+7yVWCCoixJkgpdVZ7JSUlPj9Dx/Ic1jvh9T8HiA1RPO7oFWAdNZSk/i18g/S67ugQcWdd97pujx9+nR56KGH5Mwzz4zA1iIZvgsHDhyQv/qrv5Ivf/nLJkEX6ftdmDp1qvziF78wsxu1tbXS2toqL7/8stx8883yD//wD6aQx3e+8x2JBUrKAkCSlR/UyiD6h+jRRx81laGQXu644w5Tbrijo8O1ZHbJkiXy4IMPxnvTECM33HCDWQLpeXQa6WfZsmWm+pfOeOjshJYWvuaaa0yVQK0qpeXpdUlULBBURIgz0vQWVba3t3uNRoN5Duv9kJrfA6SGaHwXNJ9KlztlZmaaPxinnHJKRLYVyfl7QZdHnHXWWWb5i/ar0H5GTU1NYW8vEvu7oCWEdbZy9erVZtkbklNplMcL+vdB86x05kJLT8cCQUWEWPMdPGmPgc7OTq+5Ek719fVmsOBtDZ2v9XdIne8BUkOkvwsaUGixBs270oBiwYIFEd1eJO/vBU3WX758uXR1dZnvCVL7u6AJueryyy93a3Kmy2CU/n7Qy3Y9C5Be44Wq40Gn/m6IBYKKCE4/Ode6etL/4Nb7eKPTVnrUaevWrbJnzx6323Sq+4UXXjDdFufPnx+pzUYCfg+QGiL5XXAGFENDQ/Lss8+a5mdIHrH4vaBr7K1VwZC634Wzzz7b5FJ5nnRZpNJ19Xr5C1/4QlTeA5Lj98LQ0JDrIINnRdGoiXdN21RqYqJ9JHw1MdEa0k4HDhxwfPTRR+Z2K5rfJbdIfQ880acifb8L77zzjuk/UFRU5Fi3bl1M3wMS67vQ0NBg+/zPPvusIycnx3xPOjs7+djS8G+Eok9F+v6N8DQ4OOj4m7/5G9O/Yvny5Y5YIaiIoJdfftn8Yi8uLnbceOONju9///umC7J+qHfccYfbfa+77jrbZiVDQ0OmG6betmjRIsff//3fO774xS86MjIyHFOnTjWNbpD634OmpiZzm/OUmZlpGh9ar9P7ILW/Cy0tLaapmV7/qU99ynHrrbeOOv30pz+NwztDPH4v6HWnnnqq4+qrr3b83d/9nWPVqlWOc88911yvz/3EE0/wwaTJd8EOQUX6/l6YM2eO46tf/aoZM+rzaFCi19fW1jp27NgRs/dDUBFhb731lvnjX1JS4hgzZozjrLPOcjz00EOj7ufrF0Vvb6/jX/7lX0z7do1Ux40b57jhhhschw4divTmIkG/B84/Dr5O1iMYSM3vQiDfA/0DhPT4vXDbbbc5Vq5c6Zg4caL525Cfn28GD9/4xjccW7ZsieE7QSKMFTwRVKTnd+EHP/iBY8mSJY6amhoToOgByLlz5zp++MMfOlpbW2P4ThyODP0nNgutAAAAAKQiErUBAAAAhIWgAgAAAEBYCCoAAAAAhIWgAgAAAEBYCCoAAAAAhIWgAgAAAEBYCCoAAAAAhIWgAgAAAEBYCCoAAAAAhIWgAgAAAEBYCCoAAEnr5ZdfljPOOEOKiork7LPPlvXr10f8NbZv3y433XSTeZ2cnBypq6uL+GsAQLIjqAAAJKWtW7fKpZdeKp2dnebnnj175OKLL5Z9+/ZF9HU+/PBD+eMf/2iCiVNPPTWizw0AqYKgAgCQlJ588kmpqKiQzZs3y8MPPyybNm2Snp4eeeWVVyL6Op/5zGdMoPLEE0/IwoULI/rcAJAqCCoAAEkpOztbjh49apY89fb2yosvvigDAwMyZcqUiL5OZiZ/KgHAH35TAgASyoYNGyQrK0u+853v+LzfV77yFZNLsWLFChkzZox89atflVtuuUWWLl0q8dDW1iaVlZVmNsPhcMRlGwAgXggqAAAJRYMJDRL+6Z/+yef9xo8fL1dffbXrcl5envzoRz+SeCktLZWbb75Z3n77bbnvvvvith0AEA8EFQCAhPHYY4/J66+/Lt/61rdk7NixfhO1f/nLX0pNTY1ZCqX5FJ988onPx9xzzz2SkZHh96TbEYpvf/vbUl1dbYKLvr6+kJ4DAJJRdrw3AAAAp5/+9Kfm59e//nW/O+Wv/uqvpL+/X26//Xa54447ZMuWLbJx40Y5+eSTvT7m85//vCxatMjvc0+cODGkDyU/P9/MnvzsZz8zyePXXnttSM8DAMmGmQoAQEh0EH/55ZdLVVWVWa6k5VZ1cD80NGTKr+oR/927dweVS6FJ1zronzVrls/7aiWm559/Xs466yy5/vrrZe7cueZ6DSr8LVE66aST/J6Ki4slVLo9avXq1SE/BwAkG4IKAEDQ1q1bZwb0ukxIB+qf+9znTI6DJkpfeeWVIe3Rp556yvy84IILfN6vu7tbvve975mg5Re/+IX5OWfOnICCiliYN2+eWQKluRUHDx6M9+YAQEyw/AkAEBQt36pLfLq6uuSv//qvzeyEVmtS77//vpx//vnS3NwcUndspZ2xfbnttttk7969ZkZAAxvlnKnQXhWRpkHMn//8Z3N+586d5rIz52LBggW2JWz1PTz99NPy0ksvmapUAJDqmKkAAATl8ccfl8bGRpk0aZL8x3/8hyugUDpj8I//+I8h7VFd/qR85URs377dBDElJSXy7//+726vqw4fPhzx2YEjR46YZV56euGFF6Spqcl12VujvVNOOcX8fO+99yK6LQCQqAgqAABBWbNmjfl5xRVXSE5Ozqjbr7vuuqD3qM566ElprwdfydlaVemf//mfTdUna2K183GRXgKl+SHad8Lu5Myf8OTcFg1yACAdEFQAAIKyb98+83Pq1Km2t5eXl5s8i2Abxzl5S5L+/e9/L88884xJ4tbgwpNztiIaS6CCpTMpSjt+A0A6IKcCABB3ZWVlrvMdHR2uQbk1j0OTs5XmNCxfvnzUc2zbti1hkrWdQZIGWACQDggqAABBcfZw8FYu9tixY24zD4EoKCiQwsJCswSqpaVlVFCh+RO7du0y5zWfQ0/eJEJQoe9BWZdoAUAqY/kTACAoy5YtMz8feeQRGRgYGHX7fffdF9IePeOMM1z9L6w0mPjJT35i8jc+/vhjr/kN7777rmvGQmcz4umDDz4wP88888y4bgcAxApBBQAgKF/60pfMbIWWdb355ptleHjYbTD94x//OKQ96lzS9MYbb7hd/93vftcsf1q1apXPpnhaNSo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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "base_dir = Path(\"../../sasbdb_bench/cache\")\n", + "\n", + "csv_files = sorted(base_dir.glob(\"*/sba_fit_grid.csv\"))\n", + "\n", + "# Select the benchmark entry to show without fitted model overlays.\n", + "selected_indices = [1]\n", + "csv_files = [csv_files[i] for i in selected_indices]\n", + "\n", + "csv_files_martini = sorted(base_dir.glob(\"*/sba_fit_martini.csv\"))\n", + "csv_files_martini = [csv_files_martini[i] for i in selected_indices]\n", + "\n", + "# Create temporary copies with i_fit removed so only experimental curves are plotted.\n", + "exp_only_files = []\n", + "for csv_path in csv_files:\n", + " tmp_df = pd.read_csv(csv_path)\n", + " if \"i_fit\" in tmp_df.columns:\n", + " tmp_df[\"i_fit\"] = np.nan\n", + " tmp_path = base_dir / f\"{csv_path.parent.name}_exp_only.csv\"\n", + " tmp_df.to_csv(tmp_path, index=False)\n", + " exp_only_files.append(tmp_path)\n", + "\n", + "csv_files = exp_only_files\n", + "\n", + "nrows, ncols = 1, 1\n", + "fig, axes = plt.subplots(nrows, ncols, figsize=(8, 6), squeeze=False)\n", + "axes = axes.ravel()\n", + "\n", + "for ax, csv_path, csv_path_martini in zip(axes, csv_files, csv_files_martini):\n", + " df = pd.read_csv(csv_path)\n", + " df_martini = pd.read_csv(csv_path_martini)\n", + " num_df = df.select_dtypes(include=[np.number])\n", + " num_df_martini = df_martini.select_dtypes(include=[np.number])\n", + " print(num_df.columns)\n", + " print(num_df_martini.columns)\n", + "\n", + " if num_df.shape[1] < 2:\n", + " ax.set_title(csv_path.parent.name)\n", + " ax.text(0.5, 0.5, \"Not enough numeric columns to plot\", ha=\"center\", va=\"center\")\n", + " ax.axis(\"off\")\n", + " continue\n", + "\n", + " x = num_df.iloc[:, 0]\n", + "\n", + " # Plot experimental columns only. Fit overlays can be restored by uncommenting below.\n", + " for col in num_df.columns[1:-2]: # Exclude last two columns, typically i_fit and sigma.\n", + " ax.plot(x, num_df[col], label=\"Experiment\", alpha=0.5)\n", + " # ax.plot(x, num_df_martini[\"i_fit\"], label=\"Martini bead fit\", linestyle=\"-\", color=\"m\")\n", + " # ax.plot(x, num_df[\"i_fit\"], label=\"Amino acid bead fit\", linestyle=\"-\", color=\"m\")\n", + "\n", + " ax.set_xlabel(r\"q ($\\AA^{-1}$)\", fontsize=16)\n", + " ax.set_ylabel(\"I(q)\", fontsize=16)\n", + " # ax.grid(True, alpha=0.3)\n", + " ax.set_yscale(\"log\")\n", + " ax.tick_params(axis=\"both\", which=\"major\", labelsize=14)\n", + " ax.legend(fontsize=16)\n", + "\n", + "for ax in axes[len(csv_files):]:\n", + " ax.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(base_dir / \"sba_fits_grid_1.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2306165b", + "metadata": {}, + "source": [ + "## Plot residuals\n", + "\n", + "Compute residuals as `i_exp - i_fit` for the currently selected files and plot them against `q`. This is useful for spotting systematic deviations that are harder to see on logarithmic intensity plots.\n", + "\n", + "Note: if the previous experiment-only cell has replaced `i_fit` with `NaN`, this residual plot will also contain `NaN` residuals for those temporary files." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "78c36b23", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Reuse nrows/ncols and csv_files from the previous selection cell.\n", + "fig_res, axes_res = plt.subplots(nrows, ncols, figsize=(14, 4 * nrows), squeeze=False)\n", + "axes_res = axes_res.ravel()\n", + "\n", + "for ax, csv_path in zip(axes_res, csv_files):\n", + " tmp_df = pd.read_csv(csv_path)\n", + " num_tmp = tmp_df.select_dtypes(include=[np.number])\n", + "\n", + " # Residuals require explicit q, experimental intensity, and fitted intensity columns.\n", + " if not {\"q\", \"i_exp\", \"i_fit\"}.issubset(num_tmp.columns):\n", + " ax.set_title(csv_path.parent.name)\n", + " ax.text(0.5, 0.5, \"Missing q/i_exp/i_fit columns\", ha=\"center\", va=\"center\")\n", + " ax.axis(\"off\")\n", + " continue\n", + "\n", + " residual = num_tmp[\"i_exp\"] - num_tmp[\"i_fit\"]\n", + "\n", + " ax.plot(num_tmp[\"q\"], residual, lw=1)\n", + " ax.axhline(0, color=\"black\", ls=\"--\", lw=0.8, alpha=0.7)\n", + " ax.set_title(csv_path.parent.name)\n", + " ax.set_xlabel(\"q\")\n", + " ax.set_ylabel(\"I_exp - I_fit\")\n", + " ax.grid(True, alpha=0.3)\n", + " # ax.set_yscale(\"log\")\n", + " # ax.set_xscale(\"log\")\n", + "\n", + "for ax in axes_res[len(csv_files):]:\n", + " ax.axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(base_dir / \"sba_residuals_grid.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f9744a16", + "metadata": {}, + "source": [ + "## Load chi-squared summaries\n", + "\n", + "Read the benchmark summary CSV files and compare the mean `chi2_per_point` values for amino-acid grid fits and Martini fits. One Martini row is dropped to align the two summary tables before comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "75974a2e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.666302040833983\n", + "6.8857041521516145\n" + ] + } + ], + "source": [ + "summary_path = base_dir / \"sba_summary_grid.csv\"\n", + "summary_path_martini = base_dir / \"sba_summary_martini.csv\"\n", + "\n", + "if not summary_path.exists():\n", + " raise FileNotFoundError(f\"File not found: {summary_path.resolve()}\")\n", + "\n", + "# Read the leading summary columns, including sid and chi2_per_point.\n", + "summary_df = pd.read_csv(summary_path, usecols=range(4))\n", + "summary_df_martini = pd.read_csv(summary_path_martini, usecols=range(4))\n", + "# summary_df = pd.read_csv(summary_path)\n", + "\n", + "# Drop the Martini outlier/mismatched row so the two tables align by row.\n", + "summary_df_martini = summary_df_martini.drop(index=11)\n", + "\n", + "print(np.mean(summary_df[\"chi2_per_point\"]))\n", + "print(np.mean(summary_df_martini[\"chi2_per_point\"]))" + ] + }, + { + "cell_type": "markdown", + "id": "bd632399", + "metadata": {}, + "source": [ + "## Export a chi-squared comparison table\n", + "\n", + "Assemble the per-entry chi-squared values into a compact table, then print both a LaTeX version for manuscripts and a plain-text version for notebook inspection." + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "50552e70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{table}\n", + "\\caption{Comparison of chi2 per point (Grid vs Martini)}\n", + "\\label{tab:chi2_comparison}\n", + "\\begin{tabular}{lrr}\n", + "\\toprule\n", + "sid & chi2_grid & chi2_martini \\\\\n", + "\\midrule\n", + "SASDA92 & 12.08 & 11.97 \\\\\n", + "SASDAW3 & 7.49 & 8.00 \\\\\n", + "SASDEM6 & 4.85 & 5.84 \\\\\n", + "SASDF86 & 12.21 & 5.54 \\\\\n", + "SASDJF5 & 5.51 & 5.70 \\\\\n", + "SASDJG5 & 3.59 & 3.20 \\\\\n", + "SASDJQ4 & 10.41 & 10.10 \\\\\n", + "SASDJU5 & 3.71 & 4.15 \\\\\n", + "SASDKG4 & 5.39 & 5.62 \\\\\n", + "SASDL82 & 2.65 & 2.52 \\\\\n", + "SASDMB5 & 6.35 & 5.89 \\\\\n", + "SASDME4 & 1.41 & -- \\\\\n", + "SASDMZ9 & 14.65 & 14.62 \\\\\n", + "SASDP39 & 2.07 & 8.89 \\\\\n", + "SASDT75 & 1.78 & 1.65 \\\\\n", + "SASDT85 & 17.08 & 14.52 \\\\\n", + "SASDT95 & 2.11 & 1.96 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\end{table}\n", + "\n", + " sid chi2_grid chi2_martini\n", + "SASDA92 12.079575 11.965604\n", + "SASDAW3 7.488310 7.996393\n", + "SASDEM6 4.854856 5.844511\n", + "SASDF86 12.207094 5.537906\n", + "SASDJF5 5.508333 5.696015\n", + "SASDJG5 3.590875 3.203928\n", + "SASDJQ4 10.406385 10.100352\n", + "SASDJU5 3.705424 4.149262\n", + "SASDKG4 5.392569 5.618287\n", + "SASDL82 2.652590 2.520915\n", + "SASDMB5 6.346724 5.888946\n", + "SASDME4 1.414645 NaN\n", + "SASDMZ9 14.649196 14.624083\n", + "SASDP39 2.071191 8.894835\n", + "SASDT75 1.778664 1.647053\n", + "SASDT85 17.075303 14.523205\n", + "SASDT95 2.105401 1.959971\n" + ] + } + ], + "source": [ + "# Keep only the identifier and chi-squared values needed for the comparison table.\n", + "comparison_df = pd.DataFrame(\n", + " {\n", + " \"sid\": summary_df[\"sid\"],\n", + " \"chi2_grid\": summary_df[\"chi2_per_point\"],\n", + " \"chi2_martini\": summary_df_martini[\"chi2_per_point\"],\n", + " }\n", + ")\n", + "\n", + "# Optional derived metrics for follow-up analysis.\n", + "# comparison_df[\"difference\"] = comparison_df[\"chi2_grid\"] - comparison_df[\"chi2_martini\"]\n", + "# comparison_df[\"percent_change\"] = (\n", + "# comparison_df[\"difference\"] / comparison_df[\"chi2_grid\"] * 100\n", + "# ).round(2)\n", + "\n", + "latex_table = comparison_df.to_latex(\n", + " index=False,\n", + " float_format=\"%.2f\",\n", + " na_rep=\"--\",\n", + " caption=\"Comparison of chi2 per point (Grid vs Martini)\",\n", + " label=\"tab:chi2_comparison\",\n", + ")\n", + "\n", + "print(latex_table)\n", + "print(comparison_df.to_string(index=False))\n", + "# summary_df[\"sid\"]\n", + "# summary_df[\"chi2_per_point\"]\n", + "# summary_df_martini[\"chi2_per_point\"]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pripps-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pripps-py/examples/testing_single-bead_approximation.ipynb b/pripps-py/examples/testing_single-bead_approximation.ipynb new file mode 100644 index 0000000..1277db1 --- /dev/null +++ b/pripps-py/examples/testing_single-bead_approximation.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5e0868b6", + "metadata": {}, + "source": [ + "## Testing Pripps-py for Lysozyme" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "44b5339f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pripps\n", + "from scipy.optimize import minimize\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74a07d07", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "82ef40af", + "metadata": {}, + "outputs": [ + { + "ename": "OSError", + "evalue": "assets/poly_atev.yaml: No such file or directory (os error 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m pr \u001b[38;5;241m=\u001b[39m pripps\u001b[38;5;241m.\u001b[39mPripps(atomfile\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/Users/isabelvinterbladh/Documents/HALRIC/pripps/assets/poly_amino_acid.yaml\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mpr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultipole_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mstructure_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/Users/isabelvinterbladh/Documents/HALRIC/pripps/tests/lysozyme/6lyz.xyz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mqmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mqmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mnq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m196\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mexcluded\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpolynomial\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mvolume_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# fit knob c₁ (scales excluded volume)\u001b[39;49;00m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mhydration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msasa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# FoXS-style SASA-weighted hydration shell\u001b[39;49;00m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mprobe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.8\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# SASA probe radius (Å)\u001b[39;49;00m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mcontrast_density\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.03\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# fit knob c₂ (scales hydration)\u001b[39;49;00m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mbulk_electron_density\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.334\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# e/ų, pure water\u001b[39;49;00m\n\u001b[1;32m 13\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Returns a dict of numpy arrays\u001b[39;00m\n\u001b[1;32m 16\u001b[0m out \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mintensity(volume_scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m, contrast_density\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.03\u001b[39m)\n", + "\u001b[0;31mOSError\u001b[0m: assets/poly_atev.yaml: No such file or directory (os error 2)" + ] + } + ], + "source": [ + "pr = pripps.Pripps(atomfile=\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/assets/poly_amino_acid.yaml\")\n", + "model = pr.multipole_model(\n", + " structure_file=\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/tests/lysozyme/6lyz.xyz\",\n", + " qmin=0.0,\n", + " qmax=0.5,\n", + " nq=196,\n", + " excluded=\"per-kind\",\n", + " volume_scale=1.0, # fit knob c₁ (scales excluded volume)\n", + " hydration=\"sasa\", # FoXS-style SASA-weighted hydration shell\n", + " probe=1.8, # SASA probe radius (Å)\n", + " contrast_density=0.03, # fit knob c₂ (scales hydration)\n", + " bulk_electron_density=0.334, # e/ų, pure water\n", + ")\n", + "\n", + "# Returns a dict of numpy arrays\n", + "out = model.intensity(volume_scale=1.0, contrast_density=0.03)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2d2f1bec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c1=1.0304, c2=0.0734, χ²/N=0.2205\n" + ] + } + ], + "source": [ + "# experimental data: q (Å⁻¹), I(q), σ(q)\n", + "data = np.loadtxt(\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/tests/lysozyme/lyzexp.dat\", comments=\"#\")\n", + "q_exp, i_exp, sigma = data[:, 0], data[:, 1], data[:, 2]\n", + "\n", + "def chi2(params):\n", + " c1, c2 = params\n", + " out = model.intensity(volume_scale=c1, contrast_density=c2)\n", + " theory = np.interp(q_exp, out[\"q\"], out[\"total\"])\n", + " scale = np.sum(theory * i_exp / sigma**2) / np.sum((theory / sigma) ** 2)\n", + " return np.sum(((scale * theory - i_exp) / sigma) ** 2) / len(q_exp)\n", + "\n", + "result = minimize(\n", + " chi2,\n", + " x0=[1.0, 0.2],\n", + " bounds=[(0.95, 1.12), (-2.0, 