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mm,85.00%,2,100,2.468,24.68,27.8469031072241 +6,Orijentacija 1,0.14,0.8 mm,20.00%,4,100,2.871,28.71,29.1606638499301 +7,Orijentacija 1,0.28,0.4 mm,85.00%,4,100,2.423,24.23,27.6870682827501 +8,Orijentacija 1,0.28,0.6 mm,20.00%,6,100,1.891,18.91,25.5338305769008 +9,Orijentacija 1,0.28,0.8 mm,55.00%,2,100,2.431,24.31,27.7156991768667 +10,Orijentacija 2,0.08,0.4 mm,85.00%,4,100,3.398,33.98,30.6244674906605 +11,Orijentacija 2,0.08,0.6 mm,20.00%,6,100,3.218,32.18,30.1517207952602 +12,Orijentacija 2,0.08,0.8 mm,55.00%,2,100,2.768,27.68,28.8433217156944 +13,Orijentacija 2,0.14,0.4 mm,20.00%,6,100,2.472,24.72,27.8609693283356 +14,Orijentacija 2,0.14,0.6 mm,55.00%,2,100,2.527,25.27,28.0521048383983 +15,Orijentacija 2,0.14,0.8 mm,85.00%,4,100,3.647,36.47,31.2387152662756 +16,Orijentacija 2,0.28,0.4 mm,55.00%,4,100,2.491,24.91,27.9274745507301 +17,Orijentacija 2,0.28,0.6 mm,85.00%,6,100,4.111,41.11,32.278949535607 +18,Orijentacija 2,0.28,0.8 mm,20.00%,2,100,2.263,22.63,27.0936910790946 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/0_raw_with_SNR.csv b/docs/obrada/ispitni_rezultati_taguchi_out/0_raw_with_SNR.csv new file mode 100644 index 0000000..9e88e8b --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/0_raw_with_SNR.csv @@ -0,0 +1,19 @@ +Eksperiment,Orijentacija,Visina sloja [mm],Širina ekstruzije [mm],Postotak ispune [%],Broj stijenki,A_ekv [mm^2],Fm kN],Sigma [Mpa],SNR [dB],SNR_LB [dB] +1,Orijentacija 1,0.08,0.4,20.0,2.0,100.0,0.778,7.78,17.8195919397938,17.81959193979378 +2,Orijentacija 1,0.08,0.6,55.0,4.0,100.0,3.299,32.99,30.3676463109069,30.367646310906878 +3,Orijentacija 1,0.08,0.8,85.0,6.0,100.0,0.794,7.94,17.9964100485419,17.996410048541925 +4,Orijentacija 1,0.14,0.4,55.0,6.0,100.0,2.792,27.92,28.9183082790225,28.91830827902247 +5,Orijentacija 1,0.14,0.6,85.0,2.0,100.0,2.468,24.68,27.8469031072241,27.84690310722408 +6,Orijentacija 1,0.14,0.8,20.0,4.0,100.0,2.871,28.71,29.1606638499301,29.16066384993012 +7,Orijentacija 1,0.28,0.4,85.0,4.0,100.0,2.423,24.23,27.6870682827501,27.687068282750126 +8,Orijentacija 1,0.28,0.6,20.0,6.0,100.0,1.891,18.91,25.5338305769008,25.533830576900797 +9,Orijentacija 1,0.28,0.8,55.0,2.0,100.0,2.431,24.31,27.7156991768667,27.715699176866714 +10,Orijentacija 2,0.08,0.4,85.0,4.0,100.0,3.398,33.98,30.6244674906605,30.624467490660535 +11,Orijentacija 2,0.08,0.6,20.0,6.0,100.0,3.218,32.18,30.1517207952602,30.151720795260214 +12,Orijentacija 2,0.08,0.8,55.0,2.0,100.0,2.768,27.68,28.8433217156944,28.843321715694405 +13,Orijentacija 2,0.14,0.4,20.0,6.0,100.0,2.472,24.72,27.8609693283356,27.860969328335564 +14,Orijentacija 2,0.14,0.6,55.0,2.0,100.0,2.527,25.27,28.0521048383983,28.052104838398293 +15,Orijentacija 2,0.14,0.8,85.0,4.0,100.0,3.647,36.47,31.2387152662756,31.238715266275626 +16,Orijentacija 2,0.28,0.4,55.0,4.0,100.0,2.491,24.91,27.9274745507301,27.92747455073013 +17,Orijentacija 2,0.28,0.6,85.0,6.0,100.0,4.111,41.11,32.278949535607,32.27894953560699 +18,Orijentacija 2,0.28,0.8,20.0,2.0,100.0,2.263,22.63,27.0936910790946,27.09369107909457 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/1_response_means_Sigma.csv b/docs/obrada/ispitni_rezultati_taguchi_out/1_response_means_Sigma.csv new file mode 100644 index 0000000..e9d5ff7 --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/1_response_means_Sigma.csv @@ -0,0 +1,15 @@ +Orijentacija,Sigma [Mpa],Delta (max-min),Faktor,Visina sloja [mm],Širina ekstruzije [mm],Postotak ispune [%],Broj stijenki +Orijentacija 1,21.941111111111113,7.94222222222222,Orijentacija,,,, +Orijentacija 2,29.883333333333333,7.94222222222222,Orijentacija,,,, +,23.758333333333336,4.203333333333326,Visina sloja [mm],0.08,,, +,27.961666666666662,4.203333333333326,Visina sloja [mm],0.14,,, +,26.016666666666666,4.203333333333326,Visina sloja [mm],0.28,,, +,23.923333333333332,5.266666666666666,Širina ekstruzije [mm],,0.4,, +,29.189999999999998,5.266666666666666,Širina ekstruzije [mm],,0.6,, +,24.623333333333335,5.266666666666666,Širina ekstruzije [mm],,0.8,, +,22.488333333333333,5.579999999999998,Postotak ispune [%],,,20.0, +,27.180000000000003,5.579999999999998,Postotak ispune [%],,,55.0, +,28.06833333333333,5.579999999999998,Postotak ispune [%],,,85.0, +,22.058333333333334,8.156666666666666,Broj stijenki,,,,2.0 +,30.215,8.156666666666666,Broj stijenki,,,,4.0 +,25.463333333333335,8.156666666666666,Broj stijenki,,,,6.0 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/2_response_means_SNR.csv b/docs/obrada/ispitni_rezultati_taguchi_out/2_response_means_SNR.csv new file mode 100644 index 0000000..a772fdf --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/2_response_means_SNR.csv @@ -0,0 +1,15 @@ +Orijentacija,SNR_LB [dB],Delta (max-min),Faktor,Visina sloja [mm],Širina ekstruzije [mm],Postotak ispune [%],Broj stijenki +Orijentacija 1,25.894013507992987,3.4472547809021563,Orijentacija,,,, +Orijentacija 2,29.341268288895144,3.4472547809021563,Orijentacija,,,, +,25.967193050142956,2.879084394721403,Visina sloja [mm],0.08,,, +,28.84627744486436,2.879084394721403,Visina sloja [mm],0.14,,, +,28.039452200324888,2.879084394721403,Visina sloja [mm],0.28,,, +,26.806313311882104,2.232212548834106,Širina ekstruzije [mm],,0.4,, +,29.03852586071621,2.232212548834106,Širina ekstruzije [mm],,0.6,, +,27.008083522733894,2.232212548834106,Širina ekstruzije [mm],,0.8,, +,26.270077928219177,2.3673478837173043,Postotak ispune [%],,,20.0, +,28.63742581193648,2.3673478837173043,Postotak ispune [%],,,55.0, +,27.945418955176546,2.3673478837173043,Postotak ispune [%],,,85.0, +,26.22855197617864,3.272453982363597,Broj stijenki,,,,2.0 +,29.501005958542237,3.272453982363597,Broj stijenki,,,,4.0 +,27.123364760611327,3.272453982363597,Broj stijenki,,,,6.0 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/3_factor_ranking.csv b/docs/obrada/ispitni_rezultati_taguchi_out/3_factor_ranking.csv new file mode 100644 index 0000000..bcb4505 --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/3_factor_ranking.csv @@ -0,0 +1,6 @@ +Faktor,Rang delta (Sigma),Rang delta (SNR) +Broj stijenki,8.156666666666666,3.272453982363597 +Orijentacija,7.94222222222222,3.4472547809021563 +Postotak ispune [%],5.579999999999998,2.3673478837173043 +Širina ekstruzije [mm],5.266666666666666,2.232212548834106 +Visina sloja [mm],4.203333333333326,2.879084394721403 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/4_optimal_levels.csv b/docs/obrada/ispitni_rezultati_taguchi_out/4_optimal_levels.csv new file mode 100644 index 0000000..1ca3427 --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/4_optimal_levels.csv @@ -0,0 +1,6 @@ +Faktor,Optimalna razina (po S/N) +Orijentacija,Orijentacija 2 +Visina sloja [mm],0.14 +Širina ekstruzije [mm],0.6 +Postotak ispune [%],55.0 +Broj stijenki,4.0 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/5_prediction.csv b/docs/obrada/ispitni_rezultati_taguchi_out/5_prediction.csv new file mode 100644 index 0000000..ee83c21 --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/5_prediction.csv @@ -0,0 +1,5 @@ +Predikcija,Vrijednost +Sigma_opt [MPa],40.781111111111116 +SNR_opt [dB],34.893939771178154 +Grand mean Sigma [MPa],25.912222222222223 +Grand mean SNR [dB],27.61764089844407 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/6_anova_sigma.csv b/docs/obrada/ispitni_rezultati_taguchi_out/6_anova_sigma.csv new file mode 100644 index 0000000..9945fae --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/6_anova_sigma.csv @@ -0,0 +1,7 @@ +Factor,SS,DOF,MS,Pct_contrib_% +Orijentacija,283.8550222222221,1,283.8550222222221,23.01723952764538 +Visina sloja [mm],53.10221111111093,2,26.551105555555466,4.305952746663593 +Širina ekstruzije [mm],98.16444444444437,2,49.082222222222185,7.959959676553248 +Postotak ispune [%],107.87454444444445,2,53.93727222222223,8.747332384591576 +Broj stijenki,201.40714444444444,2,100.70357222222222,16.33170500195548 +Error,488.8245444444449,8,61.10306805555561,39.63781066259073 diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Broj stijenki.png b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Broj stijenki.png new file mode 100644 index 0000000..9bed1af Binary files /dev/null and b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Broj stijenki.png differ diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Orijentacija.png b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Orijentacija.png new file mode 100644 index 0000000..3dae98a Binary files /dev/null and b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Orijentacija.png differ diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Postotak ispune [%].png b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Postotak ispune [%].png new file mode 100644 index 0000000..5f482c4 Binary files /dev/null and b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Postotak ispune [%].png differ diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Visina sloja [mm].png b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Visina sloja [mm].png new file mode 100644 index 0000000..2c71d29 Binary files /dev/null and b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Visina sloja [mm].png differ diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Širina ekstruzije [mm].png b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Širina ekstruzije [mm].png new file mode 100644 index 0000000..d800007 Binary files /dev/null and b/docs/obrada/ispitni_rezultati_taguchi_out/main_effect_SNR_Širina ekstruzije [mm].png differ diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/summary.json b/docs/obrada/ispitni_rezultati_taguchi_out/summary.json new file mode 100644 index 0000000..772430e --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/summary.json @@ -0,0 +1,19 @@ +{ + "outdir": "ispitni_rezultati_taguchi_out", + "factors": [ + "Orijentacija", + "Visina sloja [mm]", + "Širina ekstruzije [mm]", + "Postotak ispune [%]", + "Broj stijenki" + ], + "opt_levels": { + "Orijentacija": "Orijentacija 2", + "Visina sloja [mm]": 0.14, + "Širina ekstruzije [mm]": 0.6, + "Postotak ispune [%]": 55.0, + "Broj stijenki": 4.0 + }, + "pred_sigma": 40.781111111111116, + "grand_mean_sigma": 25.912222222222223 +} \ No newline at end of file diff --git a/docs/obrada/ispitni_rezultati_taguchi_out/taguchi_results.tex b/docs/obrada/ispitni_rezultati_taguchi_out/taguchi_results.tex new file mode 100644 index 0000000..36214e0 --- /dev/null +++ b/docs/obrada/ispitni_rezultati_taguchi_out/taguchi_results.tex @@ -0,0 +1,67 @@ +% --- Taguchi rezultati (S = Sigma [MPa], S/N larger-the-better) --- + +\subsection{Rezultati Taguchijeve metode} + +U skladu s ortogonalnom matricom provedena je analiza s kriterijem \textbf{što-veće-to-bolje}. Za svaku kombinaciju izračunat je S/N omjer \((\mathrm{S/N}=20\log_{10}(\sigma))\) te su određeni glavni učinci po razinama i optimalna kombinacija. + +\paragraph{Optimalne razine (po S/N).} + +\begin{tabular}{ll} +\toprule +Faktor & Optimalna razina (po S/N) \\ +\midrule +Orijentacija & Orijentacija 2 \\ +Visina sloja [mm] & 0.140000 \\ +Širina ekstruzije [mm] & 0.600000 \\ +Postotak ispune [%] & 55.000000 \\ +Broj stijenki & 4.000000 \\ +\bottomrule +\end{tabular} + + +\paragraph{Predikcija odziva na optimalnoj kombinaciji.} + +\begin{tabular}{lr} +\toprule +Predikcija & Vrijednost \\ +\midrule +Sigma_opt [MPa] & 40.78 \\ +SNR_opt [dB] & 34.89 \\ +Grand mean Sigma [MPa] & 25.91 \\ +Grand mean SNR [dB] & 27.62 \\ +\bottomrule +\end{tabular} + + +\paragraph{Rang utjecaja faktora.} + +\begin{tabular}{lrr} +\toprule +Faktor & Rang delta (Sigma) & Rang delta (SNR) \\ +\midrule +Broj stijenki & 8.157 & 3.272 \\ +Orijentacija & 7.942 & 3.447 \\ +Postotak ispune [%] & 5.580 & 2.367 \\ +Širina ekstruzije [mm] & 5.267 & 2.232 \\ +Visina sloja [mm] & 4.203 & 2.879 \\ +\bottomrule +\end{tabular} + + +\paragraph{ANOVA (Taguchi).} + +\begin{tabular}{lrrrr} +\toprule +Factor & SS & DOF & MS & Pct_contrib_% \\ +\midrule +Orijentacija & 283.855000 & 1 & 283.855000 & 23.017000 \\ +Visina sloja [mm] & 53.102000 & 2 & 26.551000 & 4.306000 \\ +Širina ekstruzije [mm] & 98.164000 & 2 & 49.082000 & 7.960000 \\ +Postotak ispune [%] & 107.875000 & 2 & 53.937000 & 8.747000 \\ +Broj stijenki & 201.407000 & 2 & 100.704000 & 16.332000 \\ +Error & 488.825000 & 8 & 61.103000 & 39.638000 \\ +\bottomrule +\end{tabular} + + +Napomena: budući da je \(n{=}1\), pogreška (Error) procijenjena je iz preostalih stupnjeva slobode (Taguchi pooling). \ No newline at end of file diff --git a/docs/obrada/obrada.py b/docs/obrada/obrada.py new file mode 100644 index 0000000..df5445d --- /dev/null +++ b/docs/obrada/obrada.py @@ -0,0 +1,229 @@ + +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Taguchi analysis pipeline for FDM experiment (per user's thesis) +- Reads a CSV with columns similar to: + 'Eksperiment','Orijentacija','Visina sloja','Širina ekstruzije','Postotak ispune', + 'Broj slojeva stijenke','A_ekv [mm^2]','Fm kN]','Sigma [Mpa]','SNR [dB]' +- Cleans units to numeric, recomputes Sigma (optional) and SNR (LB, n=1), +- Builds response tables (means, Δ), ranks factors, selects optimal levels by SNR, +- Predicts response at optimal combination (additive model), +- Runs Taguchi-style ANOVA on Sigma, +- Saves CSV outputs + main-effects plots + LaTeX snippet. +Usage: + python taguchi_from_csv.py --input ispitni_rezultati.csv --outdir out_tlak +""" +import argparse, os, re, json +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +def norm_num(x): + if pd.isna(x): + return np.nan + if isinstance(x, (int, float, np.number)): + return float(x) + s = str(x).strip() + s = s.replace(',', '.') + s = s.replace('%','') + s = s.replace(' mm','') + s = s.replace('MPa','').replace('Mpa','') + s = s.replace('kN','').replace('kN]','').replace('[','').replace(']','') + try: + return float(s) + except: + return np.nan + +def compute_snr_lb(y): + # larger-the-better; handles n=1 case + y = pd.to_numeric(y, errors='coerce') + return 20.0*np.log10(y.clip(lower=1e-12)) + +def response_table(df, factor, col): + t = df.groupby(factor, as_index=False)[col].mean() + t["Delta (max-min)"] = t[col].max() - t[col].min() + t["Faktor"] = factor + return t + +def taguchi_anova(df, response, factors): + y = df[response].astype(float) + mu = y.mean() + total_ss = ((y - mu)**2).sum() + rows = [] + dof_used = 0 + ss_used = 0.0 + for f in factors: + grp = df.groupby(f)[response].agg(['mean','count']) + ss_f = (grp['count']*(grp['mean']-mu)**2).sum() + dof_f = grp.shape[0]-1 + rows.append([f, ss_f, dof_f]) + dof_used += dof_f + ss_used += ss_f + err_ss = max(total_ss - ss_used, 0.0) + err_dof = max(len(df)-1 - dof_used, 0) + an = pd.DataFrame(rows, columns=["Factor","SS","DOF"]) + an["MS"] = an["SS"]/an["DOF"] + an["Pct_contrib_%"] = (an["SS"]/total_ss*100.0) if total_ss>0 else np.nan + err_row = pd.DataFrame([["Error", err_ss, err_dof, (err_ss/err_dof) if err_dof>0 else np.nan, (err_ss/total_ss*100.0) if total_ss>0 else np.nan]], + columns=["Factor","SS","DOF","MS","Pct_contrib_%"]) + an = pd.concat([an, err_row], ignore_index=True) + return an, mu, total_ss + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--input", required=True, help="Path to CSV with results") + ap.add_argument("--outdir", default=None, help="Output directory") + ap.add_argument("--response", default="Sigma [Mpa]", help="Response column to analyze (default Sigma [Mpa])") + ap.add_argument("--area_col", default="A_ekv [mm^2]", help="Area column if Sigma should be recomputed from Fm/Area") + ap.add_argument("--fm_col", default="Fm kN]", help="Force column (kN)") + ap.add_argument("--recompute_sigma", action="store_true", help="If set, recompute Sigma = Fm*1000/Area") + ap.add_argument("--sn_type", default="LB", choices=["LB"], help="S/N type (only LB supported here)") + args = ap.parse_args() + + in_path = args.input + outdir = args.outdir or (os.path.splitext(os.path.basename(in_path))[0] + "_taguchi_out") + os.makedirs(outdir, exist_ok=True) + + df = pd.read_csv(in_path) + + # Standard column mapping / cleanup for known names + rename_map = { + "Visina sloja":"Visina sloja [mm]", + "Širina ekstruzije":"Širina ekstruzije [mm]", + "Postotak ispune":"Postotak ispune [%]", + "Broj slojeva stijenke":"Broj stijenki", + "Sigma [MPa]":"Sigma [Mpa]", + "Fm [kN]":"Fm kN]", + } + df = df.rename(columns={k:v for k,v in rename_map.items() if k in df.columns}) + + # Ensure numeric for relevant columns + if "Visina sloja [mm]" in df.columns: + df["Visina sloja [mm]"] = df["Visina sloja [mm]"].apply(norm_num) + if "Širina ekstruzije [mm]" in df.columns: + df["Širina ekstruzije [mm]"] = df["Širina ekstruzije [mm]"].apply(norm_num) + if "Postotak ispune [%]" in df.columns: + df["Postotak ispune [%]"] = df["Postotak ispune [%]"].apply(norm_num) + if "Broj stijenki" in df.columns: + df["Broj stijenki"] = df["Broj stijenki"].apply(norm_num) + if args.area_col in df.columns: + df[args.area_col] = df[args.area_col].apply(norm_num) + if args.fm_col in df.columns: + df[args.fm_col] = df[args.fm_col].apply(norm_num) + if args.response in df.columns: + df[args.response] = df[args.response].apply(norm_num) + + # Compute Sigma if asked or missing + if args.recompute_sigma or args.response not in df.columns or df[args.response].isna().all(): + if args.fm_col in df.columns and args.area_col in df.columns: + df[args.response] = (df[args.fm_col] * 1000.0) / df[args.area_col] + else: + raise SystemExit("Cannot recompute Sigma: missing Fm or Area columns") + + # Compute SNR (LB) + df["SNR_LB [dB]"] = compute_snr_lb(df[args.response]) + + # Save cleaned raw + raw_out = os.path.join(outdir, "0_raw_with_SNR.csv") + df.to_csv(raw_out, index=False) + + # Factors to analyze (auto detect from known list) + candidate_factors = ["Orijentacija","Visina sloja [mm]","Širina ekstruzije [mm]","Postotak ispune [%]","Broj stijenki"] + factors = [f for f in candidate_factors if f in df.columns] + if len(factors) == 0: + raise SystemExit("No known factor columns found. Expected some of: " + ", ".join(candidate_factors)) + + # Response tables and deltas + resp_mu = pd.concat([response_table(df, f, args.response) for f in factors], ignore_index=True) + resp_sn = pd.concat([response_table(df, f, "SNR_LB [dB]") for f in factors], ignore_index=True) + resp_mu.to_csv(os.path.join(outdir, "1_response_means_Sigma.csv"), index=False) + resp_sn.to_csv(os.path.join(outdir, "2_response_means_SNR.csv"), index=False) + + # Ranking (by Delta) + rank_mu = resp_mu.groupby("Faktor")["Delta (max-min)"].max().sort_values(ascending=False).reset_index().rename(columns={"Delta (max-min)":"Rang delta (Sigma)"}) + rank_sn = resp_sn.groupby("Faktor")["Delta (max-min)"].max().sort_values(ascending=False).reset_index().rename(columns={"Delta (max-min)":"Rang delta (SNR)"}) + ranking = pd.merge(rank_mu, rank_sn, on="Faktor") + ranking.to_csv(os.path.join(outdir, "3_factor_ranking.csv"), index=False) + + # Optimal levels by SNR + opt_levels = {f: df.groupby(f)["SNR_LB [dB]"].mean().sort_values(ascending=False).index[0] for f in factors} + opt_table = pd.DataFrame({"Faktor": list(opt_levels.keys()), "Optimalna razina (po S/N)": list(opt_levels.values())}) + opt_table.to_csv(os.path.join(outdir, "4_optimal_levels.csv"), index=False) + + # Prediction at optimal combo (additive model) on response + grand_mean = df[args.response].mean() + k = len(factors) + pred_sigma = sum(df.groupby(f)[args.response].mean().loc[opt_levels[f]] for f in factors) - (k-1)*grand_mean + grand_mean_snr = df["SNR_LB [dB]"].mean() + pred_snr = sum(df.groupby(f)["SNR_LB [dB]"].mean().loc[opt_levels[f]] for f in factors) - (k-1)*grand_mean_snr + pred_df = pd.DataFrame({ + "Predikcija": ["Sigma_opt [MPa]","SNR_opt [dB]","Grand mean Sigma [MPa]","Grand mean SNR [dB]"], + "Vrijednost": [pred_sigma, pred_snr, grand_mean, grand_mean_snr] + }) + pred_df.to_csv(os.path.join(outdir, "5_prediction.csv"), index=False) + + # ANOVA (Taguchi-style) on response + anova_df, mu_sigma, totss = taguchi_anova(df, args.response, factors) + anova_df.to_csv(os.path.join(outdir, "6_anova_sigma.csv"), index=False) + + # Plots: main effects for SNR + for f in factors: + means = df.groupby(f)["SNR_LB [dB]"].mean().reset_index() + # numeric sort if possible + try: + means[f] = pd.to_numeric(means[f], errors="ignore") + means = means.sort_values(by=f) + except: + pass + plt.figure() + plt.plot(means[f], means["SNR_LB [dB]"], marker="o") + plt.xlabel(f) + plt.ylabel("S/N (LB) [dB]") + plt.title(f"Main effect (S/N): {f}") + plt.tight_layout() + plt.savefig(os.path.join(outdir, f"main_effect_SNR_{f}.png"), dpi=150) + plt.close() + + # LaTeX snippet + latex_lines = [] + latex_lines.append(r"% --- Taguchi rezultati (S = Sigma [MPa], S/N larger-the-better) ---") + latex_lines.append(r"\subsection{Rezultati Taguchijeve metode}") + latex_lines.append(r"U skladu s ortogonalnom matricom provedena je analiza s kriterijem \textbf{što-veće-to-bolje}. Za svaku kombinaciju izračunat je S/N omjer \((\mathrm{S/N}=20\log_{10}(\sigma))\) te su određeni glavni učinci po razinama i optimalna kombinacija.") + + # Optimal levels + latex_lines.append(r"\paragraph{Optimalne razine (po S/N).}") + latex_lines.append(opt_table.to_latex(index=False, escape=False)) + # Prediction + latex_lines.append(r"\paragraph{Predikcija odziva na optimalnoj kombinaciji.}") + latex_lines.append(pred_df.to_latex(index=False, escape=False, float_format='%.2f')) + # Ranking + latex_lines.append(r"\paragraph{Rang utjecaja faktora.}") + latex_lines.append(ranking.to_latex(index=False, escape=False, float_format='%.3f')) + # ANOVA + an_fmt = anova_df.copy() + for c in ["SS","MS","Pct_contrib_%"]: + if c in an_fmt.columns: + an_fmt[c] = an_fmt[c].astype(float).round(3) + latex_lines.append(r"\paragraph{ANOVA (Taguchi).