4.0)],\n", + " method=\"L-BFGS-B\",\n", + ")\n", + "print(f\"c1={result.x[0]:.4f}, c2={result.x[1]:.4f}, χ²/N={result.fun:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d65ba913", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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TsgIzM0JERETZ7/rNjBARERGFLdYIERGRE4vFApPJxLNCIU3WFDQYDLf8OgyEiIjI3nslPj4eFy9e5BmhLCFfvnxqXcFb6fXHQIiIiBRbEFS4cGHkypWLjWQppIP2q1ev4syZM+px0aJFM/xaDISIiEgNh9mCoIIFC/KMUMjLmTOn+irBkPzeZnSYjMXSRERkrwmSTBBRVmH7fb2VmjYGQm6wjxARhSuuq0jh9vvKQMiN3r17Y/fu3di4ceMtn2AiIiIKXQyEiIiIAqRHjx7o1KkTz28IYyBERER+ZbFqWHfoHH7Z+q/6Ko8DHWzIEInr7d5770Wwffrpp5g6dSpCgZyTuXPnBvswQg5njQWDpgFXzgKGCMAYBRgiAf2tN4UiIgq2RTtP4d1fd+NUwnX7tqIxOTCkfVXce3vGpzinR4KeKVOmOG2LiopCMGfhSeAhyzxQaGNGKBgsycCoCsCI0sAHccB7BYB3CwAfFAVGVwEmNgW+aQ/M6gEseA34czyw51fg1HbgekJQDpmIyJsg6IVpm52CIBGfcF1tl+cDRYIeaazneMufPz9WrVqFyMhI/PHHH/Z9R44cqaZbnz59Wj1u1qwZ+vTpo24SuMTGxuLtt99WvWpskpKSMHDgQBQvXhy5c+dGgwYN1GvbSNZHmvvNmzdPLdotx3Ps2LFUQ2Pys/r27Yv+/fur4ytSpAgmTZqEK1euoGfPnsibNy8qVKiAhQsXOr2/nTt34r777kOePHnU93Tv3h1nz551et2XXnoJr732GgoUKKDe/9ChQ+3PlylTRn194IEHVIBme0wMhIIXCLnSLIDpKnDpJHB6B3B4NbDrZ2DDF8CSN4GZ3YAv7gQ+KgWMqgR89wCw5G1g20zg9C7AaslSqWwiyl7k3wzJBLn7l8O2TZ7P7H9bJECQoEMCB1mAc8uWLSrI+eqrr1RAYfPNN9/AaDRiw4YNajjrk08+UfvYSJC0bt06/PDDD9i+fTsefvhhlYU6cOCAfR9p8DdixAj1fbt27VLBljvysyTYkp8lQdELL7ygXq9x48bYvHkz7rnnHnW88npC+ju1aNECtWvXxt9//41FixapIO6RRx5J9boSpP31118q2HvvvfewdOlS9Zxt8o9kzU6dOsXJQA64+nywVp+XvzQsJsCSlPLVnASYrwPXLwJXzwFXz6d8vRQPXDwKXDia8lW2uROZFyhZDyjVCCjZAChxBxCZO6RT2UQUOq5fv47Dhw+jbNmyyJEjh8/fL39AdZm0Pt39ZjzTEI3K+7dho2Rdpk2bluq433jjDXVLTk5WGZxKlSqpzEqTJk3w5ZdfOgVL0pRPghfbdOzBgwer7I7MIJbMTrly5dTXYsWK2b+vVatWqF+/Pj788EOVEZKMztatW1GzZk2nY5NAxlabIz9Lhs1sGSq5L9eZBx98EN9++629w7d0SpbAq2HDhnj//ffV/osXL7a/7okTJ1CyZEns27dPvS/X1xVybBJAffTRR+qxvLeff/45WxVvX/fwe+vL9Zs1QsEi/7MZI1Nuvki6BPy3Dzi9E4jfmZINit8BJF8CDq1IuQmpO5KgqGJroEJroFDllJ/pIZXt+jeaLZU9sVsdBkNElK4zl677dT9fNW/eHBMnTnTaJkNEQobGvv/+e9SoUQOlS5fGmDFjUn2/BByOPWkaNWqE0aNHq+Bix44d6qsEHI5kuMyxC7f8HPkZ6XHcR7ohy2tUr17dvs2WqbItH7Ft2zasXLlSDYu5OnTokP24XH+2BFO21yDPGAhlNVF5U7I9crORYbEzu4Fj62/eEk8Ah39PuS15C4gpCVS+D6j2AFCyIaDXe5XKHjpvF1pXjYNBf+tNq4go+yqcN4df9/OVDAlJbY0nf/75p/p6/vx5dZP9vXX58mUVsGzatCnVMg6OwYks+eBNgz9ZNd2RfI/jNttrWK1W+89v3769GnZz5bjGlrvXtb0GecZAyENnabnJXwBZgsw4i6uecqv/TMqw27lDwMGlwIGlwJE1QMJxYMOXKbe8RYGqnbA7XwucSpB6Jc//48YnJqH395tQr0wBFMgThbjoHKhftgADIyJyIv8uyJC6ZJPd/XEl/8rExaT8+5HZJGsyYMAAVZQ8c+ZMPPnkk1i2bBn0+pvzhaSuxtH69etRsWJFFfhIbY5cDyS7cuedd2b68depUwezZ89WBc5Sx5RREihlmetaJuKssezYWVr+moitADR8Aeg+Bxh0BOgyE6jZBYiKBi6dAv6aiOqLH8byyIF41vArYuF5NtqiXacxbP4eDJi5VdUANB2xIqCzP4go65GssdQVws2fVrbH8nygsssyTCW1NY43mVUlF/5u3bqhTZs2qoZHioWl2FmGvRxJ/c/LL7+sam5mzJiBcePGoV+/fuo5GXp6/PHH8cQTT2DOnDmqJkUKnYcPH4758+cjM65JksXq0qWLui5JYCf1QvJ+fAlsJJBavny5OjcXLlwI6DFnJQyEwkFkLqDyvcADnwOvHgS6/ADUeBQWYy6U15/CGxEzsC6qDz6PGIO79dugQ9qp1FOZMBWWiLIemVwhdYWS+XEkjwNdbygzqWSYyPHWtGlTfPDBBzh69Ci++OILtZ9sl0Lpt956S9Xe2EiQc+3aNVVgLIGHBEHPPvus/XkJoGSfV155BZUrV1YFxxKUlCpVCoEmBdpr165VQY/MKJN6IpkJJ9P1HbNa6ZHgT2aRSZG1ZLkoBWeNBWvWWAiwXEvEiNEf4t7kpaijP2jffsBaHFMs92KOpSmuw3NDsrjoKKwd3JLDZETZwK3OGnMk9YcbDp9XhdFSExTqw+ky46pWrVoYO3ZssA+FgjBrjBmhMGbIGY06nfrhweT30DppJCab70WilhMV9f/iw4iv8WdUX7xi/NHjsJnUDw36aTt7DhGR878tep2aIt+xVnH1NZSDICIGQmFOUtUDWlXEAa0E3jM/gcZJ4/CeqTuOWQuhgO4y+hrn4o+ofnjb+B0KIfWY8k+bT6Du+0s5TEZERFkSh8bCeGjMMY3d5KMViE+82d9DDyta6//G88bfUPvGsFmSFoHplhb43Nwep5F65kfPxqVxT7WiIZ8GJ6LADo0RZRYOjZFfSNAytENVp5keVuix2FofDyS/i+7Jg/G3tRKidCb0NC7G6qgBGGycjmhcdnqdKX8eVbPK6g5bik+X7eeQGRERhTwOjZHTbA/pA+JMhz+sNfBQ8hB0TX4Df1lvUwGRZIp+j3oZvQwLEAmT03dcvGbCmGUHUH3oYgZEREQU0jg0loZwGRpzN9tj6e54zN16EuevuC4Qq6GZfiteN85AZf0JtUXqiUaYu2C+tYHb5oz5ckXgowerc6kOohDGoTEK16ExBkJpCMdAyDUoWn/oHF6cvhkJ15yzPgZY0NmwGq8YZ6GI7qLattZSDe+Ye+CQVtzt633WtTba1ri5YCERhQ4GQpQVsUYoQGR5japVq6JevXoI99qhJhVjMaLzzcUAbSww4EdLczRL+gRjTJ1xXYtAE8MuLIocjMHGGciF1Asr9pmxBQu2swkjERGFDmaE0hDuGSFH0kV68JwduHjVOTNkU0J3BkOM36G1YZN6fEorgHdMPbDU6rA47A2fc0V7opDDjBBlRcwIUaYWU296qzX6tazodonWE1phPGN6BU8lD1Q1Q0V15zEp8hP8L2IcCiDRad83ft6B2ZtO4Os//sHPW/7FukPnOMOMiIJm6tSparmKrGDo0KGqC7YvZBX6uXPnIlS6ePfv3z+kPhvOGiOfhsoGtK6ECV3reNxnhbUOWid/jM/MHWDRdOhgWIelUa/ifv06VWgtzl8x4ZVZ27iQKxH5RY8ePdTFXm6RkZGoUKEC3nvvPZjN5mx3hgcOHKgWTiX/YSBEPmtbo6ga3ko91T5FEiIx0vwYOiUPwx5rSRTUXcL4yHH4ImJMquyQDRdyJaJbce+99+LUqVM4cOCAWhhVMicff/xxtjupefLkQcGCBYN9GNkKAyHK8FDZmkEtMOOZhhjzSE08VKc4ckUanPbZoZVDh+QPMNb8IEyaAW0Mf2Nx1CC1wr07ki8aOm8Xh8mIyGdRUVGIi4tD6dKl8cILL6BVq1aYN2+eeu7ChQtq5fj8+fMjV65cuO+++1TA5M6RI0fUiu5///2303ZZkFVe22q1YtWqVSr7JJmZO+64Q71m48aNsW/fPqfvmThxIsqXL6+yVLJi/Xfffef0vLzGF198gfvvv1+9RpUqVbBu3TocPHhQDSHlzp1bve6hQ4c8Do1t3LgRrVu3RmxsrKppvfvuu7F582afzl2zZs3Qt29fNWQl56hIkSKYNGkSrly5gp49eyJv3rwqy7Zw4UKn7/v9999Rv359de6LFi2KwYMHO2Xh5PvlvEvwJs+PHj061c9OSkpSWa7ixYur99ugQQN1fjMTAyG65YUVH6hTAqMeqYUdQ9ukqiEywYix5ofQIfl97LcWRyFdAr6JHIEhxm8QhWS3C7mOX5GypAcRBZmmAclXgnOTn30LcubMieTkZPvQmQQ2EhhJoKFpGtq2bQuTKfXkjzJlyqggasqUKU7b5bG8jgRJNm+++aa6uMtrG41GPPXUU/bnfv75Z/Tr109lp3bu3InnnntOBRUrV650et1hw4apYGHr1q247bbb0LVrV7Xv66+/rl5XjrVPnz4e3+elS5fw5JNPYs2aNVi/fj0qVqyo3pts98U333yjgqkNGzaooEiCyYcfflgFYhJY3XPPPejevTuuXr2q9v/333/Vz5HZ1du2bVNB39dff43333/f/pqvvvqqCpZ++eUXLFmyRAU4rkGavDf5TH744Qds375d/UzJ7nkKVAOBs8bSwFljGSNT5KX3kCsJfF43TkcP4xL1eK+1JPqZemOfVirVvuw5RBQCs28kIPkwSL2/3jgJROb2alcJUC5evKgKgiVwkEyNZFnkgv7ss8+iUqVKWLt2rbqoi3PnzqFkyZLq4i8XXinIlWyIvIb48ccf8fzzz6uhNsl2yMVbMj///POPCpTkgt68eXMsW7YMLVu2VN+zYMECtGvXDteuXVPnr0mTJqhWrRq+/PJL+3E+8sgjKksyf/58e0borbfeUsGQkECmUaNGKqCwBVUSIEgAJa9rywjJ+5TAyR3JWElx8fTp09U5sP0cCcw6derkMSNksVjwxx9/qMdyX7JLDz74IL799lu1LT4+XmV1JGhp2LChCgJnz56NPXv2qNcXn332GQYNGqRmWkvAJEN406ZNU+dYnD9/HiVKlFCfiWTYjh07hnLlyqmvxYrd/D2TQFQyTR9++GGqz8YVZ41RyNYQ9WpSxm3t0FBzD/RIfhX/adG4TX8ccyPfQWf96lT79p6+BWOXcr0yIvLOb7/9poZgJAiRoa9HH31UBQ1yoZZsjQy52MgFWoaq5Dl3JGAwGAwqeBByMZbAR4IgRzVq1LDflyBBnDlzRn2V15ZgyJE8dv2Zjq8hQ1KievXqTtvkYi9/mLtz+vRpPPPMMyoTJMGLtHq5fPmyCi58UcPhOOS9yzlyPQ7X9ydBmy0Isr0/+dknTpxQw3mSkXM87wUKFFDn3WbHjh0q6JJAVT47202ySI7DgYFmzLSfRGGlVdU4fL32iNvnVllr496kEfgkYiLuNmzH6MjPcYd5H4aan1TBkpCk+NjlB/DlH//gubvKoU+LilzRniizReRKycwE62f7QAIVGZ6RehzJLkjwk1HyGjJcJcNhkhWR7Mqnn36a+hAjIuz3bQGBZGR84e41fHldGRaTDJccn9QwSQZLAhTbsGBGjsP2c/3x/tIiQZMEXZs2bVJfHUlAlFlYI0QBUb9sATWrzF3PIXEOMehheg2jTQ/BqunQxbgScyKHoJTutNN+V5MtagHXuu8vVU0diSgTycVPhqeCcXPINHhDCm2loLdUqVJOQZAUIEsB719//WXfJoGDFDbLCgKePP3002roS4Z75PslIPKF/FwZjnMkj9P6mRkhr/nSSy+peh0ZipNA6OzZswi0KjcKu2Uo0vFYpLBahr+kSFwCKcfzLkXr+/fvtz+uXbu2yghJlkk+O8ebFL5nFgZCFLBC6iHtU/6H9/TPmQY9xlkeRHfTYJzVolFNfxS/Rb6BFvrU9UXS0fqFaZsZDBGRT2TIqGPHjmr4SAqKpbC3W7duapaSbE/rQi+1MFLz0qVLF1V87QspFJYhNclSSeHvJ598gjlz5qgZUv5+fzIbTYaqJOh4/PHHfT7WjHjxxRdx/PhxVYe1d+9eVRA9ZMgQvPzyy6qgXDI6vXr1UudhxYoVqmDctdhchsTkeCX7JudGatSkWHv48OH2OqrMwECIAjrFfmK3Oohz6TeUP5fRaar9Wmt1tEv6EButlRCtu4avIkbjeYNMe3WeNcLp9USUETLEVbduXVU8LMNGksWQ4mbX4SBXciGXISbH2WDekjojGa4aNWqUytTINHk5DilM9icprJZMS506ddSsLskOFS5cGIFWvHhxdQ4lcKlZs6YqLpfzJcXfNtLH6c4770T79u1VAXTTpk3V5+BIzokEQjK7TuqH5LxJSwDJ7GUWzhpLA2eN+W8V+w2Hz+PMpesonDeHGjZbujsez09zzvwYYcZQ4zfoZkzpmjrX0hiDTM/a64ZsBrSqhH6tKvrp6IhIcK2x1GQ216xZs9S0bgpNnDUWIFx9PjD9hjrWKq6+ymPJFskUeb3DuJkZRrxl7oW3TD1VA8ZOhj/xY+R7KILzTq83Ztl+LNgepAJOIsr2pIhXhnLGjx+vhn4oe+PQmBu9e/fG7t27VXqOAqdtjWIY3yX1umXTLK3xhGkwzmt5UFP/D+ZFvYVqOucZaH1mbFH9ioiI/E2a/MkQjgxjZWRYjLIWBkIUEuuW5cvlPFa/zloNHZOHYZ+1BIroLmJm5HtOS3NYNaimjZxJRkT+JkXOsvTDzJkzU03rpuyHgRAFnQyTbXqrtar9cSyiPq4VwcPJQ7DWUg15dNfxdcTHeMTg3J6ea5MREdGtYCBEIUHqhqQAWtYre6hOCfv2RORGD9MgzLY0hVFnxciISRhgnGWfUSZrkw36aTsXaiXyE8e+METh8PvKQIhCLiAa8VANxEXncFq49RXTC/jU/IB63M/4M0Yav4QBFvX4p80n2HCR6BbZppLbFtUkygpsv6/ptUJIC6fPp4HT54NHan9cp9eLRw0r8YHxa5UdWmiph36mPkjGzf8BBrSqyOU4iDJIFhmVxS2lD02uXLmc1pEiCrVMkARB0pVaFpm1rfWWkes3A6E0MBAKLpkiL7PDpDDa0T36jRgXMQ5ROjP+sNyO50wv4ypuZpAkmzS0Q1VVe0REvl1cZJVxTyt9E4UaCYJkOQ7XoJ2BkJ8wEAo+mSIvs8NcNdLvwlcRo5Bbl4TN1gromfwaEnBzkT75X0K6WjMYIvKdrP9kMpl46iikyXCYp1l9DIT8hIFQaPh02X618KqrmrqDmBo5Evl1l7HXWhLdk1/Hf8hnfz4uOgprB7fkqvVERGEm0YehMRZLU8jr06KiU/G0zTatAh5JfgfxWn7cpj+OGZHvoxAu2J+XGWXjVxzM5KMlIqKshIEQZYmZZFLz484BrYQKhv7VCqKC/iR+iHwfhR2CIS7HQUREaWEgRFmC1Pq460AtjmlF8FjyWzihxaK8/hR+iBzmtD4Zl+MgIiJPGAhRluxAHZPD6PScdKF+LPltFQyV08erYCgO55yW4+BCrURE5IqBEGXJDtSb37lHBUSOTmiF8GjS2zhuLYSy+tOphsmYGSIiIlcMhChLB0TSQNHRvyiER5PfxjFrIZTRn8a0yA9RAInqOWaGiIjIFQMhynYzyk4iFl1Nb+KUVgCV9P/i28iPEI0rN79nxhbVn4iIiIiBEGXLGWUntMJ4PPkN/KdF43b9EUyNHIFcuK6eY2aIiIhsGAi5MWHCBFStWhX16tVz9zSFYBH1Z11rQ++yLNI/WjF0T34DF7XcqKM/qDpRRyHZ/jwzQ0RExLXG0sDO0tljOY4aukP4PvJD5NVdw1JLHTxvGgALbrZllyCqbY1imXy0REQUKOwsTWGpbQ33maHtWnn0Sh6I61oEWhs2q9XrgZsruTIzREQUvjg0RtmKZHbGd6mTavsGrQr6mvrCounwmHEVXjHOsj9nqxlatJMF1ERE4YaBEIVNZmip9Q68ae6l7vc1zsUThsVOz7/7625YJCoiIqKwwUCIwioz9IOlBUabHlL3hxq/RTv9evtzpxKuY+rawwyGiIjCCAMhCrvM0DjLA/jW3Bp6nYZPIj5Dfd0e+3PD5u9B0xErOExGRBQmGAhRGGaGdBhqfhILLfUQpTPji8gxKKs75ZQZemEaa4aIiMIBAyEKy8yQFXoMML2IrdbyyK+7jMkRI5H/xlIcNqwZIiLK/hgIUdhmhq4jCk8nD1Qr1ssirZIZioRJPafdyAx9smQf1h06x7ohIqJsioEQhVVmqFeTMk7bziIGPZJfQ6KWC/X1+zAy4gunHkMTVh1Cl0nrWTdERJRNMRCisNKqalyqbQe1EnjB1A8mzYBOhj8xwDg71T6sGyIiyp4YCFFYqV+2AIrGOK9WL9Zaq+NN81Pqfj/jHLR1mFZvI3miofN2cZiMiCgbYSBEYbda/ZD2VeEyo1750dIcX5nvU/dHRXyBqrojqfaJT0zC+BUHM+FIiYgoMzAQorBcrX5itzpuM0PDzV2x2lIduXRJ+DLyExRwmUkmxizbzz5DRETZBAMhCttgaM2gFpjxTEP0blbevl1Wpe9j6ovD1iIooTuLiZFjEQFzqu/n1HoiouyBgRCF9TBZo/IF8fI9lZ2yQ4nIg6dNA3FJy4kG+r0YYvzGbfH0hsPnM/mIiYjI3xgIUdhzVzd0SCuOfqbesGo6dDMux2OGFanO09Ld8WF/7oiIsjoGQkQe6oZWWOtglPkRdf9d41TU0B1yOleT1x5hrRARURbHQIjIpW7o+14NEJMzQm37zNIBSyx11Zpkn0V+6rQMh2SQWCtERJS1MRAichkma1IxFiM6V7+xRYdXTC/Yi6c/jZgAPaxOy3CwVoiIKOtiIETkITtkW47jEnLhedMAXNMicZdhB/q5dJ4+c+k6zyERURbFQIjIi+U49mmlMNj0tLrfz/gzWug32587cvYqzyERURbFQIgoneU4bLPJfrE2xVTzPer+mIjPUEL3X8r9ZfuxYPtJnkcioiyIgRBROtPqb65FD3xg7obN1gqI0V3FuIhx9maLvadvwdil+7kOGRFRFsNAiCidWqEBrSraH5tgxEumvkjQcqG2/iAGGmeq7RIsjV1+AHXfX8op9UREWQgDITcmTJiAqlWrol69epn/iVDIKROb2+nxCa0QXjU9p+4/Z5yP5vot9ucuXjXhhWmbGQwREWURDITc6N27N3bv3o2NGzdm/idCIadw3tSLsy6x1sMUcxt1f3TERMThnP05yQ6xvxARUdbAQIjIy6JpdyvV77CWQQHdZfwvcjwMsNifY38hIqKsgYEQkZdF066SEYE+ppfU4qz19fvQn/2FiIiyHAZCRF4WTX/WtTb0jiuzAjiqxeENUy91v7fhF9TT7bU/98f+s5xFRkQU4hgIEXmpbY1iGN+lTqrtv1ob4yfLXdDrNIyJ/AzRuKK2/7T5BGeRERGFOAZCRD5oW6MoPu9WB/lypSzKajPU9ASOWgur9cjei5hi385ZZEREoY2BEFEGhsk2vdUaD9UpYd92GbkwwPQizJoenQx/oqN+jf05ziIjIgpdDISIMlhAfWelWKdtm7VK+J/5QXV/WMQUlNCdsT/HWWRERKGJgRCRH/sLTbB0xN/WSojWXVPrkTlOqecq9UREoYeBEJEf+wtZYEB/04tqSn09/X48Z/jN/hxXqSciCj0MhIj83F/ohFYYQ01Pqvv9jT/hNt0xdX/ssv1ceoOIKMQwECK6xcJpd7PIZlvvxBJLXUTqLPgkYqJapV6Kpl/9aRvmbDqBdYfOsccQEVEI0GmaJv8+kxuJiYmIiYlBQkICoqOjeY7II4tVw6CftqveQTaxSMCSqFfVEhzjzJ0w2vyI0/fIsJpklCSYIiKi4Fy/mREiCtAssrOIwZs3uk6/aPgFtXQHnZ6XmWRcqZ6IKLgYCBEFcBbZQmsDzLU0hkGnqVXqo5Ccah+uVE9EFDwMhIj8PIvMZTkyDDH1wGktH8rrT+E140yn52Rcmj2GiIiCh4EQUYBnkSUgDwaZnlH3exoWoa5uX6p92GOIiCg4GAgR+ZEUPk/sVgcFcjvPIltlrY1Z5pSFWUdGfJlqiMzdsBoREQUeAyGiAARD619vhQK5I522DzN3w5kbQ2QvGefc/J9QB1y4krp2iIiIAo+BEFEARBr1+PCB253qhRKRB2+beqr70nG6mu6wum/VgBenb8aC7Sf5WRARZTIGQkQBHiZzXIZjsbUefrM0gFFnVUNkRpjtz/WZsQULtp/i50FElIkYCBEFOBhaM6gF3m5Xxb5tqKkHzmt5UE1/1GktMltmaNFOBkNERJmFgRBRJswmi80b5dRo8V3TE+q+1ApV0N3sRi2GztvF5TeIiDIJAyGiTOA6K+wXaxMss9RGlM6M4RFfQQer/bn4xCSMX+HchZqIiAKDgRBRJjZbvEmHt01P4YoWhXr6/XjMsNJp/zFcqZ6IKFMwECIKUrPFUyhoX4h1sHEGCuGi0/McIiMiCjwGQkSZWDj9Wdfaqm+QzVRLG2y3lkWM7irejvjOaX8OkRERBR4DIaJM1LZGMYzvUsf+2Ao9Xjc9DYumQwfDOtyt3+a0P4fIiIgCi4EQUSZrW6MoBrSqaH+8SyuLKZZ71f33jZORA0lO+3N1eiKiwGEgRBQEfVpURFz0zeLpT8wP41+tIErq/0N/h+U3BFenJyIKHAZCREEqnh7a4Wbx9FXkwDumHur+04b5qKw75rT/0t3xmX6MREThgIEQURCLpx2HyJZb62KRpZ5afuO9iKkANPtzk9ceYcdpIqIAYCBEFEJDZO+ZuuOqFoUG+r14QL/Gvl0mmrFWiIjI/xgIEYXQENlJxGKc+QF1/42I7xGNK+q+5IZYK0RE5H8MhIhCYIisV5My9sdfWdrikLUoCukSMcD4k9O+Zy5dD8IREhFlXwyEiEJAq6px9vsmGPG2uae6/4RhCarpjtifO3L2alCOj4gou8r2gdBvv/2GypUro2LFivjqq6+CfThEaa5FZms6/af1dvxqaQiDTsN7EVPsi7JKg8UF20/yLBIR+Um2DoTMZjNefvllrFixAlu2bMHHH3+Mc+fOBfuwiDyuRXZznhjwvqkbLms5UFd/AA8ZVtu395mxBQu2n+JZJCLyg2wdCG3YsAHVqlVD8eLFkSdPHtx3331YsmRJsA+LyKvp9KdRAGPNndX9QcYfkBcpw2JWDXhx+mZOpyciyu6B0OrVq9G+fXsUK1YMOp0Oc+fOTbXPhAkTUKZMGeTIkQMNGjRQwY/NyZMnVRBkI/f//fffTDt+Il+Vic3t9FgWZT1oLYZYXSL6Gn92eu6Nn3fg5y3/Yt2hc7BIdERERNkrELpy5Qpq1qypgh13Zs6cqYa+hgwZgs2bN6t927RpgzNnzmT6sRL5Q+G8N3sKCTOMGGburu73NCxCWd3NIbHzV0wYMHMrukxaj6YjVjBDRESU3QIhGcp6//338cADKX1VXH3yySd45pln0LNnT1StWhWff/45cuXKhcmTJ6vnJZPkmAGS+7LNk6SkJCQmJjrdiIJRNO3od2tNLLfURoTOgjeN09x+X3zCdbwwjcNlRETZKhBKS3JyMjZt2oRWrVrZt+n1evV43bp16nH9+vWxc+dOFQBdvnwZCxcuVBkjT4YPH46YmBj7rWTJkpnyXohci6ZdfWB+HCbNgFaGLbhbvy3V87aBMXafJiIKk0Do7NmzsFgsKFKkiNN2eRwfn7JApdFoxOjRo9G8eXPUqlULr7zyCgoWLOjxNV9//XUkJCTYb8ePHw/4+yByVzT9Wdfa0Nvm0gP4Ryum6oXE28bvYIQ51fex+zQRke+MyOY6dOigbt6IiopSN6Jga1ujGMZDp2aH2cjSGw8Y1qCC/iS6G5ZiiuU+t9/L7tNERGGQEYqNjYXBYMDp06edtsvjuLibXXqJsqq2NYri82517DVDiciN0eaH1f3+xtnIj0SvCq6JiCgbBkKRkZGoW7culi9fbt9mtVrV40aNGgX12Ij8OUy2ZlALDGhVCTE5jJhpaY7d1tKI0V3Fyy7rkIm46ChVcE1ERNkgEJIC561bt6qbOHz4sLp/7Ngx9Vimzk+aNAnffPMN9uzZgxdeeEFNuZdZZETZxdLd8Ri7bD8SrpthhR7vmp5Q27saluM2Xcr/CzbXzVa1PxERZYNA6O+//0bt2rXVzRb4yP133nlHPX700UcxatQo9ViKoSVIWrRoUaoCaqKsSholykwwx3aJf2lVMN9SX61D9o7xW4c5Y0DCVROn0RMR+UCnaRpb0rqQBo5yk1lp+/fvVzPIoqOjfTmvRH4hXaOlYaKrErr/sDxyIKJ0JjyX3B+LrfXtz8lks7iYHGpITabjExGFm8TERNUGx5vrd0hnhIKld+/e2L17NzZu3BjsQ6Ew52kG2AmtEL6wtFP3XzfOQITDdHpOoyci8h4DIaIQltYMsM/NHfCfFoMy+tOqXsgVp9ETEaWPgRBRFlhyw90A11XkwBjzQ+p+P+Ns++r0NpxGT0SUPgZCRFlkyQ13wdBMSzO1On0B3WU8b5xn3y6lQReuJGfikRIRZU0MhIiyQC+hid3qqAJoVxYY8JG5i7rfy7AQRXFO3bdqQO/pXISViCg9nDXmp6pzosyYSr/h8HlV+yPDXucuJeGlmVtg1TTMjByGBvq9mGW+C6+an1f7c/YYEYWrRM4auzUydb5q1aqoV6+enz4SIv8MkzUqXxAdaxVXXwvmjVKZHwl5hpu6qn06G/6wN1nk7DEiovRxaMwNTp+nrMBxVthWrQJ+szSEXqfhdeN0j/sREZEzBkJEWZTrrLCR5keRrBlwt2E7mup3eNyPiIhuYiBElE2m1h/TimCapbW6L1khHazIlzNC1RBJfREREaXGQIgoG02tH2fuhEQtJ6rpj6KTfi0uXjPh8a/+QtMRK7Bo56mgHi8RUShiIESUjabWX0A0Jpo7qvsDI35EFFJ6CZ1KuI7np23Ggu0ng3q8REShhoEQUTYIhmSB1X4tK6rM0GTLvTipFUBx3Tn0MCx22rfPjC1YsJ2ZISIiGwZCRNnA0t3x+HT5ATVlPgmRGG16RG1/wTjPaekNKRV6kY0WiYjsGAgRZXFSCP3ur7udtv1sbYoD1uLIp7uCp43zU32P7M8CaiIiBkJusaEiZSXSbVpqgBxZocco88Pq/tOGBSiIBKfnZX/5PiKicMeMkBtsqEhZiaeGiYut9bDNWg65dUl40WFB1vS+j4gonDAQIsriPDdM1OFj86PqXjfDUhTDWadnj5y9WTtERBSuGAgRZbPGio7WWG/HOktVROnM6Gv82em5scv2s7cQEYU9BkJE2bCxonNWKGUG2cOG31FWd3PqvMwwGzpvF4umiSisMRAiyoaNFR1t1iphmaU2jDorBhh/cnouPjEJ41cczMQjJSIKLTpN07gIkQeJiYmIiYlBQkICoqOjM/eTIcoAmRIvs8GkEPrA6csYvzIlyKmiO4qFUa+r+22TPsRurYzT933WtTba1ijGc05EYXf9ZkaIKJsNkzUqXxAdaxVHkwqx9u17tNL4xdJY3X/FOCvV97HjNBGFKwZCRNm8iNpmjLkzzJoeLQ1bUFe3z2lfdpwmonDFQMgNNlSk7FZELY5oRfGj5W51/7WImTfKpZ2x4zQRhRsGQm6woSJlpyLqAa0q2h+PMz+IJC0CDfR7cZd+e6r92XGaiMINAyGibK5Pi4qIi04ZIjuFgvjO0krdf1nVCqXOCrHjNBGFEwZCRGEwRDa0w80hsonmDriqRaGW/h80129Ntb/MNlt36Bz7CxFRWGAgRBQmQ2QyRV6vA84hBt9aWqvt/Y2zU2WFZMp9l0nr0XTECnaeJqJsj4EQUZiQPkHju9RR9780348rWhRq6v9BS/1mt/vHJ1zHC9M2MxgiomyNgRBRGGlbIyUzdFEXjW8t93jMCsFhC2eSEVF2xkCIKMzkzx2l+gZ9aW6Hy1oOVNcfQWv9Jrf7SjDEmWRElJ0xECIKM7ZZYRcQjW/SyQrZLN0dn2nHR0SUmRgIEYWZwnlvdpueZG6HS1pOVNMfRRv93x6/Z/LaI6wVIqJsiYEQUZguvaEDcBF5MdXSxp4V0sHq9ntkX9YKEVF2xECIKIyX3pAA5ytzWyRqOVFFf8xjVoi1QkSUXRkz8k2HDx/GH3/8gaNHj+Lq1asoVKgQateujUaNGiFHjptp96y81pjcLBZLsA+FKGB9hSZ2q6OyPKcSgCmWe9HP+LPKCi1OvgOah7+R2HWaiLIbnaZpniskXXz//ff49NNP8ffff6NIkSIoVqwYcubMifPnz+PQoUMqCHr88ccxaNAglC5dGlldYmIiYmJikJCQgOjo6GAfDpHfWawaNhw+j7/3HsKTGzoiWncVvZNfwnxrQ7f7z3imIRqVL8hPgoiyzfXb66Exyfj873//Q48ePVQm6NSpU9i0aRPWrFmD3bt3qx/6yy+/wGq14o477sCsWbKOERGF+jCZBDYv3lcPPxrvV9v6GWdD71IrJENoUlck9UVERGGZEVq8eDHatEkpqkzPuXPncOTIEdStWxdZGTNCFE6Wbd6H+r80U1mhvsl98Ku1sT0IEjKUJkNqRERhmRHyNggSBQsWzPJBEFG4aVWnMk5X66Xu9zHOtc8gi4vJwSCIiLItY0YjLW+xtoYo66jYfiC0Q9+gctIJzLzrHCyV26vhMBlCc60rksJp6Unk+jwRUbYPhPLlywedLu1/+GTETfbhzCuiLCRnPugaPAes/hj1j30N3PckcOP/dQmAxq84iClrD+PiNZP9W6R2SKbjc9iMiMImEJoyZQoGDx6sCqdlyrxYt24dvvnmGwwfPhxlypTx93ESUWZp+CKwfiIQvx3YvxiofK/qKj14zg5cvHozAHJdpZ41RESU7afP27Rs2RJPP/00unTp4rR9+vTp+PLLL7Fq1SpkByyWprC19B1g7ae4VLAmxpT+DJP/PJrm7robtURrBrXgMBkRZc9iaUeS/ZEp8q5k24YNGzLykkQUQlbkfwTXEYm857bhwPpf092fnaeJKKvKUCBUsmRJTJo0KdX2r776Sj1HRFmXDIP1+ukopptbqMd9jT+nuTK9I3aeJqKwqBEaM2YMOnfujIULF6JBgwZqm2SCDhw4gNmzZ/v7GIkok0hBtCy7IWHPF+b78bhhGerr96Ghfg/WW1PWJ/N2ZXsiomybEWrbtq0Kejp06KCW15Bb+/btsX//fvUcEWVNMi3+VMJ1df80CuBHSzN1v69BskJpY+dpIgqbjJAoUaIEPvjgA/8eDREFlevQ1ufm9njMsBJNDLtQx7wfm7VKHoulZQo9+wkRUbbNCB07dsynF/7333+RVcnK81WrVkW9evWCfShEmcp1aOtfFMJsy53q/kuqVii1/LkiOHWeiLJ/ICRBwXPPPYeNGzd63EemqUkR9e23356la4V69+6tFpJN670SZUfSJVqGuBzbpX5m6QizpkczwzbU0B2yb8+XMwIDWlXE32+1ZjNFIsr+Q2MSGMhQWOvWrZEjRw61llixYsXU/QsXLqjnd+3ahTp16mDkyJGsFSLKgmRoS4a4pEGiBENSNH1MK4JfrI3R2bAGfY1zsb7+OLSqGselNYgoPBsqXrt2DfPnz8eaNWtw9OhR9Tg2Nha1a9dWC7NKNii7YENFCucp9DJ7zFY4XV73L5ZGvQa9hEbPrwHiqgf7EImI/HL9zlBn6XDBQIjCmeviqg02D4R+1xygakfgkW+DfXhERH65fvs0a+zBBx/0ar85c+b48rJEFKLDZI3KF7y5Ic9AQAKh3fOAM3uBwrcF8/CIiPzCp0BIoisiCtfMUD40LXkPCh5fAvwxGuicurs8EVFWw6GxNHBojMKda61QNd1hzI96Exr00PX9GyhYPtiHSESU+YuuElF4BEEye8wWBIldWlkst9SGDlac+PX9oB4fEZE/MBAiojTXHHM1zvyA+hp35BdYzh9x+p51h87hl63/qq/ymIgo2y6xQUThseaYq61aBfxhuR13GnYiftFIxHX9LNUQmpDGjNKT6N7bi2bikRMR+YYZISJKd80xV+NvZIViD/yIT2avwvMuQ2giPuG6GlqTIImIKFQxECKidNccc/WXdhs2WCvDqJkQveVzt/vYBsYkU8RhMiIKVQyEiMirNcec6TDe3Ende9ywHAWQ6DEYkkyRDLUREYUiBkJE5HHNMeEpGFptrYFt1nLIqUtGL+OCNM9ifMI19ZUF1UQUathHKA3sI0Thzl0RtKPW+r8xKfITXNJyoknSp0hEHrf7FcgdiYfrFse8badYUE1EAce1xoJwIonCYc2xA6cvY/zKg/bnpJ/QgsjXUUV/HJ+YHsL/LN4tw3Pz+1NM7FaHs8uIyG/YUJGI/L7mWMdaxdGkQqzTc9Jh2jaD7CnjQuRGyhCYt1hQTUTBxhohNyZMmICqVauiXr16mf+JEGWxIuqF1vo4ZC2KfLor6GZY5vNrsqCaiIKJgZAbvXv3xu7du7Fx48bM/0SIskgRtY0Vekwwd1T3nzbORw4kBaR3ERFRIDAQIiKfSKfoCV1rQ++QFppnbYxj1kIopEvEY4aVAeldREQUCAyEiMhn+XNHwXEpMTOMmGjpoO4/Z/wNkTB5/VoST8lwmwy7ERFlNgZCROSXYazZlrtwSiuAorrz6GxY7dXr2JJKMtwmw25ERJmNgRAR+WUYKxkR+MJ8v7r/omEejDDbn5OMz3N3lVVfHcXF5ODUeSIKKq4+T0QZnj0mC6s6jJDhB0tz9DbORUn9f+iScwPqdHgRcdEpw16S8Xnt3ir2nkQSTNm2ExEFCzNCROS3JTiuIwpfm9up+4PyzMcDNeNUDyJbsOPYk8hxOxFRsDAQIqIMzx6TjtAyvOVoae77kRwRgzyXDgO7f+HZJaKQxqExIrqlYKh11bjUw12rewOrPgRWjwKqdgL0/JuLiEITAyEiuiW24S4nDZ4F/hwHnNkF7F8E3NaWZ5mIQhL/TCMi/8uZH6j/TMr91R8DmpZqIdd1h87hl63/qq/ymIgoGJgRIqLAaNQbWD8ROLkZOLQCqNBSbV608xTe/XU3TiXc7EUkM9Ck+FqG2oiIMhMzQkQUGLljgTueSrkvtUI3gqAXpm12CoKETMOX7fI8EVFmYiBERIHTuC9giASO/QnL4TUqE+RuEMy2TZ7nMBkRZSYGQkQUMJY8cYgv/7C6f3jO0FSZINdgSJ6XGWhERJmFgRARBYQMczUdsQIP7agPk2ZAhUsbUUt3MEPrmBERBQoDISLyO8daoBNaIfxsaaq29zH+nKF1zIiIAoWBEBH5ldT4uNYCfWbpAIumQyvDFlTTHXH7fbobs8ekISMRUWZhIEREfiU1Pq61QEe0ovjV2kjdf9E4N9X32FYckyn0XH+MiDITAyEi8itPNT4TzJ3U1/v0G1FBd8LpOVmvTNYtYx8hIspsDISIyK881fgc0EpgoaUe9DoNLxrn2be/3a4K1gxqwSCIiIKCgRAR+ZXU+Eitjzvjb2SFOurXorTutNqvR5OyHA4joqBhIEREfiU1PlLrY6v7cbRLK4sVllow6DS8YJjHmiAiCjoGQkTkd1LrIzU/7jJDtqzQIxF/4N7iyTz7RBRUXHTVjQkTJqibxWLJ/E+EKBsFQ62rxqlZZPEJ13D+SjIK5IlCXHRDaGt/h/6flSkr03ccH+xDJaIwptM0zd3SPwQgMTERMTExSEhIQHR0NM8Jkb8c3wB83RrQGYC+fwMFyvHcElFQrt8cGiOizFeyPlChNaBZgN8/5idAREHDQIiIgqP56ylft/8Ay38HsO7QOfyy9V/1lSvQE1FmYY0QEQVH8bpApfuA/QuxbOIAPHf1BftTUmQtM8/YYJGIAo0ZISIKmrUln1FfW1vWOHWbjk+4rhZtlcVbiYgCiYEQEQWFDH8NXAN7t+n+xjn257Qbt6HzdnGYjIgCioEQEQV1cdax5s7q8f2G9aisO+a0T3xiEsavOMhPiIgChoEQEQV1cdZ9Win8Zmmo7g80zkq135hl+zlERkQBw0CIiIK+OOsYc2dYNB1aGzahjm5/qn3f/XU3h8iIKCAYCBFRUFy4kgT9jQXJDmnFMctyt7o/KOKHGxVCN8kQmgylERH5GwMhIsp0Mhus9/QtsDrEO1IrdF2LQAP9XjTXb/U4lEZE5E8MhIgo02eLyVCX69o+8SiIqZY26v5rxh+gh9XjUBoRkb8wECKioMwWc2eiuQMStFyooj+Ojvq1apvuRoPF+mUL8JMiIr9jIEREmSqtIa4E5MHn5g7q/isRsxAJk8ocvd2uCgy2giIiIj9iIEREmSq9Ia4pljaI1/KjhO4sHjcsU9uGzd/DKfREFBAMhIgoU8kQlwx1ecrvXEcUPjU/qO73Mc5FHlxVQ2nPT9uMBdtPqhojLtBKRP6i0zTNtWaRbkhMTERMTAwSEhIQHR3N80Lkx1ljspaYcPcPkAEWLIl8DeX1p/C5uT0+MndJ+QcLQEyuCFy8arLvywVaiehWrt/MCBFRppNV5Sd2q4O4GPfDZBYY8IH5cXX/KcMClNbF24MmxyBIcIFWIroVzAilgRkhosCSYS6ZRSYF1AdOX8b4lY7rimn4JmIE7jZsxxJLXTxresXj60imSIKqNYNasKiaiMCMEBFlCTITrFH5guhYqziaVIh1eVaHYeZuMGt63GPYhCb6HR5fRzJF7D5NRBnBoTEiCqkiakcHtRL4ztJa3X/H+J2qHUoLu08Tka8YCBFRyGSHhrSvmmq7LL1xQcuDyvoT6GJYkeZrsPs0EfmKgRARhVQR9Wdda9sXY7U1WfzE/JC6/4pxFmJw2e33svs0EWUEAyEiCiltaxTD+C51nLZNt7TEPmsJ5NddRn/jbLffJ9kkdp8mIl8xECKikNO2RlF8LtPro6Ps0+nfM3dX958wLEE13WH7vpI9+qxrHZVNIiLyFQMhIgpJEtisHdwSA1pVUo/XWqvjV0tDGHQaPoz42r46/fgutVXgRESUEQyEiChkyVBXv1YVVXZIaoDeM3VHopYTNfX/oHeeVWq7DKUREWUUGyqmgQ0ViUKv+WKubVNQc/swaJF5sfn+RThhza9mi8n0e9YIEZGv129jms8SEYVY80WUHYCLJ+Yh3/ltuDirD/qZBqrmi/lyRqBnkzLo06IiAyIi8hqHxogoS1m0+wwePvU4kjQjWhq2oJN+rdp+8ZoJY5YdQN33l6pFXYmIvMFAiIiy1PDYu7/uxgGtBD41P6i2DY34BoVw0b6PLMoqK9szGCIibzAQIqIsQ2qEZE0x8aXlfuy0lkE+3RV8FDHpxopjKeSeBEwSOJEzOSfrDp3DL1v/VV95jijcsUaIiLIMx7XEzDDiZdML+DXyLTVE1s26DNNurEsGh0VYVV0RKZIlkwDRFkwKmY0nzSjZh4nCFTNCRJRluK4ltl8riRHmx9T9N43fo7zu32y1CKs/szcSBMmQoWMQJOITrnMokcIaM0JElOVWqHe8mE+xtEEz/VbcZdiB/0WMx4PJ7yIJkeq5s5eSVBCRFafX+zN7Y6utchdGyTY5K/J866pxWeocEfkD+wilgX2EiEIzQHh+2manbYVxAQuiXkesLhHTzc3xhvkZdXF3vPDLch1d6pdCmdjcIR8Y2bI3roGL7WgndvNtSRHJJnWZtD7d/WY805BDiRR2128OjRFRliIBgHSUzpcrwr7tDPKjn6k3rJoOXY0r8aB+daogIj4xSU2v7/fDVhUUNB2xIiRmlrkOfyWbrWlmb5CBQnBvhwiz+lAiUUaERSD0wAMPIH/+/HjooYeCfShE5KdgaNNbrdU6ZNJI0bYWmW1K/QcRk3G77p80XyMUamPkZ0tAJoGZLUBrOHxZqjoeR5pDIXhGa6tudT9vcYYaZQVhEQj169cP3377bbAPg4gCsA7Zprdbo1/LimrYaJzlAay01EROXTImRX6CQrjg8fszml3xF0/Fy+evmPyevbHVVqU1EChDh7JfIIO8UMnCEYVdINSsWTPkzZs32IdBRAGwdHc8Pl1+QAU2VujxkqkvDliLo6juPCZFjkYUkv2aXfGHtIqXveVL9kaCRimyFp6CoetmqzqX/sAZapSVBD0QWr16Ndq3b49ixYpBp9Nh7ty5qfaZMGECypQpgxw5cqBBgwbYsGFDUI6ViEKLLaBwdAm50Ms0EBe0PKil/wfjI/4HAywhVRvj2BjSVxLISHbH1+yNDCdKkXWMQ22VowSXjtwZHdZKb4aaYLNLCiVBnz5/5coV1KxZE0899RQefDBlfN/RzJkz8fLLL+Pzzz9XQdDYsWPRpk0b7Nu3D4ULF1b71KpVC2azOdX3LlmyRAVYRJQ9eQoojmlF8Gzyy/gucjhaGzZjJL7EQNNz0Dz87RebO0pd7CUgyowZZRkNvGxHJNmdtI5PghE5N67vR6bHD50ngaPJY5AyaPZ27D11CT9sPI74RN+n7qcX5NmycFPXHkZs3iin4/N03ETZOhC677771M2TTz75BM888wx69uypHktANH/+fEyePBmDBw9W27Zu3eqXY0lKSlI3x+l3RBS60gooNmq3obfpJXwRMQadDX/gspYDQ8w9nAaH5J5kSF6ZtS1DF31vuLu4ezusVSB3JM5fuTm0F+fFcaXVfygmZ6TT+3Qn4ZoZY5cf8FhcntbUfXmvaw+e9eq9DZu/x+n4OtQsinnbTrHrNYVfIJSW5ORkbNq0Ca+//rp9m16vR6tWrbBu3Tq//7zhw4fj3Xff9fvrElFgpBdQLLfWxWumZ/FJ5Od40rgUEbDgTfNTKjNk6zMki7S6Zki8ueh7w1NQ8na7Kuqr/Bx3Q0i6G0HP7682x6ajF7zOkHjqP2R7P081KZPh9+LaeFE4BngXriRj2Hzn9+ot+Z4vVh9Otd1fn4MrZp4oywRCZ8+ehcViQZEiRZy2y+O9e/d6/ToSOG3btk0Nw5UoUQKzZs1Co0aNUu0nAZcMwzlmhEqWLHmL74KIAsU2G8pTQCF+tt6F2kVi0PX0x+hqXIEoXTIGm55FwejcqkA4JRByZnutofN2ub3opxWQ2C6yUng8ee2RVM/LsfaevgXP3lUWX64+nKrxo+PwV6RR73WDQ2+6R/+81XkJEl/ZhrUG/bQNaw6eSze7dKts7+WNn3egxW1F1Pm4VVxvjbJUIOQvy5Yt82q/qKgodSOirME2G0qyBq4Bhc2ErrXRtkY7WLdXgvbzs+hsWINGsUmYXf4DjF6T9jCONGHs98MWlZXxZqkLdxdZT0GJDANN6FonVRalyI0O2Elmq6pbchd0uctoeFObc+FKEirluooc106jkO4iCusuohASUFCXgDy6a8iLa8iDa8itu44omKCHFQZYoVM5NA1JiMB1ROL6zkjcr0XhSkQUzmkxOI+8OKvF4JwWjXitAI5phXEO0s3XP/U90lKgwYfL0KNxmVvqDJ5exszfmSfKGkI6EIqNjYXBYMDp06edtsvjuLiUv9KIKLzZZkOlty6XvsZD+Pu0BVXW9kOxCxvRdsOT+FXXXy3cmpbftqfue+PuwunpIptWZiV/7kisGdTCHtQcOXsVMzYcUx2wPb0PTxmNtrff/DcxD66isu44btMfR3ndSZTSnUYp3Rl1y2E1AZnw994VLUoFRFK4vk8rgb3WUtirlcIRLU61OfDVhaumNM9LINZb4xBaeAjpQCgyMhJ169bF8uXL0alTJ7XNarWqx3369An24RFRiJCLoVzA0hq+UoHK8jyopBuCryNHobz+FOZFvoVh5u743tLSp+yFu3qZjPQFWnvwP3WcMvwlxzd22f40sxXCOdjSUFp3GtUvHUa+v45jUsQx3KY7jpL6/zwfu86AyxEF8c/1PPhPi8EZLR/OIgaXtFy4jJy4ouXEJeRU2R8JWGTZkpSckA6ROrN6JieSkQPJKotUQJeIWCSioE5uCSimO4eiOI/cuiRU0R1HFRxHG/xt//nXtEgVEG2yVsTf1srq63/I7+OZSwkkZc25z1TGL/3Zwd7OZpP9bJ+Hvxa9pdAW9EVXL1++jIMHD6r7tWvXVrPEmjdvjgIFCqBUqVJq+vyTTz6JL774AvXr11fT53/88UdVI+RaO+RvXHSVKHuQv+ylq7HtolYQCRgV8TmaG7apx9KN+h1zDxzXfP83RRYqFd4sauqOrXhaZlF5ulDrbgyZ5dKuIe7KbtTWHURt/QHU1h9EQd0lt99zUiuAfdaSOKCVwFGtCI5rhXE1TynMfPVhwBCBJh+tCFiNTyRMKK47qzJQZXWnVHaqiv4oKutOqK7fro5ZC2G9tSpWW2tgjfV2XIT3DXAl1h3fpQ7a1kg7OJF+SNLhOj2fPlYLUUa9Xxe9pczny/U76IHQqlWrVODjSoKfqVOnqvvjx4/Hxx9/jPj4eNUz6H//+5/qKRQo0sBRblKovX//fq9OJBGFLnerr0ue4ynDQrxmnIkonRnXtQh8Zu6Iry334Qpyev3acuEU3lxk3fFU2yTHV053CnUk4NGlBD0SSOh1znsnaUbs1spgj7WkyrTsU0NQJZGAPE4/Q2R0KM9fpOaojC4eNXT/oK5+P+rqD+A23TGn9yQZqO1aWfxurYklljuwSyvjVbbu83SCE3e/A+5836sBBv60Lc2gVGb0yZAmexyFriwVCIUyZoSIsoe0sgHldf9imHEKGhtSOlSf1/LgK3M7TLe08CozIRkhq6bh8a/+uqVjzIdLqKn/R2V66ugOoJb+EKJ1V1Ptd0KLxWZrRWyxVlBf92ilkQz33aL9UdwdaFLPJMFeU/1O3KXfruqaHB23FsIiaz0sstTDZq2ix6aYslba2sEt05zNJ1nB9FoWjHq4plefpXzu3s7oo8zHQCgIJ5KIQpNcAKWLsWMDv9Q03K9fjwHGn1TtkEjSIjDf2gDzLI2wzloNSYh0O1z1WL1SmLL2MBKup+5u70k0ruB2/WGVGbF9LeWmrkfqabZr5bDFWhGbrRVU8ONrPU33hqVQp3QBxEW7n2llKwiOT7immiEu3XMGCde8W/j1VrnLhhXGBdxl2I4W+i1opt+GXLqbTW5PaQUw19IEsy134qBWItXrDWhVCX1aVPBYK2bLgsFDywLJmMlsPW+H0DrWKp6h902Bx0AoCCeSiEKPrxkPWZOsg/5P9DIuxO36mz2ApCv1X9YqKhCRwOSwVhQntYKIiozE1WR365hpqqi4AC6pwmWpk5EhoXK6eFTQnUBZvfNMWJvD1iIq6yGBj/ysfVpJmP04p8WbYt/0+iB5kjvKgCtJaa/pJjrVKoa7KxfG+ctJ6QSnQA4k4W79dtxr2ICW+s2I1l2zP7fdWhZzLHeqwMgxc5cvV4RTbyhvZ93Z9vF2CI0ZodDGQCgIJ5KIQsut1cBoKkvzkGE1Whs2qZXsXZk1PS4iDxK03CpbJD8nAmbk011BDC6ruqO0SIGwBFU7rOXU113WMkhEbgSSr8W+aQWScTf6HUn2aO7Wk05LgXgTQHhbvOxYgN1cv0Utl9JcvxUROos9c/ebtSGmmVthi1YhVT2Ru/ec1rR4b4fQWCMU2hgIBeFEElHozhK7NRqq6Y6gvn5vSi1L3tPIdfWEajiYnmTNgH+1WNU754hWFP9ocfhHK6qCHl9mRvmTrxdyx6EzCXYK5ImyD7NJ1sjbYNP153qbeXGnABLR3rAOjxhWoZr+qH37TmsZTLO0ws+Wpk5Dmb6+Z2+G0DhrLLQxEArCiSSi0HErF9m0yDX0pRYV8enyfaojc37dJeTDFUTozGpGlAlGXNRSskSSLbqKKDzRqAzuu72oWour9/TUF9dgyejQjmNgJENb3mSC3AUQC7afRJ8ZW2C9pZOhoZbuELobl6oaryhdSnB6VovGd+bW+M7SGudVh2vf33N6Q2hstph9rt8h3VCRiMjfq9I7ypczAhcdCoNd60tcyUU7ZWV2Pc4gP85oNwqX07iYSxBku/hO1KfugC3BladgQAKI6JxGXLpuvsWAIePnyB+zzOLc1OnIemu3/pZ02KpVwFZTBQxDNzxs+B1PGpeghO4sBkTMxgvGeZhtuQuTLG1VRs6X95xWk042W8xeGAil00eIiLLfqvQ2staXXq9zutAt3nnKD5mKmxkEec20Lq6eMkW2GVUJ17yfjeYLWc4jM2qu+jQvjwGtKzvV4KTXhdtTb6W0yFDjJMv9mGy5D/fpN+BZ42+ooT+Mx43L8ZhhBeZZG6O0dQgA72d6yTG7ZpC4Xln2w0DIjd69e6ubLbVGRNlrVXpbzUjD8gVT1Yzkzx3llyBIXlWyIK6v7+7i6i5TJFPzr5utaWao5KWfbFQGU/70fnaXjSznUTkuj1e1Lt4EL540qVDI6Rykt9SFuJXTb4EBv1kb4bfkhmig24vnjL+ihWErHjCshTavDc5ua4f8970BQ1w131/bi/XKhs7bhbw5InD2clKGF4elzMVAiIjCalV62yXJXZCS0SEjV/lzRWD4g9W9Lqh1lynypkmjBGwl8nvfBduV6yKjnngTvHgKNh0zYr4OW8qMtIwHRTr8pVXBX6YquN38D/oa56KN4W/EHv0N+Pw3nCrZFkUf+AAoUM7rV/RmvbL4xCSnz43rk4U+35cAJiLKQqvSy8XYkTxOa9aPt8Nqni7+/VtWxN9vtfZ5VpEtUyRN+uSrZBS8USB3pLrY+ppzcFxkND2+BodpBZvent+eTco6vZbra/tip1YOz5lexn1JwzHfUl9tK3p8Aazj6gHzBwKXz3j1OhkJkm2L5sqQGoUmZoSIKKxXpfd1WC0tsn+DcqmH2zLC24AhLianx+yXvy7uvgaHrsXRGRm2lA7RMnTnOmQoz7W9PQ5fe9HsMU+UAZcdmjzKciS9Tf3xmfkIXjP+gLsN24GNk4Ct04HGfWBp2BsbTpo9/q5kJEi2DZl5m33zBmes+RcDISLK1tzV5KS3f6ADC28uZt4GDLbvkyxXRmZ0eXNx9yY4LJA7Am/fX83jUh4ZGbb0FMjKY28CIccgyJEs5PqkaTAaWXbhy6K/Ie+5bcDvI5D4+0QsMXXENEtr1QrBdVgro0GyY/btVtcn44w1/+Oiq2lgHyGi8JXRqeLe9Krx9mLma2M/x+AqNncUXpm1DacT/dMh2d9NBm/lgp5e92dffPpoTZQ6vQwxf36EcjfWmTtkLYph5m743Vo71XvzdB68nUFXsUjeDBdRe5qxxkaPqbGhop8wECIKb76su+VtYOHrxexWAoZQCl78PcQjx/L8jfd2K77v1QADf9qGMwlXVB+iV4yzUEiXoJ5bYamF983dcS26rNPnmtEg2ZGv5y29bulc+sMZAyE/9hHav38/O0sThTFvl+vQeRFYZPRi5ilg8CaQCKXgxd8+XbYfY5ZJg8uMkfXSRj9Sy2mWl/QD72Oci6cMCxGps6hlUqZa7kWtxz9A/SopBdy+Zt/c8TUY5WKwvmFn6VvEPkJE5OvU8f6tKqV7QfNm+rW7WhJPjf28CXAyUjDuz5qrQCoTe2uL1Eqfpslr/nHadhm58JG5K2ZamuMt4zS0NGzBs8b5uD53PaxXh+KvmPtw5nJyqvM4tINvdWW+FlF7W3vmj/YP4YbF0kREfriwlInNle4+/rqY+drdOJSCF3+6lVYHQppVLt/7n9vnDmtF0cv0KppZtuJt43con3QK+LUvIq0V8bnpKTUDTQLPt9tVUU04k8xWFQzP2HAM8Ynefc62wHfq2sPo0aSsx2BIsk9nLyV5fU5CKWuXFTAQIiLyw8XWm/388VredDf251TtUHYrrQ68tcpaC38m344nDIvR3zgbdfUH8Gvkm2opj7EJnfHi9C2phtsGtKqoslUHTl/G+JUH0/0ZsnjtV2sOux2u9LYeyTasKku2uA6/sqlj2thQkYjIi4utp5BC52ZNMU8BjNWqqY7JuIXX8mV4LbuzTcUXgQz5kmHEV5Z2aJX0sWrIaNRZ1XDZsqiBuEe/0WkwTDpLS91ShF6HJhVib6nxoi3z500QJDrULKrWrXPdn00d08ZAiIgogxfb9JbrcLygyV/pj3/9l9Nq9xl5LdaKeNdBPBDiUVA1ZOyR/CqOWQuhmO48vowcg68iRqGEznmITRbuPXcpyeuu37ZQSrI/EjT7sr5bTM4IvNSyAn7ZespjptDxtckZAyEiogAt1+HLX/XevJa/h+qyCzlnMtNO6nUywyprbdyTPBLjzJ3UrLJWhi1YGvkqnjbMhwEpTRwl3ujzwxaVpRHeBkO2bJ4v67tJcP3p8oNp1iaFU6bQV6wRIiLyQkZmX3nzV70MlU14vA4aerk0hy8dp8OJnDspOJZam0DWDNlcRxRGmx/BXEsTfBAxGQ31e/BWxPdob1iHwaZnVDG1mLftFCZ0rYNh873vOxTImV+cVZYaM0JERBlcGNUfq7bLX/N6nc7rwmZ/DNVlV5lVM+TokFYcjyW/hddMzyBRy4Wa+n8wL/ItDDTORBSS1eefP3ekTxmrffGXMHvTiYAcbzhlCr3FQIiIKEACVc9zK0N12Z2nc5MvV4S6OZLM2nN3lVVfb40OP1qao2XSx1hgqY8InQV9jL9gQeTrqK/boz5fW8bKm5qhz1YdwuoDZ+Fvsh6cDJ9Jc0bWCt3EtcbcYGdpIvKHQHcDZr8Y386NcNxWt3R+bDp6AfEJ13D2chLGrzyEBA/F7L5oo9+I9yKmoIjuonocX7EL4jqPAHLE3NJaZf6U3afUJyYmIiYmxquVIRgI+elEEhH5ujgo14cKHnf9eSRjJE0W0+sOLXVdPZuUQfnYPHhp5hZVGO0qGlcw2DgDXY0rUjbkLQq0HQVL5XYYv+Igpqw97HEGYWYIxkKtlkxs9MglNoiIQqhmxd3SC+FezxNMnjpzJ1xNCUxibgREjk0Su9QvpZokul7A9XodXpyeevHXS8iNN81Po3zLHmiw413g/CFg5uP4Xd8I0652x0XkU/vljjTgSnLKTDN/iclhRJ8WFVEgdyQ+WLAH568kp9rH9t4Hz96BvDkivC7Wzyh/r3nnT8wIpYEZISLK7heBcOPNwrdFbizGKsNl3mQu0v18Tdfwz09vo9Ter1UzxgQtF943d8Msy923VNadXuZKaoLOX/Eu61TUi99HW0ZHhhIluCqQJwpx0d6dH3eBZyCzUhwaC8KJJCJKC+t5snfdlqfPV7avP3ROdXwudv0ARkR8ier6I+p71lqq4XXz0zimFfHpPaSscVbVpyn56dGlE5SktdRHWkGUN4GnFLbLrDp/ZqQ4NEZEFGKy68KnWU2gZvK5+3xdg4eLKINOycPwlGEhXjb+hCaGXViifw1jzZ3xlaUtzF609pNQwRZ0tLk9zh5k3Wq9kZbGOnWeMjo2pzws9uvrkjDB+v+D0+eJiChsZFZnbk8dxS0wYJLlfrRJHqEyQjl0JgyO+AG/Rr6Fmrq0F2jNnyvCKdiQYEVqlPxVdK256T7t7VIf2o16o7UHzzpNzc8KS8KwszQREYWNzOjM7U3wIMNhj5veQGfLH3grYhqq6I/h56gh+MZ8D0aZH8EV5Ew1S00KoF2HjwIRQKw9+J99aG/9P+d8Wurj8a/+ciouP3spKeQbPTIQIiKisJEZM/m8XydMh9nWu7AyqRbezTEd7bEaPY2L8WiebVhbaRAul23jVIwsAZbUODnWIR05e8WrY8oTZcTlJLNX+45feQizN/+r1kmbudH3DtfxiUkYs+yA/bGcSk9rvYbCkjCcNZYGFksTEWVPgZzJ98vWf9Hvh60+f19T/Q58FDkZJXA6ZUOV9sB9I4HoYmn2PUqLbRac3EtrUdZg4KyxLICBEBFR9hWomXzezkxzJyeS0Nf4M56PmA+9ZgGiorGran90WFcJlgyU9cq76d+qolOGJlj0LpmhQLaQ4KwxPy6xQURE2VOgZvKlV4eUVg+ga4jCx+bH8GfOZviu0PfQndyEalvew5zIchhi6oGtWgWfjqV/q0ooE5sLocCqQS08G5s3KuCdpX3BWWNu9O7dG7t378bGjRsz/xMhIqJsUYckXC/ztsdpFVLLc2suxWF98x9wuN4QJGo51ar2c6PewUjjFyiIBK+PRYKgUFpxPjZvFDrWKq4C0FAIggQDISIiIj+T4R6Z6i6FwI7kca8mZbx6jTNXTNhe/FG0TBqNnyx3qW2PGH/HyqhX0MOwCAakP2phy7x4s+q9r2R5EF+FUlBmw1ljREREAag/iskZidfaVE61HIXUJX29NqW7tDdBw3/Ih4Gm5zHd3ALvRkxVnamHRnyLRw0rMdTUA39pVdKcjZXWTLmM0N1Yjy3lhbwvISka5NlhnjAQIiIiyqQZaRKU+NrLyLbvZq0SOia/j8cMK/GqcSaq6I9jZtQwLLTUwwjzYziiFfXYBsCWofI080znZYBk2y+92WrutL09TgWBoVIbZMPp82ngrDEiIvKFtwuM2vaDh15Gjh2k3e2bD5cw0PgjuhhWwKDTYNIM+N7SEv8zP4iomMJprv3lOlNu6e54t4Gb9BGat+2U03ZplnjdbPUpENLpAC2TZovZcNHVIJxIIiIKb74uMOpLLyNP+358VwSaHB4H3cElaps5Ig/0TQdA3/B5ICqP1y0CLGksGuu43appqnu0N3JFGnA12ZKp/YNsGAgF4UQSEVF4y8jK9r70Mkpz339+B5a8BcRvT3mcqyD2ln8Kz++rgyOJmt+yMb942SzyxWblMGfzSY9NHAO16rwN+wgRERFlsowsMOpLL6M09y13N/Ds78DOn4BVw4Hz/+C2HR9jlhaDzwwdMN3SEkmIVLVGnlaK9+esr4K5o9LsZB0Kq87bcPo8ERFRFlrZ3iO9HqjxCCwvbsAwYx8csxZCIV0ChkR8h7VRL6GP4WdE47LaVYbZHFeJ91Z6U/Fluzwvs+RCfdV5GwZCREREfuBtkBDoKeQbjibi68uN0SJ5NAabnsYJLRaxukQMjJiFP6P64m3jt9AnHFfZmEA0i5TnpVVAVukrxECIiIjID7wNEgI9ddyWZTHDiB8sLXB30hi8lNwHu6ylkVuXhKeMi7A6qj/KLH0aOLAMsFr91izSNuQWKkGhNzh9Pg0sliYiolBa2f7WirY1NNXvxHOGX3GnYad96/U8JXGo1MO4VvVR1K5a2etALb1Cb19aBPgbZ40F4UQSEREFemV7X6bxp9WwsUHesxhe6m8UPDQb0biS8n2aDhv1NZGr7qOo0aobkCM6ywaFDIT8uPr8/v37GQgREVGWkl425tm7yuLL1YcRhSS0N6xDV8MK1NYftO9n0UfBcNu9wG33AxVaAbkKZKmgkIFQEE4kERFRKPGUjXm7XRUMm78nVePH0rp4dND/iY6GP1FBf/LmEzo9UKIeUPGelKAorjqg933B1czEQCgIJ5KIiCjUuMvGyOO0Gz9qqKo7inaG9bgnYhsqakedn47MC5SsD5RuBJRqBBStpbpYh9LwIBsqEhERkdsmjOn37tFht1YGu81lMMr8GIriHCY1Podql9cDR9YCyZeAQ8tTbjf2R4GyQJFqQJHqQJGqWHMuL4asvoxDibqgFIz7gqvPExERhRFfevdo0gEaBfH0ruJYM6g/DLACp3cBx9bduK0HLp1SnazVbc+v6vuaApAw6XxUHhzTCiNeK4jzV/LiwA/RKF23KqpUKA9ERQNReVOySUVuT1mdNQg4fT4NHBojIqLsJr1ZZd6skebkylng9E4gfiesp3di7/YNiLOeRgFdShfrdOkjgHfOwp84NEZERERpNn6UWWU6l1llafE4pJY7FijXTN3+kh5Gf6XUH+XBVZTU/YeSujMorLuIgkhEAV0iCuoScWcxHWL014CkS4A+uINTHBojIiIKM7bu0O+6zCq71SE1x2DpMnJhj1Za3Vx92qgWOtYqjlDAJTaIiIjCNBhaM6gFvu/VAPlyRnjcz5flMIK+8GwGMBAiIiIK42GyJhVj8VHn6irgudU10rLSGmM2DISIiIjC3L1eLKSalRae9QVnjaWBs8aIiCicWPzUBDHYC8+ys3QQTiQRERHdxM7SREREFLZBjMFNV+tQxOnzREREFDLDWpmNxdJERERkD4Kk0aJrbyHpQi3b5fnshoEQERERQYbDJBPkrtO0bZs8L/tlJwyEiIiICFITlFaXabUAa8J1tV92wkDIjQkTJqBq1aqoV69e5n8iREREQXDG01piGdwvq2Ag5Ebv3r2xe/dubNy4MfM/ESIioiAonAWXx/AHBkJERESErLg8hj8wECIiIiJkxeUx/IGBEBEREfl1zbGshA0ViYiIyE6CndZV4/yy5lhWwECIiIiIsuTyGP7AoTEiIiIKWwyEiIiIKGwxECIiIqKwxUCIiIiIwhYDISIiIgpbDISIiIgobDEQIiIiorDFQIiIiIjCFgMhIiIiClvsLJ0GTdPU18TExMz6PIiIiOgW2a7btut4WhgIpeHSpUvqa8mSJW/1MyEiIqIgXMdjYmLS3EeneRMuhSmr1YqTJ08ib9680Ol0fo9WJcA6fvw4oqOj/fraxPOc2fj7zHOd3fB3OmufZwltJAgqVqwY9Pq0q4CYEUqDnLwSJUogkOSDZyAUeDzPmYPnOfPwXPM8ZyfRAbgWppcJsmGxNBEREYUtBkJEREQUthgIBUlUVBSGDBmivhLPc1bH32ee6+yGv9Phc55ZLE1ERERhixkhIiIiClsMhIiIiChsMRAiIiKisMVAiIiIiMIWA6EAmjBhAsqUKYMcOXKgQYMG2LBhQ5r7z5o1C7fddpvav3r16liwYEEgDy8sz/OuXbvQuXNntb90Cx87dmymHmu4nOdJkybhzjvvRP78+dWtVatW6f7+k+/nec6cObjjjjuQL18+5M6dG7Vq1cJ3333HUxmA32lHP/zwg/r3o1OnTjzXfj7PU6dOVefW8SbfF1CyxAb53w8//KBFRkZqkydP1nbt2qU988wzWr58+bTTp0+73X/t2rWawWDQRo4cqe3evVt76623tIiICG3Hjh38ePx4njds2KANHDhQmzFjhhYXF6eNGTOG5zcAv89du3bVJkyYoG3ZskXbs2eP1qNHDy0mJkY7ceIEz7cfz/PKlSu1OXPmqH8zDh48qI0dO1b9O7Jo0SKeZz//TtscPnxYK168uHbnnXdqHTt25Hn283meMmWKFh0drZ06dcp+i4+P1wKJgVCA1K9fX+vdu7f9scVi0YoVK6YNHz7c7f6PPPKI1q5dO6dtDRo00J577rlAHWJYnmdHpUuXZiCUCedZmM1mLW/evNo333zj7Y8MS7d6nkXt2rXVH1Lk/3Mtv8eNGzfWvvrqK+3JJ59kIBSA8yyBkPzRlJk4NBYAycnJ2LRpkxoOcFy3TB6vW7fO7ffIdsf9RZs2bTzuTxk7zxSc83z16lWYTCYUKFCAH0GAzrP8Ybt8+XLs27cPd911F89zAH6n33vvPRQuXBi9evXi+Q3geb58+TJKly6tFmPt2LGjKmkIJAZCAXD27FlYLBYUKVLEabs8jo+Pd/s9st2X/Slj55mCc54HDRqkVoF2Dfbp1s9zQkIC8uTJg8jISLRr1w7jxo1D69ateWr9/Du9Zs0afP3116r+jQJ3nitXrozJkyfjl19+wbRp02C1WtG4cWOcOHECgcLV54kooD766CNVXLpq1arAFz2Gobx582Lr1q3qr2jJCL388ssoV64cmjVrFuxDyzYuXbqE7t27qyAoNjY22IeTrTVq1EjdbCQIqlKlCr744gsMGzYsID+TgVAAyP8oBoMBp0+fdtouj+Pi4tx+j2z3ZX/K2HmmzD3Po0aNUoHQsmXLUKNGDZ7+AJxnGWqoUKGCui+zxvbs2YPhw4czEPLjuT506BCOHDmC9u3b27dJpkIYjUY1HFm+fHn+fgfg3+iIiAjUrl0bBw8eRKBwaCwAJEVdt25d9deZ4/808tgx0nUk2x33F0uXLvW4P2XsPFPmneeRI0eqv+AWLVqkpnhTYM6zK/mepKQknm4/nmtpa7Jjxw6VebPdOnTogObNm6v7UstCgfmdlqE1OfdFixZFwGRqaXaYTRmMiorSpk6dqqa2Pvvss2rKoG0aYPfu3bXBgwc7TZ83Go3aqFGj1HTjIUOGcPp8AM5zUlKSmtItt6JFi6qp9HL/wIED/v8lCOPz/NFHH6kpsz/99JPTNNhLly4F8V1kv/P84YcfakuWLNEOHTqk9pd/P+TfkUmTJgXxXWTPc+2Ks8YCc57fffddbfHixep3etOmTdpjjz2m5ciRQ029DxQGQgE0btw4rVSpUuqCIFMI169fb3/u7rvvVv8jOfrxxx+1SpUqqf2rVaumzZ8/P5CHF5bnWXqASPzvepP9yH/nWVoTuDvPEuCT/87zm2++qVWoUEFdKPLnz681atRIXXgoMP9GO2IgFJjz3L9/f/u+RYoU0dq2batt3rxZCySd/Cdw+SYiIiKi0MUaISIiIgpbDISIiIgobDEQIiIiorDFQIiIiIjCFgMhIiIiClsMhIiIiChsMRAiIiKisMVAiIjCjrT5f/bZZ1XbfvnKdmpE4YuBEBGFncWLF2P//v1YuHAh9u7dq9ZDI6LwxECIiLK1u+66C9OnT3faFhMTg/z586tV2wsUKKBut+rzzz93Wp2ciLIGBkJElG3NmzcPp0+fxmOPPea0vXHjxkhOTlYBkaxu3aBBA69fU1bOfvjhh5E7d24cOXLEvv2pp57C5s2b8ccff/j1PRBRYDEQIqJs63//+x969uwJvd75nzqTyYSNGzfitddeU1/NZrPXr9myZUvMmjULhQoVctoeGRmJrl27qp9JRFkHAyEiCnlXrlzBE088gTx58qgC59GjR6NZs2bo37+/x+/577//sGLFCrfDVfPnz1eBy3vvvQeDwYAFCxY4Pf/qq6+qLJLjbcKECekep/wsyUJdu3Ytg++UiDKbMdN/IhGRjyQw+f333/HLL7+gcOHCeOONN9QwVK1atTx+z5o1a5ArVy5UqVIl1XNTpkxBly5dEBERob7K4w4dOtif//jjjzP0Gd1xxx0qu/TXX3+pQI2IQh8zQkQU0i5fvoyvv/4ao0aNUsNS1atXxzfffJPucNbRo0dRpEiRVMNiUjMkGaBu3bqpx/JVMkSSQfLG1q1bVZATHx+vMkVybDYSeEndkfxsIsoamBEiopB26NAhVdjsWNAss7wqV66c5vfJ8FSOHDlSbZ82bRpuu+021KxZUz2WrFKlSpXw/fffpznUZiP7r1q1yuPzOXPmxNWrV9N9HSIKDcwIEVG2FBsbiwsXLqTaLsNgu3btgtFotN92796NqVOn+uXnnj9/PlUhNRGFLgZCRBTSypcvr2p5pO7GRgIcaYiYltq1a6vhK8dgSGaISdAjGR0Z4rLdVq9eje3bt2PLli23nL26fv26+tlElDVwaIyIQprMFOvVq5cqmC5YsKAqln7zzTdT1f64kmBEskJr167F/fffb88G1a9fXzVZdNWoUSP1/K0EMdJDqFy5cip4I6KsgRkhIgp5MovrzjvvVNPTW7VqhaZNm6Ju3bppfo9Mi5ceQlL7IyRTM2PGDHTu3Nnt/rJdOlBLPVJGyes/88wzGf5+Isp8Oo2rDRJRFiQzt6RweezYsR73kaGxatWqqan2pUuXDujxSN1RixYt1JCdzBwjoqyBGSEiyrbi4uLU9PZjx44F/GedOnUK3377LYMgoiyGNUJElK116tQpU36ODNkRUdbDoTEiIiIKWxwaIyIiorDFQIiIiIjCFgMhIiIiClsMhIiIiChsMRAiIiKisMVAiIiIiMIWAyEiIiIKWwyEiIiIKGwxECIiIiKEq/8DSbqfFz/GueUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "out2 = model.intensity(volume_scale=result.x[0], contrast_density=result.x[1])\n", + "theory = np.interp(q_exp, out2[\"q\"], out2[\"total\"])\n", + "scale = np.sum(theory * i_exp / sigma**2) / np.sum((theory / sigma) ** 2)\n", + "plt.plot(q_exp, i_exp, \"o\", label=\"Experiment\")\n", + "plt.plot(out2[\"q\"], scale*out2[\"total\"], label=\"Polynomial model\")\n", + "plt.legend()\n", + "plt.xlabel(\"q (Å⁻¹)\")\n", + "plt.ylabel(\"I(q)\")\n", + "plt.yscale(\"log\")\n", + "#plt.xscale(\"log\")\n", + "plt.title(\"SBA fit to lysozyme data\")\n", + "plt.savefig(\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/tests/lysozyme/sba_fit_excl+hydration.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5e0c824b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0.0025641 0.00512821] [260710.30492072 260534.6764846 260001.94048118]\n" + ] + } + ], + "source": [ + "pr = pripps.Pripps(atomfile=\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/assets/poly_amino_acid.yaml\")\n", + "model = pr.multipole_model(\n", + " structure_file=\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/tests/lysozyme/6lyz.xyz\",\n", + " qmin=0.0,\n", + " qmax=0.5,\n", + " nq=196,\n", + " excluded=\"fraser\", # FoXS-style excluded volume (16π Fraser)\n", + " volume_scale=1.0, # fit knob c₁ (scales excluded volume)\n", + " hydration=\"sasa\", # FoXS-style SASA-weighted hydration shell\n", + " probe=1.8, # SASA probe radius (Å)\n", + " contrast_density=0.03, # fit knob c₂ (scales hydration)\n", + " bulk_electron_density=0.334, # e/ų, pure water\n", + ")\n", + "\n", + "# Returns a dict of numpy arrays\n", + "out = model.intensity(volume_scale=1.0, contrast_density=0.03)\n", + "print(out[\"q\"][:3], out[\"total\"][:3])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "489d560c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c1=0.9500, c2=4.0000, χ²/N=10.6151\n" + ] + } + ], + "source": [ + "# experimental data: q (Å⁻¹), I(q), σ(q)\n", + "data = np.loadtxt(\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/tests/lysozyme/lyzexp.dat\", comments=\"#\")\n", + "q_exp, i_exp, sigma = data[:, 0], data[:, 1], data[:, 2]\n", + "\n", + "def chi2(params):\n", + " c1, c2 = params\n", + " out = model.intensity(volume_scale=c1, contrast_density=c2)\n", + " theory = np.interp(q_exp, out[\"q\"], out[\"total\"])\n", + " scale = np.sum(theory * i_exp / sigma**2) / np.sum((theory / sigma) ** 2)\n", + " return np.sum(((scale * theory - i_exp) / sigma) ** 2) / len(q_exp)\n", + "\n", + "result = minimize(\n", + " chi2,\n", + " x0=[1.0, 0.2],\n", + " bounds=[(0.95, 1.12), (-2.0, 4.0)],\n", + " method=\"L-BFGS-B\",\n", + ")\n", + "print(f\"c1={result.x[0]:.4f}, c2={result.x[1]:.4f}, χ²/N={result.fun:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3da694dd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ZMHHiRHVJS0tz/TdCRORm8m9fSkqKuw+DyCZZUzAoKAh5xSU2csg6j46OxpUrV5gsTUR+MfdKfHw8Ll++7O5DITKkYMGCal3BrEnR9py/2SNERESKHgQVLVoU+fLl40Sy5NFB+/Xr13Hu3Dl1v3jx4rneFwMhIiJSw2F6EFSkSBG2CHm8iIgIdS3BkPxucztMxmRpIiLKyAmSniAib6H/XvOS08ZAiIiIMnBdRfK33ysDISIiIvJbDISIiIic5Mknn8QDDzzA9vVgDIQskDmEqlevjoYNG7r+GyEi8nJp6SZsPHIBP+86pa7lvrODDRkiyXq599574W6fffYZpk+fDk8gbfLTTz+5+zA8DqvGLBg8eLC66PMQOJzJBCT+CwSHAcHhQFCo/EId/z5ERC62dN8ZvP1LHM5cuZnxWPHocIzqUh333pX7EuecSNAzbdq0TI+FhYXBnVV4Eng45RxCDsUeIXdISwE+rgx8UAZ4tyjwdiHg3Vjgg7LA5/WBaZ2AeU8CS4YD68cDfy4Czh/SXkdE5MFB0MCZOzIFQSL+yk31uDzvLBL0yMR65pdChQphzZo1CA0Nxbp16zK2/fDDD1W59dmzZ9X91q1bY8iQIeoigUtMTAxGjhyp5qrRJSUl4ZVXXkHJkiURGRmJxo0bq33rpNdHJvdbuHChGlGQ4zlx4kS2oTF5r+eeew4vvPCCOr5ixYph8uTJSExMRN++fVGgQAFUqlQJS5YsyfT59u3bh/vuuw/58+dXr3nsscdw/vz5TPt9/vnnMWzYMBQuXFh9/rfeeivj+XLlyqnr7t27qwBNv08MhNwjVf6RMO8BMgGpN4Cbl4ELh4HjG4D9PwKbvwRWjALm9AYmNADeiwUmNNSCpI1fAP9sBVKT+DsmIreT4S/pCbI0CKY/Js87e5gsKwkQJOiQwEFmGd65c6cKcqZMmaICCt2MGTMQHByMLVu2qOGsTz/9VG2jkyBp48aNmDNnDvbs2YOePXuqXqhDhw5lbCMT/I0dO1a9bv/+/SrYskTeS4IteS8JigYOHKj216xZM+zYsQMdOnRQxyv7EzK/U9u2bVG3bl1s27YNS5cuVUHcww8/nG2/EqRt3rxZBXvvvPMOli9frp7bunWrupZeszNnzmTcJy6x4b4lNuQvDenhkQBIghkJjlJuAInngWtngWvntOvLJ4ALh7QeoRTtf4pMZFgtthZQoTVQqT1QqiEQxBFPIrLPzZs3cfToUZQvXx7h4eF2N5/kAvWavCnH7Wb3b4KmFR07YaP0usycOTPbcb/++uvqkpycrHpwqlSponpWmjdvjq+//jpTsCST8knwopdjjxgxQvXuxMXFqZ6dChUqqOsSJUpkvK59+/Zo1KgR3n//fdUjJD06u3btQu3atTMdmwQyem6OvJcMm+k9VHJbzjM9evTAt99+mzHDt8yULIFXkyZN8O6776rtf/vtt4z9njx5EqVLl8Zff/2lPlfW/Qo5NgmgPvjgA3VfPtuPP/7oU8nbN638brnEhjeQ/9mCQ7WLuTuqWt4+PR24ehr490/g9C7g5Dbg5Bbg+gXg1Dbtsu5jICwaqNgaqNwBuLMzEFHI0OHIX2lbjl7Euas3UbRAOBqVL4ygQOYtEZEx8m+HI7ezV5s2bTBp0qRMj8kQkZChse+//x61atVC2bJlMW7cuGyvl4DDfE6apk2b4pNPPlHBxd69e9W1BBzmZLjMfBZueR95j5yYbyOzIcs+atasmfGY3lOlLx+xe/durF69Wg2LZXXkyJGM48r63hJM6fsg69h14C0CA4HoUtpFen70XqVLx4ATG4HDK4EjK4Ebl4C4n7XLLy9o29Z8CKhyLxCW/X8idyY3EpHvkD+gHLmdvWRISHJrrPnjjz/U9cWLF9VFtjfq2rVrKmDZvn17tmUczIMTWfLByAR/smq6OXmN+WP6PtLlD+Bb79+lSxc17JaV+Rpblvar74OsYyDkzeR/lsLltUud3kB6mtZbdHg5ELcQOLcfOLhEuwRHANW7Ag36AaUbZVSp6cmNWUft9eTGSX3qMRgiohxJL7L8ASX/dljKApJ/cWKjtd5mV5NekxdffFElJc+dOxdPPPEEVqxYgUD5A/MWyasxt2nTJlSuXFkFPpKbIz1C0rvSsmVLlx9/vXr18MMPP6gEZ8ljyi0JlORzUGasGvMlgUFAqfpA6xHAoD+AQZuAVq8ChcpruUh75gJTOwBftgC2TkHajQSbyY1yeWvhfpcnNxKR95GhdOlFFln7RPT78ryzhtxlmEpya8wvUlUlJ/4+ffqgY8eOKodHkoUl2VmGvcxJ/s9LL72kcm5mz56Nzz//HEOHDlXPydDTo48+iscffxwLFixQOSmS6DxmzBgsWrQIzibTuUgvVq9evVSSswR2ki8kn8eewEYCqZUrV6q2uXTpklOP2ZswEPLlCRWLVgPavgk8vxN4eiVQp482b9HZfcCil4FP7kTfxG9QFNb/h4hPSMLg77fjm3V/48edrpkcjYi8kwylSy+y9PyYk/vO7l2WSioZJjK/tGjRAu+99x6OHz+Or776Sm0nj0ui9Jtvvqlyb3QS5Ny4cUMlGEvgIUHQgAEDMp6XAEq2efnll1G1alWVcCxBSZkyZeBskqC9YcMGFfRIRZnkE0klnJTrm/dq5USCP6kikyRr6eUiTYDJfKIEcl3VmLtIDtHuOcC2qcD5g+qhJFMwfkhria/T7scxU87/UDF/iMj35LVqzJuLL6Tiqk6dOhg/fry7D4XcUDXGHiF/I1VkTQYCg7fgQNup2Jx+J8ICUtE7eDVWhr6Cz0ImoGxAvM1dnHHB5GhE5L0k6JES+W51SqprTw6CiBgI+auAAFRp0QMvRLyPh5L+D6vS6iAowIRuQX9gReireCd4GmJwxerLmT9ERES+gIGQH9OTG7eZ7sRTKcPQOel9rEmrjZCANDwevBy/h72AF4PnIxI3rOYPDZ+/hzlDROTVZKkMDov5LwZCfk6SF19sX1nd3m8qhydThqNX8hvYlV4RkQFJGBq8ACvDXsH9gRvNJsq/bf6Ok6j/7nIOkxERkVdiIEQY0rYyYqNuJ5ltTK+BB5LfwbPJL+BYejHEBlzChNDP8X3I+6gYcCpbi12+noJnZ+7A2wv3saqMiIi8CgMhUkNkb3WtnmXujwAsTW+Ejslj8UnKQ7hpCkHzoP1YGjoCI4JnIxzZF3ud9sdxtdZQi7Gr2ENERERegYEQZZr/Q0rjzSUhFJ+n9UD75I+wPK2+yh96NvgXLA59DfUCtPJ7S1Vl7CEiIiJvwHmE/G0eIYPzfyyPi8dPu07jYmJypufbBW7HeyFT1XBZuikAU9I64ZPUnipgsobzDhH51zxCRK7CeYTIafN//F+XGtj6Rnt8368xoiNuL+S3Mr0+OiR9iHmprRAYYMKA4EWqd6huwCGr+9R7iBbvOc1vjIiIPAqHxshmUNS8cgzGPlgz0+MJiMSrqc+ib/KriDcVQsXAM5gX+jYGBf2MAFhf6XjI7J1YvIeTMBKRZ5k+fbparsIbvPXWW2oWbHvIKvQ//fQTPGUW7xdeeMGjvhsGQr681pgD84e+7FMPBfPd7hkSq9ProkPSWPyU1gzBAekYFjIXM0LGWp2IUZYoGzSLM1ITkWM9+eST6mQvl9DQUFSqVAnvvPMOUlNTfa6pX3nlFbVwKjkOAyELZMG9uLg4taAe3Q6Gtr95D4a2q5ypuiwB+fFCymC8mjIA101haBW0F0vCRqBF4F6rTScr3ienpqtS+593cSFXIsq7e++9F2fOnMGhQ4fUwqjSc/LRRx/5XNPmz58fRYoUcfdh+BQGQmTXUNmL91TBxN71sjwTgHlprdEl+V0cSC+NOwKu4NuQD/BC8HyLQ2WSM1Rv9DJVaj90zi6W3BNRnoWFhSE2NhZly5bFwIED0b59eyxcuFA9d+nSJbVyfKFChZAvXz7cd999KmCy5NixY2pF923btmV6XGaeln2np6ermail90l6Zho0aKD22axZM/z111+ZXjNp0iRUrFhR9VLJivXfffddpudlH1999RXuv/9+tY9q1aph48aNOHz4sBpCioyMVPs9cuSI1aEx+YP9nnvuQUxMjCruufvuu7Fjxw672q5169Z47rnn1JCVtFGxYsUwefJkJCYmom/fvihQoIDqZVuyZEmm1/3+++9o1KiRavvixYtjxIgRmXrh5PXS7hK8yfOffPJJtvdOSkpSvVwlS5ZUn7dx48aqfV2JgRDZrVMtbagsa6n9EVNJPJA8GjNT26lE6heCF+DrkHHIj+vZ9nEtKS3T/Xgu5ErkeUwmIDnRPRd57zyIiIhAcnJyxtCZBDYSGEmgYTKZ0KlTJ6SkpGR7Xbly5VQQNW3atEyPy33ZjwRJujfeeEOd3GXfwcHBeOqppzKe+/HHHzF06FDVO7Vv3z4888wzKqhYvXp1pv2OHj1aBQu7du3CnXfeid69e6ttX3vtNbVfOdYhQ4ZY/ZxXr17FE088gfXr12PTpk2oXLmy+mzyuD1mzJihgqktW7aooEiCyZ49e6pATAKrDh064LHHHsP169q/56dOnVLvIykku3fvVkHfN998g3fffTdjn6+++qoKln7++WcsW7ZMBThZgzT5bPKdzJkzB3v27FHvKb171gJVZ2D5vA3+WD6fm1L7ZfvPqMkUzT0U9DveC56KsIAUHEoviQEpL+GoqbjN/cmQW2x0ONYPb8vVqok8oQxZApL3S7jnu3j9NBAaaWhTCVAuX76sEoIlcJCeGullkRP6gAEDUKVKFWzYsEGd1MWFCxdQunRpdfKXE68k5EpviOxD/O9//8Ozzz6rhtqkt0NO3tLz8/fff6tASU7obdq0wYoVK9CuXTv1msWLF6Nz5864ceOGar/mzZujRo0a+PrrrzOO8+GHH1a9JIsWLcroEXrzzTdVMCQkkGnatKkKKPSgSgIECaBkv3qPkHxOCZwskR4rSS6eNWuWagP9fSQwe+CBB6z2CKWlpWHdunXqvtyWc1+PHj3w7bffqsfi4+NVr44ELU2aNFFB4A8//IADBw6o/YsvvvgCw4cPV+dMCZhkCG/mzJmqjcXFixdRqlQp9Z1ID9uJEydQoUIFdV2ixO3fmQSi0tP0/vvvZ/tusmL5PHlEqf2orndl6yGan3Y3Hk4eiTOmwqgceAo/h45E68CdNvdnujVsJsEVEZE9fv31VzUEI0GIDH395z//UUGDnKilt0aGXHRygpahKnnOEgkYgoKCVPAg5GQsgY8EQeZq1aqVcVuCBHHu3Dl1LfuWYMic3M/6nub7kCEpUbNmzUyPycle/jC35OzZs+jfv7/qCZLgRf5ov3btmgou7FHL7Djks0sbZT2OrJ9PgjY9CNI/n7z3yZMn1XCe9MiZt3vhwoVVu+v27t2rgi4JVOW70y/Si2Q+HOhswS57J/L5ZOp7qsdm6iHabaqErknvYlLoeDQIPIipIR/jrdTH8W1aR5v7+nbjUTQqX5i9QkTuFpJP65lx13vbQQIVGZ6RfBzpXZDgJ7dkHzJcJcNh0isivSufffZZ9kMMuV1JqwcE0iNjD0v7sGe/MiwmPVxyfJLDJD1YEqDow4K5OQ79fR3x+WyRoEmCru3bt6trcxIQuQpzhMgpPUT9mmt/Of2LguiV/CZmp7ZReUPvhMzA68Hf25xvaMm+s6j51m/4bMVBNfxGRG4iJz8ZnnLHxaynwQhJtJWE3jJlymQKgiQBWRJ4N2/enPGYBA6S2CzTpFjz9NNPq6EvGe6R10tAZA95XxmOMyf3bb1nbsg+n3/+eZWvI0NxEgidP38ezlbtVmK3DEWaH4skVsvwlySJSyBl3u6StH7w4O2lmerWrat6hKSXSb4784skvrsKAyFyivbVb/+IUxCM11Kfxocp/1H3ZTbqz0M+Rxis/8VyPTkN41YcYkBERHkiQ0bdunVTw0eSUCyJvX369FFVSvK4rRO95MJIzkuvXr1U8rU9JFFYhtSkl0oSfz/99FMsWLBAVUg5+vNJNZoMVUnQ8eijj9p9rLkxaNAg/PPPPyoP688//1QJ0aNGjcJLL72kEsqlR6dfv36qHVatWqUSxrMmm8uQmByv9L5J20iOmiRrjxkzJiOPyhUYCJFTyNCW5Azd/psuAF+kdcPzyYORbArC/UGbMTP0fRSE7coGPSCq/+5yrmhPRLkiQ1z169dXycMybCS9GJLcnHU4KCs5kcsQk3k1mFGSZyTDVR9//LHqqZEyeTkOSUx2JEmslp6WevXqqaou6R0qWrQonK1kyZKqDSVwqV27tkoul/aS5G+dzOPUsmVLdOnSRSVAt2jRQn0P5qRNJBCS6jrJH5J2kykBpGfPVVg1ZgOrxvJm6b4zGDhTK5U0H+BqEhiHr0M+RVTAdRxOL4FHk1/HWRTO+ccq83L0qafykYjIsbjoanZSzTVv3jxV1k2eiVVj5NEkYJHARUrizW1Kr44eyW/htKkwKgWeVuuUlQ44m+P+TLdmpWbeEBE5kyTxylDOhAkT1NAP+TZWjZHLqsnOXb2JmMgwpJtMeG52CHreGIXvQ99HucCzmBf6Dvokv4bDplI296eX10tSNhGRM8gkf7Nnz1bDNLkZFiPvwhwhclk1Wbc6JdVq9i2r3IEPHqyJU7gDPZP/D3+ml0ZswCXMDR2NGgFHc9yflNezV4iInEWSnGXph7lz52Yr6ybfw0DIAq4+77oV7VPyFcUjyW9iV3oFFAm4itmh76JewO3ySmvl9UyeJiIiR2AgZAFXn3ftivZ929dHf4zE5vQ7ERVwA9NDx6J2wGGbr718PUUlYktCNhE5jvm8MET+8HtlIERuHzYb2r4yNr3VHduaT8YWU3UVDH0X+gHuCvjb5mvl5//Wwv0cJiNyAL2UXF9Uk8gb6L/XnKZCsIXl8zawfN710m5exdHx96HSzb24bIrEo8lvYL8p8/o+WT1UrxTGPlSLS3IQ5ZEsMiqLW8o8NPny5cu0jhSRp/UESRAks1LLIrP6Wm+5OX8zELKBgZB7bP7zOIJnPYj6gYdwyZQfvZPfwAFTWZuvKZgvBB/0qMk5hojyeHKRVcatrfRN5GkkCJLlOLIG7QyEHISBkHtIRVjHD37BxzffQp3AI7hgKoCeyaPwt6mEzddxwkUiB/0/mJaGlJQUNid5NBkOs1bVx0DIQRgIuY8kQQ+buQ7fh76HmoHHcNIUg4eSRiEetucPKhwZgk2vtUdoMNPfiIj8VYIdQ2M8W5DHVpR92Kclng96E0fSi6NUwHl8G/pBjmuTXUxMQZMxK1lNRkREhjAQIo8OhlaMfAhzqnyGeFMhVAk8hamhHyECN22+7mJiMp6duQOL95x22bESEZF3YiBEHl9eP6J3B7wYMkpVkdULPIxJIZ8hBKk5vnbI7J1YvIfzDBERkXUMhMgrgqEnHrgXTyW/iuumMLQO2o2xIV9nWdM+u3QTMGgWe4aIiMg6BkLkNcNkAx59BK8EvoxUUyB6BK3H0KAFhl7LniEiIrKGgRB5VTD0+chhWFP5NXX/xZAf8EDg+hxfx54hIiKyhoEQed0wWfs+w5DW9Hl1X4bIGgUcMPRa9gwREVFWDITIKwXd8zbiS3ZAWEAqvgodh/IBOSdFs2eIiIiyYiBE3ikwELFPfotLhWqhUMA1TA35MMc5hnTsGSIiIh0DIfJeIREo1O8HXM9XEuUDz+LzkM8RhLQcX8aeISIi0jEQIu+WvyjyPTEPqUERaBm0D8OD5xh+KXuGiIiIgZAFEydORPXq1dGwYUP+QrxBsRoI7vGlujkgeBG6GagkE+wZIiKiAJPJZHtWOj/GRVe9zMrRwLqPcdMUgoeSR2GfqYKhlwUGABN61UOnWsWdfohEROR8XHSV/FOb14HKHREekKIqyYrgil09Q7LiPRER+RcOjZHvCAwCHpwMFKmEkgEX8EXoZ4aSp3Vv/xKHNImKiIjIbzAQIt8SHg08MhsILYDGgX/i5eB5hl965spNTN9wlMEQEZEfYSBEvueOKkC3z9XNQcEL0S5op+GXjl50AC3GruIwGRGRn2AgRL6pRneg0TPq5qR8X6Mk/rWrZ2jgTOYMERH5AwZC5Ls6jAZK1ENoyhX8WvwbtRyHPZgzRETk+xgIke8KDgN6TgfCC6LQpT1YWn2F4ZeabvUMfbrsL2w8coF5Q0REPoqBEPm2QmWB7l+pm+WPfIuPqh+z6+UT1xxBr8mbmDdEROSjGAiR76t6L9B8qLrZ/eRYFMcFu3fBvCEiIt/EQIj8Q9uRKl8oOPkKJkR8hUCk270LGS57a+F+DpMREfkQBkLkH4JCgAenACGRqG/ah2eCfs3VbuITkjBh1WGHHx4REbkHAyHyH0UqAp0+VDdfDZ2PtgX+ydVuxq04yHmGiIh8BAMh8i91HgWqP4BAUyq+KfAV5j5ZE4NbV7R7NyytJyLyDQyEyL8EBABdxgNRpRBw8W80/nMsXupQFcWjw+1Ont5y9KLTDpOIiFyDgRD5n4hCQA8pqQ8Ads1E0F+/YlSX6nLPLsvj4p10gERE5CoMhMg/lWuRUVKPX1/EveVDMalPPbt6hqZuOMZcISIiL8dAiPxXm9eBO6oBif8Ci17CvXcVx/rhbfF9v8aIjgjJ8eXSg8RcISIi78ZAiPx7CY7uk4CAICDuJ2DfDwgKDEDzyjEY+2BNw8twMFeIiMh7MRAi/1aiLtDqFe32opeBq2fVTekd6te8nKFdnLt605lHSERETsRAiKjlK0BsLeDGJeCXoYBJ+nqA9tVjDbXNsfPX2YZERF6KgRBRcCjQ/UsgMAQ4uATYPVu1SaPyhVXydICBCRYX7znNdiQi8kIMhIhEsRpAm9e0tlg6Qg2RSb6QlNVr/UO2DZ61E+OXH+Q6ZEREXoaBEJGu2VCgeG3g5hVgybCMXKEX21fOsY0kWBq/8hDqv7ucJfVERF6EgZAFEydORPXq1dGwYUPXfyPkPkHBQNfPb1eR/blYPVwuJtLwLi5fT8HAmTsYDBEReQkGQhYMHjwYcXFx2Lp1q+u/EXIv6RFq9tztKrKbCShawL7lN6R3iPMLERF5BwZCRFm1HgEUKg9cPQ2sfDsjadoenF+IiMg7MBAiyiokAuj6X+321ikIOrlZJU3bi/MLERF5PgZCRJaUbwXUfUy7vfA53HtnYXzRuy4C7ViZdd3B86wiIyLycAyEiKzpMBqILAqcPwisH49OtUpgQq96httr/o6TrCIjIvJwDISIrIkoBNz3gXZ73SfAxb/RqVZxfNmnHgrmy3lRVsEqMiIiz8ZAiMiWGj2ACq2BtCRgyXC1/IbMLbT9zXvwUL1ShtqOVWRERJ6LgRCRLQEBQKePteU3Di0D/lykHpZZp1tWiTHcdqwiIyLyTAyEiHISUxlo/rx2W3qFkhPVTXvnF1oeF8+2JiLyMAyEiIyuUB9dBkg4Caz9SD1k7/xCUzcc44zTREQehoEQkRGh+YD7xmq3/5gA/PtXxqKs9nj9x734cecpbDxygaX1REQegIEQkVF3dgKq3Aukp2jLb9xKnLaniuxiYgpenLsLvSZvQouxq9hDRETkZgyEiOwhvULB4cCxdcCBheohvYrsvrti7dpV/JWbXKCViMjNGAgR2aNQOaD5UO32sjeBlBvqpgyTPd60nF27krJ6wQVaiYjch4EQkb0kEIoqCVw+AWyckPGwnjwdYGcwxNJ6IiL3YSBEZK/QSKD929rtdZ8CCafVzdwkT+u4QCsRkXswECLKjZoPAaUbAynXgRW3gqJb+UKTJHk6wljytM7eOYmIiMgxGAgR5XbG6XtvrUO2Zw7wz9ZMwdDER40vzhobFaaG1YiIyPUYCBHlVsl6QJ1HtdtLhwPp6RlPNalQxHC+UMLNVExYdYjzChERuQEDIaK8aPd/QGh+4NR2YM/cjIfN84VyCoauJ6dh3IpDqP/ucs4rRETkYgyEiPKiQCzQ8mXt9sp3MsrpzfOFYg0uw3H5egrnFSIicjEGQkR51WQQEF0auHoa2PRFpqckGFo/vC1Gdq5muJye8woREbkOAyGivAoJB9qO1G6vGwckns/0tAyTxRQIM7w7zitEROQ6DISIHKFmT6B4bSD5KvD7rcVZ81Aevzwunt8LEZELMBAicsj/SYHAPaO129umAheOZHpan3XaqKkbjmHxHm2iRiIich4GQkSOUuFuoNI9QHoqsOKtTE/lZtbpIbN3YvGeM/x+iIiciIEQkSPd8w4QEKitTH9ic7bE6S9l1ul8xmadTjcBg2btYEk9EZETMRAicqRi1W9Psiir05v0NeZvB0Pb37wH990Va3iXrCIjInIeBkJEjtbmDSA4Aji5Bfjz12xPyzDZ403LGd4dq8iIiJyHgRCRo0UVB5oO0m6vehdIT8u2ib3J01ydnojIORgIETlDs+eB8Gjg3z+BvfOyPW1v8vSx89cdfIBERCQYCBE5Q0RBoPkL2u3V7wOpydk2kXyhL3rXRaCBlVnHrzjIpGkiIidgIETkLI2fASKLApePAzu/tbhJp1olMKFXPUO7Y9I0EZHjMRAicpbQSKDVq9rt3z8Cki0Pb3WqVRwvtq9sc1dSe8akaSIix2MgRORM9Z8ECpYBrsUDW762ulm5mEhDu+PSG0REjsVAiMiZgkOB1q9pt9ePA25esbiZ0bXIZOmNpfs42zQRkaP4fCD066+/omrVqqhcuTKmTJni7sMhf1TrP0BMVeDmZeCPCRY3saec/q2F+5Em004TEVGe+XQglJqaipdeegmrVq3Czp078dFHH+HChQvuPizyN4FBQNs3tNubvgASL+SpnD4+IQkTVh129FESEfklnw6EtmzZgho1aqBkyZLInz8/7rvvPixbtszdh0X+qFpXILYmkHwN2Gi5V0jK6fs1Nzbj9DiW0xMR+X4gtHbtWnTp0gUlSpRAQEAAfvrpp2zbTJw4EeXKlUN4eDgaN26sgh/d6dOnVRCkk9unTp1y2fETZQgIuJ0rJEnTFnqFRPvqxtcge/3Hvfhx5ylsPHKBQ2VERL4YCCUmJqJ27doq2LFk7ty5auhr1KhR2LFjh9q2Y8eOOHfunMuPlShHVTsBsbVu9Qp9nudcoYuJKXhx7i70mrwJLcauYhI1EZGvBUIylPXuu++ie/fuFp//9NNP0b9/f/Tt2xfVq1fHl19+iXz58mHq1KnqeelJMu8BktvymDVJSUlISEjIdCFySq/QZukVOp/npTd08VduYuDMHQyGiIh8KRCyJTk5Gdu3b0f79u0zHgsMDFT3N27cqO43atQI+/btUwHQtWvXsGTJEtVjZM2YMWMQHR2dcSldurRLPgv5kar3AcVrAymJwB+fW80VymmCxaz0GjLOPk1E5CeB0Pnz55GWloZixYplelzux8fHq9vBwcH45JNP0KZNG9SpUwcvv/wyihQpYnWfr732Gq5cuZJx+eeff5z+Ocifc4UmW+wVEkPaVkZslPHV6QVnnyYi8qNAyKiuXbvi4MGDOHz4MAYMGGBz27CwMERFRWW6EDlclXuB4nVu9Qr91+ImMkT2VtfqkPVYDazJmsm5qzcdcphERP7AawOhmJgYBAUF4ezZs5kel/uxscYrb4jc3it07V+rQ2ST+tRDrMHkaXtnqSYiIi8OhEJDQ1G/fn2sXLky47H09HR1v2nTpm49NqIcVekIlKgLpFy32iukB0MjOxtPng4MAC4lJvMLICLyhUBIEpx37dqlLuLo0aPq9okTJ9R9KZ2fPHkyZsyYgQMHDmDgwIGq5F6qyIg8vlfo7uHa7W1TgesXLW4mS2mMXhRneLey8sbgWaweIyLyiUBo27ZtqFu3rrrogY/c/r//+z91/z//+Q8+/vhjdV+SoSVIWrp0abYEanvJvEVSjt+wYUOHfA4iq7lCxW7NNr35K4ubbDl6EWeu2J/zw+oxIiJjAkwmE1dvtELmEZIyeqkgY+I0OcW+BcD8vkB4QeDFfUBYgUxP/7zrFIbO0XpE7TW7fxM0rWi9SpKIyFfZc/726B4hIp9XvRtQpLK2Mv3Wbxya+MzqMSKinDEQInL3yvQtX9Juy2KsKTcsLrlhbwm9YPUYEVHOGAgRuVvNnkDBMkDiv8COb60uuWFPMMTqMSIiYxgIEblbUAjQfKh2e8NnQGpynucTYvUYEZExTJa2gcnS5DIpN4HPagPX4oEu/wXqP2GxlF6qyCT3R4a9LlxNwvNzd6qgxxLpQZLgaf3wtqpniYjIXyQwWZrIy4SEA82e026vHwekpWbbRIIZqQLrVqekui5SIMxqECS49hgRUc44NGYB5xEit2jQF4goDFw6Chz42WFVYaweIyKyjoGQBYMHD0ZcXBy2bt1qo+mIHCw0Emj8jHZ7w3+BHKb4MloVxuoxIiLrGAgReZKG/YHgCODMLuDoWpub5lRaL4/L87IdERFZxkCIyJNEFgHq9rldQWZDTqX10p/U6a5YlWAtidZERJQdq8ZsYNUYucXFo8Dn9QBTOvDseiC2ps3Nl+47o9YWM1+TTIrEzGOfghEh6Nu8HIa0rcwKMiLyeQmsGiPyYoXLA9UfuJ0rlAOZZ0hK5GVtsb7NyqrHsnYAXb6RgnErDqH+u8tV4ERERBoOjRF5oubPa9f7fgAun8hxcxkmu5SYhBkbj9vc7vL1FAycuYPBEBHRLQyEiDxRibpA+VaAKQ3YNCnHzaWXZ9As65MrmpNNZCiNeUNERAyELOI8QuQR9GU3ts8Arl+0upkENBLY2EPyiSSJmojI37FHyALOI0QeoWI7oNhdQEoisO0bq5tJQGOeKG0UJ1okImIgROS5AgJu9wpt/kpbj8yBAc2x89fzcnRERD6BPUJEnqxGdyCqFJD4L7B7tkNnjh6/4iCTponI7zEQIvJkQSFA08Ha7T8+B9LT7J5h2hYmTRORv2MgROTp6j0OhBcELh4B/lxk9wzT1nB1eiIiBkJEni8sP9Dwae32hvEWF2OVSRUn9amH2Gj7h8m+3XiUpfRE5Le4xIYNXGKDPMa1c8C4u4C0JOCp34AyTayW0ksVmSRQn7+ahNGLDhjafcF8IfigR00VUBEReTsusUHka/IXBWo9rN3eONHqZjJM1rRiEXSrUxJPNi+vcoeM4IzTROSvmCNE5C2aDNKu//wVuHQsx83Nc4eM4IzTROSPGAhZwJmlySMVqw5UaK2tSr9lsqGXyFBXv+blDL8FZ5wmIn/DQMgCzixNHqvJrVL6Hd8CSVcNvaR99Vi73oIzThORP2EgRORNKrUHilQGkhKAnTMNvUSfZ8ioQ2evYeORC6wkIyK/wECIyJsEBgJNBmq3ZVV6CxMs5jVXaMLqw+g1eRNajF3FmaeJyOcxECLyNrUf0SZYvHwc+GuJ4VyhL/vUU2XyRsVfuYmBM3cwGCIin8ZAiMjbhEYCDfpqtzd9YfhlEgxtf/MeDG1X2dAM1Pq0jVyGg4h8GQMhIm/UaAAQGAwc3wCc3mX4ZTJM1qRCkYwgJydchoOIfB0DISJvFFVCW5nezl6h3FaFLY+Lt/s1RETegIEQkbfSk6b3LQASzhh+WdEC9q9HNnXDMeYKEZFPYiBE5K1K1gdKNwHSU4CtUwy/TC+nt2eletmWuUJE5IuCc/Oio0ePYt26dTh+/DiuX7+OO+64A3Xr1kXTpk0RHm7/X5tElEtNBwH/bAK2TQVavQKERBgup5eKMAlwTHbmCslaZkREfhkIff/99/jss8+wbds2FCtWDCVKlEBERAQuXryII0eOqCDo0UcfxfDhw1G2bFl48xIbcklLy3mOFiK3uvN+oGAZ4PIJYPec29VkBirIJvWpp3p5JMAxirNOE5GvCTCZTIYKSKTHJzQ0FE888QS6dOmC0qVLZ3o+KSkJGzduxJw5c/DDDz/giy++QM+ePeHNEhISEB0djStXriAqKsrdh0Nk2R8TgGVvADFVgcGbgQDjg15p6SbVy7Ph8L+YsPpIjtvP7t+EPUJE5FPnb8OB0G+//YaOHTsaOoALFy7g2LFjqF+/PrwZAyHyCjevAJ9WB5KvAX0WAJXa2b0LCYhkJmmZRNHSPwgSWsVGh2P98LZqaI2IyFfO34aTpY0GQaJIkSJeHwQReY3waKDOo9rtzV/mahfmy3BkDXP0+/I8gyAi8jWBuY20jF6IyAUaP6OFLIeWAecP52oXet6Q9PyYk/vyuDxPRORrclU1VrBgQQTkkIcgI26yDROOiVygSEWgSkfg4FJgy1dAp49ytRsJdu6pHqvyhiQxWuYcknJ7854gPa/I2vNERD4fCE2bNg0jRozAk08+qUrmhSRKz5gxA2PGjEG5cuUcfZxElJPGz2qB0M7vgTZvABEFc9VmEtRYK5Ffuu9MtkozmZNIhs3YY0RE3shwsrS5du3a4emnn0avXr0yPT5r1ix8/fXXWLNmDXwBk6XJq8j/yl80Bf49AHR4D2g2xKG7X7znDAbN2pHtcb0viMNnROTTydLmpPenQYMG2R6Xx7Zs2ZKbXRJRXslwtcoVgjY8lu64ebAW7zmNIbOzB0GCq9QTkTfLVSAkcwhNnjw52+NTpkzJNr8QEblQrf8AEYW0CRb/WpKnXUku0MYjF/DOL/sxaNZOpNvoO+Yq9UTkVzlC48aNw4MPPoglS5agcePG6jHpCTp06JCaTJGI3CQ0H1DvCWDDeK2Uvtr9udqNpVwgIzjzNBH5RY9Qp06dVNDTtWtXtbyGXGS26YMHD6rniMiNGvUHAoKAY+uA+H25CoJkHTJ7g6DcrmxPROR1PUKiVKlSeO+99xx7NESUd9GlgGpdgLifgM2TgG4T7RoOk54guysoblWPSSk9EZFP9gidOHHCrh2fOnUqN8dDRI7QZKB2vWcekHje8MtkfqDc9AQJzjxNRD4dCDVs2BDPPPMMtm7danUbKVOTJOq77rrLq3OFZOX56tWrq89M5JVKNwZK1AXSkoDt05ya4yNzKX7RmzNPE5GPzyMkC6nKUNjUqVMRHh6u1hIrUaKEun3p0iXExcVh//79qFevHkaOHOkTuUKcR4i82u65wI8DgALFgaF7gODQHF8iVWK9Jm+y622+6F0XnWqVyMOBEhF5werzuhs3bmDRokVYv349jh8/ru7HxMSgbt26amFW6Q3yFQyEyKulJgPj7wKunQV6TAFq9czzKvTmOKM0EfllIORPGAiR11szFljzPlCyPtB/lV1VY8LSPw79mpdD++qxXGOMiPwvEOrRo4eh7RYsWABfwECIvN61c8C4GkBaMtBvBVDaWN4b1xQjIn85f9tVPi87JSIvkr8ocNdDwO5ZWim9wUDIyCr0RES+gENjNrBHiHzCmd3AV62AwGDghb1AFBObici3JTh70VUi8iLFawNlmgHpqcDWKblec+znXafUtdwnIoK/zyxNRF6kybPAiT+AbdOAVq8CIRGGXsZcISLydewRIvIHVTsD0WWAGxeBvfPytOaYlNbL4/K8OfYcEZE3Yo8QkT8ICtYWY10+Etj0JVD3MSAgIFdrjslj8kp5XhKqJYGaPUdE5K3YI0TkL+o9BoTkA87tB46uzdOaYxIMyfPTNxzFO7/sx7N29BwREXkSBkJE/iKiEFC7l3Z785cOWXNs9KIDmLrhmMXn9N4k6TligjUReSoGQkT+pPGz2vVfS4CLf1vdTOYNcgS950h6mIiIPBEDISJ/ckcVoGI7LUTZMtnqZjJ5oqwl5qjpE+Ov3FDXTKgmIk/DCRVt4ISK5JMOLQe+fwgIiwJeigPCCuRqzTF7FI4MRc/6JbFw95lMuURcuJWInIETKhKRddIjVKQSkJQA7Jplc5mNSX3qITY678NkFxOT8dXao0yoJiKPwx4hG9gjRD5LhsUWvwIUrggM2QYEWh8ll+Esfc2x81eTVIK0I8nwmwRb64e35VpmROQQ7BHKo4kTJ6J69epo2NDYApVEXkeqx8KigYtHgMPLbW4q8wQ1rVgE3eqUxJPNyzs0d0gwoZqI3InJ0hYMHjwYcXFx2Lp1q+u/ESJXCMuvzSskNk0y/DIJikZ1qe6UQzJask9E5EgMhIj8VaMBQEAg8Pdq4Jzx4S7JHZrYuy4CHdkt5MCSfSIiezAQIvJXhcoCVTsZmmAx20sjw+CoReglnpLhNinZJyJyNQZCRP6syUDtevdc4PpFlw9j6Z1KMtwmw25ERK7GQIjIn5VtDhSrCaTeAHbMcNowlvT4PNNKS7Q2J9ViUqIvw21ERO7A1eeJ/JmsQN/kWeDnwcCWKUDT57SV6nOgzzwtC6taGyErHBmCkffXQGyUNuwlPT7D7q2WUYovwZT+OBGRu7BHiMjf3fUQkC8GSDgJ/PmL3dVjWcOYgFuX97vXRPe6JVXpvR7smJfimz9OROQuDISI/F1IONCgr3Z7k/GkaWszT3O4i4i8CWeWtoEzS5PfSDgDjK8JpKcAA9YAJeoafqn5zNMc7iIiT8CZpYnIPlHFgRrd7e4VEhzuIiJvxqExItJI0rTY9wNwNd6prSK9SBuPXMDPu06pa7lPROQOrBojIk3J+kCpRsDJLcDWKUDbN53SMkv3ncHbv8RlWoleKtAk+Zpl9ETkauwRIqLbmg7Wrrd8DSRddUoQNHDmjkxBkJAyfHlcniciciUGQkR0W7UuQJHKwM0rwLapDm0ZGf6SniBLg2D6Y/I8h8mIyJUYCBGR2b8IQUCLF7TbGycCKTcdkgv0446TeGvhvmw9QVmDIXleKtCIiFyFOUJElFnNh4HVY7QJFnd9DzTs57BcIFeuY0ZEZAR7hIgos+BQoPnz2u0NnwFpqQ7LBXLGOmZERHnBQIiIsqv7mLbsxuXjwP4FDssFsiXgVvWYrD9GROQqDISIKLvQfECTgdrt9eOA9HTDrSQ5Pvb2BOkrjkkJPdcfIyJXYiBERJY1fBoIiwLOxQEHlzo1x4frkxGRuzAQIiLLIgreTpRe9wlgMjklx2dk52pYP7wtJ1MkIrdgIERE1jUZBASHA6e2AX+vNtRSkuMjuT5Gc4KebF6ew2GOJkOZBgNXIn/H8nkisi5/UaDBU8CmL4DV7wMV2gABekaPZZLjI7k+UjWW06mYOUEGpacBl08AF48AF48C184C184Bif9qt29cBlJuACnXgdSb2kUEBAIBQUBgMBAUog11Sk9feEHtOjIGiC4FRJXSrtWlNBDEUwP5jwCTiX82WJOQkIDo6GhcuXIFUVFRLv1iiDzG1bPAZ7WB1BtA73lAlQ55nkeIa4vZkHgeOL0LOLMTOLMb+Pcv4NIxIC0ZLhEUBsRUBu64EygqlxraOnQFirnm/YlcfP5mIGTBxIkT1SUtLQ0HDx5kIES0bCTwx3+B4nWAAWty7BUyL6WXKrL4KzdwMTEZhfOHITZKK5FndZhMp23Sgpyja4Fj64ATm4Ar/1gPUAqXBwpXAAoU13rr5BJZFMhXGAjJd+sSDgRHaL1B6anaxZQGpCZrS6fcvKRdSy+S9CpdOam9p7o+CaQlWX5/6Skq1QAo2QAo0xQoUUebiZzIAzEQckNDEvl8L8X4WkBKIvCf74Fq97v7iLxX8nXgyCrg4BLg77XAlRPZtylSCShRVws8i1XX7keVdH7goQ/B/fundjn3JxC/F/j3AGDKMoVCeDRQvhVQobU2ZFqkonOPjcgODIQchIEQkZmVo4F1HwMxVYCBG5lHYo/rF4G/FgN/LtKCID2HR0j+TqmGQLmWQLkWWk+LBBmeJOkqcHoncHIr8M9W4PgfQNKVzNvIYr0SIN/ZRQviAlmLQ+7DQMgNDUnk82Q45bM6wI2LQJf/AvWfcNiu9SE0mYNIyu99YugsNQk4tAzYNRs49Js2RKUrWBa4szNQqT1QpgkQGgmvIsuunNkFHFkN/L0G+GczkJ5y+/kCJbSgSNatk+E0g0OpRI7CQMgNDUnkFzZ+Afz2mpaj8twObQbqPLKUVO3VydQylLR9OrDvB+DGpduPx9bUekskACpWw7eCg5sJWtD356/AoeVA8rXbzxWuCNT6D1DrYS3HicgFGAi5oSGJ/IL0ckxooOWRtH0TaPVqnnanL86atcxeDxEm9annHcFQWgpw4Bdgy2TgxB+3H5eAUQKA2r2AotXgF1JuAkd/1wJBaRMp6deVbQE0fEoLCGVxXyInYSDkhoYk8ht75wM/9ANCIoHntgNRxXM9HNZi7Cqb65LFRoVhw4h2njtMJrk/W78Btn0DXD1zO+enWhdt4VpJJPbnyqqka1owtGcO8PfvUianPR55h9Y+9Z8ECpV191GSD2Ig5IaGJPKrWYundgRObtGGPHp8navdbDxyAb0mb8pxuxfbV8HQ9pXhUa7GAxsnANum3R4GkjL2Bn2B+n1zHRz6NCnN3/EtsH0GcC3+1oMBWi5Rs+eB0o3cfIDkSxgIuaEhifzKqR3A5LbaX/hP/aYl/Nrp512nMHTOLkPbfukpQ2SXjgMbxgM7v789306xmkDzoUD1bhzuMTqM+NcSYNvUzMu2lG4CNH8eqHIfK87Ipedv1jcSkf1K1gPqPabdXvyqNv+MnY6dTzS8rSRTy1CaW3uAFr0CfF5fO4FLEFS6sTbT9rPrgFo9GQQZJUt9VO8KPP4TMGgTULcPEBQK/LMJmNMb+KIxsGdern5TRLnBmaVtYI8QkQ3X/tUCA5lPpsN7QLMhdiVJPztzh13NO7t/EzStWMT1OUDSA7T5a22JESF5P62GAWWb+VbllztJoLn5S2Dr1NvzE8m8RHcPB+7q4d95VpQr7BEiIufLfwfQ4R3t9qp3tcVADZCeHenhsZfMMeTS6rgNn2lrrMm1BEGlGgFP/AI8/jNQrjmDIEcqEAu0fwt4cR/QdiQQUQi4cAhY8DQwsbFWgSa5aUROwKExIsq9ek9oMyJLoPDrC9raWTmQiRNtVYpZIxMtOp0cv+SvfNEEWP5/QFICUOwuoNdcoN8ybUkJcp7wKKDVK8ALe4F2/3c7IJr/FDC5jTZ5I5GDMRAiotyToaEunwHB4dpJSiYSdHDPTsCtCRZltmmnklXeZz4IzH4EuPi3VgXW7QvgmXVA1XvZA+RKYQWAli9rAVGbN4DQAtpM1t92A759ADhtLMmeyAgGQkSUN7LYpkyuKJa+pgUUDuzZkT6mkZ2rOW8uIZn9eckI4IumwJGVWuJu8xe0OZLqPsoKJncHRHcPA4buAhoPBAJDtEqzr+8GfhoMXD3r1sMj38BAiIjyrslgbQVyGSKb30+bXdgK6dmRHh57wprRiw6oBGs9x0jmIJLye7nOdTWZVCVJBZgkfG+eBJjSgKqdtEqme97WhmnIM0TGAPd9ADy3DajZU3ts10ztu9vwXyA12d1HSF6MVWM2sGqMyM7Kn0nNgOsXgIZPA50/yXFpDWFPGNO/ZXn8uudM3tclO7Ze6wU6u1e7H1MVuHcMUKmdHUdDbvPPVmDJMOD0rcrDIpWAjmOAKh34pZDCCRUdhIEQkZ0OLgNm3fqL/f5xQIOn7FpsVUa/7O3gsWtdMlkjbdlIIO4n7X54NND6daBhP21+G/IeUkW2exaw4m0g8Zz2WOWOWkArw7Xk1xLsmFCRPUIOakgiumXtR1o5vay59dhPQPmWVptGhrWkikwSqM9fTVJDYLkhwVBsdDjWD29rOZcoOVErg1el8DeBgEBtKQxJxI108dxE5PiV79d+CGyaBKSnanlELV7Ukq1DXFBpSB6J8wgRkfu0fAW46yHtpDS3DxB/a/jJAglaZJLEbnVKIqZAWK7fUjqRpGdJgqpsvQZ7/gdMaAj8PlYLgqTcXyrB7v+UQZAvkFyuDu9quV2V2gPpKVpgJMO0LLcnA5gsTUSOL6nvNkGbgPDmZa3k+WycS+YJylSaf3wjMKUdsKA/kHAKiC4DPPytNili7F15fi/yMDGVgUfna99x/ljg4hHtt7fgGSDxvLuPjjwYAyEicryQCKDPfKBEXS15ekaXHOd+0avJ8kIFUzLD9f+eAKbdqyXThubXJucbskVbGJXLYvgu+W7lO5bvumF/bdB0zxxgQgNgx3eGJvwk/8McIRuYI0TkgDl6ZnQF4vcAwRFAj6+0E5UD1yDT1Y+6jHnV/0CgnPhkWE7ygOo+ps1xlL9oHj4Eea2T24Ffht6uDizbHOj6OZOp/UACk6Vd35BEZMXNK8C8vtpkhaLZc1qSsvQaWbB4z2kMmb3TcPVY2YB4DAn6CQ+GbECgzAUkKrYF7hnNITAC0lK1eaJWvw+kXNdmQZffX9PBXMzVhyUwEHJ9QxJRDiejZW9qJyRRuALQ6WMtYLEwVLV4zxkMmmWrZ8iEpoFx6BO0HB0DtyE44NaCnBXbAa1HAKUb8eugzC4d13qHZGZqUbI+0G0iULQaW8oHMRByQ0MSkQGyoOmvLwFXT2v3S9TTeoiqdARCI23OMxSIdFQPOIb7grbg/sBNKBt4a+4YUeke4O7hQOmG/BrIOskR2vkd8NubQNIVrdRefjctXuA8Uj6GgZAbGpKI7BgqWz0G2D5NK2cXQWFAuebaSu+FywNhUeqklX7zCuKPHUDiyf2ITdiNAriesZtEhONChe4o02EIh8DIzn/cTwO/vggcXKrdL1YTeGAiULw2W9JHMBByQ0MSkZ2u/Qts+Uqb5+fycUMvSQ3Jj3MxTXCtUldUbP4ggsLzs9kp971De+cDS17VkvoDgrSeoVbDOBGjD2Ag5IaGJKI8nJBkxfqjvwMX/9bK32XxVqn6CskHFCqn5RSVaqD95R4UnGlW6vgrN3AxMRmF84chNipcleE7baV68j3XzgGLX7297IqsOye5Qxxm9WoMhNzQkETkOpbWKcvTIqxEcT8Di165tW5ZANBkkDb1Qmg+to0X4hIbROSz9JXrLQVBQh6X52U7IsNkfqvBm4Faj2iLtmyaCExqChxdy0b0cZxZmoi8hgyHSU+QkSmGZDvZnrK34cYjF/DzrlPqmm1kJl9hbdLP3vOAqJLApWParOhSdi9J/uSTtMF2IiIvIDlB1nqCrC3CKou6kvUhRQ4lWlClg7aI64pRwLapwPbpwMFlwP3jgKr38ufkY9gjREReI9Oiqk7Y3h+HFOM5lGh9VXsJfJ5cpCXry9xXs/8DzO/HRVx9DAMhIvIa9q5Q74gV7X1hGMvWkKL+GIcSrSjXAnh2A9Dsea2Scd98YGIjrfSei7j6BA6NEZHX0Feoz2l4TIrni0WFId1kUkGEBETeVlbvyGGsnIYUOZSYA6kc6zAaqNEd+HkIcG4/8EM/YO88oPOnQHRJu74P8iwMhIjIa0ggI4GADPHY6huR567eTMWjUzZnPBYbFYZejcqgXEykRwVG+nxIMoynH9fyuHiLn1EfxprUp55dwZDRIUIOJeagZD1gwBpgw2fA2g+1mamPbQDavAY0eiZjjivyLgEmk+/37XXv3h1r1qxBu3btMH/+fMOv4zxCRN43j5CENkb+UfOEJGFLn0MCtpup6bh8PcXia+TzxUaHY/3wtoYDORlW6zV5U47bze7fxKHJ5ZaCPE8IPh3i3J/AwueAk1u0+0VrAJ0/Aco2dfeRETihYjYSBF29ehUzZsxgIETkI7LOLH3i4nXM2GhsqQ6hn47t7V1xdPJybv8StSdokbZqMXaV6lEyOSi4yolfVKilpwO7ZgLLRwE3LmqP1e4N3PMOkP8Odx+dX0uwY0Jkv0iWbt26NQoUKODuwyAiB5ITtgQC3euVUifY7zYZD4LcnSRsz3xIjhjG0ocUhaUwR45jZOdqDg2C/KJCLTAQqPc48Nx2oP6TWuvungVMqA9smQykp7n7CMkAtwdCa9euRZcuXVCiRAkEBATgp59urfdiZuLEiShXrhzCw8PRuHFjbNlyqyuSiPyenFQHzdqJ3MQy5knCnjgfkiMr4qQXRnq/pOfHktGLDmQKUHJbseaXFWoyEWOXz4CnV2gr2Mvki4tfAb6+G/h7jbuPjnLg9syuxMRE1K5dG0899RR69OiR7fm5c+fipZdewpdffqmCoPHjx6Njx47466+/ULRoUbVNnTp1kJqamu21y5YtUwEWEfkm/aSbVzK8Jid7V+Wy5CUpWR/GkmO0lwRDMpozaNaObM+ZJ2KL3A5rGa1Qm77hKGIKhPlW7pAsDNx/tTYJ48rRQPxe4NtuQOUO2nBZ0WruPkLy9GRp6RH68ccf8cADD2Q8JsFPw4YNMWHCBHU/PT0dpUuXxnPPPYcRI0bYlSck+7CVLJ2UlKQu5mOM8l5cdJXIMxlNAs5J4chQlWfkjFwWSwnDcj83x200r8lakrKeK2QrUMkXGojryem5em/Z/7jlBzFh9WG7Ppfe3vdUj/Wd5OrEC8DvY4Ft3wDpqdocRHUfA1qPAKL4B7on5Qi5vUfIluTkZGzfvh2vvfZaxmOBgYFo3749Nm7c6PD3GzNmDN5++22H75eInMNR5d7mQVBeytSNJgxLPo5c20pejs4XgvDgIMQnmFWUGQjQbCUpR0eE5jgkZykIEqZbxyX7loBFmActlxKTMXqR5Uq+nEg7PDtzBwrmC8lULefVydWRRYBOHwKNn9GW6jjwC7BjBrB7jpZX1OJFzj/kITy6R+j06dMoWbIk/vjjDzRterskcdiwYfj999+xefPtOUJskcBp9+7dahiucOHCmDdvXqb96dgjROSfPULWSCn7hhHt1G17eyqsVYXprxrQqjy+XntU3TZZ6Xmxt4ckp/d8qnk5fLPhGPLqoXolsf7whUxBmjM4q7LPLWX9J2TtsreBE39o94NCzQKiUs59bz+U4Cs9Qo6yYsUKQ9uFhYWpCxF510zT1npWhJzeOtcsjm3HL2U6cUeGBiEx2XZVT3xCEobO2Yntxy8ZzpeRk+ymIxcw/Ie9VhOG5ZgW7j6Dib3rZetFKXZr4sek1HR1sjZ6ks4pSVn28OOuU3CE+Tscs5+c6J/l9R/34u4qRbHrn8t5Dl7cVtZfpgnQdzFwbB2w5gPg+AZg6xRtQVeZsbrxQKBUffiNpKvAqe3AP1uBpARt5m438ehAKCYmBkFBQTh79mymx+V+bKzWNUtE/st8pmlrEylO7F0XnWqVUIHChFWHMW3DUVy+kZJjEKT7dU/2Um