}") + latex_lines.append(an_fmt.to_latex(index=False, escape=False)) + latex_lines.append(r"Napomena: budući da je \(n{=}1\), pogreška (Error) procijenjena je iz preostalih stupnjeva slobode (Taguchi pooling).") + + with open(os.path.join(outdir, "taguchi_results.tex"), "w", encoding="utf-8") as f: + f.write("\n\n".join(latex_lines)) + + # Small JSON summary + summary = { + "outdir": outdir, + "factors": factors, + "opt_levels": opt_levels, + "pred_sigma": pred_sigma, + "grand_mean_sigma": grand_mean, + } + with open(os.path.join(outdir, "summary.json"), "w", encoding="utf-8") as f: + json.dump(summary, f, ensure_ascii=False, indent=2) + + print("Done. Outputs in:", outdir) + +if __name__ == "__main__": + main() diff --git a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux index d469d5c..7dd1e1c 100644 --- a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux +++ b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux @@ -95,6 +95,7 @@ \abx@aux@segm{0}{0}{zandi2020mechanical} \@writefile{toc}{\contentsline {subsection}{\numberline {2.7}Taguchijeva metoda}{10}{}\protected@file@percent } \newlabel{subsec:taguchi_metoda}{{2.7}{10}{}{subsection.2.7}{}} +\newlabel{eq:sn_ratio}{{1}{10}{}{equation.1}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {2.8}ANOVA analiza (analiza varijance)}{11}{}\protected@file@percent } \newlabel{subsec:anova}{{2.8}{11}{}{subsection.2.8}{}} \@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Primjer ANOVA tablice}}{12}{}\protected@file@percent } @@ -238,25 +239,46 @@ 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\abx@aux@defaultrefcontext{0}{aboelella2025layer}{nty/global//global/global/global} \abx@aux@defaultrefcontext{0}{aoyagi2002viscosity}{nty/global//global/global/global} @@ -279,4 +301,4 @@ \abx@aux@defaultrefcontext{0}{Stamopoulos2020}{nty/global//global/global/global} \abx@aux@defaultrefcontext{0}{sun2008effect}{nty/global//global/global/global} \abx@aux@defaultrefcontext{0}{zandi2020mechanical}{nty/global//global/global/global} -\gdef \@abspage@last{41} +\gdef \@abspage@last{45} diff --git a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.blg b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.blg index 5b78c6b..819ceae 100644 --- a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.blg +++ b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.blg @@ -1,17 +1,17 @@ [0] Config.pm:308> INFO - This is Biber 2.20 [0] Config.pm:311> INFO - Logfile is 'ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.blg' -[49] biber:342> INFO - === Sun Aug 24, 2025, 17:44:24 -[59] Biber.pm:420> INFO - Reading 'ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bcf' -[91] Biber.pm:994> INFO - Found 21 citekeys in bib section 0 -[101] Biber.pm:4463> INFO - Processing section 0 -[105] Biber.pm:4654> INFO - Looking for bibtex file 'literatura.bib' for section 0 -[106] bibtex.pm:1713> INFO - LaTeX decoding ... -[115] bibtex.pm:1519> INFO - Found BibTeX data source 'literatura.bib' -[203] UCollate.pm:68> INFO - Overriding locale 'hr-HR' defaults 'variable = shifted' with 'variable = non-ignorable' -[204] UCollate.pm:68> INFO - Overriding locale 'hr-HR' defaults 'normalization = NFD' with 'normalization = prenormalized' -[204] Biber.pm:4283> INFO - Sorting list 'nty/global//global/global/global' of type 'entry' with template 'nty' and locale 'hr-HR' -[217] bbl.pm:677> INFO - Writing 'ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl' with encoding 'UTF-8' -[221] bbl.pm:780> INFO - Output to ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl -[221] Biber.pm:131> WARN - Duplicate entry key: 'kuznetsov2018strengthPLA' in file 'literatura.bib', skipping ... -[221] Biber.pm:131> WARN - legacy year field '1963.' in entry 'bazjanacNauka1' is not an integer - this will probably not sort properly. -[221] Biber.pm:133> INFO - WARNINGS: 2 +[43] biber:342> INFO - === Sat Sep 6, 2025, 21:08:47 +[54] Biber.pm:420> INFO - Reading 'ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bcf' +[85] Biber.pm:994> INFO - Found 21 citekeys in bib section 0 +[95] Biber.pm:4463> INFO - Processing section 0 +[101] Biber.pm:4654> INFO - Looking for bibtex file 'literatura.bib' for section 0 +[102] bibtex.pm:1713> INFO - LaTeX decoding ... +[111] bibtex.pm:1519> INFO - Found BibTeX data source 'literatura.bib' +[199] UCollate.pm:68> INFO - Overriding locale 'hr-HR' defaults 'variable = shifted' with 'variable = non-ignorable' +[199] UCollate.pm:68> INFO - Overriding locale 'hr-HR' defaults 'normalization = NFD' with 'normalization = prenormalized' +[199] Biber.pm:4283> INFO - Sorting list 'nty/global//global/global/global' of type 'entry' with template 'nty' and locale 'hr-HR' +[210] bbl.pm:677> INFO - Writing 'ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl' with encoding 'UTF-8' +[214] bbl.pm:780> INFO - Output to ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl +[215] Biber.pm:131> WARN - Duplicate entry key: 'kuznetsov2018strengthPLA' in file 'literatura.bib', skipping ... +[215] Biber.pm:131> WARN - legacy year field '1963.' in entry 'bazjanacNauka1' is not an integer - this will probably not sort properly. +[215] Biber.pm:133> INFO - WARNINGS: 2 diff --git a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fdb_latexmk b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fdb_latexmk index 1d7ab1d..90ecd09 100644 --- a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fdb_latexmk +++ b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fdb_latexmk @@ -1,15 +1,14 @@ # Fdb version 4 -["biber ispitivanje_cvrstoce_fdm_3d_printanog_uzorka"] 1756050264.57186 "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bcf" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka" 1756106077.05661 0 - "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bcf" 1756106076.9728 110930 78500a82cbacb8c6073d8b4b0765071c "pdflatex" +["biber ispitivanje_cvrstoce_fdm_3d_printanog_uzorka"] 1757185726.92069 "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bcf" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka" 1757196943.46214 0 + "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bcf" 1757196943.37322 110930 78500a82cbacb8c6073d8b4b0765071c "pdflatex" "literatura.bib" 1755950408.49282 8363 10f2631a3c522628af031baf6131e8ab "" (generated) "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.blg" (rewritten before read) -["pdflatex"] 1756106075.25098 "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.pdf" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka" 1756106077.05683 0 +["pdflatex"] 1757196941.46789 "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.pdf" "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka" 1757196943.46242 0 "/etc/texmf/web2c/texmf.cnf" 1741769514 43179 d4d8625c4224f516afc2b9ae03b45f2d "" "/usr/share/texmf/fonts/enc/dvips/base/8r.enc" 1165713224 4850 80dc9bab7f31fb78a000ccfed0e27cab "" - "/usr/share/texmf/fonts/enc/dvips/cm-super/cm-super-ts1.enc" 1136849721 2900 1537cc8184ad1792082cd229ecc269f4 "" "/usr/share/texmf/fonts/enc/dvips/inconsolata/i4-ot1-0.enc" 1561323594 2496 4d35740f3a177992ff7b134746c0a1db "" "/usr/share/texmf/fonts/map/fontname/texfonts.map" 1577235249 3524 cb3e574dea2d1052e39280babc910dc8 "" "/usr/share/texmf/fonts/tfm/adobe/helvetic/phvb7t.tfm" 1136768653 2240 eb56c13537f4d8a0bd3fafc25572b1bd "" @@ -22,7 +21,6 @@ "/usr/share/texmf/fonts/tfm/adobe/helvetic/phvrc7t.tfm" 1136768653 2736 b64ca876d1295aa2796677cdb20bd1c9 "" "/usr/share/texmf/fonts/tfm/adobe/helvetic/phvro7t.tfm" 1136768653 2772 ab6561c8ff5ee69ff6d5961b9356db5a "" "/usr/share/texmf/fonts/tfm/adobe/helvetic/phvro8r.tfm" 1136768653 4964 f223217e5e1f85fa3742fb0480aba9e8 "" - "/usr/share/texmf/fonts/tfm/jknappen/ec/tcss1200.tfm" 1136768653 1536 809a177113b9dd743dafe00d0870078f "" "/usr/share/texmf/fonts/tfm/public/amsfonts/cmextra/cmex7.tfm" 1246382020 1004 54797486969f23fa377b128694d548df "" "/usr/share/texmf/fonts/tfm/public/amsfonts/cmextra/cmex8.tfm" 1246382020 988 bdf658c3bfc2d96d3c8b02cfc1c94c20 "" "/usr/share/texmf/fonts/tfm/public/cm/cmex10.tfm" 1136768653 992 662f679a0b3d2d53c1b94050fdaa3f50 "" @@ -32,7 +30,6 @@ "/usr/share/texmf/fonts/tfm/public/cm/cmr12.tfm" 1136768653 1288 655e228510b4c2a1abe905c368440826 "" "/usr/share/texmf/fonts/tfm/public/cm/cmr6.tfm" 1136768653 1300 b62933e007d01cfd073f79b963c01526 "" "/usr/share/texmf/fonts/tfm/public/cm/cmr8.tfm" 1136768653 1292 21c1c5bfeaebccffdb478fd231a0997d "" - "/usr/share/texmf/fonts/tfm/public/cm/cmss12.tfm" 1136768653 1324 37b971caf729d7edd9cbb9f9b0ea76eb "" "/usr/share/texmf/fonts/tfm/public/cm/cmsy10.tfm" 1136768653 1124 6c73e740cf17375f03eec0ee63599741 "" "/usr/share/texmf/fonts/tfm/public/cm/cmsy6.tfm" 1136768653 1116 933a60c408fc0a863a92debe84b2d294 "" "/usr/share/texmf/fonts/tfm/public/cm/cmsy8.tfm" 1136768653 1120 8b7d695260f3cff42e636090a8002094 "" @@ -44,7 +41,7 @@ "/usr/share/texmf/fonts/type1/public/amsfonts/cm/cmr12.pfb" 1248133631 32722 d7379af29a190c3f453aba36302ff5a9 "" "/usr/share/texmf/fonts/type1/public/amsfonts/cm/cmr8.pfb" 1248133631 32726 0a1aea6fcd6468ee2cf64d891f5c43c8 "" "/usr/share/texmf/fonts/type1/public/amsfonts/cm/cmsy10.pfb" 1248133631 32569 5e5ddc8df908dea60932f3c484a54c0d "" - "/usr/share/texmf/fonts/type1/public/cm-super/sfss1200.pfb" 1215737283 95792 fb800ffa2babe7bd5fafc1817d8f1313 "" + "/usr/share/texmf/fonts/type1/public/amsfonts/cm/cmsy8.pfb" 1248133631 32626 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"ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.run.xml" 1756106076.9741 2586 0df33ae4847716b3dce228e82b7d5d25 "pdflatex" - "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex" 1756106074.55074 85579 aa1cb77876dbaaeab61f78fe3bd2bc2d "" + "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux" 1757196943.36922 26161 ef29c3d82a064c7d4141a7a142bbd8d0 "pdflatex" + "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl" 1757185727.46654 35091 3569ab5a48cadd3873a346b2982f931d "biber ispitivanje_cvrstoce_fdm_3d_printanog_uzorka" + "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.run.xml" 1757196943.37543 2586 0df33ae4847716b3dce228e82b7d5d25 "pdflatex" + "ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex" 1757196938.38714 97489 e13b9b151d488cda59dbdb37d87dd2c3 "" "media/imgs/analiza_podataka/promjena_povrsine_10x10.jpg" 1755792541.22554 94970 a002c4ee68ebf13e3d4cf13f399439c6 "" "media/imgs/analiza_podataka/promjena_povrsine_10x70.jpg" 1755792747.65033 117458 05930e6248bd5070412ced98667c5457 "" "media/imgs/analiza_podataka/vlak_2d_lezeci.jpg" 1755791868.36192 74633 29d884550cbf67019223836feec6d66e "" diff --git a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fls b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fls index 6e17412..314cc68 100644 --- a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fls +++ b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.fls @@ -450,12 +450,14 @@ INPUT /usr/share/texmf/tex/latex/inconsolata/ot1zi4.fd INPUT /usr/share/texmf/tex/latex/inconsolata/ot1zi4.fd INPUT /usr/share/texmf/tex/latex/inconsolata/ot1zi4.fd INPUT /usr/share/texmf/fonts/tfm/public/inconsolata/ot1-zi4r-0.tfm -INPUT /usr/share/texmf/fonts/tfm/public/cm/cmss12.tfm -INPUT /usr/share/texmf/fonts/tfm/jknappen/ec/tcss1200.tfm INPUT /usr/share/texmf/fonts/enc/dvips/inconsolata/i4-ot1-0.enc INPUT /usr/share/texmf/fonts/vf/adobe/helvetic/phvr7t.vf INPUT /usr/share/texmf/fonts/tfm/adobe/helvetic/phvr8r.tfm -INPUT /usr/share/texmf/fonts/enc/dvips/cm-super/cm-super-ts1.enc +INPUT /usr/share/texmf/fonts/tfm/public/amsfonts/cmextra/cmex7.tfm +INPUT /usr/share/texmf/fonts/tfm/adobe/helvetic/phvr8c.tfm +INPUT /usr/share/texmf/fonts/tfm/adobe/helvetic/phvb7t.tfm +INPUT /usr/share/texmf/fonts/vf/adobe/helvetic/phvb7t.vf +INPUT /usr/share/texmf/fonts/tfm/adobe/helvetic/phvb8r.tfm INPUT /usr/share/texmf/fonts/tfm/adobe/helvetic/phvrc7t.tfm INPUT /usr/share/texmf/fonts/vf/adobe/helvetic/phvrc7t.vf INPUT /usr/share/texmf/fonts/tfm/adobe/helvetic/phvr8r.tfm @@ -480,7 +482,7 @@ INPUT /usr/share/texmf/fonts/type1/public/amsfonts/cm/cmmi8.pfb INPUT /usr/share/texmf/fonts/type1/public/amsfonts/cm/cmr12.pfb INPUT /usr/share/texmf/fonts/type1/public/amsfonts/cm/cmr8.pfb INPUT /usr/share/texmf/fonts/type1/public/amsfonts/cm/cmsy10.pfb -INPUT /usr/share/texmf/fonts/type1/public/cm-super/sfss1200.pfb +INPUT /usr/share/texmf/fonts/type1/public/amsfonts/cm/cmsy8.pfb INPUT /usr/share/texmf/fonts/type1/urw/helvetic/uhvb8a.pfb INPUT /usr/share/texmf/fonts/type1/urw/helvetic/uhvbo8a.pfb INPUT /usr/share/texmf/fonts/type1/urw/helvetic/uhvr8a.pfb diff --git a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.log b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.log index 99133f5..c526d67 100644 --- a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.log +++ b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.log @@ -1,4 +1,4 @@ -This is pdfTeX, Version 3.141592653-2.6-1.40.27 (TeX Live 2025/TeX Live for SUSE Linux) (preloaded format=pdflatex 2025.3.28) 25 AUG 2025 09:14 +This is pdfTeX, Version 3.141592653-2.6-1.40.27 (TeX Live 2025/TeX Live for SUSE Linux) (preloaded format=pdflatex 2025.3.28) 7 SEP 2025 00:15 entering extended mode restricted \write18 enabled. %&-line parsing enabled. @@ -719,43 +719,46 @@ Package biblatex Warning: 'babel/polyglossia' detected but 'csquotes' missing. \@quotelevel=\count443 \@quotereset=\count444 LaTeX Font Info: Trying to load font information for OT1+phv on input line 8 -4. +5. (/usr/share/texmf/tex/latex/psnfss/ot1phv.fd File: ot1phv.fd 2020/03/25 scalable font definitions for OT1/phv. ) LaTeX Font Info: Font shape `OT1/phv/m/n' will be -(Font) scaled to size 11.03998pt on input line 84. +(Font) scaled to size 11.03998pt on input line 85. -(./ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux) +(./ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux + +LaTeX Warning: Label `eq:sn_ratio_vlak' multiply defined. + +) \openout1 = `ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.aux'. -LaTeX Font Info: Checking defaults for OML/cmm/m/it on input line 84. -LaTeX Font Info: ... okay on input line 84. -LaTeX Font Info: Checking defaults for OMS/cmsy/m/n on input line 84. -LaTeX Font Info: ... okay on input line 84. -LaTeX Font Info: Checking defaults for OT1/cmr/m/n on input line 84. -LaTeX Font Info: ... okay on input line 84. -LaTeX Font Info: Checking defaults for T1/cmr/m/n on input line 84. -LaTeX Font Info: ... okay on input line 84. -LaTeX Font Info: Checking defaults for TS1/cmr/m/n on input line 84. -LaTeX Font Info: ... okay on input line 84. -LaTeX Font Info: Checking defaults for OMX/cmex/m/n on input line 84. -LaTeX Font Info: ... okay on input line 84. -LaTeX Font Info: Checking defaults for U/cmr/m/n on input line 84. -LaTeX Font Info: ... okay on input line 84. +LaTeX Font Info: Checking defaults for OML/cmm/m/it on input line 85. +LaTeX Font Info: ... okay on input line 85. +LaTeX Font Info: Checking defaults for OMS/cmsy/m/n on input line 85. +LaTeX Font Info: ... okay on input line 85. +LaTeX Font Info: Checking defaults for OT1/cmr/m/n on input line 85. +LaTeX Font Info: ... okay on input line 85. +LaTeX Font Info: Checking defaults for T1/cmr/m/n on input line 85. +LaTeX Font Info: ... okay on input line 85. +LaTeX Font Info: Checking defaults for TS1/cmr/m/n on input line 85. +LaTeX Font Info: ... okay on input line 85. +LaTeX Font Info: Checking defaults for OMX/cmex/m/n on input line 85. +LaTeX Font Info: ... okay on input line 85. +LaTeX Font Info: Checking defaults for U/cmr/m/n on input line 85. +LaTeX Font Info: ... okay on input line 85. \symgns@font=\mathgroup4 LaTeX Font Info: Overwriting symbol font `gns@font' in version `bold' -(Font) TS1/phv/m/n --> TS1/phv/b/n on input line 84. -Package gensymb Info: Math companion symbols declared on input line 84. -LaTeX Info: Redefining \degree on input line 84. -LaTeX Info: Redefining \celsius on input line 84. +(Font) TS1/phv/m/n --> TS1/phv/b/n on input line 85. +Package gensymb Info: Math companion symbols declared on input line 85. +LaTeX Info: Redefining \degree on input line 85. +LaTeX Info: Redefining \celsius on input line 85. Package gensymb Info: Using text companion symbols for \degree, \celsius and \p -erthousand on input line 84. -LaTeX Info: Redefining \ohm on input line 84. -Package gensymb Info: Using \textohm for \ohm on input line 84. -Package gensymb Info: Using \textmu for \micro on input line 84. - -(/usr/share/texmf/tex/context/base/mkii/supp-pdf.mkii +erthousand on input line 85. +LaTeX Info: Redefining \ohm on input line 85. +Package gensymb Info: Using \textohm for \ohm on input line 85. +Package gensymb Info: Using \textmu for \micro on input line 85. + (/usr/share/texmf/tex/context/base/mkii/supp-pdf.mkii [Loading MPS to PDF converter (version 2006.09.02).] \scratchcounter=\count445 \scratchdimen=\dimen264 @@ -829,8 +832,8 @@ Package biblatex Info: ... file 'ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.b bl' found. (./ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.bbl) -Package biblatex Info: Reference section=0 on input line 84. -Package biblatex Info: Reference segment=0 on input line 84. +Package biblatex Info: Reference section=0 on input line 85. +Package biblatex Info: Reference segment=0 on input line 85. \c@lstlisting=\count451 (/usr/share/texmf/tex/latex/upquote/upquote.sty @@ -838,17 +841,17 @@ Package: upquote 2012/04/19 v1.3 upright-quote and grave-accent glyphs in verba tim ) LaTeX Font Info: Font shape `OT1/phv/m/n' will be -(Font) scaled to size 15.89755pt on input line 86. +(Font) scaled to size 15.89755pt on input line 87. LaTeX Font Info: Font shape `OT1/phv/b/n' will be -(Font) scaled to size 15.89755pt on input line 86. +(Font) scaled to size 15.89755pt on input line 87. LaTeX Font Info: Font shape `OT1/phv/b/n' will be -(Font) scaled to size 12.87997pt on input line 86. +(Font) scaled to size 12.87997pt on input line 87. 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LaTeX Font Info: Font shape `OT1/phv/b/n' will be -(Font) scaled to size 11.03998pt on input line 100. +(Font) scaled to size 11.03998pt on input line 101. -Underfull \hbox (badness 10000) in paragraph at lines 123--127 +Underfull \hbox (badness 10000) in paragraph at lines 124--128 [] -Underfull \hbox (badness 10000) in paragraph at lines 129--131 +Underfull \hbox (badness 10000) in paragraph at lines 130--132 [] -Underfull \hbox (badness 10000) in paragraph at lines 132--135 +Underfull \hbox (badness 10000) in paragraph at lines 133--136 [] @@ -897,11 +900,11 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): [2] LaTeX Font Info: Font shape `OT1/phv/b/it' in size <12> not available -(Font) Font shape `OT1/phv/b/sl' tried instead on input line 139. +(Font) Font shape `OT1/phv/b/sl' tried instead on input line 140. 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File: media/imgs/infills/hilbert_curve_infill.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/infills/hilbert_curve_infill.jpg used on i -nput line 309. +nput line 310. (pdftex.def) Requested size: 71.13188pt x 53.33192pt. File: media/imgs/infills/archimedean_chords_infill.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/infills/archimedean_chords_infill.jpg used - on input line 315. + on input line 316. 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File: media/imgs/infills/support_cubic_infill.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/infills/support_cubic_infill.jpg used on i -nput line 339. +nput line 340. (pdftex.def) Requested size: 71.13188pt x 53.33633pt. File: media/imgs/infills/adaptive_cubic_infill.png Graphic file (type png) Package pdftex.def Info: media/imgs/infills/adaptive_cubic_infill.png used on -input line 345. +input line 346. (pdftex.def) Requested size: 71.13188pt x 58.81096pt. File: media/imgs/infills/3d_honeycomb_infill.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/infills/3d_honeycomb_infill.jpg used on in -put line 351. +put line 352. (pdftex.def) Requested size: 71.13188pt x 53.33192pt. File: media/imgs/infills/gyroid_infill.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/infills/gyroid_infill.jpg used on input li -ne 357. +ne 358. (pdftex.def) Requested size: 71.13188pt x 53.33192pt. -Overfull \hbox (51.82632pt too wide) in paragraph at lines 326--361 +Overfull \hbox (51.82632pt too wide) in paragraph at lines 327--362 [][] [] @@ -1136,7 +1139,7 @@ edean_chords_infill.jpg> <./media/imgs/infills/cubic_infill.jpg> <./media/imgs/ infills/support_cubic_infill.jpg> <./media/imgs/infills/adaptive_cubic_infill.p ng> <./media/imgs/infills/3d_honeycomb_infill.jpg> <./media/imgs/infills/gyroid _infill.jpg>] -Underfull \hbox (badness 10000) in paragraph at lines 366--372 +Underfull \hbox (badness 10000) in paragraph at lines 367--373 [] @@ -1147,10 +1150,10 @@ pg Graphic file (type jpg) Package pdftex.def Info: media/imgs/parameters_description/orijentacija_modela_ -na_radnoj_podlozi.jpg used on input line 375. +na_radnoj_podlozi.jpg used on input line 376. 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(pdftex.def) Requested size: 409.71692pt x 118.67303pt. -Underfull \hbox (badness 10000) in paragraph at lines 398--401 +Underfull \hbox (badness 10000) in paragraph at lines 399--402 [] @@ -1178,7 +1181,7 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): [8 <./media/imgs/parameters_description/orijentacija_modela_na_radnoj_podlozi.j pg> <./media/imgs/parameters_description/broj_slojeva_stijenke.jpg>] -Overfull \hbox (6.92891pt too wide) in paragraph at lines 403--408 +Overfull \hbox (6.92891pt too wide) in paragraph at lines 404--409 \OT1/phv/m/n/12 Tijekom talo[]zenja slo-jeva, novi sloj ras-top-lje-nog fi-la-m enta do-lazi u kon-takt s ve[]c ohla[]denim [] @@ -1225,7 +1228,7 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): (fancyhdr) \addtolength{\topmargin}{-2.49998pt}. 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(pdftex.def) Requested size: 364.19667pt x 155.519pt. @@ -1393,7 +1396,7 @@ File: media/imgs/planiranje_eksperimenta/suzenje_struka.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/suzenje_struka.jpg - used on input line 993. + used on input line 1000. (pdftex.def) Requested size: 364.19667pt x 167.23065pt. @@ -1401,7 +1404,7 @@ File: media/imgs/planiranje_eksperimenta/prihvat_epruvete.jpg Graphic file (typ e jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/prihvat_epruvete.jp -g used on input line 1010. +g used on input line 1017. (pdftex.def) Requested size: 364.19667pt x 260.88644pt. @@ -1419,7 +1422,7 @@ File: media/imgs/planiranje_eksperimenta/epruveta_s_prihvatom.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/epruveta_s_prihvato -m.jpg used on input line 1017. +m.jpg used on input line 1024. (pdftex.def) Requested size: 364.19667pt x 187.17839pt. @@ -1432,7 +1435,7 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): [22 <./media/imgs/planiranje_eksperimenta/prihvat_epruvete.jpg> <./media/imgs/p laniranje_eksperimenta/epruveta_s_prihvatom.jpg>] -Underfull \hbox (badness 10000) in paragraph at lines 1025--1027 +Underfull \hbox (badness 10000) in paragraph at lines 1032--1034 [] @@ -1442,7 +1445,7 @@ File: media/imgs/planiranje_eksperimenta/epruveta_vlak_3d.jpg Graphic file (typ e jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/epruveta_vlak_3d.jp -g used on input line 1031. +g used on input line 1038. (pdftex.def) Requested size: 318.66948pt x 252.1718pt. @@ -1450,10 +1453,10 @@ File: media/imgs/planiranje_eksperimenta/epruveta_smik_3d.jpg Graphic file (typ e jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/epruveta_smik_3d.jp -g used on input line 1038. +g used on input line 1045. (pdftex.def) Requested size: 318.66948pt x 256.60207pt. -Underfull \hbox (badness 10000) in paragraph at lines 1043--1044 +Underfull \hbox (badness 10000) in paragraph at lines 1050--1051 [] @@ -1463,7 +1466,7 @@ File: media/imgs/planiranje_eksperimenta/epruveta_smik_skica.jpg Graphic file ( type jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/epruveta_smik_skica -.jpg used on input line 1047. +.jpg used on input line 1054. (pdftex.def) Requested size: 455.24411pt x 256.79521pt. @@ -1482,7 +1485,7 @@ File: media/imgs/planiranje_eksperimenta/epruveta_vlak_skica.jpg Graphic file ( type jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/epruveta_vlak_skica -.jpg used on input line 1054. +.jpg used on input line 1061. (pdftex.def) Requested size: 455.24411pt x 260.64543pt. @@ -1511,7 +1514,7 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): (fancyhdr) \addtolength{\topmargin}{-2.49998pt}. [25] -Underfull \hbox (badness 10000) in paragraph at lines 1134--1139 +Underfull \hbox (badness 10000) in paragraph at lines 1141--1146 [] @@ -1521,7 +1524,7 @@ File: media/imgs/planiranje_eksperimenta/naprava_smik.jpg Graphic file (type jp g) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/naprava_smik.jpg u -sed on input line 1142. +sed on input line 1149. (pdftex.def) Requested size: 455.24411pt x 285.54791pt. @@ -1539,14 +1542,14 @@ File: media/imgs/planiranje_eksperimenta/naprava_smik_sliceano.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/planiranje_eksperimenta/naprava_smik_slicea -no.jpg used on input line 1162. +no.jpg used on input line 1169. (pdftex.def) Requested size: 455.24411pt x 276.9331pt. -LaTeX Warning: Command \O invalid in math mode on input line 1168. +LaTeX Warning: Command \O invalid in math mode on input line 1175. -LaTeX Warning: Command \O invalid in math mode on input line 1169. +LaTeX Warning: Command \O invalid in math mode on input line 1176. @@ -1558,12 +1561,12 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): (fancyhdr) \addtolength{\topmargin}{-2.49998pt}. [27 <./media/imgs/planiranje_eksperimenta/naprava_smik_sliceano.jpg>] -Underfull \hbox (badness 10000) in paragraph at lines 1182--1185 +Underfull \hbox (badness 10000) in paragraph at lines 1189--1192 [] -Underfull \hbox (badness 10000) in paragraph at lines 1224--1227 +Underfull \hbox (badness 10000) in paragraph at lines 1231--1234 [] @@ -1573,7 +1576,7 @@ File: media/imgs/provedba_eksperimenta/tekst_epruvete.jpg Graphic file (type jp g) Package pdftex.def Info: media/imgs/provedba_eksperimenta/tekst_epruvete.jpg u -sed on input line 1230. +sed on input line 1237. (pdftex.def) Requested size: 409.71692pt x 190.7674pt. @@ -1585,12 +1588,12 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): (fancyhdr) \addtolength{\topmargin}{-2.49998pt}. [28] -Underfull \hbox (badness 10000) in paragraph at lines 1236--1238 +Underfull \hbox (badness 10000) in paragraph at lines 1243--1245 [] -Underfull \hbox (badness 10000) in paragraph at lines 1241--1243 +Underfull \hbox (badness 10000) in paragraph at lines 1248--1250 [] @@ -1604,7 +1607,7 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): (fancyhdr) \addtolength{\topmargin}{-2.49998pt}. [29 <./media/imgs/provedba_eksperimenta/tekst_epruvete.jpg>] -Underfull \hbox (badness 10000) in paragraph at lines 1260--1263 +Underfull \hbox (badness 10000) in paragraph at lines 1266--1269 [] @@ -1613,14 +1616,14 @@ pt> File: media/imgs/analiza_podataka/vlak_2d_stojeci.jpg Graphic file (type jpg) Package pdftex.def Info: media/imgs/analiza_podataka/vlak_2d_stojeci.jpg used -on input line 1276. +on input line 1282. (pdftex.def) Requested size: 227.62206pt x 234.79387pt. 