9rQ2e2Jnq0lDBcKDJUzd2j904cO38ds7ecwLgVh2yepK3lHeWUpHwxMSVbPpQ3kOOuPmpppqW9chO8WOsxc9RQaI4CAoDyrbTL0XVaDpEERrJUh1xKNgCaDASqdQWCQ+EzTCbg0lEt6PlnM/DPFm2ZEtOtYdjgcKDtSLd9Zo8OhEJDQ1G/fn2sXLkyY7hMkqXl/pAhQ9x9eETkAfSy8Jz+ypdlK8avOJinuXus5ctIQJabCRI3HP5XBTEyH5K83tLxZT1JW5uNum6ZQobe84E6JTDVAcNjrpY1iePMrbyiL24Fuo7oMTP/Pp0+hFa+pXY5vRPY/LW2mOupbdoaZhGFgbt6ADUfBko30gIob3IzATi9Azi57dZlK3D9fPbtoktrn69UIy2hHH4aCF27dg2HD9+uMDh69Ch27dqlcnnKlCmjSuefeOIJNGjQAI0aNVLl85Lr07dvX7ceNxF5DgkQbOXTOGICQ1tzEMl75Wb/E1YfwQ87TqnkaZnHJ6eTtJS+D55loUcjIQlL9sUbek9pp+iIkEy9Tt5syOydmIAAdKrlmLJ+2U4PTF0yhFaiLtB9EnDP29ow2dZvgGvx2rCZXAqWBWr2BKrcq611FhgEj5KeBvz7lxbsyEWGu84dyN4/GxgClKgDlG4MlGqoBUAeUj3n9mRpKWtv06ZNtscl+Jk+fbq6LWXvH330EeLj49WcQf/9739VWb2zca0xIt/gzKTqzx6powKv3O7f6NpoIi/DWubLaIjmH6xyerKzK32Zw7CWTAw5dM4uQ99nWHCgzaRzpw6hpaUCx9YCe/6nVZolX7v9XHg0UKE1ULGtNrxWqLxre4uSrgJn9wNn9wHx+7Tb5+IyH6Muuow2r5IEPXIdWwsIsW8SUL9JlpblL3KKxWQYzJVDYTKTtVzS0jg9OpEvcOaq6hIE5WX/9vwlmpcgSEhvht5L9lZXLbfKY8qG8+ithfszDWvldibumMgwvDJ/t11DaA4lK9hLoCOXzp8Cfy0G4n4Gjv6uzVgtt+WiB0Yyk7W61AEKV9CGmyJjch8gpdwAEk4DF49qeT36tQQ8l6wMqYZEar1VeuAjuU4FisFbuD0Q8kSDBw9WFz2iJCLvJcNi56/enijVUcxneN709wV4MhkK69u8XMb8P7Zyq7yVDA9KMvzQ9pVzVWGof59yw54hNKcKzQfUfEi7SE+R5BMdWQkcWaXdlsDo6FrtYk6Sj6NKAgWKA6GR2n5Cbl1EWhKQmqxdp9wErl8AEs8Biect9+6YK1ACiL0LKFYDKCbXdwExlT1vyM6bhsY8GYfGiLyb0SouewWYVaQdOpeIqev/xpWb2Zf58TS2KtBkmZENh89j+YFzuHLj9qSGRhUID8LVm+7vRZfk6UKRYRZzxfSEdltzN8m0BUaH0LrVKQm3kUDm3z+BM7uAM7u15TwunwCuSq5YHk/rwRFA4fLa0Ju6LgfcUVULemRdNR87fzMQclBDEpFnyU0Vlz0BRdfaxTF328lMMyF7OqPLZEhgJFV2Ul1mLYepYEQw+jYvr4Kmn3adNjxsN6RNRVQuVgCHzl6zeykOIyTmMV/PNWvwl1MStNF8stn9mzi/Ryi3AdLV08CVk1pQlHJdG+5KTtRui6AwrVRdXYcB+YoA+YsCkXdow2phUd5XqZYFAyE3NCQReQ4ja2rllpzIqxePtrhwqT0naHcxT5o2Mjt21qCh4K1htiFtK6tgyd5gUw8gnD0ruK3gz1ZZvP7byWkIzUj7kft4VbI0EZGj5VQqnRdBgYFqNmijBreuiBaV71BrcUnpu3BnPGRPjoulaQnqly2kZtpeuOuU1ZL/nHKqxKXEJJcEh5YSnOVi7bPbmqTTPOlcSDDnEwvE+jkGQkTkc4xWcUnvhswyrTNyYv5spX3z71SJLZBx0p0UmD052dZ7ymk1KiIYV2+mOjxgMNpG5kGD9BDd/dFqu4PMrFVrsp/Bs3a6LCC0N8HZWiK5vuityNrj6NULxPo5BkIWsHyeyLsZLZWWtb4CAwMyraBu75CXPcdiqYfFWk+R3htx5UaqW9vIETlXegAhn9/I5Jb2zK1kD3umObA2Sae14UCXLdNBDsdAyAKWzxN5N6Ol0k0qFsk2nPHiucoOm3W5uNlQkM7SsIylniJZfPVmarrNZGw59CealsO0P+xbMkNeJwGYUXmZmVtmzX6yefmMdjYybOmsniJJ0JbhLKPDWFm/KyPLdMh8RgXCQ3D+WhKHzLwEAyEi8jlG8zwsnQzLxUQ65BgCbLyHkd6HdJMJj07ZbPN1MlxWqlCE3ccmr5NeKAnAjPRe5CbnSg82zYMge4ctpSLNkUGRVKnJJbfDWEaW6ZD5jMy/Nw6Zeb5Adx8AEZEz6HkeapI8M3Lf1vCFvUNGlhTKF2L3EIne+yBz08i19CgYXXZDTra5SdOV3g3p5ciJvTNn2wo2jbavlOab7yvrvvNCH8aS4T575GYG8dy+F7kOe4SIyG8XY83NsJotsteh7SrjuXaV81xBZDRgiI2OsNr75agEYnuDQ/OcoNwOWw5pWwlVY/NbTFjudFcsvtlg33CgI5bKyE2Q7IxlOWyV/5P9GAgRkU+zVSptbfvcBBa4tW3jCtnzjnJzMjMaMOivy+1yGUZ6OYwEh4UjQzDy/hqIjbJ9YrZn2NJaICv38xIIWQsE8/qd2PNeuZXThJBkPwZCRERZ5GUdLiOBhZGTmb15TlmDBllfTeb5cUQvh5Fjeb97TcMn4pzK0833YymQNRKQGA1i9e8rr9+JKxb/tVa5x4q1vOESGzZwZmki/5Z1uQkjclp6wdrJzNryF7ntAXDGDMmO7o3IyxCPo5ZQke/ryo3kPH8nRivoYgqE5Wo4K6fZ0jnjdWZcYsOB8wgdPHiQS2wQ+TGjy3UYORHl9mRmLWDIKZAwssiovQGMJ+WnLN5zGkNm78z1ZJOxUWFYO6wtWn24GvEJuf9OYiLD8PK83TibYL2HKqc10HLi9WuguRiX2MgjziNERPaWjpsMlMsbKb+2lEtiaXjISO+MPUNQzsq5ciZZZT4vM27LPE0v/2+n1SAo63ei5ydZCgLf6mp7yCzrcdo7nGV0WC2vw2/+iDlCREQOOLE81bxcjic0R53M7MkVyU3lnLfI60lfJqv8ZU+8oW2/3XgUA2duz7Qki3ngaS3otLaEij3VZNL7JDlfRsj360m9dt6AgRARkQNKpuVk5qh92drOyOzGWU+untSL40iOmPPJqCX7zmZ7TALPZ2fuwIvtK6uJOOV4fn+1jVqU1kjCut7bNH3D0WwTT9qbj6QP4cmM4VwHzT6cUJGIyAa9Qsna39MBVpbScNa+7Ble83U5taez6cGoLMkydM4ulcPT6L0V2HL0Au6vVUIlRhshwZIEL1knXdR7/owEQaJr7eJqxvCs23NSR9sYCBER2aCXTNua5djIUhr6cMV9d8Vm9NzkZl/MFTH23biLDJ1JYFT/3eU4dj7R8OuyBiv2rO8mPUETe9fFwt1nrPYU2jOTuL9hIERE5KTlOnRycpO/+KXHQC/DD8hy5ja6L0cMr/nDd+Nukn8kAVHBfCGGgrSswYrRJP3HmpTBsHvvVNuypzB3mCNERGRAbpOOrSU263+Y92teDu2rxxpOaLVnxml/+242HbmghobME5o9hZEJGM2HNY32/H236YS6GMWqsuzYI0RElMuFUY0Mh9ka3pBXL94Xb1dVj6OG6nyNfN7mlWPwwYM1VTsEeFDP0Avtq9jVYyXBij3Davbwl55CezAQIiJyEmclNud1qM6XWWsbGaKSiznpWXumVXl17UzlYvKpCRllZmkjFu89o4bVHE3Wg5M5k2RyRuYK3cahsRxmliYiyi1nJjb78vxAeWWtbYSl9hp2bzVVwm5kbbbckPeS95ES+Snrj+a4aOtv+7OX6jvCxcQUvDh3l7rNhVpv41pjNnCtMSLKCy6L4NnsXSZDVzAiBC0qx+DXPZnL3Y0sz7F4zxkMmqUte+JOeVlmJbdcOdEjl9ggIvIATGz2XJYmKpShM31qA0vBUNbE9vtrncGIBXtVHhAM5GvJe45eFAdXkGqyOqUL4b3FB3AxMTnb8/rnG/HDXhQID0GTCjnnvOWFoxfsdST2CNnAHiEiyitnLHxKjvlOLK02b7oVEJkHN7ZO2NLLMWHVYUzbcNTq8hu23tOZJCdIhsOMKG4gKNF7dOKv3FDBVeH8YYiNyrlnx1Z7O+v/Aa4+74aGJCLyxr+G/Y2czLMuQZH15FwsKgyfPFwH568lGR7CsTbsI487uqxfArUrtwI1RwVWATkEJbaW+sgpUMypvbMOHzoCh8aIiDwIE5u9q5IvPiEJgQEBapoEoyyt52Z0nTB7fdCjprp25L5NNhaBzak364yFxX5zUznprvXwWDVGROQCvrrwqbdx1RIlzhgKk9hkQq/bwYYELI6sdjNZCErsWerDUhDlDUvCcB4hIiLyG65YosSe4MEeE3rVRadat3tcJOAwurCrPTYc/jdjnqFNf18w1OukB1ESmJnPUeQNS8KwR4iIiPyGKyr5jK4TZi5faBDCQ4IsVnjZysExGkAUjgy1uG9LJqw+gh92nFKr2c/dehL2kN6pyev+Rq9GZVAuJlJNSyAJ1damJfCEJWEYCBERkd/QlyiRYausZfKOWqIkN8M815PT1EUqvbrVLoFShfJlq8qylJB9KVHymW6vXWct0Hi9UzU8N3un4eM5c+Umvlp7FLkhOVbmM2Nbm5bAU5aEYSBERER+uQxH1mTjWAdV8uVlmOdSYgqm/3E8W+KxtXmPLM1hlJUs7eGsWbON0CvcorMcr6PaO68YCFnAJTaIiHybMyv5chp+g41JGy1Vb1lLvM4pCNKTqwtFhjq8cs0e+mcKDw7E9083tmtaAldgsrQFgwcPRlxcHLZu3er6b4SIiFxaySdl8nLtqJOyPvwmsu5Rv28yWL2Vl8RrGS6TIMidFVnWpiVwZHvnFQMhIiIiJw2/yfCPObkvS3UYIQFMbhKvs+7DnRVZWXlCUJYVh8aIiIgcTHpyoiNCMaxj1WzLUUhw882GYznuQwKYvAYO+hBUTkN19pB+nKiIYFy9mWo1SdsaTwrKdAyEiIiIXLSkigwH2VPCL0FTbpjvw1alnL3011+5kWr3awtGhCDdZFJBoqcMiwkOjRERETmInticdThLgh55XJ43kkOUNWiyJ2ywVJZubaiueHQ4nmlVXl0beVzWYZNqtdyQtdYenbJZrT0m7eApuPq8DVx0lYiIjLJ3gVGji/HqwRUszMNjslBGb76PrHMP1S9bCNuPX7K4OOwWK4vGmj8uPToSzOSFM1ed13HRVSIiIhezd4FRoyX8Oc17ZG0ftgKtrAvKBllZCy/r4z/vOmW4PeyZIsCdmCNERETkALlZYNToYrw5BU1Z92Ft7iF9iC63vTFFDSY7P9akDL7bdMKjV53XMUeIiIjIAZy9wKjReY9szT2kPybPmy+OalROOUvyuDxfr2xhrymnZyBERETkAEaDBGcvMGrPEJ29jCZ6y1QB3lJOz0CIiIjIAeypBvO0ITpHTRapD7l5SlBoBHOEiIiIvGRBV0cP0aVZqRbLa86SrbmLPGXVeR0DISIiIi9Z0NUIoxM2XkpMzlbub6l835qcEr09ISg0gvMI5bD6/MGDB3HlyhVERUW5/tshIiLKBVtzD4kBrcrj67VHswVKzpjjJ7e9Tq6aR4iBkIMakoiIyJNYm0doZOdqGL3ogM2E6oIRIZj4aD00qeA5q8TbgxMqEhER+TlrQ3RGVrS/fGs5DHuGyjyhJyg3mCNERETkoyzl8dhTLRafywkYjS4f4glYPk9ERORH7Jm7x5SLCRiNLDzrSRgIERER+RF7V7Q32TEBozNntXYWBkJERER+xNbEj7YYGVJz5qzWzsJAiIiIyM9Ymx06r0Nqzp7V2hkYCBEREflpMLR+eFt836+xKpeHA5bDcPbCs87AQIiIiMiPh8maV47BBw/WVAFPXtdI86Y1xnQMhIiIiPzcvQYWUvWmhWftwZmlbeDM0kRE5E/SHDQJorvnEeISG25oSCIiIvKMmaW5xAYRERG5NYgJymF1ek/BJTaIiIjIY4a1XI3J0kREROSVy2M4AgMhIiIigjcuj+EIDISIiIgI3rg8hiMwELJg4sSJqF69Oho2bOj6b4SIiMgNznnh8hiOwEDIgsGDByMuLg5bt251/TdCRETkBkW9cHkMR2AgRERERPDG5TEcgYEQERERwRuXx3AEBkJERETk0DXHvAknVCQiIqIMEuzcUz3WbctjuBoDISIiIvLK5TEcgUNjRERE5LcYCBEREZHfYiBEREREfouBEBEREfktBkJERETktxgIERERkd9iIERERER+i4EQERER+S0GQkREROS3OLO0DSaTSV0nJCS46vsgIiKiPNLP2/p53BYGQjZcvXpVXZcuXTqv3wkRERG54TweHR1tc5sAk5FwyU+lp6fj9OnTKFCgAAICAhwerUqA9c8//yAqKsqh+ya2s6vx98y29jX8TXt3O0toI0FQiRIlEBhoOwuIPUI2SOOVKlUKziRfPAMh52M7uwbb2XXY1mxnXxLlhHNhTj1BOiZLExERkd9iIERERER+i4GQm4SFhWHUqFHqmtjO3o6/Z7a1r+Fv2n/amcnSRERE5LfYI0RERER+i4EQERER+S0GQkREROS3GAgRERGR32Ig5EQTJ05EuXLlEB4ejsaNG2PLli02t583bx7uvPNOtX3NmjWxePFiZx6eX7bz/v378eCDD6rtZbbw8ePHu/RY/aWdJ0+ejJYtW6JQoULq0r59+xx//2R/Oy9YsAANGjRAwYIFERkZiTp16uC7775jUzrhN21uzpw56t+PBx54gG3t4HaePn26alvzi7zOqWSJDXK8OXPmmEJDQ01Tp0417d+/39S/f39TwYIFTWfPnrW4/YYNG0xBQUGmDz/80BQXF2d68803TSEhIaa9e/fy63FgO2/ZssX0yiuvmGbPnm2KjY01jRs3ju3rhN9z7969TRMnTjTt3LnTdODAAdOTTz5pio6ONp08eZLt7cB2Xr16tWnBggXq34zDhw+bxo8fr/4dWbp0KdvZwb9p3dGjR00lS5Y0tWzZ0tStWze2s4Pbedq0aaaoqCjTmTNnMi7x8fEmZ2Ig5CSNGjUyDR48OON+WlqaqUSJEqYxY8ZY3P7hhx82de7cOdNjjRs3Nj3zzDPOOkS/bGdzZcuWZSDkgnYWqamppgIFCphmzJhh9C39Ul7bWdStW1f9IUWOb2v5HTdr1sw0ZcoU0xNPPMFAyAntLIGQ/NHkShwac4Lk5GRs375dDQeYr1sm9zdu3GjxNfK4+faiY8eOVren3LUzuaedr1+/jpSUFBQuXJhfgZPaWf6wXblyJf766y+0atWK7eyE3/Q777yDokWLol+/fmxfJ7bztWvXULZsWbUYa7du3VRKgzMxEHKC8+fPIy0tDcWKFcv0uNyPj4+3+Bp53J7tKXftTO5p5+HDh6tVoLMG+5T3dr5y5Qry58+P0NBQdO7cGZ9//jnuueceNq2Df9Pr16/HN998o/LfyHntXLVqVUydOhU///wzZs6cifT0dDRr1gwnT56Es3D1eSJyqg8++EAll65Zs8b5SY9+qECBAti1a5f6K1p6hF566SVUqFABrVu3dveh+YyrV6/iscceU0FQTEyMuw/HpzVt2lRddBIEVatWDV999RVGjx7tlPdkIOQE8j9KUFAQzp49m+lxuR8bG2vxNfK4PdtT7tqZXNvOH3/8sQqEVqxYgVq1arH5ndDOMtRQqVIldVuqxg4cOIAxY8YwEHJgWx85cgTHjh1Dly5dMh6TngoRHByshiMrVqzI37cT/o0OCQlB3bp1cfjwYTgLh8acQLqo69evr/46M/+fRu6bR7rm5HHz7cXy5cutbk+5a2dyXTt/+OGH6i+4pUuXqhJvck47ZyWvSUpKYnM7sK1lWpO9e/eqnjf90rVrV7Rp00bdllwWcs5vWobWpO2LFy8Op3FparaflQyGhYWZpk+frkpbBwwYoEoG9TLAxx57zDRixIhM5fPBwcGmjz/+WJUbjxo1iuXzTmjnpKQkVdItl+LFi6tSerl96NAhx/8I/LidP/jgA1UyO3/+/ExlsFevXnXjp/C9dn7//fdNy5YtMx05ckRtL/9+yL8jkydPduOn8M22zopVY85p57ffftv022+/qd/09u3bTY888ogpPDxcld47CwMhJ/r8889NZcqUUScEKSHctGlTxnN33323+h/J3P/+9z9TlSpV1PY1atQwLVq0yJmH55ftLHOASPyf9SLbkePaWaYmsNTOEuCT49r5jTfeMFWqVEmdKAoVKmRq2rSpOvGQc/6NNsdAyDnt/MILL2RsW6xYMVOnTp1MO3bsMDlTgPzHef1NRERERJ6LOUJERETktxgIERERkd9iIERERER+i4EQERER+S0GQkREROS3GAgRERGR32IgRERERH6LgRAR+R2Z5n/AgAFq2n655nRqRP6LgRAR+Z3ffvsNBw8exJIlS/Dnn3+q9dCIyD8xECIin9aqVSvMmjUr02PR0dEoVKiQWrW9cOHC6pJXX375ZabVyYnIOzAQIiKftXDhQpw9exaPPPJIpsebNWuG5ORkFRDJ6taNGzc2vE9ZObtnz56IjIzEsWPHMh5/6qmnsGPHDqxbt86hn4GInIuBEBH5rP/+97/o27cvAgMz/1OXkpKCrVu3YtiwYeo6NTXV8D7btWuHefPm4Y477sj0eGhoKHr37q3ek4i8BwMhIvJ4iYmJePzxx5E/f36V4PzJJ5+gdevWeOGFF6y+5t9//8WqVassDlctWrRIBS7vvPMOgoKCsHjx4kzPv/rqq6oXyfwyceLEHI9T3kt6oW7cuJHLT0pErhbs8nckIrKTBCa///47fv75ZxQtWhSvv/66GoaqU6eO1desX78e+fLlQ7Vq1bI9N23aNPTq1QshISHqWu537do14/mPPvooV99RgwYNVO/S5s2bVaBGRJ6PPUJE5NGuXbuGb775Bh9//LEalqpZsyZmzJiR43DW8ePHUaxYsWzDYpIzJD1Affr0UfflWnqIpAfJiF27dqkgJz4+XvUUybHpJPCSvCN5byLyDuwRIiKPduTIEZXYbJ7QLFVeVatWtfk6GZ4KDw/P9vjMmTNx5513onbt2uq+9CpVqVIF33//vc2hNp1sv2bNGqvPR0RE4Pr16znuh4g8A3uEiMgnxcTE4NKlS9kel2Gw/fv3Izg4OOMSFxeH6dOnO+R9L168mC2Rmog8FwMhIvJoFStWVLk8knejkwBHJkS0pW7dumr4yjwYkgoxCXqkR0eGuPTL2rVrsWfPHuzcuTPPvVc3b95U701E3oFDY0Tk0aRSrF+/fiphukiRIipZ+o033siW+5OVBCPSK7Rhwwbcf//9Gb1BjRo1UpMsZtW0aVP1fF6CGJlDqEKFCip4IyLvwB4hIvJ4UsXVsmVLVZ7evn17tGjRAvXr17f5GimLlzmEJPdHSE/N7Nmz8eCDD1rcXh6XGaglHym3ZP/9+/fP9euJyPUCTFxtkIi8kFRuSeLy+PHjrW4jQ2M1atRQpfZly5Z16vFI3lHbtm3VkJ1UjhGRd2CPEBH5rNjYWFXefuLECae/15kzZ/Dtt98yCCLyMswRIiKf9sADD7jkfWTIjoi8D4fGiIiIyG9xaIyIiIj8FgMhIiIi8lsMhIiIiMhvMRAiIiIiv8VAiIiIiPwWAyEiIiLyWwyEiIiIyG8xECIiIiK/xUCIiIiI4K/+H4gN7fj126+RAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "out2 = model.intensity(volume_scale=result.x[0], contrast_density=result.x[1])\n", + "theory = np.interp(q_exp, out2[\"q\"], out2[\"total\"])\n", + "scale = np.sum(theory * i_exp / sigma**2) / np.sum((theory / sigma) ** 2)\n", + "plt.plot(q_exp, i_exp, \"o\", label=\"Experiment\")\n", + "plt.plot(out2[\"q\"], scale*out2[\"total\"], label=\"Polynomial model\")\n", + "plt.legend()\n", + "plt.xlabel(\"q (Å⁻¹)\")\n", + "plt.ylabel(\"I(q)\")\n", + "plt.yscale(\"log\")\n", + "#plt.xscale(\"log\")\n", + "plt.title(\"SBA fit to lysozyme data\")\n", + "#plt.savefig(\"/Users/isabelvinterbladh/Documents/HALRIC/pripps/tests/lysozyme/sba_fit_no_hydration.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ead57af1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pripps-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pripps-py/src/lib.rs b/pripps-py/src/lib.rs index fe665d4..7519b66 100644 --- a/pripps-py/src/lib.rs +++ b/pripps-py/src/lib.rs @@ -3,7 +3,10 @@ use pyo3::exceptions::{PyIOError, PyRuntimeError, PyValueError}; use pyo3::prelude::*; use pyo3::types::PyDict; -use ::pripps::{ExcludedVolume, Hydration, Intensity, MultipoleModel, Pripps, SolventConfig}; +use ::pripps::{ + DebyeModel, DirectModel, ExcludedVolume, Hydration, Intensity, MultipoleModel, Pripps, + SolventConfig, +}; #[pyclass(name = "Pripps", module = "pripps")] struct PyPripps(Pripps); @@ -72,6 +75,110 @@ impl PyPripps { .map(PyMultipoleModel) .map_err(into_py_err) } + + /// Prepare a stateful Debye fit. Caches exact per-q Debye terms + /// so `DebyeModel.intensity` is cheap to call repeatedly with + /// different `(volume_scale, contrast_density)`. + #[allow(clippy::too_many_arguments)] + #[pyo3(signature = ( + structure_file, + *, + qmin = 0.0, + qmax = 0.5, + nq = 100, + excluded = "none", + volume_scale = 1.0, + excluded_spacing = 1.0, + hydration = "none", + probe = 1.8, + spacing = 3.10516, + thickness = 3.0, + contrast_density = 0.03, + bulk_electron_density = 0.334, + ))] + fn debye_model( + &self, + structure_file: &str, + qmin: f64, + qmax: f64, + nq: usize, + excluded: &str, + volume_scale: f64, + excluded_spacing: f64, + hydration: &str, + probe: f64, + spacing: f64, + thickness: f64, + contrast_density: f64, + bulk_electron_density: f64, + ) -> PyResult { + let cfg = solvent_from_kwargs( + excluded, + volume_scale, + excluded_spacing, + hydration, + probe, + spacing, + thickness, + contrast_density, + bulk_electron_density, + )?; + self.0 + .debye_model(structure_file.as_ref(), qmin, qmax, nq, &cfg) + .map(PyDebyeModel) + .map_err(into_py_err) + } + + /// Prepare a stateful direct/explicit fit. Caches per-q-vector + /// amplitudes so `DirectModel.intensity` is cheap to call + /// repeatedly with different `(volume_scale, contrast_density)`. + #[allow(clippy::too_many_arguments)] + #[pyo3(signature = ( + structure_file, + *, + pmax = 6, + side_length = 200.0, + excluded = "none", + volume_scale = 1.0, + excluded_spacing = 1.0, + hydration = "none", + probe = 1.8, + spacing = 3.10516, + thickness = 3.0, + contrast_density = 0.03, + bulk_electron_density = 0.334, + ))] + fn direct_model( + &self, + structure_file: &str, + pmax: usize, + side_length: f64, + excluded: &str, + volume_scale: f64, + excluded_spacing: f64, + hydration: &str, + probe: f64, + spacing: f64, + thickness: f64, + contrast_density: f64, + bulk_electron_density: f64, + ) -> PyResult { + let cfg = solvent_from_kwargs( + excluded, + volume_scale, + excluded_spacing, + hydration, + probe, + spacing, + thickness, + contrast_density, + bulk_electron_density, + )?; + self.0 + .direct_model(structure_file.as_ref(), pmax, [side_length; 3], &cfg) + .map(PyDirectModel) + .map_err(into_py_err) + } } #[pyclass(name = "MultipoleModel", module = "pripps")] @@ -98,6 +205,54 @@ impl PyMultipoleModel { } } +#[pyclass(name = "DebyeModel", module = "pripps")] +struct PyDebyeModel(DebyeModel); + +#[pymethods] +impl PyDebyeModel { + /// Compute I(q) for the given fit parameters. Returns a dict of + /// numpy arrays: `q`, `total`, `atoms`, `excluded`, `hydration`. + fn intensity<'py>( + &self, + py: Python<'py>, + volume_scale: f64, + contrast_density: f64, + ) -> PyResult> { + let out = self.0.intensity(volume_scale, contrast_density); + intensity_to_dict(py, out) + } + + /// The q grid the fit was prepared over. + #[getter] + fn q_values<'py>(&self, py: Python<'py>) -> Bound<'py, numpy::PyArray1> { + self.0.q_values().to_vec().into_pyarray(py) + } +} + +#[pyclass(name = "DirectModel", module = "pripps")] +struct PyDirectModel(DirectModel); + +#[pymethods] +impl PyDirectModel { + /// Compute I(q) for the given fit parameters. Returns a dict of + /// numpy arrays: `q`, `total`, `atoms`, `excluded`, `hydration`. + fn intensity<'py>( + &self, + py: Python<'py>, + volume_scale: f64, + contrast_density: f64, + ) -> PyResult> { + let out = self.0.intensity(volume_scale, contrast_density); + intensity_to_dict(py, out) + } + + /// The duplicate-averaged q grid the fit was prepared over. + #[getter] + fn q_values<'py>(&self, py: Python<'py>) -> Bound<'py, numpy::PyArray1> { + self.0.q_values().to_vec().into_pyarray(py) + } +} + #[allow(clippy::too_many_arguments)] fn solvent_from_kwargs( excluded: &str, @@ -119,9 +274,10 @@ fn solvent_from_kwargs( volume_scale, }, "voronoi" => ExcludedVolume::Voronoi { volume_scale }, + "per-kind" | "perkind" => ExcludedVolume::PerKind { volume_scale }, other => { return Err(PyValueError::new_err(format!( - "unknown excluded-volume model: {other} (expected `none`, `fraser`, `foxs`, `grid`, or `voronoi`)" + "unknown excluded-volume model: {other} (expected `none`, `fraser`, `foxs`, `grid`, `voronoi`, or `per-kind`)" ))); } }; @@ -191,5 +347,7 @@ fn pripps(m: &Bound<'_, PyModule>) -> PyResult<()> { pyo3_log::init(); m.add_class::()?; m.add_class::()?; + m.add_class::()?; + m.add_class::()?; Ok(()) } diff --git a/pripps-py/tests/test_smoke.py b/pripps-py/tests/test_smoke.py index dd7d975..5c72ad8 100644 --- a/pripps-py/tests/test_smoke.py +++ b/pripps-py/tests/test_smoke.py @@ -36,7 +36,7 @@ def chi2_per_point(q_theory, i_theory, exp): class TestMultipoleModel(unittest.TestCase): def test_prepare_and_call(self): - pr = pripps.Pripps("../assets/single_bead.yaml") + pr = pripps.Pripps("../assets/poly_amino_acid.yaml") fit = pr.multipole_model( "../tests/lysozyme/4lzt.xyz", qmin=0.0, @@ -52,7 +52,7 @@ def test_prepare_and_call(self): self.assertTrue(np.all(out["atoms"] > 0)) def test_refit_produces_different_curves(self): - pr = pripps.Pripps("../assets/single_bead.yaml") + pr = pripps.Pripps("../assets/poly_amino_acid.yaml") fit = pr.multipole_model( "../tests/lysozyme/4lzt.xyz", qmin=0.0, @@ -66,7 +66,7 @@ def test_refit_produces_different_curves(self): self.assertFalse(np.allclose(a["total"], b["total"])) def test_bad_excluded_raises(self): - pr = pripps.Pripps("../assets/single_bead.yaml") + pr = pripps.Pripps("../assets/poly_amino_acid.yaml") with self.assertRaises(ValueError) as ctx: pr.multipole_model( "../tests/lysozyme/4lzt.xyz", @@ -101,7 +101,7 @@ class TestPepsiFit(unittest.TestCase): def setUpClass(cls): cls.exp = load_lyzexp() q_max = float(cls.exp[-1, 0]) - pr = pripps.Pripps("../assets/single_bead.yaml") + pr = pripps.Pripps("../assets/gaussian_atoms.yaml") cls.model = pr.multipole_model( "../tests/lysozyme/4lzt.pdb", qmin=0.0, diff --git a/scripts/sasbdb_bench.py b/scripts/sasbdb_bench.py index c560104..b34779e 100755 --- a/scripts/sasbdb_bench.py +++ b/scripts/sasbdb_bench.py @@ -49,8 +49,8 @@ BENCH = REPO / "sasbdb_bench" CACHE = BENCH / "cache" FF_YAML_DEFAULT = REPO / "assets" / "foxs_formfactors.yaml" -# Override via --atomfile if you want single_bead.yaml (needed for -# `--excluded voronoi` to match PepsiSAXS's K=4π convention). +# Override via --atomfile if you want gaussian_atoms.yaml for the +# PepsiSAXS-style K=4π convention. SASBDB_API = "https://www.sasbdb.org/rest-api/entry/summary/{}/" diff --git a/src/cli.rs b/src/cli.rs index b4dd589..d7ba387 100644 --- a/src/cli.rs +++ b/src/cli.rs @@ -40,6 +40,8 @@ pub enum Commands { /// Number of q values #[clap(long, default_value = "100")] nq: usize, + #[clap(flatten)] + solvent: SolventArgs, }, /// Scattering intensity using discrete q vectors Direct { @@ -76,7 +78,7 @@ pub enum Commands { }, } -/// Solvent correction parameters shared by Multipole and Direct. +/// Solvent correction parameters shared by Debye, Multipole, and Direct. #[derive(Debug, Clone, clap::Args)] pub struct SolventArgs { /// Excluded-volume model @@ -116,7 +118,7 @@ pub struct SolventArgs { impl SolventArgs { /// Build a [`SolventConfig`], returning `None` when neither /// excluded volume nor hydration is active. - fn to_config(&self) -> Option { + fn to_config(&self) -> Result> { let excluded = match self.excluded { ExcludedKind::Disabled => ExcludedVolume::Disabled, ExcludedKind::Fraser => ExcludedVolume::Fraser { @@ -132,6 +134,9 @@ impl SolventArgs { ExcludedKind::Voronoi => ExcludedVolume::Voronoi { volume_scale: self.volume_scale, }, + ExcludedKind::PerKind => ExcludedVolume::PerKind { + volume_scale: self.volume_scale, + }, }; let hydration = match self.hydration { HydrationKind::Disabled => Hydration::Disabled, @@ -155,7 +160,7 @@ impl SolventArgs { hydration, bulk_electron_density: self.bulk_electron_density, }; - cfg.is_active().then_some(cfg) + Ok(cfg.is_active().then_some(cfg)) } } @@ -168,6 +173,7 @@ pub enum ExcludedKind { Foxs, Grid, Voronoi, + PerKind, } /// Which hydration-shell generator to use. @@ -239,7 +245,7 @@ pub fn do_main() -> Result<()> { pretty_env_logger::init(); let pripps = Pripps::from_yaml(&args.atomfile)?; - let (scheme, box_override, solvent) = command_to_scheme(args.command); + let (scheme, box_override, solvent) = command_to_scheme(args.command)?; if let Some(fit_path) = &args.fit { // Fit mode: optimise (c1, c2) against experimental data @@ -247,6 +253,25 @@ pub fn do_main() -> Result<()> { let solvent = solvent.unwrap_or_default(); match scheme { + IntensityScheme::Debye { + qmin: _, + qmax: _, + nq, + } => { + let model = pripps.debye_model(&args.input, 0.0, exp.q_max(), nq, &solvent)?; + let result = crate::fit::fit_parameters(&exp, |c1, c2| model.intensity(c1, c2))?; + let out = model.intensity(result.volume_scale, result.contrast_density); + crate::fit::write_fit_csv(&args.output, &exp, &out, result.scale)?; + } + IntensityScheme::Direct { pmax } => { + let box_sides = box_override.ok_or_else(|| { + crate::Error::usage("direct fitting requires box side lengths") + })?; + let model = pripps.direct_model(&args.input, pmax, box_sides, &solvent)?; + let result = crate::fit::fit_parameters(&exp, |c1, c2| model.intensity(c1, c2))?; + let out = model.intensity(result.volume_scale, result.contrast_density); + crate::fit::write_fit_csv(&args.output, &exp, &out, result.scale)?; + } IntensityScheme::Multipole { qmin: _, qmax: _, @@ -259,11 +284,6 @@ pub fn do_main() -> Result<()> { let out = model.intensity(result.volume_scale, result.contrast_density); crate::fit::write_fit_csv(&args.output, &exp, &out, result.scale)?; } - _ => { - return Err(crate::Error::usage( - "fitting is currently only supported by the Multipole scheme", - )); - } }; } else { let xtc_opts = args @@ -290,31 +310,37 @@ pub fn do_main() -> Result<()> { /// Translate parsed CLI arguments into an [`IntensityScheme`], an /// optional explicit box override (used by `Direct`), and an optional -/// [`SolventConfig`] (only produced by `Multipole` when an excluded or -/// hydration model is selected). +/// [`SolventConfig`] when an excluded-volume or hydration model is selected. fn command_to_scheme( command: Commands, -) -> (IntensityScheme, Option<[f64; 3]>, Option) { +) -> Result<(IntensityScheme, Option<[f64; 3]>, Option)> { match command { - Commands::Debye { qmin, qmax, nq } => { - (IntensityScheme::Debye { qmin, qmax, nq }, None, None) - } + Commands::Debye { + qmin, + qmax, + nq, + solvent, + } => Ok(( + IntensityScheme::Debye { qmin, qmax, nq }, + None, + solvent.to_config()?, + )), Commands::Direct { pmax, side_length, solvent, - } => ( + } => Ok(( IntensityScheme::Direct { pmax }, side_length.map(|s| [s; 3]), - solvent.to_config(), - ), + solvent.to_config()?, + )), Commands::Multipole { qmin, qmax, nq, lmax, solvent, - } => ( + } => Ok(( IntensityScheme::Multipole { qmin, qmax, @@ -322,8 +348,8 @@ fn command_to_scheme( l_max: lmax, }, None, - solvent.to_config(), - ), + solvent.to_config()?, + )), } } diff --git a/src/debye.rs b/src/debye.rs index 8d258e0..0cba383 100644 --- a/src/debye.rs +++ b/src/debye.rs @@ -4,10 +4,13 @@ // you may not use this file except in compliance with the license. use crate::q_sampler::QSampler; +use crate::solvent::SolventConfig; use crate::{ - Intensity, IntensityCalculator, Result, Structure, Vector3, + Contributions, Intensity, IntensityCalculator, Result, Structure, Vector3, formfactor::{self, FormFactorMap}, }; +use itertools::Itertools; +use rayon::prelude::*; /// Returns sinc(x) = sin(x)/x with graceful handling of x -> 0 #[inline] @@ -22,16 +25,28 @@ pub fn sinc(x: f64) -> f64 { pub(crate) struct DebyeIntensity { sampler: QSampler, + solvent: Option, } impl DebyeIntensity { + #[cfg(test)] pub(crate) fn new( formfactors: FormFactorMap, q_minmax: (f64, f64), num_q: usize, + ) -> Result { + Self::new_with_solvent(formfactors, q_minmax, num_q, None) + } + + pub(crate) fn new_with_solvent( + formfactors: FormFactorMap, + q_minmax: (f64, f64), + num_q: usize, + solvent: Option, ) -> Result { Ok(Self { sampler: QSampler::linear(formfactors, q_minmax.0, q_minmax.1, num_q), + solvent, }) } } @@ -39,9 +54,142 @@ impl DebyeIntensity { impl IntensityCalculator for DebyeIntensity { /// Calculate I(q) over a range of q values using the Debye formula fn calc_intensities(&self, structure: &Structure) -> Result { - self.sampler.run_parallel(|q| { - intensity_at_q(&structure.ids, &structure.pos, &self.sampler.formfactors, q) + match self.solvent.as_ref().filter(|cfg| cfg.is_active()) { + None => self.sampler.run_parallel(|q| { + intensity_at_q(&structure.ids, &structure.pos, &self.sampler.formfactors, q) + }), + Some(cfg) => { + let model = build_model(&self.sampler, structure, cfg)?; + Ok(model.intensity( + cfg.excluded.volume_scale(), + cfg.hydration.contrast_density(), + )) + } + } + } +} + +/// Precompute Debye pair sums for atoms, excluded volume, and hydration. +/// +/// Unlike the vacuum Debye path, fitting needs repeated evaluations at +/// different `(volume_scale, contrast_density)`. The full Debye sum is +/// quadratic in those two scalar knobs, so caching the three self terms +/// and three cross terms per q makes refits cheap. +pub(crate) fn build_model( + sampler: &QSampler, + structure: &Structure, + cfg: &SolventConfig, +) -> Result { + let excluded_dummies = cfg.excluded.unit_scale_dummies( + structure, + &sampler.formfactors, + cfg.bulk_electron_density, + )?; + let hydration_dummies = cfg.hydration.dummies(structure, &sampler.formfactors)?; + + let terms = sampler + .q_values + .par_iter() + .map(|&q| { + let atom_ff = formfactor::formfactor_by_index(&sampler.formfactors, q, &structure.ids)?; + + let mut excluded_ff = Vec::new(); + excluded_dummies.form_factors_at(q, &mut excluded_ff)?; + + let mut hydration_ff = Vec::new(); + hydration_dummies.form_factors_at(q, &mut hydration_ff)?; + + let atom_ff = atom_ff.as_slice(); + Ok(DebyeTerms { + atoms: self_intensity_at_q(&structure.pos, atom_ff, q), + excluded: self_intensity_at_q(excluded_dummies.positions(), &excluded_ff, q), + hydration: self_intensity_at_q(hydration_dummies.positions(), &hydration_ff, q), + atom_excluded: cross_intensity_at_q( + &structure.pos, + atom_ff, + excluded_dummies.positions(), + &excluded_ff, + q, + ), + atom_hydration: cross_intensity_at_q( + &structure.pos, + atom_ff, + hydration_dummies.positions(), + &hydration_ff, + q, + ), + excluded_hydration: cross_intensity_at_q( + excluded_dummies.positions(), + &excluded_ff, + hydration_dummies.positions(), + &hydration_ff, + q, + ), + }) }) + .collect::>>()?; + + Ok(DebyeModel { + q_values: sampler.q_values.clone(), + terms, + }) +} + +/// Cached Debye state for solvent fitting. +pub struct DebyeModel { + q_values: Vec, + terms: Vec, +} + +impl DebyeModel { + /// Compute `I(q)` for the given solvent fit knobs. + pub fn intensity(&self, volume_scale: f64, contrast_density: f64) -> Intensity { + let per_q: Vec<(f64, f64, f64, f64)> = self + .terms + .iter() + .map(|terms| { + let total = terms.combine(volume_scale, contrast_density); + (total, terms.atoms, terms.excluded, terms.hydration) + }) + .collect(); + let (total, atoms, excluded, hydration): (Vec<_>, Vec<_>, Vec<_>, Vec<_>) = + per_q.into_iter().multiunzip(); + + Intensity { + q: self.q_values.clone(), + total, + contributions: Some(Contributions { + atoms, + excluded, + hydration, + }), + } + } + + /// The q grid the fit was prepared over. + pub fn q_values(&self) -> &[f64] { + &self.q_values + } +} + +#[derive(Debug, Clone, Copy, Default)] +struct DebyeTerms { + atoms: f64, + excluded: f64, + hydration: f64, + atom_excluded: f64, + atom_hydration: f64, + excluded_hydration: f64, +} + +impl DebyeTerms { + fn combine(self, volume_scale: f64, contrast_density: f64) -> f64 { + self.atoms + + volume_scale * volume_scale * self.excluded + + contrast_density * contrast_density * self.hydration + - 2.0 * volume_scale * self.atom_excluded + + 2.0 * contrast_density * self.atom_hydration + - 2.0 * volume_scale * contrast_density * self.excluded_hydration } } @@ -58,24 +206,44 @@ fn intensity_at_q( // TODO: Optimize by storing in static table, see std::sync::OnceLock let ff = formfactor::formfactor_by_index(formfactors, q, atom_ids)?; - let n = ff.len(); - let ff = ff.as_slice(); + Ok(self_intensity_at_q(positions, ff.as_slice(), q)) +} - // Compute I(q) = Σ_i f_i² + 2 Σ_{i f64 { + let n = form_factors.len(); + let diagonal: f64 = form_factors.iter().map(|f| f * f).sum(); if n < 2 { - return Ok(diagonal); + return diagonal; } let mut off_diagonal = 0.0; for i in 0..n - 1 { - let fi = ff[i]; + let fi = form_factors[i]; let pi = &positions[i]; for j in i + 1..n { let r = (pi - positions[j]).norm(); - off_diagonal += fi * ff[j] * sinc(q * r); + off_diagonal += fi * form_factors[j] * sinc(q * r); } } - Ok(2.0f64.mul_add(off_diagonal, diagonal)) + 2.0f64.mul_add(off_diagonal, diagonal) +} + +/// Cross Debye sum between two disjoint scatterer groups: +/// Σ_i∈a Σ_j∈b f_i f_j sinc(q r_ij). +fn cross_intensity_at_q( + a_positions: &[Vector3], + a_form_factors: &[f64], + b_positions: &[Vector3], + b_form_factors: &[f64], + q: f64, +) -> f64 { + let mut total = 0.0; + for (pa, &fa) in a_positions.iter().zip(a_form_factors) { + for (pb, &fb) in b_positions.iter().zip(b_form_factors) { + total += fa * fb * sinc(q * (pa - pb).norm()); + } + } + total } diff --git a/src/explicit.rs b/src/explicit.rs index b728f43..051b494 100644 --- a/src/explicit.rs +++ b/src/explicit.rs @@ -156,68 +156,116 @@ impl DirectTransform { box_sides: &Vector3, cfg: &SolventConfig, ) -> Result { - let excluded_dummies = - cfg.excluded - .dummies(structure, &self.formfactors, cfg.bulk_electron_density)?; - let hydration_dummies = cfg.hydration.dummies(structure, &self.formfactors)?; + let model = build_model( + &self.formfactors, + &self.directions, + structure, + box_sides, + cfg, + )?; + Ok(model.intensity( + cfg.excluded.volume_scale(), + cfg.hydration.contrast_density(), + )) + } +} - let volume_scale = cfg.excluded.volume_scale(); - let contrast_density = cfg.hydration.contrast_density(); +pub(crate) fn directions(p_max: usize) -> Vec { + MILLER_INDEX + .iter() + .flat_map(|dir| (1..=p_max).map(move |p| 2.0 * PI * p as f64 * dir)) + .collect() +} - // Per q-vector: compute three amplitudes A, C, B and combine - // as |A − c₁·C + c₂·B|² - let table = self - .directions - .par_iter() - .map(|dir| dir.component_div(box_sides)) - .map(|q| { - let qnorm = q.norm(); +/// Precompute the atomic, excluded-volume, and hydration amplitudes +/// for every explicit q-vector. The cached q-vector rows are combined +/// and only then averaged by q magnitude, matching the one-shot +/// [`DirectTransform`] path. +pub(crate) fn build_model( + formfactors: &FormFactorMap, + directions: &[Vector3], + structure: &Structure, + box_sides: &Vector3, + cfg: &SolventConfig, +) -> Result { + let excluded_dummies = + cfg.excluded + .unit_scale_dummies(structure, formfactors, cfg.bulk_electron_density)?; + let hydration_dummies = cfg.hydration.dummies(structure, formfactors)?; - let atom_ff = eval_atom_form_factors(qnorm, &structure.ids, &self.formfactors)?; - let a = complex_amplitude(&q, &structure.pos, &atom_ff); + let rows = directions + .par_iter() + .map(|dir| dir.component_div(box_sides)) + .map(|q| { + let qnorm = q.norm(); - let mut ev_ff = Vec::new(); - excluded_dummies.form_factors_at(qnorm, &mut ev_ff)?; - let c = complex_amplitude(&q, excluded_dummies.positions(), &ev_ff); + let atom_ff = eval_atom_form_factors(qnorm, &structure.ids, formfactors)?; + let atoms = complex_amplitude(&q, &structure.pos, &atom_ff); - let mut hyd_ff = Vec::new(); - hydration_dummies.form_factors_at(qnorm, &mut hyd_ff)?; - let b = complex_amplitude(&q, hydration_dummies.positions(), &hyd_ff); + let mut excluded_ff = Vec::new(); + excluded_dummies.form_factors_at(qnorm, &mut excluded_ff)?; + let excluded = complex_amplitude(&q, excluded_dummies.positions(), &excluded_ff); - let total = (a - volume_scale * c + contrast_density * b).norm_sqr(); - Ok((qnorm, total, a.norm_sqr(), c.norm_sqr(), b.norm_sqr())) - }) - .collect::>>()?; - - // Average duplicate q-norms (from different Miller directions) - let mut grouped = average_duplicates_5col(table); - grouped.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(Ordering::Equal)); - - let mut q = Vec::with_capacity(grouped.len()); - let mut total = Vec::with_capacity(grouped.len()); - let mut atoms = Vec::with_capacity(grouped.len()); - let mut excluded = Vec::with_capacity(grouped.len()); - let mut hydration = Vec::with_capacity(grouped.len()); - for (qi, t, a, e, h) in grouped { - q.push(qi); - total.push(t); - atoms.push(a); - excluded.push(e); - hydration.push(h); - } + let mut hydration_ff = Vec::new(); + hydration_dummies.form_factors_at(qnorm, &mut hydration_ff)?; + let hydration = complex_amplitude(&q, hydration_dummies.positions(), &hydration_ff); - Ok(Intensity { - q, - total, - contributions: Some(Contributions { + Ok(DirectTerms { + qnorm, atoms, excluded, hydration, - }), + }) }) + .collect::>>()?; + + let q_values = unique_q_values(&rows); + Ok(DirectModel { q_values, rows }) +} + +/// Cached explicit-transform state for solvent fitting. +pub struct DirectModel { + q_values: Vec, + rows: Vec, +} + +impl DirectModel { + /// Compute `I(q)` for the given solvent fit knobs. + pub fn intensity(&self, volume_scale: f64, contrast_density: f64) -> Intensity { + let table = self + .rows + .iter() + .map(|row| { + let total = (row.atoms - volume_scale * row.excluded + + contrast_density * row.hydration) + .norm_sqr(); + ( + row.qnorm, + total, + row.atoms.norm_sqr(), + row.excluded.norm_sqr(), + row.hydration.norm_sqr(), + ) + }) + .collect(); + + intensity_from_5col(table) + } + + /// The q grid the fit was prepared over after duplicate q-norms are averaged. + pub fn q_values(&self) -> &[f64] { + &self.q_values } } +#[derive(Debug, Clone, Copy)] +struct DirectTerms { + qnorm: f64, + atoms: Complex, + excluded: Complex, + hydration: Complex, +} + /// Evaluate form factors for each atom at a given q. fn eval_atom_form_factors( q: f64, @@ -267,6 +315,41 @@ fn average_duplicates_5col(data: Vec<(f64, f64, f64, f64, f64)>) -> Vec<(f64, f6 .collect() } +fn intensity_from_5col(table: Vec<(f64, f64, f64, f64, f64)>) -> Intensity { + let mut grouped = average_duplicates_5col(table); + grouped.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(Ordering::Equal)); + + let mut q = Vec::with_capacity(grouped.len()); + let mut total = Vec::with_capacity(grouped.len()); + let mut atoms = Vec::with_capacity(grouped.len()); + let mut excluded = Vec::with_capacity(grouped.len()); + let mut hydration = Vec::with_capacity(grouped.len()); + for (qi, t, a, e, h) in grouped { + q.push(qi); + total.push(t); + atoms.push(a); + excluded.push(e); + hydration.push(h); + } + + Intensity { + q, + total, + contributions: Some(Contributions { + atoms, + excluded, + hydration, + }), + } +} + +fn unique_q_values(rows: &[DirectTerms]) -> Vec { + average_duplicates(rows.iter().map(|row| (row.qnorm, 0.0)).collect()) + .into_iter() + .map(|(q, _)| q) + .collect() +} + #[cfg(test)] mod tests { use super::*; diff --git a/src/formfactor.rs b/src/formfactor.rs index 8b5c11c..99dceb4 100644 --- a/src/formfactor.rs +++ b/src/formfactor.rs @@ -30,7 +30,14 @@ use std::{cmp::Ordering, collections::BTreeMap, fs::File, ops::Add, path::Path}; // without churning call sites. #[derive(Debug, Clone, Default, PartialEq)] pub struct FormFactorMap { - entries: Vec<(AtomKind, FormFactor)>, + entries: Vec, +} + +#[derive(Debug, Clone, PartialEq)] +struct FormFactorEntry { + kind: AtomKind, + formfactor: FormFactor, + excluded_solvent: Option, } impl FormFactorMap { @@ -42,21 +49,23 @@ impl FormFactorMap { self.entries.is_empty() } - pub fn iter(&self) -> impl Iterator { - self.entries.iter() + pub fn iter(&self) -> impl Iterator { + self.entries + .iter() + .map(|entry| (&entry.kind, &entry.formfactor)) } /// Atom kind at id `id`. Panics on out-of-range id. pub(crate) fn kind(&self, id: usize) -> &AtomKind { - &self.entries[id].0 + &self.entries[id].kind } pub(crate) fn kinds(&self) -> impl Iterator { - self.entries.iter().map(|(k, _)| k) + self.entries.iter().map(|entry| &entry.kind) } pub(crate) fn form_factors(&self) -> impl Iterator { - self.entries.iter().map(|(_, f)| f) + self.entries.iter().map(|entry| &entry.formfactor) } /// Linear-search the table for the kind named `name` and return @@ -66,12 +75,37 @@ impl FormFactorMap { pub(crate) fn form_factor_by_name(&self, name: &str) -> Option<&FormFactor> { self.entries .iter() - .find(|(k, _)| k.name() == name) - .map(|(_, f)| f) + .find(|entry| entry.kind.name() == name) + .map(|entry| &entry.formfactor) + } + + pub(crate) fn excluded_solvent(&self, id: usize) -> Option<&FormFactor> { + self.entries[id].excluded_solvent.as_ref() + } + + #[cfg(test)] + pub(crate) fn from_entries(entries: Vec<(AtomKind, FormFactor)>) -> Self { + Self::from_entries_with_excluded( + entries + .into_iter() + .map(|(kind, formfactor)| (kind, formfactor, None)) + .collect(), + ) } - pub(crate) const fn from_entries(entries: Vec<(AtomKind, FormFactor)>) -> Self { - Self { entries } + pub(crate) fn from_entries_with_excluded( + entries: Vec<(AtomKind, FormFactor, Option)>, + ) -> Self { + Self { + entries: entries + .into_iter() + .map(|(kind, formfactor, excluded_solvent)| FormFactorEntry { + kind, + formfactor, + excluded_solvent, + }) + .collect(), + } } } @@ -262,6 +296,31 @@ struct AtomSpec { /// Form factor for the atom. #[serde(alias = "ff")] formfactor: FormFactorSpec, + /// Optional excluded-solvent form factor used by + /// `ExcludedVolume::PerKind`. + excluded_solvent: Option, +} + +#[derive(Copy, Clone)] +enum SpecRole { + FormFactor, + ExcludedSolvent, +} + +impl AtomSpec { + fn spec(&self, role: SpecRole) -> Option<&FormFactorSpec> { + match role { + SpecRole::FormFactor => Some(&self.formfactor), + SpecRole::ExcludedSolvent => self.excluded_solvent.as_ref(), + } + } + + fn spec_mut(&mut self, role: SpecRole) -> Option<&mut FormFactorSpec> { + match role { + SpecRole::FormFactor => Some(&mut self.formfactor), + SpecRole::ExcludedSolvent => self.excluded_solvent.as_mut(), + } + } } // If `displaced_volume` is absent but `radius` is present, @@ -334,23 +393,26 @@ pub fn read_formfactors(path: &Path) -> Result { let file = File::open(path).with_path(path)?; let mut specs: BTreeMap = yaml_serde::from_reader(file)?; - convert_alias(&mut specs)?; - convert_linear(&mut specs)?; - convert_united(&mut specs)?; + resolve_symbolic_specs(&mut specs, SpecRole::FormFactor)?; + resolve_symbolic_specs(&mut specs, SpecRole::ExcludedSolvent)?; // Spec → runtime: the three resolvers above guarantee every remaining // variant is one of the four concrete ones, so `try_from` only errors if // a resolver pass has a bug. - let entries: Vec<(AtomKind, FormFactor)> = specs + let entries: Vec<(AtomKind, FormFactor, Option)> = specs .into_iter() .map(|(name, spec)| { let kind = AtomKind::from((&*name, &spec)); let ff = FormFactor::try_from(spec.formfactor)?; - Ok((kind, ff)) + let excluded_solvent = spec + .excluded_solvent + .map(FormFactor::try_from) + .transpose()?; + Ok((kind, ff, excluded_solvent)) }) .collect::>()?; - let formfactor = FormFactorMap::from_entries(entries); + let formfactor = FormFactorMap::from_entries_with_excluded(entries); log::info!