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PDF statistics: - 269 PDF objects out of 1000 (max. 8388607) - 136 compressed objects within 2 object streams + 281 PDF objects out of 1000 (max. 8388607) + 144 compressed objects within 2 object streams 0 named destinations out of 1000 (max. 500000) 191 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.pdf b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.pdf index 038ca03..0247150 100644 Binary files a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.pdf and b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.pdf differ diff --git a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex index d901a7a..a7df6d4 100644 --- a/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex +++ b/radno/ispitivanje_cvrstoce_fdm_3d_printanog_uzorka.tex @@ -24,6 +24,7 @@ \usepackage{listings} \usepackage{xcolor} \usepackage{pdflscape} +\usepackage{multirow} % dodaj u preambulu \lstdefinestyle{python}{ language=Python, @@ -454,23 +455,23 @@ Taguchijeva metoda je eksperimentalna metoda za analizu utjecaja više ulaznih p \bigskip \textbf{Osnovni pojmovi:} \begin{itemize} - \item \textbf{Faktori (engl. factors):} Varijable koje se kontroliraju u eksperimentu. U ovom radu, to su npr. visina sloja, širina ekstruzije, postotak - ispune itd. - - \item \textbf{Razine (engl. levels):} Odabrane vrijednosti koje pojedini faktor može poprimiti. Na primjer, visina sloja može imati 3 - razine: 0.1\,mm, 0.2\,mm i 0.3\,mm. + \item \textbf{Faktori:} Varijable koje se kontroliraju u eksperimentu. U ovom radu, to su odabrani parametri ispisa + \item \textbf{Razine:} Odabrane vrijednosti koje pojedini faktor može poprimiti. + \item \textbf{Ortogonalna matrica:} Matrica koja omogućuje sustavno variranje faktora i njihovih razina tako da se s minimalnim brojem eksperimenata - obuhvati maksimalna količina informacija. Najčešće korištene su matrice tipa L9, L18, L27 itd., ovisno o broju faktora i razina. + obuhvati maksimalna količina informacija. \item \textbf{Signal-šum omjer (S/N omjer):} Kvantitativna mjera kojom se procjenjuje kvaliteta odziva. Povezuje korisni signal (željeni odziv) s - neželjenim šumom (varijacijom). Za slučaj kada se želi maksimizirati odziv (npr. čvrstoća), koristi se S/N omjer za kriterij što je vrijednost viša, to je bolja: + neželjenim šumom (varijacijom). Za slučaj kada se želi maksimizirati odziv (u ovom slučaju čvrstoća), koristi se S/N omjer za kriterij što + je vrijednost viša, to je bolja: - \begin{equation} + \begin{equation}\label{eq:sn_ratio} \text{S/N} = -10 \cdot \log_{10}\left( \frac{1}{n} \sum_{i=1}^{n} \frac{1}{y_i^2} \right) \end{equation} gdje je $y_i$ izmjerena vrijednost odziva u $i$-tom ponavljanju, a $n$ broj ponavljanja eksperimenta za danu kombinaciju parametara.\\ + \end{itemize} \textbf{Zašto se koristi u optimizaciji procesa:} @@ -492,8 +493,14 @@ Njezine glavne prednosti su: U ovom radu koristit će se Taguchijeva metoda kako bi se utvrdio utjecaj 5 faktora ispisa (visina sloja, širina ekstruzije, postotak ispune, orijentacija modela i broj slojeva stijenke) na mehaničku čvrstoću ispitnih uzoraka izrađenih FDM tehnologijom. Za svaki eksperimentalni uzorak mjerit će se vlačna i smična čvrstoća, a S/N omjer koristit će se za procjenu optimalne kombinacije parametara i njihove robusnosti.\\ + +Cilj nam je, dakle, pronaći razine faktora koje daju velik signal (visoke vrijednosti čvrstoće) i mali šum (stabilno, malo rasipanje), zato maksimiziramo S/N +omjer.\\ + + \end{flushleft} + \subsection{ANOVA analiza (analiza varijance)} \label{subsec:anova} Analiza varijance (engl. \textit{Analysis of Variance} – ANOVA) je statistička metoda kojom se utvrđuje značajnost utjecaja pojedinih faktora na @@ -624,9 +631,9 @@ kvalitetna raspodjela podataka, biti će odabrane tri visine sloja prema tablici \hline 0.08mm & 20\% & 0.4mm \\ \hline - 0.22mm & 55\% & 0.4mm \\ + 0.14mm & 55\% & 0.4mm \\ \hline - 0.36mm & 90\% & 0.4mm \\ + 0.28mm & 90\% & 0.4mm \\ \hline \end{tabular} \end{table} @@ -715,8 +722,8 @@ U Tablici~\ref{tab:sumarni_parametri} prikazan je sažetak svih ispitivanih para \hline \multirow{3}{*}{Visina sloja} & 0.08\,mm \\ - & 0.22\,mm \\ - & 0.36\,mm \\ + & 0.14\,mm \\ + & 0.28\,mm \\ \hline \multirow{3}{*}{Širina ekstruzije} & 0.4\,mm \\ @@ -783,8 +790,8 @@ Kada znamo koje kombinacije su nam potrebne, možemo napraviti tablicu \ref{tab: \hline \multirow{3}{*}{Visina sloja} & 0.08\,mm & \multirow{3}{*}{3} \\ - & 0.22\,mm & \\ - & 0.36\,mm & \\ + & 0.14\,mm & \\ + & 0.28\,mm & \\ \hline \multirow{3}{*}{Širina ekstruzije} & 0.4\,mm & \multirow{3}{*}{3} \\ @@ -828,21 +835,21 @@ kombinacije koje je potrebno ispitati.\\ 1 & Orijentacija 1 & 0.08\,mm & 0.4\,mm & 20\% & 2 \\ 2 & Orijentacija 1 & 0.08\,mm & 0.6\,mm & 55\% & 4 \\ 3 & Orijentacija 1 & 0.08\,mm & 0.8\,mm & 85\% & 6 \\ -4 & Orijentacija 1 & 0.22\,mm & 0.4\,mm & 55\% & 6 \\ -5 & Orijentacija 1 & 0.22\,mm & 0.6\,mm & 85\% & 2 \\ -6 & Orijentacija 1 & 0.22\,mm & 0.8\,mm & 20\% & 4 \\ -7 & Orijentacija 1 & 0.36\,mm & 0.4\,mm & 85\% & 4 \\ -8 & Orijentacija 1 & 0.36\,mm & 0.6\,mm & 20\% & 6 \\ -9 & Orijentacija 1 & 0.36\,mm & 0.8\,mm & 55\% & 2 \\ +4 & Orijentacija 1 & 0.14\,mm & 0.4\,mm & 55\% & 6 \\ +5 & Orijentacija 1 & 0.14\,mm & 0.6\,mm & 85\% & 2 \\ +6 & Orijentacija 1 & 0.14\,mm & 0.8\,mm & 20\% & 4 \\ +7 & Orijentacija 1 & 0.28\,mm & 0.4\,mm & 85\% & 4 \\ +8 & Orijentacija 1 & 0.28\,mm & 0.6\,mm & 20\% & 6 \\ +9 & Orijentacija 1 & 0.28\,mm & 0.8\,mm & 55\% & 2 \\ 10 & Orijentacija 2 & 0.08\,mm & 0.4\,mm & 85\% & 4 \\ 11 & Orijentacija 2 & 0.08\,mm & 0.6\,mm & 20\% & 6 \\ 12 & Orijentacija 2 & 0.08\,mm & 0.8\,mm & 55\% & 2 \\ -13 & Orijentacija 2 & 0.22\,mm & 0.4\,mm & 20\% & 6 \\ -14 & Orijentacija 2 & 0.22\,mm & 0.6\,mm & 55\% & 2 \\ -15 & Orijentacija 2 & 0.22\,mm & 0.8\,mm & 85\% & 4 \\ -16 & Orijentacija 2 & 0.36\,mm & 0.4\,mm & 55\% & 4 \\ -17 & Orijentacija 2 & 0.36\,mm & 0.6\,mm & 85\% & 6 \\ -18 & Orijentacija 2 & 0.36\,mm & 0.8\,mm & 20\% & 2 \\ +13 & Orijentacija 2 & 0.14\,mm & 0.4\,mm & 20\% & 6 \\ +14 & Orijentacija 2 & 0.14\,mm & 0.6\,mm & 55\% & 2 \\ +15 & Orijentacija 2 & 0.14\,mm & 0.8\,mm & 85\% & 4 \\ +16 & Orijentacija 2 & 0.28\,mm & 0.4\,mm & 55\% & 4 \\ +17 & Orijentacija 2 & 0.28\,mm & 0.6\,mm & 85\% & 6 \\ +18 & Orijentacija 2 & 0.28\,mm & 0.8\,mm & 20\% & 2 \\ \hline \end{tabular} } @@ -867,8 +874,8 @@ parametri za smično ispitivanje.\\ \hline \multirow{3}{*}{Visina sloja} & 0.08\,mm & \multirow{3}{*}{3} \\ - & 0.22\,mm & \\ - & 0.36\,mm & \\ + & 0.14\,mm & \\ + & 0.28\,mm & \\ \hline \multirow{3}{*}{Širina ekstruzije} & 0.4\,mm & \multirow{3}{*}{3} \\ @@ -917,24 +924,24 @@ odgovara matrici L27 koja sadržava pet faktora po tri razine, te zahtjeva 27 ek 7 & Orijentacija 3 & 0.08\,mm & 0.8\,mm & 20\% & 4 \\ 8 & Orijentacija 1 & 0.08\,mm & 0.8\,mm & 55\% & 6 \\ 9 & Orijentacija 2 & 0.08\,mm & 0.8\,mm & 85\% & 2 \\ -10 & Orijentacija 2 & 0.22\,mm & 0.4\,mm & 20\% & 6 \\ -11 & Orijentacija 3 & 0.22\,mm & 0.4\,mm & 55\% & 2 \\ -12 & Orijentacija 1 & 0.22\,mm & 0.4\,mm & 85\% & 4 \\ -13 & Orijentacija 3 & 0.22\,mm & 0.6\,mm & 20\% & 4 \\ -14 & Orijentacija 1 & 0.22\,mm & 0.6\,mm & 55\% & 6 \\ -15 & Orijentacija 2 & 0.22\,mm & 0.6\,mm & 85\% & 2 \\ -16 & Orijentacija 1 & 0.22\,mm & 0.8\,mm & 20\% & 2 \\ -17 & Orijentacija 2 & 0.22\,mm & 0.8\,mm & 55\% & 4 \\ -18 & Orijentacija 3 & 0.22\,mm & 0.8\,mm & 85\% & 6 \\ -19 & Orijentacija 3 & 0.36\,mm & 0.4\,mm & 20\% & 4 \\ -20 & Orijentacija 1 & 0.36\,mm & 0.4\,mm & 55\% & 6 \\ -21 & Orijentacija 2 & 0.36\,mm & 0.4\,mm & 85\% & 2 \\ -22 & Orijentacija 1 & 0.36\,mm & 0.6\,mm & 20\% & 4 \\ -23 & Orijentacija 2 & 0.36\,mm & 0.6\,mm & 55\% & 6 \\ -24 & Orijentacija 3 & 0.36\,mm & 0.6\,mm & 85\% & 2 \\ -25 & Orijentacija 2 & 0.36\,mm & 0.8\,mm & 20\% & 2 \\ -26 & Orijentacija 3 & 0.36\,mm & 0.8\,mm & 55\% & 4 \\ -27 & Orijentacija 1 & 0.36\,mm & 0.8\,mm & 85\% & 6 \\ +10 & Orijentacija 2 & 0.14\,mm & 0.4\,mm & 20\% & 6 \\ +11 & Orijentacija 3 & 0.14\,mm & 0.4\,mm & 55\% & 2 \\ +12 & Orijentacija 1 & 0.14\,mm & 0.4\,mm & 85\% & 4 \\ +13 & Orijentacija 3 & 0.14\,mm & 0.6\,mm & 20\% & 4 \\ +14 & Orijentacija 1 & 0.14\,mm & 0.6\,mm & 55\% & 6 \\ +15 & Orijentacija 2 & 0.14\,mm & 0.6\,mm & 85\% & 2 \\ +16 & Orijentacija 1 & 0.14\,mm & 0.8\,mm & 20\% & 2 \\ +17 & Orijentacija 2 & 0.14\,mm & 0.8\,mm & 55\% & 4 \\ +18 & Orijentacija 3 & 0.14\,mm & 0.8\,mm & 85\% & 6 \\ +19 & Orijentacija 3 & 0.28\,mm & 0.4\,mm & 20\% & 4 \\ +20 & Orijentacija 1 & 0.28\,mm & 0.4\,mm & 55\% & 6 \\ +21 & Orijentacija 2 & 0.28\,mm & 0.4\,mm & 85\% & 2 \\ +22 & Orijentacija 1 & 0.28\,mm & 0.6\,mm & 20\% & 4 \\ +23 & Orijentacija 2 & 0.28\,mm & 0.6\,mm & 55\% & 6 \\ +24 & Orijentacija 3 & 0.28\,mm & 0.6\,mm & 85\% & 2 \\ +25 & Orijentacija 2 & 0.28\,mm & 0.8\,mm & 20\% & 2 \\ +26 & Orijentacija 3 & 0.28\,mm & 0.8\,mm & 55\% & 4 \\ +27 & Orijentacija 1 & 0.28\,mm & 0.8\,mm & 85\% & 6 \\ \hline \end{tabular} } @@ -1248,7 +1255,6 @@ Za svaki ispitni uzorak zabilježeni su: \item konfiguracija parametara definirana ortogonalnom matricom \item vrijednosti sile \item izračunata naprezanja - \item vrijednosti pomaka \end{itemize} Na taj način dobiven je skup podataka koji se dalje koristi u analizi prema Taguchijevoj metodi. @@ -1339,38 +1345,251 @@ Izračun površina i geometrijskih momenata presjeka proveden je pomoću vlastit \subsection{Obrada rezultata vlačnog ispitivanja}\label{subsec:obrada_rezultata_vlačnog_ispitivanja} -Primjenom Taguchijeve metode i izračuna signal-šum (S/N omjer) moguće je odrediti koji parametri ispisa imaju najveći utjecaj na čvrstoću uzoraka te identificirati -optimalne kombinacije parametara. Za svaki uzorak izmjerena je maksimalna sila $F_\text{max}$ pri lomu, te je izračunata vlačna čvrstoća prema izrazu:\\ -\begin{equation} -\sigma = \frac{F_\text{max}}{A} -\end{equation} -gdje je $A$ površina poprečnog presjeka epruvete u zoni loma.\\ +Nakon odrađenog vlačnog ispitivanja na kidalici, zabilježene su sile prikazane u tablici \ref{tab:rezultati_vlacni}. Svaki uzorak ispitivan je do loma, te je +zabilježena najveća sila u procesu ispitivanja. \\ +\subsubsection{Signal-šum omjer rezultata vlačnog ispitivanja}\label{subsubsec:signal_sum_omjer_rezultata_vlacnog_ispitivanja} + +Primjenom Taguchijeve metode i izračuna signal-šum (S/N omjer) moguće je odrediti koji parametri ispisa imaju najveći utjecaj na čvrstoću uzoraka te identificirati +optimalne kombinacije parametara. Za svaki uzorak, pomoću maksimalne sile $F_\text{m}$ pri lomu, te je izračunata vlačna čvrstoća prema izrazu:\\ +\begin{equation} +\sigma = \frac{F_\text{m}}{A_{ekv}} +\end{equation} +gdje je $A_{ekv}$ površina ekvivalentnog poprečnog presjeka epruvete kada bi bila potpuno ispunjena.\\ +Ekvivalentu površinu koristimo kako bi svi uzorci imali istu referencu za računanje naprezanja. Pošto je unutrašnjost ispitnog uzorka kompleksna, te površina +poprečnog presjeka nije konstanta, niti je jednostavnog oblika, koristeći program opisan u poglavlju \ref{subsec:racunalna_analiza_poprecnog_presjeka} kako bi se +odredila maksimalna površina poprečnog presjeka, te uzročno posljedično i naprezanje ($\sigma ''$) koje bi se pojavilo kada bi ispitni uzorak zbilja puknuo u presjeku +najvećom površinom (najgori slučaj koji nam po metodi "što više to bolje" daje najniži rezultat). Također je zabilježena i minimalna površina poprečnog presjeka +svakog ispitnog uzorka kako bi se mogao odrediti lokalni maksimum naprezanja ($\sigma '$) koji se pri zabilježenoj sili dogodio (opisano u daljnjoj analizi). + +\begin{flushleft} +Za svaku eksperimentalnu kombinaciju iz ortogonalne matrice L18 (tablica \ref{tab:taguchi_l18_vlacni} izračunat je signal-šum (SNR) omjer prema izrazu +\ref{eq:sn_ratio}. Gdje je $y_i$ izmjerena vrijednost čvrstoće u $i$-tom ponavljanju, a $n$ broj ponavljanja. Budući da +je svaki ispitni uzorak (zbog ograničenih resursa) ispitivan samo jednom, izraz \ref{eq:sn_ratio} možemo pretvoriti u izraz \ref{eq:sn_ratio_vlak} (za +vlak). + +Za svaki rezultat izračunat je S/N omjer (pripadajuč za taj slučaj) prema kriteriju što je vrijednost viša, to je bolja.\\ + +\end{flushleft} + +\begin{equation} + \text{S/N} = -10 \cdot \log_{10}\left ( \frac{1}{\sigma^2}\right) = 20\cdot log_{10} \left (\sigma \right ) +\label{eq:sn_ratio_vlak} +\end{equation} + +\begin{table}[H] +\centering +\resizebox{\textwidth}{!}{% +\begin{tabular}{|c|c|c|c|c|c|c|c|c|} +\hline +Eksperiment & $A_{ekv}$ [mm$^2$] & $A_{min}$ [mm$^2$] & $A_{max}$ [mm$^2$] & $F_m$ [kN] & $\sigma$ [MPa] & $\sigma'$ & $\sigma''$ & SNR [dB] \\ \hline +1 & 100 & 44.493 & 44.493 & 0.778 & 7.78 & 17.4859 & 17.4859 & 17.8196 \\ \hline +2 & 100 & 87.819 & 87.819 & 3.299 & 32.99 & 37.5659 & 37.5659 & 30.3676 \\ \hline +3 & 100 & 100 & 100 & 0.794 & 7.94 & 7.9400 & 7.9400 & 17.9964 \\ \hline +4 & 100 & 87.6308 & 87.6308 & 2.792 & 27.92 & 31.8609 & 31.8609 & 28.9183 \\ \hline +5 & 100 & 91.5613 & 91.5613 & 2.468 & 24.68 & 26.9546 & 26.9546 & 27.8469 \\ \hline +6 & 100 & 93.9905 & 93.9905 & 2.871 & 28.71 & 30.5456 & 30.5456 & 29.1607 \\ \hline +7 & 100 & 92.7473 & 92.7473 & 2.423 & 24.23 & 26.1247 & 26.1247 & 27.6871 \\ \hline +8 & 100 & 96.3896 & 96.3896 & 1.891 & 18.91 & 19.6183 & 19.6183 & 25.5338 \\ \hline +9 & 100 & 79.2649 & 79.2649 & 2.431 & 24.31 & 30.6693 & 30.6693 & 27.7157 \\ \hline +10 & 100 & 35.669 & 71.34 & 3.398 & 33.98 & 95.2648 & 47.6311 & 30.6245 \\ \hline +11 & 100 & 36.045 & 72.09 & 3.218 & 32.18 & 89.2773 & 44.6386 & 30.1517 \\ \hline +12 & 100 & 26.908 & 53.817 & 2.768 & 27.68 & 102.869 & 51.4336 & 28.8433 \\ \hline +13 & 100 & 32.415 & 52.865 & 2.472 & 24.72 & 76.2610 & 46.7606 & 27.8610 \\ \hline +14 & 100 & 24.155 & 48.31 & 2.527 & 25.27 & 104.616 & 52.3080 & 28.0521 \\ \hline +15 & 100 & 42.804 & 85.607 & 3.647 & 36.47 & 85.2023 & 42.6017 & 31.2387 \\ \hline +16 & 100 & 26.909 & 53.817 & 2.491 & 24.91 & 92.5713 & 46.2865 & 27.9275 \\ \hline +17 & 100 & 44.555 & 89.111 & 4.111 & 41.11 & 92.2680 & 46.1335 & 32.2789 \\ \hline +18 & 100 & 15.895 & 31.79 & 2.263 & 22.63 & 142.372 & 71.1859 & 27.0937 \\ \hline +\end{tabular} +} +\caption{Rezultati vlačnog ispitivanja} +\label{tab:rezultati_vlacni} +\end{table} + +\begin{flushleft} +Nakon što smo odredili sve vrijednosti za signal-šum omjere, moguće je onda odrediti srednji signal-šum za svaku od razina svakog faktora u tablici +\ref{tab:taguchi_l18_vlacni}, srednju čvrstoću svake razine svakog parametra, te finalno i razliku maksimalnog i minimalnog signal-šum omjera svake razine svakog +faktora. Sa tim podacima, možemo napraviti predikciju optimalne kombinacije signal-šum omjera. Svi navedeni podaci prikazani su u tablici \ref{tab:snr_vlak}. +\end{flushleft} + +\begin{table}[H] +\centering +\scriptsize +\begin{tabular}{|c|c|c|c|c|c|} +\hline +Faktor & Razina & Srednji SNR [dB] & Srednja čvrstoća [MPa] & $\Delta$ (SNR) & Optimalna kombinacija SNR \\ \hline + +\multirow{2}{*}{Orijentacija} +& 1 & 25.8940 & 21.94 & \multirow{2}{*}{3.4473} & \multirow{2}{*}{2} \\ \cline{2-4} +& 2 & 29.3413 & 29.88 & & \\ \hline + +\multirow{3}{*}{Visina sloja [mm]} +& 0.08 & 25.9672 & 23.76 & \multirow{3}{*}{3.2725} & \multirow{3}{*}{0.14} \\ \cline{2-4} +& 0.14 & 28.8463 & 27.96 & & \\ \cline{2-4} +& 0.28 & 28.0395 & 26.02 & & \\ \hline + +\multirow{3}{*}{Širina ekstruzije [mm]} +& 0.4 & 26.8063 & 23.92 & \multirow{3}{*}{2.8791} & \multirow{3}{*}{0.6} \\ \cline{2-4} +& 0.6 & 29.0385 & 29.19 & & \\ \cline{2-4} +& 0.8 & 27.0081 & 24.62 & & \\ \hline + +\multirow{3}{*}{Postotak ispune [\%]} +& 20 & 26.2701 & 22.49 & \multirow{3}{*}{2.3673} & \multirow{3}{*}{55} \\ \cline{2-4} +& 55 & 28.6374 & 27.18 & & \\ \cline{2-4} +& 85 & 27.9454 & 28.07 & & \\ \hline + +\multirow{3}{*}{Broj slojeva stijenke} +& 2 & 26.2286 & 22.06 & \multirow{3}{*}{2.2322} & \multirow{3}{*}{4} \\ \cline{2-4} +& 4 & 29.5010 & 30.22 & & \\ \cline{2-4} +& 6 & 27.1234 & 25.46 & & \\ \hline + +\end{tabular} +\caption{Analiza faktora s prosječnim vrijednostima SNR i čvrstoće} +\label{tab:snr_vlak} +\end{table} + + +\subsubsection{ANOVA analiza rezultata vlačnog ispitivanja}\label{subsubsec:anova_analiza_rezultata_vlacnog_ispitivanja} + +ANOVA metodu provesti ćemo po postupku opisanom u poglavlju \ref{subsec:anova_analiza} + +Izračunamo li srednju srednju vrijednost ($\mu$) naprezanja $\sigma$ svakog eksperimenta te zbroj kvadrata odstupanja točaka od srednje vrijednosti za skup svih +$\sigma$, uz broj uzoraka $\mathrm{N}=18$, dobivamo vrijednosti iz izraza \ref{eq:sst} te izraza \ref{eq:sigma_avg}.\\ + +\begin{equation}\label{eq:sigma_avg} + \mu = \bar{\sigma} = \frac{\sum_{i=1}^{N} (\sigma_i)}{N} = 25.912 \mathrm{MPa} +\end{equation} + +\begin{equation}\label{eq:sst} + SS_{ukupno} = \sum_{i=1}^{N} (\sigma_i) = 1233.228 \mathrm{MPa^2} +\end{equation} + + +Sada je moguće izračunati zbrojeve kvadrata za svaki faktor ($\mathrm{SS_f}$) prema izrazu \ref{eq:ssf} koji predstavlja dio ukupne varijacije koji se može pripisati +glavnom učinku faktora za koji se računa.\\ + +\begin{equation}\label{eq:ssf} + SS_f = \sum_{j=1}^{l} n_j (\bar{\sigma}_j - \bar{\sigma})^2 +\end{equation} + +Srednji kvadrat definiran je izrazima \ref{eq:msf} i \ref{eq:mse} te opisuje prosječni varijabilitet koji objašnjava faktor po jednom stupnju slobode i "ostatak", tj. +preostali varijabilitet koji model nije objasnio.\\ + +\begin{equation}\label{eq:msf} + \mathrm{MS_f} = \frac{\mathrm{SS_f}}{\mathrm{SL_f}} +\end{equation} + +\begin{equation}\label{eq:mse} + \mathrm{MS_E} = \frac{\mathrm{SS_E}}{\mathrm{SL_E}} +\end{equation} + +Stupnjevi slobode govore koliko je neovisnih informacija dostupno za procjenu varijabilnosti. Ukupni broj stupnjeva slobode (definiran izrazom \ref{eq:sl_ukupno} +je zapravo ukupan broj ispitnih uzoraka, umanjen za jedan jer jedno od $\mathrm{N}$ mjerenja koristimo za procjenu ukupnog prosjeka $\mu$, dok je broj +stupnjeva slobode faktora s $\mathrm{a}$ razina (definiran izrazom \ref{eq:sl_faktor}) umanjen za jedan jer se jedna razina koristi za kontrast u odnosu na $\mu$.\\ + + +\begin{equation}\label{eq:sl_ukupno} + \mathrm{SL_{ukupno}} = \mathrm{N} - 1 +\end{equation} + +\begin{equation}\label{eq:sl_faktor} + \mathrm{SL_{faktor}} = \mathrm{a} -1 +\end{equation} + +U tablici \ref{tab:anova_ssf_vlak} prikazane su vrijednosti zbroja kvadrata, stupnjeva slobode te srednjih kvadrata za svaki faktor i njegove razine.\\ \begin{table}[H] \centering -\caption{Rezultati vlačnih ispitivanja (primjer vrijednosti → moram ubaciti prave nakon kidanja, ali ovo bi trebao biti format tablice)} -\label{tab:rezultati_vlacni} -\begin{tabular}{|c|c|c|c|c|} -\hline -Eksperiment & Orijentacija & $F_\text{max}$ [N] & Površina presjeka $A$ [mm$^2$] & $\sigma$ [MPa] \\ -\hline -1 & Orijentacija 1 & 1420 & 25.0 & 56.8 \\ -2 & Orijentacija 1 & 1280 & 25.0 & 51.2 \\ -3 & Orijentacija 1 & 1630 & 25.0 & 65.2 \\ -... & ... & ... & ... & ... \\ +\begin{tabular}{|c|c|c|c|c|c|} \hline + Faktor & $\bar{\sigma}$ & n & $\mu^2$ & $\mathrm{n} \cdot \mu^2$ \\ \hline + +\multicolumn{5}{|l|}{\textbf{Orijentacija}} \\ \hline +1 & 21.9411 & 9 & 15.7697 & 141.9275 \\ \hline +2 & 29.8833 & 9 & 15.7697 & 141.9275 \\ \hline +\textbf{Zbroj kvadrata} & & & & 283.8550 \\ \hline +\textbf{Stupnjevi slobode} & & & & 1 \\ \hline +\textbf{Srednji kvadrat} & & & & 283.8550 \\ \hline + +\multicolumn{5}{|l|}{\textbf{Visina sloja [mm]}} \\ \hline +0.08 & 23.7583 & 6 & 4.6392 & 27.8354 \\ \hline +0.14 & 27.9617 & 6 & 4.2002 & 25.2013 \\ \hline +0.28 & 26.0167 & 6 & 0.0109 & 0.0655 \\ \hline +\textbf{Zbroj kvadrata} & & & & 53.1022 \\ \hline +\textbf{Stupnjevi slobode} & & & & 2 \\ \hline +\textbf{Srednji kvadrat} & & & & 26.5511 \\ \hline + +\multicolumn{5}{|l|}{\textbf{Širina ekstruzije [mm]}} \\ \hline +0.4 & 23.9233 & 6 & 3.9557 & 23.7347 \\ \hline +0.6 & 29.19 & 6 & 10.7438 & 64.4629 \\ \hline +0.8 & 24.6233 & 6 & 1.6612 & 9.9674 \\ \hline +\textbf{Zbroj kvadrata} & & & & 98.1644 \\ \hline +\textbf{Stupnjevi slobode} & & & & 2 \\ \hline +\textbf{Srednji kvadrat} & & & & 49.0822 \\ \hline + +\multicolumn{5}{|l|}{\textbf{Postotak ispune [\%]}} \\ \hline +20 & 22.4883 & 6 & 11.7230 & 70.3381 \\ \hline +55 & 27.18 & 6 & 1.6073 & 9.6436 \\ \hline +85 & 28.0683 & 6 & 4.6489 & 27.8929 \\ \hline +\textbf{Zbroj kvadrata} & & & & 107.8745 \\ \hline +\textbf{Stupnjevi slobode} & & & & 2 \\ \hline +\textbf{Srednji kvadrat} & & & & 53.9377 \\ \hline + +\multicolumn{5}{|l|}{\textbf{Broj slojeva stijenke}} \\ \hline +2 & 22.0583 & 6 & 14.8525 & 89.1148 \\ \hline +4 & 30.215 & 6 & 18.5139 & 111.0834 \\ \hline +6 & 25.4633 & 6 & 0.2015 & 1.2090 \\ \hline +\textbf{Zbroj kvadrata} & & & & 201.4071 \\ \hline +\textbf{Stupnjevi slobode} & & & & 2 \\ \hline +\textbf{Srednji kvadrat} & & & & 100.7036 \\ \hline + \end{tabular} +\caption{Sume kvadrata faktora vlačnog testa} +\label{tab:anova_ssf_vlak} \end{table} -Za svaki rezultat izračunat je S/N omjer (pripadajuč za taj slučaj) prema kriteriju što je vrijednost viša, to je bolja.\\ -\begin{equation} -\text{S/N} = -10 \cdot \log_{10}\left( \frac{1}{n} \sum_{i=1}^{n} \frac{1}{y_i^2} \right) -\label{eq:sn_ratio} +Pogreška predstavlja sve što nije objašnjeno glavnim učincima u modelu, te je definirana izrazom \ref{eq:pogreska}. Uzevši u obzir da (zbog ograničennja eksperimenta) +nemamo više ispitivanja istih epruveta, pogreška će sadržavati interakcije među faktorima (jer ih ne procjenjujemo), stohastični šum procesa (varijacije u ispisu, +temperaturama, mjerenju itd.) te neubrojene kovarijante (neidealnu adheziju, točne poprečne presjeke, interakciju geometrije ispune i stijenke itd.). Pogreška u +ovome slučaju zato neće predstavljati grešku u mjerenju, već neobjašnjene varijacije u rezultatu. + +\begin{equation}\label{eq:pogreska} + \mathrm{SS_E} = \mathrm{SS_{ukupno}} - \sum_f \mathrm{SS_f} \end{equation} +F-omjer, opisan izrazom \ref{eq:f-ratio}, predstavlja usporedbu varijabilnosti zbog faktora te preostale (rezidualne) varijabilnosti. Velika F vrijednost govori +da se razine u tome faktoru razlikuju više nego što bi se očekivalo od slučajnog šuma, te je kao takva pokazatelj statističke značajnosti faktora na finalni rezultat.\\ + +\begin{equation}\label{eq:f-ratio} + \mathrm{F_f} = \frac{\mathrm{MS_f}}{\mathrm{MS_e}} +\end{equation} + + +Sažetak cijele analize, te udio varijabiliteta svakog faktora prikazan je u tablici \ref{tab:anova_sazetak_vlak}.\\ + +\begin{table}[H] +\centering +\scriptsize +\begin{tabular}{|l|c|c|c|c|c|} +\hline +\textbf{Faktor} & \textbf{Zbroj kvadrata (SS)} & \textbf{Stupnjevi slobode (df)} & \textbf{Srednji kvadrat (MS)} & \textbf{F-omjer} & \textbf{Udio varijabiliteta [\%]} \\ \hline +Orijentacija & 283.8550 & 1 & 283.8550 & 4.6455 & 23.0172 \\ \hline +Visina sloja & 53.1022 & 2 & 26.5511 & 0.4345 & 4.3060 \\ \hline +Širina ekstruzije & 98.1644 & 2 & 49.0822 & 0.8033 & 7.9600 \\ \hline +Postotak ispune & 107.8745 & 2 & 53.9373 & 0.8827 & 8.7473 \\ \hline +Broj slojeva stijenke & 201.4071 & 2 & 100.7036 & 1.6481 & 16.3317 \\ \hline +Pogreška (rezidual) & 488.8245 & 8 & 61.1031 & & 39.6378 \\ \hline +\textbf{Ukupno} & 1233.2279 & 17 & & & 100.0000 \\ \hline +\end{tabular} +\caption{ANOVA tablica sa SS, df, MS, F-omjerom i udjelom varijabiliteta} +\label{tab:anova_sazetak_vlak} +\end{table} + + + \subsection{Obrada rezultata smičnog ispitivanja}\label{subsec:obrada_rezultata_smičnog_ispitivanja} Rezultati smičnog ispitivanja obratiti će se na isti način kao i kod vlačnog, dakle prema poglavlju \ref{subsec:obrada_rezultata_vlačnog_ispitivanja}. \\ @@ -1399,12 +1618,28 @@ Eksperiment & Orijentacija & $F_\text{max}$ [N] & Površina presjeka $A$ [mm$^2$ \subsection{Obrada rezultata i S/N omjer}\label{subsec:obrada_rezultata_i_sn_omjer} Za svaku eksperimentalnu kombinaciju iz ortogonalnih matrica L18 i L27 (tablice \ref{tab:taguchi_l18_vlacni} i \ref{tab:taguchi_l27}) prikupljeni su podaci vlačne i smične čvrstoće (prema poglavljima \ref{subsec:obrada_rezultata_vlačnog_ispitivanja} i \ref{subsec:obrada_rezultata_smičnog_ispitivanja}) te je na temelju istih -izračunat signal-šum (S/N) omjer prema izrazu \ref{eq:sn_ratio}. Gdje je $y_i$ izmjerena vrijednost čvrstoće u $i$-tom ponavljanju, a $n$ broj ponavljanja.\\ +izračunat signal-šum (S/N) omjer prema izrazu \ref{eq:sn_ratio}. Gdje je $y_i$ izmjerena vrijednost čvrstoće u $i$-tom ponavljanju, a $n$ broj ponavljanja. Budući da +je svaki ispitni uzorak (zbog ograničenih resursa) ispitivan samo jednom, izraz \ref{eq:sn_ratio} možemo pretvoriti u izraz \ref{eq:sn_ratio_vlak} (za +vlak) te izraz \ref{eq:sn_ratio_smik} (za smik).\\ + +\begin{equation} + \text{S/N} = -10 \cdot \log_{10}\left ( \frac{1}{\sigma^2}\right) = 20\cdot log_{10} \left (\sigma \right ) +\label{eq:sn_ratio_vlak} +\end{equation} + +\begin{equation} + \text{S/N} = -10 \cdot \log_{10}\left ( \frac{1}{\tau^2}\right) = 20\cdot log_{10} \left (\tau \right ) +\label{eq:sn_ratio_smik} +\end{equation} + + + + \subsection{ANOVA analiza}\label{subsec:anova_analiza} Kako bi se kvantificirao doprinos svakog pojedinog parametra, na ukupnu varijabilnost čvstoće, provedena je analiza varijance (ANOVA).\\ -ANOVA postupak obuhvaća sljedeće postupke:\\ +ANOVA postupak obuhvaća sljedeće korake:\\ \begin{enumerate} \item Izračun ukupne sume kvadrata (SST), koja predstavlja ukupnu varijabilnost rezultata: \begin{equation} diff --git a/software/.ipynb_checkpoints/GRID_FINAL-checkpoint.ipynb b/software/.ipynb_checkpoints/GRID_FINAL-checkpoint.ipynb index 363fcab..d6fbb15 100644 --- a/software/.ipynb_checkpoints/GRID_FINAL-checkpoint.ipynb +++ b/software/.ipynb_checkpoints/GRID_FINAL-checkpoint.ipynb @@ -1,6 +1,304 @@ { - "cells": [], - "metadata": {}, + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "id": "4ba3c61a-034e-4f09-85b5-33b3071fe265", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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6TkxCCpIEa73GbsrcHa1hY6kwdhhEREQkwsPnacjIVhs7DIORywBbtaD18UZPZMeMGYNu3bqhffv2eRLZ8PBwZGVloX379pptfn5+8Pb2xsmTJwtNZDMyMpCRkaH5PikpCQDQc8kpyK1si+kuTI+bgxX2T3wLVkrxyWxuYeLcP6lo2G7isc3EY5tph+0mHttMPF3bbOWxaMwLuaHPkMxCp8p2Wh9r1ER206ZNOHfuHM6cOZPvtUePHsHS0hLOzs55tru5ueHRo0eFnnP27NmYMWNGvu1yCLBRaJ/xm5M0lQxxLzIQvDMELlbanyc0NFR/QUkI2008tpl4bDPtsN3EY5uJp22bhUbJAcihlAmwMInBn8VLJQCZahnCb8drfQ6jJbL37t3D+PHjERoaCmtr/X3kP23aNEyaNEnzfVJSEry8vNC0kgvWf9hKb9cxZbVm7ENGthpt2rRBOWcb0cdnZWUhNDQUHTp04BJ7IrDdxGObicc20w7bTTy2mXi6tlnIiws4/zQOn3etjmHNvIshQtNyKjoBQ34/Czt77T8xN1oiGx4ejvj4eDRo0ECzTaVS4ciRI1i0aBFCQkKQmZmJ58+f5+mVjYuLg7u7e6HntbKygpVV/m5IuUwumR/E3KWKFQoLne5ZqVRKps30ie0mHttMPLaZdthu4rHNxNO2zWSynG5YC4VCEm2uUOSmoTKtz2G0RLZdu3a4dOlSnm0jRoyAn58fpk6dCi8vLyiVSuzfvx99+/YFAERGRiImJgb+/v7iL6h9G5kdmZRuloiIqIQQkDMEUiaRX+O59ynoMPLTaImsg4MDatWqlWebnZ0dSpcurdk+atQoTJo0CS4uLnB0dMQnn3wCf39/0RULAEnlsXp5MIiIiMiwcn9vSyVneXmfZly14HXmz58PuVyOvn37IiMjA506dcKSJUu0OpdU3t0ALx8MQYcHg4iIiAxL0wElkaRF9u99mmWPbEEOHTqU53tra2ssXrwYixcv1vnc0ngkcsj/fTB0KMtGREREBqb+N6OTSyRp0XyCrMM5JFDcIYdE3tzk0AwtYCZLRERkLjQdshLpftN8gqxDuiKdRNbYARjQy6EFREREZC40Y2QlkrTo4z4lk8hKiT7GnBAREZGh/Vu1wMhRGJouc3okk8jKpPL2Bq++w2EmS0REZC6k1iObm7JzaAHlkfv8c7IXERGR+ZDcGFlO9io66by7eVm1gEMLiIiIzEdu1QKp5CwvP0Dm0II3ksgzAeDVdzjMZImIiMzFy6EF0shaNHN6dDiHqDqy165dw6ZNm3D06FHcvXsXqampKFu2LOrXr49OnTqhb9++sLKy0iGc4iOVhyIHe2SJiIjMzcuhBdJgsPJb586dQ/v27VG/fn0cO3YMTZs2xYQJE/Ddd99hyJAhEAQBX3zxBTw9PTF37lxkZGRoH1ExkcpDAXCJWiIiInMkSG1ogR7us0g9sn379sWUKVOwZcsWODs7F7rfyZMnsXDhQvz000+YPn267tGRVrhELRERkfmSSiKbq9iHFkRFRUGpVL5xP39/f/j7+yMrK0uHkIqHlB4K9sgSERGZH80YWYl8jizTDIUs5sleRUliddnfEKTyUACsWkBERGSOJFe1wNAre6WlpeHYsWO4evVqvtfS09Oxbt063SMinXFoARERkfmRWtUCDUMsiBAVFYXq1aujZcuWqF27Nlq1aoXY2FjN64mJiRgxYoT2kRQzKT0SXKKWiIjI/AgSW6LWoAsiTJ06FbVq1UJ8fDwiIyPh4OCAFi1aICYmRofLG47U3twAXKCWiIjInEhtiVqDjZEFgBMnTmD27NkoU6YMfH198c8//6BTp054++23cfv2ba0DMBQpjZF9OdmLqSwREZG54BK14hU5kU1LS4OFxcsiBzKZDEuXLkVAQABatWqFqKgoHcIgfcqd7KVmHktERGQ+/v29LZdGHqsXRV7Zy8/PD2fPnkX16tXzbF+0aBEAoEePHvqNTN8k9FC8/EiCmSwREZG5kGrVgmJf2QsAevfujY0bNxb42qJFizBo0CCT/ihbIs8EAP0s+UZERESG9fLXtjSyFs0YWR063oqcyE6bNg27du0q9PUlS5ZArVZrHUhxk8q7G+CVqgVGjoOIiIiKTqpL1BqkR9bsSeWpAHtkiYiIzNHLyV7SoI/7FJ3I3rhxQw+XNTypPBQANDerZiZLRERkNqS2IILBe2TDw8PRunVr7a9mRBJ5JgBwiVoiIiJzlDu0QDpVCww4RvbAgQNo37495syZo/XFyDC4RC0REZH50QwtkEwiq7siJbLbtm1D9+7dMXv2bAwdOrS4YyoWUikuDLzyA8A8loiIyGxohhZIJGcx2IIIAwYMwBdffIEPP/xQh0vlt3TpUtSpUweOjo5wdHSEv78/du/erXm9devWkMlkeb60jUFK725elrMgIiIic6H5JFUiOYs++t2KlMhWrlwZe/fuRVpamg6Xyq98+fKYM2cOwsPDcfbsWbRt2xY9e/bElStXNPu8//77iI2N1XzNmzdPq2tJ5JkAoJ/B00RERGRYgrTy2JflQot7stexY8eQmpqK3r17IysrS/ur/UdAQAC6du2KKlWqoGrVqvjhhx9gb2+PsLAwzT62trZwd3fXfDk6Omp1LSn1yOZi1QIiIiLzIbmqBf/+qcucniItUVumTBkcPHgQPXr0QP/+/bF9+3atL1gYlUqF4OBgpKSkwN/fX7N9w4YNWL9+Pdzd3REQEICvvvoKtra2hZ4nIyMDGRkZmu+TkpIAAGq1oNck3JTlPhjZ2dla3XPuMVJpL31hu4nHNhOPbaYdtpt4bDPxdG2z3IWl1CqVJNo9W5UNQLceWZkgYl3ZjIwMvPvuu9i6dav2V/yPS5cuwd/fH+np6bC3t0dQUBC6du0KAFixYgUqVKgAT09PXLx4EVOnTkWTJk2wbdu2Qs/37bffYsaMGfm2vzNrE4bUsNZb3Kbs/y4qcD9Fhg/9VKheir2yRERE5mDOBQViU2X4uLoK1ZxL/u/vJ+nAd+ctoMhKwe2fByAxMVH0J++iElkg592CXK6/BcEyMzMRExODxMREbNmyBb/99hsOHz6MGjVq5Nv3wIEDaNeuHW7evInKlSsXeL6CemS9vLwwbs0R/PhuM73Fbcp6LT2JKw9f4Leh9dGqalnRx2dlZSE0NBQdOnSAUqkshghLJrabeGwz8dhm2mG7icc2E0/XNuv26wlExSdj7fCGaF65dDFEaFpiElLRbv4xWKrTceP/3tEqkS3S0IJX6TOJBQBLS0v4+voCABo2bIgzZ85g4cKFWL58eb59mzZtCgCvTWStrKxgZWWVb7tCLpfMD6JclvNvpFBY6HTPSqVSMm2mT2w38dhm4rHNtMN2E49tJp7Wbfbv2EClhW6/v82FpR7uUXQimys+Ph7x8fGa8Ry56tSpo1NAarU6T4/qqyIiIgAAHh4eos8rkXHTAF6ty1byP5YgIiIqKTSfkUsoZwF0K78lOpENDw9HYGAgrl27pllKTSaTQRAEyGQyqFSqIp9r2rRp6NKlC7y9vfHixQsEBQXh0KFDCAkJwa1btzTjZUuXLo2LFy9i4sSJaNmypVbJslSKCwMvn///vMcgIiIiE/Yyj5VGzqKPcqGiE9mRI0eiatWqWLVqFdzc3HQqEREfH49hw4YhNjYWTk5OqFOnDkJCQtChQwfcu3cP+/btw4IFC5CSkgIvLy/07dsXX375pVbXklaPLBdEICIiMje5ZTPlEslZ9JGviE5kb9++ja1bt2rGtepi1apVhb7m5eWFw4cP63yNXBJ5JgC8+g6HqSwREZHZkGgdWV26ZEXP3GrXrh0uXLig9QWNRiIPBaCfJd+IiIjIsDRDCySSsujjPkX3yP72228IDAzE5cuXUatWrXyz6nr06KF7VKQTfSz5RkRERIalmXtk5DgMzaBjZE+ePInjx49j9+7d+V4TO9nLkKT0ULy8V2ayRERE5kJyPbJ6yM5EDy345JNPMGTIEMTGxkKtVuf5MtUkFpDOQwG8vFc181giIiKz8bJnUhpJy8tyodoTncg+ffoUEydOhJubmw6XNTxpPBI5OLSAiIjI/EiuasG/f+oyOV10ItunTx8cPHhQ6wsajYS6ZF9O9mImS0REZC4EiVUtgB56ZEWPka1atSqmTZuGY8eOoXbt2vkme40bN06HcIqPRB4JAPopMExERETGIZWcJXeMrEEne/3222+wt7fH4cOH89V5lclkppvISuWpwCsPhpHjICIioqJ7uWKqkQMxEKOU34qOjtb9qlSsuCACERGR+ZHaErX6IHqMLJk+Di0gIiIyPy/HyBo3DkPRx22K7pEVBAFbtmzBwYMHER8fD7Vanef1bdu26SEs/ZPMwGkAcs3axcxkiYiIzIVackMLdL9R0YnshAkTsHz5crRp0wZubm5mkyCaR5T6xR5ZIiIi8yG1oQVG6ZH9448/sG3bNnTt2lUPlzccM8m39YJ1ZImIiMyP5IYW6OE+RY+RdXJyQqVKlXS/soFJ5d0N8GodWSIiIjIfEhtaYIwlar/99lvMmDEDaWlpOl+cisfLJWqZyhIREZkLTY+shDrfdCV6aEH//v2xceNGuLq6omLFivkWRDh37pzegtMnqby7AV4Zc8I8loiIyGxoxshKJWcxRh3ZwMBAhIeHY8iQIZzsZaJYtYCIiMj85H6SKpdI0mKUBRH+97//ISQkBG+99ZbuVzckiTwUAOvIEhERmaOXv7elkbTo4y5Fj5H18vKCo6OjHi5tWNIab8IlaomIiMyN9JaoNcJkr59++gmfffYZ7ty5o/PFDUkizwQA9sgSERGZo5d1ZKXBKHVkhwwZgtTUVFSuXBm2trb5JnslJCToISz9k8pDAby8V1YtICIiMiOaOrLSyFqMMkZ2wYIFul+VitXLyV5ERERkLnJ/b0tlspc+aFW1wBxJ5M0NgFfulT2yREREZiP3k1SpzOsx2IIIKSkpok4qdn9DkMpDAbwyRta4YRAREZEIXKJWvCIlsr6+vpgzZw5iY2ML3UcQBISGhqJLly745ZdfdI9M3yTyUAAvk3Z2yBIREZkP1n8Xr0iJ7KFDh3DmzBn4+PigadOmGDNmDH744Qf89NNP+PLLL9GnTx94enpi5MiRCAgIwGeffVakiy9duhR16tSBo6MjHB0d4e/vj927d2teT09Px5gxY1C6dGnY29ujb9++iIuL0+pGJZTHam5WYCZLRERkNtgjK16RxshWq1YNW7duRUxMDIKDg3H06FGcOHECaWlpKFOmDOrXr4+VK1eiS5cuUCgURb54+fLlMWfOHFSpUgWCIGDt2rXo2bMnzp8/j5o1a2LixIn43//+h+DgYDg5OWHs2LHo06cPjh8/LvpGpTIDEHi1aoFRwyAiIiIRXi5RK42cRR/DPkVN9vL29sbkyZMxefJknS8MAAEBAXm+/+GHH7B06VKEhYWhfPnyWLVqFYKCgtC2bVsAwOrVq1G9enWEhYWhWbNmoq4ljUciB6sWEBERmaF/f3FLpWqBUcpvFReVSoXg4GCkpKTA398f4eHhyMrKQvv27TX7+Pn5wdvbGydPniw0kc3IyEBGRobm+6SkpJzzq1XIysoq3pswEYKgBpDTptrcc+4xUmkvfWG7icc2E49tph22m3hsM/F0bbPcqgXZ2dmSaPcslVrncxg9kb106RL8/f2Rnp4Oe3t7bN++HTVq1EBERAQsLS3h7OycZ383Nzc8evSo0PPNnj0bM2bMyLc9+vZt7NpV+GS1kuThQzkAOa5evYpdz69ofZ7Q0FD9BSUhbDfx2Gbisc20w3YTj20mnrZtplYrAMhw8MABOFnqNyZTpBIAXVNRoyey1apVQ0REBBITE7FlyxYEBgbi8OHDWp9v2rRpmDRpkub7pKQkeHl5oXLlyujatZ4eIjZ9B7ZcQviTWPhVr46uLSqKPj4rKwuhoaHo0KFDvpXbqHBsN/HYZuKxzbTDdhOPbSaerm02MWwvIADt2rWDq4NVMURoWlRqAZPCdHujZPRE1tLSEr6+vgCAhg0b4syZM1i4cCEGDBiAzMxMPH/+PE+vbFxcHNzd3Qs9n5WVFays8v/jKxQKyfwgKuQ5xShkMrlO96xUKiXTZvrEdhOPbSYe20w7bDfx2GbiadtmuXNblEoLSbS5Qg+z0otUfsuQ1Go1MjIy0LBhQyiVSuzfv1/zWmRkJGJiYuDv72/ECM0AF0QgIiIyO5ryWxKZom7UyV6pqamIiYlBZmZmnu116tQp8jmmTZuGLl26wNvbGy9evEBQUBAOHTqEkJAQODk5YdSoUZg0aRJcXFzg6OiITz75BP7+/qIrFgDSqckGvFK1gJksERGRWXi19rt0qhYYuPwWADx+/BgjRozIs3DBq1QqVZHPFR8fj2HDhiE2NhZOTk6oU6cOQkJC0KFDBwDA/PnzIZfL0bdvX2RkZKBTp05YsmSJ2JABSCuRzb1VrhBCRERkHl7tfJJKHVl9EJ3ITpgwAc+fP8epU6fQunVrbN++HXFxcfj+++/x008/iTrXqlWrXvu6tbU1Fi9ejMWLF4sNU9JkmpW9jBsHERERFc2rv7KZxhad6ET2wIED+Ouvv9CoUSPI5XJUqFABHTp0gKOjI2bPno1u3boVR5w6k8p4E0Ba90pERFQSvDq0QEodsrreq+jJXikpKXB1dQUAlCpVCo8fPwYA1K5dG+fOndMtmmIkxYdCzTVqiYiIzELeHlnpJC263qnoRLZatWqIjIwEANStWxfLly/HgwcPsGzZMnh4eOgYTvGRziPxytAC44ZBRERERSRIdGyBruOBRQ8tGD9+PGJjc1bI+uabb9C5c2ds2LABlpaWWLNmjU7BFCdp9ciyagEREZE5eXWCtlSqFgC65+yiE9khQ4Zo/t6wYUPcvXsX169fh7e3N8qUKaNjOKQPrFpARERkXqRatcDgY2Q3btyY53tbW1s0aNAAZcqUwZQpU3SLphhJ6Jlg1QIiIiIzkyeRNV4YBqfreGDRiexHH31UYA3ZiRMnYv369ToFQ/qR+1AwjyUiIjIPr36KKqXON12JTmQ3bNiAQYMG4dixY5ptn3zyCTZv3oyDBw/qNTh9ktIz8bJHlqksERGROcjbIyuhrMXQQwu6deuGJUuWoEePHggPD8fHH3+Mbdu24eDBg/Dz89MtmmIkqfEm//7JPJaIiMg85ClaIJ2UxfCTvQDg3XffxfPnz9GiRQuULVsWhw8fhq+vr46hFC8JPRMvqxZwcAEREZFZ4III2ilSIjtp0qQCt5ctWxYNGjTAkiVLNNt+/vln3SIqJlJ8KNgjS0REZB7UEh1aoOu9FimRPX/+fIHbfX19kZSUpHndlD++N93I9I+TvYiIiMxMnvJbxgvD0AzSI2vKk7iKTEJPBXtkiYiIzEueqgVGjMPQDL5Eba6bN28iJCQEaWlpADhD3pS8nOzFfxMiIiJzINUFEXQlOpF9+vQp2rVrh6pVq6Jr166a5WpHjRqFyZMn6z1AfZHSI6HpkTVuGERERFREeaoWGC0Kw9M1aRedyE6cOBFKpRIxMTGwtbXVbB8wYAD27NmjUzDFSUpvbuS5VQvYI0tERGQWJFu1QMfjRZff2rt3L0JCQlC+fPk826tUqYK7d+/qGE7xkdIMQHCMLBERkVlRS3VogaEXREhJScnTE5srISEBVlZWukVTjKT1TLBqARERkTnJnewlpXwFMMJkr7fffhvr1q17GYBMBrVajXnz5qFNmzY6hkP6kPtDoGaXLBERkXn491e2xPJYnXufRQ8tmDdvHtq1a4ezZ88iMzMTn332Ga5cuYKEhAQcP35cp2CKk5Te4XCJWiIiIvOS+ytbUsMKoHt+JrpHtlatWoiKisJbb72Fnj17IiUlBX369MH58+dRuXJl3aIhvZDYzwAREZHZEyTaI6sr0T2yAODk5IQvvvhC37EUM+k8GqxaQEREZF5yx8jKJdYbZZCqBRcvXkStWrUgl8tx8eLF1+5rb28PLy8vKJVKHUPTLyk9F5qhBUaNgoiIiIpKU7VAQvkKYKAxsvXq1cOjR4/g6uqKevXqQSaTvba3z8nJCcuWLcOAAQN0Ck6fJPVcaHpkjRwHERERFUluXiWpfAUG6pGNjo5G2bJlNX9/nYyMDAQHB2Pq1KlMZI0k915ZtYCIiMg8aMbISilhgYEme1WoUEHT9VuhQoXXflWtWhUff/wxGjRo8Mbzzp49G40bN4aDgwNcXV3Rq1cvREZG5tmndevWkMlkeb4+/PBD0TcqpQURuEQtERGReZJSvpLDwEvU7tmzB8eOHdN8v3jxYtSrVw/vvvsunj17BgAoVaoUtm3b9sZzHT58GGPGjEFYWBhCQ0ORlZWFjh07IiUlJc9+77//PmJjYzVf8+bNExu2pN7hyDm0gIiIyKzk/s6WSyhfAYxQfmvKlClISkoCAFy6dAmTJ09G165dER0djUmTJok61549ezB8+HDUrFkTdevWxZo1axATE4Pw8PA8+9na2sLd3V3z5ejoKDZsSXn5TDCTJSIiMgcvV/aSWCarI9Hlt6Kjo1GjRg0AwNatW9G9e3fMmjUL586dQ9euXXUKJjExEQDg4uKSZ/uGDRuwfv16uLu7IyAgAF999VWBy+QCOWN0MzIyNN/nJt0qtQpZWVk6xWcu1Go1AEClUmt1z7nHSKW99IXtJh7bTDy2mXbYbuKxzcTTpc0yXzlGSm1ukMler7K0tERqaioAYN++fRg2bBiAnOQzN2nUhlqtxoQJE9CiRQvUqlVLs/3dd99FhQoV4OnpiYsXL2Lq1KmIjIwsdOjC7NmzMWPGjHzbr1+9hl3Jd7SOz5xE3ZcBUCDm3j3s2nVX6/OEhobqLygJYbuJxzYTj22mHbabeGwz8bRps/g0ALCAKjsLu3bt0ntMpiojXaHT8aIT2bfeeguTJk1CixYtcPr0afz5558AgKioKJQvX17rQMaMGYPLly/nGX8LAKNHj9b8vXbt2vDw8EC7du1w69atAlcSmzZtWp4hDklJSfDy8kKNmjXQ9e3qWsdnTmIO38b/7t1EufLl0bVrrTcf8B9ZWVkIDQ1Fhw4dTK4esClju4nHNhOPbaYdtpt4bDPxdGmz249T8EPEcSiVSnTt2qmYIjQ9s64cBpLTtD5edCK7aNEifPzxx9iyZQuWLl2KcuXKAQB2796Nzp07axXE2LFjsXPnThw5cuSNyXDTpk0BADdv3iwwkbWysoKVlVW+7RYKhWR+EBUWOe9uZDK5TvesVCol02b6xHYTj20mHttMO2w38dhm4mnTZgqLnJRMJpNJqr11XclMdCLr7e2NnTt35ts+f/580RcXBAGffPIJtm/fjkOHDsHHx+eNx0RERAAAPDw8RF1LSkOnWbWAiIjI3OQuUWvkMAxM19sVXbUAAG7duoUvv/wSgwYNQnx8PICcHtkrV66IOs+YMWOwfv16BAUFwcHBAY8ePcKjR4+Qlpamuc53332H8PBw3LlzB3///TeGDRuGli1bok6dOqKuJaVZgC+XqGUmS0REZA5eLoggnXwF0P1+RSeyhw8fRu3atXHq1Cls27YNycnJAIALFy7gm2++EXWupUuXIjExEa1bt4aHh4fmK3fcraWlJfbt24eOHTvCz88PkydPRt++ffHPP/+IDVtSZC8zWSIiIjID6txE1rhhmB3RQws+//xzfP/995g0aRIcHBw029u2bYtFixaJOpfwhs++vby8cPjwYbEhFkhKD0buqiDMY4mIiMzDyzqyRg7EzIjukb106RJ69+6db7urqyuePHmil6CKg5QejNx7VXOQLBERkVl4+StbQgkLjLCyl7OzM2JjY/NtP3/+vKaCAZkG5rFERETm4eUYWePGYWgGT2QHDhyIqVOn4tGjR5DJZFCr1Th+/Dg+/fRTzeIIpkhKg6c1VQuMHAcREREVjSDZqgUGnuw1a9Ys+Pn5wcvLC8nJyahRowZatmyJ5s2b48svv9QpmOIkpeciN2d/0xhkIiIiMg2aHllJZSy698iKmuwlCAIePXqEX375BV9//TUuXbqE5ORk1K9fH1WqVNEtkmImoQ5ZsGgBERGReZHs0AIdjxedyPr6+uLKlSuoUqUKvLy8dLw8FQeZZkEEprJERETmQFO1wMhxmBtRQwvkcjmqVKmCp0+fFlc8xUZKXfUvhxYYNw4iIiIqGi6IoB3RY2TnzJmDKVOm4PLlyzpd2NCk9FxohhYwkSUiIjILUv2VbdChBQAwbNgwpKamom7durC0tISNjU2e1xMSEnQMiXSlGVog2R8LIiIi85I7HFAuuovRzBlyshcALFiwQLcrGouUemQ5tICIiMis5P7KltJQSMAIPbKBgYE6XtI4pDTmhEvUEhERmZfcHlkJpSsAdM/PRCeyAKBSqbB9+3Zcu3YNAFCjRg307NkTFhZanc4gpPRcsI4sERGReXlZR1ZaDN4je+XKFfTo0QOPHj1CtWrVAABz585F2bJl8c8//6BWrVo6hkS64mQvIiIi86IZWiC1LlkdiR5S/N5776FmzZq4f/8+zp07h3PnzuHevXuoU6cORo8eXRwx6oWUngtNj6xxwyAiIqIikmyPrKEne0VERODs2bMoVaqUZlupUqXwww8/oHHjxrpFU4yk9GBwQQQiIiLzItkxsjpmaKJ7ZKtWrYq4uLh82+Pj4+Hr66tTMMVJSl31XKKWiIjIvEh1aIGutys6kZ09ezbGjRuHLVu24P79+7h//z62bNmCCRMmYO7cuUhKStJ8mRIpPRYve2SNHAgREREViVrgErXaED20oHv37gCA/v375/sIOyAgQPO9TCaDSqXSV5y6k9CTkXuramayRERE5kGzRK1xwzA0g5ffOnjwoE4XNBYpFRiW2g8BERGRueOCCNoRnci2atVKx0tScePKXkREROZFkGiPrK4ks6KvlB4Mee6QD073IiIiMgu5v7M52Usc6SSyxg7ACNgjS0REZB5YR1Y70klkJfRksGoBERGReVGzjqxWpJPISug9DqsWEBERmZeXdWSNGobBGaxH9o8//kBCQkKhr6ekpGDmzJm6RVOMpPRgcIlaIiIiM6MZWiChhAW6D6UociIbGBiIJk2a4PLlywW+npycjBkzZoi6+OzZs9G4cWM4ODjA1dUVvXr1QmRkZJ590tPTMWbMGJQuXRr29vbo27dvgSuL0UuaHwJmskRERGbh5WQvIwdiaDresKihBZUqVULz5s2xbds2nS6a6/DhwxgzZgzCwsIQGhqKrKwsdOzYESkpKZp9Jk6ciH/++QfBwcE4fPgwHj58iD59+ujl+iWVXJPHMpMlIiIyBy/Lb0ktk9VNkevIymQyrF+/HitXrkT//v3xxRdfiO6B/a89e/bk+X7NmjVwdXVFeHg4WrZsicTERKxatQpBQUFo27YtAGD16tWoXr06wsLC0KxZsyJfS0rPBevIEhERmRe1VKsW6Hh8kRPZ3GVov/jiC9StWxdDhgzBxYsXsX79etjZ2ekYRo7ExEQAgIuLCwAgPDwcWVlZaN++vWYfPz8/eHt74+TJkwUmshkZGcjIyNB8n5SUBABQZauQlZWllzhNXe7SwCq1Wqt7zj1GKu2lL2w38dhm4rHNtMN2E49tJp4ubZadnf3v3wSJtbluvW6iV/YCgO7duyMsLAy9evVC06ZN8ffff+uczKrVakyYMAEtWrRArVq1AACPHj2CpaUlnJ2d8+zr5uaGR48eFXie2bNnF9hTfP78OWQ+vK5TjObiYoIMgALPnj3Hrl27tD5PaGio/oKSELabeGwz8dhm2mG7icc2E0+bNsv93f1cx9/d5ub5M4VOx4saWvAqPz8/nD59GoMGDULjxo2xYMECnQIZM2YMLl++jGPHjul0nmnTpmHSpEma75OSkuDl5YWGDRqgbd1KOp3bXFhei8eqyAg4OTuja9emoo/PyspCaGgoOnToAKVSWQwRlkxsN/HYZuKxzbTDdhOPbSaeLm1mcTUOqyIvwMWlFLp2bVJMEZqetQ9O4/bTNK2PFz204FWOjo7YuXMnvvjiCwwfPlzrIMaOHYudO3fiyJEjKF++vGa7u7s7MjMz8fz58zy9snFxcXB3dy/wXFZWVrCyssq3XWmhlMwPotLi339WmUyne1YqpdNm+sR2E49tJh7bTDtsN/HYZuJp02YKRU7PpEKu2+9ucyM3VNWCwMBA2NjY5Nsuk8kwa9YsbNy4Ea1atRJ1cUEQMHbsWGzfvh0HDhyAj49PntcbNmwIpVKJ/fv3a7ZFRkYiJiYG/v7+oq4lJZpngrO9iIiIzIIg1TqyOt5ukXtkV69e/drX+/fvj969e4u6+JgxYxAUFIS//voLDg4OmnGvTk5OsLGxgZOTE0aNGoVJkybBxcUFjo6O+OSTT+Dv7y+qYoHUcEEEIiIi86LWLO1l1DDMTpF7ZIcOHaqpAFCQs2fPon79+qIuvnTpUiQmJqJ169bw8PDQfP3555+afebPn4/u3bujb9++aNmyJdzd3bWrYyuhByP33RyXqCUiIjIPmgURjByHoenaA13kRPbq1auoUaMGQkJC8mzPysrCtGnT0Lx5c7Ro0ULUxQVBKPDr1fG21tbWWLx4MRISEpCSkoJt27YVOj72dST1YLCOLBERkVl5uSCCceMwOB3vt8iJ7KlTpzBy5EgEBATggw8+QHJyMs6ePYt69eohKCgIO3fuxPLly3WLphhJaaUMfQ2RzVKpsetSLP6KeID0LJXOcRU3QRBw8Ho8gs/eQ2KalGrwERGVHGG3n2LT6RjEv0g3dihFcvlBItaH3UXM01SdzvNyZIF08hXAgAsiWFhYYObMmejRoweGDx+OqlWr4smTJxg6dCjmz58PR0dHHUMpXlJ6LHJnAOqSx2aqgI+DInAo6gkAoHHFUvgtsDGcbExzJqVKLeDLHZex8XQMAGDp4VtYP6opPJ3zT1AkIiLTIwgCftl/E/P3RQEAXB2s8Meopqjm7mDkyAq3Jfw+PttyAWoBsLNUYNlgcUMsX5VbHUpe5C7GkkHXfkbRzWVtbQ2lUonExERYWlqiTZs2Jp/EAtLqqn+5RK12qeyL9Cwsu6bQJLEAcObOMwxaEYYnyRmvOdI4MrPVGL/pvCaJBYDbj1PwztITuP042YiRERFRUajVAmbuvKpJYgEg/kUG+i8/ifMxz4wYWeF+PxaNT4MvaCZppWSqMOqPc7iUoFvCIb0eWQONkRUEAbNnz0ajRo1Qr149PHjwAPPmzcNHH32EPn364PHjxzoFUtyk9GDocq9PkjMw5PezuPVCBnsrC2z+wB//G/cWythb4mpsEvovO4nYRO0LF+tbepYKo/84i50XY6FUyLDo3fo4/nlbVCpjh4eJ6ei37CQuP0g0dphERFSIbJUaU7ZcxOrjdwAA3wTUQMTXHVDf2xmJaVkY/NspnLj55PUnMbAF+6Iwc+dVAMB7b/ng+ned0bGGGzKz1fg9Uo7t5x+KPmfuBG0pdbwBBuyRbdasGX799VcEBwdj9erVcHZ2xscff4wLFy7gyZMnqFGjRp5qA2Q8uQ+F2KoFD56nof+yk7ga+wL2FgLWj2yEJj4uqOnphOAPm6Ocsw1uP0nBF9svF0PU2llx5DYORT6GjVKB3wIbo3sdT5RztsHmD/1R09MRT1MyMWhFGE5HJxg7VCIi+o/0LBU+2nAOW8/dh0Iuw8/962JECx8421pi/aimeLtKGaRmqjBuUwRSMrKNHS4A4NTtp1iw7wYA4NOOVfFFt+qwViqwZHAD9K7vCTVk+GzbZfx+LFrUeTlBWztFTmR9fHxw+fJlBAQE5NleqVIlHD58GNOnT8eoUaP0HqC+SOkdjjaTvW49Tka/pSdw+0kKPJ2sMb6WCjU9Xw4Z8Sljh3WjmkAhl+HA9Xicuv1Uv0Fr4WlyBlYcuQ0AmNO3NlpVLat5rYy9FTaOboYmPi54kZGNoatO4cD1OGOFSkRE/5GckY0Rq88g9GocLC3kWDakIfo0eLm6p52VBX4LbIQKpW3xJDlDdGJYHARBwJw91wEA7zb1xti2VTSTyS0UcszpVROt3NUAkDNUIjSqyMP8XlYtkFDCAgP2yG7atAkuLi6FBCHDxIkTcf78ed2iIf0QuSDC5QeJ6LfsJB4mpqNyWTtser8JXAuYI1W5rD0GNvYCAMzZc13rMbj6sujgTSRnZKNWOUcE1PHM97qjtRLrRjZBOz9XZGSrMXpdOP6KeGCESImI6FUJKZl4d2UYTt5+CnsrC6wd0QQdarjl28/KQoHJHasBAJYfuY2nRp6nEXIlDudjnsNGqcCE9lXyvS6Xy9C7ohrj21YGACzcfwMz/rkKtfrNvy+luh6CwcbIFkWVKvn/UU2FlN7gaKoWFCHRPHX7KQauCENCSiZql8sZQuDhZF3o/uPbVYGNUoHzMc+x96rxejjvJaRifdhdAMDnnatDLi/4H9haqcCyoQ3Rq54nstUCJvwZgT/+PY6IiAwvNjEN/ZefxMX7iShlq0TQ+03hX7l0oft3r+2BWuUckZyRjcUHbxkw0ryyVWr8X0hOb+x7b/vA1aHg35UyGTC2TWXM6FETALDmxB18GnwBWSr1a8+vqVogoXwFMELVAnMlpedCM7TgDfvtvxaHYb+fRnJGNpr6uCDo/aZwsbN87TGujtYY9ZYPAGDenuvIfsMPZnH5OTQKWSoBb/mWwVtVyrx2X6VCjp/718Mw/woQBOCrHZex6MANo/coExFJTfSTFLyz9CRuxifDw8kawR/6o05559ceI5fLMLWzHwDgj7A7uJegW71WbW0Jv49bj1NQylaJ0S0rvXH/wOYVsWBAPSjkMmw7/wAfrT/32prsmh5ZKfW86YF0ElkJPReaH4LX5Gk7zj/A6D/CkZGtRvvqrlg7sgkcrItWI3Z0q0ooZavErccp2BJ+Xw8Ri3P1YRJ2/DtEIPc/tzeRy2WY0aMmxrX1BQD8uDcKs3ZdYzJLRGQgVx8mod+yk3jwPA0+ZewQ/KE/fF2LViP27Spl8ZZvGWSpBPwcGvXmA/QsLVOlKQ02tm2VIv++7FW/HJYPaQgrCzn2XYvD8NWn8SK94AV7cn8fSShdAaB74i6dRNbYARjQm6oWrDt5BxP+jIBKLaB3/XJYOqQhrJWKIp/f0VqJMW1yEsIF+24gLdOwq37NC7kOQQC61/FA7fJORT5OJpNhUsdq+LJbdQDAyqPRmLr1otF6lYmIpOLsnQQMWHEST5IzUMPDEZs/8Ef5UraizpHbcbEj4gGuPkwqjjALtebEHcQlZaCcsw2GNPMWdWz7Gm5YO7IJ7K0sEHY7AYN/O4WElMx8+0l1iVpdb1cyiayUFDa0QBAE/Lr/Br7+6woAYHjzivipX10oFeIfg6H+FVDO2QaPktKx5sQdneIV4+StpzgU+RgWchk+/XcCgFjvvV0J896pA7kM2Hz2PsYGnUdGtukvwUtEZI4ORcZjyKpTeJGejcYVS2Hj6GYo62Al+jy1yzuhex0PCEJOh4ahPE/NxJJDNwEAkztWhZVF0Tt+cjWrVBob328GFztLXLyfiH7LTuSryf7yd7bEMlkdaZXIpqSkYNeuXVi2bBl++eWXPF+mSkrvcF6u7PVym1ot4Pv/XcNP/34kM75dFXwTUKPQSVJvYmWhwKQOVQEASw/dxPPU/O8u9e3VsieDmnijYhk7rc/Vv5EXlgxuCEuFHHuuPMLINWdMpkYhEVFJ8c+Fh3h/3VmkZ6nRulpZrBvZVKelzj/tWA0WchkORT7GyVuGKQO59NAtvEjPhp+7A3rWK6f1eWqXd8LmD/zh4WSNW49zxgq/uvqkZHtkDT3Z6/z58/D19cWgQYMwduxYfP/995gwYQKmT5+OBQsW6BZNMZLUyl65VQv+fX+XrVLjs60XserfGnxfd6+BiR2q6jwupVf9cvBzd0BSejaWHir+maR7Lj/ChXvPYWupwCftfHU+X+da7lg9ojFsLRU4fvMpBv92yiAJORGRFASdisG4TeeRpRIQUNcTK4Y2go2l+N7MV1UsY4dBTXI+2jdEGciHz9Ow+t9PHad29oNCx5ICvq722PJRc1QqY5ezCNHyk7jyMGf1ydzf2ZKrWqDj8aIT2YkTJyIgIADPnj2DjY0NwsLCcPfuXTRs2BA//vijjuEUIwk9GK8uiJCepcKYoHPYEp6zaspP/epi5L9VB3SleGUm6eoTd/DwefEtXZtT9iQSQM7QgMLKnojVwrcMgt5vBmdbJSLuPUf/5ScRl5Sul3MTEUnVkkM3MX37JQgCMLipNxYMqAdLC/2MZvyknS9sLRW4cO85Qq480ss5C7NgXxQys9Vo6uOC1tXKvvmAInh19cknyZkYuDwMZ+4kvOyRlVLCAiNM9oqIiMDkyZMhl8uhUCiQkZEBLy8vzJs3D9OnT9cpmOIkpcci96FIz1Jj1NozCLmSs2rK0sEN0Ldh+TccLU7ramXRxMcFmdlqLNhXfDNJN5+9j9tPUuBiZ4n339ZPIp6rnpczNn/gDzdHK0TFJeOdZSdw92mKXq9BRCQFgiBg9u5rmLcnp+NhTJvK+L5XLZ17Ml/l6mCN93LLQIZEFtuE3RtxLzSVeaZ28dNrWSzN6pMVX64+efTGYwASHFqg4/GiE1mlUgm5POcwV1dXxMTEAACcnJxw7949HcMpPlKqy5Z7p0+SM3D85lPYWSqwZkRjdKzprv9ryWT4vEtOr+yW8Pu4EfdC79dIy1RpkuRP2voWueyJGFXdHLDlw+aoUNoW9xLS8M6yk7j+yLCzYomIzJlKLWDatktYfjhn6fDpXf0wpZN+E8Bc77esBBc7S9x+nILgYioDOS8kEmoB6FzTHQ28S+n9/I7WSqwd2QRtqpVFepYaIVdyFhmSULoCwAhjZOvXr48zZ84AAFq1aoWvv/4aGzZswIQJE1CrVi3doilGUnouXn0oclZNaYbmlV+/aIAuGniXQqeablALOT/4+vb78WjEv8hA+VI2eLepuLInYni52CL4A3/4uTvg8YsM9F92EuF3nxXb9YiISoqMbBXGbTyPTWfuQS4D5vatjdEtKxfb9RyslRj7bxnI+aFRei8DefZOAkKvxkEuAz7tpF2FnKKwsVRgxbBG6FH35TLrUhtaoGuGJjqRnTVrFjw8PAAAP/zwA0qVKoWPPvoIjx8/xooVK3QKhvTDw8kGSoUM7o7W2PyBP+p6ORf7Nad08oNcBoRejcPZOwl6O++zlEws+3ci2acdq2lV9kQMV0dr/DnaHw28nZGUno0hv53CkajHxXpNIiJzlpqZjffWnsX/LsVCqZBh0bsNMKBx8XU65BrczBvlS9kg/kUGfj8erbfzCoKAObtzKuQMaOwFX1d7vZ27IEqFHAsG1MPQZhUAAN6lxdXXlTrRiWyjRo3Qpk0bADlDC/bs2YOkpCSEh4ejbt26eg9QX6TUVV/WwQpHP2uLA5+2QhW3oq2aoitfV3v0b+QFAJirx5mkSw7dxIuMbFT3cMzzjrU4Odkqsf69pni7ShmkZakwau0Z7LoUa5BrExGZk8TULAz57RSO3ngCG6UCvw9vjK61PQxybSsLBSZ3zCkDuezwLTwrYJEBbey/Fo+zd5/BykKO8e2q6uWcbyKXy/Bdr1o4PKW11jXSzZXBhxaYKyklsgDg7mQNW0sLg15zQvuqsLKQ48ydZ9h/LV7n8z14noa1J+4CAKZ2rqZ1zVtt2Fpa4LfARuhW2wNZKgFjg87hzzMxBrs+EZGpi3+RjgErTuJczHM42eR2AOhnZn9R9axbDtU9HPEiPVuzaIEuVGpBs9jCyLd84O6knwo5RVWhtJ1eJ8aZA4NP9oqLi8PQoUPh6ekJCwsLKBSKPF+mS1oPhjG4O1ljRIvcmaTXoVLr1is7PzQKmSo1mlVyQauqhv3PEch5t//LoPoY1MQLagGYuvUSVhwp/nq5RESm7l5CKvotO4nrj16grIMV/vygGRpW0P+EqDeRy2X4rHNOD+bak3fxQMcykNvO3UdUXDKcbJT4sFXxjfGll3TtaBTdZTd8+HDExMTgq6++goeHh/lUAzCTMM3dR60qY+PpGETFJWPbufvo9+9wA7EiH73A1nM5M1E/71LdaM+ZQi7DrN614WijxPLDtzFr13U8T83ClE7VzOfZJyLSo6i4Fxjy2ynEv8iAl4sNNoxqZtRxna2rlkWzSi4Iu52A+aFR+LGfdsMc07NUmP/v6pdj2lTWaQUyKjpdJ7eJTmSPHTuGo0ePol69ejpd2NCYchiGk60SY9pUxqxd1zE/NAoBdT1hrRTfU/9/IdchCEDX2u6oZ4DJaq8jk8kwrUt1ONtYYu6e61hy6BYS07Iws6d+ayMSEZm68zHPMGLNGTxPzUI1NwesG9UEbo6G/fj9v3LKQFZHr8XHse3cfbz/diVUcxc/P+SPk3fxMDEdnk7WGOZfUf+BUoEMPkbWy8ur2JeEKw7sPDOcYf4V4eFkjYeJ6fjj5F3Rx5+5k4B91+KhkMtMatD7R60rY1bv2pDJgA2nYjB+03lkZhdPIW4iIlNz/OaTf5fyzkI9L2f8+UEzoyexuep5OaNLLXeohZyOELES07Kw6GDOGNsJHapq1QFD2jF4IrtgwQJ8/vnnuHPnjm5XBnDkyBEEBATA09MTMpkMO3bsyPP68OHDIZPJ8nx17txZ5+tS8bJWKjCxQ85Mz8WHbiIxLavIx/637EmlssVb9kSsd5t645eB9aFUyLDzYixG/3FW7/ULiYhMzZ7LjzBi9RmkZqrwlm8ZbHivKZxtLY0dVh6fdqoGhVyGfdficUZkGcjlh3M+aavqZo++DfS7AiYVL9GJ7IABA3Do0CFUrlwZDg4OcHFxyfMlRkpKCurWrYvFixcXuk/nzp0RGxur+dq4caPYkAFIscCwcfVtUB5VXO3xPDULyw8XfYJU6NU4hN99BmulHOPbVSnGCLUXUNcTK4c1grVSjkORjzF01SlRyToRkTnZeu4BPt4QjkyVGp1rumPV8EawszJsVZyiqFz2ZRnIObuLXgYyLildU4d2Sic/DhkzMIOPkV2wYIFOF3xVly5d0KVLl9fuY2VlBXd33ZdW5dACw1LIZfissx/eX3cWvx+PRmDzim/8CCpbpcb//bsy2Ki3fEzmI6uCtK7mivWjmmLEmjM4e/cZBq4Iw+/D6hs7LCIivToUK8P2k1cAAP0blces3rVhoTDdyp0T2lfB9vP3EX73GfZdi0eHGm5vPGbBvhtIz1KjUYVSaF/d1QBRUh6GrloQGBio2xVFOnToEFxdXVGqVCm0bdsW33//PUqXLl3o/hkZGcjIyNB8n5SUBADIzs5GVhZ7zYoit510ba9WvqXQ0NsZ4THP8fPeSHzfs8Zr9w8Of4Ab8clwtlFiVHNvk//3qlvOARtGNsbIdeG4FpuEAStPY0RF3dtNSvT1rEkJ20w7bDdxBEHA/NAobL+TM1Z0VIsKmNqpKgS1Cllq0x1O5WKjwHD/Clh2JBpzd1/DW5WcX5t433qcgs1n7wEAPu3gi+zsbJ2uz+dMCzqW6pQJReh7T0pKgqOjo+bvr5O7n+hAZDJs374dvXr10mzbtGkTbG1t4ePjg1u3bmH69Omwt7fHyZMnC61Z++2332LGjBn5ti9ZHQTPUlz2zdBuJQG/XLGAHAI+r6eCm03B+2WqgO8jFEjMlKFXBRXaeJrPhML4NGDpNQUSMmRwshTwcXUV3PmoEZGZUgvAtmg5jsblJIDdvFToUE4wm082U7OB784rkJotw6DKKjRzLfz3yapIOS4myFGrlBrv+3HyrjGsjZLj7MN03FvQH4mJiaLzyCL1yJYqVQqxsbFwdXWFs7NzgfUzBUGATCaDSqW/d2oDBw7U/L127dqoU6cOKleujEOHDqFdu3YFHjNt2jRMmjRJ831SUhK8vLzQvIU/alQsp7fYSrKsrCyEhoaiQ4cOUCp1r6N3ef15HIh8jPBMTyzqW6/AfVYei0Zi5g14OFnj+8AWsDKzGaOdk9IRuPosbj9JxdIoG/we2AC1yzkZOyyTp+9nTQrYZtphuxVNlkqNqdsu42jcI8gAvOOjwoyh7c2uzRJL38GcPVE4+NgW0we/VWAVgvP3nuPiydOQy4B5g99CFTfdJxfzORMvNPki8PC21scXKZE9cOCAZiLXwYMHtb6YripVqoQyZcrg5s2bhSayVlZWsLKyyrddqVTyoRJJX202tUt1HIp6jJCr8bgcm4z63nlXf0lMzcKywzkD7Sd3rAZ7W9MdG1sYr9JKbHyvCfr9ehAxKVkY+vtZrAxshOaVyxg7NLPAn0/x2GbaYbsVLj1LhbGbInDgejws5DLM61sLivvnzbLNhreohHUnY/AwMR1BZx7gg/+s0iUIAn4KzSm31bdBedQor99VycyxzYxFoeOY6yIlsq1atSrw74Z2//59PH36FB4eHkaLgcSr5u6Avg3KIzj8Pubsvo5No5vl6dVfevgWktKzUc3NAb3rm2+vuYudJcbUVGH747IIi36G4avPYNGg+uhYU/fJikRExSkpPQvvrT2L09EJsLKQY+mQBni7sgt23T9v7NC0Yq1UYFLHavg0+AKWHLqFgY294WT7MrE8FPUYp6ITYGkh15SLJPMkOg3es2cPjh07pvl+8eLFqFevHt599108e/ZM1LmSk5MRERGBiIgIAEB0dDQiIiIQExOD5ORkTJkyBWFhYbhz5w7279+Pnj17wtfXF506dRIbNotvGdnEDlVhaSHHqegEHIp6rNkem5iG1f+WPfmsczWzL3tirQB+G9oAHWq4ITNbjY82nMPW8PvGDouIqFBPkzPw7sownI5OgIOVBf4Y1RRt/d4829/U9a5fDtXcHJCYloWlr5SBVKsFzP23Xvnw5hXh6VzI5A0yCF1/64tOZKdMmaKZ8HXp0iVMmjQJXbt2RXR0dJ6xqUVx9uxZ1K9fH/Xr55QtmjRpEurXr4+vv/4aCoUCFy9eRI8ePVC1alWMGjUKDRs2xNGjRwscOvAmrCNrXJ7ONhjevCIAYO7u61D/O0tx4b4byMhWo0lFF7T1KxllT6yUCiwd3AB9G5SHSi1gcvAFTbJORGRKHjxPQ7/lJ3H5QRJK21li4+hmaOIjria8qcopA5mzOuTq49F4lJgOAPjrwgNcf/QCDtYW+Lh15dedggygoHlXYoguvxUdHY0aNXLKKG3duhUBAQGYNWsWzp07h65du4o6V+vWrV9bsDgkJERseIViGmt8H7eujI2nY3D90Qv8deEBapdz0pQ9mdrFT+eH2ZRYKOT4v3fqwNHGAquP38GMf64iMS0L49tVKVH3SUTm69bjZAz97RQeJqajnLMN/hjVxORWU9RVWz9XNK5YCmfuPMOCfVGY0bMmfgyJApCz7LiprU4mRQbvkbW0tERqaioAYN++fejYsSMAwMXF5Y2luYyJuYPxOdta4qN/3/3+tDcKs3ddh1oAOtZwQ8MK+h1obwrkchm+7l4Dk/4df7Vg3w3M+OeqpjeaiMhYLj9IRP9lJ/EwMR2Vy9oh+EP/EpfEAjm9fZ938QMAbD57D9/tvIoHz9Pg5miFEc19jBwdAdA5kxWdyLZo0QKTJk3Cd999h9OnT6Nbt24AgKioKJQvb7rrEzOPNQ0jmvvAzdEK95+lYf/1eMhl0Hz0UxLJZDKMa1cF3wbkfIqx5sQdfBp8AVkq1iskIuM4dfspBq0Iw9OUzJxPxj7wL9HjRBtWcEGHGm5QC8D6sBgAwIT2VWFjaV5lHksqgy9Ru3jxYowZMwZbtmzB0qVLUa5czizz3bt3o3PnzjoFU6zYJWsSbCwVmNC+KqZtuwQA6NfQC76uDkaOqvgNb+EDJ1slPg2+iG3nH2DX5Vgo+EwCALKzFZgevt/YYRSLCqXtsHBgPVRxM+1nXK0WMGfPdWw6HQNVCf7EoCQ/a2KkZamgFoCmPi74LbARHKxLfpmozzpVw/5rcVALQKWydujX0HQ73qRG11+FohLZ7OxsHDp0CCtXroS7e96SQvPnz9ctEpKMfg3LY+PpGDx4loYJHaoYOxyD6V2/PByslBi36TxSM013iUfDkyGjhLbH1dgk9Ft+EmtHNEFdL2djh1OgLJUakzdfwN8XHho7FAMouc+aWB1ruOGXQfULXCigJKri5oDhzX2w9uQdfNW9xmuXrSXzIiqRtbCwwIcffohr164VVzzFhn1fpsNCIceWD5tDpRYk99FO+xpuOP1FezxNzjB2KCYhKzsbhw8dQqvWraG0EP0BkUnLyFZjypaLuHDvOd5dGYaVwxqhua9pLZCRlqnCxxvCcTDyMSzkMszuU7vEzFj/r5L8rImlVMhL9FCCwnzVvTomdqgiiR5oc6Jrfib6p7lJkyY4f/48KlSooOOlDYuf4poWSwvpvhu2t7KAvZW0f5HmysrKQhlroIKLbYlcBWfDe00xet1ZnLj1FMNXn8Gv79ZHJxNZICMxLQvvrT2DM3eewVopx9IhDdGmWskogVeQkv6s0ZvJZDImsSbIoEMLAODjjz/G5MmTcf/+fTRs2BB2dnZ5Xq9Tp45uERUT1pElIkOzt7LA78MbY9zG89h7NQ4frQ/HvHfq4h0jj897/CIDgb+fxtXYJDhY58TYuGLJ7IklItNm8MleAwcOBACMGzfuZRAyGQRBgEwmg0plmuOP2CNLRMZgrVRgyeAG+HzbJWwJv49Pgy8gKS0LI98yTumf+89SMXTVaUQ/SUEZe0usHdkENT2djBILEZHBe2Sjo81zhSLmsURkLBYKOeb1rQMnGyVWHYvGzJ1X8TwtCxPbG3aBjJvxyRi66hRi/y2Av/69pvApY/fmA4mIionBE1k3NzdYW1vrdlVjYCZLREYkl8vwZbfqKGWrxI97o/DL/htISsvC191rQC4v/v+gLt1PRODq00hIyYSvqz3+GNUEHk7Sm/BDRKZGt///RM+4cXV1RWBgIEJDQ6FWs6g7EVFRyWQyjG1bBTN71gSQs0DGZAMskHHy1lMMWhmGhJRM1CmfUwCfSSwRlQSiE9m1a9ciNTUVPXv2RLly5TBhwgScPXu2OGLTM3bJEpFpGOZfEQsH1oOFXIbt5x/go/XhSM8qnvkFoVfjELj6NJIzsuFfqTSC3m8GFzuuL09EpkHXoQWiE9nevXsjODgYcXFxmDVrFq5evYpmzZqhatWqmDlzpm7RFCNO9iIiU9KzXjmsGNYQVhZy7LsWj8DfT+NFepZer7Ht3H18uD4cmdlqdKjhhtUjGrP0GxGZFF3TM62LeTo4OGDEiBHYu3cvLl68CDs7O8yYMUPHcIoP81giMjVt/dywbmQTOFhZ4FR0AgatDNPbYhlrjkdj0uYLUKkF9GlQDksHN5DMKk5EZD4M3iObKz09HZs3b0avXr3QoEEDJCQkYMqUKbpFU4zYI0tEpqhppdLYOLoZSttZ4vKDnCVtHz5P0/p8giBg4b4b+PafqwCA4c0r4sd36nJJTiIySbrWkRX9P1tISAgCAwPh5uaGjz76CG5ubti7dy/u3r2LOXPm6BRMceKCCERkqmqVc8LmD/3h6WSN249T8M7SE7j9OFn0edRqATN3XsX8fVEAgIntq+KbAMNURSAi0oZRxsimpaVh3bp1ePToEZYvX46WLVvqFgURkcRVLmuP4I+ao1JZOzxMTEe/ZSdx+UFikY/PVqkxZctFrD5+BwDwbUANjDdwnVoiIkMTPeo/Li4ODg4OxRFLseL/5URk6so52yD4A38Erj6Nyw+SMGhFGFYNb4wmPq9fPjY9S4VPNp5H6NU4KOQy/NivDnrXN+4yuERERaFreiY6kXVwcIBKpcKOHTtw7do1AECNGjXQs2dPKBSmO5GAeSwRmYPS9lbY+H4zjFp7FqejEzB01SksG9IQbfxcC9w/OSMb7689i5O3n8LSQo4l7zZA+xpuBo6aiEg7un5qJHpowc2bN1G9enUMGzYM27Ztw7Zt2zB06FDUrFkTt27d0imYYsUuWSIyEw7WSqwb2QTt/FyRka3G++vO4q+IB/n2S0jJxOCVYTh5+ynsrSywdkQTJrFEJCmiE9lx48ahcuXKuHfvHs6dO4dz584hJiYGPj4+GDduXHHEqBdMY4nInFgrFVg2tCF61fNEtlrAhD8j8EfYXc3rsYnp6L/8JC7cT0QpWyWC3m8K/8qljRgxEZF4uvYzih5acPjwYYSFhcHF5eWYrdKlS2POnDlo0aKFbtEUI3bIEpG5USrk+Ll/PTjZKLH25F18teMyEl6kwzYNGPTbaTx4ng4PJ2v8MaoJfF3Nb+4CEZGuVaVEJ7JWVlZ48eJFvu3JycmwtDTdZQ+ZxxKROZLLZfi2R0042Vril/03MH//TeT8150OnzJ2+GNUE5QvZWvsMImItGLw8lvdu3fH6NGjcerUKQiCAEEQEBYWhg8//BA9evTQLRoiIspHJpNhUoeq+Kp7Dc226u4OCP7Qn0ksEUma6ET2l19+QeXKleHv7w9ra2tYW1ujRYsW8PX1xcKFC4sjRr1gLUUiMnej3vLBrrHN8aGfCsGjm6CMvZWxQyIi0omu2ZnoRNbZ2Rl//fUXoqKisGXLFmzZsgWRkZHYvn07nJycRJ3ryJEjCAgIgKenJ2QyGXbs2JHndUEQ8PXXX8PDwwM2NjZo3749bty4ITZkABxaQEQlQxU3e1QvJcBKabrlDomIispgQwvUajXmzp2LFi1aoHHjxvjtt9/Qvn17BAQEwNfXV6uLp6SkoG7duli8eHGBr8+bNw+//PILli1bhlOnTsHOzg6dOnVCenq66GuxQ5aIiIjItOj6iXmRJ3v98MMP+Pbbb9G+fXvY2Nhg4cKFiI+Px++//671xbt06YIuXboU+JogCFiwYAG+/PJL9OzZEwCwbt06uLm5YceOHRg4cKCoa3FoAREREZFpMdjKXuvWrcOSJUvwwQcfAAD27duHbt264bfffoNcLnqEwhtFR0fj0aNHaN++vWabk5MTmjZtipMnTxaayGZkZCAjI0PzfVJSEgAgKysLWVlZeo+zJMptJ7aXOGw38dhm4rHNtMN2E49tJh7bTDy1Wq3T8UVOZGNiYtC1a1fN9+3bt4dMJsPDhw9Rvrz+1/R+9OgRAMDNLe8qNW5ubprXCjJ79mzMmDEj3/aDBw/C1paze8UIDQ01dghmie0mHttMPLaZdthu4rHNxGObFd3tu7p1hhY5kc3Ozoa1tXWebUql0uTedUybNg2TJk3SfJ+UlAQvLy+0adMGpUtz1ZuiyMrKQmhoKDp06AClUmnscMwG2008tpl4bDPtsN3EY5uJxzYT78reKIRGX9X6+CInsoIgYPjw4bCyelnuJT09HR9++CHs7Ow027Zt26Z1MK9yd3cHAMTFxcHDw0OzPS4uDvXq1Sv0OCsrqzwx5lIqlXyoRGKbaYftJh7bTDy2mXbYbuKxzcRjmxWdQqFbBZYiJ7KBgYH5tg0ZMkSni7+Oj48P3N3dsX//fk3impSUhFOnTuGjjz4qtusSERERkWEYbLLX6tWrdbxUfsnJybh586bm++joaERERMDFxQXe3t6YMGECvv/+e1SpUgU+Pj746quv4OnpiV69euk9FiIiIiIyLF2LShU5kS0OZ8+eRZs2bTTf545tDQwMxJo1a/DZZ58hJSUFo0ePxvPnz/HWW29hz549+cbqEhEREZH5kenYJ2vURLZ169YQBKHQ12UyGWbOnImZM2caMCoiIiIiMgSDrexFRERERKRPuo6RZSJLRERERGaJiSwRERERGYeOYwuYyBIRERGRUXBoARERERGZJU72IiIiIiKzpGv5LSayRERERGQU7JElIiIiIrPEMbJEREREJElMZImIiIjIKAY09sKGkY21Pt6oS9QSERERkXS5OlpD4emg9fHskSUiIiIis8REloiIiIjMEhNZIiIiIjJLTGSJiIiIyCwxkSUiIiIis8REloiIiIjMEhNZIiIiIjJLTGSJiIiIyCyV+AURBEEAALx48QJKpdLI0ZiHrKwspKamIikpiW0mAttNPLaZeGwz7bDdxGObicc2086LFy8AvMzZxCjxiezTp08BAD4+PkaOhIiIiIgK8/TpUzg5OYk6psQnsi4uLgCAmJgY0Y0jVUlJSfDy8sK9e/fg6Oho7HDMBttNPLaZeGwz7bDdxGObicc2005iYiK8vb01OZsYJT6RlctzhgE7OTnxoRLJ0dGRbaYFtpt4bDPx2GbaYbuJxzYTj22mndycTdQxxRAHEREREVGxYyJLRERERGapxCeyVlZW+Oabb2BlZWXsUMwG20w7bDfx2Gbisc20w3YTj20mHttMO7q0m0zQptYBEREREZGRlfgeWSIiIiIqmZjIEhEREZFZYiJLRERERGapRCeyixcvRsWKFWFtbY2mTZvi9OnTxg7JpB05cgQBAQHw9PSETCbDjh07jB2SyZs9ezYaN24MBwcHuLq6olevXoiMjDR2WCZv6dKlqFOnjqbWor+/P3bv3m3ssMzKnDlzIJPJMGHCBGOHYrK+/fZbyGSyPF9+fn7GDsvkPXjwAEOGDEHp0qVhY2OD2rVr4+zZs8YOy6RVrFgx37Mmk8kwZswYY4dmslQqFb766iv4+PjAxsYGlStXxnfffSd6mdoSm8j++eefmDRpEr755hucO3cOdevWRadOnRAfH2/s0ExWSkoK6tati8WLFxs7FLNx+PBhjBkzBmFhYQgNDUVWVhY6duyIlJQUY4dm0sqXL485c+YgPDwcZ8+eRdu2bdGzZ09cuXLF2KGZhTNnzmD58uWoU6eOsUMxeTVr1kRsbKzm69ixY8YOyaQ9e/YMLVq0gFKpxO7du3H16lX89NNPKFWqlLFDM2lnzpzJ85yFhoYCAPr162fkyEzX3LlzsXTpUixatAjXrl3D3LlzMW/ePPz666+izlNiqxY0bdoUjRs3xqJFiwAAarUaXl5e+OSTT/D5558bOTrTJ5PJsH37dvTq1cvYoZiVx48fw9XVFYcPH0bLli2NHY5ZcXFxwf/93/9h1KhRxg7FpCUnJ6NBgwZYsmQJvv/+e9SrVw8LFiwwdlgm6dtvv8WOHTsQERFh7FDMxueff47jx4/j6NGjxg7FrE2YMAE7d+7EjRs3IJPJjB2OSerevTvc3NywatUqzba+ffvCxsYG69evL/J5SmSPbGZmJsLDw9G+fXvNNrlcjvbt2+PkyZNGjIxKusTERADQar1oqVKpVNi0aRNSUlLg7+9v7HBM3pgxY9CtW7c8/79R4W7cuAFPT09UqlQJgwcPRkxMjLFDMml///03GjVqhH79+sHV1RX169fHypUrjR2WWcnMzMT69esxcuRIJrGv0bx5c+zfvx9RUVEAgAsXLuDYsWPo0qWLqPNYFEdwxvbkyROoVCq4ubnl2e7m5obr168bKSoq6dRqNSZMmIAWLVqgVq1axg7H5F26dAn+/v5IT0+Hvb09tm/fjho1ahg7LJO2adMmnDt3DmfOnDF2KGahadOmWLNmDapVq4bY2FjMmDEDb7/9Ni5fvgwHBwdjh2eSbt++jaVLl2LSpEmYPn06zpw5g3HjxsHS0hKBgYHGDs8s7NixA8+fP8fw4cONHYpJ+/zzz5GUlAQ/Pz8oFAqoVCr88MMPGDx4sKjzlMhElsgYxowZg8uXL3MMXhFVq1YNERERSExMxJYtWxAYGIjDhw8zmS3EvXv3MH78eISGhsLa2trY4ZiFV3t26tSpg6ZNm6JChQrYvHkzh7AUQq1Wo1GjRpg1axYAoH79+rh8+TKWLVvGRLaIVq1ahS5dusDT09PYoZi0zZs3Y8OGDQgKCkLNmjURERGBCRMmwNPTU9SzViIT2TJlykChUCAuLi7P9ri4OLi7uxspKirJxo4di507d+LIkSMoX768scMxC5aWlvD19QUANGzYEGfOnMHChQuxfPlyI0dmmsLDwxEfH48GDRpotqlUKhw5cgSLFi1CRkYGFAqFESM0fc7OzqhatSpu3rxp7FBMloeHR743k9WrV8fWrVuNFJF5uXv3Lvbt24dt27YZOxSTN2XKFHz++ecYOHAgAKB27dq4e/cuZs+eLSqRLZFjZC0tLdGwYUPs379fs02tVmP//v0cg0d6JQgCxo4di+3bt+PAgQPw8fExdkhmS61WIyMjw9hhmKx27drh0qVLiIiI0Hw1atQIgwcPRkREBJPYIkhOTsatW7fg4eFh7FBMVosWLfKVEIyKikKFChWMFJF5Wb16NVxdXdGtWzdjh2LyUlNTIZfnTUMVCgXUarWo85TIHlkAmDRpEgIDA9GoUSM0adIECxYsQEpKCkaMGGHs0ExWcnJynp6K6OhoREREwMXFBd7e3kaMzHSNGTMGQUFB+Ouvv+Dg4IBHjx4BAJycnGBjY2Pk6EzXtGnT0KVLF3h7e+PFixcICgrCoUOHEBISYuzQTJaDg0O+sdd2dnYoXbo0x2QX4tNPP0VAQAAqVKiAhw8f4ptvvoFCocCgQYOMHZrJmjhxIpo3b45Zs2ahf//+OH36NFasWIEVK1YYOzSTp1arsXr1agQGBsLCosSmV3oTEBCAH374Ad7e3qhZsybOnz+Pn3/+GSNHjhR3IqEE+/XXXwVvb2/B0tJSaNKkiRAWFmbskEzawYMHBQD5vgIDA40dmskqqL0ACKtXrzZ2aCZt5MiRQoUKFQRLS0uhbNmyQrt27YS9e/caOyyz06pVK2H8+PHGDsNkDRgwQPDw8BAsLS2FcuXKCQMGDBBu3rxp7LBM3j///CPUqlVLsLKyEvz8/IQVK1YYOySzEBISIgAQIiMjjR2KWUhKShLGjx8veHt7C9bW1kKlSpWEL774QsjIyBB1nhJbR5aIiIiISrYSOUaWiIiIiEo+JrJEREREZJaYyBIRERGRWWIiS0RERERmiYksEREREZklJrJEREREZJaYyBIRERGRWWIiS0RERERmiYksEUnC8OHD0atXL1HHrFmzBs7OznqN486dO5DJZIiIiNDrefUdw6FDhyCTyfD8+XOdrzd06FDMmjVL5/Noo1mzZti6datRrk1ExY+JLBGRAXl5eSE2Nha1atWSRAwXLlzArl27MG7cuGK/VkG+/PJLfP7551Cr1Ua5PhEVLyayREQGpFAo4O7uDgsLC0nE8Ouvv6Jfv36wt7cv9msVpEuXLnjx4gV2795tlOsTUfFiIktERrNu3TqULl0aGRkZebb36tULQ4cOLfS4S5cuoW3btrCxsUHp0qUxevRoJCcna15XqVSYNGkSnJ2dUbp0aXz22WcQBOGN8axZswbe3t6wtbVF79698fTp03z7LF26FJUrV4alpSWqVauGP/74I8/rMpkMv/32G3r37g1bW1tUqVIFf//9t+b1gj7Wv3z5Mrp06QJ7e3u4ublh6NChePLkieb1Fy9eYPDgwbCzs4OHhwfmz5+P1q1bY8KECYXeS8WKFSGTyfJ9FRbDrl27ULVqVdjY2KBNmza4c+dOnvM9ffoUgwYNQrly5WBra4vatWtj48aNr21PlUqFLVu2ICAgIF9s33//PYYNGwZ7e3tUqFABf//9Nx4/foyePXvC3t4ederUwdmzZzXH5A7z2LlzJ6pVqwZbW1u88847SE1Nxdq1a1GxYkWUKlUK48aNg0ql0hynUCjQtWtXbNq06bWxEpGZEoiIjCQ1NVVwcnISNm/erNkWFxcnWFhYCAcOHCjwmOTkZMHDw0Po06ePcOnSJWH//v2Cj4+PEBgYqNln7ty5QqlSpYStW7cKV69eFUaNGiU4ODgIPXv2LDSWsLAwQS6XC3PnzhUiIyOFhQsXCs7OzoKTk5Nmn23btglKpVJYvHixEBkZKfz000+CQqHIEysAoXz58kJQUJBw48YNYdy4cYK9vb3w9OlTQRAEITo6WgAgnD9/XhAEQXj27JlQtmxZYdq0acK1a9eEc+fOCR06dBDatGmjOed7770nVKhQQdi3b59w6dIloXfv3oKDg4Mwfvz4Qu8nPj5eiI2NFWJjY4X79+8LzZo1E95+++0CY4iJiRGsrKyESZMmCdevXxfWr18vuLm5CQCEZ8+eCYIgCPfv3xf+7//+Tzh//rxw69Yt4ZdffhEUCoVw6tSpQmM4d+6cAEB49OhRnu0VKlQQXFxchGXLlglRUVHCRx99JDg6OgqdO3cWNm/eLERGRgq9evUSqlevLqjVakEQBGH16tWCUqkUOnToIJw7d044fPiwULp0aaFjx45C//79hStXrgj//POPYGlpKWzatCnP9ZYuXSpUqFCh0DiJyHwxkSUio/roo4+ELl26aL7/6aefhEqVKmkSmP9asWKFUKpUKSE5OVmz7X//+58gl8s1CZOHh4cwb948zetZWVlC+fLlX5vIDho0SOjatWuebQMGDMiTyDZv3lx4//338+zTr1+/PMcBEL788kvN98nJyQIAYffu3YIg5E8iv/vuO6Fjx455znnv3j0BgBAZGSkkJSUJSqVSCA4O1rz+/PlzwdbW9rWJ7KvGjRsnVKhQQYiPjy8whmnTpgk1atTIc8zUqVPzJLIF6datmzB58uRCX9++fbugUCjy/VtWqFBBGDJkiOb72NhYAYDw1VdfabadPHlSACDExsYKgpCTyAIQbt68qdnngw8+EGxtbYUXL15otnXq1En44IMP8lzvr7/+EuRyuaBSqQqNlYjME4cWEJFRvf/++9i7dy8ePHgAIOcj5OHDh2s+Bv+va9euoW7durCzs9Nsa9GiBdRqNSIjI5GYmIjY2Fg0bdpU87qFhQUaNWr02jiuXbuW5xgA8Pf3z7dPixYt8mxr0aIFrl27lmdbnTp1NH+3s7ODo6Mj4uPjC7zuhQsXcPDgQdjb22u+/Pz8AAC3bt3C7du3kZWVhSZNmmiOcXJyQrVq1V57P7lWrFiBVatW4e+//0bZsmUL3Kco965SqfDdd9+hdu3acHFxgb29PUJCQhATE1PotdPS0mBlZVXgv+WrbeTm5gYAqF27dr5tr7abra0tKleunGefihUr5hl/6+bmlq+tbWxsoFar8w1hISLzZ7zZBkREAOrXr4+6deti3bp16NixI65cuYL//e9/xg5LJ0qlMs/3Mpms0FnzycnJCAgIwNy5c/O95uHhgZs3b2odx8GDB/HJJ59g48aNeRJHbfzf//0fFi5ciAULFqB27dqws7PDhAkTkJmZWegxZcqUQWpqKjIzM2FpaZnntVfbKDfRLWjbq+1WULsWpa0TEhJgZ2cHGxubotwqEZkR9sgSkdG99957WLNmDVavXo327dvDy8ur0H2rV6+OCxcuICUlRbPt+PHjkMvlqFatGpycnODh4YFTp05pXs/OzkZ4ePhrY6hevXqeYwAgLCws3z7Hjx/Ps+348eOoUaPGG++xMA0aNMCVK1dQsWJF+Pr65vmys7NDpUqVoFQqcebMGc0xiYmJiIqKeu15b968iXfeeQfTp09Hnz59Xrtv9erVcfr06Tzb/nvvx48fR8+ePTFkyBDUrVsXlSpVemMM9erVAwBcvXr1tfsVt8uXL6N+/fpGjYGIigcTWSIyunfffRf379/HypUrMXLkyNfuO3jwYFhbWyMwMBCXL1/W9DoOHTpU83H0+PHjMWfOHOzYsQPXr1/Hxx9//MbC/uPGjcOePXvw448/4saNG1i0aBH27NmTZ58pU6ZgzZo1WLp0KW7cuIGff/4Z27Ztw6effqr1vY8ZMwYJCQkYNGgQzpw5g1u3biEkJAQjRoyASqWCg4MDAgMDMWXKFBw8eBBXrlzBqFGjIJfLCx1+kZaWhoCAANSvXx+jR4/Go0ePNF8F+fDDD3Hjxg1MmTIFkZGRCAoKwpo1a/LsU6VKFYSGhuLEiRO4du0aPvjgA8TFxb323sqWLYsGDRrg2LFjWrWNvhw9ehQdO3Y0agxEVDyYyBKR0Tk5OaFv376wt7d/4+pbtra2CAkJQUJCAho3box33nkH7dq1w6JFizT7TJ48GUOHDkVgYCD8/f3h4OCA3r17v/a8zZo1w8qVK7Fw4ULUrVsXe/fuxZdffplnn169emHhwoX48ccfUbNmTSxfvhyrV69G69attb11eHp64vjx41CpVOjYsSNq166NCRMmwNnZGXJ5zn/RP//8M/z9/dG9e3e0b98eLVq0QPXq1WFtbV3gOePi4nD9+nXs378fnp6e8PDw0HwVxNvbG1u3bsWOHTtQt25dLFu2LN9KXF9++SUaNGiATp06oXXr1nB3dy/SSmnvvfceNmzYIK5R9OjBgwc4ceIERowYYbQYiKj4yAShCMUViYiKWbt27VCzZk388ssvxg6lWEVGRsLPzw83btyAr6+vVudISUlBuXLl8NNPP2HUqFF6jlC/0tLSUK1aNfz555/5JpAZwtSpU/Hs2TOsWLHC4NcmouLHyV5EZFTPnj3DoUOHcOjQISxZssTY4RSrhIQEbNmyBY6Ojq8dB/xf58+fx/Xr19GkSRMkJiZi5syZAICePXsWV6h6Y2Njg3Xr1uVZ4MGQXF1dMWnSJKNcm4iKHxNZIjKq+vXr49mzZ5g7d26RS0qZq1GjRiE8PBxLly6FlZWVqGN//PFHREZGwtLSEg0bNsTRo0dRpkyZYopUv3QZeqGryZMnG+3aRFT8OLSAiIiIiMwSJ3sRERERkVliIktEREREZomJLBERERGZJSayRERERGSWmMgSERERkVliIktEREREZomJLBERERGZJSayRERERGSWmMgSERERkVn6fzFDd4xyI51IAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==== Grid ispuna 20.0% ====\n", + "XY ukupna povrsina = 31.5777 mm^2\n", + " Povrsina ljuski = 22.8407 mm^2\n", + " Povrsina ispune = 8.7370 mm^2\n", + "-- Presjeci kroz Z (uzorak konstantan po Z) --\n", + "Duzina po X @ y=1.000 mm: 2.7234 mm\n", + "Duzina po Y @ x=-2.000 mm: 2.9837 mm\n", + "Povrsina XZ @ y=1.000: 13.6170 mm^2 (Z=5.000 mm)\n", + "Povrsina YZ @ x=-2.000: 14.9186 mm^2 (Z=5.000 mm)\n", + "\n", + "A_xz(y=1mm) = 13.617021276595658 mm^2\n", + "A_yz(x=-2mm) = 14.91864831038789 mm^2\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def _udaljenost_mod(u, razmak):\n", + " r = np.mod(u, razmak)\n", + " return np.minimum(r, razmak - r)\n", + "\n", + "def _pravocrtna_maska(XX, YY, razmak, sirina_linije, kut_stupnjevi=0.0, faza=0.0):\n", + "\n", + " th = np.deg2rad(kut_stupnjevi)\n", + " u = XX * np.cos(th) + YY * np.sin(th)\n", + " dist = _udaljenost_mod(u + faza, razmak)\n", + " return dist <= (sirina_linije / 2.0)\n", + "\n", + "def _razmak_za_gustocu_mreze(sirina_linije, f):\n", + "\n", + " f = float(np.clip(f, 0.0, 1.0))\n", + " if f <= 0.0:\n", + " return np.inf\n", + " if f >= 1.0:\n", + " return sirina_linije\n", + " r = 1.0 - np.sqrt(1.0 - f)\n", + " return sirina_linije / r\n", + "\n", + "def izracun_povrsine(XX, YY, maska):\n", + " if not np.any(maska):\n", + " return {\"A\": 0.0}\n", + "\n", + " dx = XX[0, 1] - XX[0, 0]\n", + " dy = YY[1, 0] - YY[0, 0]\n", + " dA = dx * dy\n", + " A = float(np.count_nonzero(maska) * dA)\n", + " return {\"A\": A}\n", + "\n", + "def prusa_mreza_ili_pravocrtna(\n", + " sirina, visina,\n", + " udio_ispune,\n", + " sirina_linije=0.42,\n", + " slojevi_ljuske=2,\n", + " osnovni_kut_ispune_stupnjevi=45.0,\n", + " mreza=True,\n", + " z_visina=0.0, \n", + " faza_po_mm=0.0,\n", + " # Poprecni presjeci kroz Z\n", + " z_visina_objekta=None, \n", + " y_ravnina=0.0, \n", + " x_ravnina=0.0, \n", + " N=800,\n", + " graficki_prikaz=True,\n", + " detaljno=True\n", + "):\n", + " xs = np.linspace(-sirina/2, sirina/2, N)\n", + " ys = np.linspace(-visina/2, visina/2, N)\n", + " XX, YY = np.meshgrid(xs, ys)\n", + "\n", + " shell_mask = np.zeros_like(XX, dtype=bool)\n", + " for i in range(slojevi_ljuske):\n", + " off = (i + 0.5) * sirina_linije\n", + " shell_mask |= np.abs(XX - (-sirina/2 + off)) <= (sirina_linije / 2)\n", + " shell_mask |= np.abs(XX - ( +sirina/2 - off)) <= (sirina_linije / 2)\n", + " shell_mask |= np.abs(YY - (-visina/2 + off)) <= (sirina_linije / 2)\n", + " shell_mask |= np.abs(YY - ( +visina/2 - off)) <= (sirina_linije / 2)\n", + "\n", + " unutarnji_pomak = slojevi_ljuske * sirina_linije\n", + " unutarnji_pravokutnik = (\n", + " (np.abs(XX) <= (sirina/2 - unutarnji_pomak)) &\n", + " (np.abs(YY) <= (visina/2 - unutarnji_pomak))\n", + " )\n", + "\n", + " if udio_ispune <= 0.0:\n", + " infill_mask = np.zeros_like(XX, dtype=bool)\n", + " elif udio_ispune >= 1.0:\n", + " razmak = sirina_linije\n", + " maske = []\n", + " kutevi = [osnovni_kut_ispune_stupnjevi] + ([osnovni_kut_ispune_stupnjevi + 90] if mreza else [])\n", + " faza = faza_po_mm * z_visina\n", + " for a in kutevi:\n", + " maske.append(_pravocrtna_maska(XX, YY, razmak, sirina_linije, kut_stupnjevi=a, faza=faza))\n", + " infill_mask = np.logical_or.reduce(maske) & unutarnji_pravokutnik\n", + " else:\n", + " razmak = _razmak_za_gustocu_mreze(sirina_linije, udio_ispune) if mreza \\\n", + " else sirina_linije / udio_ispune\n", + " maske = []\n", + " kutevi = [osnovni_kut_ispune_stupnjevi] + ([osnovni_kut_ispune_stupnjevi + 90] if mreza else [])\n", + " faza = faza_po_mm * z_visina\n", + " for a in kutevi:\n", + " maske.append(_pravocrtna_maska(XX, YY, razmak, sirina_linije, kut_stupnjevi=a, faza=faza))\n", + " infill_mask = np.logical_or.reduce(maske) & unutarnji_pravokutnik\n", + "\n", + " konacna_maska = shell_mask | infill_mask\n", + "\n", + " if graficki_prikaz:\n", + " plt.figure(figsize=(6, 6))\n", + " img = np.where(konacna_maska, 1.0, np.nan)\n", + " plt.imshow(img, origin='lower',\n", + " extent=[-sirina/2, sirina/2, -visina/2, visina/2],\n", + " interpolation='nearest')\n", + " naslov = \"Grid\" if mreza else \"Pravocrtna\"\n", + " plt.title(f\"{naslov} @ {udio_ispune*100:.1f}% | ljuske={slojevi_ljuske}×{sirina_linije:.2f} kut={osnovni_kut_ispune_stupnjevi:.0f}°\")\n", + " plt.xlabel(\"X (mm)\")\n", + " plt.ylabel(\"Y (mm)\")\n", + " plt.gca().set_aspect('equal', 'box')\n", + " plt.grid(True)\n", + " # vodilice\n", + " plt.hlines(y_ravnina, -sirina/2, sirina/2, linestyles='--')\n", + " plt.vlines(x_ravnina, -visina/2, visina/2, linestyles='--')\n", + " plt.show()\n", + "\n", + " total = izracun_povrsine(XX, YY, konacna_maska)\n", + " ljuske = izracun_povrsine(XX, YY, shell_mask)\n", + " A_ispuna = total[\"A\"] - ljuske[\"A\"]\n", + "\n", + " dx = XX[0, 1] - XX[0, 0]\n", + " dy = YY[1, 0] - YY[0, 0]\n", + " ys_centered = YY[:, 0]\n", + " xs_centered = XX[0, :]\n", + " row = int(np.argmin(np.abs(ys_centered - y_ravnina)))\n", + " col = int(np.argmin(np.abs(xs_centered - x_ravnina)))\n", + "\n", + " duzina_x_na_y = float(np.count_nonzero(konacna_maska[row, :]) * dx)\n", + " duzina_y_na_x = float(np.count_nonzero(konacna_maska[:, col]) * dy)\n", + "\n", + " povrsina_xz_na_y = None\n", + " povrsina_yz_na_x = None\n", + " if z_visina_objekta is not None and z_visina_objekta > 0:\n", + " povrsina_xz_na_y = duzina_x_na_y * z_visina_objekta\n", + " povrsina_yz_na_x = duzina_y_na_x * z_visina_objekta\n", + "\n", + " duzina_x_vs_y = np.count_nonzero(konacna_maska, axis=1) * dx\n", + " duzina_y_vs_x = np.count_nonzero(konacna_maska, axis=0) * dy\n", + "\n", + " if z_visina_objekta is not None and z_visina_objekta > 0:\n", + " povrsina_xz_vs_y = duzina_x_vs_y * z_visina_objekta\n", + " povrsina_yz_vs_x = duzina_y_vs_x * z_visina_objekta\n", + " y_oznaka = \"Povrsina XZ presjeka (mm^2)\"\n", + " x_oznaka = \"Povrsina YZ presjeka (mm^2)\"\n", + " else:\n", + " povrsina_xz_vs_y = duzina_x_vs_y\n", + " povrsina_yz_vs_x = duzina_y_vs_x\n", + " y_oznaka = \"Duzina po X (mm) [postavi z_visina_objekta za povrsinu]\"\n", + " x_oznaka = \"Duzina po Y (mm) [postavi z_visina_objekta za povrsinu]\"\n", + "\n", + " y_os_0_do_H = ys_centered + visina/2.0\n", + " x_os_0_do_W = xs_centered + sirina/2.0\n", + "\n", + " if graficki_prikaz:\n", + " plt.figure(figsize=(7, 3.5))\n", + " plt.plot(y_os_0_do_H, povrsina_xz_vs_y)\n", + " plt.xlabel(\"y od donjeg zida (mm)\")\n", + " plt.ylabel(y_oznaka)\n", + " plt.title(\"Varijacija prema y\")\n", + " plt.grid(True)\n", + " plt.xlim(0, visina)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " plt.figure(figsize=(7, 3.5))\n", + " plt.plot(x_os_0_do_W, povrsina_yz_vs_x)\n", + " plt.xlabel(\"x od lijevog zida (mm)\")\n", + " plt.ylabel(x_oznaka)\n", + " plt.title(\"Varijacija prema x\")\n", + " plt.grid(True)\n", + " plt.xlim(0, sirina)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " if detaljno:\n", + " print(f\"==== {('Grid' if mreza else 'Pravocrtna')} ispuna {udio_ispune*100:.1f}% ====\")\n", + " print(f\"XY ukupna povrsina = {total['A']:.4f} mm^2\")\n", + " print(f\" Povrsina ljuski = {ljuske['A']:.4f} mm^2\")\n", + " print(f\" Povrsina ispune = {A_ispuna:.4f} mm^2\")\n", + " print(f\"-- Presjeci kroz Z (uzorak konstantan po Z) --\")\n", + " print(f\"Duzina po X @ y={y_ravnina:.3f} mm: {duzina_x_na_y:.4f} mm\")\n", + " print(f\"Duzina po Y @ x={x_ravnina:.3f} mm: {duzina_y_na_x:.4f} mm\")\n", + " if povrsina_xz_na_y is not None:\n", + " print(f\"Povrsina XZ @ y={y_ravnina:.3f}: {povrsina_xz_na_y:.4f} mm^2 (Z={z_visina_objekta:.3f} mm)\")\n", + " if povrsina_yz_na_x is not None:\n", + " print(f\"Povrsina YZ @ x={x_ravnina:.3f}: {povrsina_yz_na_x:.4f} mm^2 (Z={z_visina_objekta:.3f} mm)\")\n", + " print()\n", + " return {\n", + " \"maska\": konacna_maska,\n", + " \"XX\": XX, \"YY\": YY,\n", + " \"dx\": dx, \"dy\": dy,\n", + " \"povrsina_ukupno_xy\": total[\"A\"],\n", + " \"povrsina_ljuske_xy\": ljuske[\"A\"],\n", + " \"povrsina_ispune_xy\": A_ispuna,\n", + " \"duzina_x_na_y\": duzina_x_na_y,\n", + " \"duzina_y_na_x\": duzina_y_na_x,\n", + " \"povrsina_xz_na_y\": povrsina_xz_na_y,\n", + " \"povrsina_yz_na_x\": povrsina_yz_na_x,\n", + " \"y_os_mm\": y_os_0_do_H,\n", + " \"x_os_mm\": x_os_0_do_W,\n", + " \"povrsina_xz_vs_y\": povrsina_xz_vs_y,\n", + " \"povrsina_yz_vs_x\": povrsina_yz_vs_x,\n", + " }\n", + "# Konfiguracija\n", + "if __name__ == \"__main__\":\n", + " W, H = 8.0, 8.0\n", + " Z = 5.0 \n", + " res = prusa_mreza_ili_pravocrtna(\n", + " sirina=W, visina=H,\n", + " udio_ispune=0.2,\n", + " sirina_linije=0.4,\n", + " slojevi_ljuske=2,\n", + " osnovni_kut_ispune_stupnjevi=45.0,\n", + " mreza=True,\n", + " z_visina_objekta=Z, \n", + " y_ravnina=+1.0, \n", + " x_ravnina=-2.0, \n", + " N=800,\n", + " graficki_prikaz=True, detaljno=True\n", + " )\n", + " print(\"A_xz(y=1mm) =\", res[\"povrsina_xz_na_y\"], \"mm^2\")\n", + " print(\"A_yz(x=-2mm) =\", res[\"povrsina_yz_na_x\"], \"mm^2\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1975128-5d1b-4522-b686-80d18a7c4fdc", + "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": "3.13.3" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/software/.ipynb_checkpoints/ispitni_rezultati-checkpoint.csv b/software/.ipynb_checkpoints/ispitni_rezultati-checkpoint.csv new file mode 100644 index 0000000..c7b17e5 --- /dev/null +++ b/software/.ipynb_checkpoints/ispitni_rezultati-checkpoint.csv @@ -0,0 +1,19 @@ +Eksperiment,Orijentacija,Visina sloja,Širina ekstruzije,Postotak ispune,Broj slojeva stijenke,A_ekv [mm^2],A_min [mm^2],A_max,Fm kN],Sigma [Mpa],Sigma’,SNR [dB] +1,Orijentacija 1,0.08,0.4,20.00%,2,100,44.493,,0.778,7.78,17.4858966579012,17.8195919397938 +2,Orijentacija 1,0.08,0.6,55.00%,4,100,87.819,,3.299,32.99,37.5659025951104,30.3676463109069 +3,Orijentacija 1,0.08,0.8,85.00%,6,100,100,,0.794,7.94,7.94,17.9964100485419 +4,Orijentacija 1,0.14,0.4,55.00%,6,100,87.6308,,2.792,27.92,31.8609438690506,28.9183082790225 +5,Orijentacija 1,0.14,0.6,85.00%,2,100,91.5613,,2.468,24.68,26.9546194735112,27.8469031072241 +6,Orijentacija 1,0.14,0.8,20.00%,4,100,93.9905,,2.871,28.71,30.5456402508764,29.1606638499301 +7,Orijentacija 1,0.28,0.4,85.00%,4,100,92.7473,,2.423,24.23,26.1247497231725,27.6870682827501 +8,Orijentacija 1,0.28,0.6,20.00%,6,100,96.3896,,1.891,18.91,19.6182990696092,25.5338305769008 +9,Orijentacija 1,0.28,0.8,55.00%,2,100,79.2649,,2.431,24.31,30.6693126465813,27.7156991768667 +10,Orijentacija 2,0.08,0.4,85.00%,4,100,35.669,,3.398,33.98,95.264795761025,30.6244674906605 +11,Orijentacija 2,0.08,0.6,20.00%,6,100,36.045,,3.218,32.18,89.277292273547,30.1517207952602 +12,Orijentacija 2,0.08,0.8,55.00%,2,100,26.908,,2.768,27.68,102.869035231158,28.8433217156944 +13,Orijentacija 2,0.14,0.4,20.00%,6,100,32.415,,2.472,24.72,76.2609902822767,27.8609693283356 +14,Orijentacija 2,0.14,0.6,55.00%,2,100,24.155,,2.527,25.27,104.616021527634,28.0521048383983 +15,Orijentacija 2,0.14,0.8,85.00%,4,100,42.804,,3.647,36.47,85.2023175404168,31.2387152662756 +16,Orijentacija 2,0.28,0.4,55.00%,4,100,26.909,,2.491,24.91,92.5712586866848,27.9274745507301 +17,Orijentacija 2,0.28,0.6,85.00%,6,100,44.555,,4.111,41.11,92.2679833913141,32.278949535607 +18,Orijentacija 2,0.28,0.8,20.00%,2,100,15.895,,2.263,22.63,142.371815036175,27.0936910790946 diff --git a/software/.ipynb_checkpoints/ispitni_rezultati_with_AminAmax-checkpoint.csv b/software/.ipynb_checkpoints/ispitni_rezultati_with_AminAmax-checkpoint.csv new file mode 100644 index 0000000..e37f09b --- /dev/null +++ b/software/.ipynb_checkpoints/ispitni_rezultati_with_AminAmax-checkpoint.csv @@ -0,0 +1,19 @@ +Eksperiment,Orijentacija,Visina sloja,Širina ekstruzije,Postotak ispune,Broj slojeva stijenke,A_ekv [mm^2],A_min [mm^2],A_max,Fm kN],Sigma [Mpa],Sigma’,SNR [dB],A_max [mm^2],Sigma' +1,Orijentacija 1,0.08,0.4,0.2,2.0,100.0,27.712854757929772,,0.778,7.78,17.4858966579012,17.8195919397938,100.1669449081799,28.073614457831436 +2,Orijentacija 1,0.08,0.6,0.55,4.0,100.0,66.11018363939874,,3.299,32.99,37.5659025951104,30.3676463109069,100.1669449081799,49.901540404040595 +3,Orijentacija 1,0.08,0.8,0.85,6.0,100.0,99.8330550918193,,0.794,7.94,7.94,17.9964100485419,100.1669449081799,7.953277591973276 +4,Orijentacija 1,0.14,0.4,0.55,6.0,100.0,67.11185308848053,,2.792,27.92,31.8609438690506,28.9183082790225,100.1669449081799,41.60218905472654 +5,Orijentacija 1,0.14,0.6,0.85,2.0,100.0,69.78297161936533,,2.468,24.68,26.9546194735112,27.8469031072241,100.1669449081799,35.366794258373346 +6,Orijentacija 1,0.14,0.8,0.2,4.0,100.0,71.78631051752893,,2.871,28.71,30.5456402508764,29.1606638499301,100.1669449081799,39.99369767441876 +7,Orijentacija 1,0.28,0.4,0.85,4.0,100.0,74.45742904841373,,2.423,24.23,26.1247497231725,27.6870682827501,100.1669449081799,32.54208520179385 +8,Orijentacija 1,0.28,0.6,0.2,6.0,100.0,78.13021702838032,,1.891,18.91,19.6182990696092,25.5338305769008,100.1669449081799,24.20318376068386 +9,Orijentacija 1,0.28,0.8,0.55,2.0,100.0,55.75959933222015,,2.431,24.31,30.6693126465813,27.7156991768667,100.1669449081799,43.597874251497174 +10,Orijentacija 2,0.08,0.4,0.85,4.0,100.0,74.45742904841373,,3.398,33.98,95.264795761025,30.6244674906605,100.1669449081799,45.63681614349794 +11,Orijentacija 2,0.08,0.6,0.2,6.0,100.0,78.13021702838032,,3.218,32.18,89.277292273547,30.1517207952602,100.1669449081799,41.18764957264974 +12,Orijentacija 2,0.08,0.8,0.55,2.0,100.0,55.75959933222015,,2.768,27.68,102.869035231158,28.8433217156944,100.1669449081799,49.641676646706784 +13,Orijentacija 2,0.14,0.4,0.2,6.0,100.0,51.75292153589295,,2.472,24.72,76.2609902822767,27.8609693283356,100.1669449081799,47.7654193548389 +14,Orijentacija 2,0.14,0.6,0.55,2.0,100.0,51.41903171953235,,2.527,25.27,104.616021527634,28.0521048383983,100.1669449081799,49.14522727272747 +15,Orijentacija 2,0.14,0.8,0.85,4.0,100.0,87.81302170283772,,3.647,36.47,85.2023175404168,31.2387152662756,100.1669449081799,41.531425855513476 +16,Orijentacija 2,0.28,0.4,0.55,4.0,100.0,51.75292153589295,,2.491,24.91,92.5712586866848,27.9274745507301,100.1669449081799,48.13254838709696 +17,Orijentacija 2,0.28,0.6,0.85,6.0,100.0,90.15025041736192,,4.111,41.11,92.2679833913141,32.278949535607,100.1669449081799,45.60164814814833 +18,Orijentacija 2,0.28,0.8,0.2,2.0,100.0,39.732888146911364,,2.263,22.63,142.371815036175,27.0936910790946,100.1669449081799,56.955336134454 diff --git a/software/GRID_FINAL.ipynb b/software/GRID_FINAL.ipynb index 86d38a4..4bdeb8d 100644 --- a/software/GRID_FINAL.ipynb +++ b/software/GRID_FINAL.ipynb @@ -2,17 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 115, "id": "4ba3c61a-034e-4f09-85b5-33b3071fe265", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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PhgwZgqqqKm6dCRMm4Pz589i+fTv++usv7N27F08//bSjfgIhhBBCCHESp44jO3ToUAwdOtTsawzDYMGCBXjrrbcwevRoAMDKlSsRERGBjRs3Yvz48bh48SK2bNmCY8eOoVu3bgCAL774AsOGDcMnn3yC6Ohou8qTUVCOEsaTt8xHrUC4n6eFdxBCCCGEkPoyiytRpdWbLNeUaUT9HpedECE9PR1ZWVkYNGgQtywgIAA9evTAoUOHMH78eBw6dAiBgYFcEAsAgwYNglwux5EjRzB27Fizn63RaKDR1FVkSUkJAGD0kiOQq71N1l/0SEcMbR8p1k/712AHPWb/T8RDdSsNqlfpUN1Kg+pVOlS3DbfycAbe//uS2df0mgpRv8tlA9msrCwAQEREBG95REQE91pWVhbCw8N5ryuVSgQHB3PrmDN37ly89957JsuVMgZqBcP9Xa0HdIwMf+4/BSbD9K6CCLN9+3ZnF+GORXUrDapX6VDdSoPqVTpUt/bbekUOQA6ljIHKKIm1Wg+IHU25bCArpdmzZ2PmzJnc3yUlJYiJicHU/nGYNrQTt/yDTZfww6EMxMfHY9h9ic4oqlvTarXYvn077rvvPpreT2RUt9KgepUO1a00qF6lQ3XbcLs3nANyb2PGfS3x9D2x3PKPt6Tgm50XRf0ulw1kIyMNj/Kzs7MRFRXFLc/Ozkbnzp25dXJycnjvq6mpQUFBAfd+c9RqNdRqtclyhULB21kVcgUAQCaX007cCCqViupPIlS30qB6lQ7VrTSoXqVDdWs/mUwGAFDWi6vkcvHHGHDZcWRjY2MRGRmJHTt2cMtKSkpw5MgRJCUlAQCSkpJQVFSE5ORkbp2dO3dCr9ejR48edn9nbb2b/M0wpusSQgghhBAzauMm07hKZrpuIzm1RbasrAxXrlzh/k5PT8epU6cQHByMZs2aYfr06fjggw+QmJiI2NhYvP3224iOjsaYMWMAAG3atMH999+P//73v1i2bBm0Wi1eeOEFjB8/3u4RCwghhBBCiHTED2OdHMgeP34cAwYM4P5m81YnT56MFStW4NVXX0V5eTmefvppFBUVoU+fPtiyZQs8PeuGw1q1ahVeeOEFDBw4EHK5HA888AAWLVrUoPKY3DnU/p8BNckSQgghhAjBRk2y+qGrBJGsUwPZ/v37g7Hy3F4mk2HOnDmYM2eOxXWCg4OxevVqUcpTv35ldZEsIYQQQggRgI3tTBsIxY9kXTZH1hnq526wf1McSwghhBAijKW4SYIUWQpkreEaZKm3FyGEEEKIIAzX2UuKrFg+CmSNmFQ3jVpACCGEEGKXuhxZPinCWgpkjVjK5aA4lhBCCCFEGIs5spRaIC1Lnb2oRZYQQgghRBjLLbLU2Uta9Tt71f6fht8ihBBCCBHIQo4stchKjFpkCSGEEEIah20AtDQ+v5jsGkf24sWLWLt2Lfbt24fr16+joqICYWFh6NKlC4YMGYIHHngAarVagmI6hxRN4IQQQgghdzJu1AIHfJegFtkTJ05g0KBB6NKlC/bv348ePXpg+vTpeP/99zFx4kQwDIM333wT0dHRmDdvHjQajdTlloSlpGQafosQQgghRBgubHJAby9BLbIPPPAAZs2ahfXr1yMwMNDieocOHcLChQvx6aef4o033hCrjA5TvwWWJvYihBBCCLEPl1pQb7nTUgsuX74MlUplc72kpCQkJSVBq9U2umDOYHKjwM7sRZEsIYQQQoggdRMi8Jc7rbOXkCC2Meu7Khq1gBBCCCHEPnXDb9V/0u3k4bcqKyuxf/9+XLhwweS1qqoqrFy5UrSCOYPlHFnHl4UQQgghxB25XIssYEgvaNOmDfr27YsOHTqgX79+yMzM5F4vLi7GlClTxC+hE9HMXoQQQggh9jKfIysFwYHsa6+9hvbt2yMnJwcpKSnw8/ND7969kZGRIWX5HMz8wL3UIksIIYQQIozFFlkJvktwIHvw4EHMnTsXoaGhSEhIwJ9//okhQ4bgnnvuQVpamgRFczzLFU6RLCGEEEKIEBZzZJ2ZWlBZWQmlsm6QA5lMhqVLl2LkyJHo168fLl++LH7pHIxm9iKEEEIIaRzGwowI9aesFYPgmb1at26N48ePo02bNrzlX375JQBg1KhR4pbMCUwDWRp+ixBCCCHEHnUtstIT3CI7duxYrFmzxuxrX375JR599FG3nwHL0rAQNPwWIYQQQogwdTmyLpRaMHv2bGzatMni60uWLIFerxelUM5Cw28RQgghhDSOpRZZp48j+29Dw28RQgghhNiHfUIvRQtsfXYHsqmpqVKUwyVQZy9CCCGEEHG41IQIAJCcnIz+/fuLXwpXUT+Xo/b/lCNLCCGEECJM3aAF5uMqMQkOZHfu3IlBgwbh448/lqAYrsFSiyzFsYQQQgghwrANgC7TIrthwwaMGDECc+fOxaRJk8QvhYswnRCBcmQJIYQQQuxhKSXTaZ29HnnkEbz55pt49tlnRS+AK7GcI0uhLCGEEEKIEC43/FZ8fDy2bduGyspK8UvgBiiMJYQQQggRhkstcMB3CQpk9+/fj4qKCowdOxZarVbqMjmN6Z0DzexFCCGEEGKPuhZZ6b9LUCAbGhqKXbt2obq6Gg8//LDUZXIa04F7DSiOJYQQQggRpm5CBPMNhGISPGqBr68vNm/eDLn8zp1DwfLMXhTKEkIIIYQIYqFF1qnDbwGAWq3GunXrJCiGa6IWWUIIIYQQ+1jKkXX6hAgA7vAWWQtN4BTJEkIIIYQIYilHVooWWWVD35iTk4OcnBzo9Xre8o4dOza6UK6iLo6lSJYQQgghRIi6qEn63l52B7LJycmYPHkyLl68yOWOymQyMAwDmUwGnU4neiEdxWJnL4pjCSGEEEIEqYsP+cul6OxldyD7xBNPoGXLlvjuu+8QEREhSaGcxeSn0PBbhBBCCCF2qRu1gE+KkNHuQDYtLQ2//vorEhISxC+Nk5kME1H7f0otIIQQQggRxuLMXhJ8l909twYOHIjTp09LUBTnszz8luPLQgghhBDijiy1yErRJGt3i+y3336LyZMn49y5c2jfvj1UKhXv9VGjRolWOGdjW2gpjiWEEEIIEchSjqwEX2V3IHvo0CEcOHAAmzdvNnnN3Tt71UctsoQQQggh9uFaZF1lilpjL774IiZOnIjMzEzo9Xref24fxFq8c6BIlhBCCCFECC5H1mSKWvG/y+5ANj8/HzNmzEBERIT4pXEy0wkRDP+nFllCCCGEEGG4TvImDYTiR7J2B7Ljxo3Drl27RC+IK7hzBhIjhBBCCHEtLjH8VsuWLTF79mzs378fHTp0MOnsNW3aNNEK52imScnU2YsQQgghxB6M+QZZ1+js9e2338LX1xd79uzBnj17eK/JZDL3DmQtLGAot4AQQgghRBCL48i6Qo5senq6xf/S0tJELZxOp8Pbb7+N2NhYeHl5IT4+Hu+//z4vsGQYBu+88w6ioqLg5eWFQYMGITU1VZTvr5sQgRBCCCGECGFxHFkJ2B3IOtK8efOwdOlSfPnll7h48SLmzZuH+fPn44svvuDWmT9/PhYtWoRly5bhyJEj8PHxwZAhQ1BVVWX395neOdAUtYQQQggh9mAsjiPrAhMiMAyD9evXY9euXcjJyYFer+e9vmHDBtEKd/DgQYwePRrDhw8HALRo0QJr1qzB0aNHubIsWLAAb731FkaPHg0AWLlyJSIiIrBx40aMHz/eru+zlMtBcSwhhBBCiH1MAldXSC2YPn06Jk2ahPT0dPj6+iIgIID3n5h69eqFHTt24PLlywCA06dPY//+/Rg6dCgAQ5pDVlYWBg0axL0nICAAPXr0wKFDh+z/QotT1FIoSwghhBAiRF2OLH+5S3T2+vHHH7FhwwYMGzZMguLwvf766ygpKUHr1q2hUCig0+nw4YcfYsKECQCArKwsADAZ0zYiIoJ7zRyNRgONRsP9XVJSAgDQ1+ig1Wq55fraCR70eoa3nAjD1hnVnfiobqVB9SodqltpUL1Kh+q24fSM4Wm9TlfDj6v04k+cZXcgGxAQgLi4ONELYs4vv/yCVatWYfXq1WjXrh1OnTqF6dOnIzo6GpMnT27w586dOxfvvfeeyfLTp08DOZfr/s6VAVAgNy8XmzZtavD3/dtt377d2UW4Y1HdSoPqVTpUt9KgepUO1a39SksVAGQ4euQoCi/VPdU+k+sCObL/93//h/feew/ff/89vLy8RC+QsVmzZuH111/ncl07dOiA69evY+7cuZg8eTIiIyMBANnZ2YiKiuLel52djc6dO1v83NmzZ2PmzJnc3yUlJYiJiUHnzp0wtGsCt7zmdCZ+vHIWoSGhGDasm8i/7s6n1Wqxfft23HfffSbjDZPGobqVBtWrdKhupUH1Kh2q24ZbdOUAUFmOnj17oEdsMLe8+tRt/Hj+qKjfZXcg+/DDD2PNmjUIDw9HixYtTDbuiRMnRCtcRUUF5HJ+Gq9CoeA6mMXGxiIyMhI7duzgAteSkhIcOXIEzz33nMXPVavVUKvVJsuVSiXv9yiVCsM/ZDLaiRtBpVJR/UmE6lYaVK/SobqVBtWrdKhuG65+XKVQKMT/DnvfMHnyZCQnJ2PixImIiIgwGbJKTCNHjsSHH36IZs2aoV27djh58iQ+++wzPPHEEwAMw2NNnz4dH3zwARITExEbG4u3334b0dHRGDNmjN3fZ5KUTMNvEUIIIYTYxdI4si4xRe3ff/+NrVu3ok+fPuKXpp4vvvgCb7/9Np5//nnk5OQgOjoazzzzDN555x1unVdffRXl5eV4+umnUVRUhD59+mDLli3w9PS0+/vqDxNRN/wWRbKEEEIIIYJYmtnLFcaRjYmJgb+/v+gFMcfPzw8LFizAggULLK4jk8kwZ84czJkzp9HfZ9oia/g/tcgSQgghhAjDtchaiKvEZPc4sp9++ileffVVXLt2TfzSuBj2zoHiWEIIIYQQYbiZvRzwXXa3yE6cOBEVFRWIj4+Ht7e3SQJ0QUGBaIVzNIt3DhTJEkIIIYQIYrlF1gVSC6w95r/TUI4sIYQQQoh96lIypW+TbdCoBXcqk85elCNLCCGEEGIXtgHQEVPUCsqRLS8vt+tD7V3fVZhWMOXIEkIIIYTYg20AdMTwW4IC2YSEBHz88cfIzMy0uA7DMNi+fTuGDh2KRYsWiVZAR7JU4Qw1yRJCCCGECMK42vBbu3fvxhtvvIH/+7//Q6dOndCtWzdER0fD09MThYWFuHDhAg4dOgSlUonZs2fjmWeeEb2gjmBa4QYUxhJCCCGE2MdlJkRo1aoVfv31V2RkZGDdunXYt28fDh48iMrKSoSGhqJLly745ptvMHToUEmmH3MWmtmLEEIIIcQ+3PBbDsiRtauzV7NmzfDyyy/j5ZdflqAorodaZAkhhBBC7FM3Ra30oxbYPSHCHc3iOLIUyhJCCCGECFGXI8tf7hIze93JLHb2cnhJCCGEEELck+Xx98WPZCmQNWKpdx01yBJCCCGECEMtsk5iUr9ciyxFsoQQQgghQljKkXXahAj/FpZ611GLLCGEEEKIMJZbZJ00jqw5FRUVyMjIQHV1NW95x44dG10oV0HDbxFCCCGE2Mv88FtSsDuQzc3NxZQpU7B582azr+t0ukYXylksNYFTHEsIIYQQIkzdFLUumFowffp0FBUV4ciRI/Dy8sKWLVvwww8/IDExEX/88YcERXQcS0nJNEUtIYQQQogwXI6sAzp72d0iu3PnTvz+++/o1q0b5HI5mjdvjvvuuw/+/v6YO3cuhg8fLn4pncQRA/kSQgghhNxJuJm96i13iVELysvLER4eDgAICgpCbm4uAKBDhw44ceKEuKVzMMstso4vCyGEEEKIO7LYIusK48i2atUKKSkpAIBOnTrhq6++wq1bt7Bs2TJERUWJXkBHMrlzqP0/Db9FCCGEECJMXQOgheGgRGR3asFLL72EzMxMAMC7776L+++/H6tWrYKHhwdWrFghdvkcyqTJm1pkCSGEEELswqUWSB/H2h/ITpw4kft3165dcf36dVy6dAnNmjVDaGioqIVzNm5mLyeXgxBCCCHEXdRNiCA9u1ML1qxZw/vb29sbd911F0JDQzFr1izRCuYMlpKSadQCQgghhBCBuAkR6g2/JUFvL7sD2eeee87sGLIzZszATz/9JEqhnMXizF4OLwkhhBBCiHuy1CLrEuPIrlq1Co8++ij279/PLXvxxRfxyy+/YNeuXaIWztFMBu7lmmSdUBhCCCGEEDdkMUfWFYbfGj58OJYsWYJRo0YhOTkZzz//PDZs2IBdu3ahdevW4pfQkSwNv+X4khBCCCGEuKW6Ftn6M3uJH8na3dkLAP7zn/+gqKgIvXv3RlhYGPbs2YOEhASxy+ZwFoffohxZQgghhBBBuClqXWVmr5kzZ5pdHhYWhrvuugtLlizhln322WfilMwFUIssIYQQQoh9HDn+vqBA9uTJk2aXJyQkoKSkhHtdit5ojmRa/trhtyiSJYQQQggRxGKLrATfJSiQdfdOXEJZHH6L2mQJIYQQQgSpm6JW+kjW7s5erCtXrmDr1q2orKwEcGfkkbp5gzIhhBBCiMuSorOX3YFsfn4+Bg4ciJYtW2LYsGHcdLVPPvkkXn75ZdEL6EgWx5F1/xidEEIIIcQx2NSCeotdYvitGTNmQKVSISMjA97e3tzyRx55BFu2bBG1cI5nfhxZCmQJIYQQQoRhUzJdJkfW2LZt27B161Y0bdqUtzwxMRHXr18XrWCugDINCCGEEELsw3X2ckAkZXeLbHl5Oa8lllVQUAC1Wi1KoZzG0oQI1CRLCCGEECJIXWcv/nIpRreyO5C95557sHLlSu5vmUwGvV6P+fPnY8CAAaIWztFMB9+qTS1wfFEIIYQQQtwSN0VtveVOmxDB2Pz58zFw4EAcP34c1dXVePXVV3H+/HkUFBTgwIED4pfQgSzNQEENsoQQQgghwnBhkwNyZO1ukW3fvj0uX76MPn36YPTo0SgvL8e4ceNw8uRJxMfHS1BEx7GUy0HjyBJCCCGECGMpR9YlWmQBICAgAG+++abYZXE6apElhBBCCBGHaeAqfiQrKJA9c+YM2rdvD7lcjjNnzlhd19fXFzExMVCpVKIU0JkoR5YQQgghRDjjDvKOGP1JUCDbuXNnZGVlITw8HJ07d4ZMJrPakz8gIADLli3DI488IlpBHcHiFLUUyRJCCCGE2GQcM9UfpcBpqQXp6ekICwvj/m2NRqPBunXr8Nprr7lfIGuxwimSJYQQQgixxThiMh0NSnyCAtnmzZub/bclzz//PJKTkxteKhfBpRZQHEsIIYQQYhMvtcAVx5HdsmUL9u/fz/29ePFidO7cGf/5z39QWFgIAAgKCsKGDRvEK6WDWOzs5fiiEEIIIYS4HX6LbL0n3RJ8n92B7KxZs1BSUgIAOHv2LF5++WUMGzYM6enpmDlzpugFdCRLTeA0sxchhBBCiG2MldwCKXJk7Q5k09PT0bZtWwDAr7/+ihEjRuCjjz7C4sWLsXnzZtELeOvWLUycOBEhISHw8vJChw4dcPz4ce51hmHwzjvvICoqCl5eXhg0aBBSU1NF+W5qkSWEEEIIEc547H0pAtf67A5kPTw8UFFRAQD4559/MHjwYABAcHAw11IrlsLCQvTu3RsqlQqbN2/GhQsX8OmnnyIoKIhbZ/78+Vi0aBGWLVuGI0eOwMfHB0OGDEFVVZXd32c6IQLlyBJCCCGECMUbtaDea5YmnmoMuydE6NOnD2bOnInevXvj6NGj+PnnnwEAly9fRtOmTUUt3Lx58xATE4Ply5dzy2JjY7l/MwyDBQsW4K233sLo0aMBACtXrkRERAQ2btyI8ePH2/V9loffokiWEEIIIcQejhh+y+4W2S+//BJKpRLr16/H0qVL0aRJEwDA5s2bcf/994tauD/++APdunXDQw89hPDwcHTp0gXffPMN93p6ejqysrIwaNAgbllAQAB69OiBQ4cO2f19Jp29av9PYSwhhBBCiG3WWmSlYHeLbLNmzfDXX3+ZLP/8889FKZCxtLQ0LF26FDNnzsQbb7yBY8eOYdq0afDw8MDkyZORlZUFAIiIiOC9LyIignvNHI1GA41Gw/3NpkTU6Gqg1Wq55TpdDQDDRjFeToRh64zqTnxUt9KgepUO1a00qF6lQ3XbMNXaGu7fNTVaaOV1kS0bV4nJ7kAWAK5evYrly5fj6tWrWLhwIcLDw7F582Y0a9YM7dq1E61wer0e3bp1w0cffQQA6NKlC86dO4dly5Zh8uTJDf7cuXPn4r333jNZfmD/AaQGeHN/51YCgBLaGi02bdrU4O/7t9u+fbuzi3DHorqVBtWrdKhupUH1Kh2qW/todAAbXm7bug0eirrXbpWL/312B7J79uzB0KFD0bt3b+zduxcffvghwsPDcfr0aXz33XdYv369aIWLioriRkhgtWnTBr/++isAIDIyEgCQnZ2NqKgobp3s7Gx07tzZ4ufOnj2bN1RYSUkJYmJicM89vRHftO5zrhdU4INT+6FUKDFs2BAxftK/ilarxfbt23HfffdBpVI5uzh3FKpbaVC9SofqVhpUr9Khum2YMk0NcHQnAOD++4fAU1UXyV7KKsXHx3aI+n12B7Kvv/46PvjgA8ycORN+fn7c8nvvvRdffvmlqIXr3bs3UlJSeMsuX77MzS4WGxuLyMhI7NixgwtcS0pKcOTIETz33HMWP1etVkOtVpssVypUvJ3VQ2n4NwPQTtwIKpWK6k8iVLfSoHqVDtWtNKhepUN1ax+lru7fhrqrC2SVygYlAlj/PnvfcPbsWaxevdpkeXh4OPLy8kQpFGvGjBno1asXPvroIzz88MM4evQovv76a3z99dcADL3hpk+fjg8++ACJiYmIjY3F22+/jejoaIwZM8bu77M4sxf19iKEEEIIsYk3H4IDJkSwO5ANDAxEZmYmbxgsADh58iQ3goFY7r77bvz222+YPXs25syZg9jYWCxYsAATJkzg1nn11VdRXl6Op59+GkVFRejTpw+2bNkCT09Pu7/P0vhmDI1bQAghhBBiE3/UgvpT1LrAOLLjx4/Ha6+9hnXr1kEmk0Gv1+PAgQN45ZVX8Nhjj4lewBEjRmDEiBEWX5fJZJgzZw7mzJnT6O+iFllCCCGEkEYwDmRdcYrajz76CK1bt0ZMTAzKysrQtm1b9O3bF7169cJbb70lfgkdyHRChNqZvRxfFEIIIYQQt8Oborbea1KMK2tXiyzDMMjKysKiRYvwzjvv4OzZsygrK0OXLl2QmJgoQfEczMKECBTJEkIIIYTYxkstcMDMXnYHsgkJCTh//jwSExMRExMjfolcCJdaQJEsIYQQQohNvM5eDvg+u1IL5HI5EhMTkZ+fL1V5nMx8UjLlyBJCCCGE2MYYBU2mLbDih7Z258h+/PHHmDVrFs6dOyd6YZzNYmcvxxeFEEIIIcTt8IffcrHUAgB47LHHUFFRgU6dOsHDwwNeXl681wsKCkQrnKNZSkpmqEmWEEIIIcQmayGT0zt7AcCCBQskKIZrMLlToBZZQgghhBDB2H5F5lpf67fQisHuQHby5MmiF8JVWBq4lxpkCSGEEEIEqI2ZzIWsLtEiCwA6nQ6//fYbLl68CABo27YtRo8eLckcus4kRS4HIYQQQsidim37k6L11Ry7I8/z589j1KhRyMrKQqtWrQAA8+bNQ1hYGP7880+0b99e9EI6iklnL6N/MwzjsI1CCCGEEOKOGGstsq4ws9dTTz2Fdu3a4ebNmzhx4gROnDiBGzduoGPHjnj66afFL6EDWZrZC6D0AkIIIYQQW6zmyEqQXGB3i+ypU6dw/PhxBAUFccuCgoLw4Ycf4u677xa1cA5Xf5gIo39THEsIIYQQYl1di6xp0OoSLbItW7ZEdna2yfKcnBwkJCSIUihnMW2Rrfs3DcFFCCGEEGIdFy05KBvT7kB27ty5mDZtGtavX4+bN2/i5s2bWL9+PaZPn4558+ahpKSE+8/dmObIGqUWOLgshBBCCCHuhm34c1SOrN2pBSNGjAAAPPzww1wOKVvokSNHcn/LZDLodDqxyukcvBZZ5xWDEEIIIcQdcKkFDmqRtTuQ3bVrlxTlcAlWUwuoTZYQQgghRBDzObIu0NmrX79+ohfCVZjMCWz0b2qRJYQQQgixzlqLrBSNtHbnyN7JrA2/RQghhBBCrOOG3zLzmkuMWnAno7iVEEIIIUQaUowjS4GsEUotIIQQQghpuLrUAhcdR/bfhDp7EUIIIYQIx0ZLjnrILTiQ/fHHH1FQUGDx9fLycsyZM0eUQrkK3jiyFMcSQgghhFjF1E3tZcKpnb0mT56M7t2749y5c2ZfLysrw3vvvSdawVwBv0WWEEIIIYRYY7VF1tmpBXFxcejVqxc2bNggfklcHE1RSwghhBBindUcWWd29pLJZPjpp5/w2muv4eGHH8a7774remFcDbXIEkIIIYTYo3b4LXOpBc5skWVbJN98801s3LgRCxcuxNixY1FeXi5+qVwE5cgSQgghhAhnJUXWdSZEGDFiBA4fPoyLFy+iR48eSEtLE7tcLoF350CBLCGEEEKIVVyOrNnht5ycWmCsdevWOHr0KJo3b467774b27ZtE71wzsaPYymSJYQQQgixxlqLrBTsTi0w5u/vj7/++gvPPPMMHn/8cTHL5RKMg3dKLSCEEEIIsY6xliMrwffZNfyWl5eXyXKZTIaPPvoIa9asQb9+/UQtnLNRZgEhhBBCiHB1DX+OmdlLKXTF5cuXW3394YcfxtixYxtdIFfCG7WAmmQJIYQQQqyqG37L9DWnDr81adIklJSUWHz9+PHj6NKliyiFchW81AInloMQQgghxB1wqQXmXnTm8FsXLlxA27ZtsXXrVt5yrVaL2bNno1evXujdu7foBXQV1CBLCCGEEGKd1RZZZwayR44cwRNPPIGRI0fimWeeQVlZGY4fP47OnTtj9erV+Ouvv/DVV1+JX0InYyudRi0ghBBCCBFGijQCcwQHskqlEnPmzMHBgwdx4MABtGzZEr169ULPnj1x9uxZDB48WMpyOg23GSiOJYQQQgixynqOrPjsnhDB09MTKpUKxcXF8PDwwIABA+Dv7y9B0VwDmydLcSwhhBBCiHXWcmSdOiECwzCYO3cuunXrhs6dO+PWrVuYP38+nnvuOYwbNw65ubmiF84VsFVOObKEEEIIIdbVtciaGX5Lgu8THMj27NkTX3zxBdatW4fly5cjMDAQzz//PE6fPo28vDy0bdsWP//8swRFdC7KkSWEEEIIEcZatOTUzl6xsbE4d+4cRo4cyVseFxeHPXv24I033sCTTz4pegGdjU1WphZZQgghhBDr2HH3XW4c2bVr1yI4ONjsazKZDDNmzMDJkydFK5jL4FpkCSGEEEKINWy8JEXrqzl2d/ayJjExUcyPcwl1ObIUyhJCCCGEWMPlyDpoilpRA9k7EZcjS3EsIYQQQogNllMLpECBrA2OGtCXEEIIIcTd1bXImqIWWSegFllCCCGEEGHqcmTNDb/lxM5e/1Zcjix19yKEEEIIscrRLbLKhrypvLwce/bsQUZGBqqrq3mvTZs2TZSCmfPxxx9j9uzZeOmll7BgwQIAQFVVFV5++WWsXbsWGo0GQ4YMwZIlSxARESHKd3Ize4kQxzIMgzJNjUlI7KGQw1OlMFm/SqtDtU5vslwpl8Hbo0GbrtGqa/SoqtGZLJfLZPBV88vEMAwqa4DSKi2U9d7irVJAqXDOfVRpldbsbYmnUgEPpWmZyjU10NXbARQyGXzUztsGZVVak7o1tw0AoEanR4XWdJs5cxvo9QzKqmtMltuzDQDLx44jVFbroNWbHp8quRxeHvwyWdoGAOCnVkoy240tlrYBAPh6KCGX88tUUV2DGr1rbQNL50hz28Da+s7aBgCg1elRaWbf8PFQQlFvGzAMg1INf5vVaA3nAq1OD5VK0qJaVKapgb7e8WnpOqWp0UFTY7oNnHlOtbQNarSmx4el6zgAeKkUUDnpnGrpHKlWyqFWmh4L5s5flq4h9mKsRbISsLvEJ0+exLBhw1BRUYHy8nIEBwcjLy8P3t7eCA8PlyyQPXbsGL766it07NiRt3zGjBn4+++/sW7dOgQEBOCFF17AuHHjcODAAVG+t65FtvGmrT2FP0/fNlmuUsjw4r2JmDawbtSHPZdz8d8fjps96cpkwFvD2+LJPrEilEq4i5kleGDpQVRUm78gP9UnFm+NaMv9/fiKZBxMU+L1Y7tM1o0O8MTm6X0R4OXYM+9r68/g5+M3zL7m56nEpmn3ICbYm1s2d9NFfLU3zez6Lw1MxIz7WkpSTkuu5JRhzOIDKNPUADCt28eSmmPO6Pbc3wXl1RiyYC9ySzUmnxXhr8aSCV3RtXmQ1MXmKdfUYOSX+5GWW27ymo+HAhun9kZihB+3bMnuK5i/JcXsZ3ko5fhhSnckxYdIVl5zfk2+iVnrT8NMXAeFXIZPH+qEMV2aAABKqrQY/NleZJVUmf2sexJD8eOTPaQsrokanR4jvzyAi5klZl9PDPfFppfu4S7K3+5Lw4ebLpq9oVcpZPjmsW7o3ypcyiKb+PtMJl5ae9JscC2XAR8/0BEPd4vhlh1Jy8ek74+i2kwQ1aVZINY9k+TwG7tdl3Iw45dTKKrQmrwWG+qDzS/dw7tJeO6nE9hyPsvMJykx/8IebJ3eD2F+aglLbOqN385i9ZEMs6+9NbwNnronjvv7zM0iPPLVYbNBIwC8MCABrwxpJUk5LblVVInhi/aZ3QYA0DVUjmFGf0//+RR+P2V6HQeAQG8VNk27B9GBXhKU1LJ5Wy5h6e6rZl/z9lDgl2eS0L5JAADDDeycvy5g5aFrZs9fT/SOxTsj25q+YAcutcDMa06d2Ys1Y8YMjBw5EoWFhfDy8sLhw4dx/fp1dO3aFZ988okERQTKysowYcIEfPPNNwgKqrvoFhcX47vvvsNnn32Ge++9F127dsXy5ctx8OBBHD58WJwv53JkGxfK3iioMBvEAoBWx+Cz7Zdx9mYxt2zv5VyzQayhLMDvp241qjwNsfHULYtBLADsTMnh/l2l1eFgWoHFdW8XV2HPZcdOa8wwDDadzbT4emlVDQ6n5XN/X8kpxTf7zAexALB091XcKKgQtYy27EvNrQ1izfvz9G3evrovNddsEAsA2SUavLTW8WM/f7MvzWwQCwDl1Tr8dPg6b9nW89kWP6u6Ro9/Llp+XSrf7EszexEAAJ2ewb7UPO7v87dKLAaxALAvNQ85pZZfl8KlrFKLQSwApOaU8fbtPZdzLT6V0uoYfH/gmsgltG3bhSyzQSwA6Bngnwv8/WL5gWtmg1gAOJlRhHXJN0UvozVVWh2mrTlpMYBKzyvHqRtFvPW3XTAXxBoUlGtx7Jrlc64UztwsshjEAsBn2y/z9u19qXkWg1gA+OuM+WuklDafzbS4DQDgYiE/9Np5McfCmkBRhRZbzlneRlLZcMLyvltRrcPBq3Xno52XcrDioPkgFgBWHbmOkirL9SGE1SlqJXjyYXeL7KlTp/DVV19BLpdDoVBAo9EgLi4O8+fPx+TJkzFu3DjRCzl16lQMHz4cgwYNwgcffMAtT05OhlarxaBBg7hlrVu3RrNmzXDo0CH07NnT7OdpNBpoNHUX95ISwwldq9VCq+VvQLbKtdoak9fssfGEoRWwZ2wQvn2sK++1B786gktZpUjNLkbrCENr4O1Cw0Vk9v0tMaFHM27d20WVGLzwAC7cLkFpRZVDH+ntqw08541rh+Edorjl6XnlGLn4EPJKNVwd5RQbTl5yGYPk2f2gUnlw63+y7TJWHMrA3pQcDG0b5rDyZ5dUoVRTA7kMSH7zXt4joHf/vIBfT9zGjYJy7jf8ffo29AzQNzEES/7ThfdZT/yQjKPXCvHHqZt4+h7HtYynZhv21Sd7NUNbXRoGDhwIpUqF6hodun20C4UVWtwqKEOEvycA4MR1w4VtQvcYzB5a19JRWFGNe/63FzcLK5FdVI5gHw/TL5MIe9L95IH2uL99JLd8/5U8PLvqFP44fRuvD0nkWscy8g1B76/P9ECryLqW2o2nbuOt3y/gwu3iRh2bxtjPsfZ5KVmluJRVCpVChr2v9IWfZ91Thd9O3sbbf1xAflkV9xm3Cw3lv7tFEJZP5h/7Y5ceQmpOOZLT8zGojeNaNI9fM9yw9YoPxtcT7+K9NuLLg7iWX4HbheWICTS07uWXGc6Xy/7TGX0SQ7l1bxRUYOgXB7E/NRe3C8qstgYKqVt7XMkpBQAseqQj7m1dV3f7U/Pw7OpTuJ5fdyyXVmmx45IhsN3wbA+0NGrxX3n4OuZvTcWvyTfwYJcoOEpqVilKNTVQKWQ4NnsArzV45roz2HYhB8nX8tE1xh8AcOFWMfQMEOStwr5X+nLJhjVaLf779S4cy5PjclYJ7msdavb7pLAh2XBdG94hEvPG1T0JAsPg4W+O4kJmKf45n4WHuhqeTtwoMBwLz/WNxdQB8dzq7PnoekEFisoqHZpiwF7XZg1OxOSk5iZlqtDJUFGlgTcMN85saseBV/vxniiuOHgdn2xPxd7LOZjUo6nDyp9ZXIXsEg0UchmOvN6fFxN8/k8qvjtwHbcKK7hj4UqO4Rpicl1jGIxachhpeeX4+/QtPHhXkwaXqaamhvvM+se73lIE3Qh27y0qlQpyueGACw8PR0ZGBtq0aYOAgADcuGH+kW1jrF27FidOnMCxY8dMXsvKyoKHhwcCAwN5yyMiIpCVZfmuaO7cuXjvvfdMlu/atQve3t68ZTVaBQAZ9uzdg0uNeFqw5ZIcgBxNmDzs2LaF95qX1vDagWOnoLhpaCG7eN3wvdlpF7Gj+AK3LsMAfioFSrXAdxu2ItYPDlGuBS5kGnaX6uunsSPzNO81QImSqhr88dcmKOXAzXLDMl8lsHfXTt5neRbJACiw8/xNbFLzW9+klFJs+N4QNYO9O7bxXqvMNbx25Gwq4isNj7GT0w3bxaMi12SbhdQY1t97MgVNSy86pPwAcKx2P6rKSYcyDNizawf3WpinAtmVMvz05y60CTKcLHafNexH8oJr2LEtnfdZQR4KFFbLsPrPfxDn75jya/XAjQJDmSqvncIOowYYHQMoZAoUVmjx8x9bEKQGKmuAwgrDfnfl5AHcNLpvKyoDACXOZOTj7783idqJYPv27RZfO5ht2PZxvjoc3buD99r1AsNr6bdzsWnTJgDA3tuGZbrSfJP9KBRypEKOX/ecQHW6+dZCKWy6YtiP/DSm5yNl7Tlv274jyL9o2I9u5xuWpZ49Dg1/N0KUtwKZFTIs/30n2gfbvkhZq1uh9AyQmmUoU1bKCewwahTMrgQAJdJzS7n94lopoNUpEaBicOP0ARhfqZjac9XFW4XcNnOEk/mG/aKJlx576p2PvMoNr209Xnd+OZxjWBam0mDH9q289SO8DTv/vtOXEVtxyQGlNzh8oXY/Kr+FHdv4rYK+NYbXDp04A59sw/XidKphWeHNK9ixLZW3vr9KgRKtDD9s3IYWDrqu6Rjg0FXDfiTLuogd2+rO5XoGkEEBBjL8sWUH/D2A4moAUEIOBkf37oBxCrOsdj86eCUXf/29CXIpnqGbcap2P4ry0uPALv6xVZRpeO1UyjVsguHp4vHrhm0gKzO9rsWp5UiDHH8eOAvvrNNoqMu119qysjKTY0qKEaDsDmS7dOmCY8eOITExEf369cM777yDvLw8/Pjjj2jfvr3tD7DDjRs38NJLL2H79u3w9PQU7XNnz56NmTNncn+XlJQgJiYGAwYMQEgIP9fu3VO7UFGpxT339EVCuG+Dv/OzlP0AKjBmQA/0jONP9XtUdxEn828gqkUihg1MAAB8fGEvgCoM7Z+EzjGBvPX/KDyJnSm58GveHsN6NoMjHLtWCBw/hqaBnhg/pi/vNYZh8O7Jf6DVMejWZwCiA72w/0o+cCYZPirgvvvug8qoF0LvSi2WXdyFAo0MfQcOFiW5XIiCIxnAhUvo2CIcw4bxW1g1J29j041zUPiFYtiwbgCAf9adAbKycHeH1hjWuwVvfeZsFjb/cgZar2AMG9bdIeUHgA/P7QGgwch+3ZF94QivbreVnsHf57LgG9Maw/rGgmEYzDq2A4Aek4b3RWyoD++z1uUmY/+VfEQkdsSwro5pQbicXQrmyCH4eSrxyOj7TB4zzT23G7ll1ejcow/aRfvj/O0S4NhhBPuoMG7kYN66VVodPju3A+U1MnS9515E+jf+HKHVarF9+3aTfdbYqc0pQNp19GoXi2FD+fl8kRlF+DblKBgPbwwbdg8A4MyWFOD6dXRpHYth9/PXLz1+E4d+v4BKzzAMG8ZvrZXSl18cAFCOB/p3xYBW/Kci28vO4MrZLDSJb8Pt968d/weAHiPuG4CmQfw7+m1lZ/D32SwEtWiNYVby9oXUrVC3iipRfXgfVAoZJo25n9eaqanRY+7pf1Ctl6FHv4EI9VVjV0oucO4kmob5Y9iwJN5nVVTX4H9ndqK8Roak/oMQ5O2YpxNpu64Cl6/irsQmGDaMf+0MSS/A798fR4HeG8OGGc63yX9fAq5moHe9/U6r1eLMz/8AADQegRg2zPyTSCn876LhOjVqQE/c3YKfa39mSwqO5l5HeEwct98vumLY7+7v2x296+W1r89Nxr4r+QiK64Bhd8fAEdLzylF9+AC8VHI8+cBQkw6O753ehaJKLTrenYS2TYJwMbMUSD6EYF81Rgzvz1tXp2ew6MIOaGr06JDUH82D+Y1iUrmwLRW4nI4+bWMwbBg/t1V5IRu/XjsNeAdh2DBDHv6BjeeB27dwV9uWGNY/jre+9nQm/ll/FlrvEAwbdneDyxR4NR+LLyTD388Pw4b1Mnl92p6NDf5sc+yOID766COUlhoe6Xz44Yd47LHH8NxzzyExMRHff/+9qIVLTk5GTk4O7rqr7tGXTqfD3r178eWXX2Lr1q2orq5GUVERr1U2OzsbkZGRZj7RQK1WQ602fQSmUqlMTrDsdVapVDb45Kup0eFmUSUAoGVUgMnnhPgZLsDFVTqoVCro9AxyavMaY0L8TNZvHeWPnSm5uJZf2egLglDXCw2pAvHhpuUBgFBfNTKLq1BUpUdzlQolGkMelK+SManXUJUKob5q5JVpcKNIg45NHZMYn55v2AYJEaa/oVmo4SbldnEV91pRpeHxSKifl8n6LSMNifNXc8uhVDqmx3NplZbbL1pG+iP7An+fTYjwA85l4Vbtbyit0nI5gYb9iJ+GkhDuh/1X8nGtoMqB+5Gh/PFhvvDwMA0Ygn3UyC2rRlm1Yb+5XWIYFaV5iI9JGVUqFVqE+iAttxzp+VWICRGvGcfcuYCVnm9I+0mM8DdZJ8zfsC8XVmi513LLDI/WIgO8TdZvEx1o+My8codtgxqdHtdrj4W2TQJNvjcywPAb8soNv6GyWocqrWE/CgvwhkrFv2wkhPsByML1fGH7kbW6FepaQSEAoEWID7w8+edylQqIDvDCraJK3C6pRlSQL0o1hvIH+6hNvjtApUJ0gCduF1fhRpEG4QH8Gz6pXCswbANz+1HTYMP5qMBoP7pVZDgHmzt/RXgZmrnS8xx3Pqqs1uFWbQpZKwHXNYZhcLv2NzQPNf0NbaIDsO9KPtLyHHldM5yP4sJ8oVabOx95oKhSixKN4XxUUrsfhZjZj1QwdNC7lFWKjMIqJEQESF5+oO581MbMNmD3o+wSjel1zd/TZP1Wtde1xp6PFArDOUIulzlkW9rd2atbt24YMGAAAENqwZYtW1BSUoLk5GR06tRJ1MINHDgQZ8+exalTp7j/unXrhgkTJnD/VqlU2LGj7vFeSkoKMjIykJSUZOWTheOG32rEZ2TkV0CnZ+CrViLcTA5ZsLdhQxdUGC7aeWUa6PQMFHKZ2Zyz+DDDznk1t6wRpbLP1RzDd8WFmT/Jh/oayplXm0tXUG74LT4W9mH2cxz6G2q/KyHMtGW9SW0v09tFVVwOT2Ht9jCXPxob6gOZDCiu1HK/VWpsB6kwPzUvL5MV6msoJ1ueQkPOBzxV5ociiq99wsBuW0dgvyvezDYAgCAf/rFwszZXPCbIfOsGuy2vOPA3sNvB3LHAtuaVVtVAW9tZk+3sEu5v7lg2fMbt4iqUW+nEJ6abhZWo1unhqZIjOsD0JpItJ3vTxG4LD4UcPtb2I4cey4ZtYGk/YluNb9QGi9aOZcD4WDDfCVEKbH3Fm9uPastZUa1DVW3nKHY7hPiY7kchasNoGeXVOmSXmO/cKbb0vHIwjKGnvrl6Da49FgrZ81GFluvoFRVg+vQkwRnXtVz2umZ+PwquPR+x+09+uaFugyxc2Lhrs1P2I9PfwNZzTmkVamrPR9yxYObJQ2ztvphXVo1iKx3gbHH0uPsuPSGCn58f2rdvz/vPx8cHISEhaN++PQICAvDkk09i5syZ2LVrF5KTkzFlyhQkJSVZ7OhlL274rUZsF+MTlrk7ZfakxR7wmbV3ueF+apNxBAHnBIFpedYvHGwQxQay7G/xtdDmz36Opd7rUmCDnXgzKSKRtQd8tU7PHehsIBjobXrS8vJQcMHvVQf9BmsXPsB4PzKU29oJy/hznHHhiA83/xuC6x0L+WWG/1vqRJTg4CCqSqvDjdrg2tyx4O+l4nLj2PpnA8JwP9OLd6C3B0Jqf3N6nmP3o9hQX5NHqQC4joI5JfxjOchHZfb85Yz9iD2WLaV7sftLfm3Z2Zs7S2kDjm4cYBiGO/eZOx/5e9aNIcv2qGe3g7mgUSkHmgWx5yPH/AbjAMradY0NwLNrR+4I8fEw20mZva458pqQZuuc6s1vHGD/b+5mwvhz0vIcsw20Oj2u17bImruxDvFVQymXQc8AufUamYLM7Ee+aiWXonW1Eb/B2qgFhuUN/miz7A5ks7OzMWnSJERHR0OpVEKhUPD+c7TPP/8cI0aMwAMPPIC+ffsiMjISGzZsEO3zuSlqG3GHYav1oP7BwvYQtnTxZu8es0s0KG3kMBlCWbvrA+rKmlfG3rlab5F19MWvpErLtVSYu/ipFHL4exqi7sLaC0eBlQsH4PiLn61tEFzvwsH+39wJy/hzbhRWQmNmkgsp2Hss2GpJY7elo1pkr+UbWqH8PZXczZsxhVyGQG/+DUVu7X5nrkUWcOZ+ZP7izR7LbEuyrSAwrjYtp7DCcU8nrtoIZNn9pahC2H7k6PNRVkkVKqp1UMplaGYml1Imk1kMooItnFQd3cBhaz+qf1NqLRA3fI5hW94qqkSFhYk6xGbrfMSdU8sFXhMc3LKfUVCBGj0Dbw+F2T4CCrkMIbXnKbZRgL2+Wbypq21kaMyTOmvjyFpb3lB258g+/vjjyMjIwNtvv42oqCiHz4aye/du3t+enp5YvHgxFi9eLNE3Nn5mr6tWWgIBowO+QljrQYCXCmF+auSWapCWW45O9TqDia1Kq+PGlLTUkhZSm1rAjlnK/hZfpfmKc/QBz97lh/up4W/msTxg2A4lVTUorKhGZbWOewxmKRCMC/PBnsu53F291Ni6sn3SFXbhCPdTw1etRJmmBhn5FbxJCKRgaIUSePHjjgXrJ11Ht8hy2yDcfCsUYBgeqaC8GgXl1bzhekItteKE++DotQKHpXik2bh4s2lC7AXPVhDIPp24VVSJq7llCPYJNruemK7kWg9kLQWBlo9lxz4hYvejZiHeFmeCCvZRIa9Mg8KKatTo9CipMuxHlm8ofLADuQ7fjyw9ljfZBjZurIN8PBDs44GC8mqk5ZZzA/hLhWGYuqd0ls6p3vVTC1ysccMo5c/c0xXAsB2ySzQoKK+GTs9wN3fW0iMOXMlv1JNGdixzS+Gh2HGj3YHs/v37sW/fPnTu3FnUgrgqrkW2MYFs7SPDuFDbj4QZhrF54QAMgUBuqQZpeWWSB7LX8yugZwwzX4X5mr8Yh9RrAWFPXr4WWmTZfKj0vHIuH1hKtk5YgGE7XMuvQEF5NbcNlHIZ/CyMqlB30nLsI2FLecpsCkFRheGExW6DQAsXPplMhvgwH5y+WYyruWWSB7LZJRqUV+ugkMvQLFjYo7y6Y8F6TlpeWTWKKqot/lax2GoVBwzH7dXcchRVVHPHg0Iug5+njf3IwakFlm6sg+rtR4U2gkDAsE/eKqpEWm4Z7m4hbSDL3iSw32tO/RsitnXccpqNoS6uF1SgukZvdppkMQnZjwKNjoWiSkP5ZTJYnA2RHZXE8alO1m+sS2rzxbkbayvHaHyYDwrKq3E1t0zyQLagvBrFlVrIZDAZ0YUVVK9Flv0NIWaexgB1n5NfXo3C8mqrx4wYbKX8AfxjoaRSy02EIGWaDdciaymQbfAnm2f30RoTE9PoWa7cSd0UtQ37zQzDIM1Wi2ztDlWt06O8WmezFQpwbFK5cUK85VYo9rE2/xGMj4VbpehAL3go5ajW6bkOPVK6aqMFB+B3TjBuwbH0mx15911jlAtluaOUofx6Biip1BrlyFruNerIYJytp+bB3hYDBdMAxPrTCR+1kuvQ4IjtICQAqTsWqrlWqEAvlcUWk7pj2VGPhK3fWLM54ex+xB7T1gMQx+9HTQK94O1h/gQTVO/pRIGNVqgIfzV8PBTQ6RlkFDjuN1gNQLzrjgX2OAjwUlmcRpfLz3TAcaDXG+X4WriZCPBScYFMUYW27rpmtYHGcS3j7L4aHeBltjMsYHo+stUia3w+ckSerK3OswD/WGB/h5+n0uKTgLpt0IjyszmyFkJWp+fILliwAK+//jquXbsmbklcVGNbZHNLNdxsUs1DzPe89vJQwFNl2BSF5dVGj4QtByBxDgyibD0OBkzzodiTlq/KfMUp5DLuQuqIk1Zdi6zl32DcOcFWRymgLs3iRkGF5DmmbE9ztVLOdTKrT6WQc61++eXVgi4cXF6dA4IoWz2EgbogistJE/B0wpF5stZGLGAZHwu2Hmkbfxb7dEJKhQJaM433owKjICrI2g2RA0fAsNZpkxVcL0/Z1g2RTCbjPu+KQxsHBJyPjLaZtfNRbGjtrJAOGAEjs6QKlVodVAoZYiyMl6qQyxDoVfdo3tbTFcCxeb5pNp5MAEYjCpnkKbvWTZ3V85FRA42Qp73sZ13Pr+BGXrEX2/DnqMxTuwPZRx55BLt370Z8fDz8/PwQHBzM++9OY+mOQig2l6tZsDfUSsud4YKNHiPZyiUCHNs5wVZCPGAcgFTz0iMstcgaf55DfgPXOcTy43PjAIRLiLdy0g3zVcPPUwk9A661VCrGQaCllj2A34JgK0cWcOxj7bpccWFBYI1Oj+JKduQI279B6kCWYRhhLbJGjyNtPdIGgKZB3vBQyKGp0eN27XjTUmFbiay1ZgL1gnEXOx9xIxYIOR9VVEOvF5qu5cjGASGPhGuDQKMAxNo2CHLgCBhsENg8xMdiyx5bJqDeTZ2LtewLaaCp/6TR0qgFxp8n9X5kOB/Z3o+MG2iEPO2N9PeEt4cCNXoGGQUNu65xoxY06N32sztHdsGCBRIUw3U1tkVWyI4GGHa228VVvBYQIY/yruVVSJ5jKjQvEGDzcGq4liVLObKGz3PMAV9do8f12gPSWmpBXX6mVlAQKJPJEBfmi9M3inA1p4w3f7vYhJx0AUN5r+dXIL/MKACx2qpc+xgppwwMw0jaeZPL5wq1vQ0KKwx5gexxZ24INJajWmSNe5pberoC8B8J23qkDRharmJDfZCSXYoruWUWW7jEwKYiWWvBAQzb4TqbLy7gWGCDyozapxPWbtoby9bQWwA/EC+pqssLtLYfOeoJUZmmhhti0eoTIqN0rboAxPrg8vFhvsgvL5A8x5TrZGQhPYUV5OMB5JXXa5EVklpQBr2esXrT3liCgkCjzl46oTdEDurInC8gxxcw6rAm8Loml8sQF+aDc7dKcDWnzGbsYg5jM0nWyZ29Jk+eLGoBXF1jc2TTBDT9A+ZbQKy1QjUJ9IJaaWjFuVlYgeYh0sxGwzCMUWum7cdgFdU63C42tCr5qBVQyi0/4nLUAX89v5ybkCLCwhBIAH/wayGtB4DhQnT6RpHkwXhdAGL9pGIcRBUJOOk2D/GGXAaUamqQW6pBuAjTvFpiT4usceukv5V8LsBxrThCepoD9XLSBFw4AEOdpGSXIi23HANaWV21UYTclAL8G1Mhx0KYA0fAEHJTx5bVuFXJV620GmA7amKH9Nr9NNTXw+o5np+iUjsQv43zUVyYY0bA4IJAG9O2GzcOCEmzaRrkxT2duFVUKe1NnR0NNFodg5uFFYJurEXJMRWAveFqGuRldlxeFi9FRUDjBmD4DedulTT4nOro4bcEpRaUlJTw/m3tP8InuEWWC0DsuWuS/sSbU2q7pzkA+KmVUNbePbPlsXnSDXXMhcPWhBQs4x7zQu68DZ/pmCCKfSQspEUWYPPqbD9GUisV3DiWVyTcDuWaGtyubYWKs9Ii6+2h4DqCsSdqW9uAbZm7UVjBzYIkBaFBoHErjvAbIscfC9bwnk4IOBbYETCMv0MKldU63Kq9wbHWIuvloYBX7cWd3Y+stYoD/G0gZYdmIbniQP0c2doUFQu95VmOShUSfkNklCMr4EmjUiHnnnakSfgbNDVGQ0paORY8VQp4yA37Qmq24TcHeKms3siyjVYZBQ3PMRWiITelQvrfGH+mVMeyUzp7BQUFIScnBwAQGBiIoKAgk//Y5XcaborahqYWCOiYANTtbHllGi4v0NaJt66jjnQHPFv+ZlZ6mgO1A3jX/ga2PNZ6ywN15c8vr2s9lIKQziGA+VYoW8M5OS4AEXZDxP6G/DLhwXicA4JxNmcvxMfDaouMTCbjLnTcDZGN8of6eiDASwWGkfaxsNCnK+Z6CdveBo7pdCd8P2Jz3jVcnq+t7eCImzpDkGm4WQixMBQgi61zdj+yFkABRk8nqmq4WZCkIDhNyGgYNLZFNkRAyz7giP1I4A2RmdZAwY0DEv4GbkhJtdLixEMsNj0utbY8traBcY6plH0n6tI7hDaSCet8ChjPstawbWBzHNkGfaplglILdu7cyXXk2rVrl8hFcA8NiWONWw9sXTjYRxXX88ttjvPGckQQJfSEBRhOvLmlGsEBCDtUSWZxFa7mlqNrc+vrN5SQMWQBSwGIrTvXurw6qXJMhYybyWJ/Q0ZBXQ94a4/BAMNv2HlJ2kdhQnrXsoJ8PJBVUiU4AJHJZEgI90Xy9UJcyS1D22j/xhfYDMFBoJ0dXIw/U8ogsLpGzz1mt/lIuHY/YkfLAGxvB0eMXCBkGD1WkI+Km6TB8Lf18nuqFGga5I2MggpczSk3O6WwGIR09AL4M/UV2JiNicV+ppTjc5dparhZEoWmOt0uqkSV1rAf2bwhCvcBzkt8XWODQCsTm7B8lECBpu46YisQNzyd8MXZW4bxuYXsqw1ha7pvVl2KirZB56OGXNdspRaITVAg269fP7P//jeo6+xlfyjLtkIFeqts7vzB9VozrY3zxnLEozyhF2+grgWZO+BtBFDs5xoC2TJ0bS5Niz77G2ydUNiDu7Sqhptn3tYB3yzEGwq5DGWaGuSUarh56sXEBpi2epoDdfsRuw18PBRW86cAR7WkCd+P2JsH9lgQMqh4fJgPkq8XOiSIEnpDVF6t4+aXF9oqnlemQXGFFgECjh17sTc3Ph4KhNtoharfKu6pklsca5MVF+qA85HAm1Kg7tite0IkbD/KKKhAWl4ZkuJDGlFSy+zdj6q0etyqHWvb0kD8rPojYEiRY8qej0J91RYnZ2Cxv4EN3lUKGXxs7Eeu1kBjGEJShis5pQBsH8vs57KBrFTsTVs0jNleyVtmSWyoD2QyoLhSi/zyam62P6G4UQssBMBOH0d2y5Yt2L9/P/f34sWL0blzZ/znP/9BYWGhqIVzBVwg24D3Cj1hAUYn3Vxhd33Gnyv1ozxAWEta/Ud5tg4W48+V6oDX64UNmQTwB/Bmb0JsbQfjHFOpgii7toE32yJruPAJCgId2JIm1bHAjVwg0X4ktKc5YOicxraE1eVnWv8NvmolN1f6VYkGUr8iYHpdVv0ARMixzI2AUduKIwVbU9MaMzkf2XNOlShdS6dnBM3GBBhuQj1qGzPYR9TBVoZ9AgwjYLQIlTbn3d6ndMbvCfK2PMEMyxHTBdtzY+2r5L/H1s0EYJSuJdF+VKXV4UYhm+Nr/TcYj1Mv9JxqeDphGK+8YdeF2tQCC686fWavWbNmcZ26zp49i5kzZ2LYsGFIT0/HzJkzRS6e87HjyDbkvGzXAe9T18sWsC8INO4dLTahj8GAujJramofRdpx4ZDqpCV0yCSAP4C3PdtB6pZxe7YB2xlEaHoKUNeSdquoEpXV0nSWEjJiAcu4x7nx39YkSByMC+1pDtTmi5v8BgFPJ8KlHf6pITcT9myD+iNgSEFovjtg7jcI2QbStgbeKqzkpsBtEmR+YhOWTCbj0oLY3yCsVVnac+pVoxsiW9indFz5BVwT2OtaTqkGJVXahhbTKiGT/LDY7LKy2kkm7LquSXRTej3fMIKCv6cSoQIC6+B6x4KtlDmgcQ1ldS2yFlZwdotseno62rZtCwD49ddfMXLkSHz00UdYvHgxNm/eLG7pXEDdhrA/krXrsXy9E5SQk663hxLREk6HV1FdIzjHFzA9wAVdOCR+jMRe+FqEWh+4m1W/1cYVWsYb0gLCEtIKFezjwV0wpRhIXadnuM8VlqJSfxvY3o8SwgzDPaVJNDuW0J7mrPplFrIdpB7Fw74ba1W9v22XX+oRMGp0em4/sjYZAsvkfGTXsSzRNqg9T8eG+AjKX63/G2yNWgA44Dc04IbI0t/m+HuquNQXKYJxoRMJsHyU/POJrVZxgN/pToqnE0KmjTdWf98X1kDT8P2oLkfWQmqByJGs3YGsh4cHKioMTdr//PMPBg8eDAAIDg6+I4ff4saRbUiLrB35XA056QLSjsVqPPyRkPLUb6kSFATWHvAZjZgOzxohU9MaMw4EPZRyeNvI5wKkT4+wL0+53jYQcDPBdk4wfJf4v+F2USU0NXp4KORoGmQ7Z69+mW21gAJAkyDDuMrVNXpuWB0x2dOCA/AvFEq5DH5q290R4iUeuaAhT1e4v4WejyS8qTMMZ8TAU2V5mmZj9W+khbRmssfyraJKSYZys+fJBMDfDh5Kuc38UuPPlno/sifdzNLflkg5ckFOqQZlmhrDkJI2ntIBppP62Bq1AABahBhyTEuqapBXJv7TUntiC4Bf7zIZbOY2A40buYCx0dvL6TmyvXv3xsyZM/H+++/j6NGjGD58OADg8uXLaNq0qbilcwHc8Ft2vk9v1Aol5ICv37NcyEkXkPbu254WHMBMK5SAIErqoUrs6eUM8C/YwQLyuQBpH+VpanRcvquQ1kB/z7rxfAF7AhDpgnH2M1uEegtqhWpIqzg7O5bx94nJnpsJgF/mIB+B+5GEj7WNp9cVsh8Z54sDwm6IDJ/duGF7rGG3QVyo9WmaWSatUAL2oxCfuqHcpHg60Zj9SOj5qK5lX9qnK0Jaxf09VTDeVLaGlGRx+5EETxqNh5QUMgOdd717UCH7kadKgZjam3ZJr80NuCEK8FJBKeDpZKNSC2zkyIrN7kB28eLFUKlUWL9+PZYuXYomTZoAADZv3oz7779f9AI6W0NbZDNLqlCp1UGlkAnqOeqpUvDutl0jAKm7cAjRkMdIhmlepfsNQofeYhnfQNjbCnWrqBIV1ZZnMmuIjPwKrqe5tVnJWMbj+QLCb4ikHEu2MRdvQNh+BEg7Va09j1MB0xsiIdjPvi7B04ncMg1Kq2ogl8FmrjhgGJjeuNXGFVpkhUxNa6x+vQu5IZJ6Ygd7Om4C/MBPaGsm+9nGY5KL5WZhBap1eqiVckQLaBWXy2W849fuBhoJnjRy28DG9Los33qpBUJaZAFpn9Q19oZICPazGzLRjK0cWad29qqpqcHu3bvxzTff4PTp03jyySe51z7//HMsWrRI5OK5gAYOv8Xe9TUPEZabCdS7+Nl54ZCiNTDNzru+xj5GkuI3CB16i8XfBsJaD4J8PLjfKvZvMJ4KUuhYfo0JxqV4lGd3EFjvRBsmcOgXqQJZe3qas4yfRghthTJ+OiF2egQbEMQEe9scjo1lvB8JPpYl7HRnbyBbf6B7oTdEUgZRaXYeCw3ZBn6eKu6mV+yWcfZYjg0VluML8M9BQtKEAGmfTgidXpdV//B19nWNYRi796OgBlwTQn094O+pBMMA1/Lt+w22c2TFZVcgq1Qq8eyzz0KjkW7WE1fDtcja+T57H8sD/J1N+F1fbStOQQWqa8RtxbH3rs+4/HKZ4TG3EFKlRxRXaJFXO0OP0N/Q1KgnsdALn+Hz2UdhYgey9p2wgHp333a27KfnlUMvcmcpe/MCjcvsq1bC30vYfiTVEFz29DRnBTUgAJHz0iPE3Y/qpjgWvh81KAAxejoh9ggY9gy9BcCkxdDWxCAsqYIow/mIndikITfW9pyPpGkZT7MzCAT4wbjQcW3Z89G1/HLUiPx0wt5rs0+904/deb4i70fZJcbTxgurT+Mhw4Sk/AG1Tyca2AfH9sxeTu7s1b17d5w8eVLUQriyhk5R25AAxMuopaR9kwBB74nwV8PHQwGdnkFGgXgnLb3e/rs+4wNcz0BQHpvx54t9wF/JNQxgHRXgCR8BnW2MywLYF8hyeWkit0Q15IaIl58p8DfEBHtDpZChUqtDZu0g/mJhg/uGpKgYcjXt3I9E7inMtUIJ7GkO8LeBPYPSS3UssBcioY9TAfAukkIfRwb7eHAXSjHzGxmGQZqdaUL1j3mhT8bYOhI7P5MdsSDS3xO+As9HDbkpBSTcj9jzkR37kfHkHkLPY9EBXvBUyaHVMbhRO4i/WOzp9AiYBrJCn2hIlaLCfl5zG9PGG2tnNNuh0JtSoO6c3dCWfYuBrLM7ez3//PN4+eWX8eWXX+LQoUM4c+YM7787TV2LrH0XxrqencIDWXaoK8AQfAlhfNd0RcRHYbd4Pc2FtUL5qJXo1NQQgLezY5pQqYYqYS/e9kwRaHyi9VYLO2EBRr9B9JOW/ftRQy5+KoUczUPE7+1cXKnlxhQVmhdoPIOUrdmkjMWG+kBe21M4t0y8p0b2dqwA+DelIztGC36fVCkedb9B+H40omMU92+hrZmANAPaZ5doUFrb05wd8F8qxq1QYj6dsPfJBNCwln3AKD9T7P3IjjFkWRqjJ4VCb+oMTycaF0SZY++QkgCgkIMbq3VkJzuO5do6ulko7ggY9g4FCIA3bXdZlfB+HA29rnE5sg7q7iXsttDI+PHjAQDTpk3jlslkMm4+Xp1OmgHVnUXWwNyChrSk8b9X+A4QH+aLMzfFnQ6PbUVrHuItqIcj6+dnkvDDwWvo1iJY8HvqD1VSP7etoa40oFXc+LtLKu044CV4lFej0yM129CqnGjHhcP4caTQ/EzA0BJ1JacMV3LK0LdlmPCCWsFehCL81fDztH/aVSHDn7E8VQrEBHvjen4FruSUIdxPnOmC7U2xAYCOMYFQymWIDvRq2E2dVC1pdvyGvi3D4OOhgFwmbJgiFjddsIi/4WKWYWjHuFAfQT3NG6NZsDeUcsPTiaySKkGdmoRgz0dCn0wADW+RTQw3jKvM1ptYGrIfGU/WI7RV3PAdPriYWYKruWUY2CZCeCGtqJupTiU4VxQA5o9rj4zCKkzs2Vzwe0J8DDmmJVU1uJZfjtaRws8D1jTkhsj4mLGnI2lDr2vcqAUOGrbA7kA2PT1dinK4LG5mLzveU1qlRXYJ2wol/ICf/2BHvLLuNN4f3d6eItblZ4oYRNk7Th3LU6XAM/3iAQBarbAes+x0eDcKKpGWWyZeIGvHLEAs4xsIoS3RAP9RXkV1Dbw97D60TFzOLkNFtQ5+aqV9ObLGHY3seIzUOsof2y5k49ytYrvKaU1KliEQt+fibcxL4GM8VkKYL67nV+BqThl6xYc26Dvra8jFu0mgF3a90h/+dqRGGH/H1dppXu15ryVVWp1RK5Twi59KIcfeVwdAq2Pgb8dNCPsbxOx0dynTsB+1jrIvGPD2UKDCzlxdw9MJb1zNLcelrBLRAln2uGod5Sf4PcbBltB+EwDQMSYAMhlwo6ASeWUahArsMGlNZnEl8muD0lg7UgtaR/rhbAPOKVJ0urO30ybrnsRQqFT23YizT0tPZhThao54gWxDf8P8Bzpi4Y5UzLivpeD3GF/X7Dkf2XqwKnaAa/fVNiIiAp6e4rR0uAMZN2qB8PccTS8AYEgPEDLwMKt3QigOzR5oT/EA1AXLp28WYcu5LLPreHsokBQfwrsjPnerGDfN5h8xWJd8E4B9d32NER/mixsFlbiUVYoecSGifCY3hqydB/zPT/fElvNZmNK7heD3NA/xRvMQQ2vglzuvoGPTQJN1PFVy9IoP5eU16fUM9l/JM3uxPZyWDwDoFBMoON8YAIJrL1p+aqVdLSBdmhnKfPJGkeD32HLgquE33N0iyK73tY3yx4XMEjxyd4xd70sI98WOSznYczkPYWZaZL08FEiKC+FtA52ewZG0fJRU1UCnq8HpfBmUF7KhUChRpdXhZEYhAPsCEMC+3FhWbKjh6URxpRYF5dUIESEAScstB8MY8o3tadUD0KDvZ4PNPZdzselsJuS1J1Hjug0P8Ea35kG8C2NltQ6H0vJQXWN6st2Xmmv47Ej7tsFz/eLx6fbLuK+tfS163ZoH42puOQ5eyce9rRvfGqjTMzh9wxDMdYkRfizwR78Qvu38PVVICPNFak4Zfjx0HW3M3AAEeKnQMy6Ytw0qqmtwOC3f6jbo3iJYcJ8DAHh9aGt4eyjwsJ3HshSd7lJqW6jtDQIbKj7MEMj+ePiaxfz6+DAfJEbU7ddVWh0OXc3npWQYu1h7U2fv096H746xexs0D/GGWilHRbUO526VoENTYX136obfcszMXnYHsuHh4Rg7diwmTpyIgQMHQi63O83WLVXWy3E5mVGItUdvQGcmwj1zswgAMLR9lMlrUjAedujZn5ItrvfGsNZ4uq+htTQ1uxQjvthv9XNlMqBfy3DxCmrF3S2CsTslF0t3X7V49969RTDvQDx7sxirjlzn5o+ujx3CyN5gvEdciN3BtEwmw5jOTbBwRyqW7L5qcb3pgxIxfVDdHfHSPVfxv60pVj+7c0ygXWUJrb3ghQiYzpL3PbXBd3peOWb+cooLQIx1ahqASUkteMv+OnMbu1NyzX7m7ks5AIA+ifalKqz+bw+cu1WC3gn2bQf24vfPxWz8czHb7Drdmgfhp6d6cJ02Npy4iVnrjfP7Ffj+8mnee+5JDBWtRcUaT5UCTQK9cLOwEo99f9RsACIDMLZLE/RKqGtx3nIuy+LvPVEbiMeH+YjSwmtLn4RQtI70w6WsUjy/6kS9V+vqduUT3bkUlqKKajz81SFczrYetLSx82biuf7x6BQTiLua23cj1ScxFD8fv4HfT99GkZmxWGUAhneMQv9WdedHvZ7B0j1XzU6kUKnVoUxTA28PBVpG2NcB2EulQKVWx+VpCtWlWSBSc8qwcEeqxXXmP9gRD3erO6d+8PdFrD6SYfVzx3RpYlc5QnzVeM/Op4xAXae7C5kleGXdabPreCjleLJPLC8w/XZfGi7VPgmqjz1G7o4VnvrWGPe2Dsf65Js4nFaAw2kFZtfxUMix//UBXCrU/7am4Lv9tp98N/Qplz1UCjkGtY3A32cy8ebGs2gZYXr8qRRyPNG7BS8Yr6iNlyydbZzeIvvDDz9g9erVGD16NAICAvDII49g4sSJ6Natm7glcxFsC8aS3VcwqE04dyF4a+M5nL9tPf9o3F32HfANlRjui8d7tbD4SDivTINr+RU4mVHELWNb3QK9VWZbLD2UcjyW1ALdHXTAT+7VAssPpCOrpArra1uD6/v1xE0MaB3OpR58tOkiDtW2WloS5qcWPA5pY03u1QIXMkt4OWGs0qoapGSXYvuFbC6QLaqoxpJdVwAA7Zv4w9NM7p+/lwrju9t3F92tRTAeuKsp7km079F6kI8HWkX4ISW7FBtO3DK7zvrkm0gI90NSvCHALCyvxoyfT0Grs/zIIsBLxbX2ChXo7YE+dpYfAO5vH4ldl3K4DmbG9AyDExlFOH69ENsuZGNUbceNA1fyABhyI8N8PVBQWIjgoLrWQm+1Eu+Namd3WRrqmb5xePv38zh/u8TiOeZIegH2vjoAgCGAevmXUyi38ghdLgP+e0+cJOWtTyGXYd4DHTF/6yVotHWtSgzDoKCwEJUyT2SVaHDgSh4XyK5PvskFsV2bB5m9AMYEe6NPgn03REqFvEH53r0TQuGhkCO3VGPxfLT9YjaOvjGIa93/88xtmzelXZsH2dXnQCaT4bX7WyEtr9zuVsQn+sQis7jK7DBo7Pno020pGNO5CTyUcjAMg+0XDIFeu2h/s2k9UYFeGGtnINtQ8WG+8FQZWgMtbQMAKNfUYOH4LgCAi5kl+ODvi1Y/Ny7MB2M6C++01RjDOkRh/oMdsT75ptmOgylZpSjV1ODszWIMbGMIZNknQInhvhaf6PZJDLWrhb4xHuzaFH+fycSZm8U4c9N8jJFVXInlU7oDMOTgfrsvDYDlob7Evp22O5AdO3Ysxo4di9LSUqxfvx5r1qxBz549ERcXh4kTJ+Kdd94RuYjO9frQ1hj15QGczChCTqkGEf6eyC3VcBeYVwa3NHtiig/zFTyEVmPJZDL8n5UL7e6UHDy+/BjvEQ2bvzamcxOr73UUX7USPz3VA3tScs3mI/946DpuFVXi1I0i7jFhao7hrvupPrEItZBX2ych1CGtUIDhpuebx8zf0OWWanD3h//g/O0S5JdpEOKrxomMQpRX69AixBt/TO1jV/qANR5KOT59uFOD3rt4QhfsuJhjdhscTS/Azks5+GJnKhfI/nXmNrQ6BrGhPhbTAJLiQuxKcWgMf08Vlk7savH119afwc/Hb3Cd6IC6m7oPxrRHUmwgNm3ahGHDutudEyeWSUktEBfma/bJhE7P4H9bU5BRUIGSKi38PVW4WViJ8modPBRyzBxsPv+tR2wwujSzr1WyMTrFBGLVUz15y7RaLTZt2oTKyAS8/tt53o315drtMe3eBMwc3Mph5bQk2McDPzzRHadrn67V993+dOSWarArJQdD2kUCAL7YabgpHdkp2mwHP6VchvvbR9pdlsd7x9r9HgBoHemPH5/sYfY1TY0O3T/cgewSDS5nl6J9kwCkZJcit1QDL5UCG57vJXmnOlu8PBRY+UQP7olCfdfzy7Hm6A1eK37ydcO6LSN8Me6upibvkcuAIe0i7bqZaKyHu8XwWr2NTV97EhtP3calrFIMbBNRO5W0oUV/0aNdzD6RcbT+LcOwcHxnZBabDstYUqnFkt1XcTitAJoaHdRKBS5mluB6fgUUchleGmT+fCT2NbnBPVL8/PwwZcoUTJkyBRcuXMCECRPw3nvv3XGBbMemgYgN9UF6Xjmu5pQhwt8TB68aWnDaRvnjhXsTnVxC29g7+Wt5hulOFXIZdyG3Z2gqqbWO9Lf4+DY9txw/H7+BkxmFuK9tBIoqqrnBxWfc19KunC1nCPNTc49bj10rwP3to7hODO2aBIgWxDZWQrgfEsLNP74d0CocOy/l4OzNYi7xf/tFQ+rAf7o3w3/7OqbFrzESI/h5d/llGlzPN6SgdLIzhUNKvRNC0TvBfIv0T4evI7O4CilZpbi7RTA3XnJcmA+ere1o6crYIfrO3CqCVqeHSiHnLt6JZh5dOktSfAh3w1ZfQXk1vt6bhu0XsjGkXSTKNDVc48CcUe0c1lrWUGqlAq0i/HD0WgGu5pahfZMArm9H99hgpwexrO6xwRafCl7LMwSy6Xll0OsZyOUy7ubo/naRbnEstInyx8ZTt3Eh09AwVlBejeJKLWQy+zrUSUkmk2F0Z/Ot8AzD4JfjN5FXpkHy9UL0ig/lzq3dmgc57Dc0+LakqqoKv/zyC8aMGYO77roLBQUFmDVrlphlcxn1BzZmD5aeInVKklp0oBfUSjmqdXrcLDRctNmhYOwZ1smZuI5ItXXPXvii7ZjswNnYsfzYsjekN7wztQj1hlwGlGpquEf37OgWdzUPdGLJhGNzaNkRPtiOEy1CvO3qmOlMbCvNpdqLX2q2fTNeOVtcqA98PBSo0uq5fFJ7J19xtrtqz0dsg0B67f4U6uvh8kEsq26oN0PZ2UC8rR3DxTlT0yAveCjkqNLqcbvY0Gn51A1Di2xnO1OZnKV1vWOZ3RZNAr0ET7zgTDKZDD3jDDcabGdGIWMNO3WKWgDYunUrJk+ejIiICDz33HOIiIjAtm3bcP36dXz88cciF8811B9LjR3+wp6kfWdS8Ka+NAwPxY5W4EotINa0iza04rDpBA0ZFNrZ6g9239ixhh1NrVRwsz1dqd2P2GGdHNHxQAzx7CDreeXQ6Zm6kS0stEK7Irbn/sXaDi2pOexNqXv8BrlcZnRDUYaC8moUVhg6VLlKK5Qt7P5ypXYSF3c8H3GzEebWPx+5x29Q1g6TBhiuzZoaHXdjxF4vXB0bQ1zPr4BWp3e7bQDUnXfS7NmPnD2z19ixY1FZWYmVK1ciKysLX331Ffr27StuqVxMXL0W2asNGJ/U2Yxnq2GH4wn28bB7OB5nYbdBXlk1iiqqjaYZdI8LH2A6baS9UyW6AuObOrb8wT7u0wrVJMgLHko5qmv0uFVYWdcS6KBh5sSQYBQEAkaBrJvcWAP8/Yg9HpoEetk1k5szNQ8xTJpQXq1DZnGVWwYgxjMqGv7vxufUnDJcz6+AnjH0twgXaSxyqUX6e8LbQ4EaPcONfw246X5U77omdCZHMdj9TDY7Oxt+fu5x5y8WdqdKyy1HZbXO7inuXIFxEBXqZwg63OVRJGCY/jYqwLP2olF38XOnm4kEo0d5heXVDRpc3Nniwnyw45IhiPL3NJw+3OnCp5DLEBfqg0tZpbiaW9agWbucrf6kCVdz3CtNCOCna7ED/bvTsaxSyBEb6oPUnDKk5pS59Y11el45Sqq0yCoxdOZxp1bl+HAf4LxhP2KndXfUMHNikMlkiK/t2Gk4H7nfjbXx+UinZ7hWcWvjtzu9s5efnx90Oh02btyIixcNw1y0bdsWo0ePhkLhHnfT9mI31K2iSpy/bcgDCfK2f3BxZzKe/YsdX9SdLnyAYTtkFlchzfiAd6OTbrNgHyjkMpRparhhw9wpxxfgn7TYKWfdaRsAhvLWBbLutx+xLR25pRpczi5DmaYGCrkMzUPc8+LHBbJuFAQChhbw1JwypGaXuuV+1DTIGx4KOTQ1euxPNXRgDvVVu02uOMBvoGFnYHOnbQAY9ns2kGXTFt0lVQvgT+Jy6kYRqnV6qJVyqzPiOT1H9sqVK2jTpg0ee+wxbNiwARs2bMCkSZPQrl07XL1qeSB4dxZk9Ah+e+2Ayu53sNQd8GznEHcLZNkLeEpWKTJqe5q703bwUMq5HFN2vEZ3aoUCjFNU3DMIBOr2o7O3irkhZdwpiPLzVCHC3/DodOt5w0x+LUK8ebOVuTouRzanrlXcnVoCgbo82cvZpQ2eNtSZjPtOcOcjNzoOAP7TUndM+QPqfsPFzNIGT+LjTOwU8wCw7YLhfBQb6mNxJjNA/AkR7D7zTZs2DfHx8bhx4wZOnDiBEydOICMjA7GxsZg2bZq4pXMh7AHOHvCOzP8QA3vCyi+vxrFrhmFW3KWjF4s94Hem5KBGz8DHQ8Fd0N2FyX7kRmkFAP/pxIXasZTd6aQL1P2Gf2q3QYiPBwK93efpClD3G9gpqd2loxereUjdCBhHap9OuFsQxaZm7bmci+oaPTyUcjQJstwK5YrY69i22hsidwsC2fLnlGpwqnY8aHfbj9g633kxG3oG8PNUOmwSH7GwLcjOaqCxO5Dds2cP5s+fj+DgurHdQkJC8PHHH2PPnj2iFs6VsBvKHTvoAIYc0+jaHCK2h7A75cgC/LtvwNCC4y65UCz2N5Rpagx/u9k2CPbx4GZrSXfDx2BA3TZgZ8Jyt2MZqCszO/6kO3X0AgwjYMTUPp1gt4O1nDpXxD7Ryi4xDEUXZ6MVyhW5+7Hg56niOnZxj+Xd7DewwbjxNnDX6xoXH9looHF6aoFarUZpqek8xmVlZfDwcK9WDXvUb3VytwMe4AdNfp7u07OTVb8V3N3uvAFzv8H99iPjC4WHQs49VnIXJtvAzVqUAdPf4G43pQB/3/dTK7mpp91FbKgPjONWd3tKB5ju+275G4z2I7kM3JBc7qJFiA/vUbs7XhNM4iNb5yORA3W7A9kRI0bg6aefxpEjR8AwDBiGweHDh/Hss89i1KhRohbOldTfudytJQ3gP8ZODHe/uz52qBKWOx7w9ccrdcffYHwD0SLU26HTPYqBHQGD5Z7bgF9m9wxk6/ajODc8H3mqFLwOdnfCfuRu/SYAfhDVLNjbZWYlE8pTpUBMUF3w7e43E+b+rs/pLbKLFi1CfHw8kpKS4OnpCU9PT/Tu3RsJCQlYuHChyMVzHcYbxletRIybtUIB/OC7pZvlxwKGgdSND3J3e4QEwGQOdnfL8QX4x4IrzAXeEMaBbGcXmppWqPo30u4YRHVpFsT9291yxVnG9e6O24D3dEUpRxMrPc1dlXFqkztuA4B/I+qOv6FNvanlbQ0pKfY9q93j/gQGBuL333/HlStXuOG32rRpg4SEBHFL5mKMH5+6YysUAHRoYpjtRCmXYUrvWCeXpmHGdG6Cc7fcs5MRAJNpB92tFQrgn2hbR7pnIKvTM9y/uzYPsrKma4ry90SgtwpFFVq0jPB1i+ks67u3dTj3b5XC/Y4DwJCb/I+bjmQDGBplWP6eKrc8Hxk3bozu0sSJJWm4Z/rGYeelHMhlpo0d7iDAmz9km60hJWUit8kKDmT1ej3+97//4Y8//kB1dTUGDhyId999F15e7ncH1xDGgWvP2BAnlqThujQLwo9PdkdCuC+iAtxzu03s2Ry/n7qNKq3O7ToZsVpF+CEluxShvu6ZU27cGtgq0j23wav3t8bk74/i/THt3fLiLZfLsO6ZJCRfL0TvhFBnF6dBPFUKzBjUEkt2X3HbG2vjR/Hu+EjY2N0t3O+GDgDuah6ESH9PJEb4YkSHKGcXp0F6xIVg1yv9kVuq4TpBupv+rcKwOyXXKd8tOJD98MMP8X//938YNGgQvLy8sHDhQuTk5OD777+Xsnwu5atJXbH1fBZm3NfS2UVpsHsSw5xdhEbxVCmwcWpvyGXu2ZoJAN9O7oaPNl3Ei/cmOrsoDWKcVuOuLbK9E0Jx+YOhkLtZL3NjiRF+bjeEXn3TBiZg2sAEtz2W20UbnnI1D/F2q4lNjK35b0+sOnId749u7+yiNIi/pwoHX78XANz6eI4N9XGrWR7r++ShTvi/P85jYs/mNtd1WmrBypUrsWTJEjzzzDMAgH/++QfDhw/Ht99+C7nc/R6zN8SQdpEY0i7S2cX413O3IW7qiwn2xtKJXZ1djAZTKuRY92wSyjQ1VmdvcXXufNG7U7hrAMtqFemHJRPucrue8saS4kOQFO+eTxlZdCw7X6ivGl/+5y5B64q9tQQHshkZGRg2bBj396BBgyCTyXD79m00bdpU5GIRQlzZ3S2Cba9EyL/AMDd9nE2IszhtZq+amhp4enrylqlUKmi1WnFLRAghhBBC7lBO6uzFMAwef/xxqNV1wwVVVVXh2WefhY9PXV7Hhg0bRCvc3LlzsWHDBly6dAleXl7o1asX5s2bh1atWvHK8PLLL2Pt2rXQaDQYMmQIlixZgoiICNHKQQghhBBCGs9pLbKTJ09GeHg4AgICuP8mTpyI6Oho3jIx7dmzB1OnTsXhw4exfft2aLVaDB48GOXl5dw6M2bMwJ9//ol169Zhz549uH37NsaNGydqOQghhBBCiOsR3CK7fPlyKcth1pYtW3h/r1ixAuHh4UhOTkbfvn1RXFyM7777DqtXr8a9997LlbNNmzY4fPgwevbs6fAyE0IIIYQQ85w+s5czFRcXAwCCgw0dTZKTk6HVajFo0CBundatW6NZs2Y4dOiQU8pICCGEEELMc/rMXs6i1+sxffp09O7dG+3bG8a7y8rKgoeHBwIDA3nrRkREICsry+JnaTQaaDQa7u+SEsNMUVqtljqviYitS6pT8VHdSoPqVTpUt9KgepUO1a17cJtAdurUqTh37hz279/f6M+aO3cu3nvvPZPlu3btgre3+44H6Kq2b9/u7CLcsahupUH1Kh2qW2lQvUqH6lZcZWUa2yvZwS0C2RdeeAF//fUX9u7dyxuzNjIyEtXV1SgqKuK1ymZnZyMy0vLEBbNnz8bMmTO5v0tKShATE4MBAwYgJMS9B4Z2JVqtFtu3b8d9990HlUpl+w1EMKpbaVC9SofqVhpUr9KhupXGogvbRP08lw5kGYbBiy++iN9++w27d+9GbCx/Pu6uXbtCpVJhx44deOCBBwAAKSkpyMjIQFJSksXPVavVvGHEWCqVinZWCVC9SofqVhpUr9KhupUG1at0qG7FJXPWOLLOMHXqVKxevRq///47/Pz8uLzXgIAAeHl5ISAgAE8++SRmzpyJ4OBg+Pv748UXX0RSUhKNWEAIIYQQ4mpE7u3l0oHs0qVLAQD9+/fnLV++fDkef/xxAMDnn38OuVyOBx54gDchAiGEEEIIcS1iD7/l0oEswzA21/H09MTixYuxePFiB5SIEEIIIYQ0mLNm9iKEEEIIIaQxnDZFLSGEEEIIIY0hdmcvCmQJIYQQQohbokCWEEIIIYQ4xPuj2oj6eRTIEkIIIYQQh4gP8xX18yiQJYQQQgghbokCWUIIIYQQ4pYokCWEEEIIIW6JAllCCCGEEOKWKJAlhBBCCCFuiQJZQgghhBDiliiQJYQQQgghbokCWUIIIYQQ4paUzi6AK2AYBgBQWloKlUrl5NLcObRaLSoqKlBSUkL1KjKqW2lQvUqH6lYaVK/SobqVRmlpKYC62KuxKJAFkJ+fDwCIjY11ckkIIYQQQu58+fn5CAgIaPTnUCALIDg4GACQkZEhSqUSg5KSEsTExODGjRvw9/d3dnHuKFS30qB6lQ7VrTSoXqVDdSuN4uJiNGvWjIu9GosCWQByuSFVOCAggHZWCfj7+1O9SoTqVhpUr9KhupUG1at0qG6lwcZejf4cUT6FEEIIIYQQB6NAlhBCCCGEuCUKZAGo1Wq8++67UKvVzi7KHYXqVTpUt9KgepUO1a00qF6lQ3UrDbHrVcaINf4BIYQQQgghDkQtsoQQQgghxC1RIEsIIYQQQtwSBbKEEEIIIcQt/esD2cWLF6NFixbw9PREjx49cPToUWcXye3s3bsXI0eORHR0NGQyGTZu3Mh7nWEYvPPOO4iKioKXlxcGDRqE1NRU5xTWjcydOxd33303/Pz8EB4ejjFjxiAlJYW3TlVVFaZOnYqQkBD4+vrigQceQHZ2tpNK7D6WLl2Kjh07cuNDJiUlYfPmzdzrVK/i+PjjjyGTyTB9+nRuGdVtw/zf//0fZDIZ77/WrVtzr1O9NtytW7cwceJEhISEwMvLCx06dMDx48e51+ka1jAtWrQw2WdlMhmmTp0KQLx99l8dyP7888+YOXMm3n33XZw4cQKdOnXCkCFDkJOT4+yiuZXy8nJ06tQJixcvNvv6/PnzsWjRIixbtgxHjhyBj48PhgwZgqqqKgeX1L3s2bMHU6dOxeHDh7F9+3ZotVoMHjwY5eXl3DozZszAn3/+iXXr1mHPnj24ffs2xo0b58RSu4emTZvi448/RnJyMo4fP457770Xo0ePxvnz5wFQvYrh2LFj+Oqrr9CxY0fecqrbhmvXrh0yMzO5//bv38+9RvXaMIWFhejduzdUKhU2b96MCxcu4NNPP0VQUBC3Dl3DGubYsWO8/XX79u0AgIceegiAiPss8y/WvXt3ZurUqdzfOp2OiY6OZubOnevEUrk3AMxvv/3G/a3X65nIyEjmf//7H7esqKiIUavVzJo1a5xQQveVk5PDAGD27NnDMIyhHlUqFbNu3TpunYsXLzIAmEOHDjmrmG4rKCiI+fbbb6leRVBaWsokJiYy27dvZ/r168e89NJLDMPQPtsY7777LtOpUyezr1G9Ntxrr73G9OnTx+LrdA0Tz0svvcTEx8czer1e1H32X9siW11djeTkZAwaNIhbJpfLMWjQIBw6dMiJJbuzpKenIysri1fPAQEB6NGjB9WznYqLiwGAm586OTkZWq2WV7etW7dGs2bNqG7toNPpsHbtWpSXlyMpKYnqVQRTp07F8OHDeXUI0D7bWKmpqYiOjkZcXBwmTJiAjIwMAFSvjfHHH3+gW7dueOihhxAeHo4uXbrgm2++4V6na5g4qqur8dNPP+GJJ56ATCYTdZ/91wayeXl50Ol0iIiI4C2PiIhAVlaWk0p152Hrkuq5cfR6PaZPn47evXujffv2AAx16+HhgcDAQN66VLfCnD17Fr6+vlCr1Xj22Wfx22+/oW3btlSvjbR27VqcOHECc+fONXmN6rbhevTogRUrVmDLli1YunQp0tPTcc8996C0tJTqtRHS0tKwdOlSJCYmYuvWrXjuuecwbdo0/PDDDwDoGiaWjRs3oqioCI8//jgAcc8FSpHKSAiR0NSpU3Hu3DleThxpnFatWuHUqVMoLi7G+vXrMXnyZOzZs8fZxXJrN27cwEsvvYTt27fD09PT2cW5owwdOpT7d8eOHdGjRw80b94cv/zyC7y8vJxYMvem1+vRrVs3fPTRRwCALl264Ny5c1i2bBkmT57s5NLdOb777jsMHToU0dHRon/2v7ZFNjQ0FAqFwqSHXHZ2NiIjI51UqjsPW5dUzw33wgsv4K+//sKuXbvQtGlTbnlkZCSqq6tRVFTEW5/qVhgPDw8kJCSga9eumDt3Ljp16oSFCxdSvTZCcnIycnJycNddd0GpVEKpVGLPnj1YtGgRlEolIiIiqG5FEhgYiJYtW+LKlSu0zzZCVFQU2rZty1vWpk0bLm2DrmGNd/36dfzzzz946qmnuGVi7rP/2kDWw8MDXbt2xY4dO7hler0eO3bsQFJSkhNLdmeJjY1FZGQkr55LSkpw5MgRqmcbGIbBCy+8gN9++w07d+5EbGws7/WuXbtCpVLx6jYlJQUZGRlUtw2g1+uh0WioXhth4MCBOHv2LE6dOsX9161bN0yYMIH7N9WtOMrKynD16lVERUXRPtsIvXv3NhnW8PLly2jevDkAuoaJYfny5QgPD8fw4cO5ZaLusyJ3SnMra9euZdRqNbNixQrmwoULzNNPP80EBgYyWVlZzi6aWyktLWVOnjzJnDx5kgHAfPbZZ8zJkyeZ69evMwzDMB9//DETGBjI/P7778yZM2eY0aNHM7GxsUxlZaWTS+7annvuOSYgIIDZvXs3k5mZyf1XUVHBrfPss88yzZo1Y3bu3MkcP36cSUpKYpKSkpxYavfw+uuvM3v27GHS09OZM2fOMK+//jojk8mYbdu2MQxD9Som41ELGIbqtqFefvllZvfu3Ux6ejpz4MABZtCgQUxoaCiTk5PDMAzVa0MdPXqUUSqVzIcffsikpqYyq1atYry9vZmffvqJW4euYQ2n0+mYZs2aMa+99prJa2Lts//qQJZhGOaLL75gmjVrxnh4eDDdu3dnDh8+7OwiuZ1du3YxAEz+mzx5MsMwhuFL3n77bSYiIoJRq9XMwIEDmZSUFOcW2g2Yq1MAzPLly7l1Kisrmeeff54JCgpivL29mbFjxzKZmZnOK7SbeOKJJ5jmzZszHh4eTFhYGDNw4EAuiGUYqlcx1Q9kqW4b5pFHHmGioqIYDw8PpkmTJswjjzzCXLlyhXud6rXh/vzzT6Z9+/aMWq1mWrduzXz99de81+ka1nBbt25lAJitL7H2WRnDMEwjWowJIYQQQghxin9tjiwhhBBCCHFvFMgSQgghhBC3RIEsIYQQQghxSxTIEkIIIYQQt0SBLCGEEEIIcUsUyBJCCCGEELdEgSwhhBBCCHFLFMgSQgghhBC3RIEsIeRf5fHHH8eYMWPses+KFSsQGBgoajmuXbsGmUyGU6dOifq5Ypdh9+7dkMlkKCoqavT3TZo0CR999FGjP6chevbsiV9//dUp300IkQ4FsoQQ4gQxMTHIzMxE+/bt/xVlOH36NDZt2oRp06ZJ/l3mvPXWW3j99deh1+ud8v2EEGlQIEsIIU6gUCgQGRkJpVL5ryjDF198gYceegi+vr6Sf5c5Q4cORWlpKTZv3uyU7yeESIMCWUKI061cuRIhISHQaDS85WPGjMGkSZMsvu/s2bO499574eXlhZCQEDz99NMoKyvjXtfpdJg5cyYCAwMREhKCV199FQzD2CzPihUr0KxZM3h7e2Ps2LHIz883WWfp0qWIj4+Hh4cHWrVqhR9//JH3ukwmw7fffouxY8fC29sbiYmJ+OOPP7jXzT3WP3fuHIYOHQpfX19ERERg0qRJyMvL414vLS3FhAkT4OPjg6ioKHz++efo378/pk+fbvG3tGjRAjKZzOQ/S2XYtGkTWrZsCS8vLwwYMADXrl3jfV5+fj4effRRNGnSBN7e3ujQoQPWrFljtT51Oh3Wr1+PkSNHmpTtgw8+wGOPPQZfX180b94cf/zxB3JzczF69Gj4+vqiY8eOOH78OPceNs3jr7/+QqtWreDt7Y0HH3wQFRUV+OGHH9CiRQsEBQVh2rRp0Ol03PsUCgWGDRuGtWvXWi0rIcTNMIQQ4mQVFRVMQEAA88svv3DLsrOzGaVSyezcudPse8rKypioqChm3LhxzNmzZ5kdO3YwsbGxzOTJk7l15s2bxwQFBTG//vorc+HCBebJJ59k/Pz8mNGjR1ssy+HDhxm5XM7MmzePSUlJYRYuXMgEBgYyAQEB3DobNmxgVCoVs3jxYiYlJYX59NNPGYVCwSsrAKZp06bM6tWrmdTUVGbatGmMr68vk5+fzzAMw6SnpzMAmJMnTzIMwzCFhYVMWFgYM3v2bObixYvMiRMnmPvuu48ZMGAA95lPPfUU07x5c+aff/5hzp49y4wdO5bx8/NjXnrpJYu/Jycnh8nMzGQyMzOZmzdvMj179mTuueces2XIyMhg1Go1M3PmTObSpUvMTz/9xERERDAAmMLCQoZhGObmzZvM//73P+bkyZPM1atXmUWLFjEKhYI5cuSIxTKcOHGCAcBkZWXxljdv3pwJDg5mli1bxly+fJl57rnnGH9/f+b+++9nfvnlFyYlJYUZM2YM06ZNG0av1zMMwzDLly9nVCoVc9999zEnTpxg9uzZw4SEhDCDBw9mHn74Yeb8+fPMn3/+yXh4eDBr167lfd/SpUuZ5s2bWywnIcT9UCBLCHEJzz33HDN06FDu708//ZSJi4vjApj6vv76ayYoKIgpKyvjlv3999+MXC7nAqaoqChm/vz53OtarZZp2rSp1UD20UcfZYYNG8Zb9sgjj/AC2V69ejH//e9/ees89NBDvPcBYN566y3u77KyMgYAs3nzZoZhTIPI999/nxk8eDDvM2/cuMEAYFJSUpiSkhJGpVIx69at414vKipivL29rQayxqZNm8Y0b96cycnJMVuG2bNnM23btuW957XXXuMFsuYMHz6cefnlly2+/ttvvzEKhcJkWzZv3pyZOHEi93dmZiYDgHn77be5ZYcOHWIAMJmZmQzDGAJZAMyVK1e4dZ555hnG29ubKS0t5ZYNGTKEeeaZZ3jf9/vvvzNyuZzR6XQWy0oIcS+UWkAIcQn//e9/sW3bNty6dQuA4RHy448/zj0Gr+/ixYvo1KkTfHx8uGW9e/eGXq9HSkoKiouLkZmZiR49enCvK5VKdOvWzWo5Ll68yHsPACQlJZms07t3b96y3r174+LFi7xlHTt25P7t4+MDf39/5OTkmP3e06dPY9euXfD19eX+a926NQDg6tWrSEtLg1arRffu3bn3BAQEoFWrVlZ/D+vrr7/Gd999hz/++ANhYWFm1xHy23U6Hd5//3106NABwcHB8PX1xdatW5GRkWHxuysrK6FWq81uS+M6ioiIAAB06NDBZJlxvXl7eyM+Pp63TosWLXj5txERESZ17eXlBb1eb5LCQghxX87rZUAIIUa6dOmCTp06YeXKlRg8eDDOnz+Pv//+29nFahSVSsX7WyaTWew1X1ZWhpEjR2LevHkmr0VFReHKlSsNLseuXbvw4osvYs2aNbzAsSH+97//YeHChViwYAE6dOgAHx8fTJ8+HdXV1RbfExoaioqKClRXV8PDw4P3mnEdsYGuuWXG9WauXoXUdUFBAXx8fODl5SXkpxJC3AC1yBJCXMZTTz2FFStWYPny5Rg0aBBiYmIsrtumTRucPn0a5eXl3LIDBw5ALpejVatWCAgIQFRUFI4cOcK9XlNTg+TkZKtlaNOmDe89AHD48GGTdQ4cOMBbduDAAbRt29bmb7Tkrrvuwvnz59GiRQskJCTw/vPx8UFcXBxUKhWOHTvGvae4uBiXL1+2+rlXrlzBgw8+iDfeeAPjxo2zum6bNm1w9OhR3rL6v/3AgQMYPXo0Jk6ciE6dOiEuLs5mGTp37gwAuHDhgtX1pHbu3Dl06dLFqWUghIiLAllCiMv4z3/+g5s3b+Kbb77BE088YXXdCRMmwNPTE5MnT8a5c+e4VsdJkyZxj6NfeuklfPzxx9i4cSMuXbqE559/3ubA/tOmTcOWLVvwySefIDU1FV9++SW2bNnCW2fWrFlYsWIFli5ditTUVHz22WfYsGEDXnnllQb/9qlTp6KgoACPPvoojh07hqtXr2Lr1q2YMmUKdDod/Pz8MHnyZMyaNQu7du3C+fPn8eSTT0Iul1tMv6isrMTIkSPRpUsXPP3008jKyuL+M+fZZ59FamoqZs2ahZSUFKxevRorVqzgrZOYmIjt27fj4MGDuHjxIp555hlkZ2db/W1hYWG46667sH///gbVjVj27duHwYMHO7UMhBBxUSBLCHEZAQEBeOCBB+Dr62tz9i1vb29s3boVBQUFuPvuu/Hggw9i4MCB+PLLL7l1Xn75ZUyaNAmTJ09GUlIS/Pz8MHbsWKuf27NnT3zzzTdYuHAhOnXqhG3btuGtt97irTNmzBgsXLgQn3zyCdq1a4evvvoKy5cvR//+/Rv60xEdHY0DBw5Ap9Nh8ODB6NChA6ZPn47AwEDI5YZT9WeffYakpCSMGDECgwYNQu/evdGmTRt4enqa/czs7GxcunQJO3bsQHR0NKKiorj/zGnWrBl+/fVXbNy4EZ06dcKyZctMZuJ66623cNddd2HIkCHo378/IiMjBc2U9tRTT2HVqlX2VYqIbt26hYMHD2LKlClOKwMhRHwyhhEwqCIhhDjIwIED0a5dOyxatMjZRZFUSkoKWrdujdTUVCQkJDToM8rLy9GkSRN8+umnePLJJ0UuobgqKyvRqlUr/PzzzyYdyBzhtddeQ2FhIb7++muHfzchRDrU2YsQ4hIKCwuxe/du7N69G0uWLHF2cSRVUFCA9evXw9/f32oecH0nT57EpUuX0L17dxQXF2POnDkAgNGjR0tVVNF4eXlh5cqVvAkeHCk8PBwzZ850yncTQqRDgSwhxCV06dIFhYWFmDdvnuAhpdzVk08+ieTkZCxduhRqtdqu937yySdISUmBh4cHunbtin379iE0NFSikoqrMakXjfXyyy877bsJIdKh1AJCCCGEEOKWqLMXIYQQQghxSxTIEkIIIYQQt0SBLCGEEEIIcUsUyBJCCCGEELdEgSwhhBBCCHFLFMgSQgghhBC3RIEsIYQQQghxSxTIEkIIIYQQt0SBLCGEEEIIcUv/Dy64EsRwxn+lAAAAAElFTkSuQmCC", + "image/png": 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", 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", 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", 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" ] @@ -45,17 +41,23 @@ "output_type": "stream", "text": [ "==== Grid ispuna 20.0% ====\n", - "XY ukupna povrsina = 245.4207 mm^2\n", - " Povrsina ljuski = 132.4033 mm^2\n", - " Povrsina ispune = 113.0174 mm^2\n", + "XY ukupna povrsina = 841.3408 mm^2\n", + " Povrsina ljuski = 594.4840 mm^2\n", + " Povrsina ispune = 246.8568 mm^2\n", "-- Presjeci kroz Z (uzorak konstantan po Z) --\n", - "Duzina po X @ y=1.000 mm: 2.8786 mm\n", - "Duzina po Y @ x=-2.000 mm: 15.9449 mm\n", - "Povrsina XZ @ y=1.000: 28.7860 mm^2 (Z=10.000 mm)\n", - "Povrsina YZ @ x=-2.000: 159.4493 mm^2 (Z=10.000 mm)\n", + "Duzina po X @ y=1.000 mm: 5.4318 mm\n", + "Duzina po Y @ x=-2.000 mm: 41.9024 mm\n", + "Povrsina XZ @ y=1.000: 54.3179 mm^2 (Z=10.000 mm)\n", + "Povrsina YZ @ x=-2.000: 419.0238 mm^2 (Z=10.000 mm)\n", + "MIN XZ povrsina = 31.7897 mm^2 @ y = 9.011 mm\n", + "MAX XZ povrsina = 54.5682 mm^2 @ y = 22.979 mm\n", + "MIN YZ povrsina = 229.7872 mm^2 @ x = 4.981 mm\n", + "MAX YZ povrsina = 1802.2528 mm^2 @ x = 0.501 mm\n", "\n", - "A_xz(y=1mm) = 28.785982478097694 mm^2\n", - "A_yz(x=-2mm) = 159.44931163954337 mm^2\n" + "A_xz(y=1mm) = 54.31789737171478 mm^2\n", + "A_yz(x=-2mm) = 419.02377972464365 mm^2\n", + "MIN/MAX XZ: 31.78973717146441 @ y = 9.01126408010012 mm | 54.56821026282867 @ y = 22.97872340425532 mm\n", + "MIN/MAX YZ: 229.78723404254652 @ x = 4.981226533166458 mm | 1802.2528160199727 @ x = 0.5006257822277851 mm\n" ] } ], @@ -68,14 +70,12 @@ " return np.minimum(r, razmak - r)\n", "\n", "def _pravocrtna_maska(XX, YY, razmak, sirina_linije, kut_stupnjevi=0.0, faza=0.0):\n", - "\n", " th = np.deg2rad(kut_stupnjevi)\n", " u = XX * np.cos(th) + YY * np.sin(th)\n", " dist = _udaljenost_mod(u + faza, razmak)\n", " return dist <= (sirina_linije / 2.0)\n", "\n", "def _razmak_za_gustocu_mreze(sirina_linije, f):\n", - "\n", " f = float(np.clip(f, 0.0, 1.0))\n", " if f <= 0.0:\n", " return np.inf\n", @@ -87,7 +87,6 @@ "def izracun_povrsine(XX, YY, maska):\n", " if not np.any(maska):\n", " return {\"A\": 0.0}\n", - "\n", " dx = XX[0, 1] - XX[0, 0]\n", " dy = YY[1, 0] - YY[0, 0]\n", " dA = dx * dy\n", @@ -103,7 +102,7 @@ " mreza=True,\n", " z_visina=0.0, \n", " faza_po_mm=0.0,\n", - " # Poprecni presjeci kroz Z\n", + " # Poprečni presjeci kroz Z\n", " z_visina_objekta=None, \n", " y_ravnina=0.0, \n", " x_ravnina=0.0, \n", @@ -202,8 +201,34 @@ " y_oznaka = \"Duzina po X (mm) [postavi z_visina_objekta za povrsinu]\"\n", " x_oznaka = \"Duzina po Y (mm) [postavi z_visina_objekta za povrsinu]\"\n", "\n", - " y_os_0_do_H = ys_centered + visina/2.0\n", - " x_os_0_do_W = xs_centered + sirina/2.0\n", + " y_os_0_do_H = ys_centered + visina/2.0 # 0 .. H\n", + " x_os_0_do_W = xs_centered + sirina/2.0 # 0 .. W\n", + "\n", + " # ==== NOVO: minimumi i maksimumi za XZ i YZ (samo srednji dio) ====\n", + " # definiraj margine koje ćeš izbaciti (npr. 5% s obje strane)\n", + " margin_frac = 0.05 \n", + " n_y = len(povrsina_xz_vs_y)\n", + " n_x = len(povrsina_yz_vs_x)\n", + " start_y, end_y = int(margin_frac * n_y), int((1 - margin_frac) * n_y)\n", + " start_x, end_x = int(margin_frac * n_x), int((1 - margin_frac) * n_x)\n", + " \n", + " # XZ presjek (po y)\n", + " idx_min_xz = start_y + int(np.argmin(povrsina_xz_vs_y[start_y:end_y]))\n", + " idx_max_xz = start_y + int(np.argmax(povrsina_xz_vs_y[start_y:end_y]))\n", + " min_povrsina_xz = float(povrsina_xz_vs_y[idx_min_xz])\n", + " max_povrsina_xz = float(povrsina_xz_vs_y[idx_max_xz])\n", + " y_min_xz_mm = float(y_os_0_do_H[idx_min_xz])\n", + " y_max_xz_mm = float(y_os_0_do_H[idx_max_xz])\n", + " \n", + " # YZ presjek (po x)\n", + " idx_min_yz = start_x + int(np.argmin(povrsina_yz_vs_x[start_x:end_x]))\n", + " idx_max_yz = start_x + int(np.argmax(povrsina_yz_vs_x[start_x:end_x]))\n", + " min_povrsina_yz = float(povrsina_yz_vs_x[idx_min_yz])\n", + " max_povrsina_yz = float(povrsina_yz_vs_x[idx_max_yz])\n", + " x_min_yz_mm = float(x_os_0_do_W[idx_min_yz])\n", + " x_max_yz_mm = float(x_os_0_do_W[idx_max_yz])\n", + " # ================================================================\n", + "\n", "\n", " if