( "Read form-factor definitions from {} for {} species", path.display(), @@ -380,7 +442,7 @@ pub fn write_formfactors(formfactor: &FormFactorMap, path: &Path) -> Result<()> // // Only evaluates kinds that are actually referenced by `atom_ids`, // not every kind in the map. Form-factor YAMLs can mix definitions -// with different q-ranges — e.g. `single_bead.yaml` has atomic +// with different q-ranges — e.g. `poly_amino_acid.yaml` has atomic // Gaussians out to q = 6 Å⁻¹ alongside residue-level Polynomials // limited to q ≤ 0.75 — and evaluating all of them at a common q // would error on unused entries with narrower ranges. @@ -416,18 +478,26 @@ pub fn formfactor_product_by_id(formfactors: &FormFactorMap, q: f64) -> Result) -> Result<()> { +fn resolve_symbolic_specs(specs: &mut BTreeMap, role: SpecRole) -> Result<()> { + convert_alias(specs, role)?; + convert_linear(specs, role)?; + convert_united(specs, role) +} + +fn convert_linear(specs: &mut BTreeMap, role: SpecRole) -> Result<()> { const TABLE_LEN: usize = 100; const MAX_Q: f64 = 2.0; // Å⁻¹ - let no_linear: BTreeMap = specs + let no_linear: BTreeMap = specs .iter() - .filter(|(_, spec)| !matches!(spec.formfactor, FormFactorSpec::Linear(_))) - .map(|(name, spec)| (name.clone(), spec.clone())) + .filter_map(|(name, spec)| { + let spec = spec.spec(role)?; + (!matches!(spec, FormFactorSpec::Linear(_))).then(|| (name.clone(), spec.clone())) + }) .collect(); for spec in specs.values_mut() { - let FormFactorSpec::Linear(components) = &spec.formfactor else { + let Some(FormFactorSpec::Linear(components)) = spec.spec(role) else { continue; }; let components = components.clone(); @@ -435,7 +505,6 @@ fn convert_linear(specs: &mut BTreeMap) -> Result<()> { let get_ff = |name: &str| { no_linear .get(name) - .map(|s| &s.formfactor) .ok_or_else(|| Error::UndefinedFormFactor(name.to_string())) }; @@ -459,38 +528,48 @@ fn convert_linear(specs: &mut BTreeMap) -> Result<()> { let qvalues = crate::q_sampler::linear_q_values(qmin, qmax, TABLE_LEN); let table = qvalues.iter().cloned().map(lin_combination).try_collect()?; - spec.formfactor = FormFactorSpec::Table(table); + *spec + .spec_mut(role) + .ok_or_else(|| Error::internal("missing linear form factor slot"))? = + FormFactorSpec::Table(table); } Ok(()) } -fn convert_alias(specs: &mut BTreeMap) -> Result<()> { +fn convert_alias(specs: &mut BTreeMap, role: SpecRole) -> Result<()> { let no_alias: BTreeMap = specs .iter() - .filter(|(_, spec)| !matches!(spec.formfactor, FormFactorSpec::Alias(_))) - .map(|(name, spec)| (name.clone(), spec.formfactor.clone())) + .filter_map(|(name, spec)| { + let spec = spec.spec(role)?; + (!matches!(spec, FormFactorSpec::Alias(_))).then(|| (name.clone(), spec.clone())) + }) .collect(); for spec in specs.values_mut() { - let FormFactorSpec::Alias(name) = &spec.formfactor else { + let Some(FormFactorSpec::Alias(name)) = spec.spec(role) else { continue; }; - spec.formfactor = no_alias - .get(name) + let name = name.clone(); + *spec + .spec_mut(role) + .ok_or_else(|| Error::internal("missing alias form factor slot"))? = no_alias + .get(&name) .ok_or_else(|| Error::UndefinedFormFactor(name.to_string()))? .clone(); } Ok(()) } -fn convert_united(specs: &mut BTreeMap) -> Result<()> { +fn convert_united(specs: &mut BTreeMap, role: SpecRole) -> Result<()> { const TABLE_SIZE: usize = 100; const MAX_Q: f64 = 2.0; // Å⁻¹ - let no_united: BTreeMap = specs + let no_united: BTreeMap = specs .iter() - .filter(|(_, spec)| !matches!(spec.formfactor, FormFactorSpec::United { .. })) - .map(|(name, spec)| (name.clone(), spec.clone())) + .filter_map(|(name, spec)| { + let spec = spec.spec(role)?; + (!matches!(spec, FormFactorSpec::United { .. })).then(|| (name.clone(), spec.clone())) + }) .collect(); // Evaluates single united form factor to a tabulated form factor @@ -500,7 +579,6 @@ fn convert_united(specs: &mut BTreeMap) -> Result<()> { }; let center_ff = &no_united .get(center) - .map(|s| &s.formfactor) .ok_or_else(|| Error::UndefinedFormFactor(center.clone()))?; let (qmin, mut qmax) = center_ff.qrange(); @@ -517,7 +595,6 @@ fn convert_united(specs: &mut BTreeMap) -> Result<()> { for (name, offset, weight) in orbit { let orbit_ff = &no_united .get(name) - .map(|s| &s.formfactor) .ok_or_else(|| Error::UndefinedFormFactor(name.clone()))?; let weight = weight.unwrap_or(1.0); for (q, f) in table.iter_mut() { @@ -528,8 +605,14 @@ fn convert_united(specs: &mut BTreeMap) -> Result<()> { }; for spec in specs.values_mut() { - if matches!(spec.formfactor, FormFactorSpec::United { .. }) { - spec.formfactor = eval(&spec.formfactor)?; + if matches!(spec.spec(role), Some(FormFactorSpec::United { .. })) { + let resolved = eval( + spec.spec(role) + .ok_or_else(|| Error::internal("missing united form factor slot"))?, + )?; + *spec + .spec_mut(role) + .ok_or_else(|| Error::internal("missing united form factor slot"))? = resolved; } } Ok(()) @@ -628,9 +711,9 @@ mod tests { /// Exercises the `Alias` / `United` / `Gaussian` / `Table` resolver paths /// end-to-end and pins the spec→runtime pipeline. #[test] - fn test_read_formfactors_single_bead() { - let path = Path::new("assets/single_bead.yaml"); - let ff = read_formfactors(path).expect("parse single_bead.yaml"); + fn test_read_formfactors_poly_amino_acid() { + let path = Path::new("assets/poly_amino_acid.yaml"); + let ff = read_formfactors(path).expect("parse poly_amino_acid.yaml"); assert!(!ff.is_empty(), "expected at least one species"); for (kind, f) in ff.iter() { let v = f.eval(0.1).unwrap_or_else(|e| { @@ -639,4 +722,52 @@ mod tests { assert!(v.is_finite(), "non-finite F(0.1) for {}", kind.name()); } } + + #[test] + fn read_formfactors_keeps_excluded_solvent() { + let yaml = r" +ALA: + radius: 3.1 + formfactor: !Polynomial { qrange: [0.0, 1.0], coeff: [10.0] } + excluded_solvent: !Polynomial { qrange: [0.0, 1.0], coeff: [3.5] } +"; + let path = std::env::temp_dir().join("pripps_excluded_solvent.yaml"); + std::fs::write(&path, yaml).unwrap(); + let ff = read_formfactors(&path).unwrap(); + let excluded = ff.excluded_solvent(0).expect("excluded_solvent"); + assert_relative_eq!(excluded.eval(0.2).unwrap(), 3.5); + } + + #[test] + fn read_formfactors_resolves_symbolic_excluded_solvent() { + let yaml = r" +H: + formfactor: !Constant 1.0 + excluded_solvent: !Constant 0.2 +O: + formfactor: !Constant 8.0 + excluded_solvent: !Constant 1.0 +ALIAS: + formfactor: !Constant 2.0 + excluded_solvent: !Alias H +LINEAR: + formfactor: !Constant 3.0 + excluded_solvent: !Linear [[H, 2.0], [O, 3.0]] +UNITED: + formfactor: !Constant 4.0 + excluded_solvent: !United { center: O, orbit: [[H, 1.0, 2]] } +"; + let path = std::env::temp_dir().join("pripps_symbolic_excluded_solvent.yaml"); + std::fs::write(&path, yaml).unwrap(); + let ff = read_formfactors(&path).unwrap(); + + let excluded_at = |name: &str| { + let id = ff.kinds().position(|kind| kind.name() == name).unwrap(); + ff.excluded_solvent(id).unwrap().eval(0.0).unwrap() + }; + + assert_relative_eq!(excluded_at("ALIAS"), 0.2); + assert_relative_eq!(excluded_at("LINEAR"), 3.4); + assert_relative_eq!(excluded_at("UNITED"), 1.4); + } } diff --git a/src/lib.rs b/src/lib.rs index 06b85af..6ba22e6 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -31,7 +31,9 @@ use structure::Structure; use trajectory::trajectory_average; pub use atomkind::{AtomKind, FindByName}; +pub use debye::DebyeModel; pub use error::{Error, Result}; +pub use explicit::DirectModel; pub use formfactor::{FormFactor, FormFactorMap, read_formfactors, write_formfactors}; pub use multipole::MultipoleModel; pub use solvent::{ExcludedVolume, Hydration, SolventConfig}; @@ -48,9 +50,9 @@ pub(crate) type Vector3 = nalgebra::Vector3; /// zero-copy `ndarray` view. All pripps calculators return this type; /// callers typically iterate via `.q.iter().zip(&curve.total)`. /// -/// `contributions` is populated when Multipole runs with an active -/// [`SolventConfig`], carrying the per-contribution diagnostic -/// columns. It is `None` for vacuum Multipole, Debye, and Direct. +/// `contributions` is populated when Debye, Multipole, or Direct runs +/// with an active [`SolventConfig`], carrying the per-contribution +/// diagnostic columns. It is `None` for vacuum calculations. #[derive(Debug, Clone, Default, PartialEq)] pub struct Intensity { pub q: Vec, @@ -194,6 +196,57 @@ impl Pripps { multipole::build_model(&sampler, &structure, &atoms_basis, solvent, &l_max_per_q) } + /// Prepare a stateful Debye fit. Caches the exact per-q Debye + /// self and cross terms for atoms, excluded volume, and hydration + /// so repeated calls to [`DebyeModel::intensity`] only recombine + /// the cached terms with different `(volume_scale, contrast_density)`. + pub fn debye_model( + &self, + structure_file: &path::Path, + qmin: f64, + qmax: f64, + nq: usize, + solvent: &SolventConfig, + ) -> Result { + let structure = Structure::from_file(structure_file, &self.atomkinds())?; + let sampler = q_sampler::QSampler::linear(self.formfactors.clone(), qmin, qmax, nq); + log::info!( + "Debye fit preparation over {} atoms, {} q-points", + structure.pos.len(), + sampler.q_values.len(), + ); + debye::build_model(&sampler, &structure, solvent) + } + + /// Prepare a stateful direct/explicit fit. Caches the complex + /// amplitudes for atoms, excluded volume, and hydration at every + /// sampled q-vector so repeated calls to [`DirectModel::intensity`] + /// only recombine the cached amplitudes with different + /// `(volume_scale, contrast_density)`. + pub fn direct_model( + &self, + structure_file: &path::Path, + pmax: usize, + box_sides: [f64; 3], + solvent: &SolventConfig, + ) -> Result { + let structure = Structure::from_file(structure_file, &self.atomkinds())?; + let directions = explicit::directions(pmax); + let box_sides = Vector3::from(box_sides); + log::info!( + "Direct fit preparation over {} atoms, {} q-vectors", + structure.pos.len(), + directions.len(), + ); + explicit::build_model( + &self.formfactors, + &directions, + &structure, + &box_sides, + solvent, + ) + } + /// Compute I(q) for a structure under the given [`IntensityScheme`]. /// /// When `xtcfile` is `Some`, the structure is used as a template (for @@ -223,19 +276,15 @@ impl Pripps { } let active_solvent = solvent.filter(|cfg| cfg.is_active()); - if active_solvent.is_some() && matches!(scheme, IntensityScheme::Debye { .. }) { - return Err(Error::usage( - "solvent correction is not supported by the Debye scheme", - )); - } let calc: Box = match scheme { IntensityScheme::Debye { qmin, qmax, nq } => { log::info!("Using Debye formula to calculate 𝐼(𝑞) for {qmin} ≤ 𝑞 ≤ {qmax} Å⁻¹"); - Box::new(DebyeIntensity::new( + Box::new(DebyeIntensity::new_with_solvent( self.formfactors.clone(), (qmin, qmax), nq, + active_solvent.cloned(), )?) } IntensityScheme::Direct { pmax } => { @@ -279,43 +328,145 @@ mod tests { use super::*; #[test] - fn debye_rejects_active_solvent() { - let yaml = r"C: { formfactor: !Constant 1.0 }"; - let path = std::env::temp_dir().join("pripps_debye_reject.yaml"); - std::fs::write(&path, yaml).unwrap(); - let pripps = Pripps::from_yaml(&path).unwrap(); - - let pdb = std::env::temp_dir().join("pripps_debye_reject.pdb"); - std::fs::write( - &pdb, - "HEADER test\n\ - ATOM 1 C ALA A 1 0.000 0.000 0.000 1.00 0.00 C\n\ - END\n", - ) - .unwrap(); - + fn debye_solvent_matches_cached_model_for_matching_knobs() { + let (pripps, xyz) = yaml_and_xyz(); let solvent = SolventConfig { excluded: ExcludedVolume::Fraser { volume_scale: 1.0 }, + hydration: Hydration::Sasa { + probe: 1.8, + contrast_density: 0.03, + }, ..Default::default() }; - let err = pripps + let one_shot = pripps .intensity( IntensityScheme::Debye { qmin: 0.0, - qmax: 0.1, - nq: 3, + qmax: 0.3, + nq: 20, }, - &pdb, + &xyz, None, None, Some(&solvent), ) - .unwrap_err(); - assert!(matches!(err, Error::Usage(_))); + .unwrap(); + + let fit = pripps.debye_model(&xyz, 0.0, 0.3, 20, &solvent).unwrap(); + let from_fit = fit.intensity(1.0, 0.03); + + assert_eq!(one_shot.total, from_fit.total); + assert_eq!(one_shot.contributions, from_fit.contributions); + } + + #[test] + fn direct_solvent_matches_cached_model_for_matching_knobs() { + let (pripps, xyz) = yaml_and_xyz(); + let solvent = SolventConfig { + excluded: ExcludedVolume::Fraser { volume_scale: 1.0 }, + hydration: Hydration::Sasa { + probe: 1.8, + contrast_density: 0.03, + }, + ..Default::default() + }; + let box_sides = [200.0; 3]; + let one_shot = pripps + .intensity( + IntensityScheme::Direct { pmax: 2 }, + &xyz, + None, + Some(box_sides), + Some(&solvent), + ) + .unwrap(); + + let fit = pripps.direct_model(&xyz, 2, box_sides, &solvent).unwrap(); + let from_fit = fit.intensity(1.0, 0.03); + + assert_eq!(one_shot.total, from_fit.total); + assert_eq!(one_shot.contributions, from_fit.contributions); + } + + #[test] + fn fraser_volume_scale_is_not_applied_twice() { + let (pripps, xyz) = yaml_and_xyz(); + let scale = 1.7; + let unit_solvent = SolventConfig { + excluded: ExcludedVolume::Fraser { volume_scale: 1.0 }, + ..Default::default() + }; + let scaled_solvent = SolventConfig { + excluded: ExcludedVolume::Fraser { + volume_scale: scale, + }, + ..Default::default() + }; + + let debye_one_shot = pripps + .intensity( + IntensityScheme::Debye { + qmin: 0.0, + qmax: 0.2, + nq: 8, + }, + &xyz, + None, + None, + Some(&scaled_solvent), + ) + .unwrap(); + let debye_model = pripps + .debye_model(&xyz, 0.0, 0.2, 8, &unit_solvent) + .unwrap(); + assert_eq!( + debye_one_shot.total, + debye_model.intensity(scale, 0.0).total + ); + + let direct_box = [200.0; 3]; + let direct_one_shot = pripps + .intensity( + IntensityScheme::Direct { pmax: 2 }, + &xyz, + None, + Some(direct_box), + Some(&scaled_solvent), + ) + .unwrap(); + let direct_model = pripps + .direct_model(&xyz, 2, direct_box, &unit_solvent) + .unwrap(); + assert_eq!( + direct_one_shot.total, + direct_model.intensity(scale, 0.0).total + ); + + let multipole_one_shot = pripps + .intensity( + IntensityScheme::Multipole { + qmin: 0.0, + qmax: 0.2, + nq: Some(8), + l_max: None, + }, + &xyz, + None, + None, + Some(&scaled_solvent), + ) + .unwrap(); + let multipole_model = pripps + .multipole_model(&xyz, 0.0, 0.2, Some(8), None, &unit_solvent) + .unwrap(); + assert_eq!( + multipole_one_shot.total, + multipole_model.intensity(scale, 0.0).total + ); } fn yaml_and_xyz() -> (Pripps, std::path::PathBuf) { - let yaml = std::path::Path::new("assets/single_bead.yaml"); + let yaml = std::path::Path::new("assets/poly_amino_acid.yaml"); let xyz = std::path::PathBuf::from("tests/lysozyme/4lzt.xyz"); (Pripps::from_yaml(yaml).unwrap(), xyz) } @@ -412,15 +563,93 @@ mod tests { fit::ExpData::from_file(std::path::Path::new("tests/lysozyme/lyzexp.dat")).unwrap() } + fn pepsi_intensity_scale(path: &std::path::Path) -> f64 { + let contents = std::fs::read_to_string(path).unwrap(); + contents + .lines() + .find(|line| line.contains("Intensity scaling")) + .and_then(|line| line.split_whitespace().last()) + .and_then(|value| value.parse().ok()) + .unwrap_or_else(|| panic!("missing intensity scaling in {}", path.display())) + } + + fn pepsi_unscaled_iat_profile(path: &std::path::Path, scale: f64) -> Vec<(f64, f64)> { + let contents = std::fs::read_to_string(path).unwrap(); + contents + .lines() + .filter(|line| { + let trimmed = line.trim(); + !trimmed.is_empty() && !trimmed.starts_with('#') + }) + .map(|line| { + let fields: Vec<_> = line.split_whitespace().collect(); + assert!( + fields.len() >= 3, + "expected at least three PEPSI output columns in {line:?}" + ); + let q = fields[0].parse::().unwrap(); + let iat = fields[2].parse::().unwrap() / scale; + (q, iat) + }) + .collect() + } + + #[test] + fn amino_acid_debye_matches_scaled_pepsi_iat_profile() { + let scale = pepsi_intensity_scale(std::path::Path::new("tests/lysozyme/pepsi.log")); + let reference = + pepsi_unscaled_iat_profile(std::path::Path::new("tests/lysozyme/pepsi.out"), scale); + let (pripps, xyz) = yaml_and_xyz(); + let intensity = pripps + .intensity( + IntensityScheme::Debye { + qmin: reference.first().unwrap().0, + qmax: reference.last().unwrap().0, + nq: reference.len(), + }, + &xyz, + None, + None, + None, + ) + .unwrap(); + + assert_eq!(intensity.q.len(), reference.len()); + + let mut max_rel = 0.0_f64; + let mut max_q = 0.0_f64; + for ((&q, &calc), &(q_ref, expected)) in intensity + .q + .iter() + .zip(&intensity.total) + .zip(reference.iter()) + { + assert!( + (q - q_ref).abs() < 1.0e-12, + "q grid mismatch: calculated {q}, reference {q_ref}" + ); + let rel = ((calc - expected) / expected).abs(); + if rel > max_rel { + max_rel = rel; + max_q = q_ref; + } + } + + assert!( + max_rel < 0.04, + "PEPSI Iat profile differs by {max_rel:.4} at q={max_q:.3}" + ); + } + #[test] fn foxs_chi2_against_experiment() { - // FoXS gets χ²/N ≈ 0.29 on 6lyz.pdb vs lyzexp.dat with + // FoXS gets χ²/N ≈ 0.29 on 4lzt.pdb vs lyzexp.dat with // excluded volume only (no hydration, c₁=1). With the // FoXS-compatible Cromer-Mann form factors and the 16π // Fraser formula, pripps should match. let pripps = Pripps::from_yaml(std::path::Path::new("assets/foxs_formfactors.yaml")).unwrap(); - let pdb = std::path::PathBuf::from("tests/lysozyme/6lyz.pdb"); + let pdb = std::path::PathBuf::from("tests/lysozyme/4lzt.pdb"); let solvent = SolventConfig { excluded: ExcludedVolume::FraserFoxs { volume_scale: 1.0 }, ..Default::default() @@ -450,14 +679,67 @@ mod tests { ); } + #[test] + fn foxs_profile_matches_reference_file() { + let reference = + fit::ExpData::from_file(std::path::Path::new("tests/lysozyme/6lyz_foxs.dat")).unwrap(); + let pripps = + Pripps::from_yaml(std::path::Path::new("assets/foxs_formfactors.yaml")).unwrap(); + let pdb = std::path::PathBuf::from("tests/lysozyme/6lyz.pdb"); + let solvent = SolventConfig { + excluded: ExcludedVolume::FraserFoxs { volume_scale: 1.0 }, + ..Default::default() + }; + let intensity = pripps + .intensity( + IntensityScheme::Multipole { + qmin: *reference.q.first().unwrap(), + qmax: *reference.q.last().unwrap(), + nq: Some(reference.q.len()), + l_max: None, + }, + &pdb, + None, + None, + Some(&solvent), + ) + .unwrap(); + + assert_eq!(intensity.q.len(), reference.q.len()); + + let mut max_rel = 0.0_f64; + let mut max_q = 0.0_f64; + for ((&q, &calc), (&q_ref, &expected)) in intensity + .q + .iter() + .zip(&intensity.total) + .zip(reference.q.iter().zip(&reference.intensity)) + { + assert!( + (q - q_ref).abs() < 1.0e-12, + "q grid mismatch: calculated {q}, reference {q_ref}" + ); + let rel = ((calc - expected) / expected).abs(); + if rel > max_rel { + max_rel = rel; + max_q = q_ref; + } + } + + assert!