graficki_prikaz:\n", " plt.figure(figsize=(7, 3.5))\n", @@ -213,6 +238,8 @@ " plt.title(\"Varijacija prema y\")\n", " plt.grid(True)\n", " plt.xlim(0, visina)\n", + " # markeri min/max\n", + " plt.scatter([y_min_xz_mm, y_max_xz_mm], [min_povrsina_xz, max_povrsina_xz])\n", " plt.tight_layout()\n", " plt.show()\n", "\n", @@ -223,8 +250,11 @@ " plt.title(\"Varijacija prema x\")\n", " plt.grid(True)\n", " plt.xlim(0, sirina)\n", + " # markeri min/max\n", + " plt.scatter([x_min_yz_mm, x_max_yz_mm], [min_povrsina_yz, max_povrsina_yz])\n", " plt.tight_layout()\n", " plt.show()\n", + "\n", " if detaljno:\n", " print(f\"==== {('Grid' if mreza else 'Pravocrtna')} ispuna {udio_ispune*100:.1f}% ====\")\n", " print(f\"XY ukupna povrsina = {total['A']:.4f} mm^2\")\n", @@ -237,7 +267,13 @@ " print(f\"Povrsina XZ @ y={y_ravnina:.3f}: {povrsina_xz_na_y:.4f} mm^2 (Z={z_visina_objekta:.3f} mm)\")\n", " if povrsina_yz_na_x is not None:\n", " print(f\"Povrsina YZ @ x={x_ravnina:.3f}: {povrsina_yz_na_x:.4f} mm^2 (Z={z_visina_objekta:.3f} mm)\")\n", + " # NOVO: ispis min/max\n", + " print(f\"MIN XZ povrsina = {min_povrsina_xz:.4f} mm^2 @ y = {y_min_xz_mm:.3f} mm\")\n", + " print(f\"MAX XZ povrsina = {max_povrsina_xz:.4f} mm^2 @ y = {y_max_xz_mm:.3f} mm\")\n", + " print(f\"MIN YZ povrsina = {min_povrsina_yz:.4f} mm^2 @ x = {x_min_yz_mm:.3f} mm\")\n", + " print(f\"MAX YZ povrsina = {max_povrsina_yz:.4f} mm^2 @ x = {x_max_yz_mm:.3f} mm\")\n", " print()\n", + "\n", " return {\n", " \"maska\": konacna_maska,\n", " \"XX\": XX, \"YY\": YY,\n", @@ -253,15 +289,25 @@ " \"x_os_mm\": x_os_0_do_W,\n", " \"povrsina_xz_vs_y\": povrsina_xz_vs_y,\n", " \"povrsina_yz_vs_x\": povrsina_yz_vs_x,\n", + " # NOVO: min/max rezultati\n", + " \"min_povrsina_xz\": min_povrsina_xz,\n", + " \"max_povrsina_xz\": max_povrsina_xz,\n", + " \"y_min_xz_mm\": y_min_xz_mm,\n", + " \"y_max_xz_mm\": y_max_xz_mm,\n", + " \"min_povrsina_yz\": min_povrsina_yz,\n", + " \"max_povrsina_yz\": max_povrsina_yz,\n", + " \"x_min_yz_mm\": x_min_yz_mm,\n", + " \"x_max_yz_mm\": x_max_yz_mm,\n", " }\n", + "\n", "# Konfiguracija\n", "if __name__ == \"__main__\":\n", - " W, H = 10.0, 70.0\n", - " Z = 10.0 \n", + " W, H = 10, 180\n", + " Z = 10\n", " res = prusa_mreza_ili_pravocrtna(\n", " sirina=W, visina=H,\n", " udio_ispune=0.2,\n", - " sirina_linije=0.42,\n", + " sirina_linije=0.8,\n", " slojevi_ljuske=2,\n", " osnovni_kut_ispune_stupnjevi=45.0,\n", " mreza=True,\n", @@ -272,8 +318,20 @@ " graficki_prikaz=True, detaljno=True\n", " )\n", " print(\"A_xz(y=1mm) =\", res[\"povrsina_xz_na_y\"], \"mm^2\")\n", - " print(\"A_yz(x=-2mm) =\", res[\"povrsina_yz_na_x\"], \"mm^2\")" + " print(\"A_yz(x=-2mm) =\", res[\"povrsina_yz_na_x\"], \"mm^2\")\n", + " print(\"MIN/MAX XZ:\", res[\"min_povrsina_xz\"], \"@ y =\", res[\"y_min_xz_mm\"], \"mm | \",\n", + " res[\"max_povrsina_xz\"], \"@ y =\", res[\"y_max_xz_mm\"], \"mm\")\n", + " print(\"MIN/MAX YZ:\", res[\"min_povrsina_yz\"], \"@ x =\", res[\"x_min_yz_mm\"], \"mm | \",\n", + " res[\"max_povrsina_yz\"], \"@ x =\", res[\"x_max_yz_mm\"], \"mm\")\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d0d580b-3b46-493b-9666-1c7d5820d608", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/software/ispitni_rezultati.csv b/software/ispitni_rezultati.csv new file mode 100644 index 0000000..c7b17e5 --- /dev/null +++ b/software/ispitni_rezultati.csv @@ -0,0 +1,19 @@ +Eksperiment,Orijentacija,Visina sloja,Širina ekstruzije,Postotak ispune,Broj slojeva stijenke,A_ekv [mm^2],A_min [mm^2],A_max,Fm kN],Sigma [Mpa],Sigma’,SNR [dB] +1,Orijentacija 1,0.08,0.4,20.00%,2,100,44.493,,0.778,7.78,17.4858966579012,17.8195919397938 +2,Orijentacija 1,0.08,0.6,55.00%,4,100,87.819,,3.299,32.99,37.5659025951104,30.3676463109069 +3,Orijentacija 1,0.08,0.8,85.00%,6,100,100,,0.794,7.94,7.94,17.9964100485419 +4,Orijentacija 1,0.14,0.4,55.00%,6,100,87.6308,,2.792,27.92,31.8609438690506,28.9183082790225 +5,Orijentacija 1,0.14,0.6,85.00%,2,100,91.5613,,2.468,24.68,26.9546194735112,27.8469031072241 +6,Orijentacija 1,0.14,0.8,20.00%,4,100,93.9905,,2.871,28.71,30.5456402508764,29.1606638499301 +7,Orijentacija 1,0.28,0.4,85.00%,4,100,92.7473,,2.423,24.23,26.1247497231725,27.6870682827501 +8,Orijentacija 1,0.28,0.6,20.00%,6,100,96.3896,,1.891,18.91,19.6182990696092,25.5338305769008 +9,Orijentacija 1,0.28,0.8,55.00%,2,100,79.2649,,2.431,24.31,30.6693126465813,27.7156991768667 +10,Orijentacija 2,0.08,0.4,85.00%,4,100,35.669,,3.398,33.98,95.264795761025,30.6244674906605 +11,Orijentacija 2,0.08,0.6,20.00%,6,100,36.045,,3.218,32.18,89.277292273547,30.1517207952602 +12,Orijentacija 2,0.08,0.8,55.00%,2,100,26.908,,2.768,27.68,102.869035231158,28.8433217156944 +13,Orijentacija 2,0.14,0.4,20.00%,6,100,32.415,,2.472,24.72,76.2609902822767,27.8609693283356 +14,Orijentacija 2,0.14,0.6,55.00%,2,100,24.155,,2.527,25.27,104.616021527634,28.0521048383983 +15,Orijentacija 2,0.14,0.8,85.00%,4,100,42.804,,3.647,36.47,85.2023175404168,31.2387152662756 +16,Orijentacija 2,0.28,0.4,55.00%,4,100,26.909,,2.491,24.91,92.5712586866848,27.9274745507301 +17,Orijentacija 2,0.28,0.6,85.00%,6,100,44.555,,4.111,41.11,92.2679833913141,32.278949535607 +18,Orijentacija 2,0.28,0.8,20.00%,2,100,15.895,,2.263,22.63,142.371815036175,27.0936910790946 diff --git a/software/ispitni_rezultati_with_AminAmax.csv b/software/ispitni_rezultati_with_AminAmax.csv new file mode 100644 index 0000000..e37f09b --- /dev/null +++ b/software/ispitni_rezultati_with_AminAmax.csv @@ -0,0 +1,19 @@ +Eksperiment,Orijentacija,Visina sloja,Širina ekstruzije,Postotak ispune,Broj slojeva stijenke,A_ekv [mm^2],A_min [mm^2],A_max,Fm kN],Sigma [Mpa],Sigma’,SNR [dB],A_max [mm^2],Sigma' +1,Orijentacija 1,0.08,0.4,0.2,2.0,100.0,27.712854757929772,,0.778,7.78,17.4858966579012,17.8195919397938,100.1669449081799,28.073614457831436 +2,Orijentacija 1,0.08,0.6,0.55,4.0,100.0,66.11018363939874,,3.299,32.99,37.5659025951104,30.3676463109069,100.1669449081799,49.901540404040595 +3,Orijentacija 1,0.08,0.8,0.85,6.0,100.0,99.8330550918193,,0.794,7.94,7.94,17.9964100485419,100.1669449081799,7.953277591973276 +4,Orijentacija 1,0.14,0.4,0.55,6.0,100.0,67.11185308848053,,2.792,27.92,31.8609438690506,28.9183082790225,100.1669449081799,41.60218905472654 +5,Orijentacija 1,0.14,0.6,0.85,2.0,100.0,69.78297161936533,,2.468,24.68,26.9546194735112,27.8469031072241,100.1669449081799,35.366794258373346 +6,Orijentacija 1,0.14,0.8,0.2,4.0,100.0,71.78631051752893,,2.871,28.71,30.5456402508764,29.1606638499301,100.1669449081799,39.99369767441876 +7,Orijentacija 1,0.28,0.4,0.85,4.0,100.0,74.45742904841373,,2.423,24.23,26.1247497231725,27.6870682827501,100.1669449081799,32.54208520179385 +8,Orijentacija 1,0.28,0.6,0.2,6.0,100.0,78.13021702838032,,1.891,18.91,19.6182990696092,25.5338305769008,100.1669449081799,24.20318376068386 +9,Orijentacija 1,0.28,0.8,0.55,2.0,100.0,55.75959933222015,,2.431,24.31,30.6693126465813,27.7156991768667,100.1669449081799,43.597874251497174 +10,Orijentacija 2,0.08,0.4,0.85,4.0,100.0,74.45742904841373,,3.398,33.98,95.264795761025,30.6244674906605,100.1669449081799,45.63681614349794 +11,Orijentacija 2,0.08,0.6,0.2,6.0,100.0,78.13021702838032,,3.218,32.18,89.277292273547,30.1517207952602,100.1669449081799,41.18764957264974 +12,Orijentacija 2,0.08,0.8,0.55,2.0,100.0,55.75959933222015,,2.768,27.68,102.869035231158,28.8433217156944,100.1669449081799,49.641676646706784 +13,Orijentacija 2,0.14,0.4,0.2,6.0,100.0,51.75292153589295,,2.472,24.72,76.2609902822767,27.8609693283356,100.1669449081799,47.7654193548389 +14,Orijentacija 2,0.14,0.6,0.55,2.0,100.0,51.41903171953235,,2.527,25.27,104.616021527634,28.0521048383983,100.1669449081799,49.14522727272747 +15,Orijentacija 2,0.14,0.8,0.85,4.0,100.0,87.81302170283772,,3.647,36.47,85.2023175404168,31.2387152662756,100.1669449081799,41.531425855513476 +16,Orijentacija 2,0.28,0.4,0.55,4.0,100.0,51.75292153589295,,2.491,24.91,92.5712586866848,27.9274745507301,100.1669449081799,48.13254838709696 +17,Orijentacija 2,0.28,0.6,0.85,6.0,100.0,90.15025041736192,,4.111,41.11,92.2679833913141,32.278949535607,100.1669449081799,45.60164814814833 +18,Orijentacija 2,0.28,0.8,0.2,2.0,100.0,39.732888146911364,,2.263,22.63,142.371815036175,27.0936910790946,100.1669449081799,56.955336134454 diff --git a/software/obrada/.ipynb_checkpoints/ispitni_rezultati-checkpoint.csv b/software/obrada/.ipynb_checkpoints/ispitni_rezultati-checkpoint.csv new file mode 100644 index 0000000..1f5949a --- /dev/null +++ b/software/obrada/.ipynb_checkpoints/ispitni_rezultati-checkpoint.csv @@ -0,0 +1,19 @@ +Eksperiment,Orijentacija,Visina sloja,Širina ekstruzije,Postotak ispune,Broj slojeva stijenke,A_ekv [mm^2],Fm kN],Sigma [Mpa],SNR [dB] +1,Orijentacija 1,0.08,0.4 mm,20.00%,2,100,0.778,7.78,17.8195919397938 +2,Orijentacija 1,0.08,0.6 mm,55.00%,4,100,3.299,32.99,30.3676463109069 +3,Orijentacija 1,0.08,0.8 mm,85.00%,6,100,0.794,7.94,17.9964100485419 +4,Orijentacija 1,0.14,0.4 mm,55.00%,6,100,2.792,27.92,28.9183082790225 +5,Orijentacija 1,0.14,0.6 mm,85.00%,2,100,2.468,24.68,27.8469031072241 +6,Orijentacija 1,0.14,0.8 mm,20.00%,4,100,2.871,28.71,29.1606638499301 +7,Orijentacija 1,0.28,0.4 mm,85.00%,4,100,2.423,24.23,27.6870682827501 +8,Orijentacija 1,0.28,0.6 mm,20.00%,6,100,1.891,18.91,25.5338305769008 +9,Orijentacija 1,0.28,0.8 mm,55.00%,2,100,2.431,24.31,27.7156991768667 +10,Orijentacija 2,0.08,0.4 mm,85.00%,4,100,3.398,33.98,30.6244674906605 +11,Orijentacija 2,0.08,0.6 mm,20.00%,6,100,3.218,32.18,30.1517207952602 +12,Orijentacija 2,0.08,0.8 mm,55.00%,2,100,2.768,27.68,28.8433217156944 +13,Orijentacija 2,0.14,0.4 mm,20.00%,6,100,2.472,24.72,27.8609693283356 +14,Orijentacija 2,0.14,0.6 mm,55.00%,2,100,2.527,25.27,28.0521048383983 +15,Orijentacija 2,0.14,0.8 mm,85.00%,4,100,3.647,36.47,31.2387152662756 +16,Orijentacija 2,0.28,0.4 mm,55.00%,4,100,2.491,24.91,27.9274745507301 +17,Orijentacija 2,0.28,0.6 mm,85.00%,6,100,4.111,41.11,32.278949535607 +18,Orijentacija 2,0.28,0.8 mm,20.00%,2,100,2.263,22.63,27.0936910790946 diff --git a/software/obrada/.ipynb_checkpoints/obrada-checkpoint.ipynb b/software/obrada/.ipynb_checkpoints/obrada-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/software/obrada/.ipynb_checkpoints/obrada-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/software/obrada/ispitni_rezultati.csv b/software/obrada/ispitni_rezultati.csv new file mode 100644 index 0000000..1f5949a --- /dev/null +++ b/software/obrada/ispitni_rezultati.csv @@ -0,0 +1,19 @@ +Eksperiment,Orijentacija,Visina sloja,Širina ekstruzije,Postotak ispune,Broj slojeva stijenke,A_ekv [mm^2],Fm kN],Sigma [Mpa],SNR [dB] +1,Orijentacija 1,0.08,0.4 mm,20.00%,2,100,0.778,7.78,17.8195919397938 +2,Orijentacija 1,0.08,0.6 mm,55.00%,4,100,3.299,32.99,30.3676463109069 +3,Orijentacija 1,0.08,0.8 mm,85.00%,6,100,0.794,7.94,17.9964100485419 +4,Orijentacija 1,0.14,0.4 mm,55.00%,6,100,2.792,27.92,28.9183082790225 +5,Orijentacija 1,0.14,0.6 mm,85.00%,2,100,2.468,24.68,27.8469031072241 +6,Orijentacija 1,0.14,0.8 mm,20.00%,4,100,2.871,28.71,29.1606638499301 +7,Orijentacija 1,0.28,0.4 mm,85.00%,4,100,2.423,24.23,27.6870682827501 +8,Orijentacija 1,0.28,0.6 mm,20.00%,6,100,1.891,18.91,25.5338305769008 +9,Orijentacija 1,0.28,0.8 mm,55.00%,2,100,2.431,24.31,27.7156991768667 +10,Orijentacija 2,0.08,0.4 mm,85.00%,4,100,3.398,33.98,30.6244674906605 +11,Orijentacija 2,0.08,0.6 mm,20.00%,6,100,3.218,32.18,30.1517207952602 +12,Orijentacija 2,0.08,0.8 mm,55.00%,2,100,2.768,27.68,28.8433217156944 +13,Orijentacija 2,0.14,0.4 mm,20.00%,6,100,2.472,24.72,27.8609693283356 +14,Orijentacija 2,0.14,0.6 mm,55.00%,2,100,2.527,25.27,28.0521048383983 +15,Orijentacija 2,0.14,0.8 mm,85.00%,4,100,3.647,36.47,31.2387152662756 +16,Orijentacija 2,0.28,0.4 mm,55.00%,4,100,2.491,24.91,27.9274745507301 +17,Orijentacija 2,0.28,0.6 mm,85.00%,6,100,4.111,41.11,32.278949535607 +18,Orijentacija 2,0.28,0.8 mm,20.00%,2,100,2.263,22.63,27.0936910790946 diff --git a/software/obrada/obrada.ipynb b/software/obrada/obrada.ipynb new file mode 100644 index 0000000..551c282 --- /dev/null +++ b/software/obrada/obrada.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "5d58304b-e94b-428b-91c8-35d649d97dd7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "usage: ipykernel_launcher.py [-h] --input INPUT [--outdir OUTDIR]\n", + " [--response RESPONSE] [--area_col AREA_COL]\n", + " [--fm_col FM_COL] [--recompute_sigma]\n", + " [--sn_type {LB}]\n", + "ipykernel_launcher.py: error: the following arguments are required: --input\n" + ] + }, + { + "ename": "SystemExit", + "evalue": "2", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.13/site-packages/IPython/core/interactiveshell.py:3585: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n", + " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n" + ] + } + ], + "source": [ + "\n", + "#!/usr/bin/env python3\n", + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Taguchi analysis pipeline for FDM experiment (per user's thesis)\n", + "- Reads a CSV with columns similar to:\n", + " 'Eksperiment','Orijentacija','Visina sloja','Širina ekstruzije','Postotak ispune',\n", + " 'Broj slojeva stijenke','A_ekv [mm^2]','Fm kN]','Sigma [Mpa]','SNR [dB]'\n", + "- Cleans units to numeric, recomputes Sigma (optional) and SNR (LB, n=1),\n", + "- Builds response tables (means, Δ), ranks factors, selects optimal levels by SNR,\n", + "- Predicts response at optimal combination (additive model),\n", + "- Runs Taguchi-style ANOVA on Sigma,\n", + "- Saves CSV outputs + main-effects plots + LaTeX snippet.\n", + "Usage:\n", + " python taguchi_from_csv.py --input ispitni_rezultati.csv --outdir out_tlak\n", + "\"\"\"\n", + "import argparse, os, re, json\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def norm_num(x):\n", + " if pd.isna(x):\n", + " return np.nan\n", + " if isinstance(x, (int, float, np.number)):\n", + " return float(x)\n", + " s = str(x).strip()\n", + " s = s.replace(',', '.')\n", + " s = s.replace('%','')\n", + " s = s.replace(' mm','')\n", + " s = s.replace('MPa','').replace('Mpa','')\n", + " s = s.replace('kN','').replace('kN]','').replace('[','').replace(']','')\n", + " try:\n", + " return float(s)\n", + " except:\n", + " return np.nan\n", + "\n", + "def compute_snr_lb(y):\n", + " # larger-the-better; handles n=1 case\n", + " y = pd.to_numeric(y, errors='coerce')\n", + " return 20.0*np.log10(y.clip(lower=1e-12))\n", + "\n", + "def response_table(df, factor, col):\n", + " t = df.groupby(factor, as_index=False)[col].mean()\n", + " t[\"Delta (max-min)\"] = t[col].max() - t[col].min()\n", + " t[\"Faktor\"] = factor\n", + " return t\n", + "\n", + "def taguchi_anova(df, response, factors):\n", + " y = df[response].astype(float)\n", + " mu = y.mean()\n", + " total_ss = ((y - mu)**2).sum()\n", + " rows = []\n", + " dof_used = 0\n", + " ss_used = 0.0\n", + " for f in factors:\n", + " grp = df.groupby(f)[response].agg(['mean','count'])\n", + " ss_f = (grp['count']*(grp['mean']-mu)**2).sum()\n", + " dof_f = grp.shape[0]-1\n", + " rows.append([f, ss_f, dof_f])\n", + " dof_used += dof_f\n", + " ss_used += ss_f\n", + " err_ss = max(total_ss - ss_used, 0.0)\n", + " err_dof = max(len(df)-1 - dof_used, 0)\n", + " an = pd.DataFrame(rows, columns=[\"Factor\",\"SS\",\"DOF\"])\n", + " an[\"MS\"] = an[\"SS\"]/an[\"DOF\"]\n", + " an[\"Pct_contrib_%\"] = (an[\"SS\"]/total_ss*100.0) if total_ss>0 else np.nan\n", + " err_row = pd.DataFrame([[\"Error\", err_ss, err_dof, (err_ss/err_dof) if err_dof>0 else np.nan, (err_ss/total_ss*100.0) if total_ss>0 else np.nan]],\n", + " columns=[\"Factor\",\"SS\",\"DOF\",\"MS\",\"Pct_contrib_%\"])\n", + " an = pd.concat([an, err_row], ignore_index=True)\n", + " return an, mu, total_ss\n", + "\n", + "def main():\n", + " ap = argparse.ArgumentParser()\n", + " ap.add_argument(\"--input\", required=True, help=\"Path to CSV with results\")\n", + " ap.add_argument(\"--outdir\", default=None, help=\"Output directory\")\n", + " ap.add_argument(\"--response\", default=\"Sigma [Mpa]\", help=\"Response column to analyze (default Sigma [Mpa])\")\n", + " ap.add_argument(\"--area_col\", default=\"A_ekv [mm^2]\", help=\"Area column if Sigma should be recomputed from Fm/Area\")\n", + " ap.add_argument(\"--fm_col\", default=\"Fm kN]\", help=\"Force column (kN)\")\n", + " ap.add_argument(\"--recompute_sigma\", action=\"store_true\", help=\"If set, recompute Sigma = Fm*1000/Area\")\n", + " ap.add_argument(\"--sn_type\", default=\"LB\", choices=[\"LB\"], help=\"S/N type (only LB supported here)\")\n", + " args = ap.parse_args()\n", + "\n", + " in_path = args.input\n", + " outdir = args.outdir or (os.path.splitext(os.path.basename(in_path))[0] + \"_taguchi_out\")\n", + " os.makedirs(outdir, exist_ok=True)\n", + "\n", + " df = pd.read_csv(in_path)\n", + "\n", + " # Standard column mapping / cleanup for known names\n", + " rename_map = {\n", + " \"Visina sloja\":\"Visina sloja [mm]\",\n", + " \"Širina ekstruzije\":\"Širina ekstruzije [mm]\",\n", + " \"Postotak ispune\":\"Postotak ispune [%]\",\n", + " \"Broj slojeva stijenke\":\"Broj stijenki\",\n", + " \"Sigma [MPa]\":\"Sigma [Mpa]\",\n", + " \"Fm [kN]\":\"Fm kN]\",\n", + " }\n", + " df = df.rename(columns={k:v for k,v in rename_map.items() if k in df.columns})\n", + "\n", + " # Ensure numeric for relevant columns\n", + " if \"Visina sloja [mm]\" in df.columns:\n", + " df[\"Visina sloja [mm]\"] = df[\"Visina sloja [mm]\"].apply(norm_num)\n", + " if \"Širina ekstruzije [mm]\" in df.columns:\n", + " df[\"Širina ekstruzije [mm]\"] = df[\"Širina ekstruzije [mm]\"].apply(norm_num)\n", + " if \"Postotak ispune [%]\" in df.columns:\n", + " df[\"Postotak ispune [%]\"] = df[\"Postotak ispune [%]\"].apply(norm_num)\n", + " if \"Broj stijenki\" in df.columns:\n", + " df[\"Broj stijenki\"] = df[\"Broj stijenki\"].apply(norm_num)\n", + " if args.area_col in df.columns:\n", + " df[args.area_col] = df[args.area_col].apply(norm_num)\n", + " if args.fm_col in df.columns:\n", + " df[args.fm_col] = df[args.fm_col].apply(norm_num)\n", + " if args.response in df.columns:\n", + " df[args.response] = df[args.response].apply(norm_num)\n", + "\n", + " # Compute Sigma if asked or missing\n", + " if args.recompute_sigma or args.response not in df.columns or df[args.response].isna().all():\n", + " if args.fm_col in df.columns and args.area_col in df.columns:\n", + " df[args.response] = (df[args.fm_col] * 1000.0) / df[args.area_col]\n", + " else:\n", + " raise SystemExit(\"Cannot recompute Sigma: missing Fm or Area columns\")\n", + "\n", + " # Compute SNR (LB)\n", + " df[\"SNR_LB [dB]\"] = compute_snr_lb(df[args.response])\n", + "\n", + " # Save cleaned raw\n", + " raw_out = os.path.join(outdir, \"0_raw_with_SNR.csv\")\n", + " df.to_csv(raw_out, index=False)\n", + "\n", + " # Factors to analyze (auto detect from known list)\n", + " candidate_factors = [\"Orijentacija\",\"Visina sloja [mm]\",\"Širina ekstruzije [mm]\",\"Postotak ispune [%]\",\"Broj stijenki\"]\n", + " factors = [f for f in candidate_factors if f in df.columns]\n", + " if len(factors) == 0:\n", + " raise SystemExit(\"No known factor columns found. Expected some of: \" + \", \".join(candidate_factors))\n", + "\n", + " # Response tables and deltas\n", + " resp_mu = pd.concat([response_table(df, f, args.response) for f in factors], ignore_index=True)\n", + " resp_sn = pd.concat([response_table(df, f, \"SNR_LB [dB]\") for f in factors], ignore_index=True)\n", + " resp_mu.to_csv(os.path.join(outdir, \"1_response_means_Sigma.csv\"), index=False)\n", + " resp_sn.to_csv(os.path.join(outdir, \"2_response_means_SNR.csv\"), index=False)\n", + "\n", + " # Ranking (by Delta)\n", + " rank_mu = resp_mu.groupby(\"Faktor\")[\"Delta (max-min)\"].max().sort_values(ascending=False).reset_index().rename(columns={\"Delta (max-min)\":\"Rang delta (Sigma)\"})\n", + " rank_sn = resp_sn.groupby(\"Faktor\")[\"Delta (max-min)\"].max().sort_values(ascending=False).reset_index().rename(columns={\"Delta (max-min)\":\"Rang delta (SNR)\"})\n", + " ranking = pd.merge(rank_mu, rank_sn, on=\"Faktor\")\n", + " ranking.to_csv(os.path.join(outdir, \"3_factor_ranking.csv\"), index=False)\n", + "\n", + " # Optimal levels by SNR\n", + " opt_levels = {f: df.groupby(f)[\"SNR_LB [dB]\"].mean().sort_values(ascending=False).index[0] for f in factors}\n", + " opt_table = pd.DataFrame({\"Faktor\": list(opt_levels.keys()), \"Optimalna razina (po S/N)\": list(opt_levels.values())})\n", + " opt_table.to_csv(os.path.join(outdir, \"4_optimal_levels.csv\"), index=False)\n", + "\n", + " # Prediction at optimal combo (additive model) on response\n", + " grand_mean = df[args.response].mean()\n", + " k = len(factors)\n", + " pred_sigma = sum(df.groupby(f)[args.response].mean().loc[opt_levels[f]] for f in factors) - (k-1)*grand_mean\n", + " grand_mean_snr = df[\"SNR_LB [dB]\"].mean()\n", + " pred_snr = sum(df.groupby(f)[\"SNR_LB [dB]\"].mean().loc[opt_levels[f]] for f in factors) - (k-1)*grand_mean_snr\n", + " pred_df = pd.DataFrame({\n", + " \"Predikcija\": [\"Sigma_opt [MPa]\",\"SNR_opt [dB]\",\"Grand mean Sigma [MPa]\",\"Grand mean SNR [dB]\"],\n", + " \"Vrijednost\": [pred_sigma, pred_snr, grand_mean, grand_mean_snr]\n", + " })\n", + " pred_df.to_csv(os.path.join(outdir, \"5_prediction.csv\"), index=False)\n", + "\n", + " # ANOVA (Taguchi-style) on response\n", + " anova_df, mu_sigma, totss = taguchi_anova(df, args.response, factors)\n", + " anova_df.to_csv(os.path.join(outdir, \"6_anova_sigma.csv\"), index=False)\n", + "\n", + " # Plots: main effects for SNR\n", + " for f in factors:\n", + " means = df.groupby(f)[\"SNR_LB [dB]\"].mean().reset_index()\n", + " # numeric sort if possible\n", + " try:\n", + " means[f] = pd.to_numeric(means[f], errors=\"ignore\")\n", + " means = means.sort_values(by=f)\n", + " except:\n", + " pass\n", + " plt.figure()\n", + " plt.plot(means[f], means[\"SNR_LB [dB]\"], marker=\"o\")\n", + " plt.xlabel(f)\n", + " plt.ylabel(\"S/N (LB) [dB]\")\n", + " plt.title(f\"Main effect (S/N): {f}\")\n", + " plt.tight_layout()\n", + " plt.savefig(os.path.join(outdir, f\"main_effect_SNR_{f}.png\"), dpi=150)\n", + " plt.close()\n", + "\n", + " # LaTeX snippet\n", + " latex_lines = []\n", + " latex_lines.append(r\"% --- Taguchi rezultati (S = Sigma [MPa], S/N larger-the-better) ---\")\n", + " latex_lines.append(r\"\\subsection{Rezultati Taguchijeve metode}\")\n", + " latex_lines.append(r\"U skladu s ortogonalnom matricom provedena je analiza s kriterijem \\textbf{što-veće-to-bolje}. Za svaku kombinaciju izračunat je S/N omjer \\((\\mathrm{S/N}=20\\log_{10}(\\sigma))\\) te su određeni glavni učinci po razinama i optimalna kombinacija.\")\n", + "\n", + " # Optimal levels\n", + " latex_lines.append(r\"\\paragraph{Optimalne razine (po S/N).}\")\n", + " latex_lines.append(opt_table.to_latex(index=False, escape=False))\n", + " # Prediction\n", + " latex_lines.append(r\"\\paragraph{Predikcija odziva na optimalnoj kombinaciji.}\")\n", + " latex_lines.append(pred_df.to_latex(index=False, escape=False, float_format='%.2f'))\n", + " # Ranking\n", + " latex_lines.append(r\"\\paragraph{Rang utjecaja faktora.}\")\n", + " latex_lines.append(ranking.to_latex(index=False, escape=False, float_format='%.3f'))\n", + " # ANOVA\n", + " an_fmt = anova_df.copy()\n", + " for c in [\"SS\",\"MS\",\"Pct_contrib_%\"]:\n", + " if c in an_fmt.columns:\n", + " an_fmt[c] = an_fmt[c].astype(float).round(3)\n", + " latex_lines.append(r\"\\paragraph{ANOVA (Taguchi).}\")\n", + " latex_lines.append(an_fmt.to_latex(index=False, escape=False))\n", + " latex_lines.append(r\"Napomena: budući da je \\(n{=}1\\), pogreška (Error) procijenjena je iz preostalih stupnjeva slobode (Taguchi pooling).\")\n", + "\n", + " with open(os.path.join(outdir, \"taguchi_results.tex\"), \"w\", encoding=\"utf-8\") as f:\n", + " f.write(\"\\n\\n\".join(latex_lines))\n", + "\n", + " # Small JSON summary\n", + " summary = {\n", + " \"outdir\": outdir,\n", + " \"factors\": factors,\n", + " \"opt_levels\": opt_levels,\n", + " \"pred_sigma\": pred_sigma,\n", + " \"grand_mean_sigma\": grand_mean,\n", + " }\n", + " with open(os.path.join(outdir, \"summary.json\"), \"w\", encoding=\"utf-8\") as f:\n", + " json.dump(summary, f, ensure_ascii=False, indent=2)\n", + "\n", + " print(\"Done. Outputs in:\", outdir)\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56399c17-5135-4fa3-8809-358fef72570b", + "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": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}