( + max_rel < 0.05, + "FoXS reference profile differs by {max_rel:.4} at q={max_q:.3}" + ); + } + #[test] fn pepsi_pipeline_reasonable_chi2() { // PepsiSAXS form factors + PepsiSAXS Fraser (4π) should // produce a physically reasonable profile. Not as good as // the FoXS pipeline (different coefficients, different // denominator) but the shape should be plausible. - let pripps = Pripps::from_yaml(std::path::Path::new("assets/single_bead.yaml")).unwrap(); - let pdb = std::path::PathBuf::from("tests/lysozyme/6lyz.pdb"); + let pripps = Pripps::from_yaml(std::path::Path::new("assets/gaussian_atoms.yaml")).unwrap(); + let pdb = std::path::PathBuf::from("tests/lysozyme/4lzt.pdb"); let solvent = SolventConfig { excluded: ExcludedVolume::Fraser { volume_scale: 1.0 }, ..Default::default() @@ -529,7 +811,7 @@ mod tests { /// rescales it back to a full average (not frame 0 × 1/N_frames). #[test] fn trajectory_stride_and_weights() { - let pripps = Pripps::from_yaml(Path::new("assets/single_bead.yaml")).unwrap(); + let pripps = Pripps::from_yaml(Path::new("assets/gaussian_atoms.yaml")).unwrap(); let template = std::path::PathBuf::from("tests/lysozyme/4lzt.xyz"); let xtc = std::env::temp_dir().join("pripps_stride_test.xtc"); write_two_frame_xtc(&xtc); diff --git a/src/multipole/mod.rs b/src/multipole/mod.rs index af66c2c..e0cae85 100644 --- a/src/multipole/mod.rs +++ b/src/multipole/mod.rs @@ -380,9 +380,11 @@ pub(crate) fn build_model( l_max_per_q: &[usize], ) -> Result { let l_max = atoms_basis.l_max; - let excluded_dummies = - cfg.excluded - .dummies(structure, &sampler.formfactors, cfg.bulk_electron_density)?; + let excluded_dummies = cfg.excluded.unit_scale_dummies( + structure, + &sampler.formfactors, + cfg.bulk_electron_density, + )?; let hydration_dummies = cfg.hydration.dummies(structure, &sampler.formfactors)?; let excluded_basis = SphericalBasis::new(excluded_dummies.positions(), l_max); diff --git a/src/solvent/fraser.rs b/src/solvent/fraser.rs index 4684ab0..ddb28b5 100644 --- a/src/solvent/fraser.rs +++ b/src/solvent/fraser.rs @@ -72,7 +72,7 @@ pub(super) fn dummies( let mut form_factors = Vec::with_capacity(structure.pos.len()); // Validate per atom rather than up front over all kinds in the - // map: coarse-grained YAML files (e.g. `single_bead.yaml`) may + // map: coarse-grained YAML files (e.g. `poly_amino_acid.yaml`) may // legally leave some residue entries without a `displaced_volume`, // and only kinds the loaded structure references need one. for (&id, &pos) in structure.ids.iter().zip(&structure.pos) { diff --git a/src/solvent/mod.rs b/src/solvent/mod.rs index 2704e8b..ed551dd 100644 --- a/src/solvent/mod.rs +++ b/src/solvent/mod.rs @@ -32,6 +32,7 @@ mod fraser; mod grid; mod grid_excluded; +mod per_kind_excluded; mod sasa; mod voronoi_excluded; mod voronoi_hydration; @@ -131,7 +132,8 @@ impl ExcludedVolume { Self::Fraser { volume_scale } | Self::FraserFoxs { volume_scale } | Self::Grid { volume_scale, .. } - | Self::Voronoi { volume_scale } => *volume_scale, + | Self::Voronoi { volume_scale } + | Self::PerKind { volume_scale, .. } => *volume_scale, } } } @@ -202,6 +204,11 @@ pub enum ExcludedVolume { Voronoi { volume_scale: f64, }, + /// Per-kind excluded-solvent form factors stored alongside the + /// atom/residue form factors in the same YAML file. + PerKind { + volume_scale: f64, + }, } /// How the hydration shell is represented. @@ -350,6 +357,33 @@ impl DummySet { } impl ExcludedVolume { + /// Generate excluded-volume dummies for cached solvent models. + /// + /// Cached Debye/Direct/Multipole models apply `volume_scale` during + /// the coherent recombination step. Fraser/FoXS dummy generation can + /// also bake that scale into the Gaussian form factors, so cached + /// builders must force the dummy basis to unit scale to avoid applying + /// the same fit knob twice. + pub(crate) fn unit_scale_dummies( + &self, + structure: &Structure, + formfactors: &FormFactorMap, + bulk_electron_density: f64, + ) -> Result { + match self { + Self::Disabled => Self::Disabled, + Self::Fraser { .. } => Self::Fraser { volume_scale: 1.0 }, + Self::FraserFoxs { .. } => Self::FraserFoxs { volume_scale: 1.0 }, + Self::Grid { spacing, .. } => Self::Grid { + spacing: *spacing, + volume_scale: 1.0, + }, + Self::Voronoi { .. } => Self::Voronoi { volume_scale: 1.0 }, + Self::PerKind { .. } => Self::PerKind { volume_scale: 1.0 }, + } + .dummies(structure, formfactors, bulk_electron_density) + } + pub(crate) fn dummies( &self, structure: &Structure, @@ -378,6 +412,7 @@ impl ExcludedVolume { Self::Voronoi { .. } => { voronoi_excluded::dummies(structure, formfactors, bulk_electron_density) } + Self::PerKind { .. } => per_kind_excluded::dummies(structure, formfactors), } } } diff --git a/src/solvent/per_kind_excluded.rs b/src/solvent/per_kind_excluded.rs new file mode 100644 index 0000000..337e6a6 --- /dev/null +++ b/src/solvent/per_kind_excluded.rs @@ -0,0 +1,90 @@ +// Copyright 2026 Mikael Lund +// +// Licensed under the Apache license, version 2.0 (the "license"); +// you may not use this file except in compliance with the license. + +//! Per-kind excluded-volume dummies from the atom form-factor table. +//! +//! For each structure atom/residue, this generator copies the per-kind +//! excluded-solvent form factor from the same YAML entry as the atomistic +//! form factor. + +use crate::formfactor::FormFactorMap; +use crate::solvent::DummySet; +use crate::{Error, Result, Structure}; + +pub(super) fn dummies(structure: &Structure, formfactors: &FormFactorMap) -> Result { + let mut positions = Vec::with_capacity(structure.pos.len()); + let mut form_factors = Vec::with_capacity(structure.pos.len()); + + for (&id, &pos) in structure.ids.iter().zip(&structure.pos) { + let ff = formfactors + .excluded_solvent(id) + .ok_or_else(|| { + Error::validation(format!( + "atom kind {} has no `excluded_solvent`; required by ExcludedVolume::PerKind", + formfactors.kind(id).name() + )) + })? + .clone(); + positions.push(pos); + form_factors.push(ff); + } + + log::info!( + "Per-kind excluded volume: {} dummies from form-factor table", + positions.len() + ); + DummySet::per_dummy(positions, form_factors) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::formfactor::FormFactor; + use crate::{AtomKind, Vector3}; + + fn polynomial(coeff0: f64) -> FormFactor { + FormFactor::Polynomial { + qrange: (0.0, 1.0), + coeff: vec![coeff0], + } + } + + fn map_with(name: &str, atom_coeff0: f64, excluded_coeff0: Option) -> FormFactorMap { + let kind = AtomKind::new(name); + FormFactorMap::from_entries_with_excluded(vec![( + kind, + polynomial(atom_coeff0), + excluded_coeff0.map(polynomial), + )]) + } + + #[test] + fn copies_excluded_solvent_from_atom_map() { + let atom_map = map_with("ALA", 10.0, Some(3.5)); + let structure = Structure { + pos: vec![Vector3::new(0.0, 0.0, 0.0)], + ids: vec![0], + box_sides: None, + }; + + let d = dummies(&structure, &atom_map).unwrap(); + let mut f = Vec::new(); + d.form_factors_at(0.2, &mut f).unwrap(); + assert_eq!(f, vec![3.5]); + } + + #[test] + fn missing_excluded_solvent_errors() { + let atom_map = map_with("ALA", 10.0, None); + let structure = Structure { + pos: vec![Vector3::new(0.0, 0.0, 0.0)], + ids: vec![0], + box_sides: None, + }; + + let err = dummies(&structure, &atom_map).unwrap_err(); + assert!(format!("{err}").contains("excluded_solvent")); + } +} diff --git a/src/structure.rs b/src/structure.rs index 674dc96..7f0ecb1 100644 --- a/src/structure.rs +++ b/src/structure.rs @@ -136,14 +136,39 @@ pub(crate) fn resolve_united_atom(residue: &str, atom: &str) -> Option<&'static }) } +fn resolve_atom_kind_id( + atomkinds: &[AtomKind], + atom_name: &str, + residue: Option<&str>, +) -> Result { + if let Some(residue) = residue { + let residue_scoped = format!("{residue}_{atom_name}"); + if let Some(id) = atomkinds.find_name(&residue_scoped) { + return Ok(id); + } + + if let Some(united) = resolve_united_atom(residue, atom_name) + && let Some(id) = atomkinds.find_name(united) + { + return Ok(id); + } + } + + atomkinds.find_name(atom_name).ok_or_else(|| { + Error::UnknownAtom(format!( + "{}; add an entry for this atom type to the form-factor YAML", + atom_name + )) + }) +} + impl Structure { /// Construct a structure from a PDB or XYZ file. Each atom is /// looked up by name in `atomkinds`. For PDB files, the lookup - /// first tries [`resolve_united_atom`] with residue context to - /// map heavy atoms to their united-atom form-factor type (e.g. - /// LYS:CB → CH₂), falling back to atom-name-only lookup for - /// atoms not in the table or for XYZ files (which have no - /// residue context). + /// first tries a residue-scoped key (`RES_ATOM`, e.g. `MET_BB`) + /// for coarse-grained Martini inputs, then [`resolve_united_atom`] + /// for atomic protein PDBs (e.g. LYS:CB → CH₂), and finally + /// falls back to atom-name-only lookup. pub fn from_file(path: &Path, atomkinds: &[AtomKind]) -> Result { let atoms = read_structure(path)?; @@ -151,18 +176,7 @@ impl Structure { let mut ids = Vec::with_capacity(atoms.len()); for atom in atoms { - let resolved_name = atom - .residue - .as_deref() - .and_then(|res| resolve_united_atom(res, &atom.name)) - .unwrap_or(&atom.name); - - let id = atomkinds.find_name(resolved_name).ok_or_else(|| { - Error::UnknownAtom(format!( - "{}; add an entry for this atom type to the form-factor YAML", - atom.name - )) - })?; + let id = resolve_atom_kind_id(atomkinds, &atom.name, atom.residue.as_deref())?; pos.push(Vector3::new( atom.position[0], atom.position[1], @@ -229,3 +243,24 @@ impl Display for Structure { write!(f, "𝑁={}", self.pos.len()) } } + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn resolves_residue_scoped_martini_names_before_atomic_fallbacks() { + let atomkinds = vec![AtomKind::new("MET_BB"), AtomKind::new("NH")]; + + let id = resolve_atom_kind_id(&atomkinds, "BB", Some("MET")).unwrap(); + assert_eq!(atomkinds[id].name(), "MET_BB"); + } + + #[test] + fn resolves_united_atom_names_for_atomic_pdbs() { + let atomkinds = vec![AtomKind::new("MET_BB"), AtomKind::new("NH")]; + + let id = resolve_atom_kind_id(&atomkinds, "N", Some("MET")).unwrap(); + assert_eq!(atomkinds[id].name(), "NH"); + } +} diff --git a/tests/lysozyme/4lzt.weights b/tests/lysozyme/4lzt.weights deleted file mode 100644 index 7d1b2ad..0000000 --- a/tests/lysozyme/4lzt.weights +++ /dev/null @@ -1,2 +0,0 @@ -0.2 -1.0 diff --git a/tests/lysozyme/6lyz.xyz b/tests/lysozyme/6lyz.xyz new file mode 100644 index 0000000..03b3826 --- /dev/null +++ b/tests/lysozyme/6lyz.xyz @@ -0,0 +1,131 @@ +129 +# lysozyme 6lyz.pdb +LYS 0.5122222222222222 10.585777777777778 8.915222222222221 +VAL 2.6317142857142857 13.928142857142857 6.715285714285715 +PHE -2.172 14.515999999999998 8.884545454545455 +GLY -3.224 17.33325 5.4775 +ARG -5.408727272727273 22.652 4.659818181818181 +CYS -8.018166666666668 19.500666666666664 4.714 +GLU -6.296444444444446 14.504666666666667 6.433222222222223 +LEU -5.4295 17.8035 10.483624999999998 +ALA -9.1286 19.5242 9.872399999999999 +ALA -11.162200000000002 16.78 9.703399999999998 +ALA -9.5976 15.159199999999998 12.511599999999998 +MET -8.529375000000002 18.575625 14.872125 +LYS -14.604222222222221 18.872777777777777 13.259555555555556 +ARG -13.659363636363638 12.38281818181818 14.735818181818184 +HIS -10.838899999999999 13.9262 17.7428 +GLY -13.58625 18.449 19.298 +LEU -10.248874999999998 20.732875 18.713875 +ASP -14.306750000000001 23.386874999999996 16.183249999999997 +ASN -14.962375 26.26525 18.891875 +TYR -11.738 22.792166666666663 23.82875 +ARG -11.423181818181819 26.679181818181817 26.945181818181823 +GLY -12.616 29.33575 21.57875 +TYR -8.916666666666668 29.014166666666664 21.044416666666667 +SER -11.032666666666666 28.167833333333334 15.569166666666666 +LEU -10.594874999999998 24.167 13.223750000000003 +GLY -7.701499999999999 27.163 12.576249999999998 +ASN -6.607874999999999 28.894375 15.796125 +TRP -6.4195714285714285 23.953357142857147 17.80578571428571 +VAL -4.573428571428572 23.815857142857144 11.411857142857144 +CYS -1.9116666666666668 27.234833333333338 12.286166666666668 +ALA -0.33659999999999995 25.567199999999996 15.174600000000002 +ALA 0.3356 22.5218 13.468799999999998 +LYS 2.1854444444444443 24.269666666666666 9.088111111111111 +PHE 3.7260909090909085 27.525454545454544 11.199454545454543 +GLU 4.7925555555555555 23.753 16.27144444444444 +SER 4.856166666666668 20.011833333333332 12.861500000000001 +ASN 5.044875 20.8345 8.4655 +PHE 0.5182727272727271 19.63081818181818 8.908363636363635 +ASN 5.345 15.364624999999998 10.960500000000001 +THR 3.1882857142857146 13.013 13.665428571428572 +GLN 7.392444444444444 10.857777777777779 13.094444444444445 +ALA 8.0794 15.475400000000002 15.360399999999998 +THR 11.182142857142855 15.622142857142858 17.461285714285715 +ASN 12.327874999999999 19.511125 18.411250000000003 +ARG 16.72127272727273 17.804363636363636 23.35763636363636 +ASN 14.274249999999999 22.162499999999998 24.204 +THR 18.06142857142857 24.325285714285712 26.244714285714288 +ASP 15.360125 22.429999999999996 29.30875 +GLY 16.17175 18.942249999999998 27.99875 +SER 13.121333333333332 19.307166666666664 27.07833333333333 +THR 11.802142857142858 16.979000000000003 24.159571428571432 +ASP 9.052000000000001 19.35675 21.992624999999997 +TYR 7.959333333333333 14.609833333333334 22.247416666666666 +GLY 4.374750000000001 16.11975 17.61925 +ILE 1.0695000000000001 17.593 15.155999999999999 +LEU 0.36750000000000005 20.113249999999997 18.7425 +GLN 5.801666666666667 19.848 19.34288888888889 +ILE 3.31175 17.996750000000002 23.65625 +ASN 7.152750000000001 19.639374999999998 26.894625 +SER 8.815 16.473166666666668 28.609499999999997 +ARG 10.129545454545456 20.483727272727272 32.894363636363636 +TRP 5.715071428571428 21.234214285714284 32.853857142857144 +TRP 1.7999999999999996 18.878000000000004 29.656071428571426 +CYS 5.178999999999999 13.727666666666666 28.461000000000002 +ASN 7.6015 10.060749999999999 30.364875 +ASP 10.931875 11.149750000000001 27.698125000000005 +GLY 12.81875 9.28575 30.559 +ARG 15.797909090909092 12.473363636363633 27.441 +THR 12.897285714285715 14.926285714285715 31.266142857142857 +PRO 14.433285714285715 15.232571428571427 35.10885714285714 +GLY 11.22375 17.244500000000002 36.57725 +SER 9.069833333333333 15.235833333333334 34.83316666666666 +ARG 6.079636363636363 17.827545454545458 37.23118181818182 +ASN 3.04975 12.692499999999999 33.125625 +LEU 0.166875 16.43125 33.99575 +CYS -1.2148333333333332 13.898166666666667 30.11616666666667 +ASN -0.559125 9.852875000000001 32.52125 +ILE 1.2777500000000002 9.80475 27.799625 +PRO 3.9159999999999995 7.905714285714285 26.96657142857143 +CYS 6.099333333333333 11.158999999999999 24.8155 +SER 5.844166666666666 7.924333333333333 22.22883333333333 +ALA 2.2041999999999997 8.4306 21.9746 +LEU 2.235125 12.334500000000002 21.556250000000002 +LEU 5.074624999999999 10.5635 17.792125000000002 +SER 1.0148333333333335 8.228666666666667 16.524666666666665 +SER -0.7610000000000001 8.181666666666667 13.035166666666669 +ASP -4.265125 8.306875000000002 16.223875 +ILE -4.387625 12.893375 16.487125 +THR -6.363857142857144 10.65542857142857 19.95428571428571 +ALA -3.3015999999999996 11.6446 22.4504 +SER -2.265333333333333 14.7975 20.950166666666664 +VAL -6.2892857142857155 16.054285714285715 21.069428571428574 +ASN -7.039125 13.93525 24.665374999999997 +CYS -2.9375 16.61616666666667 25.951666666666668 +ALA -4.1335999999999995 19.6486 24.0618 +LYS -8.511 18.980555555555554 25.049444444444447 +LYS -5.365444444444445 18.779222222222224 30.346555555555554 +ILE -2.157625 22.186 27.400624999999998 +VAL -5.902285714285715 24.930000000000003 26.331428571428575 +SER -7.880333333333333 24.440833333333334 29.789166666666663 +ASP -4.602374999999999 24.446499999999997 31.592750000000002 +GLY -4.59425 29.249499999999998 31.314500000000002 +ASN -1.7429999999999997 30.4125 29.339250000000003 +GLY -3.9345000000000003 28.94475 26.3185 +MET -3.377375 28.439625000000003 21.89225 +ASN -0.45425000000000004 31.87975 24.474749999999997 +ALA 1.4384000000000001 27.494999999999997 25.1242 +TRP 0.49399999999999994 25.44571428571428 21.939428571428575 +VAL 5.691428571428571 29.79942857142857 20.70885714285714 +ALA 3.9814000000000007 29.3072 17.319 +TRP -1.4782857142857142 30.96464285714286 18.6195 +ARG 2.887909090909091 33.40109090909092 21.357727272727274 +ASN 4.9098749999999995 34.506249999999994 17.55725 +ARG 4.395545454545455 31.769545454545458 12.544818181818185 +CYS -1.2428333333333332 32.282 14.351333333333335 +LYS -2.145222222222222 35.67555555555556 18.545222222222222 +GLY -4.0992500000000005 37.690749999999994 14.42175 +THR -3.9265714285714286 35.91785714285714 11.949428571428571 +ASP -7.348374999999999 35.091875 8.627875 +VAL -6.970857142857143 31.403428571428574 11.296142857142858 +GLN -11.23711111111111 31.485666666666663 8.82888888888889 +ALA -7.2136 30.238 5.6996 +TRP -3.9581428571428567 27.70157142857143 7.593285714285714 +ILE -10.503499999999999 25.809 7.287875 +ARG -9.800545454545455 29.533818181818184 2.885363636363636 +GLY -11.81 23.7925 0.9970000000000001 +CYS -12.322666666666665 22.100333333333328 3.6685 +ARG -14.368636363636364 17.548000000000002 1.6985454545454546 +LEU -15.547 20.791444444444444 7.384888888888889 diff --git a/tests/lysozyme/lyzexp.dat b/tests/lysozyme/lyzexp.dat index 1ee6934..54f4fd1 100644 --- a/tests/lysozyme/lyzexp.dat +++ b/tests/lysozyme/lyzexp.dat @@ -1,4 +1,4 @@ -lizosyme, high angles (>.22) 46 mg/ml, small angles (<.22) 15 mg/ml +# lysozyme, high angles (>.22) 46 mg/ml, small angles (<.22) 15 mg/ml 4.138455E-02 5.904029 1.555333E-01 4.371607E-02 5.652469 1.527037E-01 4.604759E-02 5.533381 1.521723E-01 diff --git a/tests/lysozyme/plot.ipynb b/tests/lysozyme/plot.ipynb deleted file mode 100644 index a0a26d1..0000000 --- a/tests/lysozyme/plot.ipynb +++ /dev/null @@ -1,124 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b55cc4d0-742a-4eba-91a0-202580b8974f", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "794f97c9-950b-4c68-887c-3a65354a821a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Pepsi (AA)\n", - "scaling = 3.34523e-09 # \"Intensity Scaling\" from pepsi.log file\n", - "q, Itotal, Iatomic, Iexcluded, Ihydration, AatAev, AatAhs, AevAhs = np.loadtxt(\n", - " \"pepsi.out\", unpack=True\n", - ")\n", - "plt.plot(q, Itotal / scaling, label=\"Pepsi (AA)\", lw=3)\n", - "\n", - "# Pripps (CG)\n", - "q, I = np.loadtxt(\"intensity_cg.dat\", unpack=True)\n", - "plt.plot(q, I, label=\"Pripps (CG, no solvation)\", lw=3)\n", - "\n", - "# Experiment\n", - "q, I, b = np.loadtxt(\"SASDMF2.dat\", unpack=True)\n", - "plt.plot(q, I * 0.7e5, \"k-\", label=\"Experiment (SASDMF2)\", alpha=0.5)\n", - "\n", - "# Plot details\n", - "plt.title(\"Calculated and measured formfactor for Lysozyme\")\n", - "plt.yscale(\"log\")\n", - "plt.legend(loc=0, frameon=False)\n", - "plt.xlim(0, 0.75)\n", - "plt.xlabel(\"q (1/Å)\")\n", - "plt.ylabel(\"Intensity\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "05e8a70d-4768-4835-9fd2-acdc0c8d445f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Pepsi (AA)\n", - "q, Itotal, Iatomic, Iexcluded, Ihydration, AatAev, AatAhs, AevAhs = np.loadtxt(\n", - " \"pepsi.out\", unpack=True\n", - ")\n", - "plt.plot(q, Itotal, label=\"total\", lw=5, alpha=0.5)\n", - "plt.plot(q, Iatomic, label=\"atomic\", lw=5, alpha=0.5)\n", - "plt.plot(q, Iexcluded, label=\"excluded\", lw=5, alpha=0.5)\n", - "plt.plot(q, Ihydration, label=\"hydration\", lw=5, alpha=0.5)\n", - "plt.yscale(\"log\")\n", - "plt.legend(loc=0, frameon=False)\n", - "plt.xlim(0, 0.5)\n", - "plt.xlabel(\"q (1/Å)\")\n", - "plt.ylabel(\"Intensity\")\n", - "plt.title(\"Pepsi contribitions (4LZT.pdb)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89b910e7-8ecf-4356-8f1d-4906ae143aa9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}